From a3f29a382338e59818b4f6e918992b2153cc8ccb Mon Sep 17 00:00:00 2001
From: wyy566 <531938832@qq.com>
Date: Thu, 9 Jun 2022 14:44:52 +0800
Subject: [PATCH] adapt to Spark 3.1.1

---
 README.md                                     |   16 +-
 ml-accelerator/pom.xml                        |    6 +-
 .../optimize/FirstOrderMinimizerX.scala       |  291 -
 .../main/scala/breeze/optimize/LBFGSX.scala   |  110 -
 .../main/scala/breeze/optimize/OWLQNX.scala   |   97 -
 .../DecisionTreeClassifier.scala              |  160 -
 .../ml/classification/GBTClassifier.scala     |  423 -
 .../spark/ml/classification/LinearSVC.scala   |  304 -
 .../classification/LogisticRegression.scala   |  703 --
 .../RandomForestClassifier.scala              |  356 -
 .../org/apache/spark/ml/feature/IDF.scala     |  195 -
 .../org/apache/spark/ml/fpm/PrefixSpan.scala  |  168 +
 .../DifferentiableLossAggregatorX.scala       |   98 -
 .../optim/aggregator/HingeAggregatorX.scala   |  112 -
 .../optim/aggregator/HuberAggregatorX.scala   |  163 -
 .../aggregator/LeastSquaresAggregatorX.scala  |  226 -
 .../aggregator/LogisticAggregatorX.scala      |  379 -
 .../ml/optim/loss/RDDLossFunctionX.scala      |   77 -
 .../apache/spark/ml/recommendation/ALS.scala  |  174 +-
 .../ml/regression/DecisionTreeRegressor.scala |  184 -
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 .../spark/ml/tree/impl/DecisionForest.scala   | 1275 ---
 .../ml/tree/impl/GradientBoostedTrees.scala   |  663 --
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 .../spark/ml/tree/impl/RandomForest.scala     | 1231 ---
 .../ml/tree/impl/RandomForest4GBDTX.scala     |  621 --
 .../spark/ml/tree/impl/RandomForestRaw.scala  | 1157 ---
 .../org/apache/spark/ml/tree/treeParams.scala |  611 --
 .../spark/mllib/clustering/KMACCm.scala       |  124 -
 .../spark/mllib/clustering/KMeans.scala       |  616 --
 .../apache/spark/mllib/clustering/LDA.scala   |   16 +-
 .../spark/mllib/clustering/LDAOptimizer.scala |   70 +-
 .../spark/mllib/clustering/LocalKMeansX.scala |  156 -
 .../org/apache/spark/mllib/feature/IDF.scala  |  231 -
 .../apache/spark/mllib/fpm/PrefixSpan.scala   |   18 +-
 .../linalg/EigenValueDecomposition.scala      |  285 -
 .../mllib/linalg/distributed/RowMatrix.scala  |  910 --
 .../mllib/stat/correlation/Correlation.scala  |  103 -
 .../stat/correlation/PearsonCorrelation.scala |  119 -
 .../correlation/SpearmanCorrelation.scala     |   65 -
 .../spark/mllib/tree/DecisionTree.scala       |  291 -
 ml-core/pom.xml                               |    4 +-
 .../main/scala/breeze/numerics/DigammaX.scala |    6 -
 .../scala/org/apache/spark/ml/tree/Node.scala |  635 --
 .../org/apache/spark/ml/tree/Split.scala      |  274 -
 .../spark/ml/tree/impl/BaggedPoint.scala      |  135 -
 .../tree/impl/DTFeatureStatsAggregator.scala  |  111 -
 .../ml/tree/impl/DTStatsAggregator.scala      |  187 -
 .../tree/impl/GradientBoostedTreesCore.scala  |  256 -
 .../spark/ml/tree/impl/TreePointX.scala       |    6 -
 .../spark/ml/tree/impl/TreePointY.scala       |    6 -
 .../spark/mllib/clustering/LDAUtilsX.scala    |    6 -
 .../clustering/OnlineLDAOptimizerXObj.scala   |   45 +-
 .../spark/mllib/fpm/LocalPrefixSpan.scala     |    6 -
 .../spark/mllib/fpm/PrefixSpanBase.scala      |    6 -
 .../spark/mllib/tree/impurity/Entropy.scala   |  171 -
 .../spark/mllib/tree/impurity/Gini.scala      |  168 -
 .../mllib/tree/impurity/Impurities.scala      |   38 -
 .../spark/mllib/tree/impurity/Impurity.scala  |  204 -
 .../spark/mllib/tree/impurity/Variance.scala  |  148 -
 ml-kernel-client-core/pom.xml                 |    2 +-
 ml-kernel-client/pom.xml                      |    4 +-
 .../scala/breeze/linalg/blas/YTYUtils.scala   |    5 +-
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 .../main/scala/breeze/optimize/OWLQNL.scala   |   78 -
 .../spark/ml/recommendation/ALSUtils.scala    |    5 +-
 .../tree/impl/GradientBoostedTreesUtil.scala  |   77 -
 .../spark/mllib.clustering/KmeansUtil.scala   |   38 -
 .../spark/mllib.clustering/LDAUtilsXOpt.scala |    3 +-
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 ml-xgboost/CONTRIBUTORS.md                    |  104 -
 ml-xgboost/Jenkinsfile                        |  435 -
 ml-xgboost/Jenkinsfile-win64                  |  151 -
 ml-xgboost/LICENSE                            |  201 -
 ml-xgboost/Makefile                           |  147 -
 ml-xgboost/NEWS.md                            | 1100 ---
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 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessHiveRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/ClusteringCoefficientRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/ConnectedComponentsRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/CycleDetectionWithConstrainsRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/DegreeRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/IncPageRankRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionHiveRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/KcoreMain.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/LabelPropagationRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/LouvainHiveRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/LouvainRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/MSSPRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationHiveRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/ModularityRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/Node2VecRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/PageRankHiveRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/PageRankRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/PersonalizedPageRankRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/StronglyConnectedComponentsRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/SubgraphMatchingRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/TrangleCountRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/TrustRankRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/Util.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/WCEHiveRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/WCERunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/graph/WeightedPageRankRunner.scala
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 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/CovRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/DTRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/GBDTRunner.scala
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 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/KNNRunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/LDARunner.scala
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 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/PCARunner.scala
 delete mode 100644 tools/kal-test/src/main/scala/com/bigdata/ml/PearsonRunner.scala
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 delete mode 100644 tools/kal-test/src/main/scala/org/apache/spark/ml/knn/KNN.scala
 delete mode 100644 tools/kal-test/src/main/scala/org/apache/spark/ml/knn/MetricTree.scala
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diff --git a/README.md b/README.md
index c1f5210..97493a2 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,9 @@
 Introduction
 ============
 
-The machine learning algorithm library running on Kunpeng processors is an acceleration library that provides a rich set of high-level tools for machine learning algorithms. It is based on the original APIs of Apache [Spark 2.3.2](https://github.com/apache/spark/tree/v2.3.2), [breeze 0.13.1](https://github.com/scalanlp/breeze/tree/releases/v0.13.1) and [xgboost 1.1.0](https://github.com/dmlc/xgboost/tree/release_1.0.0). The acceleration library for greatly improves the computing power in big data scenarios.
+The machine learning algorithm library running on Kunpeng processors is an acceleration library that provides a rich set of high-level tools for machine learning algorithms. It is based on the original APIs of Apache [Spark 3.1.1](https://github.com/apache/spark/tree/v3.1.1). The acceleration library for greatly improves the computing power in big data scenarios.
 
-The library provides 21 machine learning algorithms: support vector machine (SVM), random forest classifier (RFC), gradient boosting decision tree (GBDT), decision tree (DT), K-means clustering, linear regression, logistic regression algorithm, principal component analysis (PCA), principal component analysis for Sparse Matrix(SPCA), singular value decomposition (SVD), latent dirichlet allocation (LDA), prefix-projected pattern prowth (Prefix-Span), alternating least squares (ALS), K-nearest neighbors (KNN), Covariance, Density-based spatial clustering of applicaitons with noise (DBSCAN), Pearson, Spearman, XGboost, Inverse Document Frequency(IDF), and SimRank. You can find the latest documentation on the project web page. This README file contains only basic setup instructions.
+The library provides 5 machine learning algorithms: latent dirichlet allocation (LDA), prefix-projected pattern prowth (Prefix-Span), alternating least squares (ALS), K-nearest neighbors (KNN), Density-based spatial clustering of applicaitons with noise (DBSCAN). You can find the latest documentation on the project web page. This README file contains only basic setup instructions.
 You can find the latest documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
 
 
@@ -21,17 +21,9 @@ Building And Packageing
 
     mvn clean package
 
-(2) Build XGBoost project under the "Spark-ml-algo-lib/ml-xgboost/jvm-packages" directory:
+(2) Obtain "boostkit-ml-core_2.12-2.1.0-spark3.1.1.jar" under the "Spark-ml-algo-lib/ml-core/target" directory.
 
-    mvn clean package
-
-(3) Obtain "boostkit-ml-core_2.11-2.1.0-spark2.3.2.jar" under the "Spark-ml-algo-lib/ml-core/target" directory.
-
-   Obtain "boostkit-ml-acc_2.11-2.1.0-spark2.3.2.jar" under the "Spark-ml-algo-lib/ml-accelerator/target" directory.
-
-   Obtain "boostkit-xgboost4j_2.11-2.1.0.jar" under the "Spark-ml-algo-lib/ml-xgboost/jvm-packages/boostkit-xgboost4j/target" directory.
-
-   Obtain "boostkit-xgboost4j-spark2.3.2_2.11-2.1.0.jar" under the "Spark-ml-algo-lib/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/target" directory.
+   Obtain "boostkit-ml-acc_2.12-2.1.0-spark3.1.1.jar" under the "Spark-ml-algo-lib/ml-accelerator/target" directory.
 
 
 Contribution Guidelines
diff --git a/ml-accelerator/pom.xml b/ml-accelerator/pom.xml
index 5c9f082..0af611b 100644
--- a/ml-accelerator/pom.xml
+++ b/ml-accelerator/pom.xml
@@ -6,7 +6,7 @@
   </parent>
 
   <modelVersion>4.0.0</modelVersion>
-  <artifactId>boostkit-ml-acc_2.11</artifactId>
+  <artifactId>boostkit-ml-acc_2.12</artifactId>
   <version>2.1.0</version>
   <name>${project.artifactId}</name>
   <description>Spark ml algo accelerator</description>
@@ -14,14 +14,14 @@
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>boostkit-ml-core_2.11</artifactId>
+      <artifactId>boostkit-ml-core_2.12</artifactId>
       <version>${project.version}</version>
       <classifier>${spark.version}</classifier>
 
     </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>boostkit-ml-kernel-client_2.11</artifactId>
+      <artifactId>boostkit-ml-kernel-client_2.12</artifactId>
       <version>${project.version}</version>
       <classifier>${spark.version}</classifier>
       <scope>compile</scope>
diff --git a/ml-accelerator/src/main/scala/breeze/optimize/FirstOrderMinimizerX.scala b/ml-accelerator/src/main/scala/breeze/optimize/FirstOrderMinimizerX.scala
deleted file mode 100644
index 053f103..0000000
--- a/ml-accelerator/src/main/scala/breeze/optimize/FirstOrderMinimizerX.scala
+++ /dev/null
@@ -1,291 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-
-package breeze.optimize
-
-import scala.language.implicitConversions
-
-import FirstOrderMinimizerX.ConvergenceCheck
-import breeze.linalg.norm
-import breeze.math.{MutableInnerProductModule, NormedModule}
-import breeze.util.Implicits._
-import breeze.util.SerializableLogging
-
-/**
- *
- * @author dlwh
- */
-abstract class FirstOrderMinimizerX[T, DF<:StochasticDiffFunction[T]]
-(val convergenceCheck: ConvergenceCheck[T])
-(implicit space: MutableInnerProductModule[T, Double])
-  extends Minimizer[T, DF] with SerializableLogging {
-
-  def this(maxIter: Int = -1, tolerance: Double = 1E-6,
-           fvalMemory: Int = 100, relativeTolerance: Boolean = true)
-          (implicit space: MutableInnerProductModule[T, Double]) =
-    this(FirstOrderMinimizerX.defaultConvergenceCheckX[T]
-      (maxIter, tolerance, relativeTolerance, fvalMemory))
-
-  var inertiaCoefficient: Double = 0.5
-  val momentumUpdateCoefficient : Double = 0.9
-
-  def setInertiaCoefficient(a: Double): Unit = {
-    this.inertiaCoefficient = a
-  }
-
-  /**
-   * Any history the derived minimization function needs to do its updates.
-   * typically an approximation
-   * to the second derivative/hessian matrix.
-   */
-  type History
-  type State = FirstOrderMinimizerX.State[T, convergenceCheck.Info, History]
-
-
-
-
-  protected def initialHistory(f: DF, init: T): History
-  protected def adjustFunction(f: DF): DF = f
-  protected def adjust(newX: T, newGrad: T, newVal: Double): (Double, T) = (newVal, newGrad)
-  protected def chooseDescentDirection(state: State, f: DF): T
-  protected def takeStep(state: State, dir: T, stepSize: Double): T
-  protected def updateHistory(newX: T, newGrad: T, newVal: Double, f: DF, oldState: State): History
-  protected def updateTheta(f: DF, state: State): (T, T)
-
-
-  protected def initialState(f: DF, init: T): State = {
-    val x = init
-    val history = initialHistory(f, init)
-    val (value, grad) = calculateObjective(f, x, history)
-    val (adjValue, adjGrad) = adjust(x, grad, value)
-    import space._
-    val copyInit = space.copy(init)
-    copyInit -= copyInit
-    FirstOrderMinimizerX.State(x,
-      value,
-      grad,
-      adjValue,
-      adjGrad,
-      0,
-      adjValue,
-      history,
-      convergenceCheck.initialInfo,
-      copyInit)
-  }
-
-
-  protected def calculateObjective(f: DF, x: T, history: History): (Double, T) = {
-    f.calculate(x)
-  }
-
-  def infiniteIterations(f: DF, state: State): Iterator[State] = {
-    var failedOnce = false
-    val adjustedFun = adjustFunction(f)
-    import space._
-
-    Iterator.iterate(state) { state => try {
-        val (x, currentMomentum) = updateTheta(adjustedFun, state)
-        // the Func used to update the theta, sub class overide it.
-        val (value, grad) = calculateObjective(adjustedFun, x, state.history)
-        val (adjValue, adjGrad) = adjust(x, grad, value)
-        val oneOffImprovement = (state.adjustedValue - adjValue)/
-          (state.adjustedValue.abs max adjValue.abs max 1E-6 * state.initialAdjVal.abs)
-        logger.info(f"Val and Grad Norm: $adjValue%.6g (rel: " +
-          f"$oneOffImprovement%.3g) ${norm(adjGrad)}%.6g")
-        val history = updateHistory(x, grad, value, adjustedFun, state)
-        val newCInfo = convergenceCheck
-          .update(x,
-            grad,
-            value,
-            state,
-            state.convergenceInfo)
-        failedOnce = false
-        FirstOrderMinimizerX
-          .State(x,
-            value,
-            grad,
-            adjValue,
-            adjGrad,
-            state.iter + 1,
-            state.initialAdjVal,
-            history,
-            newCInfo,
-            currentMomentum)
-    } catch {
-        case x: FirstOrderException if !failedOnce =>
-          failedOnce = true
-          logger.error(s"Failure! Resetting history: $x")
-          state.copy(history = initialHistory(adjustedFun, state.x))
-        case x: FirstOrderException =>
-          logger.error("Failure again! Giving up and returning. " +
-            "Maybe the objective is just poorly behaved?")
-          state.copy(searchFailed = true)
-      }
-    }
-  }
-
-  def iterations(f: DF, init: T): Iterator[State] = {
-    val adjustedFun = adjustFunction(f)
-    infiniteIterations(f, initialState(adjustedFun, init))
-      .takeUpToWhere{s =>
-      convergenceCheck.apply(s, s.convergenceInfo) match {
-        case Some(converged) =>
-          logger.info(s"Converged because ${converged.reason}")
-          true
-        case None =>
-          false
-      }
-    }
-  }
-
-  def minimize(f: DF, init: T): T = {
-    minimizeAndReturnState(f, init).x
-  }
-
-
-  def minimizeAndReturnState(f: DF, init: T): State = {
-    iterations(f, init).last
-  }
-}
-
-
-object FirstOrderMinimizerX {
-
-  /**
-   * Tracks the information about the optimizer, including the current point,
-   * its value, gradient, and then any history.
-   * Also includes information for checking convergence.
-   * @param x the current point being considered
-   * @param value f(x)
-   * @param grad f.gradientAt(x)
-   * @param adjustedValue  f(x) + r(x)
-   * @param adjustedGradient f'(x) + r'(x)
-   * @param iter what iteration number we are on.
-   * @param initialAdjVal f(x_0) + r(x_0), used for checking convergence
-   * @param history any information needed by the optimizer to do updates.
-   * @param searchFailed did the line search fail?
-   */
-  case class State[+T, +ConvergenceInfo, +History](x: T,
-                                                   value: Double,
-                                                   grad: T,
-                                                   adjustedValue: Double,
-                                                   adjustedGradient: T,
-                                                   iter: Int,
-                                                   initialAdjVal: Double,
-                                                   history: History,
-                                                   convergenceInfo: ConvergenceInfo,
-                                                   momentum: T,
-                                                   searchFailed: Boolean = false) {
-  }
-
-  trait ConvergenceCheck[T] {
-    type Info
-    def initialInfo: Info
-    def apply(state: State[T, _, _], info: Info): Option[ConvergenceReason]
-    def update(newX: T,
-               newGrad: T,
-               newVal: Double,
-               oldState: State[T, _, _],
-               oldInfo: Info): Info
-    def ||(otherCheck: ConvergenceCheck[T]):
-    ConvergenceCheck[T] = orElse(otherCheck)
-
-    def orElse(other: ConvergenceCheck[T]):
-    ConvergenceCheck[T] = {
-      SequenceConvergenceCheck(asChecks ++ other.asChecks)
-    }
-
-    protected def asChecks:
-    IndexedSeq[ConvergenceCheck[T]] = IndexedSeq(this)
-  }
-
-  object ConvergenceCheck {
-    implicit def fromPartialFunction[T](pf: PartialFunction[State[T, _, _], ConvergenceReason]):
-    ConvergenceCheck[T] = new ConvergenceCheck[T] {
-      override type Info = Unit
-
-      def update(newX: T,
-                 newGrad: T,
-                 newVal: Double,
-                 oldState: State[T, _, _],
-                 oldInfo: Info):
-      Info = oldInfo
-
-      override def apply(state: State[T, _, _], info: Info):
-      Option[ConvergenceReason] = pf.lift(state)
-
-      override def initialInfo: Info = ()
-    }
-  }
-
-  case class SequenceConvergenceCheck[T](checks: IndexedSeq[ConvergenceCheck[T]])
-    extends ConvergenceCheck[T] {
-    type Info = IndexedSeq[ConvergenceCheck[T]#Info]
-
-    override def initialInfo: IndexedSeq[ConvergenceCheck[T]#Info] = checks.map(_.initialInfo)
-
-    override def update(newX: T,
-                        newGrad: T,
-                        newVal: Double,
-                        oldState: State[T, _, _],
-                        oldInfo: Info):
-    Info = {
-      require(oldInfo.length == checks.length)
-      (checks zip oldInfo).map { case (c, i) =>
-        c.update(newX,
-          newGrad,
-          newVal,
-          oldState,
-          i.asInstanceOf[c.Info]) }
-    }
-
-    override def apply(state: State[T, _, _],
-                       info: IndexedSeq[ConvergenceCheck[T]#Info]): Option[ConvergenceReason] = {
-      (checks zip info).iterator.flatMap { case (c, i) =>
-        c(state, i.asInstanceOf[c.Info])}.toStream.headOption
-    }
-  }
-
-
-  trait ConvergenceReason {
-    def reason: String
-  }
-  case object MaxIterations extends ConvergenceReason {
-    override def reason: String = "max iterations reached"
-  }
-
-  case object GradientConverged extends ConvergenceReason {
-    override def reason: String = "gradient converged"
-  }
-
-  def maxIterationsReached[T](maxIter: Int):
-  ConvergenceCheck[T] = ConvergenceCheck.fromPartialFunction {
-    case s: State[_, _, _] if (s.iter >= maxIter && maxIter >= 0) =>
-      MaxIterations
-  }
-
-  def gradientConverged[T](tolerance: Double, relative: Boolean = true)
-                          (implicit space: NormedModule[T, Double]): ConvergenceCheck[T] = {
-    import space.normImpl
-    ConvergenceCheck.fromPartialFunction[T] {
-      case s: State[T, _, _] if (norm(s.adjustedGradient) < tolerance
-        * (if (relative) s.adjustedValue else 1.0)) =>
-        GradientConverged
-    }
-  }
-
-
-  def defaultConvergenceCheckX[T](maxIter: Int, tolerance: Double,
-                                 relative: Boolean = true, fvalMemory: Int = 20)
-                                (implicit space: NormedModule[T, Double]): ConvergenceCheck[T] =
-    (
-      maxIterationsReached[T](maxIter) ||
-      gradientConverged[T](tolerance, relative)
-    )
-
-}
diff --git a/ml-accelerator/src/main/scala/breeze/optimize/LBFGSX.scala b/ml-accelerator/src/main/scala/breeze/optimize/LBFGSX.scala
deleted file mode 100644
index e0b99d7..0000000
--- a/ml-accelerator/src/main/scala/breeze/optimize/LBFGSX.scala
+++ /dev/null
@@ -1,110 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-
-package breeze.optimize
-
-/*
- Copyright 2009 David Hall, Daniel Ramage
-
- Licensed under the Apache License, Version 2.0 (the "License")
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
-*/
-
-import breeze.linalg._
-import breeze.math.MutableInnerProductModule
-import breeze.optimize.FirstOrderMinimizerX.ConvergenceCheck
-import breeze.util.SerializableLogging
-
-/**
- * Port of LBFGS to Scala.
- *
- * Special note for LBFGS:
- *  If you use it in published work, you must cite one of:
- *     * J. Nocedal. Updating  Quasi-Newton  Matrices  with  Limited  Storage
- *    (1980), Mathematics of Computation 35, pp. 773-782.
- *  * D.C. Liu and J. Nocedal. On the  Limited  mem  Method  for  Large
- *    Scale  Optimization  (1989),  Mathematical  Programming  B,  45,  3,
- *    pp. 503-528.
- *
- * @param m: The memory of the search. 3 to 7 is usually sufficient.
- */
-class LBFGSX[T](convergenceCheck: ConvergenceCheck[T], m: Int)
-               (implicit space: MutableInnerProductModule[T, Double]) extends
-  FirstOrderMinimizerX[T, DiffFunction[T]](convergenceCheck) with SerializableLogging {
-
-  def this(maxIter: Int = -1, m: Int = 7, tolerance: Double = 1E-9)
-          (implicit space: MutableInnerProductModule[T, Double]) =
-    this(FirstOrderMinimizerX.defaultConvergenceCheckX(maxIter, tolerance), m )
-  import space._
-  require(m > 0)
-
-  type History = LBFGSX.ApproximateInverseHessianX[T]
-
-  override protected def adjustFunction(f: DiffFunction[T]): DiffFunction[T] = f.cached
-
-  def takeStep(state: State, dir: T, stepSize: Double): T = state.x + dir * stepSize
-  protected def initialHistory(f: DiffFunction[T], x: T):
-  History = new LBFGSX.ApproximateInverseHessianX(m)
-  protected def chooseDescentDirection(state: State, fn: DiffFunction[T]): T = {
-    state.history * state.grad
-  }
-
-  protected def updateHistory(newX: T, newGrad: T, newVal: Double,
-                              f: DiffFunction[T], oldState: State): History = {
-    oldState.history.updated(newX - oldState.x, newGrad -:- oldState.grad)
-  }
-
-
-  override def updateTheta(f: DiffFunction[T], state: State): (T, T) = {
-    val adjustedFun = adjustFunction(f)
-    val dir = chooseDescentDirection(state, adjustedFun)
-    val currentMomentum = ACC
-      .updateMomentum(state.momentum, dir, inertiaCoefficient, momentumUpdateCoefficient)(space)
-    val stepSize = 1.0
-    logger.info(f"Step Size: $stepSize%.4g")
-    val x = takeStep(state, currentMomentum, stepSize)
-    (x, currentMomentum)
-  }
-}
-
-object LBFGSX {
-  case class ApproximateInverseHessianX[T](m: Int,
-    private[LBFGSX] val memStep: IndexedSeq[T] = IndexedSeq.empty,
-    private[LBFGSX] val memGradDelta: IndexedSeq[T] = IndexedSeq.empty)
-   (implicit space: MutableInnerProductModule[T, Double])
-    extends NumericOps[ApproximateInverseHessianX[T]] {
-
-    import space._
-
-    def repr: ApproximateInverseHessianX[T] = this
-
-    def updated(step: T, gradDelta: T): ApproximateInverseHessianX[T] = {
-      val (a, b) = ACC.update(step, gradDelta, this.memStep, this.memGradDelta, m)(space)
-      new ApproximateInverseHessianX(m, a, b)
-    }
-
-
-    def historyLength: Int = memStep.length
-
-    def *(grad: T): T = {
-      val a = ACC.getInverseOfHessian(grad, this.memStep, this.memGradDelta, m, historyLength)
-      a
-    }
-  }
-
-}
-
diff --git a/ml-accelerator/src/main/scala/breeze/optimize/OWLQNX.scala b/ml-accelerator/src/main/scala/breeze/optimize/OWLQNX.scala
deleted file mode 100644
index 8e55560..0000000
--- a/ml-accelerator/src/main/scala/breeze/optimize/OWLQNX.scala
+++ /dev/null
@@ -1,97 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-
-package breeze.optimize
-
-import breeze.math._
-import breeze.numerics._
-import breeze.util._
-
-/**
- * Implements the Orthant-wise Limited Memory QuasiNewton method,
- * which is a variant of LBFGS that handles L1 regularization.
- *
- * Paper is Andrew and Gao (2007) Scalable Training of L1-Regularized Log-Linear Models
- *
- * @author dlwh
- */
-class OWLQNX[K, T](maxIter: Int, m: Int, l1reg: K => Double, tolerance: Double)
-                  (implicit space: MutableEnumeratedCoordinateField[T, K, Double])
-  extends LBFGSX[T](maxIter, m, tolerance = tolerance) with SerializableLogging {
-
-  def this(maxIter: Int, m: Int, l1reg: K => Double)
-          (implicit space: MutableEnumeratedCoordinateField[T, K, Double])
-            = this(maxIter, m, l1reg, 1E-8)
-
-  def this(maxIter: Int, m: Int, l1reg: Double, tolerance: Double = 1E-8)
-          (implicit space: MutableEnumeratedCoordinateField[T, K, Double])
-            = this(maxIter, m, (_: K) => l1reg, tolerance)
-
-  def this(maxIter: Int, m: Int, l1reg: Double)
-          (implicit space: MutableEnumeratedCoordinateField[T, K, Double])
-            = this(maxIter, m, (_: K) => l1reg, 1E-8)
-
-  def this(maxIter: Int, m: Int)(implicit space: MutableEnumeratedCoordinateField[T, K, Double])
-            = this(maxIter, m, (_: K) => 1.0, 1E-8)
-
-  require(m > 0)
-
-  import space._
-
-  override def chooseDescentDirection(state: State, fn: DiffFunction[T]): T = {
-    val descentDir = super.chooseDescentDirection(state.copy(grad = state.adjustedGradient), fn)
-    val correctedDir = space.zipMapValues.map(descentDir, state.adjustedGradient, { case (d, g)
-    => if (d * g < 0) d else 0.0 })
-
-    correctedDir
-  }
-
-
-  // projects x to be on the same orthant as y
-  // this basically requires that x'_i = x_i if sign(x_i) == sign(y_i), and 0 otherwise.
-
-  override def takeStep(state: State, dir: T, stepSize: Double): T = {
-    val stepped = state.x + dir * stepSize
-    val orthant = computeOrthant(state.x, state.adjustedGradient)
-    space.zipMapValues.map(stepped, orthant, { case (v, ov) =>
-      v * I(math.signum(v) == math.signum(ov))
-    })
-  }
-
-  // Adds in the regularization stuff to the gradient
-  override def adjust(newX: T, newGrad: T, newVal: Double): (Double, T) = {
-    var adjValue = newVal
-    val res = space.zipMapKeyValues.mapActive(newX, newGrad, {case (i, xv, v) =>
-      val l1regValue = l1reg(i)
-      require(l1regValue >= 0.0)
-
-      if(l1regValue == 0.0) {
-        v
-      } else {
-        adjValue += Math.abs(l1regValue * xv)
-        xv match {
-          case 0.0 =>
-            val delta_+ = v + l1regValue
-            val delta_- = v - l1regValue
-            if (delta_- > 0) delta_- else if (delta_+ < 0) delta_+ else 0.0
-          case _ => v + math.signum(xv) * l1regValue
-        }
-      }
-    })
-    adjValue -> res
-  }
-
-  private def computeOrthant(x: T, grad: T) = {
-    val orth = space.zipMapValues.map(x, grad, {case (v, gv) =>
-      if (v != 0) math.signum(v)
-      else math.signum(-gv)
-    })
-    orth
-  }
-
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
deleted file mode 100644
index 53ee5ee..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.classification
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.DecisionForest
-import org.apache.spark.ml.util._
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.Dataset
-
-
-/**
- * Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning)
- * for classification.
- * It supports both binary and multiclass labels, as well as both continuous and categorical
- * features.
- */
-@Since("1.4.0")
-class DecisionTreeClassifier @Since("1.4.0") (
-    @Since("1.4.0") override val uid: String)
-  extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
-  with DecisionTreeClassifierParams with DefaultParamsWritable {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("dtc"))
-
-  // Override parameter setters from parent trait for Java API compatibility.
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be at least 1.
-   * (default = 10)
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setImpurity(value: String): this.type = set(impurity, value)
-
-  /** @group setParam */
-  @Since("1.6.0")
-  override def setSeed(value: Long): this.type = set(seed, value)
-
-  override protected def train(dataset: Dataset[_]): DecisionTreeClassificationModel = {
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val numClasses: Int = getNumClasses(dataset)
-
-    if (isDefined(thresholds)) {
-      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
-        ".train() called with non-matching numClasses and thresholds.length." +
-        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
-    }
-
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
-    val strategy = getOldStrategy(categoricalFeatures, numClasses)
-
-    val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(params: _*)
-
-    val trees = DecisionForest.run(oldDataset, strategy, numTrees = 1,
-      featureSubsetStrategy = "all",
-      seed = $(seed), instr = Some(instr), parentUID = Some(uid))
-
-    val m = trees.head.asInstanceOf[DecisionTreeClassificationModel]
-    instr.logSuccess(m)
-    m
-  }
-
-  /** (private[ml]) Train a decision tree on an RDD */
-  private[ml] def train(data: RDD[LabeledPoint],
-      oldStrategy: OldStrategy): DecisionTreeClassificationModel = {
-    val instr = Instrumentation.create(this, data)
-    instr.logParams(params: _*)
-
-    val trees = DecisionForest.run(data, oldStrategy, numTrees = 1,
-      featureSubsetStrategy = "all",
-      seed = 0L, instr = Some(instr), parentUID = Some(uid))
-
-    val m = trees.head.asInstanceOf[DecisionTreeClassificationModel]
-    instr.logSuccess(m)
-    m
-  }
-
-  /** (private[ml]) Create a Strategy instance to use with the old API. */
-  private[ml] def getOldStrategy(
-      categoricalFeatures: Map[Int, Int],
-      numClasses: Int): OldStrategy = {
-    super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity,
-      subsamplingRate = 1.0)
-  }
-
-  @Since("1.4.1")
-  override def copy(extra: ParamMap): DecisionTreeClassifier = defaultCopy(extra)
-}
-
-@Since("1.4.0")
-object DecisionTreeClassifier extends DefaultParamsReadable[DecisionTreeClassifier] {
-  /** Accessor for supported impurities: entropy, gini */
-  @Since("1.4.0")
-  final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
-
-  @Since("2.0.0")
-  override def load(path: String): DecisionTreeClassifier = super.load(path)
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
deleted file mode 100644
index 9b62352..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ /dev/null
@@ -1,423 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.classification
-
-import com.github.fommil.netlib.BLAS.{getInstance => blas}
-import org.json4s.{DefaultFormats, JObject}
-import org.json4s.JsonDSL._
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.GradientBoostedTrees
-import org.apache.spark.ml.util._
-import org.apache.spark.ml.util.DefaultParamsReader.Metadata
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.sql.functions._
-
-/**
- * Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting)
- * learning algorithm for classification.
- * It supports binary labels, as well as both continuous and categorical features.
- *
- * The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.
- *
- * Notes on Gradient Boosting vs. TreeBoost:
- *  - This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
- *  - Both algorithms learn tree ensembles by minimizing loss functions.
- *  - TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes
- *    based on the loss function, whereas the original gradient boosting method does not.
- *  - We expect to implement TreeBoost in the future:
- *    [https://issues.apache.org/jira/browse/SPARK-4240]
- *
- * @note Multiclass labels are not currently supported.
- */
-@Since("1.4.0")
-class GBTClassifier @Since("1.4.0") (
-    @Since("1.4.0") override val uid: String)
-  extends ProbabilisticClassifier[Vector, GBTClassifier, GBTClassificationModel]
-  with GBTClassifierParams with DefaultParamsWritable with Logging {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("gbtc"))
-
-  // Override parameter setters from parent trait for Java API compatibility.
-
-  // Parameters from TreeClassifierParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be at least 1.
-   * (default = 10)
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /**
-   * The impurity setting is ignored for GBT models.
-   * Individual trees are built using impurity "Variance."
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setImpurity(value: String): this.type = {
-    logWarning("GBTClassifier.setImpurity should NOT be used")
-    this
-  }
-
-  // Parameters from TreeEnsembleParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSeed(value: Long): this.type = set(seed, value)
-
-  // Parameters from GBTParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxIter(value: Int): this.type = set(maxIter, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setStepSize(value: Double): this.type = set(stepSize, value)
-
-  /** @group setParam */
-  @Since("2.3.0")
-  override def setFeatureSubsetStrategy(value: String): this.type =
-    set(featureSubsetStrategy, value)
-
-  // Parameters from GBTClassifierParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setLossType(value: String): this.type = set(lossType, value)
-
-  override protected def train(dataset: Dataset[_]): GBTClassificationModel = {
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    // We copy and modify this from Classifier.extractLabeledPoints since GBT only supports
-    // 2 classes now.  This lets us provide a more precise error message.
-    val oldDataset: RDD[LabeledPoint] =
-      dataset.select(col($(labelCol)), col($(featuresCol))).rdd.map {
-        case Row(label: Double, features: Vector) =>
-          require(label == 0 || label == 1, s"GBTClassifier was given" +
-            s" dataset with invalid label $label.  Labels must be in {0,1}; note that" +
-            s" GBTClassifier currently only supports binary classification.")
-          LabeledPoint(label, features)
-      }
-    val numFeatures = oldDataset.first().features.size
-    val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification)
-
-    val numClasses = 2
-    if (isDefined(thresholds)) {
-      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
-        ".train() called with non-matching numClasses and thresholds.length." +
-        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
-    }
-
-    val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, impurity, lossType,
-      maxDepth, maxBins, maxIter, maxMemoryInMB, minInfoGain, minInstancesPerNode,
-      seed, stepSize, subsamplingRate, cacheNodeIds, checkpointInterval, featureSubsetStrategy)
-    instr.logNumFeatures(numFeatures)
-    instr.logNumClasses(numClasses)
-
-    val (doUseAcc, setUseAccFlag) = super.getDoUseAcc
-    val (baseLearners, learnerWeights) = if (setUseAccFlag) {
-      GradientBoostedTrees.run(oldDataset, boostingStrategy,
-        $(seed), $(featureSubsetStrategy), doUseAcc)
-    } else {
-      GradientBoostedTrees.run(oldDataset, boostingStrategy,
-        $(seed), $(featureSubsetStrategy))
-    }
-
-    val m = new GBTClassificationModel(uid, baseLearners, learnerWeights, numFeatures)
-    instr.logSuccess(m)
-    m
-  }
-
-  @Since("1.4.1")
-  override def copy(extra: ParamMap): GBTClassifier = defaultCopy(extra)
-}
-
-@Since("1.4.0")
-object GBTClassifier extends DefaultParamsReadable[GBTClassifier] {
-
-  /** Accessor for supported loss settings: logistic */
-  @Since("1.4.0")
-  final val supportedLossTypes: Array[String] = GBTClassifierParams.supportedLossTypes
-
-  @Since("2.0.0")
-  override def load(path: String): GBTClassifier = super.load(path)
-}
-
-/**
- * Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting)
- * model for classification.
- * It supports binary labels, as well as both continuous and categorical features.
- *
- * @param _trees  Decision trees in the ensemble.
- * @param _treeWeights  Weights for the decision trees in the ensemble.
- *
- * @note Multiclass labels are not currently supported.
- */
-@Since("1.6.0")
-class GBTClassificationModel private[ml](
-    @Since("1.6.0") override val uid: String,
-    private val _trees: Array[DecisionTreeRegressionModel],
-    private val _treeWeights: Array[Double],
-    @Since("1.6.0") override val numFeatures: Int,
-    @Since("2.2.0") override val numClasses: Int)
-  extends ProbabilisticClassificationModel[Vector, GBTClassificationModel]
-  with GBTClassifierParams with TreeEnsembleModel[DecisionTreeRegressionModel]
-  with MLWritable with Serializable {
-
-  require(_trees.nonEmpty, "GBTClassificationModel requires at least 1 tree.")
-  require(_trees.length == _treeWeights.length, "GBTClassificationModel given trees, treeWeights" +
-    s" of non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
-
-  /**
-   * Construct a GBTClassificationModel
-   *
-   * @param _trees  Decision trees in the ensemble.
-   * @param _treeWeights  Weights for the decision trees in the ensemble.
-   * @param numFeatures  The number of features.
-   */
-  private[ml] def this(
-      uid: String,
-      _trees: Array[DecisionTreeRegressionModel],
-      _treeWeights: Array[Double],
-      numFeatures: Int) =
-  this(uid, _trees, _treeWeights, numFeatures, 2)
-
-  /**
-   * Construct a GBTClassificationModel
-   *
-   * @param _trees  Decision trees in the ensemble.
-   * @param _treeWeights  Weights for the decision trees in the ensemble.
-   */
-  @Since("1.6.0")
-  def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) =
-    this(uid, _trees, _treeWeights, -1, 2)
-
-  @Since("1.4.0")
-  override def trees: Array[DecisionTreeRegressionModel] = _trees
-
-  /**
-   * Number of trees in ensemble
-   */
-  @Since("2.0.0")
-  val getNumTrees: Int = trees.length
-
-  @Since("1.4.0")
-  override def treeWeights: Array[Double] = _treeWeights
-
-  override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
-    val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
-    }
-    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
-  }
-
-  override protected def predict(features: Vector): Double = {
-    // If thresholds defined, use predictRaw to get probabilities, otherwise use optimization
-    if (isDefined(thresholds)) {
-      super.predict(features)
-    } else {
-      if (margin(features) > 0.0) 1.0 else 0.0
-    }
-  }
-
-  override protected def predictRaw(features: Vector): Vector = {
-    val prediction: Double = margin(features)
-    Vectors.dense(Array(-prediction, prediction))
-  }
-
-  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
-    rawPrediction match {
-      case dv: DenseVector =>
-        dv.values(0) = loss.computeProbability(dv.values(0))
-        dv.values(1) = 1.0 - dv.values(0)
-        dv
-      case sv: SparseVector =>
-        throw new RuntimeException("Unexpected error in GBTClassificationModel:" +
-          " raw2probabilityInPlace encountered SparseVector")
-    }
-  }
-
-  /** Number of trees in ensemble */
-  val numTrees: Int = trees.length
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): GBTClassificationModel = {
-    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights, numFeatures, numClasses),
-      extra).setParent(parent)
-  }
-
-  @Since("1.4.0")
-  override def toString: String = {
-    s"GBTClassificationModel (uid=$uid) with $numTrees trees"
-  }
-
-  /**
-   * Estimate of the importance of each feature.
-   *
-   * Each feature's importance is the average of its importance across all trees in the ensemble
-   * The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
-   * (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
-   * and follows the implementation from scikit-learn.
-
-   * See `DecisionTreeClassificationModel.featureImportances`
-   */
-  @Since("2.0.0")
-  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
-
-  /** Raw prediction for the positive class. */
-  private def margin(features: Vector): Double = {
-    val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
-    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
-  }
-
-  /** (private[ml]) Convert to a model in the old API */
-  private[ml] def toOld: OldGBTModel = {
-    new OldGBTModel(OldAlgo.Classification, _trees.map(_.toOld), _treeWeights)
-  }
-
-  // hard coded loss, which is not meant to be changed in the model
-  private val loss = getOldLossType
-
-  @Since("2.0.0")
-  override def write: MLWriter = new GBTClassificationModel.GBTClassificationModelWriter(this)
-}
-
-@Since("2.0.0")
-object GBTClassificationModel extends MLReadable[GBTClassificationModel] {
-
-  private val numFeaturesKey: String = "numFeatures"
-  private val numTreesKey: String = "numTrees"
-
-  @Since("2.0.0")
-  override def read: MLReader[GBTClassificationModel] = new GBTClassificationModelReader
-
-  @Since("2.0.0")
-  override def load(path: String): GBTClassificationModel = super.load(path)
-
-  private[GBTClassificationModel]
-  class GBTClassificationModelWriter(instance: GBTClassificationModel) extends MLWriter {
-
-    override protected def saveImpl(path: String): Unit = {
-
-      val extraMetadata: JObject = Map(
-        numFeaturesKey -> instance.numFeatures,
-        numTreesKey -> instance.getNumTrees)
-      EnsembleModelReadWrite.saveImpl(instance, path, sparkSession, extraMetadata)
-    }
-  }
-
-  private class GBTClassificationModelReader extends MLReader[GBTClassificationModel] {
-
-    /** Checked against metadata when loading model */
-    private val className = classOf[GBTClassificationModel].getName
-    private val treeClassName = classOf[DecisionTreeRegressionModel].getName
-
-    override def load(path: String): GBTClassificationModel = {
-      implicit val format = DefaultFormats
-      val (metadata: Metadata, treesData: Array[(Metadata, Node)], treeWeights: Array[Double]) =
-        EnsembleModelReadWrite.loadImpl(path, sparkSession, className, treeClassName)
-      val numFeatures = (metadata.metadata \ numFeaturesKey).extract[Int]
-      val numTrees = (metadata.metadata \ numTreesKey).extract[Int]
-
-      val trees: Array[DecisionTreeRegressionModel] = treesData.map {
-        case (treeMetadata, root) =>
-          val tree =
-            new DecisionTreeRegressionModel(treeMetadata.uid, root, numFeatures)
-          DefaultParamsReader.getAndSetParams(tree, treeMetadata)
-          tree
-      }
-      require(numTrees == trees.length, s"GBTClassificationModel.load expected $numTrees" +
-        s" trees based on metadata but found ${trees.length} trees.")
-      val model = new GBTClassificationModel(metadata.uid,
-        trees, treeWeights, numFeatures)
-      DefaultParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-
-  /** Convert a model from the old API */
-  private[ml] def fromOld(
-      oldModel: OldGBTModel,
-      parent: GBTClassifier,
-      categoricalFeatures: Map[Int, Int],
-      numFeatures: Int = -1,
-      numClasses: Int = 2): GBTClassificationModel = {
-    require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" +
-      s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).")
-    val newTrees = oldModel.trees.map { tree =>
-      // parent for each tree is null since there is no good way to set this.
-      DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
-    }
-    val uid = if (parent != null) parent.uid else Identifiable.randomUID("gbtc")
-    new GBTClassificationModel(uid, newTrees, oldModel.treeWeights, numFeatures, numClasses)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
deleted file mode 100644
index 6689c08..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
+++ /dev/null
@@ -1,304 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.classification
-
-import scala.collection.mutable
-
-import breeze.linalg.{DenseVector => BDV}
-import breeze.optimize.{CachedDiffFunction, OWLQNX => BreezeOWLQN}
-
-import org.apache.spark.SparkException
-import org.apache.spark.annotation.{Experimental, Since}
-import org.apache.spark.ml.StaticUtils
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg._
-import org.apache.spark.ml.optim.aggregator.HingeAggregatorX
-import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunctionX}
-import org.apache.spark.ml.param._
-import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.VectorImplicits._
-import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions.{col, lit}
-
-
-
-/**
- * :: Experimental ::
- *
- * <a href = "https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM">
- *   Linear SVM Classifier</a>
- *
- * This binary classifier optimizes the Hinge Loss using the OWLQN optimizer.
- * Only supports L2 regularization currently.
- *
- */
-@Since("2.2.0")
-@Experimental
-class LinearSVC @Since("2.2.0") (
-                                            @Since("2.2.0") override val uid: String)
-  extends Classifier[Vector, LinearSVC, LinearSVCModel]
-    with LinearSVCParams with DefaultParamsWritable {
-
-  @Since("2.2.0")
-  def this() = this(Identifiable.randomUID("linearsvc"))
-
-  /**
-   * Set the regularization parameter.
-   * Default is 0.0.
-   *
-   * @group setParam
-   */
-
-
-  var ic = 0.5
-  var iters = -1
-  def setIc(a: Double): Unit = {
-    this.ic = a
-  }
-
-  @Since("2.2.0")
-  def setRegParam(value: Double): this.type = set(regParam, value)
-  setDefault(regParam -> 0.0)
-
-  /**
-   * Set the maximum number of iterations.
-   * Default is 100.
-   *
-   * @group setParam
-   */
-  @Since("2.2.0")
-  def setMaxIter(value: Int): this.type = set(maxIter, value)
-  setDefault(maxIter -> 100)
-
-  /**
-   * Whether to fit an intercept term.
-   * Default is true.
-   *
-   * @group setParam
-   */
-  @Since("2.2.0")
-  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
-  setDefault(fitIntercept -> true)
-
-  /**
-   * Set the convergence tolerance of iterations.
-   * Smaller values will lead to higher accuracy at the cost of more iterations.
-   * Default is 1E-6.
-   *
-   * @group setParam
-   */
-  @Since("2.2.0")
-  def setTol(value: Double): this.type = set(tol, value)
-  setDefault(tol -> 0.0)
-
-  /**
-   * Whether to standardize the training features before fitting the model.
-   * Default is true.
-   *
-   * @group setParam
-   */
-  @Since("2.2.0")
-  def setStandardization(value: Boolean): this.type = set(standardization, value)
-  setDefault(standardization -> true)
-
-  /**
-   * Set the value of param [[weightCol]].
-   * If this is not set or empty, we treat all instance weights as 1.0.
-   * Default is not set, so all instances have weight one.
-   *
-   * @group setParam
-   */
-  @Since("2.2.0")
-  def setWeightCol(value: String): this.type = set(weightCol, value)
-
-  /**
-   * Set threshold in binary classification.
-   *
-   * @group setParam
-   */
-  @Since("2.2.0")
-  def setThreshold(value: Double): this.type = set(threshold, value)
-  setDefault(threshold -> 0.0)
-
-  /**
-   * Suggested depth for treeAggregate (greater than or equal to 2).
-   * If the dimensions of features or the number of partitions are large,
-   * this param could be adjusted to a larger size.
-   * Default is 2.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.2.0")
-  def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
-  setDefault(aggregationDepth -> 2)
-
-  @Since("2.2.0")
-  override def copy(extra: ParamMap): LinearSVC = defaultCopy(extra)
-
-  override protected def train(dataset: Dataset[_]): LinearSVCModel = {
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances: RDD[Instance] =
-      dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          Instance(label, weight, features)
-      }
-
-    val instr = Instrumentation.create(this, instances)
-    instr.logParams(regParam, maxIter, fitIntercept, tol, standardization, threshold,
-      aggregationDepth)
-
-    val (summarizer, labelSummarizer) = {
-      val seqOp = (c: (MultivariateOnlineSummarizer, MultiClassSummarizer),
-                   instance: Instance) => {
-        (c._1.add(instance.features, instance.weight),
-          c._2.add(instance.label, instance.weight + StaticUtils.ZERO_DOUBLE))
-      }
-
-      val combOp = (c1: (MultivariateOnlineSummarizer, MultiClassSummarizer),
-                    c2: (MultivariateOnlineSummarizer, MultiClassSummarizer)) =>
-        (c1._1.merge(c2._1), c1._2.merge(c2._2))
-
-      instances.treeAggregate(
-        (new MultivariateOnlineSummarizer, new MultiClassSummarizer)
-      )(seqOp, combOp, $(aggregationDepth))
-    }
-
-    val histogram = labelSummarizer.histogram
-    val numInvalid = labelSummarizer.countInvalid
-    val numFeatures = summarizer.mean.size
-    val numFeaturesPlusIntercept = if (getFitIntercept) numFeatures + 1 else numFeatures
-
-    val numClasses = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
-      case Some(n: Int) =>
-        require(n >= histogram.length, s"Specified number of classes $n was " +
-          s"less than the number of unique labels ${histogram.length}.")
-        n
-      case None => histogram.length
-    }
-    require(numClasses == 2, s"LinearSVC only supports binary classification." +
-      s" $numClasses classes detected in $labelCol")
-    instr.logNumClasses(numClasses)
-    instr.logNumFeatures(numFeatures)
-
-    val (coefficientVector, interceptVector, objectiveHistory) = {
-      if (numInvalid != 0) {
-        val msg = s"Classification labels should be in [0 to ${numClasses - 1}]. " +
-          s"Found $numInvalid invalid labels."
-        logError(msg)
-        throw new SparkException(msg)
-      }
-
-      val featuresStd = summarizer.variance.toArray.map(math.sqrt)
-      val getFeaturesStd = (j: Int) => featuresStd(j)
-      val regParamL2 = $(regParam)
-      val bcFeaturesStd = instances.context.broadcast(featuresStd.map{t => if (t!=0.0)1/t else 0.0})
-      val regularization = if (regParamL2 != 0.0) {
-        val shouldApply = (idx: Int) => idx >= 0 && idx < numFeatures
-        Some(new L2Regularization(regParamL2, shouldApply,
-          if ($(standardization)) None else Some(getFeaturesStd)))
-      } else {
-        None
-      }
-
-      val getAggregatorFunc = new HingeAggregatorX(bcFeaturesStd, $(fitIntercept))(_)
-      val costFun = new RDDLossFunctionX(instances, getAggregatorFunc, regularization,
-        $(aggregationDepth))
-
-      def regParamL1Fun = (index: Int) => 0D
-      val optimizer = new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol))
-
-      var u = ic
-      try {
-        u = instances.sparkContext.getConf
-          .getDouble("spark.boostkit.LinearSVC.inertiaCoefficient", ic)
-        if (u < 0.0) {
-          throw new Exception
-        }
-      }
-      catch {
-        case x: Exception =>
-          throw new Exception("'spark.boostkit.LinearSVC.inertiaCoefficient' value is invalid")
-      }
-      this.ic = u
-
-      optimizer.setInertiaCoefficient(ic)
-      val initialCoefWithIntercept = Vectors.zeros(numFeaturesPlusIntercept)
-
-      val states = optimizer.iterations(new CachedDiffFunction(costFun),
-        initialCoefWithIntercept.asBreeze.toDenseVector)
-
-      val scaledObjectiveHistory = mutable.ArrayBuilder.make[Double]
-      var state: optimizer.State = null
-      while (states.hasNext) {
-        state = states.next()
-        scaledObjectiveHistory += state.adjustedValue
-        iters += 1
-      }
-
-      bcFeaturesStd.destroy(blocking = false)
-      if (state == null) {
-        val msg = s"${optimizer.getClass.getName} failed."
-        logError(msg)
-        throw new SparkException(msg)
-      }
-
-      /*
-         The coefficients are trained in the scaled space; we're converting them back to
-         the original space.
-         Note that the intercept in scaled space and original space is the same;
-         as a result, no scaling is needed.
-       */
-      val rawCoefficients = state.x.toArray
-      val coefficientArray = Array.tabulate(numFeatures) { i =>
-        if (featuresStd(i) != 0.0) {
-          rawCoefficients(i) / featuresStd(i)
-        } else {
-          0.0
-        }
-      }
-
-      val intercept = if ($(fitIntercept)) {
-        rawCoefficients(numFeaturesPlusIntercept - 1)
-      } else {
-        0.0
-      }
-      (Vectors.dense(coefficientArray), intercept, scaledObjectiveHistory.result())
-    }
-
-    val model = copyValues(new LinearSVCModel(uid, coefficientVector, interceptVector))
-    instr.logSuccess(model)
-    model
-  }
-
-  def getIters: Int = iters
-}
-
-@Since("2.2.0")
-object LinearSVC extends DefaultParamsReadable[LinearSVC] {
-
-  @Since("2.2.0")
-  override def load(path: String): LinearSVC = super.load(path)
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
deleted file mode 100644
index a47a709..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ /dev/null
@@ -1,703 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.classification
-
-import java.util.Locale
-
-import scala.collection.mutable
-
-import breeze.linalg.{DenseVector => BDV}
-import breeze.optimize.{CachedDiffFunction, LBFGSL, OWLQNL}
-
-import org.apache.spark.SparkException
-import org.apache.spark.annotation.Since
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.StaticUtils
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg._
-import org.apache.spark.ml.optim.aggregator.LogisticAggregatorX
-import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunctionX}
-import org.apache.spark.ml.param._
-import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.VectorImplicits._
-import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions.{col, lit}
-import org.apache.spark.storage.StorageLevel
-
-/**
- * Logistic regression. Supports:
- *  - Multinomial logistic (softmax) regression.
- *  - Binomial logistic regression.
- *
- * This class supports fitting traditional logistic regression model by LBFGS/OWLQN and
- * bound (box) constrained logistic regression model by LBFGSB.
- */
-@Since("1.2.0")
-class LogisticRegression @Since("1.2.0") (
-    @Since("1.4.0") override val uid: String)
-  extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel]
-  with LogisticRegressionParams with DefaultParamsWritable with Logging {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("logreg"))
-
-  /**
-   * Set the regularization parameter.
-   * Default is 0.0.
-   *
-   * @group setParam
-   */
-  @Since("1.2.0")
-  def setRegParam(value: Double): this.type = set(regParam, value)
-  setDefault(regParam -> 0.0)
-
-  /**
-   * Set the ElasticNet mixing parameter.
-   * For alpha = 0, the penalty is an L2 penalty.
-   * For alpha = 1, it is an L1 penalty.
-   * For alpha in (0,1), the penalty is a combination of L1 and L2.
-   * Default is 0.0 which is an L2 penalty.
-   *
-   * Note: Fitting under bound constrained optimization only supports L2 regularization,
-   * so throws exception if this param is non-zero value.
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
-  setDefault(elasticNetParam -> 0.0)
-
-  /**
-   * Set the maximum number of iterations.
-   * Default is 100.
-   *
-   * @group setParam
-   */
-  @Since("1.2.0")
-  def setMaxIter(value: Int): this.type = set(maxIter, value)
-  setDefault(maxIter -> 100)
-
-  /**
-   * Set the convergence tolerance of iterations.
-   * Smaller value will lead to higher accuracy at the cost of more iterations.
-   * Default is 1E-6.
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  def setTol(value: Double): this.type = set(tol, value)
-  setDefault(tol -> 1E-6)
-
-  /**
-   * Whether to fit an intercept term.
-   * Default is true.
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
-  setDefault(fitIntercept -> true)
-
-  /**
-   * Sets the value of param [[family]].
-   * Default is "auto".
-   *
-   * @group setParam
-   */
-  @Since("2.1.0")
-  def setFamily(value: String): this.type = set(family, value)
-  setDefault(family -> "auto")
-
-  /**
-   * Whether to standardize the training features before fitting the model.
-   * The coefficients of models will be always returned on the original scale,
-   * so it will be transparent for users. Note that with/without standardization,
-   * the models should be always converged to the same solution when no regularization
-   * is applied. In R's GLMNET package, the default behavior is true as well.
-   * Default is true.
-   *
-   * @group setParam
-   */
-  @Since("1.5.0")
-  def setStandardization(value: Boolean): this.type = set(standardization, value)
-  setDefault(standardization -> true)
-
-  @Since("1.5.0")
-  override def setThreshold(value: Double): this.type = super.setThreshold(value)
-  setDefault(threshold -> 0.5)
-
-  @Since("1.5.0")
-  override def getThreshold: Double = super.getThreshold
-
-  /**
-   * Sets the value of param [[weightCol]].
-   * If this is not set or empty, we treat all instance weights as 1.0.
-   * Default is not set, so all instances have weight one.
-   *
-   * @group setParam
-   */
-  @Since("1.6.0")
-  def setWeightCol(value: String): this.type = set(weightCol, value)
-
-  @Since("1.5.0")
-  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
-
-  @Since("1.5.0")
-  override def getThresholds: Array[Double] = super.getThresholds
-
-  /**
-   * Suggested depth for treeAggregate (greater than or equal to 2).
-   * If the dimensions of features or the number of partitions are large,
-   * this param could be adjusted to a larger size.
-   * Default is 2.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.1.0")
-  def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
-  setDefault(aggregationDepth -> 2)
-
-  /**
-   * Set the lower bounds on coefficients if fitting under bound constrained optimization.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.2.0")
-  def setLowerBoundsOnCoefficients(value: Matrix): this.type = set(lowerBoundsOnCoefficients, value)
-
-  /**
-   * Set the upper bounds on coefficients if fitting under bound constrained optimization.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.2.0")
-  def setUpperBoundsOnCoefficients(value: Matrix): this.type = set(upperBoundsOnCoefficients, value)
-
-  /**
-   * Set the lower bounds on intercepts if fitting under bound constrained optimization.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.2.0")
-  def setLowerBoundsOnIntercepts(value: Vector): this.type = set(lowerBoundsOnIntercepts, value)
-
-  /**
-   * Set the upper bounds on intercepts if fitting under bound constrained optimization.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.2.0")
-  def setUpperBoundsOnIntercepts(value: Vector): this.type = set(upperBoundsOnIntercepts, value)
-
-  private def assertBoundConstrainedOptimizationParamsValid(
-      numCoefficientSets: Int,
-      numFeatures: Int): Unit = {
-    if (isSet(lowerBoundsOnCoefficients)) {
-      require($(lowerBoundsOnCoefficients).numRows == numCoefficientSets &&
-        $(lowerBoundsOnCoefficients).numCols == numFeatures,
-        "The shape of LowerBoundsOnCoefficients must be compatible with (1, number of features) " +
-          "for binomial regression, or (number of classes, number of features) for multinomial " +
-          "regression, but found: " +
-          s"(${getLowerBoundsOnCoefficients.numRows}, ${getLowerBoundsOnCoefficients.numCols}).")
-    }
-    if (isSet(upperBoundsOnCoefficients)) {
-      require($(upperBoundsOnCoefficients).numRows == numCoefficientSets &&
-        $(upperBoundsOnCoefficients).numCols == numFeatures,
-        "The shape of upperBoundsOnCoefficients must be compatible with (1, number of features) " +
-          "for binomial regression, or (number of classes, number of features) for multinomial " +
-          "regression, but found: " +
-          s"(${getUpperBoundsOnCoefficients.numRows}, ${getUpperBoundsOnCoefficients.numCols}).")
-    }
-    if (isSet(lowerBoundsOnIntercepts)) {
-      require($(lowerBoundsOnIntercepts).size == numCoefficientSets, "The size of " +
-        "lowerBoundsOnIntercepts must be equal to 1 for binomial regression, or the number of " +
-        s"classes for multinomial regression, but found: ${getLowerBoundsOnIntercepts.size}.")
-    }
-    if (isSet(upperBoundsOnIntercepts)) {
-      require($(upperBoundsOnIntercepts).size == numCoefficientSets, "The size of " +
-        "upperBoundsOnIntercepts must be equal to 1 for binomial regression, or the number of " +
-        s"classes for multinomial regression, but found: ${getUpperBoundsOnIntercepts.size}.")
-    }
-    if (isSet(lowerBoundsOnCoefficients) && isSet(upperBoundsOnCoefficients)) {
-      require($(lowerBoundsOnCoefficients).toArray.zip($(upperBoundsOnCoefficients).toArray)
-        .forall(x => x._1 <= x._2), "LowerBoundsOnCoefficients should always be " +
-        "less than or equal to upperBoundsOnCoefficients, but found: " +
-        s"lowerBoundsOnCoefficients = $getLowerBoundsOnCoefficients, " +
-        s"upperBoundsOnCoefficients = $getUpperBoundsOnCoefficients.")
-    }
-    if (isSet(lowerBoundsOnIntercepts) && isSet(upperBoundsOnIntercepts)) {
-      require($(lowerBoundsOnIntercepts).toArray.zip($(upperBoundsOnIntercepts).toArray)
-        .forall(x => x._1 <= x._2), "LowerBoundsOnIntercepts should always be " +
-        "less than or equal to upperBoundsOnIntercepts, but found: " +
-        s"lowerBoundsOnIntercepts = $getLowerBoundsOnIntercepts, " +
-        s"upperBoundsOnIntercepts = $getUpperBoundsOnIntercepts.")
-    }
-  }
-
-  private var optInitialModel: Option[LogisticRegressionModel] = None
-
-  private[spark] def setInitialModel(model: LogisticRegressionModel): this.type = {
-    this.optInitialModel = Some(model)
-    this
-  }
-
-  override protected[spark] def train(dataset: Dataset[_]): LogisticRegressionModel = {
-    val handlePersistence = dataset.storageLevel == StorageLevel.NONE
-    train(dataset, handlePersistence)
-  }
-
-  protected[spark] def train(
-      dataset: Dataset[_],
-      handlePersistence: Boolean): LogisticRegressionModel = {
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances: RDD[Instance] =
-      dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          Instance(label, weight + StaticUtils.ZERO_DOUBLE, features)
-      }
-
-    if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
-
-    val instr = Instrumentation.create(this, instances)
-    instr.logParams(regParam, elasticNetParam, standardization, threshold,
-      maxIter, tol, fitIntercept)
-
-    val (summarizer, labelSummarizer) = {
-      val seqOp = (c: (MultivariateOnlineSummarizer, MultiClassSummarizer),
-        instance: Instance) =>
-        (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight))
-
-      val combOp = (c1: (MultivariateOnlineSummarizer, MultiClassSummarizer),
-        c2: (MultivariateOnlineSummarizer, MultiClassSummarizer)) =>
-        (c1._1.merge(c2._1), c1._2.merge(c2._2))
-
-      instances.treeAggregate(
-        (new MultivariateOnlineSummarizer, new MultiClassSummarizer)
-      )(seqOp, combOp, $(aggregationDepth))
-    }
-
-    val histogram = labelSummarizer.histogram
-    val numInvalid = labelSummarizer.countInvalid
-    val numFeatures = summarizer.mean.size
-    val numFeaturesPlusIntercept = if (getFitIntercept) numFeatures + 1 else numFeatures
-
-    val numClasses = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
-      case Some(n: Int) =>
-        require(n >= histogram.length, s"Specified number of classes $n was " +
-          s"less than the number of unique labels ${histogram.length}.")
-        n
-      case None => histogram.length
-    }
-
-    val isMultinomial = getFamily.toLowerCase(Locale.ROOT) match {
-      case "binomial" =>
-        require(numClasses == 1 || numClasses == 2, s"Binomial family only supports 1 or 2 " +
-          s"outcome classes but found $numClasses.")
-        false
-      case "multinomial" => true
-      case "auto" => numClasses > 2
-      case other => throw new IllegalArgumentException(s"Unsupported family: $other")
-    }
-    val numCoefficientSets = if (isMultinomial) numClasses else 1
-
-    // Check params interaction is valid if fitting under bound constrained optimization.
-    if (usingBoundConstrainedOptimization) {
-      assertBoundConstrainedOptimizationParamsValid(numCoefficientSets, numFeatures)
-    }
-
-    if (isDefined(thresholds)) {
-      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
-        ".train() called with non-matching numClasses and thresholds.length." +
-        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
-    }
-
-    instr.logNumClasses(numClasses)
-    instr.logNumFeatures(numFeatures)
-
-    val (coefficientMatrix, interceptVector, objectiveHistory) = {
-      if (numInvalid != 0) {
-        val msg = s"Classification labels should be in [0 to ${numClasses - 1}]. " +
-          s"Found $numInvalid invalid labels."
-        logError(msg)
-        throw new SparkException(msg)
-      }
-
-      val isConstantLabel = histogram.count(_ != 0.0) == 1
-
-      if ($(fitIntercept) && isConstantLabel && !usingBoundConstrainedOptimization) {
-        logWarning(s"All labels are the same value and fitIntercept=true, so the coefficients " +
-          s"will be zeros. Training is not needed.")
-        val constantLabelIndex = Vectors.dense(histogram).argmax
-        val coefMatrix = new SparseMatrix(numCoefficientSets, numFeatures,
-          new Array[Int](numCoefficientSets + 1), Array.empty[Int], Array.empty[Double],
-          isTransposed = true).compressed
-        val interceptVec = if (isMultinomial) {
-          Vectors.sparse(numClasses, Seq((constantLabelIndex, Double.PositiveInfinity)))
-        } else {
-          Vectors.dense(if (numClasses == 2) Double.PositiveInfinity else Double.NegativeInfinity)
-        }
-        (coefMatrix, interceptVec, Array.empty[Double])
-      } else {
-        if (!$(fitIntercept) && isConstantLabel) {
-          logWarning(s"All labels belong to a single class and fitIntercept=false. It's a " +
-            s"dangerous ground, so the algorithm may not converge.")
-        }
-
-        val featuresMean = summarizer.mean.toArray
-        val featuresStd = summarizer.variance.toArray.map(math.sqrt)
-
-        if (!$(fitIntercept) && (0 until numFeatures).exists { i =>
-          featuresStd(i) == 0.0 && featuresMean(i) != 0.0 }) {
-          logWarning("Fitting LogisticRegressionModel without intercept on dataset with " +
-            "constant nonzero column, Spark MLlib outputs zero coefficients for constant " +
-            "nonzero columns. This behavior is the same as R glmnet but different from LIBSVM.")
-        }
-
-        val regParamL1 = $(elasticNetParam) * $(regParam)
-        val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
-
-        val bcFeaturesStd = instances.context.broadcast(featuresStd.map(t =>
-          if (t != 0d) 1d / t else 0d))
-        val getAggregatorFunc = new LogisticAggregatorX(bcFeaturesStd, numClasses, $(fitIntercept),
-          multinomial = isMultinomial)(_)
-        val getFeaturesStd = (j: Int) => if (j >= 0 && j < numCoefficientSets * numFeatures) {
-          featuresStd(j / numCoefficientSets)
-        } else {
-          0.0
-        }
-
-        val regularization = if (regParamL2 != 0.0) {
-          val shouldApply = (idx: Int) => idx >= 0 && idx < numFeatures * numCoefficientSets
-          Some(new L2Regularization(regParamL2, shouldApply,
-            if ($(standardization)) None else Some(getFeaturesStd)))
-        } else {
-          None
-        }
-
-        val costFun = new RDDLossFunctionX(instances, getAggregatorFunc, regularization,
-          $(aggregationDepth))
-
-        val numCoeffsPlusIntercepts = numFeaturesPlusIntercept * numCoefficientSets
-
-        val (lowerBounds, upperBounds): (Array[Double], Array[Double]) = {
-          if (usingBoundConstrainedOptimization) {
-            val lowerBounds = Array.fill[Double](numCoeffsPlusIntercepts)(Double.MinValue)
-            val upperBounds = Array.fill[Double](numCoeffsPlusIntercepts)(Double.MaxValue)
-            val isSetLowerBoundsOnCoefficients = isSet(lowerBoundsOnCoefficients)
-            val isSetUpperBoundsOnCoefficients = isSet(upperBoundsOnCoefficients)
-            val isSetLowerBoundsOnIntercepts = isSet(lowerBoundsOnIntercepts)
-            val isSetUpperBoundsOnIntercepts = isSet(upperBoundsOnIntercepts)
-
-            var i = 0
-            while (i < numCoeffsPlusIntercepts) {
-              val coefficientSetIndex = i % numCoefficientSets
-              val featureIndex = i / numCoefficientSets
-              if (featureIndex < numFeatures) {
-                if (isSetLowerBoundsOnCoefficients) {
-                  lowerBounds(i) = $(lowerBoundsOnCoefficients)(
-                    coefficientSetIndex, featureIndex) * featuresStd(featureIndex)
-                }
-                if (isSetUpperBoundsOnCoefficients) {
-                  upperBounds(i) = $(upperBoundsOnCoefficients)(
-                    coefficientSetIndex, featureIndex) * featuresStd(featureIndex)
-                }
-              } else {
-                if (isSetLowerBoundsOnIntercepts) {
-                  lowerBounds(i) = $(lowerBoundsOnIntercepts)(coefficientSetIndex)
-                }
-                if (isSetUpperBoundsOnIntercepts) {
-                  upperBounds(i) = $(upperBoundsOnIntercepts)(coefficientSetIndex)
-                }
-              }
-              i += 1
-            }
-            (lowerBounds, upperBounds)
-          } else {
-            (null, null)
-          }
-        }
-
-        val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) {
-          if (lowerBounds != null && upperBounds != null) {
-            new LBFGSL(BDV[Double](lowerBounds), BDV[Double](upperBounds), $(maxIter), 10, $(tol))
-          } else {
-            new LBFGSL($(maxIter), 10, $(tol))
-          }
-        } else {
-          val standardizationParam = $(standardization)
-          val effectiveL1Reg = Array.fill[Double](numCoeffsPlusIntercepts)(0d)
-            .zipWithIndex.map{case (_, index) =>
-            // Remove the L1 penalization on the intercept
-            val isIntercept = $(fitIntercept) && index >= numFeatures * numCoefficientSets
-            if (isIntercept) {
-              0.0
-            } else {
-              if (standardizationParam) {
-                regParamL1
-              } else {
-                val featureIndex = index / numCoefficientSets
-                // If `standardization` is false, we still standardize the data
-                // to improve the rate of convergence; as a result, we have to
-                // perform this reverse standardization by penalizing each component
-                // differently to get effectively the same objective function when
-                // the training dataset is not standardized.
-                if (featuresStd(featureIndex) != 0.0) {
-                  regParamL1 / featuresStd(featureIndex)
-                } else {
-                  0.0
-                }
-              }
-            }
-          }
-          new OWLQNL($(maxIter), 10, $(tol), BDV[Double](effectiveL1Reg))
-        }
-
-        /*
-          The coefficients are laid out in column major order during training. Here we initialize
-          a column major matrix of initial coefficients.
-         */
-        val initialCoefWithInterceptMatrix =
-          Matrices.zeros(numCoefficientSets, numFeaturesPlusIntercept)
-
-        val initialModelIsValid = optInitialModel match {
-          case Some(_initialModel) =>
-            val providedCoefs = _initialModel.coefficientMatrix
-            val modelIsValid = (providedCoefs.numRows == numCoefficientSets) &&
-              (providedCoefs.numCols == numFeatures) &&
-              (_initialModel.interceptVector.size == numCoefficientSets) &&
-              (_initialModel.getFitIntercept == $(fitIntercept))
-            if (!modelIsValid) {
-              logWarning(s"Initial coefficients will be ignored! Its dimensions " +
-                s"(${providedCoefs.numRows}, ${providedCoefs.numCols}) did not match the " +
-                s"expected size ($numCoefficientSets, $numFeatures)")
-            }
-            modelIsValid
-          case None => false
-        }
-
-        if (initialModelIsValid) {
-          val providedCoef = optInitialModel.get.coefficientMatrix
-          providedCoef.foreachActive { (classIndex, featureIndex, value) =>
-            // We need to scale the coefficients since they will be trained in the scaled space
-            initialCoefWithInterceptMatrix.update(classIndex, featureIndex,
-              value * featuresStd(featureIndex))
-          }
-          if ($(fitIntercept)) {
-            optInitialModel.get.interceptVector.foreachActive { (classIndex, value) =>
-              initialCoefWithInterceptMatrix.update(classIndex, numFeatures, value)
-            }
-          }
-        } else if ($(fitIntercept) && isMultinomial) {
-          /*
-             For multinomial logistic regression, when we initialize the coefficients as zeros,
-             it will converge faster if we initialize the intercepts such that
-             it follows the distribution of the labels.
-             {{{
-               P(1) = \exp(b_1) / Z
-               ...
-               P(K) = \exp(b_K) / Z
-               where Z = \sum_{k=1}^{K} \exp(b_k)
-             }}}
-             Since this doesn't have a unique solution, one of the solutions that satisfies the
-             above equations is
-             {{{
-               \exp(b_k) = count_k * \exp(\lambda)
-               b_k = \log(count_k) * \lambda
-             }}}
-             \lambda is a free parameter, so choose the phase \lambda such that the
-             mean is centered. This yields
-             {{{
-               b_k = \log(count_k)
-               b_k' = b_k - \mean(b_k)
-             }}}
-           */
-          val rawIntercepts = histogram.map(math.log1p) // add 1 for smoothing (log1p(x) = log(1+x))
-          val rawMean = rawIntercepts.sum / rawIntercepts.length
-          rawIntercepts.indices.foreach { i =>
-            initialCoefWithInterceptMatrix.update(i, numFeatures, rawIntercepts(i) - rawMean)
-          }
-        } else if ($(fitIntercept)) {
-          /*
-             For binary logistic regression, when we initialize the coefficients as zeros,
-             it will converge faster if we initialize the intercept such that
-             it follows the distribution of the labels.
-
-             {{{
-               P(0) = 1 / (1 + \exp(b)), and
-               P(1) = \exp(b) / (1 + \exp(b))
-             }}}, hence
-             {{{
-               b = \log{P(1) / P(0)} = \log{count_1 / count_0}
-             }}}
-           */
-          initialCoefWithInterceptMatrix.update(0, numFeatures,
-            math.log(histogram(1) / histogram(0)))
-        }
-
-        if (usingBoundConstrainedOptimization) {
-          // Make sure all initial values locate in the corresponding bound.
-          var i = 0
-          while (i < numCoeffsPlusIntercepts) {
-            val coefficientSetIndex = i % numCoefficientSets
-            val featureIndex = i / numCoefficientSets
-            if (initialCoefWithInterceptMatrix(coefficientSetIndex, featureIndex) < lowerBounds(i))
-            {
-              initialCoefWithInterceptMatrix.update(
-                coefficientSetIndex, featureIndex, lowerBounds(i))
-            } else if (
-              initialCoefWithInterceptMatrix(coefficientSetIndex, featureIndex) > upperBounds(i))
-            {
-              initialCoefWithInterceptMatrix.update(
-                coefficientSetIndex, featureIndex, upperBounds(i))
-            }
-            i += 1
-          }
-        }
-
-        val states = optimizer.iterations(new CachedDiffFunction(costFun),
-          new BDV[Double](initialCoefWithInterceptMatrix.toArray))
-
-        /*
-           Note that in Logistic Regression, the objective history (loss + regularization)
-           is log-likelihood which is invariant under feature standardization. As a result,
-           the objective history from optimizer is the same as the one in the original space.
-         */
-        val arrayBuilder = mutable.ArrayBuilder.make[Double]
-        var state: optimizer.State = null
-        while (states.hasNext) {
-          state = states.next()
-          arrayBuilder += state.adjustedValue
-        }
-        bcFeaturesStd.destroy(blocking = false)
-
-        if (state == null) {
-          val msg = s"${optimizer.getClass.getName} failed."
-          logError(msg)
-          throw new SparkException(msg)
-        }
-
-        /*
-           The coefficients are trained in the scaled space; we're converting them back to
-           the original space.
-
-           Additionally, since the coefficients were laid out in column major order during training
-           to avoid extra computation, we convert them back to row major before passing them to the
-           model.
-
-           Note that the intercept in scaled space and original space is the same;
-           as a result, no scaling is needed.
-         */
-        val allCoefficients = state.x.toArray.clone()
-        val allCoefMatrix = new DenseMatrix(numCoefficientSets, numFeaturesPlusIntercept,
-          allCoefficients)
-        val denseCoefficientMatrix = new DenseMatrix(numCoefficientSets, numFeatures,
-          new Array[Double](numCoefficientSets * numFeatures), isTransposed = true)
-        val interceptVec = if ($(fitIntercept) || !isMultinomial) {
-          Vectors.zeros(numCoefficientSets)
-        } else {
-          Vectors.sparse(numCoefficientSets, Seq.empty)
-        }
-        // separate intercepts and coefficients from the combined matrix
-        allCoefMatrix.foreachActive { (classIndex, featureIndex, value) =>
-          val isIntercept = $(fitIntercept) && (featureIndex == numFeatures)
-          if (!isIntercept && featuresStd(featureIndex) != 0.0) {
-            denseCoefficientMatrix.update(classIndex, featureIndex,
-              value / featuresStd(featureIndex))
-          }
-          if (isIntercept) interceptVec.toArray(classIndex) = value
-        }
-
-        if ($(regParam) == 0.0 && isMultinomial && !usingBoundConstrainedOptimization) {
-          /*
-            When no regularization is applied, the multinomial coefficients lack identifiability
-            because we do not use a pivot class. We can add any constant value to the coefficients
-            and get the same likelihood. So here, we choose the mean centered coefficients for
-            reproducibility. This method follows the approach in glmnet, described here:
-
-            Friedman, et al. "Regularization Paths for Generalized Linear Models via
-              Coordinate Descent," https://core.ac.uk/download/files/153/6287975.pdf
-           */
-          val centers = Array.fill(numFeatures)(0.0)
-          denseCoefficientMatrix.foreachActive { case (i, j, v) =>
-            centers(j) += v
-          }
-          centers.transform(_ / numCoefficientSets)
-          denseCoefficientMatrix.foreachActive { case (i, j, v) =>
-            denseCoefficientMatrix.update(i, j, v - centers(j))
-          }
-        }
-
-        // center the intercepts when using multinomial algorithm
-        if ($(fitIntercept) && isMultinomial && !usingBoundConstrainedOptimization) {
-          val interceptArray = interceptVec.toArray
-          val interceptMean = interceptArray.sum / interceptArray.length
-          (0 until interceptVec.size).foreach { i => interceptArray(i) -= interceptMean }
-        }
-        (denseCoefficientMatrix.compressed, interceptVec.compressed, arrayBuilder.result())
-      }
-    }
-
-    if (handlePersistence) instances.unpersist()
-
-    val model = copyValues(new LogisticRegressionModel(uid, coefficientMatrix, interceptVector,
-      numClasses, isMultinomial))
-
-    val (summaryModel, probabilityColName, predictionColName) = model.findSummaryModel()
-    val logRegSummary = if (numClasses <= 2) {
-      new BinaryLogisticRegressionTrainingSummaryImpl(
-        summaryModel.transform(dataset),
-        probabilityColName,
-        predictionColName,
-        $(labelCol),
-        $(featuresCol),
-        objectiveHistory)
-    } else {
-      new LogisticRegressionTrainingSummaryImpl(
-        summaryModel.transform(dataset),
-        probabilityColName,
-        predictionColName,
-        $(labelCol),
-        $(featuresCol),
-        objectiveHistory)
-    }
-    model.setSummary(Some(logRegSummary))
-    instr.logSuccess(model)
-    model
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra)
-}
-
-@Since("1.6.0")
-object LogisticRegression extends DefaultParamsReadable[LogisticRegression] {
-
-  @Since("1.6.0")
-  override def load(path: String): LogisticRegression = super.load(path)
-
-  private[classification] val supportedFamilyNames =
-    Array("auto", "binomial", "multinomial").map(_.toLowerCase(Locale.ROOT))
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
deleted file mode 100644
index d2dd1ec..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ /dev/null
@@ -1,356 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.classification
-
-import org.json4s.{DefaultFormats, JObject}
-import org.json4s.JsonDSL._
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.RandomForest
-import org.apache.spark.ml.util._
-import org.apache.spark.ml.util.DefaultParamsReader.Metadata
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset}
-import org.apache.spark.sql.functions._
-
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> learning algorithm for
- * classification.
- * It supports both binary and multiclass labels, as well as both continuous and categorical
- * features.
- */
-@Since("1.4.0")
-class RandomForestClassifier @Since("1.4.0") (
-    @Since("1.4.0") override val uid: String)
-  extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel]
-  with RandomForestClassifierParams with DefaultParamsWritable {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("rfc"))
-
-  // Override parameter setters from parent trait for Java API compatibility.
-
-  // Parameters from TreeClassifierParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be at least 1.
-   * (default = 10)
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setImpurity(value: String): this.type = set(impurity, value)
-
-  // Parameters from TreeEnsembleParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSeed(value: Long): this.type = set(seed, value)
-
-  // Parameters from RandomForestParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setNumTrees(value: Int): this.type = set(numTrees, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setFeatureSubsetStrategy(value: String): this.type =
-    set(featureSubsetStrategy, value)
-
-  override protected def train(dataset: Dataset[_]): RandomForestClassificationModel = {
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val numClasses: Int = getNumClasses(dataset)
-
-    if (isDefined(thresholds)) {
-      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
-        ".train() called with non-matching numClasses and thresholds.length." +
-        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
-    }
-
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
-    val strategy =
-      super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
-
-    val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, probabilityCol, rawPredictionCol,
-      impurity, numTrees, featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
-      minInstancesPerNode, seed, subsamplingRate, thresholds, cacheNodeIds, checkpointInterval)
-
-    val trees = RandomForest
-      .run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
-      .map(_.asInstanceOf[DecisionTreeClassificationModel])
-
-    val numFeatures = oldDataset.first().features.size
-    val m = new RandomForestClassificationModel(uid, trees, numFeatures, numClasses)
-    instr.logSuccess(m)
-    m
-  }
-
-  @Since("1.4.1")
-  override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
-}
-
-@Since("1.4.0")
-object RandomForestClassifier extends DefaultParamsReadable[RandomForestClassifier] {
-  /** Accessor for supported impurity settings: entropy, gini */
-  @Since("1.4.0")
-  final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
-
-  /** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */
-  @Since("1.4.0")
-  final val supportedFeatureSubsetStrategies: Array[String] =
-    TreeEnsembleParams.supportedFeatureSubsetStrategies
-
-  @Since("2.0.0")
-  override def load(path: String): RandomForestClassifier = super.load(path)
-}
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> model for classification.
- * It supports both binary and multiclass labels, as well as both continuous and categorical
- * features.
- *
- * @param _trees  Decision trees in the ensemble.
- *                Warning: These have null parents.
- */
-@Since("1.4.0")
-class RandomForestClassificationModel private[ml] (
-    @Since("1.5.0") override val uid: String,
-    private val _trees: Array[DecisionTreeClassificationModel],
-    @Since("1.6.0") override val numFeatures: Int,
-    @Since("1.5.0") override val numClasses: Int)
-  extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]
-  with RandomForestClassifierParams with TreeEnsembleModel[DecisionTreeClassificationModel]
-  with MLWritable with Serializable {
-
-  require(_trees.nonEmpty, "RandomForestClassificationModel requires at least 1 tree.")
-
-  /**
-   * Construct a random forest classification model, with all trees weighted equally.
-   *
-   * @param trees  Component trees
-   */
-  private[ml] def this(
-      trees: Array[DecisionTreeClassificationModel],
-      numFeatures: Int,
-      numClasses: Int) =
-    this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
-
-  @Since("1.4.0")
-  override def trees: Array[DecisionTreeClassificationModel] = _trees
-
-  // Note: We may add support for weights (based on tree performance) later on.
-  private lazy val _treeWeights: Array[Double] = Array.fill[Double](_trees.length)(1.0)
-
-  @Since("1.4.0")
-  override def treeWeights: Array[Double] = _treeWeights
-
-  override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
-    val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
-    }
-    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
-  }
-
-  override protected def predictRaw(features: Vector): Vector = {
-    // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128
-    // Classifies using majority votes.
-    // Ignore the tree weights since all are 1.0 for now.
-    val votes = Array.fill[Double](numClasses)(0.0)
-    _trees.view.foreach { tree =>
-      val classCounts: Array[Double] = tree.rootNode.predictImpl(features).impurityStats.stats
-      val total = classCounts.sum
-      if (total != 0) {
-        var i = 0
-        while (i < numClasses) {
-          votes(i) += classCounts(i) / total
-          i += 1
-        }
-      }
-    }
-    Vectors.dense(votes)
-  }
-
-  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
-    rawPrediction match {
-      case dv: DenseVector =>
-        ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(dv)
-        dv
-      case sv: SparseVector =>
-        throw new RuntimeException("Unexpected error in RandomForestClassificationModel:" +
-          " raw2probabilityInPlace encountered SparseVector")
-    }
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): RandomForestClassificationModel = {
-    copyValues(new RandomForestClassificationModel(uid, _trees, numFeatures, numClasses), extra)
-      .setParent(parent)
-  }
-
-  @Since("1.4.0")
-  override def toString: String = {
-    s"RandomForestClassificationModel (uid=$uid) with $getNumTrees trees"
-  }
-
-  /**
-   * Estimate of the importance of each feature.
-   *
-   * Each feature's importance is the average of its importance across all trees in the ensemble
-   * The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
-   * (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
-   * and follows the implementation from scikit-learn.
-   *
-   * @see `DecisionTreeClassificationModel.featureImportances`
-   */
-  @Since("1.5.0")
-  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
-
-  /** (private[ml]) Convert to a model in the old API */
-  private[ml] def toOld: OldRandomForestModel = {
-    new OldRandomForestModel(OldAlgo.Classification, _trees.map(_.toOld))
-  }
-
-  @Since("2.0.0")
-  override def write: MLWriter =
-    new RandomForestClassificationModel.RandomForestClassificationModelWriter(this)
-}
-
-@Since("2.0.0")
-object RandomForestClassificationModel extends MLReadable[RandomForestClassificationModel] {
-
-  @Since("2.0.0")
-  override def read: MLReader[RandomForestClassificationModel] =
-    new RandomForestClassificationModelReader
-
-  @Since("2.0.0")
-  override def load(path: String): RandomForestClassificationModel = super.load(path)
-
-  private[RandomForestClassificationModel]
-  class RandomForestClassificationModelWriter(instance: RandomForestClassificationModel)
-    extends MLWriter {
-
-    override protected def saveImpl(path: String): Unit = {
-      // Note: numTrees is not currently used, but could be nice to store for fast querying.
-      val extraMetadata: JObject = Map(
-        "numFeatures" -> instance.numFeatures,
-        "numClasses" -> instance.numClasses,
-        "numTrees" -> instance.getNumTrees)
-      EnsembleModelReadWrite.saveImpl(instance, path, sparkSession, extraMetadata)
-    }
-  }
-
-  private class RandomForestClassificationModelReader
-    extends MLReader[RandomForestClassificationModel] {
-
-    /** Checked against metadata when loading model */
-    private val className = classOf[RandomForestClassificationModel].getName
-    private val treeClassName = classOf[DecisionTreeClassificationModel].getName
-
-    override def load(path: String): RandomForestClassificationModel = {
-      implicit val format = DefaultFormats
-      val (metadata: Metadata, treesData: Array[(Metadata, Node)], _) =
-        EnsembleModelReadWrite.loadImpl(path, sparkSession, className, treeClassName)
-      val numFeatures = (metadata.metadata \ "numFeatures").extract[Int]
-      val numClasses = (metadata.metadata \ "numClasses").extract[Int]
-      val numTrees = (metadata.metadata \ "numTrees").extract[Int]
-
-      val trees: Array[DecisionTreeClassificationModel] = treesData.map {
-        case (treeMetadata, root) =>
-          val tree =
-            new DecisionTreeClassificationModel(treeMetadata.uid, root, numFeatures, numClasses)
-          DefaultParamsReader.getAndSetParams(tree, treeMetadata)
-          tree
-      }
-      require(numTrees == trees.length, s"RandomForestClassificationModel.load expected $numTrees" +
-        s" trees based on metadata but found ${trees.length} trees.")
-
-      val model = new RandomForestClassificationModel(metadata.uid, trees, numFeatures, numClasses)
-      DefaultParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-
-  /** Convert a model from the old API */
-  private[ml] def fromOld(
-      oldModel: OldRandomForestModel,
-      parent: RandomForestClassifier,
-      categoricalFeatures: Map[Int, Int],
-      numClasses: Int,
-      numFeatures: Int = -1): RandomForestClassificationModel = {
-    require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" +
-      s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).")
-    val newTrees = oldModel.trees.map { tree =>
-      // parent for each tree is null since there is no good way to set this.
-      DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures)
-    }
-    val uid = if (parent != null) parent.uid else Identifiable.randomUID("rfc")
-    new RandomForestClassificationModel(uid, newTrees, numFeatures, numClasses)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/feature/IDF.scala
deleted file mode 100644
index 46a0730..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ /dev/null
@@ -1,195 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.feature
-
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.ml._
-import org.apache.spark.ml.linalg.{Vector, VectorUDT}
-import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.util._
-import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.StructType
-
-/**
- * Params for [[IDF]] and [[IDFModel]].
- */
-private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol {
-
-  /**
-   * The minimum number of documents in which a term should appear.
-   * Default: 0
-   * @group param
-   */
-  final val minDocFreq = new IntParam(
-    this, "minDocFreq", "minimum number of documents in which a term should appear for filtering" +
-      " (>= 0)", ParamValidators.gtEq(0))
-
-  setDefault(minDocFreq -> 0)
-
-  /** @group getParam */
-  def getMinDocFreq: Int = $(minDocFreq)
-
-  /**
-   * Validate and transform the input schema.
-   */
-  protected def validateAndTransformSchema(schema: StructType): StructType = {
-    SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
-    SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
-  }
-}
-
-/**
- * Compute the Inverse Document Frequency (IDF) given a collection of documents.
- */
-@Since("1.4.0")
-final class IDF @Since("1.4.0") (@Since("1.4.0") override val uid: String)
-  extends Estimator[IDFModel] with IDFBase with DefaultParamsWritable {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("idf"))
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setInputCol(value: String): this.type = set(inputCol, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setOutputCol(value: String): this.type = set(outputCol, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setMinDocFreq(value: Int): this.type = set(minDocFreq, value)
-
-  @Since("2.0.0")
-  override def fit(dataset: Dataset[_]): IDFModel = {
-    transformSchema(dataset.schema, logging = true)
-    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
-      case Row(v: Vector) => OldVectors.fromML(v)
-    }
-    val idf = new feature.IDF($(minDocFreq)).fit(input)
-    copyValues(new IDFModel(uid, idf).setParent(this))
-  }
-
-  @Since("1.4.0")
-  override def transformSchema(schema: StructType): StructType = {
-    validateAndTransformSchema(schema)
-  }
-
-  @Since("1.4.1")
-  override def copy(extra: ParamMap): IDF = defaultCopy(extra)
-}
-
-@Since("1.6.0")
-object IDF extends DefaultParamsReadable[IDF] {
-
-  @Since("1.6.0")
-  override def load(path: String): IDF = super.load(path)
-}
-
-/**
- * Model fitted by [[IDF]].
- */
-@Since("1.4.0")
-class IDFModel private[ml] (
-    @Since("1.4.0") override val uid: String,
-    idfModel: feature.IDFModel)
-  extends Model[IDFModel] with IDFBase with MLWritable {
-
-  import IDFModel._
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setInputCol(value: String): this.type = set(inputCol, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setOutputCol(value: String): this.type = set(outputCol, value)
-
-  @Since("2.0.0")
-  override def transform(dataset: Dataset[_]): DataFrame = {
-    transformSchema(dataset.schema, logging = true)
-    // TODO: Make the idfModel.transform natively in ml framework to avoid extra conversion.
-    val idf = udf { vec: Vector => idfModel.transform(OldVectors.fromML(vec)).asML }
-    dataset.withColumn($(outputCol), idf(col($(inputCol))))
-  }
-
-  @Since("1.4.0")
-  override def transformSchema(schema: StructType): StructType = {
-    validateAndTransformSchema(schema)
-  }
-
-  @Since("1.4.1")
-  override def copy(extra: ParamMap): IDFModel = {
-    val copied = new IDFModel(uid, idfModel)
-    copyValues(copied, extra).setParent(parent)
-  }
-
-  /** Returns the IDF vector. */
-  @Since("2.0.0")
-  def idf: Vector = idfModel.idf.asML
-
-  @Since("1.6.0")
-  override def write: MLWriter = new IDFModelWriter(this)
-}
-
-@Since("1.6.0")
-object IDFModel extends MLReadable[IDFModel] {
-
-  private[IDFModel] class IDFModelWriter(instance: IDFModel) extends MLWriter {
-
-    private case class Data(idf: Vector)
-
-    override protected def saveImpl(path: String): Unit = {
-      DefaultParamsWriter.saveMetadata(instance, path, sc)
-      val data = Data(instance.idf)
-      val dataPath = new Path(path, "data").toString
-      sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
-    }
-  }
-
-  private class IDFModelReader extends MLReader[IDFModel] {
-
-    private val className = classOf[IDFModel].getName
-
-    override def load(path: String): IDFModel = {
-      val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
-      val dataPath = new Path(path, "data").toString
-      val data = sparkSession.read.parquet(dataPath)
-      val Row(idf: Vector) = MLUtils.convertVectorColumnsToML(data, "idf")
-        .select("idf")
-        .head()
-      val model = new IDFModel(metadata.uid, new feature.IDFModel(OldVectors.fromML(idf)))
-      DefaultParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-
-  @Since("1.6.0")
-  override def read: MLReader[IDFModel] = new IDFModelReader
-
-  @Since("1.6.0")
-  override def load(path: String): IDFModel = super.load(path)
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/fpm/PrefixSpan.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/fpm/PrefixSpan.scala
new file mode 100644
index 0000000..10a569a
--- /dev/null
+++ b/ml-accelerator/src/main/scala/org/apache/spark/ml/fpm/PrefixSpan.scala
@@ -0,0 +1,168 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.fpm
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.util.Instrumentation.instrumented
+import org.apache.spark.mllib.fpm.{PrefixSpan => mllibPrefixSpan}
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.types.{ArrayType, LongType, StructField, StructType}
+
+/**
+ * A parallel PrefixSpan algorithm to mine frequent sequential patterns.
+ * The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns
+ * Efficiently by Prefix-Projected Pattern Growth
+ * (see <a href="https://doi.org/10.1109/ICDE.2001.914830">here</a>).
+ * This class is not yet an Estimator/Transformer, use `findFrequentSequentialPatterns` method to
+ * run the PrefixSpan algorithm.
+ *
+ * @see <a href="https://en.wikipedia.org/wiki/Sequential_Pattern_Mining">Sequential Pattern Mining
+ * (Wikipedia)</a>
+ */
+@Since("2.4.0")
+final class PrefixSpan(@Since("2.4.0") override val uid: String) extends Params {
+
+  @Since("2.4.0")
+  def this() = this(Identifiable.randomUID("prefixSpan"))
+
+  /**
+   * Param for the minimal support level (default: `0.1`).
+   * Sequential patterns that appear more than (minSupport * size-of-the-dataset) times are
+   * identified as frequent sequential patterns.
+   * @group param
+   */
+  @Since("2.4.0")
+  val minSupport = new DoubleParam(this, "minSupport", "The minimal support level of the " +
+    "sequential pattern. Sequential pattern that appears more than " +
+    "(minSupport * size-of-the-dataset) " +
+    "times will be output.", ParamValidators.gtEq(0.0))
+
+  /** @group getParam */
+  @Since("2.4.0")
+  def getMinSupport: Double = $(minSupport)
+
+  /** @group setParam */
+  @Since("2.4.0")
+  def setMinSupport(value: Double): this.type = set(minSupport, value)
+
+  /**
+   * Param for the maximal pattern length (default: `10`).
+   * @group param
+   */
+  @Since("2.4.0")
+  val maxPatternLength = new IntParam(this, "maxPatternLength",
+    "The maximal length of the sequential pattern.",
+    ParamValidators.gt(0))
+
+  /** @group getParam */
+  @Since("2.4.0")
+  def getMaxPatternLength: Int = $(maxPatternLength)
+
+  /** @group setParam */
+  @Since("2.4.0")
+  def setMaxPatternLength(value: Int): this.type = set(maxPatternLength, value)
+
+  /**
+   * Param for the maximum number of items (including delimiters used in the internal storage
+   * format) allowed in a projected database before local processing (default: `32000000`).
+   * If a projected database exceeds this size, another iteration of distributed prefix growth
+   * is run.
+   * @group param
+   */
+  @Since("2.4.0")
+  val maxLocalProjDBSize = new LongParam(this, "maxLocalProjDBSize",
+    "The maximum number of items (including delimiters used in the internal storage format) " +
+    "allowed in a projected database before local processing. If a projected database exceeds " +
+    "this size, another iteration of distributed prefix growth is run.",
+    ParamValidators.gt(0))
+
+  /** @group getParam */
+  @Since("2.4.0")
+  def getMaxLocalProjDBSize: Long = $(maxLocalProjDBSize)
+
+  /** @group setParam */
+  @Since("2.4.0")
+  def setMaxLocalProjDBSize(value: Long): this.type = set(maxLocalProjDBSize, value)
+
+  /**
+   * Param for the name of the sequence column in dataset (default "sequence"), rows with
+   * nulls in this column are ignored.
+   * @group param
+   */
+  @Since("2.4.0")
+  val sequenceCol = new Param[String](this, "sequenceCol", "The name of the sequence column in " +
+    "dataset, rows with nulls in this column are ignored.")
+
+  /** @group getParam */
+  @Since("2.4.0")
+  def getSequenceCol: String = $(sequenceCol)
+
+  /** @group setParam */
+  @Since("2.4.0")
+  def setSequenceCol(value: String): this.type = set(sequenceCol, value)
+
+  setDefault(minSupport -> 0.1, maxPatternLength -> 10, maxLocalProjDBSize -> 32000000,
+    sequenceCol -> "sequence")
+
+  /**
+   * Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
+   *
+   * @param dataset A dataset or a dataframe containing a sequence column which is
+   *                {{{ArrayType(ArrayType(T))}}} type, T is the item type for the input dataset.
+   * @return A `DataFrame` that contains columns of sequence and corresponding frequency.
+   *         The schema of it will be:
+   *          - `sequence: ArrayType(ArrayType(T))` (T is the item type)
+   *          - `freq: Long`
+   */
+  @Since("2.4.0")
+  def findFrequentSequentialPatterns(dataset: Dataset[_]): DataFrame = instrumented { instr =>
+    instr.logDataset(dataset)
+    instr.logParams(this, params: _*)
+
+    val sequenceColParam = $(sequenceCol)
+    val inputType = dataset.schema(sequenceColParam).dataType
+    require(inputType.isInstanceOf[ArrayType] &&
+      inputType.asInstanceOf[ArrayType].elementType.isInstanceOf[ArrayType],
+      s"The input column must be ArrayType and the array element type must also be ArrayType, " +
+      s"but got $inputType.")
+
+    val data = dataset.select(sequenceColParam)
+    val sequences = data.where(col(sequenceColParam).isNotNull).rdd
+      .map(r => r.getSeq[scala.collection.Seq[Any]](0).map(_.toArray).toArray)
+
+    val mllibPrefixSpan = new mllibPrefixSpan()
+      .setMinSupport($(minSupport))
+      .setMaxPatternLength($(maxPatternLength))
+      .setMaxLocalProjDBSize($(maxLocalProjDBSize))
+
+    val rows = mllibPrefixSpan.run(sequences).freqSequences.map(f => Row(f.sequence, f.freq))
+    val schema = StructType(Seq(
+      StructField("sequence", dataset.schema(sequenceColParam).dataType, nullable = false),
+      StructField("freq", LongType, nullable = false)))
+    val freqSequences = dataset.sparkSession.createDataFrame(rows, schema)
+
+    freqSequences
+  }
+
+  @Since("2.4.0")
+  override def copy(extra: ParamMap): PrefixSpan = defaultCopy(extra)
+
+}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/DifferentiableLossAggregatorX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/DifferentiableLossAggregatorX.scala
deleted file mode 100644
index 48bc3b8..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/DifferentiableLossAggregatorX.scala
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.optim.aggregator
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-
-import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors}
-
-/**
- * A parent trait for aggregators used in fitting MLlib models. This parent trait implements
- * some of the common code shared between concrete instances of aggregators. Subclasses of this
- * aggregator need only implement the `add` method.
- *
- * @tparam Datum The type of the instances added to the aggregator to update the loss and gradient.
- * @tparam Agg Specialization of [[DifferentiableLossAggregator]]. Classes that subclass this
- *             type need to use this parameter to specify the concrete type of the aggregator.
- */
-private[ml] trait DifferentiableLossAggregatorX[
-    Datum,
-    Agg <: DifferentiableLossAggregatorX[Datum, Agg]] extends Serializable {
-
-  self: Agg => // enforce classes that extend this to be the same type as `Agg`
-
-  protected var weightSum: Double = 0.0
-  protected var lossSum: Double = 0.0
-
-  /** The dimension of the gradient array. */
-  protected val dim: Int
-
-  /** Array of gradient values that are mutated when new instances are added to the aggregator. */
-  protected lazy val gradientSumArray: DoubleArrayList =
-    DoubleArrayList.wrap(Array.ofDim[Double](dim))
-
-  /** Add a single data point to this aggregator. */
-  def add(instance: Datum): Agg
-
-  /** Merge two aggregators. The `this` object will be modified in place and returned. */
-  def merge(other: Agg): Agg = {
-    require(dim == other.dim, s"Dimensions mismatch when merging with another " +
-      s"${getClass.getSimpleName}. Expecting $dim but got ${other.dim}.")
-
-    if (other.weightSum != 0) {
-      weightSum += other.weightSum
-      lossSum += other.lossSum
-
-      var i = 0
-      val localThisGradientSumArray = this.gradientSumArray
-      val localOtherGradientSumArray = other.gradientSumArray
-      while (i < dim) {
-        val e = localThisGradientSumArray.getDouble(i)
-        localThisGradientSumArray.set(i, e + localOtherGradientSumArray.getDouble(i))
-        i += 1
-      }
-    }
-    this
-  }
-
-  /** The current weighted averaged gradient. */
-  def gradient: Vector = {
-    require(weightSum > 0.0, s"The effective number of instances should be " +
-      s"greater than 0.0, but was $weightSum.")
-    val result = Vectors.dense(gradientSumArray.elements().clone())
-    BLAS.scal(1.0 / weightSum, result)
-    result
-  }
-
-  /** Weighted count of instances in this aggregator. */
-  def weight: Double = weightSum
-
-  /** The current loss value of this aggregator. */
-  def loss: Double = {
-    require(weightSum > 0.0, s"The effective number of instances should be " +
-      s"greater than 0.0, but was $weightSum.")
-    lossSum / weightSum
-  }
-
-}
-
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/HingeAggregatorX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/HingeAggregatorX.scala
deleted file mode 100644
index d4a4f1b..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/HingeAggregatorX.scala
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.optim.aggregator
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg._
-
-/**
- * HingeAggregator computes the gradient and loss for Hinge loss function as used in
- * binary classification for instances in sparse or dense vector in an online fashion.
- *
- * Two HingeAggregators can be merged together to have a summary of loss and gradient of
- * the corresponding joint dataset.
- *
- * This class standardizes feature values during computation using bcFeaturesStd.
- *
- * @param bcCoefficients The coefficients corresponding to the features.
- * @param fitIntercept Whether to fit an intercept term.
- * @param bcFeaturesStd The standard deviation values of the features.
- */
-private[ml] class HingeAggregatorX(
-    bcFeaturesStd: Broadcast[Array[Double]],
-    fitIntercept: Boolean)(bcCoefficients: Broadcast[Vector])
-  extends DifferentiableLossAggregatorX[Instance, HingeAggregatorX] {
-
-  private val numFeatures: Int = bcFeaturesStd.value.length
-  private val numFeaturesPlusIntercept: Int = if (fitIntercept) numFeatures + 1 else numFeatures
-  @transient private lazy val coefficientsArray = bcCoefficients.value match {
-    case DenseVector(values) => values
-    case _ => throw new IllegalArgumentException(s"coefficients only supports dense vector" +
-      s" but got type ${bcCoefficients.value.getClass}.")
-  }
-  protected override val dim: Int = numFeaturesPlusIntercept
-
-  /**
-   * Add a new training instance to this HingeAggregator, and update the loss and gradient
-   * of the objective function.
-   *
-   * @param instance The instance of data point to be added.
-   * @return This HingeAggregator object.
-   */
-  def add(instance: Instance): this.type = {
-    instance match { case Instance(label, weight, features) =>
-      require(numFeatures == features.size, s"Dimensions mismatch when adding new instance." +
-        s" Expecting $numFeatures but got ${features.size}.")
-      require(weight >= 0.0, s"instance weight, $weight has to be >= 0.0")
-
-      if (weight == 0.0) return this
-      val localFeaturesStd = DoubleArrayList.wrap(bcFeaturesStd.value)
-      val localCoefficients = DoubleArrayList.wrap(coefficientsArray)
-      val localGradientSumArray = gradientSumArray
-
-      val dotProduct = {
-        var sum = 0.0
-        features.foreachActive { (index, value) =>
-            sum += localCoefficients.getDouble(index) * value * localFeaturesStd.getDouble(index)
-        }
-        if (fitIntercept) sum += localCoefficients.getDouble(numFeaturesPlusIntercept - 1)
-        sum
-      }
-      // Our loss function with {0, 1} labels is max(0, 1 - (2y - 1) (f_w(x)))
-      // Therefore the gradient is -(2y - 1)*x
-      val labelScaled = 2 * label - 1.0
-      val loss = if (1.0 > labelScaled * dotProduct) {
-        (1.0 - labelScaled * dotProduct) * weight
-      } else {
-        0.0
-      }
-
-      if (1.0 > labelScaled * dotProduct) {
-        val gradientScale = -labelScaled * weight
-        features.foreachActive { (index, value) =>
-          val e = localGradientSumArray.getDouble(index)
-          localGradientSumArray.set(index, e + value * gradientScale
-            * localFeaturesStd.getDouble(index))
-        }
-        if (fitIntercept) {
-          val e = localGradientSumArray.getDouble(localGradientSumArray.size() - 1)
-          localGradientSumArray.set(localGradientSumArray.size() - 1, e + gradientScale)
-        }
-      }
-
-      lossSum += loss
-      weightSum += weight
-      this
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/HuberAggregatorX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/HuberAggregatorX.scala
deleted file mode 100644
index c35cdb5..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/HuberAggregatorX.scala
+++ /dev/null
@@ -1,163 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.optim.aggregator
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg.{DenseVector, Vector}
-
-/**
- * HuberAggregator computes the gradient and loss for a huber loss function,
- * as used in robust regression for samples in sparse or dense vector in an online fashion.
- *
- * The huber loss function based on:
- * <a href="http://statweb.stanford.edu/~owen/reports/hhu.pdf">Art B. Owen (2006),
- * A robust hybrid of lasso and ridge regression</a>.
- *
- * Two HuberAggregator can be merged together to have a summary of loss and gradient of
- * the corresponding joint dataset.
- *
- * The huber loss function is given by
- *
- * <blockquote>
- *   $$
- *   \begin{align}
- *   \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma +
- *   H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2}
- *   \end{align}
- *   $$
- * </blockquote>
- *
- * where
- *
- * <blockquote>
- *   $$
- *   \begin{align}
- *   H_m(z) = \begin{cases}
- *            z^2, & \text {if } |z| &lt; \epsilon, \\
- *            2\epsilon|z| - \epsilon^2, & \text{otherwise}
- *            \end{cases}
- *   \end{align}
- *   $$
- * </blockquote>
- *
- * It is advised to set the parameter $\epsilon$ to 1.35 to achieve 95% statistical efficiency
- * for normally distributed data. Please refer to chapter 2 of
- * <a href="http://statweb.stanford.edu/~owen/reports/hhu.pdf">
- * A robust hybrid of lasso and ridge regression</a> for more detail.
- *
- * @param fitIntercept Whether to fit an intercept term.
- * @param epsilon The shape parameter to control the amount of robustness.
- * @param bcFeaturesStd The broadcast standard deviation values of the features.
- * @param bcParameters including three parts: the regression coefficients corresponding
- *                     to the features, the intercept (if fitIntercept is ture)
- *                     and the scale parameter (sigma).
- */
-private[ml] class HuberAggregatorX(
-    fitIntercept: Boolean,
-    epsilon: Double,
-    bcFeaturesStd: Broadcast[Array[Double]])(bcParameters: Broadcast[Vector])
-  extends DifferentiableLossAggregatorX[Instance, HuberAggregatorX] {
-
-  protected override val dim: Int = bcParameters.value.size
-  private val numFeatures: Int = if (fitIntercept) dim - 2 else dim - 1
-
-  @transient private lazy val parametersArray = DoubleArrayList.wrap(bcParameters.value match {
-    case DenseVector(values) => values
-    case _ => throw new IllegalArgumentException(s"coefficients only supports dense vector but " +
-      s"got type ${bcParameters.value.getClass}.)")
-  })
-  @transient private lazy val featuresStd = DoubleArrayList.wrap(bcFeaturesStd.value)
-  @transient private lazy val sigma: Double = parametersArray.getDouble(dim - 1)
-  @transient private lazy val intercept: Double = if (fitIntercept) {
-    parametersArray.getDouble(dim - 2)
-  } else {
-    0.0
-  }
-
-  /**
-   * Add a new training instance to this HuberAggregator, and update the loss and gradient
-   * of the objective function.
-   *
-   * @param instance The instance of data point to be added.
-   * @return This HuberAggregator object.
-   */
-  def add(instance: Instance): HuberAggregatorX = {
-    instance match { case Instance(label, weight, features) =>
-      require(numFeatures == features.size, s"Dimensions mismatch when adding new sample." +
-        s" Expecting $numFeatures but got ${features.size}.")
-      require(weight >= 0.0, s"instance weight, $weight has to be >= 0.0")
-
-      if (weight == 0.0) return this
-      val localFeaturesStd = featuresStd
-      val localCoefficients = parametersArray
-      val localGradientSumArray = gradientSumArray
-
-      val margin = {
-        var sum = 0.0
-        features.foreachActive { (index, value) =>
-          sum += localCoefficients.getDouble(index) * value * localFeaturesStd.getDouble(index)
-        }
-        if (fitIntercept) sum += intercept
-        sum
-      }
-      val linearLoss = label - margin
-
-      if (math.abs(linearLoss) <= sigma * epsilon) {
-        lossSum += 0.5 * weight * (sigma + math.pow(linearLoss, 2.0) / sigma)
-        val linearLossDivSigma = linearLoss / sigma
-
-        features.foreachActive { (index, value) =>
-          localGradientSumArray.set(index, localGradientSumArray.getDouble(index)
-            - 1.0 * weight * linearLossDivSigma * value * localFeaturesStd.getDouble(index))
-        }
-        if (fitIntercept) {
-          localGradientSumArray.set(dim - 2, localGradientSumArray.getDouble(dim - 2)
-            - 1.0 * weight * linearLossDivSigma)
-        }
-        localGradientSumArray.set(dim - 1, localGradientSumArray.getDouble(dim - 1)
-          + 0.5 * weight * (1.0 - math.pow(linearLossDivSigma, 2.0)))
-      } else {
-        val sign = if (linearLoss >= 0) -1.0 else 1.0
-        lossSum += 0.5 * weight *
-          (sigma + 2.0 * epsilon * math.abs(linearLoss) - sigma * epsilon * epsilon)
-
-        features.foreachActive { (index, value) =>
-          localGradientSumArray.set(index, localGradientSumArray.getDouble(index)
-            + weight * sign * epsilon * value * localFeaturesStd.getDouble(index))
-        }
-        if (fitIntercept) {
-          localGradientSumArray.set(dim - 2, localGradientSumArray.getDouble(dim - 2)
-            + weight * sign * epsilon)
-        }
-        localGradientSumArray.set(dim - 1, localGradientSumArray.getDouble(dim - 1)
-          + 0.5 * weight * (1.0 - epsilon * epsilon))
-      }
-
-      weightSum += weight
-      this
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/LeastSquaresAggregatorX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/LeastSquaresAggregatorX.scala
deleted file mode 100644
index 9677969..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/LeastSquaresAggregatorX.scala
+++ /dev/null
@@ -1,226 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.optim.aggregator
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors}
-
-/**
- * LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function,
- * as used in linear regression for samples in sparse or dense vector in an online fashion.
- *
- * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of
- * the corresponding joint dataset.
- *
- * For improving the convergence rate during the optimization process, and also preventing against
- * features with very large variances exerting an overly large influence during model training,
- * package like R's GLMNET performs the scaling to unit variance and removing the mean to reduce
- * the condition number, and then trains the model in scaled space but returns the coefficients in
- * the original scale. See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf
- *
- * However, we don't want to apply the `StandardScaler` on the training dataset, and then cache
- * the standardized dataset since it will create a lot of overhead. As a result, we perform the
- * scaling implicitly when we compute the objective function. The following is the mathematical
- * derivation.
- *
- * Note that we don't deal with intercept by adding bias here, because the intercept
- * can be computed using closed form after the coefficients are converged.
- * See this discussion for detail.
- * http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
- *
- * When training with intercept enabled,
- * The objective function in the scaled space is given by
- *
- * <blockquote>
- *    $$
- *    L = 1/2n ||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2,
- *    $$
- * </blockquote>
- *
- * where $\bar{x_i}$ is the mean of $x_i$, $\hat{x_i}$ is the standard deviation of $x_i$,
- * $\bar{y}$ is the mean of label, and $\hat{y}$ is the standard deviation of label.
- *
- * If we fitting the intercept disabled (that is forced through 0.0),
- * we can use the same equation except we set $\bar{y}$ and $\bar{x_i}$ to 0 instead
- * of the respective means.
- *
- * This can be rewritten as
- *
- * <blockquote>
- *    $$
- *    \begin{align}
- *     L &= 1/2n ||\sum_i (w_i/\hat{x_i})x_i - \sum_i (w_i/\hat{x_i})\bar{x_i} - y / \hat{y}
- *          + \bar{y} / \hat{y}||^2 \\
- *       &= 1/2n ||\sum_i w_i^\prime x_i - y / \hat{y} + offset||^2 = 1/2n diff^2
- *    \end{align}
- *    $$
- * </blockquote>
- *
- * where $w_i^\prime$ is the effective coefficients defined by $w_i/\hat{x_i}$, offset is
- *
- * <blockquote>
- *    $$
- *    - \sum_i (w_i/\hat{x_i})\bar{x_i} + \bar{y} / \hat{y}.
- *    $$
- * </blockquote>
- *
- * and diff is
- *
- * <blockquote>
- *    $$
- *    \sum_i w_i^\prime x_i - y / \hat{y} + offset
- *    $$
- * </blockquote>
- *
- * Note that the effective coefficients and offset don't depend on training dataset,
- * so they can be precomputed.
- *
- * Now, the first derivative of the objective function in scaled space is
- *
- * <blockquote>
- *    $$
- *    \frac{\partial L}{\partial w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i}
- *    $$
- * </blockquote>
- *
- * However, $(x_i - \bar{x_i})$ will densify the computation, so it's not
- * an ideal formula when the training dataset is sparse format.
- *
- * This can be addressed by adding the dense $\bar{x_i} / \hat{x_i}$ terms
- * in the end by keeping the sum of diff. The first derivative of total
- * objective function from all the samples is
- *
- *
- * <blockquote>
- *    $$
- *    \begin{align}
- *       \frac{\partial L}{\partial w_i} &=
- *           1/N \sum_j diff_j (x_{ij} - \bar{x_i}) / \hat{x_i} \\
- *         &= 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) - diffSum \bar{x_i} / \hat{x_i}) \\
- *         &= 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + correction_i)
- *    \end{align}
- *    $$
- * </blockquote>
- *
- * where $correction_i = - diffSum \bar{x_i} / \hat{x_i}$
- *
- * A simple math can show that diffSum is actually zero, so we don't even
- * need to add the correction terms in the end. From the definition of diff,
- *
- * <blockquote>
- *    $$
- *    \begin{align}
- *       diffSum &= \sum_j (\sum_i w_i(x_{ij} - \bar{x_i})
- *                    / \hat{x_i} - (y_j - \bar{y}) / \hat{y}) \\
- *         &= N * (\sum_i w_i(\bar{x_i} - \bar{x_i}) / \hat{x_i} - (\bar{y} - \bar{y}) / \hat{y}) \\
- *         &= 0
- *    \end{align}
- *    $$
- * </blockquote>
- *
- * As a result, the first derivative of the total objective function only depends on
- * the training dataset, which can be easily computed in distributed fashion, and is
- * sparse format friendly.
- *
- * <blockquote>
- *    $$
- *    \frac{\partial L}{\partial w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i})
- *    $$
- * </blockquote>
- *
- * @note The constructor is curried, since the cost function will repeatedly create new versions
- *       of this class for different coefficient vectors.
- *
- * @param labelStd The standard deviation value of the label.
- * @param labelMean The mean value of the label.
- * @param fitIntercept Whether to fit an intercept term.
- * @param bcFeaturesStd The broadcast standard deviation values of the features.
- * @param bcFeaturesMean The broadcast mean values of the features.
- * @param bcCoefficients The broadcast coefficients corresponding to the features.
- */
-private[ml] class LeastSquaresAggregatorX(
-    labelStd: Double,
-    labelMean: Double,
-    fitIntercept: Boolean,
-    bcFeaturesStd: Broadcast[Array[Double]],
-    bcFeaturesMean: Broadcast[Array[Double]])(bcCoefficients: Broadcast[Vector])
-  extends DifferentiableLossAggregatorX[Instance, LeastSquaresAggregatorX] {
-  require(labelStd > 0.0, s"${this.getClass.getName} requires the label standard " +
-    s"deviation to be positive.")
-
-  private val numFeatures = bcFeaturesStd.value.length
-  protected override val dim: Int = numFeatures
-  // make transient so we do not serialize between aggregation stages
-  @transient private lazy val featuresStd = DoubleArrayList.wrap(bcFeaturesStd.value)
-  @transient private lazy val effectiveCoefAndOffset = {
-    val coefficientsArray = bcCoefficients.value.toArray.clone()
-    val featuresMean = bcFeaturesMean.value
-    var sum = 0.0
-    var i = 0
-    val len = coefficientsArray.length
-    while (i < len) {
-      coefficientsArray(i) *= featuresStd.getDouble(i)
-      sum += coefficientsArray(i) * featuresMean(i)
-      i += 1
-    }
-    val offset = if (fitIntercept) labelMean / labelStd - sum else 0.0
-    (Vectors.dense(coefficientsArray), offset)
-  }
-  // do not use tuple assignment above because it will circumvent the @transient tag
-  @transient private lazy val effectiveCoefficientsVector = effectiveCoefAndOffset._1
-  @transient private lazy val offset = effectiveCoefAndOffset._2
-
-  /**
-   * Add a new training instance to this LeastSquaresAggregator, and update the loss and gradient
-   * of the objective function.
-   *
-   * @param instance The instance of data point to be added.
-   * @return This LeastSquaresAggregator object.
-   */
-  def add(instance: Instance): LeastSquaresAggregatorX = {
-    instance match { case Instance(label, weight, features) =>
-      require(numFeatures == features.size, s"Dimensions mismatch when adding new sample." +
-        s" Expecting $numFeatures but got ${features.size}.")
-      require(weight >= 0.0, s"instance weight, $weight has to be >= 0.0")
-
-      if (weight == 0.0) return this
-
-      val diff = BLAS.dot(features, effectiveCoefficientsVector) - label / labelStd + offset
-
-      if (diff != 0) {
-        val localGradientSumArray = gradientSumArray
-        val localFeaturesStd = featuresStd
-        features.foreachActive { (index, value) =>
-          localGradientSumArray.set(index, localGradientSumArray.getDouble(index)
-            + weight * diff * value * localFeaturesStd.getDouble(index))
-        }
-        lossSum += weight * diff * diff / 2.0
-      }
-      weightSum += weight
-      this
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorX.scala
deleted file mode 100644
index 6ac09f6..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorX.scala
+++ /dev/null
@@ -1,379 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.optim.aggregator
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg.{DenseVector, Vector}
-import org.apache.spark.mllib.util.MLUtils
-
-/**
- * LogisticAggregator computes the gradient and loss for binary or multinomial logistic (softmax)
- * loss function, as used in classification for instances in sparse or dense vector in an online
- * fashion.
- *
- * Two LogisticAggregators can be merged together to have a summary of loss and gradient of
- * the corresponding joint dataset.
- *
- * For improving the convergence rate during the optimization process and also to prevent against
- * features with very large variances exerting an overly large influence during model training,
- * packages like R's GLMNET perform the scaling to unit variance and remove the mean in order to
- * reduce the condition number. The model is then trained in this scaled space, but returns the
- * coefficients in the original scale. See page 9 in
- * http://cran.r-project.org/web/packages/glmnet/glmnet.pdf
- *
- * However, we don't want to apply the [[org.apache.spark.ml.feature.StandardScaler]] on the
- * training dataset, and then cache the standardized dataset since it will create a lot of overhead.
- * As a result, we perform the scaling implicitly when we compute the objective function (though
- * we do not subtract the mean).
- *
- * Note that there is a difference between multinomial (softmax) and binary loss. The binary case
- * uses one outcome class as a "pivot" and regresses the other class against the pivot. In the
- * multinomial case, the softmax loss function is used to model each class probability
- * independently. Using softmax loss produces `K` sets of coefficients, while using a pivot class
- * produces `K - 1` sets of coefficients (a single coefficient vector in the binary case). In the
- * binary case, we can say that the coefficients are shared between the positive and negative
- * classes. When regularization is applied, multinomial (softmax) loss will produce a result
- * different from binary loss since the positive and negative don't share the coefficients while the
- * binary regression shares the coefficients between positive and negative.
- *
- * The following is a mathematical derivation for the multinomial (softmax) loss.
- *
- * The probability of the multinomial outcome $y$ taking on any of the K possible outcomes is:
- *
- * <blockquote>
- *    $$
- *    P(y_i=0|\vec{x}_i, \beta) = \frac{e^{\vec{x}_i^T \vec{\beta}_0}}{\sum_{k=0}^{K-1}
- *       e^{\vec{x}_i^T \vec{\beta}_k}} \\
- *    P(y_i=1|\vec{x}_i, \beta) = \frac{e^{\vec{x}_i^T \vec{\beta}_1}}{\sum_{k=0}^{K-1}
- *       e^{\vec{x}_i^T \vec{\beta}_k}}\\
- *    P(y_i=K-1|\vec{x}_i, \beta) = \frac{e^{\vec{x}_i^T \vec{\beta}_{K-1}}\,}{\sum_{k=0}^{K-1}
- *       e^{\vec{x}_i^T \vec{\beta}_k}}
- *    $$
- * </blockquote>
- *
- * The model coefficients $\beta = (\beta_0, \beta_1, \beta_2, ..., \beta_{K-1})$ become a matrix
- * which has dimension of $K \times (N+1)$ if the intercepts are added. If the intercepts are not
- * added, the dimension will be $K \times N$.
- *
- * Note that the coefficients in the model above lack identifiability. That is, any constant scalar
- * can be added to all of the coefficients and the probabilities remain the same.
- *
- * <blockquote>
- *    $$
- *    \begin{align}
- *    \frac{e^{\vec{x}_i^T \left(\vec{\beta}_0 + \vec{c}\right)}}{\sum_{k=0}^{K-1}
- *       e^{\vec{x}_i^T \left(\vec{\beta}_k + \vec{c}\right)}}
- *    = \frac{e^{\vec{x}_i^T \vec{\beta}_0}e^{\vec{x}_i^T \vec{c}}\,}{e^{\vec{x}_i^T \vec{c}}
- *       \sum_{k=0}^{K-1} e^{\vec{x}_i^T \vec{\beta}_k}}
- *    = \frac{e^{\vec{x}_i^T \vec{\beta}_0}}{\sum_{k=0}^{K-1} e^{\vec{x}_i^T \vec{\beta}_k}}
- *    \end{align}
- *    $$
- * </blockquote>
- *
- * However, when regularization is added to the loss function, the coefficients are indeed
- * identifiable because there is only one set of coefficients which minimizes the regularization
- * term. When no regularization is applied, we choose the coefficients with the minimum L2
- * penalty for consistency and reproducibility. For further discussion see:
- *
- * Friedman, et al. "Regularization Paths for Generalized Linear Models via Coordinate Descent"
- *
- * The loss of objective function for a single instance of data (we do not include the
- * regularization term here for simplicity) can be written as
- *
- * <blockquote>
- *    $$
- *    \begin{align}
- *    \ell\left(\beta, x_i\right) &= -log{P\left(y_i \middle| \vec{x}_i, \beta\right)} \\
- *    &= log\left(\sum_{k=0}^{K-1}e^{\vec{x}_i^T \vec{\beta}_k}\right) - \vec{x}_i^T \vec{\beta}_y\\
- *    &= log\left(\sum_{k=0}^{K-1} e^{margins_k}\right) - margins_y
- *    \end{align}
- *    $$
- * </blockquote>
- *
- * where ${margins}_k = \vec{x}_i^T \vec{\beta}_k$.
- *
- * For optimization, we have to calculate the first derivative of the loss function, and a simple
- * calculation shows that
- *
- * <blockquote>
- *    $$
- *    \begin{align}
- *    \frac{\partial \ell(\beta, \vec{x}_i, w_i)}{\partial \beta_{j, k}}
- *    &= x_{i,j} \cdot w_i \cdot \left(\frac{e^{\vec{x}_i \cdot \vec{\beta}_k}}{\sum_{k'=0}^{K-1}
- *      e^{\vec{x}_i \cdot \vec{\beta}_{k'}}\,} - I_{y=k}\right) \\
- *    &= x_{i, j} \cdot w_i \cdot multiplier_k
- *    \end{align}
- *    $$
- * </blockquote>
- *
- * where $w_i$ is the sample weight, $I_{y=k}$ is an indicator function
- *
- *  <blockquote>
- *    $$
- *    I_{y=k} = \begin{cases}
- *          1 & y = k \\
- *          0 & else
- *       \end{cases}
- *    $$
- * </blockquote>
- *
- * and
- *
- * <blockquote>
- *    $$
- *    multiplier_k = \left(\frac{e^{\vec{x}_i \cdot \vec{\beta}_k}}{\sum_{k=0}^{K-1}
- *       e^{\vec{x}_i \cdot \vec{\beta}_k}} - I_{y=k}\right)
- *    $$
- * </blockquote>
- *
- * If any of margins is larger than 709.78, the numerical computation of multiplier and loss
- * function will suffer from arithmetic overflow. This issue occurs when there are outliers in
- * data which are far away from the hyperplane, and this will cause the failing of training once
- * infinity is introduced. Note that this is only a concern when max(margins) &gt; 0.
- *
- * Fortunately, when max(margins) = maxMargin &gt; 0, the loss function and the multiplier can
- * easily be rewritten into the following equivalent numerically stable formula.
- *
- * <blockquote>
- *    $$
- *    \ell\left(\beta, x\right) = log\left(\sum_{k=0}^{K-1} e^{margins_k - maxMargin}\right) -
- *       margins_{y} + maxMargin
- *    $$
- * </blockquote>
- *
- * Note that each term, $(margins_k - maxMargin)$ in the exponential is no greater than zero; as a
- * result, overflow will not happen with this formula.
- *
- * For $multiplier$, a similar trick can be applied as the following,
- *
- * <blockquote>
- *    $$
- *    multiplier_k = \left(\frac{e^{\vec{x}_i \cdot \vec{\beta}_k - maxMargin}}{\sum_{k'=0}^{K-1}
- *       e^{\vec{x}_i \cdot \vec{\beta}_{k'} - maxMargin}} - I_{y=k}\right)
- *    $$
- * </blockquote>
- *
- *
- * @param bcCoefficients The broadcast coefficients corresponding to the features.
- * @param bcFeaturesStd The broadcast standard deviation values of the features.
- * @param numClasses the number of possible outcomes for k classes classification problem in
- *                   Multinomial Logistic Regression.
- * @param fitIntercept Whether to fit an intercept term.
- * @param multinomial Whether to use multinomial (softmax) or binary loss
- * @note In order to avoid unnecessary computation during calculation of the gradient updates
- * we lay out the coefficients in column major order during training. This allows us to
- * perform feature standardization once, while still retaining sequential memory access
- * for speed. We convert back to row major order when we create the model,
- * since this form is optimal for the matrix operations used for prediction.
- */
-private[ml] class LogisticAggregatorX(
-    bcFeaturesStd: Broadcast[Array[Double]],
-    numClasses: Int,
-    fitIntercept: Boolean,
-    multinomial: Boolean)(bcCoefficients: Broadcast[Vector])
-  extends DifferentiableLossAggregatorX[Instance, LogisticAggregatorX] with Logging {
-
-  private val numFeatures = bcFeaturesStd.value.length
-  private val numFeaturesPlusIntercept = if (fitIntercept) numFeatures + 1 else numFeatures
-  private val coefficientSize = bcCoefficients.value.size
-  protected override val dim: Int = coefficientSize
-  if (multinomial) {
-    require(numClasses ==  coefficientSize / numFeaturesPlusIntercept, s"The number of " +
-      s"coefficients should be ${numClasses * numFeaturesPlusIntercept} but was $coefficientSize")
-  } else {
-    require(coefficientSize == numFeaturesPlusIntercept, s"Expected $numFeaturesPlusIntercept " +
-      s"coefficients but got $coefficientSize")
-    require(numClasses == 1 || numClasses == 2, s"Binary logistic aggregator requires numClasses " +
-      s"in {1, 2} but found $numClasses.")
-  }
-
-  @transient private lazy val coefficientsArray = DoubleArrayList.wrap(bcCoefficients.value match {
-    case DenseVector(values) => values
-    case _ => throw new IllegalArgumentException(s"coefficients only supports dense vector but " +
-      s"got type ${bcCoefficients.value.getClass}.)")
-  })
-  @transient private lazy val featuresStdArray = DoubleArrayList.wrap(bcFeaturesStd.value)
-
-  if (multinomial && numClasses <= 2) {
-    logInfo(s"Multinomial logistic regression for binary classification yields separate " +
-      s"coefficients for positive and negative classes. When no regularization is applied, the" +
-      s"result will be effectively the same as binary logistic regression. When regularization" +
-      s"is applied, multinomial loss will produce a result different from binary loss.")
-  }
-
-  /** Update gradient and loss using binary loss function. */
-  private def binaryUpdateInPlace(features: Vector, weight: Double, label: Double): Unit = {
-
-    val localFeaturesStd = featuresStdArray
-    val localCoefficients = coefficientsArray
-    val localGradientArray = gradientSumArray
-    val margin = - {
-      var sum = 0.0
-      features.foreachActive { (index, value) =>
-        sum += localCoefficients.getDouble(index) * value * localFeaturesStd.getDouble(index)
-      }
-      if (fitIntercept) sum += localCoefficients.getDouble(numFeaturesPlusIntercept - 1)
-      sum
-    }
-
-    val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - label)
-
-    features.foreachActive { (index, value) =>
-      localGradientArray.set(index, localGradientArray.getDouble(index)
-        + multiplier * value * localFeaturesStd.getDouble(index))
-    }
-
-    if (fitIntercept) {
-      localGradientArray.set(numFeaturesPlusIntercept - 1,
-        localGradientArray.getDouble(numFeaturesPlusIntercept - 1) + multiplier)
-    }
-
-    if (label > 0) {
-      // The following is equivalent to log(1 + exp(margin)) but more numerically stable.
-      lossSum += weight * MLUtils.log1pExp(margin)
-    } else {
-      lossSum += weight * (MLUtils.log1pExp(margin) - margin)
-    }
-  }
-
-  /** Update gradient and loss using multinomial (softmax) loss function. */
-  private def multinomialUpdateInPlace(features: Vector, weight: Double, label: Double): Unit = {
-    // TODO: use level 2 BLAS operations
-    /*
-      Note: this can still be used when numClasses = 2 for binary
-      logistic regression without pivoting.
-     */
-    val localFeaturesStd = featuresStdArray
-    val localCoefficients = coefficientsArray
-    val localGradientArray = gradientSumArray
-
-    // marginOfLabel is margins(label) in the formula
-    var marginOfLabel = 0.0
-    var maxMargin = Double.NegativeInfinity
-
-    val margins = new Array[Double](numClasses)
-    features.foreachActive { (index, value) =>
-      val localFeaturesStdValue = localFeaturesStd.getDouble(index)
-      if (localFeaturesStdValue != 0.0 && value != 0.0) {
-        val stdValue = value * localFeaturesStdValue
-        var j = 0
-        while (j < numClasses) {
-          margins(j) += localCoefficients.getDouble(index * numClasses + j) * stdValue
-          j += 1
-        }
-      }
-    }
-    var i = 0
-    while (i < numClasses) {
-      if (fitIntercept) {
-        margins(i) += localCoefficients.getDouble(numClasses * numFeatures + i)
-      }
-      if (i == label.toInt) marginOfLabel = margins(i)
-      if (margins(i) > maxMargin) {
-        maxMargin = margins(i)
-      }
-      i += 1
-    }
-
-    /**
-     * When maxMargin is greater than 0, the original formula could cause overflow.
-     * We address this by subtracting maxMargin from all the margins, so it's guaranteed
-     * that all of the new margins will be smaller than zero to prevent arithmetic overflow.
-     */
-    val multipliers = new Array[Double](numClasses)
-    val sum = {
-      var temp = 0.0
-      var i = 0
-      while (i < numClasses) {
-        if (maxMargin > 0) margins(i) -= maxMargin
-        val exp = math.exp(margins(i))
-        temp += exp
-        multipliers(i) = exp
-        i += 1
-      }
-      temp
-    }
-
-    margins.indices.foreach { i =>
-      multipliers(i) = multipliers(i) / sum - (if (label == i) 1.0 else 0.0)
-    }
-    features.foreachActive { (index, value) =>
-      val localFeaturesStdValue = localFeaturesStd.getDouble(index)
-      if (localFeaturesStdValue != 0.0 && value != 0.0) {
-        val stdValue = value * localFeaturesStdValue
-        var j = 0
-        while (j < numClasses) {
-          val id = index * numClasses + j
-          localGradientArray.set(id, localGradientArray.getDouble(id)
-            + weight * multipliers(j) * stdValue)
-          j += 1
-        }
-      }
-    }
-    if (fitIntercept) {
-      var i = 0
-      while (i < numClasses) {
-        val id = numFeatures * numClasses + i
-        localGradientArray.set(id, localGradientArray.getDouble(id)
-          + weight * multipliers(i))
-        i += 1
-      }
-    }
-
-    val loss = if (maxMargin > 0) {
-      math.log(sum) - marginOfLabel + maxMargin
-    } else {
-      math.log(sum) - marginOfLabel
-    }
-    lossSum += weight * loss
-  }
-
-  /**
-   * Add a new training instance to this LogisticAggregator, and update the loss and gradient
-   * of the objective function.
-   *
-   * @param instance The instance of data point to be added.
-   * @return This LogisticAggregator object.
-   */
-  def add(instance: Instance): this.type = {
-    instance match { case Instance(label, weight, features) =>
-      require(numFeatures == features.size, s"Dimensions mismatch when adding new instance." +
-        s" Expecting $numFeatures but got ${features.size}.")
-      require(weight >= 0.0, s"instance weight, $weight has to be >= 0.0")
-
-      if (weight == 0.0) return this
-
-      if (multinomial) {
-        multinomialUpdateInPlace(features, weight, label)
-      } else {
-        binaryUpdateInPlace(features, weight, label)
-      }
-      weightSum += weight
-      this
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/loss/RDDLossFunctionX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/loss/RDDLossFunctionX.scala
deleted file mode 100644
index c548b90..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/optim/loss/RDDLossFunctionX.scala
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.optim.loss
-
-import scala.reflect.ClassTag
-
-import breeze.linalg.{DenseVector => BDV}
-import breeze.optimize.DiffFunction
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors}
-import org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregatorX
-import org.apache.spark.rdd.RDD
-
-/**
- * This class computes the gradient and loss of a differentiable loss function by mapping a
- * [[DifferentiableLossAggregatorX]] over an [[RDD]]. The loss function is the
- * sum of the loss computed on a single instance across all points in the RDD. Therefore, the actual
- * analytical form of the loss function is specified by the aggregator, which computes each points
- * contribution to the overall loss.
- *
- * A differentiable regularization component can also be added by providing a
- * [[DifferentiableRegularization]] loss function.
- *
- * @param instances RDD containing the data to compute the loss function over.
- * @param getAggregator A function which gets a new loss aggregator in every tree aggregate step.
- * @param regularization An option representing the regularization loss function to apply to the
- *                       coefficients.
- * @param aggregationDepth The aggregation depth of the tree aggregation step.
- * @tparam Agg Specialization of [[DifferentiableLossAggregatorX]], representing the concrete type
- *             of the aggregator.
- */
-private[ml] class RDDLossFunctionX[
-    T: ClassTag,
-    Agg <: DifferentiableLossAggregatorX[T, Agg]: ClassTag](
-    instances: RDD[T],
-    getAggregator: (Broadcast[Vector] => Agg),
-    regularization: Option[DifferentiableRegularization[Vector]],
-    aggregationDepth: Int = 2)
-  extends DiffFunction[BDV[Double]] {
-
-  override def calculate(coefficients: BDV[Double]): (Double, BDV[Double]) = {
-    val bcCoefficients = instances.context.broadcast(Vectors.fromBreeze(coefficients))
-    val thisAgg = getAggregator(bcCoefficients)
-    val seqOp = (agg: Agg, x: T) => agg.add(x)
-    val combOp = (agg1: Agg, agg2: Agg) => agg1.merge(agg2)
-    val newAgg = instances.treeAggregate(thisAgg)(seqOp, combOp, aggregationDepth)
-    val gradient = newAgg.gradient
-    val regLoss = regularization.map { regFun =>
-      val (regLoss, regGradient) = regFun.calculate(Vectors.fromBreeze(coefficients))
-      BLAS.axpy(1.0, regGradient, gradient)
-      regLoss
-    }.getOrElse(0.0)
-    bcCoefficients.destroy(blocking = false)
-    (newAgg.loss + regLoss, gradient.asBreeze.toDenseVector)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 917bb52..e3c4bae 100644
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/ml-accelerator/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -38,17 +32,18 @@ import org.apache.hadoop.fs.Path
 import org.json4s.DefaultFormats
 import org.json4s.JsonDSL._
 
-import org.apache.spark.{Dependency, Partitioner, ShuffleDependency, SparkContext}
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.{Partitioner, SparkException}
+import org.apache.spark.annotation.Since
 import org.apache.spark.internal.Logging
-import org.apache.spark.ml.{Estimator, Model, StaticUtils}
+import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.linalg.BLAS
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.linalg.CholeskyDecomposition
 import org.apache.spark.mllib.optimization.NNLS
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{DeterministicLevel, RDD}
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
@@ -57,11 +52,11 @@ import org.apache.spark.util.{BoundedPriorityQueue, Utils}
 import org.apache.spark.util.collection.{OpenHashMap, OpenHashSet, SortDataFormat, Sorter}
 import org.apache.spark.util.random.XORShiftRandom
 
-
 /**
  * Common params for ALS and ALSModel.
  */
-private[recommendation] trait ALSModelParams extends Params with HasPredictionCol {
+private[recommendation] trait ALSModelParams extends Params with HasPredictionCol
+  with HasBlockSize {
   /**
    * Param for the column name for user ids. Ids must be integers. Other
    * numeric types are supported for this column, but will be cast to integers as long as they
@@ -132,13 +127,15 @@ private[recommendation] trait ALSModelParams extends Params with HasPredictionCo
 
   /** @group expertGetParam */
   def getColdStartStrategy: String = $(coldStartStrategy).toLowerCase(Locale.ROOT)
+
+  setDefault(blockSize -> 4096)
 }
 
 /**
  * Common params for ALS.
  */
 private[recommendation] trait ALSParams extends ALSModelParams with HasMaxIter with HasRegParam
-  with HasPredictionCol with HasCheckpointInterval with HasSeed {
+  with HasCheckpointInterval with HasSeed {
 
   /**
    * Param for rank of the matrix factorization (positive).
@@ -295,6 +292,15 @@ class ALSModel private[ml] (
   @Since("2.2.0")
   def setColdStartStrategy(value: String): this.type = set(coldStartStrategy, value)
 
+  /**
+   * Set block size for stacking input data in matrices.
+   * Default is 4096.
+   *
+   * @group expertSetParam
+   */
+  @Since("3.0.0")
+  def setBlockSize(value: Int): this.type = set(blockSize, value)
+
   private val predict = udf { (featuresA: Seq[Float], featuresB: Seq[Float]) =>
     if (featuresA != null && featuresB != null) {
       var dotProduct = 0.0f
@@ -345,6 +351,11 @@ class ALSModel private[ml] (
   @Since("1.6.0")
   override def write: MLWriter = new ALSModel.ALSModelWriter(this)
 
+  @Since("3.0.0")
+  override def toString: String = {
+    s"ALSModel: uid=$uid, rank=$rank"
+  }
+
   /**
    * Returns top `numItems` items recommended for each user, for all users.
    * @param numItems max number of recommendations for each user
@@ -353,7 +364,7 @@ class ALSModel private[ml] (
    */
   @Since("2.2.0")
   def recommendForAllUsers(numItems: Int): DataFrame = {
-    recommendForAll(userFactors, itemFactors, $(userCol), $(itemCol), numItems)
+    recommendForAll(userFactors, itemFactors, $(userCol), $(itemCol), numItems, $(blockSize))
   }
 
   /**
@@ -368,7 +379,7 @@ class ALSModel private[ml] (
   @Since("2.3.0")
   def recommendForUserSubset(dataset: Dataset[_], numItems: Int): DataFrame = {
     val srcFactorSubset = getSourceFactorSubset(dataset, userFactors, $(userCol))
-    recommendForAll(srcFactorSubset, itemFactors, $(userCol), $(itemCol), numItems)
+    recommendForAll(srcFactorSubset, itemFactors, $(userCol), $(itemCol), numItems, $(blockSize))
   }
 
   /**
@@ -379,7 +390,7 @@ class ALSModel private[ml] (
    */
   @Since("2.2.0")
   def recommendForAllItems(numUsers: Int): DataFrame = {
-    recommendForAll(itemFactors, userFactors, $(itemCol), $(userCol), numUsers)
+    recommendForAll(itemFactors, userFactors, $(itemCol), $(userCol), numUsers, $(blockSize))
   }
 
   /**
@@ -394,7 +405,7 @@ class ALSModel private[ml] (
   @Since("2.3.0")
   def recommendForItemSubset(dataset: Dataset[_], numUsers: Int): DataFrame = {
     val srcFactorSubset = getSourceFactorSubset(dataset, itemFactors, $(itemCol))
-    recommendForAll(srcFactorSubset, userFactors, $(itemCol), $(userCol), numUsers)
+    recommendForAll(srcFactorSubset, userFactors, $(itemCol), $(userCol), numUsers, $(blockSize))
   }
 
   /**
@@ -443,11 +454,12 @@ class ALSModel private[ml] (
       dstFactors: DataFrame,
       srcOutputColumn: String,
       dstOutputColumn: String,
-      num: Int): DataFrame = {
+      num: Int,
+      blockSize: Int): DataFrame = {
     import srcFactors.sparkSession.implicits._
 
-    val srcFactorsBlocked = blockify(srcFactors.as[(Int, Array[Float])])
-    val dstFactorsBlocked = blockify(dstFactors.as[(Int, Array[Float])])
+    val srcFactorsBlocked = blockify(srcFactors.as[(Int, Array[Float])], blockSize)
+    val dstFactorsBlocked = blockify(dstFactors.as[(Int, Array[Float])], blockSize)
     val ratings = srcFactorsBlocked.crossJoin(dstFactorsBlocked)
       .as[(Seq[(Int, Array[Float])], Seq[(Int, Array[Float])])]
       .flatMap { case (srcIter, dstIter) =>
@@ -485,11 +497,10 @@ class ALSModel private[ml] (
 
   /**
    * Blockifies factors to improve the efficiency of cross join
-   * TODO: SPARK-20443 - expose blockSize as a param?
    */
   private def blockify(
       factors: Dataset[(Int, Array[Float])],
-      blockSize: Int = 4096): Dataset[Seq[(Int, Array[Float])]] = {
+      blockSize: Int): Dataset[Seq[(Int, Array[Float])]] = {
     import factors.sparkSession.implicits._
     factors.mapPartitions(_.grouped(blockSize))
   }
@@ -537,7 +548,7 @@ object ALSModel extends MLReadable[ALSModel] {
 
       val model = new ALSModel(metadata.uid, rank, userFactors, itemFactors)
 
-      DefaultParamsReader.getAndSetParams(model, metadata)
+      metadata.getAndSetParams(model)
       model
     }
   }
@@ -564,13 +575,20 @@ object ALSModel extends MLReadable[ALSModel] {
  *
  * For implicit preference data, the algorithm used is based on
  * "Collaborative Filtering for Implicit Feedback Datasets", available at
- * http://dx.doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here.
+ * https://doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here.
  *
  * Essentially instead of finding the low-rank approximations to the rating matrix `R`,
  * this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if
  * r is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence'
  * values related to strength of indicated user
  * preferences rather than explicit ratings given to items.
+ *
+ * Note: the input rating dataset to the ALS implementation should be deterministic.
+ * Nondeterministic data can cause failure during fitting ALS model.
+ * For example, an order-sensitive operation like sampling after a repartition makes dataset
+ * output nondeterministic, like `dataset.repartition(2).sample(false, 0.5, 1618)`.
+ * Checkpointing sampled dataset or adding a sort before sampling can help make the dataset
+ * deterministic.
  */
 @Since("1.3.0")
 class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel] with ALSParams
@@ -649,6 +667,15 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
   @Since("2.2.0")
   def setColdStartStrategy(value: String): this.type = set(coldStartStrategy, value)
 
+  /**
+   * Set block size for stacking input data in matrices.
+   * Default is 4096.
+   *
+   * @group expertSetParam
+   */
+  @Since("3.0.0")
+  def setBlockSize(value: Int): this.type = set(blockSize, value)
+
   /**
    * Sets both numUserBlocks and numItemBlocks to the specific value.
    *
@@ -661,10 +688,8 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
     this
   }
 
-
-
   @Since("2.0.0")
-  override def fit(dataset: Dataset[_]): ALSModel = {
+  override def fit(dataset: Dataset[_]): ALSModel = instrumented { instr =>
     transformSchema(dataset.schema)
     import dataset.sparkSession.implicits._
 
@@ -676,11 +701,11 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
         Rating(row.getInt(0), row.getInt(1), row.getFloat(2))
       }
 
-
-    val instr = Instrumentation.create(this, ratings)
-    instr.logParams(rank, numUserBlocks, numItemBlocks, implicitPrefs, alpha, userCol,
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, rank, numUserBlocks, numItemBlocks, implicitPrefs, alpha, userCol,
       itemCol, ratingCol, predictionCol, maxIter, regParam, nonnegative, checkpointInterval,
-      seed, intermediateStorageLevel, finalStorageLevel)
+      seed, intermediateStorageLevel, finalStorageLevel, blockSize)
 
     val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank),
       numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks),
@@ -691,8 +716,8 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
       checkpointInterval = $(checkpointInterval), seed = $(seed))
     val userDF = userFactors.toDF("id", "features")
     val itemDF = itemFactors.toDF("id", "features")
-    val model = new ALSModel(uid, $(rank), userDF, itemDF).setParent(this)
-    instr.logSuccess(model)
+    val model = new ALSModel(uid, $(rank), userDF, itemDF).setBlockSize($(blockSize))
+      .setParent(this)
     copyValues(model)
   }
 
@@ -707,21 +732,17 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
 
 
 /**
- * :: DeveloperApi ::
  * An implementation of ALS that supports generic ID types, specialized for Int and Long. This is
  * exposed as a developer API for users who do need other ID types. But it is not recommended
  * because it increases the shuffle size and memory requirement during training. For simplicity,
  * users and items must have the same type. The number of distinct users/items should be smaller
  * than 2 billion.
  */
-@DeveloperApi
 object ALS extends DefaultParamsReadable[ALS] with Logging {
 
   /**
-   * :: DeveloperApi ::
    * Rating class for better code readability.
    */
-  @DeveloperApi
   case class Rating[@specialized(Int, Long) ID](user: ID, item: ID, rating: Float)
 
   @Since("1.6.0")
@@ -804,7 +825,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
      * Given a triangular matrix in the order of fillXtX above, compute the full symmetric square
      * matrix that it represents, storing it into destMatrix.
      */
-    private def fillAtA(triAtA: Array[Double], lambda: Double) {
+    private def fillAtA(triAtA: Array[Double], lambda: Double): Unit = {
       var i = 0
       var pos = 0
       var a = 0.0
@@ -862,7 +883,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
     }
 
     /** Adds an observation. */
-    def add(a: Array[Float], b: Double, c: Double = 1.0): this.type = {
+    def add(a: Array[Float], b: Double, c: Double = 1.0): NormalEquation = {
       require(c >= 0.0)
       require(a.length == k)
       copyToDouble(a)
@@ -874,7 +895,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
     }
 
     /** Merges another normal equation object. */
-    def merge(other: NormalEquation): this.type = {
+    def merge(other: NormalEquation): NormalEquation = {
       require(other.k == k)
       blas.daxpy(ata.length, 1.0, other.ata, 1, ata, 1)
       blas.daxpy(atb.length, 1.0, other.atb, 1, atb, 1)
@@ -892,7 +913,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
   val DEFAULT_UNPERSIST_CYCLE = 300
 
   /**
-   * :: DeveloperApi ::
    * Implementation of the ALS algorithm.
    *
    * This implementation of the ALS factorization algorithm partitions the two sets of factors among
@@ -917,7 +937,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
    * "block" as referring to a subset of an RDD containing the ratings rather than a contiguous
    * submatrix of the ratings matrix.
    */
-  @DeveloperApi
   def train[ID: ClassTag]( // scalastyle:ignore
       ratings: RDD[Rating[ID]],
       rank: Int = 10,
@@ -964,7 +983,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
     val joinIU = mergedIU.join(userInBlocks).persist()
     joinIU.foreachPartition(_)
 
-
     // Encoders for storing each user/item's partition ID and index within its partition using a
     // single integer; used as an optimization
     val userLocalIndexEncoder = new LocalIndexEncoder(userPart.numPartitions)
@@ -1017,6 +1035,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
       case x: Exception =>
         throw new Exception("'spark.boostkit.ALS.blockMaxRow' value is invalid")
     }
+
     if (implicitPrefs) {
       val dataIterI = new Array[RDD[(Int, ALS.FactorBlock)]](unpersistCycle)
       val dataIterU = new Array[RDD[(Int, ALS.FactorBlock)]](unpersistCycle)
@@ -1068,14 +1087,16 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
 
       }
     }
-
     val userIdAndFactors = userInBlocks
       .mapValues(_.srcIds)
       .join(userFactors)
       .mapPartitions({ items =>
         items.flatMap { case (_, (ids, factors)) =>
-          ids.view.zip(factors)
+          ids.iterator.zip(factors.iterator)
         }
+        // Preserve the partitioning because IDs
+        // are consistent with the partitioners in userInBlocks
+        // and userFactors.
       }, preservesPartitioning = true)
       .setName("userFactors")
       .persist(finalRDDStorageLevel)
@@ -1084,20 +1105,20 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
       .join(itemFactors)
       .mapPartitions({ items =>
         items.flatMap { case (_, (ids, factors)) =>
-          ids.view.zip(factors)
+          ids.iterator.zip(factors.iterator)
         }
       }, preservesPartitioning = true)
       .setName("itemFactors")
       .persist(finalRDDStorageLevel)
     if (finalRDDStorageLevel != StorageLevel.NONE) {
       userIdAndFactors.count()
-      itemFactors.unpersist()
-      itemIdAndFactors.count()
       userInBlocks.unpersist()
       userOutBlocks.unpersist()
-      itemInBlocks.unpersist()
       itemOutBlocks.unpersist()
       blockRatings.unpersist()
+      itemIdAndFactors.count()
+      itemFactors.unpersist()
+      itemInBlocks.unpersist()
     }
     (userIdAndFactors, itemIdAndFactors)
   }
@@ -1291,16 +1312,19 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
     // elements distributed as Normal(0,1) and taking the absolute value, and then normalizing.
     // This appears to create factorizations that have a slightly better reconstruction
     // (<1%) compared picking elements uniformly at random in [0,1].
-    inBlocks.map { case (srcBlockId, inBlock) =>
-      val random = new XORShiftRandom(byteswap64(seed ^ srcBlockId))
-      val factors = Array.fill(inBlock.srcIds.length) {
-        val factor = Array.fill(rank)(random.nextGaussian().toFloat)
-        val nrm = blas.snrm2(rank, factor, 1)
-        blas.sscal(rank, 1.0f / nrm, factor, 1)
-        factor
+    inBlocks.mapPartitions({ iter =>
+      iter.map {
+        case (srcBlockId, inBlock) =>
+          val random = new XORShiftRandom(byteswap64(seed ^ srcBlockId))
+          val factors = Array.fill(inBlock.srcIds.length) {
+            val factor = Array.fill(rank)(random.nextGaussian().toFloat)
+            val nrm = blas.snrm2(rank, factor, 1)
+            blas.sscal(rank, 1.0f / nrm, factor, 1)
+            factor
+          }
+          (srcBlockId, factors)
       }
-      (srcBlockId, factors)
-    }
+    }, preservesPartitioning = true)
   }
 
   /**
@@ -1396,7 +1420,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
           Iterator.empty
         }
       } ++ {
-        builders.view.zipWithIndex.filter(_._1.size > 0).map { case (block, idx) =>
+        builders.iterator.zipWithIndex.filter(_._1.size > 0).map { case (block, idx) =>
           val srcBlockId = idx % srcPart.numPartitions
           val dstBlockId = idx / srcPart.numPartitions
           ((srcBlockId, dstBlockId), block.build())
@@ -1632,7 +1656,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
         val dstIdSet = new OpenHashSet[ID](1 << 20)
         dstIds.foreach(dstIdSet.add)
         val sortedDstIds = new Array[ID](dstIdSet.size)
-        var i = StaticUtils.ZERO_INT
+        var i = 0
         var pos = dstIdSet.nextPos(0)
         while (pos != -1) {
           sortedDstIds(i) = dstIdSet.getValue(pos)
@@ -1784,6 +1808,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
     ne.copyATA(d)
   }
 
+
   /**
    * Encoder for storing (blockId, localIndex) into a single integer.
    *
@@ -1827,31 +1852,4 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
    * satisfies this requirement, we simply use a type alias here.
    */
   private[recommendation] type ALSPartitioner = org.apache.spark.HashPartitioner
-
-  /**
-   * Private function to clean up all of the shuffles files from the dependencies and their parents.
-   */
-  private[spark] def cleanShuffleDependencies[T](
-      sc: SparkContext,
-      deps: Seq[Dependency[_]],
-      blocking: Boolean = false): Unit = {
-    // If there is no reference tracking we skip clean up.
-    sc.cleaner.foreach { cleaner =>
-      /**
-       * Clean the shuffles & all of its parents.
-       */
-      def cleanEagerly(dep: Dependency[_]): Unit = {
-        if (dep.isInstanceOf[ShuffleDependency[_, _, _]]) {
-          val shuffleId = dep.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
-          cleaner.doCleanupShuffle(shuffleId, blocking)
-        }
-        val rdd = dep.rdd
-        val rddDeps = rdd.dependencies
-        if (rdd.getStorageLevel == StorageLevel.NONE && rddDeps != null) {
-          rddDeps.foreach(cleanEagerly)
-        }
-      }
-      deps.foreach(cleanEagerly)
-    }
-  }
-}
+}
\ No newline at end of file
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
deleted file mode 100644
index c6f1aa4..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ /dev/null
@@ -1,184 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.regression
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-import it.unimi.dsi.fastutil.ints.{Int2ObjectOpenHashMap, IntArrayList}
-import it.unimi.dsi.fastutil.objects.ObjectArrayList
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.Predictor
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.DecisionForest
-import org.apache.spark.ml.tree.impl.DecisionTreeMetadata
-import org.apache.spark.ml.tree.impl.RandomForest4GBDTX
-import org.apache.spark.ml.tree.impl.TreePoint
-import org.apache.spark.ml.util._
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.Dataset
-
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Decision_tree_learning">Decision tree</a>
- * learning algorithm for regression.
- * It supports both continuous and categorical features.
- */
-@Since("1.4.0")
-class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
-  extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
-  with DecisionTreeRegressorParams with DefaultParamsWritable {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("dtr"))
-
-  // Override parameter setters from parent trait for Java API compatibility.
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be at least 1.
-   * (default = 10)
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setImpurity(value: String): this.type = set(impurity, value)
-
-  /** @group setParam */
-  @Since("1.6.0")
-  override def setSeed(value: Long): this.type = set(seed, value)
-
-  /** @group setParam */
-  @Since("2.0.0")
-  def setVarianceCol(value: String): this.type = set(varianceCol, value)
-
-  override protected def train(dataset: Dataset[_]): DecisionTreeRegressionModel = {
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
-    val strategy = getOldStrategy(categoricalFeatures)
-
-    val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(params: _*)
-
-    val trees = DecisionForest.run(oldDataset, strategy, numTrees = 1,
-      featureSubsetStrategy = "all", seed = $(seed), instr = Some(instr), parentUID = Some(uid))
-
-    val m = trees.head.asInstanceOf[DecisionTreeRegressionModel]
-    instr.logSuccess(m)
-    m
-  }
-
-  /** (private[ml]) Train a decision tree on an RDD */
-  private[ml] def train(
-      data: RDD[LabeledPoint],
-      oldStrategy: OldStrategy,
-      featureSubsetStrategy: String): DecisionTreeRegressionModel = {
-    val instr = Instrumentation.create(this, data)
-    instr.logParams(params: _*)
-
-    val trees = DecisionForest.run(data, oldStrategy, numTrees = 1, featureSubsetStrategy,
-      seed = $(seed), instr = Some(instr), parentUID = Some(uid))
-
-    val m = trees.head.asInstanceOf[DecisionTreeRegressionModel]
-    instr.logSuccess(m)
-    m
-  }
-
-  /** (private[ml]) Train a decision tree on an RDD */
-  private[ml] def train4GBDTX(
-    labelArrayBc: Broadcast[DoubleArrayList],
-    processedInput: RDD[(Int, (IntArrayList, ObjectArrayList[Split]))],
-    metadata: DecisionTreeMetadata,
-    splits: Array[Array[Split]],
-    oldStrategy: OldStrategy,
-    featureSubsetStrategy: String,
-    input: RDD[TreePoint],
-    rawPartInfoBc: Broadcast[Int2ObjectOpenHashMap[IntArrayList]]):
-    DecisionTreeRegressionModel = {
-      val instr = Instrumentation.create(this, processedInput)
-      instr.logParams(params: _*)
-
-      val trees = RandomForest4GBDTX.runX(labelArrayBc, processedInput, metadata,
-        splits, oldStrategy, numTrees = 1, seed = $(seed), input,
-        rawPartInfoBc, parentUID = Some(uid))
-
-      val m = trees.head.asInstanceOf[DecisionTreeRegressionModel]
-      instr.logSuccess(m)
-      m
-    }
-
-
-  /** (private[ml]) Create a Strategy instance to use with the old API. */
-  private[ml] def getOldStrategy(categoricalFeatures: Map[Int, Int]): OldStrategy = {
-    super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity,
-      subsamplingRate = 1.0)
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): DecisionTreeRegressor = defaultCopy(extra)
-}
-
-@Since("1.4.0")
-object DecisionTreeRegressor extends DefaultParamsReadable[DecisionTreeRegressor] {
-  /** Accessor for supported impurities: variance */
-  final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities
-
-  @Since("2.0.0")
-  override def load(path: String): DecisionTreeRegressor = super.load(path)
-}
-
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
deleted file mode 100644
index ac7c2ba..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ /dev/null
@@ -1,354 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.regression
-
-import com.github.fommil.netlib.BLAS.{getInstance => blas}
-import org.json4s.{DefaultFormats, JObject}
-import org.json4s.JsonDSL._
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.{PredictionModel, Predictor}
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.GradientBoostedTrees
-import org.apache.spark.ml.util._
-import org.apache.spark.ml.util.DefaultParamsReader.Metadata
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset}
-import org.apache.spark.sql.functions._
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a>
- * learning algorithm for regression.
- * It supports both continuous and categorical features.
- *
- * The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.
- *
- * Notes on Gradient Boosting vs. TreeBoost:
- *  - This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
- *  - Both algorithms learn tree ensembles by minimizing loss functions.
- *  - TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes
- *    based on the loss function, whereas the original gradient boosting method does not.
- *     - When the loss is SquaredError, these methods give the same result, but they could differ
- *       for other loss functions.
- *  - We expect to implement TreeBoost in the future:
- *    [https://issues.apache.org/jira/browse/SPARK-4240]
- */
-@Since("1.4.0")
-class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
-  extends Predictor[Vector, GBTRegressor, GBTRegressionModel]
-  with GBTRegressorParams with DefaultParamsWritable with Logging {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("gbtr"))
-
-  // Override parameter setters from parent trait for Java API compatibility.
-
-  // Parameters from TreeRegressorParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be at least 1.
-   * (default = 10)
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /**
-   * The impurity setting is ignored for GBT models.
-   * Individual trees are built using impurity "Variance."
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setImpurity(value: String): this.type = {
-    logWarning("GBTRegressor.setImpurity should NOT be used")
-    this
-  }
-
-  // Parameters from TreeEnsembleParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSeed(value: Long): this.type = set(seed, value)
-
-  // Parameters from GBTParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxIter(value: Int): this.type = set(maxIter, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setStepSize(value: Double): this.type = set(stepSize, value)
-
-  // Parameters from GBTRegressorParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  def setLossType(value: String): this.type = set(lossType, value)
-
-  /** @group setParam */
-  @Since("2.3.0")
-  override def setFeatureSubsetStrategy(value: String): this.type =
-    set(featureSubsetStrategy, value)
-
-  override protected def train(dataset: Dataset[_]): GBTRegressionModel = {
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
-    val numFeatures = oldDataset.first().features.size
-    val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression)
-
-    val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, impurity, lossType,
-      maxDepth, maxBins, maxIter, maxMemoryInMB, minInfoGain, minInstancesPerNode,
-      seed, stepSize, subsamplingRate, cacheNodeIds, checkpointInterval, featureSubsetStrategy)
-    instr.logNumFeatures(numFeatures)
-
-    val (doUseAcc, setUseAccFlag) = super.getDoUseAcc
-    val (baseLearners, learnerWeights) = if (setUseAccFlag) {
-      GradientBoostedTrees.run(oldDataset, boostingStrategy,
-      $(seed), $(featureSubsetStrategy), doUseAcc)
-    } else {
-       GradientBoostedTrees.run(oldDataset, boostingStrategy,
-         $(seed), $(featureSubsetStrategy))
-    }
-    val m = new GBTRegressionModel(uid, baseLearners, learnerWeights, numFeatures)
-    instr.logSuccess(m)
-    m
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): GBTRegressor = defaultCopy(extra)
-}
-
-@Since("1.4.0")
-object GBTRegressor extends DefaultParamsReadable[GBTRegressor] {
-
-  /** Accessor for supported loss settings: squared (L2), absolute (L1) */
-  @Since("1.4.0")
-  final val supportedLossTypes: Array[String] = GBTRegressorParams.supportedLossTypes
-
-  @Since("2.0.0")
-  override def load(path: String): GBTRegressor = super.load(path)
-}
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a>
- * model for regression.
- * It supports both continuous and categorical features.
- * @param _trees  Decision trees in the ensemble.
- * @param _treeWeights  Weights for the decision trees in the ensemble.
- */
-@Since("1.4.0")
-class GBTRegressionModel private[ml](
-    override val uid: String,
-    private val _trees: Array[DecisionTreeRegressionModel],
-    private val _treeWeights: Array[Double],
-    override val numFeatures: Int)
-  extends PredictionModel[Vector, GBTRegressionModel]
-  with GBTRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel]
-  with MLWritable with Serializable {
-
-  require(_trees.nonEmpty, "GBTRegressionModel requires at least 1 tree.")
-  require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
-    s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
-
-  /**
-   * Construct a GBTRegressionModel
-   * @param _trees  Decision trees in the ensemble.
-   * @param _treeWeights  Weights for the decision trees in the ensemble.
-   */
-  @Since("1.4.0")
-  def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) =
-    this(uid, _trees, _treeWeights, -1)
-
-  @Since("1.4.0")
-  override def trees: Array[DecisionTreeRegressionModel] = _trees
-
-  /**
-   * Number of trees in ensemble
-   */
-  @Since("2.0.0")
-  val getNumTrees: Int = trees.length
-
-  @Since("1.4.0")
-  override def treeWeights: Array[Double] = _treeWeights
-
-  override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
-    val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
-    }
-    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
-  }
-
-  override protected def predict(features: Vector): Double = {
-    // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
-    // Classifies by thresholding sum of weighted tree predictions
-    val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
-    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
-  }
-
-  /** Number of trees in ensemble */
-  val numTrees: Int = trees.length
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): GBTRegressionModel = {
-    copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures),
-      extra).setParent(parent)
-  }
-
-  @Since("1.4.0")
-  override def toString: String = {
-    s"GBTRegressionModel (uid=$uid) with $numTrees trees"
-  }
-
-  /**
-   * Estimate of the importance of each feature.
-   *
-   * Each feature's importance is the average of its importance across all trees in the ensemble
-   * The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
-   * (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
-   * and follows the implementation from scikit-learn.
-   *
-   * @see `DecisionTreeRegressionModel.featureImportances`
-   */
-  @Since("2.0.0")
-  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
-
-  /** (private[ml]) Convert to a model in the old API */
-  private[ml] def toOld: OldGBTModel = {
-    new OldGBTModel(OldAlgo.Regression, _trees.map(_.toOld), _treeWeights)
-  }
-
-  @Since("2.0.0")
-  override def write: MLWriter = new GBTRegressionModel.GBTRegressionModelWriter(this)
-}
-
-@Since("2.0.0")
-object GBTRegressionModel extends MLReadable[GBTRegressionModel] {
-
-  @Since("2.0.0")
-  override def read: MLReader[GBTRegressionModel] = new GBTRegressionModelReader
-
-  @Since("2.0.0")
-  override def load(path: String): GBTRegressionModel = super.load(path)
-
-  private[GBTRegressionModel]
-  class GBTRegressionModelWriter(instance: GBTRegressionModel) extends MLWriter {
-
-    override protected def saveImpl(path: String): Unit = {
-      val extraMetadata: JObject = Map(
-        "numFeatures" -> instance.numFeatures,
-        "numTrees" -> instance.getNumTrees)
-      EnsembleModelReadWrite.saveImpl(instance, path, sparkSession, extraMetadata)
-    }
-  }
-
-  private class GBTRegressionModelReader extends MLReader[GBTRegressionModel] {
-
-    /** Checked against metadata when loading model */
-    private val className = classOf[GBTRegressionModel].getName
-    private val treeClassName = classOf[DecisionTreeRegressionModel].getName
-
-    override def load(path: String): GBTRegressionModel = {
-      implicit val format = DefaultFormats
-      val (metadata: Metadata, treesData: Array[(Metadata, Node)], treeWeights: Array[Double]) =
-        EnsembleModelReadWrite.loadImpl(path, sparkSession, className, treeClassName)
-
-      val numFeatures = (metadata.metadata \ "numFeatures").extract[Int]
-      val numTrees = (metadata.metadata \ "numTrees").extract[Int]
-
-      val trees: Array[DecisionTreeRegressionModel] = treesData.map {
-        case (treeMetadata, root) =>
-          val tree =
-            new DecisionTreeRegressionModel(treeMetadata.uid, root, numFeatures)
-          DefaultParamsReader.getAndSetParams(tree, treeMetadata)
-          tree
-      }
-
-      require(numTrees == trees.length, s"GBTRegressionModel.load expected $numTrees" +
-        s" trees based on metadata but found ${trees.length} trees.")
-
-      val model = new GBTRegressionModel(metadata.uid, trees, treeWeights, numFeatures)
-      DefaultParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-
-  /** Convert a model from the old API */
-  private[ml] def fromOld(
-      oldModel: OldGBTModel,
-      parent: GBTRegressor,
-      categoricalFeatures: Map[Int, Int],
-      numFeatures: Int = -1): GBTRegressionModel = {
-    require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" +
-      s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).")
-    val newTrees = oldModel.trees.map { tree =>
-      // parent for each tree is null since there is no good way to set this.
-      DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
-    }
-    val uid = if (parent != null) parent.uid else Identifiable.randomUID("gbtr")
-    new GBTRegressionModel(uid, newTrees, oldModel.treeWeights, numFeatures)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
deleted file mode 100644
index 0f00bce..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ /dev/null
@@ -1,565 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.regression
-
-import scala.collection.mutable
-
-import breeze.linalg.{DenseVector => BDV}
-import breeze.optimize.{CachedDiffFunction, LBFGSL, OWLQNL}
-
-import org.apache.spark.SparkException
-import org.apache.spark.annotation.Since
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.StaticUtils
-import org.apache.spark.ml.feature.Instance
-import org.apache.spark.ml.linalg.{Vector, Vectors}
-import org.apache.spark.ml.linalg.BLAS._
-import org.apache.spark.ml.optim.WeightedLeastSquares
-import org.apache.spark.ml.optim.aggregator.{HuberAggregatorX, LeastSquaresAggregatorX}
-import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunctionX}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.VectorImplicits._
-import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions._
-import org.apache.spark.storage.StorageLevel
-
-/**
- * Linear regression.
- *
- * The learning objective is to minimize the specified loss function, with regularization.
- * This supports two kinds of loss:
- *  - squaredError (a.k.a squared loss)
- *  - huber (a hybrid of squared error for relatively small errors and absolute error for
- *  relatively large ones, and we estimate the scale parameter from training data)
- *
- * This supports multiple types of regularization:
- *  - none (a.k.a. ordinary least squares)
- *  - L2 (ridge regression)
- *  - L1 (Lasso)
- *  - L2 + L1 (elastic net)
- *
- * The squared error objective function is:
- *
- * <blockquote>
- *   $$
- *   \begin{align}
- *   \min_{w}\frac{1}{2n}{\sum_{i=1}^n(X_{i}w - y_{i})^{2} +
- *   \lambda\left[\frac{1-\alpha}{2}{||w||_{2}}^{2} + \alpha{||w||_{1}}\right]}
- *   \end{align}
- *   $$
- * </blockquote>
- *
- * The huber objective function is:
- *
- * <blockquote>
- *   $$
- *   \begin{align}
- *   \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma +
- *   H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2}
- *   \end{align}
- *   $$
- * </blockquote>
- *
- * where
- *
- * <blockquote>
- *   $$
- *   \begin{align}
- *   H_m(z) = \begin{cases}
- *            z^2, & \text {if } |z| &lt; \epsilon, \\
- *            2\epsilon|z| - \epsilon^2, & \text{otherwise}
- *            \end{cases}
- *   \end{align}
- *   $$
- * </blockquote>
- *
- * Note: Fitting with huber loss only supports none and L2 regularization.
- */
-@Since("1.3.0")
-class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String)
-  extends Regressor[Vector, LinearRegression, LinearRegressionModel]
-  with LinearRegressionParams with DefaultParamsWritable with Logging {
-
-  import LinearRegression._
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("linReg"))
-
-  /**
-   * Set the regularization parameter.
-   * Default is 0.0.
-   *
-   * @group setParam
-   */
-  @Since("1.3.0")
-  def setRegParam(value: Double): this.type = set(regParam, value)
-  setDefault(regParam -> 0.0)
-
-  /**
-   * Set if we should fit the intercept.
-   * Default is true.
-   *
-   * @group setParam
-   */
-  @Since("1.5.0")
-  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
-  setDefault(fitIntercept -> true)
-
-  /**
-   * Whether to standardize the training features before fitting the model.
-   * The coefficients of models will be always returned on the original scale,
-   * so it will be transparent for users.
-   * Default is true.
-   *
-   * @note With/without standardization, the models should be always converged
-   * to the same solution when no regularization is applied. In R's GLMNET package,
-   * the default behavior is true as well.
-   *
-   * @group setParam
-   */
-  @Since("1.5.0")
-  def setStandardization(value: Boolean): this.type = set(standardization, value)
-  setDefault(standardization -> true)
-
-  /**
-   * Set the ElasticNet mixing parameter.
-   * For alpha = 0, the penalty is an L2 penalty.
-   * For alpha = 1, it is an L1 penalty.
-   * For alpha in (0,1), the penalty is a combination of L1 and L2.
-   * Default is 0.0 which is an L2 penalty.
-   *
-   * Note: Fitting with huber loss only supports None and L2 regularization,
-   * so throws exception if this param is non-zero value.
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
-  setDefault(elasticNetParam -> 0.0)
-
-  /**
-   * Set the maximum number of iterations.
-   * Default is 100.
-   *
-   * @group setParam
-   */
-  @Since("1.3.0")
-  def setMaxIter(value: Int): this.type = set(maxIter, value)
-  setDefault(maxIter -> 100)
-
-  /**
-   * Set the convergence tolerance of iterations.
-   * Smaller value will lead to higher accuracy with the cost of more iterations.
-   * Default is 1E-6.
-   *
-   * @group setParam
-   */
-  @Since("1.4.0")
-  def setTol(value: Double): this.type = set(tol, value)
-  setDefault(tol -> 1E-6)
-
-  /**
-   * Whether to over-/under-sample training instances according to the given weights in weightCol.
-   * If not set or empty, all instances are treated equally (weight 1.0).
-   * Default is not set, so all instances have weight one.
-   *
-   * @group setParam
-   */
-  @Since("1.6.0")
-  def setWeightCol(value: String): this.type = set(weightCol, value)
-
-  /**
-   * Set the solver algorithm used for optimization.
-   * In case of linear regression, this can be "l-bfgs", "normal" and "auto".
-   *  - "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton
-   *    optimization method.
-   *  - "normal" denotes using Normal Equation as an analytical solution to the linear regression
-   *    problem.  This solver is limited to `LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER`.
-   *  - "auto" (default) means that the solver algorithm is selected automatically.
-   *    The Normal Equations solver will be used when possible, but this will automatically fall
-   *    back to iterative optimization methods when needed.
-   *
-   * Note: Fitting with huber loss doesn't support normal solver,
-   * so throws exception if this param was set with "normal".
-   * @group setParam
-   */
-  @Since("1.6.0")
-  def setSolver(value: String): this.type = set(solver, value)
-  setDefault(solver -> Auto)
-
-  /**
-   * Suggested depth for treeAggregate (greater than or equal to 2).
-   * If the dimensions of features or the number of partitions are large,
-   * this param could be adjusted to a larger size.
-   * Default is 2.
-   *
-   * @group expertSetParam
-   */
-  @Since("2.1.0")
-  def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
-  setDefault(aggregationDepth -> 2)
-
-  /**
-   * Sets the value of param [[loss]].
-   * Default is "squaredError".
-   *
-   * @group setParam
-   */
-  @Since("2.3.0")
-  def setLoss(value: String): this.type = set(loss, value)
-  setDefault(loss -> SquaredError)
-
-  /**
-   * Sets the value of param [[epsilon]].
-   * Default is 1.35.
-   *
-   * @group setExpertParam
-   */
-  @Since("2.3.0")
-  def setEpsilon(value: Double): this.type = set(epsilon, value)
-  setDefault(epsilon -> 1.35)
-
-  override protected def train(dataset: Dataset[_]): LinearRegressionModel = {
-    // Extract the number of features before deciding optimization solver.
-    val numFeatures = dataset.select(col($(featuresCol))).first()
-      .getAs[Vector](StaticUtils.ZERO_INT).size
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-
-    val instances: RDD[Instance] = dataset.select(
-      col($(labelCol)), w, col($(featuresCol))).rdd.map {
-      case Row(label: Double, weight: Double, features: Vector) =>
-        Instance(label, weight + StaticUtils.ZERO_DOUBLE, features)
-    }
-
-    val instr = Instrumentation.create(this, dataset)
-    instr.logParams(labelCol, featuresCol, weightCol, predictionCol, solver, tol, elasticNetParam,
-      fitIntercept, maxIter, regParam, standardization, aggregationDepth, loss, epsilon)
-    instr.logNumFeatures(numFeatures)
-
-    if ($(loss) == SquaredError && (($(solver) == Auto &&
-      numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == Normal)) {
-      // For low dimensional data, WeightedLeastSquares is more efficient since the
-      // training algorithm only requires one pass through the data. (SPARK-10668)
-
-      val optimizer = new WeightedLeastSquares($(fitIntercept), $(regParam),
-        elasticNetParam = $(elasticNetParam), $(standardization), true,
-        solverType = WeightedLeastSquares.Auto, maxIter = $(maxIter), tol = $(tol))
-      val model = optimizer.fit(instances)
-      // When it is trained by WeightedLeastSquares, training summary does not
-      // attach returned model.
-      val lrModel = copyValues(new LinearRegressionModel(uid, model.coefficients, model.intercept))
-      val (summaryModel, predictionColName) = lrModel.findSummaryModelAndPredictionCol()
-      val trainingSummary = new LinearRegressionTrainingSummary(
-        summaryModel.transform(dataset),
-        predictionColName,
-        $(labelCol),
-        $(featuresCol),
-        summaryModel,
-        model.diagInvAtWA.toArray,
-        model.objectiveHistory)
-
-      lrModel.setSummary(Some(trainingSummary))
-      instr.logSuccess(lrModel)
-      return lrModel
-    }
-
-    val handlePersistence = dataset.storageLevel == StorageLevel.NONE
-    if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
-
-    val (featuresSummarizer, ySummarizer) = {
-      val seqOp = (c: (MultivariateOnlineSummarizer, MultivariateOnlineSummarizer),
-        instance: Instance) =>
-        (c._1.add(instance.features, instance.weight),
-          c._2.add(Vectors.dense(instance.label), instance.weight))
-
-      val combOp = (c1: (MultivariateOnlineSummarizer, MultivariateOnlineSummarizer),
-        c2: (MultivariateOnlineSummarizer, MultivariateOnlineSummarizer)) =>
-        (c1._1.merge(c2._1), c1._2.merge(c2._2))
-
-      instances.treeAggregate(
-        (new MultivariateOnlineSummarizer, new MultivariateOnlineSummarizer)
-      )(seqOp, combOp, $(aggregationDepth))
-    }
-
-    val yMean = ySummarizer.mean(0)
-    val rawYStd = math.sqrt(ySummarizer.variance(0))
-    if (rawYStd == 0.0) {
-      if ($(fitIntercept) || yMean == 0.0) {
-        // If the rawYStd==0 and fitIntercept==true, then the intercept is yMean with
-        // zero coefficient; as a result, training is not needed.
-        // Also, if yMean==0 and rawYStd==0, all the coefficients are zero regardless of
-        // the fitIntercept.
-        if (yMean == 0.0) {
-          logWarning(s"Mean and standard deviation of the label are zero, so the coefficients " +
-            s"and the intercept will all be zero; as a result, training is not needed.")
-        } else {
-          logWarning(s"The standard deviation of the label is zero, so the coefficients will be " +
-            s"zeros and the intercept will be the mean of the label; as a result, " +
-            s"training is not needed.")
-        }
-        if (handlePersistence) instances.unpersist()
-        val coefficients = Vectors.sparse(numFeatures, Seq.empty)
-        val intercept = yMean
-
-        val model = copyValues(new LinearRegressionModel(uid, coefficients, intercept))
-        // Handle possible missing or invalid prediction columns
-        val (summaryModel, predictionColName) = model.findSummaryModelAndPredictionCol()
-
-        val trainingSummary = new LinearRegressionTrainingSummary(
-          summaryModel.transform(dataset),
-          predictionColName,
-          $(labelCol),
-          $(featuresCol),
-          model,
-          Array(0D),
-          Array(0D))
-
-        model.setSummary(Some(trainingSummary))
-        instr.logSuccess(model)
-        return model
-      } else {
-        require($(regParam) == 0.0, "The standard deviation of the label is zero. " +
-          "Model cannot be regularized.")
-        logWarning(s"The standard deviation of the label is zero. " +
-          "Consider setting fitIntercept=true.")
-      }
-    }
-
-    // if y is constant (rawYStd is zero), then y cannot be scaled. In this case
-    // setting yStd=abs(yMean) ensures that y is not scaled anymore in l-bfgs algorithm.
-    val yStd = if (rawYStd > 0) rawYStd else math.abs(yMean)
-    val featuresMean = featuresSummarizer.mean.toArray
-    val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)
-    val bcFeaturesMean = instances.context.broadcast(featuresMean)
-    val bcFeaturesStd = instances.context.broadcast(featuresStd.map(t =>
-      if (t != 0d) 1d / t else 0d))
-
-    if (!$(fitIntercept) && (0 until numFeatures).exists { i =>
-      featuresStd(i) == 0.0 && featuresMean(i) != 0.0 }) {
-      logWarning("Fitting LinearRegressionModel without intercept on dataset with " +
-        "constant nonzero column, Spark MLlib outputs zero coefficients for constant nonzero " +
-        "columns. This behavior is the same as R glmnet but different from LIBSVM.")
-    }
-
-    // Since we implicitly do the feature scaling when we compute the cost function
-    // to improve the convergence, the effective regParam will be changed.
-    val effectiveRegParam = $(loss) match {
-      case SquaredError => $(regParam) / yStd
-      case Huber => $(regParam)
-    }
-    val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam
-    val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam
-
-    val getFeaturesStd = (j: Int) => if (j >= 0 && j < numFeatures) featuresStd(j) else 0.0
-    val regularization = if (effectiveL2RegParam != 0.0) {
-      val shouldApply = (idx: Int) => idx >= 0 && idx < numFeatures
-      Some(new L2Regularization(effectiveL2RegParam, shouldApply,
-        if ($(standardization)) None else Some(getFeaturesStd)))
-    } else {
-      None
-    }
-
-    val costFun = $(loss) match {
-      case SquaredError =>
-        val getAggregatorFunc = new LeastSquaresAggregatorX(yStd, yMean, $(fitIntercept),
-          bcFeaturesStd, bcFeaturesMean)(_)
-        new RDDLossFunctionX(instances, getAggregatorFunc, regularization, $(aggregationDepth))
-      case Huber =>
-        val getAggregatorFunc = new HuberAggregatorX($(fitIntercept), $(epsilon), bcFeaturesStd)(_)
-        new RDDLossFunctionX(instances, getAggregatorFunc, regularization, $(aggregationDepth))
-    }
-
-    val optimizer = $(loss) match {
-      case SquaredError =>
-        val dim = numFeatures
-        if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
-          new LBFGSL($(maxIter), 10, $(tol))
-        } else {
-          val standardizationParam = $(standardization)
-          val effectiveL1Reg =
-            if (standardizationParam) {
-              BDV[Double](Array.fill(dim)(effectiveL1RegParam))
-            } else {
-              // If `standardization` is false, we still standardize the data
-              // to improve the rate of convergence; as a result, we have to
-              // perform this reverse standardization by penalizing each component
-              // differently to get effectively the same objective function when
-              // the training dataset is not standardized.
-              BDV[Double](featuresStd.map(x => if (x != 0.0) effectiveL1RegParam / x else 0.0))
-            }
-          new OWLQNL($(maxIter), 10, $(tol), effectiveL1Reg)
-        }
-      case Huber =>
-        val dim = if ($(fitIntercept)) numFeatures + 2 else numFeatures + 1
-        val lowerBounds = BDV[Double](Array.fill(dim)(Double.MinValue))
-        // Optimize huber loss in space "\sigma > 0"
-        lowerBounds(dim - 1) = Double.MinPositiveValue
-        val upperBounds = BDV[Double](Array.fill(dim)(Double.MaxValue))
-        new LBFGSL(lowerBounds, upperBounds, $(maxIter), 10, $(tol))
-    }
-
-    val initialValues = $(loss) match {
-      case SquaredError =>
-        Vectors.zeros(numFeatures)
-      case Huber =>
-        val dim = if ($(fitIntercept)) numFeatures + 2 else numFeatures + 1
-        Vectors.dense(Array.fill(dim)(1.0))
-    }
-
-    val states = optimizer.iterations(new CachedDiffFunction(costFun),
-      initialValues.asBreeze.toDenseVector)
-
-    val (coefficients, intercept, scale, objectiveHistory) = {
-      /*
-         Note that in Linear Regression, the objective history (loss + regularization) returned
-         from optimizer is computed in the scaled space given by the following formula.
-         <blockquote>
-            $$
-            L &= 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2
-                 + regTerms \\
-            $$
-         </blockquote>
-       */
-      val arrayBuilder = mutable.ArrayBuilder.make[Double]
-      var state: optimizer.State = null
-      while (states.hasNext) {
-        state = states.next()
-        arrayBuilder += state.adjustedValue
-      }
-      if (state == null) {
-        val msg = s"${optimizer.getClass.getName} failed."
-        logError(msg)
-        throw new SparkException(msg)
-      }
-
-      bcFeaturesMean.destroy(blocking = false)
-      bcFeaturesStd.destroy(blocking = false)
-
-      val parameters = state.x.toArray.clone()
-
-      /*
-         The coefficients are trained in the scaled space; we're converting them back to
-         the original space.
-       */
-      val rawCoefficients: Array[Double] = $(loss) match {
-        case SquaredError => parameters
-        case Huber => parameters.slice(0, numFeatures)
-      }
-
-      var i = 0
-      val len = rawCoefficients.length
-      val multiplier = $(loss) match {
-        case SquaredError => yStd
-        case Huber => 1.0
-      }
-      while (i < len) {
-        rawCoefficients(i) *= { if (featuresStd(i) != 0.0) multiplier / featuresStd(i) else 0.0 }
-        i += 1
-      }
-
-      val interceptValue: Double = if ($(fitIntercept)) {
-        $(loss) match {
-          case SquaredError =>
-            /*
-            The intercept of squared error in R's GLMNET is computed using closed form
-            after the coefficients are converged. See the following discussion for detail.
-            http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet
-            */
-            yMean - dot(Vectors.dense(rawCoefficients), Vectors.dense(featuresMean))
-          case Huber => parameters(numFeatures)
-        }
-      } else {
-        0.0
-      }
-
-      val scaleValue: Double = $(loss) match {
-        case SquaredError => 1.0
-        case Huber => parameters.last
-      }
-
-      (Vectors.dense(rawCoefficients).compressed, interceptValue, scaleValue, arrayBuilder.result())
-    }
-
-    if (handlePersistence) instances.unpersist()
-
-    val model = copyValues(new LinearRegressionModel(uid, coefficients, intercept, scale))
-    // Handle possible missing or invalid prediction columns
-    val (summaryModel, predictionColName) = model.findSummaryModelAndPredictionCol()
-
-    val trainingSummary = new LinearRegressionTrainingSummary(
-      summaryModel.transform(dataset),
-      predictionColName,
-      $(labelCol),
-      $(featuresCol),
-      model,
-      Array(0D),
-      objectiveHistory)
-
-    model.setSummary(Some(trainingSummary))
-    instr.logSuccess(model)
-    model
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra)
-}
-
-@Since("1.6.0")
-object LinearRegression extends DefaultParamsReadable[LinearRegression] {
-
-  @Since("1.6.0")
-  override def load(path: String): LinearRegression = super.load(path)
-
-  /**
-   * When using `LinearRegression.solver` == "normal", the solver must limit the number of
-   * features to at most this number.  The entire covariance matrix X^T^X will be collected
-   * to the driver. This limit helps prevent memory overflow errors.
-   */
-  @Since("2.1.0")
-  val MAX_FEATURES_FOR_NORMAL_SOLVER: Int = WeightedLeastSquares.MAX_NUM_FEATURES
-
-  /** String name for "auto". */
-  private[regression] val Auto = "auto"
-
-  /** String name for "normal". */
-  private[regression] val Normal = "normal"
-
-  /** String name for "l-bfgs". */
-  private[regression] val LBFGS = "l-bfgs"
-
-  /** Set of solvers that LinearRegression supports. */
-  private[regression] val supportedSolvers = Array(Auto, Normal, LBFGS)
-
-  /** String name for "squaredError". */
-  private[regression] val SquaredError = "squaredError"
-
-  /** String name for "huber". */
-  private[regression] val Huber = "huber"
-
-  /** Set of loss function names that LinearRegression supports. */
-  private[regression] val supportedLosses = Array(SquaredError, Huber)
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
deleted file mode 100644
index ada1b5e..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ /dev/null
@@ -1,312 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.regression
-
-import org.json4s.{DefaultFormats, JObject}
-import org.json4s.JsonDSL._
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.ml.{PredictionModel, Predictor}
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.RandomForest
-import org.apache.spark.ml.util._
-import org.apache.spark.ml.util.DefaultParamsReader.Metadata
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset}
-import org.apache.spark.sql.functions._
-
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a>
- * learning algorithm for regression.
- * It supports both continuous and categorical features.
- */
-@Since("1.4.0")
-class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
-  extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel]
-  with RandomForestRegressorParams with DefaultParamsWritable {
-
-  @Since("1.4.0")
-  def this() = this(Identifiable.randomUID("rfr"))
-
-  // Override parameter setters from parent trait for Java API compatibility.
-
-  // Parameters from TreeRegressorParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertSetParam */
-  @Since("1.4.0")
-  override def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be at least 1.
-   * (default = 10)
-   * @group setParam
-   */
-  @Since("1.4.0")
-  override def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setImpurity(value: String): this.type = set(impurity, value)
-
-  // Parameters from TreeEnsembleParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setSeed(value: Long): this.type = set(seed, value)
-
-  // Parameters from RandomForestParams:
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setNumTrees(value: Int): this.type = set(numTrees, value)
-
-  /** @group setParam */
-  @Since("1.4.0")
-  override def setFeatureSubsetStrategy(value: String): this.type =
-    set(featureSubsetStrategy, value)
-
-  override protected def train(dataset: Dataset[_]): RandomForestRegressionModel = {
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
-    val strategy =
-      super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)
-
-    val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, impurity, numTrees,
-      featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
-      minInstancesPerNode, seed, subsamplingRate, cacheNodeIds, checkpointInterval)
-
-    val trees = RandomForest
-      .run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
-      .map(_.asInstanceOf[DecisionTreeRegressionModel])
-
-    val numFeatures = oldDataset.first().features.size
-    val m = new RandomForestRegressionModel(uid, trees, numFeatures)
-    instr.logSuccess(m)
-    m
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra)
-}
-
-@Since("1.4.0")
-object RandomForestRegressor extends DefaultParamsReadable[RandomForestRegressor]{
-  /** Accessor for supported impurity settings: variance */
-  @Since("1.4.0")
-  final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities
-
-  /** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */
-  @Since("1.4.0")
-  final val supportedFeatureSubsetStrategies: Array[String] =
-    TreeEnsembleParams.supportedFeatureSubsetStrategies
-
-  @Since("2.0.0")
-  override def load(path: String): RandomForestRegressor = super.load(path)
-
-}
-
-/**
- * <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> model for regression.
- * It supports both continuous and categorical features.
- *
- * @param _trees  Decision trees in the ensemble.
- * @param numFeatures  Number of features used by this model
- */
-@Since("1.4.0")
-class RandomForestRegressionModel private[ml] (
-    override val uid: String,
-    private val _trees: Array[DecisionTreeRegressionModel],
-    override val numFeatures: Int)
-  extends PredictionModel[Vector, RandomForestRegressionModel]
-  with RandomForestRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel]
-  with MLWritable with Serializable {
-
-  require(_trees.nonEmpty, "RandomForestRegressionModel requires at least 1 tree.")
-
-  /**
-   * Construct a random forest regression model, with all trees weighted equally.
-   *
-   * @param trees  Component trees
-   */
-  private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
-    this(Identifiable.randomUID("rfr"), trees, numFeatures)
-
-  @Since("1.4.0")
-  override def trees: Array[DecisionTreeRegressionModel] = _trees
-
-  // Note: We may add support for weights (based on tree performance) later on.
-  private lazy val _treeWeights: Array[Double] = Array.fill[Double](_trees.length)(1.0)
-
-  @Since("1.4.0")
-  override def treeWeights: Array[Double] = _treeWeights
-
-  override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
-    val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
-    }
-    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
-  }
-
-  override protected def predict(features: Vector): Double = {
-    // TODO: When we add a generic Bagging class, handle transform there.  SPARK-7128
-    // Predict average of tree predictions.
-    // Ignore the weights since all are 1.0 for now.
-    _trees.map(_.rootNode.predictImpl(features).prediction).sum / getNumTrees
-  }
-
-  @Since("1.4.0")
-  override def copy(extra: ParamMap): RandomForestRegressionModel = {
-    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra).setParent(parent)
-  }
-
-  @Since("1.4.0")
-  override def toString: String = {
-    s"RandomForestRegressionModel (uid=$uid) with $getNumTrees trees"
-  }
-
-  /**
-   * Estimate of the importance of each feature.
-   *
-   * Each feature's importance is the average of its importance across all trees in the ensemble
-   * The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
-   * (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
-   * and follows the implementation from scikit-learn.
-   *
-   * @see `DecisionTreeRegressionModel.featureImportances`
-   */
-  @Since("1.5.0")
-  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
-
-  /** (private[ml]) Convert to a model in the old API */
-  private[ml] def toOld: OldRandomForestModel = {
-    new OldRandomForestModel(OldAlgo.Regression, _trees.map(_.toOld))
-  }
-
-  @Since("2.0.0")
-  override def write: MLWriter =
-    new RandomForestRegressionModel.RandomForestRegressionModelWriter(this)
-}
-
-@Since("2.0.0")
-object RandomForestRegressionModel extends MLReadable[RandomForestRegressionModel] {
-
-  @Since("2.0.0")
-  override def read: MLReader[RandomForestRegressionModel] = new RandomForestRegressionModelReader
-
-  @Since("2.0.0")
-  override def load(path: String): RandomForestRegressionModel = super.load(path)
-
-  private[RandomForestRegressionModel]
-  class RandomForestRegressionModelWriter(instance: RandomForestRegressionModel)
-    extends MLWriter {
-
-    override protected def saveImpl(path: String): Unit = {
-      val extraMetadata: JObject = Map(
-        "numFeatures" -> instance.numFeatures,
-        "numTrees" -> instance.getNumTrees)
-      EnsembleModelReadWrite.saveImpl(instance, path, sparkSession, extraMetadata)
-    }
-  }
-
-  private class RandomForestRegressionModelReader extends MLReader[RandomForestRegressionModel] {
-
-    /** Checked against metadata when loading model */
-    private val className = classOf[RandomForestRegressionModel].getName
-    private val treeClassName = classOf[DecisionTreeRegressionModel].getName
-
-    override def load(path: String): RandomForestRegressionModel = {
-      implicit val format = DefaultFormats
-      val (metadata: Metadata, treesData: Array[(Metadata, Node)], treeWeights: Array[Double]) =
-        EnsembleModelReadWrite.loadImpl(path, sparkSession, className, treeClassName)
-      val numFeatures = (metadata.metadata \ "numFeatures").extract[Int]
-      val numTrees = (metadata.metadata \ "numTrees").extract[Int]
-
-      val trees: Array[DecisionTreeRegressionModel] = treesData.map { case (treeMetadata, root) =>
-        val tree =
-          new DecisionTreeRegressionModel(treeMetadata.uid, root, numFeatures)
-        DefaultParamsReader.getAndSetParams(tree, treeMetadata)
-        tree
-      }
-      require(numTrees == trees.length, s"RandomForestRegressionModel.load expected $numTrees" +
-        s" trees based on metadata but found ${trees.length} trees.")
-
-      val model = new RandomForestRegressionModel(metadata.uid, trees, numFeatures)
-      DefaultParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-
-  /** Convert a model from the old API */
-  private[ml] def fromOld(
-      oldModel: OldRandomForestModel,
-      parent: RandomForestRegressor,
-      categoricalFeatures: Map[Int, Int],
-      numFeatures: Int = -1): RandomForestRegressionModel = {
-    require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" +
-      s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).")
-    val newTrees = oldModel.trees.map { tree =>
-      // parent for each tree is null since there is no good way to set this.
-      DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
-    }
-    val uid = if (parent != null) parent.uid else Identifiable.randomUID("rfr")
-    new RandomForestRegressionModel(uid, newTrees, numFeatures)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/stat/Correlation.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/stat/Correlation.scala
deleted file mode 100644
index d84f291..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/stat/Correlation.scala
+++ /dev/null
@@ -1,93 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.stat
-
-import scala.collection.JavaConverters._
-
-import org.apache.spark.annotation.{Experimental, Since}
-import org.apache.spark.ml.linalg.{SQLDataTypes, Vector}
-import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
-import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.sql.types.{StructField, StructType}
-
-/**
- * API for correlation functions in MLlib, compatible with DataFrames and Datasets.
- *
- * The functions in this package generalize the functions in [[org.apache.spark.sql.Dataset#stat]]
- * to spark.ml's Vector types.
- */
-@Since("2.2.0")
-@Experimental
-object Correlation {
-
-  /**
-   * :: Experimental ::
-   * Compute the correlation matrix for the input Dataset of Vectors using the specified method.
-   * Methods currently supported: `pearson` (default), `spearman`.
-   *
-   * @param dataset A dataset or a dataframe
-   * @param column The name of the column of vectors for which the correlation coefficient needs
-   *               to be computed. This must be a column of the dataset, and it must contain
-   *               Vector objects.
-   * @param method String specifying the method to use for computing correlation.
-   *               Supported: `pearson` (default), `spearman`
-   * @return A dataframe that contains the correlation matrix of the column of vectors. This
-   *         dataframe contains a single row and a single column of name
-   *         '$METHODNAME($COLUMN)'.
-   * @throws IllegalArgumentException if the column is not a valid column in the dataset, or if
-   *                                  the content of this column is not of type Vector.
-   *
-   *  Here is how to access the correlation coefficient:
-   *  {{{
-   *    val data: Dataset[Vector] = ...
-   *    val Row(coeff: Matrix) = Correlation.corr(data, "value").head
-   *    // coeff now contains the Pearson correlation matrix.
-   *  }}}
-   *
-   * @note For Spearman, a rank correlation, we need to create an RDD[Double] for each column
-   * and sort it in order to retrieve the ranks and then join the columns back into an RDD[Vector],
-   * which is fairly costly. Cache the input Dataset before calling corr with `method = "spearman"`
-   * to avoid recomputing the common lineage.
-   */
-  @Since("2.2.0")
-  def corr(dataset: Dataset[_], column: String, method: String): DataFrame = {
-    val rdd = dataset.select(column).rdd.map {
-      case Row(v: Vector) => OldVectors.fromML(v)
-    }
-    val oldM = OldStatistics.corr(rdd, method)
-    val name = s"$method($column)"
-    val schema = StructType(Array(StructField(name, SQLDataTypes.MatrixType, nullable = false)))
-    dataset.sparkSession.createDataFrame(Seq(Row(oldM.asML)).asJava, schema)
-  }
-
-  /**
-   * Compute the Pearson correlation matrix for the input Dataset of Vectors.
-   */
-  @Since("2.2.0")
-  def corr(dataset: Dataset[_], column: String): DataFrame = {
-    corr(dataset, column, "pearson")
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/DecisionForest.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/DecisionForest.scala
deleted file mode 100644
index ebce220..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/DecisionForest.scala
+++ /dev/null
@@ -1,1275 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import java.io.IOException
-
-import scala.collection.mutable
-import scala.util.Random
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.classification.DecisionTreeClassificationModel
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.RandomForest.NodeIndexInfo
-import org.apache.spark.ml.util.Instrumentation
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
-import org.apache.spark.mllib.tree.model.ImpurityStats
-import org.apache.spark.rdd.RDD
-import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
-
-/**
- * ALGORITHM
- *
- * This is a sketch of the algorithm to help new developers.
- *
- * The algorithm partitions data by instances (rows).
- * On each iteration, the algorithm splits a set of nodes.  In order to choose the best split
- * for a given node, sufficient statistics are collected from the distributed data.
- * For each node, the statistics are collected to some worker node, and that worker selects
- * the best split.
- *
- * This setup requires discretization of continuous features.  This binning is done in the
- * findSplits() method during initialization, after which each continuous feature becomes
- * an ordered discretized feature with at most maxBins possible values.
- *
- * The main loop in the algorithm operates on a queue of nodes (nodeStack).  These nodes
- * lie at the periphery of the tree being trained.  If multiple trees are being trained at once,
- * then this queue contains nodes from all of them.  Each iteration works roughly as follows:
- *   On the master node:
- *     - Some number of nodes are pulled off of the queue (based on the amount of memory
- *       required for their sufficient statistics).
- *     - For random forests, if featureSubsetStrategy is not "all," then a subset of candidate
- *       features are chosen for each node.  See method selectNodesToSplit().
- *   On worker nodes, via method findBestSplits():
- *     - The worker makes one pass over its subset of instances.
- *     - For each (tree, node, feature, split) tuple, the worker collects statistics about
- *       splitting.  Note that the set of (tree, node) pairs is limited to the nodes selected
- *       from the queue for this iteration.  The set of features considered can also be limited
- *       based on featureSubsetStrategy.
- *     - For each node, the statistics for that node are aggregated to a particular worker
- *       via reduceByKey().  The designated worker chooses the best (feature, split) pair,
- *       or chooses to stop splitting if the stopping criteria are met.
- *   On the master node:
- *     - The master collects all decisions about splitting nodes and updates the model.
- *     - The updated model is passed to the workers on the next iteration.
- * This process continues until the node queue is empty.
- *
- * Most of the methods in this implementation support the statistics aggregation, which is
- * the heaviest part of the computation.  In general, this implementation is bound by either
- * the cost of statistics computation on workers or by communicating the sufficient statistics.
- */
-private[spark] object DecisionForest extends Logging {
-
-  /**
-   * Train a random forest.
-   *
-   * @param input Training data: RDD of `LabeledPoint`
-   * @return an unweighted set of trees
-   */
-  def run(
-      input: RDD[LabeledPoint],
-      strategy: OldStrategy,
-      numTrees: Int,
-      featureSubsetStrategy: String,
-      seed: Long,
-      instr: Option[Instrumentation[_]],
-      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
-    val exParams = DTUtils.parseExtraParams(input, strategy)
-    runX(input, strategy, numTrees, featureSubsetStrategy, seed, instr, exParams, parentUID)
-  }
-
-  /**
-   * Train a random forest.
-   *
-   * @param input Training data: RDD of `LabeledPoint`
-   * @return an unweighted set of trees
-   */
-  def runX(
-      input: RDD[LabeledPoint],
-      strategy: OldStrategy,
-      numTrees: Int,
-      featureSubsetStrategy: String,
-      seed: Long,
-      instr: Option[Instrumentation[_]],
-      extraParams: DFExtraParams,
-      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
-
-    DecisionForestInfo.timerResult = ""
-    val timer = new TimeTracker()
-
-    timer.start("total")
-
-    timer.start("init")
-
-    val binnedFeaturesType = BinnedFeaturesDataType.withName(extraParams.rfParams.featuresDataType)
-    val retaggedInput = input.retag(classOf[LabeledPoint])
-    // featureSubsetStrategy: The number of features to consider for splits at each tree node.
-    // featureSubsetStrategy: default value is "auto" for random forest.
-    // impurity: default value is "gini" for random forest.
-    val metadata =
-      DecisionTreeMetadata.buildMetadata(retaggedInput, strategy, numTrees, featureSubsetStrategy)
-    logWarning(s"decisionTreeMetadata details: ${metadata.numFeatures}," +
-      s" ${metadata.numExamples}, ${metadata.numClasses: Int}, ${metadata.maxBins: Int}," +
-      s" ${metadata.featureArity}, ${metadata.unorderedFeatures.mkString("[", ";", "]")}," +
-      s" ${metadata.impurity}, ${metadata.quantileStrategy}, ${metadata.maxDepth: Int}," +
-      s" ${metadata.minInstancesPerNode: Int}, ${metadata.minInfoGain: Double}," +
-      s" ${metadata.numTrees: Int}, ${metadata.numFeaturesPerNode: Int},${binnedFeaturesType}")
-    instr match {
-      case Some(instrumentation) =>
-        instrumentation.logNumFeatures(metadata.numFeatures)
-        instrumentation.logNumClasses(metadata.numClasses)
-      case None =>
-        logInfo("numFeatures: " + metadata.numFeatures)
-        logInfo("numClasses: " + metadata.numClasses)
-    }
-
-    // Find the splits and the corresponding bins (interval between the splits) using a sample
-    // of the input data.
-    timer.start("findSplits")
-    val splits = findSplits(retaggedInput, metadata, seed, extraParams.numFeaturesOptFindSplits)
-    timer.stop("findSplits")
-    logDebug("numBins: feature: number of bins")
-    logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
-      s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
-    }.mkString("\n"))
-
-    // Bin feature values (TreePoint representation).
-    // Cache input RDD for speedup during multiple passes.
-    val treeInput = TreePointY.convertToTreeRDD(retaggedInput, splits, metadata, binnedFeaturesType)
-
-    val withReplacement = numTrees > 1
-
-    // Default value of subsamplingRate is 1 for random forest.
-    val baggedInputOri = BaggedPoint.convertToBaggedRDD(treeInput, strategy.subsamplingRate,
-      numTrees, withReplacement, seed)
-
-    val baggedInput = DTUtils.transformBaggedRDD(baggedInputOri, extraParams)
-
-    // depth of the decision tree
-    val maxDepth = strategy.maxDepth
-    require(maxDepth <= 30,
-      s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
-
-    // Max memory usage for aggregates
-    // TODO: Calculate memory usage more precisely.
-    val maxMemoryUsage: Long = strategy.maxMemoryInMB * 1024L * 1024L
-    logDebug("max memory usage for aggregates = " + maxMemoryUsage + " bytes.")
-
-    /*
-     * The main idea here is to perform group-wise training of the decision tree nodes thus
-     * reducing the passes over the data from (# nodes) to (# nodes / maxNumberOfNodesPerGroup).
-     * Each data sample is handled by a particular node (or it reaches a leaf and is not used
-     * in lower levels).
-     */
-
-    // Create an RDD of node Id cache.
-    // At first, all the rows belong to the root nodes (node Id == 1).
-    // Default value of useNodeIdCache is false for random forest.
-    val nodeIdCache = if (strategy.useNodeIdCache) {
-      Some(NodeIdCache.init(
-        data = baggedInput,
-        numTrees = numTrees,
-        checkpointInterval = strategy.checkpointInterval,
-        initVal = 1))
-    } else {
-      None
-    }
-
-    /*
-      Stack of nodes to train: (treeIndex, node)
-      The reason this is a stack is that we train many trees at once, but we want to focus on
-      completing trees, rather than training all simultaneously.  If we are splitting nodes from
-      1 tree, then the new nodes to split will be put at the top of this stack, so we will continue
-      training the same tree in the next iteration.  This focus allows us to send fewer trees to
-      workers on each iteration; see topNodesForGroup below.
-     */
-    val nodeStack = new mutable.ArrayStack[(Int, LearningNode)]
-    val localTrainingStack = new mutable.ListBuffer[LocalTrainingTask]
-
-    val rng = new Random()
-    rng.setSeed(seed)
-
-    // Allocate and queue root nodes.
-    val topNodes = Array.fill[LearningNode](numTrees)(LearningNode.emptyNode(nodeIndex = 1))
-    Range(0, numTrees).foreach(treeIndex => nodeStack.push((treeIndex, topNodes(treeIndex))))
-
-    timer.stop("init")
-
-    while (nodeStack.nonEmpty) {
-      // Collect some nodes to split, and choose features for each node (if subsampling).
-      // Each group of nodes may come from one or multiple trees, and at multiple levels.
-      // nodesForGroup: treeIndex --> learningNodes in tree
-      // treeToNodeToIndexInfo: treeIndex --> (global) learningNodes index in tree
-      //    --> (node index in group, feature indices).
-      val (nodesForGroup, treeToNodeToIndexInfo) =
-      DecisionForest.selectNodesToSplit(nodeStack, maxMemoryUsage, metadata, rng)
-      // Sanity check (should never occur):
-      assert(nodesForGroup.nonEmpty,
-        s"DecisionForest selected empty nodesForGroup.  Error for unknown reason.")
-
-      // Only send trees to worker if they contain nodes being split this iteration.
-      // topNodesForGroup: treeIndex --> top node in tree
-      val topNodesForGroup: Map[Int, LearningNode] =
-      nodesForGroup.keys.map(treeIdx => treeIdx -> topNodes(treeIdx)).toMap
-
-      // Choose node splits, and enqueue new nodes as needed.
-      timer.start("findBestSplits")
-      DecisionForest.findBestSplits(baggedInput, metadata, topNodesForGroup, nodesForGroup,
-        treeToNodeToIndexInfo, splits, (nodeStack, localTrainingStack),
-        extraParams, timer, nodeIdCache)
-      timer.stop("findBestSplits")
-    }
-
-    baggedInput.unpersist()
-
-    timer.stop("total")
-
-    logInfo("Internal timing for DecisionTree:")
-    logInfo(s"$timer")
-    DecisionForestInfo.timerResult = timer.toString()
-
-    // Delete any remaining checkpoints used for node Id cache.
-    if (nodeIdCache.nonEmpty) {
-      try {
-        nodeIdCache.get.deleteAllCheckpoints()
-      } catch {
-        case e: IOException =>
-          logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}")
-      }
-    }
-
-    val numFeatures = metadata.numFeatures
-
-    parentUID match {
-      case Some(uid) =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(uid, rootNode.toNode, numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map { rootNode =>
-            new DecisionTreeRegressionModel(uid, rootNode.toNode, numFeatures)
-          }
-        }
-      case None =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(rootNode.toNode, numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map { rootNode =>
-            new DecisionTreeRegressionModel(rootNode.toNode, numFeatures)
-          }
-        }
-    }
-  }
-
-  /**
-   * Helper for binSeqOp, for data which can contain a mix of ordered and unordered features.
-   *
-   * For ordered features, a single bin is updated.
-   * For unordered features, bins correspond to subsets of categories; either the left or right bin
-   * for each subset is updated.
-   *
-   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-   *             each (feature, bin).
-   * @param treePoint  Data point being aggregated.
-   * @param splits possible splits indexed (numFeatures)(numSplits)
-   * @param unorderedFeatures  Set of indices of unordered features.
-   * @param instanceWeight  Weight (importance) of instance in dataset.
-   */
-  private def mixedBinSeqOp(
-      agg: DTStatsAggregator,
-      treePoint: TreePointY,
-      splits: Array[Array[Split]],
-      unorderedFeatures: Set[Int],
-      instanceWeight: Int,
-      featuresForNode: Option[Array[Int]]): Unit = {
-    val numFeaturesPerNode = if (featuresForNode.nonEmpty) {
-      // Use subsampled features
-      featuresForNode.get.length
-    } else {
-      // Use all features
-      agg.metadata.numFeatures
-    }
-    // Iterate over features.
-    var featureIndexIdx = 0
-    while (featureIndexIdx < numFeaturesPerNode) {
-      val featureIndex = if (featuresForNode.nonEmpty) {
-        featuresForNode.get.apply(featureIndexIdx)
-      } else {
-        featureIndexIdx
-      }
-      // TODO: we can use AggUpdateUtils to update histogram.
-      if (unorderedFeatures.contains(featureIndex)) {
-        // Unordered feature
-        val featureValue = treePoint.binnedFeatures.get(featureIndex)
-        val leftNodeFeatureOffset = agg.getFeatureOffset(featureIndexIdx)
-        // Update the left or right bin for each split.
-        val numSplits = agg.metadata.numSplits(featureIndex)
-        val featureSplits = splits(featureIndex)
-        var splitIndex = 0
-        while (splitIndex < numSplits) {
-          if (featureSplits(splitIndex).shouldGoLeft(featureValue, featureSplits)) {
-            agg.featureUpdate(leftNodeFeatureOffset, splitIndex, treePoint.label, instanceWeight)
-          }
-          splitIndex += 1
-        }
-      } else {
-        // Ordered feature
-        val binIndex = treePoint.binnedFeatures.get(featureIndex)
-        agg.update(featureIndexIdx, binIndex, treePoint.label, instanceWeight)
-      }
-      featureIndexIdx += 1
-    }
-  }
-
-  /**
-   * Helper for binSeqOp, for regression and for classification with only ordered features.
-   *
-   * For each feature, the sufficient statistics of one bin are updated.
-   *
-   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-   *             each (feature, bin).
-   * @param treePoint  Data point being aggregated.
-   * @param instanceWeight  Weight (importance) of instance in dataset.
-   */
-  private def orderedBinSeqOp(
-      agg: DTStatsAggregator,
-      treePoint: TreePointY,
-      instanceWeight: Int,
-      featuresForNode: Option[Array[Int]]): Unit = {
-    val label = treePoint.label
-
-    // Iterate over features.
-    if (featuresForNode.nonEmpty) {
-      // Use subsampled features
-      var featureIndexIdx = 0
-      while (featureIndexIdx < featuresForNode.get.length) {
-        val binIndex = treePoint.binnedFeatures.get(featuresForNode.get.apply(featureIndexIdx))
-        agg.update(featureIndexIdx, binIndex, label, instanceWeight)
-        featureIndexIdx += 1
-      }
-    } else {
-      // Use all features
-      val numFeatures = agg.metadata.numFeatures
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        val binIndex = treePoint.binnedFeatures.get(featureIndex)
-        agg.update(featureIndex, binIndex, label, instanceWeight)
-        featureIndex += 1
-      }
-    }
-  }
-
-  /**
-   * Given a group of nodes, this finds the best split for each node.
-   *
-   * @param input Training data: RDD of [[TreePointX]]
-   * @param metadata Learning and dataset metadata
-   * @param topNodesForGroup For each tree in group, tree index -> root node.
-   *                         Used for matching instances with nodes.
-   * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree
-   * @param treeToNodeToIndexInfo Mapping: treeIndex --> (global) learningNodes index in tree
-   *                              --> (node index in group, feature indices)
-   *                              feature indices: probably parts of full features.
-   *                              Mapping: treeIndex --> nodeIndex --> nodeIndexInfo,
-   *                              where nodeIndexInfo stores the index in the group and the
-   *                              feature subsets (if using feature subsets).
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @param stacks  Queue of nodes to split, with values (treeIndex, node).
-   *                   Updated with new non-leaf nodes which are created.
-   * @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
-   *                    each value in the array is the data point's node Id
-   *                    for a corresponding tree. This is used to prevent the need
-   *                    to pass the entire tree to the executors during
-   *                    the node stat aggregation phase.
-   *
-   */
-  private[tree] def findBestSplits(
-      input: RDD[BaggedPoint[TreePointY]],
-      metadata: DecisionTreeMetadata,
-      topNodesForGroup: Map[Int, LearningNode],
-      nodesForGroup: Map[Int, Array[LearningNode]],
-      treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
-      splits: Array[Array[Split]],
-      stacks: (mutable.ArrayStack[(Int, LearningNode)], mutable.ListBuffer[LocalTrainingTask]),
-      extraParams: DFExtraParams,
-      timer: TimeTracker = new TimeTracker,
-      nodeIdCache: Option[NodeIdCache] = None): Unit = {
-
-    /*
-     * The high-level descriptions of the best split optimizations are noted here.
-     *
-     * *Group-wise training*
-     * We perform bin calculations for groups of nodes to reduce the number of
-     * passes over the data.  Each iteration requires more computation and storage,
-     * but saves several iterations over the data.
-     *
-     * *Bin-wise computation*
-     * We use a bin-wise best split computation strategy instead of a straightforward best split
-     * computation strategy. Instead of analyzing each sample for contribution to the left/right
-     * child node impurity of every split, we first categorize each feature of a sample into a
-     * bin. We exploit this structure to calculate aggregates for bins and then use these aggregates
-     * to calculate information gain for each split.
-     *
-     * *Aggregation over partitions*
-     * Instead of performing a flatMap/reduceByKey operation, we exploit the fact that we know
-     * the number of splits in advance. Thus, we store the aggregates (at the appropriate
-     * indices) in a single array for all bins and rely upon the RDD aggregate method to
-     * drastically reduce the communication overhead.
-     */
-
-    val bcVariables = if (null == extraParams.rfParams) false else extraParams.rfParams.bcVariables
-    val (nodeStack, _) = stacks
-    /** numNodes:  Number of nodes in this group */
-    val numNodes = nodesForGroup.values.map(_.length).sum
-    logDebug("numNodes = " + numNodes)
-    logDebug("numFeatures = " + metadata.numFeatures)
-    logDebug("numClasses = " + metadata.numClasses)
-    logDebug("isMulticlass = " + metadata.isMulticlass)
-    logDebug("isMulticlassWithCategoricalFeatures = " +
-      metadata.isMulticlassWithCategoricalFeatures)
-    logDebug("using nodeIdCache = " + nodeIdCache.nonEmpty.toString)
-
-    val groupInfo =
-      DTUtils.getGroupInfo(numNodes, treeToNodeToIndexInfo, extraParams, nodesForGroup)
-
-    val splitsBc = if (bcVariables) Some(input.sparkContext.broadcast(splits)) else Option.empty
-    val splitsOption = if (bcVariables) Option.empty else Some(splits)
-
-    /**
-     * Performs a sequential aggregation over a partition for a particular tree and node.
-     *
-     * For each feature, the aggregate sufficient statistics are updated for the relevant
-     * bins.
-     *
-     * @param treeIndex Index of the tree that we want to perform aggregation for.
-     * @param nodeInfo The node info for the tree node.
-     * @param agg Array storing aggregate calculation, with a set of sufficient statistics
-     *            for each (node, feature, bin).
-     * @param baggedPoint Data point being aggregated.
-     */
-    def nodeBinSeqOp(
-        treeIndex: Int,
-        nodeInfo: NodeIndexInfo,
-        agg: Array[DTStatsAggregator],
-        splitsBcv: Array[Array[Split]],
-        baggedPoint: BaggedPoint[TreePointY],
-        sampleId: Short = 0): Unit = {
-      if (DTUtils.isValidNodeInfo(nodeInfo, agg, groupInfo, baggedPoint, sampleId)) {
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val featuresForNode = nodeInfo.featureSubset
-        val instanceWeight = baggedPoint.subsampleWeights(treeIndex)
-        if (metadata.unorderedFeatures.isEmpty) {
-          orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, instanceWeight, featuresForNode)
-        } else {
-          mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splitsBcv,
-            metadata.unorderedFeatures, instanceWeight, featuresForNode)
-        }
-        agg(aggNodeIndex).updateParent(baggedPoint.datum.label, instanceWeight)
-      }
-    }
-
-    /**
-     * Performs a sequential aggregation over a partition.
-     *
-     * Each data point contributes to one node. For each feature,
-     * the aggregate sufficient statistics are updated for the relevant bins.
-     *
-     * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-     *             each (node, feature, bin).
-     * @param baggedPoint   Data point being aggregated.
-     * @return  agg
-     */
-    def binSeqOp(
-        agg: Array[DTStatsAggregator],
-        baggedPoint: BaggedPoint[TreePointY],
-        splitsBcv: Array[Array[Split]],
-        sampleId: Short): Array[DTStatsAggregator] = {
-      // TODO: treeToNodeToIndexInfo and topNodesForGroup(include sub-nodes) weren't broadcast.
-      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
-        if (DTUtils.isSubSampled(baggedPoint, groupInfo, treeIndex, sampleId)) {
-          val nodeIndex =
-            topNodesForGroup(treeIndex).predictImpl(baggedPoint.datum.binnedFeatures, splitsBcv)
-          nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null),
-            agg, splitsBcv, baggedPoint, sampleId)
-        }
-      }
-      agg
-    }
-
-    /**
-     * Do the same thing as binSeqOp, but with nodeIdCache.
-     */
-    def binSeqOpWithNodeIdCache(
-        agg: Array[DTStatsAggregator],
-        splitsBcv: Array[Array[Split]],
-        dataPoint: (BaggedPoint[TreePointY], Array[Int])): Array[DTStatsAggregator] = {
-      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
-        val baggedPoint = dataPoint._1
-        val nodeIdCache = dataPoint._2
-        val nodeIndex = nodeIdCache(treeIndex)
-        nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null),
-          agg, splitsBcv, baggedPoint)
-      }
-
-      agg
-    }
-
-    /**
-     * Get node index in group --> features indices map,
-     * which is a short cut to find feature indices for a node given node index in group.
-     */
-    def getNodeToFeatures(
-        treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]]): Option[Map[Int, Array[Int]]] = {
-      if (!metadata.subsamplingFeatures) {
-        None
-      } else {
-        val mutableNodeToFeatures = new mutable.HashMap[Int, Array[Int]]()
-        treeToNodeToIndexInfo.values.foreach { nodeIdToNodeInfo =>
-          nodeIdToNodeInfo.values.foreach { nodeIndexInfo =>
-            assert(nodeIndexInfo.featureSubset.isDefined)
-            mutableNodeToFeatures(nodeIndexInfo.nodeIndexInGroup) = nodeIndexInfo.featureSubset.get
-          }
-        }
-        Some(mutableNodeToFeatures.toMap)
-      }
-    }
-
-    /** array of nodes to train indexed by node index in group */
-    val nodes = new Array[LearningNode](numNodes)
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        nodes(treeToNodeToIndexInfo(treeIndex)(node.id).nodeIndexInGroup) = node
-      }
-    }
-
-    // Calculate best splits for all nodes in the group
-    timer.start("chooseSplits")
-
-    // In each partition, iterate all instances and compute aggregate stats for each node,
-    // yield a (nodeIndex, nodeAggregateStats) pair for each node.
-    // After a `reduceByKey` operation,
-    // stats of a node will be shuffled to a particular partition and be combined together,
-    // then best splits for nodes are found there.
-    // Finally, only best Splits for nodes are collected to driver to construct decision tree.
-    // nodeToFeatures: node index in group -> selected feature indexes
-    val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
-    val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
-
-    /** partitionAggregates RDD: node index in group --> nodeStats */
-    val partitionAggregates: RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
-      input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions { points =>
-        // Construct a nodeStatsAggregators array to hold node aggregate stats,
-        // each node will have a nodeStatsAggregator
-        val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
-          val featuresForNode = nodeToFeaturesBc.value.map { nodeToFeatures =>
-            nodeToFeatures(nodeIndex)
-          }
-          new DTStatsAggregator(metadata, featuresForNode)
-        }
-
-        val splitsBcv = if (bcVariables) splitsBc.get.value else splitsOption.get
-        // iterator all instances in current partition and update aggregate stats
-        points.foreach(binSeqOpWithNodeIdCache(nodeStatsAggregators, splitsBcv, _))
-
-        // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
-        // which can be combined with other partition using `reduceByKey`
-        nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
-      }
-    } else {
-      input.mapPartitions { points =>
-          val (firstPointOption, nodeStatsAggregators) =
-            DTUtils.initNodeStatsAgg(numNodes, nodeToFeaturesBc, metadata, points, groupInfo)
-          if (firstPointOption.isEmpty) {
-            Iterator.empty
-          } else {
-            val firstPoint = firstPointOption.get
-            val sampleId = firstPoint.sampleId
-
-            val splitsBcv = if (bcVariables) splitsBc.get.value else splitsOption.get
-            binSeqOp(nodeStatsAggregators, firstPoint, splitsBcv, sampleId)
-            // iterator all instances in current partition and update aggregate stats
-            points.foreach(binSeqOp(nodeStatsAggregators, _, splitsBcv, sampleId))
-
-            // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
-            // which can be combined with other partition using `reduceByKey`
-            nodeStatsAggregators.view.zipWithIndex
-              .filter(v => RFUtils.isValidAgg(v._1)).map(_.swap).iterator
-          }
-      }
-    }
-
-    val reducedAggregates = if (extraParams.useDFCollPtner) {
-      val partitioner = new DFCollectionPartitioner(input.partitions.length,
-        DTUtils.maxNumParallelThreads(), metadata.numFeatures)
-      partitionAggregates.reduceByKey(partitioner, (a, b) => a.merge(b))
-    } else {
-      logInfo("DFCollectionPartitioner discarded.")
-      partitionAggregates.reduceByKey((a, b) => a.merge(b))
-    }
-    val nodeToBestSplits = reducedAggregates.map {
-      case (nodeIndex, aggStats) =>
-        val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
-          Some(nodeToFeatures(nodeIndex))
-        }
-
-        val splitsBcv = if (bcVariables) splitsBc.get.value else splitsOption.get
-        // find best split for each node
-        val (split: Split, stats: ImpurityStats) =
-          binsToBestSplit(aggStats, splitsBcv, featuresForNode, nodes(nodeIndex))
-        (nodeIndex, (split, stats))
-    }.collectAsMap()
-
-    timer.stop("chooseSplits")
-
-    val nodeIdUpdaters = if (nodeIdCache.nonEmpty) {
-      Array.fill[mutable.Map[Int, NodeIndexUpdaterRaw]](
-        metadata.numTrees)(mutable.Map[Int, NodeIndexUpdaterRaw]())
-    } else {
-      null
-    }
-    // Iterate over all nodes in this group.
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        val nodeIndex = node.id
-        val nodeLevel = LearningNode.indexToLevel(nodeIndex)
-        val nodeInfo = treeToNodeToIndexInfo(treeIndex)(nodeIndex)
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val (split: Split, stats: ImpurityStats) =
-          nodeToBestSplits(aggNodeIndex)
-        logDebug("best split = " + split)
-
-        // Extract info for this node.  Create children if not leaf.
-        val isLeaf =
-          (stats.gain <= 0) || (nodeLevel == metadata.maxDepth)
-        node.isLeaf = isLeaf
-        node.stats = stats
-        logDebug("Node = " + node)
-
-        if (!isLeaf) {
-          node.split = Some(split)
-          val childIsLeaf = (nodeLevel + 1) == metadata.maxDepth
-          val leftChildIsLeaf = childIsLeaf || (stats.leftImpurity == 0.0)
-          val rightChildIsLeaf = childIsLeaf || (stats.rightImpurity == 0.0)
-          node.leftChild = Some(LearningNode(LearningNode.leftChildIndex(nodeIndex),
-            leftChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.leftImpurityCalculator)))
-          node.rightChild = Some(LearningNode(LearningNode.rightChildIndex(nodeIndex),
-            rightChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.rightImpurityCalculator)))
-
-          if (nodeIdCache.nonEmpty) {
-            val nodeIndexUpdater = NodeIndexUpdaterRaw(
-              split = split,
-              nodeIndex = nodeIndex)
-            nodeIdUpdaters(treeIndex).put(nodeIndex, nodeIndexUpdater)
-          }
-
-          // enqueue left child and right child if they are not leaves
-          if (!leftChildIsLeaf) {
-            nodeStack.push((treeIndex, node.leftChild.get))
-          }
-          if (!rightChildIsLeaf) {
-            nodeStack.push((treeIndex, node.rightChild.get))
-          }
-
-          logDebug("leftChildIndex = " + node.leftChild.get.id +
-            ", impurity = " + stats.leftImpurity)
-          logDebug("rightChildIndex = " + node.rightChild.get.id +
-            ", impurity = " + stats.rightImpurity)
-        }
-      }
-    }
-
-    if (nodeIdCache.nonEmpty) {
-      // Update the cache if needed.
-      nodeIdCache.get.updateNodeIndicesY(input, nodeIdUpdaters, splits)
-    }
-  }
-
-  /**
-   * Calculate the impurity statistics for a given (feature, split) based upon left/right
-   * aggregates.
-   *
-   * @param stats the recycle impurity statistics for this feature's all splits,
-   *              only 'impurity' and 'impurityCalculator' are valid between each iteration
-   * @param leftImpurityCalculator left node aggregates for this (feature, split)
-   * @param rightImpurityCalculator right node aggregate for this (feature, split)
-   * @param metadata learning and dataset metadata for DecisionTree
-   * @return Impurity statistics for this (feature, split)
-   */
-  private def calculateImpurityStats(
-      stats: ImpurityStats,
-      leftImpurityCalculator: ImpurityCalculator,
-      rightImpurityCalculator: ImpurityCalculator,
-      metadata: DecisionTreeMetadata): ImpurityStats = {
-
-    val parentImpurityCalculator: ImpurityCalculator = if (stats == null) {
-      leftImpurityCalculator.copy.add(rightImpurityCalculator)
-    } else {
-      stats.impurityCalculator
-    }
-
-    val impurity: Double = if (stats == null) {
-      parentImpurityCalculator.calculate()
-    } else {
-      stats.impurity
-    }
-
-    val leftCount = leftImpurityCalculator.count
-    val rightCount = rightImpurityCalculator.count
-
-    val totalCount = leftCount + rightCount
-
-    // If left child or right child doesn't satisfy minimum instances per node,
-    // then this split is invalid, return invalid information gain stats.
-    if ((leftCount < metadata.minInstancesPerNode) ||
-      (rightCount < metadata.minInstancesPerNode)) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
-    val rightImpurity = rightImpurityCalculator.calculate()
-
-    val leftWeight = leftCount / totalCount.toDouble
-    val rightWeight = rightCount / totalCount.toDouble
-
-    val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
-
-    // if information gain doesn't satisfy minimum information gain,
-    // then this split is invalid, return invalid information gain stats.
-    if (gain < metadata.minInfoGain) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    new ImpurityStats(gain, impurity, parentImpurityCalculator,
-      leftImpurityCalculator, rightImpurityCalculator)
-  }
-
-  /**
-   * Find the best split for a node.
-   *
-   * @param binAggregates Bin statistics.
-   * @return tuple for best split: (Split, information gain, prediction at node)
-   */
-  private[tree] def binsToBestSplit(
-      binAggregates: DTStatsAggregator,
-      splits: Array[Array[Split]],
-      featuresForNode: Option[Array[Int]],
-      node: LearningNode): (Split, ImpurityStats) = {
-
-    // Calculate InformationGain and ImpurityStats if current node is top node
-    val level = LearningNode.indexToLevel(node.id)
-    var gainAndImpurityStats: ImpurityStats = if (level == 0) {
-      null
-    } else {
-      node.stats
-    }
-
-    val validFeatureSplits =
-      Range(0, binAggregates.metadata.numFeaturesPerNode).view.map { featureIndexIdx =>
-        featuresForNode.map(features => (featureIndexIdx, features(featureIndexIdx)))
-          .getOrElse((featureIndexIdx, featureIndexIdx))
-      }.withFilter { case (_, featureIndex) =>
-        binAggregates.metadata.numSplits(featureIndex) != 0
-      }
-
-    // For each (feature, split), calculate the gain, and select the best (feature, split).
-    val splitsAndImpurityInfo =
-      validFeatureSplits.map { case (featureIndexIdx, featureIndex) =>
-        val numSplits = binAggregates.metadata.numSplits(featureIndex)
-        if (binAggregates.metadata.isContinuous(featureIndex)) {
-          // Cumulative sum (scanLeft) of bin statistics.
-          // Afterwards, binAggregates for a bin is the sum of aggregates for
-          // that bin + all preceding bins.
-          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          var splitIndex = 0
-          while (splitIndex < numSplits) {
-            binAggregates.mergeForFeature(nodeFeatureOffset, splitIndex + 1, splitIndex)
-            splitIndex += 1
-          }
-          // Find best split.
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { case splitIdx =>
-              val leftChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, splitIdx)
-              val rightChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
-              rightChildStats.subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIdx, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
-        } else if (binAggregates.metadata.isUnordered(featureIndex)) {
-          // Unordered categorical feature
-          val leftChildOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { splitIndex =>
-              val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
-              val rightChildStats = binAggregates.getParentImpurityCalculator()
-                .subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIndex, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
-        } else {
-          // Ordered categorical feature
-          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          val numCategories = binAggregates.metadata.numBins(featureIndex)
-
-          /* Each bin is one category (feature value).
-           * The bins are ordered based on centroidForCategories, and this ordering determines which
-           * splits are considered.  (With K categories, we consider K - 1 possible splits.)
-           *
-           * centroidForCategories is a list: (category, centroid)
-           */
-          val centroidForCategories = Range(0, numCategories).map { case featureValue =>
-            val categoryStats =
-              binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
-            val centroid = if (categoryStats.count != 0) {
-              if (binAggregates.metadata.isMulticlass) {
-                // multiclass classification
-                // For categorical variables in multiclass classification,
-                // the bins are ordered by the impurity of their corresponding labels.
-                categoryStats.calculate()
-              } else if (binAggregates.metadata.isClassification) {
-                // binary classification
-                // For categorical variables in binary classification,
-                // the bins are ordered by the count of class 1.
-                categoryStats.stats(1)
-              } else {
-                // regression
-                // For categorical variables in regression and binary classification,
-                // the bins are ordered by the prediction.
-                categoryStats.predict
-              }
-            } else {
-              Double.MaxValue
-            }
-            (featureValue, centroid)
-          }
-
-          logDebug("Centroids for categorical variable: " + centroidForCategories.mkString(","))
-
-          // bins sorted by centroids
-          val categoriesSortedByCentroid = centroidForCategories.toList.sortBy(_._2)
-
-          logDebug("Sorted centroids for categorical variable = " +
-            categoriesSortedByCentroid.mkString(","))
-
-          // Cumulative sum (scanLeft) of bin statistics.
-          // Afterwards, binAggregates for a bin is the sum of aggregates for
-          // that bin + all preceding bins.
-          var splitIndex = 0
-          while (splitIndex < numSplits) {
-            val currentCategory = categoriesSortedByCentroid(splitIndex)._1
-            val nextCategory = categoriesSortedByCentroid(splitIndex + 1)._1
-            binAggregates.mergeForFeature(nodeFeatureOffset, nextCategory, currentCategory)
-            splitIndex += 1
-          }
-          // lastCategory = index of bin with total aggregates for this (node, feature)
-          val lastCategory = categoriesSortedByCentroid.last._1
-          // Find best split.
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { splitIndex =>
-              val featureValue = categoriesSortedByCentroid(splitIndex)._1
-              val leftChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
-              val rightChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
-              rightChildStats.subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIndex, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          val categoriesForSplit =
-            categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
-          val bestFeatureSplit =
-            new CategoricalSplit(featureIndex, categoriesForSplit.toArray, numCategories)
-          (bestFeatureSplit, bestFeatureGainStats)
-        }
-      }
-
-    val (bestSplit, bestSplitStats) =
-      if (splitsAndImpurityInfo.isEmpty) {
-        // If no valid splits for features, then this split is invalid,
-        // return invalid information gain stats.  Take any split and continue.
-        // Splits is empty, so arbitrarily choose to split on any threshold
-        val dummyFeatureIndex = featuresForNode.map(_.head).getOrElse(0)
-        val parentImpurityCalculator = binAggregates.getParentImpurityCalculator()
-        if (binAggregates.metadata.isContinuous(dummyFeatureIndex)) {
-          (new ContinuousSplit(dummyFeatureIndex, 0),
-            ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-        } else {
-          val numCategories = binAggregates.metadata.featureArity(dummyFeatureIndex)
-          (new CategoricalSplit(dummyFeatureIndex, Array(), numCategories),
-            ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-        }
-      } else {
-        splitsAndImpurityInfo.maxBy(_._2.gain)
-      }
-    (bestSplit, bestSplitStats)
-  }
-
-  /**
-   * Returns splits for decision tree calculation.
-   * Continuous and categorical features are handled differently.
-   *
-   * Continuous features:
-   *   For each feature, there are numBins - 1 possible splits representing the possible binary
-   *   decisions at each node in the tree.
-   *   This finds locations (feature values) for splits using a subsample of the data.
-   *
-   * Categorical features:
-   *   For each feature, there is 1 bin per split.
-   *   Splits and bins are handled in 2 ways:
-   *   (a) "unordered features"
-   *       For multiclass classification with a low-arity feature
-   *       (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits),
-   *       the feature is split based on subsets of categories.
-   *   (b) "ordered features"
-   *       For regression and binary classification,
-   *       and for multiclass classification with a high-arity feature,
-   *       there is one bin per category.
-   *
-   * @param input Training data: RDD of [[LabeledPoint]]
-   * @param metadata Learning and dataset metadata
-   * @param seed random seed
-   * @return Splits, an Array of [[Split]]
-   *          of size (numFeatures, numSplits)
-   */
-  protected[tree] def findSplits(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      seed: Long,
-      numFeaturesOptFindSplits: Int = 8192): Array[Array[Split]] = {
-
-    logDebug("isMulticlass = " + metadata.isMulticlass)
-
-    val numFeatures = metadata.numFeatures
-
-    // Sample the input only if there are continuous features.
-    val continuousFeatures = Range(0, numFeatures).filter(metadata.isContinuous)
-    val sampledInput = if (continuousFeatures.nonEmpty) {
-      // Calculate the number of samples for approximate quantile calculation.
-      val requiredSamples = math.max(metadata.maxBins * metadata.maxBins, 10000)
-      val fraction = if (requiredSamples < metadata.numExamples) {
-        requiredSamples.toDouble / metadata.numExamples
-      } else {
-        1.0
-      }
-      logDebug("fraction of data used for calculating quantiles = " + fraction)
-      input.sample(withReplacement = false, fraction, new XORShiftRandom(seed).nextInt())
-    } else {
-      input.sparkContext.emptyRDD[LabeledPoint]
-    }
-
-    findSplitsBySorting(sampledInput, metadata, continuousFeatures, numFeaturesOptFindSplits)
-  }
-
-  private def findSplitsBySorting(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      continuousFeatures: IndexedSeq[Int],
-      numFeaturesOptFindSplits: Int = 8192): Array[Array[Split]] = {
-
-    val continuousSplits: scala.collection.Map[Int, Array[Split]] = {
-      // reduce the parallelism for split computations when there are less
-      // continuous features than input partitions. this prevents tasks from
-      // being spun up that will definitely do no work.
-      val numPartitions = math.min(continuousFeatures.length, input.partitions.length)
-
-      if (continuousFeatures.length < numFeaturesOptFindSplits) {
-        input
-          .flatMap(point => continuousFeatures.map(idx => (idx, point.features(idx))))
-          .groupByKey(numPartitions)
-          .map { case (idx, samples) =>
-            val thresholds = findSplitsForContinuousFeature(samples, metadata, idx)
-            val splits: Array[Split] = thresholds.map(thresh => new ContinuousSplit(idx, thresh))
-            logDebug(s"featureIndex = $idx, numSplits = ${splits.length}")
-            (idx, splits)
-          }.collectAsMap()
-      } else {
-        val numSamples = input.count
-        require(numSamples < Int.MaxValue)
-        input.mapPartitions { points =>
-          val partialRes = points.foldLeft(Array.empty[(Int, Array[Double])]) { case (res, point) =>
-            var resNew = res
-            continuousFeatures.foreach { idx =>
-              val featureValue = point.features(idx)
-              if (featureValue != 0.0) {
-                resNew = resNew :+ (idx, Array(featureValue))
-              }
-            }
-            resNew
-          }
-          val restRes = continuousFeatures.indices.diff(partialRes.map(_._1).distinct)
-            .toArray.map(idx => (idx, Array.empty[Double]))
-          (partialRes ++ restRes).iterator
-        }.reduceByKey(_ ++ _).map { case (idx, partialSamples) =>
-          val thresholds = findSplitsForContinuousFeature(partialSamples, metadata, idx,
-            numSamples.toInt - partialSamples.length)
-          val splits: Array[Split] = thresholds.map(thresh => new ContinuousSplit(idx, thresh))
-          logDebug(s"featureIndex = $idx, numSplits = ${splits.length}")
-          (idx, splits)
-        }.collectAsMap()
-      }
-    }
-
-    val numFeatures = metadata.numFeatures
-    val splits: Array[Array[Split]] = Array.tabulate(numFeatures) {
-      case i if metadata.isContinuous(i) =>
-        val split = continuousSplits(i)
-        metadata.setNumSplits(i, split.length)
-        split
-
-      case i if metadata.isCategorical(i) && metadata.isUnordered(i) =>
-        // Unordered features
-        // 2^(maxFeatureValue - 1) - 1 combinations
-        val featureArity = metadata.featureArity(i)
-        Array.tabulate[Split](metadata.numSplits(i)) { splitIndex =>
-          val categories = extractMultiClassCategories(splitIndex + 1, featureArity)
-          new CategoricalSplit(i, categories.toArray, featureArity)
-        }
-
-      case i if metadata.isCategorical(i) =>
-        // Ordered features
-        //   Splits are constructed as needed during training.
-        Array.empty[Split]
-    }
-    splits
-  }
-
-  /**
-   * Nested method to extract list of eligible categories given an index. It extracts the
-   * position of ones in a binary representation of the input. If binary
-   * representation of an number is 01101 (13), the output list should (3.0, 2.0,
-   * 0.0). The maxFeatureValue depict the number of rightmost digits that will be tested for ones.
-   */
-  private[tree] def extractMultiClassCategories(
-      input: Int,
-      maxFeatureValue: Int): List[Double] = {
-    var categories = List[Double]()
-    var j = 0
-    var bitShiftedInput = input
-    while (j < maxFeatureValue) {
-      if (bitShiftedInput % 2 != 0) {
-        // updating the list of categories.
-        categories = j.toDouble :: categories
-      }
-      // Right shift by one
-      bitShiftedInput = bitShiftedInput >> 1
-      j += 1
-    }
-    categories
-  }
-
-  /**
-   * Find splits for a continuous feature
-   * NOTE: Returned number of splits is set based on `featureSamples` and
-   *       could be different from the specified `numSplits`.
-   *       The `numSplits` attribute in the `DecisionTreeMetadata` class will be set accordingly.
-   *
-   * @param featureSamples feature values of each sample
-   * @param metadata decision tree metadata
-   *                 NOTE: `metadata.numbins` will be changed accordingly
-   *                       if there are not enough splits to be found
-   * @param featureIndex feature index to find splits
-   * @return array of split thresholds
-   */
-  private[tree] def findSplitsForContinuousFeature(
-      featureSamples: Iterable[Double],
-      metadata: DecisionTreeMetadata,
-      featureIndex: Int,
-      numSamplesOfZeroFeature: Int = 0): Array[Double] = {
-    require(metadata.isContinuous(featureIndex),
-      "findSplitsForContinuousFeature can only be used to find splits for a continuous feature.")
-
-    val splits: Array[Double] = if (featureSamples.isEmpty && 0 == numSamplesOfZeroFeature) {
-      Array.empty[Double]
-    } else {
-      val numSplits = metadata.numSplits(featureIndex)
-
-      // get count for each distinct value
-      val startValueCountPair = if (0 == numSamplesOfZeroFeature) {
-        (Map.empty[Double, Int], 0)
-      } else {
-        (Map(0.0 -> numSamplesOfZeroFeature), numSamplesOfZeroFeature)
-      }
-      val (valueCountMap, numSamples) = featureSamples.foldLeft(startValueCountPair) {
-        case ((m, cnt), x) =>
-          (m + ((x, m.getOrElse(x, 0) + 1)), cnt + 1)
-      }
-      // sort distinct values
-      val valueCounts = valueCountMap.toSeq.sortBy(_._1).toArray
-
-      val possibleSplits = valueCounts.length - 1
-      if (possibleSplits == 0) {
-        // constant feature
-        Array.empty[Double]
-      } else if (possibleSplits <= numSplits) {
-        // if possible splits is not enough or just enough, just return all possible splits
-        (1 to possibleSplits)
-          .map(index => (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0)
-          .toArray
-      } else {
-        // stride between splits
-        val stride: Double = numSamples.toDouble / (numSplits + 1)
-        logDebug("stride = " + stride)
-
-        // iterate `valueCount` to find splits
-        val splitsBuilder = mutable.ArrayBuilder.make[Double]
-        var index = 1
-        // currentCount: sum of counts of values that have been visited
-        var currentCount = valueCounts(0)._2
-        // targetCount: target value for `currentCount`.
-        // If `currentCount` is closest value to `targetCount`,
-        // then current value is a split threshold.
-        // After finding a split threshold, `targetCount` is added by stride.
-        var targetCount = stride
-        while (index < valueCounts.length) {
-          val previousCount = currentCount
-          currentCount += valueCounts(index)._2
-          val previousGap = math.abs(previousCount - targetCount)
-          val currentGap = math.abs(currentCount - targetCount)
-          // If adding count of current value to currentCount
-          // makes the gap between currentCount and targetCount smaller,
-          // previous value is a split threshold.
-          if (previousGap < currentGap) {
-            splitsBuilder += (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0
-            targetCount += stride
-          }
-          index += 1
-        }
-
-        splitsBuilder.result()
-      }
-    }
-    splits
-  }
-
-  /**
-   * Pull nodes off of the queue, and collect a group of nodes to be split on this iteration.
-   * This tracks the memory usage for aggregates and stops adding nodes when too much memory
-   * will be needed; this allows an adaptive number of nodes since different nodes may require
-   * different amounts of memory (if featureSubsetStrategy is not "all").
-   *
-   * @param nodeStack  Queue of nodes to split.
-   * @param maxMemoryUsage  Bound on size of aggregate statistics.
-   * @return  (nodesForGroup, treeToNodeToIndexInfo).
-   *          nodesForGroup holds the nodes to split: treeIndex --> nodes in tree.
-   *
-   *          treeToNodeToIndexInfo holds indices selected features for each node:
-   *            treeIndex --> (global) node index --> (node index in group, feature indices).
-   *          The (global) node index is the index in the tree; the node index in group is the
-   *           index in [0, numNodesInGroup) of the node in this group.
-   *          The feature indices are None if not subsampling features.
-   */
-  private[tree] def selectNodesToSplit(
-      nodeStack: mutable.ArrayStack[(Int, LearningNode)],
-      maxMemoryUsage: Long,
-      metadata: DecisionTreeMetadata,
-      rng: Random): (Map[Int, Array[LearningNode]], Map[Int, Map[Int, NodeIndexInfo]]) = {
-    // Collect some nodes to split:
-    //  nodesForGroup(treeIndex) = nodes to split
-    val mutableNodesForGroup = new mutable.HashMap[Int, mutable.ArrayBuffer[LearningNode]]()
-    val mutableTreeToNodeToIndexInfo =
-      new mutable.HashMap[Int, mutable.HashMap[Int, NodeIndexInfo]]()
-    var memUsage: Long = 0L
-    var numNodesInGroup = 0
-    // If maxMemoryInMB is set very small, we want to still try to split 1 node,
-    // so we allow one iteration if memUsage == 0.
-    var groupDone = false
-    while (nodeStack.nonEmpty && !groupDone) {
-      val (treeIndex, node) = nodeStack.top
-      // Choose subset of features for node (if subsampling).
-      val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) {
-        Some(SamplingUtils.reservoirSampleAndCount(Range(0,
-          metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong())._1)
-      } else {
-        None
-      }
-      // Check if enough memory remains to add this node to the group.
-      val nodeMemUsage = DecisionForest.aggregateSizeForNode(metadata, featureSubset) * 8L
-      if (memUsage + nodeMemUsage <= maxMemoryUsage || memUsage == 0) {
-        nodeStack.pop()
-        mutableNodesForGroup.getOrElseUpdate(treeIndex, new mutable.ArrayBuffer[LearningNode]()) +=
-          node
-        mutableTreeToNodeToIndexInfo
-          .getOrElseUpdate(treeIndex, new mutable.HashMap[Int, NodeIndexInfo]())(node.id)
-          = new NodeIndexInfo(numNodesInGroup, featureSubset)
-        numNodesInGroup += 1
-        memUsage += nodeMemUsage
-      } else {
-        groupDone = true
-      }
-    }
-    if (memUsage > maxMemoryUsage) {
-      // If maxMemoryUsage is 0, we should still allow splitting 1 node.
-      logWarning(s"Tree learning is using approximately $memUsage bytes per iteration, which" +
-        s" exceeds requested limit maxMemoryUsage=$maxMemoryUsage. This allows splitting" +
-        s" $numNodesInGroup nodes in this iteration.")
-    }
-    logWarning(f"[this group] actualMemUsage: ${memUsage/(1024d*1024d)}%.2f MB," +
-      f" maxMemoryUsage: ${maxMemoryUsage/(1024d*1024d)}%.2f MB.")
-    // Convert mutable maps to immutable ones.
-    val nodesForGroup: Map[Int, Array[LearningNode]] =
-      mutableNodesForGroup.mapValues(_.toArray).toMap
-    val treeToNodeToIndexInfo = mutableTreeToNodeToIndexInfo.mapValues(_.toMap).toMap
-    (nodesForGroup, treeToNodeToIndexInfo)
-  }
-
-  /**
-   * Get the number of values to be stored for this node in the bin aggregates.
-   *
-   * @param featureSubset  Indices of features which may be split at this node.
-   *                       If None, then use all features.
-   */
-  private def aggregateSizeForNode(
-      metadata: DecisionTreeMetadata,
-      featureSubset: Option[Array[Int]]): Long = {
-    val totalBins = if (featureSubset.nonEmpty) {
-      featureSubset.get.map(featureIndex => metadata.numBins(featureIndex).toLong).sum
-    } else {
-      metadata.numBins.map(_.toLong).sum
-    }
-    if (metadata.isClassification) {
-      metadata.numClasses * totalBins
-    } else {
-      3 * totalBins
-    }
-  }
-}
-
-object DecisionForestInfo {
-  var timerResult: String = ""
-}
-
-case class LocalTrainingTask(node: LearningNode)
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
deleted file mode 100644
index ddb6925..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
+++ /dev/null
@@ -1,663 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-
-import org.apache.spark.SparkContext
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, DecisionTreeRegressor}
-import org.apache.spark.ml.tree.Split
-import org.apache.spark.ml.tree.impl.RandomForest4GBDTX.findSplits
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.configuration.{BoostingStrategy => OldBoostingStrategy}
-import org.apache.spark.mllib.tree.impurity.{Variance => OldVariance}
-import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.rdd.util.PeriodicRDDCheckpointer
-import org.apache.spark.storage.StorageLevel
-
-
-private[spark] object GradientBoostedTrees extends Logging {
-
-  /**
-   * Method to train a gradient boosting model
-   * @param input Training dataset: RDD of `LabeledPoint`.
-   * @param seed Random seed.
-   * @return tuple of ensemble models and weights:
-   *         (array of decision tree models, array of model weights)
-   */
-  def run(
-      input: RDD[LabeledPoint],
-      boostingStrategy: OldBoostingStrategy,
-      seed: Long,
-      featureSubsetStrategy: String): (Array[DecisionTreeRegressionModel], Array[Double]) = {
-    val doUseAcc = getDoUseAccFromSparkConf(input.sparkContext)
-    run(input, boostingStrategy, seed, featureSubsetStrategy, doUseAcc)
-  }
-
-  /** Run with extended parameters */
-  def run(
-      input: RDD[LabeledPoint],
-      boostingStrategy: OldBoostingStrategy,
-      seed: Long,
-      featureSubsetStrategy: String,
-      doUseAcc: Boolean): (Array[DecisionTreeRegressionModel], Array[Double]) = {
-    val algo = boostingStrategy.treeStrategy.algo
-    algo match {
-      case OldAlgo.Regression =>
-        if (doUseAcc) {
-          GradientBoostedTrees.boostX(input, input, boostingStrategy, validate = false,
-            seed, featureSubsetStrategy)
-        } else {
-          GradientBoostedTrees.boost(input, input, boostingStrategy, validate = false,
-            seed, featureSubsetStrategy)
-        }
-      case OldAlgo.Classification =>
-        // Map labels to -1, +1 so binary classification can be treated as regression.
-        val remappedInput = input.map(x => new LabeledPoint((x.label * 2) - 1, x.features))
-        if (doUseAcc) {
-          GradientBoostedTrees.boostX(remappedInput, remappedInput,
-            boostingStrategy, validate = false, seed, featureSubsetStrategy)
-        } else {
-          GradientBoostedTrees.boost(remappedInput, remappedInput,
-            boostingStrategy, validate = false, seed, featureSubsetStrategy)
-        }
-      // algo is enumerate value, this case may be unreachable
-      case _ =>
-        throw new IllegalArgumentException(s"$algo is not supported by gradient boosting.")
-    }
-  }
-
-  /**
-   * Method to validate a gradient boosting model
-   * @param input Training dataset: RDD of `LabeledPoint`.
-   * @param validationInput Validation dataset.
-   *                        This dataset should be different from the training dataset,
-   *                        but it should follow the same distribution.
-   *                        E.g., these two datasets could be created from an original dataset
-   *                        by using `org.apache.spark.rdd.RDD.randomSplit()`
-   * @param seed Random seed.
-   * @return tuple of ensemble models and weights:
-   *         (array of decision tree models, array of model weights)
-   */
-  def runWithValidation(
-      input: RDD[LabeledPoint],
-      validationInput: RDD[LabeledPoint],
-      boostingStrategy: OldBoostingStrategy,
-      seed: Long,
-      featureSubsetStrategy: String): (Array[DecisionTreeRegressionModel], Array[Double]) = {
-    val doUseAcc = getDoUseAccFromSparkConf(input.sparkContext)
-    runWithValidation(input, validationInput, boostingStrategy, seed, featureSubsetStrategy,
-      doUseAcc)
-  }
-
-  /** Run with validation dataset and extended parameters */
-  def runWithValidation(
-      input: RDD[LabeledPoint],
-      validationInput: RDD[LabeledPoint],
-      boostingStrategy: OldBoostingStrategy,
-      seed: Long,
-      featureSubsetStrategy: String,
-      doUseAcc: Boolean): (Array[DecisionTreeRegressionModel], Array[Double]) = {
-    val algo = boostingStrategy.treeStrategy.algo
-    algo match {
-      case OldAlgo.Regression =>
-        if (doUseAcc) {
-          GradientBoostedTrees.boostX(input, validationInput, boostingStrategy,
-            validate = true, seed, featureSubsetStrategy)
-        } else {
-          GradientBoostedTrees.boost(input, validationInput, boostingStrategy,
-            validate = true, seed, featureSubsetStrategy)
-        }
-      case OldAlgo.Classification =>
-        // Map labels to -1, +1 so binary classification can be treated as regression.
-        val remappedInput = input.map(
-          x => new LabeledPoint((x.label * 2) - 1, x.features))
-        val remappedValidationInput = validationInput.map(
-          x => new LabeledPoint((x.label * 2) - 1, x.features))
-        if (doUseAcc) {
-          GradientBoostedTrees.boostX(remappedInput, remappedValidationInput, boostingStrategy,
-            validate = true, seed, featureSubsetStrategy)
-        } else {
-          GradientBoostedTrees.boost(remappedInput, remappedValidationInput, boostingStrategy,
-            validate = true, seed, featureSubsetStrategy)
-        }
-
-      // algo is enumerate value, this case may be unreachable
-      case _ =>
-        throw new IllegalArgumentException(s"$algo is not supported by the gradient boosting.")
-    }
-  }
-
-  private val extraParamKey = "spark.boostkit.ml.gbdt.doUseAcc"
-  private val doUseAccDefault = true
-
-  private def getDoUseAccFromSparkConf(sc: SparkContext): Boolean = {
-    val doUseAcctStr = sc.conf.getOption(extraParamKey)
-    if (doUseAcctStr.nonEmpty) {
-      try {
-        doUseAcctStr.get.toBoolean
-      } catch {
-        case ex: Exception =>
-          throw new IllegalArgumentException(s"Parse boostkit parameter" +
-            s"($extraParamKey) failed, Error reason: ${ex.getMessage}")
-      }
-    } else {
-      doUseAccDefault
-    }
-  }
-
-  /**
-   * Compute the initial predictions and errors for a dataset for the first
-   * iteration of gradient boosting.
-   * @param data: training data.
-   * @param initTreeWeight: learning rate assigned to the first tree.
-   * @param initTree: first DecisionTreeModel.
-   * @param loss: evaluation metric.
-   * @return an RDD with each element being a zip of the prediction and error
-   *         corresponding to every sample.
-   */
-  def computeInitialPredictionAndError(
-      data: RDD[LabeledPoint],
-      initTreeWeight: Double,
-      initTree: DecisionTreeRegressionModel,
-      loss: OldLoss): RDD[(Double, Double)] = {
-    data.map { lp =>
-      val pred = updatePrediction(lp.features, 0.0, initTree, initTreeWeight)
-      val error = loss.computeError(pred, lp.label)
-      (pred, error)
-    }
-  }
-
-  def computeInitialPredictionAndErrorX(
-      data: RDD[TreePoint],
-      initTreeWeight: Double,
-      initTree: DecisionTreeRegressionModel,
-      loss: OldLoss,
-      splits: Array[Array[Split]]): RDD[(Double, Double)] = {
-    data.map { lp =>
-      val pred = updatePredictionX(lp.binnedFeatures, 0.0, initTree, initTreeWeight, splits)
-      val error = loss.computeError(pred, lp.label)
-      (pred, error)
-    }
-  }
-
-  /**
-   * Update a zipped predictionError RDD
-   * (as obtained with computeInitialPredictionAndError)
-   * @param data: training data.
-   * @param predictionAndError: predictionError RDD
-   * @param treeWeight: Learning rate.
-   * @param tree: Tree using which the prediction and error should be updated.
-   * @param loss: evaluation metric.
-   * @return an RDD with each element being a zip of the prediction and error
-   *         corresponding to each sample.
-   */
-  def updatePredictionError(
-      data: RDD[LabeledPoint],
-      predictionAndError: RDD[(Double, Double)],
-      treeWeight: Double,
-      tree: DecisionTreeRegressionModel,
-      loss: OldLoss): RDD[(Double, Double)] = {
-
-    val newPredError = data.zip(predictionAndError).mapPartitions { iter =>
-      iter.map { case (lp, (pred, error)) =>
-        val newPred = updatePrediction(lp.features, pred, tree, treeWeight)
-        val newError = loss.computeError(newPred, lp.label)
-        (newPred, newError)
-      }
-    }
-    newPredError
-  }
-
-  def updatePredictionErrorX(
-      data: RDD[TreePoint],
-      predictionAndError: RDD[(Double, Double)],
-      treeWeight: Double,
-      tree: DecisionTreeRegressionModel,
-      loss: OldLoss,
-      splits: Array[Array[Split]]): RDD[(Double, Double)] = {
-
-    val newPredError = data.zip(predictionAndError).mapPartitions { iter =>
-      iter.map { case (lp, (pred, error)) =>
-        val newPred = updatePredictionX(lp.binnedFeatures, pred, tree, treeWeight, splits)
-        val newError = loss.computeError(newPred, lp.label)
-        (newPred, newError)
-      }
-    }
-    newPredError
-  }
-
-  /**
-   * Add prediction from a new boosting iteration to an existing prediction.
-   *
-   * @param features Vector of features representing a single data point.
-   * @param prediction The existing prediction.
-   * @param tree New Decision Tree model.
-   * @param weight Tree weight.
-   * @return Updated prediction.
-   */
-  def updatePrediction(
-      features: Vector,
-      prediction: Double,
-      tree: DecisionTreeRegressionModel,
-      weight: Double): Double = {
-    prediction + tree.rootNode.predictImpl(features).prediction * weight
-  }
-
-  def updatePredictionX(
-      features: Array[Int],
-      prediction: Double,
-      tree: DecisionTreeRegressionModel,
-      weight: Double,
-      splits: Array[Array[Split]]): Double = {
-    prediction + tree.rootNode.predictImplX(features, splits).prediction * weight
-  }
-
-  /**
-   * Method to calculate error of the base learner for the gradient boosting calculation.
-   * Note: This method is not used by the gradient boosting algorithm but is useful for debugging
-   * purposes.
-   * @param data Training dataset: RDD of `LabeledPoint`.
-   * @param trees Boosted Decision Tree models
-   * @param treeWeights Learning rates at each boosting iteration.
-   * @param loss evaluation metric.
-   * @return Measure of model error on data
-   */
-  def computeError(
-      data: RDD[LabeledPoint],
-      trees: Array[DecisionTreeRegressionModel],
-      treeWeights: Array[Double],
-      loss: OldLoss): Double = {
-    data.map { lp =>
-      val predicted = trees.zip(treeWeights).foldLeft(0.0) { case (acc, (model, weight)) =>
-        updatePrediction(lp.features, acc, model, weight)
-      }
-      loss.computeError(predicted, lp.label)
-    }.mean()
-  }
-
-  /**
-   * Method to compute error or loss for every iteration of gradient boosting.
-   *
-   * @param data RDD of `LabeledPoint`
-   * @param trees Boosted Decision Tree models
-   * @param treeWeights Learning rates at each boosting iteration.
-   * @param loss evaluation metric.
-   * @param algo algorithm for the ensemble, either Classification or Regression
-   * @return an array with index i having the losses or errors for the ensemble
-   *         containing the first i+1 trees
-   */
-  def evaluateEachIteration(
-      data: RDD[LabeledPoint],
-      trees: Array[DecisionTreeRegressionModel],
-      treeWeights: Array[Double],
-      loss: OldLoss,
-      algo: OldAlgo.Value): Array[Double] = {
-
-    val sc = data.sparkContext
-    val remappedData = algo match {
-      case OldAlgo.Classification => data.map(x => new LabeledPoint((x.label * 2) - 1, x.features))
-      case _ => data
-    }
-
-    val broadcastTrees = sc.broadcast(trees)
-    val localTreeWeights = treeWeights
-    val treesIndices = trees.indices
-
-    val dataCount = remappedData.count()
-    val evaluation = remappedData.map { point =>
-      treesIndices.map { idx =>
-        val prediction = broadcastTrees.value(idx)
-          .rootNode
-          .predictImpl(point.features)
-          .prediction
-        prediction * localTreeWeights(idx)
-      }
-      .scanLeft(0.0)(_ + _).drop(1)
-      .map(prediction => loss.computeError(prediction, point.label))
-    }
-    .aggregate(treesIndices.map(_ => 0.0))(
-      (aggregated, row) => treesIndices.map(idx => aggregated(idx) + row(idx)),
-      (a, b) => treesIndices.map(idx => a(idx) + b(idx)))
-    .map(_ / dataCount)
-
-    broadcastTrees.destroy(blocking = false)
-    evaluation.toArray
-  }
-
-  /**
-   * Internal method for performing regression using trees as base learners.
-   * @param input training dataset
-   * @param validationInput validation dataset, ignored if validate is set to false.
-   * @param boostingStrategy boosting parameters
-   * @param validate whether or not to use the validation dataset.
-   * @param seed Random seed.
-   * @return tuple of ensemble models and weights:
-   *         (array of decision tree models, array of model weights)
-   */
-  def boost(
-      input: RDD[LabeledPoint],
-      validationInput: RDD[LabeledPoint],
-      boostingStrategy: OldBoostingStrategy,
-      validate: Boolean,
-      seed: Long,
-      featureSubsetStrategy: String): (Array[DecisionTreeRegressionModel], Array[Double]) = {
-    val timer = new TimeTracker()
-    timer.start("total")
-    timer.start("init")
-
-    boostingStrategy.assertValid()
-
-    // Initialize gradient boosting parameters
-    val numIterations = boostingStrategy.numIterations
-    val baseLearners = new Array[DecisionTreeRegressionModel](numIterations)
-    val baseLearnerWeights = new Array[Double](numIterations)
-    val loss = boostingStrategy.loss
-    val learningRate = boostingStrategy.learningRate
-
-    // Prepare strategy for individual trees, which use regression with variance impurity.
-    val treeStrategy = boostingStrategy.treeStrategy.copy
-    val validationTol = boostingStrategy.validationTol
-    treeStrategy.algo = OldAlgo.Regression
-    treeStrategy.impurity = OldVariance
-    treeStrategy.assertValid()
-
-    // Cache input
-    val persistedInput = if (input.getStorageLevel == StorageLevel.NONE) {
-      input.persist(StorageLevel.MEMORY_AND_DISK)
-      true
-    } else {
-      false
-    }
-
-    // Prepare periodic checkpointers
-    val predErrorCheckpointer = new PeriodicRDDCheckpointer[(Double, Double)](
-      treeStrategy.getCheckpointInterval, input.sparkContext)
-    val validatePredErrorCheckpointer = new PeriodicRDDCheckpointer[(Double, Double)](
-      treeStrategy.getCheckpointInterval, input.sparkContext)
-
-    timer.stop("init")
-
-    logDebug("##########")
-    logDebug("Building tree 0")
-    logDebug("##########")
-
-    // Initialize tree
-    timer.start("building tree 0")
-    val firstTree = new DecisionTreeRegressor().setSeed(seed)
-    val firstTreeModel = firstTree.train(input, treeStrategy, featureSubsetStrategy)
-    val firstTreeWeight = 1.0
-    baseLearners(0) = firstTreeModel
-    baseLearnerWeights(0) = firstTreeWeight
-
-    var predError: RDD[(Double, Double)] =
-      computeInitialPredictionAndError(input, firstTreeWeight, firstTreeModel, loss)
-    predErrorCheckpointer.update(predError)
-    logDebug(s"error of gbt = ${predError.values.mean()}")
-
-    // Note: A model of type regression is used since we require raw prediction
-    timer.stop("building tree 0")
-
-    var validatePredError: RDD[(Double, Double)] =
-      computeInitialPredictionAndError(validationInput, firstTreeWeight, firstTreeModel, loss)
-    if (validate) validatePredErrorCheckpointer.update(validatePredError)
-    var bestValidateError = if (validate) validatePredError.values.mean() else 0.0
-    var bestM = 1
-
-    var m = 1
-    var doneLearning = false
-    while (m < numIterations && !doneLearning) {
-      // Update data with pseudo-residuals
-      val data = predError.zip(input).map { case ((pred, _), point) =>
-        LabeledPoint(-loss.gradient(pred, point.label), point.features)
-      }
-
-      timer.start(s"building tree $m")
-      logDebug("###################################################")
-      logDebug(s"Gradient boosting tree iteration ${m}")
-      logDebug("###################################################")
-
-      val dt = new DecisionTreeRegressor().setSeed(seed + m)
-      val model = dt.train(data, treeStrategy, featureSubsetStrategy)
-      timer.stop(s"building tree $m")
-      // Update partial model
-      baseLearners(m) = model
-      // Note: The setting of baseLearnerWeights is incorrect for losses other than SquaredError.
-      //       Technically, the weight should be optimized for the particular loss.
-      //       However, the behavior should be reasonable, though not optimal.
-      baseLearnerWeights(m) = learningRate
-
-      predError = updatePredictionError(
-        input, predError, baseLearnerWeights(m), baseLearners(m), loss)
-      predErrorCheckpointer.update(predError)
-      logDebug(s"error of gbt = ${predError.values.mean()}")
-
-      if (validate) {
-        // Stop training early if
-        // 1. Reduction in error is less than the validationTol or
-        // 2. If the error increases, that is if the model is overfit.
-        // We want the model returned corresponding to the best validation error.
-
-        validatePredError = updatePredictionError(
-          validationInput, validatePredError, baseLearnerWeights(m), baseLearners(m), loss)
-        validatePredErrorCheckpointer.update(validatePredError)
-        val currentValidateError = validatePredError.values.mean()
-        if (bestValidateError - currentValidateError < validationTol * Math.max(
-          currentValidateError, 0.01)) {
-          doneLearning = true
-        } else if (currentValidateError < bestValidateError) {
-          bestValidateError = currentValidateError
-          bestM = m + 1
-        }
-      }
-      m += 1
-    }
-
-    timer.stop("total")
-
-    logInfo("Internal timing for DecisionTree:")
-    logInfo(s"$timer")
-
-    predErrorCheckpointer.unpersistDataSet()
-    predErrorCheckpointer.deleteAllCheckpoints()
-    validatePredErrorCheckpointer.unpersistDataSet()
-    validatePredErrorCheckpointer.deleteAllCheckpoints()
-    if (persistedInput) input.unpersist()
-
-    if (validate) {
-      (baseLearners.slice(0, bestM), baseLearnerWeights.slice(0, bestM))
-    } else {
-      (baseLearners, baseLearnerWeights)
-    }
-  }
-
-  /**
-   * Internal method for performing regression using trees as base learners.
-   * @param input training dataset
-   * @param validationInput validation dataset, ignored if validate is set to false.
-   * @param boostingStrategy boosting parameters
-   * @param validate whether or not to use the validation dataset.
-   * @param seed Random seed.
-   * @return tuple of ensemble models and weights:
-   *         (array of decision tree models, array of model weights)
-   */
-  def boostX(
-      input: RDD[LabeledPoint],
-      validationInput: RDD[LabeledPoint],
-      boostingStrategy: OldBoostingStrategy,
-      validate: Boolean,
-      seed: Long,
-      featureSubsetStrategy: String): (Array[DecisionTreeRegressionModel], Array[Double]) = {
-    val timer = new TimeTracker()
-    timer.start("total")
-    timer.start("init")
-
-    boostingStrategy.assertValid()
-
-    // Initialize gradient boosting parameters
-    val numIterations = boostingStrategy.numIterations
-    val baseLearners = new Array[DecisionTreeRegressionModel](numIterations)
-    val baseLearnerWeights = new Array[Double](numIterations)
-    val loss = boostingStrategy.loss
-    val learningRate = boostingStrategy.learningRate
-
-    // Prepare strategy for individual trees, which use regression with variance impurity.
-    val treeStrategy = boostingStrategy.treeStrategy.copy
-    val validationTol = boostingStrategy.validationTol
-    treeStrategy.algo = OldAlgo.Regression
-    treeStrategy.impurity = OldVariance
-    treeStrategy.assertValid()
-
-    // Prepare periodic checkpointers
-    val predErrorCheckpointer = new PeriodicRDDCheckpointer[(Double, Double)](
-      treeStrategy.getCheckpointInterval, input.sparkContext)
-    val validatePredErrorCheckpointer = new PeriodicRDDCheckpointer[(Double, Double)](
-      treeStrategy.getCheckpointInterval, input.sparkContext)
-
-    // X
-    val retaggedInput = input.retag(classOf[LabeledPoint])
-    val metadata =
-      DecisionTreeMetadata.buildMetadata(retaggedInput, treeStrategy, 1, featureSubsetStrategy)
-
-    // Find the splits and the corresponding bins (interval between the splits) using a sample
-    // of the input data.
-    timer.start("findSplits")
-    val splits = findSplits(retaggedInput, metadata, seed)
-    timer.stop("findSplits")
-    logDebug("numBins: feature: number of bins")
-    logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
-      s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
-    }.mkString("\n"))
-
-    val (treeInput, processedInput, labelArrayBcTmp, rawPartInfoBcTmp) =
-      GradientBoostedTreesUtil.dataProcessX(retaggedInput, splits, treeStrategy, metadata, timer,
-        seed)
-    var rawPartInfoBc = rawPartInfoBcTmp
-    var labelArrayBc = labelArrayBcTmp
-
-    // X
-    timer.stop("init")
-
-    logDebug("##########")
-    logDebug("Building tree 0")
-    logDebug("##########")
-
-    // Initialize tree
-    timer.start("building tree 0")
-    val firstTree = new DecisionTreeRegressor().setSeed(seed)
-    val firstTreeModel = firstTree.train4GBDTX(labelArrayBc, processedInput, metadata, splits,
-      treeStrategy, featureSubsetStrategy, treeInput, rawPartInfoBc)
-    val firstTreeWeight = 1.0
-    baseLearners(0) = firstTreeModel
-    baseLearnerWeights(0) = firstTreeWeight
-
-    var predError: RDD[(Double, Double)] =
-      computeInitialPredictionAndErrorX(treeInput, firstTreeWeight, firstTreeModel, loss, splits)
-    predErrorCheckpointer.update(predError)
-    logDebug(s"error of gbt = ${predError.values.mean()}")
-
-    // Note: A model of type regression is used since we require raw prediction
-    timer.stop("building tree 0")
-
-    var validatePredError: RDD[(Double, Double)] =
-      computeInitialPredictionAndError(validationInput, firstTreeWeight, firstTreeModel, loss)
-    if (validate) validatePredErrorCheckpointer.update(validatePredError)
-    var bestValidateError = if (validate) validatePredError.values.mean() else 0.0
-    var bestM = 1
-
-    var m = 1
-    var doneLearning = false
-    while (m < numIterations && !doneLearning) {
-      labelArrayBc = treeInput.sparkContext.broadcast(
-        DoubleArrayList.wrap(
-          predError.zip(treeInput).map { case ((pred, _), point) =>
-            -loss.gradient(pred, point.label)}.collect()
-        )
-      )
-
-      timer.start(s"building tree $m")
-      logDebug("###################################################")
-      logDebug(s"Gradient boosting tree iteration ${m}")
-      logDebug("###################################################")
-
-      val dt = new DecisionTreeRegressor().setSeed(seed + m)
-      val model = dt.train4GBDTX(labelArrayBc, processedInput, metadata, splits, treeStrategy,
-        featureSubsetStrategy, treeInput, rawPartInfoBc)
-      timer.stop(s"building tree $m")
-      // Update partial model
-      baseLearners(m) = model
-      // Note: The setting of baseLearnerWeights is incorrect for losses other than SquaredError.
-      //       Technically, the weight should be optimized for the particular loss.
-      //       However, the behavior should be reasonable, though not optimal.
-      baseLearnerWeights(m) = learningRate
-
-      predError = updatePredictionErrorX(
-        treeInput, predError, baseLearnerWeights(m), baseLearners(m), loss, splits)
-      predErrorCheckpointer.update(predError)
-      logDebug(s"error of gbt = ${predError.values.mean()}")
-
-      if (validate) {
-        // Stop training early if
-        // 1. Reduction in error is less than the validationTol or
-        // 2. If the error increases, that is if the model is overfit.
-        // We want the model returned corresponding to the best validation error.
-
-        validatePredError = updatePredictionError(
-          validationInput, validatePredError, baseLearnerWeights(m), baseLearners(m), loss)
-        validatePredErrorCheckpointer.update(validatePredError)
-        val currentValidateError = validatePredError.values.mean()
-        if (bestValidateError - currentValidateError < validationTol * Math.max(
-          currentValidateError, 0.01)) {
-          doneLearning = true
-        } else if (currentValidateError < bestValidateError) {
-          bestValidateError = currentValidateError
-          bestM = m + 1
-        }
-      }
-      m += 1
-    }
-
-    timer.stop("total")
-
-    logInfo("Internal timing for DecisionTree:")
-    logInfo(s"$timer")
-
-    predErrorCheckpointer.unpersistDataSet()
-    predErrorCheckpointer.deleteAllCheckpoints()
-    validatePredErrorCheckpointer.unpersistDataSet()
-    validatePredErrorCheckpointer.deleteAllCheckpoints()
-    treeInput.unpersist()
-    processedInput.unpersist()
-
-    if (validate) {
-      (baseLearners.slice(0, bestM), baseLearnerWeights.slice(0, bestM))
-    } else {
-      (baseLearners, baseLearnerWeights)
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala
deleted file mode 100644
index 7fb606d..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala
+++ /dev/null
@@ -1,360 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import java.io.IOException
-
-import scala.collection.mutable
-
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.tree.{LearningNode, Split, SplitBase}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-
-/**
- * This is used by the node id cache to find the child id that a data point would belong to.
- * @param split Split information.
- * @param nodeIndex The current node index of a data point that this will update.
- */
-private[tree] case class NodeIndexUpdaterRaw(split: Split, nodeIndex: Int) {
-
-  /**
-   * Determine a child node index based on the feature value and the split.
-   * @param binnedFeature Binned feature value.
-   * @param splits Split information to convert the bin indices to approximate feature values.
-   * @return Child node index to update to.
-   */
-  def updateNodeIndex(binnedFeature: Int, splits: Array[Split]): Int = {
-    if (split.shouldGoLeft(binnedFeature, splits)) {
-      LearningNode.leftChildIndex(nodeIndex)
-    } else {
-      LearningNode.rightChildIndex(nodeIndex)
-    }
-  }
-}
-
-/**
- * This is used by the node id cache to find the child id that a data point would belong to.
- * @param split Split information.
- * @param nodeIndex The current node index of a data point that this will update.
- */
-private[tree] case class NodeIndexUpdater(split: SplitBase, nodeIndex: Int) {
-
-  /**
-   * Determine a child node index based on the feature value and the split.
-   * @param binnedFeature Binned feature value.
-   * @param splits Split information to convert the bin indices to approximate feature values.
-   * @return Child node index to update to.
-   */
-  def updateNodeIndex(binnedFeature: Char, splits: Array[SplitBase]): Int = {
-    if (split.shouldGoLeft(binnedFeature, splits)) {
-      LearningNode.leftChildIndex(nodeIndex)
-    } else {
-      LearningNode.rightChildIndex(nodeIndex)
-    }
-  }
-}
-
-/**
- * Each TreePoint belongs to a particular node per tree.
- * Each row in the nodeIdsForInstances RDD is an array over trees of the node index
- * in each tree. Initially, values should all be 1 for root node.
- * The nodeIdsForInstances RDD needs to be updated at each iteration.
- * @param nodeIdsForInstances The initial values in the cache
- *                           (should be an Array of all 1's (meaning the root nodes)).
- * @param checkpointInterval The checkpointing interval
- *                           (how often should the cache be checkpointed.).
- */
-private[spark] class NodeIdCache(
-  var nodeIdsForInstances: RDD[Array[Int]],
-  val checkpointInterval: Int) extends Logging {
-
-  // Keep a reference to a previous node Ids for instances.
-  // Because we will keep on re-persisting updated node Ids,
-  // we want to unpersist the previous RDD.
-  private var prevNodeIdsForInstances: RDD[Array[Int]] = null
-
-  // To keep track of the past checkpointed RDDs.
-  private val checkpointQueue = mutable.Queue[RDD[Array[Int]]]()
-  private var rddUpdateCount = 0
-
-  // Indicates whether we can checkpoint
-  private val canCheckpoint = nodeIdsForInstances.sparkContext.getCheckpointDir.nonEmpty
-
-  // Hadoop Configuration for deleting checkpoints as needed
-  private val hadoopConf = nodeIdsForInstances.sparkContext.hadoopConfiguration
-
-  /**
-   * Update the node index values in the cache.
-   * This updates the RDD and its lineage.
-   * TODO: Passing bin information to executors seems unnecessary and costly.
-   * @param data The RDD of training rows.
-   * @param nodeIdUpdaters A map of node index updaters.
-   *                       The key is the indices of nodes that we want to update.
-   * @param splits  Split information needed to find child node indices.
-   */
-  def updateNodeIndices(
-      data: RDD[BaggedPoint[TreePointX]],
-      nodeIdUpdaters: Array[mutable.Map[Int, NodeIndexUpdater]],
-      splits: Array[Array[SplitBase]]): Unit = {
-    if (prevNodeIdsForInstances != null) {
-      // Unpersist the previous one if one exists.
-      prevNodeIdsForInstances.unpersist()
-    }
-
-    prevNodeIdsForInstances = nodeIdsForInstances
-    nodeIdsForInstances = data.zip(nodeIdsForInstances).map { case (point, ids) =>
-      var treeId = 0
-      while (treeId < nodeIdUpdaters.length) {
-        val nodeIdUpdater = nodeIdUpdaters(treeId).getOrElse(ids(treeId), null)
-        if (nodeIdUpdater != null) {
-          val featureIndex = nodeIdUpdater.split.featureIndex
-          val newNodeIndex = nodeIdUpdater.updateNodeIndex(
-            binnedFeature = point.datum.binnedFeatures.get(featureIndex),
-            splits = splits(featureIndex))
-          ids(treeId) = newNodeIndex
-        }
-        treeId += 1
-      }
-      ids
-    }
-
-    // Keep on persisting new ones.
-    nodeIdsForInstances.persist(StorageLevel.MEMORY_AND_DISK)
-    rddUpdateCount += 1
-
-    // Handle checkpointing if the directory is not None.
-    if (canCheckpoint && checkpointInterval != -1 && (rddUpdateCount % checkpointInterval) == 0) {
-      // Let's see if we can delete previous checkpoints.
-      var canDelete = true
-      while (checkpointQueue.size > 1 && canDelete) {
-        // We can delete the oldest checkpoint iff
-        // the next checkpoint actually exists in the file system.
-        if (checkpointQueue(1).getCheckpointFile.isDefined) {
-          val old = checkpointQueue.dequeue()
-          // Since the old checkpoint is not deleted by Spark, we'll manually delete it here.
-          try {
-            val path = new Path(old.getCheckpointFile.get)
-            val fs = path.getFileSystem(hadoopConf)
-            fs.delete(path, true)
-          } catch {
-            case e: IOException =>
-              logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
-                s" file: ${old.getCheckpointFile.get}")
-          }
-        } else {
-          canDelete = false
-        }
-      }
-
-      nodeIdsForInstances.checkpoint()
-      checkpointQueue.enqueue(nodeIdsForInstances)
-    }
-  }
-
-  /**
-   * Update the node index values in the cache.
-   * This updates the RDD and its lineage.
-   * TODO: Passing bin information to executors seems unnecessary and costly.
-   * @param data The RDD of training rows.
-   * @param nodeIdUpdaters A map of node index updaters.
-   *                       The key is the indices of nodes that we want to update.
-   * @param splits  Split information needed to find child node indices.
-   */
-  def updateNodeIndicesRaw(
-      data: RDD[BaggedPoint[TreePoint]],
-      nodeIdUpdaters: Array[mutable.Map[Int, NodeIndexUpdaterRaw]],
-      splits: Array[Array[Split]]): Unit = {
-    if (prevNodeIdsForInstances != null) {
-      // Unpersist the previous one if one exists.
-      prevNodeIdsForInstances.unpersist()
-    }
-
-    prevNodeIdsForInstances = nodeIdsForInstances
-    nodeIdsForInstances = data.zip(nodeIdsForInstances).map { case (point, ids) =>
-      var treeId = 0
-      while (treeId < nodeIdUpdaters.length) {
-        val nodeIdUpdater = nodeIdUpdaters(treeId).getOrElse(ids(treeId), null)
-        if (nodeIdUpdater != null) {
-          val featureIndex = nodeIdUpdater.split.featureIndex
-          val newNodeIndex = nodeIdUpdater.updateNodeIndex(
-            binnedFeature = point.datum.binnedFeatures(featureIndex),
-            splits = splits(featureIndex))
-          ids(treeId) = newNodeIndex
-        }
-        treeId += 1
-      }
-      ids
-    }
-
-    // Keep on persisting new ones.
-    nodeIdsForInstances.persist(StorageLevel.MEMORY_AND_DISK)
-    rddUpdateCount += 1
-
-    // Handle checkpointing if the directory is not None.
-    if (canCheckpoint && checkpointInterval != -1 && (rddUpdateCount % checkpointInterval) == 0) {
-      // Let's see if we can delete previous checkpoints.
-      var canDelete = true
-      while (checkpointQueue.size > 1 && canDelete) {
-        // We can delete the oldest checkpoint iff
-        // the next checkpoint actually exists in the file system.
-        if (checkpointQueue(1).getCheckpointFile.isDefined) {
-          val old = checkpointQueue.dequeue()
-          // Since the old checkpoint is not deleted by Spark, we'll manually delete it here.
-          try {
-            val path = new Path(old.getCheckpointFile.get)
-            val fs = path.getFileSystem(hadoopConf)
-            fs.delete(path, true)
-          } catch {
-            case e: IOException =>
-              logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
-                s" file: ${old.getCheckpointFile.get}")
-          }
-        } else {
-          canDelete = false
-        }
-      }
-
-      nodeIdsForInstances.checkpoint()
-      checkpointQueue.enqueue(nodeIdsForInstances)
-    }
-  }
-
-  /**
-   * Update the node index values in the cache.
-   * This updates the RDD and its lineage.
-   * TODO: Passing bin information to executors seems unnecessary and costly.
-   * @param data The RDD of training rows.
-   * @param nodeIdUpdaters A map of node index updaters.
-   *                       The key is the indices of nodes that we want to update.
-   * @param splits  Split information needed to find child node indices.
-   */
-  def updateNodeIndicesY(
-      data: RDD[BaggedPoint[TreePointY]],
-      nodeIdUpdaters: Array[mutable.Map[Int, NodeIndexUpdaterRaw]],
-      splits: Array[Array[Split]]): Unit = {
-    if (prevNodeIdsForInstances != null) {
-      // Unpersist the previous one if one exists.
-      prevNodeIdsForInstances.unpersist()
-    }
-
-    prevNodeIdsForInstances = nodeIdsForInstances
-    nodeIdsForInstances = data.zip(nodeIdsForInstances).map { case (point, ids) =>
-      var treeId = 0
-      while (treeId < nodeIdUpdaters.length) {
-        val nodeIdUpdater = nodeIdUpdaters(treeId).getOrElse(ids(treeId), null)
-        if (nodeIdUpdater != null) {
-          val featureIndex = nodeIdUpdater.split.featureIndex
-          val newNodeIndex = nodeIdUpdater.updateNodeIndex(
-            binnedFeature = point.datum.binnedFeatures.get(featureIndex),
-            splits = splits(featureIndex))
-          ids(treeId) = newNodeIndex
-        }
-        treeId += 1
-      }
-      ids
-    }
-
-    // Keep on persisting new ones.
-    nodeIdsForInstances.persist(StorageLevel.MEMORY_AND_DISK)
-    rddUpdateCount += 1
-
-    // Handle checkpointing if the directory is not None.
-    if (canCheckpoint && checkpointInterval != -1 && (rddUpdateCount % checkpointInterval) == 0) {
-      // Let's see if we can delete previous checkpoints.
-      var canDelete = true
-      while (checkpointQueue.size > 1 && canDelete) {
-        // We can delete the oldest checkpoint iff
-        // the next checkpoint actually exists in the file system.
-        if (checkpointQueue(1).getCheckpointFile.isDefined) {
-          val old = checkpointQueue.dequeue()
-          // Since the old checkpoint is not deleted by Spark, we'll manually delete it here.
-          try {
-            val path = new Path(old.getCheckpointFile.get)
-            val fs = path.getFileSystem(hadoopConf)
-            fs.delete(path, true)
-          } catch {
-            case e: IOException =>
-              logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
-                s" file: ${old.getCheckpointFile.get}")
-          }
-        } else {
-          canDelete = false
-        }
-      }
-
-      nodeIdsForInstances.checkpoint()
-      checkpointQueue.enqueue(nodeIdsForInstances)
-    }
-  }
-
-
-  /**
-   * Call this after training is finished to delete any remaining checkpoints.
-   */
-  def deleteAllCheckpoints(): Unit = {
-    while (checkpointQueue.nonEmpty) {
-      val old = checkpointQueue.dequeue()
-      if (old.getCheckpointFile.isDefined) {
-        try {
-          val path = new Path(old.getCheckpointFile.get)
-          val fs = path.getFileSystem(hadoopConf)
-          fs.delete(path, true)
-        } catch {
-          case e: IOException =>
-            logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
-              s" file: ${old.getCheckpointFile.get}")
-        }
-      }
-    }
-    if (prevNodeIdsForInstances != null) {
-      // Unpersist the previous one if one exists.
-      prevNodeIdsForInstances.unpersist()
-    }
-  }
-}
-
-private[spark] object NodeIdCache {
-  /**
-   * Initialize the node Id cache with initial node Id values.
-   * @param data The RDD of training rows.
-   * @param numTrees The number of trees that we want to create cache for.
-   * @param checkpointInterval The checkpointing interval
-   *                           (how often should the cache be checkpointed.).
-   * @param initVal The initial values in the cache.
-   * @return A node Id cache containing an RDD of initial root node Indices.
-   */
-  def init[T](
-      data: RDD[T],
-      numTrees: Int,
-      checkpointInterval: Int,
-      initVal: Int = 1): NodeIdCache = {
-    new NodeIdCache(
-      data.map(_ => Array.fill[Int](numTrees)(initVal)),
-      checkpointInterval)
-  }
-}
-
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
deleted file mode 100644
index 17c8775..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
+++ /dev/null
@@ -1,1231 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import java.io.IOException
-
-import scala.collection.mutable
-import scala.util.Random
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.classification.DecisionTreeClassificationModel
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.util.Instrumentation
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
-import org.apache.spark.mllib.tree.model.ImpurityStats
-import org.apache.spark.rdd.RDD
-import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
-
-
-/**
- * ALGORITHM
- *
- * This is a sketch of the algorithm to help new developers.
- *
- * The algorithm partitions data by instances (rows).
- * On each iteration, the algorithm splits a set of nodes.  In order to choose the best split
- * for a given node, sufficient statistics are collected from the distributed data.
- * For each node, the statistics are collected to some worker node, and that worker selects
- * the best split.
- *
- * This setup requires discretization of continuous features.  This binning is done in the
- * findSplits() method during initialization, after which each continuous feature becomes
- * an ordered discretized feature with at most maxBins possible values.
- *
- * The main loop in the algorithm operates on a queue of nodes (nodeStack).  These nodes
- * lie at the periphery of the tree being trained.  If multiple trees are being trained at once,
- * then this queue contains nodes from all of them.  Each iteration works roughly as follows:
- *   On the master node:
- *     - Some number of nodes are pulled off of the queue (based on the amount of memory
- *       required for their sufficient statistics).
- *     - For random forests, if featureSubsetStrategy is not "all," then a subset of candidate
- *       features are chosen for each node.  See method selectNodesToSplit().
- *   On worker nodes, via method findBestSplits():
- *     - The worker makes one pass over its subset of instances.
- *     - For each (tree, node, feature, split) tuple, the worker collects statistics about
- *       splitting.  Note that the set of (tree, node) pairs is limited to the nodes selected
- *       from the queue for this iteration.  The set of features considered can also be limited
- *       based on featureSubsetStrategy.
- *     - For each node, the statistics for that node are aggregated to a particular worker
- *       via reduceByKey().  The designated worker chooses the best (feature, split) pair,
- *       or chooses to stop splitting if the stopping criteria are met.
- *   On the master node:
- *     - The master collects all decisions about splitting nodes and updates the model.
- *     - The updated model is passed to the workers on the next iteration.
- * This process continues until the node queue is empty.
- *
- * Most of the methods in this implementation support the statistics aggregation, which is
- * the heaviest part of the computation.  In general, this implementation is bound by either
- * the cost of statistics computation on workers or by communicating the sufficient statistics.
- */
-private[spark] object RandomForest extends Logging {
-
-  /**
-   * Train a random forest.
-   *
-   * @param input Training data: RDD of `LabeledPoint`
-   * @return an unweighted set of trees
-   */
-  def run(
-      input: RDD[LabeledPoint],
-      strategy: OldStrategy,
-      numTrees: Int,
-      featureSubsetStrategy: String,
-      seed: Long,
-      instr: Option[Instrumentation[_]],
-      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
-    val exParams = RFUtils.parseExtraParams(input, strategy)
-    runX(input, strategy, numTrees, featureSubsetStrategy, seed, instr, exParams, parentUID)
-  }
-
-  /**
-   * Train a random forest.
-   *
-   * @param input Training data: RDD of `LabeledPoint`
-   * @return an unweighted set of trees
-   */
-  def runX(
-      input: RDD[LabeledPoint],
-      strategy: OldStrategy,
-      numTrees: Int,
-      featureSubsetStrategy: String,
-      seed: Long,
-      instr: Option[Instrumentation[_]],
-      extraParams: RFExtraParams,
-      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
-
-    RandomForestInfo.timerResult = ""
-    val timer = new TimeTracker()
-
-    timer.start("total")
-
-    timer.start("init")
-
-    val binnedFeaturesType = BinnedFeaturesDataType.withName(extraParams.featuresDataType)
-    val retaggedInput = input.retag(classOf[LabeledPoint])
-    // featureSubsetStrategy: The number of features to consider for splits at each tree node.
-    // featureSubsetStrategy: default value is "auto" for random forest.
-    // impurity: default value is "gini" for random forest.
-    val metadata =
-      DecisionTreeMetadata.buildMetadata(retaggedInput, strategy, numTrees, featureSubsetStrategy)
-    logWarning(s"decisionTreeMetadata details: ${metadata.numFeatures}," +
-      s" ${metadata.numExamples}, ${metadata.numClasses: Int}, ${metadata.maxBins: Int}," +
-      s" ${metadata.featureArity}, ${metadata.unorderedFeatures.mkString("[", ";", "]")}," +
-      s" ${metadata.impurity}, ${metadata.quantileStrategy}, ${metadata.maxDepth: Int}," +
-      s" ${metadata.minInstancesPerNode: Int}, ${metadata.minInfoGain: Double}," +
-      s" ${metadata.numTrees: Int}, ${metadata.numFeaturesPerNode: Int}, ${binnedFeaturesType}")
-    instr match {
-      case Some(instrumentation) =>
-        instrumentation.logNumFeatures(metadata.numFeatures)
-        instrumentation.logNumClasses(metadata.numClasses)
-      case None =>
-        logInfo(s"numFeatures: ${metadata.numFeatures}")
-        logInfo(s"numClasses: ${metadata.numClasses}")
-    }
-
-    // Find the splits and the corresponding bins (interval between the splits) using a sample
-    // of the input data.
-    timer.start("findSplits")
-    val splits = findSplits(retaggedInput, metadata, seed)
-    val baseSplits =
-      splits.map(v => v.zipWithIndex.map{case (split, binIdx) => Split.toBase(split, binIdx)})
-    timer.stop("findSplits")
-    logDebug("numBins: feature: number of bins")
-    logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
-      s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
-    }.mkString("\n"))
-
-    // Bin feature values (TreePoint representation).
-    // Cache input RDD for speedup during multiple passes.
-    val treeInput = TreePointX.convertToTreeRDD(retaggedInput, splits, metadata, binnedFeaturesType)
-
-    val withReplacement = numTrees > 1
-
-    // Default value of subsamplingRate is 1 for random forest.
-    val baggedInputOri = BaggedPoint.convertToBaggedRDD(treeInput, strategy.subsamplingRate,
-      numTrees, withReplacement, seed)
-
-    val baggedInput = RFUtils.transformBaggedRDD(baggedInputOri, extraParams)
-
-    // depth of the decision tree
-    val maxDepth = strategy.maxDepth
-    require(maxDepth <= 30,
-      s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
-
-    // Max memory usage for aggregates
-    // TODO: Calculate memory usage more precisely.
-    val maxMemoryUsage: Long = strategy.maxMemoryInMB * 1024L * 1024L
-    logDebug(s"max memory usage for aggregates = ${maxMemoryUsage} bytes.")
-
-    /*
-     * The main idea here is to perform group-wise training of the decision tree nodes thus
-     * reducing the passes over the data from (# nodes) to (# nodes / maxNumberOfNodesPerGroup).
-     * Each data sample is handled by a particular node (or it reaches a leaf and is not used
-     * in lower levels).
-     */
-
-    // Create an RDD of node Id cache.
-    // At first, all the rows belong to the root nodes (node Id == 1).
-    // Default value of useNodeIdCache is false for random forest.
-    val nodeIdCache = if (strategy.useNodeIdCache) {
-      Some(NodeIdCache.init(
-        data = baggedInput,
-        numTrees = numTrees,
-        checkpointInterval = strategy.checkpointInterval,
-        initVal = 1))
-    } else {
-      None
-    }
-
-    /*
-      Stack of nodes to train: (treeIndex, node)
-      The reason this is a stack is that we train many trees at once, but we want to focus on
-      completing trees, rather than training all simultaneously.  If we are splitting nodes from
-      1 tree, then the new nodes to split will be put at the top of this stack, so we will continue
-      training the same tree in the next iteration.  This focus allows us to send fewer trees to
-      workers on each iteration; see topNodesForGroup below.
-     */
-    val nodeStack = new mutable.ArrayStack[(Int, LearningNodeX)]
-
-    val rng = new Random()
-    rng.setSeed(seed)
-
-    // Allocate and queue root nodes.
-    val topNodes = Array.fill[LearningNodeX](numTrees)(LearningNodeX.emptyNode(nodeIndex = 1))
-    Range(0, numTrees).foreach(treeIndex => nodeStack.push((treeIndex, topNodes(treeIndex))))
-
-    timer.stop("init")
-
-    while (nodeStack.nonEmpty) {
-      // Collect some nodes to split, and choose features for each node (if subsampling).
-      // Each group of nodes may come from one or multiple trees, and at multiple levels.
-      // nodesForGroup: treeIndex --> learningNodes in tree
-      // treeToNodeToIndexInfo: treeIndex --> (global) learningNodes index in tree
-      //    --> (node index in group, feature indices).
-      val (nodesForGroup, treeToNodeToIndexInfo) =
-        RandomForest.selectNodesToSplit(nodeStack, maxMemoryUsage, metadata, rng)
-      // Sanity check (should never occur):
-      assert(nodesForGroup.nonEmpty,
-        s"RandomForest selected empty nodesForGroup.  Error for unknown reason.")
-
-      // Only send trees to worker if they contain nodes being split this iteration.
-      // topNodesForGroup: treeIndex --> top node in tree
-      val topNodesForGroup: Map[Int, LearningNodeX] =
-        nodesForGroup.keys.map(treeIdx => treeIdx -> topNodes(treeIdx)).toMap
-
-      // Choose node splits, and enqueue new nodes as needed.
-      timer.start("findBestSplits")
-      RandomForest.findBestSplits(baggedInput, metadata, topNodesForGroup, nodesForGroup,
-        treeToNodeToIndexInfo, baseSplits, nodeStack, timer, nodeIdCache, Some(extraParams))
-      timer.stop("findBestSplits")
-    }
-
-    baggedInput.unpersist()
-
-    timer.stop("total")
-
-    logInfo("Internal timing for DecisionTree:")
-    logInfo(s"$timer")
-    RandomForestInfo.timerResult = timer.toString()
-
-    // Delete any remaining checkpoints used for node Id cache.
-    if (nodeIdCache.nonEmpty) {
-      try {
-        nodeIdCache.get.deleteAllCheckpoints()
-      } catch {
-        case e: IOException =>
-          logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}")
-      }
-    }
-
-    val numFeatures = metadata.numFeatures
-
-    parentUID match {
-      case Some(uid) =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(uid, rootNode.toNode(splits), numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map { rootNode =>
-            new DecisionTreeRegressionModel(uid, rootNode.toNode(splits), numFeatures)
-          }
-        }
-      case None =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(rootNode.toNode(splits), numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map(rootNode =>
-            new DecisionTreeRegressionModel(rootNode.toNode(splits), numFeatures))
-        }
-    }
-  }
-
-  /**
-   * Helper for binSeqOp, for data which can contain a mix of ordered and unordered features.
-   *
-   * For ordered features, a single bin is updated.
-   * For unordered features, bins correspond to subsets of categories; either the left or right bin
-   * for each subset is updated.
-   *
-   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-   *             each (feature, bin).
-   * @param treePoint  Data point being aggregated.
-   * @param splits possible splits indexed (numFeatures)(numSplits)
-   * @param unorderedFeatures  Set of indices of unordered features.
-   * @param instanceWeight  Weight (importance) of instance in dataset.
-   */
-  private def mixedBinSeqOp(
-      agg: DTStatsAggregator,
-      treePoint: TreePointX,
-      splits: Array[Array[SplitBase]],
-      unorderedFeatures: Set[Int],
-      instanceWeight: Int,
-      featuresForNode: Option[Array[Int]]): Unit = {
-    val numFeaturesPerNode = if (featuresForNode.nonEmpty) {
-      // Use subsampled features
-      featuresForNode.get.length
-    } else {
-      // Use all features
-      agg.metadata.numFeatures
-    }
-    // Iterate over features.
-    var featureIndexIdx = 0
-    while (featureIndexIdx < numFeaturesPerNode) {
-      val featureIndex = if (featuresForNode.nonEmpty) {
-        featuresForNode.get.apply(featureIndexIdx)
-      } else {
-        featureIndexIdx
-      }
-      if (unorderedFeatures.contains(featureIndex)) {
-        // Unordered feature
-        val featureValue = treePoint.binnedFeatures.get(featureIndex)
-        val leftNodeFeatureOffset = agg.getFeatureOffset(featureIndexIdx)
-        // Update the left or right bin for each split.
-        val numSplits = agg.metadata.numSplits(featureIndex)
-        val featureSplits = splits(featureIndex)
-        var splitIndex = 0
-        while (splitIndex < numSplits) {
-          if (featureSplits(splitIndex).shouldGoLeft(featureValue, featureSplits)) {
-            agg.featureUpdate(leftNodeFeatureOffset, splitIndex, treePoint.label, instanceWeight)
-          }
-          splitIndex += 1
-        }
-      } else {
-        // Ordered feature
-        val binIndex = treePoint.binnedFeatures.get(featureIndex)
-        agg.update(featureIndexIdx, binIndex, treePoint.label, instanceWeight)
-      }
-      featureIndexIdx += 1
-    }
-  }
-
-  /**
-   * Helper for binSeqOp, for regression and for classification with only ordered features.
-   *
-   * For each feature, the sufficient statistics of one bin are updated.
-   *
-   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-   *             each (feature, bin).
-   * @param treePoint  Data point being aggregated.
-   * @param instanceWeight  Weight (importance) of instance in dataset.
-   */
-  private def orderedBinSeqOp(
-      agg: DTStatsAggregator,
-      treePoint: TreePointX,
-      instanceWeight: Int,
-      featuresForNode: Option[Array[Int]]): Unit = {
-    val label = treePoint.label
-
-    // Iterate over features.
-    if (featuresForNode.nonEmpty) {
-      // Use subsampled features
-      var featureIndexIdx = 0
-      while (featureIndexIdx < featuresForNode.get.length) {
-        val binIndex = treePoint.binnedFeatures.get(featuresForNode.get.apply(featureIndexIdx))
-        agg.update(featureIndexIdx, binIndex, label, instanceWeight)
-        featureIndexIdx += 1
-      }
-    } else {
-      // Use all features
-      val numFeatures = agg.metadata.numFeatures
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        val binIndex = treePoint.binnedFeatures.get(featureIndex)
-        agg.update(featureIndex, binIndex, label, instanceWeight)
-        featureIndex += 1
-      }
-    }
-  }
-
-  /**
-   * Given a group of nodes, this finds the best split for each node.
-   *
-   * @param input Training data: RDD of [[TreePointX]]
-   * @param metadata Learning and dataset metadata
-   * @param topNodesForGroup For each tree in group, tree index -> root node.
-   *                         Used for matching instances with nodes.
-   * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree
-   * @param treeToNodeToIndexInfo Mapping: treeIndex --> (global) learningNodes index in tree
-   *                              --> (node index in group, feature indices)
-   *                              feature indices: probably parts of full features.
-   *                              Mapping: treeIndex --> nodeIndex --> nodeIndexInfo,
-   *                              where nodeIndexInfo stores the index in the group and the
-   *                              feature subsets (if using feature subsets).
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @param nodeStack  Queue of nodes to split, with values (treeIndex, node).
-   *                   Updated with new non-leaf nodes which are created.
-   * @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
-   *                    each value in the array is the data point's node Id
-   *                    for a corresponding tree. This is used to prevent the need
-   *                    to pass the entire tree to the executors during
-   *                    the node stat aggregation phase.
-   */
-  private[tree] def findBestSplits(
-      input: RDD[BaggedPoint[TreePointX]],
-      metadata: DecisionTreeMetadata,
-      topNodesForGroup: Map[Int, LearningNodeX],
-      nodesForGroup: Map[Int, Array[LearningNodeX]],
-      treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
-      splits: Array[Array[SplitBase]],
-      nodeStack: mutable.ArrayStack[(Int, LearningNodeX)],
-      timer: TimeTracker = new TimeTracker,
-      nodeIdCache: Option[NodeIdCache] = None,
-      extraParams: Option[RFExtraParams] = None): Unit = {
-
-    /*
-     * The high-level descriptions of the best split optimizations are noted here.
-     *
-     * *Group-wise training*
-     * We perform bin calculations for groups of nodes to reduce the number of
-     * passes over the data.  Each iteration requires more computation and storage,
-     * but saves several iterations over the data.
-     *
-     * *Bin-wise computation*
-     * We use a bin-wise best split computation strategy instead of a straightforward best split
-     * computation strategy. Instead of analyzing each sample for contribution to the left/right
-     * child node impurity of every split, we first categorize each feature of a sample into a
-     * bin. We exploit this structure to calculate aggregates for bins and then use these aggregates
-     * to calculate information gain for each split.
-     *
-     * *Aggregation over partitions*
-     * Instead of performing a flatMap/reduceByKey operation, we exploit the fact that we know
-     * the number of splits in advance. Thus, we store the aggregates (at the appropriate
-     * indices) in a single array for all bins and rely upon the RDD aggregate method to
-     * drastically reduce the communication overhead.
-     */
-
-    val bcVariables = if (extraParams.isEmpty) false else extraParams.get.bcVariables
-    /** numNodes:  Number of nodes in this group */
-    val numNodes = nodesForGroup.values.map(_.length).sum
-    logDebug(s"numNodes = ${numNodes}")
-    logDebug(s"numFeatures = ${metadata.numFeatures}")
-    logDebug(s"numClasses = ${metadata.numClasses}")
-    logDebug(s"isMulticlass = ${metadata.isMulticlass}")
-    logDebug(s"isMulticlassWithCategoricalFeatures = " +
-      s"${metadata.isMulticlassWithCategoricalFeatures}")
-    logDebug(s"using nodeIdCache = ${ nodeIdCache.nonEmpty.toString}")
-
-    val groupInfo = RFUtils.getGroupInfo(numNodes, treeToNodeToIndexInfo, extraParams)
-
-    val splitsBc = if (bcVariables) Some(input.sparkContext.broadcast(splits)) else Option.empty
-    val splitsOption = if (bcVariables) Option.empty else Some(splits)
-
-
-    /**
-     * Performs a sequential aggregation over a partition for a particular tree and node.
-     *
-     * For each feature, the aggregate sufficient statistics are updated for the relevant
-     * bins.
-     *
-     * @param treeIndex Index of the tree that we want to perform aggregation for.
-     * @param nodeInfo The node info for the tree node.
-     * @param agg Array storing aggregate calculation, with a set of sufficient statistics
-     *            for each (node, feature, bin).
-     * @param baggedPoint Data point being aggregated.
-     */
-    def nodeBinSeqOp(
-        treeIndex: Int,
-        nodeInfo: NodeIndexInfo,
-        agg: Array[DTStatsAggregator],
-        splitsBcv: Array[Array[SplitBase]],
-        baggedPoint: BaggedPoint[TreePointX]): Unit = {
-      if (RFUtils.isValidNodeInfo(nodeInfo, agg)) {
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val featuresForNode = nodeInfo.featureSubset
-        val instanceWeight = baggedPoint.subsampleWeights(treeIndex)
-        if (metadata.unorderedFeatures.isEmpty) {
-          orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, instanceWeight, featuresForNode)
-        } else {
-          mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splitsBcv,
-            metadata.unorderedFeatures, instanceWeight, featuresForNode)
-        }
-        agg(aggNodeIndex).updateParent(baggedPoint.datum.label, instanceWeight)
-      }
-    }
-
-    /**
-     * Performs a sequential aggregation over a partition.
-     *
-     * Each data point contributes to one node. For each feature,
-     * the aggregate sufficient statistics are updated for the relevant bins.
-     *
-     * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-     *             each (node, feature, bin).
-     * @param baggedPoint   Data point being aggregated.
-     * @return  agg
-     */
-    def binSeqOp(
-        agg: Array[DTStatsAggregator],
-        baggedPoint: BaggedPoint[TreePointX],
-        splitsBcv: Array[Array[SplitBase]],
-        sampleId: Short): Array[DTStatsAggregator] = {
-      // TODO: treeToNodeToIndexInfo and topNodesForGroup(include sub-nodes) weren't broadcast.
-      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
-        if (RFUtils.isSubSampled(baggedPoint, groupInfo, treeIndex, sampleId)) {
-          val nodeIndex =
-            topNodesForGroup(treeIndex).predictImpl(baggedPoint.datum.binnedFeatures, splitsBcv)
-          nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null),
-            agg, splitsBcv, baggedPoint)
-        }
-      }
-      agg
-    }
-
-    /**
-     * Do the same thing as binSeqOp, but with nodeIdCache.
-     */
-    def binSeqOpWithNodeIdCache(
-        agg: Array[DTStatsAggregator],
-        splitsBcv: Array[Array[SplitBase]],
-        dataPoint: (BaggedPoint[TreePointX], Array[Int])): Array[DTStatsAggregator] = {
-      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
-        val baggedPoint = dataPoint._1
-        val nodeIdCache = dataPoint._2
-        val nodeIndex = nodeIdCache(treeIndex)
-        nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null),
-          agg, splitsBcv, baggedPoint)
-      }
-
-      agg
-    }
-
-    /**
-     * Get node index in group --> features indices map,
-     * which is a short cut to find feature indices for a node given node index in group.
-     */
-    def getNodeToFeatures(
-        treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]]): Option[Map[Int, Array[Int]]] = {
-      if (!metadata.subsamplingFeatures) {
-        None
-      } else {
-        val mutableNodeToFeatures = new mutable.HashMap[Int, Array[Int]]()
-        treeToNodeToIndexInfo.values.foreach { nodeIdToNodeInfo =>
-          nodeIdToNodeInfo.values.foreach { nodeIndexInfo =>
-            assert(nodeIndexInfo.featureSubset.isDefined)
-            mutableNodeToFeatures(nodeIndexInfo.nodeIndexInGroup) = nodeIndexInfo.featureSubset.get
-          }
-        }
-        Some(mutableNodeToFeatures.toMap)
-      }
-    }
-
-    // array of nodes to train indexed by node index in group
-    val nodes = new Array[LearningNodeX](numNodes)
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        nodes(treeToNodeToIndexInfo(treeIndex)(node.id).nodeIndexInGroup) = node
-      }
-    }
-
-    // Calculate best splits for all nodes in the group
-    timer.start("chooseSplits")
-
-    // In each partition, iterate all instances and compute aggregate stats for each node,
-    // yield a (nodeIndex, nodeAggregateStats) pair for each node.
-    // After a `reduceByKey` operation,
-    // stats of a node will be shuffled to a particular partition and be combined together,
-    // then best splits for nodes are found there.
-    // Finally, only best Splits for nodes are collected to driver to construct decision tree.
-    // nodeToFeatures: node index in group -> selected feature indexes
-    val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
-    val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
-
-    /** partitionAggregates RDD: node index in group --> nodeStats  */
-    val partitionAggregates: RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
-      input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions { points =>
-        // Construct a nodeStatsAggregators array to hold node aggregate stats,
-        // each node will have a nodeStatsAggregator
-        val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
-          val featuresForNode = nodeToFeaturesBc.value.map { nodeToFeatures =>
-            nodeToFeatures(nodeIndex)
-          }
-          new DTStatsAggregator(metadata, featuresForNode)
-        }
-
-        val splitsBcv = if (bcVariables) splitsBc.get.value else splitsOption.get
-        // iterator all instances in current partition and update aggregate stats
-        points.foreach(binSeqOpWithNodeIdCache(nodeStatsAggregators, splitsBcv, _))
-
-        // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
-        // which can be combined with other partition using `reduceByKey`
-        nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
-      }
-    } else {
-      input.mapPartitions { points =>
-        val (firstPointOption, nodeStatsAggregators) =
-          RFUtils.initNodeStatsAgg(numNodes, nodeToFeaturesBc, metadata, points, groupInfo)
-        if (firstPointOption.isEmpty) {
-          Iterator.empty
-        } else {
-          val firstPoint = firstPointOption.get
-          val sampleId = firstPoint.sampleId
-
-          val splitsBcv = if (bcVariables) splitsBc.get.value else splitsOption.get
-          binSeqOp(nodeStatsAggregators, firstPoint, splitsBcv, sampleId)
-
-
-          // iterator all instances in current partition and update aggregate stats
-          points.foreach(binSeqOp(nodeStatsAggregators, _, splitsBcv, sampleId))
-
-          // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
-          // which can be combined with other partition using `reduceByKey`
-          nodeStatsAggregators.view.zipWithIndex
-            .filter(v => RFUtils.isValidAgg(v._1)).map(_.swap).iterator
-        }
-      }
-    }
-
-    val nodeToBestSplits = partitionAggregates.reduceByKey((a, b) => a.merge(b)).map {
-      case (nodeIndex, aggStats) =>
-        val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
-          Some(nodeToFeatures(nodeIndex))
-        }
-
-        val splitsBcv = if (bcVariables) splitsBc.get.value else splitsOption.get
-        // find best split for each node
-        val (split: SplitBase, stats: ImpurityStats) =
-          binsToBestSplit(aggStats, splitsBcv, featuresForNode, nodes(nodeIndex))
-        (nodeIndex, (split, stats))
-    }.collectAsMap()
-
-    timer.stop("chooseSplits")
-
-    val nodeIdUpdaters = if (nodeIdCache.nonEmpty) {
-      Array.fill[mutable.Map[Int, NodeIndexUpdater]](
-        metadata.numTrees)(mutable.Map[Int, NodeIndexUpdater]())
-    } else {
-      null
-    }
-    // Iterate over all nodes in this group.
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        val nodeIndex = node.id
-        val nodeInfo = treeToNodeToIndexInfo(treeIndex)(nodeIndex)
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val (split: SplitBase, stats: ImpurityStats) =
-          nodeToBestSplits(aggNodeIndex)
-        logDebug(s"best split = ${split}")
-
-        // Extract info for this node.  Create children if not leaf.
-        val isLeaf =
-          (stats.gain <= 0) || (LearningNodeX.indexToLevel(nodeIndex) == metadata.maxDepth)
-        node.isLeaf = isLeaf
-        node.stats = stats
-        logDebug(s"Node = ${node}")
-
-        if (!isLeaf) {
-          node.split = Some(split)
-          val childIsLeaf = (LearningNodeX.indexToLevel(nodeIndex) + 1) == metadata.maxDepth
-          val leftChildIsLeaf = childIsLeaf || (stats.leftImpurity == 0.0)
-          val rightChildIsLeaf = childIsLeaf || (stats.rightImpurity == 0.0)
-          node.leftChild = Some(LearningNodeX(LearningNodeX.leftChildIndex(nodeIndex),
-            leftChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.leftImpurityCalculator)))
-          node.rightChild = Some(LearningNodeX(LearningNodeX.rightChildIndex(nodeIndex),
-            rightChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.rightImpurityCalculator)))
-
-          if (nodeIdCache.nonEmpty) {
-            val nodeIndexUpdater = NodeIndexUpdater(
-              split = split,
-              nodeIndex = nodeIndex)
-            nodeIdUpdaters(treeIndex).put(nodeIndex, nodeIndexUpdater)
-          }
-
-          // enqueue left child and right child if they are not leaves
-          if (!leftChildIsLeaf) {
-            nodeStack.push((treeIndex, node.leftChild.get))
-          }
-          if (!rightChildIsLeaf) {
-            nodeStack.push((treeIndex, node.rightChild.get))
-          }
-
-          logDebug(s"leftChildIndex = ${node.leftChild.get.id}" +
-            s", impurity = ${stats.leftImpurity}")
-          logDebug(s"rightChildIndex = ${node.rightChild.get.id}" +
-            s", impurity = ${stats.rightImpurity}")
-        }
-      }
-    }
-
-    if (nodeIdCache.nonEmpty) {
-      // Update the cache if needed.
-      nodeIdCache.get.updateNodeIndices(input, nodeIdUpdaters, splits)
-    }
-  }
-
-  /**
-   * Calculate the impurity statistics for a given (feature, split) based upon left/right
-   * aggregates.
-   *
-   * @param stats the recycle impurity statistics for this feature's all splits,
-   *              only 'impurity' and 'impurityCalculator' are valid between each iteration
-   * @param leftImpurityCalculator left node aggregates for this (feature, split)
-   * @param rightImpurityCalculator right node aggregate for this (feature, split)
-   * @param metadata learning and dataset metadata for DecisionTree
-   * @return Impurity statistics for this (feature, split)
-   */
-  private def calculateImpurityStats(
-      stats: ImpurityStats,
-      leftImpurityCalculator: ImpurityCalculator,
-      rightImpurityCalculator: ImpurityCalculator,
-      metadata: DecisionTreeMetadata): ImpurityStats = {
-
-    val parentImpurityCalculator: ImpurityCalculator = if (stats == null) {
-      leftImpurityCalculator.copy.add(rightImpurityCalculator)
-    } else {
-      stats.impurityCalculator
-    }
-
-    val impurity: Double = if (stats == null) {
-      parentImpurityCalculator.calculate()
-    } else {
-      stats.impurity
-    }
-
-    val leftCount = leftImpurityCalculator.count
-    val rightCount = rightImpurityCalculator.count
-
-    val totalCount = leftCount + rightCount
-
-    // If left child or right child doesn't satisfy minimum instances per node,
-    // then this split is invalid, return invalid information gain stats.
-    if ((leftCount < metadata.minInstancesPerNode) ||
-      (rightCount < metadata.minInstancesPerNode)) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
-    val rightImpurity = rightImpurityCalculator.calculate()
-
-    val leftWeight = leftCount / totalCount.toDouble
-    val rightWeight = rightCount / totalCount.toDouble
-
-    val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
-
-    // if information gain doesn't satisfy minimum information gain,
-    // then this split is invalid, return invalid information gain stats.
-    if (gain < metadata.minInfoGain) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    new ImpurityStats(gain, impurity, parentImpurityCalculator,
-      leftImpurityCalculator, rightImpurityCalculator)
-  }
-
-  /**
-   * Find the best split for a node.
-   *
-   * @param binAggregates Bin statistics.
-   * @return tuple for best split: (Split, information gain, prediction at node)
-   */
-  private[tree] def binsToBestSplit(
-      binAggregates: DTStatsAggregator,
-      splits: Array[Array[SplitBase]],
-      featuresForNode: Option[Array[Int]],
-      node: LearningNodeX): (SplitBase, ImpurityStats) = {
-
-    // Calculate InformationGain and ImpurityStats if current node is top node
-    val level = LearningNodeX.indexToLevel(node.id)
-    var gainAndImpurityStats: ImpurityStats = if (level == 0) {
-      null
-    } else {
-      node.stats
-    }
-
-    val validFeatureSplits =
-      Range(0, binAggregates.metadata.numFeaturesPerNode).view.map { featureIndexIdx =>
-        featuresForNode.map(features => (featureIndexIdx, features(featureIndexIdx)))
-          .getOrElse((featureIndexIdx, featureIndexIdx))
-      }.withFilter { case (_, featureIndex) =>
-        binAggregates.metadata.numSplits(featureIndex) != 0
-      }
-
-    // For each (feature, split), calculate the gain, and select the best (feature, split).
-    val splitsAndImpurityInfo =
-      validFeatureSplits.map { case (featureIndexIdx, featureIndex) =>
-        val numSplits = binAggregates.metadata.numSplits(featureIndex)
-        if (binAggregates.metadata.isContinuous(featureIndex)) {
-          // Cumulative sum (scanLeft) of bin statistics.
-          // Afterwards, binAggregates for a bin is the sum of aggregates for
-          // that bin + all preceding bins.
-          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          var splitIndex = 0
-          while (splitIndex < numSplits) {
-            binAggregates.mergeForFeature(nodeFeatureOffset, splitIndex + 1, splitIndex)
-            splitIndex += 1
-          }
-          // Find best split.
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { case splitIdx =>
-              val leftChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, splitIdx)
-              val rightChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
-              rightChildStats.subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIdx, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
-        } else if (binAggregates.metadata.isUnordered(featureIndex)) {
-          // Unordered categorical feature
-          val leftChildOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { splitIndex =>
-              val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
-              val rightChildStats = binAggregates.getParentImpurityCalculator()
-                .subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIndex, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
-        } else {
-          // Ordered categorical feature
-          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          val numCategories = binAggregates.metadata.numBins(featureIndex)
-
-          /* Each bin is one category (feature value).
-           * The bins are ordered based on centroidForCategories, and this ordering determines which
-           * splits are considered.  (With K categories, we consider K - 1 possible splits.)
-           *
-           * centroidForCategories is a list: (category, centroid)
-           */
-          val centroidForCategories = Range(0, numCategories).map { case featureValue =>
-            val categoryStats =
-              binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
-            val centroid = if (categoryStats.count != 0) {
-              if (binAggregates.metadata.isMulticlass) {
-                // multiclass classification
-                // For categorical variables in multiclass classification,
-                // the bins are ordered by the impurity of their corresponding labels.
-                categoryStats.calculate()
-              } else if (binAggregates.metadata.isClassification) {
-                // binary classification
-                // For categorical variables in binary classification,
-                // the bins are ordered by the count of class 1.
-                categoryStats.stats(1)
-              } else {
-                // regression
-                // For categorical variables in regression and binary classification,
-                // the bins are ordered by the prediction.
-                categoryStats.predict
-              }
-            } else {
-              Double.MaxValue
-            }
-            (featureValue, centroid)
-          }
-
-          logDebug(s"Centroids for categorical variable: ${centroidForCategories.mkString(",")}")
-
-          // bins sorted by centroids
-          val categoriesSortedByCentroid = centroidForCategories.toList.sortBy(_._2)
-
-          logDebug("Sorted centroids for categorical variable = " +
-            categoriesSortedByCentroid.mkString(","))
-
-          // Cumulative sum (scanLeft) of bin statistics.
-          // Afterwards, binAggregates for a bin is the sum of aggregates for
-          // that bin + all preceding bins.
-          var splitIndex = 0
-          while (splitIndex < numSplits) {
-            val currentCategory = categoriesSortedByCentroid(splitIndex)._1
-            val nextCategory = categoriesSortedByCentroid(splitIndex + 1)._1
-            binAggregates.mergeForFeature(nodeFeatureOffset, nextCategory, currentCategory)
-            splitIndex += 1
-          }
-          // lastCategory = index of bin with total aggregates for this (node, feature)
-          val lastCategory = categoriesSortedByCentroid.last._1
-          // Find best split.
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { splitIndex =>
-              val featureValue = categoriesSortedByCentroid(splitIndex)._1
-              val leftChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
-              val rightChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
-              rightChildStats.subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIndex, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          val categoriesForSplit =
-            categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
-          val bestFeatureSplit =
-            new CategoricalSplit(featureIndex, categoriesForSplit.toArray, numCategories)
-          (bestFeatureSplit, bestFeatureGainStats)
-        }
-      }
-
-    val (bestSplit, bestSplitStats) =
-      if (splitsAndImpurityInfo.isEmpty) {
-        // If no valid splits for features, then this split is invalid,
-        // return invalid information gain stats.  Take any split and continue.
-        // Splits is empty, so arbitrarily choose to split on any threshold
-        val dummyFeatureIndex = featuresForNode.map(_.head).getOrElse(0)
-        val parentImpurityCalculator = binAggregates.getParentImpurityCalculator()
-        if (binAggregates.metadata.isContinuous(dummyFeatureIndex)) {
-          (new ContinuousSplit(dummyFeatureIndex, 0),
-            ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-        } else {
-          val numCategories = binAggregates.metadata.featureArity(dummyFeatureIndex)
-          (new CategoricalSplit(dummyFeatureIndex, Array(), numCategories),
-            ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-        }
-      } else {
-        splitsAndImpurityInfo.maxBy(_._2.gain)
-      }
-    (bestSplit, bestSplitStats)
-  }
-
-  /**
-   * Returns splits for decision tree calculation.
-   * Continuous and categorical features are handled differently.
-   *
-   * Continuous features:
-   *   For each feature, there are numBins - 1 possible splits representing the possible binary
-   *   decisions at each node in the tree.
-   *   This finds locations (feature values) for splits using a subsample of the data.
-   *
-   * Categorical features:
-   *   For each feature, there is 1 bin per split.
-   *   Splits and bins are handled in 2 ways:
-   *   (a) "unordered features"
-   *       For multiclass classification with a low-arity feature
-   *       (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits),
-   *       the feature is split based on subsets of categories.
-   *   (b) "ordered features"
-   *       For regression and binary classification,
-   *       and for multiclass classification with a high-arity feature,
-   *       there is one bin per category.
-   *
-   * @param input Training data: RDD of [[LabeledPoint]]
-   * @param metadata Learning and dataset metadata
-   * @param seed random seed
-   * @return Splits, an Array of [[Split]]
-   *          of size (numFeatures, numSplits)
-   */
-  protected[tree] def findSplits(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      seed: Long): Array[Array[Split]] = {
-
-    logDebug(s"isMulticlass = ${metadata.isMulticlass}")
-
-    val numFeatures = metadata.numFeatures
-
-    // Sample the input only if there are continuous features.
-    val continuousFeatures = Range(0, numFeatures).filter(metadata.isContinuous)
-    val sampledInput = if (continuousFeatures.nonEmpty) {
-      // Calculate the number of samples for approximate quantile calculation.
-      val requiredSamples = math.max(metadata.maxBins * metadata.maxBins, 10000)
-      val fraction = if (requiredSamples < metadata.numExamples) {
-        requiredSamples.toDouble / metadata.numExamples
-      } else {
-        1.0
-      }
-      logDebug(s"fraction of data used for calculating quantiles = ${fraction}")
-      input.sample(withReplacement = false, fraction, new XORShiftRandom(seed).nextInt())
-    } else {
-      input.sparkContext.emptyRDD[LabeledPoint]
-    }
-
-    findSplitsBySorting(sampledInput, metadata, continuousFeatures)
-  }
-
-  private def findSplitsBySorting(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      continuousFeatures: IndexedSeq[Int]): Array[Array[Split]] = {
-
-    val continuousSplits: scala.collection.Map[Int, Array[Split]] = {
-      // reduce the parallelism for split computations when there are less
-      // continuous features than input partitions. this prevents tasks from
-      // being spun up that will definitely do no work.
-      val numPartitions = math.min(continuousFeatures.length, input.partitions.length)
-
-      input
-        .flatMap(point => continuousFeatures.map(idx => (idx, point.features(idx))))
-        .groupByKey(numPartitions)
-        .map { case (idx, samples) =>
-          val thresholds = findSplitsForContinuousFeature(samples, metadata, idx)
-          val splits: Array[Split] = thresholds.map(thresh => new ContinuousSplit(idx, thresh))
-          logDebug(s"featureIndex = $idx, numSplits = ${splits.length}")
-          (idx, splits)
-        }.collectAsMap()
-    }
-
-    val numFeatures = metadata.numFeatures
-    val splits: Array[Array[Split]] = Array.tabulate(numFeatures) {
-      case i if metadata.isContinuous(i) =>
-        val split = continuousSplits(i)
-        metadata.setNumSplits(i, split.length)
-        split
-
-      case i if metadata.isCategorical(i) && metadata.isUnordered(i) =>
-        // Unordered features
-        // 2^(maxFeatureValue - 1) - 1 combinations
-        val featureArity = metadata.featureArity(i)
-        Array.tabulate[Split](metadata.numSplits(i)) { splitIndex =>
-          val categories = extractMultiClassCategories(splitIndex + 1, featureArity)
-          new CategoricalSplit(i, categories.toArray, featureArity)
-        }
-
-      case i if metadata.isCategorical(i) =>
-        // Ordered features
-        //   Splits are constructed as needed during training.
-        Array.empty[Split]
-    }
-    splits
-  }
-
-  /**
-   * Nested method to extract list of eligible categories given an index. It extracts the
-   * position of ones in a binary representation of the input. If binary
-   * representation of an number is 01101 (13), the output list should (3.0, 2.0,
-   * 0.0). The maxFeatureValue depict the number of rightmost digits that will be tested for ones.
-   */
-  private[tree] def extractMultiClassCategories(
-      input: Int,
-      maxFeatureValue: Int): List[Double] = {
-    var categories = List[Double]()
-    var j = 0
-    var bitShiftedInput = input
-    while (j < maxFeatureValue) {
-      if (bitShiftedInput % 2 != 0) {
-        // updating the list of categories.
-        categories = j.toDouble :: categories
-      }
-      // Right shift by one
-      bitShiftedInput = bitShiftedInput >> 1
-      j += 1
-    }
-    categories
-  }
-
-  /**
-   * Find splits for a continuous feature
-   * NOTE: Returned number of splits is set based on `featureSamples` and
-   *       could be different from the specified `numSplits`.
-   *       The `numSplits` attribute in the `DecisionTreeMetadata` class will be set accordingly.
-   *
-   * @param featureSamples feature values of each sample
-   * @param metadata decision tree metadata
-   *                 NOTE: `metadata.numbins` will be changed accordingly
-   *                       if there are not enough splits to be found
-   * @param featureIndex feature index to find splits
-   * @return array of split thresholds
-   */
-  private[tree] def findSplitsForContinuousFeature(
-      featureSamples: Iterable[Double],
-      metadata: DecisionTreeMetadata,
-      featureIndex: Int): Array[Double] = {
-    require(metadata.isContinuous(featureIndex),
-      "findSplitsForContinuousFeature can only be used to find splits for a continuous feature.")
-
-    val splits: Array[Double] = if (featureSamples.isEmpty) {
-      Array.empty[Double]
-    } else {
-      val numSplits = metadata.numSplits(featureIndex)
-
-      // get count for each distinct value
-      val (valueCountMap, numSamples) = featureSamples.foldLeft((Map.empty[Double, Int], 0)) {
-        case ((m, cnt), x) =>
-          (m + ((x, m.getOrElse(x, 0) + 1)), cnt + 1)
-      }
-      // sort distinct values
-      val valueCounts = valueCountMap.toSeq.sortBy(_._1).toArray
-
-      val possibleSplits = valueCounts.length - 1
-      if (possibleSplits == 0) {
-        // constant feature
-        Array.empty[Double]
-      } else if (possibleSplits <= numSplits) {
-        // if possible splits is not enough or just enough, just return all possible splits
-        (1 to possibleSplits)
-          .map(index => (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0)
-          .toArray
-      } else {
-        // stride between splits
-        val stride: Double = numSamples.toDouble / (numSplits + 1)
-        logDebug(s"stride = ${stride}")
-
-        // iterate `valueCount` to find splits
-        val splitsBuilder = mutable.ArrayBuilder.make[Double]
-        var index = 1
-        // currentCount: sum of counts of values that have been visited
-        var currentCount = valueCounts(0)._2
-        // targetCount: target value for `currentCount`.
-        // If `currentCount` is closest value to `targetCount`,
-        // then current value is a split threshold.
-        // After finding a split threshold, `targetCount` is added by stride.
-        var targetCount = stride
-        while (index < valueCounts.length) {
-          val previousCount = currentCount
-          currentCount += valueCounts(index)._2
-          val previousGap = math.abs(previousCount - targetCount)
-          val currentGap = math.abs(currentCount - targetCount)
-          // If adding count of current value to currentCount
-          // makes the gap between currentCount and targetCount smaller,
-          // previous value is a split threshold.
-          if (previousGap < currentGap) {
-            splitsBuilder += (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0
-            targetCount += stride
-          }
-          index += 1
-        }
-
-        splitsBuilder.result()
-      }
-    }
-    splits
-  }
-
-  private[tree] class NodeIndexInfo(
-      val nodeIndexInGroup: Int,
-      val featureSubset: Option[Array[Int]]) extends Serializable
-
-  /**
-   * Pull nodes off of the queue, and collect a group of nodes to be split on this iteration.
-   * This tracks the memory usage for aggregates and stops adding nodes when too much memory
-   * will be needed; this allows an adaptive number of nodes since different nodes may require
-   * different amounts of memory (if featureSubsetStrategy is not "all").
-   *
-   * @param nodeStack  Queue of nodes to split.
-   * @param maxMemoryUsage  Bound on size of aggregate statistics.
-   * @return  (nodesForGroup, treeToNodeToIndexInfo).
-   *          nodesForGroup holds the nodes to split: treeIndex --> nodes in tree.
-   *
-   *          treeToNodeToIndexInfo holds indices selected features for each node:
-   *            treeIndex --> (global) node index --> (node index in group, feature indices).
-   *          The (global) node index is the index in the tree; the node index in group is the
-   *           index in [0, numNodesInGroup) of the node in this group.
-   *          The feature indices are None if not subsampling features.
-   */
-  private[tree] def selectNodesToSplit(
-      nodeStack: mutable.ArrayStack[(Int, LearningNodeX)],
-      maxMemoryUsage: Long,
-      metadata: DecisionTreeMetadata,
-      rng: Random): (Map[Int, Array[LearningNodeX]], Map[Int, Map[Int, NodeIndexInfo]]) = {
-    // Collect some nodes to split:
-    //  nodesForGroup(treeIndex) = nodes to split
-    val mutableNodesForGroup = new mutable.HashMap[Int, mutable.ArrayBuffer[LearningNodeX]]()
-    val mutableTreeToNodeToIndexInfo =
-      new mutable.HashMap[Int, mutable.HashMap[Int, NodeIndexInfo]]()
-    var memUsage: Long = 0L
-    var numNodesInGroup = 0
-    // If maxMemoryInMB is set very small, we want to still try to split 1 node,
-    // so we allow one iteration if memUsage == 0.
-    var groupDone = false
-    while (nodeStack.nonEmpty && !groupDone) {
-      val (treeIndex, node) = nodeStack.top
-      // Choose subset of features for node (if subsampling).
-      val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) {
-        Some(SamplingUtils.reservoirSampleAndCount(Range(0,
-          metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong())._1)
-      } else {
-        None
-      }
-      // Check if enough memory remains to add this node to the group.
-      val nodeMemUsage = RandomForest.aggregateSizeForNode(metadata, featureSubset) * 8L
-      if (memUsage + nodeMemUsage <= maxMemoryUsage || memUsage == 0) {
-        nodeStack.pop()
-        mutableNodesForGroup.getOrElseUpdate(treeIndex, new mutable.ArrayBuffer[LearningNodeX]()) +=
-          node
-        mutableTreeToNodeToIndexInfo
-          .getOrElseUpdate(treeIndex, new mutable.HashMap[Int, NodeIndexInfo]())(node.id)
-          = new NodeIndexInfo(numNodesInGroup, featureSubset)
-        numNodesInGroup += 1
-        memUsage += nodeMemUsage
-      } else {
-        groupDone = true
-      }
-    }
-    if (memUsage > maxMemoryUsage) {
-      // If maxMemoryUsage is 0, we should still allow splitting 1 node.
-      logWarning(s"Tree learning is using approximately $memUsage bytes per iteration, which" +
-        s" exceeds requested limit maxMemoryUsage=$maxMemoryUsage. This allows splitting" +
-        s" $numNodesInGroup nodes in this iteration.")
-    }
-    logWarning(f"[this group] actualMemUsage: ${memUsage/(1024d*1024d)}%.2f MB," +
-      f" maxMemoryUsage: ${maxMemoryUsage/(1024d*1024d)}%.2f MB.")
-    // Convert mutable maps to immutable ones.
-    val nodesForGroup: Map[Int, Array[LearningNodeX]] =
-      mutableNodesForGroup.mapValues(_.toArray).toMap
-    val treeToNodeToIndexInfo = mutableTreeToNodeToIndexInfo.mapValues(_.toMap).toMap
-    (nodesForGroup, treeToNodeToIndexInfo)
-  }
-
-  /**
-   * Get the number of values to be stored for this node in the bin aggregates.
-   *
-   * @param featureSubset  Indices of features which may be split at this node.
-   *                       If None, then use all features.
-   */
-  private def aggregateSizeForNode(
-      metadata: DecisionTreeMetadata,
-      featureSubset: Option[Array[Int]]): Long = {
-    val totalBins = if (featureSubset.nonEmpty) {
-      featureSubset.get.map(featureIndex => metadata.numBins(featureIndex).toLong).sum
-    } else {
-      metadata.numBins.map(_.toLong).sum
-    }
-    if (metadata.isClassification) {
-      metadata.numClasses * totalBins
-    } else {
-      3 * totalBins
-    }
-  }
-}
-
-object RandomForestInfo {
-  var timerResult: String = ""
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest4GBDTX.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest4GBDTX.scala
deleted file mode 100644
index 101adf1..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest4GBDTX.scala
+++ /dev/null
@@ -1,621 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-import it.unimi.dsi.fastutil.ints.{Int2ObjectOpenHashMap, IntArrayList}
-import it.unimi.dsi.fastutil.objects.ObjectArrayList
-import scala.collection.mutable
-import scala.util.Random
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.classification.DecisionTreeClassificationModel
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.impl.GradientBoostedTreesCore.NodeIndexInfo
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.model.ImpurityStats
-import org.apache.spark.rdd.RDD
-import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
-
-
-/**
- * ALGORITHM
- *
- * This is a sketch of the algorithm to help new developers.
- *
- * The algorithm partitions data by instances (rows).
- * On each iteration, the algorithm splits a set of nodes.  In order to choose the best split
- * for a given node, sufficient statistics are collected from the distributed data.
- * For each node, the statistics are collected to some worker node, and that worker selects
- * the best split.
- *
- * This setup requires discretization of continuous features.  This binning is done in the
- * findSplits() method during initialization, after which each continuous feature becomes
- * an ordered discretized feature with at most maxBins possible values.
- *
- * The main loop in the algorithm operates on a queue of nodes (nodeStack).  These nodes
- * lie at the periphery of the tree being trained.  If multiple trees are being trained at once,
- * then this queue contains nodes from all of them.  Each iteration works roughly as follows:
- *   On the master node:
- *     - Some number of nodes are pulled off of the queue (based on the amount of memory
- *       required for their sufficient statistics).
- *     - For random forests, if featureSubsetStrategy is not "all," then a subset of candidate
- *       features are chosen for each node.  See method selectNodesToSplit().
- *   On worker nodes, via method findBestSplits():
- *     - The worker makes one pass over its subset of instances.
- *     - For each (tree, node, feature, split) tuple, the worker collects statistics about
- *       splitting.  Note that the set of (tree, node) pairs is limited to the nodes selected
- *       from the queue for this iteration.  The set of features considered can also be limited
- *       based on featureSubsetStrategy.
- *     - For each node, the statistics for that node are aggregated to a particular worker
- *       via reduceByKey().  The designated worker chooses the best (feature, split) pair,
- *       or chooses to stop splitting if the stopping criteria are met.
- *   On the master node:
- *     - The master collects all decisions about splitting nodes and updates the model.
- *     - The updated model is passed to the workers on the next iteration.
- * This process continues until the node queue is empty.
- *
- * Most of the methods in this implementation support the statistics aggregation, which is
- * the heaviest part of the computation.  In general, this implementation is bound by either
- * the cost of statistics computation on workers or by communicating the sufficient statistics.
- */
-private[spark] object RandomForest4GBDTX extends Logging {
-
-  /**
-   * Train a random forest.
-   *
-   * @param input Training data: RDD of `LabeledPoint`
-   * @return an unweighted set of trees
-   */
-  def runX(
-      labelArrayBc: Broadcast[DoubleArrayList],
-      processedInput: RDD[(Int, (IntArrayList, ObjectArrayList[Split]))],
-      metadata: DecisionTreeMetadata,
-      splits: Array[Array[Split]],
-      strategy: OldStrategy,
-      numTrees: Int,
-      seed: Long,
-      input: RDD[TreePoint],
-      rawPartInfoBc: Broadcast[Int2ObjectOpenHashMap[IntArrayList]],
-      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
-
-    val timer = new TimeTracker()
-
-    timer.start("total")
-
-    timer.start("init")
-
-    // depth of the decision tree
-    val maxDepth = strategy.maxDepth
-    require(maxDepth <= 30,
-      s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
-
-    // Max memory usage for aggregates
-    // TODO: Calculate memory usage more precisely.
-    val maxMemoryUsage: Long = strategy.maxMemoryInMB * 1024L * 1024L
-    logDebug(s"max memory usage for aggregates = ${maxMemoryUsage} bytes.")
-
-    /*
-      Stack of nodes to train: (treeIndex, node)
-      The reason this is a stack is that we train many trees at once, but we want to focus on
-      completing trees, rather than training all simultaneously.  If we are splitting nodes from
-      1 tree, then the new nodes to split will be put at the top of this stack, so we will continue
-      training the same tree in the next iteration.  This focus allows us to send fewer trees to
-      workers on each iteration; see topNodesForGroup below.
-     */
-    val nodeStack = new mutable.ArrayStack[(Int, LearningNode)]
-
-    val rng = new Random()
-    rng.setSeed(seed)
-
-    // Allocate and queue root nodes.
-    val topNodes = Array.fill[LearningNode](numTrees)(LearningNode.emptyNode(nodeIndex = 1))
-    Range(0, numTrees).foreach(treeIndex => nodeStack.push((treeIndex, topNodes(treeIndex))))
-
-    val nodeIdCacheX = GradientBoostedTreesUtil.nodeIdCacheXConstruction(topNodes, rawPartInfoBc)
-    timer.stop("init")
-
-    while (nodeStack.nonEmpty) {
-      // Collect some nodes to split, and choose features for each node (if subsampling).
-      // Each group of nodes may come from one or multiple trees, and at multiple levels.
-      val (nodesForGroup, treeToNodeToIndexInfo) =
-        RandomForest4GBDTX.selectNodesToSplitX(nodeStack, maxMemoryUsage, metadata, rng)
-      // Sanity check (should never occur):
-      assert(nodesForGroup.nonEmpty,
-        s"RandomForest selected empty nodesForGroup.  Error for unknown reason.")
-
-      // Only send trees to worker if they contain nodes being split this iteration.
-      val topNodesForGroup: Map[Int, LearningNode] =
-        nodesForGroup.keys.map(treeIdx => treeIdx -> topNodes(treeIdx)).toMap
-
-      // Choose node splits, and enqueue new nodes as needed.
-      timer.start("findBestSplits")
-      RandomForest4GBDTX.findBestSplitsX(labelArrayBc, processedInput, metadata,
-        (nodesForGroup, treeToNodeToIndexInfo), splits, nodeStack, nodeIdCacheX, input,
-        rawPartInfoBc, timer)
-      timer.stop("findBestSplits")
-    }
-
-    timer.stop("total")
-
-    logInfo("Internal timing for DecisionTree:")
-    logInfo(s"$timer")
-
-    val numFeatures = metadata.numFeatures
-
-    parentUID match {
-      case Some(uid) =>
-        if (strategy.algo == OldAlgo.Classification) {
-          // unreachable for GBDT
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(uid, rootNode.toNode, numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map { rootNode =>
-            new DecisionTreeRegressionModel(uid, rootNode.toNode, numFeatures)
-          }
-        }
-      // unreachable for GBDT
-      case None =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(rootNode.toNode, numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map(rootNode => new DecisionTreeRegressionModel(rootNode.toNode, numFeatures))
-        }
-    }
-  }
-
-  /**
-   * Given a group of nodes, this finds the best split for each node.
-   *
-   * @param input Training data: RDD of [[TreePoint]]
-   * @param metadata Learning and dataset metadata
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @param nodeStack  Queue of nodes to split, with values (treeIndex, node).
-   *                   Updated with new non-leaf nodes which are created.
-   * @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
-   *                    each value in the array is the data point's node Id
-   *                    for a corresponding tree. This is used to prevent the need
-   *                    to pass the entire tree to the executors during
-   *                    the node stat aggregation phase.
-   */
-  private[tree] def findBestSplitsX(
-      labelArrayBc: Broadcast[DoubleArrayList],
-      processedInput: RDD[(Int, (IntArrayList, ObjectArrayList[Split]))],
-      metadata: DecisionTreeMetadata,
-      packagedNodeInfo: (Map[Int, Array[LearningNode]], Map[Int, Map[Int, NodeIndexInfo]]),
-      splits: Array[Array[Split]],
-      nodeStack: mutable.ArrayStack[(Int, LearningNode)],
-      nodeIdCache: Int2ObjectOpenHashMap[Int2ObjectOpenHashMap[IntArrayList]],
-      input: RDD[TreePoint],
-      rawPartInfoBc: Broadcast[Int2ObjectOpenHashMap[IntArrayList]],
-      timer: TimeTracker = new TimeTracker) : Unit = {
-
-    /*
-     * The high-level descriptions of the best split optimizations are noted here.
-     *
-     * *Group-wise training*
-     * We perform bin calculations for groups of nodes to reduce the number of
-     * passes over the data.  Each iteration requires more computation and storage,
-     * but saves several iterations over the data.
-     *
-     * *Bin-wise computation*
-     * We use a bin-wise best split computation strategy instead of a straightforward best split
-     * computation strategy. Instead of analyzing each sample for contribution to the left/right
-     * child node impurity of every split, we first categorize each feature of a sample into a
-     * bin. We exploit this structure to calculate aggregates for bins and then use these aggregates
-     * to calculate information gain for each split.
-     *
-     * *Aggregation over partitions*
-     * Instead of performing a flatMap/reduceByKey operation, we exploit the fact that we know
-     * the number of splits in advance. Thus, we store the aggregates (at the appropriate
-     * indices) in a single array for all bins and rely upon the RDD aggregate method to
-     * drastically reduce the communication overhead.
-     */
-
-    // Un-package node info
-    val (nodesForGroup, treeToNodeToIndexInfo) = packagedNodeInfo
-    // numNodes:  Number of nodes in this group
-    val numNodes = nodesForGroup.values.map(_.length).sum
-    logDebug(s"numNodes = ${numNodes}")
-    logDebug(s"numFeatures = ${metadata.numFeatures}")
-    logDebug(s"numClasses = ${metadata.numClasses}")
-    logDebug(s"isMulticlass = ${metadata.isMulticlass}")
-    logDebug(s"isMulticlassWithCategoricalFeatures =" +
-      s"${metadata.isMulticlassWithCategoricalFeatures}")
-
-    // array of nodes to train indexed by node index in group
-    val nodes = new Array[LearningNode](numNodes)
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        nodes(treeToNodeToIndexInfo(treeIndex)(node.id).nodeIndexInGroup) = node
-      }
-    }
-
-    timer.start("broadcast")
-    val nodeIdCacheBc = processedInput.sparkContext.broadcast(nodeIdCache)
-    timer.stop("broadcast")
-
-    // Calculate best splits for all nodes in the group
-    timer.start("chooseSplits")
-
-    val nodeToBestSplits = GradientBoostedTreesUtil.chooseBestSplits(processedInput,
-      treeToNodeToIndexInfo, metadata, nodeIdCacheBc, labelArrayBc, nodes)
-
-    timer.stop("chooseSplits")
-
-    // Iterate over all nodes in this group.
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        val nodeIndex = node.id
-        val nodeInfo = treeToNodeToIndexInfo(treeIndex)(nodeIndex)
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val (split: Split, stats: ImpurityStats) =
-          nodeToBestSplits(nodeIndex)
-        logDebug(s"best split = ${split}")
-
-        // Extract info for this node.  Create children if not leaf.
-        val isLeaf =
-          (stats.gain <= 0) || (LearningNode.indexToLevel(nodeIndex) == metadata.maxDepth)
-        node.isLeaf = isLeaf
-        node.stats = stats
-        logDebug(s"Node = ${node}")
-
-        if (!isLeaf) {
-          node.split = Some(split)
-          val childIsLeaf = (LearningNode.indexToLevel(nodeIndex) + 1) == metadata.maxDepth
-          val leftChildIsLeaf = childIsLeaf || (stats.leftImpurity == 0.0)
-          val rightChildIsLeaf = childIsLeaf || (stats.rightImpurity == 0.0)
-          node.leftChild = Some(LearningNode(LearningNode.leftChildIndex(nodeIndex),
-            leftChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.leftImpurityCalculator)))
-          node.rightChild = Some(LearningNode(LearningNode.rightChildIndex(nodeIndex),
-            rightChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.rightImpurityCalculator)))
-
-          // enqueue left child and right child if they are not leaves
-          if (!leftChildIsLeaf) {
-            nodeStack.push((treeIndex, node.leftChild.get))
-          }
-          if (!rightChildIsLeaf) {
-            nodeStack.push((treeIndex, node.rightChild.get))
-          }
-
-          logDebug(s"leftChildIndex = ${node.leftChild.get.id}" +
-            s", impurity = ${stats.leftImpurity}")
-          logDebug(s"rightChildIndex = ${node.rightChild.get.id}" +
-            s", impurity = ${stats.rightImpurity}")
-        }
-      }
-    }
-
-    GradientBoostedTreesUtil.updateNodeIdCache(nodeIdCache, nodeIdCacheBc, input, nodesForGroup,
-      treeToNodeToIndexInfo, splits, rawPartInfoBc, metadata, timer)
-  }
-
-  /**
-   * Returns splits for decision tree calculation.
-   * Continuous and categorical features are handled differently.
-   *
-   * Continuous features:
-   *   For each feature, there are numBins - 1 possible splits representing the possible binary
-   *   decisions at each node in the tree.
-   *   This finds locations (feature values) for splits using a subsample of the data.
-   *
-   * Categorical features:
-   *   For each feature, there is 1 bin per split.
-   *   Splits and bins are handled in 2 ways:
-   *   (a) "unordered features"
-   *       For multiclass classification with a low-arity feature
-   *       (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits),
-   *       the feature is split based on subsets of categories.
-   *   (b) "ordered features"
-   *       For regression and binary classification,
-   *       and for multiclass classification with a high-arity feature,
-   *       there is one bin per category.
-   *
-   * @param input Training data: RDD of [[LabeledPoint]]
-   * @param metadata Learning and dataset metadata
-   * @param seed random seed
-   * @return Splits, an Array of [[Split]]
-   *          of size (numFeatures, numSplits)
-   */
-  protected[tree] def findSplits(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      seed: Long): Array[Array[Split]] = {
-
-    logDebug(s"isMulticlass = ${metadata.isMulticlass}")
-
-    val numFeatures = metadata.numFeatures
-
-    // Sample the input only if there are continuous features.
-    val continuousFeatures = Range(0, numFeatures).filter(metadata.isContinuous)
-    val sampledInput = if (continuousFeatures.nonEmpty) {
-      // Calculate the number of samples for approximate quantile calculation.
-      val requiredSamples = math.max(metadata.maxBins * metadata.maxBins, 10000)
-      val fraction = if (requiredSamples < metadata.numExamples) {
-        requiredSamples.toDouble / metadata.numExamples
-      } else {
-        1.0
-      }
-      logDebug(s"fraction of data used for calculating quantiles = ${fraction}")
-      input.sample(withReplacement = false, fraction, new XORShiftRandom(seed).nextInt())
-    } else {
-      input.sparkContext.emptyRDD[LabeledPoint]
-    }
-
-    findSplitsBySorting(sampledInput, metadata, continuousFeatures)
-  }
-
-  private def findSplitsBySorting(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      continuousFeatures: IndexedSeq[Int]): Array[Array[Split]] = {
-
-    val continuousSplits: scala.collection.Map[Int, Array[Split]] = {
-      // reduce the parallelism for split computations when there are less
-      // continuous features than input partitions. this prevents tasks from
-      // being spun up that will definitely do no work.
-      val numPartitions = math.min(continuousFeatures.length, input.partitions.length)
-
-      input
-        .flatMap(point => continuousFeatures.map(idx => (idx, point.features(idx))))
-        .groupByKey(numPartitions)
-        .map { case (idx, samples) =>
-          val thresholds = findSplitsForContinuousFeature(samples, metadata, idx)
-          val splits: Array[Split] = thresholds.map(thresh => new ContinuousSplit(idx, thresh))
-          logDebug(s"featureIndex = $idx, numSplits = ${splits.length}")
-          (idx, splits)
-        }.collectAsMap()
-    }
-
-    val numFeatures = metadata.numFeatures
-    val splits: Array[Array[Split]] = Array.tabulate(numFeatures) {
-      case i if metadata.isContinuous(i) =>
-        val split = continuousSplits(i)
-        metadata.setNumSplits(i, split.length)
-        split
-
-      // unreachable for GBDT
-      case i if metadata.isCategorical(i) && metadata.isUnordered(i) =>
-        // Unordered features
-        // 2^(maxFeatureValue - 1) - 1 combinations
-        val featureArity = metadata.featureArity(i)
-        Array.tabulate[Split](metadata.numSplits(i)) { splitIndex =>
-          val categories = extractMultiClassCategories(splitIndex + 1, featureArity)
-          new CategoricalSplit(i, categories.toArray, featureArity)
-        }
-
-      case i if metadata.isCategorical(i) =>
-        // Ordered features
-        //   Splits are constructed as needed during training.
-        Array.empty[Split]
-    }
-    splits
-  }
-
-  /**
-   * Nested method to extract list of eligible categories given an index. It extracts the
-   * position of ones in a binary representation of the input. If binary
-   * representation of an number is 01101 (13), the output list should (3.0, 2.0,
-   * 0.0). The maxFeatureValue depict the number of rightmost digits that will be tested for ones.
-   */
-  private[tree] def extractMultiClassCategories(
-      input: Int,
-      maxFeatureValue: Int): List[Double] = {
-    var categories = List[Double]()
-    var j = 0
-    var bitShiftedInput = input
-    while (j < maxFeatureValue) {
-      if (bitShiftedInput % 2 != 0) {
-        // updating the list of categories.
-        categories = j.toDouble :: categories
-      }
-      // Right shift by one
-      bitShiftedInput = bitShiftedInput >> 1
-      j += 1
-    }
-    categories
-  }
-
-  /**
-   * Find splits for a continuous feature
-   * NOTE: Returned number of splits is set based on `featureSamples` and
-   *       could be different from the specified `numSplits`.
-   *       The `numSplits` attribute in the `DecisionTreeMetadata` class will be set accordingly.
-   *
-   * @param featureSamples feature values of each sample
-   * @param metadata decision tree metadata
-   *                 NOTE: `metadata.numbins` will be changed accordingly
-   *                       if there are not enough splits to be found
-   * @param featureIndex feature index to find splits
-   * @return array of split thresholds
-   */
-  private[tree] def findSplitsForContinuousFeature(
-      featureSamples: Iterable[Double],
-      metadata: DecisionTreeMetadata,
-      featureIndex: Int): Array[Double] = {
-    require(metadata.isContinuous(featureIndex),
-      "findSplitsForContinuousFeature can only be used to find splits for a continuous feature.")
-
-    val splits: Array[Double] = if (featureSamples.isEmpty) {
-      Array.empty[Double]
-    } else {
-      val numSplits = metadata.numSplits(featureIndex)
-
-      // get count for each distinct value
-      val (valueCountMap, numSamples) = featureSamples.foldLeft((Map.empty[Double, Int], 0)) {
-        case ((m, cnt), x) =>
-          (m + ((x, m.getOrElse(x, 0) + 1)), cnt + 1)
-      }
-      // sort distinct values
-      val valueCounts = valueCountMap.toSeq.sortBy(_._1).toArray
-
-      val possibleSplits = valueCounts.length - 1
-      if (possibleSplits == 0) {
-        // constant feature
-        Array.empty[Double]
-      } else if (possibleSplits <= numSplits) {
-        // if possible splits is not enough or just enough, just return all possible splits
-        (1 to possibleSplits)
-          .map(index => (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0)
-          .toArray
-      } else {
-        // stride between splits
-        val stride: Double = numSamples.toDouble / (numSplits + 1)
-        logDebug(s"stride = ${stride}")
-
-        // iterate `valueCount` to find splits
-        val splitsBuilder = mutable.ArrayBuilder.make[Double]
-        var index = 1
-        // currentCount: sum of counts of values that have been visited
-        var currentCount = valueCounts(0)._2
-        // targetCount: target value for `currentCount`.
-        // If `currentCount` is closest value to `targetCount`,
-        // then current value is a split threshold.
-        // After finding a split threshold, `targetCount` is added by stride.
-        var targetCount = stride
-        while (index < valueCounts.length) {
-          val previousCount = currentCount
-          currentCount += valueCounts(index)._2
-          val previousGap = math.abs(previousCount - targetCount)
-          val currentGap = math.abs(currentCount - targetCount)
-          // If adding count of current value to currentCount
-          // makes the gap between currentCount and targetCount smaller,
-          // previous value is a split threshold.
-          if (previousGap < currentGap) {
-            splitsBuilder += (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0
-            targetCount += stride
-          }
-          index += 1
-        }
-
-        splitsBuilder.result()
-      }
-    }
-    splits
-  }
-
-  /**
-   * Pull nodes off of the queue, and collect a group of nodes to be split on this iteration.
-   * This tracks the memory usage for aggregates and stops adding nodes when too much memory
-   * will be needed; this allows an adaptive number of nodes since different nodes may require
-   * different amounts of memory (if featureSubsetStrategy is not "all").
-   *
-   * @param nodeStack  Queue of nodes to split.
-   * @param maxMemoryUsage  Bound on size of aggregate statistics.
-   * @return  (nodesForGroup, treeToNodeToIndexInfo).
-   *          nodesForGroup holds the nodes to split: treeIndex --> nodes in tree.
-   *
-   *          treeToNodeToIndexInfo holds indices selected features for each node:
-   *            treeIndex --> (global) node index --> (node index in group, feature indices).
-   *          The (global) node index is the index in the tree; the node index in group is the
-   *           index in [0, numNodesInGroup) of the node in this group.
-   *          The feature indices are None if not subsampling features.
-   */
-  private[tree] def selectNodesToSplitX(
-      nodeStack: mutable.ArrayStack[(Int, LearningNode)],
-      maxMemoryUsage: Long,
-      metadata: DecisionTreeMetadata,
-      rng: Random): (Map[Int, Array[LearningNode]], Map[Int, Map[Int, NodeIndexInfo]]) = {
-    // Collect some nodes to split:
-    //  nodesForGroup(treeIndex) = nodes to split
-    val mutableNodesForGroup = new mutable.HashMap[Int, mutable.ArrayBuffer[LearningNode]]()
-    val mutableTreeToNodeToIndexInfo =
-      new mutable.HashMap[Int, mutable.HashMap[Int, NodeIndexInfo]]()
-    var memUsage: Long = 0L
-    var numNodesInGroup = 0
-    // If maxMemoryInMB is set very small, we want to still try to split 1 node,
-    // so we allow one iteration if memUsage == 0.
-    var groupDone = false
-    while (nodeStack.nonEmpty && !groupDone) {
-      val (treeIndex, node) = nodeStack.top
-      // Choose subset of features for node (if subsampling).
-      val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) {
-        Some(SamplingUtils.reservoirSampleAndCount(Range(0,
-          metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong())._1)
-      } else {
-        None
-      }
-      val featureSubsetHashSetX: Option[mutable.HashSet[Int]] = if (metadata.subsamplingFeatures) {
-        Some(scala.collection.mutable.HashSet(featureSubset.get: _*))
-      } else {
-        None
-      }
-      // Check if enough memory remains to add this node to the group.
-      val nodeMemUsage = RandomForest4GBDTX.aggregateSizeForNode(metadata, featureSubset) * 8L
-      if (memUsage + nodeMemUsage <= maxMemoryUsage || memUsage == 0) {
-        nodeStack.pop()
-        mutableNodesForGroup.getOrElseUpdate(treeIndex, new mutable.ArrayBuffer[LearningNode]()) +=
-          node
-        mutableTreeToNodeToIndexInfo
-          .getOrElseUpdate(treeIndex, new mutable.HashMap[Int, NodeIndexInfo]())(node.id)
-          = new NodeIndexInfo(numNodesInGroup, featureSubset, featureSubsetHashSetX)
-        numNodesInGroup += 1
-        memUsage += nodeMemUsage
-      } else {
-        groupDone = true
-      }
-    }
-    if (memUsage > maxMemoryUsage) {
-      // If maxMemoryUsage is 0, we should still allow splitting 1 node.
-      logWarning(s"Tree learning is using approximately $memUsage bytes per iteration, which" +
-        s" exceeds requested limit maxMemoryUsage=$maxMemoryUsage. This allows splitting" +
-        s" $numNodesInGroup nodes in this iteration.")
-    }
-    // Convert mutable maps to immutable ones.
-    val nodesForGroup: Map[Int, Array[LearningNode]] =
-      mutableNodesForGroup.mapValues(_.toArray).toMap
-    val treeToNodeToIndexInfo = mutableTreeToNodeToIndexInfo.mapValues(_.toMap).toMap
-    (nodesForGroup, treeToNodeToIndexInfo)
-  }
-
-  /**
-   * Get the number of values to be stored for this node in the bin aggregates.
-   *
-   * @param featureSubset  Indices of features which may be split at this node.
-   *                       If None, then use all features.
-   */
-  private def aggregateSizeForNode(
-      metadata: DecisionTreeMetadata,
-      featureSubset: Option[Array[Int]]): Long = {
-    val totalBins = if (featureSubset.nonEmpty) {
-      featureSubset.get.map(featureIndex => metadata.numBins(featureIndex).toLong).sum
-    } else {
-      metadata.numBins.map(_.toLong).sum
-    }
-    if (metadata.isClassification) {
-      // unreachable for GBDT
-      metadata.numClasses * totalBins
-    } else {
-      3 * totalBins
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForestRaw.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForestRaw.scala
deleted file mode 100644
index 25dfd36..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/impl/RandomForestRaw.scala
+++ /dev/null
@@ -1,1157 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import java.io.IOException
-
-import scala.collection.mutable
-import scala.util.Random
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.classification.DecisionTreeClassificationModel
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree._
-import org.apache.spark.ml.util.Instrumentation
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
-import org.apache.spark.mllib.tree.model.ImpurityStats
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
-
-
-/**
- * ALGORITHM
- *
- * This is a sketch of the algorithm to help new developers.
- *
- * The algorithm partitions data by instances (rows).
- * On each iteration, the algorithm splits a set of nodes.  In order to choose the best split
- * for a given node, sufficient statistics are collected from the distributed data.
- * For each node, the statistics are collected to some worker node, and that worker selects
- * the best split.
- *
- * This setup requires discretization of continuous features.  This binning is done in the
- * findSplits() method during initialization, after which each continuous feature becomes
- * an ordered discretized feature with at most maxBins possible values.
- *
- * The main loop in the algorithm operates on a queue of nodes (nodeStack).  These nodes
- * lie at the periphery of the tree being trained.  If multiple trees are being trained at once,
- * then this queue contains nodes from all of them.  Each iteration works roughly as follows:
- *   On the master node:
- *     - Some number of nodes are pulled off of the queue (based on the amount of memory
- *       required for their sufficient statistics).
- *     - For random forests, if featureSubsetStrategy is not "all," then a subset of candidate
- *       features are chosen for each node.  See method selectNodesToSplit().
- *   On worker nodes, via method findBestSplits():
- *     - The worker makes one pass over its subset of instances.
- *     - For each (tree, node, feature, split) tuple, the worker collects statistics about
- *       splitting.  Note that the set of (tree, node) pairs is limited to the nodes selected
- *       from the queue for this iteration.  The set of features considered can also be limited
- *       based on featureSubsetStrategy.
- *     - For each node, the statistics for that node are aggregated to a particular worker
- *       via reduceByKey().  The designated worker chooses the best (feature, split) pair,
- *       or chooses to stop splitting if the stopping criteria are met.
- *   On the master node:
- *     - The master collects all decisions about splitting nodes and updates the model.
- *     - The updated model is passed to the workers on the next iteration.
- * This process continues until the node queue is empty.
- *
- * Most of the methods in this implementation support the statistics aggregation, which is
- * the heaviest part of the computation.  In general, this implementation is bound by either
- * the cost of statistics computation on workers or by communicating the sufficient statistics.
- */
-private[spark] object RandomForestRaw extends Logging {
-
-  /**
-   * Train a random forest.
-   *
-   * @param input Training data: RDD of `LabeledPoint`
-   * @return an unweighted set of trees
-   */
-  def run(
-      input: RDD[LabeledPoint],
-      strategy: OldStrategy,
-      numTrees: Int,
-      featureSubsetStrategy: String,
-      seed: Long,
-      instr: Option[Instrumentation[_]],
-      parentUID: Option[String] = None): Array[DecisionTreeModel] = {
-
-    val timer = new TimeTracker()
-
-    timer.start("total")
-
-    timer.start("init")
-
-    val retaggedInput = input.retag(classOf[LabeledPoint])
-    val metadata =
-      DecisionTreeMetadata.buildMetadata(retaggedInput, strategy, numTrees, featureSubsetStrategy)
-    instr match {
-      case Some(instrumentation) =>
-        instrumentation.logNumFeatures(metadata.numFeatures)
-        instrumentation.logNumClasses(metadata.numClasses)
-      case None =>
-        logInfo(s"numFeatures: ${metadata.numFeatures}")
-        logInfo(s"numClasses: ${metadata.numClasses}")
-    }
-
-    // Find the splits and the corresponding bins (interval between the splits) using a sample
-    // of the input data.
-    timer.start("findSplits")
-    val splits = findSplits(retaggedInput, metadata, seed)
-    timer.stop("findSplits")
-    logDebug("numBins: feature: number of bins")
-    logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
-      s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
-    }.mkString("\n"))
-
-    // Bin feature values (TreePoint representation).
-    // Cache input RDD for speedup during multiple passes.
-    val treeInput = TreePoint.convertToTreeRDD(retaggedInput, splits, metadata)
-
-    val withReplacement = numTrees > 1
-
-    val baggedInput = BaggedPoint
-      .convertToBaggedRDD(treeInput, strategy.subsamplingRate, numTrees, withReplacement, seed)
-      .persist(StorageLevel.MEMORY_AND_DISK)
-
-    // depth of the decision tree
-    val maxDepth = strategy.maxDepth
-    require(maxDepth <= 30,
-      s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
-
-    // Max memory usage for aggregates
-    // TODO: Calculate memory usage more precisely.
-    val maxMemoryUsage: Long = strategy.maxMemoryInMB * 1024L * 1024L
-    logDebug(s"max memory usage for aggregates = ${maxMemoryUsage} bytes.")
-
-    /*
-     * The main idea here is to perform group-wise training of the decision tree nodes thus
-     * reducing the passes over the data from (# nodes) to (# nodes / maxNumberOfNodesPerGroup).
-     * Each data sample is handled by a particular node (or it reaches a leaf and is not used
-     * in lower levels).
-     */
-
-    // Create an RDD of node Id cache.
-    // At first, all the rows belong to the root nodes (node Id == 1).
-    val nodeIdCache = if (strategy.useNodeIdCache) {
-      Some(NodeIdCache.init(
-        data = baggedInput,
-        numTrees = numTrees,
-        checkpointInterval = strategy.checkpointInterval,
-        initVal = 1))
-    } else {
-      None
-    }
-
-    /*
-      Stack of nodes to train: (treeIndex, node)
-      The reason this is a stack is that we train many trees at once, but we want to focus on
-      completing trees, rather than training all simultaneously.  If we are splitting nodes from
-      1 tree, then the new nodes to split will be put at the top of this stack, so we will continue
-      training the same tree in the next iteration.  This focus allows us to send fewer trees to
-      workers on each iteration; see topNodesForGroup below.
-     */
-    val nodeStack = new mutable.ArrayStack[(Int, LearningNode)]
-
-    val rng = new Random()
-    rng.setSeed(seed)
-
-    // Allocate and queue root nodes.
-    val topNodes = Array.fill[LearningNode](numTrees)(LearningNode.emptyNode(nodeIndex = 1))
-    Range(0, numTrees).foreach(treeIndex => nodeStack.push((treeIndex, topNodes(treeIndex))))
-
-    timer.stop("init")
-
-    while (nodeStack.nonEmpty) {
-      // Collect some nodes to split, and choose features for each node (if subsampling).
-      // Each group of nodes may come from one or multiple trees, and at multiple levels.
-      val (nodesForGroup, treeToNodeToIndexInfo) =
-        RandomForestRaw.selectNodesToSplit(nodeStack, maxMemoryUsage, metadata, rng)
-      // Sanity check (should never occur):
-      assert(nodesForGroup.nonEmpty,
-        s"RandomForest selected empty nodesForGroup.  Error for unknown reason.")
-
-      // Only send trees to worker if they contain nodes being split this iteration.
-      val topNodesForGroup: Map[Int, LearningNode] =
-        nodesForGroup.keys.map(treeIdx => treeIdx -> topNodes(treeIdx)).toMap
-
-      // Choose node splits, and enqueue new nodes as needed.
-      timer.start("findBestSplits")
-      RandomForestRaw.findBestSplits(baggedInput, metadata, topNodesForGroup, nodesForGroup,
-        treeToNodeToIndexInfo, splits, nodeStack, timer, nodeIdCache)
-      timer.stop("findBestSplits")
-    }
-
-    baggedInput.unpersist()
-
-    timer.stop("total")
-
-    logInfo("Internal timing for DecisionTree:")
-    logInfo(s"$timer")
-
-    // Delete any remaining checkpoints used for node Id cache.
-    if (nodeIdCache.nonEmpty) {
-      try {
-        nodeIdCache.get.deleteAllCheckpoints()
-      } catch {
-        case e: IOException =>
-          logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}")
-      }
-    }
-
-    val numFeatures = metadata.numFeatures
-
-    parentUID match {
-      case Some(uid) =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(uid, rootNode.toNode, numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map { rootNode =>
-            new DecisionTreeRegressionModel(uid, rootNode.toNode, numFeatures)
-          }
-        }
-      case None =>
-        if (strategy.algo == OldAlgo.Classification) {
-          topNodes.map { rootNode =>
-            new DecisionTreeClassificationModel(rootNode.toNode, numFeatures,
-              strategy.getNumClasses)
-          }
-        } else {
-          topNodes.map(rootNode => new DecisionTreeRegressionModel(rootNode.toNode, numFeatures))
-        }
-    }
-  }
-
-  /**
-   * Helper for binSeqOp, for data which can contain a mix of ordered and unordered features.
-   *
-   * For ordered features, a single bin is updated.
-   * For unordered features, bins correspond to subsets of categories; either the left or right bin
-   * for each subset is updated.
-   *
-   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-   *             each (feature, bin).
-   * @param treePoint  Data point being aggregated.
-   * @param splits possible splits indexed (numFeatures)(numSplits)
-   * @param unorderedFeatures  Set of indices of unordered features.
-   * @param instanceWeight  Weight (importance) of instance in dataset.
-   */
-  private def mixedBinSeqOp(
-      agg: DTStatsAggregator,
-      treePoint: TreePoint,
-      splits: Array[Array[Split]],
-      unorderedFeatures: Set[Int],
-      instanceWeight: Double,
-      featuresForNode: Option[Array[Int]]): Unit = {
-    val numFeaturesPerNode = if (featuresForNode.nonEmpty) {
-      // Use subsampled features
-      featuresForNode.get.length
-    } else {
-      // Use all features
-      agg.metadata.numFeatures
-    }
-    // Iterate over features.
-    var featureIndexIdx = 0
-    while (featureIndexIdx < numFeaturesPerNode) {
-      val featureIndex = if (featuresForNode.nonEmpty) {
-        featuresForNode.get.apply(featureIndexIdx)
-      } else {
-        featureIndexIdx
-      }
-      if (unorderedFeatures.contains(featureIndex)) {
-        // Unordered feature
-        val featureValue = treePoint.binnedFeatures(featureIndex)
-        val leftNodeFeatureOffset = agg.getFeatureOffset(featureIndexIdx)
-        // Update the left or right bin for each split.
-        val numSplits = agg.metadata.numSplits(featureIndex)
-        val featureSplits = splits(featureIndex)
-        var splitIndex = 0
-        while (splitIndex < numSplits) {
-          if (featureSplits(splitIndex).shouldGoLeft(featureValue, featureSplits)) {
-            agg.featureUpdate(leftNodeFeatureOffset, splitIndex,
-              treePoint.label, instanceWeight.toInt)
-          }
-          splitIndex += 1
-        }
-      } else {
-        // Ordered feature
-        val binIndex = treePoint.binnedFeatures(featureIndex)
-        agg.update(featureIndexIdx, binIndex, treePoint.label, instanceWeight.toInt)
-      }
-      featureIndexIdx += 1
-    }
-  }
-
-  /**
-   * Helper for binSeqOp, for regression and for classification with only ordered features.
-   *
-   * For each feature, the sufficient statistics of one bin are updated.
-   *
-   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-   *             each (feature, bin).
-   * @param treePoint  Data point being aggregated.
-   * @param instanceWeight  Weight (importance) of instance in dataset.
-   */
-  private def orderedBinSeqOp(
-      agg: DTStatsAggregator,
-      treePoint: TreePoint,
-      instanceWeight: Double,
-      featuresForNode: Option[Array[Int]]): Unit = {
-    val label = treePoint.label
-
-    // Iterate over features.
-    if (featuresForNode.nonEmpty) {
-      // Use subsampled features
-      var featureIndexIdx = 0
-      while (featureIndexIdx < featuresForNode.get.length) {
-        val binIndex = treePoint.binnedFeatures(featuresForNode.get.apply(featureIndexIdx))
-        agg.update(featureIndexIdx, binIndex, label, instanceWeight.toInt)
-        featureIndexIdx += 1
-      }
-    } else {
-      // Use all features
-      val numFeatures = agg.metadata.numFeatures
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        val binIndex = treePoint.binnedFeatures(featureIndex)
-        agg.update(featureIndex, binIndex, label, instanceWeight.toInt)
-        featureIndex += 1
-      }
-    }
-  }
-
-  /**
-   * Given a group of nodes, this finds the best split for each node.
-   *
-   * @param input Training data: RDD of [[TreePoint]]
-   * @param metadata Learning and dataset metadata
-   * @param topNodesForGroup For each tree in group, tree index -> root node.
-   *                         Used for matching instances with nodes.
-   * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree
-   * @param treeToNodeToIndexInfo Mapping: treeIndex --> nodeIndex --> nodeIndexInfo,
-   *                              where nodeIndexInfo stores the index in the group and the
-   *                              feature subsets (if using feature subsets).
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @param nodeStack  Queue of nodes to split, with values (treeIndex, node).
-   *                   Updated with new non-leaf nodes which are created.
-   * @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
-   *                    each value in the array is the data point's node Id
-   *                    for a corresponding tree. This is used to prevent the need
-   *                    to pass the entire tree to the executors during
-   *                    the node stat aggregation phase.
-   */
-  private[tree] def findBestSplits(
-      input: RDD[BaggedPoint[TreePoint]],
-      metadata: DecisionTreeMetadata,
-      topNodesForGroup: Map[Int, LearningNode],
-      nodesForGroup: Map[Int, Array[LearningNode]],
-      treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
-      splits: Array[Array[Split]],
-      nodeStack: mutable.ArrayStack[(Int, LearningNode)],
-      timer: TimeTracker = new TimeTracker,
-      nodeIdCache: Option[NodeIdCache] = None): Unit = {
-
-    /*
-     * The high-level descriptions of the best split optimizations are noted here.
-     *
-     * *Group-wise training*
-     * We perform bin calculations for groups of nodes to reduce the number of
-     * passes over the data.  Each iteration requires more computation and storage,
-     * but saves several iterations over the data.
-     *
-     * *Bin-wise computation*
-     * We use a bin-wise best split computation strategy instead of a straightforward best split
-     * computation strategy. Instead of analyzing each sample for contribution to the left/right
-     * child node impurity of every split, we first categorize each feature of a sample into a
-     * bin. We exploit this structure to calculate aggregates for bins and then use these aggregates
-     * to calculate information gain for each split.
-     *
-     * *Aggregation over partitions*
-     * Instead of performing a flatMap/reduceByKey operation, we exploit the fact that we know
-     * the number of splits in advance. Thus, we store the aggregates (at the appropriate
-     * indices) in a single array for all bins and rely upon the RDD aggregate method to
-     * drastically reduce the communication overhead.
-     */
-
-    // numNodes:  Number of nodes in this group
-    val numNodes = nodesForGroup.values.map(_.length).sum
-    logDebug(s"numNodes = ${numNodes}")
-    logDebug(s"numFeatures = ${metadata.numFeatures}")
-    logDebug(s"numClasses = ${metadata.numClasses}")
-    logDebug(s"isMulticlass = ${metadata.isMulticlass}")
-    logDebug("isMulticlassWithCategoricalFeatures = " +
-      s"${metadata.isMulticlassWithCategoricalFeatures}")
-    logDebug(s"using nodeIdCache = ${nodeIdCache.nonEmpty.toString}")
-
-    /**
-     * Performs a sequential aggregation over a partition for a particular tree and node.
-     *
-     * For each feature, the aggregate sufficient statistics are updated for the relevant
-     * bins.
-     *
-     * @param treeIndex Index of the tree that we want to perform aggregation for.
-     * @param nodeInfo The node info for the tree node.
-     * @param agg Array storing aggregate calculation, with a set of sufficient statistics
-     *            for each (node, feature, bin).
-     * @param baggedPoint Data point being aggregated.
-     */
-    def nodeBinSeqOp(
-        treeIndex: Int,
-        nodeInfo: NodeIndexInfo,
-        agg: Array[DTStatsAggregator],
-        baggedPoint: BaggedPoint[TreePoint]): Unit = {
-      if (nodeInfo != null) {
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val featuresForNode = nodeInfo.featureSubset
-        val instanceWeight = baggedPoint.subsampleWeights(treeIndex)
-        if (metadata.unorderedFeatures.isEmpty) {
-          orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, instanceWeight, featuresForNode)
-        } else {
-          mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splits,
-            metadata.unorderedFeatures, instanceWeight, featuresForNode)
-        }
-        agg(aggNodeIndex).updateParent(baggedPoint.datum.label, instanceWeight)
-      }
-    }
-
-    /**
-     * Performs a sequential aggregation over a partition.
-     *
-     * Each data point contributes to one node. For each feature,
-     * the aggregate sufficient statistics are updated for the relevant bins.
-     *
-     * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
-     *             each (node, feature, bin).
-     * @param baggedPoint   Data point being aggregated.
-     * @return  agg
-     */
-    def binSeqOp(
-        agg: Array[DTStatsAggregator],
-        baggedPoint: BaggedPoint[TreePoint]): Array[DTStatsAggregator] = {
-      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
-        val nodeIndex =
-          topNodesForGroup(treeIndex).predictImpl(baggedPoint.datum.binnedFeatures, splits)
-        nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null), agg, baggedPoint)
-      }
-      agg
-    }
-
-    /**
-     * Do the same thing as binSeqOp, but with nodeIdCache.
-     */
-    def binSeqOpWithNodeIdCache(
-        agg: Array[DTStatsAggregator],
-        dataPoint: (BaggedPoint[TreePoint], Array[Int])): Array[DTStatsAggregator] = {
-      treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
-        val baggedPoint = dataPoint._1
-        val nodeIdCache = dataPoint._2
-        val nodeIndex = nodeIdCache(treeIndex)
-        nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex, null), agg, baggedPoint)
-      }
-
-      agg
-    }
-
-    /**
-     * Get node index in group --> features indices map,
-     * which is a short cut to find feature indices for a node given node index in group.
-     */
-    def getNodeToFeatures(
-        treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]]): Option[Map[Int, Array[Int]]] = {
-      if (!metadata.subsamplingFeatures) {
-        None
-      } else {
-        val mutableNodeToFeatures = new mutable.HashMap[Int, Array[Int]]()
-        treeToNodeToIndexInfo.values.foreach { nodeIdToNodeInfo =>
-          nodeIdToNodeInfo.values.foreach { nodeIndexInfo =>
-            assert(nodeIndexInfo.featureSubset.isDefined)
-            mutableNodeToFeatures(nodeIndexInfo.nodeIndexInGroup) = nodeIndexInfo.featureSubset.get
-          }
-        }
-        Some(mutableNodeToFeatures.toMap)
-      }
-    }
-
-    // array of nodes to train indexed by node index in group
-    val nodes = new Array[LearningNode](numNodes)
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        nodes(treeToNodeToIndexInfo(treeIndex)(node.id).nodeIndexInGroup) = node
-      }
-    }
-
-    // Calculate best splits for all nodes in the group
-    timer.start("chooseSplits")
-
-    // In each partition, iterate all instances and compute aggregate stats for each node,
-    // yield a (nodeIndex, nodeAggregateStats) pair for each node.
-    // After a `reduceByKey` operation,
-    // stats of a node will be shuffled to a particular partition and be combined together,
-    // then best splits for nodes are found there.
-    // Finally, only best Splits for nodes are collected to driver to construct decision tree.
-    val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
-    val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
-
-    val partitionAggregates: RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
-      input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions { points =>
-        // Construct a nodeStatsAggregators array to hold node aggregate stats,
-        // each node will have a nodeStatsAggregator
-        val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
-          val featuresForNode = nodeToFeaturesBc.value.map { nodeToFeatures =>
-            nodeToFeatures(nodeIndex)
-          }
-          new DTStatsAggregator(metadata, featuresForNode)
-        }
-
-        // iterator all instances in current partition and update aggregate stats
-        points.foreach(binSeqOpWithNodeIdCache(nodeStatsAggregators, _))
-
-        // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
-        // which can be combined with other partition using `reduceByKey`
-        nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
-      }
-    } else {
-      input.mapPartitions { points =>
-        // Construct a nodeStatsAggregators array to hold node aggregate stats,
-        // each node will have a nodeStatsAggregator
-        val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
-          val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
-            Some(nodeToFeatures(nodeIndex))
-          }
-          new DTStatsAggregator(metadata, featuresForNode)
-        }
-
-        // iterator all instances in current partition and update aggregate stats
-        points.foreach(binSeqOp(nodeStatsAggregators, _))
-
-        // transform nodeStatsAggregators array to (nodeIndex, nodeAggregateStats) pairs,
-        // which can be combined with other partition using `reduceByKey`
-        nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
-      }
-    }
-
-    val nodeToBestSplits = partitionAggregates.reduceByKey((a, b) => a.merge(b)).map {
-      case (nodeIndex, aggStats) =>
-        val featuresForNode = nodeToFeaturesBc.value.flatMap { nodeToFeatures =>
-          Some(nodeToFeatures(nodeIndex))
-        }
-
-        // find best split for each node
-        val (split: Split, stats: ImpurityStats) =
-          binsToBestSplit(aggStats, splits, featuresForNode, nodes(nodeIndex))
-        (nodeIndex, (split, stats))
-    }.collectAsMap()
-
-    timer.stop("chooseSplits")
-
-    val nodeIdUpdaters = if (nodeIdCache.nonEmpty) {
-      Array.fill[mutable.Map[Int, NodeIndexUpdaterRaw]](
-        metadata.numTrees)(mutable.Map[Int, NodeIndexUpdaterRaw]())
-    } else {
-      null
-    }
-    // Iterate over all nodes in this group.
-    nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
-      nodesForTree.foreach { node =>
-        val nodeIndex = node.id
-        val nodeInfo = treeToNodeToIndexInfo(treeIndex)(nodeIndex)
-        val aggNodeIndex = nodeInfo.nodeIndexInGroup
-        val (split: Split, stats: ImpurityStats) =
-          nodeToBestSplits(aggNodeIndex)
-        logDebug(s"best split = ${split}")
-
-        // Extract info for this node.  Create children if not leaf.
-        val isLeaf =
-          (stats.gain <= 0) || (LearningNode.indexToLevel(nodeIndex) == metadata.maxDepth)
-        node.isLeaf = isLeaf
-        node.stats = stats
-        logDebug(s"Node = ${node}")
-
-        if (!isLeaf) {
-          node.split = Some(split)
-          val childIsLeaf = (LearningNode.indexToLevel(nodeIndex) + 1) == metadata.maxDepth
-          val leftChildIsLeaf = childIsLeaf || (stats.leftImpurity == 0.0)
-          val rightChildIsLeaf = childIsLeaf || (stats.rightImpurity == 0.0)
-          node.leftChild = Some(LearningNode(LearningNode.leftChildIndex(nodeIndex),
-            leftChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.leftImpurityCalculator)))
-          node.rightChild = Some(LearningNode(LearningNode.rightChildIndex(nodeIndex),
-            rightChildIsLeaf, ImpurityStats.getEmptyImpurityStats(stats.rightImpurityCalculator)))
-
-          if (nodeIdCache.nonEmpty) {
-            val nodeIndexUpdater = NodeIndexUpdaterRaw(
-              split = split,
-              nodeIndex = nodeIndex)
-            nodeIdUpdaters(treeIndex).put(nodeIndex, nodeIndexUpdater)
-          }
-
-          // enqueue left child and right child if they are not leaves
-          if (!leftChildIsLeaf) {
-            nodeStack.push((treeIndex, node.leftChild.get))
-          }
-          if (!rightChildIsLeaf) {
-            nodeStack.push((treeIndex, node.rightChild.get))
-          }
-
-          logDebug(s"leftChildIndex = ${node.leftChild.get.id}" +
-            s", impurity = ${stats.leftImpurity}")
-          logDebug(s"rightChildIndex = ${node.rightChild.get.id}" +
-            s", impurity = ${stats.rightImpurity}")
-        }
-      }
-    }
-
-    if (nodeIdCache.nonEmpty) {
-      // Update the cache if needed.
-      nodeIdCache.get.updateNodeIndicesRaw(input, nodeIdUpdaters, splits)
-    }
-  }
-
-  /**
-   * Calculate the impurity statistics for a given (feature, split) based upon left/right
-   * aggregates.
-   *
-   * @param stats the recycle impurity statistics for this feature's all splits,
-   *              only 'impurity' and 'impurityCalculator' are valid between each iteration
-   * @param leftImpurityCalculator left node aggregates for this (feature, split)
-   * @param rightImpurityCalculator right node aggregate for this (feature, split)
-   * @param metadata learning and dataset metadata for DecisionTree
-   * @return Impurity statistics for this (feature, split)
-   */
-  private def calculateImpurityStats(
-      stats: ImpurityStats,
-      leftImpurityCalculator: ImpurityCalculator,
-      rightImpurityCalculator: ImpurityCalculator,
-      metadata: DecisionTreeMetadata): ImpurityStats = {
-
-    val parentImpurityCalculator: ImpurityCalculator = if (stats == null) {
-      leftImpurityCalculator.copy.add(rightImpurityCalculator)
-    } else {
-      stats.impurityCalculator
-    }
-
-    val impurity: Double = if (stats == null) {
-      parentImpurityCalculator.calculate()
-    } else {
-      stats.impurity
-    }
-
-    val leftCount = leftImpurityCalculator.count
-    val rightCount = rightImpurityCalculator.count
-
-    val totalCount = leftCount + rightCount
-
-    // If left child or right child doesn't satisfy minimum instances per node,
-    // then this split is invalid, return invalid information gain stats.
-    if ((leftCount < metadata.minInstancesPerNode) ||
-      (rightCount < metadata.minInstancesPerNode)) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
-    val rightImpurity = rightImpurityCalculator.calculate()
-
-    val leftWeight = leftCount / totalCount.toDouble
-    val rightWeight = rightCount / totalCount.toDouble
-
-    val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
-
-    // if information gain doesn't satisfy minimum information gain,
-    // then this split is invalid, return invalid information gain stats.
-    if (gain < metadata.minInfoGain) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    new ImpurityStats(gain, impurity, parentImpurityCalculator,
-      leftImpurityCalculator, rightImpurityCalculator)
-  }
-
-  /**
-   * Find the best split for a node.
-   *
-   * @param binAggregates Bin statistics.
-   * @return tuple for best split: (Split, information gain, prediction at node)
-   */
-  private[tree] def binsToBestSplit(
-      binAggregates: DTStatsAggregator,
-      splits: Array[Array[Split]],
-      featuresForNode: Option[Array[Int]],
-      node: LearningNode): (Split, ImpurityStats) = {
-
-    // Calculate InformationGain and ImpurityStats if current node is top node
-    val level = LearningNode.indexToLevel(node.id)
-    var gainAndImpurityStats: ImpurityStats = if (level == 0) {
-      null
-    } else {
-      node.stats
-    }
-
-    val validFeatureSplits =
-      Range(0, binAggregates.metadata.numFeaturesPerNode).view.map { featureIndexIdx =>
-        featuresForNode.map(features => (featureIndexIdx, features(featureIndexIdx)))
-          .getOrElse((featureIndexIdx, featureIndexIdx))
-      }.withFilter { case (_, featureIndex) =>
-        binAggregates.metadata.numSplits(featureIndex) != 0
-      }
-
-    // For each (feature, split), calculate the gain, and select the best (feature, split).
-    val splitsAndImpurityInfo =
-      validFeatureSplits.map { case (featureIndexIdx, featureIndex) =>
-        val numSplits = binAggregates.metadata.numSplits(featureIndex)
-        if (binAggregates.metadata.isContinuous(featureIndex)) {
-          // Cumulative sum (scanLeft) of bin statistics.
-          // Afterwards, binAggregates for a bin is the sum of aggregates for
-          // that bin + all preceding bins.
-          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          var splitIndex = 0
-          while (splitIndex < numSplits) {
-            binAggregates.mergeForFeature(nodeFeatureOffset, splitIndex + 1, splitIndex)
-            splitIndex += 1
-          }
-          // Find best split.
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { case splitIdx =>
-              val leftChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, splitIdx)
-              val rightChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
-              rightChildStats.subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIdx, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
-        } else if (binAggregates.metadata.isUnordered(featureIndex)) {
-          // Unordered categorical feature
-          val leftChildOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { splitIndex =>
-              val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
-              val rightChildStats = binAggregates.getParentImpurityCalculator()
-                .subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIndex, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
-        } else {
-          // Ordered categorical feature
-          val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-          val numCategories = binAggregates.metadata.numBins(featureIndex)
-
-          /* Each bin is one category (feature value).
-           * The bins are ordered based on centroidForCategories, and this ordering determines which
-           * splits are considered.  (With K categories, we consider K - 1 possible splits.)
-           *
-           * centroidForCategories is a list: (category, centroid)
-           */
-          val centroidForCategories = Range(0, numCategories).map { case featureValue =>
-            val categoryStats =
-              binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
-            val centroid = if (categoryStats.count != 0) {
-              if (binAggregates.metadata.isMulticlass) {
-                // multiclass classification
-                // For categorical variables in multiclass classification,
-                // the bins are ordered by the impurity of their corresponding labels.
-                categoryStats.calculate()
-              } else if (binAggregates.metadata.isClassification) {
-                // binary classification
-                // For categorical variables in binary classification,
-                // the bins are ordered by the count of class 1.
-                categoryStats.stats(1)
-              } else {
-                // regression
-                // For categorical variables in regression and binary classification,
-                // the bins are ordered by the prediction.
-                categoryStats.predict
-              }
-            } else {
-              Double.MaxValue
-            }
-            (featureValue, centroid)
-          }
-
-          logDebug(s"Centroids for categorical variable: ${ centroidForCategories.mkString(",")}")
-
-          // bins sorted by centroids
-          val categoriesSortedByCentroid = centroidForCategories.toList.sortBy(_._2)
-
-          logDebug("Sorted centroids for categorical variable = " +
-            categoriesSortedByCentroid.mkString(","))
-
-          // Cumulative sum (scanLeft) of bin statistics.
-          // Afterwards, binAggregates for a bin is the sum of aggregates for
-          // that bin + all preceding bins.
-          var splitIndex = 0
-          while (splitIndex < numSplits) {
-            val currentCategory = categoriesSortedByCentroid(splitIndex)._1
-            val nextCategory = categoriesSortedByCentroid(splitIndex + 1)._1
-            binAggregates.mergeForFeature(nodeFeatureOffset, nextCategory, currentCategory)
-            splitIndex += 1
-          }
-          // lastCategory = index of bin with total aggregates for this (node, feature)
-          val lastCategory = categoriesSortedByCentroid.last._1
-          // Find best split.
-          val (bestFeatureSplitIndex, bestFeatureGainStats) =
-            Range(0, numSplits).map { splitIndex =>
-              val featureValue = categoriesSortedByCentroid(splitIndex)._1
-              val leftChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
-              val rightChildStats =
-                binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
-              rightChildStats.subtract(leftChildStats)
-              gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-                leftChildStats, rightChildStats, binAggregates.metadata)
-              (splitIndex, gainAndImpurityStats)
-            }.maxBy(_._2.gain)
-          val categoriesForSplit =
-            categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
-          val bestFeatureSplit =
-            new CategoricalSplit(featureIndex, categoriesForSplit.toArray, numCategories)
-          (bestFeatureSplit, bestFeatureGainStats)
-        }
-      }
-
-    val (bestSplit, bestSplitStats) =
-      if (splitsAndImpurityInfo.isEmpty) {
-        // If no valid splits for features, then this split is invalid,
-        // return invalid information gain stats.  Take any split and continue.
-        // Splits is empty, so arbitrarily choose to split on any threshold
-        val dummyFeatureIndex = featuresForNode.map(_.head).getOrElse(0)
-        val parentImpurityCalculator = binAggregates.getParentImpurityCalculator()
-        if (binAggregates.metadata.isContinuous(dummyFeatureIndex)) {
-          (new ContinuousSplit(dummyFeatureIndex, 0),
-            ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-        } else {
-          val numCategories = binAggregates.metadata.featureArity(dummyFeatureIndex)
-          (new CategoricalSplit(dummyFeatureIndex, Array(), numCategories),
-            ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-        }
-      } else {
-        splitsAndImpurityInfo.maxBy(_._2.gain)
-      }
-    (bestSplit, bestSplitStats)
-  }
-
-  /**
-   * Returns splits for decision tree calculation.
-   * Continuous and categorical features are handled differently.
-   *
-   * Continuous features:
-   *   For each feature, there are numBins - 1 possible splits representing the possible binary
-   *   decisions at each node in the tree.
-   *   This finds locations (feature values) for splits using a subsample of the data.
-   *
-   * Categorical features:
-   *   For each feature, there is 1 bin per split.
-   *   Splits and bins are handled in 2 ways:
-   *   (a) "unordered features"
-   *       For multiclass classification with a low-arity feature
-   *       (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits),
-   *       the feature is split based on subsets of categories.
-   *   (b) "ordered features"
-   *       For regression and binary classification,
-   *       and for multiclass classification with a high-arity feature,
-   *       there is one bin per category.
-   *
-   * @param input Training data: RDD of [[LabeledPoint]]
-   * @param metadata Learning and dataset metadata
-   * @param seed random seed
-   * @return Splits, an Array of [[Split]]
-   *          of size (numFeatures, numSplits)
-   */
-  protected[tree] def findSplits(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      seed: Long): Array[Array[Split]] = {
-
-    logDebug(s"isMulticlass = ${metadata.isMulticlass}")
-
-    val numFeatures = metadata.numFeatures
-
-    // Sample the input only if there are continuous features.
-    val continuousFeatures = Range(0, numFeatures).filter(metadata.isContinuous)
-    val sampledInput = if (continuousFeatures.nonEmpty) {
-      // Calculate the number of samples for approximate quantile calculation.
-      val requiredSamples = math.max(metadata.maxBins * metadata.maxBins, 10000)
-      val fraction = if (requiredSamples < metadata.numExamples) {
-        requiredSamples.toDouble / metadata.numExamples
-      } else {
-        1.0
-      }
-      logDebug(s"fraction of data used for calculating quantiles = ${fraction}")
-      input.sample(withReplacement = false, fraction, new XORShiftRandom(seed).nextInt())
-    } else {
-      input.sparkContext.emptyRDD[LabeledPoint]
-    }
-
-    findSplitsBySorting(sampledInput, metadata, continuousFeatures)
-  }
-
-  private def findSplitsBySorting(
-      input: RDD[LabeledPoint],
-      metadata: DecisionTreeMetadata,
-      continuousFeatures: IndexedSeq[Int]): Array[Array[Split]] = {
-
-    val continuousSplits: scala.collection.Map[Int, Array[Split]] = {
-      // reduce the parallelism for split computations when there are less
-      // continuous features than input partitions. this prevents tasks from
-      // being spun up that will definitely do no work.
-      val numPartitions = math.min(continuousFeatures.length, input.partitions.length)
-
-      input
-        .flatMap(point => continuousFeatures.map(idx => (idx, point.features(idx))))
-        .groupByKey(numPartitions)
-        .map { case (idx, samples) =>
-          val thresholds = findSplitsForContinuousFeature(samples, metadata, idx)
-          val splits: Array[Split] = thresholds.map(thresh => new ContinuousSplit(idx, thresh))
-          logDebug(s"featureIndex = $idx, numSplits = ${splits.length}")
-          (idx, splits)
-        }.collectAsMap()
-    }
-
-    val numFeatures = metadata.numFeatures
-    val splits: Array[Array[Split]] = Array.tabulate(numFeatures) {
-      case i if metadata.isContinuous(i) =>
-        val split = continuousSplits(i)
-        metadata.setNumSplits(i, split.length)
-        split
-
-      case i if metadata.isCategorical(i) && metadata.isUnordered(i) =>
-        // Unordered features
-        // 2^(maxFeatureValue - 1) - 1 combinations
-        val featureArity = metadata.featureArity(i)
-        Array.tabulate[Split](metadata.numSplits(i)) { splitIndex =>
-          val categories = extractMultiClassCategories(splitIndex + 1, featureArity)
-          new CategoricalSplit(i, categories.toArray, featureArity)
-        }
-
-      case i if metadata.isCategorical(i) =>
-        // Ordered features
-        //   Splits are constructed as needed during training.
-        Array.empty[Split]
-    }
-    splits
-  }
-
-  /**
-   * Nested method to extract list of eligible categories given an index. It extracts the
-   * position of ones in a binary representation of the input. If binary
-   * representation of an number is 01101 (13), the output list should (3.0, 2.0,
-   * 0.0). The maxFeatureValue depict the number of rightmost digits that will be tested for ones.
-   */
-  private[tree] def extractMultiClassCategories(
-      input: Int,
-      maxFeatureValue: Int): List[Double] = {
-    var categories = List[Double]()
-    var j = 0
-    var bitShiftedInput = input
-    while (j < maxFeatureValue) {
-      if (bitShiftedInput % 2 != 0) {
-        // updating the list of categories.
-        categories = j.toDouble :: categories
-      }
-      // Right shift by one
-      bitShiftedInput = bitShiftedInput >> 1
-      j += 1
-    }
-    categories
-  }
-
-  /**
-   * Find splits for a continuous feature
-   * NOTE: Returned number of splits is set based on `featureSamples` and
-   *       could be different from the specified `numSplits`.
-   *       The `numSplits` attribute in the `DecisionTreeMetadata` class will be set accordingly.
-   *
-   * @param featureSamples feature values of each sample
-   * @param metadata decision tree metadata
-   *                 NOTE: `metadata.numbins` will be changed accordingly
-   *                       if there are not enough splits to be found
-   * @param featureIndex feature index to find splits
-   * @return array of split thresholds
-   */
-  private[tree] def findSplitsForContinuousFeature(
-      featureSamples: Iterable[Double],
-      metadata: DecisionTreeMetadata,
-      featureIndex: Int): Array[Double] = {
-    require(metadata.isContinuous(featureIndex),
-      "findSplitsForContinuousFeature can only be used to find splits for a continuous feature.")
-
-    val splits: Array[Double] = if (featureSamples.isEmpty) {
-      Array.empty[Double]
-    } else {
-      val numSplits = metadata.numSplits(featureIndex)
-
-      // get count for each distinct value
-      val (valueCountMap, numSamples) = featureSamples.foldLeft((Map.empty[Double, Int], 0)) {
-        case ((m, cnt), x) =>
-          (m + ((x, m.getOrElse(x, 0) + 1)), cnt + 1)
-      }
-      // sort distinct values
-      val valueCounts = valueCountMap.toSeq.sortBy(_._1).toArray
-
-      val possibleSplits = valueCounts.length - 1
-      if (possibleSplits == 0) {
-        // constant feature
-        Array.empty[Double]
-      } else if (possibleSplits <= numSplits) {
-        // if possible splits is not enough or just enough, just return all possible splits
-        (1 to possibleSplits)
-          .map(index => (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0)
-          .toArray
-      } else {
-        // stride between splits
-        val stride: Double = numSamples.toDouble / (numSplits + 1)
-        logDebug(s"stride = ${stride}")
-
-        // iterate `valueCount` to find splits
-        val splitsBuilder = mutable.ArrayBuilder.make[Double]
-        var index = 1
-        // currentCount: sum of counts of values that have been visited
-        var currentCount = valueCounts(0)._2
-        // targetCount: target value for `currentCount`.
-        // If `currentCount` is closest value to `targetCount`,
-        // then current value is a split threshold.
-        // After finding a split threshold, `targetCount` is added by stride.
-        var targetCount = stride
-        while (index < valueCounts.length) {
-          val previousCount = currentCount
-          currentCount += valueCounts(index)._2
-          val previousGap = math.abs(previousCount - targetCount)
-          val currentGap = math.abs(currentCount - targetCount)
-          // If adding count of current value to currentCount
-          // makes the gap between currentCount and targetCount smaller,
-          // previous value is a split threshold.
-          if (previousGap < currentGap) {
-            splitsBuilder += (valueCounts(index - 1)._1 + valueCounts(index)._1) / 2.0
-            targetCount += stride
-          }
-          index += 1
-        }
-
-        splitsBuilder.result()
-      }
-    }
-    splits
-  }
-
-  private[tree] class NodeIndexInfo(
-      val nodeIndexInGroup: Int,
-      val featureSubset: Option[Array[Int]]) extends Serializable
-
-  /**
-   * Pull nodes off of the queue, and collect a group of nodes to be split on this iteration.
-   * This tracks the memory usage for aggregates and stops adding nodes when too much memory
-   * will be needed; this allows an adaptive number of nodes since different nodes may require
-   * different amounts of memory (if featureSubsetStrategy is not "all").
-   *
-   * @param nodeStack  Queue of nodes to split.
-   * @param maxMemoryUsage  Bound on size of aggregate statistics.
-   * @return  (nodesForGroup, treeToNodeToIndexInfo).
-   *          nodesForGroup holds the nodes to split: treeIndex --> nodes in tree.
-   *
-   *          treeToNodeToIndexInfo holds indices selected features for each node:
-   *            treeIndex --> (global) node index --> (node index in group, feature indices).
-   *          The (global) node index is the index in the tree; the node index in group is the
-   *           index in [0, numNodesInGroup) of the node in this group.
-   *          The feature indices are None if not subsampling features.
-   */
-  private[tree] def selectNodesToSplit(
-      nodeStack: mutable.ArrayStack[(Int, LearningNode)],
-      maxMemoryUsage: Long,
-      metadata: DecisionTreeMetadata,
-      rng: Random): (Map[Int, Array[LearningNode]], Map[Int, Map[Int, NodeIndexInfo]]) = {
-    // Collect some nodes to split:
-    //  nodesForGroup(treeIndex) = nodes to split
-    val mutableNodesForGroup = new mutable.HashMap[Int, mutable.ArrayBuffer[LearningNode]]()
-    val mutableTreeToNodeToIndexInfo =
-      new mutable.HashMap[Int, mutable.HashMap[Int, NodeIndexInfo]]()
-    var memUsage: Long = 0L
-    var numNodesInGroup = 0
-    // If maxMemoryInMB is set very small, we want to still try to split 1 node,
-    // so we allow one iteration if memUsage == 0.
-    var groupDone = false
-    while (nodeStack.nonEmpty && !groupDone) {
-      val (treeIndex, node) = nodeStack.top
-      // Choose subset of features for node (if subsampling).
-      val featureSubset: Option[Array[Int]] = if (metadata.subsamplingFeatures) {
-        Some(SamplingUtils.reservoirSampleAndCount(Range(0,
-          metadata.numFeatures).iterator, metadata.numFeaturesPerNode, rng.nextLong())._1)
-      } else {
-        None
-      }
-      // Check if enough memory remains to add this node to the group.
-      val nodeMemUsage = RandomForestRaw.aggregateSizeForNode(metadata, featureSubset) * 8L
-      if (memUsage + nodeMemUsage <= maxMemoryUsage || memUsage == 0) {
-        nodeStack.pop()
-        mutableNodesForGroup.getOrElseUpdate(treeIndex, new mutable.ArrayBuffer[LearningNode]()) +=
-          node
-        mutableTreeToNodeToIndexInfo
-          .getOrElseUpdate(treeIndex, new mutable.HashMap[Int, NodeIndexInfo]())(node.id)
-          = new NodeIndexInfo(numNodesInGroup, featureSubset)
-        numNodesInGroup += 1
-        memUsage += nodeMemUsage
-      } else {
-        groupDone = true
-      }
-    }
-    if (memUsage > maxMemoryUsage) {
-      // If maxMemoryUsage is 0, we should still allow splitting 1 node.
-      logWarning(s"Tree learning is using approximately $memUsage bytes per iteration, which" +
-        s" exceeds requested limit maxMemoryUsage=$maxMemoryUsage. This allows splitting" +
-        s" $numNodesInGroup nodes in this iteration.")
-    }
-    // Convert mutable maps to immutable ones.
-    val nodesForGroup: Map[Int, Array[LearningNode]] =
-      mutableNodesForGroup.mapValues(_.toArray).toMap
-    val treeToNodeToIndexInfo = mutableTreeToNodeToIndexInfo.mapValues(_.toMap).toMap
-    (nodesForGroup, treeToNodeToIndexInfo)
-  }
-
-  /**
-   * Get the number of values to be stored for this node in the bin aggregates.
-   *
-   * @param featureSubset  Indices of features which may be split at this node.
-   *                       If None, then use all features.
-   */
-  private def aggregateSizeForNode(
-      metadata: DecisionTreeMetadata,
-      featureSubset: Option[Array[Int]]): Long = {
-    val totalBins = if (featureSubset.nonEmpty) {
-      featureSubset.get.map(featureIndex => metadata.numBins(featureIndex).toLong).sum
-    } else {
-      metadata.numBins.map(_.toLong).sum
-    }
-    if (metadata.isClassification) {
-      metadata.numClasses * totalBins
-    } else {
-      3 * totalBins
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
deleted file mode 100644
index 38b79ec..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ /dev/null
@@ -1,611 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree
-
-import java.util.Locale
-
-import scala.util.Try
-
-import org.apache.spark.ml.PredictorParams
-import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
-import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, ClassificationLoss => OldClassificationLoss, LogLoss => OldLogLoss, Loss => OldLoss, SquaredError => OldSquaredError}
-import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
-
-/**
- * Parameters for Decision Tree-based algorithms.
- *
- * Note: Marked as private and DeveloperApi since this may be made public in the future.
- */
-private[ml] trait DecisionTreeParams extends PredictorParams
-  with HasCheckpointInterval with HasSeed {
-
-  /**
-   * Maximum depth of the tree (>= 0).
-   * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   * (default = 5)
-   * @group param
-   */
-  final val maxDepth: IntParam =
-    new IntParam(this, "maxDepth", "Maximum depth of the tree. (>= 0)" +
-      " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.",
-      ParamValidators.gtEq(0))
-
-  /**
-   * Maximum number of bins used for discretizing continuous features and for choosing how to split
-   * on features at each node.  More bins give higher granularity.
-   * Must be >= 2 and >= number of categories in any categorical feature.
-   * (default = 32)
-   * @group param
-   */
-  final val maxBins: IntParam = new IntParam(this, "maxBins", "Max number of bins for" +
-    " discretizing continuous features.  Must be >=2 and >= number of categories for any" +
-    " categorical feature.", ParamValidators.gtEq(2))
-
-  /**
-   * Minimum number of instances each child must have after split.
-   * If a split causes the left or right child to have fewer than minInstancesPerNode,
-   * the split will be discarded as invalid.
-   * Should be >= 1.
-   * (default = 1)
-   * @group param
-   */
-  final val minInstancesPerNode: IntParam = new IntParam(this, "minInstancesPerNode", "Minimum" +
-    " number of instances each child must have after split.  If a split causes the left or right" +
-    " child to have fewer than minInstancesPerNode, the split will be discarded as invalid." +
-    " Should be >= 1.", ParamValidators.gtEq(1))
-
-  /**
-   * Minimum information gain for a split to be considered at a tree node.
-   * Should be >= 0.0.
-   * (default = 0.0)
-   * @group param
-   */
-  final val minInfoGain: DoubleParam = new DoubleParam(this, "minInfoGain",
-    "Minimum information gain for a split to be considered at a tree node.",
-    ParamValidators.gtEq(0.0))
-
-  /**
-   * Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be
-   * split per iteration, and its aggregates may exceed this size.
-   * (default = 256 MB)
-   * @group expertParam
-   */
-  final val maxMemoryInMB: IntParam = new IntParam(this, "maxMemoryInMB",
-    "Maximum memory in MB allocated to histogram aggregation.",
-    ParamValidators.gtEq(0))
-
-  /**
-   * If false, the algorithm will pass trees to executors to match instances with nodes.
-   * If true, the algorithm will cache node IDs for each instance.
-   * Caching can speed up training of deeper trees. Users can set how often should the
-   * cache be checkpointed or disable it by setting checkpointInterval.
-   * (default = false)
-   * @group expertParam
-   */
-  final val cacheNodeIds: BooleanParam = new BooleanParam(this, "cacheNodeIds", "If false, the" +
-    " algorithm will pass trees to executors to match instances with nodes. If true, the" +
-    " algorithm will cache node IDs for each instance. Caching can speed up training of deeper" +
-    " trees.")
-
-  setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0,
-    maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  /** @group getParam */
-  final def getMaxDepth: Int = $(maxDepth)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  /** @group getParam */
-  final def getMaxBins: Int = $(maxBins)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
-
-  /** @group getParam */
-  final def getMinInstancesPerNode: Int = $(minInstancesPerNode)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
-
-  /** @group getParam */
-  final def getMinInfoGain: Double = $(minInfoGain)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setSeed(value: Long): this.type = set(seed, value)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group expertSetParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
-
-  /** @group expertGetParam */
-  final def getMaxMemoryInMB: Int = $(maxMemoryInMB)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group expertSetParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
-
-  /** @group expertGetParam */
-  final def getCacheNodeIds: Boolean = $(cacheNodeIds)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  /** (private[ml]) Create a Strategy instance to use with the old API. */
-  private[ml] def getOldStrategy(
-      categoricalFeatures: Map[Int, Int],
-      numClasses: Int,
-      oldAlgo: OldAlgo.Algo,
-      oldImpurity: OldImpurity,
-      subsamplingRate: Double): OldStrategy = {
-    val strategy = OldStrategy.defaultStrategy(oldAlgo)
-    strategy.impurity = oldImpurity
-    strategy.checkpointInterval = getCheckpointInterval
-    strategy.maxBins = getMaxBins
-    strategy.maxDepth = getMaxDepth
-    strategy.maxMemoryInMB = getMaxMemoryInMB
-    strategy.minInfoGain = getMinInfoGain
-    strategy.minInstancesPerNode = getMinInstancesPerNode
-    strategy.useNodeIdCache = getCacheNodeIds
-    strategy.numClasses = numClasses
-    strategy.categoricalFeaturesInfo = categoricalFeatures
-    strategy.subsamplingRate = subsamplingRate
-    strategy
-  }
-}
-
-/**
- * Parameters for Decision Tree-based classification algorithms.
- */
-private[ml] trait TreeClassifierParams extends Params {
-
-  /**
-   * Criterion used for information gain calculation (case-insensitive).
-   * Supported: "entropy" and "gini".
-   * (default = gini)
-   * @group param
-   */
-  final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" +
-    " information gain calculation (case-insensitive). Supported options:" +
-    s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}",
-    (value: String) =>
-      TreeClassifierParams.supportedImpurities.contains(value.toLowerCase(Locale.ROOT)))
-
-  setDefault(impurity -> "gini")
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setImpurity(value: String): this.type = set(impurity, value)
-
-  /** @group getParam */
-  final def getImpurity: String = $(impurity).toLowerCase(Locale.ROOT)
-
-  /** Convert new impurity to old impurity. */
-  private[ml] def getOldImpurity: OldImpurity = {
-    getImpurity match {
-      case "entropy" => OldEntropy
-      case "gini" => OldGini
-      case _ =>
-        // Should never happen because of check in setter method.
-        throw new RuntimeException(
-          s"TreeClassifierParams was given unrecognized impurity: $impurity.")
-    }
-  }
-}
-
-private[ml] object TreeClassifierParams {
-  // These options should be lowercase.
-  final val supportedImpurities: Array[String] =
-    Array("entropy", "gini").map(_.toLowerCase(Locale.ROOT))
-}
-
-private[ml] trait DecisionTreeClassifierParams
-  extends DecisionTreeParams with TreeClassifierParams
-
-/**
- * Parameters for Decision Tree-based regression algorithms.
- */
-private[ml] trait TreeRegressorParams extends Params {
-
-  /**
-   * Criterion used for information gain calculation (case-insensitive).
-   * Supported: "variance".
-   * (default = variance)
-   * @group param
-   */
-  final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" +
-    " information gain calculation (case-insensitive). Supported options:" +
-    s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}",
-    (value: String) =>
-      TreeRegressorParams.supportedImpurities.contains(value.toLowerCase(Locale.ROOT)))
-
-  setDefault(impurity -> "variance")
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setImpurity(value: String): this.type = set(impurity, value)
-
-  /** @group getParam */
-  final def getImpurity: String = $(impurity).toLowerCase(Locale.ROOT)
-
-  /** Convert new impurity to old impurity. */
-  private[ml] def getOldImpurity: OldImpurity = {
-    getImpurity match {
-      case "variance" => OldVariance
-      case _ =>
-        // Should never happen because of check in setter method.
-        throw new RuntimeException(
-          s"TreeRegressorParams was given unrecognized impurity: $impurity")
-    }
-  }
-}
-
-private[ml] object TreeRegressorParams {
-  // These options should be lowercase.
-  final val supportedImpurities: Array[String] =
-    Array("variance").map(_.toLowerCase(Locale.ROOT))
-}
-
-private[ml] trait DecisionTreeRegressorParams extends DecisionTreeParams
-  with TreeRegressorParams with HasVarianceCol {
-
-  override protected def validateAndTransformSchema(
-      schema: StructType,
-      fitting: Boolean,
-      featuresDataType: DataType): StructType = {
-    val newSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType)
-    if (isDefined(varianceCol) && $(varianceCol).nonEmpty) {
-      SchemaUtils.appendColumn(newSchema, $(varianceCol), DoubleType)
-    } else {
-      newSchema
-    }
-  }
-}
-
-private[spark] object TreeEnsembleParams {
-  // These options should be lowercase.
-  final val supportedFeatureSubsetStrategies: Array[String] =
-    Array("auto", "all", "onethird", "sqrt", "log2").map(_.toLowerCase(Locale.ROOT))
-}
-
-/**
- * Parameters for Decision Tree-based ensemble algorithms.
- *
- * Note: Marked as private and DeveloperApi since this may be made public in the future.
- */
-private[ml] trait TreeEnsembleParams extends DecisionTreeParams {
-
-  /**
-   * Fraction of the training data used for learning each decision tree, in range (0, 1].
-   * (default = 1.0)
-   * @group param
-   */
-  final val subsamplingRate: DoubleParam = new DoubleParam(this, "subsamplingRate",
-    "Fraction of the training data used for learning each decision tree, in range (0, 1].",
-    ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true))
-
-  setDefault(subsamplingRate -> 1.0)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value)
-
-  /** @group getParam */
-  final def getSubsamplingRate: Double = $(subsamplingRate)
-
-  /**
-   * Create a Strategy instance to use with the old API.
-   * NOTE: The caller should set impurity and seed.
-   */
-  private[ml] def getOldStrategy(
-      categoricalFeatures: Map[Int, Int],
-      numClasses: Int,
-      oldAlgo: OldAlgo.Algo,
-      oldImpurity: OldImpurity): OldStrategy = {
-    super.getOldStrategy(categoricalFeatures, numClasses, oldAlgo, oldImpurity, getSubsamplingRate)
-  }
-
-  /**
-   * The number of features to consider for splits at each tree node.
-   * Supported options:
-   *  - "auto": Choose automatically for task:
-   *            If numTrees == 1, set to "all."
-   *            If numTrees > 1 (forest), set to "sqrt" for classification and
-   *              to "onethird" for regression.
-   *  - "all": use all features
-   *  - "onethird": use 1/3 of the features
-   *  - "sqrt": use sqrt(number of features)
-   *  - "log2": use log2(number of features)
-   *  - "n": when n is in the range (0, 1.0], use n * number of features. When n
-   *         is in the range (1, number of features), use n features.
-   * (default = "auto")
-   *
-   * These various settings are based on the following references:
-   *  - log2: tested in Breiman (2001)
-   *  - sqrt: recommended by Breiman manual for random forests
-   *  - The defaults of sqrt (classification) and onethird (regression) match the R randomForest
-   *    package.
-   * @see <a href="http://www.stat.berkeley.edu/~breiman/randomforest2001.pdf">Breiman (2001)</a>
-   * @see <a href="http://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf">
-   * Breiman manual for random forests</a>
-   *
-   * @group param
-   */
-  final val featureSubsetStrategy: Param[String] = new Param[String](this, "featureSubsetStrategy",
-    "The number of features to consider for splits at each tree node." +
-      s" Supported options: ${TreeEnsembleParams.supportedFeatureSubsetStrategies.mkString(", ")}" +
-      s", (0.0-1.0], [1-n].",
-    (value: String) =>
-      TreeEnsembleParams.supportedFeatureSubsetStrategies.contains(
-        value.toLowerCase(Locale.ROOT))
-      || Try(value.toInt).filter(_ > 0).isSuccess
-      || Try(value.toDouble).filter(_ > 0).filter(_ <= 1.0).isSuccess)
-
-  setDefault(featureSubsetStrategy -> "auto")
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setFeatureSubsetStrategy(value: String): this.type = set(featureSubsetStrategy, value)
-
-  /** @group getParam */
-  final def getFeatureSubsetStrategy: String = $(featureSubsetStrategy).toLowerCase(Locale.ROOT)
-}
-
-
-
-/**
- * Parameters for Random Forest algorithms.
- */
-private[ml] trait RandomForestParams extends TreeEnsembleParams {
-
-  /**
-   * Number of trees to train (>= 1).
-   * If 1, then no bootstrapping is used.  If > 1, then bootstrapping is done.
-   * TODO: Change to always do bootstrapping (simpler).  SPARK-7130
-   * (default = 20)
-   *
-   * Note: The reason that we cannot add this to both GBT and RF (i.e. in TreeEnsembleParams)
-   * is the param `maxIter` controls how many trees a GBT has. The semantics in the algorithms
-   * are a bit different.
-   * @group param
-   */
-  final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)",
-    ParamValidators.gtEq(1))
-
-  setDefault(numTrees -> 20)
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setNumTrees(value: Int): this.type = set(numTrees, value)
-
-  /** @group getParam */
-  final def getNumTrees: Int = $(numTrees)
-}
-
-private[ml] trait RandomForestClassifierParams
-  extends RandomForestParams with TreeClassifierParams
-
-private[ml] trait RandomForestRegressorParams
-  extends RandomForestParams with TreeRegressorParams
-
-/**
- * Parameters for Gradient-Boosted Tree algorithms.
- *
- * Note: Marked as private and DeveloperApi since this may be made public in the future.
- */
-private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter with HasStepSize {
-
-  /* TODO: Add this doc when we add this param.  SPARK-7132
-   * Threshold for stopping early when runWithValidation is used.
-   * If the error rate on the validation input changes by less than the validationTol,
-   * then learning will stop early (before [[numIterations]]).
-   * This parameter is ignored when run is used.
-   * (default = 1e-5)
-   * @group param
-   */
-  // final val validationTol: DoubleParam = new DoubleParam(this, "validationTol", "")
-  // validationTol -> 1e-5
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setMaxIter(value: Int): this.type = set(maxIter, value)
-
-  /**
-   * Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking
-   * the contribution of each estimator.
-   * (default = 0.1)
-   * @group param
-   */
-  final override val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size " +
-    "(a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator.",
-    ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true))
-
-  /**
-   * @deprecated This method is deprecated and will be removed in 3.0.0.
-   * @group setParam
-   */
-  @deprecated("This method is deprecated and will be removed in 3.0.0.", "2.1.0")
-  def setStepSize(value: Double): this.type = set(stepSize, value)
-
-  setDefault(maxIter -> 20, stepSize -> 0.1)
-
-  setDefault(featureSubsetStrategy -> "all")
-
-  /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */
-  private[ml] def getOldBoostingStrategy(
-      categoricalFeatures: Map[Int, Int],
-      oldAlgo: OldAlgo.Algo): OldBoostingStrategy = {
-    val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 2, oldAlgo, OldVariance)
-    // NOTE: The old API does not support "seed" so we ignore it.
-    new OldBoostingStrategy(strategy, getOldLossType, getMaxIter, getStepSize)
-  }
-
-  /** Get old Gradient Boosting Loss type */
-  private[ml] def getOldLossType: OldLoss
-
-  final val doUseAcc: BooleanParam = new BooleanParam(this, "doUseAcc",
-    "If true, use the optimized algorithm; otherwise, use the raw version")
-
-  var setUseAccFlag = false
-
-  /** Set algorithm to the raw version. */
-  def setDoUseAcc(value: Boolean): this.type = {
-    setUseAccFlag = true
-    set(doUseAcc, value)
-  }
-  setDefault(doUseAcc -> true)
-
-  /** Get algorithm type. */
-  def getDoUseAcc: (Boolean, Boolean) = ($(doUseAcc), setUseAccFlag)
-}
-
-private[ml] object GBTClassifierParams {
-  // The losses below should be lowercase.
-  /** Accessor for supported loss settings: logistic */
-  final val supportedLossTypes: Array[String] =
-    Array("logistic").map(_.toLowerCase(Locale.ROOT))
-}
-
-private[ml] trait GBTClassifierParams extends GBTParams with TreeClassifierParams {
-
-  /**
-   * Loss function which GBT tries to minimize. (case-insensitive)
-   * Supported: "logistic"
-   * (default = logistic)
-   * @group param
-   */
-  val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" +
-    " tries to minimize (case-insensitive). Supported options:" +
-    s" ${GBTClassifierParams.supportedLossTypes.mkString(", ")}",
-    (value: String) =>
-      GBTClassifierParams.supportedLossTypes.contains(value.toLowerCase(Locale.ROOT)))
-
-  setDefault(lossType -> "logistic")
-
-  /** @group getParam */
-  def getLossType: String = $(lossType).toLowerCase(Locale.ROOT)
-
-  /** (private[ml]) Convert new loss to old loss. */
-  override private[ml] def getOldLossType: OldClassificationLoss = {
-    getLossType match {
-      case "logistic" => OldLogLoss
-      case _ =>
-        // Should never happen because of check in setter method.
-        throw new RuntimeException(s"GBTClassifier was given bad loss type: $getLossType")
-    }
-  }
-}
-
-private[ml] object GBTRegressorParams {
-  // The losses below should be lowercase.
-  /** Accessor for supported loss settings: squared (L2), absolute (L1) */
-  final val supportedLossTypes: Array[String] =
-    Array("squared", "absolute").map(_.toLowerCase(Locale.ROOT))
-}
-
-private[ml] trait GBTRegressorParams extends GBTParams with TreeRegressorParams {
-
-  /**
-   * Loss function which GBT tries to minimize. (case-insensitive)
-   * Supported: "squared" (L2) and "absolute" (L1)
-   * (default = squared)
-   * @group param
-   */
-  val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" +
-    " tries to minimize (case-insensitive). Supported options:" +
-    s" ${GBTRegressorParams.supportedLossTypes.mkString(", ")}",
-    (value: String) =>
-      GBTRegressorParams.supportedLossTypes.contains(value.toLowerCase(Locale.ROOT)))
-
-  setDefault(lossType -> "squared")
-
-  /** @group getParam */
-  def getLossType: String = $(lossType).toLowerCase(Locale.ROOT)
-
-  /** (private[ml]) Convert new loss to old loss. */
-  override private[ml] def getOldLossType: OldLoss = {
-    getLossType match {
-      case "squared" => OldSquaredError
-      case "absolute" => OldAbsoluteError
-      case _ =>
-        // Should never happen because of check in setter method.
-        throw new RuntimeException(s"GBTRegressorParams was given bad loss type: $getLossType")
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/KMACCm.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/KMACCm.scala
deleted file mode 100644
index 2f9094c..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/KMACCm.scala
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.mllib.clustering
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.SparkSession
-
-object KMACCm {
-  val DEFAULT_SAMPLE_RATE = 0.05
-
-  def generateNewCenters(
-      data: RDD[VectorWithNorm],
-      bcs: Broadcast[Array[Double]],
-      bcCenters: Broadcast[Array[VectorWithNorm]]): Map[Int, VectorWithNorm] = {
-    val newCenters = data
-      .mapPartitions { points =>
-        val thisS = bcs.value
-        val thisCenters = bcCenters.value
-        val dims = thisCenters.head.vector.size
-        val sums = Array.fill(thisCenters.length)(Vectors.zeros(dims))
-        val counts = Array.fill(thisCenters.length)(0L)
-
-        points.foreach { point =>
-          val (bestCenter, cost) = KmeansUtil.findClosest(thisCenters, point, thisS)
-          val sum = sums(bestCenter)
-          axpy(1.0, point.vector, sum)
-          counts(bestCenter) += 1
-        }
-        counts.indices.filter(counts(_) > 0).map(j => (j, (sums(j), counts(j)))).iterator
-      }.reduceByKey { case ((sum1, count1), (sum2, count2)) =>
-      axpy(1.0, sum2, sum1)
-      (sum1, count1 + count2)
-    }.mapValues { case (sum, count) =>
-      scal(1.0 / count, sum)
-      new VectorWithNorm(sum)
-    }.collectAsMap()
-    newCenters.toMap
-  }
-
-  def compute(
-      data: RDD[VectorWithNorm],
-      centers: Array[VectorWithNorm],
-      maxIterations: Int,
-      epsilon: Double,
-      enableMiniBatch: Boolean): Unit = {
-    var converged = false
-    var iteration = 0
-    val cl = centers.length
-    val p = Array.fill(cl)(0.0)
-    val sc = data.sparkContext
-
-    var sampleRate = DEFAULT_SAMPLE_RATE
-    try {
-      sampleRate = sc.getConf.getDouble("spark.boostkit.Kmeans.sampleRate",
-        DEFAULT_SAMPLE_RATE)
-      if (sampleRate < 0.0) {
-        throw new Exception
-      }
-    }
-    catch {
-      case x: Exception =>
-        throw new Exception("'spark.boostkit.Kmeans.sampleRate' value is invalid")
-    }
-
-    val DEFAULT_PAR_LEVEL = 100
-    var customParLevel = DEFAULT_PAR_LEVEL
-    try{
-      customParLevel = SparkSession.builder().getOrCreate()
-        .sparkContext.getConf.getInt("spark.boostkit.Kmeans.parLevel",
-        DEFAULT_PAR_LEVEL)
-      if (customParLevel < 1) {
-        throw new Exception
-      }
-    }
-    catch {
-      case x: Exception =>
-        throw new Exception("'spark.boostkit.Kmeans.parLevel' value is invalid")
-    }
-
-    while (iteration < maxIterations && !converged) {
-      val s = KmeansUtil.generateDisMatrix(centers, customParLevel)
-      val bcCenters = sc.broadcast(centers)
-      val bcs = sc.broadcast(s)
-
-      // Find the new centers
-      val newCenters = if (!enableMiniBatch) generateNewCenters(data, bcs, bcCenters)
-      else generateNewCenters(data.sample(false, sampleRate), bcs, bcCenters)
-      converged = true
-      newCenters.foreach { case (j, newCenter) =>
-        p(j) = KmeansUtil.fastDistance(newCenter, centers(j))
-        if (converged && p(j) > epsilon) {
-          converged = false
-        }
-        centers(j) = newCenter
-      }
-      bcCenters.destroy(blocking = false)
-      bcs.destroy(blocking = false)
-      iteration += 1
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
deleted file mode 100644
index 4ecbcc3..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ /dev/null
@@ -1,616 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.clustering
-
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.clustering.{KMeans => NewKMeans}
-import org.apache.spark.ml.util.Instrumentation
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.Utils
-import org.apache.spark.util.random.XORShiftRandom
-
-/**
- * K-means clustering with a k-means++ like initialization mode
- * (the k-means|| algorithm by Bahmani et al).
- *
- * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given
- * to it should be cached by the user.
- */
-@Since("0.8.0")
-class KMeans private(
-    private var k: Int,
-    private var maxIterations: Int,
-    private var initializationMode: String,
-    private var initializationSteps: Int,
-    private var epsilon: Double,
-    private var seed: Long) extends Serializable with Logging {
-
-  /**
-   * Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20,
-   * initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random}.
-   */
-  @Since("0.8.0")
-  def this() = this(2, 20, KMeans.K_MEANS_PARALLEL, 2, 1e-4, Utils.random.nextLong())
-
-  /**
-   * Number of clusters to create (k).
-   *
-   * @note It is possible for fewer than k clusters to
-   *       be returned, for example, if there are fewer than k distinct points to cluster.
-   */
-  @Since("1.4.0")
-  def getK: Int = k
-
-  /**
-   * Set the number of clusters to create (k).
-   *
-   * @note It is possible for fewer than k clusters to
-   * be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.
-   */
-  @Since("0.8.0")
-  def setK(k: Int): this.type = {
-    require(k > 0 && k < Math.sqrt(Int.MaxValue),
-      s"Number of clusters must be positive and less than sqrt of Int.MaxValue, but got ${k}")
-    this.k = k
-    this
-  }
-
-  /**
-   * Maximum number of iterations allowed.
-   */
-  @Since("1.4.0")
-  def getMaxIterations: Int = maxIterations
-
-  /**
-   * Set maximum number of iterations allowed. Default: 20.
-   */
-  @Since("0.8.0")
-  def setMaxIterations(maxIterations: Int): this.type = {
-    require(maxIterations >= 0,
-      s"Maximum of iterations must be nonnegative but got ${maxIterations}")
-    this.maxIterations = maxIterations
-    this
-  }
-
-  /**
-   * The initialization algorithm. This can be either "random" or "k-means||".
-   */
-  @Since("1.4.0")
-  def getInitializationMode: String = initializationMode
-
-  /**
-   * Set the initialization algorithm. This can be either "random" to choose random points as
-   * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
-   * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
-   */
-  @Since("0.8.0")
-  def setInitializationMode(initializationMode: String): this.type = {
-    KMeans.validateInitMode(initializationMode)
-    this.initializationMode = initializationMode
-    this
-  }
-
-  /**
-   * This function has no effect since Spark 2.0.0.
-   */
-  @Since("1.4.0")
-  @deprecated("This has no effect and always returns 1", "2.1.0")
-  def getRuns: Int = {
-    logWarning("Getting number of runs has no effect since Spark 2.0.0.")
-    1
-  }
-
-  /**
-   * This function has no effect since Spark 2.0.0.
-   */
-  @Since("0.8.0")
-  @deprecated("This has no effect", "2.1.0")
-  def setRuns(runs: Int): this.type = {
-    logWarning("Setting number of runs has no effect since Spark 2.0.0.")
-    this
-  }
-
-  /**
-   * Number of steps for the k-means|| initialization mode
-   */
-  @Since("1.4.0")
-  def getInitializationSteps: Int = initializationSteps
-
-  /**
-   * Set the number of steps for the k-means|| initialization mode. This is an advanced
-   * setting -- the default of 2 is almost always enough. Default: 2.
-   */
-  @Since("0.8.0")
-  def setInitializationSteps(initializationSteps: Int): this.type = {
-    require(initializationSteps > 0,
-      s"Number of initialization steps must be positive but got ${initializationSteps}")
-    this.initializationSteps = initializationSteps
-    this
-  }
-
-  /**
-   * The distance threshold within which we've consider centers to have converged.
-   */
-  @Since("1.4.0")
-  def getEpsilon: Double = epsilon
-
-  /**
-   * Set the distance threshold within which we've consider centers to have converged.
-   * If all centers move less than this Euclidean distance, we stop iterating one run.
-   */
-  @Since("0.8.0")
-  def setEpsilon(epsilon: Double): this.type = {
-    require(epsilon >= 0,
-      s"Distance threshold must be nonnegative but got ${epsilon}")
-    this.epsilon = epsilon
-    this
-  }
-
-  /**
-   * The random seed for cluster initialization.
-   */
-  @Since("1.4.0")
-  def getSeed: Long = seed
-
-  /**
-   * Set the random seed for cluster initialization.
-   */
-  @Since("1.4.0")
-  def setSeed(seed: Long): this.type = {
-    this.seed = seed
-    this
-  }
-
-  // Initial cluster centers can be provided as a KMeansModel object rather than using the
-  // random or k-means|| initializationMode
-  private var initialModel: Option[KMeansModel] = None
-
-  /**
-   * Set the initial starting point, bypassing the random initialization or k-means||
-   * The condition model.k == this.k must be met, failure results
-   * in an IllegalArgumentException.
-   */
-  @Since("1.4.0")
-  def setInitialModel(model: KMeansModel): this.type = {
-    require(model.k == k, "mismatched cluster count")
-    initialModel = Some(model)
-    this
-  }
-
-  /**
-   * Train a K-means model on the given set of points; `data` should be cached for high
-   * performance, because this is an iterative algorithm.
-   */
-  @Since("0.8.0")
-  def run(data: RDD[Vector]): KMeansModel = {
-    run(data, None)
-  }
-
-  private[spark] def run(
-      data: RDD[Vector],
-      instr: Option[Instrumentation[NewKMeans]]): KMeansModel = {
-
-    if (data.getStorageLevel == StorageLevel.NONE) {
-      logWarning("The input data is not directly cached, which may hurt performance if its"
-        + " parent RDDs are also uncached.")
-    }
-
-    // Compute squared norms and cache them.
-    val norms = data.map(Vectors.norm(_, 2.0))
-    norms.persist()
-    val zippedData = data.zip(norms).map { case (v, norm) =>
-      new VectorWithNorm(v, norm)
-    }
-    val model = runAlgorithm(zippedData, instr)
-    norms.unpersist()
-
-    // Warn at the end of the run as well, for increased visibility.
-    if (data.getStorageLevel == StorageLevel.NONE) {
-      logWarning("The input data was not directly cached, which may hurt performance if its"
-        + " parent RDDs are also uncached.")
-    }
-    model
-  }
-
-  /**
-   * Implementation of K-Means algorithm.
-   */
-  private def runAlgorithm(
-      data: RDD[VectorWithNorm],
-      instr: Option[Instrumentation[NewKMeans]]): KMeansModel = {
-
-    val sc = data.sparkContext
-    val initStartTime = System.nanoTime()
-    val centers = initialModel match {
-      case Some(kMeansCenters) =>
-        kMeansCenters.clusterCenters.map(new VectorWithNorm(_))
-      case None =>
-        if (initializationMode == KMeans.RANDOM) {
-          initRandom(data)
-        } else {
-          initKMeansParallel(data)
-        }
-    }
-    val centersR = centers.clone()
-    val cl = centers.length
-    val initTimeInSeconds = (System.nanoTime() - initStartTime) / 1e9
-    logInfo(f"Initialization with $initializationMode took $initTimeInSeconds%.3f seconds.")
-    var converged = false
-    var cost = 0.0
-    var iteration = 0
-    val iterationStartTime = System.nanoTime()
-    instr.foreach(_.logNumFeatures(centers.head.vector.size))
-
-    // Execute iterations of Lloyd's algorithm until converged
-    if (cl > 1) {
-      val methodEnum = Array("default", "allData")
-      val method = sc.getConf.get("spark.boostkit.Kmeans.optMethod", "default")
-      if (!methodEnum.contains(method)) {
-        throw new Exception("'spark.boostkit.Kmeans.optMethod' value is invalid")
-      }
-      if (method == "allData") {
-        KMACCm.compute(data, centers, maxIterations, epsilon, false)
-      } else {
-        KMACCm.compute(data, centers, maxIterations, epsilon, true)
-      }
-    } else {
-      iteration = 0
-      converged = false
-      cost = 0.0
-      while (iteration < maxIterations && !converged) {
-        val costAccum = sc.doubleAccumulator
-        val bcCenters = sc.broadcast(centersR)
-
-        // Find the new centers
-        val newCenters = data.mapPartitions { points =>
-          val thisCenters = bcCenters.value
-          val dims = thisCenters.head.vector.size
-          val sums = Array.fill(thisCenters.length)(Vectors.zeros(dims))
-          val counts = Array.fill(thisCenters.length)(0L)
-          points.foreach { point =>
-            val (bestCenter, cost) = KMeans.findClosest(thisCenters, point)
-            costAccum.add(cost)
-            val sum = sums(bestCenter)
-            axpy(1.0, point.vector, sum)
-            counts(bestCenter) += 1
-          }
-          counts.indices.filter(counts(_) > 0).map(j => (j, (sums(j), counts(j)))).iterator
-        }.reduceByKey { case ((sum1, count1), (sum2, count2)) =>
-          axpy(1.0, sum2, sum1)
-          (sum1, count1 + count2)
-        }.mapValues { case (sum, count) =>
-          scal(1.0 / count, sum)
-          new VectorWithNorm(sum)
-        }.collectAsMap()
-        bcCenters.destroy(blocking = false)
-
-        // Update the cluster centers and costs
-        converged = true
-        newCenters.foreach { case (j, newCenter) =>
-          if (converged && KMeans.fastSquaredDistance(newCenter, centersR(j)) > epsilon * epsilon) {
-            converged = false
-          }
-          centersR(j) = newCenter
-        }
-        cost = costAccum.value
-        iteration += 1
-      }
-    }
-
-    val iterationTimeInSeconds = (System.nanoTime() - iterationStartTime) / 1e9
-    logInfo(f"Iterations took $iterationTimeInSeconds%.3f seconds.")
-    if (iteration == maxIterations) {
-      logInfo(s"KMeansX reached the max number of iterations: $maxIterations.")
-    } else {
-      logInfo(s"KMeansX converged in $iteration iterations.")
-    }
-    if (cl > 1) {
-      new KMeansModel(centers.map(_.vector))
-    }
-    else {
-      new KMeansModel(centersR.map(_.vector))
-    }
-  }
-
-  /**
-   * Initialize a set of cluster centers at random.
-   */
-  private def initRandom(data: RDD[VectorWithNorm]): Array[VectorWithNorm] = {
-    // Select without replacement; may still produce duplicates if the data has < k distinct
-    // points, so deduplicate the centroids to match the behavior of k-means|| in the same situation
-    data.takeSample(false, k, new XORShiftRandom(this.seed).nextInt())
-      .map(_.vector).distinct.map(new VectorWithNorm(_))
-  }
-
-
-  /**
-   * Initialize a set of cluster centers using the k-means|| algorithm by Bahmani et al.
-   * (Bahmani et al., Scalable K-Means++, VLDB 2012). This is a variant of k-means++ that tries
-   * to find dissimilar cluster centers by starting with a random center and then doing
-   * passes where more centers are chosen with probability proportional to their squared distance
-   * to the current cluster set. It results in a provable approximation to an optimal clustering.
-   *
-   * The original paper can be found at http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf.
-   */
-  private[clustering] def initKMeansParallel(data: RDD[VectorWithNorm]): Array[VectorWithNorm] = {
-    // Initialize empty centers and point costs.
-    var costs = data.map(_ => Double.PositiveInfinity)
-
-    // Initialize the first center to a random point.
-    val seed = new XORShiftRandom(this.seed).nextInt()
-    val sample = data.takeSample(false, 1, seed)
-    // Could be empty if data is empty; fail with a better message early:
-    require(sample.nonEmpty, s"No samples available from $data")
-
-    val centers = ArrayBuffer[VectorWithNorm]()
-    var newCenters = Seq(sample.head.toDense)
-    centers ++= newCenters
-
-    // On each step, sample 2 * k points on average with probability proportional
-    // to their squared distance from the centers. Note that only distances between points
-    // and new centers are computed in each iteration.
-    var step = 0
-    val bcNewCentersList = ArrayBuffer[Broadcast[_]]()
-    while (step < initializationSteps) {
-      val bcNewCenters = data.context.broadcast(newCenters)
-      bcNewCentersList += bcNewCenters
-      val preCosts = costs
-      costs = data.zip(preCosts).map { case (point, cost) =>
-        math.min(KMeans.pointCost(bcNewCenters.value, point), cost)
-      }.persist(StorageLevel.MEMORY_AND_DISK)
-      val sumCosts = costs.sum()
-
-      bcNewCenters.unpersist(blocking = false)
-      preCosts.unpersist(blocking = false)
-
-      val chosen = data.zip(costs).mapPartitionsWithIndex { (index, pointCosts) =>
-        val rand = new XORShiftRandom(seed ^ (step << 16) ^ index)
-        pointCosts.filter { case (_, c) => rand.nextDouble() < 2.0 * c * k / sumCosts }.map(_._1)
-      }.collect()
-      newCenters = chosen.map(_.toDense)
-      centers ++= newCenters
-      step += 1
-    }
-
-    costs.unpersist(blocking = false)
-    bcNewCentersList.foreach(_.destroy(false))
-
-    val distinctCenters = centers.map(_.vector).distinct.map(new VectorWithNorm(_))
-
-    if (distinctCenters.size <= k) {
-      distinctCenters.toArray
-    } else {
-      // Finally, we might have a set of more than k distinct candidate centers; weight each
-      // candidate by the number of points in the dataset mapping to it and run a local k-means++
-      // on the weighted centers to pick k of them
-      val bcCenters = data.context.broadcast(distinctCenters)
-      val countMap = data.map(KMeans.findClosest(bcCenters.value, _)._1).countByValue()
-
-      bcCenters.destroy(blocking = false)
-
-      val myWeights = distinctCenters.indices.map(countMap.getOrElse(_, 0L).toDouble).toArray
-      LocalKMeansX.kMeansPlusPlus(0, distinctCenters.toArray, myWeights, k, 30)
-    }
-  }
-}
-
-
-/**
- * Top-level methods for calling K-means clustering.
- */
-@Since("0.8.0")
-object KMeans {
-
-  // Initialization mode names
-  @Since("0.8.0")
-  val RANDOM = "random"
-  @Since("0.8.0")
-  val K_MEANS_PARALLEL = "k-means||"
-
-  /**
-   * Trains a k-means model using the given set of parameters.
-   *
-   * @param data               Training points as an `RDD` of `Vector` types.
-   * @param k                  Number of clusters to create.
-   * @param maxIterations      Maximum number of iterations allowed.
-   * @param initializationMode The initialization algorithm. This can either be "random" or
-   *                           "k-means||". (default: "k-means||")
-   * @param seed  Random seed for cluster initialization. Default is to generate seed based
-   *              on system time.
-   */
-  @Since("2.1.0")
-  def train(
-      data: RDD[Vector],
-      k: Int,
-      maxIterations: Int,
-      initializationMode: String,
-      seed: Long): KMeansModel = {
-    new KMeans().setK(k)
-      .setMaxIterations(maxIterations)
-      .setInitializationMode(initializationMode)
-      .setSeed(seed)
-      .run(data)
-  }
-
-  /**
-   * Trains a k-means model using the given set of parameters.
-   *
-   * @param data               Training points as an `RDD` of `Vector` types.
-   * @param k                  Number of clusters to create.
-   * @param maxIterations      Maximum number of iterations allowed.
-   * @param initializationMode The initialization algorithm. This can either be "random" or
-   *                           "k-means||". (default: "k-means||")
-   */
-  @Since("2.1.0")
-  def train(
-      data: RDD[Vector],
-      k: Int,
-      maxIterations: Int,
-      initializationMode: String): KMeansModel = {
-    new KMeans().setK(k)
-      .setMaxIterations(maxIterations)
-      .setInitializationMode(initializationMode)
-      .run(data)
-  }
-
-  /**
-   * Trains a k-means model using the given set of parameters.
-   *
-   * @param data               Training points as an `RDD` of `Vector` types.
-   * @param k                  Number of clusters to create.
-   * @param maxIterations      Maximum number of iterations allowed.
-   * @param runs               This param has no effect since Spark 2.0.0.
-   * @param initializationMode The initialization algorithm. This can either be "random" or
-   *                           "k-means||". (default: "k-means||")
-   * @param seed  Random seed for cluster initialization. Default is to generate seed based
-   *              on system time.
-   */
-  @Since("1.3.0")
-  @deprecated("Use train method without 'runs'", "2.1.0")
-  def train(
-      data: RDD[Vector],
-      k: Int,
-      maxIterations: Int,
-      runs: Int,
-      initializationMode: String,
-      seed: Long): KMeansModel = {
-    new KMeans().setK(k)
-      .setMaxIterations(maxIterations)
-      .setInitializationMode(initializationMode)
-      .setSeed(seed)
-      .run(data)
-  }
-
-  /**
-   * Trains a k-means model using the given set of parameters.
-   *
-   * @param data               Training points as an `RDD` of `Vector` types.
-   * @param k                  Number of clusters to create.
-   * @param maxIterations      Maximum number of iterations allowed.
-   * @param runs               This param has no effect since Spark 2.0.0.
-   * @param initializationMode The initialization algorithm. This can either be "random" or
-   *                           "k-means||". (default: "k-means||")
-   */
-  @Since("0.8.0")
-  @deprecated("Use train method without 'runs'", "2.1.0")
-  def train(
-      data: RDD[Vector],
-      k: Int,
-      maxIterations: Int,
-      runs: Int,
-      initializationMode: String): KMeansModel = {
-    new KMeans().setK(k)
-      .setMaxIterations(maxIterations)
-      .setInitializationMode(initializationMode)
-      .run(data)
-  }
-
-  /**
-   * Trains a k-means model using specified parameters and the default values for unspecified.
-   */
-  @Since("0.8.0")
-  def train(
-      data: RDD[Vector],
-      k: Int,
-      maxIterations: Int): KMeansModel = {
-    new KMeans().setK(k)
-      .setMaxIterations(maxIterations)
-      .run(data)
-  }
-
-  /**
-   * Trains a k-means model using specified parameters and the default values for unspecified.
-   */
-  @Since("0.8.0")
-  @deprecated("Use train method without 'runs'", "2.1.0")
-  def train(
-      data: RDD[Vector],
-      k: Int,
-      maxIterations: Int,
-      runs: Int): KMeansModel = {
-    new KMeans().setK(k)
-      .setMaxIterations(maxIterations)
-      .run(data)
-  }
-
-  /**
-   * Returns the index of the closest center to the given point, as well as the squared distance.
-   */
-  private[mllib] def findClosest(
-      centers: TraversableOnce[VectorWithNorm],
-      point: VectorWithNorm): (Int, Double) = {
-    var bestDistance = Double.PositiveInfinity
-    var bestIndex = 0
-    var i = 0
-    centers.foreach { center =>
-      // Since `\|a - b\| \geq |\|a\| - \|b\||`, we can use this lower bound to avoid unnecessary
-      // distance computation.
-      var lowerBoundOfSqDist = center.norm - point.norm
-      lowerBoundOfSqDist = lowerBoundOfSqDist * lowerBoundOfSqDist
-      if (lowerBoundOfSqDist < bestDistance) {
-        val distance: Double = fastSquaredDistance(center, point)
-        if (distance < bestDistance) {
-          bestDistance = distance
-          bestIndex = i
-        }
-      }
-      i += 1
-    }
-    (bestIndex, bestDistance)
-  }
-
-  /**
-   * Returns the K-means cost of a given point against the given cluster centers.
-   */
-  private[mllib] def pointCost(
-      centers: TraversableOnce[VectorWithNorm],
-      point: VectorWithNorm): Double =
-    findClosest(centers, point)._2
-
-  /**
-   * Returns the squared Euclidean distance between two vectors computed by
-   * [[org.apache.spark.mllib.util.MLUtils#fastSquaredDistance]].
-   */
-  private[clustering] def fastSquaredDistance(
-      v1: VectorWithNorm,
-      v2: VectorWithNorm): Double = {
-    MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
-  }
-
-  private[spark] def validateInitMode(initMode: String): Boolean = {
-    initMode match {
-      case KMeans.RANDOM => true
-      case KMeans.K_MEANS_PARALLEL => true
-      case _ => false
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 35f087f..ea40a85 100644
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -27,7 +21,7 @@ import java.util.Locale
 
 import breeze.linalg.{DenseVector => BDV}
 
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
 import org.apache.spark.internal.Logging
@@ -288,21 +282,15 @@ class LDA private (
 
 
   /**
-   * :: DeveloperApi ::
-   *
    * LDAOptimizer used to perform the actual calculation
    */
   @Since("1.4.0")
-  @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
 
   /**
-   * :: DeveloperApi ::
-   *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
    */
   @Since("1.4.0")
-  @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
     this.ldaOptimizer = optimizer
     this
@@ -341,7 +329,7 @@ class LDA private (
     val state = ldaOptimizer.initialize(documents, this)
     timer.stop("initialize")
     var iter = 0
-    val iterationTimes = Array.fill[Double](maxIterations)(0)
+    val iterationTimes = Array.ofDim[Double](maxIterations)
 
     timer.start("train")
     LDAUtilsXOpt.init(documents.sparkContext)
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 64edba0..42fd9de 100644
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -29,20 +23,18 @@ import breeze.linalg.{all, sum, DenseMatrix => BDM, DenseVector => BDV}
 import breeze.numerics.{abs, exp, trigamma}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.tree.impl.TimeTracker
 import org.apache.spark.mllib.linalg.{DenseVector, Matrices, SparseVector, Vector, Vectors}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
 
 /**
- * :: DeveloperApi ::
- *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
  */
 @Since("1.4.0")
-@DeveloperApi
 trait LDAOptimizer {
 
   /*
@@ -67,8 +59,6 @@ trait LDAOptimizer {
 }
 
 /**
- * :: DeveloperApi ::
- *
  * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA
  * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic
  * distribution adaptively.
@@ -77,7 +67,6 @@ trait LDAOptimizer {
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
  */
 @Since("1.4.0")
-@DeveloperApi
 final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
 
   // LDA common parameters
@@ -254,6 +243,10 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
     this.randomGenerator = new Random(lda.getSeed)
 
     this.docs = docs
+    if (this.docs.getStorageLevel == StorageLevel.NONE) {
+      logWarning("The input data is not directly cached, which may hurt performance if its"
+        + " parent RDDs are also uncached.")
+    }
 
     // Initialize the variational distribution q(beta|lambda)
     this.lambda = getGammaMatrix(k, vocabSize)
@@ -296,9 +289,10 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
     val alpha = this.alpha.asBreeze
     val gammaShape = this.gammaShape
     val optimizeDocConcentration = this.optimizeDocConcentration
+    val seed = randomGenerator.nextLong()
     // If and only if optimizeDocConcentration is set true,
     // we calculate logphat in the same pass as other statistics.
-    // No calculation of loghat happens otherwise.
+    // No calculation of logphat happens otherwise.
     val logphatPartOptionBase = () => if (optimizeDocConcentration) {
                                         Some(BDV.zeros[Double](k))
                                       } else {
@@ -309,9 +303,10 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
     val stats: RDD[(BDM[Double], Option[BDV[Double]], Long)] =
       if (LDAUtilsXOpt.useOptimizedCalc()) {
         LDAUtilsXOpt.optimizedCalcStats(batch, expElogbetaBc, k, vocabSize, logphatPartOptionBase,
-          alpha, gammaShape)
+          alpha, gammaShape, seed)
     } else {
-      batch.mapPartitions { docs =>
+      batch.mapPartitionsWithIndex {
+        (index, docs) =>
         val nonEmptyDocs = docs.filter(_._2.numNonzeros > 0)
 
         val stat = BDM.zeros[Double](k, vocabSize)
@@ -321,7 +316,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
         nonEmptyDocs.foreach { case (_, termCounts: Vector) =>
           nonEmptyDocCount += 1
           val (gammad, sstats, ids) = OnlineLDAOptimizerXObj.variationalTopicInference(
-            termCounts, expElogbetaBcValue, alpha, gammaShape, k)
+            termCounts, expElogbetaBcValue, alpha, gammaShape, k, seed + index)
           stat(::, ids) := stat(::, ids) + sstats
           logphatPartOption.foreach(_ += LDAUtilsX.dirichletExpectation(gammad))
         }
@@ -357,6 +352,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
 
     if (nonEmptyDocsN == 0) {
       logWarning("No non-empty documents were submitted in the batch.")
+      timer.stop("update-lambda")
       // Therefore, there is no need to update any of the model parameters
       return this
     }
@@ -439,7 +435,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
   }
 
   override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
-    new LocalLDAModel(Matrices.fromBreeze(lambda).transpose, alpha, eta, gammaShape)
+    new LocalLDAModel(Matrices.fromBreeze(lambda).transpose, alpha, eta)
+      .setSeed(randomGenerator.nextLong())
   }
 
 }
@@ -448,7 +445,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer with Logging {
  * Serializable companion object containing helper methods and shared code for
  * [[OnlineLDAOptimizer]] and [[LocalLDAModel]].
  */
-private[clustering] object OnlineLDAOptimizer {
+private[spark] object OnlineLDAOptimizer {
   /**
    * Uses variational inference to infer the topic distribution `gammad` given the term counts
    * for a document. `termCounts` must contain at least one non-zero entry, otherwise Breeze will
@@ -461,25 +458,24 @@ private[clustering] object OnlineLDAOptimizer {
    * @return Returns a tuple of `gammad` - estimate of gamma, the topic distribution, `sstatsd` -
    *         statistics for updating lambda and `ids` - list of termCounts vector indices.
    */
-  private[clustering] def variationalTopicInference(
-      termCounts: Vector,
+  private[spark] def variationalTopicInference(
+      indices: List[Int],
+      values: Array[Double],
       expElogbeta: BDM[Double],
       alpha: breeze.linalg.Vector[Double],
       gammaShape: Double,
-      k: Int): (BDV[Double], BDM[Double], List[Int]) = {
-    val (ids: List[Int], cts: Array[Double]) = termCounts match {
-      case v: DenseVector => ((0 until v.size).toList, v.values)
-      case v: SparseVector => (v.indices.toList, v.values)
-    }
+      k: Int,
+      seed: Long): (BDV[Double], BDM[Double], List[Int]) = {
     // Initialize the variational distribution q(theta|gamma) for the mini-batch
+    val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister(seed))
     val gammad: BDV[Double] =
-      new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k)                   // K
+      new Gamma(gammaShape, 1.0 / gammaShape)(randBasis).samplesVector(k) // K
     val expElogthetad: BDV[Double] = exp(LDAUtils.dirichletExpectation(gammad))  // K
-    val expElogbetad = expElogbeta(ids, ::).toDenseMatrix                        // ids * K
+    val expElogbetad = expElogbeta(indices, ::).toDenseMatrix                        // ids * K
 
     val phiNorm: BDV[Double] = expElogbetad * expElogthetad +:+ 1e-100            // ids
     var meanGammaChange = 1D
-    val ctsVector = new BDV[Double](cts)                                         // ids
+    val ctsVector = new BDV[Double](values)                                         // ids
 
     // Iterate between gamma and phi until convergence
     while (meanGammaChange > 1e-3) {
@@ -493,6 +489,20 @@ private[clustering] object OnlineLDAOptimizer {
     }
 
     val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector /:/ phiNorm).asDenseMatrix
-    (gammad, sstatsd, ids)
+    (gammad, sstatsd, indices)
+  }
+
+  private[clustering] def variationalTopicInference(
+      termCounts: Vector,
+      expElogbeta: BDM[Double],
+      alpha: breeze.linalg.Vector[Double],
+      gammaShape: Double,
+      k: Int,
+      seed: Long): (BDV[Double], BDM[Double], List[Int]) = {
+    val (ids: List[Int], cts: Array[Double]) = termCounts match {
+      case v: DenseVector => (List.range(0, v.size), v.values)
+      case v: SparseVector => (v.indices.toList, v.values)
+    }
+    variationalTopicInference(ids, cts, expElogbeta, alpha, gammaShape, k, seed)
   }
 }
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeansX.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeansX.scala
deleted file mode 100644
index 4d7c18a..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeansX.scala
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.clustering
-
-import scala.util.Random
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.sql.SparkSession
-
-/**
- * An utility object to run K-means locally. This is private to the ML package because it's used
- * in the initialization of KMeans but not meant to be publicly exposed.
- */
-private[mllib] object LocalKMeansX extends Logging {
-
-  /**
-   * Run K-means++ on the weighted point set `points`. This first does the K-means++
-   * initialization procedure and then rounds of Lloyd's algorithm.
-   */
-  def kMeansPlusPlus(
-      seed: Int,
-      points: Array[VectorWithNorm],
-      weights: Array[Double],
-      k: Int,
-      maxIterations: Int
-  ): Array[VectorWithNorm] = {
-    val DEFAULT_PAR_LEVEL = 100
-    val rand = new Random(seed)
-    val dimensions = points(0).vector.size
-    val centers = new Array[VectorWithNorm](k)
-
-    // Initialize centers by sampling using the k-means++ procedure.
-    centers(0) = pickWeighted(rand, points, weights).toDense
-    var costArray = points.map(KMeans.fastSquaredDistance(_, centers(0)))
-
-    var customParLevel = DEFAULT_PAR_LEVEL
-    try{
-      customParLevel = SparkSession.builder().getOrCreate()
-        .sparkContext.getConf.getInt("spark.boostkit.Kmeans.parLevel",
-        DEFAULT_PAR_LEVEL)
-      if (customParLevel < 1) {
-        throw new Exception
-      }
-    }
-    catch {
-      case x: Exception =>
-        throw new Exception("'spark.boostkit.Kmeans.parLevel' value is invalid")
-    }
-    val customForkJoinPool = new scala.concurrent.forkjoin.ForkJoinPool(customParLevel)
-    val customTaskSupport = new scala.collection.parallel.ForkJoinTaskSupport(customForkJoinPool)
-
-    for (i <- 1 until k) {
-      val sum = costArray.zip(weights).map(p => p._1 * p._2).sum
-      val r = rand.nextDouble() * sum
-      var cumulativeScore = 0.0
-      var j = 0
-      while (j < points.length && cumulativeScore < r) {
-        cumulativeScore += weights(j) * costArray(j)
-        j += 1
-      }
-      if (j == 0) {
-        logWarning("kMeansPlusPlus initialization ran out of distinct points for centers." +
-          s" Using duplicate point for center k = $i.")
-        centers(i) = points(0).toDense
-      } else {
-        centers(i) = points(j - 1).toDense
-      }
-
-      val costArrayPar = points.zipWithIndex.par
-      costArrayPar.tasksupport = customTaskSupport
-      costArray = costArrayPar.map{
-        t =>
-          math.min(KMeans.fastSquaredDistance(points(t._2), centers(i)), costArray(t._2))
-      }.toArray
-    }
-
-    // Run up to maxIterations iterations of Lloyd's algorithm
-    val oldClosest = Array.fill(points.length)(-1)
-    var iteration = 0
-    var moved = true
-    while (moved && iteration < maxIterations) {
-      moved = false
-      val counts = Array.fill(k)(0.0)
-      val sums = Array.fill(k)(Vectors.zeros(dimensions))
-
-      val assignPar = points.par
-      assignPar.tasksupport = customTaskSupport
-      val assign = assignPar.map{
-        case point =>
-          val index = KMeans.findClosest(centers, point)._1
-          (point, index)
-      }.toArray
-
-      var idx = 0
-      while(idx < assign.size) {
-        val index = assign(idx)._2
-        if (index != oldClosest(idx)) {
-          moved = true
-          oldClosest(idx) = index
-        }
-        axpy(weights(idx), assign(idx)._1.vector, sums(index))
-        counts(index) += weights(idx)
-        idx += 1
-      }
-
-      // Update centers
-      var j = 0
-      while (j < k) {
-        if (counts(j) == 0.0) {
-          // Assign center to a random point
-          centers(j) = points(rand.nextInt(points.length)).toDense
-        } else {
-          scal(1.0 / counts(j), sums(j))
-          centers(j) = new VectorWithNorm(sums(j))
-        }
-        j += 1
-      }
-      iteration += 1
-    }
-
-    if (iteration == maxIterations) {
-      logInfo(s"Local KMeans++ reached the max number of iterations: $maxIterations.")
-    } else {
-      logInfo(s"Local KMeans++ converged in $iteration iterations.")
-    }
-    centers
-  }
-
-  private def pickWeighted[T](rand: Random, data: Array[T], weights: Array[Double]): T = {
-    val r = rand.nextDouble() * weights.sum
-    var i = 0
-    var curWeight = 0.0
-    while (i < data.length && curWeight < r) {
-      curWeight += weights(i)
-      i += 1
-    }
-    data(i - 1)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
deleted file mode 100644
index f633c1b..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ /dev/null
@@ -1,231 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.feature
-
-import breeze.linalg.{DenseVector => BDV}
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.api.java.JavaRDD
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.rdd.RDD
-
-/**
- * Inverse document frequency (IDF).
- * The standard formulation is used: `idf = log((m + 1) / (d(t) + 1))`, where `m` is the total
- * number of documents and `d(t)` is the number of documents that contain term `t`.
- *
- * This implementation supports filtering out terms which do not appear in a minimum number
- * of documents (controlled by the variable `minDocFreq`). For terms that are not in
- * at least `minDocFreq` documents, the IDF is found as 0, resulting in TF-IDFs of 0.
- *
- * @param minDocFreq minimum of documents in which a term
- *                   should appear for filtering
- */
-@Since("1.1.0")
-class IDF @Since("1.2.0") (@Since("1.2.0") val minDocFreq: Int) {
-
-  @Since("1.1.0")
-  def this() = this(0)
-
-  // TODO: Allow different IDF formulations.
-
-  /**
-   * Computes the inverse document frequency.
-   * @param dataset an RDD of term frequency vectors
-   */
-  @Since("1.1.0")
-  def fit(dataset: RDD[Vector]): IDFModel = {
-    val (idf, docFreq, numDocs) = IDFUtils.train(dataset, minDocFreq)
-    new IDFModel(idf)
-  }
-
-  /**
-   * Computes the inverse document frequency.
-   * @param dataset a JavaRDD of term frequency vectors
-   */
-  @Since("1.1.0")
-  def fit(dataset: JavaRDD[Vector]): IDFModel = {
-    fit(dataset.rdd)
-  }
-}
-
-private object IDF {
-
-  /** Document frequency aggregator. */
-  class DocumentFrequencyAggregator(val minDocFreq: Int) extends Serializable {
-
-    /** number of documents */
-    private var m = 0L
-    /** document frequency vector */
-    private var df: BDV[Long] = _
-
-
-    def this() = this(0)
-
-    /** Adds a new document. */
-    def add(doc: Vector): this.type = {
-      if (isEmpty) {
-        df = BDV.zeros(doc.size)
-      }
-      doc match {
-        case SparseVector(size, indices, values) =>
-          val nnz = indices.length
-          var k = 0
-          while (k < nnz) {
-            if (values(k) > 0) {
-              df(indices(k)) += 1L
-            }
-            k += 1
-          }
-        case DenseVector(values) =>
-          val n = values.length
-          var j = 0
-          while (j < n) {
-            if (values(j) > 0.0) {
-              df(j) += 1L
-            }
-            j += 1
-          }
-        case other =>
-          throw new UnsupportedOperationException(
-            s"Only sparse and dense vectors are supported but got ${other.getClass}.")
-      }
-      m += 1L
-      this
-    }
-
-    /** Merges another. */
-    def merge(other: DocumentFrequencyAggregator): this.type = {
-      if (!other.isEmpty) {
-        m += other.m
-        if (df == null) {
-          df = other.df.copy
-        } else {
-          df += other.df
-        }
-      }
-      this
-    }
-
-    private def isEmpty: Boolean = m == 0L
-
-    /** Returns the current IDF vector. */
-    def idf(): Vector = {
-      if (isEmpty) {
-        throw new IllegalStateException("Haven't seen any document yet.")
-      }
-      val n = df.length
-      val inv = new Array[Double](n)
-      var j = 0
-      while (j < n) {
-        /*
-         * If the term is not present in the minimum
-         * number of documents, set IDF to 0. This
-         * will cause multiplication in IDFModel to
-         * set TF-IDF to 0.
-         *
-         * Since arrays are initialized to 0 by default,
-         * we just omit changing those entries.
-         */
-        if (df(j) >= minDocFreq) {
-          inv(j) = math.log((m + 1.0) / (df(j) + 1.0))
-        }
-        j += 1
-      }
-      Vectors.dense(inv)
-    }
-  }
-}
-
-/**
- * Represents an IDF model that can transform term frequency vectors.
- */
-@Since("1.1.0")
-class IDFModel private[spark] (@Since("1.1.0") val idf: Vector) extends Serializable {
-
-  /**
-   * Transforms term frequency (TF) vectors to TF-IDF vectors.
-   *
-   * If `minDocFreq` was set for the IDF calculation,
-   * the terms which occur in fewer than `minDocFreq`
-   * documents will have an entry of 0.
-   *
-   * @param dataset an RDD of term frequency vectors
-   * @return an RDD of TF-IDF vectors
-   */
-  @Since("1.1.0")
-  def transform(dataset: RDD[Vector]): RDD[Vector] = {
-    val bcIdf = dataset.context.broadcast(idf)
-    dataset.mapPartitions(iter => iter.map(v => IDFModel.transform(bcIdf.value, v)))
-  }
-
-  /**
-   * Transforms a term frequency (TF) vector to a TF-IDF vector
-   *
-   * @param v a term frequency vector
-   * @return a TF-IDF vector
-   */
-  @Since("1.3.0")
-  def transform(v: Vector): Vector = IDFModel.transform(idf, v)
-
-  /**
-   * Transforms term frequency (TF) vectors to TF-IDF vectors (Java version).
-   * @param dataset a JavaRDD of term frequency vectors
-   * @return a JavaRDD of TF-IDF vectors
-   */
-  @Since("1.1.0")
-  def transform(dataset: JavaRDD[Vector]): JavaRDD[Vector] = {
-    transform(dataset.rdd).toJavaRDD()
-  }
-}
-
-private object IDFModel {
-
-  /**
-   * Transforms a term frequency (TF) vector to a TF-IDF vector with a IDF vector
-   *
-   * @param idf an IDF vector
-   * @param v a term frequency vector
-   * @return a TF-IDF vector
-   */
-  def transform(idf: Vector, v: Vector): Vector = {
-    val n = v.size
-    v match {
-      case SparseVector(size, indices, values) =>
-        val nnz = indices.length
-        val newValues = new Array[Double](nnz)
-        var k = 0
-        while (k < nnz) {
-          newValues(k) = values(k) * idf(indices(k))
-          k += 1
-        }
-        Vectors.sparse(n, indices, newValues)
-      case DenseVector(values) =>
-        val newValues = new Array[Double](n)
-        var j = 0
-        while (j < n) {
-          newValues(j) = values(j) * idf(j)
-          j += 1
-        }
-        Vectors.dense(newValues)
-      case other =>
-        throw new UnsupportedOperationException(
-          s"Only sparse and dense vectors are supported but got ${other.getClass}.")
-    }
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 0a30a2e..23fabc6 100644
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/ml-accelerator/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -40,12 +34,11 @@ import org.apache.spark.storage.StorageLevel
  * A parallel PrefixSpan algorithm to mine frequent sequential patterns.
  * The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns
  * Efficiently by Prefix-Projected Pattern Growth
- * (see <a href="http://doi.org/10.1109/ICDE.2001.914830">here</a>).
+ * (see <a href="https://doi.org/10.1109/ICDE.2001.914830">here</a>).
  *
  * @param minSupport the minimal support level of the sequential pattern, any pattern that appears
  *                   more than (minSupport * size-of-the-dataset) times will be output
- * @param maxPatternLength the maximal length of the sequential pattern, any pattern that appears
- *                         less than maxPatternLength will be output
+ * @param maxPatternLength the maximal length of the sequential pattern
  * @param maxLocalProjDBSize The maximum number of items (including delimiters used in the internal
  *                           storage format) allowed in a projected database before local
  *                           processing. If a projected database exceeds this size, another
@@ -170,6 +163,13 @@ class PrefixSpan private (
     val freqSequences = results.map { case (seq: Array[Int], count: Long) =>
       new FreqSequence(toPublicRepr(seq), count)
     }
+    // Cache the final RDD to the same storage level as input
+    if (data.getStorageLevel != StorageLevel.NONE) {
+      freqSequences.persist(data.getStorageLevel)
+      freqSequences.count()
+    }
+    dataInternalRepr.unpersist()
+
     new PrefixSpanModel(freqSequences)
   }
 
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala
deleted file mode 100644
index d121ad1..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala
+++ /dev/null
@@ -1,285 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.linalg
-
-import breeze.linalg.{DenseMatrix => BDM, DenseMatrixUtil, DenseVector => BDV}
-import breeze.linalg.blas.Dgemv
-import com.github.fommil.netlib.ARPACK
-import org.netlib.util.{doubleW, intW}
-
-/**
- * Compute eigen-decomposition.
- */
-private[mllib] object EigenValueDecomposition {
-
-  private val DEFAULT_THREAD_NUM = 35
-
-  /**
-   * Compute the leading k eigenvalues and eigenvectors on a symmetric square matrix using ARPACK.
-   * The caller needs to ensure that the input matrix is real symmetric. This function requires
-   * memory for `n*(4*k+4)` doubles.
-   *
-   * @param mul a function that multiplies the symmetric matrix with a DenseVector.
-   * @param n dimension of the square matrix (maximum Int.MaxValue).
-   * @param k number of leading eigenvalues required, where k must be positive and less than n.
-   * @param tol tolerance of the eigs computation.
-   * @param maxIterations the maximum number of Arnoldi update iterations.
-   * @return a dense vector of eigenvalues in descending order and a dense matrix of eigenvectors
-   *         (columns of the matrix).
-   * @note The number of computed eigenvalues might be smaller than k when some Ritz values do not
-   *       satisfy the convergence criterion specified by tol (see ARPACK Users Guide, Chapter 4.6
-   *       for more details). The maximum number of Arnoldi update iterations is set to 300 in this
-   *       function.
-   */
-  def symmetricEigs(
-      mul: BDV[Double] => BDV[Double],
-      n: Int,
-      k: Int,
-      tol: Double,
-      maxIterations: Int): (BDV[Double], BDM[Double]) = {
-    // TODO: remove this function and use eigs in breeze when switching breeze version
-    require(n > k, s"Number of required eigenvalues $k must be smaller than matrix dimension $n")
-
-    val arpack = ARPACK.getInstance()
-
-    // tolerance used in stopping criterion
-    val tolW = new doubleW(tol)
-    // number of desired eigenvalues, 0 < nev < n
-    val nev = new intW(k)
-    // nev Lanczos vectors are generated in the first iteration
-    // ncv-nev Lanczos vectors are generated in each subsequent iteration
-    // ncv must be smaller than n
-    val ncv = math.min(2 * k, n)
-
-    // "I" for standard eigenvalue problem, "G" for generalized eigenvalue problem
-    val bmat = "I"
-    // "LM" : compute the NEV largest (in magnitude) eigenvalues
-    val which = "LM"
-
-    var iparam = new Array[Int](11)
-    // use exact shift in each iteration
-    iparam(0) = 1
-    // maximum number of Arnoldi update iterations, or the actual number of iterations on output
-    iparam(2) = maxIterations
-    // Mode 1: A*x = lambda*x, A symmetric
-    iparam(6) = 1
-
-    require(n * ncv.toLong <= Integer.MAX_VALUE && ncv * (ncv.toLong + 8) <= Integer.MAX_VALUE,
-      s"k = $k and/or n = $n are too large to compute an eigendecomposition")
-
-    val ido = new intW(0)
-    val info = new intW(0)
-    val resid = new Array[Double](n)
-    val v = new Array[Double](n * ncv)
-    val workd = new Array[Double](n * 3)
-    val workl = new Array[Double](ncv * (ncv + 8))
-    val ipntr = new Array[Int](11)
-
-    // call ARPACK's reverse communication, first iteration with ido = 0
-    arpack.dsaupd(ido, bmat, n, which, nev.`val`, tolW, resid, ncv, v, n, iparam, ipntr, workd,
-      workl, workl.length, info)
-
-    val w = BDV(workd)
-
-    // ido = 99 : done flag in reverse communication
-    while (ido.`val` != 99) {
-      if (ido.`val` != -1 && ido.`val` != 1) {
-        throw new IllegalStateException("ARPACK returns ido = " + ido.`val` +
-          " This flag is not compatible with Mode 1: A*x = lambda*x, A symmetric.")
-      }
-      // multiply working vector with the matrix
-      val inputOffset = ipntr(0) - 1
-      val outputOffset = ipntr(1) - 1
-      val x = w.slice(inputOffset, inputOffset + n)
-      val y = w.slice(outputOffset, outputOffset + n)
-      y := mul(x)
-      // call ARPACK's reverse communication
-      arpack.dsaupd(ido, bmat, n, which, nev.`val`, tolW, resid, ncv, v, n, iparam, ipntr,
-        workd, workl, workl.length, info)
-    }
-
-    if (info.`val` != 0) {
-      info.`val` match {
-        case 1 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
-          " Maximum number of iterations taken. (Refer ARPACK user guide for details)")
-        case 3 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
-          " No shifts could be applied. Try to increase NCV. " +
-          "(Refer ARPACK user guide for details)")
-        case _ => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
-          " Please refer ARPACK user guide for error message.")
-      }
-    }
-
-    val d = new Array[Double](nev.`val`)
-    val select = new Array[Boolean](ncv)
-    // copy the Ritz vectors
-    val z = java.util.Arrays.copyOfRange(v, 0, nev.`val` * n)
-
-    // call ARPACK's post-processing for eigenvectors
-    arpack.dseupd(true, "A", select, d, z, n, 0.0, bmat, n, which, nev, tol, resid, ncv, v, n,
-      iparam, ipntr, workd, workl, workl.length, info)
-
-    // number of computed eigenvalues, might be smaller than k
-    val computed = iparam(4)
-
-    val eigenPairs = java.util.Arrays.copyOfRange(d, 0, computed).zipWithIndex.map { r =>
-      (r._1, java.util.Arrays.copyOfRange(z, r._2 * n, r._2 * n + n))
-    }
-
-    // sort the eigen-pairs in descending order
-    val sortedEigenPairs = eigenPairs.sortBy(- _._1)
-
-    // copy eigenvectors in descending order of eigenvalues
-    val sortedU = BDM.zeros[Double](n, computed)
-    sortedEigenPairs.zipWithIndex.foreach { r =>
-      val b = r._2 * n
-      var i = 0
-      while (i < n) {
-        sortedU.data(b + i) = r._1._2(i)
-        i += 1
-      }
-    }
-
-    (BDV[Double](sortedEigenPairs.map(_._1)), sortedU)
-  }
-
-  def symmetricEigsLocal(
-      matrix: BDM[Double],
-      n: Int,
-      k: Int,
-      tol: Double,
-      maxIterations: Int,
-      driverCores: Int): (BDV[Double], BDM[Double]) = {
-    // TODO: remove this function and use eigs in breeze when switching breeze version
-    require(n > k, s"Number of required eigenvalues $k must be smaller than matrix dimension $n")
-
-    val threadNum = math.min(
-      if (driverCores < 2) DEFAULT_THREAD_NUM else driverCores, matrix.rows)
-    val blocks = DenseMatrixUtil.blockByRow(matrix, threadNum)
-
-    val arpack = ARPACK.getInstance()
-
-    // tolerance used in stopping criterion
-    val tolW = new doubleW(tol)
-    // number of desired eigenvalues, 0 < nev < n
-    val nev = new intW(k)
-    // nev Lanczos vectors are generated in the first iteration
-    // ncv-nev Lanczos vectors are generated in each subsequent iteration
-    // ncv must be smaller than n
-    val ncv = math.min(2 * k, n)
-
-    // "I" for standard eigenvalue problem, "G" for generalized eigenvalue problem
-    val bmat = "I"
-    // "LM" : compute the NEV largest (in magnitude) eigenvalues
-    val which = "LM"
-
-    var iparam = new Array[Int](11)
-    // use exact shift in each iteration
-    iparam(0) = 1
-    // maximum number of Arnoldi update iterations, or the actual number of iterations on output
-    iparam(2) = maxIterations
-    // Mode 1: A*x = lambda*x, A symmetric
-    iparam(6) = 1
-
-    require(n * ncv.toLong <= Integer.MAX_VALUE && ncv * (ncv.toLong + 8) <= Integer.MAX_VALUE,
-      s"k = $k and/or n = $n are too large to compute an eigendecomposition")
-
-    val ido = new intW(0)
-    val info = new intW(0)
-    val resid = new Array[Double](n)
-    val v = new Array[Double](n * ncv)
-    val workd = new Array[Double](n * 3)
-    val workl = new Array[Double](ncv * (ncv + 8))
-    val ipntr = new Array[Int](11)
-
-    // call ARPACK's reverse communication, first iteration with ido = 0
-    arpack.dsaupd(ido, bmat, n, which, nev.`val`, tolW, resid, ncv, v, n, iparam, ipntr, workd,
-      workl, workl.length, info)
-
-    val w = BDV(workd)
-
-    // ido = 99 : done flag in reverse communication
-    while (ido.`val` != 99) {
-      if (ido.`val` != -1 && ido.`val` != 1) {
-        throw new IllegalStateException("ARPACK returns ido = " + ido.`val` +
-          " This flag is not compatible with Mode 1: A*x = lambda*x, A symmetric.")
-      }
-
-      // multiply working vector with the matrix
-      val inputOffset = ipntr(0) - 1
-      val outputOffset = ipntr(1) - 1
-      val input = w.slice(inputOffset, inputOffset + n)
-      val output = Dgemv.compute(blocks, input)
-      System.arraycopy(output.data, 0, workd, outputOffset, n)
-
-      // call ARPACK's reverse communication
-      arpack.dsaupd(ido, bmat, n, which, nev.`val`, tolW, resid, ncv, v, n, iparam, ipntr,
-        workd, workl, workl.length, info)
-    }
-
-    if (info.`val` != 0) {
-      info.`val` match {
-        case 1 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
-          " Maximum number of iterations taken. (Refer ARPACK user guide for details)")
-        case 3 => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
-          " No shifts could be applied. Try to increase NCV. " +
-          "(Refer ARPACK user guide for details)")
-        case _ => throw new IllegalStateException("ARPACK returns non-zero info = " + info.`val` +
-          " Please refer ARPACK user guide for error message.")
-      }
-    }
-
-    val d = new Array[Double](nev.`val`)
-    val select = new Array[Boolean](ncv)
-    // copy the Ritz vectors
-    val z = java.util.Arrays.copyOfRange(v, 0, nev.`val` * n)
-
-    // call ARPACK's post-processing for eigenvectors
-    arpack.dseupd(true, "A", select, d, z, n, 0.0, bmat, n, which, nev, tol, resid, ncv, v, n,
-      iparam, ipntr, workd, workl, workl.length, info)
-
-    // number of computed eigenvalues, might be smaller than k
-    val computed = iparam(4)
-
-    val eigenPairs = java.util.Arrays.copyOfRange(d, 0, computed).zipWithIndex.map { r =>
-      (r._1, java.util.Arrays.copyOfRange(z, r._2 * n, r._2 * n + n))
-    }
-
-    // sort the eigen-pairs in descending order
-    val sortedEigenPairs = eigenPairs.sortBy(- _._1)
-
-    // copy eigenvectors in descending order of eigenvalues
-    val sortedU = BDM.zeros[Double](n, computed)
-    sortedEigenPairs.zipWithIndex.foreach { r =>
-      val b = r._2 * n
-      var i = 0
-      while (i < n) {
-        sortedU.data(b + i) = r._1._2(i)
-        i += 1
-      }
-    }
-
-    (BDV[Double](sortedEigenPairs.map(_._1)), sortedU)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
deleted file mode 100644
index 0d62720..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ /dev/null
@@ -1,910 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.linalg.distributed
-
-
-import java.util.Arrays
-
-import scala.collection.mutable.ListBuffer
-
-import breeze.linalg.{axpy => brzAxpy, inv, DenseMatrix => BDM, DenseVector => BDV, MatrixSingularException, SparseVector => BSV}
-import breeze.linalg.blas.Gramian
-import breeze.linalg.lapack.EigenDecomposition
-import breeze.linalg.lapack.EigenDecomposition.Eigen
-import breeze.numerics.{sqrt => brzSqrt}
-import com.github.fommil.netlib.BLAS.{getInstance => blas}
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.StaticUtils
-import org.apache.spark.mllib.feature.SPCA
-import org.apache.spark.mllib.linalg._
-import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary, Statistics}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.random.XORShiftRandom
-
-
-
-/**
- * Represents a row-oriented distributed Matrix with no meaningful row indices.
- *
- * @param rows  rows stored as an RDD[Vector]
- * @param nRows number of rows. A non-positive value means unknown, and then the number of rows will
- *              be determined by the number of records in the RDD `rows`.
- * @param nCols number of columns. A non-positive value means unknown, and then the number of
- *              columns will be determined by the size of the first row.
- */
-@Since("1.0.0")
-class RowMatrix @Since("1.0.0")(
-    @Since("1.0.0") val rows: RDD[Vector],
-    private var nRows: Long,
-    private var nCols: Int) extends DistributedMatrix with Logging {
-
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
-  @Since("1.0.0")
-  def this(rows: RDD[Vector]) = this(rows, 0L, 0)
-
-  /** Gets or computes the number of columns. */
-  @Since("1.0.0")
-  override def numCols(): Long = {
-    if (nCols <= 0) {
-      try {
-        // Calling `first` will throw an exception if `rows` is empty.
-        nCols = rows.first().size
-      } catch {
-        case err: UnsupportedOperationException =>
-          sys.error("Cannot determine the number of cols because it is not specified in the " +
-            "constructor and the rows RDD is empty.")
-      }
-    }
-    nCols
-  }
-
-  /** Gets or computes the number of rows. */
-  @Since("1.0.0")
-  override def numRows(): Long = {
-    if (nRows <= 0L) {
-      nRows = rows.count()
-      if (nRows == 0L) {
-        sys.error("Cannot determine the number of rows because it is not specified in the " +
-          "constructor and the rows RDD is empty.")
-      }
-    }
-    nRows
-  }
-
-  /**
-   * Multiplies the Gramian matrix `A^T A` by a dense vector on the right without computing `A^T A`.
-   *
-   * @param v a dense vector whose length must match the number of columns of this matrix
-   * @return a dense vector representing the product
-   */
-  private[mllib] def multiplyGramianMatrixBy(v: BDV[Double]): BDV[Double] = {
-    val n = numCols().toInt
-    val vbr = rows.context.broadcast(v)
-    rows.treeAggregate(BDV.zeros[Double](n))(
-      seqOp = (U, r) => {
-        val rBrz = r.asBreeze
-        val a = rBrz.dot(vbr.value)
-        rBrz match {
-          // use specialized axpy for better performance
-          case _: BDV[_] => brzAxpy(a, rBrz.asInstanceOf[BDV[Double]], U)
-          case _: BSV[_] => brzAxpy(a, rBrz.asInstanceOf[BSV[Double]], U)
-          case _ => throw new UnsupportedOperationException(
-            s"Do not support vector operation from type ${rBrz.getClass.getName}.")
-        }
-        U
-      }, combOp = (U1, U2) => U1 += U2)
-  }
-
-  /**
-   * Computes the Gramian matrix `A^T A`.
-   *
-   * @note This cannot be computed on matrices with more than 65535 columns.
-   */
-  @Since("1.0.0")
-  def computeGramianMatrix(): Matrix = {
-    if (rows.map(_.isInstanceOf[SparseVector]).reduce((x, y) => x && y)) {
-      RowMatrixUtil.computeGramMatrixAsDenseMatrix(
-        rows.map(_.asInstanceOf[SparseVector]), numCols().toInt)
-    } else {
-      computeDenseGramianMatrix()
-    }
-  }
-
-
-  /**
-   * Compute the leading k eigenvalues and eigenvectors on a symmetric square sparse matrix.
-   *
-   * @param n dimension of the square matrix (maximum Int.MaxValue).
-   * @param k number of leading eigenvalues required, where k must be positive and less than n.
-   * @param tol tolerance of the eigs computation.
-   * @param maxIter the maximum number of Arnoldi update iterations.
-   * @return a dense vector of eigenvalues in descending order and a dense matrix of eigenvectors
-   *         (columns of the matrix).
-   * @note The number of computed eigenvalues might be smaller than k when some Ritz values do not
-   *       satisfy the convergence criterion specified by tol (see ARPACK Users Guide, Chapter 4.6
-   *       for more details). The maximum number of Arnoldi update iterations is set to 300 in this
-   *       function.
-   */
-  def eigenValueDecompositionOnSparseMatrix(
-    n: Int,
-    k: Int,
-    tol: Double,
-    maxIter: Int): (BDV[Double], BDM[Double]) = {
-    val result = RowMatrixUtil.computeGramMatrix(
-      rows.map(_.asInstanceOf[SparseVector]), n)
-    EigenValueDecomposition.symmetricEigs(
-      RowMatrixUtil.multiplySparseGramMatrixBy(result),
-      n, k, tol, maxIter)
-  }
-
-  /**
-   * Compute the leading k eigenvalues and eigenvectors on a symmetric square dense matrix.
-   *
-   * @param n dimension of the square matrix (maximum Int.MaxValue).
-   * @param k number of leading eigenvalues required, where k must be positive and less than n.
-   * @param tol tolerance of the eigs computation.
-   * @param maxIter the maximum number of Arnoldi update iterations.
-   * @return a dense vector of eigenvalues in descending order and a dense matrix of eigenvectors
-   *         (columns of the matrix).
-   */
-  def eigenValueDecompositionOnDenseMatrix(
-    n: Int,
-    k: Int,
-    tol: Double,
-    maxIter: Int): (BDV[Double], BDM[Double]) = {
-    val result = RowMatrixUtil.computeGramMatrix(
-      rows.map(_.asInstanceOf[SparseVector]), n)
-    val resultDenseMatrix = result._3.map{case ((i, j), sp) =>
-      ((i, j), new BDM[Double](sp.numRows, sp.numCols, sp.toArray))}
-    val newResult = (result._1, result._2, resultDenseMatrix)
-    EigenValueDecomposition.symmetricEigs(
-      RowMatrixUtil.multiplyDenseGramMatrixBy(newResult),
-      n, k, tol, maxIter)
-  }
-
-  /**
-   * Computes the Gramian matrix `A^T A` of dense matrix.
-   * @return Gramian matrix
-   */
-  def computeDenseGramianMatrix(): Matrix = {
-    val n = numCols().toInt
-    checkNumColumns(n)
-
-    // compute the upper triangular matrix
-    val gramianLen = n * (n + 1) / 2
-    val gramian = rows.mapPartitions(iter => {
-      val subMatrixValues = iter.map(_.toArray).toArray
-      val subMatrixRow = subMatrixValues.length
-      val localCovariance = new Array[Double](gramianLen)
-      Gramian.compute(subMatrixValues.flatten, localCovariance, subMatrixRow, n)
-      Array(localCovariance).iterator
-    }).treeReduce((cov1, cov2) => {
-      blas.daxpy(cov1.length, 1.0, cov2, 1, cov1, 1)
-      cov1
-    }, depth = 4)
-
-    // full fill the gramian matrix
-    val fullGramian = new Array[Double](n * n)
-    for(i <- 0 until n) {
-      val srcOffset = (2 * n - i + 1) * i / 2
-      fullGramian(i * n + i) = gramian(srcOffset)
-      for(j <- i until n) {
-        val v = gramian(srcOffset + j - i)
-        fullGramian(i * n + j) = v
-        fullGramian(j * n + i) = v
-      }
-    }
-
-    new DenseMatrix(n, n, fullGramian)
-  }
-
-
-  private def checkNumColumns(cols: Int): Unit = {
-    if (cols > 65535) {
-      throw new IllegalArgumentException(s"Argument with more than 65535 cols: $cols")
-    }
-    if (cols > 10000) {
-      val memMB = (cols.toLong * cols) / 125000
-      logWarning(s"$cols columns will require at least $memMB megabytes of memory!")
-    }
-  }
-
-  /**
-   * Computes singular value decomposition of this matrix. Denote this matrix by A (m x n). This
-   * will compute matrices U, S, V such that A ~= U * S * V', where S contains the leading k
-   * singular values, U and V contain the corresponding singular vectors.
-   *
-   * At most k largest non-zero singular values and associated vectors are returned. If there are k
-   * such values, then the dimensions of the return will be:
-   *  - U is a RowMatrix of size m x k that satisfies U' * U = eye(k),
-   *  - s is a Vector of size k, holding the singular values in descending order,
-   *  - V is a Matrix of size n x k that satisfies V' * V = eye(k).
-   *
-   * We assume n is smaller than m, though this is not strictly required.
-   * The singular values and the right singular vectors are derived
-   * from the eigenvalues and the eigenvectors of the Gramian matrix A' * A. U, the matrix
-   * storing the right singular vectors, is computed via matrix multiplication as
-   * U = A * (V * S^-1^), if requested by user. The actual method to use is determined
-   * automatically based on the cost:
-   *  - If n is small (n &lt; 100) or k is large compared with n (k &gt; n / 2), we compute
-   * the Gramian matrix first and then compute its top eigenvalues and eigenvectors locally
-   * on the driver. This requires a single pass with O(n^2^) storage on each executor and
-   * on the driver, and O(n^2^ k) time on the driver.
-   *  - Otherwise, we compute (A' * A) * v in a distributive way and send it to ARPACK's DSAUPD to
-   * compute (A' * A)'s top eigenvalues and eigenvectors on the driver node. This requires O(k)
-   * passes, O(n) storage on each executor, and O(n k) storage on the driver.
-   *
-   * Several internal parameters are set to default values. The reciprocal condition number rCond
-   * is set to 1e-9. All singular values smaller than rCond * sigma(0) are treated as zeros, where
-   * sigma(0) is the largest singular value. The maximum number of Arnoldi update iterations for
-   * ARPACK is set to 300 or k * 3, whichever is larger. The numerical tolerance for ARPACK's
-   * eigen-decomposition is set to 1e-10.
-   *
-   * @param k number of leading singular values to keep (0 &lt; k &lt;= n).
-   *          It might return less than k if
-   *          there are numerically zero singular values or there are not enough Ritz values
-   *          converged before the maximum number of Arnoldi update iterations is reached (in case
-   *          that matrix A is ill-conditioned).
-   * @param computeU whether to compute U
-   * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
-   *              are treated as zero, where sigma(0) is the largest singular value.
-   * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
-   * @note The conditions that decide which method to use internally and the default parameters are
-   *       subject to change.
-   */
-  @Since("1.0.0")
-  def computeSVD(
-      k: Int,
-      computeU: Boolean = false,
-      rCond: Double = 1e-9): SingularValueDecomposition[RowMatrix, Matrix] = {
-    // maximum number of Arnoldi update iterations for invoking ARPACK
-    val maxIter = math.max(300, k * 3)
-    // numerical tolerance for invoking ARPACK
-    val tol = 1e-10
-    computeSVD(k, computeU, rCond, maxIter, tol, "auto")
-  }
-
-  /**
-   * The actual SVD implementation, visible for testing.
-   *
-   * @param k        number of leading singular values to keep (0 &lt; k &lt;= n)
-   * @param computeU whether to compute U
-   * @param rCond    the reciprocal condition number
-   * @param maxIter  max number of iterations (if ARPACK is used)
-   * @param tol      termination tolerance (if ARPACK is used)
-   * @param mode     computation mode (auto: determine automatically which mode to use,
-   *                 local-svd: compute gram matrix and computes its full SVD locally,
-   *                 local-eigs: compute gram matrix and computes its top eigenvalues locally,
-   *                 dist-eigs: compute the top eigenvalues of the gram matrix distributively)
-   * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
-   */
-  private[mllib] def computeSVD(
-      k: Int,
-      computeU: Boolean,
-      rCond: Double,
-      maxIter: Int,
-      tol: Double,
-      mode: String): SingularValueDecomposition[RowMatrix, Matrix] = {
-    val n = numCols().toInt
-    require(k > 0 && k <= n, s"Requested k singular values but got k=$k and numCols=$n.")
-
-    object SVDMode extends Enumeration {
-      val LocalARPACK, LocalLAPACK, DistARPACK = Value
-    }
-
-    val modeStr = if (mode == "auto") RowMatrixUtil.selectSVDBranch(n, k) else mode
-    val computeMode = modeStr match {
-      case "local-svd" => SVDMode.LocalLAPACK
-      case "local-eigs" => SVDMode.LocalARPACK
-      case "dist-eigs" => SVDMode.DistARPACK
-      case _ => throw new IllegalArgumentException(s"Do not support mode $mode.")
-    }
-
-    val isSparse: Boolean = rows.map(_.isInstanceOf[SparseVector]).reduce((x, y) => x && y)
-
-    // Compute the eigen-decomposition of A' * A.
-    val (sigmaSquares: BDV[Double], u: BDM[Double]) = computeMode match {
-      case SVDMode.LocalARPACK =>
-        require(k < n, s"k must be smaller than n in local-eigs mode but got k=$k and n=$n.")
-        if (isSparse) {
-          eigenValueDecompositionOnDenseMatrix(n, k, tol, maxIter)
-        } else {
-          val G = computeDenseGramianMatrix().asBreeze.asInstanceOf[BDM[Double]]
-          val driverCores = RowMatrixUtil.parseExtraParams(rows.sparkContext, -1)
-          EigenValueDecomposition.symmetricEigsLocal(G, n, k, tol, maxIter, driverCores)
-        }
-      case SVDMode.LocalLAPACK =>
-        // svd latent constraint, 2 * n * n + 6 * n + 1 < Int.MaxValue
-        require(n < 32767, s"$n exceeds the breeze svd capability")
-        val G = computeGramianMatrix().asBreeze.asInstanceOf[BDM[Double]]
-        val Eigen(uFull, sigmaSquaresFull) = EigenDecomposition.symmetricEigenDecomposition(G)
-        (sigmaSquaresFull, uFull)
-      case SVDMode.DistARPACK =>
-        if (rows.getStorageLevel == StorageLevel.NONE) {
-          logWarning("The input data is not directly cached, which may hurt performance if its"
-            + " parent RDDs are also uncached.")
-        }
-        require(k < n, s"k must be smaller than n in dist-eigs mode but got k=$k and n=$n.")
-        if (isSparse) {
-          eigenValueDecompositionOnSparseMatrix(n, k, tol, maxIter)
-        } else {
-          EigenValueDecomposition.symmetricEigs(multiplyGramianMatrixBy, n, k, tol, maxIter)
-        }
-    }
-
-    val sigmas: BDV[Double] = brzSqrt(sigmaSquares)
-
-    // Determine the effective rank.
-    val sigma0 = sigmas(0)
-    val threshold = rCond * sigma0
-    var i = 0
-    // sigmas might have a length smaller than k, if some Ritz values do not satisfy the convergence
-    // criterion specified by tol after max number of iterations.
-    // Thus use i < min(k, sigmas.length) instead of i < k.
-    if (sigmas.length < k) {
-      logWarning(s"Requested $k singular values but only found ${sigmas.length} converged.")
-    }
-    while (i < math.min(k, sigmas.length) && sigmas(i) >= threshold) {
-      i += 1
-    }
-    val sk = i
-
-    if (sk < k) {
-      logWarning(s"Requested $k singular values but only found $sk nonzeros.")
-    }
-
-    // Warn at the end of the run as well, for increased visibility.
-    if (computeMode == SVDMode.DistARPACK && rows.getStorageLevel == StorageLevel.NONE) {
-      logWarning("The input data was not directly cached, which may hurt performance if its"
-        + " parent RDDs are also uncached.")
-    }
-
-    val s = Vectors.dense(Arrays.copyOfRange(sigmas.data, 0, sk))
-    val V = Matrices.dense(n, sk, Arrays.copyOfRange(u.data, 0, n * sk))
-
-    if (computeU) {
-      // N = Vk * Sk^{-1}
-      val N = new BDM[Double](n, sk, Arrays.copyOfRange(u.data, 0, n * sk))
-      var i = 0
-      var j = 0
-      while (j < sk) {
-        i = 0
-        val sigma = sigmas(j)
-        while (i < n) {
-          N(i, j) /= sigma
-          i += 1
-        }
-        j += 1
-      }
-      val U = this.multiply(Matrices.fromBreeze(N))
-      SingularValueDecomposition(U, s, V)
-    } else {
-      SingularValueDecomposition(null, s, V)
-    }
-  }
-
-  /**
-   * Distributed algorithm of computing covariance matrix for a dense matrix with dimension (m,n).
-   * @param mean Mean value vector of size n
-   * @param n Column number
-   * @param m Row number
-   * @return Covariance matrix
-   */
-  private def computeDenseVectorCovariance(mean: Vector, n: Int, m: Long): Matrix = {
-    val meanBroadcast = rows.context.broadcast(mean)
-
-    // centralize matrix
-    val centralizedRows = rows.map(row => {
-      val mean = meanBroadcast.value
-      val centralizedRow = new Array[Double](n)
-      for (idx <- 0 until n)
-        centralizedRow(idx) = row(idx) - mean(idx)
-      Vectors.dense(centralizedRow)
-    })
-
-    // compute the upper triangular matrix
-    val covarianceLen = n * (n + 1) / 2
-    val covariance = centralizedRows.mapPartitions(iter => {
-      val subMatrixValues = iter.map(_.toArray).toArray
-      val subMatrixRow = subMatrixValues.length
-      val localCovariance = new Array[Double](covarianceLen)
-      Gramian.compute(subMatrixValues.flatten, localCovariance, subMatrixRow, n)
-      Array(localCovariance).iterator
-    }).treeReduce((cov1, cov2) => {
-      blas.daxpy(cov1.length, 1.0, cov2, 1, cov1, 1)
-      cov1
-    }, depth = 4)
-
-    // full fill the covariance matrix
-    val fullCovariance = new Array[Double](n * n)
-    val m1 = m - 1.0
-    for(i <- StaticUtils.ZERO_INT until n) {
-      val srcOffset = (2 * n - i + 1) * i / 2
-      fullCovariance(i * n + i) = covariance(srcOffset) / m1
-      for(j <- i + 1 until n) {
-        val v = covariance(srcOffset + j - i) / m1
-        fullCovariance(i * n + j) = v
-        fullCovariance(j * n + i) = v
-      }
-    }
-
-    new DenseMatrix(n, n, fullCovariance)
-  }
-
-  /**
-   * Distributed algorithm of computing covariance matrix for a sparse matrix with dimension (m,n).
-   * @param mean Mean value vector of size n
-   * @param n Column number
-   * @param m Row number
-   * @return Covariance matrix
-   */
-  def computeSparseVectorCovariance(mean: Vector, n: Int, m: Long): Matrix = {
-    val G = RowMatrixUtil.computeGramMatrixAsDenseMatrix(
-      rows.map(_.asInstanceOf[SparseVector]), n)
-    var i = 0
-    var j = 0
-    val m1 = m - 1.0
-    var alpha = 0.0
-    while (i < n) {
-      alpha = m / m1 * mean(i)
-      j = i
-      while (j < n) {
-        val Gij = G(i, j) / m1 - alpha * mean(j)
-        G(i, j) = Gij
-        G(j, i) = Gij
-        j += 1
-      }
-      i += 1
-    }
-    G
-  }
-
-  /**
-   * Compute covariance matrix with formula Cov(X, Y) = E[(X-E(X))(Y-E(Y))]
-   * @return Covariance matrix
-   */
-  def computeCovariance(): Matrix = {
-    val isSparse = rows.map(_.isInstanceOf[SparseVector]).reduce((x, y) => x && y)
-
-    val n = numCols().toInt
-    checkNumColumns(n)
-    val summary = computeColumnSummaryStatistics()
-    val m = summary.count
-    require(m > 1, s"RowMatrix.computeCovariance called on matrix with only $m rows." +
-      "  Cannot compute the covariance of a RowMatrix with <= 1 row.")
-    val mean = summary.mean
-
-    if (isSparse) {
-      computeSparseVectorCovariance(mean, n, m)
-    } else {
-      computeDenseVectorCovariance(mean, n, m)
-    }
-  }
-
-  /**
-   * Computes the top k principal components and a vector of proportions of
-   * variance explained by each principal component.
-   * Rows correspond to observations and columns correspond to variables.
-   * The principal components are stored a local matrix of size n-by-k.
-   * Each column corresponds for one principal component,
-   * and the columns are in descending order of component variance.
-   * The row data do not need to be "centered" first; it is not necessary for
-   * the mean of each column to be 0.
-   *
-   * @param k number of top principal components.
-   * @param mode number of top principal components.
-   * @return a matrix of size n-by-k, whose columns are principal components, and
-   *         a vector of values which indicate how much variance each principal component
-   *         explains
-   */
-  @Since("1.6.0")
-  def computePrincipalComponentsAndExplainedVarianceBody(
-      k: Int,
-      mode: String = "auto"): (Matrix, Vector) = {
-    val n = numCols().toInt
-    require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]")
-
-    object PCAMode extends Enumeration {
-      val Correlation, SVD, SparseSVD = Value
-    }
-    val checkSparseBranch = if (rows.map(_.isInstanceOf[SparseVector])
-      .reduce((x, y) => x && y)) {
-        PCAMode.SparseSVD
-      } else {
-        PCAMode.SVD
-      }
-    val computeMode = mode match {
-        case "Correlation" => PCAMode.Correlation
-        case "SVD" => checkSparseBranch
-        case _ =>
-          if (n == k || n < 1500) {
-            PCAMode.Correlation
-          } else {
-            checkSparseBranch
-          }
-        }
-    computeMode match {
-      case PCAMode.Correlation =>
-        val cov = computeCovariance().asBreeze.asInstanceOf[BDM[Double]]
-        val Eigen(u, s) = EigenDecomposition.symmetricEigenDecomposition(cov)
-
-        val eigenSum = s.data.sum
-        val explainedVariance = s.data.map(_ / eigenSum)
-        if (k == n) {
-          (Matrices.dense(n, k, u.data), Vectors.dense(explainedVariance))
-        } else {
-          (Matrices.dense(n, k, Arrays.copyOfRange(u.data, 0, n * k)),
-            Vectors.dense(Arrays.copyOfRange(explainedVariance, 0, k)))
-        }
-      case PCAMode.SVD =>
-        val stas = Statistics.colStats(rows)
-        val meanVector = stas.mean.asBreeze
-        val centredMatrix = new RowMatrix(rows.map { rowVector =>
-          Vectors.fromBreeze(rowVector.asBreeze - meanVector)
-        })
-        val svd = centredMatrix.computeSVD(k)
-        val s = svd.s.toArray.map(eigValue => eigValue * eigValue / (numRows().toInt - 1))
-        val eigenSum = stas.variance.toArray.sum
-        val explainedVariance = s.map(_ / eigenSum)
-        (svd.V, Vectors.dense(explainedVariance))
-      case PCAMode.SparseSVD =>
-        val model = new SPCA(k).fit(rows)
-        (model.pc.asInstanceOf[Matrix], model.explainedVariance.asInstanceOf[Vector])
-    }
-  }
-
-  /**
-   * Computes the top k principal components and a vector of proportions of
-   * variance explained by each principal component.
-   *
-   * @param k number of top principal components.
-   * @return a matrix of size n-by-k, whose columns are principal components, and
-   *         a vector of values which indicate how much variance each principal component
-   *         explains
-   */
-  def computePrincipalComponentsAndExplainedVariance(k: Int): (Matrix, Vector) = {
-    computePrincipalComponentsAndExplainedVarianceBody(k)
-  }
-
-  /**
-   * Computes the top k principal components only.
-   *
-   * @param k number of top principal components.
-   * @return a matrix of size n-by-k, whose columns are principal components
-   * @see computePrincipalComponentsAndExplainedVariance
-   */
-  @Since("1.0.0")
-  def computePrincipalComponents(k: Int): Matrix = {
-    computePrincipalComponentsAndExplainedVariance(k)._1
-  }
-
-  /**
-   * Computes column-wise summary statistics.
-   */
-  @Since("1.0.0")
-  def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
-    val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
-      (aggregator, data) => aggregator.add(data),
-      (aggregator1, aggregator2) => aggregator1.merge(aggregator2))
-    updateNumRows(summary.count)
-    summary
-  }
-
-  /**
-   * Multiply this matrix by a local matrix on the right.
-   *
-   * @param B a local matrix whose number of rows must match the number of columns of this matrix
-   * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product,
-   *         which preserves partitioning
-   */
-  @Since("1.0.0")
-  def multiply(B: Matrix): RowMatrix = {
-    val n = numCols().toInt
-    val k = B.numCols
-    require(n == B.numRows, s"Dimension mismatch: $n vs ${B.numRows}")
-
-    require(B.isInstanceOf[DenseMatrix],
-      s"Only support dense matrix at this time but found ${B.getClass.getName}.")
-
-    val Bb = rows.context.broadcast(B.asBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
-    val AB = rows.mapPartitions { iter =>
-      val Bi = Bb.value
-      iter.map { row =>
-        val v = BDV.zeros[Double](k)
-        var i = 0
-        while (i < k) {
-          v(i) = row.asBreeze.dot(new BDV(Bi, i * n, 1, n))
-          i += 1
-        }
-        Vectors.fromBreeze(v)
-      }
-    }
-
-    new RowMatrix(AB, nRows, B.numCols)
-  }
-
-  /**
-   * Compute all cosine similarities between columns of this matrix using the brute-force
-   * approach of computing normalized dot products.
-   *
-   * @return An n x n sparse upper-triangular matrix of cosine similarities between
-   *         columns of this matrix.
-   */
-  @Since("1.2.0")
-  def columnSimilarities(): CoordinateMatrix = {
-    columnSimilarities(0.0)
-  }
-
-  /**
-   * Compute similarities between columns of this matrix using a sampling approach.
-   *
-   * The threshold parameter is a trade-off knob between estimate quality and computational cost.
-   *
-   * Setting a threshold of 0 guarantees deterministic correct results, but comes at exactly
-   * the same cost as the brute-force approach. Setting the threshold to positive values
-   * incurs strictly less computational cost than the brute-force approach, however the
-   * similarities computed will be estimates.
-   *
-   * The sampling guarantees relative-error correctness for those pairs of columns that have
-   * similarity greater than the given similarity threshold.
-   *
-   * To describe the guarantee, we set some notation:
-   * Let A be the smallest in magnitude non-zero element of this matrix.
-   * Let B be the largest  in magnitude non-zero element of this matrix.
-   * Let L be the maximum number of non-zeros per row.
-   *
-   * For example, for {0,1} matrices: A=B=1.
-   * Another example, for the Netflix matrix: A=1, B=5
-   *
-   * For those column pairs that are above the threshold,
-   * the computed similarity is correct to within 20% relative error with probability
-   * at least 1 - (0.981)^10/B^
-   *
-   * The shuffle size is bounded by the *smaller* of the following two expressions:
-   *
-   * O(n log(n) L / (threshold * A))
-   * O(m L^2^)
-   *
-   * The latter is the cost of the brute-force approach, so for non-zero thresholds,
-   * the cost is always cheaper than the brute-force approach.
-   *
-   * @param threshold Set to 0 for deterministic guaranteed correctness.
-   *                  Similarities above this threshold are estimated
-   *                  with the cost vs estimate quality trade-off described above.
-   * @return An n x n sparse upper-triangular matrix of cosine similarities
-   *         between columns of this matrix.
-   */
-  @Since("1.2.0")
-  def columnSimilarities(threshold: Double): CoordinateMatrix = {
-    require(threshold >= 0, s"Threshold cannot be negative: $threshold")
-
-    if (threshold > 1) {
-      logWarning(s"Threshold is greater than 1: $threshold " +
-        "Computation will be more efficient with promoted sparsity, " +
-        " however there is no correctness guarantee.")
-    }
-
-    val gamma = if (threshold < 1e-6) {
-      Double.PositiveInfinity
-    } else {
-      10 * math.log(numCols()) / threshold
-    }
-
-    columnSimilaritiesDIMSUM(computeColumnSummaryStatistics().normL2.toArray, gamma)
-  }
-
-  /**
-   * Compute QR decomposition for [[RowMatrix]]. The implementation is designed to optimize the QR
-   * decomposition (factorization) for the [[RowMatrix]] of a tall and skinny shape.
-   * Reference:
-   * Paul G. Constantine, David F. Gleich. "Tall and skinny QR factorizations in MapReduce
-   * architectures" (see <a href="http://dx.doi.org/10.1145/1996092.1996103">here</a>)
-   *
-   * @param computeQ whether to computeQ
-   * @return QRDecomposition(Q, R), Q = null if computeQ = false.
-   */
-  @Since("1.5.0")
-  def tallSkinnyQR(computeQ: Boolean = false): QRDecomposition[RowMatrix, Matrix] = {
-    val col = numCols().toInt
-    // split rows horizontally into smaller matrices, and compute QR for each of them
-    val blockQRs = rows.retag(classOf[Vector]).glom().filter(_.length != 0).map { partRows =>
-      val bdm = BDM.zeros[Double](partRows.length, col)
-      var i = 0
-      partRows.foreach { row =>
-        bdm(i, ::) := row.asBreeze.t
-        i += 1
-      }
-      breeze.linalg.qr.reduced(bdm).r
-    }
-
-    // combine the R part from previous results vertically into a tall matrix
-    val combinedR = blockQRs.treeReduce { (r1, r2) =>
-      val stackedR = BDM.vertcat(r1, r2)
-      breeze.linalg.qr.reduced(stackedR).r
-    }
-
-    val finalR = Matrices.fromBreeze(combinedR.toDenseMatrix)
-    val finalQ = if (computeQ) {
-      try {
-        val invR = inv(combinedR)
-        this.multiply(Matrices.fromBreeze(invR))
-      } catch {
-        case err: MatrixSingularException =>
-          logWarning("R is not invertible and return Q as null")
-          null
-      }
-    } else {
-      null
-    }
-    QRDecomposition(finalQ, finalR)
-  }
-
-  /**
-   * Find all similar columns using the DIMSUM sampling algorithm, described in two papers
-   *
-   * http://arxiv.org/abs/1206.2082
-   * http://arxiv.org/abs/1304.1467
-   *
-   * @param colMags A vector of column magnitudes
-   * @param gamma   The oversampling parameter. For provable results, set to 10 * log(n) / s,
-   *                where s is the smallest similarity score to be estimated,
-   *                and n is the number of columns
-   * @return An n x n sparse upper-triangular matrix of cosine similarities
-   *         between columns of this matrix.
-   */
-  private[mllib] def columnSimilaritiesDIMSUM(
-      colMags: Array[Double],
-      gamma: Double): CoordinateMatrix = {
-    require(gamma > 1.0, s"Oversampling should be greater than 1: $gamma")
-    require(colMags.size == this.numCols(), "Number of magnitudes didn't match column dimension")
-    val sg = math.sqrt(gamma) // sqrt(gamma) used many times
-
-    // Don't divide by zero for those columns with zero magnitude
-    val colMagsCorrected = colMags.map(x => if (x == 0) 1.0 else x)
-
-    val sc = rows.context
-    val pBV = sc.broadcast(colMagsCorrected.map(c => sg / c))
-    val qBV = sc.broadcast(colMagsCorrected.map(c => math.min(sg, c)))
-
-    val sims = rows.mapPartitionsWithIndex { (indx, iter) =>
-      val p = pBV.value
-      val q = qBV.value
-
-      val rand = new XORShiftRandom(indx)
-      val scaled = new Array[Double](p.size)
-      iter.flatMap { row =>
-        row match {
-          case SparseVector(size, indices, values) =>
-            val nnz = indices.size
-            var k = 0
-            while (k < nnz) {
-              scaled(k) = values(k) / q(indices(k))
-              k += 1
-            }
-
-            Iterator.tabulate(nnz) { k =>
-              val buf = new ListBuffer[((Int, Int), Double)]()
-              val i = indices(k)
-              val iVal = scaled(k)
-              if (iVal != 0 && rand.nextDouble() < p(i)) {
-                var l = k + 1
-                while (l < nnz) {
-                  val j = indices(l)
-                  val jVal = scaled(l)
-                  if (jVal != 0 && rand.nextDouble() < p(j)) {
-                    buf += (((i, j), iVal * jVal))
-                  }
-                  l += 1
-                }
-              }
-              buf
-            }.flatten
-          case DenseVector(values) =>
-            val n = values.size
-            var i = 0
-            while (i < n) {
-              scaled(i) = values(i) / q(i)
-              i += 1
-            }
-            Iterator.tabulate(n) { i =>
-              val buf = new ListBuffer[((Int, Int), Double)]()
-              val iVal = scaled(i)
-              if (iVal != 0 && rand.nextDouble() < p(i)) {
-                var j = i + 1
-                while (j < n) {
-                  val jVal = scaled(j)
-                  if (jVal != 0 && rand.nextDouble() < p(j)) {
-                    buf += (((i, j), iVal * jVal))
-                  }
-                  j += 1
-                }
-              }
-              buf
-            }.flatten
-        }
-      }
-    }.reduceByKey(_ + _).map { case ((i, j), sim) =>
-      MatrixEntry(i.toLong, j.toLong, sim)
-    }
-    new CoordinateMatrix(sims, numCols(), numCols())
-  }
-
-  private[mllib] override def toBreeze(): BDM[Double] = {
-    val m = numRows().toInt
-    val n = numCols().toInt
-    val mat = BDM.zeros[Double](m, n)
-    var i = 0
-    rows.collect().foreach { vector =>
-      vector.foreachActive { case (j, v) =>
-        mat(i, j) = v
-      }
-      i += 1
-    }
-    mat
-  }
-
-  /** Updates or verifies the number of rows. */
-  private def updateNumRows(m: Long) {
-    if (nRows <= 0) {
-      nRows = m
-    } else {
-      require(nRows == m,
-        s"The number of rows $m is different from what specified or previously computed: ${nRows}.")
-    }
-  }
-}
-
-@Since("1.0.0")
-object RowMatrix {
-
-  /**
-   * Fills a full square matrix from its upper triangular part.
-   */
-  private def triuToFull(n: Int, U: Array[Double]): Matrix = {
-    val G = new BDM[Double](n, n)
-
-    var row = 0
-    var col = 0
-    var idx = 0
-    var value = 0.0
-    while (col < n) {
-      row = 0
-      while (row < col) {
-        value = U(idx)
-        G(row, col) = value
-        G(col, row) = value
-        idx += 1
-        row += 1
-      }
-      G(col, col) = U(idx)
-      idx += 1
-      col += 1
-    }
-
-    Matrices.dense(n, n, G.data)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
deleted file mode 100644
index 5d58eed..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
+++ /dev/null
@@ -1,103 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.stat.correlation
-
-import org.apache.spark.mllib.linalg.{DenseVector, Matrix, Vector}
-import org.apache.spark.rdd.RDD
-
-/**
- * Trait for correlation algorithms.
- */
-private[stat] trait Correlation {
-
-  /**
-   * Compute correlation for two datasets.
-   */
-  def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double
-
-  /**
-   * Compute the correlation matrix S, for the input matrix, where S(i, j) is the correlation
-   * between column i and j. S(i, j) can be NaN if the correlation is undefined for column i and j.
-   */
-  def computeCorrelationMatrix(X: RDD[Vector]): Matrix
-
-  /**
-   * Combine the two input RDD[Double]s into an RDD[Vector] and compute the correlation using the
-   * correlation implementation for RDD[Vector]. Can be NaN if correlation is undefined for the
-   * input vectors.
-   */
-  def computeCorrelationWithMatrixImpl(x: RDD[Double], y: RDD[Double]): Double = {
-    val mat: RDD[Vector] = x.zip(y).map { case (xi, yi) => new DenseVector(Array(xi, yi)) }
-    computeCorrelationMatrix(mat)(0, 1)
-  }
-
-}
-
-/**
- * Delegates computation to the specific correlation object based on the input method name.
- */
-private[stat] object Correlations {
-
-  def corr(x: RDD[Double],
-       y: RDD[Double],
-       method: String = CorrelationNames.defaultCorrName): Double = {
-    val correlation = getCorrelationFromName(method)
-    correlation.computeCorrelation(x, y)
-  }
-
-  def corrMatrix(X: RDD[Vector],
-      method: String = CorrelationNames.defaultCorrName): Matrix = {
-    val correlation = getCorrelationFromName(method)
-    correlation.computeCorrelationMatrix(X)
-  }
-
-  // Match input correlation name with a known name via simple string matching.
-  def getCorrelationFromName(method: String): Correlation = {
-    try {
-      CorrelationNames.nameToObjectMap(method)
-    } catch {
-      case nse: NoSuchElementException =>
-        throw new IllegalArgumentException("Unrecognized method name. Supported correlations: "
-          + CorrelationNames.nameToObjectMap.keys.mkString(", "))
-    }
-  }
-}
-
-/**
- * Maintains supported and default correlation names.
- *
- * Currently supported correlations: `pearson`, `spearman`.
- * Current default correlation: `pearson`.
- *
- * After new correlation algorithms are added, please update the documentation here and in
- * Statistics.scala for the correlation APIs.
- */
-private[mllib] object CorrelationNames {
-
-  // Note: after new types of correlations are implemented, please update this map.
-  val nameToObjectMap = Map(("pearson", PearsonCorrelation), ("spearman", SpearmanCorrelation))
-  val defaultCorrName: String = "pearson"
-
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
deleted file mode 100644
index 5bee973..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
+++ /dev/null
@@ -1,119 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.stat.correlation
-
-import breeze.linalg.{DenseMatrix => BDM}
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.mllib.linalg.{DenseVector, Matrices, Matrix, SparseVector, Vector}
-import org.apache.spark.mllib.linalg.distributed.RowMatrix
-import org.apache.spark.rdd.RDD
-
-/**
- * Compute Pearson correlation for two RDDs of the type RDD[Double] or the correlation matrix
- * for an RDD of the type RDD[Vector].
- *
- * Definition of Pearson correlation can be found at
- * http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
- */
-private[stat] object PearsonCorrelation extends Correlation with Logging {
-
-  /**
-   * Compute the Pearson correlation for two datasets. NaN if either vector has 0 variance.
-   */
-  override def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double = {
-    computeCorrelationWithMatrixImpl(x, y)
-  }
-
-  /**
-   * Compute the Pearson correlation matrix S, for the input matrix, where S(i, j) is the
-   * correlation between column i and j. 0 covariance results in a correlation value of Double.NaN.
-   */
-  override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
-    val isSparse: Boolean = X.map(_.isInstanceOf[SparseVector]).treeReduce((x, y) => x && y)
-    if (isSparse) {
-      val rowMatrix = new RowMatrix(X)
-      val cov = rowMatrix.computeCovariance()
-      computeCorrelationMatrixFromCovariance(cov)
-    } else {
-      PearsonCorrelationUtil.computeDenseVectorCorrelation(X.map(_.asInstanceOf[DenseVector]))
-    }
-  }
-
-  /**
-   * Compute the Pearson correlation matrix from the covariance matrix.
-   * 0 variance results in a correlation value of Double.NaN.
-   */
-  def computeCorrelationMatrixFromCovariance(covarianceMatrix: Matrix): Matrix = {
-    val cov = covarianceMatrix.asBreeze.asInstanceOf[BDM[Double]]
-    val n = cov.cols
-
-    // Compute the standard deviation on the diagonals first
-    var i = 0
-    while (i < n) {
-      // TODO remove once covariance numerical issue resolved.
-      cov(i, i) = if (closeToZero(cov(i, i))) 0.0 else math.sqrt(cov(i, i))
-      i +=1
-    }
-
-    // Loop through columns since cov is column major
-    var j = 0
-    var sigma = 0.0
-    var containNaN = false
-    while (j < n) {
-      sigma = cov(j, j)
-      i = 0
-      while (i < j) {
-        val corr = if (sigma == 0.0 || cov(i, i) == 0.0) {
-          containNaN = true
-          Double.NaN
-        } else {
-          cov(i, j) / (sigma * cov(i, i))
-        }
-        cov(i, j) = corr
-        cov(j, i) = corr
-        i += 1
-      }
-      j += 1
-    }
-
-    // put 1.0 on the diagonals
-    i = 0
-    while (i < n) {
-      cov(i, i) = 1.0
-      i +=1
-    }
-
-    if (containNaN) {
-      logWarning("Pearson correlation matrix contains NaN values.")
-    }
-
-    Matrices.fromBreeze(cov)
-  }
-
-  private def closeToZero(value: Double, threshold: Double = 1e-12): Boolean = {
-    math.abs(value) <= threshold
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
deleted file mode 100644
index eaf0afc..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala
+++ /dev/null
@@ -1,65 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.stat.correlation
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.mllib.linalg.{Matrix, Vector}
-import org.apache.spark.mllib.linalg.distributed.RowMatrix
-import org.apache.spark.rdd.RDD
-import org.apache.spark.storage.StorageLevel
-
-/**
- * Compute Spearman's correlation for two RDDs of the type RDD[Double] or the correlation matrix
- * for an RDD of the type RDD[Vector].
- *
- * Definition of Spearman's correlation can be found at
- * http://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient
- */
-private[stat] object SpearmanCorrelation extends Correlation with Logging {
-
-  /**
-   * Compute Spearman's correlation for two datasets.
-   */
-  override def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double = {
-    computeCorrelationWithMatrixImpl(x, y)
-  }
-
-  /**
-   * Compute Spearman's correlation matrix S, for the input matrix, where S(i, j) is the
-   * correlation between column i and j.
-   */
-  override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
-    val groupedRanks = SpearmanCorrelationUtil.getRanks(X)
-      .persist(StorageLevel.MEMORY_AND_DISK)
-      .setName("groupedRanks")
-    groupedRanks.foreach(_ => {})
-    X.sparkContext.getPersistentRDDs.foreach{case (_, rdd) =>
-      if (!rdd.name.contains("groupedRanks")) rdd.unpersist()
-    }
-    val rowMatrix = new RowMatrix(groupedRanks)
-    val cov = rowMatrix.computeCovariance()
-    PearsonCorrelation.computeCorrelationMatrixFromCovariance(cov)
-  }
-}
diff --git a/ml-accelerator/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/ml-accelerator/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
deleted file mode 100644
index bf402c3..0000000
--- a/ml-accelerator/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ /dev/null
@@ -1,291 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.tree
-
-import scala.collection.JavaConverters._
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.api.java.JavaRDD
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.tree.impl.{DecisionForest, DTUtils}
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.configuration.Algo._
-import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
-import org.apache.spark.mllib.tree.configuration.Strategy
-import org.apache.spark.mllib.tree.impurity._
-import org.apache.spark.mllib.tree.model._
-import org.apache.spark.rdd.RDD
-
-
-/**
- * A class which implements a decision tree learning algorithm for classification and regression.
- * It supports both continuous and categorical features.
- *
- * @param strategy The configuration parameters for the tree algorithm which specify the type
- *                 of decision tree (classification or regression), feature type (continuous,
- *                 categorical), depth of the tree, quantile calculation strategy, etc.
- * @param seed Random seed.
- */
-@Since("1.0.0")
-class DecisionTree private[spark] (private val strategy: Strategy, private val seed: Int)
-  extends Serializable with Logging {
-
-  /**
-   * @param strategy The configuration parameters for the tree algorithm which specify the type
-   *                 of decision tree (classification or regression), feature type (continuous,
-   *                 categorical), depth of the tree, quantile calculation strategy, etc.
-   */
-  @Since("1.0.0")
-  def this(strategy: Strategy) = this(strategy, seed = 0)
-
-  strategy.assertValid()
-
-  /**
-   * Method to train a decision tree model over an RDD
-   *
-   * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   * @return DecisionTreeModel that can be used for prediction.
-   */
-  @Since("1.2.0")
-  def run(input: RDD[LabeledPoint]): DecisionTreeModel = {
-    val trees = DecisionForest.run(input.map(_.asML), strategy, numTrees = 1,
-      featureSubsetStrategy = "all", seed = seed.toLong, None)
-    val rfModel = new RandomForestModel(strategy.algo, trees.map(_.toOld))
-    rfModel.trees(0)
-  }
-}
-
-@Since("1.0.0")
-object DecisionTree extends Serializable with Logging {
-
-  /**
-   * Method to train a decision tree model.
-   * The method supports binary and multiclass classification and regression.
-   *
-   * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   *              For classification, labels should take values {0, 1, ..., numClasses-1}.
-   *              For regression, labels are real numbers.
-   * @param strategy The configuration parameters for the tree algorithm which specify the type
-   *                 of decision tree (classification or regression), feature type (continuous,
-   *                 categorical), depth of the tree, quantile calculation strategy, etc.
-   * @return DecisionTreeModel that can be used for prediction.
-   *
-   * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier`
-   * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor`
-   * is recommended to clearly separate classification and regression.
-   */
-  @Since("1.0.0")
-  def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
-    new DecisionTree(strategy).run(input)
-  }
-
-  /**
-   * Method to train a decision tree model.
-   * The method supports binary and multiclass classification and regression.
-   *
-   * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   *              For classification, labels should take values {0, 1, ..., numClasses-1}.
-   *              For regression, labels are real numbers.
-   * @param algo Type of decision tree, either classification or regression.
-   * @param impurity Criterion used for information gain calculation.
-   * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
-   *                 1 internal node + 2 leaf nodes).
-   * @return DecisionTreeModel that can be used for prediction.
-   *
-   * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier`
-   * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor`
-   * is recommended to clearly separate classification and regression.
-   */
-  @Since("1.0.0")
-  def train(
-      input: RDD[LabeledPoint],
-      algo: Algo,
-      impurity: Impurity,
-      maxDepth: Int): DecisionTreeModel = {
-    val (_, maxMemInMB) = DTUtils.getInvisibleParamsForMLLib(input)
-    val strategy =
-      new Strategy(algo, impurity, maxDepth, maxMemoryInMB = maxMemInMB)
-    new DecisionTree(strategy).run(input)
-  }
-
-  /**
-   * Method to train a decision tree model.
-   * The method supports binary and multiclass classification and regression.
-   *
-   * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   *              For classification, labels should take values {0, 1, ..., numClasses-1}.
-   *              For regression, labels are real numbers.
-   * @param algo Type of decision tree, either classification or regression.
-   * @param impurity Criterion used for information gain calculation.
-   * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
-   *                 1 internal node + 2 leaf nodes).
-   * @param numClasses Number of classes for classification. Default value of 2.
-   * @return DecisionTreeModel that can be used for prediction.
-   *
-   * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier`
-   * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor`
-   * is recommended to clearly separate classification and regression.
-   */
-  @Since("1.2.0")
-  def train(
-      input: RDD[LabeledPoint],
-      algo: Algo,
-      impurity: Impurity,
-      maxDepth: Int,
-      numClasses: Int): DecisionTreeModel = {
-    val (_, maxMemInMB) = DTUtils.getInvisibleParamsForMLLib(input)
-    val strategy = new Strategy(algo, impurity, maxDepth, numClasses,
-      maxMemoryInMB = maxMemInMB)
-    new DecisionTree(strategy).run(input)
-  }
-
-  /**
-   * Method to train a decision tree model.
-   * The method supports binary and multiclass classification and regression.
-   *
-   * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   *              For classification, labels should take values {0, 1, ..., numClasses-1}.
-   *              For regression, labels are real numbers.
-   * @param algo Type of decision tree, either classification or regression.
-   * @param impurity Criterion used for information gain calculation.
-   * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
-   *                 1 internal node + 2 leaf nodes).
-   * @param numClasses Number of classes for classification. Default value of 2.
-   * @param maxBins Maximum number of bins used for splitting features.
-   * @param quantileCalculationStrategy  Algorithm for calculating quantiles.
-   * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n to k)
-   *                                indicates that feature n is categorical with k categories
-   *                                indexed from 0: {0, 1, ..., k-1}.
-   * @return DecisionTreeModel that can be used for prediction.
-   *
-   * @note Using `org.apache.spark.mllib.tree.DecisionTree.trainClassifier`
-   * and `org.apache.spark.mllib.tree.DecisionTree.trainRegressor`
-   * is recommended to clearly separate classification and regression.
-   */
-  @Since("1.0.0")
-  def train(
-      input: RDD[LabeledPoint],
-      algo: Algo,
-      impurity: Impurity,
-      maxDepth: Int,
-      numClasses: Int,
-      maxBins: Int,
-      quantileCalculationStrategy: QuantileStrategy,
-      categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel = {
-    val (_, maxMemInMB) = DTUtils.getInvisibleParamsForMLLib(input)
-    val strategy = new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
-      quantileCalculationStrategy, categoricalFeaturesInfo, maxMemoryInMB = maxMemInMB)
-    new DecisionTree(strategy).run(input)
-  }
-
-  /**
-   * Method to train a decision tree model for binary or multiclass classification.
-   *
-   * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   *              Labels should take values {0, 1, ..., numClasses-1}.
-   * @param numClasses Number of classes for classification.
-   * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n to k)
-   *                                indicates that feature n is categorical with k categories
-   *                                indexed from 0: {0, 1, ..., k-1}.
-   * @param impurity Criterion used for information gain calculation.
-   *                 Supported values: "gini" (recommended) or "entropy".
-   * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
-   *                 1 internal node + 2 leaf nodes).
-   *                 (suggested value: 5)
-   * @param maxBins Maximum number of bins used for splitting features.
-   *                (suggested value: 32)
-   * @return DecisionTreeModel that can be used for prediction.
-   */
-  @Since("1.1.0")
-  def trainClassifier(
-      input: RDD[LabeledPoint],
-      numClasses: Int,
-      categoricalFeaturesInfo: Map[Int, Int],
-      impurity: String,
-      maxDepth: Int,
-      maxBins: Int): DecisionTreeModel = {
-    val impurityType = Impurities.fromString(impurity)
-    train(input, Classification, impurityType, maxDepth, numClasses, maxBins, Sort,
-      categoricalFeaturesInfo)
-  }
-
-  /**
-   * Java-friendly API for `org.apache.spark.mllib.tree.DecisionTree.trainClassifier`
-   */
-  @Since("1.1.0")
-  def trainClassifier(
-      input: JavaRDD[LabeledPoint],
-      numClasses: Int,
-      categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
-      impurity: String,
-      maxDepth: Int,
-      maxBins: Int): DecisionTreeModel = {
-    trainClassifier(input.rdd, numClasses,
-      categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap,
-      impurity, maxDepth, maxBins)
-  }
-
-  /**
-   * Method to train a decision tree model for regression.
-   *
-   * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   *              Labels are real numbers.
-   * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n to k)
-   *                                indicates that feature n is categorical with k categories
-   *                                indexed from 0: {0, 1, ..., k-1}.
-   * @param impurity Criterion used for information gain calculation.
-   *                 The only supported value for regression is "variance".
-   * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
-   *                 1 internal node + 2 leaf nodes).
-   *                 (suggested value: 5)
-   * @param maxBins Maximum number of bins used for splitting features.
-   *                (suggested value: 32)
-   * @return DecisionTreeModel that can be used for prediction.
-   */
-  @Since("1.1.0")
-  def trainRegressor(
-      input: RDD[LabeledPoint],
-      categoricalFeaturesInfo: Map[Int, Int],
-      impurity: String,
-      maxDepth: Int,
-      maxBins: Int): DecisionTreeModel = {
-    val impurityType = Impurities.fromString(impurity)
-    train(input, Regression, impurityType, maxDepth, 0, maxBins, Sort, categoricalFeaturesInfo)
-  }
-
-  /**
-   * Java-friendly API for `org.apache.spark.mllib.tree.DecisionTree.trainRegressor`
-   */
-  @Since("1.1.0")
-  def trainRegressor(
-      input: JavaRDD[LabeledPoint],
-      categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
-      impurity: String,
-      maxDepth: Int,
-      maxBins: Int): DecisionTreeModel = {
-    trainRegressor(input.rdd,
-      categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap,
-      impurity, maxDepth, maxBins)
-  }
-}
diff --git a/ml-core/pom.xml b/ml-core/pom.xml
index 6aad5f9..443d9b3 100644
--- a/ml-core/pom.xml
+++ b/ml-core/pom.xml
@@ -6,7 +6,7 @@
   </parent>
 
   <modelVersion>4.0.0</modelVersion>
-  <artifactId>boostkit-ml-core_2.11</artifactId>
+  <artifactId>boostkit-ml-core_2.12</artifactId>
   <version>2.1.0</version>
   <name>${project.artifactId}</name>
   <description>Spark ml core</description>
@@ -14,7 +14,7 @@
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>boostkit-ml-kernel-client-core_2.11</artifactId>
+      <artifactId>boostkit-ml-kernel-client-core_2.12</artifactId>
       <version>2.1.0</version>
       <classifier>${spark.version}</classifier>
       <scope>compile</scope>
diff --git a/ml-core/src/main/scala/breeze/numerics/DigammaX.scala b/ml-core/src/main/scala/breeze/numerics/DigammaX.scala
index 4fd0f02..7dce00d 100644
--- a/ml-core/src/main/scala/breeze/numerics/DigammaX.scala
+++ b/ml-core/src/main/scala/breeze/numerics/DigammaX.scala
@@ -1,10 +1,4 @@
 // scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  Copyright 2012 David Hall
 
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/Node.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/Node.scala
deleted file mode 100644
index bbaaff8..0000000
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ /dev/null
@@ -1,635 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree
-
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.tree.impl.BinnedFeature
-import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
-import org.apache.spark.mllib.tree.model.{ImpurityStats,
-  InformationGainStats => OldInformationGainStats, Node => OldNode, Predict => OldPredict}
-
-/**
- * Decision tree node interface.
- */
-sealed abstract class Node extends Serializable {
-
-  // TODO: Add aggregate stats (once available).  This will happen after we move the DecisionTree
-  //       code into the new API and deprecate the old API.  SPARK-3727
-
-  /** Prediction a leaf node makes, or which an internal node would make if it were a leaf node */
-  def prediction: Double
-
-  /** Impurity measure at this node (for training data) */
-  def impurity: Double
-
-  /**
-   * Statistics aggregated from training data at this node, used to compute prediction, impurity,
-   * and probabilities.
-   * For classification, the array of class counts must be normalized to a probability distribution.
-   */
-  private[ml] def impurityStats: ImpurityCalculator
-
-  /** Recursive prediction helper method */
-  private[ml] def predictImpl(features: Vector): LeafNode
-
-  private[ml] def predictImplX(binnedFeatures: Array[Int], splits: Array[Array[Split]]): LeafNode
-
-  /**
-   * Get the number of nodes in tree below this node, including leaf nodes.
-   * E.g., if this is a leaf, returns 0.  If both children are leaves, returns 2.
-   */
-  private[tree] def numDescendants: Int
-
-  /**
-   * Recursive print function.
-   * @param indentFactor  The number of spaces to add to each level of indentation.
-   */
-  private[tree] def subtreeToString(indentFactor: Int = 0): String
-
-  /**
-   * Get depth of tree from this node.
-   * E.g.: Depth 0 means this is a leaf node.  Depth 1 means 1 internal and 2 leaf nodes.
-   */
-  private[tree] def subtreeDepth: Int
-
-  /**
-   * Create a copy of this node in the old Node format, recursively creating child nodes as needed.
-   * @param id  Node ID using old format IDs
-   */
-  private[ml] def toOld(id: Int): OldNode
-
-  /**
-   * Trace down the tree, and return the largest feature index used in any split.
-   * @return  Max feature index used in a split, or -1 if there are no splits (single leaf node).
-   */
-  private[ml] def maxSplitFeatureIndex(): Int
-
-  /** Returns a deep copy of the subtree rooted at this node. */
-  private[tree] def deepCopy(): Node
-}
-
-private[ml] object Node {
-
-  /**
-   * Create a new Node from the old Node format, recursively creating child nodes as needed.
-   */
-  def fromOld(oldNode: OldNode, categoricalFeatures: Map[Int, Int]): Node = {
-    if (oldNode.isLeaf) {
-      // TODO: Once the implementation has been moved to this API, then include sufficient
-      //       statistics here.
-      new LeafNode(prediction = oldNode.predict.predict,
-        impurity = oldNode.impurity, impurityStats = null)
-    } else {
-      val gain = if (oldNode.stats.nonEmpty) {
-        oldNode.stats.get.gain
-      } else {
-        0.0
-      }
-      new InternalNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity,
-        gain = gain, leftChild = fromOld(oldNode.leftNode.get, categoricalFeatures),
-        rightChild = fromOld(oldNode.rightNode.get, categoricalFeatures),
-        split = Split.fromOld(oldNode.split.get, categoricalFeatures), impurityStats = null)
-    }
-  }
-}
-
-/**
- * Decision tree leaf node.
- * @param prediction  Prediction this node makes
- * @param impurity  Impurity measure at this node (for training data)
- */
-class LeafNode private[ml] (
-    override val prediction: Double,
-    override val impurity: Double,
-    override private[ml] val impurityStats: ImpurityCalculator) extends Node {
-
-  override def toString: String =
-    s"LeafNode(prediction = $prediction, impurity = $impurity)"
-
-  override private[ml] def predictImpl(features: Vector): LeafNode = this
-
-  override private[ml] def predictImplX(binnedFeatures: Array[Int], splits: Array[Array[Split]]
-                                        ): LeafNode = this
-
-  override private[tree] def numDescendants: Int = 0
-
-  override private[tree] def subtreeToString(indentFactor: Int = 0): String = {
-    val prefix: String = " " * indentFactor
-    s"$prefix" + s"Predict: $prediction\n"
-  }
-
-  override private[tree] def subtreeDepth: Int = 0
-
-  override private[ml] def toOld(id: Int): OldNode = {
-    new OldNode(id, new OldPredict(prediction, prob = impurityStats.prob(prediction)),
-      impurity, isLeaf = true, None, None, None, None)
-  }
-
-  override private[ml] def maxSplitFeatureIndex(): Int = -1
-
-  override private[tree] def deepCopy(): Node = {
-    new LeafNode(prediction, impurity, impurityStats)
-  }
-}
-
-/**
- * Internal Decision Tree node.
- * @param prediction  Prediction this node would make if it were a leaf node
- * @param impurity  Impurity measure at this node (for training data)
- * @param gain Information gain value. Values less than 0 indicate missing values;
- *             this quirk will be removed with future updates.
- * @param leftChild  Left-hand child node
- * @param rightChild  Right-hand child node
- * @param split  Information about the test used to split to the left or right child.
- */
-class InternalNode private[ml] (
-    override val prediction: Double,
-    override val impurity: Double,
-    val gain: Double,
-    val leftChild: Node,
-    val rightChild: Node,
-    val split: Split,
-    override private[ml] val impurityStats: ImpurityCalculator) extends Node {
-
-  // Note to developers: The constructor argument impurityStats should be reconsidered before we
-  //                     make the constructor public.  We may be able to improve the representation.
-
-  override def toString: String = {
-    s"InternalNode(prediction = $prediction, impurity = $impurity, split = $split)"
-  }
-
-  override private[ml] def predictImpl(features: Vector): LeafNode = {
-    if (split.shouldGoLeft(features)) {
-      leftChild.predictImpl(features)
-    } else {
-      rightChild.predictImpl(features)
-    }
-  }
-
-  private[ml] def predictImplX(binnedFeatures: Array[Int], splits: Array[Array[Split]]
-                              ): LeafNode = {
-    if (split.shouldGoLeft(binnedFeatures(split.featureIndex).toChar, splits(split.featureIndex))) {
-      leftChild.predictImplX(binnedFeatures, splits)
-    } else {
-      rightChild.predictImplX(binnedFeatures, splits)
-    }
-  }
-
-  override private[tree] def numDescendants: Int = {
-    2 + leftChild.numDescendants + rightChild.numDescendants
-  }
-
-  override private[tree] def subtreeToString(indentFactor: Int = 0): String = {
-    val prefix: String = " " * indentFactor
-    s"$prefix If (${InternalNode.splitToString(split, left = true)})\n" +
-      leftChild.subtreeToString(indentFactor + 1) +
-      s"$prefix Else (${InternalNode.splitToString(split, left = false)})\n" +
-      rightChild.subtreeToString(indentFactor + 1)
-  }
-
-  override private[tree] def subtreeDepth: Int = {
-    1 + math.max(leftChild.subtreeDepth, rightChild.subtreeDepth)
-  }
-
-  override private[ml] def toOld(id: Int): OldNode = {
-    assert(id.toLong * 2 < Int.MaxValue, "Decision Tree could not be converted from new to old API"
-      + " since the old API does not support deep trees.")
-    new OldNode(id, new OldPredict(prediction, prob = impurityStats.prob(prediction)), impurity,
-      isLeaf = false, Some(split.toOld), Some(leftChild.toOld(OldNode.leftChildIndex(id))),
-      Some(rightChild.toOld(OldNode.rightChildIndex(id))),
-      Some(new OldInformationGainStats(gain, impurity, leftChild.impurity, rightChild.impurity,
-        new OldPredict(leftChild.prediction, prob = 0.0),
-        new OldPredict(rightChild.prediction, prob = 0.0))))
-  }
-
-  override private[ml] def maxSplitFeatureIndex(): Int = {
-    math.max(split.featureIndex,
-      math.max(leftChild.maxSplitFeatureIndex(), rightChild.maxSplitFeatureIndex()))
-  }
-
-  override private[tree] def deepCopy(): Node = {
-    new InternalNode(prediction, impurity, gain, leftChild.deepCopy(), rightChild.deepCopy(),
-      split, impurityStats)
-  }
-}
-
-private object InternalNode {
-
-  /**
-   * Helper method for [[Node.subtreeToString()]].
-   * @param split  Split to print
-   * @param left  Indicates whether this is the part of the split going to the left,
-   *              or that going to the right.
-   */
-  private def splitToString(split: Split, left: Boolean): String = {
-    val featureStr = s"feature ${split.featureIndex}"
-    split match {
-      case contSplit: ContinuousSplit =>
-        if (left) {
-          s"$featureStr <= ${contSplit.threshold}"
-        } else {
-          s"$featureStr > ${contSplit.threshold}"
-        }
-      case catSplit: CategoricalSplit =>
-        val categoriesStr = catSplit.leftCategories.mkString("{", ",", "}")
-        if (left) {
-          s"$featureStr in $categoriesStr"
-        } else {
-          s"$featureStr not in $categoriesStr"
-        }
-    }
-  }
-}
-
-/**
- * Version of a node used in learning.  This uses vars so that we can modify nodes as we split the
- * tree by adding children, etc.
- *
- * For now, we use node IDs.  These will be kept internal since we hope to remove node IDs
- * in the future, or at least change the indexing (so that we can support much deeper trees).
- *
- * This node can either be:
- *  - a leaf node, with leftChild, rightChild, split set to null, or
- *  - an internal node, with all values set
- *
- * @param id  We currently use the same indexing as the old implementation in
- *            [[org.apache.spark.mllib.tree.model.Node]], but this will change later.
- * @param isLeaf  Indicates whether this node will definitely be a leaf in the learned tree,
- *                so that we do not need to consider splitting it further.
- * @param stats  Impurity statistics for this node.
- */
-private[tree] class LearningNodeX(
-    var id: Int,
-    var leftChild: Option[LearningNodeX],
-    var rightChild: Option[LearningNodeX],
-    var split: Option[SplitBase],
-    var isLeaf: Boolean,
-    var stats: ImpurityStats) extends Serializable {
-
-  /**
-   * Convert this [[LearningNodeX]] to a regular [[Node]], and recurse on any children.
-   */
-  def toNode(splits: Array[Array[Split]]): Node = {
-    if (leftChild.nonEmpty) {
-      assert(rightChild.nonEmpty && split.nonEmpty && stats != null,
-        "Unknown error during Decision Tree learning.  Could not convert LearningNodeX to Node.")
-      val normalSplit = Split.fromBase(split.get, splits)
-      new InternalNode(stats.impurityCalculator.predict, stats.impurity, stats.gain,
-        leftChild.get.toNode(splits), rightChild.get.toNode(splits),
-        normalSplit, stats.impurityCalculator)
-    } else {
-      if (stats.valid) {
-        new LeafNode(stats.impurityCalculator.predict, stats.impurity,
-          stats.impurityCalculator)
-      } else {
-        // Here we want to keep same behavior with the old mllib.DecisionTreeModel
-        new LeafNode(stats.impurityCalculator.predict, -1.0, stats.impurityCalculator)
-      }
-
-    }
-  }
-
-  /**
-   * Get the node index corresponding to this data point.
-   * This function mimics prediction, passing an example from the root node down to a leaf
-   * or unsplit node; that node's index is returned.
-   *
-   * @param binnedFeatures  Binned feature vector for data point.
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @return Leaf index if the data point reaches a leaf.
-   *         Otherwise, last node reachable in tree matching this example.
-   *         Note: This is the global node index, i.e., the index used in the tree.
-   *         This index is different from the index used during training a particular
-   *         group of nodes on one call to
-   *         [[org.apache.spark.ml.tree.impl.RandomForest.findBestSplits()]].
-   */
-  def predictImpl(binnedFeatures: BinnedFeature, splits: Array[Array[SplitBase]]): Int = {
-    if (this.isLeaf || this.split.isEmpty) {
-      this.id
-    } else {
-      val split = this.split.get
-      val featureIndex = split.featureIndex
-      val splitLeft = split.shouldGoLeft(binnedFeatures.get(featureIndex), splits(featureIndex))
-      if (this.leftChild.isEmpty) {
-        // Not yet split. Return next layer of nodes to train
-        if (splitLeft) {
-          LearningNodeX.leftChildIndex(this.id)
-        } else {
-          LearningNodeX.rightChildIndex(this.id)
-        }
-      } else {
-        if (splitLeft) {
-          this.leftChild.get.predictImpl(binnedFeatures, splits)
-        } else {
-          this.rightChild.get.predictImpl(binnedFeatures, splits)
-        }
-      }
-    }
-  }
-
-}
-
-private[tree] object LearningNodeX {
-
-  /** Create a node with some of its fields set. */
-  def apply(
-      id: Int,
-      isLeaf: Boolean,
-      stats: ImpurityStats): LearningNodeX = {
-    // todo: this is a bug at spark 2.3.2 (isLeaf are always assigned false)
-    new LearningNodeX(id, None, None, None, isLeaf, stats)
-  }
-
-  /** Create an empty node with the given node index.  Values must be set later on. */
-  def emptyNode(nodeIndex: Int): LearningNodeX = {
-    new LearningNodeX(nodeIndex, None, None, None, false, null)
-  }
-
-  // The below indexing methods were copied from spark.mllib.tree.model.Node
-
-  /**
-   * Return the index of the left child of this node.
-   */
-  def leftChildIndex(nodeIndex: Int): Int = nodeIndex << 1
-
-  /**
-   * Return the index of the right child of this node.
-   */
-  def rightChildIndex(nodeIndex: Int): Int = (nodeIndex << 1) + 1
-
-  /**
-   * Get the parent index of the given node, or 0 if it is the root.
-   */
-  def parentIndex(nodeIndex: Int): Int = nodeIndex >> 1
-
-  /**
-   * Return the level of a tree which the given node is in.
-   */
-  def indexToLevel(nodeIndex: Int): Int = if (nodeIndex == 0) {
-    throw new IllegalArgumentException(s"0 is not a valid node index.")
-  } else {
-    java.lang.Integer.numberOfTrailingZeros(java.lang.Integer.highestOneBit(nodeIndex))
-  }
-
-  /**
-   * Returns true if this is a left child.
-   * Note: Returns false for the root.
-   */
-  def isLeftChild(nodeIndex: Int): Boolean = nodeIndex > 1 && nodeIndex % 2 == 0
-
-  /**
-   * Return the maximum number of nodes which can be in the given level of the tree.
-   * @param level  Level of tree (0 = root).
-   */
-  def maxNodesInLevel(level: Int): Int = 1 << level
-
-  /**
-   * Return the index of the first node in the given level.
-   * @param level  Level of tree (0 = root).
-   */
-  def startIndexInLevel(level: Int): Int = 1 << level
-
-  /**
-   * Traces down from a root node to get the node with the given node index.
-   * This assumes the node exists.
-   */
-  def getNode(nodeIndex: Int, rootNode: LearningNodeX): LearningNodeX = {
-    var tmpNode: LearningNodeX = rootNode
-    var levelsToGo = indexToLevel(nodeIndex)
-    while (levelsToGo > 0) {
-      if ((nodeIndex & (1 << levelsToGo - 1)) == 0) {
-        tmpNode = tmpNode.leftChild.get
-      } else {
-        tmpNode = tmpNode.rightChild.get
-      }
-      levelsToGo -= 1
-    }
-    tmpNode
-  }
-
-}
-
-/**
- * Version of a node used in learning.  This uses vars so that we can modify nodes as we split the
- * tree by adding children, etc.
- *
- * For now, we use node IDs.  These will be kept internal since we hope to remove node IDs
- * in the future, or at least change the indexing (so that we can support much deeper trees).
- *
- * This node can either be:
- *  - a leaf node, with leftChild, rightChild, split set to null, or
- *  - an internal node, with all values set
- *
- * @param id  We currently use the same indexing as the old implementation in
- *            [[org.apache.spark.mllib.tree.model.Node]], but this will change later.
- * @param isLeaf  Indicates whether this node will definitely be a leaf in the learned tree,
- *                so that we do not need to consider splitting it further.
- * @param stats  Impurity statistics for this node.
- */
-private[tree] class LearningNode(
-    var id: Int,
-    var leftChild: Option[LearningNode],
-    var rightChild: Option[LearningNode],
-    var split: Option[Split],
-    var isLeaf: Boolean,
-    var stats: ImpurityStats) extends Serializable {
-
-  /**
-   * Convert this [[LearningNode]] to a regular [[Node]], and recurse on any children.
-   */
-  def toNode: Node = {
-    if (leftChild.nonEmpty) {
-      assert(rightChild.nonEmpty && split.nonEmpty && stats != null,
-        "Unknown error during Decision Tree learning.  Could not convert LearningNode to Node.")
-      new InternalNode(stats.impurityCalculator.predict, stats.impurity, stats.gain,
-        leftChild.get.toNode, rightChild.get.toNode, split.get, stats.impurityCalculator)
-    } else {
-      if (stats.valid) {
-        new LeafNode(stats.impurityCalculator.predict, stats.impurity,
-          stats.impurityCalculator)
-      } else {
-        // Here we want to keep same behavior with the old mllib.DecisionTreeModel
-        new LeafNode(stats.impurityCalculator.predict, -1.0, stats.impurityCalculator)
-      }
-
-    }
-  }
-
-  /**
-   * Get the node index corresponding to this data point.
-   * This function mimics prediction, passing an example from the root node down to a leaf
-   * or unsplit node; that node's index is returned.
-   *
-   * @param binnedFeatures  Binned feature vector for data point.
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @return Leaf index if the data point reaches a leaf.
-   *         Otherwise, last node reachable in tree matching this example.
-   *         Note: This is the global node index, i.e., the index used in the tree.
-   *         This index is different from the index used during training a particular
-   *         group of nodes on one call to
-   *         [[org.apache.spark.ml.tree.impl.RandomForest.findBestSplits()]].
-   */
-  def predictImpl(binnedFeatures: Array[Int], splits: Array[Array[Split]]): Int = {
-    if (this.isLeaf || this.split.isEmpty) {
-      this.id
-    } else {
-      val split = this.split.get
-      val featureIndex = split.featureIndex
-      val splitLeft = split.shouldGoLeft(binnedFeatures(featureIndex), splits(featureIndex))
-      if (this.leftChild.isEmpty) {
-        // Not yet split. Return next layer of nodes to train
-        if (splitLeft) {
-          LearningNode.leftChildIndex(this.id)
-        } else {
-          LearningNode.rightChildIndex(this.id)
-        }
-      } else {
-        if (splitLeft) {
-          this.leftChild.get.predictImpl(binnedFeatures, splits)
-        } else {
-          this.rightChild.get.predictImpl(binnedFeatures, splits)
-        }
-      }
-    }
-  }
-
-  /**
-   * Get the node index corresponding to this data point.
-   * This function mimics prediction, passing an example from the root node down to a leaf
-   * or unsplit node; that node's index is returned.
-   *
-   * @param binnedFeatures  Binned feature vector for data point.
-   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
-   * @return Leaf index if the data point reaches a leaf.
-   *         Otherwise, last node reachable in tree matching this example.
-   *         Note: This is the global node index, i.e., the index used in the tree.
-   *         This index is different from the index used during training a particular
-   *         group of nodes on one call to
-   *         [[org.apache.spark.ml.tree.impl.RandomForest.findBestSplits()]].
-   */
-  def predictImpl(binnedFeatures: BinnedFeature, splits: Array[Array[Split]]): Int = {
-    if (this.isLeaf || this.split.isEmpty) {
-      this.id
-    } else {
-      val split = this.split.get
-      val featureIndex = split.featureIndex
-      val splitLeft = split.shouldGoLeft(binnedFeatures.get(featureIndex), splits(featureIndex))
-      if (this.leftChild.isEmpty) {
-        // Not yet split. Return next layer of nodes to train
-        if (splitLeft) {
-          LearningNode.leftChildIndex(this.id)
-        } else {
-          LearningNode.rightChildIndex(this.id)
-        }
-      } else {
-        if (splitLeft) {
-          this.leftChild.get.predictImpl(binnedFeatures, splits)
-        } else {
-          this.rightChild.get.predictImpl(binnedFeatures, splits)
-        }
-      }
-    }
-  }
-
-}
-
-
-private[tree] object LearningNode {
-
-  /** Create a node with some of its fields set. */
-  def apply(
-      id: Int,
-      isLeaf: Boolean,
-      stats: ImpurityStats): LearningNode = {
-    // todo: this is a bug at spark 2.3.2 (isLeaf are always assigned false)
-    new LearningNode(id, None, None, None, isLeaf, stats)
-  }
-
-  /** Create an empty node with the given node index.  Values must be set later on. */
-  def emptyNode(nodeIndex: Int): LearningNode = {
-    new LearningNode(nodeIndex, None, None, None, false, null)
-  }
-
-  // The below indexing methods were copied from spark.mllib.tree.model.Node
-
-  /**
-   * Return the index of the left child of this node.
-   */
-  def leftChildIndex(nodeIndex: Int): Int = nodeIndex << 1
-
-  /**
-   * Return the index of the right child of this node.
-   */
-  def rightChildIndex(nodeIndex: Int): Int = (nodeIndex << 1) + 1
-
-  /**
-   * Get the parent index of the given node, or 0 if it is the root.
-   */
-  def parentIndex(nodeIndex: Int): Int = nodeIndex >> 1
-
-  /**
-   * Return the level of a tree which the given node is in.
-   */
-  def indexToLevel(nodeIndex: Int): Int = if (nodeIndex == 0) {
-    throw new IllegalArgumentException(s"0 is not a valid node index.")
-  } else {
-    java.lang.Integer.numberOfTrailingZeros(java.lang.Integer.highestOneBit(nodeIndex))
-  }
-
-  /**
-   * Returns true if this is a left child.
-   * Note: Returns false for the root.
-   */
-  def isLeftChild(nodeIndex: Int): Boolean = nodeIndex > 1 && nodeIndex % 2 == 0
-
-  /**
-   * Return the maximum number of nodes which can be in the given level of the tree.
-   * @param level  Level of tree (0 = root).
-   */
-  def maxNodesInLevel(level: Int): Int = 1 << level
-
-  /**
-   * Return the index of the first node in the given level.
-   * @param level  Level of tree (0 = root).
-   */
-  def startIndexInLevel(level: Int): Int = 1 << level
-
-  /**
-   * Traces down from a root node to get the node with the given node index.
-   * This assumes the node exists.
-   */
-  def getNode(nodeIndex: Int, rootNode: LearningNode): LearningNode = {
-    var tmpNode: LearningNode = rootNode
-    var levelsToGo = indexToLevel(nodeIndex)
-    while (levelsToGo > 0) {
-      if ((nodeIndex & (1 << levelsToGo - 1)) == 0) {
-        tmpNode = tmpNode.leftChild.get
-      } else {
-        tmpNode = tmpNode.rightChild.get
-      }
-      levelsToGo -= 1
-    }
-    tmpNode
-  }
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/Split.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/Split.scala
deleted file mode 100644
index dbea593..0000000
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/Split.scala
+++ /dev/null
@@ -1,274 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree
-
-import java.util.Objects
-
-import org.apache.spark.annotation.Since
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.mllib.tree.configuration.{FeatureType => OldFeatureType}
-import org.apache.spark.mllib.tree.model.{Split => OldSplit}
-
-
-/**
- * Interface for a "Split," which specifies a test made at a decision tree node
- * to choose the left or right path.
- */
-sealed trait SplitBase extends Serializable {
-
-  /** Index of feature which this split tests */
-  def featureIndex: Int
-
-  /**
-   * Return true (split to left) or false (split to right).
-   * @param binnedFeature Binned feature value.
-   * @param splits All splits for the given feature.
-   */
-  private[tree] def shouldGoLeft(binnedFeature: Char, splits: Array[SplitBase]): Boolean
-}
-
-sealed trait Split extends SplitBase {
-  /**
-   * Return true (split to left) or false (split to right).
-   * @param features  Vector of features (original values, not binned).
-   */
-  private[ml] def shouldGoLeft(features: Vector): Boolean
-
-  /**
-   * Return true (split to left) or false (split to right).
-   * @param binnedFeature Binned feature value.
-   * @param splits All splits for the given feature.
-   */
-  private[tree] def shouldGoLeft(binnedFeature: Int, splits: Array[Split]): Boolean
-
-  /** Convert to old Split format */
-  private[tree] def toOld: OldSplit
-}
-
-private[tree] object Split {
-
-  def fromOld(oldSplit: OldSplit, categoricalFeatures: Map[Int, Int]): Split = {
-    oldSplit.featureType match {
-      case OldFeatureType.Categorical =>
-        new CategoricalSplit(featureIndex = oldSplit.feature,
-          _leftCategories = oldSplit.categories.toArray, categoricalFeatures(oldSplit.feature))
-      case OldFeatureType.Continuous =>
-        new ContinuousSplit(featureIndex = oldSplit.feature, threshold = oldSplit.threshold)
-    }
-  }
-
-  def toBase(split: Split, binIdx: Int): SplitBase = {
-    split match {
-      case value: CategoricalSplit =>
-        value
-      case value: ContinuousSplit =>
-        new ContinuousSplitLearning(value.featureIndex, binIdx)
-    }
-  }
-
-  def fromBase(baseSplit: SplitBase, splits: Array[Array[Split]]): Split = {
-    baseSplit match {
-      case value: CategoricalSplit =>
-        value
-      case value: ContinuousSplit =>
-        value
-      case value: ContinuousSplitLearning =>
-        val thresh =
-          splits(value.featureIndex)(value.binIndex).asInstanceOf[ContinuousSplit].threshold
-        new ContinuousSplit(value.featureIndex, thresh)
-    }
-  }
-}
-
-/**
- * Split which tests a categorical feature.
- * @param featureIndex  Index of the feature to test
- * @param _leftCategories  If the feature value is in this set of categories, then the split goes
- *                         left. Otherwise, it goes right.
- * @param numCategories  Number of categories for this feature.
- */
-class CategoricalSplit private[ml] (
-    override val featureIndex: Int,
-    _leftCategories: Array[Double],
-    @Since("2.0.0") val numCategories: Int)
-  extends Split {
-
-  require(_leftCategories.forall(cat => 0 <= cat && cat < numCategories), "Invalid leftCategories" +
-    s" (should be in range [0, $numCategories)): ${_leftCategories.mkString(",")}")
-
-  /**
-   * If true, then "categories" is the set of categories for splitting to the left, and vice versa.
-   */
-  private val isLeft: Boolean = _leftCategories.length <= numCategories / 2
-
-  /** Set of categories determining the splitting rule, along with [[isLeft]]. */
-  private val categories: Set[Double] = {
-    if (isLeft) {
-      _leftCategories.toSet
-    } else {
-      setComplement(_leftCategories.toSet)
-    }
-  }
-
-  override private[ml] def shouldGoLeft(features: Vector): Boolean = {
-    if (isLeft) {
-      categories.contains(features(featureIndex))
-    } else {
-      !categories.contains(features(featureIndex))
-    }
-  }
-
-  override private[tree] def shouldGoLeft(binnedFeature: Int, splits: Array[Split]): Boolean = {
-    if (isLeft) {
-      categories.contains(binnedFeature.toDouble)
-    } else {
-      !categories.contains(binnedFeature.toDouble)
-    }
-  }
-
-  override private[tree] def shouldGoLeft(
-      binnedFeature: Char,
-      splits: Array[SplitBase]): Boolean = {
-    if (isLeft) {
-      categories.contains(binnedFeature.toDouble)
-    } else {
-      !categories.contains(binnedFeature.toDouble)
-    }
-  }
-
-  override def hashCode(): Int = {
-    val state = Seq(featureIndex, isLeft, categories)
-    state.map(Objects.hashCode).foldLeft(0)((a, b) => 31 * a + b)
-  }
-
-  override def equals(o: Any): Boolean = o match {
-    case other: CategoricalSplit => featureIndex == other.featureIndex &&
-      isLeft == other.isLeft && categories == other.categories
-    case _ => false
-  }
-
-  override private[tree] def toOld: OldSplit = {
-    val oldCats = if (isLeft) {
-      categories
-    } else {
-      setComplement(categories)
-    }
-    OldSplit(featureIndex, threshold = 0.0, OldFeatureType.Categorical, oldCats.toList)
-  }
-
-  /** Get sorted categories which split to the left */
-  def leftCategories: Array[Double] = {
-    val cats = if (isLeft) categories else setComplement(categories)
-    cats.toArray.sorted
-  }
-
-  /** Get sorted categories which split to the right */
-  def rightCategories: Array[Double] = {
-    val cats = if (isLeft) setComplement(categories) else categories
-    cats.toArray.sorted
-  }
-
-  /** [0, numCategories) \ cats */
-  private def setComplement(cats: Set[Double]): Set[Double] = {
-    Range(0, numCategories).map(_.toDouble).filter(cat => !cats.contains(cat)).toSet
-  }
-}
-
-/**
- * Split which tests a continuous feature.
- * @param featureIndex  Index of the feature to test
- * @param threshold  If the feature value is less than or equal to this threshold, then the
- *                   split goes left. Otherwise, it goes right.
- */
-class ContinuousSplit private[ml] (override val featureIndex: Int, val threshold: Double)
-  extends Split {
-
-  override private[ml] def shouldGoLeft(features: Vector): Boolean = {
-    features(featureIndex) <= threshold
-  }
-
-  override private[tree] def shouldGoLeft(binnedFeature: Int, splits: Array[Split]): Boolean = {
-    if (binnedFeature == splits.length) {
-      // > last split, so split right
-      false
-    } else {
-      val featureValueUpperBound = splits(binnedFeature).asInstanceOf[ContinuousSplit].threshold
-      featureValueUpperBound <= threshold
-    }
-  }
-
-  override private[tree] def shouldGoLeft(
-      binnedFeature: Char,
-      splits: Array[SplitBase]): Boolean = {
-
-    if (binnedFeature == splits.length) {
-      // > last split, so split right
-      false
-    } else {
-      val featureValueUpperBound = splits(binnedFeature).asInstanceOf[ContinuousSplit].threshold
-      featureValueUpperBound <= threshold
-    }
-  }
-
-  override def equals(o: Any): Boolean = {
-    o match {
-      case other: ContinuousSplit =>
-        featureIndex == other.featureIndex && threshold == other.threshold
-      case _ =>
-        false
-    }
-  }
-
-  override def hashCode(): Int = {
-    val state = Seq(featureIndex, threshold)
-    state.map(Objects.hashCode).foldLeft(0)((a, b) => 31 * a + b)
-  }
-
-  override private[tree] def toOld: OldSplit = {
-    OldSplit(featureIndex, threshold, OldFeatureType.Continuous, List.empty[Double])
-  }
-}
-
-/**
- * Split which tests a continuous feature.
- * @param featureIndex  Index of the feature to test
- * @param binIndex  If the binned feature value is less than or equal to this bin index, then the
- *                   split goes left. Otherwise, it goes right.
- */
-class ContinuousSplitLearning private[ml] (override val featureIndex: Int, val binIndex: Int)
-  extends SplitBase {
-
-  override private[tree] def shouldGoLeft(
-      binnedFeature: Char,
-      splits: Array[SplitBase]): Boolean = {
-
-    if (binnedFeature == splits.length) {
-      // > last split, so split right
-      false
-    } else {
-      binnedFeature <= binIndex
-    }
-  }
-
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/BaggedPoint.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/BaggedPoint.scala
deleted file mode 100644
index c2f8822..0000000
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/BaggedPoint.scala
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import org.apache.commons.math3.distribution.PoissonDistribution
-
-import org.apache.spark.rdd.RDD
-import org.apache.spark.util.Utils
-import org.apache.spark.util.random.XORShiftRandom
-
-/**
- * Internal representation of a datapoint which belongs to several subsamples of the same dataset,
- * particularly for bagging (e.g., for random forests).
- *
- * This holds one instance, as well as an array of weights which represent the (weighted)
- * number of times which this instance appears in each subsamplingRate.
- * E.g., (datum, [1, 0, 4]) indicates that there are 3 subsamples of the dataset and that
- * this datum has 1 copy, 0 copies, and 4 copies in the 3 subsamples, respectively.
- *
- * @param datum  Data instance
- * @param subsampleWeights  Weight of this instance in each subsampled dataset.
- * @param sampleId ID of sample
- *
- * TODO: This does not currently support (Double) weighted instances.  Once MLlib has weighted
- *       dataset support, update.  (We store subsampleWeights as Double for this future extension.)
- */
-private[spark] class BaggedPoint[Datum](
-    val datum: Datum,
-    val subsampleWeights: Array[Int],
-    var sampleId: Short = 0)
-  extends Serializable
-
-private[spark] object BaggedPoint {
-
-  /**
-   * Convert an input dataset into its BaggedPoint representation,
-   * choosing subsamplingRate counts for each instance.
-   * Each subsamplingRate has the same number of instances as the original dataset,
-   * and is created by subsampling without replacement.
-   * @param input Input dataset.
-   * @param subsamplingRate Fraction of the training data used for learning decision tree.
-   * @param numSubsamples Number of subsamples of this RDD to take.
-   * @param withReplacement Sampling with/without replacement.
-   * @param seed Random seed.
-   * @return BaggedPoint dataset representation.
-   */
-  def convertToBaggedRDD[Datum] (
-      input: RDD[Datum],
-      subsamplingRate: Double,
-      numSubsamples: Int,
-      withReplacement: Boolean,
-      seed: Long = Utils.random.nextLong()): RDD[BaggedPoint[Datum]] = {
-    if (withReplacement) {
-      convertToBaggedRDDSamplingWithReplacement(input, subsamplingRate, numSubsamples, seed)
-    } else {
-      if (numSubsamples == 1 && subsamplingRate == 1.0) {
-        convertToBaggedRDDWithoutSampling(input)
-      } else {
-        convertToBaggedRDDSamplingWithoutReplacement(input, subsamplingRate, numSubsamples, seed)
-      }
-    }
-  }
-
-  private def convertToBaggedRDDSamplingWithoutReplacement[Datum] (
-      input: RDD[Datum],
-      subsamplingRate: Double,
-      numSubsamples: Int,
-      seed: Long): RDD[BaggedPoint[Datum]] = {
-    input.mapPartitionsWithIndex { (partitionIndex, instances) =>
-      // Use random seed = seed + partitionIndex + 1 to make generation reproducible.
-      val rng = new XORShiftRandom
-      rng.setSeed(seed + partitionIndex + 1)
-      instances.map { instance =>
-        val subsampleWeights = new Array[Int](numSubsamples)
-        var subsampleIndex = 0
-        while (subsampleIndex < numSubsamples) {
-          val x = rng.nextDouble()
-          subsampleWeights(subsampleIndex) = {
-            if (x < subsamplingRate) 1 else 0
-          }
-          subsampleIndex += 1
-        }
-        new BaggedPoint(instance, subsampleWeights)
-      }
-    }
-  }
-
-  private def convertToBaggedRDDSamplingWithReplacement[Datum] (
-      input: RDD[Datum],
-      subsample: Double,
-      numSubsamples: Int,
-      seed: Long): RDD[BaggedPoint[Datum]] = {
-    input.mapPartitionsWithIndex { (partitionIndex, instances) =>
-      // Use random seed = seed + partitionIndex + 1 to make generation reproducible.
-      val poisson = new PoissonDistribution(subsample)
-      poisson.reseedRandomGenerator(seed + partitionIndex + 1)
-      instances.map { instance =>
-        val subsampleWeights = new Array[Int](numSubsamples)
-        var subsampleIndex = 0
-        while (subsampleIndex < numSubsamples) {
-          subsampleWeights(subsampleIndex) = poisson.sample()
-          subsampleIndex += 1
-        }
-        new BaggedPoint(instance, subsampleWeights)
-      }
-    }
-  }
-
-  private def convertToBaggedRDDWithoutSampling[Datum] (
-      input: RDD[Datum]): RDD[BaggedPoint[Datum]] = {
-    input.map(datum => new BaggedPoint(datum, Array(1)))
-  }
-
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/DTFeatureStatsAggregator.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/DTFeatureStatsAggregator.scala
deleted file mode 100644
index 3d98a41..0000000
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/DTFeatureStatsAggregator.scala
+++ /dev/null
@@ -1,111 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import org.apache.spark.mllib.tree.impurity._
-
-
-/**
- * DecisionTree statistics aggregator for a feature for a node.
- * This class is abstract to support learning with and without feature subsampling.
- */
-private[spark] class DTFeatureStatsAggregator(
-    val metadata: DecisionTreeMetadata,
-    val _featureIndex: Int) extends Serializable {
-
-  /**
-   * [[ImpurityAggregator]] instance specifying the impurity type.
-   */
-
-  val impurityAggregator = new VarianceAggregator()
-
-  val featureIndex: Int = _featureIndex
-
-  /**
-   * Number of elements (Double values) used for the sufficient statistics of each bin.
-   */
-  private val statsSize: Int = impurityAggregator.statsSize
-
-  /**
-   * Number of bins for the feature.
-   */
-  private val numBins: Int = {
-      metadata.numBins(featureIndex)
-  }
-
-  /**
-   * Total number of elements stored in this aggregator
-   */
-  private val allStatsSize: Int = numBins * statsSize
-
-  /**
-   * Flat array of elements.
-   */
-  private val allStats: Array[Double] = new Array[Double](allStatsSize)
-
-  /**
-   * Array of parent node sufficient stats.
-   */
-  private val parentStats: Array[Double] = new Array[Double](statsSize)
-
-  /**
-   * Get an [[ImpurityCalculator]] for a given (node, feature, bin).
-   */
-  def getImpurityCalculator(featureOffset: Int, binIndex: Int): ImpurityCalculator = {
-    impurityAggregator.getCalculator(allStats, binIndex * statsSize)
-  }
-
-  /**
-   * Get an [[ImpurityCalculator]] for the parent node.
-   */
-  def getParentImpurityCalculator(): ImpurityCalculator = {
-    impurityAggregator.getCalculator(parentStats, 0)
-  }
-
-  /**
-   * Update the stats for a given bin for ordered features, using the given label.
-   */
-  def updateX(featureIndex: Int, binIndex: Int, label: Double): Unit = {
-    val i = binIndex * statsSize
-    impurityAggregator.updateX(allStats, i, label)
-  }
-
-  /**
-   * Pre-compute feature offset for use with [[featureUpdate]].
-   * For ordered features only.
-   */
-  def getFeatureOffset(featureIndex: Int): Int = 0
-
-  /**
-   * For a given feature, merge the stats for two bins.
-   *
-   * @param featureOffset  This is a pre-computed feature offset
-   *                           from [[getFeatureOffset]].
-   * @param binIndex  The other bin is merged into this bin.
-   * @param otherBinIndex  This bin is not modified.  X
-   */
-  def mergeForFeature(featureOffset: Int, binIndex: Int, otherBinIndex: Int): Unit = {
-    impurityAggregator.merge(allStats, binIndex * statsSize, otherBinIndex * statsSize)
-  }
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/DTStatsAggregator.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/DTStatsAggregator.scala
deleted file mode 100644
index e18154f..0000000
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/DTStatsAggregator.scala
+++ /dev/null
@@ -1,187 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import org.apache.spark.mllib.tree.impurity._
-
-
-
-/**
- * DecisionTree statistics aggregator for a node.
- * This holds a flat array of statistics for a set of (features, bins)
- * and helps with indexing.
- * This class is abstract to support learning with and without feature subsampling.
- */
-private[spark] class DTStatsAggregator(
-    val metadata: DecisionTreeMetadata,
-    featureSubset: Option[Array[Int]]) extends Serializable {
-
-  /**
-   * [[ImpurityAggregator]] instance specifying the impurity type.
-   */
-  val impurityAggregator: ImpurityAggregator = metadata.impurity match {
-    case Gini => new GiniAggregator(metadata.numClasses)
-    case Entropy => new EntropyAggregator(metadata.numClasses)
-    case Variance => new VarianceAggregator()
-    case _ => throw new IllegalArgumentException(s"Bad impurity parameter: ${metadata.impurity}")
-  }
-
-  /**
-   * Number of elements (Double values) used for the sufficient statistics of each bin.
-   */
-  private val statsSize: Int = impurityAggregator.statsSize
-
-  /**
-   * Number of bins for each feature.  This is indexed by the feature index.
-   */
-  private val numBins: Array[Int] = {
-    if (featureSubset.isDefined) {
-      featureSubset.get.map(metadata.numBins(_))
-    } else {
-      metadata.numBins
-    }
-  }
-
-  /**
-   * Offset for each feature for calculating indices into the [[allStats]] array.
-   */
-  private val featureOffsets: Array[Int] = {
-    numBins.scanLeft(0)((total, nBins) => total + statsSize * nBins)
-  }
-
-  /**
-   * Total number of elements stored in this aggregator
-   */
-  private val allStatsSize: Int = featureOffsets.last
-
-  /**
-   * Flat array of elements.
-   * Index for start of stats for a (feature, bin) is:
-   *   index = featureOffsets(featureIndex) + binIndex * statsSize
-   */
-  private val allStats: Array[Double] = new Array[Double](allStatsSize)
-
-  /**
-   * Array of parent node sufficient stats.
-   * Note: parent stats need to be explicitly tracked in the [[DTStatsAggregator]] for unordered
-   *       categorical features, because the parent [[Node]] object does not have [[ImpurityStats]]
-   *       on the first iteration.
-   */
-  private val parentStats: Array[Double] = new Array[Double](statsSize)
-
-  /**
-   * Get an [[ImpurityCalculator]] for a given (node, feature, bin).
-   *
-   * @param featureOffset  This is a pre-computed (node, feature) offset
-   *                           from [[getFeatureOffset]].
-   */
-  def getImpurityCalculator(featureOffset: Int, binIndex: Int): ImpurityCalculator = {
-    impurityAggregator.getCalculator(allStats, featureOffset + binIndex * statsSize)
-  }
-
-  /**
-   * Get an [[ImpurityCalculator]] for the parent node.
-   */
-  def getParentImpurityCalculator(): ImpurityCalculator = {
-    impurityAggregator.getCalculator(parentStats, 0)
-  }
-
-  /**
-   * Update the stats for a given (feature, bin) for ordered features, using the given label.
-   */
-  def update(featureIndex: Int, binIndex: Int, label: Double, instanceWeight: Int): Unit = {
-    val i = featureOffsets(featureIndex) + binIndex * statsSize
-    impurityAggregator.update(allStats, i, label, instanceWeight)
-  }
-
-  /**
-   * Update the parent node stats using the given label.
-   */
-  def updateParent(label: Double, instanceWeight: Int): Unit = {
-    impurityAggregator.update(parentStats, 0, label, instanceWeight)
-  }
-
-  /**
-   * Faster version of [[update]].
-   * Update the stats for a given (feature, bin), using the given label.
-   *
-   * @param featureOffset  This is a pre-computed feature offset
-   *                           from [[getFeatureOffset]].
-   */
-  def featureUpdate(
-      featureOffset: Int,
-      binIndex: Int,
-      label: Double,
-      instanceWeight: Int): Unit = {
-    impurityAggregator.update(allStats, featureOffset + binIndex * statsSize,
-      label, instanceWeight)
-  }
-
-  /**
-   * Pre-compute feature offset for use with [[featureUpdate]].
-   * For ordered features only.
-   */
-  def getFeatureOffset(featureIndex: Int): Int = featureOffsets(featureIndex)
-
-  /**
-   * For a given feature, merge the stats for two bins.
-   *
-   * @param featureOffset  This is a pre-computed feature offset
-   *                           from [[getFeatureOffset]].
-   * @param binIndex  The other bin is merged into this bin.
-   * @param otherBinIndex  This bin is not modified.
-   */
-  def mergeForFeature(featureOffset: Int, binIndex: Int, otherBinIndex: Int): Unit = {
-    impurityAggregator.merge(allStats, featureOffset + binIndex * statsSize,
-      featureOffset + otherBinIndex * statsSize)
-  }
-
-  /**
-   * Merge this aggregator with another, and returns this aggregator.
-   * This method modifies this aggregator in-place.
-   */
-  def merge(other: DTStatsAggregator): DTStatsAggregator = {
-    require(allStatsSize == other.allStatsSize,
-      s"DTStatsAggregator.merge requires that both aggregators have the same length stats vectors."
-        + s" This aggregator is of length $allStatsSize, but the other is ${other.allStatsSize}.")
-    var i = 0
-    // TODO: Test BLAS.axpy
-    while (i < allStatsSize) {
-      allStats(i) += other.allStats(i)
-      i += 1
-    }
-
-    require(statsSize == other.statsSize,
-      s"DTStatsAggregator.merge requires that both aggregators have the same length parent " +
-        s"stats vectors. This aggregator's parent stats are length $statsSize, " +
-        s"but the other is ${other.statsSize}.")
-    var j = 0
-    while (j < statsSize) {
-      parentStats(j) += other.parentStats(j)
-      j += 1
-    }
-
-    this
-  }
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesCore.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesCore.scala
deleted file mode 100644
index 24e1930..0000000
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesCore.scala
+++ /dev/null
@@ -1,256 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.ml.tree.impl
-
-import it.unimi.dsi.fastutil.objects.ObjectArrayList
-
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.tree.{CategoricalSplit, ContinuousSplit, LearningNode, Split}
-import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
-import org.apache.spark.mllib.tree.model.ImpurityStats
-
-object GradientBoostedTreesCore extends Logging{
-  private[tree] class NodeIndexInfo(
-      val nodeIndexInGroup: Int,
-      val featureSubset: Option[Array[Int]],
-      val featureSubsetHashSetX: Option[scala.collection.mutable.HashSet[Int]] = None)
-    extends Serializable
-
-  /**
-   * Calculate the impurity statistics for a given (feature, split) based upon left/right
-   * aggregates.
-   *
-   * @param stats the recycle impurity statistics for this feature's all splits,
-   *              only 'impurity' and 'impurityCalculator' are valid between each iteration
-   * @param leftImpurityCalculator left node aggregates for this (feature, split)
-   * @param rightImpurityCalculator right node aggregate for this (feature, split)
-   * @param metadata learning and dataset metadata for DecisionTree
-   * @return Impurity statistics for this (feature, split)
-   */
-  private def calculateImpurityStats(
-      stats: ImpurityStats,
-      leftImpurityCalculator: ImpurityCalculator,
-      rightImpurityCalculator: ImpurityCalculator,
-      metadata: DecisionTreeMetadata): ImpurityStats = {
-
-    val parentImpurityCalculator: ImpurityCalculator = if (stats == null) {
-      leftImpurityCalculator.copy.add(rightImpurityCalculator)
-    } else {
-      stats.impurityCalculator
-    }
-
-    val impurity: Double = if (stats == null) {
-      parentImpurityCalculator.calculate()
-    } else {
-      stats.impurity
-    }
-
-    val leftCount = leftImpurityCalculator.count
-    val rightCount = rightImpurityCalculator.count
-
-    val totalCount = leftCount + rightCount
-
-    // If left child or right child doesn't satisfy minimum instances per node,
-    // then this split is invalid, return invalid information gain stats.
-    if ((leftCount < metadata.minInstancesPerNode) ||
-      (rightCount < metadata.minInstancesPerNode)) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
-    val rightImpurity = rightImpurityCalculator.calculate()
-
-    val leftWeight = leftCount / totalCount.toDouble
-    val rightWeight = rightCount / totalCount.toDouble
-
-    val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
-
-    // if information gain doesn't satisfy minimum information gain,
-    // then this split is invalid, return invalid information gain stats.
-    if (gain < metadata.minInfoGain) {
-      return ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator)
-    }
-
-    new ImpurityStats(gain, impurity, parentImpurityCalculator,
-      leftImpurityCalculator, rightImpurityCalculator)
-  }
-
-  /**
-   * Find the best split for a node.
-   *
-   * @param binAggregates Bin statistics.
-   * @return tuple for best split: (Split, information gain, prediction at node)
-   */
-  private[tree] def binsToBestSplitX(
-      binAggregates: DTFeatureStatsAggregator,
-      splits: ObjectArrayList[Split],
-      featureIndex: Int,
-      node: LearningNode): (Split, ImpurityStats) = {
-
-    // Calculate InformationGain and ImpurityStats if current node is top node
-    val level = LearningNode.indexToLevel(node.id)
-    var gainAndImpurityStats: ImpurityStats = if (level == 0) {
-      null
-    } else {
-      node.stats
-    }
-
-    if (binAggregates.metadata.numSplits(featureIndex) != 0) {
-      val featureIndexIdx = featureIndex
-      val numSplits = binAggregates.metadata.numSplits(featureIndex)
-      if (binAggregates.metadata.isContinuous(featureIndex)) {
-        // Cumulative sum (scanLeft) of bin statistics.
-        // Afterwards, binAggregates for a bin is the sum of aggregates for
-        // that bin + all preceding bins.
-        var splitIndex = 0
-        while (splitIndex < numSplits) {
-          binAggregates.mergeForFeature(0, splitIndex + 1, splitIndex)
-          splitIndex += 1
-        }
-        // Find best split.
-        val (bestFeatureSplitIndex, bestFeatureGainStats) =
-          Range(0, numSplits).map { case splitIdx =>
-            val leftChildStats = binAggregates.getImpurityCalculator(0, splitIdx)
-            val rightChildStats =
-              binAggregates.getImpurityCalculator(0, numSplits)
-            rightChildStats.subtract(leftChildStats)
-            gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-              leftChildStats, rightChildStats, binAggregates.metadata)
-            (splitIdx, gainAndImpurityStats)
-          }.maxBy(_._2.gain)
-        (splits.get(bestFeatureSplitIndex), bestFeatureGainStats)
-      } else if (binAggregates.metadata.isUnordered(featureIndex)) {
-        // unreachable for GBDT
-        // Unordered categorical feature
-        // val leftChildOffset = binAggregates.getFeatureOffset(featureIndexIdx)
-        val (bestFeatureSplitIndex, bestFeatureGainStats) =
-          Range(0, numSplits).map { splitIndex =>
-            val leftChildStats = binAggregates.getImpurityCalculator(0, splitIndex)
-            val rightChildStats = binAggregates.getImpurityCalculator(0, numSplits)
-              .subtract(leftChildStats)
-            gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-              leftChildStats, rightChildStats, binAggregates.metadata)
-            (splitIndex, gainAndImpurityStats)
-          }.maxBy(_._2.gain)
-        (splits.get(bestFeatureSplitIndex), bestFeatureGainStats)
-      } else {
-        // Ordered categorical feature
-        val numCategories = binAggregates.metadata.numBins(featureIndex)
-
-        /* Each bin is one category (feature value).
-         * The bins are ordered based on centroidForCategories, and this ordering determines which
-         * splits are considered.  (With K categories, we consider K - 1 possible splits.)
-         *
-         * centroidForCategories is a list: (category, centroid)
-         */
-        val centroidForCategories = Range(0, numCategories).map { case featureValue =>
-          val categoryStats =
-            binAggregates.getImpurityCalculator(0, featureValue)
-          val centroid = if (categoryStats.count != 0) {
-            if (binAggregates.metadata.isMulticlass) {
-              // unreachable for GBDT
-              // multiclass classification
-              // For categorical variables in multiclass classification,
-              // the bins are ordered by the impurity of their corresponding labels.
-              categoryStats.calculate()
-            } else if (binAggregates.metadata.isClassification) {
-              // unreachable for GBDT
-              // binary classification
-              // For categorical variables in binary classification,
-              // the bins are ordered by the count of class 1.
-              categoryStats.stats(1)
-            } else {
-              // regression
-              // For categorical variables in regression and binary classification,
-              // the bins are ordered by the prediction.
-              categoryStats.predict
-            }
-          } else {
-            Double.MaxValue
-          }
-          (featureValue, centroid)
-        }
-
-        logDebug(s"Centroids for categorical variable: ${centroidForCategories.mkString(",")}")
-
-        // bins sorted by centroids
-        val categoriesSortedByCentroid = centroidForCategories.toList.sortBy(_._2)
-
-        logDebug("Sorted centroids for categorical variable = " +
-          categoriesSortedByCentroid.mkString(","))
-
-        // Cumulative sum (scanLeft) of bin statistics.
-        // Afterwards, binAggregates for a bin is the sum of aggregates for
-        // that bin + all preceding bins.
-        var splitIndex = 0
-        while (splitIndex < numSplits) {
-          val currentCategory = categoriesSortedByCentroid(splitIndex)._1
-          val nextCategory = categoriesSortedByCentroid(splitIndex + 1)._1
-          binAggregates.mergeForFeature(0, nextCategory, currentCategory)
-          splitIndex += 1
-        }
-        // lastCategory = index of bin with total aggregates for this (node, feature)
-        val lastCategory = categoriesSortedByCentroid.last._1
-        // Find best split.
-        val (bestFeatureSplitIndex, bestFeatureGainStats) =
-          Range(0, numSplits).map { splitIndex =>
-            val featureValue = categoriesSortedByCentroid(splitIndex)._1
-            val leftChildStats =
-              binAggregates.getImpurityCalculator(0, featureValue)
-            val rightChildStats =
-              binAggregates.getImpurityCalculator(0, lastCategory)
-            rightChildStats.subtract(leftChildStats)
-            gainAndImpurityStats = calculateImpurityStats(gainAndImpurityStats,
-              leftChildStats, rightChildStats, binAggregates.metadata)
-            (splitIndex, gainAndImpurityStats)
-          }.maxBy(_._2.gain)
-        val categoriesForSplit =
-          categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
-        val bestFeatureSplit =
-          new CategoricalSplit(featureIndex, categoriesForSplit.toArray, numCategories)
-        (bestFeatureSplit, bestFeatureGainStats)
-      }
-    } else {
-      // If no valid splits for features, then this split is invalid,
-      // return invalid information gain stats.  Take any split and continue.
-      // Splits is empty, so arbitrarily choose to split on any threshold
-      // val parentImpurityCalculator = binAggregates.getParentImpurityCalculator()
-      // No split, no need to merge
-      val featureIndexIdx = featureIndex
-      val numSplits = binAggregates.metadata.numSplits(featureIndex)
-      val parentImpurityCalculator = binAggregates.getImpurityCalculator(0, numSplits)
-      if (binAggregates.metadata.isContinuous(featureIndex)) {
-        (new ContinuousSplit(featureIndex, 0),
-          ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-      } else {
-        // Seems like unreachable for GBDT (as well as RF)
-        val numCategories = binAggregates.metadata.featureArity(featureIndex)
-        (new CategoricalSplit(featureIndex, Array(), numCategories),
-          ImpurityStats.getInvalidImpurityStats(parentImpurityCalculator))
-      }
-    }
-
-    // For each (feature, split), calculate the gain, and select the best (feature, split).
-  }
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointX.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointX.scala
index 7329562..0b8392a 100644
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointX.scala
+++ b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointX.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
diff --git a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointY.scala b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointY.scala
index 036cb97..4272edb 100644
--- a/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointY.scala
+++ b/ml-core/src/main/scala/org/apache/spark/ml/tree/impl/TreePointY.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/clustering/LDAUtilsX.scala b/ml-core/src/main/scala/org/apache/spark/mllib/clustering/LDAUtilsX.scala
index 4a30b9e..fc9420a 100644
--- a/ml-core/src/main/scala/org/apache/spark/mllib/clustering/LDAUtilsX.scala
+++ b/ml-core/src/main/scala/org/apache/spark/mllib/clustering/LDAUtilsX.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/clustering/OnlineLDAOptimizerXObj.scala b/ml-core/src/main/scala/org/apache/spark/mllib/clustering/OnlineLDAOptimizerXObj.scala
index 50f3337..c2b1589 100644
--- a/ml-core/src/main/scala/org/apache/spark/mllib/clustering/OnlineLDAOptimizerXObj.scala
+++ b/ml-core/src/main/scala/org/apache/spark/mllib/clustering/OnlineLDAOptimizerXObj.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -25,7 +19,7 @@ package org.apache.spark.mllib.clustering
 
 import breeze.linalg.{sum, DenseMatrix => BDM, DenseVector => BDV}
 import breeze.numerics.{abs, exp}
-import breeze.stats.distributions.Gamma
+import breeze.stats.distributions.{Gamma, RandBasis}
 
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector}
 
@@ -34,7 +28,7 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector}
  * Serializable companion object containing helper methods and shared code for
  * [[OnlineLDAOptimizer]] and [[LocalLDAModel]].
  */
-private[clustering] object OnlineLDAOptimizerXObj {
+private[spark] object OnlineLDAOptimizerXObj {
   /**
    * Uses variational inference to infer the topic distribution `gammad` given the term counts
    * for a document. `termCounts` must contain at least one non-zero entry, otherwise Breeze will
@@ -47,25 +41,24 @@ private[clustering] object OnlineLDAOptimizerXObj {
    * @return Returns a tuple of `gammad` - estimate of gamma, the topic distribution, `sstatsd` -
    *         statistics for updating lambda and `ids` - list of termCounts vector indices.
    */
-  private[clustering] def variationalTopicInference(
-      termCounts: Vector,
+  private[spark] def variationalTopicInference(
+      indices: List[Int],
+      values: Array[Double],
       expElogbeta: BDM[Double],
       alpha: breeze.linalg.Vector[Double],
       gammaShape: Double,
-      k: Int): (BDV[Double], BDM[Double], List[Int]) = {
-    val (ids: List[Int], cts: Array[Double]) = termCounts match {
-      case v: DenseVector => ((0 until v.size).toList, v.values)
-      case v: SparseVector => (v.indices.toList, v.values)
-    }
+      k: Int,
+      seed: Long): (BDV[Double], BDM[Double], List[Int]) = {
     // Initialize the variational distribution q(theta|gamma) for the mini-batch
+    val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister(seed))
     val gammad: BDV[Double] =
-      new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k)                   // K
+      new Gamma(gammaShape, 1.0 / gammaShape)(randBasis).samplesVector(k)  // K
     val expElogthetad: BDV[Double] = exp(LDAUtilsX.dirichletExpectation(gammad))  // K
-    val expElogbetad = expElogbeta(ids, ::).toDenseMatrix                        // ids * K
+    val expElogbetad = expElogbeta(indices, ::).toDenseMatrix                        // ids * K
 
     val phiNorm: BDV[Double] = expElogbetad * expElogthetad +:+ 1e-100            // ids
     var meanGammaChange = 1D
-    val ctsVector = new BDV[Double](cts)                                         // ids
+    val ctsVector = new BDV[Double](values)                                         // ids
 
     // Iterate between gamma and phi until convergence
     while (meanGammaChange > 1e-3) {
@@ -79,6 +72,20 @@ private[clustering] object OnlineLDAOptimizerXObj {
     }
 
     val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector /:/ phiNorm).asDenseMatrix
-    (gammad, sstatsd, ids)
+    (gammad, sstatsd, indices)
+  }
+
+  private[clustering] def variationalTopicInference(
+      termCounts: Vector,
+      expElogbeta: BDM[Double],
+      alpha: breeze.linalg.Vector[Double],
+      gammaShape: Double,
+      k: Int,
+      seed: Long): (BDV[Double], BDM[Double], List[Int]) = {
+    val (ids: List[Int], cts: Array[Double]) = termCounts match {
+      case v: DenseVector => (List.range(0, v.size), v.values)
+      case v: SparseVector => (v.indices.toList, v.values)
+    }
+    variationalTopicInference(ids, cts, expElogbeta, alpha, gammaShape, k, seed)
   }
 }
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/ml-core/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
index 43e76b2..789c299 100644
--- a/ml-core/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
+++ b/ml-core/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpanBase.scala b/ml-core/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpanBase.scala
index 573aace..313dfa5 100644
--- a/ml-core/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpanBase.scala
+++ b/ml-core/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpanBase.scala
@@ -1,9 +1,3 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
deleted file mode 100644
index 7406c0d..0000000
--- a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ /dev/null
@@ -1,171 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.tree.impurity
-
-import org.apache.spark.annotation.{DeveloperApi, Since}
-
-/**
- * Class for calculating entropy during multiclass classification.
- */
-@Since("1.0.0")
-object Entropy extends Impurity {
-
-  private[tree] def log2(x: Double) = scala.math.log(x) / scala.math.log(2)
-
-  /**
-   * :: DeveloperApi ::
-   * information calculation for multiclass classification
-   * @param counts Array[Double] with counts for each label
-   * @param totalCount sum of counts for all labels
-   * @return information value, or 0 if totalCount = 0
-   */
-  @Since("1.1.0")
-  @DeveloperApi
-  override def calculate(counts: Array[Double], totalCount: Double): Double = {
-    if (totalCount == 0) {
-      return 0
-    }
-    val numClasses = counts.length
-    var impurity = 0.0
-    var classIndex = 0
-    while (classIndex < numClasses) {
-      val classCount = counts(classIndex)
-      if (classCount != 0) {
-        val freq = classCount / totalCount
-        impurity -= freq * log2(freq)
-      }
-      classIndex += 1
-    }
-    impurity
-  }
-
-  /**
-   * :: DeveloperApi ::
-   * variance calculation
-   * @param count number of instances
-   * @param sum sum of labels
-   * @param sumSquares summation of squares of the labels
-   * @return information value, or 0 if count = 0
-   */
-  @Since("1.0.0")
-  @DeveloperApi
-  override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
-    throw new UnsupportedOperationException("Entropy.calculate")
-
-  /**
-   * Get this impurity instance.
-   * This is useful for passing impurity parameters to a Strategy in Java.
-   */
-  @Since("1.1.0")
-  def instance: this.type = this
-
-}
-
-/**
- * Class for updating views of a vector of sufficient statistics,
- * in order to compute impurity from a sample.
- * Note: Instances of this class do not hold the data; they operate on views of the data.
- * @param numClasses  Number of classes for label.
- */
-private[spark] class EntropyAggregator(numClasses: Int)
-  extends ImpurityAggregator(numClasses) with Serializable {
-
-  /**
-   * Update stats for one (node, feature, bin) with the given label.
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def update(allStats: Array[Double], offset: Int, label: Double, instanceWeight: Int): Unit = {
-    if (label >= statsSize) {
-      throw new IllegalArgumentException(s"EntropyAggregator given label $label" +
-        s" but requires label < numClasses (= $statsSize).")
-    }
-    if (label < 0) {
-      throw new IllegalArgumentException(s"EntropyAggregator given label $label" +
-        s"but requires label is non-negative.")
-    }
-    allStats(offset + label.toInt) += instanceWeight
-  }
-
-  /**
-   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def getCalculator(allStats: Array[Double], offset: Int): EntropyCalculator = {
-    new EntropyCalculator(allStats.view(offset, offset + statsSize).toArray)
-  }
-}
-
-/**
- * Stores statistics for one (node, feature, bin) for calculating impurity.
- * Unlike [[EntropyAggregator]], this class stores its own data and is for a specific
- * (node, feature, bin).
- * @param stats  Array of sufficient statistics for a (node, feature, bin).
- */
-private[spark] class EntropyCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
-
-  /**
-   * Make a deep copy of this [[ImpurityCalculator]].
-   */
-  def copy: EntropyCalculator = new EntropyCalculator(stats.clone())
-
-  /**
-   * Calculate the impurity from the stored sufficient statistics.
-   */
-  def calculate(): Double = Entropy.calculate(stats, stats.sum)
-
-  /**
-   * Number of data points accounted for in the sufficient statistics.
-   */
-  def count: Long = stats.sum.toLong
-
-  /**
-   * Prediction which should be made based on the sufficient statistics.
-   */
-  def predict: Double = if (count == 0) {
-    0
-  } else {
-    indexOfLargestArrayElement(stats)
-  }
-
-  /**
-   * Probability of the label given by [[predict]].
-   */
-  override def prob(label: Double): Double = {
-    val lbl = label.toInt
-    require(lbl < stats.length,
-      s"EntropyCalculator.prob given invalid label: $lbl (should be < ${stats.length}")
-    require(lbl >= 0, "Entropy does not support negative labels")
-    val cnt = count
-    if (cnt == 0) {
-      0
-    } else {
-      stats(lbl) / cnt
-    }
-  }
-
-  override def toString: String = s"EntropyCalculator(stats = [${stats.mkString(", ")}])"
-
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
deleted file mode 100644
index f182519..0000000
--- a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ /dev/null
@@ -1,168 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.tree.impurity
-
-import org.apache.spark.annotation.{DeveloperApi, Since}
-
-/**
- * Class for calculating the Gini impurity
- * (http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity)
- * during multiclass classification.
- */
-@Since("1.0.0")
-object Gini extends Impurity {
-
-  /**
-   * :: DeveloperApi ::
-   * information calculation for multiclass classification
-   * @param counts Array[Double] with counts for each label
-   * @param totalCount sum of counts for all labels
-   * @return information value, or 0 if totalCount = 0
-   */
-  @Since("1.1.0")
-  @DeveloperApi
-  override def calculate(counts: Array[Double], totalCount: Double): Double = {
-    if (totalCount == 0) {
-      return 0
-    }
-    val numClasses = counts.length
-    var impurity = 1.0
-    var classIndex = 0
-    while (classIndex < numClasses) {
-      val freq = counts(classIndex) / totalCount
-      impurity -= freq * freq
-      classIndex += 1
-    }
-    impurity
-  }
-
-  /**
-   * :: DeveloperApi ::
-   * variance calculation
-   * @param count number of instances
-   * @param sum sum of labels
-   * @param sumSquares summation of squares of the labels
-   * @return information value, or 0 if count = 0
-   */
-  @Since("1.0.0")
-  @DeveloperApi
-  override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
-    throw new UnsupportedOperationException("Gini.calculate")
-
-  /**
-   * Get this impurity instance.
-   * This is useful for passing impurity parameters to a Strategy in Java.
-   */
-  @Since("1.1.0")
-  def instance: this.type = this
-
-}
-
-/**
- * Class for updating views of a vector of sufficient statistics,
- * in order to compute impurity from a sample.
- * Note: Instances of this class do not hold the data; they operate on views of the data.
- * @param numClasses  Number of classes for label.
- */
-private[spark] class GiniAggregator(numClasses: Int)
-  extends ImpurityAggregator(numClasses) with Serializable {
-
-  /**
-   * Update stats for one (node, feature, bin) with the given label.
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def update(allStats: Array[Double], offset: Int, label: Double, instanceWeight: Int): Unit = {
-    if (label >= statsSize) {
-      throw new IllegalArgumentException(s"GiniAggregator given label $label" +
-        s" but requires label < numClasses (= $statsSize).")
-    }
-    if (label < 0) {
-      throw new IllegalArgumentException(s"GiniAggregator given label $label" +
-        s"but requires label is non-negative.")
-    }
-    allStats(offset + label.toInt) += instanceWeight
-  }
-
-  /**
-   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def getCalculator(allStats: Array[Double], offset: Int): GiniCalculator = {
-    new GiniCalculator(allStats.view(offset, offset + statsSize).toArray)
-  }
-}
-
-/**
- * Stores statistics for one (node, feature, bin) for calculating impurity.
- * Unlike [[GiniAggregator]], this class stores its own data and is for a specific
- * (node, feature, bin).
- * @param stats  Array of sufficient statistics for a (node, feature, bin).
- */
-private[spark] class GiniCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
-
-  /**
-   * Make a deep copy of this [[ImpurityCalculator]].
-   */
-  def copy: GiniCalculator = new GiniCalculator(stats.clone())
-
-  /**
-   * Calculate the impurity from the stored sufficient statistics.
-   */
-  def calculate(): Double = Gini.calculate(stats, stats.sum)
-
-  /**
-   * Number of data points accounted for in the sufficient statistics.
-   */
-  def count: Long = stats.sum.toLong
-
-  /**
-   * Prediction which should be made based on the sufficient statistics.
-   */
-  def predict: Double = if (count == 0) {
-    0
-  } else {
-    indexOfLargestArrayElement(stats)
-  }
-
-  /**
-   * Probability of the label given by [[predict]].
-   */
-  override def prob(label: Double): Double = {
-    val lbl = label.toInt
-    require(lbl < stats.length,
-      s"GiniCalculator.prob given invalid label: $lbl (should be < ${stats.length}")
-    require(lbl >= 0, "GiniImpurity does not support negative labels")
-    val cnt = count
-    if (cnt == 0) {
-      0
-    } else {
-      stats(lbl) / cnt
-    }
-  }
-
-  override def toString: String = s"GiniCalculator(stats = [${stats.mkString(", ")}])"
-
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurities.scala b/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurities.scala
deleted file mode 100644
index f470091..0000000
--- a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurities.scala
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.tree.impurity
-
-/**
- * Factory for Impurity instances.
- */
-private[mllib] object Impurities {
-
-  def fromString(name: String): Impurity = name match {
-    case "gini" => Gini
-    case "entropy" => Entropy
-    case "variance" => Variance
-    case _ => throw new IllegalArgumentException(s"Did not recognize Impurity name: $name")
-  }
-
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
deleted file mode 100644
index 0492e1c..0000000
--- a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ /dev/null
@@ -1,204 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.tree.impurity
-
-import java.util.Locale
-
-import org.apache.spark.annotation.{DeveloperApi, Since}
-
-/**
- * Trait for calculating information gain.
- * This trait is used for
- *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
- *  (b) calculating impurity values from sufficient statistics.
- */
-@Since("1.0.0")
-trait Impurity extends Serializable {
-
-  /**
-   * :: DeveloperApi ::
-   * information calculation for multiclass classification
-   * @param counts Array[Double] with counts for each label
-   * @param totalCount sum of counts for all labels
-   * @return information value, or 0 if totalCount = 0
-   */
-  @Since("1.1.0")
-  @DeveloperApi
-  def calculate(counts: Array[Double], totalCount: Double): Double
-
-  /**
-   * :: DeveloperApi ::
-   * information calculation for regression
-   * @param count number of instances
-   * @param sum sum of labels
-   * @param sumSquares summation of squares of the labels
-   * @return information value, or 0 if count = 0
-   */
-  @Since("1.0.0")
-  @DeveloperApi
-  def calculate(count: Double, sum: Double, sumSquares: Double): Double
-}
-
-/**
- * Interface for updating views of a vector of sufficient statistics,
- * in order to compute impurity from a sample.
- * Note: Instances of this class do not hold the data; they operate on views of the data.
- * @param statsSize  Length of the vector of sufficient statistics for one bin.
- */
-private[spark] abstract class ImpurityAggregator(val statsSize: Int) extends Serializable {
-
-  /**
-   * Merge the stats from one bin into another.
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for (node, feature, bin) which is modified by the merge.
-   * @param otherOffset  Start index of stats for (node, feature, other bin) which is not modified.
-   */
-  def merge(allStats: Array[Double], offset: Int, otherOffset: Int): Unit = {
-    var i = 0
-    while (i < statsSize) {
-      allStats(offset + i) += allStats(otherOffset + i)
-      i += 1
-    }
-  }
-
-  /**
-   * Update stats for one (node, feature, bin) with the given label.
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def update(allStats: Array[Double], offset: Int, label: Double, instanceWeight: Int): Unit
-
-  /**
-   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def getCalculator(allStats: Array[Double], offset: Int): ImpurityCalculator
-}
-
-/**
- * Stores statistics for one (node, feature, bin) for calculating impurity.
- * Unlike [[ImpurityAggregator]], this class stores its own data and is for a specific
- * (node, feature, bin).
- * @param stats  Array of sufficient statistics for a (node, feature, bin).
- */
-private[spark] abstract class ImpurityCalculator(val stats: Array[Double]) extends Serializable {
-
-  /**
-   * Make a deep copy of this [[ImpurityCalculator]].
-   */
-  def copy: ImpurityCalculator
-
-  /**
-   * Calculate the impurity from the stored sufficient statistics.
-   */
-  def calculate(): Double
-
-  /**
-   * Add the stats from another calculator into this one, modifying and returning this calculator.
-   */
-  def add(other: ImpurityCalculator): ImpurityCalculator = {
-    require(stats.length == other.stats.length,
-      s"Two ImpurityCalculator instances cannot be added with different counts sizes." +
-        s"  Sizes are ${stats.length} and ${other.stats.length}.")
-    var i = 0
-    val len = other.stats.length
-    while (i < len) {
-      stats(i) += other.stats(i)
-      i += 1
-    }
-    this
-  }
-
-  /**
-   * Subtract the stats from another calculator from this one, modifying and returning this
-   * calculator.
-   */
-  def subtract(other: ImpurityCalculator): ImpurityCalculator = {
-    require(stats.length == other.stats.length,
-      s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." +
-      s"  Sizes are ${stats.length} and ${other.stats.length}.")
-    var i = 0
-    val len = other.stats.length
-    while (i < len) {
-      stats(i) -= other.stats(i)
-      i += 1
-    }
-    this
-  }
-
-  /**
-   * Number of data points accounted for in the sufficient statistics.
-   */
-  def count: Long
-
-  /**
-   * Prediction which should be made based on the sufficient statistics.
-   */
-  def predict: Double
-
-  /**
-   * Probability of the label given by [[predict]], or -1 if no probability is available.
-   */
-  def prob(label: Double): Double = -1
-
-  /**
-   * Return the index of the largest array element.
-   * Fails if the array is empty.
-   */
-  protected def indexOfLargestArrayElement(array: Array[Double]): Int = {
-    val result = array.foldLeft((-1, Double.MinValue, 0)) {
-      case ((maxIndex, maxValue, currentIndex), currentValue) =>
-        if (currentValue > maxValue) {
-          (currentIndex, currentValue, currentIndex + 1)
-        } else {
-          (maxIndex, maxValue, currentIndex + 1)
-        }
-    }
-    if (result._1 < 0) {
-      throw new RuntimeException("ImpurityCalculator internal error:" +
-        " indexOfLargestArrayElement failed")
-    }
-    result._1
-  }
-
-}
-
-private[spark] object ImpurityCalculator {
-
-  /**
-   * Create an [[ImpurityCalculator]] instance of the given impurity type and with
-   * the given stats.
-   */
-  def getCalculator(impurity: String, stats: Array[Double]): ImpurityCalculator = {
-    impurity.toLowerCase(Locale.ROOT) match {
-      case "gini" => new GiniCalculator(stats)
-      case "entropy" => new EntropyCalculator(stats)
-      case "variance" => new VarianceCalculator(stats)
-      case _ =>
-        throw new IllegalArgumentException(
-          s"ImpurityCalculator builder did not recognize impurity type: $impurity")
-    }
-  }
-}
diff --git a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
deleted file mode 100644
index 732ceaa..0000000
--- a/ml-core/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ /dev/null
@@ -1,148 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.mllib.tree.impurity
-
-import org.apache.spark.annotation.{DeveloperApi, Since}
-
-/**
- * Class for calculating variance during regression
- */
-@Since("1.0.0")
-object Variance extends Impurity {
-
-  /**
-   * :: DeveloperApi ::
-   * information calculation for multiclass classification
-   * @param counts Array[Double] with counts for each label
-   * @param totalCount sum of counts for all labels
-   * @return information value, or 0 if totalCount = 0
-   */
-  @Since("1.1.0")
-  @DeveloperApi
-  override def calculate(counts: Array[Double], totalCount: Double): Double =
-     throw new UnsupportedOperationException("Variance.calculate")
-
-  /**
-   * :: DeveloperApi ::
-   * variance calculation
-   * @param count number of instances
-   * @param sum sum of labels
-   * @param sumSquares summation of squares of the labels
-   * @return information value, or 0 if count = 0
-   */
-  @Since("1.0.0")
-  @DeveloperApi
-  override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
-    if (count == 0) {
-      return 0
-    }
-    val squaredLoss = sumSquares - (sum * sum) / count
-    squaredLoss / count
-  }
-
-  /**
-   * Get this impurity instance.
-   * This is useful for passing impurity parameters to a Strategy in Java.
-   */
-  @Since("1.0.0")
-  def instance: this.type = this
-
-}
-
-/**
- * Class for updating views of a vector of sufficient statistics,
- * in order to compute impurity from a sample.
- * Note: Instances of this class do not hold the data; they operate on views of the data.
- */
-private[spark] class VarianceAggregator()
-  extends ImpurityAggregator(statsSize = 3) with Serializable {
-
-  /**
-   * Update stats for one (node, feature, bin) with the given label.
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def update(allStats: Array[Double], offset: Int, label: Double, instanceWeight: Int): Unit = {
-    allStats(offset) += instanceWeight
-    allStats(offset + 1) += instanceWeight * label
-    allStats(offset + 2) += instanceWeight * label * label
-  }
-
-  def updateX(allStats: Array[Double], offset: Int, label: Double): Unit = {
-    allStats(offset) += 1.0
-    allStats(offset + 1) += label
-    allStats(offset + 2) += label * label
-  }
-
-  /**
-   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
-   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
-   * @param offset    Start index of stats for this (node, feature, bin).
-   */
-  def getCalculator(allStats: Array[Double], offset: Int): VarianceCalculator = {
-    new VarianceCalculator(allStats.view(offset, offset + statsSize).toArray)
-  }
-}
-
-/**
- * Stores statistics for one (node, feature, bin) for calculating impurity.
- * Unlike [[GiniAggregator]], this class stores its own data and is for a specific
- * (node, feature, bin).
- * @param stats  Array of sufficient statistics for a (node, feature, bin).
- */
-private[spark] class VarianceCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
-
-  require(stats.length == 3,
-    s"VarianceCalculator requires sufficient statistics array stats to be of length 3," +
-    s" but was given array of length ${stats.length}.")
-
-  /**
-   * Make a deep copy of this [[ImpurityCalculator]].
-   */
-  def copy: VarianceCalculator = new VarianceCalculator(stats.clone())
-
-  /**
-   * Calculate the impurity from the stored sufficient statistics.
-   */
-  def calculate(): Double = Variance.calculate(stats(0), stats(1), stats(2))
-
-  /**
-   * Number of data points accounted for in the sufficient statistics.
-   */
-  def count: Long = stats(0).toLong
-
-  /**
-   * Prediction which should be made based on the sufficient statistics.
-   */
-  def predict: Double = if (count == 0) {
-    0
-  } else {
-    stats(1) / count
-  }
-
-  override def toString: String = {
-    s"VarianceAggregator(cnt = ${stats(0)}, sum = ${stats(1)}, sum2 = ${stats(2)})"
-  }
-
-}
diff --git a/ml-kernel-client-core/pom.xml b/ml-kernel-client-core/pom.xml
index 561c79e..e0e7f74 100644
--- a/ml-kernel-client-core/pom.xml
+++ b/ml-kernel-client-core/pom.xml
@@ -6,7 +6,7 @@
   </parent>
 
   <modelVersion>4.0.0</modelVersion>
-  <artifactId>boostkit-ml-kernel-client-core_2.11</artifactId>
+  <artifactId>boostkit-ml-kernel-client-core_2.12</artifactId>
   <version>2.1.0</version>
   <name>${project.artifactId}</name>
   <description>Spark ml core</description>
diff --git a/ml-kernel-client/pom.xml b/ml-kernel-client/pom.xml
index 2be4e38..7434007 100644
--- a/ml-kernel-client/pom.xml
+++ b/ml-kernel-client/pom.xml
@@ -6,7 +6,7 @@
   </parent>
 
   <modelVersion>4.0.0</modelVersion>
-  <artifactId>boostkit-ml-kernel-client_2.11</artifactId>
+  <artifactId>boostkit-ml-kernel-client_2.12</artifactId>
   <version>2.1.0</version>
   <name>${project.artifactId}</name>
   <description>Spark ml core</description>
@@ -14,7 +14,7 @@
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>boostkit-ml-core_2.11</artifactId>
+      <artifactId>boostkit-ml-core_2.12</artifactId>
       <version>${project.version}</version>
       <classifier>${spark.version}</classifier>
     </dependency>
diff --git a/ml-kernel-client/src/main/scala/breeze/linalg/blas/YTYUtils.scala b/ml-kernel-client/src/main/scala/breeze/linalg/blas/YTYUtils.scala
index 8dd48df..8c43aa4 100644
--- a/ml-kernel-client/src/main/scala/breeze/linalg/blas/YTYUtils.scala
+++ b/ml-kernel-client/src/main/scala/breeze/linalg/blas/YTYUtils.scala
@@ -6,7 +6,10 @@
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 * */
 /*
- * Copyright (c). Huawei Technologies Co., Ltd. 2020-2020. All rights reserved.
+ * This file to You under the Apache License, Version 2.0;
+ * you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *    http://www.apache.org/licenses/LICENSE-2.0
  */
 
 package breeze.linalg.blas
diff --git a/ml-kernel-client/src/main/scala/breeze/optimize/ACC.scala b/ml-kernel-client/src/main/scala/breeze/optimize/ACC.scala
deleted file mode 100644
index bd2f43a..0000000
--- a/ml-kernel-client/src/main/scala/breeze/optimize/ACC.scala
+++ /dev/null
@@ -1,47 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * This file to You under the Apache License, Version 2.0;
- * you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *    http://www.apache.org/licenses/LICENSE-2.0
- */
-
-package breeze.optimize
-
-import breeze.math.MutableInnerProductModule
-
-object ACC{
-
-  def update[T](
-     step: T,
-     delta: T,
-     mStep: IndexedSeq[T],
-     g: IndexedSeq[T],
-     m: Int) (implicit space: MutableInnerProductModule[T, Double]):
-       (IndexedSeq[T], IndexedSeq[T]) = {
-    null
-  }
-
-  def updateMomentum[T](
-      m: T,
-      dir: T,
-      coeff: Double,
-      uCoeff: Double) (implicit space: MutableInnerProductModule[T, Double]): T = {
-    null.asInstanceOf[T]
-  }
-
-  def getInverseOfHessian[T](
-      g: T,
-      deltaA: IndexedSeq[T],
-      deltaB: IndexedSeq[T],
-      m: Int,
-      size: Int)(implicit space: MutableInnerProductModule[T, Double]): T = {
-    null.asInstanceOf[T]
-  }
-}
diff --git a/ml-kernel-client/src/main/scala/breeze/optimize/LBFGSL.scala b/ml-kernel-client/src/main/scala/breeze/optimize/LBFGSL.scala
deleted file mode 100644
index ac14511..0000000
--- a/ml-kernel-client/src/main/scala/breeze/optimize/LBFGSL.scala
+++ /dev/null
@@ -1,84 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * This file to You under the Apache License, Version 2.0;
- * you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *    http://www.apache.org/licenses/LICENSE-2.0
- */
-
-package breeze.optimize
-
-import breeze.linalg.DenseVector
-import breeze.util.SerializableLogging
-
-class LBFGSL(
-    var lowerBounds: DenseVector[Double],
-    var upperBounds: DenseVector[Double],
-    maxIterations: Int,
-    maxFCalls: Int,
-    m: Int,
-    fTolerance: Double,
-    xTolerance: Double)
-  extends FirstOrderMinimizer[DenseVector[Double], DiffFunction[DenseVector[Double]]](null)
-    with SerializableLogging {
-
-  def this(
-      lowerBounds: DenseVector[Double],
-      upperBounds: DenseVector[Double],
-      maxIterations: Int,
-      m: Int,
-      fTolerance: Double) = {
-    this(lowerBounds, upperBounds, maxIterations, 0, m, fTolerance, 0.0)
-  }
-
-  def this(maxIterations: Int, m: Int, fTolerance: Double) = {
-    this(null, null, maxIterations, 0, m, fTolerance, 0.0)
-  }
-
-  override def iterations(f: DiffFunction[DenseVector[Double]], x0: DenseVector[Double]):
-  Iterator[FirstOrderMinimizer.State[DenseVector[Double], convergenceCheck.Info, History]] = {
-    null
-  }
-
-  case class History()
-
-  override def initialHistory(
-      f: DiffFunction[DenseVector[Double]],
-      init: DenseVector[Double]): History = {
-    null
-  }
-
-  override def updateHistory(
-      newX: DenseVector[Double],
-      newGrad: DenseVector[Double],
-      newVal: Double,
-      f: DiffFunction[DenseVector[Double]],
-      oldState: State): History = {
-    null
-  }
-
-  override def chooseDescentDirection(state: State, f: DiffFunction[DenseVector[Double]]):
-  DenseVector[Double] = {
-    null
-  }
-
-  override def determineStepSize(
-      state: State,
-      f: DiffFunction[DenseVector[Double]],
-      direction: DenseVector[Double]): Double = {
-    0.0
-  }
-
-  override def takeStep(
-      state: State,
-      dir: DenseVector[Double],
-      stepSize: Double): DenseVector[Double] = {
-    null
-  }
-}
diff --git a/ml-kernel-client/src/main/scala/breeze/optimize/OWLQNL.scala b/ml-kernel-client/src/main/scala/breeze/optimize/OWLQNL.scala
deleted file mode 100644
index abb342d..0000000
--- a/ml-kernel-client/src/main/scala/breeze/optimize/OWLQNL.scala
+++ /dev/null
@@ -1,78 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * This file to You under the Apache License, Version 2.0;
- * you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *    http://www.apache.org/licenses/LICENSE-2.0
- */
-
-package breeze.optimize
-
-import breeze.linalg.DenseVector
-import breeze.util.SerializableLogging
-
-class OWLQNL(
-    maxIterations: Int,
-    maxFCalls: Int,
-    m: Int,
-    fTolerance: Double,
-    xTolerance: Double,
-    var l1RegParam: DenseVector[Double])
-  extends FirstOrderMinimizer[DenseVector[Double], DiffFunction[DenseVector[Double]]](null)
-    with SerializableLogging {
-
-  def this(
-      maxIterations: Int,
-      m: Int,
-      fTolerance: Double,
-      l1RegParam: DenseVector[Double]) = {
-    this(maxIterations, 0, m, fTolerance, 0.0, l1RegParam)
-  }
-
-  override def iterations(f: DiffFunction[DenseVector[Double]], x0: DenseVector[Double]):
-  Iterator[FirstOrderMinimizer.State[DenseVector[Double], convergenceCheck.Info, History]] = {
-    null
-  }
-
-  case class History()
-
-  override def initialHistory(
-      f: DiffFunction[DenseVector[Double]],
-      init: DenseVector[Double]): History = {
-    null
-  }
-
-  override def updateHistory(
-      newX: DenseVector[Double],
-      newGrad: DenseVector[Double],
-      newVal: Double,
-      f: DiffFunction[DenseVector[Double]],
-      oldState: State): History = {
-    null
-  }
-
-  override def chooseDescentDirection(state: State, f: DiffFunction[DenseVector[Double]]):
-  DenseVector[Double] = {
-    null
-  }
-
-  override def determineStepSize(
-      state: State,
-      f: DiffFunction[DenseVector[Double]],
-      direction: DenseVector[Double]): Double = {
-    0.0
-  }
-
-  override def takeStep(
-      state: State,
-      dir: DenseVector[Double],
-      stepSize: Double): DenseVector[Double] = {
-    null
-  }
-}
diff --git a/ml-kernel-client/src/main/scala/org/apache/spark/ml/recommendation/ALSUtils.scala b/ml-kernel-client/src/main/scala/org/apache/spark/ml/recommendation/ALSUtils.scala
index dca6a58..bf33ee6 100644
--- a/ml-kernel-client/src/main/scala/org/apache/spark/ml/recommendation/ALSUtils.scala
+++ b/ml-kernel-client/src/main/scala/org/apache/spark/ml/recommendation/ALSUtils.scala
@@ -6,7 +6,10 @@
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 * */
 /*
- * Copyright (c). Huawei Technologies Co., Ltd. 2020-2020. All rights reserved.
+ * This file to You under the Apache License, Version 2.0;
+ * you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *    http://www.apache.org/licenses/LICENSE-2.0
  */
 
 package org.apache.spark.ml.recommendation
diff --git a/ml-kernel-client/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesUtil.scala b/ml-kernel-client/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesUtil.scala
deleted file mode 100644
index 736cfaf..0000000
--- a/ml-kernel-client/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesUtil.scala
+++ /dev/null
@@ -1,77 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * This file to You under the Apache License, Version 2.0;
- * you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *    http://www.apache.org/licenses/LICENSE-2.0
- */
-
-package org.apache.spark.ml.tree.impl
-
-import it.unimi.dsi.fastutil.doubles.DoubleArrayList
-import it.unimi.dsi.fastutil.ints.Int2ObjectOpenHashMap
-import it.unimi.dsi.fastutil.ints.IntArrayList
-import it.unimi.dsi.fastutil.objects.ObjectArrayList
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.internal.Logging
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.tree.LearningNode
-import org.apache.spark.ml.tree.Split
-import org.apache.spark.ml.tree.impl.GradientBoostedTreesCore.NodeIndexInfo
-import org.apache.spark.mllib.tree.configuration.{Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.model.ImpurityStats
-import org.apache.spark.rdd.RDD
-
-object GradientBoostedTreesUtil extends Logging {
-
-  def dataProcessX(
-      input: RDD[LabeledPoint],
-      splits: Array[Array[Split]],
-      treeStrategy: OldStrategy,
-      metadata: DecisionTreeMetadata,
-      timer: TimeTracker,
-      seed: Long): (RDD[TreePoint], RDD[(Int, (IntArrayList, ObjectArrayList[Split]))],
-    Broadcast[DoubleArrayList], Broadcast[Int2ObjectOpenHashMap[IntArrayList]]) = {
-    null
-  }
-
-  def nodeIdCacheXConstruction(
-      nodes: Array[LearningNode],
-      rawPartInfoBc: Broadcast[Int2ObjectOpenHashMap[IntArrayList]])
-    : Int2ObjectOpenHashMap[Int2ObjectOpenHashMap[IntArrayList]] = {
-    null
-  }
-
-  def chooseBestSplits(
-      input: RDD[(Int, (IntArrayList, ObjectArrayList[Split]))],
-      nodeIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
-      metadata: DecisionTreeMetadata,
-      nodeIdCacheBc: Broadcast[Int2ObjectOpenHashMap[Int2ObjectOpenHashMap[IntArrayList]]],
-      labelArrayBc: Broadcast[DoubleArrayList],
-      nodes: Array[LearningNode]): scala.collection.Map[Int, (Split, ImpurityStats)] = {
-    null
-  }
-
-
-
-
-  def updateNodeIdCache(
-      nodeIdCache: Int2ObjectOpenHashMap[Int2ObjectOpenHashMap[IntArrayList]],
-      nodeIdCacheBc: Broadcast[Int2ObjectOpenHashMap[Int2ObjectOpenHashMap[IntArrayList]]],
-      input: RDD[TreePoint],
-      nodesForGroup: Map[Int, Array[LearningNode]],
-      nodeIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
-      splits: Array[Array[Split]],
-      rawPartInfoBc: Broadcast[Int2ObjectOpenHashMap[IntArrayList]],
-      metadata: DecisionTreeMetadata,
-      timer: TimeTracker): Unit = {
-  }
-
-}
diff --git a/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/KmeansUtil.scala b/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/KmeansUtil.scala
deleted file mode 100644
index 83de295..0000000
--- a/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/KmeansUtil.scala
+++ /dev/null
@@ -1,38 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * This file to You under the Apache License, Version 2.0;
- * you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *    http://www.apache.org/licenses/LICENSE-2.0
- */
-
-package org.apache.spark.mllib.clustering
-
-object KmeansUtil {
-
-  def generateDisMatrix(
-      centers: Array[VectorWithNorm], parLevel: Int): Array[Double] = {
-    val cl = centers.length
-    Array.fill(cl * cl)(0.0)
-  }
-
-  def findClosest(
-      centers: TraversableOnce[VectorWithNorm],
-      point: VectorWithNorm,
-      s: Array[Double]): (Int, Double) = {
-    (-1, -1.0)
-  }
-
-  def fastDistance(
-      v1: VectorWithNorm,
-      v2: VectorWithNorm): Double = {
-    -1.0
-  }
-
-}
diff --git a/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/LDAUtilsXOpt.scala b/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/LDAUtilsXOpt.scala
index e5c042f..e06a3d6 100644
--- a/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/LDAUtilsXOpt.scala
+++ b/ml-kernel-client/src/main/scala/org/apache/spark/mllib.clustering/LDAUtilsXOpt.scala
@@ -50,7 +50,8 @@ object LDAUtilsXOpt {
       vocabSize: Int,
       logphatPartOptionBase: () => Option[BDV[Double]],
       alpha: BV[Double],
-      gammaShape: Double): RDD[(BDM[Double], Option[BDV[Double]], Long)] = {
+      gammaShape: Double,
+      seed: Long): RDD[(BDM[Double], Option[BDV[Double]], Long)] = {
     null
   }
 
diff --git a/ml-xgboost/.clang-tidy b/ml-xgboost/.clang-tidy
deleted file mode 100644
index 3be1d9e..0000000
--- a/ml-xgboost/.clang-tidy
+++ /dev/null
@@ -1,21 +0,0 @@
-Checks: 'modernize-*,-modernize-make-*,-modernize-use-auto,-modernize-raw-string-literal,-modernize-avoid-c-arrays,-modernize-use-trailing-return-type,google-*,-google-default-arguments,-clang-diagnostic-#pragma-messages,readability-identifier-naming'
-CheckOptions:
-  - { key: readability-identifier-naming.ClassCase,                 value: CamelCase  }
-  - { key: readability-identifier-naming.StructCase,                value: CamelCase  }
-  - { key: readability-identifier-naming.TypeAliasCase,             value: CamelCase  }
-  - { key: readability-identifier-naming.TypedefCase,               value: CamelCase  }
-  - { key: readability-identifier-naming.TypeTemplateParameterCase, value: CamelCase  }
-  - { key: readability-identifier-naming.MemberCase,                value: lower_case }
-  - { key: readability-identifier-naming.PrivateMemberSuffix,       value: '_'        }
-  - { key: readability-identifier-naming.ProtectedMemberSuffix,     value: '_'        }
-  - { key: readability-identifier-naming.EnumCase,                  value: CamelCase  }
-  - { key: readability-identifier-naming.EnumConstant,              value: CamelCase  }
-  - { key: readability-identifier-naming.EnumConstantPrefix,        value: k          }
-  - { key: readability-identifier-naming.GlobalConstantCase,        value: CamelCase  }
-  - { key: readability-identifier-naming.GlobalConstantPrefix,      value: k          }
-  - { key: readability-identifier-naming.StaticConstantCase,        value: CamelCase  }
-  - { key: readability-identifier-naming.StaticConstantPrefix,      value: k          }
-  - { key: readability-identifier-naming.ConstexprVariableCase,     value: CamelCase  }
-  - { key: readability-identifier-naming.ConstexprVariablePrefix,   value: k          }
-  - { key: readability-identifier-naming.FunctionCase,              value: CamelCase  }
-  - { key: readability-identifier-naming.NamespaceCase,             value: lower_case }
diff --git a/ml-xgboost/.editorconfig b/ml-xgboost/.editorconfig
deleted file mode 100644
index 97a7bc1..0000000
--- a/ml-xgboost/.editorconfig
+++ /dev/null
@@ -1,11 +0,0 @@
-root = true
-
-[*]
-charset=utf-8
-indent_style = space
-indent_size = 2
-insert_final_newline = true
-
-[*.py]
-indent_style = space
-indent_size = 4
diff --git a/ml-xgboost/.gitmodules b/ml-xgboost/.gitmodules
deleted file mode 100644
index 35afd1d..0000000
--- a/ml-xgboost/.gitmodules
+++ /dev/null
@@ -1,3 +0,0 @@
-[submodule "cub"]
-	path = cub
-	url = https://github.com/NVlabs/cub
diff --git a/ml-xgboost/.travis.yml b/ml-xgboost/.travis.yml
deleted file mode 100644
index de23566..0000000
--- a/ml-xgboost/.travis.yml
+++ /dev/null
@@ -1,79 +0,0 @@
-# disable sudo for container build.
-sudo: required
-
-# Enabling test OS X
-os:
-  - linux
-  - osx
-
-osx_image: xcode10.1
-dist: bionic
-
-# Use Build Matrix to do lint and build seperately
-env:
-  matrix:
-    # python package test
-    - TASK=python_test
-    # test installation of Python source distribution
-    - TASK=python_sdist_test
-    # java package test
-    - TASK=java_test
-    # cmake test
-    - TASK=cmake_test
-
-  global:
-    - secure: "PR16i9F8QtNwn99C5NDp8nptAS+97xwDtXEJJfEiEVhxPaaRkOp0MPWhogCaK0Eclxk1TqkgWbdXFknwGycX620AzZWa/A1K3gAs+GrpzqhnPMuoBJ0Z9qxXTbSJvCyvMbYwVrjaxc/zWqdMU8waWz8A7iqKGKs/SqbQ3rO6v7c="
-    - secure: "dAGAjBokqm/0nVoLMofQni/fWIBcYSmdq4XvCBX1ZAMDsWnuOfz/4XCY6h2lEI1rVHZQ+UdZkc9PioOHGPZh5BnvE49/xVVWr9c4/61lrDOlkD01ZjSAeoV0fAZq+93V/wPl4QV+MM+Sem9hNNzFSbN5VsQLAiWCSapWsLdKzqA="
-
-matrix:
-  exclude:
-    - os: linux
-      env: TASK=python_test
-    - os: linux
-      env: TASK=java_test
-    - os: linux
-      env: TASK=cmake_test
-
-# dependent brew packages
-addons:
-  homebrew:
-    packages:
-      - cmake
-      - libomp
-      - graphviz
-      - openssl
-      - libgit2
-      - wget
-      - r
-    update: true
-
-before_install:
-  - source tests/travis/travis_setup_env.sh
-  - if [ "${TASK}" != "python_sdist_test" ]; then export PYTHONPATH=${PYTHONPATH}:${PWD}/python-package; fi
-  - echo "MAVEN_OPTS='-Xmx2g -XX:MaxPermSize=1024m -XX:ReservedCodeCacheSize=512m -Dorg.slf4j.simpleLogger.defaultLogLevel=error'" > ~/.mavenrc
-
-install:
-  - source tests/travis/setup.sh
-
-script:
-  - tests/travis/run_test.sh
-
-cache:
-  directories:
-    - ${HOME}/.cache/usr
-    - ${HOME}/.cache/pip
-
-before_cache:
-  - tests/travis/travis_before_cache.sh
-
-after_failure:
-  - tests/travis/travis_after_failure.sh
-
-after_success:
-  - tree build
-  - bash <(curl -s https://codecov.io/bash) -a '-o src/ src/*.c'
-
-notifications:
-  email:
-    on_success: change
-    on_failure: always
diff --git a/ml-xgboost/CITATION b/ml-xgboost/CITATION
deleted file mode 100644
index 1890625..0000000
--- a/ml-xgboost/CITATION
+++ /dev/null
@@ -1,18 +0,0 @@
-@inproceedings{Chen:2016:XST:2939672.2939785,
- author = {Chen, Tianqi and Guestrin, Carlos},
- title = {{XGBoost}: A Scalable Tree Boosting System},
- booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
- series = {KDD '16},
- year = {2016},
- isbn = {978-1-4503-4232-2},
- location = {San Francisco, California, USA},
- pages = {785--794},
- numpages = {10},
- url = {http://doi.acm.org/10.1145/2939672.2939785},
- doi = {10.1145/2939672.2939785},
- acmid = {2939785},
- publisher = {ACM},
- address = {New York, NY, USA},
- keywords = {large-scale machine learning},
-}
-
diff --git a/ml-xgboost/CMakeLists.txt b/ml-xgboost/CMakeLists.txt
deleted file mode 100644
index dbc7ae0..0000000
--- a/ml-xgboost/CMakeLists.txt
+++ /dev/null
@@ -1,310 +0,0 @@
-cmake_minimum_required(VERSION 3.13)
-set(CMAKE_CXX_FLAGS "-fstack-protector-all -D_FORTIFY_SOURCE=2 -O2 -Wl,-z,relro,-z,now,-z,noexecstack -s ${CMAKE_CXX_FLAGS}")
-set(CMAKE_SKIP_RPATH TRUE)
-project(xgboost LANGUAGES CXX C VERSION 1.1.0)
-include(cmake/Utils.cmake)
-list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
-cmake_policy(SET CMP0022 NEW)
-cmake_policy(SET CMP0079 NEW)
-cmake_policy(SET CMP0063 NEW)
-
-if ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
-  cmake_policy(SET CMP0077 NEW)
-endif ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
-
-message(STATUS "CMake version ${CMAKE_VERSION}")
-
-if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.0)
-  message(FATAL_ERROR "GCC version must be at least 5.0!")
-endif()
-
-include(${xgboost_SOURCE_DIR}/cmake/FindPrefetchIntrinsics.cmake)
-find_prefetch_intrinsics()
-include(${xgboost_SOURCE_DIR}/cmake/Version.cmake)
-write_version()
-set_default_configuration_release()
-
-#-- Options
-option(BUILD_C_DOC "Build documentation for C APIs using Doxygen." OFF)
-option(USE_OPENMP "Build with OpenMP support." ON)
-option(BUILD_STATIC_LIB "Build static library" OFF)
-## Bindings
-option(JVM_BINDINGS "Build JVM bindings" ON)
-option(R_LIB "Build shared library for R package" OFF)
-## Dev
-option(USE_DEBUG_OUTPUT "Dump internal training results like gradients and predictions to stdout.
-Should only be used for debugging." OFF)
-option(GOOGLE_TEST "Build google tests" OFF)
-option(WITH_CODE_COVERAGE "with code coverage" OFF)
-option(USE_DMLC_GTEST "Use google tests bundled with dmlc-core submodule" OFF)
-option(USE_NVTX "Build with cuda pwrofiling annotations. Developers only." OFF)
-set(NVTX_HEADER_DIR "" CACHE PATH "Path to the stand-alone nvtx header")
-option(RABIT_MOCK "Build rabit with mock" OFF)
-option(HIDE_CXX_SYMBOLS "Build shared library and hide all C++ symbols" OFF)
-## CUDA
-option(USE_CUDA  "Build with GPU acceleration" OFF)
-option(USE_NCCL  "Build with NCCL to enable distributed GPU support." OFF)
-option(BUILD_WITH_SHARED_NCCL "Build with shared NCCL library." OFF)
-set(GPU_COMPUTE_VER "" CACHE STRING
-  "Semicolon separated list of compute versions to be built against, e.g. '35;61'")
-## Copied From dmlc
-option(USE_HDFS "Build with HDFS support" OFF)
-option(USE_AZURE "Build with AZURE support" OFF)
-option(USE_S3 "Build with S3 support" OFF)
-## Sanitizers
-option(USE_SANITIZER "Use santizer flags" OFF)
-option(SANITIZER_PATH "Path to sanitizes.")
-set(ENABLED_SANITIZERS "address" "leak" CACHE STRING
-  "Semicolon separated list of sanitizer names. E.g 'address;leak'. Supported sanitizers are
-address, leak, undefined and thread.")
-## Plugins
-option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
-option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)
-## Force AVX
-option(USE_AVX "Use AVX" ON) 
-
-#-- Checks for building XGBoost
-if (USE_DEBUG_OUTPUT AND (NOT (CMAKE_BUILD_TYPE MATCHES Debug)))
-  message(SEND_ERROR "Do not enable `USE_DEBUG_OUTPUT' with release build.")
-endif (USE_DEBUG_OUTPUT AND (NOT (CMAKE_BUILD_TYPE MATCHES Debug)))
-if (USE_NCCL AND NOT (USE_CUDA))
-  message(SEND_ERROR "`USE_NCCL` must be enabled with `USE_CUDA` flag.")
-endif (USE_NCCL AND NOT (USE_CUDA))
-if (BUILD_WITH_SHARED_NCCL AND (NOT USE_NCCL))
-  message(SEND_ERROR "Build XGBoost with -DUSE_NCCL=ON to enable BUILD_WITH_SHARED_NCCL.")
-endif (BUILD_WITH_SHARED_NCCL AND (NOT USE_NCCL))
-if (JVM_BINDINGS AND R_LIB)
-  message(SEND_ERROR "`R_LIB' is not compatible with `JVM_BINDINGS' as they both have customized configurations.")
-endif (JVM_BINDINGS AND R_LIB)
-if (R_LIB AND GOOGLE_TEST)
-  message(WARNING "Some C++ unittests will fail with `R_LIB` enabled,
- as R package redirects some functions to R runtime implementation.")
-endif (R_LIB AND GOOGLE_TEST)
-
-include(CheckSymbolExists)
-include(CheckIncludeFile)
-include(CheckIncludeFileCXX)
-if(USE_AVX)
-  if(MSVC)
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
-  else()
-    EXECUTE_PROCESS(COMMAND uname -a OUTPUT_VARIABLE ARCH)
-    message(STATUS "Current architechture is ${ARCH}")
-
-    if (${ARCH} MATCHES ".*aarch64.*")
-      message(STATUS "Turn on ARM intrinsics")
-      add_compile_options(-DUSE_ARM_INTRINSICS)
-    endif()
-    if (${ARCH} MATCHES ".*x86.*")
-      message(STATUS "Turn on Intel intrinsics")
-      add_compile_options(-mavx -DUSE_INTEL_INTRINSICS)
-    endif()
-  endif()
-endif()
-
-if(WITH_CODE_COVERAGE)
-    message(STATUS "Enabled code coverage options")
-    add_compile_options("-ftest-coverage" "-fprofile-arcs")
-    # actually these options are for compiling but for some targets they are needed in link options
-    add_link_options("-ftest-coverage" "-fprofile-arcs")
-    add_link_options("-lgcov")
-endif()
-
-#-- Sanitizer
-if (USE_SANITIZER)
-  include(cmake/Sanitizer.cmake)
-  enable_sanitizers("${ENABLED_SANITIZERS}")
-endif (USE_SANITIZER)
-
-if (USE_CUDA)
-  SET(USE_OPENMP ON CACHE BOOL "CUDA requires OpenMP" FORCE)
-  # `export CXX=' is ignored by CMake CUDA.
-  set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_CXX_COMPILER})
-  message(STATUS "Configured CUDA host compiler: ${CMAKE_CUDA_HOST_COMPILER}")
-
-  enable_language(CUDA)
-  set(GEN_CODE "")
-  format_gencode_flags("${GPU_COMPUTE_VER}" GEN_CODE)
-  message(STATUS "CUDA GEN_CODE: ${GEN_CODE}")
-endif (USE_CUDA)
-
-find_package(Threads REQUIRED)
-
-if (USE_OPENMP)
-  if (APPLE)
-    # Require CMake 3.16+ on Mac OSX, as previous versions of CMake had trouble locating
-    # OpenMP on Mac. See https://github.com/dmlc/xgboost/pull/5146#issuecomment-568312706
-    cmake_minimum_required(VERSION 3.16)
-  endif (APPLE)
-  find_package(OpenMP REQUIRED)
-endif (USE_OPENMP)
-
-# dmlc-core
-msvc_use_static_runtime()
-add_subdirectory(${xgboost_SOURCE_DIR}/dmlc-core)
-set_target_properties(dmlc PROPERTIES
-  CXX_STANDARD 14
-  CXX_STANDARD_REQUIRED ON
-  POSITION_INDEPENDENT_CODE ON)
-list(APPEND LINKED_LIBRARIES_PRIVATE dmlc)
-
-# rabit
-set(RABIT_BUILD_DMLC OFF)
-set(DMLC_ROOT ${xgboost_SOURCE_DIR}/dmlc-core)
-set(RABIT_WITH_R_LIB ${R_LIB})
-add_subdirectory(rabit)
-
-if (RABIT_MOCK)
-  list(APPEND LINKED_LIBRARIES_PRIVATE rabit_mock_static)
-else()
-  list(APPEND LINKED_LIBRARIES_PRIVATE rabit)
-endif(RABIT_MOCK)
-foreach(lib rabit rabit_base rabit_empty rabit_mock rabit_mock_static)
-  # Explicitly link dmlc to rabit, so that configured header (build_config.h)
-  # from dmlc is correctly applied to rabit.
-  if (TARGET ${lib})
-    target_link_libraries(${lib} dmlc ${CMAKE_THREAD_LIBS_INIT})
-    if (HIDE_CXX_SYMBOLS)  # Hide all C++ symbols from Rabit
-      set_target_properties(${lib} PROPERTIES CXX_VISIBILITY_PRESET hidden)
-    endif (HIDE_CXX_SYMBOLS)
-  endif (TARGET ${lib})
-endforeach()
-
-# Exports some R specific definitions and objects
-if (R_LIB)
-  add_subdirectory(${xgboost_SOURCE_DIR}/R-package)
-endif (R_LIB)
-
-# core xgboost
-list(APPEND LINKED_LIBRARIES_PRIVATE Threads::Threads ${CMAKE_THREAD_LIBS_INIT})
-add_subdirectory(${xgboost_SOURCE_DIR}/plugin)
-add_subdirectory(${xgboost_SOURCE_DIR}/src)
-target_link_libraries(objxgboost PUBLIC dmlc)
-set(XGBOOST_OBJ_SOURCES "${XGBOOST_OBJ_SOURCES};$<TARGET_OBJECTS:objxgboost>")
-
-#-- library
-if (BUILD_STATIC_LIB)
-  add_library(xgboost STATIC ${XGBOOST_OBJ_SOURCES})
-  string(TIMESTAMP time_stamp "%s")
-  add_custom_command(TARGET xgboost
-          POST_BUILD
-          COMMAND mkdir ${CMAKE_CURRENT_SOURCE_DIR}/lib/tmp_${time_stamp}
-          COMMAND mv ${CMAKE_CURRENT_SOURCE_DIR}/lib/libxgboost.a ${CMAKE_CURRENT_SOURCE_DIR}/lib/tmp_${time_stamp}/
-          COMMAND cp ${CMAKE_CURRENT_SOURCE_DIR}/lib/libboostkit_xgboost_kernel.a ${CMAKE_CURRENT_SOURCE_DIR}/lib/tmp_${time_stamp}/
-          VERBATIM
-          )
-  add_custom_command(TARGET xgboost
-          POST_BUILD
-          COMMAND ar -x libxgboost.a
-          COMMAND ar -x libboostkit_xgboost_kernel.a
-          COMMAND sh -c "ar -qcs ${CMAKE_CURRENT_SOURCE_DIR}/lib/libxgboost.a ${CMAKE_CURRENT_SOURCE_DIR}/lib/tmp_${time_stamp}/*.o"
-          WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/lib/tmp_${time_stamp}/
-          VERBATIM
-          )
-  add_custom_command(TARGET xgboost
-          POST_BUILD
-          COMMAND rm -rf ${CMAKE_CURRENT_SOURCE_DIR}/lib/tmp_${time_stamp}
-          VERBATIM
-          )
-else (BUILD_STATIC_LIB)
-  add_library(xgboost SHARED ${XGBOOST_OBJ_SOURCES})
-  target_link_libraries(xgboost PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/lib/libboostkit_xgboost_kernel.so)
-endif (BUILD_STATIC_LIB)
-
-#-- Hide all C++ symbols
-if (HIDE_CXX_SYMBOLS)
-  set_target_properties(objxgboost PROPERTIES CXX_VISIBILITY_PRESET hidden)
-  set_target_properties(xgboost PROPERTIES CXX_VISIBILITY_PRESET hidden)
-endif (HIDE_CXX_SYMBOLS)
-
-target_include_directories(xgboost
-  INTERFACE
-  $<INSTALL_INTERFACE:${CMAKE_INSTALL_PREFIX}/include>
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/include>)
-target_link_libraries(xgboost PRIVATE ${LINKED_LIBRARIES_PRIVATE})
-
-# This creates its own shared library `xgboost4j'.
-if (JVM_BINDINGS)
-  # To ensure open source independently compliation
-  add_subdirectory(${PROJECT_SOURCE_DIR}/kernel_include/boostkit_xgboost_kernel_client)
-  add_subdirectory(${xgboost_SOURCE_DIR}/jvm-packages)
-endif (JVM_BINDINGS)
-#-- End shared library
-
-#-- CLI for xgboost
-add_executable(runxgboost ${xgboost_SOURCE_DIR}/src/cli_main.cc ${XGBOOST_OBJ_SOURCES})
-
-target_include_directories(runxgboost
-  PRIVATE
-  ${xgboost_SOURCE_DIR}/include
-  ${xgboost_SOURCE_DIR}/dmlc-core/include
-  ${xgboost_SOURCE_DIR}/rabit/include
-  ${xgboost_SOURCE_DIR}/kernel_include)
-target_link_libraries(runxgboost PRIVATE ${LINKED_LIBRARIES_PRIVATE})
-target_link_libraries(runxgboost PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/lib/libboostkit_xgboost_kernel.so)
-set_target_properties(
-  runxgboost PROPERTIES
-  OUTPUT_NAME xgboost
-  CXX_STANDARD 14
-  CXX_STANDARD_REQUIRED ON)
-#-- End CLI for xgboost
-
-set_output_directory(runxgboost ${xgboost_SOURCE_DIR})
-set_output_directory(xgboost ${xgboost_SOURCE_DIR}/lib)
-# Ensure these two targets do not build simultaneously, as they produce outputs with conflicting names
-add_dependencies(xgboost runxgboost)
-
-#-- Installing XGBoost
-if (R_LIB)
-  set_target_properties(xgboost PROPERTIES PREFIX "")
-  if (APPLE)
-    set_target_properties(xgboost PROPERTIES SUFFIX ".so")
-  endif (APPLE)
-  setup_rpackage_install_target(xgboost ${CMAKE_CURRENT_BINARY_DIR})
-  set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_BINARY_DIR}/dummy_inst")
-endif (R_LIB)
-if (MINGW)
-  set_target_properties(xgboost PROPERTIES PREFIX "")
-endif (MINGW)
-
-if (BUILD_C_DOC)
-  include(cmake/Doc.cmake)
-  run_doxygen()
-endif (BUILD_C_DOC)
-
-include(GNUInstallDirs)
-# Install all headers.  Please note that currently the C++ headers does not form an "API".
-install(DIRECTORY ${xgboost_SOURCE_DIR}/include/xgboost
-  DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
-
-install(TARGETS xgboost runxgboost
-  EXPORT XGBoostTargets
-  ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
-  INCLUDES DESTINATION ${LIBLEGACY_INCLUDE_DIRS})
-install(EXPORT XGBoostTargets
-  FILE XGBoostTargets.cmake
-  NAMESPACE xgboost::
-  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/xgboost)
-
-include(CMakePackageConfigHelpers)
-configure_package_config_file(
-  ${CMAKE_CURRENT_LIST_DIR}/cmake/xgboost-config.cmake.in
-  ${CMAKE_CURRENT_BINARY_DIR}/cmake/xgboost-config.cmake
-  INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/xgboost)
-write_basic_package_version_file(
-  ${CMAKE_BINARY_DIR}/cmake/xgboost-config-version.cmake
-  VERSION ${XGBOOST_VERSION}
-  COMPATIBILITY AnyNewerVersion)
-install(
-  FILES
-  ${CMAKE_BINARY_DIR}/cmake/xgboost-config.cmake
-  ${CMAKE_BINARY_DIR}/cmake/xgboost-config-version.cmake
-  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/xgboost)
-
-
-# For MSVC: Call msvc_use_static_runtime() once again to completely
-# replace /MD with /MT. See https://github.com/dmlc/xgboost/issues/4462
-# for issues caused by mixing of /MD and /MT flags
-msvc_use_static_runtime()
diff --git a/ml-xgboost/CONTRIBUTORS.md b/ml-xgboost/CONTRIBUTORS.md
deleted file mode 100644
index e426f85..0000000
--- a/ml-xgboost/CONTRIBUTORS.md
+++ /dev/null
@@ -1,104 +0,0 @@
-Contributors of DMLC/XGBoost
-============================
-XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.
-
-Project Management Committee(PMC) 
-----------
-The Project Management Committee(PMC) consists group of active committers that moderate the discussion, manage the project release, and proposes new committer/PMC members. 
-
-* [Tianqi Chen](https://github.com/tqchen), University of Washington
-  - Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
-* [Michael Benesty](https://github.com/pommedeterresautee)
-  - Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
-* [Yuan Tang](https://github.com/terrytangyuan), Ant Financial
-  - Yuan is a software engineer in Ant Financial. He contributed mostly in R and Python packages.
-* [Nan Zhu](https://github.com/CodingCat), Uber
-  - Nan is a software engineer in Uber. He contributed mostly in JVM packages.
-* [Jiaming Yuan](https://github.com/trivialfis)
-  - Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C++ codebase.
-* [Hyunsu Cho](http://hyunsu-cho.io/), NVIDIA
-  - Hyunsu is the maintainer of the XGBoost Python package. He also manages the Jenkins continuous integration system (https://xgboost-ci.net/). He is the initial author of the CPU 'hist' updater.
-* [Rory Mitchell](https://github.com/RAMitchell), University of Waikato
-  - Rory is a Ph.D. student at University of Waikato. He is the original creator of the GPU training algorithms. He improved the CMake build system and continuous integration. 
-* [Hongliang Liu](https://github.com/phunterlau)
-
-
-Committers
-----------
-Committers are people who have made substantial contribution to the project and granted write access to the project.
-
-* [Tong He](https://github.com/hetong007), Amazon AI
-  - Tong is an applied scientist in Amazon AI. He is the maintainer of XGBoost R package.
-* [Vadim Khotilovich](https://github.com/khotilov)
-  - Vadim contributes many improvements in R and core packages.
-* [Bing Xu](https://github.com/antinucleon)
-  - Bing is the original creator of XGBoost Python package and currently the maintainer of [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl).
-* [Sergei Lebedev](https://github.com/superbobry), Criteo
-  - Sergei is a software engineer in Criteo. He contributed mostly in JVM packages.
-* [Scott Lundberg](http://scottlundberg.com/), University of Washington
-  - Scott is a Ph.D. student at University of Washington. He is the creator of SHAP, a unified approach to explain the output of machine learning models such as decision tree ensembles. He also helps maintain the XGBoost Julia package.
-
-
-Become a Committer
-------------------
-XGBoost is a opensource project and we are actively looking for new committers who are willing to help maintaining and lead the project.
-Committers comes from contributors who:
-* Made substantial contribution to the project.
-* Willing to spent time on maintaining and lead the project.
-
-New committers will be proposed by current committer members, with support from more than two of current committers.
-
-List of Contributors
---------------------
-* [Full List of Contributors](https://github.com/dmlc/xgboost/graphs/contributors)
-  - To contributors: please add your name to the list when you submit a patch to the project:)
-* [Kailong Chen](https://github.com/kalenhaha)
-  - Kailong is an early contributor of XGBoost, he is creator of ranking objectives in XGBoost.
-* [Skipper Seabold](https://github.com/jseabold)
-  - Skipper is the major contributor to the scikit-learn module of XGBoost.
-* [Zygmunt Zając](https://github.com/zygmuntz)
-  - Zygmunt is the master behind the early stopping feature frequently used by kagglers.
-* [Ajinkya Kale](https://github.com/ajkl)
-* [Boliang Chen](https://github.com/cblsjtu)
-* [Yangqing Men](https://github.com/yanqingmen)
-  - Yangqing is the creator of XGBoost java package.
-* [Engpeng Yao](https://github.com/yepyao)
-* [Giulio](https://github.com/giuliohome)
-  - Giulio is the creator of Windows project of XGBoost
-* [Jamie Hall](https://github.com/nerdcha)
-  - Jamie is the initial creator of XGBoost scikit-learn module.
-* [Yen-Ying Lee](https://github.com/white1033)
-* [Masaaki Horikoshi](https://github.com/sinhrks)
-  - Masaaki is the initial creator of XGBoost Python plotting module.
-* [daiyl0320](https://github.com/daiyl0320)
-  - daiyl0320 contributed patch to XGBoost distributed version more robust, and scales stably on TB scale datasets.
-* [Huayi Zhang](https://github.com/irachex)
-* [Johan Manders](https://github.com/johanmanders)
-* [yoori](https://github.com/yoori)
-* [Mathias Müller](https://github.com/far0n)
-* [Sam Thomson](https://github.com/sammthomson)
-* [ganesh-krishnan](https://github.com/ganesh-krishnan)
-* [Damien Carol](https://github.com/damiencarol)
-* [Alex Bain](https://github.com/convexquad)
-* [Baltazar Bieniek](https://github.com/bbieniek)
-* [Adam Pocock](https://github.com/Craigacp)
-* [Gideon Whitehead](https://github.com/gaw89)
-* [Yi-Lin Juang](https://github.com/frankyjuang)
-* [Andrew Hannigan](https://github.com/andrewhannigan)
-* [Andy Adinets](https://github.com/canonizer)
-* [Henry Gouk](https://github.com/henrygouk)
-* [Pierre de Sahb](https://github.com/pdesahb)
-* [liuliang01](https://github.com/liuliang01)
-  - liuliang01 added support for the qid column for LibSVM input format. This makes ranking task easier in distributed setting.
-* [Andrew Thia](https://github.com/BlueTea88)
-  - Andrew Thia implemented feature interaction constraints
-* [Wei Tian](https://github.com/weitian)
-* [Chen Qin](https://github.com/chenqin)
-* [Sam Wilkinson](https://samwilkinson.io)
-* [Matthew Jones](https://github.com/mt-jones)
-* [Jiaxiang Li](https://github.com/JiaxiangBU)
-* [Bryan Woods](https://github.com/bryan-woods)
-  - Bryan added support for cross-validation for the ranking objective
-* [Haoda Fu](https://github.com/fuhaoda)
-* [Evan Kepner](https://github.com/EvanKepner)
-  - Evan Kepner added support for os.PathLike file paths in Python
diff --git a/ml-xgboost/Jenkinsfile b/ml-xgboost/Jenkinsfile
deleted file mode 100644
index f2bd51c..0000000
--- a/ml-xgboost/Jenkinsfile
+++ /dev/null
@@ -1,435 +0,0 @@
-#!/usr/bin/groovy
-// -*- mode: groovy -*-
-// Jenkins pipeline
-// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
-
-// Command to run command inside a docker container
-dockerRun = 'tests/ci_build/ci_build.sh'
-
-import groovy.transform.Field
-
-@Field
-def commit_id   // necessary to pass a variable from one stage to another
-
-pipeline {
-  // Each stage specify its own agent
-  agent none
-
-  environment {
-    DOCKER_CACHE_ECR_ID = '492475357299'
-    DOCKER_CACHE_ECR_REGION = 'us-west-2'
-  }
-
-  // Setup common job properties
-  options {
-    ansiColor('xterm')
-    timestamps()
-    timeout(time: 240, unit: 'MINUTES')
-    buildDiscarder(logRotator(numToKeepStr: '10'))
-    preserveStashes()
-  }
-
-  // Build stages
-  stages {
-    stage('Jenkins Linux: Get sources') {
-      agent { label 'linux && cpu' }
-      steps {
-        script {
-          checkoutSrcs()
-          commit_id = "${GIT_COMMIT}"
-        }
-        stash name: 'srcs'
-        milestone ordinal: 1
-      }
-    }
-    stage('Jenkins Linux: Formatting Check') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'clang-tidy': { ClangTidy() },
-            'lint': { Lint() },
-            'sphinx-doc': { SphinxDoc() },
-            'doxygen': { Doxygen() }
-          ])
-        }
-        milestone ordinal: 2
-      }
-    }
-    stage('Jenkins Linux: Build') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'build-cpu': { BuildCPU() },
-            'build-cpu-rabit-mock': { BuildCPUMock() },
-            'build-cpu-non-omp': { BuildCPUNonOmp() },
-            'build-gpu-cuda10.0': { BuildCUDA(cuda_version: '10.0') },
-            'build-gpu-cuda10.1': { BuildCUDA(cuda_version: '10.1') },
-            'build-jvm-packages': { BuildJVMPackages(spark_version: '2.4.3') },
-            'build-jvm-doc': { BuildJVMDoc() }
-          ])
-        }
-        milestone ordinal: 3
-      }
-    }
-    stage('Jenkins Linux: Test') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'test-python-cpu': { TestPythonCPU() },
-            'test-python-gpu-cuda9.0': { TestPythonGPU(cuda_version: '9.0') },
-            'test-python-gpu-cuda10.0': { TestPythonGPU(cuda_version: '10.0') },
-            'test-python-gpu-cuda10.1': { TestPythonGPU(cuda_version: '10.1') },
-            'test-python-mgpu-cuda10.1': { TestPythonGPU(cuda_version: '10.1', multi_gpu: true) },
-            'test-cpp-gpu': { TestCppGPU(cuda_version: '10.1') },
-            'test-cpp-mgpu': { TestCppGPU(cuda_version: '10.1', multi_gpu: true) },
-            'test-jvm-jdk8': { CrossTestJVMwithJDK(jdk_version: '8', spark_version: '2.4.3') },
-            'test-jvm-jdk11': { CrossTestJVMwithJDK(jdk_version: '11') },
-            'test-jvm-jdk12': { CrossTestJVMwithJDK(jdk_version: '12') },
-            'test-r-3.5.3': { TestR(use_r35: true) }
-          ])
-        }
-        milestone ordinal: 4
-      }
-    }
-    stage('Jenkins Linux: Deploy') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'deploy-jvm-packages': { DeployJVMPackages(spark_version: '2.4.3') }
-          ])
-        }
-        milestone ordinal: 5
-      }
-    }
-  }
-}
-
-// check out source code from git
-def checkoutSrcs() {
-  retry(5) {
-    try {
-      timeout(time: 2, unit: 'MINUTES') {
-        checkout scm
-        sh 'git submodule update --init'
-      }
-    } catch (exc) {
-      deleteDir()
-      error "Failed to fetch source codes"
-    }
-  }
-}
-
-def ClangTidy() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running clang-tidy job..."
-    def container_type = "clang_tidy"
-    def docker_binary = "docker"
-    def dockerArgs = "--build-arg CUDA_VERSION=10.1"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} ${dockerArgs} python3 tests/ci_build/tidy.py
-    """
-    deleteDir()
-  }
-}
-
-def Lint() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running lint..."
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} make lint
-    """
-    deleteDir()
-  }
-}
-
-def SphinxDoc() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running sphinx-doc..."
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='-e SPHINX_GIT_BRANCH=${BRANCH_NAME}'"
-    sh """#!/bin/bash
-    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} make -C doc html
-    """
-    deleteDir()
-  }
-}
-
-def Doxygen() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running doxygen..."
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/doxygen.sh ${BRANCH_NAME}
-    """
-    echo 'Uploading doc...'
-    s3Upload file: "build/${BRANCH_NAME}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "doxygen/${BRANCH_NAME}.tar.bz2"
-    deleteDir()
-  }
-}
-
-def BuildCPU() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Build CPU"
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} rm -fv dmlc-core/include/dmlc/build_config_default.h
-      # This step is not necessary, but here we include it, to ensure that DMLC_CORE_USE_CMAKE flag is correctly propagated
-      # We want to make sure that we use the configured header build/dmlc/build_config.h instead of include/dmlc/build_config_default.h.
-      # See discussion at https://github.com/dmlc/xgboost/issues/5510
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_via_cmake.sh
-    ${dockerRun} ${container_type} ${docker_binary} build/testxgboost
-    """
-    // Sanitizer test
-    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='-e ASAN_SYMBOLIZER_PATH=/usr/bin/llvm-symbolizer -e ASAN_OPTIONS=symbolize=1 -e UBSAN_OPTIONS=print_stacktrace=1:log_path=ubsan_error.log --cap-add SYS_PTRACE'"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_via_cmake.sh -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak;undefined" \
-      -DCMAKE_BUILD_TYPE=Debug -DSANITIZER_PATH=/usr/lib/x86_64-linux-gnu/
-    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} build/testxgboost
-    """
-
-    stash name: 'xgboost_cli', includes: 'xgboost'
-    deleteDir()
-  }
-}
-
-def BuildCPUMock() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Build CPU with rabit mock"
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_mock_cmake.sh
-    """
-    echo 'Stashing rabit C++ test executable (xgboost)...'
-    stash name: 'xgboost_rabit_tests', includes: 'xgboost'
-    deleteDir()
-  }
-}
-
-def BuildCPUNonOmp() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Build CPU without OpenMP"
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_via_cmake.sh -DUSE_OPENMP=OFF
-    """
-    echo "Running Non-OpenMP C++ test..."
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} build/testxgboost
-    """
-    deleteDir()
-  }
-}
-
-def BuildCUDA(args) {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Build with CUDA ${args.cuda_version}"
-    def container_type = "gpu_build"
-    def docker_binary = "docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_via_cmake.sh -DUSE_CUDA=ON -DUSE_NCCL=ON -DOPEN_MP:BOOL=ON -DHIDE_CXX_SYMBOLS=ON
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} bash -c "cd python-package && rm -rf dist/* && python setup.py bdist_wheel --universal"
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} python3 tests/ci_build/rename_whl.py python-package/dist/*.whl ${commit_id} manylinux2010_x86_64
-    """
-    // Stash wheel for CUDA 10.0 target
-    if (args.cuda_version == '10.0') {
-      echo 'Stashing Python wheel...'
-      stash name: 'xgboost_whl_cuda10', includes: 'python-package/dist/*.whl'
-      path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
-      s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', workingDir: 'python-package/dist', includePathPattern:'**/*.whl'
-      echo 'Stashing C++ test executable (testxgboost)...'
-      stash name: 'xgboost_cpp_tests', includes: 'build/testxgboost'
-    }
-    deleteDir()
-  }
-}
-
-def BuildJVMPackages(args) {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Build Boostkit-XGBoost4J-Spark with Spark ${args.spark_version}"
-    def container_type = "jvm"
-    def docker_binary = "docker"
-    // Use only 4 CPU cores
-    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='--cpuset-cpus 0-3'"
-    sh """
-    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_jvm_packages.sh ${args.spark_version}
-    """
-    echo 'Stashing Boostkit-XGBoost4J JAR...'
-    stash name: 'boostkit-xgboost4j_jar', includes: "jvm-packages/boostkit-xgboost4j/target/*.jar,jvm-packages/boostkit-xgboost4j-spark/target/*.jar,jvm-packages/boostkit-xgboost4j-example/target/*.jar"
-    deleteDir()
-  }
-}
-
-def BuildJVMDoc() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Building JVM doc..."
-    def container_type = "jvm"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_jvm_doc.sh ${BRANCH_NAME}
-    """
-    echo 'Uploading doc...'
-    s3Upload file: "jvm-packages/${BRANCH_NAME}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "${BRANCH_NAME}.tar.bz2"
-    deleteDir()
-  }
-}
-
-def TestPythonCPU() {
-  node('linux && cpu') {
-    unstash name: 'xgboost_whl_cuda10'
-    unstash name: 'srcs'
-    unstash name: 'xgboost_cli'
-    echo "Test Python CPU"
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/test_python.sh cpu
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/test_python.sh cpu-py35
-    """
-    deleteDir()
-  }
-}
-
-def TestPythonGPU(args) {
-  nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
-  node(nodeReq) {
-    unstash name: 'xgboost_whl_cuda10'
-    unstash name: 'srcs'
-    echo "Test Python GPU: CUDA ${args.cuda_version}"
-    def container_type = "gpu"
-    def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
-    if (args.multi_gpu) {
-      echo "Using multiple GPUs"
-      sh """
-      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_python.sh mgpu
-      """
-      if (args.cuda_version != '9.0') {
-        echo "Running tests with cuDF..."
-        sh """
-        ${dockerRun} cudf ${docker_binary} ${docker_args} tests/ci_build/test_python.sh mgpu-cudf
-        """
-      }
-    } else {
-      echo "Using a single GPU"
-      sh """
-      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_python.sh gpu
-      """
-      if (args.cuda_version != '9.0') {
-        echo "Running tests with cuDF..."
-        sh """
-        ${dockerRun} cudf ${docker_binary} ${docker_args} tests/ci_build/test_python.sh cudf
-        """
-      }
-    }
-    // For CUDA 10.0 target, run cuDF tests too
-    deleteDir()
-  }
-}
-
-def TestCppRabit() {
-  node(nodeReq) {
-    unstash name: 'xgboost_rabit_tests'
-    unstash name: 'srcs'
-    echo "Test C++, rabit mock on"
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/runxgb.sh xgboost tests/ci_build/approx.conf.in
-    """
-    deleteDir()
-  }
-}
-
-def TestCppGPU(args) {
-  nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
-  node(nodeReq) {
-    unstash name: 'xgboost_cpp_tests'
-    unstash name: 'srcs'
-    echo "Test C++, CUDA ${args.cuda_version}"
-    def container_type = "gpu"
-    def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
-    if (args.multi_gpu) {
-      echo "Using multiple GPUs"
-      sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost --gtest_filter=*.MGPU_*"
-    } else {
-      echo "Using a single GPU"
-      sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost --gtest_filter=-*.MGPU_*"
-    }
-    deleteDir()
-  }
-}
-
-def CrossTestJVMwithJDK(args) {
-  node('linux && cpu') {
-    unstash name: 'boostkit-xgboost4j_jar'
-    unstash name: 'srcs'
-    if (args.spark_version != null) {
-      echo "Test Boostkit-XGBoost4J on a machine with JDK ${args.jdk_version}, Spark ${args.spark_version}"
-    } else {
-      echo "Test Boostkit-XGBoost4J on a machine with JDK ${args.jdk_version}"
-    }
-    def container_type = "jvm_cross"
-    def docker_binary = "docker"
-    def spark_arg = (args.spark_version != null) ? "--build-arg SPARK_VERSION=${args.spark_version}" : ""
-    def docker_args = "--build-arg JDK_VERSION=${args.jdk_version} ${spark_arg}"
-    // Run integration tests only when spark_version is given
-    def docker_extra_params = (args.spark_version != null) ? "CI_DOCKER_EXTRA_PARAMS_INIT='-e RUN_INTEGRATION_TEST=1'" : ""
-    sh """
-    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_jvm_cross.sh
-    """
-    deleteDir()
-  }
-}
-
-def TestR(args) {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Test R package"
-    def container_type = "rproject"
-    def docker_binary = "docker"
-    def use_r35_flag = (args.use_r35) ? "1" : "0"
-    def docker_args = "--build-arg USE_R35=${use_r35_flag}"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_test_rpkg.sh || tests/ci_build/print_r_stacktrace.sh
-    """
-    deleteDir()
-  }
-}
-
-def DeployJVMPackages(args) {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    if (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release')) {
-      echo 'Deploying to xgboost-maven-repo S3 repo...'
-      def container_type = "jvm"
-      def docker_binary = "docker"
-      sh """
-      ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/deploy_jvm_packages.sh ${args.spark_version}
-      """
-    }
-    deleteDir()
-  }
-}
diff --git a/ml-xgboost/Jenkinsfile-win64 b/ml-xgboost/Jenkinsfile-win64
deleted file mode 100644
index 15dc345..0000000
--- a/ml-xgboost/Jenkinsfile-win64
+++ /dev/null
@@ -1,151 +0,0 @@
-#!/usr/bin/groovy
-// -*- mode: groovy -*-
-
-/* Jenkins pipeline for Windows AMD64 target */
-
-import groovy.transform.Field
-
-@Field
-def commit_id   // necessary to pass a variable from one stage to another
-
-pipeline {
-  agent none
-  // Build stages
-  stages {
-    stage('Jenkins Win64: Get sources') {
-      agent { label 'win64 && build' }
-      steps {
-        script {
-          checkoutSrcs()
-          commit_id = "${GIT_COMMIT}"
-        }
-        stash name: 'srcs'
-        milestone ordinal: 1
-      }
-    }
-    stage('Jenkins Win64: Build') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'build-win64-cuda9.0': { BuildWin64() }
-          ])
-        }
-        milestone ordinal: 2
-      }
-    }
-    stage('Jenkins Win64: Test') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'test-win64-cpu': { TestWin64CPU() },
-            'test-win64-gpu-cuda9.0': { TestWin64GPU(cuda_target: 'cuda9') },
-            'test-win64-gpu-cuda10.0': { TestWin64GPU(cuda_target: 'cuda10_0') },
-            'test-win64-gpu-cuda10.1': { TestWin64GPU(cuda_target: 'cuda10_1') }
-          ])
-        }
-        milestone ordinal: 3
-      }
-    }
-  }
-}
-
-// check out source code from git
-def checkoutSrcs() {
-  retry(5) {
-    try {
-      timeout(time: 2, unit: 'MINUTES') {
-        checkout scm
-        sh 'git submodule update --init'
-      }
-    } catch (exc) {
-      deleteDir()
-      error "Failed to fetch source codes"
-    }
-  }
-}
-
-def BuildWin64() {
-  node('win64 && build') {
-    unstash name: 'srcs'
-    echo "Building XGBoost for Windows AMD64 target..."
-    bat "nvcc --version"
-    bat """
-    mkdir build
-    cd build
-    cmake .. -G"Visual Studio 15 2017 Win64" -DUSE_CUDA=ON -DCMAKE_VERBOSE_MAKEFILE=ON -DGOOGLE_TEST=ON -DUSE_DMLC_GTEST=ON
-    """
-    bat """
-    cd build
-    "C:\\Program Files (x86)\\Microsoft Visual Studio\\2017\\Community\\MSBuild\\15.0\\Bin\\MSBuild.exe" xgboost.sln /m /p:Configuration=Release /nodeReuse:false
-    """
-    bat """
-    cd python-package
-    conda activate && python setup.py bdist_wheel --universal && for /R %%i in (dist\\*.whl) DO python ../tests/ci_build/rename_whl.py "%%i" ${commit_id} win_amd64
-    """
-    echo "Insert vcomp140.dll (OpenMP runtime) into the wheel..."
-    bat """
-    cd python-package\\dist
-    COPY /B ..\\..\\tests\\ci_build\\insert_vcomp140.py
-    conda activate && python insert_vcomp140.py *.whl
-    """
-    echo 'Stashing Python wheel...'
-    stash name: 'xgboost_whl', includes: 'python-package/dist/*.whl'
-    path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
-    s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', workingDir: 'python-package/dist', includePathPattern:'**/*.whl'
-    echo 'Stashing C++ test executable (testxgboost)...'
-    stash name: 'xgboost_cpp_tests', includes: 'build/testxgboost.exe'
-    stash name: 'xgboost_cli', includes: 'xgboost.exe'
-    deleteDir()
-  }
-}
-
-def TestWin64CPU() {
-  node('win64 && cpu') {
-    unstash name: 'srcs'
-    unstash name: 'xgboost_whl'
-    unstash name: 'xgboost_cli'
-    echo "Test Win64 CPU"
-    echo "Installing Python wheel..."
-    bat "conda activate && (python -m pip uninstall -y xgboost || cd .)"
-    bat """
-    conda activate && for /R %%i in (python-package\\dist\\*.whl) DO python -m pip install "%%i"
-    """
-    echo "Installing Python dependencies..."
-    bat """
-     conda activate && conda upgrade scikit-learn pandas numpy
-    """
-    echo "Running Python tests..."
-    bat "conda activate && python -m pytest -v -s --fulltrace tests\\python"
-    bat "conda activate && python -m pip uninstall -y xgboost"
-    deleteDir()
-  }
-}
-
-def TestWin64GPU(args) {
-  node("win64 && gpu && ${args.cuda_target}") {
-    unstash name: 'srcs'
-    unstash name: 'xgboost_whl'
-    unstash name: 'xgboost_cpp_tests'
-    echo "Test Win64 GPU (${args.cuda_target})"
-    bat "nvcc --version"
-    echo "Running C++ tests..."
-    bat "build\\testxgboost.exe"
-    echo "Installing Python wheel..."
-    bat "conda activate && (python -m pip uninstall -y xgboost || cd .)"
-    bat """
-    conda activate && for /R %%i in (python-package\\dist\\*.whl) DO python -m pip install "%%i"
-    """
-    echo "Installing Python dependencies..."
-    bat """
-     conda activate && conda upgrade scikit-learn pandas numpy
-    """
-    echo "Running Python tests..."
-    bat """
-    conda activate && python -m pytest -v -s --fulltrace -m "(not slow) and (not mgpu)" tests\\python-gpu
-    """
-    bat "conda activate && python -m pip uninstall -y xgboost"
-    deleteDir()
-  }
-}
diff --git a/ml-xgboost/LICENSE b/ml-xgboost/LICENSE
deleted file mode 100644
index 90c0ff9..0000000
--- a/ml-xgboost/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
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diff --git a/ml-xgboost/Makefile b/ml-xgboost/Makefile
deleted file mode 100644
index a49d307..0000000
--- a/ml-xgboost/Makefile
+++ /dev/null
@@ -1,147 +0,0 @@
-ifndef DMLC_CORE
-	DMLC_CORE = dmlc-core
-endif
-
-ifndef RABIT
-	RABIT = rabit
-endif
-
-ROOTDIR = $(CURDIR)
-
-# workarounds for some buggy old make & msys2 versions seen in windows
-ifeq (NA, $(shell test ! -d "$(ROOTDIR)" && echo NA ))
-        $(warning Attempting to fix non-existing ROOTDIR [$(ROOTDIR)])
-        ROOTDIR := $(shell pwd)
-        $(warning New ROOTDIR [$(ROOTDIR)] $(shell test -d "$(ROOTDIR)" && echo " is OK" ))
-endif
-MAKE_OK := $(shell "$(MAKE)" -v 2> /dev/null)
-ifndef MAKE_OK
-        $(warning Attempting to recover non-functional MAKE [$(MAKE)])
-        MAKE := $(shell which make 2> /dev/null)
-        MAKE_OK := $(shell "$(MAKE)" -v 2> /dev/null)
-endif
-$(warning MAKE [$(MAKE)] - $(if $(MAKE_OK),checked OK,PROBLEM))
-
-include $(DMLC_CORE)/make/dmlc.mk
-
-# set compiler defaults for OSX versus *nix
-# let people override either
-OS := $(shell uname)
-ifeq ($(OS), Darwin)
-ifndef CC
-export CC = $(if $(shell which clang), clang, gcc)
-endif
-ifndef CXX
-export CXX = $(if $(shell which clang++), clang++, g++)
-endif
-else
-# linux defaults
-ifndef CC
-export CC = gcc
-endif
-ifndef CXX
-export CXX = g++
-endif
-endif
-
-export CFLAGS= -DDMLC_LOG_CUSTOMIZE=1 -std=c++11 -Wall -Wno-unknown-pragmas -Iinclude $(ADD_CFLAGS)
-CFLAGS += -I$(DMLC_CORE)/include -I$(RABIT)/include -I$(GTEST_PATH)/include
-
-ifeq ($(TEST_COVER), 1)
-	CFLAGS += -g -O0 -fprofile-arcs -ftest-coverage
-else
-	CFLAGS += -O3 -funroll-loops
-endif
-
-ifndef LINT_LANG
-	LINT_LANG= "all"
-endif
-
-# specify tensor path
-.PHONY: clean all lint clean_all doxygen rcpplint pypack Rpack Rbuild Rcheck
-
-build/%.o: src/%.cc
-	@mkdir -p $(@D)
-	$(CXX) $(CFLAGS) -MM -MT build/$*.o $< >build/$*.d
-	$(CXX) -c $(CFLAGS) $< -o $@
-
-# The should be equivalent to $(ALL_OBJ)  except for build/cli_main.o
-amalgamation/xgboost-all0.o: amalgamation/xgboost-all0.cc
-	$(CXX) -c $(CFLAGS) $< -o $@
-
-rcpplint:
-	python3 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
-
-lint: rcpplint
-	python3 dmlc-core/scripts/lint.py --exclude_path python-package/xgboost/dmlc-core \
-	  python-package/xgboost/include python-package/xgboost/lib \
-	  python-package/xgboost/make python-package/xgboost/rabit \
-	  python-package/xgboost/src --pylint-rc ${PWD}/python-package/.pylintrc xgboost \
-	  ${LINT_LANG} include src python-package
-
-ifeq ($(TEST_COVER), 1)
-cover: check
-	@- $(foreach COV_OBJ, $(COVER_OBJ), \
-		gcov -pbcul -o $(shell dirname $(COV_OBJ)) $(COV_OBJ) > gcov.log || cat gcov.log; \
-	)
-endif
-
-clean:
-	$(RM) -rf build lib bin *~ */*~ */*/*~ */*/*/*~ */*.o */*/*.o */*/*/*.o #xgboost
-	$(RM) -rf build_tests *.gcov tests/cpp/xgboost_test
-	if [ -d "R-package/src" ]; then \
-		cd R-package/src; \
-		$(RM) -rf rabit src include dmlc-core amalgamation *.so *.dll; \
-		cd $(ROOTDIR); \
-	fi
-
-clean_all: clean
-	cd $(DMLC_CORE); "$(MAKE)" clean; cd $(ROOTDIR)
-	cd $(RABIT); "$(MAKE)" clean; cd $(ROOTDIR)
-
-# create pip source dist (sdist) pack for PyPI
-pippack: clean_all
-	cd python-package; python setup.py sdist; mv dist/*.tar.gz ..; cd ..
-
-# Script to make a clean installable R package.
-Rpack: clean_all
-	rm -rf xgboost xgboost*.tar.gz
-	cp -r R-package xgboost
-	rm -rf xgboost/src/*.o xgboost/src/*.so xgboost/src/*.dll
-	rm -rf xgboost/src/*/*.o
-	rm -rf xgboost/demo/*.model xgboost/demo/*.buffer xgboost/demo/*.txt
-	rm -rf xgboost/demo/runall.R
-	cp -r src xgboost/src/src
-	cp -r include xgboost/src/include
-	cp -r amalgamation xgboost/src/amalgamation
-	mkdir -p xgboost/src/rabit
-	cp -r rabit/include xgboost/src/rabit/include
-	cp -r rabit/src xgboost/src/rabit/src
-	rm -rf xgboost/src/rabit/src/*.o
-	mkdir -p xgboost/src/dmlc-core
-	cp -r dmlc-core/include xgboost/src/dmlc-core/include
-	cp -r dmlc-core/src xgboost/src/dmlc-core/src
-	cp ./LICENSE xgboost
-# Modify PKGROOT in Makevars.in
-	cat R-package/src/Makevars.in|sed '2s/.*/PKGROOT=./' > xgboost/src/Makevars.in
-# Configure Makevars.win (Windows-specific Makevars, likely using MinGW)
-	cp xgboost/src/Makevars.in xgboost/src/Makevars.win
-	cat xgboost/src/Makevars.in| sed '3s/.*/ENABLE_STD_THREAD=0/' > xgboost/src/Makevars.win
-	sed -i -e 's/@OPENMP_CXXFLAGS@/$$\(SHLIB_OPENMP_CXXFLAGS\)/g' xgboost/src/Makevars.win
-	sed -i -e 's/-pthread/$$\(SHLIB_PTHREAD_FLAGS\)/g' xgboost/src/Makevars.win
-	sed -i -e 's/@ENDIAN_FLAG@/-DDMLC_CMAKE_LITTLE_ENDIAN=1/g' xgboost/src/Makevars.win
-	sed -i -e 's/@BACKTRACE_LIB@//g' xgboost/src/Makevars.win
-	sed -i -e 's/@OPENMP_LIB@//g' xgboost/src/Makevars.win
-	rm -f xgboost/src/Makevars.win-e   # OSX sed create this extra file; remove it
-	bash R-package/remove_warning_suppression_pragma.sh
-	rm xgboost/remove_warning_suppression_pragma.sh
-
-Rbuild: Rpack
-	R CMD build --no-build-vignettes xgboost
-	rm -rf xgboost
-
-Rcheck: Rbuild
-	R CMD check xgboost*.tar.gz
-
--include build/*.d
--include build/*/*.d
diff --git a/ml-xgboost/NEWS.md b/ml-xgboost/NEWS.md
deleted file mode 100644
index 777fa6c..0000000
--- a/ml-xgboost/NEWS.md
+++ /dev/null
@@ -1,1100 +0,0 @@
-XGBoost Change Log
-==================
-
-This file records the changes in xgboost library in reverse chronological order.
-
-## v1.0.0 (2020.02.19)
-This release marks a major milestone for the XGBoost project.
-
-### Apache-style governance, contribution policy, and semantic versioning (#4646, #4659)
-* Starting with 1.0.0 release, the XGBoost Project is adopting Apache-style governance. The full community guideline is [available in the doc website](https://xgboost.readthedocs.io/en/release_1.0.0/contrib/community.html). Note that we now have Project Management Committee (PMC) who would steward the project on the long-term basis. The PMC is also entrusted to run and fund the project's continuous integration (CI) infrastructure (https://xgboost-ci.net).
-* We also adopt the [semantic versioning](https://semver.org/). See [our release versioning policy](https://xgboost.readthedocs.io/en/release_1.0.0/contrib/release.html).
-
-### Better performance scaling for multi-core CPUs (#4502, #4529, #4716, #4851, #5008, #5107, #5138, #5156)
-* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). Previous effort #4529 was replaced with a series of pull requests (#5107, #5138, #5156) aimed at achieving the same performance benefits while keeping the C++ codebase legible. The latest performance benchmark results show [up to 5x speedup on Intel CPUs with many cores](https://github.com/dmlc/xgboost/pull/5156#issuecomment-580024413). Note: #5244, which concludes the effort, will become part of the upcoming release 1.1.0.
-
-### Improved installation experience on Mac OSX (#4672, #5074, #5080, #5146, #5240)
-* It used to be quite complicated to install XGBoost on Mac OSX. XGBoost uses OpenMP to distribute work among multiple CPU cores, and Mac's default C++ compiler (Apple Clang) does not come with OpenMP. Existing work-around (using another C++ compiler) was complex and prone to fail with cryptic diagnosis (#4933, #4949, #4969).
-* Now it only takes two commands to install XGBoost: `brew install libomp` followed by `pip install xgboost`. The installed XGBoost will use all CPU cores.
-* Even better, XGBoost is now available from Homebrew: `brew install xgboost`. See Homebrew/homebrew-core#50467.
-* Previously, if you installed the XGBoost R package using the command `install.packages('xgboost')`, it could only use a single CPU core and you would experience slow training performance. With 1.0.0 release, the R package will use all CPU cores out of box.
-
-### Distributed XGBoost now available on Kubernetes (#4621, #4939)
-* Check out the [tutorial for setting up distributed XGBoost on a Kubernetes cluster](https://xgboost.readthedocs.io/en/release_1.0.0/tutorials/kubernetes.html).
-
-### Ruby binding for XGBoost (#4856)
-
-### New Native Dask interface for multi-GPU and multi-node scaling (#4473, #4507, #4617, #4819, #4907, #4914, #4941, #4942, #4951, #4973, #5048, #5077, #5144, #5270)
-* XGBoost now integrates seamlessly with [Dask](https://dask.org/), a lightweight distributed framework for data processing. Together with the first-class support for cuDF data frames (see below), it is now easier than ever to create end-to-end data pipeline running on one or more NVIDIA GPUs.
-* Multi-GPU training with Dask is now up to 20% faster than the previous release (#4914, #4951).
-
-### First-class support for cuDF data frames and cuPy arrays (#4737, #4745, #4794, #4850, #4891, #4902, #4918, #4927, #4928, #5053, #5189, #5194, #5206, #5219, #5225)
-* [cuDF](https://github.com/rapidsai/cudf) is a data frame library for loading and processing tabular data on NVIDIA GPUs. It provides a Pandas-like API.
-* [cuPy](https://github.com/cupy/cupy) implements a NumPy-compatible multi-dimensional array on NVIDIA GPUs.
-* Now users can keep the data on the GPU memory throughout the end-to-end data pipeline, obviating the need for copying data between the main memory and GPU memory.
-* XGBoost can accept any data structure that exposes `__array_interface__` signature, opening way to support other columar formats that are compatible with Apache Arrow.
-
-### [Feature interaction constraint](https://xgboost.readthedocs.io/en/release_1.0.0/tutorials/feature_interaction_constraint.html) is now available with `approx` and `gpu_hist` algorithms (#4534, #4587, #4596, #5034).
-
-### Learning to rank is now GPU accelerated (#4873, #5004, #5129)
-* Supported ranking objectives: NDGC, Map, Pairwise.
-* [Up to 2x improved training performance on GPUs](https://devblogs.nvidia.com/learning-to-rank-with-xgboost-and-gpu/).
-
-### Enable `gamma` parameter for GPU training (#4874, #4953)
-* The `gamma` parameter specifies the minimum loss reduction required to add a new split in a tree. A larger value for `gamma` has the effect of pre-pruning the tree, by making harder to add splits.
-
-### External memory for GPU training (#4486, #4526, #4747, #4833, #4879, #5014)
-* It is now possible to use NVIDIA GPUs even when the size of training data exceeds the available GPU memory. Note that the external memory support for GPU is still experimental. #5093 will further improve performance and will become part of the upcoming release 1.1.0.
-* RFC for enabling external memory with GPU algorithms: #4357
-
-### Improve Scikit-Learn interface (#4558, #4842, #4929, #5049, #5151, #5130, #5227)
-* Many users of XGBoost enjoy the convenience and breadth of Scikit-Learn ecosystem. In this release, we revise the Scikit-Learn API of XGBoost (`XGBRegressor`, `XGBClassifier`, and `XGBRanker`) to achieve feature parity with the traditional XGBoost interface (`xgboost.train()`).
-* Insert check to validate data shapes.
-* Produce an error message if `eval_set` is not a tuple. An error message is better than silently crashing.
-* Allow using `numpy.RandomState` object.
-* Add `n_jobs` as an alias of `nthread`.
-* Roadmap: #5152
-
-### XGBoost4J-Spark: Redesigning checkpointing mechanism
-* RFC is available at #4786
-* Clean up checkpoint file after a successful training job (#4754): The current implementation in XGBoost4J-Spark does not clean up the checkpoint file after a successful training job. If the user runs another job with the same checkpointing directory, she will get a wrong model because the second job will re-use the checkpoint file left over from the first job. To prevent this scenario, we propose to always clean up the checkpoint file after every successful training job.
-* Avoid Multiple Jobs for Checkpointing (#5082): The current method for checkpoint is to collect the booster produced at the last iteration of each checkpoint internal to Driver and persist it in HDFS. The major issue with this approach is that it needs to re-perform the data preparation for training if the user did not choose to cache the training dataset. To avoid re-performing data prep, we build external-memory checkpointing in the XGBoost4J layer as well.
-* Enable deterministic repartitioning when checkpoint is enabled (#4807): Distributed algorithm for gradient boosting assumes a fixed partition of the training data between multiple iterations. In previous versions, there was no guarantee that data partition would stay the same, especially when a worker goes down and some data had to recovered from previous checkpoint. In this release, we make data partition deterministic by using the data hash value of each data row in computing the partition.
-
-### XGBoost4J-Spark: handle errors thrown by the native code (#4560)
-* All core logic of XGBoost is written in C++, so XGBoost4J-Spark internally uses the C++ code via Java Native Interface (JNI). #4560 adds a proper error handling for any errors or exceptions arising from the C++ code, so that the XGBoost Spark application can be torn down in an orderly fashion.
-
-### XGBoost4J-Spark: Refine method to count the number of alive cores  (#4858)
-* The `SparkParallelismTracker` class ensures that sufficient number of executor cores are alive. To that end, it is important to query the number of alive cores reliably.
-
-### XGBoost4J: Add `BigDenseMatrix` to store more than `Integer.MAX_VALUE` elements (#4383)
-
-### Robust model serialization with JSON (#4632, #4708, #4739, #4868, #4936, #4945, #4974, #5086, #5087, #5089, #5091, #5094, #5110, #5111, #5112, #5120, #5137, #5218, #5222, #5236, #5245, #5248, #5281)
-* In this release, we introduce an experimental support of using [JSON](https://www.json.org/json-en.html) for serializing (saving/loading) XGBoost models and related hyperparameters for training. We would like to eventually replace the old binary format with JSON, since it is an open format and parsers are available in many programming languages and platforms. See [the documentation for model I/O using JSON](https://xgboost.readthedocs.io/en/release_1.0.0/tutorials/saving_model.html). #3980 explains why JSON was chosen over other alternatives.
-* To maximize interoperability and compatibility of the serialized models, we now split serialization into two parts (#4855):
-  1. Model, e.g. decision trees and strictly related metadata like `num_features`.
-  2. Internal configuration, consisting of training parameters and other configurable parameters. For example, `max_delta_step`, `tree_method`, `objective`, `predictor`, `gpu_id`.
-
-  Previously, users often ran into issues where the model file produced by one machine could not load or run on another machine. For example, models trained using a machine with an NVIDIA GPU could not run on another machine without a GPU (#5291, #5234). The reason is that the old binary format saved some internal configuration that were not universally applicable to all machines, e.g. `predictor='gpu_predictor'`.
-
-  Now, model saving function (`Booster.save_model()` in Python) will save only the model, without internal configuration. This will guarantee that your model file would be used anywhere. Internal configuration will be serialized in limited circumstances such as:
-  * Multiple nodes in a distributed system exchange model details over the network.
-  * Model checkpointing, to recover from possible crashes.
-
-  This work proved to be useful for parameter validation as well (see below).
-* Starting with 1.0.0 release, we will use semantic versioning to indicate whether the model produced by one version of XGBoost would be compatible with another version of XGBoost. Any change in the major version indicates a breaking change in the serialization format.
-* We now provide a robust method to save and load scikit-learn related attributes (#5245). Previously, we used Python pickle to save Python attributes related to `XGBClassifier`, `XGBRegressor`, and `XGBRanker` objects. The attributes are necessary to properly interact with scikit-learn. See #4639 for more details. The use of pickling hampered interoperability, as a pickle from one machine may not necessarily work on another machine. Starting with this release, we use an alternative method to serialize the scikit-learn related attributes. The use of Python pickle is now discouraged (#5236, #5281).
-
-### Parameter validation: detection of unused or incorrect parameters (#4553, #4577, #4738, #4801, #4961, #5101, #5157, #5167, #5256)
-* Mis-spelled training parameter is a common user mistake. In previous versions of XGBoost, mis-spelled parameters were silently ignored. Starting with 1.0.0 release, XGBoost will produce a warning message if there is any unused training parameters. Currently, parameter validation is available to R users and Python XGBoost API users. We are working to extend its support to scikit-learn users.
-* Configuration steps now have well-defined semantics (#4542, #4738), so we know exactly where and how the internal configurable parameters are changed.
-* The user can now use `save_config()` function to inspect all (used) training parameters. This is helpful for debugging model performance.
-
-### Allow individual workers to recover from faults (#4808, #4966)
-* Status quo: if a worker fails, all workers are shut down and restarted, and learning resumes from the last checkpoint. This involves requesting resources from the scheduler (e.g. Spark) and shuffling all the data again from scratch. Both of these operations can be quite costly and block training for extended periods of time, especially if the training data is big and the number of worker nodes is in the hundreds.
-* The proposed solution is to recover the single node that failed, instead of shutting down all workers. The rest of the clusters wait until the single failed worker is bootstrapped and catches up with the rest.
-* See roadmap at #4753. Note that this is work in progress. In particular, the feature is not yet available from XGBoost4J-Spark.
-
-### Accurate prediction for DART models
-* Use DART tree weights when computing SHAPs (#5050)
-* Don't drop trees during DART prediction by default (#5115)
-* Fix DART prediction in R (#5204)
-
-### Make external memory more robust
-* Fix issues with training with external memory on cpu (#4487)
-* Fix crash with approx tree method on cpu (#4510)
-* Fix external memory race in `exact` (#4980). Note: `dmlc::ThreadedIter` is not actually thread-safe. We would like to re-design it in the long term.
-
-### Major refactoring of the `DMatrix` class (#4686, #4744, #4748, #5044, #5092, #5108, #5188, #5198)
-* Goal 1: improve performance and reduce memory consumption. Right now, if the user trains a model with a NumPy array as training data, the array gets copies 2-3 times before training begins. We'd like to reduce duplication of the data matrix.
-* Goal 2: Expose a common interface to external data, unify the way DMatrix objects are constructed and simplify the process of adding new external data sources. This work is essential for ingesting cuPy arrays.
-* Goal 3: Handle missing values consistently.
-* RFC: #4354, Roadmap: #5143
-* This work is also relevant to external memory support on GPUs.
-
-### Breaking: XGBoost Python package now requires Python 3.5 or newer (#5021, #5274)
-* Python 3.4 has reached its end-of-life on March 16, 2019, so we now require Python 3.5 or newer.
-
-### Breaking: GPU algorithm now requires CUDA 9.0 and higher (#4527, #4580)
-
-### Breaking: `n_gpus` parameter removed; multi-GPU training now requires a distributed framework (#4579, #4749, #4773, #4810, #4867, #4908)
-* #4531 proposed removing support for single-process multi-GPU training. Contributors would focus on multi-GPU support through distributed frameworks such as Dask and Spark, where the framework would be expected to assign a worker process for each GPU independently. By delegating GPU management and data movement to the distributed framework, we can greatly simplify the core XGBoost codebase, make multi-GPU training more robust, and reduce burden for future development.
-
-### Breaking: Some deprecated features have been removed
-* ``gpu_exact`` training method (#4527, #4742, #4777). Use ``gpu_hist`` instead.
-* ``learning_rates`` parameter in Python (#5155). Use the callback API instead.
-* ``num_roots`` (#5059, #5165), since the current training code always uses a single root node.
-* GPU-specific objectives (#4690), such as `gpu:reg:linear`. Use objectives without `gpu:` prefix; GPU will be used automatically if your machine has one.
-
-### Breaking: the C API function `XGBoosterPredict()` now asks for an extra parameter `training`.
-
-### Breaking: We now use CMake exclusively to build XGBoost. `Makefile` is being sunset.
-* Exception: the R package uses Autotools, as the CRAN ecosystem did not yet adopt CMake widely.
-
-### Performance improvements
-* Smarter choice of histogram construction for distributed `gpu_hist` (#4519)
-* Optimizations for quantization on device (#4572)
-* Introduce caching memory allocator to avoid latency associated with GPU memory allocation (#4554, #4615)
-* Optimize the initialization stage of the CPU `hist` algorithm for sparse datasets (#4625)
-* Prevent unnecessary data copies from GPU memory to the host (#4795)
-* Improve operation efficiency for single prediction (#5016)
-* Group builder modified for incremental building, to speed up building large `DMatrix` (#5098)
-
-### Bug-fixes
-* Eliminate `FutureWarning: Series.base is deprecated` (#4337)
-* Ensure pandas DataFrame column names are treated as strings in type error message (#4481)
-* [jvm-packages] Add back `reg:linear` for scala, as it is only deprecated and not meant to be removed yet (#4490)
-* Fix library loading for Cygwin users (#4499)
-* Fix prediction from loaded pickle (#4516)
-* Enforce exclusion between `pred_interactions=True` and `pred_interactions=True` (#4522)
-* Do not return dangling reference to local `std::string` (#4543)
-* Set the appropriate device before freeing device memory (#4566)
-* Mark `SparsePageDmatrix` destructor default. (#4568)
-* Choose the appropriate tree method only when the tree method is 'auto' (#4571)
-* Fix `benchmark_tree.py` (#4593)
-* [jvm-packages] Fix silly bug in feature scoring (#4604)
-* Fix GPU predictor when the test data matrix has different number of features than the training data matrix used to train the model (#4613)
-* Fix external memory for get column batches. (#4622)
-* [R] Use built-in label when xgb.DMatrix is given to xgb.cv() (#4631)
-* Fix early stopping in the Python package (#4638)
-* Fix AUC error in distributed mode caused by imbalanced dataset (#4645, #4798)
-* [jvm-packages] Expose `setMissing` method in `XGBoostClassificationModel` / `XGBoostRegressionModel` (#4643)
-* Remove initializing stringstream reference. (#4788)
-* [R] `xgb.get.handle` now checks all class listed of `object` (#4800)
-* Do not use `gpu_predictor` unless data comes from GPU (#4836)
-* Fix data loading (#4862)
-* Workaround `isnan` across different environments. (#4883)
-* [jvm-packages] Handle Long-type parameter (#4885)
-* Don't `set_params` at the end of `set_state` (#4947). Ensure that the model does not change after pickling and unpickling multiple times.
-* C++ exceptions should not crash OpenMP loops (#4960)
-* Fix `usegpu` flag in DART. (#4984)
-* Run training with empty `DMatrix` (#4990, #5159)
-* Ensure that no two processes can use the same GPU (#4990)
-* Fix repeated split and 0 cover nodes (#5010)
-* Reset histogram hit counter between multiple data batches (#5035)
-* Fix `feature_name` crated from int64index dataframe. (#5081)
-* Don't use 0 for "fresh leaf" (#5084)
-* Throw error when user attempts to use multi-GPU training and XGBoost has not been compiled with NCCL (#5170)
-* Fix metric name loading (#5122)
-* Quick fix for memory leak in CPU `hist` algorithm (#5153)
-* Fix wrapping GPU ID and prevent data copying (#5160)
-* Fix signature of Span constructor (#5166)
-* Lazy initialization of device vector, so that XGBoost compiled with CUDA can run on a machine without any GPU (#5173)
-* Model loading should not change system locale (#5314)
-* Distributed training jobs would sometimes hang; revert Rabit to fix this regression (dmlc/rabit#132, #5237)
-
-### API changes
-* Add support for cross-validation using query ID (#4474)
-* Enable feature importance property for DART model (#4525)
-* Add `rmsle` metric and `reg:squaredlogerror` objective (#4541)
-* All objective and evaluation metrics are now exposed to JVM packages (#4560)
-* `dump_model()` and `get_dump()` now support exporting in GraphViz language (#4602)
-* Support metrics `ndcg-` and `map-` (#4635)
-* [jvm-packages] Allow chaining prediction (transform) in XGBoost4J-Spark (#4667)
-* [jvm-packages] Add option to bypass missing value check in the Spark layer (#4805). Only use this option if you know what you are doing.
-* [jvm-packages] Add public group getter (#4838)
-* `XGDMatrixSetGroup` C API is now deprecated (#4864). Use `XGDMatrixSetUIntInfo` instead.
-* [R] Added new `train_folds` parameter to `xgb.cv()` (#5114)
-* Ingest meta information from Pandas DataFrame, such as data weights (#5216)
-
-### Maintenance: Refactor code for legibility and maintainability
-* De-duplicate GPU parameters (#4454)
-* Simplify INI-style config reader using C++11 STL (#4478, #4521)
-* Refactor histogram building code for `gpu_hist` (#4528)
-* Overload device memory allocator, to enable instrumentation for compiling memory usage statistics (#4532)
-* Refactor out row partitioning logic from `gpu_hist` (#4554)
-* Remove an unused variable (#4588)
-* Implement tree model dump with code generator, to de-duplicate code for generating dumps in 3 different formats (#4602)
-* Remove `RowSet` class which is no longer being used (#4697)
-* Remove some unused functions as reported by cppcheck (#4743)
-* Mimic CUDA assert output in Span check (#4762)
-* [jvm-packages] Refactor `XGBoost.scala` to put all params processing in one place (#4815)
-* Add some comments for GPU row partitioner (#4832)
-* Span: use `size_t' for index_type,  add `front' and `back'. (#4935)
-* Remove dead code in `exact` algorithm (#5034, #5105)
-* Unify integer types used for row and column indices (#5034)
-* Extract feature interaction constraint from `SplitEvaluator` class. (#5034)
-* [Breaking] De-duplicate paramters and docstrings in the constructors of Scikit-Learn models (#5130)
-* Remove benchmark code from GPU tests (#5141)
-* Clean up Python 2 compatibility code. (#5161)
-* Extensible binary serialization format for `DMatrix::MetaInfo` (#5187). This will be useful for implementing censored labels for survival analysis applications.
-* Cleanup clang-tidy warnings. (#5247)
-
-### Maintenance: testing, continuous integration, build system
-* Use `yaml.safe_load` instead of `yaml.load`. (#4537)
-* Ensure GCC is at least 5.x (#4538)
-* Remove all mention of `reg:linear` from tests (#4544)
-* [jvm-packages] Upgrade to Scala 2.12 (#4574)
-* [jvm-packages] Update kryo dependency to 2.22 (#4575)
-* [CI] Specify account ID when logging into ECR Docker registry (#4584)
-* Use Sphinx 2.1+ to compile documentation (#4609)
-* Make Pandas optional for running Python unit tests (#4620)
-* Fix spark tests on machines with many cores (#4634)
-* [jvm-packages] Update local dev build process (#4640)
-* Add optional dependencies to setup.py (#4655)
-* [jvm-packages] Fix maven warnings (#4664)
-* Remove extraneous files from the R package, to comply with CRAN policy (#4699)
-* Remove VC-2013 support, since it is not C++11 compliant (#4701)
-* [CI] Fix broken installation of Pandas (#4704, #4722)
-* [jvm-packages] Clean up temporary files afer running tests (#4706)
-* Specify version macro in CMake. (#4730)
-* Include dmlc-tracker into XGBoost Python package (#4731)
-* [CI] Use long key ID for Ubuntu repository fingerprints. (#4783)
-* Remove plugin, cuda related code in automake & autoconf files (#4789)
-* Skip related tests when scikit-learn is not installed. (#4791)
-* Ignore vscode and clion files (#4866)
-* Use bundled Google Test by default (#4900)
-* [CI] Raise timeout threshold in Jenkins (#4938)
-* Copy CMake parameter from dmlc-core. (#4948)
-* Set correct file permission. (#4964)
-* [CI] Update lint configuration to support latest pylint convention (#4971)
-* [CI] Upload nightly builds to S3 (#4976, #4979)
-* Add asan.so.5 to cmake script. (#4999)
-* [CI] Fix Travis tests. (#5062)
-* [CI] Locate vcomp140.dll from System32 directory (#5078)
-* Implement training observer to dump internal states of objects (#5088). This will be useful for debugging.
-* Fix visual studio output library directories (#5119)
-* [jvm-packages] Comply with scala style convention + fix broken unit test (#5134)
-* [CI] Repair download URL for Maven 3.6.1 (#5139)
-* Don't use modernize-use-trailing-return-type in clang-tidy. (#5169)
-* Explicitly use UTF-8 codepage when using MSVC (#5197)
-* Add CMake option to run Undefined Behavior Sanitizer (UBSan) (#5211)
-* Make some GPU tests deterministic (#5229)
-* [R] Robust endian detection in CRAN xgboost build (#5232)
-* Support FreeBSD (#5233)
-* Make `pip install xgboost*.tar.gz` work by fixing build-python.sh (#5241)
-* Fix compilation error due to 64-bit integer narrowing to `size_t` (#5250)
-* Remove use of `std::cout` from R package, to comply with CRAN policy (#5261)
-* Update DMLC-Core submodule (#4674, #4688, #4726, #4924)
-* Update Rabit submodule (#4560, #4667, #4718, #4808, #4966, #5237)
-
-### Usability Improvements, Documentation
-* Add Random Forest API to Python API doc (#4500)
-* Fix Python demo and doc. (#4545)
-* Remove doc about not supporting cuda 10.1 (#4578)
-* Address some sphinx warnings and errors, add doc for building doc. (#4589)
-* Add instruction to run formatting checks locally (#4591)
-* Fix docstring for `XGBModel.predict()` (#4592)
-* Doc and demo for customized metric and objective (#4598, #4608)
-* Add to documentation how to run tests locally (#4610)
-* Empty evaluation list in early stopping should produce meaningful error message (#4633)
-* Fixed year to 2019 in conf.py, helpers.h and LICENSE (#4661)
-* Minor updates to links and grammar (#4673)
-* Remove `silent` in doc (#4689)
-* Remove old Python trouble shooting doc (#4729)
-* Add `os.PathLike` support for file paths to DMatrix and Booster Python classes (#4757)
-* Update XGBoost4J-Spark doc (#4804)
-* Regular formatting for evaluation metrics (#4803)
-* [jvm-packages] Refine documentation for handling missing values in XGBoost4J-Spark (#4805)
-* Monitor for distributed envorinment (#4829). This is useful for identifying performance bottleneck.
-* Add check for length of weights and produce a good error message (#4872)
-* Fix DMatrix doc (#4884)
-* Export C++ headers in CMake installation (#4897)
-* Update license year in README.md to 2019 (#4940)
-* Fix incorrectly displayed Note in the doc (#4943)
-* Follow PEP 257 Docstring Conventions (#4959)
-* Document minimum version required for Google Test (#5001)
-* Add better error message for invalid feature names (#5024)
-* Some guidelines on device memory usage (#5038)
-* [doc] Some notes for external memory. (#5065)
-* Update document for `tree_method` (#5106)
-* Update demo for ranking. (#5154)
-* Add new lines for Spark XGBoost missing values section (#5180)
-* Fix simple typo: utilty -> utility (#5182)
-* Update R doc by roxygen2 (#5201)
-* [R] Direct user to use `set.seed()` instead of setting `seed` parameter (#5125)
-* Add Optuna badge to `README.md` (#5208)
-* Fix compilation error in `c-api-demo.c` (#5215)
-
-### Acknowledgement
-**Contributors**: Nan Zhu (@CodingCat), Crissman Loomis (@Crissman), Cyprien Ricque (@Cyprien-Ricque), Evan Kepner (@EvanKepner), K.O. (@Hi-king), KaiJin Ji (@KerryJi), Peter Badida (@KeyWeeUsr), Kodi Arfer (@Kodiologist), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Jacob Kim (@TheJacobKim), Vibhu Jawa (@VibhuJawa), Marcos (@astrowonk), Andy Adinets (@canonizer), Chen Qin (@chenqin), Christopher Cowden (@cowden), @cpfarrell, @david-cortes, Liangcai Li (@firestarman), @fuhaoda, Philip Hyunsu Cho (@hcho3), @here-nagini, Tong He (@hetong007), Michal Kurka (@michalkurka), Honza Sterba (@honzasterba), @iblumin, @koertkuipers, mattn (@mattn), Mingjie Tang (@merlintang), OrdoAbChao (@mglowacki100), Matthew Jones (@mt-jones), mitama (@nigimitama), Nathan Moore (@nmoorenz), Daniel Stahl (@phillyfan1138), Michaël Benesty (@pommedeterresautee), Rong Ou (@rongou), Sebastian (@sfahnens), Xu Xiao (@sperlingxx), @sriramch, Sean Owen (@srowen), Stephanie Yang (@stpyang), Yuan Tang (@terrytangyuan), Mathew Wicks (@thesuperzapper), Tim Gates (@timgates42), TinkleG (@tinkle1129), Oleksandr Pryimak (@trams), Jiaming Yuan (@trivialfis), Matvey Turkov (@turk0v), Bobby Wang (@wbo4958), yage (@yage99), @yellowdolphin
-
-**Reviewers**: Nan Zhu (@CodingCat), Crissman Loomis (@Crissman), Cyprien Ricque (@Cyprien-Ricque), Evan Kepner (@EvanKepner), John Zedlewski (@JohnZed), KOLANICH (@KOLANICH), KaiJin Ji (@KerryJi), Kodi Arfer (@Kodiologist), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Nikita Titov (@StrikerRUS), Jacob Kim (@TheJacobKim), Vibhu Jawa (@VibhuJawa), Andrew Kane (@ankane), Arno Candel (@arnocandel), Marcos (@astrowonk), Bryan Woods (@bryan-woods), Andy Adinets (@canonizer), Chen Qin (@chenqin), Thomas Franke (@coding-komek), Peter  (@codingforfun), @cpfarrell, Joshua Patterson (@datametrician), @fuhaoda, Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), Honza Sterba (@honzasterba), @iblumin, @jakirkham, Vadim Khotilovich (@khotilov), Keith Kraus (@kkraus14), @koertkuipers, @melonki, Mingjie Tang (@merlintang), OrdoAbChao (@mglowacki100), Daniel Mahler (@mhlr), Matthew Rocklin (@mrocklin), Matthew Jones (@mt-jones), Michaël Benesty (@pommedeterresautee), PSEUDOTENSOR / Jonathan McKinney (@pseudotensor), Rong Ou (@rongou), Vladimir (@sh1ng), Scott Lundberg (@slundberg), Xu Xiao (@sperlingxx), @sriramch, Pasha Stetsenko (@st-pasha), Stephanie Yang (@stpyang), Yuan Tang (@terrytangyuan), Mathew Wicks (@thesuperzapper), Theodore Vasiloudis (@thvasilo), TinkleG (@tinkle1129), Oleksandr Pryimak (@trams), Jiaming Yuan (@trivialfis), Bobby Wang (@wbo4958), yage (@yage99), @yellowdolphin, Yin Lou (@yinlou)
-
-## v0.90 (2019.05.18)
-
-### XGBoost Python package drops Python 2.x (#4379, #4381)
-Python 2.x is reaching its end-of-life at the end of this year. [Many scientific Python packages are now moving to drop Python 2.x](https://python3statement.org/).
-
-### XGBoost4J-Spark now requires Spark 2.4.x (#4377)
-* Spark 2.3 is reaching its end-of-life soon. See discussion at #4389.
-* **Consistent handling of missing values** (#4309, #4349, #4411): Many users had reported issue with inconsistent predictions between XGBoost4J-Spark and the Python XGBoost package. The issue was caused by Spark mis-handling non-zero missing values (NaN, -1, 999 etc). We now alert the user whenever Spark doesn't handle missing values correctly (#4309, #4349). See [the tutorial for dealing with missing values in XGBoost4J-Spark](https://xgboost.readthedocs.io/en/release_0.90/jvm/xgboost4j_spark_tutorial.html#dealing-with-missing-values). This fix also depends on the availability of Spark 2.4.x.
-
-### Roadmap: better performance scaling for multi-core CPUs (#4310)
-* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). #4310 optimizes quantile sketches and other pre-processing tasks. Special thanks to @SmirnovEgorRu.
-
-### Roadmap: Harden distributed training (#4250)
-* Make distributed training in XGBoost more robust by hardening [Rabit](https://github.com/dmlc/rabit), which implements [the AllReduce primitive](https://en.wikipedia.org/wiki/Reduce_%28parallel_pattern%29). In particular, improve test coverage on mechanisms for fault tolerance and recovery. Special thanks to @chenqin.
-
-### New feature: Multi-class metric functions for GPUs (#4368)
-* Metrics for multi-class classification have been ported to GPU: `merror`, `mlogloss`. Special thanks to @trivialfis.
-* With supported metrics, XGBoost will select the correct devices based on your system and `n_gpus` parameter.
-
-### New feature: Scikit-learn-like random forest API (#4148, #4255, #4258)
-* XGBoost Python package now offers `XGBRFClassifier` and `XGBRFRegressor` API to train random forests. See [the tutorial](https://xgboost.readthedocs.io/en/release_0.90/tutorials/rf.html). Special thanks to @canonizer
-
-### New feature: use external memory in GPU predictor (#4284, #4396, #4438, #4457)
-* It is now possible to make predictions on GPU when the input is read from external memory. This is useful when you want to make predictions with big dataset that does not fit into the GPU memory. Special thanks to @rongou, @canonizer, @sriramch.
-
-  ```python
-  dtest = xgboost.DMatrix('test_data.libsvm#dtest.cache')
-  bst.set_param('predictor', 'gpu_predictor')
-  bst.predict(dtest)
-  ```
-
-* Coming soon: GPU training (`gpu_hist`) with external memory
-
-### New feature: XGBoost can now handle comments in LIBSVM files (#4430)
-* Special thanks to @trivialfis and @hcho3
-
-### New feature: Embed XGBoost in your C/C++ applications using CMake (#4323, #4333, #4453)
-* It is now easier than ever to embed XGBoost in your C/C++ applications. In your CMakeLists.txt, add `xgboost::xgboost` as a linked library:
-
-  ```cmake
-  find_package(xgboost REQUIRED)
-  add_executable(api-demo c-api-demo.c)
-  target_link_libraries(api-demo xgboost::xgboost)
-  ```
-
-  [XGBoost C API documentation is available.](https://xgboost.readthedocs.io/en/release_0.90/dev) Special thanks to @trivialfis
-
-### Performance improvements
-* Use feature interaction constraints to narrow split search space (#4341, #4428)
-* Additional optimizations for `gpu_hist` (#4248, #4283)
-* Reduce OpenMP thread launches in `gpu_hist` (#4343)
-* Additional optimizations for multi-node multi-GPU random forests. (#4238)
-* Allocate unique prediction buffer for each input matrix, to avoid re-sizing GPU array (#4275)
-* Remove various synchronisations from CUDA API calls (#4205)
-* XGBoost4J-Spark
-  - Allow the user to control whether to cache partitioned training data, to potentially reduce execution time (#4268)
-
-### Bug-fixes
-* Fix node reuse in `hist` (#4404)
-* Fix GPU histogram allocation (#4347)
-* Fix matrix attributes not sliced (#4311)
-* Revise AUC and AUCPR metrics now work with weighted ranking task (#4216, #4436)
-* Fix timer invocation for InitDataOnce() in `gpu_hist` (#4206)
-* Fix R-devel errors (#4251)
-* Make gradient update in GPU linear updater thread-safe (#4259)
-* Prevent out-of-range access in column matrix (#4231)
-* Don't store DMatrix handle in Python object until it's initialized, to improve exception safety (#4317)
-* XGBoost4J-Spark
-  - Fix non-deterministic order within a zipped partition on prediction (#4388)
-  - Remove race condition on tracker shutdown (#4224)
-  - Allow set the parameter `maxLeaves`. (#4226)
-  - Allow partial evaluation of dataframe before prediction (#4407)
-  - Automatically set `maximize_evaluation_metrics` if not explicitly given (#4446)
-
-### API changes
-* Deprecate `reg:linear` in favor of `reg:squarederror`. (#4267, #4427)
-* Add attribute getter and setter to the Booster object in XGBoost4J (#4336)
-
-### Maintenance: Refactor C++ code for legibility and maintainability
-* Fix clang-tidy warnings. (#4149)
-* Remove deprecated C APIs. (#4266)
-* Use Monitor class to time functions in `hist`. (#4273)
-* Retire DVec class in favour of c++20 style span for device memory. (#4293)
-* Improve HostDeviceVector exception safety (#4301)
-
-### Maintenance: testing, continuous integration, build system
-* **Major refactor of CMakeLists.txt** (#4323, #4333, #4453): adopt modern CMake and export XGBoost as a target
-* **Major improvement in Jenkins CI pipeline** (#4234)
-  - Migrate all Linux tests to Jenkins (#4401)
-  - Builds and tests are now de-coupled, to test an artifact against multiple versions of CUDA, JDK, and other dependencies (#4401)
-  - Add Windows GPU to Jenkins CI pipeline (#4463, #4469)
-* Support CUDA 10.1 (#4223, #4232, #4265, #4468)
-* Python wheels are now built with CUDA 9.0, so that JIT is not required on Volta architecture (#4459)
-* Integrate with NVTX CUDA profiler (#4205)
-* Add a test for cpu predictor using external memory (#4308)
-* Refactor tests to get rid of duplication (#4358)
-* Remove test dependency on `craigcitro/r-travis`, since it's deprecated (#4353)
-* Add files from local R build to `.gitignore` (#4346)
-* Make XGBoost4J compatible with Java 9+ by revising NativeLibLoader (#4351)
-* Jenkins build for CUDA 10.0 (#4281)
-* Remove remaining `silent` and `debug_verbose` in Python tests (#4299)
-* Use all cores to build XGBoost4J lib on linux (#4304)
-* Upgrade Jenkins Linux build environment to GCC 5.3.1, CMake 3.6.0 (#4306)
-* Make CMakeLists.txt compatible with CMake 3.3 (#4420)
-* Add OpenMP option in CMakeLists.txt (#4339)
-* Get rid of a few trivial compiler warnings (#4312)
-* Add external Docker build cache, to speed up builds on Jenkins CI (#4331, #4334, #4458)
-* Fix Windows tests (#4403)
-* Fix a broken python test (#4395)
-* Use a fixed seed to split data in XGBoost4J-Spark tests, for reproducibility (#4417)
-* Add additional Python tests to test training under constraints (#4426)
-* Enable building with shared NCCL. (#4447)
-
-### Usability Improvements, Documentation
-* Document limitation of one-split-at-a-time Greedy tree learning heuristic (#4233)
-* Update build doc: PyPI wheel now support multi-GPU (#4219)
-* Fix docs for `num_parallel_tree` (#4221)
-* Fix document about `colsample_by*` parameter (#4340)
-* Make the train and test input with same colnames. (#4329)
-* Update R contribute link. (#4236)
-* Fix travis R tests (#4277)
-* Log version number in crash log in XGBoost4J-Spark (#4271, #4303)
-* Allow supression of Rabit output in Booster::train in XGBoost4J (#4262)
-* Add tutorial on handling missing values in XGBoost4J-Spark (#4425)
-* Fix typos (#4345, #4393, #4432, #4435)
-* Added language classifier in setup.py (#4327)
-* Added Travis CI badge (#4344)
-* Add BentoML to use case section (#4400)
-* Remove subtly sexist remark (#4418)
-* Add R vignette about parsing JSON dumps (#4439)
-
-### Acknowledgement
-**Contributors**: Nan Zhu (@CodingCat), Adam Pocock (@Craigacp), Daniel Hen (@Daniel8hen), Jiaxiang Li (@JiaxiangBU), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Andy Adinets (@canonizer), Jonas (@elcombato), Harry Braviner (@harrybraviner), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), James Lamb (@jameslamb), Jean-Francois Zinque (@jeffzi), Yang Yang (@jokerkeny), Mayank Suman (@mayanksuman), jess (@monkeywithacupcake), Hajime Morrita (@omo), Ravi Kalia (@project-delphi), @ras44, Rong Ou (@rongou), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), @sriramch, Jiaming Yuan (@trivialfis), Christopher Suchanek (@wsuchy), Bozhao (@yubozhao)
-
-**Reviewers**: Nan Zhu (@CodingCat), Adam Pocock (@Craigacp), Daniel Hen (@Daniel8hen), Jiaxiang Li (@JiaxiangBU), Laurae (@Laurae2), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), @alois-bissuel, Andy Adinets (@canonizer), Chen Qin (@chenqin), Harry Braviner (@harrybraviner), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), @jakirkham, James Lamb (@jameslamb), Julien Schueller (@jschueller), Mayank Suman (@mayanksuman), Hajime Morrita (@omo), Rong Ou (@rongou), Sara Robinson (@sararob), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), @sriramch, Sean Owen (@srowen), Sergei Lebedev (@superbobry), Yuan (Terry) Tang (@terrytangyuan), Theodore Vasiloudis (@thvasilo), Matthew Tovbin (@tovbinm), Jiaming Yuan (@trivialfis), Xin Yin (@xydrolase)
-
-## v0.82 (2019.03.03)
-This release is packed with many new features and bug fixes.
-
-### Roadmap: better performance scaling for multi-core CPUs (#3957)
-* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). #3957 marks an important step toward better performance scaling, by using software pre-fetching and replacing STL vectors with C-style arrays. Special thanks to @Laurae2 and @SmirnovEgorRu.
-* See #3810 for latest progress on this roadmap.
-
-### New feature: Distributed Fast Histogram Algorithm (`hist`) (#4011, #4102, #4140, #4128)
-* It is now possible to run the `hist` algorithm in distributed setting. Special thanks to @CodingCat. The benefits include:
-  1. Faster local computation via feature binning
-  2. Support for monotonic constraints and feature interaction constraints
-  3. Simpler codebase than `approx`, allowing for future improvement
-* Depth-wise tree growing is now performed in a separate code path, so that cross-node syncronization is performed only once per level.
-
-### New feature: Multi-Node, Multi-GPU training (#4095)
-* Distributed training is now able to utilize clusters equipped with NVIDIA GPUs. In particular, the rabit AllReduce layer will communicate GPU device information. Special thanks to @mt-jones, @RAMitchell, @rongou, @trivialfis, @canonizer, and @jeffdk.
-* Resource management systems will be able to assign a rank for each GPU in the cluster.
-* In Dask, users will be able to construct a collection of XGBoost processes over an inhomogeneous device cluster (i.e. workers with different number and/or kinds of GPUs).
-
-### New feature: Multiple validation datasets in XGBoost4J-Spark (#3904, #3910)
-* You can now track the performance of the model during training with multiple evaluation datasets. By specifying `eval_sets` or call `setEvalSets` over a `XGBoostClassifier` or `XGBoostRegressor`, you can pass in multiple evaluation datasets typed as a `Map` from `String` to `DataFrame`. Special thanks to @CodingCat.
-* See the usage of multiple validation datasets [here](https://github.com/dmlc/xgboost/blob/0c1d5f1120c0a159f2567b267f0ec4ffadee00d0/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala#L66-L78)
-
-### New feature: Additional metric functions for GPUs (#3952)
-* Element-wise metrics have been ported to GPU: `rmse`, `mae`, `logloss`, `poisson-nloglik`, `gamma-deviance`, `gamma-nloglik`, `error`, `tweedie-nloglik`. Special thanks to @trivialfis and @RAMitchell.
-* With supported metrics, XGBoost will select the correct devices based on your system and `n_gpus` parameter.
-
-### New feature: Column sampling at individual nodes (splits) (#3971)
-* Columns (features) can now be sampled at individual tree nodes, in addition to per-tree and per-level sampling. To enable per-node sampling, set `colsample_bynode` parameter, which represents the fraction of columns sampled at each node. This parameter is set to 1.0 by default (i.e. no sampling per node). Special thanks to @canonizer.
-* The `colsample_bynode` parameter works cumulatively with other `colsample_by*` parameters: for example, `{'colsample_bynode':0.5, 'colsample_bytree':0.5}` with 100 columns will give 25 features to choose from at each split.
-
-### Major API change: consistent logging level via `verbosity` (#3982, #4002, #4138)
-* XGBoost now allows fine-grained control over logging. You can set `verbosity` to 0 (silent), 1 (warning), 2 (info), and 3 (debug). This is useful for controlling the amount of logging outputs. Special thanks to @trivialfis.
-* Parameters `silent` and `debug_verbose` are now deprecated.
-* Note: Sometimes XGBoost tries to change configurations based on heuristics, which is displayed as warning message.  If there's unexpected behaviour, please try to increase value of verbosity.
-
-### Major bug fix: external memory (#4040, #4193)
-* Clarify object ownership in multi-threaded prefetcher, to avoid memory error.
-* Correctly merge two column batches (which uses [CSC layout](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS))).
-* Add unit tests for external memory.
-* Special thanks to @trivialfis and @hcho3.
-
-### Major bug fix: early stopping fixed in XGBoost4J and XGBoost4J-Spark (#3928, #4176)
-* Early stopping in XGBoost4J and XGBoost4J-Spark is now consistent with its counterpart in the Python package. Training stops if the current iteration is `earlyStoppingSteps` away from the best iteration. If there are multiple evaluation sets, only the last one is used to determinate early stop.
-* See the updated documentation [here](https://xgboost.readthedocs.io/en/release_0.82/jvm/xgboost4j_spark_tutorial.html#early-stopping)
-* Special thanks to @CodingCat, @yanboliang, and @mingyang.
-
-### Major bug fix: infrequent features should not crash distributed training (#4045)
-* For infrequently occuring features, some partitions may not get any instance. This scenario used to crash distributed training due to mal-formed ranges. The problem has now been fixed.
-* In practice, one-hot-encoded categorical variables tend to produce rare features, particularly when the cardinality is high.
-* Special thanks to @CodingCat.
-
-### Performance improvements
-* Faster, more space-efficient radix sorting in `gpu_hist` (#3895)
-* Subtraction trick in histogram calculation in `gpu_hist` (#3945)
-* More performant re-partition in XGBoost4J-Spark (#4049)
-
-### Bug-fixes
-* Fix semantics of `gpu_id` when running multiple XGBoost processes on a multi-GPU machine (#3851)
-* Fix page storage path for external memory on Windows (#3869)
-* Fix configuration setup so that DART utilizes GPU (#4024)
-* Eliminate NAN values from SHAP prediction (#3943)
-* Prevent empty quantile sketches in `hist` (#4155)
-* Enable running objectives with 0 GPU (#3878)
-* Parameters are no longer dependent on system locale (#3891, #3907)
-* Use consistent data type in the GPU coordinate descent code (#3917)
-* Remove undefined behavior in the CLI config parser on the ARM platform (#3976)
-* Initialize counters in GPU AllReduce (#3987)
-* Prevent deadlocks in GPU AllReduce (#4113)
-* Load correct values from sliced NumPy arrays (#4147, #4165)
-* Fix incorrect GPU device selection (#4161)
-* Make feature binning logic in `hist` aware of query groups when running a ranking task (#4115). For ranking task, query groups are weighted, not individual instances.
-* Generate correct C++ exception type for `LOG(FATAL)` macro (#4159)
-* Python package
-  - Python package should run on system without `PATH` environment variable (#3845)
-  - Fix `coef_` and `intercept_` signature to be compatible with `sklearn.RFECV` (#3873)
-  - Use UTF-8 encoding in Python package README, to support non-English locale (#3867)
-  - Add AUC-PR to list of metrics to maximize for early stopping (#3936)
-  - Allow loading pickles without `self.booster` attribute, for backward compatibility (#3938, #3944)
-  - White-list DART for feature importances (#4073)
-  - Update usage of [h2oai/datatable](https://github.com/h2oai/datatable) (#4123)
-* XGBoost4J-Spark
-  - Address scalability issue in prediction (#4033)
-  - Enforce the use of per-group weights for ranking task (#4118)
-  - Fix vector size of `rawPredictionCol` in `XGBoostClassificationModel` (#3932)
-  - More robust error handling in Spark tracker (#4046, #4108)
-  - Fix return type of `setEvalSets` (#4105)
-  - Return correct value of `getMaxLeaves` (#4114)
-
-### API changes
-* Add experimental parameter `single_precision_histogram` to use single-precision histograms for the `gpu_hist` algorithm (#3965)
-* Python package
-  - Add option to select type of feature importances in the scikit-learn inferface (#3876)
-  - Add `trees_to_df()` method to dump decision trees as Pandas data frame (#4153)
-  - Add options to control node shapes in the GraphViz plotting function (#3859)
-  - Add `xgb_model` option to `XGBClassifier`, to load previously saved model (#4092)
-  - Passing lists into `DMatrix` is now deprecated (#3970)
-* XGBoost4J
-  - Support multiple feature importance features (#3801)
-
-### Maintenance: Refactor C++ code for legibility and maintainability
-* Refactor `hist` algorithm code and add unit tests (#3836)
-* Minor refactoring of split evaluator in `gpu_hist` (#3889)
-* Removed unused leaf vector field in the tree model (#3989)
-* Simplify the tree representation by combining `TreeModel` and `RegTree` classes (#3995)
-* Simplify and harden tree expansion code (#4008, #4015)
-* De-duplicate parameter classes in the linear model algorithms (#4013)
-* Robust handling of ranges with C++20 span in `gpu_exact` and `gpu_coord_descent` (#4020, #4029)
-* Simplify tree training code (#3825). Also use Span class for robust handling of ranges.
-
-### Maintenance: testing, continuous integration, build system
-* Disallow `std::regex` since it's not supported by GCC 4.8.x (#3870)
-* Add multi-GPU tests for coordinate descent algorithm for linear models (#3893, #3974)
-* Enforce naming style in Python lint (#3896)
-* Refactor Python tests (#3897, #3901): Use pytest exclusively, display full trace upon failure
-* Address `DeprecationWarning` when using Python collections (#3909)
-* Use correct group for maven site plugin (#3937)
-* Jenkins CI is now using on-demand EC2 instances exclusively, due to unreliability of Spot instances (#3948)
-* Better GPU performance logging (#3945)
-* Fix GPU tests on machines with only 1 GPU (#4053)
-* Eliminate CRAN check warnings and notes (#3988)
-* Add unit tests for tree serialization (#3989)
-* Add unit tests for tree fitting functions in `hist` (#4155)
-* Add a unit test for `gpu_exact` algorithm (#4020)
-* Correct JVM CMake GPU flag (#4071)
-* Fix failing Travis CI on Mac (#4086)
-* Speed up Jenkins by not compiling CMake (#4099)
-* Analyze C++ and CUDA code using clang-tidy, as part of Jenkins CI pipeline (#4034)
-* Fix broken R test: Install Homebrew GCC (#4142)
-* Check for empty datasets in GPU unit tests (#4151)
-* Fix Windows compilation (#4139)
-* Comply with latest convention of cpplint (#4157)
-* Fix a unit test in `gpu_hist` (#4158)
-* Speed up data generation in Python tests (#4164)
-
-### Usability Improvements
-* Add link to [InfoWorld 2019 Technology of the Year Award](https://www.infoworld.com/article/3336072/application-development/infoworlds-2019-technology-of-the-year-award-winners.html) (#4116)
-* Remove outdated AWS YARN tutorial (#3885)
-* Document current limitation in number of features (#3886)
-* Remove unnecessary warning when `gblinear` is selected (#3888)
-* Document limitation of CSV parser: header not supported (#3934)
-* Log training parameters in XGBoost4J-Spark (#4091)
-* Clarify early stopping behavior in the scikit-learn interface (#3967)
-* Clarify behavior of `max_depth` parameter (#4078)
-* Revise Python docstrings for ranking task (#4121). In particular, weights must be per-group in learning-to-rank setting.
-* Document parameter `num_parallel_tree` (#4022)
-* Add Jenkins status badge (#4090)
-* Warn users against using internal functions of `Booster` object (#4066)
-* Reformat `benchmark_tree.py` to comply with Python style convention (#4126)
-* Clarify a comment in `objectiveTrait` (#4174)
-* Fix typos and broken links in documentation (#3890, #3872, #3902, #3919, #3975, #4027, #4156, #4167)
-
-### Acknowledgement
-**Contributors** (in no particular order): Jiaming Yuan (@trivialfis), Hyunsu Cho (@hcho3), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Yanbo Liang (@yanboliang), Andy Adinets (@canonizer), Tong He (@hetong007), Yuan Tang (@terrytangyuan)
-
-**First-time Contributors** (in no particular order): Jelle Zijlstra (@JelleZijlstra), Jiacheng Xu (@jiachengxu), @ajing, Kashif Rasul (@kashif), @theycallhimavi, Joey Gao (@pjgao), Prabakaran Kumaresshan (@nixphix), Huafeng Wang (@huafengw), @lyxthe, Sam Wilkinson (@scwilkinson), Tatsuhito Kato (@stabacov), Shayak Banerjee (@shayakbanerjee), Kodi Arfer (@Kodiologist), @KyleLi1985, Egor Smirnov (@SmirnovEgorRu), @tmitanitky, Pasha Stetsenko (@st-pasha), Kenichi Nagahara (@keni-chi), Abhai Kollara Dilip (@abhaikollara), Patrick Ford (@pford221), @hshujuan, Matthew Jones (@mt-jones), Thejaswi Rao (@teju85), Adam November (@anovember)
-
-**First-time Reviewers** (in no particular order): Mingyang Hu (@mingyang), Theodore Vasiloudis (@thvasilo), Jakub Troszok (@troszok), Rong Ou (@rongou), @Denisevi4, Matthew Jones (@mt-jones), Jeff Kaplan (@jeffdk)
-
-## v0.81 (2018.11.04)
-### New feature: feature interaction constraints
-* Users are now able to control which features (independent variables) are allowed to interact by specifying feature interaction constraints (#3466).
-* [Tutorial](https://xgboost.readthedocs.io/en/release_0.81/tutorials/feature_interaction_constraint.html) is available, as well as [R](https://github.com/dmlc/xgboost/blob/9254c58e4dfff6a59dc0829a2ceb02e45ed17cd0/R-package/demo/interaction_constraints.R) and [Python](https://github.com/dmlc/xgboost/blob/9254c58e4dfff6a59dc0829a2ceb02e45ed17cd0/tests/python/test_interaction_constraints.py) examples.
-
-### New feature: learning to rank using scikit-learn interface
-* Learning to rank task is now available for the scikit-learn interface of the Python package (#3560, #3848). It is now possible to integrate the XGBoost ranking model into the scikit-learn learning pipeline.
-* Examples of using `XGBRanker` class is found at [demo/rank/rank_sklearn.py](https://github.com/dmlc/xgboost/blob/24a268a2e3cb17302db3d72da8f04016b7d352d9/demo/rank/rank_sklearn.py).
-
-### New feature: R interface for SHAP interactions
-* SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. Previously, this feature was only available from the Python package; now it is available from the R package as well (#3636).
-
-### New feature: GPU predictor now use multiple GPUs to predict
-* GPU predictor is now able to utilize multiple GPUs at once to accelerate prediction (#3738)
-
-### New feature: Scale distributed XGBoost to large-scale clusters
-* Fix OS file descriptor limit assertion error on large cluster (#3835, dmlc/rabit#73) by replacing `select()` based AllReduce/Broadcast with `poll()` based implementation.
-* Mitigate tracker "thundering herd" issue on large cluster. Add exponential backoff retry when workers connect to tracker.
-* With this change, we were able to scale to 1.5k executors on a 12 billion row dataset after some tweaks here and there.
-
-### New feature: Additional objective functions for GPUs
-* New objective functions ported to GPU: `hinge`, `multi:softmax`, `multi:softprob`, `count:poisson`, `reg:gamma`, `"reg:tweedie`.
-* With supported objectives, XGBoost will select the correct devices based on your system and `n_gpus` parameter.
-
-### Major bug fix: learning to rank with XGBoost4J-Spark
-* Previously, `repartitionForData` would shuffle data and lose ordering necessary for ranking task.
-* To fix this issue, data points within each RDD partition is explicitly group by their group (query session) IDs (#3654). Also handle empty RDD partition carefully (#3750).
-
-### Major bug fix: early stopping fixed in XGBoost4J-Spark
-* Earlier implementation of early stopping had incorrect semantics and didn't let users to specify direction for optimizing (maximize / minimize)
-* A parameter `maximize_evaluation_metrics` is defined so as to tell whether a metric should be maximized or minimized as part of early stopping criteria (#3808). Also early stopping now has correct semantics.
-
-### API changes
-* Column sampling by level (`colsample_bylevel`) is now functional for `hist` algorithm (#3635, #3862)
-* GPU tag `gpu:` for regression objectives are now deprecated. XGBoost will select the correct devices automatically (#3643)
-* Add `disable_default_eval_metric` parameter to disable default metric (#3606)
-* Experimental AVX support for gradient computation is removed (#3752)
-* XGBoost4J-Spark
-  - Add `rank:ndcg` and `rank:map` to supported objectives (#3697)
-* Python package
-  - Add `callbacks` argument to `fit()` function of sciki-learn API (#3682)
-  - Add `XGBRanker` to scikit-learn interface (#3560, #3848)
-  - Add `validate_features` argument to `predict()` function of scikit-learn API (#3653)
-  - Allow scikit-learn grid search over parameters specified as keyword arguments (#3791)
-  - Add `coef_` and `intercept_` as properties of scikit-learn wrapper (#3855). Some scikit-learn functions expect these properties.
-
-### Performance improvements
-* Address very high GPU memory usage for large data (#3635)
-* Fix performance regression within `EvaluateSplits()` of `gpu_hist` algorithm. (#3680)
-
-### Bug-fixes
-* Fix a problem in GPU quantile sketch with tiny instance weights. (#3628)
-* Fix copy constructor for `HostDeviceVectorImpl` to prevent dangling pointers (#3657)
-* Fix a bug in partitioned file loading (#3673)
-* Fixed an uninitialized pointer in `gpu_hist` (#3703)
-* Reshared data among GPUs when number of GPUs is changed (#3721)
-* Add back `max_delta_step` to split evaluation (#3668)
-* Do not round up integer thresholds for integer features in JSON dump (#3717)
-* Use `dmlc::TemporaryDirectory` to handle temporaries in cross-platform way (#3783)
-* Fix accuracy problem with `gpu_hist` when `min_child_weight` and `lambda` are set to 0 (#3793)
-* Make sure that `tree_method` parameter is recognized and not silently ignored (#3849)
-* XGBoost4J-Spark
-  - Make sure `thresholds` are considered when executing `predict()` method (#3577)
-  - Avoid losing precision when computing probabilities by converting to `Double` early (#3576)
-  - `getTreeLimit()` should return `Int` (#3602)
-  - Fix checkpoint serialization on HDFS (#3614)
-  - Throw `ControlThrowable` instead of `InterruptedException` so that it is properly re-thrown (#3632)
-  - Remove extraneous output to stdout (#3665)
-  - Allow specification of task type for custom objectives and evaluations (#3646)
-  - Fix distributed updater check (#3739)
-  - Fix issue when spark job execution thread cannot return before we execute `first()` (#3758)
-* Python package
-  - Fix accessing `DMatrix.handle` before it is set (#3599)
-  - `XGBClassifier.predict()` should return margin scores when `output_margin` is set to true (#3651)
-  - Early stopping callback should maximize metric of form `NDCG@n-` (#3685)
-  - Preserve feature names when slicing `DMatrix` (#3766)
-* R package
-  - Replace `nround` with `nrounds` to match actual parameter (#3592)
-  - Amend `xgb.createFolds` to handle classes of a single element (#3630)
-  - Fix buggy random generator and make `colsample_bytree` functional (#3781)
-
-### Maintenance: testing, continuous integration, build system
-* Add sanitizers tests to Travis CI (#3557)
-* Add NumPy, Matplotlib, Graphviz as requirements for doc build (#3669)
-* Comply with CRAN submission policy (#3660, #3728)
-* Remove copy-paste error in JVM test suite (#3692)
-* Disable flaky tests in `R-package/tests/testthat/test_update.R` (#3723)
-* Make Python tests compatible with scikit-learn 0.20 release (#3731)
-* Separate out restricted and unrestricted tasks, so that pull requests don't build downloadable artifacts (#3736)
-* Add multi-GPU unit test environment (#3741)
-* Allow plug-ins to be built by CMake (#3752)
-* Test wheel compatibility on CPU containers for pull requests (#3762)
-* Fix broken doc build due to Matplotlib 3.0 release (#3764)
-* Produce `xgboost.so` for XGBoost-R on Mac OSX, so that `make install` works (#3767)
-* Retry Jenkins CI tests up to 3 times to improve reliability (#3769, #3769, #3775, #3776, #3777)
-* Add basic unit tests for `gpu_hist` algorithm (#3785)
-* Fix Python environment for distributed unit tests (#3806)
-* Test wheels on CUDA 10.0 container for compatibility (#3838)
-* Fix JVM doc build (#3853)
-
-### Maintenance: Refactor C++ code for legibility and maintainability
-* Merge generic device helper functions into `GPUSet` class (#3626)
-* Re-factor column sampling logic into `ColumnSampler` class (#3635, #3637)
-* Replace `std::vector` with `HostDeviceVector` in `MetaInfo` and `SparsePage` (#3446)
-* Simplify `DMatrix` class (#3395)
-* De-duplicate CPU/GPU code using `Transform` class (#3643, #3751)
-* Remove obsoleted `QuantileHistMaker` class (#3761)
-* Remove obsoleted `NoConstraint` class (#3792)
-
-### Other Features
-* C++20-compliant Span class for safe pointer indexing (#3548, #3588)
-* Add helper functions to manipulate multiple GPU devices (#3693)
-* XGBoost4J-Spark
-  - Allow specifying host ip from the `xgboost-tracker.properties file` (#3833). This comes in handy when `hosts` files doesn't correctly define localhost.
-
-### Usability Improvements
-* Add reference to GitHub repository in `pom.xml` of JVM packages (#3589)
-* Add R demo of multi-class classification (#3695)
-* Document JSON dump functionality (#3600, #3603)
-* Document CUDA requirement and lack of external memory for GPU algorithms (#3624)
-* Document LambdaMART objectives, both pairwise and listwise (#3672)
-* Document `aucpr` evaluation metric (#3687)
-* Document gblinear parameters: `feature_selector` and `top_k` (#3780)
-* Add instructions for using MinGW-built XGBoost with Python. (#3774)
-* Removed nonexistent parameter `use_buffer` from documentation (#3610)
-* Update Python API doc to include all classes and members (#3619, #3682)
-* Fix typos and broken links in documentation (#3618, #3640, #3676, #3713, #3759, #3784, #3843, #3852)
-* Binary classification demo should produce LIBSVM with 0-based indexing (#3652)
-* Process data once for Python and CLI examples of learning to rank (#3666)
-* Include full text of Apache 2.0 license in the repository (#3698)
-* Save predictor parameters in model file (#3856)
-* JVM packages
-  - Let users specify feature names when calling `getModelDump` and `getFeatureScore` (#3733)
-  - Warn the user about the lack of over-the-wire encryption (#3667)
-  - Fix errors in examples (#3719)
-  - Document choice of trackers (#3831)
-  - Document that vanilla Apache Spark is required (#3854)
-* Python package
-  - Document that custom objective can't contain colon (:) (#3601)
-  - Show a better error message for failed library loading (#3690)
-  - Document that feature importance is unavailable for non-tree learners (#3765)
-  - Document behavior of `get_fscore()` for zero-importance features (#3763)
-  - Recommend pickling as the way to save `XGBClassifier` / `XGBRegressor` / `XGBRanker` (#3829)
-* R package
-  - Enlarge variable importance plot to make it more visible (#3820)
-
-### BREAKING CHANGES
-* External memory page files have changed, breaking backwards compatibility for temporary storage used during external memory training. This only affects external memory users upgrading their xgboost version - we recommend clearing all `*.page` files before resuming training. Model serialization is unaffected.
-
-### Known issues
-* Quantile sketcher fails to produce any quantile for some edge cases (#2943)
-* The `hist` algorithm leaks memory when used with learning rate decay callback (#3579)
-* Using custom evaluation funciton together with early stopping causes assertion failure in XGBoost4J-Spark (#3595)
-* Early stopping doesn't work with `gblinear` learner (#3789)
-* Label and weight vectors are not reshared upon the change in number of GPUs (#3794). To get around this issue, delete the `DMatrix` object and re-load.
-* The `DMatrix` Python objects are initialized with incorrect values when given array slices (#3841)
-* The `gpu_id` parameter is broken and not yet properly supported (#3850)
-
-### Acknowledgement
-**Contributors** (in no particular order): Hyunsu Cho (@hcho3), Jiaming Yuan (@trivialfis), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Andy Adinets (@canonizer), Vadim Khotilovich (@khotilov), Sergei Lebedev (@superbobry)
-
-**First-time Contributors** (in no particular order): Matthew Tovbin (@tovbinm), Jakob Richter (@jakob-r), Grace Lam (@grace-lam), Grant W Schneider (@grantschneider), Andrew Thia (@BlueTea88), Sergei Chipiga (@schipiga), Joseph Bradley (@jkbradley), Chen Qin (@chenqin), Jerry Lin (@linjer), Dmitriy Rybalko (@rdtft), Michael Mui (@mmui), Takahiro Kojima (@515hikaru), Bruce Zhao (@BruceZhaoR), Wei Tian (@weitian), Saumya Bhatnagar (@Sam1301), Juzer Shakir (@JuzerShakir), Zhao Hang (@cleghom), Jonathan Friedman (@jontonsoup), Bruno Tremblay (@meztez), Boris Filippov (@frenzykryger), @Shiki-H, @mrgutkun, @gorogm, @htgeis, @jakehoare, @zengxy, @KOLANICH
-
-**First-time Reviewers** (in no particular order): Nikita Titov (@StrikerRUS), Xiangrui Meng (@mengxr), Nirmal Borah (@Nirmal-Neel)
-
-
-## v0.80 (2018.08.13)
-* **JVM packages received a major upgrade**: To consolidate the APIs and improve the user experience, we refactored the design of XGBoost4J-Spark in a significant manner. (#3387)
-  - Consolidated APIs: It is now much easier to integrate XGBoost models into a Spark ML pipeline. Users can control behaviors like output leaf prediction results by setting corresponding column names. Training is now more consistent with other Estimators in Spark MLLIB: there is now one single method `fit()` to train decision trees.
-  - Better user experience: we refactored the parameters relevant modules in XGBoost4J-Spark to provide both camel-case (Spark ML style) and underscore (XGBoost style) parameters
-  - A brand-new tutorial is [available](https://xgboost.readthedocs.io/en/release_0.80/jvm/xgboost4j_spark_tutorial.html) for XGBoost4J-Spark.
-  - Latest API documentation is now hosted at https://xgboost.readthedocs.io/.
-* XGBoost documentation now keeps track of multiple versions:
-  - Latest master: https://xgboost.readthedocs.io/en/latest
-  - 0.80 stable: https://xgboost.readthedocs.io/en/release_0.80
-  - 0.72 stable: https://xgboost.readthedocs.io/en/release_0.72
-* Support for per-group weights in ranking objective (#3379)
-* Fix inaccurate decimal parsing (#3546)
-* New functionality
-  - Query ID column support in LIBSVM data files (#2749). This is convenient for performing ranking task in distributed setting.
-  - Hinge loss for binary classification (`binary:hinge`) (#3477)
-  - Ability to specify delimiter and instance weight column for CSV files (#3546)
-  - Ability to use 1-based indexing instead of 0-based (#3546)
-* GPU support
-  - Quantile sketch, binning, and index compression are now performed on GPU, eliminating PCIe transfer for 'gpu_hist' algorithm (#3319, #3393)
-  - Upgrade to NCCL2 for multi-GPU training (#3404).
-  - Use shared memory atomics for faster training (#3384).
-  - Dynamically allocate GPU memory, to prevent large allocations for deep trees (#3519)
-  - Fix memory copy bug for large files (#3472)
-* Python package
-  - Importing data from Python datatable (#3272)
-  - Pre-built binary wheels available for 64-bit Linux and Windows (#3424, #3443)
-  - Add new importance measures 'total_gain', 'total_cover' (#3498)
-  - Sklearn API now supports saving and loading models (#3192)
-  - Arbitrary cross validation fold indices (#3353)
-  - `predict()` function in Sklearn API uses `best_ntree_limit` if available, to make early stopping easier to use (#3445)
-  - Informational messages are now directed to Python's `print()` rather than standard output (#3438). This way, messages appear inside Jupyter notebooks.
-* R package
-  - Oracle Solaris support, per CRAN policy (#3372)
-* JVM packages
-  - Single-instance prediction (#3464)
-  - Pre-built JARs are now available from Maven Central (#3401)
-  - Add NULL pointer check (#3021)
-  - Consider `spark.task.cpus` when controlling parallelism (#3530)
-  - Handle missing values in prediction (#3529)
-  - Eliminate outputs of `System.out` (#3572)
-* Refactored C++ DMatrix class for simplicity and de-duplication (#3301)
-* Refactored C++ histogram facilities (#3564)
-* Refactored constraints / regularization mechanism for split finding (#3335, #3429). Users may specify an elastic net (L2 + L1 regularization) on leaf weights as well as monotonic constraints on test nodes. The refactor will be useful for a future addition of feature interaction constraints.
-* Statically link `libstdc++` for MinGW32 (#3430)
-* Enable loading from `group`, `base_margin` and `weight` (see [here](http://xgboost.readthedocs.io/en/latest/tutorials/input_format.html#auxiliary-files-for-additional-information)) for Python, R, and JVM packages (#3431)
-* Fix model saving for `count:possion` so that `max_delta_step` doesn't get truncated (#3515)
-* Fix loading of sparse CSC matrix (#3553)
-* Fix incorrect handling of `base_score` parameter for Tweedie regression (#3295)
-
-## v0.72.1 (2018.07.08)
-This version is only applicable for the Python package. The content is identical to that of v0.72.
-
-## v0.72 (2018.06.01)
-* Starting with this release, we plan to make a new release every two months. See #3252 for more details.
-* Fix a pathological behavior (near-zero second-order gradients) in multiclass objective (#3304)
-* Tree dumps now use high precision in storing floating-point values (#3298)
-* Submodules `rabit` and `dmlc-core` have been brought up to date, bringing bug fixes (#3330, #3221).
-* GPU support
-  - Continuous integration tests for GPU code (#3294, #3309)
-  - GPU accelerated coordinate descent algorithm (#3178)
-  - Abstract 1D vector class now works with multiple GPUs (#3287)
-  - Generate PTX code for most recent architecture (#3316)
-  - Fix a memory bug on NVIDIA K80 cards (#3293)
-  - Address performance instability for single-GPU, multi-core machines (#3324)
-* Python package
-  - FreeBSD support (#3247)
-  - Validation of feature names in `Booster.predict()` is now optional (#3323)
-* Updated Sklearn API
-  - Validation sets now support instance weights (#2354)
-  - `XGBClassifier.predict_proba()` should not support `output_margin` option. (#3343) See BREAKING CHANGES below.
-* R package:
-  - Better handling of NULL in `print.xgb.Booster()` (#3338)
-  - Comply with CRAN policy by removing compiler warning suppression (#3329)
-  - Updated CRAN submission
-* JVM packages
-  - JVM packages will now use the same versioning scheme as other packages (#3253)
-  - Update Spark to 2.3 (#3254)
-  - Add scripts to cross-build and deploy artifacts (#3276, #3307)
-  - Fix a compilation error for Scala 2.10 (#3332)
-* BREAKING CHANGES
-  - `XGBClassifier.predict_proba()` no longer accepts paramter `output_margin`. The paramater makes no sense for `predict_proba()` because the method is to predict class probabilities, not raw margin scores.
-
-## v0.71 (2018.04.11)
-* This is a minor release, mainly motivated by issues concerning `pip install`, e.g. #2426, #3189, #3118, and #3194.
-  With this release, users of Linux and MacOS will be able to run `pip install` for the most part.
-* Refactored linear booster class (`gblinear`), so as to support multiple coordinate descent updaters (#3103, #3134). See BREAKING CHANGES below.
-* Fix slow training for multiclass classification with high number of classes (#3109)
-* Fix a corner case in approximate quantile sketch (#3167). Applicable for 'hist' and 'gpu_hist' algorithms
-* Fix memory leak in DMatrix (#3182)
-* New functionality
-  - Better linear booster class (#3103, #3134)
-  - Pairwise SHAP interaction effects (#3043)
-  - Cox loss (#3043)
-  - AUC-PR metric for ranking task (#3172)
-  - Monotonic constraints for 'hist' algorithm (#3085)
-* GPU support
-  - Create an abtract 1D vector class that moves data seamlessly between the main and GPU memory (#2935, #3116, #3068). This eliminates unnecessary PCIe data transfer during training time.
-  - Fix minor bugs (#3051, #3217)
-  - Fix compatibility error for CUDA 9.1 (#3218)
-* Python package:
-  - Correctly handle parameter `verbose_eval=0` (#3115)
-* R package:
-  - Eliminate segmentation fault on 32-bit Windows platform (#2994)
-* JVM packages
-  - Fix a memory bug involving double-freeing Booster objects (#3005, #3011)
-  - Handle empty partition in predict (#3014)
-  - Update docs and unify terminology (#3024)
-  - Delete cache files after job finishes (#3022)
-  - Compatibility fixes for latest Spark versions (#3062, #3093)
-* BREAKING CHANGES: Updated linear modelling algorithms. In particular L1/L2 regularisation penalties are now normalised to number of training examples. This makes the implementation consistent with sklearn/glmnet. L2 regularisation has also been removed from the intercept. To produce linear models with the old regularisation behaviour, the alpha/lambda regularisation parameters can be manually scaled by dividing them by the number of training examples.
-
-## v0.7 (2017.12.30)
-* **This version represents a major change from the last release (v0.6), which was released one year and half ago.**
-* Updated Sklearn API
-  - Add compatibility layer for scikit-learn v0.18: `sklearn.cross_validation` now deprecated
-  - Updated to allow use of all XGBoost parameters via `**kwargs`.
-  - Updated `nthread` to `n_jobs` and `seed` to `random_state` (as per Sklearn convention); `nthread` and `seed` are now marked as deprecated
-  - Updated to allow choice of Booster (`gbtree`, `gblinear`, or `dart`)
-  - `XGBRegressor` now supports instance weights (specify `sample_weight` parameter)
-  - Pass `n_jobs` parameter to the `DMatrix` constructor
-  - Add `xgb_model` parameter to `fit` method, to allow continuation of training
-* Refactored gbm to allow more friendly cache strategy
-  - Specialized some prediction routine
-* Robust `DMatrix` construction from a sparse matrix
-* Faster consturction of `DMatrix` from 2D NumPy matrices: elide copies, use of multiple threads
-* Automatically remove nan from input data when it is sparse.
-  - This can solve some of user reported problem of istart != hist.size
-* Fix the single-instance prediction function to obtain correct predictions
-* Minor fixes
-  - Thread local variable is upgraded so it is automatically freed at thread exit.
-  - Fix saving and loading `count::poisson` models
-  - Fix CalcDCG to use base-2 logarithm
-  - Messages are now written to stderr instead of stdout
-  - Keep built-in evaluations while using customized evaluation functions
-  - Use `bst_float` consistently to minimize type conversion
-  - Copy the base margin when slicing `DMatrix`
-  - Evaluation metrics are now saved to the model file
-  - Use `int32_t` explicitly when serializing version
-  - In distributed training, synchronize the number of features after loading a data matrix.
-* Migrate to C++11
-  - The current master version now requires C++11 enabled compiled(g++4.8 or higher)
-* Predictor interface was factored out (in a manner similar to the updater interface).
-* Makefile support for Solaris and ARM
-* Test code coverage using Codecov
-* Add CPP tests
-* Add `Dockerfile` and `Jenkinsfile` to support continuous integration for GPU code
-* New functionality
-  - Ability to adjust tree model's statistics to a new dataset without changing tree structures.
-  - Ability to extract feature contributions from individual predictions, as described in [here](http://blog.datadive.net/interpreting-random-forests/) and [here](https://arxiv.org/abs/1706.06060).
-  - Faster, histogram-based tree algorithm (`tree_method='hist'`) .
-  - GPU/CUDA accelerated tree algorithms (`tree_method='gpu_hist'` or `'gpu_exact'`), including the GPU-based predictor.
-  - Monotonic constraints: when other features are fixed, force the prediction to be monotonic increasing with respect to a certain specified feature.
-  - Faster gradient caculation using AVX SIMD
-  - Ability to export models in JSON format
-  - Support for Tweedie regression
-  - Additional dropout options for DART: binomial+1, epsilon
-  - Ability to update an existing model in-place: this is useful for many applications, such as determining feature importance
-* Python package:
-  - New parameters:
-    - `learning_rates` in `cv()`
-    - `shuffle` in `mknfold()`
-    - `max_features` and `show_values` in `plot_importance()`
-    - `sample_weight` in `XGBRegressor.fit()`
-  - Support binary wheel builds
-  - Fix `MultiIndex` detection to support Pandas 0.21.0 and higher
-  - Support metrics and evaluation sets whose names contain `-`
-  - Support feature maps when plotting trees
-  - Compatibility fix for Python 2.6
-  - Call `print_evaluation` callback at last iteration
-  - Use appropriate integer types when calling native code, to prevent truncation and memory error
-  - Fix shared library loading on Mac OS X
-* R package:
-  - New parameters:
-    - `silent` in `xgb.DMatrix()`
-    - `use_int_id` in `xgb.model.dt.tree()`
-    - `predcontrib` in `predict()`
-    - `monotone_constraints` in `xgb.train()`
-  - Default value of the `save_period` parameter in `xgboost()` changed to NULL (consistent with `xgb.train()`).
-  - It's possible to custom-build the R package with GPU acceleration support.
-  - Enable JVM build for Mac OS X and Windows
-  - Integration with AppVeyor CI
-  - Improved safety for garbage collection
-  - Store numeric attributes with higher precision
-  - Easier installation for devel version
-  - Improved `xgb.plot.tree()`
-  - Various minor fixes to improve user experience and robustness
-  - Register native code to pass CRAN check
-  - Updated CRAN submission
-* JVM packages
-  - Add Spark pipeline persistence API
-  - Fix data persistence: loss evaluation on test data had wrongly used caches for training data.
-  - Clean external cache after training
-  - Implement early stopping
-  - Enable training of multiple models by distinguishing stage IDs
-  - Better Spark integration: support RDD / dataframe / dataset, integrate with Spark ML package
-  - XGBoost4j now supports ranking task
-  - Support training with missing data
-  - Refactor JVM package to separate regression and classification models to be consistent with other machine learning libraries
-  - Support XGBoost4j compilation on Windows
-  - Parameter tuning tool
-  - Publish source code for XGBoost4j to maven local repo
-  - Scala implementation of the Rabit tracker (drop-in replacement for the Java implementation)
-  - Better exception handling for the Rabit tracker
-  - Persist `num_class`, number of classes (for classification task)
-  - `XGBoostModel` now holds `BoosterParams`
-  - libxgboost4j is now part of CMake build
-  - Release `DMatrix` when no longer needed, to conserve memory
-  - Expose `baseMargin`, to allow initialization of boosting with predictions from an external model
-  - Support instance weights
-  - Use `SparkParallelismTracker` to prevent jobs from hanging forever
-  - Expose train-time evaluation metrics via `XGBoostModel.summary`
-  - Option to specify `host-ip` explicitly in the Rabit tracker
-* Documentation
-  - Better math notation for gradient boosting
-  - Updated build instructions for Mac OS X
-  - Template for GitHub issues
-  - Add `CITATION` file for citing XGBoost in scientific writing
-  - Fix dropdown menu in xgboost.readthedocs.io
-  - Document `updater_seq` parameter
-  - Style fixes for Python documentation
-  - Links to additional examples and tutorials
-  - Clarify installation requirements
-* Changes that break backward compatibility
-  - [#1519](https://github.com/dmlc/xgboost/pull/1519) XGBoost-spark no longer contains APIs for DMatrix; use the public booster interface instead.
-  - [#2476](https://github.com/dmlc/xgboost/pull/2476) `XGBoostModel.predict()` now has a different signature
-
-
-## v0.6 (2016.07.29)
-* Version 0.5 is skipped due to major improvements in the core
-* Major refactor of core library.
-  - Goal: more flexible and modular code as a portable library.
-  - Switch to use of c++11 standard code.
-  - Random number generator defaults to ```std::mt19937```.
-  - Share the data loading pipeline and logging module from dmlc-core.
-  - Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader.
-    - Future plugin modules can be put into xgboost/plugin and register back to the library.
-  - Remove most of the raw pointers to smart ptrs, for RAII safety.
-* Add official option to approximate algorithm `tree_method` to parameter.
-  - Change default behavior to switch to prefer faster algorithm.
-  - User will get a message when approximate algorithm is chosen.
-* Change library name to libxgboost.so
-* Backward compatiblity
-  - The binary buffer file is not backward compatible with previous version.
-  - The model file is backward compatible on 64 bit platforms.
-* The model file is compatible between 64/32 bit platforms(not yet tested).
-* External memory version and other advanced features will be exposed to R library as well on linux.
-  - Previously some of the features are blocked due to C++11 and threading limits.
-  - The windows version is still blocked due to Rtools do not support ```std::thread```.
-* rabit and dmlc-core are maintained through git submodule
-  - Anyone can open PR to update these dependencies now.
-* Improvements
-  - Rabit and xgboost libs are not thread-safe and use thread local PRNGs
-  - This could fix some of the previous problem which runs xgboost on multiple threads.
-* JVM Package
-  - Enable xgboost4j for java and scala
-  - XGBoost distributed now runs on Flink and Spark.
-* Support model attributes listing for meta data.
-  - https://github.com/dmlc/xgboost/pull/1198
-  - https://github.com/dmlc/xgboost/pull/1166
-* Support callback API
-  - https://github.com/dmlc/xgboost/issues/892
-  - https://github.com/dmlc/xgboost/pull/1211
-  - https://github.com/dmlc/xgboost/pull/1264
-* Support new booster DART(dropout in tree boosting)
-  - https://github.com/dmlc/xgboost/pull/1220
-* Add CMake build system
-  - https://github.com/dmlc/xgboost/pull/1314
-
-## v0.47 (2016.01.14)
-
-* Changes in R library
-  - fixed possible problem of poisson regression.
-  - switched from 0 to NA for missing values.
-  - exposed access to additional model parameters.
-* Changes in Python library
-  - throws exception instead of crash terminal when a parameter error happens.
-  - has importance plot and tree plot functions.
-  - accepts different learning rates for each boosting round.
-  - allows model training continuation from previously saved model.
-  - allows early stopping in CV.
-  - allows feval to return a list of tuples.
-  - allows eval_metric to handle additional format.
-  - improved compatibility in sklearn module.
-  - additional parameters added for sklearn wrapper.
-  - added pip installation functionality.
-  - supports more Pandas DataFrame dtypes.
-  - added best_ntree_limit attribute, in addition to best_score and best_iteration.
-* Java api is ready for use
-* Added more test cases and continuous integration to make each build more robust.
-
-## v0.4 (2015.05.11)
-
-* Distributed version of xgboost that runs on YARN, scales to billions of examples
-* Direct save/load data and model from/to S3 and HDFS
-* Feature importance visualization in R module, by Michael Benesty
-* Predict leaf index
-* Poisson regression for counts data
-* Early stopping option in training
-* Native save load support in R and python
-  - xgboost models now can be saved using save/load in R
-  - xgboost python model is now pickable
-* sklearn wrapper is supported in python module
-* Experimental External memory version
-
-
-## v0.3 (2014.09.07)
-
-* Faster tree construction module
-  - Allows subsample columns during tree construction via ```bst:col_samplebytree=ratio```
-* Support for boosting from initial predictions
-* Experimental version of LambdaRank
-* Linear booster is now parallelized, using parallel coordinated descent.
-* Add [Code Guide](src/README.md) for customizing objective function and evaluation
-* Add R module
-
-
-## v0.2x (2014.05.20)
-
-* Python module
-* Weighted samples instances
-* Initial version of pairwise rank
-
-
-## v0.1 (2014.03.26)
-
-* Initial release
diff --git a/ml-xgboost/R-package/.Rbuildignore b/ml-xgboost/R-package/.Rbuildignore
deleted file mode 100644
index b37d627..0000000
--- a/ml-xgboost/R-package/.Rbuildignore
+++ /dev/null
@@ -1,6 +0,0 @@
-\.o$
-\.so$
-\.dll$
-^.*\.Rproj$
-^\.Rproj\.user$
-README.md
diff --git a/ml-xgboost/R-package/CMakeLists.txt b/ml-xgboost/R-package/CMakeLists.txt
deleted file mode 100644
index 96776a0..0000000
--- a/ml-xgboost/R-package/CMakeLists.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-find_package(LibR REQUIRED)
-message(STATUS "LIBR_CORE_LIBRARY " ${LIBR_CORE_LIBRARY})
-
-file(GLOB_RECURSE R_SOURCES
-  ${CMAKE_CURRENT_LIST_DIR}/src/*.cc
-  ${CMAKE_CURRENT_LIST_DIR}/src/*.c)
-# Use object library to expose symbols
-add_library(xgboost-r OBJECT ${R_SOURCES})
-
-set(R_DEFINITIONS
-  -DXGBOOST_STRICT_R_MODE=1
-  -DXGBOOST_CUSTOMIZE_GLOBAL_PRNG=1
-  -DDMLC_LOG_BEFORE_THROW=0
-  -DDMLC_DISABLE_STDIN=1
-  -DDMLC_LOG_CUSTOMIZE=1
-  -DRABIT_CUSTOMIZE_MSG_
-  -DRABIT_STRICT_CXX98_)
-target_compile_definitions(xgboost-r
-  PRIVATE ${R_DEFINITIONS})
-target_include_directories(xgboost-r
-  PRIVATE
-  ${LIBR_INCLUDE_DIRS}
-  ${PROJECT_SOURCE_DIR}/include
-  ${PROJECT_SOURCE_DIR}/dmlc-core/include
-  ${PROJECT_SOURCE_DIR}/rabit/include)
-set_target_properties(
-  xgboost-r PROPERTIES
-  CXX_STANDARD 14
-  CXX_STANDARD_REQUIRED ON
-  POSITION_INDEPENDENT_CODE ON)
-
-set(XGBOOST_DEFINITIONS "${XGBOOST_DEFINITIONS};${R_DEFINITIONS}" PARENT_SCOPE)
-set(XGBOOST_OBJ_SOURCES $<TARGET_OBJECTS:xgboost-r> PARENT_SCOPE)
-set(LINKED_LIBRARIES_PRIVATE ${LINKED_LIBRARIES_PRIVATE} ${LIBR_CORE_LIBRARY} PARENT_SCOPE)
-
-if (USE_OPENMP)
-  target_link_libraries(xgboost-r PRIVATE OpenMP::OpenMP_CXX)
-endif ()
diff --git a/ml-xgboost/R-package/DESCRIPTION b/ml-xgboost/R-package/DESCRIPTION
deleted file mode 100644
index 794abdb..0000000
--- a/ml-xgboost/R-package/DESCRIPTION
+++ /dev/null
@@ -1,67 +0,0 @@
-Package: xgboost
-Type: Package
-Title: Extreme Gradient Boosting
-Version: 1.1.0.1
-Date: 2020-02-21
-Authors@R: c(
-  person("Tianqi", "Chen", role = c("aut"),
-         email = "tianqi.tchen@gmail.com"),
-  person("Tong", "He", role = c("aut", "cre"),
-         email = "hetong007@gmail.com"),
-  person("Michael", "Benesty", role = c("aut"),
-         email = "michael@benesty.fr"),
-  person("Vadim", "Khotilovich", role = c("aut"),
-         email = "khotilovich@gmail.com"),
-  person("Yuan", "Tang", role = c("aut"),
-         email = "terrytangyuan@gmail.com",
-         comment = c(ORCID = "0000-0001-5243-233X")),
-  person("Hyunsu", "Cho", role = c("aut"),
-         email = "chohyu01@cs.washington.edu"),
-  person("Kailong", "Chen", role = c("aut")),
-  person("Rory", "Mitchell", role = c("aut")),
-  person("Ignacio", "Cano", role = c("aut")),
-  person("Tianyi", "Zhou", role = c("aut")),
-  person("Mu", "Li", role = c("aut")),
-  person("Junyuan", "Xie", role = c("aut")),
-  person("Min", "Lin", role = c("aut")),
-  person("Yifeng", "Geng", role = c("aut")),
-  person("Yutian", "Li", role = c("aut")),
-  person("XGBoost contributors", role = c("cph"),
-         comment = "base XGBoost implementation")
-  )
-Description: Extreme Gradient Boosting, which is an efficient implementation
-    of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
-    This package is its R interface. The package includes efficient linear 
-    model solver and tree learning algorithms. The package can automatically 
-    do parallel computation on a single machine which could be more than 10 
-    times faster than existing gradient boosting packages. It supports
-    various objective functions, including regression, classification and ranking.
-    The package is made to be extensible, so that users are also allowed to define
-    their own objectives easily.
-License: Apache License (== 2.0) | file LICENSE
-URL: https://github.com/dmlc/xgboost
-BugReports: https://github.com/dmlc/xgboost/issues
-NeedsCompilation: yes
-VignetteBuilder: knitr
-Suggests:
-    knitr,
-    rmarkdown,
-    ggplot2 (>= 1.0.1),
-    DiagrammeR (>= 0.9.0),
-    Ckmeans.1d.dp (>= 3.3.1),
-    vcd (>= 1.3),
-    testthat,
-    lintr,
-    igraph (>= 1.0.1),
-    jsonlite,
-    float
-Depends:
-    R (>= 3.3.0)
-Imports:
-    Matrix (>= 1.1-0),
-    methods,
-    data.table (>= 1.9.6),
-    magrittr (>= 1.5),
-    stringi (>= 0.5.2)
-RoxygenNote: 7.1.0
-SystemRequirements: GNU make, C++11
diff --git a/ml-xgboost/R-package/LICENSE b/ml-xgboost/R-package/LICENSE
deleted file mode 100644
index b9f38c3..0000000
--- a/ml-xgboost/R-package/LICENSE
+++ /dev/null
@@ -1,13 +0,0 @@
-Copyright (c) 2014 by Tianqi Chen and Contributors 
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-    
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
diff --git a/ml-xgboost/R-package/NAMESPACE b/ml-xgboost/R-package/NAMESPACE
deleted file mode 100644
index fb0ac54..0000000
--- a/ml-xgboost/R-package/NAMESPACE
+++ /dev/null
@@ -1,91 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-S3method("[",xgb.DMatrix)
-S3method("dimnames<-",xgb.DMatrix)
-S3method(dim,xgb.DMatrix)
-S3method(dimnames,xgb.DMatrix)
-S3method(getinfo,xgb.DMatrix)
-S3method(predict,xgb.Booster)
-S3method(predict,xgb.Booster.handle)
-S3method(print,xgb.Booster)
-S3method(print,xgb.DMatrix)
-S3method(print,xgb.cv.synchronous)
-S3method(setinfo,xgb.DMatrix)
-S3method(slice,xgb.DMatrix)
-export("xgb.attr<-")
-export("xgb.attributes<-")
-export("xgb.config<-")
-export("xgb.parameters<-")
-export(cb.cv.predict)
-export(cb.early.stop)
-export(cb.evaluation.log)
-export(cb.gblinear.history)
-export(cb.print.evaluation)
-export(cb.reset.parameters)
-export(cb.save.model)
-export(getinfo)
-export(setinfo)
-export(slice)
-export(xgb.Booster.complete)
-export(xgb.DMatrix)
-export(xgb.DMatrix.save)
-export(xgb.attr)
-export(xgb.attributes)
-export(xgb.config)
-export(xgb.create.features)
-export(xgb.cv)
-export(xgb.dump)
-export(xgb.gblinear.history)
-export(xgb.ggplot.deepness)
-export(xgb.ggplot.importance)
-export(xgb.importance)
-export(xgb.load)
-export(xgb.load.raw)
-export(xgb.model.dt.tree)
-export(xgb.plot.deepness)
-export(xgb.plot.importance)
-export(xgb.plot.multi.trees)
-export(xgb.plot.shap)
-export(xgb.plot.tree)
-export(xgb.save)
-export(xgb.save.raw)
-export(xgb.serialize)
-export(xgb.train)
-export(xgb.unserialize)
-export(xgboost)
-import(methods)
-importClassesFrom(Matrix,dgCMatrix)
-importClassesFrom(Matrix,dgeMatrix)
-importFrom(Matrix,colSums)
-importFrom(Matrix,sparse.model.matrix)
-importFrom(Matrix,sparseMatrix)
-importFrom(Matrix,sparseVector)
-importFrom(Matrix,t)
-importFrom(data.table,":=")
-importFrom(data.table,as.data.table)
-importFrom(data.table,data.table)
-importFrom(data.table,is.data.table)
-importFrom(data.table,rbindlist)
-importFrom(data.table,setkey)
-importFrom(data.table,setkeyv)
-importFrom(data.table,setnames)
-importFrom(grDevices,rgb)
-importFrom(graphics,barplot)
-importFrom(graphics,grid)
-importFrom(graphics,lines)
-importFrom(graphics,par)
-importFrom(graphics,points)
-importFrom(graphics,title)
-importFrom(magrittr,"%>%")
-importFrom(stats,median)
-importFrom(stats,predict)
-importFrom(stringi,stri_detect_regex)
-importFrom(stringi,stri_match_first_regex)
-importFrom(stringi,stri_replace_all_regex)
-importFrom(stringi,stri_replace_first_regex)
-importFrom(stringi,stri_split_regex)
-importFrom(utils,head)
-importFrom(utils,object.size)
-importFrom(utils,str)
-importFrom(utils,tail)
-useDynLib(xgboost, .registration = TRUE)
diff --git a/ml-xgboost/R-package/R/callbacks.R b/ml-xgboost/R-package/R/callbacks.R
deleted file mode 100644
index e6f9f04..0000000
--- a/ml-xgboost/R-package/R/callbacks.R
+++ /dev/null
@@ -1,831 +0,0 @@
-#' Callback closures for booster training.
-#'
-#' These are used to perform various service tasks either during boosting iterations or at the end.
-#' This approach helps to modularize many of such tasks without bloating the main training methods,
-#' and it offers .
-#'
-#' @details
-#' By default, a callback function is run after each boosting iteration.
-#' An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
-#'
-#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after
-#' the boosting is completed.
-#'
-#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in
-#' the environment from which they are called from, which is a fairly uncommon thing to do in R.
-#'
-#' To write a custom callback closure, make sure you first understand the main concepts about R environments.
-#' Check either R documentation on \code{\link[base]{environment}} or the
-#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
-#' book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
-#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
-#' with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
-#'
-#' @seealso
-#' \code{\link{cb.print.evaluation}},
-#' \code{\link{cb.evaluation.log}},
-#' \code{\link{cb.reset.parameters}},
-#' \code{\link{cb.early.stop}},
-#' \code{\link{cb.save.model}},
-#' \code{\link{cb.cv.predict}},
-#' \code{\link{xgb.train}},
-#' \code{\link{xgb.cv}}
-#'
-#' @name callbacks
-NULL
-
-#
-# Callbacks -------------------------------------------------------------------
-#
-
-#' Callback closure for printing the result of evaluation
-#'
-#' @param period  results would be printed every number of periods
-#' @param showsd  whether standard deviations should be printed (when available)
-#'
-#' @details
-#' The callback function prints the result of evaluation at every \code{period} iterations.
-#' The initial and the last iteration's evaluations are always printed.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{bst_evaluation} (also \code{bst_evaluation_err} when available),
-#' \code{iteration},
-#' \code{begin_iteration},
-#' \code{end_iteration}.
-#'
-#' @seealso
-#' \code{\link{callbacks}}
-#'
-#' @export
-cb.print.evaluation <- function(period = 1, showsd = TRUE) {
-
-  callback <- function(env = parent.frame()) {
-    if (length(env$bst_evaluation) == 0 ||
-        period == 0 ||
-        NVL(env$rank, 0) != 0 )
-      return()
-
-    i <- env$iteration
-    if ((i-1) %% period == 0 ||
-        i == env$begin_iteration ||
-        i == env$end_iteration) {
-      stdev <- if (showsd) env$bst_evaluation_err else NULL
-      msg <- format.eval.string(i, env$bst_evaluation, stdev)
-      cat(msg, '\n')
-    }
-  }
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.print.evaluation'
-  callback
-}
-
-
-#' Callback closure for logging the evaluation history
-#'
-#' @details
-#' This callback function appends the current iteration evaluation results \code{bst_evaluation}
-#' available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
-#'
-#' The finalizer callback (called with \code{finalize = TURE} in the end) converts
-#' the \code{evaluation_log} list into a final data.table.
-#'
-#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
-#'
-#' Note: in the column names of the final data.table, the dash '-' character is replaced with
-#' the underscore '_' in order to make the column names more like regular R identifiers.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{evaluation_log},
-#' \code{bst_evaluation},
-#' \code{iteration}.
-#'
-#' @seealso
-#' \code{\link{callbacks}}
-#'
-#' @export
-cb.evaluation.log <- function() {
-
-  mnames <- NULL
-
-  init <- function(env) {
-    if (!is.list(env$evaluation_log))
-      stop("'evaluation_log' has to be a list")
-    mnames <<- names(env$bst_evaluation)
-    if (is.null(mnames) || any(mnames == ""))
-      stop("bst_evaluation must have non-empty names")
-
-    mnames <<- gsub('-', '_', names(env$bst_evaluation))
-    if(!is.null(env$bst_evaluation_err))
-      mnames <<- c(paste0(mnames, '_mean'), paste0(mnames, '_std'))
-  }
-
-  finalizer <- function(env) {
-    env$evaluation_log <- as.data.table(t(simplify2array(env$evaluation_log)))
-    setnames(env$evaluation_log, c('iter', mnames))
-
-    if(!is.null(env$bst_evaluation_err)) {
-      # rearrange col order from _mean,_mean,...,_std,_std,...
-      # to be _mean,_std,_mean,_std,...
-      len <- length(mnames)
-      means <- mnames[seq_len(len/2)]
-      stds <- mnames[(len/2 + 1):len]
-      cnames <- numeric(len)
-      cnames[c(TRUE, FALSE)] <- means
-      cnames[c(FALSE, TRUE)] <- stds
-      env$evaluation_log <- env$evaluation_log[, c('iter', cnames), with = FALSE]
-    }
-  }
-
-  callback <- function(env = parent.frame(), finalize = FALSE) {
-    if (is.null(mnames))
-      init(env)
-
-    if (finalize)
-      return(finalizer(env))
-
-    ev <- env$bst_evaluation
-    if(!is.null(env$bst_evaluation_err))
-      ev <- c(ev, env$bst_evaluation_err)
-    env$evaluation_log <- c(env$evaluation_log,
-                            list(c(iter = env$iteration, ev)))
-  }
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.evaluation.log'
-  callback
-}
-
-#' Callback closure for resetting the booster's parameters at each iteration.
-#'
-#' @param new_params a list where each element corresponds to a parameter that needs to be reset.
-#'        Each element's value must be either a vector of values of length \code{nrounds}
-#'        to be set at each iteration,
-#'        or a function of two parameters \code{learning_rates(iteration, nrounds)}
-#'        which returns a new parameter value by using the current iteration number
-#'        and the total number of boosting rounds.
-#'
-#' @details
-#' This is a "pre-iteration" callback function used to reset booster's parameters
-#' at the beginning of each iteration.
-#'
-#' Note that when training is resumed from some previous model, and a function is used to
-#' reset a parameter value, the \code{nrounds} argument in this function would be the
-#' the number of boosting rounds in the current training.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{bst} or \code{bst_folds},
-#' \code{iteration},
-#' \code{begin_iteration},
-#' \code{end_iteration}.
-#'
-#' @seealso
-#' \code{\link{callbacks}}
-#'
-#' @export
-cb.reset.parameters <- function(new_params) {
-
-  if (typeof(new_params) != "list")
-    stop("'new_params' must be a list")
-  pnames <- gsub("\\.", "_", names(new_params))
-  nrounds <- NULL
-
-  # run some checks in the begining
-  init <- function(env) {
-    nrounds <<- env$end_iteration - env$begin_iteration + 1
-
-    if (is.null(env$bst) && is.null(env$bst_folds))
-      stop("Parent frame has neither 'bst' nor 'bst_folds'")
-
-    # Some parameters are not allowed to be changed,
-    # since changing them would simply wreck some chaos
-    not_allowed <- pnames %in%
-      c('num_class', 'num_output_group', 'size_leaf_vector', 'updater_seq')
-    if (any(not_allowed))
-      stop('Parameters ', paste(pnames[not_allowed]), " cannot be changed during boosting.")
-
-    for (n in pnames) {
-      p <- new_params[[n]]
-      if (is.function(p)) {
-        if (length(formals(p)) != 2)
-          stop("Parameter '", n, "' is a function but not of two arguments")
-      } else if (is.numeric(p) || is.character(p)) {
-        if (length(p) != nrounds)
-          stop("Length of '", n, "' has to be equal to 'nrounds'")
-      } else {
-        stop("Parameter '", n, "' is not a function or a vector")
-      }
-    }
-  }
-
-  callback <- function(env = parent.frame()) {
-    if (is.null(nrounds))
-      init(env)
-
-    i <- env$iteration
-    pars <- lapply(new_params, function(p) {
-      if (is.function(p))
-        return(p(i, nrounds))
-      p[i]
-    })
-
-    if (!is.null(env$bst)) {
-      xgb.parameters(env$bst$handle) <- pars
-    } else {
-      for (fd in env$bst_folds)
-        xgb.parameters(fd$bst) <- pars
-    }
-  }
-  attr(callback, 'is_pre_iteration') <- TRUE
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.reset.parameters'
-  callback
-}
-
-
-#' Callback closure to activate the early stopping.
-#'
-#' @param stopping_rounds The number of rounds with no improvement in
-#'        the evaluation metric in order to stop the training.
-#' @param maximize whether to maximize the evaluation metric
-#' @param metric_name the name of an evaluation column to use as a criteria for early
-#'        stopping. If not set, the last column would be used.
-#'        Let's say the test data in \code{watchlist} was labelled as \code{dtest},
-#'        and one wants to use the AUC in test data for early stopping regardless of where
-#'        it is in the \code{watchlist}, then one of the following would need to be set:
-#'        \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
-#'        All dash '-' characters in metric names are considered equivalent to '_'.
-#' @param verbose whether to print the early stopping information.
-#'
-#' @details
-#' This callback function determines the condition for early stopping
-#' by setting the \code{stop_condition = TRUE} flag in its calling frame.
-#'
-#' The following additional fields are assigned to the model's R object:
-#' \itemize{
-#' \item \code{best_score} the evaluation score at the best iteration
-#' \item \code{best_iteration} at which boosting iteration the best score has occurred (1-based index)
-#' \item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
-#'      It differs from \code{best_iteration} in multiclass or random forest settings.
-#' }
-#'
-#' The Same values are also stored as xgb-attributes:
-#' \itemize{
-#' \item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
-#' \item \code{best_msg} message string is also stored.
-#' }
-#'
-#' At least one data element is required in the evaluation watchlist for early stopping to work.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{stop_condition},
-#' \code{bst_evaluation},
-#' \code{rank},
-#' \code{bst} (or \code{bst_folds} and \code{basket}),
-#' \code{iteration},
-#' \code{begin_iteration},
-#' \code{end_iteration},
-#' \code{num_parallel_tree}.
-#'
-#' @seealso
-#' \code{\link{callbacks}},
-#' \code{\link{xgb.attr}}
-#'
-#' @export
-cb.early.stop <- function(stopping_rounds, maximize = FALSE,
-                          metric_name = NULL, verbose = TRUE) {
-  # state variables
-  best_iteration <- -1
-  best_ntreelimit <- -1
-  best_score <- Inf
-  best_msg <- NULL
-  metric_idx <- 1
-
-  init <- function(env) {
-    if (length(env$bst_evaluation) == 0)
-      stop("For early stopping, watchlist must have at least one element")
-
-    eval_names <- gsub('-', '_', names(env$bst_evaluation))
-    if (!is.null(metric_name)) {
-      metric_idx <<- which(gsub('-', '_', metric_name) == eval_names)
-      if (length(metric_idx) == 0)
-        stop("'metric_name' for early stopping is not one of the following:\n",
-             paste(eval_names, collapse = ' '), '\n')
-    }
-    if (is.null(metric_name) &&
-        length(env$bst_evaluation) > 1) {
-      metric_idx <<- length(eval_names)
-      if (verbose)
-        cat('Multiple eval metrics are present. Will use ',
-            eval_names[metric_idx], ' for early stopping.\n', sep = '')
-    }
-
-    metric_name <<- eval_names[metric_idx]
-
-    # maximize is usually NULL when not set in xgb.train and built-in metrics
-    if (is.null(maximize))
-      maximize <<- grepl('(_auc|_map|_ndcg)', metric_name)
-
-    if (verbose && NVL(env$rank, 0) == 0)
-      cat("Will train until ", metric_name, " hasn't improved in ",
-          stopping_rounds, " rounds.\n\n", sep = '')
-
-    best_iteration <<- 1
-    if (maximize) best_score <<- -Inf
-
-    env$stop_condition <- FALSE
-
-    if (!is.null(env$bst)) {
-      if (!inherits(env$bst, 'xgb.Booster'))
-        stop("'bst' in the parent frame must be an 'xgb.Booster'")
-      if (!is.null(best_score <- xgb.attr(env$bst$handle, 'best_score'))) {
-        best_score <<- as.numeric(best_score)
-        best_iteration <<- as.numeric(xgb.attr(env$bst$handle, 'best_iteration')) + 1
-        best_msg <<- as.numeric(xgb.attr(env$bst$handle, 'best_msg'))
-      } else {
-        xgb.attributes(env$bst$handle) <- list(best_iteration = best_iteration - 1,
-                                               best_score = best_score)
-      }
-    } else if (is.null(env$bst_folds) || is.null(env$basket)) {
-      stop("Parent frame has neither 'bst' nor ('bst_folds' and 'basket')")
-    }
-  }
-
-  finalizer <- function(env) {
-    if (!is.null(env$bst)) {
-      attr_best_score = as.numeric(xgb.attr(env$bst$handle, 'best_score'))
-      if (best_score != attr_best_score)
-        stop("Inconsistent 'best_score' values between the closure state: ", best_score,
-             " and the xgb.attr: ", attr_best_score)
-      env$bst$best_iteration = best_iteration
-      env$bst$best_ntreelimit = best_ntreelimit
-      env$bst$best_score = best_score
-    } else {
-      env$basket$best_iteration <- best_iteration
-      env$basket$best_ntreelimit <- best_ntreelimit
-    }
-  }
-
-  callback <- function(env = parent.frame(), finalize = FALSE) {
-    if (best_iteration < 0)
-      init(env)
-
-    if (finalize)
-      return(finalizer(env))
-
-    i <- env$iteration
-    score = env$bst_evaluation[metric_idx]
-
-    if (( maximize && score > best_score) ||
-        (!maximize && score < best_score)) {
-
-      best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
-      best_score <<- score
-      best_iteration <<- i
-      best_ntreelimit <<- best_iteration * env$num_parallel_tree
-      # save the property to attributes, so they will occur in checkpoint
-      if (!is.null(env$bst)) {
-        xgb.attributes(env$bst) <- list(
-          best_iteration = best_iteration - 1, # convert to 0-based index
-          best_score = best_score,
-          best_msg = best_msg,
-          best_ntreelimit = best_ntreelimit)
-      }
-    } else if (i - best_iteration >= stopping_rounds) {
-      env$stop_condition <- TRUE
-      env$end_iteration <- i
-      if (verbose && NVL(env$rank, 0) == 0)
-        cat("Stopping. Best iteration:\n", best_msg, "\n\n", sep = '')
-    }
-  }
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.early.stop'
-  callback
-}
-
-
-#' Callback closure for saving a model file.
-#'
-#' @param save_period save the model to disk after every
-#'        \code{save_period} iterations; 0 means save the model at the end.
-#' @param save_name the name or path for the saved model file.
-#'        It can contain a \code{\link[base]{sprintf}} formatting specifier
-#'        to include the integer iteration number in the file name.
-#'        E.g., with \code{save_name} = 'xgboost_%04d.model',
-#'        the file saved at iteration 50 would be named "xgboost_0050.model".
-#'
-#' @details
-#' This callback function allows to save an xgb-model file, either periodically after each \code{save_period}'s or at the end.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{bst},
-#' \code{iteration},
-#' \code{begin_iteration},
-#' \code{end_iteration}.
-#'
-#' @seealso
-#' \code{\link{callbacks}}
-#'
-#' @export
-cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
-
-  if (save_period < 0)
-    stop("'save_period' cannot be negative")
-
-  callback <- function(env = parent.frame()) {
-    if (is.null(env$bst))
-      stop("'save_model' callback requires the 'bst' booster object in its calling frame")
-
-    if ((save_period > 0 && (env$iteration - env$begin_iteration) %% save_period == 0) ||
-        (save_period == 0 && env$iteration == env$end_iteration))
-      xgb.save(env$bst, sprintf(save_name, env$iteration))
-  }
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.save.model'
-  callback
-}
-
-
-#' Callback closure for returning cross-validation based predictions.
-#'
-#' @param save_models a flag for whether to save the folds' models.
-#'
-#' @details
-#' This callback function saves predictions for all of the test folds,
-#' and also allows to save the folds' models.
-#'
-#' It is a "finalizer" callback and it uses early stopping information whenever it is available,
-#' thus it must be run after the early stopping callback if the early stopping is used.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{bst_folds},
-#' \code{basket},
-#' \code{data},
-#' \code{end_iteration},
-#' \code{params},
-#' \code{num_parallel_tree},
-#' \code{num_class}.
-#'
-#' @return
-#' Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
-#' depending on the number of prediction outputs per data row. The order of predictions corresponds
-#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is
-#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
-#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
-#' meaningful when user-provided folds have overlapping indices as in, e.g., random sampling splits.
-#' When some of the indices in the training dataset are not included into user-provided \code{folds},
-#' their prediction value would be \code{NA}.
-#'
-#' @seealso
-#' \code{\link{callbacks}}
-#'
-#' @export
-cb.cv.predict <- function(save_models = FALSE) {
-
-  finalizer <- function(env) {
-    if (is.null(env$basket) || is.null(env$bst_folds))
-      stop("'cb.cv.predict' callback requires 'basket' and 'bst_folds' lists in its calling frame")
-
-    N <- nrow(env$data)
-    pred <-
-      if (env$num_class > 1) {
-        matrix(NA_real_, N, env$num_class)
-      } else {
-        rep(NA_real_, N)
-      }
-
-    ntreelimit <- NVL(env$basket$best_ntreelimit,
-                      env$end_iteration * env$num_parallel_tree)
-    if (NVL(env$params[['booster']], '') == 'gblinear') {
-      ntreelimit <- 0 # must be 0 for gblinear
-    }
-    for (fd in env$bst_folds) {
-      pr <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit, reshape = TRUE)
-      if (is.matrix(pred)) {
-        pred[fd$index,] <- pr
-      } else {
-        pred[fd$index] <- pr
-      }
-    }
-    env$basket$pred <- pred
-    if (save_models) {
-      env$basket$models <- lapply(env$bst_folds, function(fd) {
-        xgb.attr(fd$bst, 'niter') <- env$end_iteration - 1
-        xgb.Booster.complete(xgb.handleToBooster(fd$bst), saveraw = TRUE)
-      })
-    }
-  }
-
-  callback <- function(env = parent.frame(), finalize = FALSE) {
-    if (finalize)
-      return(finalizer(env))
-  }
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.cv.predict'
-  callback
-}
-
-
-#' Callback closure for collecting the model coefficients history of a gblinear booster
-#' during its training.
-#'
-#' @param sparse when set to FALSE/TURE, a dense/sparse matrix is used to store the result.
-#'       Sparse format is useful when one expects only a subset of coefficients to be non-zero,
-#'       when using the "thrifty" feature selector with fairly small number of top features
-#'       selected per iteration.
-#'
-#' @details
-#' To keep things fast and simple, gblinear booster does not internally store the history of linear
-#' model coefficients at each boosting iteration. This callback provides a workaround for storing
-#' the coefficients' path, by extracting them after each training iteration.
-#'
-#' Callback function expects the following values to be set in its calling frame:
-#' \code{bst} (or \code{bst_folds}).
-#'
-#' @return
-#' Results are stored in the \code{coefs} element of the closure.
-#' The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
-#' With \code{xgb.train}, it is either a dense of a sparse matrix.
-#' While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
-#'
-#' @seealso
-#' \code{\link{callbacks}}, \code{\link{xgb.gblinear.history}}.
-#'
-#' @examples
-#' #### Binary classification:
-#' #
-#' # In the iris dataset, it is hard to linearly separate Versicolor class from the rest
-#' # without considering the 2nd order interactions:
-#' require(magrittr)
-#' x <- model.matrix(Species ~ .^2, iris)[,-1]
-#' colnames(x)
-#' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
-#' param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
-#'               lambda = 0.0003, alpha = 0.0003, nthread = 2)
-#' # For 'shotgun', which is a default linear updater, using high eta values may result in
-#' # unstable behaviour in some datasets. With this simple dataset, however, the high learning
-#' # rate does not break the convergence, but allows us to illustrate the typical pattern of
-#' # "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
-#' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 1.,
-#'                  callbacks = list(cb.gblinear.history()))
-#' # Extract the coefficients' path and plot them vs boosting iteration number:
-#' coef_path <- xgb.gblinear.history(bst)
-#' matplot(coef_path, type = 'l')
-#'
-#' # With the deterministic coordinate descent updater, it is safer to use higher learning rates.
-#' # Will try the classical componentwise boosting which selects a single best feature per round:
-#' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
-#'                  updater = 'coord_descent', feature_selector = 'thrifty', top_k = 1,
-#'                  callbacks = list(cb.gblinear.history()))
-#' xgb.gblinear.history(bst) %>% matplot(type = 'l')
-#' #  Componentwise boosting is known to have similar effect to Lasso regularization.
-#' # Try experimenting with various values of top_k, eta, nrounds,
-#' # as well as different feature_selectors.
-#'
-#' # For xgb.cv:
-#' bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
-#'              callbacks = list(cb.gblinear.history()))
-#' # coefficients in the CV fold #3
-#' xgb.gblinear.history(bst)[[3]] %>% matplot(type = 'l')
-#'
-#'
-#' #### Multiclass classification:
-#' #
-#' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
-#' param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
-#'               lambda = 0.0003, alpha = 0.0003, nthread = 2)
-#' # For the default linear updater 'shotgun' it sometimes is helpful
-#' # to use smaller eta to reduce instability
-#' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 70, eta = 0.5,
-#'                  callbacks = list(cb.gblinear.history()))
-#' # Will plot the coefficient paths separately for each class:
-#' xgb.gblinear.history(bst, class_index = 0) %>% matplot(type = 'l')
-#' xgb.gblinear.history(bst, class_index = 1) %>% matplot(type = 'l')
-#' xgb.gblinear.history(bst, class_index = 2) %>% matplot(type = 'l')
-#'
-#' # CV:
-#' bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
-#'               callbacks = list(cb.gblinear.history(FALSE)))
-#' # 1st forld of 1st class
-#' xgb.gblinear.history(bst, class_index = 0)[[1]] %>% matplot(type = 'l')
-#'
-#' @export
-cb.gblinear.history <- function(sparse=FALSE) {
-  coefs <- NULL
-
-  init <- function(env) {
-    if (!is.null(env$bst)) { # xgb.train:
-      coef_path <- list()
-    } else if (!is.null(env$bst_folds)) { # xgb.cv:
-      coef_path <- rep(list(), length(env$bst_folds))
-    } else stop("Parent frame has neither 'bst' nor 'bst_folds'")
-  }
-
-  # convert from list to (sparse) matrix
-  list2mat <- function(coef_list) {
-    if (sparse) {
-      coef_mat <- sparseMatrix(x = unlist(lapply(coef_list, slot, "x")),
-                               i = unlist(lapply(coef_list, slot, "i")),
-                               p = c(0, cumsum(sapply(coef_list, function(x) length(x@x)))),
-                               dims = c(length(coef_list[[1]]), length(coef_list)))
-      return(t(coef_mat))
-    } else {
-      return(do.call(rbind, coef_list))
-    }
-  }
-
-  finalizer <- function(env) {
-    if (length(coefs) == 0)
-      return()
-    if (!is.null(env$bst)) { # # xgb.train:
-      coefs <<- list2mat(coefs)
-    } else { # xgb.cv:
-      # first lapply transposes the list
-      coefs <<- lapply(seq_along(coefs[[1]]), function(i) lapply(coefs, "[[", i)) %>%
-                lapply(function(x) list2mat(x))
-    }
-  }
-
-  extract.coef <- function(env) {
-    if (!is.null(env$bst)) { # # xgb.train:
-      cf <- as.numeric(grep('(booster|bias|weigh)', xgb.dump(env$bst), invert = TRUE, value = TRUE))
-      if (sparse) cf <- as(cf, "sparseVector")
-    } else { # xgb.cv:
-      cf <- vector("list", length(env$bst_folds))
-      for (i in seq_along(env$bst_folds)) {
-        dmp <- xgb.dump(xgb.handleToBooster(env$bst_folds[[i]]$bst))
-        cf[[i]] <- as.numeric(grep('(booster|bias|weigh)', dmp, invert = TRUE, value = TRUE))
-        if (sparse) cf[[i]] <- as(cf[[i]], "sparseVector")
-      }
-    }
-    cf
-  }
-
-  callback <- function(env = parent.frame(), finalize = FALSE) {
-    if (is.null(coefs)) init(env)
-    if (finalize) return(finalizer(env))
-    cf <- extract.coef(env)
-    coefs <<- c(coefs, list(cf))
-  }
-
-  attr(callback, 'call') <- match.call()
-  attr(callback, 'name') <- 'cb.gblinear.history'
-  callback
-}
-
-#' Extract gblinear coefficients history.
-#'
-#' A helper function to extract the matrix of linear coefficients' history
-#' from a gblinear model created while using the \code{cb.gblinear.history()}
-#' callback.
-#'
-#' @param model either an \code{xgb.Booster} or a result of \code{xgb.cv()}, trained
-#'        using the \code{cb.gblinear.history()} callback.
-#' @param class_index zero-based class index to extract the coefficients for only that
-#'        specific class in a multinomial multiclass model. When it is NULL, all the
-#'        coefficients are returned. Has no effect in non-multiclass models.
-#'
-#' @return
-#' For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns
-#' corresponding to iteration's coefficients (in the order as \code{xgb.dump()} would
-#' return) and the rows corresponding to boosting iterations.
-#'
-#' For an \code{xgb.cv} result, a list of such matrices is returned with the elements
-#' corresponding to CV folds.
-#'
-#' @export
-xgb.gblinear.history <- function(model, class_index = NULL) {
-
-  if (!(inherits(model, "xgb.Booster") ||
-        inherits(model, "xgb.cv.synchronous")))
-    stop("model must be an object of either xgb.Booster or xgb.cv.synchronous class")
-  is_cv <- inherits(model, "xgb.cv.synchronous")
-
-  if (is.null(model[["callbacks"]]) || is.null(model$callbacks[["cb.gblinear.history"]]))
-    stop("model must be trained while using the cb.gblinear.history() callback")
-
-  if (!is_cv) {
-    # extract num_class & num_feat from the internal model
-    dmp <- xgb.dump(model)
-    if(length(dmp) < 2 || dmp[2] != "bias:")
-      stop("It does not appear to be a gblinear model")
-    dmp <- dmp[-c(1,2)]
-    n <- which(dmp == 'weight:')
-    if(length(n) != 1)
-      stop("It does not appear to be a gblinear model")
-    num_class <- n - 1
-    num_feat <- (length(dmp) - 4) / num_class
-  } else {
-    # in case of CV, the object is expected to have this info
-    if (model$params$booster != "gblinear")
-      stop("It does not appear to be a gblinear model")
-    num_class <- NVL(model$params$num_class, 1)
-    num_feat <- model$nfeatures
-    if (is.null(num_feat))
-      stop("This xgb.cv result does not have nfeatures info")
-  }
-
-  if (!is.null(class_index) &&
-      num_class > 1 &&
-      (class_index[1] < 0 || class_index[1] >= num_class))
-    stop("class_index has to be within [0,", num_class - 1, "]")
-
-  coef_path <- environment(model$callbacks$cb.gblinear.history)[["coefs"]]
-  if (!is.null(class_index) && num_class > 1) {
-    coef_path <- if (is.list(coef_path)) {
-      lapply(coef_path,
-             function(x) x[, seq(1 + class_index, by=num_class, length.out=num_feat)])
-    } else {
-      coef_path <- coef_path[, seq(1 + class_index, by=num_class, length.out=num_feat)]
-    }
-  }
-  coef_path
-}
-
-
-#
-# Internal utility functions for callbacks ------------------------------------
-#
-
-# Format the evaluation metric string
-format.eval.string <- function(iter, eval_res, eval_err = NULL) {
-  if (length(eval_res) == 0)
-    stop('no evaluation results')
-  enames <- names(eval_res)
-  if (is.null(enames))
-    stop('evaluation results must have names')
-  iter <- sprintf('[%d]\t', iter)
-  if (!is.null(eval_err)) {
-    if (length(eval_res) != length(eval_err))
-      stop('eval_res & eval_err lengths mismatch')
-    res <- paste0(sprintf("%s:%f+%f", enames, eval_res, eval_err), collapse = '\t')
-  } else {
-    res <- paste0(sprintf("%s:%f", enames, eval_res), collapse = '\t')
-  }
-  return(paste0(iter, res))
-}
-
-# Extract callback names from the list of callbacks
-callback.names <- function(cb_list) {
-  unlist(lapply(cb_list, function(x) attr(x, 'name')))
-}
-
-# Extract callback calls from the list of callbacks
-callback.calls <- function(cb_list) {
-  unlist(lapply(cb_list, function(x) attr(x, 'call')))
-}
-
-# Add a callback cb to the list and make sure that
-# cb.early.stop and cb.cv.predict are at the end of the list
-# with cb.cv.predict being the last (when present)
-add.cb <- function(cb_list, cb) {
-  cb_list <- c(cb_list, cb)
-  names(cb_list) <- callback.names(cb_list)
-  if ('cb.early.stop' %in% names(cb_list)) {
-    cb_list <- c(cb_list, cb_list['cb.early.stop'])
-    # this removes only the first one
-    cb_list['cb.early.stop'] <- NULL
-  }
-  if ('cb.cv.predict' %in% names(cb_list)) {
-    cb_list <- c(cb_list, cb_list['cb.cv.predict'])
-    cb_list['cb.cv.predict'] <- NULL
-  }
-  cb_list
-}
-
-# Sort callbacks list into categories
-categorize.callbacks <- function(cb_list) {
-  list(
-    pre_iter = Filter(function(x) {
-        pre <- attr(x, 'is_pre_iteration')
-        !is.null(pre) && pre
-      }, cb_list),
-    post_iter = Filter(function(x) {
-        pre <- attr(x, 'is_pre_iteration')
-        is.null(pre) || !pre
-      }, cb_list),
-    finalize = Filter(function(x) {
-        'finalize' %in% names(formals(x))
-      }, cb_list)
-  )
-}
-
-# Check whether all callback functions with names given by 'query_names' are present in the 'cb_list'.
-has.callbacks <- function(cb_list, query_names) {
-  if (length(cb_list) < length(query_names))
-    return(FALSE)
-  if (!is.list(cb_list) ||
-      any(sapply(cb_list, class) != 'function')) {
-    stop('`cb_list` must be a list of callback functions')
-  }
-  cb_names <- callback.names(cb_list)
-  if (!is.character(cb_names) ||
-      length(cb_names) != length(cb_list) ||
-      any(cb_names == "")) {
-    stop('All callbacks in the `cb_list` must have a non-empty `name` attribute')
-  }
-  if (!is.character(query_names) ||
-      length(query_names) == 0 ||
-      any(query_names == "")) {
-    stop('query_names must be a non-empty vector of non-empty character names')
-  }
-  return(all(query_names %in% cb_names))
-}
diff --git a/ml-xgboost/R-package/R/utils.R b/ml-xgboost/R-package/R/utils.R
deleted file mode 100644
index 0edbf12..0000000
--- a/ml-xgboost/R-package/R/utils.R
+++ /dev/null
@@ -1,352 +0,0 @@
-#
-# This file is for the low level reuseable utility functions
-# that are not supposed to be visibe to a user.
-#
-
-#
-# General helper utilities ----------------------------------------------------
-#
-
-# SQL-style NVL shortcut.
-NVL <- function(x, val) {
-  if (is.null(x))
-    return(val)
-  if (is.vector(x)) {
-    x[is.na(x)] <- val
-    return(x)
-  }
-  if (typeof(x) == 'closure')
-    return(x)
-  stop("typeof(x) == ", typeof(x), " is not supported by NVL")
-}
-
-
-#
-# Low-level functions for boosting --------------------------------------------
-#
-
-# Merges booster params with whatever is provided in ...
-# plus runs some checks
-check.booster.params <- function(params, ...) {
-  if (!identical(class(params), "list"))
-    stop("params must be a list")
-
-  # in R interface, allow for '.' instead of '_' in parameter names
-  names(params) <- gsub("\\.", "_", names(params))
-
-  # merge parameters from the params and the dots-expansion
-  dot_params <- list(...)
-  names(dot_params) <- gsub("\\.", "_", names(dot_params))
-  if (length(intersect(names(params),
-                       names(dot_params))) > 0)
-    stop("Same parameters in 'params' and in the call are not allowed. Please check your 'params' list.")
-  params <- c(params, dot_params)
-
-  # providing a parameter multiple times makes sense only for 'eval_metric'
-  name_freqs <- table(names(params))
-  multi_names <- setdiff(names(name_freqs[name_freqs > 1]), 'eval_metric')
-  if (length(multi_names) > 0) {
-    warning("The following parameters were provided multiple times:\n\t",
-            paste(multi_names, collapse = ', '), "\n  Only the last value for each of them will be used.\n")
-    # While xgboost internals would choose the last value for a multiple-times parameter,
-    # enforce it here in R as well (b/c multi-parameters might be used further in R code,
-    # and R takes the 1st value when multiple elements with the same name are present in a list).
-    for (n in multi_names) {
-      del_idx <- which(n == names(params))
-      del_idx <- del_idx[-length(del_idx)]
-      params[[del_idx]] <- NULL
-    }
-  }
-
-  # for multiclass, expect num_class to be set
-  if (typeof(params[['objective']]) == "character" &&
-      substr(NVL(params[['objective']], 'x'), 1, 6) == 'multi:' &&
-      as.numeric(NVL(params[['num_class']], 0)) < 2) {
-        stop("'num_class' > 1 parameter must be set for multiclass classification")
-  }
-
-  # monotone_constraints parser
-
-  if (!is.null(params[['monotone_constraints']]) &&
-      typeof(params[['monotone_constraints']]) != "character") {
-        vec2str = paste(params[['monotone_constraints']], collapse = ',')
-        vec2str = paste0('(', vec2str, ')')
-        params[['monotone_constraints']] = vec2str
-  }
-
-  # interaction constraints parser (convert from list of column indices to string)
-  if (!is.null(params[['interaction_constraints']]) &&
-      typeof(params[['interaction_constraints']]) != "character"){
-    # check input class
-    if (!identical(class(params[['interaction_constraints']]),'list')) stop('interaction_constraints should be class list')
-    if (!all(unique(sapply(params[['interaction_constraints']], class)) %in% c('numeric','integer'))) {
-      stop('interaction_constraints should be a list of numeric/integer vectors')
-    }
-
-    # recast parameter as string
-    interaction_constraints <- sapply(params[['interaction_constraints']], function(x) paste0('[', paste(x, collapse=','), ']'))
-    params[['interaction_constraints']] <- paste0('[', paste(interaction_constraints, collapse=','), ']')
-  }
-  return(params)
-}
-
-
-# Performs some checks related to custom objective function.
-# WARNING: has side-effects and can modify 'params' and 'obj' in its calling frame
-check.custom.obj <- function(env = parent.frame()) {
-  if (!is.null(env$params[['objective']]) && !is.null(env$obj))
-    stop("Setting objectives in 'params' and 'obj' at the same time is not allowed")
-
-  if (!is.null(env$obj) && typeof(env$obj) != 'closure')
-    stop("'obj' must be a function")
-
-  # handle the case when custom objective function was provided through params
-  if (!is.null(env$params[['objective']]) &&
-      typeof(env$params$objective) == 'closure') {
-    env$obj <- env$params$objective
-    env$params$objective <- NULL
-  }
-}
-
-# Performs some checks related to custom evaluation function.
-# WARNING: has side-effects and can modify 'params' and 'feval' in its calling frame
-check.custom.eval <- function(env = parent.frame()) {
-  if (!is.null(env$params[['eval_metric']]) && !is.null(env$feval))
-    stop("Setting evaluation metrics in 'params' and 'feval' at the same time is not allowed")
-
-  if (!is.null(env$feval) && typeof(env$feval) != 'closure')
-    stop("'feval' must be a function")
-
-  # handle a situation when custom eval function was provided through params
-  if (!is.null(env$params[['eval_metric']]) &&
-      typeof(env$params$eval_metric) == 'closure') {
-    env$feval <- env$params$eval_metric
-    env$params$eval_metric <- NULL
-  }
-
-  # require maximize to be set when custom feval and early stopping are used together
-  if (!is.null(env$feval) &&
-      is.null(env$maximize) && (
-        !is.null(env$early_stopping_rounds) ||
-        has.callbacks(env$callbacks, 'cb.early.stop')))
-    stop("Please set 'maximize' to indicate whether the evaluation metric needs to be maximized or not")
-}
-
-
-# Update a booster handle for an iteration with dtrain data
-xgb.iter.update <- function(booster_handle, dtrain, iter, obj = NULL) {
-  if (!identical(class(booster_handle), "xgb.Booster.handle")) {
-    stop("booster_handle must be of xgb.Booster.handle class")
-  }
-  if (!inherits(dtrain, "xgb.DMatrix")) {
-    stop("dtrain must be of xgb.DMatrix class")
-  }
-
-  if (is.null(obj)) {
-    .Call(XGBoosterUpdateOneIter_R, booster_handle, as.integer(iter), dtrain)
-  } else {
-    pred <- predict(booster_handle, dtrain, outputmargin = TRUE, training = TRUE)
-    gpair <- obj(pred, dtrain)
-    .Call(XGBoosterBoostOneIter_R, booster_handle, dtrain, gpair$grad, gpair$hess)
-  }
-  return(TRUE)
-}
-
-
-# Evaluate one iteration.
-# Returns a named vector of evaluation metrics
-# with the names in a 'datasetname-metricname' format.
-xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
-  if (!identical(class(booster_handle), "xgb.Booster.handle"))
-    stop("class of booster_handle must be xgb.Booster.handle")
-
-  if (length(watchlist) == 0)
-    return(NULL)
-
-  evnames <- names(watchlist)
-  if (is.null(feval)) {
-    msg <- .Call(XGBoosterEvalOneIter_R, booster_handle, as.integer(iter), watchlist, as.list(evnames))
-    msg <- stri_split_regex(msg, '(\\s+|:|\\s+)')[[1]][-1]
-    res <- as.numeric(msg[c(FALSE,TRUE)]) # even indices are the values
-    names(res) <- msg[c(TRUE,FALSE)]      # odds are the names
-  } else {
-    res <- sapply(seq_along(watchlist), function(j) {
-      w <- watchlist[[j]]
-      preds <- predict(booster_handle, w) # predict using all trees
-      eval_res <- feval(preds, w)
-      out <- eval_res$value
-      names(out) <- paste0(evnames[j], "-", eval_res$metric)
-      out
-    })
-  }
-  return(res)
-}
-
-
-#
-# Helper functions for cross validation ---------------------------------------
-#
-
-# Generates random (stratified if needed) CV folds
-generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
-
-  # cannot do it for rank
-  if (exists('objective', where = params) &&
-      is.character(params$objective) &&
-      strtrim(params$objective, 5) == 'rank:') {
-    stop("\n\tAutomatic generation of CV-folds is not implemented for ranking!\n",
-         "\tConsider providing pre-computed CV-folds through the 'folds=' parameter.\n")
-  }
-  # shuffle
-  rnd_idx <- sample.int(nrows)
-  if (stratified &&
-      length(label) == length(rnd_idx)) {
-    y <- label[rnd_idx]
-    # WARNING: some heuristic logic is employed to identify classification setting!
-    #  - For classification, need to convert y labels to factor before making the folds,
-    #    and then do stratification by factor levels.
-    #  - For regression, leave y numeric and do stratification by quantiles.
-    if (exists('objective', where = params) &&
-        is.character(params$objective)) {
-      # If 'objective' provided in params, assume that y is a classification label
-      # unless objective is reg:squarederror
-      if (params$objective != 'reg:squarederror')
-        y <- factor(y)
-    } else {
-      # If no 'objective' given in params, it means that user either wants to
-      # use the default 'reg:squarederror' objective or has provided a custom
-      # obj function.  Here, assume classification setting when y has 5 or less
-      # unique values:
-      if (length(unique(y)) <= 5)
-        y <- factor(y)
-    }
-    folds <- xgb.createFolds(y, nfold)
-  } else {
-    # make simple non-stratified folds
-    kstep <- length(rnd_idx) %/% nfold
-    folds <- list()
-    for (i in seq_len(nfold - 1)) {
-      folds[[i]] <- rnd_idx[seq_len(kstep)]
-      rnd_idx <- rnd_idx[-seq_len(kstep)]
-    }
-    folds[[nfold]] <- rnd_idx
-  }
-  return(folds)
-}
-
-# Creates CV folds stratified by the values of y.
-# It was borrowed from caret::createFolds and simplified
-# by always returning an unnamed list of fold indices.
-xgb.createFolds <- function(y, k = 10)
-{
-  if (is.numeric(y)) {
-    ## Group the numeric data based on their magnitudes
-    ## and sample within those groups.
-
-    ## When the number of samples is low, we may have
-    ## issues further slicing the numeric data into
-    ## groups. The number of groups will depend on the
-    ## ratio of the number of folds to the sample size.
-    ## At most, we will use quantiles. If the sample
-    ## is too small, we just do regular unstratified
-    ## CV
-    cuts <- floor(length(y) / k)
-    if (cuts < 2) cuts <- 2
-    if (cuts > 5) cuts <- 5
-    y <- cut(y,
-             unique(stats::quantile(y, probs = seq(0, 1, length = cuts))),
-             include.lowest = TRUE)
-  }
-
-  if (k < length(y)) {
-    ## reset levels so that the possible levels and
-    ## the levels in the vector are the same
-    y <- factor(as.character(y))
-    numInClass <- table(y)
-    foldVector <- vector(mode = "integer", length(y))
-
-    ## For each class, balance the fold allocation as far
-    ## as possible, then resample the remainder.
-    ## The final assignment of folds is also randomized.
-    for (i in seq_along(numInClass)) {
-      ## create a vector of integers from 1:k as many times as possible without
-      ## going over the number of samples in the class. Note that if the number
-      ## of samples in a class is less than k, nothing is producd here.
-      seqVector <- rep(seq_len(k), numInClass[i] %/% k)
-      ## add enough random integers to get  length(seqVector) == numInClass[i]
-      if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
-      ## shuffle the integers for fold assignment and assign to this classes's data
-      ## seqVector[sample.int(length(seqVector))] is used to handle length(seqVector) == 1
-      foldVector[y == dimnames(numInClass)$y[i]] <- seqVector[sample.int(length(seqVector))]
-    }
-  } else {
-    foldVector <- seq(along = y)
-  }
-
-  out <- split(seq(along = y), foldVector)
-  names(out) <- NULL
-  out
-}
-
-
-#
-# Deprectaion notice utilities ------------------------------------------------
-#
-
-#' Deprecation notices.
-#'
-#' At this time, some of the parameter names were changed in order to make the code style more uniform.
-#' The deprecated parameters would be removed in the next release.
-#'
-#' To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
-#'
-#' A deprecation warning is shown when any of the deprecated parameters is used in a call.
-#' An additional warning is shown when there was a partial match to a deprecated parameter
-#' (as R is able to partially match parameter names).
-#'
-#' @name xgboost-deprecated
-NULL
-
-# Lookup table for the deprecated parameters bookkeeping
-depr_par_lut <- matrix(c(
-  'print.every.n', 'print_every_n',
-  'early.stop.round', 'early_stopping_rounds',
-  'training.data', 'data',
-  'with.stats', 'with_stats',
-  'numberOfClusters', 'n_clusters',
-  'features.keep', 'features_keep',
-  'plot.height','plot_height',
-  'plot.width','plot_width',
-  'n_first_tree', 'trees',
-  'dummy', 'DUMMY'
-), ncol = 2, byrow = TRUE)
-colnames(depr_par_lut) <- c('old', 'new')
-
-# Checks the dot-parameters for deprecated names
-# (including partial matching), gives a deprecation warning,
-# and sets new parameters to the old parameters' values within its parent frame.
-# WARNING: has side-effects
-check.deprecation <- function(..., env = parent.frame()) {
-  pars <- list(...)
-  # exact and partial matches
-  all_match <- pmatch(names(pars), depr_par_lut[,1])
-  # indices of matched pars' names
-  idx_pars <- which(!is.na(all_match))
-  if (length(idx_pars) == 0) return()
-  # indices of matched LUT rows
-  idx_lut <- all_match[idx_pars]
-  # which of idx_lut were the exact matches?
-  ex_match <- depr_par_lut[idx_lut,1] %in% names(pars)
-  for (i in seq_along(idx_pars)) {
-    pars_par <- names(pars)[idx_pars[i]]
-    old_par <- depr_par_lut[idx_lut[i], 1]
-    new_par <- depr_par_lut[idx_lut[i], 2]
-    if (!ex_match[i]) {
-      warning("'", pars_par, "' was partially matched to '", old_par,"'")
-    }
-    .Deprecated(new_par, old = old_par, package = 'xgboost')
-    if (new_par != 'NULL') {
-      eval(parse(text = paste(new_par, '<-', pars[[pars_par]])), envir = env)
-    }
-  }
-}
diff --git a/ml-xgboost/R-package/R/xgb.Booster.R b/ml-xgboost/R-package/R/xgb.Booster.R
deleted file mode 100644
index dcc4469..0000000
--- a/ml-xgboost/R-package/R/xgb.Booster.R
+++ /dev/null
@@ -1,711 +0,0 @@
-# Construct an internal xgboost Booster and return a handle to it.
-# internal utility function
-xgb.Booster.handle <- function(params = list(), cachelist = list(), modelfile = NULL) {
-  if (typeof(cachelist) != "list" ||
-      !all(vapply(cachelist, inherits, logical(1), what = 'xgb.DMatrix'))) {
-    stop("cachelist must be a list of xgb.DMatrix objects")
-  }
-  ## Load existing model, dispatch for on disk model file and in memory buffer
-  if (!is.null(modelfile)) {
-    if (typeof(modelfile) == "character") {
-      ## A filename
-      handle <- .Call(XGBoosterCreate_R, cachelist)
-      .Call(XGBoosterLoadModel_R, handle, modelfile[1])
-      class(handle) <- "xgb.Booster.handle"
-      if (length(params) > 0) {
-        xgb.parameters(handle) <- params
-      }
-      return(handle)
-    } else if (typeof(modelfile) == "raw") {
-      ## A memory buffer
-      bst <- xgb.unserialize(modelfile)
-      xgb.parameters(bst) <- params
-      return (bst)
-    } else if (inherits(modelfile, "xgb.Booster")) {
-      ## A booster object
-      bst <- xgb.Booster.complete(modelfile, saveraw = TRUE)
-      bst <- xgb.unserialize(bst$raw)
-      xgb.parameters(bst) <- params
-      return (bst)
-    } else {
-      stop("modelfile must be either character filename, or raw booster dump, or xgb.Booster object")
-    }
-  }
-  ## Create new model
-  handle <- .Call(XGBoosterCreate_R, cachelist)
-  class(handle) <- "xgb.Booster.handle"
-  if (length(params) > 0) {
-    xgb.parameters(handle) <- params
-  }
-  return(handle)
-}
-
-# Convert xgb.Booster.handle to xgb.Booster
-# internal utility function
-xgb.handleToBooster <- function(handle, raw = NULL) {
-  bst <- list(handle = handle, raw = raw)
-  class(bst) <- "xgb.Booster"
-  return(bst)
-}
-
-# Check whether xgb.Booster.handle is null
-# internal utility function
-is.null.handle <- function(handle) {
-  if (is.null(handle)) return(TRUE)
-
-  if (!identical(class(handle), "xgb.Booster.handle"))
-    stop("argument type must be xgb.Booster.handle")
-
-  if (.Call(XGCheckNullPtr_R, handle))
-    return(TRUE)
-
-  return(FALSE)
-}
-
-# Return a verified to be valid handle out of either xgb.Booster.handle or xgb.Booster
-# internal utility function
-xgb.get.handle <- function(object) {
-  if (inherits(object, "xgb.Booster")) {
-    handle <- object$handle
-  } else if (inherits(object, "xgb.Booster.handle")) {
-    handle <- object
-  } else {
-    stop("argument must be of either xgb.Booster or xgb.Booster.handle class")
-  }
-  if (is.null.handle(handle)) {
-    stop("invalid xgb.Booster.handle")
-  }
-  handle
-}
-
-#' Restore missing parts of an incomplete xgb.Booster object.
-#'
-#' It attempts to complete an \code{xgb.Booster} object by restoring either its missing
-#' raw model memory dump (when it has no \code{raw} data but its \code{xgb.Booster.handle} is valid)
-#' or its missing internal handle (when its \code{xgb.Booster.handle} is not valid
-#' but it has a raw Booster memory dump).
-#'
-#' @param object object of class \code{xgb.Booster}
-#' @param saveraw a flag indicating whether to append \code{raw} Booster memory dump data
-#'                when it doesn't already exist.
-#'
-#' @details
-#'
-#' While this method is primarily for internal use, it might be useful in some practical situations.
-#'
-#' E.g., when an \code{xgb.Booster} model is saved as an R object and then is loaded as an R object,
-#' its handle (pointer) to an internal xgboost model would be invalid. The majority of xgboost methods
-#' should still work for such a model object since those methods would be using
-#' \code{xgb.Booster.complete} internally. However, one might find it to be more efficient to call the
-#' \code{xgb.Booster.complete} function explicitly once after loading a model as an R-object.
-#' That would prevent further repeated implicit reconstruction of an internal booster model.
-#'
-#' @return
-#' An object of \code{xgb.Booster} class.
-#'
-#' @examples
-#'
-#' data(agaricus.train, package='xgboost')
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#' saveRDS(bst, "xgb.model.rds")
-#'
-#' bst1 <- readRDS("xgb.model.rds")
-#' if (file.exists("xgb.model.rds")) file.remove("xgb.model.rds")
-#' # the handle is invalid:
-#' print(bst1$handle)
-#'
-#' bst1 <- xgb.Booster.complete(bst1)
-#' # now the handle points to a valid internal booster model:
-#' print(bst1$handle)
-#'
-#' @export
-xgb.Booster.complete <- function(object, saveraw = TRUE) {
-  if (!inherits(object, "xgb.Booster"))
-    stop("argument type must be xgb.Booster")
-
-  if (is.null.handle(object$handle)) {
-    object$handle <- xgb.Booster.handle(modelfile = object$raw)
-  } else {
-    if (is.null(object$raw) && saveraw) {
-      object$raw <- xgb.serialize(object$handle)
-    }
-  }
-
-  attrs <- xgb.attributes(object)
-  if (!is.null(attrs$best_ntreelimit)) {
-    object$best_ntreelimit <- as.integer(attrs$best_ntreelimit)
-  }
-  if (!is.null(attrs$best_iteration)) {
-    ## Convert from 0 based back to 1 based.
-    object$best_iteration <- as.integer(attrs$best_iteration) + 1
-  }
-  if (!is.null(attrs$best_score)) {
-    object$best_score <- as.numeric(attrs$best_score)
-  }
-  if (!is.null(attrs$best_msg)) {
-    object$best_msg <- attrs$best_msg
-  }
-  if (!is.null(attrs$niter)) {
-    object$niter <- as.integer(attrs$niter)
-  }
-
-  return(object)
-}
-
-#' Predict method for eXtreme Gradient Boosting model
-#'
-#' Predicted values based on either xgboost model or model handle object.
-#'
-#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}
-#' @param newdata takes \code{matrix}, \code{dgCMatrix}, local data file or \code{xgb.DMatrix}.
-#' @param missing Missing is only used when input is dense matrix. Pick a float value that represents
-#'        missing values in data (e.g., sometimes 0 or some other extreme value is used).
-#' @param outputmargin whether the prediction should be returned in the for of original untransformed
-#'        sum of predictions from boosting iterations' results. E.g., setting \code{outputmargin=TRUE} for
-#'        logistic regression would result in predictions for log-odds instead of probabilities.
-#' @param ntreelimit limit the number of model's trees or boosting iterations used in prediction (see Details).
-#'        It will use all the trees by default (\code{NULL} value).
-#' @param predleaf whether predict leaf index.
-#' @param predcontrib whether to return feature contributions to individual predictions (see Details).
-#' @param approxcontrib whether to use a fast approximation for feature contributions (see Details).
-#' @param predinteraction whether to return contributions of feature interactions to individual predictions (see Details).
-#' @param reshape whether to reshape the vector of predictions to a matrix form when there are several
-#'        prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
-#'        or predinteraction flags is TRUE.
-#' @param training whether is the prediction result used for training.  For dart booster,
-#'        training predicting will perform dropout.
-#' @param ... Parameters passed to \code{predict.xgb.Booster}
-#'
-#' @details
-#' Note that \code{ntreelimit} is not necessarily equal to the number of boosting iterations
-#' and it is not necessarily equal to the number of trees in a model.
-#' E.g., in a random forest-like model, \code{ntreelimit} would limit the number of trees.
-#' But for multiclass classification, while there are multiple trees per iteration,
-#' \code{ntreelimit} limits the number of boosting iterations.
-#'
-#' Also note that \code{ntreelimit} would currently do nothing for predictions from gblinear,
-#' since gblinear doesn't keep its boosting history.
-#'
-#' One possible practical applications of the \code{predleaf} option is to use the model
-#' as a generator of new features which capture non-linearity and interactions,
-#' e.g., as implemented in \code{\link{xgb.create.features}}.
-#'
-#' Setting \code{predcontrib = TRUE} allows to calculate contributions of each feature to
-#' individual predictions. For "gblinear" booster, feature contributions are simply linear terms
-#' (feature_beta * feature_value). For "gbtree" booster, feature contributions are SHAP
-#' values (Lundberg 2017) that sum to the difference between the expected output
-#' of the model and the current prediction (where the hessian weights are used to compute the expectations).
-#' Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
-#' in \url{http://blog.datadive.net/interpreting-random-forests/}.
-#'
-#' With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
-#' are computed. Note that this operation might be rather expensive in terms of compute and memory.
-#' Since it quadratically depends on the number of features, it is recommended to perform selection
-#' of the most important features first. See below about the format of the returned results.
-#'
-#' @return
-#' For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
-#' For multiclass classification, either a \code{num_class * nrows(newdata)} vector or
-#' a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on
-#' the \code{reshape} value.
-#'
-#' When \code{predleaf = TRUE}, the output is a matrix object with the
-#' number of columns corresponding to the number of trees.
-#'
-#' When \code{predcontrib = TRUE} and it is not a multiclass setting, the output is a matrix object with
-#' \code{num_features + 1} columns. The last "+ 1" column in a matrix corresponds to bias.
-#' For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-#' such a matrix. The contribution values are on the scale of untransformed margin
-#' (e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
-#'
-#' When \code{predinteraction = TRUE} and it is not a multiclass setting, the output is a 3d array with
-#' dimensions \code{c(nrow, num_features + 1, num_features + 1)}. The off-diagonal (in the last two dimensions)
-#' elements represent different features interaction contributions. The array is symmetric WRT the last
-#' two dimensions. The "+ 1" columns corresponds to bias. Summing this array along the last dimension should
-#' produce practically the same result as predict with \code{predcontrib = TRUE}.
-#' For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-#' such an array.
-#'
-#' @seealso
-#' \code{\link{xgb.train}}.
-#'
-#' @references
-#'
-#' Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
-#'
-#' Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
-#'
-#' @examples
-#' ## binary classification:
-#'
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#'
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
-#' # use all trees by default
-#' pred <- predict(bst, test$data)
-#' # use only the 1st tree
-#' pred1 <- predict(bst, test$data, ntreelimit = 1)
-#'
-#' # Predicting tree leafs:
-#' # the result is an nsamples X ntrees matrix
-#' pred_leaf <- predict(bst, test$data, predleaf = TRUE)
-#' str(pred_leaf)
-#'
-#' # Predicting feature contributions to predictions:
-#' # the result is an nsamples X (nfeatures + 1) matrix
-#' pred_contr <- predict(bst, test$data, predcontrib = TRUE)
-#' str(pred_contr)
-#' # verify that contributions' sums are equal to log-odds of predictions (up to float precision):
-#' summary(rowSums(pred_contr) - qlogis(pred))
-#' # for the 1st record, let's inspect its features that had non-zero contribution to prediction:
-#' contr1 <- pred_contr[1,]
-#' contr1 <- contr1[-length(contr1)]    # drop BIAS
-#' contr1 <- contr1[contr1 != 0]        # drop non-contributing features
-#' contr1 <- contr1[order(abs(contr1))] # order by contribution magnitude
-#' old_mar <- par("mar")
-#' par(mar = old_mar + c(0,7,0,0))
-#' barplot(contr1, horiz = TRUE, las = 2, xlab = "contribution to prediction in log-odds")
-#' par(mar = old_mar)
-#'
-#'
-#' ## multiclass classification in iris dataset:
-#'
-#' lb <- as.numeric(iris$Species) - 1
-#' num_class <- 3
-#' set.seed(11)
-#' bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-#'                max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
-#'                objective = "multi:softprob", num_class = num_class)
-#' # predict for softmax returns num_class probability numbers per case:
-#' pred <- predict(bst, as.matrix(iris[, -5]))
-#' str(pred)
-#' # reshape it to a num_class-columns matrix
-#' pred <- matrix(pred, ncol=num_class, byrow=TRUE)
-#' # convert the probabilities to softmax labels
-#' pred_labels <- max.col(pred) - 1
-#' # the following should result in the same error as seen in the last iteration
-#' sum(pred_labels != lb)/length(lb)
-#'
-#' # compare that to the predictions from softmax:
-#' set.seed(11)
-#' bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-#'                max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
-#'                objective = "multi:softmax", num_class = num_class)
-#' pred <- predict(bst, as.matrix(iris[, -5]))
-#' str(pred)
-#' all.equal(pred, pred_labels)
-#' # prediction from using only 5 iterations should result
-#' # in the same error as seen in iteration 5:
-#' pred5 <- predict(bst, as.matrix(iris[, -5]), ntreelimit=5)
-#' sum(pred5 != lb)/length(lb)
-#'
-#'
-#' ## random forest-like model of 25 trees for binary classification:
-#'
-#' set.seed(11)
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 5,
-#'                nthread = 2, nrounds = 1, objective = "binary:logistic",
-#'                num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1)
-#' # Inspect the prediction error vs number of trees:
-#' lb <- test$label
-#' dtest <- xgb.DMatrix(test$data, label=lb)
-#' err <- sapply(1:25, function(n) {
-#'   pred <- predict(bst, dtest, ntreelimit=n)
-#'   sum((pred > 0.5) != lb)/length(lb)
-#' })
-#' plot(err, type='l', ylim=c(0,0.1), xlab='#trees')
-#'
-#' @rdname predict.xgb.Booster
-#' @export
-predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
-                                predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
-                                reshape = FALSE, training = FALSE, ...) {
-
-  object <- xgb.Booster.complete(object, saveraw = FALSE)
-  if (!inherits(newdata, "xgb.DMatrix"))
-    newdata <- xgb.DMatrix(newdata, missing = missing)
-  if (!is.null(object[["feature_names"]]) &&
-      !is.null(colnames(newdata)) &&
-      !identical(object[["feature_names"]], colnames(newdata)))
-    stop("Feature names stored in `object` and `newdata` are different!")
-  if (is.null(ntreelimit))
-    ntreelimit <- NVL(object$best_ntreelimit, 0)
-  if (NVL(object$params[['booster']], '') == 'gblinear')
-    ntreelimit <- 0
-  if (ntreelimit < 0)
-    stop("ntreelimit cannot be negative")
-
-  option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) +
-    8L * as.logical(approxcontrib) + 16L * as.logical(predinteraction)
-
-  ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1],
-               as.integer(ntreelimit), as.integer(training))
-
-  n_ret <- length(ret)
-  n_row <- nrow(newdata)
-  npred_per_case <- n_ret / n_row
-
-  if (n_ret %% n_row != 0)
-    stop("prediction length ", n_ret, " is not multiple of nrows(newdata) ", n_row)
-
-  if (predleaf) {
-    ret <- if (n_ret == n_row) {
-      matrix(ret, ncol = 1)
-    } else {
-      matrix(ret, nrow = n_row, byrow = TRUE)
-    }
-  } else if (predcontrib) {
-    n_col1 <- ncol(newdata) + 1
-    n_group <- npred_per_case / n_col1
-    cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
-    ret <- if (n_ret == n_row) {
-      matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
-    } else if (n_group == 1) {
-      matrix(ret, nrow = n_row, byrow = TRUE, dimnames = list(NULL, cnames))
-    } else {
-      arr <- array(ret, c(n_col1, n_group, n_row),
-                   dimnames = list(cnames, NULL, NULL)) %>% aperm(c(2,3,1)) # [group, row, col]
-      lapply(seq_len(n_group), function(g) arr[g,,])
-    }
-  } else if (predinteraction) {
-    n_col1 <- ncol(newdata) + 1
-    n_group <- npred_per_case / n_col1^2
-    cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
-    ret <- if (n_ret == n_row) {
-      matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
-    } else if (n_group == 1) {
-      array(ret, c(n_col1, n_col1, n_row), dimnames = list(cnames, cnames, NULL)) %>% aperm(c(3,1,2))
-    } else {
-      arr <- array(ret, c(n_col1, n_col1, n_group, n_row),
-                   dimnames = list(cnames, cnames, NULL, NULL)) %>% aperm(c(3,4,1,2)) # [group, row, col1, col2]
-      lapply(seq_len(n_group), function(g) arr[g,,,])
-    }
-  } else if (reshape && npred_per_case > 1) {
-    ret <- matrix(ret, nrow = n_row, byrow = TRUE)
-  }
-  return(ret)
-}
-
-#' @rdname predict.xgb.Booster
-#' @export
-predict.xgb.Booster.handle <- function(object, ...) {
-
-  bst <- xgb.handleToBooster(object)
-
-  ret <- predict(bst, ...)
-  return(ret)
-}
-
-
-#' Accessors for serializable attributes of a model.
-#'
-#' These methods allow to manipulate the key-value attribute strings of an xgboost model.
-#'
-#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.
-#' @param name a non-empty character string specifying which attribute is to be accessed.
-#' @param value a value of an attribute for \code{xgb.attr<-}; for \code{xgb.attributes<-}
-#'        it's a list (or an object coercible to a list) with the names of attributes to set
-#'        and the elements corresponding to attribute values.
-#'        Non-character values are converted to character.
-#'        When attribute value is not a scalar, only the first index is used.
-#'        Use \code{NULL} to remove an attribute.
-#'
-#' @details
-#' The primary purpose of xgboost model attributes is to store some meta-data about the model.
-#' Note that they are a separate concept from the object attributes in R.
-#' Specifically, they refer to key-value strings that can be attached to an xgboost model,
-#' stored together with the model's binary representation, and accessed later
-#' (from R or any other interface).
-#' In contrast, any R-attribute assigned to an R-object of \code{xgb.Booster} class
-#' would not be saved by \code{xgb.save} because an xgboost model is an external memory object
-#' and its serialization is handled externally.
-#' Also, setting an attribute that has the same name as one of xgboost's parameters wouldn't
-#' change the value of that parameter for a model.
-#' Use \code{\link{xgb.parameters<-}} to set or change model parameters.
-#'
-#' The attribute setters would usually work more efficiently for \code{xgb.Booster.handle}
-#' than for \code{xgb.Booster}, since only just a handle (pointer) would need to be copied.
-#' That would only matter if attributes need to be set many times.
-#' Note, however, that when feeding a handle of an \code{xgb.Booster} object to the attribute setters,
-#' the raw model cache of an \code{xgb.Booster} object would not be automatically updated,
-#' and it would be user's responsibility to call \code{xgb.serialize} to update it.
-#'
-#' The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
-#' but it doesn't delete the other existing attributes.
-#'
-#' @return
-#' \code{xgb.attr} returns either a string value of an attribute
-#' or \code{NULL} if an attribute wasn't stored in a model.
-#'
-#' \code{xgb.attributes} returns a list of all attribute stored in a model
-#' or \code{NULL} if a model has no stored attributes.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#'
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#'
-#' xgb.attr(bst, "my_attribute") <- "my attribute value"
-#' print(xgb.attr(bst, "my_attribute"))
-#' xgb.attributes(bst) <- list(a = 123, b = "abc")
-#'
-#' xgb.save(bst, 'xgb.model')
-#' bst1 <- xgb.load('xgb.model')
-#' if (file.exists('xgb.model')) file.remove('xgb.model')
-#' print(xgb.attr(bst1, "my_attribute"))
-#' print(xgb.attributes(bst1))
-#'
-#' # deletion:
-#' xgb.attr(bst1, "my_attribute") <- NULL
-#' print(xgb.attributes(bst1))
-#' xgb.attributes(bst1) <- list(a = NULL, b = NULL)
-#' print(xgb.attributes(bst1))
-#'
-#' @rdname xgb.attr
-#' @export
-xgb.attr <- function(object, name) {
-  if (is.null(name) || nchar(as.character(name[1])) == 0) stop("invalid attribute name")
-  handle <- xgb.get.handle(object)
-  .Call(XGBoosterGetAttr_R, handle, as.character(name[1]))
-}
-
-#' @rdname xgb.attr
-#' @export
-`xgb.attr<-` <- function(object, name, value) {
-  if (is.null(name) || nchar(as.character(name[1])) == 0) stop("invalid attribute name")
-  handle <- xgb.get.handle(object)
-  if (!is.null(value)) {
-    # Coerce the elements to be scalar strings.
-    # Q: should we warn user about non-scalar elements?
-    if (is.numeric(value[1])) {
-      value <- format(value[1], digits = 17)
-    } else {
-      value <- as.character(value[1])
-    }
-  }
-  .Call(XGBoosterSetAttr_R, handle, as.character(name[1]), value)
-  if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.serialize(object$handle)
-  }
-  object
-}
-
-#' @rdname xgb.attr
-#' @export
-xgb.attributes <- function(object) {
-  handle <- xgb.get.handle(object)
-  attr_names <- .Call(XGBoosterGetAttrNames_R, handle)
-  if (is.null(attr_names)) return(NULL)
-  res <- lapply(attr_names, function(x) {
-    .Call(XGBoosterGetAttr_R, handle, x)
-  })
-  names(res) <- attr_names
-  res
-}
-
-#' @rdname xgb.attr
-#' @export
-`xgb.attributes<-` <- function(object, value) {
-  a <- as.list(value)
-  if (is.null(names(a)) || any(nchar(names(a)) == 0)) {
-    stop("attribute names cannot be empty strings")
-  }
-  # Coerce the elements to be scalar strings.
-  # Q: should we warn a user about non-scalar elements?
-  a <- lapply(a, function(x) {
-    if (is.null(x)) return(NULL)
-    if (is.numeric(x[1])) {
-      format(x[1], digits = 17)
-    } else {
-      as.character(x[1])
-    }
-  })
-  handle <- xgb.get.handle(object)
-  for (i in seq_along(a)) {
-    .Call(XGBoosterSetAttr_R, handle, names(a[i]), a[[i]])
-  }
-  if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.serialize(object$handle)
-  }
-  object
-}
-
-#' Accessors for model parameters as JSON string.
-#'
-#' @param object Object of class \code{xgb.Booster}
-#' @param value A JSON string.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#'
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#' config <- xgb.config(bst)
-#'
-#' @rdname xgb.config
-#' @export
-xgb.config <- function(object) {
-  handle <- xgb.get.handle(object)
-  .Call(XGBoosterSaveJsonConfig_R, handle);
-}
-
-#' @rdname xgb.config
-#' @export
-`xgb.config<-` <- function(object, value) {
-  handle <- xgb.get.handle(object)
-  .Call(XGBoosterLoadJsonConfig_R, handle, value)
-  object$raw <- NULL  # force renew the raw buffer
-  object <- xgb.Booster.complete(object)
-  object
-}
-
-#' Accessors for model parameters.
-#'
-#' Only the setter for xgboost parameters is currently implemented.
-#'
-#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.
-#' @param value a list (or an object coercible to a list) with the names of parameters to set
-#'        and the elements corresponding to parameter values.
-#'
-#' @details
-#' Note that the setter would usually work more efficiently for \code{xgb.Booster.handle}
-#' than for \code{xgb.Booster}, since only just a handle would need to be copied.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#'
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#'
-#' xgb.parameters(bst) <- list(eta = 0.1)
-#'
-#' @rdname xgb.parameters
-#' @export
-`xgb.parameters<-` <- function(object, value) {
-  if (length(value) == 0) return(object)
-  p <- as.list(value)
-  if (is.null(names(p)) || any(nchar(names(p)) == 0)) {
-    stop("parameter names cannot be empty strings")
-  }
-  names(p) <- gsub("\\.", "_", names(p))
-  p <- lapply(p, function(x) as.character(x)[1])
-  handle <- xgb.get.handle(object)
-  for (i in seq_along(p)) {
-    .Call(XGBoosterSetParam_R, handle, names(p[i]), p[[i]])
-  }
-  if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.serialize(object$handle)
-  }
-  object
-}
-
-# Extract the number of trees in a model.
-# TODO: either add a getter to C-interface, or simply set an 'ntree' attribute after each iteration.
-# internal utility function
-xgb.ntree <- function(bst) {
-  length(grep('^booster', xgb.dump(bst)))
-}
-
-
-#' Print xgb.Booster
-#'
-#' Print information about xgb.Booster.
-#'
-#' @param x an xgb.Booster object
-#' @param verbose whether to print detailed data (e.g., attribute values)
-#' @param ... not currently used
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#' attr(bst, 'myattr') <- 'memo'
-#'
-#' print(bst)
-#' print(bst, verbose=TRUE)
-#'
-#' @method print xgb.Booster
-#' @export
-print.xgb.Booster <- function(x, verbose = FALSE, ...) {
-  cat('##### xgb.Booster\n')
-
-  valid_handle <- !is.null.handle(x$handle)
-  if (!valid_handle)
-    cat("Handle is invalid! Suggest using xgb.Booster.complete\n")
-
-  cat('raw: ')
-  if (!is.null(x$raw)) {
-    cat(format(object.size(x$raw), units = "auto"), '\n')
-  } else {
-    cat('NULL\n')
-  }
-  if (!is.null(x$call)) {
-    cat('call:\n  ')
-    print(x$call)
-  }
-
-  if (!is.null(x$params)) {
-    cat('params (as set within xgb.train):\n')
-    cat( '  ',
-         paste(names(x$params),
-               paste0('"', unlist(x$params), '"'),
-               sep = ' = ', collapse = ', '), '\n', sep = '')
-  }
-  # TODO: need an interface to access all the xgboosts parameters
-
-  attrs <- character(0)
-  if (valid_handle)
-    attrs <- xgb.attributes(x)
-  if (length(attrs) > 0) {
-    cat('xgb.attributes:\n')
-    if (verbose) {
-      cat( paste(paste0('  ',names(attrs)),
-                 paste0('"', unlist(attrs), '"'),
-                 sep = ' = ', collapse = '\n'), '\n', sep = '')
-    } else {
-      cat('  ', paste(names(attrs), collapse = ', '), '\n', sep = '')
-    }
-  }
-
-  if (!is.null(x$callbacks) && length(x$callbacks) > 0) {
-    cat('callbacks:\n')
-    lapply(callback.calls(x$callbacks), function(x) {
-      cat('  ')
-      print(x)
-    })
-  }
-
-  if (!is.null(x$feature_names))
-    cat('# of features:', length(x$feature_names), '\n')
-
-  cat('niter: ', x$niter, '\n', sep = '')
-  # TODO: uncomment when faster xgb.ntree is implemented
-  #cat('ntree: ', xgb.ntree(x), '\n', sep='')
-
-  for (n in setdiff(names(x), c('handle', 'raw', 'call', 'params', 'callbacks',
-                                'evaluation_log','niter','feature_names'))) {
-    if (is.atomic(x[[n]])) {
-      cat(n, ':', x[[n]], '\n', sep = ' ')
-    } else {
-      cat(n, ':\n\t', sep = ' ')
-      print(x[[n]])
-    }
-  }
-
-  if (!is.null(x$evaluation_log)) {
-    cat('evaluation_log:\n')
-    print(x$evaluation_log, row.names = FALSE, topn = 2)
-  }
-
-  invisible(x)
-}
diff --git a/ml-xgboost/R-package/R/xgb.DMatrix.R b/ml-xgboost/R-package/R/xgb.DMatrix.R
deleted file mode 100644
index 4201a83..0000000
--- a/ml-xgboost/R-package/R/xgb.DMatrix.R
+++ /dev/null
@@ -1,380 +0,0 @@
-#' Construct xgb.DMatrix object
-#'
-#' Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file.
-#' Supported input file formats are either a libsvm text file or a binary file that was created previously by
-#' \code{\link{xgb.DMatrix.save}}).
-#'
-#' @param data a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character
-#'        string representing a filename.
-#' @param info a named list of additional information to store in the \code{xgb.DMatrix} object.
-#'        See \code{\link{setinfo}} for the specific allowed kinds of
-#' @param missing a float value to represents missing values in data (used only when input is a dense matrix).
-#'        It is useful when a 0 or some other extreme value represents missing values in data.
-#' @param silent whether to suppress printing an informational message after loading from a file.
-#' @param ... the \code{info} data could be passed directly as parameters, without creating an \code{info} list.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
-#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
-#' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
-#' @export
-xgb.DMatrix <- function(data, info = list(), missing = NA, silent = FALSE, ...) {
-  cnames <- NULL
-  if (typeof(data) == "character") {
-    if (length(data) > 1)
-      stop("'data' has class 'character' and length ", length(data),
-           ".\n  'data' accepts either a numeric matrix or a single filename.")
-    handle <- .Call(XGDMatrixCreateFromFile_R, data, as.integer(silent))
-  } else if (is.matrix(data)) {
-    handle <- .Call(XGDMatrixCreateFromMat_R, data, missing)
-    cnames <- colnames(data)
-  } else if (inherits(data, "dgCMatrix")) {
-    handle <- .Call(XGDMatrixCreateFromCSC_R, data@p, data@i, data@x, nrow(data))
-    cnames <- colnames(data)
-  } else {
-    stop("xgb.DMatrix does not support construction from ", typeof(data))
-  }
-  dmat <- handle
-  attributes(dmat) <- list(.Dimnames = list(NULL, cnames), class = "xgb.DMatrix")
-
-  info <- append(info, list(...))
-  for (i in seq_along(info)) {
-    p <- info[i]
-    setinfo(dmat, names(p), p[[1]])
-  }
-  return(dmat)
-}
-
-
-# get dmatrix from data, label
-# internal helper method
-xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
-  if (inherits(data, "dgCMatrix") || is.matrix(data)) {
-    if (is.null(label)) {
-      stop("label must be provided when data is a matrix")
-    }
-    dtrain <- xgb.DMatrix(data, label = label, missing = missing)
-    if (!is.null(weight)){
-      setinfo(dtrain, "weight", weight)
-    }
-  } else {
-    if (!is.null(label)) {
-      warning("xgboost: label will be ignored.")
-    }
-    if (is.character(data)) {
-      dtrain <- xgb.DMatrix(data[1])
-    } else if (inherits(data, "xgb.DMatrix")) {
-      dtrain <- data
-    } else if (inherits(data, "data.frame")) {
-      stop("xgboost doesn't support data.frame as input. Convert it to matrix first.")
-    } else {
-      stop("xgboost: invalid input data")
-    }
-  }
-  return (dtrain)
-}
-
-
-#' Dimensions of xgb.DMatrix
-#'
-#' Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
-#' @param x Object of class \code{xgb.DMatrix}
-#'
-#' @details
-#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
-#' be directly used with an \code{xgb.DMatrix} object.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
-#' stopifnot(nrow(dtrain) == nrow(train$data))
-#' stopifnot(ncol(dtrain) == ncol(train$data))
-#' stopifnot(all(dim(dtrain) == dim(train$data)))
-#'
-#' @export
-dim.xgb.DMatrix <- function(x) {
-  c(.Call(XGDMatrixNumRow_R, x), .Call(XGDMatrixNumCol_R, x))
-}
-
-
-#' Handling of column names of \code{xgb.DMatrix}
-#'
-#' Only column names are supported for \code{xgb.DMatrix}, thus setting of
-#' row names would have no effect and returned row names would be NULL.
-#'
-#' @param x object of class \code{xgb.DMatrix}
-#' @param value a list of two elements: the first one is ignored
-#'        and the second one is column names
-#'
-#' @details
-#' Generic \code{dimnames} methods are used by \code{colnames}.
-#' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' dimnames(dtrain)
-#' colnames(dtrain)
-#' colnames(dtrain) <- make.names(1:ncol(train$data))
-#' print(dtrain, verbose=TRUE)
-#'
-#' @rdname dimnames.xgb.DMatrix
-#' @export
-dimnames.xgb.DMatrix <- function(x) {
-  attr(x, '.Dimnames')
-}
-
-#' @rdname dimnames.xgb.DMatrix
-#' @export
-`dimnames<-.xgb.DMatrix` <- function(x, value) {
-  if (!is.list(value) || length(value) != 2L)
-    stop("invalid 'dimnames' given: must be a list of two elements")
-  if (!is.null(value[[1L]]))
-    stop("xgb.DMatrix does not have rownames")
-  if (is.null(value[[2]])) {
-    attr(x, '.Dimnames') <- NULL
-    return(x)
-  }
-  if (ncol(x) != length(value[[2]]))
-    stop("can't assign ", length(value[[2]]), " colnames to a ",
-         ncol(x), " column xgb.DMatrix")
-  attr(x, '.Dimnames') <- value
-  x
-}
-
-
-#' Get information of an xgb.DMatrix object
-#'
-#' Get information of an xgb.DMatrix object
-#' @param object Object of class \code{xgb.DMatrix}
-#' @param name the name of the information field to get (see details)
-#' @param ... other parameters
-#'
-#' @details
-#' The \code{name} field can be one of the following:
-#'
-#' \itemize{
-#'     \item \code{label}: label Xgboost learn from ;
-#'     \item \code{weight}: to do a weight rescale ;
-#'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
-#'     \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
-#'
-#' }
-#'
-#' \code{group} can be setup by \code{setinfo} but can't be retrieved by \code{getinfo}.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
-#' labels <- getinfo(dtrain, 'label')
-#' setinfo(dtrain, 'label', 1-labels)
-#'
-#' labels2 <- getinfo(dtrain, 'label')
-#' stopifnot(all(labels2 == 1-labels))
-#' @rdname getinfo
-#' @export
-getinfo <- function(object, ...) UseMethod("getinfo")
-
-#' @rdname getinfo
-#' @export
-getinfo.xgb.DMatrix <- function(object, name, ...) {
-  if (typeof(name) != "character" ||
-      length(name) != 1 ||
-      !name %in% c('label', 'weight', 'base_margin', 'nrow',
-                   'label_lower_bound', 'label_upper_bound')) {
-    stop("getinfo: name must be one of the following\n",
-         "    'label', 'weight', 'base_margin', 'nrow', 'label_lower_bound', 'label_upper_bound'")
-  }
-  if (name != "nrow"){
-    ret <- .Call(XGDMatrixGetInfo_R, object, name)
-  } else {
-    ret <- nrow(object)
-  }
-  if (length(ret) == 0) return(NULL)
-  return(ret)
-}
-
-
-#' Set information of an xgb.DMatrix object
-#'
-#' Set information of an xgb.DMatrix object
-#'
-#' @param object Object of class "xgb.DMatrix"
-#' @param name the name of the field to get
-#' @param info the specific field of information to set
-#' @param ... other parameters
-#'
-#' @details
-#' The \code{name} field can be one of the following:
-#'
-#' \itemize{
-#'     \item \code{label}: label Xgboost learn from ;
-#'     \item \code{weight}: to do a weight rescale ;
-#'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
-#'     \item \code{group}: number of rows in each group (to use with \code{rank:pairwise} objective).
-#' }
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
-#' labels <- getinfo(dtrain, 'label')
-#' setinfo(dtrain, 'label', 1-labels)
-#' labels2 <- getinfo(dtrain, 'label')
-#' stopifnot(all.equal(labels2, 1-labels))
-#' @rdname setinfo
-#' @export
-setinfo <- function(object, ...) UseMethod("setinfo")
-
-#' @rdname setinfo
-#' @export
-setinfo.xgb.DMatrix <- function(object, name, info, ...) {
-  if (name == "label") {
-    if (length(info) != nrow(object))
-      stop("The length of labels must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
-    return(TRUE)
-  }
-  if (name == "label_lower_bound") {
-    if (length(info) != nrow(object))
-      stop("The length of lower-bound labels must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
-    return(TRUE)
-  }
-  if (name == "label_upper_bound") {
-    if (length(info) != nrow(object))
-      stop("The length of upper-bound labels must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
-    return(TRUE)
-  }
-  if (name == "weight") {
-    if (length(info) != nrow(object))
-      stop("The length of weights must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
-    return(TRUE)
-  }
-  if (name == "base_margin") {
-    # if (length(info)!=nrow(object))
-    #   stop("The length of base margin must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
-    return(TRUE)
-  }
-  if (name == "group") {
-    if (sum(info) != nrow(object))
-      stop("The sum of groups must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.integer(info))
-    return(TRUE)
-  }
-  stop("setinfo: unknown info name ", name)
-  return(FALSE)
-}
-
-
-#' Get a new DMatrix containing the specified rows of
-#' original xgb.DMatrix object
-#'
-#' Get a new DMatrix containing the specified rows of
-#' original xgb.DMatrix object
-#'
-#' @param object Object of class "xgb.DMatrix"
-#' @param idxset a integer vector of indices of rows needed
-#' @param colset currently not used (columns subsetting is not available)
-#' @param ... other parameters (currently not used)
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
-#' dsub <- slice(dtrain, 1:42)
-#' labels1 <- getinfo(dsub, 'label')
-#' dsub <- dtrain[1:42, ]
-#' labels2 <- getinfo(dsub, 'label')
-#' all.equal(labels1, labels2)
-#'
-#' @rdname slice.xgb.DMatrix
-#' @export
-slice <- function(object, ...) UseMethod("slice")
-
-#' @rdname slice.xgb.DMatrix
-#' @export
-slice.xgb.DMatrix <- function(object, idxset, ...) {
-  if (!inherits(object, "xgb.DMatrix")) {
-    stop("object must be xgb.DMatrix")
-  }
-  ret <- .Call(XGDMatrixSliceDMatrix_R, object, idxset)
-
-  attr_list <- attributes(object)
-  nr <- nrow(object)
-  len <- sapply(attr_list, NROW)
-  ind <- which(len == nr)
-  if (length(ind) > 0) {
-    nms <- names(attr_list)[ind]
-    for (i in seq_along(ind)) {
-      obj_attr <- attr(object, nms[i])
-      if (NCOL(obj_attr) > 1) {
-        attr(ret, nms[i]) <- obj_attr[idxset,]
-      } else {
-        attr(ret, nms[i]) <- obj_attr[idxset]
-      }
-    }
-  }
-  return(structure(ret, class = "xgb.DMatrix"))
-}
-
-#' @rdname slice.xgb.DMatrix
-#' @export
-`[.xgb.DMatrix` <- function(object, idxset, colset = NULL) {
-  slice(object, idxset)
-}
-
-
-#' Print xgb.DMatrix
-#'
-#' Print information about xgb.DMatrix.
-#' Currently it displays dimensions and presence of info-fields and colnames.
-#'
-#' @param x an xgb.DMatrix object
-#' @param verbose whether to print colnames (when present)
-#' @param ... not currently used
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
-#' dtrain
-#' print(dtrain, verbose=TRUE)
-#'
-#' @method print xgb.DMatrix
-#' @export
-print.xgb.DMatrix <- function(x, verbose = FALSE, ...) {
-  cat('xgb.DMatrix  dim:', nrow(x), 'x', ncol(x), ' info: ')
-  infos <- c()
-  if(length(getinfo(x, 'label')) > 0) infos <- 'label'
-  if(length(getinfo(x, 'weight')) > 0) infos <- c(infos, 'weight')
-  if(length(getinfo(x, 'base_margin')) > 0) infos <- c(infos, 'base_margin')
-  if (length(infos) == 0) infos <- 'NA'
-  cat(infos)
-  cnames <- colnames(x)
-  cat('  colnames:')
-  if (verbose & !is.null(cnames)) {
-    cat("\n'")
-    cat(cnames, sep = "','")
-    cat("'")
-  } else {
-    if (is.null(cnames)) cat(' no')
-    else cat(' yes')
-  }
-  cat("\n")
-  invisible(x)
-}
diff --git a/ml-xgboost/R-package/R/xgb.DMatrix.save.R b/ml-xgboost/R-package/R/xgb.DMatrix.save.R
deleted file mode 100644
index 1c659e5..0000000
--- a/ml-xgboost/R-package/R/xgb.DMatrix.save.R
+++ /dev/null
@@ -1,24 +0,0 @@
-#' Save xgb.DMatrix object to binary file
-#' 
-#' Save xgb.DMatrix object to binary file
-#' 
-#' @param dmatrix the \code{xgb.DMatrix} object
-#' @param fname the name of the file to write.
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
-#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
-#' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
-#' @export
-xgb.DMatrix.save <- function(dmatrix, fname) {
-  if (typeof(fname) != "character")
-    stop("fname must be character")
-  if (!inherits(dmatrix, "xgb.DMatrix"))
-    stop("dmatrix must be xgb.DMatrix")
-  
-  .Call(XGDMatrixSaveBinary_R, dmatrix, fname[1], 0L)
-  return(TRUE)
-}
diff --git a/ml-xgboost/R-package/R/xgb.create.features.R b/ml-xgboost/R-package/R/xgb.create.features.R
deleted file mode 100644
index b8be649..0000000
--- a/ml-xgboost/R-package/R/xgb.create.features.R
+++ /dev/null
@@ -1,87 +0,0 @@
-#' Create new features from a previously learned model
-#' 
-#' May improve the learning by adding new features to the training data based on the decision trees from a previously learned model.
-#' 
-#' @param model decision tree boosting model learned on the original data
-#' @param data original data (usually provided as a \code{dgCMatrix} matrix)
-#' @param ... currently not used
-#' 
-#' @return \code{dgCMatrix} matrix including both the original data and the new features.
-#'
-#' @details 
-#' This is the function inspired from the paragraph 3.1 of the paper:
-#' 
-#' \strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
-#' 
-#' \emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, 
-#' Joaquin Quinonero Candela)}
-#'  
-#' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
-#' 
-#' \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
-#' 
-#' Extract explaining the method:
-#' 
-#' "We found that boosted decision trees are a powerful and very
-#' convenient way to implement non-linear and tuple transformations
-#' of the kind we just described. We treat each individual
-#' tree as a categorical feature that takes as value the
-#' index of the leaf an instance ends up falling in. We use 
-#' 1-of-K coding of this type of features. 
-#' 
-#' For example, consider the boosted tree model in Figure 1 with 2 subtrees, 
-#' where the first subtree has 3 leafs and the second 2 leafs. If an
-#' instance ends up in leaf 2 in the first subtree and leaf 1 in
-#' second subtree, the overall input to the linear classifier will
-#' be the binary vector \code{[0, 1, 0, 1, 0]}, where the first 3 entries
-#' correspond to the leaves of the first subtree and last 2 to
-#' those of the second subtree.
-#' 
-#' [...]
-#' 
-#' We can understand boosted decision tree
-#' based transformation as a supervised feature encoding that
-#' converts a real-valued vector into a compact binary-valued
-#' vector. A traversal from root node to a leaf node represents
-#' a rule on certain features."
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
-#' dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
-#'
-#' param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-#' nrounds = 4
-#'
-#' bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
-#' 
-#' # Model accuracy without new features
-#' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
-#'                    length(agaricus.test$label)
-#' 
-#' # Convert previous features to one hot encoding
-#' new.features.train <- xgb.create.features(model = bst, agaricus.train$data)
-#' new.features.test <- xgb.create.features(model = bst, agaricus.test$data)
-#' 
-#' # learning with new features
-#' new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
-#' new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
-#' watchlist <- list(train = new.dtrain)
-#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
-#' 
-#' # Model accuracy with new features
-#' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
-#'                   length(agaricus.test$label)
-#' 
-#' # Here the accuracy was already good and is now perfect.
-#' cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
-#'           accuracy.after, "!\n"))
-#' 
-#' @export
-xgb.create.features <- function(model, data, ...){
-  check.deprecation(...)
-  pred_with_leaf <- predict(model, data, predleaf = TRUE)
-  cols <- lapply(as.data.frame(pred_with_leaf), factor)
-  cbind(data, sparse.model.matrix( ~ . -1, cols))
-}
diff --git a/ml-xgboost/R-package/R/xgb.cv.R b/ml-xgboost/R-package/R/xgb.cv.R
deleted file mode 100644
index fdfcb59..0000000
--- a/ml-xgboost/R-package/R/xgb.cv.R
+++ /dev/null
@@ -1,319 +0,0 @@
-#' Cross Validation
-#'
-#' The cross validation function of xgboost
-#'
-#' @param params the list of parameters. Commonly used ones are:
-#' \itemize{
-#'   \item \code{objective} objective function, common ones are
-#'   \itemize{
-#'     \item \code{reg:squarederror} Regression with squared loss
-#'     \item \code{binary:logistic} logistic regression for classification
-#'   }
-#'   \item \code{eta} step size of each boosting step
-#'   \item \code{max_depth} maximum depth of the tree
-#'   \item \code{nthread} number of thread used in training, if not set, all threads are used
-#' }
-#'
-#'   See \code{\link{xgb.train}} for further details.
-#'   See also demo/ for walkthrough example in R.
-#' @param data takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.
-#' @param nrounds the max number of iterations
-#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
-#' @param label vector of response values. Should be provided only when data is an R-matrix.
-#' @param missing is only used when input is a dense matrix. By default is set to NA, which means
-#'        that NA values should be considered as 'missing' by the algorithm.
-#'        Sometimes, 0 or other extreme value might be used to represent missing values.
-#' @param prediction A logical value indicating whether to return the test fold predictions
-#'        from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.
-#' @param showsd \code{boolean}, whether to show standard deviation of cross validation
-#' @param metrics, list of evaluation metrics to be used in cross validation,
-#'   when it is not specified, the evaluation metric is chosen according to objective function.
-#'   Possible options are:
-#' \itemize{
-#'   \item \code{error} binary classification error rate
-#'   \item \code{rmse} Rooted mean square error
-#'   \item \code{logloss} negative log-likelihood function
-#'   \item \code{auc} Area under curve
-#'   \item \code{aucpr} Area under PR curve
-#'   \item \code{merror} Exact matching error, used to evaluate multi-class classification
-#' }
-#' @param obj customized objective function. Returns gradient and second order
-#'        gradient with given prediction and dtrain.
-#' @param feval customized evaluation function. Returns
-#'        \code{list(metric='metric-name', value='metric-value')} with given
-#'        prediction and dtrain.
-#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
-#'        by the values of outcome labels.
-#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
-#'        (each element must be a vector of test fold's indices). When folds are supplied,
-#'        the \code{nfold} and \code{stratified} parameters are ignored.
-#' @param train_folds \code{list} list specifying which indicies to use for training. If \code{NULL}
-#'        (the default) all indices not specified in \code{folds} will be used for training.
-#' @param verbose \code{boolean}, print the statistics during the process
-#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the
-#'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance
-#'        doesn't improve for \code{k} rounds.
-#'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
-#' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
-#'        then this parameter must be set as well.
-#'        When it is \code{TRUE}, it means the larger the evaluation score the better.
-#'        This parameter is passed to the \code{\link{cb.early.stop}} callback.
-#' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-#'        parameters' values. User can provide either existing or their own callback methods in order
-#'        to customize the training process.
-#' @param ... other parameters to pass to \code{params}.
-#'
-#' @details
-#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
-#'
-#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
-#'
-#' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
-#'
-#' All observations are used for both training and validation.
-#'
-#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
-#'
-#' @return
-#' An object of class \code{xgb.cv.synchronous} with the following elements:
-#' \itemize{
-#'   \item \code{call} a function call.
-#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
-#'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or
-#'         explicitly passed.
-#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
-#'         first column corresponding to iteration number and the rest corresponding to the
-#'         CV-based evaluation means and standard deviations for the training and test CV-sets.
-#'         It is created by the \code{\link{cb.evaluation.log}} callback.
-#'   \item \code{niter} number of boosting iterations.
-#'   \item \code{nfeatures} number of features in training data.
-#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
-#'         parameter or randomly generated.
-#'   \item \code{best_iteration} iteration number with the best evaluation metric value
-#'         (only available with early stopping).
-#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
-#'         which could further be used in \code{predict} method
-#'         (only available with early stopping).
-#'   \item \code{pred} CV prediction values available when \code{prediction} is set.
-#'         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-#'   \item \code{models} a list of the CV folds' models. It is only available with the explicit
-#'         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
-#' }
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-#' cv <- xgb.cv(data = dtrain, nrounds = 3, nthread = 2, nfold = 5, metrics = list("rmse","auc"),
-#'                   max_depth = 3, eta = 1, objective = "binary:logistic")
-#' print(cv)
-#' print(cv, verbose=TRUE)
-#'
-#' @export
-xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
-                   prediction = FALSE, showsd = TRUE, metrics=list(),
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, train_folds = NULL,
-                   verbose = TRUE, print_every_n=1L,
-                   early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
-
-  check.deprecation(...)
-
-  params <- check.booster.params(params, ...)
-  # TODO: should we deprecate the redundant 'metrics' parameter?
-  for (m in metrics)
-    params <- c(params, list("eval_metric" = m))
-
-  check.custom.obj()
-  check.custom.eval()
-
-  #if (is.null(params[['eval_metric']]) && is.null(feval))
-  #  stop("Either 'eval_metric' or 'feval' must be provided for CV")
-
-  # Check the labels
-  if ( (inherits(data, 'xgb.DMatrix') && is.null(getinfo(data, 'label'))) ||
-       (!inherits(data, 'xgb.DMatrix') && is.null(label))) {
-    stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
-  } else if (inherits(data, 'xgb.DMatrix')) {
-    if (!is.null(label))
-      warning("xgb.cv: label will be ignored, since data is of type xgb.DMatrix")
-    cv_label = getinfo(data, 'label')
-  } else {
-    cv_label = label
-  }
-
-  # CV folds
-  if(!is.null(folds)) {
-    if(!is.list(folds) || length(folds) < 2)
-      stop("'folds' must be a list with 2 or more elements that are vectors of indices for each CV-fold")
-    nfold <- length(folds)
-  } else {
-    if (nfold <= 1)
-      stop("'nfold' must be > 1")
-    folds <- generate.cv.folds(nfold, nrow(data), stratified, cv_label, params)
-  }
-
-  # Potential TODO: sequential CV
-  #if (strategy == 'sequential')
-  #  stop('Sequential CV strategy is not yet implemented')
-
-  # verbosity & evaluation printing callback:
-  params <- c(params, list(silent = 1))
-  print_every_n <- max( as.integer(print_every_n), 1L)
-  if (!has.callbacks(callbacks, 'cb.print.evaluation') && verbose) {
-    callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n, showsd = showsd))
-  }
-  # evaluation log callback: always is on in CV
-  evaluation_log <- list()
-  if (!has.callbacks(callbacks, 'cb.evaluation.log')) {
-    callbacks <- add.cb(callbacks, cb.evaluation.log())
-  }
-  # Early stopping callback
-  stop_condition <- FALSE
-  if (!is.null(early_stopping_rounds) &&
-      !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
-                                                 maximize = maximize, verbose = verbose))
-  }
-  # CV-predictions callback
-  if (prediction &&
-      !has.callbacks(callbacks, 'cb.cv.predict')) {
-    callbacks <- add.cb(callbacks, cb.cv.predict(save_models = FALSE))
-  }
-  # Sort the callbacks into categories
-  cb <- categorize.callbacks(callbacks)
-
-
-  # create the booster-folds
-  # train_folds
-  dall <- xgb.get.DMatrix(data, label, missing)
-  bst_folds <- lapply(seq_along(folds), function(k) {
-    dtest  <- slice(dall, folds[[k]])
-    # code originally contributed by @RolandASc on stackoverflow
-    if(is.null(train_folds))
-       dtrain <- slice(dall, unlist(folds[-k]))
-    else
-       dtrain <- slice(dall, train_folds[[k]])
-    handle <- xgb.Booster.handle(params, list(dtrain, dtest))
-    list(dtrain = dtrain, bst = handle, watchlist = list(train = dtrain, test=dtest), index = folds[[k]])
-  })
-  rm(dall)
-  # a "basket" to collect some results from callbacks
-  basket <- list()
-
-  # extract parameters that can affect the relationship b/w #trees and #iterations
-  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
-  num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
-
-  # those are fixed for CV (no training continuation)
-  begin_iteration <- 1
-  end_iteration <- nrounds
-
-  # synchronous CV boosting: run CV folds' models within each iteration
-  for (iteration in begin_iteration:end_iteration) {
-
-    for (f in cb$pre_iter) f()
-
-    msg <- lapply(bst_folds, function(fd) {
-      xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, obj)
-      xgb.iter.eval(fd$bst, fd$watchlist, iteration - 1, feval)
-    })
-    msg <- simplify2array(msg)
-    bst_evaluation <- rowMeans(msg)
-    bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2)
-
-    for (f in cb$post_iter) f()
-
-    if (stop_condition) break
-  }
-  for (f in cb$finalize) f(finalize = TRUE)
-
-  # the CV result
-  ret <- list(
-    call = match.call(),
-    params = params,
-    callbacks = callbacks,
-    evaluation_log = evaluation_log,
-    niter = end_iteration,
-    nfeatures = ncol(data),
-    folds = folds
-  )
-  ret <- c(ret, basket)
-
-  class(ret) <- 'xgb.cv.synchronous'
-  invisible(ret)
-}
-
-
-
-#' Print xgb.cv result
-#'
-#' Prints formatted results of \code{xgb.cv}.
-#'
-#' @param x an \code{xgb.cv.synchronous} object
-#' @param verbose whether to print detailed data
-#' @param ... passed to \code{data.table.print}
-#'
-#' @details
-#' When not verbose, it would only print the evaluation results,
-#' including the best iteration (when available).
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' cv <- xgb.cv(data = train$data, label = train$label, nfold = 5, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#' print(cv)
-#' print(cv, verbose=TRUE)
-#'
-#' @rdname print.xgb.cv
-#' @method print xgb.cv.synchronous
-#' @export
-print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
-  cat('##### xgb.cv ', length(x$folds), '-folds\n', sep = '')
-
-  if (verbose) {
-    if (!is.null(x$call)) {
-      cat('call:\n  ')
-      print(x$call)
-    }
-    if (!is.null(x$params)) {
-      cat('params (as set within xgb.cv):\n')
-      cat( '  ',
-           paste(names(x$params),
-                 paste0('"', unlist(x$params), '"'),
-                 sep = ' = ', collapse = ', '), '\n', sep = '')
-    }
-    if (!is.null(x$callbacks) && length(x$callbacks) > 0) {
-      cat('callbacks:\n')
-      lapply(callback.calls(x$callbacks), function(x) {
-        cat('  ')
-        print(x)
-      })
-    }
-
-    for (n in c('niter', 'best_iteration', 'best_ntreelimit')) {
-      if (is.null(x[[n]]))
-        next
-      cat(n, ': ', x[[n]], '\n', sep = '')
-    }
-
-    if (!is.null(x$pred)) {
-      cat('pred:\n')
-      str(x$pred)
-    }
-  }
-
-  if (verbose)
-    cat('evaluation_log:\n')
-  print(x$evaluation_log, row.names = FALSE, ...)
-
-  if (!is.null(x$best_iteration)) {
-    cat('Best iteration:\n')
-    print(x$evaluation_log[x$best_iteration], row.names = FALSE, ...)
-  }
-  invisible(x)
-}
diff --git a/ml-xgboost/R-package/R/xgb.dump.R b/ml-xgboost/R-package/R/xgb.dump.R
deleted file mode 100644
index ffa3cbc..0000000
--- a/ml-xgboost/R-package/R/xgb.dump.R
+++ /dev/null
@@ -1,72 +0,0 @@
-#' Dump an xgboost model in text format.
-#' 
-#' Dump an xgboost model in text format.
-#' 
-#' @param model the model object.
-#' @param fname the name of the text file where to save the model text dump. 
-#'        If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.
-#' @param fmap feature map file representing feature types.
-#'        Detailed description could be found at 
-#'        \url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
-#'        See demo/ for walkthrough example in R, and
-#'        \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} 
-#'        for example Format.
-#' @param with_stats whether to dump some additional statistics about the splits.
-#'        When this option is on, the model dump contains two additional values:
-#'        gain is the approximate loss function gain we get in each split;
-#'        cover is the sum of second order gradient in each node.
-#' @param dump_format either 'text' or 'json' format could be specified.
-#' @param ... currently not used
-#'
-#' @return
-#' If fname is not provided or set to \code{NULL} the function will return the model
-#' as a \code{character} vector. Otherwise it will return \code{TRUE}.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#' # save the model in file 'xgb.model.dump'
-#' dump_path = file.path(tempdir(), 'model.dump')
-#' xgb.dump(bst, dump_path, with_stats = TRUE)
-#' 
-#' # print the model without saving it to a file
-#' print(xgb.dump(bst, with_stats = TRUE))
-#' 
-#' # print in JSON format:
-#' cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
-#' 
-#' @export
-xgb.dump <- function(model, fname = NULL, fmap = "", with_stats=FALSE,
-                     dump_format = c("text", "json"), ...) {
-  check.deprecation(...)
-  dump_format <- match.arg(dump_format)
-  if (!inherits(model, "xgb.Booster"))
-    stop("model: argument must be of type xgb.Booster")
-  if (!(is.null(fname) || is.character(fname)))
-    stop("fname: argument must be a character string (when provided)")
-  if (!(is.null(fmap) || is.character(fmap)))
-    stop("fmap: argument must be a character string (when provided)")
-  
-  model <- xgb.Booster.complete(model)
-  model_dump <- .Call(XGBoosterDumpModel_R, model$handle, NVL(fmap, "")[1], as.integer(with_stats),
-                      as.character(dump_format))
-
-  if (is.null(fname)) 
-    model_dump <- stri_replace_all_regex(model_dump, '\t', '')
-  
-  if (dump_format == "text")
-    model_dump <- unlist(stri_split_regex(model_dump, '\n'))
-  
-  model_dump <- grep('^\\s*$', model_dump, invert = TRUE, value = TRUE)
-  
-  if (is.null(fname)) {
-    return(model_dump)
-  } else {
-    writeLines(model_dump, fname[1])
-    return(TRUE)
-  }
-}
diff --git a/ml-xgboost/R-package/R/xgb.ggplot.R b/ml-xgboost/R-package/R/xgb.ggplot.R
deleted file mode 100644
index eceb5c4..0000000
--- a/ml-xgboost/R-package/R/xgb.ggplot.R
+++ /dev/null
@@ -1,135 +0,0 @@
-# ggplot backend for the xgboost plotting facilities
-
-
-#' @rdname xgb.plot.importance
-#' @export
-xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, 
-                                  rel_to_first = FALSE, n_clusters = c(1:10), ...) {
-  
-  importance_matrix <- xgb.plot.importance(importance_matrix, top_n = top_n, measure = measure,
-                                           rel_to_first = rel_to_first, plot = FALSE, ...)
-  if (!requireNamespace("ggplot2", quietly = TRUE)) {
-    stop("ggplot2 package is required", call. = FALSE)
-  }
-  if (!requireNamespace("Ckmeans.1d.dp", quietly = TRUE)) {
-    stop("Ckmeans.1d.dp package is required", call. = FALSE)
-  }
-  
-  clusters <- suppressWarnings(
-    Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix$Importance, n_clusters)
-  )
-  importance_matrix[, Cluster := as.character(clusters$cluster)]
-
-  plot <-
-    ggplot2::ggplot(importance_matrix, 
-                    ggplot2::aes(x = factor(Feature, levels = rev(Feature)), y = Importance, width = 0.5),
-                    environment = environment()) + 
-    ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position = "identity") + 
-    ggplot2::coord_flip() + 
-    ggplot2::xlab("Features") + 
-    ggplot2::ggtitle("Feature importance") + 
-    ggplot2::theme(plot.title = ggplot2::element_text(lineheight = .9, face = "bold"), 
-                   panel.grid.major.y = ggplot2::element_blank())
-  return(plot)
-}
-
-
-#' @rdname xgb.plot.deepness
-#' @export
-xgb.ggplot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med.depth", "med.weight")) {
-
-  if (!requireNamespace("ggplot2", quietly = TRUE))
-    stop("ggplot2 package is required for plotting the graph deepness.", call. = FALSE)
-
-  which <- match.arg(which)
-  
-  dt_depths <- xgb.plot.deepness(model = model, plot = FALSE)
-  dt_summaries <- dt_depths[, .(.N, Cover = mean(Cover)), Depth]
-  setkey(dt_summaries, 'Depth')
-
-  if (which == "2x1") {
-    p1 <-
-      ggplot2::ggplot(dt_summaries) +
-      ggplot2::geom_bar(ggplot2::aes(x = Depth, y = N), stat = "Identity") +
-      ggplot2::xlab("") +
-      ggplot2::ylab("Number of leafs") +
-      ggplot2::ggtitle("Model complexity") +
-      ggplot2::theme(
-        plot.title = ggplot2::element_text(lineheight = 0.9, face = "bold"),
-        panel.grid.major.y = ggplot2::element_blank(),
-        axis.ticks = ggplot2::element_blank(),
-        axis.text.x = ggplot2::element_blank()
-      )
-  
-    p2 <- 
-      ggplot2::ggplot(dt_summaries) +
-      ggplot2::geom_bar(ggplot2::aes(x = Depth, y = Cover), stat = "Identity") + 
-      ggplot2::xlab("Leaf depth") +
-      ggplot2::ylab("Weighted cover")
-  
-    multiplot(p1, p2, cols = 1)
-    return(invisible(list(p1, p2)))
-    
-  } else if (which == "max.depth") {
-    p <-
-      ggplot2::ggplot(dt_depths[, max(Depth), Tree]) +
-      ggplot2::geom_jitter(ggplot2::aes(x = Tree, y = V1),
-                           height = 0.15, alpha=0.4, size=3, stroke=0) + 
-      ggplot2::xlab("tree #") +
-      ggplot2::ylab("Max tree leaf depth")
-    return(p)
-    
-  } else if (which == "med.depth") {
-    p <-
-      ggplot2::ggplot(dt_depths[, median(as.numeric(Depth)), Tree]) +
-      ggplot2::geom_jitter(ggplot2::aes(x = Tree, y = V1),
-                           height = 0.15, alpha=0.4, size=3, stroke=0) + 
-      ggplot2::xlab("tree #") +
-      ggplot2::ylab("Median tree leaf depth")
-    return(p)
-
-  } else if (which == "med.weight") {
-    p <-
-      ggplot2::ggplot(dt_depths[, median(abs(Weight)), Tree]) +
-      ggplot2::geom_point(ggplot2::aes(x = Tree, y = V1),
-                          alpha=0.4, size=3, stroke=0) + 
-      ggplot2::xlab("tree #") +
-      ggplot2::ylab("Median absolute leaf weight")
-    return(p)
-  }
-}
-
-# Plot multiple ggplot graph aligned by rows and columns.
-# ... the plots
-# cols number of columns
-# internal utility function
-multiplot <- function(..., cols = 1) {
-  plots <- list(...)
-  num_plots = length(plots)
-  
-  layout <- matrix(seq(1, cols * ceiling(num_plots / cols)),
-                   ncol = cols, nrow = ceiling(num_plots / cols))
-  
-  if (num_plots == 1) {
-    print(plots[[1]])
-  } else {
-    grid::grid.newpage()
-    grid::pushViewport(grid::viewport(layout = grid::grid.layout(nrow(layout), ncol(layout))))
-    for (i in 1:num_plots) {
-      # Get the i,j matrix positions of the regions that contain this subplot
-      matchidx <- as.data.table(which(layout == i, arr.ind = TRUE))
-      
-      print(
-        plots[[i]], vp = grid::viewport(
-          layout.pos.row = matchidx$row,
-          layout.pos.col = matchidx$col
-        )
-      )
-    }
-  }
-}
-
-globalVariables(c(
-  "Cluster", "ggplot", "aes", "geom_bar", "coord_flip", "xlab", "ylab", "ggtitle", "theme",
-  "element_blank", "element_text", "V1", "Weight"
-))
diff --git a/ml-xgboost/R-package/R/xgb.importance.R b/ml-xgboost/R-package/R/xgb.importance.R
deleted file mode 100644
index 62e37e8..0000000
--- a/ml-xgboost/R-package/R/xgb.importance.R
+++ /dev/null
@@ -1,139 +0,0 @@
-#' Importance of features in a model.
-#' 
-#' Creates a \code{data.table} of feature importances in a model.
-#' 
-#' @param feature_names character vector of feature names. If the model already
-#'       contains feature names, those would be used when \code{feature_names=NULL} (default value).
-#'       Non-null \code{feature_names} could be provided to override those in the model.
-#' @param model object of class \code{xgb.Booster}.
-#' @param trees (only for the gbtree booster) an integer vector of tree indices that should be included
-#'          into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
-#'          It could be useful, e.g., in multiclass classification to get feature importances 
-#'          for each class separately. IMPORTANT: the tree index in xgboost models
-#'          is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).
-#' @param data deprecated.
-#' @param label deprecated.
-#' @param target deprecated.
-#'
-#' @details 
-#' 
-#' This function works for both linear and tree models.
-#' 
-#' For linear models, the importance is the absolute magnitude of linear coefficients. 
-#' For that reason, in order to obtain a meaningful ranking by importance for a linear model, 
-#' the features need to be on the same scale (which you also would want to do when using either 
-#' L1 or L2 regularization).
-#' 
-#' @return
-#' 
-#' For a tree model, a \code{data.table} with the following columns:
-#' \itemize{
-#'   \item \code{Features} names of the features used in the model;
-#'   \item \code{Gain} represents fractional contribution of each feature to the model based on
-#'        the total gain of this feature's splits. Higher percentage means a more important 
-#'        predictive feature.
-#'   \item \code{Cover} metric of the number of observation related to this feature;
-#'   \item \code{Frequency} percentage representing the relative number of times
-#'        a feature have been used in trees.
-#' }
-#' 
-#' A linear model's importance \code{data.table} has the following columns:
-#' \itemize{
-#'   \item \code{Features} names of the features used in the model;
-#'   \item \code{Weight} the linear coefficient of this feature;
-#'   \item \code{Class} (only for multiclass models) class label.
-#' }
-#' 
-#' If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names}, 
-#' index of the features will be used instead. Because the index is extracted from the model dump
-#' (based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
-#' 
-#' @examples
-#' 
-#' # binomial classification using gbtree:
-#' data(agaricus.train, package='xgboost')
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#' xgb.importance(model = bst)
-#' 
-#' # binomial classification using gblinear:
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear", 
-#'                eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
-#' xgb.importance(model = bst)
-#' 
-#' # multiclass classification using gbtree:
-#' nclass <- 3
-#' nrounds <- 10
-#' mbst <- xgboost(data = as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1,
-#'                max_depth = 3, eta = 0.2, nthread = 2, nrounds = nrounds,
-#'                objective = "multi:softprob", num_class = nclass)
-#' # all classes clumped together:
-#' xgb.importance(model = mbst)
-#' # inspect importances separately for each class:
-#' xgb.importance(model = mbst, trees = seq(from=0, by=nclass, length.out=nrounds))
-#' xgb.importance(model = mbst, trees = seq(from=1, by=nclass, length.out=nrounds))
-#' xgb.importance(model = mbst, trees = seq(from=2, by=nclass, length.out=nrounds))
-#' 
-#' # multiclass classification using gblinear:
-#' mbst <- xgboost(data = scale(as.matrix(iris[, -5])), label = as.numeric(iris$Species) - 1,
-#'                booster = "gblinear", eta = 0.2, nthread = 1, nrounds = 15,
-#'                objective = "multi:softprob", num_class = nclass)
-#' xgb.importance(model = mbst)
-#'
-#' @export
-xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
-                           data = NULL, label = NULL, target = NULL){
-  
-  if (!(is.null(data) && is.null(label) && is.null(target)))
-    warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
-  
-  if (!inherits(model, "xgb.Booster"))
-    stop("model: must be an object of class xgb.Booster")
-  
-  if (is.null(feature_names) && !is.null(model$feature_names))
-    feature_names <- model$feature_names
-  
-  if (!(is.null(feature_names) || is.character(feature_names)))
-    stop("feature_names: Has to be a character vector")
-
-  model_text_dump <- xgb.dump(model = model, with_stats = TRUE)
-  
-  # linear model
-  if(model_text_dump[2] == "bias:"){
-    weights <- which(model_text_dump == "weight:") %>%
-               {model_text_dump[(. + 1):length(model_text_dump)]} %>%
-               as.numeric
-    
-    num_class <- NVL(model$params$num_class, 1)
-    if(is.null(feature_names)) 
-      feature_names <- seq(to = length(weights) / num_class) - 1
-    if (length(feature_names) * num_class != length(weights))
-      stop("feature_names length does not match the number of features used in the model")
-    
-    result <- if (num_class == 1) {
-      data.table(Feature = feature_names, Weight = weights)[order(-abs(Weight))]
-    } else {
-      data.table(Feature = rep(feature_names, each = num_class),
-                 Weight = weights,
-                 Class = seq_len(num_class) - 1)[order(Class, -abs(Weight))]
-    }
-  } else { 
-  # tree model
-    result <- xgb.model.dt.tree(feature_names = feature_names,
-                                text = model_text_dump,
-                                trees = trees)[
-      Feature != "Leaf", .(Gain = sum(Quality), 
-                           Cover = sum(Cover), 
-                           Frequency = .N), by = Feature][
-      ,`:=`(Gain = Gain / sum(Gain), 
-            Cover = Cover / sum(Cover),
-            Frequency = Frequency / sum(Frequency))][
-      order(Gain, decreasing = TRUE)]
-  }
-  result
-}
-
-# Avoid error messages during CRAN check.
-# The reason is that these variables are never declared
-# They are mainly column names inferred by Data.table...
-globalVariables(c(".", ".N", "Gain", "Cover", "Frequency", "Feature", "Class"))
diff --git a/ml-xgboost/R-package/R/xgb.load.R b/ml-xgboost/R-package/R/xgb.load.R
deleted file mode 100644
index bda4e7e..0000000
--- a/ml-xgboost/R-package/R/xgb.load.R
+++ /dev/null
@@ -1,47 +0,0 @@
-#' Load xgboost model from binary file
-#'
-#' Load xgboost model from the binary model file.
-#'
-#' @param modelfile the name of the binary input file.
-#'
-#' @details
-#' The input file is expected to contain a model saved in an xgboost-internal binary format
-#' using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some
-#' appropriate methods from other xgboost interfaces. E.g., a model trained in Python and
-#' saved from there in xgboost format, could be loaded from R.
-#'
-#' Note: a model saved as an R-object, has to be loaded using corresponding R-methods,
-#' not \code{xgb.load}.
-#'
-#' @return
-#' An object of \code{xgb.Booster} class.
-#'
-#' @seealso
-#' \code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' xgb.save(bst, 'xgb.model')
-#' bst <- xgb.load('xgb.model')
-#' if (file.exists('xgb.model')) file.remove('xgb.model')
-#' pred <- predict(bst, test$data)
-#' @export
-xgb.load <- function(modelfile) {
-  if (is.null(modelfile))
-    stop("xgb.load: modelfile cannot be NULL")
-
-  handle <- xgb.Booster.handle(modelfile = modelfile)
-  # re-use modelfile if it is raw so we do not need to serialize
-  if (typeof(modelfile) == "raw") {
-    bst <- xgb.handleToBooster(handle, modelfile)
-  } else {
-    bst <- xgb.handleToBooster(handle, NULL)
-  }
-  bst <- xgb.Booster.complete(bst, saveraw = TRUE)
-  return(bst)
-}
diff --git a/ml-xgboost/R-package/R/xgb.load.raw.R b/ml-xgboost/R-package/R/xgb.load.raw.R
deleted file mode 100644
index 2a7d375..0000000
--- a/ml-xgboost/R-package/R/xgb.load.raw.R
+++ /dev/null
@@ -1,14 +0,0 @@
-#' Load serialised xgboost model from R's raw vector
-#'
-#' User can generate raw memory buffer by calling xgb.save.raw
-#'
-#' @param buffer the buffer returned by xgb.save.raw
-#'
-#' @export
-xgb.load.raw <- function(buffer) {
-  cachelist <- list()
-  handle <- .Call(XGBoosterCreate_R, cachelist)
-  .Call(XGBoosterLoadModelFromRaw_R, handle, buffer)
-  class(handle) <- "xgb.Booster.handle"
-  return (handle)
-}
diff --git a/ml-xgboost/R-package/R/xgb.model.dt.tree.R b/ml-xgboost/R-package/R/xgb.model.dt.tree.R
deleted file mode 100644
index 6a00797..0000000
--- a/ml-xgboost/R-package/R/xgb.model.dt.tree.R
+++ /dev/null
@@ -1,159 +0,0 @@
-#' Parse a boosted tree model text dump
-#' 
-#' Parse a boosted tree model text dump into a \code{data.table} structure.
-#' 
-#' @param feature_names character vector of feature names. If the model already
-#'          contains feature names, those would be used when \code{feature_names=NULL} (default value).
-#'          Non-null \code{feature_names} could be provided to override those in the model.
-#' @param model object of class \code{xgb.Booster}
-#' @param text \code{character} vector previously generated by the \code{xgb.dump} 
-#'          function  (where parameter \code{with_stats = TRUE} should have been set).
-#'          \code{text} takes precedence over \code{model}.
-#' @param trees an integer vector of tree indices that should be parsed.
-#'          If set to \code{NULL}, all trees of the model are parsed.
-#'          It could be useful, e.g., in multiclass classification to get only
-#'          the trees of one certain class. IMPORTANT: the tree index in xgboost models
-#'          is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).
-#' @param use_int_id a logical flag indicating whether nodes in columns "Yes", "No", "Missing" should be
-#'          represented as integers (when FALSE) or as "Tree-Node" character strings (when FALSE).
-#' @param ... currently not used.
-#'
-#' @return 
-#' A \code{data.table} with detailed information about model trees' nodes.
-#'
-#' The columns of the \code{data.table} are:
-#' 
-#' \itemize{
-#'  \item \code{Tree}: integer ID of a tree in a model (zero-based index)
-#'  \item \code{Node}: integer ID of a node in a tree (zero-based index)
-#'  \item \code{ID}: character identifier of a node in a model (only when \code{use_int_id=FALSE})
-#'  \item \code{Feature}: for a branch node, it's a feature id or name (when available);
-#'              for a leaf note, it simply labels it as \code{'Leaf'}
-#'  \item \code{Split}: location of the split for a branch node (split condition is always "less than")
-#'  \item \code{Yes}: ID of the next node when the split condition is met
-#'  \item \code{No}: ID of the next node when the split condition is not met
-#'  \item \code{Missing}: ID of the next node when branch value is missing
-#'  \item \code{Quality}: either the split gain (change in loss) or the leaf value
-#'  \item \code{Cover}: metric related to the number of observation either seen by a split
-#'                      or collected by a leaf during training.
-#' } 
-#' 
-#' When \code{use_int_id=FALSE}, columns "Yes", "No", and "Missing" point to model-wide node identifiers
-#' in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from 
-#' the corresponding trees in the "Node" column.
-#' 
-#' @examples
-#' # Basic use:
-#' 
-#' data(agaricus.train, package='xgboost')
-#' 
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
-#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' 
-#' (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
-#' 
-#' # This bst model already has feature_names stored with it, so those would be used when 
-#' # feature_names is not set:
-#' (dt <- xgb.model.dt.tree(model = bst))
-#' 
-#' # How to match feature names of splits that are following a current 'Yes' branch:
-#' 
-#' merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
-#'  
-#' @export
-xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
-                              trees = NULL, use_int_id = FALSE, ...){
-  check.deprecation(...)
-  
-  if (!inherits(model, "xgb.Booster") && !is.character(text)) {
-    stop("Either 'model' must be an object of class xgb.Booster\n",
-         "  or 'text' must be a character vector with the result of xgb.dump\n",
-         "  (or NULL if 'model' was provided).")
-  }
-  
-  if (is.null(feature_names) && !is.null(model) && !is.null(model$feature_names))
-    feature_names <- model$feature_names
-  
-  if (!(is.null(feature_names) || is.character(feature_names))) {
-    stop("feature_names: must be a character vector")
-  }
-  
-  if (!(is.null(trees) || is.numeric(trees))) {
-    stop("trees: must be a vector of integers.")
-  }
-  
-  if (is.null(text)){
-    text <- xgb.dump(model = model, with_stats = TRUE)
-  }
-  
-  if (length(text) < 2 ||
-      sum(stri_detect_regex(text, 'yes=(\\d+),no=(\\d+)')) < 1) {
-    stop("Non-tree model detected! This function can only be used with tree models.")
-  }
-  
-  position <- which(!is.na(stri_match_first_regex(text, "booster")))
-  
-  add.tree.id <- function(node, tree) if (use_int_id) node else paste(tree, node, sep = "-")
-  
-  anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
-  
-  td <- data.table(t = text)
-  td[position, Tree := 1L]
-  td[, Tree := cumsum(ifelse(is.na(Tree), 0L, Tree)) - 1L]
-  
-  if (is.null(trees)) {
-    trees <- 0:max(td$Tree)
-  } else {
-    trees <- trees[trees >= 0 & trees <= max(td$Tree)]
-  }
-  td <- td[Tree %in% trees & !grepl('^booster', t)]
-  
-  td[, Node := stri_match_first_regex(t, "(\\d+):")[,2] %>% as.integer ]
-  if (!use_int_id) td[, ID := add.tree.id(Node, Tree)]
-  td[, isLeaf := !is.na(stri_match_first_regex(t, "leaf"))]
-
-  # parse branch lines
-  branch_rx <- paste0("f(\\d+)<(", anynumber_regex, ")\\] yes=(\\d+),no=(\\d+),missing=(\\d+),",
-                      "gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
-  branch_cols <- c("Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
-  td[isLeaf == FALSE, 
-     (branch_cols) := {
-      # skip some indices with spurious capture groups from anynumber_regex
-      xtr <- stri_match_first_regex(t, branch_rx)[, c(2,3,5,6,7,8,10), drop = FALSE]
-      xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
-      lapply(seq_len(ncol(xtr)), function(i) xtr[,i])
-    }]
-  # assign feature_names when available
-  if (!is.null(feature_names)) {
-    if (length(feature_names) <= max(as.numeric(td$Feature), na.rm = TRUE))
-      stop("feature_names has less elements than there are features used in the model")
-    td[isLeaf == FALSE, Feature := feature_names[as.numeric(Feature) + 1] ]
-  }
-  
-  # parse leaf lines
-  leaf_rx <- paste0("leaf=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
-  leaf_cols <- c("Feature", "Quality", "Cover")
-  td[isLeaf == TRUE,
-     (leaf_cols) := {
-      xtr <- stri_match_first_regex(t, leaf_rx)[, c(2,4)]
-      c("Leaf", lapply(seq_len(ncol(xtr)), function(i) xtr[,i]))
-    }]
-  
-  # convert some columns to numeric
-  numeric_cols <- c("Split", "Quality", "Cover")
-  td[, (numeric_cols) := lapply(.SD, as.numeric), .SDcols = numeric_cols]
-  if (use_int_id) {
-    int_cols <- c("Yes", "No", "Missing")
-    td[, (int_cols) := lapply(.SD, as.integer), .SDcols = int_cols]
-  }
-  
-  td[, t := NULL]
-  td[, isLeaf := NULL]
-  
-  td[order(Tree, Node)]
-}
-
-# Avoid error messages during CRAN check.
-# The reason is that these variables are never declared
-# They are mainly column names inferred by Data.table...
-globalVariables(c("Tree", "Node", "ID", "Feature", "t", "isLeaf",".SD", ".SDcols"))
diff --git a/ml-xgboost/R-package/R/xgb.plot.deepness.R b/ml-xgboost/R-package/R/xgb.plot.deepness.R
deleted file mode 100644
index 87d632a..0000000
--- a/ml-xgboost/R-package/R/xgb.plot.deepness.R
+++ /dev/null
@@ -1,150 +0,0 @@
-#' Plot model trees deepness
-#'
-#' Visualizes distributions related to depth of tree leafs.
-#' \code{xgb.plot.deepness} uses base R graphics, while \code{xgb.ggplot.deepness} uses the ggplot backend.
-#' 
-#' @param model either an \code{xgb.Booster} model generated by the \code{xgb.train} function
-#'        or a data.table result of the \code{xgb.model.dt.tree} function.
-#' @param plot (base R barplot) whether a barplot should be produced. 
-#'        If FALSE, only a data.table is returned.
-#' @param which which distribution to plot (see details).
-#' @param ... other parameters passed to \code{barplot} or \code{plot}.
-#' 
-#' @details
-#' 
-#' When \code{which="2x1"}, two distributions with respect to the leaf depth
-#' are plotted on top of each other:
-#' \itemize{
-#'  \item the distribution of the number of leafs in a tree model at a certain depth;
-#'  \item the distribution of average weighted number of observations ("cover") 
-#'        ending up in leafs at certain depth.
-#' }
-#' Those could be helpful in determining sensible ranges of the \code{max_depth} 
-#' and \code{min_child_weight} parameters.
-#' 
-#' When \code{which="max.depth"} or \code{which="med.depth"}, plots of either maximum or median depth
-#' per tree with respect to tree number are created. And \code{which="med.weight"} allows to see how
-#' a tree's median absolute leaf weight changes through the iterations.
-#'
-#' This function was inspired by the blog post
-#' \url{https://github.com/aysent/random-forest-leaf-visualization}.
-#' 
-#' @return
-#' 
-#' Other than producing plots (when \code{plot=TRUE}), the \code{xgb.plot.deepness} function
-#' silently returns a processed data.table where each row corresponds to a terminal leaf in a tree model,
-#' and contains information about leaf's depth, cover, and weight (which is used in calculating predictions).
-#' 
-#' The \code{xgb.ggplot.deepness} silently returns either a list of two ggplot graphs when \code{which="2x1"}
-#' or a single ggplot graph for the other \code{which} options.
-#'
-#' @seealso 
-#' 
-#' \code{\link{xgb.train}}, \code{\link{xgb.model.dt.tree}}.
-#' 
-#' @examples
-#' 
-#' data(agaricus.train, package='xgboost')
-#'
-#' # Change max_depth to a higher number to get a more significant result
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 6,
-#'                eta = 0.1, nthread = 2, nrounds = 50, objective = "binary:logistic",
-#'                subsample = 0.5, min_child_weight = 2)
-#'
-#' xgb.plot.deepness(bst)
-#' xgb.ggplot.deepness(bst)
-#' 
-#' xgb.plot.deepness(bst, which='max.depth', pch=16, col=rgb(0,0,1,0.3), cex=2)
-#' 
-#' xgb.plot.deepness(bst, which='med.weight', pch=16, col=rgb(0,0,1,0.3), cex=2)
-#'
-#' @rdname xgb.plot.deepness
-#' @export
-xgb.plot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med.depth", "med.weight"),
-                              plot = TRUE, ...) {
-  
-  if (!(inherits(model, "xgb.Booster") || is.data.table(model)))
-    stop("model: Has to be either an xgb.Booster model generaged by the xgb.train function\n",
-         "or a data.table result of the xgb.importance function")
-
-  if (!requireNamespace("igraph", quietly = TRUE))
-    stop("igraph package is required for plotting the graph deepness.", call. = FALSE)
-
-  which <- match.arg(which)
-  
-  dt_tree <- model
-  if (inherits(model, "xgb.Booster"))
-    dt_tree <- xgb.model.dt.tree(model = model)
-  
-  if (!all(c("Feature", "Tree", "ID", "Yes", "No", "Cover") %in% colnames(dt_tree)))
-    stop("Model tree columns are not as expected!\n",
-         "  Note that this function works only for tree models.")
-  
-  dt_depths <- merge(get.leaf.depth(dt_tree), dt_tree[, .(ID, Cover, Weight = Quality)], by = "ID")
-  setkeyv(dt_depths, c("Tree", "ID"))
-  # count by depth levels, and also calculate average cover at a depth
-  dt_summaries <- dt_depths[, .(.N, Cover = mean(Cover)), Depth]
-  setkey(dt_summaries, "Depth")
-  
-  if (plot) {
-    if (which == "2x1") {
-      op <- par(no.readonly = TRUE)
-      par(mfrow = c(2,1),
-          oma = c(3,1,3,1) + 0.1,
-          mar = c(1,4,1,0) + 0.1)
-
-      dt_summaries[, barplot(N, border = NA, ylab = 'Number of leafs', ...)]
-
-      dt_summaries[, barplot(Cover, border = NA, ylab = "Weighted cover", names.arg = Depth, ...)]
-    
-      title("Model complexity", xlab = "Leaf depth", outer = TRUE, line = 1)
-      par(op)
-    } else if (which == "max.depth") {
-      dt_depths[, max(Depth), Tree][
-                , plot(jitter(V1, amount = 0.1) ~ Tree, ylab = 'Max tree leaf depth', xlab = "tree #", ...)]
-    } else if (which == "med.depth") {
-      dt_depths[, median(as.numeric(Depth)), Tree][
-                , plot(jitter(V1, amount = 0.1) ~ Tree, ylab = 'Median tree leaf depth', xlab = "tree #", ...)]
-    } else if (which == "med.weight") {
-      dt_depths[, median(abs(Weight)), Tree][
-                , plot(V1 ~ Tree, ylab = 'Median absolute leaf weight', xlab = "tree #", ...)]
-    }
-  }
-  invisible(dt_depths)
-}
-
-# Extract path depths from root to leaf
-# from data.table containing the nodes and edges of the trees.
-# internal utility function
-get.leaf.depth <- function(dt_tree) {
-  # extract tree graph's edges
-  dt_edges <- rbindlist(list(
-      dt_tree[Feature != "Leaf", .(ID, To = Yes, Tree)],
-      dt_tree[Feature != "Leaf", .(ID, To = No, Tree)]
-    ))
-  # whether "To" is a leaf:
-  dt_edges <- 
-    merge(dt_edges,
-          dt_tree[Feature == "Leaf", .(ID, Leaf = TRUE)],
-          all.x = TRUE, by.x = "To", by.y = "ID")
-  dt_edges[is.na(Leaf), Leaf := FALSE]
-
-  dt_edges[, {
-    graph <- igraph::graph_from_data_frame(.SD[,.(ID, To)])
-    # min(ID) in a tree is a root node
-    paths_tmp <- igraph::shortest_paths(graph, from = min(ID), to = To[Leaf == TRUE])
-    # list of paths to each leaf in a tree
-    paths <- lapply(paths_tmp$vpath, names)
-    # combine into a resulting path lengths table for a tree
-    data.table(Depth = sapply(paths, length), ID = To[Leaf == TRUE])
-  }, by = Tree]
-}
-
-# Avoid error messages during CRAN check.
-# The reason is that these variables are never declared
-# They are mainly column names inferred by Data.table...
-globalVariables(
-  c(
-    ".N", "N", "Depth", "Quality", "Cover", "Tree", "ID", "Yes", "No", "Feature", "Leaf", "Weight"
-  )
-)
diff --git a/ml-xgboost/R-package/R/xgb.plot.importance.R b/ml-xgboost/R-package/R/xgb.plot.importance.R
deleted file mode 100644
index 598bd3b..0000000
--- a/ml-xgboost/R-package/R/xgb.plot.importance.R
+++ /dev/null
@@ -1,125 +0,0 @@
-#' Plot feature importance as a bar graph
-#'
-#' Represents previously calculated feature importance as a bar graph.
-#' \code{xgb.plot.importance} uses base R graphics, while \code{xgb.ggplot.importance} uses the ggplot backend.
-#'
-#' @param importance_matrix a \code{data.table} returned by \code{\link{xgb.importance}}.
-#' @param top_n maximal number of top features to include into the plot.
-#' @param measure the name of importance measure to plot.
-#'        When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.
-#' @param rel_to_first whether importance values should be represented as relative to the highest ranked feature.
-#'        See Details.
-#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.
-#'        When it is NULL, the existing \code{par('mar')} is used.
-#' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}.
-#' @param plot (base R barplot) whether a barplot should be produced.
-#'        If FALSE, only a data.table is returned.
-#' @param n_clusters (ggplot only) a \code{numeric} vector containing the min and the max range
-#'        of the possible number of clusters of bars.
-#' @param ... other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).
-#'
-#' @details
-#' The graph represents each feature as a horizontal bar of length proportional to the importance of a feature.
-#' Features are shown ranked in a decreasing importance order.
-#' It works for importances from both \code{gblinear} and \code{gbtree} models.
-#'
-#' When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
-#' For gbtree model, that would mean being normalized to the total of 1
-#' ("what is feature's importance contribution relative to the whole model?").
-#' For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
-#' Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
-#' "what is feature's importance contribution relative to the most important feature?"
-#'
-#' The ggplot-backend method also performs 1-D clustering of the importance values,
-#' with bar colors corresponding to different clusters that have somewhat similar importance values.
-#'
-#' @return
-#' The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
-#' and silently returns a processed data.table with \code{n_top} features sorted by importance.
-#'
-#' The \code{xgb.ggplot.importance} function returns a ggplot graph which could be customized afterwards.
-#' E.g., to change the title of the graph, add \code{+ ggtitle("A GRAPH NAME")} to the result.
-#'
-#' @seealso
-#' \code{\link[graphics]{barplot}}.
-#'
-#' @examples
-#' data(agaricus.train)
-#'
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-#'
-#' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
-#'
-#' xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
-#'
-#' (gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
-#' gg + ggplot2::ylab("Frequency")
-#'
-#' @rdname xgb.plot.importance
-#' @export
-xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
-                                rel_to_first = FALSE, left_margin = 10, cex = NULL, plot = TRUE, ...) {
-  check.deprecation(...)
-  if (!is.data.table(importance_matrix))  {
-    stop("importance_matrix: must be a data.table")
-  }
-
-  imp_names <- colnames(importance_matrix)
-  if (is.null(measure)) {
-    if (all(c("Feature", "Gain") %in% imp_names)) {
-      measure <- "Gain"
-    } else if (all(c("Feature", "Weight") %in% imp_names)) {
-      measure <- "Weight"
-    } else {
-      stop("Importance matrix column names are not as expected!")
-    }
-  } else {
-    if (!measure %in% imp_names)
-      stop("Invalid `measure`")
-    if (!"Feature" %in% imp_names)
-      stop("Importance matrix column names are not as expected!")
-  }
-
-  # also aggregate, just in case when the values were not yet summed up by feature
-  importance_matrix <- importance_matrix[, Importance := sum(get(measure)), by = Feature]
-
-  # make sure it's ordered
-  importance_matrix <- importance_matrix[order(-abs(Importance))]
-
-  if (!is.null(top_n)) {
-    top_n <- min(top_n, nrow(importance_matrix))
-    importance_matrix <- head(importance_matrix, top_n)
-  }
-  if (rel_to_first) {
-    importance_matrix[, Importance := Importance/max(abs(Importance))]
-  }
-  if (is.null(cex)) {
-    cex <- 2.5/log2(1 + nrow(importance_matrix))
-  }
-
-  if (plot) {
-    op <- par(no.readonly = TRUE)
-    mar <- op$mar
-    if (!is.null(left_margin))
-      mar[2] <- left_margin
-    par(mar = mar)
-
-    # reverse the order of rows to have the highest ranked at the top
-    importance_matrix[nrow(importance_matrix):1,
-                      barplot(Importance, horiz = TRUE, border = NA, cex.names = cex,
-                              names.arg = Feature, las = 1, ...)]
-    grid(NULL, NA)
-    # redraw over the grid
-    importance_matrix[nrow(importance_matrix):1,
-                      barplot(Importance, horiz = TRUE, border = NA, add = TRUE)]
-    par(op)
-  }
-
-  invisible(importance_matrix)
-}
-
-# Avoid error messages during CRAN check.
-# The reason is that these variables are never declared
-# They are mainly column names inferred by Data.table...
-globalVariables(c("Feature", "Importance"))
diff --git a/ml-xgboost/R-package/R/xgb.plot.multi.trees.R b/ml-xgboost/R-package/R/xgb.plot.multi.trees.R
deleted file mode 100644
index 3e7b04b..0000000
--- a/ml-xgboost/R-package/R/xgb.plot.multi.trees.R
+++ /dev/null
@@ -1,148 +0,0 @@
-#' Project all trees on one tree and plot it
-#'
-#' Visualization of the ensemble of trees as a single collective unit.
-#'
-#' @param model produced by the \code{xgb.train} function.
-#' @param feature_names names of each feature as a \code{character} vector.
-#' @param features_keep number of features to keep in each position of the multi trees.
-#' @param plot_width width in pixels of the graph to produce
-#' @param plot_height height in pixels of the graph to produce
-#' @param render a logical flag for whether the graph should be rendered (see Value).
-#' @param ... currently not used
-#' 
-#' @details
-#'
-#' This function tries to capture the complexity of a gradient boosted tree model
-#' in a cohesive way by compressing an ensemble of trees into a single tree-graph representation.
-#' The goal is to improve the interpretability of a model generally seen as black box.
-#'
-#' Note: this function is applicable to tree booster-based models only.
-#'
-#' It takes advantage of the fact that the shape of a binary tree is only defined by
-#' its depth (therefore, in a boosting model, all trees have similar shape).
-#'
-#' Moreover, the trees tend to reuse the same features.
-#'
-#' The function projects each tree onto one, and keeps for each position the
-#' \code{features_keep} first features (based on the Gain per feature measure).
-#'
-#' This function is inspired by this blog post:
-#' \url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
-#'
-#' @return
-#'
-#' When \code{render = TRUE}:
-#' returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
-#' Similar to ggplot objects, it needs to be printed to see it when not running from command line.
-#'
-#' When \code{render = FALSE}:
-#' silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
-#' This could be useful if one wants to modify some of the graph attributes
-#' before rendering the graph with \code{\link[DiagrammeR]{render_graph}}.
-#'
-#' @examples
-#'
-#' data(agaricus.train, package='xgboost')
-#'
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
-#'                eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
-#'                min_child_weight = 50, verbose = 0)
-#'
-#' p <- xgb.plot.multi.trees(model = bst, features_keep = 3)
-#' print(p)
-#'
-#' \dontrun{
-#' # Below is an example of how to save this plot to a file.
-#' # Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
-#' library(DiagrammeR)
-#' gr <- xgb.plot.multi.trees(model=bst, features_keep = 3, render=FALSE)
-#' export_graph(gr, 'tree.pdf', width=1500, height=600)
-#' }
-#'
-#' @export
-xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5, plot_width = NULL, plot_height = NULL,
-                                 render = TRUE, ...){
-  check.deprecation(...)
-  tree.matrix <- xgb.model.dt.tree(feature_names = feature_names, model = model)
-
-  # first number of the path represents the tree, then the following numbers are related to the path to follow
-  # root init
-  root.nodes <- tree.matrix[stri_detect_regex(ID, "\\d+-0"), ID]
-  tree.matrix[ID %in% root.nodes, abs.node.position := root.nodes]
-
-  precedent.nodes <- root.nodes
-
-  while(tree.matrix[,sum(is.na(abs.node.position))] > 0) {
-    yes.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(Yes)]
-    no.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(No)]
-    yes.nodes.abs.pos <- yes.row.nodes[, abs.node.position] %>% paste0("_0")
-    no.nodes.abs.pos <- no.row.nodes[, abs.node.position] %>% paste0("_1")
-    
-    tree.matrix[ID %in% yes.row.nodes[, Yes], abs.node.position := yes.nodes.abs.pos]
-    tree.matrix[ID %in% no.row.nodes[, No], abs.node.position := no.nodes.abs.pos]
-    precedent.nodes <- c(yes.nodes.abs.pos, no.nodes.abs.pos)
-  }
-  
-  tree.matrix[!is.na(Yes), Yes := paste0(abs.node.position, "_0")]
-  tree.matrix[!is.na(No), No := paste0(abs.node.position, "_1")]
-  
-  remove.tree <- . %>% stri_replace_first_regex(pattern = "^\\d+-", replacement = "")
-  
-  tree.matrix[,`:=`(abs.node.position = remove.tree(abs.node.position),
-                    Yes = remove.tree(Yes),
-                    No = remove.tree(No))]
-  
-  nodes.dt <- tree.matrix[
-        , .(Quality = sum(Quality))
-        , by = .(abs.node.position, Feature)
-      ][, .(Text = paste0(Feature[1:min(length(Feature), features_keep)],
-                          " (",
-                          format(Quality[1:min(length(Quality), features_keep)], digits=5),
-                          ")") %>%
-                   paste0(collapse = "\n"))
-        , by = abs.node.position]
-  
-  edges.dt <- tree.matrix[Feature != "Leaf", .(abs.node.position, Yes)] %>%
-    list(tree.matrix[Feature != "Leaf",.(abs.node.position, No)]) %>%
-    rbindlist() %>%
-    setnames(c("From", "To")) %>%
-    .[, .N, .(From, To)] %>%
-    .[, N:=NULL]
-  
-  nodes <- DiagrammeR::create_node_df(
-    n = nrow(nodes.dt),
-    label = nodes.dt[,Text]
-  )
-  
-  edges <- DiagrammeR::create_edge_df(
-    from = match(edges.dt[,From], nodes.dt[,abs.node.position]),
-    to = match(edges.dt[,To], nodes.dt[,abs.node.position]),
-    rel = "leading_to")
-  
-  graph <- DiagrammeR::create_graph(
-      nodes_df = nodes,
-      edges_df = edges,
-      attr_theme = NULL
-      ) %>%
-    DiagrammeR::add_global_graph_attrs(
-      attr_type = "graph",
-      attr  = c("layout", "rankdir"),
-      value = c("dot", "LR")
-      ) %>%
-    DiagrammeR::add_global_graph_attrs(
-      attr_type = "node",
-      attr  = c("color", "fillcolor", "style", "shape", "fontname"),
-      value = c("DimGray", "beige", "filled", "rectangle", "Helvetica")
-      ) %>%
-    DiagrammeR::add_global_graph_attrs(
-      attr_type = "edge",
-      attr  = c("color", "arrowsize", "arrowhead", "fontname"),
-      value = c("DimGray", "1.5", "vee", "Helvetica"))
-
-  if (!render) return(invisible(graph))
-
-  DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
-}
-
-globalVariables(c(".N", "N", "From", "To", "Text", "Feature", "no.nodes.abs.pos",
-                  "ID", "Yes", "No", "Tree", "yes.nodes.abs.pos", "abs.node.position"))
diff --git a/ml-xgboost/R-package/R/xgb.plot.shap.R b/ml-xgboost/R-package/R/xgb.plot.shap.R
deleted file mode 100644
index 18f6aaa..0000000
--- a/ml-xgboost/R-package/R/xgb.plot.shap.R
+++ /dev/null
@@ -1,218 +0,0 @@
-#' SHAP contribution dependency plots
-#'
-#' Visualizing the SHAP feature contribution to prediction dependencies on feature value.
-#'
-#' @param data data as a \code{matrix} or \code{dgCMatrix}.
-#' @param shap_contrib a matrix of SHAP contributions that was computed earlier for the above
-#'          \code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.
-#' @param features a vector of either column indices or of feature names to plot. When it is NULL,
-#'          feature importance is calculated, and \code{top_n} high ranked features are taken.
-#' @param top_n when \code{features} is NULL, top_n [1, 100] most important features in a model are taken.
-#' @param model an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
-#'          or \code{features} is missing.
-#' @param trees passed to \code{\link{xgb.importance}} when \code{features = NULL}.
-#' @param target_class is only relevant for multiclass models. When it is set to a 0-based class index,
-#'          only SHAP contributions for that specific class are used.
-#'          If it is not set, SHAP importances are averaged over all classes.
-#' @param approxcontrib passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.
-#' @param subsample a random fraction of data points to use for plotting. When it is NULL,
-#'          it is set so that up to 100K data points are used.
-#' @param n_col a number of columns in a grid of plots.
-#' @param col color of the scatterplot markers.
-#' @param pch scatterplot marker.
-#' @param discrete_n_uniq a maximal number of unique values in a feature to consider it as discrete.
-#' @param discrete_jitter an \code{amount} parameter of jitter added to discrete features' positions.
-#' @param ylab a y-axis label in 1D plots.
-#' @param plot_NA whether the contributions of cases with missing values should also be plotted.
-#' @param col_NA a color of marker for missing value contributions.
-#' @param pch_NA a marker type for NA values.
-#' @param pos_NA a relative position of the x-location where NA values are shown:
-#'          \code{min(x) + (max(x) - min(x)) * pos_NA}.
-#' @param plot_loess whether to plot loess-smoothed curves. The smoothing is only done for features with
-#'          more than 5 distinct values.
-#' @param col_loess a color to use for the loess curves.
-#' @param span_loess the \code{span} parameter in \code{\link[stats]{loess}}'s call.
-#' @param which whether to do univariate or bivariate plotting. NOTE: only 1D is implemented so far.
-#' @param plot whether a plot should be drawn. If FALSE, only a lits of matrices is returned.
-#' @param ... other parameters passed to \code{plot}.
-#'
-#' @details
-#'
-#' These scatterplots represent how SHAP feature contributions depend of feature values.
-#' The similarity to partial dependency plots is that they also give an idea for how feature values
-#' affect predictions. However, in partial dependency plots, we usually see marginal dependencies
-#' of model prediction on feature value, while SHAP contribution dependency plots display the estimated
-#' contributions of a feature to model prediction for each individual case.
-#'
-#' When \code{plot_loess = TRUE} is set, feature values are rounded to 3 significant digits and
-#' weighted LOESS is computed and plotted, where weights are the numbers of data points
-#' at each rounded value.
-#'
-#' Note: SHAP contributions are shown on the scale of model margin. E.g., for a logistic binomial objective,
-#' the margin is prediction before a sigmoidal transform into probability-like values.
-#' Also, since SHAP stands for "SHapley Additive exPlanation" (model prediction = sum of SHAP
-#' contributions for all features + bias), depending on the objective used, transforming SHAP
-#' contributions for a feature from the marginal to the prediction space is not necessarily
-#' a meaningful thing to do.
-#'
-#' @return
-#'
-#' In addition to producing plots (when \code{plot=TRUE}), it silently returns a list of two matrices:
-#' \itemize{
-#'  \item \code{data} the values of selected features;
-#'  \item \code{shap_contrib} the contributions of selected features.
-#' }
-#'
-#' @references
-#'
-#' Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
-#'
-#' Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
-#'
-#' @examples
-#'
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#'
-#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
-#'                eta = 0.1, max_depth = 3, subsample = .5,
-#'                method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
-#'
-#' xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
-#' contr <- predict(bst, agaricus.test$data, predcontrib = TRUE)
-#' xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
-#'
-#' # multiclass example - plots for each class separately:
-#' nclass <- 3
-#' nrounds <- 20
-#' x <- as.matrix(iris[, -5])
-#' set.seed(123)
-#' is.na(x[sample(nrow(x) * 4, 30)]) <- TRUE # introduce some missing values
-#' mbst <- xgboost(data = x, label = as.numeric(iris$Species) - 1, nrounds = nrounds,
-#'                 max_depth = 2, eta = 0.3, subsample = .5, nthread = 2,
-#'                 objective = "multi:softprob", num_class = nclass, verbose = 0)
-#' trees0 <- seq(from=0, by=nclass, length.out=nrounds)
-#' col <- rgb(0, 0, 1, 0.5)
-#' xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4,
-#'               n_col = 2, col = col, pch = 16, pch_NA = 17)
-#' xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
-#'               n_col = 2, col = col, pch = 16, pch_NA = 17)
-#' xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
-#'               n_col = 2, col = col, pch = 16, pch_NA = 17)
-#'
-#' @rdname xgb.plot.shap
-#' @export
-xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL,
-                          trees = NULL, target_class = NULL, approxcontrib = FALSE,
-                          subsample = NULL, n_col = 1, col = rgb(0, 0, 1, 0.2), pch = '.',
-                          discrete_n_uniq = 5, discrete_jitter = 0.01, ylab = "SHAP",
-                          plot_NA = TRUE, col_NA = rgb(0.7, 0, 1, 0.6), pch_NA = '.', pos_NA = 1.07,
-                          plot_loess = TRUE, col_loess = 2, span_loess = 0.5,
-                          which = c("1d", "2d"), plot = TRUE, ...) {
-
-  if (!is.matrix(data) && !inherits(data, "dgCMatrix"))
-    stop("data: must be either matrix or dgCMatrix")
-
-  if (is.null(shap_contrib) && (is.null(model) || !inherits(model, "xgb.Booster")))
-    stop("when shap_contrib is not provided, one must provide an xgb.Booster model")
-
-  if (is.null(features) && (is.null(model) || !inherits(model, "xgb.Booster")))
-    stop("when features are not provided, one must provide an xgb.Booster model to rank the features")
-
-  if (!is.null(shap_contrib) &&
-      (!is.matrix(shap_contrib) || nrow(shap_contrib) != nrow(data) || ncol(shap_contrib) != ncol(data) + 1))
-    stop("shap_contrib is not compatible with the provided data")
-
-  nsample <- if (is.null(subsample)) min(100000, nrow(data)) else as.integer(subsample * nrow(data))
-  idx <- sample(1:nrow(data), nsample)
-  data <- data[idx,]
-
-  if (is.null(shap_contrib)) {
-    shap_contrib <- predict(model, data, predcontrib = TRUE, approxcontrib = approxcontrib)
-  } else {
-    shap_contrib <- shap_contrib[idx,]
-  }
-
-  which <- match.arg(which)
-  if (which == "2d")
-    stop("2D plots are not implemented yet")
-
-  if (is.null(features)) {
-    imp <- xgb.importance(model = model, trees = trees)
-    top_n <- as.integer(top_n[1])
-    if (top_n < 1 && top_n > 100)
-      stop("top_n: must be an integer within [1, 100]")
-    features <- imp$Feature[1:min(top_n, NROW(imp))]
-  }
-
-  if (is.character(features)) {
-    if (is.null(colnames(data)))
-      stop("Either provide `data` with column names or provide `features` as column indices")
-    features <- match(features, colnames(data))
-  }
-
-  if (n_col > length(features)) n_col <- length(features)
-
-  if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
-    shap_contrib <- if (!is.null(target_class)) shap_contrib[[target_class + 1]]
-                    else Reduce("+", lapply(shap_contrib, abs))
-  }
-
-  shap_contrib <- shap_contrib[, features, drop = FALSE]
-  data <- data[, features, drop = FALSE]
-  cols <- colnames(data)
-  if (is.null(cols)) cols <- colnames(shap_contrib)
-  if (is.null(cols)) cols <- paste0('X', 1:ncol(data))
-  colnames(data) <- cols
-  colnames(shap_contrib) <- cols
-
-  if (plot && which == "1d") {
-    op <- par(mfrow = c(ceiling(length(features) / n_col), n_col),
-              oma = c(0,0,0,0) + 0.2,
-              mar = c(3.5,3.5,0,0) + 0.1,
-              mgp = c(1.7, 0.6, 0))
-    for (f in cols) {
-      ord <- order(data[, f])
-      x <- data[, f][ord]
-      y <- shap_contrib[, f][ord]
-      x_lim <- range(x, na.rm = TRUE)
-      y_lim <- range(y, na.rm = TRUE)
-      do_na <- plot_NA && any(is.na(x))
-      if (do_na) {
-        x_range <- diff(x_lim)
-        loc_na <- min(x, na.rm = TRUE) + x_range * pos_NA
-        x_lim <- range(c(x_lim, loc_na))
-      }
-      x_uniq <- unique(x)
-      x2plot <- x
-      # add small jitter for discrete features with <= 5 distinct values
-      if (length(x_uniq) <= discrete_n_uniq)
-        x2plot <- jitter(x, amount = discrete_jitter * min(diff(x_uniq), na.rm = TRUE))
-      plot(x2plot, y, pch = pch, xlab = f, col = col, xlim = x_lim, ylim = y_lim, ylab = ylab, ...)
-      grid()
-      if (plot_loess) {
-        # compress x to 3 digits, and mean-aggredate y
-        zz <- data.table(x = signif(x, 3), y)[, .(.N, y=mean(y)), x]
-        if (nrow(zz) <= 5) {
-          lines(zz$x, zz$y, col = col_loess)
-        } else {
-          lo <- stats::loess(y ~ x, data = zz, weights = zz$N, span = span_loess)
-          zz$y_lo <- predict(lo, zz, type = "link")
-          lines(zz$x, zz$y_lo, col = col_loess)
-        }
-      }
-      if (do_na) {
-        i_na <- which(is.na(x))
-        x_na <- rep(loc_na, length(i_na))
-        x_na <- jitter(x_na, amount = x_range * 0.01)
-        points(x_na, y[i_na], pch = pch_NA, col = col_NA)
-      }
-    }
-    par(op)
-  }
-  if (plot && which == "2d") {
-    # TODO
-    warning("Bivariate plotting is currently not available.")
-  }
-  invisible(list(data = data, shap_contrib = shap_contrib))
-}
diff --git a/ml-xgboost/R-package/R/xgb.plot.tree.R b/ml-xgboost/R-package/R/xgb.plot.tree.R
deleted file mode 100644
index 29c37d6..0000000
--- a/ml-xgboost/R-package/R/xgb.plot.tree.R
+++ /dev/null
@@ -1,138 +0,0 @@
-#' Plot a boosted tree model
-#' 
-#' Read a tree model text dump and plot the model. 
-#' 
-#' @param feature_names names of each feature as a \code{character} vector.
-#' @param model produced by the \code{xgb.train} function.
-#' @param trees an integer vector of tree indices that should be visualized.
-#'          If set to \code{NULL}, all trees of the model are included.
-#'          IMPORTANT: the tree index in xgboost model is zero-based
-#'          (e.g., use \code{trees = 0:2} for the first 3 trees in a model).
-#' @param plot_width  the width of the diagram in pixels.
-#' @param plot_height	the height of the diagram in pixels.
-#' @param render a logical flag for whether the graph should be rendered (see Value).
-#' @param show_node_id a logical flag for whether to show node id's in the graph.
-#' @param ... currently not used.
-#'
-#' @details 
-#' 
-#' The content of each node is organised that way:
-#' 
-#' \itemize{
-#'  \item Feature name.
-#'  \item \code{Cover}: The sum of second order gradient of training data classified to the leaf.
-#'        If it is square loss, this simply corresponds to the number of instances seen by a split
-#'        or collected by a leaf during training.
-#'        The deeper in the tree a node is, the lower this metric will be.
-#'  \item \code{Gain} (for split nodes): the information gain metric of a split
-#'        (corresponds to the importance of the node in the model).
-#'  \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
-#' } 
-#' The tree root nodes also indicate the Tree index (0-based).
-#' 
-#' The "Yes" branches are marked by the "< split_value" label.
-#' The branches that also used for missing values are marked as bold
-#' (as in "carrying extra capacity").
-#' 
-#' This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
-#'  
-#' @return
-#' 
-#' When \code{render = TRUE}:
-#' returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
-#' Similar to ggplot objects, it needs to be printed to see it when not running from command line.
-#' 
-#' When \code{render = FALSE}:
-#' silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
-#' This could be useful if one wants to modify some of the graph attributes
-#' before rendering the graph with \code{\link[DiagrammeR]{render_graph}}.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' 
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
-#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' # plot all the trees
-#' xgb.plot.tree(model = bst)
-#' # plot only the first tree and display the node ID:
-#' xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
-#' 
-#' \dontrun{
-#' # Below is an example of how to save this plot to a file. 
-#' # Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
-#' library(DiagrammeR)
-#' gr <- xgb.plot.tree(model=bst, trees=0:1, render=FALSE)
-#' export_graph(gr, 'tree.pdf', width=1500, height=1900)
-#' export_graph(gr, 'tree.png', width=1500, height=1900)
-#' }
-#' 
-#' @export
-xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot_width = NULL, plot_height = NULL,
-                          render = TRUE, show_node_id = FALSE, ...){
-  check.deprecation(...)
-  if (!inherits(model, "xgb.Booster")) {
-    stop("model: Has to be an object of class xgb.Booster")
-  }
-
-  if (!requireNamespace("DiagrammeR", quietly = TRUE)) {
-    stop("DiagrammeR package is required for xgb.plot.tree", call. = FALSE)
-  }
-  
-  dt <- xgb.model.dt.tree(feature_names = feature_names, model = model, trees = trees)
-
-  dt[, label:= paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Quality)]
-  if (show_node_id)
-    dt[, label := paste0(ID, ": ", label)]
-  dt[Node == 0, label := paste0("Tree ", Tree, "\n", label)]
-  dt[, shape:= "rectangle"][Feature == "Leaf", shape:= "oval"]
-  dt[, filledcolor:= "Beige"][Feature == "Leaf", filledcolor:= "Khaki"]
-  # in order to draw the first tree on top:
-  dt <- dt[order(-Tree)]
-  
-  nodes <- DiagrammeR::create_node_df(
-    n         = nrow(dt),
-    ID        = dt$ID,
-    label     = dt$label,
-    fillcolor = dt$filledcolor,
-    shape     = dt$shape,
-    data      = dt$Feature,
-    fontcolor = "black")
-  
-  edges <- DiagrammeR::create_edge_df(
-    from  = match(dt[Feature != "Leaf", c(ID)] %>% rep(2), dt$ID),
-    to    = match(dt[Feature != "Leaf", c(Yes, No)], dt$ID),
-    label = dt[Feature != "Leaf", paste("<", Split)] %>%
-            c(rep("", nrow(dt[Feature != "Leaf"]))),
-    style = dt[Feature != "Leaf", ifelse(Missing == Yes, "bold", "solid")] %>%
-            c(dt[Feature != "Leaf", ifelse(Missing == No, "bold", "solid")]),
-    rel   = "leading_to")
-
-  graph <- DiagrammeR::create_graph(
-      nodes_df = nodes,
-      edges_df = edges,
-      attr_theme = NULL
-      ) %>%
-    DiagrammeR::add_global_graph_attrs(
-      attr_type = "graph",
-      attr  = c("layout", "rankdir"),
-      value = c("dot", "LR")
-      ) %>%
-    DiagrammeR::add_global_graph_attrs(
-      attr_type = "node",
-      attr  = c("color", "style", "fontname"),
-      value = c("DimGray", "filled", "Helvetica")
-      ) %>%
-    DiagrammeR::add_global_graph_attrs(
-      attr_type = "edge",
-      attr  = c("color", "arrowsize", "arrowhead", "fontname"),
-      value = c("DimGray", "1.5", "vee", "Helvetica"))
-  
-  if (!render) return(invisible(graph))
-  
-  DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
-}
-
-# Avoid error messages during CRAN check.
-# The reason is that these variables are never declared
-# They are mainly column names inferred by Data.table...
-globalVariables(c("Feature", "ID", "Cover", "Quality", "Split", "Yes", "No", "Missing", ".", "shape", "filledcolor", "label"))
diff --git a/ml-xgboost/R-package/R/xgb.save.R b/ml-xgboost/R-package/R/xgb.save.R
deleted file mode 100644
index d969dae..0000000
--- a/ml-xgboost/R-package/R/xgb.save.R
+++ /dev/null
@@ -1,43 +0,0 @@
-#' Save xgboost model to binary file
-#' 
-#' Save xgboost model to a file in binary format.
-#' 
-#' @param model model object of \code{xgb.Booster} class.
-#' @param fname name of the file to write.
-#' 
-#' @details 
-#' This methods allows to save a model in an xgboost-internal binary format which is universal 
-#' among the various xgboost interfaces. In R, the saved model file could be read-in later
-#' using either the \code{\link{xgb.load}} function or the \code{xgb_model} parameter 
-#' of \code{\link{xgb.train}}.
-#' 
-#' Note: a model can also be saved as an R-object (e.g., by using \code{\link[base]{readRDS}} 
-#' or \code{\link[base]{save}}). However, it would then only be compatible with R, and 
-#' corresponding R-methods would need to be used to load it.
-#' 
-#' @seealso 
-#' \code{\link{xgb.load}}, \code{\link{xgb.Booster.complete}}. 
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
-#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' xgb.save(bst, 'xgb.model')
-#' bst <- xgb.load('xgb.model')
-#' if (file.exists('xgb.model')) file.remove('xgb.model')
-#' pred <- predict(bst, test$data)
-#' @export
-xgb.save <- function(model, fname) {
-  if (typeof(fname) != "character")
-    stop("fname must be character")
-  if (!inherits(model, "xgb.Booster")) {
-    stop("model must be xgb.Booster.",
-         if (inherits(model, "xgb.DMatrix")) " Use xgb.DMatrix.save to save an xgb.DMatrix object." else "")
-  }
-  model <- xgb.Booster.complete(model, saveraw = FALSE)
-  .Call(XGBoosterSaveModel_R, model$handle, fname[1])
-  return(TRUE)
-}
diff --git a/ml-xgboost/R-package/R/xgb.save.raw.R b/ml-xgboost/R-package/R/xgb.save.raw.R
deleted file mode 100644
index 967a314..0000000
--- a/ml-xgboost/R-package/R/xgb.save.raw.R
+++ /dev/null
@@ -1,23 +0,0 @@
-#' Save xgboost model to R's raw vector,
-#' user can call xgb.load.raw to load the model back from raw vector
-#'
-#' Save xgboost model from xgboost or xgb.train
-#'
-#' @param model the model object.
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' raw <- xgb.save.raw(bst)
-#' bst <- xgb.load.raw(raw)
-#' pred <- predict(bst, test$data)
-#'
-#' @export
-xgb.save.raw <- function(model) {
-  handle <- xgb.get.handle(model)
-  .Call(XGBoosterModelToRaw_R, handle)
-}
diff --git a/ml-xgboost/R-package/R/xgb.serialize.R b/ml-xgboost/R-package/R/xgb.serialize.R
deleted file mode 100644
index 00bbb42..0000000
--- a/ml-xgboost/R-package/R/xgb.serialize.R
+++ /dev/null
@@ -1,21 +0,0 @@
-#' Serialize the booster instance into R's raw vector.  The serialization method differs
-#' from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
-#' parameters.  This serialization format is not stable across different xgboost versions.
-#'
-#' @param booster the booster instance
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' raw <- xgb.serialize(bst)
-#' bst <- xgb.unserialize(raw)
-#'
-#' @export
-xgb.serialize <- function(booster) {
-  handle <- xgb.get.handle(booster)
-  .Call(XGBoosterSerializeToBuffer_R, handle)
-}
diff --git a/ml-xgboost/R-package/R/xgb.train.R b/ml-xgboost/R-package/R/xgb.train.R
deleted file mode 100644
index 8733bcc..0000000
--- a/ml-xgboost/R-package/R/xgb.train.R
+++ /dev/null
@@ -1,377 +0,0 @@
-#' eXtreme Gradient Boosting Training
-#'
-#' \code{xgb.train} is an advanced interface for training an xgboost model.
-#' The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
-#'
-#' @param params the list of parameters.
-#'        The complete list of parameters is available at \url{http://xgboost.readthedocs.io/en/latest/parameter.html}.
-#'        Below is a shorter summary:
-#'
-#' 1. General Parameters
-#'
-#' \itemize{
-#'   \item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
-#' }
-#'
-#' 2. Booster Parameters
-#'
-#' 2.1. Parameter for Tree Booster
-#'
-#' \itemize{
-#'   \item \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1} when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model more robust to overfitting but slower to compute. Default: 0.3
-#'   \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
-#'   \item \code{max_depth} maximum depth of a tree. Default: 6
-#'   \item \code{min_child_weight} minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1
-#'   \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1
-#'   \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
-#'   \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample  < 1}  and \code{round = 1}) accordingly. Default: 1
-#'   \item \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length equals to the number of features in the training data. \code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.
-#'   \item \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions. Each item of the list represents one permitted interaction where specified features are allowed to interact with each other. Feature index values should start from \code{0} (\code{0} references the first column).  Leave argument unspecified for no interaction constraints.
-#' }
-#'
-#' 2.2. Parameter for Linear Booster
-#'
-#' \itemize{
-#'   \item \code{lambda} L2 regularization term on weights. Default: 0
-#'   \item \code{lambda_bias} L2 regularization term on bias. Default: 0
-#'   \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
-#' }
-#'
-#' 3. Task Parameters
-#'
-#' \itemize{
-#' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
-#'   \itemize{
-#'     \item \code{reg:squarederror} Regression with squared loss (Default).
-#'     \item \code{reg:logistic} logistic regression.
-#'     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
-#'     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
-#'     \item \code{num_class} set the number of classes. To use only with multiclass objectives.
-#'     \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{num_class - 1}.
-#'     \item \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
-#'     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
-#'   }
-#'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
-#'   \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
-#' }
-#'
-#' @param data training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
-#'        \code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.
-#' @param nrounds max number of boosting iterations.
-#' @param watchlist named list of xgb.DMatrix datasets to use for evaluating model performance.
-#'        Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
-#'        of these datasets during each boosting iteration, and stored in the end as a field named
-#'        \code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
-#'        \code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
-#'        printed out during the training.
-#'        E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
-#'        the performance of each round's model on mat1 and mat2.
-#' @param obj customized objective function. Returns gradient and second order
-#'        gradient with given prediction and dtrain.
-#' @param feval customized evaluation function. Returns
-#'        \code{list(metric='metric-name', value='metric-value')} with given
-#'        prediction and dtrain.
-#' @param verbose If 0, xgboost will stay silent. If 1, it will print information about performance.
-#'        If 2, some additional information will be printed out.
-#'        Note that setting \code{verbose > 0} automatically engages the
-#'        \code{cb.print.evaluation(period=1)} callback function.
-#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the
-#'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance
-#'        doesn't improve for \code{k} rounds.
-#'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
-#' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
-#'        then this parameter must be set as well.
-#'        When it is \code{TRUE}, it means the larger the evaluation score the better.
-#'        This parameter is passed to the \code{\link{cb.early.stop}} callback.
-#' @param save_period when it is non-NULL, model is saved to disk after every \code{save_period} rounds,
-#'        0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.
-#' @param save_name the name or path for periodically saved model file.
-#' @param xgb_model a previously built model to continue the training from.
-#'        Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
-#'        file with a previously saved model.
-#' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-#'        parameters' values. User can provide either existing or their own callback methods in order
-#'        to customize the training process.
-#' @param ... other parameters to pass to \code{params}.
-#' @param label vector of response values. Should not be provided when data is
-#'        a local data file name or an \code{xgb.DMatrix}.
-#' @param missing by default is set to NA, which means that NA values should be considered as 'missing'
-#'        by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
-#'        This parameter is only used when input is a dense matrix.
-#' @param weight a vector indicating the weight for each row of the input.
-#'
-#' @details
-#' These are the training functions for \code{xgboost}.
-#'
-#' The \code{xgb.train} interface supports advanced features such as \code{watchlist},
-#' customized objective and evaluation metric functions, therefore it is more flexible
-#' than the \code{xgboost} interface.
-#'
-#' Parallelization is automatically enabled if \code{OpenMP} is present.
-#' Number of threads can also be manually specified via \code{nthread} parameter.
-#'
-#' The evaluation metric is chosen automatically by Xgboost (according to the objective)
-#' when the \code{eval_metric} parameter is not provided.
-#' User may set one or several \code{eval_metric} parameters.
-#' Note that when using a customized metric, only this single metric can be used.
-#' The following is the list of built-in metrics for which Xgboost provides optimized implementation:
-#'   \itemize{
-#'      \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
-#'      \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
-#'      \item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
-#'      \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-#'            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
-#'            Different threshold (e.g., 0.) could be specified as "error@0."
-#'      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-#'      \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
-#'      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
-#'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
-#'   }
-#'
-#' The following callbacks are automatically created when certain parameters are set:
-#' \itemize{
-#'   \item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
-#'         and the \code{print_every_n} parameter is passed to it.
-#'   \item \code{cb.evaluation.log} is on when \code{watchlist} is present.
-#'   \item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
-#'   \item \code{cb.save.model}: when \code{save_period > 0} is set.
-#' }
-#'
-#' @return
-#' An object of class \code{xgb.Booster} with the following elements:
-#' \itemize{
-#'   \item \code{handle} a handle (pointer) to the xgboost model in memory.
-#'   \item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
-#'   \item \code{niter} number of boosting iterations.
-#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
-#'         first column corresponding to iteration number and the rest corresponding to evaluation
-#'         metrics' values. It is created by the \code{\link{cb.evaluation.log}} callback.
-#'   \item \code{call} a function call.
-#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
-#'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or
-#'         explicitly passed.
-#'   \item \code{best_iteration} iteration number with the best evaluation metric value
-#'         (only available with early stopping).
-#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
-#'         which could further be used in \code{predict} method
-#'         (only available with early stopping).
-#'   \item \code{best_score} the best evaluation metric value during early stopping.
-#'         (only available with early stopping).
-#'   \item \code{feature_names} names of the training dataset features
-#'         (only when column names were defined in training data).
-#'   \item \code{nfeatures} number of features in training data.
-#' }
-#'
-#' @seealso
-#' \code{\link{callbacks}},
-#' \code{\link{predict.xgb.Booster}},
-#' \code{\link{xgb.cv}}
-#'
-#' @references
-#'
-#' Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
-#' 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, \url{https://arxiv.org/abs/1603.02754}
-#'
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#'
-#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-#' dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-#' watchlist <- list(train = dtrain, eval = dtest)
-#'
-#' ## A simple xgb.train example:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
-#'               objective = "binary:logistic", eval_metric = "auc")
-#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
-#'
-#'
-#' ## An xgb.train example where custom objective and evaluation metric are used:
-#' logregobj <- function(preds, dtrain) {
-#'    labels <- getinfo(dtrain, "label")
-#'    preds <- 1/(1 + exp(-preds))
-#'    grad <- preds - labels
-#'    hess <- preds * (1 - preds)
-#'    return(list(grad = grad, hess = hess))
-#' }
-#' evalerror <- function(preds, dtrain) {
-#'   labels <- getinfo(dtrain, "label")
-#'   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
-#'   return(list(metric = "error", value = err))
-#' }
-#'
-#' # These functions could be used by passing them either:
-#' #  as 'objective' and 'eval_metric' parameters in the params list:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
-#'               objective = logregobj, eval_metric = evalerror)
-#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
-#'
-#' #  or through the ... arguments:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2)
-#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
-#'                  objective = logregobj, eval_metric = evalerror)
-#'
-#' #  or as dedicated 'obj' and 'feval' parameters of xgb.train:
-#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
-#'                  obj = logregobj, feval = evalerror)
-#'
-#'
-#' ## An xgb.train example of using variable learning rates at each iteration:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
-#'               objective = "binary:logistic", eval_metric = "auc")
-#' my_etas <- list(eta = c(0.5, 0.1))
-#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
-#'                  callbacks = list(cb.reset.parameters(my_etas)))
-#'
-#' ## Early stopping:
-#' bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
-#'                  early_stopping_rounds = 3)
-#'
-#' ## An 'xgboost' interface example:
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
-#'                max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
-#'                objective = "binary:logistic")
-#' pred <- predict(bst, agaricus.test$data)
-#'
-#' @rdname xgb.train
-#' @export
-xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
-                      obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L,
-                      early_stopping_rounds = NULL, maximize = NULL,
-                      save_period = NULL, save_name = "xgboost.model",
-                      xgb_model = NULL, callbacks = list(), ...) {
-
-  check.deprecation(...)
-
-  params <- check.booster.params(params, ...)
-
-  check.custom.obj()
-  check.custom.eval()
-
-  # data & watchlist checks
-  dtrain <- data
-  if (!inherits(dtrain, "xgb.DMatrix"))
-    stop("second argument dtrain must be xgb.DMatrix")
-  if (length(watchlist) > 0) {
-    if (typeof(watchlist) != "list" ||
-        !all(vapply(watchlist, inherits, logical(1), what = 'xgb.DMatrix')))
-      stop("watchlist must be a list of xgb.DMatrix elements")
-    evnames <- names(watchlist)
-    if (is.null(evnames) || any(evnames == ""))
-      stop("each element of the watchlist must have a name tag")
-  }
-
-  # evaluation printing callback
-  params <- c(params)
-  print_every_n <- max( as.integer(print_every_n), 1L)
-  if (!has.callbacks(callbacks, 'cb.print.evaluation') &&
-      verbose) {
-    callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n))
-  }
-  # evaluation log callback:  it is automatically enabled when watchlist is provided
-  evaluation_log <- list()
-  if (!has.callbacks(callbacks, 'cb.evaluation.log') &&
-      length(watchlist) > 0) {
-    callbacks <- add.cb(callbacks, cb.evaluation.log())
-  }
-  # Model saving callback
-  if (!is.null(save_period) &&
-      !has.callbacks(callbacks, 'cb.save.model')) {
-    callbacks <- add.cb(callbacks, cb.save.model(save_period, save_name))
-  }
-  # Early stopping callback
-  stop_condition <- FALSE
-  if (!is.null(early_stopping_rounds) &&
-      !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
-                                                 maximize = maximize, verbose = verbose))
-  }
-
-  # Sort the callbacks into categories
-  cb <- categorize.callbacks(callbacks)
-  params['validate_parameters'] <- TRUE
-  if (!is.null(params[['seed']])) {
-    warning("xgb.train: `seed` is ignored in R package.  Use `set.seed()` instead.")
-  }
-
-  # The tree updating process would need slightly different handling
-  is_update <- NVL(params[['process_type']], '.') == 'update'
-
-  # Construct a booster (either a new one or load from xgb_model)
-  handle <- xgb.Booster.handle(params, append(watchlist, dtrain), xgb_model)
-  bst <- xgb.handleToBooster(handle)
-
-  # extract parameters that can affect the relationship b/w #trees and #iterations
-  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
-  num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
-
-  # When the 'xgb_model' was set, find out how many boosting iterations it has
-  niter_init <- 0
-  if (!is.null(xgb_model)) {
-    niter_init <- as.numeric(xgb.attr(bst, 'niter')) + 1
-    if (length(niter_init) == 0) {
-      niter_init <- xgb.ntree(bst) %/% (num_parallel_tree * num_class)
-    }
-  }
-  if(is_update && nrounds > niter_init)
-    stop("nrounds cannot be larger than ", niter_init, " (nrounds of xgb_model)")
-
-  # TODO: distributed code
-  rank <- 0
-
-  niter_skip <- ifelse(is_update, 0, niter_init)
-  begin_iteration <- niter_skip + 1
-  end_iteration <- niter_skip + nrounds
-
-  # the main loop for boosting iterations
-  for (iteration in begin_iteration:end_iteration) {
-
-    for (f in cb$pre_iter) f()
-
-    xgb.iter.update(bst$handle, dtrain, iteration - 1, obj)
-
-    bst_evaluation <- numeric(0)
-    if (length(watchlist) > 0)
-      bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval)
-
-    xgb.attr(bst$handle, 'niter') <- iteration - 1
-
-    for (f in cb$post_iter) f()
-
-    if (stop_condition) break
-  }
-  for (f in cb$finalize) f(finalize = TRUE)
-
-  bst <- xgb.Booster.complete(bst, saveraw = TRUE)
-
-  # store the total number of boosting iterations
-  bst$niter = end_iteration
-
-  # store the evaluation results
-  if (length(evaluation_log) > 0 &&
-      nrow(evaluation_log) > 0) {
-    # include the previous compatible history when available
-    if (inherits(xgb_model, 'xgb.Booster') &&
-        !is_update &&
-        !is.null(xgb_model$evaluation_log) &&
-        isTRUE(all.equal(colnames(evaluation_log),
-                         colnames(xgb_model$evaluation_log)))) {
-      evaluation_log <- rbindlist(list(xgb_model$evaluation_log, evaluation_log))
-    }
-    bst$evaluation_log <- evaluation_log
-  }
-
-  bst$call <- match.call()
-  bst$params <- params
-  bst$callbacks <- callbacks
-  if (!is.null(colnames(dtrain)))
-    bst$feature_names <- colnames(dtrain)
-  bst$nfeatures <- ncol(dtrain)
-
-  return(bst)
-}
diff --git a/ml-xgboost/R-package/R/xgb.unserialize.R b/ml-xgboost/R-package/R/xgb.unserialize.R
deleted file mode 100644
index 1a62e4c..0000000
--- a/ml-xgboost/R-package/R/xgb.unserialize.R
+++ /dev/null
@@ -1,12 +0,0 @@
-#' Load the instance back from \code{\link{xgb.serialize}}
-#'
-#' @param buffer the buffer containing booster instance saved by \code{\link{xgb.serialize}}
-#'
-#' @export
-xgb.unserialize <- function(buffer) {
-  cachelist <- list()
-  handle <- .Call(XGBoosterCreate_R, cachelist)
-  .Call(XGBoosterUnserializeFromBuffer_R, handle, buffer)
-  class(handle) <- "xgb.Booster.handle"
-  return (handle)
-}
diff --git a/ml-xgboost/R-package/R/xgboost.R b/ml-xgboost/R-package/R/xgboost.R
deleted file mode 100644
index 2fddfa4..0000000
--- a/ml-xgboost/R-package/R/xgboost.R
+++ /dev/null
@@ -1,113 +0,0 @@
-# Simple interface for training an xgboost model that wraps \code{xgb.train}.
-# Its documentation is combined with xgb.train.
-#
-#' @rdname xgb.train
-#' @export
-xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
-                    params = list(), nrounds,
-                    verbose = 1, print_every_n = 1L,
-                    early_stopping_rounds = NULL, maximize = NULL,
-                    save_period = NULL, save_name = "xgboost.model",
-                    xgb_model = NULL, callbacks = list(), ...) {
-
-  dtrain <- xgb.get.DMatrix(data, label, missing, weight)
-
-  watchlist <- list(train = dtrain)
-
-  bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, print_every_n = print_every_n,
-                   early_stopping_rounds = early_stopping_rounds, maximize = maximize,
-                   save_period = save_period, save_name = save_name,
-                   xgb_model = xgb_model, callbacks = callbacks, ...)
-  return (bst)
-}
-
-#' Training part from Mushroom Data Set
-#'
-#' This data set is originally from the Mushroom data set,
-#' UCI Machine Learning Repository.
-#'
-#' This data set includes the following fields:
-#'
-#' \itemize{
-#'  \item \code{label} the label for each record
-#'  \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
-#' }
-#'
-#' @references
-#' https://archive.ics.uci.edu/ml/datasets/Mushroom
-#'
-#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
-#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
-#' School of Information and Computer Science.
-#'
-#' @docType data
-#' @keywords datasets
-#' @name agaricus.train
-#' @usage data(agaricus.train)
-#' @format A list containing a label vector, and a dgCMatrix object with 6513
-#' rows and 127 variables
-NULL
-
-#' Test part from Mushroom Data Set
-#'
-#' This data set is originally from the Mushroom data set,
-#' UCI Machine Learning Repository.
-#'
-#' This data set includes the following fields:
-#'
-#' \itemize{
-#'  \item \code{label} the label for each record
-#'  \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
-#' }
-#'
-#' @references
-#' https://archive.ics.uci.edu/ml/datasets/Mushroom
-#'
-#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
-#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
-#' School of Information and Computer Science.
-#'
-#' @docType data
-#' @keywords datasets
-#' @name agaricus.test
-#' @usage data(agaricus.test)
-#' @format A list containing a label vector, and a dgCMatrix object with 1611
-#' rows and 126 variables
-NULL
-
-# Various imports
-#' @importClassesFrom Matrix dgCMatrix dgeMatrix
-#' @importFrom Matrix colSums
-#' @importFrom Matrix sparse.model.matrix
-#' @importFrom Matrix sparseVector
-#' @importFrom Matrix sparseMatrix
-#' @importFrom Matrix t
-#' @importFrom data.table data.table
-#' @importFrom data.table is.data.table
-#' @importFrom data.table as.data.table
-#' @importFrom data.table :=
-#' @importFrom data.table rbindlist
-#' @importFrom data.table setkey
-#' @importFrom data.table setkeyv
-#' @importFrom data.table setnames
-#' @importFrom magrittr %>%
-#' @importFrom stringi stri_detect_regex
-#' @importFrom stringi stri_match_first_regex
-#' @importFrom stringi stri_replace_first_regex
-#' @importFrom stringi stri_replace_all_regex
-#' @importFrom stringi stri_split_regex
-#' @importFrom utils object.size str tail
-#' @importFrom stats predict
-#' @importFrom stats median
-#' @importFrom utils head
-#' @importFrom graphics barplot
-#' @importFrom graphics lines
-#' @importFrom graphics points
-#' @importFrom graphics grid
-#' @importFrom graphics par
-#' @importFrom graphics title
-#' @importFrom grDevices rgb
-#'
-#' @import methods
-#' @useDynLib xgboost, .registration = TRUE
-NULL
diff --git a/ml-xgboost/R-package/README.md b/ml-xgboost/R-package/README.md
deleted file mode 100644
index c548731..0000000
--- a/ml-xgboost/R-package/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
-XGBoost R Package for Scalable GBM
-==================================
-
-[![CRAN Status Badge](http://www.r-pkg.org/badges/version/xgboost)](https://cran.r-project.org/web/packages/xgboost)
-[![CRAN Downloads](http://cranlogs.r-pkg.org/badges/xgboost)](https://cran.rstudio.com/web/packages/xgboost/index.html)
-[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](http://xgboost.readthedocs.org/en/latest/R-package/index.html)
-
-Resources
----------
-* [XGBoost R Package Online Documentation](http://xgboost.readthedocs.org/en/latest/R-package/index.html)
-  - Check this out for detailed documents, examples and tutorials.
-
-Installation
-------------
-
-We are [on CRAN](https://cran.r-project.org/web/packages/xgboost/index.html) now. For stable/pre-compiled(for Windows and OS X) version, please install from CRAN:
-
-```r
-install.packages('xgboost')
-```
-
-For more detailed installation instructions, please see [here](http://xgboost.readthedocs.org/en/latest/build.html#r-package-installation).
-
-Examples
---------
-
-* Please visit [walk through example](demo).
-* See also the [example scripts](../demo/kaggle-higgs) for Kaggle Higgs Challenge, including [speedtest script](../demo/kaggle-higgs/speedtest.R) on this dataset and the one related to [Otto challenge](../demo/kaggle-otto), including a [RMarkdown documentation](../demo/kaggle-otto/understandingXGBoostModel.Rmd).
-
-Development
------------
-
-* See the [R Package section](https://xgboost.readthedocs.io/en/latest/contribute.html#r-package) of the contributors guide.
diff --git a/ml-xgboost/R-package/cleanup b/ml-xgboost/R-package/cleanup
deleted file mode 100644
index eb86699..0000000
--- a/ml-xgboost/R-package/cleanup
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/sh
-
-rm -f src/Makevars
-rm -f CMakeLists.txt
diff --git a/ml-xgboost/R-package/configure b/ml-xgboost/R-package/configure
deleted file mode 100644
index 8dab660..0000000
--- a/ml-xgboost/R-package/configure
+++ /dev/null
@@ -1,3891 +0,0 @@
-#! /bin/sh
-# Guess values for system-dependent variables and create Makefiles.
-# Generated by GNU Autoconf 2.69 for xgboost 0.6-3.
-#
-#
-# Copyright (C) 1992-1996, 1998-2012 Free Software Foundation, Inc.
-#
-#
-# This configure script is free software; the Free Software Foundation
-# gives unlimited permission to copy, distribute and modify it.
-## -------------------- ##
-## M4sh Initialization. ##
-## -------------------- ##
-
-# Be more Bourne compatible
-DUALCASE=1; export DUALCASE # for MKS sh
-if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then :
-  emulate sh
-  NULLCMD=:
-  # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which
-  # is contrary to our usage.  Disable this feature.
-  alias -g '${1+"$@"}'='"$@"'
-  setopt NO_GLOB_SUBST
-else
-  case `(set -o) 2>/dev/null` in #(
-  *posix*) :
-    set -o posix ;; #(
-  *) :
-     ;;
-esac
-fi
-
-
-as_nl='
-'
-export as_nl
-# Printing a long string crashes Solaris 7 /usr/bin/printf.
-as_echo='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'
-as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo
-as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo$as_echo
-# Prefer a ksh shell builtin over an external printf program on Solaris,
-# but without wasting forks for bash or zsh.
-if test -z "$BASH_VERSION$ZSH_VERSION" \
-    && (test "X`print -r -- $as_echo`" = "X$as_echo") 2>/dev/null; then
-  as_echo='print -r --'
-  as_echo_n='print -rn --'
-elif (test "X`printf %s $as_echo`" = "X$as_echo") 2>/dev/null; then
-  as_echo='printf %s\n'
-  as_echo_n='printf %s'
-else
-  if test "X`(/usr/ucb/echo -n -n $as_echo) 2>/dev/null`" = "X-n $as_echo"; then
-    as_echo_body='eval /usr/ucb/echo -n "$1$as_nl"'
-    as_echo_n='/usr/ucb/echo -n'
-  else
-    as_echo_body='eval expr "X$1" : "X\\(.*\\)"'
-    as_echo_n_body='eval
-      arg=$1;
-      case $arg in #(
-      *"$as_nl"*)
-	expr "X$arg" : "X\\(.*\\)$as_nl";
-	arg=`expr "X$arg" : ".*$as_nl\\(.*\\)"`;;
-      esac;
-      expr "X$arg" : "X\\(.*\\)" | tr -d "$as_nl"
-    '
-    export as_echo_n_body
-    as_echo_n='sh -c $as_echo_n_body as_echo'
-  fi
-  export as_echo_body
-  as_echo='sh -c $as_echo_body as_echo'
-fi
-
-# The user is always right.
-if test "${PATH_SEPARATOR+set}" != set; then
-  PATH_SEPARATOR=:
-  (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && {
-    (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 ||
-      PATH_SEPARATOR=';'
-  }
-fi
-
-
-# IFS
-# We need space, tab and new line, in precisely that order.  Quoting is
-# there to prevent editors from complaining about space-tab.
-# (If _AS_PATH_WALK were called with IFS unset, it would disable word
-# splitting by setting IFS to empty value.)
-IFS=" ""	$as_nl"
-
-# Find who we are.  Look in the path if we contain no directory separator.
-as_myself=
-case $0 in #((
-  *[\\/]* ) as_myself=$0 ;;
-  *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR
-for as_dir in $PATH
-do
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-  test -z "$as_dir" && as_dir=.
-    test -r "$as_dir/$0" && as_myself=$as_dir/$0 && break
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-IFS=$as_save_IFS
-
-     ;;
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-if test "x$as_myself" = x; then
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-if test ! -f "$as_myself"; then
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-
-# Unset variables that we do not need and which cause bugs (e.g. in
-# pre-3.0 UWIN ksh).  But do not cause bugs in bash 2.01; the "|| exit 1"
-# suppresses any "Segmentation fault" message there.  '((' could
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-for as_var in BASH_ENV ENV MAIL MAILPATH
-do eval test x\${$as_var+set} = xset \
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-PS1='$ '
-PS2='> '
-PS4='+ '
-
-# NLS nuisances.
-LC_ALL=C
-export LC_ALL
-LANGUAGE=C
-export LANGUAGE
-
-# CDPATH.
-(unset CDPATH) >/dev/null 2>&1 && unset CDPATH
-
-# Use a proper internal environment variable to ensure we don't fall
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-  if test x"${_as_can_reexec}" != xno && test "x$CONFIG_SHELL" != x; then
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-# Preserve -v and -x to the replacement shell.
-BASH_ENV=/dev/null
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-(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV
-case $- in # ((((
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-# out after a failed `exec'.
-$as_echo "$0: could not re-execute with $CONFIG_SHELL" >&2
-as_fn_exit 255
-  fi
-  # We don't want this to propagate to other subprocesses.
-          { _as_can_reexec=; unset _as_can_reexec;}
-if test "x$CONFIG_SHELL" = x; then
-  as_bourne_compatible="if test -n \"\${ZSH_VERSION+set}\" && (emulate sh) >/dev/null 2>&1; then :
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-  NULLCMD=:
-  # Pre-4.2 versions of Zsh do word splitting on \${1+\"\$@\"}, which
-  # is contrary to our usage.  Disable this feature.
-  alias -g '\${1+\"\$@\"}'='\"\$@\"'
-  setopt NO_GLOB_SUBST
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-  case \`(set -o) 2>/dev/null\` in #(
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-if ( set x; as_fn_ret_success y && test x = \"\$1\" ); then :
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-  if (eval "$as_required") 2>/dev/null; then :
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-fi; }
-IFS=$as_save_IFS
-
-
-      if test "x$CONFIG_SHELL" != x; then :
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-             # We cannot yet assume a decent shell, so we have to provide a
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-# works around shells that cannot unset nonexistent variables.
-# Preserve -v and -x to the replacement shell.
-BASH_ENV=/dev/null
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-(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV
-case $- in # ((((
-  *v*x* | *x*v* ) as_opts=-vx ;;
-  *v* ) as_opts=-v ;;
-  *x* ) as_opts=-x ;;
-  * ) as_opts= ;;
-esac
-exec $CONFIG_SHELL $as_opts "$as_myself" ${1+"$@"}
-# Admittedly, this is quite paranoid, since all the known shells bail
-# out after a failed `exec'.
-$as_echo "$0: could not re-execute with $CONFIG_SHELL" >&2
-exit 255
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-  va_end (v);
-  return s;
-}
-
-/* OSF 4.0 Compaq cc is some sort of almost-ANSI by default.  It has
-   function prototypes and stuff, but not '\xHH' hex character constants.
-   These don't provoke an error unfortunately, instead are silently treated
-   as 'x'.  The following induces an error, until -std is added to get
-   proper ANSI mode.  Curiously '\x00'!='x' always comes out true, for an
-   array size at least.  It's necessary to write '\x00'==0 to get something
-   that's true only with -std.  */
-int osf4_cc_array ['\x00' == 0 ? 1 : -1];
-
-/* IBM C 6 for AIX is almost-ANSI by default, but it replaces macro parameters
-   inside strings and character constants.  */
-#define FOO(x) 'x'
-int xlc6_cc_array[FOO(a) == 'x' ? 1 : -1];
-
-int test (int i, double x);
-struct s1 {int (*f) (int a);};
-struct s2 {int (*f) (double a);};
-int pairnames (int, char **, FILE *(*)(struct buf *, struct stat *, int), int, int);
-int argc;
-char **argv;
-int
-main ()
-{
-return f (e, argv, 0) != argv[0]  ||  f (e, argv, 1) != argv[1];
-  ;
-  return 0;
-}
-_ACEOF
-for ac_arg in '' -qlanglvl=extc89 -qlanglvl=ansi -std \
-	-Ae "-Aa -D_HPUX_SOURCE" "-Xc -D__EXTENSIONS__"
-do
-  CC="$ac_save_CC $ac_arg"
-  if ac_fn_c_try_compile "$LINENO"; then :
-  ac_cv_prog_cc_c89=$ac_arg
-fi
-rm -f core conftest.err conftest.$ac_objext
-  test "x$ac_cv_prog_cc_c89" != "xno" && break
-done
-rm -f conftest.$ac_ext
-CC=$ac_save_CC
-
-fi
-# AC_CACHE_VAL
-case "x$ac_cv_prog_cc_c89" in
-  x)
-    { $as_echo "$as_me:${as_lineno-$LINENO}: result: none needed" >&5
-$as_echo "none needed" >&6; } ;;
-  xno)
-    { $as_echo "$as_me:${as_lineno-$LINENO}: result: unsupported" >&5
-$as_echo "unsupported" >&6; } ;;
-  *)
-    CC="$CC $ac_cv_prog_cc_c89"
-    { $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_cv_prog_cc_c89" >&5
-$as_echo "$ac_cv_prog_cc_c89" >&6; } ;;
-esac
-if test "x$ac_cv_prog_cc_c89" != xno; then :
-
-fi
-
-ac_ext=c
-ac_cpp='$CPP $CPPFLAGS'
-ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5'
-ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5'
-ac_compiler_gnu=$ac_cv_c_compiler_gnu
-
-
-### Check whether backtrace() is part of libc or the external lib libexecinfo
-{ $as_echo "$as_me:${as_lineno-$LINENO}: checking Backtrace lib" >&5
-$as_echo_n "checking Backtrace lib... " >&6; }
-{ $as_echo "$as_me:${as_lineno-$LINENO}: result: " >&5
-$as_echo "" >&6; }
-
-{ $as_echo "$as_me:${as_lineno-$LINENO}: checking for backtrace in -lexecinfo" >&5
-$as_echo_n "checking for backtrace in -lexecinfo... " >&6; }
-if ${ac_cv_lib_execinfo_backtrace+:} false; then :
-  $as_echo_n "(cached) " >&6
-else
-  ac_check_lib_save_LIBS=$LIBS
-LIBS="-lexecinfo  $LIBS"
-cat confdefs.h - <<_ACEOF >conftest.$ac_ext
-/* end confdefs.h.  */
-
-/* Override any GCC internal prototype to avoid an error.
-   Use char because int might match the return type of a GCC
-   builtin and then its argument prototype would still apply.  */
-#ifdef __cplusplus
-extern "C"
-#endif
-char backtrace ();
-int
-main ()
-{
-return backtrace ();
-  ;
-  return 0;
-}
-_ACEOF
-if ac_fn_c_try_link "$LINENO"; then :
-  ac_cv_lib_execinfo_backtrace=yes
-else
-  ac_cv_lib_execinfo_backtrace=no
-fi
-rm -f core conftest.err conftest.$ac_objext \
-    conftest$ac_exeext conftest.$ac_ext
-LIBS=$ac_check_lib_save_LIBS
-fi
-{ $as_echo "$as_me:${as_lineno-$LINENO}: result: $ac_cv_lib_execinfo_backtrace" >&5
-$as_echo "$ac_cv_lib_execinfo_backtrace" >&6; }
-if test "x$ac_cv_lib_execinfo_backtrace" = xyes; then :
-  BACKTRACE_LIB=-lexecinfo
-else
-  BACKTRACE_LIB=''
-fi
-
-
-### Endian detection
-{ $as_echo "$as_me:${as_lineno-$LINENO}: checking endian" >&5
-$as_echo_n "checking endian... " >&6; }
-{ $as_echo "$as_me:${as_lineno-$LINENO}: result: " >&5
-$as_echo "" >&6; }
-if test "$cross_compiling" = yes; then :
-  { { $as_echo "$as_me:${as_lineno-$LINENO}: error: in \`$ac_pwd':" >&5
-$as_echo "$as_me: error: in \`$ac_pwd':" >&2;}
-as_fn_error $? "cannot run test program while cross compiling
-See \`config.log' for more details" "$LINENO" 5; }
-else
-  cat confdefs.h - <<_ACEOF >conftest.$ac_ext
-/* end confdefs.h.  */
-#include <stdint.h>
-int
-main ()
-{
-const uint16_t endianness = 256; return !!(*(const uint8_t *)&endianness);
-  ;
-  return 0;
-}
-_ACEOF
-if ac_fn_c_try_run "$LINENO"; then :
-  ENDIAN_FLAG="-DDMLC_CMAKE_LITTLE_ENDIAN=1"
-else
-  ENDIAN_FLAG="-DDMLC_CMAKE_LITTLE_ENDIAN=0"
-fi
-rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \
-  conftest.$ac_objext conftest.beam conftest.$ac_ext
-fi
-
-
-OPENMP_CXXFLAGS=""
-
-if test `uname -s` = "Linux"
-then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
-fi
-
-if test `uname -s` = "Darwin"
-then
-  OPENMP_CXXFLAGS='-Xclang -fopenmp'
-  OPENMP_LIB='/usr/local/lib/libomp.dylib'
-  ac_pkg_openmp=no
-  { $as_echo "$as_me:${as_lineno-$LINENO}: checking whether OpenMP will work in a package" >&5
-$as_echo_n "checking whether OpenMP will work in a package... " >&6; }
-  cat confdefs.h - <<_ACEOF >conftest.$ac_ext
-/* end confdefs.h.  */
-#include <omp.h>
-int
-main ()
-{
- return (omp_get_max_threads() <= 1);
-  ;
-  return 0;
-}
-_ACEOF
-  ${CC} -o conftest conftest.c /usr/local/lib/libomp.dylib -Xclang -fopenmp 2>/dev/null && ./conftest && ac_pkg_openmp=yes
-  { $as_echo "$as_me:${as_lineno-$LINENO}: result: ${ac_pkg_openmp}" >&5
-$as_echo "${ac_pkg_openmp}" >&6; }
-  if test "${ac_pkg_openmp}" = no; then
-    OPENMP_CXXFLAGS=''
-    OPENMP_LIB=''
-    echo '*****************************************************************************************'
-    echo 'WARNING: OpenMP is unavailable on this Mac OSX system. Training speed may be suboptimal.'
-    echo '         To use all CPU cores for training jobs, you should install OpenMP by running\n'
-    echo '             brew install libomp'
-    echo '*****************************************************************************************'
-  fi
-fi
-
-
-
-
-
-ac_config_files="$ac_config_files src/Makevars"
-
-cat >confcache <<\_ACEOF
-# This file is a shell script that caches the results of configure
-# tests run on this system so they can be shared between configure
-# scripts and configure runs, see configure's option --config-cache.
-# It is not useful on other systems.  If it contains results you don't
-# want to keep, you may remove or edit it.
-#
-# config.status only pays attention to the cache file if you give it
-# the --recheck option to rerun configure.
-#
-# `ac_cv_env_foo' variables (set or unset) will be overridden when
-# loading this file, other *unset* `ac_cv_foo' will be assigned the
-# following values.
-
-_ACEOF
-
-# The following way of writing the cache mishandles newlines in values,
-# but we know of no workaround that is simple, portable, and efficient.
-# So, we kill variables containing newlines.
-# Ultrix sh set writes to stderr and can't be redirected directly,
-# and sets the high bit in the cache file unless we assign to the vars.
-(
-  for ac_var in `(set) 2>&1 | sed -n 's/^\([a-zA-Z_][a-zA-Z0-9_]*\)=.*/\1/p'`; do
-    eval ac_val=\$$ac_var
-    case $ac_val in #(
-    *${as_nl}*)
-      case $ac_var in #(
-      *_cv_*) { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: cache variable $ac_var contains a newline" >&5
-$as_echo "$as_me: WARNING: cache variable $ac_var contains a newline" >&2;} ;;
-      esac
-      case $ac_var in #(
-      _ | IFS | as_nl) ;; #(
-      BASH_ARGV | BASH_SOURCE) eval $ac_var= ;; #(
-      *) { eval $ac_var=; unset $ac_var;} ;;
-      esac ;;
-    esac
-  done
-
-  (set) 2>&1 |
-    case $as_nl`(ac_space=' '; set) 2>&1` in #(
-    *${as_nl}ac_space=\ *)
-      # `set' does not quote correctly, so add quotes: double-quote
-      # substitution turns \\\\ into \\, and sed turns \\ into \.
-      sed -n \
-	"s/'/'\\\\''/g;
-	  s/^\\([_$as_cr_alnum]*_cv_[_$as_cr_alnum]*\\)=\\(.*\\)/\\1='\\2'/p"
-      ;; #(
-    *)
-      # `set' quotes correctly as required by POSIX, so do not add quotes.
-      sed -n "/^[_$as_cr_alnum]*_cv_[_$as_cr_alnum]*=/p"
-      ;;
-    esac |
-    sort
-) |
-  sed '
-     /^ac_cv_env_/b end
-     t clear
-     :clear
-     s/^\([^=]*\)=\(.*[{}].*\)$/test "${\1+set}" = set || &/
-     t end
-     s/^\([^=]*\)=\(.*\)$/\1=${\1=\2}/
-     :end' >>confcache
-if diff "$cache_file" confcache >/dev/null 2>&1; then :; else
-  if test -w "$cache_file"; then
-    if test "x$cache_file" != "x/dev/null"; then
-      { $as_echo "$as_me:${as_lineno-$LINENO}: updating cache $cache_file" >&5
-$as_echo "$as_me: updating cache $cache_file" >&6;}
-      if test ! -f "$cache_file" || test -h "$cache_file"; then
-	cat confcache >"$cache_file"
-      else
-        case $cache_file in #(
-        */* | ?:*)
-	  mv -f confcache "$cache_file"$$ &&
-	  mv -f "$cache_file"$$ "$cache_file" ;; #(
-        *)
-	  mv -f confcache "$cache_file" ;;
-	esac
-      fi
-    fi
-  else
-    { $as_echo "$as_me:${as_lineno-$LINENO}: not updating unwritable cache $cache_file" >&5
-$as_echo "$as_me: not updating unwritable cache $cache_file" >&6;}
-  fi
-fi
-rm -f confcache
-
-test "x$prefix" = xNONE && prefix=$ac_default_prefix
-# Let make expand exec_prefix.
-test "x$exec_prefix" = xNONE && exec_prefix='${prefix}'
-
-# Transform confdefs.h into DEFS.
-# Protect against shell expansion while executing Makefile rules.
-# Protect against Makefile macro expansion.
-#
-# If the first sed substitution is executed (which looks for macros that
-# take arguments), then branch to the quote section.  Otherwise,
-# look for a macro that doesn't take arguments.
-ac_script='
-:mline
-/\\$/{
- N
- s,\\\n,,
- b mline
-}
-t clear
-:clear
-s/^[	 ]*#[	 ]*define[	 ][	 ]*\([^	 (][^	 (]*([^)]*)\)[	 ]*\(.*\)/-D\1=\2/g
-t quote
-s/^[	 ]*#[	 ]*define[	 ][	 ]*\([^	 ][^	 ]*\)[	 ]*\(.*\)/-D\1=\2/g
-t quote
-b any
-:quote
-s/[	 `~#$^&*(){}\\|;'\''"<>?]/\\&/g
-s/\[/\\&/g
-s/\]/\\&/g
-s/\$/$$/g
-H
-:any
-${
-	g
-	s/^\n//
-	s/\n/ /g
-	p
-}
-'
-DEFS=`sed -n "$ac_script" confdefs.h`
-
-
-ac_libobjs=
-ac_ltlibobjs=
-U=
-for ac_i in : $LIBOBJS; do test "x$ac_i" = x: && continue
-  # 1. Remove the extension, and $U if already installed.
-  ac_script='s/\$U\././;s/\.o$//;s/\.obj$//'
-  ac_i=`$as_echo "$ac_i" | sed "$ac_script"`
-  # 2. Prepend LIBOBJDIR.  When used with automake>=1.10 LIBOBJDIR
-  #    will be set to the directory where LIBOBJS objects are built.
-  as_fn_append ac_libobjs " \${LIBOBJDIR}$ac_i\$U.$ac_objext"
-  as_fn_append ac_ltlibobjs " \${LIBOBJDIR}$ac_i"'$U.lo'
-done
-LIBOBJS=$ac_libobjs
-
-LTLIBOBJS=$ac_ltlibobjs
-
-
-
-: "${CONFIG_STATUS=./config.status}"
-ac_write_fail=0
-ac_clean_files_save=$ac_clean_files
-ac_clean_files="$ac_clean_files $CONFIG_STATUS"
-{ $as_echo "$as_me:${as_lineno-$LINENO}: creating $CONFIG_STATUS" >&5
-$as_echo "$as_me: creating $CONFIG_STATUS" >&6;}
-as_write_fail=0
-cat >$CONFIG_STATUS <<_ASEOF || as_write_fail=1
-#! $SHELL
-# Generated by $as_me.
-# Run this file to recreate the current configuration.
-# Compiler output produced by configure, useful for debugging
-# configure, is in config.log if it exists.
-
-debug=false
-ac_cs_recheck=false
-ac_cs_silent=false
-
-SHELL=\${CONFIG_SHELL-$SHELL}
-export SHELL
-_ASEOF
-cat >>$CONFIG_STATUS <<\_ASEOF || as_write_fail=1
-## -------------------- ##
-## M4sh Initialization. ##
-## -------------------- ##
-
-# Be more Bourne compatible
-DUALCASE=1; export DUALCASE # for MKS sh
-if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then :
-  emulate sh
-  NULLCMD=:
-  # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which
-  # is contrary to our usage.  Disable this feature.
-  alias -g '${1+"$@"}'='"$@"'
-  setopt NO_GLOB_SUBST
-else
-  case `(set -o) 2>/dev/null` in #(
-  *posix*) :
-    set -o posix ;; #(
-  *) :
-     ;;
-esac
-fi
-
-
-as_nl='
-'
-export as_nl
-# Printing a long string crashes Solaris 7 /usr/bin/printf.
-as_echo='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'
-as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo
-as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo$as_echo
-# Prefer a ksh shell builtin over an external printf program on Solaris,
-# but without wasting forks for bash or zsh.
-if test -z "$BASH_VERSION$ZSH_VERSION" \
-    && (test "X`print -r -- $as_echo`" = "X$as_echo") 2>/dev/null; then
-  as_echo='print -r --'
-  as_echo_n='print -rn --'
-elif (test "X`printf %s $as_echo`" = "X$as_echo") 2>/dev/null; then
-  as_echo='printf %s\n'
-  as_echo_n='printf %s'
-else
-  if test "X`(/usr/ucb/echo -n -n $as_echo) 2>/dev/null`" = "X-n $as_echo"; then
-    as_echo_body='eval /usr/ucb/echo -n "$1$as_nl"'
-    as_echo_n='/usr/ucb/echo -n'
-  else
-    as_echo_body='eval expr "X$1" : "X\\(.*\\)"'
-    as_echo_n_body='eval
-      arg=$1;
-      case $arg in #(
-      *"$as_nl"*)
-	expr "X$arg" : "X\\(.*\\)$as_nl";
-	arg=`expr "X$arg" : ".*$as_nl\\(.*\\)"`;;
-      esac;
-      expr "X$arg" : "X\\(.*\\)" | tr -d "$as_nl"
-    '
-    export as_echo_n_body
-    as_echo_n='sh -c $as_echo_n_body as_echo'
-  fi
-  export as_echo_body
-  as_echo='sh -c $as_echo_body as_echo'
-fi
-
-# The user is always right.
-if test "${PATH_SEPARATOR+set}" != set; then
-  PATH_SEPARATOR=:
-  (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && {
-    (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 ||
-      PATH_SEPARATOR=';'
-  }
-fi
-
-
-# IFS
-# We need space, tab and new line, in precisely that order.  Quoting is
-# there to prevent editors from complaining about space-tab.
-# (If _AS_PATH_WALK were called with IFS unset, it would disable word
-# splitting by setting IFS to empty value.)
-IFS=" ""	$as_nl"
-
-# Find who we are.  Look in the path if we contain no directory separator.
-as_myself=
-case $0 in #((
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-  *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR
-for as_dir in $PATH
-do
-  IFS=$as_save_IFS
-  test -z "$as_dir" && as_dir=.
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-  done
-IFS=$as_save_IFS
-
-     ;;
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-  exit 1
-fi
-
-# Unset variables that we do not need and which cause bugs (e.g. in
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-# suppresses any "Segmentation fault" message there.  '((' could
-# trigger a bug in pdksh 5.2.14.
-for as_var in BASH_ENV ENV MAIL MAILPATH
-do eval test x\${$as_var+set} = xset \
-  && ( (unset $as_var) || exit 1) >/dev/null 2>&1 && unset $as_var || :
-done
-PS1='$ '
-PS2='> '
-PS4='+ '
-
-# NLS nuisances.
-LC_ALL=C
-export LC_ALL
-LANGUAGE=C
-export LANGUAGE
-
-# CDPATH.
-(unset CDPATH) >/dev/null 2>&1 && unset CDPATH
-
-
-# as_fn_error STATUS ERROR [LINENO LOG_FD]
-# ----------------------------------------
-# Output "`basename $0`: error: ERROR" to stderr. If LINENO and LOG_FD are
-# provided, also output the error to LOG_FD, referencing LINENO. Then exit the
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-as_fn_error ()
-{
-  as_status=$1; test $as_status -eq 0 && as_status=1
-  if test "$4"; then
-    as_lineno=${as_lineno-"$3"} as_lineno_stack=as_lineno_stack=$as_lineno_stack
-    $as_echo "$as_me:${as_lineno-$LINENO}: error: $2" >&$4
-  fi
-  $as_echo "$as_me: error: $2" >&2
-  as_fn_exit $as_status
-} # as_fn_error
-
-
-# as_fn_set_status STATUS
-# -----------------------
-# Set $? to STATUS, without forking.
-as_fn_set_status ()
-{
-  return $1
-} # as_fn_set_status
-
-# as_fn_exit STATUS
-# -----------------
-# Exit the shell with STATUS, even in a "trap 0" or "set -e" context.
-as_fn_exit ()
-{
-  set +e
-  as_fn_set_status $1
-  exit $1
-} # as_fn_exit
-
-# as_fn_unset VAR
-# ---------------
-# Portably unset VAR.
-as_fn_unset ()
-{
-  { eval $1=; unset $1;}
-}
-as_unset=as_fn_unset
-# as_fn_append VAR VALUE
-# ----------------------
-# Append the text in VALUE to the end of the definition contained in VAR. Take
-# advantage of any shell optimizations that allow amortized linear growth over
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-# implementations.
-if (eval "as_var=1; as_var+=2; test x\$as_var = x12") 2>/dev/null; then :
-  eval 'as_fn_append ()
-  {
-    eval $1+=\$2
-  }'
-else
-  as_fn_append ()
-  {
-    eval $1=\$$1\$2
-  }
-fi # as_fn_append
-
-# as_fn_arith ARG...
-# ------------------
-# Perform arithmetic evaluation on the ARGs, and store the result in the
-# global $as_val. Take advantage of shells that can avoid forks. The arguments
-# must be portable across $(()) and expr.
-if (eval "test \$(( 1 + 1 )) = 2") 2>/dev/null; then :
-  eval 'as_fn_arith ()
-  {
-    as_val=$(( $* ))
-  }'
-else
-  as_fn_arith ()
-  {
-    as_val=`expr "$@" || test $? -eq 1`
-  }
-fi # as_fn_arith
-
-
-if expr a : '\(a\)' >/dev/null 2>&1 &&
-   test "X`expr 00001 : '.*\(...\)'`" = X001; then
-  as_expr=expr
-else
-  as_expr=false
-fi
-
-if (basename -- /) >/dev/null 2>&1 && test "X`basename -- / 2>&1`" = "X/"; then
-  as_basename=basename
-else
-  as_basename=false
-fi
-
-if (as_dir=`dirname -- /` && test "X$as_dir" = X/) >/dev/null 2>&1; then
-  as_dirname=dirname
-else
-  as_dirname=false
-fi
-
-as_me=`$as_basename -- "$0" ||
-$as_expr X/"$0" : '.*/\([^/][^/]*\)/*$' \| \
-	 X"$0" : 'X\(//\)$' \| \
-	 X"$0" : 'X\(/\)' \| . 2>/dev/null ||
-$as_echo X/"$0" |
-    sed '/^.*\/\([^/][^/]*\)\/*$/{
-	    s//\1/
-	    q
-	  }
-	  /^X\/\(\/\/\)$/{
-	    s//\1/
-	    q
-	  }
-	  /^X\/\(\/\).*/{
-	    s//\1/
-	    q
-	  }
-	  s/.*/./; q'`
-
-# Avoid depending upon Character Ranges.
-as_cr_letters='abcdefghijklmnopqrstuvwxyz'
-as_cr_LETTERS='ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-as_cr_Letters=$as_cr_letters$as_cr_LETTERS
-as_cr_digits='0123456789'
-as_cr_alnum=$as_cr_Letters$as_cr_digits
-
-ECHO_C= ECHO_N= ECHO_T=
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-cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1
-  ac_datarootdir_hack='
-  s&@datadir@&$datadir&g
-  s&@docdir@&$docdir&g
-  s&@infodir@&$infodir&g
-  s&@localedir@&$localedir&g
-  s&@mandir@&$mandir&g
-  s&\\\${datarootdir}&$datarootdir&g' ;;
-esac
-_ACEOF
-
-# Neutralize VPATH when `$srcdir' = `.'.
-# Shell code in configure.ac might set extrasub.
-# FIXME: do we really want to maintain this feature?
-cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1
-ac_sed_extra="$ac_vpsub
-$extrasub
-_ACEOF
-cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1
-:t
-/@[a-zA-Z_][a-zA-Z_0-9]*@/!b
-s|@configure_input@|$ac_sed_conf_input|;t t
-s&@top_builddir@&$ac_top_builddir_sub&;t t
-s&@top_build_prefix@&$ac_top_build_prefix&;t t
-s&@srcdir@&$ac_srcdir&;t t
-s&@abs_srcdir@&$ac_abs_srcdir&;t t
-s&@top_srcdir@&$ac_top_srcdir&;t t
-s&@abs_top_srcdir@&$ac_abs_top_srcdir&;t t
-s&@builddir@&$ac_builddir&;t t
-s&@abs_builddir@&$ac_abs_builddir&;t t
-s&@abs_top_builddir@&$ac_abs_top_builddir&;t t
-$ac_datarootdir_hack
-"
-eval sed \"\$ac_sed_extra\" "$ac_file_inputs" | $AWK -f "$ac_tmp/subs.awk" \
-  >$ac_tmp/out || as_fn_error $? "could not create $ac_file" "$LINENO" 5
-
-test -z "$ac_datarootdir_hack$ac_datarootdir_seen" &&
-  { ac_out=`sed -n '/\${datarootdir}/p' "$ac_tmp/out"`; test -n "$ac_out"; } &&
-  { ac_out=`sed -n '/^[	 ]*datarootdir[	 ]*:*=/p' \
-      "$ac_tmp/out"`; test -z "$ac_out"; } &&
-  { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: $ac_file contains a reference to the variable \`datarootdir'
-which seems to be undefined.  Please make sure it is defined" >&5
-$as_echo "$as_me: WARNING: $ac_file contains a reference to the variable \`datarootdir'
-which seems to be undefined.  Please make sure it is defined" >&2;}
-
-  rm -f "$ac_tmp/stdin"
-  case $ac_file in
-  -) cat "$ac_tmp/out" && rm -f "$ac_tmp/out";;
-  *) rm -f "$ac_file" && mv "$ac_tmp/out" "$ac_file";;
-  esac \
-  || as_fn_error $? "could not create $ac_file" "$LINENO" 5
- ;;
-
-
-
-  esac
-
-done # for ac_tag
-
-
-as_fn_exit 0
-_ACEOF
-ac_clean_files=$ac_clean_files_save
-
-test $ac_write_fail = 0 ||
-  as_fn_error $? "write failure creating $CONFIG_STATUS" "$LINENO" 5
-
-
-# configure is writing to config.log, and then calls config.status.
-# config.status does its own redirection, appending to config.log.
-# Unfortunately, on DOS this fails, as config.log is still kept open
-# by configure, so config.status won't be able to write to it; its
-# output is simply discarded.  So we exec the FD to /dev/null,
-# effectively closing config.log, so it can be properly (re)opened and
-# appended to by config.status.  When coming back to configure, we
-# need to make the FD available again.
-if test "$no_create" != yes; then
-  ac_cs_success=:
-  ac_config_status_args=
-  test "$silent" = yes &&
-    ac_config_status_args="$ac_config_status_args --quiet"
-  exec 5>/dev/null
-  $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false
-  exec 5>>config.log
-  # Use ||, not &&, to avoid exiting from the if with $? = 1, which
-  # would make configure fail if this is the last instruction.
-  $ac_cs_success || as_fn_exit 1
-fi
-if test -n "$ac_unrecognized_opts" && test "$enable_option_checking" != no; then
-  { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: unrecognized options: $ac_unrecognized_opts" >&5
-$as_echo "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2;}
-fi
-
-
diff --git a/ml-xgboost/R-package/configure.ac b/ml-xgboost/R-package/configure.ac
deleted file mode 100644
index c683a94..0000000
--- a/ml-xgboost/R-package/configure.ac
+++ /dev/null
@@ -1,55 +0,0 @@
-### configure.ac					-*- Autoconf -*-
-
-AC_PREREQ(2.62)
-
-AC_INIT([xgboost],[0.6-3],[],[xgboost],[])
-
-# Use this line to set CC variable to a C compiler
-AC_PROG_CC
-
-### Check whether backtrace() is part of libc or the external lib libexecinfo
-AC_MSG_CHECKING([Backtrace lib])
-AC_MSG_RESULT([])
-AC_CHECK_LIB([execinfo], [backtrace], [BACKTRACE_LIB=-lexecinfo], [BACKTRACE_LIB=''])
-
-### Endian detection
-AC_MSG_CHECKING([endian])
-AC_MSG_RESULT([])
-AC_RUN_IFELSE([AC_LANG_PROGRAM([[#include <stdint.h>]], [[const uint16_t endianness = 256; return !!(*(const uint8_t *)&endianness);]])],
-  [ENDIAN_FLAG="-DDMLC_CMAKE_LITTLE_ENDIAN=1"],
-  [ENDIAN_FLAG="-DDMLC_CMAKE_LITTLE_ENDIAN=0"])
-
-OPENMP_CXXFLAGS=""
-
-if test `uname -s` = "Linux"
-then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
-fi
-
-if test `uname -s` = "Darwin"
-then
-  OPENMP_CXXFLAGS='-Xclang -fopenmp'
-  OPENMP_LIB='/usr/local/lib/libomp.dylib'
-  ac_pkg_openmp=no
-  AC_MSG_CHECKING([whether OpenMP will work in a package])
-  AC_LANG_CONFTEST([AC_LANG_PROGRAM([[#include <omp.h>]], [[ return (omp_get_max_threads() <= 1); ]])])
-  ${CC} -o conftest conftest.c /usr/local/lib/libomp.dylib -Xclang -fopenmp 2>/dev/null && ./conftest && ac_pkg_openmp=yes
-  AC_MSG_RESULT([${ac_pkg_openmp}])
-  if test "${ac_pkg_openmp}" = no; then
-    OPENMP_CXXFLAGS=''
-    OPENMP_LIB=''
-    echo '*****************************************************************************************'
-    echo 'WARNING: OpenMP is unavailable on this Mac OSX system. Training speed may be suboptimal.'
-    echo '         To use all CPU cores for training jobs, you should install OpenMP by running\n'
-    echo '             brew install libomp'
-    echo '*****************************************************************************************'
-  fi
-fi
-
-AC_SUBST(OPENMP_CXXFLAGS)
-AC_SUBST(OPENMP_LIB)
-AC_SUBST(ENDIAN_FLAG)
-AC_SUBST(BACKTRACE_LIB)
-AC_CONFIG_FILES([src/Makevars])
-AC_OUTPUT
-
diff --git a/ml-xgboost/R-package/configure.win b/ml-xgboost/R-package/configure.win
deleted file mode 100644
index e69de29..0000000
diff --git a/ml-xgboost/R-package/demo/00Index b/ml-xgboost/R-package/demo/00Index
deleted file mode 100644
index 5c949d0..0000000
--- a/ml-xgboost/R-package/demo/00Index
+++ /dev/null
@@ -1,15 +0,0 @@
-basic_walkthrough               Basic feature walkthrough
-caret_wrapper                   Use xgboost to train in caret library
-custom_objective                Cutomize loss function, and evaluation metric
-boost_from_prediction           Boosting from existing prediction
-predict_first_ntree             Predicting using first n trees
-generalized_linear_model        Generalized Linear Model
-cross_validation                Cross validation
-create_sparse_matrix            Create Sparse Matrix
-predict_leaf_indices            Predicting the corresponding leaves
-early_stopping                  Early Stop in training
-poisson_regression              Poisson Regression on count data
-tweedie_regression              Tweddie Regression
-gpu_accelerated                 GPU-accelerated tree building algorithms
-interaction_constraints         Interaction constraints among features
-
diff --git a/ml-xgboost/R-package/demo/README.md b/ml-xgboost/R-package/demo/README.md
deleted file mode 100644
index e53afea..0000000
--- a/ml-xgboost/R-package/demo/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-XGBoost R Feature Walkthrough
-====
-* [Basic walkthrough of wrappers](basic_walkthrough.R)
-* [Train a xgboost model from caret library](caret_wrapper.R)
-* [Cutomize loss function, and evaluation metric](custom_objective.R)
-* [Boosting from existing prediction](boost_from_prediction.R)
-* [Predicting using first n trees](predict_first_ntree.R)
-* [Generalized Linear Model](generalized_linear_model.R)
-* [Cross validation](cross_validation.R)
-* [Create a sparse matrix from a dense one](create_sparse_matrix.R)
-* [Use GPU-accelerated tree building algorithms](gpu_accelerated.R)
-
-Benchmarks
-====
-* [Starter script for Kaggle Higgs Boson](../../demo/kaggle-higgs)
- 
-Notes
-====
-* Contribution of examples, benchmarks is more than welcomed!
-* If you like to share how you use xgboost to solve your problem, send a pull request:)
diff --git a/ml-xgboost/R-package/demo/basic_walkthrough.R b/ml-xgboost/R-package/demo/basic_walkthrough.R
deleted file mode 100644
index bb6b850..0000000
--- a/ml-xgboost/R-package/demo/basic_walkthrough.R
+++ /dev/null
@@ -1,112 +0,0 @@
-require(xgboost)
-require(methods)
-
-# we load in the agaricus dataset
-# In this example, we are aiming to predict whether a mushroom is edible
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-# the loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1}
-class(train$label)
-class(train$data)
-
-#-------------Basic Training using XGBoost-----------------
-# this is the basic usage of xgboost you can put matrix in data field
-# note: we are putting in sparse matrix here, xgboost naturally handles sparse input
-# use sparse matrix when your feature is sparse(e.g. when you are using one-hot encoding vector)
-print("Training xgboost with sparseMatrix")
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, nrounds = 2,
-               nthread = 2, objective = "binary:logistic")
-# alternatively, you can put in dense matrix, i.e. basic R-matrix
-print("Training xgboost with Matrix")
-bst <- xgboost(data = as.matrix(train$data), label = train$label, max_depth = 2, eta = 1, nrounds = 2,
-               nthread = 2, objective = "binary:logistic")
-
-# you can also put in xgb.DMatrix object, which stores label, data and other meta datas needed for advanced features
-print("Training xgboost with xgb.DMatrix")
-dtrain <- xgb.DMatrix(data = train$data, label = train$label)
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, nthread = 2, 
-               objective = "binary:logistic")
-
-# Verbose = 0,1,2
-print("Train xgboost with verbose 0, no message")
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
-               nthread = 2, objective = "binary:logistic", verbose = 0)
-print("Train xgboost with verbose 1, print evaluation metric")
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
-               nthread = 2, objective = "binary:logistic", verbose = 1)
-print("Train xgboost with verbose 2, also print information about tree")
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
-               nthread = 2, objective = "binary:logistic", verbose = 2)
-
-# you can also specify data as file path to a LibSVM format input
-# since we do not have this file with us, the following line is just for illustration
-# bst <- xgboost(data = 'agaricus.train.svm', max_depth = 2, eta = 1, nrounds = 2,objective = "binary:logistic")
-
-#--------------------basic prediction using xgboost--------------
-# you can do prediction using the following line
-# you can put in Matrix, sparseMatrix, or xgb.DMatrix 
-pred <- predict(bst, test$data)
-err <- mean(as.numeric(pred > 0.5) != test$label)
-print(paste("test-error=", err))
-
-#-------------------save and load models-------------------------
-# save model to binary local file
-xgb.save(bst, "xgboost.model")
-# load binary model to R
-bst2 <- xgb.load("xgboost.model")
-pred2 <- predict(bst2, test$data)
-# pred2 should be identical to pred
-print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
-
-# save model to R's raw vector
-raw = xgb.save.raw(bst)
-# load binary model to R
-bst3 <- xgb.load(raw)
-pred3 <- predict(bst3, test$data)
-# pred3 should be identical to pred
-print(paste("sum(abs(pred3-pred))=", sum(abs(pred3-pred))))
-
-#----------------Advanced features --------------
-# to use advanced features, we need to put data in xgb.DMatrix
-dtrain <- xgb.DMatrix(data = train$data, label=train$label)
-dtest <- xgb.DMatrix(data = test$data, label=test$label)
-#---------------Using watchlist----------------
-# watchlist is a list of xgb.DMatrix, each of them is tagged with name
-watchlist <- list(train=dtrain, test=dtest)
-# to train with watchlist, use xgb.train, which contains more advanced features
-# watchlist allows us to monitor the evaluation result on all data in the list 
-print("Train xgboost using xgb.train with watchlist")
-bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nrounds=2, watchlist=watchlist,
-                 nthread = 2, objective = "binary:logistic")
-# we can change evaluation metrics, or use multiple evaluation metrics
-print("train xgboost using xgb.train with watchlist, watch logloss and error")
-bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nrounds=2, watchlist=watchlist,
-                 eval_metric = "error", eval_metric = "logloss",
-                 nthread = 2, objective = "binary:logistic")
-
-# xgb.DMatrix can also be saved using xgb.DMatrix.save
-xgb.DMatrix.save(dtrain, "dtrain.buffer")
-# to load it in, simply call xgb.DMatrix
-dtrain2 <- xgb.DMatrix("dtrain.buffer")
-bst <- xgb.train(data=dtrain2, max_depth=2, eta=1, nrounds=2, watchlist=watchlist,
-                 nthread = 2, objective = "binary:logistic")
-# information can be extracted from xgb.DMatrix using getinfo
-label = getinfo(dtest, "label")
-pred <- predict(bst, dtest)
-err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
-print(paste("test-error=", err))
-
-# You can dump the tree you learned using xgb.dump into a text file
-dump_path = file.path(tempdir(), 'dump.raw.txt')
-xgb.dump(bst, dump_path, with_stats = T)
-
-# Finally, you can check which features are the most important.
-print("Most important features (look at column Gain):")
-imp_matrix <- xgb.importance(feature_names = colnames(train$data), model = bst)
-print(imp_matrix)
-
-# Feature importance bar plot by gain
-print("Feature importance Plot : ")
-print(xgb.plot.importance(importance_matrix = imp_matrix))
diff --git a/ml-xgboost/R-package/demo/boost_from_prediction.R b/ml-xgboost/R-package/demo/boost_from_prediction.R
deleted file mode 100644
index 1765650..0000000
--- a/ml-xgboost/R-package/demo/boost_from_prediction.R
+++ /dev/null
@@ -1,26 +0,0 @@
-require(xgboost)
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-watchlist <- list(eval = dtest, train = dtrain)
-###
-# advanced: start from a initial base prediction
-#
-print('start running example to start from a initial prediction')
-# train xgboost for 1 round
-param <- list(max_depth=2, eta=1, nthread = 2, silent=1, objective='binary:logistic')
-bst <- xgb.train(param, dtrain, 1, watchlist)
-# Note: we need the margin value instead of transformed prediction in set_base_margin
-# do predict with output_margin=TRUE, will always give you margin values before logistic transformation
-ptrain <- predict(bst, dtrain, outputmargin=TRUE)
-ptest  <- predict(bst, dtest, outputmargin=TRUE)
-# set the base_margin property of dtrain and dtest
-# base margin is the base prediction we will boost from
-setinfo(dtrain, "base_margin", ptrain)
-setinfo(dtest, "base_margin", ptest)
-
-print('this is result of boost from initial prediction')
-bst <- xgb.train(params = param, data = dtrain, nrounds = 1, watchlist = watchlist)
diff --git a/ml-xgboost/R-package/demo/caret_wrapper.R b/ml-xgboost/R-package/demo/caret_wrapper.R
deleted file mode 100644
index 751b202..0000000
--- a/ml-xgboost/R-package/demo/caret_wrapper.R
+++ /dev/null
@@ -1,35 +0,0 @@
-# install development version of caret library that contains xgboost models
-devtools::install_github("topepo/caret/pkg/caret") 
-require(caret)
-require(xgboost)
-require(data.table)
-require(vcd)
-require(e1071)
-
-# Load Arthritis dataset in memory.
-data(Arthritis)
-# Create a copy of the dataset with data.table package (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent and its performance are really good).
-df <- data.table(Arthritis, keep.rownames = F)
-
-# Let's add some new categorical features to see if it helps. Of course these feature are highly correlated to the Age feature. Usually it's not a good thing in ML, but Tree algorithms (including boosted trees) are able to select the best features, even in case of highly correlated features.
-# For the first feature we create groups of age by rounding the real age. Note that we transform it to factor (categorical data) so the algorithm treat them as independant values.
-df[,AgeDiscret:= as.factor(round(Age/10,0))]
-
-# Here is an even stronger simplification of the real age with an arbitrary split at 30 years old. I choose this value based on nothing. We will see later if simplifying the information based on arbitrary values is a good strategy (I am sure you already have an idea of how well it will work!).
-df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
-
-# We remove ID as there is nothing to learn from this feature (it will just add some noise as the dataset is small).
-df[,ID:=NULL]
-
-#-------------Basic Training using XGBoost in caret Library-----------------
-# Set up control parameters for caret::train
-# Here we use 10-fold cross-validation, repeating twice, and using random search for tuning hyper-parameters.
-fitControl <- trainControl(method = "repeatedcv", number = 10, repeats = 2, search = "random")
-# train a xgbTree model using caret::train
-model <- train(factor(Improved)~., data = df, method = "xgbTree", trControl = fitControl)
-
-# Instead of tree for our boosters, you can also fit a linear regression or logistic regression model using xgbLinear
-# model <- train(factor(Improved)~., data = df, method = "xgbLinear", trControl = fitControl)
-
-# See model results
-print(model)
diff --git a/ml-xgboost/R-package/demo/create_sparse_matrix.R b/ml-xgboost/R-package/demo/create_sparse_matrix.R
deleted file mode 100644
index 6069f33..0000000
--- a/ml-xgboost/R-package/demo/create_sparse_matrix.R
+++ /dev/null
@@ -1,89 +0,0 @@
-require(xgboost)
-require(Matrix)
-require(data.table)
-if (!require(vcd)) {
-  install.packages('vcd') #Available in Cran. Used for its dataset with categorical values.
-  require(vcd)
-}
-# According to its documentation, Xgboost works only on numbers.
-# Sometimes the dataset we have to work on have categorical data. 
-# A categorical variable is one which have a fixed number of values. By example, if for each observation a variable called "Colour" can have only "red", "blue" or "green" as value, it is a categorical variable.
-#
-# In R, categorical variable is called Factor. 
-# Type ?factor in console for more information.
-#
-# In this demo we will see how to transform a dense dataframe with categorical variables to a sparse matrix before analyzing it in Xgboost.
-# The method we are going to see is usually called "one hot encoding".
-
-#load Arthritis dataset in memory.
-data(Arthritis)
-
-# create a copy of the dataset with data.table package (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent and its performance are really good).
-df <- data.table(Arthritis, keep.rownames = F)
-
-# Let's have a look to the data.table
-cat("Print the dataset\n")
-print(df)
-
-# 2 columns have factor type, one has ordinal type (ordinal variable is a categorical variable with values wich can be ordered, here: None > Some > Marked).
-cat("Structure of the dataset\n")
-str(df)
-
-# Let's add some new categorical features to see if it helps. Of course these feature are highly correlated to the Age feature. Usually it's not a good thing in ML, but Tree algorithms (including boosted trees) are able to select the best features, even in case of highly correlated features.
-
-# For the first feature we create groups of age by rounding the real age. Note that we transform it to factor (categorical data) so the algorithm treat them as independant values.
-df[,AgeDiscret:= as.factor(round(Age/10,0))]
-
-# Here is an even stronger simplification of the real age with an arbitrary split at 30 years old. I choose this value based on nothing. We will see later if simplifying the information based on arbitrary values is a good strategy (I am sure you already have an idea of how well it will work!).
-df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
-
-# We remove ID as there is nothing to learn from this feature (it will just add some noise as the dataset is small).
-df[,ID:=NULL]
-
-# List the different values for the column Treatment: Placebo, Treated.
-cat("Values of the categorical feature Treatment\n")
-print(levels(df[,Treatment]))
-
-# Next step, we will transform the categorical data to dummy variables.
-# This method is also called one hot encoding.
-# The purpose is to transform each value of each categorical feature in one binary feature.
-#
-# Let's take, the column Treatment will be replaced by two columns, Placebo, and Treated. Each of them will be binary. For example an observation which had the value Placebo in column Treatment before the transformation will have, after the transformation, the value 1 in the new column Placebo and the value 0 in the new column  Treated.
-#
-# Formulae Improved~.-1 used below means transform all categorical features but column Improved to binary values.
-# Column Improved is excluded because it will be our output column, the one we want to predict.
-sparse_matrix = sparse.model.matrix(Improved~.-1, data = df)
-
-cat("Encoding of the sparse Matrix\n")
-print(sparse_matrix)
-
-# Create the output vector (not sparse)
-# 1. Set, for all rows, field in Y column to 0; 
-# 2. set Y to 1 when Improved == Marked; 
-# 3. Return Y column
-output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
-
-# Following is the same process as other demo
-cat("Learning...\n")
-bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 9,
-               eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic")
-
-importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
-print(importance)
-# According to the matrix below, the most important feature in this dataset to predict if the treatment will work is the Age. The second most important feature is having received a placebo or not. The sex is third. Then we see our generated features (AgeDiscret). We can see that their contribution is very low (Gain column).
-
-# Does these result make sense?
-# Let's check some Chi2 between each of these features and the outcome.
-
-print(chisq.test(df$Age, df$Y))
-# Pearson correlation between Age and illness disappearing is 35
-
-print(chisq.test(df$AgeDiscret, df$Y))
-# Our first simplification of Age gives a Pearson correlation of 8.
-
-print(chisq.test(df$AgeCat, df$Y))
-# The perfectly random split I did between young and old at 30 years old have a low correlation of 2. It's a result we may expect as may be in my mind > 30 years is being old (I am 32 and starting feeling old, this may explain that), but  for the illness we are studying, the age to be vulnerable is not the same. Don't let your "gut" lower the quality of your model. In "data science", there is science :-)
-
-# As you can see, in general destroying information by simplifying it won't improve your model. Chi2 just demonstrates that. But in more complex cases, creating a new feature based on existing one which makes link with the outcome more obvious may help the algorithm and improve the model. The case studied here is not enough complex to show that. Check Kaggle forum for some challenging datasets.
-# However it's almost always worse when you add some arbitrary rules.
-# Moreover, you can notice that even if we have added some not useful new features highly correlated with other features, the boosting tree algorithm have been able to choose the best one, which in this case is the Age. Linear model may not be that strong in these scenario.
diff --git a/ml-xgboost/R-package/demo/cross_validation.R b/ml-xgboost/R-package/demo/cross_validation.R
deleted file mode 100644
index d074552..0000000
--- a/ml-xgboost/R-package/demo/cross_validation.R
+++ /dev/null
@@ -1,51 +0,0 @@
-require(xgboost)
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-nrounds <- 2
-param <- list(max_depth=2, eta=1, silent=1, nthread=2, objective='binary:logistic')
-
-cat('running cross validation\n')
-# do cross validation, this will print result out as
-# [iteration]  metric_name:mean_value+std_value
-# std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold=5, metrics={'error'})
-
-cat('running cross validation, disable standard deviation display\n')
-# do cross validation, this will print result out as
-# [iteration]  metric_name:mean_value+std_value
-# std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold=5,
-       metrics='error', showsd = FALSE)
-
-###
-# you can also do cross validation with cutomized loss function
-# See custom_objective.R
-##
-print ('running cross validation, with cutomsized loss function')
-
-logregobj <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  preds <- 1/(1 + exp(-preds))
-  grad <- preds - labels
-  hess <- preds * (1 - preds)
-  return(list(grad = grad, hess = hess))
-}
-evalerror <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
-  return(list(metric = "error", value = err))
-}
-
-param <- list(max_depth=2, eta=1, silent=1,
-              objective = logregobj, eval_metric = evalerror)
-# train with customized objective
-xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5)
-
-# do cross validation with prediction values for each fold
-res <- xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5, prediction = TRUE)
-res$evaluation_log
-length(res$pred)
diff --git a/ml-xgboost/R-package/demo/custom_objective.R b/ml-xgboost/R-package/demo/custom_objective.R
deleted file mode 100644
index ec7e7e8..0000000
--- a/ml-xgboost/R-package/demo/custom_objective.R
+++ /dev/null
@@ -1,65 +0,0 @@
-require(xgboost)
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-# note: for customized objective function, we leave objective as default
-# note: what we are getting is margin value in prediction
-# you must know what you are doing
-watchlist <- list(eval = dtest, train = dtrain)
-num_round <- 2
-
-# user define objective function, given prediction, return gradient and second order gradient
-# this is loglikelihood loss
-logregobj <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  preds <- 1/(1 + exp(-preds))
-  grad <- preds - labels
-  hess <- preds * (1 - preds)
-  return(list(grad = grad, hess = hess))
-}
-
-# user defined evaluation function, return a pair metric_name, result
-# NOTE: when you do customized loss function, the default prediction value is margin
-# this may make buildin evalution metric not function properly
-# for example, we are doing logistic loss, the prediction is score before logistic transformation
-# the buildin evaluation error assumes input is after logistic transformation
-# Take this in mind when you use the customization, and maybe you need write customized evaluation function
-evalerror <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
-  return(list(metric = "error", value = err))
-}
-
-param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0, 
-              objective=logregobj, eval_metric=evalerror)
-print ('start training with user customized objective')
-# training with customized objective, we can also do step by step training
-# simply look at xgboost.py's implementation of train
-bst <- xgb.train(param, dtrain, num_round, watchlist)
-
-#
-# there can be cases where you want additional information 
-# being considered besides the property of DMatrix you can get by getinfo
-# you can set additional information as attributes if DMatrix
-
-# set label attribute of dtrain to be label, we use label as an example, it can be anything 
-attr(dtrain, 'label') <- getinfo(dtrain, 'label')
-# this is new customized objective, where you can access things you set
-# same thing applies to customized evaluation function
-logregobjattr <- function(preds, dtrain) {
-  # now you can access the attribute in customized function
-  labels <- attr(dtrain, 'label')
-  preds <- 1/(1 + exp(-preds))
-  grad <- preds - labels
-  hess <- preds * (1 - preds)
-  return(list(grad = grad, hess = hess))
-}
-param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0, 
-              objective=logregobjattr, eval_metric=evalerror)
-print ('start training with user customized objective, with additional attributes in DMatrix')
-# training with customized objective, we can also do step by step training
-# simply look at xgboost.py's implementation of train
-bst <- xgb.train(param, dtrain, num_round, watchlist)
diff --git a/ml-xgboost/R-package/demo/early_stopping.R b/ml-xgboost/R-package/demo/early_stopping.R
deleted file mode 100644
index 92a3ee8..0000000
--- a/ml-xgboost/R-package/demo/early_stopping.R
+++ /dev/null
@@ -1,40 +0,0 @@
-require(xgboost)
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-# note: for customized objective function, we leave objective as default
-# note: what we are getting is margin value in prediction
-# you must know what you are doing
-param <- list(max_depth=2, eta=1, nthread=2, verbosity=0)
-watchlist <- list(eval = dtest)
-num_round <- 20
-# user define objective function, given prediction, return gradient and second order gradient
-# this is loglikelihood loss
-logregobj <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  preds <- 1/(1 + exp(-preds))
-  grad <- preds - labels
-  hess <- preds * (1 - preds)
-  return(list(grad = grad, hess = hess))
-}
-# user defined evaluation function, return a pair metric_name, result
-# NOTE: when you do customized loss function, the default prediction value is margin
-# this may make buildin evalution metric not function properly
-# for example, we are doing logistic loss, the prediction is score before logistic transformation
-# the buildin evaluation error assumes input is after logistic transformation
-# Take this in mind when you use the customization, and maybe you need write customized evaluation function
-evalerror <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
-  return(list(metric = "error", value = err))
-}
-print ('start training with early Stopping setting')
-
-bst <- xgb.train(param, dtrain, num_round, watchlist,
-                 objective = logregobj, eval_metric = evalerror, maximize = FALSE,
-                 early_stopping_round = 3)
-bst <- xgb.cv(param, dtrain, num_round, nfold = 5,
-              objective = logregobj, eval_metric = evalerror,
-              maximize = FALSE, early_stopping_rounds = 3)
diff --git a/ml-xgboost/R-package/demo/generalized_linear_model.R b/ml-xgboost/R-package/demo/generalized_linear_model.R
deleted file mode 100644
index 3c2cdb5..0000000
--- a/ml-xgboost/R-package/demo/generalized_linear_model.R
+++ /dev/null
@@ -1,34 +0,0 @@
-require(xgboost)
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-##
-#  this script demonstrate how to fit generalized linear model in xgboost
-#  basically, we are using linear model, instead of tree for our boosters
-#  you can fit a linear regression, or logistic regression model
-##
-
-# change booster to gblinear, so that we are fitting a linear model
-# alpha is the L1 regularizer 
-# lambda is the L2 regularizer
-# you can also set lambda_bias which is L2 regularizer on the bias term
-param <- list(objective = "binary:logistic", booster = "gblinear",
-              nthread = 2, alpha = 0.0001, lambda = 1)
-
-# normally, you do not need to set eta (step_size)
-# XGBoost uses a parallel coordinate descent algorithm (shotgun), 
-# there could be affection on convergence with parallelization on certain cases
-# setting eta to be smaller value, e.g 0.5 can make the optimization more stable
-
-##
-# the rest of settings are the same
-##
-watchlist <- list(eval = dtest, train = dtrain)
-num_round <- 2
-bst <- xgb.train(param, dtrain, num_round, watchlist)
-ypred <- predict(bst, dtest)
-labels <- getinfo(dtest, 'label')
-cat('error of preds=', mean(as.numeric(ypred>0.5)!=labels),'\n')
-
diff --git a/ml-xgboost/R-package/demo/gpu_accelerated.R b/ml-xgboost/R-package/demo/gpu_accelerated.R
deleted file mode 100644
index 321255c..0000000
--- a/ml-xgboost/R-package/demo/gpu_accelerated.R
+++ /dev/null
@@ -1,45 +0,0 @@
-# An example of using GPU-accelerated tree building algorithms
-# 
-# NOTE: it can only run if you have a CUDA-enable GPU and the package was 
-#       specially compiled with GPU support.
-#
-# For the current functionality, see 
-# https://xgboost.readthedocs.io/en/latest/gpu/index.html
-#
-
-library('xgboost')
-
-# Simulate N x p random matrix with some binomial response dependent on pp columns
-set.seed(111)
-N <- 1000000
-p <- 50
-pp <- 25
-X <- matrix(runif(N * p), ncol = p)
-betas <- 2 * runif(pp) - 1
-sel <- sort(sample(p, pp))
-m <- X[, sel] %*% betas - 1 + rnorm(N)
-y <- rbinom(N, 1, plogis(m))
-
-tr <- sample.int(N, N * 0.75)
-dtrain <- xgb.DMatrix(X[tr,], label = y[tr])
-dtest <- xgb.DMatrix(X[-tr,], label = y[-tr])
-wl <- list(train = dtrain, test = dtest)
-
-# An example of running 'gpu_hist' algorithm
-# which is
-# - similar to the 'hist'
-# - the fastest option for moderately large datasets
-# - current limitations: max_depth < 16, does not implement guided loss
-# You can use tree_method = 'gpu_hist' for another GPU accelerated algorithm,
-# which is slower, more memory-hungry, but does not use binning.
-param <- list(objective = 'reg:logistic', eval_metric = 'auc', subsample = 0.5, nthread = 4,
-              max_bin = 64, tree_method = 'gpu_hist')
-pt <- proc.time()
-bst_gpu <- xgb.train(param, dtrain, watchlist = wl, nrounds = 50)
-proc.time() - pt
-
-# Compare to the 'hist' algorithm:
-param$tree_method <- 'hist'
-pt <- proc.time()
-bst_hist <- xgb.train(param, dtrain, watchlist = wl, nrounds = 50)
-proc.time() - pt
diff --git a/ml-xgboost/R-package/demo/interaction_constraints.R b/ml-xgboost/R-package/demo/interaction_constraints.R
deleted file mode 100644
index 2f2edb1..0000000
--- a/ml-xgboost/R-package/demo/interaction_constraints.R
+++ /dev/null
@@ -1,105 +0,0 @@
-library(xgboost)
-library(data.table)
-
-set.seed(1024)
-
-# Function to obtain a list of interactions fitted in trees, requires input of maximum depth
-treeInteractions <- function(input_tree, input_max_depth){
-  trees <- copy(input_tree)  # copy tree input to prevent overwriting
-  if (input_max_depth < 2) return(list())  # no interactions if max depth < 2
-  if (nrow(input_tree) == 1) return(list())
-
-  # Attach parent nodes
-  for (i in 2:input_max_depth){
-    if (i == 2) trees[, ID_merge:=ID] else trees[, ID_merge:=get(paste0('parent_',i-2))]
-    parents_left <- trees[!is.na(Split), list(i.id=ID, i.feature=Feature, ID_merge=Yes)]
-    parents_right <- trees[!is.na(Split), list(i.id=ID, i.feature=Feature, ID_merge=No)]
-
-    setorderv(trees, 'ID_merge')
-    setorderv(parents_left, 'ID_merge')
-    setorderv(parents_right, 'ID_merge')
-
-    trees <- merge(trees, parents_left, by='ID_merge', all.x=T)
-    trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
-    trees[, c('i.id','i.feature'):=NULL]
-
-    trees <- merge(trees, parents_right, by='ID_merge', all.x=T)
-    trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
-    trees[, c('i.id','i.feature'):=NULL]
-  }
-
-  # Extract nodes with interactions
-  interaction_trees <- trees[!is.na(Split) & !is.na(parent_1), 
-                             c('Feature',paste0('parent_feat_',1:(input_max_depth-1))), with=F]
-  interaction_trees_split <- split(interaction_trees, 1:nrow(interaction_trees))
-  interaction_list <- lapply(interaction_trees_split, as.character)
-
-  # Remove NAs (no parent interaction)
-  interaction_list <- lapply(interaction_list, function(x) x[!is.na(x)])
-
-  # Remove non-interactions (same variable)
-  interaction_list <- lapply(interaction_list, unique)  # remove same variables
-  interaction_length <- sapply(interaction_list, length)
-  interaction_list <- interaction_list[interaction_length > 1]
-  interaction_list <- unique(lapply(interaction_list, sort))
-  return(interaction_list)
-}
-
-# Generate sample data
-x <- list()
-for (i in 1:10){
-  x[[i]] = i*rnorm(1000, 10)
-}
-x <- as.data.table(x)
-
-y = -1*x[, rowSums(.SD)] + x[['V1']]*x[['V2']] + x[['V3']]*x[['V4']]*x[['V5']] + rnorm(1000, 0.001) + 3*sin(x[['V7']])
-
-train = as.matrix(x)
-
-# Interaction constraint list (column names form)
-interaction_list <- list(c('V1','V2'),c('V3','V4','V5'))
-
-# Convert interaction constraint list into feature index form
-cols2ids <- function(object, col_names) {
-  LUT <- seq_along(col_names) - 1
-  names(LUT) <- col_names
-  rapply(object, function(x) LUT[x], classes="character", how="replace")
-}
-interaction_list_fid = cols2ids(interaction_list, colnames(train))
-
-# Fit model with interaction constraints
-bst = xgboost(data = train, label = y, max_depth = 4,
-              eta = 0.1, nthread = 2, nrounds = 1000,
-              interaction_constraints = interaction_list_fid)
-
-bst_tree <- xgb.model.dt.tree(colnames(train), bst)
-bst_interactions <- treeInteractions(bst_tree, 4)  # interactions constrained to combinations of V1*V2 and V3*V4*V5
-
-# Fit model without interaction constraints
-bst2 = xgboost(data = train, label = y, max_depth = 4,
-               eta = 0.1, nthread = 2, nrounds = 1000)
-
-bst2_tree <- xgb.model.dt.tree(colnames(train), bst2)
-bst2_interactions <- treeInteractions(bst2_tree, 4)  # much more interactions
-
-# Fit model with both interaction and monotonicity constraints
-bst3 = xgboost(data = train, label = y, max_depth = 4,
-               eta = 0.1, nthread = 2, nrounds = 1000,
-               interaction_constraints = interaction_list_fid,
-               monotone_constraints = c(-1,0,0,0,0,0,0,0,0,0))
-
-bst3_tree <- xgb.model.dt.tree(colnames(train), bst3)
-bst3_interactions <- treeInteractions(bst3_tree, 4)  # interactions still constrained to combinations of V1*V2 and V3*V4*V5
-
-# Show monotonic constraints still apply by checking scores after incrementing V1
-x1 <- sort(unique(x[['V1']]))
-for (i in 1:length(x1)){
-  testdata <- copy(x[, -c('V1')])
-  testdata[['V1']] <- x1[i]
-  testdata <- testdata[, paste0('V',1:10), with=F]
-  pred <- predict(bst3, as.matrix(testdata))
-  
-  # Should not print out anything due to monotonic constraints
-  if (i > 1) if (any(pred > prev_pred)) print(i)
-  prev_pred <- pred 
-}
diff --git a/ml-xgboost/R-package/demo/poisson_regression.R b/ml-xgboost/R-package/demo/poisson_regression.R
deleted file mode 100644
index f9dc4ac..0000000
--- a/ml-xgboost/R-package/demo/poisson_regression.R
+++ /dev/null
@@ -1,7 +0,0 @@
-data(mtcars)
-head(mtcars)
-bst = xgboost(data=as.matrix(mtcars[,-11]),label=mtcars[,11],
-              objective='count:poisson',nrounds=5)
-pred = predict(bst,as.matrix(mtcars[,-11]))
-sqrt(mean((pred-mtcars[,11])^2))
-
diff --git a/ml-xgboost/R-package/demo/predict_first_ntree.R b/ml-xgboost/R-package/demo/predict_first_ntree.R
deleted file mode 100644
index 8934c55..0000000
--- a/ml-xgboost/R-package/demo/predict_first_ntree.R
+++ /dev/null
@@ -1,23 +0,0 @@
-require(xgboost)
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-watchlist <- list(eval = dtest, train = dtrain)
-nrounds = 2
-
-# training the model for two rounds
-bst = xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
-cat('start testing prediction from first n trees\n')
-labels <- getinfo(dtest,'label')
-
-### predict using first 1 tree
-ypred1 = predict(bst, dtest, ntreelimit=1)
-# by default, we predict using all the trees
-ypred2 = predict(bst, dtest)
-
-cat('error of ypred1=', mean(as.numeric(ypred1>0.5)!=labels),'\n')
-cat('error of ypred2=', mean(as.numeric(ypred2>0.5)!=labels),'\n')
diff --git a/ml-xgboost/R-package/demo/predict_leaf_indices.R b/ml-xgboost/R-package/demo/predict_leaf_indices.R
deleted file mode 100644
index 054bde7..0000000
--- a/ml-xgboost/R-package/demo/predict_leaf_indices.R
+++ /dev/null
@@ -1,53 +0,0 @@
-require(xgboost)
-require(data.table)
-require(Matrix)
-
-set.seed(1982)
-
-# load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
-
-param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-nrounds = 4
-
-# training the model for two rounds
-bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
-
-# Model accuracy without new features
-accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
-
-# by default, we predict using all the trees
-
-pred_with_leaf = predict(bst, dtest, predleaf = TRUE)
-head(pred_with_leaf)
-
-create.new.tree.features <- function(model, original.features){
-  pred_with_leaf <- predict(model, original.features, predleaf = TRUE)
-  cols <- list()
-  for(i in 1:model$niter){
-    # max is not the real max but it s not important for the purpose of adding features
-    leaf.id <- sort(unique(pred_with_leaf[,i]))
-    cols[[i]] <- factor(x = pred_with_leaf[,i], level = leaf.id)
-  }
-  cbind(original.features, sparse.model.matrix( ~ . -1, as.data.frame(cols)))
-}
-
-# Convert previous features to one hot encoding
-new.features.train <- create.new.tree.features(bst, agaricus.train$data)
-new.features.test <- create.new.tree.features(bst, agaricus.test$data)
-colnames(new.features.test) <- colnames(new.features.train)
-
-# learning with new features
-new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
-new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
-watchlist <- list(train = new.dtrain)
-bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
-
-# Model accuracy with new features
-accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
-
-# Here the accuracy was already good and is now perfect.
-cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!\n"))
diff --git a/ml-xgboost/R-package/demo/runall.R b/ml-xgboost/R-package/demo/runall.R
deleted file mode 100644
index 0c1392a..0000000
--- a/ml-xgboost/R-package/demo/runall.R
+++ /dev/null
@@ -1,14 +0,0 @@
-# running all scripts in demo folder
-demo(basic_walkthrough)
-demo(custom_objective)
-demo(boost_from_prediction)
-demo(predict_first_ntree)
-demo(generalized_linear_model)
-demo(cross_validation)
-demo(create_sparse_matrix)
-demo(predict_leaf_indices)
-demo(early_stopping)
-demo(poisson_regression)
-demo(caret_wrapper)
-demo(tweedie_regression)
-#demo(gpu_accelerated) # can only run when built with GPU support
\ No newline at end of file
diff --git a/ml-xgboost/R-package/demo/tweedie_regression.R b/ml-xgboost/R-package/demo/tweedie_regression.R
deleted file mode 100644
index 4d272f6..0000000
--- a/ml-xgboost/R-package/demo/tweedie_regression.R
+++ /dev/null
@@ -1,49 +0,0 @@
-library(xgboost)
-library(data.table)
-library(cplm)
-
-data(AutoClaim)
-
-# auto insurance dataset analyzed by Yip and Yau (2005)
-dt <- data.table(AutoClaim)
-
-# exclude these columns from the model matrix
-exclude <-  c('POLICYNO', 'PLCYDATE', 'CLM_FREQ5', 'CLM_AMT5', 'CLM_FLAG', 'IN_YY')
-
-# retains the missing values
-# NOTE: this dataset is comes ready out of the box
-options(na.action = 'na.pass')
-x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = F])
-options(na.action = 'na.omit')
-
-# response
-y <- dt[, CLM_AMT5]
-
-d_train <- xgb.DMatrix(data = x, label = y, missing = NA)
-
-# the tweedie_variance_power parameter determines the shape of 
-# distribution
-# - closer to 1 is more poisson like and the mass
-#   is more concentrated near zero 
-# - closer to 2 is more gamma like and the mass spreads to the 
-#   the right with less concentration near zero
-
-params <- list(
-  objective = 'reg:tweedie',
-  eval_metric = 'rmse', 
-  tweedie_variance_power = 1.4,
-  max_depth = 6,
-  eta = 1)
-
-bst <- xgb.train(
-  data = d_train, 
-  params = params, 
-  maximize = FALSE,
-  watchlist = list(train = d_train), 
-  nrounds = 20)
-
-var_imp <- xgb.importance(attr(x, 'Dimnames')[[2]], model = bst)
-
-preds <- predict(bst, d_train)
-
-rmse <- sqrt(sum(mean((y - preds)^2)))
\ No newline at end of file
diff --git a/ml-xgboost/R-package/man/agaricus.test.Rd b/ml-xgboost/R-package/man/agaricus.test.Rd
deleted file mode 100644
index e3694ae..0000000
--- a/ml-xgboost/R-package/man/agaricus.test.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgboost.R
-\docType{data}
-\name{agaricus.test}
-\alias{agaricus.test}
-\title{Test part from Mushroom Data Set}
-\format{
-A list containing a label vector, and a dgCMatrix object with 1611
-rows and 126 variables
-}
-\usage{
-data(agaricus.test)
-}
-\description{
-This data set is originally from the Mushroom data set,
-UCI Machine Learning Repository.
-}
-\details{
-This data set includes the following fields:
-
-\itemize{
- \item \code{label} the label for each record
- \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
-}
-}
-\references{
-https://archive.ics.uci.edu/ml/datasets/Mushroom
-
-Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
-[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
-School of Information and Computer Science.
-}
-\keyword{datasets}
diff --git a/ml-xgboost/R-package/man/agaricus.train.Rd b/ml-xgboost/R-package/man/agaricus.train.Rd
deleted file mode 100644
index 92692c9..0000000
--- a/ml-xgboost/R-package/man/agaricus.train.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgboost.R
-\docType{data}
-\name{agaricus.train}
-\alias{agaricus.train}
-\title{Training part from Mushroom Data Set}
-\format{
-A list containing a label vector, and a dgCMatrix object with 6513
-rows and 127 variables
-}
-\usage{
-data(agaricus.train)
-}
-\description{
-This data set is originally from the Mushroom data set,
-UCI Machine Learning Repository.
-}
-\details{
-This data set includes the following fields:
-
-\itemize{
- \item \code{label} the label for each record
- \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
-}
-}
-\references{
-https://archive.ics.uci.edu/ml/datasets/Mushroom
-
-Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
-[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
-School of Information and Computer Science.
-}
-\keyword{datasets}
diff --git a/ml-xgboost/R-package/man/callbacks.Rd b/ml-xgboost/R-package/man/callbacks.Rd
deleted file mode 100644
index 9f6f690..0000000
--- a/ml-xgboost/R-package/man/callbacks.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{callbacks}
-\alias{callbacks}
-\title{Callback closures for booster training.}
-\description{
-These are used to perform various service tasks either during boosting iterations or at the end.
-This approach helps to modularize many of such tasks without bloating the main training methods,
-and it offers .
-}
-\details{
-By default, a callback function is run after each boosting iteration.
-An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
-
-When a callback function has \code{finalize} parameter, its finalizer part will also be run after
-the boosting is completed.
-
-WARNING: side-effects!!! Be aware that these callback functions access and modify things in
-the environment from which they are called from, which is a fairly uncommon thing to do in R.
-
-To write a custom callback closure, make sure you first understand the main concepts about R environments.
-Check either R documentation on \code{\link[base]{environment}} or the
-\href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
-book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
-choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
-with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
-}
-\seealso{
-\code{\link{cb.print.evaluation}},
-\code{\link{cb.evaluation.log}},
-\code{\link{cb.reset.parameters}},
-\code{\link{cb.early.stop}},
-\code{\link{cb.save.model}},
-\code{\link{cb.cv.predict}},
-\code{\link{xgb.train}},
-\code{\link{xgb.cv}}
-}
diff --git a/ml-xgboost/R-package/man/cb.cv.predict.Rd b/ml-xgboost/R-package/man/cb.cv.predict.Rd
deleted file mode 100644
index ded899e..0000000
--- a/ml-xgboost/R-package/man/cb.cv.predict.Rd
+++ /dev/null
@@ -1,43 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.cv.predict}
-\alias{cb.cv.predict}
-\title{Callback closure for returning cross-validation based predictions.}
-\usage{
-cb.cv.predict(save_models = FALSE)
-}
-\arguments{
-\item{save_models}{a flag for whether to save the folds' models.}
-}
-\value{
-Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
-depending on the number of prediction outputs per data row. The order of predictions corresponds
-to the order of rows in the original dataset. Note that when a custom \code{folds} list is
-provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
-non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
-meaningful when user-provided folds have overlapping indices as in, e.g., random sampling splits.
-When some of the indices in the training dataset are not included into user-provided \code{folds},
-their prediction value would be \code{NA}.
-}
-\description{
-Callback closure for returning cross-validation based predictions.
-}
-\details{
-This callback function saves predictions for all of the test folds,
-and also allows to save the folds' models.
-
-It is a "finalizer" callback and it uses early stopping information whenever it is available,
-thus it must be run after the early stopping callback if the early stopping is used.
-
-Callback function expects the following values to be set in its calling frame:
-\code{bst_folds},
-\code{basket},
-\code{data},
-\code{end_iteration},
-\code{params},
-\code{num_parallel_tree},
-\code{num_class}.
-}
-\seealso{
-\code{\link{callbacks}}
-}
diff --git a/ml-xgboost/R-package/man/cb.early.stop.Rd b/ml-xgboost/R-package/man/cb.early.stop.Rd
deleted file mode 100644
index 1a099d7..0000000
--- a/ml-xgboost/R-package/man/cb.early.stop.Rd
+++ /dev/null
@@ -1,66 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.early.stop}
-\alias{cb.early.stop}
-\title{Callback closure to activate the early stopping.}
-\usage{
-cb.early.stop(
-  stopping_rounds,
-  maximize = FALSE,
-  metric_name = NULL,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{stopping_rounds}{The number of rounds with no improvement in
-the evaluation metric in order to stop the training.}
-
-\item{maximize}{whether to maximize the evaluation metric}
-
-\item{metric_name}{the name of an evaluation column to use as a criteria for early
-stopping. If not set, the last column would be used.
-Let's say the test data in \code{watchlist} was labelled as \code{dtest},
-and one wants to use the AUC in test data for early stopping regardless of where
-it is in the \code{watchlist}, then one of the following would need to be set:
-\code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
-All dash '-' characters in metric names are considered equivalent to '_'.}
-
-\item{verbose}{whether to print the early stopping information.}
-}
-\description{
-Callback closure to activate the early stopping.
-}
-\details{
-This callback function determines the condition for early stopping
-by setting the \code{stop_condition = TRUE} flag in its calling frame.
-
-The following additional fields are assigned to the model's R object:
-\itemize{
-\item \code{best_score} the evaluation score at the best iteration
-\item \code{best_iteration} at which boosting iteration the best score has occurred (1-based index)
-\item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
-     It differs from \code{best_iteration} in multiclass or random forest settings.
-}
-
-The Same values are also stored as xgb-attributes:
-\itemize{
-\item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
-\item \code{best_msg} message string is also stored.
-}
-
-At least one data element is required in the evaluation watchlist for early stopping to work.
-
-Callback function expects the following values to be set in its calling frame:
-\code{stop_condition},
-\code{bst_evaluation},
-\code{rank},
-\code{bst} (or \code{bst_folds} and \code{basket}),
-\code{iteration},
-\code{begin_iteration},
-\code{end_iteration},
-\code{num_parallel_tree}.
-}
-\seealso{
-\code{\link{callbacks}},
-\code{\link{xgb.attr}}
-}
diff --git a/ml-xgboost/R-package/man/cb.evaluation.log.Rd b/ml-xgboost/R-package/man/cb.evaluation.log.Rd
deleted file mode 100644
index 94f8a02..0000000
--- a/ml-xgboost/R-package/man/cb.evaluation.log.Rd
+++ /dev/null
@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.evaluation.log}
-\alias{cb.evaluation.log}
-\title{Callback closure for logging the evaluation history}
-\usage{
-cb.evaluation.log()
-}
-\description{
-Callback closure for logging the evaluation history
-}
-\details{
-This callback function appends the current iteration evaluation results \code{bst_evaluation}
-available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
-
-The finalizer callback (called with \code{finalize = TURE} in the end) converts
-the \code{evaluation_log} list into a final data.table.
-
-The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
-
-Note: in the column names of the final data.table, the dash '-' character is replaced with
-the underscore '_' in order to make the column names more like regular R identifiers.
-
-Callback function expects the following values to be set in its calling frame:
-\code{evaluation_log},
-\code{bst_evaluation},
-\code{iteration}.
-}
-\seealso{
-\code{\link{callbacks}}
-}
diff --git a/ml-xgboost/R-package/man/cb.gblinear.history.Rd b/ml-xgboost/R-package/man/cb.gblinear.history.Rd
deleted file mode 100644
index 35ebeb6..0000000
--- a/ml-xgboost/R-package/man/cb.gblinear.history.Rd
+++ /dev/null
@@ -1,95 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.gblinear.history}
-\alias{cb.gblinear.history}
-\title{Callback closure for collecting the model coefficients history of a gblinear booster
-during its training.}
-\usage{
-cb.gblinear.history(sparse = FALSE)
-}
-\arguments{
-\item{sparse}{when set to FALSE/TURE, a dense/sparse matrix is used to store the result.
-Sparse format is useful when one expects only a subset of coefficients to be non-zero,
-when using the "thrifty" feature selector with fairly small number of top features
-selected per iteration.}
-}
-\value{
-Results are stored in the \code{coefs} element of the closure.
-The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
-With \code{xgb.train}, it is either a dense of a sparse matrix.
-While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
-}
-\description{
-Callback closure for collecting the model coefficients history of a gblinear booster
-during its training.
-}
-\details{
-To keep things fast and simple, gblinear booster does not internally store the history of linear
-model coefficients at each boosting iteration. This callback provides a workaround for storing
-the coefficients' path, by extracting them after each training iteration.
-
-Callback function expects the following values to be set in its calling frame:
-\code{bst} (or \code{bst_folds}).
-}
-\examples{
-#### Binary classification:
-#
-# In the iris dataset, it is hard to linearly separate Versicolor class from the rest
-# without considering the 2nd order interactions:
-require(magrittr)
-x <- model.matrix(Species ~ .^2, iris)[,-1]
-colnames(x)
-dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
-param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
-              lambda = 0.0003, alpha = 0.0003, nthread = 2)
-# For 'shotgun', which is a default linear updater, using high eta values may result in
-# unstable behaviour in some datasets. With this simple dataset, however, the high learning
-# rate does not break the convergence, but allows us to illustrate the typical pattern of
-# "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
-bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 1.,
-                 callbacks = list(cb.gblinear.history()))
-# Extract the coefficients' path and plot them vs boosting iteration number:
-coef_path <- xgb.gblinear.history(bst)
-matplot(coef_path, type = 'l')
-
-# With the deterministic coordinate descent updater, it is safer to use higher learning rates.
-# Will try the classical componentwise boosting which selects a single best feature per round:
-bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
-                 updater = 'coord_descent', feature_selector = 'thrifty', top_k = 1,
-                 callbacks = list(cb.gblinear.history()))
-xgb.gblinear.history(bst) \%>\% matplot(type = 'l')
-#  Componentwise boosting is known to have similar effect to Lasso regularization.
-# Try experimenting with various values of top_k, eta, nrounds,
-# as well as different feature_selectors.
-
-# For xgb.cv:
-bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
-             callbacks = list(cb.gblinear.history()))
-# coefficients in the CV fold #3
-xgb.gblinear.history(bst)[[3]] \%>\% matplot(type = 'l')
-
-
-#### Multiclass classification:
-#
-dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
-param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
-              lambda = 0.0003, alpha = 0.0003, nthread = 2)
-# For the default linear updater 'shotgun' it sometimes is helpful
-# to use smaller eta to reduce instability
-bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 70, eta = 0.5,
-                 callbacks = list(cb.gblinear.history()))
-# Will plot the coefficient paths separately for each class:
-xgb.gblinear.history(bst, class_index = 0) \%>\% matplot(type = 'l')
-xgb.gblinear.history(bst, class_index = 1) \%>\% matplot(type = 'l')
-xgb.gblinear.history(bst, class_index = 2) \%>\% matplot(type = 'l')
-
-# CV:
-bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
-              callbacks = list(cb.gblinear.history(FALSE)))
-# 1st forld of 1st class
-xgb.gblinear.history(bst, class_index = 0)[[1]] \%>\% matplot(type = 'l')
-
-}
-\seealso{
-\code{\link{callbacks}}, \code{\link{xgb.gblinear.history}}.
-}
diff --git a/ml-xgboost/R-package/man/cb.print.evaluation.Rd b/ml-xgboost/R-package/man/cb.print.evaluation.Rd
deleted file mode 100644
index 59b9ba6..0000000
--- a/ml-xgboost/R-package/man/cb.print.evaluation.Rd
+++ /dev/null
@@ -1,29 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.print.evaluation}
-\alias{cb.print.evaluation}
-\title{Callback closure for printing the result of evaluation}
-\usage{
-cb.print.evaluation(period = 1, showsd = TRUE)
-}
-\arguments{
-\item{period}{results would be printed every number of periods}
-
-\item{showsd}{whether standard deviations should be printed (when available)}
-}
-\description{
-Callback closure for printing the result of evaluation
-}
-\details{
-The callback function prints the result of evaluation at every \code{period} iterations.
-The initial and the last iteration's evaluations are always printed.
-
-Callback function expects the following values to be set in its calling frame:
-\code{bst_evaluation} (also \code{bst_evaluation_err} when available),
-\code{iteration},
-\code{begin_iteration},
-\code{end_iteration}.
-}
-\seealso{
-\code{\link{callbacks}}
-}
diff --git a/ml-xgboost/R-package/man/cb.reset.parameters.Rd b/ml-xgboost/R-package/man/cb.reset.parameters.Rd
deleted file mode 100644
index ee0a5d1..0000000
--- a/ml-xgboost/R-package/man/cb.reset.parameters.Rd
+++ /dev/null
@@ -1,36 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.reset.parameters}
-\alias{cb.reset.parameters}
-\title{Callback closure for resetting the booster's parameters at each iteration.}
-\usage{
-cb.reset.parameters(new_params)
-}
-\arguments{
-\item{new_params}{a list where each element corresponds to a parameter that needs to be reset.
-Each element's value must be either a vector of values of length \code{nrounds}
-to be set at each iteration,
-or a function of two parameters \code{learning_rates(iteration, nrounds)}
-which returns a new parameter value by using the current iteration number
-and the total number of boosting rounds.}
-}
-\description{
-Callback closure for resetting the booster's parameters at each iteration.
-}
-\details{
-This is a "pre-iteration" callback function used to reset booster's parameters
-at the beginning of each iteration.
-
-Note that when training is resumed from some previous model, and a function is used to
-reset a parameter value, the \code{nrounds} argument in this function would be the
-the number of boosting rounds in the current training.
-
-Callback function expects the following values to be set in its calling frame:
-\code{bst} or \code{bst_folds},
-\code{iteration},
-\code{begin_iteration},
-\code{end_iteration}.
-}
-\seealso{
-\code{\link{callbacks}}
-}
diff --git a/ml-xgboost/R-package/man/cb.save.model.Rd b/ml-xgboost/R-package/man/cb.save.model.Rd
deleted file mode 100644
index fd564b3..0000000
--- a/ml-xgboost/R-package/man/cb.save.model.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{cb.save.model}
-\alias{cb.save.model}
-\title{Callback closure for saving a model file.}
-\usage{
-cb.save.model(save_period = 0, save_name = "xgboost.model")
-}
-\arguments{
-\item{save_period}{save the model to disk after every
-\code{save_period} iterations; 0 means save the model at the end.}
-
-\item{save_name}{the name or path for the saved model file.
-It can contain a \code{\link[base]{sprintf}} formatting specifier
-to include the integer iteration number in the file name.
-E.g., with \code{save_name} = 'xgboost_%04d.model',
-the file saved at iteration 50 would be named "xgboost_0050.model".}
-}
-\description{
-Callback closure for saving a model file.
-}
-\details{
-This callback function allows to save an xgb-model file, either periodically after each \code{save_period}'s or at the end.
-
-Callback function expects the following values to be set in its calling frame:
-\code{bst},
-\code{iteration},
-\code{begin_iteration},
-\code{end_iteration}.
-}
-\seealso{
-\code{\link{callbacks}}
-}
diff --git a/ml-xgboost/R-package/man/dim.xgb.DMatrix.Rd b/ml-xgboost/R-package/man/dim.xgb.DMatrix.Rd
deleted file mode 100644
index 76c53de..0000000
--- a/ml-xgboost/R-package/man/dim.xgb.DMatrix.Rd
+++ /dev/null
@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{dim.xgb.DMatrix}
-\alias{dim.xgb.DMatrix}
-\title{Dimensions of xgb.DMatrix}
-\usage{
-\method{dim}{xgb.DMatrix}(x)
-}
-\arguments{
-\item{x}{Object of class \code{xgb.DMatrix}}
-}
-\description{
-Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
-}
-\details{
-Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
-be directly used with an \code{xgb.DMatrix} object.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-
-stopifnot(nrow(dtrain) == nrow(train$data))
-stopifnot(ncol(dtrain) == ncol(train$data))
-stopifnot(all(dim(dtrain) == dim(train$data)))
-
-}
diff --git a/ml-xgboost/R-package/man/dimnames.xgb.DMatrix.Rd b/ml-xgboost/R-package/man/dimnames.xgb.DMatrix.Rd
deleted file mode 100644
index 032cb95..0000000
--- a/ml-xgboost/R-package/man/dimnames.xgb.DMatrix.Rd
+++ /dev/null
@@ -1,35 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{dimnames.xgb.DMatrix}
-\alias{dimnames.xgb.DMatrix}
-\alias{dimnames<-.xgb.DMatrix}
-\title{Handling of column names of \code{xgb.DMatrix}}
-\usage{
-\method{dimnames}{xgb.DMatrix}(x)
-
-\method{dimnames}{xgb.DMatrix}(x) <- value
-}
-\arguments{
-\item{x}{object of class \code{xgb.DMatrix}}
-
-\item{value}{a list of two elements: the first one is ignored
-and the second one is column names}
-}
-\description{
-Only column names are supported for \code{xgb.DMatrix}, thus setting of
-row names would have no effect and returned row names would be NULL.
-}
-\details{
-Generic \code{dimnames} methods are used by \code{colnames}.
-Since row names are irrelevant, it is recommended to use \code{colnames} directly.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-dimnames(dtrain)
-colnames(dtrain)
-colnames(dtrain) <- make.names(1:ncol(train$data))
-print(dtrain, verbose=TRUE)
-
-}
diff --git a/ml-xgboost/R-package/man/getinfo.Rd b/ml-xgboost/R-package/man/getinfo.Rd
deleted file mode 100644
index 1751c48..0000000
--- a/ml-xgboost/R-package/man/getinfo.Rd
+++ /dev/null
@@ -1,45 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{getinfo}
-\alias{getinfo}
-\alias{getinfo.xgb.DMatrix}
-\title{Get information of an xgb.DMatrix object}
-\usage{
-getinfo(object, ...)
-
-\method{getinfo}{xgb.DMatrix}(object, name, ...)
-}
-\arguments{
-\item{object}{Object of class \code{xgb.DMatrix}}
-
-\item{...}{other parameters}
-
-\item{name}{the name of the information field to get (see details)}
-}
-\description{
-Get information of an xgb.DMatrix object
-}
-\details{
-The \code{name} field can be one of the following:
-
-\itemize{
-    \item \code{label}: label Xgboost learn from ;
-    \item \code{weight}: to do a weight rescale ;
-    \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
-    \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
-
-}
-
-\code{group} can be setup by \code{setinfo} but can't be retrieved by \code{getinfo}.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-
-labels <- getinfo(dtrain, 'label')
-setinfo(dtrain, 'label', 1-labels)
-
-labels2 <- getinfo(dtrain, 'label')
-stopifnot(all(labels2 == 1-labels))
-}
diff --git a/ml-xgboost/R-package/man/predict.xgb.Booster.Rd b/ml-xgboost/R-package/man/predict.xgb.Booster.Rd
deleted file mode 100644
index 6430eab..0000000
--- a/ml-xgboost/R-package/man/predict.xgb.Booster.Rd
+++ /dev/null
@@ -1,202 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.Booster.R
-\name{predict.xgb.Booster}
-\alias{predict.xgb.Booster}
-\alias{predict.xgb.Booster.handle}
-\title{Predict method for eXtreme Gradient Boosting model}
-\usage{
-\method{predict}{xgb.Booster}(
-  object,
-  newdata,
-  missing = NA,
-  outputmargin = FALSE,
-  ntreelimit = NULL,
-  predleaf = FALSE,
-  predcontrib = FALSE,
-  approxcontrib = FALSE,
-  predinteraction = FALSE,
-  reshape = FALSE,
-  training = FALSE,
-  ...
-)
-
-\method{predict}{xgb.Booster.handle}(object, ...)
-}
-\arguments{
-\item{object}{Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}}
-
-\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or \code{xgb.DMatrix}.}
-
-\item{missing}{Missing is only used when input is dense matrix. Pick a float value that represents
-missing values in data (e.g., sometimes 0 or some other extreme value is used).}
-
-\item{outputmargin}{whether the prediction should be returned in the for of original untransformed
-sum of predictions from boosting iterations' results. E.g., setting \code{outputmargin=TRUE} for
-logistic regression would result in predictions for log-odds instead of probabilities.}
-
-\item{ntreelimit}{limit the number of model's trees or boosting iterations used in prediction (see Details).
-It will use all the trees by default (\code{NULL} value).}
-
-\item{predleaf}{whether predict leaf index.}
-
-\item{predcontrib}{whether to return feature contributions to individual predictions (see Details).}
-
-\item{approxcontrib}{whether to use a fast approximation for feature contributions (see Details).}
-
-\item{predinteraction}{whether to return contributions of feature interactions to individual predictions (see Details).}
-
-\item{reshape}{whether to reshape the vector of predictions to a matrix form when there are several
-prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
-or predinteraction flags is TRUE.}
-
-\item{training}{whether is the prediction result used for training.  For dart booster,
-training predicting will perform dropout.}
-
-\item{...}{Parameters passed to \code{predict.xgb.Booster}}
-}
-\value{
-For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
-For multiclass classification, either a \code{num_class * nrows(newdata)} vector or
-a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on
-the \code{reshape} value.
-
-When \code{predleaf = TRUE}, the output is a matrix object with the
-number of columns corresponding to the number of trees.
-
-When \code{predcontrib = TRUE} and it is not a multiclass setting, the output is a matrix object with
-\code{num_features + 1} columns. The last "+ 1" column in a matrix corresponds to bias.
-For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-such a matrix. The contribution values are on the scale of untransformed margin
-(e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
-
-When \code{predinteraction = TRUE} and it is not a multiclass setting, the output is a 3d array with
-dimensions \code{c(nrow, num_features + 1, num_features + 1)}. The off-diagonal (in the last two dimensions)
-elements represent different features interaction contributions. The array is symmetric WRT the last
-two dimensions. The "+ 1" columns corresponds to bias. Summing this array along the last dimension should
-produce practically the same result as predict with \code{predcontrib = TRUE}.
-For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-such an array.
-}
-\description{
-Predicted values based on either xgboost model or model handle object.
-}
-\details{
-Note that \code{ntreelimit} is not necessarily equal to the number of boosting iterations
-and it is not necessarily equal to the number of trees in a model.
-E.g., in a random forest-like model, \code{ntreelimit} would limit the number of trees.
-But for multiclass classification, while there are multiple trees per iteration,
-\code{ntreelimit} limits the number of boosting iterations.
-
-Also note that \code{ntreelimit} would currently do nothing for predictions from gblinear,
-since gblinear doesn't keep its boosting history.
-
-One possible practical applications of the \code{predleaf} option is to use the model
-as a generator of new features which capture non-linearity and interactions,
-e.g., as implemented in \code{\link{xgb.create.features}}.
-
-Setting \code{predcontrib = TRUE} allows to calculate contributions of each feature to
-individual predictions. For "gblinear" booster, feature contributions are simply linear terms
-(feature_beta * feature_value). For "gbtree" booster, feature contributions are SHAP
-values (Lundberg 2017) that sum to the difference between the expected output
-of the model and the current prediction (where the hessian weights are used to compute the expectations).
-Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
-in \url{http://blog.datadive.net/interpreting-random-forests/}.
-
-With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
-are computed. Note that this operation might be rather expensive in terms of compute and memory.
-Since it quadratically depends on the number of features, it is recommended to perform selection
-of the most important features first. See below about the format of the returned results.
-}
-\examples{
-## binary classification:
-
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
-# use all trees by default
-pred <- predict(bst, test$data)
-# use only the 1st tree
-pred1 <- predict(bst, test$data, ntreelimit = 1)
-
-# Predicting tree leafs:
-# the result is an nsamples X ntrees matrix
-pred_leaf <- predict(bst, test$data, predleaf = TRUE)
-str(pred_leaf)
-
-# Predicting feature contributions to predictions:
-# the result is an nsamples X (nfeatures + 1) matrix
-pred_contr <- predict(bst, test$data, predcontrib = TRUE)
-str(pred_contr)
-# verify that contributions' sums are equal to log-odds of predictions (up to float precision):
-summary(rowSums(pred_contr) - qlogis(pred))
-# for the 1st record, let's inspect its features that had non-zero contribution to prediction:
-contr1 <- pred_contr[1,]
-contr1 <- contr1[-length(contr1)]    # drop BIAS
-contr1 <- contr1[contr1 != 0]        # drop non-contributing features
-contr1 <- contr1[order(abs(contr1))] # order by contribution magnitude
-old_mar <- par("mar")
-par(mar = old_mar + c(0,7,0,0))
-barplot(contr1, horiz = TRUE, las = 2, xlab = "contribution to prediction in log-odds")
-par(mar = old_mar)
-
-
-## multiclass classification in iris dataset:
-
-lb <- as.numeric(iris$Species) - 1
-num_class <- 3
-set.seed(11)
-bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-               max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
-               objective = "multi:softprob", num_class = num_class)
-# predict for softmax returns num_class probability numbers per case:
-pred <- predict(bst, as.matrix(iris[, -5]))
-str(pred)
-# reshape it to a num_class-columns matrix
-pred <- matrix(pred, ncol=num_class, byrow=TRUE)
-# convert the probabilities to softmax labels
-pred_labels <- max.col(pred) - 1
-# the following should result in the same error as seen in the last iteration
-sum(pred_labels != lb)/length(lb)
-
-# compare that to the predictions from softmax:
-set.seed(11)
-bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-               max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
-               objective = "multi:softmax", num_class = num_class)
-pred <- predict(bst, as.matrix(iris[, -5]))
-str(pred)
-all.equal(pred, pred_labels)
-# prediction from using only 5 iterations should result
-# in the same error as seen in iteration 5:
-pred5 <- predict(bst, as.matrix(iris[, -5]), ntreelimit=5)
-sum(pred5 != lb)/length(lb)
-
-
-## random forest-like model of 25 trees for binary classification:
-
-set.seed(11)
-bst <- xgboost(data = train$data, label = train$label, max_depth = 5,
-               nthread = 2, nrounds = 1, objective = "binary:logistic",
-               num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1)
-# Inspect the prediction error vs number of trees:
-lb <- test$label
-dtest <- xgb.DMatrix(test$data, label=lb)
-err <- sapply(1:25, function(n) {
-  pred <- predict(bst, dtest, ntreelimit=n)
-  sum((pred > 0.5) != lb)/length(lb)
-})
-plot(err, type='l', ylim=c(0,0.1), xlab='#trees')
-
-}
-\references{
-Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
-
-Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
-}
-\seealso{
-\code{\link{xgb.train}}.
-}
diff --git a/ml-xgboost/R-package/man/print.xgb.Booster.Rd b/ml-xgboost/R-package/man/print.xgb.Booster.Rd
deleted file mode 100644
index d684882..0000000
--- a/ml-xgboost/R-package/man/print.xgb.Booster.Rd
+++ /dev/null
@@ -1,29 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.Booster.R
-\name{print.xgb.Booster}
-\alias{print.xgb.Booster}
-\title{Print xgb.Booster}
-\usage{
-\method{print}{xgb.Booster}(x, verbose = FALSE, ...)
-}
-\arguments{
-\item{x}{an xgb.Booster object}
-
-\item{verbose}{whether to print detailed data (e.g., attribute values)}
-
-\item{...}{not currently used}
-}
-\description{
-Print information about xgb.Booster.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-attr(bst, 'myattr') <- 'memo'
-
-print(bst)
-print(bst, verbose=TRUE)
-
-}
diff --git a/ml-xgboost/R-package/man/print.xgb.DMatrix.Rd b/ml-xgboost/R-package/man/print.xgb.DMatrix.Rd
deleted file mode 100644
index b1dd01b..0000000
--- a/ml-xgboost/R-package/man/print.xgb.DMatrix.Rd
+++ /dev/null
@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{print.xgb.DMatrix}
-\alias{print.xgb.DMatrix}
-\title{Print xgb.DMatrix}
-\usage{
-\method{print}{xgb.DMatrix}(x, verbose = FALSE, ...)
-}
-\arguments{
-\item{x}{an xgb.DMatrix object}
-
-\item{verbose}{whether to print colnames (when present)}
-
-\item{...}{not currently used}
-}
-\description{
-Print information about xgb.DMatrix.
-Currently it displays dimensions and presence of info-fields and colnames.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-
-dtrain
-print(dtrain, verbose=TRUE)
-
-}
diff --git a/ml-xgboost/R-package/man/print.xgb.cv.Rd b/ml-xgboost/R-package/man/print.xgb.cv.Rd
deleted file mode 100644
index 05ad61e..0000000
--- a/ml-xgboost/R-package/man/print.xgb.cv.Rd
+++ /dev/null
@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.cv.R
-\name{print.xgb.cv.synchronous}
-\alias{print.xgb.cv.synchronous}
-\title{Print xgb.cv result}
-\usage{
-\method{print}{xgb.cv.synchronous}(x, verbose = FALSE, ...)
-}
-\arguments{
-\item{x}{an \code{xgb.cv.synchronous} object}
-
-\item{verbose}{whether to print detailed data}
-
-\item{...}{passed to \code{data.table.print}}
-}
-\description{
-Prints formatted results of \code{xgb.cv}.
-}
-\details{
-When not verbose, it would only print the evaluation results,
-including the best iteration (when available).
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-cv <- xgb.cv(data = train$data, label = train$label, nfold = 5, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-print(cv)
-print(cv, verbose=TRUE)
-
-}
diff --git a/ml-xgboost/R-package/man/setinfo.Rd b/ml-xgboost/R-package/man/setinfo.Rd
deleted file mode 100644
index e133d3a..0000000
--- a/ml-xgboost/R-package/man/setinfo.Rd
+++ /dev/null
@@ -1,43 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{setinfo}
-\alias{setinfo}
-\alias{setinfo.xgb.DMatrix}
-\title{Set information of an xgb.DMatrix object}
-\usage{
-setinfo(object, ...)
-
-\method{setinfo}{xgb.DMatrix}(object, name, info, ...)
-}
-\arguments{
-\item{object}{Object of class "xgb.DMatrix"}
-
-\item{...}{other parameters}
-
-\item{name}{the name of the field to get}
-
-\item{info}{the specific field of information to set}
-}
-\description{
-Set information of an xgb.DMatrix object
-}
-\details{
-The \code{name} field can be one of the following:
-
-\itemize{
-    \item \code{label}: label Xgboost learn from ;
-    \item \code{weight}: to do a weight rescale ;
-    \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
-    \item \code{group}: number of rows in each group (to use with \code{rank:pairwise} objective).
-}
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-
-labels <- getinfo(dtrain, 'label')
-setinfo(dtrain, 'label', 1-labels)
-labels2 <- getinfo(dtrain, 'label')
-stopifnot(all.equal(labels2, 1-labels))
-}
diff --git a/ml-xgboost/R-package/man/slice.xgb.DMatrix.Rd b/ml-xgboost/R-package/man/slice.xgb.DMatrix.Rd
deleted file mode 100644
index 9f27d4b..0000000
--- a/ml-xgboost/R-package/man/slice.xgb.DMatrix.Rd
+++ /dev/null
@@ -1,40 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{slice}
-\alias{slice}
-\alias{slice.xgb.DMatrix}
-\alias{[.xgb.DMatrix}
-\title{Get a new DMatrix containing the specified rows of
-original xgb.DMatrix object}
-\usage{
-slice(object, ...)
-
-\method{slice}{xgb.DMatrix}(object, idxset, ...)
-
-\method{[}{xgb.DMatrix}(object, idxset, colset = NULL)
-}
-\arguments{
-\item{object}{Object of class "xgb.DMatrix"}
-
-\item{...}{other parameters (currently not used)}
-
-\item{idxset}{a integer vector of indices of rows needed}
-
-\item{colset}{currently not used (columns subsetting is not available)}
-}
-\description{
-Get a new DMatrix containing the specified rows of
-original xgb.DMatrix object
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-
-dsub <- slice(dtrain, 1:42)
-labels1 <- getinfo(dsub, 'label')
-dsub <- dtrain[1:42, ]
-labels2 <- getinfo(dsub, 'label')
-all.equal(labels1, labels2)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.Booster.complete.Rd b/ml-xgboost/R-package/man/xgb.Booster.complete.Rd
deleted file mode 100644
index 2b38b4c..0000000
--- a/ml-xgboost/R-package/man/xgb.Booster.complete.Rd
+++ /dev/null
@@ -1,50 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.Booster.R
-\name{xgb.Booster.complete}
-\alias{xgb.Booster.complete}
-\title{Restore missing parts of an incomplete xgb.Booster object.}
-\usage{
-xgb.Booster.complete(object, saveraw = TRUE)
-}
-\arguments{
-\item{object}{object of class \code{xgb.Booster}}
-
-\item{saveraw}{a flag indicating whether to append \code{raw} Booster memory dump data
-when it doesn't already exist.}
-}
-\value{
-An object of \code{xgb.Booster} class.
-}
-\description{
-It attempts to complete an \code{xgb.Booster} object by restoring either its missing
-raw model memory dump (when it has no \code{raw} data but its \code{xgb.Booster.handle} is valid)
-or its missing internal handle (when its \code{xgb.Booster.handle} is not valid
-but it has a raw Booster memory dump).
-}
-\details{
-While this method is primarily for internal use, it might be useful in some practical situations.
-
-E.g., when an \code{xgb.Booster} model is saved as an R object and then is loaded as an R object,
-its handle (pointer) to an internal xgboost model would be invalid. The majority of xgboost methods
-should still work for such a model object since those methods would be using
-\code{xgb.Booster.complete} internally. However, one might find it to be more efficient to call the
-\code{xgb.Booster.complete} function explicitly once after loading a model as an R-object.
-That would prevent further repeated implicit reconstruction of an internal booster model.
-}
-\examples{
-
-data(agaricus.train, package='xgboost')
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-saveRDS(bst, "xgb.model.rds")
-
-bst1 <- readRDS("xgb.model.rds")
-if (file.exists("xgb.model.rds")) file.remove("xgb.model.rds")
-# the handle is invalid:
-print(bst1$handle)
-
-bst1 <- xgb.Booster.complete(bst1)
-# now the handle points to a valid internal booster model:
-print(bst1$handle)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.DMatrix.Rd b/ml-xgboost/R-package/man/xgb.DMatrix.Rd
deleted file mode 100644
index c3d47a9..0000000
--- a/ml-xgboost/R-package/man/xgb.DMatrix.Rd
+++ /dev/null
@@ -1,35 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.R
-\name{xgb.DMatrix}
-\alias{xgb.DMatrix}
-\title{Construct xgb.DMatrix object}
-\usage{
-xgb.DMatrix(data, info = list(), missing = NA, silent = FALSE, ...)
-}
-\arguments{
-\item{data}{a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character
-string representing a filename.}
-
-\item{info}{a named list of additional information to store in the \code{xgb.DMatrix} object.
-See \code{\link{setinfo}} for the specific allowed kinds of}
-
-\item{missing}{a float value to represents missing values in data (used only when input is a dense matrix).
-It is useful when a 0 or some other extreme value represents missing values in data.}
-
-\item{silent}{whether to suppress printing an informational message after loading from a file.}
-
-\item{...}{the \code{info} data could be passed directly as parameters, without creating an \code{info} list.}
-}
-\description{
-Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file.
-Supported input file formats are either a libsvm text file or a binary file that was created previously by
-\code{\link{xgb.DMatrix.save}}).
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
-dtrain <- xgb.DMatrix('xgb.DMatrix.data')
-if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
-}
diff --git a/ml-xgboost/R-package/man/xgb.DMatrix.save.Rd b/ml-xgboost/R-package/man/xgb.DMatrix.save.Rd
deleted file mode 100644
index 7f25c5a..0000000
--- a/ml-xgboost/R-package/man/xgb.DMatrix.save.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.DMatrix.save.R
-\name{xgb.DMatrix.save}
-\alias{xgb.DMatrix.save}
-\title{Save xgb.DMatrix object to binary file}
-\usage{
-xgb.DMatrix.save(dmatrix, fname)
-}
-\arguments{
-\item{dmatrix}{the \code{xgb.DMatrix} object}
-
-\item{fname}{the name of the file to write.}
-}
-\description{
-Save xgb.DMatrix object to binary file
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
-dtrain <- xgb.DMatrix('xgb.DMatrix.data')
-if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
-}
diff --git a/ml-xgboost/R-package/man/xgb.attr.Rd b/ml-xgboost/R-package/man/xgb.attr.Rd
deleted file mode 100644
index 03779e4..0000000
--- a/ml-xgboost/R-package/man/xgb.attr.Rd
+++ /dev/null
@@ -1,86 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.Booster.R
-\name{xgb.attr}
-\alias{xgb.attr}
-\alias{xgb.attr<-}
-\alias{xgb.attributes}
-\alias{xgb.attributes<-}
-\title{Accessors for serializable attributes of a model.}
-\usage{
-xgb.attr(object, name)
-
-xgb.attr(object, name) <- value
-
-xgb.attributes(object)
-
-xgb.attributes(object) <- value
-}
-\arguments{
-\item{object}{Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.}
-
-\item{name}{a non-empty character string specifying which attribute is to be accessed.}
-
-\item{value}{a value of an attribute for \code{xgb.attr<-}; for \code{xgb.attributes<-}
-it's a list (or an object coercible to a list) with the names of attributes to set
-and the elements corresponding to attribute values.
-Non-character values are converted to character.
-When attribute value is not a scalar, only the first index is used.
-Use \code{NULL} to remove an attribute.}
-}
-\value{
-\code{xgb.attr} returns either a string value of an attribute
-or \code{NULL} if an attribute wasn't stored in a model.
-
-\code{xgb.attributes} returns a list of all attribute stored in a model
-or \code{NULL} if a model has no stored attributes.
-}
-\description{
-These methods allow to manipulate the key-value attribute strings of an xgboost model.
-}
-\details{
-The primary purpose of xgboost model attributes is to store some meta-data about the model.
-Note that they are a separate concept from the object attributes in R.
-Specifically, they refer to key-value strings that can be attached to an xgboost model,
-stored together with the model's binary representation, and accessed later
-(from R or any other interface).
-In contrast, any R-attribute assigned to an R-object of \code{xgb.Booster} class
-would not be saved by \code{xgb.save} because an xgboost model is an external memory object
-and its serialization is handled externally.
-Also, setting an attribute that has the same name as one of xgboost's parameters wouldn't
-change the value of that parameter for a model.
-Use \code{\link{xgb.parameters<-}} to set or change model parameters.
-
-The attribute setters would usually work more efficiently for \code{xgb.Booster.handle}
-than for \code{xgb.Booster}, since only just a handle (pointer) would need to be copied.
-That would only matter if attributes need to be set many times.
-Note, however, that when feeding a handle of an \code{xgb.Booster} object to the attribute setters,
-the raw model cache of an \code{xgb.Booster} object would not be automatically updated,
-and it would be user's responsibility to call \code{xgb.serialize} to update it.
-
-The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
-but it doesn't delete the other existing attributes.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-
-xgb.attr(bst, "my_attribute") <- "my attribute value"
-print(xgb.attr(bst, "my_attribute"))
-xgb.attributes(bst) <- list(a = 123, b = "abc")
-
-xgb.save(bst, 'xgb.model')
-bst1 <- xgb.load('xgb.model')
-if (file.exists('xgb.model')) file.remove('xgb.model')
-print(xgb.attr(bst1, "my_attribute"))
-print(xgb.attributes(bst1))
-
-# deletion:
-xgb.attr(bst1, "my_attribute") <- NULL
-print(xgb.attributes(bst1))
-xgb.attributes(bst1) <- list(a = NULL, b = NULL)
-print(xgb.attributes(bst1))
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.config.Rd b/ml-xgboost/R-package/man/xgb.config.Rd
deleted file mode 100644
index a5187c8..0000000
--- a/ml-xgboost/R-package/man/xgb.config.Rd
+++ /dev/null
@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.Booster.R
-\name{xgb.config}
-\alias{xgb.config}
-\alias{xgb.config<-}
-\title{Accessors for model parameters as JSON string.}
-\usage{
-xgb.config(object)
-
-xgb.config(object) <- value
-}
-\arguments{
-\item{object}{Object of class \code{xgb.Booster}}
-
-\item{value}{A JSON string.}
-}
-\description{
-Accessors for model parameters as JSON string.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-config <- xgb.config(bst)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.create.features.Rd b/ml-xgboost/R-package/man/xgb.create.features.Rd
deleted file mode 100644
index 9c59d90..0000000
--- a/ml-xgboost/R-package/man/xgb.create.features.Rd
+++ /dev/null
@@ -1,92 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.create.features.R
-\name{xgb.create.features}
-\alias{xgb.create.features}
-\title{Create new features from a previously learned model}
-\usage{
-xgb.create.features(model, data, ...)
-}
-\arguments{
-\item{model}{decision tree boosting model learned on the original data}
-
-\item{data}{original data (usually provided as a \code{dgCMatrix} matrix)}
-
-\item{...}{currently not used}
-}
-\value{
-\code{dgCMatrix} matrix including both the original data and the new features.
-}
-\description{
-May improve the learning by adding new features to the training data based on the decision trees from a previously learned model.
-}
-\details{
-This is the function inspired from the paragraph 3.1 of the paper:
-
-\strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
-
-\emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, 
-Joaquin Quinonero Candela)}
- 
-International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
-
-\url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
-
-Extract explaining the method:
-
-"We found that boosted decision trees are a powerful and very
-convenient way to implement non-linear and tuple transformations
-of the kind we just described. We treat each individual
-tree as a categorical feature that takes as value the
-index of the leaf an instance ends up falling in. We use 
-1-of-K coding of this type of features. 
-
-For example, consider the boosted tree model in Figure 1 with 2 subtrees, 
-where the first subtree has 3 leafs and the second 2 leafs. If an
-instance ends up in leaf 2 in the first subtree and leaf 1 in
-second subtree, the overall input to the linear classifier will
-be the binary vector \code{[0, 1, 0, 1, 0]}, where the first 3 entries
-correspond to the leaves of the first subtree and last 2 to
-those of the second subtree.
-
-[...]
-
-We can understand boosted decision tree
-based transformation as a supervised feature encoding that
-converts a real-valued vector into a compact binary-valued
-vector. A traversal from root node to a leaf node represents
-a rule on certain features."
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
-
-param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-nrounds = 4
-
-bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
-
-# Model accuracy without new features
-accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
-                   length(agaricus.test$label)
-
-# Convert previous features to one hot encoding
-new.features.train <- xgb.create.features(model = bst, agaricus.train$data)
-new.features.test <- xgb.create.features(model = bst, agaricus.test$data)
-
-# learning with new features
-new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
-new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
-watchlist <- list(train = new.dtrain)
-bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
-
-# Model accuracy with new features
-accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
-                  length(agaricus.test$label)
-
-# Here the accuracy was already good and is now perfect.
-cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
-          accuracy.after, "!\n"))
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.cv.Rd b/ml-xgboost/R-package/man/xgb.cv.Rd
deleted file mode 100644
index 8cb03a5..0000000
--- a/ml-xgboost/R-package/man/xgb.cv.Rd
+++ /dev/null
@@ -1,164 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.cv.R
-\name{xgb.cv}
-\alias{xgb.cv}
-\title{Cross Validation}
-\usage{
-xgb.cv(
-  params = list(),
-  data,
-  nrounds,
-  nfold,
-  label = NULL,
-  missing = NA,
-  prediction = FALSE,
-  showsd = TRUE,
-  metrics = list(),
-  obj = NULL,
-  feval = NULL,
-  stratified = TRUE,
-  folds = NULL,
-  train_folds = NULL,
-  verbose = TRUE,
-  print_every_n = 1L,
-  early_stopping_rounds = NULL,
-  maximize = NULL,
-  callbacks = list(),
-  ...
-)
-}
-\arguments{
-\item{params}{the list of parameters. Commonly used ones are:
-\itemize{
-  \item \code{objective} objective function, common ones are
-  \itemize{
-    \item \code{reg:squarederror} Regression with squared loss
-    \item \code{binary:logistic} logistic regression for classification
-  }
-  \item \code{eta} step size of each boosting step
-  \item \code{max_depth} maximum depth of the tree
-  \item \code{nthread} number of thread used in training, if not set, all threads are used
-}
-
-  See \code{\link{xgb.train}} for further details.
-  See also demo/ for walkthrough example in R.}
-
-\item{data}{takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.}
-
-\item{nrounds}{the max number of iterations}
-
-\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}
-
-\item{label}{vector of response values. Should be provided only when data is an R-matrix.}
-
-\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means
-that NA values should be considered as 'missing' by the algorithm.
-Sometimes, 0 or other extreme value might be used to represent missing values.}
-
-\item{prediction}{A logical value indicating whether to return the test fold predictions
-from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.}
-
-\item{showsd}{\code{boolean}, whether to show standard deviation of cross validation}
-
-\item{metrics, }{list of evaluation metrics to be used in cross validation,
-  when it is not specified, the evaluation metric is chosen according to objective function.
-  Possible options are:
-\itemize{
-  \item \code{error} binary classification error rate
-  \item \code{rmse} Rooted mean square error
-  \item \code{logloss} negative log-likelihood function
-  \item \code{auc} Area under curve
-  \item \code{aucpr} Area under PR curve
-  \item \code{merror} Exact matching error, used to evaluate multi-class classification
-}}
-
-\item{obj}{customized objective function. Returns gradient and second order
-gradient with given prediction and dtrain.}
-
-\item{feval}{customized evaluation function. Returns
-\code{list(metric='metric-name', value='metric-value')} with given
-prediction and dtrain.}
-
-\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
-by the values of outcome labels.}
-
-\item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
-(each element must be a vector of test fold's indices). When folds are supplied,
-the \code{nfold} and \code{stratified} parameters are ignored.}
-
-\item{train_folds}{\code{list} list specifying which indicies to use for training. If \code{NULL}
-(the default) all indices not specified in \code{folds} will be used for training.}
-
-\item{verbose}{\code{boolean}, print the statistics during the process}
-
-\item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
-Default is 1 which means all messages are printed. This parameter is passed to the
-\code{\link{cb.print.evaluation}} callback.}
-
-\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
-If set to an integer \code{k}, training with a validation set will stop if the performance
-doesn't improve for \code{k} rounds.
-Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
-
-\item{maximize}{If \code{feval} and \code{early_stopping_rounds} are set,
-then this parameter must be set as well.
-When it is \code{TRUE}, it means the larger the evaluation score the better.
-This parameter is passed to the \code{\link{cb.early.stop}} callback.}
-
-\item{callbacks}{a list of callback functions to perform various task during boosting.
-See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-parameters' values. User can provide either existing or their own callback methods in order
-to customize the training process.}
-
-\item{...}{other parameters to pass to \code{params}.}
-}
-\value{
-An object of class \code{xgb.cv.synchronous} with the following elements:
-\itemize{
-  \item \code{call} a function call.
-  \item \code{params} parameters that were passed to the xgboost library. Note that it does not
-        capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-  \item \code{callbacks} callback functions that were either automatically assigned or
-        explicitly passed.
-  \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
-        first column corresponding to iteration number and the rest corresponding to the
-        CV-based evaluation means and standard deviations for the training and test CV-sets.
-        It is created by the \code{\link{cb.evaluation.log}} callback.
-  \item \code{niter} number of boosting iterations.
-  \item \code{nfeatures} number of features in training data.
-  \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
-        parameter or randomly generated.
-  \item \code{best_iteration} iteration number with the best evaluation metric value
-        (only available with early stopping).
-  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
-        which could further be used in \code{predict} method
-        (only available with early stopping).
-  \item \code{pred} CV prediction values available when \code{prediction} is set.
-        It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-  \item \code{models} a list of the CV folds' models. It is only available with the explicit
-        setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
-}
-}
-\description{
-The cross validation function of xgboost
-}
-\details{
-The original sample is randomly partitioned into \code{nfold} equal size subsamples.
-
-Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
-
-The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
-
-All observations are used for both training and validation.
-
-Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
-}
-\examples{
-data(agaricus.train, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-cv <- xgb.cv(data = dtrain, nrounds = 3, nthread = 2, nfold = 5, metrics = list("rmse","auc"),
-                  max_depth = 3, eta = 1, objective = "binary:logistic")
-print(cv)
-print(cv, verbose=TRUE)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.dump.Rd b/ml-xgboost/R-package/man/xgb.dump.Rd
deleted file mode 100644
index 210c6e2..0000000
--- a/ml-xgboost/R-package/man/xgb.dump.Rd
+++ /dev/null
@@ -1,62 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.dump.R
-\name{xgb.dump}
-\alias{xgb.dump}
-\title{Dump an xgboost model in text format.}
-\usage{
-xgb.dump(
-  model,
-  fname = NULL,
-  fmap = "",
-  with_stats = FALSE,
-  dump_format = c("text", "json"),
-  ...
-)
-}
-\arguments{
-\item{model}{the model object.}
-
-\item{fname}{the name of the text file where to save the model text dump. 
-If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.}
-
-\item{fmap}{feature map file representing feature types.
-Detailed description could be found at 
-\url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
-See demo/ for walkthrough example in R, and
-\url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} 
-for example Format.}
-
-\item{with_stats}{whether to dump some additional statistics about the splits.
-When this option is on, the model dump contains two additional values:
-gain is the approximate loss function gain we get in each split;
-cover is the sum of second order gradient in each node.}
-
-\item{dump_format}{either 'text' or 'json' format could be specified.}
-
-\item{...}{currently not used}
-}
-\value{
-If fname is not provided or set to \code{NULL} the function will return the model
-as a \code{character} vector. Otherwise it will return \code{TRUE}.
-}
-\description{
-Dump an xgboost model in text format.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-# save the model in file 'xgb.model.dump'
-dump_path = file.path(tempdir(), 'model.dump')
-xgb.dump(bst, dump_path, with_stats = TRUE)
-
-# print the model without saving it to a file
-print(xgb.dump(bst, with_stats = TRUE))
-
-# print in JSON format:
-cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.gblinear.history.Rd b/ml-xgboost/R-package/man/xgb.gblinear.history.Rd
deleted file mode 100644
index bc8d467..0000000
--- a/ml-xgboost/R-package/man/xgb.gblinear.history.Rd
+++ /dev/null
@@ -1,29 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/callbacks.R
-\name{xgb.gblinear.history}
-\alias{xgb.gblinear.history}
-\title{Extract gblinear coefficients history.}
-\usage{
-xgb.gblinear.history(model, class_index = NULL)
-}
-\arguments{
-\item{model}{either an \code{xgb.Booster} or a result of \code{xgb.cv()}, trained
-using the \code{cb.gblinear.history()} callback.}
-
-\item{class_index}{zero-based class index to extract the coefficients for only that
-specific class in a multinomial multiclass model. When it is NULL, all the
-coefficients are returned. Has no effect in non-multiclass models.}
-}
-\value{
-For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns
-corresponding to iteration's coefficients (in the order as \code{xgb.dump()} would
-return) and the rows corresponding to boosting iterations.
-
-For an \code{xgb.cv} result, a list of such matrices is returned with the elements
-corresponding to CV folds.
-}
-\description{
-A helper function to extract the matrix of linear coefficients' history
-from a gblinear model created while using the \code{cb.gblinear.history()}
-callback.
-}
diff --git a/ml-xgboost/R-package/man/xgb.importance.Rd b/ml-xgboost/R-package/man/xgb.importance.Rd
deleted file mode 100644
index 84a18e1..0000000
--- a/ml-xgboost/R-package/man/xgb.importance.Rd
+++ /dev/null
@@ -1,101 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.importance.R
-\name{xgb.importance}
-\alias{xgb.importance}
-\title{Importance of features in a model.}
-\usage{
-xgb.importance(
-  feature_names = NULL,
-  model = NULL,
-  trees = NULL,
-  data = NULL,
-  label = NULL,
-  target = NULL
-)
-}
-\arguments{
-\item{feature_names}{character vector of feature names. If the model already
-contains feature names, those would be used when \code{feature_names=NULL} (default value).
-Non-null \code{feature_names} could be provided to override those in the model.}
-
-\item{model}{object of class \code{xgb.Booster}.}
-
-\item{trees}{(only for the gbtree booster) an integer vector of tree indices that should be included
-into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
-It could be useful, e.g., in multiclass classification to get feature importances 
-for each class separately. IMPORTANT: the tree index in xgboost models
-is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).}
-
-\item{data}{deprecated.}
-
-\item{label}{deprecated.}
-
-\item{target}{deprecated.}
-}
-\value{
-For a tree model, a \code{data.table} with the following columns:
-\itemize{
-  \item \code{Features} names of the features used in the model;
-  \item \code{Gain} represents fractional contribution of each feature to the model based on
-       the total gain of this feature's splits. Higher percentage means a more important 
-       predictive feature.
-  \item \code{Cover} metric of the number of observation related to this feature;
-  \item \code{Frequency} percentage representing the relative number of times
-       a feature have been used in trees.
-}
-
-A linear model's importance \code{data.table} has the following columns:
-\itemize{
-  \item \code{Features} names of the features used in the model;
-  \item \code{Weight} the linear coefficient of this feature;
-  \item \code{Class} (only for multiclass models) class label.
-}
-
-If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names}, 
-index of the features will be used instead. Because the index is extracted from the model dump
-(based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
-}
-\description{
-Creates a \code{data.table} of feature importances in a model.
-}
-\details{
-This function works for both linear and tree models.
-
-For linear models, the importance is the absolute magnitude of linear coefficients. 
-For that reason, in order to obtain a meaningful ranking by importance for a linear model, 
-the features need to be on the same scale (which you also would want to do when using either 
-L1 or L2 regularization).
-}
-\examples{
-
-# binomial classification using gbtree:
-data(agaricus.train, package='xgboost')
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-xgb.importance(model = bst)
-
-# binomial classification using gblinear:
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear", 
-               eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
-xgb.importance(model = bst)
-
-# multiclass classification using gbtree:
-nclass <- 3
-nrounds <- 10
-mbst <- xgboost(data = as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1,
-               max_depth = 3, eta = 0.2, nthread = 2, nrounds = nrounds,
-               objective = "multi:softprob", num_class = nclass)
-# all classes clumped together:
-xgb.importance(model = mbst)
-# inspect importances separately for each class:
-xgb.importance(model = mbst, trees = seq(from=0, by=nclass, length.out=nrounds))
-xgb.importance(model = mbst, trees = seq(from=1, by=nclass, length.out=nrounds))
-xgb.importance(model = mbst, trees = seq(from=2, by=nclass, length.out=nrounds))
-
-# multiclass classification using gblinear:
-mbst <- xgboost(data = scale(as.matrix(iris[, -5])), label = as.numeric(iris$Species) - 1,
-               booster = "gblinear", eta = 0.2, nthread = 1, nrounds = 15,
-               objective = "multi:softprob", num_class = nclass)
-xgb.importance(model = mbst)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.load.Rd b/ml-xgboost/R-package/man/xgb.load.Rd
deleted file mode 100644
index 3f743e1..0000000
--- a/ml-xgboost/R-package/man/xgb.load.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.load.R
-\name{xgb.load}
-\alias{xgb.load}
-\title{Load xgboost model from binary file}
-\usage{
-xgb.load(modelfile)
-}
-\arguments{
-\item{modelfile}{the name of the binary input file.}
-}
-\value{
-An object of \code{xgb.Booster} class.
-}
-\description{
-Load xgboost model from the binary model file.
-}
-\details{
-The input file is expected to contain a model saved in an xgboost-internal binary format
-using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some 
-appropriate methods from other xgboost interfaces. E.g., a model trained in Python and 
-saved from there in xgboost format, could be loaded from R.
-
-Note: a model saved as an R-object, has to be loaded using corresponding R-methods,
-not \code{xgb.load}.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
-               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-xgb.save(bst, 'xgb.model')
-bst <- xgb.load('xgb.model')
-if (file.exists('xgb.model')) file.remove('xgb.model')
-pred <- predict(bst, test$data)
-}
-\seealso{
-\code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}.
-}
diff --git a/ml-xgboost/R-package/man/xgb.load.raw.Rd b/ml-xgboost/R-package/man/xgb.load.raw.Rd
deleted file mode 100644
index f0248cd..0000000
--- a/ml-xgboost/R-package/man/xgb.load.raw.Rd
+++ /dev/null
@@ -1,14 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.load.raw.R
-\name{xgb.load.raw}
-\alias{xgb.load.raw}
-\title{Load serialised xgboost model from R's raw vector}
-\usage{
-xgb.load.raw(buffer)
-}
-\arguments{
-\item{buffer}{the buffer returned by xgb.save.raw}
-}
-\description{
-User can generate raw memory buffer by calling xgb.save.raw
-}
diff --git a/ml-xgboost/R-package/man/xgb.model.dt.tree.Rd b/ml-xgboost/R-package/man/xgb.model.dt.tree.Rd
deleted file mode 100644
index cf17501..0000000
--- a/ml-xgboost/R-package/man/xgb.model.dt.tree.Rd
+++ /dev/null
@@ -1,83 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.model.dt.tree.R
-\name{xgb.model.dt.tree}
-\alias{xgb.model.dt.tree}
-\title{Parse a boosted tree model text dump}
-\usage{
-xgb.model.dt.tree(
-  feature_names = NULL,
-  model = NULL,
-  text = NULL,
-  trees = NULL,
-  use_int_id = FALSE,
-  ...
-)
-}
-\arguments{
-\item{feature_names}{character vector of feature names. If the model already
-contains feature names, those would be used when \code{feature_names=NULL} (default value).
-Non-null \code{feature_names} could be provided to override those in the model.}
-
-\item{model}{object of class \code{xgb.Booster}}
-
-\item{text}{\code{character} vector previously generated by the \code{xgb.dump} 
-function  (where parameter \code{with_stats = TRUE} should have been set).
-\code{text} takes precedence over \code{model}.}
-
-\item{trees}{an integer vector of tree indices that should be parsed.
-If set to \code{NULL}, all trees of the model are parsed.
-It could be useful, e.g., in multiclass classification to get only
-the trees of one certain class. IMPORTANT: the tree index in xgboost models
-is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).}
-
-\item{use_int_id}{a logical flag indicating whether nodes in columns "Yes", "No", "Missing" should be
-represented as integers (when FALSE) or as "Tree-Node" character strings (when FALSE).}
-
-\item{...}{currently not used.}
-}
-\value{
-A \code{data.table} with detailed information about model trees' nodes.
-
-The columns of the \code{data.table} are:
-
-\itemize{
- \item \code{Tree}: integer ID of a tree in a model (zero-based index)
- \item \code{Node}: integer ID of a node in a tree (zero-based index)
- \item \code{ID}: character identifier of a node in a model (only when \code{use_int_id=FALSE})
- \item \code{Feature}: for a branch node, it's a feature id or name (when available);
-             for a leaf note, it simply labels it as \code{'Leaf'}
- \item \code{Split}: location of the split for a branch node (split condition is always "less than")
- \item \code{Yes}: ID of the next node when the split condition is met
- \item \code{No}: ID of the next node when the split condition is not met
- \item \code{Missing}: ID of the next node when branch value is missing
- \item \code{Quality}: either the split gain (change in loss) or the leaf value
- \item \code{Cover}: metric related to the number of observation either seen by a split
-                     or collected by a leaf during training.
-} 
-
-When \code{use_int_id=FALSE}, columns "Yes", "No", and "Missing" point to model-wide node identifiers
-in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from 
-the corresponding trees in the "Node" column.
-}
-\description{
-Parse a boosted tree model text dump into a \code{data.table} structure.
-}
-\examples{
-# Basic use:
-
-data(agaricus.train, package='xgboost')
-
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
-               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-
-(dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
-
-# This bst model already has feature_names stored with it, so those would be used when 
-# feature_names is not set:
-(dt <- xgb.model.dt.tree(model = bst))
-
-# How to match feature names of splits that are following a current 'Yes' branch:
-
-merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
- 
-}
diff --git a/ml-xgboost/R-package/man/xgb.parameters.Rd b/ml-xgboost/R-package/man/xgb.parameters.Rd
deleted file mode 100644
index ab26956..0000000
--- a/ml-xgboost/R-package/man/xgb.parameters.Rd
+++ /dev/null
@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.Booster.R
-\name{xgb.parameters<-}
-\alias{xgb.parameters<-}
-\title{Accessors for model parameters.}
-\usage{
-xgb.parameters(object) <- value
-}
-\arguments{
-\item{object}{Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.}
-
-\item{value}{a list (or an object coercible to a list) with the names of parameters to set
-and the elements corresponding to parameter values.}
-}
-\description{
-Only the setter for xgboost parameters is currently implemented.
-}
-\details{
-Note that the setter would usually work more efficiently for \code{xgb.Booster.handle}
-than for \code{xgb.Booster}, since only just a handle would need to be copied.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-
-xgb.parameters(bst) <- list(eta = 0.1)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.plot.deepness.Rd b/ml-xgboost/R-package/man/xgb.plot.deepness.Rd
deleted file mode 100644
index b642398..0000000
--- a/ml-xgboost/R-package/man/xgb.plot.deepness.Rd
+++ /dev/null
@@ -1,80 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.deepness.R
-\name{xgb.ggplot.deepness}
-\alias{xgb.ggplot.deepness}
-\alias{xgb.plot.deepness}
-\title{Plot model trees deepness}
-\usage{
-xgb.ggplot.deepness(
-  model = NULL,
-  which = c("2x1", "max.depth", "med.depth", "med.weight")
-)
-
-xgb.plot.deepness(
-  model = NULL,
-  which = c("2x1", "max.depth", "med.depth", "med.weight"),
-  plot = TRUE,
-  ...
-)
-}
-\arguments{
-\item{model}{either an \code{xgb.Booster} model generated by the \code{xgb.train} function
-or a data.table result of the \code{xgb.model.dt.tree} function.}
-
-\item{which}{which distribution to plot (see details).}
-
-\item{plot}{(base R barplot) whether a barplot should be produced. 
-If FALSE, only a data.table is returned.}
-
-\item{...}{other parameters passed to \code{barplot} or \code{plot}.}
-}
-\value{
-Other than producing plots (when \code{plot=TRUE}), the \code{xgb.plot.deepness} function
-silently returns a processed data.table where each row corresponds to a terminal leaf in a tree model,
-and contains information about leaf's depth, cover, and weight (which is used in calculating predictions).
-
-The \code{xgb.ggplot.deepness} silently returns either a list of two ggplot graphs when \code{which="2x1"}
-or a single ggplot graph for the other \code{which} options.
-}
-\description{
-Visualizes distributions related to depth of tree leafs.
-\code{xgb.plot.deepness} uses base R graphics, while \code{xgb.ggplot.deepness} uses the ggplot backend.
-}
-\details{
-When \code{which="2x1"}, two distributions with respect to the leaf depth
-are plotted on top of each other:
-\itemize{
- \item the distribution of the number of leafs in a tree model at a certain depth;
- \item the distribution of average weighted number of observations ("cover") 
-       ending up in leafs at certain depth.
-}
-Those could be helpful in determining sensible ranges of the \code{max_depth} 
-and \code{min_child_weight} parameters.
-
-When \code{which="max.depth"} or \code{which="med.depth"}, plots of either maximum or median depth
-per tree with respect to tree number are created. And \code{which="med.weight"} allows to see how
-a tree's median absolute leaf weight changes through the iterations.
-
-This function was inspired by the blog post
-\url{https://github.com/aysent/random-forest-leaf-visualization}.
-}
-\examples{
-
-data(agaricus.train, package='xgboost')
-
-# Change max_depth to a higher number to get a more significant result
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 6,
-               eta = 0.1, nthread = 2, nrounds = 50, objective = "binary:logistic",
-               subsample = 0.5, min_child_weight = 2)
-
-xgb.plot.deepness(bst)
-xgb.ggplot.deepness(bst)
-
-xgb.plot.deepness(bst, which='max.depth', pch=16, col=rgb(0,0,1,0.3), cex=2)
-
-xgb.plot.deepness(bst, which='med.weight', pch=16, col=rgb(0,0,1,0.3), cex=2)
-
-}
-\seealso{
-\code{\link{xgb.train}}, \code{\link{xgb.model.dt.tree}}.
-}
diff --git a/ml-xgboost/R-package/man/xgb.plot.importance.Rd b/ml-xgboost/R-package/man/xgb.plot.importance.Rd
deleted file mode 100644
index 691a8fd..0000000
--- a/ml-xgboost/R-package/man/xgb.plot.importance.Rd
+++ /dev/null
@@ -1,94 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.importance.R
-\name{xgb.ggplot.importance}
-\alias{xgb.ggplot.importance}
-\alias{xgb.plot.importance}
-\title{Plot feature importance as a bar graph}
-\usage{
-xgb.ggplot.importance(
-  importance_matrix = NULL,
-  top_n = NULL,
-  measure = NULL,
-  rel_to_first = FALSE,
-  n_clusters = c(1:10),
-  ...
-)
-
-xgb.plot.importance(
-  importance_matrix = NULL,
-  top_n = NULL,
-  measure = NULL,
-  rel_to_first = FALSE,
-  left_margin = 10,
-  cex = NULL,
-  plot = TRUE,
-  ...
-)
-}
-\arguments{
-\item{importance_matrix}{a \code{data.table} returned by \code{\link{xgb.importance}}.}
-
-\item{top_n}{maximal number of top features to include into the plot.}
-
-\item{measure}{the name of importance measure to plot.
-When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.}
-
-\item{rel_to_first}{whether importance values should be represented as relative to the highest ranked feature.
-See Details.}
-
-\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range
-of the possible number of clusters of bars.}
-
-\item{...}{other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).}
-
-\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.
-When it is NULL, the existing \code{par('mar')} is used.}
-
-\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
-
-\item{plot}{(base R barplot) whether a barplot should be produced.
-If FALSE, only a data.table is returned.}
-}
-\value{
-The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
-and silently returns a processed data.table with \code{n_top} features sorted by importance.
-
-The \code{xgb.ggplot.importance} function returns a ggplot graph which could be customized afterwards.
-E.g., to change the title of the graph, add \code{+ ggtitle("A GRAPH NAME")} to the result.
-}
-\description{
-Represents previously calculated feature importance as a bar graph.
-\code{xgb.plot.importance} uses base R graphics, while \code{xgb.ggplot.importance} uses the ggplot backend.
-}
-\details{
-The graph represents each feature as a horizontal bar of length proportional to the importance of a feature.
-Features are shown ranked in a decreasing importance order.
-It works for importances from both \code{gblinear} and \code{gbtree} models.
-
-When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
-For gbtree model, that would mean being normalized to the total of 1
-("what is feature's importance contribution relative to the whole model?").
-For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
-Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
-"what is feature's importance contribution relative to the most important feature?"
-
-The ggplot-backend method also performs 1-D clustering of the importance values,
-with bar colors corresponding to different clusters that have somewhat similar importance values.
-}
-\examples{
-data(agaricus.train)
-
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
-               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-
-importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
-
-xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
-
-(gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
-gg + ggplot2::ylab("Frequency")
-
-}
-\seealso{
-\code{\link[graphics]{barplot}}.
-}
diff --git a/ml-xgboost/R-package/man/xgb.plot.multi.trees.Rd b/ml-xgboost/R-package/man/xgb.plot.multi.trees.Rd
deleted file mode 100644
index 74c4a06..0000000
--- a/ml-xgboost/R-package/man/xgb.plot.multi.trees.Rd
+++ /dev/null
@@ -1,82 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.plot.multi.trees.R
-\name{xgb.plot.multi.trees}
-\alias{xgb.plot.multi.trees}
-\title{Project all trees on one tree and plot it}
-\usage{
-xgb.plot.multi.trees(
-  model,
-  feature_names = NULL,
-  features_keep = 5,
-  plot_width = NULL,
-  plot_height = NULL,
-  render = TRUE,
-  ...
-)
-}
-\arguments{
-\item{model}{produced by the \code{xgb.train} function.}
-
-\item{feature_names}{names of each feature as a \code{character} vector.}
-
-\item{features_keep}{number of features to keep in each position of the multi trees.}
-
-\item{plot_width}{width in pixels of the graph to produce}
-
-\item{plot_height}{height in pixels of the graph to produce}
-
-\item{render}{a logical flag for whether the graph should be rendered (see Value).}
-
-\item{...}{currently not used}
-}
-\value{
-When \code{render = TRUE}:
-returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
-Similar to ggplot objects, it needs to be printed to see it when not running from command line.
-
-When \code{render = FALSE}:
-silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
-This could be useful if one wants to modify some of the graph attributes
-before rendering the graph with \code{\link[DiagrammeR]{render_graph}}.
-}
-\description{
-Visualization of the ensemble of trees as a single collective unit.
-}
-\details{
-This function tries to capture the complexity of a gradient boosted tree model
-in a cohesive way by compressing an ensemble of trees into a single tree-graph representation.
-The goal is to improve the interpretability of a model generally seen as black box.
-
-Note: this function is applicable to tree booster-based models only.
-
-It takes advantage of the fact that the shape of a binary tree is only defined by
-its depth (therefore, in a boosting model, all trees have similar shape).
-
-Moreover, the trees tend to reuse the same features.
-
-The function projects each tree onto one, and keeps for each position the
-\code{features_keep} first features (based on the Gain per feature measure).
-
-This function is inspired by this blog post:
-\url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
-}
-\examples{
-
-data(agaricus.train, package='xgboost')
-
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
-               eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
-               min_child_weight = 50, verbose = 0)
-
-p <- xgb.plot.multi.trees(model = bst, features_keep = 3)
-print(p)
-
-\dontrun{
-# Below is an example of how to save this plot to a file.
-# Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
-library(DiagrammeR)
-gr <- xgb.plot.multi.trees(model=bst, features_keep = 3, render=FALSE)
-export_graph(gr, 'tree.pdf', width=1500, height=600)
-}
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.plot.shap.Rd b/ml-xgboost/R-package/man/xgb.plot.shap.Rd
deleted file mode 100644
index 3cd3a89..0000000
--- a/ml-xgboost/R-package/man/xgb.plot.shap.Rd
+++ /dev/null
@@ -1,158 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.plot.shap.R
-\name{xgb.plot.shap}
-\alias{xgb.plot.shap}
-\title{SHAP contribution dependency plots}
-\usage{
-xgb.plot.shap(
-  data,
-  shap_contrib = NULL,
-  features = NULL,
-  top_n = 1,
-  model = NULL,
-  trees = NULL,
-  target_class = NULL,
-  approxcontrib = FALSE,
-  subsample = NULL,
-  n_col = 1,
-  col = rgb(0, 0, 1, 0.2),
-  pch = ".",
-  discrete_n_uniq = 5,
-  discrete_jitter = 0.01,
-  ylab = "SHAP",
-  plot_NA = TRUE,
-  col_NA = rgb(0.7, 0, 1, 0.6),
-  pch_NA = ".",
-  pos_NA = 1.07,
-  plot_loess = TRUE,
-  col_loess = 2,
-  span_loess = 0.5,
-  which = c("1d", "2d"),
-  plot = TRUE,
-  ...
-)
-}
-\arguments{
-\item{data}{data as a \code{matrix} or \code{dgCMatrix}.}
-
-\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above
-\code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.}
-
-\item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
-feature importance is calculated, and \code{top_n} high ranked features are taken.}
-
-\item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are taken.}
-
-\item{model}{an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
-or \code{features} is missing.}
-
-\item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
-
-\item{target_class}{is only relevant for multiclass models. When it is set to a 0-based class index,
-only SHAP contributions for that specific class are used.
-If it is not set, SHAP importances are averaged over all classes.}
-
-\item{approxcontrib}{passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.}
-
-\item{subsample}{a random fraction of data points to use for plotting. When it is NULL,
-it is set so that up to 100K data points are used.}
-
-\item{n_col}{a number of columns in a grid of plots.}
-
-\item{col}{color of the scatterplot markers.}
-
-\item{pch}{scatterplot marker.}
-
-\item{discrete_n_uniq}{a maximal number of unique values in a feature to consider it as discrete.}
-
-\item{discrete_jitter}{an \code{amount} parameter of jitter added to discrete features' positions.}
-
-\item{ylab}{a y-axis label in 1D plots.}
-
-\item{plot_NA}{whether the contributions of cases with missing values should also be plotted.}
-
-\item{col_NA}{a color of marker for missing value contributions.}
-
-\item{pch_NA}{a marker type for NA values.}
-
-\item{pos_NA}{a relative position of the x-location where NA values are shown:
-\code{min(x) + (max(x) - min(x)) * pos_NA}.}
-
-\item{plot_loess}{whether to plot loess-smoothed curves. The smoothing is only done for features with
-more than 5 distinct values.}
-
-\item{col_loess}{a color to use for the loess curves.}
-
-\item{span_loess}{the \code{span} parameter in \code{\link[stats]{loess}}'s call.}
-
-\item{which}{whether to do univariate or bivariate plotting. NOTE: only 1D is implemented so far.}
-
-\item{plot}{whether a plot should be drawn. If FALSE, only a lits of matrices is returned.}
-
-\item{...}{other parameters passed to \code{plot}.}
-}
-\value{
-In addition to producing plots (when \code{plot=TRUE}), it silently returns a list of two matrices:
-\itemize{
- \item \code{data} the values of selected features;
- \item \code{shap_contrib} the contributions of selected features.
-}
-}
-\description{
-Visualizing the SHAP feature contribution to prediction dependencies on feature value.
-}
-\details{
-These scatterplots represent how SHAP feature contributions depend of feature values.
-The similarity to partial dependency plots is that they also give an idea for how feature values
-affect predictions. However, in partial dependency plots, we usually see marginal dependencies
-of model prediction on feature value, while SHAP contribution dependency plots display the estimated
-contributions of a feature to model prediction for each individual case.
-
-When \code{plot_loess = TRUE} is set, feature values are rounded to 3 significant digits and
-weighted LOESS is computed and plotted, where weights are the numbers of data points
-at each rounded value.
-
-Note: SHAP contributions are shown on the scale of model margin. E.g., for a logistic binomial objective,
-the margin is prediction before a sigmoidal transform into probability-like values.
-Also, since SHAP stands for "SHapley Additive exPlanation" (model prediction = sum of SHAP
-contributions for all features + bias), depending on the objective used, transforming SHAP
-contributions for a feature from the marginal to the prediction space is not necessarily
-a meaningful thing to do.
-}
-\examples{
-
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-
-bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
-               eta = 0.1, max_depth = 3, subsample = .5,
-               method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
-
-xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
-contr <- predict(bst, agaricus.test$data, predcontrib = TRUE)
-xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
-
-# multiclass example - plots for each class separately:
-nclass <- 3
-nrounds <- 20
-x <- as.matrix(iris[, -5])
-set.seed(123)
-is.na(x[sample(nrow(x) * 4, 30)]) <- TRUE # introduce some missing values
-mbst <- xgboost(data = x, label = as.numeric(iris$Species) - 1, nrounds = nrounds,
-                max_depth = 2, eta = 0.3, subsample = .5, nthread = 2,
-                objective = "multi:softprob", num_class = nclass, verbose = 0)
-trees0 <- seq(from=0, by=nclass, length.out=nrounds)
-col <- rgb(0, 0, 1, 0.5)
-xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4,
-              n_col = 2, col = col, pch = 16, pch_NA = 17)
-xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
-              n_col = 2, col = col, pch = 16, pch_NA = 17)
-xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
-              n_col = 2, col = col, pch = 16, pch_NA = 17)
-
-}
-\references{
-Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
-
-Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
-}
diff --git a/ml-xgboost/R-package/man/xgb.plot.tree.Rd b/ml-xgboost/R-package/man/xgb.plot.tree.Rd
deleted file mode 100644
index 3f9f99a..0000000
--- a/ml-xgboost/R-package/man/xgb.plot.tree.Rd
+++ /dev/null
@@ -1,91 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.plot.tree.R
-\name{xgb.plot.tree}
-\alias{xgb.plot.tree}
-\title{Plot a boosted tree model}
-\usage{
-xgb.plot.tree(
-  feature_names = NULL,
-  model = NULL,
-  trees = NULL,
-  plot_width = NULL,
-  plot_height = NULL,
-  render = TRUE,
-  show_node_id = FALSE,
-  ...
-)
-}
-\arguments{
-\item{feature_names}{names of each feature as a \code{character} vector.}
-
-\item{model}{produced by the \code{xgb.train} function.}
-
-\item{trees}{an integer vector of tree indices that should be visualized.
-If set to \code{NULL}, all trees of the model are included.
-IMPORTANT: the tree index in xgboost model is zero-based
-(e.g., use \code{trees = 0:2} for the first 3 trees in a model).}
-
-\item{plot_width}{the width of the diagram in pixels.}
-
-\item{plot_height}{the height of the diagram in pixels.}
-
-\item{render}{a logical flag for whether the graph should be rendered (see Value).}
-
-\item{show_node_id}{a logical flag for whether to show node id's in the graph.}
-
-\item{...}{currently not used.}
-}
-\value{
-When \code{render = TRUE}:
-returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
-Similar to ggplot objects, it needs to be printed to see it when not running from command line.
-
-When \code{render = FALSE}:
-silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
-This could be useful if one wants to modify some of the graph attributes
-before rendering the graph with \code{\link[DiagrammeR]{render_graph}}.
-}
-\description{
-Read a tree model text dump and plot the model.
-}
-\details{
-The content of each node is organised that way:
-
-\itemize{
- \item Feature name.
- \item \code{Cover}: The sum of second order gradient of training data classified to the leaf.
-       If it is square loss, this simply corresponds to the number of instances seen by a split
-       or collected by a leaf during training.
-       The deeper in the tree a node is, the lower this metric will be.
- \item \code{Gain} (for split nodes): the information gain metric of a split
-       (corresponds to the importance of the node in the model).
- \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
-} 
-The tree root nodes also indicate the Tree index (0-based).
-
-The "Yes" branches are marked by the "< split_value" label.
-The branches that also used for missing values are marked as bold
-(as in "carrying extra capacity").
-
-This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
-               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-# plot all the trees
-xgb.plot.tree(model = bst)
-# plot only the first tree and display the node ID:
-xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
-
-\dontrun{
-# Below is an example of how to save this plot to a file. 
-# Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
-library(DiagrammeR)
-gr <- xgb.plot.tree(model=bst, trees=0:1, render=FALSE)
-export_graph(gr, 'tree.pdf', width=1500, height=1900)
-export_graph(gr, 'tree.png', width=1500, height=1900)
-}
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.save.Rd b/ml-xgboost/R-package/man/xgb.save.Rd
deleted file mode 100644
index 7d1842d..0000000
--- a/ml-xgboost/R-package/man/xgb.save.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.save.R
-\name{xgb.save}
-\alias{xgb.save}
-\title{Save xgboost model to binary file}
-\usage{
-xgb.save(model, fname)
-}
-\arguments{
-\item{model}{model object of \code{xgb.Booster} class.}
-
-\item{fname}{name of the file to write.}
-}
-\description{
-Save xgboost model to a file in binary format.
-}
-\details{
-This methods allows to save a model in an xgboost-internal binary format which is universal 
-among the various xgboost interfaces. In R, the saved model file could be read-in later
-using either the \code{\link{xgb.load}} function or the \code{xgb_model} parameter 
-of \code{\link{xgb.train}}.
-
-Note: a model can also be saved as an R-object (e.g., by using \code{\link[base]{readRDS}} 
-or \code{\link[base]{save}}). However, it would then only be compatible with R, and 
-corresponding R-methods would need to be used to load it.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
-               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-xgb.save(bst, 'xgb.model')
-bst <- xgb.load('xgb.model')
-if (file.exists('xgb.model')) file.remove('xgb.model')
-pred <- predict(bst, test$data)
-}
-\seealso{
-\code{\link{xgb.load}}, \code{\link{xgb.Booster.complete}}.
-}
diff --git a/ml-xgboost/R-package/man/xgb.save.raw.Rd b/ml-xgboost/R-package/man/xgb.save.raw.Rd
deleted file mode 100644
index 6f2faa0..0000000
--- a/ml-xgboost/R-package/man/xgb.save.raw.Rd
+++ /dev/null
@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.save.raw.R
-\name{xgb.save.raw}
-\alias{xgb.save.raw}
-\title{Save xgboost model to R's raw vector,
-user can call xgb.load.raw to load the model back from raw vector}
-\usage{
-xgb.save.raw(model)
-}
-\arguments{
-\item{model}{the model object.}
-}
-\description{
-Save xgboost model from xgboost or xgb.train
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-raw <- xgb.save.raw(bst)
-bst <- xgb.load.raw(raw)
-pred <- predict(bst, test$data)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.serialize.Rd b/ml-xgboost/R-package/man/xgb.serialize.Rd
deleted file mode 100644
index 952441d..0000000
--- a/ml-xgboost/R-package/man/xgb.serialize.Rd
+++ /dev/null
@@ -1,29 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.serialize.R
-\name{xgb.serialize}
-\alias{xgb.serialize}
-\title{Serialize the booster instance into R's raw vector.  The serialization method differs
-from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
-parameters.  This serialization format is not stable across different xgboost versions.}
-\usage{
-xgb.serialize(booster)
-}
-\arguments{
-\item{booster}{the booster instance}
-}
-\description{
-Serialize the booster instance into R's raw vector.  The serialization method differs
-from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
-parameters.  This serialization format is not stable across different xgboost versions.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-raw <- xgb.serialize(bst)
-bst <- xgb.unserialize(raw)
-
-}
diff --git a/ml-xgboost/R-package/man/xgb.train.Rd b/ml-xgboost/R-package/man/xgb.train.Rd
deleted file mode 100644
index a6c91cc..0000000
--- a/ml-xgboost/R-package/man/xgb.train.Rd
+++ /dev/null
@@ -1,299 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.train.R, R/xgboost.R
-\name{xgb.train}
-\alias{xgb.train}
-\alias{xgboost}
-\title{eXtreme Gradient Boosting Training}
-\usage{
-xgb.train(
-  params = list(),
-  data,
-  nrounds,
-  watchlist = list(),
-  obj = NULL,
-  feval = NULL,
-  verbose = 1,
-  print_every_n = 1L,
-  early_stopping_rounds = NULL,
-  maximize = NULL,
-  save_period = NULL,
-  save_name = "xgboost.model",
-  xgb_model = NULL,
-  callbacks = list(),
-  ...
-)
-
-xgboost(
-  data = NULL,
-  label = NULL,
-  missing = NA,
-  weight = NULL,
-  params = list(),
-  nrounds,
-  verbose = 1,
-  print_every_n = 1L,
-  early_stopping_rounds = NULL,
-  maximize = NULL,
-  save_period = NULL,
-  save_name = "xgboost.model",
-  xgb_model = NULL,
-  callbacks = list(),
-  ...
-)
-}
-\arguments{
-\item{params}{the list of parameters.
-       The complete list of parameters is available at \url{http://xgboost.readthedocs.io/en/latest/parameter.html}.
-       Below is a shorter summary:
-
-1. General Parameters
-
-\itemize{
-  \item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
-}
-
-2. Booster Parameters
-
-2.1. Parameter for Tree Booster
-
-\itemize{
-  \item \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1} when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model more robust to overfitting but slower to compute. Default: 0.3
-  \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
-  \item \code{max_depth} maximum depth of a tree. Default: 6
-  \item \code{min_child_weight} minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1
-  \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1
-  \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
-  \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample  < 1}  and \code{round = 1}) accordingly. Default: 1
-  \item \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length equals to the number of features in the training data. \code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.
-  \item \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions. Each item of the list represents one permitted interaction where specified features are allowed to interact with each other. Feature index values should start from \code{0} (\code{0} references the first column).  Leave argument unspecified for no interaction constraints.
-}
-
-2.2. Parameter for Linear Booster
-
-\itemize{
-  \item \code{lambda} L2 regularization term on weights. Default: 0
-  \item \code{lambda_bias} L2 regularization term on bias. Default: 0
-  \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
-}
-
-3. Task Parameters
-
-\itemize{
-\item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
-  \itemize{
-    \item \code{reg:squarederror} Regression with squared loss (Default).
-    \item \code{reg:logistic} logistic regression.
-    \item \code{binary:logistic} logistic regression for binary classification. Output probability.
-    \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
-    \item \code{num_class} set the number of classes. To use only with multiclass objectives.
-    \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{num_class - 1}.
-    \item \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
-    \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
-  }
-  \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
-  \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
-}}
-
-\item{data}{training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
-\code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.}
-
-\item{nrounds}{max number of boosting iterations.}
-
-\item{watchlist}{named list of xgb.DMatrix datasets to use for evaluating model performance.
-Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
-of these datasets during each boosting iteration, and stored in the end as a field named
-\code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
-\code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
-printed out during the training.
-E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
-the performance of each round's model on mat1 and mat2.}
-
-\item{obj}{customized objective function. Returns gradient and second order
-gradient with given prediction and dtrain.}
-
-\item{feval}{customized evaluation function. Returns
-\code{list(metric='metric-name', value='metric-value')} with given
-prediction and dtrain.}
-
-\item{verbose}{If 0, xgboost will stay silent. If 1, it will print information about performance.
-If 2, some additional information will be printed out.
-Note that setting \code{verbose > 0} automatically engages the
-\code{cb.print.evaluation(period=1)} callback function.}
-
-\item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
-Default is 1 which means all messages are printed. This parameter is passed to the
-\code{\link{cb.print.evaluation}} callback.}
-
-\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
-If set to an integer \code{k}, training with a validation set will stop if the performance
-doesn't improve for \code{k} rounds.
-Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
-
-\item{maximize}{If \code{feval} and \code{early_stopping_rounds} are set,
-then this parameter must be set as well.
-When it is \code{TRUE}, it means the larger the evaluation score the better.
-This parameter is passed to the \code{\link{cb.early.stop}} callback.}
-
-\item{save_period}{when it is non-NULL, model is saved to disk after every \code{save_period} rounds,
-0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.}
-
-\item{save_name}{the name or path for periodically saved model file.}
-
-\item{xgb_model}{a previously built model to continue the training from.
-Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
-file with a previously saved model.}
-
-\item{callbacks}{a list of callback functions to perform various task during boosting.
-See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-parameters' values. User can provide either existing or their own callback methods in order
-to customize the training process.}
-
-\item{...}{other parameters to pass to \code{params}.}
-
-\item{label}{vector of response values. Should not be provided when data is
-a local data file name or an \code{xgb.DMatrix}.}
-
-\item{missing}{by default is set to NA, which means that NA values should be considered as 'missing'
-by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
-This parameter is only used when input is a dense matrix.}
-
-\item{weight}{a vector indicating the weight for each row of the input.}
-}
-\value{
-An object of class \code{xgb.Booster} with the following elements:
-\itemize{
-  \item \code{handle} a handle (pointer) to the xgboost model in memory.
-  \item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
-  \item \code{niter} number of boosting iterations.
-  \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
-        first column corresponding to iteration number and the rest corresponding to evaluation
-        metrics' values. It is created by the \code{\link{cb.evaluation.log}} callback.
-  \item \code{call} a function call.
-  \item \code{params} parameters that were passed to the xgboost library. Note that it does not
-        capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-  \item \code{callbacks} callback functions that were either automatically assigned or
-        explicitly passed.
-  \item \code{best_iteration} iteration number with the best evaluation metric value
-        (only available with early stopping).
-  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
-        which could further be used in \code{predict} method
-        (only available with early stopping).
-  \item \code{best_score} the best evaluation metric value during early stopping.
-        (only available with early stopping).
-  \item \code{feature_names} names of the training dataset features
-        (only when column names were defined in training data).
-  \item \code{nfeatures} number of features in training data.
-}
-}
-\description{
-\code{xgb.train} is an advanced interface for training an xgboost model.
-The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
-}
-\details{
-These are the training functions for \code{xgboost}.
-
-The \code{xgb.train} interface supports advanced features such as \code{watchlist},
-customized objective and evaluation metric functions, therefore it is more flexible
-than the \code{xgboost} interface.
-
-Parallelization is automatically enabled if \code{OpenMP} is present.
-Number of threads can also be manually specified via \code{nthread} parameter.
-
-The evaluation metric is chosen automatically by Xgboost (according to the objective)
-when the \code{eval_metric} parameter is not provided.
-User may set one or several \code{eval_metric} parameters.
-Note that when using a customized metric, only this single metric can be used.
-The following is the list of built-in metrics for which Xgboost provides optimized implementation:
-  \itemize{
-     \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
-     \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
-     \item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
-     \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-           By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
-           Different threshold (e.g., 0.) could be specified as "error@0."
-     \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-     \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
-     \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
-     \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
-  }
-
-The following callbacks are automatically created when certain parameters are set:
-\itemize{
-  \item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
-        and the \code{print_every_n} parameter is passed to it.
-  \item \code{cb.evaluation.log} is on when \code{watchlist} is present.
-  \item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
-  \item \code{cb.save.model}: when \code{save_period > 0} is set.
-}
-}
-\examples{
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-watchlist <- list(train = dtrain, eval = dtest)
-
-## A simple xgb.train example:
-param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
-              objective = "binary:logistic", eval_metric = "auc")
-bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
-
-
-## An xgb.train example where custom objective and evaluation metric are used:
-logregobj <- function(preds, dtrain) {
-   labels <- getinfo(dtrain, "label")
-   preds <- 1/(1 + exp(-preds))
-   grad <- preds - labels
-   hess <- preds * (1 - preds)
-   return(list(grad = grad, hess = hess))
-}
-evalerror <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
-  return(list(metric = "error", value = err))
-}
-
-# These functions could be used by passing them either:
-#  as 'objective' and 'eval_metric' parameters in the params list:
-param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
-              objective = logregobj, eval_metric = evalerror)
-bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
-
-#  or through the ... arguments:
-param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2)
-bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
-                 objective = logregobj, eval_metric = evalerror)
-
-#  or as dedicated 'obj' and 'feval' parameters of xgb.train:
-bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
-                 obj = logregobj, feval = evalerror)
-
-
-## An xgb.train example of using variable learning rates at each iteration:
-param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
-              objective = "binary:logistic", eval_metric = "auc")
-my_etas <- list(eta = c(0.5, 0.1))
-bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
-                 callbacks = list(cb.reset.parameters(my_etas)))
-
-## Early stopping:
-bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
-                 early_stopping_rounds = 3)
-
-## An 'xgboost' interface example:
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
-               max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
-               objective = "binary:logistic")
-pred <- predict(bst, agaricus.test$data)
-
-}
-\references{
-Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
-22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, \url{https://arxiv.org/abs/1603.02754}
-}
-\seealso{
-\code{\link{callbacks}},
-\code{\link{predict.xgb.Booster}},
-\code{\link{xgb.cv}}
-}
diff --git a/ml-xgboost/R-package/man/xgb.unserialize.Rd b/ml-xgboost/R-package/man/xgb.unserialize.Rd
deleted file mode 100644
index 7a11c5c..0000000
--- a/ml-xgboost/R-package/man/xgb.unserialize.Rd
+++ /dev/null
@@ -1,14 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/xgb.unserialize.R
-\name{xgb.unserialize}
-\alias{xgb.unserialize}
-\title{Load the instance back from \code{\link{xgb.serialize}}}
-\usage{
-xgb.unserialize(buffer)
-}
-\arguments{
-\item{buffer}{the buffer containing booster instance saved by \code{\link{xgb.serialize}}}
-}
-\description{
-Load the instance back from \code{\link{xgb.serialize}}
-}
diff --git a/ml-xgboost/R-package/man/xgboost-deprecated.Rd b/ml-xgboost/R-package/man/xgboost-deprecated.Rd
deleted file mode 100644
index 6ab0c6c..0000000
--- a/ml-xgboost/R-package/man/xgboost-deprecated.Rd
+++ /dev/null
@@ -1,16 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils.R
-\name{xgboost-deprecated}
-\alias{xgboost-deprecated}
-\title{Deprecation notices.}
-\description{
-At this time, some of the parameter names were changed in order to make the code style more uniform.
-The deprecated parameters would be removed in the next release.
-}
-\details{
-To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
-
-A deprecation warning is shown when any of the deprecated parameters is used in a call.
-An additional warning is shown when there was a partial match to a deprecated parameter
-(as R is able to partially match parameter names).
-}
diff --git a/ml-xgboost/R-package/remove_warning_suppression_pragma.sh b/ml-xgboost/R-package/remove_warning_suppression_pragma.sh
deleted file mode 100644
index 5399ac9..0000000
--- a/ml-xgboost/R-package/remove_warning_suppression_pragma.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-# remove all #pragma's that suppress compiler warnings
-set -e
-set -x
-for file in xgboost/src/dmlc-core/include/dmlc/*.h
-do
-  sed -i.bak -e 's/^.*#pragma GCC diagnostic.*$//' -e 's/^.*#pragma clang diagnostic.*$//' -e 's/^.*#pragma warning.*$//' "${file}"
-done
-for file in xgboost/src/dmlc-core/include/dmlc/*.h.bak
-do
-  rm "${file}"
-done
-set +x
-set +e
diff --git a/ml-xgboost/R-package/src/Makevars.in b/ml-xgboost/R-package/src/Makevars.in
deleted file mode 100644
index ac25b50..0000000
--- a/ml-xgboost/R-package/src/Makevars.in
+++ /dev/null
@@ -1,24 +0,0 @@
-# package root
-PKGROOT=../../
-ENABLE_STD_THREAD=1
-# _*_ mode: Makefile; _*_
-
-CXX_STD = CXX11
-
-XGB_RFLAGS = -DXGBOOST_STRICT_R_MODE=1 -DDMLC_LOG_BEFORE_THROW=0\
-           -DDMLC_ENABLE_STD_THREAD=$(ENABLE_STD_THREAD) -DDMLC_DISABLE_STDIN=1\
-           -DDMLC_LOG_CUSTOMIZE=1 -DXGBOOST_CUSTOMIZE_LOGGER=1\
-           -DRABIT_CUSTOMIZE_MSG_ -DRABIT_STRICT_CXX98_
-
-# disable the use of thread_local for 32 bit windows:
-ifeq ($(R_OSTYPE)$(WIN),windows)
-    XGB_RFLAGS += -DDMLC_CXX11_THREAD_LOCAL=0
-endif
-$(foreach v, $(XGB_RFLAGS), $(warning $(v)))
-
-PKG_CPPFLAGS=  -I$(PKGROOT)/include -I$(PKGROOT)/dmlc-core/include -I$(PKGROOT)/rabit/include -I$(PKGROOT) $(XGB_RFLAGS)
-PKG_CXXFLAGS= @OPENMP_CXXFLAGS@ @ENDIAN_FLAG@ -pthread
-PKG_LIBS = @OPENMP_CXXFLAGS@ @OPENMP_LIB@ @ENDIAN_FLAG@ @BACKTRACE_LIB@ -pthread
-OBJECTS= ./xgboost_R.o ./xgboost_custom.o ./xgboost_assert.o ./init.o\
-         $(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o\
-         $(PKGROOT)/rabit/src/engine_empty.o $(PKGROOT)/rabit/src/c_api.o
diff --git a/ml-xgboost/R-package/src/Makevars.win b/ml-xgboost/R-package/src/Makevars.win
deleted file mode 100644
index 2e24116..0000000
--- a/ml-xgboost/R-package/src/Makevars.win
+++ /dev/null
@@ -1,38 +0,0 @@
-# package root
-PKGROOT=./
-ENABLE_STD_THREAD=0
-# _*_ mode: Makefile; _*_
-
-# This file is only used for windows compilation from github
-# It will be replaced with Makevars.in for the CRAN version
-.PHONY: all xgblib
-all: $(SHLIB)
-$(SHLIB): xgblib
-xgblib:
-	cp -r ../../src .
-	cp -r ../../rabit .
-	cp -r ../../dmlc-core .
-	cp -r ../../include .
-	cp -r ../../amalgamation .
-
-CXX_STD = CXX11
-
-XGB_RFLAGS = -DXGBOOST_STRICT_R_MODE=1 -DDMLC_LOG_BEFORE_THROW=0\
-           -DDMLC_ENABLE_STD_THREAD=$(ENABLE_STD_THREAD) -DDMLC_DISABLE_STDIN=1\
-           -DDMLC_LOG_CUSTOMIZE=1 -DXGBOOST_CUSTOMIZE_LOGGER=1\
-           -DRABIT_CUSTOMIZE_MSG_ -DRABIT_STRICT_CXX98_
-
-# disable the use of thread_local for 32 bit windows:
-ifeq ($(R_OSTYPE)$(WIN),windows)
-    XGB_RFLAGS += -DDMLC_CXX11_THREAD_LOCAL=0
-endif
-$(foreach v, $(XGB_RFLAGS), $(warning $(v)))
-
-PKG_CPPFLAGS=  -I$(PKGROOT)/include -I$(PKGROOT)/dmlc-core/include -I$(PKGROOT)/rabit/include -I$(PKGROOT) $(XGB_RFLAGS)
-PKG_CXXFLAGS= $(SHLIB_OPENMP_CXXFLAGS) $(SHLIB_PTHREAD_FLAGS)
-PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(SHLIB_PTHREAD_FLAGS)
-OBJECTS= ./xgboost_R.o ./xgboost_custom.o ./xgboost_assert.o ./init.o\
-         $(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o\
-         $(PKGROOT)/rabit/src/engine_empty.o $(PKGROOT)/rabit/src/c_api.o
-
-$(OBJECTS) : xgblib
diff --git a/ml-xgboost/R-package/src/init.c b/ml-xgboost/R-package/src/init.c
deleted file mode 100644
index 2093059..0000000
--- a/ml-xgboost/R-package/src/init.c
+++ /dev/null
@@ -1,85 +0,0 @@
-/* Copyright (c) 2015 by Contributors
- *
- * This file was initially generated using the following R command:
- * tools::package_native_routine_registration_skeleton('.', con = 'src/init.c', character_only = F)
- * and edited to conform to xgboost C linter requirements. For details, see
- * https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Registering-native-routines
- */
-#include <R.h>
-#include <Rinternals.h>
-#include <stdlib.h>
-#include <R_ext/Rdynload.h>
-
-/* FIXME:
-Check these declarations against the C/Fortran source code.
-*/
-
-/* .Call calls */
-extern SEXP XGBoosterBoostOneIter_R(SEXP, SEXP, SEXP, SEXP);
-extern SEXP XGBoosterCreate_R(SEXP);
-extern SEXP XGBoosterDumpModel_R(SEXP, SEXP, SEXP, SEXP);
-extern SEXP XGBoosterEvalOneIter_R(SEXP, SEXP, SEXP, SEXP);
-extern SEXP XGBoosterGetAttrNames_R(SEXP);
-extern SEXP XGBoosterGetAttr_R(SEXP, SEXP);
-extern SEXP XGBoosterLoadModelFromRaw_R(SEXP, SEXP);
-extern SEXP XGBoosterLoadModel_R(SEXP, SEXP);
-extern SEXP XGBoosterSaveJsonConfig_R(SEXP handle);
-extern SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value);
-extern SEXP XGBoosterSerializeToBuffer_R(SEXP handle);
-extern SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw);
-extern SEXP XGBoosterModelToRaw_R(SEXP);
-extern SEXP XGBoosterPredict_R(SEXP, SEXP, SEXP, SEXP, SEXP);
-extern SEXP XGBoosterSaveModel_R(SEXP, SEXP);
-extern SEXP XGBoosterSetAttr_R(SEXP, SEXP, SEXP);
-extern SEXP XGBoosterSetParam_R(SEXP, SEXP, SEXP);
-extern SEXP XGBoosterUpdateOneIter_R(SEXP, SEXP, SEXP);
-extern SEXP XGCheckNullPtr_R(SEXP);
-extern SEXP XGDMatrixCreateFromCSC_R(SEXP, SEXP, SEXP, SEXP);
-extern SEXP XGDMatrixCreateFromFile_R(SEXP, SEXP);
-extern SEXP XGDMatrixCreateFromMat_R(SEXP, SEXP);
-extern SEXP XGDMatrixGetInfo_R(SEXP, SEXP);
-extern SEXP XGDMatrixNumCol_R(SEXP);
-extern SEXP XGDMatrixNumRow_R(SEXP);
-extern SEXP XGDMatrixSaveBinary_R(SEXP, SEXP, SEXP);
-extern SEXP XGDMatrixSetInfo_R(SEXP, SEXP, SEXP);
-extern SEXP XGDMatrixSliceDMatrix_R(SEXP, SEXP);
-
-static const R_CallMethodDef CallEntries[] = {
-  {"XGBoosterBoostOneIter_R",     (DL_FUNC) &XGBoosterBoostOneIter_R,     4},
-  {"XGBoosterCreate_R",           (DL_FUNC) &XGBoosterCreate_R,           1},
-  {"XGBoosterDumpModel_R",        (DL_FUNC) &XGBoosterDumpModel_R,        4},
-  {"XGBoosterEvalOneIter_R",      (DL_FUNC) &XGBoosterEvalOneIter_R,      4},
-  {"XGBoosterGetAttrNames_R",     (DL_FUNC) &XGBoosterGetAttrNames_R,     1},
-  {"XGBoosterGetAttr_R",          (DL_FUNC) &XGBoosterGetAttr_R,          2},
-  {"XGBoosterLoadModelFromRaw_R", (DL_FUNC) &XGBoosterLoadModelFromRaw_R, 2},
-  {"XGBoosterLoadModel_R",        (DL_FUNC) &XGBoosterLoadModel_R,        2},
-  {"XGBoosterSaveJsonConfig_R",   (DL_FUNC) &XGBoosterSaveJsonConfig_R,   1},
-  {"XGBoosterLoadJsonConfig_R",   (DL_FUNC) &XGBoosterLoadJsonConfig_R,   2},
-  {"XGBoosterSerializeToBuffer_R",     (DL_FUNC) &XGBoosterSerializeToBuffer_R,     1},
-  {"XGBoosterUnserializeFromBuffer_R", (DL_FUNC) &XGBoosterUnserializeFromBuffer_R, 2},
-  {"XGBoosterModelToRaw_R",       (DL_FUNC) &XGBoosterModelToRaw_R,       1},
-  {"XGBoosterPredict_R",          (DL_FUNC) &XGBoosterPredict_R,          5},
-  {"XGBoosterSaveModel_R",        (DL_FUNC) &XGBoosterSaveModel_R,        2},
-  {"XGBoosterSetAttr_R",          (DL_FUNC) &XGBoosterSetAttr_R,          3},
-  {"XGBoosterSetParam_R",         (DL_FUNC) &XGBoosterSetParam_R,         3},
-  {"XGBoosterUpdateOneIter_R",    (DL_FUNC) &XGBoosterUpdateOneIter_R,    3},
-  {"XGCheckNullPtr_R",            (DL_FUNC) &XGCheckNullPtr_R,            1},
-  {"XGDMatrixCreateFromCSC_R",    (DL_FUNC) &XGDMatrixCreateFromCSC_R,    4},
-  {"XGDMatrixCreateFromFile_R",   (DL_FUNC) &XGDMatrixCreateFromFile_R,   2},
-  {"XGDMatrixCreateFromMat_R",    (DL_FUNC) &XGDMatrixCreateFromMat_R,    2},
-  {"XGDMatrixGetInfo_R",          (DL_FUNC) &XGDMatrixGetInfo_R,          2},
-  {"XGDMatrixNumCol_R",           (DL_FUNC) &XGDMatrixNumCol_R,           1},
-  {"XGDMatrixNumRow_R",           (DL_FUNC) &XGDMatrixNumRow_R,           1},
-  {"XGDMatrixSaveBinary_R",       (DL_FUNC) &XGDMatrixSaveBinary_R,       3},
-  {"XGDMatrixSetInfo_R",          (DL_FUNC) &XGDMatrixSetInfo_R,          3},
-  {"XGDMatrixSliceDMatrix_R",     (DL_FUNC) &XGDMatrixSliceDMatrix_R,     2},
-  {NULL, NULL, 0}
-};
-
-#if defined(_WIN32)
-__declspec(dllexport)
-#endif  // defined(_WIN32)
-void R_init_xgboost(DllInfo *dll) {
-  R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-  R_useDynamicSymbols(dll, FALSE);
-}
diff --git a/ml-xgboost/R-package/src/xgboost_R.cc b/ml-xgboost/R-package/src/xgboost_R.cc
deleted file mode 100644
index cb86ef4..0000000
--- a/ml-xgboost/R-package/src/xgboost_R.cc
+++ /dev/null
@@ -1,491 +0,0 @@
-// Copyright (c) 2014 by Contributors
-#include <dmlc/logging.h>
-#include <dmlc/omp.h>
-#include <xgboost/c_api.h>
-#include <vector>
-#include <string>
-#include <utility>
-#include <cstring>
-#include <cstdio>
-#include <sstream>
-#include "./xgboost_R.h"
-
-/*!
- * \brief macro to annotate begin of api
- */
-#define R_API_BEGIN()                           \
-  GetRNGstate();                                \
-  try {
-/*!
- * \brief macro to annotate end of api
- */
-#define R_API_END()                             \
-  } catch(dmlc::Error& e) {                     \
-    PutRNGstate();                              \
-    error(e.what());                            \
-  }                                             \
-  PutRNGstate();
-
-/*!
- * \brief macro to check the call.
- */
-#define CHECK_CALL(x)                           \
-  if ((x) != 0) {                               \
-    error(XGBGetLastError());                   \
-  }
-
-
-using namespace dmlc;
-
-SEXP XGCheckNullPtr_R(SEXP handle) {
-  return ScalarLogical(R_ExternalPtrAddr(handle) == NULL);
-}
-
-void _DMatrixFinalizer(SEXP ext) {
-  R_API_BEGIN();
-  if (R_ExternalPtrAddr(ext) == NULL) return;
-  CHECK_CALL(XGDMatrixFree(R_ExternalPtrAddr(ext)));
-  R_ClearExternalPtr(ext);
-  R_API_END();
-}
-
-SEXP XGDMatrixCreateFromFile_R(SEXP fname, SEXP silent) {
-  SEXP ret;
-  R_API_BEGIN();
-  DMatrixHandle handle;
-  CHECK_CALL(XGDMatrixCreateFromFile(CHAR(asChar(fname)), asInteger(silent), &handle));
-  ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
-  R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGDMatrixCreateFromMat_R(SEXP mat,
-                              SEXP missing) {
-  SEXP ret;
-  R_API_BEGIN();
-  SEXP dim = getAttrib(mat, R_DimSymbol);
-  size_t nrow = static_cast<size_t>(INTEGER(dim)[0]);
-  size_t ncol = static_cast<size_t>(INTEGER(dim)[1]);
-  const bool is_int = TYPEOF(mat) == INTSXP;
-  double *din;
-  int *iin;
-  if (is_int) {
-    iin = INTEGER(mat);
-  } else {
-    din = REAL(mat);
-  }
-  std::vector<float> data(nrow * ncol);
-  #pragma omp parallel for schedule(static)
-  for (omp_ulong i = 0; i < nrow; ++i) {
-    for (size_t j = 0; j < ncol; ++j) {
-      data[i * ncol +j] = is_int ? static_cast<float>(iin[i + nrow * j]) : din[i + nrow * j];
-    }
-  }
-  DMatrixHandle handle;
-  CHECK_CALL(XGDMatrixCreateFromMat(BeginPtr(data), nrow, ncol, asReal(missing), &handle));
-  ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
-  R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGDMatrixCreateFromCSC_R(SEXP indptr,
-                              SEXP indices,
-                              SEXP data,
-                              SEXP num_row) {
-  SEXP ret;
-  R_API_BEGIN();
-  const int *p_indptr = INTEGER(indptr);
-  const int *p_indices = INTEGER(indices);
-  const double *p_data = REAL(data);
-  size_t nindptr = static_cast<size_t>(length(indptr));
-  size_t ndata = static_cast<size_t>(length(data));
-  size_t nrow = static_cast<size_t>(INTEGER(num_row)[0]);
-  std::vector<size_t> col_ptr_(nindptr);
-  std::vector<unsigned> indices_(ndata);
-  std::vector<float> data_(ndata);
-
-  for (size_t i = 0; i < nindptr; ++i) {
-    col_ptr_[i] = static_cast<size_t>(p_indptr[i]);
-  }
-  #pragma omp parallel for schedule(static)
-  for (int64_t i = 0; i < static_cast<int64_t>(ndata); ++i) {
-    indices_[i] = static_cast<unsigned>(p_indices[i]);
-    data_[i] = static_cast<float>(p_data[i]);
-  }
-  DMatrixHandle handle;
-  CHECK_CALL(XGDMatrixCreateFromCSCEx(BeginPtr(col_ptr_), BeginPtr(indices_),
-                                      BeginPtr(data_), nindptr, ndata,
-                                      nrow, &handle));
-  ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
-  R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGDMatrixSliceDMatrix_R(SEXP handle, SEXP idxset) {
-  SEXP ret;
-  R_API_BEGIN();
-  int len = length(idxset);
-  std::vector<int> idxvec(len);
-  for (int i = 0; i < len; ++i) {
-    idxvec[i] = INTEGER(idxset)[i] - 1;
-  }
-  DMatrixHandle res;
-  CHECK_CALL(XGDMatrixSliceDMatrixEx(R_ExternalPtrAddr(handle),
-                                     BeginPtr(idxvec), len,
-                                     &res,
-                                     0));
-  ret = PROTECT(R_MakeExternalPtr(res, R_NilValue, R_NilValue));
-  R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGDMatrixSaveBinary_R(SEXP handle, SEXP fname, SEXP silent) {
-  R_API_BEGIN();
-  CHECK_CALL(XGDMatrixSaveBinary(R_ExternalPtrAddr(handle),
-                                 CHAR(asChar(fname)),
-                                 asInteger(silent)));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGDMatrixSetInfo_R(SEXP handle, SEXP field, SEXP array) {
-  R_API_BEGIN();
-  int len = length(array);
-  const char *name = CHAR(asChar(field));
-  if (!strcmp("group", name)) {
-    std::vector<unsigned> vec(len);
-    #pragma omp parallel for schedule(static)
-    for (int i = 0; i < len; ++i) {
-      vec[i] = static_cast<unsigned>(INTEGER(array)[i]);
-    }
-    CHECK_CALL(XGDMatrixSetUIntInfo(R_ExternalPtrAddr(handle),
-                                    CHAR(asChar(field)),
-                                    BeginPtr(vec), len));
-  } else {
-    std::vector<float> vec(len);
-    #pragma omp parallel for schedule(static)
-    for (int i = 0; i < len; ++i) {
-      vec[i] = REAL(array)[i];
-    }
-    CHECK_CALL(XGDMatrixSetFloatInfo(R_ExternalPtrAddr(handle),
-                                     CHAR(asChar(field)),
-                                     BeginPtr(vec), len));
-  }
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field) {
-  SEXP ret;
-  R_API_BEGIN();
-  bst_ulong olen;
-  const float *res;
-  CHECK_CALL(XGDMatrixGetFloatInfo(R_ExternalPtrAddr(handle),
-                                   CHAR(asChar(field)),
-                                 &olen,
-                                 &res));
-  ret = PROTECT(allocVector(REALSXP, olen));
-  for (size_t i = 0; i < olen; ++i) {
-    REAL(ret)[i] = res[i];
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGDMatrixNumRow_R(SEXP handle) {
-  bst_ulong nrow;
-  R_API_BEGIN();
-  CHECK_CALL(XGDMatrixNumRow(R_ExternalPtrAddr(handle), &nrow));
-  R_API_END();
-  return ScalarInteger(static_cast<int>(nrow));
-}
-
-SEXP XGDMatrixNumCol_R(SEXP handle) {
-  bst_ulong ncol;
-  R_API_BEGIN();
-  CHECK_CALL(XGDMatrixNumCol(R_ExternalPtrAddr(handle), &ncol));
-  R_API_END();
-  return ScalarInteger(static_cast<int>(ncol));
-}
-
-// functions related to booster
-void _BoosterFinalizer(SEXP ext) {
-  if (R_ExternalPtrAddr(ext) == NULL) return;
-  CHECK_CALL(XGBoosterFree(R_ExternalPtrAddr(ext)));
-  R_ClearExternalPtr(ext);
-}
-
-SEXP XGBoosterCreate_R(SEXP dmats) {
-  SEXP ret;
-  R_API_BEGIN();
-  int len = length(dmats);
-  std::vector<void*> dvec;
-  for (int i = 0; i < len; ++i) {
-    dvec.push_back(R_ExternalPtrAddr(VECTOR_ELT(dmats, i)));
-  }
-  BoosterHandle handle;
-  CHECK_CALL(XGBoosterCreate(BeginPtr(dvec), dvec.size(), &handle));
-  ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
-  R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE);
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGBoosterSetParam_R(SEXP handle, SEXP name, SEXP val) {
-  R_API_BEGIN();
-  CHECK_CALL(XGBoosterSetParam(R_ExternalPtrAddr(handle),
-                               CHAR(asChar(name)),
-                               CHAR(asChar(val))));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterUpdateOneIter_R(SEXP handle, SEXP iter, SEXP dtrain) {
-  R_API_BEGIN();
-  CHECK_CALL(XGBoosterUpdateOneIter(R_ExternalPtrAddr(handle),
-                                  asInteger(iter),
-                                  R_ExternalPtrAddr(dtrain)));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterBoostOneIter_R(SEXP handle, SEXP dtrain, SEXP grad, SEXP hess) {
-  R_API_BEGIN();
-  CHECK_EQ(length(grad), length(hess))
-      << "gradient and hess must have same length";
-  int len = length(grad);
-  std::vector<float> tgrad(len), thess(len);
-  #pragma omp parallel for schedule(static)
-  for (int j = 0; j < len; ++j) {
-    tgrad[j] = REAL(grad)[j];
-    thess[j] = REAL(hess)[j];
-  }
-  CHECK_CALL(XGBoosterBoostOneIter(R_ExternalPtrAddr(handle),
-                                 R_ExternalPtrAddr(dtrain),
-                                 BeginPtr(tgrad), BeginPtr(thess),
-                                 len));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames) {
-  const char *ret;
-  R_API_BEGIN();
-  CHECK_EQ(length(dmats), length(evnames))
-      << "dmats and evnams must have same length";
-  int len = length(dmats);
-  std::vector<void*> vec_dmats;
-  std::vector<std::string> vec_names;
-  std::vector<const char*> vec_sptr;
-  for (int i = 0; i < len; ++i) {
-    vec_dmats.push_back(R_ExternalPtrAddr(VECTOR_ELT(dmats, i)));
-    vec_names.push_back(std::string(CHAR(asChar(VECTOR_ELT(evnames, i)))));
-  }
-  for (int i = 0; i < len; ++i) {
-    vec_sptr.push_back(vec_names[i].c_str());
-  }
-  CHECK_CALL(XGBoosterEvalOneIter(R_ExternalPtrAddr(handle),
-                                  asInteger(iter),
-                                  BeginPtr(vec_dmats),
-                                  BeginPtr(vec_sptr),
-                                  len, &ret));
-  R_API_END();
-  return mkString(ret);
-}
-
-SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask,
-                        SEXP ntree_limit, SEXP training) {
-  SEXP ret;
-  R_API_BEGIN();
-  bst_ulong olen;
-  const float *res;
-  CHECK_CALL(XGBoosterPredict(R_ExternalPtrAddr(handle),
-                              R_ExternalPtrAddr(dmat),
-                              asInteger(option_mask),
-                              asInteger(ntree_limit),
-                              asInteger(training),
-                              &olen, &res));
-  ret = PROTECT(allocVector(REALSXP, olen));
-  for (size_t i = 0; i < olen; ++i) {
-    REAL(ret)[i] = res[i];
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGBoosterLoadModel_R(SEXP handle, SEXP fname) {
-  R_API_BEGIN();
-  CHECK_CALL(XGBoosterLoadModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname))));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterSaveModel_R(SEXP handle, SEXP fname) {
-  R_API_BEGIN();
-  CHECK_CALL(XGBoosterSaveModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname))));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterModelToRaw_R(SEXP handle) {
-  SEXP ret;
-  R_API_BEGIN();
-  bst_ulong olen;
-  const char *raw;
-  CHECK_CALL(XGBoosterGetModelRaw(R_ExternalPtrAddr(handle), &olen, &raw));
-  ret = PROTECT(allocVector(RAWSXP, olen));
-  if (olen != 0) {
-    memcpy(RAW(ret), raw, olen);
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw) {
-  R_API_BEGIN();
-  CHECK_CALL(XGBoosterLoadModelFromBuffer(R_ExternalPtrAddr(handle),
-                                          RAW(raw),
-                                          length(raw)));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterSaveJsonConfig_R(SEXP handle) {
-  const char* ret;
-  R_API_BEGIN();
-  bst_ulong len {0};
-  CHECK_CALL(XGBoosterSaveJsonConfig(R_ExternalPtrAddr(handle),
-                                     &len,
-                                     &ret));
-  R_API_END();
-  return mkString(ret);
-}
-
-SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value) {
-  R_API_BEGIN();
-  XGBoosterLoadJsonConfig(R_ExternalPtrAddr(handle), CHAR(asChar(value)));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterSerializeToBuffer_R(SEXP handle) {
-  SEXP ret;
-  R_API_BEGIN();
-  bst_ulong out_len;
-  const char *raw;
-  CHECK_CALL(XGBoosterSerializeToBuffer(R_ExternalPtrAddr(handle), &out_len, &raw));
-  ret = PROTECT(allocVector(RAWSXP, out_len));
-  if (out_len != 0) {
-    memcpy(RAW(ret), raw, out_len);
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return ret;
-}
-
-SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw) {
-  R_API_BEGIN();
-  XGBoosterUnserializeFromBuffer(R_ExternalPtrAddr(handle),
-                                 RAW(raw),
-                                 length(raw));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterDumpModel_R(SEXP handle, SEXP fmap, SEXP with_stats, SEXP dump_format) {
-  SEXP out;
-  R_API_BEGIN();
-  bst_ulong olen;
-  const char **res;
-  const char *fmt = CHAR(asChar(dump_format));
-  CHECK_CALL(XGBoosterDumpModelEx(R_ExternalPtrAddr(handle),
-                                CHAR(asChar(fmap)),
-                                asInteger(with_stats),
-                                fmt,
-                                &olen, &res));
-  out = PROTECT(allocVector(STRSXP, olen));
-  if (!strcmp("json", fmt)) {
-    std::stringstream stream;
-    stream <<  "[\n";
-    for (size_t i = 0; i < olen; ++i) {
-      stream << res[i];
-      if (i < olen - 1) {
-        stream << ",\n";
-      } else {
-        stream << "\n";
-      }
-    }
-    stream <<  "]";
-    SET_STRING_ELT(out, 0, mkChar(stream.str().c_str()));
-  } else {
-    for (size_t i = 0; i < olen; ++i) {
-      std::stringstream stream;
-      stream <<  "booster[" << i <<"]\n" << res[i];
-      SET_STRING_ELT(out, i, mkChar(stream.str().c_str()));
-    }
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return out;
-}
-
-SEXP XGBoosterGetAttr_R(SEXP handle, SEXP name) {
-  SEXP out;
-  R_API_BEGIN();
-  int success;
-  const char *val;
-  CHECK_CALL(XGBoosterGetAttr(R_ExternalPtrAddr(handle),
-                              CHAR(asChar(name)),
-                              &val,
-                              &success));
-  if (success) {
-    out = PROTECT(allocVector(STRSXP, 1));
-    SET_STRING_ELT(out, 0, mkChar(val));
-  } else {
-    out = PROTECT(R_NilValue);
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return out;
-}
-
-SEXP XGBoosterSetAttr_R(SEXP handle, SEXP name, SEXP val) {
-  R_API_BEGIN();
-  const char *v = isNull(val) ? nullptr : CHAR(asChar(val));
-  CHECK_CALL(XGBoosterSetAttr(R_ExternalPtrAddr(handle),
-                              CHAR(asChar(name)), v));
-  R_API_END();
-  return R_NilValue;
-}
-
-SEXP XGBoosterGetAttrNames_R(SEXP handle) {
-  SEXP out;
-  R_API_BEGIN();
-  bst_ulong len;
-  const char **res;
-  CHECK_CALL(XGBoosterGetAttrNames(R_ExternalPtrAddr(handle),
-                                   &len, &res));
-  if (len > 0) {
-    out = PROTECT(allocVector(STRSXP, len));
-    for (size_t i = 0; i < len; ++i) {
-      SET_STRING_ELT(out, i, mkChar(res[i]));
-    }
-  } else {
-    out = PROTECT(R_NilValue);
-  }
-  R_API_END();
-  UNPROTECT(1);
-  return out;
-}
diff --git a/ml-xgboost/R-package/src/xgboost_R.h b/ml-xgboost/R-package/src/xgboost_R.h
deleted file mode 100644
index be16ff9..0000000
--- a/ml-xgboost/R-package/src/xgboost_R.h
+++ /dev/null
@@ -1,247 +0,0 @@
-/*!
- * Copyright 2014 (c) by Contributors
- * \file xgboost_R.h
- * \author Tianqi Chen
- * \brief R wrapper of xgboost
- */
-#ifndef XGBOOST_R_H_ // NOLINT(*)
-#define XGBOOST_R_H_ // NOLINT(*)
-
-
-#include <Rinternals.h>
-#include <R_ext/Random.h>
-#include <Rmath.h>
-
-#include <xgboost/c_api.h>
-
-/*!
- * \brief check whether a handle is NULL
- * \param handle
- * \return whether it is null ptr
- */
-XGB_DLL SEXP XGCheckNullPtr_R(SEXP handle);
-
-/*!
- * \brief load a data matrix
- * \param fname name of the content
- * \param silent whether print messages
- * \return a loaded data matrix
- */
-XGB_DLL SEXP XGDMatrixCreateFromFile_R(SEXP fname, SEXP silent);
-
-/*!
- * \brief create matrix content from dense matrix
- * This assumes the matrix is stored in column major format
- * \param data R Matrix object
- * \param missing which value to represent missing value
- * \return created dmatrix
- */
-XGB_DLL SEXP XGDMatrixCreateFromMat_R(SEXP mat,
-                                      SEXP missing);
-/*!
- * \brief create a matrix content from CSC format
- * \param indptr pointer to column headers
- * \param indices row indices
- * \param data content of the data
- * \param num_row numer of rows (when it's set to 0, then guess from data)
- * \return created dmatrix
- */
-XGB_DLL SEXP XGDMatrixCreateFromCSC_R(SEXP indptr,
-                                      SEXP indices,
-                                      SEXP data,
-                                      SEXP num_row);
-
-/*!
- * \brief create a new dmatrix from sliced content of existing matrix
- * \param handle instance of data matrix to be sliced
- * \param idxset index set
- * \return a sliced new matrix
- */
-XGB_DLL SEXP XGDMatrixSliceDMatrix_R(SEXP handle, SEXP idxset);
-
-/*!
- * \brief load a data matrix into binary file
- * \param handle a instance of data matrix
- * \param fname file name
- * \param silent print statistics when saving
- * \return R_NilValue
- */
-XGB_DLL SEXP XGDMatrixSaveBinary_R(SEXP handle, SEXP fname, SEXP silent);
-
-/*!
- * \brief set information to dmatrix
- * \param handle a instance of data matrix
- * \param field field name, can be label, weight
- * \param array pointer to float vector
- * \return R_NilValue
- */
-XGB_DLL SEXP XGDMatrixSetInfo_R(SEXP handle, SEXP field, SEXP array);
-
-/*!
- * \brief get info vector from matrix
- * \param handle a instance of data matrix
- * \param field field name
- * \return info vector
- */
-XGB_DLL SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field);
-
-/*!
- * \brief return number of rows
- * \param handle an instance of data matrix
- */
-XGB_DLL SEXP XGDMatrixNumRow_R(SEXP handle);
-
-/*!
- * \brief return number of columns
- * \param handle an instance of data matrix
- */
-XGB_DLL SEXP XGDMatrixNumCol_R(SEXP handle);
-
-/*!
- * \brief create xgboost learner
- * \param dmats a list of dmatrix handles that will be cached
- */
-XGB_DLL SEXP XGBoosterCreate_R(SEXP dmats);
-
-/*!
- * \brief set parameters
- * \param handle handle
- * \param name  parameter name
- * \param val value of parameter
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterSetParam_R(SEXP handle, SEXP name, SEXP val);
-
-/*!
- * \brief update the model in one round using dtrain
- * \param handle handle
- * \param iter current iteration rounds
- * \param dtrain training data
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterUpdateOneIter_R(SEXP ext, SEXP iter, SEXP dtrain);
-
-/*!
- * \brief update the model, by directly specify gradient and second order gradient,
- *        this can be used to replace UpdateOneIter, to support customized loss function
- * \param handle handle
- * \param dtrain training data
- * \param grad gradient statistics
- * \param hess second order gradient statistics
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterBoostOneIter_R(SEXP handle, SEXP dtrain, SEXP grad, SEXP hess);
-
-/*!
- * \brief get evaluation statistics for xgboost
- * \param handle handle
- * \param iter current iteration rounds
- * \param dmats list of handles to dmatrices
- * \param evname name of evaluation
- * \return the string containing evaluation stats
- */
-XGB_DLL SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames);
-
-/*!
- * \brief make prediction based on dmat
- * \param handle handle
- * \param dmat data matrix
- * \param option_mask output_margin:1 predict_leaf:2
- * \param ntree_limit limit number of trees used in prediction
- * \param training Whether the prediction value is used for training.
- */
-XGB_DLL SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask,
-                                SEXP ntree_limit, SEXP training);
-/*!
- * \brief load model from existing file
- * \param handle handle
- * \param fname file name
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterLoadModel_R(SEXP handle, SEXP fname);
-
-/*!
- * \brief save model into existing file
- * \param handle handle
- * \param fname file name
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterSaveModel_R(SEXP handle, SEXP fname);
-
-/*!
- * \brief load model from raw array
- * \param handle handle
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw);
-
-/*!
- * \brief save model into R's raw array
- * \param handle handle
- * \return raw array
- */
-XGB_DLL SEXP XGBoosterModelToRaw_R(SEXP handle);
-
-/*!
- * \brief Save internal parameters as a JSON string
- * \param handle handle
- * \return JSON string
- */
-
-XGB_DLL SEXP XGBoosterSaveJsonConfig_R(SEXP handle);
-/*!
- * \brief Load the JSON string returnd by XGBoosterSaveJsonConfig_R
- * \param handle handle
- * \param value JSON string
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value);
-
-/*!
-  * \brief Memory snapshot based serialization method.  Saves everything states
-  *        into buffer.
-  * \param handle handle to booster
-  */
-XGB_DLL SEXP XGBoosterSerializeToBuffer_R(SEXP handle);
-
-/*!
- * \brief Memory snapshot based serialization method.  Loads the buffer returned
- *        from `XGBoosterSerializeToBuffer'.
- * \param handle handle to booster
- * \return raw byte array
- */
-XGB_DLL SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw);
-
-/*!
- * \brief dump model into a string
- * \param handle handle
- * \param fmap  name to fmap can be empty string
- * \param with_stats whether dump statistics of splits
- * \param dump_format the format to dump the model in
- */
-XGB_DLL SEXP XGBoosterDumpModel_R(SEXP handle, SEXP fmap, SEXP with_stats, SEXP dump_format);
-
-/*!
- * \brief get learner attribute value
- * \param handle handle
- * \param name  attribute name
- * \return character containing attribute value
- */
-XGB_DLL SEXP XGBoosterGetAttr_R(SEXP handle, SEXP name);
-
-/*!
- * \brief set learner attribute value
- * \param handle handle
- * \param name  attribute name
- * \param val attribute value; NULL value would delete an attribute
- * \return R_NilValue
- */
-XGB_DLL SEXP XGBoosterSetAttr_R(SEXP handle, SEXP name, SEXP val);
-
-/*!
- * \brief get the names of learner attributes
- * \return string vector containing attribute names
- */
-XGB_DLL SEXP XGBoosterGetAttrNames_R(SEXP handle);
-
-#endif  // XGBOOST_WRAPPER_R_H_ // NOLINT(*)
diff --git a/ml-xgboost/R-package/src/xgboost_assert.c b/ml-xgboost/R-package/src/xgboost_assert.c
deleted file mode 100644
index 4706a03..0000000
--- a/ml-xgboost/R-package/src/xgboost_assert.c
+++ /dev/null
@@ -1,26 +0,0 @@
-// Copyright (c) 2014 by Contributors
-#include <stdio.h>
-#include <stdarg.h>
-#include <Rinternals.h>
-
-// implements error handling
-void XGBoostAssert_R(int exp, const char *fmt, ...) {
-  char buf[1024];
-  if (exp == 0) {
-    va_list args;
-    va_start(args, fmt);
-    vsprintf(buf, fmt, args);
-    va_end(args);
-    error("AssertError:%s\n", buf);
-  }
-}
-void XGBoostCheck_R(int exp, const char *fmt, ...) {
-  char buf[1024];
-  if (exp == 0) {
-    va_list args;
-    va_start(args, fmt);
-    vsprintf(buf, fmt, args);
-    va_end(args);
-    error("%s\n", buf);
-  }
-}
diff --git a/ml-xgboost/R-package/src/xgboost_custom.cc b/ml-xgboost/R-package/src/xgboost_custom.cc
deleted file mode 100644
index 2387e72..0000000
--- a/ml-xgboost/R-package/src/xgboost_custom.cc
+++ /dev/null
@@ -1,69 +0,0 @@
-// Copyright (c) 2015 by Contributors
-// This file contains the customization implementations of R module
-// to change behavior of libxgboost
-
-#include <xgboost/logging.h>
-#include "../../src/common/random.h"
-#include "./xgboost_R.h"
-
-// redirect the messages to R's console.
-namespace dmlc {
-void CustomLogMessage::Log(const std::string& msg) {
-  Rprintf("%s\n", msg.c_str());
-}
-}  // namespace dmlc
-
-// implements rabit error handling.
-extern "C" {
-  void XGBoostAssert_R(int exp, const char *fmt, ...);
-  void XGBoostCheck_R(int exp, const char *fmt, ...);
-}
-
-namespace rabit {
-namespace utils {
-extern "C" {
-  void (*Printf)(const char *fmt, ...) = Rprintf;
-  void (*Assert)(int exp, const char *fmt, ...) = XGBoostAssert_R;
-  void (*Check)(int exp, const char *fmt, ...) = XGBoostCheck_R;
-  void (*Error)(const char *fmt, ...) = error;
-}
-}
-}
-
-namespace xgboost {
-ConsoleLogger::~ConsoleLogger() {
-  if (cur_verbosity_ == LogVerbosity::kIgnore ||
-      cur_verbosity_ <= global_verbosity_) {
-    dmlc::CustomLogMessage::Log(log_stream_.str());
-  }
-}
-TrackerLogger::~TrackerLogger() {
-  dmlc::CustomLogMessage::Log(log_stream_.str());
-}
-}  // namespace xgboost
-
-namespace xgboost {
-namespace common {
-
-// redirect the nath functions.
-bool CheckNAN(double v) {
-  return ISNAN(v);
-}
-#if !defined(XGBOOST_USE_CUDA)
-double LogGamma(double v) {
-  return lgammafn(v);
-}
-#endif  // !defined(XGBOOST_USE_CUDA)
-// customize random engine.
-void CustomGlobalRandomEngine::seed(CustomGlobalRandomEngine::result_type val) {
-  // ignore the seed
-}
-
-// use R's PRNG to replacd
-CustomGlobalRandomEngine::result_type
-CustomGlobalRandomEngine::operator()() {
-  return static_cast<result_type>(
-      std::floor(unif_rand() * CustomGlobalRandomEngine::max()));
-}
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/R-package/tests/testthat.R b/ml-xgboost/R-package/tests/testthat.R
deleted file mode 100644
index 53cc6ca..0000000
--- a/ml-xgboost/R-package/tests/testthat.R
+++ /dev/null
@@ -1,4 +0,0 @@
-library(testthat)
-library(xgboost)
-
-test_check("xgboost")
diff --git a/ml-xgboost/R-package/tests/testthat/test_basic.R b/ml-xgboost/R-package/tests/testthat/test_basic.R
deleted file mode 100644
index b23e4dd..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_basic.R
+++ /dev/null
@@ -1,384 +0,0 @@
-require(xgboost)
-
-context("basic functions")
-
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-set.seed(1994)
-
-# disable some tests for Win32
-windows_flag = .Platform$OS.type == "windows" &&
-               .Machine$sizeof.pointer != 8
-solaris_flag = (Sys.info()['sysname'] == "SunOS")
-
-test_that("train and predict binary classification", {
-  nrounds = 2
-  expect_output(
-    bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-                  eta = 1, nthread = 2, nrounds = nrounds, objective = "binary:logistic")
-  , "train-error")
-  expect_equal(class(bst), "xgb.Booster")
-  expect_equal(bst$niter, nrounds)
-  expect_false(is.null(bst$evaluation_log))
-  expect_equal(nrow(bst$evaluation_log), nrounds)
-  expect_lt(bst$evaluation_log[, min(train_error)], 0.03)
-
-  pred <- predict(bst, test$data)
-  expect_length(pred, 1611)
-
-  pred1 <- predict(bst, train$data, ntreelimit = 1)
-  expect_length(pred1, 6513)
-  err_pred1 <- sum((pred1 > 0.5) != train$label)/length(train$label)
-  err_log <- bst$evaluation_log[1, train_error]
-  expect_lt(abs(err_pred1 - err_log), 10e-6)
-})
-
-test_that("parameter validation works", {
-  p <- list(foo = "bar")
-  nrounds = 1
-  set.seed(1994)
-
-  d <- cbind(
-    x1 = rnorm(10),
-    x2 = rnorm(10),
-    x3 = rnorm(10))
-  y <- d[,"x1"] + d[,"x2"]^2 +
-    ifelse(d[,"x3"] > .5, d[,"x3"]^2, 2^d[,"x3"]) +
-    rnorm(10)
-  dtrain <- xgb.DMatrix(data=d, info = list(label=y))
-
-  correct <- function() {
-    params <- list(max_depth = 2, booster = "dart",
-                   rate_drop = 0.5, one_drop = TRUE,
-                   objective = "reg:squarederror")
-    xgb.train(params = params, data = dtrain, nrounds = nrounds)
-  }
-  expect_silent(correct())
-  incorrect <- function() {
-    params <- list(max_depth = 2, booster = "dart",
-                   rate_drop = 0.5, one_drop = TRUE,
-                   objective = "reg:squarederror",
-                   foo = "bar", bar = "foo")
-    output <- capture.output(
-      xgb.train(params = params, data = dtrain, nrounds = nrounds))
-    print(output)
-  }
-  expect_output(incorrect(), "bar, foo")
-})
-
-
-test_that("dart prediction works", {
-  nrounds = 32
-  set.seed(1994)
-
-  d <- cbind(
-    x1 = rnorm(100),
-    x2 = rnorm(100),
-    x3 = rnorm(100))
-  y <- d[,"x1"] + d[,"x2"]^2 +
-    ifelse(d[,"x3"] > .5, d[,"x3"]^2, 2^d[,"x3"]) +
-    rnorm(100)
-
-  set.seed(1994)
-  booster_by_xgboost <- xgboost(data = d, label = y, max_depth = 2, booster = "dart",
-                                rate_drop = 0.5, one_drop = TRUE,
-                                eta = 1, nthread = 2, nrounds = nrounds, objective = "reg:squarederror")
-  pred_by_xgboost_0 <- predict(booster_by_xgboost, newdata = d, ntreelimit = 0)
-  pred_by_xgboost_1 <- predict(booster_by_xgboost, newdata = d, ntreelimit = nrounds)
-  expect_true(all(matrix(pred_by_xgboost_0, byrow=TRUE) == matrix(pred_by_xgboost_1, byrow=TRUE)))
-
-  pred_by_xgboost_2 <- predict(booster_by_xgboost, newdata = d, training = TRUE)
-  expect_false(all(matrix(pred_by_xgboost_0, byrow=TRUE) == matrix(pred_by_xgboost_2, byrow=TRUE)))
-
-  set.seed(1994)
-  dtrain <- xgb.DMatrix(data=d, info = list(label=y))
-  booster_by_train <- xgb.train( params = list(
-                                   booster = "dart",
-                                   max_depth = 2,
-                                   eta = 1,
-                                   rate_drop = 0.5,
-                                   one_drop = TRUE,
-                                   nthread = 1,
-                                   tree_method= "exact",
-                                   objective = "reg:squarederror"
-                                 ),
-                                data = dtrain,
-                                nrounds = nrounds
-                                )
-  pred_by_train_0 <- predict(booster_by_train, newdata = dtrain, ntreelimit = 0)
-  pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, ntreelimit = nrounds)
-  pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
-
-  expect_true(all(matrix(pred_by_train_0, byrow=TRUE) == matrix(pred_by_xgboost_0, byrow=TRUE)))
-  expect_true(all(matrix(pred_by_train_1, byrow=TRUE) == matrix(pred_by_xgboost_1, byrow=TRUE)))
-  expect_true(all(matrix(pred_by_train_2, byrow=TRUE) == matrix(pred_by_xgboost_2, byrow=TRUE)))
-})
-
-test_that("train and predict softprob", {
-  lb <- as.numeric(iris$Species) - 1
-  set.seed(11)
-  expect_output(
-    bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-                   max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
-                   objective = "multi:softprob", num_class=3)
-  , "train-merror")
-  expect_false(is.null(bst$evaluation_log))
-  expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
-  expect_equal(bst$niter * 3, xgb.ntree(bst))
-  pred <- predict(bst, as.matrix(iris[, -5]))
-  expect_length(pred, nrow(iris) * 3)
-  # row sums add up to total probability of 1:
-  expect_equal(rowSums(matrix(pred, ncol=3, byrow=TRUE)), rep(1, nrow(iris)), tolerance = 1e-7)
-  # manually calculate error at the last iteration:
-  mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE)
-  expect_equal(as.numeric(t(mpred)), pred)
-  pred_labels <- max.col(mpred) - 1
-  err <- sum(pred_labels != lb)/length(lb)
-  expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
-  # manually calculate error at the 1st iteration:
-  mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 1)
-  pred_labels <- max.col(mpred) - 1
-  err <- sum(pred_labels != lb)/length(lb)
-  expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
-})
-
-test_that("train and predict softmax", {
-  lb <- as.numeric(iris$Species) - 1
-  set.seed(11)
-  expect_output(
-    bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-                   max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
-                   objective = "multi:softmax", num_class=3)
-  , "train-merror")
-  expect_false(is.null(bst$evaluation_log))
-  expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
-  expect_equal(bst$niter * 3, xgb.ntree(bst))
-
-  pred <- predict(bst, as.matrix(iris[, -5]))
-  expect_length(pred, nrow(iris))
-  err <- sum(pred != lb)/length(lb)
-  expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
-})
-
-test_that("train and predict RF", {
-  set.seed(11)
-  lb <- train$label
-  # single iteration
-  bst <- xgboost(data = train$data, label = lb, max_depth = 5,
-                 nthread = 2, nrounds = 1, objective = "binary:logistic",
-                 num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1)
-  expect_equal(bst$niter, 1)
-  expect_equal(xgb.ntree(bst), 20)
-
-  pred <- predict(bst, train$data)
-  pred_err <- sum((pred > 0.5) != lb)/length(lb)
-  expect_lt(abs(bst$evaluation_log[1, train_error] - pred_err), 10e-6)
-  #expect_lt(pred_err, 0.03)
-
-  pred <- predict(bst, train$data, ntreelimit = 20)
-  pred_err_20 <- sum((pred > 0.5) != lb)/length(lb)
-  expect_equal(pred_err_20, pred_err)
-
-  #pred <- predict(bst, train$data, ntreelimit = 1)
-  #pred_err_1 <- sum((pred > 0.5) != lb)/length(lb)
-  #expect_lt(pred_err, pred_err_1)
-  #expect_lt(pred_err, 0.08)
-})
-
-test_that("train and predict RF with softprob", {
-  lb <- as.numeric(iris$Species) - 1
-  nrounds <- 15
-  set.seed(11)
-  bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
-                 max_depth = 3, eta = 0.9, nthread = 2, nrounds = nrounds,
-                 objective = "multi:softprob", num_class=3, verbose = 0,
-                 num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5)
-  expect_equal(bst$niter, 15)
-  expect_equal(xgb.ntree(bst), 15*3*4)
-  # predict for all iterations:
-  pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE)
-  expect_equal(dim(pred), c(nrow(iris), 3))
-  pred_labels <- max.col(pred) - 1
-  err <- sum(pred_labels != lb)/length(lb)
-  expect_equal(bst$evaluation_log[nrounds, train_merror], err, tolerance = 5e-6)
-  # predict for 7 iterations and adjust for 4 parallel trees per iteration
-  pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE, ntreelimit = 7 * 4)
-  err <- sum((max.col(pred) - 1) != lb)/length(lb)
-  expect_equal(bst$evaluation_log[7, train_merror], err, tolerance = 5e-6)
-})
-
-test_that("use of multiple eval metrics works", {
-  expect_output(
-    bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-                  eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
-                  eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
-  , "train-error.*train-auc.*train-logloss")
-  expect_false(is.null(bst$evaluation_log))
-  expect_equal(dim(bst$evaluation_log), c(2, 4))
-  expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
-})
-
-
-test_that("training continuation works", {
-  dtrain <- xgb.DMatrix(train$data, label = train$label)
-  watchlist = list(train=dtrain)
-  param <- list(objective = "binary:logistic", max_depth = 2, eta = 1, nthread = 2)
-
-  # for the reference, use 4 iterations at once:
-  set.seed(11)
-  bst <- xgb.train(param, dtrain, nrounds = 4, watchlist, verbose = 0)
-  # first two iterations:
-  set.seed(11)
-  bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0)
-  # continue for two more:
-  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1)
-  if (!windows_flag && !solaris_flag)
-    expect_equal(bst$raw, bst2$raw)
-  expect_false(is.null(bst2$evaluation_log))
-  expect_equal(dim(bst2$evaluation_log), c(4, 2))
-  expect_equal(bst2$evaluation_log, bst$evaluation_log)
-  # test continuing from raw model data
-  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1$raw)
-  if (!windows_flag && !solaris_flag)
-    expect_equal(bst$raw, bst2$raw)
-  expect_equal(dim(bst2$evaluation_log), c(2, 2))
-  # test continuing from a model in file
-  xgb.save(bst1, "xgboost.model")
-  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = "xgboost.model")
-  if (!windows_flag && !solaris_flag)
-    expect_equal(bst$raw, bst2$raw)
-  expect_equal(dim(bst2$evaluation_log), c(2, 2))
-})
-
-test_that("model serialization works", {
-  out_path <- "model_serialization"
-  dtrain <- xgb.DMatrix(train$data, label = train$label)
-  watchlist = list(train=dtrain)
-  param <- list(objective = "binary:logistic")
-  booster <- xgb.train(param, dtrain, nrounds = 4, watchlist)
-  raw <- xgb.serialize(booster)
-  saveRDS(raw, out_path)
-  raw <- readRDS(out_path)
-
-  loaded <- xgb.unserialize(raw)
-  raw_from_loaded <- xgb.serialize(loaded)
-  expect_equal(raw, raw_from_loaded)
-  file.remove(out_path)
-})
-
-test_that("xgb.cv works", {
-  set.seed(11)
-  expect_output(
-    cv <- xgb.cv(data = train$data, label = train$label, max_depth = 2, nfold = 5,
-                 eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
-                 verbose=TRUE)
-  , "train-error:")
-  expect_is(cv, 'xgb.cv.synchronous')
-  expect_false(is.null(cv$evaluation_log))
-  expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
-  expect_lt(cv$evaluation_log[, min(test_error_std)], 0.008)
-  expect_equal(cv$niter, 2)
-  expect_false(is.null(cv$folds) && is.list(cv$folds))
-  expect_length(cv$folds, 5)
-  expect_false(is.null(cv$params) && is.list(cv$params))
-  expect_false(is.null(cv$callbacks))
-  expect_false(is.null(cv$call))
-})
-
-test_that("xgb.cv works with stratified folds", {
-  dtrain <- xgb.DMatrix(train$data, label = train$label)
-  set.seed(314159)
-  cv <- xgb.cv(data = dtrain, max_depth = 2, nfold = 5,
-               eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
-               verbose=TRUE, stratified = FALSE)
-  set.seed(314159)
-  cv2 <- xgb.cv(data = dtrain, max_depth = 2, nfold = 5,
-                eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
-                verbose=TRUE, stratified = TRUE)
-  # Stratified folds should result in a different evaluation logs
-  expect_true(all(cv$evaluation_log[, test_error_mean] != cv2$evaluation_log[, test_error_mean]))
-})
-
-test_that("train and predict with non-strict classes", {
-  # standard dense matrix input
-  train_dense <- as.matrix(train$data)
-  bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
-                 eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
-  pr0 <- predict(bst, train_dense)
-
-  # dense matrix-like input of non-matrix class
-  class(train_dense) <- 'shmatrix'
-  expect_true(is.matrix(train_dense))
-  expect_error(
-    bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
-                   eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
-    , regexp = NA)
-  expect_error(pr <- predict(bst, train_dense), regexp = NA)
-  expect_equal(pr0, pr)
-
-  # dense matrix-like input of non-matrix class with some inheritance
-  class(train_dense) <- c('pphmatrix','shmatrix')
-  expect_true(is.matrix(train_dense))
-  expect_error(
-    bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
-                   eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
-    , regexp = NA)
-  expect_error(pr <- predict(bst, train_dense), regexp = NA)
-  expect_equal(pr0, pr)
-
-  # when someone inhertis from xgb.Booster, it should still be possible to use it as xgb.Booster
-  class(bst) <- c('super.Booster', 'xgb.Booster')
-  expect_error(pr <- predict(bst, train_dense), regexp = NA)
-  expect_equal(pr0, pr)
-})
-
-test_that("max_delta_step works", {
-  dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-  watchlist <- list(train = dtrain)
-  param <- list(objective = "binary:logistic", eval_metric="logloss", max_depth = 2, nthread = 2, eta = 0.5)
-  nrounds = 5
-  # model with no restriction on max_delta_step
-  bst1 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1)
-  # model with restricted max_delta_step
-  bst2 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1, max_delta_step = 1)
-  # the no-restriction model is expected to have consistently lower loss during the initial interations
-  expect_true(all(bst1$evaluation_log$train_logloss < bst2$evaluation_log$train_logloss))
-  expect_lt(mean(bst1$evaluation_log$train_logloss)/mean(bst2$evaluation_log$train_logloss), 0.8)
-})
-
-test_that("colsample_bytree works", {
-  # Randomly generate data matrix by sampling from uniform distribution [-1, 1]
-  set.seed(1)
-  train_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
-  train_y <- as.numeric(rowSums(train_x) > 0)
-  test_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
-  test_y <- as.numeric(rowSums(test_x) > 0)
-  colnames(train_x) <- paste0("Feature_", sprintf("%03d", 1:100))
-  colnames(test_x) <- paste0("Feature_", sprintf("%03d", 1:100))
-  dtrain <- xgb.DMatrix(train_x, label = train_y)
-  dtest <- xgb.DMatrix(test_x, label = test_y)
-  watchlist <- list(train = dtrain, eval = dtest)
-  ## Use colsample_bytree = 0.01, so that roughly one out of 100 features is chosen for
-  ## each tree
-  param <- list(max_depth = 2, eta = 0, nthread = 2,
-                colsample_bytree = 0.01, objective = "binary:logistic",
-                eval_metric = "auc")
-  set.seed(2)
-  bst <- xgb.train(param, dtrain, nrounds = 100, watchlist, verbose = 0)
-  xgb.importance(model = bst)
-  # If colsample_bytree works properly, a variety of features should be used
-  # in the 100 trees
-  expect_gte(nrow(xgb.importance(model = bst)), 30)
-})
-
-test_that("Configuration works", {
-  bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-                 eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
-                 eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
-  config <- xgb.config(bst)
-  xgb.config(bst) <- config
-  reloaded_config <- xgb.config(bst)
-  expect_equal(config, reloaded_config);
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_callbacks.R b/ml-xgboost/R-package/tests/testthat/test_callbacks.R
deleted file mode 100644
index e7230d1..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_callbacks.R
+++ /dev/null
@@ -1,330 +0,0 @@
-# More specific testing of callbacks
-
-require(xgboost)
-require(data.table)
-
-context("callbacks")
-
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-
-# add some label noise for early stopping tests
-add.noise <- function(label, frac) {
-  inoise <- sample(length(label), length(label) * frac)
-  label[inoise] <- !label[inoise]
-  label
-}
-set.seed(11)
-ltrain <- add.noise(train$label, 0.2)
-ltest <- add.noise(test$label, 0.2)
-dtrain <- xgb.DMatrix(train$data, label = ltrain)
-dtest <- xgb.DMatrix(test$data, label = ltest)
-watchlist = list(train=dtrain, test=dtest)
-
-
-err <- function(label, pr) sum((pr > 0.5) != label)/length(label)
-
-param <- list(objective = "binary:logistic", max_depth = 2, nthread = 2)
-
-
-test_that("cb.print.evaluation works as expected", {
-
-  bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
-  bst_evaluation_err <- NULL
-  begin_iteration <- 1
-  end_iteration <- 7
-
-  f0 <- cb.print.evaluation(period=0)
-  f1 <- cb.print.evaluation(period=1)
-  f5 <- cb.print.evaluation(period=5)
-
-  expect_false(is.null(attr(f1, 'call')))
-  expect_equal(attr(f1, 'name'), 'cb.print.evaluation')
-
-  iteration <- 1
-  expect_silent(f0())
-  expect_output(f1(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
-  expect_output(f5(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
-  expect_null(f1())
-
-  iteration <- 2
-  expect_output(f1(), "\\[2\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
-  expect_silent(f5())
-
-  iteration <- 7
-  expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
-  expect_output(f5(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
-
-  bst_evaluation_err  <- c('train-auc'=0.1, 'test-auc'=0.2)
-  expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\\+0.100000\ttest-auc:0.800000\\+0.200000")
-})
-
-test_that("cb.evaluation.log works as expected", {
-
-  bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
-  bst_evaluation_err <- NULL
-
-  evaluation_log <- list()
-  f <- cb.evaluation.log()
-
-  expect_false(is.null(attr(f, 'call')))
-  expect_equal(attr(f, 'name'), 'cb.evaluation.log')
-
-  iteration <- 1
-  expect_silent(f())
-  expect_equal(evaluation_log,
-               list(c(iter=1, bst_evaluation)))
-  iteration <- 2
-  expect_silent(f())
-  expect_equal(evaluation_log,
-               list(c(iter=1, bst_evaluation), c(iter=2, bst_evaluation)))
-  expect_silent(f(finalize = TRUE))
-  expect_equal(evaluation_log,
-               data.table(iter=1:2, train_auc=c(0.9,0.9), test_auc=c(0.8,0.8)))
-
-  bst_evaluation_err  <- c('train-auc'=0.1, 'test-auc'=0.2)
-  evaluation_log <- list()
-  f <- cb.evaluation.log()
-
-  iteration <- 1
-  expect_silent(f())
-  expect_equal(evaluation_log,
-               list(c(iter=1, c(bst_evaluation, bst_evaluation_err))))
-  iteration <- 2
-  expect_silent(f())
-  expect_equal(evaluation_log,
-               list(c(iter=1, c(bst_evaluation, bst_evaluation_err)),
-                    c(iter=2, c(bst_evaluation, bst_evaluation_err))))
-  expect_silent(f(finalize = TRUE))
-  expect_equal(evaluation_log,
-               data.table(iter=1:2,
-                          train_auc_mean=c(0.9,0.9), train_auc_std=c(0.1,0.1),
-                          test_auc_mean=c(0.8,0.8), test_auc_std=c(0.2,0.2)))
-})
-
-
-param <- list(objective = "binary:logistic", max_depth = 4, nthread = 2)
-
-test_that("can store evaluation_log without printing", {
-  expect_silent(
-    bst <- xgb.train(param, dtrain, nrounds = 10, watchlist, eta = 1, verbose = 0)
-  )
-  expect_false(is.null(bst$evaluation_log))
-  expect_false(is.null(bst$evaluation_log$train_error))
-  expect_lt(bst$evaluation_log[, min(train_error)], 0.2)
-})
-
-test_that("cb.reset.parameters works as expected", {
-
-  # fixed eta
-  set.seed(111)
-  bst0 <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 0.9, verbose = 0)
-  expect_false(is.null(bst0$evaluation_log))
-  expect_false(is.null(bst0$evaluation_log$train_error))
-
-  # same eta but re-set as a vector parameter in the callback
-  set.seed(111)
-  my_par <- list(eta = c(0.9, 0.9))
-  bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
-                    callbacks = list(cb.reset.parameters(my_par)))
-  expect_false(is.null(bst1$evaluation_log$train_error))
-  expect_equal(bst0$evaluation_log$train_error,
-               bst1$evaluation_log$train_error)
-
-  # same eta but re-set via a function in the callback
-  set.seed(111)
-  my_par <- list(eta = function(itr, itr_end) 0.9)
-  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
-                    callbacks = list(cb.reset.parameters(my_par)))
-  expect_false(is.null(bst2$evaluation_log$train_error))
-  expect_equal(bst0$evaluation_log$train_error,
-               bst2$evaluation_log$train_error)
-
-  # different eta re-set as a vector parameter in the callback
-  set.seed(111)
-  my_par <- list(eta = c(0.6, 0.5))
-  bst3 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
-                    callbacks = list(cb.reset.parameters(my_par)))
-  expect_false(is.null(bst3$evaluation_log$train_error))
-  expect_false(all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
-
-  # resetting multiple parameters at the same time runs with no error
-  my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
-  expect_error(
-    bst4 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
-                      callbacks = list(cb.reset.parameters(my_par)))
-  , NA) # NA = no error
-  # CV works as well
-  expect_error(
-    bst4 <- xgb.cv(param, dtrain, nfold = 2, nrounds = 2, verbose = 0,
-                   callbacks = list(cb.reset.parameters(my_par)))
-  , NA) # NA = no error
-
-  # expect no learning with 0 learning rate
-  my_par <- list(eta = c(0., 0.))
-  bstX <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
-                    callbacks = list(cb.reset.parameters(my_par)))
-  expect_false(is.null(bstX$evaluation_log$train_error))
-  er <- unique(bstX$evaluation_log$train_error)
-  expect_length(er, 1)
-  expect_gt(er, 0.4)
-})
-
-test_that("cb.save.model works as expected", {
-  files <- c('xgboost_01.model', 'xgboost_02.model', 'xgboost.model')
-  for (f in files) if (file.exists(f)) file.remove(f)
-
-  bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
-                   save_period = 1, save_name = "xgboost_%02d.model")
-  expect_true(file.exists('xgboost_01.model'))
-  expect_true(file.exists('xgboost_02.model'))
-  b1 <- xgb.load('xgboost_01.model')
-  expect_equal(xgb.ntree(b1), 1)
-  b2 <- xgb.load('xgboost_02.model')
-  expect_equal(xgb.ntree(b2), 2)
-
-  xgb.config(b2) <- xgb.config(bst)
-  expect_equal(xgb.config(bst), xgb.config(b2))
-  expect_equal(bst$raw, b2$raw)
-
-  # save_period = 0 saves the last iteration's model
-  bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
-                   save_period = 0)
-  expect_true(file.exists('xgboost.model'))
-  b2 <- xgb.load('xgboost.model')
-  xgb.config(b2) <- xgb.config(bst)
-  expect_equal(bst$raw, b2$raw)
-
-  for (f in files) if (file.exists(f)) file.remove(f)
-})
-
-test_that("early stopping xgb.train works", {
-  set.seed(11)
-  expect_output(
-    bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
-                     early_stopping_rounds = 3, maximize = FALSE)
-  , "Stopping. Best iteration")
-  expect_false(is.null(bst$best_iteration))
-  expect_lt(bst$best_iteration, 19)
-  expect_equal(bst$best_iteration, bst$best_ntreelimit)
-
-  pred <- predict(bst, dtest)
-  expect_equal(length(pred), 1611)
-  err_pred <- err(ltest, pred)
-  err_log <- bst$evaluation_log[bst$best_iteration, test_error]
-  expect_equal(err_log, err_pred, tolerance = 5e-6)
-
-  set.seed(11)
-  expect_silent(
-    bst0 <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
-                      early_stopping_rounds = 3, maximize = FALSE, verbose = 0)
-  )
-  expect_equal(bst$evaluation_log, bst0$evaluation_log)
-
-  xgb.save(bst, "model.bin")
-  loaded <- xgb.load("model.bin")
-
-  expect_false(is.null(loaded$best_iteration))
-  expect_equal(loaded$best_iteration, bst$best_ntreelimit)
-  expect_equal(loaded$best_ntreelimit, bst$best_ntreelimit)
-
-  file.remove("model.bin")
-})
-
-test_that("early stopping using a specific metric works", {
-  set.seed(11)
-  expect_output(
-    bst <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.6,
-                     eval_metric="logloss", eval_metric="auc",
-                     callbacks = list(cb.early.stop(stopping_rounds = 3, maximize = FALSE,
-                                                    metric_name = 'test_logloss')))
-  , "Stopping. Best iteration")
-  expect_false(is.null(bst$best_iteration))
-  expect_lt(bst$best_iteration, 19)
-  expect_equal(bst$best_iteration, bst$best_ntreelimit)
-
-  pred <- predict(bst, dtest, ntreelimit = bst$best_ntreelimit)
-  expect_equal(length(pred), 1611)
-  logloss_pred <- sum(-ltest * log(pred) - (1 - ltest) * log(1 - pred)) / length(ltest)
-  logloss_log <- bst$evaluation_log[bst$best_iteration, test_logloss]
-  expect_equal(logloss_log, logloss_pred, tolerance = 1e-5)
-})
-
-test_that("early stopping xgb.cv works", {
-  set.seed(11)
-  expect_output(
-    cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.3, nrounds = 20,
-                 early_stopping_rounds = 3, maximize = FALSE)
-  , "Stopping. Best iteration")
-  expect_false(is.null(cv$best_iteration))
-  expect_lt(cv$best_iteration, 19)
-  expect_equal(cv$best_iteration, cv$best_ntreelimit)
-  # the best error is min error:
-  expect_true(cv$evaluation_log[, test_error_mean[cv$best_iteration] == min(test_error_mean)])
-})
-
-test_that("prediction in xgb.cv works", {
-  set.seed(11)
-  nrounds = 4
-  cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0)
-  expect_false(is.null(cv$evaluation_log))
-  expect_false(is.null(cv$pred))
-  expect_length(cv$pred, nrow(train$data))
-  err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
-  err_log <- cv$evaluation_log[nrounds, test_error_mean]
-  expect_equal(err_pred, err_log, tolerance = 1e-6)
-
-  # save CV models
-  set.seed(11)
-  cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0,
-                callbacks = list(cb.cv.predict(save_models = TRUE)))
-  expect_equal(cv$evaluation_log, cvx$evaluation_log)
-  expect_length(cvx$models, 5)
-  expect_true(all(sapply(cvx$models, class) == 'xgb.Booster'))
-})
-
-test_that("prediction in xgb.cv works for gblinear too", {
-  set.seed(11)
-  p <- list(booster = 'gblinear', objective = "reg:logistic", nthread = 2)
-  cv <- xgb.cv(p, dtrain, nfold = 5, eta = 0.5, nrounds = 2, prediction = TRUE, verbose = 0)
-  expect_false(is.null(cv$evaluation_log))
-  expect_false(is.null(cv$pred))
-  expect_length(cv$pred, nrow(train$data))
-})
-
-test_that("prediction in early-stopping xgb.cv works", {
-  set.seed(11)
-  expect_output(
-    cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.1, nrounds = 20,
-                 early_stopping_rounds = 5, maximize = FALSE, stratified = FALSE,
-                 prediction = TRUE)
-  , "Stopping. Best iteration")
-
-  expect_false(is.null(cv$best_iteration))
-  expect_lt(cv$best_iteration, 19)
-  expect_false(is.null(cv$evaluation_log))
-  expect_false(is.null(cv$pred))
-  expect_length(cv$pred, nrow(train$data))
-
-  err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
-  err_log <- cv$evaluation_log[cv$best_iteration, test_error_mean]
-  expect_equal(err_pred, err_log, tolerance = 1e-6)
-  err_log_last <- cv$evaluation_log[cv$niter, test_error_mean]
-  expect_gt(abs(err_pred - err_log_last), 1e-4)
-})
-
-test_that("prediction in xgb.cv for softprob works", {
-  lb <- as.numeric(iris$Species) - 1
-  set.seed(11)
-  expect_warning(
-    cv <- xgb.cv(data = as.matrix(iris[, -5]), label = lb, nfold = 4,
-                 eta = 0.5, nrounds = 5, max_depth = 3, nthread = 2,
-                 subsample = 0.8, gamma = 2, verbose = 0,
-                 prediction = TRUE, objective = "multi:softprob", num_class = 3)
-  , NA)
-  expect_false(is.null(cv$pred))
-  expect_equal(dim(cv$pred), c(nrow(iris), 3))
-  expect_lt(diff(range(rowSums(cv$pred))), 1e-6)
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_custom_objective.R b/ml-xgboost/R-package/tests/testthat/test_custom_objective.R
deleted file mode 100644
index ab01147..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_custom_objective.R
+++ /dev/null
@@ -1,77 +0,0 @@
-context('Test models with custom objective')
-
-require(xgboost)
-
-set.seed(1994)
-
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-watchlist <- list(eval = dtest, train = dtrain)
-
-logregobj <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  preds <- 1 / (1 + exp(-preds))
-  grad <- preds - labels
-  hess <- preds * (1 - preds)
-  return(list(grad = grad, hess = hess))
-}
-
-evalerror <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
-  return(list(metric = "error", value = err))
-}
-
-param <- list(max_depth=2, eta=1, nthread = 2,
-              objective=logregobj, eval_metric=evalerror)
-num_round <- 2
-
-test_that("custom objective works", {
-  bst <- xgb.train(param, dtrain, num_round, watchlist)
-  expect_equal(class(bst), "xgb.Booster")
-  expect_false(is.null(bst$evaluation_log))
-  expect_false(is.null(bst$evaluation_log$eval_error))
-  expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
-})
-
-test_that("custom objective in CV works", {
-  cv <- xgb.cv(param, dtrain, num_round, nfold=10, verbose=FALSE)
-  expect_false(is.null(cv$evaluation_log))
-  expect_equal(dim(cv$evaluation_log), c(2, 5))
-  expect_lt(cv$evaluation_log[num_round, test_error_mean], 0.03)
-})
-
-test_that("custom objective using DMatrix attr works", {
-
-  attr(dtrain, 'label') <- getinfo(dtrain, 'label')
-
-  logregobjattr <- function(preds, dtrain) {
-    labels <- attr(dtrain, 'label')
-    preds <- 1 / (1 + exp(-preds))
-    grad <- preds - labels
-    hess <- preds * (1 - preds)
-    return(list(grad = grad, hess = hess))
-  }
-  param$objective = logregobjattr
-  bst <- xgb.train(param, dtrain, num_round, watchlist)
-  expect_equal(class(bst), "xgb.Booster")
-})
-
-test_that("custom objective with multi-class works", {
-  data = as.matrix(iris[, -5])
-  label =  as.numeric(iris$Species) - 1
-  dtrain <- xgb.DMatrix(data = data, label = label)
-  nclasses <- 3
-
-  fake_softprob <- function(preds, dtrain) {
-    expect_true(all(matrix(preds) == 0.5))
-    grad <- rnorm(dim(as.matrix(preds))[1])
-    expect_equal(dim(data)[1] * nclasses, dim(as.matrix(preds))[1])
-    hess <- rnorm(dim(as.matrix(preds))[1])
-    return (list(grad = grad, hess = hess))
-  }
-  param$objective = fake_softprob
-  bst <- xgb.train(param, dtrain, 1, num_class=nclasses)
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_dmatrix.R b/ml-xgboost/R-package/tests/testthat/test_dmatrix.R
deleted file mode 100644
index c063589..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_dmatrix.R
+++ /dev/null
@@ -1,117 +0,0 @@
-require(xgboost)
-require(Matrix)
-
-context("testing xgb.DMatrix functionality")
-
-data(agaricus.test, package='xgboost')
-test_data <- agaricus.test$data[1:100,]
-test_label <- agaricus.test$label[1:100]
-
-test_that("xgb.DMatrix: basic construction", {
-  # from sparse matrix
-  dtest1 <- xgb.DMatrix(test_data, label=test_label)
-
-  # from dense matrix
-  dtest2 <- xgb.DMatrix(as.matrix(test_data), label=test_label)
-  expect_equal(getinfo(dtest1, 'label'), getinfo(dtest2, 'label'))
-  expect_equal(dim(dtest1), dim(dtest2))
-
-  #from dense integer matrix
-  int_data <- as.matrix(test_data)
-  storage.mode(int_data) <- "integer"
-  dtest3 <- xgb.DMatrix(int_data, label=test_label)
-  expect_equal(dim(dtest1), dim(dtest3))
-})
-
-test_that("xgb.DMatrix: saving, loading", {
-  # save to a local file
-  dtest1 <- xgb.DMatrix(test_data, label=test_label)
-  tmp_file <- tempfile('xgb.DMatrix_')
-  expect_true(xgb.DMatrix.save(dtest1, tmp_file))
-  # read from a local file
-  expect_output(dtest3 <- xgb.DMatrix(tmp_file), "entries loaded from")
-  expect_output(dtest3 <- xgb.DMatrix(tmp_file, silent = TRUE), NA)
-  unlink(tmp_file)
-  expect_equal(getinfo(dtest1, 'label'), getinfo(dtest3, 'label'))
-
-  # from a libsvm text file
-  tmp <- c("0 1:1 2:1","1 3:1","0 1:1")
-  tmp_file <- 'tmp.libsvm'
-  writeLines(tmp, tmp_file)
-  dtest4 <- xgb.DMatrix(tmp_file, silent = TRUE)
-  expect_equal(dim(dtest4), c(3, 4))
-  expect_equal(getinfo(dtest4, 'label'), c(0,1,0))
-  unlink(tmp_file)
-})
-
-test_that("xgb.DMatrix: getinfo & setinfo", {
-  dtest <- xgb.DMatrix(test_data)
-  expect_true(setinfo(dtest, 'label', test_label))
-  labels <- getinfo(dtest, 'label')
-  expect_equal(test_label, getinfo(dtest, 'label'))
-
-  expect_true(setinfo(dtest, 'label_lower_bound', test_label))
-  expect_equal(test_label, getinfo(dtest, 'label_lower_bound'))
-
-  expect_true(setinfo(dtest, 'label_upper_bound', test_label))
-  expect_equal(test_label, getinfo(dtest, 'label_upper_bound'))
-
-  expect_true(length(getinfo(dtest, 'weight')) == 0)
-  expect_true(length(getinfo(dtest, 'base_margin')) == 0)
-
-  expect_true(setinfo(dtest, 'weight', test_label))
-  expect_true(setinfo(dtest, 'base_margin', test_label))
-  expect_true(setinfo(dtest, 'group', c(50,50)))
-  expect_error(setinfo(dtest, 'group', test_label))
-
-  # providing character values will give a warning
-  expect_warning(setinfo(dtest, 'weight', rep('a', nrow(test_data))))
-
-  # any other label should error
-  expect_error(setinfo(dtest, 'asdf', test_label))
-})
-
-test_that("xgb.DMatrix: slice, dim", {
-  dtest <- xgb.DMatrix(test_data, label=test_label)
-  expect_equal(dim(dtest), dim(test_data))
-  dsub1 <- slice(dtest, 1:42)
-  expect_equal(nrow(dsub1), 42)
-  expect_equal(ncol(dsub1), ncol(test_data))
-
-  dsub2 <- dtest[1:42,]
-  expect_equal(dim(dtest), dim(test_data))
-  expect_equal(getinfo(dsub1, 'label'), getinfo(dsub2, 'label'))
-})
-
-test_that("xgb.DMatrix: slice, trailing empty rows", {
-  data(agaricus.train, package='xgboost')
-  train_data <- agaricus.train$data
-  train_label <- agaricus.train$label
-  dtrain <- xgb.DMatrix(data=train_data, label=train_label)
-  slice(dtrain, 6513L)
-  train_data[6513, ] <- 0
-  dtrain <- xgb.DMatrix(data=train_data, label=train_label)
-  slice(dtrain, 6513L)
-  expect_equal(nrow(dtrain), 6513)
-})
-
-test_that("xgb.DMatrix: colnames", {
-  dtest <- xgb.DMatrix(test_data, label=test_label)
-  expect_equal(colnames(dtest), colnames(test_data))
-  expect_error( colnames(dtest) <- 'asdf')
-  new_names <- make.names(1:ncol(test_data))
-  expect_silent( colnames(dtest) <- new_names)
-  expect_equal(colnames(dtest), new_names)
-  expect_silent(colnames(dtest) <- NULL)
-  expect_null(colnames(dtest))
-})
-
-test_that("xgb.DMatrix: nrow is correct for a very sparse matrix", {
-  set.seed(123)
-  nr <- 1000
-  x <- rsparsematrix(nr, 100, density=0.0005)
-  # we want it very sparse, so that last rows are empty
-  expect_lt(max(x@i), nr)
-  dtest <- xgb.DMatrix(x)
-  expect_equal(dim(dtest), dim(x))
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_gc_safety.R b/ml-xgboost/R-package/tests/testthat/test_gc_safety.R
deleted file mode 100644
index b90f0f4..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_gc_safety.R
+++ /dev/null
@@ -1,15 +0,0 @@
-require(xgboost)
-
-context("Garbage Collection Safety Check")
-
-test_that("train and prediction when gctorture is on", {
-  data(agaricus.train, package='xgboost')
-  data(agaricus.test, package='xgboost')
-  train <- agaricus.train
-  test <- agaricus.test
-  gctorture(TRUE)
-  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
-  eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-  pred <- predict(bst, test$data)
-  gctorture(FALSE)
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_glm.R b/ml-xgboost/R-package/tests/testthat/test_glm.R
deleted file mode 100644
index 9b4aa73..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_glm.R
+++ /dev/null
@@ -1,48 +0,0 @@
-context('Test generalized linear models')
-
-require(xgboost)
-
-test_that("gblinear works", {
-  data(agaricus.train, package='xgboost')
-  data(agaricus.test, package='xgboost')
-  dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-  dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-  param <- list(objective = "binary:logistic", booster = "gblinear",
-                nthread = 2, eta = 0.8, alpha = 0.0001, lambda = 0.0001)
-  watchlist <- list(eval = dtest, train = dtrain)
-
-  n <- 5         # iterations
-  ERR_UL <- 0.005 # upper limit for the test set error
-  VERB <- 0      # chatterbox switch
-
-  param$updater = 'shotgun'
-  bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'shuffle')
-  ypred <- predict(bst, dtest)
-  expect_equal(length(getinfo(dtest, 'label')), 1611)
-  expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
-
-  bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'cyclic',
-                   callbacks = list(cb.gblinear.history()))
-  expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
-  h <- xgb.gblinear.history(bst)
-  expect_equal(dim(h), c(n, ncol(dtrain) + 1))
-  expect_is(h, "matrix")
-
-  param$updater = 'coord_descent'
-  bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'cyclic')
-  expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
-
-  bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'shuffle')
-  expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
-
-  bst <- xgb.train(param, dtrain, 2, watchlist, verbose = VERB, feature_selector = 'greedy')
-  expect_lt(bst$evaluation_log$eval_error[2], ERR_UL)
-
-  bst <- xgb.train(param, dtrain, n, watchlist, verbose = VERB, feature_selector = 'thrifty',
-                   top_k = 50, callbacks = list(cb.gblinear.history(sparse = TRUE)))
-  expect_lt(bst$evaluation_log$eval_error[n], ERR_UL)
-  h <- xgb.gblinear.history(bst)
-  expect_equal(dim(h), c(n, ncol(dtrain) + 1))
-  expect_s4_class(h, "dgCMatrix")
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_helpers.R b/ml-xgboost/R-package/tests/testthat/test_helpers.R
deleted file mode 100644
index 5c14d53..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_helpers.R
+++ /dev/null
@@ -1,376 +0,0 @@
-context('Test helper functions')
-
-require(xgboost)
-require(data.table)
-require(Matrix)
-require(vcd, quietly = TRUE)
-
-float_tolerance = 5e-6
-
-# disable some tests for 32-bit environment
-flag_32bit = .Machine$sizeof.pointer != 8
-
-set.seed(1982)
-data(Arthritis)
-df <- data.table(Arthritis, keep.rownames = F)
-df[,AgeDiscret := as.factor(round(Age / 10,0))]
-df[,AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
-df[,ID := NULL]
-sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
-label <- df[, ifelse(Improved == "Marked", 1, 0)]
-
-# binary
-nrounds <- 12
-bst.Tree <- xgboost(data = sparse_matrix, label = label, max_depth = 9,
-                    eta = 1, nthread = 2, nrounds = nrounds, verbose = 0,
-                    objective = "binary:logistic", booster = "gbtree")
-
-bst.GLM <- xgboost(data = sparse_matrix, label = label,
-                   eta = 1, nthread = 1, nrounds = nrounds, verbose = 0,
-                   objective = "binary:logistic", booster = "gblinear")
-
-feature.names <- colnames(sparse_matrix)
-
-# multiclass
-mlabel <- as.numeric(iris$Species) - 1
-nclass <- 3
-mbst.Tree <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
-                     max_depth = 3, eta = 0.5, nthread = 2, nrounds = nrounds,
-                     objective = "multi:softprob", num_class = nclass, base_score = 0)
-
-mbst.GLM <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
-                    booster = "gblinear", eta = 0.1, nthread = 1, nrounds = nrounds,
-                    objective = "multi:softprob", num_class = nclass, base_score = 0)
-
-
-test_that("xgb.dump works", {
-  if (!flag_32bit)
-    expect_length(xgb.dump(bst.Tree), 200)
-  dump_file = file.path(tempdir(), 'xgb.model.dump')
-  expect_true(xgb.dump(bst.Tree, dump_file, with_stats = T))
-  expect_true(file.exists(dump_file))
-  expect_gt(file.size(dump_file), 8000)
-
-  # JSON format
-  dmp <- xgb.dump(bst.Tree, dump_format = "json")
-  expect_length(dmp, 1)
-  if (!flag_32bit)
-    expect_length(grep('nodeid', strsplit(dmp, '\n')[[1]]), 188)
-})
-
-test_that("xgb.dump works for gblinear", {
-  expect_length(xgb.dump(bst.GLM), 14)
-  # also make sure that it works properly for a sparse model where some coefficients
-  # are 0 from setting large L1 regularization:
-  bst.GLM.sp <- xgboost(data = sparse_matrix, label = label, eta = 1, nthread = 2, nrounds = 1,
-                        alpha=2, objective = "binary:logistic", booster = "gblinear")
-  d.sp <- xgb.dump(bst.GLM.sp)
-  expect_length(d.sp, 14)
-  expect_gt(sum(d.sp == "0"), 0)
-
-  # JSON format
-  dmp <- xgb.dump(bst.GLM.sp, dump_format = "json")
-  expect_length(dmp, 1)
-  expect_length(grep('\\d', strsplit(dmp, '\n')[[1]]), 11)
-})
-
-test_that("predict leafs works", {
-  # no error for gbtree
-  expect_error(pred_leaf <- predict(bst.Tree, sparse_matrix, predleaf = TRUE), regexp = NA)
-  expect_equal(dim(pred_leaf), c(nrow(sparse_matrix), nrounds))
-  # error for gblinear
-  expect_error(predict(bst.GLM, sparse_matrix, predleaf = TRUE))
-})
-
-test_that("predict feature contributions works", {
-  # gbtree binary classifier
-  expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE), regexp = NA)
-  expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
-  expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
-  pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
-  expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
-  # must work with data that has no column names
-  X <- sparse_matrix
-  colnames(X) <- NULL
-  expect_error(pred_contr_ <- predict(bst.Tree, X, predcontrib = TRUE), regexp = NA)
-  expect_equal(pred_contr, pred_contr_, check.attributes = FALSE,
-               tolerance = float_tolerance)
-
-  # gbtree binary classifier (approximate method)
-  expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE, approxcontrib = TRUE), regexp = NA)
-  expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
-  expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
-  pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
-  expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
-
-  # gblinear binary classifier
-  expect_error(pred_contr <- predict(bst.GLM, sparse_matrix, predcontrib = TRUE), regexp = NA)
-  expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
-  expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
-  pred <- predict(bst.GLM, sparse_matrix, outputmargin = TRUE)
-  expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
-  # manual calculation of linear terms
-  coefs <- xgb.dump(bst.GLM)[-c(1,2,4)] %>% as.numeric
-  coefs <- c(coefs[-1], coefs[1]) # intercept must be the last
-  pred_contr_manual <- sweep(cbind(sparse_matrix, 1), 2, coefs, FUN="*")
-  expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual),
-               tolerance = float_tolerance)
-
-  # gbtree multiclass
-  pred <- predict(mbst.Tree, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
-  pred_contr <- predict(mbst.Tree, as.matrix(iris[, -5]), predcontrib = TRUE)
-  expect_is(pred_contr, "list")
-  expect_length(pred_contr, 3)
-  for (g in seq_along(pred_contr)) {
-    expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
-    expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 1e-5)
-  }
-
-  # gblinear multiclass (set base_score = 0, which is base margin in multiclass)
-  pred <- predict(mbst.GLM, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
-  pred_contr <- predict(mbst.GLM, as.matrix(iris[, -5]), predcontrib = TRUE)
-  expect_length(pred_contr, 3)
-  coefs_all <- xgb.dump(mbst.GLM)[-c(1,2,6)] %>% as.numeric %>% matrix(ncol = 3, byrow = TRUE)
-  for (g in seq_along(pred_contr)) {
-    expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
-    expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), float_tolerance)
-    # manual calculation of linear terms
-    coefs <- c(coefs_all[-1, g], coefs_all[1, g]) # intercept needs to be the last
-    pred_contr_manual <- sweep(as.matrix(cbind(iris[,-5], 1)), 2, coefs, FUN="*")
-    expect_equal(as.numeric(pred_contr[[g]]), as.numeric(pred_contr_manual),
-                 tolerance = float_tolerance)
-  }
-})
-
-test_that("SHAPs sum to predictions, with or without DART", {
-  d <- cbind(
-    x1 = rnorm(100),
-    x2 = rnorm(100),
-    x3 = rnorm(100))
-  y <- d[,"x1"] + d[,"x2"]^2 +
-    ifelse(d[,"x3"] > .5, d[,"x3"]^2, 2^d[,"x3"]) +
-    rnorm(100)
-  nrounds <- 30
-
-  for (booster in list("gbtree", "dart")) {
-    fit <- xgboost(
-      params = c(
-        list(
-          booster = booster,
-          objective = "reg:squarederror",
-          eval_metric = "rmse"),
-        if (booster == "dart")
-          list(rate_drop = .01, one_drop = T)),
-      data = d,
-      label = y,
-      nrounds = nrounds)
-
-    pr <- function(...)
-      predict(fit, newdata = d, ...)
-    pred <- pr()
-    shap <- pr(predcontrib = T)
-    shapi <- pr(predinteraction = T)
-    tol = 1e-5
-
-    expect_equal(rowSums(shap), pred, tol = tol)
-    expect_equal(apply(shapi, 1, sum), pred, tol = tol)
-    for (i in 1 : nrow(d))
-      for (f in list(rowSums, colSums))
-        expect_equal(f(shapi[i,,]), shap[i,], tol = tol)
-  }
-})
-
-test_that("xgb-attribute functionality", {
-  val <- "my attribute value"
-  list.val <- list(my_attr=val, a=123, b='ok')
-  list.ch <- list.val[order(names(list.val))]
-  list.ch <- lapply(list.ch, as.character)
-  # note: iter is 0-index in xgb attributes
-  list.default <- list(niter = as.character(nrounds - 1))
-  list.ch <- c(list.ch, list.default)
-  # proper input:
-  expect_error(xgb.attr(bst.Tree, NULL))
-  expect_error(xgb.attr(val, val))
-  # set & get:
-  expect_null(xgb.attr(bst.Tree, "asdf"))
-  expect_equal(xgb.attributes(bst.Tree), list.default)
-  xgb.attr(bst.Tree, "my_attr") <- val
-  expect_equal(xgb.attr(bst.Tree, "my_attr"), val)
-  xgb.attributes(bst.Tree) <- list.val
-  expect_equal(xgb.attributes(bst.Tree), list.ch)
-  # serializing:
-  xgb.save(bst.Tree, 'xgb.model')
-  bst <- xgb.load('xgb.model')
-  if (file.exists('xgb.model')) file.remove('xgb.model')
-  expect_equal(xgb.attr(bst, "my_attr"), val)
-  expect_equal(xgb.attributes(bst), list.ch)
-  # deletion:
-  xgb.attr(bst, "my_attr") <- NULL
-  expect_null(xgb.attr(bst, "my_attr"))
-  expect_equal(xgb.attributes(bst), list.ch[c("a", "b", "niter")])
-  xgb.attributes(bst) <- list(a=NULL, b=NULL)
-  expect_equal(xgb.attributes(bst), list.default)
-  xgb.attributes(bst) <- list(niter=NULL)
-  expect_null(xgb.attributes(bst))
-})
-
-if (grepl('Windows', Sys.info()[['sysname']]) ||
-    grepl('Linux', Sys.info()[['sysname']]) ||
-    grepl('Darwin', Sys.info()[['sysname']])) {
-    test_that("xgb-attribute numeric precision", {
-      # check that lossless conversion works with 17 digits
-      # numeric -> character -> numeric
-      X <- 10^runif(100, -20, 20)
-      if (capabilities('long.double')) {
-          X2X <- as.numeric(format(X, digits = 17))
-          expect_identical(X, X2X)
-      }
-      # retrieved attributes to be the same as written
-      for (x in X) {
-        xgb.attr(bst.Tree, "x") <- x
-        expect_equal(as.numeric(xgb.attr(bst.Tree, "x")), x, tolerance = float_tolerance)
-        xgb.attributes(bst.Tree) <- list(a = "A", b = x)
-        expect_equal(as.numeric(xgb.attr(bst.Tree, "b")), x, tolerance = float_tolerance)
-      }
-    })
-}
-
-test_that("xgb.Booster serializing as R object works", {
-  saveRDS(bst.Tree, 'xgb.model.rds')
-  bst <- readRDS('xgb.model.rds')
-  if (file.exists('xgb.model.rds')) file.remove('xgb.model.rds')
-  dtrain <- xgb.DMatrix(sparse_matrix, label = label)
-  expect_equal(predict(bst.Tree, dtrain), predict(bst, dtrain), tolerance = float_tolerance)
-  expect_equal(xgb.dump(bst.Tree), xgb.dump(bst))
-  xgb.save(bst, 'xgb.model')
-  if (file.exists('xgb.model')) file.remove('xgb.model')
-  nil_ptr <- new("externalptr")
-  class(nil_ptr) <- "xgb.Booster.handle"
-  expect_true(identical(bst$handle, nil_ptr))
-  bst <- xgb.Booster.complete(bst)
-  expect_true(!identical(bst$handle, nil_ptr))
-  expect_equal(predict(bst.Tree, dtrain), predict(bst, dtrain), tolerance = float_tolerance)
-})
-
-test_that("xgb.model.dt.tree works with and without feature names", {
-  names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
-  dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
-  expect_equal(names.dt.trees, names(dt.tree))
-  if (!flag_32bit)
-    expect_equal(dim(dt.tree), c(188, 10))
-  expect_output(str(dt.tree), 'Feature.*\\"Age\\"')
-
-  dt.tree.0 <- xgb.model.dt.tree(model = bst.Tree)
-  expect_equal(dt.tree, dt.tree.0)
-
-  # when model contains no feature names:
-  bst.Tree.x <- bst.Tree
-  bst.Tree.x$feature_names <- NULL
-  dt.tree.x <- xgb.model.dt.tree(model = bst.Tree.x)
-  expect_output(str(dt.tree.x), 'Feature.*\\"3\\"')
-  expect_equal(dt.tree[, -4, with=FALSE], dt.tree.x[, -4, with=FALSE])
-
-  # using integer node ID instead of character
-  dt.tree.int <- xgb.model.dt.tree(model = bst.Tree, use_int_id = TRUE)
-  expect_equal(as.integer(tstrsplit(dt.tree$Yes, '-')[[2]]), dt.tree.int$Yes)
-  expect_equal(as.integer(tstrsplit(dt.tree$No, '-')[[2]]), dt.tree.int$No)
-  expect_equal(as.integer(tstrsplit(dt.tree$Missing, '-')[[2]]), dt.tree.int$Missing)
-})
-
-test_that("xgb.model.dt.tree throws error for gblinear", {
-  expect_error(xgb.model.dt.tree(model = bst.GLM))
-})
-
-test_that("xgb.importance works with and without feature names", {
-  importance.Tree <- xgb.importance(feature_names = feature.names, model = bst.Tree)
-  if (!flag_32bit)
-    expect_equal(dim(importance.Tree), c(7, 4))
-  expect_equal(colnames(importance.Tree), c("Feature", "Gain", "Cover", "Frequency"))
-  expect_output(str(importance.Tree), 'Feature.*\\"Age\\"')
-
-  importance.Tree.0 <- xgb.importance(model = bst.Tree)
-  expect_equal(importance.Tree, importance.Tree.0, tolerance = float_tolerance)
-
-  # when model contains no feature names:
-  bst.Tree.x <- bst.Tree
-  bst.Tree.x$feature_names <- NULL
-  importance.Tree.x <- xgb.importance(model = bst.Tree)
-  expect_equal(importance.Tree[, -1, with=FALSE], importance.Tree.x[, -1, with=FALSE],
-               tolerance = float_tolerance)
-
-  imp2plot <- xgb.plot.importance(importance_matrix = importance.Tree)
-  expect_equal(colnames(imp2plot), c("Feature", "Gain", "Cover", "Frequency", "Importance"))
-  xgb.ggplot.importance(importance_matrix = importance.Tree)
-
-  # for multiclass
-  imp.Tree <- xgb.importance(model = mbst.Tree)
-  expect_equal(dim(imp.Tree), c(4, 4))
-  xgb.importance(model = mbst.Tree, trees = seq(from=0, by=nclass, length.out=nrounds))
-})
-
-test_that("xgb.importance works with GLM model", {
-  importance.GLM <- xgb.importance(feature_names = feature.names, model = bst.GLM)
-  expect_equal(dim(importance.GLM), c(10, 2))
-  expect_equal(colnames(importance.GLM), c("Feature", "Weight"))
-  xgb.importance(model = bst.GLM)
-  imp2plot <- xgb.plot.importance(importance.GLM)
-  expect_equal(colnames(imp2plot), c("Feature", "Weight", "Importance"))
-  xgb.ggplot.importance(importance.GLM)
-
-  # for multiclass
-  imp.GLM <- xgb.importance(model = mbst.GLM)
-  expect_equal(dim(imp.GLM), c(12, 3))
-  expect_equal(imp.GLM$Class, rep(0:2, each=4))
-})
-
-test_that("xgb.model.dt.tree and xgb.importance work with a single split model", {
-  bst1 <- xgboost(data = sparse_matrix, label = label, max_depth = 1,
-                  eta = 1, nthread = 2, nrounds = 1, verbose = 0,
-                  objective = "binary:logistic")
-  expect_error(dt <- xgb.model.dt.tree(model = bst1), regexp = NA) # no error
-  expect_equal(nrow(dt), 3)
-  expect_error(imp <- xgb.importance(model = bst1), regexp = NA) # no error
-  expect_equal(nrow(imp), 1)
-  expect_equal(imp$Gain, 1)
-})
-
-test_that("xgb.plot.tree works with and without feature names", {
-  xgb.plot.tree(feature_names = feature.names, model = bst.Tree)
-  xgb.plot.tree(model = bst.Tree)
-})
-
-test_that("xgb.plot.multi.trees works with and without feature names", {
-  xgb.plot.multi.trees(model = bst.Tree, feature_names = feature.names, features_keep = 3)
-  xgb.plot.multi.trees(model = bst.Tree, features_keep = 3)
-})
-
-test_that("xgb.plot.deepness works", {
-  d2p <- xgb.plot.deepness(model = bst.Tree)
-  expect_equal(colnames(d2p), c("ID", "Tree", "Depth", "Cover", "Weight"))
-  xgb.plot.deepness(model = bst.Tree, which = "med.depth")
-  xgb.ggplot.deepness(model = bst.Tree)
-})
-
-test_that("xgb.plot.shap works", {
-  sh <- xgb.plot.shap(data = sparse_matrix, model = bst.Tree, top_n = 2, col = 4)
-  expect_equal(names(sh), c("data", "shap_contrib"))
-  expect_equal(NCOL(sh$data), 2)
-  expect_equal(NCOL(sh$shap_contrib), 2)
-})
-
-test_that("check.deprecation works", {
-  ttt <- function(a = NNULL, DUMMY=NULL, ...) {
-    check.deprecation(...)
-    as.list((environment()))
-  }
-  res <- ttt(a = 1, DUMMY = 2, z = 3)
-  expect_equal(res, list(a = 1, DUMMY = 2))
-  expect_warning(
-    res <- ttt(a = 1, dummy = 22, z = 3)
-  , "\'dummy\' is deprecated")
-  expect_equal(res, list(a = 1, DUMMY = 22))
-  expect_warning(
-    res <- ttt(a = 1, dumm = 22, z = 3)
-  , "\'dumm\' was partially matched to \'dummy\'")
-  expect_equal(res, list(a = 1, DUMMY = 22))
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_interaction_constraints.R b/ml-xgboost/R-package/tests/testthat/test_interaction_constraints.R
deleted file mode 100644
index 9a3ddf4..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_interaction_constraints.R
+++ /dev/null
@@ -1,55 +0,0 @@
-require(xgboost)
-
-context("interaction constraints")
-
-set.seed(1024)
-x1 <- rnorm(1000, 1)
-x2 <- rnorm(1000, 1)
-x3 <- sample(c(1,2,3), size=1000, replace=TRUE)
-y <- x1 + x2 + x3 + x1*x2*x3 + rnorm(1000, 0.001) + 3*sin(x1)
-train <- matrix(c(x1,x2,x3), ncol = 3)
-
-test_that("interaction constraints for regression", {
-  # Fit a model that only allows interaction between x1 and x2
-  bst <- xgboost(data = train, label = y, max_depth = 3,
-                 eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
-                 interaction_constraints = list(c(0,1)))
-
-  # Set all observations to have the same x3 values then increment
-  #  by the same amount
-  preds <- lapply(c(1,2,3), function(x){
-    tmat <- matrix(c(x1,x2,rep(x,1000)), ncol=3)
-    return(predict(bst, tmat))
-  })
-
-  # Check incrementing x3 has the same effect on all observations
-  #   since x3 is constrained to be independent of x1 and x2
-  #   and all observations start off from the same x3 value
-  diff1 <- preds[[2]] - preds[[1]]
-  test1 <- all(abs(diff1 - diff1[1]) < 1e-4)
-
-  diff2 <- preds[[3]] - preds[[2]]
-  test2 <- all(abs(diff2 - diff2[1]) < 1e-4)
-
-  expect_true({
-    test1 & test2
-  }, "Interaction Contraint Satisfied")
-})
-
-test_that("interaction constraints scientific representation", {
-  rows <- 10
-  ## When number exceeds 1e5, R paste function uses scientific representation.
-  ## See: https://github.com/dmlc/xgboost/issues/5179
-  cols <- 1e5+10
-
-  d <- matrix(rexp(rows, rate=.1), nrow=rows, ncol=cols)
-  y <- rnorm(rows)
-
-  dtrain <- xgb.DMatrix(data=d, info = list(label=y))
-  inc <- list(c(seq.int(from = 0, to = cols, by = 1)))
-
-  with_inc <- xgb.train(data=dtrain, tree_method='hist',
-                        interaction_constraints=inc, nrounds=10)
-  without_inc <- xgb.train(data=dtrain, tree_method='hist', nrounds=10)
-  expect_equal(xgb.save.raw(with_inc), xgb.save.raw(without_inc))
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_interactions.R b/ml-xgboost/R-package/tests/testthat/test_interactions.R
deleted file mode 100644
index 20ee90c..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_interactions.R
+++ /dev/null
@@ -1,141 +0,0 @@
-context('Test prediction of feature interactions')
-
-require(xgboost)
-require(magrittr)
-
-set.seed(123)
-
-test_that("predict feature interactions works", {
-  # simulate some binary data and a linear outcome with an interaction term
-  N <- 1000
-  P <- 5
-  X <- matrix(rbinom(N * P, 1, 0.5), ncol=P, dimnames = list(NULL, letters[1:P]))
-  # center the data (as contributions are computed WRT feature means)
-  X <- scale(X, scale=FALSE)
-
-  # outcome without any interactions, without any noise:
-  f <- function(x) 2 * x[, 1] - 3 * x[, 2]
-  # outcome with interactions, without noise:
-  f_int <- function(x) f(x) + 2 * x[, 2] * x[, 3]
-  # outcome with interactions, with noise:
-  #f_int_noise <- function(x) f_int(x) + rnorm(N, 0, 0.3)
-
-  y <- f_int(X)
-
-  dm <- xgb.DMatrix(X, label = y)
-  param <- list(eta=0.1, max_depth=4, base_score=mean(y), lambda=0, nthread=2)
-  b <- xgb.train(param, dm, 100)
-  
-  pred = predict(b, dm, outputmargin=TRUE)
-
-  # SHAP contributions:
-  cont <- predict(b, dm, predcontrib=TRUE)
-  expect_equal(dim(cont), c(N, P+1))
-  # make sure for each row they add up to marginal predictions
-  max(abs(rowSums(cont) - pred)) %>% expect_lt(0.001)
-  # Hand-construct the 'ground truth' feature contributions:
-  gt_cont <- cbind(
-      2. * X[, 1],
-     -3. * X[, 2] + 1. * X[, 2] * X[, 3], # attribute a HALF of the interaction term to feature #2
-      1. * X[, 2] * X[, 3]               # and another HALF of the interaction term to feature #3
-     )
-  gt_cont <- cbind(gt_cont, matrix(0, nrow=N, ncol=P + 1 - 3))
-  # These should be relatively close:
-  expect_lt(max(abs(cont - gt_cont)), 0.05)
-
-
-  # SHAP interaction contributions:
-  intr <- predict(b, dm, predinteraction=TRUE)
-  expect_equal(dim(intr), c(N, P+1, P+1))
-  # check assigned colnames
-  cn <- c(letters[1:P], "BIAS")
-  expect_equal(dimnames(intr), list(NULL, cn, cn))
-
-  # check the symmetry
-  max(abs(aperm(intr, c(1,3,2)) - intr)) %>% expect_lt(0.00001)
-
-  # sums WRT columns must be close to feature contributions
-  max(abs(apply(intr, c(1,2), sum) - cont)) %>% expect_lt(0.00001)
-
-  # diagonal terms for features 3,4,5 must be close to zero
-  Reduce(max, sapply(3:P, function(i) max(abs(intr[, i, i])))) %>% expect_lt(0.05)
-
-  # BIAS must have no interactions
-  max(abs(intr[, 1:P, P+1])) %>% expect_lt(0.00001)
-
-  # interactions other than 2 x 3 must be close to zero
-  intr23 <- intr
-  intr23[,2,3] <- 0
-  Reduce(max, sapply(1:P, function(i) max(abs(intr23[, i, (i+1):(P+1)])))) %>% expect_lt(0.05)
-
-  # Construct the 'ground truth' contributions of interactions directly from the linear terms:
-  gt_intr <- array(0, c(N, P+1, P+1))
-  gt_intr[,2,3] <- 1. * X[, 2] * X[, 3] # attribute a HALF of the interaction term to each symmetric element
-  gt_intr[,3,2] <- gt_intr[, 2, 3]
-  # merge-in the diagonal based on 'ground truth' feature contributions
-  intr_diag = gt_cont - apply(gt_intr, c(1,2), sum)
-  for(j in seq_len(P)) {
-    gt_intr[,j,j] = intr_diag[,j]
-  }
-  # These should be relatively close:
-  expect_lt(max(abs(intr - gt_intr)), 0.1)
-})
-
-test_that("SHAP contribution values are not NAN", {
-  d <- data.frame(
-    x1 = c(-2.3, 1.4, 5.9, 2, 2.5, 0.3, -3.6, -0.2, 0.5, -2.8, -4.6, 3.3, -1.2,
-           -1.1, -2.3, 0.4, -1.5, -0.2, -1, 3.7),
-    x2 = c(291.179171, 269.198331, 289.942097, 283.191669, 269.673332,
-           294.158346, 287.255835, 291.530838, 285.899586, 269.290833,
-           268.649586, 291.530841, 280.074593, 269.484168, 293.94042,
-           294.327506, 296.20709, 295.441669, 283.16792, 270.227085),
-    y = c(9, 15, 5.7, 9.2, 22.4, 5, 9, 3.2, 7.2, 13.1, 7.8, 16.9, 6.5, 22.1,
-          5.3, 10.4, 11.1, 13.9, 11, 20.5),
-    fold = c(2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2))
-
-  ivs <- c("x1", "x2")
-
-  fit <- xgboost(
-    verbose = 0,
-    params = list(
-      objective = "reg:squarederror",
-      eval_metric = "rmse"),
-    data = as.matrix(subset(d, fold == 2)[, ivs]),
-    label = subset(d, fold == 2)$y,
-    nthread = 1,
-    nrounds = 3)
-
-  shaps <- as.data.frame(predict(fit,
-    newdata = as.matrix(subset(d, fold == 1)[, ivs]),
-    predcontrib = T))
-  result <- cbind(shaps, sum = rowSums(shaps), pred = predict(fit,
-      newdata = as.matrix(subset(d, fold == 1)[, ivs])))
-
-  expect_true(identical(TRUE, all.equal(result$sum, result$pred, tol = 1e-6)))
-})
-
-
-test_that("multiclass feature interactions work", {
-  dm <- xgb.DMatrix(as.matrix(iris[,-5]), label=as.numeric(iris$Species)-1)
-  param <- list(eta=0.1, max_depth=4, objective='multi:softprob', num_class=3)
-  b <- xgb.train(param, dm, 40)
-  pred = predict(b, dm, outputmargin=TRUE) %>% array(c(3, 150)) %>% t
-
-  # SHAP contributions:
-  cont <- predict(b, dm, predcontrib=TRUE)
-  expect_length(cont, 3)
-  # rewrap them as a 3d array
-  cont <- unlist(cont) %>% array(c(150, 5, 3))
-  # make sure for each row they add up to marginal predictions
-  max(abs(apply(cont, c(1,3), sum) - pred)) %>% expect_lt(0.001)
-
-  # SHAP interaction contributions:
-  intr <- predict(b, dm, predinteraction=TRUE)
-  expect_length(intr, 3)
-  # rewrap them as a 4d array
-  intr <- unlist(intr) %>% array(c(150, 5, 5, 3)) %>% aperm(c(4, 1, 2, 3)) # [grp, row, col, col]
-  # check the symmetry
-  max(abs(aperm(intr, c(1,2,4,3)) - intr)) %>% expect_lt(0.00001)
-  # sums WRT columns must be close to feature contributions
-  max(abs(apply(intr, c(1,2,3), sum) - aperm(cont, c(3,1,2)))) %>% expect_lt(0.00001)
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_lint.R b/ml-xgboost/R-package/tests/testthat/test_lint.R
deleted file mode 100644
index 2f2a07d..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_lint.R
+++ /dev/null
@@ -1,27 +0,0 @@
-context("Code is of high quality and lint free")
-test_that("Code Lint", {
-  skip_on_cran()
-  skip_on_travis()
-  skip_if_not_installed("lintr")
-  my_linters <- list(
-    absolute_paths_linter=lintr::absolute_paths_linter,
-    assignment_linter=lintr::assignment_linter,
-    closed_curly_linter=lintr::closed_curly_linter,
-    commas_linter=lintr::commas_linter,
-    # commented_code_linter=lintr::commented_code_linter,
-    infix_spaces_linter=lintr::infix_spaces_linter,
-    line_length_linter=lintr::line_length_linter,
-    no_tab_linter=lintr::no_tab_linter,
-    object_usage_linter=lintr::object_usage_linter,
-    # snake_case_linter=lintr::snake_case_linter,
-    # multiple_dots_linter=lintr::multiple_dots_linter,
-    object_length_linter=lintr::object_length_linter,
-    open_curly_linter=lintr::open_curly_linter,
-    # single_quotes_linter=lintr::single_quotes_linter,
-    spaces_inside_linter=lintr::spaces_inside_linter,
-    spaces_left_parentheses_linter=lintr::spaces_left_parentheses_linter,
-    trailing_blank_lines_linter=lintr::trailing_blank_lines_linter,
-    trailing_whitespace_linter=lintr::trailing_whitespace_linter
-  )
-  # lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_monotone.R b/ml-xgboost/R-package/tests/testthat/test_monotone.R
deleted file mode 100644
index 9991e91..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_monotone.R
+++ /dev/null
@@ -1,24 +0,0 @@
-require(xgboost)
-
-context("monotone constraints")
-
-set.seed(1024)
-x = rnorm(1000, 10)
-y = -1*x + rnorm(1000, 0.001) + 3*sin(x)
-train = matrix(x, ncol = 1)
-
-
-test_that("monotone constraints for regression", {
-  bst = xgboost(data = train, label = y, max_depth = 2,
-                eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
-                monotone_constraints = -1)
-  
-  pred = predict(bst, train)
-  
-  ind = order(train[,1])
-  pred.ord = pred[ind]
-  expect_true({
-    !any(diff(pred.ord) > 0)
-  }, "Monotone Contraint Satisfied")
-  
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_parameter_exposure.R b/ml-xgboost/R-package/tests/testthat/test_parameter_exposure.R
deleted file mode 100644
index 1a0dcb3..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_parameter_exposure.R
+++ /dev/null
@@ -1,30 +0,0 @@
-context('Test model params and call are exposed to R')
-
-require(xgboost)
-
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-bst <- xgboost(data = dtrain,
-               max_depth = 2,
-               eta = 1,
-               nrounds = 10,
-               nthread = 1,
-               verbose = 0,
-               objective = "binary:logistic")
-
-test_that("call is exposed to R", {
-  expect_false(is.null(bst$call))
-  expect_is(bst$call, "call")
-})
-
-test_that("params is exposed to R", {
-  model_params <- bst$params
-  expect_is(model_params, "list")
-  expect_equal(model_params$eta, 1)
-  expect_equal(model_params$max_depth, 2)
-  expect_equal(model_params$objective, "binary:logistic")
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_poisson_regression.R b/ml-xgboost/R-package/tests/testthat/test_poisson_regression.R
deleted file mode 100644
index a48f2fc..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_poisson_regression.R
+++ /dev/null
@@ -1,14 +0,0 @@
-context('Test poisson regression model')
-
-require(xgboost)
-set.seed(1994)
-
-test_that("poisson regression works", {
-  data(mtcars)
-  bst <- xgboost(data = as.matrix(mtcars[,-11]), label = mtcars[,11],
-                objective = 'count:poisson', nrounds=10, verbose=0)
-  expect_equal(class(bst), "xgb.Booster")
-  pred <- predict(bst, as.matrix(mtcars[, -11]))
-  expect_equal(length(pred), 32)
-  expect_lt(sqrt(mean( (pred - mtcars[,11])^2 )), 1.2)
-})
diff --git a/ml-xgboost/R-package/tests/testthat/test_update.R b/ml-xgboost/R-package/tests/testthat/test_update.R
deleted file mode 100644
index fa48c91..0000000
--- a/ml-xgboost/R-package/tests/testthat/test_update.R
+++ /dev/null
@@ -1,107 +0,0 @@
-require(xgboost)
-
-context("update trees in an existing model")
-
-data(agaricus.train, package = 'xgboost')
-data(agaricus.test, package = 'xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
-
-# Disable flaky tests for 32-bit Windows.
-# See https://github.com/dmlc/xgboost/issues/3720
-win32_flag = .Platform$OS.type == "windows" && .Machine$sizeof.pointer != 8
-
-test_that("updating the model works", {
-  watchlist = list(train = dtrain, test = dtest)
-
-  # no-subsampling
-  p1 <- list(objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2)
-  set.seed(11)
-  bst1 <- xgb.train(p1, dtrain, nrounds = 10, watchlist, verbose = 0)
-  tr1 <- xgb.model.dt.tree(model = bst1)
-  
-  # with subsampling
-  p2 <- modifyList(p1, list(subsample = 0.1))
-  set.seed(11)
-  bst2 <- xgb.train(p2, dtrain, nrounds = 10, watchlist, verbose = 0)
-  tr2 <- xgb.model.dt.tree(model = bst2)
-  
-  # the same no-subsampling boosting with an extra 'refresh' updater:
-  p1r <- modifyList(p1, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE))
-  set.seed(11)
-  bst1r <- xgb.train(p1r, dtrain, nrounds = 10, watchlist, verbose = 0)
-  tr1r <- xgb.model.dt.tree(model = bst1r)
-  # all should be the same when no subsampling
-  expect_equal(bst1$evaluation_log, bst1r$evaluation_log)
-  if (!win32_flag) {
-    expect_equal(tr1, tr1r, tolerance = 0.00001, check.attributes = FALSE)
-  }
-
-  # the same boosting with subsampling with an extra 'refresh' updater:
-  p2r <- modifyList(p2, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE))
-  set.seed(11)
-  bst2r <- xgb.train(p2r, dtrain, nrounds = 10, watchlist, verbose = 0)
-  tr2r <- xgb.model.dt.tree(model = bst2r)
-  # should be the same evaluation but different gains and larger cover
-  expect_equal(bst2$evaluation_log, bst2r$evaluation_log)
-  if (!win32_flag) {
-    expect_equal(tr2[Feature == 'Leaf']$Quality, tr2r[Feature == 'Leaf']$Quality)
-  }
-  expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2r[Feature != 'Leaf']$Quality)), 100)
-  expect_gt(sum(tr2r$Cover) / sum(tr2$Cover), 1.5)
-
-  # process type 'update' for no-subsampling model, refreshing the tree stats AND leaves from training data:
-  p1u <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = TRUE))
-  bst1u <- xgb.train(p1u, dtrain, nrounds = 10, watchlist, verbose = 0, xgb_model = bst1)
-  tr1u <- xgb.model.dt.tree(model = bst1u)
-  # all should be the same when no subsampling
-  expect_equal(bst1$evaluation_log, bst1u$evaluation_log)
-  expect_equal(tr1, tr1u, tolerance = 0.00001, check.attributes = FALSE)
-  
-  # process type 'update' for model with subsampling, refreshing only the tree stats from training data:
-  p2u <- modifyList(p2, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE))
-  bst2u <- xgb.train(p2u, dtrain, nrounds = 10, watchlist, verbose = 0, xgb_model = bst2)
-  tr2u <- xgb.model.dt.tree(model = bst2u)
-  # should be the same evaluation but different gains and larger cover
-  expect_equal(bst2$evaluation_log, bst2u$evaluation_log)
-  expect_equal(tr2[Feature == 'Leaf']$Quality, tr2u[Feature == 'Leaf']$Quality)
-  expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2u[Feature != 'Leaf']$Quality)), 100)
-  expect_gt(sum(tr2u$Cover) / sum(tr2$Cover), 1.5)
-  # the results should be the same as for the model with an extra 'refresh' updater
-  expect_equal(bst2r$evaluation_log, bst2u$evaluation_log)
-  if (!win32_flag) {
-    expect_equal(tr2r, tr2u, tolerance = 0.00001, check.attributes = FALSE)
-  }
-  
-  # process type 'update' for no-subsampling model, refreshing only the tree stats from TEST data:
-  p1ut <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE))
-  bst1ut <- xgb.train(p1ut, dtest, nrounds = 10, watchlist, verbose = 0, xgb_model = bst1)
-  tr1ut <- xgb.model.dt.tree(model = bst1ut)
-  # should be the same evaluations but different gains and smaller cover (test data is smaller)
-  expect_equal(bst1$evaluation_log, bst1ut$evaluation_log)
-  expect_equal(tr1[Feature == 'Leaf']$Quality, tr1ut[Feature == 'Leaf']$Quality)
-  expect_gt(sum(abs(tr1[Feature != 'Leaf']$Quality - tr1ut[Feature != 'Leaf']$Quality)), 100)
-  expect_lt(sum(tr1ut$Cover) / sum(tr1$Cover), 0.5)
-})
-
-test_that("updating works for multiclass & multitree", {
-  dtr <- xgb.DMatrix(as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1)
-  watchlist <- list(train = dtr)
-  p0 <- list(max_depth = 2, eta = 0.5, nthread = 2, subsample = 0.6,
-             objective = "multi:softprob", num_class = 3, num_parallel_tree = 2,
-             base_score = 0)
-  set.seed(121)
-  bst0 <- xgb.train(p0, dtr, 5, watchlist, verbose = 0)
-  tr0 <- xgb.model.dt.tree(model = bst0)
-  
-  # run update process for an original model with subsampling
-  p0u <- modifyList(p0, list(process_type='update', updater='refresh', refresh_leaf=FALSE))
-  bst0u <- xgb.train(p0u, dtr, nrounds = bst0$niter, watchlist, xgb_model = bst0, verbose = 0)
-  tr0u <- xgb.model.dt.tree(model = bst0u)
-  
-  # should be the same evaluation but different gains and larger cover
-  expect_equal(bst0$evaluation_log, bst0u$evaluation_log)
-  expect_equal(tr0[Feature == 'Leaf']$Quality, tr0u[Feature == 'Leaf']$Quality)
-  expect_gt(sum(abs(tr0[Feature != 'Leaf']$Quality - tr0u[Feature != 'Leaf']$Quality)), 100)
-  expect_gt(sum(tr0u$Cover) / sum(tr0$Cover), 1.5)
-})
diff --git a/ml-xgboost/R-package/vignettes/discoverYourData.Rmd b/ml-xgboost/R-package/vignettes/discoverYourData.Rmd
deleted file mode 100644
index 67b7340..0000000
--- a/ml-xgboost/R-package/vignettes/discoverYourData.Rmd
+++ /dev/null
@@ -1,338 +0,0 @@
----
-title: "Understand your dataset with Xgboost"
-output:
-  rmarkdown::html_vignette:
-    css: vignette.css
-    number_sections: yes
-    toc: yes
-author: Tianqi Chen, Tong He, Michaël Benesty, Yuan Tang
-vignette: >
-  %\VignetteIndexEntry{Discover your data}
-  %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
----
-
-Understand your dataset with XGBoost
-====================================
-
-Introduction
-------------
-
-The purpose of this vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
-
-This vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
-
-Package loading:
-
-```{r libLoading, results='hold', message=F, warning=F}
-require(xgboost)
-require(Matrix)
-require(data.table)
-if (!require('vcd')) install.packages('vcd')
-```
-
-> **VCD** package is used for one of its embedded dataset only.
-
-Preparation of the dataset
---------------------------
-
-### Numeric v.s. categorical variables
-
-
-**Xgboost** manages only `numeric` vectors.
-
-What to do when you have *categorical* data?
-
-A *categorical* variable has a fixed number of different values. For instance, if a variable called *Colour* can have only one of these three values, *red*, *blue* or *green*, then *Colour* is a *categorical* variable.
-
-> In **R**, a *categorical* variable is called `factor`.
->
-> Type `?factor` in the console for more information.
-
-To answer the question above we will convert *categorical* variables to `numeric` one.
-
-### Conversion from categorical to numeric variables
-
-#### Looking at the raw data
-
-In this Vignette we will see how to transform a *dense* `data.frame` (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features.
-
-The method we are going to see is usually called [one-hot encoding](http://en.wikipedia.org/wiki/One-hot).
-
-The first step is to load `Arthritis` dataset in memory and wrap it with `data.table` package.
-
-```{r, results='hide'}
-data(Arthritis)
-df <- data.table(Arthritis, keep.rownames = F)
-```
-
-> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
-
-The first thing we want to do is to have a look to the first few lines of the `data.table`:
-
-```{r}
-head(df)
-```
-
-Now we will check the format of each column.
-
-```{r}
-str(df)
-```
-
-2 columns have `factor` type, one has `ordinal` type.
-
-> `ordinal` variable :
->
-> * can take a limited number of values (like `factor`) ;
-> * these values are ordered (unlike `factor`). Here these ordered values are: `Marked > Some > None`
-
-#### Creation of new features based on old ones
-
-We will add some new *categorical* features to see if it helps.
-
-##### Grouping per 10 years
-
-For the first feature we create groups of age by rounding the real age.
-
-Note that we transform it to `factor` so the algorithm treat these age groups as independent values.
-
-Therefore, 20 is not closer to 30 than 60. To make it short, the distance between ages is lost in this transformation.
-
-```{r}
-head(df[,AgeDiscret := as.factor(round(Age/10,0))])
-```
-
-##### Random split into two groups
-
-Following is an even stronger simplification of the real age with an arbitrary split at 30 years old. We choose this value **based on nothing**. We will see later if simplifying the information based on arbitrary values is a good strategy (you may already have an idea of how well it will work...).
-
-```{r}
-head(df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))])
-```
-
-##### Risks in adding correlated features
-
-These new features are highly correlated to the `Age` feature because they are simple transformations of this feature.
-
-For many machine learning algorithms, using correlated features is not a good idea. It may sometimes make prediction less accurate, and most of the time make interpretation of the model almost impossible. GLM, for instance, assumes that the features are uncorrelated.
-
-Fortunately, decision tree algorithms (including boosted trees) are very robust to these features. Therefore we have nothing to do to manage this situation.
-
-##### Cleaning data
-
-We remove ID as there is nothing to learn from this feature (it would just add some noise).
-
-```{r, results='hide'}
-df[,ID:=NULL]
-```
-
-We will list the different values for the column `Treatment`:
-
-```{r}
-levels(df[,Treatment])
-```
-
-
-#### Encoding categorical features
-
-Next step, we will transform the categorical data to dummy variables.
-Several encoding methods exist, e.g., [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) is a common approach.
-We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
-
-The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.
-
-For example, the column `Treatment` will be replaced by two columns, `TreatmentPlacebo`, and `TreatmentTreated`. Each of them will be *binary*. Therefore, an observation which has the value `Placebo` in column `Treatment` before the transformation will have after the transformation the value `1` in the new column `TreatmentPlacebo` and the value `0` in the new column `TreatmentTreated`. The column `TreatmentPlacebo` will disappear during the contrast encoding, as it would be absorbed into a common constant intercept column.
-
-Column `Improved` is excluded because it will be our `label` column, the one we want to predict.
-
-```{r, warning=FALSE,message=FALSE}
-sparse_matrix <- sparse.model.matrix(Improved ~ ., data = df)[,-1]
-head(sparse_matrix)
-```
-
-> Formula `Improved ~ .` used above means transform all *categorical* features but column `Improved` to binary values. The `-1` column selection removes the intercept column which is full of `1` (this column is generated by the conversion). For more information, you can type `?sparse.model.matrix` in the console.
-
-Create the output `numeric` vector (not as a sparse `Matrix`):
-
-```{r}
-output_vector = df[,Improved] == "Marked"
-```
-
-1. set `Y` vector to `0`;
-2. set `Y` to `1` for rows where `Improved == Marked` is `TRUE` ;
-3. return `Y` vector.
-
-Build the model
----------------
-
-The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
-
-```{r}
-bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 4,
-               eta = 1, nthread = 2, nrounds = 10,objective = "binary:logistic")
-
-```
-
-You can see some `train-error: 0.XXXXX` lines followed by a number. It decreases. Each line shows how well the model explains your data. Lower is better.
-
-A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
-
-> Here you can see the numbers decrease until line 7 and then increase.
->
-> It probably means we are overfitting. To fix that I should reduce the number of rounds to `nrounds = 4`. I will let things like that because I don't really care for the purpose of this example :-)
-
-Feature importance
-------------------
-
-## Measure feature importance
-
-
-### Build the feature importance data.table
-
-Remember, each binary column corresponds to a single value of one of *categorical* features.
-
-```{r}
-importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
-head(importance)
-```
-
-> The column `Gain` provide the information we are looking for.
->
-> As you can see, features are classified by `Gain`.
-
-`Gain` is the improvement in accuracy brought by a feature to the branches it is on. The idea is that before adding a new split on a feature X to the branch there was some wrongly classified elements, after adding the split on this feature, there are two new branches, and each of these branch is more accurate (one branch saying if your observation is on this branch then it should be classified as `1`, and the other branch saying the exact opposite).
-
-`Cover` measures the relative quantity of observations concerned by a feature.
-
-`Frequency` is a simpler way to measure the `Gain`. It just counts the number of times a feature is used in all generated trees. You should not use it (unless you know why you want to use it).
-
-#### Improvement in the interpretability of feature importance data.table
-
-We can go deeper in the analysis of the model. In the `data.table` above, we have discovered which features counts to predict if the illness will go or not. But we don't yet know the role of these features. For instance, one of the question we may want to answer would be: does receiving a placebo treatment helps to recover from the illness?
-
-One simple solution is to count the co-occurrences of a feature and a class of the classification.
-
-For that purpose we will execute the same function as above but using two more parameters, `data` and `label`.
-
-```{r}
-importanceRaw <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst, data = sparse_matrix, label = output_vector)
-
-# Cleaning for better display
-importanceClean <- importanceRaw[,`:=`(Cover=NULL, Frequency=NULL)]
-
-head(importanceClean)
-```
-
-> In the table above we have removed two not needed columns and select only the first lines.
-
-First thing you notice is the new column `Split`. It is the split applied to the feature on a branch of one of the tree. Each split is present, therefore a feature can appear several times in this table. Here we can see the feature `Age` is used several times with different splits.
-
-How the split is applied to count the co-occurrences? It is always `<`. For instance, in the second line, we measure the number of persons under 61.5 years with the illness gone after the treatment.
-
-The two other new columns are `RealCover` and `RealCover %`. In the first column it measures the number of observations in the dataset where the split is respected and the label marked as `1`. The second column is the percentage of the whole population that `RealCover` represents.
-
-Therefore, according to our findings, getting a placebo doesn't seem to help but being younger than 61 years may help (seems logic).
-
-> You may wonder how to interpret the `< 1.00001` on the first line. Basically, in a sparse `Matrix`, there is no `0`, therefore, looking for one hot-encoded categorical observations validating the rule `< 1.00001` is like just looking for `1` for this feature.
-
-### Plotting the feature importance
-
-
-All these things are nice, but it would be even better to plot the results.
-
-```{r, fig.width=8, fig.height=5, fig.align='center'}
-xgb.plot.importance(importance_matrix = importance)
-```
-
-Feature have automatically been divided in 2 clusters: the interesting features... and the others.
-
-> Depending of the dataset and the learning parameters you may have more than two clusters. Default value is to limit them to `10`, but you can increase this limit. Look at the function documentation for more information.
-
-According to the plot above, the most important features in this dataset to predict if the treatment will work are :
-
-* the Age ;
-* having received a placebo or not ;
-* the sex is third but already included in the not interesting features group ;
-* then we see our generated features (AgeDiscret). We can see that their contribution is very low.
-
-### Do these results make sense?
-
-
-Let's check some **Chi2** between each of these features and the label.
-
-Higher **Chi2** means better correlation.
-
-```{r, warning=FALSE, message=FALSE}
-c2 <- chisq.test(df$Age, output_vector)
-print(c2)
-```
-
-Pearson correlation between Age and illness disappearing is **`r round(c2$statistic, 2 )`**.
-
-```{r, warning=FALSE, message=FALSE}
-c2 <- chisq.test(df$AgeDiscret, output_vector)
-print(c2)
-```
-
-Our first simplification of Age gives a Pearson correlation is **`r round(c2$statistic, 2)`**.
-
-```{r, warning=FALSE, message=FALSE}
-c2 <- chisq.test(df$AgeCat, output_vector)
-print(c2)
-```
-
-The perfectly random split I did between young and old at 30 years old have a low correlation of **`r round(c2$statistic, 2)`**. It's a result we may expect as may be in my mind > 30 years is being old (I am 32 and starting feeling old, this may explain that), but for the illness we are studying, the age to be vulnerable is not the same.
-
-Morality: don't let your *gut* lower the quality of your model.
-
-In *data science* expression, there is the word *science* :-)
-
-Conclusion
-----------
-
-As you can see, in general *destroying information by simplifying it won't improve your model*. **Chi2** just demonstrates that.
-
-But in more complex cases, creating a new feature based on existing one which makes link with the outcome more obvious may help the algorithm and improve the model.
-
-The case studied here is not enough complex to show that. Check [Kaggle website](http://www.kaggle.com/) for some challenging datasets. However it's almost always worse when you add some arbitrary rules.
-
-Moreover, you can notice that even if we have added some not useful new features highly correlated with other features, the boosting tree algorithm have been able to choose the best one, which in this case is the Age.
-
-Linear model may not be that smart in this scenario.
-
-Special Note: What about Random Forests™?
------------------------------------------
-
-As you may know, [Random Forests™](http://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](http://en.wikipedia.org/wiki/Ensemble_learning) family.
-
-Both trains several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).
-
-This difference have an impact on a corner case in feature importance analysis: the *correlated features*.
-
-Imagine two features perfectly correlated, feature `A` and feature `B`. For one specific tree, if the algorithm needs one of them, it will choose randomly (true in both boosting and Random Forests™).
-
-However, in Random Forests™ this random choice will be done for each tree, because each tree is independent from the others. Therefore, approximatively, depending of your parameters, 50% of the trees will choose feature `A` and the other 50% will choose feature `B`. So the *importance* of the information contained in `A` and `B` (which is the same, because they are perfectly correlated) is diluted in `A` and `B`. So you won't easily know this information is important to predict what you want to predict! It is even worse when you have 10 correlated features...
-
-In boosting, when a specific link between feature and outcome have been learned by the algorithm, it will try to not refocus on it (in theory it is what happens, reality is not always that simple). Therefore, all the importance will be on feature `A` or on feature `B` (but not both). You will know that one feature have an important role in the link between the observations and the label. It is still up to you to search for the correlated features to the one detected as important if you need to know all of them.
-
-If you want to try Random Forests™ algorithm, you can tweak Xgboost parameters!
-
-For instance, to compute a model with 1000 trees, with a 0.5 factor on sampling rows and columns:
-
-```{r, warning=FALSE, message=FALSE}
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-
-#Random Forest™ - 1000 trees
-bst <- xgboost(data = train$data, label = train$label, max_depth = 4, num_parallel_tree = 1000, subsample = 0.5, colsample_bytree =0.5, nrounds = 1, objective = "binary:logistic")
-
-#Boosting - 3 rounds
-bst <- xgboost(data = train$data, label = train$label, max_depth = 4, nrounds = 3, objective = "binary:logistic")
-```
-
-> Note that the parameter `round` is set to `1`.
-
-> [**Random Forests™**](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_papers.htm) is a trademark of Leo Breiman and Adele Cutler and is licensed exclusively to Salford Systems for the commercial release of the software.
diff --git a/ml-xgboost/R-package/vignettes/vignette.css b/ml-xgboost/R-package/vignettes/vignette.css
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-body {
-    margin: 0 auto;
-    background-color: white;
-
-/*  --------- FONT FAMILY --------
-   following are some optional font families. Usually a family 
-	is safer to choose than a specific font, 
-	which may not be on the users computer		*/
-/    font-family:Georgia, Palatino, serif;
-    font-family: "Open Sans", "Book Antiqua", Palatino, serif;
-/    font-family:Arial, Helvetica, sans-serif;
-/    font-family:Tahoma, Verdana, Geneva, sans-serif;
-/    font-family:Courier, monospace;
-/    font-family:"Times New Roman", Times, serif;
-
-/*	-------------- COLOR OPTIONS ------------
- 	following are additional color options for base font
-	you could uncomment another one to easily change the base color 
-	or add one to a specific element style below         */		
-    color: #333333; /* dark gray not black */
-/    color: #000000; /* black */
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-/    color: #E3E3E3; /* very light gray */
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-    padding: 5px;
-    display: inline-block;
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-  background: #CDCDCD;
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-    color: #666;
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diff --git a/ml-xgboost/R-package/vignettes/xgboost.Rnw b/ml-xgboost/R-package/vignettes/xgboost.Rnw
deleted file mode 100644
index dfbb2f1..0000000
--- a/ml-xgboost/R-package/vignettes/xgboost.Rnw
+++ /dev/null
@@ -1,222 +0,0 @@
-\documentclass{article}
-\RequirePackage{url}
-\usepackage{hyperref}
-\RequirePackage{amsmath}
-\RequirePackage{natbib}
-\RequirePackage[a4paper,lmargin={1.25in},rmargin={1.25in},tmargin={1in},bmargin={1in}]{geometry}
-
-\makeatletter
-% \VignetteIndexEntry{xgboost: eXtreme Gradient Boosting}
-%\VignetteKeywords{xgboost, gbm, gradient boosting machines}
-%\VignettePackage{xgboost}
-% \VignetteEngine{knitr::knitr}
-\makeatother
-
-\begin{document}
-%\SweaveOpts{concordance=TRUE}
-
-<<knitropts,echo=FALSE,message=FALSE>>=
-if (require('knitr')) opts_chunk$set(fig.width = 5, fig.height = 5, fig.align = 'center', tidy = FALSE, warning = FALSE, cache = TRUE)
-@
-
-%
-<<prelim,echo=FALSE>>=
-xgboost.version <- packageDescription("xgboost")$Version
-
-@
-%
-
-    \begin{center}
-    \vspace*{6\baselineskip}
-    \rule{\textwidth}{1.6pt}\vspace*{-\baselineskip}\vspace*{2pt}
-    \rule{\textwidth}{0.4pt}\\[2\baselineskip]
-    {\LARGE \textbf{xgboost: eXtreme Gradient Boosting}}\\[1.2\baselineskip]
-    \rule{\textwidth}{0.4pt}\vspace*{-\baselineskip}\vspace{3.2pt}
-    \rule{\textwidth}{1.6pt}\\[2\baselineskip]
-    {\Large Tianqi Chen, Tong He}\\[\baselineskip]
-    {\large Package Version: \Sexpr{xgboost.version}}\\[\baselineskip]
-    {\large \today}\par
-    \vfill
-    \end{center}
-
-\thispagestyle{empty}
-
-\clearpage
-
-\setcounter{page}{1}
-
-\section{Introduction}
-
-This is an introductory document of using the \verb@xgboost@ package in R. 
-
-\verb@xgboost@ is short for eXtreme Gradient Boosting package. It is an efficient
- and scalable implementation of gradient boosting framework by \citep{friedman2001greedy} \citep{friedman2000additive}. 
-The package includes efficient linear model solver and tree learning algorithm.
-It supports various objective functions, including regression, classification
-and ranking. The package is made to be extendible, so that users are also allowed to define their own objectives easily. It has several features:
-\begin{enumerate}
-    \item{Speed: }{\verb@xgboost@ can automatically do parallel computation on 
-    Windows and Linux, with openmp. It is generally over 10 times faster than
-    \verb@gbm@.}
-    \item{Input Type: }{\verb@xgboost@ takes several types of input data:}
-    \begin{itemize}
-        \item{Dense Matrix: }{R's dense matrix, i.e. \verb@matrix@}
-        \item{Sparse Matrix: }{R's sparse matrix \verb@Matrix::dgCMatrix@}
-        \item{Data File: }{Local data files}
-        \item{xgb.DMatrix: }{\verb@xgboost@'s own class. Recommended.}
-    \end{itemize}
-    \item{Sparsity: }{\verb@xgboost@ accepts sparse input for both tree booster 
-    and linear booster, and is optimized for sparse input.}
-    \item{Customization: }{\verb@xgboost@ supports customized objective function 
-    and evaluation function}
-    \item{Performance: }{\verb@xgboost@ has better performance on several different
-    datasets.}
-\end{enumerate}
-
-
-\section{Example with Mushroom data}
-
-In this section, we will illustrate some common usage of \verb@xgboost@. The 
-Mushroom data is cited from UCI Machine Learning Repository. \citep{Bache+Lichman:2013} 
-
-<<Training and prediction with iris>>=
-library(xgboost)
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, 
-               nrounds = 2, objective = "binary:logistic")
-xgb.save(bst, 'model.save')
-bst = xgb.load('model.save')
-pred <- predict(bst, test$data)
-@
-
-\verb@xgboost@ is the main function to train a \verb@Booster@, i.e. a model.
-\verb@predict@ does prediction on the model.
-
-Here we can save the model to a binary local file, and load it when needed.
-We can't inspect the trees inside. However we have another function to save the
-model in plain text. 
-<<Dump Model>>=
-xgb.dump(bst, 'model.dump')
-@
-
-The output looks like 
-
-\begin{verbatim}
-booster[0]:
-0:[f28<1.00001] yes=1,no=2,missing=2
-  1:[f108<1.00001] yes=3,no=4,missing=4
-    3:leaf=1.85965
-    4:leaf=-1.94071
-  2:[f55<1.00001] yes=5,no=6,missing=6
-    5:leaf=-1.70044
-    6:leaf=1.71218
-booster[1]:
-0:[f59<1.00001] yes=1,no=2,missing=2
-  1:leaf=-6.23624
-  2:[f28<1.00001] yes=3,no=4,missing=4
-    3:leaf=-0.96853
-    4:leaf=0.784718
-\end{verbatim}
-
-It is important to know \verb@xgboost@'s own data type: \verb@xgb.DMatrix@.
-It speeds up \verb@xgboost@, and is needed for advanced features such as 
-training from initial prediction value, weighted training instance. 
-
-We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object:
-<<xgb.DMatrix>>=
-dtrain <- xgb.DMatrix(train$data, label = train$label)
-class(dtrain)
-head(getinfo(dtrain,'label'))
-@
-
-We can also save the matrix to a binary file. Then load it simply with 
-\verb@xgb.DMatrix@
-<<save model>>=
-xgb.DMatrix.save(dtrain, 'xgb.DMatrix')
-dtrain = xgb.DMatrix('xgb.DMatrix')
-@
-
-\section{Advanced Examples}
-
-The function \verb@xgboost@ is a simple function with less parameter, in order
-to be R-friendly. The core training function is wrapped in \verb@xgb.train@. It is more flexible than \verb@xgboost@, but it requires users to read the document a bit more carefully.
-
-\verb@xgb.train@ only accept a \verb@xgb.DMatrix@ object as its input, while it supports advanced features as custom objective and evaluation functions.
-
-<<Customized loss function>>=
-logregobj <- function(preds, dtrain) {
-   labels <- getinfo(dtrain, "label")
-   preds <- 1/(1 + exp(-preds))
-   grad <- preds - labels
-   hess <- preds * (1 - preds)
-   return(list(grad = grad, hess = hess))
-}
-
-evalerror <- function(preds, dtrain) {
-  labels <- getinfo(dtrain, "label")
-  err <- sqrt(mean((preds-labels)^2))
-  return(list(metric = "MSE", value = err))
-}
-
-dtest <- xgb.DMatrix(test$data, label = test$label)
-watchlist <- list(eval = dtest, train = dtrain)
-param <- list(max_depth = 2, eta = 1, silent = 1)
-
-bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, logregobj, evalerror, maximize = FALSE)
-@
-
-The gradient and second order gradient is required for the output of customized 
-objective function. 
-
-We also have \verb@slice@ for row extraction. It is useful in 
-cross-validation.
-
-For a walkthrough demo, please see \verb@R-package/demo/@ for further 
-details.
-
-\section{The Higgs Boson competition}
-
-We have made a demo for \href{http://www.kaggle.com/c/higgs-boson}{the Higgs 
-Boson Machine Learning Challenge}. 
-
-Here are the instructions to make a submission
-\begin{enumerate}
-    \item Download the \href{http://www.kaggle.com/c/higgs-boson/data}{datasets}
-    and extract them to \verb@data/@.
-    \item Run scripts under \verb@xgboost/demo/kaggle-higgs/@: 
-    \href{https://github.com/tqchen/xgboost/blob/master/demo/kaggle-higgs/higgs-train.R}{higgs-train.R} 
-    and \href{https://github.com/tqchen/xgboost/blob/master/demo/kaggle-higgs/higgs-pred.R}{higgs-pred.R}. 
-    The computation will take less than a minute on Intel i7. 
-    \item Go to the \href{http://www.kaggle.com/c/higgs-boson/submissions/attach}{submission page} 
-    and submit your result.
-\end{enumerate}
-
-We provide \href{https://github.com/tqchen/xgboost/blob/master/demo/kaggle-higgs/speedtest.R}{a script}
-to compare the time cost on the higgs dataset with \verb@gbm@ and \verb@xgboost@. 
-The training set contains 350000 records and 30 features. 
-
-\verb@xgboost@ can automatically do parallel computation. On a machine with Intel
-i7-4700MQ and 24GB memories, we found that \verb@xgboost@ costs about 35 seconds, which is about 20 times faster
-than \verb@gbm@. When we limited \verb@xgboost@ to use only one thread, it was 
-still about two times faster than \verb@gbm@. 
-
-Meanwhile, the result from \verb@xgboost@ reaches 
-\href{http://www.kaggle.com/c/higgs-boson/details/evaluation}{3.60@AMS} with a 
-single model. This results stands in the 
-\href{http://www.kaggle.com/c/higgs-boson/leaderboard}{top 30\%} of the 
-competition. 
-
-\bibliographystyle{jss}
-\nocite{*} % list uncited references
-\bibliography{xgboost}
-
-\end{document}
-
-<<Temp file cleaning, include=FALSE>>=
-file.remove("xgb.DMatrix")
-file.remove("model.dump")
-file.remove("model.save")
-@
diff --git a/ml-xgboost/R-package/vignettes/xgboost.bib b/ml-xgboost/R-package/vignettes/xgboost.bib
deleted file mode 100644
index f21bdae..0000000
--- a/ml-xgboost/R-package/vignettes/xgboost.bib
+++ /dev/null
@@ -1,30 +0,0 @@
-@article{friedman2001greedy,
-    title={Greedy function approximation: a gradient boosting machine},
-    author={Friedman, Jerome H},
-    journal={Annals of Statistics},
-    pages={1189--1232},
-    year={2001},
-    publisher={JSTOR}
-}
-
-@article{friedman2000additive,
-  title={Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)},
-  author={Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert and others},
-  journal={The annals of statistics},
-  volume={28},
-  number={2},
-  pages={337--407},
-  year={2000},
-  publisher={Institute of Mathematical Statistics}
-}
-
-
-@misc{
-    Bache+Lichman:2013 ,
-    author = "K. Bache and M. Lichman",
-    year = "2013",
-    title = "{UCI} Machine Learning Repository",
-    url = "http://archive.ics.uci.edu/ml",
-    institution = "University of California, Irvine, School of Information and Computer Sciences" 
-}
-
diff --git a/ml-xgboost/R-package/vignettes/xgboostPresentation.Rmd b/ml-xgboost/R-package/vignettes/xgboostPresentation.Rmd
deleted file mode 100644
index 6d1bab7..0000000
--- a/ml-xgboost/R-package/vignettes/xgboostPresentation.Rmd
+++ /dev/null
@@ -1,428 +0,0 @@
----
-title: "Xgboost presentation"
-output:
-  rmarkdown::html_vignette:
-    css: vignette.css
-    number_sections: yes
-    toc: yes
-bibliography: xgboost.bib
-author: Tianqi Chen, Tong He, Michaël Benesty
-vignette: >
-  %\VignetteIndexEntry{Xgboost presentation}
-  %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
----
-
-XGBoost R Tutorial
-==================
-
-## Introduction
-
-
-**Xgboost** is short for e**X**treme **G**radient **Boost**ing package.
-
-The purpose of this Vignette is to show you how to use **Xgboost** to build a model and make predictions.
-
-It is an efficient and scalable implementation of gradient boosting framework by @friedman2000additive and @friedman2001greedy. Two solvers are included:
-
-- *linear* model ;
-- *tree learning* algorithm.
-
-It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objective functions easily.
-
-It has been [used](https://github.com/dmlc/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions.
-
-It has several features:
-
-* Speed: it can automatically do parallel computation on *Windows* and *Linux*, with *OpenMP*. It is generally over 10 times faster than the classical `gbm`.
-* Input Type: it takes several types of input data:
-    * *Dense* Matrix: *R*'s *dense* matrix, i.e. `matrix` ;
-    * *Sparse* Matrix: *R*'s *sparse* matrix, i.e. `Matrix::dgCMatrix` ;
-    * Data File: local data files ;
-    * `xgb.DMatrix`: its own class (recommended).
-* Sparsity: it accepts *sparse* input for both *tree booster*  and *linear booster*, and is optimized for *sparse* input ;
-* Customization: it supports customized objective functions and evaluation functions.
-
-## Installation
-
-
-### Github version
-
-
-For weekly updated version (highly recommended), install from *Github*:
-
-```{r installGithub, eval=FALSE}
-install.packages("drat", repos="https://cran.rstudio.com")
-drat:::addRepo("dmlc")
-install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
-```
-
-> *Windows* user will need to install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) first.
-
-### CRAN version
-
-
-The version 0.4-2 is on CRAN, and you can install it by:
-
-```{r, eval=FALSE}
-install.packages("xgboost")
-```
-
-Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost)
-
-## Learning
-
-
-For the purpose of this tutorial we will load **XGBoost** package.
-
-```{r libLoading, results='hold', message=F, warning=F}
-require(xgboost)
-```
-
-### Dataset presentation
-
-
-In this example, we are aiming to predict whether a mushroom can be eaten or not (like in many tutorials, example data are the the same as you will use on in your every day life :-).
-
-Mushroom data is cited from UCI Machine Learning Repository. @Bache+Lichman:2013.
-
-### Dataset loading
-
-
-We will load the `agaricus` datasets embedded with the package and will link them to variables.
-
-The datasets are already split in:
-
-* `train`: will be used to build the model ;
-* `test`: will be used to assess the quality of our model.
-
-Why *split* the dataset in two parts?
-
-In the first part we will build our model. In the second part we will want to test it and assess its quality. Without dividing the dataset we would test the model on the data which the algorithm have already seen.
-
-```{r datasetLoading, results='hold', message=F, warning=F}
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-```
-
-> In the real world, it would be up to you to make this division between `train` and `test` data. The way to do it is out of the purpose of this article, however `caret` package may [help](http://topepo.github.io/caret/data-splitting.html).
-
-Each variable is a `list` containing two things, `label` and `data`:
-
-```{r dataList, message=F, warning=F}
-str(train)
-```
-
-`label` is the outcome of our dataset meaning it is the binary *classification* we will try to predict.
-
-Let's discover the dimensionality of our datasets.
-
-```{r dataSize, message=F, warning=F}
-dim(train$data)
-dim(test$data)
-```
-
-This dataset is very small to not make the **R** package too heavy, however **XGBoost** is built to manage huge dataset very efficiently.
-
-As seen below, the `data` are stored in a `dgCMatrix` which is a *sparse* matrix and `label` vector is a `numeric` vector (`{0,1}`):
-
-```{r dataClass, message=F, warning=F}
-class(train$data)[1]
-class(train$label)
-```
-
-### Basic Training using XGBoost
-
-
-This step is the most critical part of the process for the quality of our model.
-
-#### Basic training
-
-We are using the `train` data. As explained above, both `data` and `label` are stored in a `list`.
-
-In a *sparse* matrix, cells containing `0` are not stored in memory. Therefore, in a dataset mainly made of `0`, memory size is reduced. It is very usual to have such dataset.
-
-We will train decision tree model using the following parameters:
-
-* `objective = "binary:logistic"`: we will train a binary classification model ;
-* `max_depth = 2`: the trees won't be deep, because our case is very simple ;
-* `nthread = 2`: the number of cpu threads we are going to use;
-* `nrounds = 2`: there will be two passes on the data, the second one will enhance the model by further reducing the difference between ground truth and prediction.
-
-```{r trainingSparse, message=F, warning=F}
-bstSparse <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-```
-
-> More complex the relationship between your features and your `label` is, more passes you need.
-
-#### Parameter variations
-
-##### Dense matrix
-
-Alternatively, you can put your dataset in a *dense* matrix, i.e. a basic **R** matrix.
-
-```{r trainingDense, message=F, warning=F}
-bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-```
-
-##### xgb.DMatrix
-
-**XGBoost** offers a way to group them in a `xgb.DMatrix`. You can even add other meta data in it. It will be useful for the most advanced features we will discover later.
-
-```{r trainingDmatrix, message=F, warning=F}
-dtrain <- xgb.DMatrix(data = train$data, label = train$label)
-bstDMatrix <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-```
-
-##### Verbose option
-
-**XGBoost** has several features to help you to view how the learning progress internally. The purpose is to help you to set the best parameters, which is the key of your model quality.
-
-One of the simplest way to see the training progress is to set the `verbose` option (see below for more advanced technics).
-
-```{r trainingVerbose0, message=T, warning=F}
-# verbose = 0, no message
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
-```
-
-```{r trainingVerbose1, message=T, warning=F}
-# verbose = 1, print evaluation metric
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 1)
-```
-
-```{r trainingVerbose2, message=T, warning=F}
-# verbose = 2, also print information about tree
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 2)
-```
-
-## Basic prediction using XGBoost
-
-
-## Perform the prediction
-
-
-The purpose of the model we have built is to classify new data. As explained before, we will use the `test` dataset for this step.
-
-```{r predicting, message=F, warning=F}
-pred <- predict(bst, test$data)
-
-# size of the prediction vector
-print(length(pred))
-
-# limit display of predictions to the first 10
-print(head(pred))
-```
-
-These numbers doesn't look like *binary classification* `{0,1}`. We need to perform a simple transformation before being able to use these results.
-
-## Transform the regression in a binary classification
-
-
-The only thing that **XGBoost** does is a *regression*. **XGBoost** is using `label` vector to build its *regression* model.
-
-How can we use a *regression* model to perform a binary classification?
-
-If we think about the meaning of a regression applied to our data, the numbers we get are probabilities that a datum will be classified as `1`. Therefore, we will set the rule that if this probability for a specific datum is `> 0.5` then the observation is classified as `1` (or `0` otherwise).
-
-```{r predictingTest, message=F, warning=F}
-prediction <- as.numeric(pred > 0.5)
-print(head(prediction))
-```
-
-## Measuring model performance
-
-
-To measure the model performance, we will compute a simple metric, the *average error*.
-
-```{r predictingAverageError, message=F, warning=F}
-err <- mean(as.numeric(pred > 0.5) != test$label)
-print(paste("test-error=", err))
-```
-
-> Note that the algorithm has not seen the `test` data during the model construction.
-
-Steps explanation:
-
-1. `as.numeric(pred > 0.5)` applies our rule that when the probability (<=> regression <=> prediction) is `> 0.5` the observation is classified as `1` and `0` otherwise ;
-2. `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
-3. `mean(vectorOfErrors)` computes the *average error* itself.
-
-The most important thing to remember is that **to do a classification, you just do a regression to the** `label` **and then apply a threshold**.
-
-*Multiclass* classification works in a similar way.
-
-This metric is **`r round(err, 2)`** and is pretty low: our yummly mushroom model works well!
-
-## Advanced features
-
-
-Most of the features below have been implemented to help you to improve your model by offering a better understanding of its content.
-
-
-### Dataset preparation
-
-
-For the following advanced features, we need to put data in `xgb.DMatrix` as explained above.
-
-```{r DMatrix, message=F, warning=F}
-dtrain <- xgb.DMatrix(data = train$data, label=train$label)
-dtest <- xgb.DMatrix(data = test$data, label=test$label)
-```
-
-### Measure learning progress with xgb.train
-
-
-Both `xgboost` (simple) and `xgb.train` (advanced) functions train models.
-
-One of the special feature of `xgb.train` is the capacity to follow the progress of the learning after each round. Because of the way boosting works, there is a time when having too many rounds lead to an overfitting. You can see this feature as a cousin of cross-validation method. The following techniques will help you to avoid overfitting or optimizing the learning time in stopping it as soon as possible.
-
-One way to measure progress in learning of a model is to provide to **XGBoost** a second dataset already classified. Therefore it can learn on the first dataset and test its model on the second one. Some metrics are measured after each round during the learning.
-
-> in some way it is similar to what we have done above with the average error. The main difference is that below it was after building the model, and now it is during the construction that we measure errors.
-
-For the purpose of this example, we use `watchlist` parameter. It is a list of `xgb.DMatrix`, each of them tagged with a name.
-
-```{r watchlist, message=F, warning=F}
-watchlist <- list(train=dtrain, test=dtest)
-
-bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
-```
-
-**XGBoost** has computed at each round the same average error metric than seen above (we set `nrounds` to 2, that is why we have two lines). Obviously, the `train-error` number is related to the training dataset (the one the algorithm learns from) and the `test-error` number to the test dataset.
-
-Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.
-
-If with your own dataset you have not such results, you should think about how you divided your dataset in training and test. May be there is something to fix. Again, `caret` package may [help](http://topepo.github.io/caret/data-splitting.html).
-
-For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.
-
-```{r watchlist2, message=F, warning=F}
-bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, eval_metric = "error", eval_metric = "logloss", objective = "binary:logistic")
-```
-
-> `eval_metric` allows us to monitor two new metrics for each round, `logloss` and `error`.
-
-### Linear boosting
-
-
-Until now, all the learnings we have performed were based on boosting trees. **XGBoost** implements a second algorithm, based on linear boosting. The only difference with previous command is `booster = "gblinear"` parameter (and removing `eta` parameter).
-
-```{r linearBoosting, message=F, warning=F}
-bst <- xgb.train(data=dtrain, booster = "gblinear", max_depth=2, nthread = 2, nrounds=2, watchlist=watchlist, eval_metric = "error", eval_metric = "logloss", objective = "binary:logistic")
-```
-
-In this specific case, *linear boosting* gets slightly better performance metrics than decision trees based algorithm.
-
-In simple cases, it will happen because there is nothing better than a linear algorithm to catch a linear link. However, decision trees are much better to catch a non linear link between predictors and outcome. Because there is no silver bullet, we advise you to check both algorithms with your own datasets to have an idea of what to use.
-
-### Manipulating xgb.DMatrix
-
-
-#### Save / Load
-
-Like saving models, `xgb.DMatrix` object (which groups both dataset and outcome) can also be saved using `xgb.DMatrix.save` function.
-
-```{r DMatrixSave, message=F, warning=F}
-xgb.DMatrix.save(dtrain, "dtrain.buffer")
-# to load it in, simply call xgb.DMatrix
-dtrain2 <- xgb.DMatrix("dtrain.buffer")
-bst <- xgb.train(data=dtrain2, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
-```
-
-```{r DMatrixDel, include=FALSE}
-file.remove("dtrain.buffer")
-```
-
-#### Information extraction
-
-Information can be extracted from `xgb.DMatrix` using `getinfo` function. Hereafter we will extract `label` data.
-
-```{r getinfo, message=F, warning=F}
-label = getinfo(dtest, "label")
-pred <- predict(bst, dtest)
-err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
-print(paste("test-error=", err))
-```
-
-### View feature importance/influence from the learnt model
-
-
-Feature importance is similar to R gbm package's relative influence (rel.inf).
-
-```
-importance_matrix <- xgb.importance(model = bst)
-print(importance_matrix)
-xgb.plot.importance(importance_matrix = importance_matrix)
-```
-
-#### View the trees from a model
-
-
-You can dump the tree you learned using `xgb.dump` into a text file.
-
-```{r dump, message=T, warning=F}
-xgb.dump(bst, with_stats = T)
-```
-
-You can plot the trees from your model using ```xgb.plot.tree``
-
-```
-xgb.plot.tree(model = bst)
-```
-
-> if you provide a path to `fname` parameter you can save the trees to your hard drive.
-
-#### Save and load models
-
-
-Maybe your dataset is big, and it takes time to train a model on it? May be you are not a big fan of losing time in redoing the same task again and again? In these very rare cases, you will want to save your model and load it when required.
-
-Hopefully for you, **XGBoost** implements such functions.
-
-```{r saveModel, message=F, warning=F}
-# save model to binary local file
-xgb.save(bst, "xgboost.model")
-```
-
-> `xgb.save` function should return `r TRUE` if everything goes well and crashes otherwise.
-
-An interesting test to see how identical our saved model is to the original one would be to compare the two predictions.
-
-```{r loadModel, message=F, warning=F}
-# load binary model to R
-bst2 <- xgb.load("xgboost.model")
-pred2 <- predict(bst2, test$data)
-
-# And now the test
-print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
-```
-
-```{r clean, include=FALSE}
-# delete the created model
-file.remove("./xgboost.model")
-```
-
-> result is `0`? We are good!
-
-In some very specific cases, like when you want to pilot **XGBoost** from `caret` package, you will want to save the model as a *R* binary vector. See below how to do it.
-
-```{r saveLoadRBinVectorModel, message=F, warning=F}
-# save model to R's raw vector
-rawVec <- xgb.serialize(bst)
-
-# print class
-print(class(rawVec))
-
-# load binary model to R
-bst3 <- xgb.load(rawVec)
-pred3 <- predict(bst3, test$data)
-
-# pred2 should be identical to pred
-print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
-```
-
-> Again `0`? It seems that `XGBoost` works pretty well!
-
-## References
diff --git a/ml-xgboost/R-package/vignettes/xgboostfromJSON.Rmd b/ml-xgboost/R-package/vignettes/xgboostfromJSON.Rmd
deleted file mode 100644
index 492f3a7..0000000
--- a/ml-xgboost/R-package/vignettes/xgboostfromJSON.Rmd
+++ /dev/null
@@ -1,189 +0,0 @@
----
-title: "XGBoost from JSON"
-output:
-  rmarkdown::html_vignette:
-    number_sections: yes
-    toc: yes
-author: Roland Stevenson
-vignette: >
-  %\VignetteIndexEntry{XGBoost from JSON}
-  %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
----
-
-XGBoost from JSON
-=================
-
-## Introduction
-
-The purpose of this Vignette is to show you how to correctly load and work with an **Xgboost** model that has been dumped to JSON.  **Xgboost** internally converts all data to [32-bit floats](https://en.wikipedia.org/wiki/Single-precision_floating-point_format), and the values dumped to JSON are decimal representations of these values.  When working with a model that has been parsed from a JSON file, care must be taken to correctly treat:
-
-- the input data, which should be converted to 32-bit floats
-- any 32-bit floats that were stored in JSON as decimal representations
-- any calculations must be done with 32-bit mathematical operators
-
-## Setup
-
-For the purpose of this tutorial we will load the xgboost, jsonlite, and float packages.  We'll also set `digits=22` in our options in case we want to inspect many digits of our results.
-
-```{r}
-require(xgboost)
-require(jsonlite)
-require(float)
-options(digits=22)
-```
-
-We will create a toy binary logistic model based on the example first provided [here](https://github.com/dmlc/xgboost/issues/3960), so that we can easily understand the structure of the dumped JSON model object.  This will allow us to understand where discrepancies can occur and how they should be handled.
-
-```{r}
-dates <- c(20180130, 20180130, 20180130,
-           20180130, 20180130, 20180130,
-           20180131, 20180131, 20180131,
-           20180131, 20180131, 20180131,
-           20180131, 20180131, 20180131,
-           20180134, 20180134, 20180134)
-
-labels <- c(1, 1, 1,
-            1, 1, 1,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0)
-
-data <- data.frame(dates = dates, labels=labels)
-
-bst <- xgboost(
-  data = as.matrix(data$dates), 
-  label = labels,
-  nthread = 2,
-  nrounds = 1,
-  objective = "binary:logistic",
-  missing = NA,
-  max_depth = 1
-)
-```
-
-## Comparing results
-We will now dump the model to JSON and attempt to illustrate a variety of issues that can arise, and how to properly deal with them.
-
-First let's dump the model to JSON:
-
-```{r}
-bst_json <- xgb.dump(bst, with_stats = FALSE, dump_format='json')
-bst_from_json <- fromJSON(bst_json, simplifyDataFrame = FALSE)
-node <- bst_from_json[[1]]
-cat(bst_json)
-```
-
-The tree JSON shown by the above code-chunk tells us that if the data is less than 20180132, the tree will output the value in the first leaf.  Otherwise it will output the value in the second leaf.  Let's try to reproduce this manually with the data we have and confirm that it matches the model predictions we've already calculated.
-
-```{r}
-bst_preds_logodds <- predict(bst,as.matrix(data$dates), outputmargin = TRUE)
-
-# calculate the logodds values using the JSON representation
-bst_from_json_logodds <- ifelse(data$dates<node$split_condition,
-                                node$children[[1]]$leaf,
-                                node$children[[2]]$leaf)
-
-bst_preds_logodds
-bst_from_json_logodds
-
-# test that values are equal
-bst_preds_logodds == bst_from_json_logodds
-
-```
-None are equal.  What happened?
-
-At this stage two things happened:
-
-- input data was not converted to 32-bit floats
-- the JSON variables were not converted to 32-bit floats
-
-### Lesson 1: All data is 32-bit floats
-
-> When working with imported JSON, all data must be converted to 32-bit floats
-
-To explain this, let's repeat the comparison and round to two decimals:
-
-```{r}
-round(bst_preds_logodds,2) == round(bst_from_json_logodds,2)
-```
-
-If we round to two decimals, we see that only the elements related to data values of `20180131` don't agree.  If we convert the data to floats, they agree:
-
-```{r}
-# now convert the dates to floats first
-bst_from_json_logodds <- ifelse(fl(data$dates)<node$split_condition,
-                                node$children[[1]]$leaf,
-                                node$children[[2]]$leaf)
-
-# test that values are equal
-round(bst_preds_logodds,2) == round(bst_from_json_logodds,2)
-```
-
-What's the lesson?  If we are going to work with an imported JSON model, any data must be converted to floats first.  In this case, since '20180131' cannot be represented as a 32-bit float, it is rounded up to 20180132, as shown here:
-
-```{r}
-fl(20180131)
-```
-
-
-### Lesson 2: JSON parameters are 32-bit floats
-
-> All JSON parameters stored as floats must be converted to floats.
-
-Let's now say we do care about numbers past the first two decimals.
-
-```{r}
-# test that values are equal
-bst_preds_logodds == bst_from_json_logodds
-```
-
-None are exactly equal.  What happened?  Although we've converted the data to 32-bit floats, we also need to convert the JSON parameters to 32-bit floats.  Let's do this:
-
-```{r}
-# now convert the dates to floats first
-bst_from_json_logodds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                                as.numeric(fl(node$children[[1]]$leaf)),
-                                as.numeric(fl(node$children[[2]]$leaf)))
-
-# test that values are equal
-bst_preds_logodds == bst_from_json_logodds
-```
-All equal.  What's the lesson?  If we are going to work with an imported JSON model, any JSON parameters that were stored as floats must also be converted to floats first.
-
-### Lesson 3: Use 32-bit math
-
-> Always use 32-bit numbers and operators
-
-We were able to get the log-odds to agree, so now let's manually calculate the sigmoid of the log-odds.  This should agree with the xgboost predictions.
-
-
-```{r}
-bst_preds <- predict(bst,as.matrix(data$dates))
-
-# calculate the predictions casting doubles to floats
-bst_from_json_preds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                              as.numeric(1/(1+exp(-1*fl(node$children[[1]]$leaf)))),
-                              as.numeric(1/(1+exp(-1*fl(node$children[[2]]$leaf))))
-)
-
-# test that values are equal
-bst_preds == bst_from_json_preds
-```
-
-None are exactly equal again.  What is going on here?  Well, since we are using the value `1` in the calcuations, we have introduced a double into the calculation.  Because of this, all float values are promoted to 64-bit doubles and the 64-bit version of the exponentiation operator `exp` is also used.  On the other hand, xgboost uses the 32-bit version of the exponentation operator in its [sigmoid function](https://github.com/dmlc/xgboost/blob/54980b8959680a0da06a3fc0ec776e47c8cbb0a1/src/common/math.h#L25-L27).
-
-How do we fix this?  We have to ensure we use the correct datatypes everywhere and the correct operators.  If we use only floats, the float library that we have loaded will ensure the 32-bit float exponention operator is applied. 
-```{r}
-# calculate the predictions casting doubles to floats
-bst_from_json_preds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                              as.numeric(fl(1)/(fl(1)+exp(fl(-1)*fl(node$children[[1]]$leaf)))),
-                              as.numeric(fl(1)/(fl(1)+exp(fl(-1)*fl(node$children[[2]]$leaf))))
-)
-
-# test that values are equal
-bst_preds == bst_from_json_preds
-```
-
-All equal.  What's the lesson?  We have to ensure that all calculations are done with 32-bit floating point operators if we want to reproduce the results that we see with xgboost.
diff --git a/ml-xgboost/README.md b/ml-xgboost/README.md
deleted file mode 100644
index 733449d..0000000
--- a/ml-xgboost/README.md
+++ /dev/null
@@ -1,67 +0,0 @@
-<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/>  eXtreme Gradient Boosting
-===========
-[![Build Status](https://xgboost-ci.net/job/xgboost/job/master/badge/icon?style=plastic)](https://xgboost-ci.net/blue/organizations/jenkins/xgboost/activity)
-[![Build Status](https://img.shields.io/travis/dmlc/xgboost.svg?label=build&logo=travis&branch=master)](https://travis-ci.org/dmlc/xgboost)
-[![Build Status](https://ci.appveyor.com/api/projects/status/5ypa8vaed6kpmli8?svg=true)](https://ci.appveyor.com/project/tqchen/xgboost)
-[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org)
-[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
-[![CRAN Status Badge](http://www.r-pkg.org/badges/version/xgboost)](http://cran.r-project.org/web/packages/xgboost)
-[![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/)
-[![Optuna](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org)
-
-[Community](https://xgboost.ai/community) |
-[Documentation](https://xgboost.readthedocs.org) |
-[Resources](demo/README.md) |
-[Contributors](CONTRIBUTORS.md) |
-[Release Notes](NEWS.md)
-
-XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
-It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
-XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
-The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
-
-License
--------
-© Contributors, 2019. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
-
-Contribute to XGBoost
----------------------
-XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
-Checkout the [Community Page](https://xgboost.ai/community).
-
-Reference
----------
-- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
-- XGBoost originates from research project at University of Washington.
-
-Sponsors
---------
-Become a sponsor and get a logo here. See details at [Sponsoring the XGBoost Project](https://xgboost.ai/sponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
-
-## Open Source Collective sponsors
-[![Backers on Open Collective](https://opencollective.com/xgboost/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/xgboost/sponsors/badge.svg)](#sponsors)
-
-### Sponsors
-[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]
-
-<!--<a href="https://opencollective.com/xgboost/sponsor/0/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/0/avatar.svg"></a>-->
-<a href="https://www.nvidia.com/en-us/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg" alt="NVIDIA" width="72" height="72"></a>
-<a href="https://opencollective.com/xgboost/sponsor/1/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/1/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/2/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/2/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/3/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/3/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/4/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/4/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/5/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/5/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/6/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/6/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/7/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/7/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/8/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/8/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/9/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/9/avatar.svg"></a>
-
-### Backers
-[[Become a backer](https://opencollective.com/xgboost#backer)]
-
-<a href="https://opencollective.com/xgboost#backers" target="_blank"><img src="https://opencollective.com/xgboost/backers.svg?width=890"></a>
-
-## Other sponsors
-The sponsors in this list are donating cloud hours in lieu of cash donation.
-
-<a href="https://aws.amazon.com/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/aws.png" alt="Amazon Web Services" width="72" height="72"></a>
diff --git a/ml-xgboost/amalgamation/dmlc-minimum0.cc b/ml-xgboost/amalgamation/dmlc-minimum0.cc
deleted file mode 100644
index d8594aa..0000000
--- a/ml-xgboost/amalgamation/dmlc-minimum0.cc
+++ /dev/null
@@ -1,16 +0,0 @@
-/*!
- * Copyright 2015 by Contributors.
- * \brief Mininum DMLC library Amalgamation, used for easy plugin of dmlc lib.
- *  Normally this is not needed.
- */
-#include "../dmlc-core/src/io/line_split.cc"
-#include "../dmlc-core/src/io/recordio_split.cc"
-#include "../dmlc-core/src/io/input_split_base.cc"
-#include "../dmlc-core/src/io/local_filesys.cc"
-#include "../dmlc-core/src/io/filesys.cc"
-#include "../dmlc-core/src/io/indexed_recordio_split.cc"
-#include "../dmlc-core/src/data.cc"
-#include "../dmlc-core/src/io.cc"
-#include "../dmlc-core/src/recordio.cc"
-
-
diff --git a/ml-xgboost/amalgamation/xgboost-all0.cc b/ml-xgboost/amalgamation/xgboost-all0.cc
deleted file mode 100644
index 5f7e75f..0000000
--- a/ml-xgboost/amalgamation/xgboost-all0.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors.
- * \brief XGBoost Amalgamation.
- *  This offers an alternative way to compile the entire library from this single file.
- *
- *  Example usage command.
- *  - $(CXX) -std=c++0x -fopenmp -o -shared libxgboost.so xgboost-all0.cc -ldmlc -lrabit
- *
- * \author Tianqi Chen.
- */
-
-// metrics
-#include "../src/metric/metric.cc"
-#include "../src/metric/elementwise_metric.cc"
-#include "../src/metric/multiclass_metric.cc"
-#include "../src/metric/rank_metric.cc"
-#include "../src/metric/survival_metric.cc"
-
-// objectives
-#include "../src/objective/objective.cc"
-#include "../src/objective/regression_obj.cc"
-#include "../src/objective/multiclass_obj.cc"
-#include "../src/objective/rank_obj.cc"
-#include "../src/objective/hinge.cc"
-#include "../src/objective/aft_obj.cc"
-
-// gbms
-#include "../src/gbm/gbm.cc"
-#include "../src/gbm/gbtree.cc"
-#include "../src/gbm/gbtree_model.cc"
-#include "../src/gbm/gblinear.cc"
-#include "../src/gbm/gblinear_model.cc"
-
-// data
-#include "../src/data/data.cc"
-#include "../src/data/simple_dmatrix.cc"
-#include "../src/data/sparse_page_raw_format.cc"
-#include "../src/data/ellpack_page.cc"
-#include "../src/data/ellpack_page_source.cc"
-
-// prediction
-#include "../src/predictor/predictor.cc"
-#include "../src/predictor/cpu_predictor.cc"
-
-#if DMLC_ENABLE_STD_THREAD
-#include "../src/data/sparse_page_dmatrix.cc"
-#endif
-
-// trees
-#include "../src/tree/param.cc"
-#include "../src/tree/split_evaluator.cc"
-#include "../src/tree/tree_model.cc"
-#include "../src/tree/tree_updater.cc"
-#include "../src/tree/updater_colmaker.cc"
-#include "../src/tree/updater_quantile_hist.cc"
-#include "../src/tree/updater_prune.cc"
-#include "../src/tree/updater_refresh.cc"
-#include "../src/tree/updater_sync.cc"
-#include "../src/tree/updater_histmaker.cc"
-#include "../src/tree/updater_skmaker.cc"
-#include "../src/tree/constraints.cc"
-
-// linear
-#include "../src/linear/linear_updater.cc"
-#include "../src/linear/updater_coordinate.cc"
-#include "../src/linear/updater_shotgun.cc"
-
-// global
-#include "../src/learner.cc"
-#include "../src/logging.cc"
-#include "../src/common/common.cc"
-#include "../src/common/timer.cc"
-#include "../src/common/host_device_vector.cc"
-#include "../src/common/hist_util.cc"
-#include "../src/common/json.cc"
-#include "../src/common/io.cc"
-#include "../src/common/survival_util.cc"
-#include "../src/common/probability_distribution.cc"
-#include "../src/common/version.cc"
-
-// c_api
-#include "../src/c_api/c_api.cc"
-#include "../src/c_api/c_api_error.cc"
diff --git a/ml-xgboost/appveyor.yml b/ml-xgboost/appveyor.yml
deleted file mode 100644
index 68933db..0000000
--- a/ml-xgboost/appveyor.yml
+++ /dev/null
@@ -1,133 +0,0 @@
-environment:
-    R_ARCH: x64
-    USE_RTOOLS: true
-    matrix:
-        - target: msvc
-          ver: 2015
-          generator: "Visual Studio 14 2015 Win64"
-          configuration: Debug
-        - target: msvc
-          ver: 2015
-          generator: "Visual Studio 14 2015 Win64"
-          configuration: Release
-        - target: mingw
-          generator: "Unix Makefiles"
-        - target: jvm
-        - target: rmsvc
-          ver: 2015
-          generator: "Visual Studio 14 2015 Win64"
-          configuration: Release
-        - target: rmingw
-          generator: "Unix Makefiles"
-
-#matrix:
-#    fast_finish: true
-
-platform:
-    - x64
-
-install:
-    - git submodule update --init --recursive
-    # MinGW
-    - set PATH=C:\msys64\mingw64\bin;C:\msys64\usr\bin;%PATH%
-    - gcc -v
-    - ls -l C:\
-    # Miniconda3
-    - call C:\Miniconda3-x64\Scripts\activate.bat
-    - conda info
-    - where python
-    - python --version
-    # do python build for mingw and one of the msvc jobs
-    - set DO_PYTHON=off
-    - if /i "%target%" == "mingw" set DO_PYTHON=on
-    - if /i "%target%_%ver%_%configuration%" == "msvc_2015_Release" set DO_PYTHON=on
-    - if /i "%DO_PYTHON%" == "on" (
-        conda config --set always_yes true &&
-        conda update -q conda &&
-        conda install -y numpy scipy pandas matplotlib pytest scikit-learn graphviz python-graphviz
-      )
-    - set PATH=C:\Miniconda3-x64\Library\bin\graphviz;%PATH%
-    # R: based on https://github.com/krlmlr/r-appveyor
-    - ps: |
-        if($env:target -eq 'rmingw' -or $env:target -eq 'rmsvc') {
-          #$ErrorActionPreference = "Stop"
-          Invoke-WebRequest https://raw.githubusercontent.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "$Env:TEMP\appveyor-tool.ps1"
-          Import-Module "$Env:TEMP\appveyor-tool.ps1"
-          Bootstrap
-          $BINARY_DEPS = "c('XML','igraph')"
-          cmd.exe /c "R.exe -q -e ""install.packages($BINARY_DEPS, repos='$CRAN', type='win.binary')"" 2>&1"
-          $DEPS = "c('data.table','magrittr','stringi','ggplot2','DiagrammeR','Ckmeans.1d.dp','vcd','testthat','lintr','knitr','rmarkdown')"
-          cmd.exe /c "R.exe -q -e ""install.packages($DEPS, repos='$CRAN', type='both')"" 2>&1"
-        }
-
-build_script:
-    - cd %APPVEYOR_BUILD_FOLDER%
-    - if /i "%target%" == "msvc" (
-        mkdir build_msvc%ver% &&
-        cd build_msvc%ver% &&
-        cmake .. -G"%generator%" -DCMAKE_CONFIGURATION_TYPES="Release;Debug;" &&
-        msbuild xgboost.sln
-      )
-    - if /i "%target%" == "mingw" (
-        mkdir build_mingw &&
-        cd build_mingw &&
-        cmake .. -G"%generator%" &&
-        make -j2
-      )
-    # Python package
-    - if /i "%DO_PYTHON%" == "on" (
-        cd %APPVEYOR_BUILD_FOLDER%\python-package &&
-        python setup.py install &&
-        mkdir wheel &&
-        python setup.py bdist_wheel --universal --plat-name win-amd64 -d wheel
-      )
-    # R package: make + mingw standard CRAN packaging (only x64 for now)
-    - if /i "%target%" == "rmingw" (
-        make Rbuild &&
-        ls -l &&
-        R.exe CMD INSTALL xgboost*.tar.gz
-      )
-    # R package: cmake + VC2015
-    - if /i "%target%" == "rmsvc" (
-        mkdir build_rmsvc%ver% &&
-        cd build_rmsvc%ver% &&
-        cmake .. -G"%generator%" -DCMAKE_CONFIGURATION_TYPES="Release" -DR_LIB=ON &&
-        cmake --build . --target install --config Release
-      )
-    - if /i "%target%" == "jvm" cd jvm-packages && mvn test -pl :boostkit-xgboost4j_2.11
-
-test_script:
-    - cd %APPVEYOR_BUILD_FOLDER%
-    - if /i "%DO_PYTHON%" == "on" python -m pytest tests/python
-    # mingw R package: run the R check (which includes unit tests), and also keep the built binary package
-    - if /i "%target%" == "rmingw" (
-        set _R_CHECK_CRAN_INCOMING_=FALSE&&
-        set _R_CHECK_FORCE_SUGGESTS_=FALSE&&
-        R.exe CMD check xgboost*.tar.gz --no-manual --no-build-vignettes --as-cran --install-args=--build
-      )
-    # MSVC R package: run only the unit tests
-    - if /i "%target%" == "rmsvc" (
-        cd build_rmsvc%ver%\R-package &&
-        R.exe -q -e "library(testthat); setwd('tests'); source('testthat.R')"
-      )
-
-on_failure:
-    # keep the whole output of R check
-    - if /i "%target%" == "rmingw" (
-        7z a failure.zip *.Rcheck\* &&
-        appveyor PushArtifact failure.zip
-      )
-
-artifacts:
-    # log from R check
-    - path: '*.Rcheck\**\*.log'
-      name: Logs
-    # source R-package
-    - path: '\xgboost_*.tar.gz'
-      name: Bits
-    # binary R-package
-    - path: '**\xgboost_*.zip'
-      name: Bits
-    # binary Python wheel package
-    - path: '**\*.whl'
-      name: Bits
diff --git a/ml-xgboost/cmake/Doc.cmake b/ml-xgboost/cmake/Doc.cmake
deleted file mode 100644
index 2ffa005..0000000
--- a/ml-xgboost/cmake/Doc.cmake
+++ /dev/null
@@ -1,16 +0,0 @@
-function (run_doxygen)
-  find_package(Doxygen REQUIRED)
-
-  if (NOT DOXYGEN_DOT_FOUND)
-    message(FATAL_ERROR "Command `dot` not found.  Please install graphviz.")
-  endif (NOT DOXYGEN_DOT_FOUND)
-
-  configure_file(
-    ${xgboost_SOURCE_DIR}/doc/Doxyfile.in
-    ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
-  add_custom_target( doc_doxygen ALL
-    COMMAND ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
-    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
-    COMMENT "Generate C APIs documentation."
-    VERBATIM)
-endfunction (run_doxygen)
diff --git a/ml-xgboost/cmake/FindPrefetchIntrinsics.cmake b/ml-xgboost/cmake/FindPrefetchIntrinsics.cmake
deleted file mode 100644
index b00ff57..0000000
--- a/ml-xgboost/cmake/FindPrefetchIntrinsics.cmake
+++ /dev/null
@@ -1,22 +0,0 @@
-function (find_prefetch_intrinsics)
-  include(CheckCXXSourceCompiles)
-  check_cxx_source_compiles("
-  #include <xmmintrin.h>
-  int main() {
-    char data = 0;
-    const char* address = &data;
-    _mm_prefetch(address, _MM_HINT_NTA);
-    return 0;
-  }
-  " XGBOOST_MM_PREFETCH_PRESENT)
-  check_cxx_source_compiles("
-  int main() {
-    char data = 0;
-    const char* address = &data;
-    __builtin_prefetch(address, 0, 0);
-    return 0;
-  }
-  " XGBOOST_BUILTIN_PREFETCH_PRESENT)
-  set(XGBOOST_MM_PREFETCH_PRESENT ${XGBOOST_MM_PREFETCH_PRESENT} PARENT_SCOPE)
-  set(XGBOOST_BUILTIN_PREFETCH_PRESENT ${XGBOOST_BUILTIN_PREFETCH_PRESENT} PARENT_SCOPE)
-endfunction (find_prefetch_intrinsics)
diff --git a/ml-xgboost/cmake/Python_version.in b/ml-xgboost/cmake/Python_version.in
deleted file mode 100644
index c55458e..0000000
--- a/ml-xgboost/cmake/Python_version.in
+++ /dev/null
@@ -1 +0,0 @@
-@xgboost_VERSION_MAJOR@.@xgboost_VERSION_MINOR@.@xgboost_VERSION_PATCH@
diff --git a/ml-xgboost/cmake/Sanitizer.cmake b/ml-xgboost/cmake/Sanitizer.cmake
deleted file mode 100644
index c1afb14..0000000
--- a/ml-xgboost/cmake/Sanitizer.cmake
+++ /dev/null
@@ -1,63 +0,0 @@
-# Set appropriate compiler and linker flags for sanitizers.
-#
-# Usage of this module:
-#  enable_sanitizers("address;leak")
-
-# Add flags
-macro(enable_sanitizer sanitizer)
-  if(${sanitizer} MATCHES "address")
-    find_package(ASan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=address")
-    link_libraries(${ASan_LIBRARY})
-
-  elseif(${sanitizer} MATCHES "thread")
-    find_package(TSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=thread")
-    link_libraries(${TSan_LIBRARY})
-
-  elseif(${sanitizer} MATCHES "leak")
-    find_package(LSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=leak")
-    link_libraries(${LSan_LIBRARY})
-
-  elseif(${sanitizer} MATCHES "undefined")
-    find_package(UBSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=undefined -fno-sanitize-recover=undefined")
-    link_libraries(${UBSan_LIBRARY})
-
-  else()
-    message(FATAL_ERROR "Santizer ${sanitizer} not supported.")
-  endif()
-endmacro()
-
-macro(enable_sanitizers SANITIZERS)
-  # Check sanitizers compatibility.
-  # Idealy, we should use if(san IN_LIST SANITIZERS) ... endif()
-  # But I haven't figure out how to make it work.
-  foreach ( _san ${SANITIZERS} )
-    string(TOLOWER ${_san} _san)
-    if (_san MATCHES "thread")
-      if (${_use_other_sanitizers})
-        message(FATAL_ERROR
-          "thread sanitizer is not compatible with ${_san} sanitizer.")
-      endif()
-      set(_use_thread_sanitizer 1)
-    else ()
-      if (${_use_thread_sanitizer})
-        message(FATAL_ERROR
-          "${_san} sanitizer is not compatible with thread sanitizer.")
-      endif()
-      set(_use_other_sanitizers 1)
-    endif()
-  endforeach()
-
-  message("Sanitizers: ${SANITIZERS}")
-
-  foreach( _san ${SANITIZERS} )
-    string(TOLOWER ${_san} _san)
-    enable_sanitizer(${_san})
-  endforeach()
-  message("Sanitizers compile flags: ${SAN_COMPILE_FLAGS}")
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SAN_COMPILE_FLAGS}")
-  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SAN_COMPILE_FLAGS}")
-endmacro()
diff --git a/ml-xgboost/cmake/Utils.cmake b/ml-xgboost/cmake/Utils.cmake
deleted file mode 100644
index 4a9e63a..0000000
--- a/ml-xgboost/cmake/Utils.cmake
+++ /dev/null
@@ -1,143 +0,0 @@
-# Automatically set source group based on folder
-function(auto_source_group SOURCES)
-
-  foreach(FILE ${SOURCES})
-      get_filename_component(PARENT_DIR "${FILE}" PATH)
-
-      # skip src or include and changes /'s to \\'s
-      string(REPLACE "${CMAKE_CURRENT_LIST_DIR}" "" GROUP "${PARENT_DIR}")
-      string(REPLACE "/" "\\\\" GROUP "${GROUP}")
-      string(REGEX REPLACE "^\\\\" "" GROUP "${GROUP}")
-
-      source_group("${GROUP}" FILES "${FILE}")
-  endforeach()
-endfunction(auto_source_group)
-
-# Force static runtime for MSVC
-function(msvc_use_static_runtime)
-  if(MSVC)
-      set(variables
-          CMAKE_C_FLAGS_DEBUG
-          CMAKE_C_FLAGS_MINSIZEREL
-          CMAKE_C_FLAGS_RELEASE
-          CMAKE_C_FLAGS_RELWITHDEBINFO
-          CMAKE_CXX_FLAGS_DEBUG
-          CMAKE_CXX_FLAGS_MINSIZEREL
-          CMAKE_CXX_FLAGS_RELEASE
-          CMAKE_CXX_FLAGS_RELWITHDEBINFO
-      )
-      foreach(variable ${variables})
-          if(${variable} MATCHES "/MD")
-              string(REGEX REPLACE "/MD" "/MT" ${variable} "${${variable}}")
-              set(${variable} "${${variable}}"  PARENT_SCOPE)
-          endif()
-      endforeach()
-      set(variables
-          CMAKE_CUDA_FLAGS
-          CMAKE_CUDA_FLAGS_DEBUG
-          CMAKE_CUDA_FLAGS_MINSIZEREL
-          CMAKE_CUDA_FLAGS_RELEASE
-          CMAKE_CUDA_FLAGS_RELWITHDEBINFO
-      )
-      foreach(variable ${variables})
-          if(${variable} MATCHES "-MD")
-              string(REGEX REPLACE "-MD" "-MT" ${variable} "${${variable}}")
-              set(${variable} "${${variable}}"  PARENT_SCOPE)
-          endif()
-          if(${variable} MATCHES "/MD")
-              string(REGEX REPLACE "/MD" "/MT" ${variable} "${${variable}}")
-              set(${variable} "${${variable}}"  PARENT_SCOPE)
-          endif()
-      endforeach()
-  endif()
-endfunction(msvc_use_static_runtime)
-
-# Set output directory of target, ignoring debug or release
-function(set_output_directory target dir)
-	set_target_properties(${target} PROPERTIES
-		RUNTIME_OUTPUT_DIRECTORY ${dir}
-		RUNTIME_OUTPUT_DIRECTORY_DEBUG ${dir}
-		RUNTIME_OUTPUT_DIRECTORY_RELEASE ${dir}
-		RUNTIME_OUTPUT_DIRECTORY_RELWITHDEBINFO ${dir}
-		RUNTIME_OUTPUT_DIRECTORY_MINSIZEREL ${dir}
-		LIBRARY_OUTPUT_DIRECTORY ${dir}
-		LIBRARY_OUTPUT_DIRECTORY_DEBUG ${dir}
-		LIBRARY_OUTPUT_DIRECTORY_RELEASE ${dir}
-		LIBRARY_OUTPUT_DIRECTORY_RELWITHDEBINFO ${dir}
-		LIBRARY_OUTPUT_DIRECTORY_MINSIZEREL ${dir}
-		ARCHIVE_OUTPUT_DIRECTORY ${dir}
-		ARCHIVE_OUTPUT_DIRECTORY_DEBUG ${dir}
-		ARCHIVE_OUTPUT_DIRECTORY_RELEASE ${dir}
-		ARCHIVE_OUTPUT_DIRECTORY_RELWITHDEBINFO ${dir}
-		ARCHIVE_OUTPUT_DIRECTORY_MINSIZEREL ${dir}
-	)
-endfunction(set_output_directory)
-
-# Set a default build type to release if none was specified
-function(set_default_configuration_release)
-    if(CMAKE_CONFIGURATION_TYPES STREQUAL "Debug;Release;MinSizeRel;RelWithDebInfo") # multiconfig generator?
-        set(CMAKE_CONFIGURATION_TYPES Release CACHE STRING "" FORCE)
-	elseif(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
-	  message(STATUS "Setting build type to 'Release' as none was specified.")
-	  set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE )
-	endif()
-endfunction(set_default_configuration_release)
-
-# Generate nvcc compiler flags given a list of architectures
-# Also generates PTX for the most recent architecture for forwards compatibility
-function(format_gencode_flags flags out)
-  if(CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
-    set(CUDA_VERSION "${CMAKE_MATCH_1}")
-  endif()
-  # Set up architecture flags
-  if(NOT flags)
-    if(CUDA_VERSION VERSION_GREATER_EQUAL "10.0")
-      set(flags "35;50;52;60;61;70;75")
-    elseif(CUDA_VERSION VERSION_GREATER_EQUAL "9.0")
-      set(flags "35;50;52;60;61;70")
-    else()
-      set(flags "35;50;52;60;61")
-    endif()
-  endif()
-  # Generate SASS
-  foreach(ver ${flags})
-    set(${out} "${${out}}--generate-code=arch=compute_${ver},code=sm_${ver};")
-  endforeach()
-  # Generate PTX for last architecture
-  list(GET flags -1 ver)
-  set(${out} "${${out}}--generate-code=arch=compute_${ver},code=compute_${ver};")
-
-  set(${out} "${${out}}" PARENT_SCOPE)
-endfunction(format_gencode_flags flags)
-
-# Assembles the R-package files in build_dir;
-# if necessary, installs the main R package dependencies;
-# runs R CMD INSTALL.
-function(setup_rpackage_install_target rlib_target build_dir)
-  # backup cmake_install.cmake
-  install(CODE "file(COPY \"${build_dir}/R-package/cmake_install.cmake\"
-DESTINATION \"${build_dir}/bak\")")
-
-  install(CODE "file(REMOVE_RECURSE \"${build_dir}/R-package\")")
-  install(
-    DIRECTORY "${xgboost_SOURCE_DIR}/R-package"
-    DESTINATION "${build_dir}"
-    REGEX "src/*" EXCLUDE
-    REGEX "R-package/configure" EXCLUDE
-  )
-  install(TARGETS ${rlib_target}
-    LIBRARY DESTINATION "${build_dir}/R-package/src/"
-    RUNTIME DESTINATION "${build_dir}/R-package/src/")
-  install(CODE "file(WRITE \"${build_dir}/R-package/src/Makevars\" \"all:\")")
-  install(CODE "file(WRITE \"${build_dir}/R-package/src/Makevars.win\" \"all:\")")
-  set(XGB_DEPS_SCRIPT
-    "deps = setdiff(c('data.table', 'magrittr', 'stringi'), rownames(installed.packages()));\
-    if(length(deps)>0) install.packages(deps, repo = 'https://cloud.r-project.org/')")
-  install(CODE "execute_process(COMMAND \"${LIBR_EXECUTABLE}\" \"-q\" \"-e\" \"${XGB_DEPS_SCRIPT}\")")
-  install(CODE "execute_process(COMMAND \"${LIBR_EXECUTABLE}\" CMD INSTALL\
-    \"--no-multiarch\" \"--build\" \"${build_dir}/R-package\")")
-
-  # restore cmake_install.cmake
-  install(CODE "file(RENAME \"${build_dir}/bak/cmake_install.cmake\"
- \"${build_dir}/R-package/cmake_install.cmake\")")
-endfunction(setup_rpackage_install_target)
diff --git a/ml-xgboost/cmake/Version.cmake b/ml-xgboost/cmake/Version.cmake
deleted file mode 100644
index f38ce3c..0000000
--- a/ml-xgboost/cmake/Version.cmake
+++ /dev/null
@@ -1,9 +0,0 @@
-function (write_version)
-  message(STATUS "xgboost VERSION: ${xgboost_VERSION}")
-  configure_file(
-    ${xgboost_SOURCE_DIR}/cmake/version_config.h.in
-    ${xgboost_SOURCE_DIR}/include/xgboost/version_config.h @ONLY)
-  configure_file(
-    ${xgboost_SOURCE_DIR}/cmake/Python_version.in
-    ${xgboost_SOURCE_DIR}/python-package/xgboost/VERSION @ONLY)
-endfunction (write_version)
diff --git a/ml-xgboost/cmake/modules/FindASan.cmake b/ml-xgboost/cmake/modules/FindASan.cmake
deleted file mode 100644
index e7b2738..0000000
--- a/ml-xgboost/cmake/modules/FindASan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(ASan_LIB_NAME ASan)
-
-find_library(ASan_LIBRARY
-  NAMES libasan.so libasan.so.5 libasan.so.4 libasan.so.3 libasan.so.2 libasan.so.1 libasan.so.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(ASan DEFAULT_MSG
-  ASan_LIBRARY)
-
-mark_as_advanced(
-  ASan_LIBRARY
-  ASan_LIB_NAME)
diff --git a/ml-xgboost/cmake/modules/FindLSan.cmake b/ml-xgboost/cmake/modules/FindLSan.cmake
deleted file mode 100644
index 3f68fb0..0000000
--- a/ml-xgboost/cmake/modules/FindLSan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(LSan_LIB_NAME lsan)
-
-find_library(LSan_LIBRARY
-  NAMES liblsan.so liblsan.so.0 liblsan.so.0.0.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(LSan DEFAULT_MSG
-  LSan_LIBRARY)
-
-mark_as_advanced(
-  LSan_LIBRARY
-  LSan_LIB_NAME)
diff --git a/ml-xgboost/cmake/modules/FindLibR.cmake b/ml-xgboost/cmake/modules/FindLibR.cmake
deleted file mode 100644
index 0110439..0000000
--- a/ml-xgboost/cmake/modules/FindLibR.cmake
+++ /dev/null
@@ -1,183 +0,0 @@
-# CMake module for R
-# Borrows ideas from RStudio's FindLibR.cmake
-#
-# Defines the following:
-#  LIBR_FOUND
-#  LIBR_HOME
-#  LIBR_EXECUTABLE
-#  LIBR_INCLUDE_DIRS
-#  LIBR_LIB_DIR
-#  LIBR_CORE_LIBRARY
-# and a cmake function to create R.lib for MSVC
-#
-# The following could be provided by user through cmake's -D options:
-#  LIBR_EXECUTABLE (for unix and win)
-#  R_VERSION (for win)
-#  R_ARCH (for win 64 when want 32 bit build)
-#
-# TODO: 
-# - someone to verify OSX detection, 
-# - possibly, add OSX detection based on current R in PATH or LIBR_EXECUTABLE
-# - improve registry-based R_HOME detection in Windows (from a set of R_VERSION's)
-
-
-# Windows users might want to change this to their R version:
-if(NOT R_VERSION)
-  set(R_VERSION "3.4.1")
-endif()
-if(NOT R_ARCH)
-  if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4")
-    set(R_ARCH "i386")
-  else()
-    set(R_ARCH "x64")
-  endif()
-endif()
-
-
-# Creates R.lib and R.def in the build directory for linking with MSVC
-function(create_rlib_for_msvc)
-  # various checks and warnings
-  if(NOT WIN32 OR NOT MSVC)
-    message(FATAL_ERROR "create_rlib_for_msvc() can only be used with MSVC")
-  endif()
-  if(NOT EXISTS "${LIBR_LIB_DIR}")
-    message(FATAL_ERROR "LIBR_LIB_DIR was not set!")
-  endif()
-  find_program(GENDEF_EXE gendef)
-  find_program(DLLTOOL_EXE dlltool)
-  if(NOT GENDEF_EXE OR NOT DLLTOOL_EXE)
-    message(FATAL_ERROR "\nEither gendef.exe or dlltool.exe not found!\
-      \nDo you have Rtools installed with its MinGW's bin/ in PATH?")
-  endif()  
-  # extract symbols from R.dll into R.def and R.lib import library
-  execute_process(COMMAND ${GENDEF_EXE}
-    "-" "${LIBR_LIB_DIR}/R.dll"
-    OUTPUT_FILE "${CMAKE_CURRENT_BINARY_DIR}/R.def")
-  execute_process(COMMAND ${DLLTOOL_EXE}
-    "--input-def" "${CMAKE_CURRENT_BINARY_DIR}/R.def"
-    "--output-lib" "${CMAKE_CURRENT_BINARY_DIR}/R.lib")
-endfunction(create_rlib_for_msvc)
-
-
-# detection for OSX
-if(APPLE)
-
-  find_library(LIBR_LIBRARIES R)
-
-  if(LIBR_LIBRARIES MATCHES ".*\\.framework")
-    set(LIBR_HOME "${LIBR_LIBRARIES}/Resources" CACHE PATH "R home directory")
-    set(LIBR_INCLUDE_DIRS "${LIBR_HOME}/include" CACHE PATH "R include directory")
-    set(LIBR_EXECUTABLE "${LIBR_HOME}/R" CACHE PATH "R executable")
-    set(LIBR_LIB_DIR "${LIBR_HOME}/lib" CACHE PATH "R lib directory")
-  else()
-    get_filename_component(_LIBR_LIBRARIES "${LIBR_LIBRARIES}" REALPATH)
-    get_filename_component(_LIBR_LIBRARIES_DIR "${_LIBR_LIBRARIES}" DIRECTORY)
-    set(LIBR_EXECUTABLE "${_LIBR_LIBRARIES_DIR}/../bin/R")
-    execute_process(
-      COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(R.home())"
-      OUTPUT_VARIABLE LIBR_HOME)
-    set(LIBR_HOME ${LIBR_HOME} CACHE PATH "R home directory")
-    set(LIBR_INCLUDE_DIRS "${LIBR_HOME}/include" CACHE PATH "R include directory")
-    set(LIBR_LIB_DIR "${LIBR_HOME}/lib" CACHE PATH "R lib directory")
-  endif()
-  
-# detection for UNIX & Win32
-else()
-
-  # attempt to find R executable
-  if(NOT LIBR_EXECUTABLE)
-    find_program(LIBR_EXECUTABLE NAMES R R.exe)
-  endif()
-  
-  if(UNIX)
-
-    if(NOT LIBR_EXECUTABLE)
-      message(FATAL_ERROR "Unable to locate R executable.\
-        \nEither add its location to PATH or provide it through the LIBR_EXECUTABLE cmake variable")
-    endif()
-
-    # ask R for the home path
-    execute_process(
-      COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(R.home())"
-      OUTPUT_VARIABLE LIBR_HOME
-    )
-    # ask R for the include dir
-    execute_process(
-      COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(R.home('include'))"
-      OUTPUT_VARIABLE LIBR_INCLUDE_DIRS
-    )
-    # ask R for the lib dir
-    execute_process(
-      COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(R.home('lib'))"
-      OUTPUT_VARIABLE LIBR_LIB_DIR
-    )
-
-  # Windows
-  else()
-    # ask R for R_HOME 
-    if(LIBR_EXECUTABLE)
-      execute_process(
-        COMMAND ${LIBR_EXECUTABLE} "--slave" "--no-save" "-e" "cat(normalizePath(R.home(),winslash='/'))"
-        OUTPUT_VARIABLE LIBR_HOME)
-    endif()
-    # if R executable not available, query R_HOME path from registry
-    if(NOT LIBR_HOME)
-      get_filename_component(LIBR_HOME
-        "[HKEY_LOCAL_MACHINE\\SOFTWARE\\R-core\\R\\${R_VERSION};InstallPath]"
-        ABSOLUTE)
-      if(NOT LIBR_HOME)
-        message(FATAL_ERROR "\nUnable to locate R executable.\
-          \nEither add its location to PATH or provide it through the LIBR_EXECUTABLE cmake variable")
-      endif()
-    endif()
-    # set exe location based on R_ARCH
-    if(NOT LIBR_EXECUTABLE)
-      set(LIBR_EXECUTABLE "${LIBR_HOME}/bin/${R_ARCH}/R.exe")
-    endif()
-    # set other R paths based on home path
-    set(LIBR_INCLUDE_DIRS "${LIBR_HOME}/include")
-    set(LIBR_LIB_DIR "${LIBR_HOME}/bin/${R_ARCH}")
- 
-message(STATUS "LIBR_HOME [${LIBR_HOME}]")
-message(STATUS "LIBR_EXECUTABLE [${LIBR_EXECUTABLE}]")
-message(STATUS "LIBR_INCLUDE_DIRS [${LIBR_INCLUDE_DIRS}]")
-message(STATUS "LIBR_LIB_DIR [${LIBR_LIB_DIR}]")
-message(STATUS "LIBR_CORE_LIBRARY [${LIBR_CORE_LIBRARY}]")
-
-  endif()
-
-endif()
-
-if(WIN32 AND MSVC)
-  # create a local R.lib import library for R.dll if it doesn't exist
-  if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/R.lib")
-    create_rlib_for_msvc()
-  endif()
-endif()
-
-# look for the core R library
-find_library(LIBR_CORE_LIBRARY NAMES R
-  HINTS "${CMAKE_CURRENT_BINARY_DIR}" "${LIBR_LIB_DIR}" "${LIBR_HOME}/bin" "${LIBR_LIBRARIES}")
-if(LIBR_CORE_LIBRARY-NOTFOUND)
-  message(STATUS "Could not find R core shared library.")
-endif()
-
-set(LIBR_HOME ${LIBR_HOME} CACHE PATH "R home directory")
-set(LIBR_EXECUTABLE ${LIBR_EXECUTABLE} CACHE PATH "R executable")
-set(LIBR_INCLUDE_DIRS ${LIBR_INCLUDE_DIRS} CACHE PATH "R include directory")
-set(LIBR_LIB_DIR ${LIBR_LIB_DIR} CACHE PATH "R shared libraries directory")
-set(LIBR_CORE_LIBRARY ${LIBR_CORE_LIBRARY} CACHE PATH "R core shared library")
-
-# define find requirements
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(LibR DEFAULT_MSG
-  LIBR_HOME
-  LIBR_EXECUTABLE
-  LIBR_INCLUDE_DIRS
-  LIBR_LIB_DIR
-  LIBR_CORE_LIBRARY
-)
-
-if(LIBR_FOUND)
-  message(STATUS "Found R: ${LIBR_EXECUTABLE}")
-endif()
diff --git a/ml-xgboost/cmake/modules/FindNVML.cmake b/ml-xgboost/cmake/modules/FindNVML.cmake
deleted file mode 100644
index a4bed00..0000000
--- a/ml-xgboost/cmake/modules/FindNVML.cmake
+++ /dev/null
@@ -1,23 +0,0 @@
-if (NVML_LIBRARY)
-  unset(NVML_LIBRARY CACHE)
-endif(NVML_LIBRARY)
-
-set(NVML_LIB_NAME nvml)
-
-find_path(NVML_INCLUDE_DIR
-  NAMES nvml.h
-  PATHS ${CUDA_HOME}/include ${CUDA_INCLUDE} /usr/local/cuda/include)
-
-find_library(NVML_LIBRARY
-  NAMES nvidia-ml)
-
-message(STATUS "Using nvml library: ${NVML_LIBRARY}")
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(NVML DEFAULT_MSG
-                                  NVML_INCLUDE_DIR NVML_LIBRARY)
-
-mark_as_advanced(
-  NVML_INCLUDE_DIR
-  NVML_LIBRARY
-)
diff --git a/ml-xgboost/cmake/modules/FindNccl.cmake b/ml-xgboost/cmake/modules/FindNccl.cmake
deleted file mode 100644
index 643c45f..0000000
--- a/ml-xgboost/cmake/modules/FindNccl.cmake
+++ /dev/null
@@ -1,65 +0,0 @@
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Tries to find NCCL headers and libraries.
-#
-# Usage of this module as follows:
-#
-#  find_package(NCCL)
-#
-# Variables used by this module, they can change the default behaviour and need
-# to be set before calling find_package:
-#
-#  NCCL_ROOT - When set, this path is inspected instead of standard library
-#              locations as the root of the NCCL installation.
-#              The environment variable NCCL_ROOT overrides this veriable.
-#
-# This module defines
-#  Nccl_FOUND, whether nccl has been found
-#  NCCL_INCLUDE_DIR, directory containing header
-#  NCCL_LIBRARY, directory containing nccl library
-#  NCCL_LIB_NAME, nccl library name
-#
-# This module assumes that the user has already called find_package(CUDA)
-
-if (NCCL_LIBRARY)
-  # Don't cache NCCL_LIBRARY to enable switching between static and shared.
-  unset(NCCL_LIBRARY CACHE)
-endif()
-
-if (BUILD_WITH_SHARED_NCCL)
-  # libnccl.so
-  set(NCCL_LIB_NAME nccl)
-else ()
-  # libnccl_static.a
-  set(NCCL_LIB_NAME nccl_static)
-endif (BUILD_WITH_SHARED_NCCL)
-
-find_path(NCCL_INCLUDE_DIR
-  NAMES nccl.h
-  PATHS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include)
-
-find_library(NCCL_LIBRARY
-  NAMES ${NCCL_LIB_NAME}
-  PATHS $ENV{NCCL_ROOT}/lib/ ${NCCL_ROOT}/lib)
-
-message(STATUS "Using nccl library: ${NCCL_LIBRARY}")
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(Nccl DEFAULT_MSG
-                                  NCCL_INCLUDE_DIR NCCL_LIBRARY)
-
-mark_as_advanced(
-  NCCL_INCLUDE_DIR
-  NCCL_LIBRARY
-)
diff --git a/ml-xgboost/cmake/modules/FindTSan.cmake b/ml-xgboost/cmake/modules/FindTSan.cmake
deleted file mode 100644
index aa01802..0000000
--- a/ml-xgboost/cmake/modules/FindTSan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(TSan_LIB_NAME tsan)
-
-find_library(TSan_LIBRARY
-  NAMES libtsan.so libtsan.so.0 libtsan.so.0.0.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(TSan DEFAULT_MSG
-  TSan_LIBRARY)
-
-mark_as_advanced(
-  TSan_LIBRARY
-  TSan_LIB_NAME)
diff --git a/ml-xgboost/cmake/modules/FindUBSan.cmake b/ml-xgboost/cmake/modules/FindUBSan.cmake
deleted file mode 100644
index e1b72eb..0000000
--- a/ml-xgboost/cmake/modules/FindUBSan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(UBSan_LIB_NAME UBSan)
-
-find_library(UBSan_LIBRARY
-  NAMES libubsan.so libubsan.so.5 libubsan.so.4 libubsan.so.3 libubsan.so.2 libubsan.so.1 libubsan.so.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(UBSan DEFAULT_MSG
-  UBSan_LIBRARY)
-
-mark_as_advanced(
-  UBSan_LIBRARY
-  UBSan_LIB_NAME)
diff --git a/ml-xgboost/cmake/version_config.h.in b/ml-xgboost/cmake/version_config.h.in
deleted file mode 100644
index dfde79a..0000000
--- a/ml-xgboost/cmake/version_config.h.in
+++ /dev/null
@@ -1,11 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#ifndef XGBOOST_VERSION_CONFIG_H_
-#define XGBOOST_VERSION_CONFIG_H_
-
-#define XGBOOST_VER_MAJOR @xgboost_VERSION_MAJOR@
-#define XGBOOST_VER_MINOR @xgboost_VERSION_MINOR@
-#define XGBOOST_VER_PATCH @xgboost_VERSION_PATCH@
-
-#endif  // XGBOOST_VERSION_CONFIG_H_
diff --git a/ml-xgboost/cmake/xgboost-config.cmake.in b/ml-xgboost/cmake/xgboost-config.cmake.in
deleted file mode 100644
index 6a155f0..0000000
--- a/ml-xgboost/cmake/xgboost-config.cmake.in
+++ /dev/null
@@ -1,5 +0,0 @@
-@PACKAGE_INIT@
-
-if(NOT TARGET xgboost::xgboost)
-  include(${CMAKE_CURRENT_LIST_DIR}/XGBoostTargets.cmake)
-endif()
diff --git a/ml-xgboost/cub/.cproject b/ml-xgboost/cub/.cproject
deleted file mode 100644
index 5e970a7..0000000
--- a/ml-xgboost/cub/.cproject
+++ /dev/null
@@ -1,1211 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<?fileVersion 4.0.0?><cproject storage_type_id="org.eclipse.cdt.core.XmlProjectDescriptionStorage">
-	<storageModule moduleId="org.eclipse.cdt.core.settings">
-		<cconfiguration id="cdt.managedbuild.toolchain.gnu.cygwin.base.1260156311">
-			<storageModule buildSystemId="org.eclipse.cdt.managedbuilder.core.configurationDataProvider" id="cdt.managedbuild.toolchain.gnu.cygwin.base.1260156311" moduleId="org.eclipse.cdt.core.settings" name="Default">
-				<externalSettings>
-					<externalSetting languages="cdt.managedbuild.tool.gnu.c.compiler.input.cygwin.1945715073"/>
-				</externalSettings>
-				<extensions>
-					<extension id="org.eclipse.cdt.core.Cygwin_PE" point="org.eclipse.cdt.core.BinaryParser"/>
-					<extension id="org.eclipse.cdt.core.GCCErrorParser" point="org.eclipse.cdt.core.ErrorParser"/>
-					<extension id="org.eclipse.cdt.core.GASErrorParser" point="org.eclipse.cdt.core.ErrorParser"/>
-					<extension id="org.eclipse.cdt.core.GLDErrorParser" point="org.eclipse.cdt.core.ErrorParser"/>
-					<extension id="org.eclipse.cdt.core.GmakeErrorParser" point="org.eclipse.cdt.core.ErrorParser"/>
-					<extension id="org.eclipse.cdt.core.CWDLocator" point="org.eclipse.cdt.core.ErrorParser"/>
-				</extensions>
-			</storageModule>
-			<storageModule moduleId="cdtBuildSystem" version="4.0.0">
-				<configuration artifactName="B40CTrunk" buildProperties="" description="" id="cdt.managedbuild.toolchain.gnu.cygwin.base.1260156311" name="Default" parent="org.eclipse.cdt.build.core.emptycfg">
-					<folderInfo id="cdt.managedbuild.toolchain.gnu.cygwin.base.1260156311.1722659113" name="/" resourcePath="">
-						<toolChain id="cdt.managedbuild.toolchain.gnu.cygwin.base.481495889" name="Cygwin GCC" superClass="cdt.managedbuild.toolchain.gnu.cygwin.base">
-							<targetPlatform archList="all" binaryParser="org.eclipse.cdt.core.Cygwin_PE" id="cdt.managedbuild.target.gnu.platform.cygwin.base.100038061" name="Debug Platform" osList="win32" superClass="cdt.managedbuild.target.gnu.platform.cygwin.base"/>
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diff --git a/ml-xgboost/cub/CHANGE_LOG.TXT b/ml-xgboost/cub/CHANGE_LOG.TXT
deleted file mode 100644
index 837dcc2..0000000
--- a/ml-xgboost/cub/CHANGE_LOG.TXT
+++ /dev/null
@@ -1,346 +0,0 @@
-1.7.0    06/07/2017
-    - Compatible with CUDA9 and SM7.x (Volta) independent thread scheduling 
-    - API change: remove cub::WarpAll() and cub::WarpAny().  These functions served to 
-      emulate __all and __any functionality for SM1.x devices, which did not have those 
-      operations.  However, the SM1.x devices are now deprecated in CUDA, and the 
-      interfaces of the these two functions are now lacking the lane-mask needed 
-      for collectives to run on Volta SMs having independent thread scheduling. 
-    - Bug fixes: 
-        - Issue #86 Incorrect results with ReduceByKey
-          		  
-//-----------------------------------------------------------------------------
-
-1.6.4    12/06/2016
-    - Updated sm_5x, sm_6x tuning policies for radix sorting (3.5B and 3.4B 
-      32b keys/s on TitanX and GTX 1080, respectively)
-    - Bug fixes: 
-        - Restore fence work-around for scan (reduce-by-key, etc.) hangs 
-          in CUDA 8.5
-        - Issue 65: DeviceSegmentedRadixSort should allow inputs to have 
-          pointer-to-const type 
-        - Mollify Clang device-side warnings
-        - Remove out-dated VC project files
-          		  
-//-----------------------------------------------------------------------------
-
-1.6.3    11/20/2016
-    - API change: BlockLoad and BlockStore are now templated by the local
-      data type, instead of the Iterator type.  This allows for output iterators
-      having \p void as their \p value_type (e.g., discard iterators).
-    - Updated GP100 tuning policies for radix sorting (6.2B 32b keys/s)
-    - Bug fixes: 
-        - Issue #74: Warpreduce executes reduction operator for out-of-bounds items
-        - Issue #72 (cub:InequalityWrapper::operator() should be non-const)
-        - Issue #71 (KeyVairPair won't work if Key has non-trivial ctor)
-		- Issue #70 1.5.3 breaks BlockScan API.  Retroactively reversioned
-		  from v1.5.3 -> v1.6 to appropriately indicate API change.
-		- Issue #69 cub::BlockStore::Store doesn't compile if OutputIteratorT::value_type != T  
-        - Issue #68 (cub::TilePrefixCallbackOp::WarpReduce doesn't permit ptx 
-          arch specialization)
-		- Improved support for Win32 platforms (warnings, alignment, etc)
-		  
-//-----------------------------------------------------------------------------
-
-1.6.2 (was 1.5.5)    10/25/2016
-    - Updated Pascal tuning policies for radix sorting
-    - Bug fixes: 
-        - Fix for arm64 compilation of caching allocator
-
-//-----------------------------------------------------------------------------
-
-1.6.1 (was 1.5.4)    10/14/2016
-    - Bug fixes: 
-        - Fix for radix sorting bug introduced by scan refactorization
-
-//-----------------------------------------------------------------------------
-
-1.6.0 (was 1.5.3)    10/11/2016
-    - API change: Device/block/warp-wide exclusive scans have been revised to now 
-      accept an "initial value" (instead of an "identity value") for seeding the 
-      computation with an arbitrary prefix.  
-    - API change: Device-wide reductions and scans can now have input sequence types that are 
-      different from output sequence types (as long as they are coercible)
-      value") for seeding the computation with an arbitrary prefix
-    - Reduce repository size (move doxygen binary to doc repository)
-    - Minor reductions in block-scan instruction count
-    - Bug fixes: 
-        - Issue #55: warning in cub/device/dispatch/dispatch_reduce_by_key.cuh 
-        - Issue #59: cub::DeviceScan::ExclusiveSum can't prefix sum of float into double
-        - Issue #58: Infinite loop in cub::CachingDeviceAllocator::NearestPowerOf
-        - Issue #47: Caching allocator needs to clean up cuda error upon successful retry 
-        - Issue #46: Very high amount of needed memory from the cub::DeviceHistogram::HistogramEven routine
-        - Issue #45: Caching Device Allocator fails with debug output enabled
-        - Fix for generic-type reduce-by-key warpscan (sm3.x and newer)
-
-//-----------------------------------------------------------------------------
-
-1.5.2    03/21/2016
-	- Improved medium-size scan performance for sm5x (Maxwell)
-    - Refactored caching allocator for device memory
-   		- Spends less time locked
-		- Failure to allocate a block from the runtime will retry once after
-		  freeing cached allocations
-		- Now respects max-bin (issue where blocks in excess of max-bin were
-		  still being retained in free cache)
-		- Uses C++11 mutex when available
-    - Bug fixes: 
-        - Fix for generic-type reduce-by-key warpscan (sm3.x and newer)
-          
-//-----------------------------------------------------------------------------
-
-1.5.1    12/28/2015
-    - Bug fixes: 
-        - Fix for incorrect DeviceRadixSort output for some small problems on 
-          Maxwell SM52 architectures
-        - Fix for macro redefinition warnings when compiling with Thrust sort
-          
-//-----------------------------------------------------------------------------
-
-1.5.0    12/14/2015
-    - New Features:
-        - Added new segmented device-wide operations for device-wide sort and 
-          reduction primitives.
-    - Bug fixes: 
-        - Fix for Git Issue 36 (Compilation error with GCC 4.8.4 nvcc 7.0.27) and
-          Forums thread (ThreadLoad generates compiler errors when loading from 
-          pointer-to-const)
-        - Fix for Git Issue 29 (DeviceRadixSort::SortKeys<bool> yields compiler 
-          errors)
-        - Fix for Git Issue 26 (CUDA error: misaligned address after 
-          cub::DeviceRadixSort::SortKeys())
-        - Fix for incorrect/crash on 0-length problems, e.g., Git Issue 25 (Floating 
-          point exception (core dumped) during cub::DeviceRadixSort::SortKeys)
-        - Fix for CUDA 7.5 issues on SM 5.2 with SHFL-based warp-scan and warp-reduction 
-          on non-primitive data types (e.g., user-defined structs)
-        - Fix for small radix sorting problems where 0 temporary bytes were 
-          required and users code was invoking malloc(0) on some systems where
-          that returns NULL.  (Impl assumed was asking for size again and was not 
-          running the sort.)
-          
-//-----------------------------------------------------------------------------
-
-1.4.1    04/13/2015
-    - Bug fixes: 
-        - Fixes for CUDA 7.0 issues with SHFL-based warp-scan and warp-reduction 
-          on non-primitive data types (e.g., user-defined structs)
-        - Fixes for minor CUDA 7.0 performance regressions in cub::DeviceScan,
-          DeviceReduceByKey
-        - Fixes to allow cub::DeviceRadixSort and cub::BlockRadixSort on bool types
-        - Remove requirement for callers to define the CUB_CDP macro 
-          when invoking CUB device-wide rountines using CUDA dynamic parallelism
-        - Fix for headers not being included in the proper order (or missing includes)
-          for some block-wide functions
-          
-//-----------------------------------------------------------------------------
-
-1.4.0    03/18/2015
-    - New Features:
-		- Support and performance tuning for new Maxwell GPU architectures
-        - Updated cub::DeviceHistogram implementation that provides the same 
-          "histogram-even" and "histogram-range" functionality as IPP/NPP.
-          Provides extremely fast and, perhaps more importantly, very 
-          uniform performance response across diverse real-world datasets, 
-          including pathological (homogeneous) sample distributions (resilience)
-        - New cub::DeviceSpmv methods for multiplying sparse matrices by 
-          dense vectors, load-balanced using a merge-based parallel decomposition.
-        - New cub::DeviceRadixSort sorting entry-points that always return
-          the sorted output into the specified buffer (as opposed to the 
-          cub::DoubleBuffer in which it could end up in either buffer)
-        - New cub::DeviceRunLengthEncode::NonTrivialRuns for finding the starting 
-          offsets and lengths of all non-trivial runs (i.e., length > 1) of keys in 
-          a given sequence.  (Useful for top-down partitioning algorithms like 
-          MSD sorting of very-large keys.)
-          
-//-----------------------------------------------------------------------------
-
-1.3.2    07/28/2014
-    - Bug fixes: 
-        - Fix for cub::DeviceReduce where reductions of small problems 
-          (small enough to only dispatch a single threadblock) would run in 
-          the default stream (stream zero) regardless of whether an alternate
-          stream was specified.  
-          
-//-----------------------------------------------------------------------------
-
-1.3.1    05/23/2014
-    - Bug fixes: 
-        - Workaround for a benign WAW race warning reported by cuda-memcheck
-          in BlockScan specialized for BLOCK_SCAN_WARP_SCANS algorithm.
-        - Fix for bug in DeviceRadixSort where the algorithm may sort more 
-          key bits than the caller specified (up to the nearest radix digit).
-        - Fix for ~3% DeviceRadixSort performance regression on Kepler and 
-          Fermi that was introduced in v1.3.0.  
-
-//-----------------------------------------------------------------------------
-
-1.3.0    05/12/2014
-    - New features:
-        - CUB's collective (block-wide, warp-wide) primitives underwent a minor 
-          interface refactoring:
-            - To provide the appropriate support for multidimensional thread blocks,
-              The interfaces for collective classes are now template-parameterized 
-              by X, Y, and Z block dimensions (with BLOCK_DIM_Y and BLOCK_DIM_Z being 
-              optional, and BLOCK_DIM_X replacing BLOCK_THREADS).  Furthermore, the 
-              constructors that accept remapped linear thread-identifiers have been 
-              removed: all primitives now assume a row-major thread-ranking for 
-              multidimensional thread blocks.  
-            - To allow the host program (compiled by the host-pass) to 
-              accurately determine the device-specific storage requirements for 
-              a given collective (compiled for each device-pass), the interfaces 
-              for collective classes are now (optionally) template-parameterized 
-              by the desired PTX compute capability. This is useful when 
-              aliasing collective storage to shared memory that has been 
-              allocated dynamically by the host at the kernel call site.   
-            - Most CUB programs having typical 1D usage should not require any 
-              changes to accomodate these updates.
-        - Added new "combination" WarpScan methods for efficiently computing 
-          both inclusive and exclusive prefix scans (and sums).
-    - Bug fixes: 
-        - Fixed bug in cub::WarpScan (which affected cub::BlockScan and 
-          cub::DeviceScan) where incorrect results (e.g., NAN) would often be 
-          returned when parameterized for floating-point types (fp32, fp64).
-        - Workaround-fix for ptxas error when compiling with with -G flag on Linux 
-          (for debug instrumentation) 
-        - Misc. workaround-fixes for certain scan scenarios (using custom 
-          scan operators) where code compiled for SM1x is run on newer 
-          GPUs of higher compute-capability: the compiler could not tell
-          which memory space was being used collective operations and was 
-          mistakenly using global ops instead of shared ops. 
-
-//-----------------------------------------------------------------------------
-
-1.2.3    04/01/2014
-    - Bug fixes: 
-        - Fixed access violation bug in DeviceReduce::ReduceByKey for non-primitive value types
-        - Fixed code-snippet bug in ArgIndexInputIteratorT documentation 
-
-//-----------------------------------------------------------------------------
-
-1.2.2    03/03/2014
-    - New features:
-        - Added MS VC++ project solutions for device-wide and block-wide examples 
-    - Performance:
-        - Added a third algorithmic variant of cub::BlockReduce for improved performance
-          when using commutative operators (e.g., numeric addition)
-    - Bug fixes: 
-        - Fixed bug where inclusion of Thrust headers in a certain order prevented CUB device-wide primitives from working properly
-
-//-----------------------------------------------------------------------------
-
-1.2.0    02/25/2014
-    - New features:
-        - Added device-wide reduce-by-key (DeviceReduce::ReduceByKey, DeviceReduce::RunLengthEncode) 
-    - Performance
-        - Improved DeviceScan, DeviceSelect, DevicePartition performance
-    - Documentation and testing:
-        - Compatible with CUDA 6.0
-        - Added performance-portability plots for many device-wide primitives to doc 
-        - Update doc and tests to reflect iterator (in)compatibilities with CUDA 5.0 (and older) and Thrust 1.6 (and older).
-    - Bug fixes 
-        - Revised the operation of temporary tile status bookkeeping for DeviceScan (and similar) to be safe for current code run on future platforms (now uses proper fences)  
-        - Fixed DeviceScan bug where Win32 alignment disagreements between host and device regarding user-defined data types would corrupt tile status
-        - Fixed BlockScan bug where certain exclusive scans on custom data types for the BLOCK_SCAN_WARP_SCANS variant would return incorrect results for the first thread in the block
-        - Added workaround for TexRefInputIteratorTto work with CUDA 6.0
-    
-//-----------------------------------------------------------------------------
-
-1.1.1    12/11/2013
-    - New features:
-        - Added TexObjInputIteratorT, TexRefInputIteratorT, CacheModifiedInputIteratorT, and CacheModifiedOutputIterator types for loading & storing arbitrary types through the cache hierarchy.  Compatible with Thrust API. 
-        - Added descending sorting to DeviceRadixSort and BlockRadixSort
-        - Added min, max, arg-min, and arg-max to DeviceReduce
-        - Added DeviceSelect (select-unique, select-if, and select-flagged)
-        - Added DevicePartition (partition-if, partition-flagged)
-        - Added generic cub::ShuffleUp(), cub::ShuffleDown(), and cub::ShuffleIndex() for warp-wide communication of arbitrary data types (SM3x+)
-        - Added cub::MaxSmOccupancy() for accurately determining SM occupancy for any given kernel function pointer
-    - Performance
-        - Improved DeviceScan and DeviceRadixSort performance for older architectures (SM10-SM30)
-    - Interface changes:
-        - Refactored block-wide I/O (BlockLoad and BlockStore), removing cache-modifiers from their interfaces.  The CacheModifiedInputIteratorTand CacheModifiedOutputIterator should now be used with BlockLoad and BlockStore to effect that behavior.
-        - Rename device-wide "stream_synchronous" param to "debug_synchronous" to avoid confusion about usage
-    - Documentation and testing:
-        - Added simple examples of device-wide methods
-        - Improved doxygen documentation and example snippets
-        - Improved test coverege to include up to 21,000 kernel variants and 851,000 unit tests (per architecture, per platform)
-    - Bug fixes 
-        - Fixed misc DeviceScan, BlockScan, DeviceReduce, and BlockReduce bugs when operating on non-primitive types for older architectures SM10-SM13
-        - Fixed DeviceScan / WarpReduction bug: SHFL-based segmented reduction producting incorrect results for multi-word types (size > 4B) on Linux 
-        - Fixed BlockScan bug: For warpscan-based scans, not all threads in the first warp were entering the prefix callback functor
-        - Fixed DeviceRadixSort bug: race condition with key-value pairs for pre-SM35 architectures
-        - Fixed DeviceRadixSort bug: incorrect bitfield-extract behavior with long keys on 64bit Linux
-        - Fixed BlockDiscontinuity bug: complation error in for types other than int32/uint32
-        - CDP (device-callable) versions of device-wide methods now report the same temporary storage allocation size requirement as their host-callable counterparts
-     
-
-//-----------------------------------------------------------------------------
-
-1.0.2    08/23/2013
-    - Corrections to code snippet examples for BlockLoad, BlockStore, and BlockDiscontinuity
-    - Cleaned up unnecessary/missing header includes.  You can now safely #inlude a specific .cuh (instead of cub.cuh)
-    - Bug/compilation fixes for BlockHistogram 
-
-//-----------------------------------------------------------------------------
-
-1.0.1    08/08/2013
-    - New collective interface idiom (specialize::construct::invoke).
-    - Added best-in-class DeviceRadixSort.  Implements short-circuiting for homogenous digit passes.
-    - Added best-in-class DeviceScan.  Implements single-pass "adaptive-lookback" strategy.
-    - Significantly improved documentation (with example code snippets) 
-    - More extensive regression test suit for aggressively testing collective variants
-    - Allow non-trially-constructed types (previously unions had prevented aliasing temporary storage of those types)
-    - Improved support for Kepler SHFL (collective ops now use SHFL for types larger than 32b)
-    - Better code generation for 64-bit addressing within BlockLoad/BlockStore
-    - DeviceHistogram now supports histograms of arbitrary bins
-    - Misc. fixes
-      - Workarounds for SM10 codegen issues in uncommonly-used WarpScan/Reduce specializations
-      - Updates to accommodate CUDA 5.5 dynamic parallelism   
-
-
-//-----------------------------------------------------------------------------
-
-0.9.4    05/07/2013
-
-    - Fixed compilation errors for SM10-SM13
-    - Fixed compilation errors for some WarpScan entrypoints on SM30+
-    - Added block-wide histogram (BlockHistogram256)
-    - Added device-wide histogram (DeviceHistogram256)
-    - Added new BlockScan algorithm variant BLOCK_SCAN_RAKING_MEMOIZE, which 
-      trades more register consumption for less shared memory I/O)
-    - Updates to BlockRadixRank to use BlockScan (which improves performance
-      on Kepler due to SHFL instruction)
-    - Allow types other than C++ primitives to be used in WarpScan::*Sum methods 
-      if they only have operator + overloaded.  (Previously they also required 
-      to support assignment from int(0).) 
-    - Update BlockReduce's BLOCK_REDUCE_WARP_REDUCTIONS algorithm to work even 
-      when block size is not an even multiple of warp size
-    - Added work management utility descriptors (GridQueue, GridEvenShare)
-    - Refactoring of DeviceAllocator interface and CachingDeviceAllocator 
-      implementation 
-    - Misc. documentation updates and corrections. 
-     
-//-----------------------------------------------------------------------------
-
-0.9.2    04/04/2013
-
-    - Added WarpReduce.  WarpReduce uses the SHFL instruction when applicable. 
-      BlockReduce now uses this WarpReduce instead of implementing its own.
-    - Misc. fixes for 64-bit Linux compilation warnings and errors.
-    - Misc. documentation updates and corrections. 
-
-//-----------------------------------------------------------------------------
-
-0.9.1    03/09/2013
-
-    - Fix for ambiguity in BlockScan::Reduce() between generic reduction and 
-      summation.  Summation entrypoints are now called ::Sum(), similar to the 
-      convention in BlockScan.
-    - Small edits to mainpage documentation and download tracking
-    
-//-----------------------------------------------------------------------------
-
-0.9.0    03/07/2013    
-
-    - Intial "preview" release.    CUB is the first durable, high-performance library 
-      of cooperative block-level, warp-level, and thread-level primitives for CUDA 
-      kernel programming.  More primitives and examples coming soon!
-    
\ No newline at end of file
diff --git a/ml-xgboost/cub/LICENSE.TXT b/ml-xgboost/cub/LICENSE.TXT
deleted file mode 100644
index 9ba3b78..0000000
--- a/ml-xgboost/cub/LICENSE.TXT
+++ /dev/null
@@ -1,24 +0,0 @@
-Copyright (c) 2010-2011, Duane Merrill.  All rights reserved.
-Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-   *  Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-   *  Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-   *  Neither the name of the NVIDIA CORPORATION nor the
-      names of its contributors may be used to endorse or promote products
-      derived from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/ml-xgboost/cub/README.md b/ml-xgboost/cub/README.md
deleted file mode 100644
index 1ffcf84..0000000
--- a/ml-xgboost/cub/README.md
+++ /dev/null
@@ -1,128 +0,0 @@
-<hr>
-<h3>About CUB</h3>
-
-Current release: v1.6.4 (12/06/2016)
-
-We recommend the [CUB Project Website](http://nvlabs.github.com/cub) and the [cub-users discussion forum](http://groups.google.com/group/cub-users) for further information and examples.
-
-CUB provides state-of-the-art, reusable software components for every layer 
-of the CUDA programming model:
-- [<b><em>Device-wide primitives</em></b>] (https://nvlabs.github.com/cub/group___device_module.html) 
-  - Sort, prefix scan, reduction, histogram, etc.  
-  - Compatible with CUDA dynamic parallelism
-- [<b><em>Block-wide "collective" primitives</em></b>] (https://nvlabs.github.com/cub/group___block_module.html)
-  - I/O, sort, prefix scan, reduction, histogram, etc.  
-  - Compatible with arbitrary thread block sizes and types 
-- [<b><em>Warp-wide "collective" primitives</em></b>] (https://nvlabs.github.com/cub/group___warp_module.html)
-  - Warp-wide prefix scan, reduction, etc.
-  - Safe and architecture-specific
-- [<b><em>Thread and resource utilities</em></b>](https://nvlabs.github.com/cub/group___thread_module.html)
-  - PTX intrinsics, device reflection, texture-caching iterators, caching memory allocators, etc. 
-
-![Orientation of collective primitives within the CUDA software stack](http://nvlabs.github.com/cub/cub_overview.png)
-
-<br><hr>
-<h3>A Simple Example</h3>
-
-```C++
-#include <cub/cub.cuh>
- 
-// Block-sorting CUDA kernel
-__global__ void BlockSortKernel(int *d_in, int *d_out)
-{
-     using namespace cub;
-
-     // Specialize BlockRadixSort, BlockLoad, and BlockStore for 128 threads 
-     // owning 16 integer items each
-     typedef BlockRadixSort<int, 128, 16>                     BlockRadixSort;
-     typedef BlockLoad<int, 128, 16, BLOCK_LOAD_TRANSPOSE>   BlockLoad;
-     typedef BlockStore<int, 128, 16, BLOCK_STORE_TRANSPOSE> BlockStore;
- 
-     // Allocate shared memory
-     __shared__ union {
-         typename BlockRadixSort::TempStorage  sort;
-         typename BlockLoad::TempStorage       load; 
-         typename BlockStore::TempStorage      store; 
-     } temp_storage; 
-
-     int block_offset = blockIdx.x * (128 * 16);	  // OffsetT for this block's ment
-
-     // Obtain a segment of 2048 consecutive keys that are blocked across threads
-     int thread_keys[16];
-     BlockLoad(temp_storage.load).Load(d_in + block_offset, thread_keys);
-     __syncthreads();
-
-     // Collectively sort the keys
-     BlockRadixSort(temp_storage.sort).Sort(thread_keys);
-     __syncthreads();
-
-     // Store the sorted segment 
-     BlockStore(temp_storage.store).Store(d_out + block_offset, thread_keys);
-}
-```
-
-Each thread block uses cub::BlockRadixSort to collectively sort 
-its own input segment.  The class is specialized by the 
-data type being sorted, by the number of threads per block, by the number of 
-keys per thread, and implicitly by the targeted compilation architecture.  
-
-The cub::BlockLoad and cub::BlockStore classes are similarly specialized.    
-Furthermore, to provide coalesced accesses to device memory, these primitives are 
-configured to access memory using a striped access pattern (where consecutive threads 
-simultaneously access consecutive items) and then <em>transpose</em> the keys into 
-a [<em>blocked arrangement</em>](index.html#sec4sec3) of elements across threads. 
-
-Once specialized, these classes expose opaque \p TempStorage member types.  
-The thread block uses these storage types to statically allocate the union of 
-shared memory needed by the thread block.  (Alternatively these storage types 
-could be aliased to global memory allocations).
-
-<br><hr>
-<h3>Stable Releases</h3>
-
-CUB releases are labeled using version identifiers having three fields: 
-*epoch.feature.update*.  The *epoch* field corresponds to support for
-a major change in the CUDA programming model.  The *feature* field 
-corresponds to a stable set of features, functionality, and interface.  The
-*update* field corresponds to a bug-fix or performance update for that
-feature set.  At the moment, we do not publicly provide non-stable releases 
-such as development snapshots, beta releases or rolling releases.  (Feel free
-to contact us if you would like such things.)  See the 
-[CUB Project Website](http://nvlabs.github.com/cub) for more information.
-
-<br><hr>
-<h3>Contributors</h3>
-
-CUB is developed as an open-source project by [NVIDIA Research](http://research.nvidia.com).  The primary contributor is [Duane Merrill](http://github.com/dumerrill).
-
-<br><hr>
-<h3>Open Source License</h3>
-
-CUB is available under the "New BSD" open-source license:
-
-```
-Copyright (c) 2010-2011, Duane Merrill.  All rights reserved.
-Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-   *  Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-   *  Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-   *  Neither the name of the NVIDIA CORPORATION nor the
-      names of its contributors may be used to endorse or promote products
-      derived from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-```
diff --git a/ml-xgboost/cub/common.mk b/ml-xgboost/cub/common.mk
deleted file mode 100644
index 8154850..0000000
--- a/ml-xgboost/cub/common.mk
+++ /dev/null
@@ -1,233 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
-
-
-#-------------------------------------------------------------------------------
-# Commandline Options
-#-------------------------------------------------------------------------------
-
-# [sm=<XXX,...>] Compute-capability to compile for, e.g., "sm=200,300,350" (SM20 by default).
-  
-COMMA = ,
-ifdef sm
-	SM_ARCH = $(subst $(COMMA),-,$(sm))
-else 
-    SM_ARCH = 200
-endif
-
-ifeq (700, $(findstring 700, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_70,code=\"sm_70,compute_70\" 
-    SM_DEF 		+= -DSM700
-    TEST_ARCH 	= 700
-endif
-ifeq (620, $(findstring 620, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_62,code=\"sm_62,compute_62\" 
-    SM_DEF 		+= -DSM620
-    TEST_ARCH 	= 620
-endif
-ifeq (610, $(findstring 610, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_61,code=\"sm_61,compute_61\" 
-    SM_DEF 		+= -DSM610
-    TEST_ARCH 	= 610
-endif
-ifeq (600, $(findstring 600, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_60,code=\"sm_60,compute_60\" 
-    SM_DEF 		+= -DSM600
-    TEST_ARCH 	= 600
-endif
-ifeq (520, $(findstring 520, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_52,code=\"sm_52,compute_52\" 
-    SM_DEF 		+= -DSM520
-    TEST_ARCH 	= 520
-endif
-ifeq (370, $(findstring 370, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_37,code=\"sm_37,compute_37\" 
-    SM_DEF 		+= -DSM370
-    TEST_ARCH 	= 370
-endif
-ifeq (350, $(findstring 350, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_35,code=\"sm_35,compute_35\" 
-    SM_DEF 		+= -DSM350
-    TEST_ARCH 	= 350
-endif
-ifeq (300, $(findstring 300, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_30,code=\"sm_30,compute_30\"
-    SM_DEF 		+= -DSM300
-    TEST_ARCH 	= 300
-endif
-ifeq (210, $(findstring 210, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_20,code=\"sm_21,compute_20\"
-    SM_DEF 		+= -DSM210
-    TEST_ARCH 	= 210
-endif
-ifeq (200, $(findstring 200, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_20,code=\"sm_20,compute_20\"
-    SM_DEF 		+= -DSM200
-    TEST_ARCH 	= 200
-endif
-ifeq (130, $(findstring 130, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_13,code=\"sm_13,compute_13\" 
-    SM_DEF 		+= -DSM130
-    TEST_ARCH 	= 130
-endif
-ifeq (120, $(findstring 120, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_12,code=\"sm_12,compute_12\" 
-    SM_DEF 		+= -DSM120
-    TEST_ARCH 	= 120
-endif
-ifeq (110, $(findstring 110, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_11,code=\"sm_11,compute_11\" 
-    SM_DEF 		+= -DSM110
-    TEST_ARCH 	= 110
-endif
-ifeq (100, $(findstring 100, $(SM_ARCH)))
-    SM_TARGETS 	+= -gencode=arch=compute_10,code=\"sm_10,compute_10\" 
-    SM_DEF 		+= -DSM100
-    TEST_ARCH 	= 100
-endif
-
-
-# [cdp=<0|1>] CDP enable option (default: no)
-ifeq ($(cdp), 1)
-	DEFINES += -DCUB_CDP
-	CDP_SUFFIX = cdp
-    NVCCFLAGS += -rdc=true -lcudadevrt
-else
-	CDP_SUFFIX = nocdp
-endif
-
-
-# [force32=<0|1>] Device addressing mode option (64-bit device pointers by default) 
-ifeq ($(force32), 1)
-	CPU_ARCH = -m32
-	CPU_ARCH_SUFFIX = i386
-else
-	CPU_ARCH = -m64
-	CPU_ARCH_SUFFIX = x86_64
-    NPPI = -lnppi
-endif
-
-
-# [abi=<0|1>] CUDA ABI option (enabled by default) 
-ifneq ($(abi), 0)
-	ABI_SUFFIX = abi
-else 
-	NVCCFLAGS += -Xptxas -abi=no
-	ABI_SUFFIX = noabi
-endif
-
-
-# [open64=<0|1>] Middle-end compiler option (nvvm by default)
-ifeq ($(open64), 1)
-	NVCCFLAGS += -open64
-	PTX_SUFFIX = open64
-else 
-	PTX_SUFFIX = nvvm
-endif
-
-
-# [verbose=<0|1>] Verbose toolchain output from nvcc option
-ifeq ($(verbose), 1)
-	NVCCFLAGS += -v
-endif
-
-
-# [keep=<0|1>] Keep intermediate compilation artifacts option
-ifeq ($(keep), 1)
-	NVCCFLAGS += -keep
-endif
-
-# [debug=<0|1>] Generate debug mode code
-ifeq ($(debug), 1)
-	NVCCFLAGS += -G
-endif
-
-
-#-------------------------------------------------------------------------------
-# Compiler and compilation platform
-#-------------------------------------------------------------------------------
-
-CUB_DIR = $(dir $(lastword $(MAKEFILE_LIST)))
-
-NVCC = "$(shell which nvcc)"
-ifdef nvccver
-    NVCC_VERSION = $(nvccver)
-else
-    NVCC_VERSION = $(strip $(shell nvcc --version | grep release | sed 's/.*release //' |  sed 's/,.*//'))
-endif
-
-# detect OS
-OSUPPER = $(shell uname -s 2>/dev/null | tr [:lower:] [:upper:])
-
-# Default flags: verbose kernel properties (regs, smem, cmem, etc.); runtimes for compilation phases 
-NVCCFLAGS += $(SM_DEF) -Xptxas -v -Xcudafe -\# 
-
-ifeq (WIN_NT, $(findstring WIN_NT, $(OSUPPER)))
-    # For MSVC
-    # Enable more warnings and treat as errors
-    NVCCFLAGS += -Xcompiler /W3 -Xcompiler /WX
-    # Disable excess x86 floating point precision that can lead to results being labeled incorrectly
-    NVCCFLAGS += -Xcompiler /fp:strict
-    # Help the compiler/linker work with huge numbers of kernels on Windows
-	NVCCFLAGS += -Xcompiler /bigobj -Xcompiler /Zm500
-	CC = cl
-	
-	# Multithreaded runtime
-	NVCCFLAGS += -Xcompiler /MT
-	
-ifneq ($(force32), 1)
-	CUDART_CYG = "$(shell dirname $(NVCC))/../lib/Win32/cudart.lib"
-else
-	CUDART_CYG = "$(shell dirname $(NVCC))/../lib/x64/cudart.lib"
-endif
-	CUDART = "$(shell cygpath -w $(CUDART_CYG))"
-else
-    # For g++
-    # Disable excess x86 floating point precision that can lead to results being labeled incorrectly
-    NVCCFLAGS += -Xcompiler -ffloat-store
-    CC = g++
-ifneq ($(force32), 1)
-    CUDART = "$(shell dirname $(NVCC))/../lib/libcudart_static.a"
-else
-    CUDART = "$(shell dirname $(NVCC))/../lib64/libcudart_static.a"
-endif
-endif
-
-# Suffix to append to each binary
-BIN_SUFFIX = sm$(SM_ARCH)_$(PTX_SUFFIX)_$(NVCC_VERSION)_$(ABI_SUFFIX)_$(CDP_SUFFIX)_$(CPU_ARCH_SUFFIX)
-
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-CUB_DEPS = 	$(call rwildcard, $(CUB_DIR),*.cuh) \
-			$(CUB_DIR)common.mk
-		
diff --git a/ml-xgboost/cub/cub/agent/agent_histogram.cuh b/ml-xgboost/cub/cub/agent/agent_histogram.cuh
deleted file mode 100644
index e42ffe2..0000000
--- a/ml-xgboost/cub/cub/agent/agent_histogram.cuh
+++ /dev/null
@@ -1,783 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentHistogram implements a stateful abstraction of CUDA thread blocks for participating in device-wide histogram .
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "../util_type.cuh"
-#include "../block/block_load.cuh"
-#include "../grid/grid_queue.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy
- ******************************************************************************/
-
-/**
- *
- */
-enum BlockHistogramMemoryPreference
-{
-    GMEM,
-    SMEM,
-    BLEND
-};
-
-
-/**
- * Parameterizable tuning policy type for AgentHistogram
- */
-template <
-    int                             _BLOCK_THREADS,                 ///< Threads per thread block
-    int                             _PIXELS_PER_THREAD,             ///< Pixels per thread (per tile of input)
-    BlockLoadAlgorithm              _LOAD_ALGORITHM,                ///< The BlockLoad algorithm to use
-    CacheLoadModifier               _LOAD_MODIFIER,                 ///< Cache load modifier for reading input elements
-    bool                            _RLE_COMPRESS,                  ///< Whether to perform localized RLE to compress samples before histogramming
-    BlockHistogramMemoryPreference  _MEM_PREFERENCE,                ///< Whether to prefer privatized shared-memory bins (versus privatized global-memory bins)
-    bool                            _WORK_STEALING>                 ///< Whether to dequeue tiles from a global work queue
-struct AgentHistogramPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,                   ///< Threads per thread block
-        PIXELS_PER_THREAD       = _PIXELS_PER_THREAD,               ///< Pixels per thread (per tile of input)
-        IS_RLE_COMPRESS            = _RLE_COMPRESS,                    ///< Whether to perform localized RLE to compress samples before histogramming
-        MEM_PREFERENCE          = _MEM_PREFERENCE,                  ///< Whether to prefer privatized shared-memory bins (versus privatized global-memory bins)
-        IS_WORK_STEALING           = _WORK_STEALING,                   ///< Whether to dequeue tiles from a global work queue
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;          ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;           ///< Cache load modifier for reading input elements
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentHistogram implements a stateful abstraction of CUDA thread blocks for participating in device-wide histogram .
- */
-template <
-    typename    AgentHistogramPolicyT,     ///< Parameterized AgentHistogramPolicy tuning policy type
-    int         PRIVATIZED_SMEM_BINS,           ///< Number of privatized shared-memory histogram bins of any channel.  Zero indicates privatized counters to be maintained in device-accessible memory.
-    int         NUM_CHANNELS,                   ///< Number of channels interleaved in the input data.  Supports up to four channels.
-    int         NUM_ACTIVE_CHANNELS,            ///< Number of channels actively being histogrammed
-    typename    SampleIteratorT,                ///< Random-access input iterator type for reading samples
-    typename    CounterT,                       ///< Integer type for counting sample occurrences per histogram bin
-    typename    PrivatizedDecodeOpT,            ///< The transform operator type for determining privatized counter indices from samples, one for each channel
-    typename    OutputDecodeOpT,                ///< The transform operator type for determining output bin-ids from privatized counter indices, one for each channel
-    typename    OffsetT,                        ///< Signed integer type for global offsets
-    int         PTX_ARCH = CUB_PTX_ARCH>        ///< PTX compute capability
-struct AgentHistogram
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// The sample type of the input iterator
-    typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-    /// The pixel type of SampleT
-    typedef typename CubVector<SampleT, NUM_CHANNELS>::Type PixelT;
-
-    /// The quad type of SampleT
-    typedef typename CubVector<SampleT, 4>::Type QuadT;
-
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS           = AgentHistogramPolicyT::BLOCK_THREADS,
-
-        PIXELS_PER_THREAD       = AgentHistogramPolicyT::PIXELS_PER_THREAD,
-        SAMPLES_PER_THREAD      = PIXELS_PER_THREAD * NUM_CHANNELS,
-        QUADS_PER_THREAD        = SAMPLES_PER_THREAD / 4,
-
-        TILE_PIXELS             = PIXELS_PER_THREAD * BLOCK_THREADS,
-        TILE_SAMPLES            = SAMPLES_PER_THREAD * BLOCK_THREADS,
-
-        IS_RLE_COMPRESS            = AgentHistogramPolicyT::IS_RLE_COMPRESS,
-
-        MEM_PREFERENCE          = (PRIVATIZED_SMEM_BINS > 0) ?
-                                        AgentHistogramPolicyT::MEM_PREFERENCE :
-                                        GMEM,
-
-        IS_WORK_STEALING           = AgentHistogramPolicyT::IS_WORK_STEALING,
-    };
-
-    /// Cache load modifier for reading input elements
-    static const CacheLoadModifier LOAD_MODIFIER = AgentHistogramPolicyT::LOAD_MODIFIER;
-
-
-    /// Input iterator wrapper type (for applying cache modifier)
-    typedef typename If<IsPointer<SampleIteratorT>::VALUE,
-            CacheModifiedInputIterator<LOAD_MODIFIER, SampleT, OffsetT>,     // Wrap the native input pointer with CacheModifiedInputIterator
-            SampleIteratorT>::Type                                           // Directly use the supplied input iterator type
-        WrappedSampleIteratorT;
-
-    /// Pixel input iterator type (for applying cache modifier)
-    typedef CacheModifiedInputIterator<LOAD_MODIFIER, PixelT, OffsetT>
-        WrappedPixelIteratorT;
-
-    /// Qaud input iterator type (for applying cache modifier)
-    typedef CacheModifiedInputIterator<LOAD_MODIFIER, QuadT, OffsetT>
-        WrappedQuadIteratorT;
-
-    /// Parameterized BlockLoad type for samples
-    typedef BlockLoad<
-            SampleT,
-            BLOCK_THREADS,
-            SAMPLES_PER_THREAD,
-            AgentHistogramPolicyT::LOAD_ALGORITHM>
-        BlockLoadSampleT;
-
-    /// Parameterized BlockLoad type for pixels
-    typedef BlockLoad<
-            PixelT,
-            BLOCK_THREADS,
-            PIXELS_PER_THREAD,
-            AgentHistogramPolicyT::LOAD_ALGORITHM>
-        BlockLoadPixelT;
-
-    /// Parameterized BlockLoad type for quads
-    typedef BlockLoad<
-            QuadT,
-            BLOCK_THREADS,
-            QUADS_PER_THREAD,
-            AgentHistogramPolicyT::LOAD_ALGORITHM>
-        BlockLoadQuadT;
-
-    /// Shared memory type required by this thread block
-    struct _TempStorage
-    {
-        CounterT histograms[NUM_ACTIVE_CHANNELS][PRIVATIZED_SMEM_BINS + 1];     // Smem needed for block-privatized smem histogram (with 1 word of padding)
-
-        int tile_idx;
-
-        union
-        {
-            typename BlockLoadSampleT::TempStorage sample_load;     // Smem needed for loading a tile of samples
-            typename BlockLoadPixelT::TempStorage pixel_load;       // Smem needed for loading a tile of pixels
-            typename BlockLoadQuadT::TempStorage quad_load;         // Smem needed for loading a tile of quads
-        };
-    };
-
-
-    /// Temporary storage type (unionable)
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    /// Reference to temp_storage
-    _TempStorage &temp_storage;
-
-    /// Sample input iterator (with cache modifier applied, if possible)
-    WrappedSampleIteratorT d_wrapped_samples;
-
-    /// Native pointer for input samples (possibly NULL if unavailable)
-    SampleT* d_native_samples;
-
-    /// The number of output bins for each channel
-    int (&num_output_bins)[NUM_ACTIVE_CHANNELS];
-
-    /// The number of privatized bins for each channel
-    int (&num_privatized_bins)[NUM_ACTIVE_CHANNELS];
-
-    /// Reference to gmem privatized histograms for each channel
-    CounterT* d_privatized_histograms[NUM_ACTIVE_CHANNELS];
-
-    /// Reference to final output histograms (gmem)
-    CounterT* (&d_output_histograms)[NUM_ACTIVE_CHANNELS];
-
-    /// The transform operator for determining output bin-ids from privatized counter indices, one for each channel
-    OutputDecodeOpT (&output_decode_op)[NUM_ACTIVE_CHANNELS];
-
-    /// The transform operator for determining privatized counter indices from samples, one for each channel
-    PrivatizedDecodeOpT (&privatized_decode_op)[NUM_ACTIVE_CHANNELS];
-
-    /// Whether to prefer privatized smem counters vs privatized global counters
-    bool prefer_smem;
-
-
-    //---------------------------------------------------------------------
-    // Initialize privatized bin counters
-    //---------------------------------------------------------------------
-
-    // Initialize privatized bin counters
-    __device__ __forceinline__ void InitBinCounters(CounterT* privatized_histograms[NUM_ACTIVE_CHANNELS])
-    {
-        // Initialize histogram bin counts to zeros
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-        {
-            for (int privatized_bin = threadIdx.x; privatized_bin < num_privatized_bins[CHANNEL]; privatized_bin += BLOCK_THREADS)
-            {
-                privatized_histograms[CHANNEL][privatized_bin] = 0;
-            }
-        }
-
-        // Barrier to make sure all threads are done updating counters
-        CTA_SYNC();
-    }
-
-
-    // Initialize privatized bin counters.  Specialized for privatized shared-memory counters
-    __device__ __forceinline__ void InitSmemBinCounters()
-    {
-        CounterT* privatized_histograms[NUM_ACTIVE_CHANNELS];
-
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-            privatized_histograms[CHANNEL] = temp_storage.histograms[CHANNEL];
-
-        InitBinCounters(privatized_histograms);
-    }
-
-
-    // Initialize privatized bin counters.  Specialized for privatized global-memory counters
-    __device__ __forceinline__ void InitGmemBinCounters()
-    {
-        InitBinCounters(d_privatized_histograms);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Update final output histograms
-    //---------------------------------------------------------------------
-
-    // Update final output histograms from privatized histograms
-    __device__ __forceinline__ void StoreOutput(CounterT* privatized_histograms[NUM_ACTIVE_CHANNELS])
-    {
-        // Barrier to make sure all threads are done updating counters
-        CTA_SYNC();
-
-        // Apply privatized bin counts to output bin counts
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-        {
-            int channel_bins = num_privatized_bins[CHANNEL];
-            for (int privatized_bin = threadIdx.x; 
-                    privatized_bin < channel_bins;  
-                    privatized_bin += BLOCK_THREADS)
-            {
-                int         output_bin  = -1;
-                CounterT    count       = privatized_histograms[CHANNEL][privatized_bin];
-                bool        is_valid    = count > 0;
-
-                output_decode_op[CHANNEL].BinSelect<LOAD_MODIFIER>((SampleT) privatized_bin, output_bin, is_valid);
-
-                if (output_bin >= 0)
-                {
-                    atomicAdd(&d_output_histograms[CHANNEL][output_bin], count);
-                }
-
-            }
-        }
-    }
-
-
-    // Update final output histograms from privatized histograms.  Specialized for privatized shared-memory counters
-    __device__ __forceinline__ void StoreSmemOutput()
-    {
-        CounterT* privatized_histograms[NUM_ACTIVE_CHANNELS];
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-            privatized_histograms[CHANNEL] = temp_storage.histograms[CHANNEL];
-
-        StoreOutput(privatized_histograms);
-    }
-
-
-    // Update final output histograms from privatized histograms.  Specialized for privatized global-memory counters
-    __device__ __forceinline__ void StoreGmemOutput()
-    {
-        StoreOutput(d_privatized_histograms);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Tile accumulation
-    //---------------------------------------------------------------------
-
-    // Accumulate pixels.  Specialized for RLE compression.
-    __device__ __forceinline__ void AccumulatePixels(
-        SampleT             samples[PIXELS_PER_THREAD][NUM_CHANNELS],
-        bool                is_valid[PIXELS_PER_THREAD],
-        CounterT*           privatized_histograms[NUM_ACTIVE_CHANNELS],
-        Int2Type<true>      is_rle_compress)
-    {
-
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-        {
-            // Bin pixels
-            int bins[PIXELS_PER_THREAD];
-
-            #pragma unroll
-            for (int PIXEL = 0; PIXEL < PIXELS_PER_THREAD; ++PIXEL)
-            {
-                bins[PIXEL] = -1;
-                privatized_decode_op[CHANNEL].BinSelect<LOAD_MODIFIER>(samples[PIXEL][CHANNEL], bins[PIXEL], is_valid[PIXEL]);
-            }
-
-            CounterT accumulator = 1;
-
-            #pragma unroll
-            for (int PIXEL = 0; PIXEL < PIXELS_PER_THREAD - 1; ++PIXEL)
-            {
-                if (bins[PIXEL] == bins[PIXEL + 1])
-                {
-                     accumulator++;
-                }
-                else
-                {
-                    if (bins[PIXEL] >= 0)
-                        atomicAdd(privatized_histograms[CHANNEL] + bins[PIXEL], accumulator);
-
-                     accumulator = 1;
-                }
-            }
-            // Last pixel
-            if (bins[PIXELS_PER_THREAD - 1] >= 0)
-                atomicAdd(privatized_histograms[CHANNEL] + bins[PIXELS_PER_THREAD - 1], accumulator);
-        }
-    }
-
-
-    // Accumulate pixels.  Specialized for individual accumulation of each pixel.
-    __device__ __forceinline__ void AccumulatePixels(
-        SampleT             samples[PIXELS_PER_THREAD][NUM_CHANNELS],
-        bool                is_valid[PIXELS_PER_THREAD],
-        CounterT*           privatized_histograms[NUM_ACTIVE_CHANNELS],
-        Int2Type<false>     is_rle_compress)
-    {
-        #pragma unroll
-        for (int PIXEL = 0; PIXEL < PIXELS_PER_THREAD; ++PIXEL)
-        {
-            #pragma unroll
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-            {
-                int bin = -1;
-                privatized_decode_op[CHANNEL].BinSelect<LOAD_MODIFIER>(samples[PIXEL][CHANNEL], bin, is_valid[PIXEL]);
-                if (bin >= 0)
-                    atomicAdd(privatized_histograms[CHANNEL] + bin, 1);
-            }
-        }
-    }
-
-
-    /**
-     * Accumulate pixel, specialized for smem privatized histogram
-     */
-    __device__ __forceinline__ void AccumulateSmemPixels(
-        SampleT             samples[PIXELS_PER_THREAD][NUM_CHANNELS],
-        bool                is_valid[PIXELS_PER_THREAD])
-    {
-        CounterT* privatized_histograms[NUM_ACTIVE_CHANNELS];
-
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-            privatized_histograms[CHANNEL] = temp_storage.histograms[CHANNEL];
-
-        AccumulatePixels(samples, is_valid, privatized_histograms, Int2Type<IS_RLE_COMPRESS>());
-    }
-
-
-    /**
-     * Accumulate pixel, specialized for gmem privatized histogram
-     */
-    __device__ __forceinline__ void AccumulateGmemPixels(
-        SampleT             samples[PIXELS_PER_THREAD][NUM_CHANNELS],
-        bool                is_valid[PIXELS_PER_THREAD])
-    {
-        AccumulatePixels(samples, is_valid, d_privatized_histograms, Int2Type<IS_RLE_COMPRESS>());
-    }
-
-
-
-    //---------------------------------------------------------------------
-    // Tile loading
-    //---------------------------------------------------------------------
-
-    // Load full, aligned tile using pixel iterator (multi-channel)
-    template <int _NUM_ACTIVE_CHANNELS>
-    __device__ __forceinline__ void LoadFullAlignedTile(
-        OffsetT                         block_offset,
-        int                             valid_samples,
-        SampleT                         (&samples)[PIXELS_PER_THREAD][NUM_CHANNELS],
-        Int2Type<_NUM_ACTIVE_CHANNELS>  num_active_channels)
-    {
-        typedef PixelT AliasedPixels[PIXELS_PER_THREAD];
-
-        WrappedPixelIteratorT d_wrapped_pixels((PixelT*) (d_native_samples + block_offset));
-
-        // Load using a wrapped pixel iterator
-        BlockLoadPixelT(temp_storage.pixel_load).Load(
-            d_wrapped_pixels,
-            reinterpret_cast<AliasedPixels&>(samples));
-    }
-
-    // Load full, aligned tile using quad iterator (single-channel)
-    __device__ __forceinline__ void LoadFullAlignedTile(
-        OffsetT                         block_offset,
-        int                             valid_samples,
-        SampleT                         (&samples)[PIXELS_PER_THREAD][NUM_CHANNELS],
-        Int2Type<1>                     num_active_channels)
-    {
-        typedef QuadT AliasedQuads[QUADS_PER_THREAD];
-
-        WrappedQuadIteratorT d_wrapped_quads((QuadT*) (d_native_samples + block_offset));
-
-        // Load using a wrapped quad iterator
-        BlockLoadQuadT(temp_storage.quad_load).Load(
-            d_wrapped_quads,
-            reinterpret_cast<AliasedQuads&>(samples));
-    }
-
-    // Load full, aligned tile
-    __device__ __forceinline__ void LoadTile(
-        OffsetT         block_offset,
-        int             valid_samples,
-        SampleT         (&samples)[PIXELS_PER_THREAD][NUM_CHANNELS],
-        Int2Type<true>  is_full_tile,
-        Int2Type<true>  is_aligned)
-    {
-        LoadFullAlignedTile(block_offset, valid_samples, samples, Int2Type<NUM_ACTIVE_CHANNELS>());
-    }
-
-    // Load full, mis-aligned tile using sample iterator
-    __device__ __forceinline__ void LoadTile(
-        OffsetT         block_offset,
-        int             valid_samples,
-        SampleT         (&samples)[PIXELS_PER_THREAD][NUM_CHANNELS],
-        Int2Type<true>  is_full_tile,
-        Int2Type<false> is_aligned)
-    {
-        typedef SampleT AliasedSamples[SAMPLES_PER_THREAD];
-
-        // Load using sample iterator
-        BlockLoadSampleT(temp_storage.sample_load).Load(
-            d_wrapped_samples + block_offset,
-            reinterpret_cast<AliasedSamples&>(samples));
-    }
-
-    // Load partially-full, aligned tile using the pixel iterator
-    __device__ __forceinline__ void LoadTile(
-        OffsetT         block_offset,
-        int             valid_samples,
-        SampleT         (&samples)[PIXELS_PER_THREAD][NUM_CHANNELS],
-        Int2Type<false> is_full_tile,
-        Int2Type<true>  is_aligned)
-    {
-        typedef PixelT AliasedPixels[PIXELS_PER_THREAD];
-
-        WrappedPixelIteratorT d_wrapped_pixels((PixelT*) (d_native_samples + block_offset));
-
-        int valid_pixels = valid_samples / NUM_CHANNELS;
-
-        // Load using a wrapped pixel iterator
-        BlockLoadPixelT(temp_storage.pixel_load).Load(
-            d_wrapped_pixels,
-            reinterpret_cast<AliasedPixels&>(samples),
-            valid_pixels);
-    }
-
-    // Load partially-full, mis-aligned tile using sample iterator
-    __device__ __forceinline__ void LoadTile(
-        OffsetT         block_offset,
-        int             valid_samples,
-        SampleT         (&samples)[PIXELS_PER_THREAD][NUM_CHANNELS],
-        Int2Type<false> is_full_tile,
-        Int2Type<false> is_aligned)
-    {
-        typedef SampleT AliasedSamples[SAMPLES_PER_THREAD];
-
-        BlockLoadSampleT(temp_storage.sample_load).Load(
-            d_wrapped_samples + block_offset,
-            reinterpret_cast<AliasedSamples&>(samples),
-            valid_samples);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Tile processing
-    //---------------------------------------------------------------------
-
-    // Consume a tile of data samples
-    template <
-        bool IS_ALIGNED,        // Whether the tile offset is aligned (quad-aligned for single-channel, pixel-aligned for multi-channel)
-        bool IS_FULL_TILE>      // Whether the tile is full
-    __device__ __forceinline__ void ConsumeTile(OffsetT block_offset, int valid_samples)
-    {
-        SampleT     samples[PIXELS_PER_THREAD][NUM_CHANNELS];
-        bool        is_valid[PIXELS_PER_THREAD];
-
-        // Load tile
-        LoadTile(
-            block_offset,
-            valid_samples,
-            samples,
-            Int2Type<IS_FULL_TILE>(),
-            Int2Type<IS_ALIGNED>());
-
-        // Set valid flags
-        #pragma unroll
-        for (int PIXEL = 0; PIXEL < PIXELS_PER_THREAD; ++PIXEL)
-            is_valid[PIXEL] = IS_FULL_TILE || (((threadIdx.x * PIXELS_PER_THREAD + PIXEL) * NUM_CHANNELS) < valid_samples);
-
-        // Accumulate samples
-#if CUB_PTX_ARCH >= 120
-        if (prefer_smem)
-            AccumulateSmemPixels(samples, is_valid);
-        else
-            AccumulateGmemPixels(samples, is_valid);
-#else
-        AccumulateGmemPixels(samples, is_valid);
-#endif
-
-    }
-
-
-    // Consume row tiles.  Specialized for work-stealing from queue
-    template <bool IS_ALIGNED>
-    __device__ __forceinline__ void ConsumeTiles(
-        OffsetT             num_row_pixels,             ///< The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                   ///< The number of rows in the region of interest
-        OffsetT             row_stride_samples,         ///< The number of samples between starts of consecutive rows in the region of interest
-        int                 tiles_per_row,              ///< Number of image tiles per row
-        GridQueue<int>      tile_queue,
-        Int2Type<true>      is_work_stealing)
-    {
-
-        int         num_tiles                   = num_rows * tiles_per_row;
-        int         tile_idx                    = (blockIdx.y  * gridDim.x) + blockIdx.x;
-        OffsetT     num_even_share_tiles        = gridDim.x * gridDim.y;
-
-        while (tile_idx < num_tiles)
-        {
-            int     row             = tile_idx / tiles_per_row;
-            int     col             = tile_idx - (row * tiles_per_row);
-            OffsetT row_offset      = row * row_stride_samples;
-            OffsetT col_offset      = (col * TILE_SAMPLES);
-            OffsetT tile_offset     = row_offset + col_offset;
-
-            if (col == tiles_per_row - 1)
-            {
-                // Consume a partially-full tile at the end of the row
-                OffsetT num_remaining = (num_row_pixels * NUM_CHANNELS) - col_offset;
-                ConsumeTile<IS_ALIGNED, false>(tile_offset, num_remaining);
-            } 
-            else
-            {
-                // Consume full tile
-                ConsumeTile<IS_ALIGNED, true>(tile_offset, TILE_SAMPLES);
-            }
-
-            CTA_SYNC();
-
-            // Get next tile
-            if (threadIdx.x == 0)
-                temp_storage.tile_idx = tile_queue.Drain(1) + num_even_share_tiles;
-
-            CTA_SYNC();
-
-            tile_idx = temp_storage.tile_idx;
-        }
-    }
-
-
-    // Consume row tiles.  Specialized for even-share (striped across thread blocks)
-    template <bool IS_ALIGNED>
-    __device__ __forceinline__ void ConsumeTiles(
-        OffsetT             num_row_pixels,             ///< The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                   ///< The number of rows in the region of interest
-        OffsetT             row_stride_samples,         ///< The number of samples between starts of consecutive rows in the region of interest
-        int                 tiles_per_row,              ///< Number of image tiles per row
-        GridQueue<int>      tile_queue,
-        Int2Type<false>     is_work_stealing)
-    {
-        for (int row = blockIdx.y; row < num_rows; row += gridDim.y)
-        {
-            OffsetT row_begin   = row * row_stride_samples;
-            OffsetT row_end     = row_begin + (num_row_pixels * NUM_CHANNELS);
-            OffsetT tile_offset = row_begin + (blockIdx.x * TILE_SAMPLES);
-
-            while (tile_offset < row_end)
-            {
-                OffsetT num_remaining = row_end - tile_offset;
-
-                if (num_remaining < TILE_SAMPLES)
-                {
-                    // Consume partial tile
-                    ConsumeTile<IS_ALIGNED, false>(tile_offset, num_remaining);
-                    break;
-                }
-
-                // Consume full tile
-                ConsumeTile<IS_ALIGNED, true>(tile_offset, TILE_SAMPLES);
-                tile_offset += gridDim.x * TILE_SAMPLES;
-            }
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Parameter extraction
-    //---------------------------------------------------------------------
-
-    // Return a native pixel pointer (specialized for CacheModifiedInputIterator types)
-    template <
-        CacheLoadModifier   _MODIFIER,
-        typename            _ValueT,
-        typename            _OffsetT>
-    __device__ __forceinline__ SampleT* NativePointer(CacheModifiedInputIterator<_MODIFIER, _ValueT, _OffsetT> itr)
-    {
-        return itr.ptr;
-    }
-
-    // Return a native pixel pointer (specialized for other types)
-    template <typename IteratorT>
-    __device__ __forceinline__ SampleT* NativePointer(IteratorT itr)
-    {
-        return NULL;
-    }
-
-
-
-    //---------------------------------------------------------------------
-    // Interface
-    //---------------------------------------------------------------------
-
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentHistogram(
-        TempStorage         &temp_storage,                                      ///< Reference to temp_storage
-        SampleIteratorT     d_samples,                                          ///< Input data to reduce
-        int                 (&num_output_bins)[NUM_ACTIVE_CHANNELS],            ///< The number bins per final output histogram
-        int                 (&num_privatized_bins)[NUM_ACTIVE_CHANNELS],        ///< The number bins per privatized histogram
-        CounterT*           (&d_output_histograms)[NUM_ACTIVE_CHANNELS],        ///< Reference to final output histograms
-        CounterT*           (&d_privatized_histograms)[NUM_ACTIVE_CHANNELS],    ///< Reference to privatized histograms
-        OutputDecodeOpT     (&output_decode_op)[NUM_ACTIVE_CHANNELS],           ///< The transform operator for determining output bin-ids from privatized counter indices, one for each channel
-        PrivatizedDecodeOpT (&privatized_decode_op)[NUM_ACTIVE_CHANNELS])       ///< The transform operator for determining privatized counter indices from samples, one for each channel
-    :
-        temp_storage(temp_storage.Alias()),
-        d_wrapped_samples(d_samples),
-        num_output_bins(num_output_bins),
-        num_privatized_bins(num_privatized_bins),
-        d_output_histograms(d_output_histograms),
-        privatized_decode_op(privatized_decode_op),
-        output_decode_op(output_decode_op),
-        d_native_samples(NativePointer(d_wrapped_samples)),
-        prefer_smem((MEM_PREFERENCE == SMEM) ?
-            true :                              // prefer smem privatized histograms
-            (MEM_PREFERENCE == GMEM) ?
-                false :                         // prefer gmem privatized histograms
-                blockIdx.x & 1)                 // prefer blended privatized histograms
-    {
-        int blockId = (blockIdx.y * gridDim.x) + blockIdx.x;
-
-        // Initialize the locations of this block's privatized histograms
-        for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-            this->d_privatized_histograms[CHANNEL] = d_privatized_histograms[CHANNEL] + (blockId * num_privatized_bins[CHANNEL]);
-    }
-
-
-    /**
-     * Consume image
-     */
-    __device__ __forceinline__ void ConsumeTiles(
-        OffsetT             num_row_pixels,             ///< The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                   ///< The number of rows in the region of interest
-        OffsetT             row_stride_samples,         ///< The number of samples between starts of consecutive rows in the region of interest
-        int                 tiles_per_row,              ///< Number of image tiles per row
-        GridQueue<int>      tile_queue)                 ///< Queue descriptor for assigning tiles of work to thread blocks
-    {
-        // Check whether all row starting offsets are quad-aligned (in single-channel) or pixel-aligned (in multi-channel)
-        size_t  row_bytes           = sizeof(SampleT) * row_stride_samples;
-        size_t  offset_mask         = size_t(d_native_samples) | row_bytes;
-        int     quad_mask           = sizeof(SampleT) * 4 - 1;
-        int     pixel_mask          = AlignBytes<PixelT>::ALIGN_BYTES - 1;
-        bool    quad_aligned_rows   = (NUM_CHANNELS == 1) && ((offset_mask & quad_mask) == 0);
-        bool    pixel_aligned_rows  = (NUM_CHANNELS > 1) && ((offset_mask & pixel_mask) == 0);
-
-        // Whether rows are aligned and can be vectorized
-        if (quad_aligned_rows || pixel_aligned_rows)
-            ConsumeTiles<true>(num_row_pixels, num_rows, row_stride_samples, tiles_per_row, tile_queue, Int2Type<IS_WORK_STEALING>());
-        else
-            ConsumeTiles<false>(num_row_pixels, num_rows, row_stride_samples, tiles_per_row, tile_queue, Int2Type<IS_WORK_STEALING>());
-    }
-
-
-    /**
-     * Initialize privatized bin counters.  Specialized for privatized shared-memory counters
-     */
-    __device__ __forceinline__ void InitBinCounters()
-    {
-        if (prefer_smem)
-            InitSmemBinCounters();
-        else
-            InitGmemBinCounters();
-    }
-
-
-    /**
-     * Store privatized histogram to device-accessible memory.  Specialized for privatized shared-memory counters
-     */
-    __device__ __forceinline__ void StoreOutput()
-    {
-        if (prefer_smem)
-            StoreSmemOutput();
-        else
-            StoreGmemOutput();
-    }
-
-
-};
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_radix_sort_downsweep.cuh b/ml-xgboost/cub/cub/agent/agent_radix_sort_downsweep.cuh
deleted file mode 100644
index 9b9931a..0000000
--- a/ml-xgboost/cub/cub/agent/agent_radix_sort_downsweep.cuh
+++ /dev/null
@@ -1,753 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * AgentRadixSortDownsweep implements a stateful abstraction of CUDA thread blocks for participating in device-wide radix sort downsweep .
- */
-
-
-#pragma once
-
-#include "../thread/thread_load.cuh"
-#include "../block/block_load.cuh"
-#include "../block/block_store.cuh"
-#include "../block/block_radix_rank.cuh"
-#include "../block/block_exchange.cuh"
-#include "../util_type.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Types of scattering strategies
- */
-enum RadixSortScatterAlgorithm
-{
-    RADIX_SORT_SCATTER_DIRECT,      ///< Scatter directly from registers to global bins
-    RADIX_SORT_SCATTER_TWO_PHASE,   ///< First scatter from registers into shared memory bins, then into global bins
-};
-
-
-/**
- * Parameterizable tuning policy type for AgentRadixSortDownsweep
- */
-template <
-    int                         _BLOCK_THREADS,             ///< Threads per thread block
-    int                         _ITEMS_PER_THREAD,          ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm          _LOAD_ALGORITHM,            ///< The BlockLoad algorithm to use
-    CacheLoadModifier           _LOAD_MODIFIER,             ///< Cache load modifier for reading keys (and values)
-    bool                        _MEMOIZE_OUTER_SCAN,        ///< Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure.  See BlockScanAlgorithm::BLOCK_SCAN_RAKING_MEMOIZE for more details.
-    BlockScanAlgorithm          _INNER_SCAN_ALGORITHM,      ///< The BlockScan algorithm algorithm to use
-    RadixSortScatterAlgorithm   _SCATTER_ALGORITHM,         ///< The scattering strategy to use
-    int                         _RADIX_BITS>                ///< The number of radix bits, i.e., log2(bins)
-struct AgentRadixSortDownsweepPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,           ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,        ///< Items per thread (per tile of input)
-        RADIX_BITS              = _RADIX_BITS,              ///< The number of radix bits, i.e., log2(bins)
-        MEMOIZE_OUTER_SCAN      = _MEMOIZE_OUTER_SCAN,      ///< Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure.  See BlockScanAlgorithm::BLOCK_SCAN_RAKING_MEMOIZE for more details.
-    };
-
-    static const BlockLoadAlgorithm         LOAD_ALGORITHM          = _LOAD_ALGORITHM;          ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier          LOAD_MODIFIER           = _LOAD_MODIFIER;           ///< Cache load modifier for reading keys (and values)
-    static const BlockScanAlgorithm         INNER_SCAN_ALGORITHM    = _INNER_SCAN_ALGORITHM;    ///< The BlockScan algorithm algorithm to use
-    static const RadixSortScatterAlgorithm  SCATTER_ALGORITHM       = _SCATTER_ALGORITHM;       ///< The scattering strategy to use
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentRadixSortDownsweep implements a stateful abstraction of CUDA thread blocks for participating in device-wide radix sort downsweep .
- */
-template <
-    typename AgentRadixSortDownsweepPolicy,     ///< Parameterized AgentRadixSortDownsweepPolicy tuning policy type
-    bool     IS_DESCENDING,                        ///< Whether or not the sorted-order is high-to-low
-    typename KeyT,                              ///< KeyT type
-    typename ValueT,                            ///< ValueT type
-    typename OffsetT>                           ///< Signed integer type for global offsets
-struct AgentRadixSortDownsweep
-{
-    //---------------------------------------------------------------------
-    // Type definitions and constants
-    //---------------------------------------------------------------------
-
-    // Appropriate unsigned-bits representation of KeyT
-    typedef typename Traits<KeyT>::UnsignedBits UnsignedBits;
-
-    static const UnsignedBits LOWEST_KEY = Traits<KeyT>::LOWEST_KEY;
-    static const UnsignedBits MAX_KEY = Traits<KeyT>::MAX_KEY;
-
-    static const BlockLoadAlgorithm         LOAD_ALGORITHM          = AgentRadixSortDownsweepPolicy::LOAD_ALGORITHM;
-    static const CacheLoadModifier          LOAD_MODIFIER           = AgentRadixSortDownsweepPolicy::LOAD_MODIFIER;
-    static const BlockScanAlgorithm         INNER_SCAN_ALGORITHM    = AgentRadixSortDownsweepPolicy::INNER_SCAN_ALGORITHM;
-    static const RadixSortScatterAlgorithm  SCATTER_ALGORITHM       = AgentRadixSortDownsweepPolicy::SCATTER_ALGORITHM;
-
-    enum
-    {
-        BLOCK_THREADS           = AgentRadixSortDownsweepPolicy::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = AgentRadixSortDownsweepPolicy::ITEMS_PER_THREAD,
-        RADIX_BITS              = AgentRadixSortDownsweepPolicy::RADIX_BITS,
-        MEMOIZE_OUTER_SCAN      = AgentRadixSortDownsweepPolicy::MEMOIZE_OUTER_SCAN,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-
-        RADIX_DIGITS            = 1 << RADIX_BITS,
-        KEYS_ONLY               = Equals<ValueT, NullType>::VALUE,
-
-        WARP_THREADS            = CUB_PTX_LOG_WARP_THREADS,
-        WARPS                   = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-
-        BYTES_PER_SIZET         = sizeof(OffsetT),
-        LOG_BYTES_PER_SIZET     = Log2<BYTES_PER_SIZET>::VALUE,
-
-        LOG_SMEM_BANKS          = CUB_PTX_LOG_SMEM_BANKS,
-        SMEM_BANKS              = 1 << LOG_SMEM_BANKS,
-
-        DIGITS_PER_SCATTER_PASS = BLOCK_THREADS / SMEM_BANKS,
-        SCATTER_PASSES          = RADIX_DIGITS / DIGITS_PER_SCATTER_PASS,
-
-        LOG_STORE_TXN_THREADS   = LOG_SMEM_BANKS,
-        STORE_TXN_THREADS       = 1 << LOG_STORE_TXN_THREADS,
-    };
-
-    // Input iterator wrapper type (for applying cache modifier)s
-    typedef CacheModifiedInputIterator<LOAD_MODIFIER, UnsignedBits, OffsetT>    KeysItr;
-    typedef CacheModifiedInputIterator<LOAD_MODIFIER, ValueT, OffsetT>          ValuesItr;
-
-    // BlockRadixRank type
-    typedef BlockRadixRank<
-        BLOCK_THREADS,
-        RADIX_BITS,
-        IS_DESCENDING,
-        MEMOIZE_OUTER_SCAN,
-        INNER_SCAN_ALGORITHM> BlockRadixRank;
-
-    // BlockLoad type (keys)
-    typedef BlockLoad<
-        UnsignedBits,
-        BLOCK_THREADS,
-        ITEMS_PER_THREAD,
-        LOAD_ALGORITHM> BlockLoadKeys;
-
-    // BlockLoad type (values)
-    typedef BlockLoad<
-        ValueT,
-        BLOCK_THREADS,
-        ITEMS_PER_THREAD,
-        LOAD_ALGORITHM> BlockLoadValues;
-
-    // BlockExchange type (keys)
-    typedef BlockExchange<
-        UnsignedBits,
-        BLOCK_THREADS,
-        ITEMS_PER_THREAD> BlockExchangeKeys;
-
-    // BlockExchange type (values)
-    typedef BlockExchange<
-        ValueT,
-        BLOCK_THREADS,
-        ITEMS_PER_THREAD> BlockExchangeValues;
-
-
-    /**
-     * Shared memory storage layout
-     */
-    union __align__(16) _TempStorage
-    {
-        typename BlockLoadKeys::TempStorage         load_keys;
-        typename BlockRadixRank::TempStorage        ranking;
-        typename BlockLoadValues::TempStorage       load_values;
-        typename BlockExchangeValues::TempStorage   exchange_values;
-
-        OffsetT     exclusive_digit_prefix[RADIX_DIGITS];
-
-        struct
-        {
-            typename BlockExchangeKeys::TempStorage     exchange_keys;
-            OffsetT     relative_bin_offsets[RADIX_DIGITS + 1];
-        };
-
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    // Shared storage for this CTA
-    _TempStorage    &temp_storage;
-
-    // Input and output device pointers
-    KeysItr         d_keys_in;
-    ValuesItr       d_values_in;
-    UnsignedBits    *d_keys_out;
-    ValueT          *d_values_out;
-
-    // The global scatter base offset for each digit (valid in the first RADIX_DIGITS threads)
-    OffsetT         bin_offset;
-
-    // The least-significant bit position of the current digit to extract
-    int             current_bit;
-
-    // Number of bits in current digit
-    int             num_bits;
-
-    // Whether to short-cirucit
-    int             short_circuit;
-
-    //---------------------------------------------------------------------
-    // Utility methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Scatter ranked keys directly to device-accessible memory
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void ScatterKeys(
-        UnsignedBits                            (&twiddled_keys)[ITEMS_PER_THREAD],
-        OffsetT                                 (&relative_bin_offsets)[ITEMS_PER_THREAD],
-        int                                     (&ranks)[ITEMS_PER_THREAD],
-        OffsetT                                 valid_items,
-        Int2Type<RADIX_SORT_SCATTER_DIRECT>     /*scatter_algorithm*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            UnsignedBits digit          = BFE(twiddled_keys[ITEM], current_bit, num_bits);
-            relative_bin_offsets[ITEM]  = temp_storage.relative_bin_offsets[digit];
-
-            // Un-twiddle
-            UnsignedBits key            = Traits<KeyT>::TwiddleOut(twiddled_keys[ITEM]);
-
-            if (FULL_TILE || (ranks[ITEM] < valid_items))
-            {
-                d_keys_out[relative_bin_offsets[ITEM] + ranks[ITEM]] = key;
-            }
-        }
-    }
-
-
-    /**
-     * Scatter ranked keys through shared memory, then to device-accessible memory
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void ScatterKeys(
-        UnsignedBits                            (&twiddled_keys)[ITEMS_PER_THREAD],
-        OffsetT                                 (&relative_bin_offsets)[ITEMS_PER_THREAD],
-        int                                     (&ranks)[ITEMS_PER_THREAD],
-        OffsetT                                 valid_items,
-        Int2Type<RADIX_SORT_SCATTER_TWO_PHASE>  /*scatter_algorithm*/)
-    {
-        UnsignedBits *smem = reinterpret_cast<UnsignedBits*>(&temp_storage.exchange_keys);
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            smem[ranks[ITEM]] = twiddled_keys[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            UnsignedBits key = smem[threadIdx.x + (ITEM * BLOCK_THREADS)];
-
-            UnsignedBits digit = BFE(key, current_bit, num_bits);
-
-            relative_bin_offsets[ITEM] = temp_storage.relative_bin_offsets[digit];
-
-            // Un-twiddle
-            key = Traits<KeyT>::TwiddleOut(key);
-
-            if (FULL_TILE || 
-                (static_cast<OffsetT>(threadIdx.x + (ITEM * BLOCK_THREADS)) < valid_items))
-            {
-                d_keys_out[relative_bin_offsets[ITEM] + threadIdx.x + (ITEM * BLOCK_THREADS)] = key;
-            }
-        }
-    }
-
-
-
-    /**
-     * Scatter ranked values directly to device-accessible memory
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void ScatterValues(
-        ValueT                                  (&values)[ITEMS_PER_THREAD],
-        OffsetT                                 (&relative_bin_offsets)[ITEMS_PER_THREAD],
-        int                                     (&ranks)[ITEMS_PER_THREAD],
-        OffsetT                                 valid_items,
-        Int2Type<RADIX_SORT_SCATTER_DIRECT>     /*scatter_algorithm*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (FULL_TILE || (ranks[ITEM] < valid_items))
-            {
-                d_values_out[relative_bin_offsets[ITEM] + ranks[ITEM]] = values[ITEM];
-            }
-        }
-    }
-
-
-    /**
-     * Scatter ranked values through shared memory, then to device-accessible memory
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void ScatterValues(
-        ValueT                                  (&values)[ITEMS_PER_THREAD],
-        OffsetT                                 (&relative_bin_offsets)[ITEMS_PER_THREAD],
-        int                                     (&ranks)[ITEMS_PER_THREAD],
-        OffsetT                                 valid_items,
-        Int2Type<RADIX_SORT_SCATTER_TWO_PHASE>  /*scatter_algorithm*/)
-    {
-        CTA_SYNC();
-
-        ValueT *smem = reinterpret_cast<ValueT*>(&temp_storage.exchange_values);
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            smem[ranks[ITEM]] = values[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            ValueT value = smem[threadIdx.x + (ITEM * BLOCK_THREADS)];
-
-            if (FULL_TILE || 
-                (static_cast<OffsetT>(threadIdx.x + (ITEM * BLOCK_THREADS)) < valid_items))
-            {
-                d_values_out[relative_bin_offsets[ITEM] + threadIdx.x + (ITEM * BLOCK_THREADS)] = value;
-            }
-        }
-    }
-
-
-    /**
-     * Load a tile of items (specialized for full tile)
-     */
-    template <typename BlockLoadT, typename T, typename InputIteratorT>
-    __device__ __forceinline__ void LoadItems(
-        BlockLoadT      &block_loader, 
-        T               (&items)[ITEMS_PER_THREAD],
-        InputIteratorT  d_in,
-        OffsetT         /*valid_items*/,
-        Int2Type<true>  /*is_full_tile*/)
-    {
-        block_loader.Load(d_in, items);
-    }
-
-
-    /**
-     * Load a tile of items (specialized for full tile)
-     */
-    template <typename BlockLoadT, typename T, typename InputIteratorT>
-    __device__ __forceinline__ void LoadItems(
-        BlockLoadT      &block_loader,
-        T               (&items)[ITEMS_PER_THREAD],
-        InputIteratorT  d_in,
-        OffsetT         /*valid_items*/,
-        T               /*oob_item*/,
-        Int2Type<true>  /*is_full_tile*/)
-    {
-        block_loader.Load(d_in, items);
-    }
-
-
-    /**
-     * Load a tile of items (specialized for partial tile)
-     */
-    template <typename BlockLoadT, typename T, typename InputIteratorT>
-    __device__ __forceinline__ void LoadItems(
-        BlockLoadT      &block_loader, 
-        T               (&items)[ITEMS_PER_THREAD],
-        InputIteratorT  d_in,
-        OffsetT         valid_items,
-        Int2Type<false> /*is_full_tile*/)
-    {
-        block_loader.Load(d_in, items, valid_items);
-    }
-
-    /**
-     * Load a tile of items (specialized for partial tile)
-     */
-    template <typename BlockLoadT, typename T, typename InputIteratorT>
-    __device__ __forceinline__ void LoadItems(
-        BlockLoadT      &block_loader,
-        T               (&items)[ITEMS_PER_THREAD],
-        InputIteratorT  d_in,
-        OffsetT         valid_items,
-        T               oob_item,
-        Int2Type<false> /*is_full_tile*/)
-    {
-        block_loader.Load(d_in, items, valid_items, oob_item);
-    }
-
-
-    /**
-     * Truck along associated values
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void GatherScatterValues(
-        OffsetT         (&relative_bin_offsets)[ITEMS_PER_THREAD],
-        int             (&ranks)[ITEMS_PER_THREAD],
-        OffsetT         block_offset,
-        OffsetT         valid_items,
-        Int2Type<false> /*is_keys_only*/)
-    {
-        CTA_SYNC();
-
-        ValueT values[ITEMS_PER_THREAD];
-
-        BlockLoadValues loader(temp_storage.load_values);
-        LoadItems(
-            loader,
-            values,
-            d_values_in + block_offset,
-            valid_items,
-            Int2Type<FULL_TILE>());
-
-        ScatterValues<FULL_TILE>(
-            values,
-            relative_bin_offsets,
-            ranks,
-            valid_items,
-            Int2Type<SCATTER_ALGORITHM>());
-    }
-
-
-    /**
-     * Truck along associated values (specialized for key-only sorting)
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void GatherScatterValues(
-        OffsetT         (&/*relative_bin_offsets*/)[ITEMS_PER_THREAD],
-        int             (&/*ranks*/)[ITEMS_PER_THREAD],
-        OffsetT         /*block_offset*/,
-        OffsetT         /*valid_items*/,
-        Int2Type<true>  /*is_keys_only*/)
-    {}
-
-
-    /**
-     * Process tile
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void ProcessTile(
-        OffsetT block_offset,
-        const OffsetT &valid_items = TILE_ITEMS)
-    {
-        // Per-thread tile data
-        UnsignedBits    keys[ITEMS_PER_THREAD];                     // Keys
-        UnsignedBits    twiddled_keys[ITEMS_PER_THREAD];            // Twiddled keys
-        int             ranks[ITEMS_PER_THREAD];                    // For each key, the local rank within the CTA
-        OffsetT         relative_bin_offsets[ITEMS_PER_THREAD];     // For each key, the global scatter base offset of the corresponding digit
-
-        // Assign default (min/max) value to all keys
-        UnsignedBits default_key = (IS_DESCENDING) ? LOWEST_KEY : MAX_KEY;
-
-        // Load tile of keys
-        BlockLoadKeys loader(temp_storage.load_keys);
-        LoadItems(
-            loader,
-            keys,
-            d_keys_in + block_offset,
-            valid_items, 
-            default_key,
-            Int2Type<FULL_TILE>());
-
-        CTA_SYNC();
-
-        // Twiddle key bits if necessary
-        #pragma unroll
-        for (int KEY = 0; KEY < ITEMS_PER_THREAD; KEY++)
-        {
-            twiddled_keys[KEY] = Traits<KeyT>::TwiddleIn(keys[KEY]);
-        }
-
-        // Rank the twiddled keys
-        int exclusive_digit_prefix;
-        BlockRadixRank(temp_storage.ranking).RankKeys(
-            twiddled_keys,
-            ranks,
-            current_bit,
-            num_bits,
-            exclusive_digit_prefix);
-
-        CTA_SYNC();
-
-        // Share exclusive digit prefix
-        if (threadIdx.x < RADIX_DIGITS)
-        {
-            // Store exclusive prefix
-            temp_storage.exclusive_digit_prefix[threadIdx.x] = exclusive_digit_prefix;
-        }
-
-        CTA_SYNC();
-
-        // Get inclusive digit prefix
-        int inclusive_digit_prefix;
-        if (threadIdx.x < RADIX_DIGITS)
-        {
-            if (IS_DESCENDING)
-            {
-                // Get inclusive digit prefix from exclusive prefix (higher bins come first)
-                inclusive_digit_prefix = (threadIdx.x == 0) ?
-                    (BLOCK_THREADS * ITEMS_PER_THREAD) :
-                    temp_storage.exclusive_digit_prefix[threadIdx.x - 1];
-            }
-            else
-            {
-                // Get inclusive digit prefix from exclusive prefix (lower bins come first)
-                inclusive_digit_prefix = (threadIdx.x == RADIX_DIGITS - 1) ?
-                    (BLOCK_THREADS * ITEMS_PER_THREAD) :
-                    temp_storage.exclusive_digit_prefix[threadIdx.x + 1];
-            }
-        }
-
-        CTA_SYNC();
-
-        // Update global scatter base offsets for each digit
-        if (threadIdx.x < RADIX_DIGITS)
-        {
-
-
-            bin_offset -= exclusive_digit_prefix;
-            temp_storage.relative_bin_offsets[threadIdx.x] = bin_offset;
-            bin_offset += inclusive_digit_prefix;
-        }
-
-        CTA_SYNC();
-
-        // Scatter keys
-        ScatterKeys<FULL_TILE>(twiddled_keys, relative_bin_offsets, ranks, valid_items, Int2Type<SCATTER_ALGORITHM>());
-
-        // Gather/scatter values
-        GatherScatterValues<FULL_TILE>(relative_bin_offsets , ranks, block_offset, valid_items, Int2Type<KEYS_ONLY>());
-    }
-
-    //---------------------------------------------------------------------
-    // Copy shortcut
-    //---------------------------------------------------------------------
-
-    /**
-     * Copy tiles within the range of input
-     */
-    template <
-        typename InputIteratorT,
-        typename T>
-    __device__ __forceinline__ void Copy(
-        InputIteratorT  d_in,
-        T               *d_out,
-        OffsetT         block_offset,
-        OffsetT         block_end)
-    {
-        // Simply copy the input
-        while (block_offset + TILE_ITEMS <= block_end)
-        {
-            T items[ITEMS_PER_THREAD];
-
-            LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_in + block_offset, items);
-            CTA_SYNC();
-            StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_out + block_offset, items);
-
-            block_offset += TILE_ITEMS;
-        }
-
-        // Clean up last partial tile with guarded-I/O
-        if (block_offset < block_end)
-        {
-            OffsetT valid_items = block_end - block_offset;
-
-            T items[ITEMS_PER_THREAD];
-
-            LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_in + block_offset, items, valid_items);
-            CTA_SYNC();
-            StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_out + block_offset, items, valid_items);
-        }
-    }
-
-
-    /**
-     * Copy tiles within the range of input (specialized for NullType)
-     */
-    template <typename InputIteratorT>
-    __device__ __forceinline__ void Copy(
-        InputIteratorT  /*d_in*/,
-        NullType        * /*d_out*/,
-        OffsetT         /*block_offset*/,
-        OffsetT         /*block_end*/)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Interface
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentRadixSortDownsweep(
-        TempStorage     &temp_storage,
-        OffsetT         num_items,
-        OffsetT         bin_offset,
-        const KeyT      *d_keys_in,
-        KeyT            *d_keys_out,
-        const ValueT    *d_values_in,
-        ValueT          *d_values_out,
-        int             current_bit,
-        int             num_bits)
-    :
-        temp_storage(temp_storage.Alias()),
-        bin_offset(bin_offset),
-        d_keys_in(reinterpret_cast<const UnsignedBits*>(d_keys_in)),
-        d_values_in(d_values_in),
-        d_keys_out(reinterpret_cast<UnsignedBits*>(d_keys_out)),
-        d_values_out(d_values_out),
-        current_bit(current_bit),
-        num_bits(num_bits),
-        short_circuit(1)
-    {
-        if (threadIdx.x < RADIX_DIGITS)
-        {
-            // Short circuit if the histogram has only bin counts of only zeros or problem-size
-            short_circuit = ((bin_offset == 0) || (bin_offset == num_items));
-        }
-
-        short_circuit = CTA_SYNC_AND(short_circuit);
-    }
-
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentRadixSortDownsweep(
-        TempStorage     &temp_storage,
-        OffsetT         num_items,
-        OffsetT         *d_spine,
-        const KeyT      *d_keys_in,
-        KeyT            *d_keys_out,
-        const ValueT    *d_values_in,
-        ValueT          *d_values_out,
-        int             current_bit,
-        int             num_bits)
-    :
-        temp_storage(temp_storage.Alias()),
-        d_keys_in(reinterpret_cast<const UnsignedBits*>(d_keys_in)),
-        d_values_in(d_values_in),
-        d_keys_out(reinterpret_cast<UnsignedBits*>(d_keys_out)),
-        d_values_out(d_values_out),
-        current_bit(current_bit),
-        num_bits(num_bits),
-        short_circuit(1)
-    {
-        // Load digit bin offsets (each of the first RADIX_DIGITS threads will load an offset for that digit)
-        if (threadIdx.x < RADIX_DIGITS)
-        {
-            int bin_idx = (IS_DESCENDING) ?
-                RADIX_DIGITS - threadIdx.x - 1 :
-                threadIdx.x;
-
-            // Short circuit if the first block's histogram has only bin counts of only zeros or problem-size
-            OffsetT first_block_bin_offset = d_spine[gridDim.x * bin_idx];
-            short_circuit = ((first_block_bin_offset == 0) || (first_block_bin_offset == num_items));
-
-            // Load my block's bin offset for my bin
-            bin_offset = d_spine[(gridDim.x * bin_idx) + blockIdx.x];
-        }
-
-        short_circuit = CTA_SYNC_AND(short_circuit);
-    }
-
-
-    /**
-     * Distribute keys from a segment of input tiles.
-     */
-    __device__ __forceinline__ void ProcessRegion(
-        OffsetT   block_offset,
-        OffsetT   block_end)
-    {
-        if (short_circuit)
-        {
-            // Copy keys
-            Copy(d_keys_in, d_keys_out, block_offset, block_end);
-
-            // Copy values
-            Copy(d_values_in, d_values_out, block_offset, block_end);
-        }
-        else
-        {
-            // Process full tiles of tile_items
-            while (block_offset + TILE_ITEMS <= block_end)
-            {
-                ProcessTile<true>(block_offset);
-                block_offset += TILE_ITEMS;
-
-                CTA_SYNC();
-            }
-
-            // Clean up last partial tile with guarded-I/O
-            if (block_offset < block_end)
-            {
-                ProcessTile<false>(block_offset, block_end - block_offset);
-            }
-        }
-    }
-
-};
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_radix_sort_upsweep.cuh b/ml-xgboost/cub/cub/agent/agent_radix_sort_upsweep.cuh
deleted file mode 100644
index 88e27d3..0000000
--- a/ml-xgboost/cub/cub/agent/agent_radix_sort_upsweep.cuh
+++ /dev/null
@@ -1,449 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * AgentRadixSortUpsweep implements a stateful abstraction of CUDA thread blocks for participating in device-wide radix sort upsweep .
- */
-
-#pragma once
-
-#include "../thread/thread_reduce.cuh"
-#include "../thread/thread_load.cuh"
-#include "../block/block_load.cuh"
-#include "../util_type.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentRadixSortUpsweep
- */
-template <
-    int                 _BLOCK_THREADS,     ///< Threads per thread block
-    int                 _ITEMS_PER_THREAD,  ///< Items per thread (per tile of input)
-    CacheLoadModifier   _LOAD_MODIFIER,     ///< Cache load modifier for reading keys
-    int                 _RADIX_BITS>        ///< The number of radix bits, i.e., log2(bins)
-struct AgentRadixSortUpsweepPolicy
-{
-    enum
-    {
-        BLOCK_THREADS       = _BLOCK_THREADS,       ///< Threads per thread block
-        ITEMS_PER_THREAD    = _ITEMS_PER_THREAD,    ///< Items per thread (per tile of input)
-        RADIX_BITS          = _RADIX_BITS,          ///< The number of radix bits, i.e., log2(bins)
-    };
-
-    static const CacheLoadModifier LOAD_MODIFIER = _LOAD_MODIFIER;      ///< Cache load modifier for reading keys
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentRadixSortUpsweep implements a stateful abstraction of CUDA thread blocks for participating in device-wide radix sort upsweep .
- */
-template <
-    typename AgentRadixSortUpsweepPolicy,   ///< Parameterized AgentRadixSortUpsweepPolicy tuning policy type
-    typename KeyT,                          ///< KeyT type
-    typename OffsetT>                       ///< Signed integer type for global offsets
-struct AgentRadixSortUpsweep
-{
-
-    //---------------------------------------------------------------------
-    // Type definitions and constants
-    //---------------------------------------------------------------------
-
-    typedef typename Traits<KeyT>::UnsignedBits UnsignedBits;
-
-    // Integer type for digit counters (to be packed into words of PackedCounters)
-    typedef unsigned char DigitCounter;
-
-    // Integer type for packing DigitCounters into columns of shared memory banks
-    typedef unsigned int PackedCounter;
-
-    static const CacheLoadModifier LOAD_MODIFIER = AgentRadixSortUpsweepPolicy::LOAD_MODIFIER;
-
-    enum
-    {
-        RADIX_BITS              = AgentRadixSortUpsweepPolicy::RADIX_BITS,
-        BLOCK_THREADS           = AgentRadixSortUpsweepPolicy::BLOCK_THREADS,
-        KEYS_PER_THREAD         = AgentRadixSortUpsweepPolicy::ITEMS_PER_THREAD,
-
-        RADIX_DIGITS            = 1 << RADIX_BITS,
-
-        LOG_WARP_THREADS        = CUB_PTX_LOG_WARP_THREADS,
-        WARP_THREADS            = 1 << LOG_WARP_THREADS,
-        WARPS                   = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-
-        TILE_ITEMS              = BLOCK_THREADS * KEYS_PER_THREAD,
-
-        BYTES_PER_COUNTER       = sizeof(DigitCounter),
-        LOG_BYTES_PER_COUNTER   = Log2<BYTES_PER_COUNTER>::VALUE,
-
-        PACKING_RATIO           = sizeof(PackedCounter) / sizeof(DigitCounter),
-        LOG_PACKING_RATIO       = Log2<PACKING_RATIO>::VALUE,
-
-        LOG_COUNTER_LANES       = CUB_MAX(0, RADIX_BITS - LOG_PACKING_RATIO),
-        COUNTER_LANES           = 1 << LOG_COUNTER_LANES,
-
-        // To prevent counter overflow, we must periodically unpack and aggregate the
-        // digit counters back into registers.  Each counter lane is assigned to a
-        // warp for aggregation.
-
-        LANES_PER_WARP          = CUB_MAX(1, (COUNTER_LANES + WARPS - 1) / WARPS),
-
-        // Unroll tiles in batches without risk of counter overflow
-        UNROLL_COUNT            = CUB_MIN(64, 255 / KEYS_PER_THREAD),
-        UNROLLED_ELEMENTS       = UNROLL_COUNT * TILE_ITEMS,
-    };
-
-
-    // Input iterator wrapper type (for applying cache modifier)s
-    typedef CacheModifiedInputIterator<LOAD_MODIFIER, UnsignedBits, OffsetT> KeysItr;
-
-    /**
-     * Shared memory storage layout
-     */
-    struct _TempStorage
-    {
-        union
-        {
-            DigitCounter    digit_counters[COUNTER_LANES][BLOCK_THREADS][PACKING_RATIO];
-            PackedCounter   packed_counters[COUNTER_LANES][BLOCK_THREADS];
-            OffsetT         digit_partials[RADIX_DIGITS][WARP_THREADS + 1];
-        };
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Thread fields (aggregate state bundle)
-    //---------------------------------------------------------------------
-
-    // Shared storage for this CTA
-    _TempStorage    &temp_storage;
-
-    // Thread-local counters for periodically aggregating composite-counter lanes
-    OffsetT         local_counts[LANES_PER_WARP][PACKING_RATIO];
-
-    // Input and output device pointers
-    KeysItr         d_keys_in;
-
-    // The least-significant bit position of the current digit to extract
-    int             current_bit;
-
-    // Number of bits in current digit
-    int             num_bits;
-
-
-
-    //---------------------------------------------------------------------
-    // Helper structure for templated iteration
-    //---------------------------------------------------------------------
-
-    // Iterate
-    template <int COUNT, int MAX>
-    struct Iterate
-    {
-        // BucketKeys
-        static __device__ __forceinline__ void BucketKeys(
-            AgentRadixSortUpsweep       &cta,
-            UnsignedBits                keys[KEYS_PER_THREAD])
-        {
-            cta.Bucket(keys[COUNT]);
-
-            // Next
-            Iterate<COUNT + 1, MAX>::BucketKeys(cta, keys);
-        }
-    };
-
-    // Terminate
-    template <int MAX>
-    struct Iterate<MAX, MAX>
-    {
-        // BucketKeys
-        static __device__ __forceinline__ void BucketKeys(AgentRadixSortUpsweep &/*cta*/, UnsignedBits /*keys*/[KEYS_PER_THREAD]) {}
-    };
-
-
-    //---------------------------------------------------------------------
-    // Utility methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Decode a key and increment corresponding smem digit counter
-     */
-    __device__ __forceinline__ void Bucket(UnsignedBits key)
-    {
-        // Perform transform op
-        UnsignedBits converted_key = Traits<KeyT>::TwiddleIn(key);
-
-        // Extract current digit bits
-        UnsignedBits digit = BFE(converted_key, current_bit, num_bits);
-
-        // Get sub-counter offset
-        UnsignedBits sub_counter = digit & (PACKING_RATIO - 1);
-
-        // Get row offset
-        UnsignedBits row_offset = digit >> LOG_PACKING_RATIO;
-
-        // Increment counter
-        temp_storage.digit_counters[row_offset][threadIdx.x][sub_counter]++;
-    }
-
-
-    /**
-     * Reset composite counters
-     */
-    __device__ __forceinline__ void ResetDigitCounters()
-    {
-        #pragma unroll
-        for (int LANE = 0; LANE < COUNTER_LANES; LANE++)
-        {
-            temp_storage.packed_counters[LANE][threadIdx.x] = 0;
-        }
-    }
-
-
-    /**
-     * Reset the unpacked counters in each thread
-     */
-    __device__ __forceinline__ void ResetUnpackedCounters()
-    {
-        #pragma unroll
-        for (int LANE = 0; LANE < LANES_PER_WARP; LANE++)
-        {
-            #pragma unroll
-            for (int UNPACKED_COUNTER = 0; UNPACKED_COUNTER < PACKING_RATIO; UNPACKED_COUNTER++)
-            {
-                local_counts[LANE][UNPACKED_COUNTER] = 0;
-            }
-        }
-    }
-
-
-    /**
-     * Extracts and aggregates the digit counters for each counter lane
-     * owned by this warp
-     */
-    __device__ __forceinline__ void UnpackDigitCounts()
-    {
-        unsigned int warp_id = threadIdx.x >> LOG_WARP_THREADS;
-        unsigned int warp_tid = threadIdx.x & (WARP_THREADS - 1);
-
-        #pragma unroll
-        for (int LANE = 0; LANE < LANES_PER_WARP; LANE++)
-        {
-            const int counter_lane = (LANE * WARPS) + warp_id;
-            if (counter_lane < COUNTER_LANES)
-            {
-                #pragma unroll
-                for (int PACKED_COUNTER = 0; PACKED_COUNTER < BLOCK_THREADS; PACKED_COUNTER += WARP_THREADS)
-                {
-                    #pragma unroll
-                    for (int UNPACKED_COUNTER = 0; UNPACKED_COUNTER < PACKING_RATIO; UNPACKED_COUNTER++)
-                    {
-                        OffsetT counter = temp_storage.digit_counters[counter_lane][warp_tid + PACKED_COUNTER][UNPACKED_COUNTER];
-                        local_counts[LANE][UNPACKED_COUNTER] += counter;
-                    }
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Places unpacked counters into smem for final digit reduction
-     */
-    __device__ __forceinline__ void ReduceUnpackedCounts(OffsetT &bin_count)
-    {
-        unsigned int warp_id = threadIdx.x >> LOG_WARP_THREADS;
-        unsigned int warp_tid = threadIdx.x & (WARP_THREADS - 1);
-
-        // Place unpacked digit counters in shared memory
-        #pragma unroll
-        for (int LANE = 0; LANE < LANES_PER_WARP; LANE++)
-        {
-            int counter_lane = (LANE * WARPS) + warp_id;
-            if (counter_lane < COUNTER_LANES)
-            {
-                int digit_row = counter_lane << LOG_PACKING_RATIO;
-
-                #pragma unroll
-                for (int UNPACKED_COUNTER = 0; UNPACKED_COUNTER < PACKING_RATIO; UNPACKED_COUNTER++)
-                {
-                    temp_storage.digit_partials[digit_row + UNPACKED_COUNTER][warp_tid] =
-                        local_counts[LANE][UNPACKED_COUNTER];
-                }
-            }
-        }
-
-        CTA_SYNC();
-
-        // Rake-reduce bin_count reductions
-        if (threadIdx.x < RADIX_DIGITS)
-        {
-            bin_count = ThreadReduce<WARP_THREADS>(
-                temp_storage.digit_partials[threadIdx.x],
-                Sum());
-        }
-    }
-
-
-    /**
-     * Processes a single, full tile
-     */
-    __device__ __forceinline__ void ProcessFullTile(OffsetT block_offset)
-    {
-        // Tile of keys
-        UnsignedBits keys[KEYS_PER_THREAD];
-
-        LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_keys_in + block_offset, keys);
-
-        // Prevent hoisting
-        CTA_SYNC();
-
-        // Bucket tile of keys
-        Iterate<0, KEYS_PER_THREAD>::BucketKeys(*this, keys);
-    }
-
-
-    /**
-     * Processes a single load (may have some threads masked off)
-     */
-    __device__ __forceinline__ void ProcessPartialTile(
-        OffsetT block_offset,
-        const OffsetT &block_end)
-    {
-        // Process partial tile if necessary using single loads
-        block_offset += threadIdx.x;
-        while (block_offset < block_end)
-        {
-            // Load and bucket key
-            UnsignedBits key = d_keys_in[block_offset];
-            Bucket(key);
-            block_offset += BLOCK_THREADS;
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Interface
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentRadixSortUpsweep(
-        TempStorage &temp_storage,
-        const KeyT  *d_keys_in,
-        int         current_bit,
-        int         num_bits)
-    :
-        temp_storage(temp_storage.Alias()),
-        d_keys_in(reinterpret_cast<const UnsignedBits*>(d_keys_in)),
-        current_bit(current_bit),
-        num_bits(num_bits)
-    {}
-
-
-    /**
-     * Compute radix digit histograms from a segment of input tiles.
-     */
-    __device__ __forceinline__ void ProcessRegion(
-        OffsetT          block_offset,
-        const OffsetT    &block_end,
-        OffsetT          &bin_count)                ///< [out] The digit count for tid'th bin (output param, valid in the first RADIX_DIGITS threads)
-    {
-        // Reset digit counters in smem and unpacked counters in registers
-        ResetDigitCounters();
-        ResetUnpackedCounters();
-
-        // Unroll batches of full tiles
-        while (block_offset + UNROLLED_ELEMENTS <= block_end)
-        {
-            for (int i = 0; i < UNROLL_COUNT; ++i)
-            {
-                ProcessFullTile(block_offset);
-                block_offset += TILE_ITEMS;
-            }
-
-            CTA_SYNC();
-
-            // Aggregate back into local_count registers to prevent overflow
-            UnpackDigitCounts();
-
-            CTA_SYNC();
-
-            // Reset composite counters in lanes
-            ResetDigitCounters();
-        }
-
-        // Unroll single full tiles
-        while (block_offset + TILE_ITEMS <= block_end)
-        {
-            ProcessFullTile(block_offset);
-            block_offset += TILE_ITEMS;
-        }
-
-        // Process partial tile if necessary
-        ProcessPartialTile(
-            block_offset,
-            block_end);
-
-        CTA_SYNC();
-
-        // Aggregate back into local_count registers
-        UnpackDigitCounts();
-
-        CTA_SYNC();
-
-        // Final raking reduction of counts by bin
-        ReduceUnpackedCounts(bin_count);
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_reduce.cuh b/ml-xgboost/cub/cub/agent/agent_reduce.cuh
deleted file mode 100644
index ad8fbcf..0000000
--- a/ml-xgboost/cub/cub/agent/agent_reduce.cuh
+++ /dev/null
@@ -1,475 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentReduce implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduction .
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "../block/block_load.cuh"
-#include "../block/block_reduce.cuh"
-#include "../grid/grid_mapping.cuh"
-#include "../grid/grid_queue.cuh"
-#include "../grid/grid_even_share.cuh"
-#include "../util_type.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentReduce
- */
-template <
-    int                     _BLOCK_THREADS,         ///< Threads per thread block
-    int                     _ITEMS_PER_THREAD,      ///< Items per thread (per tile of input)
-    int                     _VECTOR_LOAD_LENGTH,    ///< Number of items per vectorized load
-    BlockReduceAlgorithm    _BLOCK_ALGORITHM,       ///< Cooperative block-wide reduction algorithm to use
-    CacheLoadModifier       _LOAD_MODIFIER,         ///< Cache load modifier for reading input elements
-    GridMappingStrategy     _GRID_MAPPING>          ///< How to map tiles of input onto thread blocks
-struct AgentReducePolicy
-{
-    enum
-    {
-        BLOCK_THREADS       = _BLOCK_THREADS,       ///< Threads per thread block
-        ITEMS_PER_THREAD    = _ITEMS_PER_THREAD,    ///< Items per thread (per tile of input)
-        VECTOR_LOAD_LENGTH  = _VECTOR_LOAD_LENGTH,  ///< Number of items per vectorized load
-    };
-
-    static const BlockReduceAlgorithm  BLOCK_ALGORITHM      = _BLOCK_ALGORITHM;     ///< Cooperative block-wide reduction algorithm to use
-    static const CacheLoadModifier     LOAD_MODIFIER        = _LOAD_MODIFIER;       ///< Cache load modifier for reading input elements
-    static const GridMappingStrategy   GRID_MAPPING         = _GRID_MAPPING;        ///< How to map tiles of input onto thread blocks
-};
-
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentReduce implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduction .
- *
- * Each thread reduces only the values it loads. If \p FIRST_TILE, this
- * partial reduction is stored into \p thread_aggregate.  Otherwise it is
- * accumulated into \p thread_aggregate.
- */
-template <
-    typename AgentReducePolicy,        ///< Parameterized AgentReducePolicy tuning policy type
-    typename InputIteratorT,           ///< Random-access iterator type for input
-    typename OutputIteratorT,          ///< Random-access iterator type for output
-    typename OffsetT,                  ///< Signed integer type for global offsets
-    typename ReductionOp>              ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-struct AgentReduce
-{
-
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    /// The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    /// Vector type of InputT for data movement
-    typedef typename CubVector<InputT, AgentReducePolicy::VECTOR_LOAD_LENGTH>::Type VectorT;
-
-    /// Input iterator wrapper type (for applying cache modifier)
-    typedef typename If<IsPointer<InputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentReducePolicy::LOAD_MODIFIER, InputT, OffsetT>,      // Wrap the native input pointer with CacheModifiedInputIterator
-            InputIteratorT>::Type                                                               // Directly use the supplied input iterator type
-        WrappedInputIteratorT;
-
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS       = AgentReducePolicy::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = AgentReducePolicy::ITEMS_PER_THREAD,
-        VECTOR_LOAD_LENGTH  = CUB_MIN(ITEMS_PER_THREAD, AgentReducePolicy::VECTOR_LOAD_LENGTH),
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,
-
-        // Can vectorize according to the policy if the input iterator is a native pointer to a primitive type
-        ATTEMPT_VECTORIZATION   = (VECTOR_LOAD_LENGTH > 1) &&
-                                    (ITEMS_PER_THREAD % VECTOR_LOAD_LENGTH == 0) &&
-                                    (IsPointer<InputIteratorT>::VALUE) && Traits<InputT>::PRIMITIVE,
-
-    };
-
-    static const CacheLoadModifier    LOAD_MODIFIER   = AgentReducePolicy::LOAD_MODIFIER;
-    static const BlockReduceAlgorithm BLOCK_ALGORITHM = AgentReducePolicy::BLOCK_ALGORITHM;
-
-    /// Parameterized BlockReduce primitive
-    typedef BlockReduce<OutputT, BLOCK_THREADS, AgentReducePolicy::BLOCK_ALGORITHM> BlockReduceT;
-
-    /// Shared memory type required by this thread block
-    struct _TempStorage
-    {
-        typename BlockReduceT::TempStorage  reduce;
-        OffsetT                             dequeue_offset;
-    };
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage&           temp_storage;       ///< Reference to temp_storage
-    InputIteratorT          d_in;               ///< Input data to reduce
-    WrappedInputIteratorT   d_wrapped_in;       ///< Wrapped input data to reduce
-    ReductionOp             reduction_op;       ///< Binary reduction operator
-
-
-    //---------------------------------------------------------------------
-    // Utility
-    //---------------------------------------------------------------------
-
-
-    // Whether or not the input is aligned with the vector type (specialized for types we can vectorize)
-    template <typename Iterator>
-    static __device__ __forceinline__ bool IsAligned(
-        Iterator        d_in,
-        Int2Type<true>  /*can_vectorize*/)
-    {
-        return (size_t(d_in) & (sizeof(VectorT) - 1)) == 0;
-    }
-
-    // Whether or not the input is aligned with the vector type (specialized for types we cannot vectorize)
-    template <typename Iterator>
-    static __device__ __forceinline__ bool IsAligned(
-        Iterator        /*d_in*/,
-        Int2Type<false> /*can_vectorize*/)
-    {
-        return false;
-    }
-
-
-    //---------------------------------------------------------------------
-    // Constructor
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentReduce(
-        TempStorage&            temp_storage,       ///< Reference to temp_storage
-        InputIteratorT          d_in,               ///< Input data to reduce
-        ReductionOp             reduction_op)       ///< Binary reduction operator
-    :
-        temp_storage(temp_storage.Alias()),
-        d_in(d_in),
-        d_wrapped_in(d_in),
-        reduction_op(reduction_op)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Tile consumption
-    //---------------------------------------------------------------------
-
-    /**
-     * Consume a full tile of input (non-vectorized)
-     */
-    template <int IS_FIRST_TILE>
-    __device__ __forceinline__ void ConsumeTile(
-        OutputT                 &thread_aggregate,
-        OffsetT                 block_offset,       ///< The offset the tile to consume
-        int                     /*valid_items*/,    ///< The number of valid items in the tile
-        Int2Type<true>          /*is_full_tile*/,   ///< Whether or not this is a full tile
-        Int2Type<false>         /*can_vectorize*/)  ///< Whether or not we can vectorize loads
-    {
-        OutputT items[ITEMS_PER_THREAD];
-
-        // Load items in striped fashion
-        LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_wrapped_in + block_offset, items);
-
-        // Reduce items within each thread stripe
-        thread_aggregate = (IS_FIRST_TILE) ?
-            ThreadReduce(items, reduction_op) :
-            ThreadReduce(items, reduction_op, thread_aggregate);
-    }
-
-
-    /**
-     * Consume a full tile of input (vectorized)
-     */
-    template <int IS_FIRST_TILE>
-    __device__ __forceinline__ void ConsumeTile(
-        OutputT                 &thread_aggregate,
-        OffsetT                 block_offset,       ///< The offset the tile to consume
-        int                     /*valid_items*/,    ///< The number of valid items in the tile
-        Int2Type<true>          /*is_full_tile*/,   ///< Whether or not this is a full tile
-        Int2Type<true>          /*can_vectorize*/)  ///< Whether or not we can vectorize loads
-    {
-        // Alias items as an array of VectorT and load it in striped fashion
-        enum { WORDS =  ITEMS_PER_THREAD / VECTOR_LOAD_LENGTH };
-
-        // Fabricate a vectorized input iterator
-        InputT *d_in_unqualified = const_cast<InputT*>(d_in) + block_offset + (threadIdx.x * VECTOR_LOAD_LENGTH);
-        CacheModifiedInputIterator<AgentReducePolicy::LOAD_MODIFIER, VectorT, OffsetT> d_vec_in(
-            reinterpret_cast<VectorT*>(d_in_unqualified));
-
-        // Load items as vector items
-        InputT input_items[ITEMS_PER_THREAD];
-        VectorT *vec_items = reinterpret_cast<VectorT*>(input_items);
-        #pragma unroll
-        for (int i = 0; i < WORDS; ++i)
-            vec_items[i] = d_vec_in[BLOCK_THREADS * i];
-
-        // Convert from input type to output type
-        OutputT items[ITEMS_PER_THREAD];
-        #pragma unroll
-        for (int i = 0; i < ITEMS_PER_THREAD; ++i)
-            items[i] = input_items[i];
-
-        // Reduce items within each thread stripe
-        thread_aggregate = (IS_FIRST_TILE) ?
-            ThreadReduce(items, reduction_op) :
-            ThreadReduce(items, reduction_op, thread_aggregate);
-    }
-
-
-    /**
-     * Consume a partial tile of input
-     */
-    template <int IS_FIRST_TILE, int CAN_VECTORIZE>
-    __device__ __forceinline__ void ConsumeTile(
-        OutputT                 &thread_aggregate,
-        OffsetT                 block_offset,       ///< The offset the tile to consume
-        int                     valid_items,        ///< The number of valid items in the tile
-        Int2Type<false>         /*is_full_tile*/,   ///< Whether or not this is a full tile
-        Int2Type<CAN_VECTORIZE> /*can_vectorize*/)  ///< Whether or not we can vectorize loads
-    {
-        // Partial tile
-        int thread_offset = threadIdx.x;
-
-        // Read first item
-        if ((IS_FIRST_TILE) && (thread_offset < valid_items))
-        {
-            thread_aggregate = d_wrapped_in[block_offset + thread_offset];
-            thread_offset += BLOCK_THREADS;
-        }
-
-        // Continue reading items (block-striped)
-        while (thread_offset < valid_items)
-        {
-            OutputT item        = d_wrapped_in[block_offset + thread_offset];
-            thread_aggregate    = reduction_op(thread_aggregate, item);
-            thread_offset       += BLOCK_THREADS;
-        }
-    }
-
-
-    //---------------------------------------------------------------
-    // Consume a contiguous segment of tiles
-    //---------------------------------------------------------------------
-
-    /**
-     * \brief Reduce a contiguous segment of input tiles
-     */
-    template <int CAN_VECTORIZE>
-    __device__ __forceinline__ OutputT ConsumeRange(
-        OffsetT block_offset,                       ///< [in] Threadblock begin offset (inclusive)
-        OffsetT block_end,                          ///< [in] Threadblock end offset (exclusive)
-        Int2Type<CAN_VECTORIZE> can_vectorize)      ///< Whether or not we can vectorize loads
-    {
-        OutputT thread_aggregate;
-
-        if (block_offset + TILE_ITEMS > block_end)
-        {
-            // First tile isn't full (not all threads have valid items)
-            int valid_items = block_end - block_offset;
-            ConsumeTile<true>(thread_aggregate, block_offset, valid_items, Int2Type<false>(), can_vectorize);
-            return BlockReduceT(temp_storage.reduce).Reduce(thread_aggregate, reduction_op, valid_items);
-        }
-
-        // At least one full block
-        ConsumeTile<true>(thread_aggregate, block_offset, TILE_ITEMS, Int2Type<true>(), can_vectorize);
-        block_offset += TILE_ITEMS;
-
-        // Consume subsequent full tiles of input
-        while (block_offset + TILE_ITEMS <= block_end)
-        {
-            ConsumeTile<false>(thread_aggregate, block_offset, TILE_ITEMS, Int2Type<true>(), can_vectorize);
-            block_offset += TILE_ITEMS;
-        }
-
-        // Consume a partially-full tile
-        if (block_offset < block_end)
-        {
-            int valid_items = block_end - block_offset;
-            ConsumeTile<false>(thread_aggregate, block_offset, valid_items, Int2Type<false>(), can_vectorize);
-        }
-
-        // Compute block-wide reduction (all threads have valid items)
-        return BlockReduceT(temp_storage.reduce).Reduce(thread_aggregate, reduction_op);
-    }
-
-
-    /**
-     * \brief Reduce a contiguous segment of input tiles
-     */
-    __device__ __forceinline__ OutputT ConsumeRange(
-        OffsetT block_offset,                       ///< [in] Threadblock begin offset (inclusive)
-        OffsetT block_end)                          ///< [in] Threadblock end offset (exclusive)
-    {
-        return (IsAligned(d_in + block_offset, Int2Type<ATTEMPT_VECTORIZATION>())) ?
-            ConsumeRange(block_offset, block_end, Int2Type<true && ATTEMPT_VECTORIZATION>()) :
-            ConsumeRange(block_offset, block_end, Int2Type<false && ATTEMPT_VECTORIZATION>());
-    }
-
-
-    /**
-     * Reduce a contiguous segment of input tiles
-     */
-    __device__ __forceinline__ OutputT ConsumeTiles(
-        OffsetT                             /*num_items*/,      ///< [in] Total number of global input items
-        GridEvenShare<OffsetT>              &even_share,        ///< [in] GridEvenShare descriptor
-        GridQueue<OffsetT>                  &/*queue*/,         ///< [in,out] GridQueue descriptor
-        Int2Type<GRID_MAPPING_EVEN_SHARE>   /*is_even_share*/)  ///< [in] Marker type indicating this is an even-share mapping
-    {
-        // Initialize even-share descriptor for this thread block
-        even_share.BlockInit();
-
-        return (IsAligned(d_in, Int2Type<ATTEMPT_VECTORIZATION>())) ?
-            ConsumeRange(even_share.block_offset, even_share.block_end, Int2Type<true && ATTEMPT_VECTORIZATION>()) :
-            ConsumeRange(even_share.block_offset, even_share.block_end, Int2Type<false && ATTEMPT_VECTORIZATION>());
-
-    }
-
-
-    //---------------------------------------------------------------------
-    // Dynamically consume tiles
-    //---------------------------------------------------------------------
-
-    /**
-     * Dequeue and reduce tiles of items as part of a inter-block reduction
-     */
-    template <int CAN_VECTORIZE>
-    __device__ __forceinline__ OutputT ConsumeTiles(
-        int                     num_items,          ///< Total number of input items
-        GridQueue<OffsetT>      queue,              ///< Queue descriptor for assigning tiles of work to thread blocks
-        Int2Type<CAN_VECTORIZE> can_vectorize)      ///< Whether or not we can vectorize loads
-    {
-        // We give each thread block at least one tile of input.
-        OutputT thread_aggregate;
-        OffsetT block_offset = blockIdx.x * TILE_ITEMS;
-        OffsetT even_share_base = gridDim.x * TILE_ITEMS;
-
-        if (block_offset + TILE_ITEMS > num_items)
-        {
-            // First tile isn't full (not all threads have valid items)
-            int valid_items = num_items - block_offset;
-            ConsumeTile<true>(thread_aggregate, block_offset, valid_items, Int2Type<false>(), can_vectorize);
-            return BlockReduceT(temp_storage.reduce).Reduce(thread_aggregate, reduction_op, valid_items);
-        }
-
-        // Consume first full tile of input
-        ConsumeTile<true>(thread_aggregate, block_offset, TILE_ITEMS, Int2Type<true>(), can_vectorize);
-
-        if (num_items > even_share_base)
-        {
-            // Dequeue a tile of items
-            if (threadIdx.x == 0)
-                temp_storage.dequeue_offset = queue.Drain(TILE_ITEMS) + even_share_base;
-
-            CTA_SYNC();
-
-            // Grab tile offset and check if we're done with full tiles
-            block_offset = temp_storage.dequeue_offset;
-
-            // Consume more full tiles
-            while (block_offset + TILE_ITEMS <= num_items)
-            {
-                ConsumeTile<false>(thread_aggregate, block_offset, TILE_ITEMS, Int2Type<true>(), can_vectorize);
-
-                CTA_SYNC();
-
-                // Dequeue a tile of items
-                if (threadIdx.x == 0)
-                    temp_storage.dequeue_offset = queue.Drain(TILE_ITEMS) + even_share_base;
-
-                CTA_SYNC();
-
-                // Grab tile offset and check if we're done with full tiles
-                block_offset = temp_storage.dequeue_offset;
-            }
-
-            // Consume partial tile
-            if (block_offset < num_items)
-            {
-                int valid_items = num_items - block_offset;
-                ConsumeTile<false>(thread_aggregate, block_offset, valid_items, Int2Type<false>(), can_vectorize);
-            }
-        }
-
-        // Compute block-wide reduction (all threads have valid items)
-        return BlockReduceT(temp_storage.reduce).Reduce(thread_aggregate, reduction_op);
-
-    }
-
-    /**
-     * Dequeue and reduce tiles of items as part of a inter-block reduction
-     */
-    __device__ __forceinline__ OutputT ConsumeTiles(
-        OffsetT                         num_items,          ///< [in] Total number of global input items
-        GridEvenShare<OffsetT>          &/*even_share*/,    ///< [in] GridEvenShare descriptor
-        GridQueue<OffsetT>              &queue,             ///< [in,out] GridQueue descriptor
-        Int2Type<GRID_MAPPING_DYNAMIC>  /*is_dynamic*/)     ///< [in] Marker type indicating this is a dynamic mapping
-    {
-        return (IsAligned(d_in, Int2Type<ATTEMPT_VECTORIZATION>())) ?
-            ConsumeTiles(num_items, queue, Int2Type<true && ATTEMPT_VECTORIZATION>()) :
-            ConsumeTiles(num_items, queue, Int2Type<false && ATTEMPT_VECTORIZATION>());
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_reduce_by_key.cuh b/ml-xgboost/cub/cub/agent/agent_reduce_by_key.cuh
deleted file mode 100644
index 28d5f49..0000000
--- a/ml-xgboost/cub/cub/agent/agent_reduce_by_key.cuh
+++ /dev/null
@@ -1,549 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentReduceByKey implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "single_pass_scan_operators.cuh"
-#include "../block/block_load.cuh"
-#include "../block/block_store.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_discontinuity.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../iterator/constant_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentReduceByKey
- */
-template <
-    int                         _BLOCK_THREADS,                 ///< Threads per thread block
-    int                         _ITEMS_PER_THREAD,              ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm          _LOAD_ALGORITHM,                ///< The BlockLoad algorithm to use
-    CacheLoadModifier           _LOAD_MODIFIER,                 ///< Cache load modifier for reading input elements
-    BlockScanAlgorithm          _SCAN_ALGORITHM>                ///< The BlockScan algorithm to use
-struct AgentReduceByKeyPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;      ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;       ///< Cache load modifier for reading input elements
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;      ///< The BlockScan algorithm to use
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentReduceByKey implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key
- */
-template <
-    typename    AgentReduceByKeyPolicyT,        ///< Parameterized AgentReduceByKeyPolicy tuning policy type
-    typename    KeysInputIteratorT,             ///< Random-access input iterator type for keys
-    typename    UniqueOutputIteratorT,          ///< Random-access output iterator type for keys
-    typename    ValuesInputIteratorT,           ///< Random-access input iterator type for values
-    typename    AggregatesOutputIteratorT,      ///< Random-access output iterator type for values
-    typename    NumRunsOutputIteratorT,         ///< Output iterator type for recording number of items selected
-    typename    EqualityOpT,                    ///< KeyT equality operator type
-    typename    ReductionOpT,                   ///< ValueT reduction operator type
-    typename    OffsetT>                        ///< Signed integer type for global offsets
-struct AgentReduceByKey
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // The input keys type
-    typedef typename std::iterator_traits<KeysInputIteratorT>::value_type KeyInputT;
-
-    // The output keys type
-    typedef typename If<(Equals<typename std::iterator_traits<UniqueOutputIteratorT>::value_type, void>::VALUE),    // KeyOutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<KeysInputIteratorT>::value_type,                                              // ... then the input iterator's value type,
-        typename std::iterator_traits<UniqueOutputIteratorT>::value_type>::Type KeyOutputT;                         // ... else the output iterator's value type
-
-    // The input values type
-    typedef typename std::iterator_traits<ValuesInputIteratorT>::value_type ValueInputT;
-
-    // The output values type
-    typedef typename If<(Equals<typename std::iterator_traits<AggregatesOutputIteratorT>::value_type, void>::VALUE),    // ValueOutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<ValuesInputIteratorT>::value_type,                                                // ... then the input iterator's value type,
-        typename std::iterator_traits<AggregatesOutputIteratorT>::value_type>::Type ValueOutputT;                       // ... else the output iterator's value type
-
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef KeyValuePair<OffsetT, ValueOutputT> OffsetValuePairT;
-
-    // Tuple type for pairing keys and values
-    typedef KeyValuePair<KeyOutputT, ValueOutputT> KeyValuePairT;
-
-    // Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<ValueOutputT, OffsetT> ScanTileStateT;
-
-    // Guarded inequality functor
-    template <typename _EqualityOpT>
-    struct GuardedInequalityWrapper
-    {
-        _EqualityOpT     op;             ///< Wrapped equality operator
-        int             num_remaining;  ///< Items remaining
-
-        /// Constructor
-        __host__ __device__ __forceinline__
-        GuardedInequalityWrapper(_EqualityOpT op, int num_remaining) : op(op), num_remaining(num_remaining) {}
-
-        /// Boolean inequality operator, returns <tt>(a != b)</tt>
-        template <typename T>
-        __host__ __device__ __forceinline__ bool operator()(const T &a, const T &b, int idx) const
-        {
-            if (idx < num_remaining)
-                return !op(a, b);   // In bounds
-
-            // Return true if first out-of-bounds item, false otherwise
-            return (idx == num_remaining);
-       }
-    };
-
-
-    // Constants
-    enum
-    {
-        BLOCK_THREADS       = AgentReduceByKeyPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = AgentReduceByKeyPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,
-        TWO_PHASE_SCATTER   = (ITEMS_PER_THREAD > 1),
-
-        // Whether or not the scan operation has a zero-valued identity value (true if we're performing addition on a primitive type)
-        HAS_IDENTITY_ZERO   = (Equals<ReductionOpT, cub::Sum>::VALUE) && (Traits<ValueOutputT>::PRIMITIVE),
-    };
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for keys
-    typedef typename If<IsPointer<KeysInputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentReduceByKeyPolicyT::LOAD_MODIFIER, KeyInputT, OffsetT>,     // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            KeysInputIteratorT>::Type                                                                   // Directly use the supplied input iterator type
-        WrappedKeysInputIteratorT;
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for values
-    typedef typename If<IsPointer<ValuesInputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentReduceByKeyPolicyT::LOAD_MODIFIER, ValueInputT, OffsetT>,   // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            ValuesInputIteratorT>::Type                                                                 // Directly use the supplied input iterator type
-        WrappedValuesInputIteratorT;
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for fixup values
-    typedef typename If<IsPointer<AggregatesOutputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentReduceByKeyPolicyT::LOAD_MODIFIER, ValueInputT, OffsetT>,   // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            AggregatesOutputIteratorT>::Type                                                            // Directly use the supplied input iterator type
-        WrappedFixupInputIteratorT;
-
-    // Reduce-value-by-segment scan operator
-    typedef ReduceBySegmentOp<ReductionOpT> ReduceBySegmentOpT;
-
-    // Parameterized BlockLoad type for keys
-    typedef BlockLoad<
-            KeyOutputT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            AgentReduceByKeyPolicyT::LOAD_ALGORITHM>
-        BlockLoadKeysT;
-
-    // Parameterized BlockLoad type for values
-    typedef BlockLoad<
-            ValueOutputT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            AgentReduceByKeyPolicyT::LOAD_ALGORITHM>
-        BlockLoadValuesT;
-
-    // Parameterized BlockDiscontinuity type for keys
-    typedef BlockDiscontinuity<
-            KeyOutputT,
-            BLOCK_THREADS>
-        BlockDiscontinuityKeys;
-
-    // Parameterized BlockScan type
-    typedef BlockScan<
-            OffsetValuePairT,
-            BLOCK_THREADS,
-            AgentReduceByKeyPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    // Callback type for obtaining tile prefix during block scan
-    typedef TilePrefixCallbackOp<
-            OffsetValuePairT,
-            ReduceBySegmentOpT,
-            ScanTileStateT>
-        TilePrefixCallbackOpT;
-
-    // Key and value exchange types
-    typedef KeyOutputT    KeyExchangeT[TILE_ITEMS + 1];
-    typedef ValueOutputT  ValueExchangeT[TILE_ITEMS + 1];
-
-    // Shared memory type for this threadblock
-    union _TempStorage
-    {
-        struct
-        {
-            typename BlockScanT::TempStorage                scan;           // Smem needed for tile scanning
-            typename TilePrefixCallbackOpT::TempStorage     prefix;         // Smem needed for cooperative prefix callback
-            typename BlockDiscontinuityKeys::TempStorage    discontinuity;  // Smem needed for discontinuity detection
-        };
-
-        // Smem needed for loading keys
-        typename BlockLoadKeysT::TempStorage load_keys;
-
-        // Smem needed for loading values
-        typename BlockLoadValuesT::TempStorage load_values;
-
-        // Smem needed for compacting key value pairs(allows non POD items in this union)
-        Uninitialized<KeyValuePairT[TILE_ITEMS + 1]> raw_exchange;
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage&                   temp_storage;       ///< Reference to temp_storage
-    WrappedKeysInputIteratorT       d_keys_in;          ///< Input keys
-    UniqueOutputIteratorT           d_unique_out;       ///< Unique output keys
-    WrappedValuesInputIteratorT     d_values_in;        ///< Input values
-    AggregatesOutputIteratorT       d_aggregates_out;   ///< Output value aggregates
-    NumRunsOutputIteratorT          d_num_runs_out;     ///< Output pointer for total number of segments identified
-    EqualityOpT                     equality_op;        ///< KeyT equality operator
-    ReductionOpT                    reduction_op;       ///< Reduction operator
-    ReduceBySegmentOpT              scan_op;            ///< Reduce-by-segment scan operator
-
-
-    //---------------------------------------------------------------------
-    // Constructor
-    //---------------------------------------------------------------------
-
-    // Constructor
-    __device__ __forceinline__
-    AgentReduceByKey(
-        TempStorage&                temp_storage,       ///< Reference to temp_storage
-        KeysInputIteratorT          d_keys_in,          ///< Input keys
-        UniqueOutputIteratorT       d_unique_out,       ///< Unique output keys
-        ValuesInputIteratorT        d_values_in,        ///< Input values
-        AggregatesOutputIteratorT   d_aggregates_out,   ///< Output value aggregates
-        NumRunsOutputIteratorT      d_num_runs_out,     ///< Output pointer for total number of segments identified
-        EqualityOpT                 equality_op,        ///< KeyT equality operator
-        ReductionOpT                reduction_op)       ///< ValueT reduction operator
-    :
-        temp_storage(temp_storage.Alias()),
-        d_keys_in(d_keys_in),
-        d_unique_out(d_unique_out),
-        d_values_in(d_values_in),
-        d_aggregates_out(d_aggregates_out),
-        d_num_runs_out(d_num_runs_out),
-        equality_op(equality_op),
-        reduction_op(reduction_op),
-        scan_op(reduction_op)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Scatter utility methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Directly scatter flagged items to output offsets
-     */
-    __device__ __forceinline__ void ScatterDirect(
-        KeyValuePairT   (&scatter_items)[ITEMS_PER_THREAD],
-        OffsetT         (&segment_flags)[ITEMS_PER_THREAD],
-        OffsetT         (&segment_indices)[ITEMS_PER_THREAD])
-    {
-        // Scatter flagged keys and values
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (segment_flags[ITEM])
-            {
-                d_unique_out[segment_indices[ITEM]]     = scatter_items[ITEM].key;
-                d_aggregates_out[segment_indices[ITEM]] = scatter_items[ITEM].value;
-            }
-        }
-    }
-
-
-    /**
-     * 2-phase scatter flagged items to output offsets
-     *
-     * The exclusive scan causes each head flag to be paired with the previous
-     * value aggregate: the scatter offsets must be decremented for value aggregates
-     */
-    __device__ __forceinline__ void ScatterTwoPhase(
-        KeyValuePairT   (&scatter_items)[ITEMS_PER_THREAD],
-        OffsetT         (&segment_flags)[ITEMS_PER_THREAD],
-        OffsetT         (&segment_indices)[ITEMS_PER_THREAD],
-        OffsetT         num_tile_segments,
-        OffsetT         num_tile_segments_prefix)
-    {
-        CTA_SYNC();
-
-        // Compact and scatter pairs
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (segment_flags[ITEM])
-            {
-                temp_storage.raw_exchange.Alias()[segment_indices[ITEM] - num_tile_segments_prefix] = scatter_items[ITEM];
-            }
-        }
-
-        CTA_SYNC();
-
-        for (int item = threadIdx.x; item < num_tile_segments; item += BLOCK_THREADS)
-        {
-            KeyValuePairT pair                                  = temp_storage.raw_exchange.Alias()[item];
-            d_unique_out[num_tile_segments_prefix + item]       = pair.key;
-            d_aggregates_out[num_tile_segments_prefix + item]   = pair.value;
-        }
-    }
-
-
-    /**
-     * Scatter flagged items
-     */
-    __device__ __forceinline__ void Scatter(
-        KeyValuePairT   (&scatter_items)[ITEMS_PER_THREAD],
-        OffsetT         (&segment_flags)[ITEMS_PER_THREAD],
-        OffsetT         (&segment_indices)[ITEMS_PER_THREAD],
-        OffsetT         num_tile_segments,
-        OffsetT         num_tile_segments_prefix)
-    {
-        // Do a one-phase scatter if (a) two-phase is disabled or (b) the average number of selected items per thread is less than one
-        if (TWO_PHASE_SCATTER && (num_tile_segments > BLOCK_THREADS))
-        {
-            ScatterTwoPhase(
-                scatter_items,
-                segment_flags,
-                segment_indices,
-                num_tile_segments,
-                num_tile_segments_prefix);
-        }
-        else
-        {
-            ScatterDirect(
-                scatter_items,
-                segment_flags,
-                segment_indices);
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Cooperatively scan a device-wide sequence of tiles with other CTAs
-    //---------------------------------------------------------------------
-
-    /**
-     * Process a tile of input (dynamic chained scan)
-     */
-    template <bool IS_LAST_TILE>                ///< Whether the current tile is the last tile
-    __device__ __forceinline__ void ConsumeTile(
-        OffsetT             num_remaining,      ///< Number of global input items remaining (including this tile)
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state)         ///< Global tile state descriptor
-    {
-        KeyOutputT          keys[ITEMS_PER_THREAD];             // Tile keys
-        KeyOutputT          prev_keys[ITEMS_PER_THREAD];        // Tile keys shuffled up
-        ValueOutputT        values[ITEMS_PER_THREAD];           // Tile values
-        OffsetT             head_flags[ITEMS_PER_THREAD];       // Segment head flags
-        OffsetT             segment_indices[ITEMS_PER_THREAD];  // Segment indices
-        OffsetValuePairT    scan_items[ITEMS_PER_THREAD];       // Zipped values and segment flags|indices
-        KeyValuePairT       scatter_items[ITEMS_PER_THREAD];    // Zipped key value pairs for scattering
-
-        // Load keys
-        if (IS_LAST_TILE)
-            BlockLoadKeysT(temp_storage.load_keys).Load(d_keys_in + tile_offset, keys, num_remaining);
-        else
-            BlockLoadKeysT(temp_storage.load_keys).Load(d_keys_in + tile_offset, keys);
-
-        // Load tile predecessor key in first thread
-        KeyOutputT tile_predecessor;
-        if (threadIdx.x == 0)
-        {
-            tile_predecessor = (tile_idx == 0) ?
-                keys[0] :                       // First tile gets repeat of first item (thus first item will not be flagged as a head)
-                d_keys_in[tile_offset - 1];     // Subsequent tiles get last key from previous tile
-        }
-
-        CTA_SYNC();
-
-        // Load values
-        if (IS_LAST_TILE)
-            BlockLoadValuesT(temp_storage.load_values).Load(d_values_in + tile_offset, values, num_remaining);
-        else
-            BlockLoadValuesT(temp_storage.load_values).Load(d_values_in + tile_offset, values);
-
-        CTA_SYNC();
-
-        // Initialize head-flags and shuffle up the previous keys
-        if (IS_LAST_TILE)
-        {
-            // Use custom flag operator to additionally flag the first out-of-bounds item
-            GuardedInequalityWrapper<EqualityOpT> flag_op(equality_op, num_remaining);
-            BlockDiscontinuityKeys(temp_storage.discontinuity).FlagHeads(
-                head_flags, keys, prev_keys, flag_op, tile_predecessor);
-        }
-        else
-        {
-            InequalityWrapper<EqualityOpT> flag_op(equality_op);
-            BlockDiscontinuityKeys(temp_storage.discontinuity).FlagHeads(
-                head_flags, keys, prev_keys, flag_op, tile_predecessor);
-        }
-
-        // Zip values and head flags
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            scan_items[ITEM].value  = values[ITEM];
-            scan_items[ITEM].key    = head_flags[ITEM];
-        }
-
-        // Perform exclusive tile scan
-        OffsetValuePairT    block_aggregate;        // Inclusive block-wide scan aggregate
-        OffsetT             num_segments_prefix;    // Number of segments prior to this tile
-        ValueOutputT        total_aggregate;        // The tile prefix folded with block_aggregate
-        if (tile_idx == 0)
-        {
-            // Scan first tile
-            BlockScanT(temp_storage.scan).ExclusiveScan(scan_items, scan_items, scan_op, block_aggregate);
-            num_segments_prefix     = 0;
-            total_aggregate         = block_aggregate.value;
-
-            // Update tile status if there are successor tiles
-            if ((!IS_LAST_TILE) && (threadIdx.x == 0))
-                tile_state.SetInclusive(0, block_aggregate);
-        }
-        else
-        {
-            // Scan non-first tile
-            TilePrefixCallbackOpT prefix_op(tile_state, temp_storage.prefix, scan_op, tile_idx);
-            BlockScanT(temp_storage.scan).ExclusiveScan(scan_items, scan_items, scan_op, prefix_op);
-
-            block_aggregate         = prefix_op.GetBlockAggregate();
-            num_segments_prefix     = prefix_op.GetExclusivePrefix().key;
-            total_aggregate         = reduction_op(
-                                        prefix_op.GetExclusivePrefix().value,
-                                        block_aggregate.value);
-        }
-
-        // Rezip scatter items and segment indices
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            scatter_items[ITEM].key     = prev_keys[ITEM];
-            scatter_items[ITEM].value   = scan_items[ITEM].value;
-            segment_indices[ITEM]       = scan_items[ITEM].key;
-        }
-
-        // At this point, each flagged segment head has:
-        //  - The key for the previous segment
-        //  - The reduced value from the previous segment
-        //  - The segment index for the reduced value
-
-        // Scatter flagged keys and values
-        OffsetT num_tile_segments = block_aggregate.key;
-        Scatter(scatter_items, head_flags, segment_indices, num_tile_segments, num_segments_prefix);
-
-        // Last thread in last tile will output final count (and last pair, if necessary)
-        if ((IS_LAST_TILE) && (threadIdx.x == BLOCK_THREADS - 1))
-        {
-            OffsetT num_segments = num_segments_prefix + num_tile_segments;
-
-            // If the last tile is a whole tile, output the final_value
-            if (num_remaining == TILE_ITEMS)
-            {
-                d_unique_out[num_segments]      = keys[ITEMS_PER_THREAD - 1];
-                d_aggregates_out[num_segments]  = total_aggregate;
-                num_segments++;
-            }
-
-            // Output the total number of items selected
-            *d_num_runs_out = num_segments;
-        }
-    }
-
-
-    /**
-     * Scan tiles of items as part of a dynamic chained scan
-     */
-    __device__ __forceinline__ void ConsumeRange(
-        int                 num_items,          ///< Total number of input items
-        ScanTileStateT&     tile_state,         ///< Global tile state descriptor
-        int                 start_tile)         ///< The starting tile for the current grid
-    {
-        // Blocks are launched in increasing order, so just assign one tile per block
-        int     tile_idx        = start_tile + blockIdx.x;          // Current tile index
-        OffsetT tile_offset     = OffsetT(TILE_ITEMS) * tile_idx;   // Global offset for the current tile
-        OffsetT num_remaining   = num_items - tile_offset;          // Remaining items (including this tile)
-
-        if (num_remaining > TILE_ITEMS)
-        {
-            // Not last tile
-            ConsumeTile<false>(num_remaining, tile_idx, tile_offset, tile_state);
-        }
-        else if (num_remaining > 0)
-        {
-            // Last tile
-            ConsumeTile<true>(num_remaining, tile_idx, tile_offset, tile_state);
-        }
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_rle.cuh b/ml-xgboost/cub/cub/agent/agent_rle.cuh
deleted file mode 100644
index e899f0b..0000000
--- a/ml-xgboost/cub/cub/agent/agent_rle.cuh
+++ /dev/null
@@ -1,830 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentRle implements a stateful abstraction of CUDA thread blocks for participating in device-wide run-length-encode.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "single_pass_scan_operators.cuh"
-#include "../block/block_load.cuh"
-#include "../block/block_store.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_exchange.cuh"
-#include "../block/block_discontinuity.cuh"
-#include "../grid/grid_queue.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../iterator/constant_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentRle
- */
-template <
-    int                         _BLOCK_THREADS,                 ///< Threads per thread block
-    int                         _ITEMS_PER_THREAD,              ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm          _LOAD_ALGORITHM,                ///< The BlockLoad algorithm to use
-    CacheLoadModifier           _LOAD_MODIFIER,                 ///< Cache load modifier for reading input elements
-    bool                        _STORE_WARP_TIME_SLICING,       ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any store-related data transpositions (versus each warp having its own storage)
-    BlockScanAlgorithm          _SCAN_ALGORITHM>                ///< The BlockScan algorithm to use
-struct AgentRlePolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-        STORE_WARP_TIME_SLICING = _STORE_WARP_TIME_SLICING,     ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any store-related data transpositions (versus each warp having its own storage)
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;      ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;       ///< Cache load modifier for reading input elements
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;      ///< The BlockScan algorithm to use
-};
-
-
-
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentRle implements a stateful abstraction of CUDA thread blocks for participating in device-wide run-length-encode 
- */
-template <
-    typename    AgentRlePolicyT,        ///< Parameterized AgentRlePolicyT tuning policy type
-    typename    InputIteratorT,         ///< Random-access input iterator type for data
-    typename    OffsetsOutputIteratorT, ///< Random-access output iterator type for offset values
-    typename    LengthsOutputIteratorT, ///< Random-access output iterator type for length values
-    typename    EqualityOpT,            ///< T equality operator type
-    typename    OffsetT>                ///< Signed integer type for global offsets
-struct AgentRle
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type T;
-
-    /// The lengths output value type
-    typedef typename If<(Equals<typename std::iterator_traits<LengthsOutputIteratorT>::value_type, void>::VALUE),   // LengthT =  (if output iterator's value type is void) ?
-        OffsetT,                                                                                                    // ... then the OffsetT type,
-        typename std::iterator_traits<LengthsOutputIteratorT>::value_type>::Type LengthT;                           // ... else the output iterator's value type
-
-    /// Tuple type for scanning (pairs run-length and run-index)
-    typedef KeyValuePair<OffsetT, LengthT> LengthOffsetPair;
-
-    /// Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<LengthT, OffsetT> ScanTileStateT;
-
-    // Constants
-    enum
-    {
-        WARP_THREADS            = CUB_WARP_THREADS(PTX_ARCH),
-        BLOCK_THREADS           = AgentRlePolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = AgentRlePolicyT::ITEMS_PER_THREAD,
-        WARP_ITEMS              = WARP_THREADS * ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-        WARPS                   = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-
-        /// Whether or not to sync after loading data
-        SYNC_AFTER_LOAD         = (AgentRlePolicyT::LOAD_ALGORITHM != BLOCK_LOAD_DIRECT),
-
-        /// Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any store-related data transpositions (versus each warp having its own storage)
-        STORE_WARP_TIME_SLICING = AgentRlePolicyT::STORE_WARP_TIME_SLICING,
-        ACTIVE_EXCHANGE_WARPS   = (STORE_WARP_TIME_SLICING) ? 1 : WARPS,
-    };
-
-
-    /**
-     * Special operator that signals all out-of-bounds items are not equal to everything else,
-     * forcing both (1) the last item to be tail-flagged and (2) all oob items to be marked
-     * trivial.
-     */
-    template <bool LAST_TILE>
-    struct OobInequalityOp
-    {
-        OffsetT         num_remaining;
-        EqualityOpT      equality_op;
-
-        __device__ __forceinline__ OobInequalityOp(
-            OffsetT     num_remaining,
-            EqualityOpT  equality_op)
-        :
-            num_remaining(num_remaining),
-            equality_op(equality_op)
-        {}
-
-        template <typename Index>
-        __device__ __forceinline__ bool operator()(T first, T second, Index idx)
-        {
-            if (!LAST_TILE || (idx < num_remaining))
-                return !equality_op(first, second);
-            else
-                return true;
-        }
-    };
-
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for data
-    typedef typename If<IsPointer<InputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentRlePolicyT::LOAD_MODIFIER, T, OffsetT>,      // Wrap the native input pointer with CacheModifiedVLengthnputIterator
-            InputIteratorT>::Type                                                       // Directly use the supplied input iterator type
-        WrappedInputIteratorT;
-
-    // Parameterized BlockLoad type for data
-    typedef BlockLoad<
-            T,
-            AgentRlePolicyT::BLOCK_THREADS,
-            AgentRlePolicyT::ITEMS_PER_THREAD,
-            AgentRlePolicyT::LOAD_ALGORITHM>
-        BlockLoadT;
-
-    // Parameterized BlockDiscontinuity type for data
-    typedef BlockDiscontinuity<T, BLOCK_THREADS> BlockDiscontinuityT;
-
-    // Parameterized WarpScan type
-    typedef WarpScan<LengthOffsetPair> WarpScanPairs;
-
-    // Reduce-length-by-run scan operator
-    typedef ReduceBySegmentOp<cub::Sum> ReduceBySegmentOpT;
-
-    // Callback type for obtaining tile prefix during block scan
-    typedef TilePrefixCallbackOp<
-            LengthOffsetPair,
-            ReduceBySegmentOpT,
-            ScanTileStateT>
-        TilePrefixCallbackOpT;
-
-    // Warp exchange types
-    typedef WarpExchange<LengthOffsetPair, ITEMS_PER_THREAD>        WarpExchangePairs;
-
-    typedef typename If<STORE_WARP_TIME_SLICING, typename WarpExchangePairs::TempStorage, NullType>::Type WarpExchangePairsStorage;
-
-    typedef WarpExchange<OffsetT, ITEMS_PER_THREAD>                 WarpExchangeOffsets;
-    typedef WarpExchange<LengthT, ITEMS_PER_THREAD>                 WarpExchangeLengths;
-
-    typedef LengthOffsetPair WarpAggregates[WARPS];
-
-    // Shared memory type for this threadblock
-    struct _TempStorage
-    {
-        union
-        {
-            struct
-            {
-                typename BlockDiscontinuityT::TempStorage       discontinuity;              // Smem needed for discontinuity detection
-                typename WarpScanPairs::TempStorage             warp_scan[WARPS];           // Smem needed for warp-synchronous scans
-                Uninitialized<LengthOffsetPair[WARPS]>          warp_aggregates;            // Smem needed for sharing warp-wide aggregates
-                typename TilePrefixCallbackOpT::TempStorage     prefix;                     // Smem needed for cooperative prefix callback
-            };
-
-            // Smem needed for input loading
-            typename BlockLoadT::TempStorage                    load;
-
-            // Smem needed for two-phase scatter
-            union
-            {
-                unsigned long long                              align;
-                WarpExchangePairsStorage                        exchange_pairs[ACTIVE_EXCHANGE_WARPS];
-                typename WarpExchangeOffsets::TempStorage       exchange_offsets[ACTIVE_EXCHANGE_WARPS];
-                typename WarpExchangeLengths::TempStorage       exchange_lengths[ACTIVE_EXCHANGE_WARPS];
-            };
-        };
-
-        OffsetT             tile_idx;                   // Shared tile index
-        LengthOffsetPair    tile_inclusive;             // Inclusive tile prefix
-        LengthOffsetPair    tile_exclusive;             // Exclusive tile prefix
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage&                   temp_storage;       ///< Reference to temp_storage
-
-    WrappedInputIteratorT           d_in;               ///< Pointer to input sequence of data items
-    OffsetsOutputIteratorT          d_offsets_out;      ///< Input run offsets
-    LengthsOutputIteratorT          d_lengths_out;      ///< Output run lengths
-
-    EqualityOpT                     equality_op;        ///< T equality operator
-    ReduceBySegmentOpT              scan_op;            ///< Reduce-length-by-flag scan operator
-    OffsetT                         num_items;          ///< Total number of input items
-
-
-    //---------------------------------------------------------------------
-    // Constructor
-    //---------------------------------------------------------------------
-
-    // Constructor
-    __device__ __forceinline__
-    AgentRle(
-        TempStorage                 &temp_storage,      ///< [in] Reference to temp_storage
-        InputIteratorT              d_in,               ///< [in] Pointer to input sequence of data items
-        OffsetsOutputIteratorT      d_offsets_out,      ///< [out] Pointer to output sequence of run offsets
-        LengthsOutputIteratorT      d_lengths_out,      ///< [out] Pointer to output sequence of run lengths
-        EqualityOpT                 equality_op,        ///< [in] T equality operator
-        OffsetT                     num_items)          ///< [in] Total number of input items
-    :
-        temp_storage(temp_storage.Alias()),
-        d_in(d_in),
-        d_offsets_out(d_offsets_out),
-        d_lengths_out(d_lengths_out),
-        equality_op(equality_op),
-        scan_op(cub::Sum()),
-        num_items(num_items)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Utility methods for initializing the selections
-    //---------------------------------------------------------------------
-
-    template <bool FIRST_TILE, bool LAST_TILE>
-    __device__ __forceinline__ void InitializeSelections(
-        OffsetT             tile_offset,
-        OffsetT             num_remaining,
-        T                   (&items)[ITEMS_PER_THREAD],
-        LengthOffsetPair    (&lengths_and_num_runs)[ITEMS_PER_THREAD])
-    {
-        bool                head_flags[ITEMS_PER_THREAD];
-        bool                tail_flags[ITEMS_PER_THREAD];
-
-        OobInequalityOp<LAST_TILE> inequality_op(num_remaining, equality_op);
-
-        if (FIRST_TILE && LAST_TILE)
-        {
-            // First-and-last-tile always head-flags the first item and tail-flags the last item
-
-            BlockDiscontinuityT(temp_storage.discontinuity).FlagHeadsAndTails(
-                head_flags, tail_flags, items, inequality_op);
-        }
-        else if (FIRST_TILE)
-        {
-            // First-tile always head-flags the first item
-
-            // Get the first item from the next tile
-            T tile_successor_item;
-            if (threadIdx.x == BLOCK_THREADS - 1)
-                tile_successor_item = d_in[tile_offset + TILE_ITEMS];
-
-            BlockDiscontinuityT(temp_storage.discontinuity).FlagHeadsAndTails(
-                head_flags, tail_flags, tile_successor_item, items, inequality_op);
-        }
-        else if (LAST_TILE)
-        {
-            // Last-tile always flags the last item
-
-            // Get the last item from the previous tile
-            T tile_predecessor_item;
-            if (threadIdx.x == 0)
-                tile_predecessor_item = d_in[tile_offset - 1];
-
-            BlockDiscontinuityT(temp_storage.discontinuity).FlagHeadsAndTails(
-                head_flags, tile_predecessor_item, tail_flags, items, inequality_op);
-        }
-        else
-        {
-            // Get the first item from the next tile
-            T tile_successor_item;
-            if (threadIdx.x == BLOCK_THREADS - 1)
-                tile_successor_item = d_in[tile_offset + TILE_ITEMS];
-
-            // Get the last item from the previous tile
-            T tile_predecessor_item;
-            if (threadIdx.x == 0)
-                tile_predecessor_item = d_in[tile_offset - 1];
-
-            BlockDiscontinuityT(temp_storage.discontinuity).FlagHeadsAndTails(
-                head_flags, tile_predecessor_item, tail_flags, tile_successor_item, items, inequality_op);
-        }
-
-        // Zip counts and runs
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            lengths_and_num_runs[ITEM].key   = head_flags[ITEM] && (!tail_flags[ITEM]);
-            lengths_and_num_runs[ITEM].value    = ((!head_flags[ITEM]) || (!tail_flags[ITEM]));
-        }
-    }
-
-    //---------------------------------------------------------------------
-    // Scan utility methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Scan of allocations
-     */
-    __device__ __forceinline__ void WarpScanAllocations(
-        LengthOffsetPair    &tile_aggregate,
-        LengthOffsetPair    &warp_aggregate,
-        LengthOffsetPair    &warp_exclusive_in_tile,
-        LengthOffsetPair    &thread_exclusive_in_warp,
-        LengthOffsetPair    (&lengths_and_num_runs)[ITEMS_PER_THREAD])
-    {
-        // Perform warpscans
-        unsigned int warp_id = ((WARPS == 1) ? 0 : threadIdx.x / WARP_THREADS);
-        int lane_id = LaneId();
-
-        LengthOffsetPair identity;
-        identity.key = 0;
-        identity.value = 0;
-
-        LengthOffsetPair thread_inclusive;
-        LengthOffsetPair thread_aggregate = ThreadReduce(lengths_and_num_runs, scan_op);
-        WarpScanPairs(temp_storage.warp_scan[warp_id]).Scan(
-            thread_aggregate,
-            thread_inclusive,
-            thread_exclusive_in_warp,
-            identity,
-            scan_op);
-
-        // Last lane in each warp shares its warp-aggregate
-        if (lane_id == WARP_THREADS - 1)
-            temp_storage.warp_aggregates.Alias()[warp_id] = thread_inclusive;
-
-        CTA_SYNC();
-
-        // Accumulate total selected and the warp-wide prefix
-        warp_exclusive_in_tile          = identity;
-        warp_aggregate                  = temp_storage.warp_aggregates.Alias()[warp_id];
-        tile_aggregate                  = temp_storage.warp_aggregates.Alias()[0];
-
-        #pragma unroll
-        for (int WARP = 1; WARP < WARPS; ++WARP)
-        {
-            if (warp_id == WARP)
-                warp_exclusive_in_tile = tile_aggregate;
-
-            tile_aggregate = scan_op(tile_aggregate, temp_storage.warp_aggregates.Alias()[WARP]);
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Utility methods for scattering selections
-    //---------------------------------------------------------------------
-
-    /**
-     * Two-phase scatter, specialized for warp time-slicing
-     */
-    template <bool FIRST_TILE>
-    __device__ __forceinline__ void ScatterTwoPhase(
-        OffsetT             tile_num_runs_exclusive_in_global,
-        OffsetT             warp_num_runs_aggregate,
-        OffsetT             warp_num_runs_exclusive_in_tile,
-        OffsetT             (&thread_num_runs_exclusive_in_warp)[ITEMS_PER_THREAD],
-        LengthOffsetPair    (&lengths_and_offsets)[ITEMS_PER_THREAD],
-        Int2Type<true>      is_warp_time_slice)
-    {
-        unsigned int warp_id = ((WARPS == 1) ? 0 : threadIdx.x / WARP_THREADS);
-        int lane_id = LaneId();
-
-        // Locally compact items within the warp (first warp)
-        if (warp_id == 0)
-        {
-            WarpExchangePairs(temp_storage.exchange_pairs[0]).ScatterToStriped(lengths_and_offsets, thread_num_runs_exclusive_in_warp);
-        }
-
-        // Locally compact items within the warp (remaining warps)
-        #pragma unroll
-        for (int SLICE = 1; SLICE < WARPS; ++SLICE)
-        {
-            CTA_SYNC();
-
-            if (warp_id == SLICE)
-            {
-                WarpExchangePairs(temp_storage.exchange_pairs[0]).ScatterToStriped(lengths_and_offsets, thread_num_runs_exclusive_in_warp);
-            }
-        }
-
-        // Global scatter
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if ((ITEM * WARP_THREADS) < warp_num_runs_aggregate - lane_id)
-            {
-                OffsetT item_offset =
-                    tile_num_runs_exclusive_in_global +
-                    warp_num_runs_exclusive_in_tile +
-                    (ITEM * WARP_THREADS) + lane_id;
-
-                // Scatter offset
-                d_offsets_out[item_offset] = lengths_and_offsets[ITEM].key;
-
-                // Scatter length if not the first (global) length
-                if ((!FIRST_TILE) || (ITEM != 0) || (threadIdx.x > 0))
-                {
-                    d_lengths_out[item_offset - 1] = lengths_and_offsets[ITEM].value;
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Two-phase scatter
-     */
-    template <bool FIRST_TILE>
-    __device__ __forceinline__ void ScatterTwoPhase(
-        OffsetT             tile_num_runs_exclusive_in_global,
-        OffsetT             warp_num_runs_aggregate,
-        OffsetT             warp_num_runs_exclusive_in_tile,
-        OffsetT             (&thread_num_runs_exclusive_in_warp)[ITEMS_PER_THREAD],
-        LengthOffsetPair    (&lengths_and_offsets)[ITEMS_PER_THREAD],
-        Int2Type<false>     is_warp_time_slice)
-    {
-        unsigned int warp_id = ((WARPS == 1) ? 0 : threadIdx.x / WARP_THREADS);
-        int lane_id = LaneId();
-
-        // Unzip
-        OffsetT run_offsets[ITEMS_PER_THREAD];
-        LengthT run_lengths[ITEMS_PER_THREAD];
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            run_offsets[ITEM] = lengths_and_offsets[ITEM].key;
-            run_lengths[ITEM] = lengths_and_offsets[ITEM].value;
-        }
-
-        WarpExchangeOffsets(temp_storage.exchange_offsets[warp_id]).ScatterToStriped(run_offsets, thread_num_runs_exclusive_in_warp);
-
-        WARP_SYNC(0xffffffff);
-
-        WarpExchangeLengths(temp_storage.exchange_lengths[warp_id]).ScatterToStriped(run_lengths, thread_num_runs_exclusive_in_warp);
-
-        // Global scatter
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if ((ITEM * WARP_THREADS) + lane_id < warp_num_runs_aggregate)
-            {
-                OffsetT item_offset =
-                    tile_num_runs_exclusive_in_global +
-                    warp_num_runs_exclusive_in_tile +
-                    (ITEM * WARP_THREADS) + lane_id;
-
-                // Scatter offset
-                d_offsets_out[item_offset] = run_offsets[ITEM];
-
-                // Scatter length if not the first (global) length
-                if ((!FIRST_TILE) || (ITEM != 0) || (threadIdx.x > 0))
-                {
-                    d_lengths_out[item_offset - 1] = run_lengths[ITEM];
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Direct scatter
-     */
-    template <bool FIRST_TILE>
-    __device__ __forceinline__ void ScatterDirect(
-        OffsetT             tile_num_runs_exclusive_in_global,
-        OffsetT             warp_num_runs_aggregate,
-        OffsetT             warp_num_runs_exclusive_in_tile,
-        OffsetT             (&thread_num_runs_exclusive_in_warp)[ITEMS_PER_THREAD],
-        LengthOffsetPair    (&lengths_and_offsets)[ITEMS_PER_THREAD])
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (thread_num_runs_exclusive_in_warp[ITEM] < warp_num_runs_aggregate)
-            {
-                OffsetT item_offset =
-                    tile_num_runs_exclusive_in_global +
-                    warp_num_runs_exclusive_in_tile +
-                    thread_num_runs_exclusive_in_warp[ITEM];
-
-                // Scatter offset
-                d_offsets_out[item_offset] = lengths_and_offsets[ITEM].key;
-
-                // Scatter length if not the first (global) length
-                if (item_offset >= 1)
-                {
-                    d_lengths_out[item_offset - 1] = lengths_and_offsets[ITEM].value;
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Scatter
-     */
-    template <bool FIRST_TILE>
-    __device__ __forceinline__ void Scatter(
-        OffsetT             tile_num_runs_aggregate,
-        OffsetT             tile_num_runs_exclusive_in_global,
-        OffsetT             warp_num_runs_aggregate,
-        OffsetT             warp_num_runs_exclusive_in_tile,
-        OffsetT             (&thread_num_runs_exclusive_in_warp)[ITEMS_PER_THREAD],
-        LengthOffsetPair    (&lengths_and_offsets)[ITEMS_PER_THREAD])
-    {
-        if ((ITEMS_PER_THREAD == 1) || (tile_num_runs_aggregate < BLOCK_THREADS))
-        {
-            // Direct scatter if the warp has any items
-            if (warp_num_runs_aggregate)
-            {
-                ScatterDirect<FIRST_TILE>(
-                    tile_num_runs_exclusive_in_global,
-                    warp_num_runs_aggregate,
-                    warp_num_runs_exclusive_in_tile,
-                    thread_num_runs_exclusive_in_warp,
-                    lengths_and_offsets);
-            }
-        }
-        else
-        {
-            // Scatter two phase
-            ScatterTwoPhase<FIRST_TILE>(
-                tile_num_runs_exclusive_in_global,
-                warp_num_runs_aggregate,
-                warp_num_runs_exclusive_in_tile,
-                thread_num_runs_exclusive_in_warp,
-                lengths_and_offsets,
-                Int2Type<STORE_WARP_TIME_SLICING>());
-        }
-    }
-
-
-
-    //---------------------------------------------------------------------
-    // Cooperatively scan a device-wide sequence of tiles with other CTAs
-    //---------------------------------------------------------------------
-
-    /**
-     * Process a tile of input (dynamic chained scan)
-     */
-    template <
-        bool                LAST_TILE>
-    __device__ __forceinline__ LengthOffsetPair ConsumeTile(
-        OffsetT             num_items,          ///< Total number of global input items
-        OffsetT             num_remaining,      ///< Number of global input items remaining (including this tile)
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,       ///< Tile offset
-        ScanTileStateT       &tile_status)       ///< Global list of tile status
-    {
-        if (tile_idx == 0)
-        {
-            // First tile
-
-            // Load items
-            T items[ITEMS_PER_THREAD];
-            if (LAST_TILE)
-                BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items, num_remaining, T());
-            else
-                BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items);
-
-            if (SYNC_AFTER_LOAD)
-                CTA_SYNC();
-
-            // Set flags
-            LengthOffsetPair    lengths_and_num_runs[ITEMS_PER_THREAD];
-
-            InitializeSelections<true, LAST_TILE>(
-                tile_offset,
-                num_remaining,
-                items,
-                lengths_and_num_runs);
-
-            // Exclusive scan of lengths and runs
-            LengthOffsetPair tile_aggregate;
-            LengthOffsetPair warp_aggregate;
-            LengthOffsetPair warp_exclusive_in_tile;
-            LengthOffsetPair thread_exclusive_in_warp;
-
-            WarpScanAllocations(
-                tile_aggregate,
-                warp_aggregate,
-                warp_exclusive_in_tile,
-                thread_exclusive_in_warp,
-                lengths_and_num_runs);
-
-            // Update tile status if this is not the last tile
-            if (!LAST_TILE && (threadIdx.x == 0))
-                tile_status.SetInclusive(0, tile_aggregate);
-
-            // Update thread_exclusive_in_warp to fold in warp run-length
-            if (thread_exclusive_in_warp.key == 0)
-                thread_exclusive_in_warp.value += warp_exclusive_in_tile.value;
-
-            LengthOffsetPair    lengths_and_offsets[ITEMS_PER_THREAD];
-            OffsetT             thread_num_runs_exclusive_in_warp[ITEMS_PER_THREAD];
-            LengthOffsetPair    lengths_and_num_runs2[ITEMS_PER_THREAD];
-
-            // Downsweep scan through lengths_and_num_runs
-            ThreadScanExclusive(lengths_and_num_runs, lengths_and_num_runs2, scan_op, thread_exclusive_in_warp);
-
-            // Zip
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                lengths_and_offsets[ITEM].value         = lengths_and_num_runs2[ITEM].value;
-                lengths_and_offsets[ITEM].key        = tile_offset + (threadIdx.x * ITEMS_PER_THREAD) + ITEM;
-                thread_num_runs_exclusive_in_warp[ITEM] = (lengths_and_num_runs[ITEM].key) ?
-                                                                lengths_and_num_runs2[ITEM].key :         // keep
-                                                                WARP_THREADS * ITEMS_PER_THREAD;            // discard
-            }
-
-            OffsetT tile_num_runs_aggregate              = tile_aggregate.key;
-            OffsetT tile_num_runs_exclusive_in_global    = 0;
-            OffsetT warp_num_runs_aggregate              = warp_aggregate.key;
-            OffsetT warp_num_runs_exclusive_in_tile      = warp_exclusive_in_tile.key;
-
-            // Scatter
-            Scatter<true>(
-                tile_num_runs_aggregate,
-                tile_num_runs_exclusive_in_global,
-                warp_num_runs_aggregate,
-                warp_num_runs_exclusive_in_tile,
-                thread_num_runs_exclusive_in_warp,
-                lengths_and_offsets);
-
-            // Return running total (inclusive of this tile)
-            return tile_aggregate;
-        }
-        else
-        {
-            // Not first tile
-
-            // Load items
-            T items[ITEMS_PER_THREAD];
-            if (LAST_TILE)
-                BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items, num_remaining, T());
-            else
-                BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items);
-
-            if (SYNC_AFTER_LOAD)
-                CTA_SYNC();
-
-            // Set flags
-            LengthOffsetPair    lengths_and_num_runs[ITEMS_PER_THREAD];
-
-            InitializeSelections<false, LAST_TILE>(
-                tile_offset,
-                num_remaining,
-                items,
-                lengths_and_num_runs);
-
-            // Exclusive scan of lengths and runs
-            LengthOffsetPair tile_aggregate;
-            LengthOffsetPair warp_aggregate;
-            LengthOffsetPair warp_exclusive_in_tile;
-            LengthOffsetPair thread_exclusive_in_warp;
-
-            WarpScanAllocations(
-                tile_aggregate,
-                warp_aggregate,
-                warp_exclusive_in_tile,
-                thread_exclusive_in_warp,
-                lengths_and_num_runs);
-
-            // First warp computes tile prefix in lane 0
-            TilePrefixCallbackOpT prefix_op(tile_status, temp_storage.prefix, Sum(), tile_idx);
-            unsigned int warp_id = ((WARPS == 1) ? 0 : threadIdx.x / WARP_THREADS);
-            if (warp_id == 0)
-            {
-                prefix_op(tile_aggregate);
-                if (threadIdx.x == 0)
-                    temp_storage.tile_exclusive = prefix_op.exclusive_prefix;
-            }
-
-            CTA_SYNC();
-
-            LengthOffsetPair tile_exclusive_in_global = temp_storage.tile_exclusive;
-
-            // Update thread_exclusive_in_warp to fold in warp and tile run-lengths
-            LengthOffsetPair thread_exclusive = scan_op(tile_exclusive_in_global, warp_exclusive_in_tile);
-            if (thread_exclusive_in_warp.key == 0)
-                thread_exclusive_in_warp.value += thread_exclusive.value;
-
-            // Downsweep scan through lengths_and_num_runs
-            LengthOffsetPair    lengths_and_num_runs2[ITEMS_PER_THREAD];
-            LengthOffsetPair    lengths_and_offsets[ITEMS_PER_THREAD];
-            OffsetT             thread_num_runs_exclusive_in_warp[ITEMS_PER_THREAD];
-
-            ThreadScanExclusive(lengths_and_num_runs, lengths_and_num_runs2, scan_op, thread_exclusive_in_warp);
-
-            // Zip
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                lengths_and_offsets[ITEM].value         = lengths_and_num_runs2[ITEM].value;
-                lengths_and_offsets[ITEM].key        = tile_offset + (threadIdx.x * ITEMS_PER_THREAD) + ITEM;
-                thread_num_runs_exclusive_in_warp[ITEM] = (lengths_and_num_runs[ITEM].key) ?
-                                                                lengths_and_num_runs2[ITEM].key :         // keep
-                                                                WARP_THREADS * ITEMS_PER_THREAD;            // discard
-            }
-
-            OffsetT tile_num_runs_aggregate              = tile_aggregate.key;
-            OffsetT tile_num_runs_exclusive_in_global    = tile_exclusive_in_global.key;
-            OffsetT warp_num_runs_aggregate              = warp_aggregate.key;
-            OffsetT warp_num_runs_exclusive_in_tile      = warp_exclusive_in_tile.key;
-
-            // Scatter
-            Scatter<false>(
-                tile_num_runs_aggregate,
-                tile_num_runs_exclusive_in_global,
-                warp_num_runs_aggregate,
-                warp_num_runs_exclusive_in_tile,
-                thread_num_runs_exclusive_in_warp,
-                lengths_and_offsets);
-
-            // Return running total (inclusive of this tile)
-            return prefix_op.inclusive_prefix;
-        }
-    }
-
-
-    /**
-     * Scan tiles of items as part of a dynamic chained scan
-     */
-    template <typename NumRunsIteratorT>            ///< Output iterator type for recording number of items selected
-    __device__ __forceinline__ void ConsumeRange(
-        int                 num_tiles,              ///< Total number of input tiles
-        ScanTileStateT&     tile_status,            ///< Global list of tile status
-        NumRunsIteratorT    d_num_runs_out)         ///< Output pointer for total number of runs identified
-    {
-        // Blocks are launched in increasing order, so just assign one tile per block
-        int     tile_idx        = (blockIdx.x * gridDim.y) + blockIdx.y;    // Current tile index
-        OffsetT tile_offset     = tile_idx * TILE_ITEMS;                  // Global offset for the current tile
-        OffsetT num_remaining   = num_items - tile_offset;                  // Remaining items (including this tile)
-
-        if (tile_idx < num_tiles - 1)
-        {
-            // Not the last tile (full)
-            ConsumeTile<false>(num_items, num_remaining, tile_idx, tile_offset, tile_status);
-        }
-        else if (num_remaining > 0)
-        {
-            // The last tile (possibly partially-full)
-            LengthOffsetPair running_total = ConsumeTile<true>(num_items, num_remaining, tile_idx, tile_offset, tile_status);
-
-            if (threadIdx.x == 0)
-            {
-                // Output the total number of items selected
-                *d_num_runs_out = running_total.key;
-
-                // The inclusive prefix contains accumulated length reduction for the last run
-                if (running_total.key > 0)
-                    d_lengths_out[running_total.key - 1] = running_total.value;
-            }
-        }
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_scan.cuh b/ml-xgboost/cub/cub/agent/agent_scan.cuh
deleted file mode 100644
index ef80ccb..0000000
--- a/ml-xgboost/cub/cub/agent/agent_scan.cuh
+++ /dev/null
@@ -1,471 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentScan implements a stateful abstraction of CUDA thread blocks for participating in device-wide prefix scan .
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "single_pass_scan_operators.cuh"
-#include "../block/block_load.cuh"
-#include "../block/block_store.cuh"
-#include "../block/block_scan.cuh"
-#include "../grid/grid_queue.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentScan
- */
-template <
-    int                         _BLOCK_THREADS,                 ///< Threads per thread block
-    int                         _ITEMS_PER_THREAD,              ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm          _LOAD_ALGORITHM,                ///< The BlockLoad algorithm to use
-    CacheLoadModifier           _LOAD_MODIFIER,                 ///< Cache load modifier for reading input elements
-    BlockStoreAlgorithm         _STORE_ALGORITHM,               ///< The BlockStore algorithm to use
-    BlockScanAlgorithm          _SCAN_ALGORITHM>                ///< The BlockScan algorithm to use
-struct AgentScanPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;          ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;           ///< Cache load modifier for reading input elements
-    static const BlockStoreAlgorithm    STORE_ALGORITHM         = _STORE_ALGORITHM;         ///< The BlockStore algorithm to use
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;          ///< The BlockScan algorithm to use
-};
-
-
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentScan implements a stateful abstraction of CUDA thread blocks for participating in device-wide prefix scan .
- */
-template <
-    typename AgentScanPolicyT,      ///< Parameterized AgentScanPolicyT tuning policy type
-    typename InputIteratorT,        ///< Random-access input iterator type
-    typename OutputIteratorT,       ///< Random-access output iterator type
-    typename ScanOpT,               ///< Scan functor type
-    typename InitValueT,            ///< The init_value element for ScanOpT type (cub::NullType for inclusive scan)
-    typename OffsetT>               ///< Signed integer type for global offsets
-struct AgentScan
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // Tile status descriptor interface type
-    typedef ScanTileState<OutputT> ScanTileStateT;
-
-    // Input iterator wrapper type (for applying cache modifier)
-    typedef typename If<IsPointer<InputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentScanPolicyT::LOAD_MODIFIER, InputT, OffsetT>,   // Wrap the native input pointer with CacheModifiedInputIterator
-            InputIteratorT>::Type                                                           // Directly use the supplied input iterator type
-        WrappedInputIteratorT;
-
-    // Constants
-    enum
-    {
-        IS_INCLUSIVE        = Equals<InitValueT, NullType>::VALUE,            // Inclusive scan if no init_value type is provided
-        BLOCK_THREADS       = AgentScanPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = AgentScanPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    // Parameterized BlockLoad type
-    typedef BlockLoad<
-            OutputT,
-            AgentScanPolicyT::BLOCK_THREADS,
-            AgentScanPolicyT::ITEMS_PER_THREAD,
-            AgentScanPolicyT::LOAD_ALGORITHM>
-        BlockLoadT;
-
-    // Parameterized BlockStore type
-    typedef BlockStore<
-            OutputT,
-            AgentScanPolicyT::BLOCK_THREADS,
-            AgentScanPolicyT::ITEMS_PER_THREAD,
-            AgentScanPolicyT::STORE_ALGORITHM>
-        BlockStoreT;
-
-    // Parameterized BlockScan type
-    typedef BlockScan<
-            OutputT,
-            AgentScanPolicyT::BLOCK_THREADS,
-            AgentScanPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    // Callback type for obtaining tile prefix during block scan
-    typedef TilePrefixCallbackOp<
-            OutputT,
-            ScanOpT,
-            ScanTileStateT>
-        TilePrefixCallbackOpT;
-
-    // Stateful BlockScan prefix callback type for managing a running total while scanning consecutive tiles
-    typedef BlockScanRunningPrefixOp<
-            OutputT,
-            ScanOpT>
-        RunningPrefixCallbackOp;
-
-    // Shared memory type for this threadblock
-    union _TempStorage
-    {
-        typename BlockLoadT::TempStorage    load;       // Smem needed for tile loading
-        typename BlockStoreT::TempStorage   store;      // Smem needed for tile storing
-
-        struct
-        {
-            typename TilePrefixCallbackOpT::TempStorage  prefix;     // Smem needed for cooperative prefix callback
-            typename BlockScanT::TempStorage             scan;       // Smem needed for tile scanning
-        };
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage&               temp_storage;       ///< Reference to temp_storage
-    WrappedInputIteratorT       d_in;               ///< Input data
-    OutputIteratorT             d_out;              ///< Output data
-    ScanOpT                     scan_op;            ///< Binary scan operator
-    InitValueT                  init_value;         ///< The init_value element for ScanOpT
-
-
-    //---------------------------------------------------------------------
-    // Block scan utility methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Exclusive scan specialization (first tile)
-     */
-    __device__ __forceinline__
-    void ScanTile(
-        OutputT             (&items)[ITEMS_PER_THREAD],
-        OutputT             init_value,
-        ScanOpT             scan_op,
-        OutputT             &block_aggregate,
-        Int2Type<false>     /*is_inclusive*/)
-    {
-        BlockScanT(temp_storage.scan).ExclusiveScan(items, items, init_value, scan_op, block_aggregate);
-        block_aggregate = scan_op(init_value, block_aggregate);
-    }
-
-
-    /**
-     * Inclusive scan specialization (first tile)
-     */
-    __device__ __forceinline__
-    void ScanTile(
-        OutputT             (&items)[ITEMS_PER_THREAD],
-        InitValueT          /*init_value*/,
-        ScanOpT             scan_op,
-        OutputT             &block_aggregate,
-        Int2Type<true>      /*is_inclusive*/)
-    {
-        BlockScanT(temp_storage.scan).InclusiveScan(items, items, scan_op, block_aggregate);
-    }
-
-
-    /**
-     * Exclusive scan specialization (subsequent tiles)
-     */
-    template <typename PrefixCallback>
-    __device__ __forceinline__
-    void ScanTile(
-        OutputT             (&items)[ITEMS_PER_THREAD],
-        ScanOpT             scan_op,
-        PrefixCallback      &prefix_op,
-        Int2Type<false>     /*is_inclusive*/)
-    {
-        BlockScanT(temp_storage.scan).ExclusiveScan(items, items, scan_op, prefix_op);
-    }
-
-
-    /**
-     * Inclusive scan specialization (subsequent tiles)
-     */
-    template <typename PrefixCallback>
-    __device__ __forceinline__
-    void ScanTile(
-        OutputT             (&items)[ITEMS_PER_THREAD],
-        ScanOpT             scan_op,
-        PrefixCallback      &prefix_op,
-        Int2Type<true>      /*is_inclusive*/)
-    {
-        BlockScanT(temp_storage.scan).InclusiveScan(items, items, scan_op, prefix_op);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Constructor
-    //---------------------------------------------------------------------
-
-    // Constructor
-    __device__ __forceinline__
-    AgentScan(
-        TempStorage&    temp_storage,       ///< Reference to temp_storage
-        InputIteratorT  d_in,               ///< Input data
-        OutputIteratorT d_out,              ///< Output data
-        ScanOpT         scan_op,            ///< Binary scan operator
-        InitValueT      init_value)         ///< Initial value to seed the exclusive scan
-    :
-        temp_storage(temp_storage.Alias()),
-        d_in(d_in),
-        d_out(d_out),
-        scan_op(scan_op),
-        init_value(init_value)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Cooperatively scan a device-wide sequence of tiles with other CTAs
-    //---------------------------------------------------------------------
-
-    /**
-     * Process a tile of input (dynamic chained scan)
-     */
-    template <bool IS_LAST_TILE>                ///< Whether the current tile is the last tile
-    __device__ __forceinline__ void ConsumeTile(
-        OffsetT             num_remaining,      ///< Number of global input items remaining (including this tile)
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state)         ///< Global tile state descriptor
-    {
-        // Load items
-        OutputT items[ITEMS_PER_THREAD];
-
-        if (IS_LAST_TILE)
-            BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items, num_remaining);
-        else
-            BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items);
-
-        CTA_SYNC();
-
-        // Perform tile scan
-        if (tile_idx == 0)
-        {
-            // Scan first tile
-            OutputT block_aggregate;
-            ScanTile(items, init_value, scan_op, block_aggregate, Int2Type<IS_INCLUSIVE>());
-            if ((!IS_LAST_TILE) && (threadIdx.x == 0))
-                tile_state.SetInclusive(0, block_aggregate);
-        }
-        else
-        {
-            // Scan non-first tile
-            TilePrefixCallbackOpT prefix_op(tile_state, temp_storage.prefix, scan_op, tile_idx);
-            ScanTile(items, scan_op, prefix_op, Int2Type<IS_INCLUSIVE>());
-        }
-
-        CTA_SYNC();
-
-        // Store items
-        if (IS_LAST_TILE)
-            BlockStoreT(temp_storage.store).Store(d_out + tile_offset, items, num_remaining);
-        else
-            BlockStoreT(temp_storage.store).Store(d_out + tile_offset, items);
-    }
-
-
-    /**
-     * Scan tiles of items as part of a dynamic chained scan
-     */
-    __device__ __forceinline__ void ConsumeRange(
-        int                 num_items,          ///< Total number of input items
-        ScanTileStateT&     tile_state,         ///< Global tile state descriptor
-        int                 start_tile)         ///< The starting tile for the current grid
-    {
-        // Blocks are launched in increasing order, so just assign one tile per block
-        int     tile_idx        = start_tile + blockIdx.x;          // Current tile index
-        OffsetT tile_offset     = OffsetT(TILE_ITEMS) * tile_idx;   // Global offset for the current tile
-        OffsetT num_remaining   = num_items - tile_offset;          // Remaining items (including this tile)
-
-        if (num_remaining > TILE_ITEMS)
-        {
-            // Not last tile
-            ConsumeTile<false>(num_remaining, tile_idx, tile_offset, tile_state);
-        }
-        else if (num_remaining > 0)
-        {
-            // Last tile
-            ConsumeTile<true>(num_remaining, tile_idx, tile_offset, tile_state);
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Scan an sequence of consecutive tiles (independent of other thread blocks)
-    //---------------------------------------------------------------------
-
-    /**
-     * Process a tile of input
-     */
-    template <
-        bool                        IS_FIRST_TILE,
-        bool                        IS_LAST_TILE>
-    __device__ __forceinline__ void ConsumeTile(
-        OffsetT                     tile_offset,                ///< Tile offset
-        RunningPrefixCallbackOp&    prefix_op,                  ///< Running prefix operator
-        int                         valid_items = TILE_ITEMS)   ///< Number of valid items in the tile
-    {
-        // Load items
-        OutputT items[ITEMS_PER_THREAD];
-
-        if (IS_LAST_TILE)
-            BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items, valid_items);
-        else
-            BlockLoadT(temp_storage.load).Load(d_in + tile_offset, items);
-
-        CTA_SYNC();
-
-        // Block scan
-        if (IS_FIRST_TILE)
-        {
-            OutputT block_aggregate;
-            ScanTile(items, init_value, scan_op, block_aggregate, Int2Type<IS_INCLUSIVE>());
-            prefix_op.running_total = block_aggregate;
-        }
-        else
-        {
-            ScanTile(items, scan_op, prefix_op, Int2Type<IS_INCLUSIVE>());
-        }
-
-        CTA_SYNC();
-
-        // Store items
-        if (IS_LAST_TILE)
-            BlockStoreT(temp_storage.store).Store(d_out + tile_offset, items, valid_items);
-        else
-            BlockStoreT(temp_storage.store).Store(d_out + tile_offset, items);
-    }
-
-
-    /**
-     * Scan a consecutive share of input tiles
-     */
-    __device__ __forceinline__ void ConsumeRange(
-        OffsetT  range_offset,      ///< [in] Threadblock begin offset (inclusive)
-        OffsetT  range_end)         ///< [in] Threadblock end offset (exclusive)
-    {
-        BlockScanRunningPrefixOp<OutputT, ScanOpT> prefix_op(scan_op);
-
-        if (range_offset + TILE_ITEMS <= range_end)
-        {
-            // Consume first tile of input (full)
-            ConsumeTile<true, true>(range_offset, prefix_op);
-            range_offset += TILE_ITEMS;
-
-            // Consume subsequent full tiles of input
-            while (range_offset + TILE_ITEMS <= range_end)
-            {
-                ConsumeTile<false, true>(range_offset, prefix_op);
-                range_offset += TILE_ITEMS;
-            }
-
-            // Consume a partially-full tile
-            if (range_offset < range_end)
-            {
-                int valid_items = range_end - range_offset;
-                ConsumeTile<false, false>(range_offset, prefix_op, valid_items);
-            }
-        }
-        else
-        {
-            // Consume the first tile of input (partially-full)
-            int valid_items = range_end - range_offset;
-            ConsumeTile<true, false>(range_offset, prefix_op, valid_items);
-        }
-    }
-
-
-    /**
-     * Scan a consecutive share of input tiles, seeded with the specified prefix value
-     */
-    __device__ __forceinline__ void ConsumeRange(
-        OffsetT range_offset,                       ///< [in] Threadblock begin offset (inclusive)
-        OffsetT range_end,                          ///< [in] Threadblock end offset (exclusive)
-        OutputT prefix)                             ///< [in] The prefix to apply to the scan segment
-    {
-        BlockScanRunningPrefixOp<OutputT, ScanOpT> prefix_op(prefix, scan_op);
-
-        // Consume full tiles of input
-        while (range_offset + TILE_ITEMS <= range_end)
-        {
-            ConsumeTile<true, false>(range_offset, prefix_op);
-            range_offset += TILE_ITEMS;
-        }
-
-        // Consume a partially-full tile
-        if (range_offset < range_end)
-        {
-            int valid_items = range_end - range_offset;
-            ConsumeTile<false, false>(range_offset, prefix_op, valid_items);
-        }
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_segment_fixup.cuh b/ml-xgboost/cub/cub/agent/agent_segment_fixup.cuh
deleted file mode 100644
index b221cad..0000000
--- a/ml-xgboost/cub/cub/agent/agent_segment_fixup.cuh
+++ /dev/null
@@ -1,375 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentSegmentFixup implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "single_pass_scan_operators.cuh"
-#include "../block/block_load.cuh"
-#include "../block/block_store.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_discontinuity.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../iterator/constant_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentSegmentFixup
- */
-template <
-    int                         _BLOCK_THREADS,                 ///< Threads per thread block
-    int                         _ITEMS_PER_THREAD,              ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm          _LOAD_ALGORITHM,                ///< The BlockLoad algorithm to use
-    CacheLoadModifier           _LOAD_MODIFIER,                 ///< Cache load modifier for reading input elements
-    BlockScanAlgorithm          _SCAN_ALGORITHM>                ///< The BlockScan algorithm to use
-struct AgentSegmentFixupPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;      ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;       ///< Cache load modifier for reading input elements
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;      ///< The BlockScan algorithm to use
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-/**
- * \brief AgentSegmentFixup implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key
- */
-template <
-    typename    AgentSegmentFixupPolicyT,       ///< Parameterized AgentSegmentFixupPolicy tuning policy type
-    typename    PairsInputIteratorT,            ///< Random-access input iterator type for keys
-    typename    AggregatesOutputIteratorT,      ///< Random-access output iterator type for values
-    typename    EqualityOpT,                    ///< KeyT equality operator type
-    typename    ReductionOpT,                   ///< ValueT reduction operator type
-    typename    OffsetT>                        ///< Signed integer type for global offsets
-struct AgentSegmentFixup
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // Data type of key-value input iterator
-    typedef typename std::iterator_traits<PairsInputIteratorT>::value_type KeyValuePairT;
-
-    // Value type
-    typedef typename KeyValuePairT::Value ValueT;
-
-    // Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<ValueT, OffsetT> ScanTileStateT;
-
-    // Constants
-    enum
-    {
-        BLOCK_THREADS       = AgentSegmentFixupPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = AgentSegmentFixupPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,
-
-        // Whether or not do fixup using RLE + global atomics
-        USE_ATOMIC_FIXUP    = (CUB_PTX_ARCH >= 350) && 
-                                (Equals<ValueT, float>::VALUE || 
-                                 Equals<ValueT, int>::VALUE ||
-                                 Equals<ValueT, unsigned int>::VALUE ||
-                                 Equals<ValueT, unsigned long long>::VALUE),
-
-        // Whether or not the scan operation has a zero-valued identity value (true if we're performing addition on a primitive type)
-        HAS_IDENTITY_ZERO   = (Equals<ReductionOpT, cub::Sum>::VALUE) && (Traits<ValueT>::PRIMITIVE),
-    };
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for keys
-    typedef typename If<IsPointer<PairsInputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentSegmentFixupPolicyT::LOAD_MODIFIER, KeyValuePairT, OffsetT>,    // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            PairsInputIteratorT>::Type                                                                      // Directly use the supplied input iterator type
-        WrappedPairsInputIteratorT;
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for fixup values
-    typedef typename If<IsPointer<AggregatesOutputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentSegmentFixupPolicyT::LOAD_MODIFIER, ValueT, OffsetT>,    // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            AggregatesOutputIteratorT>::Type                                                        // Directly use the supplied input iterator type
-        WrappedFixupInputIteratorT;
-
-    // Reduce-value-by-segment scan operator
-    typedef ReduceByKeyOp<cub::Sum> ReduceBySegmentOpT;
-
-    // Parameterized BlockLoad type for pairs
-    typedef BlockLoad<
-            KeyValuePairT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            AgentSegmentFixupPolicyT::LOAD_ALGORITHM>
-        BlockLoadPairs;
-
-    // Parameterized BlockScan type
-    typedef BlockScan<
-            KeyValuePairT,
-            BLOCK_THREADS,
-            AgentSegmentFixupPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    // Callback type for obtaining tile prefix during block scan
-    typedef TilePrefixCallbackOp<
-            KeyValuePairT,
-            ReduceBySegmentOpT,
-            ScanTileStateT>
-        TilePrefixCallbackOpT;
-
-    // Shared memory type for this threadblock
-    union _TempStorage
-    {
-        struct
-        {
-            typename BlockScanT::TempStorage                scan;           // Smem needed for tile scanning
-            typename TilePrefixCallbackOpT::TempStorage     prefix;         // Smem needed for cooperative prefix callback
-        };
-
-        // Smem needed for loading keys
-        typename BlockLoadPairs::TempStorage load_pairs;
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage&                   temp_storage;       ///< Reference to temp_storage
-    WrappedPairsInputIteratorT      d_pairs_in;          ///< Input keys
-    AggregatesOutputIteratorT       d_aggregates_out;   ///< Output value aggregates
-    WrappedFixupInputIteratorT      d_fixup_in;         ///< Fixup input values
-    InequalityWrapper<EqualityOpT>  inequality_op;      ///< KeyT inequality operator
-    ReductionOpT                    reduction_op;       ///< Reduction operator
-    ReduceBySegmentOpT              scan_op;            ///< Reduce-by-segment scan operator
-
-
-    //---------------------------------------------------------------------
-    // Constructor
-    //---------------------------------------------------------------------
-
-    // Constructor
-    __device__ __forceinline__
-    AgentSegmentFixup(
-        TempStorage&                temp_storage,       ///< Reference to temp_storage
-        PairsInputIteratorT         d_pairs_in,          ///< Input keys
-        AggregatesOutputIteratorT   d_aggregates_out,   ///< Output value aggregates
-        EqualityOpT                 equality_op,        ///< KeyT equality operator
-        ReductionOpT                reduction_op)       ///< ValueT reduction operator
-    :
-        temp_storage(temp_storage.Alias()),
-        d_pairs_in(d_pairs_in),
-        d_aggregates_out(d_aggregates_out),
-        d_fixup_in(d_aggregates_out),
-        inequality_op(equality_op),
-        reduction_op(reduction_op),
-        scan_op(reduction_op)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Cooperatively scan a device-wide sequence of tiles with other CTAs
-    //---------------------------------------------------------------------
-
-
-    /**
-     * Process input tile.  Specialized for atomic-fixup
-     */
-    template <bool IS_LAST_TILE>
-    __device__ __forceinline__ void ConsumeTile(
-        OffsetT             num_remaining,      ///< Number of global input items remaining (including this tile)
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state,         ///< Global tile state descriptor
-        Int2Type<true>      use_atomic_fixup)   ///< Marker whether to use atomicAdd (instead of reduce-by-key)
-    {
-        KeyValuePairT   pairs[ITEMS_PER_THREAD];
-
-        // Load pairs
-        KeyValuePairT oob_pair;
-        oob_pair.key = -1;
-
-        if (IS_LAST_TILE)
-            BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs, num_remaining, oob_pair);
-        else
-            BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs);
-
-        // RLE 
-        #pragma unroll
-        for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            ValueT* d_scatter = d_aggregates_out + pairs[ITEM - 1].key;
-            if (pairs[ITEM].key != pairs[ITEM - 1].key)
-                atomicAdd(d_scatter, pairs[ITEM - 1].value);
-            else
-                pairs[ITEM].value = reduction_op(pairs[ITEM - 1].value, pairs[ITEM].value);
-        }
-
-        // Flush last item if valid
-        ValueT* d_scatter = d_aggregates_out + pairs[ITEMS_PER_THREAD - 1].key;
-        if ((!IS_LAST_TILE) || (pairs[ITEMS_PER_THREAD - 1].key >= 0))
-            atomicAdd(d_scatter, pairs[ITEMS_PER_THREAD - 1].value);
-    }
-
-
-    /**
-     * Process input tile.  Specialized for reduce-by-key fixup
-     */
-    template <bool IS_LAST_TILE>
-    __device__ __forceinline__ void ConsumeTile(
-        OffsetT             num_remaining,      ///< Number of global input items remaining (including this tile)
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state,         ///< Global tile state descriptor
-        Int2Type<false>     use_atomic_fixup)   ///< Marker whether to use atomicAdd (instead of reduce-by-key)
-    {
-        KeyValuePairT   pairs[ITEMS_PER_THREAD];
-        KeyValuePairT   scatter_pairs[ITEMS_PER_THREAD];
-
-        // Load pairs
-        KeyValuePairT oob_pair;
-        oob_pair.key = -1;
-
-        if (IS_LAST_TILE)
-            BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs, num_remaining, oob_pair);
-        else
-            BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs);
-
-        CTA_SYNC();
-
-        KeyValuePairT tile_aggregate;
-        if (tile_idx == 0)
-        {
-            // Exclusive scan of values and segment_flags
-            BlockScanT(temp_storage.scan).ExclusiveScan(pairs, scatter_pairs, scan_op, tile_aggregate);
-
-            // Update tile status if this is not the last tile
-            if (threadIdx.x == 0)
-            {
-                // Set first segment id to not trigger a flush (invalid from exclusive scan)
-                scatter_pairs[0].key = pairs[0].key;
-
-                if (!IS_LAST_TILE)
-                    tile_state.SetInclusive(0, tile_aggregate);
-
-            }
-        }
-        else
-        {
-            // Exclusive scan of values and segment_flags
-            TilePrefixCallbackOpT prefix_op(tile_state, temp_storage.prefix, scan_op, tile_idx);
-            BlockScanT(temp_storage.scan).ExclusiveScan(pairs, scatter_pairs, scan_op, prefix_op);
-            tile_aggregate = prefix_op.GetBlockAggregate();
-        }
-
-        // Scatter updated values
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (scatter_pairs[ITEM].key != pairs[ITEM].key)
-            {
-                // Update the value at the key location
-                ValueT value    = d_fixup_in[scatter_pairs[ITEM].key];
-                value           = reduction_op(value, scatter_pairs[ITEM].value);
-
-                d_aggregates_out[scatter_pairs[ITEM].key] = value;
-            }
-        }
-
-        // Finalize the last item
-        if (IS_LAST_TILE)
-        {
-            // Last thread will output final count and last item, if necessary
-            if (threadIdx.x == BLOCK_THREADS - 1)
-            {
-                // If the last tile is a whole tile, the inclusive prefix contains accumulated value reduction for the last segment
-                if (num_remaining == TILE_ITEMS)
-                {
-                    // Update the value at the key location
-                    OffsetT last_key = pairs[ITEMS_PER_THREAD - 1].key;
-                    d_aggregates_out[last_key] = reduction_op(tile_aggregate.value, d_fixup_in[last_key]);
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Scan tiles of items as part of a dynamic chained scan
-     */
-    __device__ __forceinline__ void ConsumeRange(
-        int                 num_items,          ///< Total number of input items
-        int                 num_tiles,          ///< Total number of input tiles
-        ScanTileStateT&     tile_state)         ///< Global tile state descriptor
-    {
-        // Blocks are launched in increasing order, so just assign one tile per block
-        int     tile_idx        = (blockIdx.x * gridDim.y) + blockIdx.y;    // Current tile index
-        OffsetT tile_offset     = tile_idx * TILE_ITEMS;                    // Global offset for the current tile
-        OffsetT num_remaining   = num_items - tile_offset;                  // Remaining items (including this tile)
-
-        if (num_remaining > TILE_ITEMS)
-        {
-            // Not the last tile (full)
-            ConsumeTile<false>(num_remaining, tile_idx, tile_offset, tile_state, Int2Type<USE_ATOMIC_FIXUP>());
-        }
-        else if (num_remaining > 0)
-        {
-            // The last tile (possibly partially-full)
-            ConsumeTile<true>(num_remaining, tile_idx, tile_offset, tile_state, Int2Type<USE_ATOMIC_FIXUP>());
-        }
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_select_if.cuh b/ml-xgboost/cub/cub/agent/agent_select_if.cuh
deleted file mode 100644
index a4e37c3..0000000
--- a/ml-xgboost/cub/cub/agent/agent_select_if.cuh
+++ /dev/null
@@ -1,703 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentSelectIf implements a stateful abstraction of CUDA thread blocks for participating in device-wide select.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "single_pass_scan_operators.cuh"
-#include "../block/block_load.cuh"
-#include "../block/block_store.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_exchange.cuh"
-#include "../block/block_discontinuity.cuh"
-#include "../grid/grid_queue.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentSelectIf
- */
-template <
-    int                         _BLOCK_THREADS,                 ///< Threads per thread block
-    int                         _ITEMS_PER_THREAD,              ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm          _LOAD_ALGORITHM,                ///< The BlockLoad algorithm to use
-    CacheLoadModifier           _LOAD_MODIFIER,                 ///< Cache load modifier for reading input elements
-    BlockScanAlgorithm          _SCAN_ALGORITHM>                ///< The BlockScan algorithm to use
-struct AgentSelectIfPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;      ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;       ///< Cache load modifier for reading input elements
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;      ///< The BlockScan algorithm to use
-};
-
-
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-
-/**
- * \brief AgentSelectIf implements a stateful abstraction of CUDA thread blocks for participating in device-wide selection
- *
- * Performs functor-based selection if SelectOpT functor type != NullType
- * Otherwise performs flag-based selection if FlagsInputIterator's value type != NullType
- * Otherwise performs discontinuity selection (keep unique)
- */
-template <
-    typename    AgentSelectIfPolicyT,           ///< Parameterized AgentSelectIfPolicy tuning policy type
-    typename    InputIteratorT,                 ///< Random-access input iterator type for selection items
-    typename    FlagsInputIteratorT,            ///< Random-access input iterator type for selections (NullType* if a selection functor or discontinuity flagging is to be used for selection)
-    typename    SelectedOutputIteratorT,        ///< Random-access input iterator type for selection_flags items
-    typename    SelectOpT,                      ///< Selection operator type (NullType if selections or discontinuity flagging is to be used for selection)
-    typename    EqualityOpT,                    ///< Equality operator type (NullType if selection functor or selections is to be used for selection)
-    typename    OffsetT,                        ///< Signed integer type for global offsets
-    bool        KEEP_REJECTS>                   ///< Whether or not we push rejected items to the back of the output
-struct AgentSelectIf
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<SelectedOutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                                  // ... then the input iterator's value type,
-        typename std::iterator_traits<SelectedOutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // The flag value type
-    typedef typename std::iterator_traits<FlagsInputIteratorT>::value_type FlagT;
-
-    // Tile status descriptor interface type
-    typedef ScanTileState<OffsetT> ScanTileStateT;
-
-    // Constants
-    enum
-    {
-        USE_SELECT_OP,
-        USE_SELECT_FLAGS,
-        USE_DISCONTINUITY,
-
-        BLOCK_THREADS           = AgentSelectIfPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = AgentSelectIfPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-        TWO_PHASE_SCATTER       = (ITEMS_PER_THREAD > 1),
-
-        SELECT_METHOD           = (!Equals<SelectOpT, NullType>::VALUE) ?
-                                    USE_SELECT_OP :
-                                    (!Equals<FlagT, NullType>::VALUE) ?
-                                        USE_SELECT_FLAGS :
-                                        USE_DISCONTINUITY
-    };
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for items
-    typedef typename If<IsPointer<InputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentSelectIfPolicyT::LOAD_MODIFIER, InputT, OffsetT>,        // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            InputIteratorT>::Type                                                               // Directly use the supplied input iterator type
-        WrappedInputIteratorT;
-
-    // Cache-modified Input iterator wrapper type (for applying cache modifier) for values
-    typedef typename If<IsPointer<FlagsInputIteratorT>::VALUE,
-            CacheModifiedInputIterator<AgentSelectIfPolicyT::LOAD_MODIFIER, FlagT, OffsetT>,    // Wrap the native input pointer with CacheModifiedValuesInputIterator
-            FlagsInputIteratorT>::Type                                                          // Directly use the supplied input iterator type
-        WrappedFlagsInputIteratorT;
-
-    // Parameterized BlockLoad type for input data
-    typedef BlockLoad<
-            OutputT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            AgentSelectIfPolicyT::LOAD_ALGORITHM>
-        BlockLoadT;
-
-    // Parameterized BlockLoad type for flags
-    typedef BlockLoad<
-            FlagT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            AgentSelectIfPolicyT::LOAD_ALGORITHM>
-        BlockLoadFlags;
-
-    // Parameterized BlockDiscontinuity type for items
-    typedef BlockDiscontinuity<
-            OutputT,
-            BLOCK_THREADS>
-        BlockDiscontinuityT;
-
-    // Parameterized BlockScan type
-    typedef BlockScan<
-            OffsetT,
-            BLOCK_THREADS,
-            AgentSelectIfPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    // Callback type for obtaining tile prefix during block scan
-    typedef TilePrefixCallbackOp<
-            OffsetT,
-            cub::Sum,
-            ScanTileStateT>
-        TilePrefixCallbackOpT;
-
-    // Item exchange type
-    typedef OutputT ItemExchangeT[TILE_ITEMS];
-
-    // Shared memory type for this threadblock
-    union _TempStorage
-    {
-        struct
-        {
-            typename BlockScanT::TempStorage                scan;           // Smem needed for tile scanning
-            typename TilePrefixCallbackOpT::TempStorage     prefix;         // Smem needed for cooperative prefix callback
-            typename BlockDiscontinuityT::TempStorage       discontinuity;  // Smem needed for discontinuity detection
-        };
-
-        // Smem needed for loading items
-        typename BlockLoadT::TempStorage load_items;
-
-        // Smem needed for loading values
-        typename BlockLoadFlags::TempStorage load_flags;
-
-        // Smem needed for compacting items (allows non POD items in this union)
-        Uninitialized<ItemExchangeT> raw_exchange;
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage&                   temp_storage;       ///< Reference to temp_storage
-    WrappedInputIteratorT           d_in;               ///< Input items
-    SelectedOutputIteratorT         d_selected_out;     ///< Unique output items
-    WrappedFlagsInputIteratorT      d_flags_in;         ///< Input selection flags (if applicable)
-    InequalityWrapper<EqualityOpT>  inequality_op;      ///< T inequality operator
-    SelectOpT                       select_op;          ///< Selection operator
-    OffsetT                         num_items;          ///< Total number of input items
-
-
-    //---------------------------------------------------------------------
-    // Constructor
-    //---------------------------------------------------------------------
-
-    // Constructor
-    __device__ __forceinline__
-    AgentSelectIf(
-        TempStorage                 &temp_storage,      ///< Reference to temp_storage
-        InputIteratorT              d_in,               ///< Input data
-        FlagsInputIteratorT         d_flags_in,         ///< Input selection flags (if applicable)
-        SelectedOutputIteratorT     d_selected_out,     ///< Output data
-        SelectOpT                   select_op,          ///< Selection operator
-        EqualityOpT                 equality_op,        ///< Equality operator
-        OffsetT                     num_items)          ///< Total number of input items
-    :
-        temp_storage(temp_storage.Alias()),
-        d_in(d_in),
-        d_flags_in(d_flags_in),
-        d_selected_out(d_selected_out),
-        select_op(select_op),
-        inequality_op(equality_op),
-        num_items(num_items)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Utility methods for initializing the selections
-    //---------------------------------------------------------------------
-
-    /**
-     * Initialize selections (specialized for selection operator)
-     */
-    template <bool IS_FIRST_TILE, bool IS_LAST_TILE>
-    __device__ __forceinline__ void InitializeSelections(
-        OffsetT                     /*tile_offset*/,
-        OffsetT                     num_tile_items,
-        OutputT                     (&items)[ITEMS_PER_THREAD],
-        OffsetT                     (&selection_flags)[ITEMS_PER_THREAD],
-        Int2Type<USE_SELECT_OP>     /*select_method*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            // Out-of-bounds items are selection_flags
-            selection_flags[ITEM] = 1;
-
-            if (!IS_LAST_TILE || (OffsetT(threadIdx.x * ITEMS_PER_THREAD) + ITEM < num_tile_items))
-                selection_flags[ITEM] = select_op(items[ITEM]);
-        }
-    }
-
-
-    /**
-     * Initialize selections (specialized for valid flags)
-     */
-    template <bool IS_FIRST_TILE, bool IS_LAST_TILE>
-    __device__ __forceinline__ void InitializeSelections(
-        OffsetT                     tile_offset,
-        OffsetT                     num_tile_items,
-        OutputT                     (&/*items*/)[ITEMS_PER_THREAD],
-        OffsetT                     (&selection_flags)[ITEMS_PER_THREAD],
-        Int2Type<USE_SELECT_FLAGS>  /*select_method*/)
-    {
-        CTA_SYNC();
-
-        FlagT flags[ITEMS_PER_THREAD];
-
-        if (IS_LAST_TILE)
-        {
-            // Out-of-bounds items are selection_flags
-            BlockLoadFlags(temp_storage.load_flags).Load(d_flags_in + tile_offset, flags, num_tile_items, 1);
-        }
-        else
-        {
-            BlockLoadFlags(temp_storage.load_flags).Load(d_flags_in + tile_offset, flags);
-        }
-
-        // Convert flag type to selection_flags type
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            selection_flags[ITEM] = flags[ITEM];
-        }
-    }
-
-
-    /**
-     * Initialize selections (specialized for discontinuity detection)
-     */
-    template <bool IS_FIRST_TILE, bool IS_LAST_TILE>
-    __device__ __forceinline__ void InitializeSelections(
-        OffsetT                     tile_offset,
-        OffsetT                     num_tile_items,
-        OutputT                     (&items)[ITEMS_PER_THREAD],
-        OffsetT                     (&selection_flags)[ITEMS_PER_THREAD],
-        Int2Type<USE_DISCONTINUITY> /*select_method*/)
-    {
-        if (IS_FIRST_TILE)
-        {
-            CTA_SYNC();
-
-            // Set head selection_flags.  First tile sets the first flag for the first item
-            BlockDiscontinuityT(temp_storage.discontinuity).FlagHeads(selection_flags, items, inequality_op);
-        }
-        else
-        {
-            OutputT tile_predecessor;
-            if (threadIdx.x == 0)
-                tile_predecessor = d_in[tile_offset - 1];
-
-            CTA_SYNC();
-
-            BlockDiscontinuityT(temp_storage.discontinuity).FlagHeads(selection_flags, items, inequality_op, tile_predecessor);
-        }
-
-        // Set selection flags for out-of-bounds items
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            // Set selection_flags for out-of-bounds items
-            if ((IS_LAST_TILE) && (OffsetT(threadIdx.x * ITEMS_PER_THREAD) + ITEM >= num_tile_items))
-                selection_flags[ITEM] = 1;
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Scatter utility methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Scatter flagged items to output offsets (specialized for direct scattering)
-     */
-    template <bool IS_LAST_TILE, bool IS_FIRST_TILE>
-    __device__ __forceinline__ void ScatterDirect(
-        OutputT (&items)[ITEMS_PER_THREAD],
-        OffsetT (&selection_flags)[ITEMS_PER_THREAD],
-        OffsetT (&selection_indices)[ITEMS_PER_THREAD],
-        OffsetT num_selections)
-    {
-        // Scatter flagged items
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (selection_flags[ITEM])
-            {
-                if ((!IS_LAST_TILE) || selection_indices[ITEM] < num_selections)
-                {
-                    d_selected_out[selection_indices[ITEM]] = items[ITEM];
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Scatter flagged items to output offsets (specialized for two-phase scattering)
-     */
-    template <bool IS_LAST_TILE, bool IS_FIRST_TILE>
-    __device__ __forceinline__ void ScatterTwoPhase(
-        OutputT         (&items)[ITEMS_PER_THREAD],
-        OffsetT         (&selection_flags)[ITEMS_PER_THREAD],
-        OffsetT         (&selection_indices)[ITEMS_PER_THREAD],
-        int             /*num_tile_items*/,                         ///< Number of valid items in this tile
-        int             num_tile_selections,                        ///< Number of selections in this tile
-        OffsetT         num_selections_prefix,                      ///< Total number of selections prior to this tile
-        OffsetT         /*num_rejected_prefix*/,                    ///< Total number of rejections prior to this tile
-        Int2Type<false> /*is_keep_rejects*/)                        ///< Marker type indicating whether to keep rejected items in the second partition
-    {
-        CTA_SYNC();
-
-        // Compact and scatter items
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int local_scatter_offset = selection_indices[ITEM] - num_selections_prefix;
-            if (selection_flags[ITEM])
-            {
-                temp_storage.raw_exchange.Alias()[local_scatter_offset] = items[ITEM];
-            }
-        }
-
-        CTA_SYNC();
-
-        for (int item = threadIdx.x; item < num_tile_selections; item += BLOCK_THREADS)
-        {
-            d_selected_out[num_selections_prefix + item] = temp_storage.raw_exchange.Alias()[item];
-        }
-    }
-
-
-    /**
-     * Scatter flagged items to output offsets (specialized for two-phase scattering)
-     */
-    template <bool IS_LAST_TILE, bool IS_FIRST_TILE>
-    __device__ __forceinline__ void ScatterTwoPhase(
-        OutputT         (&items)[ITEMS_PER_THREAD],
-        OffsetT         (&selection_flags)[ITEMS_PER_THREAD],
-        OffsetT         (&selection_indices)[ITEMS_PER_THREAD],
-        int             num_tile_items,                             ///< Number of valid items in this tile
-        int             num_tile_selections,                        ///< Number of selections in this tile
-        OffsetT         num_selections_prefix,                      ///< Total number of selections prior to this tile
-        OffsetT         num_rejected_prefix,                        ///< Total number of rejections prior to this tile
-        Int2Type<true>  /*is_keep_rejects*/)                        ///< Marker type indicating whether to keep rejected items in the second partition
-    {
-        CTA_SYNC();
-
-        int tile_num_rejections = num_tile_items - num_tile_selections;
-
-        // Scatter items to shared memory (rejections first)
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int item_idx                = (threadIdx.x * ITEMS_PER_THREAD) + ITEM;
-            int local_selection_idx     = selection_indices[ITEM] - num_selections_prefix;
-            int local_rejection_idx     = item_idx - local_selection_idx;
-            int local_scatter_offset    = (selection_flags[ITEM]) ?
-                                            tile_num_rejections + local_selection_idx :
-                                            local_rejection_idx;
-
-            temp_storage.raw_exchange.Alias()[local_scatter_offset] = items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        // Gather items from shared memory and scatter to global
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int item_idx            = (ITEM * BLOCK_THREADS) + threadIdx.x;
-            int rejection_idx       = item_idx;
-            int selection_idx       = item_idx - tile_num_rejections;
-            OffsetT scatter_offset  = (item_idx < tile_num_rejections) ?
-                                        num_items - num_rejected_prefix - rejection_idx - 1 :
-                                        num_selections_prefix + selection_idx;
-
-            OutputT item = temp_storage.raw_exchange.Alias()[item_idx];
-
-            if (!IS_LAST_TILE || (item_idx < num_tile_items))
-            {
-                d_selected_out[scatter_offset] = item;
-            }
-        }
-    }
-
-
-    /**
-     * Scatter flagged items
-     */
-    template <bool IS_LAST_TILE, bool IS_FIRST_TILE>
-    __device__ __forceinline__ void Scatter(
-        OutputT         (&items)[ITEMS_PER_THREAD],
-        OffsetT         (&selection_flags)[ITEMS_PER_THREAD],
-        OffsetT         (&selection_indices)[ITEMS_PER_THREAD],
-        int             num_tile_items,                             ///< Number of valid items in this tile
-        int             num_tile_selections,                        ///< Number of selections in this tile
-        OffsetT         num_selections_prefix,                      ///< Total number of selections prior to this tile
-        OffsetT         num_rejected_prefix,                        ///< Total number of rejections prior to this tile
-        OffsetT         num_selections)                             ///< Total number of selections including this tile
-    {
-        // Do a two-phase scatter if (a) keeping both partitions or (b) two-phase is enabled and the average number of selection_flags items per thread is greater than one
-        if (KEEP_REJECTS || (TWO_PHASE_SCATTER && (num_tile_selections > BLOCK_THREADS)))
-        {
-            ScatterTwoPhase<IS_LAST_TILE, IS_FIRST_TILE>(
-                items,
-                selection_flags,
-                selection_indices,
-                num_tile_items,
-                num_tile_selections,
-                num_selections_prefix,
-                num_rejected_prefix,
-                Int2Type<KEEP_REJECTS>());
-        }
-        else
-        {
-            ScatterDirect<IS_LAST_TILE, IS_FIRST_TILE>(
-                items,
-                selection_flags,
-                selection_indices,
-                num_selections);
-        }
-    }
-
-    //---------------------------------------------------------------------
-    // Cooperatively scan a device-wide sequence of tiles with other CTAs
-    //---------------------------------------------------------------------
-
-
-    /**
-     * Process first tile of input (dynamic chained scan).  Returns the running count of selections (including this tile)
-     */
-    template <bool IS_LAST_TILE>
-    __device__ __forceinline__ OffsetT ConsumeFirstTile(
-        int                 num_tile_items,      ///< Number of input items comprising this tile
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state)         ///< Global tile state descriptor
-    {
-        OutputT     items[ITEMS_PER_THREAD];
-        OffsetT     selection_flags[ITEMS_PER_THREAD];
-        OffsetT     selection_indices[ITEMS_PER_THREAD];
-
-        // Load items
-        if (IS_LAST_TILE)
-            BlockLoadT(temp_storage.load_items).Load(d_in + tile_offset, items, num_tile_items);
-        else
-            BlockLoadT(temp_storage.load_items).Load(d_in + tile_offset, items);
-
-        // Initialize selection_flags
-        InitializeSelections<true, IS_LAST_TILE>(
-            tile_offset,
-            num_tile_items,
-            items,
-            selection_flags,
-            Int2Type<SELECT_METHOD>());
-
-        CTA_SYNC();
-
-        // Exclusive scan of selection_flags
-        OffsetT num_tile_selections;
-        BlockScanT(temp_storage.scan).ExclusiveSum(selection_flags, selection_indices, num_tile_selections);
-
-        if (threadIdx.x == 0)
-        {
-            // Update tile status if this is not the last tile
-            if (!IS_LAST_TILE)
-                tile_state.SetInclusive(0, num_tile_selections);
-        }
-
-        // Discount any out-of-bounds selections
-        if (IS_LAST_TILE)
-            num_tile_selections -= (TILE_ITEMS - num_tile_items);
-
-        // Scatter flagged items
-        Scatter<IS_LAST_TILE, true>(
-            items,
-            selection_flags,
-            selection_indices,
-            num_tile_items,
-            num_tile_selections,
-            0,
-            0,
-            num_tile_selections);
-
-        return num_tile_selections;
-    }
-
-
-    /**
-     * Process subsequent tile of input (dynamic chained scan).  Returns the running count of selections (including this tile)
-     */
-    template <bool IS_LAST_TILE>
-    __device__ __forceinline__ OffsetT ConsumeSubsequentTile(
-        int                 num_tile_items,      ///< Number of input items comprising this tile
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state)         ///< Global tile state descriptor
-    {
-        OutputT     items[ITEMS_PER_THREAD];
-        OffsetT     selection_flags[ITEMS_PER_THREAD];
-        OffsetT     selection_indices[ITEMS_PER_THREAD];
-
-        // Load items
-        if (IS_LAST_TILE)
-            BlockLoadT(temp_storage.load_items).Load(d_in + tile_offset, items, num_tile_items);
-        else
-            BlockLoadT(temp_storage.load_items).Load(d_in + tile_offset, items);
-
-        // Initialize selection_flags
-        InitializeSelections<false, IS_LAST_TILE>(
-            tile_offset,
-            num_tile_items,
-            items,
-            selection_flags,
-            Int2Type<SELECT_METHOD>());
-
-        CTA_SYNC();
-
-        // Exclusive scan of values and selection_flags
-        TilePrefixCallbackOpT prefix_op(tile_state, temp_storage.prefix, cub::Sum(), tile_idx);
-        BlockScanT(temp_storage.scan).ExclusiveSum(selection_flags, selection_indices, prefix_op);
-
-        OffsetT num_tile_selections     = prefix_op.GetBlockAggregate();
-        OffsetT num_selections          = prefix_op.GetInclusivePrefix();
-        OffsetT num_selections_prefix   = prefix_op.GetExclusivePrefix();
-        OffsetT num_rejected_prefix     = (tile_idx * TILE_ITEMS) - num_selections_prefix;
-
-        // Discount any out-of-bounds selections
-        if (IS_LAST_TILE)
-        {
-            int num_discount    = TILE_ITEMS - num_tile_items;
-            num_selections      -= num_discount;
-            num_tile_selections -= num_discount;
-        }
-
-        // Scatter flagged items
-        Scatter<IS_LAST_TILE, false>(
-            items,
-            selection_flags,
-            selection_indices,
-            num_tile_items,
-            num_tile_selections,
-            num_selections_prefix,
-            num_rejected_prefix,
-            num_selections);
-
-        return num_selections;
-    }
-
-
-    /**
-     * Process a tile of input
-     */
-    template <bool IS_LAST_TILE>
-    __device__ __forceinline__ OffsetT ConsumeTile(
-        int                 num_tile_items,         ///< Number of input items comprising this tile
-        int                 tile_idx,           ///< Tile index
-        OffsetT             tile_offset,        ///< Tile offset
-        ScanTileStateT&     tile_state)         ///< Global tile state descriptor
-    {
-        OffsetT num_selections;
-        if (tile_idx == 0)
-        {
-            num_selections = ConsumeFirstTile<IS_LAST_TILE>(num_tile_items, tile_offset, tile_state);
-        }
-        else
-        {
-            num_selections = ConsumeSubsequentTile<IS_LAST_TILE>(num_tile_items, tile_idx, tile_offset, tile_state);
-        }
-
-        return num_selections;
-    }
-
-
-    /**
-     * Scan tiles of items as part of a dynamic chained scan
-     */
-    template <typename NumSelectedIteratorT>        ///< Output iterator type for recording number of items selection_flags
-    __device__ __forceinline__ void ConsumeRange(
-        int                     num_tiles,          ///< Total number of input tiles
-        ScanTileStateT&         tile_state,         ///< Global tile state descriptor
-        NumSelectedIteratorT    d_num_selected_out) ///< Output total number selection_flags
-    {
-        // Blocks are launched in increasing order, so just assign one tile per block
-        int     tile_idx        = (blockIdx.x * gridDim.y) + blockIdx.y;    // Current tile index
-        OffsetT tile_offset     = tile_idx * TILE_ITEMS;                    // Global offset for the current tile
-
-        if (tile_idx < num_tiles - 1)
-        {
-            // Not the last tile (full)
-            ConsumeTile<false>(TILE_ITEMS, tile_idx, tile_offset, tile_state);
-        }
-        else
-        {
-            // The last tile (possibly partially-full)
-            OffsetT num_remaining   = num_items - tile_offset;
-            OffsetT num_selections  = ConsumeTile<true>(num_remaining, tile_idx, tile_offset, tile_state);
-
-            if (threadIdx.x == 0)
-            {
-                // Output the total number of items selection_flags
-                *d_num_selected_out = num_selections;
-            }
-        }
-    }
-
-};
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_spmv_csrt.cuh b/ml-xgboost/cub/cub/agent/agent_spmv_csrt.cuh
deleted file mode 100644
index 29ab6ea..0000000
--- a/ml-xgboost/cub/cub/agent/agent_spmv_csrt.cuh
+++ /dev/null
@@ -1,638 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentSpmv implements a stateful abstraction of CUDA thread blocks for participating in device-wide SpMV.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "../util_type.cuh"
-#include "../block/block_reduce.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_exchange.cuh"
-#include "../thread/thread_search.cuh"
-#include "../thread/thread_operators.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../iterator/counting_input_iterator.cuh"
-#include "../iterator/tex_ref_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentSpmv
- */
-template <
-    int                             _BLOCK_THREADS,                         ///< Threads per thread block
-    int                             _ITEMS_PER_THREAD,                      ///< Items per thread (per tile of input)
-    CacheLoadModifier               _ROW_OFFSETS_SEARCH_LOAD_MODIFIER,      ///< Cache load modifier for reading CSR row-offsets during search
-    CacheLoadModifier               _ROW_OFFSETS_LOAD_MODIFIER,             ///< Cache load modifier for reading CSR row-offsets
-    CacheLoadModifier               _COLUMN_INDICES_LOAD_MODIFIER,          ///< Cache load modifier for reading CSR column-indices
-    CacheLoadModifier               _VALUES_LOAD_MODIFIER,                  ///< Cache load modifier for reading CSR values
-    CacheLoadModifier               _VECTOR_VALUES_LOAD_MODIFIER,           ///< Cache load modifier for reading vector values
-    bool                            _DIRECT_LOAD_NONZEROS,                  ///< Whether to load nonzeros directly from global during sequential merging (vs. pre-staged through shared memory)
-    BlockScanAlgorithm              _SCAN_ALGORITHM>                        ///< The BlockScan algorithm to use
-struct AgentSpmvPolicy
-{
-    enum
-    {
-        BLOCK_THREADS                                                   = _BLOCK_THREADS,                       ///< Threads per thread block
-        ITEMS_PER_THREAD                                                = _ITEMS_PER_THREAD,                    ///< Items per thread (per tile of input)
-        DIRECT_LOAD_NONZEROS                                            = _DIRECT_LOAD_NONZEROS,                ///< Whether to load nonzeros directly from global during sequential merging (pre-staged through shared memory)
-    };
-
-    static const CacheLoadModifier  ROW_OFFSETS_SEARCH_LOAD_MODIFIER    = _ROW_OFFSETS_SEARCH_LOAD_MODIFIER;    ///< Cache load modifier for reading CSR row-offsets
-    static const CacheLoadModifier  ROW_OFFSETS_LOAD_MODIFIER           = _ROW_OFFSETS_LOAD_MODIFIER;           ///< Cache load modifier for reading CSR row-offsets
-    static const CacheLoadModifier  COLUMN_INDICES_LOAD_MODIFIER        = _COLUMN_INDICES_LOAD_MODIFIER;        ///< Cache load modifier for reading CSR column-indices
-    static const CacheLoadModifier  VALUES_LOAD_MODIFIER                = _VALUES_LOAD_MODIFIER;                ///< Cache load modifier for reading CSR values
-    static const CacheLoadModifier  VECTOR_VALUES_LOAD_MODIFIER         = _VECTOR_VALUES_LOAD_MODIFIER;         ///< Cache load modifier for reading vector values
-    static const BlockScanAlgorithm SCAN_ALGORITHM                      = _SCAN_ALGORITHM;                      ///< The BlockScan algorithm to use
-
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-template <
-    typename        ValueT,              ///< Matrix and vector value type
-    typename        OffsetT>             ///< Signed integer type for sequence offsets
-struct SpmvParams
-{
-    ValueT*         d_values;            ///< Pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-    OffsetT*        d_row_end_offsets;   ///< Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values
-    OffsetT*        d_column_indices;    ///< Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-    ValueT*         d_vector_x;          ///< Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-    ValueT*         d_vector_y;          ///< Pointer to the array of \p num_rows values corresponding to the dense output vector <em>y</em>
-    int             num_rows;            ///< Number of rows of matrix <b>A</b>.
-    int             num_cols;            ///< Number of columns of matrix <b>A</b>.
-    int             num_nonzeros;        ///< Number of nonzero elements of matrix <b>A</b>.
-    ValueT          alpha;               ///< Alpha multiplicand
-    ValueT          beta;                ///< Beta addend-multiplicand
-
-    TexRefInputIterator<ValueT, 66778899, OffsetT>  t_vector_x;
-};
-
-
-/**
- * \brief AgentSpmv implements a stateful abstraction of CUDA thread blocks for participating in device-wide SpMV.
- */
-template <
-    typename    AgentSpmvPolicyT,           ///< Parameterized AgentSpmvPolicy tuning policy type
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT,                    ///< Signed integer type for sequence offsets
-    bool        HAS_ALPHA,                  ///< Whether the input parameter \p alpha is 1
-    bool        HAS_BETA,                   ///< Whether the input parameter \p beta is 0
-    int         PTX_ARCH = CUB_PTX_ARCH>    ///< PTX compute capability
-struct AgentSpmv
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS           = AgentSpmvPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = AgentSpmvPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    /// 2D merge path coordinate type
-    typedef typename CubVector<OffsetT, 2>::Type CoordinateT;
-
-    /// Input iterator wrapper types (for applying cache modifiers)
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::ROW_OFFSETS_SEARCH_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsSearchIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::ROW_OFFSETS_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::COLUMN_INDICES_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        ColumnIndicesIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        ValueIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VECTOR_VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        VectorValueIteratorT;
-
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef KeyValuePair<OffsetT, ValueT> KeyValuePairT;
-
-    // Reduce-value-by-key scan operator
-    typedef ReduceByKeyOp<cub::Sum> ReduceBySegmentOpT;
-
-    // BlockReduce specialization
-    typedef BlockReduce<
-            ValueT,
-            BLOCK_THREADS,
-            BLOCK_REDUCE_WARP_REDUCTIONS>
-        BlockReduceT;
-
-    // BlockScan specialization
-    typedef BlockScan<
-            KeyValuePairT,
-            BLOCK_THREADS,
-            AgentSpmvPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    /// Merge item type (either a non-zero value or a row-end offset)
-    union MergeItem
-    {
-        // Value type to pair with index type OffsetT (NullType if loading values directly during merge)
-        typedef typename If<AgentSpmvPolicyT::DIRECT_LOAD_NONZEROS, NullType, ValueT>::Type MergeValueT;
-
-        OffsetT     row_end_offset;
-        MergeValueT nonzero;
-    };
-
-    /// Shared memory type required by this thread block
-    struct _TempStorage
-    {
-        union {
-            CoordinateT tile_coord;
-            OffsetT turnstile;
-        };
-
-        union
-        {
-            // Smem needed for tile of merge items
-            MergeItem merge_items[ITEMS_PER_THREAD + TILE_ITEMS + 1];
-
-            // Smem needed for block-wide reduction
-            typename BlockReduceT::TempStorage reduce;
-
-            // Smem needed for tile scanning
-            typename BlockScanT::TempStorage scan;
-        };
-    };
-
-    /// Temporary storage type (unionable)
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-
-    _TempStorage&                   temp_storage;         /// Reference to temp_storage
-
-    SpmvParams<ValueT, OffsetT>&    spmv_params;
-
-    ValueIteratorT                  wd_values;            ///< Wrapped pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-    RowOffsetsIteratorT             wd_row_end_offsets;   ///< Wrapped Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values
-    ColumnIndicesIteratorT          wd_column_indices;    ///< Wrapped Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-    VectorValueIteratorT            wd_vector_x;          ///< Wrapped Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-    VectorValueIteratorT            wd_vector_y;          ///< Wrapped Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-
-
-    //---------------------------------------------------------------------
-    // Interface
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentSpmv(
-        TempStorage&                    temp_storage,           ///< Reference to temp_storage
-        SpmvParams<ValueT, OffsetT>&    spmv_params)            ///< SpMV input parameter bundle
-    :
-        temp_storage(temp_storage.Alias()),
-        spmv_params(spmv_params),
-        wd_values(spmv_params.d_values),
-        wd_row_end_offsets(spmv_params.d_row_end_offsets),
-        wd_column_indices(spmv_params.d_column_indices),
-        wd_vector_x(spmv_params.d_vector_x),
-        wd_vector_y(spmv_params.d_vector_y)
-    {}
-
-
-
-
-    /**
-     * Consume a merge tile, specialized for direct-load of nonzeros
-     * /
-    __device__ __forceinline__ KeyValuePairT ConsumeTile(
-        int             tile_idx,
-        CoordinateT     tile_start_coord,
-        CoordinateT     tile_end_coord,
-        Int2Type<true>  is_direct_load)     ///< Marker type indicating whether to load nonzeros directly during path-discovery or beforehand in batch
-    {
-        int         tile_num_rows           = tile_end_coord.x - tile_start_coord.x;
-        int         tile_num_nonzeros       = tile_end_coord.y - tile_start_coord.y;
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-
-        // Gather the row end-offsets for the merge tile into shared memory
-        for (int item = threadIdx.x; item <= tile_num_rows; item += BLOCK_THREADS)
-        {
-            s_tile_row_end_offsets[item] = wd_row_end_offsets[tile_start_coord.x + item];
-        }
-
-        CTA_SYNC();
-
-        // Search for the thread's starting coordinate within the merge tile
-        CountingInputIterator<OffsetT>  tile_nonzero_indices(tile_start_coord.y);
-        CoordinateT                     thread_start_coord;
-
-        MergePathSearch(
-            OffsetT(threadIdx.x * ITEMS_PER_THREAD),    // Diagonal
-            s_tile_row_end_offsets,                     // List A
-            tile_nonzero_indices,                       // List B
-            tile_num_rows,
-            tile_num_nonzeros,
-            thread_start_coord);
-
-        CTA_SYNC();            // Perf-sync
-
-        // Compute the thread's merge path segment
-        CoordinateT     thread_current_coord = thread_start_coord;
-        KeyValuePairT   scan_segment[ITEMS_PER_THREAD];
-
-        ValueT          running_total = 0.0;
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            OffsetT nonzero_idx         = CUB_MIN(tile_nonzero_indices[thread_current_coord.y], spmv_params.num_nonzeros - 1);
-            OffsetT column_idx          = wd_column_indices[nonzero_idx];
-            ValueT  value               = wd_values[nonzero_idx];
-            ValueT  vector_value        = wd_vector_x[column_idx];
-            ValueT  nonzero             = value * vector_value;
-
-            OffsetT row_end_offset      = s_tile_row_end_offsets[thread_current_coord.x];
-
-            if (tile_nonzero_indices[thread_current_coord.y] < row_end_offset)
-            {
-                // Move down (accumulate)
-                running_total += nonzero;
-                scan_segment[ITEM].value    = running_total;
-                scan_segment[ITEM].key      = tile_num_rows;
-                ++thread_current_coord.y;
-            }
-            else
-            {
-                // Move right (reset)
-                scan_segment[ITEM].value    = running_total;
-                scan_segment[ITEM].key      = thread_current_coord.x;
-                running_total               = 0.0;
-                ++thread_current_coord.x;
-            }
-        }
-
-        CTA_SYNC();
-
-        // Block-wide reduce-value-by-segment
-        KeyValuePairT       tile_carry;
-        ReduceBySegmentOpT  scan_op;
-        KeyValuePairT       scan_item;
-
-        scan_item.value = running_total;
-        scan_item.key   = thread_current_coord.x;
-
-        BlockScanT(temp_storage.scan).ExclusiveScan(scan_item, scan_item, scan_op, tile_carry);
-
-        if (tile_num_rows > 0)
-        {
-            if (threadIdx.x == 0)
-                scan_item.key = -1;
-
-            // Direct scatter
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                if (scan_segment[ITEM].key < tile_num_rows)
-                {
-                    if (scan_item.key == scan_segment[ITEM].key)
-                        scan_segment[ITEM].value = scan_item.value + scan_segment[ITEM].value;
-
-                    if (HAS_ALPHA)
-                    {
-                        scan_segment[ITEM].value *= spmv_params.alpha;
-                    }
-
-                    if (HAS_BETA)
-                    {
-                        // Update the output vector element
-                        ValueT addend = spmv_params.beta * wd_vector_y[tile_start_coord.x + scan_segment[ITEM].key];
-                        scan_segment[ITEM].value += addend;
-                    }
-
-                    // Set the output vector element
-                    spmv_params.d_vector_y[tile_start_coord.x + scan_segment[ITEM].key] = scan_segment[ITEM].value;
-                }
-            }
-        }
-
-        // Return the tile's running carry-out
-        return tile_carry;
-    }
-*/
-
-
-    /**
-     * Consume a merge tile, specialized for indirect load of nonzeros
-     * /
-    __device__ __forceinline__ KeyValuePairT ConsumeTile(
-        int             tile_idx,
-        CoordinateT     tile_start_coord,
-        CoordinateT     tile_end_coord,
-        Int2Type<false> is_direct_load)     ///< Marker type indicating whether to load nonzeros directly during path-discovery or beforehand in batch
-    {
-        int         tile_num_rows           = tile_end_coord.x - tile_start_coord.x;
-        int         tile_num_nonzeros       = tile_end_coord.y - tile_start_coord.y;
-
-#if (CUB_PTX_ARCH >= 520)
-
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-        ValueT*     s_tile_nonzeros         = &temp_storage.merge_items[tile_num_rows + ITEMS_PER_THREAD].nonzero;
-
-        // Gather the nonzeros for the merge tile into shared memory
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int nonzero_idx = threadIdx.x + (ITEM * BLOCK_THREADS);
-
-            ValueIteratorT a                = wd_values + tile_start_coord.y + nonzero_idx;
-            ColumnIndicesIteratorT ci       = wd_column_indices + tile_start_coord.y + nonzero_idx;
-            ValueT* s                       = s_tile_nonzeros + nonzero_idx;
-
-            if (nonzero_idx < tile_num_nonzeros)
-            {
-
-                OffsetT column_idx              = *ci;
-                ValueT  value                   = *a;
-                ValueT  vector_value            = spmv_params.t_vector_x[column_idx];
-                vector_value                    = wd_vector_x[column_idx];
-                ValueT  nonzero                 = value * vector_value;
-                *s    = nonzero;
-            }
-        }
-
-
-#else
-
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-        ValueT*     s_tile_nonzeros         = &temp_storage.merge_items[tile_num_rows + ITEMS_PER_THREAD].nonzero;
-
-        // Gather the nonzeros for the merge tile into shared memory
-        if (tile_num_nonzeros > 0)
-        {
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                int     nonzero_idx             = threadIdx.x + (ITEM * BLOCK_THREADS);
-                nonzero_idx                     = CUB_MIN(nonzero_idx, tile_num_nonzeros - 1);
-
-                OffsetT column_idx              = wd_column_indices[tile_start_coord.y + nonzero_idx];
-                ValueT  value                   = wd_values[tile_start_coord.y + nonzero_idx];
-
-                ValueT  vector_value            = wd_vector_x[column_idx];
-                ValueT  nonzero                 = value * vector_value;
-
-                s_tile_nonzeros[nonzero_idx]    = nonzero;
-            }
-        }
-
-#endif
-
-        // Gather the row end-offsets for the merge tile into shared memory
-        #pragma unroll 1
-        for (int item = threadIdx.x; item <= tile_num_rows; item += BLOCK_THREADS)
-        {
-            s_tile_row_end_offsets[item] = wd_row_end_offsets[tile_start_coord.x + item];
-        }
-
-        CTA_SYNC();
-
-        // Search for the thread's starting coordinate within the merge tile
-        CountingInputIterator<OffsetT>  tile_nonzero_indices(tile_start_coord.y);
-        CoordinateT                     thread_start_coord;
-
-        MergePathSearch(
-            OffsetT(threadIdx.x * ITEMS_PER_THREAD),    // Diagonal
-            s_tile_row_end_offsets,                     // List A
-            tile_nonzero_indices,                       // List B
-            tile_num_rows,
-            tile_num_nonzeros,
-            thread_start_coord);
-
-        CTA_SYNC();            // Perf-sync
-
-        // Compute the thread's merge path segment
-        CoordinateT     thread_current_coord = thread_start_coord;
-        KeyValuePairT   scan_segment[ITEMS_PER_THREAD];
-        ValueT          running_total = 0.0;
-
-        OffsetT row_end_offset  = s_tile_row_end_offsets[thread_current_coord.x];
-        ValueT  nonzero         = s_tile_nonzeros[thread_current_coord.y];
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (tile_nonzero_indices[thread_current_coord.y] < row_end_offset)
-            {
-                // Move down (accumulate)
-                scan_segment[ITEM].value    = nonzero;
-                running_total               += nonzero;
-                ++thread_current_coord.y;
-                nonzero                     = s_tile_nonzeros[thread_current_coord.y];
-            }
-            else
-            {
-                // Move right (reset)
-                scan_segment[ITEM].value    = 0.0;
-                running_total               = 0.0;
-                ++thread_current_coord.x;
-                row_end_offset              = s_tile_row_end_offsets[thread_current_coord.x];
-            }
-
-            scan_segment[ITEM].key = thread_current_coord.x;
-        }
-
-        CTA_SYNC();
-
-        // Block-wide reduce-value-by-segment
-        KeyValuePairT       tile_carry;
-        ReduceBySegmentOpT  scan_op;
-        KeyValuePairT       scan_item;
-
-        scan_item.value = running_total;
-        scan_item.key = thread_current_coord.x;
-
-        BlockScanT(temp_storage.scan).ExclusiveScan(scan_item, scan_item, scan_op, tile_carry);
-
-        if (threadIdx.x == 0)
-        {
-            scan_item.key = thread_start_coord.x;
-            scan_item.value = 0.0;
-        }
-
-        if (tile_num_rows > 0)
-        {
-
-            CTA_SYNC();
-
-            // Scan downsweep and scatter
-            ValueT* s_partials = &temp_storage.merge_items[0].nonzero;
-
-            if (scan_item.key != scan_segment[0].key)
-            {
-                s_partials[scan_item.key] = scan_item.value;
-            }
-            else
-            {
-                scan_segment[0].value += scan_item.value;
-            }
-
-            #pragma unroll
-            for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                if (scan_segment[ITEM - 1].key != scan_segment[ITEM].key)
-                {
-                    s_partials[scan_segment[ITEM - 1].key] = scan_segment[ITEM - 1].value;
-                }
-                else
-                {
-                    scan_segment[ITEM].value += scan_segment[ITEM - 1].value;
-                }
-            }
-
-            CTA_SYNC();
-
-            #pragma unroll 1
-            for (int item = threadIdx.x; item < tile_num_rows; item += BLOCK_THREADS)
-            {
-                spmv_params.d_vector_y[tile_start_coord.x + item] = s_partials[item];
-            }
-        }
-
-        // Return the tile's running carry-out
-        return tile_carry;
-    }
-*/
-
-    /**
-     * Consume input tile
-     */
-    __device__ __forceinline__ void ConsumeTile(
-        int             merge_items_per_block,  ///< [in] Number of merge tiles per block
-        KeyValuePairT*  d_tile_carry_pairs)     ///< [out] Pointer to the temporary array carry-out dot product row-ids, one per block
-    {
-        // Read our starting coordinates
-        if (threadIdx.x == 0)
-        {
-            // Search our starting coordinates
-            OffsetT                         diagonal = blockIdx.x * merge_items_per_block;
-            CoordinateT                     tile_coord;
-            CountingInputIterator<OffsetT>  nonzero_indices(0);
-
-            // Search the merge path
-            MergePathSearch(
-                diagonal,
-                RowOffsetsSearchIteratorT(spmv_params.d_row_end_offsets),
-                nonzero_indices,
-                spmv_params.num_rows,
-                spmv_params.num_nonzeros,
-                tile_coord);
-
-            temp_storage.tile_coord = tile_coord;
-        }
-
-        CTA_SYNC();
-
-        CoordinateT tile_start_coord = temp_storage.tile_coord;
-
-
-        // Mooch
-        __shared__ volatile OffsetT x;
-        x = tile_start_coord.x;
-
-
-        // Turnstile
-        if (threadIdx.x == 0)
-        {
-            __threadfence();
-            temp_storage.turnstile = atomicAdd(spmv_params.d_row_end_offsets - 1, 1);
-        }
-        
-        CTA_SYNC();
-
-        // Last block through turnstile does fixup
-        if (temp_storage.turnstile == gridDim.x - 1)
-        {
-            if (threadIdx.x == 0)
-            {
-                spmv_params.d_row_end_offsets[-1] = 0;
-            }
-
-        }
-
-
-    }
-
-
-};
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_spmv_orig.cuh b/ml-xgboost/cub/cub/agent/agent_spmv_orig.cuh
deleted file mode 100644
index 5179b09..0000000
--- a/ml-xgboost/cub/cub/agent/agent_spmv_orig.cuh
+++ /dev/null
@@ -1,924 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentSpmv implements a stateful abstraction of CUDA thread blocks for participating in device-wide SpMV.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "../util_type.cuh"
-#include "../block/block_reduce.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_exchange.cuh"
-#include "../thread/thread_search.cuh"
-#include "../thread/thread_operators.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../iterator/counting_input_iterator.cuh"
-#include "../iterator/tex_ref_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentSpmv
- */
-template <
-    int                             _BLOCK_THREADS,                         ///< Threads per thread block
-    int                             _ITEMS_PER_THREAD,                      ///< Items per thread (per tile of input)
-    CacheLoadModifier               _ROW_OFFSETS_SEARCH_LOAD_MODIFIER,      ///< Cache load modifier for reading CSR row-offsets during search
-    CacheLoadModifier               _ROW_OFFSETS_LOAD_MODIFIER,             ///< Cache load modifier for reading CSR row-offsets
-    CacheLoadModifier               _COLUMN_INDICES_LOAD_MODIFIER,          ///< Cache load modifier for reading CSR column-indices
-    CacheLoadModifier               _VALUES_LOAD_MODIFIER,                  ///< Cache load modifier for reading CSR values
-    CacheLoadModifier               _VECTOR_VALUES_LOAD_MODIFIER,           ///< Cache load modifier for reading vector values
-    bool                            _DIRECT_LOAD_NONZEROS,                  ///< Whether to load nonzeros directly from global during sequential merging (vs. pre-staged through shared memory)
-    BlockScanAlgorithm              _SCAN_ALGORITHM>                        ///< The BlockScan algorithm to use
-struct AgentSpmvPolicy
-{
-    enum
-    {
-        BLOCK_THREADS                                                   = _BLOCK_THREADS,                       ///< Threads per thread block
-        ITEMS_PER_THREAD                                                = _ITEMS_PER_THREAD,                    ///< Items per thread (per tile of input)
-        DIRECT_LOAD_NONZEROS                                            = _DIRECT_LOAD_NONZEROS,                ///< Whether to load nonzeros directly from global during sequential merging (pre-staged through shared memory)
-    };
-
-    static const CacheLoadModifier  ROW_OFFSETS_SEARCH_LOAD_MODIFIER    = _ROW_OFFSETS_SEARCH_LOAD_MODIFIER;    ///< Cache load modifier for reading CSR row-offsets
-    static const CacheLoadModifier  ROW_OFFSETS_LOAD_MODIFIER           = _ROW_OFFSETS_LOAD_MODIFIER;           ///< Cache load modifier for reading CSR row-offsets
-    static const CacheLoadModifier  COLUMN_INDICES_LOAD_MODIFIER        = _COLUMN_INDICES_LOAD_MODIFIER;        ///< Cache load modifier for reading CSR column-indices
-    static const CacheLoadModifier  VALUES_LOAD_MODIFIER                = _VALUES_LOAD_MODIFIER;                ///< Cache load modifier for reading CSR values
-    static const CacheLoadModifier  VECTOR_VALUES_LOAD_MODIFIER         = _VECTOR_VALUES_LOAD_MODIFIER;         ///< Cache load modifier for reading vector values
-    static const BlockScanAlgorithm SCAN_ALGORITHM                      = _SCAN_ALGORITHM;                      ///< The BlockScan algorithm to use
-
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-template <
-    typename        ValueT,              ///< Matrix and vector value type
-    typename        OffsetT>             ///< Signed integer type for sequence offsets
-struct SpmvParams
-{
-    ValueT*         d_values;            ///< Pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-    OffsetT*        d_row_end_offsets;   ///< Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values
-    OffsetT*        d_column_indices;    ///< Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-    ValueT*         d_vector_x;          ///< Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-    ValueT*         d_vector_y;          ///< Pointer to the array of \p num_rows values corresponding to the dense output vector <em>y</em>
-    int             num_rows;            ///< Number of rows of matrix <b>A</b>.
-    int             num_cols;            ///< Number of columns of matrix <b>A</b>.
-    int             num_nonzeros;        ///< Number of nonzero elements of matrix <b>A</b>.
-    ValueT          alpha;               ///< Alpha multiplicand
-    ValueT          beta;                ///< Beta addend-multiplicand
-
-    TexRefInputIterator<ValueT, 66778899, OffsetT>  t_vector_x;
-};
-
-
-/**
- * \brief AgentSpmv implements a stateful abstraction of CUDA thread blocks for participating in device-wide SpMV.
- */
-template <
-    typename    AgentSpmvPolicyT,           ///< Parameterized AgentSpmvPolicy tuning policy type
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT,                    ///< Signed integer type for sequence offsets
-    bool        HAS_ALPHA,                  ///< Whether the input parameter \p alpha is 1
-    bool        HAS_BETA,                   ///< Whether the input parameter \p beta is 0
-    int         PTX_ARCH = CUB_PTX_ARCH>    ///< PTX compute capability
-struct AgentSpmv
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS           = AgentSpmvPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = AgentSpmvPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    /// 2D merge path coordinate type
-    typedef typename CubVector<OffsetT, 2>::Type CoordinateT;
-
-    /// Input iterator wrapper types (for applying cache modifiers)
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::ROW_OFFSETS_SEARCH_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsSearchIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::ROW_OFFSETS_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::COLUMN_INDICES_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        ColumnIndicesIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        ValueIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VECTOR_VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        VectorValueIteratorT;
-
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef KeyValuePair<OffsetT, ValueT> KeyValuePairT;
-
-    // Reduce-value-by-segment scan operator
-    typedef ReduceByKeyOp<cub::Sum> ReduceBySegmentOpT;
-
-    // BlockReduce specialization
-    typedef BlockReduce<
-            ValueT,
-            BLOCK_THREADS,
-            BLOCK_REDUCE_WARP_REDUCTIONS>
-        BlockReduceT;
-
-    // BlockScan specialization
-    typedef BlockScan<
-            KeyValuePairT,
-            BLOCK_THREADS,
-            AgentSpmvPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    // BlockScan specialization
-    typedef BlockScan<
-            ValueT,
-            BLOCK_THREADS,
-            AgentSpmvPolicyT::SCAN_ALGORITHM>
-        BlockPrefixSumT;
-
-    // BlockExchange specialization
-    typedef BlockExchange<
-            ValueT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD>
-        BlockExchangeT;
-
-    /// Merge item type (either a non-zero value or a row-end offset)
-    union MergeItem
-    {
-        // Value type to pair with index type OffsetT (NullType if loading values directly during merge)
-        typedef typename If<AgentSpmvPolicyT::DIRECT_LOAD_NONZEROS, NullType, ValueT>::Type MergeValueT;
-
-        OffsetT     row_end_offset;
-        MergeValueT nonzero;
-    };
-
-    /// Shared memory type required by this thread block
-    struct _TempStorage
-    {
-        CoordinateT tile_coords[2];
-
-        union
-        {
-            // Smem needed for tile of merge items
-            MergeItem merge_items[ITEMS_PER_THREAD + TILE_ITEMS + 1];
-
-            // Smem needed for block exchange
-            typename BlockExchangeT::TempStorage exchange;
-
-            // Smem needed for block-wide reduction
-            typename BlockReduceT::TempStorage reduce;
-
-            // Smem needed for tile scanning
-            typename BlockScanT::TempStorage scan;
-
-            // Smem needed for tile prefix sum
-            typename BlockPrefixSumT::TempStorage prefix_sum;
-        };
-    };
-
-    /// Temporary storage type (unionable)
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-
-    _TempStorage&                   temp_storage;         /// Reference to temp_storage
-
-    SpmvParams<ValueT, OffsetT>&    spmv_params;
-
-    ValueIteratorT                  wd_values;            ///< Wrapped pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-    RowOffsetsIteratorT             wd_row_end_offsets;   ///< Wrapped Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values
-    ColumnIndicesIteratorT          wd_column_indices;    ///< Wrapped Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-    VectorValueIteratorT            wd_vector_x;          ///< Wrapped Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-    VectorValueIteratorT            wd_vector_y;          ///< Wrapped Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-
-
-    //---------------------------------------------------------------------
-    // Interface
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentSpmv(
-        TempStorage&                    temp_storage,           ///< Reference to temp_storage
-        SpmvParams<ValueT, OffsetT>&    spmv_params)            ///< SpMV input parameter bundle
-    :
-        temp_storage(temp_storage.Alias()),
-        spmv_params(spmv_params),
-        wd_values(spmv_params.d_values),
-        wd_row_end_offsets(spmv_params.d_row_end_offsets),
-        wd_column_indices(spmv_params.d_column_indices),
-        wd_vector_x(spmv_params.d_vector_x),
-        wd_vector_y(spmv_params.d_vector_y)
-    {}
-
-
-
-
-    /**
-     * Consume a merge tile, specialized for direct-load of nonzeros
-     */
-    __device__ __forceinline__ KeyValuePairT ConsumeTile(
-        int             tile_idx,
-        CoordinateT     tile_start_coord,
-        CoordinateT     tile_end_coord,
-        Int2Type<true>  is_direct_load)     ///< Marker type indicating whether to load nonzeros directly during path-discovery or beforehand in batch
-    {
-        int         tile_num_rows           = tile_end_coord.x - tile_start_coord.x;
-        int         tile_num_nonzeros       = tile_end_coord.y - tile_start_coord.y;
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-
-        // Gather the row end-offsets for the merge tile into shared memory
-        for (int item = threadIdx.x; item <= tile_num_rows; item += BLOCK_THREADS)
-        {
-            s_tile_row_end_offsets[item] = wd_row_end_offsets[tile_start_coord.x + item];
-        }
-
-        CTA_SYNC();
-
-        // Search for the thread's starting coordinate within the merge tile
-        CountingInputIterator<OffsetT>  tile_nonzero_indices(tile_start_coord.y);
-        CoordinateT                     thread_start_coord;
-
-        MergePathSearch(
-            OffsetT(threadIdx.x * ITEMS_PER_THREAD),    // Diagonal
-            s_tile_row_end_offsets,                     // List A
-            tile_nonzero_indices,                       // List B
-            tile_num_rows,
-            tile_num_nonzeros,
-            thread_start_coord);
-
-        CTA_SYNC();            // Perf-sync
-
-        // Compute the thread's merge path segment
-        CoordinateT     thread_current_coord = thread_start_coord;
-        KeyValuePairT   scan_segment[ITEMS_PER_THREAD];
-
-        ValueT          running_total = 0.0;
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            OffsetT nonzero_idx         = CUB_MIN(tile_nonzero_indices[thread_current_coord.y], spmv_params.num_nonzeros - 1);
-            OffsetT column_idx          = wd_column_indices[nonzero_idx];
-            ValueT  value               = wd_values[nonzero_idx];
-
-            ValueT  vector_value        = spmv_params.t_vector_x[column_idx];
-#if (CUB_PTX_ARCH >= 350)
-            vector_value                = wd_vector_x[column_idx];
-#endif
-            ValueT  nonzero             = value * vector_value;
-
-            OffsetT row_end_offset      = s_tile_row_end_offsets[thread_current_coord.x];
-
-            if (tile_nonzero_indices[thread_current_coord.y] < row_end_offset)
-            {
-                // Move down (accumulate)
-                running_total += nonzero;
-                scan_segment[ITEM].value    = running_total;
-                scan_segment[ITEM].key      = tile_num_rows;
-                ++thread_current_coord.y;
-            }
-            else
-            {
-                // Move right (reset)
-                scan_segment[ITEM].value    = running_total;
-                scan_segment[ITEM].key      = thread_current_coord.x;
-                running_total               = 0.0;
-                ++thread_current_coord.x;
-            }
-        }
-
-        CTA_SYNC();
-
-        // Block-wide reduce-value-by-segment
-        KeyValuePairT       tile_carry;
-        ReduceBySegmentOpT  scan_op;
-        KeyValuePairT       scan_item;
-
-        scan_item.value = running_total;
-        scan_item.key   = thread_current_coord.x;
-
-        BlockScanT(temp_storage.scan).ExclusiveScan(scan_item, scan_item, scan_op, tile_carry);
-
-        if (tile_num_rows > 0)
-        {
-            if (threadIdx.x == 0)
-                scan_item.key = -1;
-
-            // Direct scatter
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                if (scan_segment[ITEM].key < tile_num_rows)
-                {
-                    if (scan_item.key == scan_segment[ITEM].key)
-                        scan_segment[ITEM].value = scan_item.value + scan_segment[ITEM].value;
-
-                    if (HAS_ALPHA)
-                    {
-                        scan_segment[ITEM].value *= spmv_params.alpha;
-                    }
-
-                    if (HAS_BETA)
-                    {
-                        // Update the output vector element
-                        ValueT addend = spmv_params.beta * wd_vector_y[tile_start_coord.x + scan_segment[ITEM].key];
-                        scan_segment[ITEM].value += addend;
-                    }
-
-                    // Set the output vector element
-                    spmv_params.d_vector_y[tile_start_coord.x + scan_segment[ITEM].key] = scan_segment[ITEM].value;
-                }
-            }
-        }
-
-        // Return the tile's running carry-out
-        return tile_carry;
-    }
-
-
-
-    /**
-     * Consume a merge tile, specialized for indirect load of nonzeros
-     */
-    __device__ __forceinline__ KeyValuePairT ConsumeTile(
-        int             tile_idx,
-        CoordinateT     tile_start_coord,
-        CoordinateT     tile_end_coord,
-        Int2Type<false> is_direct_load)     ///< Marker type indicating whether to load nonzeros directly during path-discovery or beforehand in batch
-    {
-        int         tile_num_rows           = tile_end_coord.x - tile_start_coord.x;
-        int         tile_num_nonzeros       = tile_end_coord.y - tile_start_coord.y;
-
-#if (CUB_PTX_ARCH >= 520)
-
-/*
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[tile_num_nonzeros].row_end_offset;
-        ValueT*     s_tile_nonzeros         = &temp_storage.merge_items[0].nonzero;
-
-        OffsetT col_indices[ITEMS_PER_THREAD];
-        ValueT mat_values[ITEMS_PER_THREAD];
-        int nonzero_indices[ITEMS_PER_THREAD];
-
-        // Gather the nonzeros for the merge tile into shared memory
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            nonzero_indices[ITEM]           = threadIdx.x + (ITEM * BLOCK_THREADS);
-
-            ValueIteratorT a                = wd_values + tile_start_coord.y + nonzero_indices[ITEM];
-            ColumnIndicesIteratorT ci       = wd_column_indices + tile_start_coord.y + nonzero_indices[ITEM];
-
-            col_indices[ITEM]               = (nonzero_indices[ITEM] < tile_num_nonzeros) ? *ci : 0;
-            mat_values[ITEM]                = (nonzero_indices[ITEM] < tile_num_nonzeros) ? *a : 0.0;
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            VectorValueIteratorT x = wd_vector_x + col_indices[ITEM];
-            mat_values[ITEM] *= *x;
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            ValueT *s = s_tile_nonzeros + nonzero_indices[ITEM];
-
-            *s = mat_values[ITEM];
-        }
-
-        CTA_SYNC();
-
-*/
-
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-        ValueT*     s_tile_nonzeros         = &temp_storage.merge_items[tile_num_rows + ITEMS_PER_THREAD].nonzero;
-
-        // Gather the nonzeros for the merge tile into shared memory
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int nonzero_idx = threadIdx.x + (ITEM * BLOCK_THREADS);
-
-            ValueIteratorT a                = wd_values + tile_start_coord.y + nonzero_idx;
-            ColumnIndicesIteratorT ci       = wd_column_indices + tile_start_coord.y + nonzero_idx;
-            ValueT* s                       = s_tile_nonzeros + nonzero_idx;
-
-            if (nonzero_idx < tile_num_nonzeros)
-            {
-
-                OffsetT column_idx              = *ci;
-                ValueT  value                   = *a;
-
-                ValueT  vector_value            = spmv_params.t_vector_x[column_idx];
-                vector_value                    = wd_vector_x[column_idx];
-
-                ValueT  nonzero                 = value * vector_value;
-
-                *s    = nonzero;
-            }
-        }
-
-
-#else
-
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-        ValueT*     s_tile_nonzeros         = &temp_storage.merge_items[tile_num_rows + ITEMS_PER_THREAD].nonzero;
-
-        // Gather the nonzeros for the merge tile into shared memory
-        if (tile_num_nonzeros > 0)
-        {
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                int     nonzero_idx             = threadIdx.x + (ITEM * BLOCK_THREADS);
-                nonzero_idx                     = CUB_MIN(nonzero_idx, tile_num_nonzeros - 1);
-
-                OffsetT column_idx              = wd_column_indices[tile_start_coord.y + nonzero_idx];
-                ValueT  value                   = wd_values[tile_start_coord.y + nonzero_idx];
-
-                ValueT  vector_value            = spmv_params.t_vector_x[column_idx];
-#if (CUB_PTX_ARCH >= 350)
-                vector_value                    = wd_vector_x[column_idx];
-#endif
-                ValueT  nonzero                 = value * vector_value;
-
-                s_tile_nonzeros[nonzero_idx]    = nonzero;
-            }
-        }
-
-#endif
-
-        // Gather the row end-offsets for the merge tile into shared memory
-        #pragma unroll 1
-        for (int item = threadIdx.x; item <= tile_num_rows; item += BLOCK_THREADS)
-        {
-            s_tile_row_end_offsets[item] = wd_row_end_offsets[tile_start_coord.x + item];
-        }
-
-        CTA_SYNC();
-
-        // Search for the thread's starting coordinate within the merge tile
-        CountingInputIterator<OffsetT>  tile_nonzero_indices(tile_start_coord.y);
-        CoordinateT                     thread_start_coord;
-
-        MergePathSearch(
-            OffsetT(threadIdx.x * ITEMS_PER_THREAD),    // Diagonal
-            s_tile_row_end_offsets,                     // List A
-            tile_nonzero_indices,                       // List B
-            tile_num_rows,
-            tile_num_nonzeros,
-            thread_start_coord);
-
-        CTA_SYNC();            // Perf-sync
-
-        // Compute the thread's merge path segment
-        CoordinateT     thread_current_coord = thread_start_coord;
-        KeyValuePairT   scan_segment[ITEMS_PER_THREAD];
-        ValueT          running_total = 0.0;
-
-        OffsetT row_end_offset  = s_tile_row_end_offsets[thread_current_coord.x];
-        ValueT  nonzero         = s_tile_nonzeros[thread_current_coord.y];
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (tile_nonzero_indices[thread_current_coord.y] < row_end_offset)
-            {
-                // Move down (accumulate)
-                scan_segment[ITEM].value    = nonzero;
-                running_total               += nonzero;
-                ++thread_current_coord.y;
-                nonzero                     = s_tile_nonzeros[thread_current_coord.y];
-            }
-            else
-            {
-                // Move right (reset)
-                scan_segment[ITEM].value    = 0.0;
-                running_total               = 0.0;
-                ++thread_current_coord.x;
-                row_end_offset              = s_tile_row_end_offsets[thread_current_coord.x];
-            }
-
-            scan_segment[ITEM].key = thread_current_coord.x;
-        }
-
-        CTA_SYNC();
-
-        // Block-wide reduce-value-by-segment
-        KeyValuePairT       tile_carry;
-        ReduceBySegmentOpT  scan_op;
-        KeyValuePairT       scan_item;
-
-        scan_item.value = running_total;
-        scan_item.key = thread_current_coord.x;
-
-        BlockScanT(temp_storage.scan).ExclusiveScan(scan_item, scan_item, scan_op, tile_carry);
-
-        if (threadIdx.x == 0)
-        {
-            scan_item.key = thread_start_coord.x;
-            scan_item.value = 0.0;
-        }
-
-        if (tile_num_rows > 0)
-        {
-
-            CTA_SYNC();
-
-            // Scan downsweep and scatter
-            ValueT* s_partials = &temp_storage.merge_items[0].nonzero;
-
-            if (scan_item.key != scan_segment[0].key)
-            {
-                s_partials[scan_item.key] = scan_item.value;
-            }
-            else
-            {
-                scan_segment[0].value += scan_item.value;
-            }
-
-            #pragma unroll
-            for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                if (scan_segment[ITEM - 1].key != scan_segment[ITEM].key)
-                {
-                    s_partials[scan_segment[ITEM - 1].key] = scan_segment[ITEM - 1].value;
-                }
-                else
-                {
-                    scan_segment[ITEM].value += scan_segment[ITEM - 1].value;
-                }
-            }
-
-            CTA_SYNC();
-
-            #pragma unroll 1
-            for (int item = threadIdx.x; item < tile_num_rows; item += BLOCK_THREADS)
-            {
-                spmv_params.d_vector_y[tile_start_coord.x + item] = s_partials[item];
-            }
-        }
-
-        // Return the tile's running carry-out
-        return tile_carry;
-    }
-
-
-
-
-
-
-
-    /**
-     * Consume a merge tile, specialized for indirect load of nonzeros
-     * /
-    template <typename IsDirectLoadT>
-    __device__ __forceinline__ KeyValuePairT ConsumeTile1(
-        int             tile_idx,
-        CoordinateT     tile_start_coord,
-        CoordinateT     tile_end_coord,
-        IsDirectLoadT   is_direct_load)     ///< Marker type indicating whether to load nonzeros directly during path-discovery or beforehand in batch
-    {
-        int         tile_num_rows           = tile_end_coord.x - tile_start_coord.x;
-        int         tile_num_nonzeros       = tile_end_coord.y - tile_start_coord.y;
-
-        OffsetT*    s_tile_row_end_offsets  = &temp_storage.merge_items[0].row_end_offset;
-
-        int warp_idx                        = threadIdx.x / WARP_THREADS;
-        int lane_idx                        = LaneId();
-
-        // Gather the row end-offsets for the merge tile into shared memory
-        #pragma unroll 1
-        for (int item = threadIdx.x; item <= tile_num_rows; item += BLOCK_THREADS)
-        {
-            s_tile_row_end_offsets[item] = wd_row_end_offsets[tile_start_coord.x + item];
-        }
-
-        CTA_SYNC();
-
-        // Search for warp start/end coords
-        if (lane_idx == 0)
-        {
-            MergePathSearch(
-                OffsetT(warp_idx * ITEMS_PER_WARP),                 // Diagonal
-                s_tile_row_end_offsets,                             // List A
-                CountingInputIterator<OffsetT>(tile_start_coord.y), // List B
-                tile_num_rows,
-                tile_num_nonzeros,
-                temp_storage.warp_coords[warp_idx]);
-
-            CoordinateT last = {tile_num_rows, tile_num_nonzeros};
-            temp_storage.warp_coords[WARPS] = last;
-        }
-
-        CTA_SYNC();
-
-        CoordinateT     warp_coord          = temp_storage.warp_coords[warp_idx];
-        CoordinateT     warp_end_coord      = temp_storage.warp_coords[warp_idx + 1];
-        OffsetT         warp_nonzero_idx    = tile_start_coord.y + warp_coord.y;
-
-        // Consume whole rows
-        #pragma unroll 1
-        for (; warp_coord.x < warp_end_coord.x; ++warp_coord.x)
-        {
-            ValueT  row_total       = 0.0;
-            OffsetT row_end_offset  = s_tile_row_end_offsets[warp_coord.x];
-
-            #pragma unroll 1
-            for (OffsetT nonzero_idx = warp_nonzero_idx + lane_idx;
-                nonzero_idx < row_end_offset;
-                nonzero_idx += WARP_THREADS)
-            {
-                OffsetT column_idx          = wd_column_indices[nonzero_idx];
-                ValueT  value               = wd_values[nonzero_idx];
-                ValueT  vector_value        = wd_vector_x[column_idx];
-                row_total                   += value * vector_value;
-            }
-
-            // Warp reduce
-            row_total = WarpReduceT(temp_storage.warp_reduce[warp_idx]).Sum(row_total);
-
-            // Output
-            if (lane_idx == 0)
-            {
-                spmv_params.d_vector_y[tile_start_coord.x + warp_coord.x] = row_total;
-            }
-
-            warp_nonzero_idx = row_end_offset;
-        }
-
-        // Consume partial portion of thread's last row
-        if (warp_nonzero_idx < tile_start_coord.y + warp_end_coord.y)
-        {
-            ValueT row_total = 0.0;
-            for (OffsetT nonzero_idx = warp_nonzero_idx + lane_idx;
-                nonzero_idx < tile_start_coord.y + warp_end_coord.y;
-                nonzero_idx += WARP_THREADS)
-            {
-
-                OffsetT column_idx          = wd_column_indices[nonzero_idx];
-                ValueT  value               = wd_values[nonzero_idx];
-                ValueT  vector_value        = wd_vector_x[column_idx];
-                row_total                   += value * vector_value;
-            }
-
-            // Warp reduce
-            row_total = WarpReduceT(temp_storage.warp_reduce[warp_idx]).Sum(row_total);
-
-            // Output
-            if (lane_idx == 0)
-            {
-                spmv_params.d_vector_y[tile_start_coord.x + warp_coord.x] = row_total;
-            }
-        }
-
-        // Return the tile's running carry-out
-        KeyValuePairT tile_carry(tile_num_rows, 0.0);
-        return tile_carry;
-    }
-*/
-
-
-
-
-
-
-
-    /**
-     * Consume a merge tile, specialized for indirect load of nonzeros
-     * /
-    __device__ __forceinline__ KeyValuePairT ConsumeTile2(
-        int             tile_idx,
-        CoordinateT     tile_start_coord,
-        CoordinateT     tile_end_coord,
-        Int2Type<false> is_direct_load)     ///< Marker type indicating whether to load nonzeros directly during path-discovery or beforehand in batch
-    {
-        int         tile_num_rows           = tile_end_coord.x - tile_start_coord.x;
-        int         tile_num_nonzeros       = tile_end_coord.y - tile_start_coord.y;
-
-        ValueT*     s_tile_nonzeros         = &temp_storage.merge_items[0].nonzero;
-
-        ValueT      nonzeros[ITEMS_PER_THREAD];
-
-        // Gather the nonzeros for the merge tile into shared memory
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int     nonzero_idx         = threadIdx.x + (ITEM * BLOCK_THREADS);
-            nonzero_idx                 = CUB_MIN(nonzero_idx, tile_num_nonzeros - 1);
-
-            OffsetT column_idx          = wd_column_indices[tile_start_coord.y + nonzero_idx];
-            ValueT  value               = wd_values[tile_start_coord.y + nonzero_idx];
-
-            ValueT  vector_value        = spmv_params.t_vector_x[column_idx];
-#if (CUB_PTX_ARCH >= 350)
-            vector_value                = wd_vector_x[column_idx];
-#endif
-
-            nonzeros[ITEM]              = value * vector_value;
-        }
-
-        // Exchange striped->blocked
-        BlockExchangeT(temp_storage.exchange).StripedToBlocked(nonzeros);
-
-        CTA_SYNC();
-
-        // Compute an inclusive prefix sum
-        BlockPrefixSumT(temp_storage.prefix_sum).InclusiveSum(nonzeros, nonzeros);
-
-        CTA_SYNC();
-
-        if (threadIdx.x == 0)
-            s_tile_nonzeros[0] = 0.0;
-
-        // Scatter back to smem
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            int item_idx = (threadIdx.x * ITEMS_PER_THREAD) + ITEM + 1;
-            s_tile_nonzeros[item_idx] = nonzeros[ITEM];
-        }
-
-        CTA_SYNC();
-
-        // Gather the row end-offsets for the merge tile into shared memory
-        #pragma unroll 1
-        for (int item = threadIdx.x; item < tile_num_rows; item += BLOCK_THREADS)
-        {
-            OffsetT start = CUB_MAX(wd_row_end_offsets[tile_start_coord.x + item - 1], tile_start_coord.y);
-            OffsetT end = wd_row_end_offsets[tile_start_coord.x + item];
-
-            start -= tile_start_coord.y;
-            end -= tile_start_coord.y;
-
-            ValueT row_partial = s_tile_nonzeros[end] - s_tile_nonzeros[start];
-
-            spmv_params.d_vector_y[tile_start_coord.x + item] = row_partial;
-        }
-
-        // Get the tile's carry-out
-        KeyValuePairT tile_carry;
-        if (threadIdx.x == 0)
-        {
-            tile_carry.key = tile_num_rows;
-
-            OffsetT start = CUB_MAX(wd_row_end_offsets[tile_end_coord.x - 1], tile_start_coord.y);
-            start -= tile_start_coord.y;
-            OffsetT end = tile_num_nonzeros;
-
-            tile_carry.value = s_tile_nonzeros[end] - s_tile_nonzeros[start];
-        }
-
-        // Return the tile's running carry-out
-        return tile_carry;
-    }
-*/
-
-
-    /**
-     * Consume input tile
-     */
-    __device__ __forceinline__ void ConsumeTile(
-        CoordinateT*    d_tile_coordinates,     ///< [in] Pointer to the temporary array of tile starting coordinates
-        KeyValuePairT*  d_tile_carry_pairs,     ///< [out] Pointer to the temporary array carry-out dot product row-ids, one per block
-        int             num_merge_tiles)        ///< [in] Number of merge tiles
-    {
-        int tile_idx = (blockIdx.x * gridDim.y) + blockIdx.y;    // Current tile index
-
-        if (tile_idx >= num_merge_tiles)
-            return;
-
-        // Read our starting coordinates
-        if (threadIdx.x < 2)
-        {
-            if (d_tile_coordinates == NULL)
-            {
-                // Search our starting coordinates
-                OffsetT                         diagonal = (tile_idx + threadIdx.x) * TILE_ITEMS;
-                CoordinateT                     tile_coord;
-                CountingInputIterator<OffsetT>  nonzero_indices(0);
-
-                // Search the merge path
-                MergePathSearch(
-                    diagonal,
-                    RowOffsetsSearchIteratorT(spmv_params.d_row_end_offsets),
-                    nonzero_indices,
-                    spmv_params.num_rows,
-                    spmv_params.num_nonzeros,
-                    tile_coord);
-
-                temp_storage.tile_coords[threadIdx.x] = tile_coord;
-            }
-            else
-            {
-                temp_storage.tile_coords[threadIdx.x] = d_tile_coordinates[tile_idx + threadIdx.x];
-            }
-        }
-
-        CTA_SYNC();
-
-        CoordinateT tile_start_coord     = temp_storage.tile_coords[0];
-        CoordinateT tile_end_coord       = temp_storage.tile_coords[1];
-
-        // Consume multi-segment tile
-        KeyValuePairT tile_carry = ConsumeTile(
-            tile_idx,
-            tile_start_coord,
-            tile_end_coord,
-            Int2Type<AgentSpmvPolicyT::DIRECT_LOAD_NONZEROS>());
-
-        // Output the tile's carry-out
-        if (threadIdx.x == 0)
-        {
-            if (HAS_ALPHA)
-                tile_carry.value *= spmv_params.alpha;
-
-            tile_carry.key += tile_start_coord.x;
-            d_tile_carry_pairs[tile_idx]    = tile_carry;
-        }
-    }
-
-
-};
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/agent_spmv_row_based.cuh b/ml-xgboost/cub/cub/agent/agent_spmv_row_based.cuh
deleted file mode 100644
index cc2bd88..0000000
--- a/ml-xgboost/cub/cub/agent/agent_spmv_row_based.cuh
+++ /dev/null
@@ -1,470 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::AgentSpmv implements a stateful abstraction of CUDA thread blocks for participating in device-wide SpMV.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "../util_type.cuh"
-#include "../block/block_reduce.cuh"
-#include "../block/block_scan.cuh"
-#include "../block/block_exchange.cuh"
-#include "../thread/thread_search.cuh"
-#include "../thread/thread_operators.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../iterator/counting_input_iterator.cuh"
-#include "../iterator/tex_ref_input_iterator.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Tuning policy
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for AgentSpmv
- */
-template <
-    int                             _BLOCK_THREADS,                         ///< Threads per thread block
-    int                             _ITEMS_PER_THREAD,                      ///< Items per thread (per tile of input)
-    CacheLoadModifier               _ROW_OFFSETS_SEARCH_LOAD_MODIFIER,      ///< Cache load modifier for reading CSR row-offsets during search
-    CacheLoadModifier               _ROW_OFFSETS_LOAD_MODIFIER,             ///< Cache load modifier for reading CSR row-offsets
-    CacheLoadModifier               _COLUMN_INDICES_LOAD_MODIFIER,          ///< Cache load modifier for reading CSR column-indices
-    CacheLoadModifier               _VALUES_LOAD_MODIFIER,                  ///< Cache load modifier for reading CSR values
-    CacheLoadModifier               _VECTOR_VALUES_LOAD_MODIFIER,           ///< Cache load modifier for reading vector values
-    bool                            _DIRECT_LOAD_NONZEROS,                  ///< Whether to load nonzeros directly from global during sequential merging (vs. pre-staged through shared memory)
-    BlockScanAlgorithm              _SCAN_ALGORITHM>                        ///< The BlockScan algorithm to use
-struct AgentSpmvPolicy
-{
-    enum
-    {
-        BLOCK_THREADS                                                   = _BLOCK_THREADS,                       ///< Threads per thread block
-        ITEMS_PER_THREAD                                                = _ITEMS_PER_THREAD,                    ///< Items per thread (per tile of input)
-        DIRECT_LOAD_NONZEROS                                            = _DIRECT_LOAD_NONZEROS,                ///< Whether to load nonzeros directly from global during sequential merging (pre-staged through shared memory)
-    };
-
-    static const CacheLoadModifier  ROW_OFFSETS_SEARCH_LOAD_MODIFIER    = _ROW_OFFSETS_SEARCH_LOAD_MODIFIER;    ///< Cache load modifier for reading CSR row-offsets
-    static const CacheLoadModifier  ROW_OFFSETS_LOAD_MODIFIER           = _ROW_OFFSETS_LOAD_MODIFIER;           ///< Cache load modifier for reading CSR row-offsets
-    static const CacheLoadModifier  COLUMN_INDICES_LOAD_MODIFIER        = _COLUMN_INDICES_LOAD_MODIFIER;        ///< Cache load modifier for reading CSR column-indices
-    static const CacheLoadModifier  VALUES_LOAD_MODIFIER                = _VALUES_LOAD_MODIFIER;                ///< Cache load modifier for reading CSR values
-    static const CacheLoadModifier  VECTOR_VALUES_LOAD_MODIFIER         = _VECTOR_VALUES_LOAD_MODIFIER;         ///< Cache load modifier for reading vector values
-    static const BlockScanAlgorithm SCAN_ALGORITHM                      = _SCAN_ALGORITHM;                      ///< The BlockScan algorithm to use
-
-};
-
-
-/******************************************************************************
- * Thread block abstractions
- ******************************************************************************/
-
-template <
-    typename        ValueT,              ///< Matrix and vector value type
-    typename        OffsetT>             ///< Signed integer type for sequence offsets
-struct SpmvParams
-{
-    ValueT*         d_values;            ///< Pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-    OffsetT*        d_row_end_offsets;   ///< Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values
-    OffsetT*        d_column_indices;    ///< Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-    ValueT*         d_vector_x;          ///< Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-    ValueT*         d_vector_y;          ///< Pointer to the array of \p num_rows values corresponding to the dense output vector <em>y</em>
-    int             num_rows;            ///< Number of rows of matrix <b>A</b>.
-    int             num_cols;            ///< Number of columns of matrix <b>A</b>.
-    int             num_nonzeros;        ///< Number of nonzero elements of matrix <b>A</b>.
-    ValueT          alpha;               ///< Alpha multiplicand
-    ValueT          beta;                ///< Beta addend-multiplicand
-
-    TexRefInputIterator<ValueT, 66778899, OffsetT>  t_vector_x;
-};
-
-
-/**
- * \brief AgentSpmv implements a stateful abstraction of CUDA thread blocks for participating in device-wide SpMV.
- */
-template <
-    typename    AgentSpmvPolicyT,           ///< Parameterized AgentSpmvPolicy tuning policy type
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT,                    ///< Signed integer type for sequence offsets
-    bool        HAS_ALPHA,                  ///< Whether the input parameter \p alpha is 1
-    bool        HAS_BETA,                   ///< Whether the input parameter \p beta is 0
-    int         PTX_ARCH = CUB_PTX_ARCH>    ///< PTX compute capability
-struct AgentSpmv
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS           = AgentSpmvPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = AgentSpmvPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    /// 2D merge path coordinate type
-    typedef typename CubVector<OffsetT, 2>::Type CoordinateT;
-
-    /// Input iterator wrapper types (for applying cache modifiers)
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::ROW_OFFSETS_SEARCH_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsSearchIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::ROW_OFFSETS_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::COLUMN_INDICES_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        ColumnIndicesIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        ValueIteratorT;
-
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VECTOR_VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        VectorValueIteratorT;
-
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef KeyValuePair<OffsetT, ValueT> KeyValuePairT;
-
-    // Reduce-value-by-segment scan operator
-    typedef ReduceBySegmentOp<cub::Sum> ReduceBySegmentOpT;
-
-    // Prefix functor type
-    typedef BlockScanRunningPrefixOp<KeyValuePairT, ReduceBySegmentOpT> PrefixOpT;
-
-    // BlockScan specialization
-    typedef BlockScan<
-            KeyValuePairT,
-            BLOCK_THREADS,
-            AgentSpmvPolicyT::SCAN_ALGORITHM>
-        BlockScanT;
-
-    /// Shared memory type required by this thread block
-    struct _TempStorage
-    {
-        OffsetT tile_nonzero_idx;
-        OffsetT tile_nonzero_idx_end;
-
-        // Smem needed for tile scanning
-        typename BlockScanT::TempStorage scan;
-
-        // Smem needed for tile of merge items
-        ValueT nonzeros[TILE_ITEMS + 1];
-
-    };
-
-    /// Temporary storage type (unionable)
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-
-    _TempStorage&                   temp_storage;         /// Reference to temp_storage
-
-    SpmvParams<ValueT, OffsetT>&    spmv_params;
-
-    ValueIteratorT                  wd_values;            ///< Wrapped pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-    RowOffsetsIteratorT             wd_row_end_offsets;   ///< Wrapped Pointer to the array of \p m offsets demarcating the end of every row in \p d_column_indices and \p d_values
-    ColumnIndicesIteratorT          wd_column_indices;    ///< Wrapped Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-    VectorValueIteratorT            wd_vector_x;          ///< Wrapped Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-    VectorValueIteratorT            wd_vector_y;          ///< Wrapped Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-
-
-    //---------------------------------------------------------------------
-    // Interface
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__ AgentSpmv(
-        TempStorage&                    temp_storage,           ///< Reference to temp_storage
-        SpmvParams<ValueT, OffsetT>&    spmv_params)            ///< SpMV input parameter bundle
-    :
-        temp_storage(temp_storage.Alias()),
-        spmv_params(spmv_params),
-        wd_values(spmv_params.d_values),
-        wd_row_end_offsets(spmv_params.d_row_end_offsets),
-        wd_column_indices(spmv_params.d_column_indices),
-        wd_vector_x(spmv_params.d_vector_x),
-        wd_vector_y(spmv_params.d_vector_y)
-    {}
-
-
-    __device__ __forceinline__ void InitNan(double& nan_token)
-    {
-        long long NAN_BITS  = 0xFFF0000000000001;
-        nan_token           = reinterpret_cast<ValueT&>(NAN_BITS); // ValueT(0.0) / ValueT(0.0);
-    } 
-
-
-    __device__ __forceinline__ void InitNan(float& nan_token)
-    {
-        int NAN_BITS        = 0xFF800001;
-        nan_token           = reinterpret_cast<ValueT&>(NAN_BITS); // ValueT(0.0) / ValueT(0.0);
-    } 
-
-
-    /**
-     *
-     */
-    template <int NNZ_PER_THREAD>
-    __device__ __forceinline__ void ConsumeStrip(
-        PrefixOpT&          prefix_op,
-        ReduceBySegmentOpT& scan_op,
-        ValueT&             row_total,
-        ValueT&             row_start,
-        OffsetT&            tile_nonzero_idx,
-        OffsetT             tile_nonzero_idx_end,
-        OffsetT             row_nonzero_idx,
-        OffsetT             row_nonzero_idx_end)
-    {
-        ValueT NAN_TOKEN;
-        InitNan(NAN_TOKEN);
-
-
-        //
-        // Gather a strip of nonzeros into shared memory
-        //
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < NNZ_PER_THREAD; ++ITEM)
-        {
-
-            ValueT nonzero = 0.0;
-
-            OffsetT                 local_nonzero_idx   = (ITEM * BLOCK_THREADS) + threadIdx.x;
-            OffsetT                 nonzero_idx         = tile_nonzero_idx + local_nonzero_idx;
-
-            bool in_range = nonzero_idx < tile_nonzero_idx_end;
-
-            OffsetT nonzero_idx2 = (in_range) ?
-                nonzero_idx :
-                tile_nonzero_idx_end - 1;
-
-            OffsetT column_idx          = wd_column_indices[nonzero_idx2];
-            ValueT  value               = wd_values[nonzero_idx2];
-            ValueT  vector_value        = wd_vector_x[column_idx];
-            nonzero                     = value * vector_value;
-
-            if (!in_range)
-                nonzero = 0.0;
-
-            temp_storage.nonzeros[local_nonzero_idx] = nonzero;
-        }
-
-        CTA_SYNC();
-
-        //
-        // Swap in NANs at local row start offsets
-        //
-
-        OffsetT local_row_nonzero_idx = row_nonzero_idx - tile_nonzero_idx;
-        if ((local_row_nonzero_idx >= 0) && (local_row_nonzero_idx < TILE_ITEMS))
-        {
-            // Thread's row starts in this strip
-            row_start = temp_storage.nonzeros[local_row_nonzero_idx];
-            temp_storage.nonzeros[local_row_nonzero_idx] = NAN_TOKEN;
-        }
-
-        CTA_SYNC();
-
-        //
-        // Segmented scan
-        //
-
-        // Read strip of nonzeros into thread-blocked order, setup segment flags
-        KeyValuePairT scan_items[NNZ_PER_THREAD];
-        for (int ITEM = 0; ITEM < NNZ_PER_THREAD; ++ITEM)
-        {
-            int     local_nonzero_idx   = (threadIdx.x * NNZ_PER_THREAD) + ITEM;
-            ValueT  value               = temp_storage.nonzeros[local_nonzero_idx];
-            bool    is_nan              = (value != value);
-
-            scan_items[ITEM].value  = (is_nan) ? 0.0 : value;
-            scan_items[ITEM].key    = is_nan;
-        }
-
-        KeyValuePairT       tile_aggregate;
-        KeyValuePairT       scan_items_out[NNZ_PER_THREAD];
-
-        BlockScanT(temp_storage.scan).ExclusiveScan(scan_items, scan_items_out, scan_op, tile_aggregate, prefix_op);
-
-        // Save the inclusive sum for the last row
-        if (threadIdx.x == 0)
-        {
-            temp_storage.nonzeros[TILE_ITEMS] = prefix_op.running_total.value;
-        }
-
-        // Store segment totals
-        for (int ITEM = 0; ITEM < NNZ_PER_THREAD; ++ITEM)
-        {
-            int local_nonzero_idx = (threadIdx.x * NNZ_PER_THREAD) + ITEM;
-
-            if (scan_items[ITEM].key)
-                temp_storage.nonzeros[local_nonzero_idx] = scan_items_out[ITEM].value;
-        }
-
-        CTA_SYNC();
-
-        //
-        // Update row totals
-        //
-
-        OffsetT local_row_nonzero_idx_end = row_nonzero_idx_end - tile_nonzero_idx;
-        if ((local_row_nonzero_idx_end >= 0) && (local_row_nonzero_idx_end < TILE_ITEMS))
-        {
-            // Thread's row ends in this strip
-            row_total = temp_storage.nonzeros[local_row_nonzero_idx_end];
-        }
-
-        tile_nonzero_idx += NNZ_PER_THREAD * BLOCK_THREADS;
-    }
-
-
-
-    /**
-     * Consume input tile
-     */
-    __device__ __forceinline__ void ConsumeTile(
-        int     tile_idx,
-        int     rows_per_tile)
-    {
-        //
-        // Read in tile of row ranges
-        //
-
-        // Row range for the thread block
-        OffsetT tile_row_idx        = tile_idx * rows_per_tile;
-        OffsetT tile_row_idx_end    = CUB_MIN(tile_row_idx + rows_per_tile, spmv_params.num_rows);
-
-        // Thread's row
-        OffsetT row_idx             = tile_row_idx + threadIdx.x;
-        ValueT  row_total           = 0.0;
-        ValueT  row_start           = 0.0;
-
-        // Nonzero range for the thread's row
-        OffsetT row_nonzero_idx     = -1;
-        OffsetT row_nonzero_idx_end = -1;
-
-        if (row_idx < tile_row_idx_end)
-        {
-            row_nonzero_idx     = wd_row_end_offsets[row_idx - 1];
-            row_nonzero_idx_end = wd_row_end_offsets[row_idx];
-
-            // Share block's starting nonzero offset
-            if (threadIdx.x == 0)
-                temp_storage.tile_nonzero_idx = row_nonzero_idx;
-
-            // Share block's ending nonzero offset
-            if (row_idx == tile_row_idx_end - 1)
-                temp_storage.tile_nonzero_idx_end = row_nonzero_idx_end;
-
-            // Zero-length rows don't participate
-            if (row_nonzero_idx == row_nonzero_idx_end)
-            {
-                row_nonzero_idx = -1;
-                row_nonzero_idx_end = -1;
-            }
-        }
-
-        CTA_SYNC();
-
-        //
-        // Process strips of nonzeros
-        //
-
-        // Nonzero range for the thread block
-        OffsetT tile_nonzero_idx        = temp_storage.tile_nonzero_idx;
-        OffsetT tile_nonzero_idx_end    = temp_storage.tile_nonzero_idx_end;
-
-        KeyValuePairT tile_prefix(0, 0.0);
-        ReduceBySegmentOpT  scan_op;
-        PrefixOpT           prefix_op(tile_prefix, scan_op);
-
-        #pragma unroll 1
-        while (tile_nonzero_idx < tile_nonzero_idx_end)
-        {
-            ConsumeStrip<ITEMS_PER_THREAD>(prefix_op, scan_op, row_total, row_start,
-                tile_nonzero_idx, tile_nonzero_idx_end, row_nonzero_idx, row_nonzero_idx_end);
-
-            CTA_SYNC();
-        }
-
-        //
-        // Output to y
-        //
-
-        if (row_idx < tile_row_idx_end)
-        {
-            if (row_nonzero_idx_end == tile_nonzero_idx_end)
-            {
-                // Last row grabs the inclusive sum
-                row_total = temp_storage.nonzeros[TILE_ITEMS];
-            }
-
-            spmv_params.d_vector_y[row_idx] = row_start + row_total;
-        }
-    }
-
-
-};
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/agent/single_pass_scan_operators.cuh b/ml-xgboost/cub/cub/agent/single_pass_scan_operators.cuh
deleted file mode 100644
index 1674318..0000000
--- a/ml-xgboost/cub/cub/agent/single_pass_scan_operators.cuh
+++ /dev/null
@@ -1,792 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Callback operator types for supplying BlockScan prefixes
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../warp/warp_reduce.cuh"
-#include "../util_arch.cuh"
-#include "../util_device.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Prefix functor type for maintaining a running prefix while scanning a
- * region independent of other thread blocks
- ******************************************************************************/
-
-/**
- * Stateful callback operator type for supplying BlockScan prefixes.
- * Maintains a running prefix that can be applied to consecutive
- * BlockScan operations.
- */
-template <
-    typename T,                 ///< BlockScan value type
-    typename ScanOpT>            ///< Wrapped scan operator type
-struct BlockScanRunningPrefixOp
-{
-    ScanOpT     op;                 ///< Wrapped scan operator
-    T           running_total;      ///< Running block-wide prefix
-
-    /// Constructor
-    __device__ __forceinline__ BlockScanRunningPrefixOp(ScanOpT op)
-    :
-        op(op)
-    {}
-
-    /// Constructor
-    __device__ __forceinline__ BlockScanRunningPrefixOp(
-        T starting_prefix,
-        ScanOpT op)
-    :
-        op(op),
-        running_total(starting_prefix)
-    {}
-
-    /**
-     * Prefix callback operator.  Returns the block-wide running_total in thread-0.
-     */
-    __device__ __forceinline__ T operator()(
-        const T &block_aggregate)              ///< The aggregate sum of the BlockScan inputs
-    {
-        T retval = running_total;
-        running_total = op(running_total, block_aggregate);
-        return retval;
-    }
-};
-
-
-/******************************************************************************
- * Generic tile status interface types for block-cooperative scans
- ******************************************************************************/
-
-/**
- * Enumerations of tile status
- */
-enum ScanTileStatus
-{
-    SCAN_TILE_OOB,          // Out-of-bounds (e.g., padding)
-    SCAN_TILE_INVALID = 99, // Not yet processed
-    SCAN_TILE_PARTIAL,      // Tile aggregate is available
-    SCAN_TILE_INCLUSIVE,    // Inclusive tile prefix is available
-};
-
-
-/**
- * Tile status interface.
- */
-template <
-    typename    T,
-    bool        SINGLE_WORD = Traits<T>::PRIMITIVE>
-struct ScanTileState;
-
-
-/**
- * Tile status interface specialized for scan status and value types
- * that can be combined into one machine word that can be
- * read/written coherently in a single access.
- */
-template <typename T>
-struct ScanTileState<T, true>
-{
-    // Status word type
-    typedef typename If<(sizeof(T) == 8),
-        long long,
-        typename If<(sizeof(T) == 4),
-            int,
-            typename If<(sizeof(T) == 2),
-                short,
-                char>::Type>::Type>::Type StatusWord;
-
-
-    // Unit word type
-    typedef typename If<(sizeof(T) == 8),
-        longlong2,
-        typename If<(sizeof(T) == 4),
-            int2,
-            typename If<(sizeof(T) == 2),
-                int,
-                uchar2>::Type>::Type>::Type TxnWord;
-
-
-    // Device word type
-    struct TileDescriptor
-    {
-        StatusWord  status;
-        T           value;
-    };
-
-
-    // Constants
-    enum
-    {
-        TILE_STATUS_PADDING = CUB_PTX_WARP_THREADS,
-    };
-
-
-    // Device storage
-    TileDescriptor *d_tile_status;
-
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    ScanTileState()
-    :
-        d_tile_status(NULL)
-    {}
-
-
-    /// Initializer
-    __host__ __device__ __forceinline__
-    cudaError_t Init(
-        int     /*num_tiles*/,                      ///< [in] Number of tiles
-        void    *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t  /*temp_storage_bytes*/)             ///< [in] Size in bytes of \t d_temp_storage allocation
-    {
-        d_tile_status = reinterpret_cast<TileDescriptor*>(d_temp_storage);
-        return cudaSuccess;
-    }
-
-
-    /**
-     * Compute device memory needed for tile status
-     */
-    __host__ __device__ __forceinline__
-    static cudaError_t AllocationSize(
-        int     num_tiles,                          ///< [in] Number of tiles
-        size_t  &temp_storage_bytes)                ///< [out] Size in bytes of \t d_temp_storage allocation
-    {
-        temp_storage_bytes = (num_tiles + TILE_STATUS_PADDING) * sizeof(TileDescriptor);       // bytes needed for tile status descriptors
-        return cudaSuccess;
-    }
-
-
-    /**
-     * Initialize (from device)
-     */
-    __device__ __forceinline__ void InitializeStatus(int num_tiles)
-    {
-        int tile_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-        if (tile_idx < num_tiles)
-        {
-            // Not-yet-set
-            d_tile_status[TILE_STATUS_PADDING + tile_idx].status = StatusWord(SCAN_TILE_INVALID);
-        }
-
-        if ((blockIdx.x == 0) && (threadIdx.x < TILE_STATUS_PADDING))
-        {
-            // Padding
-            d_tile_status[threadIdx.x].status = StatusWord(SCAN_TILE_OOB);
-        }
-    }
-
-
-    /**
-     * Update the specified tile's inclusive value and corresponding status
-     */
-    __device__ __forceinline__ void SetInclusive(int tile_idx, T tile_inclusive)
-    {
-        TileDescriptor tile_descriptor;
-        tile_descriptor.status = SCAN_TILE_INCLUSIVE;
-        tile_descriptor.value = tile_inclusive;
-
-        TxnWord alias;
-        *reinterpret_cast<TileDescriptor*>(&alias) = tile_descriptor;
-        ThreadStore<STORE_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx), alias);
-    }
-
-
-    /**
-     * Update the specified tile's partial value and corresponding status
-     */
-    __device__ __forceinline__ void SetPartial(int tile_idx, T tile_partial)
-    {
-        TileDescriptor tile_descriptor;
-        tile_descriptor.status = SCAN_TILE_PARTIAL;
-        tile_descriptor.value = tile_partial;
-
-        TxnWord alias;
-        *reinterpret_cast<TileDescriptor*>(&alias) = tile_descriptor;
-        ThreadStore<STORE_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx), alias);
-    }
-
-    /**
-     * Wait for the corresponding tile to become non-invalid
-     */
-    __device__ __forceinline__ void WaitForValid(
-        int             tile_idx,
-        StatusWord      &status,
-        T               &value)
-    {
-        TileDescriptor  tile_descriptor;
-        do
-        {
-            __threadfence_block(); // prevent hoisting loads from loop
-            TxnWord alias = ThreadLoad<LOAD_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx));
-            tile_descriptor = reinterpret_cast<TileDescriptor&>(alias);
-
-        } while (WARP_ANY((tile_descriptor.status == SCAN_TILE_INVALID), 0xffffffff));
-
-        status = tile_descriptor.status;
-        value = tile_descriptor.value;
-    }
-
-};
-
-
-
-/**
- * Tile status interface specialized for scan status and value types that
- * cannot be combined into one machine word.
- */
-template <typename T>
-struct ScanTileState<T, false>
-{
-    // Status word type
-    typedef char StatusWord;
-
-    // Constants
-    enum
-    {
-        TILE_STATUS_PADDING = CUB_PTX_WARP_THREADS,
-    };
-
-    // Device storage
-    StatusWord  *d_tile_status;
-    T           *d_tile_partial;
-    T           *d_tile_inclusive;
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    ScanTileState()
-    :
-        d_tile_status(NULL),
-        d_tile_partial(NULL),
-        d_tile_inclusive(NULL)
-    {}
-
-
-    /// Initializer
-    __host__ __device__ __forceinline__
-    cudaError_t Init(
-        int     num_tiles,                          ///< [in] Number of tiles
-        void    *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t  temp_storage_bytes)                 ///< [in] Size in bytes of \t d_temp_storage allocation
-    {
-        cudaError_t error = cudaSuccess;
-        do
-        {
-            void*   allocations[3];
-            size_t  allocation_sizes[3];
-
-            allocation_sizes[0] = (num_tiles + TILE_STATUS_PADDING) * sizeof(StatusWord);           // bytes needed for tile status descriptors
-            allocation_sizes[1] = (num_tiles + TILE_STATUS_PADDING) * sizeof(Uninitialized<T>);     // bytes needed for partials
-            allocation_sizes[2] = (num_tiles + TILE_STATUS_PADDING) * sizeof(Uninitialized<T>);     // bytes needed for inclusives
-
-            // Compute allocation pointers into the single storage blob
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-
-            // Alias the offsets
-            d_tile_status       = reinterpret_cast<StatusWord*>(allocations[0]);
-            d_tile_partial      = reinterpret_cast<T*>(allocations[1]);
-            d_tile_inclusive    = reinterpret_cast<T*>(allocations[2]);
-        }
-        while (0);
-
-        return error;
-    }
-
-
-    /**
-     * Compute device memory needed for tile status
-     */
-    __host__ __device__ __forceinline__
-    static cudaError_t AllocationSize(
-        int     num_tiles,                          ///< [in] Number of tiles
-        size_t  &temp_storage_bytes)                ///< [out] Size in bytes of \t d_temp_storage allocation
-    {
-        // Specify storage allocation requirements
-        size_t  allocation_sizes[3];
-        allocation_sizes[0] = (num_tiles + TILE_STATUS_PADDING) * sizeof(StatusWord);         // bytes needed for tile status descriptors
-        allocation_sizes[1] = (num_tiles + TILE_STATUS_PADDING) * sizeof(Uninitialized<T>);   // bytes needed for partials
-        allocation_sizes[2] = (num_tiles + TILE_STATUS_PADDING) * sizeof(Uninitialized<T>);   // bytes needed for inclusives
-
-        // Set the necessary size of the blob
-        void* allocations[3];
-        return CubDebug(AliasTemporaries(NULL, temp_storage_bytes, allocations, allocation_sizes));
-    }
-
-
-    /**
-     * Initialize (from device)
-     */
-    __device__ __forceinline__ void InitializeStatus(int num_tiles)
-    {
-        int tile_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-        if (tile_idx < num_tiles)
-        {
-            // Not-yet-set
-            d_tile_status[TILE_STATUS_PADDING + tile_idx] = StatusWord(SCAN_TILE_INVALID);
-        }
-
-        if ((blockIdx.x == 0) && (threadIdx.x < TILE_STATUS_PADDING))
-        {
-            // Padding
-            d_tile_status[threadIdx.x] = StatusWord(SCAN_TILE_OOB);
-        }
-    }
-
-
-    /**
-     * Update the specified tile's inclusive value and corresponding status
-     */
-    __device__ __forceinline__ void SetInclusive(int tile_idx, T tile_inclusive)
-    {
-        // Update tile inclusive value
-        ThreadStore<STORE_CG>(d_tile_inclusive + TILE_STATUS_PADDING + tile_idx, tile_inclusive);
-
-        // Fence
-        __threadfence();
-
-        // Update tile status
-        ThreadStore<STORE_CG>(d_tile_status + TILE_STATUS_PADDING + tile_idx, StatusWord(SCAN_TILE_INCLUSIVE));
-    }
-
-
-    /**
-     * Update the specified tile's partial value and corresponding status
-     */
-    __device__ __forceinline__ void SetPartial(int tile_idx, T tile_partial)
-    {
-        // Update tile partial value
-        ThreadStore<STORE_CG>(d_tile_partial + TILE_STATUS_PADDING + tile_idx, tile_partial);
-
-        // Fence
-        __threadfence();
-
-        // Update tile status
-        ThreadStore<STORE_CG>(d_tile_status + TILE_STATUS_PADDING + tile_idx, StatusWord(SCAN_TILE_PARTIAL));
-    }
-
-    /**
-     * Wait for the corresponding tile to become non-invalid
-     */
-    __device__ __forceinline__ void WaitForValid(
-        int             tile_idx,
-        StatusWord      &status,
-        T               &value)
-    {
-        do {
-            status = ThreadLoad<LOAD_CG>(d_tile_status + TILE_STATUS_PADDING + tile_idx);
-
-            __threadfence();    // prevent hoisting loads from loop or loads below above this one
-
-        } while (status == SCAN_TILE_INVALID);
-
-        if (status == StatusWord(SCAN_TILE_PARTIAL)) 
-            value = ThreadLoad<LOAD_CG>(d_tile_partial + TILE_STATUS_PADDING + tile_idx);
-        else
-            value = ThreadLoad<LOAD_CG>(d_tile_inclusive + TILE_STATUS_PADDING + tile_idx);
-    }
-};
-
-
-/******************************************************************************
- * ReduceByKey tile status interface types for block-cooperative scans
- ******************************************************************************/
-
-/**
- * Tile status interface for reduction by key.
- *
- */
-template <
-    typename    ValueT,
-    typename    KeyT,
-    bool        SINGLE_WORD = (Traits<ValueT>::PRIMITIVE) && (sizeof(ValueT) + sizeof(KeyT) < 16)>
-struct ReduceByKeyScanTileState;
-
-
-/**
- * Tile status interface for reduction by key, specialized for scan status and value types that
- * cannot be combined into one machine word.
- */
-template <
-    typename    ValueT,
-    typename    KeyT>
-struct ReduceByKeyScanTileState<ValueT, KeyT, false> :
-    ScanTileState<KeyValuePair<KeyT, ValueT> >
-{
-    typedef ScanTileState<KeyValuePair<KeyT, ValueT> > SuperClass;
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    ReduceByKeyScanTileState() : SuperClass() {}
-};
-
-
-/**
- * Tile status interface for reduction by key, specialized for scan status and value types that
- * can be combined into one machine word that can be read/written coherently in a single access.
- */
-template <
-    typename ValueT,
-    typename KeyT>
-struct ReduceByKeyScanTileState<ValueT, KeyT, true>
-{
-    typedef KeyValuePair<KeyT, ValueT>KeyValuePairT;
-
-    // Constants
-    enum
-    {
-        PAIR_SIZE           = sizeof(ValueT) + sizeof(KeyT),
-        TXN_WORD_SIZE       = 1 << Log2<PAIR_SIZE + 1>::VALUE,
-        STATUS_WORD_SIZE    = TXN_WORD_SIZE - PAIR_SIZE,
-
-        TILE_STATUS_PADDING = CUB_PTX_WARP_THREADS,
-    };
-
-    // Status word type
-    typedef typename If<(STATUS_WORD_SIZE == 8),
-        long long,
-        typename If<(STATUS_WORD_SIZE == 4),
-            int,
-            typename If<(STATUS_WORD_SIZE == 2),
-                short,
-                char>::Type>::Type>::Type StatusWord;
-
-    // Status word type
-    typedef typename If<(TXN_WORD_SIZE == 16),
-        longlong2,
-        typename If<(TXN_WORD_SIZE == 8),
-            long long,
-            int>::Type>::Type TxnWord;
-
-    // Device word type (for when sizeof(ValueT) == sizeof(KeyT))
-    struct TileDescriptorBigStatus
-    {
-        KeyT        key;
-        ValueT      value;
-        StatusWord  status;
-    };
-
-    // Device word type (for when sizeof(ValueT) != sizeof(KeyT))
-    struct TileDescriptorLittleStatus
-    {
-        ValueT      value;
-        StatusWord  status;
-        KeyT        key;
-    };
-
-    // Device word type
-    typedef typename If<
-            (sizeof(ValueT) == sizeof(KeyT)),
-            TileDescriptorBigStatus,
-            TileDescriptorLittleStatus>::Type
-        TileDescriptor;
-
-
-    // Device storage
-    TileDescriptor *d_tile_status;
-
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    ReduceByKeyScanTileState()
-    :
-        d_tile_status(NULL)
-    {}
-
-
-    /// Initializer
-    __host__ __device__ __forceinline__
-    cudaError_t Init(
-        int     /*num_tiles*/,                      ///< [in] Number of tiles
-        void    *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t  /*temp_storage_bytes*/)             ///< [in] Size in bytes of \t d_temp_storage allocation
-    {
-        d_tile_status = reinterpret_cast<TileDescriptor*>(d_temp_storage);
-        return cudaSuccess;
-    }
-
-
-    /**
-     * Compute device memory needed for tile status
-     */
-    __host__ __device__ __forceinline__
-    static cudaError_t AllocationSize(
-        int     num_tiles,                          ///< [in] Number of tiles
-        size_t  &temp_storage_bytes)                ///< [out] Size in bytes of \t d_temp_storage allocation
-    {
-        temp_storage_bytes = (num_tiles + TILE_STATUS_PADDING) * sizeof(TileDescriptor);       // bytes needed for tile status descriptors
-        return cudaSuccess;
-    }
-
-
-    /**
-     * Initialize (from device)
-     */
-    __device__ __forceinline__ void InitializeStatus(int num_tiles)
-    {
-        int tile_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-        if (tile_idx < num_tiles)
-        {
-            // Not-yet-set
-            d_tile_status[TILE_STATUS_PADDING + tile_idx].status = StatusWord(SCAN_TILE_INVALID);
-        }
-
-        if ((blockIdx.x == 0) && (threadIdx.x < TILE_STATUS_PADDING))
-        {
-            // Padding
-            d_tile_status[threadIdx.x].status = StatusWord(SCAN_TILE_OOB);
-        }
-    }
-
-
-    /**
-     * Update the specified tile's inclusive value and corresponding status
-     */
-    __device__ __forceinline__ void SetInclusive(int tile_idx, KeyValuePairT tile_inclusive)
-    {
-        TileDescriptor tile_descriptor;
-        tile_descriptor.status  = SCAN_TILE_INCLUSIVE;
-        tile_descriptor.value   = tile_inclusive.value;
-        tile_descriptor.key     = tile_inclusive.key;
-
-        TxnWord alias;
-        *reinterpret_cast<TileDescriptor*>(&alias) = tile_descriptor;
-        ThreadStore<STORE_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx), alias);
-    }
-
-
-    /**
-     * Update the specified tile's partial value and corresponding status
-     */
-    __device__ __forceinline__ void SetPartial(int tile_idx, KeyValuePairT tile_partial)
-    {
-        TileDescriptor tile_descriptor;
-        tile_descriptor.status  = SCAN_TILE_PARTIAL;
-        tile_descriptor.value   = tile_partial.value;
-        tile_descriptor.key     = tile_partial.key;
-
-        TxnWord alias;
-        *reinterpret_cast<TileDescriptor*>(&alias) = tile_descriptor;
-        ThreadStore<STORE_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx), alias);
-    }
-
-    /**
-     * Wait for the corresponding tile to become non-invalid
-     */
-    __device__ __forceinline__ void WaitForValid(
-        int                     tile_idx,
-        StatusWord              &status,
-        KeyValuePairT           &value)
-    {
-        TxnWord         alias           = ThreadLoad<LOAD_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx));
-        TileDescriptor  tile_descriptor = reinterpret_cast<TileDescriptor&>(alias);
-
-        while (tile_descriptor.status == SCAN_TILE_INVALID)
-        {
-            __threadfence_block(); // prevent hoisting loads from loop
-
-            alias           = ThreadLoad<LOAD_CG>(reinterpret_cast<TxnWord*>(d_tile_status + TILE_STATUS_PADDING + tile_idx));
-            tile_descriptor = reinterpret_cast<TileDescriptor&>(alias);
-        }
-
-        status      = tile_descriptor.status;
-        value.value = tile_descriptor.value;
-        value.key   = tile_descriptor.key;
-    }
-
-};
-
-
-/******************************************************************************
- * Prefix call-back operator for coupling local block scan within a
- * block-cooperative scan
- ******************************************************************************/
-
-/**
- * Stateful block-scan prefix functor.  Provides the the running prefix for
- * the current tile by using the call-back warp to wait on on
- * aggregates/prefixes from predecessor tiles to become available.
- */
-template <
-    typename    T,
-    typename    ScanOpT,
-    typename    ScanTileStateT,
-    int         PTX_ARCH = CUB_PTX_ARCH>
-struct TilePrefixCallbackOp
-{
-    // Parameterized warp reduce
-    typedef WarpReduce<T, CUB_PTX_WARP_THREADS, PTX_ARCH> WarpReduceT;
-
-    // Temporary storage type
-    struct _TempStorage
-    {
-        typename WarpReduceT::TempStorage   warp_reduce;
-        T                                   exclusive_prefix;
-        T                                   inclusive_prefix;
-        T                                   block_aggregate;
-    };
-
-    // Alias wrapper allowing temporary storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-    // Type of status word
-    typedef typename ScanTileStateT::StatusWord StatusWord;
-
-    // Fields
-    _TempStorage&               temp_storage;       ///< Reference to a warp-reduction instance
-    ScanTileStateT&             tile_status;        ///< Interface to tile status
-    ScanOpT                     scan_op;            ///< Binary scan operator
-    int                         tile_idx;           ///< The current tile index
-    T                           exclusive_prefix;   ///< Exclusive prefix for the tile
-    T                           inclusive_prefix;   ///< Inclusive prefix for the tile
-
-    // Constructor
-    __device__ __forceinline__
-    TilePrefixCallbackOp(
-        ScanTileStateT       &tile_status,
-        TempStorage         &temp_storage,
-        ScanOpT              scan_op,
-        int                 tile_idx)
-    :
-        temp_storage(temp_storage.Alias()),
-        tile_status(tile_status),
-        scan_op(scan_op),
-        tile_idx(tile_idx) {}
-
-
-    // Block until all predecessors within the warp-wide window have non-invalid status
-    __device__ __forceinline__
-    void ProcessWindow(
-        int         predecessor_idx,        ///< Preceding tile index to inspect
-        StatusWord  &predecessor_status,    ///< [out] Preceding tile status
-        T           &window_aggregate)      ///< [out] Relevant partial reduction from this window of preceding tiles
-    {
-        T value;
-        tile_status.WaitForValid(predecessor_idx, predecessor_status, value);
-
-        // Perform a segmented reduction to get the prefix for the current window.
-        // Use the swizzled scan operator because we are now scanning *down* towards thread0.
-
-        int tail_flag = (predecessor_status == StatusWord(SCAN_TILE_INCLUSIVE));
-        window_aggregate = WarpReduceT(temp_storage.warp_reduce).TailSegmentedReduce(
-            value,
-            tail_flag,
-            SwizzleScanOp<ScanOpT>(scan_op));
-    }
-
-
-    // BlockScan prefix callback functor (called by the first warp)
-    __device__ __forceinline__
-    T operator()(T block_aggregate)
-    {
-
-        // Update our status with our tile-aggregate
-        if (threadIdx.x == 0)
-        {
-            temp_storage.block_aggregate = block_aggregate;
-            tile_status.SetPartial(tile_idx, block_aggregate);
-        }
-
-        int         predecessor_idx = tile_idx - threadIdx.x - 1;
-        StatusWord  predecessor_status;
-        T           window_aggregate;
-
-        // Wait for the warp-wide window of predecessor tiles to become valid
-        ProcessWindow(predecessor_idx, predecessor_status, window_aggregate);
-
-        // The exclusive tile prefix starts out as the current window aggregate
-        exclusive_prefix = window_aggregate;
-
-        // Keep sliding the window back until we come across a tile whose inclusive prefix is known
-        while (WARP_ALL((predecessor_status != StatusWord(SCAN_TILE_INCLUSIVE)), 0xffffffff))
-        {
-            predecessor_idx -= CUB_PTX_WARP_THREADS;
-
-            // Update exclusive tile prefix with the window prefix
-            ProcessWindow(predecessor_idx, predecessor_status, window_aggregate);
-            exclusive_prefix = scan_op(window_aggregate, exclusive_prefix);
-        }
-
-        // Compute the inclusive tile prefix and update the status for this tile
-        if (threadIdx.x == 0)
-        {
-            inclusive_prefix = scan_op(exclusive_prefix, block_aggregate);
-            tile_status.SetInclusive(tile_idx, inclusive_prefix);
-
-            temp_storage.exclusive_prefix = exclusive_prefix;
-            temp_storage.inclusive_prefix = inclusive_prefix;
-        }
-
-        // Return exclusive_prefix
-        return exclusive_prefix;
-    }
-
-    // Get the exclusive prefix stored in temporary storage
-    __device__ __forceinline__
-    T GetExclusivePrefix()
-    {
-        return temp_storage.exclusive_prefix;
-    }
-
-    // Get the inclusive prefix stored in temporary storage
-    __device__ __forceinline__
-    T GetInclusivePrefix()
-    {
-        return temp_storage.inclusive_prefix;
-    }
-
-    // Get the block aggregate stored in temporary storage
-    __device__ __forceinline__
-    T GetBlockAggregate()
-    {
-        return temp_storage.block_aggregate;
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_adjacent_difference.cuh b/ml-xgboost/cub/cub/block/block_adjacent_difference.cuh
deleted file mode 100644
index 7aeaaab..0000000
--- a/ml-xgboost/cub/cub/block/block_adjacent_difference.cuh
+++ /dev/null
@@ -1,596 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockDiscontinuity class provides [<em>collective</em>](index.html#sec0) methods for flagging discontinuities within an ordered set of items partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "../util_type.cuh"
-#include "../util_ptx.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-template <
-    typename    T,
-    int         BLOCK_DIM_X,
-    int         BLOCK_DIM_Y     = 1,
-    int         BLOCK_DIM_Z     = 1,
-    int         PTX_ARCH        = CUB_PTX_ARCH>
-class BlockAdjacentDifference
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-
-    /// Shared memory storage layout type (last element from each thread's input)
-    struct _TempStorage
-    {
-        T first_items[BLOCK_THREADS];
-        T last_items[BLOCK_THREADS];
-    };
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /// Specialization for when FlagOp has third index param
-    template <typename FlagOp, bool HAS_PARAM = BinaryOpHasIdxParam<T, FlagOp>::HAS_PARAM>
-    struct ApplyOp
-    {
-        // Apply flag operator
-        static __device__ __forceinline__ T FlagT(FlagOp flag_op, const T &a, const T &b, int idx)
-        {
-            return flag_op(b, a, idx);
-        }
-    };
-
-    /// Specialization for when FlagOp does not have a third index param
-    template <typename FlagOp>
-    struct ApplyOp<FlagOp, false>
-    {
-        // Apply flag operator
-        static __device__ __forceinline__ T FlagT(FlagOp flag_op, const T &a, const T &b, int /*idx*/)
-        {
-            return flag_op(b, a);
-        }
-    };
-
-    /// Templated unrolling of item comparison (inductive case)
-    template <int ITERATION, int MAX_ITERATIONS>
-    struct Iterate
-    {
-        // Head flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagHeads(
-            int                     linear_tid,
-            FlagT                   (&flags)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            T                       (&preds)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-            FlagOp                  flag_op)                            ///< [in] Binary boolean flag predicate
-        {
-            preds[ITERATION] = input[ITERATION - 1];
-
-            flags[ITERATION] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                preds[ITERATION],
-                input[ITERATION],
-                (linear_tid * ITEMS_PER_THREAD) + ITERATION);
-
-            Iterate<ITERATION + 1, MAX_ITERATIONS>::FlagHeads(linear_tid, flags, input, preds, flag_op);
-        }
-
-        // Tail flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagTails(
-            int                     linear_tid,
-            FlagT                   (&flags)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            FlagOp                  flag_op)                            ///< [in] Binary boolean flag predicate
-        {
-            flags[ITERATION] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITERATION],
-                input[ITERATION + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITERATION + 1);
-
-            Iterate<ITERATION + 1, MAX_ITERATIONS>::FlagTails(linear_tid, flags, input, flag_op);
-        }
-
-    };
-
-    /// Templated unrolling of item comparison (termination case)
-    template <int MAX_ITERATIONS>
-    struct Iterate<MAX_ITERATIONS, MAX_ITERATIONS>
-    {
-        // Head flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagHeads(
-            int                     /*linear_tid*/,
-            FlagT                   (&/*flags*/)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&/*input*/)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            T                       (&/*preds*/)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-            FlagOp                  /*flag_op*/)                            ///< [in] Binary boolean flag predicate
-        {}
-
-        // Tail flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagTails(
-            int                     /*linear_tid*/,
-            FlagT                   (&/*flags*/)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&/*input*/)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            FlagOp                  /*flag_op*/)                            ///< [in] Binary boolean flag predicate
-        {}
-    };
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-
-public:
-
-    /// \smemstorage{BlockDiscontinuity}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockAdjacentDifference()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockAdjacentDifference(
-        TempStorage &temp_storage)  ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Head flag operations
-     *********************************************************************/
-    //@{
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        T               (&preds)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share last item
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        if (linear_tid == 0)
-        {
-            // Set flag for first thread-item (preds[0] is undefined)
-            head_flags[0] = 1;
-        }
-        else
-        {
-            preds[0] = temp_storage.last_items[linear_tid - 1];
-            head_flags[0] = ApplyOp<FlagOp>::FlagT(flag_op, preds[0], input[0], linear_tid * ITEMS_PER_THREAD);
-        }
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-    }
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        T               (&preds)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-        FlagOp          flag_op,                            ///< [in] Binary boolean flag predicate
-        T               tile_predecessor_item)              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-    {
-        // Share last item
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        // Set flag for first thread-item
-        preds[0] = (linear_tid == 0) ?
-            tile_predecessor_item :              // First thread
-            temp_storage.last_items[linear_tid - 1];
-
-        head_flags[0] = ApplyOp<FlagOp>::FlagT(flag_op, preds[0], input[0], linear_tid * ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-    }
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        T preds[ITEMS_PER_THREAD];
-        FlagHeads(head_flags, input, preds, flag_op);
-    }
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op,                            ///< [in] Binary boolean flag predicate
-        T               tile_predecessor_item)              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-    {
-        T preds[ITEMS_PER_THREAD];
-        FlagHeads(head_flags, input, preds, flag_op, tile_predecessor_item);
-    }
-
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagTails(
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first item
-        temp_storage.first_items[linear_tid] = input[0];
-
-        CTA_SYNC();
-
-        // Set flag for last thread-item
-        tail_flags[ITEMS_PER_THREAD - 1] = (linear_tid == BLOCK_THREADS - 1) ?
-            1 :                             // Last thread
-            ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITEMS_PER_THREAD - 1],
-                temp_storage.first_items[linear_tid + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagTails(
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op,                            ///< [in] Binary boolean flag predicate
-        T               tile_successor_item)                ///< [in] <b>[<em>thread</em><sub><tt>BLOCK_THREADS</tt>-1</sub> only]</b> Item with which to compare the last tile item (<tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> from <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>).
-    {
-        // Share first item
-        temp_storage.first_items[linear_tid] = input[0];
-
-        CTA_SYNC();
-
-        // Set flag for last thread-item
-        T successor_item = (linear_tid == BLOCK_THREADS - 1) ?
-            tile_successor_item :              // Last thread
-            temp_storage.first_items[linear_tid + 1];
-
-        tail_flags[ITEMS_PER_THREAD - 1] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            input[ITEMS_PER_THREAD - 1],
-            successor_item,
-            (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        preds[0] = temp_storage.last_items[linear_tid - 1];
-        if (linear_tid == 0)
-        {
-            head_flags[0] = 1;
-        }
-        else
-        {
-            head_flags[0] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                preds[0],
-                input[0],
-                linear_tid * ITEMS_PER_THREAD);
-        }
-
-
-        // Set flag for last thread-item
-        tail_flags[ITEMS_PER_THREAD - 1] = (linear_tid == BLOCK_THREADS - 1) ?
-            1 :                             // Last thread
-            ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITEMS_PER_THREAD - 1],
-                temp_storage.first_items[linear_tid + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               tile_successor_item,                ///< [in] <b>[<em>thread</em><sub><tt>BLOCK_THREADS</tt>-1</sub> only]</b> Item with which to compare the last tile item (<tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> from <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>).
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        if (linear_tid == 0)
-        {
-            head_flags[0] = 1;
-        }
-        else
-        {
-            preds[0] = temp_storage.last_items[linear_tid - 1];
-            head_flags[0] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                preds[0],
-                input[0],
-                linear_tid * ITEMS_PER_THREAD);
-        }
-
-        // Set flag for last thread-item
-        T successor_item = (linear_tid == BLOCK_THREADS - 1) ?
-            tile_successor_item :              // Last thread
-            temp_storage.first_items[linear_tid + 1];
-
-        tail_flags[ITEMS_PER_THREAD - 1] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            input[ITEMS_PER_THREAD - 1],
-            successor_item,
-            (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               tile_predecessor_item,              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        preds[0] = (linear_tid == 0) ?
-            tile_predecessor_item :              // First thread
-            temp_storage.last_items[linear_tid - 1];
-
-        head_flags[0] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            preds[0],
-            input[0],
-            linear_tid * ITEMS_PER_THREAD);
-
-        // Set flag for last thread-item
-        tail_flags[ITEMS_PER_THREAD - 1] = (linear_tid == BLOCK_THREADS - 1) ?
-            1 :                             // Last thread
-            ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITEMS_PER_THREAD - 1],
-                temp_storage.first_items[linear_tid + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               tile_predecessor_item,              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               tile_successor_item,                ///< [in] <b>[<em>thread</em><sub><tt>BLOCK_THREADS</tt>-1</sub> only]</b> Item with which to compare the last tile item (<tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> from <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>).
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        preds[0] = (linear_tid == 0) ?
-            tile_predecessor_item :              // First thread
-            temp_storage.last_items[linear_tid - 1];
-
-        head_flags[0] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            preds[0],
-            input[0],
-            linear_tid * ITEMS_PER_THREAD);
-
-        // Set flag for last thread-item
-        T successor_item = (linear_tid == BLOCK_THREADS - 1) ?
-            tile_successor_item :              // Last thread
-            temp_storage.first_items[linear_tid + 1];
-
-        tail_flags[ITEMS_PER_THREAD - 1] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            input[ITEMS_PER_THREAD - 1],
-            successor_item,
-            (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/block/block_discontinuity.cuh b/ml-xgboost/cub/cub/block/block_discontinuity.cuh
deleted file mode 100644
index 2f3bb79..0000000
--- a/ml-xgboost/cub/cub/block/block_discontinuity.cuh
+++ /dev/null
@@ -1,1148 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockDiscontinuity class provides [<em>collective</em>](index.html#sec0) methods for flagging discontinuities within an ordered set of items partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "../util_type.cuh"
-#include "../util_ptx.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief The BlockDiscontinuity class provides [<em>collective</em>](index.html#sec0) methods for flagging discontinuities within an ordered set of items partitioned across a CUDA thread block. ![](discont_logo.png)
- * \ingroup BlockModule
- *
- * \tparam T                The data type to be flagged.
- * \tparam BLOCK_DIM_X      The thread block length in threads along the X dimension
- * \tparam BLOCK_DIM_Y      <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z      <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH         <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - A set of "head flags" (or "tail flags") is often used to indicate corresponding items
- *   that differ from their predecessors (or successors).  For example, head flags are convenient
- *   for demarcating disjoint data segments as part of a segmented scan or reduction.
- * - \blocked
- *
- * \par Performance Considerations
- * - \granularity
- *
- * \par A Simple Example
- * \blockcollective{BlockDiscontinuity}
- * \par
- * The code snippet below illustrates the head flagging of 512 integer items that
- * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
- * where each thread owns 4 consecutive items.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
- *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
- *
- *     // Allocate shared memory for BlockDiscontinuity
- *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
- *
- *     // Obtain a segment of consecutive items that are blocked across threads
- *     int thread_data[4];
- *     ...
- *
- *     // Collectively compute head flags for discontinuities in the segment
- *     int head_flags[4];
- *     BlockDiscontinuity(temp_storage).FlagHeads(head_flags, thread_data, cub::Inequality());
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the block of threads is
- * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], [3,4,4,4], ... }</tt>.
- * The corresponding output \p head_flags in those threads will be
- * <tt>{ [1,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
- *
- * \par Performance Considerations
- * - Incurs zero bank conflicts for most types
- *
- */
-template <
-    typename    T,
-    int         BLOCK_DIM_X,
-    int         BLOCK_DIM_Y     = 1,
-    int         BLOCK_DIM_Z     = 1,
-    int         PTX_ARCH        = CUB_PTX_ARCH>
-class BlockDiscontinuity
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-
-    /// Shared memory storage layout type (last element from each thread's input)
-    struct _TempStorage
-    {
-        T first_items[BLOCK_THREADS];
-        T last_items[BLOCK_THREADS];
-    };
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /// Specialization for when FlagOp has third index param
-    template <typename FlagOp, bool HAS_PARAM = BinaryOpHasIdxParam<T, FlagOp>::HAS_PARAM>
-    struct ApplyOp
-    {
-        // Apply flag operator
-        static __device__ __forceinline__ bool FlagT(FlagOp flag_op, const T &a, const T &b, int idx)
-        {
-            return flag_op(a, b, idx);
-        }
-    };
-
-    /// Specialization for when FlagOp does not have a third index param
-    template <typename FlagOp>
-    struct ApplyOp<FlagOp, false>
-    {
-        // Apply flag operator
-        static __device__ __forceinline__ bool FlagT(FlagOp flag_op, const T &a, const T &b, int /*idx*/)
-        {
-            return flag_op(a, b);
-        }
-    };
-
-    /// Templated unrolling of item comparison (inductive case)
-    template <int ITERATION, int MAX_ITERATIONS>
-    struct Iterate
-    {
-        // Head flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagHeads(
-            int                     linear_tid,
-            FlagT                   (&flags)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            T                       (&preds)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-            FlagOp                  flag_op)                            ///< [in] Binary boolean flag predicate
-        {
-            preds[ITERATION] = input[ITERATION - 1];
-
-            flags[ITERATION] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                preds[ITERATION],
-                input[ITERATION],
-                (linear_tid * ITEMS_PER_THREAD) + ITERATION);
-
-            Iterate<ITERATION + 1, MAX_ITERATIONS>::FlagHeads(linear_tid, flags, input, preds, flag_op);
-        }
-
-        // Tail flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagTails(
-            int                     linear_tid,
-            FlagT                   (&flags)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            FlagOp                  flag_op)                            ///< [in] Binary boolean flag predicate
-        {
-            flags[ITERATION] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITERATION],
-                input[ITERATION + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITERATION + 1);
-
-            Iterate<ITERATION + 1, MAX_ITERATIONS>::FlagTails(linear_tid, flags, input, flag_op);
-        }
-
-    };
-
-    /// Templated unrolling of item comparison (termination case)
-    template <int MAX_ITERATIONS>
-    struct Iterate<MAX_ITERATIONS, MAX_ITERATIONS>
-    {
-        // Head flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagHeads(
-            int                     /*linear_tid*/,
-            FlagT                   (&/*flags*/)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&/*input*/)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            T                       (&/*preds*/)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-            FlagOp                  /*flag_op*/)                            ///< [in] Binary boolean flag predicate
-        {}
-
-        // Tail flags
-        template <
-            int             ITEMS_PER_THREAD,
-            typename        FlagT,
-            typename        FlagOp>
-        static __device__ __forceinline__ void FlagTails(
-            int                     /*linear_tid*/,
-            FlagT                   (&/*flags*/)[ITEMS_PER_THREAD],         ///< [out] Calling thread's discontinuity head_flags
-            T                       (&/*input*/)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-            FlagOp                  /*flag_op*/)                            ///< [in] Binary boolean flag predicate
-        {}
-    };
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-
-public:
-
-    /// \smemstorage{BlockDiscontinuity}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockDiscontinuity()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockDiscontinuity(
-        TempStorage &temp_storage)  ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Head flag operations
-     *********************************************************************/
-    //@{
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        T               (&preds)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share last item
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        if (linear_tid == 0)
-        {
-            // Set flag for first thread-item (preds[0] is undefined)
-            head_flags[0] = 1;
-        }
-        else
-        {
-            preds[0] = temp_storage.last_items[linear_tid - 1];
-            head_flags[0] = ApplyOp<FlagOp>::FlagT(flag_op, preds[0], input[0], linear_tid * ITEMS_PER_THREAD);
-        }
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-    }
-
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        T               (&preds)[ITEMS_PER_THREAD],         ///< [out] Calling thread's predecessor items
-        FlagOp          flag_op,                            ///< [in] Binary boolean flag predicate
-        T               tile_predecessor_item)              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-    {
-        // Share last item
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        // Set flag for first thread-item
-        preds[0] = (linear_tid == 0) ?
-            tile_predecessor_item :              // First thread
-            temp_storage.last_items[linear_tid - 1];
-
-        head_flags[0] = ApplyOp<FlagOp>::FlagT(flag_op, preds[0], input[0], linear_tid * ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-    }
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-    /**
-     * \brief Sets head flags indicating discontinuities between items partitioned across the thread block, for which the first item has no reference and is always flagged.
-     *
-     * \par
-     * - The flag <tt>head_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(</tt><em>previous-item</em><tt>, input<sub><em>i</em></sub>)</tt>
-     *   returns \p true (where <em>previous-item</em> is either the preceding item
-     *   in the same thread or the last item in the previous thread).
-     * - For <em>thread</em><sub>0</sub>, item <tt>input<sub>0</sub></tt> is always flagged.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the head-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute head flags for discontinuities in the segment
-     *     int head_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagHeads(head_flags, thread_data, cub::Inequality());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], [3,4,4,4], ... }</tt>.
-     * The corresponding output \p head_flags in those threads will be
-     * <tt>{ [1,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        T preds[ITEMS_PER_THREAD];
-        FlagHeads(head_flags, input, preds, flag_op);
-    }
-
-
-    /**
-     * \brief Sets head flags indicating discontinuities between items partitioned across the thread block.
-     *
-     * \par
-     * - The flag <tt>head_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(</tt><em>previous-item</em><tt>, input<sub><em>i</em></sub>)</tt>
-     *   returns \p true (where <em>previous-item</em> is either the preceding item
-     *   in the same thread or the last item in the previous thread).
-     * - For <em>thread</em><sub>0</sub>, item <tt>input<sub>0</sub></tt> is compared
-     *   against \p tile_predecessor_item.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the head-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Have thread0 obtain the predecessor item for the entire tile
-     *     int tile_predecessor_item;
-     *     if (threadIdx.x == 0) tile_predecessor_item == ...
-     *
-     *     // Collectively compute head flags for discontinuities in the segment
-     *     int head_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagHeads(
-     *         head_flags, thread_data, cub::Inequality(), tile_predecessor_item);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], [3,4,4,4], ... }</tt>,
-     * and that \p tile_predecessor_item is \p 0.  The corresponding output \p head_flags in those threads will be
-     * <tt>{ [0,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeads(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op,                            ///< [in] Binary boolean flag predicate
-        T               tile_predecessor_item)              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-    {
-        T preds[ITEMS_PER_THREAD];
-        FlagHeads(head_flags, input, preds, flag_op, tile_predecessor_item);
-    }
-
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Tail flag operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Sets tail flags indicating discontinuities between items partitioned across the thread block, for which the last item has no reference and is always flagged.
-     *
-     * \par
-     * - The flag <tt>tail_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(input<sub><em>i</em></sub>, </tt><em>next-item</em><tt>)</tt>
-     *   returns \p true (where <em>next-item</em> is either the next item
-     *   in the same thread or the first item in the next thread).
-     * - For <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>, item
-     *   <tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> is always flagged.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the tail-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute tail flags for discontinuities in the segment
-     *     int tail_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagTails(tail_flags, thread_data, cub::Inequality());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], ..., [124,125,125,125] }</tt>.
-     * The corresponding output \p tail_flags in those threads will be
-     * <tt>{ [0,1,0,0], [0,0,0,1], [1,0,0,...], ..., [1,0,0,1] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagTails(
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first item
-        temp_storage.first_items[linear_tid] = input[0];
-
-        CTA_SYNC();
-
-        // Set flag for last thread-item
-        tail_flags[ITEMS_PER_THREAD - 1] = (linear_tid == BLOCK_THREADS - 1) ?
-            1 :                             // Last thread
-            ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITEMS_PER_THREAD - 1],
-                temp_storage.first_items[linear_tid + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    /**
-     * \brief Sets tail flags indicating discontinuities between items partitioned across the thread block.
-     *
-     * \par
-     * - The flag <tt>tail_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(input<sub><em>i</em></sub>, </tt><em>next-item</em><tt>)</tt>
-     *   returns \p true (where <em>next-item</em> is either the next item
-     *   in the same thread or the first item in the next thread).
-     * - For <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>, item
-     *   <tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> is compared
-     *   against \p tile_successor_item.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the tail-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Have thread127 obtain the successor item for the entire tile
-     *     int tile_successor_item;
-     *     if (threadIdx.x == 127) tile_successor_item == ...
-     *
-     *     // Collectively compute tail flags for discontinuities in the segment
-     *     int tail_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagTails(
-     *         tail_flags, thread_data, cub::Inequality(), tile_successor_item);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], ..., [124,125,125,125] }</tt>
-     * and that \p tile_successor_item is \p 125.  The corresponding output \p tail_flags in those threads will be
-     * <tt>{ [0,1,0,0], [0,0,0,1], [1,0,0,...], ..., [1,0,0,0] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagTails(
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op,                            ///< [in] Binary boolean flag predicate
-        T               tile_successor_item)                ///< [in] <b>[<em>thread</em><sub><tt>BLOCK_THREADS</tt>-1</sub> only]</b> Item with which to compare the last tile item (<tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> from <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>).
-    {
-        // Share first item
-        temp_storage.first_items[linear_tid] = input[0];
-
-        CTA_SYNC();
-
-        // Set flag for last thread-item
-        T successor_item = (linear_tid == BLOCK_THREADS - 1) ?
-            tile_successor_item :              // Last thread
-            temp_storage.first_items[linear_tid + 1];
-
-        tail_flags[ITEMS_PER_THREAD - 1] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            input[ITEMS_PER_THREAD - 1],
-            successor_item,
-            (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Head & tail flag operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Sets both head and tail flags indicating discontinuities between items partitioned across the thread block.
-     *
-     * \par
-     * - The flag <tt>head_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(</tt><em>previous-item</em><tt>, input<sub><em>i</em></sub>)</tt>
-     *   returns \p true (where <em>previous-item</em> is either the preceding item
-     *   in the same thread or the last item in the previous thread).
-     * - For <em>thread</em><sub>0</sub>, item <tt>input<sub>0</sub></tt> is always flagged.
-     * - The flag <tt>tail_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(input<sub><em>i</em></sub>, </tt><em>next-item</em><tt>)</tt>
-     *   returns \p true (where <em>next-item</em> is either the next item
-     *   in the same thread or the first item in the next thread).
-     * - For <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>, item
-     *   <tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> is always flagged.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the head- and tail-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute head and flags for discontinuities in the segment
-     *     int head_flags[4];
-     *     int tail_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagTails(
-     *         head_flags, tail_flags, thread_data, cub::Inequality());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], ..., [124,125,125,125] }</tt>
-     * and that the tile_successor_item is \p 125.  The corresponding output \p head_flags
-     * in those threads will be <tt>{ [1,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
-     * and the corresponding output \p tail_flags in those threads will be
-     * <tt>{ [0,1,0,0], [0,0,0,1], [1,0,0,...], ..., [1,0,0,1] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        preds[0] = temp_storage.last_items[linear_tid - 1];
-        if (linear_tid == 0)
-        {
-            head_flags[0] = 1;
-        }
-        else
-        {
-            head_flags[0] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                preds[0],
-                input[0],
-                linear_tid * ITEMS_PER_THREAD);
-        }
-
-
-        // Set flag for last thread-item
-        tail_flags[ITEMS_PER_THREAD - 1] = (linear_tid == BLOCK_THREADS - 1) ?
-            1 :                             // Last thread
-            ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITEMS_PER_THREAD - 1],
-                temp_storage.first_items[linear_tid + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    /**
-     * \brief Sets both head and tail flags indicating discontinuities between items partitioned across the thread block.
-     *
-     * \par
-     * - The flag <tt>head_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(</tt><em>previous-item</em><tt>, input<sub><em>i</em></sub>)</tt>
-     *   returns \p true (where <em>previous-item</em> is either the preceding item
-     *   in the same thread or the last item in the previous thread).
-     * - For <em>thread</em><sub>0</sub>, item <tt>input<sub>0</sub></tt> is always flagged.
-     * - The flag <tt>tail_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(input<sub><em>i</em></sub>, </tt><em>next-item</em><tt>)</tt>
-     *   returns \p true (where <em>next-item</em> is either the next item
-     *   in the same thread or the first item in the next thread).
-     * - For <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>, item
-     *   <tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> is compared
-     *   against \p tile_predecessor_item.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the head- and tail-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Have thread127 obtain the successor item for the entire tile
-     *     int tile_successor_item;
-     *     if (threadIdx.x == 127) tile_successor_item == ...
-     *
-     *     // Collectively compute head and flags for discontinuities in the segment
-     *     int head_flags[4];
-     *     int tail_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagTails(
-     *         head_flags, tail_flags, tile_successor_item, thread_data, cub::Inequality());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], ..., [124,125,125,125] }</tt>
-     * and that the tile_successor_item is \p 125.  The corresponding output \p head_flags
-     * in those threads will be <tt>{ [1,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
-     * and the corresponding output \p tail_flags in those threads will be
-     * <tt>{ [0,1,0,0], [0,0,0,1], [1,0,0,...], ..., [1,0,0,0] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               tile_successor_item,                ///< [in] <b>[<em>thread</em><sub><tt>BLOCK_THREADS</tt>-1</sub> only]</b> Item with which to compare the last tile item (<tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> from <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>).
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        if (linear_tid == 0)
-        {
-            head_flags[0] = 1;
-        }
-        else
-        {
-            preds[0] = temp_storage.last_items[linear_tid - 1];
-            head_flags[0] = ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                preds[0],
-                input[0],
-                linear_tid * ITEMS_PER_THREAD);
-        }
-
-        // Set flag for last thread-item
-        T successor_item = (linear_tid == BLOCK_THREADS - 1) ?
-            tile_successor_item :              // Last thread
-            temp_storage.first_items[linear_tid + 1];
-
-        tail_flags[ITEMS_PER_THREAD - 1] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            input[ITEMS_PER_THREAD - 1],
-            successor_item,
-            (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    /**
-     * \brief Sets both head and tail flags indicating discontinuities between items partitioned across the thread block.
-     *
-     * \par
-     * - The flag <tt>head_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(</tt><em>previous-item</em><tt>, input<sub><em>i</em></sub>)</tt>
-     *   returns \p true (where <em>previous-item</em> is either the preceding item
-     *   in the same thread or the last item in the previous thread).
-     * - For <em>thread</em><sub>0</sub>, item <tt>input<sub>0</sub></tt> is compared
-     *   against \p tile_predecessor_item.
-     * - The flag <tt>tail_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(input<sub><em>i</em></sub>, </tt><em>next-item</em><tt>)</tt>
-     *   returns \p true (where <em>next-item</em> is either the next item
-     *   in the same thread or the first item in the next thread).
-     * - For <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>, item
-     *   <tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> is always flagged.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the head- and tail-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Have thread0 obtain the predecessor item for the entire tile
-     *     int tile_predecessor_item;
-     *     if (threadIdx.x == 0) tile_predecessor_item == ...
-     *
-     *     // Have thread127 obtain the successor item for the entire tile
-     *     int tile_successor_item;
-     *     if (threadIdx.x == 127) tile_successor_item == ...
-     *
-     *     // Collectively compute head and flags for discontinuities in the segment
-     *     int head_flags[4];
-     *     int tail_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagTails(
-     *         head_flags, tile_predecessor_item, tail_flags, tile_successor_item,
-     *         thread_data, cub::Inequality());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], ..., [124,125,125,125] }</tt>,
-     * that the \p tile_predecessor_item is \p 0, and that the
-     * \p tile_successor_item is \p 125.  The corresponding output \p head_flags
-     * in those threads will be <tt>{ [0,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
-     * and the corresponding output \p tail_flags in those threads will be
-     * <tt>{ [0,1,0,0], [0,0,0,1], [1,0,0,...], ..., [1,0,0,1] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               tile_predecessor_item,              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        preds[0] = (linear_tid == 0) ?
-            tile_predecessor_item :              // First thread
-            temp_storage.last_items[linear_tid - 1];
-
-        head_flags[0] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            preds[0],
-            input[0],
-            linear_tid * ITEMS_PER_THREAD);
-
-        // Set flag for last thread-item
-        tail_flags[ITEMS_PER_THREAD - 1] = (linear_tid == BLOCK_THREADS - 1) ?
-            1 :                             // Last thread
-            ApplyOp<FlagOp>::FlagT(
-                flag_op,
-                input[ITEMS_PER_THREAD - 1],
-                temp_storage.first_items[linear_tid + 1],
-                (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-    /**
-     * \brief Sets both head and tail flags indicating discontinuities between items partitioned across the thread block.
-     *
-     * \par
-     * - The flag <tt>head_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(</tt><em>previous-item</em><tt>, input<sub><em>i</em></sub>)</tt>
-     *   returns \p true (where <em>previous-item</em> is either the preceding item
-     *   in the same thread or the last item in the previous thread).
-     * - For <em>thread</em><sub>0</sub>, item <tt>input<sub>0</sub></tt> is compared
-     *   against \p tile_predecessor_item.
-     * - The flag <tt>tail_flags<sub><em>i</em></sub></tt> is set for item
-     *   <tt>input<sub><em>i</em></sub></tt> when
-     *   <tt>flag_op(input<sub><em>i</em></sub>, </tt><em>next-item</em><tt>)</tt>
-     *   returns \p true (where <em>next-item</em> is either the next item
-     *   in the same thread or the first item in the next thread).
-     * - For <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>, item
-     *   <tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> is compared
-     *   against \p tile_successor_item.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the head- and tail-flagging of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_discontinuity.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockDiscontinuity for a 1D block of 128 threads on type int
-     *     typedef cub::BlockDiscontinuity<int, 128> BlockDiscontinuity;
-     *
-     *     // Allocate shared memory for BlockDiscontinuity
-     *     __shared__ typename BlockDiscontinuity::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Have thread0 obtain the predecessor item for the entire tile
-     *     int tile_predecessor_item;
-     *     if (threadIdx.x == 0) tile_predecessor_item == ...
-     *
-     *     // Have thread127 obtain the successor item for the entire tile
-     *     int tile_successor_item;
-     *     if (threadIdx.x == 127) tile_successor_item == ...
-     *
-     *     // Collectively compute head and flags for discontinuities in the segment
-     *     int head_flags[4];
-     *     int tail_flags[4];
-     *     BlockDiscontinuity(temp_storage).FlagTails(
-     *         head_flags, tile_predecessor_item, tail_flags, tile_successor_item,
-     *         thread_data, cub::Inequality());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,0,1,1], [1,1,1,1], [2,3,3,3], ..., [124,125,125,125] }</tt>,
-     * that the \p tile_predecessor_item is \p 0, and that the
-     * \p tile_successor_item is \p 125.  The corresponding output \p head_flags
-     * in those threads will be <tt>{ [0,0,1,0], [0,0,0,0], [1,1,0,0], [0,1,0,0], ... }</tt>.
-     * and the corresponding output \p tail_flags in those threads will be
-     * <tt>{ [0,1,0,0], [0,0,0,1], [1,0,0,...], ..., [1,0,0,0] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam FlagT                <b>[inferred]</b> The flag type (must be an integer type)
-     * \tparam FlagOp               <b>[inferred]</b> Binary predicate functor type having member <tt>T operator()(const T &a, const T &b)</tt> or member <tt>T operator()(const T &a, const T &b, unsigned int b_index)</tt>, and returning \p true if a discontinuity exists between \p a and \p b, otherwise \p false.  \p b_index is the rank of b in the aggregate tile of data.
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        FlagT,
-        typename        FlagOp>
-    __device__ __forceinline__ void FlagHeadsAndTails(
-        FlagT           (&head_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity head_flags
-        T               tile_predecessor_item,              ///< [in] <b>[<em>thread</em><sub>0</sub> only]</b> Item with which to compare the first tile item (<tt>input<sub>0</sub></tt> from <em>thread</em><sub>0</sub>).
-        FlagT           (&tail_flags)[ITEMS_PER_THREAD],    ///< [out] Calling thread's discontinuity tail_flags
-        T               tile_successor_item,                ///< [in] <b>[<em>thread</em><sub><tt>BLOCK_THREADS</tt>-1</sub> only]</b> Item with which to compare the last tile item (<tt>input</tt><sub><em>ITEMS_PER_THREAD</em>-1</sub> from <em>thread</em><sub><em>BLOCK_THREADS</em>-1</sub>).
-        T               (&input)[ITEMS_PER_THREAD],         ///< [in] Calling thread's input items
-        FlagOp          flag_op)                            ///< [in] Binary boolean flag predicate
-    {
-        // Share first and last items
-        temp_storage.first_items[linear_tid] = input[0];
-        temp_storage.last_items[linear_tid] = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        T preds[ITEMS_PER_THREAD];
-
-        // Set flag for first thread-item
-        preds[0] = (linear_tid == 0) ?
-            tile_predecessor_item :              // First thread
-            temp_storage.last_items[linear_tid - 1];
-
-        head_flags[0] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            preds[0],
-            input[0],
-            linear_tid * ITEMS_PER_THREAD);
-
-        // Set flag for last thread-item
-        T successor_item = (linear_tid == BLOCK_THREADS - 1) ?
-            tile_successor_item :              // Last thread
-            temp_storage.first_items[linear_tid + 1];
-
-        tail_flags[ITEMS_PER_THREAD - 1] = ApplyOp<FlagOp>::FlagT(
-            flag_op,
-            input[ITEMS_PER_THREAD - 1],
-            successor_item,
-            (linear_tid * ITEMS_PER_THREAD) + ITEMS_PER_THREAD);
-
-        // Set head_flags for remaining items
-        Iterate<1, ITEMS_PER_THREAD>::FlagHeads(linear_tid, head_flags, input, preds, flag_op);
-
-        // Set tail_flags for remaining items
-        Iterate<0, ITEMS_PER_THREAD - 1>::FlagTails(linear_tid, tail_flags, input, flag_op);
-    }
-
-
-
-
-    //@}  end member group
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/block/block_exchange.cuh b/ml-xgboost/cub/cub/block/block_exchange.cuh
deleted file mode 100644
index 5217976..0000000
--- a/ml-xgboost/cub/cub/block/block_exchange.cuh
+++ /dev/null
@@ -1,1248 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockExchange class provides [<em>collective</em>](index.html#sec0) methods for rearranging data partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "../util_ptx.cuh"
-#include "../util_arch.cuh"
-#include "../util_macro.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief The BlockExchange class provides [<em>collective</em>](index.html#sec0) methods for rearranging data partitioned across a CUDA thread block. ![](transpose_logo.png)
- * \ingroup BlockModule
- *
- * \tparam T                    The data type to be exchanged.
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam ITEMS_PER_THREAD     The number of items partitioned onto each thread.
- * \tparam WARP_TIME_SLICING    <b>[optional]</b> When \p true, only use enough shared memory for a single warp's worth of tile data, time-slicing the block-wide exchange over multiple synchronized rounds.  Yields a smaller memory footprint at the expense of decreased parallelism.  (Default: false)
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - It is commonplace for blocks of threads to rearrange data items between
- *   threads.  For example, the device-accessible memory subsystem prefers access patterns
- *   where data items are "striped" across threads (where consecutive threads access consecutive items),
- *   yet most block-wide operations prefer a "blocked" partitioning of items across threads
- *   (where consecutive items belong to a single thread).
- * - BlockExchange supports the following types of data exchanges:
- *   - Transposing between [<em>blocked</em>](index.html#sec5sec3) and [<em>striped</em>](index.html#sec5sec3) arrangements
- *   - Transposing between [<em>blocked</em>](index.html#sec5sec3) and [<em>warp-striped</em>](index.html#sec5sec3) arrangements
- *   - Scattering ranked items to a [<em>blocked arrangement</em>](index.html#sec5sec3)
- *   - Scattering ranked items to a [<em>striped arrangement</em>](index.html#sec5sec3)
- * - \rowmajor
- *
- * \par A Simple Example
- * \blockcollective{BlockExchange}
- * \par
- * The code snippet below illustrates the conversion from a "blocked" to a "striped" arrangement
- * of 512 integer items partitioned across 128 threads where each thread owns 4 items.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_exchange.cuh>
- *
- * __global__ void ExampleKernel(int *d_data, ...)
- * {
- *     // Specialize BlockExchange for a 1D block of 128 threads owning 4 integer items each
- *     typedef cub::BlockExchange<int, 128, 4> BlockExchange;
- *
- *     // Allocate shared memory for BlockExchange
- *     __shared__ typename BlockExchange::TempStorage temp_storage;
- *
- *     // Load a tile of data striped across threads
- *     int thread_data[4];
- *     cub::LoadDirectStriped<128>(threadIdx.x, d_data, thread_data);
- *
- *     // Collectively exchange data into a blocked arrangement across threads
- *     BlockExchange(temp_storage).StripedToBlocked(thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of striped input \p thread_data across the block of threads is
- * <tt>{ [0,128,256,384], [1,129,257,385], ..., [127,255,383,511] }</tt>.
- * The corresponding output \p thread_data in those threads will be
- * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
- *
- * \par Performance Considerations
- * - Proper device-specific padding ensures zero bank conflicts for most types.
- *
- */
-template <
-    typename    InputT,
-    int         BLOCK_DIM_X,
-    int         ITEMS_PER_THREAD,
-    bool        WARP_TIME_SLICING   = false,
-    int         BLOCK_DIM_Y         = 1,
-    int         BLOCK_DIM_Z         = 1,
-    int         PTX_ARCH            = CUB_PTX_ARCH>
-class BlockExchange
-{
-private:
-
-    /******************************************************************************
-     * Constants
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS               = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        LOG_WARP_THREADS            = CUB_LOG_WARP_THREADS(PTX_ARCH),
-        WARP_THREADS                = 1 << LOG_WARP_THREADS,
-        WARPS                       = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-
-        LOG_SMEM_BANKS              = CUB_LOG_SMEM_BANKS(PTX_ARCH),
-        SMEM_BANKS                  = 1 << LOG_SMEM_BANKS,
-
-        TILE_ITEMS                  = BLOCK_THREADS * ITEMS_PER_THREAD,
-
-        TIME_SLICES                 = (WARP_TIME_SLICING) ? WARPS : 1,
-
-        TIME_SLICED_THREADS         = (WARP_TIME_SLICING) ? CUB_MIN(BLOCK_THREADS, WARP_THREADS) : BLOCK_THREADS,
-        TIME_SLICED_ITEMS           = TIME_SLICED_THREADS * ITEMS_PER_THREAD,
-
-        WARP_TIME_SLICED_THREADS    = CUB_MIN(BLOCK_THREADS, WARP_THREADS),
-        WARP_TIME_SLICED_ITEMS      = WARP_TIME_SLICED_THREADS * ITEMS_PER_THREAD,
-
-        // Insert padding to avoid bank conflicts during raking when items per thread is a power of two and > 4 (otherwise we can typically use 128b loads)
-        INSERT_PADDING              = (ITEMS_PER_THREAD > 4) && (PowerOfTwo<ITEMS_PER_THREAD>::VALUE),
-        PADDING_ITEMS               = (INSERT_PADDING) ? (TIME_SLICED_ITEMS >> LOG_SMEM_BANKS) : 0,
-    };
-
-    /******************************************************************************
-     * Type definitions
-     ******************************************************************************/
-
-    /// Shared memory storage layout type
-    struct __align__(16) _TempStorage
-    {
-        InputT buff[TIME_SLICED_ITEMS + PADDING_ITEMS];
-    };
-
-public:
-
-    /// \smemstorage{BlockExchange}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-private:
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-    unsigned int lane_id;
-    unsigned int warp_id;
-    unsigned int warp_offset;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /**
-     * Transposes data items from <em>blocked</em> arrangement to <em>striped</em> arrangement.  Specialized for no timeslicing.
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void BlockedToStriped(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<false> /*time_slicing*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = (linear_tid * ITEMS_PER_THREAD) + ITEM;
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = int(ITEM * BLOCK_THREADS) + linear_tid;
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-
-    /**
-     * Transposes data items from <em>blocked</em> arrangement to <em>striped</em> arrangement.  Specialized for warp-timeslicing.
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void BlockedToStriped(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<true>  /*time_slicing*/)
-    {
-        InputT temp_items[ITEMS_PER_THREAD];
-
-        #pragma unroll
-        for (int SLICE = 0; SLICE < TIME_SLICES; SLICE++)
-        {
-            const int SLICE_OFFSET  = SLICE * TIME_SLICED_ITEMS;
-            const int SLICE_OOB     = SLICE_OFFSET + TIME_SLICED_ITEMS;
-
-            CTA_SYNC();
-
-            if (warp_id == SLICE)
-            {
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = (lane_id * ITEMS_PER_THREAD) + ITEM;
-                    if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                    temp_storage.buff[item_offset] = input_items[ITEM];
-                }
-            }
-
-            CTA_SYNC();
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                // Read a strip of items
-                const int STRIP_OFFSET  = ITEM * BLOCK_THREADS;
-                const int STRIP_OOB     = STRIP_OFFSET + BLOCK_THREADS;
-
-                if ((SLICE_OFFSET < STRIP_OOB) && (SLICE_OOB > STRIP_OFFSET))
-                {
-                    int item_offset = STRIP_OFFSET + linear_tid - SLICE_OFFSET;
-                    if ((item_offset >= 0) && (item_offset < TIME_SLICED_ITEMS))
-                    {
-                        if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                        temp_items[ITEM] = temp_storage.buff[item_offset];
-                    }
-                }
-            }
-        }
-
-        // Copy
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            output_items[ITEM] = temp_items[ITEM];
-        }
-    }
-
-
-    /**
-     * Transposes data items from <em>blocked</em> arrangement to <em>warp-striped</em> arrangement. Specialized for no timeslicing
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void BlockedToWarpStriped(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<false> /*time_slicing*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = warp_offset + ITEM + (lane_id * ITEMS_PER_THREAD);
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        WARP_SYNC(0xffffffff);
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = warp_offset + (ITEM * WARP_TIME_SLICED_THREADS) + lane_id;
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-    /**
-     * Transposes data items from <em>blocked</em> arrangement to <em>warp-striped</em> arrangement. Specialized for warp-timeslicing
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void BlockedToWarpStriped(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<true>  /*time_slicing*/)
-    {
-        if (warp_id == 0)
-        {
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                int item_offset = ITEM + (lane_id * ITEMS_PER_THREAD);
-                if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                temp_storage.buff[item_offset] = input_items[ITEM];
-            }
-
-            WARP_SYNC(0xffffffff);
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                int item_offset = (ITEM * WARP_TIME_SLICED_THREADS) + lane_id;
-                if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                output_items[ITEM] = temp_storage.buff[item_offset];
-            }
-        }
-
-        #pragma unroll
-        for (unsigned int SLICE = 1; SLICE < TIME_SLICES; ++SLICE)
-        {
-            CTA_SYNC();
-
-            if (warp_id == SLICE)
-            {
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = ITEM + (lane_id * ITEMS_PER_THREAD);
-                    if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                    temp_storage.buff[item_offset] = input_items[ITEM];
-                }
-
-                WARP_SYNC(0xffffffff);
-
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = (ITEM * WARP_TIME_SLICED_THREADS) + lane_id;
-                    if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                    output_items[ITEM] = temp_storage.buff[item_offset];
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Transposes data items from <em>striped</em> arrangement to <em>blocked</em> arrangement.  Specialized for no timeslicing.
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void StripedToBlocked(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<false> /*time_slicing*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = int(ITEM * BLOCK_THREADS) + linear_tid;
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        // No timeslicing
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = (linear_tid * ITEMS_PER_THREAD) + ITEM;
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-
-    /**
-     * Transposes data items from <em>striped</em> arrangement to <em>blocked</em> arrangement.  Specialized for warp-timeslicing.
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void StripedToBlocked(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<true>  /*time_slicing*/)
-    {
-        // Warp time-slicing
-        InputT temp_items[ITEMS_PER_THREAD];
-
-        #pragma unroll
-        for (int SLICE = 0; SLICE < TIME_SLICES; SLICE++)
-        {
-            const int SLICE_OFFSET  = SLICE * TIME_SLICED_ITEMS;
-            const int SLICE_OOB     = SLICE_OFFSET + TIME_SLICED_ITEMS;
-
-            CTA_SYNC();
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                // Write a strip of items
-                const int STRIP_OFFSET  = ITEM * BLOCK_THREADS;
-                const int STRIP_OOB     = STRIP_OFFSET + BLOCK_THREADS;
-
-                if ((SLICE_OFFSET < STRIP_OOB) && (SLICE_OOB > STRIP_OFFSET))
-                {
-                    int item_offset = STRIP_OFFSET + linear_tid - SLICE_OFFSET;
-                    if ((item_offset >= 0) && (item_offset < TIME_SLICED_ITEMS))
-                    {
-                        if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                        temp_storage.buff[item_offset] = input_items[ITEM];
-                    }
-                }
-            }
-
-            CTA_SYNC();
-
-            if (warp_id == SLICE)
-            {
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = (lane_id * ITEMS_PER_THREAD) + ITEM;
-                    if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                    temp_items[ITEM] = temp_storage.buff[item_offset];
-                }
-            }
-        }
-
-        // Copy
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            output_items[ITEM] = temp_items[ITEM];
-        }
-    }
-
-
-    /**
-     * Transposes data items from <em>warp-striped</em> arrangement to <em>blocked</em> arrangement.  Specialized for no timeslicing
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void WarpStripedToBlocked(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<false> /*time_slicing*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = warp_offset + (ITEM * WARP_TIME_SLICED_THREADS) + lane_id;
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        WARP_SYNC(0xffffffff);
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = warp_offset + ITEM + (lane_id * ITEMS_PER_THREAD);
-            if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-
-    /**
-     * Transposes data items from <em>warp-striped</em> arrangement to <em>blocked</em> arrangement.  Specialized for warp-timeslicing
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void WarpStripedToBlocked(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        Int2Type<true>  /*time_slicing*/)
-    {
-        #pragma unroll
-        for (unsigned int SLICE = 0; SLICE < TIME_SLICES; ++SLICE)
-        {
-            CTA_SYNC();
-
-            if (warp_id == SLICE)
-            {
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = (ITEM * WARP_TIME_SLICED_THREADS) + lane_id;
-                    if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                    temp_storage.buff[item_offset] = input_items[ITEM];
-                }
-
-                WARP_SYNC(0xffffffff);
-
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = ITEM + (lane_id * ITEMS_PER_THREAD);
-                    if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                    output_items[ITEM] = temp_storage.buff[item_offset];
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Exchanges data items annotated by rank into <em>blocked</em> arrangement.  Specialized for no timeslicing.
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToBlocked(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OffsetT         ranks[ITEMS_PER_THREAD],    ///< [in] Corresponding scatter ranks
-        Int2Type<false> /*time_slicing*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = ranks[ITEM];
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = (linear_tid * ITEMS_PER_THREAD) + ITEM;
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-    /**
-     * Exchanges data items annotated by rank into <em>blocked</em> arrangement.  Specialized for warp-timeslicing.
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToBlocked(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OffsetT         ranks[ITEMS_PER_THREAD],    ///< [in] Corresponding scatter ranks
-        Int2Type<true>  /*time_slicing*/)
-    {
-        InputT temp_items[ITEMS_PER_THREAD];
-
-        #pragma unroll
-        for (int SLICE = 0; SLICE < TIME_SLICES; SLICE++)
-        {
-            CTA_SYNC();
-
-            const int SLICE_OFFSET = TIME_SLICED_ITEMS * SLICE;
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                int item_offset = ranks[ITEM] - SLICE_OFFSET;
-                if ((item_offset >= 0) && (item_offset < WARP_TIME_SLICED_ITEMS))
-                {
-                    if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-                    temp_storage.buff[item_offset] = input_items[ITEM];
-                }
-            }
-
-            CTA_SYNC();
-
-            if (warp_id == SLICE)
-            {
-                #pragma unroll
-                for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-                {
-                    int item_offset = (lane_id * ITEMS_PER_THREAD) + ITEM;
-                    if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-                    temp_items[ITEM] = temp_storage.buff[item_offset];
-                }
-            }
-        }
-
-        // Copy
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            output_items[ITEM] = temp_items[ITEM];
-        }
-    }
-
-
-    /**
-     * Exchanges data items annotated by rank into <em>striped</em> arrangement.  Specialized for no timeslicing.
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToStriped(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OffsetT         ranks[ITEMS_PER_THREAD],    ///< [in] Corresponding scatter ranks
-        Int2Type<false> /*time_slicing*/)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = ranks[ITEM];
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = int(ITEM * BLOCK_THREADS) + linear_tid;
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-
-    /**
-     * Exchanges data items annotated by rank into <em>striped</em> arrangement.  Specialized for warp-timeslicing.
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToStriped(
-        InputT          input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OutputT         output_items[ITEMS_PER_THREAD],     ///< [out] Items to exchange, converting between <em>blocked</em> and <em>striped</em> arrangements.
-        OffsetT         ranks[ITEMS_PER_THREAD],    ///< [in] Corresponding scatter ranks
-        Int2Type<true> /*time_slicing*/)
-    {
-        InputT temp_items[ITEMS_PER_THREAD];
-
-        #pragma unroll
-        for (int SLICE = 0; SLICE < TIME_SLICES; SLICE++)
-        {
-            const int SLICE_OFFSET  = SLICE * TIME_SLICED_ITEMS;
-            const int SLICE_OOB     = SLICE_OFFSET + TIME_SLICED_ITEMS;
-
-            CTA_SYNC();
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                int item_offset = ranks[ITEM] - SLICE_OFFSET;
-                if ((item_offset >= 0) && (item_offset < WARP_TIME_SLICED_ITEMS))
-                {
-                    if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-                    temp_storage.buff[item_offset] = input_items[ITEM];
-                }
-            }
-
-            CTA_SYNC();
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-            {
-                // Read a strip of items
-                const int STRIP_OFFSET  = ITEM * BLOCK_THREADS;
-                const int STRIP_OOB     = STRIP_OFFSET + BLOCK_THREADS;
-
-                if ((SLICE_OFFSET < STRIP_OOB) && (SLICE_OOB > STRIP_OFFSET))
-                {
-                    int item_offset = STRIP_OFFSET + linear_tid - SLICE_OFFSET;
-                    if ((item_offset >= 0) && (item_offset < TIME_SLICED_ITEMS))
-                    {
-                        if (INSERT_PADDING) item_offset += item_offset >> LOG_SMEM_BANKS;
-                        temp_items[ITEM] = temp_storage.buff[item_offset];
-                    }
-                }
-            }
-        }
-
-        // Copy
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            output_items[ITEM] = temp_items[ITEM];
-        }
-    }
-
-
-public:
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockExchange()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)),
-        warp_id((WARPS == 1) ? 0 : linear_tid / WARP_THREADS),
-        lane_id(LaneId()),
-        warp_offset(warp_id * WARP_TIME_SLICED_ITEMS)
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockExchange(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)),
-        lane_id(LaneId()),
-        warp_id((WARPS == 1) ? 0 : linear_tid / WARP_THREADS),
-        warp_offset(warp_id * WARP_TIME_SLICED_ITEMS)
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Structured exchanges
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Transposes data items from <em>striped</em> arrangement to <em>blocked</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the conversion from a "striped" to a "blocked" arrangement
-     * of 512 integer items partitioned across 128 threads where each thread owns 4 items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_exchange.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, ...)
-     * {
-     *     // Specialize BlockExchange for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockExchange<int, 128, 4> BlockExchange;
-     *
-     *     // Allocate shared memory for BlockExchange
-     *     __shared__ typename BlockExchange::TempStorage temp_storage;
-     *
-     *     // Load a tile of ordered data into a striped arrangement across block threads
-     *     int thread_data[4];
-     *     cub::LoadDirectStriped<128>(threadIdx.x, d_data, thread_data);
-     *
-     *     // Collectively exchange data into a blocked arrangement across threads
-     *     BlockExchange(temp_storage).StripedToBlocked(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of striped input \p thread_data across the block of threads is
-     * <tt>{ [0,128,256,384], [1,129,257,385], ..., [127,255,383,511] }</tt> after loading from device-accessible memory.
-     * The corresponding output \p thread_data in those threads will be
-     * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
-     *
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void StripedToBlocked(
-        InputT      input_items[ITEMS_PER_THREAD],    ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD])   ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        StripedToBlocked(input_items, output_items, Int2Type<WARP_TIME_SLICING>());
-    }
-
-
-    /**
-     * \brief Transposes data items from <em>blocked</em> arrangement to <em>striped</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the conversion from a "blocked" to a "striped" arrangement
-     * of 512 integer items partitioned across 128 threads where each thread owns 4 items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_exchange.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, ...)
-     * {
-     *     // Specialize BlockExchange for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockExchange<int, 128, 4> BlockExchange;
-     *
-     *     // Allocate shared memory for BlockExchange
-     *     __shared__ typename BlockExchange::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively exchange data into a striped arrangement across threads
-     *     BlockExchange(temp_storage).BlockedToStriped(thread_data, thread_data);
-     *
-     *     // Store data striped across block threads into an ordered tile
-     *     cub::StoreDirectStriped<STORE_DEFAULT, 128>(threadIdx.x, d_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of blocked input \p thread_data across the block of threads is
-     * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
-     * The corresponding output \p thread_data in those threads will be
-     * <tt>{ [0,128,256,384], [1,129,257,385], ..., [127,255,383,511] }</tt> in
-     * preparation for storing to device-accessible memory.
-     *
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void BlockedToStriped(
-        InputT      input_items[ITEMS_PER_THREAD],    ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD])   ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        BlockedToStriped(input_items, output_items, Int2Type<WARP_TIME_SLICING>());
-    }
-
-
-
-    /**
-     * \brief Transposes data items from <em>warp-striped</em> arrangement to <em>blocked</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the conversion from a "warp-striped" to a "blocked" arrangement
-     * of 512 integer items partitioned across 128 threads where each thread owns 4 items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_exchange.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, ...)
-     * {
-     *     // Specialize BlockExchange for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockExchange<int, 128, 4> BlockExchange;
-     *
-     *     // Allocate shared memory for BlockExchange
-     *     __shared__ typename BlockExchange::TempStorage temp_storage;
-     *
-     *     // Load a tile of ordered data into a warp-striped arrangement across warp threads
-     *     int thread_data[4];
-     *     cub::LoadSWarptriped<LOAD_DEFAULT>(threadIdx.x, d_data, thread_data);
-     *
-     *     // Collectively exchange data into a blocked arrangement across threads
-     *     BlockExchange(temp_storage).WarpStripedToBlocked(thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of warp-striped input \p thread_data across the block of threads is
-     * <tt>{ [0,32,64,96], [1,33,65,97], [2,34,66,98], ..., [415,447,479,511] }</tt>
-     * after loading from device-accessible memory.  (The first 128 items are striped across
-     * the first warp of 32 threads, the second 128 items are striped across the second warp, etc.)
-     * The corresponding output \p thread_data in those threads will be
-     * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
-     *
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void WarpStripedToBlocked(
-        InputT      input_items[ITEMS_PER_THREAD],    ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD])   ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        WarpStripedToBlocked(input_items, output_items, Int2Type<WARP_TIME_SLICING>());
-    }
-
-
-
-    /**
-     * \brief Transposes data items from <em>blocked</em> arrangement to <em>warp-striped</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the conversion from a "blocked" to a "warp-striped" arrangement
-     * of 512 integer items partitioned across 128 threads where each thread owns 4 items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_exchange.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, ...)
-     * {
-     *     // Specialize BlockExchange for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockExchange<int, 128, 4> BlockExchange;
-     *
-     *     // Allocate shared memory for BlockExchange
-     *     __shared__ typename BlockExchange::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively exchange data into a warp-striped arrangement across threads
-     *     BlockExchange(temp_storage).BlockedToWarpStriped(thread_data, thread_data);
-     *
-     *     // Store data striped across warp threads into an ordered tile
-     *     cub::StoreDirectStriped<STORE_DEFAULT, 128>(threadIdx.x, d_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of blocked input \p thread_data across the block of threads is
-     * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
-     * The corresponding output \p thread_data in those threads will be
-     * <tt>{ [0,32,64,96], [1,33,65,97], [2,34,66,98], ..., [415,447,479,511] }</tt>
-     * in preparation for storing to device-accessible memory. (The first 128 items are striped across
-     * the first warp of 32 threads, the second 128 items are striped across the second warp, etc.)
-     *
-     */
-    template <typename OutputT>
-    __device__ __forceinline__ void BlockedToWarpStriped(
-        InputT      input_items[ITEMS_PER_THREAD],    ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD])   ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        BlockedToWarpStriped(input_items, output_items, Int2Type<WARP_TIME_SLICING>());
-    }
-
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Scatter exchanges
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Exchanges data items annotated by rank into <em>blocked</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \tparam OffsetT                              <b>[inferred]</b> Signed integer type for local offsets
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToBlocked(
-        InputT      input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD],     ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD])            ///< [in] Corresponding scatter ranks
-    {
-        ScatterToBlocked(input_items, output_items, ranks, Int2Type<WARP_TIME_SLICING>());
-    }
-
-
-
-    /**
-     * \brief Exchanges data items annotated by rank into <em>striped</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \tparam OffsetT                              <b>[inferred]</b> Signed integer type for local offsets
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToStriped(
-        InputT      input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD],     ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD])            ///< [in] Corresponding scatter ranks
-    {
-        ScatterToStriped(input_items, output_items, ranks, Int2Type<WARP_TIME_SLICING>());
-    }
-
-
-
-    /**
-     * \brief Exchanges data items annotated by rank into <em>striped</em> arrangement.  Items with rank -1 are not exchanged.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \tparam OffsetT                              <b>[inferred]</b> Signed integer type for local offsets
-     */
-    template <typename OutputT, typename OffsetT>
-    __device__ __forceinline__ void ScatterToStripedGuarded(
-        InputT      input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD],     ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD])            ///< [in] Corresponding scatter ranks
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = ranks[ITEM];
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            if (ranks[ITEM] >= 0)
-                temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = int(ITEM * BLOCK_THREADS) + linear_tid;
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-
-
-
-    /**
-     * \brief Exchanges valid data items annotated by rank into <em>striped</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \tparam OffsetT                              <b>[inferred]</b> Signed integer type for local offsets
-     * \tparam ValidFlag                            <b>[inferred]</b> FlagT type denoting which items are valid
-     */
-    template <typename OutputT, typename OffsetT, typename ValidFlag>
-    __device__ __forceinline__ void ScatterToStripedFlagged(
-        InputT      input_items[ITEMS_PER_THREAD],      ///< [in] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OutputT     output_items[ITEMS_PER_THREAD],     ///< [out] Items from exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD],            ///< [in] Corresponding scatter ranks
-        ValidFlag   is_valid[ITEMS_PER_THREAD])         ///< [in] Corresponding flag denoting item validity
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = ranks[ITEM];
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            if (is_valid[ITEM])
-                temp_storage.buff[item_offset] = input_items[ITEM];
-        }
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = int(ITEM * BLOCK_THREADS) + linear_tid;
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            output_items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-
-    //@}  end member group
-
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-    __device__ __forceinline__ void StripedToBlocked(
-        InputT      items[ITEMS_PER_THREAD])   ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        StripedToBlocked(items, items);
-    }
-
-    __device__ __forceinline__ void BlockedToStriped(
-        InputT      items[ITEMS_PER_THREAD])   ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        BlockedToStriped(items, items);
-    }
-
-    __device__ __forceinline__ void WarpStripedToBlocked(
-        InputT      items[ITEMS_PER_THREAD])    ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        WarpStripedToBlocked(items, items);
-    }
-
-    __device__ __forceinline__ void BlockedToWarpStriped(
-        InputT      items[ITEMS_PER_THREAD])    ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-    {
-        BlockedToWarpStriped(items, items);
-    }
-
-    template <typename OffsetT>
-    __device__ __forceinline__ void ScatterToBlocked(
-        InputT      items[ITEMS_PER_THREAD],    ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD])    ///< [in] Corresponding scatter ranks
-    {
-        ScatterToBlocked(items, items, ranks);
-    }
-
-    template <typename OffsetT>
-    __device__ __forceinline__ void ScatterToStriped(
-        InputT      items[ITEMS_PER_THREAD],    ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD])    ///< [in] Corresponding scatter ranks
-    {
-        ScatterToStriped(items, items, ranks);
-    }
-
-    template <typename OffsetT>
-    __device__ __forceinline__ void ScatterToStripedGuarded(
-        InputT      items[ITEMS_PER_THREAD],    ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD])    ///< [in] Corresponding scatter ranks
-    {
-        ScatterToStripedGuarded(items, items, ranks);
-    }
-
-    template <typename OffsetT, typename ValidFlag>
-    __device__ __forceinline__ void ScatterToStripedFlagged(
-        InputT      items[ITEMS_PER_THREAD],        ///< [in-out] Items to exchange, converting between <em>striped</em> and <em>blocked</em> arrangements.
-        OffsetT     ranks[ITEMS_PER_THREAD],        ///< [in] Corresponding scatter ranks
-        ValidFlag   is_valid[ITEMS_PER_THREAD])     ///< [in] Corresponding flag denoting item validity
-    {
-        ScatterToStriped(items, items, ranks, is_valid);
-    }
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-};
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-template <
-    typename    T,
-    int         ITEMS_PER_THREAD,
-    int         LOGICAL_WARP_THREADS    = CUB_PTX_WARP_THREADS,
-    int         PTX_ARCH                = CUB_PTX_ARCH>
-class WarpExchange
-{
-private:
-
-    /******************************************************************************
-     * Constants
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        // Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        WARP_ITEMS                  = (ITEMS_PER_THREAD * LOGICAL_WARP_THREADS) + 1,
-
-        LOG_SMEM_BANKS              = CUB_LOG_SMEM_BANKS(PTX_ARCH),
-        SMEM_BANKS                  = 1 << LOG_SMEM_BANKS,
-
-        // Insert padding if the number of items per thread is a power of two and > 4 (otherwise we can typically use 128b loads)
-        INSERT_PADDING              = (ITEMS_PER_THREAD > 4) && (PowerOfTwo<ITEMS_PER_THREAD>::VALUE),
-        PADDING_ITEMS               = (INSERT_PADDING) ? (WARP_ITEMS >> LOG_SMEM_BANKS) : 0,
-    };
-
-    /******************************************************************************
-     * Type definitions
-     ******************************************************************************/
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        T buff[WARP_ITEMS + PADDING_ITEMS];
-    };
-
-public:
-
-    /// \smemstorage{WarpExchange}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-private:
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    _TempStorage    &temp_storage;
-    int             lane_id;
-
-public:
-
-    /******************************************************************************
-     * Construction
-     ******************************************************************************/
-
-    /// Constructor
-    __device__ __forceinline__ WarpExchange(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        lane_id(IS_ARCH_WARP ?
-            LaneId() :
-            LaneId() % LOGICAL_WARP_THREADS)
-    {}
-
-
-    /******************************************************************************
-     * Interface
-     ******************************************************************************/
-
-    /**
-     * \brief Exchanges valid data items annotated by rank into <em>striped</em> arrangement.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \tparam OffsetT                              <b>[inferred]</b> Signed integer type for local offsets
-     */
-    template <typename OffsetT>
-    __device__ __forceinline__ void ScatterToStriped(
-        T               items[ITEMS_PER_THREAD],        ///< [in-out] Items to exchange
-        OffsetT         ranks[ITEMS_PER_THREAD])        ///< [in] Corresponding scatter ranks
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if (INSERT_PADDING) ranks[ITEM] = SHR_ADD(ranks[ITEM], LOG_SMEM_BANKS, ranks[ITEM]);
-            temp_storage.buff[ranks[ITEM]] = items[ITEM];
-        }
-
-        WARP_SYNC(0xffffffff);
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            int item_offset = (ITEM * LOGICAL_WARP_THREADS) + lane_id;
-            if (INSERT_PADDING) item_offset = SHR_ADD(item_offset, LOG_SMEM_BANKS, item_offset);
-            items[ITEM] = temp_storage.buff[item_offset];
-        }
-    }
-
-};
-
-
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_histogram.cuh b/ml-xgboost/cub/cub/block/block_histogram.cuh
deleted file mode 100644
index b8bce77..0000000
--- a/ml-xgboost/cub/cub/block/block_histogram.cuh
+++ /dev/null
@@ -1,415 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockHistogram class provides [<em>collective</em>](index.html#sec0) methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "specializations/block_histogram_sort.cuh"
-#include "specializations/block_histogram_atomic.cuh"
-#include "../util_ptx.cuh"
-#include "../util_arch.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Algorithmic variants
- ******************************************************************************/
-
-/**
- * \brief BlockHistogramAlgorithm enumerates alternative algorithms for the parallel construction of block-wide histograms.
- */
-enum BlockHistogramAlgorithm
-{
-
-    /**
-     * \par Overview
-     * Sorting followed by differentiation.  Execution is comprised of two phases:
-     * -# Sort the data using efficient radix sort
-     * -# Look for "runs" of same-valued keys by detecting discontinuities; the run-lengths are histogram bin counts.
-     *
-     * \par Performance Considerations
-     * Delivers consistent throughput regardless of sample bin distribution.
-     */
-    BLOCK_HISTO_SORT,
-
-
-    /**
-     * \par Overview
-     * Use atomic addition to update byte counts directly
-     *
-     * \par Performance Considerations
-     * Performance is strongly tied to the hardware implementation of atomic
-     * addition, and may be significantly degraded for non uniformly-random
-     * input distributions where many concurrent updates are likely to be
-     * made to the same bin counter.
-     */
-    BLOCK_HISTO_ATOMIC,
-};
-
-
-
-/******************************************************************************
- * Block histogram
- ******************************************************************************/
-
-
-/**
- * \brief The BlockHistogram class provides [<em>collective</em>](index.html#sec0) methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block. ![](histogram_logo.png)
- * \ingroup BlockModule
- *
- * \tparam T                    The sample type being histogrammed (must be castable to an integer bin identifier)
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam ITEMS_PER_THREAD     The number of items per thread
- * \tparam BINS                 The number bins within the histogram
- * \tparam ALGORITHM            <b>[optional]</b> cub::BlockHistogramAlgorithm enumerator specifying the underlying algorithm to use (default: cub::BLOCK_HISTO_SORT)
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - A <a href="http://en.wikipedia.org/wiki/Histogram"><em>histogram</em></a>
- *   counts the number of observations that fall into each of the disjoint categories (known as <em>bins</em>).
- * - BlockHistogram can be optionally specialized to use different algorithms:
- *   -# <b>cub::BLOCK_HISTO_SORT</b>.  Sorting followed by differentiation. [More...](\ref cub::BlockHistogramAlgorithm)
- *   -# <b>cub::BLOCK_HISTO_ATOMIC</b>.  Use atomic addition to update byte counts directly. [More...](\ref cub::BlockHistogramAlgorithm)
- *
- * \par Performance Considerations
- * - \granularity
- *
- * \par A Simple Example
- * \blockcollective{BlockHistogram}
- * \par
- * The code snippet below illustrates a 256-bin histogram of 512 integer samples that
- * are partitioned across 128 threads where each thread owns 4 samples.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_histogram.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 character samples each
- *     typedef cub::BlockHistogram<unsigned char, 128, 4, 256> BlockHistogram;
- *
- *     // Allocate shared memory for BlockHistogram
- *     __shared__ typename BlockHistogram::TempStorage temp_storage;
- *
- *     // Allocate shared memory for block-wide histogram bin counts
- *     __shared__ unsigned int smem_histogram[256];
- *
- *     // Obtain input samples per thread
- *     unsigned char data[4];
- *     ...
- *
- *     // Compute the block-wide histogram
- *     BlockHistogram(temp_storage).Histogram(data, smem_histogram);
- *
- * \endcode
- *
- * \par Performance and Usage Considerations
- * - The histogram output can be constructed in shared or device-accessible memory
- * - See cub::BlockHistogramAlgorithm for performance details regarding algorithmic alternatives
- *
- */
-template <
-    typename                T,
-    int                     BLOCK_DIM_X,
-    int                     ITEMS_PER_THREAD,
-    int                     BINS,
-    BlockHistogramAlgorithm ALGORITHM           = BLOCK_HISTO_SORT,
-    int                     BLOCK_DIM_Y         = 1,
-    int                     BLOCK_DIM_Z         = 1,
-    int                     PTX_ARCH            = CUB_PTX_ARCH>
-class BlockHistogram
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    /**
-     * Ensure the template parameterization meets the requirements of the
-     * targeted device architecture.  BLOCK_HISTO_ATOMIC can only be used
-     * on version SM120 or later.  Otherwise BLOCK_HISTO_SORT is used
-     * regardless.
-     */
-    static const BlockHistogramAlgorithm SAFE_ALGORITHM =
-        ((ALGORITHM == BLOCK_HISTO_ATOMIC) && (PTX_ARCH < 120)) ?
-            BLOCK_HISTO_SORT :
-            ALGORITHM;
-
-    /// Internal specialization.
-    typedef typename If<(SAFE_ALGORITHM == BLOCK_HISTO_SORT),
-        BlockHistogramSort<T, BLOCK_DIM_X, ITEMS_PER_THREAD, BINS, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH>,
-        BlockHistogramAtomic<BINS> >::Type InternalBlockHistogram;
-
-    /// Shared memory storage layout type for BlockHistogram
-    typedef typename InternalBlockHistogram::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-public:
-
-    /// \smemstorage{BlockHistogram}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockHistogram()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockHistogram(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Histogram operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Initialize the shared histogram counters to zero.
-     *
-     * \par Snippet
-     * The code snippet below illustrates a the initialization and update of a
-     * histogram of 512 integer samples that are partitioned across 128 threads
-     * where each thread owns 4 samples.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_histogram.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 character samples each
-     *     typedef cub::BlockHistogram<unsigned char, 128, 4, 256> BlockHistogram;
-     *
-     *     // Allocate shared memory for BlockHistogram
-     *     __shared__ typename BlockHistogram::TempStorage temp_storage;
-     *
-     *     // Allocate shared memory for block-wide histogram bin counts
-     *     __shared__ unsigned int smem_histogram[256];
-     *
-     *     // Obtain input samples per thread
-     *     unsigned char thread_samples[4];
-     *     ...
-     *
-     *     // Initialize the block-wide histogram
-     *     BlockHistogram(temp_storage).InitHistogram(smem_histogram);
-     *
-     *     // Update the block-wide histogram
-     *     BlockHistogram(temp_storage).Composite(thread_samples, smem_histogram);
-     *
-     * \endcode
-     *
-     * \tparam CounterT              <b>[inferred]</b> Histogram counter type
-     */
-    template <typename CounterT     >
-    __device__ __forceinline__ void InitHistogram(CounterT      histogram[BINS])
-    {
-        // Initialize histogram bin counts to zeros
-        int histo_offset = 0;
-
-        #pragma unroll
-        for(; histo_offset + BLOCK_THREADS <= BINS; histo_offset += BLOCK_THREADS)
-        {
-            histogram[histo_offset + linear_tid] = 0;
-        }
-        // Finish up with guarded initialization if necessary
-        if ((BINS % BLOCK_THREADS != 0) && (histo_offset + linear_tid < BINS))
-        {
-            histogram[histo_offset + linear_tid] = 0;
-        }
-    }
-
-
-    /**
-     * \brief Constructs a block-wide histogram in shared/device-accessible memory.  Each thread contributes an array of input elements.
-     *
-     * \par
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a 256-bin histogram of 512 integer samples that
-     * are partitioned across 128 threads where each thread owns 4 samples.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_histogram.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 character samples each
-     *     typedef cub::BlockHistogram<unsigned char, 128, 4, 256> BlockHistogram;
-     *
-     *     // Allocate shared memory for BlockHistogram
-     *     __shared__ typename BlockHistogram::TempStorage temp_storage;
-     *
-     *     // Allocate shared memory for block-wide histogram bin counts
-     *     __shared__ unsigned int smem_histogram[256];
-     *
-     *     // Obtain input samples per thread
-     *     unsigned char thread_samples[4];
-     *     ...
-     *
-     *     // Compute the block-wide histogram
-     *     BlockHistogram(temp_storage).Histogram(thread_samples, smem_histogram);
-     *
-     * \endcode
-     *
-     * \tparam CounterT              <b>[inferred]</b> Histogram counter type
-     */
-    template <
-        typename            CounterT     >
-    __device__ __forceinline__ void Histogram(
-        T                   (&items)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input values to histogram
-        CounterT             histogram[BINS])                ///< [out] Reference to shared/device-accessible memory histogram
-    {
-        // Initialize histogram bin counts to zeros
-        InitHistogram(histogram);
-
-        CTA_SYNC();
-
-        // Composite the histogram
-        InternalBlockHistogram(temp_storage).Composite(items, histogram);
-    }
-
-
-
-    /**
-     * \brief Updates an existing block-wide histogram in shared/device-accessible memory.  Each thread composites an array of input elements.
-     *
-     * \par
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a the initialization and update of a
-     * histogram of 512 integer samples that are partitioned across 128 threads
-     * where each thread owns 4 samples.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_histogram.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 character samples each
-     *     typedef cub::BlockHistogram<unsigned char, 128, 4, 256> BlockHistogram;
-     *
-     *     // Allocate shared memory for BlockHistogram
-     *     __shared__ typename BlockHistogram::TempStorage temp_storage;
-     *
-     *     // Allocate shared memory for block-wide histogram bin counts
-     *     __shared__ unsigned int smem_histogram[256];
-     *
-     *     // Obtain input samples per thread
-     *     unsigned char thread_samples[4];
-     *     ...
-     *
-     *     // Initialize the block-wide histogram
-     *     BlockHistogram(temp_storage).InitHistogram(smem_histogram);
-     *
-     *     // Update the block-wide histogram
-     *     BlockHistogram(temp_storage).Composite(thread_samples, smem_histogram);
-     *
-     * \endcode
-     *
-     * \tparam CounterT              <b>[inferred]</b> Histogram counter type
-     */
-    template <
-        typename            CounterT     >
-    __device__ __forceinline__ void Composite(
-        T                   (&items)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input values to histogram
-        CounterT             histogram[BINS])                 ///< [out] Reference to shared/device-accessible memory histogram
-    {
-        InternalBlockHistogram(temp_storage).Composite(items, histogram);
-    }
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_load.cuh b/ml-xgboost/cub/cub/block/block_load.cuh
deleted file mode 100644
index dac487f..0000000
--- a/ml-xgboost/cub/cub/block/block_load.cuh
+++ /dev/null
@@ -1,1268 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Operations for reading linear tiles of data into the CUDA thread block.
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "block_exchange.cuh"
-#include "../iterator/cache_modified_input_iterator.cuh"
-#include "../util_ptx.cuh"
-#include "../util_macro.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIo
- * @{
- */
-
-
-/******************************************************************//**
- * \name Blocked arrangement I/O (direct)
- *********************************************************************/
-//@{
-
-
-/**
- * \brief Load a linear segment of items into a blocked arrangement across the thread block.
- *
- * \blocked
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    typename        InputT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectBlocked(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-{
-    InputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
-
-    // Load directly in thread-blocked order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        items[ITEM] = thread_itr[ITEM];
-    }
-}
-
-
-/**
- * \brief Load a linear segment of items into a blocked arrangement across the thread block, guarded by range.
- *
- * \blocked
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    typename        InputT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectBlocked(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-    int             valid_items)                ///< [in] Number of valid items to load
-{
-    InputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
-
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        if ((linear_tid * ITEMS_PER_THREAD) + ITEM < valid_items)
-        {
-            items[ITEM] = thread_itr[ITEM];
-        }
-    }
-}
-
-
-/**
- * \brief Load a linear segment of items into a blocked arrangement across the thread block, guarded by range, with a fall-back assignment of out-of-bound elements..
- *
- * \blocked
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    typename        InputT,
-    typename        DefaultT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectBlocked(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-    int             valid_items,                ///< [in] Number of valid items to load
-    DefaultT        oob_default)                ///< [in] Default value to assign out-of-bound items
-{
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        items[ITEM] = oob_default;
-
-    LoadDirectBlocked(linear_tid, block_itr, items, valid_items);
-}
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-/**
- * Internal implementation for load vectorization
- */
-template <
-    CacheLoadModifier   MODIFIER,
-    typename            T,
-    int                 ITEMS_PER_THREAD>
-__device__ __forceinline__ void InternalLoadDirectBlockedVectorized(
-    int    linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    T      *block_ptr,                 ///< [in] Input pointer for loading from
-    T      (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-{
-    // Biggest memory access word that T is a whole multiple of
-    typedef typename UnitWord<T>::DeviceWord DeviceWord;
-
-    enum
-    {
-        TOTAL_WORDS = sizeof(items) / sizeof(DeviceWord),
-
-        VECTOR_SIZE = (TOTAL_WORDS % 4 == 0) ?
-            4 :
-            (TOTAL_WORDS % 2 == 0) ?
-                2 :
-                1,
-
-        VECTORS_PER_THREAD = TOTAL_WORDS / VECTOR_SIZE,
-    };
-
-    // Vector type
-    typedef typename CubVector<DeviceWord, VECTOR_SIZE>::Type Vector;
-
-    // Vector items
-    Vector vec_items[VECTORS_PER_THREAD];
-
-    // Aliased input ptr
-    Vector* vec_ptr = reinterpret_cast<Vector*>(block_ptr) + (linear_tid * VECTORS_PER_THREAD);
-
-    // Load directly in thread-blocked order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < VECTORS_PER_THREAD; ITEM++)
-    {
-        vec_items[ITEM] = ThreadLoad<MODIFIER>(vec_ptr + ITEM);
-    }
-
-    // Copy
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        items[ITEM] = *(reinterpret_cast<T*>(vec_items) + ITEM);
-    }
-}
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/**
- * \brief Load a linear segment of items into a blocked arrangement across the thread block.
- *
- * \blocked
- *
- * The input offset (\p block_ptr + \p block_offset) must be quad-item aligned
- *
- * The following conditions will prevent vectorization and loading will fall back to cub::BLOCK_LOAD_DIRECT:
- *   - \p ITEMS_PER_THREAD is odd
- *   - The data type \p T is not a built-in primitive or CUDA vector type (e.g., \p short, \p int2, \p double, \p float2, etc.)
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- */
-template <
-    typename        T,
-    int             ITEMS_PER_THREAD>
-__device__ __forceinline__ void LoadDirectBlockedVectorized(
-    int linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    T   *block_ptr,                 ///< [in] Input pointer for loading from
-    T   (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-{
-    InternalLoadDirectBlockedVectorized<LOAD_DEFAULT>(linear_tid, block_ptr, items);
-}
-
-
-//@}  end member group
-/******************************************************************//**
- * \name Striped arrangement I/O (direct)
- *********************************************************************/
-//@{
-
-
-/**
- * \brief Load a linear segment of items into a striped arrangement across the thread block.
- *
- * \striped
- *
- * \tparam BLOCK_THREADS        The thread block size in threads
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    int             BLOCK_THREADS,
-    typename        InputT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectStriped(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-{
-    InputIteratorT thread_itr = block_itr + linear_tid;
-
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        items[ITEM] = thread_itr[ITEM * BLOCK_THREADS];
-    }
-}
-
-
-/**
- * \brief Load a linear segment of items into a striped arrangement across the thread block, guarded by range
- *
- * \striped
- *
- * \tparam BLOCK_THREADS        The thread block size in threads
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    int             BLOCK_THREADS,
-    typename        InputT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectStriped(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-    int             valid_items)                ///< [in] Number of valid items to load
-{
-    InputIteratorT thread_itr = block_itr + linear_tid;
-
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        if (linear_tid + (ITEM * BLOCK_THREADS) < valid_items)
-        {
-            items[ITEM] = thread_itr[ITEM * BLOCK_THREADS];
-        }
-    }
-}
-
-
-/**
- * \brief Load a linear segment of items into a striped arrangement across the thread block, guarded by range, with a fall-back assignment of out-of-bound elements.
- *
- * \striped
- *
- * \tparam BLOCK_THREADS        The thread block size in threads
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    int             BLOCK_THREADS,
-    typename        InputT,
-    typename        DefaultT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectStriped(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-    int             valid_items,                ///< [in] Number of valid items to load
-    DefaultT        oob_default)                ///< [in] Default value to assign out-of-bound items
-{
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        items[ITEM] = oob_default;
-
-    LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, valid_items);
-}
-
-
-
-//@}  end member group
-/******************************************************************//**
- * \name Warp-striped arrangement I/O (direct)
- *********************************************************************/
-//@{
-
-
-/**
- * \brief Load a linear segment of items into a warp-striped arrangement across the thread block.
- *
- * \warpstriped
- *
- * \par Usage Considerations
- * The number of threads in the thread block must be a multiple of the architecture's warp size.
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT       <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    typename        InputT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectWarpStriped(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-{
-    int tid                = linear_tid & (CUB_PTX_WARP_THREADS - 1);
-    int wid                = linear_tid >> CUB_PTX_LOG_WARP_THREADS;
-    int warp_offset        = wid * CUB_PTX_WARP_THREADS * ITEMS_PER_THREAD;
-
-    InputIteratorT thread_itr = block_itr + warp_offset + tid ;
-
-    // Load directly in warp-striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        items[ITEM] = thread_itr[(ITEM * CUB_PTX_WARP_THREADS)];
-    }
-}
-
-
-/**
- * \brief Load a linear segment of items into a warp-striped arrangement across the thread block, guarded by range
- *
- * \warpstriped
- *
- * \par Usage Considerations
- * The number of threads in the thread block must be a multiple of the architecture's warp size.
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT        <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    typename        InputT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectWarpStriped(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-    int             valid_items)                ///< [in] Number of valid items to load
-{
-    int tid                = linear_tid & (CUB_PTX_WARP_THREADS - 1);
-    int wid                = linear_tid >> CUB_PTX_LOG_WARP_THREADS;
-    int warp_offset        = wid * CUB_PTX_WARP_THREADS * ITEMS_PER_THREAD;
-
-    InputIteratorT thread_itr = block_itr + warp_offset + tid ;
-
-    // Load directly in warp-striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        if (warp_offset + tid + (ITEM * CUB_PTX_WARP_THREADS) < valid_items)
-        {
-            items[ITEM] = thread_itr[(ITEM * CUB_PTX_WARP_THREADS)];
-        }
-    }
-}
-
-
-/**
- * \brief Load a linear segment of items into a warp-striped arrangement across the thread block, guarded by range, with a fall-back assignment of out-of-bound elements.
- *
- * \warpstriped
- *
- * \par Usage Considerations
- * The number of threads in the thread block must be a multiple of the architecture's warp size.
- *
- * \tparam T                    <b>[inferred]</b> The data type to load.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam InputIteratorT        <b>[inferred]</b> The random-access iterator type for input \iterator.
- */
-template <
-    typename        InputT,
-    typename        DefaultT,
-    int             ITEMS_PER_THREAD,
-    typename        InputIteratorT>
-__device__ __forceinline__ void LoadDirectWarpStriped(
-    int             linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-    InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-    int             valid_items,                ///< [in] Number of valid items to load
-    DefaultT        oob_default)                ///< [in] Default value to assign out-of-bound items
-{
-    // Load directly in warp-striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        items[ITEM] = oob_default;
-
-    LoadDirectWarpStriped(linear_tid, block_itr, items, valid_items);
-}
-
-
-
-//@}  end member group
-
-/** @} */       // end group UtilIo
-
-
-
-//-----------------------------------------------------------------------------
-// Generic BlockLoad abstraction
-//-----------------------------------------------------------------------------
-
-/**
- * \brief cub::BlockLoadAlgorithm enumerates alternative algorithms for cub::BlockLoad to read a linear segment of data from memory into a blocked arrangement across a CUDA thread block.
- */
-
-/**
- * \brief cub::BlockLoadAlgorithm enumerates alternative algorithms for cub::BlockLoad to read a linear segment of data from memory into a blocked arrangement across a CUDA thread block.
- */
-enum BlockLoadAlgorithm
-{
-    /**
-     * \par Overview
-     *
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is read
-     * directly from memory.
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) decreases as the
-     *   access stride between threads increases (i.e., the number items per thread).
-     */
-    BLOCK_LOAD_DIRECT,
-
-    /**
-     * \par Overview
-     *
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is read
-     * from memory using CUDA's built-in vectorized loads as a coalescing optimization.
-     * For example, <tt>ld.global.v4.s32</tt> instructions will be generated
-     * when \p T = \p int and \p ITEMS_PER_THREAD % 4 == 0.
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high until the the
-     *   access stride between threads (i.e., the number items per thread) exceeds the
-     *   maximum vector load width (typically 4 items or 64B, whichever is lower).
-     * - The following conditions will prevent vectorization and loading will fall back to cub::BLOCK_LOAD_DIRECT:
-     *   - \p ITEMS_PER_THREAD is odd
-     *   - The \p InputIteratorTis not a simple pointer type
-     *   - The block input offset is not quadword-aligned
-     *   - The data type \p T is not a built-in primitive or CUDA vector type (e.g., \p short, \p int2, \p double, \p float2, etc.)
-     */
-    BLOCK_LOAD_VECTORIZE,
-
-    /**
-     * \par Overview
-     *
-     * A [<em>striped arrangement</em>](index.html#sec5sec3) of data is read
-     * efficiently from memory and then locally transposed into a
-     * [<em>blocked arrangement</em>](index.html#sec5sec3).
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high regardless
-     *   of items loaded per thread.
-     * - The local reordering incurs slightly longer latencies and throughput than the
-     *   direct cub::BLOCK_LOAD_DIRECT and cub::BLOCK_LOAD_VECTORIZE alternatives.
-     */
-    BLOCK_LOAD_TRANSPOSE,
-
-
-    /**
-     * \par Overview
-     *
-     * A [<em>warp-striped arrangement</em>](index.html#sec5sec3) of data is
-     * read efficiently from memory and then locally transposed into a
-     * [<em>blocked arrangement</em>](index.html#sec5sec3).
-     *
-     * \par Usage Considerations
-     * - BLOCK_THREADS must be a multiple of WARP_THREADS
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high regardless
-     *   of items loaded per thread.
-     * - The local reordering incurs slightly larger latencies than the
-     *   direct cub::BLOCK_LOAD_DIRECT and cub::BLOCK_LOAD_VECTORIZE alternatives.
-     * - Provisions more shared storage, but incurs smaller latencies than the
-     *   BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED alternative.
-     */
-    BLOCK_LOAD_WARP_TRANSPOSE,
-
-
-    /**
-     * \par Overview
-     *
-     * Like \p BLOCK_LOAD_WARP_TRANSPOSE, a [<em>warp-striped arrangement</em>](index.html#sec5sec3)
-     * of data is read directly from memory and then is locally transposed into a
-     * [<em>blocked arrangement</em>](index.html#sec5sec3). To reduce the shared memory
-     * requirement, only one warp's worth of shared memory is provisioned and is
-     * subsequently time-sliced among warps.
-     *
-     * \par Usage Considerations
-     * - BLOCK_THREADS must be a multiple of WARP_THREADS
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high regardless
-     *   of items loaded per thread.
-     * - Provisions less shared memory temporary storage, but incurs larger
-     *   latencies than the BLOCK_LOAD_WARP_TRANSPOSE alternative.
-     */
-    BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED,
-};
-
-
-/**
- * \brief The BlockLoad class provides [<em>collective</em>](index.html#sec0) data movement methods for loading a linear segment of items from memory into a [<em>blocked arrangement</em>](index.html#sec5sec3) across a CUDA thread block.  ![](block_load_logo.png)
- * \ingroup BlockModule
- * \ingroup UtilIo
- *
- * \tparam InputT               The data type to read into (which must be convertible from the input iterator's value type).
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam ITEMS_PER_THREAD     The number of consecutive items partitioned onto each thread.
- * \tparam ALGORITHM            <b>[optional]</b> cub::BlockLoadAlgorithm tuning policy.  default: cub::BLOCK_LOAD_DIRECT.
- * \tparam WARP_TIME_SLICING    <b>[optional]</b> Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage). (default: false)
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - The BlockLoad class provides a single data movement abstraction that can be specialized
- *   to implement different cub::BlockLoadAlgorithm strategies.  This facilitates different
- *   performance policies for different architectures, data types, granularity sizes, etc.
- * - BlockLoad can be optionally specialized by different data movement strategies:
- *   -# <b>cub::BLOCK_LOAD_DIRECT</b>.  A [<em>blocked arrangement</em>](index.html#sec5sec3)
- *      of data is read directly from memory.  [More...](\ref cub::BlockLoadAlgorithm)
- *   -# <b>cub::BLOCK_LOAD_VECTORIZE</b>.  A [<em>blocked arrangement</em>](index.html#sec5sec3)
- *      of data is read directly from memory using CUDA's built-in vectorized loads as a
- *      coalescing optimization.    [More...](\ref cub::BlockLoadAlgorithm)
- *   -# <b>cub::BLOCK_LOAD_TRANSPOSE</b>.  A [<em>striped arrangement</em>](index.html#sec5sec3)
- *      of data is read directly from memory and is then locally transposed into a
- *      [<em>blocked arrangement</em>](index.html#sec5sec3).  [More...](\ref cub::BlockLoadAlgorithm)
- *   -# <b>cub::BLOCK_LOAD_WARP_TRANSPOSE</b>.  A [<em>warp-striped arrangement</em>](index.html#sec5sec3)
- *      of data is read directly from memory and is then locally transposed into a
- *      [<em>blocked arrangement</em>](index.html#sec5sec3).  [More...](\ref cub::BlockLoadAlgorithm)
- *   -# <b>cub::BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED,</b>.  A [<em>warp-striped arrangement</em>](index.html#sec5sec3)
- *      of data is read directly from memory and is then locally transposed into a
- *      [<em>blocked arrangement</em>](index.html#sec5sec3) one warp at a time.  [More...](\ref cub::BlockLoadAlgorithm)
- * - \rowmajor
- *
- * \par A Simple Example
- * \blockcollective{BlockLoad}
- * \par
- * The code snippet below illustrates the loading of a linear
- * segment of 512 integers into a "blocked" arrangement across 128 threads where each
- * thread owns 4 consecutive items.  The load is specialized for \p BLOCK_LOAD_WARP_TRANSPOSE,
- * meaning memory references are efficiently coalesced using a warp-striped access
- * pattern (after which items are locally reordered among threads).
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>
- *
- * __global__ void ExampleKernel(int *d_data, ...)
- * {
- *     // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
- *     typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
- *
- *     // Allocate shared memory for BlockLoad
- *     __shared__ typename BlockLoad::TempStorage temp_storage;
- *
- *     // Load a segment of consecutive items that are blocked across threads
- *     int thread_data[4];
- *     BlockLoad(temp_storage).Load(d_data, thread_data);
- *
- * \endcode
- * \par
- * Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, ...</tt>.
- * The set of \p thread_data across the block of threads in those threads will be
- * <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
- *
- */
-template <
-    typename            InputT,
-    int                 BLOCK_DIM_X,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  ALGORITHM           = BLOCK_LOAD_DIRECT,
-    int                 BLOCK_DIM_Y         = 1,
-    int                 BLOCK_DIM_Z         = 1,
-    int                 PTX_ARCH            = CUB_PTX_ARCH>
-class BlockLoad
-{
-private:
-
-    /******************************************************************************
-     * Constants and typed definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-
-    /******************************************************************************
-     * Algorithmic variants
-     ******************************************************************************/
-
-    /// Load helper
-    template <BlockLoadAlgorithm _POLICY, int DUMMY>
-    struct LoadInternal;
-
-
-    /**
-     * BLOCK_LOAD_DIRECT specialization of load helper
-     */
-    template <int DUMMY>
-    struct LoadInternal<BLOCK_LOAD_DIRECT, DUMMY>
-    {
-        /// Shared memory storage layout type
-        typedef NullType TempStorage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ LoadInternal(
-            TempStorage &/*temp_storage*/,
-            int linear_tid)
-        :
-            linear_tid(linear_tid)
-        {}
-
-        /// Load a linear segment of items from memory
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load
-        {
-            LoadDirectBlocked(linear_tid, block_itr, items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items)                    ///< [in] Number of valid items to load
-        {
-            LoadDirectBlocked(linear_tid, block_itr, items, valid_items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
-        template <typename InputIteratorT, typename DefaultT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items,                    ///< [in] Number of valid items to load
-            DefaultT        oob_default)                    ///< [in] Default value to assign out-of-bound items
-        {
-            LoadDirectBlocked(linear_tid, block_itr, items, valid_items, oob_default);
-        }
-
-    };
-
-
-    /**
-     * BLOCK_LOAD_VECTORIZE specialization of load helper
-     */
-    template <int DUMMY>
-    struct LoadInternal<BLOCK_LOAD_VECTORIZE, DUMMY>
-    {
-        /// Shared memory storage layout type
-        typedef NullType TempStorage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ LoadInternal(
-            TempStorage &/*temp_storage*/,
-            int linear_tid)
-        :
-            linear_tid(linear_tid)
-        {}
-
-        /// Load a linear segment of items from memory, specialized for native pointer types (attempts vectorization)
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputT               *block_ptr,                     ///< [in] The thread block's base input iterator for loading from
-            InputT               (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load
-        {
-            InternalLoadDirectBlockedVectorized<LOAD_DEFAULT>(linear_tid, block_ptr, items);
-        }
-
-        /// Load a linear segment of items from memory, specialized for native pointer types (attempts vectorization)
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            const InputT         *block_ptr,                     ///< [in] The thread block's base input iterator for loading from
-            InputT               (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load
-        {
-            InternalLoadDirectBlockedVectorized<LOAD_DEFAULT>(linear_tid, block_ptr, items);
-        }
-
-        /// Load a linear segment of items from memory, specialized for native pointer types (attempts vectorization)
-        template <
-            CacheLoadModifier   MODIFIER,
-            typename            ValueType,
-            typename            OffsetT>
-        __device__ __forceinline__ void Load(
-            CacheModifiedInputIterator<MODIFIER, ValueType, OffsetT>    block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT                                                     (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load
-        {
-            InternalLoadDirectBlockedVectorized<MODIFIER>(linear_tid, block_itr.ptr, items);
-        }
-
-        /// Load a linear segment of items from memory, specialized for opaque input iterators (skips vectorization)
-        template <typename _InputIteratorT>
-        __device__ __forceinline__ void Load(
-            _InputIteratorT   block_itr,                    ///< [in] The thread block's base input iterator for loading from
-            InputT           (&items)[ITEMS_PER_THREAD])   ///< [out] Data to load
-        {
-            LoadDirectBlocked(linear_tid, block_itr, items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range (skips vectorization)
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items)                    ///< [in] Number of valid items to load
-        {
-            LoadDirectBlocked(linear_tid, block_itr, items, valid_items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements (skips vectorization)
-        template <typename InputIteratorT, typename DefaultT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items,                    ///< [in] Number of valid items to load
-            DefaultT          oob_default)                    ///< [in] Default value to assign out-of-bound items
-        {
-            LoadDirectBlocked(linear_tid, block_itr, items, valid_items, oob_default);
-        }
-
-    };
-
-
-    /**
-     * BLOCK_LOAD_TRANSPOSE specialization of load helper
-     */
-    template <int DUMMY>
-    struct LoadInternal<BLOCK_LOAD_TRANSPOSE, DUMMY>
-    {
-        // BlockExchange utility type for keys
-        typedef BlockExchange<InputT, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
-
-        /// Shared memory storage layout type
-        struct _TempStorage : BlockExchange::TempStorage
-        {
-            /// Temporary storage for partially-full block guard
-            volatile int valid_items;
-        };
-
-        /// Alias wrapper allowing storage to be unioned
-        struct TempStorage : Uninitialized<_TempStorage> {};
-
-        /// Thread reference to shared storage
-        _TempStorage &temp_storage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ LoadInternal(
-            TempStorage &temp_storage,
-            int linear_tid)
-        :
-            temp_storage(temp_storage.Alias()),
-            linear_tid(linear_tid)
-        {}
-
-        /// Load a linear segment of items from memory
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load{
-        {
-            LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items);
-            BlockExchange(temp_storage).StripedToBlocked(items, items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items)                    ///< [in] Number of valid items to load
-        {
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, temp_storage.valid_items);
-            BlockExchange(temp_storage).StripedToBlocked(items, items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
-        template <typename InputIteratorT, typename DefaultT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items,                    ///< [in] Number of valid items to load
-            DefaultT        oob_default)                    ///< [in] Default value to assign out-of-bound items
-        {
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, temp_storage.valid_items, oob_default);
-            BlockExchange(temp_storage).StripedToBlocked(items, items);
-        }
-
-    };
-
-
-    /**
-     * BLOCK_LOAD_WARP_TRANSPOSE specialization of load helper
-     */
-    template <int DUMMY>
-    struct LoadInternal<BLOCK_LOAD_WARP_TRANSPOSE, DUMMY>
-    {
-        enum
-        {
-            WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH)
-        };
-
-        // Assert BLOCK_THREADS must be a multiple of WARP_THREADS
-        CUB_STATIC_ASSERT((BLOCK_THREADS % WARP_THREADS == 0), "BLOCK_THREADS must be a multiple of WARP_THREADS");
-
-        // BlockExchange utility type for keys
-        typedef BlockExchange<InputT, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
-
-        /// Shared memory storage layout type
-        struct _TempStorage : BlockExchange::TempStorage
-        {
-            /// Temporary storage for partially-full block guard
-            volatile int valid_items;
-        };
-
-        /// Alias wrapper allowing storage to be unioned
-        struct TempStorage : Uninitialized<_TempStorage> {};
-
-        /// Thread reference to shared storage
-        _TempStorage &temp_storage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ LoadInternal(
-            TempStorage &temp_storage,
-            int linear_tid)
-        :
-            temp_storage(temp_storage.Alias()),
-            linear_tid(linear_tid)
-        {}
-
-        /// Load a linear segment of items from memory
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load{
-        {
-            LoadDirectWarpStriped(linear_tid, block_itr, items);
-            BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items)                    ///< [in] Number of valid items to load
-        {
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
-            BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
-        }
-
-
-        /// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
-        template <typename InputIteratorT, typename DefaultT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items,                    ///< [in] Number of valid items to load
-            DefaultT        oob_default)                    ///< [in] Default value to assign out-of-bound items
-        {
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items, oob_default);
-            BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
-        }
-    };
-
-
-    /**
-     * BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED specialization of load helper
-     */
-    template <int DUMMY>
-    struct LoadInternal<BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED, DUMMY>
-    {
-        enum
-        {
-            WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH)
-        };
-
-        // Assert BLOCK_THREADS must be a multiple of WARP_THREADS
-        CUB_STATIC_ASSERT((BLOCK_THREADS % WARP_THREADS == 0), "BLOCK_THREADS must be a multiple of WARP_THREADS");
-
-        // BlockExchange utility type for keys
-        typedef BlockExchange<InputT, BLOCK_DIM_X, ITEMS_PER_THREAD, true, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
-
-        /// Shared memory storage layout type
-        struct _TempStorage : BlockExchange::TempStorage
-        {
-            /// Temporary storage for partially-full block guard
-            volatile int valid_items;
-        };
-
-        /// Alias wrapper allowing storage to be unioned
-        struct TempStorage : Uninitialized<_TempStorage> {};
-
-        /// Thread reference to shared storage
-        _TempStorage &temp_storage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ LoadInternal(
-            TempStorage &temp_storage,
-            int linear_tid)
-        :
-            temp_storage(temp_storage.Alias()),
-            linear_tid(linear_tid)
-        {}
-
-        /// Load a linear segment of items from memory
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD])     ///< [out] Data to load{
-        {
-            LoadDirectWarpStriped(linear_tid, block_itr, items);
-            BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
-        }
-
-        /// Load a linear segment of items from memory, guarded by range
-        template <typename InputIteratorT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items)                    ///< [in] Number of valid items to load
-        {
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
-            BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
-        }
-
-
-        /// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
-        template <typename InputIteratorT, typename DefaultT>
-        __device__ __forceinline__ void Load(
-            InputIteratorT  block_itr,                      ///< [in] The thread block's base input iterator for loading from
-            InputT          (&items)[ITEMS_PER_THREAD],     ///< [out] Data to load
-            int             valid_items,                    ///< [in] Number of valid items to load
-            DefaultT        oob_default)                    ///< [in] Default value to assign out-of-bound items
-        {
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items, oob_default);
-            BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
-        }
-    };
-
-
-    /******************************************************************************
-     * Type definitions
-     ******************************************************************************/
-
-    /// Internal load implementation to use
-    typedef LoadInternal<ALGORITHM, 0> InternalLoad;
-
-
-    /// Shared memory storage layout type
-    typedef typename InternalLoad::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Thread reference to shared storage
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    int linear_tid;
-
-public:
-
-    /// \smemstorage{BlockLoad}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockLoad()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockLoad(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Data movement
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Load a linear segment of items from memory.
-     *
-     * \par
-     * - \blocked
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the loading of a linear
-     * segment of 512 integers into a "blocked" arrangement across 128 threads where each
-     * thread owns 4 consecutive items.  The load is specialized for \p BLOCK_LOAD_WARP_TRANSPOSE,
-     * meaning memory references are efficiently coalesced using a warp-striped access
-     * pattern (after which items are locally reordered among threads).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, ...)
-     * {
-     *     // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
-     *
-     *     // Allocate shared memory for BlockLoad
-     *     __shared__ typename BlockLoad::TempStorage temp_storage;
-     *
-     *     // Load a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     BlockLoad(temp_storage).Load(d_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, ...</tt>.
-     * The set of \p thread_data across the block of threads in those threads will be
-     * <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
-     *
-     */
-    template <typename InputIteratorT>
-    __device__ __forceinline__ void Load(
-        InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-        InputT          (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-    {
-        InternalLoad(temp_storage, linear_tid).Load(block_itr, items);
-    }
-
-
-    /**
-     * \brief Load a linear segment of items from memory, guarded by range.
-     *
-     * \par
-     * - \blocked
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the guarded loading of a linear
-     * segment of 512 integers into a "blocked" arrangement across 128 threads where each
-     * thread owns 4 consecutive items.  The load is specialized for \p BLOCK_LOAD_WARP_TRANSPOSE,
-     * meaning memory references are efficiently coalesced using a warp-striped access
-     * pattern (after which items are locally reordered among threads).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, int valid_items, ...)
-     * {
-     *     // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
-     *
-     *     // Allocate shared memory for BlockLoad
-     *     __shared__ typename BlockLoad::TempStorage temp_storage;
-     *
-     *     // Load a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     BlockLoad(temp_storage).Load(d_data, thread_data, valid_items);
-     *
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, 6...</tt> and \p valid_items is \p 5.
-     * The set of \p thread_data across the block of threads in those threads will be
-     * <tt>{ [0,1,2,3], [4,?,?,?], ..., [?,?,?,?] }</tt>, with only the first two threads
-     * being unmasked to load portions of valid data (and other items remaining unassigned).
-     *
-     */
-    template <typename InputIteratorT>
-    __device__ __forceinline__ void Load(
-        InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-        InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-        int             valid_items)                ///< [in] Number of valid items to load
-    {
-        InternalLoad(temp_storage, linear_tid).Load(block_itr, items, valid_items);
-    }
-
-
-    /**
-     * \brief Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
-     *
-     * \par
-     * - \blocked
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the guarded loading of a linear
-     * segment of 512 integers into a "blocked" arrangement across 128 threads where each
-     * thread owns 4 consecutive items.  The load is specialized for \p BLOCK_LOAD_WARP_TRANSPOSE,
-     * meaning memory references are efficiently coalesced using a warp-striped access
-     * pattern (after which items are locally reordered among threads).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, int valid_items, ...)
-     * {
-     *     // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
-     *
-     *     // Allocate shared memory for BlockLoad
-     *     __shared__ typename BlockLoad::TempStorage temp_storage;
-     *
-     *     // Load a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     BlockLoad(temp_storage).Load(d_data, thread_data, valid_items, -1);
-     *
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, 6...</tt>,
-     * \p valid_items is \p 5, and the out-of-bounds default is \p -1.
-     * The set of \p thread_data across the block of threads in those threads will be
-     * <tt>{ [0,1,2,3], [4,-1,-1,-1], ..., [-1,-1,-1,-1] }</tt>, with only the first two threads
-     * being unmasked to load portions of valid data (and other items are assigned \p -1)
-     *
-     */
-    template <typename InputIteratorT, typename DefaultT>
-    __device__ __forceinline__ void Load(
-        InputIteratorT  block_itr,                  ///< [in] The thread block's base input iterator for loading from
-        InputT          (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
-        int             valid_items,                ///< [in] Number of valid items to load
-        DefaultT        oob_default)                ///< [in] Default value to assign out-of-bound items
-    {
-        InternalLoad(temp_storage, linear_tid).Load(block_itr, items, valid_items, oob_default);
-    }
-
-
-    //@}  end member group
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_radix_rank.cuh b/ml-xgboost/cub/cub/block/block_radix_rank.cuh
deleted file mode 100644
index 8ca925f..0000000
--- a/ml-xgboost/cub/cub/block/block_radix_rank.cuh
+++ /dev/null
@@ -1,432 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockRadixRank provides operations for ranking unsigned integer types within a CUDA threadblock
- */
-
-#pragma once
-
-#include "../thread/thread_reduce.cuh"
-#include "../thread/thread_scan.cuh"
-#include "../block/block_scan.cuh"
-#include "../util_ptx.cuh"
-#include "../util_arch.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief BlockRadixRank provides operations for ranking unsigned integer types within a CUDA threadblock.
- * \ingroup BlockModule
- *
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam RADIX_BITS           The number of radix bits per digit place
- * \tparam DESCENDING           Whether or not the sorted-order is high-to-low
- * \tparam MEMOIZE_OUTER_SCAN   <b>[optional]</b> Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure (default: true for architectures SM35 and newer, false otherwise).  See BlockScanAlgorithm::BLOCK_SCAN_RAKING_MEMOIZE for more details.
- * \tparam INNER_SCAN_ALGORITHM <b>[optional]</b> The cub::BlockScanAlgorithm algorithm to use (default: cub::BLOCK_SCAN_WARP_SCANS)
- * \tparam SMEM_CONFIG          <b>[optional]</b> Shared memory bank mode (default: \p cudaSharedMemBankSizeFourByte)
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * Blah...
- * - Keys must be in a form suitable for radix ranking (i.e., unsigned bits).
- * - \blocked
- *
- * \par Performance Considerations
- * - \granularity
- *
- * \par Examples
- * \par
- * - <b>Example 1:</b> Simple radix rank of 32-bit integer keys
- *      \code
- *      #include <cub/cub.cuh>
- *
- *      template <int BLOCK_THREADS>
- *      __global__ void ExampleKernel(...)
- *      {
- *
- *      \endcode
- */
-template <
-    int                     BLOCK_DIM_X,
-    int                     RADIX_BITS,
-    bool                    DESCENDING,
-    bool                    MEMOIZE_OUTER_SCAN      = (CUB_PTX_ARCH >= 350) ? true : false,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM    = BLOCK_SCAN_WARP_SCANS,
-    cudaSharedMemConfig     SMEM_CONFIG             = cudaSharedMemBankSizeFourByte,
-    int                     BLOCK_DIM_Y             = 1,
-    int                     BLOCK_DIM_Z             = 1,
-    int                     PTX_ARCH                = CUB_PTX_ARCH>
-class BlockRadixRank
-{
-private:
-
-    /******************************************************************************
-     * Type definitions and constants
-     ******************************************************************************/
-
-    // Integer type for digit counters (to be packed into words of type PackedCounters)
-    typedef unsigned short DigitCounter;
-
-    // Integer type for packing DigitCounters into columns of shared memory banks
-    typedef typename If<(SMEM_CONFIG == cudaSharedMemBankSizeEightByte),
-        unsigned long long,
-        unsigned int>::Type PackedCounter;
-
-    enum
-    {
-        // The thread block size in threads
-        BLOCK_THREADS               = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        RADIX_DIGITS                = 1 << RADIX_BITS,
-
-        LOG_WARP_THREADS            = CUB_LOG_WARP_THREADS(PTX_ARCH),
-        WARP_THREADS                = 1 << LOG_WARP_THREADS,
-        WARPS                       = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-
-        BYTES_PER_COUNTER           = sizeof(DigitCounter),
-        LOG_BYTES_PER_COUNTER       = Log2<BYTES_PER_COUNTER>::VALUE,
-
-        PACKING_RATIO               = sizeof(PackedCounter) / sizeof(DigitCounter),
-        LOG_PACKING_RATIO           = Log2<PACKING_RATIO>::VALUE,
-
-        LOG_COUNTER_LANES           = CUB_MAX((RADIX_BITS - LOG_PACKING_RATIO), 0),                // Always at least one lane
-        COUNTER_LANES               = 1 << LOG_COUNTER_LANES,
-
-        // The number of packed counters per thread (plus one for padding)
-        PADDED_COUNTER_LANES        = COUNTER_LANES + 1,
-        RAKING_SEGMENT              = PADDED_COUNTER_LANES,
-
-        LOG_SMEM_BANKS              = CUB_LOG_SMEM_BANKS(PTX_ARCH),
-        SMEM_BANKS                  = 1 << LOG_SMEM_BANKS,
-    };
-
-
-    /// BlockScan type
-    typedef BlockScan<
-            PackedCounter,
-            BLOCK_DIM_X,
-            INNER_SCAN_ALGORITHM,
-            BLOCK_DIM_Y,
-            BLOCK_DIM_Z,
-            PTX_ARCH>
-        BlockScan;
-
-
-    /// Shared memory storage layout type for BlockRadixRank
-    struct __align__(16) _TempStorage
-    {
-        union
-        {
-            DigitCounter            digit_counters[PADDED_COUNTER_LANES][BLOCK_THREADS][PACKING_RATIO];
-            PackedCounter           raking_grid[BLOCK_THREADS][RAKING_SEGMENT];
-        };
-
-        // Storage for scanning local ranks
-        typename BlockScan::TempStorage block_scan;
-    };
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-    /// Copy of raking segment, promoted to registers
-    PackedCounter cached_segment[RAKING_SEGMENT];
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /**
-     * Internal storage allocator
-     */
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /**
-     * Performs upsweep raking reduction, returning the aggregate
-     */
-    __device__ __forceinline__ PackedCounter Upsweep()
-    {
-        PackedCounter *smem_raking_ptr = temp_storage.raking_grid[linear_tid];
-        PackedCounter *raking_ptr;
-
-        if (MEMOIZE_OUTER_SCAN)
-        {
-            // Copy data into registers
-            #pragma unroll
-            for (int i = 0; i < RAKING_SEGMENT; i++)
-            {
-                cached_segment[i] = smem_raking_ptr[i];
-            }
-            raking_ptr = cached_segment;
-        }
-        else
-        {
-            raking_ptr = smem_raking_ptr;
-        }
-
-        return ThreadReduce<RAKING_SEGMENT>(raking_ptr, Sum());
-    }
-
-
-    /// Performs exclusive downsweep raking scan
-    __device__ __forceinline__ void ExclusiveDownsweep(
-        PackedCounter raking_partial)
-    {
-        PackedCounter *smem_raking_ptr = temp_storage.raking_grid[linear_tid];
-
-        PackedCounter *raking_ptr = (MEMOIZE_OUTER_SCAN) ?
-            cached_segment :
-            smem_raking_ptr;
-
-        // Exclusive raking downsweep scan
-        ThreadScanExclusive<RAKING_SEGMENT>(raking_ptr, raking_ptr, Sum(), raking_partial);
-
-        if (MEMOIZE_OUTER_SCAN)
-        {
-            // Copy data back to smem
-            #pragma unroll
-            for (int i = 0; i < RAKING_SEGMENT; i++)
-            {
-                smem_raking_ptr[i] = cached_segment[i];
-            }
-        }
-    }
-
-
-    /**
-     * Reset shared memory digit counters
-     */
-    __device__ __forceinline__ void ResetCounters()
-    {
-        // Reset shared memory digit counters
-        #pragma unroll
-        for (int LANE = 0; LANE < PADDED_COUNTER_LANES; LANE++)
-        {
-            *((PackedCounter*) temp_storage.digit_counters[LANE][linear_tid]) = 0;
-        }
-    }
-
-
-    /**
-     * Block-scan prefix callback
-     */
-    struct PrefixCallBack
-    {
-        __device__ __forceinline__ PackedCounter operator()(PackedCounter block_aggregate)
-        {
-            PackedCounter block_prefix = 0;
-
-            // Propagate totals in packed fields
-            #pragma unroll
-            for (int PACKED = 1; PACKED < PACKING_RATIO; PACKED++)
-            {
-                block_prefix += block_aggregate << (sizeof(DigitCounter) * 8 * PACKED);
-            }
-
-            return block_prefix;
-        }
-    };
-
-
-    /**
-     * Scan shared memory digit counters.
-     */
-    __device__ __forceinline__ void ScanCounters()
-    {
-        // Upsweep scan
-        PackedCounter raking_partial = Upsweep();
-
-        // Compute exclusive sum
-        PackedCounter exclusive_partial;
-        PrefixCallBack prefix_call_back;
-        BlockScan(temp_storage.block_scan).ExclusiveSum(raking_partial, exclusive_partial, prefix_call_back);
-
-        // Downsweep scan with exclusive partial
-        ExclusiveDownsweep(exclusive_partial);
-    }
-
-public:
-
-    /// \smemstorage{BlockScan}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockRadixRank()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockRadixRank(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Raking
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Rank keys.
-     */
-    template <
-        typename        UnsignedBits,
-        int             KEYS_PER_THREAD>
-    __device__ __forceinline__ void RankKeys(
-        UnsignedBits    (&keys)[KEYS_PER_THREAD],           ///< [in] Keys for this tile
-        int             (&ranks)[KEYS_PER_THREAD],          ///< [out] For each key, the local rank within the tile
-        int             current_bit,                        ///< [in] The least-significant bit position of the current digit to extract
-        int             num_bits)                           ///< [in] The number of bits in the current digit
-    {
-        DigitCounter    thread_prefixes[KEYS_PER_THREAD];   // For each key, the count of previous keys in this tile having the same digit
-        DigitCounter*   digit_counters[KEYS_PER_THREAD];    // For each key, the byte-offset of its corresponding digit counter in smem
-
-        // Reset shared memory digit counters
-        ResetCounters();
-
-        for (int ITEM = 0; ITEM < KEYS_PER_THREAD; ++ITEM)
-        {
-            // Get digit
-            unsigned int digit = BFE(keys[ITEM], current_bit, num_bits);
-
-            // Get sub-counter
-            unsigned int sub_counter = digit >> LOG_COUNTER_LANES;
-
-            // Get counter lane
-            unsigned int counter_lane = digit & (COUNTER_LANES - 1);
-
-            if (DESCENDING)
-            {
-                sub_counter = PACKING_RATIO - 1 - sub_counter;
-                counter_lane = COUNTER_LANES - 1 - counter_lane;
-            }
-
-            // Pointer to smem digit counter
-            digit_counters[ITEM] = &temp_storage.digit_counters[counter_lane][linear_tid][sub_counter];
-
-            // Load thread-exclusive prefix
-            thread_prefixes[ITEM] = *digit_counters[ITEM];
-
-            // Store inclusive prefix
-            *digit_counters[ITEM] = thread_prefixes[ITEM] + 1;
-        }
-
-        CTA_SYNC();
-
-        // Scan shared memory counters
-        ScanCounters();
-
-        CTA_SYNC();
-
-        // Extract the local ranks of each key
-        for (int ITEM = 0; ITEM < KEYS_PER_THREAD; ++ITEM)
-        {
-            // Add in threadblock exclusive prefix
-            ranks[ITEM] = thread_prefixes[ITEM] + *digit_counters[ITEM];
-        }
-    }
-
-
-    /**
-     * \brief Rank keys.  For the lower \p RADIX_DIGITS threads, digit counts for each digit are provided for the corresponding thread.
-     */
-    template <
-        typename        UnsignedBits,
-        int             KEYS_PER_THREAD>
-    __device__ __forceinline__ void RankKeys(
-        UnsignedBits    (&keys)[KEYS_PER_THREAD],           ///< [in] Keys for this tile
-        int             (&ranks)[KEYS_PER_THREAD],          ///< [out] For each key, the local rank within the tile (out parameter)
-        int             current_bit,                        ///< [in] The least-significant bit position of the current digit to extract
-        int             num_bits,                           ///< [in] The number of bits in the current digit
-        int             &exclusive_digit_prefix)            ///< [out] The exclusive prefix sum for the digit threadIdx.x
-    {
-        // Rank keys
-        RankKeys(keys, ranks, current_bit, num_bits);
-
-        // Get the inclusive and exclusive digit totals corresponding to the calling thread.
-        if ((BLOCK_THREADS == RADIX_DIGITS) || (linear_tid < RADIX_DIGITS))
-        {
-            unsigned int bin_idx = (DESCENDING) ?
-                RADIX_DIGITS - linear_tid - 1 :
-                linear_tid;
-
-            // Obtain ex/inclusive digit counts.  (Unfortunately these all reside in the
-            // first counter column, resulting in unavoidable bank conflicts.)
-            unsigned int counter_lane   = (bin_idx & (COUNTER_LANES - 1));
-            unsigned int sub_counter    = bin_idx >> (LOG_COUNTER_LANES);
-
-            exclusive_digit_prefix      = temp_storage.digit_counters[counter_lane][0][sub_counter];
-        }
-    }
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/block/block_radix_sort.cuh b/ml-xgboost/cub/cub/block/block_radix_sort.cuh
deleted file mode 100644
index b82d956..0000000
--- a/ml-xgboost/cub/cub/block/block_radix_sort.cuh
+++ /dev/null
@@ -1,865 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockRadixSort class provides [<em>collective</em>](index.html#sec0) methods for radix sorting of items partitioned across a CUDA thread block.
- */
-
-
-#pragma once
-
-#include "block_exchange.cuh"
-#include "block_radix_rank.cuh"
-#include "../util_ptx.cuh"
-#include "../util_arch.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief The BlockRadixSort class provides [<em>collective</em>](index.html#sec0) methods for sorting items partitioned across a CUDA thread block using a radix sorting method.  ![](sorting_logo.png)
- * \ingroup BlockModule
- *
- * \tparam KeyT                 KeyT type
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam ITEMS_PER_THREAD     The number of items per thread
- * \tparam ValueT               <b>[optional]</b> ValueT type (default: cub::NullType, which indicates a keys-only sort)
- * \tparam RADIX_BITS           <b>[optional]</b> The number of radix bits per digit place (default: 4 bits)
- * \tparam MEMOIZE_OUTER_SCAN   <b>[optional]</b> Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure (default: true for architectures SM35 and newer, false otherwise).
- * \tparam INNER_SCAN_ALGORITHM <b>[optional]</b> The cub::BlockScanAlgorithm algorithm to use (default: cub::BLOCK_SCAN_WARP_SCANS)
- * \tparam SMEM_CONFIG          <b>[optional]</b> Shared memory bank mode (default: \p cudaSharedMemBankSizeFourByte)
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - The [<em>radix sorting method</em>](http://en.wikipedia.org/wiki/Radix_sort) arranges
- *   items into ascending order.  It relies upon a positional representation for
- *   keys, i.e., each key is comprised of an ordered sequence of symbols (e.g., digits,
- *   characters, etc.) specified from least-significant to most-significant.  For a
- *   given input sequence of keys and a set of rules specifying a total ordering
- *   of the symbolic alphabet, the radix sorting method produces a lexicographic
- *   ordering of those keys.
- * - BlockRadixSort can sort all of the built-in C++ numeric primitive types, e.g.:
- *   <tt>unsigned char</tt>, \p int, \p double, etc.  Within each key, the implementation treats fixed-length
- *   bit-sequences of \p RADIX_BITS as radix digit places.  Although the direct radix sorting
- *   method can only be applied to unsigned integral types, BlockRadixSort
- *   is able to sort signed and floating-point types via simple bit-wise transformations
- *   that ensure lexicographic key ordering.
- * - \rowmajor
- *
- * \par Performance Considerations
- * - \granularity
- *
- * \par A Simple Example
- * \blockcollective{BlockRadixSort}
- * \par
- * The code snippet below illustrates a sort of 512 integer keys that
- * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
- * where each thread owns 4 consecutive items.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer items each
- *     typedef cub::BlockRadixSort<int, 128, 4> BlockRadixSort;
- *
- *     // Allocate shared memory for BlockRadixSort
- *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
- *
- *     // Obtain a segment of consecutive items that are blocked across threads
- *     int thread_keys[4];
- *     ...
- *
- *     // Collectively sort the keys
- *     BlockRadixSort(temp_storage).Sort(thread_keys);
- *
- *     ...
- * \endcode
- * \par
- * Suppose the set of input \p thread_keys across the block of threads is
- * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
- * corresponding output \p thread_keys in those threads will be
- * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
- *
- */
-template <
-    typename                KeyT,
-    int                     BLOCK_DIM_X,
-    int                     ITEMS_PER_THREAD,
-    typename                ValueT                   = NullType,
-    int                     RADIX_BITS              = 4,
-    bool                    MEMOIZE_OUTER_SCAN      = (CUB_PTX_ARCH >= 350) ? true : false,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM    = BLOCK_SCAN_WARP_SCANS,
-    cudaSharedMemConfig     SMEM_CONFIG             = cudaSharedMemBankSizeFourByte,
-    int                     BLOCK_DIM_Y             = 1,
-    int                     BLOCK_DIM_Z             = 1,
-    int                     PTX_ARCH                = CUB_PTX_ARCH>
-class BlockRadixSort
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    enum
-    {
-        // The thread block size in threads
-        BLOCK_THREADS               = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        // Whether or not there are values to be trucked along with keys
-        KEYS_ONLY                   = Equals<ValueT, NullType>::VALUE,
-    };
-
-    // KeyT traits and unsigned bits type
-    typedef Traits<KeyT>                        KeyTraits;
-    typedef typename KeyTraits::UnsignedBits    UnsignedBits;
-
-    /// Ascending BlockRadixRank utility type
-    typedef BlockRadixRank<
-            BLOCK_DIM_X,
-            RADIX_BITS,
-            false,
-            MEMOIZE_OUTER_SCAN,
-            INNER_SCAN_ALGORITHM,
-            SMEM_CONFIG,
-            BLOCK_DIM_Y,
-            BLOCK_DIM_Z,
-            PTX_ARCH>
-        AscendingBlockRadixRank;
-
-    /// Descending BlockRadixRank utility type
-    typedef BlockRadixRank<
-            BLOCK_DIM_X,
-            RADIX_BITS,
-            true,
-            MEMOIZE_OUTER_SCAN,
-            INNER_SCAN_ALGORITHM,
-            SMEM_CONFIG,
-            BLOCK_DIM_Y,
-            BLOCK_DIM_Z,
-            PTX_ARCH>
-        DescendingBlockRadixRank;
-
-    /// BlockExchange utility type for keys
-    typedef BlockExchange<KeyT, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchangeKeys;
-
-    /// BlockExchange utility type for values
-    typedef BlockExchange<ValueT, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchangeValues;
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        union
-        {
-            typename AscendingBlockRadixRank::TempStorage  asending_ranking_storage;
-            typename DescendingBlockRadixRank::TempStorage descending_ranking_storage;
-            typename BlockExchangeKeys::TempStorage        exchange_keys;
-            typename BlockExchangeValues::TempStorage      exchange_values;
-        };
-    };
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-    /// Rank keys (specialized for ascending sort)
-    __device__ __forceinline__ void RankKeys(
-        UnsignedBits    (&unsigned_keys)[ITEMS_PER_THREAD],
-        int             (&ranks)[ITEMS_PER_THREAD],
-        int             begin_bit,
-        int             pass_bits,
-        Int2Type<false> /*is_descending*/)
-    {
-        AscendingBlockRadixRank(temp_storage.asending_ranking_storage).RankKeys(
-            unsigned_keys,
-            ranks,
-            begin_bit,
-            pass_bits);
-    }
-
-    /// Rank keys (specialized for descending sort)
-    __device__ __forceinline__ void RankKeys(
-        UnsignedBits    (&unsigned_keys)[ITEMS_PER_THREAD],
-        int             (&ranks)[ITEMS_PER_THREAD],
-        int             begin_bit,
-        int             pass_bits,
-        Int2Type<true>  /*is_descending*/)
-    {
-        DescendingBlockRadixRank(temp_storage.descending_ranking_storage).RankKeys(
-            unsigned_keys,
-            ranks,
-            begin_bit,
-            pass_bits);
-    }
-
-    /// ExchangeValues (specialized for key-value sort, to-blocked arrangement)
-    __device__ __forceinline__ void ExchangeValues(
-        ValueT          (&values)[ITEMS_PER_THREAD],
-        int             (&ranks)[ITEMS_PER_THREAD],
-        Int2Type<false> /*is_keys_only*/,
-        Int2Type<true>  /*is_blocked*/)
-    {
-        CTA_SYNC();
-
-        // Exchange values through shared memory in blocked arrangement
-        BlockExchangeValues(temp_storage.exchange_values).ScatterToBlocked(values, ranks);
-    }
-
-    /// ExchangeValues (specialized for key-value sort, to-striped arrangement)
-    __device__ __forceinline__ void ExchangeValues(
-        ValueT          (&values)[ITEMS_PER_THREAD],
-        int             (&ranks)[ITEMS_PER_THREAD],
-        Int2Type<false> /*is_keys_only*/,
-        Int2Type<false> /*is_blocked*/)
-    {
-        CTA_SYNC();
-
-        // Exchange values through shared memory in blocked arrangement
-        BlockExchangeValues(temp_storage.exchange_values).ScatterToStriped(values, ranks);
-    }
-
-    /// ExchangeValues (specialized for keys-only sort)
-    template <int IS_BLOCKED>
-    __device__ __forceinline__ void ExchangeValues(
-        ValueT                  (&/*values*/)[ITEMS_PER_THREAD],
-        int                     (&/*ranks*/)[ITEMS_PER_THREAD],
-        Int2Type<true>          /*is_keys_only*/,
-        Int2Type<IS_BLOCKED>    /*is_blocked*/)
-    {}
-
-    /// Sort blocked arrangement
-    template <int DESCENDING, int KEYS_ONLY>
-    __device__ __forceinline__ void SortBlocked(
-        KeyT                    (&keys)[ITEMS_PER_THREAD],          ///< Keys to sort
-        ValueT                  (&values)[ITEMS_PER_THREAD],        ///< Values to sort
-        int                     begin_bit,                          ///< The beginning (least-significant) bit index needed for key comparison
-        int                     end_bit,                            ///< The past-the-end (most-significant) bit index needed for key comparison
-        Int2Type<DESCENDING>    is_descending,                      ///< Tag whether is a descending-order sort
-        Int2Type<KEYS_ONLY>     is_keys_only)                       ///< Tag whether is keys-only sort
-    {
-        UnsignedBits (&unsigned_keys)[ITEMS_PER_THREAD] =
-            reinterpret_cast<UnsignedBits (&)[ITEMS_PER_THREAD]>(keys);
-
-        // Twiddle bits if necessary
-        #pragma unroll
-        for (int KEY = 0; KEY < ITEMS_PER_THREAD; KEY++)
-        {
-            unsigned_keys[KEY] = KeyTraits::TwiddleIn(unsigned_keys[KEY]);
-        }
-
-        // Radix sorting passes
-        while (true)
-        {
-            int pass_bits = CUB_MIN(RADIX_BITS, end_bit - begin_bit);
-
-            // Rank the blocked keys
-            int ranks[ITEMS_PER_THREAD];
-            RankKeys(unsigned_keys, ranks, begin_bit, pass_bits, is_descending);
-            begin_bit += RADIX_BITS;
-
-            CTA_SYNC();
-
-            // Exchange keys through shared memory in blocked arrangement
-            BlockExchangeKeys(temp_storage.exchange_keys).ScatterToBlocked(keys, ranks);
-
-            // Exchange values through shared memory in blocked arrangement
-            ExchangeValues(values, ranks, is_keys_only, Int2Type<true>());
-
-            // Quit if done
-            if (begin_bit >= end_bit) break;
-
-            CTA_SYNC();
-        }
-
-        // Untwiddle bits if necessary
-        #pragma unroll
-        for (int KEY = 0; KEY < ITEMS_PER_THREAD; KEY++)
-        {
-            unsigned_keys[KEY] = KeyTraits::TwiddleOut(unsigned_keys[KEY]);
-        }
-    }
-
-public:
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-    /// Sort blocked -> striped arrangement
-    template <int DESCENDING, int KEYS_ONLY>
-    __device__ __forceinline__ void SortBlockedToStriped(
-        KeyT                    (&keys)[ITEMS_PER_THREAD],          ///< Keys to sort
-        ValueT                  (&values)[ITEMS_PER_THREAD],        ///< Values to sort
-        int                     begin_bit,                          ///< The beginning (least-significant) bit index needed for key comparison
-        int                     end_bit,                            ///< The past-the-end (most-significant) bit index needed for key comparison
-        Int2Type<DESCENDING>    is_descending,                      ///< Tag whether is a descending-order sort
-        Int2Type<KEYS_ONLY>     is_keys_only)                       ///< Tag whether is keys-only sort
-    {
-        UnsignedBits (&unsigned_keys)[ITEMS_PER_THREAD] =
-            reinterpret_cast<UnsignedBits (&)[ITEMS_PER_THREAD]>(keys);
-
-        // Twiddle bits if necessary
-        #pragma unroll
-        for (int KEY = 0; KEY < ITEMS_PER_THREAD; KEY++)
-        {
-            unsigned_keys[KEY] = KeyTraits::TwiddleIn(unsigned_keys[KEY]);
-        }
-
-        // Radix sorting passes
-        while (true)
-        {
-            int pass_bits = CUB_MIN(RADIX_BITS, end_bit - begin_bit);
-
-            // Rank the blocked keys
-            int ranks[ITEMS_PER_THREAD];
-            RankKeys(unsigned_keys, ranks, begin_bit, pass_bits, is_descending);
-            begin_bit += RADIX_BITS;
-
-            CTA_SYNC();
-
-            // Check if this is the last pass
-            if (begin_bit >= end_bit)
-            {
-                // Last pass exchanges keys through shared memory in striped arrangement
-                BlockExchangeKeys(temp_storage.exchange_keys).ScatterToStriped(keys, ranks);
-
-                // Last pass exchanges through shared memory in striped arrangement
-                ExchangeValues(values, ranks, is_keys_only, Int2Type<false>());
-
-                // Quit
-                break;
-            }
-
-            // Exchange keys through shared memory in blocked arrangement
-            BlockExchangeKeys(temp_storage.exchange_keys).ScatterToBlocked(keys, ranks);
-
-            // Exchange values through shared memory in blocked arrangement
-            ExchangeValues(values, ranks, is_keys_only, Int2Type<true>());
-
-            CTA_SYNC();
-        }
-
-        // Untwiddle bits if necessary
-        #pragma unroll
-        for (int KEY = 0; KEY < ITEMS_PER_THREAD; KEY++)
-        {
-            unsigned_keys[KEY] = KeyTraits::TwiddleOut(unsigned_keys[KEY]);
-        }
-    }
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-    /// \smemstorage{BlockRadixSort}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockRadixSort()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockRadixSort(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Sorting (blocked arrangements)
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Performs an ascending block-wide radix sort over a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys.
-     *
-     * \par
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys each
-     *     typedef cub::BlockRadixSort<int, 128, 4> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     ...
-     *
-     *     // Collectively sort the keys
-     *     BlockRadixSort(temp_storage).Sort(thread_keys);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.
-     * The corresponding output \p thread_keys in those threads will be
-     * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
-     */
-    __device__ __forceinline__ void Sort(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        NullType values[ITEMS_PER_THREAD];
-
-        SortBlocked(keys, values, begin_bit, end_bit, Int2Type<false>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    /**
-     * \brief Performs an ascending block-wide radix sort across a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys and values.
-     *
-     * \par
-     * - BlockRadixSort can only accommodate one associated tile of values. To "truck along"
-     *   more than one tile of values, simply perform a key-value sort of the keys paired
-     *   with a temporary value array that enumerates the key indices.  The reordered indices
-     *   can then be used as a gather-vector for exchanging other associated tile data through
-     *   shared memory.
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys and values that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive pairs.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys and values each
-     *     typedef cub::BlockRadixSort<int, 128, 4, int> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     int thread_values[4];
-     *     ...
-     *
-     *     // Collectively sort the keys and values among block threads
-     *     BlockRadixSort(temp_storage).Sort(thread_keys, thread_values);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
-     * corresponding output \p thread_keys in those threads will be
-     * <tt>{ [0,1,2,3], [4,5,6,7], [8,9,10,11], ..., [508,509,510,511] }</tt>.
-     *
-     */
-    __device__ __forceinline__ void Sort(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        ValueT  (&values)[ITEMS_PER_THREAD],        ///< [in-out] Values to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        SortBlocked(keys, values, begin_bit, end_bit, Int2Type<false>(), Int2Type<KEYS_ONLY>());
-    }
-
-    /**
-     * \brief Performs a descending block-wide radix sort over a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys.
-     *
-     * \par
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys each
-     *     typedef cub::BlockRadixSort<int, 128, 4> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     ...
-     *
-     *     // Collectively sort the keys
-     *     BlockRadixSort(temp_storage).Sort(thread_keys);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.
-     * The corresponding output \p thread_keys in those threads will be
-     * <tt>{ [511,510,509,508], [11,10,9,8], [7,6,5,4], ..., [3,2,1,0] }</tt>.
-     */
-    __device__ __forceinline__ void SortDescending(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        NullType values[ITEMS_PER_THREAD];
-
-        SortBlocked(keys, values, begin_bit, end_bit, Int2Type<true>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    /**
-     * \brief Performs a descending block-wide radix sort across a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys and values.
-     *
-     * \par
-     * - BlockRadixSort can only accommodate one associated tile of values. To "truck along"
-     *   more than one tile of values, simply perform a key-value sort of the keys paired
-     *   with a temporary value array that enumerates the key indices.  The reordered indices
-     *   can then be used as a gather-vector for exchanging other associated tile data through
-     *   shared memory.
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys and values that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive pairs.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys and values each
-     *     typedef cub::BlockRadixSort<int, 128, 4, int> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     int thread_values[4];
-     *     ...
-     *
-     *     // Collectively sort the keys and values among block threads
-     *     BlockRadixSort(temp_storage).Sort(thread_keys, thread_values);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
-     * corresponding output \p thread_keys in those threads will be
-     * <tt>{ [511,510,509,508], [11,10,9,8], [7,6,5,4], ..., [3,2,1,0] }</tt>.
-     *
-     */
-    __device__ __forceinline__ void SortDescending(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        ValueT  (&values)[ITEMS_PER_THREAD],        ///< [in-out] Values to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        SortBlocked(keys, values, begin_bit, end_bit, Int2Type<true>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Sorting (blocked arrangement -> striped arrangement)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Performs an ascending radix sort across a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys, leaving them in a [<em>striped arrangement</em>](index.html#sec5sec3).
-     *
-     * \par
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys that
-     * are initially partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive keys.  The final partitioning is striped.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys each
-     *     typedef cub::BlockRadixSort<int, 128, 4> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     ...
-     *
-     *     // Collectively sort the keys
-     *     BlockRadixSort(temp_storage).SortBlockedToStriped(thread_keys);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
-     * corresponding output \p thread_keys in those threads will be
-     * <tt>{ [0,128,256,384], [1,129,257,385], [2,130,258,386], ..., [127,255,383,511] }</tt>.
-     *
-     */
-    __device__ __forceinline__ void SortBlockedToStriped(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        NullType values[ITEMS_PER_THREAD];
-
-        SortBlockedToStriped(keys, values, begin_bit, end_bit, Int2Type<false>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    /**
-     * \brief Performs an ascending radix sort across a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys and values, leaving them in a [<em>striped arrangement</em>](index.html#sec5sec3).
-     *
-     * \par
-     * - BlockRadixSort can only accommodate one associated tile of values. To "truck along"
-     *   more than one tile of values, simply perform a key-value sort of the keys paired
-     *   with a temporary value array that enumerates the key indices.  The reordered indices
-     *   can then be used as a gather-vector for exchanging other associated tile data through
-     *   shared memory.
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys and values that
-     * are initially partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive pairs.  The final partitioning is striped.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys and values each
-     *     typedef cub::BlockRadixSort<int, 128, 4, int> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     int thread_values[4];
-     *     ...
-     *
-     *     // Collectively sort the keys and values among block threads
-     *     BlockRadixSort(temp_storage).SortBlockedToStriped(thread_keys, thread_values);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
-     * corresponding output \p thread_keys in those threads will be
-     * <tt>{ [0,128,256,384], [1,129,257,385], [2,130,258,386], ..., [127,255,383,511] }</tt>.
-     *
-     */
-    __device__ __forceinline__ void SortBlockedToStriped(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        ValueT  (&values)[ITEMS_PER_THREAD],        ///< [in-out] Values to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        SortBlockedToStriped(keys, values, begin_bit, end_bit, Int2Type<false>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    /**
-     * \brief Performs a descending radix sort across a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys, leaving them in a [<em>striped arrangement</em>](index.html#sec5sec3).
-     *
-     * \par
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys that
-     * are initially partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive keys.  The final partitioning is striped.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys each
-     *     typedef cub::BlockRadixSort<int, 128, 4> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     ...
-     *
-     *     // Collectively sort the keys
-     *     BlockRadixSort(temp_storage).SortBlockedToStriped(thread_keys);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
-     * corresponding output \p thread_keys in those threads will be
-     * <tt>{ [511,383,255,127], [386,258,130,2], [385,257,128,1], ..., [384,256,128,0] }</tt>.
-     *
-     */
-    __device__ __forceinline__ void SortDescendingBlockedToStriped(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        NullType values[ITEMS_PER_THREAD];
-
-        SortBlockedToStriped(keys, values, begin_bit, end_bit, Int2Type<true>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    /**
-     * \brief Performs a descending radix sort across a [<em>blocked arrangement</em>](index.html#sec5sec3) of keys and values, leaving them in a [<em>striped arrangement</em>](index.html#sec5sec3).
-     *
-     * \par
-     * - BlockRadixSort can only accommodate one associated tile of values. To "truck along"
-     *   more than one tile of values, simply perform a key-value sort of the keys paired
-     *   with a temporary value array that enumerates the key indices.  The reordered indices
-     *   can then be used as a gather-vector for exchanging other associated tile data through
-     *   shared memory.
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sort of 512 integer keys and values that
-     * are initially partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive pairs.  The final partitioning is striped.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_radix_sort.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockRadixSort for a 1D block of 128 threads owning 4 integer keys and values each
-     *     typedef cub::BlockRadixSort<int, 128, 4, int> BlockRadixSort;
-     *
-     *     // Allocate shared memory for BlockRadixSort
-     *     __shared__ typename BlockRadixSort::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_keys[4];
-     *     int thread_values[4];
-     *     ...
-     *
-     *     // Collectively sort the keys and values among block threads
-     *     BlockRadixSort(temp_storage).SortBlockedToStriped(thread_keys, thread_values);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_keys across the block of threads is
-     * <tt>{ [0,511,1,510], [2,509,3,508], [4,507,5,506], ..., [254,257,255,256] }</tt>.  The
-     * corresponding output \p thread_keys in those threads will be
-     * <tt>{ [511,383,255,127], [386,258,130,2], [385,257,128,1], ..., [384,256,128,0] }</tt>.
-     *
-     */
-    __device__ __forceinline__ void SortDescendingBlockedToStriped(
-        KeyT    (&keys)[ITEMS_PER_THREAD],          ///< [in-out] Keys to sort
-        ValueT  (&values)[ITEMS_PER_THREAD],        ///< [in-out] Values to sort
-        int     begin_bit   = 0,                    ///< [in] <b>[optional]</b> The beginning (least-significant) bit index needed for key comparison
-        int     end_bit     = sizeof(KeyT) * 8)      ///< [in] <b>[optional]</b> The past-the-end (most-significant) bit index needed for key comparison
-    {
-        SortBlockedToStriped(keys, values, begin_bit, end_bit, Int2Type<true>(), Int2Type<KEYS_ONLY>());
-    }
-
-
-    //@}  end member group
-
-};
-
-/**
- * \example example_block_radix_sort.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_raking_layout.cuh b/ml-xgboost/cub/cub/block/block_raking_layout.cuh
deleted file mode 100644
index 9997eca..0000000
--- a/ml-xgboost/cub/cub/block/block_raking_layout.cuh
+++ /dev/null
@@ -1,153 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockRakingLayout provides a conflict-free shared memory layout abstraction for warp-raking across thread block data.
- */
-
-
-#pragma once
-
-#include "../util_macro.cuh"
-#include "../util_arch.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief BlockRakingLayout provides a conflict-free shared memory layout abstraction for 1D raking across thread block data.    ![](raking.png)
- * \ingroup BlockModule
- *
- * \par Overview
- * This type facilitates a shared memory usage pattern where a block of CUDA
- * threads places elements into shared memory and then reduces the active
- * parallelism to one "raking" warp of threads for serially aggregating consecutive
- * sequences of shared items.  Padding is inserted to eliminate bank conflicts
- * (for most data types).
- *
- * \tparam T                        The data type to be exchanged.
- * \tparam BLOCK_THREADS            The thread block size in threads.
- * \tparam PTX_ARCH                 <b>[optional]</b> \ptxversion
- */
-template <
-    typename    T,
-    int         BLOCK_THREADS,
-    int         PTX_ARCH = CUB_PTX_ARCH>
-struct BlockRakingLayout
-{
-    //---------------------------------------------------------------------
-    // Constants and type definitions
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        /// The total number of elements that need to be cooperatively reduced
-        SHARED_ELEMENTS = BLOCK_THREADS,
-
-        /// Maximum number of warp-synchronous raking threads
-        MAX_RAKING_THREADS = CUB_MIN(BLOCK_THREADS, CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// Number of raking elements per warp-synchronous raking thread (rounded up)
-        SEGMENT_LENGTH = (SHARED_ELEMENTS + MAX_RAKING_THREADS - 1) / MAX_RAKING_THREADS,
-
-        /// Never use a raking thread that will have no valid data (e.g., when BLOCK_THREADS is 62 and SEGMENT_LENGTH is 2, we should only use 31 raking threads)
-        RAKING_THREADS = (SHARED_ELEMENTS + SEGMENT_LENGTH - 1) / SEGMENT_LENGTH,
-
-        /// Whether we will have bank conflicts (technically we should find out if the GCD is > 1)
-        HAS_CONFLICTS = (CUB_SMEM_BANKS(PTX_ARCH) % SEGMENT_LENGTH == 0),
-
-        /// Degree of bank conflicts (e.g., 4-way)
-        CONFLICT_DEGREE = (HAS_CONFLICTS) ?
-            (MAX_RAKING_THREADS * SEGMENT_LENGTH) / CUB_SMEM_BANKS(PTX_ARCH) :
-            1,
-
-        /// Pad each segment length with one element if degree of bank conflicts is greater than 4-way (heuristic)
-        SEGMENT_PADDING = (CONFLICT_DEGREE > CUB_PREFER_CONFLICT_OVER_PADDING(PTX_ARCH)) ? 1 : 0,
-//        SEGMENT_PADDING = (HAS_CONFLICTS) ? 1 : 0,
-
-        /// Total number of elements in the raking grid
-        GRID_ELEMENTS = RAKING_THREADS * (SEGMENT_LENGTH + SEGMENT_PADDING),
-
-        /// Whether or not we need bounds checking during raking (the number of reduction elements is not a multiple of the number of raking threads)
-        UNGUARDED = (SHARED_ELEMENTS % RAKING_THREADS == 0),
-    };
-
-
-    /**
-     * \brief Shared memory storage type
-     */
-    struct __align__(16) _TempStorage
-    {
-        T buff[BlockRakingLayout::GRID_ELEMENTS];
-    };
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /**
-     * \brief Returns the location for the calling thread to place data into the grid
-     */
-    static __device__ __forceinline__ T* PlacementPtr(
-        TempStorage &temp_storage,
-        unsigned int linear_tid)
-    {
-        // Offset for partial
-        unsigned int offset = linear_tid;
-
-        // Add in one padding element for every segment
-        if (SEGMENT_PADDING > 0)
-        {
-            offset += offset / SEGMENT_LENGTH;
-        }
-
-        // Incorporating a block of padding partials every shared memory segment
-        return temp_storage.Alias().buff + offset;
-    }
-
-
-    /**
-     * \brief Returns the location for the calling thread to begin sequential raking
-     */
-    static __device__ __forceinline__ T* RakingPtr(
-        TempStorage &temp_storage,
-        unsigned int linear_tid)
-    {
-        return temp_storage.Alias().buff + (linear_tid * (SEGMENT_LENGTH + SEGMENT_PADDING));
-    }
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_reduce.cuh b/ml-xgboost/cub/cub/block/block_reduce.cuh
deleted file mode 100644
index aa7921e..0000000
--- a/ml-xgboost/cub/cub/block/block_reduce.cuh
+++ /dev/null
@@ -1,607 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockReduce class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel reduction of items partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "specializations/block_reduce_raking.cuh"
-#include "specializations/block_reduce_raking_commutative_only.cuh"
-#include "specializations/block_reduce_warp_reductions.cuh"
-#include "../util_ptx.cuh"
-#include "../util_type.cuh"
-#include "../thread/thread_operators.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-
-/******************************************************************************
- * Algorithmic variants
- ******************************************************************************/
-
-/**
- * BlockReduceAlgorithm enumerates alternative algorithms for parallel
- * reduction across a CUDA threadblock.
- */
-enum BlockReduceAlgorithm
-{
-
-    /**
-     * \par Overview
-     * An efficient "raking" reduction algorithm that only supports commutative
-     * reduction operators (true for most operations, e.g., addition).
-     *
-     * \par
-     * Execution is comprised of three phases:
-     * -# Upsweep sequential reduction in registers (if threads contribute more
-     *    than one input each).  Threads in warps other than the first warp place
-     *    their partial reductions into shared memory.
-     * -# Upsweep sequential reduction in shared memory.  Threads within the first
-     *    warp continue to accumulate by raking across segments of shared partial reductions
-     * -# A warp-synchronous Kogge-Stone style reduction within the raking warp.
-     *
-     * \par
-     * \image html block_reduce.png
-     * <div class="centercaption">\p BLOCK_REDUCE_RAKING data flow for a hypothetical 16-thread threadblock and 4-thread raking warp.</div>
-     *
-     * \par Performance Considerations
-     * - This variant performs less communication than BLOCK_REDUCE_RAKING_NON_COMMUTATIVE
-     *   and is preferable when the reduction operator is commutative.  This variant
-     *   applies fewer reduction operators  than BLOCK_REDUCE_WARP_REDUCTIONS, and can provide higher overall
-     *   throughput across the GPU when suitably occupied.  However, turn-around latency may be
-     *   higher than to BLOCK_REDUCE_WARP_REDUCTIONS and thus less-desirable
-     *   when the GPU is under-occupied.
-     */
-    BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY,
-
-
-    /**
-     * \par Overview
-     * An efficient "raking" reduction algorithm that supports commutative
-     * (e.g., addition) and non-commutative (e.g., string concatenation) reduction
-     * operators. \blocked.
-     *
-     * \par
-     * Execution is comprised of three phases:
-     * -# Upsweep sequential reduction in registers (if threads contribute more
-     *    than one input each).  Each thread then places the partial reduction
-     *    of its item(s) into shared memory.
-     * -# Upsweep sequential reduction in shared memory.  Threads within a
-     *    single warp rake across segments of shared partial reductions.
-     * -# A warp-synchronous Kogge-Stone style reduction within the raking warp.
-     *
-     * \par
-     * \image html block_reduce.png
-     * <div class="centercaption">\p BLOCK_REDUCE_RAKING data flow for a hypothetical 16-thread threadblock and 4-thread raking warp.</div>
-     *
-     * \par Performance Considerations
-     * - This variant performs more communication than BLOCK_REDUCE_RAKING
-     *   and is only preferable when the reduction operator is non-commutative.  This variant
-     *   applies fewer reduction operators than BLOCK_REDUCE_WARP_REDUCTIONS, and can provide higher overall
-     *   throughput across the GPU when suitably occupied.  However, turn-around latency may be
-     *   higher than to BLOCK_REDUCE_WARP_REDUCTIONS and thus less-desirable
-     *   when the GPU is under-occupied.
-     */
-    BLOCK_REDUCE_RAKING,
-
-
-    /**
-     * \par Overview
-     * A quick "tiled warp-reductions" reduction algorithm that supports commutative
-     * (e.g., addition) and non-commutative (e.g., string concatenation) reduction
-     * operators.
-     *
-     * \par
-     * Execution is comprised of four phases:
-     * -# Upsweep sequential reduction in registers (if threads contribute more
-     *    than one input each).  Each thread then places the partial reduction
-     *    of its item(s) into shared memory.
-     * -# Compute a shallow, but inefficient warp-synchronous Kogge-Stone style
-     *    reduction within each warp.
-     * -# A propagation phase where the warp reduction outputs in each warp are
-     *    updated with the aggregate from each preceding warp.
-     *
-     * \par
-     * \image html block_scan_warpscans.png
-     * <div class="centercaption">\p BLOCK_REDUCE_WARP_REDUCTIONS data flow for a hypothetical 16-thread threadblock and 4-thread raking warp.</div>
-     *
-     * \par Performance Considerations
-     * - This variant applies more reduction operators than BLOCK_REDUCE_RAKING
-     *   or BLOCK_REDUCE_RAKING_NON_COMMUTATIVE, which may result in lower overall
-     *   throughput across the GPU.  However turn-around latency may be lower and
-     *   thus useful when the GPU is under-occupied.
-     */
-    BLOCK_REDUCE_WARP_REDUCTIONS,
-};
-
-
-/******************************************************************************
- * Block reduce
- ******************************************************************************/
-
-/**
- * \brief The BlockReduce class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel reduction of items partitioned across a CUDA thread block. ![](reduce_logo.png)
- * \ingroup BlockModule
- *
- * \tparam T                Data type being reduced
- * \tparam BLOCK_DIM_X      The thread block length in threads along the X dimension
- * \tparam ALGORITHM        <b>[optional]</b> cub::BlockReduceAlgorithm enumerator specifying the underlying algorithm to use (default: cub::BLOCK_REDUCE_WARP_REDUCTIONS)
- * \tparam BLOCK_DIM_Y      <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z      <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH         <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - A <a href="http://en.wikipedia.org/wiki/Reduce_(higher-order_function)"><em>reduction</em></a> (or <em>fold</em>)
- *   uses a binary combining operator to compute a single aggregate from a list of input elements.
- * - \rowmajor
- * - BlockReduce can be optionally specialized by algorithm to accommodate different latency/throughput workload profiles:
- *   -# <b>cub::BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY</b>.  An efficient "raking" reduction algorithm that only supports commutative reduction operators. [More...](\ref cub::BlockReduceAlgorithm)
- *   -# <b>cub::BLOCK_REDUCE_RAKING</b>.  An efficient "raking" reduction algorithm that supports commutative and non-commutative reduction operators. [More...](\ref cub::BlockReduceAlgorithm)
- *   -# <b>cub::BLOCK_REDUCE_WARP_REDUCTIONS</b>.  A quick "tiled warp-reductions" reduction algorithm that supports commutative and non-commutative reduction operators. [More...](\ref cub::BlockReduceAlgorithm)
- *
- * \par Performance Considerations
- * - \granularity
- * - Very efficient (only one synchronization barrier).
- * - Incurs zero bank conflicts for most types
- * - Computation is slightly more efficient (i.e., having lower instruction overhead) for:
- *   - Summation (<b><em>vs.</em></b> generic reduction)
- *   - \p BLOCK_THREADS is a multiple of the architecture's warp size
- *   - Every thread has a valid input (i.e., full <b><em>vs.</em></b> partial-tiles)
- * - See cub::BlockReduceAlgorithm for performance details regarding algorithmic alternatives
- *
- * \par A Simple Example
- * \blockcollective{BlockReduce}
- * \par
- * The code snippet below illustrates a sum reduction of 512 integer items that
- * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
- * where each thread owns 4 consecutive items.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize BlockReduce for a 1D block of 128 threads on type int
- *     typedef cub::BlockReduce<int, 128> BlockReduce;
- *
- *     // Allocate shared memory for BlockReduce
- *     __shared__ typename BlockReduce::TempStorage temp_storage;
- *
- *     // Obtain a segment of consecutive items that are blocked across threads
- *     int thread_data[4];
- *     ...
- *
- *     // Compute the block-wide sum for thread0
- *     int aggregate = BlockReduce(temp_storage).Sum(thread_data);
- *
- * \endcode
- *
- */
-template <
-    typename                T,
-    int                     BLOCK_DIM_X,
-    BlockReduceAlgorithm    ALGORITHM       = BLOCK_REDUCE_WARP_REDUCTIONS,
-    int                     BLOCK_DIM_Y     = 1,
-    int                     BLOCK_DIM_Z     = 1,
-    int                     PTX_ARCH        = CUB_PTX_ARCH>
-class BlockReduce
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    typedef BlockReduceWarpReductions<T, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH>           WarpReductions;
-    typedef BlockReduceRakingCommutativeOnly<T, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH>    RakingCommutativeOnly;
-    typedef BlockReduceRaking<T, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH>                   Raking;
-
-    /// Internal specialization type
-    typedef typename If<(ALGORITHM == BLOCK_REDUCE_WARP_REDUCTIONS),
-        WarpReductions,
-        typename If<(ALGORITHM == BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY),
-            RakingCommutativeOnly,
-            Raking>::Type>::Type InternalBlockReduce;     // BlockReduceRaking
-
-    /// Shared memory storage layout type for BlockReduce
-    typedef typename InternalBlockReduce::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-
-public:
-
-    /// \smemstorage{BlockReduce}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockReduce()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockReduce(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Generic reductions
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes a block-wide reduction for thread<sub>0</sub> using the specified binary reduction functor.  Each thread contributes one input element.
-     *
-     * \par
-     * - The return value is undefined in threads other than thread<sub>0</sub>.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a max reduction of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockReduce for a 1D block of 128 threads on type int
-     *     typedef cub::BlockReduce<int, 128> BlockReduce;
-     *
-     *     // Allocate shared memory for BlockReduce
-     *     __shared__ typename BlockReduce::TempStorage temp_storage;
-     *
-     *     // Each thread obtains an input item
-     *     int thread_data;
-     *     ...
-     *
-     *     // Compute the block-wide max for thread0
-     *     int aggregate = BlockReduce(temp_storage).Reduce(thread_data, cub::Max());
-     *
-     * \endcode
-     *
-     * \tparam ReductionOp          <b>[inferred]</b> Binary reduction functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T               input,                      ///< [in] Calling thread's input
-        ReductionOp     reduction_op)               ///< [in] Binary reduction functor 
-    {
-        return InternalBlockReduce(temp_storage).template Reduce<true>(input, BLOCK_THREADS, reduction_op);
-    }
-
-
-    /**
-     * \brief Computes a block-wide reduction for thread<sub>0</sub> using the specified binary reduction functor.  Each thread contributes an array of consecutive input elements.
-     *
-     * \par
-     * - The return value is undefined in threads other than thread<sub>0</sub>.
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a max reduction of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockReduce for a 1D block of 128 threads on type int
-     *     typedef cub::BlockReduce<int, 128> BlockReduce;
-     *
-     *     // Allocate shared memory for BlockReduce
-     *     __shared__ typename BlockReduce::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Compute the block-wide max for thread0
-     *     int aggregate = BlockReduce(temp_storage).Reduce(thread_data, cub::Max());
-     *
-     * \endcode
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ReductionOp          <b>[inferred]</b> Binary reduction functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int ITEMS_PER_THREAD,
-        typename ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T               (&inputs)[ITEMS_PER_THREAD],    ///< [in] Calling thread's input segment
-        ReductionOp     reduction_op)                   ///< [in] Binary reduction functor 
-    {
-        // Reduce partials
-        T partial = ThreadReduce(inputs, reduction_op);
-        return Reduce(partial, reduction_op);
-    }
-
-
-    /**
-     * \brief Computes a block-wide reduction for thread<sub>0</sub> using the specified binary reduction functor.  The first \p num_valid threads each contribute one input element.
-     *
-     * \par
-     * - The return value is undefined in threads other than thread<sub>0</sub>.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a max reduction of a partially-full tile of integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
-     *
-     * __global__ void ExampleKernel(int num_valid, ...)
-     * {
-     *     // Specialize BlockReduce for a 1D block of 128 threads on type int
-     *     typedef cub::BlockReduce<int, 128> BlockReduce;
-     *
-     *     // Allocate shared memory for BlockReduce
-     *     __shared__ typename BlockReduce::TempStorage temp_storage;
-     *
-     *     // Each thread obtains an input item
-     *     int thread_data;
-     *     if (threadIdx.x < num_valid) thread_data = ...
-     *
-     *     // Compute the block-wide max for thread0
-     *     int aggregate = BlockReduce(temp_storage).Reduce(thread_data, cub::Max(), num_valid);
-     *
-     * \endcode
-     *
-     * \tparam ReductionOp          <b>[inferred]</b> Binary reduction functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   input,                  ///< [in] Calling thread's input
-        ReductionOp         reduction_op,           ///< [in] Binary reduction functor 
-        int                 num_valid)              ///< [in] Number of threads containing valid elements (may be less than BLOCK_THREADS)
-    {
-        // Determine if we scan skip bounds checking
-        if (num_valid >= BLOCK_THREADS)
-        {
-            return InternalBlockReduce(temp_storage).template Reduce<true>(input, num_valid, reduction_op);
-        }
-        else
-        {
-            return InternalBlockReduce(temp_storage).template Reduce<false>(input, num_valid, reduction_op);
-        }
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Summation reductions
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes a block-wide reduction for thread<sub>0</sub> using addition (+) as the reduction operator.  Each thread contributes one input element.
-     *
-     * \par
-     * - The return value is undefined in threads other than thread<sub>0</sub>.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sum reduction of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockReduce for a 1D block of 128 threads on type int
-     *     typedef cub::BlockReduce<int, 128> BlockReduce;
-     *
-     *     // Allocate shared memory for BlockReduce
-     *     __shared__ typename BlockReduce::TempStorage temp_storage;
-     *
-     *     // Each thread obtains an input item
-     *     int thread_data;
-     *     ...
-     *
-     *     // Compute the block-wide sum for thread0
-     *     int aggregate = BlockReduce(temp_storage).Sum(thread_data);
-     *
-     * \endcode
-     *
-     */
-    __device__ __forceinline__ T Sum(
-        T   input)                      ///< [in] Calling thread's input
-    {
-        return InternalBlockReduce(temp_storage).template Sum<true>(input, BLOCK_THREADS);
-    }
-
-    /**
-     * \brief Computes a block-wide reduction for thread<sub>0</sub> using addition (+) as the reduction operator.  Each thread contributes an array of consecutive input elements.
-     *
-     * \par
-     * - The return value is undefined in threads other than thread<sub>0</sub>.
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sum reduction of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockReduce for a 1D block of 128 threads on type int
-     *     typedef cub::BlockReduce<int, 128> BlockReduce;
-     *
-     *     // Allocate shared memory for BlockReduce
-     *     __shared__ typename BlockReduce::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Compute the block-wide sum for thread0
-     *     int aggregate = BlockReduce(temp_storage).Sum(thread_data);
-     *
-     * \endcode
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ T Sum(
-        T   (&inputs)[ITEMS_PER_THREAD])    ///< [in] Calling thread's input segment
-    {
-        // Reduce partials
-        T partial = ThreadReduce(inputs, cub::Sum());
-        return Sum(partial);
-    }
-
-
-    /**
-     * \brief Computes a block-wide reduction for thread<sub>0</sub> using addition (+) as the reduction operator.  The first \p num_valid threads each contribute one input element.
-     *
-     * \par
-     * - The return value is undefined in threads other than thread<sub>0</sub>.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sum reduction of a partially-full tile of integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_reduce.cuh>
-     *
-     * __global__ void ExampleKernel(int num_valid, ...)
-     * {
-     *     // Specialize BlockReduce for a 1D block of 128 threads on type int
-     *     typedef cub::BlockReduce<int, 128> BlockReduce;
-     *
-     *     // Allocate shared memory for BlockReduce
-     *     __shared__ typename BlockReduce::TempStorage temp_storage;
-     *
-     *     // Each thread obtains an input item (up to num_items)
-     *     int thread_data;
-     *     if (threadIdx.x < num_valid)
-     *         thread_data = ...
-     *
-     *     // Compute the block-wide sum for thread0
-     *     int aggregate = BlockReduce(temp_storage).Sum(thread_data, num_valid);
-     *
-     * \endcode
-     *
-     */
-    __device__ __forceinline__ T Sum(
-        T   input,                  ///< [in] Calling thread's input
-        int num_valid)              ///< [in] Number of threads containing valid elements (may be less than BLOCK_THREADS)
-    {
-        // Determine if we scan skip bounds checking
-        if (num_valid >= BLOCK_THREADS)
-        {
-            return InternalBlockReduce(temp_storage).template Sum<true>(input, num_valid);
-        }
-        else
-        {
-            return InternalBlockReduce(temp_storage).template Sum<false>(input, num_valid);
-        }
-    }
-
-
-    //@}  end member group
-};
-
-/**
- * \example example_block_reduce.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_scan.cuh b/ml-xgboost/cub/cub/block/block_scan.cuh
deleted file mode 100644
index 0f7aaf0..0000000
--- a/ml-xgboost/cub/cub/block/block_scan.cuh
+++ /dev/null
@@ -1,2126 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockScan class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel prefix sum/scan of items partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "specializations/block_scan_raking.cuh"
-#include "specializations/block_scan_warp_scans.cuh"
-#include "../util_arch.cuh"
-#include "../util_type.cuh"
-#include "../util_ptx.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Algorithmic variants
- ******************************************************************************/
-
-/**
- * \brief BlockScanAlgorithm enumerates alternative algorithms for cub::BlockScan to compute a parallel prefix scan across a CUDA thread block.
- */
-enum BlockScanAlgorithm
-{
-
-    /**
-     * \par Overview
-     * An efficient "raking reduce-then-scan" prefix scan algorithm.  Execution is comprised of five phases:
-     * -# Upsweep sequential reduction in registers (if threads contribute more than one input each).  Each thread then places the partial reduction of its item(s) into shared memory.
-     * -# Upsweep sequential reduction in shared memory.  Threads within a single warp rake across segments of shared partial reductions.
-     * -# A warp-synchronous Kogge-Stone style exclusive scan within the raking warp.
-     * -# Downsweep sequential exclusive scan in shared memory.  Threads within a single warp rake across segments of shared partial reductions, seeded with the warp-scan output.
-     * -# Downsweep sequential scan in registers (if threads contribute more than one input), seeded with the raking scan output.
-     *
-     * \par
-     * \image html block_scan_raking.png
-     * <div class="centercaption">\p BLOCK_SCAN_RAKING data flow for a hypothetical 16-thread threadblock and 4-thread raking warp.</div>
-     *
-     * \par Performance Considerations
-     * - Although this variant may suffer longer turnaround latencies when the
-     *   GPU is under-occupied, it can often provide higher overall throughput
-     *   across the GPU when suitably occupied.
-     */
-    BLOCK_SCAN_RAKING,
-
-
-    /**
-     * \par Overview
-     * Similar to cub::BLOCK_SCAN_RAKING, but with fewer shared memory reads at
-     * the expense of higher register pressure.  Raking threads preserve their
-     * "upsweep" segment of values in registers while performing warp-synchronous
-     * scan, allowing the "downsweep" not to re-read them from shared memory.
-     */
-    BLOCK_SCAN_RAKING_MEMOIZE,
-
-
-    /**
-     * \par Overview
-     * A quick "tiled warpscans" prefix scan algorithm.  Execution is comprised of four phases:
-     * -# Upsweep sequential reduction in registers (if threads contribute more than one input each).  Each thread then places the partial reduction of its item(s) into shared memory.
-     * -# Compute a shallow, but inefficient warp-synchronous Kogge-Stone style scan within each warp.
-     * -# A propagation phase where the warp scan outputs in each warp are updated with the aggregate from each preceding warp.
-     * -# Downsweep sequential scan in registers (if threads contribute more than one input), seeded with the raking scan output.
-     *
-     * \par
-     * \image html block_scan_warpscans.png
-     * <div class="centercaption">\p BLOCK_SCAN_WARP_SCANS data flow for a hypothetical 16-thread threadblock and 4-thread raking warp.</div>
-     *
-     * \par Performance Considerations
-     * - Although this variant may suffer lower overall throughput across the
-     *   GPU because due to a heavy reliance on inefficient warpscans, it can
-     *   often provide lower turnaround latencies when the GPU is under-occupied.
-     */
-    BLOCK_SCAN_WARP_SCANS,
-};
-
-
-/******************************************************************************
- * Block scan
- ******************************************************************************/
-
-/**
- * \brief The BlockScan class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel prefix sum/scan of items partitioned across a CUDA thread block. ![](block_scan_logo.png)
- * \ingroup BlockModule
- *
- * \tparam T                Data type being scanned
- * \tparam BLOCK_DIM_X      The thread block length in threads along the X dimension
- * \tparam ALGORITHM        <b>[optional]</b> cub::BlockScanAlgorithm enumerator specifying the underlying algorithm to use (default: cub::BLOCK_SCAN_RAKING)
- * \tparam BLOCK_DIM_Y      <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z      <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH         <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - Given a list of input elements and a binary reduction operator, a [<em>prefix scan</em>](http://en.wikipedia.org/wiki/Prefix_sum)
- *   produces an output list where each element is computed to be the reduction
- *   of the elements occurring earlier in the input list.  <em>Prefix sum</em>
- *   connotes a prefix scan with the addition operator. The term \em inclusive indicates
- *   that the <em>i</em><sup>th</sup> output reduction incorporates the <em>i</em><sup>th</sup> input.
- *   The term \em exclusive indicates the <em>i</em><sup>th</sup> input is not incorporated into
- *   the <em>i</em><sup>th</sup> output reduction.
- * - \rowmajor
- * - BlockScan can be optionally specialized by algorithm to accommodate different workload profiles:
- *   -# <b>cub::BLOCK_SCAN_RAKING</b>.  An efficient (high throughput) "raking reduce-then-scan" prefix scan algorithm. [More...](\ref cub::BlockScanAlgorithm)
- *   -# <b>cub::BLOCK_SCAN_RAKING_MEMOIZE</b>.  Similar to cub::BLOCK_SCAN_RAKING, but having higher throughput at the expense of additional register pressure for intermediate storage. [More...](\ref cub::BlockScanAlgorithm)
- *   -# <b>cub::BLOCK_SCAN_WARP_SCANS</b>.  A quick (low latency) "tiled warpscans" prefix scan algorithm. [More...](\ref cub::BlockScanAlgorithm)
- *
- * \par Performance Considerations
- * - \granularity
- * - Uses special instructions when applicable (e.g., warp \p SHFL)
- * - Uses synchronization-free communication between warp lanes when applicable
- * - Invokes a minimal number of minimal block-wide synchronization barriers (only
- *   one or two depending on algorithm selection)
- * - Incurs zero bank conflicts for most types
- * - Computation is slightly more efficient (i.e., having lower instruction overhead) for:
- *   - Prefix sum variants (<b><em>vs.</em></b> generic scan)
- *   - \blocksize
- * - See cub::BlockScanAlgorithm for performance details regarding algorithmic alternatives
- *
- * \par A Simple Example
- * \blockcollective{BlockScan}
- * \par
- * The code snippet below illustrates an exclusive prefix sum of 512 integer items that
- * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
- * where each thread owns 4 consecutive items.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize BlockScan for a 1D block of 128 threads on type int
- *     typedef cub::BlockScan<int, 128> BlockScan;
- *
- *     // Allocate shared memory for BlockScan
- *     __shared__ typename BlockScan::TempStorage temp_storage;
- *
- *     // Obtain a segment of consecutive items that are blocked across threads
- *     int thread_data[4];
- *     ...
- *
- *     // Collectively compute the block-wide exclusive prefix sum
- *     BlockScan(temp_storage).ExclusiveSum(thread_data, thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the block of threads is
- * <tt>{[1,1,1,1], [1,1,1,1], ..., [1,1,1,1]}</tt>.
- * The corresponding output \p thread_data in those threads will be
- * <tt>{[0,1,2,3], [4,5,6,7], ..., [508,509,510,511]}</tt>.
- *
- */
-template <
-    typename            T,
-    int                 BLOCK_DIM_X,
-    BlockScanAlgorithm  ALGORITHM       = BLOCK_SCAN_RAKING,
-    int                 BLOCK_DIM_Y     = 1,
-    int                 BLOCK_DIM_Z     = 1,
-    int                 PTX_ARCH        = CUB_PTX_ARCH>
-class BlockScan
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    /**
-     * Ensure the template parameterization meets the requirements of the
-     * specified algorithm. Currently, the BLOCK_SCAN_WARP_SCANS policy
-     * cannot be used with threadblock sizes not a multiple of the
-     * architectural warp size.
-     */
-    static const BlockScanAlgorithm SAFE_ALGORITHM =
-        ((ALGORITHM == BLOCK_SCAN_WARP_SCANS) && (BLOCK_THREADS % CUB_WARP_THREADS(PTX_ARCH) != 0)) ?
-            BLOCK_SCAN_RAKING :
-            ALGORITHM;
-
-    typedef BlockScanWarpScans<T, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> WarpScans;
-    typedef BlockScanRaking<T, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, (SAFE_ALGORITHM == BLOCK_SCAN_RAKING_MEMOIZE), PTX_ARCH> Raking;
-
-    /// Define the delegate type for the desired algorithm
-    typedef typename If<(SAFE_ALGORITHM == BLOCK_SCAN_WARP_SCANS),
-        WarpScans,
-        Raking>::Type InternalBlockScan;
-
-    /// Shared memory storage layout type for BlockScan
-    typedef typename InternalBlockScan::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /******************************************************************************
-     * Public types
-     ******************************************************************************/
-public:
-
-    /// \smemstorage{BlockScan}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockScan()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockScan(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Exclusive prefix sum operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes one input element.  The value of 0 is applied as the initial value, and is assigned to \p output in <em>thread</em><sub>0</sub>.
-     *
-     * \par
-     * - \identityzero
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix sum of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix sum
-     *     BlockScan(temp_storage).ExclusiveSum(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>1, 1, ..., 1</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>0, 1, ..., 127</tt>.
-     *
-     */
-    __device__ __forceinline__ void ExclusiveSum(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output)                        ///< [out] Calling thread's output item (may be aliased to \p input)
-    {
-        T initial_value = 0;
-        ExclusiveScan(input, output, initial_value, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes one input element.  The value of 0 is applied as the initial value, and is assigned to \p output in <em>thread</em><sub>0</sub>.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - \identityzero
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix sum of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix sum
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).ExclusiveSum(thread_data, thread_data, block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>1, 1, ..., 1</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>0, 1, ..., 127</tt>.
-     * Furthermore the value \p 128 will be stored in \p block_aggregate for all threads.
-     *
-     */
-    __device__ __forceinline__ void ExclusiveSum(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        T initial_value = 0;
-        ExclusiveScan(input, output, initial_value, cub::Sum(), block_aggregate);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes one input element.  Instead of using 0 as the block-wide prefix, the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - \identityzero
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an exclusive prefix sum over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 128 integer items that are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total += block_aggregate;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(0);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data = d_data[block_offset];
-     *
-     *         // Collectively compute the block-wide exclusive prefix sum
-     *         BlockScan(temp_storage).ExclusiveSum(
-     *             thread_data, thread_data, prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         d_data[block_offset] = thread_data;
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>1, 1, 1, 1, 1, 1, 1, 1, ...</tt>.
-     * The corresponding output for the first segment will be <tt>0, 1, ..., 127</tt>.
-     * The output for the second segment will be <tt>128, 129, ..., 255</tt>.
-     *
-     * \tparam BlockPrefixCallbackOp        <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveSum(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        ExclusiveScan(input, output, cub::Sum(), block_prefix_callback_op);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Exclusive prefix sum operations (multiple data per thread)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes an array of consecutive input elements.  The value of 0 is applied as the initial value, and is assigned to \p output[0] in <em>thread</em><sub>0</sub>.
-     *
-     * \par
-     * - \identityzero
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix sum of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix sum
-     *     BlockScan(temp_storage).ExclusiveSum(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{ [1,1,1,1], [1,1,1,1], ..., [1,1,1,1] }</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void ExclusiveSum(
-        T                 (&input)[ITEMS_PER_THREAD],   ///< [in] Calling thread's input items
-        T                 (&output)[ITEMS_PER_THREAD])  ///< [out] Calling thread's output items (may be aliased to \p input)
-    {
-        T initial_value = 0;
-        ExclusiveScan(input, output, initial_value, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes an array of consecutive input elements.  The value of 0 is applied as the initial value, and is assigned to \p output[0] in <em>thread</em><sub>0</sub>.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - \identityzero
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix sum of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix sum
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).ExclusiveSum(thread_data, thread_data, block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{ [1,1,1,1], [1,1,1,1], ..., [1,1,1,1] }</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
-     * Furthermore the value \p 512 will be stored in \p block_aggregate for all threads.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void ExclusiveSum(
-        T                 (&input)[ITEMS_PER_THREAD],       ///< [in] Calling thread's input items
-        T                 (&output)[ITEMS_PER_THREAD],      ///< [out] Calling thread's output items (may be aliased to \p input)
-        T                 &block_aggregate)                 ///< [out] block-wide aggregate reduction of input items
-    {
-        // Reduce consecutive thread items in registers
-        T initial_value = 0;
-        ExclusiveScan(input, output, initial_value, cub::Sum(), block_aggregate);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes an array of consecutive input elements.  Instead of using 0 as the block-wide prefix, the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - \identityzero
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an exclusive prefix sum over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 512 integer items that are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3)
-     * across 128 threads where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total += block_aggregate;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockLoad, BlockStore, and BlockScan for a 1D block of 128 threads, 4 ints per thread
-     *     typedef cub::BlockLoad<int*, 128, 4, BLOCK_LOAD_TRANSPOSE>   BlockLoad;
-     *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_TRANSPOSE>  BlockStore;
-     *     typedef cub::BlockScan<int, 128>                             BlockScan;
-     *
-     *     // Allocate aliased shared memory for BlockLoad, BlockStore, and BlockScan
-     *     __shared__ union {
-     *         typename BlockLoad::TempStorage     load;
-     *         typename BlockScan::TempStorage     scan;
-     *         typename BlockStore::TempStorage    store;
-     *     } temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(0);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128 * 4)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data[4];
-     *         BlockLoad(temp_storage.load).Load(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *
-     *         // Collectively compute the block-wide exclusive prefix sum
-     *         int block_aggregate;
-     *         BlockScan(temp_storage.scan).ExclusiveSum(
-     *             thread_data, thread_data, prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         BlockStore(temp_storage.store).Store(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>1, 1, 1, 1, 1, 1, 1, 1, ...</tt>.
-     * The corresponding output for the first segment will be <tt>0, 1, 2, 3, ..., 510, 511</tt>.
-     * The output for the second segment will be <tt>512, 513, 514, 515, ..., 1022, 1023</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam BlockPrefixCallbackOp        <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <
-        int ITEMS_PER_THREAD,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveSum(
-        T                       (&input)[ITEMS_PER_THREAD],   ///< [in] Calling thread's input items
-        T                       (&output)[ITEMS_PER_THREAD],  ///< [out] Calling thread's output items (may be aliased to \p input)
-        BlockPrefixCallbackOp   &block_prefix_callback_op)    ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        ExclusiveScan(input, output, cub::Sum(), block_prefix_callback_op);
-    }
-
-
-
-    //@}  end member group        // Exclusive prefix sums
-    /******************************************************************//**
-     * \name Exclusive prefix scan operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix max scan of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix max scan
-     *     BlockScan(temp_storage).ExclusiveScan(thread_data, thread_data, INT_MIN, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>0, -1, 2, -3, ..., 126, -127</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>INT_MIN, 0, 0, 2, ..., 124, 126</tt>.
-     *
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        T               initial_value,                  ///< [in] Initial value to seed the exclusive scan (and is assigned to \p output[0] in <em>thread</em><sub>0</sub>)
-        ScanOp          scan_op)                        ///< [in] Binary scan functor 
-    {
-        InternalBlockScan(temp_storage).ExclusiveScan(input, output, initial_value, scan_op);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix max scan of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix max scan
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).ExclusiveScan(thread_data, thread_data, INT_MIN, cub::Max(), block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>0, -1, 2, -3, ..., 126, -127</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>INT_MIN, 0, 0, 2, ..., 124, 126</tt>.
-     * Furthermore the value \p 126 will be stored in \p block_aggregate for all threads.
-     *
-     * \tparam ScanOp   <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &output,            ///< [out] Calling thread's output items (may be aliased to \p input)
-        T               initial_value,      ///< [in] Initial value to seed the exclusive scan (and is assigned to \p output[0] in <em>thread</em><sub>0</sub>)
-        ScanOp          scan_op,            ///< [in] Binary scan functor 
-        T               &block_aggregate)   ///< [out] block-wide aggregate reduction of input items
-    {
-        InternalBlockScan(temp_storage).ExclusiveScan(input, output, initial_value, scan_op, block_aggregate);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an exclusive prefix max scan over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 128 integer items that are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total = (block_aggregate > old_prefix) ? block_aggregate : old_prefix;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(INT_MIN);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data = d_data[block_offset];
-     *
-     *         // Collectively compute the block-wide exclusive prefix max scan
-     *         BlockScan(temp_storage).ExclusiveScan(
-     *             thread_data, thread_data, INT_MIN, cub::Max(), prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         d_data[block_offset] = thread_data;
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, -1, 2, -3, 4, -5, ...</tt>.
-     * The corresponding output for the first segment will be <tt>INT_MIN, 0, 0, 2, ..., 124, 126</tt>.
-     * The output for the second segment will be <tt>126, 128, 128, 130, ..., 252, 254</tt>.
-     *
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     * \tparam BlockPrefixCallbackOp        <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan functor 
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        InternalBlockScan(temp_storage).ExclusiveScan(input, output, scan_op, block_prefix_callback_op);
-    }
-
-
-    //@}  end member group        // Inclusive prefix sums
-    /******************************************************************//**
-     * \name Exclusive prefix scan operations (multiple data per thread)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix max scan of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix max scan
-     *     BlockScan(temp_storage).ExclusiveScan(thread_data, thread_data, INT_MIN, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,-1,2,-3], [4,-5,6,-7], ..., [508,-509,510,-511] }</tt>.
-     * The corresponding output \p thread_data in those threads will be
-     * <tt>{ [INT_MIN,0,0,2], [2,4,4,6], ..., [506,508,508,510] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                 (&input)[ITEMS_PER_THREAD],   ///< [in] Calling thread's input items
-        T                 (&output)[ITEMS_PER_THREAD],  ///< [out] Calling thread's output items (may be aliased to \p input)
-        T                 initial_value,                ///< [in] Initial value to seed the exclusive scan (and is assigned to \p output[0] in <em>thread</em><sub>0</sub>)
-        ScanOp            scan_op)                      ///< [in] Binary scan functor
-    {
-        // Reduce consecutive thread items in registers
-        T thread_prefix = ThreadReduce(input, scan_op);
-
-        // Exclusive threadblock-scan
-        ExclusiveScan(thread_prefix, thread_prefix, initial_value, scan_op);
-
-        // Exclusive scan in registers with prefix as seed
-        ThreadScanExclusive(input, output, scan_op, thread_prefix);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an exclusive prefix max scan of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide exclusive prefix max scan
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).ExclusiveScan(thread_data, thread_data, INT_MIN, cub::Max(), block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{ [0,-1,2,-3], [4,-5,6,-7], ..., [508,-509,510,-511] }</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>{ [INT_MIN,0,0,2], [2,4,4,6], ..., [506,508,508,510] }</tt>.
-     * Furthermore the value \p 510 will be stored in \p block_aggregate for all threads.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                 (&input)[ITEMS_PER_THREAD],   ///< [in] Calling thread's input items
-        T                 (&output)[ITEMS_PER_THREAD],  ///< [out] Calling thread's output items (may be aliased to \p input)
-        T                 initial_value,                ///< [in] Initial value to seed the exclusive scan (and is assigned to \p output[0] in <em>thread</em><sub>0</sub>)
-        ScanOp            scan_op,                      ///< [in] Binary scan functor
-        T                 &block_aggregate)             ///< [out] block-wide aggregate reduction of input items
-    {
-        // Reduce consecutive thread items in registers
-        T thread_prefix = ThreadReduce(input, scan_op);
-
-        // Exclusive threadblock-scan
-        ExclusiveScan(thread_prefix, thread_prefix, initial_value, scan_op, block_aggregate);
-
-        // Exclusive scan in registers with prefix as seed
-        ThreadScanExclusive(input, output, scan_op, thread_prefix);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an exclusive prefix max scan over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 128 integer items that are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total = (block_aggregate > old_prefix) ? block_aggregate : old_prefix;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockLoad, BlockStore, and BlockScan for a 1D block of 128 threads, 4 ints per thread
-     *     typedef cub::BlockLoad<int*, 128, 4, BLOCK_LOAD_TRANSPOSE>   BlockLoad;
-     *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_TRANSPOSE>  BlockStore;
-     *     typedef cub::BlockScan<int, 128>                             BlockScan;
-     *
-     *     // Allocate aliased shared memory for BlockLoad, BlockStore, and BlockScan
-     *     __shared__ union {
-     *         typename BlockLoad::TempStorage     load;
-     *         typename BlockScan::TempStorage     scan;
-     *         typename BlockStore::TempStorage    store;
-     *     } temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(0);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128 * 4)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data[4];
-     *         BlockLoad(temp_storage.load).Load(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *
-     *         // Collectively compute the block-wide exclusive prefix max scan
-     *         BlockScan(temp_storage.scan).ExclusiveScan(
-     *             thread_data, thread_data, INT_MIN, cub::Max(), prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         BlockStore(temp_storage.store).Store(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, -1, 2, -3, 4, -5, ...</tt>.
-     * The corresponding output for the first segment will be <tt>INT_MIN, 0, 0, 2, 2, 4, ..., 508, 510</tt>.
-     * The output for the second segment will be <tt>510, 512, 512, 514, 514, 516, ..., 1020, 1022</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD         <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp                   <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     * \tparam BlockPrefixCallbackOp    <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp,
-        typename        BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                       (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T                       (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan functor
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        // Reduce consecutive thread items in registers
-        T thread_prefix = ThreadReduce(input, scan_op);
-
-        // Exclusive threadblock-scan
-        ExclusiveScan(thread_prefix, thread_prefix, scan_op, block_prefix_callback_op);
-
-        // Exclusive scan in registers with prefix as seed
-        ThreadScanExclusive(input, output, scan_op, thread_prefix);
-    }
-
-
-    //@}  end member group
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document no-initial-value scans
-
-    /******************************************************************//**
-     * \name Exclusive prefix scan operations (no initial value, single datum per thread)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan functor
-    {
-        InternalBlockScan(temp_storage).ExclusiveScan(input, output, scan_op);
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \tparam ScanOp   <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan functor
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        InternalBlockScan(temp_storage).ExclusiveScan(input, output, scan_op, block_aggregate);
-    }
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Exclusive prefix scan operations (no initial value, multiple data per thread)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                 (&input)[ITEMS_PER_THREAD],   ///< [in] Calling thread's input items
-        T                 (&output)[ITEMS_PER_THREAD],  ///< [out] Calling thread's output items (may be aliased to \p input)
-        ScanOp            scan_op)                      ///< [in] Binary scan functor
-    {
-        // Reduce consecutive thread items in registers
-        T thread_partial = ThreadReduce(input, scan_op);
-
-        // Exclusive threadblock-scan
-        ExclusiveScan(thread_partial, thread_partial, scan_op);
-
-        // Exclusive scan in registers with prefix
-        ThreadScanExclusive(input, output, scan_op, thread_partial, (linear_tid != 0));
-    }
-
-
-    /**
-     * \brief Computes an exclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  Also provides every thread with the block-wide \p block_aggregate of all inputs.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T               (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan functor
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        // Reduce consecutive thread items in registers
-        T thread_partial = ThreadReduce(input, scan_op);
-
-        // Exclusive threadblock-scan
-        ExclusiveScan(thread_partial, thread_partial, scan_op, block_aggregate);
-
-        // Exclusive scan in registers with prefix
-        ThreadScanExclusive(input, output, scan_op, thread_partial, (linear_tid != 0));
-    }
-
-
-    //@}  end member group
-#endif // DOXYGEN_SHOULD_SKIP_THIS  // Do not document no-initial-value scans
-
-    /******************************************************************//**
-     * \name Inclusive prefix sum operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes one input element.
-     *
-     * \par
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix sum of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix sum
-     *     BlockScan(temp_storage).InclusiveSum(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>1, 1, ..., 1</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>1, 2, ..., 128</tt>.
-     *
-     */
-    __device__ __forceinline__ void InclusiveSum(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output)                        ///< [out] Calling thread's output item (may be aliased to \p input)
-    {
-        InclusiveScan(input, output, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix sum of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix sum
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).InclusiveSum(thread_data, thread_data, block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>1, 1, ..., 1</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>1, 2, ..., 128</tt>.
-     * Furthermore the value \p 128 will be stored in \p block_aggregate for all threads.
-     *
-     */
-    __device__ __forceinline__ void InclusiveSum(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        InclusiveScan(input, output, cub::Sum(), block_aggregate);
-    }
-
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes one input element.  Instead of using 0 as the block-wide prefix, the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an inclusive prefix sum over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 128 integer items that are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total += block_aggregate;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(0);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data = d_data[block_offset];
-     *
-     *         // Collectively compute the block-wide inclusive prefix sum
-     *         BlockScan(temp_storage).InclusiveSum(
-     *             thread_data, thread_data, prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         d_data[block_offset] = thread_data;
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>1, 1, 1, 1, 1, 1, 1, 1, ...</tt>.
-     * The corresponding output for the first segment will be <tt>1, 2, ..., 128</tt>.
-     * The output for the second segment will be <tt>129, 130, ..., 256</tt>.
-     *
-     * \tparam BlockPrefixCallbackOp          <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveSum(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        InclusiveScan(input, output, cub::Sum(), block_prefix_callback_op);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Inclusive prefix sum operations (multiple data per thread)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes an array of consecutive input elements.
-     *
-     * \par
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix sum of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix sum
-     *     BlockScan(temp_storage).InclusiveSum(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{ [1,1,1,1], [1,1,1,1], ..., [1,1,1,1] }</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>{ [1,2,3,4], [5,6,7,8], ..., [509,510,511,512] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void InclusiveSum(
-        T               (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T               (&output)[ITEMS_PER_THREAD])    ///< [out] Calling thread's output items (may be aliased to \p input)
-    {
-        if (ITEMS_PER_THREAD == 1)
-        {
-            InclusiveSum(input[0], output[0]);
-        }
-        else
-        {
-            // Reduce consecutive thread items in registers
-            Sum scan_op;
-            T thread_prefix = ThreadReduce(input, scan_op);
-
-            // Exclusive threadblock-scan
-            ExclusiveSum(thread_prefix, thread_prefix);
-
-            // Inclusive scan in registers with prefix as seed
-            ThreadScanInclusive(input, output, scan_op, thread_prefix, (linear_tid != 0));
-        }
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes an array of consecutive input elements.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix sum of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix sum
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).InclusiveSum(thread_data, thread_data, block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [1,1,1,1], [1,1,1,1], ..., [1,1,1,1] }</tt>.  The
-     * corresponding output \p thread_data in those threads will be
-     * <tt>{ [1,2,3,4], [5,6,7,8], ..., [509,510,511,512] }</tt>.
-     * Furthermore the value \p 512 will be stored in \p block_aggregate for all threads.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void InclusiveSum(
-        T               (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T               (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        if (ITEMS_PER_THREAD == 1)
-        {
-            InclusiveSum(input[0], output[0], block_aggregate);
-        }
-        else
-        {
-            // Reduce consecutive thread items in registers
-            Sum scan_op;
-            T thread_prefix = ThreadReduce(input, scan_op);
-
-            // Exclusive threadblock-scan
-            ExclusiveSum(thread_prefix, thread_prefix, block_aggregate);
-
-            // Inclusive scan in registers with prefix as seed
-            ThreadScanInclusive(input, output, scan_op, thread_prefix, (linear_tid != 0));
-        }
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using addition (+) as the scan operator.  Each thread contributes an array of consecutive input elements.  Instead of using 0 as the block-wide prefix, the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an inclusive prefix sum over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 512 integer items that are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3)
-     * across 128 threads where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total += block_aggregate;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockLoad, BlockStore, and BlockScan for a 1D block of 128 threads, 4 ints per thread
-     *     typedef cub::BlockLoad<int*, 128, 4, BLOCK_LOAD_TRANSPOSE>   BlockLoad;
-     *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_TRANSPOSE>  BlockStore;
-     *     typedef cub::BlockScan<int, 128>                             BlockScan;
-     *
-     *     // Allocate aliased shared memory for BlockLoad, BlockStore, and BlockScan
-     *     __shared__ union {
-     *         typename BlockLoad::TempStorage     load;
-     *         typename BlockScan::TempStorage     scan;
-     *         typename BlockStore::TempStorage    store;
-     *     } temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(0);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128 * 4)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data[4];
-     *         BlockLoad(temp_storage.load).Load(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *
-     *         // Collectively compute the block-wide inclusive prefix sum
-     *         BlockScan(temp_storage.scan).IncluisveSum(
-     *             thread_data, thread_data, prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         BlockStore(temp_storage.store).Store(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>1, 1, 1, 1, 1, 1, 1, 1, ...</tt>.
-     * The corresponding output for the first segment will be <tt>1, 2, 3, 4, ..., 511, 512</tt>.
-     * The output for the second segment will be <tt>513, 514, 515, 516, ..., 1023, 1024</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam BlockPrefixCallbackOp        <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <
-        int ITEMS_PER_THREAD,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveSum(
-        T                       (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T                       (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        if (ITEMS_PER_THREAD == 1)
-        {
-            InclusiveSum(input[0], output[0], block_prefix_callback_op);
-        }
-        else
-        {
-            // Reduce consecutive thread items in registers
-            Sum scan_op;
-            T thread_prefix = ThreadReduce(input, scan_op);
-
-            // Exclusive threadblock-scan
-            ExclusiveSum(thread_prefix, thread_prefix, block_prefix_callback_op);
-
-            // Inclusive scan in registers with prefix as seed
-            ThreadScanInclusive(input, output, scan_op, thread_prefix);
-        }
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Inclusive prefix scan operations
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix max scan of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix max scan
-     *     BlockScan(temp_storage).InclusiveScan(thread_data, thread_data, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>0, -1, 2, -3, ..., 126, -127</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>0, 0, 2, 2, ..., 126, 126</tt>.
-     *
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan functor 
-    {
-        InternalBlockScan(temp_storage).InclusiveScan(input, output, scan_op);
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix max scan of 128 integer items that
-     * are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain input item for each thread
-     *     int thread_data;
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix max scan
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).InclusiveScan(thread_data, thread_data, cub::Max(), block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>0, -1, 2, -3, ..., 126, -127</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>0, 0, 2, 2, ..., 126, 126</tt>.
-     * Furthermore the value \p 126 will be stored in \p block_aggregate for all threads.
-     *
-     * \tparam ScanOp   <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan functor 
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        InternalBlockScan(temp_storage).InclusiveScan(input, output, scan_op, block_aggregate);
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - Supports non-commutative scan operators.
-     * - \rowmajor
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an inclusive prefix max scan over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 128 integer items that are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total = (block_aggregate > old_prefix) ? block_aggregate : old_prefix;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(INT_MIN);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data = d_data[block_offset];
-     *
-     *         // Collectively compute the block-wide inclusive prefix max scan
-     *         BlockScan(temp_storage).InclusiveScan(
-     *             thread_data, thread_data, cub::Max(), prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         d_data[block_offset] = thread_data;
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, -1, 2, -3, 4, -5, ...</tt>.
-     * The corresponding output for the first segment will be <tt>0, 0, 2, 2, ..., 126, 126</tt>.
-     * The output for the second segment will be <tt>128, 128, 130, 130, ..., 254, 254</tt>.
-     *
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     * \tparam BlockPrefixCallbackOp        <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan functor 
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        InternalBlockScan(temp_storage).InclusiveScan(input, output, scan_op, block_prefix_callback_op);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Inclusive prefix scan operations (multiple data per thread)
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix max scan of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix max scan
-     *     BlockScan(temp_storage).InclusiveScan(thread_data, thread_data, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{ [0,-1,2,-3], [4,-5,6,-7], ..., [508,-509,510,-511] }</tt>.  The
-     * corresponding output \p thread_data in those threads will be <tt>{ [0,0,2,2], [4,4,6,6], ..., [508,508,510,510] }</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T               (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan functor 
-    {
-        if (ITEMS_PER_THREAD == 1)
-        {
-            InclusiveScan(input[0], output[0], scan_op);
-        }
-        else
-        {
-            // Reduce consecutive thread items in registers
-            T thread_prefix = ThreadReduce(input, scan_op);
-
-            // Exclusive threadblock-scan
-            ExclusiveScan(thread_prefix, thread_prefix, scan_op);
-
-            // Inclusive scan in registers with prefix as seed (first thread does not seed)
-            ThreadScanInclusive(input, output, scan_op, thread_prefix, (linear_tid != 0));
-        }
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates an inclusive prefix max scan of 512 integer items that
-     * are partitioned in a [<em>blocked arrangement</em>](index.html#sec5sec3) across 128 threads
-     * where each thread owns 4 consecutive items.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize BlockScan for a 1D block of 128 threads on type int
-     *     typedef cub::BlockScan<int, 128> BlockScan;
-     *
-     *     // Allocate shared memory for BlockScan
-     *     __shared__ typename BlockScan::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Collectively compute the block-wide inclusive prefix max scan
-     *     int block_aggregate;
-     *     BlockScan(temp_storage).InclusiveScan(thread_data, thread_data, cub::Max(), block_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is
-     * <tt>{ [0,-1,2,-3], [4,-5,6,-7], ..., [508,-509,510,-511] }</tt>.
-     * The corresponding output \p thread_data in those threads will be
-     * <tt>{ [0,0,2,2], [4,4,6,6], ..., [508,508,510,510] }</tt>.
-     * Furthermore the value \p 510 will be stored in \p block_aggregate for all threads.
-     *
-     * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp               <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename         ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T               (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan functor 
-        T               &block_aggregate)               ///< [out] block-wide aggregate reduction of input items
-    {
-        if (ITEMS_PER_THREAD == 1)
-        {
-            InclusiveScan(input[0], output[0], scan_op, block_aggregate);
-        }
-        else
-        {
-            // Reduce consecutive thread items in registers
-            T thread_prefix = ThreadReduce(input, scan_op);
-
-            // Exclusive threadblock-scan (with no initial value)
-            ExclusiveScan(thread_prefix, thread_prefix, scan_op, block_aggregate);
-
-            // Inclusive scan in registers with prefix as seed (first thread does not seed)
-            ThreadScanInclusive(input, output, scan_op, thread_prefix, (linear_tid != 0));
-        }
-    }
-
-
-    /**
-     * \brief Computes an inclusive block-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-     *
-     * \par
-     * - The \p block_prefix_callback_op functor must implement a member function <tt>T operator()(T block_aggregate)</tt>.
-     *   The functor's input parameter \p block_aggregate is the same value also returned by the scan operation.
-     *   The functor will be invoked by the first warp of threads in the block, however only the return value from
-     *   <em>lane</em><sub>0</sub> is applied as the block-wide prefix.  Can be stateful.
-     * - Supports non-commutative scan operators.
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a single thread block that progressively
-     * computes an inclusive prefix max scan over multiple "tiles" of input using a
-     * prefix functor to maintain a running total between block-wide scans.  Each tile consists
-     * of 128 integer items that are partitioned across 128 threads.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_scan.cuh>
-     *
-     * // A stateful callback functor that maintains a running prefix to be applied
-     * // during consecutive scan operations.
-     * struct BlockPrefixCallbackOp
-     * {
-     *     // Running prefix
-     *     int running_total;
-     *
-     *     // Constructor
-     *     __device__ BlockPrefixCallbackOp(int running_total) : running_total(running_total) {}
-     *
-     *     // Callback operator to be entered by the first warp of threads in the block.
-     *     // Thread-0 is responsible for returning a value for seeding the block-wide scan.
-     *     __device__ int operator()(int block_aggregate)
-     *     {
-     *         int old_prefix = running_total;
-     *         running_total = (block_aggregate > old_prefix) ? block_aggregate : old_prefix;
-     *         return old_prefix;
-     *     }
-     * };
-     *
-     * __global__ void ExampleKernel(int *d_data, int num_items, ...)
-     * {
-     *     // Specialize BlockLoad, BlockStore, and BlockScan for a 1D block of 128 threads, 4 ints per thread
-     *     typedef cub::BlockLoad<int*, 128, 4, BLOCK_LOAD_TRANSPOSE>   BlockLoad;
-     *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_TRANSPOSE>  BlockStore;
-     *     typedef cub::BlockScan<int, 128>                             BlockScan;
-     *
-     *     // Allocate aliased shared memory for BlockLoad, BlockStore, and BlockScan
-     *     __shared__ union {
-     *         typename BlockLoad::TempStorage     load;
-     *         typename BlockScan::TempStorage     scan;
-     *         typename BlockStore::TempStorage    store;
-     *     } temp_storage;
-     *
-     *     // Initialize running total
-     *     BlockPrefixCallbackOp prefix_op(0);
-     *
-     *     // Have the block iterate over segments of items
-     *     for (int block_offset = 0; block_offset < num_items; block_offset += 128 * 4)
-     *     {
-     *         // Load a segment of consecutive items that are blocked across threads
-     *         int thread_data[4];
-     *         BlockLoad(temp_storage.load).Load(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *
-     *         // Collectively compute the block-wide inclusive prefix max scan
-     *         BlockScan(temp_storage.scan).InclusiveScan(
-     *             thread_data, thread_data, cub::Max(), prefix_op);
-     *         CTA_SYNC();
-     *
-     *         // Store scanned items to output segment
-     *         BlockStore(temp_storage.store).Store(d_data + block_offset, thread_data);
-     *         CTA_SYNC();
-     *     }
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>0, -1, 2, -3, 4, -5, ...</tt>.
-     * The corresponding output for the first segment will be <tt>0, 0, 2, 2, 4, 4, ..., 510, 510</tt>.
-     * The output for the second segment will be <tt>512, 512, 514, 514, 516, 516, ..., 1022, 1022</tt>.
-     *
-     * \tparam ITEMS_PER_THREAD         <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
-     * \tparam ScanOp                   <b>[inferred]</b> Binary scan functor  type having member <tt>T operator()(const T &a, const T &b)</tt>
-     * \tparam BlockPrefixCallbackOp    <b>[inferred]</b> Call-back functor type having member <tt>T operator()(T block_aggregate)</tt>
-     */
-    template <
-        int             ITEMS_PER_THREAD,
-        typename        ScanOp,
-        typename        BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
-        T                       (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan functor 
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide prefix to be applied to the logical input sequence.
-    {
-        if (ITEMS_PER_THREAD == 1)
-        {
-            InclusiveScan(input[0], output[0], scan_op, block_prefix_callback_op);
-        }
-        else
-        {
-            // Reduce consecutive thread items in registers
-            T thread_prefix = ThreadReduce(input, scan_op);
-
-            // Exclusive threadblock-scan
-            ExclusiveScan(thread_prefix, thread_prefix, scan_op, block_prefix_callback_op);
-
-            // Inclusive scan in registers with prefix as seed
-            ThreadScanInclusive(input, output, scan_op, thread_prefix);
-        }
-    }
-
-    //@}  end member group
-
-
-};
-
-/**
- * \example example_block_scan.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_shuffle.cuh b/ml-xgboost/cub/cub/block/block_shuffle.cuh
deleted file mode 100644
index d605191..0000000
--- a/ml-xgboost/cub/cub/block/block_shuffle.cuh
+++ /dev/null
@@ -1,305 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockShuffle class provides [<em>collective</em>](index.html#sec0) methods for shuffling data partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "../util_arch.cuh"
-#include "../util_ptx.cuh"
-#include "../util_macro.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief The BlockShuffle class provides [<em>collective</em>](index.html#sec0) methods for shuffling data partitioned across a CUDA thread block.
- * \ingroup BlockModule
- *
- * \tparam T                    The data type to be exchanged.
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * It is commonplace for blocks of threads to rearrange data items between
- * threads.  The BlockShuffle abstraction allows threads to efficiently shift items
- * either (a) up to their successor or (b) down to their predecessor.
- *
- */
-template <
-    typename            T,
-    int                 BLOCK_DIM_X,
-    int                 BLOCK_DIM_Y         = 1,
-    int                 BLOCK_DIM_Z         = 1,
-    int                 PTX_ARCH            = CUB_PTX_ARCH>
-class BlockShuffle
-{
-private:
-
-    /******************************************************************************
-     * Constants
-     ******************************************************************************/
-
-    enum
-    {
-        BLOCK_THREADS               = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        LOG_WARP_THREADS            = CUB_LOG_WARP_THREADS(PTX_ARCH),
-        WARP_THREADS                = 1 << LOG_WARP_THREADS,
-        WARPS                       = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-    };
-
-    /******************************************************************************
-     * Type definitions
-     ******************************************************************************/
-
-    /// Shared memory storage layout type (last element from each thread's input)
-    struct _TempStorage
-    {
-        T prev[BLOCK_THREADS];
-        T next[BLOCK_THREADS];
-    };
-
-
-public:
-
-    /// \smemstorage{BlockShuffle}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-private:
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    unsigned int linear_tid;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-public:
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockShuffle()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockShuffle(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Shuffle movement
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Each <em>thread<sub>i</sub></em> obtains the \p input provided by <em>thread</em><sub><em>i</em>+<tt>distance</tt></sub>. The offset \p distance may be negative.
-     *
-     * \par
-     * - \smemreuse
-     */
-    __device__ __forceinline__ void Offset(
-        T   input,                  ///< [in] The input item from the calling thread (<em>thread<sub>i</sub></em>)
-        T&  output,                 ///< [out] The \p input item from the successor (or predecessor) thread <em>thread</em><sub><em>i</em>+<tt>distance</tt></sub> (may be aliased to \p input).  This value is only updated for for <em>thread<sub>i</sub></em> when 0 <= (<em>i</em> + \p distance) < <tt>BLOCK_THREADS-1</tt>
-        int distance = 1)           ///< [in] Offset distance (may be negative)
-    {
-        temp_storage[linear_tid].prev = input;
-
-        CTA_SYNC();
-
-        if ((linear_tid + distance >= 0) && (linear_tid + distance < BLOCK_THREADS))
-            output = temp_storage[linear_tid + distance].prev;
-    }
-
-
-    /**
-     * \brief Each <em>thread<sub>i</sub></em> obtains the \p input provided by <em>thread</em><sub><em>i</em>+<tt>distance</tt></sub>.
-     *
-     * \par
-     * - \smemreuse
-     */
-    __device__ __forceinline__ void Rotate(
-        T   input,                  ///< [in] The calling thread's input item
-        T&  output,                 ///< [out] The \p input item from thread <em>thread</em><sub>(<em>i</em>+<tt>distance></tt>)%<tt><BLOCK_THREADS></tt></sub> (may be aliased to \p input).  This value is not updated for <em>thread</em><sub>BLOCK_THREADS-1</sub>
-        unsigned int distance = 1)  ///< [in] Offset distance (0 < \p distance < <tt>BLOCK_THREADS</tt>)
-    {
-        temp_storage[linear_tid].prev = input;
-
-        CTA_SYNC();
-
-        unsigned int offset = threadIdx.x + distance;
-        if (offset >= BLOCK_THREADS)
-            offset -= BLOCK_THREADS;
-
-        output = temp_storage[offset].prev;
-    }
-
-
-    /**
-     * \brief The thread block rotates its [<em>blocked arrangement</em>](index.html#sec5sec3) of \p input items, shifting it up by one item
-     *
-     * \par
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void Up(
-        T (&input)[ITEMS_PER_THREAD],   ///< [in] The calling thread's input items
-        T (&prev)[ITEMS_PER_THREAD])    ///< [out] The corresponding predecessor items (may be aliased to \p input).  The item \p prev[0] is not updated for <em>thread</em><sub>0</sub>.
-    {
-        temp_storage[linear_tid].prev = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = ITEMS_PER_THREAD - 1; ITEM > 0; --ITEM)
-            prev[ITEM] = input[ITEM - 1];
-
-
-        if (linear_tid > 0)
-            prev[0] = temp_storage[linear_tid - 1].prev;
-    }
-
-
-    /**
-     * \brief The thread block rotates its [<em>blocked arrangement</em>](index.html#sec5sec3) of \p input items, shifting it up by one item.  All threads receive the \p input provided by <em>thread</em><sub><tt>BLOCK_THREADS-1</tt></sub>.
-     *
-     * \par
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void Up(
-        T (&input)[ITEMS_PER_THREAD],   ///< [in] The calling thread's input items
-        T (&prev)[ITEMS_PER_THREAD],    ///< [out] The corresponding predecessor items (may be aliased to \p input).  The item \p prev[0] is not updated for <em>thread</em><sub>0</sub>.
-        T &block_suffix)                ///< [out] The item \p input[ITEMS_PER_THREAD-1] from <em>thread</em><sub><tt>BLOCK_THREADS-1</tt></sub>, provided to all threads
-    {
-        Up(input, prev);
-        block_suffix = temp_storage[BLOCK_THREADS - 1].prev;
-    }
-
-
-    /**
-     * \brief The thread block rotates its [<em>blocked arrangement</em>](index.html#sec5sec3) of \p input items, shifting it down by one item
-     *
-     * \par
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void Down(
-        T (&input)[ITEMS_PER_THREAD],   ///< [in] The calling thread's input items
-        T (&prev)[ITEMS_PER_THREAD])    ///< [out] The corresponding predecessor items (may be aliased to \p input).  The value \p prev[0] is not updated for <em>thread</em><sub>BLOCK_THREADS-1</sub>.
-    {
-        temp_storage[linear_tid].prev = input[ITEMS_PER_THREAD - 1];
-
-        CTA_SYNC();
-
-        #pragma unroll
-        for (int ITEM = ITEMS_PER_THREAD - 1; ITEM > 0; --ITEM)
-            prev[ITEM] = input[ITEM - 1];
-
-        if (linear_tid > 0)
-            prev[0] = temp_storage[linear_tid - 1].prev;
-    }
-
-
-    /**
-     * \brief The thread block rotates its [<em>blocked arrangement</em>](index.html#sec5sec3) of input items, shifting it down by one item.  All threads receive \p input[0] provided by <em>thread</em><sub><tt>0</tt></sub>.
-     *
-     * \par
-     * - \blocked
-     * - \granularity
-     * - \smemreuse
-     */
-    template <int ITEMS_PER_THREAD>
-    __device__ __forceinline__ void Down(
-        T (&input)[ITEMS_PER_THREAD],   ///< [in] The calling thread's input items
-        T (&prev)[ITEMS_PER_THREAD],    ///< [out] The corresponding predecessor items (may be aliased to \p input).  The value \p prev[0] is not updated for <em>thread</em><sub>BLOCK_THREADS-1</sub>.
-        T &block_prefix)                ///< [out] The item \p input[0] from <em>thread</em><sub><tt>0</tt></sub>, provided to all threads
-    {
-        Up(input, prev);
-        block_prefix = temp_storage[BLOCK_THREADS - 1].prev;
-    }
-
-    //@}  end member group
-
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/block_store.cuh b/ml-xgboost/cub/cub/block/block_store.cuh
deleted file mode 100644
index e821f8e..0000000
--- a/ml-xgboost/cub/cub/block/block_store.cuh
+++ /dev/null
@@ -1,1000 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Operations for writing linear segments of data from the CUDA thread block
- */
-
-#pragma once
-
-#include <iterator>
-
-#include "block_exchange.cuh"
-#include "../util_ptx.cuh"
-#include "../util_macro.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIo
- * @{
- */
-
-
-/******************************************************************//**
- * \name Blocked arrangement I/O (direct)
- *********************************************************************/
-//@{
-
-/**
- * \brief Store a blocked arrangement of items across a thread block into a linear segment of items.
- *
- * \blocked
- *
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam OutputIteratorT      <b>[inferred]</b> The random-access iterator type for output \iterator.
- */
-template <
-    typename            T,
-    int                 ITEMS_PER_THREAD,
-    typename            OutputIteratorT>
-__device__ __forceinline__ void StoreDirectBlocked(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-    T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-{
-    OutputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
-
-    // Store directly in thread-blocked order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        thread_itr[ITEM] = items[ITEM];
-    }
-}
-
-
-/**
- * \brief Store a blocked arrangement of items across a thread block into a linear segment of items, guarded by range
- *
- * \blocked
- *
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam OutputIteratorT      <b>[inferred]</b> The random-access iterator type for output \iterator.
- */
-template <
-    typename            T,
-    int                 ITEMS_PER_THREAD,
-    typename            OutputIteratorT>
-__device__ __forceinline__ void StoreDirectBlocked(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-    T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-    int                 valid_items)                ///< [in] Number of valid items to write
-{
-    OutputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
-
-    // Store directly in thread-blocked order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        if (ITEM + (linear_tid * ITEMS_PER_THREAD) < valid_items)
-        {
-            thread_itr[ITEM] = items[ITEM];
-        }
-    }
-}
-
-
-/**
- * \brief Store a blocked arrangement of items across a thread block into a linear segment of items.
- *
- * \blocked
- *
- * The output offset (\p block_ptr + \p block_offset) must be quad-item aligned,
- * which is the default starting offset returned by \p cudaMalloc()
- *
- * \par
- * The following conditions will prevent vectorization and storing will fall back to cub::BLOCK_STORE_DIRECT:
- *   - \p ITEMS_PER_THREAD is odd
- *   - The data type \p T is not a built-in primitive or CUDA vector type (e.g., \p short, \p int2, \p double, \p float2, etc.)
- *
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- *
- */
-template <
-    typename            T,
-    int                 ITEMS_PER_THREAD>
-__device__ __forceinline__ void StoreDirectBlockedVectorized(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    T                   *block_ptr,                 ///< [in] Input pointer for storing from
-    T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-{
-    enum
-    {
-        // Maximum CUDA vector size is 4 elements
-        MAX_VEC_SIZE = CUB_MIN(4, ITEMS_PER_THREAD),
-
-        // Vector size must be a power of two and an even divisor of the items per thread
-        VEC_SIZE = ((((MAX_VEC_SIZE - 1) & MAX_VEC_SIZE) == 0) && ((ITEMS_PER_THREAD % MAX_VEC_SIZE) == 0)) ?
-            MAX_VEC_SIZE :
-            1,
-
-        VECTORS_PER_THREAD = ITEMS_PER_THREAD / VEC_SIZE,
-    };
-
-    // Vector type
-    typedef typename CubVector<T, VEC_SIZE>::Type Vector;
-
-    // Alias global pointer
-    Vector *block_ptr_vectors = reinterpret_cast<Vector*>(const_cast<T*>(block_ptr));
-
-    // Alias pointers (use "raw" array here which should get optimized away to prevent conservative PTXAS lmem spilling)
-    Vector raw_vector[VECTORS_PER_THREAD];
-    T *raw_items = reinterpret_cast<T*>(raw_vector);
-
-    // Copy
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        raw_items[ITEM] = items[ITEM];
-    }
-
-    // Direct-store using vector types
-    StoreDirectBlocked(linear_tid, block_ptr_vectors, raw_vector);
-}
-
-
-
-//@}  end member group
-/******************************************************************//**
- * \name Striped arrangement I/O (direct)
- *********************************************************************/
-//@{
-
-
-/**
- * \brief Store a striped arrangement of data across the thread block into a linear segment of items.
- *
- * \striped
- *
- * \tparam BLOCK_THREADS        The thread block size in threads
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam OutputIteratorT      <b>[inferred]</b> The random-access iterator type for output \iterator.
- */
-template <
-    int                 BLOCK_THREADS,
-    typename            T,
-    int                 ITEMS_PER_THREAD,
-    typename            OutputIteratorT>
-__device__ __forceinline__ void StoreDirectStriped(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-    T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-{
-    OutputIteratorT thread_itr = block_itr + linear_tid;
-
-    // Store directly in striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        thread_itr[(ITEM * BLOCK_THREADS)] = items[ITEM];
-    }
-}
-
-
-/**
- * \brief Store a striped arrangement of data across the thread block into a linear segment of items, guarded by range
- *
- * \striped
- *
- * \tparam BLOCK_THREADS        The thread block size in threads
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam OutputIteratorT      <b>[inferred]</b> The random-access iterator type for output \iterator.
- */
-template <
-    int                 BLOCK_THREADS,
-    typename            T,
-    int                 ITEMS_PER_THREAD,
-    typename            OutputIteratorT>
-__device__ __forceinline__ void StoreDirectStriped(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-    T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-    int                 valid_items)                ///< [in] Number of valid items to write
-{
-    OutputIteratorT thread_itr = block_itr + linear_tid;
-
-    // Store directly in striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        if ((ITEM * BLOCK_THREADS) + linear_tid < valid_items)
-        {
-            thread_itr[(ITEM * BLOCK_THREADS)] = items[ITEM];
-        }
-    }
-}
-
-
-
-//@}  end member group
-/******************************************************************//**
- * \name Warp-striped arrangement I/O (direct)
- *********************************************************************/
-//@{
-
-
-/**
- * \brief Store a warp-striped arrangement of data across the thread block into a linear segment of items.
- *
- * \warpstriped
- *
- * \par Usage Considerations
- * The number of threads in the thread block must be a multiple of the architecture's warp size.
- *
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam OutputIteratorT      <b>[inferred]</b> The random-access iterator type for output \iterator.
- */
-template <
-    typename            T,
-    int                 ITEMS_PER_THREAD,
-    typename            OutputIteratorT>
-__device__ __forceinline__ void StoreDirectWarpStriped(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-    T                   (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
-{
-    int tid         = linear_tid & (CUB_PTX_WARP_THREADS - 1);
-    int wid         = linear_tid >> CUB_PTX_LOG_WARP_THREADS;
-    int warp_offset = wid * CUB_PTX_WARP_THREADS * ITEMS_PER_THREAD;
-
-    OutputIteratorT thread_itr = block_itr + warp_offset + tid;
-
-    // Store directly in warp-striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        thread_itr[(ITEM * CUB_PTX_WARP_THREADS)] = items[ITEM];
-    }
-}
-
-
-/**
- * \brief Store a warp-striped arrangement of data across the thread block into a linear segment of items, guarded by range
- *
- * \warpstriped
- *
- * \par Usage Considerations
- * The number of threads in the thread block must be a multiple of the architecture's warp size.
- *
- * \tparam T                    <b>[inferred]</b> The data type to store.
- * \tparam ITEMS_PER_THREAD     <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
- * \tparam OutputIteratorT      <b>[inferred]</b> The random-access iterator type for output \iterator.
- */
-template <
-    typename            T,
-    int                 ITEMS_PER_THREAD,
-    typename            OutputIteratorT>
-__device__ __forceinline__ void StoreDirectWarpStriped(
-    int                 linear_tid,                 ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
-    OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-    T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-    int                 valid_items)                ///< [in] Number of valid items to write
-{
-    int tid         = linear_tid & (CUB_PTX_WARP_THREADS - 1);
-    int wid         = linear_tid >> CUB_PTX_LOG_WARP_THREADS;
-    int warp_offset = wid * CUB_PTX_WARP_THREADS * ITEMS_PER_THREAD;
-
-    OutputIteratorT thread_itr = block_itr + warp_offset + tid;
-
-    // Store directly in warp-striped order
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-    {
-        if (warp_offset + tid + (ITEM * CUB_PTX_WARP_THREADS) < valid_items)
-        {
-            thread_itr[(ITEM * CUB_PTX_WARP_THREADS)] = items[ITEM];
-        }
-    }
-}
-
-
-//@}  end member group
-
-
-/** @} */       // end group UtilIo
-
-
-//-----------------------------------------------------------------------------
-// Generic BlockStore abstraction
-//-----------------------------------------------------------------------------
-
-/**
- * \brief cub::BlockStoreAlgorithm enumerates alternative algorithms for cub::BlockStore to write a blocked arrangement of items across a CUDA thread block to a linear segment of memory.
- */
-enum BlockStoreAlgorithm
-{
-    /**
-     * \par Overview
-     *
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is written
-     * directly to memory.
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) decreases as the
-     *   access stride between threads increases (i.e., the number items per thread).
-     */
-    BLOCK_STORE_DIRECT,
-
-    /**
-     * \par Overview
-     *
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is written directly
-     * to memory using CUDA's built-in vectorized stores as a coalescing optimization.
-     * For example, <tt>st.global.v4.s32</tt> instructions will be generated
-     * when \p T = \p int and \p ITEMS_PER_THREAD % 4 == 0.
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high until the the
-     *   access stride between threads (i.e., the number items per thread) exceeds the
-     *   maximum vector store width (typically 4 items or 64B, whichever is lower).
-     * - The following conditions will prevent vectorization and writing will fall back to cub::BLOCK_STORE_DIRECT:
-     *   - \p ITEMS_PER_THREAD is odd
-     *   - The \p OutputIteratorT is not a simple pointer type
-     *   - The block output offset is not quadword-aligned
-     *   - The data type \p T is not a built-in primitive or CUDA vector type (e.g., \p short, \p int2, \p double, \p float2, etc.)
-     */
-    BLOCK_STORE_VECTORIZE,
-
-    /**
-     * \par Overview
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) is locally
-     * transposed and then efficiently written to memory as a [<em>striped arrangement</em>](index.html#sec5sec3).
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high regardless
-     *   of items written per thread.
-     * - The local reordering incurs slightly longer latencies and throughput than the
-     *   direct cub::BLOCK_STORE_DIRECT and cub::BLOCK_STORE_VECTORIZE alternatives.
-     */
-    BLOCK_STORE_TRANSPOSE,
-
-    /**
-     * \par Overview
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) is locally
-     * transposed and then efficiently written to memory as a
-     * [<em>warp-striped arrangement</em>](index.html#sec5sec3)
-     *
-     * \par Usage Considerations
-     * - BLOCK_THREADS must be a multiple of WARP_THREADS
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high regardless
-     *   of items written per thread.
-     * - The local reordering incurs slightly longer latencies and throughput than the
-     *   direct cub::BLOCK_STORE_DIRECT and cub::BLOCK_STORE_VECTORIZE alternatives.
-     */
-    BLOCK_STORE_WARP_TRANSPOSE,
-
-    /**
-     * \par Overview
-     * A [<em>blocked arrangement</em>](index.html#sec5sec3) is locally
-     * transposed and then efficiently written to memory as a
-     * [<em>warp-striped arrangement</em>](index.html#sec5sec3)
-     * To reduce the shared memory requirement, only one warp's worth of shared
-     * memory is provisioned and is subsequently time-sliced among warps.
-     *
-     * \par Usage Considerations
-     * - BLOCK_THREADS must be a multiple of WARP_THREADS
-     *
-     * \par Performance Considerations
-     * - The utilization of memory transactions (coalescing) remains high regardless
-     *   of items written per thread.
-     * - Provisions less shared memory temporary storage, but incurs larger
-     *   latencies than the BLOCK_STORE_WARP_TRANSPOSE alternative.
-     */
-    BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED,
-
-};
-
-
-/**
- * \brief The BlockStore class provides [<em>collective</em>](index.html#sec0) data movement methods for writing a [<em>blocked arrangement</em>](index.html#sec5sec3) of items partitioned across a CUDA thread block to a linear segment of memory.  ![](block_store_logo.png)
- * \ingroup BlockModule
- * \ingroup UtilIo
- *
- * \tparam T                    The type of data to be written.
- * \tparam BLOCK_DIM_X          The thread block length in threads along the X dimension
- * \tparam ITEMS_PER_THREAD     The number of consecutive items partitioned onto each thread.
- * \tparam ALGORITHM            <b>[optional]</b> cub::BlockStoreAlgorithm tuning policy enumeration.  default: cub::BLOCK_STORE_DIRECT.
- * \tparam WARP_TIME_SLICING    <b>[optional]</b> Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage). (default: false)
- * \tparam BLOCK_DIM_Y          <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
- * \tparam BLOCK_DIM_Z          <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
- * \tparam PTX_ARCH             <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - The BlockStore class provides a single data movement abstraction that can be specialized
- *   to implement different cub::BlockStoreAlgorithm strategies.  This facilitates different
- *   performance policies for different architectures, data types, granularity sizes, etc.
- * - BlockStore can be optionally specialized by different data movement strategies:
- *   -# <b>cub::BLOCK_STORE_DIRECT</b>.  A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is written
- *      directly to memory. [More...](\ref cub::BlockStoreAlgorithm)
- *   -# <b>cub::BLOCK_STORE_VECTORIZE</b>.  A [<em>blocked arrangement</em>](index.html#sec5sec3)
- *      of data is written directly to memory using CUDA's built-in vectorized stores as a
- *      coalescing optimization.  [More...](\ref cub::BlockStoreAlgorithm)
- *   -# <b>cub::BLOCK_STORE_TRANSPOSE</b>.  A [<em>blocked arrangement</em>](index.html#sec5sec3)
- *      is locally transposed into a [<em>striped arrangement</em>](index.html#sec5sec3) which is
- *      then written to memory.  [More...](\ref cub::BlockStoreAlgorithm)
- *   -# <b>cub::BLOCK_STORE_WARP_TRANSPOSE</b>.  A [<em>blocked arrangement</em>](index.html#sec5sec3)
- *      is locally transposed into a [<em>warp-striped arrangement</em>](index.html#sec5sec3) which is
- *      then written to memory.  [More...](\ref cub::BlockStoreAlgorithm)
- * - \rowmajor
- *
- * \par A Simple Example
- * \blockcollective{BlockStore}
- * \par
- * The code snippet below illustrates the storing of a "blocked" arrangement
- * of 512 integers across 128 threads (where each thread owns 4 consecutive items)
- * into a linear segment of memory.  The store is specialized for \p BLOCK_STORE_WARP_TRANSPOSE,
- * meaning items are locally reordered among threads so that memory references will be
- * efficiently coalesced using a warp-striped access pattern.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/block/block_store.cuh>
- *
- * __global__ void ExampleKernel(int *d_data, ...)
- * {
- *     // Specialize BlockStore for a 1D block of 128 threads owning 4 integer items each
- *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_WARP_TRANSPOSE> BlockStore;
- *
- *     // Allocate shared memory for BlockStore
- *     __shared__ typename BlockStore::TempStorage temp_storage;
- *
- *     // Obtain a segment of consecutive items that are blocked across threads
- *     int thread_data[4];
- *     ...
- *
- *     // Store items to linear memory
- *     int thread_data[4];
- *     BlockStore(temp_storage).Store(d_data, thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of \p thread_data across the block of threads is
- * <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
- * The output \p d_data will be <tt>0, 1, 2, 3, 4, 5, ...</tt>.
- *
- */
-template <
-    typename                T,
-    int                     BLOCK_DIM_X,
-    int                     ITEMS_PER_THREAD,
-    BlockStoreAlgorithm     ALGORITHM           = BLOCK_STORE_DIRECT,
-    int                     BLOCK_DIM_Y         = 1,
-    int                     BLOCK_DIM_Z         = 1,
-    int                     PTX_ARCH            = CUB_PTX_ARCH>
-class BlockStore
-{
-private:
-    /******************************************************************************
-     * Constants and typed definitions
-     ******************************************************************************/
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-
-    /******************************************************************************
-     * Algorithmic variants
-     ******************************************************************************/
-
-    /// Store helper
-    template <BlockStoreAlgorithm _POLICY, int DUMMY>
-    struct StoreInternal;
-
-
-    /**
-     * BLOCK_STORE_DIRECT specialization of store helper
-     */
-    template <int DUMMY>
-    struct StoreInternal<BLOCK_STORE_DIRECT, DUMMY>
-    {
-        /// Shared memory storage layout type
-        typedef NullType TempStorage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ StoreInternal(
-            TempStorage &/*temp_storage*/,
-            int linear_tid)
-        :
-            linear_tid(linear_tid)
-        {}
-
-        /// Store items into a linear segment of memory
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-        {
-            StoreDirectBlocked(linear_tid, block_itr, items);
-        }
-
-        /// Store items into a linear segment of memory, guarded by range
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-            int                 valid_items)                ///< [in] Number of valid items to write
-        {
-            StoreDirectBlocked(linear_tid, block_itr, items, valid_items);
-        }
-    };
-
-
-    /**
-     * BLOCK_STORE_VECTORIZE specialization of store helper
-     */
-    template <int DUMMY>
-    struct StoreInternal<BLOCK_STORE_VECTORIZE, DUMMY>
-    {
-        /// Shared memory storage layout type
-        typedef NullType TempStorage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ StoreInternal(
-            TempStorage &/*temp_storage*/,
-            int linear_tid)
-        :
-            linear_tid(linear_tid)
-        {}
-
-        /// Store items into a linear segment of memory, specialized for native pointer types (attempts vectorization)
-        __device__ __forceinline__ void Store(
-            T                   *block_ptr,                 ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-        {
-            StoreDirectBlockedVectorized(linear_tid, block_ptr, items);
-        }
-
-        /// Store items into a linear segment of memory, specialized for opaque input iterators (skips vectorization)
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT    block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-        {
-            StoreDirectBlocked(linear_tid, block_itr, items);
-        }
-
-        /// Store items into a linear segment of memory, guarded by range
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-            int                 valid_items)                ///< [in] Number of valid items to write
-        {
-            StoreDirectBlocked(linear_tid, block_itr, items, valid_items);
-        }
-    };
-
-
-    /**
-     * BLOCK_STORE_TRANSPOSE specialization of store helper
-     */
-    template <int DUMMY>
-    struct StoreInternal<BLOCK_STORE_TRANSPOSE, DUMMY>
-    {
-        // BlockExchange utility type for keys
-        typedef BlockExchange<T, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
-
-        /// Shared memory storage layout type
-        struct _TempStorage : BlockExchange::TempStorage
-        {
-            /// Temporary storage for partially-full block guard
-            volatile int valid_items;
-        };
-
-        /// Alias wrapper allowing storage to be unioned
-        struct TempStorage : Uninitialized<_TempStorage> {};
-
-        /// Thread reference to shared storage
-        _TempStorage &temp_storage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ StoreInternal(
-            TempStorage &temp_storage,
-            int linear_tid)
-        :
-            temp_storage(temp_storage.Alias()),
-            linear_tid(linear_tid)
-        {}
-
-        /// Store items into a linear segment of memory
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-        {
-            BlockExchange(temp_storage).BlockedToStriped(items);
-            StoreDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items);
-        }
-
-        /// Store items into a linear segment of memory, guarded by range
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT   block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-            int                 valid_items)                ///< [in] Number of valid items to write
-        {
-            BlockExchange(temp_storage).BlockedToStriped(items);
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            StoreDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, temp_storage.valid_items);
-        }
-    };
-
-
-    /**
-     * BLOCK_STORE_WARP_TRANSPOSE specialization of store helper
-     */
-    template <int DUMMY>
-    struct StoreInternal<BLOCK_STORE_WARP_TRANSPOSE, DUMMY>
-    {
-        enum
-        {
-            WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH)
-        };
-
-        // Assert BLOCK_THREADS must be a multiple of WARP_THREADS
-        CUB_STATIC_ASSERT((BLOCK_THREADS % WARP_THREADS == 0), "BLOCK_THREADS must be a multiple of WARP_THREADS");
-
-        // BlockExchange utility type for keys
-        typedef BlockExchange<T, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
-
-        /// Shared memory storage layout type
-        struct _TempStorage : BlockExchange::TempStorage
-        {
-            /// Temporary storage for partially-full block guard
-            volatile int valid_items;
-        };
-
-        /// Alias wrapper allowing storage to be unioned
-        struct TempStorage : Uninitialized<_TempStorage> {};
-
-        /// Thread reference to shared storage
-        _TempStorage &temp_storage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ StoreInternal(
-            TempStorage &temp_storage,
-            int linear_tid)
-        :
-            temp_storage(temp_storage.Alias()),
-            linear_tid(linear_tid)
-        {}
-
-        /// Store items into a linear segment of memory
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT   block_itr,                    ///< [in] The thread block's base output iterator for storing to
-            T                 (&items)[ITEMS_PER_THREAD])   ///< [in] Data to store
-        {
-            BlockExchange(temp_storage).BlockedToWarpStriped(items);
-            StoreDirectWarpStriped(linear_tid, block_itr, items);
-        }
-
-        /// Store items into a linear segment of memory, guarded by range
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT   block_itr,                    ///< [in] The thread block's base output iterator for storing to
-            T                 (&items)[ITEMS_PER_THREAD],   ///< [in] Data to store
-            int               valid_items)                  ///< [in] Number of valid items to write
-        {
-            BlockExchange(temp_storage).BlockedToWarpStriped(items);
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            StoreDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
-        }
-    };
-
-
-    /**
-     * BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED specialization of store helper
-     */
-    template <int DUMMY>
-    struct StoreInternal<BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED, DUMMY>
-    {
-        enum
-        {
-            WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH)
-        };
-
-        // Assert BLOCK_THREADS must be a multiple of WARP_THREADS
-        CUB_STATIC_ASSERT((BLOCK_THREADS % WARP_THREADS == 0), "BLOCK_THREADS must be a multiple of WARP_THREADS");
-
-        // BlockExchange utility type for keys
-        typedef BlockExchange<T, BLOCK_DIM_X, ITEMS_PER_THREAD, true, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
-
-        /// Shared memory storage layout type
-        struct _TempStorage : BlockExchange::TempStorage
-        {
-            /// Temporary storage for partially-full block guard
-            volatile int valid_items;
-        };
-
-        /// Alias wrapper allowing storage to be unioned
-        struct TempStorage : Uninitialized<_TempStorage> {};
-
-        /// Thread reference to shared storage
-        _TempStorage &temp_storage;
-
-        /// Linear thread-id
-        int linear_tid;
-
-        /// Constructor
-        __device__ __forceinline__ StoreInternal(
-            TempStorage &temp_storage,
-            int linear_tid)
-        :
-            temp_storage(temp_storage.Alias()),
-            linear_tid(linear_tid)
-        {}
-
-        /// Store items into a linear segment of memory
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-        {
-            BlockExchange(temp_storage).BlockedToWarpStriped(items);
-            StoreDirectWarpStriped(linear_tid, block_itr, items);
-        }
-
-        /// Store items into a linear segment of memory, guarded by range
-        template <typename OutputIteratorT>
-        __device__ __forceinline__ void Store(
-            OutputIteratorT   block_itr,                  ///< [in] The thread block's base output iterator for storing to
-            T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-            int                 valid_items)                ///< [in] Number of valid items to write
-        {
-            BlockExchange(temp_storage).BlockedToWarpStriped(items);
-            if (linear_tid == 0)
-                temp_storage.valid_items = valid_items;     // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
-            CTA_SYNC();
-            StoreDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
-        }
-    };
-
-    /******************************************************************************
-     * Type definitions
-     ******************************************************************************/
-
-    /// Internal load implementation to use
-    typedef StoreInternal<ALGORITHM, 0> InternalStore;
-
-
-    /// Shared memory storage layout type
-    typedef typename InternalStore::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Internal storage allocator
-    __device__ __forceinline__ _TempStorage& PrivateStorage()
-    {
-        __shared__ _TempStorage private_storage;
-        return private_storage;
-    }
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Thread reference to shared storage
-    _TempStorage &temp_storage;
-
-    /// Linear thread-id
-    int linear_tid;
-
-public:
-
-
-    /// \smemstorage{BlockStore}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using a private static allocation of shared memory as temporary storage.
-     */
-    __device__ __forceinline__ BlockStore()
-    :
-        temp_storage(PrivateStorage()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.
-     */
-    __device__ __forceinline__ BlockStore(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Data movement
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Store items into a linear segment of memory.
-     *
-     * \par
-     * - \blocked
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the storing of a "blocked" arrangement
-     * of 512 integers across 128 threads (where each thread owns 4 consecutive items)
-     * into a linear segment of memory.  The store is specialized for \p BLOCK_STORE_WARP_TRANSPOSE,
-     * meaning items are locally reordered among threads so that memory references will be
-     * efficiently coalesced using a warp-striped access pattern.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_store.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, ...)
-     * {
-     *     // Specialize BlockStore for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_WARP_TRANSPOSE> BlockStore;
-     *
-     *     // Allocate shared memory for BlockStore
-     *     __shared__ typename BlockStore::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Store items to linear memory
-     *     int thread_data[4];
-     *     BlockStore(temp_storage).Store(d_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of \p thread_data across the block of threads is
-     * <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
-     * The output \p d_data will be <tt>0, 1, 2, 3, 4, 5, ...</tt>.
-     *
-     */
-    template <typename OutputIteratorT>
-    __device__ __forceinline__ void Store(
-        OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-        T                   (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
-    {
-        InternalStore(temp_storage, linear_tid).Store(block_itr, items);
-    }
-
-    /**
-     * \brief Store items into a linear segment of memory, guarded by range.
-     *
-     * \par
-     * - \blocked
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the guarded storing of a "blocked" arrangement
-     * of 512 integers across 128 threads (where each thread owns 4 consecutive items)
-     * into a linear segment of memory.  The store is specialized for \p BLOCK_STORE_WARP_TRANSPOSE,
-     * meaning items are locally reordered among threads so that memory references will be
-     * efficiently coalesced using a warp-striped access pattern.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/block/block_store.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, int valid_items, ...)
-     * {
-     *     // Specialize BlockStore for a 1D block of 128 threads owning 4 integer items each
-     *     typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_WARP_TRANSPOSE> BlockStore;
-     *
-     *     // Allocate shared memory for BlockStore
-     *     __shared__ typename BlockStore::TempStorage temp_storage;
-     *
-     *     // Obtain a segment of consecutive items that are blocked across threads
-     *     int thread_data[4];
-     *     ...
-     *
-     *     // Store items to linear memory
-     *     int thread_data[4];
-     *     BlockStore(temp_storage).Store(d_data, thread_data, valid_items);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of \p thread_data across the block of threads is
-     * <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt> and \p valid_items is \p 5.
-     * The output \p d_data will be <tt>0, 1, 2, 3, 4, ?, ?, ?, ...</tt>, with
-     * only the first two threads being unmasked to store portions of valid data.
-     *
-     */
-    template <typename OutputIteratorT>
-    __device__ __forceinline__ void Store(
-        OutputIteratorT     block_itr,                  ///< [in] The thread block's base output iterator for storing to
-        T                   (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
-        int                 valid_items)                ///< [in] Number of valid items to write
-    {
-        InternalStore(temp_storage, linear_tid).Store(block_itr, items, valid_items);
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_histogram_atomic.cuh b/ml-xgboost/cub/cub/block/specializations/block_histogram_atomic.cuh
deleted file mode 100644
index 8744efb..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_histogram_atomic.cuh
+++ /dev/null
@@ -1,82 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockHistogramAtomic class provides atomic-based methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief The BlockHistogramAtomic class provides atomic-based methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
- */
-template <int BINS>
-struct BlockHistogramAtomic
-{
-    /// Shared memory storage layout type
-    struct TempStorage {};
-
-
-    /// Constructor
-    __device__ __forceinline__ BlockHistogramAtomic(
-        TempStorage &temp_storage)
-    {}
-
-
-    /// Composite data onto an existing histogram
-    template <
-        typename            T,
-        typename            CounterT,     
-        int                 ITEMS_PER_THREAD>
-    __device__ __forceinline__ void Composite(
-        T                   (&items)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input values to histogram
-        CounterT             histogram[BINS])                 ///< [out] Reference to shared/device-accessible memory histogram
-    {
-        // Update histogram
-        #pragma unroll
-        for (int i = 0; i < ITEMS_PER_THREAD; ++i)
-        {
-              atomicAdd(histogram + items[i], 1);
-        }
-    }
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_histogram_sort.cuh b/ml-xgboost/cub/cub/block/specializations/block_histogram_sort.cuh
deleted file mode 100644
index 5f88b4f..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_histogram_sort.cuh
+++ /dev/null
@@ -1,226 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::BlockHistogramSort class provides sorting-based methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
- */
-
-#pragma once
-
-#include "../../block/block_radix_sort.cuh"
-#include "../../block/block_discontinuity.cuh"
-#include "../../util_ptx.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-
-/**
- * \brief The BlockHistogramSort class provides sorting-based methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
- */
-template <
-    typename    T,                  ///< Sample type
-    int         BLOCK_DIM_X,        ///< The thread block length in threads along the X dimension
-    int         ITEMS_PER_THREAD,   ///< The number of samples per thread
-    int         BINS,               ///< The number of bins into which histogram samples may fall
-    int         BLOCK_DIM_Y,        ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,        ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>           ///< The PTX compute capability for which to to specialize this collective
-struct BlockHistogramSort
-{
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    // Parameterize BlockRadixSort type for our thread block
-    typedef BlockRadixSort<
-            T,
-            BLOCK_DIM_X,
-            ITEMS_PER_THREAD,
-            NullType,
-            4,
-            (PTX_ARCH >= 350) ? true : false,
-            BLOCK_SCAN_WARP_SCANS,
-            cudaSharedMemBankSizeFourByte,
-            BLOCK_DIM_Y,
-            BLOCK_DIM_Z,
-            PTX_ARCH>
-        BlockRadixSortT;
-
-    // Parameterize BlockDiscontinuity type for our thread block
-    typedef BlockDiscontinuity<
-            T,
-            BLOCK_DIM_X,
-            BLOCK_DIM_Y,
-            BLOCK_DIM_Z,
-            PTX_ARCH>
-        BlockDiscontinuityT;
-
-    /// Shared memory
-    union _TempStorage
-    {
-        // Storage for sorting bin values
-        typename BlockRadixSortT::TempStorage sort;
-
-        struct
-        {
-            // Storage for detecting discontinuities in the tile of sorted bin values
-            typename BlockDiscontinuityT::TempStorage flag;
-
-            // Storage for noting begin/end offsets of bin runs in the tile of sorted bin values
-            unsigned int run_begin[BINS];
-            unsigned int run_end[BINS];
-        };
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    // Thread fields
-    _TempStorage &temp_storage;
-    unsigned int linear_tid;
-
-
-    /// Constructor
-    __device__ __forceinline__ BlockHistogramSort(
-        TempStorage     &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    // Discontinuity functor
-    struct DiscontinuityOp
-    {
-        // Reference to temp_storage
-        _TempStorage &temp_storage;
-
-        // Constructor
-        __device__ __forceinline__ DiscontinuityOp(_TempStorage &temp_storage) :
-            temp_storage(temp_storage)
-        {}
-
-        // Discontinuity predicate
-        __device__ __forceinline__ bool operator()(const T &a, const T &b, int b_index)
-        {
-            if (a != b)
-            {
-                // Note the begin/end offsets in shared storage
-                temp_storage.run_begin[b] = b_index;
-                temp_storage.run_end[a] = b_index;
-
-                return true;
-            }
-            else
-            {
-                return false;
-            }
-        }
-    };
-
-
-    // Composite data onto an existing histogram
-    template <
-        typename            CounterT     >
-    __device__ __forceinline__ void Composite(
-        T                   (&items)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input values to histogram
-        CounterT            histogram[BINS])                 ///< [out] Reference to shared/device-accessible memory histogram
-    {
-        enum { TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD };
-
-        // Sort bytes in blocked arrangement
-        BlockRadixSortT(temp_storage.sort).Sort(items);
-
-        CTA_SYNC();
-
-        // Initialize the shared memory's run_begin and run_end for each bin
-        int histo_offset = 0;
-
-        #pragma unroll
-        for(; histo_offset + BLOCK_THREADS <= BINS; histo_offset += BLOCK_THREADS)
-        {
-            temp_storage.run_begin[histo_offset + linear_tid] = TILE_SIZE;
-            temp_storage.run_end[histo_offset + linear_tid] = TILE_SIZE;
-        }
-        // Finish up with guarded initialization if necessary
-        if ((BINS % BLOCK_THREADS != 0) && (histo_offset + linear_tid < BINS))
-        {
-            temp_storage.run_begin[histo_offset + linear_tid] = TILE_SIZE;
-            temp_storage.run_end[histo_offset + linear_tid] = TILE_SIZE;
-        }
-
-        CTA_SYNC();
-
-        int flags[ITEMS_PER_THREAD];    // unused
-
-        // Compute head flags to demarcate contiguous runs of the same bin in the sorted tile
-        DiscontinuityOp flag_op(temp_storage);
-        BlockDiscontinuityT(temp_storage.flag).FlagHeads(flags, items, flag_op);
-
-        // Update begin for first item
-        if (linear_tid == 0) temp_storage.run_begin[items[0]] = 0;
-
-        CTA_SYNC();
-
-        // Composite into histogram
-        histo_offset = 0;
-
-        #pragma unroll
-        for(; histo_offset + BLOCK_THREADS <= BINS; histo_offset += BLOCK_THREADS)
-        {
-            int thread_offset = histo_offset + linear_tid;
-            CounterT      count = temp_storage.run_end[thread_offset] - temp_storage.run_begin[thread_offset];
-            histogram[thread_offset] += count;
-        }
-
-        // Finish up with guarded composition if necessary
-        if ((BINS % BLOCK_THREADS != 0) && (histo_offset + linear_tid < BINS))
-        {
-            int thread_offset = histo_offset + linear_tid;
-            CounterT      count = temp_storage.run_end[thread_offset] - temp_storage.run_begin[thread_offset];
-            histogram[thread_offset] += count;
-        }
-    }
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_reduce_raking.cuh b/ml-xgboost/cub/cub/block/specializations/block_reduce_raking.cuh
deleted file mode 100644
index 4baf7f2..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_reduce_raking.cuh
+++ /dev/null
@@ -1,222 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockReduceRaking provides raking-based methods of parallel reduction across a CUDA thread block.  Supports non-commutative reduction operators.
- */
-
-#pragma once
-
-#include "../../block/block_raking_layout.cuh"
-#include "../../warp/warp_reduce.cuh"
-#include "../../thread/thread_reduce.cuh"
-#include "../../util_ptx.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief BlockReduceRaking provides raking-based methods of parallel reduction across a CUDA thread block.  Supports non-commutative reduction operators.
- *
- * Supports non-commutative binary reduction operators.  Unlike commutative
- * reduction operators (e.g., addition), the application of a non-commutative
- * reduction operator (e.g, string concatenation) across a sequence of inputs must
- * honor the relative ordering of items and partial reductions when applying the
- * reduction operator.
- *
- * Compared to the implementation of BlockReduceRaking (which does not support
- * non-commutative operators), this implementation requires a few extra
- * rounds of inter-thread communication.
- */
-template <
-    typename    T,              ///< Data type being reduced
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockReduceRaking
-{
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    /// Layout type for padded thread block raking grid
-    typedef BlockRakingLayout<T, BLOCK_THREADS, PTX_ARCH> BlockRakingLayout;
-
-    ///  WarpReduce utility type
-    typedef typename WarpReduce<T, BlockRakingLayout::RAKING_THREADS, PTX_ARCH>::InternalWarpReduce WarpReduce;
-
-    /// Constants
-    enum
-    {
-        /// Number of raking threads
-        RAKING_THREADS = BlockRakingLayout::RAKING_THREADS,
-
-        /// Number of raking elements per warp synchronous raking thread
-        SEGMENT_LENGTH = BlockRakingLayout::SEGMENT_LENGTH,
-
-        /// Cooperative work can be entirely warp synchronous
-        WARP_SYNCHRONOUS = (RAKING_THREADS == BLOCK_THREADS),
-
-        /// Whether or not warp-synchronous reduction should be unguarded (i.e., the warp-reduction elements is a power of two
-        WARP_SYNCHRONOUS_UNGUARDED = PowerOfTwo<RAKING_THREADS>::VALUE,
-
-        /// Whether or not accesses into smem are unguarded
-        RAKING_UNGUARDED = BlockRakingLayout::UNGUARDED,
-
-    };
-
-
-    /// Shared memory storage layout type
-    union _TempStorage
-    {
-        typename WarpReduce::TempStorage            warp_storage;        ///< Storage for warp-synchronous reduction
-        typename BlockRakingLayout::TempStorage     raking_grid;         ///< Padded threadblock raking grid
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    // Thread fields
-    _TempStorage &temp_storage;
-    unsigned int linear_tid;
-
-
-    /// Constructor
-    __device__ __forceinline__ BlockReduceRaking(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    template <bool IS_FULL_TILE, typename ReductionOp, int ITERATION>
-    __device__ __forceinline__ T RakingReduction(
-        ReductionOp                 reduction_op,       ///< [in] Binary scan operator
-        T                           *raking_segment,
-        T                           partial,            ///< [in] <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
-        int                         num_valid,          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        Int2Type<ITERATION>         /*iteration*/)
-    {
-        // Update partial if addend is in range
-        if ((IS_FULL_TILE && RAKING_UNGUARDED) || ((linear_tid * SEGMENT_LENGTH) + ITERATION < num_valid))
-        {
-            T addend = raking_segment[ITERATION];
-            partial = reduction_op(partial, addend);
-        }
-        return RakingReduction<IS_FULL_TILE>(reduction_op, raking_segment, partial, num_valid, Int2Type<ITERATION + 1>());
-    }
-
-    template <bool IS_FULL_TILE, typename ReductionOp>
-    __device__ __forceinline__ T RakingReduction(
-        ReductionOp                 /*reduction_op*/,   ///< [in] Binary scan operator
-        T                           * /*raking_segment*/,
-        T                           partial,            ///< [in] <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
-        int                         /*num_valid*/,      ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        Int2Type<SEGMENT_LENGTH>    /*iteration*/)
-    {
-        return partial;
-    }
-
-
-
-    /// Computes a threadblock-wide reduction using the specified reduction operator. The first num_valid threads each contribute one reduction partial.  The return value is only valid for thread<sub>0</sub>.
-    template <
-        bool                IS_FULL_TILE,
-        typename            ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   partial,            ///< [in] Calling thread's input partial reductions
-        int                 num_valid,          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        ReductionOp         reduction_op)       ///< [in] Binary reduction operator
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp synchronous reduction (unguarded if active threads is a power-of-two)
-            partial = WarpReduce(temp_storage.warp_storage).template Reduce<IS_FULL_TILE, SEGMENT_LENGTH>(
-                partial,
-                num_valid,
-                reduction_op);
-        }
-        else
-        {
-            // Place partial into shared memory grid.
-            *BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid) = partial;
-
-            CTA_SYNC();
-
-            // Reduce parallelism to one warp
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking reduction in grid
-                T *raking_segment = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
-                partial = raking_segment[0];
-
-                partial = RakingReduction<IS_FULL_TILE>(reduction_op, raking_segment, partial, num_valid, Int2Type<1>());
-
-                partial = WarpReduce(temp_storage.warp_storage).template Reduce<IS_FULL_TILE && RAKING_UNGUARDED, SEGMENT_LENGTH>(
-                    partial,
-                    num_valid,
-                    reduction_op);
-
-            }
-        }
-
-        return partial;
-    }
-
-
-    /// Computes a threadblock-wide reduction using addition (+) as the reduction operator. The first num_valid threads each contribute one reduction partial.  The return value is only valid for thread<sub>0</sub>.
-    template <bool IS_FULL_TILE>
-    __device__ __forceinline__ T Sum(
-        T                   partial,            ///< [in] Calling thread's input partial reductions
-        int                 num_valid)          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-    {
-        cub::Sum reduction_op;
-
-        return Reduce<IS_FULL_TILE>(partial, num_valid, reduction_op);
-    }
-
-
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_reduce_raking_commutative_only.cuh b/ml-xgboost/cub/cub/block/specializations/block_reduce_raking_commutative_only.cuh
deleted file mode 100644
index 9093236..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_reduce_raking_commutative_only.cuh
+++ /dev/null
@@ -1,202 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockReduceRakingCommutativeOnly provides raking-based methods of parallel reduction across a CUDA thread block.  Does not support non-commutative reduction operators.
- */
-
-#pragma once
-
-#include "block_reduce_raking.cuh"
-#include "../../warp/warp_reduce.cuh"
-#include "../../thread/thread_reduce.cuh"
-#include "../../util_ptx.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief BlockReduceRakingCommutativeOnly provides raking-based methods of parallel reduction across a CUDA thread block.  Does not support non-commutative reduction operators.  Does not support block sizes that are not a multiple of the warp size.
- */
-template <
-    typename    T,              ///< Data type being reduced
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockReduceRakingCommutativeOnly
-{
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    // The fall-back implementation to use when BLOCK_THREADS is not a multiple of the warp size or not all threads have valid values
-    typedef BlockReduceRaking<T, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> FallBack;
-
-    /// Constants
-    enum
-    {
-        /// Number of warp threads
-        WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH),
-
-        /// Whether or not to use fall-back
-        USE_FALLBACK = ((BLOCK_THREADS % WARP_THREADS != 0) || (BLOCK_THREADS <= WARP_THREADS)),
-
-        /// Number of raking threads
-        RAKING_THREADS = WARP_THREADS,
-
-        /// Number of threads actually sharing items with the raking threads
-        SHARING_THREADS = CUB_MAX(1, BLOCK_THREADS - RAKING_THREADS),
-
-        /// Number of raking elements per warp synchronous raking thread
-        SEGMENT_LENGTH = SHARING_THREADS / WARP_THREADS,
-    };
-
-    ///  WarpReduce utility type
-    typedef WarpReduce<T, RAKING_THREADS, PTX_ARCH> WarpReduce;
-
-    /// Layout type for padded thread block raking grid
-    typedef BlockRakingLayout<T, SHARING_THREADS, PTX_ARCH> BlockRakingLayout;
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        union
-        {
-            struct
-            {
-                typename WarpReduce::TempStorage        warp_storage;        ///< Storage for warp-synchronous reduction
-                typename BlockRakingLayout::TempStorage raking_grid;         ///< Padded threadblock raking grid
-            };
-            typename FallBack::TempStorage              fallback_storage;    ///< Fall-back storage for non-commutative block scan
-        };
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    // Thread fields
-    _TempStorage &temp_storage;
-    unsigned int linear_tid;
-
-
-    /// Constructor
-    __device__ __forceinline__ BlockReduceRakingCommutativeOnly(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    /// Computes a threadblock-wide reduction using addition (+) as the reduction operator. The first num_valid threads each contribute one reduction partial.  The return value is only valid for thread<sub>0</sub>.
-    template <bool FULL_TILE>
-    __device__ __forceinline__ T Sum(
-        T                   partial,            ///< [in] Calling thread's input partial reductions
-        int                 num_valid)          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-    {
-        if (USE_FALLBACK || !FULL_TILE)
-        {
-            return FallBack(temp_storage.fallback_storage).template Sum<FULL_TILE>(partial, num_valid);
-        }
-        else
-        {
-            // Place partial into shared memory grid
-            if (linear_tid >= RAKING_THREADS)
-                *BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid - RAKING_THREADS) = partial;
-
-            CTA_SYNC();
-
-            // Reduce parallelism to one warp
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking reduction in grid
-                T *raking_segment = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
-                partial = ThreadReduce<SEGMENT_LENGTH>(raking_segment, cub::Sum(), partial);
-
-                // Warpscan
-                partial = WarpReduce(temp_storage.warp_storage).Sum(partial);
-            }
-        }
-
-        return partial;
-    }
-
-
-    /// Computes a threadblock-wide reduction using the specified reduction operator. The first num_valid threads each contribute one reduction partial.  The return value is only valid for thread<sub>0</sub>.
-    template <
-        bool                FULL_TILE,
-        typename            ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   partial,            ///< [in] Calling thread's input partial reductions
-        int                 num_valid,          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        ReductionOp         reduction_op)       ///< [in] Binary reduction operator
-    {
-        if (USE_FALLBACK || !FULL_TILE)
-        {
-            return FallBack(temp_storage.fallback_storage).template Reduce<FULL_TILE>(partial, num_valid, reduction_op);
-        }
-        else
-        {
-            // Place partial into shared memory grid
-            if (linear_tid >= RAKING_THREADS)
-                *BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid - RAKING_THREADS) = partial;
-
-            CTA_SYNC();
-
-            // Reduce parallelism to one warp
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking reduction in grid
-                T *raking_segment = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
-                partial = ThreadReduce<SEGMENT_LENGTH>(raking_segment, reduction_op, partial);
-
-                // Warpscan
-                partial = WarpReduce(temp_storage.warp_storage).Reduce(partial, reduction_op);
-            }
-        }
-
-        return partial;
-    }
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_reduce_warp_reductions.cuh b/ml-xgboost/cub/cub/block/specializations/block_reduce_warp_reductions.cuh
deleted file mode 100644
index bcca3a8..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_reduce_warp_reductions.cuh
+++ /dev/null
@@ -1,222 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockReduceWarpReductions provides variants of warp-reduction-based parallel reduction across a CUDA threadblock.  Supports non-commutative reduction operators.
- */
-
-#pragma once
-
-#include "../../warp/warp_reduce.cuh"
-#include "../../util_ptx.cuh"
-#include "../../util_arch.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief BlockReduceWarpReductions provides variants of warp-reduction-based parallel reduction across a CUDA threadblock.  Supports non-commutative reduction operators.
- */
-template <
-    typename    T,              ///< Data type being reduced
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockReduceWarpReductions
-{
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        /// Number of warp threads
-        WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH),
-
-        /// Number of active warps
-        WARPS = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-
-        /// The logical warp size for warp reductions
-        LOGICAL_WARP_SIZE = CUB_MIN(BLOCK_THREADS, WARP_THREADS),
-
-        /// Whether or not the logical warp size evenly divides the threadblock size
-        EVEN_WARP_MULTIPLE = (BLOCK_THREADS % LOGICAL_WARP_SIZE == 0)
-    };
-
-
-    ///  WarpReduce utility type
-    typedef typename WarpReduce<T, LOGICAL_WARP_SIZE, PTX_ARCH>::InternalWarpReduce WarpReduce;
-
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        typename WarpReduce::TempStorage    warp_reduce[WARPS];                ///< Buffer for warp-synchronous scan
-        T                                   warp_aggregates[WARPS];     ///< Shared totals from each warp-synchronous scan
-        T                                   block_prefix;               ///< Shared prefix for the entire threadblock
-    };
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    // Thread fields
-    _TempStorage &temp_storage;
-    unsigned int linear_tid;
-    unsigned int warp_id;
-    unsigned int lane_id;
-
-
-    /// Constructor
-    __device__ __forceinline__ BlockReduceWarpReductions(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)),
-        warp_id((WARPS == 1) ? 0 : linear_tid / WARP_THREADS),
-        lane_id(LaneId())
-    {}
-
-
-    template <bool FULL_TILE, typename ReductionOp, int SUCCESSOR_WARP>
-    __device__ __forceinline__ T ApplyWarpAggregates(
-        ReductionOp                 reduction_op,       ///< [in] Binary scan operator
-        T                           warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
-        int                         num_valid,          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        Int2Type<SUCCESSOR_WARP>    /*successor_warp*/)
-    {
-        if (FULL_TILE || (SUCCESSOR_WARP * LOGICAL_WARP_SIZE < num_valid))
-        {
-            T addend = temp_storage.warp_aggregates[SUCCESSOR_WARP];
-            warp_aggregate = reduction_op(warp_aggregate, addend);
-        }
-        return ApplyWarpAggregates<FULL_TILE>(reduction_op, warp_aggregate, num_valid, Int2Type<SUCCESSOR_WARP + 1>());
-    }
-
-    template <bool FULL_TILE, typename ReductionOp>
-    __device__ __forceinline__ T ApplyWarpAggregates(
-        ReductionOp         /*reduction_op*/,   ///< [in] Binary scan operator
-        T                   warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
-        int                 /*num_valid*/,      ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        Int2Type<WARPS>     /*successor_warp*/)
-    {
-        return warp_aggregate;
-    }
-
-
-    /// Returns block-wide aggregate in <em>thread</em><sub>0</sub>.
-    template <
-        bool                FULL_TILE,
-        typename            ReductionOp>
-    __device__ __forceinline__ T ApplyWarpAggregates(
-        ReductionOp         reduction_op,       ///< [in] Binary scan operator
-        T                   warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
-        int                 num_valid)          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-    {
-        // Share lane aggregates
-        if (lane_id == 0)
-        {
-            temp_storage.warp_aggregates[warp_id] = warp_aggregate;
-        }
-
-        CTA_SYNC();
-
-        // Update total aggregate in warp 0, lane 0
-        if (linear_tid == 0)
-        {
-            warp_aggregate = ApplyWarpAggregates<FULL_TILE>(reduction_op, warp_aggregate, num_valid, Int2Type<1>());
-        }
-
-        return warp_aggregate;
-    }
-
-
-    /// Computes a threadblock-wide reduction using addition (+) as the reduction operator. The first num_valid threads each contribute one reduction partial.  The return value is only valid for thread<sub>0</sub>.
-    template <bool FULL_TILE>
-    __device__ __forceinline__ T Sum(
-        T                   input,          ///< [in] Calling thread's input partial reductions
-        int                 num_valid)      ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-    {
-        cub::Sum        reduction_op;
-        unsigned int    warp_offset = warp_id * LOGICAL_WARP_SIZE;
-        unsigned int    warp_num_valid = (FULL_TILE && EVEN_WARP_MULTIPLE) ?
-                            LOGICAL_WARP_SIZE :
-                            (warp_offset < num_valid) ?
-                                num_valid - warp_offset :
-                                0;
-
-        // Warp reduction in every warp
-        T warp_aggregate = WarpReduce(temp_storage.warp_reduce[warp_id]).template Reduce<(FULL_TILE && EVEN_WARP_MULTIPLE), 1>(
-            input,
-            warp_num_valid,
-            cub::Sum());
-
-        // Update outputs and block_aggregate with warp-wide aggregates from lane-0s
-        return ApplyWarpAggregates<FULL_TILE>(reduction_op, warp_aggregate, num_valid);
-    }
-
-
-    /// Computes a threadblock-wide reduction using the specified reduction operator. The first num_valid threads each contribute one reduction partial.  The return value is only valid for thread<sub>0</sub>.
-    template <
-        bool                FULL_TILE,
-        typename            ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   input,              ///< [in] Calling thread's input partial reductions
-        int                 num_valid,          ///< [in] Number of valid elements (may be less than BLOCK_THREADS)
-        ReductionOp         reduction_op)       ///< [in] Binary reduction operator
-    {
-        unsigned int    warp_offset = warp_id * LOGICAL_WARP_SIZE;
-        unsigned int    warp_num_valid = (FULL_TILE && EVEN_WARP_MULTIPLE) ?
-                            LOGICAL_WARP_SIZE :
-                            (warp_offset < static_cast<unsigned int>(num_valid)) ?
-                                num_valid - warp_offset :
-                                0;
-
-        // Warp reduction in every warp
-        T warp_aggregate = WarpReduce(temp_storage.warp_reduce[warp_id]).template Reduce<(FULL_TILE && EVEN_WARP_MULTIPLE), 1>(
-            input,
-            warp_num_valid,
-            reduction_op);
-
-        // Update outputs and block_aggregate with warp-wide aggregates from lane-0s
-        return ApplyWarpAggregates<FULL_TILE>(reduction_op, warp_aggregate, num_valid);
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_scan_raking.cuh b/ml-xgboost/cub/cub/block/specializations/block_scan_raking.cuh
deleted file mode 100644
index c0d0f51..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_scan_raking.cuh
+++ /dev/null
@@ -1,666 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-
-/**
- * \file
- * cub::BlockScanRaking provides variants of raking-based parallel prefix scan across a CUDA threadblock.
- */
-
-#pragma once
-
-#include "../../util_ptx.cuh"
-#include "../../util_arch.cuh"
-#include "../../block/block_raking_layout.cuh"
-#include "../../thread/thread_reduce.cuh"
-#include "../../thread/thread_scan.cuh"
-#include "../../warp/warp_scan.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief BlockScanRaking provides variants of raking-based parallel prefix scan across a CUDA threadblock.
- */
-template <
-    typename    T,              ///< Data type being scanned
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    bool        MEMOIZE,        ///< Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockScanRaking
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-    };
-
-    /// Layout type for padded threadblock raking grid
-    typedef BlockRakingLayout<T, BLOCK_THREADS, PTX_ARCH> BlockRakingLayout;
-
-    /// Constants
-    enum
-    {
-        /// Number of raking threads
-        RAKING_THREADS = BlockRakingLayout::RAKING_THREADS,
-
-        /// Number of raking elements per warp synchronous raking thread
-        SEGMENT_LENGTH = BlockRakingLayout::SEGMENT_LENGTH,
-
-        /// Cooperative work can be entirely warp synchronous
-        WARP_SYNCHRONOUS = (BLOCK_THREADS == RAKING_THREADS),
-    };
-
-    ///  WarpScan utility type
-    typedef WarpScan<T, RAKING_THREADS, PTX_ARCH> WarpScan;
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        typename WarpScan::TempStorage              warp_scan;          ///< Buffer for warp-synchronous scan
-        typename BlockRakingLayout::TempStorage     raking_grid;        ///< Padded threadblock raking grid
-        T                                           block_aggregate;    ///< Block aggregate
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    // Thread fields
-    _TempStorage    &temp_storage;
-    unsigned int    linear_tid;
-    T               cached_segment[SEGMENT_LENGTH];
-
-
-    //---------------------------------------------------------------------
-    // Utility methods
-    //---------------------------------------------------------------------
-
-    /// Templated reduction
-    template <int ITERATION, typename ScanOp>
-    __device__ __forceinline__ T GuardedReduce(
-        T*                  raking_ptr,         ///< [in] Input array
-        ScanOp              scan_op,            ///< [in] Binary reduction operator
-        T                   raking_partial,     ///< [in] Prefix to seed reduction with
-        Int2Type<ITERATION> /*iteration*/)
-    {
-        if ((BlockRakingLayout::UNGUARDED) || (((linear_tid * SEGMENT_LENGTH) + ITERATION) < BLOCK_THREADS))
-        {
-            T addend = raking_ptr[ITERATION];
-            raking_partial = scan_op(raking_partial, addend);
-        }
-
-        return GuardedReduce(raking_ptr, scan_op, raking_partial, Int2Type<ITERATION + 1>());
-    }
-
-
-    /// Templated reduction (base case)
-    template <typename ScanOp>
-    __device__ __forceinline__ T GuardedReduce(
-        T*                          /*raking_ptr*/,    ///< [in] Input array
-        ScanOp                      /*scan_op*/,       ///< [in] Binary reduction operator
-        T                           raking_partial,    ///< [in] Prefix to seed reduction with
-        Int2Type<SEGMENT_LENGTH>    /*iteration*/)
-    {
-        return raking_partial;
-    }
-
-
-    /// Templated copy
-    template <int ITERATION>
-    __device__ __forceinline__ void CopySegment(
-        T*                  out,            ///< [out] Out array
-        T*                  in,             ///< [in] Input array
-        Int2Type<ITERATION> /*iteration*/)
-    {
-        out[ITERATION] = in[ITERATION];
-        CopySegment(out, in, Int2Type<ITERATION + 1>());
-    }
-
- 
-    /// Templated copy (base case)
-    __device__ __forceinline__ void CopySegment(
-        T*                  /*out*/,            ///< [out] Out array
-        T*                  /*in*/,             ///< [in] Input array
-        Int2Type<SEGMENT_LENGTH> /*iteration*/)
-    {}
-
-
-    /// Performs upsweep raking reduction, returning the aggregate
-    template <typename ScanOp>
-    __device__ __forceinline__ T Upsweep(
-        ScanOp scan_op)
-    {
-        T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
-
-        // Read data into registers
-        CopySegment(cached_segment, smem_raking_ptr, Int2Type<0>());
-
-        T raking_partial = cached_segment[0];
-
-        return GuardedReduce(cached_segment, scan_op, raking_partial, Int2Type<1>());
-    }
-
-
-    /// Performs exclusive downsweep raking scan
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveDownsweep(
-        ScanOp          scan_op,
-        T               raking_partial,
-        bool            apply_prefix = true)
-    {
-        T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
-
-        // Read data back into registers
-        if (!MEMOIZE)
-        {
-            CopySegment(cached_segment, smem_raking_ptr, Int2Type<0>());
-        }
-
-        ThreadScanExclusive(cached_segment, cached_segment, scan_op, raking_partial, apply_prefix);
-
-        // Write data back to smem
-        CopySegment(smem_raking_ptr, cached_segment, Int2Type<0>());
-    }
-
-
-    /// Performs inclusive downsweep raking scan
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveDownsweep(
-        ScanOp          scan_op,
-        T               raking_partial,
-        bool            apply_prefix = true)
-    {
-        T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
-
-        // Read data back into registers
-        if (!MEMOIZE)
-        {
-            CopySegment(cached_segment, smem_raking_ptr, Int2Type<0>());
-        }
-
-        ThreadScanInclusive(cached_segment, cached_segment, scan_op, raking_partial, apply_prefix);
-
-        // Write data back to smem
-        CopySegment(smem_raking_ptr, cached_segment, Int2Type<0>());
-    }
-
-
-    //---------------------------------------------------------------------
-    // Constructors
-    //---------------------------------------------------------------------
-
-    /// Constructor
-    __device__ __forceinline__ BlockScanRaking(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Exclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &exclusive_output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            WarpScan(temp_storage.warp_scan).ExclusiveScan(input, exclusive_output, scan_op);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Warp-synchronous scan
-                T exclusive_partial;
-                WarpScan(temp_storage.warp_scan).ExclusiveScan(upsweep_partial, exclusive_partial, scan_op);
-
-                // Exclusive raking downsweep scan
-                ExclusiveDownsweep(scan_op, exclusive_partial, (linear_tid != 0));
-            }
-
-            CTA_SYNC();
-
-            // Grab thread prefix from shared memory
-            exclusive_output = *placement_ptr;
-        }
-    }
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &output,            ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            WarpScan(temp_storage.warp_scan).ExclusiveScan(input, output, initial_value, scan_op);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Exclusive Warp-synchronous scan
-                T exclusive_partial;
-                WarpScan(temp_storage.warp_scan).ExclusiveScan(upsweep_partial, exclusive_partial, initial_value, scan_op);
-
-                // Exclusive raking downsweep scan
-                ExclusiveDownsweep(scan_op, exclusive_partial);
-            }
-
-            CTA_SYNC();
-
-            // Grab exclusive partial from shared memory
-            output = *placement_ptr;
-        }
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan operator
-        T               &block_aggregate)               ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            WarpScan(temp_storage.warp_scan).ExclusiveScan(input, output, scan_op, block_aggregate);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial= Upsweep(scan_op);
-
-                // Warp-synchronous scan
-                T inclusive_partial;
-                T exclusive_partial;
-                WarpScan(temp_storage.warp_scan).Scan(upsweep_partial, inclusive_partial, exclusive_partial, scan_op);
-
-                // Exclusive raking downsweep scan
-                ExclusiveDownsweep(scan_op, exclusive_partial, (linear_tid != 0));
-
-                // Broadcast aggregate to all threads
-                if (linear_tid == RAKING_THREADS - 1)
-                    temp_storage.block_aggregate = inclusive_partial;
-            }
-
-            CTA_SYNC();
-
-            // Grab thread prefix from shared memory
-            output = *placement_ptr;
-
-            // Retrieve block aggregate
-            block_aggregate = temp_storage.block_aggregate;
-        }
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &output,            ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            WarpScan(temp_storage.warp_scan).ExclusiveScan(input, output, initial_value, scan_op, block_aggregate);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Warp-synchronous scan
-                T exclusive_partial;
-                WarpScan(temp_storage.warp_scan).ExclusiveScan(upsweep_partial, exclusive_partial, initial_value, scan_op, block_aggregate);
-
-                // Exclusive raking downsweep scan
-                ExclusiveDownsweep(scan_op, exclusive_partial);
-
-                // Broadcast aggregate to other threads
-                if (linear_tid == 0)
-                    temp_storage.block_aggregate = block_aggregate;
-            }
-
-            CTA_SYNC();
-
-            // Grab exclusive partial from shared memory
-            output = *placement_ptr;
-
-            // Retrieve block aggregate
-            block_aggregate = temp_storage.block_aggregate;
-        }
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            T block_aggregate;
-            WarpScan warp_scan(temp_storage.warp_scan);
-            warp_scan.ExclusiveScan(input, output, scan_op, block_aggregate);
-
-            // Obtain warp-wide prefix in lane0, then broadcast to other lanes
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            block_prefix = warp_scan.Broadcast(block_prefix, 0);
-
-            output = scan_op(block_prefix, output);
-            if (linear_tid == 0)
-                output = block_prefix;
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                WarpScan warp_scan(temp_storage.warp_scan);
-
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Warp-synchronous scan
-                T exclusive_partial, block_aggregate;
-                warp_scan.ExclusiveScan(upsweep_partial, exclusive_partial, scan_op, block_aggregate);
-
-                // Obtain block-wide prefix in lane0, then broadcast to other lanes
-                T block_prefix = block_prefix_callback_op(block_aggregate);
-                block_prefix = warp_scan.Broadcast(block_prefix, 0);
-
-                // Update prefix with warpscan exclusive partial
-                T downsweep_prefix = scan_op(block_prefix, exclusive_partial);
-                if (linear_tid == 0)
-                    downsweep_prefix = block_prefix;
-
-                // Exclusive raking downsweep scan
-                ExclusiveDownsweep(scan_op, downsweep_prefix);
-            }
-
-            CTA_SYNC();
-
-            // Grab thread prefix from shared memory
-            output = *placement_ptr;
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Inclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            WarpScan(temp_storage.warp_scan).InclusiveScan(input, output, scan_op);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Exclusive Warp-synchronous scan
-                T exclusive_partial;
-                WarpScan(temp_storage.warp_scan).ExclusiveScan(upsweep_partial, exclusive_partial, scan_op);
-
-                // Inclusive raking downsweep scan
-                InclusiveDownsweep(scan_op, exclusive_partial, (linear_tid != 0));
-            }
-
-            CTA_SYNC();
-
-            // Grab thread prefix from shared memory
-            output = *placement_ptr;
-        }
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan operator
-        T               &block_aggregate)               ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            WarpScan(temp_storage.warp_scan).InclusiveScan(input, output, scan_op, block_aggregate);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Warp-synchronous scan
-                T inclusive_partial;
-                T exclusive_partial;
-                WarpScan(temp_storage.warp_scan).Scan(upsweep_partial, inclusive_partial, exclusive_partial, scan_op);
-
-                // Inclusive raking downsweep scan
-                InclusiveDownsweep(scan_op, exclusive_partial, (linear_tid != 0));
-
-                // Broadcast aggregate to all threads
-                if (linear_tid == RAKING_THREADS - 1)
-                    temp_storage.block_aggregate = inclusive_partial;
-            }
-
-            CTA_SYNC();
-
-            // Grab thread prefix from shared memory
-            output = *placement_ptr;
-
-            // Retrieve block aggregate
-            block_aggregate = temp_storage.block_aggregate;
-        }
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &output,                        ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        if (WARP_SYNCHRONOUS)
-        {
-            // Short-circuit directly to warp-synchronous scan
-            T block_aggregate;
-            WarpScan warp_scan(temp_storage.warp_scan);
-            warp_scan.InclusiveScan(input, output, scan_op, block_aggregate);
-
-            // Obtain warp-wide prefix in lane0, then broadcast to other lanes
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            block_prefix = warp_scan.Broadcast(block_prefix, 0);
-
-            // Update prefix with exclusive warpscan partial
-            output = scan_op(block_prefix, output);
-        }
-        else
-        {
-            // Place thread partial into shared memory raking grid
-            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
-            *placement_ptr = input;
-
-            CTA_SYNC();
-
-            // Reduce parallelism down to just raking threads
-            if (linear_tid < RAKING_THREADS)
-            {
-                WarpScan warp_scan(temp_storage.warp_scan);
-
-                // Raking upsweep reduction across shared partials
-                T upsweep_partial = Upsweep(scan_op);
-
-                // Warp-synchronous scan
-                T exclusive_partial, block_aggregate;
-                warp_scan.ExclusiveScan(upsweep_partial, exclusive_partial, scan_op, block_aggregate);
-
-                // Obtain block-wide prefix in lane0, then broadcast to other lanes
-                T block_prefix = block_prefix_callback_op(block_aggregate);
-                block_prefix = warp_scan.Broadcast(block_prefix, 0);
-
-                // Update prefix with warpscan exclusive partial
-                T downsweep_prefix = scan_op(block_prefix, exclusive_partial);
-                if (linear_tid == 0)
-                    downsweep_prefix = block_prefix;
-
-                // Inclusive raking downsweep scan
-                InclusiveDownsweep(scan_op, downsweep_prefix);
-            }
-
-            CTA_SYNC();
-
-            // Grab thread prefix from shared memory
-            output = *placement_ptr;
-        }
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans.cuh b/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans.cuh
deleted file mode 100644
index e40f735..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans.cuh
+++ /dev/null
@@ -1,392 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockScanWarpscans provides warpscan-based variants of parallel prefix scan across a CUDA threadblock.
- */
-
-#pragma once
-
-#include "../../util_arch.cuh"
-#include "../../util_ptx.cuh"
-#include "../../warp/warp_scan.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief BlockScanWarpScans provides warpscan-based variants of parallel prefix scan across a CUDA threadblock.
- */
-template <
-    typename    T,
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockScanWarpScans
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        /// Number of warp threads
-        WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH),
-
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        /// Number of active warps
-        WARPS = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-    };
-
-    ///  WarpScan utility type
-    typedef WarpScan<T, WARP_THREADS, PTX_ARCH> WarpScanT;
-
-    ///  WarpScan utility type
-    typedef WarpScan<T, WARPS, PTX_ARCH> WarpAggregateScan;
-
-    /// Shared memory storage layout type
-
-    struct __align__(32) _TempStorage
-    {
-        T                               warp_aggregates[WARPS];
-        typename WarpScanT::TempStorage warp_scan[WARPS];           ///< Buffer for warp-synchronous scans
-        T                               block_prefix;               ///< Shared prefix for the entire threadblock
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    // Thread fields
-    _TempStorage    &temp_storage;
-    unsigned int    linear_tid;
-    unsigned int    warp_id;
-    unsigned int    lane_id;
-
-
-    //---------------------------------------------------------------------
-    // Constructors
-    //---------------------------------------------------------------------
-
-    /// Constructor
-    __device__ __forceinline__ BlockScanWarpScans(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)),
-        warp_id((WARPS == 1) ? 0 : linear_tid / WARP_THREADS),
-        lane_id(LaneId())
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Utility methods
-    //---------------------------------------------------------------------
-
-    template <typename ScanOp, int WARP>
-    __device__ __forceinline__ void ApplyWarpAggregates(
-        T               &warp_prefix,           ///< [out] The calling thread's partial reduction
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate,   ///< [out] Threadblock-wide aggregate reduction of input items
-        Int2Type<WARP>  /*addend_warp*/)
-    {
-        if (warp_id == WARP)
-            warp_prefix = block_aggregate;
-
-        T addend = temp_storage.warp_aggregates[WARP];
-        block_aggregate = scan_op(block_aggregate, addend);
-
-        ApplyWarpAggregates(warp_prefix, scan_op, block_aggregate, Int2Type<WARP + 1>());
-    }
-
-    template <typename ScanOp>
-    __device__ __forceinline__ void ApplyWarpAggregates(
-        T               &/*warp_prefix*/,       ///< [out] The calling thread's partial reduction
-        ScanOp          /*scan_op*/,            ///< [in] Binary scan operator
-        T               &/*block_aggregate*/,   ///< [out] Threadblock-wide aggregate reduction of input items
-        Int2Type<WARPS> /*addend_warp*/)
-    {}
-
-
-    /// Use the warp-wide aggregates to compute the calling warp's prefix.  Also returns block-wide aggregate in all threads.
-    template <typename ScanOp>
-    __device__ __forceinline__ T ComputeWarpPrefix(
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>WARP_THREADS - 1</sub> only]</b> Warp-wide aggregate reduction of input items
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Last lane in each warp shares its warp-aggregate
-        if (lane_id == WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = warp_aggregate;
-
-        CTA_SYNC();
-
-        // Accumulate block aggregates and save the one that is our warp's prefix
-        T warp_prefix;
-        block_aggregate = temp_storage.warp_aggregates[0];
-
-        // Use template unrolling (since the PTX backend can't handle unrolling it for SM1x)
-        ApplyWarpAggregates(warp_prefix, scan_op, block_aggregate, Int2Type<1>());
-/*
-        #pragma unroll
-        for (int WARP = 1; WARP < WARPS; ++WARP)
-        {
-            if (warp_id == WARP)
-                warp_prefix = block_aggregate;
-
-            T addend = temp_storage.warp_aggregates[WARP];
-            block_aggregate = scan_op(block_aggregate, addend);
-        }
-*/
-
-        return warp_prefix;
-    }
-
-
-    /// Use the warp-wide aggregates and initial-value to compute the calling warp's prefix.  Also returns block-wide aggregate in all threads.
-    template <typename ScanOp>
-    __device__ __forceinline__ T ComputeWarpPrefix(
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>WARP_THREADS - 1</sub> only]</b> Warp-wide aggregate reduction of input items
-        T               &block_aggregate,   ///< [out] Threadblock-wide aggregate reduction of input items
-        const T         &initial_value)     ///< [in] Initial value to seed the exclusive scan
-    {
-        T warp_prefix = ComputeWarpPrefix(scan_op, warp_aggregate, block_aggregate);
-
-        warp_prefix = scan_op(initial_value, warp_prefix);
-
-        if (warp_id == 0)
-            warp_prefix = initial_value;
-
-        return warp_prefix;
-    }
-
-    //---------------------------------------------------------------------
-    // Exclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        // Compute block-wide exclusive scan.  The exclusive output from tid0 is invalid.
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &exclusive_output,  ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, initial_value, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item
-        T               &exclusive_output,  ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        WarpScanT(temp_storage.warp_scan[warp_id]).Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Compute the warp-wide prefix and block-wide aggregate for each warp.  Warp prefix for warp0 is invalid.
-        T warp_prefix = ComputeWarpPrefix(scan_op, inclusive_output, block_aggregate);
-
-        // Apply warp prefix to our lane's partial
-        if (warp_id != 0)
-        {
-            exclusive_output = scan_op(warp_prefix, exclusive_output);
-            if (lane_id == 0)
-                exclusive_output = warp_prefix;
-        }
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &exclusive_output,  ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        WarpScanT(temp_storage.warp_scan[warp_id]).Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Compute the warp-wide prefix and block-wide aggregate for each warp
-        T warp_prefix = ComputeWarpPrefix(scan_op, inclusive_output, block_aggregate, initial_value);
-
-        // Apply warp prefix to our lane's partial
-        exclusive_output = scan_op(warp_prefix, exclusive_output);
-        if (lane_id == 0)
-            exclusive_output = warp_prefix;
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        // Compute block-wide exclusive scan.  The exclusive output from tid0 is invalid.
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-
-        // Use the first warp to determine the threadblock prefix, returning the result in lane0
-        if (warp_id == 0)
-        {
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            if (lane_id == 0)
-            {
-                // Share the prefix with all threads
-                temp_storage.block_prefix = block_prefix;
-                exclusive_output = block_prefix;                // The block prefix is the exclusive output for tid0
-            }
-        }
-
-        CTA_SYNC();
-
-        // Incorporate threadblock prefix into outputs
-        T block_prefix = temp_storage.block_prefix;
-        if (linear_tid > 0)
-        {
-            exclusive_output = scan_op(block_prefix, exclusive_output);
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Inclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        T block_aggregate;
-        InclusiveScan(input, inclusive_output, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan operator
-        T               &block_aggregate)               ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        WarpScanT(temp_storage.warp_scan[warp_id]).InclusiveScan(input, inclusive_output, scan_op);
-
-        // Compute the warp-wide prefix and block-wide aggregate for each warp.  Warp prefix for warp0 is invalid.
-        T warp_prefix = ComputeWarpPrefix(scan_op, inclusive_output, block_aggregate);
-
-        // Apply warp prefix to our lane's partial
-        if (warp_id != 0)
-        {
-            inclusive_output = scan_op(warp_prefix, inclusive_output);
-        }
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        T block_aggregate;
-        InclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-
-        // Use the first warp to determine the threadblock prefix, returning the result in lane0
-        if (warp_id == 0)
-        {
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            if (lane_id == 0)
-            {
-                // Share the prefix with all threads
-                temp_storage.block_prefix = block_prefix;
-            }
-        }
-
-        CTA_SYNC();
-
-        // Incorporate threadblock prefix into outputs
-        T block_prefix = temp_storage.block_prefix;
-        exclusive_output = scan_op(block_prefix, exclusive_output);
-    }
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans2.cuh b/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans2.cuh
deleted file mode 100644
index 154833b..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans2.cuh
+++ /dev/null
@@ -1,436 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockScanWarpscans provides warpscan-based variants of parallel prefix scan across a CUDA threadblock.
- */
-
-#pragma once
-
-#include "../../util_arch.cuh"
-#include "../../util_ptx.cuh"
-#include "../../warp/warp_scan.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief BlockScanWarpScans provides warpscan-based variants of parallel prefix scan across a CUDA threadblock.
- */
-template <
-    typename    T,
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockScanWarpScans
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        /// Number of warp threads
-        WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH),
-
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        /// Number of active warps
-        WARPS = (BLOCK_THREADS + WARP_THREADS - 1) / WARP_THREADS,
-    };
-
-    ///  WarpScan utility type
-    typedef WarpScan<T, WARP_THREADS, PTX_ARCH> WarpScanT;
-
-    ///  WarpScan utility type
-    typedef WarpScan<T, WARPS, PTX_ARCH> WarpAggregateScanT;
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        typename WarpAggregateScanT::TempStorage    inner_scan[WARPS];          ///< Buffer for warp-synchronous scans
-        typename WarpScanT::TempStorage             warp_scan[WARPS];           ///< Buffer for warp-synchronous scans
-        T                                           warp_aggregates[WARPS];
-        T                                           block_prefix;               ///< Shared prefix for the entire threadblock
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    // Thread fields
-    _TempStorage    &temp_storage;
-    unsigned int    linear_tid;
-    unsigned int    warp_id;
-    unsigned int    lane_id;
-
-
-    //---------------------------------------------------------------------
-    // Constructors
-    //---------------------------------------------------------------------
-
-    /// Constructor
-    __device__ __forceinline__ BlockScanWarpScans(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)),
-        warp_id((WARPS == 1) ? 0 : linear_tid / WARP_THREADS),
-        lane_id(LaneId())
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Utility methods
-    //---------------------------------------------------------------------
-
-    template <typename ScanOp, int WARP>
-    __device__ __forceinline__ void ApplyWarpAggregates(
-        T               &warp_prefix,           ///< [out] The calling thread's partial reduction
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate,   ///< [out] Threadblock-wide aggregate reduction of input items
-        Int2Type<WARP>  addend_warp)
-    {
-        if (warp_id == WARP)
-            warp_prefix = block_aggregate;
-
-        T addend = temp_storage.warp_aggregates[WARP];
-        block_aggregate = scan_op(block_aggregate, addend);
-
-        ApplyWarpAggregates(warp_prefix, scan_op, block_aggregate, Int2Type<WARP + 1>());
-    }
-
-    template <typename ScanOp>
-    __device__ __forceinline__ void ApplyWarpAggregates(
-        T               &warp_prefix,           ///< [out] The calling thread's partial reduction
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate,   ///< [out] Threadblock-wide aggregate reduction of input items
-        Int2Type<WARPS> addend_warp)
-    {}
-
-
-    /// Use the warp-wide aggregates to compute the calling warp's prefix.  Also returns block-wide aggregate in all threads.
-    template <typename ScanOp>
-    __device__ __forceinline__ T ComputeWarpPrefix(
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>WARP_THREADS - 1</sub> only]</b> Warp-wide aggregate reduction of input items
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Last lane in each warp shares its warp-aggregate
-        if (lane_id == WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = warp_aggregate;
-
-        CTA_SYNC();
-
-        // Accumulate block aggregates and save the one that is our warp's prefix
-        T warp_prefix;
-        block_aggregate = temp_storage.warp_aggregates[0];
-
-        // Use template unrolling (since the PTX backend can't handle unrolling it for SM1x)
-        ApplyWarpAggregates(warp_prefix, scan_op, block_aggregate, Int2Type<1>());
-/*
-        #pragma unroll
-        for (int WARP = 1; WARP < WARPS; ++WARP)
-        {
-            if (warp_id == WARP)
-                warp_prefix = block_aggregate;
-
-            T addend = temp_storage.warp_aggregates[WARP];
-            block_aggregate = scan_op(block_aggregate, addend);
-        }
-*/
-
-        return warp_prefix;
-    }
-
-
-    /// Use the warp-wide aggregates and initial-value to compute the calling warp's prefix.  Also returns block-wide aggregate in all threads.
-    template <typename ScanOp>
-    __device__ __forceinline__ T ComputeWarpPrefix(
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               warp_aggregate,     ///< [in] <b>[<em>lane</em><sub>WARP_THREADS - 1</sub> only]</b> Warp-wide aggregate reduction of input items
-        T               &block_aggregate,   ///< [out] Threadblock-wide aggregate reduction of input items
-        const T         &initial_value)     ///< [in] Initial value to seed the exclusive scan
-    {
-        T warp_prefix = ComputeWarpPrefix(scan_op, warp_aggregate, block_aggregate);
-
-        warp_prefix = scan_op(initial_value, warp_prefix);
-
-        if (warp_id == 0)
-            warp_prefix = initial_value;
-
-        return warp_prefix;
-    }
-
-    //---------------------------------------------------------------------
-    // Exclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        // Compute block-wide exclusive scan.  The exclusive output from tid0 is invalid.
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &exclusive_output,  ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, initial_value, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item
-        T               &exclusive_output,  ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        WarpScanT my_warp_scan(temp_storage.warp_scan[warp_id]);
-
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        my_warp_scan.Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Compute the warp-wide prefix and block-wide aggregate for each warp.  Warp prefix for warp0 is invalid.
-//        T warp_prefix = ComputeWarpPrefix(scan_op, inclusive_output, block_aggregate);
-
-//--------------------------------------------------
-        // Last lane in each warp shares its warp-aggregate
-        if (lane_id == WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-
-        CTA_SYNC();
-
-        // Get the warp scan partial
-        T warp_inclusive, warp_prefix;
-        if (lane_id < WARPS)
-        {
-            // Scan the warpscan partials
-            T warp_val = temp_storage.warp_aggregates[lane_id];
-            WarpAggregateScanT(temp_storage.inner_scan[warp_id]).Scan(warp_val, warp_inclusive, warp_prefix, scan_op);
-        }
-
-        warp_prefix         = my_warp_scan.Broadcast(warp_prefix, warp_id);
-        block_aggregate     = my_warp_scan.Broadcast(warp_inclusive, WARPS - 1);
-//--------------------------------------------------
-
-        // Apply warp prefix to our lane's partial
-        if (warp_id != 0)
-        {
-            exclusive_output = scan_op(warp_prefix, exclusive_output);
-            if (lane_id == 0)
-                exclusive_output = warp_prefix;
-        }
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &exclusive_output,  ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        WarpScanT my_warp_scan(temp_storage.warp_scan[warp_id]);
-
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        my_warp_scan.Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Compute the warp-wide prefix and block-wide aggregate for each warp
-//        T warp_prefix = ComputeWarpPrefix(scan_op, inclusive_output, block_aggregate, initial_value);
-
-//--------------------------------------------------
-        // Last lane in each warp shares its warp-aggregate
-        if (lane_id == WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-
-        CTA_SYNC();
-
-        // Get the warp scan partial
-        T warp_inclusive, warp_prefix;
-        if (lane_id < WARPS)
-        {
-            // Scan the warpscan partials
-            T warp_val = temp_storage.warp_aggregates[lane_id];
-            WarpAggregateScanT(temp_storage.inner_scan[warp_id]).Scan(warp_val, warp_inclusive, warp_prefix, initial_value, scan_op);
-        }
-
-        warp_prefix         = my_warp_scan.Broadcast(warp_prefix, warp_id);
-        block_aggregate     = my_warp_scan.Broadcast(warp_inclusive, WARPS - 1);
-//--------------------------------------------------
-
-        // Apply warp prefix to our lane's partial
-        exclusive_output = scan_op(warp_prefix, exclusive_output);
-        if (lane_id == 0)
-            exclusive_output = warp_prefix;
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        // Compute block-wide exclusive scan.  The exclusive output from tid0 is invalid.
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-
-        // Use the first warp to determine the threadblock prefix, returning the result in lane0
-        if (warp_id == 0)
-        {
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            if (lane_id == 0)
-            {
-                // Share the prefix with all threads
-                temp_storage.block_prefix = block_prefix;
-                exclusive_output = block_prefix;                // The block prefix is the exclusive output for tid0
-            }
-        }
-
-        CTA_SYNC();
-
-        // Incorporate threadblock prefix into outputs
-        T block_prefix = temp_storage.block_prefix;
-        if (linear_tid > 0)
-        {
-            exclusive_output = scan_op(block_prefix, exclusive_output);
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Inclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        T block_aggregate;
-        InclusiveScan(input, inclusive_output, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan operator
-        T               &block_aggregate)               ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        WarpScanT(temp_storage.warp_scan[warp_id]).InclusiveScan(input, inclusive_output, scan_op);
-
-        // Compute the warp-wide prefix and block-wide aggregate for each warp.  Warp prefix for warp0 is invalid.
-        T warp_prefix = ComputeWarpPrefix(scan_op, inclusive_output, block_aggregate);
-
-        // Apply warp prefix to our lane's partial
-        if (warp_id != 0)
-        {
-            inclusive_output = scan_op(warp_prefix, inclusive_output);
-        }
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        T block_aggregate;
-        InclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-
-        // Use the first warp to determine the threadblock prefix, returning the result in lane0
-        if (warp_id == 0)
-        {
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            if (lane_id == 0)
-            {
-                // Share the prefix with all threads
-                temp_storage.block_prefix = block_prefix;
-            }
-        }
-
-        CTA_SYNC();
-
-        // Incorporate threadblock prefix into outputs
-        T block_prefix = temp_storage.block_prefix;
-        exclusive_output = scan_op(block_prefix, exclusive_output);
-    }
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans3.cuh b/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans3.cuh
deleted file mode 100644
index 811d84f..0000000
--- a/ml-xgboost/cub/cub/block/specializations/block_scan_warp_scans3.cuh
+++ /dev/null
@@ -1,412 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::BlockScanWarpscans provides warpscan-based variants of parallel prefix scan across a CUDA threadblock.
- */
-
-#pragma once
-
-#include "../../util_arch.cuh"
-#include "../../util_ptx.cuh"
-#include "../../warp/warp_scan.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief BlockScanWarpScans provides warpscan-based variants of parallel prefix scan across a CUDA threadblock.
- */
-template <
-    typename    T,
-    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
-    int         BLOCK_DIM_Y,    ///< The thread block length in threads along the Y dimension
-    int         BLOCK_DIM_Z,    ///< The thread block length in threads along the Z dimension
-    int         PTX_ARCH>       ///< The PTX compute capability for which to to specialize this collective
-struct BlockScanWarpScans
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// Constants
-    enum
-    {
-        /// The thread block size in threads
-        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
-
-        /// Number of warp threads
-        INNER_WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH),
-        OUTER_WARP_THREADS = BLOCK_THREADS / INNER_WARP_THREADS,
-
-        /// Number of outer scan warps
-        OUTER_WARPS = INNER_WARP_THREADS
-    };
-
-    ///  Outer WarpScan utility type
-    typedef WarpScan<T, OUTER_WARP_THREADS, PTX_ARCH> OuterWarpScanT;
-
-    ///  Inner WarpScan utility type
-    typedef WarpScan<T, INNER_WARP_THREADS, PTX_ARCH> InnerWarpScanT;
-
-    typedef typename OuterWarpScanT::TempStorage OuterScanArray[OUTER_WARPS];
-
-
-    /// Shared memory storage layout type
-    struct _TempStorage
-    {
-        union
-        {
-            Uninitialized<OuterScanArray>           outer_warp_scan;  ///< Buffer for warp-synchronous outer scans
-            typename InnerWarpScanT::TempStorage    inner_warp_scan;  ///< Buffer for warp-synchronous inner scan
-        };
-        T                               warp_aggregates[OUTER_WARPS];
-        T                               block_aggregate;                           ///< Shared prefix for the entire threadblock
-    };
-
-
-    /// Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Per-thread fields
-    //---------------------------------------------------------------------
-
-    // Thread fields
-    _TempStorage    &temp_storage;
-    unsigned int    linear_tid;
-    unsigned int    warp_id;
-    unsigned int    lane_id;
-
-
-    //---------------------------------------------------------------------
-    // Constructors
-    //---------------------------------------------------------------------
-
-    /// Constructor
-    __device__ __forceinline__ BlockScanWarpScans(
-        TempStorage &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)),
-        warp_id((OUTER_WARPS == 1) ? 0 : linear_tid / OUTER_WARP_THREADS),
-        lane_id((OUTER_WARPS == 1) ? linear_tid : linear_tid % OUTER_WARP_THREADS)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Exclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        // Compute block-wide exclusive scan.  The exclusive output from tid0 is invalid.
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &exclusive_output,  ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        T block_aggregate;
-        ExclusiveScan(input, exclusive_output, initial_value, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.  With no initial value, the output computed for <em>thread</em><sub>0</sub> is undefined.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item
-        T               &exclusive_output,  ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        OuterWarpScanT(temp_storage.outer_warp_scan.Alias()[warp_id]).Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Share outer warp total
-        if (lane_id == OUTER_WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-
-        CTA_SYNC();
-
-        if (linear_tid < INNER_WARP_THREADS)
-        {
-            T outer_warp_input = temp_storage.warp_aggregates[linear_tid];
-            T outer_warp_exclusive;
-
-            InnerWarpScanT(temp_storage.inner_warp_scan).ExclusiveScan(
-                outer_warp_input, outer_warp_exclusive, scan_op, block_aggregate);
-
-            temp_storage.block_aggregate                = block_aggregate;
-            temp_storage.warp_aggregates[linear_tid]    = outer_warp_exclusive;
-        }
-
-        CTA_SYNC();
-
-        if (warp_id != 0)
-        {
-            // Retrieve block aggregate
-            block_aggregate = temp_storage.block_aggregate;
-
-            // Apply warp prefix to our lane's partial
-            T outer_warp_exclusive = temp_storage.warp_aggregates[warp_id];
-            exclusive_output = scan_op(outer_warp_exclusive, exclusive_output);
-            if (lane_id == 0)
-                exclusive_output = outer_warp_exclusive;
-        }
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input items
-        T               &exclusive_output,  ///< [out] Calling thread's output items (may be aliased to \p input)
-        const T         &initial_value,     ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &block_aggregate)   ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        OuterWarpScanT(temp_storage.outer_warp_scan.Alias()[warp_id]).Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Share outer warp total
-        if (lane_id == OUTER_WARP_THREADS - 1)
-        {
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-        }
-
-        CTA_SYNC();
-
-        if (linear_tid < INNER_WARP_THREADS)
-        {
-            T outer_warp_input = temp_storage.warp_aggregates[linear_tid];
-            T outer_warp_exclusive;
-
-            InnerWarpScanT(temp_storage.inner_warp_scan).ExclusiveScan(
-                outer_warp_input, outer_warp_exclusive, initial_value, scan_op, block_aggregate);
-
-            temp_storage.block_aggregate                = block_aggregate;
-            temp_storage.warp_aggregates[linear_tid]    = outer_warp_exclusive;
-        }
-
-        CTA_SYNC();
-
-        // Retrieve block aggregate
-        block_aggregate = temp_storage.block_aggregate;
-
-        // Apply warp prefix to our lane's partial
-        T outer_warp_exclusive = temp_storage.warp_aggregates[warp_id];
-        exclusive_output = scan_op(outer_warp_exclusive, exclusive_output);
-        if (lane_id == 0)
-            exclusive_output = outer_warp_exclusive;
-    }
-
-
-    /// Computes an exclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  The call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        T inclusive_output;
-        OuterWarpScanT(temp_storage.outer_warp_scan.Alias()[warp_id]).Scan(input, inclusive_output, exclusive_output, scan_op);
-
-        // Share outer warp total
-        if (lane_id == OUTER_WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-
-        CTA_SYNC();
-
-        if (linear_tid < INNER_WARP_THREADS)
-        {
-            InnerWarpScanT inner_scan(temp_storage.inner_warp_scan);
-
-            T upsweep = temp_storage.warp_aggregates[linear_tid];
-            T downsweep_prefix, block_aggregate;
-
-            inner_scan.ExclusiveScan(upsweep, downsweep_prefix, scan_op, block_aggregate);
-
-            // Use callback functor to get block prefix in lane0 and then broadcast to other lanes
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            block_prefix = inner_scan.Broadcast(block_prefix, 0);
-
-            downsweep_prefix = scan_op(block_prefix, downsweep_prefix);
-            if (linear_tid == 0)
-                downsweep_prefix = block_prefix;
-
-            temp_storage.warp_aggregates[linear_tid] = downsweep_prefix;
-        }
-
-        CTA_SYNC();
-
-        // Apply warp prefix to our lane's partial (or assign it if partial is invalid)
-        T outer_warp_exclusive = temp_storage.warp_aggregates[warp_id];
-        exclusive_output = scan_op(outer_warp_exclusive, exclusive_output);
-        if (lane_id == 0)
-            exclusive_output = outer_warp_exclusive;
-    }
-
-
-    //---------------------------------------------------------------------
-    // Inclusive scans
-    //---------------------------------------------------------------------
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op)                        ///< [in] Binary scan operator
-    {
-        T block_aggregate;
-        InclusiveScan(input, inclusive_output, scan_op, block_aggregate);
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,                          ///< [in] Calling thread's input item
-        T               &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp          scan_op,                        ///< [in] Binary scan operator
-        T               &block_aggregate)               ///< [out] Threadblock-wide aggregate reduction of input items
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        OuterWarpScanT(temp_storage.outer_warp_scan.Alias()[warp_id]).InclusiveScan(
-            input, inclusive_output, scan_op);
-
-        // Share outer warp total
-        if (lane_id == OUTER_WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-
-        CTA_SYNC();
-
-        if (linear_tid < INNER_WARP_THREADS)
-        {
-            T outer_warp_input = temp_storage.warp_aggregates[linear_tid];
-            T outer_warp_exclusive;
-
-            InnerWarpScanT(temp_storage.inner_warp_scan).ExclusiveScan(
-                outer_warp_input, outer_warp_exclusive, scan_op, block_aggregate);
-
-            temp_storage.block_aggregate                = block_aggregate;
-            temp_storage.warp_aggregates[linear_tid]    = outer_warp_exclusive;
-        }
-
-        CTA_SYNC();
-
-        if (warp_id != 0)
-        {
-            // Retrieve block aggregate
-            block_aggregate = temp_storage.block_aggregate;
-
-            // Apply warp prefix to our lane's partial
-            T outer_warp_exclusive = temp_storage.warp_aggregates[warp_id];
-            inclusive_output = scan_op(outer_warp_exclusive, inclusive_output);
-        }
-    }
-
-
-    /// Computes an inclusive threadblock-wide prefix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_prefix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically prefixes the threadblock's scan inputs.
-    template <
-        typename ScanOp,
-        typename BlockPrefixCallbackOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,                          ///< [in] Calling thread's input item
-        T                       &inclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
-        ScanOp                  scan_op,                        ///< [in] Binary scan operator
-        BlockPrefixCallbackOp   &block_prefix_callback_op)      ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a threadblock-wide prefix to be applied to all inputs.
-    {
-        // Compute warp scan in each warp.  The exclusive output from each lane0 is invalid.
-        OuterWarpScanT(temp_storage.outer_warp_scan.Alias()[warp_id]).InclusiveScan(
-            input, inclusive_output, scan_op);
-
-        // Share outer warp total
-        if (lane_id == OUTER_WARP_THREADS - 1)
-            temp_storage.warp_aggregates[warp_id] = inclusive_output;
-
-        CTA_SYNC();
-
-        if (linear_tid < INNER_WARP_THREADS)
-        {
-            InnerWarpScanT inner_scan(temp_storage.inner_warp_scan);
-
-            T upsweep = temp_storage.warp_aggregates[linear_tid];
-            T downsweep_prefix, block_aggregate;
-            inner_scan.ExclusiveScan(upsweep, downsweep_prefix, scan_op, block_aggregate);
-
-            // Use callback functor to get block prefix in lane0 and then broadcast to other lanes
-            T block_prefix = block_prefix_callback_op(block_aggregate);
-            block_prefix = inner_scan.Broadcast(block_prefix, 0);
-
-            downsweep_prefix = scan_op(block_prefix, downsweep_prefix);
-            if (linear_tid == 0)
-                downsweep_prefix = block_prefix;
-
-            temp_storage.warp_aggregates[linear_tid]    = downsweep_prefix;
-        }
-
-        CTA_SYNC();
-
-        // Apply warp prefix to our lane's partial
-        T outer_warp_exclusive = temp_storage.warp_aggregates[warp_id];
-        inclusive_output = scan_op(outer_warp_exclusive, inclusive_output);
-    }
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/cub.cuh b/ml-xgboost/cub/cub/cub.cuh
deleted file mode 100644
index 1150784..0000000
--- a/ml-xgboost/cub/cub/cub.cuh
+++ /dev/null
@@ -1,96 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * CUB umbrella include file
- */
-
-#pragma once
-
-
-// Block
-#include "block/block_histogram.cuh"
-#include "block/block_discontinuity.cuh"
-#include "block/block_exchange.cuh"
-#include "block/block_load.cuh"
-#include "block/block_radix_rank.cuh"
-#include "block/block_radix_sort.cuh"
-#include "block/block_reduce.cuh"
-#include "block/block_scan.cuh"
-#include "block/block_store.cuh"
-//#include "block/block_shift.cuh"
-
-// Device
-#include "device/device_histogram.cuh"
-#include "device/device_partition.cuh"
-#include "device/device_radix_sort.cuh"
-#include "device/device_reduce.cuh"
-#include "device/device_run_length_encode.cuh"
-#include "device/device_scan.cuh"
-#include "device/device_segmented_radix_sort.cuh"
-#include "device/device_segmented_reduce.cuh"
-#include "device/device_select.cuh"
-#include "device/device_spmv.cuh"
-
-// Grid
-//#include "grid/grid_barrier.cuh"
-#include "grid/grid_even_share.cuh"
-#include "grid/grid_mapping.cuh"
-#include "grid/grid_queue.cuh"
-
-// Thread
-#include "thread/thread_load.cuh"
-#include "thread/thread_operators.cuh"
-#include "thread/thread_reduce.cuh"
-#include "thread/thread_scan.cuh"
-#include "thread/thread_store.cuh"
-
-// Warp
-#include "warp/warp_reduce.cuh"
-#include "warp/warp_scan.cuh"
-
-// Iterator
-#include "iterator/arg_index_input_iterator.cuh"
-#include "iterator/cache_modified_input_iterator.cuh"
-#include "iterator/cache_modified_output_iterator.cuh"
-#include "iterator/constant_input_iterator.cuh"
-#include "iterator/counting_input_iterator.cuh"
-#include "iterator/tex_obj_input_iterator.cuh"
-#include "iterator/tex_ref_input_iterator.cuh"
-#include "iterator/transform_input_iterator.cuh"
-
-// Util
-#include "util_allocator.cuh"
-#include "util_arch.cuh"
-#include "util_debug.cuh"
-#include "util_device.cuh"
-#include "util_macro.cuh"
-#include "util_ptx.cuh"
-#include "util_type.cuh"
-
diff --git a/ml-xgboost/cub/cub/device/device_histogram.cuh b/ml-xgboost/cub/cub/device/device_histogram.cuh
deleted file mode 100644
index 9a4e099..0000000
--- a/ml-xgboost/cub/cub/device/device_histogram.cuh
+++ /dev/null
@@ -1,866 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceHistogram provides device-wide parallel operations for constructing histogram(s) from a sequence of samples data residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-#include <limits>
-
-#include "dispatch/dispatch_histogram.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceHistogram provides device-wide parallel operations for constructing histogram(s) from a sequence of samples data residing within device-accessible memory. ![](histogram_logo.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * A <a href="http://en.wikipedia.org/wiki/Histogram"><em>histogram</em></a>
- * counts the number of observations that fall into each of the disjoint categories (known as <em>bins</em>).
- *
- * \par Usage Considerations
- * \cdp_class{DeviceHistogram}
- *
- */
-struct DeviceHistogram
-{
-    /******************************************************************//**
-     * \name Evenly-segmented bin ranges
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Computes an intensity histogram from a sequence of data samples using equal-width bins.
-     *
-     * \par
-     * - The number of histogram bins is (\p num_levels - 1)
-     * - All bins comprise the same width of sample values: (\p upper_level - \p lower_level) / (\p num_levels - 1)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of a six-bin histogram
-     * from a sequence of float samples
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples and
-     * // output histogram
-     * int      num_samples;    // e.g., 10
-     * float*   d_samples;      // e.g., [2.2, 6.0, 7.1, 2.9, 3.5, 0.3, 2.9, 2.0, 6.1, 999.5]
-     * int*     d_histogram;    // e.g., [ -, -, -, -, -, -, -, -]
-     * int      num_levels;     // e.g., 7       (seven level boundaries for six bins)
-     * float    lower_level;    // e.g., 0.0     (lower sample value boundary of lowest bin)
-     * float    upper_level;    // e.g., 12.0    (upper sample value boundary of upper bin)
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::HistogramEven(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level, num_samples);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::HistogramEven(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level, num_samples);
-     *
-     * // d_histogram   <-- [1, 0, 5, 0, 3, 0, 0, 0];
-     *
-     * \endcode
-     *
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t HistogramEven(
-        void*               d_temp_storage,                             ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the input sequence of data samples.
-        CounterT*           d_histogram,                                ///< [out] The pointer to the histogram counter output array of length <tt>num_levels</tt> - 1.
-        int                 num_levels,                                 ///< [in] The number of boundaries (levels) for delineating histogram samples.  Implies that the number of bins is <tt>num_levels</tt> - 1.
-        LevelT              lower_level,                                ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin.
-        LevelT              upper_level,                                ///< [in] The upper sample value bound (exclusive) for the highest histogram bin.
-        OffsetT             num_samples,                                ///< [in] The number of input samples (i.e., the length of \p d_samples)
-        cudaStream_t        stream                  = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous       = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        /// The sample value type of the input iterator
-        typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-        CounterT*           d_histogram1[1]     = {d_histogram};
-        int                 num_levels1[1]      = {num_levels};
-        LevelT              lower_level1[1]     = {lower_level};
-        LevelT              upper_level1[1]     = {upper_level};
-
-        return MultiHistogramEven<1, 1>(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_samples,
-            d_histogram1,
-            num_levels1,
-            lower_level1,
-            upper_level1,
-            num_samples,
-            1,
-            sizeof(SampleT) * num_samples,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes an intensity histogram from a sequence of data samples using equal-width bins.
-     *
-     * \par
-     * - A two-dimensional <em>region of interest</em> within \p d_samples can be specified
-     *   using the \p num_row_samples, num_rows, and \p row_stride_bytes parameters.
-     * - The row stride must be a whole multiple of the sample data type
-     *   size, i.e., <tt>(row_stride_bytes % sizeof(SampleT)) == 0</tt>.
-     * - The number of histogram bins is (\p num_levels - 1)
-     * - All bins comprise the same width of sample values: (\p upper_level - \p lower_level) / (\p num_levels - 1)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of a six-bin histogram
-     * from a 2x5 region of interest within a flattened 2x7 array of float samples.
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples and
-     * // output histogram
-     * int      num_row_samples;    // e.g., 5
-     * int      num_rows;           // e.g., 2;
-     * size_t   row_stride_bytes;   // e.g., 7 * sizeof(float)
-     * float*   d_samples;          // e.g., [2.2, 6.0, 7.1, 2.9, 3.5,   -, -,
-     *                              //        0.3, 2.9, 2.0, 6.1, 999.5, -, -]
-     * int*     d_histogram;        // e.g., [ -, -, -, -, -, -, -, -]
-     * int      num_levels;         // e.g., 7       (seven level boundaries for six bins)
-     * float    lower_level;        // e.g., 0.0     (lower sample value boundary of lowest bin)
-     * float    upper_level;        // e.g., 12.0    (upper sample value boundary of upper bin)
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage  = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::HistogramEven(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level,
-     *     num_row_samples, num_rows, row_stride_bytes);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::HistogramEven(d_temp_storage, temp_storage_bytes, d_samples, d_histogram,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level,
-     *     num_row_samples, num_rows, row_stride_bytes);
-     *
-     * // d_histogram   <-- [1, 0, 5, 0, 3, 0, 0, 0];
-     *
-     * \endcode
-     *
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t HistogramEven(
-        void*               d_temp_storage,                             ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the input sequence of data samples.
-        CounterT*           d_histogram,                                ///< [out] The pointer to the histogram counter output array of length <tt>num_levels</tt> - 1.
-        int                 num_levels,                                 ///< [in] The number of boundaries (levels) for delineating histogram samples.  Implies that the number of bins is <tt>num_levels</tt> - 1.
-        LevelT              lower_level,                                ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin.
-        LevelT              upper_level,                                ///< [in] The upper sample value bound (exclusive) for the highest histogram bin.
-        OffsetT             num_row_samples,                            ///< [in] The number of data samples per row in the region of interest
-        OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-        size_t              row_stride_bytes,                           ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-        cudaStream_t        stream                  = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous       = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        CounterT*           d_histogram1[1]     = {d_histogram};
-        int                 num_levels1[1]      = {num_levels};
-        LevelT              lower_level1[1]     = {lower_level};
-        LevelT              upper_level1[1]     = {upper_level};
-
-        return MultiHistogramEven<1, 1>(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_samples,
-            d_histogram1,
-            num_levels1,
-            lower_level1,
-            upper_level1,
-            num_row_samples,
-            num_rows,
-            row_stride_bytes,
-            stream,
-            debug_synchronous);
-    }
-
-    /**
-     * \brief Computes per-channel intensity histograms from a sequence of multi-channel "pixel" data samples using equal-width bins.
-     *
-     * \par
-     * - The input is a sequence of <em>pixel</em> structures, where each pixel comprises
-     *   a record of \p NUM_CHANNELS consecutive data samples (e.g., an <em>RGBA</em> pixel).
-     * - Of the \p NUM_CHANNELS specified, the function will only compute histograms
-     *   for the first \p NUM_ACTIVE_CHANNELS (e.g., only <em>RGB</em> histograms from <em>RGBA</em>
-     *   pixel samples).
-     * - The number of histogram bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-     * - For channel<sub><em>i</em></sub>, the range of values for all histogram bins
-     *   have the same width: (<tt>upper_level[i]</tt> - <tt>lower_level[i]</tt>) / (<tt> num_levels[i]</tt> - 1)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of three 256-bin <em>RGB</em> histograms
-     * from a quad-channel sequence of <em>RGBA</em> pixels (8 bits per channel per pixel)
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples
-     * // and output histograms
-     * int              num_pixels;         // e.g., 5
-     * unsigned char*   d_samples;          // e.g., [(2, 6, 7, 5), (3, 0, 2, 1), (7, 0, 6, 2),
-     *                                      //        (0, 6, 7, 5), (3, 0, 2, 6)]
-     * int*             d_histogram[3];     // e.g., three device pointers to three device buffers,
-     *                                      //       each allocated with 256 integer counters
-     * int              num_levels[3];      // e.g., {257, 257, 257};
-     * unsigned int     lower_level[3];     // e.g., {0, 0, 0};
-     * unsigned int     upper_level[3];     // e.g., {256, 256, 256};
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::MultiHistogramEven<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level, num_pixels);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::MultiHistogramEven<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level, num_pixels);
-     *
-     * // d_histogram   <-- [ [1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, ..., 0],
-     * //                     [0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, ..., 0],
-     * //                     [0, 0, 2, 0, 0, 0, 1, 2, 0, 0, 0, ..., 0] ]
-     *
-     * \endcode
-     *
-     * \tparam NUM_CHANNELS             Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
-     * \tparam NUM_ACTIVE_CHANNELS      <b>[inferred]</b> Number of channels actively being histogrammed
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        int                 NUM_CHANNELS,
-        int                 NUM_ACTIVE_CHANNELS,
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t MultiHistogramEven(
-        void*               d_temp_storage,                             ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four <em>RGBA</em> 8-bit samples).
-        CounterT*           d_histogram[NUM_ACTIVE_CHANNELS],           ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histogram[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-        int                 num_levels[NUM_ACTIVE_CHANNELS],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-        LevelT              lower_level[NUM_ACTIVE_CHANNELS],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-        LevelT              upper_level[NUM_ACTIVE_CHANNELS],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-        OffsetT             num_pixels,                                 ///< [in] The number of multi-channel pixels (i.e., the length of \p d_samples / NUM_CHANNELS)
-        cudaStream_t        stream                  = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous       = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        /// The sample value type of the input iterator
-        typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-        return MultiHistogramEven<NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_samples,
-            d_histogram,
-            num_levels,
-            lower_level,
-            upper_level,
-            num_pixels,
-            1,
-            sizeof(SampleT) * NUM_CHANNELS * num_pixels,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes per-channel intensity histograms from a sequence of multi-channel "pixel" data samples using equal-width bins.
-     *
-     * \par
-     * - The input is a sequence of <em>pixel</em> structures, where each pixel comprises
-     *   a record of \p NUM_CHANNELS consecutive data samples (e.g., an <em>RGBA</em> pixel).
-     * - Of the \p NUM_CHANNELS specified, the function will only compute histograms
-     *   for the first \p NUM_ACTIVE_CHANNELS (e.g., only <em>RGB</em> histograms from <em>RGBA</em>
-     *   pixel samples).
-     * - A two-dimensional <em>region of interest</em> within \p d_samples can be specified
-     *   using the \p num_row_samples, num_rows, and \p row_stride_bytes parameters.
-     * - The row stride must be a whole multiple of the sample data type
-     *   size, i.e., <tt>(row_stride_bytes % sizeof(SampleT)) == 0</tt>.
-     * - The number of histogram bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-     * - For channel<sub><em>i</em></sub>, the range of values for all histogram bins
-     *   have the same width: (<tt>upper_level[i]</tt> - <tt>lower_level[i]</tt>) / (<tt> num_levels[i]</tt> - 1)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of three 256-bin <em>RGB</em> histograms from a 2x3 region of
-     * interest of within a flattened 2x4 array of quad-channel <em>RGBA</em> pixels (8 bits per channel per pixel).
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples
-     * // and output histograms
-     * int              num_row_pixels;     // e.g., 3
-     * int              num_rows;           // e.g., 2
-     * size_t           row_stride_bytes;   // e.g., 4 * sizeof(unsigned char) * NUM_CHANNELS
-     * unsigned char*   d_samples;          // e.g., [(2, 6, 7, 5), (3, 0, 2, 1), (7, 0, 6, 2), (-, -, -, -),
-     *                                      //        (0, 6, 7, 5), (3, 0, 2, 6), (1, 1, 1, 1), (-, -, -, -)]
-     * int*             d_histogram[3];     // e.g., three device pointers to three device buffers,
-     *                                      //       each allocated with 256 integer counters
-     * int              num_levels[3];      // e.g., {257, 257, 257};
-     * unsigned int     lower_level[3];     // e.g., {0, 0, 0};
-     * unsigned int     upper_level[3];     // e.g., {256, 256, 256};
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::MultiHistogramEven<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level,
-     *     num_row_pixels, num_rows, row_stride_bytes);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::MultiHistogramEven<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, lower_level, upper_level,
-     *     num_row_pixels, num_rows, row_stride_bytes);
-     *
-     * // d_histogram   <-- [ [1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, ..., 0],
-     * //                     [0, 4, 0, 0, 0, 0, 2, 0, 0, 0, 0, ..., 0],
-     * //                     [0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, ..., 0] ]
-     *
-     * \endcode
-     *
-     * \tparam NUM_CHANNELS             Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
-     * \tparam NUM_ACTIVE_CHANNELS      <b>[inferred]</b> Number of channels actively being histogrammed
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        int                 NUM_CHANNELS,
-        int                 NUM_ACTIVE_CHANNELS,
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t MultiHistogramEven(
-        void*               d_temp_storage,                             ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four <em>RGBA</em> 8-bit samples).
-        CounterT*           d_histogram[NUM_ACTIVE_CHANNELS],           ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histogram[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-        int                 num_levels[NUM_ACTIVE_CHANNELS],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-        LevelT              lower_level[NUM_ACTIVE_CHANNELS],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-        LevelT              upper_level[NUM_ACTIVE_CHANNELS],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-        OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-        size_t              row_stride_bytes,                           ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-        cudaStream_t        stream                  = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous       = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        /// The sample value type of the input iterator
-        typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-        Int2Type<sizeof(SampleT) == 1> is_byte_sample;
-
-        if ((sizeof(OffsetT) > sizeof(int)) &&
-            ((unsigned long long) (num_rows * row_stride_bytes) < (unsigned long long) std::numeric_limits<int>::max()))
-        {
-            // Down-convert OffsetT data type
-
-
-            return DipatchHistogram<NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, LevelT, int>::DispatchEven(
-                d_temp_storage, temp_storage_bytes, d_samples, d_histogram, num_levels, lower_level, upper_level,
-                (int) num_row_pixels, (int) num_rows, (int) (row_stride_bytes / sizeof(SampleT)),
-                stream, debug_synchronous, is_byte_sample);
-        }
-
-        return DipatchHistogram<NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, LevelT, OffsetT>::DispatchEven(
-            d_temp_storage, temp_storage_bytes, d_samples, d_histogram, num_levels, lower_level, upper_level,
-            num_row_pixels, num_rows, (OffsetT) (row_stride_bytes / sizeof(SampleT)),
-            stream, debug_synchronous, is_byte_sample);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Custom bin ranges
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Computes an intensity histogram from a sequence of data samples using the specified bin boundary levels.
-     *
-     * \par
-     * - The number of histogram bins is (\p num_levels - 1)
-     * - The value range for bin<sub><em>i</em></sub> is [<tt>level[i]</tt>, <tt>level[i+1]</tt>)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of an six-bin histogram
-     * from a sequence of float samples
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples and
-     * // output histogram
-     * int      num_samples;    // e.g., 10
-     * float*   d_samples;      // e.g., [2.2, 6.0, 7.1, 2.9, 3.5, 0.3, 2.9, 2.0, 6.1, 999.5]
-     * int*     d_histogram;    // e.g., [ -, -, -, -, -, -, -, -]
-     * int      num_levels      // e.g., 7 (seven level boundaries for six bins)
-     * float*   d_levels;       // e.g., [0.0, 2.0, 4.0, 6.0, 8.0, 12.0, 16.0]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::HistogramRange(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels, num_samples);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::HistogramRange(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels, num_samples);
-     *
-     * // d_histogram   <-- [1, 0, 5, 0, 3, 0, 0, 0];
-     *
-     * \endcode
-     *
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t HistogramRange(
-        void*               d_temp_storage,                         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                              ///< [in] The pointer to the input sequence of data samples.
-        CounterT*           d_histogram,                            ///< [out] The pointer to the histogram counter output array of length <tt>num_levels</tt> - 1.
-        int                 num_levels,                             ///< [in] The number of boundaries (levels) for delineating histogram samples.  Implies that the number of bins is <tt>num_levels</tt> - 1.
-        LevelT*             d_levels,                               ///< [in] The pointer to the array of boundaries (levels).  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-        OffsetT             num_samples,                            ///< [in] The number of data samples per row in the region of interest
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        /// The sample value type of the input iterator
-        typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-        CounterT*           d_histogram1[1] = {d_histogram};
-        int                 num_levels1[1]  = {num_levels};
-        LevelT*             d_levels1[1]    = {d_levels};
-
-        return MultiHistogramRange<1, 1>(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_samples,
-            d_histogram1,
-            num_levels1,
-            d_levels1,
-            num_samples,
-            1,
-            sizeof(SampleT) * num_samples,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes an intensity histogram from a sequence of data samples using the specified bin boundary levels.
-     *
-     * \par
-     * - A two-dimensional <em>region of interest</em> within \p d_samples can be specified
-     *   using the \p num_row_samples, num_rows, and \p row_stride_bytes parameters.
-     * - The row stride must be a whole multiple of the sample data type
-     *   size, i.e., <tt>(row_stride_bytes % sizeof(SampleT)) == 0</tt>.
-     * - The number of histogram bins is (\p num_levels - 1)
-     * - The value range for bin<sub><em>i</em></sub> is [<tt>level[i]</tt>, <tt>level[i+1]</tt>)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of a six-bin histogram
-     * from a 2x5 region of interest within a flattened 2x7 array of float samples.
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples and
-     * // output histogram
-     * int      num_row_samples;    // e.g., 5
-     * int      num_rows;           // e.g., 2;
-     * int      row_stride_bytes;   // e.g., 7 * sizeof(float)
-     * float*   d_samples;          // e.g., [2.2, 6.0, 7.1, 2.9, 3.5,   -, -,
-     *                              //        0.3, 2.9, 2.0, 6.1, 999.5, -, -]
-     * int*     d_histogram;        // e.g., [ , , , , , , , ]
-     * int      num_levels          // e.g., 7 (seven level boundaries for six bins)
-     * float    *d_levels;          // e.g., [0.0, 2.0, 4.0, 6.0, 8.0, 12.0, 16.0]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::HistogramRange(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels,
-     *     num_row_samples, num_rows, row_stride_bytes);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::HistogramRange(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels,
-     *     num_row_samples, num_rows, row_stride_bytes);
-     *
-     * // d_histogram   <-- [1, 0, 5, 0, 3, 0, 0, 0];
-     *
-     * \endcode
-     *
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t HistogramRange(
-        void*               d_temp_storage,                         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                              ///< [in] The pointer to the input sequence of data samples.
-        CounterT*           d_histogram,                            ///< [out] The pointer to the histogram counter output array of length <tt>num_levels</tt> - 1.
-        int                 num_levels,                             ///< [in] The number of boundaries (levels) for delineating histogram samples.  Implies that the number of bins is <tt>num_levels</tt> - 1.
-        LevelT*             d_levels,                               ///< [in] The pointer to the array of boundaries (levels).  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-        OffsetT             num_row_samples,                        ///< [in] The number of data samples per row in the region of interest
-        OffsetT             num_rows,                               ///< [in] The number of rows in the region of interest
-        size_t              row_stride_bytes,                       ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        CounterT*           d_histogram1[1]     = {d_histogram};
-        int                 num_levels1[1]      = {num_levels};
-        LevelT*             d_levels1[1]        = {d_levels};
-
-        return MultiHistogramRange<1, 1>(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_samples,
-            d_histogram1,
-            num_levels1,
-            d_levels1,
-            num_row_samples,
-            num_rows,
-            row_stride_bytes,
-            stream,
-            debug_synchronous);
-    }
-
-    /**
-     * \brief Computes per-channel intensity histograms from a sequence of multi-channel "pixel" data samples using the specified bin boundary levels.
-     *
-     * \par
-     * - The input is a sequence of <em>pixel</em> structures, where each pixel comprises
-     *   a record of \p NUM_CHANNELS consecutive data samples (e.g., an <em>RGBA</em> pixel).
-     * - Of the \p NUM_CHANNELS specified, the function will only compute histograms
-     *   for the first \p NUM_ACTIVE_CHANNELS (e.g., <em>RGB</em> histograms from <em>RGBA</em>
-     *   pixel samples).
-     * - The number of histogram bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-     * - For channel<sub><em>i</em></sub>, the range of values for all histogram bins
-     *   have the same width: (<tt>upper_level[i]</tt> - <tt>lower_level[i]</tt>) / (<tt> num_levels[i]</tt> - 1)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of three 4-bin <em>RGB</em> histograms
-     * from a quad-channel sequence of <em>RGBA</em> pixels (8 bits per channel per pixel)
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples
-     * // and output histograms
-     * int            num_pixels;       // e.g., 5
-     * unsigned char  *d_samples;       // e.g., [(2, 6, 7, 5),(3, 0, 2, 1),(7, 0, 6, 2),
-     *                                  //        (0, 6, 7, 5),(3, 0, 2, 6)]
-     * unsigned int   *d_histogram[3];  // e.g., [[ -, -, -, -],[ -, -, -, -],[ -, -, -, -]];
-     * int            num_levels[3];    // e.g., {5, 5, 5};
-     * unsigned int   *d_levels[3];     // e.g., [ [0, 2, 4, 6, 8],
-     *                                  //         [0, 2, 4, 6, 8],
-     *                                  //         [0, 2, 4, 6, 8] ];
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::MultiHistogramRange<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels, num_pixels);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::MultiHistogramRange<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels, num_pixels);
-     *
-     * // d_histogram   <-- [ [1, 3, 0, 1],
-     * //                     [3, 0, 0, 2],
-     * //                     [0, 2, 0, 3] ]
-     *
-     * \endcode
-     *
-     * \tparam NUM_CHANNELS             Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
-     * \tparam NUM_ACTIVE_CHANNELS      <b>[inferred]</b> Number of channels actively being histogrammed
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        int                 NUM_CHANNELS,
-        int                 NUM_ACTIVE_CHANNELS,
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t MultiHistogramRange(
-        void*               d_temp_storage,                         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                              ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four <em>RGBA</em> 8-bit samples).
-        CounterT*           d_histogram[NUM_ACTIVE_CHANNELS],       ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histogram[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-        int                 num_levels[NUM_ACTIVE_CHANNELS],        ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-        LevelT*             d_levels[NUM_ACTIVE_CHANNELS],          ///< [in] The pointers to the arrays of boundaries (levels), one for each active channel.  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-        OffsetT             num_pixels,                             ///< [in] The number of multi-channel pixels (i.e., the length of \p d_samples / NUM_CHANNELS)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        /// The sample value type of the input iterator
-        typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-        return MultiHistogramRange<NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_samples,
-            d_histogram,
-            num_levels,
-            d_levels,
-            num_pixels,
-            1,
-            sizeof(SampleT) * NUM_CHANNELS * num_pixels,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes per-channel intensity histograms from a sequence of multi-channel "pixel" data samples using the specified bin boundary levels.
-     *
-     * \par
-     * - The input is a sequence of <em>pixel</em> structures, where each pixel comprises
-     *   a record of \p NUM_CHANNELS consecutive data samples (e.g., an <em>RGBA</em> pixel).
-     * - Of the \p NUM_CHANNELS specified, the function will only compute histograms
-     *   for the first \p NUM_ACTIVE_CHANNELS (e.g., <em>RGB</em> histograms from <em>RGBA</em>
-     *   pixel samples).
-     * - A two-dimensional <em>region of interest</em> within \p d_samples can be specified
-     *   using the \p num_row_samples, num_rows, and \p row_stride_bytes parameters.
-     * - The row stride must be a whole multiple of the sample data type
-     *   size, i.e., <tt>(row_stride_bytes % sizeof(SampleT)) == 0</tt>.
-     * - The number of histogram bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-     * - For channel<sub><em>i</em></sub>, the range of values for all histogram bins
-     *   have the same width: (<tt>upper_level[i]</tt> - <tt>lower_level[i]</tt>) / (<tt> num_levels[i]</tt> - 1)
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the computation of three 4-bin <em>RGB</em> histograms from a 2x3 region of
-     * interest of within a flattened 2x4 array of quad-channel <em>RGBA</em> pixels (8 bits per channel per pixel).
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_histogram.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input samples
-     * // and output histograms
-     * int              num_row_pixels;     // e.g., 3
-     * int              num_rows;           // e.g., 2
-     * size_t           row_stride_bytes;   // e.g., 4 * sizeof(unsigned char) * NUM_CHANNELS
-     * unsigned char*   d_samples;          // e.g., [(2, 6, 7, 5),(3, 0, 2, 1),(1, 1, 1, 1),(-, -, -, -),
-     *                                      //        (7, 0, 6, 2),(0, 6, 7, 5),(3, 0, 2, 6),(-, -, -, -)]
-     * int*             d_histogram[3];     // e.g., [[ -, -, -, -],[ -, -, -, -],[ -, -, -, -]];
-     * int              num_levels[3];      // e.g., {5, 5, 5};
-     * unsigned int*    d_levels[3];        // e.g., [ [0, 2, 4, 6, 8],
-     *                                      //         [0, 2, 4, 6, 8],
-     *                                      //         [0, 2, 4, 6, 8] ];
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceHistogram::MultiHistogramRange<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels, num_row_pixels, num_rows, row_stride_bytes);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Compute histograms
-     * cub::DeviceHistogram::MultiHistogramRange<4, 3>(d_temp_storage, temp_storage_bytes,
-     *     d_samples, d_histogram, num_levels, d_levels, num_row_pixels, num_rows, row_stride_bytes);
-     *
-     * // d_histogram   <-- [ [2, 3, 0, 1],
-     * //                     [3, 0, 0, 2],
-     * //                     [1, 2, 0, 3] ]
-     *
-     * \endcode
-     *
-     * \tparam NUM_CHANNELS             Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
-     * \tparam NUM_ACTIVE_CHANNELS      <b>[inferred]</b> Number of channels actively being histogrammed
-     * \tparam SampleIteratorT          <b>[inferred]</b> Random-access input iterator type for reading input samples. \iterator
-     * \tparam CounterT                 <b>[inferred]</b> Integer type for histogram bin counters
-     * \tparam LevelT                   <b>[inferred]</b> Type for specifying boundaries (levels)
-     * \tparam OffsetT                  <b>[inferred]</b> Signed integer type for sequence offsets, list lengths, pointer differences, etc.  \offset_size1
-     */
-    template <
-        int                 NUM_CHANNELS,
-        int                 NUM_ACTIVE_CHANNELS,
-        typename            SampleIteratorT,
-        typename            CounterT,
-        typename            LevelT,
-        typename            OffsetT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t MultiHistogramRange(
-        void*               d_temp_storage,                         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                              ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four <em>RGBA</em> 8-bit samples).
-        CounterT*           d_histogram[NUM_ACTIVE_CHANNELS],       ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histogram[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-        int                 num_levels[NUM_ACTIVE_CHANNELS],        ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-        LevelT*             d_levels[NUM_ACTIVE_CHANNELS],          ///< [in] The pointers to the arrays of boundaries (levels), one for each active channel.  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-        OffsetT             num_row_pixels,                         ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                               ///< [in] The number of rows in the region of interest
-        size_t              row_stride_bytes,                       ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        /// The sample value type of the input iterator
-        typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-        Int2Type<sizeof(SampleT) == 1> is_byte_sample;
-
-        if ((sizeof(OffsetT) > sizeof(int)) &&
-            ((unsigned long long) (num_rows * row_stride_bytes) < (unsigned long long) std::numeric_limits<int>::max()))
-        {
-            // Down-convert OffsetT data type
-            return DipatchHistogram<NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, LevelT, int>::DispatchRange(
-                d_temp_storage, temp_storage_bytes, d_samples, d_histogram, num_levels, d_levels,
-                (int) num_row_pixels, (int) num_rows, (int) (row_stride_bytes / sizeof(SampleT)),
-                stream, debug_synchronous, is_byte_sample);
-        }
-
-        return DipatchHistogram<NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, LevelT, OffsetT>::DispatchRange(
-            d_temp_storage, temp_storage_bytes, d_samples, d_histogram, num_levels, d_levels,
-            num_row_pixels, num_rows, (OffsetT) (row_stride_bytes / sizeof(SampleT)),
-            stream, debug_synchronous, is_byte_sample);
-    }
-
-
-
-    //@}  end member group
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_partition.cuh b/ml-xgboost/cub/cub/device/device_partition.cuh
deleted file mode 100644
index d51969e..0000000
--- a/ml-xgboost/cub/cub/device/device_partition.cuh
+++ /dev/null
@@ -1,273 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DevicePartition provides device-wide, parallel operations for partitioning sequences of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch/dispatch_select_if.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DevicePartition provides device-wide, parallel operations for partitioning sequences of data items residing within device-accessible memory. ![](partition_logo.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * These operations apply a selection criterion to construct a partitioned output sequence from items selected/unselected from
- * a specified input sequence.
- *
- * \par Usage Considerations
- * \cdp_class{DevicePartition}
- *
- * \par Performance
- * \linear_performance{partition}
- *
- * \par
- * The following chart illustrates DevicePartition::If
- * performance across different CUDA architectures for \p int32 items,
- * where 50% of the items are randomly selected for the first partition.
- * \plots_below
- *
- * \image html partition_if_int32_50_percent.png
- *
- */
-struct DevicePartition
-{
-    /**
-     * \brief Uses the \p d_flags sequence to split the corresponding items from \p d_in into a partitioned sequence \p d_out.  The total number of items copied into the first partition is written to \p d_num_selected_out. ![](partition_flags_logo.png)
-     *
-     * \par
-     * - The value type of \p d_flags must be castable to \p bool (e.g., \p bool, \p char, \p int, etc.).
-     * - Copies of the selected items are compacted into \p d_out and maintain their original
-     *   relative ordering, however copies of the unselected items are compacted into the
-     *   rear of \p d_out in reverse order.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the compaction of items selected from an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>       // or equivalently <cub/device/device_partition.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input, flags, and output
-     * int  num_items;              // e.g., 8
-     * int  *d_in;                  // e.g., [1, 2, 3, 4, 5, 6, 7, 8]
-     * char *d_flags;               // e.g., [1, 0, 0, 1, 0, 1, 1, 0]
-     * int  *d_out;                 // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int  *d_num_selected_out;    // e.g., [ ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DevicePartition::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run selection
-     * cub::DevicePartition::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items);
-     *
-     * // d_out                 <-- [1, 4, 6, 7, 8, 5, 3, 2]
-     * // d_num_selected_out    <-- [4]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam FlagIterator         <b>[inferred]</b> Random-access input iterator type for reading selection flags \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Random-access output iterator type for writing output items \iterator
-     * \tparam NumSelectedIteratorT  <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    FlagIterator,
-        typename                    OutputIteratorT,
-        typename                    NumSelectedIteratorT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Flagged(
-        void*               d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        FlagIterator                d_flags,                        ///< [in] Pointer to the input sequence of selection flags
-        OutputIteratorT             d_out,                          ///< [out] Pointer to the output sequence of partitioned data items
-        NumSelectedIteratorT        d_num_selected_out,             ///< [out] Pointer to the output total number of items selected (i.e., the offset of the unselected partition)
-        int                         num_items,                      ///< [in] Total number of items to select from
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int                     OffsetT;         // Signed integer type for global offsets
-        typedef NullType                SelectOp;       // Selection op (not used)
-        typedef NullType                EqualityOp;     // Equality operator (not used)
-
-        return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, true>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_flags,
-            d_out,
-            d_num_selected_out,
-            SelectOp(),
-            EqualityOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Uses the \p select_op functor to split the corresponding items from \p d_in into a partitioned sequence \p d_out.  The total number of items copied into the first partition is written to \p d_num_selected_out. ![](partition_logo.png)
-     *
-     * \par
-     * - Copies of the selected items are compacted into \p d_out and maintain their original
-     *   relative ordering, however copies of the unselected items are compacted into the
-     *   rear of \p d_out in reverse order.
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated partition-if performance across different
-     * CUDA architectures for \p int32 and \p int64 items, respectively.  Items are
-     * selected for the first partition with 50% probability.
-     *
-     * \image html partition_if_int32_50_percent.png
-     * \image html partition_if_int64_50_percent.png
-     *
-     * \par
-     * The following charts are similar, but 5% selection probability for the first partition:
-     *
-     * \image html partition_if_int32_5_percent.png
-     * \image html partition_if_int64_5_percent.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the compaction of items selected from an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_partition.cuh>
-     *
-     * // Functor type for selecting values less than some criteria
-     * struct LessThan
-     * {
-     *     int compare;
-     *
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     LessThan(int compare) : compare(compare) {}
-     *
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     bool operator()(const int &a) const {
-     *         return (a < compare);
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int      num_items;              // e.g., 8
-     * int      *d_in;                  // e.g., [0, 2, 3, 9, 5, 2, 81, 8]
-     * int      *d_out;                 // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int      *d_num_selected_out;    // e.g., [ ]
-     * LessThan select_op(7);
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run selection
-     * cub::DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op);
-     *
-     * // d_out                 <-- [0, 2, 3, 5, 2, 8, 81, 9]
-     * // d_num_selected_out    <-- [5]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Random-access output iterator type for writing output items \iterator
-     * \tparam NumSelectedIteratorT  <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
-     * \tparam SelectOp             <b>[inferred]</b> Selection functor type having member <tt>bool operator()(const T &a)</tt>
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT,
-        typename                    NumSelectedIteratorT,
-        typename                    SelectOp>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t If(
-        void*               d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                          ///< [out] Pointer to the output sequence of partitioned data items
-        NumSelectedIteratorT        d_num_selected_out,             ///< [out] Pointer to the output total number of items selected (i.e., the offset of the unselected partition)
-        int                         num_items,                      ///< [in] Total number of items to select from
-        SelectOp                    select_op,                      ///< [in] Unary selection operator
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int                     OffsetT;         // Signed integer type for global offsets
-        typedef NullType*               FlagIterator;   // FlagT iterator type (not used)
-        typedef NullType                EqualityOp;     // Equality operator (not used)
-
-        return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, true>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            NULL,
-            d_out,
-            d_num_selected_out,
-            select_op,
-            EqualityOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-};
-
-/**
- * \example example_device_partition_flagged.cu
- * \example example_device_partition_if.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_radix_sort.cuh b/ml-xgboost/cub/cub/device/device_radix_sort.cuh
deleted file mode 100644
index 1cd2609..0000000
--- a/ml-xgboost/cub/cub/device/device_radix_sort.cuh
+++ /dev/null
@@ -1,796 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceRadixSort provides device-wide, parallel operations for computing a radix sort across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch/dispatch_radix_sort.cuh"
-#include "../util_arch.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceRadixSort provides device-wide, parallel operations for computing a radix sort across a sequence of data items residing within device-accessible memory. ![](sorting_logo.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * The [<em>radix sorting method</em>](http://en.wikipedia.org/wiki/Radix_sort) arranges
- * items into ascending (or descending) order.  The algorithm relies upon a positional representation for
- * keys, i.e., each key is comprised of an ordered sequence of symbols (e.g., digits,
- * characters, etc.) specified from least-significant to most-significant.  For a
- * given input sequence of keys and a set of rules specifying a total ordering
- * of the symbolic alphabet, the radix sorting method produces a lexicographic
- * ordering of those keys.
- *
- * \par
- * DeviceRadixSort can sort all of the built-in C++ numeric primitive types, e.g.:
- * <tt>unsigned char</tt>, \p int, \p double, etc.  Although the direct radix sorting
- * method can only be applied to unsigned integral types, DeviceRadixSort
- * is able to sort signed and floating-point types via simple bit-wise transformations
- * that ensure lexicographic key ordering.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceRadixSort}
- *
- * \par Performance
- * \linear_performance{radix sort} The following chart illustrates DeviceRadixSort::SortKeys
- * performance across different CUDA architectures for uniform-random \p uint32 keys.
- * \plots_below
- *
- * \image html lsb_radix_sort_int32_keys.png
- *
- */
-struct DeviceRadixSort
-{
-
-    /******************************************************************//**
-     * \name KeyT-value pairs
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Sorts key-value pairs into ascending order. (~<em>2N </em>auxiliary storage required)
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated sorting performance across different
-     * CUDA architectures for uniform-random <tt>uint32,uint32</tt> and
-     * <tt>uint64,uint64</tt> pairs, respectively.
-     *
-     * \image html lsb_radix_sort_int32_pairs.png
-     * \image html lsb_radix_sort_int64_pairs.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [        ...        ]
-     * int  *d_values_in;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_values_out;      // e.g., [        ...        ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out, num_items);
-     *
-     * // d_keys_out            <-- [0, 3, 5, 6, 7, 8, 9]
-     * // d_values_out          <-- [5, 4, 3, 1, 2, 0, 6]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     * \tparam ValueT    <b>[inferred]</b> ValueT type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairs(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] Pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] Pointer to the sorted output sequence of key data
-        const ValueT        *d_values_in,                           ///< [in] Pointer to the corresponding input sequence of associated value items
-        ValueT              *d_values_out,                          ///< [out] Pointer to the correspondingly-reordered output sequence of associated value items
-        int                 num_items,                              ///< [in] Number of items to sort
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        DoubleBuffer<KeyT>       d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<ValueT>     d_values(const_cast<ValueT*>(d_values_in), d_values_out);
-
-        return DispatchRadixSort<false, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts key-value pairs into ascending order. (~<em>N </em>auxiliary storage required)
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers and a corresponding
-     *   pair of associated value buffers.  Each pair is managed by a DoubleBuffer
-     *   structure that indicates which of the two buffers is "current" (and thus
-     *   contains the input data to be sorted).
-     * - The contents of both buffers within each pair may be altered by the sorting
-     *   operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within each DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated sorting performance across different
-     * CUDA architectures for uniform-random <tt>uint32,uint32</tt> and
-     * <tt>uint64,uint64</tt> pairs, respectively.
-     *
-     * \image html lsb_radix_sort_int32_pairs.png
-     * \image html lsb_radix_sort_int64_pairs.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [        ...        ]
-     * int  *d_value_buf;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_value_alt_buf;   // e.g., [        ...        ]
-     * ...
-     *
-     * // Create a set of DoubleBuffers to wrap pairs of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     * cub::DoubleBuffer<int> d_values(d_value_buf, d_value_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items);
-     *
-     * // d_keys.Current()      <-- [0, 3, 5, 6, 7, 8, 9]
-     * // d_values.Current()    <-- [5, 4, 3, 1, 2, 0, 6]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     * \tparam ValueT    <b>[inferred]</b> ValueT type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairs(
-        void                    *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>      &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        DoubleBuffer<ValueT>    &d_values,                              ///< [in,out] Double-buffer of values whose "current" device-accessible buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-        int                     num_items,                              ///< [in] Number of items to sort
-        int                     begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                     end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t            stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchRadixSort<false, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts key-value pairs into descending order. (~<em>2N</em> auxiliary storage required).
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Performance
-     * Performance is similar to DeviceRadixSort::SortPairs.
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [        ...        ]
-     * int  *d_values_in;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_values_out;      // e.g., [        ...        ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out, num_items);
-     *
-     * // d_keys_out            <-- [9, 8, 7, 6, 5, 3, 0]
-     * // d_values_out          <-- [6, 0, 2, 1, 3, 4, 5]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     * \tparam ValueT    <b>[inferred]</b> ValueT type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairsDescending(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] Pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] Pointer to the sorted output sequence of key data
-        const ValueT        *d_values_in,                           ///< [in] Pointer to the corresponding input sequence of associated value items
-        ValueT              *d_values_out,                          ///< [out] Pointer to the correspondingly-reordered output sequence of associated value items
-        int                 num_items,                              ///< [in] Number of items to sort
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        DoubleBuffer<KeyT>       d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<ValueT>     d_values(const_cast<ValueT*>(d_values_in), d_values_out);
-
-        return DispatchRadixSort<true, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts key-value pairs into descending order. (~<em>N </em>auxiliary storage required).
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers and a corresponding
-     *   pair of associated value buffers.  Each pair is managed by a DoubleBuffer
-     *   structure that indicates which of the two buffers is "current" (and thus
-     *   contains the input data to be sorted).
-     * - The contents of both buffers within each pair may be altered by the sorting
-     *   operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within each DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Performance
-     * Performance is similar to DeviceRadixSort::SortPairs.
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [        ...        ]
-     * int  *d_value_buf;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_value_alt_buf;   // e.g., [        ...        ]
-     * ...
-     *
-     * // Create a set of DoubleBuffers to wrap pairs of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     * cub::DoubleBuffer<int> d_values(d_value_buf, d_value_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items);
-     *
-     * // d_keys.Current()      <-- [9, 8, 7, 6, 5, 3, 0]
-     * // d_values.Current()    <-- [6, 0, 2, 1, 3, 4, 5]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     * \tparam ValueT    <b>[inferred]</b> ValueT type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairsDescending(
-        void                    *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>      &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        DoubleBuffer<ValueT>    &d_values,                              ///< [in,out] Double-buffer of values whose "current" device-accessible buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-        int                     num_items,                              ///< [in] Number of items to sort
-        int                     begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                     end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t            stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchRadixSort<true, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Keys-only
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Sorts keys into ascending order. (~<em>2N </em>auxiliary storage required)
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated sorting performance across different
-     * CUDA architectures for uniform-random \p uint32 and \p uint64 keys, respectively.
-     *
-     * \image html lsb_radix_sort_int32_keys.png
-     * \image html lsb_radix_sort_int64_keys.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [        ...        ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, num_items);
-     *
-     * // d_keys_out            <-- [0, 3, 5, 6, 7, 8, 9]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeys(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] Pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] Pointer to the sorted output sequence of key data
-        int                 num_items,                              ///< [in] Number of items to sort
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Null value type
-        DoubleBuffer<KeyT>      d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<NullType>  d_values;
-
-        return DispatchRadixSort<false, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts keys into ascending order. (~<em>N </em>auxiliary storage required).
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers managed by a
-     *   DoubleBuffer structure that indicates which of the two buffers is
-     *   "current" (and thus contains the input data to be sorted).
-     * - The contents of both buffers may be altered by the sorting operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within the DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated sorting performance across different
-     * CUDA architectures for uniform-random \p uint32 and \p uint64 keys, respectively.
-     *
-     * \image html lsb_radix_sort_int32_keys.png
-     * \image html lsb_radix_sort_int64_keys.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [        ...        ]
-     * ...
-     *
-     * // Create a DoubleBuffer to wrap the pair of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys, num_items);
-     *
-     * // d_keys.Current()      <-- [0, 3, 5, 6, 7, 8, 9]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeys(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>  &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        int                 num_items,                              ///< [in] Number of items to sort
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Null value type
-        DoubleBuffer<NullType> d_values;
-
-        return DispatchRadixSort<false, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-    /**
-     * \brief Sorts keys into descending order. (~<em>2N</em> auxiliary storage required).
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Performance
-     * Performance is similar to DeviceRadixSort::SortKeys.
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [        ...        ]
-     * ...
-     *
-     * // Create a DoubleBuffer to wrap the pair of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, num_items);
-     *
-     * // d_keys_out            <-- [9, 8, 7, 6, 5, 3, 0]s
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeysDescending(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] Pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] Pointer to the sorted output sequence of key data
-        int                 num_items,                              ///< [in] Number of items to sort
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        DoubleBuffer<KeyT>      d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<NullType>  d_values;
-
-        return DispatchRadixSort<true, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts keys into descending order. (~<em>N </em>auxiliary storage required).
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers managed by a
-     *   DoubleBuffer structure that indicates which of the two buffers is
-     *   "current" (and thus contains the input data to be sorted).
-     * - The contents of both buffers may be altered by the sorting operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within the DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Performance
-     * Performance is similar to DeviceRadixSort::SortKeys.
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sorting of a device vector of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [        ...        ]
-     * ...
-     *
-     * // Create a DoubleBuffer to wrap the pair of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys, num_items);
-     *
-     * // d_keys.Current()      <-- [9, 8, 7, 6, 5, 3, 0]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> KeyT type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeysDescending(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>  &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        int                 num_items,                              ///< [in] Number of items to sort
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Null value type
-        DoubleBuffer<NullType> d_values;
-
-        return DispatchRadixSort<true, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-
-    //@}  end member group
-
-
-};
-
-/**
- * \example example_device_radix_sort.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_reduce.cuh b/ml-xgboost/cub/cub/device/device_reduce.cuh
deleted file mode 100644
index 13f5bbb..0000000
--- a/ml-xgboost/cub/cub/device/device_reduce.cuh
+++ /dev/null
@@ -1,699 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceReduce provides device-wide, parallel operations for computing a reduction across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-#include <limits>
-
-#include "../iterator/arg_index_input_iterator.cuh"
-#include "dispatch/dispatch_reduce.cuh"
-#include "dispatch/dispatch_reduce_by_key.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceReduce provides device-wide, parallel operations for computing a reduction across a sequence of data items residing within device-accessible memory. ![](reduce_logo.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * A <a href="http://en.wikipedia.org/wiki/Reduce_(higher-order_function)"><em>reduction</em></a> (or <em>fold</em>)
- * uses a binary combining operator to compute a single aggregate from a sequence of input elements.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceReduce}
- *
- * \par Performance
- * \linear_performance{reduction, reduce-by-key, and run-length encode}
- *
- * \par
- * The following chart illustrates DeviceReduce::Sum
- * performance across different CUDA architectures for \p int32 keys.
- *
- * \image html reduce_int32.png
- *
- * \par
- * The following chart illustrates DeviceReduce::ReduceByKey (summation)
- * performance across different CUDA architectures for \p fp32
- * values.  Segments are identified by \p int32 keys, and have lengths uniformly sampled from [1,1000].
- *
- * \image html reduce_by_key_fp32_len_500.png
- *
- * \par
- * \plots_below
- *
- */
-struct DeviceReduce
-{
-    /**
-     * \brief Computes a device-wide reduction using the specified binary \p reduction_op functor and initial value \p init.
-     *
-     * \par
-     * - Does not support binary reduction operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates a user-defined min-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // CustomMin functor
-     * struct CustomMin
-     * {
-     *     template <typename T>
-     *     __device__ __forceinline__
-     *     T operator()(const T &a, const T &b) const {
-     *         return (b < a) ? b : a;
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_items;  // e.g., 7
-     * int          *d_in;      // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int          *d_out;     // e.g., [-]
-     * CustomMin    min_op;
-     * int          init;       // e.g., INT_MAX
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::Reduce(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, min_op, init);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run reduction
-     * cub::DeviceReduce::Reduce(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, min_op, init);
-     *
-     * // d_out <-- [0]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     * \tparam ReductionOpT         <b>[inferred]</b> Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt> 
-     * \tparam T                    <b>[inferred]</b> Data element type that is convertible to the \p value type of \p InputIteratorT
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT,
-        typename                    ReductionOpT,
-        typename                    T>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Reduce(
-        void                        *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                              ///< [out] Pointer to the output aggregate
-        int                         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        ReductionOpT                reduction_op,                       ///< [in] Binary reduction functor
-        T                           init,                               ///< [in] Initial value of the reduction
-        cudaStream_t                stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, ReductionOpT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_items,
-            reduction_op,
-            init,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide sum using the addition (\p +) operator.
-     *
-     * \par
-     * - Uses \p 0 as the initial value of the reduction.
-     * - Does not support \p + operators that are non-commutative..
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated sum-reduction performance across different
-     * CUDA architectures for \p int32 and \p int64 items, respectively.
-     *
-     * \image html reduce_int32.png
-     * \image html reduce_int64.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sum-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int  num_items;      // e.g., 7
-     * int  *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_out;         // e.g., [-]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sum-reduction
-     * cub::DeviceReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // d_out <-- [38]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Sum(
-        void                        *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                              ///< [out] Pointer to the output aggregate
-        int                         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The output value type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-        return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, cub::Sum>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_items,
-            cub::Sum(),
-            OutputT(),            // zero-initialize
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide minimum using the less-than ('<') operator.
-     *
-     * \par
-     * - Uses <tt>std::numeric_limits<T>::max()</tt> as the initial value of the reduction.
-     * - Does not support \p < operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the min-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int  num_items;      // e.g., 7
-     * int  *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_out;         // e.g., [-]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::Min(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run min-reduction
-     * cub::DeviceReduce::Min(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // d_out <-- [0]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Min(
-        void                        *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                              ///< [out] Pointer to the output aggregate
-        int                         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input value type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-        return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, cub::Min>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_items,
-            cub::Min(),
-            Traits<InputT>::Max(), // replace with std::numeric_limits<T>::max() when C++11 support is more prevalent
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Finds the first device-wide minimum using the less-than ('<') operator, also returning the index of that item.
-     *
-     * \par
-     * - The output value type of \p d_out is cub::KeyValuePair <tt><int, T></tt> (assuming the value type of \p d_in is \p T)
-     *   - The minimum is written to <tt>d_out.value</tt> and its offset in the input array is written to <tt>d_out.key</tt>.
-     *   - The <tt>{1, std::numeric_limits<T>::max()}</tt> tuple is produced for zero-length inputs
-     * - Does not support \p < operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the argmin-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int                      num_items;      // e.g., 7
-     * int                      *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * KeyValuePair<int, int>   *d_out;         // e.g., [{-,-}]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::ArgMin(d_temp_storage, temp_storage_bytes, d_in, d_argmin, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run argmin-reduction
-     * cub::DeviceReduce::ArgMin(d_temp_storage, temp_storage_bytes, d_in, d_argmin, num_items);
-     *
-     * // d_out <-- [{5, 0}]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items (of some type \p T) \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate (having value type <tt>cub::KeyValuePair<int, T></tt>) \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t ArgMin(
-        void                        *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                              ///< [out] Pointer to the output aggregate
-        int                         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputValueT;
-
-        // The output tuple type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            KeyValuePair<OffsetT, InputValueT>,                                                                 // ... then the key value pair OffsetT + InputValueT
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputTupleT;                     // ... else the output iterator's value type
-
-        // The output value type
-        typedef typename OutputTupleT::Value OutputValueT;
-
-        // Wrapped input iterator to produce index-value <OffsetT, InputT> tuples
-        typedef ArgIndexInputIterator<InputIteratorT, OffsetT, OutputValueT> ArgIndexInputIteratorT;
-        ArgIndexInputIteratorT d_indexed_in(d_in);
-
-        // Initial value
-        OutputTupleT initial_value(1, Traits<InputValueT>::Max());   // replace with std::numeric_limits<T>::max() when C++11 support is more prevalent
-
-        return DispatchReduce<ArgIndexInputIteratorT, OutputIteratorT, OffsetT, cub::ArgMin>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_indexed_in,
-            d_out,
-            num_items,
-            cub::ArgMin(),
-            initial_value,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide maximum using the greater-than ('>') operator.
-     *
-     * \par
-     * - Uses <tt>std::numeric_limits<T>::lowest()</tt> as the initial value of the reduction.
-     * - Does not support \p > operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the max-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int  num_items;      // e.g., 7
-     * int  *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_out;         // e.g., [-]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_max, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run max-reduction
-     * cub::DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_max, num_items);
-     *
-     * // d_out <-- [9]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Max(
-        void                        *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                              ///< [out] Pointer to the output aggregate
-        int                         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input value type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-        return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, cub::Max>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_items,
-            cub::Max(),
-            Traits<InputT>::Lowest(),    // replace with std::numeric_limits<T>::lowest() when C++11 support is more prevalent
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Finds the first device-wide maximum using the greater-than ('>') operator, also returning the index of that item
-     *
-     * \par
-     * - The output value type of \p d_out is cub::KeyValuePair <tt><int, T></tt> (assuming the value type of \p d_in is \p T)
-     *   - The maximum is written to <tt>d_out.value</tt> and its offset in the input array is written to <tt>d_out.key</tt>.
-     *   - The <tt>{1, std::numeric_limits<T>::lowest()}</tt> tuple is produced for zero-length inputs
-     * - Does not support \p > operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the argmax-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_reduce.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int                      num_items;      // e.g., 7
-     * int                      *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * KeyValuePair<int, int>   *d_out;         // e.g., [{-,-}]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::ArgMax(d_temp_storage, temp_storage_bytes, d_in, d_argmax, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run argmax-reduction
-     * cub::DeviceReduce::ArgMax(d_temp_storage, temp_storage_bytes, d_in, d_argmax, num_items);
-     *
-     * // d_out <-- [{6, 9}]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items (of some type \p T) \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate (having value type <tt>cub::KeyValuePair<int, T></tt>) \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t ArgMax(
-        void                        *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                              ///< [out] Pointer to the output aggregate
-        int                         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputValueT;
-
-        // The output tuple type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            KeyValuePair<OffsetT, InputValueT>,                                                                 // ... then the key value pair OffsetT + InputValueT
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputTupleT;                     // ... else the output iterator's value type
-
-        // The output value type
-        typedef typename OutputTupleT::Value OutputValueT;
-
-        // Wrapped input iterator to produce index-value <OffsetT, InputT> tuples
-        typedef ArgIndexInputIterator<InputIteratorT, OffsetT, OutputValueT> ArgIndexInputIteratorT;
-        ArgIndexInputIteratorT d_indexed_in(d_in);
-
-        // Initial value
-        OutputTupleT initial_value(1, Traits<InputValueT>::Lowest());     // replace with std::numeric_limits<T>::lowest() when C++11 support is more prevalent
-
-        return DispatchReduce<ArgIndexInputIteratorT, OutputIteratorT, OffsetT, cub::ArgMax>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_indexed_in,
-            d_out,
-            num_items,
-            cub::ArgMax(),
-            initial_value,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Reduces segments of values, where segments are demarcated by corresponding runs of identical keys.
-     *
-     * \par
-     * This operation computes segmented reductions within \p d_values_in using
-     * the specified binary \p reduction_op functor.  The segments are identified by
-     * "runs" of corresponding keys in \p d_keys_in, where runs are maximal ranges of
-     * consecutive, identical keys.  For the <em>i</em><sup>th</sup> run encountered,
-     * the first key of the run and the corresponding value aggregate of that run are
-     * written to <tt>d_unique_out[<em>i</em>]</tt> and <tt>d_aggregates_out[<em>i</em>]</tt>,
-     * respectively. The total number of runs encountered is written to \p d_num_runs_out.
-     *
-     * \par
-     * - The <tt>==</tt> equality operator is used to determine whether keys are equivalent
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following chart illustrates reduction-by-key (sum) performance across
-     * different CUDA architectures for \p fp32 and \p fp64 values, respectively.  Segments
-     * are identified by \p int32 keys, and have lengths uniformly sampled from [1,1000].
-     *
-     * \image html reduce_by_key_fp32_len_500.png
-     * \image html reduce_by_key_fp64_len_500.png
-     *
-     * \par
-     * The following charts are similar, but with segment lengths uniformly sampled from [1,10]:
-     *
-     * \image html reduce_by_key_fp32_len_5.png
-     * \image html reduce_by_key_fp64_len_5.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the segmented reduction of \p int values grouped
-     * by runs of associated \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_reduce.cuh>
-     *
-     * // CustomMin functor
-     * struct CustomMin
-     * {
-     *     template <typename T>
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     T operator()(const T &a, const T &b) const {
-     *         return (b < a) ? b : a;
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_items;          // e.g., 8
-     * int          *d_keys_in;         // e.g., [0, 2, 2, 9, 5, 5, 5, 8]
-     * int          *d_values_in;       // e.g., [0, 7, 1, 6, 2, 5, 3, 4]
-     * int          *d_unique_out;      // e.g., [-, -, -, -, -, -, -, -]
-     * int          *d_aggregates_out;  // e.g., [-, -, -, -, -, -, -, -]
-     * int          *d_num_runs_out;    // e.g., [-]
-     * CustomMin    reduction_op;
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceReduce::ReduceByKey(d_temp_storage, temp_storage_bytes, d_keys_in, d_unique_out, d_values_in, d_aggregates_out, d_num_runs_out, reduction_op, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run reduce-by-key
-     * cub::DeviceReduce::ReduceByKey(d_temp_storage, temp_storage_bytes, d_keys_in, d_unique_out, d_values_in, d_aggregates_out, d_num_runs_out, reduction_op, num_items);
-     *
-     * // d_unique_out      <-- [0, 2, 9, 5, 8]
-     * // d_aggregates_out  <-- [0, 1, 6, 2, 4]
-     * // d_num_runs_out    <-- [5]
-     *
-     * \endcode
-     *
-     * \tparam KeysInputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input keys \iterator
-     * \tparam UniqueOutputIteratorT    <b>[inferred]</b> Random-access output iterator type for writing unique output keys \iterator
-     * \tparam ValuesInputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input values \iterator
-     * \tparam AggregatesOutputIterator <b>[inferred]</b> Random-access output iterator type for writing output value aggregates \iterator
-     * \tparam NumRunsOutputIteratorT   <b>[inferred]</b> Output iterator type for recording the number of runs encountered \iterator
-     * \tparam ReductionOpT              <b>[inferred]</b> Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt> 
-     */
-    template <
-        typename                    KeysInputIteratorT,
-        typename                    UniqueOutputIteratorT,
-        typename                    ValuesInputIteratorT,
-        typename                    AggregatesOutputIteratorT,
-        typename                    NumRunsOutputIteratorT,
-        typename                    ReductionOpT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t ReduceByKey(
-        void                        *d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        KeysInputIteratorT          d_keys_in,                      ///< [in] Pointer to the input sequence of keys
-        UniqueOutputIteratorT       d_unique_out,                   ///< [out] Pointer to the output sequence of unique keys (one key per run)
-        ValuesInputIteratorT        d_values_in,                    ///< [in] Pointer to the input sequence of corresponding values
-        AggregatesOutputIteratorT   d_aggregates_out,               ///< [out] Pointer to the output sequence of value aggregates (one aggregate per run)
-        NumRunsOutputIteratorT      d_num_runs_out,                 ///< [out] Pointer to total number of runs encountered (i.e., the length of d_unique_out)
-        ReductionOpT                reduction_op,                   ///< [in] Binary reduction functor
-        int                         num_items,                      ///< [in] Total number of associated key+value pairs (i.e., the length of \p d_in_keys and \p d_in_values)
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // FlagT iterator type (not used)
-
-        // Selection op (not used)
-
-        // Default == operator
-        typedef Equality EqualityOp;
-
-        return DispatchReduceByKey<KeysInputIteratorT, UniqueOutputIteratorT, ValuesInputIteratorT, AggregatesOutputIteratorT, NumRunsOutputIteratorT, EqualityOp, ReductionOpT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys_in,
-            d_unique_out,
-            d_values_in,
-            d_aggregates_out,
-            d_num_runs_out,
-            EqualityOp(),
-            reduction_op,
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-};
-
-/**
- * \example example_device_reduce.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_run_length_encode.cuh b/ml-xgboost/cub/cub/device/device_run_length_encode.cuh
deleted file mode 100644
index ee1a539..0000000
--- a/ml-xgboost/cub/cub/device/device_run_length_encode.cuh
+++ /dev/null
@@ -1,278 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceRunLengthEncode provides device-wide, parallel operations for computing a run-length encoding across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch/dispatch_rle.cuh"
-#include "dispatch/dispatch_reduce_by_key.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceRunLengthEncode provides device-wide, parallel operations for demarcating "runs" of same-valued items within a sequence residing within device-accessible memory. ![](run_length_encode_logo.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * A <a href="http://en.wikipedia.org/wiki/Run-length_encoding"><em>run-length encoding</em></a>
- * computes a simple compressed representation of a sequence of input elements such that each
- * maximal "run" of consecutive same-valued data items is encoded as a single data value along with a
- * count of the elements in that run.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceRunLengthEncode}
- *
- * \par Performance
- * \linear_performance{run-length encode}
- *
- * \par
- * The following chart illustrates DeviceRunLengthEncode::RunLengthEncode performance across
- * different CUDA architectures for \p int32 items.
- * Segments have lengths uniformly sampled from [1,1000].
- *
- * \image html rle_int32_len_500.png
- *
- * \par
- * \plots_below
- *
- */
-struct DeviceRunLengthEncode
-{
-
-    /**
-     * \brief Computes a run-length encoding of the sequence \p d_in.
-     *
-     * \par
-     * - For the <em>i</em><sup>th</sup> run encountered, the first key of the run and its length are written to
-     *   <tt>d_unique_out[<em>i</em>]</tt> and <tt>d_counts_out[<em>i</em>]</tt>,
-     *   respectively.
-     * - The total number of runs encountered is written to \p d_num_runs_out.
-     * - The <tt>==</tt> equality operator is used to determine whether values are equivalent
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated encode performance across different
-     * CUDA architectures for \p int32 and \p int64 items, respectively.  Segments have
-     * lengths uniformly sampled from [1,1000].
-     *
-     * \image html rle_int32_len_500.png
-     * \image html rle_int64_len_500.png
-     *
-     * \par
-     * The following charts are similar, but with segment lengths uniformly sampled from [1,10]:
-     *
-     * \image html rle_int32_len_5.png
-     * \image html rle_int64_len_5.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the run-length encoding of a sequence of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_run_length_encode.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_items;          // e.g., 8
-     * int          *d_in;              // e.g., [0, 2, 2, 9, 5, 5, 5, 8]
-     * int          *d_unique_out;      // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int          *d_counts_out;      // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int          *d_num_runs_out;    // e.g., [ ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRunLengthEncode::Encode(d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_counts_out, d_num_runs_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run encoding
-     * cub::DeviceRunLengthEncode::Encode(d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_counts_out, d_num_runs_out, num_items);
-     *
-     * // d_unique_out      <-- [0, 2, 9, 5, 8]
-     * // d_counts_out      <-- [1, 2, 1, 3, 1]
-     * // d_num_runs_out    <-- [5]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT           <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam UniqueOutputIteratorT    <b>[inferred]</b> Random-access output iterator type for writing unique output items \iterator
-     * \tparam LengthsOutputIteratorT   <b>[inferred]</b> Random-access output iterator type for writing output counts \iterator
-     * \tparam NumRunsOutputIteratorT   <b>[inferred]</b> Output iterator type for recording the number of runs encountered \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    UniqueOutputIteratorT,
-        typename                    LengthsOutputIteratorT,
-        typename                    NumRunsOutputIteratorT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Encode(
-        void*                       d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of keys
-        UniqueOutputIteratorT       d_unique_out,                   ///< [out] Pointer to the output sequence of unique keys (one key per run)
-        LengthsOutputIteratorT      d_counts_out,                   ///< [out] Pointer to the output sequence of run-lengths (one count per run)
-        NumRunsOutputIteratorT      d_num_runs_out,                     ///< [out] Pointer to total number of runs
-        int                         num_items,                      ///< [in] Total number of associated key+value pairs (i.e., the length of \p d_in_keys and \p d_in_values)
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int         OffsetT;                    // Signed integer type for global offsets
-        typedef NullType*   FlagIterator;               // FlagT iterator type (not used)
-        typedef NullType    SelectOp;                   // Selection op (not used)
-        typedef Equality    EqualityOp;                 // Default == operator
-        typedef cub::Sum    ReductionOp;                // Value reduction operator
-
-        // The lengths output value type
-        typedef typename If<(Equals<typename std::iterator_traits<LengthsOutputIteratorT>::value_type, void>::VALUE),   // LengthT =  (if output iterator's value type is void) ?
-            OffsetT,                                                                                                    // ... then the OffsetT type,
-            typename std::iterator_traits<LengthsOutputIteratorT>::value_type>::Type LengthT;                           // ... else the output iterator's value type
-
-        // Generator type for providing 1s values for run-length reduction
-        typedef ConstantInputIterator<LengthT, OffsetT> LengthsInputIteratorT;
-
-        return DispatchReduceByKey<InputIteratorT, UniqueOutputIteratorT, LengthsInputIteratorT, LengthsOutputIteratorT, NumRunsOutputIteratorT, EqualityOp, ReductionOp, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_unique_out,
-            LengthsInputIteratorT((LengthT) 1),
-            d_counts_out,
-            d_num_runs_out,
-            EqualityOp(),
-            ReductionOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Enumerates the starting offsets and lengths of all non-trivial runs (of length > 1) of same-valued keys in the sequence \p d_in.
-     *
-     * \par
-     * - For the <em>i</em><sup>th</sup> non-trivial run, the run's starting offset
-     *   and its length are written to <tt>d_offsets_out[<em>i</em>]</tt> and
-     *   <tt>d_lengths_out[<em>i</em>]</tt>, respectively.
-     * - The total number of runs encountered is written to \p d_num_runs_out.
-     * - The <tt>==</tt> equality operator is used to determine whether values are equivalent
-     * - \devicestorage
-     *
-     * \par Performance
-     *
-     * \par Snippet
-     * The code snippet below illustrates the identification of non-trivial runs within a sequence of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_run_length_encode.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_items;          // e.g., 8
-     * int          *d_in;              // e.g., [0, 2, 2, 9, 5, 5, 5, 8]
-     * int          *d_offsets_out;     // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int          *d_lengths_out;     // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int          *d_num_runs_out;    // e.g., [ ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceRunLengthEncode::NonTrivialRuns(d_temp_storage, temp_storage_bytes, d_in, d_offsets_out, d_lengths_out, d_num_runs_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run encoding
-     * cub::DeviceRunLengthEncode::NonTrivialRuns(d_temp_storage, temp_storage_bytes, d_in, d_offsets_out, d_lengths_out, d_num_runs_out, num_items);
-     *
-     * // d_offsets_out         <-- [1, 4]
-     * // d_lengths_out         <-- [2, 3]
-     * // d_num_runs_out        <-- [2]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT           <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OffsetsOutputIteratorT   <b>[inferred]</b> Random-access output iterator type for writing run-offset values \iterator
-     * \tparam LengthsOutputIteratorT   <b>[inferred]</b> Random-access output iterator type for writing run-length values \iterator
-     * \tparam NumRunsOutputIteratorT   <b>[inferred]</b> Output iterator type for recording the number of runs encountered \iterator
-     */
-    template <
-        typename                InputIteratorT,
-        typename                OffsetsOutputIteratorT,
-        typename                LengthsOutputIteratorT,
-        typename                NumRunsOutputIteratorT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t NonTrivialRuns(
-        void*               d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT          d_in,                           ///< [in] Pointer to input sequence of data items
-        OffsetsOutputIteratorT  d_offsets_out,                  ///< [out] Pointer to output sequence of run-offsets (one offset per non-trivial run)
-        LengthsOutputIteratorT  d_lengths_out,                  ///< [out] Pointer to output sequence of run-lengths (one count per non-trivial run)
-        NumRunsOutputIteratorT  d_num_runs_out,                 ///< [out] Pointer to total number of runs (i.e., length of \p d_offsets_out)
-        int                     num_items,                      ///< [in] Total number of associated key+value pairs (i.e., the length of \p d_in_keys and \p d_in_values)
-        cudaStream_t            stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int         OffsetT;                    // Signed integer type for global offsets
-        typedef Equality    EqualityOp;                 // Default == operator
-
-        return DeviceRleDispatch<InputIteratorT, OffsetsOutputIteratorT, LengthsOutputIteratorT, NumRunsOutputIteratorT, EqualityOp, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_offsets_out,
-            d_lengths_out,
-            d_num_runs_out,
-            EqualityOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_scan.cuh b/ml-xgboost/cub/cub/device/device_scan.cuh
deleted file mode 100644
index 1395040..0000000
--- a/ml-xgboost/cub/cub/device/device_scan.cuh
+++ /dev/null
@@ -1,423 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceScan provides device-wide, parallel operations for computing a prefix scan across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch/dispatch_scan.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceScan provides device-wide, parallel operations for computing a prefix scan across a sequence of data items residing within device-accessible memory. ![](device_scan.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * Given a sequence of input elements and a binary reduction operator, a [<em>prefix scan</em>](http://en.wikipedia.org/wiki/Prefix_sum)
- * produces an output sequence where each element is computed to be the reduction
- * of the elements occurring earlier in the input sequence.  <em>Prefix sum</em>
- * connotes a prefix scan with the addition operator. The term \em inclusive indicates
- * that the <em>i</em><sup>th</sup> output reduction incorporates the <em>i</em><sup>th</sup> input.
- * The term \em exclusive indicates the <em>i</em><sup>th</sup> input is not incorporated into
- * the <em>i</em><sup>th</sup> output reduction.
- *
- * \par
- * As of CUB 1.0.1 (2013), CUB's device-wide scan APIs have implemented our <em>"decoupled look-back"</em> algorithm
- * for performing global prefix scan with only a single pass through the
- * input data, as described in our 2016 technical report [1].  The central
- * idea is to leverage a small, constant factor of redundant work in order to overlap the latencies
- * of global prefix propagation with local computation.  As such, our algorithm requires only
- * ~2<em>n</em> data movement (<em>n</em> inputs are read, <em>n</em> outputs are written), and typically
- * proceeds at "memcpy" speeds.
- *
- * \par
- * [1] [Duane Merrill and Michael Garland.  "Single-pass Parallel Prefix Scan with Decoupled Look-back", <em>NVIDIA Technical Report NVR-2016-002</em>, 2016.](https://research.nvidia.com/publication/single-pass-parallel-prefix-scan-decoupled-look-back)
- *
- * \par Usage Considerations
- * \cdp_class{DeviceScan}
- *
- * \par Performance
- * \linear_performance{prefix scan}
- *
- * \par
- * The following chart illustrates DeviceScan::ExclusiveSum
- * performance across different CUDA architectures for \p int32 keys.
- * \plots_below
- *
- * \image html scan_int32.png
- *
- */
-struct DeviceScan
-{
-    /******************************************************************//**
-     * \name Exclusive scans
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Computes a device-wide exclusive prefix sum.  The value of 0 is applied as the initial value, and is assigned to *d_out.
-     *
-     * \par
-     * - Supports non-commutative sum operators.
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated exclusive sum performance across different
-     * CUDA architectures for \p int32 and \p int64 items, respectively.
-     *
-     * \image html scan_int32.png
-     * \image html scan_int64.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the exclusive prefix sum of an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_scan.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int  num_items;      // e.g., 7
-     * int  *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_out;         // e.g., [ ,  ,  ,  ,  ,  ,  ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run exclusive prefix sum
-     * cub::DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // d_out s<-- [0, 8, 14, 21, 26, 29, 29]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading scan inputs \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Random-access output iterator type for writing scan outputs \iterator
-     */
-    template <
-        typename        InputIteratorT,
-        typename        OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t ExclusiveSum(
-        void            *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t          &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT  d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT d_out,                              ///< [out] Pointer to the output sequence of data items
-        int             num_items,                          ///< [in] Total number of input items (i.e., the length of \p d_in)
-        cudaStream_t    stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool            debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The output value type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-        // Initial value
-        OutputT init_value = 0;
-
-        return DispatchScan<InputIteratorT, OutputIteratorT, Sum, OutputT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            Sum(),
-            init_value,
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide exclusive prefix scan using the specified binary \p scan_op functor.  The \p init_value value is applied as the initial value, and is assigned to *d_out.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the exclusive prefix min-scan of an \p int device vector
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_scan.cuh>
-     *
-     * // CustomMin functor
-     * struct CustomMin
-     * {
-     *     template <typename T>
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     T operator()(const T &a, const T &b) const {
-     *         return (b < a) ? b : a;
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_items;      // e.g., 7
-     * int          *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int          *d_out;         // e.g., [ ,  ,  ,  ,  ,  ,  ]
-     * CustomMin    min_op
-     * ...
-     *
-     * // Determine temporary device storage requirements for exclusive prefix scan
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceScan::ExclusiveScan(d_temp_storage, temp_storage_bytes, d_in, d_out, min_op, (int) MAX_INT, num_items);
-     *
-     * // Allocate temporary storage for exclusive prefix scan
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run exclusive prefix min-scan
-     * cub::DeviceScan::ExclusiveScan(d_temp_storage, temp_storage_bytes, d_in, d_out, min_op, (int) MAX_INT, num_items);
-     *
-     * // d_out <-- [2147483647, 8, 6, 6, 5, 3, 0]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT   <b>[inferred]</b> Random-access input iterator type for reading scan inputs \iterator
-     * \tparam OutputIteratorT  <b>[inferred]</b> Random-access output iterator type for writing scan outputs \iterator
-     * \tparam ScanOp           <b>[inferred]</b> Binary scan functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-     * \tparam Identity         <b>[inferred]</b> Type of the \p identity value used Binary scan functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        typename        InputIteratorT,
-        typename        OutputIteratorT,
-        typename        ScanOpT,
-        typename        InitValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t ExclusiveScan(
-        void            *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t          &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT  d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT d_out,                              ///< [out] Pointer to the output sequence of data items
-        ScanOpT         scan_op,                            ///< [in] Binary scan functor
-        InitValueT      init_value,                         ///< [in] Initial value to seed the exclusive scan (and is assigned to *d_out)
-        int             num_items,                          ///< [in] Total number of input items (i.e., the length of \p d_in)
-        cudaStream_t    stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool            debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchScan<InputIteratorT, OutputIteratorT, ScanOpT, InitValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            scan_op,
-            init_value,
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Inclusive scans
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes a device-wide inclusive prefix sum.
-     *
-     * \par
-     * - Supports non-commutative sum operators.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the inclusive prefix sum of an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_scan.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int  num_items;      // e.g., 7
-     * int  *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_out;         // e.g., [ ,  ,  ,  ,  ,  ,  ]
-     * ...
-     *
-     * // Determine temporary device storage requirements for inclusive prefix sum
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceScan::InclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // Allocate temporary storage for inclusive prefix sum
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run inclusive prefix sum
-     * cub::DeviceScan::InclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items);
-     *
-     * // d_out <-- [8, 14, 21, 26, 29, 29, 38]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading scan inputs \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Random-access output iterator type for writing scan outputs \iterator
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t InclusiveSum(
-        void*               d_temp_storage,                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                           ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                          ///< [out] Pointer to the output sequence of data items
-        int                 num_items,                      ///< [in] Total number of input items (i.e., the length of \p d_in)
-        cudaStream_t        stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchScan<InputIteratorT, OutputIteratorT, Sum, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            Sum(),
-            NullType(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide inclusive prefix scan using the specified binary \p scan_op functor.
-     *
-     * \par
-     * - Supports non-commutative scan operators.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the inclusive prefix min-scan of an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_scan.cuh>
-     *
-     * // CustomMin functor
-     * struct CustomMin
-     * {
-     *     template <typename T>
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     T operator()(const T &a, const T &b) const {
-     *         return (b < a) ? b : a;
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_items;      // e.g., 7
-     * int          *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int          *d_out;         // e.g., [ ,  ,  ,  ,  ,  ,  ]
-     * CustomMin    min_op;
-     * ...
-     *
-     * // Determine temporary device storage requirements for inclusive prefix scan
-     * void *d_temp_storage = NULL;
-     * size_t temp_storage_bytes = 0;
-     * cub::DeviceScan::InclusiveScan(d_temp_storage, temp_storage_bytes, d_in, d_out, min_op, num_items);
-     *
-     * // Allocate temporary storage for inclusive prefix scan
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run inclusive prefix min-scan
-     * cub::DeviceScan::InclusiveScan(d_temp_storage, temp_storage_bytes, d_in, d_out, min_op, num_items);
-     *
-     * // d_out <-- [8, 6, 6, 5, 3, 0, 0]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT   <b>[inferred]</b> Random-access input iterator type for reading scan inputs \iterator
-     * \tparam OutputIteratorT  <b>[inferred]</b> Random-access output iterator type for writing scan outputs \iterator
-     * \tparam ScanOp           <b>[inferred]</b> Binary scan functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        typename        InputIteratorT,
-        typename        OutputIteratorT,
-        typename        ScanOpT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t InclusiveScan(
-        void            *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t          &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT  d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT d_out,                              ///< [out] Pointer to the output sequence of data items
-        ScanOpT         scan_op,                            ///< [in] Binary scan functor
-        int             num_items,                          ///< [in] Total number of input items (i.e., the length of \p d_in)
-        cudaStream_t    stream             = 0,             ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool            debug_synchronous  = false)         ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchScan<InputIteratorT, OutputIteratorT, ScanOpT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            scan_op,
-            NullType(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-    //@}  end member group
-
-};
-
-/**
- * \example example_device_scan.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_segmented_radix_sort.cuh b/ml-xgboost/cub/cub/device/device_segmented_radix_sort.cuh
deleted file mode 100644
index 2d6a8f4..0000000
--- a/ml-xgboost/cub/cub/device/device_segmented_radix_sort.cuh
+++ /dev/null
@@ -1,855 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSegmentedRadixSort provides device-wide, parallel operations for computing a batched radix sort across multiple, non-overlapping sequences of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch/dispatch_radix_sort.cuh"
-#include "../util_arch.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceSegmentedRadixSort provides device-wide, parallel operations for computing a batched radix sort across multiple, non-overlapping sequences of data items residing within device-accessible memory. ![](segmented_sorting_logo.png)
- * \ingroup SegmentedModule
- *
- * \par Overview
- * The [<em>radix sorting method</em>](http://en.wikipedia.org/wiki/Radix_sort) arranges
- * items into ascending (or descending) order.  The algorithm relies upon a positional representation for
- * keys, i.e., each key is comprised of an ordered sequence of symbols (e.g., digits,
- * characters, etc.) specified from least-significant to most-significant.  For a
- * given input sequence of keys and a set of rules specifying a total ordering
- * of the symbolic alphabet, the radix sorting method produces a lexicographic
- * ordering of those keys.
- *
- * \par
- * DeviceSegmentedRadixSort can sort all of the built-in C++ numeric primitive types, e.g.:
- * <tt>unsigned char</tt>, \p int, \p double, etc.  Although the direct radix sorting
- * method can only be applied to unsigned integral types, DeviceSegmentedRadixSort
- * is able to sort signed and floating-point types via simple bit-wise transformations
- * that ensure lexicographic key ordering.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceSegmentedRadixSort}
- *
- */
-struct DeviceSegmentedRadixSort
-{
-
-    /******************************************************************//**
-     * \name Key-value pairs
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Sorts segments of key-value pairs into ascending order. (~<em>2N </em>auxiliary storage required)
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [-, -, -, -, -, -, -]
-     * int  *d_values_in;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_values_out;      // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortPairs(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortPairs(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys_out            <-- [6, 7, 8, 0, 3, 5, 9]
-     * // d_values_out          <-- [1, 2, 0, 5, 4, 3, 6]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     * \tparam ValueT    <b>[inferred]</b> Value type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairs(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] %Device-accessible pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] %Device-accessible pointer to the sorted output sequence of key data
-        const ValueT        *d_values_in,                           ///< [in] %Device-accessible pointer to the corresponding input sequence of associated value items
-        ValueT              *d_values_out,                          ///< [out] %Device-accessible pointer to the correspondingly-reordered output sequence of associated value items
-        int                 num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                 num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int           *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int           *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        DoubleBuffer<KeyT>       d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<ValueT>     d_values(const_cast<ValueT*>(d_values_in), d_values_out);
-
-        return DispatchSegmentedRadixSort<false, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts segments of key-value pairs into ascending order. (~<em>N </em>auxiliary storage required)
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers and a corresponding
-     *   pair of associated value buffers.  Each pair is managed by a DoubleBuffer
-     *   structure that indicates which of the two buffers is "current" (and thus
-     *   contains the input data to be sorted).
-     * - The contents of both buffers within each pair may be altered by the sorting
-     *   operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within each DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [-, -, -, -, -, -, -]
-     * int  *d_value_buf;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_value_alt_buf;   // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Create a set of DoubleBuffers to wrap pairs of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     * cub::DoubleBuffer<int> d_values(d_value_buf, d_value_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys.Current()      <-- [6, 7, 8, 0, 3, 5, 9]
-     * // d_values.Current()    <-- [5, 4, 3, 1, 2, 0, 6]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     * \tparam ValueT    <b>[inferred]</b> Value type
-     */
-    template <
-        typename                KeyT,
-        typename                ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairs(
-        void                    *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>      &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        DoubleBuffer<ValueT>    &d_values,                              ///< [in,out] Double-buffer of values whose "current" device-accessible buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-        int                     num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                     num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int               *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int               *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                     begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                     end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t            stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchSegmentedRadixSort<false, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts segments of key-value pairs into descending order. (~<em>2N</em> auxiliary storage required).
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [-, -, -, -, -, -, -]
-     * int  *d_values_in;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_values_out;      // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes,
-     *     d_keys_in, d_keys_out, d_values_in, d_values_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys_out            <-- [8, 7, 6, 9, 5, 3, 0]
-     * // d_values_out          <-- [0, 2, 1, 6, 3, 4, 5]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     * \tparam ValueT    <b>[inferred]</b> Value type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairsDescending(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] %Device-accessible pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] %Device-accessible pointer to the sorted output sequence of key data
-        const ValueT        *d_values_in,                           ///< [in] %Device-accessible pointer to the corresponding input sequence of associated value items
-        ValueT              *d_values_out,                          ///< [out] %Device-accessible pointer to the correspondingly-reordered output sequence of associated value items
-        int                 num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                 num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int           *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int           *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        DoubleBuffer<KeyT>       d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<ValueT>     d_values(const_cast<ValueT*>(d_values_in), d_values_out);
-
-        return DispatchSegmentedRadixSort<true, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts segments of key-value pairs into descending order. (~<em>N </em>auxiliary storage required).
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers and a corresponding
-     *   pair of associated value buffers.  Each pair is managed by a DoubleBuffer
-     *   structure that indicates which of the two buffers is "current" (and thus
-     *   contains the input data to be sorted).
-     * - The contents of both buffers within each pair may be altered by the sorting
-     *   operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within each DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys
-     * with associated vector of \p int values.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [-, -, -, -, -, -, -]
-     * int  *d_value_buf;       // e.g., [0, 1, 2, 3, 4, 5, 6]
-     * int  *d_value_alt_buf;   // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Create a set of DoubleBuffers to wrap pairs of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     * cub::DoubleBuffer<int> d_values(d_value_buf, d_value_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, d_keys, d_values,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, d_keys, d_values,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys.Current()      <-- [8, 7, 6, 9, 5, 3, 0]
-     * // d_values.Current()    <-- [0, 2, 1, 6, 3, 4, 5]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     * \tparam ValueT    <b>[inferred]</b> Value type
-     */
-    template <
-        typename            KeyT,
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortPairsDescending(
-        void                    *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>      &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        DoubleBuffer<ValueT>    &d_values,                              ///< [in,out] Double-buffer of values whose "current" device-accessible buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-        int                     num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                     num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int               *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int               *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                     begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                     end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t            stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchSegmentedRadixSort<true, KeyT, ValueT, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Keys-only
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Sorts segments of keys into ascending order. (~<em>2N </em>auxiliary storage required)
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys_out            <-- [6, 7, 8, 0, 3, 5, 9]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeys(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] %Device-accessible pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] %Device-accessible pointer to the sorted output sequence of key data
-        int                 num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                 num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int           *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int           *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Null value type
-        DoubleBuffer<KeyT>      d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<NullType>  d_values;
-
-        return DispatchSegmentedRadixSort<false, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts segments of keys into ascending order. (~<em>N </em>auxiliary storage required).
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers managed by a
-     *   DoubleBuffer structure that indicates which of the two buffers is
-     *   "current" (and thus contains the input data to be sorted).
-     * - The contents of both buffers may be altered by the sorting operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within the DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Create a DoubleBuffer to wrap the pair of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortKeys(d_temp_storage, temp_storage_bytes, d_keys,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys.Current()      <-- [6, 7, 8, 0, 3, 5, 9]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeys(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>  &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        int                 num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                 num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int           *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int           *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Null value type
-        DoubleBuffer<NullType> d_values;
-
-        return DispatchSegmentedRadixSort<false, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-    /**
-     * \brief Sorts segments of keys into descending order. (~<em>2N</em> auxiliary storage required).
-     *
-     * \par
-     * - The contents of the input data are not altered by the sorting operation
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageNP  For sorting using only <em>O</em>(<tt>P</tt>) temporary storage, see the sorting interface using DoubleBuffer wrappers below.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_keys_in;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_keys_out;        // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Create a DoubleBuffer to wrap the pair of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys_out            <-- [8, 7, 6, 9, 5, 3, 0]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeysDescending(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        const KeyT          *d_keys_in,                             ///< [in] %Device-accessible pointer to the input data of key data to sort
-        KeyT                *d_keys_out,                            ///< [out] %Device-accessible pointer to the sorted output sequence of key data
-        int                 num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                 num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int           *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int           *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        DoubleBuffer<KeyT>      d_keys(const_cast<KeyT*>(d_keys_in), d_keys_out);
-        DoubleBuffer<NullType>  d_values;
-
-        return DispatchSegmentedRadixSort<true, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            false,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Sorts segments of keys into descending order. (~<em>N </em>auxiliary storage required).
-     *
-     * \par
-     * - The sorting operation is given a pair of key buffers managed by a
-     *   DoubleBuffer structure that indicates which of the two buffers is
-     *   "current" (and thus contains the input data to be sorted).
-     * - The contents of both buffers may be altered by the sorting operation.
-     * - Upon completion, the sorting operation will update the "current" indicator
-     *   within the DoubleBuffer wrapper to reference which of the two buffers
-     *   now contains the sorted output sequence (a function of the number of key bits
-     *   specified and the targeted device architecture).
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - An optional bit subrange <tt>[begin_bit, end_bit)</tt> of differentiating key bits can be specified.  This can reduce overall sorting overhead and yield a corresponding performance improvement.
-     * - \devicestorageP
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the batched sorting of three segments (with one zero-length segment) of \p int keys.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_segmentd_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for sorting data
-     * int  num_items;          // e.g., 7
-     * int  num_segments;       // e.g., 3
-     * int  *d_offsets;         // e.g., [0, 3, 3, 7]
-     * int  *d_key_buf;         // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int  *d_key_alt_buf;     // e.g., [-, -, -, -, -, -, -]
-     * ...
-     *
-     * // Create a DoubleBuffer to wrap the pair of device pointers
-     * cub::DoubleBuffer<int> d_keys(d_key_buf, d_key_alt_buf);
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sorting operation
-     * cub::DeviceSegmentedRadixSort::SortKeysDescending(d_temp_storage, temp_storage_bytes, d_keys,
-     *     num_items, num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_keys.Current()      <-- [8, 7, 6, 9, 5, 3, 0]
-     *
-     * \endcode
-     *
-     * \tparam KeyT      <b>[inferred]</b> Key type
-     */
-    template <typename KeyT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t SortKeysDescending(
-        void                *d_temp_storage,                        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>  &d_keys,                                ///< [in,out] Reference to the double-buffer of keys whose "current" device-accessible buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        int                 num_items,                              ///< [in] The total number of items to sort (across all segments)
-        int                 num_segments,                           ///< [in] The number of segments that comprise the sorting data
-        const int           *d_begin_offsets,                       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int           *d_end_offsets,                         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                 begin_bit           = 0,                ///< [in] <b>[optional]</b> The least-significant bit index (inclusive)  needed for key comparison
-        int                 end_bit             = sizeof(KeyT) * 8, ///< [in] <b>[optional]</b> The most-significant bit index (exclusive) needed for key comparison (e.g., sizeof(unsigned int) * 8)
-        cudaStream_t        stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Null value type
-        DoubleBuffer<NullType> d_values;
-
-        return DispatchSegmentedRadixSort<true, KeyT, NullType, OffsetT>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys,
-            d_values,
-            num_items,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            begin_bit,
-            end_bit,
-            true,
-            stream,
-            debug_synchronous);
-    }
-
-
-    //@}  end member group
-
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_segmented_reduce.cuh b/ml-xgboost/cub/cub/device/device_segmented_reduce.cuh
deleted file mode 100644
index 66f1cdf..0000000
--- a/ml-xgboost/cub/cub/device/device_segmented_reduce.cuh
+++ /dev/null
@@ -1,607 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSegmentedReduce provides device-wide, parallel operations for computing a batched reduction across multiple sequences of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "../iterator/arg_index_input_iterator.cuh"
-#include "dispatch/dispatch_reduce.cuh"
-#include "dispatch/dispatch_reduce_by_key.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceSegmentedReduce provides device-wide, parallel operations for computing a reduction across multiple sequences of data items residing within device-accessible memory. ![](reduce_logo.png)
- * \ingroup SegmentedModule
- *
- * \par Overview
- * A <a href="http://en.wikipedia.org/wiki/Reduce_(higher-order_function)"><em>reduction</em></a> (or <em>fold</em>)
- * uses a binary combining operator to compute a single aggregate from a sequence of input elements.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceSegmentedReduce}
- *
- */
-struct DeviceSegmentedReduce
-{
-    /**
-     * \brief Computes a device-wide segmented reduction using the specified binary \p reduction_op functor.
-     *
-     * \par
-     * - Does not support binary reduction operators that are non-commutative.
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates a custom min-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // CustomMin functor
-     * struct CustomMin
-     * {
-     *     template <typename T>
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     T operator()(const T &a, const T &b) const {
-     *         return (b < a) ? b : a;
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int          num_segments;   // e.g., 3
-     * int          *d_offsets;     // e.g., [0, 3, 3, 7]
-     * int          *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int          *d_out;         // e.g., [-, -, -]
-     * CustomMin    min_op;
-     * int          initial_value;           // e.g., INT_MAX
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedReduce::Reduce(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1, min_op, initial_value);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run reduction
-     * cub::DeviceSegmentedReduce::Reduce(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1, min_op, initial_value);
-     *
-     * // d_out <-- [6, INT_MAX, 0]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     * \tparam ReductionOp          <b>[inferred]</b> Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-     * \tparam T                    <b>[inferred]</b> Data element type that is convertible to the \p value type of \p InputIteratorT
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT,
-        typename            ReductionOp,
-        typename            T>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Reduce(
-        void                *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                              ///< [out] Pointer to the output aggregate
-        int                 num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int                 *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int                 *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        ReductionOp         reduction_op,                       ///< [in] Binary reduction functor 
-        T                   initial_value,                               ///< [in] Initial value of the reduction for each segment
-        cudaStream_t        stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        return DispatchSegmentedReduce<InputIteratorT, OutputIteratorT, OffsetT, ReductionOp>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            reduction_op,
-            initial_value,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide segmented sum using the addition ('+') operator.
-     *
-     * \par
-     * - Uses \p 0 as the initial value of the reduction for each segment.
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - Does not support \p + operators that are non-commutative..
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the sum reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int num_segments;   // e.g., 3
-     * int *d_offsets;     // e.g., [0, 3, 3, 7]
-     * int *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int *d_out;         // e.g., [-, -, -]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run sum-reduction
-     * cub::DeviceSegmentedReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_out <-- [21, 0, 17]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Sum(
-        void                *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                              ///< [out] Pointer to the output aggregate
-        int                 num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int                 *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int                 *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        cudaStream_t        stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The output value type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-        return DispatchSegmentedReduce<InputIteratorT, OutputIteratorT, OffsetT, cub::Sum>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            cub::Sum(),
-            OutputT(),            // zero-initialize
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide segmented minimum using the less-than ('<') operator.
-     *
-     * \par
-     * - Uses <tt>std::numeric_limits<T>::max()</tt> as the initial value of the reduction for each segment.
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - Does not support \p < operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the min-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int num_segments;   // e.g., 3
-     * int *d_offsets;     // e.g., [0, 3, 3, 7]
-     * int *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int *d_out;         // e.g., [-, -, -]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedReduce::Min(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run min-reduction
-     * cub::DeviceSegmentedReduce::Min(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_out <-- [6, INT_MAX, 0]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Min(
-        void                *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                              ///< [out] Pointer to the output aggregate
-        int                 num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int                 *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int                 *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        cudaStream_t        stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input value type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-        return DispatchSegmentedReduce<InputIteratorT, OutputIteratorT, OffsetT, cub::Min>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            cub::Min(),
-            Traits<InputT>::Max(),    // replace with std::numeric_limits<T>::max() when C++11 support is more prevalent
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Finds the first device-wide minimum in each segment using the less-than ('<') operator, also returning the in-segment index of that item.
-     *
-     * \par
-     * - The output value type of \p d_out is cub::KeyValuePair <tt><int, T></tt> (assuming the value type of \p d_in is \p T)
-     *   - The minimum of the <em>i</em><sup>th</sup> segment is written to <tt>d_out[i].value</tt> and its offset in that segment is written to <tt>d_out[i].key</tt>.
-     *   - The <tt>{1, std::numeric_limits<T>::max()}</tt> tuple is produced for zero-length inputs
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - Does not support \p < operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the argmin-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int                      num_segments;   // e.g., 3
-     * int                      *d_offsets;     // e.g., [0, 3, 3, 7]
-     * int                      *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * KeyValuePair<int, int>   *d_out;         // e.g., [{-,-}, {-,-}, {-,-}]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedReduce::ArgMin(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run argmin-reduction
-     * cub::DeviceSegmentedReduce::ArgMin(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_out <-- [{1,6}, {1,INT_MAX}, {2,0}]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items (of some type \p T) \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate (having value type <tt>KeyValuePair<int, T></tt>) \iterator
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t ArgMin(
-        void                *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                              ///< [out] Pointer to the output aggregate
-        int                 num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int                 *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int                 *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        cudaStream_t        stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputValueT;
-
-        // The output tuple type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            KeyValuePair<OffsetT, InputValueT>,                                                                 // ... then the key value pair OffsetT + InputValueT
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputTupleT;                     // ... else the output iterator's value type
-
-        // The output value type
-        typedef typename OutputTupleT::Value OutputValueT;
-
-        // Wrapped input iterator to produce index-value <OffsetT, InputT> tuples
-        typedef ArgIndexInputIterator<InputIteratorT, OffsetT, OutputValueT> ArgIndexInputIteratorT;
-        ArgIndexInputIteratorT d_indexed_in(d_in);
-
-        // Initial value
-        OutputTupleT initial_value(1, Traits<InputValueT>::Max());   // replace with std::numeric_limits<T>::max() when C++11 support is more prevalent
-
-        return DispatchSegmentedReduce<ArgIndexInputIteratorT, OutputIteratorT, OffsetT, cub::ArgMin>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_indexed_in,
-            d_out,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            cub::ArgMin(),
-            initial_value,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide segmented maximum using the greater-than ('>') operator.
-     *
-     * \par
-     * - Uses <tt>std::numeric_limits<T>::lowest()</tt> as the initial value of the reduction.
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - Does not support \p > operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the max-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int num_segments;   // e.g., 3
-     * int *d_offsets;     // e.g., [0, 3, 3, 7]
-     * int *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * int *d_out;         // e.g., [-, -, -]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run max-reduction
-     * cub::DeviceSegmentedReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_out <-- [8, INT_MIN, 9]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate \iterator
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t Max(
-        void                *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                              ///< [out] Pointer to the output aggregate
-        int                 num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int                 *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int                 *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        cudaStream_t        stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input value type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-        return DispatchSegmentedReduce<InputIteratorT, OutputIteratorT, OffsetT, cub::Max>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_out,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            cub::Max(),
-            Traits<InputT>::Lowest(),    // replace with std::numeric_limits<T>::lowest() when C++11 support is more prevalent
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Finds the first device-wide maximum in each segment using the greater-than ('>') operator, also returning the in-segment index of that item
-     *
-     * \par
-     * - The output value type of \p d_out is cub::KeyValuePair <tt><int, T></tt> (assuming the value type of \p d_in is \p T)
-     *   - The maximum of the <em>i</em><sup>th</sup> segment is written to <tt>d_out[i].value</tt> and its offset in that segment is written to <tt>d_out[i].key</tt>.
-     *   - The <tt>{1, std::numeric_limits<T>::lowest()}</tt> tuple is produced for zero-length inputs
-     * - When input a contiguous sequence of segments, a single sequence
-     *   \p segment_offsets (of length <tt>num_segments+1</tt>) can be aliased
-     *   for both the \p d_begin_offsets and \p d_end_offsets parameters (where
-     *   the latter is specified as <tt>segment_offsets+1</tt>).
-     * - Does not support \p > operators that are non-commutative.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the argmax-reduction of a device vector of \p int data elements.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_reduce.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int                      num_segments;   // e.g., 3
-     * int                      *d_offsets;     // e.g., [0, 3, 3, 7]
-     * int                      *d_in;          // e.g., [8, 6, 7, 5, 3, 0, 9]
-     * KeyValuePair<int, int>   *d_out;         // e.g., [{-,-}, {-,-}, {-,-}]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSegmentedReduce::ArgMax(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run argmax-reduction
-     * cub::DeviceSegmentedReduce::ArgMax(d_temp_storage, temp_storage_bytes, d_in, d_out,
-     *     num_segments, d_offsets, d_offsets + 1);
-     *
-     * // d_out <-- [{0,8}, {1,INT_MIN}, {3,9}]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT     <b>[inferred]</b> Random-access input iterator type for reading input items (of some type \p T) \iterator
-     * \tparam OutputIteratorT    <b>[inferred]</b> Output iterator type for recording the reduced aggregate (having value type <tt>KeyValuePair<int, T></tt>) \iterator
-     */
-    template <
-        typename            InputIteratorT,
-        typename            OutputIteratorT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t ArgMax(
-        void                *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t              &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                              ///< [out] Pointer to the output aggregate
-        int                 num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int                 *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int                 *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        cudaStream_t        stream              = 0,            ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous   = false)        ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // The input type
-        typedef typename std::iterator_traits<InputIteratorT>::value_type InputValueT;
-
-        // The output tuple type
-        typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            KeyValuePair<OffsetT, InputValueT>,                                                                 // ... then the key value pair OffsetT + InputValueT
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputTupleT;                     // ... else the output iterator's value type
-
-        // The output value type
-        typedef typename OutputTupleT::Value OutputValueT;
-
-        // Wrapped input iterator to produce index-value <OffsetT, InputT> tuples
-        typedef ArgIndexInputIterator<InputIteratorT, OffsetT, OutputValueT> ArgIndexInputIteratorT;
-        ArgIndexInputIteratorT d_indexed_in(d_in);
-
-        // Initial value
-        OutputTupleT initial_value(1, Traits<InputValueT>::Lowest());     // replace with std::numeric_limits<T>::lowest() when C++11 support is more prevalent
-
-        return DispatchSegmentedReduce<ArgIndexInputIteratorT, OutputIteratorT, OffsetT, cub::ArgMax>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_indexed_in,
-            d_out,
-            num_segments,
-            d_begin_offsets,
-            d_end_offsets,
-            cub::ArgMax(),
-            initial_value,
-            stream,
-            debug_synchronous);
-    }
-
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_select.cuh b/ml-xgboost/cub/cub/device/device_select.cuh
deleted file mode 100644
index 237b595..0000000
--- a/ml-xgboost/cub/cub/device/device_select.cuh
+++ /dev/null
@@ -1,369 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSelect provides device-wide, parallel operations for compacting selected items from sequences of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch/dispatch_select_if.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceSelect provides device-wide, parallel operations for compacting selected items from sequences of data items residing within device-accessible memory. ![](select_logo.png)
- * \ingroup SingleModule
- *
- * \par Overview
- * These operations apply a selection criterion to selectively copy
- * items from a specified input sequence to a compact output sequence.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceSelect}
- *
- * \par Performance
- * \linear_performance{select-flagged, select-if, and select-unique}
- *
- * \par
- * The following chart illustrates DeviceSelect::If
- * performance across different CUDA architectures for \p int32 items,
- * where 50% of the items are randomly selected.
- *
- * \image html select_if_int32_50_percent.png
- *
- * \par
- * The following chart illustrates DeviceSelect::Unique
- * performance across different CUDA architectures for \p int32 items
- * where segments have lengths uniformly sampled from [1,1000].
- *
- * \image html select_unique_int32_len_500.png
- *
- * \par
- * \plots_below
- *
- */
-struct DeviceSelect
-{
-    /**
-     * \brief Uses the \p d_flags sequence to selectively copy the corresponding items from \p d_in into \p d_out.  The total number of items selected is written to \p d_num_selected_out. ![](select_flags_logo.png)
-     *
-     * \par
-     * - The value type of \p d_flags must be castable to \p bool (e.g., \p bool, \p char, \p int, etc.).
-     * - Copies of the selected items are compacted into \p d_out and maintain their original relative ordering.
-     * - \devicestorage
-     *
-     * \par Snippet
-     * The code snippet below illustrates the compaction of items selected from an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>       // or equivalently <cub/device/device_select.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input, flags, and output
-     * int  num_items;              // e.g., 8
-     * int  *d_in;                  // e.g., [1, 2, 3, 4, 5, 6, 7, 8]
-     * char *d_flags;               // e.g., [1, 0, 0, 1, 0, 1, 1, 0]
-     * int  *d_out;                 // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int  *d_num_selected_out;    // e.g., [ ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSelect::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run selection
-     * cub::DeviceSelect::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items);
-     *
-     * // d_out                 <-- [1, 4, 6, 7]
-     * // d_num_selected_out    <-- [4]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam FlagIterator         <b>[inferred]</b> Random-access input iterator type for reading selection flags \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Random-access output iterator type for writing selected items \iterator
-     * \tparam NumSelectedIteratorT  <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    FlagIterator,
-        typename                    OutputIteratorT,
-        typename                    NumSelectedIteratorT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Flagged(
-        void*               d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        FlagIterator                d_flags,                        ///< [in] Pointer to the input sequence of selection flags
-        OutputIteratorT             d_out,                          ///< [out] Pointer to the output sequence of selected data items
-        NumSelectedIteratorT         d_num_selected_out,                 ///< [out] Pointer to the output total number of items selected (i.e., length of \p d_out)
-        int                         num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int                     OffsetT;         // Signed integer type for global offsets
-        typedef NullType                SelectOp;       // Selection op (not used)
-        typedef NullType                EqualityOp;     // Equality operator (not used)
-
-        return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, false>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_flags,
-            d_out,
-            d_num_selected_out,
-            SelectOp(),
-            EqualityOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Uses the \p select_op functor to selectively copy items from \p d_in into \p d_out.  The total number of items selected is written to \p d_num_selected_out. ![](select_logo.png)
-     *
-     * \par
-     * - Copies of the selected items are compacted into \p d_out and maintain their original relative ordering.
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated select-if performance across different
-     * CUDA architectures for \p int32 and \p int64 items, respectively.  Items are
-     * selected with 50% probability.
-     *
-     * \image html select_if_int32_50_percent.png
-     * \image html select_if_int64_50_percent.png
-     *
-     * \par
-     * The following charts are similar, but 5% selection probability:
-     *
-     * \image html select_if_int32_5_percent.png
-     * \image html select_if_int64_5_percent.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the compaction of items selected from an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_select.cuh>
-     *
-     * // Functor type for selecting values less than some criteria
-     * struct LessThan
-     * {
-     *     int compare;
-     *
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     LessThan(int compare) : compare(compare) {}
-     *
-     *     CUB_RUNTIME_FUNCTION __forceinline__
-     *     bool operator()(const int &a) const {
-     *         return (a < compare);
-     *     }
-     * };
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int      num_items;              // e.g., 8
-     * int      *d_in;                  // e.g., [0, 2, 3, 9, 5, 2, 81, 8]
-     * int      *d_out;                 // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int      *d_num_selected_out;    // e.g., [ ]
-     * LessThan select_op(7);
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run selection
-     * cub::DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op);
-     *
-     * // d_out                 <-- [0, 2, 3, 5, 2]
-     * // d_num_selected_out    <-- [5]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Random-access output iterator type for writing selected items \iterator
-     * \tparam NumSelectedIteratorT  <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
-     * \tparam SelectOp             <b>[inferred]</b> Selection operator type having member <tt>bool operator()(const T &a)</tt>
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT,
-        typename                    NumSelectedIteratorT,
-        typename                    SelectOp>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t If(
-        void*               d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                          ///< [out] Pointer to the output sequence of selected data items
-        NumSelectedIteratorT         d_num_selected_out,                 ///< [out] Pointer to the output total number of items selected (i.e., length of \p d_out)
-        int                         num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        SelectOp                    select_op,                      ///< [in] Unary selection operator
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int                     OffsetT;         // Signed integer type for global offsets
-        typedef NullType*               FlagIterator;   // FlagT iterator type (not used)
-        typedef NullType                EqualityOp;     // Equality operator (not used)
-
-        return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, false>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            NULL,
-            d_out,
-            d_num_selected_out,
-            select_op,
-            EqualityOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Given an input sequence \p d_in having runs of consecutive equal-valued keys, only the first key from each run is selectively copied to \p d_out.  The total number of items selected is written to \p d_num_selected_out. ![](unique_logo.png)
-     *
-     * \par
-     * - The <tt>==</tt> equality operator is used to determine whether keys are equivalent
-     * - Copies of the selected items are compacted into \p d_out and maintain their original relative ordering.
-     * - \devicestorage
-     *
-     * \par Performance
-     * The following charts illustrate saturated select-unique performance across different
-     * CUDA architectures for \p int32 and \p int64 items, respectively.  Segments have
-     * lengths uniformly sampled from [1,1000].
-     *
-     * \image html select_unique_int32_len_500.png
-     * \image html select_unique_int64_len_500.png
-     *
-     * \par
-     * The following charts are similar, but with segment lengths uniformly sampled from [1,10]:
-     *
-     * \image html select_unique_int32_len_5.png
-     * \image html select_unique_int64_len_5.png
-     *
-     * \par Snippet
-     * The code snippet below illustrates the compaction of items selected from an \p int device vector.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>       // or equivalently <cub/device/device_select.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input and output
-     * int  num_items;              // e.g., 8
-     * int  *d_in;                  // e.g., [0, 2, 2, 9, 5, 5, 5, 8]
-     * int  *d_out;                 // e.g., [ ,  ,  ,  ,  ,  ,  ,  ]
-     * int  *d_num_selected_out;    // e.g., [ ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void     *d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSelect::Unique(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run selection
-     * cub::DeviceSelect::Unique(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items);
-     *
-     * // d_out                 <-- [0, 2, 9, 5, 8]
-     * // d_num_selected_out    <-- [5]
-     *
-     * \endcode
-     *
-     * \tparam InputIteratorT       <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
-     * \tparam OutputIteratorT      <b>[inferred]</b> Random-access output iterator type for writing selected items \iterator
-     * \tparam NumSelectedIteratorT  <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
-     */
-    template <
-        typename                    InputIteratorT,
-        typename                    OutputIteratorT,
-        typename                    NumSelectedIteratorT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Unique(
-        void*               d_temp_storage,                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                      &temp_storage_bytes,            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT             d_out,                          ///< [out] Pointer to the output sequence of selected data items
-        NumSelectedIteratorT         d_num_selected_out,             ///< [out] Pointer to the output total number of items selected (i.e., length of \p d_out)
-        int                         num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream             = 0,         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous  = false)     ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        typedef int                     OffsetT;         // Signed integer type for global offsets
-        typedef NullType*               FlagIterator;   // FlagT iterator type (not used)
-        typedef NullType                SelectOp;       // Selection op (not used)
-        typedef Equality                EqualityOp;     // Default == operator
-
-        return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, false>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            NULL,
-            d_out,
-            d_num_selected_out,
-            SelectOp(),
-            EqualityOp(),
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-
-};
-
-/**
- * \example example_device_select_flagged.cu
- * \example example_device_select_if.cu
- * \example example_device_select_unique.cu
- */
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/device_spmv.cuh b/ml-xgboost/cub/cub/device/device_spmv.cuh
deleted file mode 100644
index b0d0e02..0000000
--- a/ml-xgboost/cub/cub/device/device_spmv.cuh
+++ /dev/null
@@ -1,174 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * vector multiplication (SpMV).
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-#include <limits>
-
-#include "dispatch/dispatch_spmv_orig.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * dense-vector multiplication (SpMV).
- * \ingroup SingleModule
- *
- * \par Overview
- * The [<em>SpMV computation</em>](http://en.wikipedia.org/wiki/Sparse_matrix-vector_multiplication)
- * performs the matrix-vector operation
- * <em>y</em> = <em>alpha</em>*<b>A</b>*<em>x</em> + <em>beta</em>*<em>y</em>,
- * where:
- *  - <b>A</b> is an <em>m</em>x<em>n</em> sparse matrix whose non-zero structure is specified in
- *    [<em>compressed-storage-row (CSR) format</em>](http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_row_Storage_.28CRS_or_CSR.29)
- *    (i.e., three arrays: <em>values</em>, <em>row_offsets</em>, and <em>column_indices</em>)
- *  - <em>x</em> and <em>y</em> are dense vectors
- *  - <em>alpha</em> and <em>beta</em> are scalar multiplicands
- *
- * \par Usage Considerations
- * \cdp_class{DeviceSpmv}
- *
- */
-struct DeviceSpmv
-{
-    /******************************************************************//**
-     * \name CSR matrix operations
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief This function performs the matrix-vector operation <em>y</em> = <b>A</b>*<em>x</em>.
-     *
-     * \par Snippet
-     * The code snippet below illustrates SpMV upon a 9x9 CSR matrix <b>A</b>
-     * representing a 3x3 lattice (24 non-zeros).
-     *
-     * \par
-     * \code
-     * #include <cub/cub.cuh>   // or equivalently <cub/device/device_spmv.cuh>
-     *
-     * // Declare, allocate, and initialize device-accessible pointers for input matrix A, input vector x,
-     * // and output vector y
-     * int    num_rows = 9;
-     * int    num_cols = 9;
-     * int    num_nonzeros = 24;
-     *
-     * float* d_values;  // e.g., [1, 1, 1, 1, 1, 1, 1, 1,
-     *                   //        1, 1, 1, 1, 1, 1, 1, 1,
-     *                   //        1, 1, 1, 1, 1, 1, 1, 1]
-     *
-     * int*   d_column_indices; // e.g., [1, 3, 0, 2, 4, 1, 5, 0,
-     *                          //        4, 6, 1, 3, 5, 7, 2, 4,
-     *                          //        8, 3, 7, 4, 6, 8, 5, 7]
-     *
-     * int*   d_row_offsets;    // e.g., [0, 2, 5, 7, 10, 14, 17, 19, 22, 24]
-     *
-     * float* d_vector_x;       // e.g., [1, 1, 1, 1, 1, 1, 1, 1, 1]
-     * float* d_vector_y;       // e.g., [ ,  ,  ,  ,  ,  ,  ,  ,  ]
-     * ...
-     *
-     * // Determine temporary device storage requirements
-     * void*    d_temp_storage = NULL;
-     * size_t   temp_storage_bytes = 0;
-     * cub::DeviceSpmv::CsrMV(d_temp_storage, temp_storage_bytes, d_values,
-     *     d_row_offsets, d_column_indices, d_vector_x, d_vector_y,
-     *     num_rows, num_cols, num_nonzeros, alpha, beta);
-     *
-     * // Allocate temporary storage
-     * cudaMalloc(&d_temp_storage, temp_storage_bytes);
-     *
-     * // Run SpMV
-     * cub::DeviceSpmv::CsrMV(d_temp_storage, temp_storage_bytes, d_values,
-     *     d_row_offsets, d_column_indices, d_vector_x, d_vector_y,
-     *     num_rows, num_cols, num_nonzeros, alpha, beta);
-     *
-     * // d_vector_y <-- [2, 3, 2, 3, 4, 3, 2, 3, 2]
-     *
-     * \endcode
-     *
-     * \tparam ValueT       <b>[inferred]</b> Matrix and vector value type (e.g., /p float, /p double, etc.)
-     */
-    template <
-        typename            ValueT>
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t CsrMV(
-        void*               d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        ValueT*             d_values,                           ///< [in] Pointer to the array of \p num_nonzeros values of the corresponding nonzero elements of matrix <b>A</b>.
-        int*                d_row_offsets,                      ///< [in] Pointer to the array of \p m + 1 offsets demarcating the start of every row in \p d_column_indices and \p d_values (with the final entry being equal to \p num_nonzeros)
-        int*                d_column_indices,                   ///< [in] Pointer to the array of \p num_nonzeros column-indices of the corresponding nonzero elements of matrix <b>A</b>.  (Indices are zero-valued.)
-        ValueT*             d_vector_x,                         ///< [in] Pointer to the array of \p num_cols values corresponding to the dense input vector <em>x</em>
-        ValueT*             d_vector_y,                         ///< [out] Pointer to the array of \p num_rows values corresponding to the dense output vector <em>y</em>
-        int                 num_rows,                           ///< [in] number of rows of matrix <b>A</b>.
-        int                 num_cols,                           ///< [in] number of columns of matrix <b>A</b>.
-        int                 num_nonzeros,                       ///< [in] number of nonzero elements of matrix <b>A</b>.
-        cudaStream_t        stream                  = 0,        ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous       = false)    ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        SpmvParams<ValueT, int> spmv_params;
-        spmv_params.d_values             = d_values;
-        spmv_params.d_row_end_offsets    = d_row_offsets + 1;
-        spmv_params.d_column_indices     = d_column_indices;
-        spmv_params.d_vector_x           = d_vector_x;
-        spmv_params.d_vector_y           = d_vector_y;
-        spmv_params.num_rows             = num_rows;
-        spmv_params.num_cols             = num_cols;
-        spmv_params.num_nonzeros         = num_nonzeros;
-        spmv_params.alpha                = 1.0;
-        spmv_params.beta                 = 0.0;
-
-        return DispatchSpmv<ValueT, int>::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            spmv_params,
-            stream,
-            debug_synchronous);
-    }
-
-    //@}  end member group
-};
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_histogram.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_histogram.cuh
deleted file mode 100644
index 52504da..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_histogram.cuh
+++ /dev/null
@@ -1,1085 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceHistogram provides device-wide parallel operations for constructing histogram(s) from a sequence of samples data residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-#include <limits>
-
-#include "../../agent/agent_histogram.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../thread/thread_search.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-
-/******************************************************************************
- * Histogram kernel entry points
- *****************************************************************************/
-
-/**
- * Histogram initialization kernel entry point
- */
-template <
-    int                                             NUM_ACTIVE_CHANNELS,            ///< Number of channels actively being histogrammed
-    typename                                        CounterT,                       ///< Integer type for counting sample occurrences per histogram bin
-    typename                                        OffsetT>                        ///< Signed integer type for global offsets
-__global__ void DeviceHistogramInitKernel(
-    ArrayWrapper<int, NUM_ACTIVE_CHANNELS>          num_output_bins_wrapper,        ///< Number of output histogram bins per channel
-    ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS>    d_output_histograms_wrapper,    ///< Histogram counter data having logical dimensions <tt>CounterT[NUM_ACTIVE_CHANNELS][num_bins.array[CHANNEL]]</tt>
-    GridQueue<int>                                  tile_queue)                     ///< Drain queue descriptor for dynamically mapping tile data onto thread blocks
-{
-    if ((threadIdx.x == 0) && (blockIdx.x == 0))
-        tile_queue.ResetDrain();
-
-    int output_bin = (blockIdx.x * blockDim.x) + threadIdx.x;
-
-    #pragma unroll
-    for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-    {
-        if (output_bin < num_output_bins_wrapper.array[CHANNEL])
-            d_output_histograms_wrapper.array[CHANNEL][output_bin] = 0;
-    }
-}
-
-
-/**
- * Histogram privatized sweep kernel entry point (multi-block).  Computes privatized histograms, one per thread block.
- */
-template <
-    typename                                            AgentHistogramPolicyT,     ///< Parameterized AgentHistogramPolicy tuning policy type
-    int                                                 PRIVATIZED_SMEM_BINS,           ///< Maximum number of histogram bins per channel (e.g., up to 256)
-    int                                                 NUM_CHANNELS,                   ///< Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
-    int                                                 NUM_ACTIVE_CHANNELS,            ///< Number of channels actively being histogrammed
-    typename                                            SampleIteratorT,                ///< The input iterator type. \iterator.
-    typename                                            CounterT,                       ///< Integer type for counting sample occurrences per histogram bin
-    typename                                            PrivatizedDecodeOpT,            ///< The transform operator type for determining privatized counter indices from samples, one for each channel
-    typename                                            OutputDecodeOpT,                ///< The transform operator type for determining output bin-ids from privatized counter indices, one for each channel
-    typename                                            OffsetT>                        ///< Signed integer type for global offsets
-__launch_bounds__ (int(AgentHistogramPolicyT::BLOCK_THREADS))
-__global__ void DeviceHistogramSweepKernel(
-    SampleIteratorT                                         d_samples,                          ///< Input data to reduce
-    ArrayWrapper<int, NUM_ACTIVE_CHANNELS>                  num_output_bins_wrapper,            ///< The number bins per final output histogram
-    ArrayWrapper<int, NUM_ACTIVE_CHANNELS>                  num_privatized_bins_wrapper,        ///< The number bins per privatized histogram
-    ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS>            d_output_histograms_wrapper,        ///< Reference to final output histograms
-    ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS>            d_privatized_histograms_wrapper,    ///< Reference to privatized histograms
-    ArrayWrapper<OutputDecodeOpT, NUM_ACTIVE_CHANNELS>      output_decode_op_wrapper,           ///< The transform operator for determining output bin-ids from privatized counter indices, one for each channel
-    ArrayWrapper<PrivatizedDecodeOpT, NUM_ACTIVE_CHANNELS>  privatized_decode_op_wrapper,       ///< The transform operator for determining privatized counter indices from samples, one for each channel
-    OffsetT                                                 num_row_pixels,                     ///< The number of multi-channel pixels per row in the region of interest
-    OffsetT                                                 num_rows,                           ///< The number of rows in the region of interest
-    OffsetT                                                 row_stride_samples,                 ///< The number of samples between starts of consecutive rows in the region of interest
-    int                                                     tiles_per_row,                      ///< Number of image tiles per row
-    GridQueue<int>                                          tile_queue)                         ///< Drain queue descriptor for dynamically mapping tile data onto thread blocks
-{
-    // Thread block type for compositing input tiles
-    typedef AgentHistogram<
-            AgentHistogramPolicyT,
-            PRIVATIZED_SMEM_BINS,
-            NUM_CHANNELS,
-            NUM_ACTIVE_CHANNELS,
-            SampleIteratorT,
-            CounterT,
-            PrivatizedDecodeOpT,
-            OutputDecodeOpT,
-            OffsetT>
-        AgentHistogramT;
-
-    // Shared memory for AgentHistogram
-    __shared__ typename AgentHistogramT::TempStorage temp_storage;
-
-    AgentHistogramT agent(
-        temp_storage,
-        d_samples,
-        num_output_bins_wrapper.array,
-        num_privatized_bins_wrapper.array,
-        d_output_histograms_wrapper.array,
-        d_privatized_histograms_wrapper.array,
-        output_decode_op_wrapper.array,
-        privatized_decode_op_wrapper.array);
-
-    // Initialize counters
-    agent.InitBinCounters();
-
-    // Consume input tiles
-    agent.ConsumeTiles(
-        num_row_pixels,
-        num_rows,
-        row_stride_samples,
-        tiles_per_row,
-        tile_queue);
-
-    // Store output to global (if necessary)
-    agent.StoreOutput();
-
-}
-
-
-
-
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceHistogram
- */
-template <
-    int         NUM_CHANNELS,               ///< Number of channels interleaved in the input data (may be greater than the number of channels being actively histogrammed)
-    int         NUM_ACTIVE_CHANNELS,        ///< Number of channels actively being histogrammed
-    typename    SampleIteratorT,            ///< Random-access input iterator type for reading input items \iterator
-    typename    CounterT,                   ///< Integer type for counting sample occurrences per histogram bin
-    typename    LevelT,                     ///< Type for specifying bin level boundaries
-    typename    OffsetT>                    ///< Signed integer type for global offsets
-struct DipatchHistogram
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    /// The sample value type of the input iterator
-    typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-    enum
-    {
-        // Maximum number of bins per channel for which we will use a privatized smem strategy
-        MAX_PRIVATIZED_SMEM_BINS = 256
-    };
-
-
-    //---------------------------------------------------------------------
-    // Transform functors for converting samples to bin-ids
-    //---------------------------------------------------------------------
-
-    // Searches for bin given a list of bin-boundary levels
-    template <typename LevelIteratorT>
-    struct SearchTransform
-    {
-        LevelIteratorT  d_levels;                   // Pointer to levels array
-        int             num_output_levels;          // Number of levels in array
-
-        // Initializer
-        __host__ __device__ __forceinline__ void Init(
-            LevelIteratorT  d_levels,               // Pointer to levels array
-            int             num_output_levels)      // Number of levels in array
-        {
-            this->d_levels          = d_levels;
-            this->num_output_levels = num_output_levels;
-        }
-
-        // Method for converting samples to bin-ids
-        template <CacheLoadModifier LOAD_MODIFIER, typename _SampleT>
-        __host__ __device__ __forceinline__ void BinSelect(_SampleT sample, int &bin, bool valid)
-        {
-            /// Level iterator wrapper type
-            typedef typename If<IsPointer<LevelIteratorT>::VALUE,
-                    CacheModifiedInputIterator<LOAD_MODIFIER, LevelT, OffsetT>,     // Wrap the native input pointer with CacheModifiedInputIterator
-                    LevelIteratorT>::Type                                           // Directly use the supplied input iterator type
-                WrappedLevelIteratorT;
-
-            WrappedLevelIteratorT wrapped_levels(d_levels);
-
-            int num_bins = num_output_levels - 1;
-            if (valid)
-            {
-                bin = UpperBound(wrapped_levels, num_output_levels, (LevelT) sample) - 1;
-                if (bin >= num_bins)
-                    bin = -1;
-            }
-        }
-    };
-
-
-    // Scales samples to evenly-spaced bins
-    struct ScaleTransform
-    {
-        int    num_bins;    // Number of levels in array
-        LevelT max;         // Max sample level (exclusive)
-        LevelT min;         // Min sample level (inclusive)
-        LevelT scale;       // Bin scaling factor
-
-        // Initializer
-        template <typename _LevelT>
-        __host__ __device__ __forceinline__ void Init(
-            int     num_output_levels,  // Number of levels in array
-            _LevelT max,                // Max sample level (exclusive)
-            _LevelT min,                // Min sample level (inclusive)
-            _LevelT scale)              // Bin scaling factor
-        {
-            this->num_bins = num_output_levels - 1;
-            this->max = max;
-            this->min = min;
-            this->scale = scale;
-        }
-
-        // Initializer (float specialization)
-        __host__ __device__ __forceinline__ void Init(
-            int    num_output_levels,   // Number of levels in array
-            float   max,                // Max sample level (exclusive)
-            float   min,                // Min sample level (inclusive)
-            float   scale)              // Bin scaling factor
-        {
-            this->num_bins = num_output_levels - 1;
-            this->max = max;
-            this->min = min;
-            this->scale = float(1.0) / scale;
-        }
-
-        // Initializer (double specialization)
-        __host__ __device__ __forceinline__ void Init(
-            int    num_output_levels,   // Number of levels in array
-            double max,                 // Max sample level (exclusive)
-            double min,                 // Min sample level (inclusive)
-            double scale)               // Bin scaling factor
-        {
-            this->num_bins = num_output_levels - 1;
-            this->max = max;
-            this->min = min;
-            this->scale = double(1.0) / scale;
-        }
-
-        // Method for converting samples to bin-ids
-        template <CacheLoadModifier LOAD_MODIFIER, typename _SampleT>
-        __host__ __device__ __forceinline__ void BinSelect(_SampleT sample, int &bin, bool valid)
-        {
-            LevelT level_sample = (LevelT) sample;
-
-            if (valid && (level_sample >= min) && (level_sample < max))
-                bin = (int) ((level_sample - min) / scale);
-        }
-
-        // Method for converting samples to bin-ids (float specialization)
-        template <CacheLoadModifier LOAD_MODIFIER>
-        __host__ __device__ __forceinline__ void BinSelect(float sample, int &bin, bool valid)
-        {
-            LevelT level_sample = (LevelT) sample;
-
-            if (valid && (level_sample >= min) && (level_sample < max))
-                bin = (int) ((level_sample - min) * scale);
-        }
-
-        // Method for converting samples to bin-ids (double specialization)
-        template <CacheLoadModifier LOAD_MODIFIER>
-        __host__ __device__ __forceinline__ void BinSelect(double sample, int &bin, bool valid)
-        {
-            LevelT level_sample = (LevelT) sample;
-
-            if (valid && (level_sample >= min) && (level_sample < max))
-                bin = (int) ((level_sample - min) * scale);
-        }
-    };
-
-
-    // Pass-through bin transform operator
-    struct PassThruTransform
-    {
-        // Method for converting samples to bin-ids
-        template <CacheLoadModifier LOAD_MODIFIER, typename _SampleT>
-        __host__ __device__ __forceinline__ void BinSelect(_SampleT sample, int &bin, bool valid)
-        {
-            if (valid)
-                bin = (int) sample;
-        }
-    };
-
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies
-    //---------------------------------------------------------------------
-
-    /// SM11
-    struct Policy110
-    {
-        // HistogramSweepPolicy
-        typedef AgentHistogramPolicy<
-                512,
-                (NUM_CHANNELS == 1) ? 8 : 2,
-                BLOCK_LOAD_DIRECT,
-                LOAD_DEFAULT,
-                true,
-                GMEM,
-                false>
-            HistogramSweepPolicy;
-    };
-
-    /// SM20
-    struct Policy200
-    {
-        // HistogramSweepPolicy
-        typedef AgentHistogramPolicy<
-                (NUM_CHANNELS == 1) ? 256 : 128,
-                (NUM_CHANNELS == 1) ? 8 : 3,
-                (NUM_CHANNELS == 1) ? BLOCK_LOAD_DIRECT : BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                true,
-                SMEM,
-                false>
-            HistogramSweepPolicy;
-    };
-
-    /// SM30
-    struct Policy300
-    {
-        // HistogramSweepPolicy
-        typedef AgentHistogramPolicy<
-                512,
-                (NUM_CHANNELS == 1) ? 8 : 2,
-                BLOCK_LOAD_DIRECT,
-                LOAD_DEFAULT,
-                true,
-                GMEM,
-                false>
-            HistogramSweepPolicy;
-    };
-
-    /// SM35
-    struct Policy350
-    {
-        // HistogramSweepPolicy
-        typedef AgentHistogramPolicy<
-                128,
-                (NUM_CHANNELS == 1) ? 8 : 7,
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                true,
-                BLEND,
-                true>
-            HistogramSweepPolicy;
-    };
-
-    /// SM50
-    struct Policy500
-    {
-        // HistogramSweepPolicy
-        typedef AgentHistogramPolicy<
-                256,
-                8,
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                true,
-                SMEM,
-                true>
-            HistogramSweepPolicy;
-    };
-
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies of current PTX compiler pass
-    //---------------------------------------------------------------------
-
-#if (CUB_PTX_ARCH >= 500)
-    typedef Policy500 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#else
-    typedef Policy110 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxHistogramSweepPolicy : PtxPolicy::HistogramSweepPolicy {};
-
-
-    //---------------------------------------------------------------------
-    // Utilities
-    //---------------------------------------------------------------------
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t InitConfigs(
-        int             ptx_version,
-        KernelConfig    &histogram_sweep_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        return histogram_sweep_config.template Init<PtxHistogramSweepPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 500)
-        {
-            return histogram_sweep_config.template Init<typename Policy500::HistogramSweepPolicy>();
-        }
-        else if (ptx_version >= 350)
-        {
-            return histogram_sweep_config.template Init<typename Policy350::HistogramSweepPolicy>();
-        }
-        else if (ptx_version >= 300)
-        {
-            return histogram_sweep_config.template Init<typename Policy300::HistogramSweepPolicy>();
-        }
-        else if (ptx_version >= 200)
-        {
-            return histogram_sweep_config.template Init<typename Policy200::HistogramSweepPolicy>();
-        }
-        else if (ptx_version >= 110)
-        {
-            return histogram_sweep_config.template Init<typename Policy110::HistogramSweepPolicy>();
-        }
-        else
-        {
-            // No global atomic support
-            return cudaErrorNotSupported;
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration
-     */
-    struct KernelConfig
-    {
-        int                             block_threads;
-        int                             pixels_per_thread;
-
-        template <typename BlockPolicy>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        cudaError_t Init()
-        {
-            block_threads               = BlockPolicy::BLOCK_THREADS;
-            pixels_per_thread           = BlockPolicy::PIXELS_PER_THREAD;
-
-            return cudaSuccess;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Dispatch entrypoints
-    //---------------------------------------------------------------------
-
-    /**
-     * Privatization-based dispatch routine
-     */
-    template <
-        typename                            PrivatizedDecodeOpT,                            ///< The transform operator type for determining privatized counter indices from samples, one for each channel
-        typename                            OutputDecodeOpT,                                ///< The transform operator type for determining output bin-ids from privatized counter indices, one for each channel
-        typename                            DeviceHistogramInitKernelT,                     ///< Function type of cub::DeviceHistogramInitKernel
-        typename                            DeviceHistogramSweepKernelT>                    ///< Function type of cub::DeviceHistogramSweepKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t PrivatizedDispatch(
-        void*                               d_temp_storage,                                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                             temp_storage_bytes,                             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT                     d_samples,                                      ///< [in] The pointer to the input sequence of sample items. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-        CounterT*                           d_output_histograms[NUM_ACTIVE_CHANNELS],       ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_output_levels[i]</tt> - 1.
-        int                                 num_privatized_levels[NUM_ACTIVE_CHANNELS],     ///< [in] The number of bin level boundaries for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_output_levels[i]</tt> - 1.
-        PrivatizedDecodeOpT                 privatized_decode_op[NUM_ACTIVE_CHANNELS],      ///< [in] Transform operators for determining bin-ids from samples, one for each channel
-        int                                 num_output_levels[NUM_ACTIVE_CHANNELS],         ///< [in] The number of bin level boundaries for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_output_levels[i]</tt> - 1.
-        OutputDecodeOpT                     output_decode_op[NUM_ACTIVE_CHANNELS],          ///< [in] Transform operators for determining bin-ids from samples, one for each channel
-        int                                 max_num_output_bins,                            ///< [in] Maximum number of output bins in any channel
-        OffsetT                             num_row_pixels,                                 ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT                             num_rows,                                       ///< [in] The number of rows in the region of interest
-        OffsetT                             row_stride_samples,                             ///< [in] The number of samples between starts of consecutive rows in the region of interest
-        DeviceHistogramInitKernelT          histogram_init_kernel,                          ///< [in] Kernel function pointer to parameterization of cub::DeviceHistogramInitKernel
-        DeviceHistogramSweepKernelT         histogram_sweep_kernel,                         ///< [in] Kernel function pointer to parameterization of cub::DeviceHistogramSweepKernel
-        KernelConfig                        histogram_sweep_config,                         ///< [in] Dispatch parameters that match the policy that \p histogram_sweep_kernel was compiled for
-        cudaStream_t                        stream,                                         ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                                debug_synchronous)                              ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-    #ifndef CUB_RUNTIME_ENABLED
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported);
-
-    #else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Get SM occupancy for histogram_sweep_kernel
-            int histogram_sweep_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                histogram_sweep_sm_occupancy,
-                histogram_sweep_kernel,
-                histogram_sweep_config.block_threads))) break;
-
-            // Get device occupancy for histogram_sweep_kernel
-            int histogram_sweep_occupancy = histogram_sweep_sm_occupancy * sm_count;
-
-            if (num_row_pixels * NUM_CHANNELS == row_stride_samples)
-            {
-                // Treat as a single linear array of samples
-                num_row_pixels      *= num_rows;
-                num_rows            = 1;
-                row_stride_samples  = num_row_pixels * NUM_CHANNELS;
-            }
-
-            // Get grid dimensions, trying to keep total blocks ~histogram_sweep_occupancy
-            int pixels_per_tile     = histogram_sweep_config.block_threads * histogram_sweep_config.pixels_per_thread;
-            int tiles_per_row       = int(num_row_pixels + pixels_per_tile - 1) / pixels_per_tile;
-            int blocks_per_row      = CUB_MIN(histogram_sweep_occupancy, tiles_per_row);
-            int blocks_per_col      = (blocks_per_row > 0) ?
-                                        int(CUB_MIN(histogram_sweep_occupancy / blocks_per_row, num_rows)) :
-                                        0;
-            int num_threadblocks    = blocks_per_row * blocks_per_col;
-
-            dim3 sweep_grid_dims;
-            sweep_grid_dims.x = (unsigned int) blocks_per_row;
-            sweep_grid_dims.y = (unsigned int) blocks_per_col;
-            sweep_grid_dims.z = 1;
-
-            // Temporary storage allocation requirements
-            const int   NUM_ALLOCATIONS = NUM_ACTIVE_CHANNELS + 1;
-            void*       allocations[NUM_ALLOCATIONS];
-            size_t      allocation_sizes[NUM_ALLOCATIONS];
-
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                allocation_sizes[CHANNEL] = size_t(num_threadblocks) * (num_privatized_levels[CHANNEL] - 1) * sizeof(CounterT);
-
-            allocation_sizes[NUM_ALLOCATIONS - 1] = GridQueue<int>::AllocationSize();
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                break;
-            }
-
-            // Construct the grid queue descriptor
-            GridQueue<int> tile_queue(allocations[NUM_ALLOCATIONS - 1]);
-
-            // Setup array wrapper for histogram channel output (because we can't pass static arrays as kernel parameters)
-            ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS> d_output_histograms_wrapper;
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                d_output_histograms_wrapper.array[CHANNEL] = d_output_histograms[CHANNEL];
-
-            // Setup array wrapper for privatized per-block histogram channel output (because we can't pass static arrays as kernel parameters)
-            ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS> d_privatized_histograms_wrapper;
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                d_privatized_histograms_wrapper.array[CHANNEL] = (CounterT*) allocations[CHANNEL];
-
-            // Setup array wrapper for sweep bin transforms (because we can't pass static arrays as kernel parameters)
-            ArrayWrapper<PrivatizedDecodeOpT, NUM_ACTIVE_CHANNELS> privatized_decode_op_wrapper;
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                privatized_decode_op_wrapper.array[CHANNEL] = privatized_decode_op[CHANNEL];
-
-            // Setup array wrapper for aggregation bin transforms (because we can't pass static arrays as kernel parameters)
-            ArrayWrapper<OutputDecodeOpT, NUM_ACTIVE_CHANNELS> output_decode_op_wrapper;
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                output_decode_op_wrapper.array[CHANNEL] = output_decode_op[CHANNEL];
-
-            // Setup array wrapper for num privatized bins (because we can't pass static arrays as kernel parameters)
-            ArrayWrapper<int, NUM_ACTIVE_CHANNELS> num_privatized_bins_wrapper;
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                num_privatized_bins_wrapper.array[CHANNEL] = num_privatized_levels[CHANNEL] - 1;
-
-            // Setup array wrapper for num output bins (because we can't pass static arrays as kernel parameters)
-            ArrayWrapper<int, NUM_ACTIVE_CHANNELS> num_output_bins_wrapper;
-            for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-                num_output_bins_wrapper.array[CHANNEL] = num_output_levels[CHANNEL] - 1;
-
-            int histogram_init_block_threads    = 256;
-            int histogram_init_grid_dims        = (max_num_output_bins + histogram_init_block_threads - 1) / histogram_init_block_threads;
-
-            // Log DeviceHistogramInitKernel configuration
-            if (debug_synchronous) _CubLog("Invoking DeviceHistogramInitKernel<<<%d, %d, 0, %lld>>>()\n",
-                histogram_init_grid_dims, histogram_init_block_threads, (long long) stream);
-
-            // Invoke histogram_init_kernel
-            histogram_init_kernel<<<histogram_init_grid_dims, histogram_init_block_threads, 0, stream>>>(
-                num_output_bins_wrapper,
-                d_output_histograms_wrapper,
-                tile_queue);
-
-            // Return if empty problem
-            if ((blocks_per_row == 0) || (blocks_per_col == 0))
-                break;
-
-            // Log histogram_sweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking histogram_sweep_kernel<<<{%d, %d, %d}, %d, 0, %lld>>>(), %d pixels per thread, %d SM occupancy\n",
-                sweep_grid_dims.x, sweep_grid_dims.y, sweep_grid_dims.z,
-                histogram_sweep_config.block_threads, (long long) stream, histogram_sweep_config.pixels_per_thread, histogram_sweep_sm_occupancy);
-
-            // Invoke histogram_sweep_kernel
-            histogram_sweep_kernel<<<sweep_grid_dims, histogram_sweep_config.block_threads, 0, stream>>>(
-                d_samples,
-                num_output_bins_wrapper,
-                num_privatized_bins_wrapper,
-                d_output_histograms_wrapper,
-                d_privatized_histograms_wrapper,
-                output_decode_op_wrapper,
-                privatized_decode_op_wrapper,
-                num_row_pixels,
-                num_rows,
-                row_stride_samples,
-                tiles_per_row,
-                tile_queue);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-        }
-        while (0);
-
-        return error;
-
-    #endif // CUB_RUNTIME_ENABLED
-    }
-
-
-
-    /**
-     * Dispatch routine for HistogramRange, specialized for sample types larger than 8bit
-     */
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t DispatchRange(
-        void*               d_temp_storage,                            ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-        CounterT*           d_output_histograms[NUM_ACTIVE_CHANNELS],  ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_output_levels[i]</tt> - 1.
-        int                 num_output_levels[NUM_ACTIVE_CHANNELS],     ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_output_levels[i]</tt> - 1.
-        LevelT              *d_levels[NUM_ACTIVE_CHANNELS],             ///< [in] The pointers to the arrays of boundaries (levels), one for each active channel.  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-        OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-        OffsetT             row_stride_samples,                         ///< [in] The number of samples between starts of consecutive rows in the region of interest
-        cudaStream_t        stream,                                     ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous,                          ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-        Int2Type<false>     is_byte_sample)                             ///< [in] Marker type indicating whether or not SampleT is a 8b type
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel dispatch configurations
-            KernelConfig histogram_sweep_config;
-            if (CubDebug(error = InitConfigs(ptx_version, histogram_sweep_config)))
-                break;
-
-            // Use the search transform op for converting samples to privatized bins
-            typedef SearchTransform<LevelT*> PrivatizedDecodeOpT;
-
-            // Use the pass-thru transform op for converting privatized bins to output bins
-            typedef PassThruTransform OutputDecodeOpT;
-
-            PrivatizedDecodeOpT     privatized_decode_op[NUM_ACTIVE_CHANNELS];
-            OutputDecodeOpT         output_decode_op[NUM_ACTIVE_CHANNELS];
-            int                     max_levels = num_output_levels[0];
-
-            for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-            {
-                privatized_decode_op[channel].Init(d_levels[channel], num_output_levels[channel]);
-                if (num_output_levels[channel] > max_levels)
-                    max_levels = num_output_levels[channel];
-            }
-            int max_num_output_bins = max_levels - 1;
-
-            // Dispatch
-            if (max_num_output_bins > MAX_PRIVATIZED_SMEM_BINS)
-            {
-                // Too many bins to keep in shared memory.
-                const int PRIVATIZED_SMEM_BINS = 0;
-
-                if (CubDebug(error = PrivatizedDispatch(
-                    d_temp_storage,
-                    temp_storage_bytes,
-                    d_samples,
-                    d_output_histograms,
-                    num_output_levels,
-                    privatized_decode_op,
-                    num_output_levels,
-                    output_decode_op,
-                    max_num_output_bins,
-                    num_row_pixels,
-                    num_rows,
-                    row_stride_samples,
-                    DeviceHistogramInitKernel<NUM_ACTIVE_CHANNELS, CounterT, OffsetT>,
-                    DeviceHistogramSweepKernel<PtxHistogramSweepPolicy, PRIVATIZED_SMEM_BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, PrivatizedDecodeOpT, OutputDecodeOpT, OffsetT>,
-                    histogram_sweep_config,
-                    stream,
-                    debug_synchronous))) break;
-            }
-            else
-            {
-                // Dispatch shared-privatized approach
-                const int PRIVATIZED_SMEM_BINS = MAX_PRIVATIZED_SMEM_BINS;
-
-                if (CubDebug(error = PrivatizedDispatch(
-                    d_temp_storage,
-                    temp_storage_bytes,
-                    d_samples,
-                    d_output_histograms,
-                    num_output_levels,
-                    privatized_decode_op,
-                    num_output_levels,
-                    output_decode_op,
-                    max_num_output_bins,
-                    num_row_pixels,
-                    num_rows,
-                    row_stride_samples,
-                    DeviceHistogramInitKernel<NUM_ACTIVE_CHANNELS, CounterT, OffsetT>,
-                    DeviceHistogramSweepKernel<PtxHistogramSweepPolicy, PRIVATIZED_SMEM_BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, PrivatizedDecodeOpT, OutputDecodeOpT, OffsetT>,
-                    histogram_sweep_config,
-                    stream,
-                    debug_synchronous))) break;
-            }
-
-        } while (0);
-
-        return error;
-    }
-
-
-    /**
-     * Dispatch routine for HistogramRange, specialized for 8-bit sample types (computes 256-bin privatized histograms and then reduces to user-specified levels)
-     */
-    CUB_RUNTIME_FUNCTION
-    static cudaError_t DispatchRange(
-        void*               d_temp_storage,                             ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                         ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-        CounterT*           d_output_histograms[NUM_ACTIVE_CHANNELS],   ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_output_levels[i]</tt> - 1.
-        int                 num_output_levels[NUM_ACTIVE_CHANNELS],     ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_output_levels[i]</tt> - 1.
-        LevelT              *d_levels[NUM_ACTIVE_CHANNELS],             ///< [in] The pointers to the arrays of boundaries (levels), one for each active channel.  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-        OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-        OffsetT             row_stride_samples,                         ///< [in] The number of samples between starts of consecutive rows in the region of interest
-        cudaStream_t        stream,                                     ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous,                          ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-        Int2Type<true>      is_byte_sample)                             ///< [in] Marker type indicating whether or not SampleT is a 8b type
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel dispatch configurations
-            KernelConfig histogram_sweep_config;
-            if (CubDebug(error = InitConfigs(ptx_version, histogram_sweep_config)))
-                break;
-
-            // Use the pass-thru transform op for converting samples to privatized bins
-            typedef PassThruTransform PrivatizedDecodeOpT;
-
-            // Use the search transform op for converting privatized bins to output bins
-            typedef SearchTransform<LevelT*> OutputDecodeOpT;
-
-            int                         num_privatized_levels[NUM_ACTIVE_CHANNELS];
-            PrivatizedDecodeOpT         privatized_decode_op[NUM_ACTIVE_CHANNELS];
-            OutputDecodeOpT             output_decode_op[NUM_ACTIVE_CHANNELS];
-            int                         max_levels = num_output_levels[0];              // Maximum number of levels in any channel
-
-            for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-            {
-                num_privatized_levels[channel] = 257;
-                output_decode_op[channel].Init(d_levels[channel], num_output_levels[channel]);
-
-                if (num_output_levels[channel] > max_levels)
-                    max_levels = num_output_levels[channel];
-            }
-            int max_num_output_bins = max_levels - 1;
-
-            const int PRIVATIZED_SMEM_BINS = 256;
-
-            if (CubDebug(error = PrivatizedDispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_samples,
-                d_output_histograms,
-                num_privatized_levels,
-                privatized_decode_op,
-                num_output_levels,
-                output_decode_op,
-                max_num_output_bins,
-                num_row_pixels,
-                num_rows,
-                row_stride_samples,
-                DeviceHistogramInitKernel<NUM_ACTIVE_CHANNELS, CounterT, OffsetT>,
-                DeviceHistogramSweepKernel<PtxHistogramSweepPolicy, PRIVATIZED_SMEM_BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, PrivatizedDecodeOpT, OutputDecodeOpT, OffsetT>,
-                histogram_sweep_config,
-                stream,
-                debug_synchronous))) break;
-
-        } while (0);
-
-        return error;
-    }
-
-
-    /**
-     * Dispatch routine for HistogramEven, specialized for sample types larger than 8-bit
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t DispatchEven(
-        void*               d_temp_storage,                            ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the input sequence of sample items. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-        CounterT*           d_output_histograms[NUM_ACTIVE_CHANNELS],  ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_output_levels[i]</tt> - 1.
-        int                 num_output_levels[NUM_ACTIVE_CHANNELS],     ///< [in] The number of bin level boundaries for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_output_levels[i]</tt> - 1.
-        LevelT              lower_level[NUM_ACTIVE_CHANNELS],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-        LevelT              upper_level[NUM_ACTIVE_CHANNELS],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-        OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-        OffsetT             row_stride_samples,                         ///< [in] The number of samples between starts of consecutive rows in the region of interest
-        cudaStream_t        stream,                                     ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous,                          ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-        Int2Type<false>     is_byte_sample)                             ///< [in] Marker type indicating whether or not SampleT is a 8b type
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel dispatch configurations
-            KernelConfig histogram_sweep_config;
-            if (CubDebug(error = InitConfigs(ptx_version, histogram_sweep_config)))
-                break;
-
-            // Use the scale transform op for converting samples to privatized bins
-            typedef ScaleTransform PrivatizedDecodeOpT;
-
-            // Use the pass-thru transform op for converting privatized bins to output bins
-            typedef PassThruTransform OutputDecodeOpT;
-
-            PrivatizedDecodeOpT         privatized_decode_op[NUM_ACTIVE_CHANNELS];
-            OutputDecodeOpT             output_decode_op[NUM_ACTIVE_CHANNELS];
-            int                         max_levels = num_output_levels[0];
-
-            for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-            {
-                int     bins    = num_output_levels[channel] - 1;
-                LevelT  scale   = (upper_level[channel] - lower_level[channel]) / bins;
-
-                privatized_decode_op[channel].Init(num_output_levels[channel], upper_level[channel], lower_level[channel], scale);
-
-                if (num_output_levels[channel] > max_levels)
-                    max_levels = num_output_levels[channel];
-            }
-            int max_num_output_bins = max_levels - 1;
-
-            if (max_num_output_bins > MAX_PRIVATIZED_SMEM_BINS)
-            {
-                // Dispatch shared-privatized approach
-                const int PRIVATIZED_SMEM_BINS = 0;
-
-                if (CubDebug(error = PrivatizedDispatch(
-                    d_temp_storage,
-                    temp_storage_bytes,
-                    d_samples,
-                    d_output_histograms,
-                    num_output_levels,
-                    privatized_decode_op,
-                    num_output_levels,
-                    output_decode_op,
-                    max_num_output_bins,
-                    num_row_pixels,
-                    num_rows,
-                    row_stride_samples,
-                    DeviceHistogramInitKernel<NUM_ACTIVE_CHANNELS, CounterT, OffsetT>,
-                    DeviceHistogramSweepKernel<PtxHistogramSweepPolicy, PRIVATIZED_SMEM_BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, PrivatizedDecodeOpT, OutputDecodeOpT, OffsetT>,
-                    histogram_sweep_config,
-                    stream,
-                    debug_synchronous))) break;
-            }
-            else
-            {
-                // Dispatch shared-privatized approach
-                const int PRIVATIZED_SMEM_BINS = MAX_PRIVATIZED_SMEM_BINS;
-
-                if (CubDebug(error = PrivatizedDispatch(
-                    d_temp_storage,
-                    temp_storage_bytes,
-                    d_samples,
-                    d_output_histograms,
-                    num_output_levels,
-                    privatized_decode_op,
-                    num_output_levels,
-                    output_decode_op,
-                    max_num_output_bins,
-                    num_row_pixels,
-                    num_rows,
-                    row_stride_samples,
-                    DeviceHistogramInitKernel<NUM_ACTIVE_CHANNELS, CounterT, OffsetT>,
-                    DeviceHistogramSweepKernel<PtxHistogramSweepPolicy, PRIVATIZED_SMEM_BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, PrivatizedDecodeOpT, OutputDecodeOpT, OffsetT>,
-                    histogram_sweep_config,
-                    stream,
-                    debug_synchronous))) break;
-            }
-        }
-        while (0);
-
-        return error;
-    }
-
-
-    /**
-     * Dispatch routine for HistogramEven, specialized for 8-bit sample types (computes 256-bin privatized histograms and then reduces to user-specified levels)
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t DispatchEven(
-        void*               d_temp_storage,                            ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,                        ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SampleIteratorT     d_samples,                                  ///< [in] The pointer to the input sequence of sample items. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-        CounterT*           d_output_histograms[NUM_ACTIVE_CHANNELS],  ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_output_levels[i]</tt> - 1.
-        int                 num_output_levels[NUM_ACTIVE_CHANNELS],     ///< [in] The number of bin level boundaries for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_output_levels[i]</tt> - 1.
-        LevelT              lower_level[NUM_ACTIVE_CHANNELS],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-        LevelT              upper_level[NUM_ACTIVE_CHANNELS],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-        OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-        OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-        OffsetT             row_stride_samples,                         ///< [in] The number of samples between starts of consecutive rows in the region of interest
-        cudaStream_t        stream,                                     ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous,                          ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-        Int2Type<true>      is_byte_sample)                             ///< [in] Marker type indicating whether or not SampleT is a 8b type
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel dispatch configurations
-            KernelConfig histogram_sweep_config;
-            if (CubDebug(error = InitConfigs(ptx_version, histogram_sweep_config)))
-                break;
-
-            // Use the pass-thru transform op for converting samples to privatized bins
-            typedef PassThruTransform PrivatizedDecodeOpT;
-
-            // Use the scale transform op for converting privatized bins to output bins
-            typedef ScaleTransform OutputDecodeOpT;
-
-            int                     num_privatized_levels[NUM_ACTIVE_CHANNELS];
-            PrivatizedDecodeOpT     privatized_decode_op[NUM_ACTIVE_CHANNELS];
-            OutputDecodeOpT         output_decode_op[NUM_ACTIVE_CHANNELS];
-            int                     max_levels = num_output_levels[0];
-
-            for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-            {
-                num_privatized_levels[channel] = 257;
-
-                int     bins    = num_output_levels[channel] - 1;
-                LevelT  scale   = (upper_level[channel] - lower_level[channel]) / bins;
-                output_decode_op[channel].Init(num_output_levels[channel], upper_level[channel], lower_level[channel], scale);
-
-                if (num_output_levels[channel] > max_levels)
-                    max_levels = num_output_levels[channel];
-            }
-            int max_num_output_bins = max_levels - 1;
-
-            const int PRIVATIZED_SMEM_BINS = 256;
-
-            if (CubDebug(error = PrivatizedDispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_samples,
-                d_output_histograms,
-                num_privatized_levels,
-                privatized_decode_op,
-                num_output_levels,
-                output_decode_op,
-                max_num_output_bins,
-                num_row_pixels,
-                num_rows,
-                row_stride_samples,
-                DeviceHistogramInitKernel<NUM_ACTIVE_CHANNELS, CounterT, OffsetT>,
-                DeviceHistogramSweepKernel<PtxHistogramSweepPolicy, PRIVATIZED_SMEM_BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, PrivatizedDecodeOpT, OutputDecodeOpT, OffsetT>,
-                histogram_sweep_config,
-                stream,
-                debug_synchronous))) break;
-
-        }
-        while (0);
-
-        return error;
-    }
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_radix_sort.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_radix_sort.cuh
deleted file mode 100644
index d2483ad..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_radix_sort.cuh
+++ /dev/null
@@ -1,1572 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceRadixSort provides device-wide, parallel operations for computing a radix sort across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "../../agent/agent_radix_sort_upsweep.cuh"
-#include "../../agent/agent_radix_sort_downsweep.cuh"
-#include "../../agent/agent_scan.cuh"
-#include "../../block/block_radix_sort.cuh"
-#include "../../grid/grid_even_share.cuh"
-#include "../../util_type.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/******************************************************************************
- * Kernel entry points
- *****************************************************************************/
-
-/**
- * Upsweep digit-counting kernel entry point (multi-block).  Computes privatized digit histograms, one per block.
- */
-template <
-    typename                ChainedPolicyT,                 ///< Chained tuning policy
-    bool                    ALT_DIGIT_BITS,                 ///< Whether or not to use the alternate (lower-bits) policy
-    bool                    IS_DESCENDING,                  ///< Whether or not the sorted-order is high-to-low
-    typename                KeyT,                           ///< Key type
-    typename                OffsetT>                        ///< Signed integer type for global offsets
-__launch_bounds__ (int((ALT_DIGIT_BITS) ?
-    ChainedPolicyT::ActivePolicy::AltUpsweepPolicy::BLOCK_THREADS :
-    ChainedPolicyT::ActivePolicy::UpsweepPolicy::BLOCK_THREADS))
-__global__ void DeviceRadixSortUpsweepKernel(
-    const KeyT              *d_keys,                        ///< [in] Input keys buffer
-    OffsetT                 *d_spine,                       ///< [out] Privatized (per block) digit histograms (striped, i.e., 0s counts from each block, then 1s counts from each block, etc.)
-    OffsetT                 /*num_items*/,                  ///< [in] Total number of input data items
-    int                     current_bit,                    ///< [in] Bit position of current radix digit
-    int                     num_bits,                       ///< [in] Number of bits of current radix digit
-    GridEvenShare<OffsetT>  even_share)                     ///< [in] Even-share descriptor for mapan equal number of tiles onto each thread block
-{
-    // Parameterize AgentRadixSortUpsweep type for the current configuration
-    typedef AgentRadixSortUpsweep<
-            typename If<(ALT_DIGIT_BITS),
-                typename ChainedPolicyT::ActivePolicy::AltUpsweepPolicy,
-                typename ChainedPolicyT::ActivePolicy::UpsweepPolicy>::Type,
-            KeyT,
-            OffsetT>
-        AgentRadixSortUpsweepT;
-
-    // Shared memory storage
-    __shared__ typename AgentRadixSortUpsweepT::TempStorage temp_storage;
-
-    // Initialize even-share descriptor for this thread block
-    even_share.BlockInit();
-
-    OffsetT bin_count;
-    AgentRadixSortUpsweepT(temp_storage, d_keys, current_bit, num_bits).ProcessRegion(
-        even_share.block_offset,
-        even_share.block_end,
-        bin_count);
-
-    // Write out digit counts (striped)
-    if (threadIdx.x < AgentRadixSortUpsweepT::RADIX_DIGITS)
-    {
-        int bin_idx = (IS_DESCENDING) ?
-            AgentRadixSortUpsweepT::RADIX_DIGITS - threadIdx.x - 1 :
-            threadIdx.x;
-
-        d_spine[(gridDim.x * bin_idx) + blockIdx.x] = bin_count;
-    }
-}
-
-
-/**
- * Spine scan kernel entry point (single-block).  Computes an exclusive prefix sum over the privatized digit histograms
- */
-template <
-    typename                ChainedPolicyT,                 ///< Chained tuning policy
-    typename                OffsetT>                        ///< Signed integer type for global offsets
-__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::ScanPolicy::BLOCK_THREADS), 1)
-__global__ void RadixSortScanBinsKernel(
-    OffsetT                 *d_spine,                       ///< [in,out] Privatized (per block) digit histograms (striped, i.e., 0s counts from each block, then 1s counts from each block, etc.)
-    int                     num_counts)                     ///< [in] Total number of bin-counts
-{
-    // Parameterize the AgentScan type for the current configuration
-    typedef AgentScan<
-            typename ChainedPolicyT::ActivePolicy::ScanPolicy,
-            OffsetT*,
-            OffsetT*,
-            cub::Sum,
-            OffsetT,
-            OffsetT>
-        AgentScanT;
-
-    // Shared memory storage
-    __shared__ typename AgentScanT::TempStorage temp_storage;
-
-    // Block scan instance
-    AgentScanT block_scan(temp_storage, d_spine, d_spine, cub::Sum(), OffsetT(0)) ;
-
-    // Process full input tiles
-    int block_offset = 0;
-    BlockScanRunningPrefixOp<OffsetT, Sum> prefix_op(0, Sum());
-    while (block_offset + AgentScanT::TILE_ITEMS <= num_counts)
-    {
-        block_scan.template ConsumeTile<false, false>(block_offset, prefix_op);
-        block_offset += AgentScanT::TILE_ITEMS;
-    }
-}
-
-
-/**
- * Downsweep pass kernel entry point (multi-block).  Scatters keys (and values) into corresponding bins for the current digit place.
- */
-template <
-    typename                ChainedPolicyT,                 ///< Chained tuning policy
-    bool                    ALT_DIGIT_BITS,                 ///< Whether or not to use the alternate (lower-bits) policy
-    bool                    IS_DESCENDING,                  ///< Whether or not the sorted-order is high-to-low
-    typename                KeyT,                           ///< Key type
-    typename                ValueT,                         ///< Value type
-    typename                OffsetT>                        ///< Signed integer type for global offsets
-__launch_bounds__ (int((ALT_DIGIT_BITS) ?
-    ChainedPolicyT::ActivePolicy::AltDownsweepPolicy::BLOCK_THREADS :
-    ChainedPolicyT::ActivePolicy::DownsweepPolicy::BLOCK_THREADS))
-__global__ void DeviceRadixSortDownsweepKernel(
-    const KeyT              *d_keys_in,                     ///< [in] Input keys buffer
-    KeyT                    *d_keys_out,                    ///< [in] Output keys buffer
-    const ValueT            *d_values_in,                   ///< [in] Input values buffer
-    ValueT                  *d_values_out,                  ///< [in] Output values buffer
-    OffsetT                 *d_spine,                       ///< [in] Scan of privatized (per block) digit histograms (striped, i.e., 0s counts from each block, then 1s counts from each block, etc.)
-    OffsetT                 num_items,                      ///< [in] Total number of input data items
-    int                     current_bit,                    ///< [in] Bit position of current radix digit
-    int                     num_bits,                       ///< [in] Number of bits of current radix digit
-    GridEvenShare<OffsetT>  even_share)                     ///< [in] Even-share descriptor for mapan equal number of tiles onto each thread block
-{
-    // Parameterize AgentRadixSortDownsweep type for the current configuration
-    typedef AgentRadixSortDownsweep<
-            typename If<(ALT_DIGIT_BITS),
-                typename ChainedPolicyT::ActivePolicy::AltDownsweepPolicy,
-                typename ChainedPolicyT::ActivePolicy::DownsweepPolicy>::Type,
-            IS_DESCENDING,
-            KeyT,
-            ValueT,
-            OffsetT>
-        AgentRadixSortDownsweepT;
-
-    // Shared memory storage
-    __shared__  typename AgentRadixSortDownsweepT::TempStorage temp_storage;
-
-    // Initialize even-share descriptor for this thread block
-    even_share.BlockInit();
-
-    // Process input tiles
-    AgentRadixSortDownsweepT(temp_storage, num_items, d_spine, d_keys_in, d_keys_out, d_values_in, d_values_out, current_bit, num_bits).ProcessRegion(
-        even_share.block_offset,
-        even_share.block_end);
-}
-
-
-/**
- * Single pass kernel entry point (single-block).  Fully sorts a tile of input.
- */
-template <
-    typename                ChainedPolicyT,                 ///< Chained tuning policy
-    bool                    IS_DESCENDING,                  ///< Whether or not the sorted-order is high-to-low
-    typename                KeyT,                           ///< Key type
-    typename                ValueT,                         ///< Value type
-    typename                OffsetT>                        ///< Signed integer type for global offsets
-__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::SingleTilePolicy::BLOCK_THREADS), 1)
-__global__ void DeviceRadixSortSingleTileKernel(
-    const KeyT              *d_keys_in,                     ///< [in] Input keys buffer
-    KeyT                    *d_keys_out,                    ///< [in] Output keys buffer
-    const ValueT            *d_values_in,                   ///< [in] Input values buffer
-    ValueT                  *d_values_out,                  ///< [in] Output values buffer
-    OffsetT                 num_items,                      ///< [in] Total number of input data items
-    int                     current_bit,                    ///< [in] Bit position of current radix digit
-    int                     end_bit)                        ///< [in] The past-the-end (most-significant) bit index needed for key comparison
-{
-    // Constants
-    enum
-    {
-        BLOCK_THREADS           = ChainedPolicyT::ActivePolicy::SingleTilePolicy::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = ChainedPolicyT::ActivePolicy::SingleTilePolicy::ITEMS_PER_THREAD,
-        KEYS_ONLY               = Equals<ValueT, NullType>::VALUE,
-    };
-
-    // BlockRadixSort type
-    typedef BlockRadixSort<
-            KeyT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            ValueT,
-            ChainedPolicyT::ActivePolicy::SingleTilePolicy::RADIX_BITS,
-            ChainedPolicyT::ActivePolicy::SingleTilePolicy::MEMOIZE_OUTER_SCAN,
-            ChainedPolicyT::ActivePolicy::SingleTilePolicy::INNER_SCAN_ALGORITHM>
-        BlockRadixSortT;
-
-    // BlockLoad type (keys)
-    typedef BlockLoad<
-        KeyT,
-        BLOCK_THREADS,
-        ITEMS_PER_THREAD,
-        ChainedPolicyT::ActivePolicy::SingleTilePolicy::LOAD_ALGORITHM> BlockLoadKeys;
-
-    // BlockLoad type (values)
-    typedef BlockLoad<
-        ValueT,
-        BLOCK_THREADS,
-        ITEMS_PER_THREAD,
-        ChainedPolicyT::ActivePolicy::SingleTilePolicy::LOAD_ALGORITHM> BlockLoadValues;
-
-    // Unsigned word for key bits
-    typedef typename Traits<KeyT>::UnsignedBits UnsignedBitsT;
-
-    // Shared memory storage
-    __shared__ union
-    {
-        typename BlockRadixSortT::TempStorage       sort;
-        typename BlockLoadKeys::TempStorage         load_keys;
-        typename BlockLoadValues::TempStorage       load_values;
-
-    } temp_storage;
-
-    // Keys and values for the block
-    KeyT            keys[ITEMS_PER_THREAD];
-    ValueT          values[ITEMS_PER_THREAD];
-
-    // Get default (min/max) value for out-of-bounds keys
-    UnsignedBitsT   default_key_bits = (IS_DESCENDING) ? Traits<KeyT>::LOWEST_KEY : Traits<KeyT>::MAX_KEY;
-    KeyT            default_key = reinterpret_cast<KeyT&>(default_key_bits);
-
-    // Load keys
-    BlockLoadKeys(temp_storage.load_keys).Load(d_keys_in, keys, num_items, default_key);
-
-    CTA_SYNC();
-
-    // Load values
-    if (!KEYS_ONLY)
-    {
-        BlockLoadValues(temp_storage.load_values).Load(d_values_in, values, num_items);
-
-        CTA_SYNC();
-    }
-
-    // Sort tile
-    BlockRadixSortT(temp_storage.sort).SortBlockedToStriped(
-        keys,
-        values,
-        current_bit,
-        end_bit,
-        Int2Type<IS_DESCENDING>(),
-        Int2Type<KEYS_ONLY>());
-
-    // Store keys and values
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-    {
-        int item_offset = ITEM * BLOCK_THREADS + threadIdx.x;
-        if (item_offset < num_items)
-        {
-            d_keys_out[item_offset] = keys[ITEM];
-            if (!KEYS_ONLY)
-                d_values_out[item_offset] = values[ITEM];
-        }
-    }
-}
-
-
-/**
- * Segmented radix sorting pass (one block per segment)
- */
-template <
-    typename                ChainedPolicyT,                 ///< Chained tuning policy
-    bool                    ALT_DIGIT_BITS,                 ///< Whether or not to use the alternate (lower-bits) policy
-    bool                    IS_DESCENDING,                  ///< Whether or not the sorted-order is high-to-low
-    typename                KeyT,                           ///< Key type
-    typename                ValueT,                         ///< Value type
-    typename                OffsetT>                        ///< Signed integer type for global offsets
-__launch_bounds__ (int((ALT_DIGIT_BITS) ?
-    ChainedPolicyT::ActivePolicy::AltSegmentedPolicy::BLOCK_THREADS :
-    ChainedPolicyT::ActivePolicy::SegmentedPolicy::BLOCK_THREADS))
-__global__ void DeviceSegmentedRadixSortKernel(
-    const KeyT              *d_keys_in,                     ///< [in] Input keys buffer
-    KeyT                    *d_keys_out,                    ///< [in] Output keys buffer
-    const ValueT            *d_values_in,                   ///< [in] Input values buffer
-    ValueT                  *d_values_out,                  ///< [in] Output values buffer
-    const int               *d_begin_offsets,               ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-    const int               *d_end_offsets,                 ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-    int                     /*num_segments*/,               ///< [in] The number of segments that comprise the sorting data
-    int                     current_bit,                    ///< [in] Bit position of current radix digit
-    int                     pass_bits)                      ///< [in] Number of bits of current radix digit
-{
-    //
-    // Constants
-    //
-
-    typedef typename If<(ALT_DIGIT_BITS),
-        typename ChainedPolicyT::ActivePolicy::AltSegmentedPolicy,
-        typename ChainedPolicyT::ActivePolicy::SegmentedPolicy>::Type SegmentedPolicyT;
-
-    enum
-    {
-        BLOCK_THREADS       = SegmentedPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = SegmentedPolicyT::ITEMS_PER_THREAD,
-        RADIX_BITS          = SegmentedPolicyT::RADIX_BITS,
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,
-        RADIX_DIGITS        = 1 << RADIX_BITS,
-        KEYS_ONLY           = Equals<ValueT, NullType>::VALUE,
-    };
-
-    // Upsweep type
-    typedef AgentRadixSortUpsweep<
-            AgentRadixSortUpsweepPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, SegmentedPolicyT::LOAD_MODIFIER, RADIX_BITS>,
-            KeyT,
-            OffsetT>
-        BlockUpsweepT;
-
-    // Digit-scan type
-    typedef BlockScan<OffsetT, BLOCK_THREADS> DigitScanT;
-
-    // Downsweep type
-    typedef AgentRadixSortDownsweep<SegmentedPolicyT, IS_DESCENDING, KeyT, ValueT, OffsetT> BlockDownsweepT;
-
-    //
-    // Process input tiles
-    //
-
-    // Shared memory storage
-    __shared__ union
-    {
-        typename BlockUpsweepT::TempStorage     upsweep;
-        typename BlockDownsweepT::TempStorage   downsweep;
-        struct
-        {
-            volatile OffsetT                        reverse_counts_in[RADIX_DIGITS];
-            volatile OffsetT                        reverse_counts_out[RADIX_DIGITS];
-            typename DigitScanT::TempStorage        scan;
-        };
-
-    } temp_storage;
-
-    OffsetT segment_begin   = d_begin_offsets[blockIdx.x];
-    OffsetT segment_end     = d_end_offsets[blockIdx.x];
-    OffsetT num_items       = segment_end - segment_begin;
-
-    // Check if empty segment
-    if (num_items <= 0)
-        return;
-
-    // Upsweep
-    OffsetT bin_count = 0;      // The count of each digit value in this pass (valid in the first RADIX_DIGITS threads)
-    BlockUpsweepT(temp_storage.upsweep, d_keys_in, current_bit, pass_bits).ProcessRegion(
-        segment_begin,
-        segment_end,
-        bin_count);
-
-    CTA_SYNC();
-
-    if (IS_DESCENDING)
-    {
-        // Reverse bin counts
-        if (threadIdx.x < RADIX_DIGITS)
-            temp_storage.reverse_counts_in[threadIdx.x] = bin_count;
-
-        CTA_SYNC();
-
-        if (threadIdx.x < RADIX_DIGITS)
-            bin_count = temp_storage.reverse_counts_in[RADIX_DIGITS - threadIdx.x - 1];
-    }
-
-    // Scan
-    OffsetT bin_offset;     // The global scatter base offset for each digit value in this pass (valid in the first RADIX_DIGITS threads)
-    DigitScanT(temp_storage.scan).ExclusiveSum(bin_count, bin_offset);
-    bin_offset += segment_begin;
-
-    if (IS_DESCENDING)
-    {
-        // Reverse bin offsets
-        if (threadIdx.x < RADIX_DIGITS)
-            temp_storage.reverse_counts_out[threadIdx.x] = bin_offset;
-
-        CTA_SYNC();
-
-        if (threadIdx.x < RADIX_DIGITS)
-            bin_offset = temp_storage.reverse_counts_out[RADIX_DIGITS - threadIdx.x - 1];
-    }
-
-    CTA_SYNC();
-
-    // Downsweep
-    BlockDownsweepT(temp_storage.downsweep, num_items, bin_offset, d_keys_in, d_keys_out, d_values_in, d_values_out, current_bit, pass_bits).ProcessRegion(
-        segment_begin, segment_end);
-}
-
-
-
-/******************************************************************************
- * Policy
- ******************************************************************************/
-
-/**
- * Tuning policy for kernel specialization
- */
-template <
-    typename KeyT,          ///< Key type
-    typename ValueT,        ///< Value type
-    typename OffsetT>       ///< Signed integer type for global offsets
-struct DeviceRadixSortPolicy
-{
-    //------------------------------------------------------------------------------
-    // Constants
-    //------------------------------------------------------------------------------
-
-    enum
-    {
-        // Whether this is a keys-only (or key-value) sort
-        KEYS_ONLY = (Equals<ValueT, NullType>::VALUE),
-
-        // Relative size of KeyT type to a 4-byte word
-        SCALE_FACTOR_4B = (CUB_MAX(sizeof(KeyT), sizeof(ValueT)) + 3) / 4,
-    };
-
-    //------------------------------------------------------------------------------
-    // Architecture-specific tuning policies
-    //------------------------------------------------------------------------------
-
-    /// SM13
-    struct Policy130 : ChainedPolicy<130, Policy130, Policy130>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 5,
-            ALT_RADIX_BITS          = PRIMARY_RADIX_BITS - 1,
-        };
-
-        // Keys-only upsweep policies
-        typedef AgentRadixSortUpsweepPolicy <128, CUB_MAX(1, 19 / SCALE_FACTOR_4B), LOAD_DEFAULT, PRIMARY_RADIX_BITS>   UpsweepPolicyKeys;
-        typedef AgentRadixSortUpsweepPolicy <128, CUB_MAX(1, 15 / SCALE_FACTOR_4B), LOAD_DEFAULT, ALT_RADIX_BITS>       AltUpsweepPolicyKeys;
-
-        // Key-value pairs upsweep policies
-        typedef AgentRadixSortUpsweepPolicy <128, CUB_MAX(1, 19 / SCALE_FACTOR_4B), LOAD_DEFAULT, PRIMARY_RADIX_BITS>   UpsweepPolicyPairs;
-        typedef AgentRadixSortUpsweepPolicy <128, CUB_MAX(1, 15 / SCALE_FACTOR_4B), LOAD_DEFAULT, ALT_RADIX_BITS>       AltUpsweepPolicyPairs;
-
-        // Upsweep policies
-        typedef typename If<KEYS_ONLY, UpsweepPolicyKeys, UpsweepPolicyPairs>::Type         UpsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltUpsweepPolicyKeys, AltUpsweepPolicyPairs>::Type   AltUpsweepPolicy;
-
-        // Scan policy
-        typedef AgentScanPolicy <256, 4, BLOCK_LOAD_VECTORIZE, LOAD_DEFAULT, BLOCK_STORE_VECTORIZE, BLOCK_SCAN_WARP_SCANS> ScanPolicy;
-
-        // Keys-only downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <64, CUB_MAX(1, 19 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>    DownsweepPolicyKeys;
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 15 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>       AltDownsweepPolicyKeys;
-
-        // Key-value pairs downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <64, CUB_MAX(1, 19 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>    DownsweepPolicyPairs;
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 15 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>       AltDownsweepPolicyPairs;
-
-        // Downsweep policies
-        typedef typename If<KEYS_ONLY, DownsweepPolicyKeys, DownsweepPolicyPairs>::Type         DownsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltDownsweepPolicyKeys, AltDownsweepPolicyPairs>::Type   AltDownsweepPolicy;
-
-        // Single-tile policy
-        typedef DownsweepPolicy SingleTilePolicy;
-
-        // Segmented policies
-        typedef DownsweepPolicy     SegmentedPolicy;
-        typedef AltDownsweepPolicy  AltSegmentedPolicy;
-    };
-
-    /// SM20
-    struct Policy200 : ChainedPolicy<200, Policy200, Policy130>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 5,
-            ALT_RADIX_BITS          = PRIMARY_RADIX_BITS - 1,
-        };
-
-        // Keys-only upsweep policies
-        typedef AgentRadixSortUpsweepPolicy <64, CUB_MAX(1, 18 / SCALE_FACTOR_4B), LOAD_DEFAULT, PRIMARY_RADIX_BITS>    UpsweepPolicyKeys;
-        typedef AgentRadixSortUpsweepPolicy <64, CUB_MAX(1, 18 / SCALE_FACTOR_4B), LOAD_DEFAULT, ALT_RADIX_BITS>        AltUpsweepPolicyKeys;
-
-        // Key-value pairs upsweep policies
-        typedef AgentRadixSortUpsweepPolicy <128, CUB_MAX(1, 13 / SCALE_FACTOR_4B), LOAD_DEFAULT, PRIMARY_RADIX_BITS>   UpsweepPolicyPairs;
-        typedef AgentRadixSortUpsweepPolicy <128, CUB_MAX(1, 13 / SCALE_FACTOR_4B), LOAD_DEFAULT, ALT_RADIX_BITS>       AltUpsweepPolicyPairs;
-
-        // Upsweep policies
-        typedef typename If<KEYS_ONLY, UpsweepPolicyKeys, UpsweepPolicyPairs>::Type         UpsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltUpsweepPolicyKeys, AltUpsweepPolicyPairs>::Type   AltUpsweepPolicy;
-
-        // Scan policy
-        typedef AgentScanPolicy <512, 4, BLOCK_LOAD_VECTORIZE, LOAD_DEFAULT, BLOCK_STORE_VECTORIZE, BLOCK_SCAN_RAKING_MEMOIZE> ScanPolicy;
-
-        // Keys-only downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <64, CUB_MAX(1, 18 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>    DownsweepPolicyKeys;
-        typedef AgentRadixSortDownsweepPolicy <64, CUB_MAX(1, 18 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>        AltDownsweepPolicyKeys;
-
-        // Key-value pairs downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 13 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>   DownsweepPolicyPairs;
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 13 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>       AltDownsweepPolicyPairs;
-
-        // Downsweep policies
-        typedef typename If<KEYS_ONLY, DownsweepPolicyKeys, DownsweepPolicyPairs>::Type         DownsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltDownsweepPolicyKeys, AltDownsweepPolicyPairs>::Type   AltDownsweepPolicy;
-
-        // Single-tile policy
-        typedef DownsweepPolicy SingleTilePolicy;
-
-        // Segmented policies
-        typedef DownsweepPolicy     SegmentedPolicy;
-        typedef AltDownsweepPolicy  AltSegmentedPolicy;
-    };
-
-    /// SM30
-    struct Policy300 : ChainedPolicy<300, Policy300, Policy200>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 5,
-            ALT_RADIX_BITS          = PRIMARY_RADIX_BITS - 1,
-        };
-
-        // Keys-only upsweep policies
-        typedef AgentRadixSortUpsweepPolicy <256, CUB_MAX(1, 7 / SCALE_FACTOR_4B), LOAD_DEFAULT, PRIMARY_RADIX_BITS>    UpsweepPolicyKeys;
-        typedef AgentRadixSortUpsweepPolicy <256, CUB_MAX(1, 7 / SCALE_FACTOR_4B), LOAD_DEFAULT, ALT_RADIX_BITS>        AltUpsweepPolicyKeys;
-
-        // Key-value pairs upsweep policies
-        typedef AgentRadixSortUpsweepPolicy <256, CUB_MAX(1, 5 / SCALE_FACTOR_4B), LOAD_DEFAULT, PRIMARY_RADIX_BITS>    UpsweepPolicyPairs;
-        typedef AgentRadixSortUpsweepPolicy <256, CUB_MAX(1, 5 / SCALE_FACTOR_4B), LOAD_DEFAULT, ALT_RADIX_BITS>        AltUpsweepPolicyPairs;
-
-        // Upsweep policies
-        typedef typename If<KEYS_ONLY, UpsweepPolicyKeys, UpsweepPolicyPairs>::Type         UpsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltUpsweepPolicyKeys, AltUpsweepPolicyPairs>::Type   AltUpsweepPolicy;
-
-        // Scan policy
-        typedef AgentScanPolicy <1024, 4, BLOCK_LOAD_VECTORIZE, LOAD_DEFAULT, BLOCK_STORE_VECTORIZE, BLOCK_SCAN_WARP_SCANS> ScanPolicy;
-
-        // Keys-only downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 14 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>   DownsweepPolicyKeys;
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 14 / SCALE_FACTOR_4B), BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>       AltDownsweepPolicyKeys;
-
-        // Key-value pairs downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 10 / SCALE_FACTOR_4B), BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>    DownsweepPolicyPairs;
-        typedef AgentRadixSortDownsweepPolicy <128, CUB_MAX(1, 10 / SCALE_FACTOR_4B), BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>        AltDownsweepPolicyPairs;
-
-        // Downsweep policies
-        typedef typename If<KEYS_ONLY, DownsweepPolicyKeys, DownsweepPolicyPairs>::Type         DownsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltDownsweepPolicyKeys, AltDownsweepPolicyPairs>::Type   AltDownsweepPolicy;
-
-        // Single-tile policy
-        typedef DownsweepPolicy SingleTilePolicy;
-
-        // Segmented policies
-        typedef DownsweepPolicy     SegmentedPolicy;
-        typedef AltDownsweepPolicy  AltSegmentedPolicy;
-    };
-
-
-    /// SM35
-    struct Policy350 : ChainedPolicy<350, Policy350, Policy300>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 5,    // 1.72B 32b keys/s, 1.17B 32b pairs/s, 1.55B 32b segmented keys/s (K40m)
-        };
-
-        // Scan policy
-        typedef AgentScanPolicy <1024, 4, BLOCK_LOAD_VECTORIZE, LOAD_DEFAULT, BLOCK_STORE_VECTORIZE, BLOCK_SCAN_WARP_SCANS> ScanPolicy;
-
-        // Keys-only downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <64,   CUB_MAX(1, 18 / SCALE_FACTOR_4B), BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS> DownsweepPolicyKeys;
-        typedef AgentRadixSortDownsweepPolicy <128,  CUB_MAX(1, 11 / SCALE_FACTOR_4B), BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS - 1> AltDownsweepPolicyKeys;
-
-        // Key-value pairs downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <128,  CUB_MAX(1, 15 / SCALE_FACTOR_4B), BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS> DownsweepPolicyPairs;
-        typedef AltDownsweepPolicyKeys AltDownsweepPolicyPairs;
-
-        // Downsweep policies
-        typedef typename If<KEYS_ONLY, DownsweepPolicyKeys, DownsweepPolicyPairs>::Type DownsweepPolicy;
-        typedef typename If<KEYS_ONLY, AltDownsweepPolicyKeys, AltDownsweepPolicyPairs>::Type AltDownsweepPolicy;
-
-        // Upsweep policies
-        typedef DownsweepPolicy UpsweepPolicy;
-        typedef AltDownsweepPolicy AltUpsweepPolicy;
-
-        // Single-tile policy
-        typedef DownsweepPolicy SingleTilePolicy;
-
-        // Segmented policies
-        typedef DownsweepPolicy     SegmentedPolicy;
-        typedef AltDownsweepPolicy  AltSegmentedPolicy;
-    };
-
-
-    /// SM50
-    struct Policy500 : ChainedPolicy<500, Policy500, Policy350>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 7,    // 3.5B 32b keys/s, 1.92B 32b pairs/s (TitanX)
-            SINGLE_TILE_RADIX_BITS  = 6,
-            SEGMENTED_RADIX_BITS    = 6,    // 3.1B 32b segmented keys/s (TitanX)
-        };
-
-        // ScanPolicy
-        typedef AgentScanPolicy <512, 23, BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, BLOCK_STORE_WARP_TRANSPOSE, BLOCK_SCAN_RAKING_MEMOIZE> ScanPolicy;
-
-        // Downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <160, CUB_MAX(1, 39 / SCALE_FACTOR_4B),  BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, false, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>  DownsweepPolicy;
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 16 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_RAKING_MEMOIZE, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS - 1>   AltDownsweepPolicy;
-
-        // Upsweep policies
-        typedef DownsweepPolicy UpsweepPolicy;
-        typedef AltDownsweepPolicy AltUpsweepPolicy;
-
-        // Single-tile policy
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 19 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, SINGLE_TILE_RADIX_BITS> SingleTilePolicy;
-
-        // Segmented policies
-        typedef AgentRadixSortDownsweepPolicy <192, CUB_MAX(1, 31 / SCALE_FACTOR_4B),  BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, SEGMENTED_RADIX_BITS>   SegmentedPolicy;
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 11 / SCALE_FACTOR_4B),  BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, SEGMENTED_RADIX_BITS - 1>       AltSegmentedPolicy;
-    };
-
-
-    /// SM60 (GP100)
-    struct Policy600 : ChainedPolicy<600, Policy600, Policy500>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 6,
-        };
-
-        // ScanPolicy
-        typedef AgentScanPolicy <512, 23, BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, BLOCK_STORE_WARP_TRANSPOSE, BLOCK_SCAN_RAKING_MEMOIZE> ScanPolicy;
-
-        // Downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <192, CUB_MAX(1, 39 / SCALE_FACTOR_4B),  BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>   DownsweepPolicy;
-        typedef AgentRadixSortDownsweepPolicy <384, CUB_MAX(1, 11 / SCALE_FACTOR_4B),  BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS - 1>       AltDownsweepPolicy;
-
-        // Upsweep policies
-        typedef DownsweepPolicy UpsweepPolicy;
-        typedef AltDownsweepPolicy AltUpsweepPolicy;
-
-        // Single-tile policy
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 19 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS> SingleTilePolicy;
-
-        // Segmented policies
-        typedef DownsweepPolicy     SegmentedPolicy;
-        typedef AltDownsweepPolicy  AltSegmentedPolicy;
-    };
-
-
-    /// SM61 (GP104)
-    struct Policy610 : ChainedPolicy<610, Policy610, Policy600>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 7,    // 3.4B 32b keys/s, 1.83B 32b pairs/s (1080)
-            SINGLE_TILE_RADIX_BITS  = 6,
-            SEGMENTED_RADIX_BITS    = 6,    // 3.3B 32b segmented keys/s (1080)
-        };
-
-        // ScanPolicy
-        typedef AgentScanPolicy <512, 23, BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, BLOCK_STORE_WARP_TRANSPOSE, BLOCK_SCAN_RAKING_MEMOIZE> ScanPolicy;
-
-        // Downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <160, CUB_MAX(1, 53 / SCALE_FACTOR_4B),  BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, KEYS_ONLY, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>  DownsweepPolicy;
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 35 / SCALE_FACTOR_4B),  BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS - 1>   AltDownsweepPolicy;
-
-        // Upsweep policies
-        typedef DownsweepPolicy UpsweepPolicy;
-        typedef AltDownsweepPolicy AltUpsweepPolicy;
-
-        // Single-tile policy
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 19 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, SINGLE_TILE_RADIX_BITS>          SingleTilePolicy;
-
-        // Segmented policies
-        typedef AgentRadixSortDownsweepPolicy <192, CUB_MAX(1, 39 / SCALE_FACTOR_4B),  BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, SEGMENTED_RADIX_BITS>   SegmentedPolicy;
-        typedef AgentRadixSortDownsweepPolicy <384, CUB_MAX(1, 11 / SCALE_FACTOR_4B),  BLOCK_LOAD_TRANSPOSE, LOAD_DEFAULT, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, SEGMENTED_RADIX_BITS - 1>       AltSegmentedPolicy;
-    };
-
-
-    /// SM62 (Tegra, less RF)
-    struct Policy620 : ChainedPolicy<620, Policy620, Policy610>
-    {
-        enum {
-            PRIMARY_RADIX_BITS      = 5,
-            ALT_RADIX_BITS          = PRIMARY_RADIX_BITS - 1,
-        };
-
-        // ScanPolicy
-        typedef AgentScanPolicy <512, 23, BLOCK_LOAD_WARP_TRANSPOSE, LOAD_DEFAULT, BLOCK_STORE_WARP_TRANSPOSE, BLOCK_SCAN_RAKING_MEMOIZE> ScanPolicy;
-
-        // Downsweep policies
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 16 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_RAKING_MEMOIZE, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS>   DownsweepPolicy;
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 16 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_RAKING_MEMOIZE, RADIX_SORT_SCATTER_TWO_PHASE, ALT_RADIX_BITS>       AltDownsweepPolicy;
-
-        // Upsweep policies
-        typedef DownsweepPolicy UpsweepPolicy;
-        typedef AltDownsweepPolicy AltUpsweepPolicy;
-
-        // Single-tile policy
-        typedef AgentRadixSortDownsweepPolicy <256, CUB_MAX(1, 19 / SCALE_FACTOR_4B),  BLOCK_LOAD_DIRECT, LOAD_LDG, true, BLOCK_SCAN_WARP_SCANS, RADIX_SORT_SCATTER_TWO_PHASE, PRIMARY_RADIX_BITS> SingleTilePolicy;
-
-        // Segmented policies
-        typedef DownsweepPolicy     SegmentedPolicy;
-        typedef AltDownsweepPolicy  AltSegmentedPolicy;
-    };
-
-    /// MaxPolicy
-    typedef Policy620 MaxPolicy;
-};
-
-
-
-/******************************************************************************
- * Single-problem dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for device-wide radix sort
- */
-template <
-    bool     IS_DESCENDING, ///< Whether or not the sorted-order is high-to-low
-    typename KeyT,          ///< Key type
-    typename ValueT,        ///< Value type
-    typename OffsetT>       ///< Signed integer type for global offsets
-struct DispatchRadixSort :
-    DeviceRadixSortPolicy<KeyT, ValueT, OffsetT>
-{
-    //------------------------------------------------------------------------------
-    // Constants
-    //------------------------------------------------------------------------------
-
-    enum
-    {
-        // Whether this is a keys-only (or key-value) sort
-        KEYS_ONLY = (Equals<ValueT, NullType>::VALUE),
-    };
-
-
-    //------------------------------------------------------------------------------
-    // Problem state
-    //------------------------------------------------------------------------------
-
-    void                    *d_temp_storage;        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-    size_t                  &temp_storage_bytes;    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-    DoubleBuffer<KeyT>      &d_keys;                ///< [in,out] Double-buffer whose current buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-    DoubleBuffer<ValueT>    &d_values;              ///< [in,out] Double-buffer whose current buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-    OffsetT                 num_items;              ///< [in] Number of items to sort
-    int                     begin_bit;              ///< [in] The beginning (least-significant) bit index needed for key comparison
-    int                     end_bit;                ///< [in] The past-the-end (most-significant) bit index needed for key comparison
-    cudaStream_t            stream;                 ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-    bool                    debug_synchronous;      ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    int                     ptx_version;            ///< [in] PTX version
-    bool                    is_overwrite_okay;      ///< [in] Whether is okay to overwrite source buffers
-
-
-    //------------------------------------------------------------------------------
-    // Constructor
-    //------------------------------------------------------------------------------
-
-    /// Constructor
-    CUB_RUNTIME_FUNCTION __forceinline__
-    DispatchRadixSort(
-        void*                   d_temp_storage,
-        size_t                  &temp_storage_bytes,
-        DoubleBuffer<KeyT>      &d_keys,
-        DoubleBuffer<ValueT>    &d_values,
-        OffsetT                 num_items,
-        int                     begin_bit,
-        int                     end_bit,
-        bool                    is_overwrite_okay,
-        cudaStream_t            stream,
-        bool                    debug_synchronous,
-        int                     ptx_version)
-    :
-        d_temp_storage(d_temp_storage),
-        temp_storage_bytes(temp_storage_bytes),
-        d_keys(d_keys),
-        d_values(d_values),
-        num_items(num_items),
-        begin_bit(begin_bit),
-        end_bit(end_bit),
-        stream(stream),
-        debug_synchronous(debug_synchronous),
-        ptx_version(ptx_version),
-        is_overwrite_okay(is_overwrite_okay)
-    {}
-
-
-    //------------------------------------------------------------------------------
-    // Small-problem (single tile) invocation
-    //------------------------------------------------------------------------------
-
-    /// Invoke a single block to sort in-core
-    template <
-        typename                ActivePolicyT,          ///< Umbrella policy active for the target device
-        typename                SingleTileKernelT>      ///< Function type of cub::DeviceRadixSortSingleTileKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokeSingleTile(
-        SingleTileKernelT       single_tile_kernel)     ///< [in] Kernel function pointer to parameterization of cub::DeviceRadixSortSingleTileKernel
-    {
-#ifndef CUB_RUNTIME_ENABLED
-        (void)single_tile_kernel;
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Return if the caller is simply requesting the size of the storage allocation
-            if (d_temp_storage == NULL)
-            {
-                temp_storage_bytes = 1;
-                break;
-            }
-
-            // Return if empty problem
-            if (num_items == 0)
-                break;
-
-            // Log single_tile_kernel configuration
-            if (debug_synchronous)
-                _CubLog("Invoking single_tile_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy, current bit %d, bit_grain %d\n",
-                    1, ActivePolicyT::SingleTilePolicy::BLOCK_THREADS, (long long) stream,
-                    ActivePolicyT::SingleTilePolicy::ITEMS_PER_THREAD, 1, begin_bit, ActivePolicyT::SingleTilePolicy::RADIX_BITS);
-
-            // Invoke upsweep_kernel with same grid size as downsweep_kernel
-            single_tile_kernel<<<1, ActivePolicyT::SingleTilePolicy::BLOCK_THREADS, 0, stream>>>(
-                d_keys.Current(),
-                d_keys.Alternate(),
-                d_values.Current(),
-                d_values.Alternate(),
-                num_items,
-                begin_bit,
-                end_bit);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Update selector
-            d_keys.selector ^= 1;
-            d_values.selector ^= 1;
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Normal problem size invocation
-    //------------------------------------------------------------------------------
-
-    /**
-     * Invoke a three-kernel sorting pass at the current bit.
-     */
-    template <typename PassConfigT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokePass(
-        const KeyT      *d_keys_in,
-        KeyT            *d_keys_out,
-        const ValueT    *d_values_in,
-        ValueT          *d_values_out,
-        OffsetT         *d_spine,
-        int             spine_length,
-        int             &current_bit,
-        PassConfigT     &pass_config)
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            int pass_bits = CUB_MIN(pass_config.radix_bits, (end_bit - current_bit));
-
-            // Log upsweep_kernel configuration
-            if (debug_synchronous)
-                _CubLog("Invoking upsweep_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy, current bit %d, bit_grain %d\n",
-                pass_config.even_share.grid_size, pass_config.upsweep_config.block_threads, (long long) stream,
-                pass_config.upsweep_config.items_per_thread, pass_config.upsweep_config.sm_occupancy, current_bit, pass_bits);
-
-            // Invoke upsweep_kernel with same grid size as downsweep_kernel
-            pass_config.upsweep_kernel<<<pass_config.even_share.grid_size, pass_config.upsweep_config.block_threads, 0, stream>>>(
-                d_keys_in,
-                d_spine,
-                num_items,
-                current_bit,
-                pass_bits,
-                pass_config.even_share);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Log scan_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking scan_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread\n",
-                1, pass_config.scan_config.block_threads, (long long) stream, pass_config.scan_config.items_per_thread);
-
-            // Invoke scan_kernel
-            pass_config.scan_kernel<<<1, pass_config.scan_config.block_threads, 0, stream>>>(
-                d_spine,
-                spine_length);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Log downsweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking downsweep_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                pass_config.even_share.grid_size, pass_config.downsweep_config.block_threads, (long long) stream,
-                pass_config.downsweep_config.items_per_thread, pass_config.downsweep_config.sm_occupancy);
-
-            // Invoke downsweep_kernel
-            pass_config.downsweep_kernel<<<pass_config.even_share.grid_size, pass_config.downsweep_config.block_threads, 0, stream>>>(
-                d_keys_in,
-                d_keys_out,
-                d_values_in,
-                d_values_out,
-                d_spine,
-                num_items,
-                current_bit,
-                pass_bits,
-                pass_config.even_share);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Update current bit
-            current_bit += pass_bits;
-        }
-        while (0);
-
-        return error;
-    }
-
-
-
-    /// Pass configuration structure
-    template <
-        typename UpsweepKernelT,
-        typename ScanKernelT,
-        typename DownsweepKernelT>
-    struct PassConfig
-    {
-        UpsweepKernelT          upsweep_kernel;
-        KernelConfig            upsweep_config;
-        ScanKernelT             scan_kernel;
-        KernelConfig            scan_config;
-        DownsweepKernelT        downsweep_kernel;
-        KernelConfig            downsweep_config;
-        int                     radix_bits;
-        int                     radix_digits;
-        int                     max_downsweep_grid_size;
-        GridEvenShare<OffsetT>  even_share;
-
-        /// Initialize pass configuration
-        template <
-            typename UpsweepPolicyT,
-            typename ScanPolicyT,
-            typename DownsweepPolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        cudaError_t InitPassConfig(
-            UpsweepKernelT      upsweep_kernel,
-            ScanKernelT         scan_kernel,
-            DownsweepKernelT    downsweep_kernel,
-            int                 ptx_version,
-            int                 sm_count,
-            int                 num_items)
-        {
-            cudaError error = cudaSuccess;
-            do
-            {
-                this->upsweep_kernel    = upsweep_kernel;
-                this->scan_kernel       = scan_kernel;
-                this->downsweep_kernel  = downsweep_kernel;
-                radix_bits              = DownsweepPolicyT::RADIX_BITS;
-                radix_digits            = 1 << radix_bits;
-
-                if (CubDebug(error = upsweep_config.Init<UpsweepPolicyT>(upsweep_kernel))) break;
-                if (CubDebug(error = scan_config.Init<ScanPolicyT>(scan_kernel))) break;
-                if (CubDebug(error = downsweep_config.Init<DownsweepPolicyT>(downsweep_kernel))) break;
-
-                max_downsweep_grid_size = (downsweep_config.sm_occupancy * sm_count) * CUB_SUBSCRIPTION_FACTOR(ptx_version);
-
-                even_share = GridEvenShare<OffsetT>(
-                    num_items,
-                    max_downsweep_grid_size,
-                    CUB_MAX(downsweep_config.tile_size, upsweep_config.tile_size));
-
-            }
-            while (0);
-            return error;
-        }
-
-    };
-
-
-    /// Invocation (run multiple digit passes)
-    template <
-        typename            ActivePolicyT,          ///< Umbrella policy active for the target device
-        typename            UpsweepKernelT,         ///< Function type of cub::DeviceRadixSortUpsweepKernel
-        typename            ScanKernelT,            ///< Function type of cub::SpineScanKernel
-        typename            DownsweepKernelT>       ///< Function type of cub::DeviceRadixSortDownsweepKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokePasses(
-        UpsweepKernelT      upsweep_kernel,         ///< [in] Kernel function pointer to parameterization of cub::DeviceRadixSortUpsweepKernel
-        UpsweepKernelT      alt_upsweep_kernel,     ///< [in] Alternate kernel function pointer to parameterization of cub::DeviceRadixSortUpsweepKernel
-        ScanKernelT         scan_kernel,            ///< [in] Kernel function pointer to parameterization of cub::SpineScanKernel
-        DownsweepKernelT    downsweep_kernel,       ///< [in] Kernel function pointer to parameterization of cub::DeviceRadixSortDownsweepKernel
-        DownsweepKernelT    alt_downsweep_kernel)   ///< [in] Alternate kernel function pointer to parameterization of cub::DeviceRadixSortDownsweepKernel
-    {
-#ifndef CUB_RUNTIME_ENABLED
-        (void)upsweep_kernel;
-        (void)alt_upsweep_kernel;
-        (void)scan_kernel;
-        (void)downsweep_kernel;
-        (void)alt_downsweep_kernel;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Init regular and alternate-digit kernel configurations
-            PassConfig<UpsweepKernelT, ScanKernelT, DownsweepKernelT> pass_config, alt_pass_config;
-            if ((error = pass_config.template InitPassConfig<
-                    typename ActivePolicyT::UpsweepPolicy, 
-                    typename ActivePolicyT::ScanPolicy, 
-                    typename ActivePolicyT::DownsweepPolicy>(
-                upsweep_kernel, scan_kernel, downsweep_kernel, ptx_version, sm_count, num_items))) break;
-
-            if ((error = alt_pass_config.template InitPassConfig<
-                    typename ActivePolicyT::AltUpsweepPolicy, 
-                    typename ActivePolicyT::ScanPolicy, 
-                    typename ActivePolicyT::AltDownsweepPolicy>(
-                alt_upsweep_kernel, scan_kernel, alt_downsweep_kernel, ptx_version, sm_count, num_items))) break;
-
-            // Get maximum spine length
-            int max_grid_size       = CUB_MAX(pass_config.max_downsweep_grid_size, alt_pass_config.max_downsweep_grid_size);
-            int spine_length        = (max_grid_size * pass_config.radix_digits) + pass_config.scan_config.tile_size;
-
-            // Temporary storage allocation requirements
-            void* allocations[3];
-            size_t allocation_sizes[3] =
-            {
-                spine_length * sizeof(OffsetT),                                         // bytes needed for privatized block digit histograms
-                (is_overwrite_okay) ? 0 : num_items * sizeof(KeyT),                     // bytes needed for 3rd keys buffer
-                (is_overwrite_okay || (KEYS_ONLY)) ? 0 : num_items * sizeof(ValueT),    // bytes needed for 3rd values buffer
-            };
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-
-            // Return if the caller is simply requesting the size of the storage allocation
-            if (d_temp_storage == NULL)
-                return cudaSuccess;
-
-            // Pass planning.  Run passes of the alternate digit-size configuration until we have an even multiple of our preferred digit size
-            int num_bits            = end_bit - begin_bit;
-            int num_passes          = (num_bits + pass_config.radix_bits - 1) / pass_config.radix_bits;
-            bool is_num_passes_odd  = num_passes & 1;
-            int max_alt_passes      = (num_passes * pass_config.radix_bits) - num_bits;
-            int alt_end_bit         = CUB_MIN(end_bit, begin_bit + (max_alt_passes * alt_pass_config.radix_bits));
-
-            // Alias the temporary storage allocations
-            OffsetT *d_spine = static_cast<OffsetT*>(allocations[0]);
-
-            DoubleBuffer<KeyT> d_keys_remaining_passes(
-                (is_overwrite_okay || is_num_passes_odd) ? d_keys.Alternate() : static_cast<KeyT*>(allocations[1]),
-                (is_overwrite_okay) ? d_keys.Current() : (is_num_passes_odd) ? static_cast<KeyT*>(allocations[1]) : d_keys.Alternate());
-
-            DoubleBuffer<ValueT> d_values_remaining_passes(
-                (is_overwrite_okay || is_num_passes_odd) ? d_values.Alternate() : static_cast<ValueT*>(allocations[2]),
-                (is_overwrite_okay) ? d_values.Current() : (is_num_passes_odd) ? static_cast<ValueT*>(allocations[2]) : d_values.Alternate());
-
-            // Run first pass, consuming from the input's current buffers
-            int current_bit = begin_bit;
-            if (CubDebug(error = InvokePass(
-                d_keys.Current(), d_keys_remaining_passes.Current(),
-                d_values.Current(), d_values_remaining_passes.Current(),
-                d_spine, spine_length, current_bit,
-                (current_bit < alt_end_bit) ? alt_pass_config : pass_config))) break;
-
-            // Run remaining passes
-            while (current_bit < end_bit)
-            {
-                if (CubDebug(error = InvokePass(
-                    d_keys_remaining_passes.d_buffers[d_keys_remaining_passes.selector],    d_keys_remaining_passes.d_buffers[d_keys_remaining_passes.selector ^ 1],
-                    d_values_remaining_passes.d_buffers[d_keys_remaining_passes.selector],  d_values_remaining_passes.d_buffers[d_keys_remaining_passes.selector ^ 1],
-                    d_spine, spine_length, current_bit,
-                    (current_bit < alt_end_bit) ? alt_pass_config : pass_config))) break;;
-
-                // Invert selectors
-                d_keys_remaining_passes.selector ^= 1;
-                d_values_remaining_passes.selector ^= 1;
-            }
-
-            // Update selector
-            if (!is_overwrite_okay) {
-                num_passes = 1; // Sorted data always ends up in the other vector
-            }
-
-            d_keys.selector = (d_keys.selector + num_passes) & 1;
-            d_values.selector = (d_values.selector + num_passes) & 1;
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Chained policy invocation
-    //------------------------------------------------------------------------------
-
-    /// Invocation
-    template <typename ActivePolicyT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t Invoke()
-    {
-        typedef typename DispatchRadixSort::MaxPolicy       MaxPolicyT;
-        typedef typename ActivePolicyT::SingleTilePolicy    SingleTilePolicyT;
-
-        // Force kernel code-generation in all compiler passes
-        if (num_items <= (SingleTilePolicyT::BLOCK_THREADS * SingleTilePolicyT::ITEMS_PER_THREAD))
-        {
-            // Small, single tile size
-            return InvokeSingleTile<ActivePolicyT>(
-                DeviceRadixSortSingleTileKernel<MaxPolicyT, IS_DESCENDING, KeyT, ValueT, OffsetT>);
-        }
-        else
-        {
-            // Regular size
-            return InvokePasses<ActivePolicyT>(
-                DeviceRadixSortUpsweepKernel<   MaxPolicyT, false,   IS_DESCENDING, KeyT, OffsetT>,
-                DeviceRadixSortUpsweepKernel<   MaxPolicyT, true,    IS_DESCENDING, KeyT, OffsetT>,
-                RadixSortScanBinsKernel<        MaxPolicyT, OffsetT>,
-                DeviceRadixSortDownsweepKernel< MaxPolicyT, false,   IS_DESCENDING, KeyT, ValueT, OffsetT>,
-                DeviceRadixSortDownsweepKernel< MaxPolicyT, true,    IS_DESCENDING, KeyT, ValueT, OffsetT>);
-        }
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Dispatch entrypoints
-    //------------------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>      &d_keys,                ///< [in,out] Double-buffer whose current buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        DoubleBuffer<ValueT>    &d_values,              ///< [in,out] Double-buffer whose current buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-        OffsetT                 num_items,              ///< [in] Number of items to sort
-        int                     begin_bit,              ///< [in] The beginning (least-significant) bit index needed for key comparison
-        int                     end_bit,                ///< [in] The past-the-end (most-significant) bit index needed for key comparison
-        bool                    is_overwrite_okay,      ///< [in] Whether is okay to overwrite source buffers
-        cudaStream_t            stream,                 ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous)      ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        typedef typename DispatchRadixSort::MaxPolicy MaxPolicyT;
-
-        cudaError_t error;
-        do {
-            // Get PTX version
-            int ptx_version;
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-
-            // Create dispatch functor
-            DispatchRadixSort dispatch(
-                d_temp_storage, temp_storage_bytes,
-                d_keys, d_values,
-                num_items, begin_bit, end_bit, is_overwrite_okay,
-                stream, debug_synchronous, ptx_version);
-
-            // Dispatch to chained policy
-            if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch))) break;
-
-        } while (0);
-
-        return error;
-    }
-};
-
-
-
-
-/******************************************************************************
- * Segmented dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for segmented device-wide radix sort
- */
-template <
-    bool     IS_DESCENDING, ///< Whether or not the sorted-order is high-to-low
-    typename KeyT,          ///< Key type
-    typename ValueT,        ///< Value type
-    typename OffsetT>       ///< Signed integer type for global offsets
-struct DispatchSegmentedRadixSort :
-    DeviceRadixSortPolicy<KeyT, ValueT, OffsetT>
-{
-    //------------------------------------------------------------------------------
-    // Constants
-    //------------------------------------------------------------------------------
-
-    enum
-    {
-        // Whether this is a keys-only (or key-value) sort
-        KEYS_ONLY = (Equals<ValueT, NullType>::VALUE),
-    };
-
-
-    //------------------------------------------------------------------------------
-    // Parameter members
-    //------------------------------------------------------------------------------
-
-    void                    *d_temp_storage;        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-    size_t                  &temp_storage_bytes;    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-    DoubleBuffer<KeyT>      &d_keys;                ///< [in,out] Double-buffer whose current buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-    DoubleBuffer<ValueT>    &d_values;              ///< [in,out] Double-buffer whose current buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-    OffsetT                 num_items;              ///< [in] Number of items to sort
-    OffsetT                 num_segments;           ///< [in] The number of segments that comprise the sorting data
-    const OffsetT           *d_begin_offsets;       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-    const OffsetT           *d_end_offsets;         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-    int                     begin_bit;              ///< [in] The beginning (least-significant) bit index needed for key comparison
-    int                     end_bit;                ///< [in] The past-the-end (most-significant) bit index needed for key comparison
-    cudaStream_t            stream;                 ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-    bool                    debug_synchronous;      ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    int                     ptx_version;            ///< [in] PTX version
-    bool                    is_overwrite_okay;      ///< [in] Whether is okay to overwrite source buffers
-
-
-    //------------------------------------------------------------------------------
-    // Constructors
-    //------------------------------------------------------------------------------
-
-    /// Constructor
-    CUB_RUNTIME_FUNCTION __forceinline__
-    DispatchSegmentedRadixSort(
-        void*                   d_temp_storage,
-        size_t                  &temp_storage_bytes,
-        DoubleBuffer<KeyT>      &d_keys,
-        DoubleBuffer<ValueT>    &d_values,
-        OffsetT                 num_items,
-        OffsetT                 num_segments,
-        const OffsetT           *d_begin_offsets,
-        const OffsetT           *d_end_offsets,
-        int                     begin_bit,
-        int                     end_bit,
-        bool                    is_overwrite_okay,
-        cudaStream_t            stream,
-        bool                    debug_synchronous,
-        int                     ptx_version)
-    :
-        d_temp_storage(d_temp_storage),
-        temp_storage_bytes(temp_storage_bytes),
-        d_keys(d_keys),
-        d_values(d_values),
-        num_items(num_items),
-        num_segments(num_segments),
-        d_begin_offsets(d_begin_offsets),
-        d_end_offsets(d_end_offsets),
-        begin_bit(begin_bit),
-        end_bit(end_bit),
-        is_overwrite_okay(is_overwrite_okay),
-        stream(stream),
-        debug_synchronous(debug_synchronous),
-        ptx_version(ptx_version)
-    {}
-
-
-    //------------------------------------------------------------------------------
-    // Multi-segment invocation
-    //------------------------------------------------------------------------------
-
-    /// Invoke a three-kernel sorting pass at the current bit.
-    template <typename PassConfigT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokePass(
-        const KeyT      *d_keys_in,
-        KeyT            *d_keys_out,
-        const ValueT    *d_values_in,
-        ValueT          *d_values_out,
-        int             &current_bit,
-        PassConfigT     &pass_config)
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            int pass_bits = CUB_MIN(pass_config.radix_bits, (end_bit - current_bit));
-
-            // Log kernel configuration
-            if (debug_synchronous)
-                _CubLog("Invoking segmented_kernels<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy, current bit %d, bit_grain %d\n",
-                    num_segments, pass_config.segmented_config.block_threads, (long long) stream,
-                pass_config.segmented_config.items_per_thread, pass_config.segmented_config.sm_occupancy, current_bit, pass_bits);
-
-            pass_config.segmented_kernel<<<num_segments, pass_config.segmented_config.block_threads, 0, stream>>>(
-                d_keys_in, d_keys_out,
-                d_values_in,  d_values_out,
-                d_begin_offsets, d_end_offsets, num_segments,
-                current_bit, pass_bits);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Update current bit
-            current_bit += pass_bits;
-        }
-        while (0);
-
-        return error;
-    }
-
-
-    /// PassConfig data structure
-    template <typename SegmentedKernelT>
-    struct PassConfig
-    {
-        SegmentedKernelT    segmented_kernel;
-        KernelConfig        segmented_config;
-        int                 radix_bits;
-        int                 radix_digits;
-
-        /// Initialize pass configuration
-        template <typename SegmentedPolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        cudaError_t InitPassConfig(SegmentedKernelT segmented_kernel)
-        {
-            this->segmented_kernel  = segmented_kernel;
-            this->radix_bits        = SegmentedPolicyT::RADIX_BITS;
-            this->radix_digits      = 1 << radix_bits;
-
-            return CubDebug(segmented_config.Init<SegmentedPolicyT>(segmented_kernel));
-        }
-    };
-
-
-    /// Invocation (run multiple digit passes)
-    template <
-        typename                ActivePolicyT,          ///< Umbrella policy active for the target device
-        typename                SegmentedKernelT>       ///< Function type of cub::DeviceSegmentedRadixSortKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokePasses(
-        SegmentedKernelT     segmented_kernel,          ///< [in] Kernel function pointer to parameterization of cub::DeviceSegmentedRadixSortKernel
-        SegmentedKernelT     alt_segmented_kernel)      ///< [in] Alternate kernel function pointer to parameterization of cub::DeviceSegmentedRadixSortKernel
-    {
-#ifndef CUB_RUNTIME_ENABLED
-      (void)segmented_kernel;
-      (void)alt_segmented_kernel;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Init regular and alternate kernel configurations
-            PassConfig<SegmentedKernelT> pass_config, alt_pass_config;
-            if ((error = pass_config.template       InitPassConfig<typename ActivePolicyT::SegmentedPolicy>(segmented_kernel))) break;
-            if ((error = alt_pass_config.template   InitPassConfig<typename ActivePolicyT::AltSegmentedPolicy>(alt_segmented_kernel))) break;
-
-            // Temporary storage allocation requirements
-            void* allocations[2];
-            size_t allocation_sizes[2] =
-            {
-                (is_overwrite_okay) ? 0 : num_items * sizeof(KeyT),                      // bytes needed for 3rd keys buffer
-                (is_overwrite_okay || (KEYS_ONLY)) ? 0 : num_items * sizeof(ValueT),     // bytes needed for 3rd values buffer
-            };
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-
-            // Return if the caller is simply requesting the size of the storage allocation
-            if (d_temp_storage == NULL)
-            {
-                if (temp_storage_bytes == 0)
-                    temp_storage_bytes = 1;
-                return cudaSuccess;
-            }
-
-            // Pass planning.  Run passes of the alternate digit-size configuration until we have an even multiple of our preferred digit size
-            int radix_bits          = ActivePolicyT::SegmentedPolicy::RADIX_BITS;
-            int alt_radix_bits      = ActivePolicyT::AltSegmentedPolicy::RADIX_BITS;
-            int num_bits            = end_bit - begin_bit;
-            int num_passes          = (num_bits + radix_bits - 1) / radix_bits;
-            bool is_num_passes_odd  = num_passes & 1;
-            int max_alt_passes      = (num_passes * radix_bits) - num_bits;
-            int alt_end_bit         = CUB_MIN(end_bit, begin_bit + (max_alt_passes * alt_radix_bits));
-
-            DoubleBuffer<KeyT> d_keys_remaining_passes(
-                (is_overwrite_okay || is_num_passes_odd) ? d_keys.Alternate() : static_cast<KeyT*>(allocations[0]),
-                (is_overwrite_okay) ? d_keys.Current() : (is_num_passes_odd) ? static_cast<KeyT*>(allocations[0]) : d_keys.Alternate());
-
-            DoubleBuffer<ValueT> d_values_remaining_passes(
-                (is_overwrite_okay || is_num_passes_odd) ? d_values.Alternate() : static_cast<ValueT*>(allocations[1]),
-                (is_overwrite_okay) ? d_values.Current() : (is_num_passes_odd) ? static_cast<ValueT*>(allocations[1]) : d_values.Alternate());
-
-            // Run first pass, consuming from the input's current buffers
-            int current_bit = begin_bit;
-
-            if (CubDebug(error = InvokePass(
-                d_keys.Current(), d_keys_remaining_passes.Current(),
-                d_values.Current(), d_values_remaining_passes.Current(),
-                current_bit,
-                (current_bit < alt_end_bit) ? alt_pass_config : pass_config))) break;
-
-            // Run remaining passes
-            while (current_bit < end_bit)
-            {
-                if (CubDebug(error = InvokePass(
-                    d_keys_remaining_passes.d_buffers[d_keys_remaining_passes.selector],    d_keys_remaining_passes.d_buffers[d_keys_remaining_passes.selector ^ 1],
-                    d_values_remaining_passes.d_buffers[d_keys_remaining_passes.selector],  d_values_remaining_passes.d_buffers[d_keys_remaining_passes.selector ^ 1],
-                    current_bit,
-                    (current_bit < alt_end_bit) ? alt_pass_config : pass_config))) break;
-
-                // Invert selectors and update current bit
-                d_keys_remaining_passes.selector ^= 1;
-                d_values_remaining_passes.selector ^= 1;
-            }
-
-            // Update selector
-            if (!is_overwrite_okay) {
-                num_passes = 1; // Sorted data always ends up in the other vector
-            }
-
-            d_keys.selector = (d_keys.selector + num_passes) & 1;
-            d_values.selector = (d_values.selector + num_passes) & 1;
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Chained policy invocation
-    //------------------------------------------------------------------------------
-
-    /// Invocation
-    template <typename ActivePolicyT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t Invoke()
-    {
-        typedef typename DispatchSegmentedRadixSort::MaxPolicy MaxPolicyT;
-
-        // Force kernel code-generation in all compiler passes
-        return InvokePasses<ActivePolicyT>(
-            DeviceSegmentedRadixSortKernel<MaxPolicyT, false,   IS_DESCENDING, KeyT, ValueT, OffsetT>,
-            DeviceSegmentedRadixSortKernel<MaxPolicyT, true,    IS_DESCENDING, KeyT, ValueT, OffsetT>);
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Dispatch entrypoints
-    //------------------------------------------------------------------------------
-
-
-    /// Internal dispatch routine
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        DoubleBuffer<KeyT>      &d_keys,                ///< [in,out] Double-buffer whose current buffer contains the unsorted input keys and, upon return, is updated to point to the sorted output keys
-        DoubleBuffer<ValueT>    &d_values,              ///< [in,out] Double-buffer whose current buffer contains the unsorted input values and, upon return, is updated to point to the sorted output values
-        int                     num_items,              ///< [in] Number of items to sort
-        int                     num_segments,           ///< [in] The number of segments that comprise the sorting data
-        const int               *d_begin_offsets,       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        const int               *d_end_offsets,         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        int                     begin_bit,              ///< [in] The beginning (least-significant) bit index needed for key comparison
-        int                     end_bit,                ///< [in] The past-the-end (most-significant) bit index needed for key comparison
-        bool                    is_overwrite_okay,      ///< [in] Whether is okay to overwrite source buffers
-        cudaStream_t            stream,                 ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous)      ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        typedef typename DispatchSegmentedRadixSort::MaxPolicy MaxPolicyT;
-
-        cudaError_t error;
-        do {
-            // Get PTX version
-            int ptx_version;
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-
-            // Create dispatch functor
-            DispatchSegmentedRadixSort dispatch(
-                d_temp_storage, temp_storage_bytes,
-                d_keys, d_values,
-                num_items, num_segments, d_begin_offsets, d_end_offsets,
-                begin_bit, end_bit, is_overwrite_okay,
-                stream, debug_synchronous, ptx_version);
-
-            // Dispatch to chained policy
-            if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch))) break;
-
-        } while (0);
-
-        return error;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_reduce.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_reduce.cuh
deleted file mode 100644
index a87f905..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_reduce.cuh
+++ /dev/null
@@ -1,928 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceReduce provides device-wide, parallel operations for computing a reduction across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "../../agent/agent_reduce.cuh"
-#include "../../iterator/arg_index_input_iterator.cuh"
-#include "../../thread/thread_operators.cuh"
-#include "../../grid/grid_even_share.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../iterator/arg_index_input_iterator.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/******************************************************************************
- * Kernel entry points
- *****************************************************************************/
-
-/**
- * Reduce region kernel entry point (multi-block).  Computes privatized reductions, one per thread block.
- */
-template <
-    typename                ChainedPolicyT,             ///< Chained tuning policy
-    typename                InputIteratorT,             ///< Random-access input iterator type for reading input items \iterator
-    typename                OutputIteratorT,            ///< Output iterator type for recording the reduced aggregate \iterator
-    typename                OffsetT,                    ///< Signed integer type for global offsets
-    typename                ReductionOpT>               ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::ReducePolicy::BLOCK_THREADS))
-__global__ void DeviceReduceKernel(
-    InputIteratorT          d_in,                       ///< [in] Pointer to the input sequence of data items
-    OutputIteratorT         d_out,                      ///< [out] Pointer to the output aggregate
-    OffsetT                 num_items,                  ///< [in] Total number of input data items
-    GridEvenShare<OffsetT>  even_share,                 ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
-    GridQueue<OffsetT>      queue,                      ///< [in] Drain queue descriptor for dynamically mapping tile data onto thread blocks
-    ReductionOpT            reduction_op)               ///< [in] Binary reduction functor
-{
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // Thread block type for reducing input tiles
-    typedef AgentReduce<
-            typename ChainedPolicyT::ActivePolicy::ReducePolicy,
-            InputIteratorT,
-            OutputIteratorT,
-            OffsetT,
-            ReductionOpT>
-        AgentReduceT;
-
-    // Shared memory storage
-    __shared__ typename AgentReduceT::TempStorage temp_storage;
-
-    // Consume input tiles
-    OutputT block_aggregate = AgentReduceT(temp_storage, d_in, reduction_op).ConsumeTiles(
-        num_items,
-        even_share,
-        queue,
-        Int2Type<ChainedPolicyT::ActivePolicy::ReducePolicy::GRID_MAPPING>());
-
-    // Output result
-    if (threadIdx.x == 0)
-        d_out[blockIdx.x] = block_aggregate;
-}
-
-
-/**
- * Reduce a single tile kernel entry point (single-block).  Can be used to aggregate privatized threadblock reductions from a previous multi-block reduction pass.
- */
-template <
-    typename                ChainedPolicyT,             ///< Chained tuning policy
-    typename                InputIteratorT,             ///< Random-access input iterator type for reading input items \iterator
-    typename                OutputIteratorT,            ///< Output iterator type for recording the reduced aggregate \iterator
-    typename                OffsetT,                    ///< Signed integer type for global offsets
-    typename                ReductionOpT,               ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename                OuputT>                     ///< Data element type that is convertible to the \p value type of \p OutputIteratorT
-__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::SingleTilePolicy::BLOCK_THREADS), 1)
-__global__ void DeviceReduceSingleTileKernel(
-    InputIteratorT          d_in,                       ///< [in] Pointer to the input sequence of data items
-    OutputIteratorT         d_out,                      ///< [out] Pointer to the output aggregate
-    OffsetT                 num_items,                  ///< [in] Total number of input data items
-    ReductionOpT            reduction_op,               ///< [in] Binary reduction functor
-    OuputT                  init)                       ///< [in] The initial value of the reduction
-{
-    // Thread block type for reducing input tiles
-    typedef AgentReduce<
-            typename ChainedPolicyT::ActivePolicy::SingleTilePolicy,
-            InputIteratorT,
-            OutputIteratorT,
-            OffsetT,
-            ReductionOpT>
-        AgentReduceT;
-
-    // Shared memory storage
-    __shared__ typename AgentReduceT::TempStorage temp_storage;
-
-    // Check if empty problem
-    if (num_items == 0)
-    {
-        if (threadIdx.x == 0)
-            *d_out = init;
-        return;
-    }
-
-    // Consume input tiles
-    OuputT block_aggregate = AgentReduceT(temp_storage, d_in, reduction_op).ConsumeRange(
-        OffsetT(0),
-        num_items);
-
-    // Output result
-    if (threadIdx.x == 0)
-        *d_out = reduction_op(init, block_aggregate);
-}
-
-
-/// Normalize input iterator to segment offset
-template <typename T, typename OffsetT, typename IteratorT>
-__device__ __forceinline__
-void NormalizeReductionOutput(
-    T &/*val*/,
-    OffsetT /*base_offset*/,
-    IteratorT /*itr*/)
-{}
-
-
-/// Normalize input iterator to segment offset (specialized for arg-index)
-template <typename KeyValuePairT, typename OffsetT, typename WrappedIteratorT, typename OutputValueT>
-__device__ __forceinline__
-void NormalizeReductionOutput(
-    KeyValuePairT &val,
-    OffsetT base_offset,
-    ArgIndexInputIterator<WrappedIteratorT, OffsetT, OutputValueT> /*itr*/)
-{
-    val.key -= base_offset;
-}
-
-
-/**
- * Segmented reduction (one block per segment)
- */
-template <
-    typename                ChainedPolicyT,             ///< Chained tuning policy
-    typename                InputIteratorT,             ///< Random-access input iterator type for reading input items \iterator
-    typename                OutputIteratorT,            ///< Output iterator type for recording the reduced aggregate \iterator
-    typename                OffsetT,                    ///< Signed integer type for global offsets
-    typename                ReductionOpT,               ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename                OutputT>                    ///< Data element type that is convertible to the \p value type of \p OutputIteratorT
-__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::ReducePolicy::BLOCK_THREADS))
-__global__ void DeviceSegmentedReduceKernel(
-    InputIteratorT          d_in,                       ///< [in] Pointer to the input sequence of data items
-    OutputIteratorT         d_out,                      ///< [out] Pointer to the output aggregate
-    int                     *d_begin_offsets,           ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-    int                     *d_end_offsets,             ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-    int                     /*num_segments*/,           ///< [in] The number of segments that comprise the sorting data
-    ReductionOpT            reduction_op,               ///< [in] Binary reduction functor 
-    OutputT                 init)                       ///< [in] The initial value of the reduction
-{
-    // Thread block type for reducing input tiles
-    typedef AgentReduce<
-            typename ChainedPolicyT::ActivePolicy::ReducePolicy,
-            InputIteratorT,
-            OutputIteratorT,
-            OffsetT,
-            ReductionOpT>
-        AgentReduceT;
-
-    // Shared memory storage
-    __shared__ typename AgentReduceT::TempStorage temp_storage;
-
-    OffsetT segment_begin   = d_begin_offsets[blockIdx.x];
-    OffsetT segment_end     = d_end_offsets[blockIdx.x];
-
-    // Check if empty problem
-    if (segment_begin == segment_end)
-    {
-        if (threadIdx.x == 0)
-            d_out[blockIdx.x] = init;
-        return;
-    }
-
-    // Consume input tiles
-    OutputT block_aggregate = AgentReduceT(temp_storage, d_in, reduction_op).ConsumeRange(
-        segment_begin,
-        segment_end);
-
-    // Normalize as needed
-    NormalizeReductionOutput(block_aggregate, segment_begin, d_in);
-
-    if (threadIdx.x == 0)
-        d_out[blockIdx.x] = reduction_op(init, block_aggregate);;
-}
-
-
-
-
-/******************************************************************************
- * Policy
- ******************************************************************************/
-
-template <
-    typename OuputT,            ///< Data type
-    typename OffsetT,           ///< Signed integer type for global offsets
-    typename ReductionOpT>      ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt> 
-struct DeviceReducePolicy
-{
-    //------------------------------------------------------------------------------
-    // Architecture-specific tuning policies
-    //------------------------------------------------------------------------------
-
-    /// SM13
-    struct Policy130 : ChainedPolicy<130, Policy130, Policy130>
-    {
-        // ReducePolicy
-        typedef AgentReducePolicy<
-                CUB_NOMINAL_CONFIG(128, 8, OuputT),      ///< Threads per block, items per thread
-                2,                                  ///< Number of items per vectorized load
-                BLOCK_REDUCE_RAKING,                ///< Cooperative block-wide reduction algorithm to use
-                LOAD_DEFAULT,                       ///< Cache load modifier
-                GRID_MAPPING_EVEN_SHARE>            ///< How to map tiles of input onto thread blocks
-            ReducePolicy;
-
-        // SingleTilePolicy
-        typedef ReducePolicy SingleTilePolicy;
-
-        // SegmentedReducePolicy
-        typedef ReducePolicy SegmentedReducePolicy;
-    };
-
-
-    /// SM20
-    struct Policy200 : ChainedPolicy<200, Policy200, Policy130>
-    {
-        // ReducePolicy (GTX 580: 178.9 GB/s @ 48M 4B items, 158.1 GB/s @ 192M 1B items)
-        typedef AgentReducePolicy<
-                CUB_NOMINAL_CONFIG(128, 8, OuputT),      ///< Threads per block, items per thread
-                4,                                  ///< Number of items per vectorized load
-                BLOCK_REDUCE_RAKING,                ///< Cooperative block-wide reduction algorithm to use
-                LOAD_DEFAULT,                       ///< Cache load modifier
-                GRID_MAPPING_DYNAMIC>               ///< How to map tiles of input onto thread blocks
-            ReducePolicy;
-
-        // SingleTilePolicy
-        typedef ReducePolicy SingleTilePolicy;
-
-        // SegmentedReducePolicy
-        typedef ReducePolicy SegmentedReducePolicy;
-    };
-
-
-    /// SM30
-    struct Policy300 : ChainedPolicy<300, Policy300, Policy200>
-    {
-        // ReducePolicy (GTX670: 154.0 @ 48M 4B items)
-        typedef AgentReducePolicy<
-                CUB_NOMINAL_CONFIG(256, 20, OuputT),     ///< Threads per block, items per thread
-                2,                                  ///< Number of items per vectorized load
-                BLOCK_REDUCE_WARP_REDUCTIONS,       ///< Cooperative block-wide reduction algorithm to use
-                LOAD_DEFAULT,                       ///< Cache load modifier
-                GRID_MAPPING_EVEN_SHARE>            ///< How to map tiles of input onto thread blocks
-            ReducePolicy;
-
-        // SingleTilePolicy
-        typedef ReducePolicy SingleTilePolicy;
-
-        // SegmentedReducePolicy
-        typedef ReducePolicy SegmentedReducePolicy;
-    };
-
-
-    /// SM35
-    struct Policy350 : ChainedPolicy<350, Policy350, Policy300>
-    {
-        // ReducePolicy (GTX Titan: 255.1 GB/s @ 48M 4B items; 228.7 GB/s @ 192M 1B items)
-        typedef AgentReducePolicy<
-                CUB_NOMINAL_CONFIG(256, 20, OuputT),     ///< Threads per block, items per thread
-                4,                                  ///< Number of items per vectorized load
-                BLOCK_REDUCE_WARP_REDUCTIONS,       ///< Cooperative block-wide reduction algorithm to use
-                LOAD_LDG,                           ///< Cache load modifier
-                GRID_MAPPING_DYNAMIC>               ///< How to map tiles of input onto thread blocks
-            ReducePolicy;
-
-        // SingleTilePolicy
-        typedef ReducePolicy SingleTilePolicy;
-
-        // SegmentedReducePolicy
-        typedef ReducePolicy SegmentedReducePolicy;
-    };
-
-    /// SM60
-    struct Policy600 : ChainedPolicy<600, Policy600, Policy350>
-    {
-        // ReducePolicy (P100: 591 GB/s @ 64M 4B items; 583 GB/s @ 256M 1B items)
-        typedef AgentReducePolicy<
-                CUB_NOMINAL_CONFIG(256, 16, OuputT),     ///< Threads per block, items per thread
-                4,                                  ///< Number of items per vectorized load
-                BLOCK_REDUCE_WARP_REDUCTIONS,       ///< Cooperative block-wide reduction algorithm to use
-                LOAD_LDG,                           ///< Cache load modifier
-                GRID_MAPPING_DYNAMIC>               ///< How to map tiles of input onto thread blocks
-            ReducePolicy;
-
-        // SingleTilePolicy
-        typedef ReducePolicy SingleTilePolicy;
-
-        // SegmentedReducePolicy
-        typedef ReducePolicy SegmentedReducePolicy;
-    };
-
-
-    /// MaxPolicy
-    typedef Policy600 MaxPolicy;
-
-};
-
-
-
-/******************************************************************************
- * Single-problem dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for device-wide reduction
- */
-template <
-    typename InputIteratorT,    ///< Random-access input iterator type for reading input items \iterator
-    typename OutputIteratorT,   ///< Output iterator type for recording the reduced aggregate \iterator
-    typename OffsetT,           ///< Signed integer type for global offsets
-    typename ReductionOpT>      ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt> 
-struct DispatchReduce :
-    DeviceReducePolicy<
-        typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-            typename std::iterator_traits<InputIteratorT>::value_type,                                  // ... then the input iterator's value type,
-            typename std::iterator_traits<OutputIteratorT>::value_type>::Type,                          // ... else the output iterator's value type
-        OffsetT,
-        ReductionOpT>
-{
-    //------------------------------------------------------------------------------
-    // Constants
-    //------------------------------------------------------------------------------
-
-    // Data type of output iterator
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-
-    //------------------------------------------------------------------------------
-    // Problem state
-    //------------------------------------------------------------------------------
-
-    void                *d_temp_storage;                ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-    size_t              &temp_storage_bytes;            ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-    InputIteratorT      d_in;                           ///< [in] Pointer to the input sequence of data items
-    OutputIteratorT     d_out;                          ///< [out] Pointer to the output aggregate
-    OffsetT             num_items;                      ///< [in] Total number of input items (i.e., length of \p d_in)
-    ReductionOpT        reduction_op;                   ///< [in] Binary reduction functor 
-    OutputT             init;                           ///< [in] The initial value of the reduction
-    cudaStream_t        stream;                         ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-    bool                debug_synchronous;              ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    int                 ptx_version;                    ///< [in] PTX version
-
-    //------------------------------------------------------------------------------
-    // Constructor
-    //------------------------------------------------------------------------------
-
-    /// Constructor
-    CUB_RUNTIME_FUNCTION __forceinline__
-    DispatchReduce(
-        void*                   d_temp_storage,
-        size_t                  &temp_storage_bytes,
-        InputIteratorT          d_in,
-        OutputIteratorT         d_out,
-        OffsetT                 num_items,
-        ReductionOpT            reduction_op,
-        OutputT                 init,
-        cudaStream_t            stream,
-        bool                    debug_synchronous,
-        int                     ptx_version)
-    :
-        d_temp_storage(d_temp_storage),
-        temp_storage_bytes(temp_storage_bytes),
-        d_in(d_in),
-        d_out(d_out),
-        num_items(num_items),
-        reduction_op(reduction_op),
-        init(init),
-        stream(stream),
-        debug_synchronous(debug_synchronous),
-        ptx_version(ptx_version)
-    {}
-
-
-    //------------------------------------------------------------------------------
-    // Small-problem (single tile) invocation
-    //------------------------------------------------------------------------------
-
-    /// Invoke a single block block to reduce in-core
-    template <
-        typename                ActivePolicyT,          ///< Umbrella policy active for the target device
-        typename                SingleTileKernelT>      ///< Function type of cub::DeviceReduceSingleTileKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokeSingleTile(
-        SingleTileKernelT       single_tile_kernel)     ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceSingleTileKernel
-    {
-#ifndef CUB_RUNTIME_ENABLED
-        (void)single_tile_kernel;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Return if the caller is simply requesting the size of the storage allocation
-            if (d_temp_storage == NULL)
-            {
-                temp_storage_bytes = 1;
-                break;
-            }
-
-            // Log single_reduce_sweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking DeviceReduceSingleTileKernel<<<1, %d, 0, %lld>>>(), %d items per thread\n",
-                ActivePolicyT::SingleTilePolicy::BLOCK_THREADS,
-                (long long) stream,
-                ActivePolicyT::SingleTilePolicy::ITEMS_PER_THREAD);
-
-            // Invoke single_reduce_sweep_kernel
-            single_tile_kernel<<<1, ActivePolicyT::SingleTilePolicy::BLOCK_THREADS, 0, stream>>>(
-                d_in,
-                d_out,
-                num_items,
-                reduction_op,
-                init);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Normal problem size invocation (two-pass)
-    //------------------------------------------------------------------------------
-
-    /// Invoke two-passes to reduce
-    template <
-        typename                ActivePolicyT,              ///< Umbrella policy active for the target device
-        typename                ReduceKernelT,              ///< Function type of cub::DeviceReduceKernel
-        typename                SingleTileKernelT,          ///< Function type of cub::DeviceReduceSingleTileKernel
-        typename                FillAndResetDrainKernelT>   ///< Function type of cub::FillAndResetDrainKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokePasses(
-        ReduceKernelT               reduce_kernel,          ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceKernel
-        SingleTileKernelT           single_tile_kernel,     ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceSingleTileKernel
-        FillAndResetDrainKernelT    prepare_drain_kernel)   ///< [in] Kernel function pointer to parameterization of cub::FillAndResetDrainKernel
-    {
-#ifndef CUB_RUNTIME_ENABLED
-        (void)               reduce_kernel;
-        (void)           single_tile_kernel;
-        (void)    prepare_drain_kernel;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Init regular kernel configuration
-            KernelConfig reduce_config;
-            if (CubDebug(error = reduce_config.Init<typename ActivePolicyT::ReducePolicy>(reduce_kernel))) break;
-            int reduce_device_occupancy = reduce_config.sm_occupancy * sm_count;
-
-            // Even-share work distribution
-            int max_blocks = reduce_device_occupancy * CUB_SUBSCRIPTION_FACTOR(ptx_version);
-            GridEvenShare<OffsetT> even_share(num_items, max_blocks, reduce_config.tile_size);
-
-            // Temporary storage allocation requirements
-            void* allocations[2];
-            size_t allocation_sizes[2] =
-            {
-                max_blocks * sizeof(OutputT),           // bytes needed for privatized block reductions
-                GridQueue<OffsetT>::AllocationSize()    // bytes needed for grid queue descriptor
-            };
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                return cudaSuccess;
-            }
-
-            // Alias the allocation for the privatized per-block reductions
-            OutputT *d_block_reductions = (OutputT*) allocations[0];
-
-            // Alias the allocation for the grid queue descriptor
-            GridQueue<OffsetT> queue(allocations[1]);
-
-            // Get grid size for device_reduce_sweep_kernel
-            int reduce_grid_size;
-            if (ActivePolicyT::ReducePolicy::GRID_MAPPING == GRID_MAPPING_EVEN_SHARE)
-            {
-                // Work is distributed evenly
-                reduce_grid_size = even_share.grid_size;
-            }
-            else if (ActivePolicyT::ReducePolicy::GRID_MAPPING == GRID_MAPPING_DYNAMIC)
-            {
-                // Work is distributed dynamically
-                int num_tiles       = (num_items + reduce_config.tile_size - 1) / reduce_config.tile_size;
-                reduce_grid_size    = (num_tiles < reduce_device_occupancy) ?
-                                        num_tiles :                 // Not enough to fill the device with threadblocks
-                                        reduce_device_occupancy;    // Fill the device with threadblocks
-
-                // Prepare the dynamic queue descriptor if necessary
-                if (debug_synchronous) _CubLog("Invoking prepare_drain_kernel<<<1, 1, 0, %lld>>>()\n", (long long) stream);
-
-                // Invoke prepare_drain_kernel
-                prepare_drain_kernel<<<1, 1, 0, stream>>>(queue, num_items);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-            else
-            {
-                error = CubDebug(cudaErrorNotSupported ); break;
-            }
-
-            // Log device_reduce_sweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking DeviceReduceKernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                reduce_grid_size,
-                ActivePolicyT::ReducePolicy::BLOCK_THREADS,
-                (long long) stream,
-                ActivePolicyT::ReducePolicy::ITEMS_PER_THREAD,
-                reduce_config.sm_occupancy);
-
-            // Invoke DeviceReduceKernel
-            reduce_kernel<<<reduce_grid_size, ActivePolicyT::ReducePolicy::BLOCK_THREADS, 0, stream>>>(
-                d_in,
-                d_block_reductions,
-                num_items,
-                even_share,
-                queue,
-                reduction_op);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Log single_reduce_sweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking DeviceReduceSingleTileKernel<<<1, %d, 0, %lld>>>(), %d items per thread\n",
-                ActivePolicyT::SingleTilePolicy::BLOCK_THREADS,
-                (long long) stream,
-                ActivePolicyT::SingleTilePolicy::ITEMS_PER_THREAD);
-
-            // Invoke DeviceReduceSingleTileKernel
-            single_tile_kernel<<<1, ActivePolicyT::SingleTilePolicy::BLOCK_THREADS, 0, stream>>>(
-                d_block_reductions,
-                d_out,
-                reduce_grid_size,
-                reduction_op,
-                init);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Chained policy invocation
-    //------------------------------------------------------------------------------
-
-    /// Invocation
-    template <typename ActivePolicyT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t Invoke()
-    {
-        typedef typename ActivePolicyT::SingleTilePolicy    SingleTilePolicyT;
-        typedef typename DispatchReduce::MaxPolicy          MaxPolicyT;
-
-        // Force kernel code-generation in all compiler passes
-        if (num_items <= (SingleTilePolicyT::BLOCK_THREADS * SingleTilePolicyT::ITEMS_PER_THREAD))
-        {
-            // Small, single tile size
-            return InvokeSingleTile<ActivePolicyT>(
-                DeviceReduceSingleTileKernel<MaxPolicyT, InputIteratorT, OutputIteratorT, OffsetT, ReductionOpT, OutputT>);
-        }
-        else
-        {
-            // Regular size
-            return InvokePasses<ActivePolicyT>(
-                DeviceReduceKernel<typename DispatchReduce::MaxPolicy, InputIteratorT, OutputT*, OffsetT, ReductionOpT>,
-                DeviceReduceSingleTileKernel<MaxPolicyT, OutputT*, OutputIteratorT, OffsetT, ReductionOpT, OutputT>,
-                FillAndResetDrainKernel<OffsetT>);
-        }
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Dispatch entrypoints
-    //------------------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void            *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t          &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT  d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT d_out,                              ///< [out] Pointer to the output aggregate
-        OffsetT         num_items,                          ///< [in] Total number of input items (i.e., length of \p d_in)
-        ReductionOpT    reduction_op,                       ///< [in] Binary reduction functor 
-        OutputT         init,                               ///< [in] The initial value of the reduction
-        cudaStream_t    stream,                             ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool            debug_synchronous)                  ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        typedef typename DispatchReduce::MaxPolicy MaxPolicyT;
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-
-            // Create dispatch functor
-            DispatchReduce dispatch(
-                d_temp_storage, temp_storage_bytes,
-                d_in, d_out, num_items, reduction_op, init,
-                stream, debug_synchronous, ptx_version);
-
-            // Dispatch to chained policy
-            if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch))) break;
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-
-/******************************************************************************
- * Segmented dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for device-wide reduction
- */
-template <
-    typename InputIteratorT,    ///< Random-access input iterator type for reading input items \iterator
-    typename OutputIteratorT,   ///< Output iterator type for recording the reduced aggregate \iterator
-    typename OffsetT,           ///< Signed integer type for global offsets
-    typename ReductionOpT>      ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt> 
-struct DispatchSegmentedReduce :
-    DeviceReducePolicy<
-        typename std::iterator_traits<InputIteratorT>::value_type,
-        OffsetT,
-        ReductionOpT>
-{
-    //------------------------------------------------------------------------------
-    // Constants
-    //------------------------------------------------------------------------------
-
-    /// The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-
-    //------------------------------------------------------------------------------
-    // Problem state
-    //------------------------------------------------------------------------------
-
-    void                *d_temp_storage;        ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-    size_t              &temp_storage_bytes;    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-    InputIteratorT      d_in;                   ///< [in] Pointer to the input sequence of data items
-    OutputIteratorT     d_out;                  ///< [out] Pointer to the output aggregate
-    OffsetT             num_segments;           ///< [in] The number of segments that comprise the sorting data
-    OffsetT             *d_begin_offsets;       ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-    OffsetT             *d_end_offsets;         ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-    ReductionOpT        reduction_op;           ///< [in] Binary reduction functor 
-    OutputT             init;                   ///< [in] The initial value of the reduction
-    cudaStream_t        stream;                 ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-    bool                debug_synchronous;      ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    int                 ptx_version;            ///< [in] PTX version
-
-    //------------------------------------------------------------------------------
-    // Constructor
-    //------------------------------------------------------------------------------
-
-    /// Constructor
-    CUB_RUNTIME_FUNCTION __forceinline__
-    DispatchSegmentedReduce(
-        void*                   d_temp_storage,
-        size_t                  &temp_storage_bytes,
-        InputIteratorT          d_in,
-        OutputIteratorT         d_out,
-        OffsetT                 num_segments,
-        OffsetT                 *d_begin_offsets,
-        OffsetT                 *d_end_offsets,
-        ReductionOpT            reduction_op,
-        OutputT                 init,
-        cudaStream_t            stream,
-        bool                    debug_synchronous,
-        int                     ptx_version)
-    :
-        d_temp_storage(d_temp_storage),
-        temp_storage_bytes(temp_storage_bytes),
-        d_in(d_in),
-        d_out(d_out),
-        num_segments(num_segments),
-        d_begin_offsets(d_begin_offsets),
-        d_end_offsets(d_end_offsets),
-        reduction_op(reduction_op),
-        init(init),
-        stream(stream),
-        debug_synchronous(debug_synchronous),
-        ptx_version(ptx_version)
-    {}
-
-
-
-    //------------------------------------------------------------------------------
-    // Chained policy invocation
-    //------------------------------------------------------------------------------
-
-    /// Invocation
-    template <
-        typename                        ActivePolicyT,                  ///< Umbrella policy active for the target device
-        typename                        DeviceSegmentedReduceKernelT>   ///< Function type of cub::DeviceSegmentedReduceKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t InvokePasses(
-        DeviceSegmentedReduceKernelT    segmented_reduce_kernel)        ///< [in] Kernel function pointer to parameterization of cub::DeviceSegmentedReduceKernel
-    {
-#ifndef CUB_RUNTIME_ENABLED
-        (void)segmented_reduce_kernel;
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Return if the caller is simply requesting the size of the storage allocation
-            if (d_temp_storage == NULL)
-            {
-                temp_storage_bytes = 1;
-                return cudaSuccess;
-            }
-
-            // Init kernel configuration
-            KernelConfig segmented_reduce_config;
-            if (CubDebug(error = segmented_reduce_config.Init<typename ActivePolicyT::SegmentedReducePolicy>(segmented_reduce_kernel))) break;
-
-            // Log device_reduce_sweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking SegmentedDeviceReduceKernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                num_segments,
-                ActivePolicyT::SegmentedReducePolicy::BLOCK_THREADS,
-                (long long) stream,
-                ActivePolicyT::SegmentedReducePolicy::ITEMS_PER_THREAD,
-                segmented_reduce_config.sm_occupancy);
-
-            // Invoke DeviceReduceKernel
-            segmented_reduce_kernel<<<num_segments, ActivePolicyT::SegmentedReducePolicy::BLOCK_THREADS, 0, stream>>>(
-                d_in,
-                d_out,
-                d_begin_offsets,
-                d_end_offsets,
-                num_segments,
-                reduction_op,
-                init);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-
-    }
-
-
-    /// Invocation
-    template <typename ActivePolicyT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t Invoke()
-    {
-        typedef typename DispatchSegmentedReduce::MaxPolicy MaxPolicyT;
-
-        // Force kernel code-generation in all compiler passes
-        return InvokePasses<ActivePolicyT>(
-            DeviceSegmentedReduceKernel<MaxPolicyT, InputIteratorT, OutputIteratorT, OffsetT, ReductionOpT, OutputT>);
-    }
-
-
-    //------------------------------------------------------------------------------
-    // Dispatch entrypoints
-    //------------------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void            *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t          &temp_storage_bytes,                ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT  d_in,                               ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT d_out,                              ///< [out] Pointer to the output aggregate
-        int             num_segments,                       ///< [in] The number of segments that comprise the sorting data
-        int             *d_begin_offsets,                   ///< [in] %Device-accessible pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
-        int             *d_end_offsets,                     ///< [in] %Device-accessible pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>.  If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
-        ReductionOpT    reduction_op,                       ///< [in] Binary reduction functor 
-        OutputT         init,                               ///< [in] The initial value of the reduction
-        cudaStream_t    stream,                             ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool            debug_synchronous)                  ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        typedef typename DispatchSegmentedReduce::MaxPolicy MaxPolicyT;
-
-        if (num_segments <= 0)
-            return cudaSuccess;
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-
-            // Create dispatch functor
-            DispatchSegmentedReduce dispatch(
-                d_temp_storage, temp_storage_bytes,
-                d_in, d_out,
-                num_segments, d_begin_offsets, d_end_offsets,
-                reduction_op, init,
-                stream, debug_synchronous, ptx_version);
-
-            // Dispatch to chained policy
-            if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch))) break;
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_reduce_by_key.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_reduce_by_key.cuh
deleted file mode 100644
index 63ba62b..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_reduce_by_key.cuh
+++ /dev/null
@@ -1,554 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceReduceByKey provides device-wide, parallel operations for reducing segments of values residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch_scan.cuh"
-#include "../../agent/agent_reduce_by_key.cuh"
-#include "../../thread/thread_operators.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/******************************************************************************
- * Kernel entry points
- *****************************************************************************/
-
-/**
- * Multi-block reduce-by-key sweep kernel entry point
- */
-template <
-    typename            AgentReduceByKeyPolicyT,                 ///< Parameterized AgentReduceByKeyPolicyT tuning policy type
-    typename            KeysInputIteratorT,                     ///< Random-access input iterator type for keys
-    typename            UniqueOutputIteratorT,                  ///< Random-access output iterator type for keys
-    typename            ValuesInputIteratorT,                   ///< Random-access input iterator type for values
-    typename            AggregatesOutputIteratorT,              ///< Random-access output iterator type for values
-    typename            NumRunsOutputIteratorT,                 ///< Output iterator type for recording number of segments encountered
-    typename            ScanTileStateT,                         ///< Tile status interface type
-    typename            EqualityOpT,                            ///< KeyT equality operator type
-    typename            ReductionOpT,                           ///< ValueT reduction operator type
-    typename            OffsetT>                                ///< Signed integer type for global offsets
-__launch_bounds__ (int(AgentReduceByKeyPolicyT::BLOCK_THREADS))
-__global__ void DeviceReduceByKeyKernel(
-    KeysInputIteratorT          d_keys_in,                      ///< Pointer to the input sequence of keys
-    UniqueOutputIteratorT       d_unique_out,                   ///< Pointer to the output sequence of unique keys (one key per run)
-    ValuesInputIteratorT        d_values_in,                    ///< Pointer to the input sequence of corresponding values
-    AggregatesOutputIteratorT   d_aggregates_out,               ///< Pointer to the output sequence of value aggregates (one aggregate per run)
-    NumRunsOutputIteratorT      d_num_runs_out,                 ///< Pointer to total number of runs encountered (i.e., the length of d_unique_out)
-    ScanTileStateT              tile_state,                     ///< Tile status interface
-    int                         start_tile,                     ///< The starting tile for the current grid
-    EqualityOpT                 equality_op,                    ///< KeyT equality operator
-    ReductionOpT                reduction_op,                   ///< ValueT reduction operator
-    OffsetT                     num_items)                      ///< Total number of items to select from
-{
-    // Thread block type for reducing tiles of value segments
-    typedef AgentReduceByKey<
-            AgentReduceByKeyPolicyT,
-            KeysInputIteratorT,
-            UniqueOutputIteratorT,
-            ValuesInputIteratorT,
-            AggregatesOutputIteratorT,
-            NumRunsOutputIteratorT,
-            EqualityOpT,
-            ReductionOpT,
-            OffsetT>
-        AgentReduceByKeyT;
-
-    // Shared memory for AgentReduceByKey
-    __shared__ typename AgentReduceByKeyT::TempStorage temp_storage;
-
-    // Process tiles
-    AgentReduceByKeyT(temp_storage, d_keys_in, d_unique_out, d_values_in, d_aggregates_out, d_num_runs_out, equality_op, reduction_op).ConsumeRange(
-        num_items,
-        tile_state,
-        start_tile);
-}
-
-
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceReduceByKey
- */
-template <
-    typename    KeysInputIteratorT,         ///< Random-access input iterator type for keys
-    typename    UniqueOutputIteratorT,      ///< Random-access output iterator type for keys
-    typename    ValuesInputIteratorT,       ///< Random-access input iterator type for values
-    typename    AggregatesOutputIteratorT,  ///< Random-access output iterator type for values
-    typename    NumRunsOutputIteratorT,     ///< Output iterator type for recording number of segments encountered
-    typename    EqualityOpT,                ///< KeyT equality operator type
-    typename    ReductionOpT,               ///< ValueT reduction operator type
-    typename    OffsetT>                    ///< Signed integer type for global offsets
-struct DispatchReduceByKey
-{
-    //-------------------------------------------------------------------------
-    // Types and constants
-    //-------------------------------------------------------------------------
-
-    // The input keys type
-    typedef typename std::iterator_traits<KeysInputIteratorT>::value_type KeyInputT;
-
-    // The output keys type
-    typedef typename If<(Equals<typename std::iterator_traits<UniqueOutputIteratorT>::value_type, void>::VALUE),    // KeyOutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<KeysInputIteratorT>::value_type,                                              // ... then the input iterator's value type,
-        typename std::iterator_traits<UniqueOutputIteratorT>::value_type>::Type KeyOutputT;                         // ... else the output iterator's value type
-
-    // The input values type
-    typedef typename std::iterator_traits<ValuesInputIteratorT>::value_type ValueInputT;
-
-    // The output values type
-    typedef typename If<(Equals<typename std::iterator_traits<AggregatesOutputIteratorT>::value_type, void>::VALUE),    // ValueOutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<ValuesInputIteratorT>::value_type,                                                // ... then the input iterator's value type,
-        typename std::iterator_traits<AggregatesOutputIteratorT>::value_type>::Type ValueOutputT;                       // ... else the output iterator's value type
-
-    enum
-    {
-        INIT_KERNEL_THREADS     = 128,
-        MAX_INPUT_BYTES         = CUB_MAX(sizeof(KeyOutputT), sizeof(ValueOutputT)),
-        COMBINED_INPUT_BYTES    = sizeof(KeyOutputT) + sizeof(ValueOutputT),
-    };
-
-    // Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<ValueOutputT, OffsetT> ScanTileStateT;
-
-
-    //-------------------------------------------------------------------------
-    // Tuning policies
-    //-------------------------------------------------------------------------
-
-    /// SM35
-    struct Policy350
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 6,
-            ITEMS_PER_THREAD            = (MAX_INPUT_BYTES <= 8) ? 6 : CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, ((NOMINAL_4B_ITEMS_PER_THREAD * 8) + COMBINED_INPUT_BYTES - 1) / COMBINED_INPUT_BYTES)),
-        };
-
-        typedef AgentReduceByKeyPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                BLOCK_SCAN_WARP_SCANS>
-            ReduceByKeyPolicyT;
-    };
-
-    /// SM30
-    struct Policy300
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 6,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, ((NOMINAL_4B_ITEMS_PER_THREAD * 8) + COMBINED_INPUT_BYTES - 1) / COMBINED_INPUT_BYTES)),
-        };
-
-        typedef AgentReduceByKeyPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            ReduceByKeyPolicyT;
-    };
-
-    /// SM20
-    struct Policy200
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 11,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, ((NOMINAL_4B_ITEMS_PER_THREAD * 8) + COMBINED_INPUT_BYTES - 1) / COMBINED_INPUT_BYTES)),
-        };
-
-        typedef AgentReduceByKeyPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            ReduceByKeyPolicyT;
-    };
-
-    /// SM13
-    struct Policy130
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 7,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, ((NOMINAL_4B_ITEMS_PER_THREAD * 8) + COMBINED_INPUT_BYTES - 1) / COMBINED_INPUT_BYTES)),
-        };
-
-        typedef AgentReduceByKeyPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            ReduceByKeyPolicyT;
-    };
-
-    /// SM11
-    struct Policy110
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 5,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 8) / COMBINED_INPUT_BYTES)),
-        };
-
-        typedef AgentReduceByKeyPolicy<
-                64,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_RAKING>
-            ReduceByKeyPolicyT;
-    };
-
-
-    /******************************************************************************
-     * Tuning policies of current PTX compiler pass
-     ******************************************************************************/
-
-#if (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 130)
-    typedef Policy130 PtxPolicy;
-
-#else
-    typedef Policy110 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxReduceByKeyPolicy : PtxPolicy::ReduceByKeyPolicyT {};
-
-
-    /******************************************************************************
-     * Utilities
-     ******************************************************************************/
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig    &reduce_by_key_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-        (void)ptx_version;
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        reduce_by_key_config.template Init<PtxReduceByKeyPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 350)
-        {
-            reduce_by_key_config.template Init<typename Policy350::ReduceByKeyPolicyT>();
-        }
-        else if (ptx_version >= 300)
-        {
-            reduce_by_key_config.template Init<typename Policy300::ReduceByKeyPolicyT>();
-        }
-        else if (ptx_version >= 200)
-        {
-            reduce_by_key_config.template Init<typename Policy200::ReduceByKeyPolicyT>();
-        }
-        else if (ptx_version >= 130)
-        {
-            reduce_by_key_config.template Init<typename Policy130::ReduceByKeyPolicyT>();
-        }
-        else
-        {
-            reduce_by_key_config.template Init<typename Policy110::ReduceByKeyPolicyT>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.
-     */
-    struct KernelConfig
-    {
-        int block_threads;
-        int items_per_thread;
-        int tile_items;
-
-        template <typename PolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads       = PolicyT::BLOCK_THREADS;
-            items_per_thread    = PolicyT::ITEMS_PER_THREAD;
-            tile_items          = block_threads * items_per_thread;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Dispatch entrypoints
-    //---------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduce-by-key using the
-     * specified kernel functions.
-     */
-    template <
-        typename                    ScanInitKernelT,         ///< Function type of cub::DeviceScanInitKernel
-        typename                    ReduceByKeyKernelT>      ///< Function type of cub::DeviceReduceByKeyKernelT
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                       d_temp_storage,             ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                     temp_storage_bytes,         ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        KeysInputIteratorT          d_keys_in,                  ///< [in] Pointer to the input sequence of keys
-        UniqueOutputIteratorT       d_unique_out,               ///< [out] Pointer to the output sequence of unique keys (one key per run)
-        ValuesInputIteratorT        d_values_in,                ///< [in] Pointer to the input sequence of corresponding values
-        AggregatesOutputIteratorT   d_aggregates_out,           ///< [out] Pointer to the output sequence of value aggregates (one aggregate per run)
-        NumRunsOutputIteratorT      d_num_runs_out,             ///< [out] Pointer to total number of runs encountered (i.e., the length of d_unique_out)
-        EqualityOpT                 equality_op,                ///< [in] KeyT equality operator
-        ReductionOpT                reduction_op,               ///< [in] ValueT reduction operator
-        OffsetT                     num_items,                  ///< [in] Total number of items to select from
-        cudaStream_t                stream,                     ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous,          ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        int                         /*ptx_version*/,            ///< [in] PTX version of dispatch kernels
-        ScanInitKernelT            	init_kernel,                ///< [in] Kernel function pointer to parameterization of cub::DeviceScanInitKernel
-        ReduceByKeyKernelT         	reduce_by_key_kernel,       ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceByKeyKernel
-        KernelConfig                reduce_by_key_config)       ///< [in] Dispatch parameters that match the policy that \p reduce_by_key_kernel was compiled for
-    {
-
-#ifndef CUB_RUNTIME_ENABLED
-      (void)d_temp_storage;
-      (void)temp_storage_bytes;
-      (void)d_keys_in;
-      (void)d_unique_out;
-      (void)d_values_in;
-      (void)d_aggregates_out;
-      (void)d_num_runs_out;
-      (void)equality_op;
-      (void)reduction_op;
-      (void)num_items;
-      (void)stream;
-      (void)debug_synchronous;
-      (void)init_kernel;
-      (void)reduce_by_key_kernel;
-      (void)reduce_by_key_config;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported);
-
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Number of input tiles
-            int tile_size = reduce_by_key_config.block_threads * reduce_by_key_config.items_per_thread;
-            int num_tiles = (num_items + tile_size - 1) / tile_size;
-
-            // Specify temporary storage allocation requirements
-            size_t  allocation_sizes[1];
-            if (CubDebug(error = ScanTileStateT::AllocationSize(num_tiles, allocation_sizes[0]))) break;    // bytes needed for tile status descriptors
-
-            // Compute allocation pointers into the single storage blob (or compute the necessary size of the blob)
-            void* allocations[1];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                break;
-            }
-
-            // Construct the tile status interface
-            ScanTileStateT tile_state;
-            if (CubDebug(error = tile_state.Init(num_tiles, allocations[0], allocation_sizes[0]))) break;
-
-            // Log init_kernel configuration
-            int init_grid_size = CUB_MAX(1, (num_tiles + INIT_KERNEL_THREADS - 1) / INIT_KERNEL_THREADS);
-            if (debug_synchronous) _CubLog("Invoking init_kernel<<<%d, %d, 0, %lld>>>()\n", init_grid_size, INIT_KERNEL_THREADS, (long long) stream);
-
-            // Invoke init_kernel to initialize tile descriptors
-            init_kernel<<<init_grid_size, INIT_KERNEL_THREADS, 0, stream>>>(
-                tile_state,
-                num_tiles,
-                d_num_runs_out);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Return if empty problem
-            if (num_items == 0)
-                break;
-
-            // Get SM occupancy for reduce_by_key_kernel
-            int reduce_by_key_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                reduce_by_key_sm_occupancy,            // out
-                reduce_by_key_kernel,
-                reduce_by_key_config.block_threads))) break;
-
-            // Get max x-dimension of grid
-            int max_dim_x;
-            if (CubDebug(error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal))) break;;
-
-            // Run grids in epochs (in case number of tiles exceeds max x-dimension
-            int scan_grid_size = CUB_MIN(num_tiles, max_dim_x);
-            for (int start_tile = 0; start_tile < num_tiles; start_tile += scan_grid_size)
-            {
-                // Log reduce_by_key_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking %d reduce_by_key_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                    start_tile, scan_grid_size, reduce_by_key_config.block_threads, (long long) stream, reduce_by_key_config.items_per_thread, reduce_by_key_sm_occupancy);
-
-                // Invoke reduce_by_key_kernel
-                reduce_by_key_kernel<<<scan_grid_size, reduce_by_key_config.block_threads, 0, stream>>>(
-                    d_keys_in,
-                    d_unique_out,
-                    d_values_in,
-                    d_aggregates_out,
-                    d_num_runs_out,
-                    tile_state,
-                    start_tile,
-                    equality_op,
-                    reduction_op,
-                    num_items);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-        }
-        while (0);
-
-        return error;
-
-#endif  // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                       d_temp_storage,                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                     temp_storage_bytes,             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        KeysInputIteratorT          d_keys_in,                      ///< [in] Pointer to the input sequence of keys
-        UniqueOutputIteratorT       d_unique_out,                   ///< [out] Pointer to the output sequence of unique keys (one key per run)
-        ValuesInputIteratorT        d_values_in,                    ///< [in] Pointer to the input sequence of corresponding values
-        AggregatesOutputIteratorT   d_aggregates_out,               ///< [out] Pointer to the output sequence of value aggregates (one aggregate per run)
-        NumRunsOutputIteratorT      d_num_runs_out,                 ///< [out] Pointer to total number of runs encountered (i.e., the length of d_unique_out)
-        EqualityOpT                 equality_op,                    ///< [in] KeyT equality operator
-        ReductionOpT                reduction_op,                   ///< [in] ValueT reduction operator
-        OffsetT                     num_items,                      ///< [in] Total number of items to select from
-        cudaStream_t                stream,                         ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous)              ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig reduce_by_key_config;
-            InitConfigs(ptx_version, reduce_by_key_config);
-
-            // Dispatch
-            if (CubDebug(error = Dispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_keys_in,
-                d_unique_out,
-                d_values_in,
-                d_aggregates_out,
-                d_num_runs_out,
-                equality_op,
-                reduction_op,
-                num_items,
-                stream,
-                debug_synchronous,
-                ptx_version,
-                DeviceCompactInitKernel<ScanTileStateT, NumRunsOutputIteratorT>,
-                DeviceReduceByKeyKernel<PtxReduceByKeyPolicy, KeysInputIteratorT, UniqueOutputIteratorT, ValuesInputIteratorT, AggregatesOutputIteratorT, NumRunsOutputIteratorT, ScanTileStateT, EqualityOpT, ReductionOpT, OffsetT>,
-                reduce_by_key_config))) break;
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_rle.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_rle.cuh
deleted file mode 100644
index f05ed4c..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_rle.cuh
+++ /dev/null
@@ -1,538 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceRle provides device-wide, parallel operations for run-length-encoding sequences of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch_scan.cuh"
-#include "../../agent/agent_rle.cuh"
-#include "../../thread/thread_operators.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Kernel entry points
- *****************************************************************************/
-
-/**
- * Select kernel entry point (multi-block)
- *
- * Performs functor-based selection if SelectOp functor type != NullType
- * Otherwise performs flag-based selection if FlagIterator's value type != NullType
- * Otherwise performs discontinuity selection (keep unique)
- */
-template <
-    typename            AgentRlePolicyT,        ///< Parameterized AgentRlePolicyT tuning policy type
-    typename            InputIteratorT,             ///< Random-access input iterator type for reading input items \iterator
-    typename            OffsetsOutputIteratorT,     ///< Random-access output iterator type for writing run-offset values \iterator
-    typename            LengthsOutputIteratorT,     ///< Random-access output iterator type for writing run-length values \iterator
-    typename            NumRunsOutputIteratorT,     ///< Output iterator type for recording the number of runs encountered \iterator
-    typename            ScanTileStateT,              ///< Tile status interface type
-    typename            EqualityOpT,                 ///< T equality operator type
-    typename            OffsetT>                    ///< Signed integer type for global offsets
-__launch_bounds__ (int(AgentRlePolicyT::BLOCK_THREADS))
-__global__ void DeviceRleSweepKernel(
-    InputIteratorT              d_in,               ///< [in] Pointer to input sequence of data items
-    OffsetsOutputIteratorT      d_offsets_out,      ///< [out] Pointer to output sequence of run-offsets
-    LengthsOutputIteratorT      d_lengths_out,      ///< [out] Pointer to output sequence of run-lengths
-    NumRunsOutputIteratorT      d_num_runs_out,     ///< [out] Pointer to total number of runs (i.e., length of \p d_offsets_out)
-    ScanTileStateT              tile_status,        ///< [in] Tile status interface
-    EqualityOpT                 equality_op,        ///< [in] Equality operator for input items
-    OffsetT                     num_items,          ///< [in] Total number of input items (i.e., length of \p d_in)
-    int                         num_tiles)          ///< [in] Total number of tiles for the entire problem
-{
-    // Thread block type for selecting data from input tiles
-    typedef AgentRle<
-        AgentRlePolicyT,
-        InputIteratorT,
-        OffsetsOutputIteratorT,
-        LengthsOutputIteratorT,
-        EqualityOpT,
-        OffsetT> AgentRleT;
-
-    // Shared memory for AgentRle
-    __shared__ typename AgentRleT::TempStorage temp_storage;
-
-    // Process tiles
-    AgentRleT(temp_storage, d_in, d_offsets_out, d_lengths_out, equality_op, num_items).ConsumeRange(
-        num_tiles,
-        tile_status,
-        d_num_runs_out);
-}
-
-
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceRle
- */
-template <
-    typename            InputIteratorT,             ///< Random-access input iterator type for reading input items \iterator
-    typename            OffsetsOutputIteratorT,     ///< Random-access output iterator type for writing run-offset values \iterator
-    typename            LengthsOutputIteratorT,     ///< Random-access output iterator type for writing run-length values \iterator
-    typename            NumRunsOutputIteratorT,     ///< Output iterator type for recording the number of runs encountered \iterator
-    typename            EqualityOpT,                ///< T equality operator type
-    typename            OffsetT>                    ///< Signed integer type for global offsets
-struct DeviceRleDispatch
-{
-    /******************************************************************************
-     * Types and constants
-     ******************************************************************************/
-
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type T;
-
-    // The lengths output value type
-    typedef typename If<(Equals<typename std::iterator_traits<LengthsOutputIteratorT>::value_type, void>::VALUE),   // LengthT =  (if output iterator's value type is void) ?
-        OffsetT,                                                                                                    // ... then the OffsetT type,
-        typename std::iterator_traits<LengthsOutputIteratorT>::value_type>::Type LengthT;                           // ... else the output iterator's value type
-
-    enum
-    {
-        INIT_KERNEL_THREADS = 128,
-    };
-
-    // Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<LengthT, OffsetT> ScanTileStateT;
-
-
-    /******************************************************************************
-     * Tuning policies
-     ******************************************************************************/
-
-    /// SM35
-    struct Policy350
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 15,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
-        };
-
-        typedef AgentRlePolicy<
-                96,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                true,
-                BLOCK_SCAN_WARP_SCANS>
-            RleSweepPolicy;
-    };
-
-    /// SM30
-    struct Policy300
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 5,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
-        };
-
-        typedef AgentRlePolicy<
-                256,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                true,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            RleSweepPolicy;
-    };
-
-    /// SM20
-    struct Policy200
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 15,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
-        };
-
-        typedef AgentRlePolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            RleSweepPolicy;
-    };
-
-    /// SM13
-    struct Policy130
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 9,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
-        };
-
-        typedef AgentRlePolicy<
-                64,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                true,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            RleSweepPolicy;
-    };
-
-    /// SM10
-    struct Policy100
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 9,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
-        };
-
-        typedef AgentRlePolicy<
-                256,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                true,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            RleSweepPolicy;
-    };
-
-
-    /******************************************************************************
-     * Tuning policies of current PTX compiler pass
-     ******************************************************************************/
-
-#if (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 130)
-    typedef Policy130 PtxPolicy;
-
-#else
-    typedef Policy100 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxRleSweepPolicy : PtxPolicy::RleSweepPolicy {};
-
-
-    /******************************************************************************
-     * Utilities
-     ******************************************************************************/
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig&   device_rle_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        device_rle_config.template Init<PtxRleSweepPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 350)
-        {
-            device_rle_config.template Init<typename Policy350::RleSweepPolicy>();
-        }
-        else if (ptx_version >= 300)
-        {
-            device_rle_config.template Init<typename Policy300::RleSweepPolicy>();
-        }
-        else if (ptx_version >= 200)
-        {
-            device_rle_config.template Init<typename Policy200::RleSweepPolicy>();
-        }
-        else if (ptx_version >= 130)
-        {
-            device_rle_config.template Init<typename Policy130::RleSweepPolicy>();
-        }
-        else
-        {
-            device_rle_config.template Init<typename Policy100::RleSweepPolicy>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.  Mirrors the constants within AgentRlePolicyT.
-     */
-    struct KernelConfig
-    {
-        int                     block_threads;
-        int                     items_per_thread;
-        BlockLoadAlgorithm      load_policy;
-        bool                    store_warp_time_slicing;
-        BlockScanAlgorithm      scan_algorithm;
-
-        template <typename AgentRlePolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads               = AgentRlePolicyT::BLOCK_THREADS;
-            items_per_thread            = AgentRlePolicyT::ITEMS_PER_THREAD;
-            load_policy                 = AgentRlePolicyT::LOAD_ALGORITHM;
-            store_warp_time_slicing     = AgentRlePolicyT::STORE_WARP_TIME_SLICING;
-            scan_algorithm              = AgentRlePolicyT::SCAN_ALGORITHM;
-        }
-
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Print()
-        {
-            printf("%d, %d, %d, %d, %d",
-                block_threads,
-                items_per_thread,
-                load_policy,
-                store_warp_time_slicing,
-                scan_algorithm);
-        }
-    };
-
-
-    /******************************************************************************
-     * Dispatch entrypoints
-     ******************************************************************************/
-
-    /**
-     * Internal dispatch routine for computing a device-wide run-length-encode using the
-     * specified kernel functions.
-     */
-    template <
-        typename                    DeviceScanInitKernelPtr,        ///< Function type of cub::DeviceScanInitKernel
-        typename                    DeviceRleSweepKernelPtr>        ///< Function type of cub::DeviceRleSweepKernelPtr
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                       d_temp_storage,                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                     temp_storage_bytes,             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        OffsetsOutputIteratorT      d_offsets_out,                  ///< [out] Pointer to the output sequence of run-offsets
-        LengthsOutputIteratorT      d_lengths_out,                  ///< [out] Pointer to the output sequence of run-lengths
-        NumRunsOutputIteratorT      d_num_runs_out,                 ///< [out] Pointer to the total number of runs encountered (i.e., length of \p d_offsets_out)
-        EqualityOpT                 equality_op,                    ///< [in] Equality operator for input items
-        OffsetT                     num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream,                         ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous,              ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        int                         ptx_version,                    ///< [in] PTX version of dispatch kernels
-        DeviceScanInitKernelPtr     device_scan_init_kernel,        ///< [in] Kernel function pointer to parameterization of cub::DeviceScanInitKernel
-        DeviceRleSweepKernelPtr     device_rle_sweep_kernel,        ///< [in] Kernel function pointer to parameterization of cub::DeviceRleSweepKernel
-        KernelConfig                device_rle_config)              ///< [in] Dispatch parameters that match the policy that \p device_rle_sweep_kernel was compiled for
-    {
-
-#ifndef CUB_RUNTIME_ENABLED
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported);
-
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Number of input tiles
-            int tile_size = device_rle_config.block_threads * device_rle_config.items_per_thread;
-            int num_tiles = (num_items + tile_size - 1) / tile_size;
-
-            // Specify temporary storage allocation requirements
-            size_t  allocation_sizes[1];
-            if (CubDebug(error = ScanTileStateT::AllocationSize(num_tiles, allocation_sizes[0]))) break;    // bytes needed for tile status descriptors
-
-            // Compute allocation pointers into the single storage blob (or compute the necessary size of the blob)
-            void* allocations[1];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                break;
-            }
-
-            // Construct the tile status interface
-            ScanTileStateT tile_status;
-            if (CubDebug(error = tile_status.Init(num_tiles, allocations[0], allocation_sizes[0]))) break;
-
-            // Log device_scan_init_kernel configuration
-            int init_grid_size = CUB_MAX(1, (num_tiles + INIT_KERNEL_THREADS - 1) / INIT_KERNEL_THREADS);
-            if (debug_synchronous) _CubLog("Invoking device_scan_init_kernel<<<%d, %d, 0, %lld>>>()\n", init_grid_size, INIT_KERNEL_THREADS, (long long) stream);
-
-            // Invoke device_scan_init_kernel to initialize tile descriptors and queue descriptors
-            device_scan_init_kernel<<<init_grid_size, INIT_KERNEL_THREADS, 0, stream>>>(
-                tile_status,
-                num_tiles,
-                d_num_runs_out);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Return if empty problem
-            if (num_items == 0)
-                break;
-
-            // Get SM occupancy for device_rle_sweep_kernel
-            int device_rle_kernel_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                device_rle_kernel_sm_occupancy,            // out
-                device_rle_sweep_kernel,
-                device_rle_config.block_threads))) break;
-
-            // Get max x-dimension of grid
-            int max_dim_x;
-            if (CubDebug(error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal))) break;;
-
-            // Get grid size for scanning tiles
-            dim3 scan_grid_size;
-            scan_grid_size.z = 1;
-            scan_grid_size.y = ((unsigned int) num_tiles + max_dim_x - 1) / max_dim_x;
-            scan_grid_size.x = CUB_MIN(num_tiles, max_dim_x);
-
-            // Log device_rle_sweep_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking device_rle_sweep_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                scan_grid_size.x, scan_grid_size.y, scan_grid_size.z, device_rle_config.block_threads, (long long) stream, device_rle_config.items_per_thread, device_rle_kernel_sm_occupancy);
-
-            // Invoke device_rle_sweep_kernel
-            device_rle_sweep_kernel<<<scan_grid_size, device_rle_config.block_threads, 0, stream>>>(
-                d_in,
-                d_offsets_out,
-                d_lengths_out,
-                d_num_runs_out,
-                tile_status,
-                equality_op,
-                num_items,
-                num_tiles);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-        }
-        while (0);
-
-        return error;
-
-#endif  // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                       d_temp_storage,                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                     temp_storage_bytes,             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to input sequence of data items
-        OffsetsOutputIteratorT      d_offsets_out,                  ///< [out] Pointer to output sequence of run-offsets
-        LengthsOutputIteratorT      d_lengths_out,                  ///< [out] Pointer to output sequence of run-lengths
-        NumRunsOutputIteratorT      d_num_runs_out,                 ///< [out] Pointer to total number of runs (i.e., length of \p d_offsets_out)
-        EqualityOpT                 equality_op,                    ///< [in] Equality operator for input items
-        OffsetT                     num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream,                         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous)              ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig device_rle_config;
-            InitConfigs(ptx_version, device_rle_config);
-
-            // Dispatch
-            if (CubDebug(error = Dispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_in,
-                d_offsets_out,
-                d_lengths_out,
-                d_num_runs_out,
-                equality_op,
-                num_items,
-                stream,
-                debug_synchronous,
-                ptx_version,
-                DeviceCompactInitKernel<ScanTileStateT, NumRunsOutputIteratorT>,
-                DeviceRleSweepKernel<PtxRleSweepPolicy, InputIteratorT, OffsetsOutputIteratorT, LengthsOutputIteratorT, NumRunsOutputIteratorT, ScanTileStateT, EqualityOpT, OffsetT>,
-                device_rle_config))) break;
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_scan.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_scan.cuh
deleted file mode 100644
index c39f3bd..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_scan.cuh
+++ /dev/null
@@ -1,563 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceScan provides device-wide, parallel operations for computing a prefix scan across a sequence of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "../../agent/agent_scan.cuh"
-#include "../../thread/thread_operators.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_arch.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Kernel entry points
- *****************************************************************************/
-
-/**
- * Initialization kernel for tile status initialization (multi-block)
- */
-template <
-    typename            ScanTileStateT>     ///< Tile status interface type
-__global__ void DeviceScanInitKernel(
-    ScanTileStateT      tile_state,         ///< [in] Tile status interface
-    int                 num_tiles)          ///< [in] Number of tiles
-{
-    // Initialize tile status
-    tile_state.InitializeStatus(num_tiles);
-}
-
-/**
- * Initialization kernel for tile status initialization (multi-block)
- */
-template <
-    typename                ScanTileStateT,         ///< Tile status interface type
-    typename                NumSelectedIteratorT>   ///< Output iterator type for recording the number of items selected
-__global__ void DeviceCompactInitKernel(
-    ScanTileStateT          tile_state,             ///< [in] Tile status interface
-    int                     num_tiles,              ///< [in] Number of tiles
-    NumSelectedIteratorT    d_num_selected_out)     ///< [out] Pointer to the total number of items selected (i.e., length of \p d_selected_out)
-{
-    // Initialize tile status
-    tile_state.InitializeStatus(num_tiles);
-
-    // Initialize d_num_selected_out
-    if ((blockIdx.x == 0) && (threadIdx.x == 0))
-        *d_num_selected_out = 0;
-}
-
-
-/**
- * Scan kernel entry point (multi-block)
- */
-template <
-    typename            ScanPolicyT,        ///< Parameterized ScanPolicyT tuning policy type
-    typename            InputIteratorT,     ///< Random-access input iterator type for reading scan inputs \iterator
-    typename            OutputIteratorT,    ///< Random-access output iterator type for writing scan outputs \iterator
-    typename            ScanTileStateT,     ///< Tile status interface type
-    typename            ScanOpT,            ///< Binary scan functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename            InitValueT,         ///< Initial value to seed the exclusive scan (cub::NullType for inclusive scans)
-    typename            OffsetT>            ///< Signed integer type for global offsets
-__launch_bounds__ (int(ScanPolicyT::BLOCK_THREADS))
-__global__ void DeviceScanKernel(
-    InputIteratorT      d_in,               ///< Input data
-    OutputIteratorT     d_out,              ///< Output data
-    ScanTileStateT      tile_state,         ///< Tile status interface
-    int                 start_tile,         ///< The starting tile for the current grid
-    ScanOpT             scan_op,            ///< Binary scan functor 
-    InitValueT          init_value,         ///< Initial value to seed the exclusive scan
-    OffsetT             num_items)          ///< Total number of scan items for the entire problem
-{
-    // Thread block type for scanning input tiles
-    typedef AgentScan<
-        ScanPolicyT,
-        InputIteratorT,
-        OutputIteratorT,
-        ScanOpT,
-        InitValueT,
-        OffsetT> AgentScanT;
-
-    // Shared memory for AgentScan
-    __shared__ typename AgentScanT::TempStorage temp_storage;
-
-    // Process tiles
-    AgentScanT(temp_storage, d_in, d_out, scan_op, init_value).ConsumeRange(
-        num_items,
-        tile_state,
-        start_tile);
-}
-
-
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceScan
- */
-template <
-    typename InputIteratorT,     ///< Random-access input iterator type for reading scan inputs \iterator
-    typename OutputIteratorT,    ///< Random-access output iterator type for writing scan outputs \iterator
-    typename ScanOpT,            ///< Binary scan functor type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename InitValueT,          ///< The init_value element type for ScanOpT (cub::NullType for inclusive scans)
-    typename OffsetT>            ///< Signed integer type for global offsets
-struct DispatchScan
-{
-    //---------------------------------------------------------------------
-    // Constants and Types
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        INIT_KERNEL_THREADS = 128
-    };
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // Tile status descriptor interface type
-    typedef ScanTileState<OutputT> ScanTileStateT;
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies
-    //---------------------------------------------------------------------
-
-    /// SM600
-    struct Policy600
-    {
-        typedef AgentScanPolicy<
-            CUB_NOMINAL_CONFIG(128, 15, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_STORE_TRANSPOSE,
-                BLOCK_SCAN_WARP_SCANS>
-            ScanPolicyT;
-    };
-
-
-    /// SM520
-    struct Policy520
-    {
-        // Titan X: 32.47B items/s @ 48M 32-bit T
-        typedef AgentScanPolicy<
-                CUB_NOMINAL_CONFIG(128, 12, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                BLOCK_STORE_WARP_TRANSPOSE,
-                BLOCK_SCAN_WARP_SCANS>
-            ScanPolicyT;
-    };
-
-
-    /// SM35
-    struct Policy350
-    {
-        // GTX Titan: 29.5B items/s (232.4 GB/s) @ 48M 32-bit T
-        typedef AgentScanPolicy<
-                CUB_NOMINAL_CONFIG(128, 12, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED,
-                BLOCK_SCAN_RAKING>
-            ScanPolicyT;
-    };
-
-    /// SM30
-    struct Policy300
-    {
-        typedef AgentScanPolicy<
-                CUB_NOMINAL_CONFIG(256, 9, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_STORE_WARP_TRANSPOSE,
-                BLOCK_SCAN_WARP_SCANS>
-            ScanPolicyT;
-    };
-
-    /// SM20
-    struct Policy200
-    {
-        // GTX 580: 20.3B items/s (162.3 GB/s) @ 48M 32-bit T
-        typedef AgentScanPolicy<
-                CUB_NOMINAL_CONFIG(128, 12, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_STORE_WARP_TRANSPOSE,
-                BLOCK_SCAN_WARP_SCANS>
-            ScanPolicyT;
-    };
-
-    /// SM13
-    struct Policy130
-    {
-        typedef AgentScanPolicy<
-                CUB_NOMINAL_CONFIG(96, 21, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_STORE_WARP_TRANSPOSE,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            ScanPolicyT;
-    };
-
-    /// SM10
-    struct Policy100
-    {
-        typedef AgentScanPolicy<
-                CUB_NOMINAL_CONFIG(64, 9, OutputT),      ///< Threads per block, items per thread
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_STORE_WARP_TRANSPOSE,
-                BLOCK_SCAN_WARP_SCANS>
-            ScanPolicyT;
-    };
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies of current PTX compiler pass
-    //---------------------------------------------------------------------
-
-#if (CUB_PTX_ARCH >= 600)
-    typedef Policy600 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 520)
-    typedef Policy520 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 130)
-    typedef Policy130 PtxPolicy;
-
-#else
-    typedef Policy100 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxAgentScanPolicy : PtxPolicy::ScanPolicyT {};
-
-
-    //---------------------------------------------------------------------
-    // Utilities
-    //---------------------------------------------------------------------
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig    &scan_kernel_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-        (void)ptx_version;
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        scan_kernel_config.template Init<PtxAgentScanPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 600)
-        {
-            scan_kernel_config.template Init<typename Policy600::ScanPolicyT>();
-        }
-        else if (ptx_version >= 520)
-        {
-            scan_kernel_config.template Init<typename Policy520::ScanPolicyT>();
-        }
-        else if (ptx_version >= 350)
-        {
-            scan_kernel_config.template Init<typename Policy350::ScanPolicyT>();
-        }
-        else if (ptx_version >= 300)
-        {
-            scan_kernel_config.template Init<typename Policy300::ScanPolicyT>();
-        }
-        else if (ptx_version >= 200)
-        {
-            scan_kernel_config.template Init<typename Policy200::ScanPolicyT>();
-        }
-        else if (ptx_version >= 130)
-        {
-            scan_kernel_config.template Init<typename Policy130::ScanPolicyT>();
-        }
-        else
-        {
-            scan_kernel_config.template Init<typename Policy100::ScanPolicyT>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.
-     */
-    struct KernelConfig
-    {
-        int block_threads;
-        int items_per_thread;
-        int tile_items;
-
-        template <typename PolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads       = PolicyT::BLOCK_THREADS;
-            items_per_thread    = PolicyT::ITEMS_PER_THREAD;
-            tile_items          = block_threads * items_per_thread;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Dispatch entrypoints
-    //---------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide prefix scan using the
-     * specified kernel functions.
-     */
-    template <
-        typename            ScanInitKernelPtrT,     ///< Function type of cub::DeviceScanInitKernel
-        typename            ScanSweepKernelPtrT>    ///< Function type of cub::DeviceScanKernelPtrT
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*               d_temp_storage,         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&             temp_storage_bytes,     ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT      d_in,                   ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT     d_out,                  ///< [out] Pointer to the output sequence of data items
-        ScanOpT             scan_op,                ///< [in] Binary scan functor 
-        InitValueT          init_value,             ///< [in] Initial value to seed the exclusive scan
-        OffsetT             num_items,              ///< [in] Total number of input items (i.e., the length of \p d_in)
-        cudaStream_t        stream,                 ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                debug_synchronous,      ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        int                 /*ptx_version*/,        ///< [in] PTX version of dispatch kernels
-        ScanInitKernelPtrT  init_kernel,            ///< [in] Kernel function pointer to parameterization of cub::DeviceScanInitKernel
-        ScanSweepKernelPtrT scan_kernel,            ///< [in] Kernel function pointer to parameterization of cub::DeviceScanKernel
-        KernelConfig        scan_kernel_config)     ///< [in] Dispatch parameters that match the policy that \p scan_kernel was compiled for
-    {
-
-#ifndef CUB_RUNTIME_ENABLED
-        (void)d_temp_storage;
-        (void)temp_storage_bytes;
-        (void)d_in;
-        (void)d_out;
-        (void)scan_op;
-        (void)init_value;
-        (void)num_items;
-        (void)stream;
-        (void)debug_synchronous;
-        (void)init_kernel;
-        (void)scan_kernel;
-        (void)scan_kernel_config;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported);
-
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Number of input tiles
-            int tile_size = scan_kernel_config.block_threads * scan_kernel_config.items_per_thread;
-            int num_tiles = (num_items + tile_size - 1) / tile_size;
-
-            // Specify temporary storage allocation requirements
-            size_t  allocation_sizes[1];
-            if (CubDebug(error = ScanTileStateT::AllocationSize(num_tiles, allocation_sizes[0]))) break;    // bytes needed for tile status descriptors
-
-            // Compute allocation pointers into the single storage blob (or compute the necessary size of the blob)
-            void* allocations[1];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                break;
-            }
-
-            // Return if empty problem
-            if (num_items == 0)
-                break;
-
-            // Construct the tile status interface
-            ScanTileStateT tile_state;
-            if (CubDebug(error = tile_state.Init(num_tiles, allocations[0], allocation_sizes[0]))) break;
-
-            // Log init_kernel configuration
-            int init_grid_size = (num_tiles + INIT_KERNEL_THREADS - 1) / INIT_KERNEL_THREADS;
-            if (debug_synchronous) _CubLog("Invoking init_kernel<<<%d, %d, 0, %lld>>>()\n", init_grid_size, INIT_KERNEL_THREADS, (long long) stream);
-
-            // Invoke init_kernel to initialize tile descriptors
-            init_kernel<<<init_grid_size, INIT_KERNEL_THREADS, 0, stream>>>(
-                tile_state,
-                num_tiles);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Get SM occupancy for scan_kernel
-            int scan_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                scan_sm_occupancy,            // out
-                scan_kernel,
-                scan_kernel_config.block_threads))) break;
-
-            // Get max x-dimension of grid
-            int max_dim_x;
-            if (CubDebug(error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal))) break;;
-
-            // Run grids in epochs (in case number of tiles exceeds max x-dimension
-            int scan_grid_size = CUB_MIN(num_tiles, max_dim_x);
-            for (int start_tile = 0; start_tile < num_tiles; start_tile += scan_grid_size)
-            {
-                // Log scan_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking %d scan_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                    start_tile, scan_grid_size, scan_kernel_config.block_threads, (long long) stream, scan_kernel_config.items_per_thread, scan_sm_occupancy);
-
-                // Invoke scan_kernel
-                scan_kernel<<<scan_grid_size, scan_kernel_config.block_threads, 0, stream>>>(
-                    d_in,
-                    d_out,
-                    tile_state,
-                    start_tile,
-                    scan_op,
-                    init_value,
-                    num_items);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-        }
-        while (0);
-
-        return error;
-
-#endif  // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*           d_temp_storage,         ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&         temp_storage_bytes,     ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT  d_in,                   ///< [in] Pointer to the input sequence of data items
-        OutputIteratorT d_out,                  ///< [out] Pointer to the output sequence of data items
-        ScanOpT         scan_op,                ///< [in] Binary scan functor 
-        InitValueT      init_value,             ///< [in] Initial value to seed the exclusive scan
-        OffsetT         num_items,              ///< [in] Total number of input items (i.e., the length of \p d_in)
-        cudaStream_t    stream,                 ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool            debug_synchronous)      ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig scan_kernel_config;
-            InitConfigs(ptx_version, scan_kernel_config);
-
-            // Dispatch
-            if (CubDebug(error = Dispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_in,
-                d_out,
-                scan_op,
-                init_value,
-                num_items,
-                stream,
-                debug_synchronous,
-                ptx_version,
-                DeviceScanInitKernel<ScanTileStateT>,
-                DeviceScanKernel<PtxAgentScanPolicy, InputIteratorT, OutputIteratorT, ScanTileStateT, ScanOpT, InitValueT, OffsetT>,
-                scan_kernel_config))) break;
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_select_if.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_select_if.cuh
deleted file mode 100644
index c6bf44d..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_select_if.cuh
+++ /dev/null
@@ -1,542 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSelect provides device-wide, parallel operations for selecting items from sequences of data items residing within device-accessible memory.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch_scan.cuh"
-#include "../../agent/agent_select_if.cuh"
-#include "../../thread/thread_operators.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/******************************************************************************
- * Kernel entry points
- *****************************************************************************/
-
-/**
- * Select kernel entry point (multi-block)
- *
- * Performs functor-based selection if SelectOpT functor type != NullType
- * Otherwise performs flag-based selection if FlagsInputIterator's value type != NullType
- * Otherwise performs discontinuity selection (keep unique)
- */
-template <
-    typename            AgentSelectIfPolicyT,       ///< Parameterized AgentSelectIfPolicyT tuning policy type
-    typename            InputIteratorT,             ///< Random-access input iterator type for reading input items
-    typename            FlagsInputIteratorT,        ///< Random-access input iterator type for reading selection flags (NullType* if a selection functor or discontinuity flagging is to be used for selection)
-    typename            SelectedOutputIteratorT,    ///< Random-access output iterator type for writing selected items
-    typename            NumSelectedIteratorT,       ///< Output iterator type for recording the number of items selected
-    typename            ScanTileStateT,             ///< Tile status interface type
-    typename            SelectOpT,                  ///< Selection operator type (NullType if selection flags or discontinuity flagging is to be used for selection)
-    typename            EqualityOpT,                ///< Equality operator type (NullType if selection functor or selection flags is to be used for selection)
-    typename            OffsetT,                    ///< Signed integer type for global offsets
-    bool                KEEP_REJECTS>               ///< Whether or not we push rejected items to the back of the output
-__launch_bounds__ (int(AgentSelectIfPolicyT::BLOCK_THREADS))
-__global__ void DeviceSelectSweepKernel(
-    InputIteratorT          d_in,                   ///< [in] Pointer to the input sequence of data items
-    FlagsInputIteratorT     d_flags,                ///< [in] Pointer to the input sequence of selection flags (if applicable)
-    SelectedOutputIteratorT d_selected_out,         ///< [out] Pointer to the output sequence of selected data items
-    NumSelectedIteratorT    d_num_selected_out,     ///< [out] Pointer to the total number of items selected (i.e., length of \p d_selected_out)
-    ScanTileStateT          tile_status,            ///< [in] Tile status interface
-    SelectOpT               select_op,              ///< [in] Selection operator
-    EqualityOpT             equality_op,            ///< [in] Equality operator
-    OffsetT                 num_items,              ///< [in] Total number of input items (i.e., length of \p d_in)
-    int                     num_tiles)              ///< [in] Total number of tiles for the entire problem
-{
-    // Thread block type for selecting data from input tiles
-    typedef AgentSelectIf<
-        AgentSelectIfPolicyT,
-        InputIteratorT,
-        FlagsInputIteratorT,
-        SelectedOutputIteratorT,
-        SelectOpT,
-        EqualityOpT,
-        OffsetT,
-        KEEP_REJECTS> AgentSelectIfT;
-
-    // Shared memory for AgentSelectIf
-    __shared__ typename AgentSelectIfT::TempStorage temp_storage;
-
-    // Process tiles
-    AgentSelectIfT(temp_storage, d_in, d_flags, d_selected_out, select_op, equality_op, num_items).ConsumeRange(
-        num_tiles,
-        tile_status,
-        d_num_selected_out);
-}
-
-
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceSelect
- */
-template <
-    typename    InputIteratorT,                 ///< Random-access input iterator type for reading input items
-    typename    FlagsInputIteratorT,            ///< Random-access input iterator type for reading selection flags (NullType* if a selection functor or discontinuity flagging is to be used for selection)
-    typename    SelectedOutputIteratorT,        ///< Random-access output iterator type for writing selected items
-    typename    NumSelectedIteratorT,           ///< Output iterator type for recording the number of items selected
-    typename    SelectOpT,                      ///< Selection operator type (NullType if selection flags or discontinuity flagging is to be used for selection)
-    typename    EqualityOpT,                    ///< Equality operator type (NullType if selection functor or selection flags is to be used for selection)
-    typename    OffsetT,                        ///< Signed integer type for global offsets
-    bool        KEEP_REJECTS>                   ///< Whether or not we push rejected items to the back of the output
-struct DispatchSelectIf
-{
-    /******************************************************************************
-     * Types and constants
-     ******************************************************************************/
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<SelectedOutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                                  // ... then the input iterator's value type,
-        typename std::iterator_traits<SelectedOutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // The flag value type
-    typedef typename std::iterator_traits<FlagsInputIteratorT>::value_type FlagT;
-
-    enum
-    {
-        INIT_KERNEL_THREADS = 128,
-    };
-
-    // Tile status descriptor interface type
-    typedef ScanTileState<OffsetT> ScanTileStateT;
-
-
-    /******************************************************************************
-     * Tuning policies
-     ******************************************************************************/
-
-    /// SM35
-    struct Policy350
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 10,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(OutputT)))),
-        };
-
-        typedef AgentSelectIfPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_DIRECT,
-                LOAD_LDG,
-                BLOCK_SCAN_WARP_SCANS>
-            SelectIfPolicyT;
-    };
-
-    /// SM30
-    struct Policy300
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 7,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(3, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(OutputT)))),
-        };
-
-        typedef AgentSelectIfPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SelectIfPolicyT;
-    };
-
-    /// SM20
-    struct Policy200
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = (KEEP_REJECTS) ? 7 : 15,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(OutputT)))),
-        };
-
-        typedef AgentSelectIfPolicy<
-                128,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SelectIfPolicyT;
-    };
-
-    /// SM13
-    struct Policy130
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 9,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(OutputT)))),
-        };
-
-        typedef AgentSelectIfPolicy<
-                64,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            SelectIfPolicyT;
-    };
-
-    /// SM10
-    struct Policy100
-    {
-        enum {
-            NOMINAL_4B_ITEMS_PER_THREAD = 9,
-            ITEMS_PER_THREAD            = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(OutputT)))),
-        };
-
-        typedef AgentSelectIfPolicy<
-                64,
-                ITEMS_PER_THREAD,
-                BLOCK_LOAD_WARP_TRANSPOSE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_RAKING>
-            SelectIfPolicyT;
-    };
-
-
-    /******************************************************************************
-     * Tuning policies of current PTX compiler pass
-     ******************************************************************************/
-
-#if (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 130)
-    typedef Policy130 PtxPolicy;
-
-#else
-    typedef Policy100 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxSelectIfPolicyT : PtxPolicy::SelectIfPolicyT {};
-
-
-    /******************************************************************************
-     * Utilities
-     ******************************************************************************/
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig    &select_if_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-        (void)ptx_version;
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        select_if_config.template Init<PtxSelectIfPolicyT>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 350)
-        {
-            select_if_config.template Init<typename Policy350::SelectIfPolicyT>();
-        }
-        else if (ptx_version >= 300)
-        {
-            select_if_config.template Init<typename Policy300::SelectIfPolicyT>();
-        }
-        else if (ptx_version >= 200)
-        {
-            select_if_config.template Init<typename Policy200::SelectIfPolicyT>();
-        }
-        else if (ptx_version >= 130)
-        {
-            select_if_config.template Init<typename Policy130::SelectIfPolicyT>();
-        }
-        else
-        {
-            select_if_config.template Init<typename Policy100::SelectIfPolicyT>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.
-     */
-    struct KernelConfig
-    {
-        int block_threads;
-        int items_per_thread;
-        int tile_items;
-
-        template <typename PolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads       = PolicyT::BLOCK_THREADS;
-            items_per_thread    = PolicyT::ITEMS_PER_THREAD;
-            tile_items          = block_threads * items_per_thread;
-        }
-    };
-
-
-    /******************************************************************************
-     * Dispatch entrypoints
-     ******************************************************************************/
-
-    /**
-     * Internal dispatch routine for computing a device-wide selection using the
-     * specified kernel functions.
-     */
-    template <
-        typename                    ScanInitKernelPtrT,             ///< Function type of cub::DeviceScanInitKernel
-        typename                    SelectIfKernelPtrT>             ///< Function type of cub::SelectIfKernelPtrT
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                       d_temp_storage,                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                     temp_storage_bytes,             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        FlagsInputIteratorT         d_flags,                        ///< [in] Pointer to the input sequence of selection flags (if applicable)
-        SelectedOutputIteratorT     d_selected_out,                 ///< [in] Pointer to the output sequence of selected data items
-        NumSelectedIteratorT        d_num_selected_out,             ///< [in] Pointer to the total number of items selected (i.e., length of \p d_selected_out)
-        SelectOpT                   select_op,                      ///< [in] Selection operator
-        EqualityOpT                 equality_op,                    ///< [in] Equality operator
-        OffsetT                     num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream,                         ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous,              ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        int                         /*ptx_version*/,                ///< [in] PTX version of dispatch kernels
-        ScanInitKernelPtrT          scan_init_kernel,               ///< [in] Kernel function pointer to parameterization of cub::DeviceScanInitKernel
-        SelectIfKernelPtrT          select_if_kernel,               ///< [in] Kernel function pointer to parameterization of cub::DeviceSelectSweepKernel
-        KernelConfig                select_if_config)               ///< [in] Dispatch parameters that match the policy that \p select_if_kernel was compiled for
-    {
-
-#ifndef CUB_RUNTIME_ENABLED
-        (void)d_temp_storage;
-        (void)temp_storage_bytes;
-        (void)d_in;
-        (void)d_flags;
-        (void)d_selected_out;
-        (void)d_num_selected_out;
-        (void)select_op;
-        (void)equality_op;
-        (void)num_items;
-        (void)stream;
-        (void)debug_synchronous;
-        (void)scan_init_kernel;
-        (void)select_if_kernel;
-        (void)select_if_config;
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported);
-
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Number of input tiles
-            int tile_size = select_if_config.block_threads * select_if_config.items_per_thread;
-            int num_tiles = (num_items + tile_size - 1) / tile_size;
-
-            // Specify temporary storage allocation requirements
-            size_t  allocation_sizes[1];
-            if (CubDebug(error = ScanTileStateT::AllocationSize(num_tiles, allocation_sizes[0]))) break;    // bytes needed for tile status descriptors
-
-            // Compute allocation pointers into the single storage blob (or compute the necessary size of the blob)
-            void* allocations[1];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                break;
-            }
-
-            // Construct the tile status interface
-            ScanTileStateT tile_status;
-            if (CubDebug(error = tile_status.Init(num_tiles, allocations[0], allocation_sizes[0]))) break;
-
-            // Log scan_init_kernel configuration
-            int init_grid_size = CUB_MAX(1, (num_tiles + INIT_KERNEL_THREADS - 1) / INIT_KERNEL_THREADS);
-            if (debug_synchronous) _CubLog("Invoking scan_init_kernel<<<%d, %d, 0, %lld>>>()\n", init_grid_size, INIT_KERNEL_THREADS, (long long) stream);
-
-            // Invoke scan_init_kernel to initialize tile descriptors
-            scan_init_kernel<<<init_grid_size, INIT_KERNEL_THREADS, 0, stream>>>(
-                tile_status,
-                num_tiles,
-                d_num_selected_out);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Return if empty problem
-            if (num_items == 0)
-                break;
-
-            // Get SM occupancy for select_if_kernel
-            int range_select_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                range_select_sm_occupancy,            // out
-                select_if_kernel,
-                select_if_config.block_threads))) break;
-
-            // Get max x-dimension of grid
-            int max_dim_x;
-            if (CubDebug(error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal))) break;;
-
-            // Get grid size for scanning tiles
-            dim3 scan_grid_size;
-            scan_grid_size.z = 1;
-            scan_grid_size.y = ((unsigned int) num_tiles + max_dim_x - 1) / max_dim_x;
-            scan_grid_size.x = CUB_MIN(num_tiles, max_dim_x);
-
-            // Log select_if_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking select_if_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                scan_grid_size.x, scan_grid_size.y, scan_grid_size.z, select_if_config.block_threads, (long long) stream, select_if_config.items_per_thread, range_select_sm_occupancy);
-
-            // Invoke select_if_kernel
-            select_if_kernel<<<scan_grid_size, select_if_config.block_threads, 0, stream>>>(
-                d_in,
-                d_flags,
-                d_selected_out,
-                d_num_selected_out,
-                tile_status,
-                select_op,
-                equality_op,
-                num_items,
-                num_tiles);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-        }
-        while (0);
-
-        return error;
-
-#endif  // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                       d_temp_storage,                 ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                     temp_storage_bytes,             ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        InputIteratorT              d_in,                           ///< [in] Pointer to the input sequence of data items
-        FlagsInputIteratorT         d_flags,                        ///< [in] Pointer to the input sequence of selection flags (if applicable)
-        SelectedOutputIteratorT     d_selected_out,                 ///< [in] Pointer to the output sequence of selected data items
-        NumSelectedIteratorT        d_num_selected_out,             ///< [in] Pointer to the total number of items selected (i.e., length of \p d_selected_out)
-        SelectOpT                   select_op,                      ///< [in] Selection operator
-        EqualityOpT                 equality_op,                    ///< [in] Equality operator
-        OffsetT                     num_items,                      ///< [in] Total number of input items (i.e., length of \p d_in)
-        cudaStream_t                stream,                         ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                        debug_synchronous)              ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig select_if_config;
-            InitConfigs(ptx_version, select_if_config);
-
-            // Dispatch
-            if (CubDebug(error = Dispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_in,
-                d_flags,
-                d_selected_out,
-                d_num_selected_out,
-                select_op,
-                equality_op,
-                num_items,
-                stream,
-                debug_synchronous,
-                ptx_version,
-                DeviceCompactInitKernel<ScanTileStateT, NumSelectedIteratorT>,
-                DeviceSelectSweepKernel<PtxSelectIfPolicyT, InputIteratorT, FlagsInputIteratorT, SelectedOutputIteratorT, NumSelectedIteratorT, ScanTileStateT, SelectOpT, EqualityOpT, OffsetT, KEEP_REJECTS>,
-                select_if_config))) break;
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_csrt.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_csrt.cuh
deleted file mode 100644
index d7c6d9e..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_csrt.cuh
+++ /dev/null
@@ -1,477 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * vector multiplication (SpMV).
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "dispatch_scan.cuh"
-#include "../../agent/agent_spmv_orig.cuh"
-#include "../../util_type.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * SpMV kernel entry points
- *****************************************************************************/
-
-/**
- * Spmv agent entry point
- */
-template <
-    typename        SpmvPolicyT,                ///< Parameterized SpmvPolicy tuning policy type
-    typename        ValueT,                     ///< Matrix and vector value type
-    typename        OffsetT,                    ///< Signed integer type for sequence offsets
-    bool            HAS_ALPHA,                  ///< Whether the input parameter Alpha is 1
-    bool            HAS_BETA>                   ///< Whether the input parameter Beta is 0
-__launch_bounds__ (int(SpmvPolicyT::BLOCK_THREADS))
-__global__ void DeviceSpmvKernel(
-    SpmvParams<ValueT, OffsetT>     spmv_params,                ///< [in] SpMV input parameter bundle
-    int                             merge_items_per_block,      ///< [in] Number of merge tiles per block
-    KeyValuePair<OffsetT,ValueT>*   d_tile_carry_pairs)         ///< [out] Pointer to the temporary array carry-out dot product row-ids, one per block
-{
-    // Spmv agent type specialization
-    typedef AgentSpmv<
-            SpmvPolicyT,
-            ValueT,
-            OffsetT,
-            HAS_ALPHA,
-            HAS_BETA>
-        AgentSpmvT;
-
-    // Shared memory for AgentSpmv
-    __shared__ typename AgentSpmvT::TempStorage temp_storage;
-
-    AgentSpmvT(temp_storage, spmv_params).ConsumeTile(
-        merge_items_per_block, d_tile_carry_pairs);
-}
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceSpmv
- */
-template <
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT>                    ///< Signed integer type for global offsets
-struct DispatchSpmv
-{
-    //---------------------------------------------------------------------
-    // Constants and Types
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        INIT_KERNEL_THREADS = 128
-    };
-
-    // SpmvParams bundle type
-    typedef SpmvParams<ValueT, OffsetT> SpmvParamsT;
-
-    // Tuple type for scanning {row id, accumulated value}
-    typedef KeyValuePair<OffsetT, ValueT> KeyValuePairT;
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies
-    //---------------------------------------------------------------------
-
-    /// SM11
-    struct Policy110
-    {
-        typedef AgentSpmvPolicy<
-                128,
-                1,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-    };
-
-    /// SM20
-    struct Policy200 
-    {
-        typedef AgentSpmvPolicy<
-                96,
-                18,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_RAKING>
-            SpmvPolicyT;
-    };
-
-
-
-    /// SM30
-    struct Policy300 
-    {
-        typedef AgentSpmvPolicy<
-                96,
-                6,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-    };
-
-
-    /// SM35
-    struct Policy350
-    {
-/*
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 96 : 128,
-                (sizeof(ValueT) > 4) ? 4 : 7,
-                LOAD_LDG,
-                LOAD_CA,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                (sizeof(ValueT) > 4) ? true : false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-*/
-        typedef AgentSpmvPolicy<
-                128,
-                5,
-                LOAD_CA,
-                LOAD_CA,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                (sizeof(ValueT) > 4) ? true : false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-    };
-
-    /// SM37
-    struct Policy370
-    {
-
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 128 : 128,
-                (sizeof(ValueT) > 4) ? 9 : 14,
-                LOAD_LDG,
-                LOAD_CA,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                false, 
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-    };
-
-    /// SM50
-    struct Policy500
-    {
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 64 : 128,
-                (sizeof(ValueT) > 4) ? 6 : 7,
-                LOAD_LDG,
-                LOAD_DEFAULT,
-                (sizeof(ValueT) > 4) ? LOAD_LDG : LOAD_DEFAULT,
-                (sizeof(ValueT) > 4) ? LOAD_LDG : LOAD_DEFAULT,
-                LOAD_LDG,
-                (sizeof(ValueT) > 4) ? true : false,
-                (sizeof(ValueT) > 4) ? BLOCK_SCAN_WARP_SCANS : BLOCK_SCAN_RAKING_MEMOIZE>
-            SpmvPolicyT;
-    };
-
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies of current PTX compiler pass
-    //---------------------------------------------------------------------
-
-#if (CUB_PTX_ARCH >= 500)
-    typedef Policy500 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 370)
-    typedef Policy370 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#else
-    typedef Policy110 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxSpmvPolicyT : PtxPolicy::SpmvPolicyT {};
-
-
-    //---------------------------------------------------------------------
-    // Utilities
-    //---------------------------------------------------------------------
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig    &spmv_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        spmv_config.template Init<PtxSpmvPolicyT>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 500)
-        {
-            spmv_config.template            Init<typename Policy500::SpmvPolicyT>();
-        }
-        else if (ptx_version >= 370)
-        {
-            spmv_config.template            Init<typename Policy370::SpmvPolicyT>();
-        }
-        else if (ptx_version >= 350)
-        {
-            spmv_config.template            Init<typename Policy350::SpmvPolicyT>();
-        }
-        else if (ptx_version >= 300)
-        {
-            spmv_config.template            Init<typename Policy300::SpmvPolicyT>();
-        }
-        else if (ptx_version >= 200)
-        {
-            spmv_config.template            Init<typename Policy200::SpmvPolicyT>();
-        }
-        else
-        {
-            spmv_config.template            Init<typename Policy110::SpmvPolicyT>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.
-     */
-    struct KernelConfig
-    {
-        int block_threads;
-        int items_per_thread;
-        int tile_items;
-
-        template <typename PolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads       = PolicyT::BLOCK_THREADS;
-            items_per_thread    = PolicyT::ITEMS_PER_THREAD;
-            tile_items          = block_threads * items_per_thread;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Dispatch entrypoints
-    //---------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction using the
-     * specified kernel functions.
-     *
-     * If the input is larger than a single tile, this method uses two-passes of
-     * kernel invocations.
-     */
-    template <
-        typename                SpmvKernelT>                        ///< Function type of cub::AgentSpmvKernel
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                 temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SpmvParamsT&            spmv_params,                        ///< SpMV input parameter bundle
-        cudaStream_t            stream,                             ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous,                  ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        SpmvKernelT             spmv_kernel,                        ///< [in] Kernel function pointer to parameterization of AgentSpmvKernel
-        KernelConfig            spmv_config)                        ///< [in] Dispatch parameters that match the policy that \p spmv_kernel was compiled for
-    {
-#ifndef CUB_RUNTIME_ENABLED
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Total number of spmv work items
-            int num_merge_items = spmv_params.num_rows + spmv_params.num_nonzeros;
-
-            // Get SM occupancy for kernels
-            int spmv_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                spmv_sm_occupancy,
-                spmv_kernel,
-                spmv_config.block_threads))) break;
-            int spmv_device_occupancy = spmv_sm_occupancy * sm_count;
-
-            // Grid dimensions
-            int spmv_grid_size = CUB_MIN(((num_merge_items + spmv_config.block_threads - 1) / spmv_config.block_threads), spmv_device_occupancy);
-
-            // Merge items per block
-            int merge_items_per_block = (num_merge_items + spmv_grid_size - 1) / spmv_grid_size;
-
-            // Get the temporary storage allocation requirements
-            size_t allocation_sizes[1];
-            allocation_sizes[0] = spmv_grid_size * sizeof(KeyValuePairT);       // bytes needed for block carry-out pairs
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            void* allocations[1];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                return cudaSuccess;
-            }
-            KeyValuePairT* d_tile_carry_pairs = (KeyValuePairT*) allocations[0];  // Agent carry-out pairs
-
-            // Log spmv_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking spmv_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                spmv_grid_size, spmv_config.block_threads, (long long) stream, spmv_config.items_per_thread, spmv_sm_occupancy);
-
-            // Invoke spmv_kernel
-            spmv_kernel<<<spmv_grid_size, spmv_config.block_threads, 0, stream>>>(
-                spmv_params,
-                merge_items_per_block,
-                d_tile_carry_pairs);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                 temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SpmvParamsT&            spmv_params,                        ///< SpMV input parameter bundle
-        cudaStream_t            stream                  = 0,        ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous       = false)    ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig spmv_config;
-            InitConfigs(ptx_version, spmv_config);
-
-            if (CubDebug(error = Dispatch(
-                d_temp_storage, 
-                temp_storage_bytes, 
-                spmv_params, 
-                stream, 
-                debug_synchronous,
-                DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, false, false>,
-                spmv_config))) break;
-
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_orig.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_orig.cuh
deleted file mode 100644
index 1650628..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_orig.cuh
+++ /dev/null
@@ -1,850 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * vector multiplication (SpMV).
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "../../agent/single_pass_scan_operators.cuh"
-#include "../../agent/agent_segment_fixup.cuh"
-#include "../../agent/agent_spmv_orig.cuh"
-#include "../../util_type.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../thread/thread_search.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * SpMV kernel entry points
- *****************************************************************************/
-
-/**
- * Spmv search kernel. Identifies merge path starting coordinates for each tile.
- */
-template <
-    typename    AgentSpmvPolicyT,           ///< Parameterized SpmvPolicy tuning policy type
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT>                    ///< Signed integer type for sequence offsets
-__global__ void DeviceSpmv1ColKernel(
-    SpmvParams<ValueT, OffsetT> spmv_params)                ///< [in] SpMV input parameter bundle
-{
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VECTOR_VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        VectorValueIteratorT;
-
-    VectorValueIteratorT wrapped_vector_x(spmv_params.d_vector_x);
-
-    int row_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-    if (row_idx < spmv_params.num_rows)
-    {
-        OffsetT     end_nonzero_idx = spmv_params.d_row_end_offsets[row_idx];
-        OffsetT     nonzero_idx = spmv_params.d_row_end_offsets[row_idx - 1];
-
-        ValueT value = 0.0;
-        if (end_nonzero_idx != nonzero_idx)
-        {
-            value = spmv_params.d_values[nonzero_idx] * wrapped_vector_x[spmv_params.d_column_indices[nonzero_idx]];
-        }
-
-        spmv_params.d_vector_y[row_idx] = value;
-    }
-}
-
-
-/**
- * Spmv search kernel. Identifies merge path starting coordinates for each tile.
- */
-template <
-    typename    SpmvPolicyT,                    ///< Parameterized SpmvPolicy tuning policy type
-    typename    OffsetT,                        ///< Signed integer type for sequence offsets
-    typename    CoordinateT,                    ///< Merge path coordinate type
-    typename    SpmvParamsT>                    ///< SpmvParams type
-__global__ void DeviceSpmvSearchKernel(
-    int             num_merge_tiles,            ///< [in] Number of SpMV merge tiles (spmv grid size)
-    CoordinateT*    d_tile_coordinates,         ///< [out] Pointer to the temporary array of tile starting coordinates
-    SpmvParamsT     spmv_params)                ///< [in] SpMV input parameter bundle
-{
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS           = SpmvPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = SpmvPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    typedef CacheModifiedInputIterator<
-            SpmvPolicyT::ROW_OFFSETS_SEARCH_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsSearchIteratorT;
-
-    // Find the starting coordinate for all tiles (plus the end coordinate of the last one)
-    int tile_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-    if (tile_idx < num_merge_tiles + 1)
-    {
-        OffsetT                         diagonal = (tile_idx * TILE_ITEMS);
-        CoordinateT                     tile_coordinate;
-        CountingInputIterator<OffsetT>  nonzero_indices(0);
-
-        // Search the merge path
-        MergePathSearch(
-            diagonal,
-            RowOffsetsSearchIteratorT(spmv_params.d_row_end_offsets),
-            nonzero_indices,
-            spmv_params.num_rows,
-            spmv_params.num_nonzeros,
-            tile_coordinate);
-
-        // Output starting offset
-        d_tile_coordinates[tile_idx] = tile_coordinate;
-    }
-}
-
-
-/**
- * Spmv agent entry point
- */
-template <
-    typename        SpmvPolicyT,                ///< Parameterized SpmvPolicy tuning policy type
-    typename        ScanTileStateT,             ///< Tile status interface type
-    typename        ValueT,                     ///< Matrix and vector value type
-    typename        OffsetT,                    ///< Signed integer type for sequence offsets
-    typename        CoordinateT,                ///< Merge path coordinate type
-    bool            HAS_ALPHA,                  ///< Whether the input parameter Alpha is 1
-    bool            HAS_BETA>                   ///< Whether the input parameter Beta is 0
-__launch_bounds__ (int(SpmvPolicyT::BLOCK_THREADS))
-__global__ void DeviceSpmvKernel(
-    SpmvParams<ValueT, OffsetT>     spmv_params,                ///< [in] SpMV input parameter bundle
-    CoordinateT*                    d_tile_coordinates,         ///< [in] Pointer to the temporary array of tile starting coordinates
-    KeyValuePair<OffsetT,ValueT>*   d_tile_carry_pairs,         ///< [out] Pointer to the temporary array carry-out dot product row-ids, one per block
-    int                             num_tiles,                  ///< [in] Number of merge tiles
-    ScanTileStateT                  tile_state,                 ///< [in] Tile status interface for fixup reduce-by-key kernel
-    int                             num_segment_fixup_tiles)    ///< [in] Number of reduce-by-key tiles (fixup grid size)
-{
-    // Spmv agent type specialization
-    typedef AgentSpmv<
-            SpmvPolicyT,
-            ValueT,
-            OffsetT,
-            HAS_ALPHA,
-            HAS_BETA>
-        AgentSpmvT;
-
-    // Shared memory for AgentSpmv
-    __shared__ typename AgentSpmvT::TempStorage temp_storage;
-
-    AgentSpmvT(temp_storage, spmv_params).ConsumeTile(
-        d_tile_coordinates,
-        d_tile_carry_pairs,
-        num_tiles);
-
-    // Initialize fixup tile status
-    tile_state.InitializeStatus(num_segment_fixup_tiles);
-
-}
-
-
-/**
- * Multi-block reduce-by-key sweep kernel entry point
- */
-template <
-    typename    AgentSegmentFixupPolicyT,       ///< Parameterized AgentSegmentFixupPolicy tuning policy type
-    typename    PairsInputIteratorT,            ///< Random-access input iterator type for keys
-    typename    AggregatesOutputIteratorT,      ///< Random-access output iterator type for values
-    typename    OffsetT,                        ///< Signed integer type for global offsets
-    typename    ScanTileStateT>                 ///< Tile status interface type
-__launch_bounds__ (int(AgentSegmentFixupPolicyT::BLOCK_THREADS))
-__global__ void DeviceSegmentFixupKernel(
-    PairsInputIteratorT         d_pairs_in,         ///< [in] Pointer to the array carry-out dot product row-ids, one per spmv block
-    AggregatesOutputIteratorT   d_aggregates_out,   ///< [in,out] Output value aggregates
-    OffsetT                     num_items,          ///< [in] Total number of items to select from
-    int                         num_tiles,          ///< [in] Total number of tiles for the entire problem
-    ScanTileStateT              tile_state)         ///< [in] Tile status interface
-{
-    // Thread block type for reducing tiles of value segments
-    typedef AgentSegmentFixup<
-            AgentSegmentFixupPolicyT,
-            PairsInputIteratorT,
-            AggregatesOutputIteratorT,
-            cub::Equality,
-            cub::Sum,
-            OffsetT>
-        AgentSegmentFixupT;
-
-    // Shared memory for AgentSegmentFixup
-    __shared__ typename AgentSegmentFixupT::TempStorage temp_storage;
-
-    // Process tiles
-    AgentSegmentFixupT(temp_storage, d_pairs_in, d_aggregates_out, cub::Equality(), cub::Sum()).ConsumeRange(
-        num_items,
-        num_tiles,
-        tile_state);
-}
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceSpmv
- */
-template <
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT>                    ///< Signed integer type for global offsets
-struct DispatchSpmv
-{
-    //---------------------------------------------------------------------
-    // Constants and Types
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        INIT_KERNEL_THREADS = 128
-    };
-
-    // SpmvParams bundle type
-    typedef SpmvParams<ValueT, OffsetT> SpmvParamsT;
-
-    // 2D merge path coordinate type
-    typedef typename CubVector<OffsetT, 2>::Type CoordinateT;
-
-    // Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<ValueT, OffsetT> ScanTileStateT;
-
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef KeyValuePair<OffsetT, ValueT> KeyValuePairT;
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies
-    //---------------------------------------------------------------------
-
-    /// SM11
-    struct Policy110
-    {
-        typedef AgentSpmvPolicy<
-                128,
-                1,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                4,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-    };
-
-    /// SM20
-    struct Policy200 
-    {
-        typedef AgentSpmvPolicy<
-                96,
-                18,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_RAKING>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                4,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-
-    };
-
-
-
-    /// SM30
-    struct Policy300 
-    {
-        typedef AgentSpmvPolicy<
-                96,
-                6,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                4,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-
-    };
-
-
-    /// SM35
-    struct Policy350
-    {
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 96 : 128,
-                (sizeof(ValueT) > 4) ? 4 : 7,
-                LOAD_LDG,
-                LOAD_CA,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                (sizeof(ValueT) > 4) ? true : false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                3,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_LDG,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-    };
-
-
-    /// SM37
-    struct Policy370
-    {
-
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 128 : 128,
-                (sizeof(ValueT) > 4) ? 9 : 14,
-                LOAD_LDG,
-                LOAD_CA,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                false, 
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                3,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_LDG,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-    };
-
-    /// SM50
-    struct Policy500
-    {
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 64 : 128,
-                (sizeof(ValueT) > 4) ? 6 : 7,
-                LOAD_LDG,
-                LOAD_DEFAULT,
-                (sizeof(ValueT) > 4) ? LOAD_LDG : LOAD_DEFAULT,
-                (sizeof(ValueT) > 4) ? LOAD_LDG : LOAD_DEFAULT,
-                LOAD_LDG,
-                (sizeof(ValueT) > 4) ? true : false,
-                (sizeof(ValueT) > 4) ? BLOCK_SCAN_WARP_SCANS : BLOCK_SCAN_RAKING_MEMOIZE>
-            SpmvPolicyT;
-
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                3,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_LDG,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            SegmentFixupPolicyT;
-    };
-
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies of current PTX compiler pass
-    //---------------------------------------------------------------------
-
-#if (CUB_PTX_ARCH >= 500)
-    typedef Policy500 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 370)
-    typedef Policy370 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#else
-    typedef Policy110 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxSpmvPolicyT : PtxPolicy::SpmvPolicyT {};
-    struct PtxSegmentFixupPolicy : PtxPolicy::SegmentFixupPolicyT {};
-
-
-    //---------------------------------------------------------------------
-    // Utilities
-    //---------------------------------------------------------------------
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig    &spmv_config,
-        KernelConfig    &segment_fixup_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        spmv_config.template Init<PtxSpmvPolicyT>();
-        segment_fixup_config.template Init<PtxSegmentFixupPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 500)
-        {
-            spmv_config.template            Init<typename Policy500::SpmvPolicyT>();
-            segment_fixup_config.template   Init<typename Policy500::SegmentFixupPolicyT>();
-        }
-        else if (ptx_version >= 370)
-        {
-            spmv_config.template            Init<typename Policy370::SpmvPolicyT>();
-            segment_fixup_config.template   Init<typename Policy370::SegmentFixupPolicyT>();
-        }
-        else if (ptx_version >= 350)
-        {
-            spmv_config.template            Init<typename Policy350::SpmvPolicyT>();
-            segment_fixup_config.template   Init<typename Policy350::SegmentFixupPolicyT>();
-        }
-        else if (ptx_version >= 300)
-        {
-            spmv_config.template            Init<typename Policy300::SpmvPolicyT>();
-            segment_fixup_config.template   Init<typename Policy300::SegmentFixupPolicyT>();
-
-        }
-        else if (ptx_version >= 200)
-        {
-            spmv_config.template            Init<typename Policy200::SpmvPolicyT>();
-            segment_fixup_config.template   Init<typename Policy200::SegmentFixupPolicyT>();
-        }
-        else
-        {
-            spmv_config.template            Init<typename Policy110::SpmvPolicyT>();
-            segment_fixup_config.template   Init<typename Policy110::SegmentFixupPolicyT>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.
-     */
-    struct KernelConfig
-    {
-        int block_threads;
-        int items_per_thread;
-        int tile_items;
-
-        template <typename PolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads       = PolicyT::BLOCK_THREADS;
-            items_per_thread    = PolicyT::ITEMS_PER_THREAD;
-            tile_items          = block_threads * items_per_thread;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Dispatch entrypoints
-    //---------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction using the
-     * specified kernel functions.
-     *
-     * If the input is larger than a single tile, this method uses two-passes of
-     * kernel invocations.
-     */
-    template <
-        typename                Spmv1ColKernelT,                    ///< Function type of cub::DeviceSpmv1ColKernel
-        typename                SpmvSearchKernelT,                  ///< Function type of cub::AgentSpmvSearchKernel
-        typename                SpmvKernelT,                        ///< Function type of cub::AgentSpmvKernel
-        typename                SegmentFixupKernelT>                 ///< Function type of cub::DeviceSegmentFixupKernelT
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                 temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SpmvParamsT&            spmv_params,                        ///< SpMV input parameter bundle
-        cudaStream_t            stream,                             ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous,                  ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        Spmv1ColKernelT         spmv_1col_kernel,                   ///< [in] Kernel function pointer to parameterization of DeviceSpmv1ColKernel
-        SpmvSearchKernelT       spmv_search_kernel,                 ///< [in] Kernel function pointer to parameterization of AgentSpmvSearchKernel
-        SpmvKernelT             spmv_kernel,                        ///< [in] Kernel function pointer to parameterization of AgentSpmvKernel
-        SegmentFixupKernelT     segment_fixup_kernel,               ///< [in] Kernel function pointer to parameterization of cub::DeviceSegmentFixupKernel
-        KernelConfig            spmv_config,                        ///< [in] Dispatch parameters that match the policy that \p spmv_kernel was compiled for
-        KernelConfig            segment_fixup_config)               ///< [in] Dispatch parameters that match the policy that \p segment_fixup_kernel was compiled for
-    {
-#ifndef CUB_RUNTIME_ENABLED
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-            if (spmv_params.num_cols == 1)
-            {
-                if (d_temp_storage == NULL)
-                {
-                    // Return if the caller is simply requesting the size of the storage allocation
-                    temp_storage_bytes = 1;
-                    break;
-                }
-
-                // Get search/init grid dims
-                int degen_col_kernel_block_size     = INIT_KERNEL_THREADS;
-                int degen_col_kernel_grid_size      = (spmv_params.num_rows + degen_col_kernel_block_size - 1) / degen_col_kernel_block_size;
-
-                if (debug_synchronous) _CubLog("Invoking spmv_1col_kernel<<<%d, %d, 0, %lld>>>()\n",
-                    degen_col_kernel_grid_size, degen_col_kernel_block_size, (long long) stream);
-
-                // Invoke spmv_search_kernel
-                spmv_1col_kernel<<<degen_col_kernel_grid_size, degen_col_kernel_block_size, 0, stream>>>(
-                    spmv_params);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-                break;
-            }
-
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Get max x-dimension of grid
-            int max_dim_x;
-            if (CubDebug(error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal))) break;;
-
-            // Total number of spmv work items
-            int num_merge_items = spmv_params.num_rows + spmv_params.num_nonzeros;
-
-            // Tile sizes of kernels
-            int merge_tile_size              = spmv_config.block_threads * spmv_config.items_per_thread;
-            int segment_fixup_tile_size     = segment_fixup_config.block_threads * segment_fixup_config.items_per_thread;
-
-            // Number of tiles for kernels
-            unsigned int num_merge_tiles            = (num_merge_items + merge_tile_size - 1) / merge_tile_size;
-            unsigned int num_segment_fixup_tiles    = (num_merge_tiles + segment_fixup_tile_size - 1) / segment_fixup_tile_size;
-
-            // Get SM occupancy for kernels
-            int spmv_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                spmv_sm_occupancy,
-                spmv_kernel,
-                spmv_config.block_threads))) break;
-
-            int segment_fixup_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                segment_fixup_sm_occupancy,
-                segment_fixup_kernel,
-                segment_fixup_config.block_threads))) break;
-
-            // Get grid dimensions
-            dim3 spmv_grid_size(
-                CUB_MIN(num_merge_tiles, max_dim_x),
-                (num_merge_tiles + max_dim_x - 1) / max_dim_x,
-                1);
-
-            dim3 segment_fixup_grid_size(
-                CUB_MIN(num_segment_fixup_tiles, max_dim_x),
-                (num_segment_fixup_tiles + max_dim_x - 1) / max_dim_x,
-                1);
-
-            // Get the temporary storage allocation requirements
-            size_t allocation_sizes[3];
-            if (CubDebug(error = ScanTileStateT::AllocationSize(num_segment_fixup_tiles, allocation_sizes[0]))) break;    // bytes needed for reduce-by-key tile status descriptors
-            allocation_sizes[1] = num_merge_tiles * sizeof(KeyValuePairT);       // bytes needed for block carry-out pairs
-            allocation_sizes[2] = (num_merge_tiles + 1) * sizeof(CoordinateT);   // bytes needed for tile starting coordinates
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            void* allocations[3];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                break;
-            }
-
-            // Construct the tile status interface
-            ScanTileStateT tile_state;
-            if (CubDebug(error = tile_state.Init(num_segment_fixup_tiles, allocations[0], allocation_sizes[0]))) break;
-
-            // Alias the other allocations
-            KeyValuePairT*  d_tile_carry_pairs      = (KeyValuePairT*) allocations[1];  // Agent carry-out pairs
-            CoordinateT*    d_tile_coordinates      = (CoordinateT*) allocations[2];    // Agent starting coordinates
-
-            // Get search/init grid dims
-            int search_block_size   = INIT_KERNEL_THREADS;
-            int search_grid_size    = (num_merge_tiles + 1 + search_block_size - 1) / search_block_size;
-
-#if (CUB_PTX_ARCH == 0)
-            // Init textures
-            if (CubDebug(error = spmv_params.t_vector_x.BindTexture(spmv_params.d_vector_x))) break;
-#endif
-
-            if (search_grid_size < sm_count)
-//            if (num_merge_tiles < spmv_sm_occupancy * sm_count)
-            {
-                // Not enough spmv tiles to saturate the device: have spmv blocks search their own staring coords
-                d_tile_coordinates = NULL;
-            }
-            else
-            {
-                // Use separate search kernel if we have enough spmv tiles to saturate the device
-
-                // Log spmv_search_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking spmv_search_kernel<<<%d, %d, 0, %lld>>>()\n",
-                    search_grid_size, search_block_size, (long long) stream);
-
-                // Invoke spmv_search_kernel
-                spmv_search_kernel<<<search_grid_size, search_block_size, 0, stream>>>(
-                    num_merge_tiles,
-                    d_tile_coordinates,
-                    spmv_params);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-
-            // Log spmv_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking spmv_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                spmv_grid_size.x, spmv_grid_size.y, spmv_grid_size.z, spmv_config.block_threads, (long long) stream, spmv_config.items_per_thread, spmv_sm_occupancy);
-
-            // Invoke spmv_kernel
-            spmv_kernel<<<spmv_grid_size, spmv_config.block_threads, 0, stream>>>(
-                spmv_params,
-                d_tile_coordinates,
-                d_tile_carry_pairs,
-                num_merge_tiles,
-                tile_state,
-                num_segment_fixup_tiles);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-            // Run reduce-by-key fixup if necessary
-            if (num_merge_tiles > 1)
-            {
-                // Log segment_fixup_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking segment_fixup_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                    segment_fixup_grid_size.x, segment_fixup_grid_size.y, segment_fixup_grid_size.z, segment_fixup_config.block_threads, (long long) stream, segment_fixup_config.items_per_thread, segment_fixup_sm_occupancy);
-
-                // Invoke segment_fixup_kernel
-                segment_fixup_kernel<<<segment_fixup_grid_size, segment_fixup_config.block_threads, 0, stream>>>(
-                    d_tile_carry_pairs,
-                    spmv_params.d_vector_y,
-                    num_merge_tiles,
-                    num_segment_fixup_tiles,
-                    tile_state);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-
-#if (CUB_PTX_ARCH == 0)
-            // Free textures
-            if (CubDebug(error = spmv_params.t_vector_x.UnbindTexture())) break;
-#endif
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                 temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SpmvParamsT&            spmv_params,                        ///< SpMV input parameter bundle
-        cudaStream_t            stream                  = 0,        ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous       = false)    ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig spmv_config, segment_fixup_config;
-            InitConfigs(ptx_version, spmv_config, segment_fixup_config);
-
-            if (CubDebug(error = Dispatch(
-                d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                DeviceSpmv1ColKernel<PtxSpmvPolicyT, ValueT, OffsetT>,
-                DeviceSpmvSearchKernel<PtxSpmvPolicyT, OffsetT, CoordinateT, SpmvParamsT>,
-                DeviceSpmvKernel<PtxSpmvPolicyT, ScanTileStateT, ValueT, OffsetT, CoordinateT, false, false>,
-                DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                spmv_config, segment_fixup_config))) break;
-
-/*
-            // Dispatch
-            if (spmv_params.beta == 0.0)
-            {
-                if (spmv_params.alpha == 1.0)
-                {
-                    // Dispatch y = A*x
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmv1ColKernel<PtxSpmvPolicyT, ValueT, OffsetT>,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ScanTileStateT, ValueT, OffsetT, CoordinateT, false, false>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, segment_fixup_config))) break;
-                }
-                else
-                {
-                    // Dispatch y = alpha*A*x
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, ScanTileStateT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, CoordinateT, true, false>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, segment_fixup_config))) break;
-                }
-            }
-            else
-            {
-                if (spmv_params.alpha == 1.0)
-                {
-                    // Dispatch y = A*x + beta*y
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, ScanTileStateT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, CoordinateT, false, true>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, segment_fixup_config))) break;
-                }
-                else
-                {
-                    // Dispatch y = alpha*A*x + beta*y
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, ScanTileStateT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, CoordinateT, true, true>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, segment_fixup_config))) break;
-                }
-            }
-*/
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_row_based.cuh b/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_row_based.cuh
deleted file mode 100644
index 81db42a..0000000
--- a/ml-xgboost/cub/cub/device/dispatch/dispatch_spmv_row_based.cuh
+++ /dev/null
@@ -1,877 +0,0 @@
-
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::DeviceSpmv provides device-wide parallel operations for performing sparse-matrix * vector multiplication (SpMV).
- */
-
-#pragma once
-
-#include <stdio.h>
-#include <iterator>
-
-#include "../../agent/single_pass_scan_operators.cuh"
-#include "../../agent/agent_segment_fixup.cuh"
-#include "../../agent/agent_spmv_row_based.cuh"
-#include "../../util_type.cuh"
-#include "../../util_debug.cuh"
-#include "../../util_device.cuh"
-#include "../../thread/thread_search.cuh"
-#include "../../grid/grid_queue.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * SpMV kernel entry points
- *****************************************************************************/
-
-/**
- * Spmv search kernel. Identifies merge path starting coordinates for each tile.
- */
-template <
-    typename    AgentSpmvPolicyT,           ///< Parameterized SpmvPolicy tuning policy type
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT>                    ///< Signed integer type for sequence offsets
-__global__ void DeviceSpmv1ColKernel(
-    SpmvParams<ValueT, OffsetT> spmv_params)                ///< [in] SpMV input parameter bundle
-{
-    typedef CacheModifiedInputIterator<
-            AgentSpmvPolicyT::VECTOR_VALUES_LOAD_MODIFIER,
-            ValueT,
-            OffsetT>
-        VectorValueIteratorT;
-
-    VectorValueIteratorT wrapped_vector_x(spmv_params.d_vector_x);
-
-    int row_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-    if (row_idx < spmv_params.num_rows)
-    {
-        OffsetT     end_nonzero_idx = spmv_params.d_row_end_offsets[row_idx];
-        OffsetT     nonzero_idx = spmv_params.d_row_end_offsets[row_idx - 1];
-
-        ValueT value = 0.0;
-        if (end_nonzero_idx != nonzero_idx)
-        {
-            value = spmv_params.d_values[nonzero_idx] * wrapped_vector_x[spmv_params.d_column_indices[nonzero_idx]];
-        }
-
-        spmv_params.d_vector_y[row_idx] = value;
-    }
-}
-
-
-/**
- * Spmv search kernel. Identifies merge path starting coordinates for each tile.
- */
-template <
-    typename    SpmvPolicyT,                    ///< Parameterized SpmvPolicy tuning policy type
-    typename    OffsetT,                        ///< Signed integer type for sequence offsets
-    typename    CoordinateT,                    ///< Merge path coordinate type
-    typename    SpmvParamsT>                    ///< SpmvParams type
-__global__ void DeviceSpmvSearchKernel(
-    int             num_spmv_tiles,            ///< [in] Number of SpMV merge tiles (spmv grid size)
-    CoordinateT*    d_tile_coordinates,         ///< [out] Pointer to the temporary array of tile starting coordinates
-    SpmvParamsT     spmv_params)                ///< [in] SpMV input parameter bundle
-{
-    /// Constants
-    enum
-    {
-        BLOCK_THREADS           = SpmvPolicyT::BLOCK_THREADS,
-        ITEMS_PER_THREAD        = SpmvPolicyT::ITEMS_PER_THREAD,
-        TILE_ITEMS              = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    typedef CacheModifiedInputIterator<
-            SpmvPolicyT::ROW_OFFSETS_SEARCH_LOAD_MODIFIER,
-            OffsetT,
-            OffsetT>
-        RowOffsetsSearchIteratorT;
-
-    // Find the starting coordinate for all tiles (plus the end coordinate of the last one)
-    int tile_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
-    if (tile_idx < num_spmv_tiles + 1)
-    {
-        OffsetT                         diagonal = (tile_idx * TILE_ITEMS);
-        CoordinateT                     tile_coordinate;
-        CountingInputIterator<OffsetT>  nonzero_indices(0);
-
-        // Search the merge path
-        MergePathSearch(
-            diagonal,
-            RowOffsetsSearchIteratorT(spmv_params.d_row_end_offsets),
-            nonzero_indices,
-            spmv_params.num_rows,
-            spmv_params.num_nonzeros,
-            tile_coordinate);
-
-        // Output starting offset
-        d_tile_coordinates[tile_idx] = tile_coordinate;
-    }
-}
-
-
-/**
- * Spmv agent entry point
- */
-template <
-    typename        SpmvPolicyT,                ///< Parameterized SpmvPolicy tuning policy type
-    typename        ScanTileStateT,             ///< Tile status interface type
-    typename        ValueT,                     ///< Matrix and vector value type
-    typename        OffsetT,                    ///< Signed integer type for sequence offsets
-    typename        CoordinateT,                ///< Merge path coordinate type
-    bool            HAS_ALPHA,                  ///< Whether the input parameter Alpha is 1
-    bool            HAS_BETA>                   ///< Whether the input parameter Beta is 0
-__launch_bounds__ (int(SpmvPolicyT::BLOCK_THREADS))
-__global__ void DeviceSpmvKernel(
-    SpmvParams<ValueT, OffsetT>     spmv_params,                ///< [in] SpMV input parameter bundle
-//    CoordinateT*                    d_tile_coordinates,         ///< [in] Pointer to the temporary array of tile starting coordinates
-//    KeyValuePair<OffsetT,ValueT>*   d_tile_carry_pairs,         ///< [out] Pointer to the temporary array carry-out dot product row-ids, one per block
-//    int                             num_tiles,                  ///< [in] Number of merge tiles
-//    ScanTileStateT                  tile_state,                 ///< [in] Tile status interface for fixup reduce-by-key kernel
-//    int                             num_fixup_tiles,    ///< [in] Number of reduce-by-key tiles (fixup grid size)
-    int                             rows_per_tile)              ///< [in] Number of rows per tile
-{
-    // Spmv agent type specialization
-    typedef AgentSpmv<
-            SpmvPolicyT,
-            ValueT,
-            OffsetT,
-            HAS_ALPHA,
-            HAS_BETA>
-        AgentSpmvT;
-
-    // Shared memory for AgentSpmv
-    __shared__ typename AgentSpmvT::TempStorage temp_storage;
-
-    AgentSpmvT(temp_storage, spmv_params).ConsumeTile(
-        blockIdx.x,
-        rows_per_tile);
-
-/*
-    AgentSpmvT(temp_storage, spmv_params).ConsumeTile(
-        d_tile_coordinates,
-        d_tile_carry_pairs,
-        num_tiles);
-
-    // Initialize fixup tile status
-    tile_state.InitializeStatus(num_fixup_tiles);
-*/
-}
-
-
-/**
- * Multi-block reduce-by-key sweep kernel entry point
- */
-template <
-    typename    AgentSegmentFixupPolicyT,       ///< Parameterized AgentSegmentFixupPolicy tuning policy type
-    typename    PairsInputIteratorT,            ///< Random-access input iterator type for keys
-    typename    AggregatesOutputIteratorT,      ///< Random-access output iterator type for values
-    typename    OffsetT,                        ///< Signed integer type for global offsets
-    typename    ScanTileStateT>                 ///< Tile status interface type
-__launch_bounds__ (int(AgentSegmentFixupPolicyT::BLOCK_THREADS))
-__global__ void DeviceSegmentFixupKernel(
-    PairsInputIteratorT         d_pairs_in,         ///< [in] Pointer to the array carry-out dot product row-ids, one per spmv block
-    AggregatesOutputIteratorT   d_aggregates_out,   ///< [in,out] Output value aggregates
-    OffsetT                     num_items,          ///< [in] Total number of items to select from
-    int                         num_tiles,          ///< [in] Total number of tiles for the entire problem
-    ScanTileStateT              tile_state)         ///< [in] Tile status interface
-{
-    // Thread block type for reducing tiles of value segments
-    typedef AgentSegmentFixup<
-            AgentSegmentFixupPolicyT,
-            PairsInputIteratorT,
-            AggregatesOutputIteratorT,
-            cub::Equality,
-            cub::Sum,
-            OffsetT>
-        AgentSegmentFixupT;
-
-    // Shared memory for AgentSegmentFixup
-    __shared__ typename AgentSegmentFixupT::TempStorage temp_storage;
-
-    // Process tiles
-    AgentSegmentFixupT(temp_storage, d_pairs_in, d_aggregates_out, cub::Equality(), cub::Sum()).ConsumeRange(
-        num_items,
-        num_tiles,
-        tile_state);
-}
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceSpmv
- */
-template <
-    typename    ValueT,                     ///< Matrix and vector value type
-    typename    OffsetT>                    ///< Signed integer type for global offsets
-struct DispatchSpmv
-{
-    //---------------------------------------------------------------------
-    // Constants and Types
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        INIT_KERNEL_THREADS = 128
-    };
-
-    // SpmvParams bundle type
-    typedef SpmvParams<ValueT, OffsetT> SpmvParamsT;
-
-    // 2D merge path coordinate type
-    typedef typename CubVector<OffsetT, 2>::Type CoordinateT;
-
-    // Tile status descriptor interface type
-    typedef ReduceByKeyScanTileState<ValueT, OffsetT> ScanTileStateT;
-
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef KeyValuePair<OffsetT, ValueT> KeyValuePairT;
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies
-    //---------------------------------------------------------------------
-
-    /// SM11
-    struct Policy110
-    {
-        typedef AgentSpmvPolicy<
-                128,
-                1,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                4,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-    };
-
-    /// SM20
-    struct Policy200 
-    {
-        typedef AgentSpmvPolicy<
-                96,
-                18,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_RAKING>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                4,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-
-    };
-
-
-
-    /// SM30
-    struct Policy300 
-    {
-        typedef AgentSpmvPolicy<
-                96,
-                6,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                4,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_DEFAULT,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-
-    };
-
-
-    /// SM35
-    struct Policy350
-    {
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 64 : 128,
-                (sizeof(ValueT) > 4) ? 7 : 7,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                false,
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                3,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_LDG,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-    };
-
-
-    /// SM37
-    struct Policy370
-    {
-
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 128 : 128,
-                (sizeof(ValueT) > 4) ? 7 : 7,
-                LOAD_LDG,
-                LOAD_CA,
-                LOAD_LDG,
-                LOAD_LDG,
-                LOAD_LDG,
-                false, 
-                BLOCK_SCAN_WARP_SCANS>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                3,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_LDG,
-                BLOCK_SCAN_WARP_SCANS>
-            SegmentFixupPolicyT;
-    };
-
-    /// SM50
-    struct Policy500
-    {
-        typedef AgentSpmvPolicy<
-                (sizeof(ValueT) > 4) ? 64 : 64,
-                7, 
-                LOAD_DEFAULT,
-                LOAD_CA,
-                LOAD_DEFAULT,
-                LOAD_DEFAULT,
-                LOAD_LDG,
-                false,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            SpmvPolicyT;
-
-        typedef AgentSegmentFixupPolicy<
-                128,
-                3,
-                BLOCK_LOAD_VECTORIZE,
-                LOAD_LDG,
-                BLOCK_SCAN_RAKING_MEMOIZE>
-            SegmentFixupPolicyT;
-    };
-
-
-
-    //---------------------------------------------------------------------
-    // Tuning policies of current PTX compiler pass
-    //---------------------------------------------------------------------
-
-#if (CUB_PTX_ARCH >= 500)
-    typedef Policy500 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 370)
-    typedef Policy370 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#else
-    typedef Policy110 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxSpmvPolicyT : PtxPolicy::SpmvPolicyT {};
-    struct PtxSegmentFixupPolicy : PtxPolicy::SegmentFixupPolicyT {};
-
-
-    //---------------------------------------------------------------------
-    // Utilities
-    //---------------------------------------------------------------------
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <typename KernelConfig>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static void InitConfigs(
-        int             ptx_version,
-        KernelConfig    &spmv_config,
-        KernelConfig    &fixup_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        spmv_config.template Init<PtxSpmvPolicyT>();
-        fixup_config.template Init<PtxSegmentFixupPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 500)
-        {
-            spmv_config.template            Init<typename Policy500::SpmvPolicyT>();
-            fixup_config.template   Init<typename Policy500::SegmentFixupPolicyT>();
-        }
-        else if (ptx_version >= 370)
-        {
-            spmv_config.template            Init<typename Policy370::SpmvPolicyT>();
-            fixup_config.template   Init<typename Policy370::SegmentFixupPolicyT>();
-        }
-        else if (ptx_version >= 350)
-        {
-            spmv_config.template            Init<typename Policy350::SpmvPolicyT>();
-            fixup_config.template   Init<typename Policy350::SegmentFixupPolicyT>();
-        }
-        else if (ptx_version >= 300)
-        {
-            spmv_config.template            Init<typename Policy300::SpmvPolicyT>();
-            fixup_config.template   Init<typename Policy300::SegmentFixupPolicyT>();
-
-        }
-        else if (ptx_version >= 200)
-        {
-            spmv_config.template            Init<typename Policy200::SpmvPolicyT>();
-            fixup_config.template   Init<typename Policy200::SegmentFixupPolicyT>();
-        }
-        else
-        {
-            spmv_config.template            Init<typename Policy110::SpmvPolicyT>();
-            fixup_config.template   Init<typename Policy110::SegmentFixupPolicyT>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * Kernel kernel dispatch configuration.
-     */
-    struct KernelConfig
-    {
-        int block_threads;
-        int items_per_thread;
-        int tile_items;
-
-        template <typename PolicyT>
-        CUB_RUNTIME_FUNCTION __forceinline__
-        void Init()
-        {
-            block_threads       = PolicyT::BLOCK_THREADS;
-            items_per_thread    = PolicyT::ITEMS_PER_THREAD;
-            tile_items          = block_threads * items_per_thread;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Dispatch entrypoints
-    //---------------------------------------------------------------------
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction using the
-     * specified kernel functions.
-     *
-     * If the input is larger than a single tile, this method uses two-passes of
-     * kernel invocations.
-     */
-    template <
-//        typename                Spmv1ColKernelT,                    ///< Function type of cub::DeviceSpmv1ColKernel
-//        typename                SpmvSearchKernelT,                  ///< Function type of cub::AgentSpmvSearchKernel
-        typename                SpmvKernelT>                        ///< Function type of cub::AgentSpmvKernel
-//        typename                SegmentFixupKernelT>                 ///< Function type of cub::DeviceSegmentFixupKernelT
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                 temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SpmvParamsT&            spmv_params,                        ///< SpMV input parameter bundle
-        cudaStream_t            stream,                             ///< [in] CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous,                  ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-//        Spmv1ColKernelT         spmv_1col_kernel,                   ///< [in] Kernel function pointer to parameterization of DeviceSpmv1ColKernel
-//        SpmvSearchKernelT       spmv_search_kernel,                 ///< [in] Kernel function pointer to parameterization of AgentSpmvSearchKernel
-        SpmvKernelT             spmv_kernel,                        ///< [in] Kernel function pointer to parameterization of AgentSpmvKernel
-//        SegmentFixupKernelT     fixup_kernel,               ///< [in] Kernel function pointer to parameterization of cub::DeviceSegmentFixupKernel
-        KernelConfig            spmv_config,                        ///< [in] Dispatch parameters that match the policy that \p spmv_kernel was compiled for
-        KernelConfig            fixup_config)               ///< [in] Dispatch parameters that match the policy that \p fixup_kernel was compiled for
-    {
-#ifndef CUB_RUNTIME_ENABLED
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-
-#else
-        cudaError error = cudaSuccess;
-        do
-        {
-/*
-            if (spmv_params.num_cols == 1)
-            {
-                if (d_temp_storage == NULL)
-                {
-                    // Return if the caller is simply requesting the size of the storage allocation
-                    temp_storage_bytes = 1;
-                    return cudaSuccess;
-                }
-
-                // Get search/init grid dims
-                int degen_col_kernel_block_size     = INIT_KERNEL_THREADS;
-                int degen_col_kernel_grid_size      = (spmv_params.num_rows + degen_col_kernel_block_size - 1) / degen_col_kernel_block_size;
-
-                if (debug_synchronous) _CubLog("Invoking spmv_1col_kernel<<<%d, %d, 0, %lld>>>()\n",
-                    degen_col_kernel_grid_size, degen_col_kernel_block_size, (long long) stream);
-
-                // Invoke spmv_search_kernel
-                spmv_1col_kernel<<<degen_col_kernel_grid_size, degen_col_kernel_block_size, 0, stream>>>(
-                    spmv_params);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-
-                break;
-            }
-*/
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Get max x-dimension of grid
-            int max_dim_x;
-            if (CubDebug(error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal))) break;;
-
-            // Get SM occupancy for kernels
-            int spmv_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                spmv_sm_occupancy,
-                spmv_kernel,
-                spmv_config.block_threads))) break;
-  
-            // Tile sizes of kernels
-            int spmv_tile_size      = spmv_config.block_threads * spmv_config.items_per_thread;
-            int fixup_tile_size     = fixup_config.block_threads * fixup_config.items_per_thread;
-
-            unsigned int rows_per_tile = spmv_config.block_threads;
-
-            if (spmv_params.num_rows < rows_per_tile * spmv_sm_occupancy * sm_count * 8)
-            {
-                // Decrease rows per tile if needed to accomodate high expansion factor
-                unsigned int expansion_factor = (spmv_params.num_nonzeros) / spmv_params.num_rows;
-
-                if ((expansion_factor > 0) && (expansion_factor > spmv_config.items_per_thread))
-                    rows_per_tile = (spmv_tile_size) / expansion_factor;
-
-                // Decrease rows per tile if needed to accomodate minimum parallelism
-                unsigned int spmv_device_occupancy = sm_count * 2;
-//                unsigned int spmv_device_occupancy = sm_count * ((spmv_sm_occupancy + 1) / 2);
-                if (spmv_params.num_rows < spmv_device_occupancy * rows_per_tile)
-                    rows_per_tile = (spmv_params.num_rows) / spmv_device_occupancy;
-            }
-
-            rows_per_tile = CUB_MAX(rows_per_tile, 2);
-
-            if (debug_synchronous) _CubLog("Rows per tile: %d\n", rows_per_tile);
-
-            // Number of tiles for kernels
-            unsigned int num_spmv_tiles     = (spmv_params.num_rows + rows_per_tile - 1) / rows_per_tile;
-//            unsigned int num_fixup_tiles    = (num_spmv_tiles + fixup_tile_size - 1) / fixup_tile_size;
-
-            // Get grid dimensions
-            dim3 spmv_grid_size(
-                CUB_MIN(num_spmv_tiles, max_dim_x),
-                (num_spmv_tiles + max_dim_x - 1) / max_dim_x,
-                1);
-
-/*
-            dim3 spmv_grid_size(
-                CUB_MIN(num_spmv_tiles, max_dim_x),
-                (num_spmv_tiles + max_dim_x - 1) / max_dim_x,
-                1);
-
-            dim3 fixup_grid_size(
-                CUB_MIN(num_fixup_tiles, max_dim_x),
-                (num_fixup_tiles + max_dim_x - 1) / max_dim_x,
-                1);
-*/
-            // Get the temporary storage allocation requirements
-            size_t allocation_sizes[3];
-//            if (CubDebug(error = ScanTileStateT::AllocationSize(num_fixup_tiles, allocation_sizes[0]))) break;    // bytes needed for reduce-by-key tile status descriptors
-            allocation_sizes[0] = 0;
-            allocation_sizes[1] = num_spmv_tiles * sizeof(KeyValuePairT);       // bytes needed for block carry-out pairs
-            allocation_sizes[2] = (num_spmv_tiles + 1) * sizeof(CoordinateT);   // bytes needed for tile starting coordinates
-
-            // Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
-            void* allocations[3];
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                return cudaSuccess;
-            }
-
-            // Construct the tile status interface
-/*
-            ScanTileStateT tile_state;
-            if (CubDebug(error = tile_state.Init(num_fixup_tiles, allocations[0], allocation_sizes[0]))) break;
-*/
-            // Alias the other allocations
-            KeyValuePairT*  d_tile_carry_pairs      = (KeyValuePairT*) allocations[1];  // Agent carry-out pairs
-            CoordinateT*    d_tile_coordinates      = (CoordinateT*) allocations[2];    // Agent starting coordinates
-
-            // Get search/init grid dims
-            int search_block_size   = INIT_KERNEL_THREADS;
-            int search_grid_size    = (num_spmv_tiles + 1 + search_block_size - 1) / search_block_size;
-
-#if (CUB_PTX_ARCH == 0)
-            // Init textures
-//            if (CubDebug(error = spmv_params.t_vector_x.BindTexture(spmv_params.d_vector_x))) break;
-#endif
-
-/*
-            if (search_grid_size < sm_count)
-            {
-                // Not enough spmv tiles to saturate the device: have spmv blocks search their own staring coords
-                d_tile_coordinates = NULL;
-            }
-            else
-            {
-                // Use separate search kernel if we have enough spmv tiles to saturate the device
-
-                // Log spmv_search_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking spmv_search_kernel<<<%d, %d, 0, %lld>>>()\n",
-                    search_grid_size, search_block_size, (long long) stream);
-
-                // Invoke spmv_search_kernel
-                spmv_search_kernel<<<search_grid_size, search_block_size, 0, stream>>>(
-                    num_spmv_tiles,
-                    d_tile_coordinates,
-                    spmv_params);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-*/
-            // Log spmv_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking spmv_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                spmv_grid_size.x, spmv_grid_size.y, spmv_grid_size.z, spmv_config.block_threads, (long long) stream, spmv_config.items_per_thread, spmv_sm_occupancy);
-
-            // Invoke spmv_kernel
-            spmv_kernel<<<spmv_grid_size, spmv_config.block_threads, 0, stream>>>(
-                spmv_params,
-//                d_tile_coordinates,
-//                d_tile_carry_pairs,
-//                num_spmv_tiles,
-//                tile_state,
-//                num_fixup_tiles,
-                rows_per_tile);
-
-            // Check for failure to launch
-            if (CubDebug(error = cudaPeekAtLastError())) break;
-
-            // Sync the stream if specified to flush runtime errors
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-/*
-            // Run reduce-by-key fixup if necessary
-            if (num_spmv_tiles > 1)
-            {
-                // Log fixup_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking fixup_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                    fixup_grid_size.x, fixup_grid_size.y, fixup_grid_size.z, fixup_config.block_threads, (long long) stream, fixup_config.items_per_thread, fixup_sm_occupancy);
-
-                // Invoke fixup_kernel
-                fixup_kernel<<<fixup_grid_size, fixup_config.block_threads, 0, stream>>>(
-                    d_tile_carry_pairs,
-                    spmv_params.d_vector_y,
-                    num_spmv_tiles,
-                    num_fixup_tiles,
-                    tile_state);
-
-                // Check for failure to launch
-                if (CubDebug(error = cudaPeekAtLastError())) break;
-
-                // Sync the stream if specified to flush runtime errors
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-*/
-#if (CUB_PTX_ARCH == 0)
-            // Free textures
-//            if (CubDebug(error = spmv_params.t_vector_x.UnbindTexture())) break;
-#endif
-        }
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine for computing a device-wide reduction
-     */
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Dispatch(
-        void*                   d_temp_storage,                     ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-        size_t&                 temp_storage_bytes,                 ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
-        SpmvParamsT&            spmv_params,                        ///< SpMV input parameter bundle
-        cudaStream_t            stream                  = 0,        ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous       = false)    ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  May cause significant slowdown.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            KernelConfig spmv_config, fixup_config;
-            InitConfigs(ptx_version, spmv_config, fixup_config);
-
-            if (CubDebug(error = Dispatch(
-                d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-//                DeviceSpmv1ColKernel<PtxSpmvPolicyT, ValueT, OffsetT>,
-//                DeviceSpmvSearchKernel<PtxSpmvPolicyT, OffsetT, CoordinateT, SpmvParamsT>,
-                DeviceSpmvKernel<PtxSpmvPolicyT, ScanTileStateT, ValueT, OffsetT, CoordinateT, false, false>,
-//                DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                spmv_config, fixup_config))) break;
-
-/*
-            // Dispatch
-            if (spmv_params.beta == 0.0)
-            {
-                if (spmv_params.alpha == 1.0)
-                {
-                    // Dispatch y = A*x
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmv1ColKernel<PtxSpmvPolicyT, ValueT, OffsetT>,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ScanTileStateT, ValueT, OffsetT, CoordinateT, false, false>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, fixup_config))) break;
-                }
-                else
-                {
-                    // Dispatch y = alpha*A*x
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, ScanTileStateT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, CoordinateT, true, false>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, fixup_config))) break;
-                }
-            }
-            else
-            {
-                if (spmv_params.alpha == 1.0)
-                {
-                    // Dispatch y = A*x + beta*y
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, ScanTileStateT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, CoordinateT, false, true>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, fixup_config))) break;
-                }
-                else
-                {
-                    // Dispatch y = alpha*A*x + beta*y
-                    if (CubDebug(error = Dispatch(
-                        d_temp_storage, temp_storage_bytes, spmv_params, stream, debug_synchronous,
-                        DeviceSpmvSearchKernel<PtxSpmvPolicyT, ScanTileStateT, OffsetT, CoordinateT, SpmvParamsT>,
-                        DeviceSpmvKernel<PtxSpmvPolicyT, ValueT, OffsetT, CoordinateT, true, true>,
-                        DeviceSegmentFixupKernel<PtxSegmentFixupPolicy, KeyValuePairT*, ValueT*, OffsetT, ScanTileStateT>,
-                        spmv_config, fixup_config))) break;
-                }
-            }
-*/
-        }
-        while (0);
-
-        return error;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/grid/grid_barrier.cuh b/ml-xgboost/cub/cub/grid/grid_barrier.cuh
deleted file mode 100644
index 0a98c26..0000000
--- a/ml-xgboost/cub/cub/grid/grid_barrier.cuh
+++ /dev/null
@@ -1,211 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::GridBarrier implements a software global barrier among thread blocks within a CUDA grid
- */
-
-#pragma once
-
-#include "../util_debug.cuh"
-#include "../util_namespace.cuh"
-#include "../thread/thread_load.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup GridModule
- * @{
- */
-
-
-/**
- * \brief GridBarrier implements a software global barrier among thread blocks within a CUDA grid
- */
-class GridBarrier
-{
-protected :
-
-    typedef unsigned int SyncFlag;
-
-    // Counters in global device memory
-    SyncFlag* d_sync;
-
-public:
-
-    /**
-     * Constructor
-     */
-    GridBarrier() : d_sync(NULL) {}
-
-
-    /**
-     * Synchronize
-     */
-    __device__ __forceinline__ void Sync() const
-    {
-        volatile SyncFlag *d_vol_sync = d_sync;
-
-        // Threadfence and syncthreads to make sure global writes are visible before
-        // thread-0 reports in with its sync counter
-        __threadfence();
-        CTA_SYNC();
-
-        if (blockIdx.x == 0)
-        {
-            // Report in ourselves
-            if (threadIdx.x == 0)
-            {
-                d_vol_sync[blockIdx.x] = 1;
-            }
-
-            CTA_SYNC();
-
-            // Wait for everyone else to report in
-            for (int peer_block = threadIdx.x; peer_block < gridDim.x; peer_block += blockDim.x)
-            {
-                while (ThreadLoad<LOAD_CG>(d_sync + peer_block) == 0)
-                {
-                    __threadfence_block();
-                }
-            }
-
-            CTA_SYNC();
-
-            // Let everyone know it's safe to proceed
-            for (int peer_block = threadIdx.x; peer_block < gridDim.x; peer_block += blockDim.x)
-            {
-                d_vol_sync[peer_block] = 0;
-            }
-        }
-        else
-        {
-            if (threadIdx.x == 0)
-            {
-                // Report in
-                d_vol_sync[blockIdx.x] = 1;
-
-                // Wait for acknowledgment
-                while (ThreadLoad<LOAD_CG>(d_sync + blockIdx.x) == 1)
-                {
-                    __threadfence_block();
-                }
-            }
-
-            CTA_SYNC();
-        }
-    }
-};
-
-
-/**
- * \brief GridBarrierLifetime extends GridBarrier to provide lifetime management of the temporary device storage needed for cooperation.
- *
- * Uses RAII for lifetime, i.e., device resources are reclaimed when
- * the destructor is called.
- */
-class GridBarrierLifetime : public GridBarrier
-{
-protected:
-
-    // Number of bytes backed by d_sync
-    size_t sync_bytes;
-
-public:
-
-    /**
-     * Constructor
-     */
-    GridBarrierLifetime() : GridBarrier(), sync_bytes(0) {}
-
-
-    /**
-     * DeviceFrees and resets the progress counters
-     */
-    cudaError_t HostReset()
-    {
-        cudaError_t retval = cudaSuccess;
-        if (d_sync)
-        {
-            CubDebug(retval = cudaFree(d_sync));
-            d_sync = NULL;
-        }
-        sync_bytes = 0;
-        return retval;
-    }
-
-
-    /**
-     * Destructor
-     */
-    virtual ~GridBarrierLifetime()
-    {
-        HostReset();
-    }
-
-
-    /**
-     * Sets up the progress counters for the next kernel launch (lazily
-     * allocating and initializing them if necessary)
-     */
-    cudaError_t Setup(int sweep_grid_size)
-    {
-        cudaError_t retval = cudaSuccess;
-        do {
-            size_t new_sync_bytes = sweep_grid_size * sizeof(SyncFlag);
-            if (new_sync_bytes > sync_bytes)
-            {
-                if (d_sync)
-                {
-                    if (CubDebug(retval = cudaFree(d_sync))) break;
-                }
-
-                sync_bytes = new_sync_bytes;
-
-                // Allocate and initialize to zero
-                if (CubDebug(retval = cudaMalloc((void**) &d_sync, sync_bytes))) break;
-                if (CubDebug(retval = cudaMemset(d_sync, 0, new_sync_bytes))) break;
-            }
-        } while (0);
-
-        return retval;
-    }
-};
-
-
-/** @} */       // end group GridModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/grid/grid_even_share.cuh b/ml-xgboost/cub/cub/grid/grid_even_share.cuh
deleted file mode 100644
index 8e4cc12..0000000
--- a/ml-xgboost/cub/cub/grid/grid_even_share.cuh
+++ /dev/null
@@ -1,185 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::GridEvenShare is a descriptor utility for distributing input among CUDA threadblocks in an "even-share" fashion.  Each threadblock gets roughly the same number of fixed-size work units (grains).
- */
-
-
-#pragma once
-
-#include "../util_namespace.cuh"
-#include "../util_macro.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup GridModule
- * @{
- */
-
-
-/**
- * \brief GridEvenShare is a descriptor utility for distributing input among CUDA threadblocks in an "even-share" fashion.  Each threadblock gets roughly the same number of fixed-size work units (grains).
- *
- * \par Overview
- * GridEvenShare indicates which sections of input are to be mapped onto which threadblocks.
- * Threadblocks may receive one of three different amounts of work: "big", "normal",
- * and "last".  The "big" workloads are one scheduling grain larger than "normal".  The "last" work unit
- * for the last threadblock may be partially-full if the input is not an even multiple of
- * the scheduling grain size.
- *
- * \par
- * Before invoking a child grid, a parent thread will typically construct an instance of
- * GridEvenShare.  The instance can be passed to child threadblocks which can
- * initialize their per-threadblock offsets using \p BlockInit().
- *
- * \tparam OffsetT      Signed integer type for global offsets
- */
-template <typename OffsetT>
-struct GridEvenShare
-{
-    OffsetT     total_grains;
-    int         big_blocks;
-    OffsetT     big_share;
-    OffsetT     normal_share;
-    OffsetT     normal_base_offset;
-
-    /// Total number of input items
-    OffsetT     num_items;
-
-    /// Grid size in threadblocks
-    int         grid_size;
-
-    /// OffsetT into input marking the beginning of the owning thread block's segment of input tiles
-    OffsetT     block_offset;
-
-    /// OffsetT into input of marking the end (one-past) of the owning thread block's segment of input tiles
-    OffsetT     block_end;
-
-    /**
-     * \brief Default constructor.  Zero-initializes block-specific fields.
-     */
-    __host__ __device__ __forceinline__ GridEvenShare() :
-        num_items(0),
-        grid_size(0),
-        block_offset(0),
-        block_end(0) {}
-
-    /**
-     * \brief Constructor.  Initializes the grid-specific members \p num_items and \p grid_size. To be called prior prior to kernel launch)
-     */
-    __host__ __device__ __forceinline__ GridEvenShare(
-        OffsetT  num_items,                 ///< Total number of input items
-        int     max_grid_size,              ///< Maximum grid size allowable (actual grid size may be less if not warranted by the the number of input items)
-        int     schedule_granularity)       ///< Granularity by which the input can be parcelled into and distributed among threablocks.  Usually the thread block's native tile size (or a multiple thereof.
-    {
-        this->num_items             = num_items;
-        this->block_offset          = num_items;
-        this->block_end             = num_items;
-        this->total_grains          = (num_items + schedule_granularity - 1) / schedule_granularity;
-        this->grid_size             = CUB_MIN(total_grains, max_grid_size);
-        OffsetT grains_per_block     = total_grains / grid_size;
-        this->big_blocks            = total_grains - (grains_per_block * grid_size);        // leftover grains go to big blocks
-        this->normal_share          = grains_per_block * schedule_granularity;
-        this->normal_base_offset    = big_blocks * schedule_granularity;
-        this->big_share             = normal_share + schedule_granularity;
-    }
-
-
-
-    /**
-     * \brief Initializes ranges for the specified partition index
-     */
-    __device__ __forceinline__ void Init(int partition_id)
-    {
-        if (partition_id < big_blocks)
-        {
-            // This threadblock gets a big share of grains (grains_per_block + 1)
-            block_offset = (partition_id * big_share);
-            block_end = block_offset + big_share;
-        }
-        else if (partition_id < total_grains)
-        {
-            // This threadblock gets a normal share of grains (grains_per_block)
-            block_offset = normal_base_offset + (partition_id * normal_share);
-            block_end = CUB_MIN(num_items, block_offset + normal_share);
-        }
-    }
-
-
-    /**
-     * \brief Initializes ranges for the current thread block (e.g., to be called by each threadblock after startup)
-     */
-    __device__ __forceinline__ void BlockInit()
-    {
-        Init(blockIdx.x);
-    }
-
-
-    /**
-     * Print to stdout
-     */
-    __host__ __device__ __forceinline__ void Print()
-    {
-        printf(
-#if (CUB_PTX_ARCH > 0)
-            "\tthreadblock(%d) "
-            "block_offset(%lu) "
-            "block_end(%lu) "
-#endif
-            "num_items(%lu)  "
-            "total_grains(%lu)  "
-            "big_blocks(%lu)  "
-            "big_share(%lu)  "
-            "normal_share(%lu)\n",
-#if (CUB_PTX_ARCH > 0)
-                blockIdx.x,
-                (unsigned long) block_offset,
-                (unsigned long) block_end,
-#endif
-                (unsigned long) num_items,
-                (unsigned long) total_grains,
-                (unsigned long) big_blocks,
-                (unsigned long) big_share,
-                (unsigned long) normal_share);
-    }
-};
-
-
-
-/** @} */       // end group GridModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/grid/grid_mapping.cuh b/ml-xgboost/cub/cub/grid/grid_mapping.cuh
deleted file mode 100644
index fa3574e..0000000
--- a/ml-xgboost/cub/cub/grid/grid_mapping.cuh
+++ /dev/null
@@ -1,95 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::GridMappingStrategy enumerates alternative strategies for mapping constant-sized tiles of device-wide data onto a grid of CUDA thread blocks.
- */
-
-#pragma once
-
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup GridModule
- * @{
- */
-
-
-/******************************************************************************
- * Mapping policies
- *****************************************************************************/
-
-
-/**
- * \brief cub::GridMappingStrategy enumerates alternative strategies for mapping constant-sized tiles of device-wide data onto a grid of CUDA thread blocks.
- */
-enum GridMappingStrategy
-{
-    /**
-     * \brief An "even-share" strategy for assigning input tiles to thread blocks.
-     *
-     * \par Overview
-     * The input is evenly partitioned into \p p segments, where \p p is
-     * constant and corresponds loosely to the number of thread blocks that may
-     * actively reside on the target device. Each segment is comprised of
-     * consecutive tiles, where a tile is a small, constant-sized unit of input
-     * to be processed to completion before the thread block terminates or
-     * obtains more work.  The kernel invokes \p p thread blocks, each
-     * of which iteratively consumes a segment of <em>n</em>/<em>p</em> elements
-     * in tile-size increments.
-     */
-    GRID_MAPPING_EVEN_SHARE,
-
-    /**
-     * \brief A dynamic "queue-based" strategy for assigning input tiles to thread blocks.
-     *
-     * \par Overview
-     * The input is treated as a queue to be dynamically consumed by a grid of
-     * thread blocks.  Work is atomically dequeued in tiles, where a tile is a
-     * unit of input to be processed to completion before the thread block
-     * terminates or obtains more work.  The grid size \p p is constant,
-     * loosely corresponding to the number of thread blocks that may actively
-     * reside on the target device.
-     */
-    GRID_MAPPING_DYNAMIC,
-};
-
-
-/** @} */       // end group GridModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/grid/grid_queue.cuh b/ml-xgboost/cub/cub/grid/grid_queue.cuh
deleted file mode 100644
index ff015ea..0000000
--- a/ml-xgboost/cub/cub/grid/grid_queue.cuh
+++ /dev/null
@@ -1,220 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::GridQueue is a descriptor utility for dynamic queue management.
- */
-
-#pragma once
-
-#include "../util_namespace.cuh"
-#include "../util_debug.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup GridModule
- * @{
- */
-
-
-/**
- * \brief GridQueue is a descriptor utility for dynamic queue management.
- *
- * \par Overview
- * GridQueue descriptors provides abstractions for "filling" or
- * "draining" globally-shared vectors.
- *
- * \par
- * A "filling" GridQueue works by atomically-adding to a zero-initialized counter,
- * returning a unique offset for the calling thread to write its items.
- * The GridQueue maintains the total "fill-size".  The fill counter must be reset
- * using GridQueue::ResetFill by the host or kernel instance prior to the kernel instance that
- * will be filling.
- *
- * \par
- * Similarly, a "draining" GridQueue works by works by atomically-incrementing a
- * zero-initialized counter, returning a unique offset for the calling thread to
- * read its items. Threads can safely drain until the array's logical fill-size is
- * exceeded.  The drain counter must be reset using GridQueue::ResetDrain or
- * GridQueue::FillAndResetDrain by the host or kernel instance prior to the kernel instance that
- * will be filling.  (For dynamic work distribution of existing data, the corresponding fill-size
- * is simply the number of elements in the array.)
- *
- * \par
- * Iterative work management can be implemented simply with a pair of flip-flopping
- * work buffers, each with an associated set of fill and drain GridQueue descriptors.
- *
- * \tparam OffsetT Signed integer type for global offsets
- */
-template <typename OffsetT>
-class GridQueue
-{
-private:
-
-    /// Counter indices
-    enum
-    {
-        FILL    = 0,
-        DRAIN   = 1,
-    };
-
-    /// Pair of counters
-    OffsetT *d_counters;
-
-public:
-
-    /// Returns the device allocation size in bytes needed to construct a GridQueue instance
-    __host__ __device__ __forceinline__
-    static size_t AllocationSize()
-    {
-        return sizeof(OffsetT) * 2;
-    }
-
-
-    /// Constructs an invalid GridQueue descriptor
-    __host__ __device__ __forceinline__ GridQueue()
-    :
-        d_counters(NULL)
-    {}
-
-
-    /// Constructs a GridQueue descriptor around the device storage allocation
-    __host__ __device__ __forceinline__ GridQueue(
-        void *d_storage)                    ///< Device allocation to back the GridQueue.  Must be at least as big as <tt>AllocationSize()</tt>.
-    :
-        d_counters((OffsetT*) d_storage)
-    {}
-
-
-    /// This operation sets the fill-size and resets the drain counter, preparing the GridQueue for draining in the next kernel instance.  To be called by the host or by a kernel prior to that which will be draining.
-    __host__ __device__ __forceinline__ cudaError_t FillAndResetDrain(
-        OffsetT fill_size,
-        cudaStream_t stream = 0)
-    {
-#if (CUB_PTX_ARCH > 0)
-        (void)stream;
-        d_counters[FILL] = fill_size;
-        d_counters[DRAIN] = 0;
-        return cudaSuccess;
-#else
-        OffsetT counters[2];
-        counters[FILL] = fill_size;
-        counters[DRAIN] = 0;
-        return CubDebug(cudaMemcpyAsync(d_counters, counters, sizeof(OffsetT) * 2, cudaMemcpyHostToDevice, stream));
-#endif
-    }
-
-
-    /// This operation resets the drain so that it may advance to meet the existing fill-size.  To be called by the host or by a kernel prior to that which will be draining.
-    __host__ __device__ __forceinline__ cudaError_t ResetDrain(cudaStream_t stream = 0)
-    {
-#if (CUB_PTX_ARCH > 0)
-        (void)stream;
-        d_counters[DRAIN] = 0;
-        return cudaSuccess;
-#else
-        return CubDebug(cudaMemsetAsync(d_counters + DRAIN, 0, sizeof(OffsetT), stream));
-#endif
-    }
-
-
-    /// This operation resets the fill counter.  To be called by the host or by a kernel prior to that which will be filling.
-    __host__ __device__ __forceinline__ cudaError_t ResetFill(cudaStream_t stream = 0)
-    {
-#if (CUB_PTX_ARCH > 0)
-        (void)stream;
-        d_counters[FILL] = 0;
-        return cudaSuccess;
-#else
-        return CubDebug(cudaMemsetAsync(d_counters + FILL, 0, sizeof(OffsetT), stream));
-#endif
-    }
-
-
-    /// Returns the fill-size established by the parent or by the previous kernel.
-    __host__ __device__ __forceinline__ cudaError_t FillSize(
-        OffsetT &fill_size,
-        cudaStream_t stream = 0)
-    {
-#if (CUB_PTX_ARCH > 0)
-        (void)stream;
-        fill_size = d_counters[FILL];
-        return cudaSuccess;
-#else
-        return CubDebug(cudaMemcpyAsync(&fill_size, d_counters + FILL, sizeof(OffsetT), cudaMemcpyDeviceToHost, stream));
-#endif
-    }
-
-
-    /// Drain \p num_items from the queue.  Returns offset from which to read items.  To be called from CUDA kernel.
-    __device__ __forceinline__ OffsetT Drain(OffsetT num_items)
-    {
-        return atomicAdd(d_counters + DRAIN, num_items);
-    }
-
-
-    /// Fill \p num_items into the queue.  Returns offset from which to write items.    To be called from CUDA kernel.
-    __device__ __forceinline__ OffsetT Fill(OffsetT num_items)
-    {
-        return atomicAdd(d_counters + FILL, num_items);
-    }
-};
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-/**
- * Reset grid queue (call with 1 block of 1 thread)
- */
-template <typename OffsetT>
-__global__ void FillAndResetDrainKernel(
-    GridQueue<OffsetT>   grid_queue,
-    OffsetT              num_items)
-{
-    grid_queue.FillAndResetDrain(num_items);
-}
-
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/** @} */       // end group GridModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-
diff --git a/ml-xgboost/cub/cub/host/mutex.cuh b/ml-xgboost/cub/cub/host/mutex.cuh
deleted file mode 100644
index 6a2aa6f..0000000
--- a/ml-xgboost/cub/cub/host/mutex.cuh
+++ /dev/null
@@ -1,167 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Simple portable mutex
- */
-
-
-#pragma once
-
-#if __cplusplus > 199711L
-    #include <mutex>
-#else
-    #if defined(_WIN32) || defined(_WIN64)
-        #include <intrin.h>
-        #include <windows.h>
-        #undef small            // Windows is terrible for polluting macro namespace
-
-        /**
-         * Compiler read/write barrier
-         */
-        #pragma intrinsic(_ReadWriteBarrier)
-
-    #endif
-#endif
-
-#include "../util_namespace.cuh"
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * Simple portable mutex
- *   - Wraps std::mutex when compiled with C++11 or newer (supported on all platforms)
- *   - Uses GNU/Windows spinlock mechanisms for pre C++11 (supported on x86/x64 when compiled with cl.exe or g++)
- */
-struct Mutex
-{
-#if __cplusplus > 199711L
-
-    std::mutex mtx;
-
-    void Lock()
-    {
-        mtx.lock();
-    }
-
-    void Unlock()
-    {
-        mtx.unlock();
-    }
-
-    void TryLock()
-    {
-        mtx.try_lock();
-    }
-
-#else       //__cplusplus > 199711L
-
-    #if defined(_MSC_VER)
-
-        // Microsoft VC++
-        typedef long Spinlock;
-
-    #else
-
-        // GNU g++
-        typedef int Spinlock;
-
-        /**
-         * Compiler read/write barrier
-         */
-        __forceinline__ void _ReadWriteBarrier()
-        {
-            __sync_synchronize();
-        }
-
-        /**
-         * Atomic exchange
-         */
-        __forceinline__ long _InterlockedExchange(volatile int * const Target, const int Value)
-        {
-            // NOTE: __sync_lock_test_and_set would be an acquire barrier, so we force a full barrier
-            _ReadWriteBarrier();
-            return __sync_lock_test_and_set(Target, Value);
-        }
-
-        /**
-         * Pause instruction to prevent excess processor bus usage
-         */
-        __forceinline__ void YieldProcessor()
-        {
-        }
-
-    #endif  // defined(_MSC_VER)
-
-        /// Lock member
-        volatile Spinlock lock;
-
-        /**
-         * Constructor
-         */
-        Mutex() : lock(0) {}
-
-        /**
-         * Return when the specified spinlock has been acquired
-         */
-        __forceinline__ void Lock()
-        {
-            while (1)
-            {
-                if (!_InterlockedExchange(&lock, 1)) return;
-                while (lock) YieldProcessor();
-            }
-        }
-
-
-        /**
-         * Release the specified spinlock
-         */
-        __forceinline__ void Unlock()
-        {
-            _ReadWriteBarrier();
-            lock = 0;
-        }
-
-#endif      // __cplusplus > 199711L
-
-};
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
diff --git a/ml-xgboost/cub/cub/iterator/arg_index_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/arg_index_input_iterator.cuh
deleted file mode 100644
index 4a7e852..0000000
--- a/ml-xgboost/cub/cub/iterator/arg_index_input_iterator.cuh
+++ /dev/null
@@ -1,259 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_namespace.cuh"
-
-#include <thrust/version.h>
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-/**
- * \brief A random-access input wrapper for pairing dereferenced values with their corresponding indices (forming \p KeyValuePair tuples).
- *
- * \par Overview
- * - ArgIndexInputIteratorTwraps a random access input iterator \p itr of type \p InputIteratorT.
- *   Dereferencing an ArgIndexInputIteratorTat offset \p i produces a \p KeyValuePair value whose
- *   \p key field is \p i and whose \p value field is <tt>itr[i]</tt>.
- * - Can be used with any data type.
- * - Can be constructed, manipulated, and exchanged within and between host and device
- *   functions.  Wrapped host memory can only be dereferenced on the host, and wrapped
- *   device memory can only be dereferenced on the device.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p ArgIndexInputIteratorTto
- * dereference an array of doubles
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/arg_index_input_iterator.cuh>
- *
- * // Declare, allocate, and initialize a device array
- * double *d_in;         // e.g., [8.0, 6.0, 7.0, 5.0, 3.0, 0.0, 9.0]
- *
- * // Create an iterator wrapper
- * cub::ArgIndexInputIterator<double*> itr(d_in);
- *
- * // Within device code:
- * typedef typename cub::ArgIndexInputIterator<double*>::value_type Tuple;
- * Tuple item_offset_pair.key = *itr;
- * printf("%f @ %d\n",
- *   item_offset_pair.value,
- *   item_offset_pair.key);   // 8.0 @ 0
- *
- * itr = itr + 6;
- * item_offset_pair.key = *itr;
- * printf("%f @ %d\n",
- *   item_offset_pair.value,
- *   item_offset_pair.key);   // 9.0 @ 6
- *
- * \endcode
- *
- * \tparam InputIteratorT       The value type of the wrapped input iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- * \tparam OutputValueT         The paired value type of the <offset,value> tuple (Default: value type of input iterator)
- */
-template <
-    typename    InputIteratorT,
-    typename    OffsetT             = ptrdiff_t,
-    typename    OutputValueT        = typename std::iterator_traits<InputIteratorT>::value_type>
-class ArgIndexInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef ArgIndexInputIterator                       self_type;              ///< My own type
-    typedef OffsetT                                     difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef KeyValuePair<difference_type, OutputValueT> value_type;             ///< The type of the element the iterator can point to
-    typedef value_type*                                 pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef value_type                                  reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::any_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    InputIteratorT  itr;
-    difference_type offset;
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ArgIndexInputIterator(
-        InputIteratorT  itr,            ///< Input iterator to wrap
-        difference_type offset = 0)     ///< OffsetT (in items) from \p itr denoting the position of the iterator
-    :
-        itr(itr),
-        offset(offset)
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        offset++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        offset++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-        value_type retval;
-        retval.value = itr[offset];
-        retval.key = offset;
-        return retval;
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(itr, offset + n);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        offset += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(itr, offset - n);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        offset -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return offset - other.offset;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        self_type offset = (*this) + n;
-        return *offset;
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return &(*(*this));
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return ((itr == rhs.itr) && (offset == rhs.offset));
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return ((itr != rhs.itr) || (offset != rhs.offset));
-    }
-
-    /// Normalize
-    __host__ __device__ __forceinline__ void normalize()
-    {
-        itr += offset;
-        offset = 0;
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& /*itr*/)
-    {
-        return os;
-    }
-};
-
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/cache_modified_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/cache_modified_input_iterator.cuh
deleted file mode 100644
index fb97e90..0000000
--- a/ml-xgboost/cub/cub/iterator/cache_modified_input_iterator.cuh
+++ /dev/null
@@ -1,240 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_namespace.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-/**
- * \brief A random-access input wrapper for dereferencing array values using a PTX cache load modifier.
- *
- * \par Overview
- * - CacheModifiedInputIteratorTis a random-access input iterator that wraps a native
- *   device pointer of type <tt>ValueType*</tt>. \p ValueType references are
- *   made by reading \p ValueType values through loads modified by \p MODIFIER.
- * - Can be used to load any data type from memory using PTX cache load modifiers (e.g., "LOAD_LDG",
- *   "LOAD_CG", "LOAD_CA", "LOAD_CS", "LOAD_CV", etc.).
- * - Can be constructed, manipulated, and exchanged within and between host and device
- *   functions, but can only be dereferenced within device functions.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p CacheModifiedInputIteratorTto
- * dereference a device array of double using the "ldg" PTX load modifier
- * (i.e., load values through texture cache).
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/cache_modified_input_iterator.cuh>
- *
- * // Declare, allocate, and initialize a device array
- * double *d_in;            // e.g., [8.0, 6.0, 7.0, 5.0, 3.0, 0.0, 9.0]
- *
- * // Create an iterator wrapper
- * cub::CacheModifiedInputIterator<cub::LOAD_LDG, double> itr(d_in);
- *
- * // Within device code:
- * printf("%f\n", itr[0]);  // 8.0
- * printf("%f\n", itr[1]);  // 6.0
- * printf("%f\n", itr[6]);  // 9.0
- *
- * \endcode
- *
- * \tparam CacheLoadModifier    The cub::CacheLoadModifier to use when accessing data
- * \tparam ValueType            The value type of this iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- */
-template <
-    CacheLoadModifier   MODIFIER,
-    typename            ValueType,
-    typename            OffsetT = ptrdiff_t>
-class CacheModifiedInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef CacheModifiedInputIterator          self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef ValueType                           value_type;             ///< The type of the element the iterator can point to
-    typedef ValueType*                          pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef ValueType                           reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::device_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-
-public:
-
-    /// Wrapped native pointer
-    ValueType* ptr;
-
-    /// Constructor
-    template <typename QualifiedValueType>
-    __host__ __device__ __forceinline__ CacheModifiedInputIterator(
-        QualifiedValueType* ptr)     ///< Native pointer to wrap
-    :
-        ptr(const_cast<typename RemoveQualifiers<QualifiedValueType>::Type *>(ptr))
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        ptr++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        ptr++;
-        return *this;
-    }
-
-    /// Indirection
-    __device__ __forceinline__ reference operator*() const
-    {
-        return ThreadLoad<MODIFIER>(ptr);
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(ptr + n);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        ptr += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(ptr - n);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        ptr -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return ptr - other.ptr;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        return ThreadLoad<MODIFIER>(ptr + n);
-    }
-
-    /// Structure dereference
-    __device__ __forceinline__ pointer operator->()
-    {
-        return &ThreadLoad<MODIFIER>(ptr);
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return (ptr == rhs.ptr);
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return (ptr != rhs.ptr);
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& /*itr*/)
-    {
-        return os;
-    }
-};
-
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/cache_modified_output_iterator.cuh b/ml-xgboost/cub/cub/iterator/cache_modified_output_iterator.cuh
deleted file mode 100644
index 1fb0bf6..0000000
--- a/ml-xgboost/cub/cub/iterator/cache_modified_output_iterator.cuh
+++ /dev/null
@@ -1,254 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_namespace.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-/**
- * \brief A random-access output wrapper for storing array values using a PTX cache-modifier.
- *
- * \par Overview
- * - CacheModifiedOutputIterator is a random-access output iterator that wraps a native
- *   device pointer of type <tt>ValueType*</tt>. \p ValueType references are
- *   made by writing \p ValueType values through stores modified by \p MODIFIER.
- * - Can be used to store any data type to memory using PTX cache store modifiers (e.g., "STORE_WB",
- *   "STORE_CG", "STORE_CS", "STORE_WT", etc.).
- * - Can be constructed, manipulated, and exchanged within and between host and device
- *   functions, but can only be dereferenced within device functions.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p CacheModifiedOutputIterator to
- * dereference a device array of doubles using the "wt" PTX load modifier
- * (i.e., write-through to system memory).
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/cache_modified_output_iterator.cuh>
- *
- * // Declare, allocate, and initialize a device array
- * double *d_out;              // e.g., [, , , , , , ]
- *
- * // Create an iterator wrapper
- * cub::CacheModifiedOutputIterator<cub::STORE_WT, double> itr(d_out);
- *
- * // Within device code:
- * itr[0]  = 8.0;
- * itr[1]  = 66.0;
- * itr[55] = 24.0;
- *
- * \endcode
- *
- * \par Usage Considerations
- * - Can only be dereferenced within device code
- *
- * \tparam CacheStoreModifier     The cub::CacheStoreModifier to use when accessing data
- * \tparam ValueType            The value type of this iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- */
-template <
-    CacheStoreModifier  MODIFIER,
-    typename            ValueType,
-    typename            OffsetT = ptrdiff_t>
-class CacheModifiedOutputIterator
-{
-private:
-
-    // Proxy object
-    struct Reference
-    {
-        ValueType* ptr;
-
-        /// Constructor
-        __host__ __device__ __forceinline__ Reference(ValueType* ptr) : ptr(ptr) {}
-
-        /// Assignment
-        __device__ __forceinline__ ValueType operator =(ValueType val)
-        {
-            ThreadStore<MODIFIER>(ptr, val);
-            return val;
-        }
-    };
-
-public:
-
-    // Required iterator traits
-    typedef CacheModifiedOutputIterator         self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef void                                value_type;             ///< The type of the element the iterator can point to
-    typedef void                                pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef Reference                           reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::device_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    ValueType* ptr;
-
-public:
-
-    /// Constructor
-    template <typename QualifiedValueType>
-    __host__ __device__ __forceinline__ CacheModifiedOutputIterator(
-        QualifiedValueType* ptr)     ///< Native pointer to wrap
-    :
-        ptr(const_cast<typename RemoveQualifiers<QualifiedValueType>::Type *>(ptr))
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        ptr++;
-        return retval;
-    }
-
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        ptr++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-        return Reference(ptr);
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(ptr + n);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        ptr += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(ptr - n);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        ptr -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return ptr - other.ptr;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        return Reference(ptr + n);
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return (ptr == rhs.ptr);
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return (ptr != rhs.ptr);
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        return os;
-    }
-};
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/constant_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/constant_input_iterator.cuh
deleted file mode 100644
index e205c43..0000000
--- a/ml-xgboost/cub/cub/iterator/constant_input_iterator.cuh
+++ /dev/null
@@ -1,235 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_namespace.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-/**
- * \brief A random-access input generator for dereferencing a sequence of homogeneous values
- *
- * \par Overview
- * - Read references to a ConstantInputIteratorTiterator always return the supplied constant
- *   of type \p ValueType.
- * - Can be used with any data type.
- * - Can be constructed, manipulated, dereferenced, and exchanged within and between host and device
- *   functions.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p ConstantInputIteratorTto
- * dereference a sequence of homogeneous doubles.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/constant_input_iterator.cuh>
- *
- * cub::ConstantInputIterator<double> itr(5.0);
- *
- * printf("%f\n", itr[0]);      // 5.0
- * printf("%f\n", itr[1]);      // 5.0
- * printf("%f\n", itr[2]);      // 5.0
- * printf("%f\n", itr[50]);     // 5.0
- *
- * \endcode
- *
- * \tparam ValueType            The value type of this iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- */
-template <
-    typename ValueType,
-    typename OffsetT = ptrdiff_t>
-class ConstantInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef ConstantInputIterator               self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef ValueType                           value_type;             ///< The type of the element the iterator can point to
-    typedef ValueType*                          pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef ValueType                           reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::any_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    ValueType   val;
-    OffsetT     offset;
-#ifdef _WIN32
-    OffsetT     pad[CUB_MAX(1, (16 / sizeof(OffsetT) - 1))];        // Workaround for win32 parameter-passing bug (ulonglong2 argmin DeviceReduce)
-#endif
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ConstantInputIterator(
-        ValueType   val,            ///< Starting value for the iterator instance to report
-        OffsetT     offset = 0)     ///< Base offset
-    :
-        val(val),
-        offset(offset)
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        offset++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        offset++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-        return val;
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(val, offset + n);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        offset += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(val, offset - n);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        offset -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return offset - other.offset;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance /*n*/) const
-    {
-        return val;
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return &val;
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return (offset == rhs.offset) && ((val == rhs.val));
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return (offset != rhs.offset) || (val!= rhs.val);
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        os << "[" << itr.val << "," << itr.offset << "]";
-        return os;
-    }
-
-};
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/counting_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/counting_input_iterator.cuh
deleted file mode 100644
index 224b664..0000000
--- a/ml-xgboost/cub/cub/iterator/counting_input_iterator.cuh
+++ /dev/null
@@ -1,228 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_namespace.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-/**
- * \brief A random-access input generator for dereferencing a sequence of incrementing integer values.
- *
- * \par Overview
- * - After initializing a CountingInputIteratorTto a certain integer \p base, read references
- *   at \p offset will return the value \p base + \p offset.
- * - Can be constructed, manipulated, dereferenced, and exchanged within and between host and device
- *   functions.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p CountingInputIteratorTto
- * dereference a sequence of incrementing integers.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/counting_input_iterator.cuh>
- *
- * cub::CountingInputIterator<int> itr(5);
- *
- * printf("%d\n", itr[0]);      // 5
- * printf("%d\n", itr[1]);      // 6
- * printf("%d\n", itr[2]);      // 7
- * printf("%d\n", itr[50]);     // 55
- *
- * \endcode
- *
- * \tparam ValueType            The value type of this iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- */
-template <
-    typename ValueType,
-    typename OffsetT = ptrdiff_t>
-class CountingInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef CountingInputIterator               self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef ValueType                           value_type;             ///< The type of the element the iterator can point to
-    typedef ValueType*                          pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef ValueType                           reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::any_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    ValueType val;
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__ CountingInputIterator(
-        const ValueType &val)          ///< Starting value for the iterator instance to report
-    :
-        val(val)
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        val++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        val++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-        return val;
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(val + (ValueType) n);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        val += (ValueType) n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(val - (ValueType) n);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        val -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return (difference_type) (val - other.val);
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        return val + (ValueType) n;
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return &val;
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return (val == rhs.val);
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return (val != rhs.val);
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        os << "[" << itr.val << "]";
-        return os;
-    }
-
-};
-
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/discard_output_iterator.cuh b/ml-xgboost/cub/cub/iterator/discard_output_iterator.cuh
deleted file mode 100644
index 9603a32..0000000
--- a/ml-xgboost/cub/cub/iterator/discard_output_iterator.cuh
+++ /dev/null
@@ -1,222 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include <thrust/iterator/discard_iterator.h>
-
-#include "../util_namespace.cuh"
-#include "../util_macro.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-/**
- * \brief A discard iterator
- */
-template <typename OffsetT = ptrdiff_t>
-class DiscardOutputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef DiscardOutputIterator   self_type;              ///< My own type
-    typedef OffsetT                 difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef void                    value_type;             ///< The type of the element the iterator can point to
-    typedef void                    pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef void                    reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::any_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    OffsetT offset;
-
-#if defined(_WIN32) || !defined(_WIN64)
-    // Workaround for win32 parameter-passing bug (ulonglong2 argmin DeviceReduce)
-    OffsetT pad[CUB_MAX(1, (16 / sizeof(OffsetT) - 1))];
-#endif
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__ DiscardOutputIterator(
-        OffsetT offset = 0)     ///< Base offset
-    :
-        offset(offset)
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        offset++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        offset++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ self_type& operator*()
-    {
-        // return self reference, which can be assigned to anything
-        return *this;
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(offset + n);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        offset += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(offset - n);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        offset -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return offset - other.offset;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator[](Distance n)
-    {
-        // return self reference, which can be assigned to anything
-        return *this;
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return;
-    }
-
-    /// Assignment to self (no-op)
-    __host__ __device__ __forceinline__ void operator=(self_type const& other)
-    {
-        offset = other.offset;
-    }
-
-    /// Assignment to anything else (no-op)
-    template<typename T>
-    __host__ __device__ __forceinline__ void operator=(T const&)
-    {}
-
-    /// Cast to void* operator
-    __host__ __device__ __forceinline__ operator void*() const { return NULL; }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return (offset == rhs.offset);
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return (offset != rhs.offset);
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        os << "[" << itr.offset << "]";
-        return os;
-    }
-
-};
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/tex_obj_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/tex_obj_input_iterator.cuh
deleted file mode 100644
index 7ae72ad..0000000
--- a/ml-xgboost/cub/cub/iterator/tex_obj_input_iterator.cuh
+++ /dev/null
@@ -1,310 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_debug.cuh"
-#include "../util_namespace.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-
-/**
- * \brief A random-access input wrapper for dereferencing array values through texture cache.  Uses newer Kepler-style texture objects.
- *
- * \par Overview
- * - TexObjInputIteratorTwraps a native device pointer of type <tt>ValueType*</tt>. References
- *   to elements are to be loaded through texture cache.
- * - Can be used to load any data type from memory through texture cache.
- * - Can be manipulated and exchanged within and between host and device
- *   functions, can only be constructed within host functions, and can only be
- *   dereferenced within device functions.
- * - With regard to nested/dynamic parallelism, TexObjInputIteratorTiterators may only be
- *   created by the host thread, but can be used by any descendant kernel.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p TexRefInputIteratorTto
- * dereference a device array of doubles through texture cache.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/tex_obj_input_iterator.cuh>
- *
- * // Declare, allocate, and initialize a device array
- * int num_items;   // e.g., 7
- * double *d_in;    // e.g., [8.0, 6.0, 7.0, 5.0, 3.0, 0.0, 9.0]
- *
- * // Create an iterator wrapper
- * cub::TexObjInputIterator<double> itr;
- * itr.BindTexture(d_in, sizeof(double) * num_items);
- * ...
- *
- * // Within device code:
- * printf("%f\n", itr[0]);      // 8.0
- * printf("%f\n", itr[1]);      // 6.0
- * printf("%f\n", itr[6]);      // 9.0
- *
- * ...
- * itr.UnbindTexture();
- *
- * \endcode
- *
- * \tparam T                    The value type of this iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- */
-template <
-    typename    T,
-    typename    OffsetT = ptrdiff_t>
-class TexObjInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef TexObjInputIterator                 self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef T                                   value_type;             ///< The type of the element the iterator can point to
-    typedef T*                                  pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef T                                   reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::device_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    // Largest texture word we can use in device
-    typedef typename UnitWord<T>::TextureWord TextureWord;
-
-    // Number of texture words per T
-    enum {
-        TEXTURE_MULTIPLE = sizeof(T) / sizeof(TextureWord)
-    };
-
-private:
-
-    T*                  ptr;
-    difference_type     tex_offset;
-    cudaTextureObject_t tex_obj;
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__ TexObjInputIterator()
-    :
-        ptr(NULL),
-        tex_offset(0),
-        tex_obj(0)
-    {}
-
-    /// Use this iterator to bind \p ptr with a texture reference
-    template <typename QualifiedT>
-    cudaError_t BindTexture(
-        QualifiedT      *ptr,               ///< Native pointer to wrap that is aligned to cudaDeviceProp::textureAlignment
-        size_t          bytes = size_t(-1),         ///< Number of bytes in the range
-        size_t          tex_offset = 0)     ///< OffsetT (in items) from \p ptr denoting the position of the iterator
-    {
-        this->ptr = const_cast<typename RemoveQualifiers<QualifiedT>::Type *>(ptr);
-        this->tex_offset = tex_offset;
-
-        cudaChannelFormatDesc   channel_desc = cudaCreateChannelDesc<TextureWord>();
-        cudaResourceDesc        res_desc;
-        cudaTextureDesc         tex_desc;
-        memset(&res_desc, 0, sizeof(cudaResourceDesc));
-        memset(&tex_desc, 0, sizeof(cudaTextureDesc));
-        res_desc.resType                = cudaResourceTypeLinear;
-        res_desc.res.linear.devPtr      = this->ptr;
-        res_desc.res.linear.desc        = channel_desc;
-        res_desc.res.linear.sizeInBytes = bytes;
-        tex_desc.readMode               = cudaReadModeElementType;
-        return cudaCreateTextureObject(&tex_obj, &res_desc, &tex_desc, NULL);
-    }
-
-    /// Unbind this iterator from its texture reference
-    cudaError_t UnbindTexture()
-    {
-        return cudaDestroyTextureObject(tex_obj);
-    }
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        tex_offset++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        tex_offset++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-#if (CUB_PTX_ARCH == 0)
-        // Simply dereference the pointer on the host
-        return ptr[tex_offset];
-#else
-        // Move array of uninitialized words, then alias and assign to return value
-        TextureWord words[TEXTURE_MULTIPLE];
-
-        #pragma unroll
-        for (int i = 0; i < TEXTURE_MULTIPLE; ++i)
-        {
-            words[i] = tex1Dfetch<TextureWord>(
-                tex_obj,
-                (tex_offset * TEXTURE_MULTIPLE) + i);
-        }
-
-        // Load from words
-        return *reinterpret_cast<T*>(words);
-#endif
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval;
-        retval.ptr          = ptr;
-        retval.tex_obj      = tex_obj;
-        retval.tex_offset   = tex_offset + n;
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        tex_offset += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval;
-        retval.ptr          = ptr;
-        retval.tex_obj      = tex_obj;
-        retval.tex_offset   = tex_offset - n;
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        tex_offset -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return tex_offset - other.tex_offset;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        self_type offset = (*this) + n;
-        return *offset;
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return &(*(*this));
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return ((ptr == rhs.ptr) && (tex_offset == rhs.tex_offset) && (tex_obj == rhs.tex_obj));
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return ((ptr != rhs.ptr) || (tex_offset != rhs.tex_offset) || (tex_obj != rhs.tex_obj));
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        return os;
-    }
-
-};
-
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/iterator/tex_ref_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/tex_ref_input_iterator.cuh
deleted file mode 100644
index f0fca2c..0000000
--- a/ml-xgboost/cub/cub/iterator/tex_ref_input_iterator.cuh
+++ /dev/null
@@ -1,374 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_debug.cuh"
-#include "../util_namespace.cuh"
-
-#if (CUDA_VERSION >= 5050) || defined(DOXYGEN_ACTIVE)  // This iterator is compatible with CUDA 5.5 and newer
-
-#if (THRUST_VERSION >= 100700)    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/******************************************************************************
- * Static file-scope Tesla/Fermi-style texture references
- *****************************************************************************/
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-// Anonymous namespace
-namespace {
-
-/// Global texture reference specialized by type
-template <typename T>
-struct IteratorTexRef
-{
-    /// And by unique ID
-    template <int UNIQUE_ID>
-    struct TexId
-    {
-        // Largest texture word we can use in device
-        typedef typename UnitWord<T>::DeviceWord DeviceWord;
-        typedef typename UnitWord<T>::TextureWord TextureWord;
-
-        // Number of texture words per T
-        enum {
-            DEVICE_MULTIPLE = sizeof(T) / sizeof(DeviceWord),
-            TEXTURE_MULTIPLE = sizeof(T) / sizeof(TextureWord)
-        };
-
-        // Texture reference type
-        typedef texture<TextureWord> TexRef;
-
-        // Texture reference
-        static TexRef ref;
-
-        /// Bind texture
-        static cudaError_t BindTexture(void *d_in, size_t &offset)
-        {
-            if (d_in)
-            {
-                cudaChannelFormatDesc tex_desc = cudaCreateChannelDesc<TextureWord>();
-                ref.channelDesc = tex_desc;
-                return (CubDebug(cudaBindTexture(&offset, ref, d_in)));
-            }
-
-            return cudaSuccess;
-        }
-
-        /// Unbind texture
-        static cudaError_t UnbindTexture()
-        {
-            return CubDebug(cudaUnbindTexture(ref));
-        }
-
-        /// Fetch element
-        template <typename Distance>
-        static __device__ __forceinline__ T Fetch(Distance tex_offset)
-        {
-            DeviceWord temp[DEVICE_MULTIPLE];
-            TextureWord *words = reinterpret_cast<TextureWord*>(temp);
-
-            #pragma unroll
-            for (int i = 0; i < TEXTURE_MULTIPLE; ++i)
-            {
-                words[i] = tex1Dfetch(ref, (tex_offset * TEXTURE_MULTIPLE) + i);
-            }
-
-            return reinterpret_cast<T&>(temp);
-        }
-    };
-};
-
-// Texture reference definitions
-template <typename  T>
-template <int       UNIQUE_ID>
-typename IteratorTexRef<T>::template TexId<UNIQUE_ID>::TexRef IteratorTexRef<T>::template TexId<UNIQUE_ID>::ref = 0;
-
-
-} // Anonymous namespace
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-
-/**
- * \brief A random-access input wrapper for dereferencing array values through texture cache.  Uses older Tesla/Fermi-style texture references.
- *
- * \par Overview
- * - TexRefInputIteratorTwraps a native device pointer of type <tt>ValueType*</tt>. References
- *   to elements are to be loaded through texture cache.
- * - Can be used to load any data type from memory through texture cache.
- * - Can be manipulated and exchanged within and between host and device
- *   functions, can only be constructed within host functions, and can only be
- *   dereferenced within device functions.
- * - The \p UNIQUE_ID template parameter is used to statically name the underlying texture
- *   reference.  Only one TexRefInputIteratorTinstance can be bound at any given time for a
- *   specific combination of (1) data type \p T, (2) \p UNIQUE_ID, (3) host
- *   thread, and (4) compilation .o unit.
- * - With regard to nested/dynamic parallelism, TexRefInputIteratorTiterators may only be
- *   created by the host thread and used by a top-level kernel (i.e. the one which is launched
- *   from the host).
- * - Compatible with Thrust API v1.7 or newer.
- * - Compatible with CUDA toolkit v5.5 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p TexRefInputIteratorTto
- * dereference a device array of doubles through texture cache.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/tex_ref_input_iterator.cuh>
- *
- * // Declare, allocate, and initialize a device array
- * int num_items;   // e.g., 7
- * double *d_in;    // e.g., [8.0, 6.0, 7.0, 5.0, 3.0, 0.0, 9.0]
- *
- * // Create an iterator wrapper
- * cub::TexRefInputIterator<double, __LINE__> itr;
- * itr.BindTexture(d_in, sizeof(double) * num_items);
- * ...
- *
- * // Within device code:
- * printf("%f\n", itr[0]);      // 8.0
- * printf("%f\n", itr[1]);      // 6.0
- * printf("%f\n", itr[6]);      // 9.0
- *
- * ...
- * itr.UnbindTexture();
- *
- * \endcode
- *
- * \tparam T                    The value type of this iterator
- * \tparam UNIQUE_ID            A globally-unique identifier (within the compilation unit) to name the underlying texture reference
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- */
-template <
-    typename    T,
-    int         UNIQUE_ID,
-    typename    OffsetT = ptrdiff_t>
-class TexRefInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef TexRefInputIterator                 self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef T                                   value_type;             ///< The type of the element the iterator can point to
-    typedef T*                                  pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef T                                   reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::device_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    T*              ptr;
-    difference_type tex_offset;
-
-    // Texture reference wrapper (old Tesla/Fermi-style textures)
-    typedef typename IteratorTexRef<T>::template TexId<UNIQUE_ID> TexId;
-
-public:
-/*
-    /// Constructor
-    __host__ __device__ __forceinline__ TexRefInputIterator()
-    :
-        ptr(NULL),
-        tex_offset(0)
-    {}
-*/
-    /// Use this iterator to bind \p ptr with a texture reference
-    template <typename QualifiedT>
-    cudaError_t BindTexture(
-        QualifiedT      *ptr,                   ///< Native pointer to wrap that is aligned to cudaDeviceProp::textureAlignment
-        size_t          bytes = size_t(-1),     ///< Number of bytes in the range
-        size_t          tex_offset = 0)         ///< OffsetT (in items) from \p ptr denoting the position of the iterator
-    {
-        this->ptr = const_cast<typename RemoveQualifiers<QualifiedT>::Type *>(ptr);
-        size_t offset;
-        cudaError_t retval = TexId::BindTexture(this->ptr + tex_offset, offset);
-        this->tex_offset = (difference_type) (offset / sizeof(QualifiedT));
-        return retval;
-    }
-
-    /// Unbind this iterator from its texture reference
-    cudaError_t UnbindTexture()
-    {
-        return TexId::UnbindTexture();
-    }
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        tex_offset++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        tex_offset++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-#if (CUB_PTX_ARCH == 0)
-        // Simply dereference the pointer on the host
-        return ptr[tex_offset];
-#else
-        // Use the texture reference
-        return TexId::Fetch(tex_offset);
-#endif
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval;
-        retval.ptr = ptr;
-        retval.tex_offset = tex_offset + n;
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        tex_offset += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval;
-        retval.ptr = ptr;
-        retval.tex_offset = tex_offset - n;
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        tex_offset -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return tex_offset - other.tex_offset;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        self_type offset = (*this) + n;
-        return *offset;
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return &(*(*this));
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return ((ptr == rhs.ptr) && (tex_offset == rhs.tex_offset));
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return ((ptr != rhs.ptr) || (tex_offset != rhs.tex_offset));
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        return os;
-    }
-
-};
-
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
-
-#endif // CUDA_VERSION
diff --git a/ml-xgboost/cub/cub/iterator/transform_input_iterator.cuh b/ml-xgboost/cub/cub/iterator/transform_input_iterator.cuh
deleted file mode 100644
index bf961f7..0000000
--- a/ml-xgboost/cub/cub/iterator/transform_input_iterator.cuh
+++ /dev/null
@@ -1,252 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Random-access iterator types
- */
-
-#pragma once
-
-#include <iterator>
-#include <iostream>
-
-#include "../thread/thread_load.cuh"
-#include "../thread/thread_store.cuh"
-#include "../util_device.cuh"
-#include "../util_namespace.cuh"
-
-#if (THRUST_VERSION >= 100700)
-    // This iterator is compatible with Thrust API 1.7 and newer
-    #include <thrust/iterator/iterator_facade.h>
-    #include <thrust/iterator/iterator_traits.h>
-#endif // THRUST_VERSION
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIterator
- * @{
- */
-
-
-/**
- * \brief A random-access input wrapper for transforming dereferenced values.
- *
- * \par Overview
- * - TransformInputIteratorTwraps a unary conversion functor of type \p
- *   ConversionOp and a random-access input iterator of type <tt>InputIteratorT</tt>,
- *   using the former to produce references of type \p ValueType from the latter.
- * - Can be used with any data type.
- * - Can be constructed, manipulated, and exchanged within and between host and device
- *   functions.  Wrapped host memory can only be dereferenced on the host, and wrapped
- *   device memory can only be dereferenced on the device.
- * - Compatible with Thrust API v1.7 or newer.
- *
- * \par Snippet
- * The code snippet below illustrates the use of \p TransformInputIteratorTto
- * dereference an array of integers, tripling the values and converting them to doubles.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/iterator/transform_input_iterator.cuh>
- *
- * // Functor for tripling integer values and converting to doubles
- * struct TripleDoubler
- * {
- *     __host__ __device__ __forceinline__
- *     double operator()(const int &a) const {
- *         return double(a * 2);
- *     }
- * };
- *
- * // Declare, allocate, and initialize a device array
- * int *d_in;                   // e.g., [8, 6, 7, 5, 3, 0, 9]
- * TripleDoubler conversion_op;
- *
- * // Create an iterator wrapper
- * cub::TransformInputIterator<double, TripleDoubler, int*> itr(d_in, conversion_op);
- *
- * // Within device code:
- * printf("%f\n", itr[0]);  // 24.0
- * printf("%f\n", itr[1]);  // 18.0
- * printf("%f\n", itr[6]);  // 27.0
- *
- * \endcode
- *
- * \tparam ValueType            The value type of this iterator
- * \tparam ConversionOp         Unary functor type for mapping objects of type \p InputType to type \p ValueType.  Must have member <tt>ValueType operator()(const InputType &datum)</tt>.
- * \tparam InputIteratorT       The type of the wrapped input iterator
- * \tparam OffsetT              The difference type of this iterator (Default: \p ptrdiff_t)
- *
- */
-template <
-    typename ValueType,
-    typename ConversionOp,
-    typename InputIteratorT,
-    typename OffsetT = ptrdiff_t>
-class TransformInputIterator
-{
-public:
-
-    // Required iterator traits
-    typedef TransformInputIterator              self_type;              ///< My own type
-    typedef OffsetT                             difference_type;        ///< Type to express the result of subtracting one iterator from another
-    typedef ValueType                           value_type;             ///< The type of the element the iterator can point to
-    typedef ValueType*                          pointer;                ///< The type of a pointer to an element the iterator can point to
-    typedef ValueType                           reference;              ///< The type of a reference to an element the iterator can point to
-
-#if (THRUST_VERSION >= 100700)
-    // Use Thrust's iterator categories so we can use these iterators in Thrust 1.7 (or newer) methods
-    typedef typename thrust::detail::iterator_facade_category<
-        thrust::any_system_tag,
-        thrust::random_access_traversal_tag,
-        value_type,
-        reference
-      >::type iterator_category;                                        ///< The iterator category
-#else
-    typedef std::random_access_iterator_tag     iterator_category;      ///< The iterator category
-#endif  // THRUST_VERSION
-
-private:
-
-    ConversionOp    conversion_op;
-    InputIteratorT  input_itr;
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__ TransformInputIterator(
-        InputIteratorT      input_itr,          ///< Input iterator to wrap
-        ConversionOp        conversion_op)      ///< Conversion functor to wrap
-    :
-        conversion_op(conversion_op),
-        input_itr(input_itr)
-    {}
-
-    /// Postfix increment
-    __host__ __device__ __forceinline__ self_type operator++(int)
-    {
-        self_type retval = *this;
-        input_itr++;
-        return retval;
-    }
-
-    /// Prefix increment
-    __host__ __device__ __forceinline__ self_type operator++()
-    {
-        input_itr++;
-        return *this;
-    }
-
-    /// Indirection
-    __host__ __device__ __forceinline__ reference operator*() const
-    {
-        return conversion_op(*input_itr);
-    }
-
-    /// Addition
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator+(Distance n) const
-    {
-        self_type retval(input_itr + n, conversion_op);
-        return retval;
-    }
-
-    /// Addition assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator+=(Distance n)
-    {
-        input_itr += n;
-        return *this;
-    }
-
-    /// Subtraction
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type operator-(Distance n) const
-    {
-        self_type retval(input_itr - n, conversion_op);
-        return retval;
-    }
-
-    /// Subtraction assignment
-    template <typename Distance>
-    __host__ __device__ __forceinline__ self_type& operator-=(Distance n)
-    {
-        input_itr -= n;
-        return *this;
-    }
-
-    /// Distance
-    __host__ __device__ __forceinline__ difference_type operator-(self_type other) const
-    {
-        return input_itr - other.input_itr;
-    }
-
-    /// Array subscript
-    template <typename Distance>
-    __host__ __device__ __forceinline__ reference operator[](Distance n) const
-    {
-        return conversion_op(input_itr[n]);
-    }
-
-    /// Structure dereference
-    __host__ __device__ __forceinline__ pointer operator->()
-    {
-        return &conversion_op(*input_itr);
-    }
-
-    /// Equal to
-    __host__ __device__ __forceinline__ bool operator==(const self_type& rhs)
-    {
-        return (input_itr == rhs.input_itr);
-    }
-
-    /// Not equal to
-    __host__ __device__ __forceinline__ bool operator!=(const self_type& rhs)
-    {
-        return (input_itr != rhs.input_itr);
-    }
-
-    /// ostream operator
-    friend std::ostream& operator<<(std::ostream& os, const self_type& itr)
-    {
-        return os;
-    }
-};
-
-
-
-/** @} */       // end group UtilIterator
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/thread/thread_load.cuh b/ml-xgboost/cub/cub/thread/thread_load.cuh
deleted file mode 100644
index 6921a05..0000000
--- a/ml-xgboost/cub/cub/thread/thread_load.cuh
+++ /dev/null
@@ -1,438 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Thread utilities for reading memory using PTX cache modifiers.
- */
-
-#pragma once
-
-#include <cuda.h>
-
-#include <iterator>
-
-#include "../util_ptx.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIo
- * @{
- */
-
-//-----------------------------------------------------------------------------
-// Tags and constants
-//-----------------------------------------------------------------------------
-
-/**
- * \brief Enumeration of cache modifiers for memory load operations.
- */
-enum CacheLoadModifier
-{
-    LOAD_DEFAULT,       ///< Default (no modifier)
-    LOAD_CA,            ///< Cache at all levels
-    LOAD_CG,            ///< Cache at global level
-    LOAD_CS,            ///< Cache streaming (likely to be accessed once)
-    LOAD_CV,            ///< Cache as volatile (including cached system lines)
-    LOAD_LDG,           ///< Cache as texture
-    LOAD_VOLATILE,      ///< Volatile (any memory space)
-};
-
-
-/**
- * \name Thread I/O (cache modified)
- * @{
- */
-
-/**
- * \brief Thread utility for reading memory using cub::CacheLoadModifier cache modifiers.  Can be used to load any data type.
- *
- * \par Example
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/thread/thread_load.cuh>
- *
- * // 32-bit load using cache-global modifier:
- * int *d_in;
- * int val = cub::ThreadLoad<cub::LOAD_CA>(d_in + threadIdx.x);
- *
- * // 16-bit load using default modifier
- * short *d_in;
- * short val = cub::ThreadLoad<cub::LOAD_DEFAULT>(d_in + threadIdx.x);
- *
- * // 256-bit load using cache-volatile modifier
- * double4 *d_in;
- * double4 val = cub::ThreadLoad<cub::LOAD_CV>(d_in + threadIdx.x);
- *
- * // 96-bit load using cache-streaming modifier
- * struct TestFoo { bool a; short b; };
- * TestFoo *d_struct;
- * TestFoo val = cub::ThreadLoad<cub::LOAD_CS>(d_in + threadIdx.x);
- * \endcode
- *
- * \tparam MODIFIER             <b>[inferred]</b> CacheLoadModifier enumeration
- * \tparam InputIteratorT       <b>[inferred]</b> Input iterator type \iterator
- */
-template <
-    CacheLoadModifier MODIFIER,
-    typename InputIteratorT>
-__device__ __forceinline__ typename std::iterator_traits<InputIteratorT>::value_type ThreadLoad(InputIteratorT itr);
-
-
-//@}  end member group
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-/// Helper structure for templated load iteration (inductive case)
-template <int COUNT, int MAX>
-struct IterateThreadLoad
-{
-    template <CacheLoadModifier MODIFIER, typename T>
-    static __device__ __forceinline__ void Load(T const *ptr, T *vals)
-    {
-        vals[COUNT] = ThreadLoad<MODIFIER>(ptr + COUNT);
-        IterateThreadLoad<COUNT + 1, MAX>::template Load<MODIFIER>(ptr, vals);
-    }
-
-    template <typename InputIteratorT, typename T>
-    static __device__ __forceinline__ void Dereference(InputIteratorT itr, T *vals)
-    {
-        vals[COUNT] = itr[COUNT];
-        IterateThreadLoad<COUNT + 1, MAX>::Dereference(itr, vals);
-    }
-};
-
-
-/// Helper structure for templated load iteration (termination case)
-template <int MAX>
-struct IterateThreadLoad<MAX, MAX>
-{
-    template <CacheLoadModifier MODIFIER, typename T>
-    static __device__ __forceinline__ void Load(T const * /*ptr*/, T * /*vals*/) {}
-
-    template <typename InputIteratorT, typename T>
-    static __device__ __forceinline__ void Dereference(InputIteratorT /*itr*/, T * /*vals*/) {}
-};
-
-
-/**
- * Define a uint4 (16B) ThreadLoad specialization for the given Cache load modifier
- */
-#define _CUB_LOAD_16(cub_modifier, ptx_modifier)                                             \
-    template<>                                                                              \
-    __device__ __forceinline__ uint4 ThreadLoad<cub_modifier, uint4 const *>(uint4 const *ptr)                   \
-    {                                                                                       \
-        uint4 retval;                                                                       \
-        asm volatile ("ld."#ptx_modifier".v4.u32 {%0, %1, %2, %3}, [%4];" :                 \
-            "=r"(retval.x),                                                                 \
-            "=r"(retval.y),                                                                 \
-            "=r"(retval.z),                                                                 \
-            "=r"(retval.w) :                                                                \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }                                                                                       \
-    template<>                                                                              \
-    __device__ __forceinline__ ulonglong2 ThreadLoad<cub_modifier, ulonglong2 const *>(ulonglong2 const *ptr)    \
-    {                                                                                       \
-        ulonglong2 retval;                                                                  \
-        asm volatile ("ld."#ptx_modifier".v2.u64 {%0, %1}, [%2];" :                         \
-            "=l"(retval.x),                                                                 \
-            "=l"(retval.y) :                                                                \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }
-
-/**
- * Define a uint2 (8B) ThreadLoad specialization for the given Cache load modifier
- */
-#define _CUB_LOAD_8(cub_modifier, ptx_modifier)                                              \
-    template<>                                                                              \
-    __device__ __forceinline__ ushort4 ThreadLoad<cub_modifier, ushort4 const *>(ushort4 const *ptr)             \
-    {                                                                                       \
-        ushort4 retval;                                                                     \
-        asm volatile ("ld."#ptx_modifier".v4.u16 {%0, %1, %2, %3}, [%4];" :                 \
-            "=h"(retval.x),                                                                 \
-            "=h"(retval.y),                                                                 \
-            "=h"(retval.z),                                                                 \
-            "=h"(retval.w) :                                                                \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }                                                                                       \
-    template<>                                                                              \
-    __device__ __forceinline__ uint2 ThreadLoad<cub_modifier, uint2 const *>(uint2 const *ptr)                   \
-    {                                                                                       \
-        uint2 retval;                                                                       \
-        asm volatile ("ld."#ptx_modifier".v2.u32 {%0, %1}, [%2];" :                         \
-            "=r"(retval.x),                                                                 \
-            "=r"(retval.y) :                                                                \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }                                                                                       \
-    template<>                                                                              \
-    __device__ __forceinline__ unsigned long long ThreadLoad<cub_modifier, unsigned long long const *>(unsigned long long const *ptr)    \
-    {                                                                                       \
-        unsigned long long retval;                                                          \
-        asm volatile ("ld."#ptx_modifier".u64 %0, [%1];" :                                  \
-            "=l"(retval) :                                                                  \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }
-
-/**
- * Define a uint (4B) ThreadLoad specialization for the given Cache load modifier
- */
-#define _CUB_LOAD_4(cub_modifier, ptx_modifier)                                              \
-    template<>                                                                              \
-    __device__ __forceinline__ unsigned int ThreadLoad<cub_modifier, unsigned int const *>(unsigned int const *ptr)                      \
-    {                                                                                       \
-        unsigned int retval;                                                                \
-        asm volatile ("ld."#ptx_modifier".u32 %0, [%1];" :                                  \
-            "=r"(retval) :                                                                  \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }
-
-
-/**
- * Define a unsigned short (2B) ThreadLoad specialization for the given Cache load modifier
- */
-#define _CUB_LOAD_2(cub_modifier, ptx_modifier)                                              \
-    template<>                                                                              \
-    __device__ __forceinline__ unsigned short ThreadLoad<cub_modifier, unsigned short const *>(unsigned short const *ptr)                \
-    {                                                                                       \
-        unsigned short retval;                                                              \
-        asm volatile ("ld."#ptx_modifier".u16 %0, [%1];" :                                  \
-            "=h"(retval) :                                                                  \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return retval;                                                                      \
-    }
-
-
-/**
- * Define an unsigned char (1B) ThreadLoad specialization for the given Cache load modifier
- */
-#define _CUB_LOAD_1(cub_modifier, ptx_modifier)                                              \
-    template<>                                                                              \
-    __device__ __forceinline__ unsigned char ThreadLoad<cub_modifier, unsigned char const *>(unsigned char const *ptr)                   \
-    {                                                                                       \
-        unsigned short retval;                                                              \
-        asm volatile (                                                                      \
-        "{"                                                                                 \
-        "   .reg .u8 datum;"                                                                \
-        "    ld."#ptx_modifier".u8 datum, [%1];"                                            \
-        "    cvt.u16.u8 %0, datum;"                                                         \
-        "}" :                                                                               \
-            "=h"(retval) :                                                                  \
-            _CUB_ASM_PTR_(ptr));                                                            \
-        return (unsigned char) retval;                                                      \
-    }
-
-
-/**
- * Define powers-of-two ThreadLoad specializations for the given Cache load modifier
- */
-#define _CUB_LOAD_ALL(cub_modifier, ptx_modifier)                                            \
-    _CUB_LOAD_16(cub_modifier, ptx_modifier)                                                 \
-    _CUB_LOAD_8(cub_modifier, ptx_modifier)                                                  \
-    _CUB_LOAD_4(cub_modifier, ptx_modifier)                                                  \
-    _CUB_LOAD_2(cub_modifier, ptx_modifier)                                                  \
-    _CUB_LOAD_1(cub_modifier, ptx_modifier)                                                  \
-
-
-/**
- * Define powers-of-two ThreadLoad specializations for the various Cache load modifiers
- */
-#if CUB_PTX_ARCH >= 200
-    _CUB_LOAD_ALL(LOAD_CA, ca)
-    _CUB_LOAD_ALL(LOAD_CG, cg)
-    _CUB_LOAD_ALL(LOAD_CS, cs)
-    _CUB_LOAD_ALL(LOAD_CV, cv)
-#else
-    _CUB_LOAD_ALL(LOAD_CA, global)
-    // Use volatile to ensure coherent reads when this PTX is JIT'd to run on newer architectures with L1
-    _CUB_LOAD_ALL(LOAD_CG, volatile.global)
-    _CUB_LOAD_ALL(LOAD_CS, global)
-    _CUB_LOAD_ALL(LOAD_CV, volatile.global)
-#endif
-
-#if CUB_PTX_ARCH >= 350
-    _CUB_LOAD_ALL(LOAD_LDG, global.nc)
-#else
-    _CUB_LOAD_ALL(LOAD_LDG, global)
-#endif
-
-
-// Macro cleanup
-#undef _CUB_LOAD_ALL
-#undef _CUB_LOAD_1
-#undef _CUB_LOAD_2
-#undef _CUB_LOAD_4
-#undef _CUB_LOAD_8
-#undef _CUB_LOAD_16
-
-
-
-/**
- * ThreadLoad definition for LOAD_DEFAULT modifier on iterator types
- */
-template <typename InputIteratorT>
-__device__ __forceinline__ typename std::iterator_traits<InputIteratorT>::value_type ThreadLoad(
-    InputIteratorT          itr,
-    Int2Type<LOAD_DEFAULT>  /*modifier*/,
-    Int2Type<false>         /*is_pointer*/)
-{
-    return *itr;
-}
-
-
-/**
- * ThreadLoad definition for LOAD_DEFAULT modifier on pointer types
- */
-template <typename T>
-__device__ __forceinline__ T ThreadLoad(
-    T                       *ptr,
-    Int2Type<LOAD_DEFAULT>  /*modifier*/,
-    Int2Type<true>          /*is_pointer*/)
-{
-    return *ptr;
-}
-
-
-/**
- * ThreadLoad definition for LOAD_VOLATILE modifier on primitive pointer types
- */
-template <typename T>
-__device__ __forceinline__ T ThreadLoadVolatilePointer(
-    T                       *ptr,
-    Int2Type<true>          /*is_primitive*/)
-{
-    T retval = *reinterpret_cast<volatile T*>(ptr);
-    return retval;
-}
-
-
-/**
- * ThreadLoad definition for LOAD_VOLATILE modifier on non-primitive pointer types
- */
-template <typename T>
-__device__ __forceinline__ T ThreadLoadVolatilePointer(
-    T                       *ptr,
-    Int2Type<false>         /*is_primitive*/)
-{
-    typedef typename UnitWord<T>::VolatileWord VolatileWord;   // Word type for memcopying
-
-    const int VOLATILE_MULTIPLE = sizeof(T) / sizeof(VolatileWord);
-/*
-    VolatileWord words[VOLATILE_MULTIPLE];
-
-    IterateThreadLoad<0, VOLATILE_MULTIPLE>::Dereference(
-        reinterpret_cast<volatile VolatileWord*>(ptr),
-        words);
-
-    return *reinterpret_cast<T*>(words);
-*/
-
-    T retval;
-    VolatileWord *words = reinterpret_cast<VolatileWord*>(&retval);
-    IterateThreadLoad<0, VOLATILE_MULTIPLE>::Dereference(
-        reinterpret_cast<volatile VolatileWord*>(ptr),
-        words);
-    return retval;
-}
-
-
-/**
- * ThreadLoad definition for LOAD_VOLATILE modifier on pointer types
- */
-template <typename T>
-__device__ __forceinline__ T ThreadLoad(
-    T                       *ptr,
-    Int2Type<LOAD_VOLATILE> /*modifier*/,
-    Int2Type<true>          /*is_pointer*/)
-{
-    // Apply tags for partial-specialization
-    return ThreadLoadVolatilePointer(ptr, Int2Type<Traits<T>::PRIMITIVE>());
-}
-
-
-/**
- * ThreadLoad definition for generic modifiers on pointer types
- */
-template <typename T, int MODIFIER>
-__device__ __forceinline__ T ThreadLoad(
-    T const                 *ptr,
-    Int2Type<MODIFIER>      /*modifier*/,
-    Int2Type<true>          /*is_pointer*/)
-{
-    typedef typename UnitWord<T>::DeviceWord DeviceWord;
-
-    const int DEVICE_MULTIPLE = sizeof(T) / sizeof(DeviceWord);
-
-    DeviceWord words[DEVICE_MULTIPLE];
-
-    IterateThreadLoad<0, DEVICE_MULTIPLE>::template Load<CacheLoadModifier(MODIFIER)>(
-        reinterpret_cast<DeviceWord*>(const_cast<T*>(ptr)),
-        words);
-
-    return *reinterpret_cast<T*>(words);
-}
-
-
-/**
- * ThreadLoad definition for generic modifiers
- */
-template <
-    CacheLoadModifier MODIFIER,
-    typename InputIteratorT>
-__device__ __forceinline__ typename std::iterator_traits<InputIteratorT>::value_type ThreadLoad(InputIteratorT itr)
-{
-    // Apply tags for partial-specialization
-    return ThreadLoad(
-        itr,
-        Int2Type<MODIFIER>(),
-        Int2Type<IsPointer<InputIteratorT>::VALUE>());
-}
-
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/** @} */       // end group UtilIo
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/thread/thread_operators.cuh b/ml-xgboost/cub/cub/thread/thread_operators.cuh
deleted file mode 100644
index 32e13f8..0000000
--- a/ml-xgboost/cub/cub/thread/thread_operators.cuh
+++ /dev/null
@@ -1,317 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Simple binary operator functor types
- */
-
-/******************************************************************************
- * Simple functor operators
- ******************************************************************************/
-
-#pragma once
-
-#include "../util_macro.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilModule
- * @{
- */
-
-/**
- * \brief Default equality functor
- */
-struct Equality
-{
-    /// Boolean equality operator, returns <tt>(a == b)</tt>
-    template <typename T>
-    __host__ __device__ __forceinline__ bool operator()(const T &a, const T &b) const
-    {
-        return a == b;
-    }
-};
-
-
-/**
- * \brief Default inequality functor
- */
-struct Inequality
-{
-    /// Boolean inequality operator, returns <tt>(a != b)</tt>
-    template <typename T>
-    __host__ __device__ __forceinline__ bool operator()(const T &a, const T &b) const
-    {
-        return a != b;
-    }
-};
-
-
-/**
- * \brief Inequality functor (wraps equality functor)
- */
-template <typename EqualityOp>
-struct InequalityWrapper
-{
-    /// Wrapped equality operator
-    EqualityOp op;
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    InequalityWrapper(EqualityOp op) : op(op) {}
-
-    /// Boolean inequality operator, returns <tt>(a != b)</tt>
-    template <typename T>
-    __host__ __device__ __forceinline__ bool operator()(const T &a, const T &b)
-    {
-        return !op(a, b);
-    }
-};
-
-
-/**
- * \brief Default sum functor
- */
-struct Sum
-{
-    /// Boolean sum operator, returns <tt>a + b</tt>
-    template <typename T>
-    __host__ __device__ __forceinline__ T operator()(const T &a, const T &b) const
-    {
-        return a + b;
-    }
-};
-
-
-/**
- * \brief Default max functor
- */
-struct Max
-{
-    /// Boolean max operator, returns <tt>(a > b) ? a : b</tt>
-    template <typename T>
-    __host__ __device__ __forceinline__ T operator()(const T &a, const T &b) const
-    {
-        return CUB_MAX(a, b);
-    }
-};
-
-
-/**
- * \brief Arg max functor (keeps the value and offset of the first occurrence of the larger item)
- */
-struct ArgMax
-{
-    /// Boolean max operator, preferring the item having the smaller offset in case of ties
-    template <typename T, typename OffsetT>
-    __host__ __device__ __forceinline__ KeyValuePair<OffsetT, T> operator()(
-        const KeyValuePair<OffsetT, T> &a,
-        const KeyValuePair<OffsetT, T> &b) const
-    {
-// Mooch BUG (device reduce argmax gk110 3.2 million random fp32)
-//        return ((b.value > a.value) || ((a.value == b.value) && (b.key < a.key))) ? b : a;
-
-        if ((b.value > a.value) || ((a.value == b.value) && (b.key < a.key)))
-            return b;
-        return a;
-    }
-};
-
-
-/**
- * \brief Default min functor
- */
-struct Min
-{
-    /// Boolean min operator, returns <tt>(a < b) ? a : b</tt>
-    template <typename T>
-    __host__ __device__ __forceinline__ T operator()(const T &a, const T &b) const
-    {
-        return CUB_MIN(a, b);
-    }
-};
-
-
-/**
- * \brief Arg min functor (keeps the value and offset of the first occurrence of the smallest item)
- */
-struct ArgMin
-{
-    /// Boolean min operator, preferring the item having the smaller offset in case of ties
-    template <typename T, typename OffsetT>
-    __host__ __device__ __forceinline__ KeyValuePair<OffsetT, T> operator()(
-        const KeyValuePair<OffsetT, T> &a,
-        const KeyValuePair<OffsetT, T> &b) const
-    {
-// Mooch BUG (device reduce argmax gk110 3.2 million random fp32)
-//        return ((b.value < a.value) || ((a.value == b.value) && (b.key < a.key))) ? b : a;
-
-        if ((b.value < a.value) || ((a.value == b.value) && (b.key < a.key)))
-            return b;
-        return a;
-    }
-};
-
-
-/**
- * \brief Default cast functor
- */
-template <typename B>
-struct Cast
-{
-    /// Cast operator, returns <tt>(B) a</tt>
-    template <typename A>
-    __host__ __device__ __forceinline__ B operator()(const A &a) const
-    {
-        return (B) a;
-    }
-};
-
-
-/**
- * \brief Binary operator wrapper for switching non-commutative scan arguments
- */
-template <typename ScanOp>
-class SwizzleScanOp
-{
-private:
-
-    /// Wrapped scan operator
-    ScanOp scan_op;
-
-public:
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    SwizzleScanOp(ScanOp scan_op) : scan_op(scan_op) {}
-
-    /// Switch the scan arguments
-    template <typename T>
-    __host__ __device__ __forceinline__
-    T operator()(const T &a, const T &b)
-    {
-      T _a(a);
-      T _b(b);
-
-      return scan_op(_b, _a);
-    }
-};
-
-
-/**
- * \brief Reduce-by-segment functor.
- *
- * Given two cub::KeyValuePair inputs \p a and \p b and a
- * binary associative combining operator \p <tt>f(const T &x, const T &y)</tt>,
- * an instance of this functor returns a cub::KeyValuePair whose \p key
- * field is <tt>a.key</tt> + <tt>a.key</tt>, and whose \p value field
- * is either b.value if b.key is non-zero, or f(a.value, b.value) otherwise.
- *
- * ReduceBySegmentOp is an associative, non-commutative binary combining operator
- * for input sequences of cub::KeyValuePair pairings.  Such
- * sequences are typically used to represent a segmented set of values to be reduced
- * and a corresponding set of {0,1}-valued integer "head flags" demarcating the
- * first value of each segment.
- *
- */
-template <typename ReductionOpT>    ///< Binary reduction operator to apply to values
-struct ReduceBySegmentOp
-{
-    /// Wrapped reduction operator
-    ReductionOpT op;
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ReduceBySegmentOp() {}
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ReduceBySegmentOp(ReductionOpT op) : op(op) {}
-
-    /// Scan operator
-    template <typename KeyValuePairT>       ///< KeyValuePair pairing of T (value) and OffsetT (head flag)
-    __host__ __device__ __forceinline__ KeyValuePairT operator()(
-        const KeyValuePairT &first,         ///< First partial reduction
-        const KeyValuePairT &second)        ///< Second partial reduction
-    {
-        KeyValuePairT retval;
-        retval.key = first.key + second.key;
-        retval.value = (second.key) ?
-                second.value :                          // The second partial reduction spans a segment reset, so it's value aggregate becomes the running aggregate
-                op(first.value, second.value);          // The second partial reduction does not span a reset, so accumulate both into the running aggregate
-        return retval;
-    }
-};
-
-
-
-template <typename ReductionOpT>    ///< Binary reduction operator to apply to values
-struct ReduceByKeyOp
-{
-    /// Wrapped reduction operator
-    ReductionOpT op;
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ReduceByKeyOp() {}
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ReduceByKeyOp(ReductionOpT op) : op(op) {}
-
-    /// Scan operator
-    template <typename KeyValuePairT>
-    __host__ __device__ __forceinline__ KeyValuePairT operator()(
-        const KeyValuePairT &first,       ///< First partial reduction
-        const KeyValuePairT &second)      ///< Second partial reduction
-    {
-        KeyValuePairT retval = second;
-
-        if (first.key == second.key)
-            retval.value = op(first.value, retval.value);
-
-        return retval;
-    }
-};
-
-
-
-
-
-
-
-/** @} */       // end group UtilModule
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/thread/thread_reduce.cuh b/ml-xgboost/cub/cub/thread/thread_reduce.cuh
deleted file mode 100644
index 525744a..0000000
--- a/ml-xgboost/cub/cub/thread/thread_reduce.cuh
+++ /dev/null
@@ -1,169 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Thread utilities for sequential reduction over statically-sized array types
- */
-
-#pragma once
-
-#include "../thread/thread_operators.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilModule
- * @{
- */
-
-/**
- * \name Sequential reduction over statically-sized array types
- * @{
- */
-
-
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ReductionOp>
-__device__ __forceinline__ T ThreadReduce(
-    T*                  input,                  ///< [in] Input array
-    ReductionOp         reduction_op,           ///< [in] Binary reduction operator
-    T                   prefix,                 ///< [in] Prefix to seed reduction with
-    Int2Type<LENGTH>    /*length*/)
-{
-    T addend = *input;
-    prefix = reduction_op(prefix, addend);
-
-    return ThreadReduce(input + 1, reduction_op, prefix, Int2Type<LENGTH - 1>());
-}
-
-template <
-    typename    T,
-    typename    ReductionOp>
-__device__ __forceinline__ T ThreadReduce(
-    T*                  /*input*/,              ///< [in] Input array
-    ReductionOp         /*reduction_op*/,       ///< [in] Binary reduction operator
-    T                   prefix,                 ///< [in] Prefix to seed reduction with
-    Int2Type<0>         /*length*/)
-{
-    return prefix;
-}
-
-
-/**
- * \brief Perform a sequential reduction over \p LENGTH elements of the \p input array, seeded with the specified \p prefix.  The aggregate is returned.
- *
- * \tparam LENGTH     LengthT of input array
- * \tparam T          <b>[inferred]</b> The data type to be reduced.
- * \tparam ScanOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ReductionOp>
-__device__ __forceinline__ T ThreadReduce(
-    T*          input,                  ///< [in] Input array
-    ReductionOp reduction_op,           ///< [in] Binary reduction operator
-    T           prefix)                 ///< [in] Prefix to seed reduction with
-{
-    return ThreadReduce(input, reduction_op, prefix, Int2Type<LENGTH>());
-}
-
-
-/**
- * \brief Perform a sequential reduction over \p LENGTH elements of the \p input array.  The aggregate is returned.
- *
- * \tparam LENGTH     LengthT of input array
- * \tparam T          <b>[inferred]</b> The data type to be reduced.
- * \tparam ScanOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ReductionOp>
-__device__ __forceinline__ T ThreadReduce(
-    T*          input,                  ///< [in] Input array
-    ReductionOp reduction_op)           ///< [in] Binary reduction operator
-{
-    T prefix = input[0];
-    return ThreadReduce<LENGTH - 1>(input + 1, reduction_op, prefix);
-}
-
-
-/**
- * \brief Perform a sequential reduction over the statically-sized \p input array, seeded with the specified \p prefix.  The aggregate is returned.
- *
- * \tparam LENGTH     <b>[inferred]</b> LengthT of \p input array
- * \tparam T          <b>[inferred]</b> The data type to be reduced.
- * \tparam ScanOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ReductionOp>
-__device__ __forceinline__ T ThreadReduce(
-    T           (&input)[LENGTH],       ///< [in] Input array
-    ReductionOp reduction_op,           ///< [in] Binary reduction operator
-    T           prefix)                 ///< [in] Prefix to seed reduction with
-{
-    return ThreadReduce(input, reduction_op, prefix, Int2Type<LENGTH>());
-}
-
-
-/**
- * \brief Serial reduction with the specified operator
- *
- * \tparam LENGTH     <b>[inferred]</b> LengthT of \p input array
- * \tparam T          <b>[inferred]</b> The data type to be reduced.
- * \tparam ScanOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ReductionOp>
-__device__ __forceinline__ T ThreadReduce(
-    T           (&input)[LENGTH],       ///< [in] Input array
-    ReductionOp reduction_op)           ///< [in] Binary reduction operator
-{
-    return ThreadReduce<LENGTH>((T*) input, reduction_op);
-}
-
-
-//@}  end member group
-
-/** @} */       // end group UtilModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/thread/thread_scan.cuh b/ml-xgboost/cub/cub/thread/thread_scan.cuh
deleted file mode 100644
index 312335a..0000000
--- a/ml-xgboost/cub/cub/thread/thread_scan.cuh
+++ /dev/null
@@ -1,283 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Thread utilities for sequential prefix scan over statically-sized array types
- */
-
-#pragma once
-
-#include "../thread/thread_operators.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilModule
- * @{
- */
-
-/**
- * \name Sequential prefix scan over statically-sized array types
- * @{
- */
-
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanExclusive(
-    T                   inclusive,
-    T                   exclusive,
-    T                   *input,                 ///< [in] Input array
-    T                   *output,                ///< [out] Output array (may be aliased to \p input)
-    ScanOp              scan_op,                ///< [in] Binary scan operator
-    Int2Type<LENGTH>    /*length*/)
-{
-    T addend = *input;
-    inclusive = scan_op(exclusive, addend);
-    *output = exclusive;
-    exclusive = inclusive;
-
-    return ThreadScanExclusive(inclusive, exclusive, input + 1, output + 1, scan_op, Int2Type<LENGTH - 1>());
-}
-
-template <
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanExclusive(
-    T                   inclusive,
-    T                   /*exclusive*/,
-    T                   * /*input*/,                ///< [in] Input array
-    T                   * /*output*/,               ///< [out] Output array (may be aliased to \p input)
-    ScanOp              /*scan_op*/,                ///< [in] Binary scan operator
-    Int2Type<0>         /*length*/)
-{
-    return inclusive;
-}
-
-
-/**
- * \brief Perform a sequential exclusive prefix scan over \p LENGTH elements of the \p input array, seeded with the specified \p prefix.  The aggregate is returned.
- *
- * \tparam LENGTH     LengthT of \p input and \p output arrays
- * \tparam T          <b>[inferred]</b> The data type to be scanned.
- * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanExclusive(
-    T           *input,                 ///< [in] Input array
-    T           *output,                ///< [out] Output array (may be aliased to \p input)
-    ScanOp      scan_op,                ///< [in] Binary scan operator
-    T           prefix,                 ///< [in] Prefix to seed scan with
-    bool        apply_prefix = true)    ///< [in] Whether or not the calling thread should apply its prefix.  If not, the first output element is undefined.  (Handy for preventing thread-0 from applying a prefix.)
-{
-    T inclusive = input[0];
-    if (apply_prefix)
-    {
-        inclusive = scan_op(prefix, inclusive);
-    }
-    output[0] = prefix;
-    T exclusive = inclusive;
-
-    return ThreadScanExclusive(inclusive, exclusive, input + 1, output + 1, scan_op, Int2Type<LENGTH - 1>());
-}
-
-
-/**
- * \brief Perform a sequential exclusive prefix scan over the statically-sized \p input array, seeded with the specified \p prefix.  The aggregate is returned.
- *
- * \tparam LENGTH     <b>[inferred]</b> LengthT of \p input and \p output arrays
- * \tparam T          <b>[inferred]</b> The data type to be scanned.
- * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanExclusive(
-    T           (&input)[LENGTH],       ///< [in] Input array
-    T           (&output)[LENGTH],      ///< [out] Output array (may be aliased to \p input)
-    ScanOp      scan_op,                ///< [in] Binary scan operator
-    T           prefix,                 ///< [in] Prefix to seed scan with
-    bool        apply_prefix = true)    ///< [in] Whether or not the calling thread should apply its prefix.  (Handy for preventing thread-0 from applying a prefix.)
-{
-    return ThreadScanExclusive<LENGTH>((T*) input, (T*) output, scan_op, prefix, apply_prefix);
-}
-
-
-
-
-
-
-
-
-
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanInclusive(
-    T                   inclusive,
-    T                   *input,                 ///< [in] Input array
-    T                   *output,                ///< [out] Output array (may be aliased to \p input)
-    ScanOp              scan_op,                ///< [in] Binary scan operator
-    Int2Type<LENGTH>    /*length*/)
-{
-    T addend = *input;
-    inclusive = scan_op(inclusive, addend);
-    output[0] = inclusive;
-
-    return ThreadScanInclusive(inclusive, input + 1, output + 1, scan_op, Int2Type<LENGTH - 1>());
-}
-
-template <
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanInclusive(
-    T                   inclusive,
-    T                   * /*input*/,                ///< [in] Input array
-    T                   * /*output*/,               ///< [out] Output array (may be aliased to \p input)
-    ScanOp              /*scan_op*/,                ///< [in] Binary scan operator
-    Int2Type<0>         /*length*/)
-{
-    return inclusive;
-}
-
-
-/**
- * \brief Perform a sequential inclusive prefix scan over \p LENGTH elements of the \p input array.  The aggregate is returned.
- *
- * \tparam LENGTH     LengthT of \p input and \p output arrays
- * \tparam T          <b>[inferred]</b> The data type to be scanned.
- * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanInclusive(
-    T           *input,                 ///< [in] Input array
-    T           *output,                ///< [out] Output array (may be aliased to \p input)
-    ScanOp      scan_op)                ///< [in] Binary scan operator
-{
-    T inclusive = input[0];
-    output[0] = inclusive;
-
-    // Continue scan
-    return ThreadScanInclusive(inclusive, input + 1, output + 1, scan_op, Int2Type<LENGTH - 1>());
-}
-
-
-/**
- * \brief Perform a sequential inclusive prefix scan over the statically-sized \p input array.  The aggregate is returned.
- *
- * \tparam LENGTH     <b>[inferred]</b> LengthT of \p input and \p output arrays
- * \tparam T          <b>[inferred]</b> The data type to be scanned.
- * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanInclusive(
-    T           (&input)[LENGTH],       ///< [in] Input array
-    T           (&output)[LENGTH],      ///< [out] Output array (may be aliased to \p input)
-    ScanOp      scan_op)                ///< [in] Binary scan operator
-{
-    return ThreadScanInclusive<LENGTH>((T*) input, (T*) output, scan_op);
-}
-
-
-/**
- * \brief Perform a sequential inclusive prefix scan over \p LENGTH elements of the \p input array, seeded with the specified \p prefix.  The aggregate is returned.
- *
- * \tparam LENGTH     LengthT of \p input and \p output arrays
- * \tparam T          <b>[inferred]</b> The data type to be scanned.
- * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanInclusive(
-    T           *input,                 ///< [in] Input array
-    T           *output,                ///< [out] Output array (may be aliased to \p input)
-    ScanOp      scan_op,                ///< [in] Binary scan operator
-    T           prefix,                 ///< [in] Prefix to seed scan with
-    bool        apply_prefix = true)    ///< [in] Whether or not the calling thread should apply its prefix.  (Handy for preventing thread-0 from applying a prefix.)
-{
-    T inclusive = input[0];
-    if (apply_prefix)
-    {
-        inclusive = scan_op(prefix, inclusive);
-    }
-    output[0] = inclusive;
-
-    // Continue scan
-    return ThreadScanInclusive(inclusive, input + 1, output + 1, scan_op, Int2Type<LENGTH - 1>());
-}
-
-
-/**
- * \brief Perform a sequential inclusive prefix scan over the statically-sized \p input array, seeded with the specified \p prefix.  The aggregate is returned.
- *
- * \tparam LENGTH     <b>[inferred]</b> LengthT of \p input and \p output arrays
- * \tparam T          <b>[inferred]</b> The data type to be scanned.
- * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
- */
-template <
-    int         LENGTH,
-    typename    T,
-    typename    ScanOp>
-__device__ __forceinline__ T ThreadScanInclusive(
-    T           (&input)[LENGTH],       ///< [in] Input array
-    T           (&output)[LENGTH],      ///< [out] Output array (may be aliased to \p input)
-    ScanOp      scan_op,                ///< [in] Binary scan operator
-    T           prefix,                 ///< [in] Prefix to seed scan with
-    bool        apply_prefix = true)    ///< [in] Whether or not the calling thread should apply its prefix.  (Handy for preventing thread-0 from applying a prefix.)
-{
-    return ThreadScanInclusive<LENGTH>((T*) input, (T*) output, scan_op, prefix, apply_prefix);
-}
-
-
-//@}  end member group
-
-/** @} */       // end group UtilModule
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/thread/thread_search.cuh b/ml-xgboost/cub/cub/thread/thread_search.cuh
deleted file mode 100644
index 6d2da00..0000000
--- a/ml-xgboost/cub/cub/thread/thread_search.cuh
+++ /dev/null
@@ -1,154 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Thread utilities for sequential search
- */
-
-#pragma once
-
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * Computes the begin offsets into A and B for the specific diagonal
- */
-template <
-    typename AIteratorT,
-    typename BIteratorT,
-    typename OffsetT,
-    typename CoordinateT>
-__host__ __device__ __forceinline__ void MergePathSearch(
-    OffsetT         diagonal,
-    AIteratorT      a,
-    BIteratorT      b,
-    OffsetT         a_len,
-    OffsetT         b_len,
-    CoordinateT&    path_coordinate)
-{
-    /// The value type of the input iterator
-    typedef typename std::iterator_traits<AIteratorT>::value_type T;
-
-    OffsetT split_min = CUB_MAX(diagonal - b_len, 0);
-    OffsetT split_max = CUB_MIN(diagonal, a_len);
-
-    while (split_min < split_max)
-    {
-        OffsetT split_pivot = (split_min + split_max) >> 1;
-        if (a[split_pivot] <= b[diagonal - split_pivot - 1])
-        {
-            // Move candidate split range up A, down B
-            split_min = split_pivot + 1;
-        }
-        else
-        {
-            // Move candidate split range up B, down A
-            split_max = split_pivot;
-        }
-    }
-
-    path_coordinate.x = CUB_MIN(split_min, a_len);
-    path_coordinate.y = diagonal - split_min;
-}
-
-
-
-/**
- * \brief Returns the offset of the first value within \p input which does not compare less than \p val
- */
-template <
-    typename InputIteratorT,
-    typename OffsetT,
-    typename T>
-__device__ __forceinline__ OffsetT LowerBound(
-    InputIteratorT      input,              ///< [in] Input sequence
-    OffsetT             num_items,          ///< [in] Input sequence length
-    T                   val)                ///< [in] Search key
-{
-    OffsetT retval = 0;
-    while (num_items > 0)
-    {
-        OffsetT half = num_items >> 1;
-        if (input[retval + half] < val)
-        {
-            retval = retval + (half + 1);
-            num_items = num_items - (half + 1);
-        }
-        else
-        {
-            num_items = half;
-        }
-    }
-
-    return retval;
-}
-
-
-/**
- * \brief Returns the offset of the first value within \p input which compares greater than \p val
- */
-template <
-    typename InputIteratorT,
-    typename OffsetT,
-    typename T>
-__device__ __forceinline__ OffsetT UpperBound(
-    InputIteratorT      input,              ///< [in] Input sequence
-    OffsetT             num_items,          ///< [in] Input sequence length
-    T                   val)                ///< [in] Search key
-{
-    OffsetT retval = 0;
-    while (num_items > 0)
-    {
-        OffsetT half = num_items >> 1;
-        if (val < input[retval + half])
-        {
-            num_items = half;
-        }
-        else
-        {
-            retval = retval + (half + 1);
-            num_items = num_items - (half + 1);
-        }
-    }
-
-    return retval;
-}
-
-
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/thread/thread_store.cuh b/ml-xgboost/cub/cub/thread/thread_store.cuh
deleted file mode 100644
index ae37f9c..0000000
--- a/ml-xgboost/cub/cub/thread/thread_store.cuh
+++ /dev/null
@@ -1,422 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Thread utilities for writing memory using PTX cache modifiers.
- */
-
-#pragma once
-
-#include <cuda.h>
-
-#include "../util_ptx.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup UtilIo
- * @{
- */
-
-
-//-----------------------------------------------------------------------------
-// Tags and constants
-//-----------------------------------------------------------------------------
-
-/**
- * \brief Enumeration of cache modifiers for memory store operations.
- */
-enum CacheStoreModifier
-{
-    STORE_DEFAULT,              ///< Default (no modifier)
-    STORE_WB,                   ///< Cache write-back all coherent levels
-    STORE_CG,                   ///< Cache at global level
-    STORE_CS,                   ///< Cache streaming (likely to be accessed once)
-    STORE_WT,                   ///< Cache write-through (to system memory)
-    STORE_VOLATILE,             ///< Volatile shared (any memory space)
-};
-
-
-/**
- * \name Thread I/O (cache modified)
- * @{
- */
-
-/**
- * \brief Thread utility for writing memory using cub::CacheStoreModifier cache modifiers.  Can be used to store any data type.
- *
- * \par Example
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/thread/thread_store.cuh>
- *
- * // 32-bit store using cache-global modifier:
- * int *d_out;
- * int val;
- * cub::ThreadStore<cub::STORE_CG>(d_out + threadIdx.x, val);
- *
- * // 16-bit store using default modifier
- * short *d_out;
- * short val;
- * cub::ThreadStore<cub::STORE_DEFAULT>(d_out + threadIdx.x, val);
- *
- * // 256-bit store using write-through modifier
- * double4 *d_out;
- * double4 val;
- * cub::ThreadStore<cub::STORE_WT>(d_out + threadIdx.x, val);
- *
- * // 96-bit store using cache-streaming cache modifier
- * struct TestFoo { bool a; short b; };
- * TestFoo *d_struct;
- * TestFoo val;
- * cub::ThreadStore<cub::STORE_CS>(d_out + threadIdx.x, val);
- * \endcode
- *
- * \tparam MODIFIER             <b>[inferred]</b> CacheStoreModifier enumeration
- * \tparam InputIteratorT       <b>[inferred]</b> Output iterator type \iterator
- * \tparam T                    <b>[inferred]</b> Data type of output value
- */
-template <
-    CacheStoreModifier  MODIFIER,
-    typename            OutputIteratorT,
-    typename            T>
-__device__ __forceinline__ void ThreadStore(OutputIteratorT itr, T val);
-
-
-//@}  end member group
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-/// Helper structure for templated store iteration (inductive case)
-template <int COUNT, int MAX>
-struct IterateThreadStore
-{
-    template <CacheStoreModifier MODIFIER, typename T>
-    static __device__ __forceinline__ void Store(T *ptr, T *vals)
-    {
-        ThreadStore<MODIFIER>(ptr + COUNT, vals[COUNT]);
-        IterateThreadStore<COUNT + 1, MAX>::template Store<MODIFIER>(ptr, vals);
-    }
-
-    template <typename OutputIteratorT, typename T>
-    static __device__ __forceinline__ void Dereference(OutputIteratorT ptr, T *vals)
-    {
-        ptr[COUNT] = vals[COUNT];
-        IterateThreadStore<COUNT + 1, MAX>::Dereference(ptr, vals);
-    }
-
-};
-
-/// Helper structure for templated store iteration (termination case)
-template <int MAX>
-struct IterateThreadStore<MAX, MAX>
-{
-    template <CacheStoreModifier MODIFIER, typename T>
-    static __device__ __forceinline__ void Store(T * /*ptr*/, T * /*vals*/) {}
-
-    template <typename OutputIteratorT, typename T>
-    static __device__ __forceinline__ void Dereference(OutputIteratorT /*ptr*/, T * /*vals*/) {}
-};
-
-
-/**
- * Define a uint4 (16B) ThreadStore specialization for the given Cache load modifier
- */
-#define _CUB_STORE_16(cub_modifier, ptx_modifier)                                            \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, uint4*, uint4>(uint4* ptr, uint4 val)                         \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".v4.u32 [%0], {%1, %2, %3, %4};" : :               \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "r"(val.x),                                                                     \
-            "r"(val.y),                                                                     \
-            "r"(val.z),                                                                     \
-            "r"(val.w));                                                                    \
-    }                                                                                       \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, ulonglong2*, ulonglong2>(ulonglong2* ptr, ulonglong2 val)     \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".v2.u64 [%0], {%1, %2};" : :                       \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "l"(val.x),                                                                     \
-            "l"(val.y));                                                                    \
-    }
-
-
-/**
- * Define a uint2 (8B) ThreadStore specialization for the given Cache load modifier
- */
-#define _CUB_STORE_8(cub_modifier, ptx_modifier)                                             \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, ushort4*, ushort4>(ushort4* ptr, ushort4 val)                 \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".v4.u16 [%0], {%1, %2, %3, %4};" : :               \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "h"(val.x),                                                                     \
-            "h"(val.y),                                                                     \
-            "h"(val.z),                                                                     \
-            "h"(val.w));                                                                    \
-    }                                                                                       \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, uint2*, uint2>(uint2* ptr, uint2 val)                         \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".v2.u32 [%0], {%1, %2};" : :                       \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "r"(val.x),                                                                     \
-            "r"(val.y));                                                                    \
-    }                                                                                       \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, unsigned long long*, unsigned long long>(unsigned long long* ptr, unsigned long long val)     \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".u64 [%0], %1;" : :                                \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "l"(val));                                                                      \
-    }
-
-/**
- * Define a unsigned int (4B) ThreadStore specialization for the given Cache load modifier
- */
-#define _CUB_STORE_4(cub_modifier, ptx_modifier)                                             \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, unsigned int*, unsigned int>(unsigned int* ptr, unsigned int val)                             \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".u32 [%0], %1;" : :                                \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "r"(val));                                                                      \
-    }
-
-
-/**
- * Define a unsigned short (2B) ThreadStore specialization for the given Cache load modifier
- */
-#define _CUB_STORE_2(cub_modifier, ptx_modifier)                                             \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, unsigned short*, unsigned short>(unsigned short* ptr, unsigned short val)                     \
-    {                                                                                       \
-        asm volatile ("st."#ptx_modifier".u16 [%0], %1;" : :                                \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "h"(val));                                                                      \
-    }
-
-
-/**
- * Define a unsigned char (1B) ThreadStore specialization for the given Cache load modifier
- */
-#define _CUB_STORE_1(cub_modifier, ptx_modifier)                                             \
-    template<>                                                                              \
-    __device__ __forceinline__ void ThreadStore<cub_modifier, unsigned char*, unsigned char>(unsigned char* ptr, unsigned char val)                         \
-    {                                                                                       \
-        asm volatile (                                                                      \
-        "{"                                                                                 \
-        "   .reg .u8 datum;"                                                                \
-        "   cvt.u8.u16 datum, %1;"                                                          \
-        "   st."#ptx_modifier".u8 [%0], datum;"                                             \
-        "}" : :                                                                             \
-            _CUB_ASM_PTR_(ptr),                                                             \
-            "h"((unsigned short) val));                                                               \
-    }
-
-/**
- * Define powers-of-two ThreadStore specializations for the given Cache load modifier
- */
-#define _CUB_STORE_ALL(cub_modifier, ptx_modifier)                                           \
-    _CUB_STORE_16(cub_modifier, ptx_modifier)                                                \
-    _CUB_STORE_8(cub_modifier, ptx_modifier)                                                 \
-    _CUB_STORE_4(cub_modifier, ptx_modifier)                                                 \
-    _CUB_STORE_2(cub_modifier, ptx_modifier)                                                 \
-    _CUB_STORE_1(cub_modifier, ptx_modifier)                                                 \
-
-
-/**
- * Define ThreadStore specializations for the various Cache load modifiers
- */
-#if CUB_PTX_ARCH >= 200
-    _CUB_STORE_ALL(STORE_WB, wb)
-    _CUB_STORE_ALL(STORE_CG, cg)
-    _CUB_STORE_ALL(STORE_CS, cs)
-    _CUB_STORE_ALL(STORE_WT, wt)
-#else
-    _CUB_STORE_ALL(STORE_WB, global)
-    _CUB_STORE_ALL(STORE_CG, global)
-    _CUB_STORE_ALL(STORE_CS, global)
-    _CUB_STORE_ALL(STORE_WT, volatile.global)
-#endif
-
-
-// Macro cleanup
-#undef _CUB_STORE_ALL
-#undef _CUB_STORE_1
-#undef _CUB_STORE_2
-#undef _CUB_STORE_4
-#undef _CUB_STORE_8
-#undef _CUB_STORE_16
-
-
-/**
- * ThreadStore definition for STORE_DEFAULT modifier on iterator types
- */
-template <typename OutputIteratorT, typename T>
-__device__ __forceinline__ void ThreadStore(
-    OutputIteratorT             itr,
-    T                           val,
-    Int2Type<STORE_DEFAULT>     /*modifier*/,
-    Int2Type<false>             /*is_pointer*/)
-{
-    *itr = val;
-}
-
-
-/**
- * ThreadStore definition for STORE_DEFAULT modifier on pointer types
- */
-template <typename T>
-__device__ __forceinline__ void ThreadStore(
-    T                           *ptr,
-    T                           val,
-    Int2Type<STORE_DEFAULT>     /*modifier*/,
-    Int2Type<true>              /*is_pointer*/)
-{
-    *ptr = val;
-}
-
-
-/**
- * ThreadStore definition for STORE_VOLATILE modifier on primitive pointer types
- */
-template <typename T>
-__device__ __forceinline__ void ThreadStoreVolatilePtr(
-    T                           *ptr,
-    T                           val,
-    Int2Type<true>              /*is_primitive*/)
-{
-    *reinterpret_cast<volatile T*>(ptr) = val;
-}
-
-
-/**
- * ThreadStore definition for STORE_VOLATILE modifier on non-primitive pointer types
- */
-template <typename T>
-__device__ __forceinline__ void ThreadStoreVolatilePtr(
-    T                           *ptr,
-    T                           val,
-    Int2Type<false>             /*is_primitive*/)
-{
-    // Create a temporary using shuffle-words, then store using volatile-words
-    typedef typename UnitWord<T>::VolatileWord  VolatileWord;  
-    typedef typename UnitWord<T>::ShuffleWord   ShuffleWord;
-
-    const int VOLATILE_MULTIPLE = sizeof(T) / sizeof(VolatileWord);
-    const int SHUFFLE_MULTIPLE  = sizeof(T) / sizeof(ShuffleWord);
-    
-    VolatileWord words[VOLATILE_MULTIPLE];
-
-    #pragma unroll
-    for (int i = 0; i < SHUFFLE_MULTIPLE; ++i)
-        reinterpret_cast<ShuffleWord*>(words)[i] = reinterpret_cast<ShuffleWord*>(&val)[i];
-
-    IterateThreadStore<0, VOLATILE_MULTIPLE>::template Dereference(
-        reinterpret_cast<volatile VolatileWord*>(ptr),
-        words);
-}
-
-
-/**
- * ThreadStore definition for STORE_VOLATILE modifier on pointer types
- */
-template <typename T>
-__device__ __forceinline__ void ThreadStore(
-    T                           *ptr,
-    T                           val,
-    Int2Type<STORE_VOLATILE>    /*modifier*/,
-    Int2Type<true>              /*is_pointer*/)
-{
-    ThreadStoreVolatilePtr(ptr, val, Int2Type<Traits<T>::PRIMITIVE>());
-}
-
-
-/**
- * ThreadStore definition for generic modifiers on pointer types
- */
-template <typename T, int MODIFIER>
-__device__ __forceinline__ void ThreadStore(
-    T                           *ptr,
-    T                           val,
-    Int2Type<MODIFIER>          /*modifier*/,
-    Int2Type<true>              /*is_pointer*/)
-{
-    // Create a temporary using shuffle-words, then store using device-words
-    typedef typename UnitWord<T>::DeviceWord    DeviceWord;  
-    typedef typename UnitWord<T>::ShuffleWord   ShuffleWord;
-
-    const int DEVICE_MULTIPLE   = sizeof(T) / sizeof(DeviceWord);
-    const int SHUFFLE_MULTIPLE  = sizeof(T) / sizeof(ShuffleWord);
-    
-    DeviceWord words[DEVICE_MULTIPLE];
-
-    #pragma unroll
-    for (int i = 0; i < SHUFFLE_MULTIPLE; ++i)
-        reinterpret_cast<ShuffleWord*>(words)[i] = reinterpret_cast<ShuffleWord*>(&val)[i];
-
-    IterateThreadStore<0, DEVICE_MULTIPLE>::template Store<CacheStoreModifier(MODIFIER)>(
-        reinterpret_cast<DeviceWord*>(ptr),
-        words);
-}
-
-
-/**
- * ThreadStore definition for generic modifiers
- */
-template <CacheStoreModifier MODIFIER, typename OutputIteratorT, typename T>
-__device__ __forceinline__ void ThreadStore(OutputIteratorT itr, T val)
-{
-    ThreadStore(
-        itr,
-        val,
-        Int2Type<MODIFIER>(),
-        Int2Type<IsPointer<OutputIteratorT>::VALUE>());
-}
-
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/** @} */       // end group UtilIo
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_allocator.cuh b/ml-xgboost/cub/cub/util_allocator.cuh
deleted file mode 100644
index 50b5d5e..0000000
--- a/ml-xgboost/cub/cub/util_allocator.cuh
+++ /dev/null
@@ -1,708 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple caching allocator for device memory allocations. The allocator is
- * thread-safe and capable of managing device allocations on multiple devices.
- ******************************************************************************/
-
-#pragma once
-
-#include "util_namespace.cuh"
-#include "util_debug.cuh"
-
-#include <set>
-#include <map>
-
-#include "host/mutex.cuh"
-#include <math.h>
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilMgmt
- * @{
- */
-
-
-/******************************************************************************
- * CachingDeviceAllocator (host use)
- ******************************************************************************/
-
-/**
- * \brief A simple caching allocator for device memory allocations.
- *
- * \par Overview
- * The allocator is thread-safe and stream-safe and is capable of managing cached
- * device allocations on multiple devices.  It behaves as follows:
- *
- * \par
- * - Allocations from the allocator are associated with an \p active_stream.  Once freed,
- *   the allocation becomes available immediately for reuse within the \p active_stream
- *   with which it was associated with during allocation, and it becomes available for
- *   reuse within other streams when all prior work submitted to \p active_stream has completed.
- * - Allocations are categorized and cached by bin size.  A new allocation request of
- *   a given size will only consider cached allocations within the corresponding bin.
- * - Bin limits progress geometrically in accordance with the growth factor
- *   \p bin_growth provided during construction.  Unused device allocations within
- *   a larger bin cache are not reused for allocation requests that categorize to
- *   smaller bin sizes.
- * - Allocation requests below (\p bin_growth ^ \p min_bin) are rounded up to
- *   (\p bin_growth ^ \p min_bin).
- * - Allocations above (\p bin_growth ^ \p max_bin) are not rounded up to the nearest
- *   bin and are simply freed when they are deallocated instead of being returned
- *   to a bin-cache.
- * - %If the total storage of cached allocations on a given device will exceed
- *   \p max_cached_bytes, allocations for that device are simply freed when they are
- *   deallocated instead of being returned to their bin-cache.
- *
- * \par
- * For example, the default-constructed CachingDeviceAllocator is configured with:
- * - \p bin_growth          = 8
- * - \p min_bin             = 3
- * - \p max_bin             = 7
- * - \p max_cached_bytes    = 6MB - 1B
- *
- * \par
- * which delineates five bin-sizes: 512B, 4KB, 32KB, 256KB, and 2MB
- * and sets a maximum of 6,291,455 cached bytes per device
- *
- */
-struct CachingDeviceAllocator
-{
-
-    //---------------------------------------------------------------------
-    // Constants
-    //---------------------------------------------------------------------
-
-    /// Out-of-bounds bin
-    static const unsigned int INVALID_BIN = (unsigned int) -1;
-
-    /// Invalid size
-    static const size_t INVALID_SIZE = (size_t) -1;
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-    /// Invalid device ordinal
-    static const int INVALID_DEVICE_ORDINAL = -1;
-
-    //---------------------------------------------------------------------
-    // Type definitions and helper types
-    //---------------------------------------------------------------------
-
-    /**
-     * Descriptor for device memory allocations
-     */
-    struct BlockDescriptor
-    {
-        void*           d_ptr;              // Device pointer
-        size_t          bytes;              // Size of allocation in bytes
-        unsigned int    bin;                // Bin enumeration
-        int             device;             // device ordinal
-        cudaStream_t    associated_stream;  // Associated associated_stream
-        cudaEvent_t     ready_event;        // Signal when associated stream has run to the point at which this block was freed
-
-        // Constructor (suitable for searching maps for a specific block, given its pointer and device)
-        BlockDescriptor(void *d_ptr, int device) :
-            d_ptr(d_ptr),
-            bytes(0),
-            bin(INVALID_BIN),
-            device(device),
-            associated_stream(0),
-            ready_event(0)
-        {}
-
-        // Constructor (suitable for searching maps for a range of suitable blocks, given a device)
-        BlockDescriptor(int device) :
-            d_ptr(NULL),
-            bytes(0),
-            bin(INVALID_BIN),
-            device(device),
-            associated_stream(0),
-            ready_event(0)
-        {}
-
-        // Comparison functor for comparing device pointers
-        static bool PtrCompare(const BlockDescriptor &a, const BlockDescriptor &b)
-        {
-            if (a.device == b.device)
-                return (a.d_ptr < b.d_ptr);
-            else
-                return (a.device < b.device);
-        }
-
-        // Comparison functor for comparing allocation sizes
-        static bool SizeCompare(const BlockDescriptor &a, const BlockDescriptor &b)
-        {
-            if (a.device == b.device)
-                return (a.bytes < b.bytes);
-            else
-                return (a.device < b.device);
-        }
-    };
-
-    /// BlockDescriptor comparator function interface
-    typedef bool (*Compare)(const BlockDescriptor &, const BlockDescriptor &);
-
-    class TotalBytes {
-    public:
-        size_t free;
-        size_t live;
-        TotalBytes() { free = live = 0; }
-    };
-
-    /// Set type for cached blocks (ordered by size)
-    typedef std::multiset<BlockDescriptor, Compare> CachedBlocks;
-
-    /// Set type for live blocks (ordered by ptr)
-    typedef std::multiset<BlockDescriptor, Compare> BusyBlocks;
-
-    /// Map type of device ordinals to the number of cached bytes cached by each device
-    typedef std::map<int, TotalBytes> GpuCachedBytes;
-
-
-    //---------------------------------------------------------------------
-    // Utility functions
-    //---------------------------------------------------------------------
-
-    /**
-     * Integer pow function for unsigned base and exponent
-     */
-    static unsigned int IntPow(
-        unsigned int base,
-        unsigned int exp)
-    {
-        unsigned int retval = 1;
-        while (exp > 0)
-        {
-            if (exp & 1) {
-                retval = retval * base;        // multiply the result by the current base
-            }
-            base = base * base;                // square the base
-            exp = exp >> 1;                    // divide the exponent in half
-        }
-        return retval;
-    }
-
-
-    /**
-     * Round up to the nearest power-of
-     */
-    void NearestPowerOf(
-        unsigned int    &power,
-        size_t          &rounded_bytes,
-        unsigned int    base,
-        size_t          value)
-    {
-        power = 0;
-        rounded_bytes = 1;
-
-        if (value * base < value)
-        {
-            // Overflow
-            power = sizeof(size_t) * 8;
-            rounded_bytes = size_t(0) - 1;
-            return;
-        }
-
-        while (rounded_bytes < value)
-        {
-            rounded_bytes *= base;
-            power++;
-        }
-    }
-
-
-    //---------------------------------------------------------------------
-    // Fields
-    //---------------------------------------------------------------------
-
-    cub::Mutex      mutex;              /// Mutex for thread-safety
-
-    unsigned int    bin_growth;         /// Geometric growth factor for bin-sizes
-    unsigned int    min_bin;            /// Minimum bin enumeration
-    unsigned int    max_bin;            /// Maximum bin enumeration
-
-    size_t          min_bin_bytes;      /// Minimum bin size
-    size_t          max_bin_bytes;      /// Maximum bin size
-    size_t          max_cached_bytes;   /// Maximum aggregate cached bytes per device
-
-    const bool      skip_cleanup;       /// Whether or not to skip a call to FreeAllCached() when destructor is called.  (The CUDA runtime may have already shut down for statically declared allocators)
-    bool            debug;              /// Whether or not to print (de)allocation events to stdout
-
-    GpuCachedBytes  cached_bytes;       /// Map of device ordinal to aggregate cached bytes on that device
-    CachedBlocks    cached_blocks;      /// Set of cached device allocations available for reuse
-    BusyBlocks      live_blocks;        /// Set of live device allocations currently in use
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-    //---------------------------------------------------------------------
-    // Methods
-    //---------------------------------------------------------------------
-
-    /**
-     * \brief Constructor.
-     */
-    CachingDeviceAllocator(
-        unsigned int    bin_growth,                             ///< Geometric growth factor for bin-sizes
-        unsigned int    min_bin             = 1,                ///< Minimum bin (default is bin_growth ^ 1)
-        unsigned int    max_bin             = INVALID_BIN,      ///< Maximum bin (default is no max bin)
-        size_t          max_cached_bytes    = INVALID_SIZE,     ///< Maximum aggregate cached bytes per device (default is no limit)
-        bool            skip_cleanup        = false,            ///< Whether or not to skip a call to \p FreeAllCached() when the destructor is called (default is to deallocate)
-        bool            debug               = false)            ///< Whether or not to print (de)allocation events to stdout (default is no stderr output)
-    :
-        bin_growth(bin_growth),
-        min_bin(min_bin),
-        max_bin(max_bin),
-        min_bin_bytes(IntPow(bin_growth, min_bin)),
-        max_bin_bytes(IntPow(bin_growth, max_bin)),
-        max_cached_bytes(max_cached_bytes),
-        skip_cleanup(skip_cleanup),
-        debug(debug),
-        cached_blocks(BlockDescriptor::SizeCompare),
-        live_blocks(BlockDescriptor::PtrCompare)
-    {}
-
-
-    /**
-     * \brief Default constructor.
-     *
-     * Configured with:
-     * \par
-     * - \p bin_growth          = 8
-     * - \p min_bin             = 3
-     * - \p max_bin             = 7
-     * - \p max_cached_bytes    = (\p bin_growth ^ \p max_bin) * 3) - 1 = 6,291,455 bytes
-     *
-     * which delineates five bin-sizes: 512B, 4KB, 32KB, 256KB, and 2MB and
-     * sets a maximum of 6,291,455 cached bytes per device
-     */
-    CachingDeviceAllocator(
-        bool skip_cleanup = false,
-        bool debug = false)
-    :
-        bin_growth(8),
-        min_bin(3),
-        max_bin(7),
-        min_bin_bytes(IntPow(bin_growth, min_bin)),
-        max_bin_bytes(IntPow(bin_growth, max_bin)),
-        max_cached_bytes((max_bin_bytes * 3) - 1),
-        skip_cleanup(skip_cleanup),
-        debug(debug),
-        cached_blocks(BlockDescriptor::SizeCompare),
-        live_blocks(BlockDescriptor::PtrCompare)
-    {}
-
-
-    /**
-     * \brief Sets the limit on the number bytes this allocator is allowed to cache per device.
-     *
-     * Changing the ceiling of cached bytes does not cause any allocations (in-use or
-     * cached-in-reserve) to be freed.  See \p FreeAllCached().
-     */
-    cudaError_t SetMaxCachedBytes(
-        size_t max_cached_bytes)
-    {
-        // Lock
-        mutex.Lock();
-
-        if (debug) _CubLog("Changing max_cached_bytes (%lld -> %lld)\n", (long long) this->max_cached_bytes, (long long) max_cached_bytes);
-
-        this->max_cached_bytes = max_cached_bytes;
-
-        // Unlock
-        mutex.Unlock();
-
-        return cudaSuccess;
-    }
-
-
-    /**
-     * \brief Provides a suitable allocation of device memory for the given size on the specified device.
-     *
-     * Once freed, the allocation becomes available immediately for reuse within the \p active_stream
-     * with which it was associated with during allocation, and it becomes available for reuse within other
-     * streams when all prior work submitted to \p active_stream has completed.
-     */
-    cudaError_t DeviceAllocate(
-        int             device,             ///< [in] Device on which to place the allocation
-        void            **d_ptr,            ///< [out] Reference to pointer to the allocation
-        size_t          bytes,              ///< [in] Minimum number of bytes for the allocation
-        cudaStream_t    active_stream = 0)  ///< [in] The stream to be associated with this allocation
-    {
-        *d_ptr                          = NULL;
-        int entrypoint_device           = INVALID_DEVICE_ORDINAL;
-        cudaError_t error               = cudaSuccess;
-
-        if (device == INVALID_DEVICE_ORDINAL)
-        {
-            if (CubDebug(error = cudaGetDevice(&entrypoint_device))) return error;
-            device = entrypoint_device;
-        }
-
-        // Create a block descriptor for the requested allocation
-        bool found = false;
-        BlockDescriptor search_key(device);
-        search_key.associated_stream = active_stream;
-        NearestPowerOf(search_key.bin, search_key.bytes, bin_growth, bytes);
-
-        if (search_key.bin > max_bin)
-        {
-            // Bin is greater than our maximum bin: allocate the request
-            // exactly and give out-of-bounds bin.  It will not be cached
-            // for reuse when returned.
-            search_key.bin      = INVALID_BIN;
-            search_key.bytes    = bytes;
-        }
-        else
-        {
-            // Search for a suitable cached allocation: lock
-            mutex.Lock();
-
-            if (search_key.bin < min_bin)
-            {
-                // Bin is less than minimum bin: round up
-                search_key.bin      = min_bin;
-                search_key.bytes    = min_bin_bytes;
-            }
-
-            // Iterate through the range of cached blocks on the same device in the same bin
-            CachedBlocks::iterator block_itr = cached_blocks.lower_bound(search_key);
-            while ((block_itr != cached_blocks.end())
-                    && (block_itr->device == device)
-                    && (block_itr->bin == search_key.bin))
-            {
-                // To prevent races with reusing blocks returned by the host but still
-                // in use by the device, only consider cached blocks that are
-                // either (from the active stream) or (from an idle stream)
-                if ((active_stream == block_itr->associated_stream) ||
-                    (cudaEventQuery(block_itr->ready_event) != cudaErrorNotReady))
-                {
-                    // Reuse existing cache block.  Insert into live blocks.
-                    found = true;
-                    search_key = *block_itr;
-                    search_key.associated_stream = active_stream;
-                    live_blocks.insert(search_key);
-
-                    // Remove from free blocks
-                    cached_bytes[device].free -= search_key.bytes;
-                    cached_bytes[device].live += search_key.bytes;
-
-                    if (debug) _CubLog("\tDevice %d reused cached block at %p (%lld bytes) for stream %lld (previously associated with stream %lld).\n",
-                        device, search_key.d_ptr, (long long) search_key.bytes, (long long) search_key.associated_stream, (long long)  block_itr->associated_stream);
-
-                    cached_blocks.erase(block_itr);
-
-                    break;
-                }
-                block_itr++;
-            }
-
-            // Done searching: unlock
-            mutex.Unlock();
-        }
-
-        // Allocate the block if necessary
-        if (!found)
-        {
-            // Set runtime's current device to specified device (entrypoint may not be set)
-            if (device != entrypoint_device)
-            {
-                if (CubDebug(error = cudaGetDevice(&entrypoint_device))) return error;
-                if (CubDebug(error = cudaSetDevice(device))) return error;
-            }
-
-            // Attempt to allocate
-            if (CubDebug(error = cudaMalloc(&search_key.d_ptr, search_key.bytes)) == cudaErrorMemoryAllocation)
-            {
-                // The allocation attempt failed: free all cached blocks on device and retry
-                if (debug) _CubLog("\tDevice %d failed to allocate %lld bytes for stream %lld, retrying after freeing cached allocations",
-                      device, (long long) search_key.bytes, (long long) search_key.associated_stream);
-
-                error = cudaSuccess;    // Reset the error we will return
-                cudaGetLastError();     // Reset CUDART's error
-
-                // Lock
-                mutex.Lock();
-
-                // Iterate the range of free blocks on the same device
-                BlockDescriptor free_key(device);
-                CachedBlocks::iterator block_itr = cached_blocks.lower_bound(free_key);
-
-                while ((block_itr != cached_blocks.end()) && (block_itr->device == device))
-                {
-                    // No need to worry about synchronization with the device: cudaFree is
-                    // blocking and will synchronize across all kernels executing
-                    // on the current device
-
-                    // Free device memory and destroy stream event.
-                    if (CubDebug(error = cudaFree(block_itr->d_ptr))) break;
-                    if (CubDebug(error = cudaEventDestroy(block_itr->ready_event))) break;
-
-                    // Reduce balance and erase entry
-                    cached_bytes[device].free -= block_itr->bytes;
-
-                    if (debug) _CubLog("\tDevice %d freed %lld bytes.\n\t\t  %lld available blocks cached (%lld bytes), %lld live blocks (%lld bytes) outstanding.\n",
-                        device, (long long) block_itr->bytes, (long long) cached_blocks.size(), (long long) cached_bytes[device].free, (long long) live_blocks.size(), (long long) cached_bytes[device].live);
-
-                    cached_blocks.erase(block_itr);
-
-                    block_itr++;
-                }
-
-                // Unlock
-                mutex.Unlock();
-
-                // Return under error
-                if (error) return error;
-
-                // Try to allocate again
-                if (CubDebug(error = cudaMalloc(&search_key.d_ptr, search_key.bytes))) return error;
-            }
-
-            // Create ready event
-            if (CubDebug(error = cudaEventCreateWithFlags(&search_key.ready_event, cudaEventDisableTiming)))
-                return error;
-
-            // Insert into live blocks
-            mutex.Lock();
-            live_blocks.insert(search_key);
-            cached_bytes[device].live += search_key.bytes;
-            mutex.Unlock();
-
-            if (debug) _CubLog("\tDevice %d allocated new device block at %p (%lld bytes associated with stream %lld).\n",
-                      device, search_key.d_ptr, (long long) search_key.bytes, (long long) search_key.associated_stream);
-
-            // Attempt to revert back to previous device if necessary
-            if ((entrypoint_device != INVALID_DEVICE_ORDINAL) && (entrypoint_device != device))
-            {
-                if (CubDebug(error = cudaSetDevice(entrypoint_device))) return error;
-            }
-        }
-
-        // Copy device pointer to output parameter
-        *d_ptr = search_key.d_ptr;
-
-        if (debug) _CubLog("\t\t%lld available blocks cached (%lld bytes), %lld live blocks outstanding(%lld bytes).\n",
-            (long long) cached_blocks.size(), (long long) cached_bytes[device].free, (long long) live_blocks.size(), (long long) cached_bytes[device].live);
-
-        return error;
-    }
-
-
-    /**
-     * \brief Provides a suitable allocation of device memory for the given size on the current device.
-     *
-     * Once freed, the allocation becomes available immediately for reuse within the \p active_stream
-     * with which it was associated with during allocation, and it becomes available for reuse within other
-     * streams when all prior work submitted to \p active_stream has completed.
-     */
-    cudaError_t DeviceAllocate(
-        void            **d_ptr,            ///< [out] Reference to pointer to the allocation
-        size_t          bytes,              ///< [in] Minimum number of bytes for the allocation
-        cudaStream_t    active_stream = 0)  ///< [in] The stream to be associated with this allocation
-    {
-        return DeviceAllocate(INVALID_DEVICE_ORDINAL, d_ptr, bytes, active_stream);
-    }
-
-
-    /**
-     * \brief Frees a live allocation of device memory on the specified device, returning it to the allocator.
-     *
-     * Once freed, the allocation becomes available immediately for reuse within the \p active_stream
-     * with which it was associated with during allocation, and it becomes available for reuse within other
-     * streams when all prior work submitted to \p active_stream has completed.
-     */
-    cudaError_t DeviceFree(
-        int             device,
-        void*           d_ptr)
-    {
-        int entrypoint_device           = INVALID_DEVICE_ORDINAL;
-        cudaError_t error               = cudaSuccess;
-
-        if (device == INVALID_DEVICE_ORDINAL)
-        {
-            if (CubDebug(error = cudaGetDevice(&entrypoint_device)))
-                return error;
-            device = entrypoint_device;
-        }
-
-        // Lock
-        mutex.Lock();
-
-        // Find corresponding block descriptor
-        bool recached = false;
-        BlockDescriptor search_key(d_ptr, device);
-        BusyBlocks::iterator block_itr = live_blocks.find(search_key);
-        if (block_itr != live_blocks.end())
-        {
-            // Remove from live blocks
-            search_key = *block_itr;
-            live_blocks.erase(block_itr);
-            cached_bytes[device].live -= search_key.bytes;
-
-            // Keep the returned allocation if bin is valid and we won't exceed the max cached threshold
-            if ((search_key.bin != INVALID_BIN) && (cached_bytes[device].free + search_key.bytes <= max_cached_bytes))
-            {
-                // Insert returned allocation into free blocks
-                recached = true;
-                cached_blocks.insert(search_key);
-                cached_bytes[device].free += search_key.bytes;
-
-                if (debug) _CubLog("\tDevice %d returned %lld bytes from associated stream %lld.\n\t\t %lld available blocks cached (%lld bytes), %lld live blocks outstanding. (%lld bytes)\n",
-                    device, (long long) search_key.bytes, (long long) search_key.associated_stream, (long long) cached_blocks.size(),
-                    (long long) cached_bytes[device].free, (long long) live_blocks.size(), (long long) cached_bytes[device].live);
-            }
-        }
-
-        // Unlock
-        mutex.Unlock();
-
-        // First set to specified device (entrypoint may not be set)
-        if (device != entrypoint_device)
-        {
-            if (CubDebug(error = cudaGetDevice(&entrypoint_device))) return error;
-            if (CubDebug(error = cudaSetDevice(device))) return error;
-        }
-
-        if (recached)
-        {
-            // Insert the ready event in the associated stream (must have current device set properly)
-            if (CubDebug(error = cudaEventRecord(search_key.ready_event, search_key.associated_stream))) return error;
-        }
-        else
-        {
-            // Free the allocation from the runtime and cleanup the event.
-            if (CubDebug(error = cudaFree(d_ptr))) return error;
-            if (CubDebug(error = cudaEventDestroy(search_key.ready_event))) return error;
-
-            if (debug) _CubLog("\tDevice %d freed %lld bytes from associated stream %lld.\n\t\t  %lld available blocks cached (%lld bytes), %lld live blocks (%lld bytes) outstanding.\n",
-                device, (long long) search_key.bytes, (long long) search_key.associated_stream, (long long) cached_blocks.size(), (long long) cached_bytes[device].free, (long long) live_blocks.size(), (long long) cached_bytes[device].live);
-        }
-
-        // Reset device
-        if ((entrypoint_device != INVALID_DEVICE_ORDINAL) && (entrypoint_device != device))
-        {
-            if (CubDebug(error = cudaSetDevice(entrypoint_device))) return error;
-        }
-
-        return error;
-    }
-
-
-    /**
-     * \brief Frees a live allocation of device memory on the current device, returning it to the allocator.
-     *
-     * Once freed, the allocation becomes available immediately for reuse within the \p active_stream
-     * with which it was associated with during allocation, and it becomes available for reuse within other
-     * streams when all prior work submitted to \p active_stream has completed.
-     */
-    cudaError_t DeviceFree(
-        void*           d_ptr)
-    {
-        return DeviceFree(INVALID_DEVICE_ORDINAL, d_ptr);
-    }
-
-
-    /**
-     * \brief Frees all cached device allocations on all devices
-     */
-    cudaError_t FreeAllCached()
-    {
-        cudaError_t error         = cudaSuccess;
-        int entrypoint_device     = INVALID_DEVICE_ORDINAL;
-        int current_device        = INVALID_DEVICE_ORDINAL;
-
-        mutex.Lock();
-
-        while (!cached_blocks.empty())
-        {
-            // Get first block
-            CachedBlocks::iterator begin = cached_blocks.begin();
-
-            // Get entry-point device ordinal if necessary
-            if (entrypoint_device == INVALID_DEVICE_ORDINAL)
-            {
-                if (CubDebug(error = cudaGetDevice(&entrypoint_device))) break;
-            }
-
-            // Set current device ordinal if necessary
-            if (begin->device != current_device)
-            {
-                if (CubDebug(error = cudaSetDevice(begin->device))) break;
-                current_device = begin->device;
-            }
-
-            // Free device memory
-            if (CubDebug(error = cudaFree(begin->d_ptr))) break;
-            if (CubDebug(error = cudaEventDestroy(begin->ready_event))) break;
-
-            // Reduce balance and erase entry
-            cached_bytes[current_device].free -= begin->bytes;
-
-            if (debug) _CubLog("\tDevice %d freed %lld bytes.\n\t\t  %lld available blocks cached (%lld bytes), %lld live blocks (%lld bytes) outstanding.\n",
-                current_device, (long long) begin->bytes, (long long) cached_blocks.size(), (long long) cached_bytes[current_device].free, (long long) live_blocks.size(), (long long) cached_bytes[current_device].live);
-
-            cached_blocks.erase(begin);
-        }
-
-        mutex.Unlock();
-
-        // Attempt to revert back to entry-point device if necessary
-        if (entrypoint_device != INVALID_DEVICE_ORDINAL)
-        {
-            if (CubDebug(error = cudaSetDevice(entrypoint_device))) return error;
-        }
-
-        return error;
-    }
-
-
-    /**
-     * \brief Destructor
-     */
-    virtual ~CachingDeviceAllocator()
-    {
-        if (!skip_cleanup)
-            FreeAllCached();
-    }
-
-};
-
-
-
-
-/** @} */       // end group UtilMgmt
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_arch.cuh b/ml-xgboost/cub/cub/util_arch.cuh
deleted file mode 100644
index 2be8b8f..0000000
--- a/ml-xgboost/cub/cub/util_arch.cuh
+++ /dev/null
@@ -1,144 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Static architectural properties by SM version.
- */
-
-#pragma once
-
-#include "util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-#if (__CUDACC_VER_MAJOR__ >= 9)
-#define CUB_USE_COOPERATIVE_GROUPS
-#endif
-
-/// CUB_PTX_ARCH reflects the PTX version targeted by the active compiler pass (or zero during the host pass).
-#ifndef CUB_PTX_ARCH
-    #ifndef __CUDA_ARCH__
-        #define CUB_PTX_ARCH 0
-    #else
-        #define CUB_PTX_ARCH __CUDA_ARCH__
-    #endif
-#endif
-
-
-/// Whether or not the source targeted by the active compiler pass is allowed to  invoke device kernels or methods from the CUDA runtime API.
-#ifndef CUB_RUNTIME_FUNCTION
-    #if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__>= 350 && defined(__CUDACC_RDC__))
-        #define CUB_RUNTIME_ENABLED
-        #define CUB_RUNTIME_FUNCTION __host__ __device__
-    #else
-        #define CUB_RUNTIME_FUNCTION __host__
-    #endif
-#endif
-
-
-/// Number of threads per warp
-#ifndef CUB_LOG_WARP_THREADS
-    #define CUB_LOG_WARP_THREADS(arch)                      \
-        (5)
-    #define CUB_WARP_THREADS(arch)                          \
-        (1 << CUB_LOG_WARP_THREADS(arch))
-
-    #define CUB_PTX_WARP_THREADS        CUB_WARP_THREADS(CUB_PTX_ARCH)
-    #define CUB_PTX_LOG_WARP_THREADS    CUB_LOG_WARP_THREADS(CUB_PTX_ARCH)
-#endif
-
-
-/// Number of smem banks
-#ifndef CUB_LOG_SMEM_BANKS
-    #define CUB_LOG_SMEM_BANKS(arch)                        \
-        ((arch >= 200) ?                                    \
-            (5) :                                           \
-            (4))
-    #define CUB_SMEM_BANKS(arch)                            \
-        (1 << CUB_LOG_SMEM_BANKS(arch))
-
-    #define CUB_PTX_LOG_SMEM_BANKS      CUB_LOG_SMEM_BANKS(CUB_PTX_ARCH)
-    #define CUB_PTX_SMEM_BANKS          CUB_SMEM_BANKS(CUB_PTX_ARCH)
-#endif
-
-
-/// Oversubscription factor
-#ifndef CUB_SUBSCRIPTION_FACTOR
-    #define CUB_SUBSCRIPTION_FACTOR(arch)                   \
-        ((arch >= 300) ?                                    \
-            (5) :                                           \
-            ((arch >= 200) ?                                \
-                (3) :                                       \
-                (10)))
-    #define CUB_PTX_SUBSCRIPTION_FACTOR             CUB_SUBSCRIPTION_FACTOR(CUB_PTX_ARCH)
-#endif
-
-
-/// Prefer padding overhead vs X-way conflicts greater than this threshold
-#ifndef CUB_PREFER_CONFLICT_OVER_PADDING
-    #define CUB_PREFER_CONFLICT_OVER_PADDING(arch)          \
-        ((arch >= 300) ?                                    \
-            (1) :                                           \
-            (4))
-    #define CUB_PTX_PREFER_CONFLICT_OVER_PADDING    CUB_PREFER_CONFLICT_OVER_PADDING(CUB_PTX_ARCH)
-#endif
-
-
-/// Scale down the number of warps to keep same amount of "tile" storage as the nominal configuration for 4B data.  Minimum of two warps.
-#define CUB_BLOCK_THREADS(NOMINAL_4B_BLOCK_THREADS, T, PTX_ARCH)                        \
-    (CUB_MIN(                                                                           \
-        NOMINAL_4B_BLOCK_THREADS * 2,                                                   \
-    	CUB_WARP_THREADS(PTX_ARCH) * CUB_MAX(                                           \
-    		(NOMINAL_4B_BLOCK_THREADS / CUB_WARP_THREADS(PTX_ARCH)) * 3 / 4,            \
-            (NOMINAL_4B_BLOCK_THREADS / CUB_WARP_THREADS(PTX_ARCH)) * 4 / sizeof(T))))
-
-/// Scale up/down number of items per thread to keep the same amount of "tile" storage as the nominal configuration for 4B data.  Minimum 1 item per thread
-#define CUB_ITEMS_PER_THREAD(NOMINAL_4B_ITEMS_PER_THREAD, NOMINAL_4B_BLOCK_THREADS, T, PTX_ARCH)    \
-	(CUB_MIN(                                                                           \
-        NOMINAL_4B_ITEMS_PER_THREAD * 2,                                                \
-		CUB_MAX(                                                                        \
-		    1,                                                                          \
-            (NOMINAL_4B_ITEMS_PER_THREAD * NOMINAL_4B_BLOCK_THREADS * 4 / sizeof(T)) / CUB_BLOCK_THREADS(NOMINAL_4B_BLOCK_THREADS, T, PTX_ARCH))))
-
-
-#define CUB_NOMINAL_CONFIG(NOMINAL_4B_BLOCK_THREADS, NOMINAL_4B_ITEMS_PER_THREAD, T)            \
-		CUB_BLOCK_THREADS(NOMINAL_4B_BLOCK_THREADS, T, 200),                            \
-		CUB_ITEMS_PER_THREAD(NOMINAL_4B_ITEMS_PER_THREAD, NOMINAL_4B_BLOCK_THREADS, T, 200)
-
-
-#endif  // Do not document
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_debug.cuh b/ml-xgboost/cub/cub/util_debug.cuh
deleted file mode 100644
index 168b433..0000000
--- a/ml-xgboost/cub/cub/util_debug.cuh
+++ /dev/null
@@ -1,145 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Error and event logging routines.
- *
- * The following macros definitions are supported:
- * - \p CUB_LOG.  Simple event messages are printed to \p stdout.
- */
-
-#pragma once
-
-#include <stdio.h>
-#include "util_namespace.cuh"
-#include "util_arch.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilMgmt
- * @{
- */
-
-
-/// CUB error reporting macro (prints error messages to stderr)
-#if (defined(DEBUG) || defined(_DEBUG)) && !defined(CUB_STDERR)
-    #define CUB_STDERR
-#endif
-
-
-
-/**
- * \brief %If \p CUB_STDERR is defined and \p error is not \p cudaSuccess, the corresponding error message is printed to \p stderr (or \p stdout in device code) along with the supplied source context.
- *
- * \return The CUDA error.
- */
-__host__ __device__ __forceinline__ cudaError_t Debug(
-    cudaError_t     error,
-    const char*     filename,
-    int             line)
-{
-    (void)filename;
-    (void)line;
-#ifdef CUB_STDERR
-    if (error)
-    {
-    #if (CUB_PTX_ARCH == 0)
-        fprintf(stderr, "CUDA error %d [%s, %d]: %s\n", error, filename, line, cudaGetErrorString(error));
-        fflush(stderr);
-    #elif (CUB_PTX_ARCH >= 200)
-        printf("CUDA error %d [block (%d,%d,%d) thread (%d,%d,%d), %s, %d]\n", error, blockIdx.z, blockIdx.y, blockIdx.x, threadIdx.z, threadIdx.y, threadIdx.x, filename, line);
-    #endif
-    }
-#endif
-    return error;
-}
-
-
-/**
- * \brief Debug macro
- */
-#ifndef CubDebug
-    #define CubDebug(e) cub::Debug((e), __FILE__, __LINE__)
-#endif
-
-
-/**
- * \brief Debug macro with exit
- */
-#ifndef CubDebugExit
-    #define CubDebugExit(e) if (cub::Debug((e), __FILE__, __LINE__)) { exit(1); }
-#endif
-
-
-/**
- * \brief Log macro for printf statements.
- */
-#if !defined(_CubLog)
-    #if !(defined(__clang__) && defined(__CUDA__))
-        #if (CUB_PTX_ARCH == 0)
-            #define _CubLog(format, ...) printf(format,__VA_ARGS__);
-        #elif (CUB_PTX_ARCH >= 200)
-            #define _CubLog(format, ...) printf("[block (%d,%d,%d), thread (%d,%d,%d)]: " format, blockIdx.z, blockIdx.y, blockIdx.x, threadIdx.z, threadIdx.y, threadIdx.x, __VA_ARGS__);
-        #endif
-    #else
-        // XXX shameless hack for clang around variadic printf...
-        //     Compilies w/o supplying -std=c++11 but shows warning,
-        //     so we sielence them :)
-        #pragma clang diagnostic ignored "-Wc++11-extensions"
-        #pragma clang diagnostic ignored "-Wunnamed-type-template-args"
-            template <class... Args>
-            inline __host__ __device__ void va_printf(char const* format, Args const&... args)
-            {
-        #ifdef __CUDA_ARCH__
-              printf(format, blockIdx.z, blockIdx.y, blockIdx.x, threadIdx.z, threadIdx.y, threadIdx.x, args...);
-        #else
-              printf(format, args...);
-        #endif
-            }
-        #ifndef __CUDA_ARCH__
-            #define _CubLog(format, ...) va_printf(format,__VA_ARGS__);
-        #else
-            #define _CubLog(format, ...) va_printf("[block (%d,%d,%d), thread (%d,%d,%d)]: " format, __VA_ARGS__);
-        #endif
-    #endif
-#endif
-
-
-
-
-/** @} */       // end group UtilMgmt
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_device.cuh b/ml-xgboost/cub/cub/util_device.cuh
deleted file mode 100644
index 38ab5b0..0000000
--- a/ml-xgboost/cub/cub/util_device.cuh
+++ /dev/null
@@ -1,347 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Properties of a given CUDA device and the corresponding PTX bundle
- */
-
-#pragma once
-
-#include "util_type.cuh"
-#include "util_arch.cuh"
-#include "util_debug.cuh"
-#include "util_namespace.cuh"
-#include "util_macro.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilMgmt
- * @{
- */
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-/**
- * Alias temporaries to externally-allocated device storage (or simply return the amount of storage needed).
- */
-template <int ALLOCATIONS>
-__host__ __device__ __forceinline__
-cudaError_t AliasTemporaries(
-    void    *d_temp_storage,                    ///< [in] %Device-accessible allocation of temporary storage.  When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
-    size_t  &temp_storage_bytes,                ///< [in,out] Size in bytes of \t d_temp_storage allocation
-    void*   (&allocations)[ALLOCATIONS],        ///< [in,out] Pointers to device allocations needed
-    size_t  (&allocation_sizes)[ALLOCATIONS])   ///< [in] Sizes in bytes of device allocations needed
-{
-    const int ALIGN_BYTES   = 256;
-    const int ALIGN_MASK    = ~(ALIGN_BYTES - 1);
-
-    // Compute exclusive prefix sum over allocation requests
-    size_t allocation_offsets[ALLOCATIONS];
-    size_t bytes_needed = 0;
-    for (int i = 0; i < ALLOCATIONS; ++i)
-    {
-        size_t allocation_bytes = (allocation_sizes[i] + ALIGN_BYTES - 1) & ALIGN_MASK;
-        allocation_offsets[i] = bytes_needed;
-        bytes_needed += allocation_bytes;
-    }
-    bytes_needed += ALIGN_BYTES - 1;
-
-    // Check if the caller is simply requesting the size of the storage allocation
-    if (!d_temp_storage)
-    {
-        temp_storage_bytes = bytes_needed;
-        return cudaSuccess;
-    }
-
-    // Check if enough storage provided
-    if (temp_storage_bytes < bytes_needed)
-    {
-        return CubDebug(cudaErrorInvalidValue);
-    }
-
-    // Alias
-    d_temp_storage = (void *) ((size_t(d_temp_storage) + ALIGN_BYTES - 1) & ALIGN_MASK);
-    for (int i = 0; i < ALLOCATIONS; ++i)
-    {
-        allocations[i] = static_cast<char*>(d_temp_storage) + allocation_offsets[i];
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Empty kernel for querying PTX manifest metadata (e.g., version) for the current device
- */
-template <typename T>
-__global__ void EmptyKernel(void) { }
-
-
-#endif  // DOXYGEN_SHOULD_SKIP_THIS
-
-/**
- * \brief Retrieves the PTX version that will be used on the current device (major * 100 + minor * 10)
- */
-CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t PtxVersion(int &ptx_version)
-{
-    struct Dummy
-    {
-        /// Type definition of the EmptyKernel kernel entry point
-        typedef void (*EmptyKernelPtr)();
-
-        /// Force EmptyKernel<void> to be generated if this class is used
-        CUB_RUNTIME_FUNCTION __forceinline__
-        EmptyKernelPtr Empty()
-        {
-            return EmptyKernel<void>;
-        }
-    };
-
-
-#ifndef CUB_RUNTIME_ENABLED
-    (void)ptx_version;
-
-    // CUDA API calls not supported from this device
-    return cudaErrorInvalidConfiguration;
-
-#elif (CUB_PTX_ARCH > 0)
-
-    ptx_version = CUB_PTX_ARCH;
-    return cudaSuccess;
-
-#else
-
-    cudaError_t error = cudaSuccess;
-    do
-    {
-        cudaFuncAttributes empty_kernel_attrs;
-        if (CubDebug(error = cudaFuncGetAttributes(&empty_kernel_attrs, EmptyKernel<void>))) break;
-        ptx_version = empty_kernel_attrs.ptxVersion * 10;
-    }
-    while (0);
-
-    return error;
-
-#endif
-}
-
-
-/**
- * \brief Retrieves the SM version (major * 100 + minor * 10)
- */
-CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t SmVersion(int &sm_version, int device_ordinal)
-{
-#ifndef CUB_RUNTIME_ENABLED
-    (void)sm_version;
-    (void)device_ordinal;
-
-    // CUDA API calls not supported from this device
-    return cudaErrorInvalidConfiguration;
-
-#else
-
-    cudaError_t error = cudaSuccess;
-    do
-    {
-        // Fill in SM version
-        int major, minor;
-        if (CubDebug(error = cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, device_ordinal))) break;
-        if (CubDebug(error = cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, device_ordinal))) break;
-        sm_version = major * 100 + minor * 10;
-    }
-    while (0);
-
-    return error;
-
-#endif
-}
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-/**
- * Synchronize the stream if specified
- */
-CUB_RUNTIME_FUNCTION __forceinline__
-static cudaError_t SyncStream(cudaStream_t stream)
-{
-#if (CUB_PTX_ARCH == 0)
-    return cudaStreamSynchronize(stream);
-#else
-    (void)stream;
-    // Device can't yet sync on a specific stream
-    return cudaDeviceSynchronize();
-#endif
-}
-
-
-/**
- * \brief Computes maximum SM occupancy in thread blocks for executing the given kernel function pointer \p kernel_ptr on the current device with \p block_threads per thread block.
- *
- * \par Snippet
- * The code snippet below illustrates the use of the MaxSmOccupancy function.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/util_device.cuh>
- *
- * template <typename T>
- * __global__ void ExampleKernel()
- * {
- *     // Allocate shared memory for BlockScan
- *     __shared__ volatile T buffer[4096];
- *
- *        ...
- * }
- *
- *     ...
- *
- * // Determine SM occupancy for ExampleKernel specialized for unsigned char
- * int max_sm_occupancy;
- * MaxSmOccupancy(max_sm_occupancy, ExampleKernel<unsigned char>, 64);
- *
- * // max_sm_occupancy  <-- 4 on SM10
- * // max_sm_occupancy  <-- 8 on SM20
- * // max_sm_occupancy  <-- 12 on SM35
- *
- * \endcode
- *
- */
-template <typename KernelPtr>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t MaxSmOccupancy(
-    int                 &max_sm_occupancy,          ///< [out] maximum number of thread blocks that can reside on a single SM
-    KernelPtr           kernel_ptr,                 ///< [in] Kernel pointer for which to compute SM occupancy
-    int                 block_threads,              ///< [in] Number of threads per thread block
-    int                 dynamic_smem_bytes = 0)
-{
-#ifndef CUB_RUNTIME_ENABLED
-    (void)dynamic_smem_bytes;
-    (void)block_threads;
-    (void)kernel_ptr;
-    (void)max_sm_occupancy;
-
-    // CUDA API calls not supported from this device
-    return CubDebug(cudaErrorInvalidConfiguration);
-
-#else
-
-    return cudaOccupancyMaxActiveBlocksPerMultiprocessor (
-        &max_sm_occupancy,
-        kernel_ptr,
-        block_threads,
-        dynamic_smem_bytes);
-
-#endif  // CUB_RUNTIME_ENABLED
-}
-
-
-/******************************************************************************
- * Policy management
- ******************************************************************************/
-
-/**
- * Kernel dispatch configuration
- */
-struct KernelConfig
-{
-    int block_threads;
-    int items_per_thread;
-    int tile_size;
-    int sm_occupancy;
-
-    CUB_RUNTIME_FUNCTION __forceinline__
-    KernelConfig() : block_threads(0), items_per_thread(0), tile_size(0), sm_occupancy(0) {}
-
-    template <typename AgentPolicyT, typename KernelPtrT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    cudaError_t Init(KernelPtrT kernel_ptr)
-    {
-        block_threads        = AgentPolicyT::BLOCK_THREADS;
-        items_per_thread     = AgentPolicyT::ITEMS_PER_THREAD;
-        tile_size            = block_threads * items_per_thread;
-        cudaError_t retval   = MaxSmOccupancy(sm_occupancy, kernel_ptr, block_threads);
-        return retval;
-    }
-};
-
-
-
-/// Helper for dispatching into a policy chain
-template <int PTX_VERSION, typename PolicyT, typename PrevPolicyT>
-struct ChainedPolicy
-{
-   /// The policy for the active compiler pass
-   typedef typename If<(CUB_PTX_ARCH < PTX_VERSION), typename PrevPolicyT::ActivePolicy, PolicyT>::Type ActivePolicy;
-
-   /// Specializes and dispatches op in accordance to the first policy in the chain of adequate PTX version
-   template <typename FunctorT>
-   CUB_RUNTIME_FUNCTION __forceinline__
-   static cudaError_t Invoke(int ptx_version, FunctorT &op)
-   {
-       if (ptx_version < PTX_VERSION) {
-           return PrevPolicyT::Invoke(ptx_version, op);
-       }
-       return op.template Invoke<PolicyT>();
-   }
-};
-
-/// Helper for dispatching into a policy chain (end-of-chain specialization)
-template <int PTX_VERSION, typename PolicyT>
-struct ChainedPolicy<PTX_VERSION, PolicyT, PolicyT>
-{
-    /// The policy for the active compiler pass
-    typedef PolicyT ActivePolicy;
-
-    /// Specializes and dispatches op in accordance to the first policy in the chain of adequate PTX version
-    template <typename FunctorT>
-    CUB_RUNTIME_FUNCTION __forceinline__
-    static cudaError_t Invoke(int /*ptx_version*/, FunctorT &op) {
-        return op.template Invoke<PolicyT>();
-    }
-};
-
-
-
-
-#endif  // Do not document
-
-
-
-
-/** @} */       // end group UtilMgmt
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_macro.cuh b/ml-xgboost/cub/cub/util_macro.cuh
deleted file mode 100644
index 8c7756d..0000000
--- a/ml-xgboost/cub/cub/util_macro.cuh
+++ /dev/null
@@ -1,103 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Common C/C++ macro utilities
- ******************************************************************************/
-
-#pragma once
-
-#include "util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilModule
- * @{
- */
-
-#ifndef CUB_ALIGN
-    #if defined(_WIN32) || defined(_WIN64)
-        /// Align struct
-        #define CUB_ALIGN(bytes) __declspec(align(32))
-    #else
-        /// Align struct
-        #define CUB_ALIGN(bytes) __attribute__((aligned(bytes)))
-    #endif
-#endif
-
-#ifndef CUB_MAX
-    /// Select maximum(a, b)
-    #define CUB_MAX(a, b) (((b) > (a)) ? (b) : (a))
-#endif
-
-#ifndef CUB_MIN
-    /// Select minimum(a, b)
-    #define CUB_MIN(a, b) (((b) < (a)) ? (b) : (a))
-#endif
-
-#ifndef CUB_QUOTIENT_FLOOR
-    /// Quotient of x/y rounded down to nearest integer
-    #define CUB_QUOTIENT_FLOOR(x, y) ((x) / (y))
-#endif
-
-#ifndef CUB_QUOTIENT_CEILING
-    /// Quotient of x/y rounded up to nearest integer
-    #define CUB_QUOTIENT_CEILING(x, y) (((x) + (y) - 1) / (y))
-#endif
-
-#ifndef CUB_ROUND_UP_NEAREST
-    /// x rounded up to the nearest multiple of y
-    #define CUB_ROUND_UP_NEAREST(x, y) ((((x) + (y) - 1) / (y)) * y)
-#endif
-
-#ifndef CUB_ROUND_DOWN_NEAREST
-    /// x rounded down to the nearest multiple of y
-    #define CUB_ROUND_DOWN_NEAREST(x, y) (((x) / (y)) * y)
-#endif
-
-
-#ifndef CUB_STATIC_ASSERT
-    #ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-        #define CUB_CAT_(a, b) a ## b
-        #define CUB_CAT(a, b) CUB_CAT_(a, b)
-    #endif // DOXYGEN_SHOULD_SKIP_THIS
-
-    /// Static assert
-    #define CUB_STATIC_ASSERT(cond, msg) typedef int CUB_CAT(cub_static_assert, __LINE__)[(cond) ? 1 : -1]
-#endif
-
-/** @} */       // end group UtilModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_namespace.cuh b/ml-xgboost/cub/cub/util_namespace.cuh
deleted file mode 100644
index 8c05718..0000000
--- a/ml-xgboost/cub/cub/util_namespace.cuh
+++ /dev/null
@@ -1,46 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Place-holder for prefixing the cub namespace
- */
-
-#pragma once
-
-// For example:
-//#define CUB_NS_PREFIX namespace thrust{ namespace detail {
-//#define CUB_NS_POSTFIX } }
-
-#ifndef CUB_NS_PREFIX
-#define CUB_NS_PREFIX
-#endif
-
-#ifndef CUB_NS_POSTFIX
-#define CUB_NS_POSTFIX
-#endif
diff --git a/ml-xgboost/cub/cub/util_ptx.cuh b/ml-xgboost/cub/cub/util_ptx.cuh
deleted file mode 100644
index a15ce66..0000000
--- a/ml-xgboost/cub/cub/util_ptx.cuh
+++ /dev/null
@@ -1,673 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * PTX intrinsics
- */
-
-
-#pragma once
-
-#include "util_type.cuh"
-#include "util_arch.cuh"
-#include "util_namespace.cuh"
-#include "util_debug.cuh"
-
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilPtx
- * @{
- */
-
-
-/******************************************************************************
- * PTX helper macros
- ******************************************************************************/
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-/**
- * Register modifier for pointer-types (for inlining PTX assembly)
- */
-#if defined(_WIN64) || defined(__LP64__)
-    #define __CUB_LP64__ 1
-    // 64-bit register modifier for inlined asm
-    #define _CUB_ASM_PTR_ "l"
-    #define _CUB_ASM_PTR_SIZE_ "u64"
-#else
-    #define __CUB_LP64__ 0
-    // 32-bit register modifier for inlined asm
-    #define _CUB_ASM_PTR_ "r"
-    #define _CUB_ASM_PTR_SIZE_ "u32"
-#endif
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/******************************************************************************
- * Inlined PTX intrinsics
- ******************************************************************************/
-
-/**
- * \brief Shift-right then add.  Returns (\p x >> \p shift) + \p addend.
- */
-__device__ __forceinline__ unsigned int SHR_ADD(
-    unsigned int x,
-    unsigned int shift,
-    unsigned int addend)
-{
-    unsigned int ret;
-#if CUB_PTX_ARCH >= 200
-    asm volatile("vshr.u32.u32.u32.clamp.add %0, %1, %2, %3;" :
-        "=r"(ret) : "r"(x), "r"(shift), "r"(addend));
-#else
-    ret = (x >> shift) + addend;
-#endif
-    return ret;
-}
-
-
-/**
- * \brief Shift-left then add.  Returns (\p x << \p shift) + \p addend.
- */
-__device__ __forceinline__ unsigned int SHL_ADD(
-    unsigned int x,
-    unsigned int shift,
-    unsigned int addend)
-{
-    unsigned int ret;
-#if CUB_PTX_ARCH >= 200
-    asm volatile("vshl.u32.u32.u32.clamp.add %0, %1, %2, %3;" :
-        "=r"(ret) : "r"(x), "r"(shift), "r"(addend));
-#else
-    ret = (x << shift) + addend;
-#endif
-    return ret;
-}
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-/**
- * Bitfield-extract.
- */
-template <typename UnsignedBits, int BYTE_LEN>
-__device__ __forceinline__ unsigned int BFE(
-    UnsignedBits            source,
-    unsigned int            bit_start,
-    unsigned int            num_bits,
-    Int2Type<BYTE_LEN>      /*byte_len*/)
-{
-    unsigned int bits;
-#if CUB_PTX_ARCH >= 200
-    asm volatile("bfe.u32 %0, %1, %2, %3;" : "=r"(bits) : "r"((unsigned int) source), "r"(bit_start), "r"(num_bits));
-#else
-    const unsigned int MASK = (1 << num_bits) - 1;
-    bits = (source >> bit_start) & MASK;
-#endif
-    return bits;
-}
-
-
-/**
- * Bitfield-extract for 64-bit types.
- */
-template <typename UnsignedBits>
-__device__ __forceinline__ unsigned int BFE(
-    UnsignedBits            source,
-    unsigned int            bit_start,
-    unsigned int            num_bits,
-    Int2Type<8>             /*byte_len*/)
-{
-    const unsigned long long MASK = (1ull << num_bits) - 1;
-    return (source >> bit_start) & MASK;
-}
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-/**
- * \brief Bitfield-extract.  Extracts \p num_bits from \p source starting at bit-offset \p bit_start.  The input \p source may be an 8b, 16b, 32b, or 64b unsigned integer type.
- */
-template <typename UnsignedBits>
-__device__ __forceinline__ unsigned int BFE(
-    UnsignedBits source,
-    unsigned int bit_start,
-    unsigned int num_bits)
-{
-    return BFE(source, bit_start, num_bits, Int2Type<sizeof(UnsignedBits)>());
-}
-
-
-/**
- * \brief Bitfield insert.  Inserts the \p num_bits least significant bits of \p y into \p x at bit-offset \p bit_start.
- */
-__device__ __forceinline__ void BFI(
-    unsigned int &ret,
-    unsigned int x,
-    unsigned int y,
-    unsigned int bit_start,
-    unsigned int num_bits)
-{
-#if CUB_PTX_ARCH >= 200
-    asm volatile("bfi.b32 %0, %1, %2, %3, %4;" :
-        "=r"(ret) : "r"(y), "r"(x), "r"(bit_start), "r"(num_bits));
-#else
-    x <<= bit_start;
-    unsigned int MASK_X = ((1 << num_bits) - 1) << bit_start;
-    unsigned int MASK_Y = ~MASK_X;
-    ret = (y & MASK_Y) | (x & MASK_X);
-#endif
-}
-
-
-/**
- * \brief Three-operand add.  Returns \p x + \p y + \p z.
- */
-__device__ __forceinline__ unsigned int IADD3(unsigned int x, unsigned int y, unsigned int z)
-{
-#if CUB_PTX_ARCH >= 200
-    asm volatile("vadd.u32.u32.u32.add %0, %1, %2, %3;" : "=r"(x) : "r"(x), "r"(y), "r"(z));
-#else
-    x = x + y + z;
-#endif
-    return x;
-}
-
-
-/**
- * \brief Byte-permute. Pick four arbitrary bytes from two 32-bit registers, and reassemble them into a 32-bit destination register.  For SM2.0 or later.
- *
- * \par
- * The bytes in the two source registers \p a and \p b are numbered from 0 to 7:
- * {\p b, \p a} = {{b7, b6, b5, b4}, {b3, b2, b1, b0}}. For each of the four bytes
- * {b3, b2, b1, b0} selected in the return value, a 4-bit selector is defined within
- * the four lower "nibbles" of \p index: {\p index } = {n7, n6, n5, n4, n3, n2, n1, n0}
- *
- * \par Snippet
- * The code snippet below illustrates byte-permute.
- * \par
- * \code
- * #include <cub/cub.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     int a        = 0x03020100;
- *     int b        = 0x07060504;
- *     int index    = 0x00007531;
- *
- *     int selected = PRMT(a, b, index);    // 0x07050301
- *
- * \endcode
- *
- */
-__device__ __forceinline__ int PRMT(unsigned int a, unsigned int b, unsigned int index)
-{
-    int ret;
-    asm volatile("prmt.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(a), "r"(b), "r"(index));
-    return ret;
-}
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-/**
- * Sync-threads barrier.
- */
-__device__ __forceinline__ void BAR(int count)
-{
-    asm volatile("bar.sync 1, %0;" : : "r"(count));
-}
-
-/**
- * CTA barrier
- */
-__device__  __forceinline__ void CTA_SYNC()
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    __barrier_sync(0);
-#else
-    __syncthreads();
-#endif
-}
-
-
-/**
- * CTA barrier with predicate
- */
-__device__  __forceinline__ int CTA_SYNC_AND(int p)
-{
-    return __syncthreads_and(p);
-}
-
-
-/**
- * Warp barrier
- */
-__device__  __forceinline__ void WARP_SYNC(unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    __syncwarp(member_mask);
-#endif
-}
-
-
-/**
- * Warp any
- */
-__device__  __forceinline__ int WARP_ANY(int predicate, unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    return __any_sync(member_mask, predicate);
-#else
-    return ::__any(predicate);
-#endif
-}
-
-
-/**
- * Warp any
- */
-__device__  __forceinline__ int WARP_ALL(int predicate, unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    return __all_sync(member_mask, predicate);
-#else
-    return ::__all(predicate);
-#endif
-}
-
-
-/**
- * Warp ballot
- */
-__device__  __forceinline__ int WARP_BALLOT(int predicate, unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    return __ballot_sync(member_mask, predicate);
-#else
-    return __ballot(predicate);
-#endif
-}
-
-/**
- * Warp synchronous shfl_up
- */
-__device__ __forceinline__ 
-unsigned int SHFL_UP_SYNC(unsigned int word, int src_offset, int first_lane, unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    asm volatile("shfl.sync.up.b32 %0, %1, %2, %3, %4;"
-        : "=r"(word) : "r"(word), "r"(src_offset), "r"(first_lane), "r"(member_mask));
-#else
-    asm volatile("shfl.up.b32 %0, %1, %2, %3;"
-        : "=r"(word) : "r"(word), "r"(src_offset), "r"(first_lane));
-#endif
-    return word;
-}
-
-/**
- * Warp synchronous shfl_down
- */
-__device__ __forceinline__ 
-unsigned int SHFL_DOWN_SYNC(unsigned int word, int src_offset, int last_lane, unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    asm volatile("shfl.sync.down.b32 %0, %1, %2, %3, %4;"
-        : "=r"(word) : "r"(word), "r"(src_offset), "r"(last_lane), "r"(member_mask));
-#else
-    asm volatile("shfl.down.b32 %0, %1, %2, %3;"
-        : "=r"(word) : "r"(word), "r"(src_offset), "r"(last_lane));
-#endif
-    return word;
-}
-
-/**
- * Warp synchronous shfl_idx
- */
-__device__ __forceinline__ 
-unsigned int SHFL_IDX_SYNC(unsigned int word, int src_lane, int last_lane, unsigned int member_mask)
-{
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-    asm volatile("shfl.sync.idx.b32 %0, %1, %2, %3, %4;"
-        : "=r"(word) : "r"(word), "r"(src_lane), "r"(last_lane), "r"(member_mask));
-#else
-    asm volatile("shfl.idx.b32 %0, %1, %2, %3;"
-        : "=r"(word) : "r"(word), "r"(src_lane), "r"(last_lane));
-#endif
-    return word;
-}
-
-/**
- * Floating point multiply. (Mantissa LSB rounds towards zero.)
- */
-__device__ __forceinline__ float FMUL_RZ(float a, float b)
-{
-    float d;
-    asm volatile("mul.rz.f32 %0, %1, %2;" : "=f"(d) : "f"(a), "f"(b));
-    return d;
-}
-
-
-/**
- * Floating point multiply-add. (Mantissa LSB rounds towards zero.)
- */
-__device__ __forceinline__ float FFMA_RZ(float a, float b, float c)
-{
-    float d;
-    asm volatile("fma.rz.f32 %0, %1, %2, %3;" : "=f"(d) : "f"(a), "f"(b), "f"(c));
-    return d;
-}
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-/**
- * \brief Terminates the calling thread
- */
-__device__ __forceinline__ void ThreadExit() {
-    asm volatile("exit;");
-}    
-
-
-/**
- * \brief  Abort execution and generate an interrupt to the host CPU
- */
-__device__ __forceinline__ void ThreadTrap() {
-    asm volatile("trap;");
-}
-
-
-/**
- * \brief Returns the row-major linear thread identifier for a multidimensional threadblock
- */
-__device__ __forceinline__ int RowMajorTid(int block_dim_x, int block_dim_y, int block_dim_z)
-{
-    return ((block_dim_z == 1) ? 0 : (threadIdx.z * block_dim_x * block_dim_y)) +
-            ((block_dim_y == 1) ? 0 : (threadIdx.y * block_dim_x)) +
-            threadIdx.x;
-}
-
-
-/**
- * \brief Returns the warp lane ID of the calling thread
- */
-__device__ __forceinline__ unsigned int LaneId()
-{
-    unsigned int ret;
-    asm volatile("mov.u32 %0, %%laneid;" : "=r"(ret) );
-    return ret;
-}
-
-
-/**
- * \brief Returns the warp ID of the calling thread.  Warp ID is guaranteed to be unique among warps, but may not correspond to a zero-based ranking within the thread block.
- */
-__device__ __forceinline__ unsigned int WarpId()
-{
-    unsigned int ret;
-    asm volatile("mov.u32 %0, %%warpid;" : "=r"(ret) );
-    return ret;
-}
-
-/**
- * \brief Returns the warp lane mask of all lanes less than the calling thread
- */
-__device__ __forceinline__ unsigned int LaneMaskLt()
-{
-    unsigned int ret;
-    asm volatile("mov.u32 %0, %%lanemask_lt;" : "=r"(ret) );
-    return ret;
-}
-
-/**
- * \brief Returns the warp lane mask of all lanes less than or equal to the calling thread
- */
-__device__ __forceinline__ unsigned int LaneMaskLe()
-{
-    unsigned int ret;
-    asm volatile("mov.u32 %0, %%lanemask_le;" : "=r"(ret) );
-    return ret;
-}
-
-/**
- * \brief Returns the warp lane mask of all lanes greater than the calling thread
- */
-__device__ __forceinline__ unsigned int LaneMaskGt()
-{
-    unsigned int ret;
-    asm volatile("mov.u32 %0, %%lanemask_gt;" : "=r"(ret) );
-    return ret;
-}
-
-/**
- * \brief Returns the warp lane mask of all lanes greater than or equal to the calling thread
- */
-__device__ __forceinline__ unsigned int LaneMaskGe()
-{
-    unsigned int ret;
-    asm volatile("mov.u32 %0, %%lanemask_ge;" : "=r"(ret) );
-    return ret;
-}
-
-/** @} */       // end group UtilPtx
-
-
-
-/**
- * \brief Shuffle-up for any data type.  Each <em>warp-lane<sub>i</sub></em> obtains the value \p input contributed by <em>warp-lane</em><sub><em>i</em>-<tt>src_offset</tt></sub>.  For thread lanes \e i < src_offset, the thread's own \p input is returned to the thread. ![](shfl_up_logo.png)
- * \ingroup WarpModule
- *
- * \par
- * - Available only for SM3.0 or newer
- *
- * \par Snippet
- * The code snippet below illustrates each thread obtaining a \p double value from the
- * predecessor of its predecessor.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/util_ptx.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Obtain one input item per thread
- *     double thread_data = ...
- *
- *     // Obtain item from two ranks below
- *     double peer_data = ShuffleUp(thread_data, 2, 0, 0xffffffff);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the first warp of threads is <tt>{1.0, 2.0, 3.0, 4.0, 5.0, ..., 32.0}</tt>.
- * The corresponding output \p peer_data will be <tt>{1.0, 2.0, 1.0, 2.0, 3.0, ..., 30.0}</tt>.
- *
- */
-template <typename T>
-__device__ __forceinline__ T ShuffleUp(
-    T               input,              ///< [in] The value to broadcast
-    int             src_offset,         ///< [in] The relative down-offset of the peer to read from
-    int             first_lane,         ///< [in] Index of first lane in segment (typically 0)
-    unsigned int    member_mask)        ///< [in] 32-bit mask of participating warp lanes
-{
-    typedef typename UnitWord<T>::ShuffleWord ShuffleWord;
-
-    const int       WORDS           = (sizeof(T) + sizeof(ShuffleWord) - 1) / sizeof(ShuffleWord);
- 
-    T               output;
-    ShuffleWord     *output_alias   = reinterpret_cast<ShuffleWord *>(&output);
-    ShuffleWord     *input_alias    = reinterpret_cast<ShuffleWord *>(&input);
-
-    unsigned int shuffle_word;
-    shuffle_word = SHFL_UP_SYNC((unsigned int)input_alias[0], src_offset, first_lane, member_mask);
-    output_alias[0] = shuffle_word;
-
-    #pragma unroll
-    for (int WORD = 1; WORD < WORDS; ++WORD)
-    {
-        shuffle_word       = SHFL_UP_SYNC((unsigned int)input_alias[WORD], src_offset, first_lane, member_mask);
-        output_alias[WORD] = shuffle_word;
-    }
-
-    return output;
-}
-
-
-/**
- * \brief Shuffle-down for any data type.  Each <em>warp-lane<sub>i</sub></em> obtains the value \p input contributed by <em>warp-lane</em><sub><em>i</em>+<tt>src_offset</tt></sub>.  For thread lanes \e i >= WARP_THREADS, the thread's own \p input is returned to the thread.  ![](shfl_down_logo.png)
- * \ingroup WarpModule
- *
- * \par
- * - Available only for SM3.0 or newer
- *
- * \par Snippet
- * The code snippet below illustrates each thread obtaining a \p double value from the
- * successor of its successor.
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/util_ptx.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Obtain one input item per thread
- *     double thread_data = ...
- *
- *     // Obtain item from two ranks below
- *     double peer_data = ShuffleDown(thread_data, 2, 31, 0xffffffff);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the first warp of threads is <tt>{1.0, 2.0, 3.0, 4.0, 5.0, ..., 32.0}</tt>.
- * The corresponding output \p peer_data will be <tt>{3.0, 4.0, 5.0, 6.0, 7.0, ..., 32.0}</tt>.
- *
- */
-template <typename T>
-__device__ __forceinline__ T ShuffleDown(
-    T               input,              ///< [in] The value to broadcast
-    int             src_offset,         ///< [in] The relative up-offset of the peer to read from
-    int             last_lane,          ///< [in] Index of first lane in segment (typically 31)
-    unsigned int    member_mask)        ///< [in] 32-bit mask of participating warp lanes
-{
-    typedef typename UnitWord<T>::ShuffleWord ShuffleWord;
-
-    const int       WORDS           = (sizeof(T) + sizeof(ShuffleWord) - 1) / sizeof(ShuffleWord);
-
-    T               output;
-    ShuffleWord     *output_alias   = reinterpret_cast<ShuffleWord *>(&output);
-    ShuffleWord     *input_alias    = reinterpret_cast<ShuffleWord *>(&input);
-
-    unsigned int shuffle_word;
-    shuffle_word    = SHFL_DOWN_SYNC((unsigned int)input_alias[0], src_offset, last_lane, member_mask);
-    output_alias[0] = shuffle_word;
-
-    #pragma unroll
-    for (int WORD = 1; WORD < WORDS; ++WORD)
-    {
-        shuffle_word       = SHFL_DOWN_SYNC((unsigned int)input_alias[WORD], src_offset, last_lane, member_mask);
-        output_alias[WORD] = shuffle_word;
-    }
-
-    return output;
-}
-
-
-/**
- * \brief Shuffle-broadcast for any data type.  Each <em>warp-lane<sub>i</sub></em> obtains the value \p input
- * contributed by <em>warp-lane</em><sub><tt>src_lane</tt></sub>.  For \p src_lane < 0 or \p src_lane >= WARP_THREADS,
- * then the thread's own \p input is returned to the thread. ![](shfl_broadcast_logo.png)
- *
- * \ingroup WarpModule
- *
- * \par
- * - Available only for SM3.0 or newer
- *
- * \par Snippet
- * The code snippet below illustrates each thread obtaining a \p double value from <em>warp-lane</em><sub>0</sub>.
- *
- * \par
- * \code
- * #include <cub/cub.cuh>   // or equivalently <cub/util_ptx.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Obtain one input item per thread
- *     double thread_data = ...
- *
- *     // Obtain item from thread 0
- *     double peer_data = ShuffleIndex(thread_data, 0, 32, 0xffffffff);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the first warp of threads is <tt>{1.0, 2.0, 3.0, 4.0, 5.0, ..., 32.0}</tt>.
- * The corresponding output \p peer_data will be <tt>{1.0, 1.0, 1.0, 1.0, 1.0, ..., 1.0}</tt>.
- *
- */
-template <typename T>
-__device__ __forceinline__ T ShuffleIndex(
-    T               input,                  ///< [in] The value to broadcast
-    int             src_lane,               ///< [in] Which warp lane is to do the broadcasting
-    int             logical_warp_threads,   ///< [in] Number of threads per logical warp
-    unsigned int    member_mask)            ///< [in] 32-bit mask of participating warp lanes
-{
-    typedef typename UnitWord<T>::ShuffleWord ShuffleWord;
-
-    const int       WORDS           = (sizeof(T) + sizeof(ShuffleWord) - 1) / sizeof(ShuffleWord);
-
-    T               output;
-    ShuffleWord     *output_alias   = reinterpret_cast<ShuffleWord *>(&output);
-    ShuffleWord     *input_alias    = reinterpret_cast<ShuffleWord *>(&input);
-
-    unsigned int shuffle_word;
-    shuffle_word = SHFL_IDX_SYNC((unsigned int)input_alias[0],
-                                 src_lane,
-                                 logical_warp_threads - 1,
-                                 member_mask);
-
-    output_alias[0] = shuffle_word;
-
-    #pragma unroll
-    for (int WORD = 1; WORD < WORDS; ++WORD)
-    {
-        shuffle_word = SHFL_IDX_SYNC((unsigned int)input_alias[WORD],
-                                     src_lane,
-                                     logical_warp_threads - 1,
-                                     member_mask);
-
-        output_alias[WORD] = shuffle_word;
-    }
-
-    return output;
-}
-
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/util_type.cuh b/ml-xgboost/cub/cub/util_type.cuh
deleted file mode 100644
index 6f822b3..0000000
--- a/ml-xgboost/cub/cub/util_type.cuh
+++ /dev/null
@@ -1,1141 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * Common type manipulation (metaprogramming) utilities
- */
-
-#pragma once
-
-#include <iostream>
-#include <limits>
-#include <cfloat>
-
-#include "util_macro.cuh"
-#include "util_arch.cuh"
-#include "util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup UtilModule
- * @{
- */
-
-
-
-/******************************************************************************
- * Type equality
- ******************************************************************************/
-
-/**
- * \brief Type selection (<tt>IF ? ThenType : ElseType</tt>)
- */
-template <bool IF, typename ThenType, typename ElseType>
-struct If
-{
-    /// Conditional type result
-    typedef ThenType Type;      // true
-};
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-template <typename ThenType, typename ElseType>
-struct If<false, ThenType, ElseType>
-{
-    typedef ElseType Type;      // false
-};
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-
-/******************************************************************************
- * Conditional types
- ******************************************************************************/
-
-/**
- * \brief Type equality test
- */
-template <typename A, typename B>
-struct Equals
-{
-    enum {
-        VALUE = 0,
-        NEGATE = 1
-    };
-};
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-template <typename A>
-struct Equals <A, A>
-{
-    enum {
-        VALUE = 1,
-        NEGATE = 0
-    };
-};
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/******************************************************************************
- * Static math
- ******************************************************************************/
-
-/**
- * \brief Statically determine log2(N), rounded up.
- *
- * For example:
- *     Log2<8>::VALUE   // 3
- *     Log2<3>::VALUE   // 2
- */
-template <int N, int CURRENT_VAL = N, int COUNT = 0>
-struct Log2
-{
-    /// Static logarithm value
-    enum { VALUE = Log2<N, (CURRENT_VAL >> 1), COUNT + 1>::VALUE };         // Inductive case
-};
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-template <int N, int COUNT>
-struct Log2<N, 0, COUNT>
-{
-    enum {VALUE = (1 << (COUNT - 1) < N) ?                                  // Base case
-        COUNT :
-        COUNT - 1 };
-};
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/**
- * \brief Statically determine if N is a power-of-two
- */
-template <int N>
-struct PowerOfTwo
-{
-    enum { VALUE = ((N & (N - 1)) == 0) };
-};
-
-
-
-/******************************************************************************
- * Pointer vs. iterator detection
- ******************************************************************************/
-
-/**
- * \brief Pointer vs. iterator
- */
-template <typename Tp>
-struct IsPointer
-{
-    enum { VALUE = 0 };
-};
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-template <typename Tp>
-struct IsPointer<Tp*>
-{
-    enum { VALUE = 1 };
-};
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-
-/******************************************************************************
- * Qualifier detection
- ******************************************************************************/
-
-/**
- * \brief Volatile modifier test
- */
-template <typename Tp>
-struct IsVolatile
-{
-    enum { VALUE = 0 };
-};
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-template <typename Tp>
-struct IsVolatile<Tp volatile>
-{
-    enum { VALUE = 1 };
-};
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/******************************************************************************
- * Qualifier removal
- ******************************************************************************/
-
-/**
- * \brief Removes \p const and \p volatile qualifiers from type \p Tp.
- *
- * For example:
- *     <tt>typename RemoveQualifiers<volatile int>::Type         // int;</tt>
- */
-template <typename Tp, typename Up = Tp>
-struct RemoveQualifiers
-{
-    /// Type without \p const and \p volatile qualifiers
-    typedef Up Type;
-};
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-template <typename Tp, typename Up>
-struct RemoveQualifiers<Tp, volatile Up>
-{
-    typedef Up Type;
-};
-
-template <typename Tp, typename Up>
-struct RemoveQualifiers<Tp, const Up>
-{
-    typedef Up Type;
-};
-
-template <typename Tp, typename Up>
-struct RemoveQualifiers<Tp, const volatile Up>
-{
-    typedef Up Type;
-};
-
-
-/******************************************************************************
- * Marker types
- ******************************************************************************/
-
-/**
- * \brief A simple "NULL" marker type
- */
-struct NullType
-{
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-    template <typename T>
-    __host__ __device__ __forceinline__ NullType& operator =(const T&) { return *this; }
-
-    __host__ __device__ __forceinline__ bool operator ==(const NullType&) { return true; }
-
-    __host__ __device__ __forceinline__ bool operator !=(const NullType&) { return false; }
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-};
-
-
-/**
- * \brief Allows for the treatment of an integral constant as a type at compile-time (e.g., to achieve static call dispatch based on constant integral values)
- */
-template <int A>
-struct Int2Type
-{
-   enum {VALUE = A};
-};
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-/******************************************************************************
- * Size and alignment
- ******************************************************************************/
-
-/// Structure alignment
-template <typename T>
-struct AlignBytes
-{
-    struct Pad
-    {
-        T       val;
-        char    byte;
-    };
-
-    enum
-    {
-        /// The "true CUDA" alignment of T in bytes
-        ALIGN_BYTES = sizeof(Pad) - sizeof(T)
-    };
-
-    /// The "truly aligned" type
-    typedef T Type;
-};
-
-// Specializations where host C++ compilers (e.g., 32-bit Windows) may disagree
-// with device C++ compilers (EDG) on types passed as template parameters through
-// kernel functions
-
-#define __CUB_ALIGN_BYTES(t, b)         \
-    template <> struct AlignBytes<t>    \
-    { enum { ALIGN_BYTES = b }; typedef __align__(b) t Type; };
-
-__CUB_ALIGN_BYTES(short4, 8)
-__CUB_ALIGN_BYTES(ushort4, 8)
-__CUB_ALIGN_BYTES(int2, 8)
-__CUB_ALIGN_BYTES(uint2, 8)
-__CUB_ALIGN_BYTES(long long, 8)
-__CUB_ALIGN_BYTES(unsigned long long, 8)
-__CUB_ALIGN_BYTES(float2, 8)
-__CUB_ALIGN_BYTES(double, 8)
-#ifdef _WIN32
-    __CUB_ALIGN_BYTES(long2, 8)
-    __CUB_ALIGN_BYTES(ulong2, 8)
-#else
-    __CUB_ALIGN_BYTES(long2, 16)
-    __CUB_ALIGN_BYTES(ulong2, 16)
-#endif
-__CUB_ALIGN_BYTES(int4, 16)
-__CUB_ALIGN_BYTES(uint4, 16)
-__CUB_ALIGN_BYTES(float4, 16)
-__CUB_ALIGN_BYTES(long4, 16)
-__CUB_ALIGN_BYTES(ulong4, 16)
-__CUB_ALIGN_BYTES(longlong2, 16)
-__CUB_ALIGN_BYTES(ulonglong2, 16)
-__CUB_ALIGN_BYTES(double2, 16)
-__CUB_ALIGN_BYTES(longlong4, 16)
-__CUB_ALIGN_BYTES(ulonglong4, 16)
-__CUB_ALIGN_BYTES(double4, 16)
-
-template <typename T> struct AlignBytes<volatile T> : AlignBytes<T> {};
-template <typename T> struct AlignBytes<const T> : AlignBytes<T> {};
-template <typename T> struct AlignBytes<const volatile T> : AlignBytes<T> {};
-
-
-/// Unit-words of data movement
-template <typename T>
-struct UnitWord
-{
-    enum {
-        ALIGN_BYTES = AlignBytes<T>::ALIGN_BYTES
-    };
-
-    template <typename Unit>
-    struct IsMultiple
-    {
-        enum {
-            UNIT_ALIGN_BYTES    = AlignBytes<Unit>::ALIGN_BYTES,
-            IS_MULTIPLE         = (sizeof(T) % sizeof(Unit) == 0) && (ALIGN_BYTES % UNIT_ALIGN_BYTES == 0)
-        };
-    };
-
-    /// Biggest shuffle word that T is a whole multiple of and is not larger than the alignment of T
-    typedef typename If<IsMultiple<int>::IS_MULTIPLE,
-        unsigned int,
-        typename If<IsMultiple<short>::IS_MULTIPLE,
-            unsigned short,
-            unsigned char>::Type>::Type         ShuffleWord;
-
-    /// Biggest volatile word that T is a whole multiple of and is not larger than the alignment of T
-    typedef typename If<IsMultiple<long long>::IS_MULTIPLE,
-        unsigned long long,
-        ShuffleWord>::Type                      VolatileWord;
-
-    /// Biggest memory-access word that T is a whole multiple of and is not larger than the alignment of T
-    typedef typename If<IsMultiple<longlong2>::IS_MULTIPLE,
-        ulonglong2,
-        VolatileWord>::Type                     DeviceWord;
-
-    /// Biggest texture reference word that T is a whole multiple of and is not larger than the alignment of T
-    typedef typename If<IsMultiple<int4>::IS_MULTIPLE,
-        uint4,
-        typename If<IsMultiple<int2>::IS_MULTIPLE,
-            uint2,
-            ShuffleWord>::Type>::Type           TextureWord;
-};
-
-
-// float2 specialization workaround (for SM10-SM13)
-template <>
-struct UnitWord <float2>
-{
-    typedef int         ShuffleWord;
-#if (CUB_PTX_ARCH > 0) && (CUB_PTX_ARCH <= 130)
-    typedef float       VolatileWord;
-    typedef uint2       DeviceWord;
-#else
-    typedef unsigned long long   VolatileWord;
-    typedef unsigned long long   DeviceWord;
-#endif
-    typedef float2      TextureWord;
-};
-
-// float4 specialization workaround (for SM10-SM13)
-template <>
-struct UnitWord <float4>
-{
-    typedef int         ShuffleWord;
-#if (CUB_PTX_ARCH > 0) && (CUB_PTX_ARCH <= 130)
-    typedef float               VolatileWord;
-    typedef uint4               DeviceWord;
-#else
-    typedef unsigned long long  VolatileWord;
-    typedef ulonglong2          DeviceWord;
-#endif
-    typedef float4              TextureWord;
-};
-
-
-// char2 specialization workaround (for SM10-SM13)
-template <>
-struct UnitWord <char2>
-{
-    typedef unsigned short      ShuffleWord;
-#if (CUB_PTX_ARCH > 0) && (CUB_PTX_ARCH <= 130)
-    typedef unsigned short      VolatileWord;
-    typedef short               DeviceWord;
-#else
-    typedef unsigned short      VolatileWord;
-    typedef unsigned short      DeviceWord;
-#endif
-    typedef unsigned short      TextureWord;
-};
-
-
-template <typename T> struct UnitWord<volatile T> : UnitWord<T> {};
-template <typename T> struct UnitWord<const T> : UnitWord<T> {};
-template <typename T> struct UnitWord<const volatile T> : UnitWord<T> {};
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-
-/******************************************************************************
- * Vector type inference utilities.
- ******************************************************************************/
-
-/**
- * \brief Exposes a member typedef \p Type that names the corresponding CUDA vector type if one exists.  Otherwise \p Type refers to the CubVector structure itself, which will wrap the corresponding \p x, \p y, etc. vector fields.
- */
-template <typename T, int vec_elements> struct CubVector;
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-enum
-{
-    /// The maximum number of elements in CUDA vector types
-    MAX_VEC_ELEMENTS = 4,
-};
-
-
-/**
- * Generic vector-1 type
- */
-template <typename T>
-struct CubVector<T, 1>
-{
-    T x;
-
-    typedef T BaseType;
-    typedef CubVector<T, 1> Type;
-};
-
-/**
- * Generic vector-2 type
- */
-template <typename T>
-struct CubVector<T, 2>
-{
-    T x;
-    T y;
-
-    typedef T BaseType;
-    typedef CubVector<T, 2> Type;
-};
-
-/**
- * Generic vector-3 type
- */
-template <typename T>
-struct CubVector<T, 3>
-{
-    T x;
-    T y;
-    T z;
-
-    typedef T BaseType;
-    typedef CubVector<T, 3> Type;
-};
-
-/**
- * Generic vector-4 type
- */
-template <typename T>
-struct CubVector<T, 4>
-{
-    T x;
-    T y;
-    T z;
-    T w;
-
-    typedef T BaseType;
-    typedef CubVector<T, 4> Type;
-};
-
-
-/**
- * Macro for expanding partially-specialized built-in vector types
- */
-#define CUB_DEFINE_VECTOR_TYPE(base_type,short_type)                                                    \
-                                                                                                        \
-    template<> struct CubVector<base_type, 1> : short_type##1                                           \
-    {                                                                                                   \
-      typedef base_type       BaseType;                                                                 \
-      typedef short_type##1   Type;                                                                     \
-      __host__ __device__ __forceinline__ CubVector operator+(const CubVector &other) const {           \
-          CubVector retval;                                                                             \
-          retval.x = x + other.x;                                                                       \
-          return retval;                                                                                \
-      }                                                                                                 \
-      __host__ __device__ __forceinline__ CubVector operator-(const CubVector &other) const {           \
-          CubVector retval;                                                                             \
-          retval.x = x - other.x;                                                                       \
-          return retval;                                                                                \
-      }                                                                                                 \
-    };                                                                                                  \
-                                                                                                        \
-    template<> struct CubVector<base_type, 2> : short_type##2                                           \
-    {                                                                                                   \
-        typedef base_type       BaseType;                                                               \
-        typedef short_type##2   Type;                                                                   \
-        __host__ __device__ __forceinline__ CubVector operator+(const CubVector &other) const {         \
-            CubVector retval;                                                                           \
-            retval.x = x + other.x;                                                                     \
-            retval.y = y + other.y;                                                                     \
-            return retval;                                                                              \
-        }                                                                                               \
-        __host__ __device__ __forceinline__ CubVector operator-(const CubVector &other) const {         \
-            CubVector retval;                                                                           \
-            retval.x = x - other.x;                                                                     \
-            retval.y = y - other.y;                                                                     \
-            return retval;                                                                              \
-        }                                                                                               \
-    };                                                                                                  \
-                                                                                                        \
-    template<> struct CubVector<base_type, 3> : short_type##3                                           \
-    {                                                                                                   \
-        typedef base_type       BaseType;                                                               \
-        typedef short_type##3   Type;                                                                   \
-        __host__ __device__ __forceinline__ CubVector operator+(const CubVector &other) const {         \
-            CubVector retval;                                                                           \
-            retval.x = x + other.x;                                                                     \
-            retval.y = y + other.y;                                                                     \
-            retval.z = z + other.z;                                                                     \
-            return retval;                                                                              \
-        }                                                                                               \
-        __host__ __device__ __forceinline__ CubVector operator-(const CubVector &other) const {         \
-            CubVector retval;                                                                           \
-            retval.x = x - other.x;                                                                     \
-            retval.y = y - other.y;                                                                     \
-            retval.z = z - other.z;                                                                     \
-            return retval;                                                                              \
-        }                                                                                               \
-    };                                                                                                  \
-                                                                                                        \
-    template<> struct CubVector<base_type, 4> : short_type##4                                           \
-    {                                                                                                   \
-        typedef base_type       BaseType;                                                               \
-        typedef short_type##4   Type;                                                                   \
-        __host__ __device__ __forceinline__ CubVector operator+(const CubVector &other) const {         \
-            CubVector retval;                                                                           \
-            retval.x = x + other.x;                                                                     \
-            retval.y = y + other.y;                                                                     \
-            retval.z = z + other.z;                                                                     \
-            retval.w = w + other.w;                                                                     \
-            return retval;                                                                              \
-        }                                                                                               \
-        __host__ __device__ __forceinline__ CubVector operator-(const CubVector &other) const {         \
-            CubVector retval;                                                                           \
-            retval.x = x - other.x;                                                                     \
-            retval.y = y - other.y;                                                                     \
-            retval.z = z - other.z;                                                                     \
-            retval.w = w - other.w;                                                                     \
-            return retval;                                                                              \
-        }                                                                                               \
-    };
-
-
-
-// Expand CUDA vector types for built-in primitives
-CUB_DEFINE_VECTOR_TYPE(char,               char)
-CUB_DEFINE_VECTOR_TYPE(signed char,        char)
-CUB_DEFINE_VECTOR_TYPE(short,              short)
-CUB_DEFINE_VECTOR_TYPE(int,                int)
-CUB_DEFINE_VECTOR_TYPE(long,               long)
-CUB_DEFINE_VECTOR_TYPE(long long,          longlong)
-CUB_DEFINE_VECTOR_TYPE(unsigned char,      uchar)
-CUB_DEFINE_VECTOR_TYPE(unsigned short,     ushort)
-CUB_DEFINE_VECTOR_TYPE(unsigned int,       uint)
-CUB_DEFINE_VECTOR_TYPE(unsigned long,      ulong)
-CUB_DEFINE_VECTOR_TYPE(unsigned long long, ulonglong)
-CUB_DEFINE_VECTOR_TYPE(float,              float)
-CUB_DEFINE_VECTOR_TYPE(double,             double)
-CUB_DEFINE_VECTOR_TYPE(bool,               uchar)
-
-// Undefine macros
-#undef CUB_DEFINE_VECTOR_TYPE
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-
-/******************************************************************************
- * Wrapper types
- ******************************************************************************/
-
-/**
- * \brief A storage-backing wrapper that allows types with non-trivial constructors to be aliased in unions
- */
-template <typename T>
-struct Uninitialized
-{
-    /// Biggest memory-access word that T is a whole multiple of and is not larger than the alignment of T
-    typedef typename UnitWord<T>::DeviceWord DeviceWord;
-
-    enum
-    {
-        WORDS = sizeof(T) / sizeof(DeviceWord)
-    };
-
-    /// Backing storage
-    DeviceWord storage[WORDS];
-
-    /// Alias
-    __host__ __device__ __forceinline__ T& Alias()
-    {
-        return reinterpret_cast<T&>(*this);
-    }
-};
-
-
-/**
- * \brief A key identifier paired with a corresponding value
- */
-template <
-    typename    _Key,
-    typename    _Value
-#if defined(_WIN32) && !defined(_WIN64)
-    , bool KeyIsLT = (AlignBytes<_Key>::ALIGN_BYTES < AlignBytes<_Value>::ALIGN_BYTES)
-    , bool ValIsLT = (AlignBytes<_Value>::ALIGN_BYTES < AlignBytes<_Key>::ALIGN_BYTES)
-#endif // #if defined(_WIN32) && !defined(_WIN64)
-    >
-struct KeyValuePair
-{
-    typedef _Key    Key;                ///< Key data type
-    typedef _Value  Value;              ///< Value data type
-
-    Key     key;                        ///< Item key
-    Value   value;                      ///< Item value
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    KeyValuePair() {}
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    KeyValuePair(Key const& key, Value const& value) : key(key), value(value) {}
-
-    /// Inequality operator
-    __host__ __device__ __forceinline__ bool operator !=(const KeyValuePair &b)
-    {
-        return (value != b.value) || (key != b.key);
-    }
-};
-
-#if defined(_WIN32) && !defined(_WIN64)
-
-/**
- * Win32 won't do 16B alignment.  This can present two problems for
- * should-be-16B-aligned (but actually 8B aligned) built-in and intrinsics members:
- * 1) If a smaller-aligned item were to be listed first, the host compiler places the
- *    should-be-16B item at too early an offset (and disagrees with device compiler)
- * 2) Or, if a smaller-aligned item lists second, the host compiler gets the size
- *    of the struct wrong (and disagrees with device compiler)
- *
- * So we put the larger-should-be-aligned item first, and explicitly pad the
- * end of the struct
- */
-
-/// Smaller key specialization
-template <typename K, typename V>
-struct KeyValuePair<K, V, true, false>
-{
-    typedef K Key;
-    typedef V Value;
-
-    typedef char Pad[AlignBytes<V>::ALIGN_BYTES - AlignBytes<K>::ALIGN_BYTES];
-
-    Value   value;  // Value has larger would-be alignment and goes first
-    Key     key;
-    Pad     pad;
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    KeyValuePair() {}
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    KeyValuePair(Key const& key, Value const& value) : key(key), value(value) {}
-
-    /// Inequality operator
-    __host__ __device__ __forceinline__ bool operator !=(const KeyValuePair &b)
-    {
-        return (value != b.value) || (key != b.key);
-    }
-};
-
-
-/// Smaller value specialization
-template <typename K, typename V>
-struct KeyValuePair<K, V, false, true>
-{
-    typedef K Key;
-    typedef V Value;
-
-    typedef char Pad[AlignBytes<K>::ALIGN_BYTES - AlignBytes<V>::ALIGN_BYTES];
-
-    Key     key;    // Key has larger would-be alignment and goes first
-    Value   value;
-    Pad     pad;
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    KeyValuePair() {}
-
-    /// Constructor
-    __host__ __device__ __forceinline__
-    KeyValuePair(Key const& key, Value const& value) : key(key), value(value) {}
-
-    /// Inequality operator
-    __host__ __device__ __forceinline__ bool operator !=(const KeyValuePair &b)
-    {
-        return (value != b.value) || (key != b.key);
-    }
-};
-
-#endif // #if defined(_WIN32) && !defined(_WIN64)
-
-
-#ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-
-/**
- * \brief A wrapper for passing simple static arrays as kernel parameters
- */
-template <typename T, int COUNT>
-struct ArrayWrapper
-{
-
-    /// Statically-sized array of type \p T
-    T array[COUNT];
-
-    /// Constructor
-    __host__ __device__ __forceinline__ ArrayWrapper() {}
-};
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-/**
- * \brief Double-buffer storage wrapper for multi-pass stream transformations that require more than one storage array for streaming intermediate results back and forth.
- *
- * Many multi-pass computations require a pair of "ping-pong" storage
- * buffers (e.g., one for reading from and the other for writing to, and then
- * vice-versa for the subsequent pass).  This structure wraps a set of device
- * buffers and a "selector" member to track which is "current".
- */
-template <typename T>
-struct DoubleBuffer
-{
-    /// Pair of device buffer pointers
-    T *d_buffers[2];
-
-    ///  Selector into \p d_buffers (i.e., the active/valid buffer)
-    int selector;
-
-    /// \brief Constructor
-    __host__ __device__ __forceinline__ DoubleBuffer()
-    {
-        selector = 0;
-        d_buffers[0] = NULL;
-        d_buffers[1] = NULL;
-    }
-
-    /// \brief Constructor
-    __host__ __device__ __forceinline__ DoubleBuffer(
-        T *d_current,         ///< The currently valid buffer
-        T *d_alternate)       ///< Alternate storage buffer of the same size as \p d_current
-    {
-        selector = 0;
-        d_buffers[0] = d_current;
-        d_buffers[1] = d_alternate;
-    }
-
-    /// \brief Return pointer to the currently valid buffer
-    __host__ __device__ __forceinline__ T* Current() { return d_buffers[selector]; }
-
-    /// \brief Return pointer to the currently invalid buffer
-    __host__ __device__ __forceinline__ T* Alternate() { return d_buffers[selector ^ 1]; }
-
-};
-
-
-
-/******************************************************************************
- * Typedef-detection
- ******************************************************************************/
-
-
-/**
- * \brief Defines a structure \p detector_name that is templated on type \p T.  The \p detector_name struct exposes a constant member \p VALUE indicating whether or not parameter \p T exposes a nested type \p nested_type_name
- */
-#define CUB_DEFINE_DETECT_NESTED_TYPE(detector_name, nested_type_name)  \
-    template <typename T>                                               \
-    struct detector_name                                                \
-    {                                                                   \
-        template <typename C>                                           \
-        static char& test(typename C::nested_type_name*);               \
-        template <typename>                                             \
-        static int& test(...);                                          \
-        enum                                                            \
-        {                                                               \
-            VALUE = sizeof(test<T>(0)) < sizeof(int)                    \
-        };                                                              \
-    };
-
-
-
-/******************************************************************************
- * Simple enable-if (similar to Boost)
- ******************************************************************************/
-
-/**
- * \brief Simple enable-if (similar to Boost)
- */
-template <bool Condition, class T = void>
-struct EnableIf
-{
-    /// Enable-if type for SFINAE dummy variables
-    typedef T Type;
-};
-
-
-template <class T>
-struct EnableIf<false, T> {};
-
-
-
-/******************************************************************************
- * Typedef-detection
- ******************************************************************************/
-
-/**
- * \brief Determine whether or not BinaryOp's functor is of the form <tt>bool operator()(const T& a, const T&b)</tt> or <tt>bool operator()(const T& a, const T&b, unsigned int idx)</tt>
- */
-template <typename T, typename BinaryOp>
-struct BinaryOpHasIdxParam
-{
-private:
-/*
-    template <typename BinaryOpT, bool (BinaryOpT::*)(const T &a, const T &b, unsigned int idx) const>  struct SFINAE1 {};
-    template <typename BinaryOpT, bool (BinaryOpT::*)(const T &a, const T &b, unsigned int idx)>        struct SFINAE2 {};
-    template <typename BinaryOpT, bool (BinaryOpT::*)(T a, T b, unsigned int idx) const>                struct SFINAE3 {};
-    template <typename BinaryOpT, bool (BinaryOpT::*)(T a, T b, unsigned int idx)>                      struct SFINAE4 {};
-*/
-    template <typename BinaryOpT, bool (BinaryOpT::*)(const T &a, const T &b, int idx) const>           struct SFINAE5 {};
-    template <typename BinaryOpT, bool (BinaryOpT::*)(const T &a, const T &b, int idx)>                 struct SFINAE6 {};
-    template <typename BinaryOpT, bool (BinaryOpT::*)(T a, T b, int idx) const>                         struct SFINAE7 {};
-    template <typename BinaryOpT, bool (BinaryOpT::*)(T a, T b, int idx)>                               struct SFINAE8 {};
-/*
-    template <typename BinaryOpT> static char Test(SFINAE1<BinaryOpT, &BinaryOpT::operator()> *);
-    template <typename BinaryOpT> static char Test(SFINAE2<BinaryOpT, &BinaryOpT::operator()> *);
-    template <typename BinaryOpT> static char Test(SFINAE3<BinaryOpT, &BinaryOpT::operator()> *);
-    template <typename BinaryOpT> static char Test(SFINAE4<BinaryOpT, &BinaryOpT::operator()> *);
-*/
-    template <typename BinaryOpT> static char Test(SFINAE5<BinaryOpT, &BinaryOpT::operator()> *);
-    template <typename BinaryOpT> static char Test(SFINAE6<BinaryOpT, &BinaryOpT::operator()> *);
-    template <typename BinaryOpT> static char Test(SFINAE7<BinaryOpT, &BinaryOpT::operator()> *);
-    template <typename BinaryOpT> static char Test(SFINAE8<BinaryOpT, &BinaryOpT::operator()> *);
-
-    template <typename BinaryOpT> static int Test(...);
-
-public:
-
-    /// Whether the functor BinaryOp has a third <tt>unsigned int</tt> index param
-    static const bool HAS_PARAM = sizeof(Test<BinaryOp>(NULL)) == sizeof(char);
-};
-
-
-
-
-/******************************************************************************
- * Simple type traits utilities.
- *
- * For example:
- *     Traits<int>::CATEGORY             // SIGNED_INTEGER
- *     Traits<NullType>::NULL_TYPE       // true
- *     Traits<uint4>::CATEGORY           // NOT_A_NUMBER
- *     Traits<uint4>::PRIMITIVE;         // false
- *
- ******************************************************************************/
-
-/**
- * \brief Basic type traits categories
- */
-enum Category
-{
-    NOT_A_NUMBER,
-    SIGNED_INTEGER,
-    UNSIGNED_INTEGER,
-    FLOATING_POINT
-};
-
-
-/**
- * \brief Basic type traits
- */
-template <Category _CATEGORY, bool _PRIMITIVE, bool _NULL_TYPE, typename _UnsignedBits, typename T>
-struct BaseTraits
-{
-    /// Category
-    static const Category CATEGORY      = _CATEGORY;
-    enum
-    {
-        PRIMITIVE       = _PRIMITIVE,
-        NULL_TYPE       = _NULL_TYPE,
-    };
-};
-
-
-/**
- * Basic type traits (unsigned primitive specialization)
- */
-template <typename _UnsignedBits, typename T>
-struct BaseTraits<UNSIGNED_INTEGER, true, false, _UnsignedBits, T>
-{
-    typedef _UnsignedBits       UnsignedBits;
-
-    static const Category       CATEGORY    = UNSIGNED_INTEGER;
-    static const UnsignedBits   LOWEST_KEY  = UnsignedBits(0);
-    static const UnsignedBits   MAX_KEY     = UnsignedBits(-1);
-
-    enum
-    {
-        PRIMITIVE       = true,
-        NULL_TYPE       = false,
-    };
-
-
-    static __device__ __forceinline__ UnsignedBits TwiddleIn(UnsignedBits key)
-    {
-        return key;
-    }
-
-    static __device__ __forceinline__ UnsignedBits TwiddleOut(UnsignedBits key)
-    {
-        return key;
-    }
-
-    static __host__ __device__ __forceinline__ T Max()
-    {
-        UnsignedBits retval = MAX_KEY;
-        return reinterpret_cast<T&>(retval);
-    }
-
-    static __host__ __device__ __forceinline__ T Lowest()
-    {
-        UnsignedBits retval = LOWEST_KEY;
-        return reinterpret_cast<T&>(retval);
-    }
-};
-
-
-/**
- * Basic type traits (signed primitive specialization)
- */
-template <typename _UnsignedBits, typename T>
-struct BaseTraits<SIGNED_INTEGER, true, false, _UnsignedBits, T>
-{
-    typedef _UnsignedBits       UnsignedBits;
-
-    static const Category       CATEGORY    = SIGNED_INTEGER;
-    static const UnsignedBits   HIGH_BIT    = UnsignedBits(1) << ((sizeof(UnsignedBits) * 8) - 1);
-    static const UnsignedBits   LOWEST_KEY  = HIGH_BIT;
-    static const UnsignedBits   MAX_KEY     = UnsignedBits(-1) ^ HIGH_BIT;
-
-    enum
-    {
-        PRIMITIVE       = true,
-        NULL_TYPE       = false,
-    };
-
-    static __device__ __forceinline__ UnsignedBits TwiddleIn(UnsignedBits key)
-    {
-        return key ^ HIGH_BIT;
-    };
-
-    static __device__ __forceinline__ UnsignedBits TwiddleOut(UnsignedBits key)
-    {
-        return key ^ HIGH_BIT;
-    };
-
-    static __host__ __device__ __forceinline__ T Max()
-    {
-        UnsignedBits retval = MAX_KEY;
-        return reinterpret_cast<T&>(retval);
-    }
-
-    static __host__ __device__ __forceinline__ T Lowest()
-    {
-        UnsignedBits retval = LOWEST_KEY;
-        return reinterpret_cast<T&>(retval);
-    }
-};
-
-template <typename _T>
-struct FpLimits;
-
-template <>
-struct FpLimits<float>
-{
-    static __host__ __device__ __forceinline__ float Max() {
-        return FLT_MAX;
-    }
-
-    static __host__ __device__ __forceinline__ float Lowest() {
-        return FLT_MAX * float(-1);
-    }
-};
-
-template <>
-struct FpLimits<double>
-{
-    static __host__ __device__ __forceinline__ double Max() {
-        return DBL_MAX;
-    }
-
-    static __host__ __device__ __forceinline__ double Lowest() {
-        return DBL_MAX  * double(-1);
-    }
-};
-
-
-/**
- * Basic type traits (fp primitive specialization)
- */
-template <typename _UnsignedBits, typename T>
-struct BaseTraits<FLOATING_POINT, true, false, _UnsignedBits, T>
-{
-    typedef _UnsignedBits       UnsignedBits;
-
-    static const Category       CATEGORY    = FLOATING_POINT;
-    static const UnsignedBits   HIGH_BIT    = UnsignedBits(1) << ((sizeof(UnsignedBits) * 8) - 1);
-    static const UnsignedBits   LOWEST_KEY  = UnsignedBits(-1);
-    static const UnsignedBits   MAX_KEY     = UnsignedBits(-1) ^ HIGH_BIT;
-
-    enum
-    {
-        PRIMITIVE       = true,
-        NULL_TYPE       = false,
-    };
-
-    static __device__ __forceinline__ UnsignedBits TwiddleIn(UnsignedBits key)
-    {
-        UnsignedBits mask = (key & HIGH_BIT) ? UnsignedBits(-1) : HIGH_BIT;
-        return key ^ mask;
-    };
-
-    static __device__ __forceinline__ UnsignedBits TwiddleOut(UnsignedBits key)
-    {
-        UnsignedBits mask = (key & HIGH_BIT) ? HIGH_BIT : UnsignedBits(-1);
-        return key ^ mask;
-    };
-
-    static __host__ __device__ __forceinline__ T Max() {
-        return FpLimits<T>::Max();
-    }
-
-    static __host__ __device__ __forceinline__ T Lowest() {
-        return FpLimits<T>::Lowest();
-    }
-};
-
-
-/**
- * \brief Numeric type traits
- */
-template <typename T> struct NumericTraits :            BaseTraits<NOT_A_NUMBER, false, false, T, T> {};
-
-template <> struct NumericTraits<NullType> :            BaseTraits<NOT_A_NUMBER, false, true, NullType, NullType> {};
-
-template <> struct NumericTraits<char> :                BaseTraits<(std::numeric_limits<char>::is_signed) ? SIGNED_INTEGER : UNSIGNED_INTEGER, true, false, unsigned char, char> {};
-template <> struct NumericTraits<signed char> :         BaseTraits<SIGNED_INTEGER, true, false, unsigned char, signed char> {};
-template <> struct NumericTraits<short> :               BaseTraits<SIGNED_INTEGER, true, false, unsigned short, short> {};
-template <> struct NumericTraits<int> :                 BaseTraits<SIGNED_INTEGER, true, false, unsigned int, int> {};
-template <> struct NumericTraits<long> :                BaseTraits<SIGNED_INTEGER, true, false, unsigned long, long> {};
-template <> struct NumericTraits<long long> :           BaseTraits<SIGNED_INTEGER, true, false, unsigned long long, long long> {};
-
-template <> struct NumericTraits<unsigned char> :       BaseTraits<UNSIGNED_INTEGER, true, false, unsigned char, unsigned char> {};
-template <> struct NumericTraits<unsigned short> :      BaseTraits<UNSIGNED_INTEGER, true, false, unsigned short, unsigned short> {};
-template <> struct NumericTraits<unsigned int> :        BaseTraits<UNSIGNED_INTEGER, true, false, unsigned int, unsigned int> {};
-template <> struct NumericTraits<unsigned long> :       BaseTraits<UNSIGNED_INTEGER, true, false, unsigned long, unsigned long> {};
-template <> struct NumericTraits<unsigned long long> :  BaseTraits<UNSIGNED_INTEGER, true, false, unsigned long long, unsigned long long> {};
-
-template <> struct NumericTraits<float> :               BaseTraits<FLOATING_POINT, true, false, unsigned int, float> {};
-template <> struct NumericTraits<double> :              BaseTraits<FLOATING_POINT, true, false, unsigned long long, double> {};
-
-template <> struct NumericTraits<bool> :                BaseTraits<UNSIGNED_INTEGER, true, false, typename UnitWord<bool>::VolatileWord, bool> {};
-
-
-
-/**
- * \brief Type traits
- */
-template <typename T>
-struct Traits : NumericTraits<typename RemoveQualifiers<T>::Type> {};
-
-
-#endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-/** @} */       // end group UtilModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/warp/specializations/warp_reduce_shfl.cuh b/ml-xgboost/cub/cub/warp/specializations/warp_reduce_shfl.cuh
deleted file mode 100644
index 71a3bf0..0000000
--- a/ml-xgboost/cub/cub/warp/specializations/warp_reduce_shfl.cuh
+++ /dev/null
@@ -1,549 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::WarpReduceShfl provides SHFL-based variants of parallel reduction of items partitioned across a CUDA thread warp.
- */
-
-#pragma once
-
-#include "../../thread/thread_operators.cuh"
-#include "../../util_ptx.cuh"
-#include "../../util_type.cuh"
-#include "../../util_macro.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \brief WarpReduceShfl provides SHFL-based variants of parallel reduction of items partitioned across a CUDA thread warp.
- *
- * LOGICAL_WARP_THREADS must be a power-of-two
- */
-template <
-    typename    T,                      ///< Data type being reduced
-    int         LOGICAL_WARP_THREADS,   ///< Number of threads per logical warp
-    int         PTX_ARCH>               ///< The PTX compute capability for which to to specialize this collective
-struct WarpReduceShfl
-{
-    //---------------------------------------------------------------------
-    // Constants and type definitions
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        /// Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// The number of warp reduction steps
-        STEPS = Log2<LOGICAL_WARP_THREADS>::VALUE,
-
-        /// Number of logical warps in a PTX warp
-        LOGICAL_WARPS = CUB_WARP_THREADS(PTX_ARCH) / LOGICAL_WARP_THREADS,
-    };
-
-    template <typename S>
-    struct IsInteger
-    {
-        enum {
-            ///Whether the data type is a small (32b or less) integer for which we can use a single SFHL instruction per exchange
-            IS_SMALL_UNSIGNED = (Traits<S>::CATEGORY == UNSIGNED_INTEGER) && (sizeof(S) <= sizeof(unsigned int))
-        };
-    };
-
-
-    // Creates a mask where the last thread in each logical warp is set
-    template <int WARP, int WARPS>
-    struct LastLaneMask
-    {
-        enum {
-            BASE_MASK   = 1 << (LOGICAL_WARP_THREADS - 1),
-            MASK        = (LastLaneMask<WARP + 1, WARPS>::MASK << LOGICAL_WARP_THREADS) | BASE_MASK,
-        };
-    };
-
-    // Creates a mask where the last thread in each logical warp is set
-    template <int WARP>
-    struct LastLaneMask<WARP, WARP>
-    {
-        enum {
-            MASK        = 1 << (LOGICAL_WARP_THREADS - 1),
-        };
-    };
-
-
-
-    /// Shared memory storage layout type
-    typedef NullType TempStorage;
-
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    int lane_id;
-
-    int member_mask;
-
-    //---------------------------------------------------------------------
-    // Construction
-    //---------------------------------------------------------------------
-
-    /// Constructor
-    __device__ __forceinline__ WarpReduceShfl(
-        TempStorage &/*temp_storage*/)
-    :
-        lane_id(LaneId()),
-
-        member_mask(IS_ARCH_WARP ?
-             0xffffffff :
-             (0xffffffff >> (32 - LOGICAL_WARP_THREADS)) << (LaneId() / LOGICAL_WARP_THREADS))
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Reduction steps
-    //---------------------------------------------------------------------
-
-    /// Reduction (specialized for summation across uint32 types)
-    __device__ __forceinline__ unsigned int ReduceStep(
-        unsigned int    input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*reduction_op*/,   ///< [in] Binary reduction operator
-        int             last_lane,          ///< [in] Index of last lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        unsigned int output;
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u32 r0;"
-            "  .reg .pred p;"
-            "  shfl.sync.down.b32 r0|p, %1, %2, %3, %5;"
-            "  @p add.u32 r0, r0, %4;"
-            "  mov.u32 %0, r0;"
-            "}"
-            : "=r"(output) : "r"(input), "r"(offset), "r"(last_lane), "r"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u32 r0;"
-            "  .reg .pred p;"
-            "  shfl.down.b32 r0|p, %1, %2, %3;"
-            "  @p add.u32 r0, r0, %4;"
-            "  mov.u32 %0, r0;"
-            "}"
-            : "=r"(output) : "r"(input), "r"(offset), "r"(last_lane), "r"(input));
-#endif
-
-        return output;
-    }
-
-
-    /// Reduction (specialized for summation across fp32 types)
-    __device__ __forceinline__ float ReduceStep(
-        float           input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*reduction_op*/,   ///< [in] Binary reduction operator
-        int             last_lane,          ///< [in] Index of last lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        float output;
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .f32 r0;"
-            "  .reg .pred p;"
-            "  shfl.sync.down.b32 r0|p, %1, %2, %3, %5;"
-            "  @p add.f32 r0, r0, %4;"
-            "  mov.f32 %0, r0;"
-            "}"
-            : "=f"(output) : "f"(input), "r"(offset), "r"(last_lane), "f"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .f32 r0;"
-            "  .reg .pred p;"
-            "  shfl.down.b32 r0|p, %1, %2, %3;"
-            "  @p add.f32 r0, r0, %4;"
-            "  mov.f32 %0, r0;"
-            "}"
-            : "=f"(output) : "f"(input), "r"(offset), "r"(last_lane), "f"(input));
-#endif
-
-        return output;
-    }
-
-
-    /// Reduction (specialized for summation across unsigned long long types)
-    __device__ __forceinline__ unsigned long long ReduceStep(
-        unsigned long long  input,              ///< [in] Calling thread's input item.
-        cub::Sum            /*reduction_op*/,   ///< [in] Binary reduction operator
-        int                 last_lane,          ///< [in] Index of last lane in segment
-        int                 offset)             ///< [in] Up-offset to pull from
-    {
-        unsigned long long output;
-
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.sync.down.b32 lo|p, lo, %2, %3, %4;"
-            "  shfl.sync.down.b32 hi|p, hi, %2, %3, %4;"
-            "  mov.b64 %0, {lo, hi};"
-            "  @p add.u64 %0, %0, %1;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(last_lane), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.down.b32 lo|p, lo, %2, %3"
-            "  shfl.down.b32 hi|p, hi, %2, %3;"
-            "  mov.b64 %0, {lo, hi};"
-            "  @p add.u64 %0, %0, %1;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(last_lane));
-#endif
-
-        return output;
-    }
-
-
-    /// Reduction (specialized for summation across long long types)
-    __device__ __forceinline__ long long ReduceStep(
-        long long           input,              ///< [in] Calling thread's input item.
-        cub::Sum            /*reduction_op*/,   ///< [in] Binary reduction operator
-        int                 last_lane,          ///< [in] Index of last lane in segment
-        int                 offset)             ///< [in] Up-offset to pull from
-    {
-        long long output;
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.sync.down.b32 lo|p, lo, %2, %3, %4;"
-            "  shfl.sync.down.b32 hi|p, hi, %2, %3, %4;"
-            "  mov.b64 %0, {lo, hi};"
-            "  @p add.s64 %0, %0, %1;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(last_lane), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.down.b32 lo|p, lo, %2, %3;"
-            "  shfl.down.b32 hi|p, hi, %2, %3;"
-            "  mov.b64 %0, {lo, hi};"
-            "  @p add.s64 %0, %0, %1;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(last_lane));
-#endif
-
-        return output;
-    }
-
-
-    /// Reduction (specialized for summation across double types)
-    __device__ __forceinline__ double ReduceStep(
-        double              input,              ///< [in] Calling thread's input item.
-        cub::Sum            /*reduction_op*/,   ///< [in] Binary reduction operator
-        int                 last_lane,          ///< [in] Index of last lane in segment
-        int                 offset)             ///< [in] Up-offset to pull from
-    {
-        double output;
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  .reg .f64 r0;"
-            "  mov.b64 %0, %1;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.sync.down.b32 lo|p, lo, %2, %3, %4;"
-            "  shfl.sync.down.b32 hi|p, hi, %2, %3, %4;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.f64 %0, %0, r0;"
-            "}"
-            : "=d"(output) : "d"(input), "r"(offset), "r"(last_lane), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  .reg .f64 r0;"
-            "  mov.b64 %0, %1;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.down.b32 lo|p, lo, %2, %3;"
-            "  shfl.down.b32 hi|p, hi, %2, %3;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.f64 %0, %0, r0;"
-            "}"
-            : "=d"(output) : "d"(input), "r"(offset), "r"(last_lane));
-#endif
-
-        return output;
-    }
-
-
-    /// Reduction (specialized for swizzled ReduceByKeyOp<cub::Sum> across KeyValuePair<KeyT, ValueT> types)
-    template <typename ValueT, typename KeyT>
-    __device__ __forceinline__ KeyValuePair<KeyT, ValueT> ReduceStep(
-        KeyValuePair<KeyT, ValueT>                  input,              ///< [in] Calling thread's input item.
-        SwizzleScanOp<ReduceByKeyOp<cub::Sum> >     /*reduction_op*/,       ///< [in] Binary reduction operator
-        int                                         last_lane,          ///< [in] Index of last lane in segment
-        int                                         offset)             ///< [in] Up-offset to pull from
-    {
-        KeyValuePair<KeyT, ValueT> output;
-
-        KeyT other_key = ShuffleDown(input.key, offset, last_lane, member_mask);
-        
-        output.key = input.key;
-        output.value = ReduceStep(
-            input.value, 
-            cub::Sum(), 
-            last_lane, 
-            offset, 
-            Int2Type<IsInteger<ValueT>::IS_SMALL_UNSIGNED>());
-
-        if (input.key != other_key)
-            output.value = input.value;
-
-        return output;
-    }
-
-
-
-    /// Reduction (specialized for swizzled ReduceBySegmentOp<cub::Sum> across KeyValuePair<OffsetT, ValueT> types)
-    template <typename ValueT, typename OffsetT>
-    __device__ __forceinline__ KeyValuePair<OffsetT, ValueT> ReduceStep(
-        KeyValuePair<OffsetT, ValueT>                 input,              ///< [in] Calling thread's input item.
-        SwizzleScanOp<ReduceBySegmentOp<cub::Sum> >   /*reduction_op*/,   ///< [in] Binary reduction operator
-        int                                           last_lane,          ///< [in] Index of last lane in segment
-        int                                           offset)             ///< [in] Up-offset to pull from
-    {
-        KeyValuePair<OffsetT, ValueT> output;
-
-        output.value = ReduceStep(input.value, cub::Sum(), last_lane, offset, Int2Type<IsInteger<ValueT>::IS_SMALL_UNSIGNED>());
-        output.key = ReduceStep(input.key, cub::Sum(), last_lane, offset, Int2Type<IsInteger<OffsetT>::IS_SMALL_UNSIGNED>());
-
-        if (input.key > 0)
-            output.value = input.value;
-
-        return output;
-    }
-
-
-    /// Reduction step (generic)
-    template <typename _T, typename ReductionOp>
-    __device__ __forceinline__ _T ReduceStep(
-        _T                  input,              ///< [in] Calling thread's input item.
-        ReductionOp         reduction_op,       ///< [in] Binary reduction operator
-        int                 last_lane,          ///< [in] Index of last lane in segment
-        int                 offset)             ///< [in] Up-offset to pull from
-    {
-        _T output = input;
-
-        _T temp = ShuffleDown(output, offset, last_lane, member_mask);
-
-        // Perform reduction op if valid
-        if (offset + lane_id <= last_lane)
-            output = reduction_op(input, temp);
-
-        return output;
-    }
-
-
-    /// Reduction step (specialized for small unsigned integers size 32b or less)
-    template <typename _T, typename ReductionOp>
-    __device__ __forceinline__ _T ReduceStep(
-        _T              input,                  ///< [in] Calling thread's input item.
-        ReductionOp     reduction_op,           ///< [in] Binary reduction operator
-        int             last_lane,              ///< [in] Index of last lane in segment
-        int             offset,                 ///< [in] Up-offset to pull from
-        Int2Type<true>  /*is_small_unsigned*/)  ///< [in] Marker type indicating whether T is a small unsigned integer
-    {
-        return ReduceStep(input, reduction_op, last_lane, offset);
-    }
-
-
-    /// Reduction step (specialized for types other than small unsigned integers size 32b or less)
-    template <typename _T, typename ReductionOp>
-    __device__ __forceinline__ _T ReduceStep(
-        _T              input,                  ///< [in] Calling thread's input item.
-        ReductionOp     reduction_op,           ///< [in] Binary reduction operator
-        int             last_lane,              ///< [in] Index of last lane in segment
-        int             offset,                 ///< [in] Up-offset to pull from
-        Int2Type<false> /*is_small_unsigned*/)  ///< [in] Marker type indicating whether T is a small unsigned integer
-    {
-        return ReduceStep(input, reduction_op, last_lane, offset);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Templated inclusive scan iteration
-    //---------------------------------------------------------------------
-
-    template <typename ReductionOp, int STEP>
-    __device__ __forceinline__ void ReduceStep(
-        T&              input,              ///< [in] Calling thread's input item.
-        ReductionOp     reduction_op,       ///< [in] Binary reduction operator
-        int             last_lane,          ///< [in] Index of last lane in segment
-        Int2Type<STEP>  /*step*/)
-    {
-        input = ReduceStep(input, reduction_op, last_lane, 1 << STEP, Int2Type<IsInteger<T>::IS_SMALL_UNSIGNED>());
-
-        ReduceStep(input, reduction_op, last_lane, Int2Type<STEP + 1>());
-    }
-
-    template <typename ReductionOp>
-    __device__ __forceinline__ void ReduceStep(
-        T&              /*input*/,              ///< [in] Calling thread's input item.
-        ReductionOp     /*reduction_op*/,       ///< [in] Binary reduction operator
-        int             /*last_lane*/,          ///< [in] Index of last lane in segment
-        Int2Type<STEPS> /*step*/)
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Reduction operations
-    //---------------------------------------------------------------------
-
-    /// Reduction
-    template <
-        bool            ALL_LANES_VALID,        ///< Whether all lanes in each warp are contributing a valid fold of items
-        int             FOLDED_ITEMS_PER_LANE,  ///< Number of items folded into each lane
-        typename        ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T               input,                  ///< [in] Calling thread's input
-        int             folded_items_per_warp,  ///< [in] Total number of valid items folded into each logical warp
-        ReductionOp     reduction_op)           ///< [in] Binary reduction operator
-    {
-        // Get the last thread in the logical warp
-        int first_warp_thread   = 0;
-        int last_warp_thread    = LOGICAL_WARP_THREADS - 1;
-        if (!IS_ARCH_WARP)
-        {
-            first_warp_thread = lane_id & (~(LOGICAL_WARP_THREADS - 1));
-            last_warp_thread |= lane_id;
-        }
-
-        // Common case is FOLDED_ITEMS_PER_LANE = 1 (or a multiple of 32)
-        int lanes_with_valid_data = (folded_items_per_warp - 1) / FOLDED_ITEMS_PER_LANE;
-
-        // Get the last valid lane
-        int last_lane = (ALL_LANES_VALID) ?
-            last_warp_thread :
-            CUB_MIN(last_warp_thread, first_warp_thread + lanes_with_valid_data);
-
-        T output = input;
-
-//        // Iterate reduction steps
-//        #pragma unroll
-//        for (int STEP = 0; STEP < STEPS; STEP++)
-//        {
-//            output = ReduceStep(output, reduction_op, last_lane, 1 << STEP, Int2Type<IsInteger<T>::IS_SMALL_UNSIGNED>());
-//        }
-
-        // Template-iterate reduction steps
-        ReduceStep(output, reduction_op, last_lane, Int2Type<0>());
-
-        return output;
-    }
-
-
-    /// Segmented reduction
-    template <
-        bool            HEAD_SEGMENTED,     ///< Whether flags indicate a segment-head or a segment-tail
-        typename        FlagT,
-        typename        ReductionOp>
-    __device__ __forceinline__ T SegmentedReduce(
-        T               input,              ///< [in] Calling thread's input
-        FlagT           flag,               ///< [in] Whether or not the current lane is a segment head/tail
-        ReductionOp     reduction_op)       ///< [in] Binary reduction operator
-    {
-        // Get the start flags for each thread in the warp.
-        int warp_flags = WARP_BALLOT(flag, member_mask);
-
-        if (HEAD_SEGMENTED)
-            warp_flags >>= 1;
-
-        // Mask in the last lanes of each logical warp
-        warp_flags |= LastLaneMask<1, LOGICAL_WARPS>::MASK;
-
-        // Mask out the bits below the current thread
-        warp_flags &= LaneMaskGe();
-
-        // Find the next set flag
-        int last_lane = __clz(__brev(warp_flags));
-
-        T output = input;
-
-//        // Iterate reduction steps
-//        #pragma unroll
-//        for (int STEP = 0; STEP < STEPS; STEP++)
-//        {
-//            output = ReduceStep(output, reduction_op, last_lane, 1 << STEP, Int2Type<IsInteger<T>::IS_SMALL_UNSIGNED>());
-//        }
-
-        // Template-iterate reduction steps
-        ReduceStep(output, reduction_op, last_lane, Int2Type<0>());
-
-        return output;
-    }
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/warp/specializations/warp_reduce_smem.cuh b/ml-xgboost/cub/cub/warp/specializations/warp_reduce_smem.cuh
deleted file mode 100644
index 871a256..0000000
--- a/ml-xgboost/cub/cub/warp/specializations/warp_reduce_smem.cuh
+++ /dev/null
@@ -1,373 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::WarpReduceSmem provides smem-based variants of parallel reduction of items partitioned across a CUDA thread warp.
- */
-
-#pragma once
-
-#include "../../thread/thread_operators.cuh"
-#include "../../thread/thread_load.cuh"
-#include "../../thread/thread_store.cuh"
-#include "../../util_type.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief WarpReduceSmem provides smem-based variants of parallel reduction of items partitioned across a CUDA thread warp.
- */
-template <
-    typename    T,                      ///< Data type being reduced
-    int         LOGICAL_WARP_THREADS,   ///< Number of threads per logical warp
-    int         PTX_ARCH>               ///< The PTX compute capability for which to to specialize this collective
-struct WarpReduceSmem
-{
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    enum
-    {
-        /// Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// Whether the logical warp size is a power-of-two
-        IS_POW_OF_TWO = PowerOfTwo<LOGICAL_WARP_THREADS>::VALUE,
-
-        /// The number of warp scan steps
-        STEPS = Log2<LOGICAL_WARP_THREADS>::VALUE,
-
-        /// The number of threads in half a warp
-        HALF_WARP_THREADS = 1 << (STEPS - 1),
-
-        /// The number of shared memory elements per warp
-        WARP_SMEM_ELEMENTS =  LOGICAL_WARP_THREADS + HALF_WARP_THREADS,
-
-        /// FlagT status (when not using ballot)
-        UNSET   = 0x0,  // Is initially unset
-        SET     = 0x1,  // Is initially set
-        SEEN    = 0x2,  // Has seen another head flag from a successor peer
-    };
-
-    /// Shared memory flag type
-    typedef unsigned char SmemFlag;
-
-    /// Shared memory storage layout type (1.5 warps-worth of elements for each warp)
-    struct _TempStorage
-    {
-        T           reduce[WARP_SMEM_ELEMENTS];
-        SmemFlag    flags[WARP_SMEM_ELEMENTS];
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    _TempStorage    &temp_storage;
-    unsigned int    lane_id;
-    unsigned int    member_mask;
-
-
-    /******************************************************************************
-     * Construction
-     ******************************************************************************/
-
-    /// Constructor
-    __device__ __forceinline__ WarpReduceSmem(
-        TempStorage     &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        lane_id(IS_ARCH_WARP ?
-            LaneId() :
-            LaneId() % LOGICAL_WARP_THREADS),
-        member_mask(!IS_POW_OF_TWO ?
-            (0xffffffff >> (32 - LOGICAL_WARP_THREADS)) :                                       // non-power-of-two subwarps cannot be tiled
-            (0xffffffff >> (32 - LOGICAL_WARP_THREADS)) << (LaneId() / LOGICAL_WARP_THREADS))
-    {}
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    //---------------------------------------------------------------------
-    // Regular reduction
-    //---------------------------------------------------------------------
-
-    /**
-     * Reduction step
-     */
-    template <
-        bool                ALL_LANES_VALID,        ///< Whether all lanes in each warp are contributing a valid fold of items
-        int                 FOLDED_ITEMS_PER_LANE,  ///< Number of items folded into each lane
-        typename            ReductionOp,
-        int                 STEP>
-    __device__ __forceinline__ T ReduceStep(
-        T                   input,                  ///< [in] Calling thread's input
-        int                 folded_items_per_warp,  ///< [in] Total number of valid items folded into each logical warp
-        ReductionOp         reduction_op,           ///< [in] Reduction operator
-        Int2Type<STEP>      /*step*/)
-    {
-        const int OFFSET = 1 << STEP;
-
-        // Share input through buffer
-        ThreadStore<STORE_VOLATILE>(&temp_storage.reduce[lane_id], input);
-
-        WARP_SYNC(member_mask);
-
-        // Update input if peer_addend is in range
-        if ((ALL_LANES_VALID && IS_POW_OF_TWO) || ((lane_id + OFFSET) * FOLDED_ITEMS_PER_LANE < folded_items_per_warp))
-        {
-            T peer_addend = ThreadLoad<LOAD_VOLATILE>(&temp_storage.reduce[lane_id + OFFSET]);
-            input = reduction_op(input, peer_addend);
-        }
-
-        WARP_SYNC(member_mask);
-
-        return ReduceStep<ALL_LANES_VALID, FOLDED_ITEMS_PER_LANE>(input, folded_items_per_warp, reduction_op, Int2Type<STEP + 1>());
-    }
-
-
-    /**
-     * Reduction step (terminate)
-     */
-    template <
-        bool                ALL_LANES_VALID,            ///< Whether all lanes in each warp are contributing a valid fold of items
-        int                 FOLDED_ITEMS_PER_LANE,      ///< Number of items folded into each lane
-        typename            ReductionOp>
-    __device__ __forceinline__ T ReduceStep(
-        T                   input,                      ///< [in] Calling thread's input
-        int                 /*folded_items_per_warp*/,  ///< [in] Total number of valid items folded into each logical warp
-        ReductionOp         /*reduction_op*/,           ///< [in] Reduction operator
-        Int2Type<STEPS>     /*step*/)
-    {
-        return input;
-    }
-
-
-    //---------------------------------------------------------------------
-    // Segmented reduction
-    //---------------------------------------------------------------------
-
-
-    /**
-     * Ballot-based segmented reduce
-     */
-    template <
-        bool            HEAD_SEGMENTED,     ///< Whether flags indicate a segment-head or a segment-tail
-        typename        FlagT,
-        typename        ReductionOp>
-    __device__ __forceinline__ T SegmentedReduce(
-        T               input,                  ///< [in] Calling thread's input
-        FlagT           flag,                   ///< [in] Whether or not the current lane is a segment head/tail
-        ReductionOp     reduction_op,           ///< [in] Reduction operator
-        Int2Type<true>  /*has_ballot*/)         ///< [in] Marker type for whether the target arch has ballot functionality
-    {
-        // Get the start flags for each thread in the warp.
-        int warp_flags = WARP_BALLOT(flag, member_mask);
-
-        if (!HEAD_SEGMENTED)
-            warp_flags <<= 1;
-
-        // Keep bits above the current thread.
-        warp_flags &= LaneMaskGt();
-
-        // Accommodate packing of multiple logical warps in a single physical warp
-        if (!IS_ARCH_WARP)
-        {
-            warp_flags >>= (LaneId() / LOGICAL_WARP_THREADS) * LOGICAL_WARP_THREADS;
-        }
-
-        // Find next flag
-        int next_flag = __clz(__brev(warp_flags));
-
-        // Clip the next segment at the warp boundary if necessary
-        if (LOGICAL_WARP_THREADS != 32)
-            next_flag = CUB_MIN(next_flag, LOGICAL_WARP_THREADS);
-
-        #pragma unroll
-        for (int STEP = 0; STEP < STEPS; STEP++)
-        {
-            const int OFFSET = 1 << STEP;
-
-            // Share input into buffer
-            ThreadStore<STORE_VOLATILE>(&temp_storage.reduce[lane_id], input);
-
-            WARP_SYNC(member_mask);
-
-            // Update input if peer_addend is in range
-            if (OFFSET + lane_id < next_flag)
-            {
-                T peer_addend = ThreadLoad<LOAD_VOLATILE>(&temp_storage.reduce[lane_id + OFFSET]);
-                input = reduction_op(input, peer_addend);
-            }
-
-            WARP_SYNC(member_mask);
-        }
-
-        return input;
-    }
-
-
-    /**
-     * Smem-based segmented reduce
-     */
-    template <
-        bool            HEAD_SEGMENTED,     ///< Whether flags indicate a segment-head or a segment-tail
-        typename        FlagT,
-        typename        ReductionOp>
-    __device__ __forceinline__ T SegmentedReduce(
-        T               input,                  ///< [in] Calling thread's input
-        FlagT           flag,                   ///< [in] Whether or not the current lane is a segment head/tail
-        ReductionOp     reduction_op,           ///< [in] Reduction operator
-        Int2Type<false> /*has_ballot*/)         ///< [in] Marker type for whether the target arch has ballot functionality
-    {
-        enum
-        {
-            UNSET   = 0x0,  // Is initially unset
-            SET     = 0x1,  // Is initially set
-            SEEN    = 0x2,  // Has seen another head flag from a successor peer
-        };
-
-        // Alias flags onto shared data storage
-        volatile SmemFlag *flag_storage = temp_storage.flags;
-
-        SmemFlag flag_status = (flag) ? SET : UNSET;
-
-        for (int STEP = 0; STEP < STEPS; STEP++)
-        {
-            const int OFFSET = 1 << STEP;
-
-            // Share input through buffer
-            ThreadStore<STORE_VOLATILE>(&temp_storage.reduce[lane_id], input);
-
-            WARP_SYNC(member_mask);
-
-            // Get peer from buffer
-            T peer_addend = ThreadLoad<LOAD_VOLATILE>(&temp_storage.reduce[lane_id + OFFSET]);
-
-            WARP_SYNC(member_mask);
-
-            // Share flag through buffer
-            flag_storage[lane_id] = flag_status;
-
-            // Get peer flag from buffer
-            SmemFlag peer_flag_status = flag_storage[lane_id + OFFSET];
-
-            // Update input if peer was in range
-            if (lane_id < LOGICAL_WARP_THREADS - OFFSET)
-            {
-                if (HEAD_SEGMENTED)
-                {
-                    // Head-segmented
-                    if ((flag_status & SEEN) == 0)
-                    {
-                        // Has not seen a more distant head flag
-                        if (peer_flag_status & SET)
-                        {
-                            // Has now seen a head flag
-                            flag_status |= SEEN;
-                        }
-                        else
-                        {
-                            // Peer is not a head flag: grab its count
-                            input = reduction_op(input, peer_addend);
-                        }
-
-                        // Update seen status to include that of peer
-                        flag_status |= (peer_flag_status & SEEN);
-                    }
-                }
-                else
-                {
-                    // Tail-segmented.  Simply propagate flag status
-                    if (!flag_status)
-                    {
-                        input = reduction_op(input, peer_addend);
-                        flag_status |= peer_flag_status;
-                    }
-
-                }
-            }
-        }
-
-        return input;
-    }
-
-
-    /******************************************************************************
-     * Interface
-     ******************************************************************************/
-
-    /**
-     * Reduction
-     */
-    template <
-        bool                ALL_LANES_VALID,        ///< Whether all lanes in each warp are contributing a valid fold of items
-        int                 FOLDED_ITEMS_PER_LANE,  ///< Number of items folded into each lane
-        typename            ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   input,                  ///< [in] Calling thread's input
-        int                 folded_items_per_warp,  ///< [in] Total number of valid items folded into each logical warp
-        ReductionOp         reduction_op)           ///< [in] Reduction operator
-    {
-        return ReduceStep<ALL_LANES_VALID, FOLDED_ITEMS_PER_LANE>(input, folded_items_per_warp, reduction_op, Int2Type<0>());
-    }
-
-
-    /**
-     * Segmented reduction
-     */
-    template <
-        bool            HEAD_SEGMENTED,     ///< Whether flags indicate a segment-head or a segment-tail
-        typename        FlagT,
-        typename        ReductionOp>
-    __device__ __forceinline__ T SegmentedReduce(
-        T               input,              ///< [in] Calling thread's input
-        FlagT            flag,               ///< [in] Whether or not the current lane is a segment head/tail
-        ReductionOp     reduction_op)       ///< [in] Reduction operator
-    {
-        return SegmentedReduce<HEAD_SEGMENTED>(input, flag, reduction_op, Int2Type<(PTX_ARCH >= 200)>());
-    }
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/warp/specializations/warp_scan_shfl.cuh b/ml-xgboost/cub/cub/warp/specializations/warp_scan_shfl.cuh
deleted file mode 100644
index b126c44..0000000
--- a/ml-xgboost/cub/cub/warp/specializations/warp_scan_shfl.cuh
+++ /dev/null
@@ -1,650 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::WarpScanShfl provides SHFL-based variants of parallel prefix scan of items partitioned across a CUDA thread warp.
- */
-
-#pragma once
-
-#include "../../thread/thread_operators.cuh"
-#include "../../util_type.cuh"
-#include "../../util_ptx.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief WarpScanShfl provides SHFL-based variants of parallel prefix scan of items partitioned across a CUDA thread warp.
- */
-template <
-    typename    T,                      ///< Data type being scanned
-    int         LOGICAL_WARP_THREADS,   ///< Number of threads per logical warp
-    int         PTX_ARCH>               ///< The PTX compute capability for which to to specialize this collective
-struct WarpScanShfl
-{
-    //---------------------------------------------------------------------
-    // Constants and type definitions
-    //---------------------------------------------------------------------
-
-    enum
-    {
-        /// Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// The number of warp scan steps
-        STEPS = Log2<LOGICAL_WARP_THREADS>::VALUE,
-
-        /// The 5-bit SHFL mask for logically splitting warps into sub-segments starts 8-bits up
-        SHFL_C = ((-1 << STEPS) & 31) << 8,
-    };
-
-    template <typename S>
-    struct IntegerTraits
-    {
-        enum {
-            ///Whether the data type is a small (32b or less) integer for which we can use a single SFHL instruction per exchange
-            IS_SMALL_UNSIGNED = (Traits<S>::CATEGORY == UNSIGNED_INTEGER) && (sizeof(S) <= sizeof(unsigned int))
-        };
-    };
-
-    /// Shared memory storage layout type
-    struct TempStorage {};
-
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    unsigned int lane_id;
-
-    unsigned int member_mask;
-
-    //---------------------------------------------------------------------
-    // Construction
-    //---------------------------------------------------------------------
-
-    /// Constructor
-    __device__ __forceinline__ WarpScanShfl(
-        TempStorage &/*temp_storage*/)
-    :
-        lane_id(IS_ARCH_WARP ?
-            LaneId() :
-            LaneId() % LOGICAL_WARP_THREADS),
-        member_mask(IS_ARCH_WARP ?
-             0xffffffff :
-             (0xffffffff >> (32 - LOGICAL_WARP_THREADS)) << (LaneId() / LOGICAL_WARP_THREADS))
-    {}
-
-
-    //---------------------------------------------------------------------
-    // Inclusive scan steps
-    //---------------------------------------------------------------------
-
-    /// Inclusive prefix scan step (specialized for summation across int32 types)
-    __device__ __forceinline__ int InclusiveScanStep(
-        int             input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*scan_op*/,        ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        int output;
-        int shfl_c = first_lane | SHFL_C;   // Shuffle control (mask and first-lane)
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .s32 r0;"
-            "  .reg .pred p;"
-            "  shfl.sync.up.b32 r0|p, %1, %2, %3, %5;"
-            "  @p add.s32 r0, r0, %4;"
-            "  mov.s32 %0, r0;"
-            "}"
-            : "=r"(output) : "r"(input), "r"(offset), "r"(shfl_c), "r"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .s32 r0;"
-            "  .reg .pred p;"
-            "  shfl.up.b32 r0|p, %1, %2, %3;"
-            "  @p add.s32 r0, r0, %4;"
-            "  mov.s32 %0, r0;"
-            "}"
-            : "=r"(output) : "r"(input), "r"(offset), "r"(shfl_c), "r"(input));
-#endif
-
-        return output;
-    }
-
-    /// Inclusive prefix scan step (specialized for summation across uint32 types)
-    __device__ __forceinline__ unsigned int InclusiveScanStep(
-        unsigned int    input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*scan_op*/,        ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        unsigned int output;
-        int shfl_c = first_lane | SHFL_C;   // Shuffle control (mask and first-lane)
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u32 r0;"
-            "  .reg .pred p;"
-            "  shfl.sync.up.b32 r0|p, %1, %2, %3, %5;"
-            "  @p add.u32 r0, r0, %4;"
-            "  mov.u32 %0, r0;"
-            "}"
-            : "=r"(output) : "r"(input), "r"(offset), "r"(shfl_c), "r"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u32 r0;"
-            "  .reg .pred p;"
-            "  shfl.up.b32 r0|p, %1, %2, %3;"
-            "  @p add.u32 r0, r0, %4;"
-            "  mov.u32 %0, r0;"
-            "}"
-            : "=r"(output) : "r"(input), "r"(offset), "r"(shfl_c), "r"(input));
-#endif
-
-        return output;
-    }
-
-
-    /// Inclusive prefix scan step (specialized for summation across fp32 types)
-    __device__ __forceinline__ float InclusiveScanStep(
-        float           input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*scan_op*/,        ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        float output;
-        int shfl_c = first_lane | SHFL_C;   // Shuffle control (mask and first-lane)
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .f32 r0;"
-            "  .reg .pred p;"
-            "  shfl.sync.up.b32 r0|p, %1, %2, %3, %5;"
-            "  @p add.f32 r0, r0, %4;"
-            "  mov.f32 %0, r0;"
-            "}"
-            : "=f"(output) : "f"(input), "r"(offset), "r"(shfl_c), "f"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .f32 r0;"
-            "  .reg .pred p;"
-            "  shfl.up.b32 r0|p, %1, %2, %3;"
-            "  @p add.f32 r0, r0, %4;"
-            "  mov.f32 %0, r0;"
-            "}"
-            : "=f"(output) : "f"(input), "r"(offset), "r"(shfl_c), "f"(input));
-#endif
-
-        return output;
-    }
-
-
-    /// Inclusive prefix scan step (specialized for summation across unsigned long long types)
-    __device__ __forceinline__ unsigned long long InclusiveScanStep(
-        unsigned long long  input,              ///< [in] Calling thread's input item.
-        cub::Sum            /*scan_op*/,        ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        unsigned long long output;
-        int shfl_c = first_lane | SHFL_C;   // Shuffle control (mask and first-lane)
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u64 r0;"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.sync.up.b32 lo|p, lo, %2, %3, %5;"
-            "  shfl.sync.up.b32 hi|p, hi, %2, %3, %5;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.u64 r0, r0, %4;"
-            "  mov.u64 %0, r0;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(shfl_c), "l"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u64 r0;"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.up.b32 lo|p, lo, %2, %3;"
-            "  shfl.up.b32 hi|p, hi, %2, %3;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.u64 r0, r0, %4;"
-            "  mov.u64 %0, r0;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(shfl_c), "l"(input));
-#endif
-
-        return output;
-    }
-
-
-    /// Inclusive prefix scan step (specialized for summation across long long types)
-    __device__ __forceinline__ long long InclusiveScanStep(
-        long long       input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*scan_op*/,        ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        long long output;
-        int shfl_c = first_lane | SHFL_C;   // Shuffle control (mask and first-lane)
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .s64 r0;"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.sync.up.b32 lo|p, lo, %2, %3, %5;"
-            "  shfl.sync.up.b32 hi|p, hi, %2, %3, %5;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.s64 r0, r0, %4;"
-            "  mov.s64 %0, r0;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(shfl_c), "l"(input), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .s64 r0;"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.up.b32 lo|p, lo, %2, %3;"
-            "  shfl.up.b32 hi|p, hi, %2, %3;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.s64 r0, r0, %4;"
-            "  mov.s64 %0, r0;"
-            "}"
-            : "=l"(output) : "l"(input), "r"(offset), "r"(shfl_c), "l"(input));
-#endif
-
-        return output;
-    }
-
-
-    /// Inclusive prefix scan step (specialized for summation across fp64 types)
-    __device__ __forceinline__ double InclusiveScanStep(
-        double          input,              ///< [in] Calling thread's input item.
-        cub::Sum        /*scan_op*/,        ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        double output;
-        int shfl_c = first_lane | SHFL_C;   // Shuffle control (mask and first-lane)
-
-        // Use predicate set from SHFL to guard against invalid peers
-#ifdef CUB_USE_COOPERATIVE_GROUPS
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  .reg .f64 r0;"
-            "  mov.b64 %0, %1;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.sync.up.b32 lo|p, lo, %2, %3, %4;"
-            "  shfl.sync.up.b32 hi|p, hi, %2, %3, %4;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.f64 %0, %0, r0;"
-            "}"
-            : "=d"(output) : "d"(input), "r"(offset), "r"(shfl_c), "r"(member_mask));
-#else
-        asm volatile(
-            "{"
-            "  .reg .u32 lo;"
-            "  .reg .u32 hi;"
-            "  .reg .pred p;"
-            "  .reg .f64 r0;"
-            "  mov.b64 %0, %1;"
-            "  mov.b64 {lo, hi}, %1;"
-            "  shfl.up.b32 lo|p, lo, %2, %3;"
-            "  shfl.up.b32 hi|p, hi, %2, %3;"
-            "  mov.b64 r0, {lo, hi};"
-            "  @p add.f64 %0, %0, r0;"
-            "}"
-            : "=d"(output) : "d"(input), "r"(offset), "r"(shfl_c));
-#endif
-
-        return output;
-    }
-
-
-/*
-    /// Inclusive prefix scan (specialized for ReduceBySegmentOp<cub::Sum> across KeyValuePair<OffsetT, Value> types)
-    template <typename Value, typename OffsetT>
-    __device__ __forceinline__ KeyValuePair<OffsetT, Value>InclusiveScanStep(
-        KeyValuePair<OffsetT, Value>    input,              ///< [in] Calling thread's input item.
-        ReduceBySegmentOp<cub::Sum>     scan_op,            ///< [in] Binary scan operator
-        int                             first_lane,         ///< [in] Index of first lane in segment
-        int                             offset)             ///< [in] Up-offset to pull from
-    {
-        KeyValuePair<OffsetT, Value> output;
-
-        output.value = InclusiveScanStep(input.value, cub::Sum(), first_lane, offset, Int2Type<IntegerTraits<Value>::IS_SMALL_UNSIGNED>());
-        output.key = InclusiveScanStep(input.key, cub::Sum(), first_lane, offset, Int2Type<IntegerTraits<OffsetT>::IS_SMALL_UNSIGNED>());
-
-        if (input.key > 0)
-            output.value = input.value;
-
-        return output;
-    }
-*/
-
-    /// Inclusive prefix scan step (generic)
-    template <typename _T, typename ScanOpT>
-    __device__ __forceinline__ _T InclusiveScanStep(
-        _T              input,              ///< [in] Calling thread's input item.
-        ScanOpT          scan_op,            ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset)             ///< [in] Up-offset to pull from
-    {
-        _T temp = ShuffleUp(input, offset, first_lane, member_mask);
-
-        // Perform scan op if from a valid peer
-        _T output = scan_op(temp, input);
-        if (static_cast<int>(lane_id) < first_lane + offset)
-            output = input;
-
-        return output;
-    }
-
-
-    /// Inclusive prefix scan step (specialized for small integers size 32b or less)
-    template <typename _T, typename ScanOpT>
-    __device__ __forceinline__ _T InclusiveScanStep(
-        _T              input,              ///< [in] Calling thread's input item.
-        ScanOpT          scan_op,            ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset,             ///< [in] Up-offset to pull from
-        Int2Type<true>  /*is_small_unsigned*/)  ///< [in] Marker type indicating whether T is a small integer
-    {
-        return InclusiveScanStep(input, scan_op, first_lane, offset);
-    }
-
-
-    /// Inclusive prefix scan step (specialized for types other than small integers size 32b or less)
-    template <typename _T, typename ScanOpT>
-    __device__ __forceinline__ _T InclusiveScanStep(
-        _T              input,              ///< [in] Calling thread's input item.
-        ScanOpT          scan_op,            ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        int             offset,             ///< [in] Up-offset to pull from
-        Int2Type<false> /*is_small_unsigned*/)  ///< [in] Marker type indicating whether T is a small integer
-    {
-        return InclusiveScanStep(input, scan_op, first_lane, offset);
-    }
-
-    //---------------------------------------------------------------------
-    // Templated inclusive scan iteration
-    //---------------------------------------------------------------------
-
-    template <typename _T, typename ScanOp, int STEP>
-    __device__ __forceinline__ void InclusiveScanStep(
-        _T&             input,              ///< [in] Calling thread's input item.
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        int             first_lane,         ///< [in] Index of first lane in segment
-        Int2Type<STEP>  /*step*/)               ///< [in] Marker type indicating scan step
-    {
-        input = InclusiveScanStep(input, scan_op, first_lane, 1 << STEP, Int2Type<IntegerTraits<T>::IS_SMALL_UNSIGNED>());
-
-        InclusiveScanStep(input, scan_op, first_lane, Int2Type<STEP + 1>());
-    }
-
-    template <typename _T, typename ScanOp>
-    __device__ __forceinline__ void InclusiveScanStep(
-        _T&             /*input*/,              ///< [in] Calling thread's input item.
-        ScanOp          /*scan_op*/,            ///< [in] Binary scan operator
-        int             /*first_lane*/,         ///< [in] Index of first lane in segment
-        Int2Type<STEPS> /*step*/)               ///< [in] Marker type indicating scan step
-    {}
-
-
-    /******************************************************************************
-     * Interface
-     ******************************************************************************/
-
-    //---------------------------------------------------------------------
-    // Broadcast
-    //---------------------------------------------------------------------
-
-    /// Broadcast
-    __device__ __forceinline__ T Broadcast(
-        T               input,              ///< [in] The value to broadcast
-        int             src_lane)           ///< [in] Which warp lane is to do the broadcasting
-    {
-        return ShuffleIndex(input, src_lane, LOGICAL_WARP_THREADS, member_mask);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Inclusive operations
-    //---------------------------------------------------------------------
-
-    /// Inclusive scan
-    template <typename _T, typename ScanOpT>
-    __device__ __forceinline__ void InclusiveScan(
-        _T              input,              ///< [in] Calling thread's input item.
-        _T              &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOpT         scan_op)            ///< [in] Binary scan operator
-    {
-        inclusive_output = input;
-
-        // Iterate scan steps
-        int segment_first_lane = 0;
-
-        // Iterate scan steps
-//        InclusiveScanStep(inclusive_output, scan_op, segment_first_lane, Int2Type<0>());
-
-        // Iterate scan steps
-        #pragma unroll
-        for (int STEP = 0; STEP < STEPS; STEP++)
-        {
-            inclusive_output = InclusiveScanStep(
-                inclusive_output,
-                scan_op,
-                segment_first_lane,
-                (1 << STEP),
-                Int2Type<IntegerTraits<T>::IS_SMALL_UNSIGNED>());
-        }
-
-    }
-
-    /// Inclusive scan, specialized for reduce-value-by-key
-    template <typename KeyT, typename ValueT, typename ReductionOpT>
-    __device__ __forceinline__ void InclusiveScan(
-        KeyValuePair<KeyT, ValueT>      input,              ///< [in] Calling thread's input item.
-        KeyValuePair<KeyT, ValueT>      &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ReduceByKeyOp<ReductionOpT >    scan_op)            ///< [in] Binary scan operator
-    {
-        inclusive_output = input;
-
-        KeyT pred_key = ShuffleUp(inclusive_output.key, 1, 0, member_mask);
-
-        unsigned int ballot = WARP_BALLOT((pred_key != inclusive_output.key), member_mask);
-
-        // Mask away all lanes greater than ours
-        ballot = ballot & LaneMaskLe();
-
-        // Find index of first set bit
-        int segment_first_lane = CUB_MAX(0, 31 - __clz(ballot));
-
-        // Iterate scan steps
-//        InclusiveScanStep(inclusive_output.value, scan_op.op, segment_first_lane, Int2Type<0>());
-
-        // Iterate scan steps
-        #pragma unroll
-        for (int STEP = 0; STEP < STEPS; STEP++)
-        {
-            inclusive_output.value = InclusiveScanStep(
-                inclusive_output.value,
-                scan_op.op,
-                segment_first_lane,
-                (1 << STEP),
-                Int2Type<IntegerTraits<T>::IS_SMALL_UNSIGNED>());
-        }
-    }
-
-
-    /// Inclusive scan with aggregate
-    template <typename ScanOpT>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOpT         scan_op,            ///< [in] Binary scan operator
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        InclusiveScan(input, inclusive_output, scan_op);
-
-        // Grab aggregate from last warp lane
-        warp_aggregate = ShuffleIndex(inclusive_output, LOGICAL_WARP_THREADS - 1, LOGICAL_WARP_THREADS, member_mask);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Get exclusive from inclusive
-    //---------------------------------------------------------------------
-
-    /// Update inclusive and exclusive using input and inclusive
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update(
-        T                       /*input*/,          ///< [in]
-        T                       &inclusive,         ///< [in, out]
-        T                       &exclusive,         ///< [out]
-        ScanOpT                 /*scan_op*/,        ///< [in]
-        IsIntegerT              /*is_integer*/)     ///< [in]
-    {
-        // initial value unknown
-        exclusive = ShuffleUp(inclusive, 1, 0, member_mask);
-    }
-
-    /// Update inclusive and exclusive using input and inclusive (specialized for summation of integer types)
-    __device__ __forceinline__ void Update(
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        cub::Sum                /*scan_op*/,
-        Int2Type<true>          /*is_integer*/)
-    {
-        // initial value presumed 0
-        exclusive = inclusive - input;
-    }
-
-    /// Update inclusive and exclusive using initial value using input, inclusive, and initial value
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update (
-        T                       /*input*/,
-        T                       &inclusive,
-        T                       &exclusive,
-        ScanOpT                 scan_op,
-        T                       initial_value,
-        IsIntegerT              /*is_integer*/)
-    {
-        inclusive = scan_op(initial_value, inclusive);
-        exclusive = ShuffleUp(inclusive, 1, 0, member_mask);
-        if (lane_id == 0)
-            exclusive = initial_value;
-    }
-
-    /// Update inclusive and exclusive using initial value using input and inclusive (specialized for summation of integer types)
-    __device__ __forceinline__ void Update (
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        cub::Sum                scan_op,
-        T                       initial_value,
-        Int2Type<true>          /*is_integer*/)
-    {
-        inclusive = scan_op(initial_value, inclusive);
-        exclusive = inclusive - input;
-    }
-
-
-    /// Update inclusive, exclusive, and warp aggregate using input and inclusive
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update (
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        T                       &warp_aggregate,
-        ScanOpT                 scan_op,
-        IsIntegerT              is_integer)
-    {
-        warp_aggregate = ShuffleIndex(inclusive, LOGICAL_WARP_THREADS - 1, LOGICAL_WARP_THREADS, member_mask);
-        Update(input, inclusive, exclusive, scan_op, is_integer);
-    }
-
-    /// Update inclusive, exclusive, and warp aggregate using input, inclusive, and initial value
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update (
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        T                       &warp_aggregate,
-        ScanOpT                 scan_op,
-        T                       initial_value,
-        IsIntegerT              is_integer)
-    {
-        warp_aggregate = ShuffleIndex(inclusive, LOGICAL_WARP_THREADS - 1, LOGICAL_WARP_THREADS, member_mask);
-        Update(input, inclusive, exclusive, scan_op, initial_value, is_integer);
-    }
-
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/warp/specializations/warp_scan_smem.cuh b/ml-xgboost/cub/cub/warp/specializations/warp_scan_smem.cuh
deleted file mode 100644
index fc96cd0..0000000
--- a/ml-xgboost/cub/cub/warp/specializations/warp_scan_smem.cuh
+++ /dev/null
@@ -1,395 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * cub::WarpScanSmem provides smem-based variants of parallel prefix scan of items partitioned across a CUDA thread warp.
- */
-
-#pragma once
-
-#include "../../thread/thread_operators.cuh"
-#include "../../thread/thread_load.cuh"
-#include "../../thread/thread_store.cuh"
-#include "../../util_type.cuh"
-#include "../../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \brief WarpScanSmem provides smem-based variants of parallel prefix scan of items partitioned across a CUDA thread warp.
- */
-template <
-    typename    T,                      ///< Data type being scanned
-    int         LOGICAL_WARP_THREADS,   ///< Number of threads per logical warp
-    int         PTX_ARCH>               ///< The PTX compute capability for which to to specialize this collective
-struct WarpScanSmem
-{
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    enum
-    {
-        /// Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// Whether the logical warp size is a power-of-two
-        IS_POW_OF_TWO = PowerOfTwo<LOGICAL_WARP_THREADS>::VALUE,
-
-        /// The number of warp scan steps
-        STEPS = Log2<LOGICAL_WARP_THREADS>::VALUE,
-
-        /// The number of threads in half a warp
-        HALF_WARP_THREADS = 1 << (STEPS - 1),
-
-        /// The number of shared memory elements per warp
-        WARP_SMEM_ELEMENTS =  LOGICAL_WARP_THREADS + HALF_WARP_THREADS,
-    };
-
-    /// Storage cell type (workaround for SM1x compiler bugs with custom-ops like Max() on signed chars)
-    typedef typename If<((Equals<T, char>::VALUE || Equals<T, signed char>::VALUE) && (PTX_ARCH < 200)), int, T>::Type CellT;
-
-    /// Shared memory storage layout type (1.5 warps-worth of elements for each warp)
-    typedef CellT _TempStorage[WARP_SMEM_ELEMENTS];
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    _TempStorage    &temp_storage;
-    unsigned int    lane_id;
-    unsigned int    member_mask;
-
-
-    /******************************************************************************
-     * Construction
-     ******************************************************************************/
-
-    /// Constructor
-    __device__ __forceinline__ WarpScanSmem(
-        TempStorage     &temp_storage)
-    :
-        temp_storage(temp_storage.Alias()),
-        lane_id(IS_ARCH_WARP ?
-            LaneId() :
-            LaneId() % LOGICAL_WARP_THREADS),
-        member_mask(!IS_POW_OF_TWO ?
-            (0xffffffff >> (32 - LOGICAL_WARP_THREADS)) :                                       // non-power-of-two subwarps cannot be tiled
-            (0xffffffff >> (32 - LOGICAL_WARP_THREADS)) << (LaneId() / LOGICAL_WARP_THREADS))
-    {}
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-    /// Basic inclusive scan iteration (template unrolled, inductive-case specialization)
-    template <
-        bool        HAS_IDENTITY,
-        int         STEP,
-        typename    ScanOp>
-    __device__ __forceinline__ void ScanStep(
-        T                       &partial,
-        ScanOp                  scan_op,
-        Int2Type<STEP>          /*step*/)
-    {
-        const int OFFSET = 1 << STEP;
-
-        // Share partial into buffer
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) partial);
-
-        WARP_SYNC(member_mask);
-
-        // Update partial if addend is in range
-        if (HAS_IDENTITY || (lane_id >= OFFSET))
-        {
-            T addend = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id - OFFSET]);
-            partial = scan_op(addend, partial);
-        }
-        WARP_SYNC(member_mask);
-
-        ScanStep<HAS_IDENTITY>(partial, scan_op, Int2Type<STEP + 1>());
-    }
-
-
-    /// Basic inclusive scan iteration(template unrolled, base-case specialization)
-    template <
-        bool        HAS_IDENTITY,
-        typename    ScanOp>
-    __device__ __forceinline__ void ScanStep(
-        T                       &/*partial*/,
-        ScanOp                  /*scan_op*/,
-        Int2Type<STEPS>         /*step*/)
-    {}
-
-
-    /// Inclusive prefix scan (specialized for summation across primitive types)
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,              ///< [in] Calling thread's input item.
-        T                       &output,            ///< [out] Calling thread's output item.  May be aliased with \p input.
-        Sum                     scan_op,            ///< [in] Binary scan operator
-        Int2Type<true>          /*is_primitive*/)   ///< [in] Marker type indicating whether T is primitive type
-    {
-        T identity = 0;
-        ThreadStore<STORE_VOLATILE>(&temp_storage[lane_id], (CellT) identity);
-
-        WARP_SYNC(member_mask);
-
-        // Iterate scan steps
-        output = input;
-        ScanStep<true>(output, scan_op, Int2Type<0>());
-    }
-
-
-    /// Inclusive prefix scan
-    template <typename ScanOp, int IS_PRIMITIVE>
-    __device__ __forceinline__ void InclusiveScan(
-        T                       input,              ///< [in] Calling thread's input item.
-        T                       &output,            ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp                  scan_op,            ///< [in] Binary scan operator
-        Int2Type<IS_PRIMITIVE>  /*is_primitive*/)   ///< [in] Marker type indicating whether T is primitive type
-    {
-        // Iterate scan steps
-        output = input;
-        ScanStep<false>(output, scan_op, Int2Type<0>());
-    }
-
-
-    /******************************************************************************
-     * Interface
-     ******************************************************************************/
-
-    //---------------------------------------------------------------------
-    // Broadcast
-    //---------------------------------------------------------------------
-
-    /// Broadcast
-    __device__ __forceinline__ T Broadcast(
-        T               input,              ///< [in] The value to broadcast
-        unsigned int    src_lane)           ///< [in] Which warp lane is to do the broadcasting
-    {
-        if (lane_id == src_lane)
-        {
-            ThreadStore<STORE_VOLATILE>(temp_storage, (CellT) input);
-        }
-
-        WARP_SYNC(member_mask);
-
-        return (T)ThreadLoad<LOAD_VOLATILE>(temp_storage);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Inclusive operations
-    //---------------------------------------------------------------------
-
-    /// Inclusive scan
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        InclusiveScan(input, inclusive_output, scan_op, Int2Type<Traits<T>::PRIMITIVE>());
-    }
-
-
-    /// Inclusive scan with aggregate
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        InclusiveScan(input, inclusive_output, scan_op);
-
-        // Retrieve aggregate
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) inclusive_output);
-
-        WARP_SYNC(member_mask);
-
-        warp_aggregate = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[WARP_SMEM_ELEMENTS - 1]);
-
-        WARP_SYNC(member_mask);
-    }
-
-
-    //---------------------------------------------------------------------
-    // Get exclusive from inclusive
-    //---------------------------------------------------------------------
-
-    /// Update inclusive and exclusive using input and inclusive
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update(
-        T                       /*input*/,      ///< [in]
-        T                       &inclusive,     ///< [in, out]
-        T                       &exclusive,     ///< [out]
-        ScanOpT                 /*scan_op*/,    ///< [in]
-        IsIntegerT              /*is_integer*/) ///< [in]
-    {
-        // initial value unknown
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) inclusive);
-
-        WARP_SYNC(member_mask);
-
-        exclusive = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id - 1]);
-    }
-
-    /// Update inclusive and exclusive using input and inclusive (specialized for summation of integer types)
-    __device__ __forceinline__ void Update(
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        cub::Sum                /*scan_op*/,
-        Int2Type<true>          /*is_integer*/)
-    {
-        // initial value presumed 0
-        exclusive = inclusive - input;
-    }
-
-    /// Update inclusive and exclusive using initial value using input, inclusive, and initial value
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update (
-        T                       /*input*/,
-        T                       &inclusive,
-        T                       &exclusive,
-        ScanOpT                 scan_op,
-        T                       initial_value,
-        IsIntegerT              /*is_integer*/)
-    {
-        inclusive = scan_op(initial_value, inclusive);
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) inclusive);
-
-        WARP_SYNC(member_mask);
-
-        exclusive = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id - 1]);
-        if (lane_id == 0)
-            exclusive = initial_value;
-    }
-
-    /// Update inclusive and exclusive using initial value using input and inclusive (specialized for summation of integer types)
-    __device__ __forceinline__ void Update (
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        cub::Sum                scan_op,
-        T                       initial_value,
-        Int2Type<true>          /*is_integer*/)
-    {
-        inclusive = scan_op(initial_value, inclusive);
-        exclusive = inclusive - input;
-    }
-
-
-    /// Update inclusive, exclusive, and warp aggregate using input and inclusive
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update (
-        T                       /*input*/,
-        T                       &inclusive,
-        T                       &exclusive,
-        T                       &warp_aggregate,
-        ScanOpT                 /*scan_op*/,
-        IsIntegerT              /*is_integer*/)
-    {
-        // Initial value presumed to be unknown or identity (either way our padding is correct)
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) inclusive);
-
-        WARP_SYNC(member_mask);
-
-        exclusive = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id - 1]);
-        warp_aggregate = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[WARP_SMEM_ELEMENTS - 1]);
-    }
-
-    /// Update inclusive, exclusive, and warp aggregate using input and inclusive (specialized for summation of integer types)
-    __device__ __forceinline__ void Update (
-        T                       input,
-        T                       &inclusive,
-        T                       &exclusive,
-        T                       &warp_aggregate,
-        cub::Sum                /*scan_o*/,
-        Int2Type<true>          /*is_integer*/)
-    {
-        // Initial value presumed to be unknown or identity (either way our padding is correct)
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) inclusive);
-
-        WARP_SYNC(member_mask);
-
-        warp_aggregate = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[WARP_SMEM_ELEMENTS - 1]);
-        exclusive = inclusive - input;
-    }
-
-    /// Update inclusive, exclusive, and warp aggregate using input, inclusive, and initial value
-    template <typename ScanOpT, typename IsIntegerT>
-    __device__ __forceinline__ void Update (
-        T                       /*input*/,
-        T                       &inclusive,
-        T                       &exclusive,
-        T                       &warp_aggregate,
-        ScanOpT                 scan_op,
-        T                       initial_value,
-        IsIntegerT              /*is_integer*/)
-    {
-        // Broadcast warp aggregate
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id], (CellT) inclusive);
-
-        WARP_SYNC(member_mask);
-
-        warp_aggregate = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[WARP_SMEM_ELEMENTS - 1]);
-
-        WARP_SYNC(member_mask);
-
-        // Update inclusive with initial value
-        inclusive = scan_op(initial_value, inclusive);
-
-        // Get exclusive from exclusive
-        ThreadStore<STORE_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id - 1], (CellT) inclusive);
-
-        WARP_SYNC(member_mask);
-
-        exclusive = (T) ThreadLoad<LOAD_VOLATILE>(&temp_storage[HALF_WARP_THREADS + lane_id - 2]);
-
-        if (lane_id == 0)
-            exclusive = initial_value;
-    }
-
-
-};
-
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/warp/warp_reduce.cuh b/ml-xgboost/cub/cub/warp/warp_reduce.cuh
deleted file mode 100644
index 1bb9afa..0000000
--- a/ml-xgboost/cub/cub/warp/warp_reduce.cuh
+++ /dev/null
@@ -1,612 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::WarpReduce class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel reduction of items partitioned across a CUDA thread warp.
- */
-
-#pragma once
-
-#include "specializations/warp_reduce_shfl.cuh"
-#include "specializations/warp_reduce_smem.cuh"
-#include "../thread/thread_operators.cuh"
-#include "../util_arch.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-
-/**
- * \addtogroup WarpModule
- * @{
- */
-
-/**
- * \brief The WarpReduce class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel reduction of items partitioned across a CUDA thread warp. ![](warp_reduce_logo.png)
- *
- * \tparam T                        The reduction input/output element type
- * \tparam LOGICAL_WARP_THREADS     <b>[optional]</b> The number of threads per "logical" warp (may be less than the number of hardware warp threads).  Default is the warp size of the targeted CUDA compute-capability (e.g., 32 threads for SM20).
- * \tparam PTX_ARCH                 <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - A <a href="http://en.wikipedia.org/wiki/Reduce_(higher-order_function)"><em>reduction</em></a> (or <em>fold</em>)
- *   uses a binary combining operator to compute a single aggregate from a list of input elements.
- * - Supports "logical" warps smaller than the physical warp size (e.g., logical warps of 8 threads)
- * - The number of entrant threads must be an multiple of \p LOGICAL_WARP_THREADS
- *
- * \par Performance Considerations
- * - Uses special instructions when applicable (e.g., warp \p SHFL instructions)
- * - Uses synchronization-free communication between warp lanes when applicable
- * - Incurs zero bank conflicts for most types
- * - Computation is slightly more efficient (i.e., having lower instruction overhead) for:
- *     - Summation (<b><em>vs.</em></b> generic reduction)
- *     - The architecture's warp size is a whole multiple of \p LOGICAL_WARP_THREADS
- *
- * \par Simple Examples
- * \warpcollective{WarpReduce}
- * \par
- * The code snippet below illustrates four concurrent warp sum reductions within a block of
- * 128 threads (one per each of the 32-thread warps).
- * \par
- * \code
- * #include <cub/cub.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize WarpReduce for type int
- *     typedef cub::WarpReduce<int> WarpReduce;
- *
- *     // Allocate WarpReduce shared memory for 4 warps
- *     __shared__ typename WarpReduce::TempStorage temp_storage[4];
- *
- *     // Obtain one input item per thread
- *     int thread_data = ...
- *
- *     // Return the warp-wide sums to each lane0 (threads 0, 32, 64, and 96)
- *     int warp_id = threadIdx.x / 32;
- *     int aggregate = WarpReduce(temp_storage[warp_id]).Sum(thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the block of threads is <tt>{0, 1, 2, 3, ..., 127}</tt>.
- * The corresponding output \p aggregate in threads 0, 32, 64, and 96 will \p 496, \p 1520,
- * \p 2544, and \p 3568, respectively (and is undefined in other threads).
- *
- * \par
- * The code snippet below illustrates a single warp sum reduction within a block of
- * 128 threads.
- * \par
- * \code
- * #include <cub/cub.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize WarpReduce for type int
- *     typedef cub::WarpReduce<int> WarpReduce;
- *
- *     // Allocate WarpReduce shared memory for one warp
- *     __shared__ typename WarpReduce::TempStorage temp_storage;
- *     ...
- *
- *     // Only the first warp performs a reduction
- *     if (threadIdx.x < 32)
- *     {
- *         // Obtain one input item per thread
- *         int thread_data = ...
- *
- *         // Return the warp-wide sum to lane0
- *         int aggregate = WarpReduce(temp_storage).Sum(thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the warp of threads is <tt>{0, 1, 2, 3, ..., 31}</tt>.
- * The corresponding output \p aggregate in thread0 will be \p 496 (and is undefined in other threads).
- *
- */
-template <
-    typename    T,
-    int         LOGICAL_WARP_THREADS    = CUB_PTX_WARP_THREADS,
-    int         PTX_ARCH                = CUB_PTX_ARCH>
-class WarpReduce
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    enum
-    {
-        /// Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// Whether the logical warp size is a power-of-two
-        IS_POW_OF_TWO = PowerOfTwo<LOGICAL_WARP_THREADS>::VALUE,
-    };
-
-public:
-
-    #ifndef DOXYGEN_SHOULD_SKIP_THIS    // Do not document
-
-    /// Internal specialization.  Use SHFL-based reduction if (architecture is >= SM30) and (LOGICAL_WARP_THREADS is a power-of-two)
-    typedef typename If<(PTX_ARCH >= 300) && (IS_POW_OF_TWO),
-        WarpReduceShfl<T, LOGICAL_WARP_THREADS, PTX_ARCH>,
-        WarpReduceSmem<T, LOGICAL_WARP_THREADS, PTX_ARCH> >::Type InternalWarpReduce;
-
-    #endif // DOXYGEN_SHOULD_SKIP_THIS
-
-
-private:
-
-    /// Shared memory storage layout type for WarpReduce
-    typedef typename InternalWarpReduce::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage &temp_storage;
-
-
-    /******************************************************************************
-     * Utility methods
-     ******************************************************************************/
-
-public:
-
-    /// \smemstorage{WarpReduce}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.  Logical warp and lane identifiers are constructed from <tt>threadIdx.x</tt>.
-     */
-    __device__ __forceinline__ WarpReduce(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias())
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Summation reductions
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes a warp-wide sum in the calling warp.  The output is valid in warp <em>lane</em><sub>0</sub>.
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp sum reductions within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for 4 warps
-     *     __shared__ typename WarpReduce::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Return the warp-wide sums to each lane0
-     *     int warp_id = threadIdx.x / 32;
-     *     int aggregate = WarpReduce(temp_storage[warp_id]).Sum(thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, 1, 2, 3, ..., 127}</tt>.
-     * The corresponding output \p aggregate in threads 0, 32, 64, and 96 will \p 496, \p 1520,
-     * \p 2544, and \p 3568, respectively (and is undefined in other threads).
-     *
-     */
-    __device__ __forceinline__ T Sum(
-        T                   input)              ///< [in] Calling thread's input
-    {
-        return InternalWarpReduce(temp_storage).Reduce<true, 1>(input, LOGICAL_WARP_THREADS, cub::Sum());
-    }
-
-    /**
-     * \brief Computes a partially-full warp-wide sum in the calling warp.  The output is valid in warp <em>lane</em><sub>0</sub>.
-     *
-     * All threads across the calling warp must agree on the same value for \p valid_items.  Otherwise the result is undefined.
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a sum reduction within a single, partially-full
-     * block of 32 threads (one warp).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, int valid_items)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for one warp
-     *     __shared__ typename WarpReduce::TempStorage temp_storage;
-     *
-     *     // Obtain one input item per thread if in range
-     *     int thread_data;
-     *     if (threadIdx.x < valid_items)
-     *         thread_data = d_data[threadIdx.x];
-     *
-     *     // Return the warp-wide sums to each lane0
-     *     int aggregate = WarpReduce(temp_storage).Sum(
-     *         thread_data, valid_items);
-     *
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>{0, 1, 2, 3, 4, ...</tt> and \p valid_items
-     * is \p 4.  The corresponding output \p aggregate in thread0 is \p 6 (and is
-     * undefined in other threads).
-     *
-     */
-    __device__ __forceinline__ T Sum(
-        T                   input,              ///< [in] Calling thread's input
-        int                 valid_items)        ///< [in] Total number of valid items in the calling thread's logical warp (may be less than \p LOGICAL_WARP_THREADS)
-    {
-        // Determine if we don't need bounds checking
-        return InternalWarpReduce(temp_storage).Reduce<false, 1>(input, valid_items, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes a segmented sum in the calling warp where segments are defined by head-flags.  The sum of each segment is returned to the first lane in that segment (which always includes <em>lane</em><sub>0</sub>).
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a head-segmented warp sum
-     * reduction within a block of 32 threads (one warp).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for one warp
-     *     __shared__ typename WarpReduce::TempStorage temp_storage;
-     *
-     *     // Obtain one input item and flag per thread
-     *     int thread_data = ...
-     *     int head_flag = ...
-     *
-     *     // Return the warp-wide sums to each lane0
-     *     int aggregate = WarpReduce(temp_storage).HeadSegmentedSum(
-     *         thread_data, head_flag);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data and \p head_flag across the block of threads
-     * is <tt>{0, 1, 2, 3, ..., 31</tt> and is <tt>{1, 0, 0, 0, 1, 0, 0, 0, ..., 1, 0, 0, 0</tt>,
-     * respectively.  The corresponding output \p aggregate in threads 0, 4, 8, etc. will be
-     * \p 6, \p 22, \p 38, etc. (and is undefined in other threads).
-     *
-     * \tparam ReductionOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     *
-     */
-    template <
-        typename            FlagT>
-    __device__ __forceinline__ T HeadSegmentedSum(
-        T                   input,              ///< [in] Calling thread's input
-        FlagT                head_flag)          ///< [in] Head flag denoting whether or not \p input is the start of a new segment
-    {
-        return HeadSegmentedReduce(input, head_flag, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes a segmented sum in the calling warp where segments are defined by tail-flags.  The sum of each segment is returned to the first lane in that segment (which always includes <em>lane</em><sub>0</sub>).
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a tail-segmented warp sum
-     * reduction within a block of 32 threads (one warp).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for one warp
-     *     __shared__ typename WarpReduce::TempStorage temp_storage;
-     *
-     *     // Obtain one input item and flag per thread
-     *     int thread_data = ...
-     *     int tail_flag = ...
-     *
-     *     // Return the warp-wide sums to each lane0
-     *     int aggregate = WarpReduce(temp_storage).TailSegmentedSum(
-     *         thread_data, tail_flag);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data and \p tail_flag across the block of threads
-     * is <tt>{0, 1, 2, 3, ..., 31</tt> and is <tt>{0, 0, 0, 1, 0, 0, 0, 1, ..., 0, 0, 0, 1</tt>,
-     * respectively.  The corresponding output \p aggregate in threads 0, 4, 8, etc. will be
-     * \p 6, \p 22, \p 38, etc. (and is undefined in other threads).
-     *
-     * \tparam ReductionOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        typename            FlagT>
-    __device__ __forceinline__ T TailSegmentedSum(
-        T                   input,              ///< [in] Calling thread's input
-        FlagT                tail_flag)          ///< [in] Head flag denoting whether or not \p input is the start of a new segment
-    {
-        return TailSegmentedReduce(input, tail_flag, cub::Sum());
-    }
-
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Generic reductions
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Computes a warp-wide reduction in the calling warp using the specified binary reduction functor.  The output is valid in warp <em>lane</em><sub>0</sub>.
-     *
-     * Supports non-commutative reduction operators
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp max reductions within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for 4 warps
-     *     __shared__ typename WarpReduce::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Return the warp-wide reductions to each lane0
-     *     int warp_id = threadIdx.x / 32;
-     *     int aggregate = WarpReduce(temp_storage[warp_id]).Reduce(
-     *         thread_data, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, 1, 2, 3, ..., 127}</tt>.
-     * The corresponding output \p aggregate in threads 0, 32, 64, and 96 will \p 31, \p 63,
-     * \p 95, and \p 127, respectively  (and is undefined in other threads).
-     *
-     * \tparam ReductionOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   input,              ///< [in] Calling thread's input
-        ReductionOp         reduction_op)       ///< [in] Binary reduction operator
-    {
-        return InternalWarpReduce(temp_storage).Reduce<true, 1>(input, LOGICAL_WARP_THREADS, reduction_op);
-    }
-
-    /**
-     * \brief Computes a partially-full warp-wide reduction in the calling warp using the specified binary reduction functor.  The output is valid in warp <em>lane</em><sub>0</sub>.
-     *
-     * All threads across the calling warp must agree on the same value for \p valid_items.  Otherwise the result is undefined.
-     *
-     * Supports non-commutative reduction operators
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a max reduction within a single, partially-full
-     * block of 32 threads (one warp).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(int *d_data, int valid_items)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for one warp
-     *     __shared__ typename WarpReduce::TempStorage temp_storage;
-     *
-     *     // Obtain one input item per thread if in range
-     *     int thread_data;
-     *     if (threadIdx.x < valid_items)
-     *         thread_data = d_data[threadIdx.x];
-     *
-     *     // Return the warp-wide reductions to each lane0
-     *     int aggregate = WarpReduce(temp_storage).Reduce(
-     *         thread_data, cub::Max(), valid_items);
-     *
-     * \endcode
-     * \par
-     * Suppose the input \p d_data is <tt>{0, 1, 2, 3, 4, ...</tt> and \p valid_items
-     * is \p 4.  The corresponding output \p aggregate in thread0 is \p 3 (and is
-     * undefined in other threads).
-     *
-     * \tparam ReductionOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ReductionOp>
-    __device__ __forceinline__ T Reduce(
-        T                   input,              ///< [in] Calling thread's input
-        ReductionOp         reduction_op,       ///< [in] Binary reduction operator
-        int                 valid_items)        ///< [in] Total number of valid items in the calling thread's logical warp (may be less than \p LOGICAL_WARP_THREADS)
-    {
-        return InternalWarpReduce(temp_storage).Reduce<false, 1>(input, valid_items, reduction_op);
-    }
-
-
-    /**
-     * \brief Computes a segmented reduction in the calling warp where segments are defined by head-flags.  The reduction of each segment is returned to the first lane in that segment (which always includes <em>lane</em><sub>0</sub>).
-     *
-     * Supports non-commutative reduction operators
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a head-segmented warp max
-     * reduction within a block of 32 threads (one warp).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for one warp
-     *     __shared__ typename WarpReduce::TempStorage temp_storage;
-     *
-     *     // Obtain one input item and flag per thread
-     *     int thread_data = ...
-     *     int head_flag = ...
-     *
-     *     // Return the warp-wide reductions to each lane0
-     *     int aggregate = WarpReduce(temp_storage).HeadSegmentedReduce(
-     *         thread_data, head_flag, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data and \p head_flag across the block of threads
-     * is <tt>{0, 1, 2, 3, ..., 31</tt> and is <tt>{1, 0, 0, 0, 1, 0, 0, 0, ..., 1, 0, 0, 0</tt>,
-     * respectively.  The corresponding output \p aggregate in threads 0, 4, 8, etc. will be
-     * \p 3, \p 7, \p 11, etc. (and is undefined in other threads).
-     *
-     * \tparam ReductionOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        typename            ReductionOp,
-        typename            FlagT>
-    __device__ __forceinline__ T HeadSegmentedReduce(
-        T                   input,              ///< [in] Calling thread's input
-        FlagT                head_flag,          ///< [in] Head flag denoting whether or not \p input is the start of a new segment
-        ReductionOp         reduction_op)       ///< [in] Reduction operator
-    {
-        return InternalWarpReduce(temp_storage).template SegmentedReduce<true>(input, head_flag, reduction_op);
-    }
-
-
-    /**
-     * \brief Computes a segmented reduction in the calling warp where segments are defined by tail-flags.  The reduction of each segment is returned to the first lane in that segment (which always includes <em>lane</em><sub>0</sub>).
-     *
-     * Supports non-commutative reduction operators
-     *
-     * \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates a tail-segmented warp max
-     * reduction within a block of 32 threads (one warp).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpReduce for type int
-     *     typedef cub::WarpReduce<int> WarpReduce;
-     *
-     *     // Allocate WarpReduce shared memory for one warp
-     *     __shared__ typename WarpReduce::TempStorage temp_storage;
-     *
-     *     // Obtain one input item and flag per thread
-     *     int thread_data = ...
-     *     int tail_flag = ...
-     *
-     *     // Return the warp-wide reductions to each lane0
-     *     int aggregate = WarpReduce(temp_storage).TailSegmentedReduce(
-     *         thread_data, tail_flag, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data and \p tail_flag across the block of threads
-     * is <tt>{0, 1, 2, 3, ..., 31</tt> and is <tt>{0, 0, 0, 1, 0, 0, 0, 1, ..., 0, 0, 0, 1</tt>,
-     * respectively.  The corresponding output \p aggregate in threads 0, 4, 8, etc. will be
-     * \p 3, \p 7, \p 11, etc. (and is undefined in other threads).
-     *
-     * \tparam ReductionOp     <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        typename            ReductionOp,
-        typename            FlagT>
-    __device__ __forceinline__ T TailSegmentedReduce(
-        T                   input,              ///< [in] Calling thread's input
-        FlagT                tail_flag,          ///< [in] Tail flag denoting whether or not \p input is the end of the current segment
-        ReductionOp         reduction_op)       ///< [in] Reduction operator
-    {
-        return InternalWarpReduce(temp_storage).template SegmentedReduce<false>(input, tail_flag, reduction_op);
-    }
-
-
-
-    //@}  end member group
-};
-
-/** @} */       // end group WarpModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/cub/warp/warp_scan.cuh b/ml-xgboost/cub/cub/warp/warp_scan.cuh
deleted file mode 100644
index 68196e3..0000000
--- a/ml-xgboost/cub/cub/warp/warp_scan.cuh
+++ /dev/null
@@ -1,936 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/**
- * \file
- * The cub::WarpScan class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel prefix scan of items partitioned across a CUDA thread warp.
- */
-
-#pragma once
-
-#include "specializations/warp_scan_shfl.cuh"
-#include "specializations/warp_scan_smem.cuh"
-#include "../thread/thread_operators.cuh"
-#include "../util_arch.cuh"
-#include "../util_type.cuh"
-#include "../util_namespace.cuh"
-
-/// Optional outer namespace(s)
-CUB_NS_PREFIX
-
-/// CUB namespace
-namespace cub {
-
-/**
- * \addtogroup WarpModule
- * @{
- */
-
-/**
- * \brief The WarpScan class provides [<em>collective</em>](index.html#sec0) methods for computing a parallel prefix scan of items partitioned across a CUDA thread warp.  ![](warp_scan_logo.png)
- *
- * \tparam T                        The scan input/output element type
- * \tparam LOGICAL_WARP_THREADS     <b>[optional]</b> The number of threads per "logical" warp (may be less than the number of hardware warp threads).  Default is the warp size associated with the CUDA Compute Capability targeted by the compiler (e.g., 32 threads for SM20).
- * \tparam PTX_ARCH                 <b>[optional]</b> \ptxversion
- *
- * \par Overview
- * - Given a list of input elements and a binary reduction operator, a [<em>prefix scan</em>](http://en.wikipedia.org/wiki/Prefix_sum)
- *   produces an output list where each element is computed to be the reduction
- *   of the elements occurring earlier in the input list.  <em>Prefix sum</em>
- *   connotes a prefix scan with the addition operator. The term \em inclusive indicates
- *   that the <em>i</em><sup>th</sup> output reduction incorporates the <em>i</em><sup>th</sup> input.
- *   The term \em exclusive indicates the <em>i</em><sup>th</sup> input is not incorporated into
- *   the <em>i</em><sup>th</sup> output reduction.
- * - Supports non-commutative scan operators
- * - Supports "logical" warps smaller than the physical warp size (e.g., a logical warp of 8 threads)
- * - The number of entrant threads must be an multiple of \p LOGICAL_WARP_THREADS
- *
- * \par Performance Considerations
- * - Uses special instructions when applicable (e.g., warp \p SHFL)
- * - Uses synchronization-free communication between warp lanes when applicable
- * - Incurs zero bank conflicts for most types
- * - Computation is slightly more efficient (i.e., having lower instruction overhead) for:
- *     - Summation (<b><em>vs.</em></b> generic scan)
- *     - The architecture's warp size is a whole multiple of \p LOGICAL_WARP_THREADS
- *
- * \par Simple Examples
- * \warpcollective{WarpScan}
- * \par
- * The code snippet below illustrates four concurrent warp prefix sums within a block of
- * 128 threads (one per each of the 32-thread warps).
- * \par
- * \code
- * #include <cub/cub.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize WarpScan for type int
- *     typedef cub::WarpScan<int> WarpScan;
- *
- *     // Allocate WarpScan shared memory for 4 warps
- *     __shared__ typename WarpScan::TempStorage temp_storage[4];
- *
- *     // Obtain one input item per thread
- *     int thread_data = ...
- *
- *     // Compute warp-wide prefix sums
- *     int warp_id = threadIdx.x / 32;
- *     WarpScan(temp_storage[warp_id]).ExclusiveSum(thread_data, thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the block of threads is <tt>{1, 1, 1, 1, ...}</tt>.
- * The corresponding output \p thread_data in each of the four warps of threads will be
- * <tt>0, 1, 2, 3, ..., 31}</tt>.
- *
- * \par
- * The code snippet below illustrates a single warp prefix sum within a block of
- * 128 threads.
- * \par
- * \code
- * #include <cub/cub.cuh>
- *
- * __global__ void ExampleKernel(...)
- * {
- *     // Specialize WarpScan for type int
- *     typedef cub::WarpScan<int> WarpScan;
- *
- *     // Allocate WarpScan shared memory for one warp
- *     __shared__ typename WarpScan::TempStorage temp_storage;
- *     ...
- *
- *     // Only the first warp performs a prefix sum
- *     if (threadIdx.x < 32)
- *     {
- *         // Obtain one input item per thread
- *         int thread_data = ...
- *
- *         // Compute warp-wide prefix sums
- *         WarpScan(temp_storage).ExclusiveSum(thread_data, thread_data);
- *
- * \endcode
- * \par
- * Suppose the set of input \p thread_data across the warp of threads is <tt>{1, 1, 1, 1, ...}</tt>.
- * The corresponding output \p thread_data will be <tt>{0, 1, 2, 3, ..., 31}</tt>.
- *
- */
-template <
-    typename    T,
-    int         LOGICAL_WARP_THREADS    = CUB_PTX_WARP_THREADS,
-    int         PTX_ARCH                = CUB_PTX_ARCH>
-class WarpScan
-{
-private:
-
-    /******************************************************************************
-     * Constants and type definitions
-     ******************************************************************************/
-
-    enum
-    {
-        /// Whether the logical warp size and the PTX warp size coincide
-        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(PTX_ARCH)),
-
-        /// Whether the logical warp size is a power-of-two
-        IS_POW_OF_TWO = ((LOGICAL_WARP_THREADS & (LOGICAL_WARP_THREADS - 1)) == 0),
-
-        /// Whether the data type is an integer (which has fully-associative addition)
-        IS_INTEGER = ((Traits<T>::CATEGORY == SIGNED_INTEGER) || (Traits<T>::CATEGORY == UNSIGNED_INTEGER))
-    };
-
-    /// Internal specialization.  Use SHFL-based scan if (architecture is >= SM30) and (LOGICAL_WARP_THREADS is a power-of-two)
-    typedef typename If<(PTX_ARCH >= 300) && (IS_POW_OF_TWO),
-        WarpScanShfl<T, LOGICAL_WARP_THREADS, PTX_ARCH>,
-        WarpScanSmem<T, LOGICAL_WARP_THREADS, PTX_ARCH> >::Type InternalWarpScan;
-
-    /// Shared memory storage layout type for WarpScan
-    typedef typename InternalWarpScan::TempStorage _TempStorage;
-
-
-    /******************************************************************************
-     * Thread fields
-     ******************************************************************************/
-
-    /// Shared storage reference
-    _TempStorage    &temp_storage;
-    unsigned int    lane_id;
-
-
-
-    /******************************************************************************
-     * Public types
-     ******************************************************************************/
-
-public:
-
-    /// \smemstorage{WarpScan}
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    /******************************************************************//**
-     * \name Collective constructors
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Collective constructor using the specified memory allocation as temporary storage.  Logical warp and lane identifiers are constructed from <tt>threadIdx.x</tt>.
-     */
-    __device__ __forceinline__ WarpScan(
-        TempStorage &temp_storage)             ///< [in] Reference to memory allocation having layout type TempStorage
-    :
-        temp_storage(temp_storage.Alias()),
-        lane_id(IS_ARCH_WARP ?
-            LaneId() :
-            LaneId() % LOGICAL_WARP_THREADS)
-    {}
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Inclusive prefix sums
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an inclusive prefix sum across the calling warp.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide inclusive prefix sums within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute inclusive warp-wide prefix sums
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).InclusiveSum(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{1, 1, 1, 1, ...}</tt>.
-     * The corresponding output \p thread_data in each of the four warps of threads will be
-     * <tt>1, 2, 3, ..., 32}</tt>.
-     */
-    __device__ __forceinline__ void InclusiveSum(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output)  ///< [out] Calling thread's output item.  May be aliased with \p input.
-    {
-        InclusiveScan(input, inclusive_output, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes an inclusive prefix sum across the calling warp.  Also provides every thread with the warp-wide \p warp_aggregate of all inputs.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide inclusive prefix sums within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute inclusive warp-wide prefix sums
-     *     int warp_aggregate;
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).InclusiveSum(thread_data, thread_data, warp_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{1, 1, 1, 1, ...}</tt>.
-     * The corresponding output \p thread_data in each of the four warps of threads will be
-     * <tt>1, 2, 3, ..., 32}</tt>.  Furthermore, \p warp_aggregate for all threads in all warps will be \p 32.
-     */
-    __device__ __forceinline__ void InclusiveSum(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        InclusiveScan(input, inclusive_output, cub::Sum(), warp_aggregate);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Exclusive prefix sums
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes an exclusive prefix sum across the calling warp.  The value of 0 is applied as the initial value, and is assigned to \p exclusive_output in <em>thread</em><sub>0</sub>.
-     *
-     * \par
-     *  - \identityzero
-     *  - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix sums within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix sums
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).ExclusiveSum(thread_data, thread_data);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{1, 1, 1, 1, ...}</tt>.
-     * The corresponding output \p thread_data in each of the four warps of threads will be
-     * <tt>0, 1, 2, ..., 31}</tt>.
-     *
-     */
-    __device__ __forceinline__ void ExclusiveSum(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &exclusive_output)  ///< [out] Calling thread's output item.  May be aliased with \p input.
-    {
-        T initial_value = 0;
-        ExclusiveScan(input, exclusive_output, initial_value, cub::Sum());
-    }
-
-
-    /**
-     * \brief Computes an exclusive prefix sum across the calling warp.  The value of 0 is applied as the initial value, and is assigned to \p exclusive_output in <em>thread</em><sub>0</sub>.  Also provides every thread with the warp-wide \p warp_aggregate of all inputs.
-     *
-     * \par
-     *  - \identityzero
-     *  - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix sums within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix sums
-     *     int warp_aggregate;
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).ExclusiveSum(thread_data, thread_data, warp_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{1, 1, 1, 1, ...}</tt>.
-     * The corresponding output \p thread_data in each of the four warps of threads will be
-     * <tt>0, 1, 2, ..., 31}</tt>.  Furthermore, \p warp_aggregate for all threads in all warps will be \p 32.
-     */
-    __device__ __forceinline__ void ExclusiveSum(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &exclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        T initial_value = 0;
-        ExclusiveScan(input, exclusive_output, initial_value, cub::Sum(), warp_aggregate);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Inclusive prefix scans
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Computes an inclusive prefix scan using the specified binary scan functor across the calling warp.
-     *
-     * \par
-     *  - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide inclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute inclusive warp-wide prefix max scans
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).InclusiveScan(thread_data, thread_data, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p thread_data in the first warp would be
-     * <tt>0, 0, 2, 2, ..., 30, 30</tt>, the output for the second warp would be <tt>32, 32, 34, 34, ..., 62, 62</tt>, etc.
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        InternalWarpScan(temp_storage).InclusiveScan(input, inclusive_output, scan_op);
-    }
-
-
-    /**
-     * \brief Computes an inclusive prefix scan using the specified binary scan functor across the calling warp.  Also provides every thread with the warp-wide \p warp_aggregate of all inputs.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide inclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute inclusive warp-wide prefix max scans
-     *     int warp_aggregate;
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).InclusiveScan(
-     *         thread_data, thread_data, cub::Max(), warp_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p thread_data in the first warp would be
-     * <tt>0, 0, 2, 2, ..., 30, 30</tt>, the output for the second warp would be <tt>32, 32, 34, 34, ..., 62, 62</tt>, etc.
-     * Furthermore, \p warp_aggregate would be assigned \p 30 for threads in the first warp, \p 62 for threads
-     * in the second warp, etc.
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void InclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        InternalWarpScan(temp_storage).InclusiveScan(input, inclusive_output, scan_op, warp_aggregate);
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Exclusive prefix scans
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Computes an exclusive prefix scan using the specified binary scan functor across the calling warp.  Because no initial value is supplied, the \p output computed for <em>warp-lane</em><sub>0</sub> is undefined.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix max scans
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).ExclusiveScan(thread_data, thread_data, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p thread_data in the first warp would be
-     * <tt>?, 0, 0, 2, ..., 28, 30</tt>, the output for the second warp would be <tt>?, 32, 32, 34, ..., 60, 62</tt>, etc.
-     * (The output \p thread_data in warp lane<sub>0</sub> is undefined.)
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &exclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        InternalWarpScan internal(temp_storage);
-
-        T inclusive_output;
-        internal.InclusiveScan(input, inclusive_output, scan_op);
-
-        internal.Update(
-            input,
-            inclusive_output,
-            exclusive_output,
-            scan_op,
-            Int2Type<IS_INTEGER>());
-    }
-
-
-    /**
-     * \brief Computes an exclusive prefix scan using the specified binary scan functor across the calling warp.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix max scans
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).ExclusiveScan(thread_data, thread_data, INT_MIN, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p thread_data in the first warp would be
-     * <tt>INT_MIN, 0, 0, 2, ..., 28, 30</tt>, the output for the second warp would be <tt>30, 32, 32, 34, ..., 60, 62</tt>, etc.
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &exclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        T               initial_value,      ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        InternalWarpScan internal(temp_storage);
-
-        T inclusive_output;
-        internal.InclusiveScan(input, inclusive_output, scan_op);
-
-        internal.Update(
-            input,
-            inclusive_output,
-            exclusive_output,
-            scan_op,
-            initial_value,
-            Int2Type<IS_INTEGER>());
-    }
-
-
-    /**
-     * \brief Computes an exclusive prefix scan using the specified binary scan functor across the calling warp.  Because no initial value is supplied, the \p output computed for <em>warp-lane</em><sub>0</sub> is undefined.  Also provides every thread with the warp-wide \p warp_aggregate of all inputs.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix max scans
-     *     int warp_aggregate;
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).ExclusiveScan(thread_data, thread_data, cub::Max(), warp_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p thread_data in the first warp would be
-     * <tt>?, 0, 0, 2, ..., 28, 30</tt>, the output for the second warp would be <tt>?, 32, 32, 34, ..., 60, 62</tt>, etc.
-     * (The output \p thread_data in warp lane<sub>0</sub> is undefined.)  Furthermore, \p warp_aggregate would be assigned \p 30 for threads in the first warp, \p 62 for threads
-     * in the second warp, etc.
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &exclusive_output,   ///< [out] Calling thread's output item.  May be aliased with \p input.
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        InternalWarpScan internal(temp_storage);
-
-        T inclusive_output;
-        internal.InclusiveScan(input, inclusive_output, scan_op);
-
-        internal.Update(
-            input,
-            inclusive_output,
-            exclusive_output,
-            warp_aggregate,
-            scan_op,
-            Int2Type<IS_INTEGER>());
-    }
-
-
-    /**
-     * \brief Computes an exclusive prefix scan using the specified binary scan functor across the calling warp.  Also provides every thread with the warp-wide \p warp_aggregate of all inputs.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix max scans
-     *     int warp_aggregate;
-     *     int warp_id = threadIdx.x / 32;
-     *     WarpScan(temp_storage[warp_id]).ExclusiveScan(thread_data, thread_data, INT_MIN, cub::Max(), warp_aggregate);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p thread_data in the first warp would be
-     * <tt>INT_MIN, 0, 0, 2, ..., 28, 30</tt>, the output for the second warp would be <tt>30, 32, 32, 34, ..., 60, 62</tt>, etc.
-     * Furthermore, \p warp_aggregate would be assigned \p 30 for threads in the first warp, \p 62 for threads
-     * in the second warp, etc.
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void ExclusiveScan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &exclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
-        T               initial_value,      ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op,            ///< [in] Binary scan operator
-        T               &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
-    {
-        InternalWarpScan internal(temp_storage);
-
-        T inclusive_output;
-        internal.InclusiveScan(input, inclusive_output, scan_op);
-
-        internal.Update(
-            input,
-            inclusive_output,
-            exclusive_output,
-            warp_aggregate,
-            scan_op,
-            initial_value,
-            Int2Type<IS_INTEGER>());
-    }
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Combination (inclusive & exclusive) prefix scans
-     *********************************************************************/
-    //@{
-
-
-    /**
-     * \brief Computes both inclusive and exclusive prefix scans using the specified binary scan functor across the calling warp.  Because no initial value is supplied, the \p exclusive_output computed for <em>warp-lane</em><sub>0</sub> is undefined.
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide exclusive prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute exclusive warp-wide prefix max scans
-     *     int inclusive_partial, exclusive_partial;
-     *     WarpScan(temp_storage[warp_id]).Scan(thread_data, inclusive_partial, exclusive_partial, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p inclusive_partial in the first warp would be
-     * <tt>0, 0, 2, 2, ..., 30, 30</tt>, the output for the second warp would be <tt>32, 32, 34, 34, ..., 62, 62</tt>, etc.
-     * The corresponding output \p exclusive_partial in the first warp would be
-     * <tt>?, 0, 0, 2, ..., 28, 30</tt>, the output for the second warp would be <tt>?, 32, 32, 34, ..., 60, 62</tt>, etc.
-     * (The output \p thread_data in warp lane<sub>0</sub> is undefined.)
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void Scan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's inclusive-scan output item.
-        T               &exclusive_output,  ///< [out] Calling thread's exclusive-scan output item.
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        InternalWarpScan internal(temp_storage);
-
-        internal.InclusiveScan(input, inclusive_output, scan_op);
-
-        internal.Update(
-            input,
-            inclusive_output,
-            exclusive_output,
-            scan_op,
-            Int2Type<IS_INTEGER>());
-    }
-
-
-    /**
-     * \brief Computes both inclusive and exclusive prefix scans using the specified binary scan functor across the calling warp.
-     *
-     * \par
-     *  - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates four concurrent warp-wide prefix max scans within a block of
-     * 128 threads (one per each of the 32-thread warps).
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Compute inclusive warp-wide prefix max scans
-     *     int warp_id = threadIdx.x / 32;
-     *     int inclusive_partial, exclusive_partial;
-     *     WarpScan(temp_storage[warp_id]).Scan(thread_data, inclusive_partial, exclusive_partial, INT_MIN, cub::Max());
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, -1, 2, -3, ..., 126, -127}</tt>.
-     * The corresponding output \p inclusive_partial in the first warp would be
-     * <tt>0, 0, 2, 2, ..., 30, 30</tt>, the output for the second warp would be <tt>32, 32, 34, 34, ..., 62, 62</tt>, etc.
-     * The corresponding output \p exclusive_partial in the first warp would be
-     * <tt>INT_MIN, 0, 0, 2, ..., 28, 30</tt>, the output for the second warp would be <tt>30, 32, 32, 34, ..., 60, 62</tt>, etc.
-     *
-     * \tparam ScanOp     <b>[inferred]</b> Binary scan operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <typename ScanOp>
-    __device__ __forceinline__ void Scan(
-        T               input,              ///< [in] Calling thread's input item.
-        T               &inclusive_output,  ///< [out] Calling thread's inclusive-scan output item.
-        T               &exclusive_output,  ///< [out] Calling thread's exclusive-scan output item.
-        T               initial_value,      ///< [in] Initial value to seed the exclusive scan
-        ScanOp          scan_op)            ///< [in] Binary scan operator
-    {
-        InternalWarpScan internal(temp_storage);
-
-        internal.InclusiveScan(input, inclusive_output, scan_op);
-
-        internal.Update(
-            input,
-            inclusive_output,
-            exclusive_output,
-            scan_op,
-            initial_value,
-            Int2Type<IS_INTEGER>());
-    }
-
-
-
-    //@}  end member group
-    /******************************************************************//**
-     * \name Data exchange
-     *********************************************************************/
-    //@{
-
-    /**
-     * \brief Broadcast the value \p input from <em>warp-lane</em><sub><tt>src_lane</tt></sub> to all lanes in the warp
-     *
-     * \par
-     * - \smemreuse
-     *
-     * \par Snippet
-     * The code snippet below illustrates the warp-wide broadcasts of values from
-     * lanes<sub>0</sub> in each of four warps to all other threads in those warps.
-     * \par
-     * \code
-     * #include <cub/cub.cuh>
-     *
-     * __global__ void ExampleKernel(...)
-     * {
-     *     // Specialize WarpScan for type int
-     *     typedef cub::WarpScan<int> WarpScan;
-     *
-     *     // Allocate WarpScan shared memory for 4 warps
-     *     __shared__ typename WarpScan::TempStorage temp_storage[4];
-     *
-     *     // Obtain one input item per thread
-     *     int thread_data = ...
-     *
-     *     // Broadcast from lane0 in each warp to all other threads in the warp
-     *     int warp_id = threadIdx.x / 32;
-     *     thread_data = WarpScan(temp_storage[warp_id]).Broadcast(thread_data, 0);
-     *
-     * \endcode
-     * \par
-     * Suppose the set of input \p thread_data across the block of threads is <tt>{0, 1, 2, 3, ..., 127}</tt>.
-     * The corresponding output \p thread_data will be
-     * <tt>{0, 0, ..., 0}</tt> in warp<sub>0</sub>,
-     * <tt>{32, 32, ..., 32}</tt> in warp<sub>1</sub>,
-     * <tt>{64, 64, ..., 64}</tt> in warp<sub>2</sub>, etc.
-     */
-    __device__ __forceinline__ T Broadcast(
-        T               input,              ///< [in] The value to broadcast
-        unsigned int    src_lane)           ///< [in] Which warp lane is to do the broadcasting
-    {
-        return InternalWarpScan(temp_storage).Broadcast(input, src_lane);
-    }
-
-    //@}  end member group
-
-};
-
-/** @} */       // end group WarpModule
-
-}               // CUB namespace
-CUB_NS_POSTFIX  // Optional outer namespace(s)
diff --git a/ml-xgboost/cub/eclipse code style profile.xml b/ml-xgboost/cub/eclipse code style profile.xml
deleted file mode 100644
index 2456fa7..0000000
--- a/ml-xgboost/cub/eclipse code style profile.xml	
+++ /dev/null
@@ -1,155 +0,0 @@
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-<setting id="org.eclipse.cdt.core.formatter.brace_position_for_namespace_declaration" value="end_of_line"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_after_colon_in_conditional" value="insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_comma_in_enum_declarations" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_after_prefix_operator" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_closing_angle_bracket_in_template_arguments" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.brace_position_for_array_initializer" value="next_line"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_colon_in_case" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_opening_paren_in_catch" value="insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_opening_brace_in_namespace_declaration" value="insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_postfix_operator" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_closing_bracket" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_new_line_before_while_in_do_statement" value="do not insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_before_opening_paren_in_for" value="insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_after_closing_angle_bracket_in_template_parameters" value="insert"/>
-<setting id="org.eclipse.cdt.core.formatter.insert_space_after_opening_angle_bracket_in_template_arguments" value="do not insert"/>
-</profile>
-</profiles>
diff --git a/ml-xgboost/cub/examples/block/.gitignore b/ml-xgboost/cub/examples/block/.gitignore
deleted file mode 100644
index 9dad963..0000000
--- a/ml-xgboost/cub/examples/block/.gitignore
+++ /dev/null
@@ -1,7 +0,0 @@
-/bin
-/Debug
-/Release
-/cuda55.sdf
-/cuda55.suo
-/cuda60.sdf
-/cuda60.suo
diff --git a/ml-xgboost/cub/examples/block/Makefile b/ml-xgboost/cub/examples/block/Makefile
deleted file mode 100644
index 85f2b2a..0000000
--- a/ml-xgboost/cub/examples/block/Makefile
+++ /dev/null
@@ -1,128 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
-
-#-------------------------------------------------------------------------------
-#
-# Makefile usage
-#
-# make <target> [sm=<XXX,...>] [cdp=<0|1>] [force32=<0|1>] [abi=<0|1>] [open64=<0|1>] [verbose=<0|1>] [keep=<0|1>]
-#
-#-------------------------------------------------------------------------------
- 
-include ../../common.mk 
- 
- 
-#-------------------------------------------------------------------------------
-# Includes
-#-------------------------------------------------------------------------------
-
-INC += -I$(CUB_DIR) -I$(CUB_DIR)test 
-
-
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-DEPS =				$(CUB_DEPS) \
-					$(CUB_DIR)test/Makefile \
-					$(CUB_DIR)test/test_util.h \
-					$(CUB_DIR)test/mersenne.h \
-		
-ALL = 	example_block_radix_sort \
-	 	example_block_reduce \
-	 	example_block_scan
-		
-
-
-#-------------------------------------------------------------------------------
-# make default
-#-------------------------------------------------------------------------------
-
-default:
-
-
-#-------------------------------------------------------------------------------
-# make clean
-#-------------------------------------------------------------------------------
-
-clean :
-	rm -f bin/*$(CPU_ARCH_SUFFIX)* 
-	rm -f *.i* *.cubin *.cu.c *.cudafe* *.fatbin.c *.ptx *.hash *.cu.cpp *.o
-
-
-#-------------------------------------------------------------------------------
-# make all
-#-------------------------------------------------------------------------------
-
-all : $(ALL)
-
-#-------------------------------------------------------------------------------
-# make run
-#-------------------------------------------------------------------------------
-
-run : 
-	for i in $(ALL); do ./bin/$${i}_$(BIN_SUFFIX) --device=$(device) || exit 1; done
-
-
-
-
-#-------------------------------------------------------------------------------
-# make example_block_reduce
-#-------------------------------------------------------------------------------
-
-example_block_reduce: bin/example_block_reduce_$(BIN_SUFFIX)
-
-bin/example_block_reduce_$(BIN_SUFFIX) : example_block_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_block_reduce_$(BIN_SUFFIX) example_block_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_block_scan
-#-------------------------------------------------------------------------------
-
-example_block_scan: bin/example_block_scan_$(BIN_SUFFIX)
-
-bin/example_block_scan_$(BIN_SUFFIX) : example_block_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_block_scan_$(BIN_SUFFIX) example_block_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_block_radix_sort
-#-------------------------------------------------------------------------------
-
-example_block_radix_sort: bin/example_block_radix_sort_$(BIN_SUFFIX)
-
-bin/example_block_radix_sort_$(BIN_SUFFIX) : example_block_radix_sort.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_block_radix_sort_$(BIN_SUFFIX) example_block_radix_sort.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-	
diff --git a/ml-xgboost/cub/examples/block/example_block_radix_sort.cu b/ml-xgboost/cub/examples/block/example_block_radix_sort.cu
deleted file mode 100644
index a4fe088..0000000
--- a/ml-xgboost/cub/examples/block/example_block_radix_sort.cu
+++ /dev/null
@@ -1,323 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple demonstration of cub::BlockRadixSort
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_block_radix_sort.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console (define before including cub.h)
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <iostream>
-#include <algorithm>
-
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/block/block_radix_sort.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-/// Verbose output
-bool g_verbose = false;
-
-/// Timing iterations
-int g_timing_iterations = 100;
-
-/// Default grid size
-int g_grid_size = 1;
-
-/// Uniform key samples
-bool g_uniform_keys;
-
-
-//---------------------------------------------------------------------
-// Kernels
-//---------------------------------------------------------------------
-
-/**
- * Simple kernel for performing a block-wide sorting over integers
- */
-template <
-    typename    Key,
-    int         BLOCK_THREADS,
-    int         ITEMS_PER_THREAD>
-__launch_bounds__ (BLOCK_THREADS)
-__global__ void BlockSortKernel(
-    Key         *d_in,          // Tile of input
-    Key         *d_out,         // Tile of output
-    clock_t     *d_elapsed)     // Elapsed cycle count of block scan
-{
-    enum { TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD };
-
-    // Specialize BlockLoad type for our thread block (uses warp-striped loads for coalescing, then transposes in shared memory to a blocked arrangement)
-    typedef BlockLoad<Key, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoadT;
-
-    // Specialize BlockRadixSort type for our thread block
-    typedef BlockRadixSort<Key, BLOCK_THREADS, ITEMS_PER_THREAD> BlockRadixSortT;
-
-    // Shared memory
-    __shared__ union
-    {
-        typename BlockLoadT::TempStorage        load;
-        typename BlockRadixSortT::TempStorage   sort;
-    } temp_storage;
-
-    // Per-thread tile items
-    Key items[ITEMS_PER_THREAD];
-
-    // Our current block's offset
-    int block_offset = blockIdx.x * TILE_SIZE;
-
-    // Load items into a blocked arrangement
-    BlockLoadT(temp_storage.load).Load(d_in + block_offset, items);
-
-    // Barrier for smem reuse
-    __syncthreads();
-
-    // Start cycle timer
-    clock_t start = clock();
-
-    // Sort keys
-    BlockRadixSortT(temp_storage.sort).SortBlockedToStriped(items);
-
-    // Stop cycle timer
-    clock_t stop = clock();
-
-    // Store output in striped fashion
-    StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_out + block_offset, items);
-
-    // Store elapsed clocks
-    if (threadIdx.x == 0)
-    {
-        d_elapsed[blockIdx.x] = (start > stop) ? start - stop : stop - start;
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Host utilities
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize sorting problem (and solution).
- */
-template <typename Key>
-void Initialize(
-    Key *h_in,
-    Key *h_reference,
-    int num_items,
-    int tile_size)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        if (g_uniform_keys)
-        {
-            h_in[i] = 0;
-        }
-        else
-        {
-            RandomBits(h_in[i]);
-        }
-        h_reference[i] = h_in[i];
-    }
-
-    // Only sort the first tile
-    std::sort(h_reference, h_reference + tile_size);
-}
-
-
-/**
- * Test BlockScan
- */
-template <
-    typename    Key,
-    int         BLOCK_THREADS,
-    int         ITEMS_PER_THREAD>
-void Test()
-{
-    const int TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Allocate host arrays
-    Key *h_in               = new Key[TILE_SIZE * g_grid_size];
-    Key *h_reference        = new Key[TILE_SIZE * g_grid_size];
-    clock_t *h_elapsed      = new clock_t[g_grid_size];
-
-    // Initialize problem and reference output on host
-    Initialize(h_in, h_reference, TILE_SIZE * g_grid_size, TILE_SIZE);
-
-    // Initialize device arrays
-    Key *d_in       = NULL;
-    Key *d_out      = NULL;
-    clock_t *d_elapsed  = NULL;
-    CubDebugExit(cudaMalloc((void**)&d_in,          sizeof(Key) * TILE_SIZE * g_grid_size));
-    CubDebugExit(cudaMalloc((void**)&d_out,         sizeof(Key) * TILE_SIZE * g_grid_size));
-    CubDebugExit(cudaMalloc((void**)&d_elapsed,     sizeof(clock_t) * g_grid_size));
-
-    // Display input problem data
-    if (g_verbose)
-    {
-        printf("Input data: ");
-        for (int i = 0; i < TILE_SIZE; i++)
-            std::cout << h_in[i] << ", ";
-        printf("\n\n");
-    }
-
-    // Kernel props
-    int max_sm_occupancy;
-    CubDebugExit(MaxSmOccupancy(max_sm_occupancy, BlockSortKernel<Key, BLOCK_THREADS, ITEMS_PER_THREAD>, BLOCK_THREADS));
-
-    // Copy problem to device
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(Key) * TILE_SIZE * g_grid_size, cudaMemcpyHostToDevice));
-
-    printf("BlockRadixSort %d items (%d timing iterations, %d blocks, %d threads, %d items per thread, %d SM occupancy):\n",
-        TILE_SIZE * g_grid_size, g_timing_iterations, g_grid_size, BLOCK_THREADS, ITEMS_PER_THREAD, max_sm_occupancy);
-    fflush(stdout);
-
-    // Run kernel once to prime caches and check result
-    BlockSortKernel<Key, BLOCK_THREADS, ITEMS_PER_THREAD><<<g_grid_size, BLOCK_THREADS>>>(
-        d_in,
-        d_out,
-        d_elapsed);
-
-    // Check for kernel errors and STDIO from the kernel, if any
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Check results
-    printf("\tOutput items: ");
-    int compare = CompareDeviceResults(h_reference, d_out, TILE_SIZE, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-    fflush(stdout);
-
-    // Run this several times and average the performance results
-    GpuTimer            timer;
-    float               elapsed_millis          = 0.0;
-    unsigned long long  elapsed_clocks          = 0;
-
-    for (int i = 0; i < g_timing_iterations; ++i)
-    {
-        timer.Start();
-
-        // Run kernel
-        BlockSortKernel<Key, BLOCK_THREADS, ITEMS_PER_THREAD><<<g_grid_size, BLOCK_THREADS>>>(
-            d_in,
-            d_out,
-            d_elapsed);
-
-        timer.Stop();
-        elapsed_millis += timer.ElapsedMillis();
-
-        // Copy clocks from device
-        CubDebugExit(cudaMemcpy(h_elapsed, d_elapsed, sizeof(clock_t) * g_grid_size, cudaMemcpyDeviceToHost));
-        for (int i = 0; i < g_grid_size; i++)
-            elapsed_clocks += h_elapsed[i];
-    }
-
-    // Check for kernel errors and STDIO from the kernel, if any
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Display timing results
-    float avg_millis            = elapsed_millis / g_timing_iterations;
-    float avg_items_per_sec     = float(TILE_SIZE * g_grid_size) / avg_millis / 1000.0f;
-    double avg_clocks           = double(elapsed_clocks) / g_timing_iterations / g_grid_size;
-    double avg_clocks_per_item  = avg_clocks / TILE_SIZE;
-
-    printf("\tAverage BlockRadixSort::SortBlocked clocks: %.3f\n", avg_clocks);
-    printf("\tAverage BlockRadixSort::SortBlocked clocks per item: %.3f\n", avg_clocks_per_item);
-    printf("\tAverage kernel millis: %.4f\n", avg_millis);
-    printf("\tAverage million items / sec: %.4f\n", avg_items_per_sec);
-    fflush(stdout);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (h_elapsed) delete[] h_elapsed;
-    if (d_in) CubDebugExit(cudaFree(d_in));
-    if (d_out) CubDebugExit(cudaFree(d_out));
-    if (d_elapsed) CubDebugExit(cudaFree(d_elapsed));
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    g_uniform_keys = args.CheckCmdLineFlag("uniform");
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("grid-size", g_grid_size);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--i=<timing iterations (default:%d)>]"
-            "[--grid-size=<grid size (default:%d)>]"
-            "[--v] "
-            "\n", argv[0], g_timing_iterations, g_grid_size);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-    fflush(stdout);
-
-    // Run tests
-    printf("\nuint32:\n"); fflush(stdout);
-    Test<unsigned int, 128, 13>();
-    printf("\n"); fflush(stdout);
-
-    printf("\nfp32:\n"); fflush(stdout);
-    Test<float, 128, 13>();
-    printf("\n"); fflush(stdout);
-
-    printf("\nuint8:\n"); fflush(stdout);
-    Test<unsigned char, 128, 13>();
-    printf("\n"); fflush(stdout);
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/examples/block/example_block_reduce.cu b/ml-xgboost/cub/examples/block/example_block_reduce.cu
deleted file mode 100644
index 723f6e2..0000000
--- a/ml-xgboost/cub/examples/block/example_block_reduce.cu
+++ /dev/null
@@ -1,290 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple demonstration of cub::BlockReduce
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_block_reduce.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console (define before including cub.h)
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <iostream>
-
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/block/block_reduce.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-/// Verbose output
-bool g_verbose = false;
-
-/// Timing iterations
-int g_timing_iterations = 100;
-
-/// Default grid size
-int g_grid_size = 1;
-
-
-
-//---------------------------------------------------------------------
-// Kernels
-//---------------------------------------------------------------------
-
-/**
- * Simple kernel for performing a block-wide exclusive prefix sum over integers
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    BlockReduceAlgorithm    ALGORITHM>
-__global__ void BlockSumKernel(
-    int         *d_in,          // Tile of input
-    int         *d_out,         // Tile aggregate
-    clock_t     *d_elapsed)     // Elapsed cycle count of block reduction
-{
-    // Specialize BlockReduce type for our thread block
-    typedef BlockReduce<int, BLOCK_THREADS, ALGORITHM> BlockReduceT;
-
-    // Shared memory
-    __shared__ typename BlockReduceT::TempStorage temp_storage;
-
-    // Per-thread tile data
-    int data[ITEMS_PER_THREAD];
-    LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_in, data);
-
-    // Start cycle timer
-    clock_t start = clock();
-
-    // Compute sum
-    int aggregate = BlockReduceT(temp_storage).Sum(data);
-
-    // Stop cycle timer
-    clock_t stop = clock();
-
-    // Store aggregate and elapsed clocks
-    if (threadIdx.x == 0)
-    {
-        *d_elapsed = (start > stop) ? start - stop : stop - start;
-        *d_out = aggregate;
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Host utilities
-//---------------------------------------------------------------------
-
-/**
- * Initialize reduction problem (and solution).
- * Returns the aggregate
- */
-int Initialize(int *h_in, int num_items)
-{
-    int inclusive = 0;
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_in[i] = i % 17;
-        inclusive += h_in[i];
-    }
-
-    return inclusive;
-}
-
-
-/**
- * Test thread block reduction
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    BlockReduceAlgorithm    ALGORITHM>
-void Test()
-{
-    const int TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Allocate host arrays
-    int *h_in           = new int[TILE_SIZE];
-    int *h_gpu          = new int[TILE_SIZE + 1];
-
-    // Initialize problem and reference output on host
-    int h_aggregate = Initialize(h_in, TILE_SIZE);
-
-    // Initialize device arrays
-    int *d_in           = NULL;
-    int *d_out          = NULL;
-    clock_t *d_elapsed  = NULL;
-    cudaMalloc((void**)&d_in,          sizeof(int) * TILE_SIZE);
-    cudaMalloc((void**)&d_out,         sizeof(int) * 1);
-    cudaMalloc((void**)&d_elapsed,     sizeof(clock_t));
-
-    // Display input problem data
-    if (g_verbose)
-    {
-        printf("Input data: ");
-        for (int i = 0; i < TILE_SIZE; i++)
-            printf("%d, ", h_in[i]);
-        printf("\n\n");
-    }
-
-    // Kernel props
-    int max_sm_occupancy;
-    CubDebugExit(MaxSmOccupancy(max_sm_occupancy, BlockSumKernel<BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM>, BLOCK_THREADS));
-
-    // Copy problem to device
-    cudaMemcpy(d_in, h_in, sizeof(int) * TILE_SIZE, cudaMemcpyHostToDevice);
-
-    printf("BlockReduce algorithm %s on %d items (%d timing iterations, %d blocks, %d threads, %d items per thread, %d SM occupancy):\n",
-        (ALGORITHM == BLOCK_REDUCE_RAKING) ? "BLOCK_REDUCE_RAKING" : "BLOCK_REDUCE_WARP_REDUCTIONS",
-        TILE_SIZE, g_timing_iterations, g_grid_size, BLOCK_THREADS, ITEMS_PER_THREAD, max_sm_occupancy);
-
-    // Run aggregate/prefix kernel
-    BlockSumKernel<BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM><<<g_grid_size, BLOCK_THREADS>>>(
-        d_in,
-        d_out,
-        d_elapsed);
-
-    // Check total aggregate
-    printf("\tAggregate: ");
-    int compare = CompareDeviceResults(&h_aggregate, d_out, 1, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Run this several times and average the performance results
-    GpuTimer    timer;
-    float       elapsed_millis          = 0.0;
-    clock_t     elapsed_clocks          = 0;
-
-    for (int i = 0; i < g_timing_iterations; ++i)
-    {
-        // Copy problem to device
-        cudaMemcpy(d_in, h_in, sizeof(int) * TILE_SIZE, cudaMemcpyHostToDevice);
-
-        timer.Start();
-
-        // Run aggregate/prefix kernel
-        BlockSumKernel<BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM><<<g_grid_size, BLOCK_THREADS>>>(
-            d_in,
-            d_out,
-            d_elapsed);
-
-        timer.Stop();
-        elapsed_millis += timer.ElapsedMillis();
-
-        // Copy clocks from device
-        clock_t clocks;
-        CubDebugExit(cudaMemcpy(&clocks, d_elapsed, sizeof(clock_t), cudaMemcpyDeviceToHost));
-        elapsed_clocks += clocks;
-
-    }
-
-    // Check for kernel errors and STDIO from the kernel, if any
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Display timing results
-    float avg_millis            = elapsed_millis / g_timing_iterations;
-    float avg_items_per_sec     = float(TILE_SIZE * g_grid_size) / avg_millis / 1000.0f;
-    float avg_clocks            = float(elapsed_clocks) / g_timing_iterations;
-    float avg_clocks_per_item   = avg_clocks / TILE_SIZE;
-
-    printf("\tAverage BlockReduce::Sum clocks: %.3f\n", avg_clocks);
-    printf("\tAverage BlockReduce::Sum clocks per item: %.3f\n", avg_clocks_per_item);
-    printf("\tAverage kernel millis: %.4f\n", avg_millis);
-    printf("\tAverage million items / sec: %.4f\n", avg_items_per_sec);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_gpu) delete[] h_gpu;
-    if (d_in) cudaFree(d_in);
-    if (d_out) cudaFree(d_out);
-    if (d_elapsed) cudaFree(d_elapsed);
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("grid-size", g_grid_size);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--i=<timing iterations>] "
-            "[--grid-size=<grid size>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Run tests
-    Test<1024, 1, BLOCK_REDUCE_RAKING>();
-    Test<512, 2, BLOCK_REDUCE_RAKING>();
-    Test<256, 4, BLOCK_REDUCE_RAKING>();
-    Test<128, 8, BLOCK_REDUCE_RAKING>();
-    Test<64, 16, BLOCK_REDUCE_RAKING>();
-    Test<32, 32, BLOCK_REDUCE_RAKING>();
-    Test<16, 64, BLOCK_REDUCE_RAKING>();
-
-    printf("-------------\n");
-
-    Test<1024, 1, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    Test<512, 2, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    Test<256, 4, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    Test<128, 8, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    Test<64, 16, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    Test<32, 32, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    Test<16, 64, BLOCK_REDUCE_WARP_REDUCTIONS>();
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/examples/block/example_block_scan.cu b/ml-xgboost/cub/examples/block/example_block_scan.cu
deleted file mode 100644
index 104d4de..0000000
--- a/ml-xgboost/cub/examples/block/example_block_scan.cu
+++ /dev/null
@@ -1,334 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple demonstration of cub::BlockScan
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_block_scan.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console (define before including cub.h)
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <iostream>
-
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/block/block_scan.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-/// Verbose output
-bool g_verbose = false;
-
-/// Timing iterations
-int g_timing_iterations = 100;
-
-/// Default grid size
-int g_grid_size = 1;
-
-
-
-//---------------------------------------------------------------------
-// Kernels
-//---------------------------------------------------------------------
-
-/**
- * Simple kernel for performing a block-wide exclusive prefix sum over integers
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    BlockScanAlgorithm      ALGORITHM>
-__global__ void BlockPrefixSumKernel(
-    int         *d_in,          // Tile of input
-    int         *d_out,         // Tile of output
-    clock_t     *d_elapsed)     // Elapsed cycle count of block scan
-{
-    // Specialize BlockLoad type for our thread block (uses warp-striped loads for coalescing, then transposes in shared memory to a blocked arrangement)
-    typedef BlockLoad<int, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoadT;
-
-    // Specialize BlockStore type for our thread block (uses warp-striped loads for coalescing, then transposes in shared memory to a blocked arrangement)
-    typedef BlockStore<int, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_STORE_WARP_TRANSPOSE> BlockStoreT;
-
-    // Specialize BlockScan type for our thread block
-    typedef BlockScan<int, BLOCK_THREADS, ALGORITHM> BlockScanT;
-
-    // Shared memory
-    __shared__ union
-    {
-        typename BlockLoadT::TempStorage    load;
-        typename BlockStoreT::TempStorage   store;
-        typename BlockScanT::TempStorage    scan;
-    } temp_storage;
-
-    // Per-thread tile data
-    int data[ITEMS_PER_THREAD];
-
-    // Load items into a blocked arrangement
-    BlockLoadT(temp_storage.load).Load(d_in, data);
-
-    // Barrier for smem reuse
-    __syncthreads();
-
-    // Start cycle timer
-    clock_t start = clock();
-
-    // Compute exclusive prefix sum
-    int aggregate;
-    BlockScanT(temp_storage.scan).ExclusiveSum(data, data, aggregate);
-
-    // Stop cycle timer
-    clock_t stop = clock();
-
-    // Barrier for smem reuse
-    __syncthreads();
-
-    // Store items from a blocked arrangement
-    BlockStoreT(temp_storage.store).Store(d_out, data);
-
-    // Store aggregate and elapsed clocks
-    if (threadIdx.x == 0)
-    {
-        *d_elapsed = (start > stop) ? start - stop : stop - start;
-        d_out[BLOCK_THREADS * ITEMS_PER_THREAD] = aggregate;
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Host utilities
-//---------------------------------------------------------------------
-
-/**
- * Initialize exclusive prefix sum problem (and solution).
- * Returns the aggregate
- */
-int Initialize(
-    int *h_in,
-    int *h_reference,
-    int num_items)
-{
-    int inclusive = 0;
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_in[i] = i % 17;
-
-        h_reference[i] = inclusive;
-        inclusive += h_in[i];
-    }
-
-    return inclusive;
-}
-
-
-/**
- * Test thread block scan
- */
-template <
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockScanAlgorithm  ALGORITHM>
-void Test()
-{
-    const int TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Allocate host arrays
-    int *h_in           = new int[TILE_SIZE];
-    int *h_reference    = new int[TILE_SIZE];
-    int *h_gpu          = new int[TILE_SIZE + 1];
-
-    // Initialize problem and reference output on host
-    int h_aggregate = Initialize(h_in, h_reference, TILE_SIZE);
-
-    // Initialize device arrays
-    int *d_in           = NULL;
-    int *d_out          = NULL;
-    clock_t *d_elapsed  = NULL;
-    cudaMalloc((void**)&d_in,          sizeof(int) * TILE_SIZE);
-    cudaMalloc((void**)&d_out,         sizeof(int) * (TILE_SIZE + 1));
-    cudaMalloc((void**)&d_elapsed,     sizeof(clock_t));
-
-    // Display input problem data
-    if (g_verbose)
-    {
-        printf("Input data: ");
-        for (int i = 0; i < TILE_SIZE; i++)
-            printf("%d, ", h_in[i]);
-        printf("\n\n");
-    }
-
-    // Kernel props
-    int max_sm_occupancy;
-    CubDebugExit(MaxSmOccupancy(max_sm_occupancy, BlockPrefixSumKernel<BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM>, BLOCK_THREADS));
-
-    // Copy problem to device
-    cudaMemcpy(d_in, h_in, sizeof(int) * TILE_SIZE, cudaMemcpyHostToDevice);
-
-    printf("BlockScan algorithm %s on %d items (%d timing iterations, %d blocks, %d threads, %d items per thread, %d SM occupancy):\n",
-        (ALGORITHM == BLOCK_SCAN_RAKING) ? "BLOCK_SCAN_RAKING" : (ALGORITHM == BLOCK_SCAN_RAKING_MEMOIZE) ? "BLOCK_SCAN_RAKING_MEMOIZE" : "BLOCK_SCAN_WARP_SCANS",
-        TILE_SIZE, g_timing_iterations, g_grid_size, BLOCK_THREADS, ITEMS_PER_THREAD, max_sm_occupancy);
-
-    // Run aggregate/prefix kernel
-    BlockPrefixSumKernel<BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM><<<g_grid_size, BLOCK_THREADS>>>(
-        d_in,
-        d_out,
-        d_elapsed);
-
-    // Check results
-    printf("\tOutput items: ");
-    int compare = CompareDeviceResults(h_reference, d_out, TILE_SIZE, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Check total aggregate
-    printf("\tAggregate: ");
-    compare = CompareDeviceResults(&h_aggregate, d_out + TILE_SIZE, 1, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Run this several times and average the performance results
-    GpuTimer    timer;
-    float       elapsed_millis          = 0.0;
-    clock_t     elapsed_clocks          = 0;
-
-    for (int i = 0; i < g_timing_iterations; ++i)
-    {
-        // Copy problem to device
-        cudaMemcpy(d_in, h_in, sizeof(int) * TILE_SIZE, cudaMemcpyHostToDevice);
-
-        timer.Start();
-
-        // Run aggregate/prefix kernel
-        BlockPrefixSumKernel<BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM><<<g_grid_size, BLOCK_THREADS>>>(
-            d_in,
-            d_out,
-            d_elapsed);
-
-        timer.Stop();
-        elapsed_millis += timer.ElapsedMillis();
-
-        // Copy clocks from device
-        clock_t clocks;
-        CubDebugExit(cudaMemcpy(&clocks, d_elapsed, sizeof(clock_t), cudaMemcpyDeviceToHost));
-        elapsed_clocks += clocks;
-
-    }
-
-    // Check for kernel errors and STDIO from the kernel, if any
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Display timing results
-    float avg_millis            = elapsed_millis / g_timing_iterations;
-    float avg_items_per_sec     = float(TILE_SIZE * g_grid_size) / avg_millis / 1000.0f;
-    float avg_clocks            = float(elapsed_clocks) / g_timing_iterations;
-    float avg_clocks_per_item   = avg_clocks / TILE_SIZE;
-
-    printf("\tAverage BlockScan::Sum clocks: %.3f\n", avg_clocks);
-    printf("\tAverage BlockScan::Sum clocks per item: %.3f\n", avg_clocks_per_item);
-    printf("\tAverage kernel millis: %.4f\n", avg_millis);
-    printf("\tAverage million items / sec: %.4f\n", avg_items_per_sec);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (h_gpu) delete[] h_gpu;
-    if (d_in) cudaFree(d_in);
-    if (d_out) cudaFree(d_out);
-    if (d_elapsed) cudaFree(d_elapsed);
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("grid-size", g_grid_size);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--i=<timing iterations (default:%d)>]"
-            "[--grid-size=<grid size (default:%d)>]"
-            "[--v] "
-            "\n", argv[0], g_timing_iterations, g_grid_size);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Run tests
-    Test<1024, 1, BLOCK_SCAN_RAKING>();
-    Test<512, 2, BLOCK_SCAN_RAKING>();
-    Test<256, 4, BLOCK_SCAN_RAKING>();
-    Test<128, 8, BLOCK_SCAN_RAKING>();
-    Test<64, 16, BLOCK_SCAN_RAKING>();
-    Test<32, 32, BLOCK_SCAN_RAKING>();
-
-    printf("-------------\n");
-
-    Test<1024, 1, BLOCK_SCAN_RAKING_MEMOIZE>();
-    Test<512, 2, BLOCK_SCAN_RAKING_MEMOIZE>();
-    Test<256, 4, BLOCK_SCAN_RAKING_MEMOIZE>();
-    Test<128, 8, BLOCK_SCAN_RAKING_MEMOIZE>();
-    Test<64, 16, BLOCK_SCAN_RAKING_MEMOIZE>();
-    Test<32, 32, BLOCK_SCAN_RAKING_MEMOIZE>();
-
-    printf("-------------\n");
-
-    Test<1024, 1, BLOCK_SCAN_WARP_SCANS>();
-    Test<512, 2, BLOCK_SCAN_WARP_SCANS>();
-    Test<256, 4, BLOCK_SCAN_WARP_SCANS>();
-    Test<128, 8, BLOCK_SCAN_WARP_SCANS>();
-    Test<64, 16, BLOCK_SCAN_WARP_SCANS>();
-    Test<32, 32, BLOCK_SCAN_WARP_SCANS>();
-
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/examples/block/reduce_by_key.cu b/ml-xgboost/cub/examples/block/reduce_by_key.cu
deleted file mode 100644
index 60351d5..0000000
--- a/ml-xgboost/cub/examples/block/reduce_by_key.cu
+++ /dev/null
@@ -1,57 +0,0 @@
-
-
-#include <cub/cub.cuh>
-
-
-template <
-    int         BLOCK_THREADS,          ///< Number of CTA threads
-    typename    KeyT,                   ///< Key type
-    typename    ValueT>                 ///< Value type
-__global__ void Kernel()
-{
-    // Tuple type for scanning (pairs accumulated segment-value with segment-index)
-    typedef cub::KeyValuePair<int, ValueT> OffsetValuePairT;
-
-    // Reduce-value-by-segment scan operator
-    typedef cub::ReduceBySegmentOp<cub::Sum> ReduceBySegmentOpT;
-
-    // Parameterized BlockDiscontinuity type for setting head flags
-    typedef cub::BlockDiscontinuity<
-            KeyT,
-            BLOCK_THREADS>
-        BlockDiscontinuityKeysT;
-
-    // Parameterized BlockScan type
-    typedef cub::BlockScan<
-            OffsetValuePairT,
-            BLOCK_THREADS,
-            cub::BLOCK_SCAN_WARP_SCANS>
-        BlockScanT;
-
-    // Shared memory
-    __shared__ union
-    {
-        typename BlockScanT::TempStorage                scan;           // Scan storage
-        typename BlockDiscontinuityKeysT::TempStorage   discontinuity;  // Discontinuity storage
-    } temp_storage;
-
-
-    // Read data (each thread gets 3 items each, every 9 items is a segment)
-    KeyT    my_keys[3]      = {threadIdx.x / 3, threadIdx.x / 3, threadIdx.x / 3};
-    ValueT  my_values[3]    = {1, 1, 1};
-
-    // Set head segment head flags
-    int     my_flags[3];
-    BlockDiscontinuityKeysT(temp_storage.discontinuity).FlagHeads(
-        my_flags,
-        my_keys,
-        cub::Inequality());
-
-    __syncthreads();
-
-
-
-
-
-
-}
diff --git a/ml-xgboost/cub/examples/device/.gitignore b/ml-xgboost/cub/examples/device/.gitignore
deleted file mode 100644
index 7032b5a..0000000
--- a/ml-xgboost/cub/examples/device/.gitignore
+++ /dev/null
@@ -1,8 +0,0 @@
-/bin
-/Debug
-/ipch
-/Release
-/cuda55.sdf
-/cuda55.suo
-/cuda60.sdf
-/cuda60.suo
diff --git a/ml-xgboost/cub/examples/device/Makefile b/ml-xgboost/cub/examples/device/Makefile
deleted file mode 100644
index 049430d..0000000
--- a/ml-xgboost/cub/examples/device/Makefile
+++ /dev/null
@@ -1,197 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
-
-#-------------------------------------------------------------------------------
-#
-# Makefile usage
-#
-# make <target> [sm=<XXX,...>] [cdp=<0|1>] [force32=<0|1>] [abi=<0|1>] [open64=<0|1>] [verbose=<0|1>] [keep=<0|1>]
-#
-#-------------------------------------------------------------------------------
- 
-include ../../common.mk 
- 
- 
-#-------------------------------------------------------------------------------
-# Includes
-#-------------------------------------------------------------------------------
-
-INC += -I$(CUB_DIR) -I$(CUB_DIR)test 
-
-
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-DEPS =				$(CUB_DEPS) \
-					$(CUB_DIR)test/Makefile \
-					$(CUB_DIR)test/test_util.h \
-					$(CUB_DIR)test/mersenne.h \
-		
-ALL = 	example_device_partition_flagged \
-		example_device_partition_if \
-	 	example_device_radix_sort \
-		example_device_reduce \
-	 	example_device_scan \
-	 	example_device_select_unique \
-		example_device_select_flagged \
-		example_device_select_if \
-		example_device_sort_find_non_trivial_runs
-		
-
-
-#-------------------------------------------------------------------------------
-# make default
-#-------------------------------------------------------------------------------
-
-default:
-
-
-#-------------------------------------------------------------------------------
-# make clean
-#-------------------------------------------------------------------------------
-
-clean :
-	rm -f bin/*$(CPU_ARCH_SUFFIX)* 
-	rm -f *.i* *.cubin *.cu.c *.cudafe* *.fatbin.c *.ptx *.hash *.cu.cpp *.o
-
-
-#-------------------------------------------------------------------------------
-# make all
-#-------------------------------------------------------------------------------
-
-all : $(ALL)
-
-#-------------------------------------------------------------------------------
-# make run
-#-------------------------------------------------------------------------------
-
-run : 
-	for i in $(ALL); do ./bin/$${i}_$(BIN_SUFFIX) --device=$(device) || exit 1; done
-
-
-#-------------------------------------------------------------------------------
-# make example_device_reduce
-#-------------------------------------------------------------------------------
-
-example_device_reduce: bin/example_device_reduce_$(BIN_SUFFIX)
-
-bin/example_device_reduce_$(BIN_SUFFIX) : example_device_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_reduce_$(BIN_SUFFIX) example_device_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_partition_flagged
-#-------------------------------------------------------------------------------
-
-example_device_partition_flagged: bin/example_device_partition_flagged_$(BIN_SUFFIX)
-
-bin/example_device_partition_flagged_$(BIN_SUFFIX) : example_device_partition_flagged.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_partition_flagged_$(BIN_SUFFIX) example_device_partition_flagged.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make example_device_partition_if
-#-------------------------------------------------------------------------------
-
-example_device_partition_if: bin/example_device_partition_if_$(BIN_SUFFIX)
-
-bin/example_device_partition_if_$(BIN_SUFFIX) : example_device_partition_if.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_partition_if_$(BIN_SUFFIX) example_device_partition_if.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make example_device_scan
-#-------------------------------------------------------------------------------
-
-example_device_scan: bin/example_device_scan_$(BIN_SUFFIX)
-
-bin/example_device_scan_$(BIN_SUFFIX) : example_device_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_scan_$(BIN_SUFFIX) example_device_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_radix_sort
-#-------------------------------------------------------------------------------
-
-example_device_radix_sort: bin/example_device_radix_sort_$(BIN_SUFFIX)
-
-bin/example_device_radix_sort_$(BIN_SUFFIX) : example_device_radix_sort.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_radix_sort_$(BIN_SUFFIX) example_device_radix_sort.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_select_unique
-#-------------------------------------------------------------------------------
-
-example_device_select_unique: bin/example_device_select_unique_$(BIN_SUFFIX)
-
-bin/example_device_select_unique_$(BIN_SUFFIX) : example_device_select_unique.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_select_unique_$(BIN_SUFFIX) example_device_select_unique.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_select_flagged
-#-------------------------------------------------------------------------------
-
-example_device_select_flagged: bin/example_device_select_flagged_$(BIN_SUFFIX)
-
-bin/example_device_select_flagged_$(BIN_SUFFIX) : example_device_select_flagged.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_select_flagged_$(BIN_SUFFIX) example_device_select_flagged.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make example_device_select_if
-#-------------------------------------------------------------------------------
-
-example_device_select_if: bin/example_device_select_if_$(BIN_SUFFIX)
-
-bin/example_device_select_if_$(BIN_SUFFIX) : example_device_select_if.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_select_if_$(BIN_SUFFIX) example_device_select_if.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_sort_find_non_trivial_runs
-#-------------------------------------------------------------------------------
-
-example_device_sort_find_non_trivial_runs: bin/example_device_sort_find_non_trivial_runs_$(BIN_SUFFIX)
-
-bin/example_device_sort_find_non_trivial_runs_$(BIN_SUFFIX) : example_device_sort_find_non_trivial_runs.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_sort_find_non_trivial_runs_$(BIN_SUFFIX) example_device_sort_find_non_trivial_runs.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_partition_flagged.cu b/ml-xgboost/cub/examples/device/example_device_partition_flagged.cu
deleted file mode 100644
index 72737b7..0000000
--- a/ml-xgboost/cub/examples/device/example_device_partition_flagged.cu
+++ /dev/null
@@ -1,233 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DevicePartition::Flagged().
- *
- * Partition flagged items from from a sequence of int keys using a
- * corresponding sequence of unsigned char flags.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_partition_flagged.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_partition.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem, setting flags at distances of random length
- * chosen from [1..max_segment]
- */
-void Initialize(
-    int             *h_in,
-    unsigned char   *h_flags,
-    int             num_items,
-    int             max_segment)
-{
-    unsigned short max_short = (unsigned short) -1;
-
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Select number of repeating occurrences
-        unsigned short repeat;
-        RandomBits(repeat);
-        repeat = (unsigned short) ((float(repeat) * (float(max_segment) / float(max_short))));
-        repeat = CUB_MAX(1, repeat);
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            h_flags[j] = 0;
-            h_in[j] = key;
-            j++;
-        }
-
-        h_flags[i] = 1;
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("Flags:\n");
-        DisplayResults(h_flags, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve unique problem
- */
-int Solve(
-    int             *h_in,
-    unsigned char   *h_flags,
-    int             *h_reference,
-    int             num_items)
-{
-    int num_selected = 0;
-    for (int i = 0; i < num_items; ++i)
-    {
-        if (h_flags[i])
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-        else
-        {
-            h_reference[num_items - (i - num_selected) - 1] = h_in[i];
-        }
-    }
-
-    return num_selected;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = 150;
-    int max_segment         = 40;       // Maximum segment length
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("maxseg", max_segment);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Allocate host arrays
-    int             *h_in        = new int[num_items];
-    int             *h_reference = new int[num_items];
-    unsigned char   *h_flags     = new unsigned char[num_items];
-
-    // Initialize problem and solution
-    Initialize(h_in, h_flags, num_items, max_segment);
-    int num_selected = Solve(h_in, h_flags, h_reference, num_items);
-
-    printf("cub::DevicePartition::Flagged %d items, %d selected (avg distance %d), %d-byte elements\n",
-        num_items, num_selected, (num_selected > 0) ? num_items / num_selected : 0, (int) sizeof(int));
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    int             *d_in = NULL;
-    unsigned char   *d_flags = NULL;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_flags, sizeof(unsigned char) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_flags, h_flags, sizeof(unsigned char) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array and num selected
-    int     *d_out            = NULL;
-    int     *d_num_selected_out   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DevicePartition::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DevicePartition::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_items, true, g_verbose);
-    printf("\t Data %s ", compare ? "FAIL" : "PASS");
-    compare |= CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s ", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_flags) CubDebugExit(g_allocator.DeviceFree(d_flags));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_partition_if.cu b/ml-xgboost/cub/examples/device/example_device_partition_if.cu
deleted file mode 100644
index 4dfa351..0000000
--- a/ml-xgboost/cub/examples/device/example_device_partition_if.cu
+++ /dev/null
@@ -1,244 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DevicePartition::If().
- *
- * Partitions items from from a sequence of int keys using a
- * section functor (greater-than)
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_select_if.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_partition.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-/// Selection functor type
-struct GreaterThan
-{
-    int compare;
-
-    __host__ __device__ __forceinline__
-    GreaterThan(int compare) : compare(compare) {}
-
-    __host__ __device__ __forceinline__
-    bool operator()(const int &a) const {
-        return (a > compare);
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Initialize problem, setting runs of random length chosen from [1..max_segment]
- */
-void Initialize(
-    int     *h_in,
-    int     num_items,
-    int     max_segment)
-{
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Randomly select number of repeating occurrences uniformly from [1..max_segment]
-        unsigned short max_short = (unsigned short) -1;
-        unsigned short repeat;
-        RandomBits(repeat);
-        repeat = (unsigned short) ((float(repeat) * (float(max_segment) / float(max_short))));
-        repeat = CUB_MAX(1, repeat);
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            h_in[j] = key;
-            j++;
-        }
-
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve unique problem
- */
-template <typename SelectOp>
-int Solve(
-    int             *h_in,
-    SelectOp        select_op,
-    int             *h_reference,
-    int             num_items)
-{
-    int num_selected = 0;
-    for (int i = 0; i < num_items; ++i)
-    {
-        if (select_op(h_in[i]))
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-        else
-        {
-            h_reference[num_items - (i - num_selected) - 1] = h_in[i];
-        }
-    }
-
-    return num_selected;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = 150;
-    int max_segment         = 40;       // Maximum segment length
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("maxseg", max_segment);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Allocate host arrays
-    int *h_in        = new int[num_items];
-    int *h_reference = new int[num_items];
-
-    // DevicePartition a pivot index
-    unsigned int pivot_index;
-    unsigned int max_int = (unsigned int) -1;
-    RandomBits(pivot_index);
-    pivot_index = (unsigned int) ((float(pivot_index) * (float(num_items - 1) / float(max_int))));
-    printf("Pivot idx: %d\n", pivot_index); fflush(stdout);
-
-    // Initialize problem and solution
-    Initialize(h_in, num_items, max_segment);
-    GreaterThan select_op(h_in[pivot_index]);
-
-    int num_selected = Solve(h_in, select_op, h_reference, num_items);
-
-    printf("cub::DevicePartition::If %d items, %d selected (avg run length %d), %d-byte elements\n",
-        num_items, num_selected, (num_selected > 0) ? num_items / num_selected : 0, (int) sizeof(int));
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    int *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array and num selected
-    int     *d_out            = NULL;
-    int     *d_num_selected_out   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DevicePartition::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DevicePartition::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_items, true, g_verbose);
-    printf("\t Data %s ", compare ? "FAIL" : "PASS");
-    compare = compare | CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s ", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_radix_sort.cu b/ml-xgboost/cub/examples/device/example_device_radix_sort.cu
deleted file mode 100644
index 7f1c158..0000000
--- a/ml-xgboost/cub/examples/device/example_device_radix_sort.cu
+++ /dev/null
@@ -1,226 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DeviceRadixSort::SortPairs().
- *
- * Sorts an array of float keys paired with a corresponding array of int values.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_radix_sort.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <algorithm>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_radix_sort.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Simple key-value pairing for floating point types.  Distinguishes
- * between positive and negative zero.
- */
-struct Pair
-{
-    float   key;
-    int     value;
-
-    bool operator<(const Pair &b) const
-    {
-        if (key < b.key)
-            return true;
-
-        if (key > b.key)
-            return false;
-
-        // Return true if key is negative zero and b.key is positive zero
-        unsigned int key_bits   = *reinterpret_cast<unsigned*>(const_cast<float*>(&key));
-        unsigned int b_key_bits = *reinterpret_cast<unsigned*>(const_cast<float*>(&b.key));
-        unsigned int HIGH_BIT   = 1u << 31;
-
-        return ((key_bits & HIGH_BIT) != 0) && ((b_key_bits & HIGH_BIT) == 0);
-    }
-};
-
-
-/**
- * Initialize key-value sorting problem.
- */
-void Initialize(
-    float           *h_keys,
-    int             *h_values,
-    float           *h_reference_keys,
-    int             *h_reference_values,
-    int             num_items)
-{
-    Pair *h_pairs = new Pair[num_items];
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        RandomBits(h_keys[i]);
-        RandomBits(h_values[i]);
-        h_pairs[i].key    = h_keys[i];
-        h_pairs[i].value  = h_values[i];
-    }
-
-    if (g_verbose)
-    {
-        printf("Input keys:\n");
-        DisplayResults(h_keys, num_items);
-        printf("\n\n");
-
-        printf("Input values:\n");
-        DisplayResults(h_values, num_items);
-        printf("\n\n");
-    }
-
-    std::stable_sort(h_pairs, h_pairs + num_items);
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_reference_keys[i]     = h_pairs[i].key;
-        h_reference_values[i]   = h_pairs[i].value;
-    }
-
-    delete[] h_pairs;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items = 150;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    printf("cub::DeviceRadixSort::SortPairs() %d items (%d-byte keys %d-byte values)\n",
-        num_items, int(sizeof(float)), int(sizeof(int)));
-    fflush(stdout);
-
-    // Allocate host arrays
-    float   *h_keys             = new float[num_items];
-    float   *h_reference_keys   = new float[num_items];
-    int     *h_values           = new int[num_items];
-    int     *h_reference_values = new int[num_items];
-
-    // Initialize problem and solution on host
-    Initialize(h_keys, h_values, h_reference_keys, h_reference_values, num_items);
-
-    // Allocate device arrays
-    DoubleBuffer<float> d_keys;
-    DoubleBuffer<int>   d_values;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys.d_buffers[0], sizeof(float) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys.d_buffers[1], sizeof(float) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values.d_buffers[0], sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values.d_buffers[1], sizeof(int) * num_items));
-
-    // Allocate temporary storage
-    size_t  temp_storage_bytes  = 0;
-    void    *d_temp_storage     = NULL;
-
-    CubDebugExit(DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Initialize device arrays
-    CubDebugExit(cudaMemcpy(d_keys.d_buffers[d_keys.selector], h_keys, sizeof(float) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_values.d_buffers[d_values.selector], h_values, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-    // Run
-    CubDebugExit(DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference_keys, d_keys.Current(), num_items, true, g_verbose);
-    printf("\t Compare keys (selector %d): %s\n", d_keys.selector, compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-    compare = CompareDeviceResults(h_reference_values, d_values.Current(), num_items, true, g_verbose);
-    printf("\t Compare values (selector %d): %s\n", d_values.selector, compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_keys) delete[] h_keys;
-    if (h_reference_keys) delete[] h_reference_keys;
-    if (h_values) delete[] h_values;
-    if (h_reference_values) delete[] h_reference_values;
-
-    if (d_keys.d_buffers[0]) CubDebugExit(g_allocator.DeviceFree(d_keys.d_buffers[0]));
-    if (d_keys.d_buffers[1]) CubDebugExit(g_allocator.DeviceFree(d_keys.d_buffers[1]));
-    if (d_values.d_buffers[0]) CubDebugExit(g_allocator.DeviceFree(d_values.d_buffers[0]));
-    if (d_values.d_buffers[1]) CubDebugExit(g_allocator.DeviceFree(d_values.d_buffers[1]));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_reduce.cu b/ml-xgboost/cub/examples/device/example_device_reduce.cu
deleted file mode 100644
index 44dc946..0000000
--- a/ml-xgboost/cub/examples/device/example_device_reduce.cu
+++ /dev/null
@@ -1,180 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DeviceReduce::Sum().
- *
- * Sums an array of int keys.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_reduce.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_reduce.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Initialize problem
- */
-void Initialize(
-    int   *h_in,
-    int     num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-        h_in[i] = i;
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Compute solution
- */
-void Solve(
-    int           *h_in,
-    int           &h_reference,
-    int             num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        if (i == 0)
-            h_reference = h_in[0];
-        else
-            h_reference += h_in[i];
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items = 150;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    printf("cub::DeviceReduce::Sum() %d items (%d-byte elements)\n",
-        num_items, (int) sizeof(int));
-    fflush(stdout);
-
-    // Allocate host arrays
-    int* h_in = new int[num_items];
-    int  h_reference;
-
-    // Initialize problem and solution
-    Initialize(h_in, num_items);
-    Solve(h_in, h_reference, num_items);
-
-    // Allocate problem device arrays
-    int *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array
-    int *d_out = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * 1));
-
-    // Request and allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DeviceReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DeviceReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(&h_reference, d_out, 1, g_verbose, g_verbose);
-    printf("\t%s", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_scan.cu b/ml-xgboost/cub/examples/device/example_device_scan.cu
deleted file mode 100644
index 7a9b476..0000000
--- a/ml-xgboost/cub/examples/device/example_device_scan.cu
+++ /dev/null
@@ -1,186 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DeviceScan::ExclusiveSum().
- *
- * Computes an exclusive sum of int keys.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_scan.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_scan.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem
- */
-void Initialize(
-    int        *h_in,
-    int          num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-        h_in[i] = i;
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-/**
- * Solve exclusive-scan problem
- */
-int Solve(
-    int           *h_in,
-    int           *h_reference,
-    int             num_items)
-{
-    int inclusive = 0;
-    int aggregate = 0;
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_reference[i] = inclusive;
-        inclusive += h_in[i];
-        aggregate += h_in[i];
-    }
-
-    return aggregate;
-}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items = 150;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    printf("cub::DeviceScan::ExclusiveSum %d items (%d-byte elements)\n",
-        num_items, (int) sizeof(int));
-    fflush(stdout);
-
-    // Allocate host arrays
-    int*  h_in = new int[num_items];
-    int*  h_reference = new int[num_items];
-
-    // Initialize problem and solution
-    Initialize(h_in, num_items);
-    Solve(h_in, h_reference, num_items);
-
-    // Allocate problem device arrays
-    int *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array
-    int *d_out = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * num_items));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_items, true, g_verbose);
-    printf("\t%s", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_select_flagged.cu b/ml-xgboost/cub/examples/device/example_device_select_flagged.cu
deleted file mode 100644
index 76012c4..0000000
--- a/ml-xgboost/cub/examples/device/example_device_select_flagged.cu
+++ /dev/null
@@ -1,233 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DeviceSelect::Flagged().
- *
- * Selects flagged items from from a sequence of int keys using a
- * corresponding sequence of unsigned char flags.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_select_flagged.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_select.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem, setting flags at distances of random length
- * chosen from [1..max_segment]
- */
-void Initialize(
-    int             *h_in,
-    unsigned char   *h_flags,
-    int             num_items,
-    int             max_segment)
-{
-    unsigned short max_short = (unsigned short) -1;
-
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Select number of repeating occurrences
-        unsigned short repeat;
-        RandomBits(repeat);
-        repeat = (unsigned short) ((float(repeat) * (float(max_segment) / float(max_short))));
-        repeat = CUB_MAX(1, repeat);
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            h_flags[j] = 0;
-            h_in[j] = key;
-            j++;
-        }
-
-        h_flags[i] = 1;
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("Flags:\n");
-        DisplayResults(h_flags, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve unique problem
- */
-int Solve(
-    int             *h_in,
-    unsigned char   *h_flags,
-    int             *h_reference,
-    int             num_items)
-{
-    int num_selected = 0;
-    for (int i = 0; i < num_items; ++i)
-    {
-        if (h_flags[i])
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-        else
-        {
-            h_reference[num_items - (i - num_selected) - 1] = h_in[i];
-        }
-    }
-
-    return num_selected;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = 150;
-    int max_segment         = 40;       // Maximum segment length
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("maxseg", max_segment);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Allocate host arrays
-    int             *h_in        = new int[num_items];
-    int             *h_reference = new int[num_items];
-    unsigned char   *h_flags     = new unsigned char[num_items];
-
-    // Initialize problem and solution
-    Initialize(h_in, h_flags, num_items, max_segment);
-    int num_selected = Solve(h_in, h_flags, h_reference, num_items);
-
-    printf("cub::DeviceSelect::Flagged %d items, %d selected (avg distance %d), %d-byte elements\n",
-        num_items, num_selected, (num_selected > 0) ? num_items / num_selected : 0, (int) sizeof(int));
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    int             *d_in = NULL;
-    unsigned char   *d_flags = NULL;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_flags, sizeof(unsigned char) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_flags, h_flags, sizeof(unsigned char) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array and num selected
-    int     *d_out            = NULL;
-    int     *d_num_selected_out   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DeviceSelect::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DeviceSelect::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_selected, true, g_verbose);
-    printf("\t Data %s ", compare ? "FAIL" : "PASS");
-    compare |= CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s ", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_flags) CubDebugExit(g_allocator.DeviceFree(d_flags));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_select_if.cu b/ml-xgboost/cub/examples/device/example_device_select_if.cu
deleted file mode 100644
index a21988e..0000000
--- a/ml-xgboost/cub/examples/device/example_device_select_if.cu
+++ /dev/null
@@ -1,242 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DeviceSelect::If().
- *
- * Selects items from from a sequence of int keys using a
- * section functor (greater-than)
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_select_if.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_select.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-/// Selection functor type
-struct GreaterThan
-{
-    int compare;
-
-    __host__ __device__ __forceinline__
-    GreaterThan(int compare) : compare(compare) {}
-
-    __host__ __device__ __forceinline__
-    bool operator()(const int &a) const {
-        return (a > compare);
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Initialize problem, setting runs of random length chosen from [1..max_segment]
- */
-void Initialize(
-    int     *h_in,
-    int     num_items,
-    int     max_segment)
-{
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Randomly select number of repeating occurrences uniformly from [1..max_segment]
-        unsigned short max_short = (unsigned short) -1;
-        unsigned short repeat;
-        RandomBits(repeat);
-        repeat = (unsigned short) ((float(repeat) * (float(max_segment) / float(max_short))));
-        repeat = CUB_MAX(1, repeat);
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            h_in[j] = key;
-            j++;
-        }
-
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve unique problem
- */
-template <typename SelectOp>
-int Solve(
-    int             *h_in,
-    SelectOp        select_op,
-    int             *h_reference,
-    int             num_items)
-{
-    int num_selected = 0;
-    for (int i = 0; i < num_items; ++i)
-    {
-        if (select_op(h_in[i]))
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-        else
-        {
-            h_reference[num_items - (i - num_selected) - 1] = h_in[i];
-        }
-    }
-
-    return num_selected;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = 150;
-    int max_segment         = 40;       // Maximum segment length
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("maxseg", max_segment);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Allocate host arrays
-    int *h_in        = new int[num_items];
-    int *h_reference = new int[num_items];
-
-    // Select a pivot index
-    unsigned int pivot_index;
-    unsigned int max_int = (unsigned int) -1;
-    RandomBits(pivot_index);
-    pivot_index = (unsigned int) ((float(pivot_index) * (float(num_items - 1) / float(max_int))));
-    printf("Pivot idx: %d\n", pivot_index); fflush(stdout);
-
-    // Initialize problem and solution
-    Initialize(h_in, num_items, max_segment);
-    GreaterThan select_op(h_in[pivot_index]);
-
-    int num_selected = Solve(h_in, select_op, h_reference, num_items);
-
-    printf("cub::DeviceSelect::If %d items, %d selected (avg run length %d), %d-byte elements\n",
-        num_items, num_selected, (num_selected > 0) ? num_items / num_selected : 0, (int) sizeof(int));
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    int *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array and num selected
-    int     *d_out            = NULL;
-    int     *d_num_selected_out   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_selected, true, g_verbose);
-    printf("\t Data %s ", compare ? "FAIL" : "PASS");
-    compare = compare | CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s ", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    printf("\n\n");
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/examples/device/example_device_select_unique.cu b/ml-xgboost/cub/examples/device/example_device_select_unique.cu
deleted file mode 100644
index ae5ea83..0000000
--- a/ml-xgboost/cub/examples/device/example_device_select_unique.cu
+++ /dev/null
@@ -1,221 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of DeviceSelect::Unique().
- *
- * Selects the first element from each run of identical values from a sequence
- * of int keys.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_select_unique.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_select.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem, setting runs of random length chosen from [1..max_segment]
- */
-void Initialize(
-    int     *h_in,
-    int     num_items,
-    int     max_segment)
-{
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Randomly select number of repeating occurrences uniformly from [1..max_segment]
-        unsigned short max_short = (unsigned short) -1;
-        unsigned short repeat;
-        RandomBits(repeat);
-        repeat = (unsigned short) ((float(repeat) * (float(max_segment) / float(max_short))));
-        repeat = CUB_MAX(1, repeat);
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            h_in[j] = key;
-            j++;
-        }
-
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve unique problem
- */
-int Solve(
-    int         *h_in,
-    int         *h_reference,
-    int         num_items)
-{
-    int num_selected = 0;
-    if (num_items > 0)
-    {
-        h_reference[num_selected] = h_in[0];
-        num_selected++;
-    }
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        if (h_in[i] != h_in[i - 1])
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-    }
-
-    return num_selected;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = 150;
-    int max_segment         = 40;       // Maximum segment length
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("maxseg", max_segment);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Allocate host arrays
-    int*  h_in        = new int[num_items];
-    int*  h_reference = new int[num_items];
-
-    // Initialize problem and solution
-    Initialize(h_in, num_items, max_segment);
-    int num_selected = Solve(h_in, h_reference, num_items);
-
-    printf("cub::DeviceSelect::Unique %d items (%d-byte elements), %d selected (avg run length %d)\n",
-        num_items, (int) sizeof(int), num_selected, num_items / num_selected);
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    int *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(int) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-    // Allocate device output array and num selected
-    int     *d_out            = NULL;
-    int     *d_num_selected_out   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(int) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(DeviceSelect::Unique(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Run
-    CubDebugExit(DeviceSelect::Unique(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_selected, true, g_verbose);
-    printf("\t Data %s ", compare ? "FAIL" : "PASS");
-    compare = compare | CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s ", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    printf("\n\n");
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/examples/device/example_device_sort_find_non_trivial_runs.cu b/ml-xgboost/cub/examples/device/example_device_sort_find_non_trivial_runs.cu
deleted file mode 100644
index 3fbe0eb..0000000
--- a/ml-xgboost/cub/examples/device/example_device_sort_find_non_trivial_runs.cu
+++ /dev/null
@@ -1,384 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Simple example of sorting a sequence of keys and values (each pair is a
- * randomly-selected int32 paired with its original offset in the unsorted sequence), and then
- * isolating all maximal, non-trivial (having length > 1) "runs" of duplicates.
- *
- * To compile using the command line:
- *   nvcc -arch=sm_XX example_device_sort_find_non_trivial_runs.cu -I../.. -lcudart -O3
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <algorithm>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_radix_sort.cuh>
-#include <cub/device/device_run_length_encode.cuh>
-
-#include "../../test/test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Simple key-value pairing for using std::sort on key-value pairs.
- */
-template <typename Key, typename Value>
-struct Pair
-{
-    Key     key;
-    Value   value;
-
-    bool operator<(const Pair &b) const
-    {
-        return (key < b.key);
-    }
-};
-
-
-/**
- * Pair ostream operator
- */
-template <typename Key, typename Value>
-std::ostream& operator<<(std::ostream& os, const Pair<Key, Value>& val)
-{
-    os << '<' << val.key << ',' << val.value << '>';
-    return os;
-}
-
-
-/**
- * Initialize problem
- */
-template <typename Key, typename Value>
-void Initialize(
-    Key    *h_keys,
-    Value  *h_values,
-    int    num_items,
-    int    max_key)
-{
-    float scale = float(max_key) / float(UINT_MAX);
-    for (int i = 0; i < num_items; ++i)
-    {
-        Key sample;
-        RandomBits(sample);
-        h_keys[i] = (max_key == -1) ? i : (Key) (scale * sample);
-        h_values[i] = i;
-    }
-
-    if (g_verbose)
-    {
-        printf("Keys:\n");
-        DisplayResults(h_keys, num_items);
-        printf("\n\n");
-
-        printf("Values:\n");
-        DisplayResults(h_values, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve sorted non-trivial subrange problem.  Returns the number
- * of non-trivial runs found.
- */
-template <typename Key, typename Value>
-int Solve(
-    Key     *h_keys,
-    Value   *h_values,
-    int     num_items,
-    int     *h_offsets_reference,
-    int     *h_lengths_reference)
-{
-    // Sort
-
-    Pair<Key, Value> *h_pairs = new Pair<Key, Value>[num_items];
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_pairs[i].key    = h_keys[i];
-        h_pairs[i].value  = h_values[i];
-    }
-
-    std::stable_sort(h_pairs, h_pairs + num_items);
-
-    if (g_verbose)
-    {
-        printf("Sorted pairs:\n");
-        DisplayResults(h_pairs, num_items);
-        printf("\n\n");
-    }
-
-    // Find non-trivial runs
-
-    Key     previous        = h_pairs[0].key;
-    int     length          = 1;
-    int     num_runs        = 0;
-    int     run_begin       = 0;
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        if (previous != h_pairs[i].key)
-        {
-            if (length > 1)
-            {
-                h_offsets_reference[num_runs]     = run_begin;
-                h_lengths_reference[num_runs]     = length;
-                num_runs++;
-            }
-            length = 1;
-            run_begin = i;
-        }
-        else
-        {
-            length++;
-        }
-        previous = h_pairs[i].key;
-    }
-
-    if (length > 1)
-    {
-        h_offsets_reference[num_runs]   = run_begin;
-        h_lengths_reference[num_runs]   = length;
-        num_runs++;
-    }
-
-    delete[] h_pairs;
-
-    return num_runs;
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    typedef unsigned int    Key;
-    typedef int             Value;
-
-    int timing_iterations   = 0;
-    int num_items           = 40;
-    Key max_key             = 20;       // Max item
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("maxkey", max_key);
-    args.GetCmdLineArgument("i", timing_iterations);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--i=<timing iterations> "
-            "[--n=<input items, default 40> "
-            "[--maxkey=<max key, default 20 (use -1 to test only unique keys)>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Allocate host arrays (problem and reference solution)
-
-    Key     *h_keys                 = new Key[num_items];
-    Value   *h_values               = new Value[num_items];
-    int     *h_offsets_reference    = new int[num_items];
-    int     *h_lengths_reference    = new int[num_items];
-
-    // Initialize key-value pairs and compute reference solution (sort them, and identify non-trivial runs)
-    printf("Computing reference solution on CPU for %d items (max key %d)\n", num_items, max_key);
-    fflush(stdout);
-
-    Initialize(h_keys, h_values, num_items, max_key);
-    int num_runs = Solve(h_keys, h_values, num_items, h_offsets_reference, h_lengths_reference);
-
-    printf("%d non-trivial runs\n", num_runs);
-    fflush(stdout);
-
-    // Repeat for performance timing
-    GpuTimer gpu_timer;
-    GpuTimer gpu_rle_timer;
-    float elapsed_millis = 0.0;
-    float elapsed_rle_millis = 0.0;
-    for (int i = 0; i <= timing_iterations; ++i)
-    {
-
-        // Allocate and initialize device arrays for sorting
-        DoubleBuffer<Key>       d_keys;
-        DoubleBuffer<Value>     d_values;
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys.d_buffers[0], sizeof(Key) * num_items));
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys.d_buffers[1], sizeof(Key) * num_items));
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values.d_buffers[0], sizeof(Value) * num_items));
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values.d_buffers[1], sizeof(Value) * num_items));
-
-        CubDebugExit(cudaMemcpy(d_keys.d_buffers[d_keys.selector], h_keys, sizeof(float) * num_items, cudaMemcpyHostToDevice));
-        CubDebugExit(cudaMemcpy(d_values.d_buffers[d_values.selector], h_values, sizeof(int) * num_items, cudaMemcpyHostToDevice));
-
-        // Start timer
-        gpu_timer.Start();
-
-        // Allocate temporary storage for sorting
-        size_t  temp_storage_bytes  = 0;
-        void    *d_temp_storage     = NULL;
-        CubDebugExit(DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items));
-        CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-        // Do the sort
-        CubDebugExit(DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, d_keys, d_values, num_items));
-
-        // Free unused buffers and sorting temporary storage
-        if (d_keys.d_buffers[d_keys.selector ^ 1]) CubDebugExit(g_allocator.DeviceFree(d_keys.d_buffers[d_keys.selector ^ 1]));
-        if (d_values.d_buffers[d_values.selector ^ 1]) CubDebugExit(g_allocator.DeviceFree(d_values.d_buffers[d_values.selector ^ 1]));
-        if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-        // Start timer
-        gpu_rle_timer.Start();
-
-        // Allocate device arrays for enumerating non-trivial runs
-        int     *d_offests_out   = NULL;
-        int     *d_lengths_out   = NULL;
-        int     *d_num_runs      = NULL;
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_offests_out, sizeof(int) * num_items));
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_lengths_out, sizeof(int) * num_items));
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_runs, sizeof(int) * 1));
-
-        // Allocate temporary storage for isolating non-trivial runs
-        d_temp_storage = NULL;
-        CubDebugExit(DeviceRunLengthEncode::NonTrivialRuns(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys.d_buffers[d_keys.selector],
-            d_offests_out,
-            d_lengths_out,
-            d_num_runs,
-            num_items));
-        CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-        // Do the isolation
-        CubDebugExit(DeviceRunLengthEncode::NonTrivialRuns(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys.d_buffers[d_keys.selector],
-            d_offests_out,
-            d_lengths_out,
-            d_num_runs,
-            num_items));
-
-        // Free keys buffer
-        if (d_keys.d_buffers[d_keys.selector]) CubDebugExit(g_allocator.DeviceFree(d_keys.d_buffers[d_keys.selector]));
-
-        //
-        // Hypothetically do stuff with the original key-indices corresponding to non-trivial runs of identical keys
-        //
-
-        // Stop sort timer
-        gpu_timer.Stop();
-        gpu_rle_timer.Stop();
-
-        if (i == 0)
-        {
-            // First iteration is a warmup: // Check for correctness (and display results, if specified)
-
-            printf("\nRUN OFFSETS: \n");
-            int compare = CompareDeviceResults(h_offsets_reference, d_offests_out, num_runs, true, g_verbose);
-            printf("\t\t %s ", compare ? "FAIL" : "PASS");
-
-            printf("\nRUN LENGTHS: \n");
-            compare |= CompareDeviceResults(h_lengths_reference, d_lengths_out, num_runs, true, g_verbose);
-            printf("\t\t %s ", compare ? "FAIL" : "PASS");
-
-            printf("\nNUM RUNS: \n");
-            compare |= CompareDeviceResults(&num_runs, d_num_runs, 1, true, g_verbose);
-            printf("\t\t %s ", compare ? "FAIL" : "PASS");
-
-            AssertEquals(0, compare);
-        }
-        else
-        {
-            elapsed_millis += gpu_timer.ElapsedMillis();
-            elapsed_rle_millis += gpu_rle_timer.ElapsedMillis();
-        }
-
-        // GPU cleanup
-
-        if (d_values.d_buffers[d_values.selector]) CubDebugExit(g_allocator.DeviceFree(d_values.d_buffers[d_values.selector]));
-        if (d_offests_out) CubDebugExit(g_allocator.DeviceFree(d_offests_out));
-        if (d_lengths_out) CubDebugExit(g_allocator.DeviceFree(d_lengths_out));
-        if (d_num_runs) CubDebugExit(g_allocator.DeviceFree(d_num_runs));
-        if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-    }
-
-    // Host cleanup
-    if (h_keys) delete[] h_keys;
-    if (h_values) delete[] h_values;
-    if (h_offsets_reference) delete[] h_offsets_reference;
-    if (h_lengths_reference) delete[] h_lengths_reference;
-
-    printf("\n\n");
-
-    if (timing_iterations > 0)
-    {
-        printf("%d timing iterations, average time to sort and isolate non-trivial duplicates: %.3f ms (%.3f ms spent in RLE isolation)\n",
-            timing_iterations,
-            elapsed_millis / timing_iterations,
-            elapsed_rle_millis / timing_iterations);
-    }
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/experimental/.gitignore b/ml-xgboost/cub/experimental/.gitignore
deleted file mode 100644
index 5e56e04..0000000
--- a/ml-xgboost/cub/experimental/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/bin
diff --git a/ml-xgboost/cub/experimental/Makefile b/ml-xgboost/cub/experimental/Makefile
deleted file mode 100644
index 91075ac..0000000
--- a/ml-xgboost/cub/experimental/Makefile
+++ /dev/null
@@ -1,125 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
-
-#-------------------------------------------------------------------------------
-#
-# Makefile usage
-#
-# make <target> [sm=<XXX,...>] [cdp=<0|1>] [force32=<0|1>] [abi=<0|1>] [open64=<0|1>] [verbose=<0|1>] [keep=<0|1>] [quicktest=<0|1>]
-#
-#-------------------------------------------------------------------------------
- 
-include ../common.mk 
-
-#-------------------------------------------------------------------------------
-# Commandline Options
-#-------------------------------------------------------------------------------
-
-# [mkl=<0|1>] compile against Intel MKL
-ifeq ($(mkl), 1)
-	DEFINES 	+= -DCUB_MKL
-
-ifeq (WIN_NT, $(findstring WIN_NT, $(OSUPPER)))
-	LIBS 		+=	mkl_intel_lp64.lib mkl_intel_thread.lib  mkl_core.lib libiomp5md.lib
-	NVCCFLAGS 	+= -Xcompiler /openmp
-else
-	LIBS		+= -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lm
-	NVCCFLAGS 	+= -Xcompiler -fopenmp
-	
-endif	
-
-endif
-
-
-#-------------------------------------------------------------------------------
-# Compiler and compilation platform
-#-------------------------------------------------------------------------------
-
-# Includes
-INC += -I$(CUB_DIR) -I$(CUB_DIR)test 
-
-# detect OS
-OSUPPER = $(shell uname -s 2>/dev/null | tr [:lower:] [:upper:])
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-exp_rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-EXP_DEPS = 	$(call rwildcard, ./,*.cuh) \
-			$(call rwildcard, ./,*.h)
-
-DEPS =				$(CUB_DEPS) \
-					$(EXP_DEPS) \
-					$(CUB_DIR)test/Makefile \
-					$(CUB_DIR)test/test_util.h \
-					$(CUB_DIR)test/mersenne.h \
-
-		
-
-#-------------------------------------------------------------------------------
-# make default
-#-------------------------------------------------------------------------------
-
-default:
-
-
-#-------------------------------------------------------------------------------
-# make clean
-#-------------------------------------------------------------------------------
-
-clean :
-	rm -f bin/*$(CPU_ARCH_SUFFIX)* 
-	rm -f *.i* *.cubin *.cu.c *.cudafe* *.fatbin.c *.ptx *.hash *.cu.cpp *.o
-
-
-
-#-------------------------------------------------------------------------------
-# make histogram_compare
-#-------------------------------------------------------------------------------
-
-histogram_compare: bin/histogram_compare_$(BIN_SUFFIX)
-
-bin/histogram_compare_$(BIN_SUFFIX) : histogram_compare.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/histogram_compare_$(BIN_SUFFIX) histogram_compare.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-	
-
-
-#-------------------------------------------------------------------------------
-# make spmv_compare
-#-------------------------------------------------------------------------------
-
-spmv_compare: bin/spmv_compare_$(BIN_SUFFIX)
-
-bin/spmv_compare_$(BIN_SUFFIX) : spmv_compare.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/spmv_compare_$(BIN_SUFFIX) spmv_compare.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -lcusparse $(MKL_LIBS) -O3
-	
-
diff --git a/ml-xgboost/cub/experimental/defunct/example_coo_spmv.cu b/ml-xgboost/cub/experimental/defunct/example_coo_spmv.cu
deleted file mode 100644
index 09af0da..0000000
--- a/ml-xgboost/cub/experimental/defunct/example_coo_spmv.cu
+++ /dev/null
@@ -1,1070 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * An implementation of COO SpMV using prefix scan to implement a
- * reduce-value-by-row strategy
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <iterator>
-#include <vector>
-#include <string>
-#include <algorithm>
-#include <stdio.h>
-
-#include <cub/cub.cuh>
-
-#include "coo_graph.cuh"
-#include "../test/test_util.h"
-
-using namespace cub;
-using namespace std;
-
-
-/******************************************************************************
- * Globals, constants, and typedefs
- ******************************************************************************/
-
-typedef int         VertexId;   // uint32s as vertex ids
-typedef double      Value;      // double-precision floating point values
-
-bool                    g_verbose       = false;
-int                     g_timing_iterations    = 1;
-CachingDeviceAllocator  g_allocator;
-
-
-/******************************************************************************
- * Texture referencing
- ******************************************************************************/
-
-/**
- * Templated texture reference type for multiplicand vector
- */
-template <typename Value>
-struct TexVector
-{
-    // Texture type to actually use (e.g., because CUDA doesn't load doubles as texture items)
-    typedef typename If<(Equals<Value, double>::VALUE), uint2, Value>::Type CastType;
-
-    // Texture reference type
-    typedef texture<CastType, cudaTextureType1D, cudaReadModeElementType> TexRef;
-
-    static TexRef ref;
-
-    /**
-     * Bind textures
-     */
-    static void BindTexture(void *d_in, int elements)
-    {
-        cudaChannelFormatDesc tex_desc = cudaCreateChannelDesc<CastType>();
-        if (d_in)
-        {
-            size_t offset;
-            size_t bytes = sizeof(CastType) * elements;
-            CubDebugExit(cudaBindTexture(&offset, ref, d_in, tex_desc, bytes));
-        }
-    }
-
-    /**
-     * Unbind textures
-     */
-    static void UnbindTexture()
-    {
-        CubDebugExit(cudaUnbindTexture(ref));
-    }
-
-    /**
-     * Load
-     */
-    static __device__ __forceinline__ Value Load(int offset)
-    {
-        Value output;
-        reinterpret_cast<typename TexVector<Value>::CastType &>(output) = tex1Dfetch(TexVector<Value>::ref, offset);
-        return output;
-    }
-};
-
-// Texture reference definitions
-template <typename Value>
-typename TexVector<Value>::TexRef TexVector<Value>::ref = 0;
-
-
-/******************************************************************************
- * Utility types
- ******************************************************************************/
-
-
-/**
- * A partial dot-product sum paired with a corresponding row-id
- */
-template <typename VertexId, typename Value>
-struct PartialProduct
-{
-    VertexId    row;            /// Row-id
-    Value       partial;        /// PartialProduct sum
-};
-
-
-/**
- * A partial dot-product sum paired with a corresponding row-id (specialized for double-int pairings)
- */
-template <>
-struct PartialProduct<int, double>
-{
-    long long   row;            /// Row-id
-    double      partial;        /// PartialProduct sum
-};
-
-
-/**
- * Reduce-value-by-row scan operator
- */
-struct ReduceByKeyOp
-{
-    template <typename PartialProduct>
-    __device__ __forceinline__ PartialProduct operator()(
-        const PartialProduct &first,
-        const PartialProduct &second)
-    {
-        PartialProduct retval;
-
-        retval.partial = (second.row != first.row) ?
-                second.partial :
-                first.partial + second.partial;
-
-        retval.row = second.row;
-        return retval;
-    }
-};
-
-
-/**
- * Stateful block-wide prefix operator for BlockScan
- */
-template <typename PartialProduct>
-struct BlockPrefixCallbackOp
-{
-    // Running block-wide prefix
-    PartialProduct running_prefix;
-
-    /**
-     * Returns the block-wide running_prefix in thread-0
-     */
-    __device__ __forceinline__ PartialProduct operator()(
-        const PartialProduct &block_aggregate)              ///< The aggregate sum of the BlockScan inputs
-    {
-        ReduceByKeyOp scan_op;
-
-        PartialProduct retval = running_prefix;
-        running_prefix = scan_op(running_prefix, block_aggregate);
-        return retval;
-    }
-};
-
-
-/**
- * Operator for detecting discontinuities in a list of row identifiers.
- */
-struct NewRowOp
-{
-    /// Returns true if row_b is the start of a new row
-    template <typename VertexId>
-    __device__ __forceinline__ bool operator()(
-        const VertexId& row_a,
-        const VertexId& row_b)
-    {
-        return (row_a != row_b);
-    }
-};
-
-
-
-/******************************************************************************
- * Persistent thread block types
- ******************************************************************************/
-
-/**
- * SpMV threadblock abstraction for processing a contiguous segment of
- * sparse COO tiles.
- */
-template <
-    int             BLOCK_THREADS,
-    int             ITEMS_PER_THREAD,
-    typename        VertexId,
-    typename        Value>
-struct PersistentBlockSpmv
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // Constants
-    enum
-    {
-        TILE_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    // Head flag type
-    typedef int HeadFlag;
-
-    // Partial dot product type
-    typedef PartialProduct<VertexId, Value> PartialProduct;
-
-    // Parameterized BlockScan type for reduce-value-by-row scan
-    typedef BlockScan<PartialProduct, BLOCK_THREADS, BLOCK_SCAN_RAKING_MEMOIZE> BlockScan;
-
-    // Parameterized BlockExchange type for exchanging rows between warp-striped -> blocked arrangements
-    typedef BlockExchange<VertexId, BLOCK_THREADS, ITEMS_PER_THREAD, true> BlockExchangeRows;
-
-    // Parameterized BlockExchange type for exchanging values between warp-striped -> blocked arrangements
-    typedef BlockExchange<Value, BLOCK_THREADS, ITEMS_PER_THREAD, true> BlockExchangeValues;
-
-    // Parameterized BlockDiscontinuity type for setting head-flags for each new row segment
-    typedef BlockDiscontinuity<HeadFlag, BLOCK_THREADS> BlockDiscontinuity;
-
-    // Shared memory type for this threadblock
-    struct TempStorage
-    {
-        union
-        {
-            typename BlockExchangeRows::TempStorage         exchange_rows;      // Smem needed for BlockExchangeRows
-            typename BlockExchangeValues::TempStorage       exchange_values;    // Smem needed for BlockExchangeValues
-            struct
-            {
-                typename BlockScan::TempStorage             scan;               // Smem needed for BlockScan
-                typename BlockDiscontinuity::TempStorage    discontinuity;      // Smem needed for BlockDiscontinuity
-            };
-        };
-
-        VertexId        first_block_row;    ///< The first row-ID seen by this thread block
-        VertexId        last_block_row;     ///< The last row-ID seen by this thread block
-        Value           first_product;      ///< The first dot-product written by this thread block
-    };
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    TempStorage                     &temp_storage;
-    BlockPrefixCallbackOp<PartialProduct>   prefix_op;
-    VertexId                        *d_rows;
-    VertexId                        *d_columns;
-    Value                           *d_values;
-    Value                           *d_vector;
-    Value                           *d_result;
-    PartialProduct                  *d_block_partials;
-    int                             block_offset;
-    int                             block_end;
-
-
-    //---------------------------------------------------------------------
-    // Operations
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__
-    PersistentBlockSpmv(
-        TempStorage                 &temp_storage,
-        VertexId                    *d_rows,
-        VertexId                    *d_columns,
-        Value                       *d_values,
-        Value                       *d_vector,
-        Value                       *d_result,
-        PartialProduct              *d_block_partials,
-        int                         block_offset,
-        int                         block_end)
-    :
-        temp_storage(temp_storage),
-        d_rows(d_rows),
-        d_columns(d_columns),
-        d_values(d_values),
-        d_vector(d_vector),
-        d_result(d_result),
-        d_block_partials(d_block_partials),
-        block_offset(block_offset),
-        block_end(block_end)
-    {
-        // Initialize scalar shared memory values
-        if (threadIdx.x == 0)
-        {
-            VertexId first_block_row            = d_rows[block_offset];
-            VertexId last_block_row             = d_rows[block_end - 1];
-
-            temp_storage.first_block_row        = first_block_row;
-            temp_storage.last_block_row         = last_block_row;
-            temp_storage.first_product          = Value(0);
-
-            // Initialize prefix_op to identity
-            prefix_op.running_prefix.row        = first_block_row;
-            prefix_op.running_prefix.partial    = Value(0);
-        }
-
-        __syncthreads();
-    }
-
-
-    /**
-     * Processes a COO input tile of edges, outputting dot products for each row
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void ProcessTile(
-        int block_offset,
-        int guarded_items = 0)
-    {
-        VertexId        columns[ITEMS_PER_THREAD];
-        VertexId        rows[ITEMS_PER_THREAD];
-        Value           values[ITEMS_PER_THREAD];
-        PartialProduct  partial_sums[ITEMS_PER_THREAD];
-        HeadFlag        head_flags[ITEMS_PER_THREAD];
-
-        // Load a threadblock-striped tile of A (sparse row-ids, column-ids, and values)
-        if (FULL_TILE)
-        {
-            // Unguarded loads
-            LoadDirectWarpStriped<LOAD_DEFAULT>(threadIdx.x, d_columns + block_offset, columns);
-            LoadDirectWarpStriped<LOAD_DEFAULT>(threadIdx.x, d_values + block_offset, values);
-            LoadDirectWarpStriped<LOAD_DEFAULT>(threadIdx.x, d_rows + block_offset, rows);
-        }
-        else
-        {
-            // This is a partial-tile (e.g., the last tile of input).  Extend the coordinates of the last
-            // vertex for out-of-bound items, but zero-valued
-            LoadDirectWarpStriped<LOAD_DEFAULT>(threadIdx.x, d_columns + block_offset, columns, guarded_items, VertexId(0));
-            LoadDirectWarpStriped<LOAD_DEFAULT>(threadIdx.x, d_values + block_offset, values, guarded_items, Value(0));
-            LoadDirectWarpStriped<LOAD_DEFAULT>(threadIdx.x, d_rows + block_offset, rows, guarded_items, temp_storage.last_block_row);
-        }
-
-        // Load the referenced values from x and compute the dot product partials sums
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-#if CUB_PTX_ARCH >= 350
-            values[ITEM] *= ThreadLoad<LOAD_LDG>(d_vector + columns[ITEM]);
-#else
-            values[ITEM] *= TexVector<Value>::Load(columns[ITEM]);
-#endif
-        }
-
-        // Transpose from warp-striped to blocked arrangement
-        BlockExchangeValues(temp_storage.exchange_values).WarpStripedToBlocked(values);
-
-        __syncthreads();
-
-        // Transpose from warp-striped to blocked arrangement
-        BlockExchangeRows(temp_storage.exchange_rows).WarpStripedToBlocked(rows);
-
-        // Barrier for smem reuse and coherence
-        __syncthreads();
-
-        // FlagT row heads by looking for discontinuities
-        BlockDiscontinuity(temp_storage.discontinuity).FlagHeads(
-            head_flags,                     // (Out) Head flags
-            rows,                           // Original row ids
-            NewRowOp(),                     // Functor for detecting start of new rows
-            prefix_op.running_prefix.row);  // Last row ID from previous tile to compare with first row ID in this tile
-
-        // Assemble partial product structures
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            partial_sums[ITEM].partial = values[ITEM];
-            partial_sums[ITEM].row = rows[ITEM];
-        }
-
-        // Reduce reduce-value-by-row across partial_sums using exclusive prefix scan
-        PartialProduct block_aggregate;
-        BlockScan(temp_storage.scan).ExclusiveScan(
-            partial_sums,                   // Scan input
-            partial_sums,                   // Scan output
-            ReduceByKeyOp(),                // Scan operator
-            block_aggregate,                // Block-wide total (unused)
-            prefix_op);                     // Prefix operator for seeding the block-wide scan with the running total
-
-        // Barrier for smem reuse and coherence
-        __syncthreads();
-
-        // Scatter an accumulated dot product if it is the head of a valid row
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if (head_flags[ITEM])
-            {
-                d_result[partial_sums[ITEM].row] = partial_sums[ITEM].partial;
-
-                // Save off the first partial product that this thread block will scatter
-                if (partial_sums[ITEM].row == temp_storage.first_block_row)
-                {
-                    temp_storage.first_product = partial_sums[ITEM].partial;
-                }
-            }
-        }
-    }
-
-
-    /**
-     * Iterate over input tiles belonging to this thread block
-     */
-    __device__ __forceinline__
-    void ProcessTiles()
-    {
-        // Process full tiles
-        while (block_offset <= block_end - TILE_ITEMS)
-        {
-            ProcessTile<true>(block_offset);
-            block_offset += TILE_ITEMS;
-        }
-
-        // Process the last, partially-full tile (if present)
-        int guarded_items = block_end - block_offset;
-        if (guarded_items)
-        {
-            ProcessTile<false>(block_offset, guarded_items);
-        }
-
-        if (threadIdx.x == 0)
-        {
-            if (gridDim.x == 1)
-            {
-                // Scatter the final aggregate (this kernel contains only 1 threadblock)
-                d_result[prefix_op.running_prefix.row] = prefix_op.running_prefix.partial;
-            }
-            else
-            {
-                // Write the first and last partial products from this thread block so
-                // that they can be subsequently "fixed up" in the next kernel.
-
-                PartialProduct first_product;
-                first_product.row       = temp_storage.first_block_row;
-                first_product.partial   = temp_storage.first_product;
-
-                d_block_partials[blockIdx.x * 2]          = first_product;
-                d_block_partials[(blockIdx.x * 2) + 1]    = prefix_op.running_prefix;
-            }
-        }
-    }
-};
-
-
-/**
- * Threadblock abstraction for "fixing up" an array of interblock SpMV partial products.
- */
-template <
-    int             BLOCK_THREADS,
-    int             ITEMS_PER_THREAD,
-    typename        VertexId,
-    typename        Value>
-struct FinalizeSpmvBlock
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // Constants
-    enum
-    {
-        TILE_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    // Head flag type
-    typedef int HeadFlag;
-
-    // Partial dot product type
-    typedef PartialProduct<VertexId, Value> PartialProduct;
-
-    // Parameterized BlockScan type for reduce-value-by-row scan
-    typedef BlockScan<PartialProduct, BLOCK_THREADS, BLOCK_SCAN_RAKING_MEMOIZE> BlockScan;
-
-    // Parameterized BlockDiscontinuity type for setting head-flags for each new row segment
-    typedef BlockDiscontinuity<HeadFlag, BLOCK_THREADS> BlockDiscontinuity;
-
-    // Shared memory type for this threadblock
-    struct TempStorage
-    {
-        typename BlockScan::TempStorage           scan;               // Smem needed for reduce-value-by-row scan
-        typename BlockDiscontinuity::TempStorage  discontinuity;      // Smem needed for head-flagging
-
-        VertexId last_block_row;
-    };
-
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    TempStorage                     &temp_storage;
-    BlockPrefixCallbackOp<PartialProduct>   prefix_op;
-    Value                           *d_result;
-    PartialProduct                  *d_block_partials;
-    int                             num_partials;
-
-
-    //---------------------------------------------------------------------
-    // Operations
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__
-    FinalizeSpmvBlock(
-        TempStorage                 &temp_storage,
-        Value                       *d_result,
-        PartialProduct              *d_block_partials,
-        int                         num_partials)
-    :
-        temp_storage(temp_storage),
-        d_result(d_result),
-        d_block_partials(d_block_partials),
-        num_partials(num_partials)
-    {
-        // Initialize scalar shared memory values
-        if (threadIdx.x == 0)
-        {
-            VertexId first_block_row            = d_block_partials[0].row;
-            VertexId last_block_row             = d_block_partials[num_partials - 1].row;
-            temp_storage.last_block_row         = last_block_row;
-
-            // Initialize prefix_op to identity
-            prefix_op.running_prefix.row        = first_block_row;
-            prefix_op.running_prefix.partial    = Value(0);
-        }
-
-        __syncthreads();
-    }
-
-
-    /**
-     * Processes a COO input tile of edges, outputting dot products for each row
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__
-    void ProcessTile(
-        int block_offset,
-        int guarded_items = 0)
-    {
-        VertexId        rows[ITEMS_PER_THREAD];
-        PartialProduct  partial_sums[ITEMS_PER_THREAD];
-        HeadFlag        head_flags[ITEMS_PER_THREAD];
-
-        // Load a tile of block partials from previous kernel
-        if (FULL_TILE)
-        {
-            // Full tile
-#if CUB_PTX_ARCH >= 350
-            LoadDirectBlocked<LOAD_LDG>(threadIdx.x, d_block_partials + block_offset, partial_sums);
-#else
-            LoadDirectBlocked(threadIdx.x, d_block_partials + block_offset, partial_sums);
-#endif
-        }
-        else
-        {
-            // Partial tile (extend zero-valued coordinates of the last partial-product for out-of-bounds items)
-            PartialProduct default_sum;
-            default_sum.row = temp_storage.last_block_row;
-            default_sum.partial = Value(0);
-
-#if CUB_PTX_ARCH >= 350
-            LoadDirectBlocked<LOAD_LDG>(threadIdx.x, d_block_partials + block_offset, partial_sums, guarded_items, default_sum);
-#else
-            LoadDirectBlocked(threadIdx.x, d_block_partials + block_offset, partial_sums, guarded_items, default_sum);
-#endif
-        }
-
-        // Copy out row IDs for row-head flagging
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            rows[ITEM] = partial_sums[ITEM].row;
-        }
-
-        // FlagT row heads by looking for discontinuities
-        BlockDiscontinuity(temp_storage.discontinuity).FlagHeads(
-            rows,                           // Original row ids
-            head_flags,                     // (Out) Head flags
-            NewRowOp(),                     // Functor for detecting start of new rows
-            prefix_op.running_prefix.row);   // Last row ID from previous tile to compare with first row ID in this tile
-
-        // Reduce reduce-value-by-row across partial_sums using exclusive prefix scan
-        PartialProduct block_aggregate;
-        BlockScan(temp_storage.scan).ExclusiveScan(
-            partial_sums,                   // Scan input
-            partial_sums,                   // Scan output
-            ReduceByKeyOp(),                // Scan operator
-            block_aggregate,                // Block-wide total (unused)
-            prefix_op);                     // Prefix operator for seeding the block-wide scan with the running total
-
-        // Scatter an accumulated dot product if it is the head of a valid row
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if (head_flags[ITEM])
-            {
-                d_result[partial_sums[ITEM].row] = partial_sums[ITEM].partial;
-            }
-        }
-    }
-
-
-    /**
-     * Iterate over input tiles belonging to this thread block
-     */
-    __device__ __forceinline__
-    void ProcessTiles()
-    {
-        // Process full tiles
-        int block_offset = 0;
-        while (block_offset <= num_partials - TILE_ITEMS)
-        {
-            ProcessTile<true>(block_offset);
-            block_offset += TILE_ITEMS;
-        }
-
-        // Process final partial tile (if present)
-        int guarded_items = num_partials - block_offset;
-        if (guarded_items)
-        {
-            ProcessTile<false>(block_offset, guarded_items);
-        }
-
-        // Scatter the final aggregate (this kernel contains only 1 threadblock)
-        if (threadIdx.x == 0)
-        {
-            d_result[prefix_op.running_prefix.row] = prefix_op.running_prefix.partial;
-        }
-    }
-};
-
-
-/******************************************************************************
- * Kernel entrypoints
- ******************************************************************************/
-
-
-
-/**
- * SpMV kernel whose thread blocks each process a contiguous segment of sparse COO tiles.
- */
-template <
-    int                             BLOCK_THREADS,
-    int                             ITEMS_PER_THREAD,
-    typename                        VertexId,
-    typename                        Value>
-__launch_bounds__ (BLOCK_THREADS)
-__global__ void CooKernel(
-    GridEvenShare<int>              even_share,
-    PartialProduct<VertexId, Value> *d_block_partials,
-    VertexId                        *d_rows,
-    VertexId                        *d_columns,
-    Value                           *d_values,
-    Value                           *d_vector,
-    Value                           *d_result)
-{
-    // Specialize SpMV threadblock abstraction type
-    typedef PersistentBlockSpmv<BLOCK_THREADS, ITEMS_PER_THREAD, VertexId, Value> PersistentBlockSpmv;
-
-    // Shared memory allocation
-    __shared__ typename PersistentBlockSpmv::TempStorage temp_storage;
-
-    // Initialize threadblock even-share to tell us where to start and stop our tile-processing
-    even_share.BlockInit();
-
-    // Construct persistent thread block
-    PersistentBlockSpmv persistent_block(
-        temp_storage,
-        d_rows,
-        d_columns,
-        d_values,
-        d_vector,
-        d_result,
-        d_block_partials,
-        even_share.block_offset,
-        even_share.block_end);
-
-    // Process input tiles
-    persistent_block.ProcessTiles();
-}
-
-
-/**
- * Kernel for "fixing up" an array of interblock SpMV partial products.
- */
-template <
-    int                             BLOCK_THREADS,
-    int                             ITEMS_PER_THREAD,
-    typename                        VertexId,
-    typename                        Value>
-__launch_bounds__ (BLOCK_THREADS,  1)
-__global__ void CooFinalizeKernel(
-    PartialProduct<VertexId, Value> *d_block_partials,
-    int                             num_partials,
-    Value                           *d_result)
-{
-    // Specialize "fix-up" threadblock abstraction type
-    typedef FinalizeSpmvBlock<BLOCK_THREADS, ITEMS_PER_THREAD, VertexId, Value> FinalizeSpmvBlock;
-
-    // Shared memory allocation
-    __shared__ typename FinalizeSpmvBlock::TempStorage temp_storage;
-
-    // Construct persistent thread block
-    FinalizeSpmvBlock persistent_block(temp_storage, d_result, d_block_partials, num_partials);
-
-    // Process input tiles
-    persistent_block.ProcessTiles();
-}
-
-
-
-//---------------------------------------------------------------------
-// Host subroutines
-//---------------------------------------------------------------------
-
-
-/**
- * Simple test of device
- */
-template <
-    int                         COO_BLOCK_THREADS,
-    int                         COO_ITEMS_PER_THREAD,
-    int                         COO_SUBSCRIPTION_FACTOR,
-    int                         FINALIZE_BLOCK_THREADS,
-    int                         FINALIZE_ITEMS_PER_THREAD,
-    typename                    VertexId,
-    typename                    Value>
-void TestDevice(
-    CooGraph<VertexId, Value>&  coo_graph,
-    Value*                      h_vector,
-    Value*                      h_reference)
-{
-    typedef PartialProduct<VertexId, Value> PartialProduct;
-
-    const int COO_TILE_SIZE = COO_BLOCK_THREADS * COO_ITEMS_PER_THREAD;
-
-    // SOA device storage
-    VertexId        *d_rows;             // SOA graph row coordinates
-    VertexId        *d_columns;          // SOA graph col coordinates
-    Value           *d_values;           // SOA graph values
-    Value           *d_vector;           // Vector multiplicand
-    Value           *d_result;           // Output row
-    PartialProduct  *d_block_partials;   // Temporary storage for communicating dot product partials between threadblocks
-
-    // Create SOA version of coo_graph on host
-    int             num_edges   = coo_graph.coo_tuples.size();
-    VertexId        *h_rows     = new VertexId[num_edges];
-    VertexId        *h_columns  = new VertexId[num_edges];
-    Value           *h_values   = new Value[num_edges];
-    for (int i = 0; i < num_edges; i++)
-    {
-        h_rows[i]       = coo_graph.coo_tuples[i].row;
-        h_columns[i]    = coo_graph.coo_tuples[i].col;
-        h_values[i]     = coo_graph.coo_tuples[i].val;
-    }
-
-    // Get CUDA properties
-    Device device_props;
-    CubDebugExit(device_props.Init());
-
-    // Determine launch configuration from kernel properties
-    int coo_sm_occupancy;
-    CubDebugExit(device_props.MaxSmOccupancy(
-        coo_sm_occupancy,
-        CooKernel<COO_BLOCK_THREADS, COO_ITEMS_PER_THREAD, VertexId, Value>,
-        COO_BLOCK_THREADS));
-    int max_coo_grid_size   = device_props.sm_count * coo_sm_occupancy * COO_SUBSCRIPTION_FACTOR;
-
-    // Construct an even-share work distribution
-    GridEvenShare<int> even_share(num_edges, max_coo_grid_size, COO_TILE_SIZE);
-    int coo_grid_size  = even_share.grid_size;
-    int num_partials   = coo_grid_size * 2;
-
-    // Allocate COO device arrays
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_rows,            sizeof(VertexId) * num_edges));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_columns,         sizeof(VertexId) * num_edges));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values,          sizeof(Value) * num_edges));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_vector,          sizeof(Value) * coo_graph.col_dim));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_result,          sizeof(Value) * coo_graph.row_dim));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_block_partials,  sizeof(PartialProduct) * num_partials));
-
-    // Copy host arrays to device
-    CubDebugExit(cudaMemcpy(d_rows,     h_rows,     sizeof(VertexId) * num_edges,       cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_columns,  h_columns,  sizeof(VertexId) * num_edges,       cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_values,   h_values,   sizeof(Value) * num_edges,          cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_vector,   h_vector,   sizeof(Value) * coo_graph.col_dim,  cudaMemcpyHostToDevice));
-
-    // Bind textures
-    TexVector<Value>::BindTexture(d_vector, coo_graph.col_dim);
-
-    // Print debug info
-    printf("CooKernel<%d, %d><<<%d, %d>>>(...), Max SM occupancy: %d\n",
-        COO_BLOCK_THREADS, COO_ITEMS_PER_THREAD, coo_grid_size, COO_BLOCK_THREADS, coo_sm_occupancy);
-    if (coo_grid_size > 1)
-    {
-        printf("CooFinalizeKernel<<<1, %d>>>(...)\n", FINALIZE_BLOCK_THREADS);
-    }
-    fflush(stdout);
-
-    CubDebugExit(cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeEightByte));
-
-    // Run kernel (always run one iteration without timing)
-    GpuTimer gpu_timer;
-    float elapsed_millis = 0.0;
-    for (int i = 0; i <= g_timing_iterations; i++)
-    {
-        gpu_timer.Start();
-
-        // Initialize output
-        CubDebugExit(cudaMemset(d_result, 0, coo_graph.row_dim * sizeof(Value)));
-
-        // Run the COO kernel
-        CooKernel<COO_BLOCK_THREADS, COO_ITEMS_PER_THREAD><<<coo_grid_size, COO_BLOCK_THREADS>>>(
-            even_share,
-            d_block_partials,
-            d_rows,
-            d_columns,
-            d_values,
-            d_vector,
-            d_result);
-
-        if (coo_grid_size > 1)
-        {
-            // Run the COO finalize kernel
-            CooFinalizeKernel<FINALIZE_BLOCK_THREADS, FINALIZE_ITEMS_PER_THREAD><<<1, FINALIZE_BLOCK_THREADS>>>(
-                d_block_partials,
-                num_partials,
-                d_result);
-        }
-
-        gpu_timer.Stop();
-
-        if (i > 0)
-            elapsed_millis += gpu_timer.ElapsedMillis();
-    }
-
-    // Force any kernel stdio to screen
-    CubDebugExit(cudaThreadSynchronize());
-    fflush(stdout);
-
-    // Display timing
-    if (g_timing_iterations > 0)
-    {
-        float avg_elapsed = elapsed_millis / g_timing_iterations;
-        int total_bytes = ((sizeof(VertexId) + sizeof(VertexId)) * 2 * num_edges) + (sizeof(Value) * coo_graph.row_dim);
-        printf("%d iterations, average elapsed (%.3f ms), utilized bandwidth (%.3f GB/s), GFLOPS(%.3f)\n",
-            g_timing_iterations,
-            avg_elapsed,
-            total_bytes / avg_elapsed / 1000.0 / 1000.0,
-            num_edges * 2 / avg_elapsed / 1000.0 / 1000.0);
-    }
-
-    // Check results
-    int compare = CompareDeviceResults(h_reference, d_result, coo_graph.row_dim, true, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    TexVector<Value>::UnbindTexture();
-    CubDebugExit(g_allocator.DeviceFree(d_block_partials));
-    CubDebugExit(g_allocator.DeviceFree(d_rows));
-    CubDebugExit(g_allocator.DeviceFree(d_columns));
-    CubDebugExit(g_allocator.DeviceFree(d_values));
-    CubDebugExit(g_allocator.DeviceFree(d_vector));
-    CubDebugExit(g_allocator.DeviceFree(d_result));
-    delete[] h_rows;
-    delete[] h_columns;
-    delete[] h_values;
-}
-
-
-/**
- * Compute reference answer on CPU
- */
-template <typename VertexId, typename Value>
-void ComputeReference(
-    CooGraph<VertexId, Value>&  coo_graph,
-    Value*                      h_vector,
-    Value*                      h_reference)
-{
-    for (VertexId i = 0; i < coo_graph.row_dim; i++)
-    {
-        h_reference[i] = 0.0;
-    }
-
-    for (VertexId i = 0; i < coo_graph.coo_tuples.size(); i++)
-    {
-        h_reference[coo_graph.coo_tuples[i].row] +=
-            coo_graph.coo_tuples[i].val *
-            h_vector[coo_graph.coo_tuples[i].col];
-    }
-}
-
-
-/**
- * Assign arbitrary values to vector items
- */
-template <typename Value>
-void AssignVectorValues(Value *vector, int col_dim)
-{
-    for (int i = 0; i < col_dim; i++)
-    {
-        vector[i] = 1.0;
-    }
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("i", g_timing_iterations);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s\n [--device=<device-id>] [--v] [--iterations=<test iterations>] [--grid-size=<grid-size>]\n"
-            "\t--type=wheel --spokes=<spokes>\n"
-            "\t--type=grid2d --width=<width> [--no-self-loops]\n"
-            "\t--type=grid3d --width=<width> [--no-self-loops]\n"
-            "\t--type=market --file=<file>\n"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get graph type
-    string type;
-    args.GetCmdLineArgument("type", type);
-
-    // Generate graph structure
-
-    CpuTimer timer;
-    timer.Start();
-    CooGraph<VertexId, Value> coo_graph;
-    if (type == string("grid2d"))
-    {
-        VertexId width;
-        args.GetCmdLineArgument("width", width);
-        bool self_loops = !args.CheckCmdLineFlag("no-self-loops");
-        printf("Generating %s grid2d width(%d)... ", (self_loops) ? "5-pt" : "4-pt", width); fflush(stdout);
-        if (coo_graph.InitGrid2d(width, self_loops)) exit(1);
-    } else if (type == string("grid3d"))
-    {
-        VertexId width;
-        args.GetCmdLineArgument("width", width);
-        bool self_loops = !args.CheckCmdLineFlag("no-self-loops");
-        printf("Generating %s grid3d width(%d)... ", (self_loops) ? "7-pt" : "6-pt", width); fflush(stdout);
-        if (coo_graph.InitGrid3d(width, self_loops)) exit(1);
-    }
-    else if (type == string("wheel"))
-    {
-        VertexId spokes;
-        args.GetCmdLineArgument("spokes", spokes);
-        printf("Generating wheel spokes(%d)... ", spokes); fflush(stdout);
-        if (coo_graph.InitWheel(spokes)) exit(1);
-    }
-    else if (type == string("market"))
-    {
-        string filename;
-        args.GetCmdLineArgument("file", filename);
-        printf("Generating MARKET for %s... ", filename.c_str()); fflush(stdout);
-        if (coo_graph.InitMarket(filename)) exit(1);
-    }
-    else
-    {
-        printf("Unsupported graph type\n");
-        exit(1);
-    }
-    timer.Stop();
-    printf("Done (%.3fs). %d non-zeros, %d rows, %d columns\n",
-        timer.ElapsedMillis() / 1000.0,
-        coo_graph.coo_tuples.size(),
-        coo_graph.row_dim,
-        coo_graph.col_dim);
-    fflush(stdout);
-
-    if (g_verbose)
-    {
-        cout << coo_graph << "\n";
-    }
-
-    // Create vector
-    Value *h_vector = new Value[coo_graph.col_dim];
-    AssignVectorValues(h_vector, coo_graph.col_dim);
-    if (g_verbose)
-    {
-        printf("Vector[%d]: ", coo_graph.col_dim);
-        DisplayResults(h_vector, coo_graph.col_dim);
-        printf("\n\n");
-    }
-
-    // Compute reference answer
-    Value *h_reference = new Value[coo_graph.row_dim];
-    ComputeReference(coo_graph, h_vector, h_reference);
-    if (g_verbose)
-    {
-        printf("Results[%d]: ", coo_graph.row_dim);
-        DisplayResults(h_reference, coo_graph.row_dim);
-        printf("\n\n");
-    }
-
-    // Parameterization for SM35
-    enum
-    {
-        COO_BLOCK_THREADS           = 64,
-        COO_ITEMS_PER_THREAD        = 10,
-        COO_SUBSCRIPTION_FACTOR     = 4,
-        FINALIZE_BLOCK_THREADS      = 256,
-        FINALIZE_ITEMS_PER_THREAD   = 4,
-    };
-
-    // Run GPU version
-    TestDevice<
-        COO_BLOCK_THREADS,
-        COO_ITEMS_PER_THREAD,
-        COO_SUBSCRIPTION_FACTOR,
-        FINALIZE_BLOCK_THREADS,
-        FINALIZE_ITEMS_PER_THREAD>(coo_graph, h_vector, h_reference);
-
-    // Cleanup
-    delete[] h_vector;
-    delete[] h_reference;
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/experimental/defunct/test_device_seg_reduce.cu b/ml-xgboost/cub/experimental/defunct/test_device_seg_reduce.cu
deleted file mode 100644
index 9a21f95..0000000
--- a/ml-xgboost/cub/experimental/defunct/test_device_seg_reduce.cu
+++ /dev/null
@@ -1,2142 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * An implementation of segmented reduction using a load-balanced parallelization
- * strategy based on the MergePath decision path.
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <iterator>
-#include <vector>
-#include <string>
-#include <algorithm>
-#include <stdio.h>
-
-#include <cub/cub.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-using namespace std;
-
-
-/******************************************************************************
- * Globals, constants, and typedefs
- ******************************************************************************/
-
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 1;
-CachingDeviceAllocator  g_allocator(true);
-
-
-/******************************************************************************
- * Utility routines
- ******************************************************************************/
-
-
-/**
- * An pair of index offsets
- */
-template <typename OffsetT>
-struct IndexPair
-{
-    OffsetT a_idx;
-    OffsetT b_idx;
-};
-
-
-/**
- * Computes the begin offsets into A and B for the specified
- * location (diagonal) along the merge decision path
- */
-template <
-    int                 BLOCK_THREADS,
-    typename            IteratorA,
-    typename            IteratorB,
-    typename            OffsetT>
-__device__ __forceinline__ void ParallelMergePathSearch(
-    OffsetT             diagonal,
-    IteratorA           a,
-    IteratorB           b,
-    IndexPair<OffsetT>  begin,          // Begin offsets into a and b
-    IndexPair<OffsetT>  end,            // End offsets into a and b
-    IndexPair<OffsetT>  &intersection)  // [out] Intersection offsets into a and b
-{
-    OffsetT a_split_min = CUB_MAX(diagonal - end.b_idx, begin.a_idx);
-    OffsetT a_split_max = CUB_MIN(diagonal, end.a_idx);
-
-    while (a_split_min < a_split_max)
-    {
-        OffsetT a_distance       = a_split_max - a_split_min;
-        OffsetT a_slice          = (a_distance + BLOCK_THREADS - 1) >> Log2<BLOCK_THREADS>::VALUE;
-        OffsetT a_split_pivot    = CUB_MIN(a_split_min + (threadIdx.x * a_slice), end.a_idx - 1);
-
-        int move_up = (a[a_split_pivot] <= b[diagonal - a_split_pivot - 1]);
-        int num_up = __syncthreads_count(move_up);
-/*
-        _CubLog("a_split_min(%d), a_split_max(%d) a_distance(%d), a_slice(%d), a_split_pivot(%d), move_up(%d), num_up(%d), a_begin(%d), a_end(%d)\n",
-            a_split_min, a_split_max, a_distance, a_slice, a_split_pivot, move_up, num_up, a_begin, a_end);
-*/
-        a_split_max = CUB_MIN(num_up * a_slice, end.a_idx);
-        a_split_min = CUB_MAX(a_split_max - a_slice, begin.a_idx) + 1;
-    }
-
-    intersection.a_idx = CUB_MIN(a_split_min, end.a_idx);
-    intersection.b_idx = CUB_MIN(diagonal - a_split_min, end.b_idx);
-}
-
-/**
- * Computes the begin offsets into A and B for the specified
- * location (diagonal) along the merge decision path
- */
-template <
-    typename            IteratorA,
-    typename            IteratorB,
-    typename            OffsetT>
-__device__ __forceinline__ void MergePathSearch(
-    OffsetT             diagonal,
-    IteratorA           a,
-    IteratorB           b,
-    IndexPair<OffsetT>  begin,          // Begin offsets into a and b
-    IndexPair<OffsetT>  end,            // End offsets into a and b
-    IndexPair<OffsetT>  &intersection)  // [out] Intersection offsets into a and b
-{
-    OffsetT split_min = CUB_MAX(diagonal - end.b_idx, begin.a_idx);
-    OffsetT split_max = CUB_MIN(diagonal, end.a_idx);
-
-    while (split_min < split_max)
-    {
-        OffsetT split_pivot = (split_min + split_max) >> 1;
-        if (a[split_pivot] <= b[diagonal - split_pivot - 1])
-        {
-            // Move candidate split range up A, down B
-            split_min = split_pivot + 1;
-        }
-        else
-        {
-            // Move candidate split range up B, down A
-            split_max = split_pivot;
-        }
-    }
-
-    intersection.a_idx = CUB_MIN(split_min, end.a_idx);
-    intersection.b_idx = CUB_MIN(diagonal - split_min, end.b_idx);
-}
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for BlockSegReduceRegion
- */
-template <
-    int                     _BLOCK_THREADS,             ///< Threads per thread block
-    int                     _ITEMS_PER_THREAD,          ///< Items per thread (per tile of input)
-    bool                    _USE_SMEM_SEGMENT_CACHE,    ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
-    bool                    _USE_SMEM_VALUE_CACHE,      ///< Whether or not to cache incoming values in shared memory before reducing each tile
-    CacheLoadModifier       _LOAD_MODIFIER_SEGMENTS,    ///< Cache load modifier for reading segment offsets
-    CacheLoadModifier       _LOAD_MODIFIER_VALUES,      ///< Cache load modifier for reading values
-    BlockReduceAlgorithm    _REDUCE_ALGORITHM,          ///< The BlockReduce algorithm to use
-    BlockScanAlgorithm      _SCAN_ALGORITHM>            ///< The BlockScan algorithm to use
-struct BlockSegReduceRegionPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-        USE_SMEM_SEGMENT_CACHE  = _USE_SMEM_SEGMENT_CACHE,      ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
-        USE_SMEM_VALUE_CACHE    = _USE_SMEM_VALUE_CACHE,        ///< Whether or not to cache incoming upcoming values in shared memory before reducing each tile
-    };
-
-    static const CacheLoadModifier      LOAD_MODIFIER_SEGMENTS  = _LOAD_MODIFIER_SEGMENTS;  ///< Cache load modifier for reading segment offsets
-    static const CacheLoadModifier      LOAD_MODIFIER_VALUES    = _LOAD_MODIFIER_VALUES;    ///< Cache load modifier for reading values
-    static const BlockReduceAlgorithm   REDUCE_ALGORITHM        = _REDUCE_ALGORITHM;        ///< The BlockReduce algorithm to use
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;          ///< The BlockScan algorithm to use
-};
-
-
-/******************************************************************************
- * Persistent thread block types
- ******************************************************************************/
-
-/**
- * \brief BlockSegReduceTiles implements a stateful abstraction of CUDA thread blocks for participating in device-wide segmented reduction.
- */
-template <
-    typename BlockSegReduceRegionPolicy,    ///< Parameterized BlockSegReduceRegionPolicy tuning policy
-    typename SegmentOffsetIterator,         ///< Random-access input iterator type for reading segment end-offsets
-    typename ValueIterator,                 ///< Random-access input iterator type for reading values
-    typename OutputIteratorT,               ///< Random-access output iterator type for writing segment reductions
-    typename ReductionOp,                   ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename OffsetT>                       ///< Signed integer type for global offsets
-struct BlockSegReduceRegion
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // Constants
-    enum
-    {
-        BLOCK_THREADS       = BlockSegReduceRegionPolicy::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = BlockSegReduceRegionPolicy::ITEMS_PER_THREAD,
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,                     /// Number of work items to be processed per tile
-
-        USE_SMEM_SEGMENT_CACHE  = BlockSegReduceRegionPolicy::USE_SMEM_SEGMENT_CACHE,      ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
-        USE_SMEM_VALUE_CACHE    = BlockSegReduceRegionPolicy::USE_SMEM_VALUE_CACHE,        ///< Whether or not to cache incoming upcoming values in shared memory before reducing each tile
-
-        SMEM_SEGMENT_CACHE_ITEMS    = USE_SMEM_SEGMENT_CACHE ? TILE_ITEMS : 1,
-        SMEM_VALUE_CACHE_ITEMS      = USE_SMEM_VALUE_CACHE ? TILE_ITEMS : 1,
-    };
-
-    // Segment offset type
-    typedef typename std::iterator_traits<SegmentOffsetIterator>::value_type SegmentOffset;
-
-    // Value type
-    typedef typename std::iterator_traits<ValueIterator>::value_type Value;
-
-    // Counting iterator type
-    typedef CountingInputIterator<SegmentOffsetT, OffsetT> CountingIterator;
-
-    // Segment offsets iterator wrapper type
-    typedef typename If<(IsPointer<SegmentOffsetIterator>::VALUE),
-            CacheModifiedInputIterator<BlockSegReduceRegionPolicy::LOAD_MODIFIER_SEGMENTS, SegmentOffsetT, OffsetT>,  // Wrap the native input pointer with CacheModifiedInputIterator
-            SegmentOffsetIterator>::Type                                                                            // Directly use the supplied input iterator type
-        WrappedSegmentOffsetIterator;
-
-    // Values iterator wrapper type
-    typedef typename If<(IsPointer<ValueIterator>::VALUE),
-            CacheModifiedInputIterator<BlockSegReduceRegionPolicy::LOAD_MODIFIER_VALUES, Value, OffsetT>,        // Wrap the native input pointer with CacheModifiedInputIterator
-            ValueIterator>::Type                                                                                // Directly use the supplied input iterator type
-        WrappedValueIterator;
-
-    // Tail flag type for marking segment discontinuities
-    typedef int TailFlag;
-
-    // Reduce-by-key data type tuple (segment-ID, value)
-    typedef KeyValuePair<OffsetT, Value> KeyValuePair;
-
-    // Index pair data type
-    typedef IndexPair<OffsetT> IndexPair;
-
-    // BlockScan scan operator for reduction-by-segment
-    typedef ReduceByKeyOp<ReductionOp> ReduceByKeyOp;
-
-    // Stateful BlockScan prefix callback type for managing a running total while scanning consecutive tiles
-    typedef RunningBlockPrefixCallbackOp<
-            KeyValuePair,
-            ReduceByKeyOp>
-        RunningPrefixCallbackOp;
-
-    // Parameterized BlockShift type for exchanging index pairs
-    typedef BlockShift<
-            IndexPair,
-            BLOCK_THREADS>
-        BlockShift;
-
-    // Parameterized BlockReduce type for block-wide reduction
-    typedef BlockReduce<
-            Value,
-            BLOCK_THREADS,
-            BlockSegReduceRegionPolicy::REDUCE_ALGORITHM>
-        BlockReduce;
-
-    // Parameterized BlockScan type for block-wide reduce-value-by-key
-    typedef BlockScan<
-            KeyValuePair,
-            BLOCK_THREADS,
-            BlockSegReduceRegionPolicy::SCAN_ALGORITHM>
-        BlockScan;
-
-    // Shared memory type for this threadblock
-    struct _TempStorage
-    {
-        union
-        {
-            // Smem needed for BlockScan
-            typename BlockScan::TempStorage scan;
-
-            // Smem needed for BlockReduce
-            typename BlockReduce::TempStorage reduce;
-
-            struct
-            {
-                // Smem needed for communicating start/end indices between threads for a given work tile
-                typename BlockShift::TempStorage shift;
-
-                // Smem needed for caching segment end-offsets
-                SegmentOffset cached_segment_end_offsets[SMEM_SEGMENT_CACHE_ITEMS + 1];
-            };
-
-            // Smem needed for caching values
-            Value cached_values[SMEM_VALUE_CACHE_ITEMS];
-        };
-
-        IndexPair block_region_idx[2];      // The starting [0] and ending [1] pairs of segment and value indices for the threadblock's region
-
-        // The first partial reduction tuple scattered by this thread block
-        KeyValuePair first_tuple;
-    };
-
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage                    &temp_storage;          ///< Reference to shared storage
-    WrappedSegmentOffsetIterator    d_segment_end_offsets;  ///< A sequence of \p num_segments segment end-offsets
-    WrappedValueIterator            d_values;               ///< A sequence of \p num_values data to reduce
-    OutputIteratorT                  d_output;               ///< A sequence of \p num_segments segment totals
-    CountingIterator                d_value_offsets;        ///< A sequence of \p num_values value-offsets
-    IndexPair                       *d_block_idx;
-    OffsetT                         num_values;             ///< Total number of values to reduce
-    OffsetT                         num_segments;           ///< Number of segments being reduced
-    Value                           identity;               ///< Identity value (for zero-length segments)
-    ReductionOp                     reduction_op;           ///< Reduction operator
-    ReduceByKeyOp                   scan_op;                ///< Reduce-by-key scan operator
-    RunningPrefixCallbackOp         prefix_op;              ///< Stateful running total for block-wide prefix scan of partial reduction tuples
-
-
-    //---------------------------------------------------------------------
-    // Operations
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__
-    BlockSegReduceRegion(
-        TempStorage             &temp_storage,          ///< Reference to shared storage
-        SegmentOffsetIterator   d_segment_end_offsets,  ///< A sequence of \p num_segments segment end-offsets
-        ValueIterator           d_values,               ///< A sequence of \p num_values values
-        OutputIteratorT          d_output,               ///< A sequence of \p num_segments segment totals
-        IndexPair               *d_block_idx,
-        OffsetT                 num_values,             ///< Number of values to reduce
-        OffsetT                 num_segments,           ///< Number of segments being reduced
-        Value                   identity,               ///< Identity value (for zero-length segments)
-        ReductionOp             reduction_op)           ///< Reduction operator
-    :
-        temp_storage(temp_storage.Alias()),
-        d_segment_end_offsets(d_segment_end_offsets),
-        d_values(d_values),
-        d_value_offsets(0),
-        d_output(d_output),
-        d_block_idx(d_block_idx),
-        num_values(num_values),
-        num_segments(num_segments),
-        identity(identity),
-        reduction_op(reduction_op),
-        scan_op(reduction_op),
-        prefix_op(scan_op)
-    {}
-
-
-    /**
-     * Fast-path single-segment tile reduction.  Perform a
-     * simple block-wide reduction and accumulate the result into
-     * the running total.
-     */
-    __device__ __forceinline__ void SingleSegmentTile(
-        IndexPair next_tile_idx,
-        IndexPair block_idx)
-    {
-        OffsetT tile_values = next_tile_idx.b_idx - block_idx.b_idx;
-
-        // Load a tile's worth of values (using identity for out-of-bounds items)
-        Value values[ITEMS_PER_THREAD];
-        LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_values + block_idx.b_idx, values, tile_values, identity);
-
-        // Barrier for smem reuse
-        __syncthreads();
-
-        // Reduce the tile of values and update the running total in thread-0
-        KeyValuePair tile_aggregate;
-        tile_aggregate.key      = block_idx.a_idx;
-        tile_aggregate.value    = BlockReduce(temp_storage.reduce).Reduce(values, reduction_op);
-
-        if (threadIdx.x == 0)
-        {
-            prefix_op.running_total = scan_op(prefix_op.running_total, tile_aggregate);
-        }
-    }
-
-    /**
-     * Fast-path empty-segment tile reduction.  Write out a tile of identity
-     * values to output.
-     */
-    __device__ __forceinline__ void EmptySegmentsTile(
-        IndexPair next_tile_idx,
-        IndexPair block_idx)
-    {
-        Value segment_reductions[ITEMS_PER_THREAD];
-
-        if (threadIdx.x == 0)
-        {
-            // The first segment gets the running segment total
-            segment_reductions[0] = prefix_op.running_total.value;
-
-            // Update the running prefix
-            prefix_op.running_total.value = identity;
-            prefix_op.running_total.key = next_tile_idx.a_idx;
-        }
-        else
-        {
-            // Remainder of segments in this tile get identity
-            segment_reductions[0] = identity;
-        }
-
-        // Remainder of segments in this tile get identity
-        #pragma unroll
-        for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            segment_reductions[ITEM] = identity;
-
-        // Store reductions
-        OffsetT tile_segments = next_tile_idx.a_idx - block_idx.a_idx;
-        StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_output + block_idx.a_idx, segment_reductions, tile_segments);
-    }
-
-
-    /**
-     * Multi-segment tile reduction.
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__ void MultiSegmentTile(
-        IndexPair block_idx,
-        IndexPair thread_idx,
-        IndexPair next_thread_idx,
-        IndexPair next_tile_idx)
-    {
-        IndexPair local_thread_idx;
-        local_thread_idx.a_idx = thread_idx.a_idx - block_idx.a_idx;
-        local_thread_idx.b_idx = thread_idx.b_idx - block_idx.b_idx;
-
-        // Check if first segment end-offset is in range
-        bool valid_segment = FULL_TILE || (thread_idx.a_idx < next_thread_idx.a_idx);
-
-        // Check if first value offset is in range
-        bool valid_value = FULL_TILE || (thread_idx.b_idx < next_thread_idx.b_idx);
-
-        // Load first segment end-offset
-        OffsetT segment_end_offset = (valid_segment) ?
-            (USE_SMEM_SEGMENT_CACHE)?
-                temp_storage.cached_segment_end_offsets[local_thread_idx.a_idx] :
-                d_segment_end_offsets[thread_idx.a_idx] :
-            -1;
-
-        OffsetT segment_ids[ITEMS_PER_THREAD];
-        OffsetT value_offsets[ITEMS_PER_THREAD];
-
-        KeyValuePair first_partial;
-        first_partial.key    = thread_idx.a_idx;
-        first_partial.value  = identity;
-
-        // Get segment IDs and gather-offsets for values
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            segment_ids[ITEM]   = -1;
-            value_offsets[ITEM] = -1;
-
-            // Whether or not we slide (a) right along the segment path or (b) down the value path
-            if (valid_segment && (!valid_value || (segment_end_offset <= thread_idx.b_idx)))
-            {
-                // Consume this segment index
-                segment_ids[ITEM] = thread_idx.a_idx;
-                thread_idx.a_idx++;
-                local_thread_idx.a_idx++;
-
-                valid_segment = FULL_TILE || (thread_idx.a_idx < next_thread_idx.a_idx);
-
-                // Read next segment end-offset (if valid)
-                if (valid_segment)
-                {
-                    if (USE_SMEM_SEGMENT_CACHE)
-                        segment_end_offset = temp_storage.cached_segment_end_offsets[local_thread_idx.a_idx];
-                    else
-                        segment_end_offset = d_segment_end_offsets[thread_idx.a_idx];
-                }
-            }
-            else if (valid_value)
-            {
-                // Consume this value index
-                value_offsets[ITEM] = thread_idx.b_idx;
-                thread_idx.b_idx++;
-                local_thread_idx.b_idx++;
-
-                valid_value = FULL_TILE || (thread_idx.b_idx < next_thread_idx.b_idx);
-            }
-        }
-
-        // Load values
-        Value values[ITEMS_PER_THREAD];
-
-        if (USE_SMEM_VALUE_CACHE)
-        {
-            // Barrier for smem reuse
-            __syncthreads();
-
-            OffsetT tile_values = next_tile_idx.b_idx - block_idx.b_idx;
-
-            // Load a tile's worth of values (using identity for out-of-bounds items)
-            LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_values + block_idx.b_idx, values, tile_values, identity);
-
-            // Store to shared
-            StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, temp_storage.cached_values, values, tile_values);
-
-            // Barrier for smem reuse
-            __syncthreads();
-
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                values[ITEM] = (value_offsets[ITEM] == -1) ?
-                    identity :
-                    temp_storage.cached_values[value_offsets[ITEM] - block_idx.b_idx];
-            }
-        }
-        else
-        {
-            #pragma unroll
-            for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-            {
-                values[ITEM] = (value_offsets[ITEM] == -1) ?
-                    identity :
-                    d_values[value_offsets[ITEM]];
-            }
-        }
-
-        // Reduce within thread segments
-        KeyValuePair running_total = first_partial;
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            if (segment_ids[ITEM] != -1)
-            {
-                // Consume this segment index
-                d_output[segment_ids[ITEM]] = running_total.value;
-
-//                _CubLog("Updating segment %d with value %lld\n", segment_ids[ITEM], running_total.value)
-
-                if (first_partial.key == segment_ids[ITEM])
-                    first_partial.value = running_total.value;
-
-                running_total.key    = segment_ids[ITEM];
-                running_total.value  = identity;
-            }
-
-            running_total.value = reduction_op(running_total.value, values[ITEM]);
-        }
-/*
-
-        // Barrier for smem reuse
-        __syncthreads();
-
-        // Use prefix scan to reduce values by segment-id.  The segment-reductions end up in items flagged as segment-tails.
-        KeyValuePair block_aggregate;
-        BlockScan(temp_storage.scan).InclusiveScan(
-            pairs,                          // Scan input
-            pairs,                          // Scan output
-            scan_op,                        // Scan operator
-            block_aggregate,                // Block-wide total (unused)
-            prefix_op);                     // Prefix operator for seeding the block-wide scan with the running total
-*/
-
-/*
-        // Check if first segment end-offset is in range
-        bool valid_segment = (thread_idx.a_idx < next_thread_idx.a_idx);
-
-        // Check if first value offset is in range
-        bool valid_value = (thread_idx.b_idx < next_thread_idx.b_idx);
-
-        // Load first segment end-offset
-        OffsetT segment_end_offset = (valid_segment) ?
-            d_segment_end_offsets[thread_idx.a_idx] :
-            num_values;                                                     // Out of range (the last segment end-offset is one-past the last value offset)
-
-        // Load first value offset
-        OffsetT value_offset = (valid_value) ?
-            d_value_offsets[thread_idx.b_idx] :
-            num_values;                                                     // Out of range (one-past the last value offset)
-
-        // Assemble segment-demarcating tail flags and partial reduction tuples
-        TailFlag        tail_flags[ITEMS_PER_THREAD];
-        KeyValuePair    partial_reductions[ITEMS_PER_THREAD];
-
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            // Default tuple and flag values
-            partial_reductions[ITEM].key    = thread_idx.a_idx;
-            partial_reductions[ITEM].value  = identity;
-            tail_flags[ITEM]                = 0;
-
-            // Whether or not we slide (a) right along the segment path or (b) down the value path
-            if (valid_segment && (!valid_value || (segment_end_offset <= value_offset)))
-            {
-                // Consume this segment index
-
-                // Set tail flag noting the end of the segment
-                tail_flags[ITEM] = 1;
-
-                // Increment segment index
-                thread_idx.a_idx++;
-
-                // Read next segment end-offset (if valid)
-                if ((valid_segment = (thread_idx.a_idx < next_thread_idx.a_idx)))
-                    segment_end_offset = d_segment_end_offsets[thread_idx.a_idx];
-            }
-            else if (valid_value)
-            {
-                // Consume this value index
-
-                // Update the tuple's value with the value at this index.
-                partial_reductions[ITEM].value = d_values[value_offset];
-
-                // Increment value index
-                thread_idx.b_idx++;
-
-                // Read next value offset (if valid)
-                if ((valid_value = (thread_idx.b_idx < next_thread_idx.b_idx)))
-                    value_offset = d_value_offsets[thread_idx.b_idx];
-            }
-        }
-
-        // Use prefix scan to reduce values by segment-id.  The segment-reductions end up in items flagged as segment-tails.
-        KeyValuePair block_aggregate;
-        BlockScan(temp_storage.scan).InclusiveScan(
-            partial_reductions,             // Scan input
-            partial_reductions,             // Scan output
-            scan_op,                        // Scan operator
-            block_aggregate,                // Block-wide total (unused)
-            prefix_op);                     // Prefix operator for seeding the block-wide scan with the running total
-
-        // The first segment index for this region (hoist?)
-        OffsetT first_segment_idx = temp_storage.block_idx.a_idx[0];
-
-        // Scatter an accumulated reduction if it is the head of a valid segment
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if (tail_flags[ITEM])
-            {
-                OffsetT segment_idx = partial_reductions[ITEM].key;
-                Value   value       = partial_reductions[ITEM].value;
-
-                // Write value reduction to corresponding segment id
-                d_output[segment_idx] = value;
-
-                // Save off the first value product that this thread block will scatter
-                if (segment_idx == first_segment_idx)
-                {
-                    temp_storage.first_tuple.value = value;
-                }
-            }
-        }
-*/
-    }
-
-
-
-    /**
-     * Have the thread block process the specified region of the MergePath decision path
-     */
-    __device__ __forceinline__ void ProcessRegion(
-        OffsetT         block_diagonal,
-        OffsetT         next_block_diagonal,
-        KeyValuePair    &first_tuple,       // [Out] Valid in thread-0
-        KeyValuePair    &last_tuple)        // [Out] Valid in thread-0
-    {
-        // Thread block initialization
-        if (threadIdx.x < 2)
-        {
-            // Retrieve block starting and ending indices
-            IndexPair block_idx = {0, 0};
-            if (gridDim.x > 1)
-            {
-                block_idx = d_block_idx[blockIdx.x + threadIdx.x];
-            }
-            else if (threadIdx.x > 0)
-            {
-                block_idx.a_idx = num_segments;
-                block_idx.b_idx = num_values;
-            }
-
-            // Share block starting and ending indices
-            temp_storage.block_region_idx[threadIdx.x] = block_idx;
-
-            // Initialize the block's running prefix
-            if (threadIdx.x == 0)
-            {
-                prefix_op.running_total.key    = block_idx.a_idx;
-                prefix_op.running_total.value  = identity;
-
-                // Initialize the "first scattered partial reduction tuple" to the prefix tuple (in case we don't actually scatter one)
-                temp_storage.first_tuple = prefix_op.running_total;
-            }
-        }
-
-        // Ensure coherence of region indices
-        __syncthreads();
-
-        // Read block's starting indices
-        IndexPair block_idx = temp_storage.block_region_idx[0];
-
-        // Have the thread block iterate over the region
-        #pragma unroll 1
-        while (block_diagonal < next_block_diagonal)
-        {
-            // Read block's ending indices (hoist?)
-            IndexPair next_block_idx = temp_storage.block_region_idx[1];
-
-            // Clamp the per-thread search range to within one work-tile of block's current indices
-            IndexPair next_tile_idx;
-            next_tile_idx.a_idx = CUB_MIN(next_block_idx.a_idx, block_idx.a_idx + TILE_ITEMS);
-            next_tile_idx.b_idx = CUB_MIN(next_block_idx.b_idx, block_idx.b_idx + TILE_ITEMS);
-
-            // Have each thread search for the end-indices of its subranges within the segment and value inputs
-            IndexPair next_thread_idx;
-            if (USE_SMEM_SEGMENT_CACHE)
-            {
-                // Search in smem cache
-                OffsetT num_segments = next_tile_idx.a_idx - block_idx.a_idx;
-
-                // Load global
-                SegmentOffset segment_offsets[ITEMS_PER_THREAD];
-                LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_segment_end_offsets + block_idx.a_idx, segment_offsets, num_segments, num_values);
-
-                // Store to shared
-                StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, temp_storage.cached_segment_end_offsets, segment_offsets);
-
-                __syncthreads();
-
-                OffsetT next_thread_diagonal = block_diagonal + ((threadIdx.x + 1) * ITEMS_PER_THREAD);
-
-                MergePathSearch(
-                    next_thread_diagonal,                       // Next thread diagonal
-                    temp_storage.cached_segment_end_offsets - block_idx.a_idx,                      // A (segment end-offsets)
-                    d_value_offsets,                            // B (value offsets)
-                    block_idx,                                  // Start indices into A and B
-                    next_tile_idx,                              // End indices into A and B
-                    next_thread_idx);                           // [out] diagonal intersection indices into A and B
-            }
-            else
-            {
-                // Search in global
-
-                OffsetT next_thread_diagonal = block_diagonal + ((threadIdx.x + 1) * ITEMS_PER_THREAD);
-
-                MergePathSearch(
-                    next_thread_diagonal,                       // Next thread diagonal
-                    d_segment_end_offsets,                      // A (segment end-offsets)
-                    d_value_offsets,                            // B (value offsets)
-                    block_idx,                                  // Start indices into A and B
-                    next_tile_idx,                              // End indices into A and B
-                    next_thread_idx);                           // [out] diagonal intersection indices into A and B
-            }
-
-            // Share thread end-indices to get thread begin-indices and tile end-indices
-            IndexPair thread_idx;
-
-            BlockShift(temp_storage.shift).Up(
-                next_thread_idx,    // Input item
-                thread_idx,         // [out] Output item
-                block_idx,          // Prefix item to be provided to <em>thread</em><sub>0</sub>
-                next_tile_idx);     // [out] Suffix item shifted out by the <em>thread</em><sub><tt>BLOCK_THREADS-1</tt></sub> to be provided to all threads
-
-//            if (block_idx.a_idx == next_tile_idx.a_idx)
-//            {
-//                // There are no segment end-offsets in this tile.  Perform a
-//                // simple block-wide reduction and accumulate the result into
-//                // the running total.
-//                SingleSegmentTile(next_tile_idx, block_idx);
-//            }
-//          else if (block_idx.b_idx == next_tile_idx.b_idx)
-//            {
-//                // There are no values in this tile (only empty segments).
-//                EmptySegmentsTile(next_tile_idx.a_idx, block_idx.a_idx);
-//            }
-//            else
-            if ((next_tile_idx.a_idx < num_segments) && (next_tile_idx.b_idx < num_values))
-            {
-                // Merge the tile's segment and value indices (full tile)
-                MultiSegmentTile<true>(block_idx, thread_idx, next_thread_idx, next_tile_idx);
-            }
-            else
-            {
-                // Merge the tile's segment and value indices (partially full tile)
-                MultiSegmentTile<false>(block_idx, thread_idx, next_thread_idx, next_tile_idx);
-            }
-
-            // Advance the block's indices in preparation for the next tile
-            block_idx = next_tile_idx;
-
-            // Advance to the next region in the decision path
-            block_diagonal += TILE_ITEMS;
-
-            // Barrier for smem reuse
-            __syncthreads();
-        }
-
-        // Get first and last tuples for the region
-        if (threadIdx.x == 0)
-        {
-            first_tuple = temp_storage.first_tuple;
-            last_tuple = prefix_op.running_total;
-        }
-
-    }
-
-
-};
-
-
-
-
-
-
-
-
-/******************************************************************************
- * Tuning policy types
- ******************************************************************************/
-
-/**
- * Parameterizable tuning policy type for BlockSegReduceRegionByKey
- */
-template <
-    int                     _BLOCK_THREADS,             ///< Threads per thread block
-    int                     _ITEMS_PER_THREAD,          ///< Items per thread (per tile of input)
-    BlockLoadAlgorithm      _LOAD_ALGORITHM,            ///< The BlockLoad algorithm to use
-    bool                    _LOAD_WARP_TIME_SLICING,    ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)
-    CacheLoadModifier       _LOAD_MODIFIER,             ///< Cache load modifier for reading input elements
-    BlockScanAlgorithm      _SCAN_ALGORITHM>            ///< The BlockScan algorithm to use
-struct BlockSegReduceRegionByKeyPolicy
-{
-    enum
-    {
-        BLOCK_THREADS           = _BLOCK_THREADS,               ///< Threads per thread block
-        ITEMS_PER_THREAD        = _ITEMS_PER_THREAD,            ///< Items per thread (per tile of input)
-        LOAD_WARP_TIME_SLICING  = _LOAD_WARP_TIME_SLICING,      ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)    };
-    };
-
-    static const BlockLoadAlgorithm     LOAD_ALGORITHM          = _LOAD_ALGORITHM;      ///< The BlockLoad algorithm to use
-    static const CacheLoadModifier      LOAD_MODIFIER           = _LOAD_MODIFIER;       ///< Cache load modifier for reading input elements
-    static const BlockScanAlgorithm     SCAN_ALGORITHM          = _SCAN_ALGORITHM;      ///< The BlockScan algorithm to use
-};
-
-
-/******************************************************************************
- * Persistent thread block types
- ******************************************************************************/
-
-/**
- * \brief BlockSegReduceRegionByKey implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key.
- */
-template <
-    typename    BlockSegReduceRegionByKeyPolicy,        ///< Parameterized BlockSegReduceRegionByKeyPolicy tuning policy
-    typename    InputIteratorT,                         ///< Random-access iterator referencing key-value input tuples
-    typename    OutputIteratorT,                        ///< Random-access iterator referencing segment output totals
-    typename    ReductionOp>                            ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-struct BlockSegReduceRegionByKey
-{
-    //---------------------------------------------------------------------
-    // Types and constants
-    //---------------------------------------------------------------------
-
-    // Constants
-    enum
-    {
-        BLOCK_THREADS       = BlockSegReduceRegionByKeyPolicy::BLOCK_THREADS,
-        ITEMS_PER_THREAD    = BlockSegReduceRegionByKeyPolicy::ITEMS_PER_THREAD,
-        TILE_ITEMS          = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-    // KeyValuePair input type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type KeyValuePair;
-
-    // Signed integer type for global offsets
-    typedef typename KeyValuePair::Key OffsetT;
-
-    // Value type
-    typedef typename KeyValuePair::Value Value;
-
-    // Head flag type
-    typedef int HeadFlag;
-
-    // Input iterator wrapper type for loading KeyValuePair elements through cache
-    typedef CacheModifiedInputIterator<
-            BlockSegReduceRegionByKeyPolicy::LOAD_MODIFIER,
-            KeyValuePair,
-            OffsetT>
-        WrappedInputIteratorT;
-
-    // Parameterized BlockLoad type
-    typedef BlockLoad<
-            WrappedInputIteratorT,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            BlockSegReduceRegionByKeyPolicy::LOAD_ALGORITHM,
-            BlockSegReduceRegionByKeyPolicy::LOAD_WARP_TIME_SLICING>
-        BlockLoad;
-
-    // BlockScan scan operator for reduction-by-segment
-    typedef ReduceByKeyOp<ReductionOp> ReduceByKeyOp;
-
-    // Stateful BlockScan prefix callback type for managing a running total while scanning consecutive tiles
-    typedef RunningBlockPrefixCallbackOp<
-            KeyValuePair,
-            ReduceByKeyOp>
-        RunningPrefixCallbackOp;
-
-    // Parameterized BlockScan type for block-wide reduce-value-by-key
-    typedef BlockScan<
-            KeyValuePair,
-            BLOCK_THREADS,
-            BlockSegReduceRegionByKeyPolicy::SCAN_ALGORITHM>
-        BlockScan;
-
-    // Parameterized BlockDiscontinuity type for identifying key discontinuities
-    typedef BlockDiscontinuity<
-            OffsetT,
-            BLOCK_THREADS>
-        BlockDiscontinuity;
-
-    // Operator for detecting discontinuities in a list of segment identifiers.
-    struct NewSegmentOp
-    {
-        /// Returns true if row_b is the start of a new row
-        __device__ __forceinline__ bool operator()(const OffsetT& b, const OffsetT& a)
-        {
-            return (a != b);
-        }
-    };
-
-    // Shared memory type for this threadblock
-    struct _TempStorage
-    {
-        union
-        {
-            typename BlockLoad::TempStorage                 load;           // Smem needed for tile loading
-            struct {
-                typename BlockScan::TempStorage             scan;           // Smem needed for reduce-value-by-segment scan
-                typename BlockDiscontinuity::TempStorage    discontinuity;  // Smem needed for head-flagging
-            };
-        };
-    };
-
-    // Alias wrapper allowing storage to be unioned
-    struct TempStorage : Uninitialized<_TempStorage> {};
-
-
-    //---------------------------------------------------------------------
-    // Thread fields
-    //---------------------------------------------------------------------
-
-    _TempStorage                &temp_storage;          ///< Reference to shared storage
-    WrappedInputIteratorT       d_tuple_partials;       ///< A sequence of partial reduction tuples to scan
-    OutputIteratorT              d_output;               ///< A sequence of segment totals
-    Value                       identity;               ///< Identity value (for zero-length segments)
-    ReduceByKeyOp               scan_op;                ///< Reduce-by-key scan operator
-    RunningPrefixCallbackOp     prefix_op;              ///< Stateful running total for block-wide prefix scan of partial reduction tuples
-
-
-    //---------------------------------------------------------------------
-    // Operations
-    //---------------------------------------------------------------------
-
-    /**
-     * Constructor
-     */
-    __device__ __forceinline__
-    BlockSegReduceRegionByKey(
-        TempStorage             &temp_storage,          ///< Reference to shared storage
-        InputIteratorT          d_tuple_partials,       ///< A sequence of partial reduction tuples to scan
-        OutputIteratorT          d_output,               ///< A sequence of segment totals
-        Value                   identity,               ///< Identity value (for zero-length segments)
-        ReductionOp             reduction_op)           ///< Reduction operator
-    :
-        temp_storage(temp_storage.Alias()),
-        d_tuple_partials(d_tuple_partials),
-        d_output(d_output),
-        identity(identity),
-        scan_op(reduction_op),
-        prefix_op(scan_op)
-    {}
-
-
-
-    /**
-     * Processes a reduce-value-by-key input tile, outputting reductions for each segment
-     */
-    template <bool FULL_TILE>
-    __device__ __forceinline__
-    void ProcessTile(
-        OffsetT block_offset,
-        OffsetT first_segment_idx,
-        OffsetT last_segment_idx,
-        int guarded_items = TILE_ITEMS)
-    {
-        KeyValuePair    partial_reductions[ITEMS_PER_THREAD];
-        OffsetT         segment_ids[ITEMS_PER_THREAD];
-        HeadFlag        head_flags[ITEMS_PER_THREAD];
-
-        // Load a tile of block partials from previous kernel
-        if (FULL_TILE)
-        {
-            // Full tile
-            BlockLoad(temp_storage.load).Load(d_tuple_partials + block_offset, partial_reductions);
-        }
-        else
-        {
-            KeyValuePair oob_default;
-            oob_default.key    = last_segment_idx;       // The last segment ID to be reduced
-            oob_default.value  = identity;
-
-            // Partially-full tile
-            BlockLoad(temp_storage.load).Load(d_tuple_partials + block_offset, partial_reductions, guarded_items, oob_default);
-        }
-
-        // Barrier for shared memory reuse
-        __syncthreads();
-
-        // Copy the segment IDs for head-flagging
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            segment_ids[ITEM] = partial_reductions[ITEM].key;
-        }
-
-        // FlagT segment heads by looking for discontinuities
-        BlockDiscontinuity(temp_storage.discontinuity).FlagHeads(
-            head_flags,                         // [out] Head flags
-            segment_ids,                        // Segment ids
-            NewSegmentOp(),                     // Functor for detecting start of new rows
-            prefix_op.running_total.key);       // Last segment ID from previous tile to compare with first segment ID in this tile
-
-        // Reduce-value-by-segment across partial_reductions using exclusive prefix scan
-        KeyValuePair block_aggregate;
-        BlockScan(temp_storage.scan).ExclusiveScan(
-            partial_reductions,                   // Scan input
-            partial_reductions,                   // Scan output
-            scan_op,                        // Scan operator
-            block_aggregate,                // Block-wide total (unused)
-            prefix_op);                     // Prefix operator for seeding the block-wide scan with the running total
-
-        // Scatter an accumulated reduction if it is the head of a valid segment
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
-        {
-            if (head_flags[ITEM])
-            {
-                d_output[partial_reductions[ITEM].key] = partial_reductions[ITEM].value;
-            }
-        }
-    }
-
-
-    /**
-     * Iterate over input tiles belonging to this thread block
-     */
-    __device__ __forceinline__
-    void ProcessRegion(
-        OffsetT block_offset,
-        OffsetT block_end,
-        OffsetT first_segment_idx,
-        OffsetT last_segment_idx)
-    {
-        if (threadIdx.x == 0)
-        {
-            // Initialize running prefix to the first segment index paired with identity
-            prefix_op.running_total.key    = first_segment_idx;
-            prefix_op.running_total.value  = identity;
-        }
-
-        // Process full tiles
-        while (block_offset + TILE_ITEMS <= block_end)
-        {
-            ProcessTile<true>(block_offset, first_segment_idx, last_segment_idx);
-            __syncthreads();
-
-            block_offset += TILE_ITEMS;
-        }
-
-        // Process final value tile (if present)
-        int guarded_items = block_end - block_offset;
-        if (guarded_items)
-        {
-            ProcessTile<false>(block_offset, first_segment_idx, last_segment_idx, guarded_items);
-        }
-    }
-};
-
-
-
-/******************************************************************************
- * Kernel entrypoints
- ******************************************************************************/
-
-/**
- * Segmented reduce region kernel entry point (multi-block).
- */
-
-template <
-    typename SegmentOffsetIterator,             ///< Random-access input iterator type for reading segment end-offsets
-    typename OffsetT>                           ///< Signed integer type for global offsets
-__global__ void SegReducePartitionKernel(
-    SegmentOffsetIterator       d_segment_end_offsets,  ///< [in] A sequence of \p num_segments segment end-offsets
-    IndexPair<OffsetT>          *d_block_idx,
-    int                         num_partition_samples,
-    OffsetT                     num_values,             ///< [in] Number of values to reduce
-    OffsetT                     num_segments,           ///< [in] Number of segments being reduced
-    GridEvenShare<OffsetT>      even_share)             ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
-{
-    // Segment offset type
-    typedef typename std::iterator_traits<SegmentOffsetIterator>::value_type SegmentOffset;
-
-    // Counting iterator type
-    typedef CountingInputIterator<SegmentOffsetT, OffsetT> CountingIterator;
-
-    // Cache-modified iterator for segment end-offsets
-    CacheModifiedInputIterator<LOAD_LDG, SegmentOffsetT, OffsetT> d_wrapped_segment_end_offsets(d_segment_end_offsets);
-
-    // Counting iterator for value offsets
-    CountingIterator d_value_offsets(0);
-
-    // Initialize even-share to tell us where to start and stop our tile-processing
-    int partition_id = (blockDim.x * blockIdx.x) + threadIdx.x;
-    even_share.Init(partition_id);
-
-    // Search for block starting and ending indices
-    IndexPair<OffsetT> start_idx = {0, 0};
-    IndexPair<OffsetT> end_idx   = {num_segments, num_values};
-    IndexPair<OffsetT> block_idx;
-
-    MergePathSearch(
-        even_share.block_offset,            // Next thread diagonal
-        d_wrapped_segment_end_offsets,      // A (segment end-offsets)
-        d_value_offsets,                    // B (value offsets)
-        start_idx,                          // Start indices into A and B
-        end_idx,                            // End indices into A and B
-        block_idx);                         // [out] diagonal intersection indices into A and B
-
-    // Write output
-    if (partition_id < num_partition_samples)
-    {
-        d_block_idx[partition_id] = block_idx;
-    }
-}
-
-
-/**
- * Segmented reduce region kernel entry point (multi-block).
- */
-template <
-    typename BlockSegReduceRegionPolicy,        ///< Parameterized BlockSegReduceRegionPolicy tuning policy
-    typename SegmentOffsetIterator,             ///< Random-access input iterator type for reading segment end-offsets
-    typename ValueIterator,                     ///< Random-access input iterator type for reading values
-    typename OutputIteratorT,                   ///< Random-access output iterator type for writing segment reductions
-    typename ReductionOp,                       ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename OffsetT,                           ///< Signed integer type for global offsets
-    typename Value>                             ///< Value type
-__launch_bounds__ (BlockSegReduceRegionPolicy::BLOCK_THREADS)
-__global__ void SegReduceRegionKernel(
-    SegmentOffsetIterator       d_segment_end_offsets,  ///< [in] A sequence of \p num_segments segment end-offsets
-    ValueIterator               d_values,               ///< [in] A sequence of \p num_values values
-    OutputIteratorT              d_output,               ///< [out] A sequence of \p num_segments segment totals
-    KeyValuePair<OffsetT, Value> *d_tuple_partials,      ///< [out] A sequence of (gridDim.x * 2) partial reduction tuples
-    IndexPair<OffsetT>          *d_block_idx,
-    OffsetT                     num_values,             ///< [in] Number of values to reduce
-    OffsetT                     num_segments,           ///< [in] Number of segments being reduced
-    Value                       identity,               ///< [in] Identity value (for zero-length segments)
-    ReductionOp                 reduction_op,           ///< [in] Reduction operator
-    GridEvenShare<OffsetT>      even_share)             ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
-{
-    typedef KeyValuePair<OffsetT, Value> KeyValuePair;
-
-    // Specialize threadblock abstraction type for reducing a range of segmented values
-    typedef BlockSegReduceRegion<
-            BlockSegReduceRegionPolicy,
-            SegmentOffsetIterator,
-            ValueIterator,
-            OutputIteratorT,
-            ReductionOp,
-            OffsetT>
-        BlockSegReduceRegion;
-
-    // Shared memory allocation
-    __shared__ typename BlockSegReduceRegion::TempStorage temp_storage;
-
-    // Initialize threadblock even-share to tell us where to start and stop our tile-processing
-    even_share.BlockInit();
-
-    // Construct persistent thread block
-    BlockSegReduceRegion thread_block(
-        temp_storage,
-        d_segment_end_offsets,
-        d_values,
-        d_output,
-        d_block_idx,
-        num_values,
-        num_segments,
-        identity,
-        reduction_op);
-
-    // First and last partial reduction tuples within the range (valid in thread-0)
-    KeyValuePair first_tuple, last_tuple;
-
-    // Consume block's region of work
-    thread_block.ProcessRegion(
-        even_share.block_offset,
-        even_share.block_end,
-        first_tuple,
-        last_tuple);
-
-    if (threadIdx.x == 0)
-    {
-        if (gridDim.x > 1)
-        {
-            // Special case where the first segment written and the carry-out are for the same segment
-            if (first_tuple.key == last_tuple.key)
-            {
-                first_tuple.value = identity;
-            }
-
-            // Write the first and last partial products from this thread block so
-            // that they can be subsequently "fixed up" in the next kernel.
-            d_tuple_partials[blockIdx.x * 2]          = first_tuple;
-            d_tuple_partials[(blockIdx.x * 2) + 1]    = last_tuple;
-        }
-    }
-
-}
-
-
-/**
- * Segmented reduce region kernel entry point (single-block).
- */
-template <
-    typename    BlockSegReduceRegionByKeyPolicy,        ///< Parameterized BlockSegReduceRegionByKeyPolicy tuning policy
-    typename    InputIteratorT,                         ///< Random-access iterator referencing key-value input tuples
-    typename    OutputIteratorT,                        ///< Random-access iterator referencing segment output totals
-    typename    ReductionOp,                            ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename    OffsetT,                                ///< Signed integer type for global offsets
-    typename    Value>                                  ///< Value type
-__launch_bounds__ (BlockSegReduceRegionByKeyPolicy::BLOCK_THREADS, 1)
-__global__ void SegReduceRegionByKeyKernel(
-    InputIteratorT          d_tuple_partials,           ///< [in] A sequence of partial reduction tuples
-    OutputIteratorT          d_output,                   ///< [out] A sequence of \p num_segments segment totals
-    OffsetT                 num_segments,               ///< [in] Number of segments in the \p d_output sequence
-    int                     num_tuple_partials,         ///< [in] Number of partial reduction tuples being reduced
-    Value                   identity,                   ///< [in] Identity value (for zero-length segments)
-    ReductionOp             reduction_op)               ///< [in] Reduction operator
-{
-    // Specialize threadblock abstraction type for reducing a range of values by key
-    typedef BlockSegReduceRegionByKey<
-            BlockSegReduceRegionByKeyPolicy,
-            InputIteratorT,
-            OutputIteratorT,
-            ReductionOp>
-        BlockSegReduceRegionByKey;
-
-    // Shared memory allocation
-    __shared__ typename BlockSegReduceRegionByKey::TempStorage temp_storage;
-
-    // Construct persistent thread block
-    BlockSegReduceRegionByKey thread_block(
-        temp_storage,
-        d_tuple_partials,
-        d_output,
-        identity,
-        reduction_op);
-
-    // Process input tiles
-    thread_block.ProcessRegion(
-        0,                          // Region start
-        num_tuple_partials,         // Region end
-        0,                          // First segment ID
-        num_segments);              // Last segment ID (one-past)
-}
-
-
-
-
-/******************************************************************************
- * Dispatch
- ******************************************************************************/
-
-/**
- * Utility class for dispatching the appropriately-tuned kernels for DeviceReduce
- */
-template <
-    typename ValueIterator,                     ///< Random-access input iterator type for reading values
-    typename SegmentOffsetIterator,             ///< Random-access input iterator type for reading segment end-offsets
-    typename OutputIteratorT,                   ///< Random-access output iterator type for writing segment reductions
-    typename ReductionOp,                       ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-    typename OffsetT>                           ///< Signed integer type for global offsets
-struct DeviceSegReduceDispatch
-{
-    // Value type
-    typedef typename std::iterator_traits<ValueIterator>::value_type Value;
-
-    // Reduce-by-key data type tuple (segment-ID, value)
-    typedef KeyValuePair<OffsetT, Value> KeyValuePair;
-
-    // Index pair data type
-    typedef IndexPair<OffsetT>IndexPair;
-
-
-    /******************************************************************************
-     * Tuning policies
-     ******************************************************************************/
-
-    /// SM35
-    struct Policy350
-    {
-        // ReduceRegionPolicy
-        typedef BlockSegReduceRegionPolicy<
-                128,                            ///< Threads per thread block
-                6,                              ///< Items per thread (per tile of input)
-                true,                           ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
-                false,                          ///< Whether or not to cache incoming values in shared memory before reducing each tile
-                LOAD_DEFAULT,                   ///< Cache load modifier for reading segment offsets
-                LOAD_LDG,                       ///< Cache load modifier for reading values
-                BLOCK_REDUCE_RAKING,            ///< The BlockReduce algorithm to use
-                BLOCK_SCAN_WARP_SCANS>          ///< The BlockScan algorithm to use
-            SegReduceRegionPolicy;
-
-        // ReduceRegionByKeyPolicy
-        typedef BlockSegReduceRegionByKeyPolicy<
-                256,                            ///< Threads per thread block
-                9,                             ///< Items per thread (per tile of input)
-                BLOCK_LOAD_DIRECT,              ///< The BlockLoad algorithm to use
-                false,                          ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)
-                LOAD_LDG,                       ///< Cache load modifier for reading input elements
-                BLOCK_SCAN_WARP_SCANS>          ///< The BlockScan algorithm to use
-            SegReduceRegionByKeyPolicy;
-    };
-
-
-    /// SM10
-    struct Policy100
-    {
-        // ReduceRegionPolicy
-        typedef BlockSegReduceRegionPolicy<
-                128,                            ///< Threads per thread block
-                3,                              ///< Items per thread (per tile of input)
-                false,                          ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
-                false,                          ///< Whether or not to cache incoming values in shared memory before reducing each tile
-                LOAD_DEFAULT,                   ///< Cache load modifier for reading segment offsets
-                LOAD_DEFAULT,                   ///< Cache load modifier for reading values
-                BLOCK_REDUCE_RAKING,            ///< The BlockReduce algorithm to use
-                BLOCK_SCAN_RAKING>              ///< The BlockScan algorithm to use
-            SegReduceRegionPolicy;
-
-        // ReduceRegionByKeyPolicy
-        typedef BlockSegReduceRegionByKeyPolicy<
-                128,                            ///< Threads per thread block
-                3,                              ///< Items per thread (per tile of input)
-                BLOCK_LOAD_WARP_TRANSPOSE,      ///< The BlockLoad algorithm to use
-                false,                          ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)
-                LOAD_DEFAULT,                   ///< Cache load modifier for reading input elements
-                BLOCK_SCAN_WARP_SCANS>          ///< The BlockScan algorithm to use
-            SegReduceRegionByKeyPolicy;
-    };
-
-
-    /******************************************************************************
-     * Tuning policies of current PTX compiler pass
-     ******************************************************************************/
-
-#if (CUB_PTX_ARCH >= 350)
-    typedef Policy350 PtxPolicy;
-/*
-#elif (CUB_PTX_ARCH >= 300)
-    typedef Policy300 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 200)
-    typedef Policy200 PtxPolicy;
-
-#elif (CUB_PTX_ARCH >= 130)
-    typedef Policy130 PtxPolicy;
-*/
-#else
-    typedef Policy100 PtxPolicy;
-
-#endif
-
-    // "Opaque" policies (whose parameterizations aren't reflected in the type signature)
-    struct PtxSegReduceRegionPolicy           : PtxPolicy::SegReduceRegionPolicy {};
-    struct PtxSegReduceRegionByKeyPolicy      : PtxPolicy::SegReduceRegionByKeyPolicy {};
-
-
-    /******************************************************************************
-     * Utilities
-     ******************************************************************************/
-
-    /**
-     * Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
-     */
-    template <
-        typename SegReduceKernelConfig,
-        typename SegReduceByKeyKernelConfig>
-    __host__ __device__ __forceinline__
-    static void InitConfigs(
-        int                         ptx_version,
-        SegReduceKernelConfig       &seg_reduce_region_config,
-        SegReduceByKeyKernelConfig  &seg_reduce_region_by_key_config)
-    {
-    #if (CUB_PTX_ARCH > 0)
-
-        // We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
-        seg_reduce_region_config.Init<PtxSegReduceRegionPolicy>();
-        seg_reduce_region_by_key_config.Init<PtxSegReduceRegionByKeyPolicy>();
-
-    #else
-
-        // We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
-        if (ptx_version >= 350)
-        {
-            seg_reduce_region_config.template          Init<typename Policy350::SegReduceRegionPolicy>();
-            seg_reduce_region_by_key_config.template   Init<typename Policy350::SegReduceRegionByKeyPolicy>();
-        }
-/*
-        else if (ptx_version >= 300)
-        {
-            seg_reduce_region_config.template          Init<typename Policy300::SegReduceRegionPolicy>();
-            seg_reduce_region_by_key_config.template   Init<typename Policy300::SegReduceRegionByKeyPolicy>();
-        }
-        else if (ptx_version >= 200)
-        {
-            seg_reduce_region_config.template          Init<typename Policy200::SegReduceRegionPolicy>();
-            seg_reduce_region_by_key_config.template   Init<typename Policy200::SegReduceRegionByKeyPolicy>();
-        }
-        else if (ptx_version >= 130)
-        {
-            seg_reduce_region_config.template          Init<typename Policy130::SegReduceRegionPolicy>();
-            seg_reduce_region_by_key_config.template   Init<typename Policy130::SegReduceRegionByKeyPolicy>();
-        }
-*/
-        else
-        {
-            seg_reduce_region_config.template          Init<typename Policy100::SegReduceRegionPolicy>();
-            seg_reduce_region_by_key_config.template   Init<typename Policy100::SegReduceRegionByKeyPolicy>();
-        }
-
-    #endif
-    }
-
-
-    /**
-     * SegReduceRegionKernel kernel dispatch configuration
-     */
-    struct SegReduceKernelConfig
-    {
-        int                     block_threads;
-        int                     items_per_thread;
-        bool                    use_smem_segment_cache;
-        bool                    use_smem_value_cache;
-        CacheLoadModifier       load_modifier_segments;
-        CacheLoadModifier       load_modifier_values;
-        BlockReduceAlgorithm    reduce_algorithm;
-        BlockScanAlgorithm      scan_algorithm;
-
-        template <typename SegReduceRegionPolicy>
-        __host__ __device__ __forceinline__
-        void Init()
-        {
-            block_threads               = SegReduceRegionPolicy::BLOCK_THREADS;
-            items_per_thread            = SegReduceRegionPolicy::ITEMS_PER_THREAD;
-            use_smem_segment_cache      = SegReduceRegionPolicy::USE_SMEM_SEGMENT_CACHE;
-            use_smem_value_cache        = SegReduceRegionPolicy::USE_SMEM_VALUE_CACHE;
-            load_modifier_segments      = SegReduceRegionPolicy::LOAD_MODIFIER_SEGMENTS;
-            load_modifier_values        = SegReduceRegionPolicy::LOAD_MODIFIER_VALUES;
-            reduce_algorithm            = SegReduceRegionPolicy::REDUCE_ALGORITHM;
-            scan_algorithm              = SegReduceRegionPolicy::SCAN_ALGORITHM;
-        }
-    };
-
-    /**
-     * SegReduceRegionByKeyKernel kernel dispatch configuration
-     */
-    struct SegReduceByKeyKernelConfig
-    {
-        int                     block_threads;
-        int                     items_per_thread;
-        BlockLoadAlgorithm      load_algorithm;
-        bool                    load_warp_time_slicing;
-        CacheLoadModifier       load_modifier;
-        BlockScanAlgorithm      scan_algorithm;
-
-        template <typename SegReduceRegionByKeyPolicy>
-        __host__ __device__ __forceinline__
-        void Init()
-        {
-            block_threads               = SegReduceRegionByKeyPolicy::BLOCK_THREADS;
-            items_per_thread            = SegReduceRegionByKeyPolicy::ITEMS_PER_THREAD;
-            load_algorithm              = SegReduceRegionByKeyPolicy::LOAD_ALGORITHM;
-            load_warp_time_slicing      = SegReduceRegionByKeyPolicy::LOAD_WARP_TIME_SLICING;
-            load_modifier               = SegReduceRegionByKeyPolicy::LOAD_MODIFIER;
-            scan_algorithm              = SegReduceRegionByKeyPolicy::SCAN_ALGORITHM;
-        }
-    };
-
-
-    /******************************************************************************
-     * Dispatch entrypoints
-     ******************************************************************************/
-
-    /**
-     * Internal dispatch routine for computing a device-wide segmented reduction.
-     */
-    template <
-        typename                        SegReducePartitionKernelPtr,
-        typename                        SegReduceRegionKernelPtr,               ///< Function type of cub::SegReduceRegionKernel
-        typename                        SegReduceRegionByKeyKernelPtr>          ///< Function type of cub::SegReduceRegionByKeyKernel
-    __host__ __device__ __forceinline__
-    static cudaError_t Dispatch(
-        void*               d_temp_storage,                        ///< [in] %Device allocation of temporary storage.  When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
-        size_t                          &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation.
-        ValueIterator                   d_values,                               ///< [in] A sequence of \p num_values data to reduce
-        SegmentOffsetIterator           d_segment_offsets,                      ///< [in] A sequence of (\p num_segments + 1) segment offsets
-        OutputIteratorT                  d_output,                               ///< [out] A sequence of \p num_segments segment totals
-        OffsetT                         num_values,                             ///< [in] Total number of values to reduce
-        OffsetT                         num_segments,                           ///< [in] Number of segments being reduced
-        Value                           identity,                               ///< [in] Identity value (for zero-length segments)
-        ReductionOp                     reduction_op,                           ///< [in] Reduction operator
-        cudaStream_t                    stream,                                 ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                            debug_synchronous,                      ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-        int                             sm_version,                             ///< [in] SM version of target device to use when computing SM occupancy
-        SegReducePartitionKernelPtr     seg_reduce_partition_kernel,            ///< [in] Kernel function pointer to parameterization of cub::SegReduceRegionKernel
-        SegReduceRegionKernelPtr        seg_reduce_region_kernel,               ///< [in] Kernel function pointer to parameterization of cub::SegReduceRegionKernel
-        SegReduceRegionByKeyKernelPtr   seg_reduce_region_by_key_kernel,        ///< [in] Kernel function pointer to parameterization of cub::SegReduceRegionByKeyKernel
-        SegReduceKernelConfig           &seg_reduce_region_config,              ///< [in] Dispatch parameters that match the policy that \p seg_reduce_region_kernel was compiled for
-        SegReduceByKeyKernelConfig      &seg_reduce_region_by_key_config)       ///< [in] Dispatch parameters that match the policy that \p seg_reduce_region_by_key_kernel was compiled for
-    {
-#ifndef CUB_RUNTIME_ENABLED
-
-        // Kernel launch not supported from this device
-        return CubDebug(cudaErrorNotSupported );
-
-#else
-
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Dispatch two kernels: (1) a multi-block segmented reduction
-            // to reduce regions by block, and (2) a single-block reduce-by-key kernel
-            // to "fix up" segments spanning more than one region.
-
-            // Tile size of seg_reduce_region_kernel
-            int tile_size = seg_reduce_region_config.block_threads * seg_reduce_region_config.items_per_thread;
-
-            // Get device ordinal
-            int device_ordinal;
-            if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
-
-            // Get SM count
-            int sm_count;
-            if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
-
-            // Get SM occupancy for histogram_region_kernel
-            int seg_reduce_region_sm_occupancy;
-            if (CubDebug(error = MaxSmOccupancy(
-                seg_reduce_region_sm_occupancy,
-                sm_version,
-                seg_reduce_region_kernel,
-                seg_reduce_region_config.block_threads))) break;
-
-            // Get device occupancy for histogram_region_kernel
-            int seg_reduce_region_occupancy = seg_reduce_region_sm_occupancy * sm_count;
-
-            // Even-share work distribution
-            int num_diagonals = num_values + num_segments;                  // Total number of work items
-            int subscription_factor = seg_reduce_region_sm_occupancy;       // Amount of CTAs to oversubscribe the device beyond actively-resident (heuristic)
-            int max_grid_size = seg_reduce_region_occupancy * subscription_factor;
-            GridEvenShare<OffsetT>even_share(
-                num_diagonals,
-                max_grid_size,
-                tile_size);
-
-            // Get grid size for seg_reduce_region_kernel
-            int seg_reduce_region_grid_size = even_share.grid_size;
-
-            // Number of "fix-up" reduce-by-key tuples (2 per thread block)
-            int num_tuple_partials = seg_reduce_region_grid_size * 2;
-            int num_partition_samples = seg_reduce_region_grid_size + 1;
-
-            // Temporary storage allocation requirements
-            void* allocations[2];
-            size_t allocation_sizes[2] =
-            {
-                num_tuple_partials * sizeof(KeyValuePair),  // bytes needed for "fix-up" reduce-by-key tuples
-                num_partition_samples * sizeof(IndexPair),  // bytes needed block indices
-            };
-
-            // Alias the temporary allocations from the single storage blob (or set the necessary size of the blob)
-            if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
-            if (d_temp_storage == NULL)
-            {
-                // Return if the caller is simply requesting the size of the storage allocation
-                return cudaSuccess;
-            }
-
-            // Alias the allocations
-            KeyValuePair    *d_tuple_partials   = (KeyValuePair*) allocations[0];           // "fix-up" tuples
-            IndexPair       *d_block_idx        = (IndexPair *) allocations[1];             // block starting/ending indices
-
-            // Array of segment end-offsets
-            SegmentOffsetIterator d_segment_end_offsets = d_segment_offsets + 1;
-
-            // Grid launch params for seg_reduce_partition_kernel
-            int partition_block_size = 32;
-            int partition_grid_size = (num_partition_samples + partition_block_size - 1) / partition_block_size;
-
-            // Partition work among multiple thread blocks if necessary
-            if (seg_reduce_region_grid_size > 1)
-            {
-                // Log seg_reduce_partition_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking seg_reduce_partition_kernel<<<%d, %d, 0, %lld>>>()\n",
-                    partition_grid_size, partition_block_size, (long long) stream);
-
-                // Invoke seg_reduce_partition_kernel
-                seg_reduce_partition_kernel<<<partition_grid_size, partition_block_size, 0, stream>>>(
-                    d_segment_end_offsets,  ///< [in] A sequence of \p num_segments segment end-offsets
-                    d_block_idx,
-                    num_partition_samples,
-                    num_values,             ///< [in] Number of values to reduce
-                    num_segments,           ///< [in] Number of segments being reduced
-                    even_share);            ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
-
-                // Sync the stream if specified
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-
-            // Log seg_reduce_region_kernel configuration
-            if (debug_synchronous) _CubLog("Invoking seg_reduce_region_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
-                seg_reduce_region_grid_size, seg_reduce_region_config.block_threads, (long long) stream, seg_reduce_region_config.items_per_thread, seg_reduce_region_sm_occupancy);
-
-            // Mooch
-            if (CubDebug(error = cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeEightByte))) break;
-
-            // Invoke seg_reduce_region_kernel
-            seg_reduce_region_kernel<<<seg_reduce_region_grid_size, seg_reduce_region_config.block_threads, 0, stream>>>(
-                d_segment_end_offsets,
-                d_values,
-                d_output,
-                d_tuple_partials,
-                d_block_idx,
-                num_values,
-                num_segments,
-                identity,
-                reduction_op,
-                even_share);
-
-            // Sync the stream if specified
-            if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-/*
-            // Perform "fix-up" of region partial reductions if grid size is greater than one thread block
-            if (seg_reduce_region_grid_size > 1)
-            {
-                // Log seg_reduce_region_by_key_kernel configuration
-                if (debug_synchronous) _CubLog("Invoking seg_reduce_region_by_key_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread\n",
-                    1, seg_reduce_region_by_key_config.block_threads, (long long) stream, seg_reduce_region_by_key_config.items_per_thread);
-
-                // Invoke seg_reduce_region_by_key_kernel
-                seg_reduce_region_by_key_kernel<<<1, seg_reduce_region_by_key_config.block_threads, 0, stream>>>(
-                    d_tuple_partials,
-                    d_output,
-                    num_segments,
-                    num_tuple_partials,
-                    identity,
-                    reduction_op);
-
-                // Sync the stream if specified
-                if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
-            }
-*/
-        }
-
-        while (0);
-
-        return error;
-
-#endif // CUB_RUNTIME_ENABLED
-    }
-
-
-    /**
-     * Internal dispatch routine for computing a device-wide segmented reduction.
-     */
-    __host__ __device__ __forceinline__
-    static cudaError_t Dispatch(
-        void*               d_temp_storage,                        ///< [in] %Device allocation of temporary storage.  When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
-        size_t                          &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation.
-        ValueIterator                   d_values,                               ///< [in] A sequence of \p num_values data to reduce
-        SegmentOffsetIterator           d_segment_offsets,                      ///< [in] A sequence of (\p num_segments + 1) segment offsets
-        OutputIteratorT                  d_output,                               ///< [out] A sequence of \p num_segments segment totals
-        OffsetT                         num_values,                             ///< [in] Total number of values to reduce
-        OffsetT                         num_segments,                           ///< [in] Number of segments being reduced
-        Value                           identity,                               ///< [in] Identity value (for zero-length segments)
-        ReductionOp                     reduction_op,                           ///< [in] Reduction operator
-        cudaStream_t                    stream,                                 ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                            debug_synchronous)                      ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        cudaError error = cudaSuccess;
-        do
-        {
-            // Get PTX version
-            int ptx_version;
-    #if (CUB_PTX_ARCH == 0)
-            if (CubDebug(error = PtxVersion(ptx_version))) break;
-    #else
-            ptx_version = CUB_PTX_ARCH;
-    #endif
-
-            // Get kernel kernel dispatch configurations
-            SegReduceKernelConfig seg_reduce_region_config;
-            SegReduceByKeyKernelConfig seg_reduce_region_by_key_config;
-
-            InitConfigs(ptx_version, seg_reduce_region_config, seg_reduce_region_by_key_config);
-
-            // Dispatch
-            if (CubDebug(error = Dispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                d_values,
-                d_segment_offsets,
-                d_output,
-                num_values,
-                num_segments,
-                identity,
-                reduction_op,
-                stream,
-                debug_synchronous,
-                ptx_version,            // Use PTX version instead of SM version because, as a statically known quantity, this improves device-side launch dramatically but at the risk of imprecise occupancy calculation for mismatches
-                SegReducePartitionKernel<SegmentOffsetIterator, OffsetT>,
-                SegReduceRegionKernel<PtxSegReduceRegionPolicy, SegmentOffsetIterator, ValueIterator, OutputIteratorT, ReductionOp, OffsetT, Value>,
-                SegReduceRegionByKeyKernel<PtxSegReduceRegionByKeyPolicy, KeyValuePair*, OutputIteratorT, ReductionOp, OffsetT, Value>,
-                seg_reduce_region_config,
-                seg_reduce_region_by_key_config))) break;
-        }
-        while (0);
-
-        return error;
-
-    }
-};
-
-
-
-
-/******************************************************************************
- * DeviceSegReduce
- *****************************************************************************/
-
-/**
- * \brief DeviceSegReduce provides operations for computing a device-wide, parallel segmented reduction across a sequence of data items residing within global memory.
- * \ingroup DeviceModule
- *
- * \par Overview
- * A <a href="http://en.wikipedia.org/wiki/Reduce_(higher-order_function)"><em>reduction</em></a> (or <em>fold</em>)
- * uses a binary combining operator to compute a single aggregate from a list of input elements.
- *
- * \par Usage Considerations
- * \cdp_class{DeviceReduce}
- *
- */
-struct DeviceSegReduce
-{
-    /**
-     * \brief Computes a device-wide segmented reduction using the specified binary \p reduction_op functor.
-     *
-     * \par
-     * Does not support non-commutative reduction operators.
-     *
-     * \devicestorage
-     *
-     * \cdp
-     *
-     * \iterator
-     *
-     * \tparam ValueIterator            <b>[inferred]</b> Random-access input iterator type for reading values
-     * \tparam SegmentOffsetIterator    <b>[inferred]</b> Random-access input iterator type for reading segment end-offsets
-     * \tparam OutputIteratorT           <b>[inferred]</b> Random-access output iterator type for writing segment reductions
-     * \tparam Value                    <b>[inferred]</b> Value type
-     * \tparam ReductionOp              <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
-     */
-    template <
-        typename                ValueIterator,
-        typename                SegmentOffsetIterator,
-        typename                OutputIteratorT,
-        typename                Value,
-        typename                ReductionOp>
-    __host__ __device__ __forceinline__
-    static cudaError_t Reduce(
-        void*               d_temp_storage,                        ///< [in] %Device allocation of temporary storage.  When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation.
-        ValueIterator           d_values,                               ///< [in] A sequence of \p num_values data to reduce
-        SegmentOffsetIterator   d_segment_offsets,                      ///< [in] A sequence of (\p num_segments + 1) segment offsets
-        OutputIteratorT          d_output,                               ///< [out] A sequence of \p num_segments segment totals
-        int                     num_values,                             ///< [in] Total number of values to reduce
-        int                     num_segments,                           ///< [in] Number of segments being reduced
-        Value                   identity,                               ///< [in] Identity value (for zero-length segments)
-        ReductionOp             reduction_op,                           ///< [in] Reduction operator
-        cudaStream_t            stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        typedef DeviceSegReduceDispatch<
-                ValueIterator,
-                SegmentOffsetIterator,
-                OutputIteratorT,
-                ReductionOp,
-                OffsetT>
-            DeviceSegReduceDispatch;
-
-        return DeviceSegReduceDispatch::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_values,
-            d_segment_offsets,
-            d_output,
-            num_values,
-            num_segments,
-            identity,
-            reduction_op,
-            stream,
-            debug_synchronous);
-    }
-
-
-    /**
-     * \brief Computes a device-wide segmented sum using the addition ('+') operator.
-     *
-     * \par
-     * Does not support non-commutative summation.
-     *
-     * \devicestorage
-     *
-     * \cdp
-     *
-     * \iterator
-     *
-     * \tparam ValueIterator            <b>[inferred]</b> Random-access input iterator type for reading values
-     * \tparam SegmentOffsetIterator    <b>[inferred]</b> Random-access input iterator type for reading segment end-offsets
-     * \tparam OutputIteratorT           <b>[inferred]</b> Random-access output iterator type for writing segment reductions
-     */
-    template <
-        typename                ValueIterator,
-        typename                SegmentOffsetIterator,
-        typename                OutputIteratorT>
-    __host__ __device__ __forceinline__
-    static cudaError_t Sum(
-        void*               d_temp_storage,                        ///< [in] %Device allocation of temporary storage.  When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
-        size_t                  &temp_storage_bytes,                    ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation.
-        ValueIterator           d_values,                               ///< [in] A sequence of \p num_values data to reduce
-        SegmentOffsetIterator   d_segment_offsets,                      ///< [in] A sequence of (\p num_segments + 1) segment offsets
-        OutputIteratorT          d_output,                               ///< [out] A sequence of \p num_segments segment totals
-        int                     num_values,                             ///< [in] Total number of values to reduce
-        int                     num_segments,                           ///< [in] Number of segments being reduced
-        cudaStream_t            stream              = 0,                ///< [in] <b>[optional]</b> CUDA stream to launch kernels within.  Default is stream<sub>0</sub>.
-        bool                    debug_synchronous   = false)            ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors.  Also causes launch configurations to be printed to the console.  Default is \p false.
-    {
-        // Signed integer type for global offsets
-        typedef int OffsetT;
-
-        // Value type
-        typedef typename std::iterator_traits<ValueIterator>::value_type Value;
-
-        Value identity = Value();
-        cub::Sum reduction_op;
-
-        typedef DeviceSegReduceDispatch<
-                ValueIterator,
-                SegmentOffsetIterator,
-                OutputIteratorT,
-                cub::Sum,
-                OffsetT>
-            DeviceSegReduceDispatch;
-
-        return DeviceSegReduceDispatch::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_values,
-            d_segment_offsets,
-            d_output,
-            num_values,
-            num_segments,
-            identity,
-            reduction_op,
-            stream,
-            debug_synchronous);
-    }
-};
-
-
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Initialize problem
- */
-template <typename OffsetT, typename Value>
-void Initialize(
-    GenMode         gen_mode,
-    Value           *h_values,
-    vector<OffsetT> &segment_offsets,
-    int             num_values,
-    int             avg_segment_size)
-{
-    // Initialize values
-//    if (g_verbose) printf("Values: ");
-    for (int i = 0; i < num_values; ++i)
-    {
-        InitValue(gen_mode, h_values[i], i);
-//        if (g_verbose) std::cout << h_values[i] << ", ";
-    }
-//    if (g_verbose) printf("\n\n");
-
-    // Initialize segment lengths
-    const unsigned int  MAX_INTEGER         = -1u;
-    const unsigned int  MAX_SEGMENT_LENGTH  = avg_segment_size * 2;
-    const double        SCALE_FACTOR        = double(MAX_SEGMENT_LENGTH) / double(MAX_INTEGER);
-
-    segment_offsets.push_back(0);
-
-    OffsetT consumed = 0;
-    OffsetT remaining = num_values;
-    while (remaining > 0)
-    {
-        // Randomly sample a 32-bit unsigned int
-        unsigned int segment_length;
-        RandomBits(segment_length);
-
-        // Scale to maximum segment length
-        segment_length = (unsigned int) (double(segment_length) * SCALE_FACTOR);
-        segment_length = CUB_MIN(segment_length, remaining);
-
-        consumed += segment_length;
-        remaining -= segment_length;
-
-        segment_offsets.push_back(consumed);
-    }
-}
-
-
-/**
- * Compute reference answer
- */
-template <typename OffsetT, typename Value>
-void ComputeReference(
-    Value       *h_values,
-    OffsetT     *h_segment_offsets,
-    Value       *h_reference,
-    int         num_segments,
-    Value       identity)
-{
-    if (g_verbose) printf("%d segment reductions: ", num_segments);
-    for (int segment = 0; segment < num_segments; ++segment)
-    {
-        h_reference[segment] = identity;
-
-        for (int i = h_segment_offsets[segment]; i < h_segment_offsets[segment + 1]; ++i)
-        {
-            h_reference[segment] += h_values[i];
-        }
-        if (g_verbose) std::cout << h_reference[segment] << ", ";
-    }
-    if (g_verbose) printf("\n\n");
-}
-
-
-/**
- * Simple test of device
- */
-template <
-    bool            CDP,
-    typename        OffsetT,
-    typename        Value,
-    typename        ReductionOp>
-void Test(
-    OffsetT         num_values,
-    int             avg_segment_size,
-    ReductionOp     reduction_op,
-    Value           identity,
-    char*           type_string)
-{
-    Value   *h_values = NULL;
-    Value   *h_reference = NULL;
-    OffsetT *h_segment_offsets = NULL;
-
-    printf("%d\n", num_values);
-
-    // Initialize problem on host
-    h_values = new Value[num_values];
-    vector<OffsetT> segment_offsets;
-    Initialize(UNIFORM, h_values, segment_offsets, num_values, avg_segment_size);
-
-    // Allocate simple offsets array and copy STL vector into it
-    h_segment_offsets = new OffsetT[segment_offsets.size()];
-    for (int i = 0; i < segment_offsets.size(); ++i)
-        h_segment_offsets[i] = segment_offsets[i];
-
-    OffsetT num_segments = segment_offsets.size() - 1;
-    if (g_verbose)
-    {
-        printf("%d segment offsets: ", num_segments);
-        for (int i = 0; i < num_segments; ++i)
-            std::cout << h_segment_offsets[i] << "(" << h_segment_offsets[i + 1] - h_segment_offsets[i] << "), ";
-        if (g_verbose) std::cout << std::endl << std::endl;
-    }
-
-    // Solve problem on host
-    h_reference = new Value[num_segments];
-    ComputeReference(h_values, h_segment_offsets, h_reference, num_segments, identity);
-
-    printf("\n\n%s cub::DeviceSegReduce::%s %d items (%d-byte %s), %d segments (%d-byte offset indices)\n",
-        (CDP) ? "CDP device invoked" : "Host-invoked",
-        (Equals<ReductionOp, Sum>::VALUE) ? "Sum" : "Reduce",
-        num_values, (int) sizeof(Value), type_string,
-        num_segments, (int) sizeof(OffsetT));
-    fflush(stdout);
-
-    // Allocate and initialize problem on device
-    Value   *d_values = NULL;
-    OffsetT *d_segment_offsets = NULL;
-    Value   *d_output = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values, sizeof(Value) * num_values));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(OffsetT) * (num_segments + 1)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_output, sizeof(Value) * num_segments));
-    CubDebugExit(cudaMemcpy(d_values, h_values, sizeof(Value) * num_values, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_segment_offsets, h_segment_offsets, sizeof(OffsetT) * (num_segments + 1), cudaMemcpyHostToDevice));
-
-    // Request and allocate temporary storage
-    void    *d_temp_storage = NULL;
-    size_t  temp_storage_bytes = 0;
-    CubDebugExit(DeviceSegReduce::Sum(d_temp_storage, temp_storage_bytes, d_values, d_segment_offsets, d_output, num_values, num_segments, 0, false));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Clear device output
-    CubDebugExit(cudaMemset(d_output, 0, sizeof(Value) * num_segments));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(DeviceSegReduce::Sum(d_temp_storage, temp_storage_bytes, d_values, d_segment_offsets, d_output, num_values, num_segments, 0, true));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_output, num_segments, true, g_verbose);
-    printf("\t%s", compare ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    for (int i = 0; i < g_timing_iterations; ++i)
-    {
-        CubDebugExit(DeviceSegReduce::Sum(d_temp_storage, temp_storage_bytes, d_values, d_segment_offsets, d_output, num_values, num_segments, 0, false));
-    }
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(num_values) / avg_millis / 1000.0 / 1000.0;
-        float giga_bandwidth = giga_rate *
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", avg_millis, giga_rate, giga_bandwidth);
-    }
-
-    // Device cleanup
-    if (d_values) CubDebugExit(g_allocator.DeviceFree(d_values));
-    if (d_segment_offsets) CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
-    if (d_output) CubDebugExit(g_allocator.DeviceFree(d_output));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Host cleanup
-    if (h_values)           delete[] h_values;
-    if (h_segment_offsets)  delete[] h_segment_offsets;
-    if (h_reference)        delete[] h_reference;
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_values          = 32 * 1024 * 1024;
-    int avg_segment_size    = 500;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_values);
-    args.GetCmdLineArgument("ss", avg_segment_size);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--v] "
-            "[--i=<timing iterations>] "
-            "[--n=<input samples>]\n"
-            "[--ss=<average segment size>]\n"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    Test<false>((int) num_values, avg_segment_size, Sum(), (long long) 0, CUB_TYPE_STRING(long long));
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/experimental/histogram/histogram_cub.h b/ml-xgboost/cub/experimental/histogram/histogram_cub.h
deleted file mode 100644
index 52556fc..0000000
--- a/ml-xgboost/cub/experimental/histogram/histogram_cub.h
+++ /dev/null
@@ -1,109 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-#include <cub/device/device_histogram.cuh>
-
-using namespace cub;
-
-template <
-    int         NUM_CHANNELS,
-    int         ACTIVE_CHANNELS,
-    int         NUM_BINS,
-    typename    PixelType>
-double run_cub_histogram(
-    PixelType *d_image,
-    int width,
-    int height,
-    unsigned int *d_hist, 
-    bool is_warmup)
-{
-    enum {
-        is_float = Equals<PixelType, float4>::VALUE,
-    };
-
-    typedef typename If<is_float, float, unsigned char>::Type    SampleT;    // Sample type
-    typedef typename If<is_float, float, unsigned int>::Type     LevelT;     // Level type (uint32 for uchar)
-
-    // Setup data structures
-    unsigned int*       d_histogram[ACTIVE_CHANNELS];
-    int                 num_levels[ACTIVE_CHANNELS];            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              lower_level[ACTIVE_CHANNELS];           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    LevelT              upper_level[ACTIVE_CHANNELS];           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-
-    for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-    {
-        d_histogram[CHANNEL] = d_hist + (CHANNEL * NUM_BINS);
-        num_levels[CHANNEL] = NUM_BINS + 1;
-        lower_level[CHANNEL] = 0;
-        upper_level[CHANNEL] = (is_float) ? 1 : 256;
-    }
-
-    // Allocate temporary storage
-    size_t temp_storage_bytes = 0;
-    void *d_temp_storage = NULL;
-
-    SampleT* d_image_samples = (SampleT*) d_image;
-
-    // Get amount of temporary storage needed
-    DeviceHistogram::MultiHistogramEven<NUM_CHANNELS, ACTIVE_CHANNELS>(
-        d_temp_storage,
-        temp_storage_bytes,
-        d_image_samples,
-        d_histogram,
-        num_levels,
-        lower_level,
-        upper_level,
-        width * height, 
-        (cudaStream_t) 0,
-        is_warmup);
-
-    cudaMalloc(&d_temp_storage, temp_storage_bytes);
-
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-
-    // Compute histogram
-    DeviceHistogram::MultiHistogramEven<NUM_CHANNELS, ACTIVE_CHANNELS>(
-        d_temp_storage,
-        temp_storage_bytes,
-        d_image_samples,
-        d_histogram,
-        num_levels,
-        lower_level,
-        upper_level,
-        width * height, 
-        (cudaStream_t) 0,
-        is_warmup);
-
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    cudaFree(d_temp_storage);
-
-    return elapsed_millis;
-}
-
diff --git a/ml-xgboost/cub/experimental/histogram/histogram_gmem_atomics.h b/ml-xgboost/cub/experimental/histogram/histogram_gmem_atomics.h
deleted file mode 100644
index cfe9247..0000000
--- a/ml-xgboost/cub/experimental/histogram/histogram_gmem_atomics.h
+++ /dev/null
@@ -1,185 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-#include <test/test_util.h>
-
-namespace histogram_gmem_atomics
-{
-    // Decode float4 pixel into bins
-    template <int NUM_BINS, int ACTIVE_CHANNELS>
-    __device__ __forceinline__ void DecodePixel(float4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-    {
-        float* samples = reinterpret_cast<float*>(&pixel);
-
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-            bins[CHANNEL] = (unsigned int) (samples[CHANNEL] * float(NUM_BINS));
-    }
-
-    // Decode uchar4 pixel into bins
-    template <int NUM_BINS, int ACTIVE_CHANNELS>
-    __device__ __forceinline__ void DecodePixel(uchar4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-    {
-        unsigned char* samples = reinterpret_cast<unsigned char*>(&pixel);
-
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-            bins[CHANNEL] = (unsigned int) (samples[CHANNEL]);
-    }
-
-    // Decode uchar1 pixel into bins
-    template <int NUM_BINS, int ACTIVE_CHANNELS>
-    __device__ __forceinline__ void DecodePixel(uchar1 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-    {
-        bins[0] = (unsigned int) pixel.x;
-    }
-
-    // First-pass histogram kernel (binning into privatized counters)
-    template <
-        int         NUM_PARTS,
-        int         ACTIVE_CHANNELS,
-        int         NUM_BINS,
-        typename    PixelType>
-    __global__ void histogram_gmem_atomics(
-        const PixelType *in,
-        int width,
-        int height,
-        unsigned int *out)
-    {
-        // global position and size
-        int x = blockIdx.x * blockDim.x + threadIdx.x;
-        int y = blockIdx.y * blockDim.y + threadIdx.y;
-        int nx = blockDim.x * gridDim.x;
-        int ny = blockDim.y * gridDim.y;
-
-        // threads in workgroup
-        int t = threadIdx.x + threadIdx.y * blockDim.x; // thread index in workgroup, linear in 0..nt-1
-        int nt = blockDim.x * blockDim.y; // total threads in workgroup
-
-        // group index in 0..ngroups-1
-        int g = blockIdx.x + blockIdx.y * gridDim.x;
-
-        // initialize smem
-        unsigned int *gmem = out + g * NUM_PARTS;
-        for (int i = t; i < ACTIVE_CHANNELS * NUM_BINS; i += nt)
-            gmem[i] = 0;
-        __syncthreads();
-
-        // process pixels (updates our group's partial histogram in gmem)
-        for (int col = x; col < width; col += nx)
-        {
-            for (int row = y; row < height; row += ny)
-            {
-                PixelType pixel = in[row * width + col];
-
-                unsigned int bins[ACTIVE_CHANNELS];
-                DecodePixel<NUM_BINS>(pixel, bins);
-
-                #pragma unroll
-                for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-                    atomicAdd(&gmem[(NUM_BINS * CHANNEL) + bins[CHANNEL]], 1);
-            }
-        }
-    }
-
-    // Second pass histogram kernel (accumulation)
-    template <
-        int         NUM_PARTS,
-        int         ACTIVE_CHANNELS,
-        int         NUM_BINS>
-    __global__ void histogram_gmem_accum(
-        const unsigned int *in,
-        int n,
-        unsigned int *out)
-    {
-        int i = blockIdx.x * blockDim.x + threadIdx.x;
-        if (i > ACTIVE_CHANNELS * NUM_BINS)
-            return; // out of range
-
-        unsigned int total = 0;
-        for (int j = 0; j < n; j++)
-            total += in[i + NUM_PARTS * j];
-
-        out[i] = total;
-    }
-
-
-}   // namespace histogram_gmem_atomics
-
-
-template <
-    int         ACTIVE_CHANNELS,
-    int         NUM_BINS,
-    typename    PixelType>
-double run_gmem_atomics(
-    PixelType *d_image,
-    int width,
-    int height,
-    unsigned int *d_hist,
-    bool warmup)
-{
-    enum
-    {
-        NUM_PARTS = 1024
-    };
-
-    cudaDeviceProp props;
-    cudaGetDeviceProperties(&props, 0);
-
-    dim3 block(32, 4);
-    dim3 grid(16, 16);
-    int total_blocks = grid.x * grid.y;
-
-    // allocate partial histogram
-    unsigned int *d_part_hist;
-    cudaMalloc(&d_part_hist, total_blocks * NUM_PARTS * sizeof(unsigned int));
-
-    dim3 block2(128);
-    dim3 grid2((3 * NUM_BINS + block.x - 1) / block.x);
-
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-
-    histogram_gmem_atomics::histogram_gmem_atomics<NUM_PARTS, ACTIVE_CHANNELS, NUM_BINS><<<grid, block>>>(
-        d_image,
-        width,
-        height,
-        d_part_hist);
-
-    histogram_gmem_atomics::histogram_gmem_accum<NUM_PARTS, ACTIVE_CHANNELS, NUM_BINS><<<grid2, block2>>>(
-        d_part_hist,
-        total_blocks,
-        d_hist);
-
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    cudaFree(d_part_hist);
-
-    return elapsed_millis;
-}
-
diff --git a/ml-xgboost/cub/experimental/histogram/histogram_smem_atomics.h b/ml-xgboost/cub/experimental/histogram/histogram_smem_atomics.h
deleted file mode 100644
index 2addba1..0000000
--- a/ml-xgboost/cub/experimental/histogram/histogram_smem_atomics.h
+++ /dev/null
@@ -1,195 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-#include <test/test_util.h>
-
-namespace histogram_smem_atomics
-{
-    // Decode float4 pixel into bins
-    template <int NUM_BINS, int ACTIVE_CHANNELS>
-    __device__ __forceinline__ void DecodePixel(float4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-    {
-        float* samples = reinterpret_cast<float*>(&pixel);
-
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-            bins[CHANNEL] = (unsigned int) (samples[CHANNEL] * float(NUM_BINS));
-    }
-
-    // Decode uchar4 pixel into bins
-    template <int NUM_BINS, int ACTIVE_CHANNELS>
-    __device__ __forceinline__ void DecodePixel(uchar4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-    {
-        unsigned char* samples = reinterpret_cast<unsigned char*>(&pixel);
-
-        #pragma unroll
-        for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-            bins[CHANNEL] = (unsigned int) (samples[CHANNEL]);
-    }
-
-    // Decode uchar1 pixel into bins
-    template <int NUM_BINS, int ACTIVE_CHANNELS>
-    __device__ __forceinline__ void DecodePixel(uchar1 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-    {
-        bins[0] = (unsigned int) pixel.x;
-    }
-
-    // First-pass histogram kernel (binning into privatized counters)
-    template <
-        int         NUM_PARTS,
-        int         ACTIVE_CHANNELS,
-        int         NUM_BINS,
-        typename    PixelType>
-    __global__ void histogram_smem_atomics(
-        const PixelType *in,
-        int width,
-        int height,
-        unsigned int *out)
-    {
-        // global position and size
-        int x = blockIdx.x * blockDim.x + threadIdx.x;
-        int y = blockIdx.y * blockDim.y + threadIdx.y;
-        int nx = blockDim.x * gridDim.x;
-        int ny = blockDim.y * gridDim.y;
-
-        // threads in workgroup
-        int t = threadIdx.x + threadIdx.y * blockDim.x; // thread index in workgroup, linear in 0..nt-1
-        int nt = blockDim.x * blockDim.y; // total threads in workgroup
-
-        // group index in 0..ngroups-1
-        int g = blockIdx.x + blockIdx.y * gridDim.x;
-
-        // initialize smem
-        __shared__ unsigned int smem[ACTIVE_CHANNELS * NUM_BINS + 3];
-        for (int i = t; i < ACTIVE_CHANNELS * NUM_BINS + 3; i += nt)
-            smem[i] = 0;
-        __syncthreads();
-
-        // process pixels
-        // updates our group's partial histogram in smem
-        for (int col = x; col < width; col += nx)
-        {
-            for (int row = y; row < height; row += ny)
-            {
-                PixelType pixel = in[row * width + col];
-
-                unsigned int bins[ACTIVE_CHANNELS];
-                DecodePixel<NUM_BINS>(pixel, bins);
-
-                #pragma unroll
-                for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-                    atomicAdd(&smem[(NUM_BINS * CHANNEL) + bins[CHANNEL] + CHANNEL], 1);
-            }
-        }
-
-        __syncthreads();
-
-        // move to our workgroup's slice of output
-        out += g * NUM_PARTS;
-
-        // store local output to global
-        for (int i = t; i < NUM_BINS; i += nt)
-        {
-            #pragma unroll
-            for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-                out[i + NUM_BINS * CHANNEL] = smem[i + NUM_BINS * CHANNEL + CHANNEL];
-        }
-    }
-
-    // Second pass histogram kernel (accumulation)
-    template <
-        int         NUM_PARTS,
-        int         ACTIVE_CHANNELS,
-        int         NUM_BINS>
-    __global__ void histogram_smem_accum(
-        const unsigned int *in,
-        int n,
-        unsigned int *out)
-    {
-        int i = blockIdx.x * blockDim.x + threadIdx.x;
-        if (i > ACTIVE_CHANNELS * NUM_BINS) return; // out of range
-        unsigned int total = 0;
-        for (int j = 0; j < n; j++)
-            total += in[i + NUM_PARTS * j];
-        out[i] = total;
-    }
-
-}   // namespace histogram_smem_atomics
-
-
-template <
-    int         ACTIVE_CHANNELS,
-    int         NUM_BINS,
-    typename    PixelType>
-double run_smem_atomics(
-    PixelType *d_image,
-    int width,
-    int height,
-    unsigned int *d_hist, 
-    bool warmup)
-{
-    enum
-    {
-        NUM_PARTS = 1024
-    };
-
-    cudaDeviceProp props;
-    cudaGetDeviceProperties(&props, 0);
-
-    dim3 block(32, 4);
-    dim3 grid(16, 16);
-    int total_blocks = grid.x * grid.y;
-
-    // allocate partial histogram
-    unsigned int *d_part_hist;
-    cudaMalloc(&d_part_hist, total_blocks * NUM_PARTS * sizeof(unsigned int));
-
-    dim3 block2(128);
-    dim3 grid2((ACTIVE_CHANNELS * NUM_BINS + block.x - 1) / block.x);
-
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-
-    histogram_smem_atomics::histogram_smem_atomics<NUM_PARTS, ACTIVE_CHANNELS, NUM_BINS><<<grid, block>>>(
-        d_image,
-        width,
-        height,
-        d_part_hist);
-
-    histogram_smem_atomics::histogram_smem_accum<NUM_PARTS, ACTIVE_CHANNELS, NUM_BINS><<<grid2, block2>>>(
-        d_part_hist,
-        total_blocks,
-        d_hist);
-
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    cudaFree(d_part_hist);
-
-    return elapsed_millis;
-}
-
diff --git a/ml-xgboost/cub/experimental/histogram_compare.cu b/ml-xgboost/cub/experimental/histogram_compare.cu
deleted file mode 100644
index 0bda006..0000000
--- a/ml-xgboost/cub/experimental/histogram_compare.cu
+++ /dev/null
@@ -1,635 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-#include <stdio.h>
-#include <map>
-#include <vector>
-#include <algorithm>
-#include <cstdio>
-#include <fstream>
-
-#include "histogram/histogram_gmem_atomics.h"
-#include "histogram/histogram_smem_atomics.h"
-#include "histogram/histogram_cub.h"
-
-#include <cub/util_allocator.cuh>
-#include <test/test_util.h>
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants, and type declarations
-//---------------------------------------------------------------------
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-bool                    g_verbose = false;  // Whether to display input/output to console
-bool                    g_report = false;   // Whether to display a full report in CSV format
-CachingDeviceAllocator  g_allocator(true);  // Caching allocator for device memory
-
-struct less_than_value
-{
-    inline bool operator()(
-        const std::pair<std::string, double> &a,
-        const std::pair<std::string, double> &b)
-    {
-        return a.second < b.second;
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Targa (.tga) image file parsing
-//---------------------------------------------------------------------
-
-/**
- * TGA image header info
- */
-struct TgaHeader
-{
-    char idlength;
-    char colormaptype;
-    char datatypecode;
-    short colormaporigin;
-    short colormaplength;
-    char colormapdepth;
-    short x_origin;
-    short y_origin;
-    short width;
-    short height;
-    char bitsperpixel;
-    char imagedescriptor;
-
-    void Parse (FILE *fptr)
-    {
-        idlength = fgetc(fptr);
-        colormaptype = fgetc(fptr);
-        datatypecode = fgetc(fptr);
-        fread(&colormaporigin, 2, 1, fptr);
-        fread(&colormaplength, 2, 1, fptr);
-        colormapdepth = fgetc(fptr);
-        fread(&x_origin, 2, 1, fptr);
-        fread(&y_origin, 2, 1, fptr);
-        fread(&width, 2, 1, fptr);
-        fread(&height, 2, 1, fptr);
-        bitsperpixel = fgetc(fptr);
-        imagedescriptor = fgetc(fptr);
-    }
-
-    void Display (FILE *fptr)
-    {
-        fprintf(fptr, "ID length:           %d\n", idlength);
-        fprintf(fptr, "Color map type:      %d\n", colormaptype);
-        fprintf(fptr, "Image type:          %d\n", datatypecode);
-        fprintf(fptr, "Color map offset:    %d\n", colormaporigin);
-        fprintf(fptr, "Color map length:    %d\n", colormaplength);
-        fprintf(fptr, "Color map depth:     %d\n", colormapdepth);
-        fprintf(fptr, "X origin:            %d\n", x_origin);
-        fprintf(fptr, "Y origin:            %d\n", y_origin);
-        fprintf(fptr, "Width:               %d\n", width);
-        fprintf(fptr, "Height:              %d\n", height);
-        fprintf(fptr, "Bits per pixel:      %d\n", bitsperpixel);
-        fprintf(fptr, "Descriptor:          %d\n", imagedescriptor);
-    }
-};
-
-
-/**
- * Decode image byte data into pixel
- */
-void ParseTgaPixel(uchar4 &pixel, unsigned char *tga_pixel, int bytes)
-{
-    if (bytes == 4)
-    {
-        pixel.x = tga_pixel[2];
-        pixel.y = tga_pixel[1];
-        pixel.z = tga_pixel[0];
-        pixel.w = tga_pixel[3];
-    }
-    else if (bytes == 3)
-    {
-        pixel.x = tga_pixel[2];
-        pixel.y = tga_pixel[1];
-        pixel.z = tga_pixel[0];
-        pixel.w = 0;
-    }
-    else if (bytes == 2)
-    {
-        pixel.x = (tga_pixel[1] & 0x7c) << 1;
-        pixel.y = ((tga_pixel[1] & 0x03) << 6) | ((tga_pixel[0] & 0xe0) >> 2);
-        pixel.z = (tga_pixel[0] & 0x1f) << 3;
-        pixel.w = (tga_pixel[1] & 0x80);
-    }
-}
-
-
-/**
- * Reads a .tga image file
- */
-void ReadTga(uchar4* &pixels, int &width, int &height, const char *filename)
-{
-    // Open the file
-    FILE *fptr;
-    if ((fptr = fopen(filename, "rb")) == NULL)
-    {
-        fprintf(stderr, "File open failed\n");
-        exit(-1);
-    }
-
-    // Parse header
-    TgaHeader header;
-    header.Parse(fptr);
-//    header.Display(stdout);
-    width = header.width;
-    height = header.height;
-
-    // Verify compatibility
-    if (header.datatypecode != 2 && header.datatypecode != 10)
-    {
-        fprintf(stderr, "Can only handle image type 2 and 10\n");
-        exit(-1);
-    }
-    if (header.bitsperpixel != 16 && header.bitsperpixel != 24 && header.bitsperpixel != 32)
-    {
-        fprintf(stderr, "Can only handle pixel depths of 16, 24, and 32\n");
-        exit(-1);
-    }
-    if (header.colormaptype != 0 && header.colormaptype != 1)
-    {
-        fprintf(stderr, "Can only handle color map types of 0 and 1\n");
-        exit(-1);
-    }
-
-    // Skip unnecessary header info
-    int skip_bytes = header.idlength + (header.colormaptype * header.colormaplength);
-    fseek(fptr, skip_bytes, SEEK_CUR);
-
-    // Read the image
-    int pixel_bytes = header.bitsperpixel / 8;
-
-    // Allocate and initialize pixel data
-    size_t image_bytes = width * height * sizeof(uchar4);
-    if ((pixels == NULL) && ((pixels = (uchar4*) malloc(image_bytes)) == NULL))
-    {
-        fprintf(stderr, "malloc of image failed\n");
-        exit(-1);
-    }
-    memset(pixels, 0, image_bytes);
-
-    // Parse pixels
-    unsigned char   tga_pixel[5];
-    int             current_pixel = 0;
-    while (current_pixel < header.width * header.height)
-    {
-        if (header.datatypecode == 2)
-        {
-            // Uncompressed
-            if (fread(tga_pixel, 1, pixel_bytes, fptr) != pixel_bytes)
-            {
-                fprintf(stderr, "Unexpected end of file at pixel %d  (uncompressed)\n", current_pixel);
-                exit(-1);
-            }
-            ParseTgaPixel(pixels[current_pixel], tga_pixel, pixel_bytes);
-            current_pixel++;
-        }
-        else if (header.datatypecode == 10)
-        {
-            // Compressed
-            if (fread(tga_pixel, 1, pixel_bytes + 1, fptr) != pixel_bytes + 1)
-            {
-                fprintf(stderr, "Unexpected end of file at pixel %d (compressed)\n", current_pixel);
-                exit(-1);
-            }
-            int run_length = tga_pixel[0] & 0x7f;
-            ParseTgaPixel(pixels[current_pixel], &(tga_pixel[1]), pixel_bytes);
-            current_pixel++;
-
-            if (tga_pixel[0] & 0x80)
-            {
-                // RLE chunk
-                for (int i = 0; i < run_length; i++)
-                {
-                    ParseTgaPixel(pixels[current_pixel], &(tga_pixel[1]), pixel_bytes);
-                    current_pixel++;
-                }
-            }
-            else
-            {
-                // Normal chunk
-                for (int i = 0; i < run_length; i++)
-                {
-                    if (fread(tga_pixel, 1, pixel_bytes, fptr) != pixel_bytes)
-                    {
-                        fprintf(stderr, "Unexpected end of file at pixel %d (normal)\n", current_pixel);
-                        exit(-1);
-                    }
-                    ParseTgaPixel(pixels[current_pixel], tga_pixel, pixel_bytes);
-                    current_pixel++;
-                }
-            }
-        }
-    }
-
-    // Close file
-    fclose(fptr);
-}
-
-
-
-//---------------------------------------------------------------------
-// Random image generation
-//---------------------------------------------------------------------
-
-/**
- * Generate a random image with specified entropy
- */
-void GenerateRandomImage(uchar4* &pixels, int width, int height, int entropy_reduction)
-{
-    int num_pixels = width * height;
-    size_t image_bytes = num_pixels * sizeof(uchar4);
-    if ((pixels == NULL) && ((pixels = (uchar4*) malloc(image_bytes)) == NULL))
-    {
-        fprintf(stderr, "malloc of image failed\n");
-        exit(-1);
-    }
-
-    for (int i = 0; i < num_pixels; ++i)
-    {
-        RandomBits(pixels[i].x, entropy_reduction);
-        RandomBits(pixels[i].y, entropy_reduction);
-        RandomBits(pixels[i].z, entropy_reduction);
-        RandomBits(pixels[i].w, entropy_reduction);
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Histogram verification
-//---------------------------------------------------------------------
-
-// Decode float4 pixel into bins
-template <int NUM_BINS, int ACTIVE_CHANNELS>
-void DecodePixelGold(float4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-{
-    float* samples = reinterpret_cast<float*>(&pixel);
-
-    for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-        bins[CHANNEL] = (unsigned int) (samples[CHANNEL] * float(NUM_BINS));
-}
-
-// Decode uchar4 pixel into bins
-template <int NUM_BINS, int ACTIVE_CHANNELS>
-void DecodePixelGold(uchar4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-{
-    unsigned char* samples = reinterpret_cast<unsigned char*>(&pixel);
-
-    for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-        bins[CHANNEL] = (unsigned int) (samples[CHANNEL]);
-}
-
-// Decode uchar1 pixel into bins
-template <int NUM_BINS, int ACTIVE_CHANNELS>
-void DecodePixelGold(uchar1 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
-{
-    bins[0] = (unsigned int) pixel.x;
-}
-
-
-// Compute reference histogram.  Specialized for uchar4
-template <
-    int         ACTIVE_CHANNELS,
-    int         NUM_BINS,
-    typename    PixelType>
-void HistogramGold(PixelType *image, int width, int height, unsigned int* hist)
-{
-    memset(hist, 0, ACTIVE_CHANNELS * NUM_BINS * sizeof(unsigned int));
-
-    for (int i = 0; i < width; i++)
-    {
-        for (int j = 0; j < height; j++)
-        {
-            PixelType pixel = image[i + j * width];
-
-            unsigned int bins[ACTIVE_CHANNELS];
-            DecodePixelGold<NUM_BINS>(pixel, bins);
-
-            for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
-            {
-                hist[(NUM_BINS * CHANNEL) + bins[CHANNEL]]++;
-            }
-        }
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Test execution
-//---------------------------------------------------------------------
-
-/**
- * Run a specific histogram implementation
- */
-template <
-    int         ACTIVE_CHANNELS,
-    int         NUM_BINS,
-    typename    PixelType>
-void RunTest(
-    std::vector<std::pair<std::string, double> >&   timings,
-    PixelType*                                      d_pixels,
-    const int                                       width,
-    const int                                       height,
-    unsigned int *                                  d_hist,
-    unsigned int *                                  h_hist,
-    int                                             timing_iterations,
-    const char *                                    long_name,
-    const char *                                    short_name,
-    double (*f)(PixelType*, int, int, unsigned int*, bool))
-{
-    if (!g_report) printf("%s ", long_name); fflush(stdout);
-
-    // Run single test to verify (and code cache)
-    (*f)(d_pixels, width, height, d_hist, !g_report);
-
-    int compare = CompareDeviceResults(h_hist, d_hist, ACTIVE_CHANNELS * NUM_BINS, true, g_verbose);
-    if (!g_report) printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
-
-    double elapsed_ms = 0;
-    for (int i = 0; i < timing_iterations; i++)
-    {
-        elapsed_ms += (*f)(d_pixels, width, height, d_hist, false);
-    }
-    double avg_us = (elapsed_ms / timing_iterations) * 1000;    // average in us
-    timings.push_back(std::pair<std::string, double>(short_name, avg_us));
-
-    if (!g_report)
-    {
-        printf("Avg time %.3f us (%d iterations)\n", avg_us, timing_iterations); fflush(stdout);
-    }
-    else
-    {
-        printf("%.3f, ", avg_us); fflush(stdout);
-    }
-
-    AssertEquals(0, compare);
-}
-
-
-/**
- * Evaluate corpus of histogram implementations
- */
-template <
-    int         NUM_CHANNELS,
-    int         ACTIVE_CHANNELS,
-    int         NUM_BINS,
-    typename    PixelType>
-void TestMethods(
-    PixelType*  h_pixels,
-    int         height,
-    int         width,
-    int         timing_iterations,
-    double      bandwidth_GBs)
-{
-    // Copy data to gpu
-    PixelType* d_pixels;
-    size_t pixel_bytes = width * height * sizeof(PixelType);
-    CubDebugExit(g_allocator.DeviceAllocate((void**) &d_pixels, pixel_bytes));
-    CubDebugExit(cudaMemcpy(d_pixels, h_pixels, pixel_bytes, cudaMemcpyHostToDevice));
-
-    if (g_report) printf("%.3f, ", double(pixel_bytes) / bandwidth_GBs / 1000);
-
-    // Allocate results arrays on cpu/gpu
-    unsigned int *h_hist;
-    unsigned int *d_hist;
-    size_t histogram_bytes = NUM_BINS * ACTIVE_CHANNELS * sizeof(unsigned int);
-    h_hist = (unsigned int *) malloc(histogram_bytes);
-    g_allocator.DeviceAllocate((void **) &d_hist, histogram_bytes);
-
-    // Compute reference cpu histogram
-    HistogramGold<ACTIVE_CHANNELS, NUM_BINS>(h_pixels, width, height, h_hist);
-
-    // Store timings
-    std::vector<std::pair<std::string, double> > timings;
-
-    // Run experiments
-    RunTest<ACTIVE_CHANNELS, NUM_BINS>(timings, d_pixels, width, height, d_hist, h_hist, timing_iterations,
-        "CUB", "CUB", run_cub_histogram<NUM_CHANNELS, ACTIVE_CHANNELS, NUM_BINS, PixelType>);
-    RunTest<ACTIVE_CHANNELS, NUM_BINS>(timings, d_pixels, width, height, d_hist, h_hist, timing_iterations,
-        "Shared memory atomics", "smem atomics", run_smem_atomics<ACTIVE_CHANNELS, NUM_BINS, PixelType>);
-    RunTest<ACTIVE_CHANNELS, NUM_BINS>(timings, d_pixels, width, height, d_hist, h_hist, timing_iterations,
-        "Global memory atomics", "gmem atomics", run_gmem_atomics<ACTIVE_CHANNELS, NUM_BINS, PixelType>);
-
-    // Report timings
-    if (!g_report)
-    {
-        std::sort(timings.begin(), timings.end(), less_than_value());
-        printf("Timings (us):\n");
-        for (int i = 0; i < timings.size(); i++)
-        {
-            double bandwidth = height * width * sizeof(PixelType) / timings[i].second / 1000;
-            printf("\t %.3f %s (%.3f GB/s, %.3f%% peak)\n", timings[i].second, timings[i].first.c_str(), bandwidth, bandwidth / bandwidth_GBs * 100);
-        }
-        printf("\n");
-    }
-
-    // Free data
-    CubDebugExit(g_allocator.DeviceFree(d_pixels));
-    CubDebugExit(g_allocator.DeviceFree(d_hist));
-    free(h_hist);
-}
-
-
-/**
- * Test different problem genres
- */
-void TestGenres(
-    uchar4*     uchar4_pixels,
-    int         height,
-    int         width,
-    int         timing_iterations,
-    double      bandwidth_GBs)
-{
-    int num_pixels = width * height;
-
-    {
-        if (!g_report) printf("1 channel uchar1 tests (256-bin):\n\n"); fflush(stdout);
-
-        size_t      image_bytes     = num_pixels * sizeof(uchar1);
-        uchar1*     uchar1_pixels   = (uchar1*) malloc(image_bytes);
-
-        // Convert to 1-channel (averaging first 3 channels)
-        for (int i = 0; i < num_pixels; ++i)
-        {
-            uchar1_pixels[i].x = (unsigned char)
-                (((unsigned int) uchar4_pixels[i].x +
-                  (unsigned int) uchar4_pixels[i].y +
-                  (unsigned int) uchar4_pixels[i].z) / 3);
-        }
-
-        TestMethods<1, 1, 256>(uchar1_pixels, width, height, timing_iterations, bandwidth_GBs);
-        free(uchar1_pixels);
-        if (g_report) printf(", ");
-    }
-
-    {
-        if (!g_report) printf("3/4 channel uchar4 tests (256-bin):\n\n"); fflush(stdout);
-        TestMethods<4, 3, 256>(uchar4_pixels, width, height, timing_iterations, bandwidth_GBs);
-        if (g_report) printf(", ");
-    }
-
-    {
-        if (!g_report) printf("3/4 channel float4 tests (256-bin):\n\n"); fflush(stdout);
-        size_t      image_bytes     = num_pixels * sizeof(float4);
-        float4*     float4_pixels   = (float4*) malloc(image_bytes);
-
-        // Convert to float4 with range [0.0, 1.0)
-        for (int i = 0; i < num_pixels; ++i)
-        {
-            float4_pixels[i].x = float(uchar4_pixels[i].x) / 256;
-            float4_pixels[i].y = float(uchar4_pixels[i].y) / 256;
-            float4_pixels[i].z = float(uchar4_pixels[i].z) / 256;
-            float4_pixels[i].w = float(uchar4_pixels[i].w) / 256;
-        }
-        TestMethods<4, 3, 256>(float4_pixels, width, height, timing_iterations, bandwidth_GBs);
-        free(float4_pixels);
-        if (g_report) printf("\n");
-    }
-}
-
-
-/**
- * Main
- */
-int main(int argc, char **argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf(
-            "%s "
-            "[--device=<device-id>] "
-            "[--v] "
-            "[--i=<timing iterations>] "
-            "\n\t"
-                "--file=<.tga filename> "
-            "\n\t"
-                "--entropy=<-1 (0%), 0 (100%), 1 (81%), 2 (54%), 3 (34%), 4 (20%), ..."
-                "[--height=<default: 1080>] "
-                "[--width=<default: 1920>] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    std::string         filename;
-    int                 timing_iterations   = 100;
-    int                 entropy_reduction   = 0;
-    int                 height              = 1080;
-    int                 width               = 1920;
-
-    g_verbose = args.CheckCmdLineFlag("v");
-    g_report = args.CheckCmdLineFlag("report");
-    args.GetCmdLineArgument("i", timing_iterations);
-    args.GetCmdLineArgument("file", filename);
-    args.GetCmdLineArgument("height", height);
-    args.GetCmdLineArgument("width", width);
-    args.GetCmdLineArgument("entropy", entropy_reduction);
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get GPU device bandwidth (GB/s)
-    int device_ordinal, bus_width, mem_clock_khz;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-    CubDebugExit(cudaDeviceGetAttribute(&bus_width, cudaDevAttrGlobalMemoryBusWidth, device_ordinal));
-    CubDebugExit(cudaDeviceGetAttribute(&mem_clock_khz, cudaDevAttrMemoryClockRate, device_ordinal));
-    double bandwidth_GBs = double(bus_width) * mem_clock_khz * 2 / 8 / 1000 / 1000;
-
-    // Run test(s)
-    uchar4* uchar4_pixels = NULL;
-    if (!g_report)
-    {
-        if (!filename.empty())
-        {
-            // Parse targa file
-            ReadTga(uchar4_pixels, width, height, filename.c_str());
-            printf("File %s: width(%d) height(%d)\n\n", filename.c_str(), width, height); fflush(stdout);
-        }
-        else
-        {
-            // Generate image
-            GenerateRandomImage(uchar4_pixels, width, height, entropy_reduction);
-            printf("Random image: entropy-reduction(%d) width(%d) height(%d)\n\n", entropy_reduction, width, height); fflush(stdout);
-        }
-
-        TestGenres(uchar4_pixels, height, width, timing_iterations, bandwidth_GBs);
-    }
-    else
-    {
-        // Run test suite
-        printf("Test, MIN, RLE CUB, SMEM, GMEM, , MIN, RLE_CUB, SMEM, GMEM, , MIN, RLE_CUB, SMEM, GMEM\n");
-
-        // Entropy reduction tests
-        for (entropy_reduction = 0; entropy_reduction < 5; ++entropy_reduction)
-        {
-            printf("entropy reduction %d, ", entropy_reduction);
-            GenerateRandomImage(uchar4_pixels, width, height, entropy_reduction);
-            TestGenres(uchar4_pixels, height, width, timing_iterations, bandwidth_GBs);
-        }
-        printf("entropy reduction -1, ");
-        GenerateRandomImage(uchar4_pixels, width, height, -1);
-        TestGenres(uchar4_pixels, height, width, timing_iterations, bandwidth_GBs);
-        printf("\n");
-
-        // File image tests
-        std::vector<std::string> file_tests;
-        file_tests.push_back("animals");
-        file_tests.push_back("apples");
-        file_tests.push_back("sunset");
-        file_tests.push_back("cheetah");
-        file_tests.push_back("nature");
-        file_tests.push_back("operahouse");
-        file_tests.push_back("austin");
-        file_tests.push_back("cityscape");
-
-        for (int i = 0; i < file_tests.size(); ++i)
-        {
-            printf("%s, ", file_tests[i].c_str());
-            std::string filename = std::string("histogram/benchmark/") + file_tests[i] + ".tga";
-            ReadTga(uchar4_pixels, width, height, filename.c_str());
-            TestGenres(uchar4_pixels, height, width, timing_iterations, bandwidth_GBs);
-        }
-    }
-
-    free(uchar4_pixels);
-
-    CubDebugExit(cudaDeviceSynchronize());
-    printf("\n\n");
-
-    return 0;
-}
diff --git a/ml-xgboost/cub/experimental/sparse_matrix.h b/ml-xgboost/cub/experimental/sparse_matrix.h
deleted file mode 100644
index 7681c64..0000000
--- a/ml-xgboost/cub/experimental/sparse_matrix.h
+++ /dev/null
@@ -1,1244 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Matrix data structures and parsing logic
- ******************************************************************************/
-
-#pragma once
-
-#include <cmath>
-#include <cstring>
-
-#include <iterator>
-#include <string>
-#include <algorithm>
-#include <iostream>
-#include <queue>
-#include <set>
-#include <fstream>
-#include <stdio.h>
-
-#ifdef CUB_MKL
-    #include <numa.h>
-    #include <mkl.h>
-#endif
-
-using namespace std;
-
-/******************************************************************************
- * COO matrix type
- ******************************************************************************/
-
-struct GraphStats
-{
-    int         num_rows;
-    int         num_cols;
-    int         num_nonzeros;
-
-    double      diag_dist_mean;         // mean
-    double      diag_dist_std_dev;      // sample std dev
-    double      pearson_r;    // coefficient of variation
-
-    double      row_length_mean;        // mean
-    double      row_length_std_dev;     // sample std_dev
-    double      row_length_variation;   // coefficient of variation
-    double      row_length_skewness;    // skewness
-
-    void Display(bool show_labels = true)
-    {
-        if (show_labels)
-            printf("\n"
-                "\t num_rows: %d\n"
-                "\t num_cols: %d\n"
-                "\t num_nonzeros: %d\n"
-                "\t diag_dist_mean: %.2f\n"
-                "\t diag_dist_std_dev: %.2f\n"
-                "\t pearson_r: %f\n"
-                "\t row_length_mean: %.5f\n"
-                "\t row_length_std_dev: %.5f\n"
-                "\t row_length_variation: %.5f\n"
-                "\t row_length_skewness: %.5f\n",
-                    num_rows,
-                    num_cols,
-                    num_nonzeros,
-                    diag_dist_mean,
-                    diag_dist_std_dev,
-                    pearson_r,
-                    row_length_mean,
-                    row_length_std_dev,
-                    row_length_variation,
-                    row_length_skewness);
-        else
-            printf(
-                "%d, "
-                "%d, "
-                "%d, "
-                "%.2f, "
-                "%.2f, "
-                "%f, "
-                "%.5f, "
-                "%.5f, "
-                "%.5f, "
-                "%.5f, ",
-                    num_rows,
-                    num_cols,
-                    num_nonzeros,
-                    diag_dist_mean,
-                    diag_dist_std_dev,
-                    pearson_r,
-                    row_length_mean,
-                    row_length_std_dev,
-                    row_length_variation,
-                    row_length_skewness);
-    }
-};
-
-
-
-/******************************************************************************
- * COO matrix type
- ******************************************************************************/
-
-
-/**
- * COO matrix type.  A COO matrix is just a vector of edge tuples.  Tuples are sorted
- * first by row, then by column.
- */
-template<typename ValueT, typename OffsetT>
-struct CooMatrix
-{
-    //---------------------------------------------------------------------
-    // Type definitions and constants
-    //---------------------------------------------------------------------
-
-    // COO edge tuple
-    struct CooTuple
-    {
-        OffsetT            row;
-        OffsetT            col;
-        ValueT             val;
-
-        CooTuple() {}
-        CooTuple(OffsetT row, OffsetT col) : row(row), col(col) {}
-        CooTuple(OffsetT row, OffsetT col, ValueT val) : row(row), col(col), val(val) {}
-
-        /**
-         * Comparator for sorting COO sparse format num_nonzeros
-         */
-        bool operator<(const CooTuple &other) const
-        {
-            if ((row < other.row) || ((row == other.row) && (col < other.col)))
-            {
-                return true;
-            }
-
-            return false;
-        }
-    };
-
-
-    //---------------------------------------------------------------------
-    // Data members
-    //---------------------------------------------------------------------
-
-    // Fields
-    int                 num_rows;
-    int                 num_cols;
-    int                 num_nonzeros;
-    CooTuple*           coo_tuples;
-
-    //---------------------------------------------------------------------
-    // Methods
-    //---------------------------------------------------------------------
-
-    // Constructor
-    CooMatrix() : num_rows(0), num_cols(0), num_nonzeros(0), coo_tuples(NULL) {}
-
-
-    /**
-     * Clear
-     */
-    void Clear()
-    {
-        if (coo_tuples) delete[] coo_tuples;
-        coo_tuples = NULL;
-    }
-
-
-    // Destructor
-    ~CooMatrix()
-    {
-        Clear();
-    }
-
-
-    // Display matrix to stdout
-    void Display()
-    {
-        cout << "COO Matrix (" << num_rows << " rows, " << num_cols << " columns, " << num_nonzeros << " non-zeros):\n";
-        cout << "Ordinal, Row, Column, Value\n";
-        for (int i = 0; i < num_nonzeros; i++)
-        {
-            cout << '\t' << i << ',' << coo_tuples[i].row << ',' << coo_tuples[i].col << ',' << coo_tuples[i].val << "\n";
-        }
-    }
-
-
-    /**
-     * Builds a symmetric COO sparse from an asymmetric CSR matrix.
-     */
-    template <typename CsrMatrixT>
-    void InitCsrSymmetric(CsrMatrixT &csr_matrix)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        num_rows        = csr_matrix.num_cols;
-        num_cols        = csr_matrix.num_rows;
-        num_nonzeros    = csr_matrix.num_nonzeros * 2;
-        coo_tuples      = new CooTuple[num_nonzeros];
-
-        for (OffsetT row = 0; row < csr_matrix.num_rows; ++row)
-        {
-            for (OffsetT nonzero = csr_matrix.row_offsets[row]; nonzero < csr_matrix.row_offsets[row + 1]; ++nonzero)
-            {
-                coo_tuples[nonzero].row = row;
-                coo_tuples[nonzero].col = csr_matrix.column_indices[nonzero];
-                coo_tuples[nonzero].val = csr_matrix.values[nonzero];
-
-                coo_tuples[csr_matrix.num_nonzeros + nonzero].row = coo_tuples[nonzero].col;
-                coo_tuples[csr_matrix.num_nonzeros + nonzero].col = coo_tuples[nonzero].row;
-                coo_tuples[csr_matrix.num_nonzeros + nonzero].val = csr_matrix.values[nonzero];
-
-            }
-        }
-
-        // Sort by rows, then columns
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-    }
-
-    /**
-     * Builds a COO sparse from a relabeled CSR matrix.
-     */
-    template <typename CsrMatrixT>
-    void InitCsrRelabel(CsrMatrixT &csr_matrix, OffsetT* relabel_indices)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        num_rows        = csr_matrix.num_rows;
-        num_cols        = csr_matrix.num_cols;
-        num_nonzeros    = csr_matrix.num_nonzeros;
-        coo_tuples      = new CooTuple[num_nonzeros];
-
-        for (OffsetT row = 0; row < num_rows; ++row)
-        {
-            for (OffsetT nonzero = csr_matrix.row_offsets[row]; nonzero < csr_matrix.row_offsets[row + 1]; ++nonzero)
-            {
-                coo_tuples[nonzero].row = relabel_indices[row];
-                coo_tuples[nonzero].col = relabel_indices[csr_matrix.column_indices[nonzero]];
-                coo_tuples[nonzero].val = csr_matrix.values[nonzero];
-            }
-        }
-
-        // Sort by rows, then columns
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-    }
-
-
-
-    /**
-     * Builds a METIS COO sparse from the given file.
-     */
-    void InitMetis(const string &metis_filename)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        // TODO
-    }
-
-
-    /**
-     * Builds a MARKET COO sparse from the given file.
-     */
-    void InitMarket(
-        const string&   market_filename,
-        ValueT          default_value       = 1.0,
-        bool            verbose             = false)
-    {
-        if (verbose) {
-            printf("Reading... "); fflush(stdout);
-        }
-
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        std::ifstream ifs;
-        ifs.open(market_filename.c_str(), std::ifstream::in);
-        if (!ifs.good())
-        {
-            fprintf(stderr, "Error opening file\n");
-            exit(1);
-        }
-
-        bool    array = false;
-        bool    symmetric = false;
-        bool    skew = false;
-        int     current_edge = -1;
-        char    line[1024];
-
-        if (verbose) {
-            printf("Parsing... "); fflush(stdout);
-        }
-
-        while (true)
-        {
-            ifs.getline(line, 1024);
-            if (!ifs.good())
-            {
-                // Done
-                break;
-            }
-
-            if (line[0] == '%')
-            {
-                // Comment
-                if (line[1] == '%')
-                {
-                    // Banner
-                    symmetric   = (strstr(line, "symmetric") != NULL);
-                    skew        = (strstr(line, "skew") != NULL);
-                    array       = (strstr(line, "array") != NULL);
-
-                    if (verbose) {
-                        printf("(symmetric: %d, skew: %d, array: %d) ", symmetric, skew, array); fflush(stdout);
-                    }
-                }
-            }
-            else if (current_edge == -1)
-            {
-                // Problem description
-                int nparsed = sscanf(line, "%d %d %d", &num_rows, &num_cols, &num_nonzeros);
-                if ((!array) && (nparsed == 3))
-                {
-                    if (symmetric)
-                        num_nonzeros *= 2;
-
-                    // Allocate coo matrix
-                    coo_tuples = new CooTuple[num_nonzeros];
-                    current_edge = 0;
-
-                }
-                else if (array && (nparsed == 2))
-                {
-                    // Allocate coo matrix
-                    num_nonzeros = num_rows * num_cols;
-                    coo_tuples = new CooTuple[num_nonzeros];
-                    current_edge = 0;
-                }
-                else
-                {
-                    fprintf(stderr, "Error parsing MARKET matrix: invalid problem description: %s\n", line);
-                    exit(1);
-                }
-
-            }
-            else
-            {
-                // Edge
-                if (current_edge >= num_nonzeros)
-                {
-                    fprintf(stderr, "Error parsing MARKET matrix: encountered more than %d num_nonzeros\n", num_nonzeros);
-                    exit(1);
-                }
-
-                int row, col;
-                double val;
-
-                if (array)
-                {
-                    if (sscanf(line, "%lf", &val) != 1)
-                    {
-                        fprintf(stderr, "Error parsing MARKET matrix: badly formed current_edge: '%s' at edge %d\n", line, current_edge);
-                        exit(1);
-                    }
-                    col = (current_edge / num_rows);
-                    row = (current_edge - (num_rows * col));
-
-                    coo_tuples[current_edge] = CooTuple(row, col, val);    // Convert indices to zero-based
-                }
-                else
-                {
-                    // Parse nonzero (note: using strtol and strtod is 2x faster than sscanf or istream parsing)
-                    char *l = line;
-                    char *t = NULL;
-
-                    // parse row
-                    row = strtol(l, &t, 0);
-                    if (t == l)
-                    {
-                        fprintf(stderr, "Error parsing MARKET matrix: badly formed row at edge %d\n", current_edge);
-                        exit(1);
-                    }
-                    l = t;
-
-                    // parse col
-                    col = strtol(l, &t, 0);
-                    if (t == l)
-                    {
-                        fprintf(stderr, "Error parsing MARKET matrix: badly formed col at edge %d\n", current_edge);
-                        exit(1);
-                    }
-                    l = t;
-
-                    // parse val
-                    val = strtod(l, &t);
-                    if (t == l)
-                    {
-                        val = default_value;
-                    }
-/*
-                    int nparsed = sscanf(line, "%d %d %lf", &row, &col, &val);
-                    if (nparsed == 2)
-                    {
-                        // No value specified
-                        val = default_value;
-                        
-                    }
-                    else if (nparsed != 3)
-                    {
-                        fprintf(stderr, "Error parsing MARKET matrix 1: badly formed current_edge: %d parsed at edge %d\n", nparsed, current_edge);
-                        exit(1);
-                    }
-*/
-
-                    coo_tuples[current_edge] = CooTuple(row - 1, col - 1, val);    // Convert indices to zero-based
-
-                }
-
-                current_edge++;
-
-                if (symmetric && (row != col))
-                {
-                    coo_tuples[current_edge].row = coo_tuples[current_edge - 1].col;
-                    coo_tuples[current_edge].col = coo_tuples[current_edge - 1].row;
-                    coo_tuples[current_edge].val = coo_tuples[current_edge - 1].val * (skew ? -1 : 1);
-                    current_edge++;
-                }
-            }
-        }
-
-        // Adjust nonzero count (nonzeros along the diagonal aren't reversed)
-        num_nonzeros = current_edge;
-
-        if (verbose) {
-            printf("done. Ordering..."); fflush(stdout);
-        }
-
-        // Sort by rows, then columns
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-
-        if (verbose) {
-            printf("done. "); fflush(stdout);
-        }
-
-        ifs.close();
-    }
-
-
-    /**
-     * Builds a dense matrix
-     */
-    int InitDense(
-        OffsetT     num_rows,
-        OffsetT     num_cols,
-        ValueT      default_value   = 1.0,
-        bool        verbose         = false)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        this->num_rows  = num_rows;
-        this->num_cols  = num_cols;
-
-        num_nonzeros    = num_rows * num_cols;
-        coo_tuples      = new CooTuple[num_nonzeros];
-
-        for (OffsetT row = 0; row < num_rows; ++row)
-        {
-            for (OffsetT col = 0; col < num_cols; ++col)
-            {
-                coo_tuples[(row * num_cols) + col] = CooTuple(row, col, default_value);
-            }
-        }
-
-        // Sort by rows, then columns
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-
-        return 0;
-    }
-
-    /**
-     * Builds a wheel COO sparse matrix having spokes spokes.
-     */
-    int InitWheel(
-        OffsetT     spokes,
-        ValueT      default_value   = 1.0,
-        bool        verbose         = false)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        num_rows        = spokes + 1;
-        num_cols        = num_rows;
-        num_nonzeros    = spokes * 2;
-        coo_tuples      = new CooTuple[num_nonzeros];
-
-        // Add spoke num_nonzeros
-        int current_edge = 0;
-        for (OffsetT i = 0; i < spokes; i++)
-        {
-            coo_tuples[current_edge] = CooTuple(0, i + 1, default_value);
-            current_edge++;
-        }
-
-        // Add rim
-        for (OffsetT i = 0; i < spokes; i++)
-        {
-            OffsetT dest = (i + 1) % spokes;
-            coo_tuples[current_edge] = CooTuple(i + 1, dest + 1, default_value);
-            current_edge++;
-        }
-
-        // Sort by rows, then columns
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-
-        return 0;
-    }
-
-
-    /**
-     * Builds a square 2D grid CSR matrix.  Interior num_vertices have degree 5 when including
-     * a self-loop.
-     *
-     * Returns 0 on success, 1 on failure.
-     */
-    int InitGrid2d(OffsetT width, bool self_loop, ValueT default_value = 1.0)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            exit(1);
-        }
-
-        int     interior_nodes  = (width - 2) * (width - 2);
-        int     edge_nodes      = (width - 2) * 4;
-        int     corner_nodes    = 4;
-        num_rows                       = width * width;
-        num_cols                       = num_rows;
-        num_nonzeros                   = (interior_nodes * 4) + (edge_nodes * 3) + (corner_nodes * 2);
-
-        if (self_loop)
-            num_nonzeros += num_rows;
-
-        coo_tuples          = new CooTuple[num_nonzeros];
-        int current_edge    = 0;
-
-        for (OffsetT j = 0; j < width; j++)
-        {
-            for (OffsetT k = 0; k < width; k++)
-            {
-                OffsetT me = (j * width) + k;
-
-                // West
-                OffsetT neighbor = (j * width) + (k - 1);
-                if (k - 1 >= 0) {
-                    coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                    current_edge++;
-                }
-
-                // East
-                neighbor = (j * width) + (k + 1);
-                if (k + 1 < width) {
-                    coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                    current_edge++;
-                }
-
-                // North
-                neighbor = ((j - 1) * width) + k;
-                if (j - 1 >= 0) {
-                    coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                    current_edge++;
-                }
-
-                // South
-                neighbor = ((j + 1) * width) + k;
-                if (j + 1 < width) {
-                    coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                    current_edge++;
-                }
-
-                if (self_loop)
-                {
-                    neighbor = me;
-                    coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                    current_edge++;
-                }
-            }
-        }
-
-        // Sort by rows, then columns, update dims
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-
-        return 0;
-    }
-
-
-    /**
-     * Builds a square 3D grid COO sparse matrix.  Interior num_vertices have degree 7 when including
-     * a self-loop.  Values are unintialized, coo_tuples are sorted.
-     */
-    int InitGrid3d(OffsetT width, bool self_loop, ValueT default_value = 1.0)
-    {
-        if (coo_tuples)
-        {
-            fprintf(stderr, "Matrix already constructed\n");
-            return -1;
-        }
-
-        OffsetT interior_nodes  = (width - 2) * (width - 2) * (width - 2);
-        OffsetT face_nodes      = (width - 2) * (width - 2) * 6;
-        OffsetT edge_nodes      = (width - 2) * 12;
-        OffsetT corner_nodes    = 8;
-        num_cols                       = width * width * width;
-        num_rows                       = num_cols;
-        num_nonzeros                     = (interior_nodes * 6) + (face_nodes * 5) + (edge_nodes * 4) + (corner_nodes * 3);
-
-        if (self_loop)
-            num_nonzeros += num_rows;
-
-        coo_tuples          = new CooTuple[num_nonzeros];
-        int current_edge    = 0;
-
-        for (OffsetT i = 0; i < width; i++)
-        {
-            for (OffsetT j = 0; j < width; j++)
-            {
-                for (OffsetT k = 0; k < width; k++)
-                {
-
-                    OffsetT me = (i * width * width) + (j * width) + k;
-
-                    // Up
-                    OffsetT neighbor = (i * width * width) + (j * width) + (k - 1);
-                    if (k - 1 >= 0) {
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-
-                    // Down
-                    neighbor = (i * width * width) + (j * width) + (k + 1);
-                    if (k + 1 < width) {
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-
-                    // West
-                    neighbor = (i * width * width) + ((j - 1) * width) + k;
-                    if (j - 1 >= 0) {
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-
-                    // East
-                    neighbor = (i * width * width) + ((j + 1) * width) + k;
-                    if (j + 1 < width) {
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-
-                    // North
-                    neighbor = ((i - 1) * width * width) + (j * width) + k;
-                    if (i - 1 >= 0) {
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-
-                    // South
-                    neighbor = ((i + 1) * width * width) + (j * width) + k;
-                    if (i + 1 < width) {
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-
-                    if (self_loop)
-                    {
-                        neighbor = me;
-                        coo_tuples[current_edge] = CooTuple(me, neighbor, default_value);
-                        current_edge++;
-                    }
-                }
-            }
-        }
-
-        // Sort by rows, then columns, update dims
-        std::stable_sort(coo_tuples, coo_tuples + num_nonzeros);
-
-        return 0;
-    }
-};
-
-
-
-/******************************************************************************
- * COO matrix type
- ******************************************************************************/
-
-
-/**
- * CSR sparse format matrix
- */
-template<
-    typename ValueT,
-    typename OffsetT>
-struct CsrMatrix
-{
-    int         num_rows;
-    int         num_cols;
-    int         num_nonzeros;
-    OffsetT*    row_offsets;
-    OffsetT*    column_indices;
-    ValueT*     values;
-    bool        numa_malloc;
-
-    /**
-     * Constructor
-     */
-    CsrMatrix() : num_rows(0), num_cols(0), num_nonzeros(0), row_offsets(NULL), column_indices(NULL), values(NULL) 
-    {
-#ifdef CUB_MKL
-        numa_malloc = ((numa_available() >= 0) && (numa_num_task_nodes() > 1));
-#else
-        numa_malloc = false;
-#endif
-    }
-
-
-    /**
-     * Clear
-     */
-    void Clear()
-    {
-#ifdef CUB_MKL
-        if (numa_malloc) 
-        {
-            numa_free(row_offsets, sizeof(OffsetT) * (num_rows + 1));
-            numa_free(values, sizeof(ValueT) * num_nonzeros);
-            numa_free(column_indices, sizeof(OffsetT) * num_nonzeros);
-        }
-        else
-        {
-            if (row_offsets)    mkl_free(row_offsets);
-            if (column_indices) mkl_free(column_indices);
-            if (values)         mkl_free(values);
-        }
-
-#else
-        if (row_offsets)    delete[] row_offsets;
-        if (column_indices) delete[] column_indices;
-        if (values)         delete[] values;
-#endif
-
-        row_offsets = NULL;
-        column_indices = NULL;
-        values = NULL;
-    }
-
-    /**
-     * Destructor
-     */
-    ~CsrMatrix()
-    {
-        Clear();
-    }
-
-    GraphStats Stats()
-    {
-        GraphStats stats;
-        stats.num_rows = num_rows;
-        stats.num_cols = num_cols;
-        stats.num_nonzeros = num_nonzeros;
-
-        //
-        // Compute diag-distance statistics
-        //
-
-        OffsetT samples     = 0;
-        double  mean        = 0.0;
-        double  ss_tot      = 0.0;
-
-        for (OffsetT row = 0; row < num_rows; ++row)
-        {
-            OffsetT nz_idx_start    = row_offsets[row];
-            OffsetT nz_idx_end      = row_offsets[row + 1];
-
-            for (int nz_idx = nz_idx_start; nz_idx < nz_idx_end; ++nz_idx)
-            {
-                OffsetT col             = column_indices[nz_idx];
-                double x                = (col > row) ? col - row : row - col;
-
-                samples++;
-                double delta            = x - mean;
-                mean                    = mean + (delta / samples);
-                ss_tot                  += delta * (x - mean);
-            }
-        }
-        stats.diag_dist_mean            = mean;
-        double variance                 = ss_tot / samples;
-        stats.diag_dist_std_dev         = sqrt(variance);
-
-
-        //
-        // Compute deming statistics
-        //
-
-        samples         = 0;
-        double mean_x   = 0.0;
-        double mean_y   = 0.0;
-        double ss_x     = 0.0;
-        double ss_y     = 0.0;
-
-        for (OffsetT row = 0; row < num_rows; ++row)
-        {
-            OffsetT nz_idx_start    = row_offsets[row];
-            OffsetT nz_idx_end      = row_offsets[row + 1];
-
-            for (int nz_idx = nz_idx_start; nz_idx < nz_idx_end; ++nz_idx)
-            {
-                OffsetT col             = column_indices[nz_idx];
-
-                samples++;
-                double x                = col;
-                double y                = row;
-                double delta;
-
-                delta                   = x - mean_x;
-                mean_x                  = mean_x + (delta / samples);
-                ss_x                    += delta * (x - mean_x);
-
-                delta                   = y - mean_y;
-                mean_y                  = mean_y + (delta / samples);
-                ss_y                    += delta * (y - mean_y);
-            }
-        }
-
-        samples         = 0;
-        double s_xy     = 0.0;
-        double s_xxy    = 0.0;
-        double s_xyy    = 0.0;
-        for (OffsetT row = 0; row < num_rows; ++row)
-        {
-            OffsetT nz_idx_start    = row_offsets[row];
-            OffsetT nz_idx_end      = row_offsets[row + 1];
-
-            for (int nz_idx = nz_idx_start; nz_idx < nz_idx_end; ++nz_idx)
-            {
-                OffsetT col             = column_indices[nz_idx];
-
-                samples++;
-                double x                = col;
-                double y                = row;
-
-                double xy =             (x - mean_x) * (y - mean_y);
-                double xxy =            (x - mean_x) * (x - mean_x) * (y - mean_y);
-                double xyy =            (x - mean_x) * (y - mean_y) * (y - mean_y);
-                double delta;
-
-                delta                   = xy - s_xy;
-                s_xy                    = s_xy + (delta / samples);
-
-                delta                   = xxy - s_xxy;
-                s_xxy                   = s_xxy + (delta / samples);
-
-                delta                   = xyy - s_xyy;
-                s_xyy                   = s_xyy + (delta / samples);
-            }
-        }
-
-        double s_xx     = ss_x / num_nonzeros;
-        double s_yy     = ss_y / num_nonzeros;
-
-        double deming_slope = (s_yy - s_xx + sqrt(((s_yy - s_xx) * (s_yy - s_xx)) + (4 * s_xy * s_xy))) / (2 * s_xy);
-
-        stats.pearson_r = (num_nonzeros * s_xy) / (sqrt(ss_x) * sqrt(ss_y));
-
-
-        //
-        // Compute row-length statistics
-        //
-
-        // Sample mean
-        stats.row_length_mean       = double(num_nonzeros) / num_rows;
-        variance                    = 0.0;
-        stats.row_length_skewness   = 0.0;
-        for (OffsetT row = 0; row < num_rows; ++row)
-        {
-            OffsetT length              = row_offsets[row + 1] - row_offsets[row];
-            double delta                = double(length) - stats.row_length_mean;
-            variance   += (delta * delta);
-            stats.row_length_skewness   += (delta * delta * delta);
-        }
-        variance                    /= num_rows;
-        stats.row_length_std_dev    = sqrt(variance);
-        stats.row_length_skewness   = (stats.row_length_skewness / num_rows) / pow(stats.row_length_std_dev, 3.0);
-        stats.row_length_variation  = stats.row_length_std_dev / stats.row_length_mean;
-
-        return stats;
-    }
-
-    /**
-     * Build CSR matrix from sorted COO matrix
-     */
-    void FromCoo(const CooMatrix<ValueT, OffsetT> &coo_matrix)
-    {
-        num_rows        = coo_matrix.num_rows;
-        num_cols        = coo_matrix.num_cols;
-        num_nonzeros    = coo_matrix.num_nonzeros;
-
-#ifdef CUB_MKL
-
-        if (numa_malloc)
-        {
-            numa_set_strict(1);
-//            numa_set_bind_policy(1);
-
-//        values          = (ValueT*) numa_alloc_interleaved(sizeof(ValueT) * num_nonzeros);
-//        row_offsets     = (OffsetT*) numa_alloc_interleaved(sizeof(OffsetT) * (num_rows + 1));
-//        column_indices  = (OffsetT*) numa_alloc_interleaved(sizeof(OffsetT) * num_nonzeros);
-
-            row_offsets     = (OffsetT*) numa_alloc_onnode(sizeof(OffsetT) * (num_rows + 1), 0);
-            column_indices  = (OffsetT*) numa_alloc_onnode(sizeof(OffsetT) * num_nonzeros, 0);
-            values          = (ValueT*) numa_alloc_onnode(sizeof(ValueT) * num_nonzeros, 1);
-        }
-        else
-        {
-            values          = (ValueT*) mkl_malloc(sizeof(ValueT) * num_nonzeros, 4096);
-            row_offsets     = (OffsetT*) mkl_malloc(sizeof(OffsetT) * (num_rows + 1), 4096);
-            column_indices  = (OffsetT*) mkl_malloc(sizeof(OffsetT) * num_nonzeros, 4096);
-
-        }
-
-#else
-        row_offsets     = new OffsetT[num_rows + 1];
-        column_indices  = new OffsetT[num_nonzeros];
-        values          = new ValueT[num_nonzeros];
-#endif
-
-        OffsetT prev_row = -1;
-        for (OffsetT current_edge = 0; current_edge < num_nonzeros; current_edge++)
-        {
-            OffsetT current_row = coo_matrix.coo_tuples[current_edge].row;
-
-            // Fill in rows up to and including the current row
-            for (OffsetT row = prev_row + 1; row <= current_row; row++)
-            {
-                row_offsets[row] = current_edge;
-            }
-            prev_row = current_row;
-
-            column_indices[current_edge]    = coo_matrix.coo_tuples[current_edge].col;
-            values[current_edge]            = coo_matrix.coo_tuples[current_edge].val;
-        }
-
-        // Fill out any trailing edgeless vertices (and the end-of-list element)
-        for (OffsetT row = prev_row + 1; row <= num_rows; row++)
-        {
-            row_offsets[row] = num_nonzeros;
-        }
-    }
-
-
-    /**
-     * Display log-histogram to stdout
-     */
-    void DisplayHistogram()
-    {
-        // Initialize
-        int log_counts[9];
-        for (int i = 0; i < 9; i++)
-        {
-            log_counts[i] = 0;
-        }
-
-        // Scan
-        int max_log_length = -1;
-        for (OffsetT row = 0; row < num_rows; row++)
-        {
-            OffsetT length = row_offsets[row + 1] - row_offsets[row];
-
-            int log_length = -1;
-            while (length > 0)
-            {
-                length /= 10;
-                log_length++;
-            }
-            if (log_length > max_log_length)
-            {
-                max_log_length = log_length;
-            }
-
-            log_counts[log_length + 1]++;
-        }
-        printf("CSR matrix (%d rows, %d columns, %d non-zeros):\n", (int) num_rows, (int) num_cols, (int) num_nonzeros);
-        for (int i = -1; i < max_log_length + 1; i++)
-        {
-            printf("\tDegree 1e%d: \t%d (%.2f%%)\n", i, log_counts[i + 1], (float) log_counts[i + 1] * 100.0 / num_cols);
-        }
-        fflush(stdout);
-    }
-
-
-    /**
-     * Display matrix to stdout
-     */
-    void Display()
-    {
-        printf("Input Matrix:\n");
-        for (OffsetT row = 0; row < num_rows; row++)
-        {
-            printf("%d [@%d, #%d]: ", row, row_offsets[row], row_offsets[row + 1] - row_offsets[row]);
-            for (OffsetT current_edge = row_offsets[row]; current_edge < row_offsets[row + 1]; current_edge++)
-            {
-                printf("%d (%f), ", column_indices[current_edge], values[current_edge]);
-            }
-            printf("\n");
-        }
-        fflush(stdout);
-    }
-
-
-};
-
-
-
-/******************************************************************************
- * Matrix transformations
- ******************************************************************************/
-
-// Comparator for ordering rows by degree (lowest first), then by row-id (lowest first)
-template <typename OffsetT>
-struct OrderByLow
-{
-    OffsetT* row_degrees;
-    OrderByLow(OffsetT* row_degrees) : row_degrees(row_degrees) {}
-
-    bool operator()(const OffsetT &a, const OffsetT &b)
-    {
-        if (row_degrees[a] < row_degrees[b])
-            return true;
-        else if (row_degrees[a] > row_degrees[b])
-            return false;
-        else
-            return (a < b);
-    }
-};
-
-// Comparator for ordering rows by degree (highest first), then by row-id (lowest first)
-template <typename OffsetT>
-struct OrderByHigh
-{
-    OffsetT* row_degrees;
-    OrderByHigh(OffsetT* row_degrees) : row_degrees(row_degrees) {}
-
-    bool operator()(const OffsetT &a, const OffsetT &b)
-    {
-        if (row_degrees[a] > row_degrees[b])
-            return true;
-        else if (row_degrees[a] < row_degrees[b])
-            return false;
-        else
-            return (a < b);
-    }
-};
-
-
-
-/**
- * Reverse Cuthill-McKee
- */
-template <typename ValueT, typename OffsetT>
-void RcmRelabel(
-    CsrMatrix<ValueT, OffsetT>&     matrix,
-    OffsetT*                        relabel_indices)
-{
-    // Initialize row degrees
-    OffsetT* row_degrees_in     = new OffsetT[matrix.num_rows];
-    OffsetT* row_degrees_out    = new OffsetT[matrix.num_rows];
-    for (OffsetT row = 0; row < matrix.num_rows; ++row)
-    {
-        row_degrees_in[row]         = 0;
-        row_degrees_out[row]        = matrix.row_offsets[row + 1] - matrix.row_offsets[row];
-    }
-    for (OffsetT nonzero = 0; nonzero < matrix.num_nonzeros; ++nonzero)
-    {
-        row_degrees_in[matrix.column_indices[nonzero]]++;
-    }
-
-    // Initialize unlabeled set 
-    typedef std::set<OffsetT, OrderByLow<OffsetT> > UnlabeledSet;
-    typename UnlabeledSet::key_compare  unlabeled_comp(row_degrees_in);
-    UnlabeledSet                        unlabeled(unlabeled_comp);
-    for (OffsetT row = 0; row < matrix.num_rows; ++row)
-    {
-        relabel_indices[row]    = -1;
-        unlabeled.insert(row);
-    }
-
-    // Initialize queue set
-    std::deque<OffsetT> q;
-
-    // Process unlabeled vertices (traverse connected components)
-    OffsetT relabel_idx = 0;
-    while (!unlabeled.empty())
-    {
-        // Seed the unvisited frontier queue with the unlabeled vertex of lowest-degree
-        OffsetT vertex = *unlabeled.begin();
-        q.push_back(vertex);
-
-        while (!q.empty())
-        {
-            vertex = q.front();
-            q.pop_front();
-
-            if (relabel_indices[vertex] == -1)
-            {
-                // Update this vertex
-                unlabeled.erase(vertex);
-                relabel_indices[vertex] = relabel_idx;
-                relabel_idx++;
-
-                // Sort neighbors by degree
-                OrderByLow<OffsetT> neighbor_comp(row_degrees_in);
-                std::sort(
-                    matrix.column_indices + matrix.row_offsets[vertex],
-                    matrix.column_indices + matrix.row_offsets[vertex + 1],
-                    neighbor_comp);
-
-                // Inspect neighbors, adding to the out frontier if unlabeled
-                for (OffsetT neighbor_idx = matrix.row_offsets[vertex];
-                    neighbor_idx < matrix.row_offsets[vertex + 1];
-                    ++neighbor_idx)
-                {
-                    OffsetT neighbor = matrix.column_indices[neighbor_idx];
-                    q.push_back(neighbor);
-                }
-            }
-        }
-    }
-
-/*
-    // Reverse labels
-    for (int row = 0; row < matrix.num_rows; ++row)
-    {
-        relabel_indices[row] = matrix.num_rows - relabel_indices[row] - 1;
-    }
-*/
-
-    // Cleanup
-    if (row_degrees_in) delete[] row_degrees_in;
-    if (row_degrees_out) delete[] row_degrees_out;
-}
-
-
-/**
- * Reverse Cuthill-McKee
- */
-template <typename ValueT, typename OffsetT>
-void RcmRelabel(
-    CsrMatrix<ValueT, OffsetT>&     matrix,
-    bool                            verbose = false)
-{
-    // Do not process if not square
-    if (matrix.num_cols != matrix.num_rows)
-    {
-        if (verbose) {
-            printf("RCM transformation ignored (not square)\n"); fflush(stdout);
-        }
-        return;
-    }
-
-    // Initialize relabel indices
-    OffsetT* relabel_indices = new OffsetT[matrix.num_rows];
-
-    if (verbose) {
-        printf("RCM relabeling... "); fflush(stdout);
-    }
-
-    RcmRelabel(matrix, relabel_indices);
-
-    if (verbose) {
-        printf("done. Reconstituting... "); fflush(stdout);
-    }
-
-    // Create a COO matrix from the relabel indices
-    CooMatrix<ValueT, OffsetT> coo_matrix;
-    coo_matrix.InitCsrRelabel(matrix, relabel_indices);
-
-    // Reconstitute the CSR matrix from the sorted COO tuples
-    if (relabel_indices) delete[] relabel_indices;
-    matrix.Clear();
-    matrix.FromCoo(coo_matrix);
-
-    if (verbose) {
-        printf("done. "); fflush(stdout);
-    }
-}
-
-
-
-
diff --git a/ml-xgboost/cub/experimental/spmv_compare.cu b/ml-xgboost/cub/experimental/spmv_compare.cu
deleted file mode 100644
index 5554ecb..0000000
--- a/ml-xgboost/cub/experimental/spmv_compare.cu
+++ /dev/null
@@ -1,917 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIAeBILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-//---------------------------------------------------------------------
-// SpMV comparison tool
-//---------------------------------------------------------------------
-
-#include <stdio.h>
-#include <map>
-#include <vector>
-#include <algorithm>
-#include <cstdio>
-#include <fstream>
-
-#include <cusparse.h>
-
-#include "sparse_matrix.h"
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <cub/device/device_spmv.cuh>
-#include <cub/util_allocator.cuh>
-#include <cub/iterator/tex_ref_input_iterator.cuh>
-#include <test/test_util.h>
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants, and type declarations
-//---------------------------------------------------------------------
-
-bool                    g_quiet     = false;        // Whether to display stats in CSV format
-bool                    g_verbose   = false;        // Whether to display output to console
-bool                    g_verbose2  = false;        // Whether to display input to console
-CachingDeviceAllocator  g_allocator(true);          // Caching allocator for device memory
-
-
-//---------------------------------------------------------------------
-// SpMV verification
-//---------------------------------------------------------------------
-
-// Compute reference SpMV y = Ax
-template <
-    typename ValueT,
-    typename OffsetT>
-void SpmvGold(
-    CsrMatrix<ValueT, OffsetT>&     a,
-    ValueT*                         vector_x,
-    ValueT*                         vector_y_in,
-    ValueT*                         vector_y_out,
-    ValueT                          alpha,
-    ValueT                          beta)
-{
-    for (OffsetT row = 0; row < a.num_rows; ++row)
-    {
-        ValueT partial = beta * vector_y_in[row];
-        for (
-            OffsetT offset = a.row_offsets[row];
-            offset < a.row_offsets[row + 1];
-            ++offset)
-        {
-            partial += alpha * a.values[offset] * vector_x[a.column_indices[offset]];
-        }
-        vector_y_out[row] = partial;
-    }
-}
-
-
-//---------------------------------------------------------------------
-// GPU I/O proxy
-//---------------------------------------------------------------------
-
-/**
- * Read every matrix nonzero value, read every corresponding vector value
- */
-template <
-    int         BLOCK_THREADS,
-    int         ITEMS_PER_THREAD,
-    typename    ValueT,
-    typename    OffsetT,
-    typename    VectorItr>
-__launch_bounds__ (int(BLOCK_THREADS))
-__global__ void NonZeroIoKernel(
-    SpmvParams<ValueT, OffsetT> params,
-    VectorItr                   d_vector_x)
-{
-    enum
-    {
-        TILE_ITEMS      = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-
-    ValueT nonzero = 0.0;
-
-    int tile_idx = blockIdx.x;
-
-    OffsetT block_offset = tile_idx * TILE_ITEMS;
-
-    OffsetT column_indices[ITEMS_PER_THREAD];
-    ValueT values[ITEMS_PER_THREAD];
-
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-    {
-        OffsetT nonzero_idx = block_offset + (ITEM * BLOCK_THREADS) + threadIdx.x;
-
-        OffsetT* ci = params.d_column_indices + nonzero_idx;
-        ValueT*a = params.d_values + nonzero_idx;
-
-        column_indices[ITEM]    = (nonzero_idx < params.num_nonzeros) ? *ci : 0;
-        values[ITEM]            = (nonzero_idx < params.num_nonzeros) ? *a : 0.0;
-    }
-
-    __syncthreads();
-
-    // Read vector
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-    {
-        ValueT vector_value    = ThreadLoad<LOAD_LDG>(params.d_vector_x + column_indices[ITEM]);
-        nonzero                += vector_value * values[ITEM];
-    }
-
-    __syncthreads();
-
-    if (block_offset < params.num_rows)
-    {
-        #pragma unroll
-        for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        {
-            OffsetT row_idx = block_offset + (ITEM * BLOCK_THREADS) + threadIdx.x;
-            if (row_idx < params.num_rows)
-            {
-                OffsetT row_end_offset = ThreadLoad<LOAD_DEFAULT>(params.d_row_end_offsets + row_idx);
-
-                if ((row_end_offset >= 0) && (nonzero == nonzero))
-                    params.d_vector_y[row_idx] = nonzero;
-            }
-        }
-    }
-
-}
-
-
-/**
- * Run GPU I/O proxy
- */
-template <
-    typename ValueT,
-    typename OffsetT>
-float TestGpuCsrIoProxy(
-    SpmvParams<ValueT, OffsetT>&    params,
-    int                             timing_iterations)
-{
-    enum {
-        BLOCK_THREADS       = 128,
-        ITEMS_PER_THREAD    = 7,
-        TILE_SIZE           = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-
-//    size_t smem = 1024 * 16;
-    size_t smem = 1024 * 0;
-
-    unsigned int nonzero_blocks = (params.num_nonzeros + TILE_SIZE - 1) / TILE_SIZE;
-    unsigned int row_blocks = (params.num_rows + TILE_SIZE - 1) / TILE_SIZE;
-    unsigned int blocks = std::max(nonzero_blocks, row_blocks);
-
-    typedef TexRefInputIterator<ValueT, 1234, int> TexItr;
-    TexItr x_itr;
-    CubDebugExit(x_itr.BindTexture(params.d_vector_x));
-
-    // Get device ordinal
-    int device_ordinal;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-
-    // Get device SM version
-    int sm_version;
-    CubDebugExit(SmVersion(sm_version, device_ordinal));
-
-    void (*kernel)(SpmvParams<ValueT, OffsetT>, TexItr) = NonZeroIoKernel<BLOCK_THREADS, ITEMS_PER_THREAD>;
-
-
-    int spmv_sm_occupancy;
-    CubDebugExit(MaxSmOccupancy(spmv_sm_occupancy, kernel, BLOCK_THREADS, smem));
-
-    if (!g_quiet)
-        printf("NonZeroIoKernel<%d,%d><<<%d, %d>>>, sm occupancy %d\n", BLOCK_THREADS, ITEMS_PER_THREAD, blocks, BLOCK_THREADS, spmv_sm_occupancy);
-
-    // Warmup
-    NonZeroIoKernel<BLOCK_THREADS, ITEMS_PER_THREAD><<<blocks, BLOCK_THREADS, smem>>>(params, x_itr);
-
-    // Check for failures
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(SyncStream(0));
-
-    // Timing
-    GpuTimer timer;
-    float elapsed_millis = 0.0;
-    timer.Start();
-    for (int it = 0; it < timing_iterations; ++it)
-    {
-        NonZeroIoKernel<BLOCK_THREADS, ITEMS_PER_THREAD><<<blocks, BLOCK_THREADS, smem>>>(params, x_itr);
-    }
-    timer.Stop();
-    elapsed_millis += timer.ElapsedMillis();
-
-    CubDebugExit(x_itr.UnbindTexture());
-
-    return elapsed_millis / timing_iterations;
-}
-
-
-
-//---------------------------------------------------------------------
-// cuSparse HybMV
-//---------------------------------------------------------------------
-
-/**
- * Run cuSparse HYB SpMV (specialized for fp32)
- */
-template <
-    typename OffsetT>
-float TestCusparseHybmv(
-    float*                          vector_y_in,
-    float*                          reference_vector_y_out,
-    SpmvParams<float, OffsetT>&     params,
-    int                             timing_iterations,
-    cusparseHandle_t                cusparse)
-{
-    CpuTimer cpu_timer;
-    cpu_timer.Start();
-
-    // Construct Hyb matrix
-    cusparseMatDescr_t mat_desc;
-    cusparseHybMat_t hyb_desc;
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreateMatDescr(&mat_desc));
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreateHybMat(&hyb_desc));
-    cusparseStatus_t status = cusparseScsr2hyb(
-        cusparse,
-        params.num_rows, params.num_cols,
-        mat_desc,
-        params.d_values, params.d_row_end_offsets, params.d_column_indices,
-        hyb_desc,
-        0,
-        CUSPARSE_HYB_PARTITION_AUTO);
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, status);
-
-    cudaDeviceSynchronize();
-    cpu_timer.Stop();
-    float elapsed_millis = cpu_timer.ElapsedMillis();
-    printf("HYB setup ms, %.5f, ", elapsed_millis);
-
-    // Reset input/output vector y
-    CubDebugExit(cudaMemcpy(params.d_vector_y, vector_y_in, sizeof(float) * params.num_rows, cudaMemcpyHostToDevice));
-
-    // Warmup
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseShybmv(
-        cusparse,
-        CUSPARSE_OPERATION_NON_TRANSPOSE,
-        &params.alpha, mat_desc,
-        hyb_desc,
-        params.d_vector_x, &params.beta, params.d_vector_y));
-
-    if (!g_quiet)
-    {
-        int compare = CompareDeviceResults(reference_vector_y_out, params.d_vector_y, params.num_rows, true, g_verbose);
-        printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-
-    // Timing
-    elapsed_millis    = 0.0;
-    GpuTimer timer;
-
-    timer.Start();
-    for(int it = 0; it < timing_iterations; ++it)
-    {
-        AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseShybmv(
-            cusparse,
-            CUSPARSE_OPERATION_NON_TRANSPOSE,
-            &params.alpha, mat_desc,
-            hyb_desc,
-            params.d_vector_x, &params.beta, params.d_vector_y));
-    }
-    timer.Stop();
-    elapsed_millis += timer.ElapsedMillis();
-
-    // Cleanup
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDestroyHybMat(hyb_desc));
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDestroyMatDescr(mat_desc));
-
-    return elapsed_millis / timing_iterations;
-}
-
-
-/**
- * Run cuSparse HYB SpMV (specialized for fp64)
- */
-template <
-    typename OffsetT>
-float TestCusparseHybmv(
-    double*                         vector_y_in,
-    double*                         reference_vector_y_out,
-    SpmvParams<double, OffsetT>&    params,
-    int                             timing_iterations,
-    cusparseHandle_t                cusparse)
-{
-    CpuTimer cpu_timer;
-    cpu_timer.Start();
-
-    // Construct Hyb matrix
-    cusparseMatDescr_t mat_desc;
-    cusparseHybMat_t hyb_desc;
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreateMatDescr(&mat_desc));
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreateHybMat(&hyb_desc));
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDcsr2hyb(
-        cusparse,
-        params.num_rows, params.num_cols,
-        mat_desc,
-        params.d_values, params.d_row_end_offsets, params.d_column_indices,
-        hyb_desc,
-        0,
-        CUSPARSE_HYB_PARTITION_AUTO));
-
-    cudaDeviceSynchronize();
-    cpu_timer.Stop();
-    float elapsed_millis = cpu_timer.ElapsedMillis();
-    printf("HYB setup ms, %.5f, ", elapsed_millis);
-
-    // Reset input/output vector y
-    CubDebugExit(cudaMemcpy(params.d_vector_y, vector_y_in, sizeof(float) * params.num_rows, cudaMemcpyHostToDevice));
-
-    // Warmup
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDhybmv(
-        cusparse,
-        CUSPARSE_OPERATION_NON_TRANSPOSE,
-        &params.alpha, mat_desc,
-        hyb_desc,
-        params.d_vector_x, &params.beta, params.d_vector_y));
-
-    if (!g_quiet)
-    {
-        int compare = CompareDeviceResults(reference_vector_y_out, params.d_vector_y, params.num_rows, true, g_verbose);
-        printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-
-    // Timing
-    elapsed_millis    = 0.0;
-    GpuTimer timer;
-
-    timer.Start();
-    for(int it = 0; it < timing_iterations; ++it)
-    {
-        AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDhybmv(
-            cusparse,
-            CUSPARSE_OPERATION_NON_TRANSPOSE,
-            &params.alpha, mat_desc,
-            hyb_desc,
-            params.d_vector_x, &params.beta, params.d_vector_y));
-    }
-    timer.Stop();
-    elapsed_millis += timer.ElapsedMillis();
-
-    // Cleanup
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDestroyHybMat(hyb_desc));
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDestroyMatDescr(mat_desc));
-
-    return elapsed_millis / timing_iterations;
-}
-
-
-
-//---------------------------------------------------------------------
-// cuSparse CsrMV
-//---------------------------------------------------------------------
-
-/**
- * Run cuSparse SpMV (specialized for fp32)
- */
-template <
-    typename OffsetT>
-float TestCusparseCsrmv(
-    float*                          vector_y_in,
-    float*                          reference_vector_y_out,
-    SpmvParams<float, OffsetT>&     params,
-    int                             timing_iterations,
-    cusparseHandle_t                cusparse)
-{
-    cusparseMatDescr_t desc;
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreateMatDescr(&desc));
-
-    // Reset input/output vector y
-    CubDebugExit(cudaMemcpy(params.d_vector_y, vector_y_in, sizeof(float) * params.num_rows, cudaMemcpyHostToDevice));
-
-    // Warmup
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseScsrmv(
-        cusparse, CUSPARSE_OPERATION_NON_TRANSPOSE,
-        params.num_rows, params.num_cols, params.num_nonzeros, &params.alpha, desc,
-        params.d_values, params.d_row_end_offsets, params.d_column_indices,
-        params.d_vector_x, &params.beta, params.d_vector_y));
-
-    if (!g_quiet)
-    {
-        int compare = CompareDeviceResults(reference_vector_y_out, params.d_vector_y, params.num_rows, true, g_verbose);
-        printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-
-    // Timing
-    float elapsed_millis    = 0.0;
-    GpuTimer timer;
-
-    timer.Start();
-    for(int it = 0; it < timing_iterations; ++it)
-    {
-        AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseScsrmv(
-            cusparse, CUSPARSE_OPERATION_NON_TRANSPOSE,
-            params.num_rows, params.num_cols, params.num_nonzeros, &params.alpha, desc,
-            params.d_values, params.d_row_end_offsets, params.d_column_indices,
-            params.d_vector_x, &params.beta, params.d_vector_y));
-    }
-    timer.Stop();
-    elapsed_millis += timer.ElapsedMillis();
-
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDestroyMatDescr(desc));
-    return elapsed_millis / timing_iterations;
-}
-
-
-/**
- * Run cuSparse SpMV (specialized for fp64)
- */
-template <
-    typename OffsetT>
-float TestCusparseCsrmv(
-    double*                         vector_y_in,
-    double*                         reference_vector_y_out,
-    SpmvParams<double, OffsetT>&    params,
-    int                             timing_iterations,
-    cusparseHandle_t                cusparse)
-{
-    cusparseMatDescr_t desc;
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreateMatDescr(&desc));
-
-    // Reset input/output vector y
-    CubDebugExit(cudaMemcpy(params.d_vector_y, vector_y_in, sizeof(float) * params.num_rows, cudaMemcpyHostToDevice));
-
-    // Warmup
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDcsrmv(
-        cusparse, CUSPARSE_OPERATION_NON_TRANSPOSE,
-        params.num_rows, params.num_cols, params.num_nonzeros, &params.alpha, desc,
-        params.d_values, params.d_row_end_offsets, params.d_column_indices,
-        params.d_vector_x, &params.beta, params.d_vector_y));
-
-    if (!g_quiet)
-    {
-        int compare = CompareDeviceResults(reference_vector_y_out, params.d_vector_y, params.num_rows, true, g_verbose);
-        printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-
-    // Timing
-    float elapsed_millis = 0.0;
-    GpuTimer timer;
-    timer.Start();
-    for(int it = 0; it < timing_iterations; ++it)
-    {
-        AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDcsrmv(
-            cusparse, CUSPARSE_OPERATION_NON_TRANSPOSE,
-            params.num_rows, params.num_cols, params.num_nonzeros, &params.alpha, desc,
-            params.d_values, params.d_row_end_offsets, params.d_column_indices,
-            params.d_vector_x, &params.beta, params.d_vector_y));
-
-    }
-    timer.Stop();
-    elapsed_millis += timer.ElapsedMillis();
-
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseDestroyMatDescr(desc));
-    return elapsed_millis / timing_iterations;
-}
-
-//---------------------------------------------------------------------
-// GPU Merge-based SpMV
-//---------------------------------------------------------------------
-
-/**
- * Run CUB SpMV
- */
-template <
-    typename ValueT,
-    typename OffsetT>
-float TestGpuMergeCsrmv(
-    ValueT*                         vector_y_in,
-    ValueT*                         reference_vector_y_out,
-    SpmvParams<ValueT, OffsetT>&    params,
-    int                             timing_iterations)
-{
-    // Allocate temporary storage
-    size_t temp_storage_bytes = 0;
-    void *d_temp_storage = NULL;
-
-    // Get amount of temporary storage needed
-    CubDebugExit(DeviceSpmv::CsrMV(
-        d_temp_storage, temp_storage_bytes,
-        params.d_values, params.d_row_end_offsets, params.d_column_indices,
-        params.d_vector_x, params.d_vector_y,
-        params.num_rows, params.num_cols, params.num_nonzeros,
-// params.alpha, params.beta,
-        (cudaStream_t) 0, false));
-
-    // Allocate
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Reset input/output vector y
-    CubDebugExit(cudaMemcpy(params.d_vector_y, vector_y_in, sizeof(ValueT) * params.num_rows, cudaMemcpyHostToDevice));
-
-    // Warmup
-    CubDebugExit(DeviceSpmv::CsrMV(
-        d_temp_storage, temp_storage_bytes,
-        params.d_values, params.d_row_end_offsets, params.d_column_indices,
-        params.d_vector_x, params.d_vector_y,
-        params.num_rows, params.num_cols, params.num_nonzeros, 
-// params.alpha, params.beta,
-        (cudaStream_t) 0, !g_quiet));
-
-    if (!g_quiet)
-    {
-        int compare = CompareDeviceResults(reference_vector_y_out, params.d_vector_y, params.num_rows, true, g_verbose);
-        printf("\t%s\n", compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-
-    // Timing
-    GpuTimer timer;
-    float elapsed_millis = 0.0;
-
-    timer.Start();
-    for(int it = 0; it < timing_iterations; ++it)
-    {
-        CubDebugExit(DeviceSpmv::CsrMV(
-            d_temp_storage, temp_storage_bytes,
-            params.d_values, params.d_row_end_offsets, params.d_column_indices,
-            params.d_vector_x, params.d_vector_y,
-            params.num_rows, params.num_cols, params.num_nonzeros, 
-// params.alpha, params.beta,
-            (cudaStream_t) 0, false));
-    }
-    timer.Stop();
-    elapsed_millis += timer.ElapsedMillis();
-
-    return elapsed_millis / timing_iterations;
-}
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-/**
- * Display perf
- */
-template <typename ValueT, typename OffsetT>
-void DisplayPerf(
-    float                           device_giga_bandwidth,
-    double                          avg_millis,
-    CsrMatrix<ValueT, OffsetT>&     csr_matrix)
-{
-    double nz_throughput, effective_bandwidth;
-    size_t total_bytes = (csr_matrix.num_nonzeros * (sizeof(ValueT) * 2 + sizeof(OffsetT))) +
-        (csr_matrix.num_rows) * (sizeof(OffsetT) + sizeof(ValueT));
-
-    nz_throughput       = double(csr_matrix.num_nonzeros) / avg_millis / 1.0e6;
-    effective_bandwidth = double(total_bytes) / avg_millis / 1.0e6;
-
-    if (!g_quiet)
-        printf("fp%d: %.4f avg ms, %.5f gflops, %.3lf effective GB/s (%.2f%% peak)\n",
-            sizeof(ValueT) * 8,
-            avg_millis,
-            2 * nz_throughput,
-            effective_bandwidth,
-            effective_bandwidth / device_giga_bandwidth * 100);
-    else
-        printf("%.5f, %.6f, %.3lf, %.2f%%, ",
-            avg_millis,
-            2 * nz_throughput,
-            effective_bandwidth,
-            effective_bandwidth / device_giga_bandwidth * 100);
-
-    fflush(stdout);
-}
-
-
-
-/**
- * Run tests
- */
-template <
-    typename ValueT,
-    typename OffsetT>
-void RunTest(
-    bool                        rcm_relabel,
-    ValueT                      alpha,
-    ValueT                      beta,
-    CooMatrix<ValueT, OffsetT>& coo_matrix,
-    int                         timing_iterations,
-    CommandLineArgs&            args)
-{
-    // Adaptive timing iterations: run 16 billion nonzeros through
-    if (timing_iterations == -1)
-        timing_iterations = std::min(50000ull, std::max(100ull, ((16ull << 30) / coo_matrix.num_nonzeros)));
-
-    if (!g_quiet)
-        printf("\t%d timing iterations\n", timing_iterations);
-
-    // Convert to CSR
-    CsrMatrix<ValueT, OffsetT> csr_matrix;
-    csr_matrix.FromCoo(coo_matrix);
-    if (!args.CheckCmdLineFlag("csrmv"))
-        coo_matrix.Clear();
-
-    // Relabel
-    if (rcm_relabel)
-    {
-        if (!g_quiet)
-        {
-            csr_matrix.Stats().Display();
-            printf("\n");
-            csr_matrix.DisplayHistogram();
-            printf("\n");
-            if (g_verbose2)
-                csr_matrix.Display();
-            printf("\n");
-        }
-
-        RcmRelabel(csr_matrix, !g_quiet);
-
-        if (!g_quiet) printf("\n");
-    }
-
-    // Display matrix info
-    csr_matrix.Stats().Display(!g_quiet);
-    if (!g_quiet)
-    {
-        printf("\n");
-        csr_matrix.DisplayHistogram();
-        printf("\n");
-        if (g_verbose2)
-            csr_matrix.Display();
-        printf("\n");
-    }
-    fflush(stdout);
-
-    // Allocate input and output vectors
-    ValueT* vector_x        = new ValueT[csr_matrix.num_cols];
-    ValueT* vector_y_in     = new ValueT[csr_matrix.num_rows];
-    ValueT* vector_y_out    = new ValueT[csr_matrix.num_rows];
-
-    for (int col = 0; col < csr_matrix.num_cols; ++col)
-        vector_x[col] = 1.0;
-
-    for (int row = 0; row < csr_matrix.num_rows; ++row)
-        vector_y_in[row] = 1.0;
-
-    // Compute reference answer
-    SpmvGold(csr_matrix, vector_x, vector_y_in, vector_y_out, alpha, beta);
-
-    float avg_millis;
-
-    if (g_quiet) {
-        printf("%s, %s, ", args.deviceProp.name, (sizeof(ValueT) > 4) ? "fp64" : "fp32"); fflush(stdout);
-    }
-
-    // Get GPU device bandwidth (GB/s)
-    float device_giga_bandwidth = args.device_giga_bandwidth;
-
-    // Allocate and initialize GPU problem
-    SpmvParams<ValueT, OffsetT> params;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void **) &params.d_values,          sizeof(ValueT) * csr_matrix.num_nonzeros));
-    CubDebugExit(g_allocator.DeviceAllocate((void **) &params.d_row_end_offsets, sizeof(OffsetT) * (csr_matrix.num_rows + 1)));
-    CubDebugExit(g_allocator.DeviceAllocate((void **) &params.d_column_indices,  sizeof(OffsetT) * csr_matrix.num_nonzeros));
-    CubDebugExit(g_allocator.DeviceAllocate((void **) &params.d_vector_x,        sizeof(ValueT) * csr_matrix.num_cols));
-    CubDebugExit(g_allocator.DeviceAllocate((void **) &params.d_vector_y,        sizeof(ValueT) * csr_matrix.num_rows));
-    params.num_rows         = csr_matrix.num_rows;
-    params.num_cols         = csr_matrix.num_cols;
-    params.num_nonzeros     = csr_matrix.num_nonzeros;
-    params.alpha            = alpha;
-    params.beta             = beta;
-
-    CubDebugExit(cudaMemcpy(params.d_values,            csr_matrix.values,          sizeof(ValueT) * csr_matrix.num_nonzeros, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(params.d_row_end_offsets,   csr_matrix.row_offsets,     sizeof(OffsetT) * (csr_matrix.num_rows + 1), cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(params.d_column_indices,    csr_matrix.column_indices,  sizeof(OffsetT) * csr_matrix.num_nonzeros, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(params.d_vector_x,          vector_x,                   sizeof(ValueT) * csr_matrix.num_cols, cudaMemcpyHostToDevice));
-
-    if (!g_quiet) printf("\n\n");
-    printf("GPU CSR I/O Prox, "); fflush(stdout);
-    avg_millis = TestGpuCsrIoProxy(params, timing_iterations);
-    DisplayPerf(device_giga_bandwidth, avg_millis, csr_matrix);
-
-    if (args.CheckCmdLineFlag("csrmv"))
-    {
-        if (!g_quiet) printf("\n\n");
-        printf("CUB, "); fflush(stdout);
-        avg_millis = TestGpuMergeCsrmv(vector_y_in, vector_y_out, params, timing_iterations);
-        DisplayPerf(device_giga_bandwidth, avg_millis, csr_matrix);
-    }
-
-    // Initialize cuSparse
-    cusparseHandle_t cusparse;
-    AssertEquals(CUSPARSE_STATUS_SUCCESS, cusparseCreate(&cusparse));
-
-    if (args.CheckCmdLineFlag("csrmv"))
-    {
-        if (!g_quiet) printf("\n\n");
-        printf("Cusparse CsrMV, "); fflush(stdout);
-        avg_millis = TestCusparseCsrmv(vector_y_in, vector_y_out, params, timing_iterations, cusparse);
-        DisplayPerf(device_giga_bandwidth, avg_millis, csr_matrix);
-    }
-
-    if (args.CheckCmdLineFlag("hybmv"))
-    {
-        if (!g_quiet) printf("\n\n");
-        printf("Cusparse HybMV, "); fflush(stdout);
-
-        avg_millis = TestCusparseHybmv(vector_y_in, vector_y_out, params, timing_iterations, cusparse);
-        DisplayPerf(device_giga_bandwidth, avg_millis, csr_matrix);
-    }
-
-
-    // Cleanup
-    if (params.d_values)            CubDebugExit(g_allocator.DeviceFree(params.d_values));
-    if (params.d_row_end_offsets)   CubDebugExit(g_allocator.DeviceFree(params.d_row_end_offsets));
-    if (params.d_column_indices)    CubDebugExit(g_allocator.DeviceFree(params.d_column_indices));
-    if (params.d_vector_x)          CubDebugExit(g_allocator.DeviceFree(params.d_vector_x));
-    if (params.d_vector_y)          CubDebugExit(g_allocator.DeviceFree(params.d_vector_y));
-
-    if (vector_x)                   delete[] vector_x;
-    if (vector_y_in)                delete[] vector_y_in;
-    if (vector_y_out)               delete[] vector_y_out;
-}
-
-/**
- * Run tests
- */
-template <
-    typename ValueT,
-    typename OffsetT>
-void RunTests(
-    bool                rcm_relabel,
-    ValueT              alpha,
-    ValueT              beta,
-    const std::string&  mtx_filename,
-    int                 grid2d,
-    int                 grid3d,
-    int                 wheel,
-    int                 dense,
-    int                 timing_iterations,
-    CommandLineArgs&    args)
-{
-    // Initialize matrix in COO form
-    CooMatrix<ValueT, OffsetT> coo_matrix;
-
-    if (!mtx_filename.empty())
-    {
-        // Parse matrix market file
-        printf("%s, ", mtx_filename.c_str()); fflush(stdout);
-        coo_matrix.InitMarket(mtx_filename, 1.0, !g_quiet);
-
-        if ((coo_matrix.num_rows == 1) || (coo_matrix.num_cols == 1) || (coo_matrix.num_nonzeros == 1))
-        {
-            if (!g_quiet) printf("Trivial dataset\n");
-            exit(0);
-        }
-    }
-    else if (grid2d > 0)
-    {
-        // Generate 2D lattice
-        printf("grid2d_%d, ", grid2d); fflush(stdout);
-        coo_matrix.InitGrid2d(grid2d, false);
-    }
-    else if (grid3d > 0)
-    {
-        // Generate 3D lattice
-        printf("grid3d_%d, ", grid3d); fflush(stdout);
-        coo_matrix.InitGrid3d(grid3d, false);
-    }
-    else if (wheel > 0)
-    {
-        // Generate wheel graph
-        printf("wheel_%d, ", grid2d); fflush(stdout);
-        coo_matrix.InitWheel(wheel);
-    }
-    else if (dense > 0)
-    {
-        // Generate dense graph
-        OffsetT size = 1 << 24; // 16M nnz
-        args.GetCmdLineArgument("size", size);
-
-        OffsetT rows = size / dense;
-        printf("dense_%d_x_%d, ", rows, dense); fflush(stdout);
-        coo_matrix.InitDense(rows, dense);
-    }
-    else
-    {
-        fprintf(stderr, "No graph type specified.\n");
-        exit(1);
-    }
-
-    RunTest(
-        rcm_relabel,
-        alpha,
-        beta,
-        coo_matrix,
-        timing_iterations,
-        args);
-}
-
-
-
-/**
- * Main
- */
-int main(int argc, char **argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf(
-            "%s "
-            "[--csrmv | --hybmv | --bsrmv ] "
-            "[--device=<device-id>] "
-            "[--quiet] "
-            "[--v] "
-            "[--i=<timing iterations>] "
-            "[--fp64] "
-            "[--rcm] "
-            "[--alpha=<alpha scalar (default: 1.0)>] "
-            "[--beta=<beta scalar (default: 0.0)>] "
-            "\n\t"
-                "--mtx=<matrix market file> "
-            "\n\t"
-                "--dense=<cols>"
-            "\n\t"
-                "--grid2d=<width>"
-            "\n\t"
-                "--grid3d=<width>"
-            "\n\t"
-                "--wheel=<spokes>"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    bool                fp64;
-    bool                rcm_relabel;
-    std::string         mtx_filename;
-    int                 grid2d              = -1;
-    int                 grid3d              = -1;
-    int                 wheel               = -1;
-    int                 dense               = -1;
-    int                 timing_iterations   = -1;
-    float               alpha               = 1.0;
-    float               beta                = 0.0;
-
-    g_verbose = args.CheckCmdLineFlag("v");
-    g_verbose2 = args.CheckCmdLineFlag("v2");
-    g_quiet = args.CheckCmdLineFlag("quiet");
-    fp64 = args.CheckCmdLineFlag("fp64");
-    rcm_relabel = args.CheckCmdLineFlag("rcm");
-    args.GetCmdLineArgument("i", timing_iterations);
-    args.GetCmdLineArgument("mtx", mtx_filename);
-    args.GetCmdLineArgument("grid2d", grid2d);
-    args.GetCmdLineArgument("grid3d", grid3d);
-    args.GetCmdLineArgument("wheel", wheel);
-    args.GetCmdLineArgument("dense", dense);
-    args.GetCmdLineArgument("alpha", alpha);
-    args.GetCmdLineArgument("beta", beta);
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Run test(s)
-    if (fp64)
-    {
-        RunTests<double, int>(rcm_relabel, alpha, beta, mtx_filename, grid2d, grid3d, wheel, dense, timing_iterations, args);
-    }
-    else
-    {
-        RunTests<float, int>(rcm_relabel, alpha, beta, mtx_filename, grid2d, grid3d, wheel, dense, timing_iterations, args);
-    }
-
-    CubDebugExit(cudaDeviceSynchronize());
-    printf("\n");
-
-    return 0;
-}
diff --git a/ml-xgboost/cub/experimental/spmv_script.sh b/ml-xgboost/cub/experimental/spmv_script.sh
deleted file mode 100644
index f432043..0000000
--- a/ml-xgboost/cub/experimental/spmv_script.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-
-for i in 1 2 4 8 16 32 64 128 256 512 1024 2048 4096 8192 16384 32768 65536 131072 262144 524288 1048576 2097152 4194304 8388608 16777216
-do
-	echo `date`, `$1 --dense=$i $2 $3 $4 $5 $6 $7`
-done
-
-echo
-echo
-
-for i in `ls /home/dumerrill/graphs/spmv/*.mtx`
-do
-    if [[ ( "`head -n 50 $i | grep complex`" = "" ) && ( "`head -n 50 $i | grep array`" = "" ) ]] 
-    then
-    	echo `date`, `$1 --mtx=$i $2 $3 $4 $5 $6 $7 2>/dev/null`
-    fi
-done
-
-echo
-echo
-
-for i in `ls /scratch/dumerrill/graphs/mtx/*.mtx`
-#for i in `ls /cygdrive/w/Dev/UFget/mtx/*.mtx`
-do 
-    if [[ ( "`head -n 50 $i | grep complex`" = "" ) && ( "`head -n 50 $i | grep array`" = "" ) ]] 
-    then
-    	echo `date`, `$1 --mtx=$i $2 $3 $4 $5 $6 $7 2>/dev/null`
-    fi
-done 
-
diff --git a/ml-xgboost/cub/test/.gitignore b/ml-xgboost/cub/test/.gitignore
deleted file mode 100644
index 51c0abb..0000000
--- a/ml-xgboost/cub/test/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-/bin
-/link_main.obj
diff --git a/ml-xgboost/cub/test/Makefile b/ml-xgboost/cub/test/Makefile
deleted file mode 100644
index 90585f6..0000000
--- a/ml-xgboost/cub/test/Makefile
+++ /dev/null
@@ -1,453 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
-
-
-#-------------------------------------------------------------------------------
-#
-# Makefile usage
-#
-# make <target> [sm=<XXX,...>] [cdp=<0|1>] [force32=<0|1>] [abi=<0|1>] [open64=<0|1>] [verbose=<0|1>] [keep=<0|1>] [quicktest=<0|1>] [quickertest=<0|1>]
-#
-#-------------------------------------------------------------------------------
-
-include ../common.mk 
- 
-#-------------------------------------------------------------------------------
-# Commandline Options
-#-------------------------------------------------------------------------------
-
-# Testing mode option (quick/thorough)
-ifeq ($(quickertest), 1)
-	NVCCFLAGS += -DQUICKER_TEST
-	TEST_SUFFIX = quicker
-else ifeq ($(quicktest), 1)
-	NVCCFLAGS += -DQUICK_TEST
-	TEST_SUFFIX = quick
-else 
-	TEST_SUFFIX = thorough
-	NPPI = 
-endif
-
-
-# CUDA memcheck (enabled by default) 
-ifeq ($(memcheck), 0)
-	MEMCHECK = 
-else 
-	MEMCHECK = cuda-memcheck
-endif
-
-
-#-------------------------------------------------------------------------------
-# Compiler and compilation platform
-#-------------------------------------------------------------------------------
-
-# Includes
-INC += -I$(CUB_DIR) -I$(CUB_DIR)test 
-
-# Suffix to append to each binary
-SUFFIX = $(BIN_SUFFIX)_$(TEST_SUFFIX)
-
-# Define test arch
-DEFINES += -DTEST_ARCH=$(TEST_ARCH)
-
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-DEPS =				$(CUB_DEPS) \
-					$(CUB_DIR)test/Makefile \
-					$(CUB_DIR)test/test_util.h \
-					$(CUB_DIR)test/mersenne.h \
-
-BLOCK_REDUCE = 		test_block_reduce_raking \
-	 				test_block_reduce_warp_reductions		
-
-
-BLOCK_SCAN = 		test_block_scan_raking \
-	 				test_block_scan_raking_memoize \
-	 				test_block_scan_warp_scans		
-
-
-BLOCK_RADIX_SORT = 	test_block_radix_sort_keys \
-	 				test_block_radix_sort_pairs	
-
-		
-ALL = 				link \
-	 				test_iterator \
-	 				test_allocator \
-	 				test_warp_scan \
-	 				test_warp_reduce \
-	 				$(BLOCK_REDUCE) \
-	 				$(BLOCK_SCAN) \
-	 				$(BLOCK_RADIX_SORT) \
-	 				test_block_load_store \
-	 				test_block_histogram \
-				 	test_device_reduce \
-			 		test_device_histogram \
-			 		test_device_scan \
-			 		test_device_radix_sort \
-					test_device_reduce_by_key\
-					test_device_run_length_encode\
-		 			test_device_select_unique \
-					test_device_select_if 
-		
-#	 	test_grid_barrier \		fails on sm110
-#	 	test_device_seg_reduce
-		
-
-
-#-------------------------------------------------------------------------------
-# make default
-#-------------------------------------------------------------------------------
-
-default:
-
-
-#-------------------------------------------------------------------------------
-# make clean
-#-------------------------------------------------------------------------------
-
-clean :
-	rm -f bin/*$(CPU_ARCH_SUFFIX)* 
-	rm -f *.i* *.cubin *.cu.c *.cudafe* *.fatbin.c *.ptx *.hash *.cu.cpp *.o
-
-
-#-------------------------------------------------------------------------------
-# make all
-#-------------------------------------------------------------------------------
-
-all : $(ALL)
-
-
-#-------------------------------------------------------------------------------
-# make run
-#-------------------------------------------------------------------------------
-
-run : 
-	for i in $(ALL); do $(MEMCHECK) ./bin/$${i}_$(SUFFIX) --device=$(device) || exit 1; done
-
-run_block_reduce : 
-	for i in $(BLOCK_REDUCE); do $(MEMCHECK) ./bin/$${i}_$(SUFFIX) --device=$(device) || exit 1; done
-
-run_block_scan : 
-	for i in $(BLOCK_SCAN); do $(MEMCHECK) ./bin/$${i}_$(SUFFIX) --device=$(device) || exit 1; done
-
-run_block_radix_sort : 
-	for i in $(BLOCK_RADIX_SORT); do $(MEMCHECK) ./bin/$${i}_$(SUFFIX) --device=$(device) || exit 1; done
-
-
-
-#-------------------------------------------------------------------------------
-# make link
-#-------------------------------------------------------------------------------
-
-link : bin/link_$(SUFFIX)
-
-bin/link_$(SUFFIX) : link_a.cu link_b.cu link_main.cpp $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(DEFINES) $(SM_TARGETS) link_a.cu -c -o bin/link_a.obj
-	$(NVCC) $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(DEFINES) $(SM_TARGETS) link_b.cu -c -o bin/link_b.obj
-	$(NVCC) $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(DEFINES) $(SM_TARGETS) link_main.cpp bin/link_a.obj bin/link_b.obj -o bin/link_$(SUFFIX)
-
-
-#-------------------------------------------------------------------------------
-# make test_iterator 
-#-------------------------------------------------------------------------------
-
-test_iterator: bin/test_iterator_$(SUFFIX)
-
-bin/test_iterator_$(SUFFIX) : test_iterator.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_iterator_$(SUFFIX) test_iterator.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_allocator 
-#-------------------------------------------------------------------------------
-
-test_allocator: bin/test_allocator_$(SUFFIX)
-
-bin/test_allocator_$(SUFFIX) : test_allocator.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_allocator_$(SUFFIX) test_allocator.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-	
-	
-#-------------------------------------------------------------------------------
-# make test_grid_barrier 
-#-------------------------------------------------------------------------------
-
-test_grid_barrier: bin/test_grid_barrier_$(SUFFIX)
-
-bin/test_grid_barrier_$(SUFFIX) : test_grid_barrier.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_grid_barrier_$(SUFFIX) test_grid_barrier.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-	
-
-#-------------------------------------------------------------------------------
-# make test_warp_scan 
-#-------------------------------------------------------------------------------
-
-test_warp_scan: bin/test_warp_scan_$(SUFFIX)
-
-bin/test_warp_scan_$(SUFFIX) : test_warp_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_warp_scan_$(SUFFIX) test_warp_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_warp_reduce 
-#-------------------------------------------------------------------------------
-
-test_warp_reduce: bin/test_warp_reduce_$(SUFFIX)
-
-bin/test_warp_reduce_$(SUFFIX) : test_warp_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_warp_reduce_$(SUFFIX) test_warp_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_block_reduce_raking
-#-------------------------------------------------------------------------------
-
-test_block_reduce_raking: bin/test_block_reduce_raking_$(SUFFIX)
-
-bin/test_block_reduce_raking_$(SUFFIX) : test_block_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) -DTEST_RAKING $(SM_TARGETS) -o bin/test_block_reduce_raking_$(SUFFIX) test_block_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_block_reduce_warp_reductions 
-#-------------------------------------------------------------------------------
-
-test_block_reduce_warp_reductions: bin/test_block_reduce_warp_reductions_$(SUFFIX)
-
-bin/test_block_reduce_warp_reductions_$(SUFFIX) : test_block_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) -DTEST_WARP_REDUCTIONS $(SM_TARGETS) -o bin/test_block_reduce_warp_reductions_$(SUFFIX) test_block_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_block_reduce 
-#-------------------------------------------------------------------------------
-
-test_block_reduce: $(BLOCK_REDUCE)
-
-
-#-------------------------------------------------------------------------------
-# make test_block_scan_raking
-#-------------------------------------------------------------------------------
-
-test_block_scan_raking: bin/test_block_scan_raking_$(SUFFIX)
-
-bin/test_block_scan_raking_$(SUFFIX) : test_block_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) -DTEST_RAKING $(SM_TARGETS) -o bin/test_block_scan_raking_$(SUFFIX) test_block_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_block_scan_raking_memoize
-#-------------------------------------------------------------------------------
-
-test_block_scan_raking_memoize: bin/test_block_scan_raking_memoize_$(SUFFIX)
-
-bin/test_block_scan_raking_memoize_$(SUFFIX) : test_block_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) -DTEST_RAKING_MEMOIZE $(SM_TARGETS) -o bin/test_block_scan_raking_memoize_$(SUFFIX) test_block_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_block_scan_warp_scans
-#-------------------------------------------------------------------------------
-
-test_block_scan_warp_scans: bin/test_block_scan_warp_scans_$(SUFFIX)
-
-bin/test_block_scan_warp_scans_$(SUFFIX) : test_block_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) -DTEST_WARP_SCANS $(SM_TARGETS) -o bin/test_block_scan_warp_scans_$(SUFFIX) test_block_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3  
-
-
-#-------------------------------------------------------------------------------
-# make test_block_scan 
-#-------------------------------------------------------------------------------
-
-test_block_scan: $(BLOCK_SCAN)
-
-
-#-------------------------------------------------------------------------------
-# make test_block_load_store 
-#-------------------------------------------------------------------------------
-
-test_block_load_store: bin/test_block_load_store_$(SUFFIX)
-
-bin/test_block_load_store_$(SUFFIX) : test_block_load_store.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_block_load_store_$(SUFFIX) test_block_load_store.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-	
-	
-#-------------------------------------------------------------------------------
-# make test_block_radix_sort_keys 
-#-------------------------------------------------------------------------------
-
-test_block_radix_sort_keys: bin/test_block_radix_sort_keys_$(SUFFIX)
-
-bin/test_block_radix_sort_keys_$(SUFFIX) : test_block_radix_sort.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) -DTEST_KEYS_ONLY $(SM_TARGETS) -o bin/test_block_radix_sort_keys_$(SUFFIX) test_block_radix_sort.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make test_block_radix_sort_pairs 
-#-------------------------------------------------------------------------------
-
-test_block_radix_sort_pairs: bin/test_block_radix_sort_pairs_$(SUFFIX)
-
-bin/test_block_radix_sort_pairs_$(SUFFIX) : test_block_radix_sort.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_block_radix_sort_pairs_$(SUFFIX) test_block_radix_sort.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_block_radix_sort
-#-------------------------------------------------------------------------------
-
-test_block_radix_sort : $(BLOCK_RADIX_SORT)
-
-
-#-------------------------------------------------------------------------------
-# make test_block_histogram 
-#-------------------------------------------------------------------------------
-
-test_block_histogram: bin/test_block_histogram_$(SUFFIX)
-
-bin/test_block_histogram_$(SUFFIX) : test_block_histogram.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_block_histogram_$(SUFFIX) test_block_histogram.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_device_reduce
-#-------------------------------------------------------------------------------
-
-test_device_reduce: bin/test_device_reduce_$(SUFFIX)
-
-bin/test_device_reduce_$(SUFFIX) : test_device_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_reduce_$(SUFFIX) test_device_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_device_histogram
-#-------------------------------------------------------------------------------
-
-test_device_histogram: bin/test_device_histogram_$(SUFFIX)
-
-bin/test_device_histogram_$(SUFFIX) : test_device_histogram.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_histogram_$(SUFFIX) test_device_histogram.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) $(NPPI) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_device_scan
-#-------------------------------------------------------------------------------
-
-test_device_scan: bin/test_device_scan_$(SUFFIX)
-
-bin/test_device_scan_$(SUFFIX) : test_device_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_scan_$(SUFFIX) test_device_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_device_radix_sort
-#-------------------------------------------------------------------------------
-
-test_device_radix_sort: bin/test_device_radix_sort_$(SUFFIX)
-
-bin/test_device_radix_sort_$(SUFFIX) : test_device_radix_sort.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_radix_sort_$(SUFFIX) test_device_radix_sort.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_device_select_unique
-#-------------------------------------------------------------------------------
-
-test_device_select_unique: bin/test_device_select_unique_$(SUFFIX)
-
-bin/test_device_select_unique_$(SUFFIX) : test_device_select_unique.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_select_unique_$(SUFFIX) test_device_select_unique.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make test_device_select_if
-#-------------------------------------------------------------------------------
-
-test_device_select_if: bin/test_device_select_if_$(SUFFIX)
-
-bin/test_device_select_if_$(SUFFIX) : test_device_select_if.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_select_if_$(SUFFIX) test_device_select_if.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make test_device_reduce_by_key
-#-------------------------------------------------------------------------------
-
-test_device_reduce_by_key: bin/test_device_reduce_by_key_$(SUFFIX)
-
-bin/test_device_reduce_by_key_$(SUFFIX) : test_device_reduce_by_key.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_reduce_by_key_$(SUFFIX) test_device_reduce_by_key.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make test_device_run_length_encode
-#-------------------------------------------------------------------------------
-
-test_device_run_length_encode: bin/test_device_run_length_encode_$(SUFFIX)
-
-bin/test_device_run_length_encode_$(SUFFIX) : test_device_run_length_encode.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_run_length_encode_$(SUFFIX) test_device_run_length_encode.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-
-
-#-------------------------------------------------------------------------------
-# make test_device_seg_reduce
-#-------------------------------------------------------------------------------
-#
-#test_device_seg_reduce: bin/test_device_seg_reduce_$(SUFFIX)
-#
-#bin/test_device_seg_reduce_$(SUFFIX) : test_device_seg_reduce.cu $(DEPS)
-#	mkdir -p bin
-#	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/test_device_seg_reduce_$(SUFFIX) test_device_seg_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
diff --git a/ml-xgboost/cub/test/link_a.cu b/ml-xgboost/cub/test/link_a.cu
deleted file mode 100644
index 8a9b19f..0000000
--- a/ml-xgboost/cub/test/link_a.cu
+++ /dev/null
@@ -1,11 +0,0 @@
-#include <cub/cub.cuh>
-
-void a()
-{
-    printf("a() called\n");
-
-    cub::DoubleBuffer<unsigned int>     d_keys;
-    cub::DoubleBuffer<cub::NullType>    d_values;
-    size_t                              temp_storage_bytes = 0;
-    cub::DeviceRadixSort::SortPairs(NULL, temp_storage_bytes, d_keys, d_values, 1024);
-}
diff --git a/ml-xgboost/cub/test/link_b.cu b/ml-xgboost/cub/test/link_b.cu
deleted file mode 100644
index a19ec40..0000000
--- a/ml-xgboost/cub/test/link_b.cu
+++ /dev/null
@@ -1,11 +0,0 @@
-#include <cub/cub.cuh>
-
-void b()
-{
-    printf("b() called\n");
-
-    cub::DoubleBuffer<unsigned int>     d_keys;
-    cub::DoubleBuffer<cub::NullType>    d_values;
-    size_t                              temp_storage_bytes = 0;
-    cub::DeviceRadixSort::SortPairs(NULL, temp_storage_bytes, d_keys, d_values, 1024);
-}
diff --git a/ml-xgboost/cub/test/link_main.cpp b/ml-xgboost/cub/test/link_main.cpp
deleted file mode 100644
index ef677ee..0000000
--- a/ml-xgboost/cub/test/link_main.cpp
+++ /dev/null
@@ -1,10 +0,0 @@
-#include <stdio.h>
-
-extern void a();
-extern void b();
-
-int main()
-{
-    printf("hello world\n");
-    return 0;
-}
diff --git a/ml-xgboost/cub/test/mersenne.h b/ml-xgboost/cub/test/mersenne.h
deleted file mode 100644
index 76aae80..0000000
--- a/ml-xgboost/cub/test/mersenne.h
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- A C-program for MT19937, with initialization improved 2002/1/26.
- Coded by Takuji Nishimura and Makoto Matsumoto.
-
- Before using, initialize the state by using init_genrand(seed)
- or init_by_array(init_key, key_length).
-
- Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
- All rights reserved.
-
- Redistribution and use in source and binary forms, with or without
- modification, are permitted provided that the following conditions
- are met:
-
- 1. Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
-
- 2. Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
- 3. The names of its contributors may not be used to endorse or promote
- products derived from this software without specific prior written
- permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
- A PARTICAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
- CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
- EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
- PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
- PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
- LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
- NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
- Any feedback is very welcome.
- http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
- email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
- */
-
-#include <stdio.h>
-
-namespace mersenne {
-
-/* Period parameters */
-const unsigned int N          = 624;
-const unsigned int M          = 397;
-const unsigned int MATRIX_A   = 0x9908b0df; /* constant vector a */
-const unsigned int UPPER_MASK = 0x80000000; /* most significant w-r bits */
-const unsigned int LOWER_MASK = 0x7fffffff; /* least significant r bits */
-
-static unsigned int mt[N];  /* the array for the state vector  */
-static int mti = N + 1;     /* mti==N+1 means mt[N] is not initialized */
-
-/* initializes mt[N] with a seed */
-void init_genrand(unsigned int s)
-{
-    mt[0] = s & 0xffffffff;
-    for (mti = 1; mti < N; mti++)
-    {
-        mt[mti] = (1812433253 * (mt[mti - 1] ^ (mt[mti - 1] >> 30)) + mti);
-
-        /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for mtiplier. */
-        /* In the previous versions, MSBs of the seed affect   */
-        /* only MSBs of the array mt[].                        */
-        /* 2002/01/09 modified by Makoto Matsumoto             */
-
-        mt[mti] &= 0xffffffff;
-        /* for >32 bit machines */
-    }
-}
-
-/* initialize by an array with array-length */
-/* init_key is the array for initializing keys */
-/* key_length is its length */
-/* slight change for C++, 2004/2/26 */
-void init_by_array(unsigned int init_key[], int key_length)
-{
-    int i, j, k;
-    init_genrand(19650218);
-    i = 1;
-    j = 0;
-    k = (N > key_length ? N : key_length);
-    for (; k; k--)
-    {
-        mt[i] = (mt[i] ^ ((mt[i - 1] ^ (mt[i - 1] >> 30)) * 1664525))
-            + init_key[j] + j;  /* non linear */
-        mt[i] &= 0xffffffff;    /* for WORDSIZE > 32 machines */
-        i++;
-        j++;
-        if (i >= N)
-        {
-            mt[0] = mt[N - 1];
-            i = 1;
-        }
-        if (j >= key_length) j = 0;
-    }
-    for (k = N - 1; k; k--)
-    {
-        mt[i] = (mt[i] ^ ((mt[i - 1] ^ (mt[i - 1] >> 30)) * 1566083941)) - i; /* non linear */
-        mt[i] &= 0xffffffff; /* for WORDSIZE > 32 machines */
-        i++;
-        if (i >= N)
-        {
-            mt[0] = mt[N - 1];
-            i = 1;
-        }
-    }
-
-    mt[0] = 0x80000000; /* MSB is 1; assuring non-zero initial array */
-}
-
-/* generates a random number on [0,0xffffffff]-interval */
-unsigned int genrand_int32(void)
-{
-    unsigned int y;
-    static unsigned int mag01[2] = { 0x0, MATRIX_A };
-
-    /* mag01[x] = x * MATRIX_A  for x=0,1 */
-
-    if (mti >= N)
-    { /* generate N words at one time */
-        int kk;
-
-        if (mti == N + 1) /* if init_genrand() has not been called, */
-        init_genrand(5489); /* a defat initial seed is used */
-
-        for (kk = 0; kk < N - M; kk++)
-        {
-            y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK);
-            mt[kk] = mt[kk + M] ^ (y >> 1) ^ mag01[y & 0x1];
-        }
-        for (; kk < N - 1; kk++)
-        {
-            y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK);
-            mt[kk] = mt[kk + (M - N)] ^ (y >> 1) ^ mag01[y & 0x1];
-        }
-        y = (mt[N - 1] & UPPER_MASK) | (mt[0] & LOWER_MASK);
-        mt[N - 1] = mt[M - 1] ^ (y >> 1) ^ mag01[y & 0x1];
-
-        mti = 0;
-    }
-
-    y = mt[mti++];
-
-    /* Tempering */
-    y ^= (y >> 11);
-    y ^= (y << 7) & 0x9d2c5680;
-    y ^= (y << 15) & 0xefc60000;
-    y ^= (y >> 18);
-
-    return y;
-}
-
-
-
-} // namespace mersenne
diff --git a/ml-xgboost/cub/test/test_allocator.cu b/ml-xgboost/cub/test/test_allocator.cu
deleted file mode 100644
index 94d9df1..0000000
--- a/ml-xgboost/cub/test/test_allocator.cu
+++ /dev/null
@@ -1,459 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test evaluation for caching allocator of device memory
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/util_allocator.cuh>
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>]"
-            "[--bytes=<timing bytes>]"
-            "[--i=<timing iterations>]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-#if (CUB_PTX_ARCH == 0)
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get number of GPUs and current GPU
-    int num_gpus;
-    int initial_gpu;
-    int timing_iterations           = 10000;
-    int timing_bytes                = 1024 * 1024;
-
-    if (CubDebug(cudaGetDeviceCount(&num_gpus))) exit(1);
-    if (CubDebug(cudaGetDevice(&initial_gpu))) exit(1);
-    args.GetCmdLineArgument("i", timing_iterations);
-    args.GetCmdLineArgument("bytes", timing_bytes);
-
-    // Create default allocator (caches up to 6MB in device allocations per GPU)
-    CachingDeviceAllocator allocator;
-    allocator.debug = true;
-
-    printf("Running single-gpu tests...\n"); fflush(stdout);
-
-    //
-    // Test0
-    //
-
-    // Create a new stream
-    cudaStream_t other_stream;
-    CubDebugExit(cudaStreamCreate(&other_stream));
-
-    // Allocate 999 bytes on the current gpu in stream0
-    char *d_999B_stream0_a;
-    char *d_999B_stream0_b;
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream0_a, 999, 0));
-
-    // Run some big kernel in stream 0
-    EmptyKernel<void><<<32000, 512, 1024 * 8, 0>>>();
-
-    // Free d_999B_stream0_a
-    CubDebugExit(allocator.DeviceFree(d_999B_stream0_a));
-
-    // Allocate another 999 bytes in stream 0
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream0_b, 999, 0));
-
-    // Check that that we have 1 live block on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    // Check that that we have no cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 0);
-
-    // Run some big kernel in stream 0
-    EmptyKernel<void><<<32000, 512, 1024 * 8, 0>>>();
-
-    // Free d_999B_stream0_b
-    CubDebugExit(allocator.DeviceFree(d_999B_stream0_b));
-
-    // Allocate 999 bytes on the current gpu in other_stream
-    char *d_999B_stream_other_a;
-    char *d_999B_stream_other_b;
-    allocator.DeviceAllocate((void **) &d_999B_stream_other_a, 999, other_stream);
-
-    // Check that that we have 1 live blocks on the initial GPU (that we allocated a new one because d_999B_stream0_b is only available for stream 0 until it becomes idle)
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    // Check that that we have one cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 1);
-
-    // Run some big kernel in other_stream
-    EmptyKernel<void><<<32000, 512, 1024 * 8, other_stream>>>();
-
-    // Free d_999B_stream_other
-    CubDebugExit(allocator.DeviceFree(d_999B_stream_other_a));
-
-    // Check that we can now use both allocations in stream 0 after synchronizing the device
-    CubDebugExit(cudaDeviceSynchronize());
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream0_a, 999, 0));
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream0_b, 999, 0));
-
-    // Check that that we have 2 live blocks on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 2);
-
-    // Check that that we have no cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 0);
-
-    // Free d_999B_stream0_a and d_999B_stream0_b
-    CubDebugExit(allocator.DeviceFree(d_999B_stream0_a));
-    CubDebugExit(allocator.DeviceFree(d_999B_stream0_b));
-
-    // Check that we can now use both allocations in other_stream
-    CubDebugExit(cudaDeviceSynchronize());
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream_other_a, 999, other_stream));
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream_other_b, 999, other_stream));
-
-    // Check that that we have 2 live blocks on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 2);
-
-    // Check that that we have no cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 0);
-
-    // Run some big kernel in other_stream
-    EmptyKernel<void><<<32000, 512, 1024 * 8, other_stream>>>();
-
-    // Free d_999B_stream_other_a and d_999B_stream_other_b
-    CubDebugExit(allocator.DeviceFree(d_999B_stream_other_a));
-    CubDebugExit(allocator.DeviceFree(d_999B_stream_other_b));
-
-    // Check that we can now use both allocations in stream 0 after synchronizing the device and destroying the other stream
-    CubDebugExit(cudaDeviceSynchronize());
-    CubDebugExit(cudaStreamDestroy(other_stream));
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream0_a, 999, 0));
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_999B_stream0_b, 999, 0));
-
-    // Check that that we have 2 live blocks on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 2);
-
-    // Check that that we have no cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 0);
-
-    // Free d_999B_stream0_a and d_999B_stream0_b
-    CubDebugExit(allocator.DeviceFree(d_999B_stream0_a));
-    CubDebugExit(allocator.DeviceFree(d_999B_stream0_b));
-
-    // Free all cached
-    CubDebugExit(allocator.FreeAllCached());
-
-    //
-    // Test1
-    //
-
-    // Allocate 5 bytes on the current gpu
-    char *d_5B;
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_5B, 5));
-
-    // Check that that we have zero free bytes cached on the initial GPU
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, 0);
-
-    // Check that that we have 1 live block on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    //
-    // Test2
-    //
-
-    // Allocate 4096 bytes on the current gpu
-    char *d_4096B;
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_4096B, 4096));
-
-    // Check that that we have 2 live blocks on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 2);
-
-    //
-    // Test3
-    //
-
-    // DeviceFree d_5B
-    CubDebugExit(allocator.DeviceFree(d_5B));
-
-    // Check that that we have min_bin_bytes free bytes cached on the initial gpu
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, allocator.min_bin_bytes);
-
-    // Check that that we have 1 live block on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    // Check that that we have 1 cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 1);
-
-    //
-    // Test4
-    //
-
-    // DeviceFree d_4096B
-    CubDebugExit(allocator.DeviceFree(d_4096B));
-
-    // Check that that we have the 4096 + min_bin free bytes cached on the initial gpu
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, allocator.min_bin_bytes + 4096);
-
-    // Check that that we have 0 live block on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 0);
-
-    // Check that that we have 2 cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 2);
-
-    //
-    // Test5
-    //
-
-    // Allocate 768 bytes on the current gpu
-    char *d_768B;
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_768B, 768));
-
-    // Check that that we have the min_bin free bytes cached on the initial gpu (4096 was reused)
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, allocator.min_bin_bytes);
-
-    // Check that that we have 1 live block on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    // Check that that we have 1 cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 1);
-
-    //
-    // Test6
-    //
-
-    // Allocate max_cached_bytes on the current gpu
-    char *d_max_cached;
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_max_cached, allocator.max_cached_bytes));
-
-    // DeviceFree d_max_cached
-    CubDebugExit(allocator.DeviceFree(d_max_cached));
-
-    // Check that that we have the min_bin free bytes cached on the initial gpu (max cached was not returned because we went over)
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, allocator.min_bin_bytes);
-
-    // Check that that we have 1 live block on the initial GPU
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    // Check that that we still have 1 cached block on the initial GPU
-    AssertEquals(allocator.cached_blocks.size(), 1);
-
-    //
-    // Test7
-    //
-
-    // Free all cached blocks on all GPUs
-    CubDebugExit(allocator.FreeAllCached());
-
-    // Check that that we have 0 bytes cached on the initial GPU
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, 0);
-
-    // Check that that we have 0 cached blocks across all GPUs
-    AssertEquals(allocator.cached_blocks.size(), 0);
-
-    // Check that that still we have 1 live block across all GPUs
-    AssertEquals(allocator.live_blocks.size(), 1);
-
-    //
-    // Test8
-    //
-
-    // Allocate max cached bytes + 1 on the current gpu
-    char *d_max_cached_plus;
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_max_cached_plus, allocator.max_cached_bytes + 1));
-
-    // DeviceFree max cached bytes
-    CubDebugExit(allocator.DeviceFree(d_max_cached_plus));
-
-    // DeviceFree d_768B
-    CubDebugExit(allocator.DeviceFree(d_768B));
-
-    unsigned int power;
-    size_t rounded_bytes;
-    allocator.NearestPowerOf(power, rounded_bytes, allocator.bin_growth, 768);
-
-    // Check that that we have 4096 free bytes cached on the initial gpu
-    AssertEquals(allocator.cached_bytes[initial_gpu].free, rounded_bytes);
-
-    // Check that that we have 1 cached blocks across all GPUs
-    AssertEquals(allocator.cached_blocks.size(), 1);
-
-    // Check that that still we have 0 live block across all GPUs
-    AssertEquals(allocator.live_blocks.size(), 0);
-
-#ifndef CUB_CDP
-    // BUG: find out why these tests fail when one GPU is CDP compliant and the other is not
-
-    if (num_gpus > 1)
-    {
-        printf("\nRunning multi-gpu tests...\n"); fflush(stdout);
-
-        //
-        // Test9
-        //
-
-        // Allocate 768 bytes on the next gpu
-        int next_gpu = (initial_gpu + 1) % num_gpus;
-        char *d_768B_2;
-        CubDebugExit(allocator.DeviceAllocate(next_gpu, (void **) &d_768B_2, 768));
-
-        // DeviceFree d_768B on the next gpu
-        CubDebugExit(allocator.DeviceFree(next_gpu, d_768B_2));
-
-        // Re-allocate 768 bytes on the next gpu
-        CubDebugExit(allocator.DeviceAllocate(next_gpu, (void **) &d_768B_2, 768));
-
-        // Re-free d_768B on the next gpu
-        CubDebugExit(allocator.DeviceFree(next_gpu, d_768B_2));
-
-        // Check that that we have 4096 free bytes cached on the initial gpu
-        AssertEquals(allocator.cached_bytes[initial_gpu].free, rounded_bytes);
-
-        // Check that that we have 4096 free bytes cached on the second gpu
-        AssertEquals(allocator.cached_bytes[next_gpu].free, rounded_bytes);
-
-        // Check that that we have 2 cached blocks across all GPUs
-        AssertEquals(allocator.cached_blocks.size(), 2);
-
-        // Check that that still we have 0 live block across all GPUs
-        AssertEquals(allocator.live_blocks.size(), 0);
-    }
-#endif  // CUB_CDP
-
-    //
-    // Performance
-    //
-
-    printf("\nCPU Performance (%d timing iterations, %d bytes):\n", timing_iterations, timing_bytes);
-    fflush(stdout); fflush(stderr);
-
-    // CPU performance comparisons vs cached.  Allocate and free a 1MB block 2000 times
-    CpuTimer    cpu_timer;
-    char        *d_1024MB                       = NULL;
-    allocator.debug                             = false;
-
-    // Prime the caching allocator and the kernel
-    CubDebugExit(allocator.DeviceAllocate((void **) &d_1024MB, timing_bytes));
-    CubDebugExit(allocator.DeviceFree(d_1024MB));
-    cub::EmptyKernel<void><<<1, 32>>>();
-
-    // CUDA
-    cpu_timer.Start();
-    for (int i = 0; i < timing_iterations; ++i)
-    {
-        CubDebugExit(cudaMalloc((void **) &d_1024MB, timing_bytes));
-        CubDebugExit(cudaFree(d_1024MB));
-    }
-    cpu_timer.Stop();
-    float cuda_malloc_elapsed_millis = cpu_timer.ElapsedMillis();
-
-    // CUB
-    cpu_timer.Start();
-    for (int i = 0; i < timing_iterations; ++i)
-    {
-        CubDebugExit(allocator.DeviceAllocate((void **) &d_1024MB, timing_bytes));
-        CubDebugExit(allocator.DeviceFree(d_1024MB));
-    }
-    cpu_timer.Stop();
-    float cub_calloc_elapsed_millis = cpu_timer.ElapsedMillis();
-
-    printf("\t CUB CachingDeviceAllocator allocation CPU speedup: %.2f (avg cudaMalloc %.4f ms vs. avg DeviceAllocate %.4f ms)\n",
-        cuda_malloc_elapsed_millis / cub_calloc_elapsed_millis,
-        cuda_malloc_elapsed_millis / timing_iterations,
-        cub_calloc_elapsed_millis / timing_iterations);
-
-    // GPU performance comparisons.  Allocate and free a 1MB block 2000 times
-    GpuTimer gpu_timer;
-
-    printf("\nGPU Performance (%d timing iterations, %d bytes):\n", timing_iterations, timing_bytes);
-    fflush(stdout); fflush(stderr);
-
-    // Kernel-only
-    gpu_timer.Start();
-    for (int i = 0; i < timing_iterations; ++i)
-    {
-        cub::EmptyKernel<void><<<1, 32>>>();
-    }
-    gpu_timer.Stop();
-    float cuda_empty_elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // CUDA
-    gpu_timer.Start();
-    for (int i = 0; i < timing_iterations; ++i)
-    {
-        CubDebugExit(cudaMalloc((void **) &d_1024MB, timing_bytes));
-        cub::EmptyKernel<void><<<1, 32>>>();
-        CubDebugExit(cudaFree(d_1024MB));
-    }
-    gpu_timer.Stop();
-    cuda_malloc_elapsed_millis = gpu_timer.ElapsedMillis() - cuda_empty_elapsed_millis;
-
-    // CUB
-    gpu_timer.Start();
-    for (int i = 0; i < timing_iterations; ++i)
-    {
-        CubDebugExit(allocator.DeviceAllocate((void **) &d_1024MB, timing_bytes));
-        cub::EmptyKernel<void><<<1, 32>>>();
-        CubDebugExit(allocator.DeviceFree(d_1024MB));
-    }
-    gpu_timer.Stop();
-    cub_calloc_elapsed_millis = gpu_timer.ElapsedMillis() - cuda_empty_elapsed_millis;
-
-    printf("\t CUB CachingDeviceAllocator allocation GPU speedup: %.2f (avg cudaMalloc %.4f ms vs. avg DeviceAllocate %.4f ms)\n",
-        cuda_malloc_elapsed_millis / cub_calloc_elapsed_millis,
-        cuda_malloc_elapsed_millis / timing_iterations,
-        cub_calloc_elapsed_millis / timing_iterations);
-
-
-#endif
-
-    printf("Success\n");
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/test/test_block_histogram.cu b/ml-xgboost/cub/test/test_block_histogram.cu
deleted file mode 100644
index 4a8ab45..0000000
--- a/ml-xgboost/cub/test/test_block_histogram.cu
+++ /dev/null
@@ -1,310 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of BlockHistogram utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <limits>
-#include <string>
-#include <typeinfo>
-
-#include <cub/block/block_histogram.cuh>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/util_allocator.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-/**
- * BlockHistogram test kernel.
- */
-template <
-    int                     BINS,
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    BlockHistogramAlgorithm ALGORITHM,
-    typename                T,
-    typename                HistoCounter>
-__global__ void BlockHistogramKernel(
-    T                       *d_samples,
-    HistoCounter            *d_histogram)
-{
-    // Parameterize BlockHistogram type for our thread block
-    typedef BlockHistogram<T, BLOCK_THREADS, ITEMS_PER_THREAD, BINS, ALGORITHM> BlockHistogram;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename BlockHistogram::TempStorage temp_storage;
-
-    // Per-thread tile data
-    T data[ITEMS_PER_THREAD];
-    LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_samples, data);
-
-    // Test histo (writing directly to histogram buffer in global)
-    BlockHistogram(temp_storage).Histogram(data, d_histogram);
-}
-
-
-/**
- * Initialize problem (and solution)
- */
-template <
-    int             BINS,
-    typename        SampleT>
-void Initialize(
-    GenMode         gen_mode,
-    SampleT         *h_samples,
-    int             *h_histograms_linear,
-    int             num_samples)
-{
-    // Init bins
-    for (int bin = 0; bin < BINS; ++bin)
-    {
-        h_histograms_linear[bin] = 0;
-    }
-
-    if (g_verbose) printf("Samples: \n");
-
-    // Initialize interleaved channel samples and histogram them correspondingly
-    for (int i = 0; i < num_samples; ++i)
-    {
-        InitValue(gen_mode, h_samples[i], i);
-        h_samples[i] %= BINS;
-
-        if (g_verbose) std::cout << CoutCast(h_samples[i]) << ", ";
-
-        h_histograms_linear[h_samples[i]]++;
-    }
-
-    if (g_verbose) printf("\n\n");
-}
-
-
-/**
- * Test BlockHistogram
- */
-template <
-    typename                    SampleT,
-    int                         BINS,
-    int                         BLOCK_THREADS,
-    int                         ITEMS_PER_THREAD,
-    BlockHistogramAlgorithm     ALGORITHM>
-void Test(
-    GenMode                     gen_mode)
-{
-    int num_samples = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    printf("cub::BlockHistogram %s %d %s samples (%dB), %d bins, %d threads, gen-mode %s\n",
-        (ALGORITHM == BLOCK_HISTO_SORT) ? "BLOCK_HISTO_SORT" : "BLOCK_HISTO_ATOMIC",
-        num_samples,
-        typeid(SampleT).name(),
-        (int) sizeof(SampleT),
-        BINS,
-        BLOCK_THREADS,
-        (gen_mode == RANDOM) ? "RANDOM" : (gen_mode == INTEGER_SEED) ? "SEQUENTIAL" : "HOMOGENOUS");
-    fflush(stdout);
-
-    // Allocate host arrays
-    SampleT         *h_samples          = new SampleT[num_samples];
-    int   *h_reference = new int[BINS];
-
-    // Initialize problem
-    Initialize<BINS>(gen_mode, h_samples, h_reference, num_samples);
-
-    // Allocate problem device arrays
-    SampleT         *d_samples = NULL;
-    int             *d_histogram = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_samples,             sizeof(SampleT) * num_samples));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_histogram,   sizeof(int) * BINS));
-
-    // Initialize/clear device arrays
-    CubDebugExit(cudaMemcpy(d_samples, h_samples, sizeof(SampleT) * num_samples, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_histogram, 0, sizeof(int) * BINS));
-
-    // Run kernel
-    BlockHistogramKernel<BINS, BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM><<<1, BLOCK_THREADS>>>(
-        d_samples,
-        d_histogram);
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults((int*) h_reference, d_histogram, BINS, g_verbose, g_verbose);
-    printf("\t%s\n\n", compare ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-    fflush(stdout);
-    fflush(stderr);
-
-    // Cleanup
-    if (h_samples) delete[] h_samples;
-    if (h_reference) delete[] h_reference;
-    if (d_samples) CubDebugExit(g_allocator.DeviceFree(d_samples));
-    if (d_histogram) CubDebugExit(g_allocator.DeviceFree(d_histogram));
-
-    // Correctness asserts
-    AssertEquals(0, compare);
-}
-
-
-/**
- * Test different sample distributions
- */
-template <
-    typename                    SampleT,
-    int                         BINS,
-    int                         BLOCK_THREADS,
-    int                         ITEMS_PER_THREAD,
-    BlockHistogramAlgorithm     ALGORITHM>
-void Test()
-{
-    Test<SampleT, BINS, BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM>(UNIFORM);
-    Test<SampleT, BINS, BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM>(INTEGER_SEED);
-    Test<SampleT, BINS, BLOCK_THREADS, ITEMS_PER_THREAD, ALGORITHM>(RANDOM);
-}
-
-
-/**
- * Test different ALGORITHM
- */
-template <
-    typename                    SampleT,
-    int                         BINS,
-    int                         BLOCK_THREADS,
-    int                         ITEMS_PER_THREAD>
-void Test()
-{
-    Test<SampleT, BINS, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_HISTO_SORT>();
-    Test<SampleT, BINS, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_HISTO_ATOMIC>();
-}
-
-
-/**
- * Test different ITEMS_PER_THREAD
- */
-template <
-    typename                    SampleT,
-    int                         BINS,
-    int                         BLOCK_THREADS>
-void Test()
-{
-    Test<SampleT, BINS, BLOCK_THREADS, 1>();
-    Test<SampleT, BINS, BLOCK_THREADS, 5>();
-}
-
-
-/**
- * Test different BLOCK_THREADS
- */
-template <
-    typename                    SampleT,
-    int                         BINS>
-void Test()
-{
-    Test<SampleT, BINS, 32>();
-    Test<SampleT, BINS, 96>();
-    Test<SampleT, BINS, 128>();
-}
-
-
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<total input samples across all channels> "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-#ifdef QUICK_TEST
-
-    // Compile/run quick tests
-    Test<unsigned char, 256, 128, 4, BLOCK_HISTO_SORT>(RANDOM);
-    Test<unsigned char, 256, 128, 4, BLOCK_HISTO_ATOMIC>(RANDOM);
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        Test<unsigned char, 32>();
-        Test<unsigned char, 256>();
-        Test<unsigned short, 1024>();
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_block_load_store.cu b/ml-xgboost/cub/test/test_block_load_store.cu
deleted file mode 100644
index 4bb139a..0000000
--- a/ml-xgboost/cub/test/test_block_load_store.cu
+++ /dev/null
@@ -1,549 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of BlockLoad and BlockStore utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <iterator>
-#include <stdio.h>
-
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/iterator/cache_modified_input_iterator.cuh>
-#include <cub/iterator/cache_modified_output_iterator.cuh>
-#include <cub/iterator/discard_output_iterator.cuh>
-#include <cub/util_allocator.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;
-CachingDeviceAllocator  g_allocator(true);
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-
-/**
- * Test load/store kernel.
- */
-template <
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  LOAD_ALGORITHM,
-    BlockStoreAlgorithm STORE_ALGORITHM,
-    typename            InputIteratorT,
-    typename            OutputIteratorT>
-__launch_bounds__ (BLOCK_THREADS, 1)
-__global__ void Kernel(
-    InputIteratorT    d_in,
-    OutputIteratorT    d_out_unguarded,
-    OutputIteratorT    d_out_guarded,
-    int               num_items)
-{
-    enum
-    {
-        TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD
-    };
-
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // Threadblock load/store abstraction types
-    typedef BlockLoad<InputT, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM> BlockLoad;
-    typedef BlockStore<OutputT, BLOCK_THREADS, ITEMS_PER_THREAD, STORE_ALGORITHM> BlockStore;
-
-    // Shared memory type for this threadblock
-    union TempStorage
-    {
-        typename BlockLoad::TempStorage     load;
-        typename BlockStore::TempStorage    store;
-    };
-
-    // Allocate temp storage in shared memory
-    __shared__ TempStorage temp_storage;
-
-    // Threadblock work bounds
-    int block_offset = blockIdx.x * TILE_SIZE;
-    int guarded_elements = num_items - block_offset;
-
-    // Tile of items
-    OutputT data[ITEMS_PER_THREAD];
-
-    // Load data
-    BlockLoad(temp_storage.load).Load(d_in + block_offset, data);
-
-    __syncthreads();
-
-    // Store data
-    BlockStore(temp_storage.store).Store(d_out_unguarded + block_offset, data);
-
-    __syncthreads();
-
-    // reset data
-    #pragma unroll
-    for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-        data[ITEM] = OutputT();
-
-    __syncthreads();
-
-    // Load data
-    BlockLoad(temp_storage.load).Load(d_in + block_offset, data, guarded_elements);
-
-    __syncthreads();
-
-    // Store data
-    BlockStore(temp_storage.store).Store(d_out_guarded + block_offset, data, guarded_elements);
-}
-
-
-//---------------------------------------------------------------------
-// Host testing subroutines
-//---------------------------------------------------------------------
-
-
-/**
- * Test load/store variants
- */
-template <
-    typename            T,
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  LOAD_ALGORITHM,
-    BlockStoreAlgorithm STORE_ALGORITHM,
-    typename            InputIteratorT,
-    typename            OutputIteratorT>
-void TestKernel(
-    T                   *h_in,
-    InputIteratorT      d_in,
-    OutputIteratorT      d_out_unguarded_itr,
-    OutputIteratorT      d_out_guarded_itr,
-    T                   *d_out_unguarded_ptr,
-    T                   *d_out_guarded_ptr,
-    int                 grid_size,
-    int                 guarded_elements)
-{
-    int compare;
-
-    int unguarded_elements = grid_size * BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Test with discard output iterator
-    typedef typename std::iterator_traits<InputIteratorT>::difference_type OffsetT;
-    DiscardOutputIterator<OffsetT> discard_itr;
-
-    Kernel<BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM>
-        <<<grid_size, BLOCK_THREADS>>>(
-            d_in,
-            discard_itr,
-            discard_itr,
-            guarded_elements);
-
-    // Test with regular output iterator
-    Kernel<BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM>
-        <<<grid_size, BLOCK_THREADS>>>(
-            d_in,
-            d_out_unguarded_itr,
-            d_out_guarded_itr,
-            guarded_elements);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Check results
-    compare = CompareDeviceResults(h_in, d_out_guarded_ptr, guarded_elements, g_verbose, g_verbose);
-    printf("\tGuarded: %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Check results
-    compare = CompareDeviceResults(h_in, d_out_unguarded_ptr, unguarded_elements, g_verbose, g_verbose);
-    printf("\tUnguarded: %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-}
-
-
-/**
- * Test native pointer.  Specialized for sufficient resources
- */
-template <
-    typename            T,
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  LOAD_ALGORITHM,
-    BlockStoreAlgorithm STORE_ALGORITHM>
-void TestNative(
-    int                 grid_size,
-    float               fraction_valid,
-    Int2Type<true>      sufficient_resources)
-{
-    int unguarded_elements = grid_size * BLOCK_THREADS * ITEMS_PER_THREAD;
-    int guarded_elements = int(fraction_valid * float(unguarded_elements));
-
-    // Allocate host arrays
-    T *h_in = (T*) malloc(unguarded_elements * sizeof(T));
-
-    // Allocate device arrays
-    T *d_in = NULL;
-    T *d_out_unguarded = NULL;
-    T *d_out_guarded = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * unguarded_elements));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out_unguarded, sizeof(T) * unguarded_elements));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out_guarded, sizeof(T) * guarded_elements));
-    CubDebugExit(cudaMemset(d_out_unguarded, 0, sizeof(T) * unguarded_elements));
-    CubDebugExit(cudaMemset(d_out_guarded, 0, sizeof(T) * guarded_elements));
-
-    // Initialize problem on host and device
-    for (int i = 0; i < unguarded_elements; ++i)
-    {
-        InitValue(INTEGER_SEED, h_in[i], i);
-    }
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * unguarded_elements, cudaMemcpyHostToDevice));
-
-    printf("TestNative "
-        "grid_size(%d) "
-        "guarded_elements(%d) "
-        "unguarded_elements(%d) "
-        "BLOCK_THREADS(%d) "
-        "ITEMS_PER_THREAD(%d) "
-        "LOAD_ALGORITHM(%d) "
-        "STORE_ALGORITHM(%d) "
-        "sizeof(T)(%d)\n",
-            grid_size, guarded_elements, unguarded_elements, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM, (int) sizeof(T));
-
-    TestKernel<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM>(
-        h_in,
-        (T const *) d_in,   // Test const
-        d_out_unguarded,
-        d_out_guarded,
-        d_out_unguarded,
-        d_out_guarded,
-        grid_size,
-        guarded_elements);
-
-    // Cleanup
-    if (h_in) free(h_in);
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out_unguarded) CubDebugExit(g_allocator.DeviceFree(d_out_unguarded));
-    if (d_out_guarded) CubDebugExit(g_allocator.DeviceFree(d_out_guarded));
-}
-
-
-/**
- * Test native pointer.  Specialized for insufficient resources
- */
-template <
-    typename            T,
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  LOAD_ALGORITHM,
-    BlockStoreAlgorithm STORE_ALGORITHM>
-void TestNative(
-    int                 grid_size,
-    float               fraction_valid,
-    Int2Type<false>      sufficient_resources)
-{}
-
-
-/**
- * Test iterator.  Specialized for sufficient resources.
- */
-template <
-    typename            T,
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  LOAD_ALGORITHM,
-    BlockStoreAlgorithm STORE_ALGORITHM,
-    CacheLoadModifier   LOAD_MODIFIER,
-    CacheStoreModifier  STORE_MODIFIER>
-void TestIterator(
-    int                 grid_size,
-    float               fraction_valid,
-    Int2Type<true>      sufficient_resources)
-{
-    int unguarded_elements = grid_size * BLOCK_THREADS * ITEMS_PER_THREAD;
-    int guarded_elements = int(fraction_valid * float(unguarded_elements));
-
-    // Allocate host arrays
-    T *h_in = (T*) malloc(unguarded_elements * sizeof(T));
-
-    // Allocate device arrays
-    T *d_in = NULL;
-    T *d_out_unguarded = NULL;
-    T *d_out_guarded = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * unguarded_elements));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out_unguarded, sizeof(T) * unguarded_elements));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out_guarded, sizeof(T) * guarded_elements));
-    CubDebugExit(cudaMemset(d_out_unguarded, 0, sizeof(T) * unguarded_elements));
-    CubDebugExit(cudaMemset(d_out_guarded, 0, sizeof(T) * guarded_elements));
-
-    // Initialize problem on host and device
-    for (int i = 0; i < unguarded_elements; ++i)
-    {
-        InitValue(INTEGER_SEED, h_in[i], i);
-    }
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * unguarded_elements, cudaMemcpyHostToDevice));
-
-    printf("TestIterator "
-        "grid_size(%d) "
-        "guarded_elements(%d) "
-        "unguarded_elements(%d) "
-        "BLOCK_THREADS(%d) "
-        "ITEMS_PER_THREAD(%d) "
-        "LOAD_ALGORITHM(%d) "
-        "STORE_ALGORITHM(%d) "
-        "LOAD_MODIFIER(%d) "
-        "STORE_MODIFIER(%d) "
-        "sizeof(T)(%d)\n",
-            grid_size, guarded_elements, unguarded_elements, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM, LOAD_MODIFIER, STORE_MODIFIER, (int) sizeof(T));
-
-    TestKernel<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM>(
-        h_in,
-        CacheModifiedInputIterator<LOAD_MODIFIER, T>(d_in),
-        CacheModifiedOutputIterator<STORE_MODIFIER, T>(d_out_unguarded),
-        CacheModifiedOutputIterator<STORE_MODIFIER, T>(d_out_guarded),
-        d_out_unguarded,
-        d_out_guarded,
-        grid_size,
-        guarded_elements);
-
-    // Cleanup
-    if (h_in) free(h_in);
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out_unguarded) CubDebugExit(g_allocator.DeviceFree(d_out_unguarded));
-    if (d_out_guarded) CubDebugExit(g_allocator.DeviceFree(d_out_guarded));
-}
-
-/**
- * Test iterator.  Specialized for insufficient resources.
- */
-template <
-    typename            T,
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    BlockLoadAlgorithm  LOAD_ALGORITHM,
-    BlockStoreAlgorithm STORE_ALGORITHM,
-    CacheLoadModifier   LOAD_MODIFIER,
-    CacheStoreModifier  STORE_MODIFIER>
-void TestIterator(
-    int                 grid_size,
-    float               fraction_valid,
-    Int2Type<false>     sufficient_resources)
-{}
-
-
-/**
- * Evaluate different pointer access types
- */
-template <
-    typename                T,
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    BlockLoadAlgorithm      LOAD_ALGORITHM,
-    BlockStoreAlgorithm     STORE_ALGORITHM>
-void TestPointerType(
-    int             grid_size,
-    float           fraction_valid)
-{
-    // Threadblock load/store abstraction types
-    typedef BlockLoad<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM> BlockLoad;
-    typedef BlockStore<T, BLOCK_THREADS, ITEMS_PER_THREAD, STORE_ALGORITHM> BlockStore;
-
-#if defined(SM100) || defined(SM110) || defined(SM130)
-    static const bool sufficient_load_smem  = sizeof(typename BlockLoad::TempStorage)   <= 1024 * 16;
-    static const bool sufficient_store_smem = sizeof(typename BlockStore::TempStorage)  <= 1024 * 16;
-    static const bool sufficient_threads    = BLOCK_THREADS <= 512;
-#else
-    static const bool sufficient_load_smem  = sizeof(typename BlockLoad::TempStorage)   <= 1024 * 48;
-    static const bool sufficient_store_smem = sizeof(typename BlockStore::TempStorage)  <= 1024 * 48;
-    static const bool sufficient_threads    = BLOCK_THREADS <= 1024;
-#endif
-
-    static const bool sufficient_resources  = sufficient_load_smem && sufficient_store_smem && sufficient_threads;
-
-    TestNative<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM>(grid_size, fraction_valid, Int2Type<sufficient_resources>());
-    TestIterator<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM, LOAD_DEFAULT, STORE_DEFAULT>(grid_size, fraction_valid, Int2Type<sufficient_resources>());
-}
-
-
-/**
- * Evaluate different time-slicing strategies
- */
-template <
-    typename                T,
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    BlockLoadAlgorithm      LOAD_ALGORITHM,
-    BlockStoreAlgorithm     STORE_ALGORITHM>
-void TestSlicedStrategy(
-    int             grid_size,
-    float           fraction_valid)
-{
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM, true>(grid_size, fraction_valid);
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, LOAD_ALGORITHM, STORE_ALGORITHM, false>(grid_size, fraction_valid);
-}
-
-
-
-/**
- * Evaluate different load/store strategies (specialized for block sizes that are not a multiple of 32)
- */
-template <
-    typename        T,
-    int             BLOCK_THREADS,
-    int             ITEMS_PER_THREAD>
-void TestStrategy(
-    int             grid_size,
-    float           fraction_valid,
-    Int2Type<false> is_warp_multiple)
-{
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_DIRECT, BLOCK_STORE_DIRECT>(grid_size, fraction_valid);
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_TRANSPOSE, BLOCK_STORE_TRANSPOSE>(grid_size, fraction_valid);
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_VECTORIZE, BLOCK_STORE_VECTORIZE>(grid_size, fraction_valid);
-}
-
-
-/**
- * Evaluate different load/store strategies (specialized for block sizes that are a multiple of 32)
- */
-template <
-    typename        T,
-    int             BLOCK_THREADS,
-    int             ITEMS_PER_THREAD>
-void TestStrategy(
-    int             grid_size,
-    float           fraction_valid,
-    Int2Type<true>  is_warp_multiple)
-{
-    TestStrategy<T, BLOCK_THREADS, ITEMS_PER_THREAD>(grid_size, fraction_valid, Int2Type<false>());
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_WARP_TRANSPOSE, BLOCK_STORE_WARP_TRANSPOSE>(grid_size, fraction_valid);
-    TestPointerType<T, BLOCK_THREADS, ITEMS_PER_THREAD, BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED, BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED>(grid_size, fraction_valid);
-}
-
-
-/**
- * Evaluate different register blocking
- */
-template <
-    typename T,
-    int BLOCK_THREADS>
-void TestItemsPerThread(
-    int grid_size,
-    float fraction_valid)
-{
-    Int2Type<BLOCK_THREADS % 32 == 0> is_warp_multiple;
-
-    TestStrategy<T, BLOCK_THREADS, 1>(grid_size, fraction_valid, is_warp_multiple);
-    TestStrategy<T, BLOCK_THREADS, 3>(grid_size, fraction_valid, is_warp_multiple);
-    TestStrategy<T, BLOCK_THREADS, 4>(grid_size, fraction_valid, is_warp_multiple);
-    TestStrategy<T, BLOCK_THREADS, 11>(grid_size, fraction_valid, is_warp_multiple);
-}
-
-
-/**
- * Evaluate different threadblock sizes
- */
-template <typename T>
-void TestThreads(
-    int grid_size,
-    float fraction_valid)
-{
-    TestItemsPerThread<T, 15>(grid_size, fraction_valid);
-    TestItemsPerThread<T, 32>(grid_size, fraction_valid);
-    TestItemsPerThread<T, 72>(grid_size, fraction_valid);
-    TestItemsPerThread<T, 96>(grid_size, fraction_valid);
-    TestItemsPerThread<T, 128>(grid_size, fraction_valid);
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-#ifdef QUICK_TEST
-
-    // Compile/run quick tests
-    TestNative<     int, 64, 2, BLOCK_LOAD_WARP_TRANSPOSE, BLOCK_STORE_WARP_TRANSPOSE>(1, 0.8f, Int2Type<true>());
-    TestIterator<   int, 64, 2, BLOCK_LOAD_WARP_TRANSPOSE, BLOCK_STORE_WARP_TRANSPOSE, LOAD_DEFAULT, STORE_DEFAULT>(1, 0.8f, Int2Type<true>());
-
-#else
-
-    // Compile/run thorough tests
-    TestThreads<char>(2, 0.8f);
-    TestThreads<int>(2, 0.8f);
-    TestThreads<long>(2, 0.8f);
-    TestThreads<long2>(2, 0.8f);
-
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-        TestThreads<double2>(2, 0.8f);
-    TestThreads<TestFoo>(2, 0.8f);
-    TestThreads<TestBar>(2, 0.8f);
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_block_radix_sort.cu b/ml-xgboost/cub/test/test_block_radix_sort.cu
deleted file mode 100644
index 36f8760..0000000
--- a/ml-xgboost/cub/test/test_block_radix_sort.cu
+++ /dev/null
@@ -1,717 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of BlockRadixSort utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <algorithm>
-#include <iostream>
-
-#include <cub/block/block_radix_sort.cuh>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/util_allocator.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;
-CachingDeviceAllocator  g_allocator(true);
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-
-/// Specialized descending, blocked -> blocked
-template <int BLOCK_THREADS, typename BlockRadixSort, int ITEMS_PER_THREAD, typename Key, typename Value>
-__device__ __forceinline__ void TestBlockSort(
-    typename BlockRadixSort::TempStorage &temp_storage,
-    Key                         (&keys)[ITEMS_PER_THREAD],
-    Value                       (&values)[ITEMS_PER_THREAD],
-    Key                         *d_keys,
-    Value                       *d_values,
-    int                         begin_bit,
-    int                         end_bit,
-    clock_t                     &stop,
-    Int2Type<true>              is_descending,
-    Int2Type<true>              is_blocked_output)
-{
-    BlockRadixSort(temp_storage).SortDescending(keys, values, begin_bit, end_bit);
-    stop = clock();
-    StoreDirectBlocked(threadIdx.x, d_keys, keys);
-    StoreDirectBlocked(threadIdx.x, d_values, values);
-}
-
-/// Specialized descending, blocked -> striped
-template <int BLOCK_THREADS, typename BlockRadixSort, int ITEMS_PER_THREAD, typename Key, typename Value>
-__device__ __forceinline__ void TestBlockSort(
-    typename BlockRadixSort::TempStorage &temp_storage,
-    Key                         (&keys)[ITEMS_PER_THREAD],
-    Value                       (&values)[ITEMS_PER_THREAD],
-    Key                         *d_keys,
-    Value                       *d_values,
-    int                         begin_bit,
-    int                         end_bit,
-    clock_t                     &stop,
-    Int2Type<true>              is_descending,
-    Int2Type<false>             is_blocked_output)
-{
-    BlockRadixSort(temp_storage).SortDescendingBlockedToStriped(keys, values, begin_bit, end_bit);
-    stop = clock();
-    StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_keys, keys);
-    StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_values, values);
-}
-
-/// Specialized ascending, blocked -> blocked
-template <int BLOCK_THREADS, typename BlockRadixSort, int ITEMS_PER_THREAD, typename Key, typename Value>
-__device__ __forceinline__ void TestBlockSort(
-    typename BlockRadixSort::TempStorage &temp_storage,
-    Key                         (&keys)[ITEMS_PER_THREAD],
-    Value                       (&values)[ITEMS_PER_THREAD],
-    Key                         *d_keys,
-    Value                       *d_values,
-    int                         begin_bit,
-    int                         end_bit,
-    clock_t                     &stop,
-    Int2Type<false>             is_descending,
-    Int2Type<true>              is_blocked_output)
-{
-    BlockRadixSort(temp_storage).Sort(keys, values, begin_bit, end_bit);
-    stop = clock();
-    StoreDirectBlocked(threadIdx.x, d_keys, keys);
-    StoreDirectBlocked(threadIdx.x, d_values, values);
-}
-
-/// Specialized ascending, blocked -> striped
-template <int BLOCK_THREADS, typename BlockRadixSort, int ITEMS_PER_THREAD, typename Key, typename Value>
-__device__ __forceinline__ void TestBlockSort(
-    typename BlockRadixSort::TempStorage &temp_storage,
-    Key                         (&keys)[ITEMS_PER_THREAD],
-    Value                       (&values)[ITEMS_PER_THREAD],
-    Key                         *d_keys,
-    Value                       *d_values,
-    int                         begin_bit,
-    int                         end_bit,
-    clock_t                     &stop,
-    Int2Type<false>             is_descending,
-    Int2Type<false>             is_blocked_output)
-{
-    BlockRadixSort(temp_storage).SortBlockedToStriped(keys, values, begin_bit, end_bit);
-    stop = clock();
-    StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_keys, keys);
-    StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_values, values);
-}
-
-
-
-/**
- * BlockRadixSort kernel
- */
-template <
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    int                 RADIX_BITS,
-    bool                MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm  INNER_SCAN_ALGORITHM,
-    cudaSharedMemConfig SMEM_CONFIG,
-    int                 DESCENDING,
-    int                 BLOCKED_OUTPUT,
-    typename            Key,
-    typename            Value>
-__launch_bounds__ (BLOCK_THREADS, 1)
-__global__ void Kernel(
-    Key                         *d_keys,
-    Value                       *d_values,
-    int                         begin_bit,
-    int                         end_bit,
-    clock_t                     *d_elapsed)
-{
-    // Threadblock load/store abstraction types
-    typedef BlockRadixSort<
-            Key,
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            Value,
-            RADIX_BITS,
-            MEMOIZE_OUTER_SCAN,
-            INNER_SCAN_ALGORITHM,
-            SMEM_CONFIG>
-        BlockRadixSortT;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename BlockRadixSortT::TempStorage temp_storage;
-
-    // Items per thread
-    Key     keys[ITEMS_PER_THREAD];
-    Value   values[ITEMS_PER_THREAD];
-
-    LoadDirectBlocked(threadIdx.x, d_keys, keys);
-    LoadDirectBlocked(threadIdx.x, d_values, values);
-
-    // Start cycle timer
-    clock_t stop;
-    clock_t start = clock();
-
-    TestBlockSort<BLOCK_THREADS, BlockRadixSortT>(
-        temp_storage, keys, values, d_keys, d_values, begin_bit, end_bit, stop, Int2Type<DESCENDING>(), Int2Type<BLOCKED_OUTPUT>());
-
-    // Store time
-    if (threadIdx.x == 0)
-        *d_elapsed = (start > stop) ? start - stop : stop - start;
-}
-
-
-
-//---------------------------------------------------------------------
-// Host testing subroutines
-//---------------------------------------------------------------------
-
-
-/**
- * Simple key-value pairing
- */
-template <
-    typename Key,
-    typename Value,
-    bool IS_FLOAT = (Traits<Key>::CATEGORY == FLOATING_POINT)>
-struct Pair
-{
-    Key     key;
-    Value   value;
-
-    bool operator<(const Pair &b) const
-    {
-        return (key < b.key);
-    }
-};
-
-/**
- * Simple key-value pairing (specialized for floating point types)
- */
-template <typename Key, typename Value>
-struct Pair<Key, Value, true>
-{
-    Key     key;
-    Value   value;
-
-    bool operator<(const Pair &b) const
-    {
-        if (key < b.key)
-            return true;
-
-        if (key > b.key)
-            return false;
-
-        // Key in unsigned bits
-        typedef typename Traits<Key>::UnsignedBits UnsignedBits;
-
-        // Return true if key is negative zero and b.key is positive zero
-        UnsignedBits key_bits   = *reinterpret_cast<UnsignedBits*>(const_cast<Key*>(&key));
-        UnsignedBits b_key_bits = *reinterpret_cast<UnsignedBits*>(const_cast<Key*>(&b.key));
-        UnsignedBits HIGH_BIT   = Traits<Key>::HIGH_BIT;
-
-        return ((key_bits & HIGH_BIT) != 0) && ((b_key_bits & HIGH_BIT) == 0);
-    }
-};
-
-
-/**
- * Initialize key-value sorting problem.
- */
-template <bool DESCENDING, typename Key, typename Value>
-void Initialize(
-    GenMode         gen_mode,
-    Key             *h_keys,
-    Value           *h_values,
-    Key             *h_reference_keys,
-    Value           *h_reference_values,
-    int             num_items,
-    int             entropy_reduction,
-    int             begin_bit,
-    int             end_bit)
-{
-    Pair<Key, Value> *h_pairs = new Pair<Key, Value>[num_items];
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_keys[i], i);
-
-        RandomBits(h_values[i]);
-
-        // Mask off unwanted portions
-        int num_bits = end_bit - begin_bit;
-        if ((begin_bit > 0) || (end_bit < sizeof(Key) * 8))
-        {
-            unsigned long long base = 0;
-            memcpy(&base, &h_keys[i], sizeof(Key));
-            base &= ((1ull << num_bits) - 1) << begin_bit;
-            memcpy(&h_keys[i], &base, sizeof(Key));
-        }
-
-        h_pairs[i].key    = h_keys[i];
-        h_pairs[i].value  = h_values[i];
-    }
-
-    if (DESCENDING) std::reverse(h_pairs, h_pairs + num_items);
-    std::stable_sort(h_pairs, h_pairs + num_items);
-    if (DESCENDING) std::reverse(h_pairs, h_pairs + num_items);
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_reference_keys[i]     = h_pairs[i].key;
-        h_reference_values[i]   = h_pairs[i].value;
-    }
-
-    delete[] h_pairs;
-}
-
-
-
-
-/**
- * Test BlockRadixSort kernel
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM,
-    cudaSharedMemConfig     SMEM_CONFIG,
-    bool                    DESCENDING,
-    bool                    BLOCKED_OUTPUT,
-    typename                Key,
-    typename                Value>
-void TestDriver(
-    GenMode                 gen_mode,
-    int                     entropy_reduction,
-    int                     begin_bit,
-    int                     end_bit)
-{
-    enum
-    {
-        TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD,
-        KEYS_ONLY = Equals<Value, NullType>::VALUE,
-    };
-
-    // Allocate host arrays
-    Key     *h_keys             = new Key[TILE_SIZE];
-    Key     *h_reference_keys   = new Key[TILE_SIZE];
-    Value   *h_values           = new Value[TILE_SIZE];
-    Value   *h_reference_values = new Value[TILE_SIZE];
-
-    // Allocate device arrays
-    Key     *d_keys     = NULL;
-    Value   *d_values   = NULL;
-    clock_t *d_elapsed  = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys, sizeof(Key) * TILE_SIZE));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values, sizeof(Value) * TILE_SIZE));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(clock_t)));
-
-    // Initialize problem and solution on host
-    Initialize<DESCENDING>(gen_mode, h_keys, h_values, h_reference_keys, h_reference_values,
-        TILE_SIZE, entropy_reduction, begin_bit, end_bit);
-
-    // Copy problem to device
-    CubDebugExit(cudaMemcpy(d_keys, h_keys, sizeof(Key) * TILE_SIZE, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_values, h_values, sizeof(Value) * TILE_SIZE, cudaMemcpyHostToDevice));
-
-    printf("%s "
-        "BLOCK_THREADS(%d) "
-        "ITEMS_PER_THREAD(%d) "
-        "RADIX_BITS(%d) "
-        "MEMOIZE_OUTER_SCAN(%d) "
-        "INNER_SCAN_ALGORITHM(%d) "
-        "SMEM_CONFIG(%d) "
-        "DESCENDING(%d) "
-        "BLOCKED_OUTPUT(%d) "
-        "sizeof(Key)(%d) "
-        "sizeof(Value)(%d) "
-        "gen_mode(%d), "
-        "entropy_reduction(%d) "
-        "begin_bit(%d) "
-        "end_bit(%d), "
-        "samples(%d)\n",
-            ((KEYS_ONLY) ? "Keys-only" : "Key-value"),
-            BLOCK_THREADS,
-            ITEMS_PER_THREAD,
-            RADIX_BITS,
-            MEMOIZE_OUTER_SCAN,
-            INNER_SCAN_ALGORITHM,
-            SMEM_CONFIG,
-            DESCENDING,
-            BLOCKED_OUTPUT,
-            (int) sizeof(Key),
-            (int) sizeof(Value),
-            gen_mode,
-            entropy_reduction,
-            begin_bit,
-            end_bit,
-            g_num_rand_samples);
-
-    // Set shared memory config
-    cudaDeviceSetSharedMemConfig(SMEM_CONFIG);
-
-    // Run kernel
-    Kernel<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, DESCENDING, BLOCKED_OUTPUT><<<1, BLOCK_THREADS>>>(
-        d_keys, d_values, begin_bit, end_bit, d_elapsed);
-
-    // Flush kernel output / errors
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Check keys results
-    printf("\tKeys: ");
-    int compare = CompareDeviceResults(h_reference_keys, d_keys, TILE_SIZE, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Check value results
-    if (!KEYS_ONLY)
-    {
-        printf("\tValues: ");
-        int compare = CompareDeviceResults(h_reference_values, d_values, TILE_SIZE, g_verbose, g_verbose);
-        printf("%s\n", compare ? "FAIL" : "PASS");
-        AssertEquals(0, compare);
-    }
-    printf("\n");
-
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-    printf("\n");
-
-    // Cleanup
-    if (h_keys)             delete[] h_keys;
-    if (h_reference_keys)   delete[] h_reference_keys;
-    if (h_values)           delete[] h_values;
-    if (h_reference_values) delete[] h_reference_values;
-    if (d_keys)             CubDebugExit(g_allocator.DeviceFree(d_keys));
-    if (d_values)           CubDebugExit(g_allocator.DeviceFree(d_values));
-    if (d_elapsed)          CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-/**
- * Test driver (valid tile size <= MAX_SMEM_BYTES)
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM,
-    cudaSharedMemConfig     SMEM_CONFIG,
-    bool                    DESCENDING,
-    bool                    BLOCKED_OUTPUT,
-    typename                Key,
-    typename                Value>
-void TestValid(Int2Type<true> fits_smem_capacity)
-{
-    // Iterate begin_bit
-    for (int begin_bit = 0; begin_bit <= 1; begin_bit++)
-    {
-        // Iterate end bit
-        for (int end_bit = begin_bit + 1; end_bit <= sizeof(Key) * 8; end_bit = end_bit * 2 + begin_bit)
-        {
-            // Uniform key distribution
-            TestDriver<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, DESCENDING, BLOCKED_OUTPUT, Key, Value>(
-                UNIFORM, 0, begin_bit, end_bit);
-
-            // Sequential key distribution
-            TestDriver<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, DESCENDING, BLOCKED_OUTPUT, Key, Value>(
-                INTEGER_SEED, 0, begin_bit, end_bit);
-
-            // Iterate random with entropy_reduction
-            for (int entropy_reduction = 0; entropy_reduction <= 9; entropy_reduction += 3)
-            {
-                TestDriver<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, DESCENDING, BLOCKED_OUTPUT, Key, Value>(
-                    RANDOM, entropy_reduction, begin_bit, end_bit);
-            }
-        }
-    }
-}
-
-
-/**
- * Test driver (invalid tile size)
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM,
-    cudaSharedMemConfig     SMEM_CONFIG,
-    bool                    DESCENDING,
-    bool                    BLOCKED_OUTPUT,
-    typename                Key,
-    typename                Value>
-void TestValid(Int2Type<false> fits_smem_capacity)
-{}
-
-
-/**
- * Test ascending/descending and to-blocked/to-striped
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM,
-    cudaSharedMemConfig     SMEM_CONFIG,
-    typename                Key,
-    typename                Value>
-void Test()
-{
-    // Check size of smem storage for the target arch to make sure it will fit
-    typedef BlockRadixSort<Key, BLOCK_THREADS, ITEMS_PER_THREAD, Value, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG> BlockRadixSortT;
-
-#if defined(SM100) || defined(SM110) || defined(SM130)
-    Int2Type<sizeof(typename BlockRadixSortT::TempStorage) <= 16 * 1024> fits_smem_capacity;
-#else
-    Int2Type<(sizeof(typename BlockRadixSortT::TempStorage) <= 48 * 1024)> fits_smem_capacity;
-#endif
-
-    // Sort-ascending, to-striped
-    TestValid<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, true, false, Key, Value>(fits_smem_capacity);
-
-    // Sort-descending, to-blocked
-    TestValid<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, false, true, Key, Value>(fits_smem_capacity);
-
-    // Not necessary
-//    TestValid<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, false, false, Key, Value>(fits_smem_capacity);
-//    TestValid<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, SMEM_CONFIG, true, true, Key, Value>(fits_smem_capacity);
-}
-
-
-/**
- * Test value type and smem config
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM,
-    typename                Key>
-void TestKeys()
-{
-    // Test keys-only sorting with both smem configs
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, cudaSharedMemBankSizeFourByte, Key, NullType>();    // Keys-only (4-byte smem bank config)
-#if !defined(SM100) && !defined(SM110) && !defined(SM130) && !defined(SM200)
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, cudaSharedMemBankSizeEightByte, Key, NullType>();   // Keys-only (8-byte smem bank config)
-#endif
-}
-
-
-/**
- * Test value type and smem config
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM,
-    typename                Key>
-void TestKeysAndPairs()
-{
-    // Test pairs sorting with only 4-byte configs
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, cudaSharedMemBankSizeFourByte, Key, char>();        // With small-values
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, cudaSharedMemBankSizeFourByte, Key, Key>();         // With same-values
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, cudaSharedMemBankSizeFourByte, Key, TestFoo>();     // With large values
-}
-
-
-/**
- * Test key type
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN,
-    BlockScanAlgorithm      INNER_SCAN_ALGORITHM>
-void Test()
-{
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-#ifdef TEST_KEYS_ONLY
-
-    // Test unsigned types with keys-only
-    TestKeys<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, unsigned char>();
-    TestKeys<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, unsigned short>();
-    TestKeys<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, unsigned int>();
-    TestKeys<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, unsigned long>();
-    TestKeys<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, unsigned long long>();
-
-#else
-
-    // Test signed and fp types with paired values
-    TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, char>();
-    TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, short>();
-    TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, int>();
-    TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, long>();
-    TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, long long>();
-    TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, float>();
-    if (ptx_version > 120)
-    {
-        // Don't check doubles on PTX120 or below because they're down-converted
-        TestKeysAndPairs<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, INNER_SCAN_ALGORITHM, double>();
-    }
-
-#endif
-}
-
-
-/**
- * Test inner scan algorithm
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS,
-    bool                    MEMOIZE_OUTER_SCAN>
-void Test()
-{
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, BLOCK_SCAN_RAKING>();
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, MEMOIZE_OUTER_SCAN, BLOCK_SCAN_WARP_SCANS>();
-}
-
-
-/**
- * Test outer scan algorithm
- */
-template <
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    int                     RADIX_BITS>
-void Test()
-{
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, true>();
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, RADIX_BITS, false>();
-}
-
-
-/**
- * Test radix bits
- */
-template <
-    int BLOCK_THREADS,
-    int ITEMS_PER_THREAD>
-void Test()
-{
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, 1>();
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, 2>();
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, 5>();
-}
-
-
-/**
- * Test items per thread
- */
-template <int BLOCK_THREADS>
-void Test()
-{
-    Test<BLOCK_THREADS, 1>();
-#if defined(SM100) || defined(SM110) || defined(SM130)
-    // Open64 compiler can't handle the number of test cases
-#else
-    Test<BLOCK_THREADS, 4>();
-#endif
-    Test<BLOCK_THREADS, 11>();
-}
-
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-#ifdef QUICK_TEST
-
-    {
-        typedef float T;
-        TestDriver<32, 4, 4, true, BLOCK_SCAN_WARP_SCANS, cudaSharedMemBankSizeFourByte, false, false, T, NullType>(INTEGER_SEED, 0, 0, sizeof(T) * 8);
-    }
-/*
-    // Compile/run quick tests
-    typedef unsigned int T;
-    TestDriver<64, 17, 4, true, BLOCK_SCAN_WARP_SCANS, cudaSharedMemBankSizeFourByte, false, false, T, NullType>(RANDOM, 0, 0, sizeof(T) * 8);
-    TestDriver<96, 8, 4, true, BLOCK_SCAN_WARP_SCANS, cudaSharedMemBankSizeFourByte, false, false, T, NullType>(RANDOM, 0, 0, sizeof(T) * 8);
-    TestDriver<128, 2, 4, true, BLOCK_SCAN_WARP_SCANS, cudaSharedMemBankSizeFourByte, false, false, T, NullType>(RANDOM, 0, 0, sizeof(T) * 8);
-*/
-
-#else
-
-    // Compile/run thorough tests
-    Test<32>();
-    Test<64>();
-    Test<160>();
-
-
-#endif  // QUICK_TEST
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_block_reduce.cu b/ml-xgboost/cub/test/test_block_reduce.cu
deleted file mode 100644
index 2ba9edd..0000000
--- a/ml-xgboost/cub/test/test_block_reduce.cu
+++ /dev/null
@@ -1,822 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of BlockReduce utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <device_functions.h>
-#include <typeinfo>
-
-#include <cub/block/block_reduce.cuh>
-#include <cub/block/block_load.cuh>
-#include <cub/util_ptx.cuh>
-#include <cub/util_allocator.cuh>
-#include <cub/util_debug.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose       = false;
-int                     g_repeat        = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-
-/// Generic reduction (full, 1)
-template <typename BlockReduceT, typename T, typename ReductionOp>
-__device__ __forceinline__ T DeviceTest(
-    BlockReduceT &block_reduce, T (&data)[1], ReductionOp &reduction_op)
-{
-    return block_reduce.Reduce(data[0], reduction_op);
-}
-
-/// Generic reduction (full, ITEMS_PER_THREAD)
-template <typename BlockReduceT, typename T, int ITEMS_PER_THREAD, typename ReductionOp>
-__device__ __forceinline__ T DeviceTest(
-    BlockReduceT &block_reduce, T (&data)[ITEMS_PER_THREAD], ReductionOp &reduction_op)
-{
-    return block_reduce.Reduce(data, reduction_op);
-}
-
-/// Generic reduction (partial, 1)
-template <typename BlockReduceT, typename T, typename ReductionOp>
-__device__ __forceinline__ T DeviceTest(
-    BlockReduceT &block_reduce, T &data, ReductionOp &reduction_op, int valid_threads)
-{
-    return block_reduce.Reduce(data, reduction_op, valid_threads);
-}
-
-/// Sum reduction (full, 1)
-template <typename BlockReduceT, typename T>
-__device__ __forceinline__ T DeviceTest(
-    BlockReduceT &block_reduce, T (&data)[1], Sum &reduction_op)
-{
-    return block_reduce.Sum(data[0]);
-}
-
-/// Sum reduction (full, ITEMS_PER_THREAD)
-template <typename BlockReduceT, typename T, int ITEMS_PER_THREAD>
-__device__ __forceinline__ T DeviceTest(
-    BlockReduceT &block_reduce, T (&data)[ITEMS_PER_THREAD], Sum &reduction_op)
-{
-    return block_reduce.Sum(data);
-}
-
-/// Sum reduction (partial, 1)
-template <typename BlockReduceT, typename T>
-__device__ __forceinline__ T DeviceTest(
-    BlockReduceT &block_reduce, T &data, Sum &reduction_op, int valid_threads)
-{
-    return block_reduce.Sum(data, valid_threads);
-}
-
-
-/**
- * Test full-tile reduction kernel (where num_items is an even
- * multiple of BLOCK_THREADS)
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    int                     ITEMS_PER_THREAD,
-    typename                T,
-    typename                ReductionOp>
-__launch_bounds__ (BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)
-__global__ void FullTileReduceKernel(
-    T                       *d_in,
-    T                       *d_out,
-    ReductionOp             reduction_op,
-    int                     tiles,
-    clock_t                 *d_elapsed)
-{
-    const int BLOCK_THREADS     = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z;
-    const int TILE_SIZE         = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Cooperative threadblock reduction utility type (returns aggregate in thread 0)
-    typedef BlockReduce<T, BLOCK_DIM_X, ALGORITHM, BLOCK_DIM_Y, BLOCK_DIM_Z> BlockReduceT;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename BlockReduceT::TempStorage temp_storage;
-
-    int linear_tid = RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z);
-
-    // Per-thread tile data
-    T data[ITEMS_PER_THREAD];
-
-    // Load first tile of data
-    int block_offset = 0;
-
-    if (block_offset < TILE_SIZE * tiles)
-    {
-        LoadDirectBlocked(linear_tid, d_in + block_offset, data);
-        block_offset += TILE_SIZE;
-
-        // Start cycle timer
-        clock_t start = clock();
-
-        // Cooperative reduce first tile
-        BlockReduceT block_reduce(temp_storage) ;
-        T block_aggregate = DeviceTest(block_reduce, data, reduction_op);
-
-        // Stop cycle timer
- #if CUB_PTX_ARCH == 100
-        // Bug: recording stop clock causes mis-write of running prefix value
-        clock_t stop = 0;
-#else
-        clock_t stop = clock();
-#endif // CUB_PTX_ARCH == 100
-        clock_t elapsed = (start > stop) ? start - stop : stop - start;
-
-        // Loop over input tiles
-        while (block_offset < TILE_SIZE * tiles)
-        {
-            // TestBarrier between threadblock reductions
-            __syncthreads();
-    
-            // Load tile of data
-            LoadDirectBlocked(linear_tid, d_in + block_offset, data);
-            block_offset += TILE_SIZE;
-
-            // Start cycle timer
-            clock_t start = clock();
-
-            // Cooperatively reduce the tile's aggregate
-            BlockReduceT block_reduce(temp_storage) ;
-            T tile_aggregate = DeviceTest(block_reduce, data, reduction_op);
-
-            // Stop cycle timer
-#if CUB_PTX_ARCH == 100
-            // Bug: recording stop clock causes mis-write of running prefix value
-            clock_t stop = 0;
-#else
-            clock_t stop = clock();
-#endif // CUB_PTX_ARCH == 100
-            elapsed += (start > stop) ? start - stop : stop - start;
-
-            // Reduce threadblock aggregate
-            block_aggregate = reduction_op(block_aggregate, tile_aggregate);
-        }
-
-        // Store data
-        if (linear_tid == 0)
-        {
-            d_out[0] = block_aggregate;
-            *d_elapsed = elapsed;
-        }
-    }
-}
-
-
-
-/**
- * Test partial-tile reduction kernel (where num_items < BLOCK_THREADS)
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    typename                T,
-    typename                ReductionOp>
-__launch_bounds__ (BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)
-__global__ void PartialTileReduceKernel(
-    T                       *d_in,
-    T                       *d_out,
-    int                     num_items,
-    ReductionOp             reduction_op,
-    clock_t                 *d_elapsed)
-{
-    // Cooperative threadblock reduction utility type (returns aggregate only in thread-0)
-    typedef BlockReduce<T, BLOCK_DIM_X, ALGORITHM, BLOCK_DIM_Y, BLOCK_DIM_Z> BlockReduceT;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename BlockReduceT::TempStorage temp_storage;
-
-    int linear_tid = RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z);
-
-    // Per-thread tile data
-    T partial;
-
-    // Load partial tile data
-    if (linear_tid < num_items)
-    {
-        partial = d_in[linear_tid];
-    }
-
-    // Start cycle timer
-    clock_t start = clock();
-
-    // Cooperatively reduce the tile's aggregate
-    BlockReduceT block_reduce(temp_storage) ;
-    T tile_aggregate = DeviceTest(block_reduce, partial, reduction_op, num_items);
-
-    // Stop cycle timer
-#if CUB_PTX_ARCH == 100
-    // Bug: recording stop clock causes mis-write of running prefix value
-    clock_t stop = 0;
-#else
-    clock_t stop = clock();
-#endif // CUB_PTX_ARCH == 100
-
-    clock_t elapsed = (start > stop) ? start - stop : stop - start;
-
-    // Store data
-    if (linear_tid == 0)
-    {
-        d_out[0] = tile_aggregate;
-        *d_elapsed = elapsed;
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Host utility subroutines
-//---------------------------------------------------------------------
-
-/**
- * Initialize problem (and solution)
- */
-template <
-    typename    T,
-    typename    ReductionOp>
-void Initialize(
-    GenMode     gen_mode,
-    T           *h_in,
-    T           h_reference[1],
-    ReductionOp reduction_op,
-    int         num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-        if (i == 0)
-            h_reference[0] = h_in[0];
-        else
-            h_reference[0] = reduction_op(h_reference[0], h_in[i]);
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n");
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Full tile test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Test full-tile reduction.  (Specialized for sufficient resources)
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    int                     ITEMS_PER_THREAD,
-    typename                T,
-    typename                ReductionOp>
-void TestFullTile(
-    GenMode                 gen_mode,
-    int                     tiles,
-    ReductionOp             reduction_op,
-    Int2Type<true>          sufficient_resources)
-{
-    const int BLOCK_THREADS     = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z;
-    const int TILE_SIZE         = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    int num_items = TILE_SIZE * tiles;
-
-    // Allocate host arrays
-    T *h_in = new T[num_items];
-    T h_reference[1];
-
-    // Initialize problem
-    Initialize(gen_mode, h_in, h_reference, reduction_op, num_items);
-
-    // Initialize/clear device arrays
-    T       *d_in = NULL;
-    T       *d_out = NULL;
-    clock_t *d_elapsed = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(unsigned long long)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * 1));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * 1));
-
-    // Test multi-tile (unguarded)
-    printf("TestFullTile %s, %s, gen-mode %d, num_items(%d), BLOCK_THREADS(%d) (%d,%d,%d), ITEMS_PER_THREAD(%d), tiles(%d), %s (%d bytes) elements:\n",
-        Equals<ReductionOp, Sum>::VALUE ? "Sum" : "Max",
-        (ALGORITHM == BLOCK_REDUCE_RAKING) ? "BLOCK_REDUCE_RAKING" : (ALGORITHM == BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY) ? "BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY" : "BLOCK_REDUCE_WARP_REDUCTIONS",
-        gen_mode,
-        num_items,
-        BLOCK_THREADS, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z,
-        ITEMS_PER_THREAD,
-        tiles,
-        typeid(T).name(),
-        (int) sizeof(T));
-    fflush(stdout);
-
-    dim3 block_dims(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z);
-    FullTileReduceKernel<ALGORITHM, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, ITEMS_PER_THREAD><<<1, block_dims>>>(
-        d_in,
-        d_out,
-        reduction_op,
-        tiles,
-        d_elapsed);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tReduction results: ");
-    int compare = CompareDeviceResults(h_reference, d_out, 1, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_elapsed) CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-/**
- * Test full-tile reduction.  (Specialized for insufficient resources)
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    int                     ITEMS_PER_THREAD,
-    typename                T,
-    typename                ReductionOp>
-void TestFullTile(
-    GenMode                 gen_mode,
-    int                     tiles,
-    ReductionOp             reduction_op,
-    Int2Type<false>         sufficient_resources)
-{}
-
-
-/**
- * Test full-tile reduction.
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    int                     ITEMS_PER_THREAD,
-    typename                T,
-    typename                ReductionOp>
-void TestFullTile(
-    GenMode                 gen_mode,
-    int                     tiles,
-    ReductionOp             reduction_op)
-{
-    // Check size of smem storage for the target arch to make sure it will fit
-    typedef BlockReduce<T, BLOCK_DIM_X, ALGORITHM, BLOCK_DIM_Y, BLOCK_DIM_Z, TEST_ARCH> BlockReduceT;
-
-    enum 
-    {
-#if defined(SM100) || defined(SM110) || defined(SM130)
-        sufficient_smem       = (sizeof(typename BlockReduceT::TempStorage) <= 16 * 1024),
-        sufficient_threads    = ((BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z) <= 512),
-#else
-        sufficient_smem       = (sizeof(typename BlockReduceT::TempStorage) <= 48 * 1024),
-        sufficient_threads    = ((BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z) <= 1024),
-#endif
-    };
-
-    TestFullTile<ALGORITHM, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, ITEMS_PER_THREAD, T>(gen_mode, tiles, reduction_op, Int2Type<sufficient_smem && sufficient_threads>());
-}
-
-
-/**
- * Run battery of tests for different threadblock dimensions
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_THREADS,
-    int                     ITEMS_PER_THREAD,
-    typename                T,
-    typename                ReductionOp>
-void TestFullTile(
-    GenMode                 gen_mode,
-    int                     tiles,
-    ReductionOp             reduction_op)
-{
-    TestFullTile<ALGORITHM, BLOCK_THREADS, 1, 1, ITEMS_PER_THREAD, T>(gen_mode, tiles, reduction_op);
-    TestFullTile<ALGORITHM, BLOCK_THREADS, 2, 2, ITEMS_PER_THREAD, T>(gen_mode, tiles, reduction_op);
-}
-
-/**
- * Run battery of tests for different thread items
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_THREADS,
-    typename                T,
-    typename                ReductionOp>
-void TestFullTile(
-    GenMode                 gen_mode,
-    int                     tiles,
-    ReductionOp             reduction_op)
-{
-    TestFullTile<ALGORITHM, BLOCK_THREADS, 1, T>(gen_mode, tiles, reduction_op);
-    TestFullTile<ALGORITHM, BLOCK_THREADS, 4, T>(gen_mode, tiles, reduction_op);
-}
-
-
-/**
- * Run battery of full-tile tests for different numbers of tiles
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_THREADS,
-    typename                T,
-    typename                ReductionOp>
-void TestFullTile(
-    GenMode                 gen_mode,
-    ReductionOp             reduction_op)
-{
-    for (int tiles = 1; tiles < 3; tiles++)
-    {
-        TestFullTile<ALGORITHM, BLOCK_THREADS, T>(gen_mode, tiles, reduction_op);
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Partial-tile test generation
-//---------------------------------------------------------------------
-
-/**
- * Test partial-tile reduction.  (Specialized for sufficient resources)
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    typename                T,
-    typename                ReductionOp>
-void TestPartialTile(
-    GenMode                 gen_mode,
-    int                     num_items,
-    ReductionOp             reduction_op,
-    Int2Type<true>          sufficient_resources)
-{
-    const int BLOCK_THREADS     = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z;
-    const int TILE_SIZE         = BLOCK_THREADS;
-
-    // Allocate host arrays
-    T *h_in = new T[num_items];
-    T h_reference[1];
-
-    // Initialize problem
-    Initialize(gen_mode, h_in, h_reference, reduction_op, num_items);
-
-    // Initialize/clear device arrays
-    T       *d_in = NULL;
-    T       *d_out = NULL;
-    clock_t *d_elapsed = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(unsigned long long)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * TILE_SIZE));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * 1));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * 1));
-
-    printf("TestPartialTile %s, gen-mode %d, num_items(%d), BLOCK_THREADS(%d) (%d,%d,%d), %s (%d bytes) elements:\n",
-        (ALGORITHM == BLOCK_REDUCE_RAKING) ? "BLOCK_REDUCE_RAKING" : (ALGORITHM == BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY) ? "BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY" : "BLOCK_REDUCE_WARP_REDUCTIONS",
-        gen_mode,
-        num_items,
-        BLOCK_THREADS, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z,
-        typeid(T).name(),
-        (int) sizeof(T));
-    fflush(stdout);
-
-    dim3 block_dims(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z);
-    PartialTileReduceKernel<ALGORITHM, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z><<<1, block_dims>>>(
-        d_in,
-        d_out,
-        num_items,
-        reduction_op,
-        d_elapsed);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tReduction results: ");
-    int compare = CompareDeviceResults(h_reference, d_out, 1, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_elapsed) CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-
-/**
- * Test partial-tile reduction (specialized for insufficient resources)
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    typename                T,
-    typename                ReductionOp>
-void TestPartialTile(
-    GenMode                 gen_mode,
-    int                     num_items,
-    ReductionOp             reduction_op,
-    Int2Type<false>         sufficient_resources)
-{}
-
-
-/**
- *  Run battery of partial-tile tests for different numbers of effective threads and thread dimensions
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_DIM_X,
-    int                     BLOCK_DIM_Y,
-    int                     BLOCK_DIM_Z,
-    typename                T,
-    typename                ReductionOp>
-void TestPartialTile(
-    GenMode                 gen_mode,
-    int                     num_items,
-    ReductionOp             reduction_op)
-{
-    // Check size of smem storage for the target arch to make sure it will fit
-    typedef BlockReduce<T, BLOCK_DIM_X, ALGORITHM, BLOCK_DIM_Y, BLOCK_DIM_Z, TEST_ARCH> BlockReduceT;
-
-    enum 
-    {
-#if defined(SM100) || defined(SM110) || defined(SM130)
-        sufficient_smem       = sizeof(typename BlockReduceT::TempStorage)  <= 16 * 1024,
-        sufficient_threads    = (BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)   <= 512,
-#else
-        sufficient_smem       = sizeof(typename BlockReduceT::TempStorage)  <= 48 * 1024,
-        sufficient_threads    = (BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)   <= 1024,
-#endif
-    };
-
-    TestPartialTile<ALGORITHM, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, T>(gen_mode, num_items, reduction_op, Int2Type<sufficient_smem && sufficient_threads>());
-}
-
-
-
-/**
- *  Run battery of partial-tile tests for different numbers of effective threads and thread dimensions
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_THREADS,
-    typename                T,
-    typename                ReductionOp>
-void TestPartialTile(
-    GenMode                 gen_mode,
-    ReductionOp             reduction_op)
-{
-    for (
-        int num_items = 1;
-        num_items < BLOCK_THREADS;
-        num_items += CUB_MAX(1, BLOCK_THREADS / 5))
-    {
-        TestPartialTile<ALGORITHM, BLOCK_THREADS, 1, 1, T>(gen_mode, num_items, reduction_op);
-        TestPartialTile<ALGORITHM, BLOCK_THREADS, 2, 2, T>(gen_mode, num_items, reduction_op);
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Run battery of full-tile tests for different gen modes
- */
-template <
-    BlockReduceAlgorithm    ALGORITHM,
-    int                     BLOCK_THREADS,
-    typename                T,
-    typename                ReductionOp>
-void Test(
-    ReductionOp             reduction_op)
-{
-    TestFullTile<ALGORITHM, BLOCK_THREADS, T>(UNIFORM, reduction_op);
-    TestPartialTile<ALGORITHM, BLOCK_THREADS, T>(UNIFORM, reduction_op);
-
-    TestFullTile<ALGORITHM, BLOCK_THREADS, T>(INTEGER_SEED, reduction_op);
-    TestPartialTile<ALGORITHM, BLOCK_THREADS, T>(INTEGER_SEED, reduction_op);
-
-    if (Traits<T>::CATEGORY != FLOATING_POINT)
-    {
-        // Don't test randomly-generated floats b/c of stability
-        TestFullTile<ALGORITHM, BLOCK_THREADS, T>(RANDOM, reduction_op);
-        TestPartialTile<ALGORITHM, BLOCK_THREADS, T>(RANDOM, reduction_op);
-    }
-}
-
-
-/**
- * Run battery of tests for different block-reduction algorithmic variants
- */
-template <
-    int             BLOCK_THREADS,
-    typename        T,
-    typename        ReductionOp>
-void Test(
-    ReductionOp     reduction_op)
-{
-#ifdef TEST_RAKING
-    Test<BLOCK_REDUCE_RAKING, BLOCK_THREADS, T>(reduction_op);
-    Test<BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY, BLOCK_THREADS, T>(reduction_op);
-#endif
-#ifdef TEST_WARP_REDUCTIONS
-    Test<BLOCK_REDUCE_WARP_REDUCTIONS, BLOCK_THREADS, T>(reduction_op);
-#endif
-}
-
-
-/**
- * Run battery of tests for different block sizes
- */
-template <
-    typename        T,
-    typename        ReductionOp>
-void Test(
-    ReductionOp     reduction_op)
-{
-    Test<7,   T>(reduction_op);
-    Test<32,  T>(reduction_op);
-    Test<63,  T>(reduction_op);
-    Test<97,  T>(reduction_op);
-    Test<128, T>(reduction_op);
-    Test<238, T>(reduction_op);
-}
-
-
-/**
- * Run battery of tests for different block sizes
- */
-template <typename T>
-void Test()
-{
-    Test<T>(Sum());
-    Test<T>(Max());
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-#ifdef QUICK_TEST
-
-    // Compile/run quick tests
-
-
-    printf("\n full tile ------------------------\n\n");
-
-    TestFullTile<BLOCK_REDUCE_RAKING,                   128, 1, 1, 4, int>(RANDOM, 1, Sum());
-    TestFullTile<BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY,  128, 1, 1, 4, int>(RANDOM, 1, Sum());
-    TestFullTile<BLOCK_REDUCE_WARP_REDUCTIONS,          128, 1, 1, 4, int>(RANDOM, 1, Sum());
-
-    TestFullTile<BLOCK_REDUCE_RAKING,                   128, 1, 1, 1, int>(RANDOM, 1, Sum());
-    TestFullTile<BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY,  128, 1, 1, 1, int>(RANDOM, 1, Sum());
-    TestFullTile<BLOCK_REDUCE_WARP_REDUCTIONS,          128, 1, 1, 1, int>(RANDOM, 1, Sum());
-
-    printf("\n partial tile ------------------------\n\n");
-
-    TestPartialTile<BLOCK_REDUCE_RAKING,                   128, 1, 1, int>(RANDOM, 7, Sum());
-    TestPartialTile<BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY,  128, 1, 1, int>(RANDOM, 7, Sum());
-    TestPartialTile<BLOCK_REDUCE_WARP_REDUCTIONS,          128, 1, 1, int>(RANDOM, 7, Sum());
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // primitives
-        Test<char>();
-        Test<short>();
-        Test<int>();
-        Test<long long>();
-        if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-            Test<double>();
-
-        Test<float>();
-
-        // vector types
-        Test<char2>();
-        Test<short2>();
-        Test<int2>();
-        Test<longlong2>();
-
-        Test<char4>();
-        Test<short4>();
-        Test<int4>();
-        Test<longlong4>();
-
-        // Complex types
-        Test<TestFoo>();
-        Test<TestBar>();
-    }
-
-#endif
-
-    return 0;
-}
-
-
diff --git a/ml-xgboost/cub/test/test_block_scan.cu b/ml-xgboost/cub/test/test_block_scan.cu
deleted file mode 100644
index ffefb0c..0000000
--- a/ml-xgboost/cub/test/test_block_scan.cu
+++ /dev/null
@@ -1,929 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of BlockScan utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <iostream>
-#include <limits>
-#include <typeinfo>
-
-#include <cub/block/block_scan.cuh>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_store.cuh>
-#include <cub/util_ptx.cuh>
-#include <cub/util_allocator.cuh>
-
-#include "test_util.h"
-
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose       = false;
-int                     g_repeat        = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-/**
- * Primitive variant to test
- */
-enum TestMode
-{
-    BASIC,
-    AGGREGATE,
-    PREFIX,
-};
-
-
-/**
- * Scan mode to test
- */
-enum ScanMode
-{
-    EXCLUSIVE,
-    INCLUSIVE
-};
-
-
-/**
- * \brief WrapperFunctor (for precluding test-specialized dispatch to *Sum variants)
- */
-template<typename OpT>
-struct WrapperFunctor
-{
-    OpT op;
-
-    WrapperFunctor(OpT op) : op(op) {}
-
-    template <typename T>
-    __host__ __device__ __forceinline__ T operator()(const T &a, const T &b) const
-    {
-        return op(a, b);
-    }
-};
-
-
-/**
- * Stateful prefix functor
- */
-template <
-    typename T,
-    typename ScanOpT>
-struct BlockPrefixCallbackOp
-{
-    int     linear_tid;
-    T       prefix;
-    ScanOpT  scan_op;
-
-    __device__ __forceinline__
-    BlockPrefixCallbackOp(int linear_tid, T prefix, ScanOpT scan_op) :
-        linear_tid(linear_tid),
-        prefix(prefix),
-        scan_op(scan_op)
-    {}
-
-    __device__ __forceinline__
-    T operator()(T block_aggregate)
-    {
-        // For testing purposes
-        T retval = (linear_tid == 0) ? prefix  : T();
-        prefix = scan_op(prefix, block_aggregate);
-        return retval;
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Exclusive scan
-//---------------------------------------------------------------------
-
-/// Exclusive scan (BASIC, 1)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.ExclusiveScan(data[0], data[0], initial_value, scan_op);
-}
-
-/// Exclusive scan (BASIC, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, int ITEMS_PER_THREAD, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.ExclusiveScan(data, data, initial_value, scan_op);
-}
-
-/// Exclusive scan (AGGREGATE, 1)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.ExclusiveScan(data[0], data[0], initial_value, scan_op, block_aggregate);
-}
-
-/// Exclusive scan (AGGREGATE, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, int ITEMS_PER_THREAD, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.ExclusiveScan(data, data, initial_value, scan_op, block_aggregate);
-}
-
-/// Exclusive scan (PREFIX, 1)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.ExclusiveScan(data[0], data[0], scan_op, prefix_op);
-}
-
-/// Exclusive scan (PREFIX, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, int ITEMS_PER_THREAD, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.ExclusiveScan(data, data, scan_op, prefix_op);
-}
-
-
-//---------------------------------------------------------------------
-// Exclusive sum
-//---------------------------------------------------------------------
-
-/// Exclusive sum (BASIC, 1)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.ExclusiveSum(data[0], data[0]);
-}
-
-/// Exclusive sum (BASIC, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp, int ITEMS_PER_THREAD>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.ExclusiveSum(data, data);
-}
-
-/// Exclusive sum (AGGREGATE, 1)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.ExclusiveSum(data[0], data[0], block_aggregate);
-}
-
-/// Exclusive sum (AGGREGATE, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp, int ITEMS_PER_THREAD>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.ExclusiveSum(data, data, block_aggregate);
-}
-
-/// Exclusive sum (PREFIX, 1)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.ExclusiveSum(data[0], data[0], prefix_op);
-}
-
-/// Exclusive sum (PREFIX, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp, int ITEMS_PER_THREAD>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<EXCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.ExclusiveSum(data, data, prefix_op);
-}
-
-
-//---------------------------------------------------------------------
-// Inclusive scan
-//---------------------------------------------------------------------
-
-/// Inclusive scan (BASIC, 1)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.InclusiveScan(data[0], data[0], scan_op);
-}
-
-/// Inclusive scan (BASIC, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, int ITEMS_PER_THREAD, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.InclusiveScan(data, data, scan_op);
-}
-
-/// Inclusive scan (AGGREGATE, 1)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.InclusiveScan(data[0], data[0], scan_op, block_aggregate);
-}
-
-/// Inclusive scan (AGGREGATE, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, int ITEMS_PER_THREAD, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.InclusiveScan(data, data, scan_op, block_aggregate);
-}
-
-/// Inclusive scan (PREFIX, 1)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.InclusiveScan(data[0], data[0], scan_op, prefix_op);
-}
-
-/// Inclusive scan (PREFIX, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename ScanOpT, typename PrefixCallbackOp, int ITEMS_PER_THREAD, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, ScanOpT &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, IsPrimitiveT is_primitive)
-{
-    block_scan.InclusiveScan(data, data, scan_op, prefix_op);
-}
-
-
-//---------------------------------------------------------------------
-// Inclusive sum
-//---------------------------------------------------------------------
-
-/// Inclusive sum (BASIC, 1)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.InclusiveSum(data[0], data[0]);
-}
-
-/// Inclusive sum (BASIC, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp, int ITEMS_PER_THREAD>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<BASIC> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.InclusiveSum(data, data);
-}
-
-/// Inclusive sum (AGGREGATE, 1)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.InclusiveSum(data[0], data[0], block_aggregate);
-}
-
-/// Inclusive sum (AGGREGATE, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp, int ITEMS_PER_THREAD>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<AGGREGATE> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.InclusiveSum(data, data, block_aggregate);
-}
-
-/// Inclusive sum (PREFIX, 1)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[1], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.InclusiveSum(data[0], data[0], prefix_op);
-}
-
-/// Inclusive sum (PREFIX, ITEMS_PER_THREAD)
-template <typename BlockScanT, typename T, typename PrefixCallbackOp, int ITEMS_PER_THREAD>
-__device__ __forceinline__ void DeviceTest(
-    BlockScanT &block_scan, T (&data)[ITEMS_PER_THREAD], T &initial_value, Sum &scan_op, T &block_aggregate, PrefixCallbackOp &prefix_op,
-    Int2Type<INCLUSIVE> scan_mode, Int2Type<PREFIX> test_mode, Int2Type<true> is_primitive)
-{
-    block_scan.InclusiveSum(data, data, prefix_op);
-}
-
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-/**
- * BlockScan test kernel.
- */
-template <
-    int                 BLOCK_DIM_X,
-    int                 BLOCK_DIM_Y,
-    int                 BLOCK_DIM_Z,
-    int                 ITEMS_PER_THREAD,
-    ScanMode            SCAN_MODE,
-    TestMode            TEST_MODE,
-    BlockScanAlgorithm  ALGORITHM,
-    typename            T,
-    typename            ScanOpT>
-__launch_bounds__ (BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)
-__global__ void BlockScanKernel(
-    T                   *d_in,
-    T                   *d_out,
-    T                   *d_aggregate,
-    ScanOpT              scan_op,
-    T                   initial_value,
-    clock_t             *d_elapsed)
-{
-    const int BLOCK_THREADS     = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z;
-    const int TILE_SIZE         = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Parameterize BlockScan type for our thread block
-    typedef BlockScan<T, BLOCK_DIM_X, ALGORITHM, BLOCK_DIM_Y, BLOCK_DIM_Z> BlockScanT;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename BlockScanT::TempStorage temp_storage;
-
-    int linear_tid = RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z);
-
-    // Per-thread tile data
-    T data[ITEMS_PER_THREAD];
-    LoadDirectBlocked(linear_tid, d_in, data);
-
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t start = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Test scan
-    T                                   block_aggregate;
-    BlockScanT                          block_scan(temp_storage);
-    BlockPrefixCallbackOp<T, ScanOpT>   prefix_op(linear_tid, initial_value, scan_op);
-
-    DeviceTest(block_scan, data, initial_value, scan_op, block_aggregate, prefix_op,
-        Int2Type<SCAN_MODE>(), Int2Type<TEST_MODE>(), Int2Type<Traits<T>::PRIMITIVE>());
-
-    // Stop cycle timer
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t stop = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Store output
-    StoreDirectBlocked(linear_tid, d_out, data);
-
-    // Store block_aggregate
-    if (TEST_MODE != BASIC)
-        d_aggregate[linear_tid] = block_aggregate;
-
-    // Store prefix
-    if (TEST_MODE == PREFIX)
-    {
-        if (linear_tid == 0)
-            d_out[TILE_SIZE] = prefix_op.prefix;
-    }
-
-    // Store time
-    if (linear_tid == 0)
-        *d_elapsed = (start > stop) ? start - stop : stop - start;
-}
-
-
-
-//---------------------------------------------------------------------
-// Host utility subroutines
-//---------------------------------------------------------------------
-
-/**
- * Initialize exclusive-scan problem (and solution)
- */
-template <typename T, typename ScanOpT>
-T Initialize(
-    GenMode     gen_mode,
-    T           *h_in,
-    T           *h_reference,
-    int         num_items,
-    ScanOpT     scan_op,
-    T           initial_value,
-    Int2Type<EXCLUSIVE>)
-{
-    InitValue(gen_mode, h_in[0], 0);
-
-    T block_aggregate   = h_in[0];
-    h_reference[0]      = initial_value;
-    T inclusive         = scan_op(initial_value, h_in[0]);
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-        h_reference[i] = inclusive;
-        inclusive = scan_op(inclusive, h_in[i]);
-        block_aggregate = scan_op(block_aggregate, h_in[i]);
-    }
-
-    return block_aggregate;
-}
-
-
-/**
- * Initialize inclusive-scan problem (and solution)
- */
-template <typename T, typename ScanOpT>
-T Initialize(
-    GenMode     gen_mode,
-    T           *h_in,
-    T           *h_reference,
-    int         num_items,
-    ScanOpT      scan_op,
-    T           initial_value,
-    Int2Type<INCLUSIVE>)
-{
-    InitValue(gen_mode, h_in[0], 0);
-
-    T block_aggregate   = h_in[0];
-    T inclusive         = scan_op(initial_value, h_in[0]);
-    h_reference[0]      = inclusive;
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-        inclusive = scan_op(inclusive, h_in[i]);
-        block_aggregate = scan_op(block_aggregate, h_in[i]);
-        h_reference[i] = inclusive;
-    }
-
-    return block_aggregate;
-}
-
-
-/**
- * Test threadblock scan.  (Specialized for sufficient resources)
- */
-template <
-    int                 BLOCK_DIM_X,
-    int                 BLOCK_DIM_Y,
-    int                 BLOCK_DIM_Z,
-    int                 ITEMS_PER_THREAD,
-    ScanMode            SCAN_MODE,
-    TestMode            TEST_MODE,
-    BlockScanAlgorithm  ALGORITHM,
-    typename            ScanOpT,
-    typename            T>
-void Test(
-    GenMode             gen_mode,
-    ScanOpT             scan_op,
-    T                   initial_value,
-    Int2Type<true>      sufficient_resources)
-{
-    const int BLOCK_THREADS     = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z;
-    const int TILE_SIZE         = BLOCK_THREADS * ITEMS_PER_THREAD;
-
-    // Allocate host arrays
-    T *h_in = new T[TILE_SIZE];
-    T *h_reference = new T[TILE_SIZE];
-    T *h_aggregate = new T[BLOCK_THREADS];
-
-    // Initialize problem
-    T block_aggregate = Initialize(
-        gen_mode,
-        h_in,
-        h_reference,
-        TILE_SIZE,
-        scan_op,
-        initial_value,
-        Int2Type<SCAN_MODE>());
-
-    // Test reference block_aggregate is returned in all threads
-    for (int i = 0; i < BLOCK_THREADS; ++i)
-    {
-        h_aggregate[i] = block_aggregate;
-    }
-
-    // Run kernel
-    printf("Test-mode %d, gen-mode %d, policy %d, %s %s BlockScan, %d (%d,%d,%d) threadblock threads, %d items per thread, %d tile size, %s (%d bytes) elements:\n",
-        TEST_MODE, gen_mode, ALGORITHM,
-        (SCAN_MODE == INCLUSIVE) ? "Inclusive" : "Exclusive", typeid(ScanOpT).name(),
-        BLOCK_THREADS, BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z,
-        ITEMS_PER_THREAD,  TILE_SIZE,
-        typeid(T).name(), (int) sizeof(T));
-    fflush(stdout);
-
-    // Initialize/clear device arrays
-    T       *d_in = NULL;
-    T       *d_out = NULL;
-    T       *d_aggregate = NULL;
-    clock_t *d_elapsed = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(unsigned long long)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * TILE_SIZE));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * (TILE_SIZE + 2)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_aggregate, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * TILE_SIZE, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * (TILE_SIZE + 1)));
-    CubDebugExit(cudaMemset(d_aggregate, 0, sizeof(T) * BLOCK_THREADS));
-
-    // Display input problem data
-    if (g_verbose)
-    {
-        printf("Input data: ");
-        for (int i = 0; i < TILE_SIZE; i++)
-        {
-            std::cout << CoutCast(h_in[i]) << ", ";
-        }
-        printf("\n\n");
-    }
-
-    // Run block_aggregate/prefix kernel
-    dim3 block_dims(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z);
-    BlockScanKernel<BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, ALGORITHM><<<1, block_dims>>>(
-        d_in,
-        d_out,
-        d_aggregate,
-        scan_op,
-        initial_value,
-        d_elapsed);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tScan results: ");
-    int compare = CompareDeviceResults(h_reference, d_out, TILE_SIZE, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    if (TEST_MODE == AGGREGATE)
-    {
-        // Copy out and display block_aggregate
-        printf("\tScan block aggregate: ");
-        compare = CompareDeviceResults(h_aggregate, d_aggregate, BLOCK_THREADS, g_verbose, g_verbose);
-        printf("%s\n", compare ? "FAIL" : "PASS");
-        AssertEquals(0, compare);
-    }
-
-    if (TEST_MODE == PREFIX)
-    {
-        // Copy out and display updated prefix
-        printf("\tScan running total: ");
-        T running_total = scan_op(initial_value, block_aggregate);
-        compare = CompareDeviceResults(&running_total, d_out + TILE_SIZE, 1, g_verbose, g_verbose);
-        printf("%s\n", compare ? "FAIL" : "PASS");
-        AssertEquals(0, compare);
-    }
-
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (h_aggregate) delete[] h_aggregate;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_aggregate) CubDebugExit(g_allocator.DeviceFree(d_aggregate));
-    if (d_elapsed) CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-/**
- * Test threadblock scan.  (Specialized for insufficient resources)
- */
-template <
-    int                 BLOCK_DIM_X,
-    int                 BLOCK_DIM_Y,
-    int                 BLOCK_DIM_Z,
-    int                 ITEMS_PER_THREAD,
-    ScanMode            SCAN_MODE,
-    TestMode            TEST_MODE,
-    BlockScanAlgorithm  ALGORITHM,
-    typename            ScanOpT,
-    typename            T>
-void Test(
-    GenMode             gen_mode,
-    ScanOpT             scan_op,
-    T                   initial_value,
-    Int2Type<false>     sufficient_resources)
-{}
-
-
-/**
- * Test threadblock scan.
- */
-template <
-    int                 BLOCK_DIM_X,
-    int                 BLOCK_DIM_Y,
-    int                 BLOCK_DIM_Z,
-    int                 ITEMS_PER_THREAD,
-    ScanMode            SCAN_MODE,
-    TestMode            TEST_MODE,
-    BlockScanAlgorithm  ALGORITHM,
-    typename            ScanOpT,
-    typename            T>
-void Test(
-    GenMode             gen_mode,
-    ScanOpT             scan_op,
-    T                   initial_value)
-{
-    // Check size of smem storage for the target arch to make sure it will fit
-    typedef BlockScan<T, BLOCK_DIM_X, ALGORITHM, BLOCK_DIM_Y, BLOCK_DIM_Z> BlockScanT;
-
-    enum
-    {
-#if defined(SM100) || defined(SM110) || defined(SM130)
-        sufficient_smem         = (sizeof(typename BlockScanT::TempStorage)     <= 16 * 1024),
-        sufficient_threads      = ((BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)    <= 512),
-#else
-        sufficient_smem         = (sizeof(typename BlockScanT::TempStorage)     <= 16 * 1024),
-        sufficient_threads      = ((BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z)    <= 1024),
-#endif
-
-#if defined(_WIN32) || defined(_WIN64)
-        // Accommodate ptxas crash bug (access violation) on Windows
-        special_skip            = ((TEST_ARCH <= 130) && (Equals<T, TestBar>::VALUE) && (BLOCK_DIM_Z > 1)),
-#else
-        special_skip            = false,
-#endif
-        sufficient_resources    = (sufficient_smem && sufficient_threads && !special_skip),
-    };
-
-    Test<BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, ALGORITHM>(
-        gen_mode, scan_op, initial_value, Int2Type<sufficient_resources>());
-}
-
-
-
-/**
- * Run test for different threadblock dimensions
- */
-template <
-    int                 BLOCK_THREADS,
-    int                 ITEMS_PER_THREAD,
-    ScanMode            SCAN_MODE,
-    TestMode            TEST_MODE,
-    BlockScanAlgorithm  ALGORITHM,
-    typename            ScanOpT,
-    typename            T>
-void Test(
-    GenMode     gen_mode,
-    ScanOpT     scan_op,
-    T           initial_value)
-{
-    Test<BLOCK_THREADS, 1, 1, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, ALGORITHM>(gen_mode, scan_op, initial_value);
-    Test<BLOCK_THREADS, 2, 2, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, ALGORITHM>(gen_mode, scan_op, initial_value);
-}
-
-
-/**
- * Run test for different policy types
- */
-template <
-    int         BLOCK_THREADS,
-    int         ITEMS_PER_THREAD,
-    ScanMode    SCAN_MODE,
-    TestMode    TEST_MODE,
-    typename    ScanOpT,
-    typename    T>
-void Test(
-    GenMode     gen_mode,
-    ScanOpT     scan_op,
-    T           initial_value)
-{
-#ifdef TEST_RAKING
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, BLOCK_SCAN_RAKING>(gen_mode, scan_op, initial_value);
-#endif
-#ifdef TEST_RAKING_MEMOIZE
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, BLOCK_SCAN_RAKING_MEMOIZE>(gen_mode, scan_op, initial_value);
-#endif
-#ifdef TEST_WARP_SCANS
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, SCAN_MODE, TEST_MODE, BLOCK_SCAN_WARP_SCANS>(gen_mode, scan_op, initial_value);
-#endif
-}
-
-
-/**
- * Run tests for different primitive variants
- */
-template <
-    int         BLOCK_THREADS,
-    int         ITEMS_PER_THREAD,
-    typename    ScanOpT,
-    typename    T>
-void Test(
-    GenMode     gen_mode,
-    ScanOpT     scan_op,
-    T           identity,
-    T           initial_value)
-{
-    // Exclusive (use identity as initial value because it will dispatch to *Sum variants that don't take initial values)
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, EXCLUSIVE, BASIC>(gen_mode, scan_op, identity);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, EXCLUSIVE, AGGREGATE>(gen_mode, scan_op, identity);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, EXCLUSIVE, PREFIX>(gen_mode, scan_op, identity);
-
-    // Exclusive (non-specialized, so we can use initial-value)
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, EXCLUSIVE, BASIC>(gen_mode, WrapperFunctor<ScanOpT>(scan_op), initial_value);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, EXCLUSIVE, AGGREGATE>(gen_mode, WrapperFunctor<ScanOpT>(scan_op), initial_value);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, EXCLUSIVE, PREFIX>(gen_mode, WrapperFunctor<ScanOpT>(scan_op), initial_value);
-
-    // Inclusive
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, INCLUSIVE, BASIC>(gen_mode, scan_op, identity);      // This scan doesn't take an initial value
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, INCLUSIVE, AGGREGATE>(gen_mode, scan_op, identity);  // This scan doesn't take an initial value
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD, INCLUSIVE, PREFIX>(gen_mode, scan_op, initial_value);
-}
-
-
-/**
- * Run tests for different problem-generation options
- */
-template <
-    int         BLOCK_THREADS,
-    int         ITEMS_PER_THREAD,
-    typename    ScanOpT,
-    typename    T>
-void Test(
-    ScanOpT     scan_op,
-    T           identity,
-    T           initial_value)
-{
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(UNIFORM, scan_op, identity, initial_value);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(INTEGER_SEED, scan_op, identity, initial_value);
-
-    // Don't test randomly-generated floats b/c of stability
-    if (Traits<T>::CATEGORY != FLOATING_POINT)
-        Test<BLOCK_THREADS, ITEMS_PER_THREAD>(RANDOM, scan_op, identity, initial_value);
-}
-
-
-/**
- * Run tests for different data types and scan ops
- */
-template <
-    int BLOCK_THREADS,
-    int ITEMS_PER_THREAD>
-void Test()
-{
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-    // primitive
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), (unsigned char) 0, (unsigned char) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), (unsigned short) 0, (unsigned short) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), (unsigned int) 0, (unsigned int) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), (unsigned long long) 0, (unsigned long long) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), (float) 0, (float) 99);
-
-    // primitive (alternative scan op)
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Max(), std::numeric_limits<char>::min(), (char) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Max(), std::numeric_limits<short>::min(), (short) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Max(), std::numeric_limits<int>::min(), (int) 99);
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Max(), std::numeric_limits<long long>::min(), (long long) 99);
-
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-        Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Max(), std::numeric_limits<double>::max() * -1, (double) 99);
-
-    // vec-1
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_uchar1(0), make_uchar1(17));
-
-    // vec-2
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_uchar2(0, 0), make_uchar2(17, 21));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_ushort2(0, 0), make_ushort2(17, 21));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_uint2(0, 0), make_uint2(17, 21));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_ulonglong2(0, 0), make_ulonglong2(17, 21));
-
-    // vec-4
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_char4(0, 0, 0, 0), make_char4(17, 21, 32, 85));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_short4(0, 0, 0, 0), make_short4(17, 21, 32, 85));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_int4(0, 0, 0, 0), make_int4(17, 21, 32, 85));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), make_longlong4(0, 0, 0, 0), make_longlong4(17, 21, 32, 85));
-
-    // complex
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), TestFoo::MakeTestFoo(0, 0, 0, 0), TestFoo::MakeTestFoo(17, 21, 32, 85));
-    Test<BLOCK_THREADS, ITEMS_PER_THREAD>(Sum(), TestBar(0, 0), TestBar(17, 21));
-
-}
-
-
-/**
- * Run tests for different items per thread
- */
-template <int BLOCK_THREADS>
-void Test()
-{
-    Test<BLOCK_THREADS, 1>();
-    Test<BLOCK_THREADS, 2>();
-    Test<BLOCK_THREADS, 9>();
-}
-
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-#ifdef QUICK_TEST
-
-    Test<128, 1, 1, 1, EXCLUSIVE, AGGREGATE, BLOCK_SCAN_WARP_SCANS>(UNIFORM, Sum(), int(0));
-
-    // Compile/run quick tests
-    Test<128, 1, 1, 4, EXCLUSIVE, AGGREGATE, BLOCK_SCAN_WARP_SCANS>(UNIFORM, Sum(), int(0));
-    Test<128, 1, 1, 4, EXCLUSIVE, AGGREGATE, BLOCK_SCAN_RAKING>(UNIFORM, Sum(), int(0));
-    Test<128, 1, 1, 4, EXCLUSIVE, AGGREGATE, BLOCK_SCAN_RAKING_MEMOIZE>(UNIFORM, Sum(), int(0));
-
-    Test<128, 1, 1, 2, INCLUSIVE, PREFIX, BLOCK_SCAN_RAKING>(INTEGER_SEED, Sum(), TestFoo::MakeTestFoo(17, 21, 32, 85));
-    Test<128, 1, 1, 1, EXCLUSIVE, AGGREGATE, BLOCK_SCAN_WARP_SCANS>(UNIFORM, Sum(), make_longlong4(17, 21, 32, 85));
-
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Run tests for different threadblock sizes
-        Test<17>();
-        Test<32>();
-        Test<62>();
-        Test<65>();
-//            Test<96>();             // TODO: file bug for UNREACHABLE error for Test<96, 9, BASIC, BLOCK_SCAN_RAKING>(UNIFORM, Sum(), NullType(), make_ulonglong2(17, 21));
-        Test<128>();
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
-
diff --git a/ml-xgboost/cub/test/test_device_histogram.cu b/ml-xgboost/cub/test/test_device_histogram.cu
deleted file mode 100644
index d213db8..0000000
--- a/ml-xgboost/cub/test/test_device_histogram.cu
+++ /dev/null
@@ -1,1564 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceHistogram utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <limits>
-#include <algorithm>
-#include <typeinfo>
-
-#if defined(QUICK_TEST) || defined(QUICKER_TEST)
-    #include <npp.h>
-#endif
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_histogram.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    NPP,        // NPP method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-bool                    g_verbose_input     = false;
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-
-
-//---------------------------------------------------------------------
-// Dispatch to NPP histogram
-//---------------------------------------------------------------------
-
-#if defined(QUICK_TEST) || defined(QUICKER_TEST)
-
-/**
- * Dispatch to single-channel 8b NPP histo-even
- */
-template <typename CounterT, typename LevelT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t DispatchEven(
-    Int2Type<1>             num_channels,
-    Int2Type<1>             num_active_channels,
-    Int2Type<NPP>           dispatch_to,
-    int                     timing_timing_iterations,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    unsigned char       *d_samples,               ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-    CounterT            *d_histogram[1],          ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    int                 num_levels[1],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              lower_level[1],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    LevelT              upper_level[1],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-    OffsetT             num_row_pixels,           ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT             num_rows,                 ///< [in] The number of rows in the region of interest
-    OffsetT             row_stride_bytes,         ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef unsigned char SampleT;
-
-    cudaError_t error = cudaSuccess;
-    NppiSize oSizeROI = {
-        num_row_pixels,
-        num_rows
-    };
-
-    if (d_temp_storage_bytes == NULL)
-    {
-        int nDeviceBufferSize;
-        nppiHistogramEvenGetBufferSize_8u_C1R(oSizeROI, num_levels[0] ,&nDeviceBufferSize);
-        temp_storage_bytes = nDeviceBufferSize;
-    }
-    else
-    {
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            // compute the histogram
-            nppiHistogramEven_8u_C1R(
-                d_samples,
-                row_stride_bytes,
-                oSizeROI,
-                d_histogram[0],
-                num_levels[0],
-                lower_level[0],
-                upper_level[0],
-                (Npp8u*) d_temp_storage);
-        }
-    }
-
-    return error;
-}
-
-
-/**
- * Dispatch to 3/4 8b NPP histo-even
- */
-template <typename CounterT, typename LevelT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t DispatchEven(
-    Int2Type<4>          num_channels,
-    Int2Type<3>   num_active_channels,
-    Int2Type<NPP>           dispatch_to,
-    int                     timing_timing_iterations,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    unsigned char       *d_samples,               ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-    CounterT            *d_histogram[3],          ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    int                 num_levels[3],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              lower_level[3],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    LevelT              upper_level[3],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-    OffsetT             num_row_pixels,           ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT             num_rows,                 ///< [in] The number of rows in the region of interest
-    OffsetT             row_stride_bytes,         ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef unsigned char SampleT;
-
-    cudaError_t error = cudaSuccess;
-    NppiSize oSizeROI = {
-        num_row_pixels,
-        num_rows
-    };
-
-    if (d_temp_storage_bytes == NULL)
-    {
-        int nDeviceBufferSize;
-        nppiHistogramEvenGetBufferSize_8u_AC4R(oSizeROI, num_levels ,&nDeviceBufferSize);
-        temp_storage_bytes = nDeviceBufferSize;
-    }
-    else
-    {
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            // compute the histogram
-            nppiHistogramEven_8u_AC4R(
-                d_samples,
-                row_stride_bytes,
-                oSizeROI,
-                d_histogram,
-                num_levels,
-                lower_level,
-                upper_level,
-                (Npp8u*) d_temp_storage);
-        }
-    }
-
-    return error;
-}
-
-
-#endif // #if defined(QUICK_TEST) || defined(QUICKER_TEST)
-
-
-//---------------------------------------------------------------------
-// Dispatch to different DeviceHistogram entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to CUB single histogram-even entrypoint
- */
-template <typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t DispatchEven(
-    Int2Type<1>             num_channels,
-    Int2Type<1>             num_active_channels,
-    Int2Type<CUB>           dispatch_to,
-    int                     timing_timing_iterations,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-    CounterT            *d_histogram[1],                            ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    int                 num_levels[1],                              ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              lower_level[1],                             ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    LevelT              upper_level[1],                             ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-    OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-    OffsetT             row_stride_bytes,                                 ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceHistogram::HistogramEven(
-            d_temp_storage,
-            temp_storage_bytes,
-            (const SampleT *) d_samples,
-            d_histogram[0],
-            num_levels[0],
-            lower_level[0],
-            upper_level[0],
-            num_row_pixels,
-            num_rows,
-            row_stride_bytes,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to CUB multi histogram-even entrypoint
- */
-template <int NUM_ACTIVE_CHANNELS, int NUM_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t DispatchEven(
-    Int2Type<NUM_CHANNELS>          num_channels,
-    Int2Type<NUM_ACTIVE_CHANNELS>   num_active_channels,
-    Int2Type<CUB>           dispatch_to,
-    int                     timing_timing_iterations,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-    CounterT            *d_histogram[NUM_ACTIVE_CHANNELS],          ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    int                 num_levels[NUM_ACTIVE_CHANNELS],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              lower_level[NUM_ACTIVE_CHANNELS],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    LevelT              upper_level[NUM_ACTIVE_CHANNELS],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-    OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-    OffsetT             row_stride_bytes,                                 ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceHistogram::MultiHistogramEven<NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(
-            d_temp_storage,
-            temp_storage_bytes,
-            (const SampleT *) d_samples,
-            d_histogram,
-            num_levels,
-            lower_level,
-            upper_level,
-            num_row_pixels,
-            num_rows,
-            row_stride_bytes,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to CUB single histogram-range entrypoint
- */
-template <typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t DispatchRange(
-    Int2Type<1>             num_channels,
-    Int2Type<1>             num_active_channels,
-    Int2Type<CUB>           dispatch_to,
-    int                     timing_timing_iterations,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-    CounterT            *d_histogram[1],                            ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    int                 num_levels[1],                              ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              *d_levels[1],                               ///< [in] The pointers to the arrays of boundaries (levels), one for each active channel.  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-    OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-    OffsetT             row_stride_bytes,                                 ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceHistogram::HistogramRange(
-            d_temp_storage,
-            temp_storage_bytes,
-            (const SampleT *) d_samples,
-            d_histogram[0],
-            num_levels[0],
-            d_levels[0],
-            num_row_pixels,
-            num_rows,
-            row_stride_bytes,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to CUB multi histogram-range entrypoint
- */
-template <int NUM_ACTIVE_CHANNELS, int NUM_CHANNELS, typename SampleIteratorT, typename CounterT, typename LevelT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t DispatchRange(
-    Int2Type<NUM_CHANNELS>          num_channels,
-    Int2Type<NUM_ACTIVE_CHANNELS>   num_active_channels,
-    Int2Type<CUB>           dispatch_to,
-    int                     timing_timing_iterations,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    SampleIteratorT     d_samples,                                  ///< [in] The pointer to the multi-channel input sequence of data samples. The samples from different channels are assumed to be interleaved (e.g., an array of 32-bit pixels where each pixel consists of four RGBA 8-bit samples).
-    CounterT            *d_histogram[NUM_ACTIVE_CHANNELS],          ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    int                 num_levels[NUM_ACTIVE_CHANNELS],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT              *d_levels[NUM_ACTIVE_CHANNELS],             ///< [in] The pointers to the arrays of boundaries (levels), one for each active channel.  Bin ranges are defined by consecutive boundary pairings: lower sample value boundaries are inclusive and upper sample value boundaries are exclusive.
-    OffsetT             num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT             num_rows,                                   ///< [in] The number of rows in the region of interest
-    OffsetT             row_stride_bytes,                                 ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef typename std::iterator_traits<SampleIteratorT>::value_type SampleT;
-
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceHistogram::MultiHistogramRange<NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(
-            d_temp_storage,
-            temp_storage_bytes,
-            (const SampleT *) d_samples,
-            d_histogram,
-            num_levels,
-            d_levels,
-            num_row_pixels,
-            num_rows,
-            row_stride_bytes,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-
-
-//---------------------------------------------------------------------
-// CUDA nested-parallelism test kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceHistogram
- * /
-template <int BINS, int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleT, typename SampleIteratorT, typename CounterT, int ALGORITHM>
-__global__ void CnpDispatchKernel(
-    Int2Type<ALGORITHM> algorithm,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t              temp_storage_bytes,
-    SampleT             *d_samples,
-    SampleIteratorT      d_sample_itr,
-    ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS> d_out_histograms,
-    int                 num_samples,
-    bool                debug_synchronous)
-{
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch<BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(algorithm, Int2Type<false>(), timing_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_samples, d_sample_itr, d_out_histograms.array, num_samples, 0, debug_synchronous);
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/ **
- * Dispatch to CDP kernel
- * /
-template <int BINS, int NUM_CHANNELS, int NUM_ACTIVE_CHANNELS, typename SampleT, typename SampleIteratorT, typename CounterT, int ALGORITHM>
-cudaError_t Dispatch(
-    Int2Type<ALGORITHM> algorithm,
-    Int2Type<true>      use_cdp,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    SampleT             *d_samples,
-    SampleIteratorT      d_sample_itr,
-    CounterT        *d_histograms[NUM_ACTIVE_CHANNELS],
-    int                 num_samples,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Setup array wrapper for histogram channel output (because we can't pass static arrays as kernel parameters)
-    ArrayWrapper<CounterT*, NUM_ACTIVE_CHANNELS> d_histo_wrapper;
-    for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-        d_histo_wrapper.array[CHANNEL] = d_histograms[CHANNEL];
-
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<BINS, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleIteratorT, CounterT, ALGORITHM><<<1,1>>>(algorithm, timing_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_samples, d_sample_itr, d_histo_wrapper, num_samples, debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-*/
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-// Searches for bin given a list of bin-boundary levels
-template <typename LevelT>
-struct SearchTransform
-{
-    LevelT          *levels;      // Pointer to levels array
-    int             num_levels;   // Number of levels in array
-
-    // Functor for converting samples to bin-ids (num_levels is returned if sample is out of range)
-    template <typename SampleT>
-    int operator()(SampleT sample)
-    {
-        int bin = int(std::upper_bound(levels, levels + num_levels, (LevelT) sample) - levels - 1);
-        if (bin < 0)
-        {
-            // Sample out of range
-            return num_levels;
-        }
-        return bin;
-    }
-};
-
-
-// Scales samples to evenly-spaced bins
-template <typename LevelT>
-struct ScaleTransform
-{
-    int    num_levels;  // Number of levels in array
-    LevelT max;         // Max sample level (exclusive)
-    LevelT min;         // Min sample level (inclusive)
-    LevelT scale;       // Bin scaling factor
-
-    void Init(
-        int    num_levels,  // Number of levels in array
-        LevelT max,         // Max sample level (exclusive)
-        LevelT min,         // Min sample level (inclusive)
-        LevelT scale)       // Bin scaling factor
-    {
-        this->num_levels = num_levels;
-        this->max = max;
-        this->min = min;
-        this->scale = scale;
-    }
-
-    // Functor for converting samples to bin-ids  (num_levels is returned if sample is out of range)
-    template <typename SampleT>
-    int operator()(SampleT sample)
-    {
-        if ((sample < min) || (sample >= max))
-        {
-            // Sample out of range
-            return num_levels;
-        }
-
-        return (int) ((((LevelT) sample) - min) / scale);
-    }
-};
-
-// Scales samples to evenly-spaced bins
-template <>
-struct ScaleTransform<float>
-{
-    int   num_levels;  // Number of levels in array
-    float max;         // Max sample level (exclusive)
-    float min;         // Min sample level (inclusive)
-    float scale;       // Bin scaling factor
-
-    void Init(
-        int    num_levels,  // Number of levels in array
-        float max,         // Max sample level (exclusive)
-        float min,         // Min sample level (inclusive)
-        float scale)       // Bin scaling factor
-    {
-        this->num_levels = num_levels;
-        this->max = max;
-        this->min = min;
-        this->scale = 1.0f / scale;
-    }
-
-    // Functor for converting samples to bin-ids  (num_levels is returned if sample is out of range)
-    template <typename SampleT>
-    int operator()(SampleT sample)
-    {
-        if ((sample < min) || (sample >= max))
-        {
-            // Sample out of range
-            return num_levels;
-        }
-
-        return (int) ((((float) sample) - min) * scale);
-    }
-};
-
-
-/**
- * Generate sample
- */
-template <typename T, typename LevelT>
-void Sample(T &datum, LevelT max_level, int entropy_reduction)
-{
-    unsigned int max = (unsigned int) -1;
-    unsigned int bits;
-    RandomBits(bits, entropy_reduction);
-    float fraction = (float(bits) / max);
-
-    datum = (T) (fraction * max_level);
-}
-
-
-/**
- * Initialize histogram problem (and solution)
- */
-template <
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        LevelT,
-    typename        SampleT,
-    typename        CounterT,
-    typename        TransformOp,
-    typename        OffsetT>
-void Initialize(
-    LevelT          max_level,
-    int             entropy_reduction,
-    SampleT         *h_samples,
-    int             num_levels[NUM_ACTIVE_CHANNELS],        ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    TransformOp     transform_op[NUM_ACTIVE_CHANNELS],      ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    CounterT        *h_histogram[NUM_ACTIVE_CHANNELS],      ///< [out] The pointers to the histogram counter output arrays, one for each active channel.  For channel<sub><em>i</em></sub>, the allocation length of <tt>d_histograms[i]</tt> should be <tt>num_levels[i]</tt> - 1.
-    OffsetT         num_row_pixels,                         ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT         num_rows,                               ///< [in] The number of rows in the region of interest
-    OffsetT         row_stride_bytes)                             ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-{
-    printf("Initializing... "); fflush(stdout);
-
-    // Init bins
-    for (int CHANNEL = 0; CHANNEL < NUM_ACTIVE_CHANNELS; ++CHANNEL)
-    {
-        for (int bin = 0; bin < num_levels[CHANNEL] - 1; ++bin)
-        {
-            h_histogram[CHANNEL][bin] = 0;
-        }
-    }
-
-    // Initialize samples
-    if (g_verbose_input) printf("Samples: \n");
-    for (OffsetT row = 0; row < num_rows; ++row)
-    {
-        for (OffsetT pixel = 0; pixel < num_row_pixels; ++pixel)
-        {
-            if (g_verbose_input) printf("[");
-            for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-            {
-                // Sample offset
-                OffsetT offset = (row * (row_stride_bytes / sizeof(SampleT))) + (pixel * NUM_CHANNELS) + channel;
-
-                // Init sample value
-                Sample(h_samples[offset], max_level, entropy_reduction);
-                if (g_verbose_input)
-                {
-                    if (channel > 0) printf(", ");
-                    std::cout << CoutCast(h_samples[offset]);
-                }
-
-                // Update sample bin
-                int bin = transform_op[channel](h_samples[offset]);
-                if (g_verbose_input) printf(" (%d)", bin); fflush(stdout);
-                if ((bin >= 0) && (bin < num_levels[channel] - 1))
-                {
-                    // valid bin
-                    h_histogram[channel][bin]++;
-                }
-            }
-            if (g_verbose_input) printf("]");
-        }
-        if (g_verbose_input) printf("\n\n");
-    }
-
-    printf("Done\n"); fflush(stdout);
-}
-
-
-/**
- * Test histogram-even
- */
-template <
-    Backend         BACKEND,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        SampleT,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void TestEven(
-    LevelT          max_level,
-    int             entropy_reduction,
-    int             num_levels[NUM_ACTIVE_CHANNELS],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT          lower_level[NUM_ACTIVE_CHANNELS],           ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    LevelT          upper_level[NUM_ACTIVE_CHANNELS],           ///< [in] The upper sample value bound (exclusive) for the highest histogram bin in each active channel.
-    OffsetT         num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT         num_rows,                                   ///< [in] The number of rows in the region of interest
-    OffsetT         row_stride_bytes)                                 ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-{
-    OffsetT total_samples = num_rows * (row_stride_bytes / sizeof(SampleT));
-
-    printf("\n----------------------------\n");
-    printf("%s cub::DeviceHistogramEven %d pixels (%d height, %d width, %d-byte row stride), %d %d-byte %s samples (entropy reduction %d), %s counters, %d/%d channels, max sample ",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == NPP) ? "NPP" : "CUB",
-        (int) (num_row_pixels * num_rows),
-        (int) num_rows,
-        (int) num_row_pixels,
-        (int) row_stride_bytes,
-        (int) total_samples,
-        (int) sizeof(SampleT),
-        typeid(SampleT).name(),
-        entropy_reduction,
-        typeid(CounterT).name(),
-        NUM_ACTIVE_CHANNELS,
-        NUM_CHANNELS);
-    std::cout << CoutCast(max_level) << "\n";
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-        std::cout << "\n\tChannel " << channel << ": " << num_levels[channel] - 1 << " bins [" << lower_level[channel] << ", " << upper_level[channel] << ")\n";
-    fflush(stdout);
-
-    // Allocate and initialize host and device data
-
-    typedef SampleT Foo;        // rename type to quelch gcc warnings (bug?)
-    SampleT*                    h_samples = new Foo[total_samples];
-    CounterT*                   h_histogram[NUM_ACTIVE_CHANNELS];
-    ScaleTransform<LevelT>      transform_op[NUM_ACTIVE_CHANNELS];
-
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        int bins = num_levels[channel] - 1;
-        h_histogram[channel] = new CounterT[bins];
-
-        transform_op[channel].Init(
-            num_levels[channel],
-            upper_level[channel],
-            lower_level[channel],
-            ((upper_level[channel] - lower_level[channel]) / bins));
-    }
-
-    Initialize<NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(
-        max_level, entropy_reduction, h_samples, num_levels, transform_op, h_histogram, num_row_pixels, num_rows, row_stride_bytes);
-
-    // Allocate and initialize device data
-
-    SampleT*        d_samples = NULL;
-    CounterT*       d_histogram[NUM_ACTIVE_CHANNELS];
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_samples, sizeof(SampleT) * total_samples));
-    CubDebugExit(cudaMemcpy(d_samples, h_samples, sizeof(SampleT) * total_samples, cudaMemcpyHostToDevice));
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_histogram[channel], sizeof(CounterT) * (num_levels[channel] - 1)));
-        CubDebugExit(cudaMemset(d_histogram[channel], 0, sizeof(CounterT) * (num_levels[channel] - 1)));
-    }
-
-    // Allocate CDP device arrays
-    size_t          *d_temp_storage_bytes = NULL;
-    cudaError_t     *d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-
-    DispatchEven(
-        Int2Type<NUM_CHANNELS>(), Int2Type<NUM_ACTIVE_CHANNELS>(), Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes,
-        d_samples, d_histogram, num_levels, lower_level, upper_level,
-        num_row_pixels, num_rows, row_stride_bytes,
-        0, true);
-
-    // Allocate temporary storage with "canary" zones
-    int 	canary_bytes 	= 256;
-    char 	canary_token 	= 8;
-    char* 	canary_zone 	= new char[canary_bytes];
-
-    memset(canary_zone, canary_token, canary_bytes);
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes + (canary_bytes * 2)));
-    CubDebugExit(cudaMemset(d_temp_storage, canary_token, temp_storage_bytes + (canary_bytes * 2)));
-
-    // Run warmup/correctness iteration
-    DispatchEven(
-        Int2Type<NUM_CHANNELS>(), Int2Type<NUM_ACTIVE_CHANNELS>(), Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error,
-        ((char *) d_temp_storage) + canary_bytes, temp_storage_bytes,
-        d_samples, d_histogram, num_levels, lower_level, upper_level,
-        num_row_pixels, num_rows, row_stride_bytes,
-        0, true);
-
-    // Check canary zones
-    int error = CompareDeviceResults(canary_zone, (char *) d_temp_storage, canary_bytes, true, g_verbose);
-    AssertEquals(0, error);
-    error = CompareDeviceResults(canary_zone, ((char *) d_temp_storage) + canary_bytes + temp_storage_bytes, canary_bytes, true, g_verbose);
-    AssertEquals(0, error);
-
-    // Flush any stdout/stderr
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-    fflush(stdout);
-    fflush(stderr);
-
-    // Check for correctness (and display results, if specified)
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        int channel_error = CompareDeviceResults(h_histogram[channel], d_histogram[channel], num_levels[channel] - 1, true, g_verbose);
-        printf("\tChannel %d %s", channel, channel_error ? "FAIL" : "PASS\n");
-        error |= channel_error;
-    }
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-
-    DispatchEven(
-        Int2Type<NUM_CHANNELS>(), Int2Type<NUM_ACTIVE_CHANNELS>(), Int2Type<BACKEND>(), g_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes,
-        d_samples, d_histogram, num_levels, lower_level, upper_level,
-        num_row_pixels, num_rows, row_stride_bytes,
-        0, false);
-
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(total_samples) / avg_millis / 1000.0f / 1000.0f;
-        float giga_bandwidth = giga_rate * sizeof(SampleT);
-        printf("\t%.3f avg ms, %.3f billion samples/s, %.3f billion bins/s, %.3f billion pixels/s, %.3f logical GB/s",
-            avg_millis,
-            giga_rate,
-            giga_rate * NUM_ACTIVE_CHANNELS / NUM_CHANNELS,
-            giga_rate / NUM_CHANNELS,
-            giga_bandwidth);
-    }
-
-    printf("\n\n");
-
-    // Cleanup
-    if (h_samples) delete[] h_samples;
-
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        if (h_histogram[channel])
-            delete[] h_histogram[channel];
-
-        if (d_histogram[channel])
-            CubDebugExit(g_allocator.DeviceFree(d_histogram[channel]));
-    }
-
-    if (d_samples) CubDebugExit(g_allocator.DeviceFree(d_samples));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, error);
-}
-
-
-
-
-
-/**
- * Test histogram-range
- */
-template <
-    Backend         BACKEND,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        SampleT,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void TestRange(
-    LevelT          max_level,
-    int             entropy_reduction,
-    int             num_levels[NUM_ACTIVE_CHANNELS],            ///< [in] The number of boundaries (levels) for delineating histogram samples in each active channel.  Implies that the number of bins for channel<sub><em>i</em></sub> is <tt>num_levels[i]</tt> - 1.
-    LevelT*         levels[NUM_ACTIVE_CHANNELS],                ///< [in] The lower sample value bound (inclusive) for the lowest histogram bin in each active channel.
-    OffsetT         num_row_pixels,                             ///< [in] The number of multi-channel pixels per row in the region of interest
-    OffsetT         num_rows,                                   ///< [in] The number of rows in the region of interest
-    OffsetT         row_stride_bytes)                                 ///< [in] The number of bytes between starts of consecutive rows in the region of interest
-{
-    OffsetT total_samples = num_rows * (row_stride_bytes / sizeof(SampleT));
-
-    printf("\n----------------------------\n");
-    printf("%s cub::DeviceHistogramRange %d pixels (%d height, %d width, %d-byte row stride), %d %d-byte %s samples (entropy reduction %d), %s counters, %d/%d channels, max sample ",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == NPP) ? "NPP" : "CUB",
-        (int) (num_row_pixels * num_rows),
-        (int) num_rows,
-        (int) num_row_pixels,
-        (int) row_stride_bytes,
-        (int) total_samples,
-        (int) sizeof(SampleT),
-        typeid(SampleT).name(),
-        entropy_reduction,
-        typeid(CounterT).name(),
-        NUM_ACTIVE_CHANNELS,
-        NUM_CHANNELS);
-    std::cout << CoutCast(max_level) << "\n";
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        printf("Channel %d: %d bins [", channel, num_levels[channel] - 1);
-        std::cout << levels[channel][0];
-        for (int level = 1; level < num_levels[channel]; ++level)
-            std::cout << ", " << levels[channel][level];
-        printf("]\n");
-    }
-    fflush(stdout);
-
-    // Allocate and initialize host and device data
-    typedef SampleT Foo;        // rename type to quelch gcc warnings (bug?)
-    SampleT*                    h_samples = new Foo[total_samples];
-    CounterT*                   h_histogram[NUM_ACTIVE_CHANNELS];
-    SearchTransform<LevelT>     transform_op[NUM_ACTIVE_CHANNELS];
-
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        transform_op[channel].levels = levels[channel];
-        transform_op[channel].num_levels = num_levels[channel];
-
-        int bins = num_levels[channel] - 1;
-        h_histogram[channel] = new CounterT[bins];
-    }
-
-    Initialize<NUM_CHANNELS, NUM_ACTIVE_CHANNELS>(
-        max_level, entropy_reduction, h_samples, num_levels, transform_op, h_histogram, num_row_pixels, num_rows, row_stride_bytes);
-
-    // Allocate and initialize device data
-    SampleT*        d_samples = NULL;
-    LevelT*         d_levels[NUM_ACTIVE_CHANNELS];
-    CounterT*       d_histogram[NUM_ACTIVE_CHANNELS];
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_samples, sizeof(SampleT) * total_samples));
-    CubDebugExit(cudaMemcpy(d_samples, h_samples, sizeof(SampleT) * total_samples, cudaMemcpyHostToDevice));
-
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_levels[channel], sizeof(LevelT) * num_levels[channel]));
-        CubDebugExit(cudaMemcpy(d_levels[channel], levels[channel],         sizeof(LevelT) * num_levels[channel], cudaMemcpyHostToDevice));
-
-        int bins = num_levels[channel] - 1;
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_histogram[channel],  sizeof(CounterT) * bins));
-        CubDebugExit(cudaMemset(d_histogram[channel], 0,                        sizeof(CounterT) * bins));
-    }
-
-    // Allocate CDP device arrays
-    size_t          *d_temp_storage_bytes = NULL;
-    cudaError_t     *d_cdp_error = NULL;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-
-    DispatchRange(
-        Int2Type<NUM_CHANNELS>(), Int2Type<NUM_ACTIVE_CHANNELS>(), Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes,
-        d_samples, d_histogram, num_levels, d_levels,
-        num_row_pixels, num_rows, row_stride_bytes,
-        0, true);
-
-    // Allocate temporary storage with "canary" zones
-    int     canary_bytes    = 256;
-    char    canary_token    = 9;
-    char*   canary_zone     = new char[canary_bytes];
-
-    memset(canary_zone, canary_token, canary_bytes);
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes + (canary_bytes * 2)));
-    CubDebugExit(cudaMemset(d_temp_storage, canary_token, temp_storage_bytes + (canary_bytes * 2)));
-
-    // Run warmup/correctness iteration
-    DispatchRange(
-        Int2Type<NUM_CHANNELS>(), Int2Type<NUM_ACTIVE_CHANNELS>(), Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error,
-        ((char *) d_temp_storage) + canary_bytes, temp_storage_bytes,
-        d_samples, d_histogram, num_levels, d_levels,
-        num_row_pixels, num_rows, row_stride_bytes,
-        0, true);
-
-    // Check canary zones
-    int error = CompareDeviceResults(canary_zone, (char *) d_temp_storage, canary_bytes, true, g_verbose);
-    AssertEquals(0, error);
-    error = CompareDeviceResults(canary_zone, ((char *) d_temp_storage) + canary_bytes + temp_storage_bytes, canary_bytes, true, g_verbose);
-    AssertEquals(0, error);
-
-    // Flush any stdout/stderr
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-    fflush(stdout);
-    fflush(stderr);
-
-    // Check for correctness (and display results, if specified)
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        int channel_error = CompareDeviceResults(h_histogram[channel], d_histogram[channel], num_levels[channel] - 1, true, g_verbose);
-        printf("\tChannel %d %s", channel, channel_error ? "FAIL" : "PASS\n");
-        error |= channel_error;
-    }
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-
-    DispatchRange(
-        Int2Type<NUM_CHANNELS>(), Int2Type<NUM_ACTIVE_CHANNELS>(), Int2Type<BACKEND>(), g_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes,
-        d_samples, d_histogram, num_levels, d_levels,
-        num_row_pixels, num_rows, row_stride_bytes,
-        0, false);
-
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(total_samples) / avg_millis / 1000.0f / 1000.0f;
-        float giga_bandwidth = giga_rate * sizeof(SampleT);
-        printf("\t%.3f avg ms, %.3f billion samples/s, %.3f billion bins/s, %.3f billion pixels/s, %.3f logical GB/s",
-            avg_millis,
-            giga_rate,
-            giga_rate * NUM_ACTIVE_CHANNELS / NUM_CHANNELS,
-            giga_rate / NUM_CHANNELS,
-            giga_bandwidth);
-    }
-
-    printf("\n\n");
-
-    // Cleanup
-    if (h_samples) delete[] h_samples;
-
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        if (h_histogram[channel])
-            delete[] h_histogram[channel];
-
-        if (d_histogram[channel])
-            CubDebugExit(g_allocator.DeviceFree(d_histogram[channel]));
-
-        if (d_levels[channel])
-            CubDebugExit(g_allocator.DeviceFree(d_levels[channel]));
-    }
-
-    if (d_samples) CubDebugExit(g_allocator.DeviceFree(d_samples));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, error);
-}
-
-
-/**
- * Test histogram-even
- */
-template <
-    Backend         BACKEND,
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void TestEven(
-    OffsetT         num_row_pixels,
-    OffsetT         num_rows,
-    OffsetT         row_stride_bytes,
-    int             entropy_reduction,
-    int             num_levels[NUM_ACTIVE_CHANNELS],
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    LevelT lower_level[NUM_ACTIVE_CHANNELS];
-    LevelT upper_level[NUM_ACTIVE_CHANNELS];
-
-    // Find smallest level increment
-    int max_bins = max_num_levels - 1;
-    LevelT min_level_increment = max_level / max_bins;
-
-    // Set upper and lower levels for each channel
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        int num_bins = num_levels[channel] - 1;
-        lower_level[channel] = (max_level - (num_bins * min_level_increment)) / 2;
-        upper_level[channel] = (max_level + (num_bins * min_level_increment)) / 2;
-    }
-
-    TestEven<BACKEND, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleT, CounterT, LevelT, OffsetT>(
-        max_level, entropy_reduction, num_levels, lower_level, upper_level, num_row_pixels, num_rows, row_stride_bytes);
-}
-
-
-
-/**
- * Test histogram-range
- */
-template <
-    Backend         BACKEND,
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void TestRange(
-    OffsetT         num_row_pixels,
-    OffsetT         num_rows,
-    OffsetT         row_stride_bytes,
-    int             entropy_reduction,
-    int             num_levels[NUM_ACTIVE_CHANNELS],
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    // Find smallest level increment
-    int max_bins = max_num_levels - 1;
-    LevelT min_level_increment = max_level / max_bins;
-
-    LevelT* levels[NUM_ACTIVE_CHANNELS];
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        levels[channel] = new LevelT[num_levels[channel]];
-
-        int num_bins = num_levels[channel] - 1;
-        LevelT lower_level = (max_level - (num_bins * min_level_increment)) / 2;
-
-        for (int level = 0; level < num_levels[channel]; ++level)
-            levels[channel][level] = lower_level + (level * min_level_increment);
-    }
-
-    TestRange<BACKEND, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, SampleT, CounterT, LevelT, OffsetT>(
-        max_level, entropy_reduction, num_levels, levels, num_row_pixels, num_rows, row_stride_bytes);
-
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-        delete[] levels[channel];
-
-}
-
-
-
-/**
- * Test different entrypoints
- */
-template <
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void Test(
-    OffsetT         num_row_pixels,
-    OffsetT         num_rows,
-    OffsetT         row_stride_bytes,
-    int             entropy_reduction,
-    int             num_levels[NUM_ACTIVE_CHANNELS],
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    TestEven<CUB, SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, max_num_levels);
-
-    TestRange<CUB, SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, max_num_levels);
-}
-
-
-/**
- * Test different number of levels
- */
-template <
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void Test(
-    OffsetT         num_row_pixels,
-    OffsetT         num_rows,
-    OffsetT         row_stride_bytes,
-    int             entropy_reduction,
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    int num_levels[NUM_ACTIVE_CHANNELS];
-
-    // All the same level
-    for (int channel = 0; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        num_levels[channel] = max_num_levels;
-    }
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, max_num_levels);
-
-    // All different levels
-    num_levels[0] = max_num_levels;
-    for (int channel = 1; channel < NUM_ACTIVE_CHANNELS; ++channel)
-    {
-        num_levels[channel] = (num_levels[channel - 1] / 2) + 1;
-    }
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, max_num_levels);
-}
-
-
-
-/**
- * Test different entropy-levels
- */
-template <
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void Test(
-    OffsetT         num_row_pixels,
-    OffsetT         num_rows,
-    OffsetT         row_stride_bytes,
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, 0,   max_level, max_num_levels);
-
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, -1,  max_level, max_num_levels);
-
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, 5,   max_level, max_num_levels);
-}
-
-
-/**
- * Test different row strides
- */
-template <
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void Test(
-    OffsetT         num_row_pixels,
-    OffsetT         num_rows,
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    OffsetT row_stride_bytes = num_row_pixels * NUM_CHANNELS * sizeof(SampleT);
-
-    // No padding
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes, max_level, max_num_levels);
-
-    // 13 samples padding
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        num_row_pixels, num_rows, row_stride_bytes + (13 * sizeof(SampleT)), max_level, max_num_levels);
-}
-
-
-/**
- * Test different problem sizes
- */
-template <
-    typename        SampleT,
-    int             NUM_CHANNELS,
-    int             NUM_ACTIVE_CHANNELS,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void Test(
-    LevelT          max_level,
-    int             max_num_levels)
-{
-    // 0 images
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        OffsetT(1920), OffsetT(0), max_level, max_num_levels);
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        OffsetT(0), OffsetT(0), max_level, max_num_levels);
-
-    // 1080 image
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        OffsetT(1920), OffsetT(1080), max_level, max_num_levels);
-
-    // 720 image
-    Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-        OffsetT(1280), OffsetT(720), max_level, max_num_levels);
-
-    // Sample different image sizes
-    for (OffsetT rows = 1; rows < 1000000; rows *= 100)
-    {
-        for (OffsetT cols = 1; cols < (1000000 / rows); cols *= 100)
-        {
-            Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-                cols, rows, max_level, max_num_levels);
-        }
-    }
-
-    // Randomly select linear problem size between 1:10,000,000
-    unsigned int max_int = (unsigned int) -1;
-    for (int i = 0; i < 10; ++i)
-    {
-        unsigned int num_items;
-        RandomBits(num_items);
-        num_items = (unsigned int) ((double(num_items) * double(10000000)) / double(max_int));
-        num_items = CUB_MAX(1, num_items);
-
-        Test<SampleT, NUM_CHANNELS, NUM_ACTIVE_CHANNELS, CounterT, LevelT, OffsetT>(
-            OffsetT(num_items), 1, max_level, max_num_levels);
-    }
-}
-
-
-
-/**
- * Test different channel interleavings (valid specialiation)
- */
-template <
-    typename        SampleT,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void TestChannels(
-    LevelT          max_level,
-    int             max_num_levels,
-    Int2Type<true>  is_valid_tag)
-{
-    Test<SampleT, 1, 1, CounterT, LevelT, OffsetT>(max_level, max_num_levels);
-    Test<SampleT, 4, 3, CounterT, LevelT, OffsetT>(max_level, max_num_levels);
-    Test<SampleT, 3, 3, CounterT, LevelT, OffsetT>(max_level, max_num_levels);
-    Test<SampleT, 4, 4, CounterT, LevelT, OffsetT>(max_level, max_num_levels);
-}
-
-
-/**
- * Test different channel interleavings (invalid specialiation)
- */
-template <
-    typename        SampleT,
-    typename        CounterT,
-    typename        LevelT,
-    typename        OffsetT>
-void TestChannels(
-    LevelT          max_level,
-    int             max_num_levels,
-    Int2Type<false> is_valid_tag)
-{}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_row_pixels = -1;
-    int entropy_reduction = 0;
-    int num_rows = 1;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    g_verbose_input = args.CheckCmdLineFlag("v2");
-    args.GetCmdLineArgument("n", num_row_pixels);
-
-    int row_stride_pixels = num_row_pixels;
-
-    args.GetCmdLineArgument("rows", num_rows);
-    args.GetCmdLineArgument("stride", row_stride_pixels);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-    args.GetCmdLineArgument("entropy", entropy_reduction);
-
-    bool compare_npp = args.CheckCmdLineFlag("npp");
-
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<pixels per row> "
-            "[--rows=<number of rows> "
-            "[--stride=<row stride in pixels> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--entropy=<entropy-reduction factor (default 0)>]"
-            "[--v] "
-            "[--cdp]"
-            "[--npp]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-    if (num_row_pixels < 0)
-    {
-        num_row_pixels      = 1920 * 1080;
-        row_stride_pixels   = num_row_pixels;
-    }
-
-#if defined(QUICKER_TEST)
-
-    // Compile/run quick tests
-    {
-        // HistogramEven: unsigned char 256 bins
-        typedef unsigned char       SampleT;
-        typedef int                 LevelT;
-
-        LevelT  max_level           = 256;
-        int     num_levels[1]       = {257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestEven<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-        if (compare_npp)
-            TestEven<NPP, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-
-#elif defined(QUICK_TEST)
-
-    // Compile/run quick tests
-    {
-        // HistogramEven: unsigned char 256 bins
-        typedef unsigned char       SampleT;
-        typedef int                 LevelT;
-
-        LevelT  max_level           = 256;
-        int     num_levels[1]       = {257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestEven<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-        if (compare_npp)
-            TestEven<NPP, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramEven: 4/4 multichannel Unsigned char 256 bins
-        typedef unsigned char       SampleT;
-        typedef int                 LevelT;
-
-        LevelT  max_level           = 256;
-        int     num_levels[4]       = {257, 257, 257, 257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 4;
-
-        TestEven<CUB, SampleT, 4, 4, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramEven: 3/4 multichannel Unsigned char 256 bins
-        typedef unsigned char       SampleT;
-        typedef int                 LevelT;
-
-        LevelT  max_level           = 256;
-        int     num_levels[3]       = {257, 257, 257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 4;
-
-        TestEven<CUB, SampleT, 4, 3, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-        if (compare_npp)
-            TestEven<NPP, SampleT, 4, 3, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramEven: short [0,1024] 256 bins
-        typedef unsigned short      SampleT;
-        typedef unsigned short      LevelT;
-
-        LevelT  max_level           = 1024;
-        int     num_levels[1]       = {257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestEven<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramEven: float [0,1.0] 256 bins
-        typedef float               SampleT;
-        typedef float               LevelT;
-
-        LevelT  max_level           = 1.0;
-        int     num_levels[1]       = {257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestEven<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramEven: 3/4 multichannel float [0,1.0] 256 bins
-        typedef float               SampleT;
-        typedef float               LevelT;
-
-         LevelT  max_level           = 1.0;
-         int     num_levels[3]       = {257, 257, 257};
-         int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 4;
-
-         TestEven<CUB, SampleT, 4, 3, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramRange: signed char 256 bins
-        typedef signed char         SampleT;
-        typedef int                 LevelT;
-
-        LevelT  max_level           = 256;
-        int     num_levels[1]       = {257};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestRange<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramRange: 3/4 channel, unsigned char, varied bins (256, 128, 64)
-        typedef unsigned char       SampleT;
-        typedef int                 LevelT;
-
-        LevelT  max_level           = 256;
-        int     num_levels[3]       = {257, 129, 65};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 4;
-
-        TestRange<CUB, SampleT, 4, 3, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-    {
-        // HistogramEven: double [0,1.0] 64 bins
-        typedef double              SampleT;
-        typedef double              LevelT;
-
-        LevelT  max_level           = 1.0;
-        int     num_levels[1]       = {65};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestEven<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-    {
-        // HistogramEven: short [0,1024] 512 bins
-        typedef unsigned short      SampleT;
-        typedef unsigned short      LevelT;
-
-        LevelT  max_level           = 1024;
-        int     num_levels[1]       = {513};
-        int     row_stride_bytes    = sizeof(SampleT) * row_stride_pixels * 1;
-
-        TestEven<CUB, SampleT, 1, 1, int, LevelT, int>(num_row_pixels, num_rows, row_stride_bytes, entropy_reduction, num_levels, max_level, num_levels[0]);
-    }
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        TestChannels <unsigned char,    int, int,   int>(256,   256 + 1, Int2Type<true>());
-        TestChannels <signed char,      int, int,   int>(256,   256 + 1, Int2Type<true>());
-        TestChannels <unsigned short,   int, int,   int>(128,   128 + 1, Int2Type<true>());
-        TestChannels <unsigned short,   int, int,   int>(8192,  8192 + 1, Int2Type<true>());
-        TestChannels <float,            int, float, int>(1.0,   256 + 1, Int2Type<true>());
-
-		// Test down-conversion of size_t offsets to int
-        TestChannels <unsigned char,    int, int,   long long>(256, 256 + 1, Int2Type<(sizeof(size_t) != sizeof(int))>());
-    }
-
-#endif
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/test/test_device_radix_sort.cu b/ml-xgboost/cub/test/test_device_radix_sort.cu
deleted file mode 100644
index af61773..0000000
--- a/ml-xgboost/cub/test/test_device_radix_sort.cu
+++ /dev/null
@@ -1,1273 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceRadixSort utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <algorithm>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_radix_sort.cuh>
-#include <cub/device/device_segmented_radix_sort.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/sort.h>
-#include <thrust/reverse.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,                        // CUB method (allows overwriting of input)
-    CUB_NO_OVERWRITE,           // CUB method (disallows overwriting of input)
-
-    CUB_SEGMENTED,              // CUB method (allows overwriting of input)
-    CUB_SEGMENTED_NO_OVERWRITE, // CUB method (disallows overwriting of input)
-
-    THRUST,                     // Thrust method
-    CDP,                        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-//---------------------------------------------------------------------
-// Dispatch to different DeviceRadixSort entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to CUB sorting entrypoint (specialized for ascending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<false>         is_descending,
-    Int2Type<CUB>           dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    return DeviceRadixSort::SortPairs(
-        d_temp_storage, temp_storage_bytes,
-        d_keys, d_values,
-        num_items, begin_bit, end_bit, stream, debug_synchronous);
-}
-
-/**
- * Dispatch to CUB_NO_OVERWRITE sorting entrypoint (specialized for ascending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<false>             is_descending,
-    Int2Type<CUB_NO_OVERWRITE>  dispatch_to,
-    int                         *d_selector,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    KeyT      const *const_keys_itr     = d_keys.Current();
-    ValueT    const *const_values_itr   = d_values.Current();
-
-    cudaError_t retval = DeviceRadixSort::SortPairs(
-        d_temp_storage, temp_storage_bytes,
-        const_keys_itr, d_keys.Alternate(), const_values_itr, d_values.Alternate(),
-        num_items, begin_bit, end_bit, stream, debug_synchronous);
-
-    d_keys.selector ^= 1;
-    d_values.selector ^= 1;
-    return retval;
-}
-
-/**
- * Dispatch to CUB sorting entrypoint (specialized for descending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<true>          is_descending,
-    Int2Type<CUB>           dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    return DeviceRadixSort::SortPairsDescending(
-        d_temp_storage, temp_storage_bytes,
-        d_keys, d_values,
-        num_items, begin_bit, end_bit, stream, debug_synchronous);
-}
-
-
-/**
- * Dispatch to CUB_NO_OVERWRITE sorting entrypoint (specialized for descending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<true>              is_descending,
-    Int2Type<CUB_NO_OVERWRITE>  dispatch_to,
-    int                         *d_selector,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    KeyT      const *const_keys_itr     = d_keys.Current();
-    ValueT    const *const_values_itr   = d_values.Current();
-
-    cudaError_t retval = DeviceRadixSort::SortPairsDescending(
-        d_temp_storage, temp_storage_bytes,
-        const_keys_itr, d_keys.Alternate(), const_values_itr, d_values.Alternate(),
-        num_items, begin_bit, end_bit, stream, debug_synchronous);
-
-    d_keys.selector ^= 1;
-    d_values.selector ^= 1;
-    return retval;
-}
-
-//---------------------------------------------------------------------
-// Dispatch to different DeviceRadixSort entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to CUB_SEGMENTED sorting entrypoint (specialized for ascending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<false>         is_descending,
-    Int2Type<CUB_SEGMENTED> dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    return DeviceSegmentedRadixSort::SortPairs(
-        d_temp_storage, temp_storage_bytes,
-        d_keys, d_values,
-        num_items, num_segments, d_segment_offsets, d_segment_offsets + 1,
-        begin_bit, end_bit, stream, debug_synchronous);
-}
-
-/**
- * Dispatch to CUB_SEGMENTED_NO_OVERWRITE sorting entrypoint (specialized for ascending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<false>                         is_descending,
-    Int2Type<CUB_SEGMENTED_NO_OVERWRITE>    dispatch_to,
-    int                                     *d_selector,
-    size_t                                  *d_temp_storage_bytes,
-    cudaError_t                             *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    KeyT      const *const_keys_itr     = d_keys.Current();
-    ValueT    const *const_values_itr   = d_values.Current();
-
-    cudaError_t retval = DeviceSegmentedRadixSort::SortPairs(
-        d_temp_storage, temp_storage_bytes,
-        const_keys_itr, d_keys.Alternate(), const_values_itr, d_values.Alternate(),
-        num_items, num_segments, d_segment_offsets, d_segment_offsets + 1,
-        begin_bit, end_bit, stream, debug_synchronous);
-
-    d_keys.selector ^= 1;
-    d_values.selector ^= 1;
-    return retval;
-}
-
-
-/**
- * Dispatch to CUB_SEGMENTED sorting entrypoint (specialized for descending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<true>          is_descending,
-    Int2Type<CUB_SEGMENTED> dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    return DeviceSegmentedRadixSort::SortPairsDescending(
-        d_temp_storage, temp_storage_bytes,
-        d_keys, d_values,
-        num_items, num_segments, d_segment_offsets, d_segment_offsets + 1,
-        begin_bit, end_bit, stream, debug_synchronous);
-}
-
-/**
- * Dispatch to CUB_SEGMENTED_NO_OVERWRITE sorting entrypoint (specialized for descending)
- */
-template <typename KeyT, typename ValueT>
-CUB_RUNTIME_FUNCTION
-__forceinline__
-cudaError_t Dispatch(
-    Int2Type<true>                          is_descending,
-    Int2Type<CUB_SEGMENTED_NO_OVERWRITE>    dispatch_to,
-    int                                     *d_selector,
-    size_t                                  *d_temp_storage_bytes,
-    cudaError_t                             *d_cdp_error,
-
-    void*                   d_temp_storage,
-    size_t&                 temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    KeyT      const *const_keys_itr     = d_keys.Current();
-    ValueT    const *const_values_itr   = d_values.Current();
-
-    cudaError_t retval = DeviceSegmentedRadixSort::SortPairsDescending(
-        d_temp_storage, temp_storage_bytes,
-        const_keys_itr, d_keys.Alternate(), const_values_itr, d_values.Alternate(),
-        num_items, num_segments, d_segment_offsets, d_segment_offsets + 1,
-        begin_bit, end_bit, stream, debug_synchronous);
-
-    d_keys.selector ^= 1;
-    d_values.selector ^= 1;
-    return retval;
-}
-
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch keys-only to Thrust sorting entrypoint
- */
-template <int IS_DESCENDING, typename KeyT>
-cudaError_t Dispatch(
-    Int2Type<IS_DESCENDING> is_descending,
-    Int2Type<THRUST>        dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void                    *d_temp_storage,
-    size_t                  &temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<NullType>  &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<KeyT> d_keys_wrapper(d_keys.Current());
-
-        if (IS_DESCENDING) thrust::reverse(d_keys_wrapper, d_keys_wrapper + num_items);
-        thrust::sort(d_keys_wrapper, d_keys_wrapper + num_items);
-        if (IS_DESCENDING) thrust::reverse(d_keys_wrapper, d_keys_wrapper + num_items);
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch key-value pairs to Thrust sorting entrypoint
- */
-template <int IS_DESCENDING, typename KeyT, typename ValueT>
-cudaError_t Dispatch(
-    Int2Type<IS_DESCENDING> is_descending,
-    Int2Type<THRUST>        dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void                    *d_temp_storage,
-    size_t                  &temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<KeyT>     d_keys_wrapper(d_keys.Current());
-        thrust::device_ptr<ValueT>   d_values_wrapper(d_values.Current());
-
-        if (IS_DESCENDING) {
-            thrust::reverse(d_keys_wrapper, d_keys_wrapper + num_items);
-            thrust::reverse(d_values_wrapper, d_values_wrapper + num_items);
-        }
-
-        thrust::sort_by_key(d_keys_wrapper, d_keys_wrapper + num_items, d_values_wrapper);
-
-        if (IS_DESCENDING) {
-            thrust::reverse(d_keys_wrapper, d_keys_wrapper + num_items);
-            thrust::reverse(d_values_wrapper, d_values_wrapper + num_items);
-        }
-    }
-
-    return cudaSuccess;
-}
-
-
-//---------------------------------------------------------------------
-// CUDA Nested Parallelism Test Kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceRadixSort
- */
-template <int IS_DESCENDING, typename KeyT, typename ValueT>
-__global__ void CnpDispatchKernel(
-    Int2Type<IS_DESCENDING> is_descending,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void                    *d_temp_storage,
-    size_t                  temp_storage_bytes,
-    DoubleBuffer<KeyT>      d_keys,
-    DoubleBuffer<ValueT>    d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    bool                    debug_synchronous)
-{
-#ifndef CUB_CDP
-    *d_cdp_error            = cudaErrorNotSupported;
-#else
-    *d_cdp_error            = Dispatch(
-                                is_descending, Int2Type<CUB>(), d_selector, d_temp_storage_bytes, d_cdp_error,
-                                d_temp_storage, temp_storage_bytes, d_keys, d_values,
-                                num_items, num_segments, d_segment_offsets,
-                                begin_bit, end_bit, 0, debug_synchronous);
-    *d_temp_storage_bytes   = temp_storage_bytes;
-    *d_selector             = d_keys.selector;
-#endif
-}
-
-
-/**
- * Dispatch to CDP kernel
- */
-template <int IS_DESCENDING, typename KeyT, typename ValueT>
-cudaError_t Dispatch(
-    Int2Type<IS_DESCENDING> is_descending,
-    Int2Type<CDP>           dispatch_to,
-    int                     *d_selector,
-    size_t                  *d_temp_storage_bytes,
-    cudaError_t             *d_cdp_error,
-
-    void                    *d_temp_storage,
-    size_t                  &temp_storage_bytes,
-    DoubleBuffer<KeyT>      &d_keys,
-    DoubleBuffer<ValueT>    &d_values,
-    int                     num_items,
-    int                     num_segments,
-    const int               *d_segment_offsets,
-    int                     begin_bit,
-    int                     end_bit,
-    cudaStream_t            stream,
-    bool                    debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(
-        is_descending, d_selector, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_keys, d_values,
-        num_items, num_segments, d_segment_offsets,
-        begin_bit, end_bit, debug_synchronous);
-
-    // Copy out selector
-    CubDebugExit(cudaMemcpy(&d_keys.selector, d_selector, sizeof(int) * 1, cudaMemcpyDeviceToHost));
-    d_values.selector = d_keys.selector;
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Problem generation
-//---------------------------------------------------------------------
-
-
-/**
- * Simple key-value pairing
- */
-template <
-    typename KeyT,
-    typename ValueT,
-    bool IS_FLOAT = (Traits<KeyT>::CATEGORY == FLOATING_POINT)>
-struct Pair
-{
-    KeyT     key;
-    ValueT   value;
-
-    bool operator<(const Pair &b) const
-    {
-        return (key < b.key);
-    }
-};
-
-
-/**
- * Simple key-value pairing (specialized for bool types)
- */
-template <typename ValueT>
-struct Pair<bool, ValueT, false>
-{
-    bool     key;
-    ValueT   value;
-
-    bool operator<(const Pair &b) const
-    {
-        return (!key && b.key);
-    }
-};
-
-
-/**
- * Simple key-value pairing (specialized for floating point types)
- */
-template <typename KeyT, typename ValueT>
-struct Pair<KeyT, ValueT, true>
-{
-    KeyT     key;
-    ValueT   value;
-
-    bool operator<(const Pair &b) const
-    {
-        if (key < b.key)
-            return true;
-
-        if (key > b.key)
-            return false;
-
-        // KeyT in unsigned bits
-        typedef typename Traits<KeyT>::UnsignedBits UnsignedBits;
-
-        // Return true if key is negative zero and b.key is positive zero
-        UnsignedBits key_bits   = *reinterpret_cast<UnsignedBits*>(const_cast<KeyT*>(&key));
-        UnsignedBits b_key_bits = *reinterpret_cast<UnsignedBits*>(const_cast<KeyT*>(&b.key));
-        UnsignedBits HIGH_BIT   = Traits<KeyT>::HIGH_BIT;
-
-        return ((key_bits & HIGH_BIT) != 0) && ((b_key_bits & HIGH_BIT) == 0);
-    }
-};
-
-
-/**
- * Initialize key data
- */
-template <typename KeyT>
-void InitializeKeyBits(
-    GenMode         gen_mode,
-    KeyT            *h_keys,
-    int             num_items,
-    int             entropy_reduction)
-{
-    for (int i = 0; i < num_items; ++i)
-        InitValue(gen_mode, h_keys[i], i);
-}
-
-
-/**
- * Initialize solution
- */
-template <bool IS_DESCENDING, typename KeyT>
-void InitializeSolution(
-    KeyT    *h_keys,
-    int     num_items,
-    int     num_segments,
-    int     *h_segment_offsets,
-    int     begin_bit,
-    int     end_bit,
-    int     *&h_reference_ranks,
-    KeyT    *&h_reference_keys)
-{
-    typedef Pair<KeyT, int> PairT;
-
-    PairT *h_pairs = new PairT[num_items];
-
-    int num_bits = end_bit - begin_bit;
-    for (int i = 0; i < num_items; ++i)
-    {
-
-        // Mask off unwanted portions
-        if (num_bits < sizeof(KeyT) * 8)
-        {
-            unsigned long long base = 0;
-            memcpy(&base, &h_keys[i], sizeof(KeyT));
-            base &= ((1ull << num_bits) - 1) << begin_bit;
-            memcpy(&h_pairs[i].key, &base, sizeof(KeyT));
-        }
-        else
-        {
-            h_pairs[i].key = h_keys[i];
-        }
-
-        h_pairs[i].value = i;
-    }
-
-    printf("\nSorting reference solution on CPU (%d segments)...", num_segments); fflush(stdout);
-
-    for (int i = 0; i < num_segments; ++i)
-    {
-        if (IS_DESCENDING) std::reverse(h_pairs + h_segment_offsets[i], h_pairs + h_segment_offsets[i + 1]);
-        std::stable_sort(               h_pairs + h_segment_offsets[i], h_pairs + h_segment_offsets[i + 1]);
-        if (IS_DESCENDING) std::reverse(h_pairs + h_segment_offsets[i], h_pairs + h_segment_offsets[i + 1]);
-    }
-
-    printf(" Done.\n"); fflush(stdout);
-
-    h_reference_ranks  = new int[num_items];
-    h_reference_keys   = new KeyT[num_items];
-
-    for (int i = 0; i < num_items; ++i)
-    {
-        h_reference_ranks[i]    = h_pairs[i].value;
-        h_reference_keys[i]     = h_keys[h_pairs[i].value];
-    }
-
-    if (h_pairs) delete[] h_pairs;
-}
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Test DeviceRadixSort
- */
-template <
-    Backend     BACKEND,
-    bool        IS_DESCENDING,
-    typename    KeyT,
-    typename    ValueT>
-void Test(
-    KeyT        *h_keys,
-    ValueT      *h_values,
-    int         num_items,
-    int         num_segments,
-    int         *h_segment_offsets,
-    int         begin_bit,
-    int         end_bit,
-    KeyT        *h_reference_keys,
-    ValueT      *h_reference_values)
-{
-    const bool KEYS_ONLY = Equals<ValueT, NullType>::VALUE;
-
-    printf("%s %s cub::DeviceRadixSort %d items, %d segments, %d-byte keys (%s) %d-byte values (%s), descending %d, begin_bit %d, end_bit %d\n",
-        (BACKEND == CUB_NO_OVERWRITE) ? "CUB_NO_OVERWRITE" : (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        (KEYS_ONLY) ? "keys-only" : "key-value",
-        num_items, num_segments,
-        (int) sizeof(KeyT), typeid(KeyT).name(), (KEYS_ONLY) ? 0 : (int) sizeof(ValueT), typeid(ValueT).name(),
-        IS_DESCENDING, begin_bit, end_bit);
-    fflush(stdout);
-
-    if (g_verbose)
-    {
-        printf("Input keys:\n");
-        DisplayResults(h_keys, num_items);
-        printf("\n\n");
-    }
-
-    // Allocate device arrays
-    DoubleBuffer<KeyT>   d_keys;
-    DoubleBuffer<ValueT> d_values;
-    int                 *d_selector;
-    int                 *d_segment_offsets;
-    size_t              *d_temp_storage_bytes;
-    cudaError_t         *d_cdp_error;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys.d_buffers[0], sizeof(KeyT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys.d_buffers[1], sizeof(KeyT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_selector, sizeof(int) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(int) * (num_segments + 1)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes, sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error, sizeof(cudaError_t) * 1));
-    if (!KEYS_ONLY)
-    {
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values.d_buffers[0], sizeof(ValueT) * num_items));
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values.d_buffers[1], sizeof(ValueT) * num_items));
-    }
-
-    // Allocate temporary storage (and make it un-aligned)
-    size_t  temp_storage_bytes  = 0;
-    void    *d_temp_storage     = NULL;
-    CubDebugExit(Dispatch(
-        Int2Type<IS_DESCENDING>(), Int2Type<BACKEND>(), d_selector, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_keys, d_values,
-        num_items, num_segments, d_segment_offsets,
-        begin_bit, end_bit, 0, true));
-
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes + 1));
-    void* mis_aligned_temp = static_cast<char*>(d_temp_storage) + 1;
-
-    // Initialize/clear device arrays
-    d_keys.selector = 0;
-    CubDebugExit(cudaMemcpy(d_keys.d_buffers[0], h_keys, sizeof(KeyT) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_keys.d_buffers[1], 0, sizeof(KeyT) * num_items));
-    if (!KEYS_ONLY)
-    {
-        d_values.selector = 0;
-        CubDebugExit(cudaMemcpy(d_values.d_buffers[0], h_values, sizeof(ValueT) * num_items, cudaMemcpyHostToDevice));
-        CubDebugExit(cudaMemset(d_values.d_buffers[1], 0, sizeof(ValueT) * num_items));
-    }
-    CubDebugExit(cudaMemcpy(d_segment_offsets, h_segment_offsets, sizeof(int) * (num_segments + 1), cudaMemcpyHostToDevice));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(
-        Int2Type<IS_DESCENDING>(), Int2Type<BACKEND>(), d_selector, d_temp_storage_bytes, d_cdp_error,
-        mis_aligned_temp, temp_storage_bytes, d_keys, d_values,
-        num_items, num_segments, d_segment_offsets,
-        begin_bit, end_bit, 0, true));
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Check for correctness (and display results, if specified)
-    printf("Warmup done.  Checking results:\n"); fflush(stdout);
-    int compare = CompareDeviceResults(h_reference_keys, d_keys.Current(), num_items, true, g_verbose);
-    printf("\t Compare keys (selector %d): %s ", d_keys.selector, compare ? "FAIL" : "PASS"); fflush(stdout);
-    if (!KEYS_ONLY)
-    {
-        int values_compare = CompareDeviceResults(h_reference_values, d_values.Current(), num_items, true, g_verbose);
-        compare |= values_compare;
-        printf("\t Compare values (selector %d): %s ", d_values.selector, values_compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-    if (BACKEND == CUB_NO_OVERWRITE)
-    {
-        // Check that input isn't overwritten
-        int input_compare = CompareDeviceResults(h_keys, d_keys.d_buffers[0], num_items, true, g_verbose);
-        compare |= input_compare;
-        printf("\t Compare input keys: %s ", input_compare ? "FAIL" : "PASS"); fflush(stdout);
-    }
-
-    // Performance
-    if (g_timing_iterations)
-        printf("\nPerforming timing iterations:\n"); fflush(stdout);
-
-    GpuTimer gpu_timer;
-    float elapsed_millis = 0.0f;
-    for (int i = 0; i < g_timing_iterations; ++i)
-    {
-        // Initialize/clear device arrays
-        CubDebugExit(cudaMemcpy(d_keys.d_buffers[d_keys.selector], h_keys, sizeof(KeyT) * num_items, cudaMemcpyHostToDevice));
-        CubDebugExit(cudaMemset(d_keys.d_buffers[d_keys.selector ^ 1], 0, sizeof(KeyT) * num_items));
-        if (!KEYS_ONLY)
-        {
-            CubDebugExit(cudaMemcpy(d_values.d_buffers[d_values.selector], h_values, sizeof(ValueT) * num_items, cudaMemcpyHostToDevice));
-            CubDebugExit(cudaMemset(d_values.d_buffers[d_values.selector ^ 1], 0, sizeof(ValueT) * num_items));
-        }
-
-        gpu_timer.Start();
-        CubDebugExit(Dispatch(
-            Int2Type<IS_DESCENDING>(), Int2Type<BACKEND>(), d_selector, d_temp_storage_bytes, d_cdp_error,
-            mis_aligned_temp, temp_storage_bytes, d_keys, d_values,
-            num_items, num_segments, d_segment_offsets,
-            begin_bit, end_bit, 0, false));
-        gpu_timer.Stop();
-        elapsed_millis += gpu_timer.ElapsedMillis();
-    }
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        float giga_bandwidth = (KEYS_ONLY) ?
-            giga_rate * sizeof(KeyT) * 2 :
-            giga_rate * (sizeof(KeyT) + sizeof(ValueT)) * 2;
-        printf("\n%.3f elapsed ms, %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", elapsed_millis, avg_millis, giga_rate, giga_bandwidth);
-    }
-
-    printf("\n\n");
-
-    // Cleanup
-    if (d_keys.d_buffers[0]) CubDebugExit(g_allocator.DeviceFree(d_keys.d_buffers[0]));
-    if (d_keys.d_buffers[1]) CubDebugExit(g_allocator.DeviceFree(d_keys.d_buffers[1]));
-    if (d_values.d_buffers[0]) CubDebugExit(g_allocator.DeviceFree(d_values.d_buffers[0]));
-    if (d_values.d_buffers[1]) CubDebugExit(g_allocator.DeviceFree(d_values.d_buffers[1]));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_selector) CubDebugExit(g_allocator.DeviceFree(d_selector));
-    if (d_segment_offsets) CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-
-    // Correctness asserts
-    AssertEquals(0, compare);
-}
-
-
-/**
- * Test backend
- */
-template <bool IS_DESCENDING, typename KeyT, typename ValueT>
-void TestBackend(
-    KeyT    *h_keys,
-    int     num_items,
-    int     num_segments,
-    int     *h_segment_offsets,
-    int     begin_bit,
-    int     end_bit,
-    KeyT    *h_reference_keys,
-    int     *h_reference_ranks)
-{
-    const bool KEYS_ONLY = Equals<ValueT, NullType>::VALUE;
-
-    ValueT *h_values             = NULL;
-    ValueT *h_reference_values   = NULL;
-
-    if (!KEYS_ONLY)
-    {
-        h_values            = new ValueT[num_items];
-        h_reference_values  = new ValueT[num_items];
-
-        for (int i = 0; i < num_items; ++i)
-        {
-            InitValue(INTEGER_SEED, h_values[i], i);
-            InitValue(INTEGER_SEED, h_reference_values[i], h_reference_ranks[i]);
-        }
-    }
-
-    if (num_segments == 1)
-    {
-        // Test single-segment implementations
-        Test<CUB, IS_DESCENDING>(               h_keys, h_values, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_values);
-        Test<CUB_NO_OVERWRITE, IS_DESCENDING>(  h_keys, h_values, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_values);
-#ifdef CUB_CDP
-        Test<CDP, IS_DESCENDING>(               h_keys, h_values, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_values);
-#endif
-    }
-
-    // Test multi-segment implementations
-    Test<CUB_SEGMENTED, IS_DESCENDING>(               h_keys, h_values, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_values);
-    Test<CUB_SEGMENTED_NO_OVERWRITE, IS_DESCENDING>(  h_keys, h_values, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_values);
-
-    if (h_values) delete[] h_values;
-    if (h_reference_values) delete[] h_reference_values;
-}
-
-
-
-
-/**
- * Test value type
- */
-template <bool IS_DESCENDING, typename KeyT>
-void TestValueTypes(
-    KeyT    *h_keys,
-    int     num_items,
-    int     num_segments,
-    int     *h_segment_offsets,
-    int     begin_bit,
-    int     end_bit)
-{
-    // Initialize the solution
-
-    int *h_reference_ranks = NULL;
-    KeyT *h_reference_keys = NULL;
-    InitializeSolution<IS_DESCENDING>(h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_ranks, h_reference_keys);
-
-    // Test value types
-
-    TestBackend<IS_DESCENDING, KeyT, NullType>              (h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_ranks);
-    TestBackend<IS_DESCENDING, KeyT, KeyT>                  (h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_ranks);
-
-    if (!Equals<KeyT, unsigned int>::VALUE)
-        TestBackend<IS_DESCENDING, KeyT, unsigned int>      (h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_ranks);
-
-    if (!Equals<KeyT, unsigned long long>::VALUE)
-        TestBackend<IS_DESCENDING, KeyT, unsigned long long>(h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_ranks);
-
-    TestBackend<IS_DESCENDING, KeyT, TestFoo>               (h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit, h_reference_keys, h_reference_ranks);
-
-    // Cleanup
-    if (h_reference_ranks) delete[] h_reference_ranks;
-    if (h_reference_keys) delete[] h_reference_keys;
-}
-
-
-
-/**
- * Test ascending/descending
- */
-template <typename KeyT>
-void TestDirection(
-    KeyT    *h_keys,
-    int     num_items,
-    int     num_segments,
-    int     *h_segment_offsets,
-    int     begin_bit,
-    int     end_bit)
-{
-    TestValueTypes<true>(h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit);
-    TestValueTypes<false>(h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit);
-}
-
-
-/**
- * Test different bit ranges
- */
-template <typename KeyT>
-void TestBits(
-    KeyT    *h_keys,
-    int     num_items,
-    int     num_segments,
-    int     *h_segment_offsets)
-{
-    // Don't test partial-word sorting for boolean, fp, or signed types (the bit-flipping techniques get in the way)
-    if ((Traits<KeyT>::CATEGORY == UNSIGNED_INTEGER) && (!Equals<KeyT, bool>::VALUE))
-    {
-        // Partial bits
-        int begin_bit = 1;
-        int end_bit = (sizeof(KeyT) * 8) - 1;
-        printf("Testing key bits [%d,%d)\n", begin_bit, end_bit); fflush(stdout);
-        TestDirection(h_keys, num_items, num_segments, h_segment_offsets, begin_bit, end_bit);
-
-        // Across subword boundaries
-        int mid_bit = sizeof(KeyT) * 4;
-        printf("Testing key bits [%d,%d)\n", mid_bit - 1, mid_bit + 1); fflush(stdout);
-        TestDirection(h_keys, num_items, num_segments, h_segment_offsets, mid_bit - 1, mid_bit + 1);
-    }
-
-    printf("Testing key bits [%d,%d)\n", 0, int(sizeof(KeyT)) * 8); fflush(stdout);
-    TestDirection(h_keys, num_items, num_segments, h_segment_offsets, 0, sizeof(KeyT) * 8);
-}
-
-
-/**
- * Test different segment compositions
- */
-template <typename KeyT>
-void TestSegments(
-    KeyT    *h_keys,
-    int     num_items,
-    int     max_segments)
-{
-    int *h_segment_offsets = new int[max_segments + 1];
-
-    for (int num_segments = max_segments; num_segments > 1; num_segments = (num_segments + 32 - 1) / 32)
-    {
-        if (num_items / num_segments < 128 * 1000) {
-            // Right now we assign a single thread block to each segment, so lets keep it to under 128K items per segment
-            InitializeSegments(num_items, num_segments, h_segment_offsets);
-            TestBits(h_keys, num_items, num_segments, h_segment_offsets);
-        }
-    }
-
-    // Test single segment
-    if (num_items < 128 * 1000) {
-        // Right now we assign a single thread block to each segment, so lets keep it to under 128K items per segment
-        InitializeSegments(num_items, 1, h_segment_offsets);
-        TestBits(h_keys, num_items, 1, h_segment_offsets);
-    }
-
-    if (h_segment_offsets) delete[] h_segment_offsets;
-}
-
-
-/**
- * Test different (sub)lengths and number of segments
- */
-template <typename KeyT>
-void TestSizes(
-    KeyT    *h_keys,
-    int     max_items,
-    int     max_segments)
-{
-    for (int num_items = max_items; num_items > 1; num_items = (num_items + 32 - 1) / 32)
-    {
-        TestSegments(h_keys, num_items, max_segments);
-    }
-    TestSegments(h_keys, 1, max_segments);
-    TestSegments(h_keys, 0, max_segments);
-}
-
-
-/**
- * Test key sampling distributions
- */
-template <typename KeyT>
-void TestGen(
-    int             max_items,
-    int             max_segments)
-{
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-    if (max_items < 0)
-        max_items = (ptx_version > 100) ? 9000003 : max_items = 5000003;
-
-    if (max_segments < 0)
-        max_segments = 5003;
-
-    KeyT *h_keys = new KeyT[max_items];
-
-    for (int entropy_reduction = 0; entropy_reduction <= 6; entropy_reduction += 3)
-    {
-        printf("\nTesting random %s keys with entropy reduction factor %d\n", typeid(KeyT).name(), entropy_reduction); fflush(stdout);
-        InitializeKeyBits(RANDOM, h_keys, max_items, entropy_reduction);
-        TestSizes(h_keys, max_items, max_segments);
-    }
-
-    printf("\nTesting uniform %s keys\n", typeid(KeyT).name()); fflush(stdout);
-    InitializeKeyBits(UNIFORM, h_keys, max_items, 0);
-    TestSizes(h_keys, max_items, max_segments);
-
-    printf("\nTesting natural number %s keys\n", typeid(KeyT).name()); fflush(stdout);
-    InitializeKeyBits(INTEGER_SEED, h_keys, max_items, 0);
-    TestSizes(h_keys, max_items, max_segments);
-
-    if (h_keys) delete[] h_keys;
-}
-
-
-//---------------------------------------------------------------------
-// Simple test
-//---------------------------------------------------------------------
-
-template <
-    Backend     BACKEND,
-    typename    KeyT,
-    typename    ValueT,
-    bool        IS_DESCENDING>
-void Test(
-    int         num_items,
-    int         num_segments,
-    GenMode     gen_mode,
-    int         entropy_reduction,
-    int         begin_bit,
-    int         end_bit)
-{
-    const bool KEYS_ONLY = Equals<ValueT, NullType>::VALUE;
-
-    KeyT    *h_keys             = new KeyT[num_items];
-    int     *h_reference_ranks  = NULL;
-    KeyT    *h_reference_keys   = NULL;
-    ValueT  *h_values           = NULL;
-    ValueT  *h_reference_values = NULL;
-    int     *h_segment_offsets  = new int[num_segments + 1];
-
-    if (end_bit < 0)
-        end_bit = sizeof(KeyT) * 8;
-
-    InitializeKeyBits(gen_mode, h_keys, num_items, entropy_reduction);
-    InitializeSegments(num_items, num_segments, h_segment_offsets);
-    InitializeSolution<IS_DESCENDING>(
-        h_keys, num_items, num_segments, h_segment_offsets,
-        begin_bit, end_bit, h_reference_ranks, h_reference_keys);
-
-    if (!KEYS_ONLY)
-    {
-        h_values            = new ValueT[num_items];
-        h_reference_values  = new ValueT[num_items];
-
-        for (int i = 0; i < num_items; ++i)
-        {
-            InitValue(INTEGER_SEED, h_values[i], i);
-            InitValue(INTEGER_SEED, h_reference_values[i], h_reference_ranks[i]);
-        }
-    }
-    if (h_reference_ranks) delete[] h_reference_ranks;
-
-    printf("\nTesting bits [%d,%d) of %s keys with gen-mode %d\n", begin_bit, end_bit, typeid(KeyT).name(), gen_mode); fflush(stdout);
-    Test<BACKEND, IS_DESCENDING>(
-        h_keys, h_values,
-        num_items, num_segments, h_segment_offsets,
-        begin_bit, end_bit, h_reference_keys, h_reference_values);
-
-    if (h_keys)             delete[] h_keys;
-    if (h_reference_keys)   delete[] h_reference_keys;
-    if (h_values)           delete[] h_values;
-    if (h_reference_values) delete[] h_reference_values;
-    if (h_segment_offsets)  delete[] h_segment_offsets;
-}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int bits = -1;
-    int num_items = -1;
-    int num_segments = -1;
-    int entropy_reduction = 0;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("s", num_segments);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-    args.GetCmdLineArgument("bits", bits);
-    args.GetCmdLineArgument("entropy", entropy_reduction);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--bits=<valid key bits>]"
-            "[--n=<input items> "
-            "[--s=<num segments> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "[--entropy=<entropy-reduction factor (default 0)>]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-#ifdef QUICKER_TEST
-
-    enum {
-        IS_DESCENDING   = false
-    };
-
-    // Compile/run basic CUB test
-    if (num_items < 0)      num_items       = 48000000;
-    if (num_segments < 0)   num_segments    = 5000;
-
-
-    Test<CUB_SEGMENTED, unsigned int,       NullType, IS_DESCENDING>(       num_items, num_segments,    RANDOM, entropy_reduction, 0, bits);
-
-    Test<CUB,           unsigned int,       NullType, IS_DESCENDING>(       num_items, 1,               RANDOM, entropy_reduction, 0, bits);
-    Test<CUB,           unsigned long long, NullType, IS_DESCENDING>(       num_items, 1,               RANDOM, entropy_reduction, 0, bits);
-
-    Test<CUB,           unsigned int,       unsigned int, IS_DESCENDING>(   num_items, 1,               RANDOM, entropy_reduction, 0, bits);
-    Test<CUB,           unsigned long long, unsigned int, IS_DESCENDING>(   num_items, 1,               RANDOM, entropy_reduction, 0, bits);
-
-#elif defined(QUICK_TEST)
-
-    // Compile/run quick tests
-    if (num_items < 0)      num_items       = 48000000;
-    if (num_segments < 0)   num_segments    = 5000;
-
-    // Compare CUB and thrust on 32b keys-only
-    Test<CUB, unsigned int, NullType, false> (                      num_items, 1, RANDOM, entropy_reduction, 0, bits);
-    Test<THRUST, unsigned int, NullType, false> (                   num_items, 1, RANDOM, entropy_reduction, 0, bits);
-
-    // Compare CUB and thrust on 64b keys-only
-    Test<CUB, unsigned long long, NullType, false> (                num_items, 1, RANDOM, entropy_reduction, 0, bits);
-    Test<THRUST, unsigned long long, NullType, false> (             num_items, 1, RANDOM, entropy_reduction, 0, bits);
-
-
-    // Compare CUB and thrust on 32b key-value pairs
-    Test<CUB, unsigned int, unsigned int, false> (                  num_items, 1, RANDOM, entropy_reduction, 0, bits);
-    Test<THRUST, unsigned int, unsigned int, false> (               num_items, 1, RANDOM, entropy_reduction, 0, bits);
-
-    // Compare CUB and thrust on 64b key-value pairs
-    Test<CUB, unsigned long long, unsigned long long, false> (      num_items, 1, RANDOM, entropy_reduction, 0, bits);
-    Test<THRUST, unsigned long long, unsigned long long, false> (   num_items, 1, RANDOM, entropy_reduction, 0, bits);
-
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        TestGen<bool>                 (num_items, num_segments);
-
-        TestGen<char>                 (num_items, num_segments);
-        TestGen<signed char>          (num_items, num_segments);
-        TestGen<unsigned char>        (num_items, num_segments);
-
-        TestGen<short>                (num_items, num_segments);
-        TestGen<unsigned short>       (num_items, num_segments);
-
-        TestGen<int>                  (num_items, num_segments);
-        TestGen<unsigned int>         (num_items, num_segments);
-
-        TestGen<long>                 (num_items, num_segments);
-        TestGen<unsigned long>        (num_items, num_segments);
-
-        TestGen<long long>            (num_items, num_segments);
-        TestGen<unsigned long long>   (num_items, num_segments);
-
-        TestGen<float>                (num_items, num_segments);
-
-        if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-            TestGen<double>           (num_items, num_segments);
-
-    }
-
-#endif
-
-    return 0;
-}
-
diff --git a/ml-xgboost/cub/test/test_device_reduce.cu b/ml-xgboost/cub/test/test_device_reduce.cu
deleted file mode 100644
index c4551a7..0000000
--- a/ml-xgboost/cub/test/test_device_reduce.cu
+++ /dev/null
@@ -1,1307 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceReduce utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <limits>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_reduce.cuh>
-#include <cub/device/device_segmented_reduce.cuh>
-#include <cub/iterator/constant_input_iterator.cuh>
-#include <cub/iterator/discard_output_iterator.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/reduce.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-int                     g_ptx_version;
-int                     g_sm_count;
-bool                    g_verbose           = false;
-bool                    g_verbose_input     = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-// Dispatch types
-enum Backend
-{
-    CUB,            // CUB method
-    CUB_SEGMENTED,  // CUB segmented method
-    CUB_CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-    THRUST,         // Thrust method
-};
-
-
-// Custom max functor
-struct CustomMax
-{
-    /// Boolean max operator, returns <tt>(a > b) ? a : b</tt>
-    template <typename OutputT>
-    __host__ __device__ __forceinline__ OutputT operator()(const OutputT &a, const OutputT &b)
-    {
-        return CUB_MAX(a, b);
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB DeviceReduce entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to reduce entrypoint (custom-max)
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename ReductionOpT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    ReductionOpT         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // Max-identity
-    OutputT identity = Traits<InputT>::Lowest(); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
-
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::Reduce(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, num_items, reduction_op, identity,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to sum entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::Sum            reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::Sum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to min entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::Min            reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::Min(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to max entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::Max            reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to argmin entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::ArgMin         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::ArgMin(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to argmax entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::ArgMax         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::ArgMax(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB DeviceSegmentedReduce entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to reduce entrypoint (custom-max)
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename ReductionOpT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_SEGMENTED>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    ReductionOpT        reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    // Max-identity
-    OutputT identity = Traits<InputT>::Lowest(); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
-
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSegmentedReduce::Reduce(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1, reduction_op, identity,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to sum entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_SEGMENTED>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::Sum            reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSegmentedReduce::Sum(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to min entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_SEGMENTED>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::Min            reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSegmentedReduce::Min(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to max entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_SEGMENTED>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::Max            reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSegmentedReduce::Max(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to argmin entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_SEGMENTED>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::ArgMin         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSegmentedReduce::ArgMin(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-/**
- * Dispatch to argmax entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_SEGMENTED>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    cub::ArgMax         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to device reduction directly
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSegmentedReduce::ArgMax(d_temp_storage, temp_storage_bytes,
-            d_in, d_out, max_segments, d_segment_offsets, d_segment_offsets + 1,
-            stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to reduction entrypoint (min or max specialization)
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename ReductionOpT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>    dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    ReductionOpT         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        OutputT init;
-        CubDebugExit(cudaMemcpy(&init, d_in + 0, sizeof(OutputT), cudaMemcpyDeviceToHost));
-
-        thrust::device_ptr<OutputT> d_in_wrapper(d_in);
-        OutputT retval;
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            retval = thrust::reduce(d_in_wrapper, d_in_wrapper + num_items, init, reduction_op);
-        }
-
-        if (!Equals<OutputIteratorT, DiscardOutputIterator<int> >::VALUE)
-            CubDebugExit(cudaMemcpy(d_out, &retval, sizeof(OutputT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-/**
- * Dispatch to reduction entrypoint (sum specialization)
- */
-template <typename InputIteratorT, typename OutputIteratorT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>    dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    Sum                 reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<OutputT> d_in_wrapper(d_in);
-        OutputT retval;
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            retval = thrust::reduce(d_in_wrapper, d_in_wrapper + num_items);
-        }
-
-        if (!Equals<OutputIteratorT, DiscardOutputIterator<int> >::VALUE)
-            CubDebugExit(cudaMemcpy(d_out, &retval, sizeof(OutputT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-
-//---------------------------------------------------------------------
-// CUDA nested-parallelism test kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceReduce
- */
-template <
-    typename            InputIteratorT,
-    typename            OutputIteratorT,
-    typename            ReductionOpT>
-__global__ void CnpDispatchKernel(
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t              temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    ReductionOpT         reduction_op,
-    bool                debug_synchronous)
-{
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch(Int2Type<CUB>(), timing_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
-        d_in, d_out, num_items, max_segments, d_segment_offsets, reduction_op, 0, debug_synchronous);
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/**
- * Dispatch to CUB_CDP kernel
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename ReductionOpT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB_CDP>       dispatch_to,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    int                 num_items,
-    int                 max_segments,
-    int                 *d_segment_offsets,
-    ReductionOpT         reduction_op,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(timing_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
-        d_in, d_out, num_items, max_segments, d_segment_offsets, reduction_op, debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Problem generation
-//---------------------------------------------------------------------
-
-/// Initialize problem
-template <typename InputT>
-void Initialize(
-    GenMode         gen_mode,
-    InputT          *h_in,
-    int             num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-    }
-
-    if (g_verbose_input)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/// Solve problem (max/custom-max functor)
-template <typename ReductionOpT, typename InputT, typename _OutputT>
-struct Solution
-{
-    typedef _OutputT OutputT;
-
-    template <typename HostInputIteratorT>
-    static void Solve(HostInputIteratorT h_in, OutputT *h_reference, int num_segments, int *h_segment_offsets,
-        ReductionOpT reduction_op)
-    {
-        for (int i = 0; i < num_segments; ++i)
-        {
-            OutputT aggregate = Traits<InputT>::Lowest(); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
-            for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
-                aggregate = reduction_op(aggregate, OutputT(h_in[j]));
-            h_reference[i] = aggregate;
-        }
-    }
-};
-
-/// Solve problem (min functor)
-template <typename InputT, typename _OutputT>
-struct Solution<cub::Min, InputT, _OutputT>
-{
-    typedef _OutputT OutputT;
-
-    template <typename HostInputIteratorT>
-    static void Solve(HostInputIteratorT h_in, OutputT *h_reference, int num_segments, int *h_segment_offsets,
-        cub::Min reduction_op)
-    {
-        for (int i = 0; i < num_segments; ++i)
-        {
-            OutputT aggregate = Traits<InputT>::Max();    // replace with std::numeric_limits<OutputT>::max() when C++ support is more prevalent
-            for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
-                aggregate = reduction_op(aggregate, OutputT(h_in[j]));
-            h_reference[i] = aggregate;
-        }
-    }
-};
-
-
-/// Solve problem (sum functor)
-template <typename InputT, typename _OutputT>
-struct Solution<cub::Sum, InputT, _OutputT>
-{
-    typedef _OutputT OutputT;
-
-    template <typename HostInputIteratorT>
-    static void Solve(HostInputIteratorT h_in, OutputT *h_reference, int num_segments, int *h_segment_offsets,
-        cub::Sum reduction_op)
-    {
-        for (int i = 0; i < num_segments; ++i)
-        {
-            OutputT aggregate;
-            InitValue(INTEGER_SEED, aggregate, 0);
-            for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
-                aggregate = reduction_op(aggregate, OutputT(h_in[j]));
-            h_reference[i] = aggregate;
-        }
-    }
-};
-
-/// Solve problem (argmin functor)
-template <typename InputValueT, typename OutputValueT>
-struct Solution<cub::ArgMin, InputValueT, OutputValueT>
-{
-    typedef KeyValuePair<int, OutputValueT> OutputT;
-
-    template <typename HostInputIteratorT>
-    static void Solve(HostInputIteratorT h_in, OutputT *h_reference, int num_segments, int *h_segment_offsets,
-        cub::ArgMin reduction_op)
-    {
-        for (int i = 0; i < num_segments; ++i)
-        {
-            OutputT aggregate(1, Traits<InputValueT>::Max()); // replace with std::numeric_limits<OutputT>::max() when C++ support is more prevalent
-            for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
-            {
-                OutputT item(j - h_segment_offsets[i], OutputValueT(h_in[j]));
-                aggregate = reduction_op(aggregate, item);
-            }
-            h_reference[i] = aggregate;
-        }
-    }
-};
-
-
-/// Solve problem (argmax functor)
-template <typename InputValueT, typename OutputValueT>
-struct Solution<cub::ArgMax, InputValueT, OutputValueT>
-{
-    typedef KeyValuePair<int, OutputValueT> OutputT;
-
-    template <typename HostInputIteratorT>
-    static void Solve(HostInputIteratorT h_in, OutputT *h_reference, int num_segments, int *h_segment_offsets,
-        cub::ArgMax reduction_op)
-    {
-        for (int i = 0; i < num_segments; ++i)
-        {
-            OutputT aggregate(1, Traits<InputValueT>::Lowest()); // replace with std::numeric_limits<OutputT>::lowest() when C++ support is more prevalent
-            for (int j = h_segment_offsets[i]; j < h_segment_offsets[i + 1]; ++j)
-            {
-                OutputT item(j - h_segment_offsets[i], OutputValueT(h_in[j]));
-                aggregate = reduction_op(aggregate, item);
-            }
-            h_reference[i] = aggregate;
-        }
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Problem generation
-//---------------------------------------------------------------------
-
-/// Test DeviceReduce for a given problem input
-template <
-    typename                BackendT,
-    typename                DeviceInputIteratorT,
-    typename                HostReferenceIteratorT,
-    typename                ReductionOpT>
-void Test(
-    BackendT                backend,
-    DeviceInputIteratorT    d_in,
-    int                     num_items,
-    int                     num_segments,
-    int                     *d_segment_offsets,
-    ReductionOpT            reduction_op,
-    HostReferenceIteratorT  h_reference)
-{
-    // Input and output data types
-    typedef typename std::iterator_traits<DeviceInputIteratorT>::value_type     InputT;
-    typedef typename std::iterator_traits<HostReferenceIteratorT>::value_type   OutputT;
-
-    // Allocate CUB_CDP device arrays for temp storage size and error
-    OutputT         *d_out = NULL;
-    size_t          *d_temp_storage_bytes = NULL;
-    cudaError_t     *d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out,                 sizeof(OutputT) * num_segments));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Inquire temp device storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(Dispatch(backend, 1,
-        d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
-        d_in, d_out, num_items, num_segments, d_segment_offsets,
-        reduction_op, 0, true));
-
-    // Allocate temp device storage
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Clear device output
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(OutputT) * num_segments));
-
-    // Run once with discard iterator
-    DiscardOutputIterator<int> discard_itr;
-    CubDebugExit(Dispatch(backend, 1,
-        d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
-        d_in, discard_itr, num_items, num_segments, d_segment_offsets,
-        reduction_op, 0, true));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(backend, 1,
-        d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
-        d_in, d_out, num_items, num_segments, d_segment_offsets,
-        reduction_op, 0, true));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_segments, g_verbose, g_verbose);
-    printf("\t%s", compare ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    if (g_timing_iterations > 0)
-    {
-        GpuTimer gpu_timer;
-        gpu_timer.Start();
-
-        CubDebugExit(Dispatch(backend, g_timing_iterations,
-            d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes,
-            d_in, d_out, num_items, num_segments, d_segment_offsets,
-            reduction_op, 0, false));
-
-        gpu_timer.Stop();
-        float elapsed_millis = gpu_timer.ElapsedMillis();
-
-        // Display performance
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        float giga_bandwidth = giga_rate * sizeof(InputT);
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", avg_millis, giga_rate, giga_bandwidth);
-    }
-
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, compare);
-}
-
-
-/// Test DeviceReduce
-template <
-    Backend                 BACKEND,
-    typename                OutputValueT,
-    typename                HostInputIteratorT,
-    typename                DeviceInputIteratorT,
-    typename                ReductionOpT>
-void SolveAndTest(
-    HostInputIteratorT      h_in,
-    DeviceInputIteratorT    d_in,
-    int                     num_items,
-    int                     num_segments,
-    int                     *h_segment_offsets,
-    int                     *d_segment_offsets,
-    ReductionOpT            reduction_op)
-{
-    typedef typename std::iterator_traits<DeviceInputIteratorT>::value_type     InputValueT;
-    typedef Solution<ReductionOpT, InputValueT, OutputValueT>                   SolutionT;
-    typedef typename SolutionT::OutputT                                         OutputT;
-
-    printf("\n\n%s cub::DeviceReduce<%s> %d items (%s), %d segments\n",
-        (BACKEND == CUB_CDP) ? "CUB_CDP" : (BACKEND == THRUST) ? "Thrust" : (BACKEND == CUB_SEGMENTED) ? "CUB_SEGMENTED" : "CUB",
-        typeid(ReductionOpT).name(), num_items, typeid(HostInputIteratorT).name(), num_segments);
-    fflush(stdout);
-
-    // Allocate and solve solution
-    OutputT *h_reference = new OutputT[num_segments];
-    SolutionT::Solve(h_in, h_reference, num_segments, h_segment_offsets, reduction_op);
-
-    // Run test
-    Test(Int2Type<BACKEND>(), d_in, num_items, num_segments, d_segment_offsets, reduction_op, h_reference);
-
-    // Cleanup
-    if (h_reference) delete[] h_reference;
-}
-
-
-/// Test specific problem type
-template <
-    Backend         BACKEND,
-    typename        InputT,
-    typename        OutputT,
-    typename        ReductionOpT>
-void TestProblem(
-    int             num_items,
-    int             num_segments,
-    GenMode         gen_mode,
-    ReductionOpT    reduction_op)
-{
-    printf("\n\nInitializing %d %s->%s (gen mode %d)... ", num_items, typeid(InputT).name(), typeid(OutputT).name(), gen_mode); fflush(stdout);
-    fflush(stdout);
-
-    // Initialize value data
-    InputT* h_in = new InputT[num_items];
-    Initialize(gen_mode, h_in, num_items);
-
-    // Initialize segment data
-    int *h_segment_offsets = new int[num_segments + 1];
-    InitializeSegments(num_items, num_segments, h_segment_offsets, g_verbose_input);
-
-    // Initialize device data
-    int     *d_segment_offsets      = NULL;
-    InputT  *d_in                   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in,              sizeof(InputT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(int) * (num_segments + 1)));
-    CubDebugExit(cudaMemcpy(d_in,               h_in,                   sizeof(InputT) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_segment_offsets,  h_segment_offsets,      sizeof(int) * (num_segments + 1), cudaMemcpyHostToDevice));
-
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, reduction_op);
-
-    if (h_segment_offsets)  delete[] h_segment_offsets;
-    if (d_segment_offsets)  CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
-    if (h_in)               delete[] h_in;
-    if (d_in)               CubDebugExit(g_allocator.DeviceFree(d_in));
-}
-
-
-/// Test different operators
-template <
-    Backend             BACKEND,
-    typename            OutputT,
-    typename            HostInputIteratorT,
-    typename            DeviceInputIteratorT>
-void TestByOp(
-    HostInputIteratorT      h_in,
-    DeviceInputIteratorT    d_in,
-    int                     num_items,
-    int                     num_segments,
-    int                     *h_segment_offsets,
-    int                     *d_segment_offsets)
-{
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, CustomMax());
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, Sum());
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, Min());
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, ArgMin());
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, Max());
-    SolveAndTest<BACKEND, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets, ArgMax());
-}
-
-
-/// Test different backends
-template <
-    typename InputT,
-    typename OutputT>
-void TestByBackend(
-    int             num_items,
-    int             max_segments,
-    GenMode         gen_mode)
-{
-    // Initialize host data
-    printf("\n\nInitializing %d %s -> %s (gen mode %d)... ",
-        num_items, typeid(InputT).name(), typeid(OutputT).name(), gen_mode); fflush(stdout);
-
-    InputT  *h_in               = new InputT[num_items];
-    int     *h_segment_offsets  = new int[max_segments + 1];
-    Initialize(gen_mode, h_in, num_items);
-
-    // Initialize device data
-    InputT  *d_in               = NULL;
-    int     *d_segment_offsets  = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(InputT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(int) * (max_segments + 1)));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(InputT) * num_items, cudaMemcpyHostToDevice));
-
-    //
-    // Test single-segment implementations
-    //
-
-    InitializeSegments(num_items, 1, h_segment_offsets, g_verbose_input);
-
-    // Page-aligned-input tests
-    TestByOp<CUB, OutputT>(h_in, d_in, num_items, 1, h_segment_offsets, NULL);                 // Host-dispatch
-#ifdef CUB_CDP
-    TestByOp<CUB_CDP, OutputT>(h_in, d_in, num_items, 1, h_segment_offsets, NULL);             // Device-dispatch
-#endif
-
-    // Non-page-aligned-input tests
-    if (num_items > 1)
-    {
-        InitializeSegments(num_items - 1, 1, h_segment_offsets, g_verbose_input);
-        TestByOp<CUB, OutputT>(h_in + 1, d_in + 1, num_items - 1, 1, h_segment_offsets, NULL);
-    }
-
-    //
-    // Test segmented implementation
-    //
-
-    // Right now we assign a single thread block to each segment, so lets keep it to under 128K items per segment
-    int max_items_per_segment = 128000;
-
-    for (int num_segments = (num_items + max_items_per_segment - 1) / max_items_per_segment;
-        num_segments < max_segments;
-        num_segments = (num_segments * 32) + 1)
-    {
-        InitializeSegments(num_items, num_segments, h_segment_offsets, g_verbose_input);
-        CubDebugExit(cudaMemcpy(d_segment_offsets, h_segment_offsets, sizeof(int) * (num_segments + 1), cudaMemcpyHostToDevice));
-        TestByOp<CUB_SEGMENTED, OutputT>(h_in, d_in, num_items, num_segments, h_segment_offsets, d_segment_offsets);
-    }
-
-    if (h_in)               delete[] h_in;
-    if (h_segment_offsets)  delete[] h_segment_offsets;
-    if (d_in)               CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_segment_offsets)  CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
-}
-
-
-/// Test different input-generation modes
-template <
-    typename InputT,
-    typename OutputT>
-void TestByGenMode(
-    int             num_items,
-    int             max_segments)
-{
-    //
-    // Test pointer support using different input-generation modes
-    //
-
-    TestByBackend<InputT, OutputT>(num_items, max_segments, UNIFORM);
-    TestByBackend<InputT, OutputT>(num_items, max_segments, INTEGER_SEED);
-    TestByBackend<InputT, OutputT>(num_items, max_segments, RANDOM);
-
-    //
-    // Test iterator support using a constant-iterator and SUM
-    //
-
-    InputT val;
-    InitValue(UNIFORM, val, 0);
-    ConstantInputIterator<InputT, int> h_in(val);
-
-    int *h_segment_offsets = new int[1 + 1];
-    InitializeSegments(num_items, 1, h_segment_offsets, g_verbose_input);
-
-    SolveAndTest<CUB, OutputT>(h_in, h_in, num_items, 1, h_segment_offsets, NULL, Sum());
-#ifdef CUB_CDP
-    SolveAndTest<CUB_CDP, OutputT>(h_in, h_in, num_items, 1, h_segment_offsets, NULL, Sum());
-#endif
-
-    if (h_segment_offsets) delete[] h_segment_offsets;
-}
-
-
-/// Test different problem sizes
-template <
-    typename InputT,
-    typename OutputT>
-struct TestBySize
-{
-    int             max_items;
-    int             max_segments;
-
-    TestBySize(int max_items, int max_segments) :
-        max_items(max_items),
-        max_segments(max_segments)
-    {}
-
-    template <typename ActivePolicyT>
-    cudaError_t Invoke()
-    {
-        //
-        // Black-box testing on all backends
-        //
-
-        // Test 0, 1, many
-        TestByGenMode<InputT, OutputT>(0,           max_segments);
-        TestByGenMode<InputT, OutputT>(1,           max_segments);
-        TestByGenMode<InputT, OutputT>(max_items,   max_segments);
-
-        // Test random problem sizes from a log-distribution [8, max_items-ish)
-        int     num_iterations = 8;
-        double  max_exp = log(double(max_items)) / log(double(2.0));
-        for (int i = 0; i < num_iterations; ++i)
-        {
-            int num_items = (int) pow(2.0, RandomValue(max_exp - 3.0) + 3.0);
-            TestByGenMode<InputT, OutputT>(num_items, max_segments);
-        }
-
-        //
-        // White-box testing of single-segment problems around specific sizes
-        //
-
-        // Tile-boundaries: multiple blocks, one tile per block
-        int tile_size = ActivePolicyT::ReducePolicy::BLOCK_THREADS * ActivePolicyT::ReducePolicy::ITEMS_PER_THREAD;
-        TestProblem<CUB, InputT, OutputT>(tile_size * 4,  1,      RANDOM, Sum());
-        TestProblem<CUB, InputT, OutputT>(tile_size * 4 + 1, 1,   RANDOM, Sum());
-        TestProblem<CUB, InputT, OutputT>(tile_size * 4 - 1, 1,   RANDOM, Sum());
-
-        // Tile-boundaries: multiple blocks, multiple tiles per block
-        int sm_occupancy = 32;
-        int occupancy = tile_size * sm_occupancy * g_sm_count;
-        TestProblem<CUB, InputT, OutputT>(occupancy,  1,      RANDOM, Sum());
-        TestProblem<CUB, InputT, OutputT>(occupancy + 1, 1,   RANDOM, Sum());
-        TestProblem<CUB, InputT, OutputT>(occupancy - 1, 1,   RANDOM, Sum());
-
-        return cudaSuccess;
-    }
-};
-
-
-/// Test problem type
-template <
-    typename InputT,
-    typename OutputT>
-void TestType(
-    int             max_items,
-    int             max_segments)
-{
-    typedef typename DeviceReducePolicy<OutputT, int, cub::Sum>::MaxPolicy MaxPolicyT;
-
-    TestBySize<InputT, OutputT> dispatch(max_items, max_segments);
-
-    MaxPolicyT::Invoke(g_ptx_version, dispatch);
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int max_items      = 27000000;
-    int max_segments   = 34000;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    g_verbose_input = args.CheckCmdLineFlag("v2");
-    args.GetCmdLineArgument("n", max_items);
-    args.GetCmdLineArgument("s", max_segments);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--s=<num segments> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "[--cdp]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get ptx version
-    CubDebugExit(PtxVersion(g_ptx_version));
-
-    // Get SM count
-    g_sm_count = args.deviceProp.multiProcessorCount;
-
-    std::numeric_limits<float>::max();
-
-#ifdef QUICKER_TEST
-
-    // Compile/run basic test
-
-
-
-    TestProblem<CUB, int, int>(     max_items, 1, RANDOM, Sum());
-
-    TestProblem<CUB, char, int>(    max_items, 1, RANDOM, Sum());
-
-    TestProblem<CUB, int, int>(     max_items, 1, RANDOM, ArgMax());
-
-    TestProblem<CUB, float, float>( max_items, 1, RANDOM, Sum());
-
-    TestProblem<CUB_SEGMENTED, int, int>(max_items, max_segments, RANDOM, Sum());
-
-
-#elif defined(QUICK_TEST)
-
-    // Compile/run quick comparison tests
-
-    TestProblem<CUB, char, char>(         max_items * 4, 1, UNIFORM, Sum());
-    TestProblem<THRUST, char, char>(      max_items * 4, 1, UNIFORM, Sum());
-
-    printf("\n----------------------------\n");
-    TestProblem<CUB, short, short>(        max_items * 2, 1, UNIFORM, Sum());
-    TestProblem<THRUST, short, short>(     max_items * 2, 1, UNIFORM, Sum());
-
-    printf("\n----------------------------\n");
-    TestProblem<CUB, int, int>(          max_items,     1, UNIFORM, Sum());
-    TestProblem<THRUST, int, int>(       max_items,     1, UNIFORM, Sum());
-
-    printf("\n----------------------------\n");
-    TestProblem<CUB, long long, long long>(    max_items / 2, 1, UNIFORM, Sum());
-    TestProblem<THRUST, long long, long long>( max_items / 2, 1, UNIFORM, Sum());
-
-    printf("\n----------------------------\n");
-    TestProblem<CUB, TestFoo, TestFoo>(      max_items / 4, 1, UNIFORM, Max());
-    TestProblem<THRUST, TestFoo, TestFoo>(   max_items / 4, 1, UNIFORM, Max());
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test different input types
-        TestType<char, char>(max_items, max_segments);
-        TestType<unsigned char, unsigned char>(max_items, max_segments);
-
-        TestType<char, int>(max_items, max_segments);
-
-        TestType<short, short>(max_items, max_segments);
-        TestType<int, int>(max_items, max_segments);
-        TestType<long, long>(max_items, max_segments);
-        TestType<long long, long long>(max_items, max_segments);
-
-        TestType<uchar2, uchar2>(max_items, max_segments);
-        TestType<uint2, uint2>(max_items, max_segments);
-        TestType<ulonglong2, ulonglong2>(max_items, max_segments);
-        TestType<ulonglong4, ulonglong4>(max_items, max_segments);
-
-        TestType<TestFoo, TestFoo>(max_items, max_segments);
-        TestType<TestBar, TestBar>(max_items, max_segments);
-    }
-
-#endif
-
-
-    printf("\n");
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_device_reduce_by_key.cu b/ml-xgboost/cub/test/test_device_reduce_by_key.cu
deleted file mode 100644
index 7daaea3..0000000
--- a/ml-xgboost/cub/test/test_device_reduce_by_key.cu
+++ /dev/null
@@ -1,853 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceReduce::ReduceByKey utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/iterator/constant_input_iterator.cuh>
-#include <cub/device/device_reduce.cuh>
-#include <cub/device/device_run_length_encode.cuh>
-#include <cub/thread/thread_operators.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/reduce.h>
-#include <thrust/iterator/constant_iterator.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    THRUST,     // Thrust method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to reduce-by-key entrypoint
- */
-template <
-    typename                    KeyInputIteratorT,
-    typename                    KeyOutputIteratorT,
-    typename                    ValueInputIteratorT,
-    typename                    ValueOutputIteratorT,
-    typename                    NumRunsIteratorT,
-    typename                    EqualityOpT,
-    typename                    ReductionOpT,
-    typename                    OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void                        *d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    KeyInputIteratorT           d_keys_in,
-    KeyOutputIteratorT          d_keys_out,
-    ValueInputIteratorT         d_values_in,
-    ValueOutputIteratorT        d_values_out,
-    NumRunsIteratorT            d_num_runs,
-    EqualityOpT                  equality_op,
-    ReductionOpT                 reduction_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceReduce::ReduceByKey(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_keys_in,
-            d_keys_out,
-            d_values_in,
-            d_values_out,
-            d_num_runs,
-            reduction_op,
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to reduce-by-key entrypoint
- */
-template <
-    typename                    KeyInputIteratorT,
-    typename                    KeyOutputIteratorT,
-    typename                    ValueInputIteratorT,
-    typename                    ValueOutputIteratorT,
-    typename                    NumRunsIteratorT,
-    typename                    EqualityOpT,
-    typename                    ReductionOpT,
-    typename                    OffsetT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>            dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void                        *d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    KeyInputIteratorT           d_keys_in,
-    KeyOutputIteratorT          d_keys_out,
-    ValueInputIteratorT         d_values_in,
-    ValueOutputIteratorT        d_values_out,
-    NumRunsIteratorT            d_num_runs,
-    EqualityOpT                 equality_op,
-    ReductionOpT                reduction_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The input keys type
-    typedef typename std::iterator_traits<KeyInputIteratorT>::value_type KeyInputT;
-
-    // The output keys type
-    typedef typename If<(Equals<typename std::iterator_traits<KeyOutputIteratorT>::value_type, void>::VALUE),   // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<KeyInputIteratorT>::value_type,                                           // ... then the input iterator's value type,
-        typename std::iterator_traits<KeyOutputIteratorT>::value_type>::Type KeyOutputT;                        // ... else the output iterator's value type
-
-    // The input values type
-    typedef typename std::iterator_traits<ValueInputIteratorT>::value_type ValueInputT;
-
-    // The output values type
-    typedef typename If<(Equals<typename std::iterator_traits<ValueOutputIteratorT>::value_type, void>::VALUE), // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<ValueInputIteratorT>::value_type,                                         // ... then the input iterator's value type,
-        typename std::iterator_traits<ValueOutputIteratorT>::value_type>::Type ValueOuputT;                     // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<KeyInputT> d_keys_in_wrapper(d_keys_in);
-        thrust::device_ptr<KeyOutputT> d_keys_out_wrapper(d_keys_out);
-
-        thrust::device_ptr<ValueInputT> d_values_in_wrapper(d_values_in);
-        thrust::device_ptr<ValueOuputT> d_values_out_wrapper(d_values_out);
-
-        thrust::pair<thrust::device_ptr<KeyOutputT>, thrust::device_ptr<ValueOuputT> > d_out_ends;
-
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_ends = thrust::reduce_by_key(
-                d_keys_in_wrapper,
-                d_keys_in_wrapper + num_items,
-                d_values_in_wrapper,
-                d_keys_out_wrapper,
-                d_values_out_wrapper);
-        }
-
-        OffsetT num_segments = d_out_ends.first - d_keys_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_runs, &num_segments, sizeof(OffsetT), cudaMemcpyHostToDevice));
-
-    }
-
-    return cudaSuccess;
-}
-
-
-
-//---------------------------------------------------------------------
-// CUDA Nested Parallelism Test Kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceSelect
- */
-template <
-    typename                    KeyInputIteratorT,
-    typename                    KeyOutputIteratorT,
-    typename                    ValueInputIteratorT,
-    typename                    ValueOutputIteratorT,
-    typename                    NumRunsIteratorT,
-    typename                    EqualityOpT,
-    typename                    ReductionOpT,
-    typename                    OffsetT>
-__global__ void CnpDispatchKernel(
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void                        *d_temp_storage,
-    size_t                      temp_storage_bytes,
-    KeyInputIteratorT           d_keys_in,
-    KeyOutputIteratorT          d_keys_out,
-    ValueInputIteratorT         d_values_in,
-    ValueOutputIteratorT        d_values_out,
-    NumRunsIteratorT            d_num_runs,
-    EqualityOpT                 equality_op,
-    ReductionOpT                reduction_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch(Int2Type<CUB>(), timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_runs, equality_op, reduction_op, num_items, 0, debug_synchronous);
-
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/**
- * Dispatch to CDP kernel
- */
-template <
-    typename                    KeyInputIteratorT,
-    typename                    KeyOutputIteratorT,
-    typename                    ValueInputIteratorT,
-    typename                    ValueOutputIteratorT,
-    typename                    NumRunsIteratorT,
-    typename                    EqualityOpT,
-    typename                    ReductionOpT,
-    typename                    OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CDP>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void                        *d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    KeyInputIteratorT           d_keys_in,
-    KeyOutputIteratorT          d_keys_out,
-    ValueInputIteratorT         d_values_in,
-    ValueOutputIteratorT        d_values_out,
-    NumRunsIteratorT            d_num_runs,
-    EqualityOpT                 equality_op,
-    ReductionOpT                reduction_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_runs, equality_op, reduction_op, num_items, 0, debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem
- */
-template <typename T>
-void Initialize(
-    int         entropy_reduction,
-    T           *h_in,
-    int         num_items,
-    int         max_segment)
-{
-    unsigned int max_int = (unsigned int) -1;
-
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Select number of repeating occurrences
-
-        int repeat;
-
-        if (max_segment < 0)
-        {
-            repeat = num_items;
-        }
-        else if (max_segment < 2)
-        {
-            repeat = 1;
-        }
-        else
-        {
-            RandomBits(repeat, entropy_reduction);
-            repeat = (int) ((double(repeat) * double(max_segment)) / double(max_int));
-            repeat = CUB_MAX(1, repeat);
-        }
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            InitValue(INTEGER_SEED, h_in[j], key);
-            j++;
-        }
-
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve problem.  Returns total number of segments identified
- */
-template <
-    typename        KeyInputIteratorT,
-    typename        ValueInputIteratorT,
-    typename        KeyT,
-    typename        ValueT,
-    typename        EqualityOpT,
-    typename        ReductionOpT>
-int Solve(
-    KeyInputIteratorT       h_keys_in,
-    KeyT                    *h_keys_reference,
-    ValueInputIteratorT     h_values_in,
-    ValueT                  *h_values_reference,
-    EqualityOpT             equality_op,
-    ReductionOpT            reduction_op,
-    int                     num_items)
-{
-    // First item
-    KeyT previous        = h_keys_in[0];
-    ValueT aggregate     = h_values_in[0];
-    int num_segments    = 0;
-
-    // Subsequent items
-    for (int i = 1; i < num_items; ++i)
-    {
-        if (!equality_op(previous, h_keys_in[i]))
-        {
-            h_keys_reference[num_segments] = previous;
-            h_values_reference[num_segments] = aggregate;
-            num_segments++;
-            aggregate = h_values_in[i];
-        }
-        else
-        {
-            aggregate = reduction_op(aggregate, h_values_in[i]);
-        }
-        previous = h_keys_in[i];
-    }
-
-    h_keys_reference[num_segments] = previous;
-    h_values_reference[num_segments] = aggregate;
-    num_segments++;
-
-    return num_segments;
-}
-
-
-
-/**
- * Test DeviceSelect for a given problem input
- */
-template <
-    Backend             BACKEND,
-    typename            DeviceKeyInputIteratorT,
-    typename            DeviceValueInputIteratorT,
-    typename            KeyT,
-    typename            ValueT,
-    typename            EqualityOpT,
-    typename            ReductionOpT>
-void Test(
-    DeviceKeyInputIteratorT     d_keys_in,
-    DeviceValueInputIteratorT   d_values_in,
-    KeyT*                       h_keys_reference,
-    ValueT*                     h_values_reference,
-    EqualityOpT                 equality_op,
-    ReductionOpT                reduction_op,
-    int                         num_segments,
-    int                         num_items)
-{
-    // Allocate device output arrays and number of segments
-    KeyT*   d_keys_out             = NULL;
-    ValueT* d_values_out           = NULL;
-    int*    d_num_runs         = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys_out, sizeof(KeyT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values_out, sizeof(ValueT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_runs, sizeof(int)));
-
-    // Allocate CDP device arrays
-    size_t          *d_temp_storage_bytes = NULL;
-    cudaError_t     *d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_runs, equality_op, reduction_op, num_items, 0, true));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Clear device output arrays
-    CubDebugExit(cudaMemset(d_keys_out, 0, sizeof(KeyT) * num_items));
-    CubDebugExit(cudaMemset(d_values_out, 0, sizeof(ValueT) * num_items));
-    CubDebugExit(cudaMemset(d_num_runs, 0, sizeof(int)));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_runs, equality_op, reduction_op, num_items, 0, true));
-
-    // Check for correctness (and display results, if specified)
-    int compare1 = CompareDeviceResults(h_keys_reference, d_keys_out, num_segments, true, g_verbose);
-    printf("\t Keys %s ", compare1 ? "FAIL" : "PASS");
-
-    int compare2 = CompareDeviceResults(h_values_reference, d_values_out, num_segments, true, g_verbose);
-    printf("\t Values %s ", compare2 ? "FAIL" : "PASS");
-
-    int compare3 = CompareDeviceResults(&num_segments, d_num_runs, 1, true, g_verbose);
-    printf("\t Count %s ", compare3 ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), g_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_runs, equality_op, reduction_op, num_items, 0, false));
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float   avg_millis  = elapsed_millis / g_timing_iterations;
-        float   giga_rate   = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        int     bytes_moved = ((num_items + num_segments) * sizeof(KeyT)) + ((num_items + num_segments) * sizeof(ValueT));
-        float   giga_bandwidth  = float(bytes_moved) / avg_millis / 1000.0f / 1000.0f;
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", avg_millis, giga_rate, giga_bandwidth);
-    }
-    printf("\n\n");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Cleanup
-    if (d_keys_out) CubDebugExit(g_allocator.DeviceFree(d_keys_out));
-    if (d_values_out) CubDebugExit(g_allocator.DeviceFree(d_values_out));
-    if (d_num_runs) CubDebugExit(g_allocator.DeviceFree(d_num_runs));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, compare1 | compare2 | compare3);
-}
-
-
-/**
- * Test DeviceSelect on pointer type
- */
-template <
-    Backend         BACKEND,
-    typename        KeyT,
-    typename        ValueT,
-    typename        ReductionOpT>
-void TestPointer(
-    int             num_items,
-    int             entropy_reduction,
-    int             max_segment,
-    ReductionOpT    reduction_op)
-{
-    // Allocate host arrays
-    KeyT* h_keys_in        = new KeyT[num_items];
-    KeyT* h_keys_reference = new KeyT[num_items];
-
-    ValueT* h_values_in        = new ValueT[num_items];
-    ValueT* h_values_reference = new ValueT[num_items];
-
-    for (int i = 0; i < num_items; ++i)
-        InitValue(INTEGER_SEED, h_values_in[i], 1);
-
-    // Initialize problem and solution
-    Equality equality_op;
-    Initialize(entropy_reduction, h_keys_in, num_items, max_segment);
-    int num_segments = Solve(h_keys_in, h_keys_reference, h_values_in, h_values_reference, equality_op, reduction_op, num_items);
-
-    printf("\nPointer %s cub::DeviceReduce::ReduceByKey %s reduction of %d items, %d segments (avg run length %.3f), {%s,%s} key value pairs, max_segment %d, entropy_reduction %d\n",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        (Equals<ReductionOpT, Sum>::VALUE) ? "Sum" : "Max",
-        num_items, num_segments, float(num_items) / num_segments,
-        typeid(KeyT).name(), typeid(ValueT).name(),
-        max_segment, entropy_reduction);
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    KeyT     *d_keys_in = NULL;
-    ValueT   *d_values_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys_in, sizeof(KeyT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values_in, sizeof(ValueT) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_keys_in, h_keys_in, sizeof(KeyT) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_values_in, h_values_in, sizeof(ValueT) * num_items, cudaMemcpyHostToDevice));
-
-    // Run Test
-    Test<BACKEND>(d_keys_in, d_values_in, h_keys_reference, h_values_reference, equality_op, reduction_op, num_segments, num_items);
-
-    // Cleanup
-    if (h_keys_in) delete[] h_keys_in;
-    if (h_values_in) delete[] h_values_in;
-    if (h_keys_reference) delete[] h_keys_reference;
-    if (h_values_reference) delete[] h_values_reference;
-    if (d_keys_in) CubDebugExit(g_allocator.DeviceFree(d_keys_in));
-    if (d_values_in) CubDebugExit(g_allocator.DeviceFree(d_values_in));
-}
-
-
-/**
- * Test on iterator type
- */
-template <
-    Backend         BACKEND,
-    typename        KeyT,
-    typename        ValueT,
-    typename        ReductionOpT>
-void TestIterator(
-    int             num_items,
-    int             entropy_reduction,
-    int             max_segment,
-    ReductionOpT    reduction_op)
-{
-    // Allocate host arrays
-    KeyT* h_keys_in        = new KeyT[num_items];
-    KeyT* h_keys_reference = new KeyT[num_items];
-
-    ValueT one_val;
-    InitValue(INTEGER_SEED, one_val, 1);
-    ConstantInputIterator<ValueT, int> h_values_in(one_val);
-    ValueT* h_values_reference = new ValueT[num_items];
-
-    // Initialize problem and solution
-    Equality equality_op;
-    Initialize(entropy_reduction, h_keys_in, num_items, max_segment);
-    int num_segments = Solve(h_keys_in, h_keys_reference, h_values_in, h_values_reference, equality_op, reduction_op, num_items);
-
-    printf("\nIterator %s cub::DeviceReduce::ReduceByKey %s reduction of %d items, %d segments (avg run length %.3f), {%s,%s} key value pairs, max_segment %d, entropy_reduction %d\n",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        (Equals<ReductionOpT, Sum>::VALUE) ? "Sum" : "Max",
-        num_items, num_segments, float(num_items) / num_segments,
-        typeid(KeyT).name(), typeid(ValueT).name(),
-        max_segment, entropy_reduction);
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    KeyT     *d_keys_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_keys_in, sizeof(KeyT) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_keys_in, h_keys_in, sizeof(KeyT) * num_items, cudaMemcpyHostToDevice));
-
-    // Run Test
-    Test<BACKEND>(d_keys_in, h_values_in, h_keys_reference, h_values_reference, equality_op, reduction_op, num_segments, num_items);
-
-    // Cleanup
-    if (h_keys_in) delete[] h_keys_in;
-    if (h_keys_reference) delete[] h_keys_reference;
-    if (h_values_reference) delete[] h_values_reference;
-    if (d_keys_in) CubDebugExit(g_allocator.DeviceFree(d_keys_in));
-}
-
-
-/**
- * Test different gen modes
- */
-template <
-    Backend         BACKEND,
-    typename        KeyT,
-    typename        ValueT,
-    typename        ReductionOpT>
-void Test(
-    int             num_items,
-    ReductionOpT    reduction_op,
-    int             max_segment)
-{
-    // 0 key-bit entropy reduction rounds
-    TestPointer<BACKEND, KeyT, ValueT>(num_items, 0, max_segment, reduction_op);
-
-    if (max_segment > 1)
-    {
-        // 2 key-bit entropy reduction rounds
-        TestPointer<BACKEND, KeyT, ValueT>(num_items, 2, max_segment, reduction_op);
-
-        // 7 key-bit entropy reduction rounds
-        TestPointer<BACKEND, KeyT, ValueT>(num_items, 7, max_segment, reduction_op);
-    }
-}
-
-
-/**
- * Test different avg segment lengths modes
- */
-template <
-    Backend         BACKEND,
-    typename        KeyT,
-    typename        ValueT,
-    typename        ReductionOpT>
-void Test(
-    int             num_items,
-    ReductionOpT    reduction_op)
-{
-    Test<BACKEND, KeyT, ValueT>(num_items, reduction_op, -1);
-    Test<BACKEND, KeyT, ValueT>(num_items, reduction_op, 1);
-
-    // Evaluate different max-segment lengths
-    for (int max_segment = 3; max_segment < CUB_MIN(num_items, (unsigned short) -1); max_segment *= 11)
-    {
-        Test<BACKEND, KeyT, ValueT>(num_items, reduction_op, max_segment);
-    }
-}
-
-
-
-/**
- * Test different dispatch
- */
-template <
-    typename        KeyT,
-    typename        ValueT,
-    typename        ReductionOpT>
-void TestDispatch(
-    int             num_items,
-    ReductionOpT    reduction_op)
-{
-    Test<CUB, KeyT, ValueT>(num_items, reduction_op);
-#ifdef CUB_CDP
-    Test<CDP, KeyT, ValueT>(num_items, reduction_op);
-#endif
-}
-
-
-/**
- * Test different input sizes
- */
-template <
-    typename        KeyT,
-    typename        ValueT,
-    typename        ReductionOpT>
-void TestSize(
-    int             num_items,
-    ReductionOpT    reduction_op)
-{
-    if (num_items < 0)
-    {
-        TestDispatch<KeyT, ValueT>(1,        reduction_op);
-        TestDispatch<KeyT, ValueT>(100,      reduction_op);
-        TestDispatch<KeyT, ValueT>(10000,    reduction_op);
-        TestDispatch<KeyT, ValueT>(1000000,  reduction_op);
-    }
-    else
-    {
-        TestDispatch<KeyT, ValueT>(num_items, reduction_op);
-    }
-
-}
-
-
-template <
-    typename        KeyT,
-    typename        ValueT>
-void TestOp(
-    int             num_items)
-{
-    TestSize<KeyT, ValueT>(num_items, cub::Sum());
-    TestSize<KeyT, ValueT>(num_items, cub::Max());
-}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = -1;
-    int entropy_reduction   = 0;
-    int maxseg              = 1000;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-    args.GetCmdLineArgument("maxseg", maxseg);
-    args.GetCmdLineArgument("entropy", entropy_reduction);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>]"
-            "[--entropy=<segment length bit entropy reduction rounds>]"
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "[--cdp]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-    printf("\n");
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-#ifdef QUICKER_TEST
-
-    // Compile/run basic CUB test
-    if (num_items < 0) num_items = 32000000;
-
-    TestPointer<CUB, int, double>(num_items, entropy_reduction, maxseg, cub::Sum());
-    TestPointer<CUB, int, int>(num_items, entropy_reduction, maxseg, cub::Sum());
-    TestIterator<CUB, int, int>(num_items, entropy_reduction, maxseg, cub::Sum());
-
-#elif defined(QUICK_TEST)
-
-    // Compile/run quick tests
-    if (num_items < 0) num_items = 32000000;
-
-    printf("---- RLE int ---- \n");
-    TestIterator<CUB, int, int>(num_items, entropy_reduction, maxseg, cub::Sum());
-
-    printf("---- RLE long long ---- \n");
-    TestIterator<CUB, long long, int>(num_items, entropy_reduction, maxseg, cub::Sum());
-
-    printf("---- int ---- \n");
-    TestPointer<CUB, int, int>(num_items, entropy_reduction, maxseg, cub::Sum());
-    TestPointer<THRUST, int, int>(num_items, entropy_reduction, maxseg, cub::Sum());
-
-    printf("---- float ---- \n");
-    TestPointer<CUB, int, float>(num_items, entropy_reduction, maxseg, cub::Sum());
-    TestPointer<THRUST, int, float>(num_items, entropy_reduction, maxseg, cub::Sum());
-
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-    {
-        printf("---- double ---- \n");
-        TestPointer<CUB, int, double>(num_items, entropy_reduction, maxseg, cub::Sum());
-        TestPointer<THRUST, int, double>(num_items, entropy_reduction, maxseg, cub::Sum());
-    }
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-
-        // Test different input types
-        TestOp<int, char>(num_items);
-        TestOp<int, short>(num_items);
-        TestOp<int, int>(num_items);
-        TestOp<int, long>(num_items);
-        TestOp<int, long long>(num_items);
-        TestOp<int, float>(num_items);
-        if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-            TestOp<int, double>(num_items);
-
-        TestOp<int, uchar2>(num_items);
-        TestOp<int, uint2>(num_items);
-        TestOp<int, uint3>(num_items);
-        TestOp<int, uint4>(num_items);
-        TestOp<int, ulonglong4>(num_items);
-        TestOp<int, TestFoo>(num_items);
-        TestOp<int, TestBar>(num_items);
-
-        TestOp<char, int>(num_items);
-        TestOp<long long, int>(num_items);
-        TestOp<TestFoo, int>(num_items);
-        TestOp<TestBar, int>(num_items);
-
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_device_run_length_encode.cu b/ml-xgboost/cub/test/test_device_run_length_encode.cu
deleted file mode 100644
index 872de15..0000000
--- a/ml-xgboost/cub/test/test_device_run_length_encode.cu
+++ /dev/null
@@ -1,890 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceReduce::RunLengthEncode utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/iterator/constant_input_iterator.cuh>
-#include <cub/device/device_reduce.cuh>
-#include <cub/device/device_run_length_encode.cuh>
-#include <cub/thread/thread_operators.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/reduce.h>
-#include <thrust/iterator/constant_iterator.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    THRUST,     // Thrust method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-// Operation types
-enum RleMethod
-{
-    RLE,                // Run length encode
-    NON_TRIVIAL,
-    CSR,
-};
-
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB entrypoints
-//---------------------------------------------------------------------
-
-
-/**
- * Dispatch to run-length encode entrypoint
- */
-template <
-    typename                    InputIteratorT,
-    typename                    UniqueOutputIteratorT,
-    typename                    OffsetsOutputIteratorT,
-    typename                    LengthsOutputIteratorT,
-    typename                    NumRunsIterator,
-    typename                    OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<RLE>               method,
-    Int2Type<CUB>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    UniqueOutputIteratorT       d_unique_out,
-    OffsetsOutputIteratorT      d_offsets_out,
-    LengthsOutputIteratorT      d_lengths_out,
-    NumRunsIterator             d_num_runs,
-    cub::Equality               equality_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceRunLengthEncode::Encode(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_unique_out,
-            d_lengths_out,
-            d_num_runs,
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to non-trivial runs entrypoint
- */
-template <
-    typename                    InputIteratorT,
-    typename                    UniqueOutputIteratorT,
-    typename                    OffsetsOutputIteratorT,
-    typename                    LengthsOutputIteratorT,
-    typename                    NumRunsIterator,
-    typename                    OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<NON_TRIVIAL>       method,
-    Int2Type<CUB>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    UniqueOutputIteratorT       d_unique_out,
-    OffsetsOutputIteratorT      d_offsets_out,
-    LengthsOutputIteratorT      d_lengths_out,
-    NumRunsIterator             d_num_runs,
-    cub::Equality               equality_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceRunLengthEncode::NonTrivialRuns(
-            d_temp_storage,
-            temp_storage_bytes,
-            d_in,
-            d_offsets_out,
-            d_lengths_out,
-            d_num_runs,
-            num_items,
-            stream,
-            debug_synchronous);
-    }
-    return error;
-}
-
-
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to run-length encode entrypoint
- */
-template <
-    typename                    InputIteratorT,
-    typename                    UniqueOutputIteratorT,
-    typename                    OffsetsOutputIteratorT,
-    typename                    LengthsOutputIteratorT,
-    typename                    NumRunsIterator,
-    typename                    OffsetT>
-cudaError_t Dispatch(
-    Int2Type<RLE>               method,
-    Int2Type<THRUST>            dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void                        *d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    UniqueOutputIteratorT       d_unique_out,
-    OffsetsOutputIteratorT      d_offsets_out,
-    LengthsOutputIteratorT      d_lengths_out,
-    NumRunsIterator             d_num_runs,
-    cub::Equality               equality_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<UniqueOutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                                // ... then the input iterator's value type,
-        typename std::iterator_traits<UniqueOutputIteratorT>::value_type>::Type UniqueT;                          // ... else the output iterator's value type
-
-    // The lengths output value type
-    typedef typename If<(Equals<typename std::iterator_traits<LengthsOutputIteratorT>::value_type, void>::VALUE),   // LengthT =  (if output iterator's value type is void) ?
-        OffsetT,                                                                                                    // ... then the OffsetT type,
-        typename std::iterator_traits<LengthsOutputIteratorT>::value_type>::Type LengthT;                           // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<InputT>      d_in_wrapper(d_in);
-        thrust::device_ptr<UniqueT>     d_unique_out_wrapper(d_unique_out);
-        thrust::device_ptr<LengthT>     d_lengths_out_wrapper(d_lengths_out);
-
-        thrust::pair<thrust::device_ptr<UniqueT>, thrust::device_ptr<LengthT> > d_out_ends;
-
-        LengthT one_val;
-        InitValue(INTEGER_SEED, one_val, 1);
-        thrust::constant_iterator<LengthT> constant_one(one_val);
-
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_ends = thrust::reduce_by_key(
-                d_in_wrapper,
-                d_in_wrapper + num_items,
-                constant_one,
-                d_unique_out_wrapper,
-                d_lengths_out_wrapper);
-        }
-
-        OffsetT num_runs = d_out_ends.first - d_unique_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_runs, &num_runs, sizeof(OffsetT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-
-
-//---------------------------------------------------------------------
-// CUDA Nested Parallelism Test Kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceRunLengthEncode
- */
-template <
-    int                         RLE_METHOD,
-    typename                    InputIteratorT,
-    typename                    UniqueOutputIteratorT,
-    typename                    OffsetsOutputIteratorT,
-    typename                    LengthsOutputIteratorT,
-    typename                    NumRunsIterator,
-    typename                    EqualityOp,
-    typename                    OffsetT>
-__global__ void CnpDispatchKernel(
-    Int2Type<RLE_METHOD>            method,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      temp_storage_bytes,
-    InputIteratorT              d_in,
-    UniqueOutputIteratorT       d_unique_out,
-    OffsetsOutputIteratorT      d_offsets_out,
-    LengthsOutputIteratorT      d_lengths_out,
-    NumRunsIterator             d_num_runs,
-    cub::Equality               equality_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch(method, Int2Type<CUB>(), timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_offsets_out, d_lengths_out, d_num_runs, equality_op, num_items, 0, debug_synchronous);
-
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/**
- * Dispatch to CDP kernel
- */
-template <
-    int                         RLE_METHOD,
-    typename                    InputIteratorT,
-    typename                    UniqueOutputIteratorT,
-    typename                    OffsetsOutputIteratorT,
-    typename                    LengthsOutputIteratorT,
-    typename                    NumRunsIterator,
-    typename                    EqualityOp,
-    typename                    OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<RLE_METHOD>        method,
-    Int2Type<CDP>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    UniqueOutputIteratorT       d_unique_out,
-    OffsetsOutputIteratorT      d_offsets_out,
-    LengthsOutputIteratorT      d_lengths_out,
-    NumRunsIterator             d_num_runs,
-    EqualityOp                  equality_op,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(method, timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_offsets_out, d_lengths_out, d_num_runs, equality_op, num_items, 0, debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem
- */
-template <typename T>
-void Initialize(
-    int         entropy_reduction,
-    T           *h_in,
-    int         num_items,
-    int         max_segment)
-{
-    unsigned int max_int = (unsigned int) -1;
-
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Select number of repeating occurrences for the current run
-        int repeat;
-        if (max_segment < 0)
-        {
-            repeat = num_items;
-        }
-        else if (max_segment < 2)
-        {
-            repeat = 1;
-        }
-        else
-        {
-            RandomBits(repeat, entropy_reduction);
-            repeat = (int) ((double(repeat) * double(max_segment)) / double(max_int));
-            repeat = CUB_MAX(1, repeat);
-        }
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            InitValue(INTEGER_SEED, h_in[j], key);
-            j++;
-        }
-
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve problem.  Returns total number of segments identified
- */
-template <
-    RleMethod       RLE_METHOD,
-    typename        InputIteratorT,
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT,
-    typename        EqualityOp>
-int Solve(
-    InputIteratorT  h_in,
-    T               *h_unique_reference,
-    OffsetT         *h_offsets_reference,
-    LengthT         *h_lengths_reference,
-    EqualityOp      equality_op,
-    int             num_items)
-{
-    if (num_items == 0) 
-        return 0;
-
-    // First item
-    T       previous        = h_in[0];
-    LengthT  length          = 1;
-    int     num_runs        = 0;
-    int     run_begin       = 0;
-
-    // Subsequent items
-    for (int i = 1; i < num_items; ++i)
-    {
-        if (!equality_op(previous, h_in[i]))
-        {
-            if ((RLE_METHOD != NON_TRIVIAL) || (length > 1))
-            {
-                h_unique_reference[num_runs]      = previous;
-                h_offsets_reference[num_runs]     = run_begin;
-                h_lengths_reference[num_runs]     = length;
-                num_runs++;
-            }
-            length = 1;
-            run_begin = i;
-        }
-        else
-        {
-            length++;
-        }
-        previous = h_in[i];
-    }
-
-    if ((RLE_METHOD != NON_TRIVIAL) || (length > 1))
-    {
-        h_unique_reference[num_runs]    = previous;
-        h_offsets_reference[num_runs]   = run_begin;
-        h_lengths_reference[num_runs]   = length;
-        num_runs++;
-    }
-
-    return num_runs;
-}
-
-
-
-/**
- * Test DeviceRunLengthEncode for a given problem input
- */
-template <
-    RleMethod           RLE_METHOD,
-    Backend             BACKEND,
-    typename            DeviceInputIteratorT,
-    typename            T,
-    typename            OffsetT,
-    typename            LengthT,
-    typename            EqualityOp>
-void Test(
-    DeviceInputIteratorT d_in,
-    T                   *h_unique_reference,
-    OffsetT             *h_offsets_reference,
-    LengthT             *h_lengths_reference,
-    EqualityOp          equality_op,
-    int                 num_runs,
-    int                 num_items)
-{
-    // Allocate device output arrays and number of segments
-    T*          d_unique_out       = NULL;
-    LengthT*    d_offsets_out      = NULL;
-    OffsetT*    d_lengths_out      = NULL;
-    int*        d_num_runs         = NULL;
-
-    if (RLE_METHOD == RLE)
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_unique_out, sizeof(T) * num_items));
-    if (RLE_METHOD == NON_TRIVIAL)
-        CubDebugExit(g_allocator.DeviceAllocate((void**)&d_offsets_out, sizeof(OffsetT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_lengths_out, sizeof(LengthT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_runs, sizeof(int)));
-
-    // Allocate CDP device arrays
-    size_t*          d_temp_storage_bytes = NULL;
-    cudaError_t*     d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void*           d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(Dispatch(Int2Type<RLE_METHOD>(), Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_offsets_out, d_lengths_out, d_num_runs, equality_op, num_items, 0, true));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Clear device output arrays
-    if (RLE_METHOD == RLE)
-        CubDebugExit(cudaMemset(d_unique_out,   0, sizeof(T) * num_items));
-    if (RLE_METHOD == NON_TRIVIAL)
-        CubDebugExit(cudaMemset(d_offsets_out,  0, sizeof(OffsetT) * num_items));
-    CubDebugExit(cudaMemset(d_lengths_out,  0, sizeof(LengthT) * num_items));
-    CubDebugExit(cudaMemset(d_num_runs,     0, sizeof(int)));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(Int2Type<RLE_METHOD>(), Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_offsets_out, d_lengths_out, d_num_runs, equality_op, num_items, 0, true));
-
-    // Check for correctness (and display results, if specified)
-    int compare0 = 0;
-    int compare1 = 0;
-    int compare2 = 0;
-    int compare3 = 0;
-
-    if (RLE_METHOD == RLE)
-    {
-        compare0 = CompareDeviceResults(h_unique_reference, d_unique_out, num_runs, true, g_verbose);
-        printf("\t Keys %s\n", compare0 ? "FAIL" : "PASS");
-    }
-
-    if (RLE_METHOD != RLE)
-    {
-        compare1 = CompareDeviceResults(h_offsets_reference, d_offsets_out, num_runs, true, g_verbose);
-        printf("\t Offsets %s\n", compare1 ? "FAIL" : "PASS");
-    }
-
-    if (RLE_METHOD != CSR)
-    {
-        compare2 = CompareDeviceResults(h_lengths_reference, d_lengths_out, num_runs, true, g_verbose);
-        printf("\t Lengths %s\n", compare2 ? "FAIL" : "PASS");
-    }
-
-    compare3 = CompareDeviceResults(&num_runs, d_num_runs, 1, true, g_verbose);
-    printf("\t Count %s\n", compare3 ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    CubDebugExit(Dispatch(Int2Type<RLE_METHOD>(), Int2Type<BACKEND>(), g_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_offsets_out, d_lengths_out, d_num_runs, equality_op, num_items, 0, false));
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        int bytes_moved = (num_items * sizeof(T)) + (num_runs * (sizeof(OffsetT) + sizeof(LengthT)));
-        float giga_bandwidth = float(bytes_moved) / avg_millis / 1000.0f / 1000.0f;
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", avg_millis, giga_rate, giga_bandwidth);
-    }
-    printf("\n\n");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Cleanup
-    if (d_unique_out) CubDebugExit(g_allocator.DeviceFree(d_unique_out));
-    if (d_offsets_out) CubDebugExit(g_allocator.DeviceFree(d_offsets_out));
-    if (d_lengths_out) CubDebugExit(g_allocator.DeviceFree(d_lengths_out));
-    if (d_num_runs) CubDebugExit(g_allocator.DeviceFree(d_num_runs));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, compare0 | compare1 | compare2 | compare3);
-}
-
-
-/**
- * Test DeviceRunLengthEncode on pointer type
- */
-template <
-    RleMethod       RLE_METHOD,
-    Backend         BACKEND,
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT>
-void TestPointer(
-    int             num_items,
-    int             entropy_reduction,
-    int             max_segment)
-{
-    // Allocate host arrays
-    T*      h_in                    = new T[num_items];
-    T*      h_unique_reference      = new T[num_items];
-    OffsetT* h_offsets_reference     = new OffsetT[num_items];
-    LengthT* h_lengths_reference     = new LengthT[num_items];
-
-    for (int i = 0; i < num_items; ++i)
-        InitValue(INTEGER_SEED, h_offsets_reference[i], 1);
-
-    // Initialize problem and solution
-    Equality equality_op;
-    Initialize(entropy_reduction, h_in, num_items, max_segment);
-
-    int num_runs = Solve<RLE_METHOD>(h_in, h_unique_reference, h_offsets_reference, h_lengths_reference, equality_op, num_items);
-
-    printf("\nPointer %s cub::%s on %d items, %d segments (avg run length %.3f), {%s key, %s offset, %s length}, max_segment %d, entropy_reduction %d\n",
-        (RLE_METHOD == RLE) ? "DeviceReduce::RunLengthEncode" : (RLE_METHOD == NON_TRIVIAL) ? "DeviceRunLengthEncode::NonTrivialRuns" : "Other",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        num_items, num_runs, float(num_items) / num_runs,
-        typeid(T).name(), typeid(OffsetT).name(), typeid(LengthT).name(),
-        max_segment, entropy_reduction);
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    T* d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * num_items, cudaMemcpyHostToDevice));
-
-    // Run Test
-    Test<RLE_METHOD, BACKEND>(d_in, h_unique_reference, h_offsets_reference, h_lengths_reference, equality_op, num_runs, num_items);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_unique_reference) delete[] h_unique_reference;
-    if (h_offsets_reference) delete[] h_offsets_reference;
-    if (h_lengths_reference) delete[] h_lengths_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-}
-
-
-/**
- * Test on iterator type
- */
-template <
-    RleMethod       RLE_METHOD,
-    Backend         BACKEND,
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT>
-void TestIterator(
-    int             num_items,
-    Int2Type<true>  is_primitive)
-{
-    // Allocate host arrays
-    T* h_unique_reference       = new T[num_items];
-    OffsetT* h_offsets_reference = new OffsetT[num_items];
-    LengthT* h_lengths_reference = new LengthT[num_items];
-
-    T one_val;
-    InitValue(INTEGER_SEED, one_val, 1);
-    ConstantInputIterator<T, int> h_in(one_val);
-
-    // Initialize problem and solution
-    Equality equality_op;
-    int num_runs = Solve<RLE_METHOD>(h_in, h_unique_reference, h_offsets_reference, h_lengths_reference, equality_op, num_items);
-
-    printf("\nIterator %s cub::%s on %d items, %d segments (avg run length %.3f), {%s key, %s offset, %s length}\n",
-        (RLE_METHOD == RLE) ? "DeviceReduce::RunLengthEncode" : (RLE_METHOD == NON_TRIVIAL) ? "DeviceRunLengthEncode::NonTrivialRuns" : "Other",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        num_items, num_runs, float(num_items) / num_runs,
-        typeid(T).name(), typeid(OffsetT).name(), typeid(LengthT).name());
-    fflush(stdout);
-
-    // Run Test
-    Test<RLE_METHOD, BACKEND>(h_in, h_unique_reference, h_offsets_reference, h_lengths_reference, equality_op, num_runs, num_items);
-
-    // Cleanup
-    if (h_unique_reference) delete[] h_unique_reference;
-    if (h_offsets_reference) delete[] h_offsets_reference;
-    if (h_lengths_reference) delete[] h_lengths_reference;
-}
-
-
-template <
-    RleMethod       RLE_METHOD,
-    Backend         BACKEND,
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT>
-void TestIterator(
-    int             num_items,
-    Int2Type<false> is_primitive)
-{}
-
-
-/**
- * Test different gen modes
- */
-template <
-    RleMethod       RLE_METHOD,
-    Backend         BACKEND,
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT>
-void Test(
-    int             num_items)
-{
-    // Test iterator (one run)
-    TestIterator<RLE_METHOD, BACKEND, T, OffsetT, LengthT>(num_items, Int2Type<Traits<T>::PRIMITIVE>());
-
-    // num_items runs
-    TestPointer<RLE_METHOD, BACKEND, T, OffsetT, LengthT>(num_items, 0, 1);
-
-    // Evaluate different run lengths
-    for (int max_segment = 3; max_segment < CUB_MIN(num_items, (unsigned short) -1); max_segment *= 3)
-    {
-        // Uniform selection run length
-        TestPointer<RLE_METHOD, BACKEND, T, OffsetT, LengthT>(num_items, 0, max_segment);
-
-        // Reduced-entropy run length
-        TestPointer<RLE_METHOD, BACKEND, T, OffsetT, LengthT>(num_items, 4, max_segment);
-    }
-}
-
-
-/**
- * Test different dispatch
- */
-template <
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT>
-void TestDispatch(
-    int             num_items)
-{
-    Test<RLE,           CUB, T, OffsetT, LengthT>(num_items);
-    Test<NON_TRIVIAL,   CUB, T, OffsetT, LengthT>(num_items);
-
-#ifdef CUB_CDP
-    Test<RLE,           CDP, T, OffsetT, LengthT>(num_items);
-    Test<NON_TRIVIAL,   CDP, T, OffsetT, LengthT>(num_items);
-#endif
-}
-
-
-/**
- * Test different input sizes
- */
-template <
-    typename        T,
-    typename        OffsetT,
-    typename        LengthT>
-void TestSize(
-    int             num_items)
-{
-    if (num_items < 0)
-    {
-        TestDispatch<T, OffsetT, LengthT>(0);
-        TestDispatch<T, OffsetT, LengthT>(1);
-        TestDispatch<T, OffsetT, LengthT>(100);
-        TestDispatch<T, OffsetT, LengthT>(10000);
-        TestDispatch<T, OffsetT, LengthT>(1000000);
-
-        // Randomly select problem size between 1:10,000,000
-        unsigned int max_int = (unsigned int) -1;
-        for (int i = 0; i < 10; ++i)
-        {
-            unsigned int num_items;
-            RandomBits(num_items);
-            num_items = (unsigned int) ((double(num_items) * double(10000000)) / double(max_int));
-            num_items = CUB_MAX(1, num_items);
-            TestDispatch<T, OffsetT, LengthT>(num_items);
-        }
-    }
-    else
-    {
-        TestDispatch<T, OffsetT, LengthT>(num_items);
-    }
-
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = -1;
-    int entropy_reduction   = 0;
-    int max_segment              = 1000;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-    args.GetCmdLineArgument("maxseg", max_segment);
-    args.GetCmdLineArgument("entropy", entropy_reduction);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>]"
-            "[--entropy=<segment length bit entropy reduction rounds>]"
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "[--cdp]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-    printf("\n");
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-#ifdef QUICKER_TEST
-
-    // Compile/run basic CUB test
-    if (num_items < 0) num_items = 32000000;
-
-    TestPointer<RLE,            CUB, int, int, int>(    num_items, entropy_reduction, max_segment);
-    TestPointer<NON_TRIVIAL,    CUB, int, int, int>(    num_items, entropy_reduction, max_segment);
-    TestIterator<RLE,           CUB, float, int, int>(  num_items, Int2Type<Traits<float>::PRIMITIVE>());
-
-
-#elif defined(QUICK_TEST)
-
-    // Compile/run quick tests
-    if (num_items < 0) num_items = 32000000;
-
-    TestPointer<RLE,            CUB, int, int, int>(    num_items, entropy_reduction, max_segment);
-    TestPointer<RLE,            THRUST, int, int, int>(    num_items, entropy_reduction, max_segment);
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test different input types
-        TestSize<char,          int, int>(num_items);
-        TestSize<short,         int, int>(num_items);
-        TestSize<int,           int, int>(num_items);
-        TestSize<long,          int, int>(num_items);
-        TestSize<long long,     int, int>(num_items);
-        TestSize<float,         int, int>(num_items);
-        TestSize<double,        int, int>(num_items);
-
-        TestSize<uchar2,        int, int>(num_items);
-        TestSize<uint2,         int, int>(num_items);
-        TestSize<uint3,         int, int>(num_items);
-        TestSize<uint4,         int, int>(num_items);
-        TestSize<ulonglong4,    int, int>(num_items);
-        TestSize<TestFoo,       int, int>(num_items);
-        TestSize<TestBar,       int, int>(num_items);
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_device_scan.cu b/ml-xgboost/cub/test/test_device_scan.cu
deleted file mode 100644
index 7a18884..0000000
--- a/ml-xgboost/cub/test/test_device_scan.cu
+++ /dev/null
@@ -1,1015 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceScan utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/iterator/constant_input_iterator.cuh>
-#include <cub/iterator/discard_output_iterator.cuh>
-#include <cub/device/device_scan.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/scan.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose           = false;
-int                     g_timing_iterations = 0;
-int                     g_repeat            = 0;
-double                  g_device_giga_bandwidth;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    THRUST,     // Thrust method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-/**
- * \brief WrapperFunctor (for precluding test-specialized dispatch to *Sum variants)
- */
-template<typename OpT>
-struct WrapperFunctor
-{
-    OpT op;
-
-    WrapperFunctor(OpT op) : op(op) {}
-
-    template <typename T>
-    __host__ __device__ __forceinline__ T operator()(const T &a, const T &b) const
-    {
-        return op(a, b);
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB DeviceScan entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to exclusive scan entrypoint
- */
-template <typename IsPrimitiveT, typename InputIteratorT, typename OutputIteratorT, typename ScanOpT, typename InitialValueT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    IsPrimitiveT        is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    ScanOpT             scan_op,
-    InitialValueT       initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceScan::ExclusiveScan(d_temp_storage, temp_storage_bytes, d_in, d_out, scan_op, initial_value, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to exclusive sum entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename InitialValueT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    Int2Type<true>      is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    Sum                 scan_op,
-    InitialValueT       initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to inclusive scan entrypoint
- */
-template <typename IsPrimitiveT, typename InputIteratorT, typename OutputIteratorT, typename ScanOpT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    IsPrimitiveT        is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    ScanOpT             scan_op,
-    NullType            initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceScan::InclusiveScan(d_temp_storage, temp_storage_bytes, d_in, d_out, scan_op, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to inclusive sum entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>       dispatch_to,
-    Int2Type<true>      is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    Sum                 scan_op,
-    NullType            initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceScan::InclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to exclusive scan entrypoint
- */
-template <typename IsPrimitiveT, typename InputIteratorT, typename OutputIteratorT, typename ScanOpT, typename InitialValueT, typename OffsetT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>    dispatch_to,
-    IsPrimitiveT        is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    ScanOpT             scan_op,
-    InitialValueT       initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<InputT> d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT> d_out_wrapper(d_out);
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            thrust::exclusive_scan(d_in_wrapper, d_in_wrapper + num_items, d_out_wrapper, initial_value, scan_op);
-        }
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch to exclusive sum entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename InitialValueT, typename OffsetT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>    dispatch_to,
-    Int2Type<true>      is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    Sum                 scan_op,
-    InitialValueT       initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<InputT> d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT> d_out_wrapper(d_out);
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            thrust::exclusive_scan(d_in_wrapper, d_in_wrapper + num_items, d_out_wrapper);
-        }
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch to inclusive scan entrypoint
- */
-template <typename IsPrimitiveT, typename InputIteratorT, typename OutputIteratorT, typename ScanOpT, typename OffsetT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>    dispatch_to,
-    IsPrimitiveT        is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    ScanOpT             scan_op,
-    NullType            initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<InputT> d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT> d_out_wrapper(d_out);
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            thrust::inclusive_scan(d_in_wrapper, d_in_wrapper + num_items, d_out_wrapper, scan_op);
-        }
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch to inclusive sum entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename OffsetT>
-cudaError_t Dispatch(
-    Int2Type<THRUST>    dispatch_to,
-    Int2Type<true>      is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    Sum                 scan_op,
-    NullType            initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<InputT> d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT> d_out_wrapper(d_out);
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            thrust::inclusive_scan(d_in_wrapper, d_in_wrapper + num_items, d_out_wrapper);
-        }
-    }
-
-    return cudaSuccess;
-}
-
-
-
-//---------------------------------------------------------------------
-// CUDA Nested Parallelism Test Kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceScan
- */
-template <typename IsPrimitiveT, typename InputIteratorT, typename OutputIteratorT, typename ScanOpT, typename InitialValueT, typename OffsetT>
-__global__ void CnpDispatchKernel(
-    IsPrimitiveT        is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t              temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    ScanOpT             scan_op,
-    InitialValueT       initial_value,
-    OffsetT             num_items,
-    bool                debug_synchronous)
-{
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch(
-        Int2Type<CUB>(),
-        is_primitive,
-        timing_timing_iterations,
-        d_temp_storage_bytes,
-        d_cdp_error,
-        d_temp_storage,
-        temp_storage_bytes,
-        d_in,
-        d_out,
-        scan_op,
-        initial_value,
-        num_items,
-        0,
-        debug_synchronous);
-
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/**
- * Dispatch to CDP kernel
- */
-template <typename IsPrimitiveT, typename InputIteratorT, typename OutputIteratorT, typename ScanOpT, typename InitialValueT, typename OffsetT>
-cudaError_t Dispatch(
-    Int2Type<CDP>       dispatch_to,
-    IsPrimitiveT        is_primitive,
-    int                 timing_timing_iterations,
-    size_t              *d_temp_storage_bytes,
-    cudaError_t         *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t&             temp_storage_bytes,
-    InputIteratorT      d_in,
-    OutputIteratorT     d_out,
-    ScanOpT             scan_op,
-    InitialValueT       initial_value,
-    OffsetT             num_items,
-    cudaStream_t        stream,
-    bool                debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(
-        is_primitive,
-        timing_timing_iterations,
-        d_temp_storage_bytes,
-        d_cdp_error,
-        d_temp_storage,
-        temp_storage_bytes,
-        d_in,
-        d_out,
-        scan_op,
-        initial_value,
-        num_items,
-        debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem
- */
-template <typename T>
-void Initialize(
-    GenMode      gen_mode,
-    T            *h_in,
-    int          num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-/**
- * Solve exclusive-scan problem
- */
-template <
-    typename        InputIteratorT,
-    typename        OutputT,
-    typename        ScanOpT>
-void Solve(
-    InputIteratorT  h_in,
-    OutputT         *h_reference,
-    int             num_items,
-    ScanOpT         scan_op,
-    OutputT         initial_value)
-{
-    if (num_items > 0)
-    {
-        OutputT val         = h_in[0];
-        h_reference[0]      = initial_value;
-        OutputT inclusive   = scan_op(initial_value, val);
-
-        for (int i = 1; i < num_items; ++i)
-        {
-            val = h_in[i];
-            h_reference[i] = inclusive;
-            inclusive = scan_op(inclusive, val);
-        }
-    }
-}
-
-
-/**
- * Solve inclusive-scan problem
- */
-template <
-    typename        InputIteratorT,
-    typename        OutputT,
-    typename        ScanOpT>
-void Solve(
-    InputIteratorT  h_in,
-    OutputT         *h_reference,
-    int             num_items,
-    ScanOpT         scan_op,
-    NullType)
-{
-    if (num_items > 0)
-    {
-        OutputT inclusive   = h_in[0];
-        h_reference[0]      = inclusive;
-
-        for (int i = 1; i < num_items; ++i)
-        {
-            OutputT val = h_in[i];
-            inclusive = scan_op(inclusive, val);
-            h_reference[i] = inclusive;
-        }
-    }
-}
-
-
-/**
- * Test DeviceScan for a given problem input
- */
-template <
-    Backend             BACKEND,
-    typename            DeviceInputIteratorT,
-    typename            OutputT,
-    typename            ScanOpT,
-    typename            InitialValueT>
-void Test(
-    DeviceInputIteratorT    d_in,
-    OutputT                 *h_reference,
-    int                     num_items,
-    ScanOpT                 scan_op,
-    InitialValueT           initial_value)
-{
-    typedef typename std::iterator_traits<DeviceInputIteratorT>::value_type InputT;
-
-    // Allocate device output array
-    OutputT *d_out = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(OutputT) * num_items));
-
-    // Allocate CDP device arrays
-    size_t          *d_temp_storage_bytes = NULL;
-    cudaError_t     *d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,   sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(Dispatch(
-        Int2Type<BACKEND>(),
-        Int2Type<Traits<OutputT>::PRIMITIVE>(),
-        1,
-        d_temp_storage_bytes,
-        d_cdp_error,
-        d_temp_storage,
-        temp_storage_bytes,
-        d_in,
-        d_out,
-        scan_op,
-        initial_value,
-        num_items,
-        0,
-        true));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Clear device output array
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(OutputT) * num_items));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(
-        Int2Type<BACKEND>(),
-        Int2Type<Traits<OutputT>::PRIMITIVE>(),
-        1,
-        d_temp_storage_bytes,
-        d_cdp_error,
-        d_temp_storage,
-        temp_storage_bytes,
-        d_in,
-        d_out,
-        scan_op,
-        initial_value,
-        num_items,
-        0,
-        true));
-
-    // Check for correctness (and display results, if specified)
-    int compare = CompareDeviceResults(h_reference, d_out, num_items, true, g_verbose);
-    printf("\t%s", compare ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(),
-        Int2Type<Traits<OutputT>::PRIMITIVE>(),
-        g_timing_iterations,
-        d_temp_storage_bytes,
-        d_cdp_error,
-        d_temp_storage,
-        temp_storage_bytes,
-        d_in,
-        d_out,
-        scan_op,
-        initial_value,
-        num_items,
-        0,
-        false));
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis = elapsed_millis / g_timing_iterations;
-        float giga_rate = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        float giga_bandwidth = giga_rate * (sizeof(InputT) + sizeof(OutputT));
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s, %.1f%% peak", avg_millis, giga_rate, giga_bandwidth, giga_bandwidth / g_device_giga_bandwidth * 100.0);
-    }
-
-    printf("\n\n");
-
-    // Cleanup
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, compare);
-}
-
-
-/**
- * Test DeviceScan on pointer type
- */
-template <
-    Backend         BACKEND,
-    typename        InputT,
-    typename        OutputT,
-    typename        ScanOpT,
-    typename        InitialValueT>
-void TestPointer(
-    int             num_items,
-    GenMode         gen_mode,
-    ScanOpT         scan_op,
-    InitialValueT   initial_value)
-{
-    printf("\nPointer %s %s cub::DeviceScan::%s %d items, %s->%s (%d->%d bytes) , gen-mode %s\n",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        (Equals<InitialValueT, NullType>::VALUE) ? "Inclusive" : "Exclusive",
-        (Equals<ScanOpT, Sum>::VALUE) ? "Sum" : "Scan",
-        num_items,
-        typeid(InputT).name(), typeid(OutputT).name(), (int) sizeof(InputT), (int) sizeof(OutputT),
-        (gen_mode == RANDOM) ? "RANDOM" : (gen_mode == INTEGER_SEED) ? "SEQUENTIAL" : "HOMOGENOUS");
-    fflush(stdout);
-
-    // Allocate host arrays
-    InputT*     h_in        = new InputT[num_items];
-    OutputT*    h_reference = new OutputT[num_items];
-
-    // Initialize problem and solution
-    Initialize(gen_mode, h_in, num_items);
-    Solve(h_in, h_reference, num_items, scan_op, initial_value);
-
-    // Allocate problem device arrays
-    InputT *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(InputT) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(InputT) * num_items, cudaMemcpyHostToDevice));
-
-    // Run Test
-    Test<BACKEND>(d_in, h_reference, num_items, scan_op, initial_value);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-}
-
-
-/**
- * Test DeviceScan on iterator type
- */
-template <
-    Backend         BACKEND,
-    typename        InputT,
-    typename        OutputT,
-    typename        ScanOpT,
-    typename        InitialValueT>
-void TestIterator(
-    int             num_items,
-    ScanOpT         scan_op,
-    InitialValueT   initial_value)
-{
-    printf("\nIterator %s %s cub::DeviceScan::%s %d items, %s->%s (%d->%d bytes)\n",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        (Equals<InitialValueT, NullType>::VALUE) ? "Inclusive" : "Exclusive",
-        (Equals<ScanOpT, Sum>::VALUE) ? "Sum" : "Scan",
-        num_items,
-        typeid(InputT).name(), typeid(OutputT).name(), (int) sizeof(InputT), (int) sizeof(OutputT));
-    fflush(stdout);
-
-    // Use a constant iterator as the input
-    InputT val = InputT();
-    ConstantInputIterator<InputT, int> h_in(val);
-
-    // Allocate host arrays
-    OutputT*  h_reference = new OutputT[num_items];
-
-    // Initialize problem and solution
-    Solve(h_in, h_reference, num_items, scan_op, initial_value);
-
-    // Run Test
-    Test<BACKEND>(h_in, h_reference, num_items, scan_op, initial_value);
-
-    // Cleanup
-    if (h_reference) delete[] h_reference;
-}
-
-
-/**
- * Test different gen modes
- */
-template <
-    Backend         BACKEND,
-    typename        InputT,
-    typename        OutputT,
-    typename        ScanOpT,
-    typename        InitialValueT>
-void Test(
-    int             num_items,
-    ScanOpT         scan_op,
-    InitialValueT   initial_value)
-{
-    TestPointer<BACKEND, InputT, OutputT>(  num_items, UNIFORM, scan_op, initial_value);
-    TestPointer<BACKEND, InputT, OutputT>(  num_items, RANDOM,  scan_op, initial_value);
-    TestIterator<BACKEND, InputT, OutputT>( num_items, scan_op, initial_value);
-}
-
-
-/**
- * Test different dispatch
- */
-template <
-    typename        InputT,
-    typename        OutputT,
-    typename        ScanOpT,
-    typename        InitialValueT>
-void Test(
-    int             num_items,
-    ScanOpT         scan_op,
-    InitialValueT   initial_value)
-{
-    Test<CUB, InputT, OutputT>(num_items, scan_op, initial_value);
-#ifdef CUB_CDP
-    Test<CDP, InputT, OutputT>(num_items, scan_op, initial_value);
-#endif
-}
-
-
-/**
- * Test different operators
- */
-template <typename InputT, typename OutputT>
-void TestOp(
-    int             num_items,
-    OutputT         identity,
-    OutputT         initial_value)
-{
-    // Exclusive (use identity as initial value because it will dispatch to *Sum variants that don't take initial values)
-    Test<InputT, OutputT>(num_items, cub::Sum(), identity);
-    Test<InputT, OutputT>(num_items, cub::Max(), identity);
-
-    // Exclusive (non-specialized, so we can test initial-value)
-    Test<InputT, OutputT>(num_items, WrapperFunctor<cub::Sum>(cub::Sum()), initial_value);
-    Test<InputT, OutputT>(num_items, WrapperFunctor<cub::Max>(cub::Max()), initial_value);
-
-    // Inclusive (no initial value)
-    Test<InputT, OutputT>(num_items, cub::Sum(), NullType());
-    Test<InputT, OutputT>(num_items, cub::Max(), NullType());
-}
-
-
-/**
- * Test different input sizes
- */
-template <
-    typename InputT,
-    typename OutputT>
-void TestSize(
-    int     num_items,
-    OutputT identity,
-    OutputT initial_value)
-{
-    if (num_items < 0)
-    {
-        TestOp<InputT>(0,        identity, initial_value);
-        TestOp<InputT>(1,        identity, initial_value);
-        TestOp<InputT>(100,      identity, initial_value);
-        TestOp<InputT>(10000,    identity, initial_value);
-        TestOp<InputT>(1000000,  identity, initial_value);
-
-        // Randomly select problem size between 1:10,000,000
-        unsigned int max_int = (unsigned int) -1;
-        for (int i = 0; i < 10; ++i)
-        {
-            unsigned int num_items;
-            RandomBits(num_items);
-            num_items = (unsigned int) ((double(num_items) * double(10000000)) / double(max_int));
-            num_items = CUB_MAX(1, num_items);
-            TestOp<InputT>(num_items,  identity, initial_value);
-        }
-    }
-    else
-    {
-        TestOp<InputT>(num_items, identity, initial_value);
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items = -1;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "[--cdp]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-    g_device_giga_bandwidth = args.device_giga_bandwidth;
-    printf("\n");
-
-#ifdef QUICKER_TEST
-
-    // Compile/run basic CUB test
-    if (num_items < 0) num_items = 32000000;
-
-    TestPointer<CUB, char, int>(         num_items    , UNIFORM, Sum(), (int) (0));
-    TestPointer<CUB, int, int>(         num_items    , UNIFORM, Sum(), (int) (0));
-
-#elif defined(QUICK_TEST)
-
-    // Get device ordinal
-    int device_ordinal;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-
-    // Get device SM version
-    int sm_version;
-    CubDebugExit(SmVersion(sm_version, device_ordinal));
-
-    // Compile/run quick tests
-    if (num_items < 0) num_items = 32000000;
-
-    TestPointer<CUB, char, char>(        num_items * ((sm_version <= 130) ? 1 : 4), UNIFORM, Sum(), char(0));
-    TestPointer<THRUST, char, char>(     num_items * ((sm_version <= 130) ? 1 : 4), UNIFORM, Sum(), char(0));
-
-    printf("----------------------------\n");
-    TestPointer<CUB, short, short>(       num_items * ((sm_version <= 130) ? 1 : 2), UNIFORM, Sum(), short(0));
-    TestPointer<THRUST, short, short>(    num_items * ((sm_version <= 130) ? 1 : 2), UNIFORM, Sum(), short(0));
-
-    printf("----------------------------\n");
-    TestPointer<CUB, int, int>(         num_items    , UNIFORM, Sum(), (int) (0));
-    TestPointer<THRUST, int, int>(      num_items    , UNIFORM, Sum(), (int) (0));
-
-    printf("----------------------------\n");
-    TestPointer<CUB, long long, long long>(   num_items / 2, UNIFORM, Sum(), (long long) (0));
-    TestPointer<THRUST, long long, long long>(num_items / 2, UNIFORM, Sum(), (long long) (0));
-
-    printf("----------------------------\n");
-    TestPointer<CUB, TestBar, TestBar>(     num_items / 4, UNIFORM, Sum(), TestBar());
-    TestPointer<THRUST, TestBar, TestBar>(  num_items / 4, UNIFORM, Sum(), TestBar());
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test different input+output data types
-        TestSize<unsigned char>(num_items,      (int) 0, (int) 99);
-
-        // Test same intput+output data types
-        TestSize<unsigned char>(num_items,      (unsigned char) 0,      (unsigned char) 99);
-        TestSize<char>(num_items,               (char) 0,               (char) 99);
-        TestSize<unsigned short>(num_items,     (unsigned short) 0,     (unsigned short)99);
-        TestSize<unsigned int>(num_items,       (unsigned int) 0,       (unsigned int) 99);
-        TestSize<unsigned long long>(num_items, (unsigned long long) 0, (unsigned long long) 99);
-
-        TestSize<uchar2>(num_items,     make_uchar2(0, 0),              make_uchar2(17, 21));
-        TestSize<char2>(num_items,      make_char2(0, 0),               make_char2(17, 21));
-        TestSize<ushort2>(num_items,    make_ushort2(0, 0),             make_ushort2(17, 21));
-        TestSize<uint2>(num_items,      make_uint2(0, 0),               make_uint2(17, 21));
-        TestSize<ulonglong2>(num_items, make_ulonglong2(0, 0),          make_ulonglong2(17, 21));
-        TestSize<uchar4>(num_items,     make_uchar4(0, 0, 0, 0),        make_uchar4(17, 21, 32, 85));
-        TestSize<char4>(num_items,      make_char4(0, 0, 0, 0),         make_char4(17, 21, 32, 85));
-
-        TestSize<ushort4>(num_items,    make_ushort4(0, 0, 0, 0),       make_ushort4(17, 21, 32, 85));
-        TestSize<uint4>(num_items,      make_uint4(0, 0, 0, 0),         make_uint4(17, 21, 32, 85));
-        TestSize<ulonglong4>(num_items, make_ulonglong4(0, 0, 0, 0),    make_ulonglong4(17, 21, 32, 85));
-
-        TestSize<TestFoo>(num_items,
-            TestFoo::MakeTestFoo(0, 0, 0, 0),
-            TestFoo::MakeTestFoo(1ll << 63, 1 << 31, short(1 << 15), char(1 << 7)));
-
-        TestSize<TestBar>(num_items,
-            TestBar(0, 0),
-            TestBar(1ll << 63, 1 << 31));
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_device_select_if.cu b/ml-xgboost/cub/test/test_device_select_if.cu
deleted file mode 100644
index ed52079..0000000
--- a/ml-xgboost/cub/test/test_device_select_if.cu
+++ /dev/null
@@ -1,1039 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceSelect::If and DevicePartition::If utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/device/device_select.cuh>
-#include <cub/device/device_partition.cuh>
-#include <cub/iterator/counting_input_iterator.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/copy.h>
-#include <thrust/partition.h>
-#include <thrust/iterator/reverse_iterator.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose               = false;
-int                     g_timing_iterations     = 0;
-int                     g_repeat                = 0;
-float                   g_device_giga_bandwidth;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    THRUST,     // Thrust method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-// Selection functor type
-template <typename T>
-struct LessThan
-{
-    T compare;
-
-    __host__ __device__ __forceinline__
-    LessThan(T compare) : compare(compare) {}
-
-    __host__ __device__ __forceinline__
-    bool operator()(const T &a) const {
-        return (a < compare);
-    }
-};
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB DeviceSelect entrypoints
-//---------------------------------------------------------------------
-
-
-/**
- * Dispatch to select if entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>               dispatch_to,
-    Int2Type<false>             is_flagged,
-    Int2Type<false>             is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to partition if entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>               dispatch_to,
-    Int2Type<false>             is_flagged,
-    Int2Type<true>              is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DevicePartition::If(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to select flagged entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>               dispatch_to,
-    Int2Type<true>              is_flagged,
-    Int2Type<false>             partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSelect::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-/**
- * Dispatch to partition flagged entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>               dispatch_to,
-    Int2Type<true>              is_flagged,
-    Int2Type<true>              partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DevicePartition::Flagged(d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-/**
- * Dispatch to select if entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-__host__ __forceinline__
-cudaError_t Dispatch(
-    Int2Type<THRUST>            dispatch_to,
-    Int2Type<false>             is_flagged,
-    Int2Type<false>             is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<OutputT>         d_out_wrapper_end;
-        thrust::device_ptr<InputT>          d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT>         d_out_wrapper(d_out);
-
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_wrapper_end = thrust::copy_if(d_in_wrapper, d_in_wrapper + num_items, d_out_wrapper, select_op);
-        }
-
-        OffsetT num_selected = d_out_wrapper_end - d_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_selected_out, &num_selected, sizeof(OffsetT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch to partition if entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-__host__ __forceinline__
-cudaError_t Dispatch(
-    Int2Type<THRUST>            dispatch_to,
-    Int2Type<false>             is_flagged,
-    Int2Type<true>              is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    typedef thrust::reverse_iterator<thrust::device_ptr<OutputT> > ReverseOutputIteratorT;
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::pair<thrust::device_ptr<OutputT>, ReverseOutputIteratorT> d_out_wrapper_end;
-
-        thrust::device_ptr<InputT>       d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT>       d_out_wrapper(d_out);
-
-        ReverseOutputIteratorT d_out_unselected(d_out_wrapper + num_items);
-
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_wrapper_end = thrust::partition_copy(
-                d_in_wrapper,
-                d_in_wrapper + num_items,
-                d_out_wrapper,
-                d_out_unselected,
-                select_op);
-        }
-
-        OffsetT num_selected = d_out_wrapper_end.first - d_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_selected_out, &num_selected, sizeof(OffsetT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch to select flagged entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-__host__ __forceinline__
-cudaError_t Dispatch(
-    Int2Type<THRUST>            dispatch_to,
-    Int2Type<true>              is_flagged,
-    Int2Type<false>             is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The flag type
-    typedef typename std::iterator_traits<FlagIteratorT>::value_type FlagT;
-
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<OutputT>     d_out_wrapper_end;
-        thrust::device_ptr<InputT>      d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT>     d_out_wrapper(d_out);
-        thrust::device_ptr<FlagT>       d_flags_wrapper(d_flags);
-
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_wrapper_end = thrust::copy_if(d_in_wrapper, d_in_wrapper + num_items, d_flags_wrapper, d_out_wrapper, Cast<bool>());
-        }
-
-        OffsetT num_selected = d_out_wrapper_end - d_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_selected_out, &num_selected, sizeof(OffsetT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-
-/**
- * Dispatch to partition flagged entrypoint
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-__host__ __forceinline__
-cudaError_t Dispatch(
-    Int2Type<THRUST>            dispatch_to,
-    Int2Type<true>              is_flagged,
-    Int2Type<true>              is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The flag type
-    typedef typename std::iterator_traits<FlagIteratorT>::value_type FlagT;
-
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    typedef thrust::reverse_iterator<thrust::device_ptr<OutputT> > ReverseOutputIteratorT;
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::pair<thrust::device_ptr<OutputT>, ReverseOutputIteratorT> d_out_wrapper_end;
-
-        thrust::device_ptr<InputT>  d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT> d_out_wrapper(d_out);
-        thrust::device_ptr<FlagT>   d_flags_wrapper(d_flags);
-        ReverseOutputIteratorT      d_out_unselected(d_out_wrapper + num_items);
-
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_wrapper_end = thrust::partition_copy(
-                d_in_wrapper,
-                d_in_wrapper + num_items,
-                d_flags_wrapper,
-                d_out_wrapper,
-                d_out_unselected,
-                Cast<bool>());
-        }
-
-        OffsetT num_selected = d_out_wrapper_end.first - d_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_selected_out, &num_selected, sizeof(OffsetT), cudaMemcpyHostToDevice));
-    }
-
-    return cudaSuccess;
-}
-
-
-//---------------------------------------------------------------------
-// CUDA Nested Parallelism Test Kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceSelect
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT, typename IsFlaggedTag, typename IsPartitionTag>
-__global__ void CnpDispatchKernel(
-    IsFlaggedTag                is_flagged,
-    IsPartitionTag              is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t                      temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    bool                        debug_synchronous)
-{
-
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch(Int2Type<CUB>(), is_flagged, is_partition, timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, select_op, 0, debug_synchronous);
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/**
- * Dispatch to CDP kernel
- */
-template <typename InputIteratorT, typename FlagIteratorT, typename SelectOpT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT, typename IsFlaggedTag, typename IsPartitionTag>
-cudaError_t Dispatch(
-    Int2Type<CDP>               dispatch_to,
-    IsFlaggedTag                is_flagged,
-    IsPartitionTag              is_partition,
-    int                         timing_timing_iterations,
-    size_t*                     d_temp_storage_bytes,
-    cudaError_t*                d_cdp_error,
-
-    void*                       d_temp_storage,
-    size_t&                     temp_storage_bytes,
-    InputIteratorT              d_in,
-    FlagIteratorT               d_flags,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    SelectOpT                   select_op,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(is_flagged, is_partition, timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, select_op, debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem
- */
-template <typename T>
-void Initialize(
-    T*  h_in,
-    int num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        // Initialize each item to a randomly selected value from [0..126]
-        unsigned int value;
-        RandomBits(value, 0, 0, 7);
-        if (value == 127)
-            value = 126;
-        InitValue(INTEGER_SEED, h_in[i], value);
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve selection problem (and set corresponding flags)
- */
-template <
-    typename        InputIteratorT,
-    typename        FlagIteratorT,
-    typename        SelectOpT,
-    typename        T>
-int Solve(
-    InputIteratorT  h_in,
-    SelectOpT       select_op,
-    T*              h_reference,
-    FlagIteratorT   h_flags,
-    int             num_items)
-{
-    int num_selected = 0;
-    for (int i = 0; i < num_items; ++i)
-    {
-        if ((h_flags[i] = select_op(h_in[i])))
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-        else
-        {
-            h_reference[num_items - (i - num_selected) - 1] = h_in[i];
-        }
-    }
-
-    return num_selected;
-}
-
-
-
-/**
- * Test DeviceSelect for a given problem input
- */
-template <
-    Backend             BACKEND,
-    bool                IS_FLAGGED,
-    bool                IS_PARTITION,
-    typename            DeviceInputIteratorT,
-    typename            FlagT,
-    typename            SelectOpT,
-    typename            T>
-void Test(
-    DeviceInputIteratorT    d_in,
-    FlagT*                  h_flags,
-    SelectOpT               select_op,
-    T*                      h_reference,
-    int                     num_selected,
-    int                     num_items)
-{
-    // Allocate device flags, output, and num-selected
-    FlagT*      d_flags = NULL;
-    T*          d_out = NULL;
-    int*        d_num_selected_out = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_flags, sizeof(FlagT) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate CDP device arrays
-    size_t*         d_temp_storage_bytes = NULL;
-    cudaError_t*    d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), Int2Type<IS_FLAGGED>(), Int2Type<IS_PARTITION>(), 1, d_temp_storage_bytes, d_cdp_error,
-    d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, select_op, 0, true));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Copy flags and clear device output array
-    CubDebugExit(cudaMemcpy(d_flags, h_flags, sizeof(FlagT) * num_items, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * num_items));
-    CubDebugExit(cudaMemset(d_num_selected_out, 0, sizeof(int)));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), Int2Type<IS_FLAGGED>(), Int2Type<IS_PARTITION>(), 1, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, select_op, 0, true));
-
-    // Check for correctness (and display results, if specified)
-    int compare1 = (IS_PARTITION) ?
-        CompareDeviceResults(h_reference, d_out, num_items, true, g_verbose) :
-        CompareDeviceResults(h_reference, d_out, num_selected, true, g_verbose);
-    printf("\t Data %s\n", compare1 ? "FAIL" : "PASS");
-
-    int compare2 = CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s\n", compare2 ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), Int2Type<IS_FLAGGED>(), Int2Type<IS_PARTITION>(), g_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_flags, d_out, d_num_selected_out, num_items, select_op, 0, false));
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float   avg_millis          = elapsed_millis / g_timing_iterations;
-        float   giga_rate           = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        int     num_output_items    = (IS_PARTITION) ? num_items : num_selected;
-        int     num_flag_items      = (IS_FLAGGED) ? num_items : 0;
-        size_t  num_bytes           = sizeof(T) * (num_items + num_output_items) + sizeof(FlagT) * num_flag_items;
-        float   giga_bandwidth      = float(num_bytes) / avg_millis / 1000.0f / 1000.0f;
-
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s, %.1f%% peak", avg_millis, giga_rate, giga_bandwidth, giga_bandwidth / g_device_giga_bandwidth * 100.0);
-    }
-    printf("\n\n");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Cleanup
-    if (d_flags) CubDebugExit(g_allocator.DeviceFree(d_flags));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, compare1 | compare2);
-}
-
-
-/**
- * Test on pointer type
- */
-template <
-    Backend         BACKEND,
-    bool            IS_FLAGGED,
-    bool            IS_PARTITION,
-    typename        T>
-void TestPointer(
-    int             num_items,
-    float           select_ratio)
-{
-    typedef char FlagT;
-
-    // Allocate host arrays
-    T*      h_in        = new T[num_items];
-    FlagT*  h_flags     = new FlagT[num_items];
-    T*      h_reference = new T[num_items];
-
-    // Initialize input
-    Initialize(h_in, num_items);
-
-    // Select a comparison value that is select_ratio through the space of [0,127]
-    T compare;
-    if (select_ratio <= 0.0)
-        InitValue(INTEGER_SEED, compare, 0);        // select none
-    else if (select_ratio >= 1.0)
-        InitValue(INTEGER_SEED, compare, 127);      // select all
-    else
-        InitValue(INTEGER_SEED, compare, int(double(double(127) * select_ratio)));
-
-    LessThan<T> select_op(compare);
-    int num_selected = Solve(h_in, select_op, h_reference, h_flags, num_items);
-
-    if (g_verbose) std::cout << "\nComparison item: " << compare << "\n";
-    printf("\nPointer %s cub::%s::%s %d items, %d selected (select ratio %.3f), %s %d-byte elements\n",
-        (IS_PARTITION) ? "DevicePartition" : "DeviceSelect",
-        (IS_FLAGGED) ? "Flagged" : "If",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        num_items, num_selected, float(num_selected) / num_items, typeid(T).name(), (int) sizeof(T));
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    T *d_in = NULL;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * num_items, cudaMemcpyHostToDevice));
-
-    // Run Test
-    Test<BACKEND, IS_FLAGGED, IS_PARTITION>(d_in, h_flags, select_op, h_reference, num_selected, num_items);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (h_flags) delete[] h_flags;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-}
-
-
-/**
- * Test on iterator type
- */
-template <
-    Backend         BACKEND,
-    bool            IS_FLAGGED,
-    bool            IS_PARTITION,
-    typename        T>
-void TestIterator(
-    int             num_items,
-    float           select_ratio)
-{
-    typedef char FlagT;
-
-    // Allocate host arrays
-    T*      h_reference = new T[num_items];
-    FlagT*  h_flags = new FlagT[num_items];
-
-    // Use counting iterator as the input
-    CountingInputIterator<T, int> h_in(0);
-
-    // Select a comparison value that is select_ratio through the space of [0,127]
-    T compare;
-    if (select_ratio <= 0.0)
-        InitValue(INTEGER_SEED, compare, 0);        // select none
-    else if (select_ratio >= 1.0)
-        InitValue(INTEGER_SEED, compare, 127);      // select all
-    else
-        InitValue(INTEGER_SEED, compare, int(double(double(127) * select_ratio)));
-
-    LessThan<T> select_op(compare);
-    int num_selected = Solve(h_in, select_op, h_reference, h_flags, num_items);
-
-    if (g_verbose) std::cout << "\nComparison item: " << compare << "\n";
-    printf("\nIterator %s cub::%s::%s %d items, %d selected (select ratio %.3f), %s %d-byte elements\n",
-        (IS_PARTITION) ? "DevicePartition" : "DeviceSelect",
-        (IS_FLAGGED) ? "Flagged" : "If",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        num_items, num_selected, float(num_selected) / num_items, typeid(T).name(), (int) sizeof(T));
-    fflush(stdout);
-
-    // Run Test
-    Test<BACKEND, IS_FLAGGED, IS_PARTITION>(h_in, h_flags, select_op, h_reference, num_selected, num_items);
-
-    // Cleanup
-    if (h_reference) delete[] h_reference;
-    if (h_flags) delete[] h_flags;
-}
-
-
-/**
- * Test different selection ratios
- */
-template <
-    Backend         BACKEND,
-    bool            IS_FLAGGED,
-    bool            IS_PARTITION,
-    typename        T>
-void Test(
-    int             num_items)
-{
-    for (float select_ratio = 0.0f; select_ratio <= 1.0f; select_ratio += 0.2f)
-    {
-        TestPointer<BACKEND, IS_FLAGGED, IS_PARTITION, T>(num_items, select_ratio);
-    }
-}
-
-
-/**
- * Test (select vs. partition) and (flagged vs. functor)
- */
-template <
-    Backend         BACKEND,
-    typename        T>
-void TestMethod(
-    int             num_items)
-{
-    // Functor
-    Test<BACKEND, false, false, T>(num_items);
-    Test<BACKEND, false, true, T>(num_items);
-
-    // Flagged
-    Test<BACKEND, true, false, T>(num_items);
-    Test<BACKEND, true, true, T>(num_items);
-}
-
-
-/**
- * Test different dispatch
- */
-template <
-    typename        T>
-void TestOp(
-    int             num_items)
-{
-    TestMethod<CUB, T>(num_items);
-#ifdef CUB_CDP
-    TestMethod<CDP, T>(num_items);
-#endif
-}
-
-
-/**
- * Test different input sizes
- */
-template <typename T>
-void Test(
-    int             num_items)
-{
-    if (num_items < 0)
-    {
-        TestOp<T>(0);
-        TestOp<T>(1);
-        TestOp<T>(100);
-        TestOp<T>(10000);
-        TestOp<T>(1000000);
-    }
-    else
-    {
-        TestOp<T>(num_items);
-    }
-}
-
-/**
- * Test select/partition on pointer types
- */
-template <typename T>
-void ComparePointer(
-    int             num_items,
-    float           select_ratio)
-{
-    printf("-- Select-if ----------------------------\n");
-    TestPointer<CUB, false, false, T>(num_items, select_ratio);
-    TestPointer<THRUST, false, false, T>(num_items, select_ratio);
-
-    printf("-- Partition-if ----------------------------\n");
-    TestPointer<CUB, false, true, T>(num_items, select_ratio);
-    TestPointer<THRUST, false, true, T>(num_items, select_ratio);
-
-    printf("-- Select-flagged ----------------------------\n");
-    TestPointer<CUB, true, false, T>(num_items, select_ratio);
-    TestPointer<THRUST, true, false, T>(num_items, select_ratio);
-
-    printf("-- Partition-flagged ----------------------------\n");
-    TestPointer<CUB, true, true, T>(num_items, select_ratio);
-    TestPointer<THRUST, true, true, T>(num_items, select_ratio);
-
-}
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = -1;
-    float select_ratio      = 0.5;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-    args.GetCmdLineArgument("ratio", select_ratio);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--ratio=<selection ratio, default 0.5>] "
-            "[--repeat=<repetitions of entire test suite>] "
-            "[--v] "
-            "[--cdp] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-    g_device_giga_bandwidth = args.device_giga_bandwidth;
-    printf("\n");
-
-#ifdef QUICKER_TEST
-
-    // Compile/run basic CUB test
-    if (num_items < 0) num_items = 32000000;
-
-    printf("-- Select-if ----------------------------\n");
-    TestPointer<CUB, false, false, int>(num_items, select_ratio);
-
-    printf("-- Partition-if ----------------------------\n");
-    TestPointer<CUB, false, true, int>(num_items, select_ratio);
-
-    printf("-- Select-flagged ----------------------------\n");
-    TestPointer<CUB, true, false, int>(num_items, select_ratio);
-
-    printf("-- Partition-flagged ----------------------------\n");
-    TestPointer<CUB, true, true, int>(num_items, select_ratio);
-
-
-#elif defined(QUICK_TEST)
-
-    // Get device ordinal
-    int device_ordinal;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-
-    // Get device SM version
-    int sm_version;
-    CubDebugExit(SmVersion(sm_version, device_ordinal));
-
-    // Compile/run quick tests
-    if (num_items < 0) num_items = 32000000;
-
-    printf("-- Iterator ----------------------------\n");
-    TestIterator<CUB, false, false, int>(num_items, select_ratio);
-
-    ComparePointer<char>(       num_items * ((sm_version <= 130) ? 1 : 4),  select_ratio);
-    ComparePointer<short>(      num_items * ((sm_version <= 130) ? 1 : 2),  select_ratio);
-    ComparePointer<int>(        num_items,                                  select_ratio);
-    ComparePointer<long long>(  num_items / 2,                              select_ratio);
-    ComparePointer<TestFoo>(    num_items / 4,                              select_ratio);
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test different input types
-        Test<unsigned char>(num_items);
-        Test<unsigned short>(num_items);
-        Test<unsigned int>(num_items);
-        Test<unsigned long long>(num_items);
-
-        Test<uchar2>(num_items);
-        Test<ushort2>(num_items);
-        Test<uint2>(num_items);
-        Test<ulonglong2>(num_items);
-
-        Test<uchar4>(num_items);
-        Test<ushort4>(num_items);
-        Test<uint4>(num_items);
-        Test<ulonglong4>(num_items);
-
-        Test<TestFoo>(num_items);
-        Test<TestBar>(num_items);
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_device_select_unique.cu b/ml-xgboost/cub/test/test_device_select_unique.cu
deleted file mode 100644
index 34a3ae9..0000000
--- a/ml-xgboost/cub/test/test_device_select_unique.cu
+++ /dev/null
@@ -1,648 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of DeviceSelect::Unique utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/util_allocator.cuh>
-#include <cub/iterator/counting_input_iterator.cuh>
-#include <cub/device/device_select.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/unique.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose               = false;
-int                     g_timing_iterations     = 0;
-int                     g_repeat                = 0;
-float                   g_device_giga_bandwidth;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    THRUST,     // Thrust method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-//---------------------------------------------------------------------
-// Dispatch to different CUB DeviceSelect entrypoints
-//---------------------------------------------------------------------
-
-
-/**
- * Dispatch to unique entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-CUB_RUNTIME_FUNCTION __forceinline__
-cudaError_t Dispatch(
-    Int2Type<CUB>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    OutputIteratorT              d_out,
-    NumSelectedIteratorT         d_num_selected_out,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    cudaError_t error = cudaSuccess;
-    for (int i = 0; i < timing_timing_iterations; ++i)
-    {
-        error = DeviceSelect::Unique(d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, stream, debug_synchronous);
-    }
-    return error;
-}
-
-
-//---------------------------------------------------------------------
-// Dispatch to different Thrust entrypoints
-//---------------------------------------------------------------------
-
-
-/**
- * Dispatch to unique entrypoint
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-__host__ __forceinline__
-cudaError_t Dispatch(
-    Int2Type<THRUST>            dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void                        *d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    OutputIteratorT             d_out,
-    NumSelectedIteratorT        d_num_selected_out,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // The input value type
-    typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
-
-    // The output value type
-    typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE),  // OutputT =  (if output iterator's value type is void) ?
-        typename std::iterator_traits<InputIteratorT>::value_type,                                          // ... then the input iterator's value type,
-        typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT;                          // ... else the output iterator's value type
-
-    if (d_temp_storage == 0)
-    {
-        temp_storage_bytes = 1;
-    }
-    else
-    {
-        thrust::device_ptr<OutputT> d_out_wrapper_end;
-        thrust::device_ptr<InputT> d_in_wrapper(d_in);
-        thrust::device_ptr<OutputT> d_out_wrapper(d_out);
-        for (int i = 0; i < timing_timing_iterations; ++i)
-        {
-            d_out_wrapper_end = thrust::unique_copy(d_in_wrapper, d_in_wrapper + num_items, d_out_wrapper);
-        }
-
-        OffsetT num_selected = d_out_wrapper_end - d_out_wrapper;
-        CubDebugExit(cudaMemcpy(d_num_selected_out, &num_selected, sizeof(OffsetT), cudaMemcpyHostToDevice));
-
-    }
-
-    return cudaSuccess;
-}
-
-
-
-//---------------------------------------------------------------------
-// CUDA Nested Parallelism Test Kernel
-//---------------------------------------------------------------------
-
-/**
- * Simple wrapper kernel to invoke DeviceSelect
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-__global__ void CnpDispatchKernel(
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      temp_storage_bytes,
-    InputIteratorT              d_in,
-    OutputIteratorT              d_out,
-    NumSelectedIteratorT         d_num_selected_out,
-    OffsetT                     num_items,
-    bool                        debug_synchronous)
-{
-
-#ifndef CUB_CDP
-    *d_cdp_error = cudaErrorNotSupported;
-#else
-    *d_cdp_error = Dispatch(Int2Type<CUB>(), timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, 0, debug_synchronous);
-    *d_temp_storage_bytes = temp_storage_bytes;
-#endif
-}
-
-
-/**
- * Dispatch to CDP kernel
- */
-template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT, typename OffsetT>
-cudaError_t Dispatch(
-    Int2Type<CDP>               dispatch_to,
-    int                         timing_timing_iterations,
-    size_t                      *d_temp_storage_bytes,
-    cudaError_t                 *d_cdp_error,
-
-    void*               d_temp_storage,
-    size_t                      &temp_storage_bytes,
-    InputIteratorT              d_in,
-    OutputIteratorT              d_out,
-    NumSelectedIteratorT         d_num_selected_out,
-    OffsetT                     num_items,
-    cudaStream_t                stream,
-    bool                        debug_synchronous)
-{
-    // Invoke kernel to invoke device-side dispatch
-    CnpDispatchKernel<<<1,1>>>(timing_timing_iterations, d_temp_storage_bytes, d_cdp_error,
-        d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, debug_synchronous);
-
-    // Copy out temp_storage_bytes
-    CubDebugExit(cudaMemcpy(&temp_storage_bytes, d_temp_storage_bytes, sizeof(size_t) * 1, cudaMemcpyDeviceToHost));
-
-    // Copy out error
-    cudaError_t retval;
-    CubDebugExit(cudaMemcpy(&retval, d_cdp_error, sizeof(cudaError_t) * 1, cudaMemcpyDeviceToHost));
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Test generation
-//---------------------------------------------------------------------
-
-
-/**
- * Initialize problem
- */
-template <typename T>
-void Initialize(
-    int         entropy_reduction,
-    T           *h_in,
-    int         num_items,
-    int         max_segment)
-{
-    unsigned int max_int = (unsigned int) -1;
-
-    int key = 0;
-    int i = 0;
-    while (i < num_items)
-    {
-        // Select number of repeating occurrences for the current run
-        int repeat;
-        if (max_segment < 0)
-        {
-            repeat = num_items;
-        }
-        else if (max_segment < 2)
-        {
-            repeat = 1;
-        }
-        else
-        {
-            RandomBits(repeat, entropy_reduction);
-            repeat = (int) ((double(repeat) * double(max_segment)) / double(max_int));
-            repeat = CUB_MAX(1, repeat);
-        }
-
-        int j = i;
-        while (j < CUB_MIN(i + repeat, num_items))
-        {
-            InitValue(INTEGER_SEED, h_in[j], key);
-            j++;
-        }
-
-        i = j;
-        key++;
-    }
-
-    if (g_verbose)
-    {
-        printf("Input:\n");
-        DisplayResults(h_in, num_items);
-        printf("\n\n");
-    }
-}
-
-
-/**
- * Solve unique problem
- */
-template <
-    typename        InputIteratorT,
-    typename        T>
-int Solve(
-    InputIteratorT  h_in,
-    T               *h_reference,
-    int             num_items)
-{
-    int num_selected = 0;
-    if (num_items > 0)
-    {
-        h_reference[num_selected] = h_in[0];
-        num_selected++;
-    }
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        if (h_in[i] != h_in[i - 1])
-        {
-            h_reference[num_selected] = h_in[i];
-            num_selected++;
-        }
-    }
-
-    return num_selected;
-}
-
-
-
-/**
- * Test DeviceSelect for a given problem input
- */
-template <
-    Backend             BACKEND,
-    typename            DeviceInputIteratorT,
-    typename            T>
-void Test(
-    DeviceInputIteratorT d_in,
-    T                   *h_reference,
-    int                 num_selected,
-    int                 num_items)
-{
-    // Allocate device output array and num selected
-    T       *d_out            = NULL;
-    int     *d_num_selected_out   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * num_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_num_selected_out, sizeof(int)));
-
-    // Allocate CDP device arrays
-    size_t          *d_temp_storage_bytes = NULL;
-    cudaError_t     *d_cdp_error = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_temp_storage_bytes,  sizeof(size_t) * 1));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_cdp_error,           sizeof(cudaError_t) * 1));
-
-    // Allocate temporary storage
-    void            *d_temp_storage = NULL;
-    size_t          temp_storage_bytes = 0;
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, 0, true));
-    CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-    // Clear device output array
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * num_items));
-    CubDebugExit(cudaMemset(d_num_selected_out, 0, sizeof(int)));
-
-    // Run warmup/correctness iteration
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), 1, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, 0, true));
-
-    // Check for correctness (and display results, if specified)
-    int compare1 = CompareDeviceResults(h_reference, d_out, num_selected, true, g_verbose);
-    printf("\t Data %s ", compare1 ? "FAIL" : "PASS");
-
-    int compare2 = CompareDeviceResults(&num_selected, d_num_selected_out, 1, true, g_verbose);
-    printf("\t Count %s ", compare2 ? "FAIL" : "PASS");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Performance
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    CubDebugExit(Dispatch(Int2Type<BACKEND>(), g_timing_iterations, d_temp_storage_bytes, d_cdp_error, d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, 0, false));
-    gpu_timer.Stop();
-    float elapsed_millis = gpu_timer.ElapsedMillis();
-
-    // Display performance
-    if (g_timing_iterations > 0)
-    {
-        float avg_millis        = elapsed_millis / g_timing_iterations;
-        float giga_rate         = float(num_items) / avg_millis / 1000.0f / 1000.0f;
-        float giga_bandwidth    = float((num_items + num_selected) * sizeof(T)) / avg_millis / 1000.0f / 1000.0f;
-        printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s, %.1f%% peak", avg_millis, giga_rate, giga_bandwidth, giga_bandwidth / g_device_giga_bandwidth * 100.0);
-    }
-    printf("\n\n");
-
-    // Flush any stdout/stderr
-    fflush(stdout);
-    fflush(stderr);
-
-    // Cleanup
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_num_selected_out) CubDebugExit(g_allocator.DeviceFree(d_num_selected_out));
-    if (d_temp_storage_bytes) CubDebugExit(g_allocator.DeviceFree(d_temp_storage_bytes));
-    if (d_cdp_error) CubDebugExit(g_allocator.DeviceFree(d_cdp_error));
-    if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-
-    // Correctness asserts
-    AssertEquals(0, compare1 | compare2);
-}
-
-
-/**
- * Test DeviceSelect on pointer type
- */
-template <
-    Backend         BACKEND,
-    typename        T>
-void TestPointer(
-    int             num_items,
-    int             entropy_reduction,
-    int             max_segment)
-{
-    // Allocate host arrays
-    T*  h_in        = new T[num_items];
-    T*  h_reference = new T[num_items];
-
-    // Initialize problem and solution
-    Initialize(entropy_reduction, h_in, num_items, max_segment);
-    int num_selected = Solve(h_in, h_reference, num_items);
-
-    printf("\nPointer %s cub::DeviceSelect::Unique %d items, %d selected (avg run length %.3f), %s %d-byte elements, entropy_reduction %d\n",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        num_items, num_selected, float(num_items) / num_selected,
-        typeid(T).name(),
-        (int) sizeof(T),
-        entropy_reduction);
-    fflush(stdout);
-
-    // Allocate problem device arrays
-    T *d_in = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * num_items));
-
-    // Initialize device input
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * num_items, cudaMemcpyHostToDevice));
-
-    // Run Test
-    Test<BACKEND>(d_in, h_reference, num_selected, num_items);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-}
-
-
-/**
- * Test DeviceSelect on iterator type
- */
-template <
-    Backend         BACKEND,
-    typename        T>
-void TestIterator(
-    int             num_items)
-{
-    // Use a counting iterator as the input
-    CountingInputIterator<T, int> h_in(0);
-
-    // Allocate host arrays
-    T*  h_reference = new T[num_items];
-
-    // Initialize problem and solution
-    int num_selected = Solve(h_in, h_reference, num_items);
-
-    printf("\nIterator %s cub::DeviceSelect::Unique %d items, %d selected (avg run length %.3f), %s %d-byte elements\n",
-        (BACKEND == CDP) ? "CDP CUB" : (BACKEND == THRUST) ? "Thrust" : "CUB",
-        num_items, num_selected, float(num_items) / num_selected,
-        typeid(T).name(),
-        (int) sizeof(T));
-    fflush(stdout);
-
-    // Run Test
-    Test<BACKEND>(h_in, h_reference, num_selected, num_items);
-
-    // Cleanup
-    if (h_reference) delete[] h_reference;
-}
-
-
-/**
- * Test different gen modes
- */
-template <
-    Backend         BACKEND,
-    typename        T>
-void Test(
-    int             num_items)
-{
-    for (int max_segment = 1; ((max_segment > 0) && (max_segment < num_items)); max_segment *= 11)
-    {
-        TestPointer<BACKEND, T>(num_items, 0, max_segment);
-        TestPointer<BACKEND, T>(num_items, 2, max_segment);
-        TestPointer<BACKEND, T>(num_items, 7, max_segment);
-    }
-}
-
-
-/**
- * Test different dispatch
- */
-template <
-    typename        T>
-void TestOp(
-    int             num_items)
-{
-    Test<CUB, T>(num_items);
-#ifdef CUB_CDP
-    Test<CDP, T>(num_items);
-#endif
-}
-
-
-/**
- * Test different input sizes
- */
-template <typename T>
-void Test(
-    int             num_items)
-{
-    if (num_items < 0)
-    {
-        TestOp<T>(0);
-        TestOp<T>(1);
-        TestOp<T>(100);
-        TestOp<T>(10000);
-        TestOp<T>(1000000);
-    }
-    else
-    {
-        TestOp<T>(num_items);
-    }
-}
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    int num_items           = -1;
-    int entropy_reduction   = 0;
-    int maxseg              = 1000;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("n", num_items);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    args.GetCmdLineArgument("repeat", g_repeat);
-    args.GetCmdLineArgument("maxseg", maxseg);
-    args.GetCmdLineArgument("entropy", entropy_reduction);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--n=<input items> "
-            "[--i=<timing iterations> "
-            "[--device=<device-id>] "
-            "[--maxseg=<max segment length>]"
-            "[--entropy=<segment length bit entropy reduction rounds>]"
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "[--cdp]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-    g_device_giga_bandwidth = args.device_giga_bandwidth;
-    printf("\n");
-
-#ifdef QUICKER_TEST
-
-    // Compile/run basic CUB test
-    if (num_items < 0) num_items = 32000000;
-    TestPointer<CUB, int>(         num_items,                                 entropy_reduction, maxseg);
-
-#elif defined(QUICK_TEST)
-
-    // Get device ordinal
-    int device_ordinal;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-
-    // Get device SM version
-    int sm_version;
-    CubDebugExit(SmVersion(sm_version, device_ordinal));
-
-    // Compile/run quick tests
-    if (num_items < 0) num_items = 32000000;
-
-    printf("-- Iterator ----------------------------\n");
-    TestIterator<CUB, int>(        num_items,                                 entropy_reduction, maxseg);
-
-    printf("----------------------------\n");
-    TestPointer<CUB, char>(        num_items * ((sm_version <= 130) ? 1 : 4), entropy_reduction, maxseg);
-    TestPointer<THRUST, char>(     num_items * ((sm_version <= 130) ? 1 : 4), entropy_reduction, maxseg);
-
-    printf("----------------------------\n");
-    TestPointer<CUB, short>(       num_items * ((sm_version <= 130) ? 1 : 2), entropy_reduction, maxseg);
-    TestPointer<THRUST, short>(    num_items * ((sm_version <= 130) ? 1 : 2), entropy_reduction, maxseg);
-
-    printf("----------------------------\n");
-    TestPointer<CUB, int>(         num_items,                                 entropy_reduction, maxseg);
-    TestPointer<THRUST, int>(      num_items,                                 entropy_reduction, maxseg);
-
-    printf("----------------------------\n");
-    TestPointer<CUB, long long>(   num_items / 2,                             entropy_reduction, maxseg);
-    TestPointer<THRUST, long long>(num_items / 2,                             entropy_reduction, maxseg);
-
-    printf("----------------------------\n");
-    TestPointer<CUB, TestFoo>(     num_items / 4,                             entropy_reduction, maxseg);
-    TestPointer<THRUST, TestFoo>(  num_items / 4,                             entropy_reduction, maxseg);
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test different input types
-        Test<unsigned char>(num_items);
-        Test<unsigned short>(num_items);
-        Test<unsigned int>(num_items);
-        Test<unsigned long long>(num_items);
-
-        Test<uchar2>(num_items);
-        Test<ushort2>(num_items);
-        Test<uint2>(num_items);
-        Test<ulonglong2>(num_items);
-
-        Test<uchar4>(num_items);
-        Test<ushort4>(num_items);
-        Test<uint4>(num_items);
-        Test<ulonglong4>(num_items);
-
-        Test<TestFoo>(num_items);
-        Test<TestBar>(num_items);
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_grid_barrier.cu b/ml-xgboost/cub/test/test_grid_barrier.cu
deleted file mode 100644
index c47f98a..0000000
--- a/ml-xgboost/cub/test/test_grid_barrier.cu
+++ /dev/null
@@ -1,152 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test evaluation for software global barrier throughput
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-
-#include <cub/grid/grid_barrier.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-/**
- * Kernel that iterates through the specified number of software global barriers
- */
-__global__ void Kernel(
-    GridBarrier global_barrier,
-    int iterations)
-{
-    for (int i = 0; i < iterations; i++)
-    {
-        global_barrier.Sync();
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    cudaError_t retval = cudaSuccess;
-
-    // Defaults
-    int iterations = 10000;
-    int block_size = 128;
-    int grid_size = -1;
-
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-
-    // Get args
-    args.GetCmdLineArgument("i", iterations);
-    args.GetCmdLineArgument("grid-size", grid_size);
-    args.GetCmdLineArgument("block-size", block_size);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>]"
-            "[--i=<iterations>]"
-            "[--grid-size<grid-size>]"
-            "[--block-size<block-size>]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get device ordinal
-    int device_ordinal;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-
-    // Get device SM version
-    int sm_version;
-    CubDebugExit(SmVersion(sm_version, device_ordinal));
-
-    // Get SM properties
-    int sm_count, max_block_threads, max_sm_occupancy;
-    CubDebugExit(cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal));
-    CubDebugExit(cudaDeviceGetAttribute(&max_block_threads, cudaDevAttrMaxThreadsPerBlock, device_ordinal));
-    CubDebugExit(MaxSmOccupancy(max_sm_occupancy, EmptyKernel<void>, 32));
-
-    // Compute grid size and occupancy
-    int occupancy = CUB_MIN((max_block_threads / block_size), max_sm_occupancy);
-
-    if (grid_size == -1)
-    {
-        grid_size = occupancy * sm_count;
-    }
-    else
-    {
-        occupancy = grid_size / sm_count;
-    }
-
-    printf("Initializing software global barrier for Kernel<<<%d,%d>>> with %d occupancy\n",
-        grid_size, block_size, occupancy);
-    fflush(stdout);
-
-    // Init global barrier
-    GridBarrierLifetime global_barrier;
-    global_barrier.Setup(grid_size);
-
-    // Time kernel
-    GpuTimer gpu_timer;
-    gpu_timer.Start();
-    Kernel<<<grid_size, block_size>>>(global_barrier, iterations);
-    gpu_timer.Stop();
-
-    retval = CubDebug(cudaThreadSynchronize());
-
-    // Output timing results
-    float avg_elapsed = gpu_timer.ElapsedMillis() / float(iterations);
-    printf("%d iterations, %f total elapsed millis, %f avg elapsed millis\n",
-        iterations,
-        gpu_timer.ElapsedMillis(),
-        avg_elapsed);
-
-    return retval;
-}
diff --git a/ml-xgboost/cub/test/test_iterator.cu b/ml-xgboost/cub/test/test_iterator.cu
deleted file mode 100644
index 2fe6063..0000000
--- a/ml-xgboost/cub/test/test_iterator.cu
+++ /dev/null
@@ -1,805 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of iterator utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <iterator>
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/iterator/arg_index_input_iterator.cuh>
-#include <cub/iterator/cache_modified_input_iterator.cuh>
-#include <cub/iterator/cache_modified_output_iterator.cuh>
-#include <cub/iterator/constant_input_iterator.cuh>
-#include <cub/iterator/counting_input_iterator.cuh>
-#include <cub/iterator/tex_obj_input_iterator.cuh>
-#include <cub/iterator/tex_ref_input_iterator.cuh>
-#include <cub/iterator/transform_input_iterator.cuh>
-
-#include <cub/util_type.cuh>
-#include <cub/util_allocator.cuh>
-
-#include <thrust/device_ptr.h>
-#include <thrust/copy.h>
-
-#include "test_util.h"
-
-using namespace cub;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose = false;
-CachingDeviceAllocator  g_allocator(true);
-
-// Dispatch types
-enum Backend
-{
-    CUB,        // CUB method
-    THRUST,     // Thrust method
-    CDP,        // GPU-based (dynamic parallelism) dispatch to CUB method
-};
-
-
-template <typename T>
-struct TransformOp
-{
-    // Increment transform
-    __host__ __device__ __forceinline__ T operator()(T input) const
-    {
-        T addend;
-        InitValue(INTEGER_SEED, addend, 1);
-        return input + addend;
-    }
-};
-
-struct SelectOp
-{
-    template <typename T>
-    __host__ __device__ __forceinline__ bool operator()(T input)
-    {
-        return true;;
-    }
-};
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-/**
- * Test random access input iterator
- */
-template <
-    typename InputIteratorT,
-    typename T>
-__global__ void Kernel(
-    InputIteratorT    d_in,
-    T                 *d_out,
-    InputIteratorT    *d_itrs)
-{
-    d_out[0] = *d_in;               // Value at offset 0
-    d_out[1] = d_in[100];           // Value at offset 100
-    d_out[2] = *(d_in + 1000);      // Value at offset 1000
-    d_out[3] = *(d_in + 10000);     // Value at offset 10000
-
-    d_in++;
-    d_out[4] = d_in[0];             // Value at offset 1
-
-    d_in += 20;
-    d_out[5] = d_in[0];             // Value at offset 21
-    d_itrs[0] = d_in;               // Iterator at offset 21
-
-    d_in -= 10;
-    d_out[6] = d_in[0];             // Value at offset 11;
-
-    d_in -= 11;
-    d_out[7] = d_in[0];             // Value at offset 0
-    d_itrs[1] = d_in;               // Iterator at offset 0
-}
-
-
-
-//---------------------------------------------------------------------
-// Host testing subroutines
-//---------------------------------------------------------------------
-
-
-/**
- * Run iterator test on device
- */
-template <
-    typename        InputIteratorT,
-    typename        T,
-    int             TEST_VALUES>
-void Test(
-    InputIteratorT  d_in,
-    T               (&h_reference)[TEST_VALUES])
-{
-    // Allocate device arrays
-    T                 *d_out    = NULL;
-    InputIteratorT    *d_itrs   = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out,     sizeof(T) * TEST_VALUES));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_itrs,    sizeof(InputIteratorT) * 2));
-
-    int compare;
-
-    // Run unguarded kernel
-    Kernel<<<1, 1>>>(d_in, d_out, d_itrs);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Check results
-    compare = CompareDeviceResults(h_reference, d_out, TEST_VALUES, g_verbose, g_verbose);
-    printf("\tValues: %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Check iterator at offset 21
-    InputIteratorT h_itr = d_in + 21;
-    compare = CompareDeviceResults(&h_itr, d_itrs, 1, g_verbose, g_verbose);
-    printf("\tIterators: %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Check iterator at offset 0
-    compare = CompareDeviceResults(&d_in, d_itrs + 1, 1, g_verbose, g_verbose);
-    printf("\tIterators: %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_itrs) CubDebugExit(g_allocator.DeviceFree(d_itrs));
-}
-
-
-/**
- * Test constant iterator
- */
-template <typename T>
-void TestConstant(T base)
-{
-    printf("\nTesting constant iterator on type %s (base: %lld)\n", typeid(T).name(), (unsigned long long) (base)); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    T h_reference[8] = {base, base, base, base, base, base, base, base};
-    ConstantInputIterator<T> d_itr(base);
-    Test(d_itr, h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    int copy_items  = 100;
-    T   *h_copy     = new T[copy_items];
-    T   *d_copy     = NULL;
-
-    for (int i = 0; i < copy_items; ++i)
-        h_copy[i] = d_itr[i];
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * copy_items));
-    thrust::device_ptr<T> d_copy_wrapper(d_copy);
-
-    thrust::copy_if(d_itr, d_itr + copy_items, d_copy_wrapper, SelectOp());
-
-    int compare = CompareDeviceResults(h_copy, d_copy, copy_items, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    if (h_copy) delete[] h_copy;
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif // THRUST_VERSION
-}
-
-
-/**
- * Test counting iterator
- */
-template <typename T>
-void TestCounting(T base)
-{
-    printf("\nTesting counting iterator on type %s (base: %d) \n", typeid(T).name(), int(base)); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    // Initialize reference data
-    T h_reference[8];
-    h_reference[0] = base + 0;          // Value at offset 0
-    h_reference[1] = base + 100;        // Value at offset 100
-    h_reference[2] = base + 1000;       // Value at offset 1000
-    h_reference[3] = base + 10000;      // Value at offset 10000
-    h_reference[4] = base + 1;          // Value at offset 1
-    h_reference[5] = base + 21;         // Value at offset 21
-    h_reference[6] = base + 11;         // Value at offset 11
-    h_reference[7] = base + 0;          // Value at offset 0;
-
-    CountingInputIterator<T> d_itr(base);
-    Test(d_itr, h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    unsigned long long  max_items   = ((1ull << ((sizeof(T) * 8) - 1)) - 1);
-    size_t  copy_items              = (size_t) CUB_MIN(max_items - base, 100);     // potential issue with differencing overflows when T is a smaller type than can handle the offset
-    T                   *h_copy     = new T[copy_items];
-    T                   *d_copy     = NULL;
-
-    for (unsigned long long i = 0; i < copy_items; ++i)
-        h_copy[i] = d_itr[i];
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * copy_items));
-    thrust::device_ptr<T> d_copy_wrapper(d_copy);
-    thrust::copy_if(d_itr, d_itr + copy_items, d_copy_wrapper, SelectOp());
-
-    int compare = CompareDeviceResults(h_copy, d_copy, copy_items, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    if (h_copy) delete[] h_copy;
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif // THRUST_VERSION
-}
-
-
-/**
- * Test modified iterator
- */
-template <typename T, typename CastT>
-void TestModified()
-{
-    printf("\nTesting cache-modified iterator on type %s\n", typeid(T).name()); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    const unsigned int TEST_VALUES = 11000;
-
-    T *h_data = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-    {
-        RandomBits(h_data[i]);
-    }
-
-    // Allocate device arrays
-    T *d_data = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_data, sizeof(T) * TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_data, h_data, sizeof(T) * TEST_VALUES, cudaMemcpyHostToDevice));
-
-    // Initialize reference data
-    T h_reference[8];
-    h_reference[0] = h_data[0];          // Value at offset 0
-    h_reference[1] = h_data[100];        // Value at offset 100
-    h_reference[2] = h_data[1000];       // Value at offset 1000
-    h_reference[3] = h_data[10000];      // Value at offset 10000
-    h_reference[4] = h_data[1];          // Value at offset 1
-    h_reference[5] = h_data[21];         // Value at offset 21
-    h_reference[6] = h_data[11];         // Value at offset 11
-    h_reference[7] = h_data[0];          // Value at offset 0;
-
-    Test(CacheModifiedInputIterator<LOAD_DEFAULT, T>((CastT*) d_data), h_reference);
-    Test(CacheModifiedInputIterator<LOAD_CA, T>((CastT*) d_data), h_reference);
-    Test(CacheModifiedInputIterator<LOAD_CG, T>((CastT*) d_data), h_reference);
-    Test(CacheModifiedInputIterator<LOAD_CS, T>((CastT*) d_data), h_reference);
-    Test(CacheModifiedInputIterator<LOAD_CV, T>((CastT*) d_data), h_reference);
-    Test(CacheModifiedInputIterator<LOAD_LDG, T>((CastT*) d_data), h_reference);
-    Test(CacheModifiedInputIterator<LOAD_VOLATILE, T>((CastT*) d_data), h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    T *d_copy = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * TEST_VALUES));
-
-    CacheModifiedInputIterator<LOAD_CG, T> d_in_itr((CastT*) d_data);
-    CacheModifiedOutputIterator<STORE_CG, T> d_out_itr((CastT*) d_copy);
-
-    thrust::copy_if(d_in_itr, d_in_itr + TEST_VALUES, d_out_itr, SelectOp());
-
-    int compare = CompareDeviceResults(h_data, d_copy, TEST_VALUES, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif // THRUST_VERSION
-
-    if (h_data) delete[] h_data;
-    if (d_data) CubDebugExit(g_allocator.DeviceFree(d_data));
-}
-
-
-/**
- * Test transform iterator
- */
-template <typename T, typename CastT>
-void TestTransform()
-{
-    printf("\nTesting transform iterator on type %s\n", typeid(T).name()); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    const unsigned int TEST_VALUES = 11000;
-
-    T *h_data = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-    {
-        InitValue(INTEGER_SEED, h_data[i], i);
-    }
-
-    // Allocate device arrays
-    T *d_data = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_data, sizeof(T) * TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_data, h_data, sizeof(T) * TEST_VALUES, cudaMemcpyHostToDevice));
-
-    TransformOp<T> op;
-
-    // Initialize reference data
-    T h_reference[8];
-    h_reference[0] = op(h_data[0]);          // Value at offset 0
-    h_reference[1] = op(h_data[100]);        // Value at offset 100
-    h_reference[2] = op(h_data[1000]);       // Value at offset 1000
-    h_reference[3] = op(h_data[10000]);      // Value at offset 10000
-    h_reference[4] = op(h_data[1]);          // Value at offset 1
-    h_reference[5] = op(h_data[21]);         // Value at offset 21
-    h_reference[6] = op(h_data[11]);         // Value at offset 11
-    h_reference[7] = op(h_data[0]);          // Value at offset 0;
-
-    TransformInputIterator<T, TransformOp<T>, CastT*> d_itr((CastT*) d_data, op);
-    Test(d_itr, h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    T *h_copy = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-        h_copy[i] = op(h_data[i]);
-
-    T *d_copy = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * TEST_VALUES));
-    thrust::device_ptr<T> d_copy_wrapper(d_copy);
-
-    thrust::copy_if(d_itr, d_itr + TEST_VALUES, d_copy_wrapper, SelectOp());
-
-    int compare = CompareDeviceResults(h_copy, d_copy, TEST_VALUES, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_copy) delete[] h_copy;
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif // THRUST_VERSION
-
-    if (h_data) delete[] h_data;
-    if (d_data) CubDebugExit(g_allocator.DeviceFree(d_data));
-}
-
-
-/**
- * Test tex-obj texture iterator
- */
-template <typename T, typename CastT>
-void TestTexObj()
-{
-    printf("\nTesting tex-obj iterator on type %s\n", typeid(T).name()); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    const unsigned int TEST_VALUES          = 11000;
-    const unsigned int DUMMY_OFFSET         = 500;
-    const unsigned int DUMMY_TEST_VALUES    = TEST_VALUES - DUMMY_OFFSET;
-
-    T *h_data = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-    {
-        RandomBits(h_data[i]);
-    }
-
-    // Allocate device arrays
-    T *d_data   = NULL;
-    T *d_dummy  = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_data, sizeof(T) * TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_data, h_data, sizeof(T) * TEST_VALUES, cudaMemcpyHostToDevice));
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_dummy, sizeof(T) * DUMMY_TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_dummy, h_data + DUMMY_OFFSET, sizeof(T) * DUMMY_TEST_VALUES, cudaMemcpyHostToDevice));
-
-    // Initialize reference data
-    T h_reference[8];
-    h_reference[0] = h_data[0];          // Value at offset 0
-    h_reference[1] = h_data[100];        // Value at offset 100
-    h_reference[2] = h_data[1000];       // Value at offset 1000
-    h_reference[3] = h_data[10000];      // Value at offset 10000
-    h_reference[4] = h_data[1];          // Value at offset 1
-    h_reference[5] = h_data[21];         // Value at offset 21
-    h_reference[6] = h_data[11];         // Value at offset 11
-    h_reference[7] = h_data[0];          // Value at offset 0;
-
-    // Create and bind obj-based test iterator
-    TexObjInputIterator<T> d_obj_itr;
-    CubDebugExit(d_obj_itr.BindTexture((CastT*) d_data, sizeof(T) * TEST_VALUES));
-
-    Test(d_obj_itr, h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    T *d_copy = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * TEST_VALUES));
-    thrust::device_ptr<T> d_copy_wrapper(d_copy);
-
-    CubDebugExit(cudaMemset(d_copy, 0, sizeof(T) * TEST_VALUES));
-    thrust::copy_if(d_obj_itr, d_obj_itr + TEST_VALUES, d_copy_wrapper, SelectOp());
-
-    int compare = CompareDeviceResults(h_data, d_copy, TEST_VALUES, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    CubDebugExit(d_obj_itr.UnbindTexture());
-
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif  // THRUST_VERSION
-
-    if (h_data) delete[] h_data;
-    if (d_data) CubDebugExit(g_allocator.DeviceFree(d_data));
-    if (d_dummy) CubDebugExit(g_allocator.DeviceFree(d_dummy));
-}
-
-
-#if CUDA_VERSION >= 5050
-
-/**
- * Test tex-ref texture iterator
- */
-template <typename T, typename CastT>
-void TestTexRef()
-{
-    printf("\nTesting tex-ref iterator on type %s\n", typeid(T).name()); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    const unsigned int TEST_VALUES          = 11000;
-    const unsigned int DUMMY_OFFSET         = 500;
-    const unsigned int DUMMY_TEST_VALUES    = TEST_VALUES - DUMMY_OFFSET;
-
-    T *h_data = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-    {
-        RandomBits(h_data[i]);
-    }
-
-    // Allocate device arrays
-    T *d_data   = NULL;
-    T *d_dummy  = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_data, sizeof(T) * TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_data, h_data, sizeof(T) * TEST_VALUES, cudaMemcpyHostToDevice));
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_dummy, sizeof(T) * DUMMY_TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_dummy, h_data + DUMMY_OFFSET, sizeof(T) * DUMMY_TEST_VALUES, cudaMemcpyHostToDevice));
-
-    // Initialize reference data
-    T h_reference[8];
-    h_reference[0] = h_data[0];          // Value at offset 0
-    h_reference[1] = h_data[100];        // Value at offset 100
-    h_reference[2] = h_data[1000];       // Value at offset 1000
-    h_reference[3] = h_data[10000];      // Value at offset 10000
-    h_reference[4] = h_data[1];          // Value at offset 1
-    h_reference[5] = h_data[21];         // Value at offset 21
-    h_reference[6] = h_data[11];         // Value at offset 11
-    h_reference[7] = h_data[0];          // Value at offset 0;
-
-    // Create and bind ref-based test iterator
-    TexRefInputIterator<T, __LINE__> d_ref_itr;
-    CubDebugExit(d_ref_itr.BindTexture((CastT*) d_data, sizeof(T) * TEST_VALUES));
-
-    // Create and bind dummy iterator of same type to check with interferance
-    TexRefInputIterator<T, __LINE__> d_ref_itr2;
-    CubDebugExit(d_ref_itr2.BindTexture((CastT*) d_dummy, sizeof(T) * DUMMY_TEST_VALUES));
-
-    Test(d_ref_itr, h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    T *d_copy = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * TEST_VALUES));
-    thrust::device_ptr<T> d_copy_wrapper(d_copy);
-
-    CubDebugExit(cudaMemset(d_copy, 0, sizeof(T) * TEST_VALUES));
-    thrust::copy_if(d_ref_itr, d_ref_itr + TEST_VALUES, d_copy_wrapper, SelectOp());
-
-    int compare = CompareDeviceResults(h_data, d_copy, TEST_VALUES, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif  // THRUST_VERSION
-
-    CubDebugExit(d_ref_itr.UnbindTexture());
-    CubDebugExit(d_ref_itr2.UnbindTexture());
-
-    if (h_data) delete[] h_data;
-    if (d_data) CubDebugExit(g_allocator.DeviceFree(d_data));
-    if (d_dummy) CubDebugExit(g_allocator.DeviceFree(d_dummy));
-}
-
-
-/**
- * Test texture transform iterator
- */
-template <typename T, typename CastT>
-void TestTexTransform()
-{
-    printf("\nTesting tex-transform iterator on type %s\n", typeid(T).name()); fflush(stdout);
-
-    //
-    // Test iterator manipulation in kernel
-    //
-
-    const unsigned int TEST_VALUES = 11000;
-
-    T *h_data = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-    {
-        InitValue(INTEGER_SEED, h_data[i], i);
-    }
-
-    // Allocate device arrays
-    T *d_data = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_data, sizeof(T) * TEST_VALUES));
-    CubDebugExit(cudaMemcpy(d_data, h_data, sizeof(T) * TEST_VALUES, cudaMemcpyHostToDevice));
-
-    TransformOp<T> op;
-
-    // Initialize reference data
-    T h_reference[8];
-    h_reference[0] = op(h_data[0]);          // Value at offset 0
-    h_reference[1] = op(h_data[100]);        // Value at offset 100
-    h_reference[2] = op(h_data[1000]);       // Value at offset 1000
-    h_reference[3] = op(h_data[10000]);      // Value at offset 10000
-    h_reference[4] = op(h_data[1]);          // Value at offset 1
-    h_reference[5] = op(h_data[21]);         // Value at offset 21
-    h_reference[6] = op(h_data[11]);         // Value at offset 11
-    h_reference[7] = op(h_data[0]);          // Value at offset 0;
-
-    // Create and bind texture iterator
-    typedef TexRefInputIterator<T, __LINE__> TextureIterator;
-
-    TextureIterator d_tex_itr;
-    CubDebugExit(d_tex_itr.BindTexture((CastT*) d_data, sizeof(T) * TEST_VALUES));
-
-    // Create transform iterator
-    TransformInputIterator<T, TransformOp<T>, TextureIterator> xform_itr(d_tex_itr, op);
-
-    Test(xform_itr, h_reference);
-
-#if (THRUST_VERSION >= 100700)  // Thrust 1.7 or newer
-
-    //
-    // Test with thrust::copy_if()
-    //
-
-    T *h_copy = new T[TEST_VALUES];
-    for (int i = 0; i < TEST_VALUES; ++i)
-        h_copy[i] = op(h_data[i]);
-
-    T *d_copy = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_copy, sizeof(T) * TEST_VALUES));
-    thrust::device_ptr<T> d_copy_wrapper(d_copy);
-
-    thrust::copy_if(xform_itr, xform_itr + TEST_VALUES, d_copy_wrapper, SelectOp());
-
-    int compare = CompareDeviceResults(h_copy, d_copy, TEST_VALUES, g_verbose, g_verbose);
-    printf("\tthrust::copy_if(): %s\n", (compare) ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Cleanup
-    if (h_copy) delete[] h_copy;
-    if (d_copy) CubDebugExit(g_allocator.DeviceFree(d_copy));
-
-#endif  // THRUST_VERSION
-
-    CubDebugExit(d_tex_itr.UnbindTexture());
-    if (h_data) delete[] h_data;
-    if (d_data) CubDebugExit(g_allocator.DeviceFree(d_data));
-}
-
-#endif  // CUDA_VERSION
-
-
-
-
-/**
- * Run non-integer tests
- */
-template <typename T, typename CastT>
-void Test(Int2Type<false> is_integer)
-{
-    TestModified<T, CastT>();
-    TestTransform<T, CastT>();
-
-#if CUB_CDP
-    // Test tex-obj iterators if CUDA dynamic parallelism enabled
-    TestTexObj<T, CastT>(type_string);
-#endif  // CUB_CDP
-
-#if CUDA_VERSION >= 5050
-    // Test tex-ref iterators for CUDA 5.5
-    TestTexRef<T, CastT>();
-    TestTexTransform<T, CastT>();
-#endif  // CUDA_VERSION
-}
-
-/**
- * Run integer tests
- */
-template <typename T, typename CastT>
-void Test(Int2Type<true> is_integer)
-{
-    TestConstant<T>(0);
-    TestConstant<T>(99);
-
-    TestCounting<T>(0);
-    TestCounting<T>(99);
-
-    // Run non-integer tests
-    Test<T, CastT>(Int2Type<false>());
-}
-
-/**
- * Run tests
- */
-template <typename T>
-void Test()
-{
-    enum {
-        IS_INTEGER = (Traits<T>::CATEGORY == SIGNED_INTEGER) || (Traits<T>::CATEGORY == UNSIGNED_INTEGER)
-    };
-
-    // Test non-const type
-    Test<T, T>(Int2Type<IS_INTEGER>());
-
-    // Test non-const type
-    Test<T, const T>(Int2Type<IS_INTEGER>());
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-    // Evaluate different data types
-    Test<char>();
-    Test<short>();
-    Test<int>();
-    Test<long>();
-    Test<long long>();
-    Test<float>();
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-        Test<double>();
-
-    Test<char2>();
-    Test<short2>();
-    Test<int2>();
-    Test<long2>();
-    Test<longlong2>();
-    Test<float2>();
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-        Test<double2>();
-
-    Test<char3>();
-    Test<short3>();
-    Test<int3>();
-    Test<long3>();
-    Test<longlong3>();
-    Test<float3>();
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-        Test<double3>();
-
-    Test<char4>();
-    Test<short4>();
-    Test<int4>();
-    Test<long4>();
-    Test<longlong4>();
-    Test<float4>();
-    if (ptx_version > 120)                          // Don't check doubles on PTX120 or below because they're down-converted
-        Test<double4>();
-
-    Test<TestFoo>();
-    Test<TestBar>();
-
-    printf("\nTest complete\n"); fflush(stdout);
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/cub/test/test_util.h b/ml-xgboost/cub/test/test_util.h
deleted file mode 100644
index f29595f..0000000
--- a/ml-xgboost/cub/test/test_util.h
+++ /dev/null
@@ -1,1600 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- * 
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- * 
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-
-#pragma once
-
-#if defined(_WIN32) || defined(_WIN64)
-    #include <windows.h>
-    #undef small            // Windows is terrible for polluting macro namespace
-#else
-    #include <sys/resource.h>
-#endif
-
-#include <cuda_runtime.h>
-
-#include <stdio.h>
-#include <math.h>
-#include <float.h>
-
-#include <string>
-#include <vector>
-#include <sstream>
-#include <iostream>
-#include <limits>
-
-#include "mersenne.h"
-
-#include "cub/util_debug.cuh"
-#include "cub/util_device.cuh"
-#include "cub/util_type.cuh"
-#include "cub/util_macro.cuh"
-
-/******************************************************************************
- * Assertion macros
- ******************************************************************************/
-
-/**
- * Assert equals
- */
-#define AssertEquals(a, b) if ((a) != (b)) { std::cerr << "\n(" << __FILE__ << ": " << __LINE__ << ")\n"; exit(1);}
-
-
-/******************************************************************************
- * Command-line parsing functionality
- ******************************************************************************/
-
-/**
- * Utility for parsing command line arguments
- */
-struct CommandLineArgs
-{
-
-    std::vector<std::string>    keys;
-    std::vector<std::string>    values;
-    std::vector<std::string>    args;
-    cudaDeviceProp              deviceProp;
-    float                       device_giga_bandwidth;
-    size_t                      device_free_physmem;
-    size_t                      device_total_physmem;
-
-    /**
-     * Constructor
-     */
-    CommandLineArgs(int argc, char **argv) :
-        keys(10),
-        values(10)
-    {
-        using namespace std;
-
-        // Initialize mersenne generator
-        unsigned int mersenne_init[4]=  {0x123, 0x234, 0x345, 0x456};
-        mersenne::init_by_array(mersenne_init, 4);
-
-        for (int i = 1; i < argc; i++)
-        {
-            string arg = argv[i];
-
-            if ((arg[0] != '-') || (arg[1] != '-'))
-            {
-                args.push_back(arg);
-                continue;
-            }
-
-            string::size_type pos;
-            string key, val;
-            if ((pos = arg.find('=')) == string::npos) {
-                key = string(arg, 2, arg.length() - 2);
-                val = "";
-            } else {
-                key = string(arg, 2, pos - 2);
-                val = string(arg, pos + 1, arg.length() - 1);
-            }
-
-            keys.push_back(key);
-            values.push_back(val);
-        }
-    }
-
-
-    /**
-     * Checks whether a flag "--<flag>" is present in the commandline
-     */
-    bool CheckCmdLineFlag(const char* arg_name)
-    {
-        using namespace std;
-
-        for (int i = 0; i < int(keys.size()); ++i)
-        {
-            if (keys[i] == string(arg_name))
-                return true;
-        }
-        return false;
-    }
-
-
-    /**
-     * Returns number of naked (non-flag and non-key-value) commandline parameters
-     */
-    template <typename T>
-    int NumNakedArgs()
-    {
-        return args.size();
-    }
-
-
-    /**
-     * Returns the commandline parameter for a given index (not including flags)
-     */
-    template <typename T>
-    void GetCmdLineArgument(int index, T &val)
-    {
-        using namespace std;
-        if (index < args.size()) {
-            istringstream str_stream(args[index]);
-            str_stream >> val;
-        }
-    }
-
-    /**
-     * Returns the value specified for a given commandline parameter --<flag>=<value>
-     */
-    template <typename T>
-    void GetCmdLineArgument(const char *arg_name, T &val)
-    {
-        using namespace std;
-
-        for (int i = 0; i < int(keys.size()); ++i)
-        {
-            if (keys[i] == string(arg_name))
-            {
-                istringstream str_stream(values[i]);
-                str_stream >> val;
-            }
-        }
-    }
-
-
-    /**
-     * Returns the values specified for a given commandline parameter --<flag>=<value>,<value>*
-     */
-    template <typename T>
-    void GetCmdLineArguments(const char *arg_name, std::vector<T> &vals)
-    {
-        using namespace std;
-
-        if (CheckCmdLineFlag(arg_name))
-        {
-            // Clear any default values
-            vals.clear();
-
-            // Recover from multi-value string
-            for (int i = 0; i < keys.size(); ++i)
-            {
-                if (keys[i] == string(arg_name))
-                {
-                    string val_string(values[i]);
-                    istringstream str_stream(val_string);
-                    string::size_type old_pos = 0;
-                    string::size_type new_pos = 0;
-
-                    // Iterate comma-separated values
-                    T val;
-                    while ((new_pos = val_string.find(',', old_pos)) != string::npos)
-                    {
-                        if (new_pos != old_pos)
-                        {
-                            str_stream.width(new_pos - old_pos);
-                            str_stream >> val;
-                            vals.push_back(val);
-                        }
-
-                        // skip over comma
-                        str_stream.ignore(1);
-                        old_pos = new_pos + 1;
-                    }
-
-                    // Read last value
-                    str_stream >> val;
-                    vals.push_back(val);
-                }
-            }
-        }
-    }
-
-
-    /**
-     * The number of pairs parsed
-     */
-    int ParsedArgc()
-    {
-        return (int) keys.size();
-    }
-
-    /**
-     * Initialize device
-     */
-    cudaError_t DeviceInit(int dev = -1)
-    {
-        cudaError_t error = cudaSuccess;
-
-        do
-        {
-            int deviceCount;
-            error = CubDebug(cudaGetDeviceCount(&deviceCount));
-            if (error) break;
-
-            if (deviceCount == 0) {
-                fprintf(stderr, "No devices supporting CUDA.\n");
-                exit(1);
-            }
-            if (dev < 0)
-            {
-                GetCmdLineArgument("device", dev);
-            }
-            if ((dev > deviceCount - 1) || (dev < 0))
-            {
-                dev = 0;
-            }
-
-            error = CubDebug(cudaSetDevice(dev));
-            if (error) break;
-
-            CubDebugExit(cudaMemGetInfo(&device_free_physmem, &device_total_physmem));
-
-            int ptx_version;
-            error = CubDebug(cub::PtxVersion(ptx_version));
-            if (error) break;
-
-            error = CubDebug(cudaGetDeviceProperties(&deviceProp, dev));
-            if (error) break;
-
-            if (deviceProp.major < 1) {
-                fprintf(stderr, "Device does not support CUDA.\n");
-                exit(1);
-            }
-
-            device_giga_bandwidth = float(deviceProp.memoryBusWidth) * deviceProp.memoryClockRate * 2 / 8 / 1000 / 1000;
-
-            if (!CheckCmdLineFlag("quiet"))
-            {
-                printf(
-                        "Using device %d: %s (PTX version %d, SM%d, %d SMs, "
-                        "%lld free / %lld total MB physmem, "
-                        "%.3f GB/s @ %d kHz mem clock, ECC %s)\n",
-                    dev,
-                    deviceProp.name,
-                    ptx_version,
-                    deviceProp.major * 100 + deviceProp.minor * 10,
-                    deviceProp.multiProcessorCount,
-                    (unsigned long long) device_free_physmem / 1024 / 1024,
-                    (unsigned long long) device_total_physmem / 1024 / 1024,
-                    device_giga_bandwidth,
-                    deviceProp.memoryClockRate,
-                    (deviceProp.ECCEnabled) ? "on" : "off");
-                fflush(stdout);
-            }
-
-        } while (0);
-
-        return error;
-    }
-};
-
-/******************************************************************************
- * Random bits generator
- ******************************************************************************/
-
-int g_num_rand_samples = 0;
-
-
-template <typename T>
-bool IsNaN(T val) { return false; }
-
-template<>
-__noinline__ bool IsNaN<float>(float val)
-{
-    volatile unsigned int bits = reinterpret_cast<unsigned int &>(val);
-
-    return (((bits >= 0x7F800001) && (bits <= 0x7FFFFFFF)) || 
-        ((bits >= 0xFF800001) && (bits <= 0xFFFFFFFF)));
-}
-
-template<>
-__noinline__ bool IsNaN<float1>(float1 val)
-{
-    return (IsNaN(val.x));
-}
-
-template<>
-__noinline__ bool IsNaN<float2>(float2 val)
-{
-    return (IsNaN(val.y) || IsNaN(val.x));
-}
-
-template<>
-__noinline__ bool IsNaN<float3>(float3 val)
-{
-    return (IsNaN(val.z) || IsNaN(val.y) || IsNaN(val.x));
-}
-
-template<>
-__noinline__ bool IsNaN<float4>(float4 val)
-{
-    return (IsNaN(val.y) || IsNaN(val.x) || IsNaN(val.w) || IsNaN(val.z));
-}
-
-template<>
-__noinline__ bool IsNaN<double>(double val)
-{
-    volatile unsigned long long bits = *reinterpret_cast<unsigned long long *>(&val);
-
-    return (((bits >= 0x7FF0000000000001) && (bits <= 0x7FFFFFFFFFFFFFFF)) || 
-        ((bits >= 0xFFF0000000000001) && (bits <= 0xFFFFFFFFFFFFFFFF)));
-}
-
-template<>
-__noinline__ bool IsNaN<double1>(double1 val)
-{
-    return (IsNaN(val.x));
-}
-
-template<>
-__noinline__ bool IsNaN<double2>(double2 val)
-{
-    return (IsNaN(val.y) || IsNaN(val.x));
-}
-
-template<>
-__noinline__ bool IsNaN<double3>(double3 val)
-{
-    return (IsNaN(val.z) || IsNaN(val.y) || IsNaN(val.x));
-}
-
-template<>
-__noinline__ bool IsNaN<double4>(double4 val)
-{
-    return (IsNaN(val.y) || IsNaN(val.x) || IsNaN(val.w) || IsNaN(val.z));
-}
-
-
-/**
- * Generates random keys.
- *
- * We always take the second-order byte from rand() because the higher-order
- * bits returned by rand() are commonly considered more uniformly distributed
- * than the lower-order bits.
- *
- * We can decrease the entropy level of keys by adopting the technique
- * of Thearling and Smith in which keys are computed from the bitwise AND of
- * multiple random samples:
- *
- * entropy_reduction    | Effectively-unique bits per key
- * -----------------------------------------------------
- * -1                   | 0
- * 0                    | 32
- * 1                    | 25.95 (81%)
- * 2                    | 17.41 (54%)
- * 3                    | 10.78 (34%)
- * 4                    | 6.42 (20%)
- * ...                  | ...
- *
- */
-template <typename K>
-void RandomBits(
-    K &key,
-    int entropy_reduction = 0,
-    int begin_bit = 0,
-    int end_bit = sizeof(K) * 8)
-{
-    const int NUM_BYTES = sizeof(K);
-    const int WORD_BYTES = sizeof(unsigned int);
-    const int NUM_WORDS = (NUM_BYTES + WORD_BYTES - 1) / WORD_BYTES;
-
-    unsigned int word_buff[NUM_WORDS];
-
-    if (entropy_reduction == -1)
-    {
-        memset((void *) &key, 0, sizeof(key));
-        return;
-    }
-
-    if (end_bit < 0)
-        end_bit = sizeof(K) * 8;
-
-    while (true) 
-    {
-        // Generate random word_buff
-        for (int j = 0; j < NUM_WORDS; j++)
-        {
-            int current_bit = j * WORD_BYTES * 8;
-
-            unsigned int word = 0xffffffff;
-            word &= 0xffffffff << CUB_MAX(0, begin_bit - current_bit);
-            word &= 0xffffffff >> CUB_MAX(0, (current_bit + (WORD_BYTES * 8)) - end_bit);
-
-            for (int i = 0; i <= entropy_reduction; i++)
-            {
-                // Grab some of the higher bits from rand (better entropy, supposedly)
-                word &= mersenne::genrand_int32();
-                g_num_rand_samples++;                
-            }
-
-            word_buff[j] = word;
-        }
-
-        memcpy(&key, word_buff, sizeof(K));
-
-        K copy = key;
-        if (!IsNaN(copy))
-            break;          // avoids NaNs when generating random floating point numbers
-    }
-}
-
-/// Randomly select number between [0:max)
-template <typename T>
-T RandomValue(T max)
-{
-    unsigned int bits;
-    unsigned int max_int = (unsigned int) -1;
-    do {
-        RandomBits(bits);
-    } while (bits == max_int);
-
-    return (T) ((double(bits) / double(max_int)) * double(max));
-}
-
-
-/******************************************************************************
- * Console printing utilities
- ******************************************************************************/
-
-/**
- * Helper for casting character types to integers for cout printing
- */
-template <typename T>
-T CoutCast(T val) { return val; }
-
-int CoutCast(char val) { return val; }
-
-int CoutCast(unsigned char val) { return val; }
-
-int CoutCast(signed char val) { return val; }
-
-
-
-/******************************************************************************
- * Test value initialization utilities
- ******************************************************************************/
-
-/**
- * Test problem generation options
- */
-enum GenMode
-{
-    UNIFORM,            // Assign to '2', regardless of integer seed
-    INTEGER_SEED,       // Assign to integer seed
-    RANDOM,             // Assign to random, regardless of integer seed
-};
-
-/**
- * Initialize value
- */
-template <typename T>
-__host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, T &value, int index = 0)
-{
-    switch (gen_mode)
-    {
-#if (CUB_PTX_ARCH == 0)
-    case RANDOM:
-         RandomBits(value);
-         break;
-#endif
-     case UNIFORM:
-        value = 2;
-        break;
-    case INTEGER_SEED:
-    default:
-         value = (T) index;
-        break;
-    }
-}
-
-
-/**
- * Initialize value (bool)
- */
-__host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, bool &value, int index = 0)
-{
-    switch (gen_mode)
-    {
-#if (CUB_PTX_ARCH == 0)
-    case RANDOM:
-        char c;
-        RandomBits(c, 0, 0, 1);
-        value = (c > 0);
-        break;
-#endif
-     case UNIFORM:
-        value = true;
-        break;
-    case INTEGER_SEED:
-    default:
-        value = (index > 0);
-        break;
-    }
-}
-
-
-/**
- * cub::NullType test initialization
- */
-__host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, cub::NullType &value, int index = 0)
-{}
-
-
-/**
- * cub::KeyValuePair<OffsetT, ValueT>test initialization
- */
-template <typename KeyT, typename ValueT>
-__host__ __device__ __forceinline__ void InitValue(
-    GenMode                             gen_mode,
-    cub::KeyValuePair<KeyT, ValueT>&    value,
-    int                                 index = 0)
-{
-    InitValue(gen_mode, value.value, index);
-
-    // Assign corresponding flag with a likelihood of the last bit being set with entropy-reduction level 3
-    RandomBits(value.key, 3);
-    value.key = (value.key & 0x1);
-}
-
-
-
-/******************************************************************************
- * Comparison and ostream operators
- ******************************************************************************/
-
-/**
- * KeyValuePair ostream operator
- */
-template <typename Key, typename Value>
-std::ostream& operator<<(std::ostream& os, const cub::KeyValuePair<Key, Value> &val)
-{
-    os << '(' << CoutCast(val.key) << ',' << CoutCast(val.value) << ')';
-    return os;
-}
-
-
-/******************************************************************************
- * Comparison and ostream operators for CUDA vector types
- ******************************************************************************/
-
-/**
- * Vector1 overloads
- */
-#define CUB_VEC_OVERLOAD_1(T, BaseT)                        \
-    /* Ostream output */                                    \
-    std::ostream& operator<<(                               \
-        std::ostream& os,                                   \
-        const T& val)                                       \
-    {                                                       \
-        os << '(' << CoutCast(val.x) << ')';                \
-        return os;                                          \
-    }                                                       \
-    /* Inequality */                                        \
-    __host__ __device__ __forceinline__ bool operator!=(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x != b.x);                                \
-    }                                                       \
-    /* Equality */                                          \
-    __host__ __device__ __forceinline__ bool operator==(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x == b.x);                                \
-    }                                                       \
-    /* Test initialization */                               \
-    __host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, T &value, int index = 0)   \
-    {                                                       \
-        InitValue(gen_mode, value.x, index);                \
-    }                                                       \
-    /* Max */                                               \
-    __host__ __device__ __forceinline__ bool operator>(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x > b.x);                                 \
-    }                                                       \
-    /* Min */                                               \
-    __host__ __device__ __forceinline__ bool operator<(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x < b.x);                                 \
-    }                                                       \
-    /* Summation (non-reference addends for VS2003 -O3 warpscan workaround */                       \
-    __host__ __device__ __forceinline__ T operator+(        \
-        T a,                                                \
-        T b)                                                \
-    {                                                       \
-        T retval = make_##T(a.x + b.x);                     \
-        return retval;                                      \
-    }                                                       \
-    namespace cub {                                         \
-    template<>                                              \
-    struct NumericTraits<T>                                 \
-    {                                                       \
-        static const Category CATEGORY = NOT_A_NUMBER;      \
-        enum {                                              \
-            PRIMITIVE       = false,                        \
-            NULL_TYPE       = false,                        \
-        };                                                  \
-        static T Max()                                      \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Max()};               \
-            return retval;                                  \
-        }                                                   \
-        static T Lowest()                                   \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Lowest()};            \
-            return retval;                                  \
-        }                                                   \
-    };                                                      \
-    } /* namespace std */
-
-
-
-/**
- * Vector2 overloads
- */
-#define CUB_VEC_OVERLOAD_2(T, BaseT)                        \
-    /* Ostream output */                                    \
-    std::ostream& operator<<(                               \
-        std::ostream& os,                                   \
-        const T& val)                                       \
-    {                                                       \
-        os << '('                                           \
-            << CoutCast(val.x) << ','                       \
-            << CoutCast(val.y) << ')';                      \
-        return os;                                          \
-    }                                                       \
-    /* Inequality */                                        \
-    __host__ __device__ __forceinline__ bool operator!=(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x != b.x) ||                              \
-            (a.y != b.y);                                   \
-    }                                                       \
-    /* Equality */                                          \
-    __host__ __device__ __forceinline__ bool operator==(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x == b.x) &&                              \
-            (a.y == b.y);                                   \
-    }                                                       \
-    /* Test initialization */                               \
-    __host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, T &value, int index = 0)   \
-    {                                                       \
-        InitValue(gen_mode, value.x, index);                \
-        InitValue(gen_mode, value.y, index);                \
-    }                                                       \
-    /* Max */                                               \
-    __host__ __device__ __forceinline__ bool operator>(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        if (a.x > b.x) return true; else if (b.x > a.x) return false;   \
-        return a.y > b.y;                                               \
-    }                                                       \
-    /* Min */                                               \
-    __host__ __device__ __forceinline__ bool operator<(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        if (a.x < b.x) return true; else if (b.x < a.x) return false;   \
-        return a.y < b.y;                                               \
-    }                                                       \
-    /* Summation (non-reference addends for VS2003 -O3 warpscan workaround */                                         \
-    __host__ __device__ __forceinline__ T operator+(        \
-        T a,                                         \
-        T b)                                         \
-    {                                                       \
-        T retval = make_##T(                                        \
-            a.x + b.x,                                      \
-            a.y + b.y);                                     \
-        return retval;                                      \
-    }                                                       \
-    namespace cub {                                         \
-    template<>                                              \
-    struct NumericTraits<T>                                 \
-    {                                                       \
-        static const Category CATEGORY = NOT_A_NUMBER;      \
-        enum {                                              \
-            PRIMITIVE       = false,                        \
-            NULL_TYPE       = false,                        \
-        };                                                  \
-        static T Max()                                      \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Max(),                \
-                NumericTraits<BaseT>::Max()};               \
-            return retval;                                  \
-        }                                                   \
-        static T Lowest()                                   \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Lowest(),             \
-                NumericTraits<BaseT>::Lowest()};            \
-            return retval;                                  \
-        }                                                   \
-    };                                                      \
-    } /* namespace cub */
-
-
-
-/**
- * Vector3 overloads
- */
-#define CUB_VEC_OVERLOAD_3(T, BaseT)                        \
-    /* Ostream output */                                    \
-    std::ostream& operator<<(                               \
-        std::ostream& os,                                   \
-        const T& val)                                       \
-    {                                                       \
-        os << '('                                           \
-            << CoutCast(val.x) << ','                       \
-            << CoutCast(val.y) << ','                       \
-            << CoutCast(val.z) << ')';                      \
-        return os;                                          \
-    }                                                       \
-    /* Inequality */                                        \
-    __host__ __device__ __forceinline__ bool operator!=(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x != b.x) ||                              \
-            (a.y != b.y) ||                                 \
-            (a.z != b.z);                                   \
-    }                                                       \
-    /* Equality */                                          \
-    __host__ __device__ __forceinline__ bool operator==(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x == b.x) &&                              \
-            (a.y == b.y) &&                                 \
-            (a.z == b.z);                                   \
-    }                                                       \
-    /* Test initialization */                               \
-    __host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, T &value, int index = 0)   \
-    {                                                       \
-        InitValue(gen_mode, value.x, index);                \
-        InitValue(gen_mode, value.y, index);                \
-        InitValue(gen_mode, value.z, index);                \
-    }                                                       \
-    /* Max */                                               \
-    __host__ __device__ __forceinline__ bool operator>(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        if (a.x > b.x) return true; else if (b.x > a.x) return false;   \
-        if (a.y > b.y) return true; else if (b.y > a.y) return false;   \
-        return a.z > b.z;                                               \
-    }                                                       \
-    /* Min */                                               \
-    __host__ __device__ __forceinline__ bool operator<(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        if (a.x < b.x) return true; else if (b.x < a.x) return false;   \
-        if (a.y < b.y) return true; else if (b.y < a.y) return false;   \
-        return a.z < b.z;                                               \
-    }                                                       \
-    /* Summation (non-reference addends for VS2003 -O3 warpscan workaround */                                         \
-    __host__ __device__ __forceinline__ T operator+(        \
-        T a,                                                \
-        T b)                                                \
-    {                                                       \
-        T retval = make_##T(                                        \
-            a.x + b.x,                                      \
-            a.y + b.y,                                      \
-            a.z + b.z);                                     \
-        return retval;                                      \
-    }                                                       \
-    namespace cub {                                         \
-    template<>                                              \
-    struct NumericTraits<T>                                 \
-    {                                                       \
-        static const Category CATEGORY = NOT_A_NUMBER;      \
-        enum {                                              \
-            PRIMITIVE       = false,                        \
-            NULL_TYPE       = false,                        \
-        };                                                  \
-        static T Max()                                      \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Max(),                \
-                NumericTraits<BaseT>::Max(),                \
-                NumericTraits<BaseT>::Max()};               \
-            return retval;                                  \
-        }                                                   \
-        static T Lowest()                                   \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Lowest(),             \
-                NumericTraits<BaseT>::Lowest(),             \
-                NumericTraits<BaseT>::Lowest()};            \
-            return retval;                                  \
-        }                                                   \
-    };                                                      \
-    } /* namespace cub */
-
-
-/**
- * Vector4 overloads
- */
-#define CUB_VEC_OVERLOAD_4(T, BaseT)                        \
-    /* Ostream output */                                    \
-    std::ostream& operator<<(                               \
-        std::ostream& os,                                   \
-        const T& val)                                       \
-    {                                                       \
-        os << '('                                           \
-            << CoutCast(val.x) << ','                       \
-            << CoutCast(val.y) << ','                       \
-            << CoutCast(val.z) << ','                       \
-            << CoutCast(val.w) << ')';                      \
-        return os;                                          \
-    }                                                       \
-    /* Inequality */                                        \
-    __host__ __device__ __forceinline__ bool operator!=(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x != b.x) ||                              \
-            (a.y != b.y) ||                                 \
-            (a.z != b.z) ||                                 \
-            (a.w != b.w);                                   \
-    }                                                       \
-    /* Equality */                                          \
-    __host__ __device__ __forceinline__ bool operator==(    \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        return (a.x == b.x) &&                              \
-            (a.y == b.y) &&                                 \
-            (a.z == b.z) &&                                 \
-            (a.w == b.w);                                   \
-    }                                                       \
-    /* Test initialization */                               \
-    __host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, T &value, int index = 0)   \
-    {                                                       \
-        InitValue(gen_mode, value.x, index);                \
-        InitValue(gen_mode, value.y, index);                \
-        InitValue(gen_mode, value.z, index);                \
-        InitValue(gen_mode, value.w, index);                \
-    }                                                       \
-    /* Max */                                               \
-    __host__ __device__ __forceinline__ bool operator>(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        if (a.x > b.x) return true; else if (b.x > a.x) return false;   \
-        if (a.y > b.y) return true; else if (b.y > a.y) return false;   \
-        if (a.z > b.z) return true; else if (b.z > a.z) return false;   \
-        return a.w > b.w;                                               \
-    }                                                       \
-    /* Min */                                               \
-    __host__ __device__ __forceinline__ bool operator<(     \
-        const T &a,                                         \
-        const T &b)                                         \
-    {                                                       \
-        if (a.x < b.x) return true; else if (b.x < a.x) return false;   \
-        if (a.y < b.y) return true; else if (b.y < a.y) return false;   \
-        if (a.z < b.z) return true; else if (b.z < a.z) return false;   \
-        return a.w < b.w;                                               \
-    }                                                       \
-    /* Summation (non-reference addends for VS2003 -O3 warpscan workaround */                                         \
-    __host__ __device__ __forceinline__ T operator+(        \
-        T a,                                                \
-        T b)                                                \
-    {                                                       \
-        T retval = make_##T(                                        \
-            a.x + b.x,                                      \
-            a.y + b.y,                                      \
-            a.z + b.z,                                      \
-            a.w + b.w);                                     \
-        return retval;                                      \
-    }                                                       \
-    namespace cub {                                         \
-    template<>                                              \
-    struct NumericTraits<T>                                 \
-    {                                                       \
-        static const Category CATEGORY = NOT_A_NUMBER;      \
-        enum {                                              \
-            PRIMITIVE       = false,                        \
-            NULL_TYPE       = false,                        \
-        };                                                  \
-        static T Max()                                      \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Max(),                \
-                NumericTraits<BaseT>::Max(),                \
-                NumericTraits<BaseT>::Max(),                \
-                NumericTraits<BaseT>::Max()};               \
-            return retval;                                  \
-        }                                                   \
-        static T Lowest()                                   \
-        {                                                   \
-            T retval = {                                    \
-                NumericTraits<BaseT>::Lowest(),             \
-                NumericTraits<BaseT>::Lowest(),             \
-                NumericTraits<BaseT>::Lowest(),             \
-                NumericTraits<BaseT>::Lowest()};            \
-            return retval;                                  \
-        }                                                   \
-    };                                                      \
-    } /* namespace cub */
-
-/**
- * All vector overloads
- */
-#define CUB_VEC_OVERLOAD(COMPONENT_T, BaseT)                    \
-    CUB_VEC_OVERLOAD_1(COMPONENT_T##1, BaseT)                   \
-    CUB_VEC_OVERLOAD_2(COMPONENT_T##2, BaseT)                   \
-    CUB_VEC_OVERLOAD_3(COMPONENT_T##3, BaseT)                   \
-    CUB_VEC_OVERLOAD_4(COMPONENT_T##4, BaseT)
-
-/**
- * Define for types
- */
-CUB_VEC_OVERLOAD(char, char)
-CUB_VEC_OVERLOAD(short, short)
-CUB_VEC_OVERLOAD(int, int)
-CUB_VEC_OVERLOAD(long, long)
-CUB_VEC_OVERLOAD(longlong, long long)
-CUB_VEC_OVERLOAD(uchar, unsigned char)
-CUB_VEC_OVERLOAD(ushort, unsigned short)
-CUB_VEC_OVERLOAD(uint, unsigned int)
-CUB_VEC_OVERLOAD(ulong, unsigned long)
-CUB_VEC_OVERLOAD(ulonglong, unsigned long long)
-CUB_VEC_OVERLOAD(float, float)
-CUB_VEC_OVERLOAD(double, double)
-
-
-//---------------------------------------------------------------------
-// Complex data type TestFoo
-//---------------------------------------------------------------------
-
-/**
- * TestFoo complex data type
- */
-struct TestFoo
-{
-    long long   x;
-    int         y;
-    short       z;
-    char        w;
-
-    // Factory
-    static __host__ __device__ __forceinline__ TestFoo MakeTestFoo(long long x, int y, short z, char w)
-    {
-        TestFoo retval = {x, y, z, w};
-        return retval;
-    }
-
-    // Assignment from int operator
-    __host__ __device__ __forceinline__ TestFoo& operator =(int b)
-    {
-        x = b;
-        y = b;
-        z = b;
-        w = b;
-        return *this;
-    }
-
-    // Summation operator
-    __host__ __device__ __forceinline__ TestFoo operator+(const TestFoo &b) const
-    {
-        return MakeTestFoo(x + b.x, y + b.y, z + b.z, w + b.w);
-    }
-
-    // Inequality operator
-    __host__ __device__ __forceinline__ bool operator !=(const TestFoo &b) const
-    {
-        return (x != b.x) || (y != b.y) || (z != b.z) || (w != b.w);
-    }
-
-    // Equality operator
-    __host__ __device__ __forceinline__ bool operator ==(const TestFoo &b) const
-    {
-        return (x == b.x) && (y == b.y) && (z == b.z) && (w == b.w);
-    }
-
-    // Less than operator
-    __host__ __device__ __forceinline__ bool operator <(const TestFoo &b) const
-    {
-        if (x < b.x) return true; else if (b.x < x) return false;
-        if (y < b.y) return true; else if (b.y < y) return false;
-        if (z < b.z) return true; else if (b.z < z) return false;
-        return w < b.w;
-    }
-
-    // Greater than operator
-    __host__ __device__ __forceinline__ bool operator >(const TestFoo &b) const
-    {
-        if (x > b.x) return true; else if (b.x > x) return false;
-        if (y > b.y) return true; else if (b.y > y) return false;
-        if (z > b.z) return true; else if (b.z > z) return false;
-        return w > b.w;
-    }
-
-};
-
-/**
- * TestFoo ostream operator
- */
-std::ostream& operator<<(std::ostream& os, const TestFoo& val)
-{
-    os << '(' << val.x << ',' << val.y << ',' << val.z << ',' << CoutCast(val.w) << ')';
-    return os;
-}
-
-/**
- * TestFoo test initialization
- */
-__host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, TestFoo &value, int index = 0)
-{
-    InitValue(gen_mode, value.x, index);
-    InitValue(gen_mode, value.y, index);
-    InitValue(gen_mode, value.z, index);
-    InitValue(gen_mode, value.w, index);
-}
-
-
-/// numeric_limits<TestFoo> specialization
-namespace cub {
-template<>
-struct NumericTraits<TestFoo>
-{
-    static const Category CATEGORY = NOT_A_NUMBER;
-    enum {
-        PRIMITIVE       = false,
-        NULL_TYPE       = false,
-    };
-    static TestFoo Max()
-    {
-        return TestFoo::MakeTestFoo(
-            NumericTraits<long long>::Max(),
-            NumericTraits<int>::Max(),
-            NumericTraits<short>::Max(),
-            NumericTraits<char>::Max());
-    }
-
-    static TestFoo Lowest()
-    {
-        return TestFoo::MakeTestFoo(
-            NumericTraits<long long>::Lowest(),
-            NumericTraits<int>::Lowest(),
-            NumericTraits<short>::Lowest(),
-            NumericTraits<char>::Lowest());
-    }
-};
-} // namespace cub
-
-
-//---------------------------------------------------------------------
-// Complex data type TestBar (with optimizations for fence-free warp-synchrony)
-//---------------------------------------------------------------------
-
-/**
- * TestBar complex data type
- */
-struct TestBar
-{
-    long long       x;
-    int             y;
-
-    // Constructor
-    __host__ __device__ __forceinline__ TestBar() : x(0), y(0)
-    {}
-
-    // Constructor
-    __host__ __device__ __forceinline__ TestBar(int b) : x(b), y(b)
-    {}
-
-    // Constructor
-    __host__ __device__ __forceinline__ TestBar(long long x, int y) : x(x), y(y)
-    {}
-
-    // Assignment from int operator
-    __host__ __device__ __forceinline__ TestBar& operator =(int b)
-    {
-        x = b;
-        y = b;
-        return *this;
-    }
-
-    // Summation operator
-    __host__ __device__ __forceinline__ TestBar operator+(const TestBar &b) const
-    {
-        return TestBar(x + b.x, y + b.y);
-    }
-
-    // Inequality operator
-    __host__ __device__ __forceinline__ bool operator !=(const TestBar &b) const
-    {
-        return (x != b.x) || (y != b.y);
-    }
-
-    // Equality operator
-    __host__ __device__ __forceinline__ bool operator ==(const TestBar &b) const
-    {
-        return (x == b.x) && (y == b.y);
-    }
-
-    // Less than operator
-    __host__ __device__ __forceinline__ bool operator <(const TestBar &b) const
-    {
-        if (x < b.x) return true; else if (b.x < x) return false;
-        return y < b.y;
-    }
-
-    // Greater than operator
-    __host__ __device__ __forceinline__ bool operator >(const TestBar &b) const
-    {
-        if (x > b.x) return true; else if (b.x > x) return false;
-        return y > b.y;
-    }
-
-};
-
-
-/**
- * TestBar ostream operator
- */
-std::ostream& operator<<(std::ostream& os, const TestBar& val)
-{
-    os << '(' << val.x << ',' << val.y << ')';
-    return os;
-}
-
-/**
- * TestBar test initialization
- */
-__host__ __device__ __forceinline__ void InitValue(GenMode gen_mode, TestBar &value, int index = 0)
-{
-    InitValue(gen_mode, value.x, index);
-    InitValue(gen_mode, value.y, index);
-}
-
-/// numeric_limits<TestBar> specialization
-namespace cub {
-template<>
-struct NumericTraits<TestBar>
-{
-    static const Category CATEGORY = NOT_A_NUMBER;
-    enum {
-        PRIMITIVE       = false,
-        NULL_TYPE       = false,
-    };
-    static TestBar Max()
-    {
-        return TestBar(
-            NumericTraits<long long>::Max(),
-            NumericTraits<int>::Max());
-    }
-
-    static TestBar Lowest()
-    {
-        return TestBar(
-            NumericTraits<long long>::Lowest(),
-            NumericTraits<int>::Lowest());
-    }
-};
-} // namespace cub
-
-
-/******************************************************************************
- * Helper routines for list comparison and display
- ******************************************************************************/
-
-
-/**
- * Compares the equivalence of two arrays
- */
-template <typename S, typename T, typename OffsetT>
-int CompareResults(T* computed, S* reference, OffsetT len, bool verbose = true)
-{
-    for (OffsetT i = 0; i < len; i++)
-    {
-        if (computed[i] != reference[i])
-        {
-            if (verbose) std::cout << "INCORRECT: [" << i << "]: "
-                << CoutCast(computed[i]) << " != "
-                << CoutCast(reference[i]);
-            return 1;
-        }
-    }
-    return 0;
-}
-
-
-/**
- * Compares the equivalence of two arrays
- */
-template <typename OffsetT>
-int CompareResults(float* computed, float* reference, OffsetT len, bool verbose = true)
-{
-    for (OffsetT i = 0; i < len; i++)
-    {
-        if (computed[i] != reference[i])
-        {
-            float difference = std::abs(computed[i]-reference[i]);
-            float fraction = difference / std::abs(reference[i]);
-
-            if (fraction > 0.0001)
-            {
-                if (verbose) std::cout << "INCORRECT: [" << i << "]: "
-                    << "(computed) " << CoutCast(computed[i]) << " != "
-                    << CoutCast(reference[i]) << " (difference:" << difference << ", fraction: " << fraction << ")";
-                return 1;
-            }
-        }
-    }
-    return 0;
-}
-
-
-/**
- * Compares the equivalence of two arrays
- */
-template <typename OffsetT>
-int CompareResults(cub::NullType* computed, cub::NullType* reference, OffsetT len, bool verbose = true)
-{
-    return 0;
-}
-
-/**
- * Compares the equivalence of two arrays
- */
-template <typename OffsetT>
-int CompareResults(double* computed, double* reference, OffsetT len, bool verbose = true)
-{
-    for (OffsetT i = 0; i < len; i++)
-    {
-        if (computed[i] != reference[i])
-        {
-            double difference = std::abs(computed[i]-reference[i]);
-            double fraction = difference / std::abs(reference[i]);
-
-            if (fraction > 0.0001)
-            {
-                if (verbose) std::cout << "INCORRECT: [" << i << "]: "
-                    << CoutCast(computed[i]) << " != "
-                    << CoutCast(reference[i]) << " (difference:" << difference << ", fraction: " << fraction << ")";
-                return 1;
-            }
-        }
-    }
-    return 0;
-}
-
-
-/**
- * Verify the contents of a device array match those
- * of a host array
- */
-int CompareDeviceResults(
-    cub::NullType *h_reference,
-    cub::NullType *d_data,
-    size_t num_items,
-    bool verbose = true,
-    bool display_data = false)
-{
-    return 0;
-}
-
-
-/**
- * Verify the contents of a device array match those
- * of a host array
- */
-template <typename S, typename T>
-int CompareDeviceResults(
-    S *h_reference,
-    T *d_data,
-    size_t num_items,
-    bool verbose = true,
-    bool display_data = false)
-{
-    // Allocate array on host
-    T *h_data = (T*) malloc(num_items * sizeof(T));
-
-    // Copy data back
-    cudaMemcpy(h_data, d_data, sizeof(T) * num_items, cudaMemcpyDeviceToHost);
-
-    // Display data
-    if (display_data)
-    {
-        printf("Reference:\n");
-        for (int i = 0; i < int(num_items); i++)
-        {
-            std::cout << CoutCast(h_reference[i]) << ", ";
-        }
-        printf("\n\nComputed:\n");
-        for (int i = 0; i < int(num_items); i++)
-        {
-            std::cout << CoutCast(h_data[i]) << ", ";
-        }
-        printf("\n\n");
-    }
-
-    // Check
-    int retval = CompareResults(h_data, h_reference, num_items, verbose);
-
-    // Cleanup
-    if (h_data) free(h_data);
-
-    return retval;
-}
-
-
-/**
- * Verify the contents of a device array match those
- * of a device array
- */
-template <typename T>
-int CompareDeviceDeviceResults(
-    T *d_reference,
-    T *d_data,
-    size_t num_items,
-    bool verbose = true,
-    bool display_data = false)
-{
-    // Allocate array on host
-    T *h_reference = (T*) malloc(num_items * sizeof(T));
-    T *h_data = (T*) malloc(num_items * sizeof(T));
-
-    // Copy data back
-    cudaMemcpy(h_reference, d_reference, sizeof(T) * num_items, cudaMemcpyDeviceToHost);
-    cudaMemcpy(h_data, d_data, sizeof(T) * num_items, cudaMemcpyDeviceToHost);
-
-    // Display data
-    if (display_data) {
-        printf("Reference:\n");
-        for (int i = 0; i < num_items; i++)
-        {
-            std::cout << CoutCast(h_reference[i]) << ", ";
-        }
-        printf("\n\nComputed:\n");
-        for (int i = 0; i < num_items; i++)
-        {
-            std::cout << CoutCast(h_data[i]) << ", ";
-        }
-        printf("\n\n");
-    }
-
-    // Check
-    int retval = CompareResults(h_data, h_reference, num_items, verbose);
-
-    // Cleanup
-    if (h_reference) free(h_reference);
-    if (h_data) free(h_data);
-
-    return retval;
-}
-
-
-/**
- * Print the contents of a host array
- */
-void DisplayResults(
-    cub::NullType   *h_data,
-    size_t          num_items)
-{}
-
-
-/**
- * Print the contents of a host array
- */
-template <typename InputIteratorT>
-void DisplayResults(
-    InputIteratorT h_data,
-    size_t num_items)
-{
-    // Display data
-    for (int i = 0; i < int(num_items); i++)
-    {
-        std::cout << CoutCast(h_data[i]) << ", ";
-    }
-    printf("\n");
-}
-
-
-/**
- * Print the contents of a device array
- */
-template <typename T>
-void DisplayDeviceResults(
-    T *d_data,
-    size_t num_items)
-{
-    // Allocate array on host
-    T *h_data = (T*) malloc(num_items * sizeof(T));
-
-    // Copy data back
-    cudaMemcpy(h_data, d_data, sizeof(T) * num_items, cudaMemcpyDeviceToHost);
-
-    DisplayResults(h_data, num_items);
-
-    // Cleanup
-    if (h_data) free(h_data);
-}
-
-
-/******************************************************************************
- * Segment descriptor generation
- ******************************************************************************/
-
-/**
- * Initialize segments
- */
-void InitializeSegments(
-    int     num_items,
-    int     num_segments,
-    int     *h_segment_offsets,
-    bool    verbose = false)
-{
-    if (num_segments <= 0)
-        return;
-
-    unsigned int expected_segment_length = (num_items + num_segments - 1) / num_segments;
-    int offset = 0;
-    for (int i = 0; i < num_segments; ++i)
-    {
-        h_segment_offsets[i] = offset;
-
-        unsigned int segment_length = RandomValue((expected_segment_length * 2) + 1);
-        offset += segment_length;
-        offset = CUB_MIN(offset, num_items);
-    }
-    h_segment_offsets[num_segments] = num_items;
-
-    if (verbose)
-    {
-        printf("Segment offsets: ");
-        DisplayResults(h_segment_offsets, num_segments + 1);
-    }
-}
-
-
-/******************************************************************************
- * Timing
- ******************************************************************************/
-
-
-struct CpuTimer
-{
-#if defined(_WIN32) || defined(_WIN64)
-
-    LARGE_INTEGER ll_freq;
-    LARGE_INTEGER ll_start;
-    LARGE_INTEGER ll_stop;
-
-    CpuTimer()
-    {
-        QueryPerformanceFrequency(&ll_freq);
-    }
-
-    void Start()
-    {
-        QueryPerformanceCounter(&ll_start);
-    }
-
-    void Stop()
-    {
-        QueryPerformanceCounter(&ll_stop);
-    }
-
-    float ElapsedMillis()
-    {
-        double start = double(ll_start.QuadPart) / double(ll_freq.QuadPart);
-        double stop  = double(ll_stop.QuadPart) / double(ll_freq.QuadPart);
-
-        return float((stop - start) * 1000);
-    }
-
-#else
-
-    rusage start;
-    rusage stop;
-
-    void Start()
-    {
-        getrusage(RUSAGE_SELF, &start);
-    }
-
-    void Stop()
-    {
-        getrusage(RUSAGE_SELF, &stop);
-    }
-
-    float ElapsedMillis()
-    {
-        float sec = stop.ru_utime.tv_sec - start.ru_utime.tv_sec;
-        float usec = stop.ru_utime.tv_usec - start.ru_utime.tv_usec;
-
-        return (sec * 1000) + (usec / 1000);
-    }
-
-#endif
-};
-
-struct GpuTimer
-{
-    cudaEvent_t start;
-    cudaEvent_t stop;
-
-    GpuTimer()
-    {
-        cudaEventCreate(&start);
-        cudaEventCreate(&stop);
-    }
-
-    ~GpuTimer()
-    {
-        cudaEventDestroy(start);
-        cudaEventDestroy(stop);
-    }
-
-    void Start()
-    {
-        cudaEventRecord(start, 0);
-    }
-
-    void Stop()
-    {
-        cudaEventRecord(stop, 0);
-    }
-
-    float ElapsedMillis()
-    {
-        float elapsed;
-        cudaEventSynchronize(stop);
-        cudaEventElapsedTime(&elapsed, start, stop);
-        return elapsed;
-    }
-};
diff --git a/ml-xgboost/cub/test/test_warp_reduce.cu b/ml-xgboost/cub/test/test_warp_reduce.cu
deleted file mode 100644
index 1af4c7f..0000000
--- a/ml-xgboost/cub/test/test_warp_reduce.cu
+++ /dev/null
@@ -1,837 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of WarpReduce utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/warp/warp_reduce.cuh>
-#include <cub/util_allocator.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose       = false;
-int                     g_repeat        = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-/**
- * \brief WrapperFunctor (for precluding test-specialized dispatch to *Sum variants)
- */
-template<
-    typename    OpT,
-    int         LOGICAL_WARP_THREADS>
-struct WrapperFunctor
-{
-    OpT op;
-    int num_valid;
-
-    inline __host__ __device__ WrapperFunctor(OpT op, int num_valid) : op(op), num_valid(num_valid) {}
-
-    template <typename T>
-    inline __host__ __device__ T operator()(const T &a, const T &b) const
-    {
-#if CUB_PTX_ARCH != 0
-        if ((cub::LaneId() % LOGICAL_WARP_THREADS) >= num_valid)
-            cub::ThreadTrap();
-#endif
-
-        return op(a, b);
-    }
-
-};
-
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-/**
- * Generic reduction
- */
-template <
-    typename    T,
-    typename    ReductionOp,
-    typename    WarpReduce,
-    bool        PRIMITIVE = Traits<T>::PRIMITIVE>
-struct DeviceTest
-{
-    static __device__ __forceinline__ T Reduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        ReductionOp                         &reduction_op)
-    {
-        return WarpReduce(temp_storage).Reduce(data, reduction_op);
-    }
-
-    static __device__ __forceinline__ T Reduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        ReductionOp                         &reduction_op,
-        const int                           &valid_warp_threads)
-    {
-        return WarpReduce(temp_storage).Reduce(data, reduction_op, valid_warp_threads);
-    }
-
-    template <typename FlagT>
-    static __device__ __forceinline__ T HeadSegmentedReduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        FlagT                                &flag,
-        ReductionOp                         &reduction_op)
-    {
-        return WarpReduce(temp_storage).HeadSegmentedReduce(data, flag, reduction_op);
-    }
-
-    template <typename FlagT>
-    static __device__ __forceinline__ T TailSegmentedReduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        FlagT                                &flag,
-        ReductionOp                         &reduction_op)
-    {
-        return WarpReduce(temp_storage).TailSegmentedReduce(data, flag, reduction_op);
-    }
-
-};
-
-
-/**
- * Summation
- */
-template <
-    typename    T,
-    typename    WarpReduce>
-struct DeviceTest<T, Sum, WarpReduce, true>
-{
-    static __device__ __forceinline__ T Reduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        Sum                              &reduction_op)
-    {
-        return WarpReduce(temp_storage).Sum(data);
-    }
-
-    static __device__ __forceinline__ T Reduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        Sum                              &reduction_op,
-        const int                           &valid_warp_threads)
-    {
-        return WarpReduce(temp_storage).Sum(data, valid_warp_threads);
-    }
-
-    template <typename FlagT>
-    static __device__ __forceinline__ T HeadSegmentedReduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        FlagT                                &flag,
-        Sum                              &reduction_op)
-    {
-        return WarpReduce(temp_storage).HeadSegmentedSum(data, flag);
-    }
-
-    template <typename FlagT>
-    static __device__ __forceinline__ T TailSegmentedReduce(
-        typename WarpReduce::TempStorage    &temp_storage,
-        T                                   &data,
-        FlagT                                &flag,
-        Sum                              &reduction_op)
-    {
-        return WarpReduce(temp_storage).TailSegmentedSum(data, flag);
-    }
-
-};
-
-
-/**
- * Full-tile warp reduction kernel
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    ReductionOp>
-__global__ void FullWarpReduceKernel(
-    T               *d_in,
-    T               *d_out,
-    ReductionOp     reduction_op,
-    clock_t         *d_elapsed)
-{
-    // Cooperative warp-reduce utility type (1 warp)
-    typedef WarpReduce<T, LOGICAL_WARP_THREADS> WarpReduce;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename WarpReduce::TempStorage temp_storage[WARPS];
-
-    // Per-thread tile data
-    T input = d_in[threadIdx.x];
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t start = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Test warp reduce
-    int warp_id = threadIdx.x / LOGICAL_WARP_THREADS;
-
-    T output = DeviceTest<T, ReductionOp, WarpReduce>::Reduce(
-        temp_storage[warp_id], input, reduction_op);
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t stop = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    *d_elapsed = stop - start;
-
-    // Store aggregate
-    d_out[threadIdx.x] = (threadIdx.x % LOGICAL_WARP_THREADS == 0) ?
-        output :
-        input;
-}
-
-/**
- * Partially-full warp reduction kernel
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    ReductionOp>
-__global__ void PartialWarpReduceKernel(
-    T           *d_in,
-    T           *d_out,
-    ReductionOp reduction_op,
-    clock_t     *d_elapsed,
-    int         valid_warp_threads)
-{
-    // Cooperative warp-reduce utility type
-    typedef WarpReduce<T, LOGICAL_WARP_THREADS> WarpReduce;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename WarpReduce::TempStorage temp_storage[WARPS];
-
-    // Per-thread tile data
-    T input = d_in[threadIdx.x];
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t start = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Test partial-warp reduce
-    int warp_id = threadIdx.x / LOGICAL_WARP_THREADS;
-    T output = DeviceTest<T, ReductionOp, WarpReduce>::Reduce(
-        temp_storage[warp_id], input, reduction_op, valid_warp_threads);
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t stop = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    *d_elapsed = stop - start;
-
-    // Store aggregate
-    d_out[threadIdx.x] = (threadIdx.x % LOGICAL_WARP_THREADS == 0) ?
-        output :
-        input;
-}
-
-
-/**
- * Head-based segmented warp reduction test kernel
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    FlagT,
-    typename    ReductionOp>
-__global__ void WarpHeadSegmentedReduceKernel(
-    T           *d_in,
-    FlagT        *d_head_flags,
-    T           *d_out,
-    ReductionOp reduction_op,
-    clock_t     *d_elapsed)
-{
-    // Cooperative warp-reduce utility type
-    typedef WarpReduce<T, LOGICAL_WARP_THREADS> WarpReduce;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename WarpReduce::TempStorage temp_storage[WARPS];
-
-    // Per-thread tile data
-    T       input       = d_in[threadIdx.x];
-    FlagT   head_flag   = d_head_flags[threadIdx.x];
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t start = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Test segmented warp reduce
-    int warp_id = threadIdx.x / LOGICAL_WARP_THREADS;
-    T output = DeviceTest<T, ReductionOp, WarpReduce>::HeadSegmentedReduce(
-        temp_storage[warp_id], input, head_flag, reduction_op);
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t stop = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    *d_elapsed = stop - start;
-
-    // Store aggregate
-    d_out[threadIdx.x] = ((threadIdx.x % LOGICAL_WARP_THREADS == 0) || head_flag) ?
-        output :
-        input;
-}
-
-
-/**
- * Tail-based segmented warp reduction test kernel
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    FlagT,
-    typename    ReductionOp>
-__global__ void WarpTailSegmentedReduceKernel(
-    T           *d_in,
-    FlagT       *d_tail_flags,
-    T           *d_out,
-    ReductionOp reduction_op,
-    clock_t     *d_elapsed)
-{
-    // Cooperative warp-reduce utility type
-    typedef WarpReduce<T, LOGICAL_WARP_THREADS> WarpReduce;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename WarpReduce::TempStorage temp_storage[WARPS];
-
-    // Per-thread tile data
-    T       input       = d_in[threadIdx.x];
-    FlagT    tail_flag   = d_tail_flags[threadIdx.x];
-    FlagT    head_flag   = (threadIdx.x == 0) ?
-                            0 :
-                            d_tail_flags[threadIdx.x - 1];
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t start = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Test segmented warp reduce
-    int warp_id = threadIdx.x / LOGICAL_WARP_THREADS;
-    T output = DeviceTest<T, ReductionOp, WarpReduce>::TailSegmentedReduce(
-        temp_storage[warp_id], input, tail_flag, reduction_op);
-
-    // Record elapsed clocks
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t stop = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    *d_elapsed = stop - start;
-
-    // Store aggregate
-    d_out[threadIdx.x] = ((threadIdx.x % LOGICAL_WARP_THREADS == 0) || head_flag) ?
-        output :
-        input;
-}
-
-
-//---------------------------------------------------------------------
-// Host utility subroutines
-//---------------------------------------------------------------------
-
-/**
- * Initialize reduction problem (and solution)
- */
-template <
-    typename    T,
-    typename    ReductionOp>
-void Initialize(
-    GenMode     gen_mode,
-    int         flag_entropy,
-    T           *h_in,
-    int         *h_flags,
-    int         warps,
-    int         warp_threads,
-    int         valid_warp_threads,
-    ReductionOp reduction_op,
-    T           *h_head_out,
-    T           *h_tail_out)
-{
-    for (int i = 0; i < warps * warp_threads; ++i)
-    {
-        // Sample a value for this item
-        InitValue(gen_mode, h_in[i], i);
-        h_head_out[i] = h_in[i];
-        h_tail_out[i] = h_in[i];
-
-        // Sample whether or not this item will be a segment head
-        char bits;
-        RandomBits(bits, flag_entropy);
-        h_flags[i] = bits & 0x1;
-    }
-
-    // Accumulate segments (lane 0 of each warp is implicitly a segment head)
-    for (int warp = 0; warp < warps; ++warp)
-    {
-        int warp_offset  = warp * warp_threads;
-        int item_offset = warp_offset + valid_warp_threads - 1;
-
-        // Last item in warp
-        T head_aggregate = h_in[item_offset];
-        T tail_aggregate = h_in[item_offset];
-
-        if (h_flags[item_offset])
-            h_head_out[item_offset] = head_aggregate;
-        item_offset--;
-
-        // Work backwards
-        while (item_offset >= warp_offset)
-        {
-            if (h_flags[item_offset + 1])
-            {
-                head_aggregate = h_in[item_offset];
-            }
-            else
-            {
-                head_aggregate = reduction_op(head_aggregate, h_in[item_offset]);
-            }
-
-            if (h_flags[item_offset])
-            {
-                h_head_out[item_offset] = head_aggregate;
-                h_tail_out[item_offset + 1] = tail_aggregate;
-                tail_aggregate = h_in[item_offset];
-            }
-            else
-            {
-                tail_aggregate = reduction_op(tail_aggregate, h_in[item_offset]);
-            }
-
-            item_offset--;
-        }
-
-        // Record last segment head_aggregate to head offset
-        h_head_out[warp_offset] = head_aggregate;
-        h_tail_out[warp_offset] = tail_aggregate;
-    }
-}
-
-
-/**
- * Test warp reduction
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    ReductionOp>
-void TestReduce(
-    GenMode     gen_mode,
-    ReductionOp reduction_op,
-    int         valid_warp_threads = LOGICAL_WARP_THREADS)
-{
-    const int BLOCK_THREADS = LOGICAL_WARP_THREADS * WARPS;
-
-    // Allocate host arrays
-    T   *h_in           = new T[BLOCK_THREADS];
-    int *h_flags        = new int[BLOCK_THREADS];
-    T   *h_out          = new T[BLOCK_THREADS];
-    T   *h_tail_out     = new T[BLOCK_THREADS];
-
-    // Initialize problem
-    Initialize(gen_mode, -1, h_in, h_flags, WARPS, LOGICAL_WARP_THREADS, valid_warp_threads, reduction_op, h_out, h_tail_out);
-
-    // Initialize/clear device arrays
-    T *d_in = NULL;
-    T *d_out = NULL;
-    clock_t *d_elapsed = NULL;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(clock_t)));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * BLOCK_THREADS, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * BLOCK_THREADS));
-
-    if (g_verbose)
-    {
-        printf("Data:\n");
-        for (int i = 0; i < WARPS; ++i)
-            DisplayResults(h_in + (i * LOGICAL_WARP_THREADS), valid_warp_threads);
-    }
-
-    // Run kernel
-    printf("\nGen-mode %d, %d warps, %d warp threads, %d valid lanes, %s (%d bytes) elements:\n",
-        gen_mode,
-        WARPS,
-        LOGICAL_WARP_THREADS,
-        valid_warp_threads,
-        typeid(T).name(),
-        (int) sizeof(T));
-    fflush(stdout);
-
-    if (valid_warp_threads == LOGICAL_WARP_THREADS)
-    {
-        // Run full-warp kernel
-        FullWarpReduceKernel<WARPS, LOGICAL_WARP_THREADS><<<1, BLOCK_THREADS>>>(
-            d_in,
-            d_out,
-            reduction_op,
-            d_elapsed);
-    }
-    else
-    {
-        // Run partial-warp kernel
-        PartialWarpReduceKernel<WARPS, LOGICAL_WARP_THREADS><<<1, BLOCK_THREADS>>>(
-            d_in,
-            d_out,
-            reduction_op,
-            d_elapsed,
-            valid_warp_threads);
-    }
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tReduction results: ");
-    int compare = CompareDeviceResults(h_out, d_out, BLOCK_THREADS, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_flags) delete[] h_flags;
-    if (h_out) delete[] h_out;
-    if (h_tail_out) delete[] h_tail_out;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_elapsed) CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-/**
- * Test warp segmented reduction
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    ReductionOp>
-void TestSegmentedReduce(
-    GenMode     gen_mode,
-    int         flag_entropy,
-    ReductionOp reduction_op)
-{
-    const int BLOCK_THREADS = LOGICAL_WARP_THREADS * WARPS;
-
-    // Allocate host arrays
-    int compare;
-    T   *h_in           = new T[BLOCK_THREADS];
-    int *h_flags        = new int[BLOCK_THREADS];
-    T   *h_head_out     = new T[BLOCK_THREADS];
-    T   *h_tail_out     = new T[BLOCK_THREADS];
-
-    // Initialize problem
-    Initialize(gen_mode, flag_entropy, h_in, h_flags, WARPS, LOGICAL_WARP_THREADS, LOGICAL_WARP_THREADS, reduction_op, h_head_out, h_tail_out);
-
-    // Initialize/clear device arrays
-    T           *d_in = NULL;
-    int         *d_flags = NULL;
-    T           *d_head_out = NULL;
-    T           *d_tail_out = NULL;
-    clock_t     *d_elapsed = NULL;
-
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_flags, sizeof(int) * BLOCK_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_head_out, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_tail_out, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(clock_t)));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * BLOCK_THREADS, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemcpy(d_flags, h_flags, sizeof(int) * BLOCK_THREADS, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_head_out, 0, sizeof(T) * BLOCK_THREADS));
-    CubDebugExit(cudaMemset(d_tail_out, 0, sizeof(T) * BLOCK_THREADS));
-
-    if (g_verbose)
-    {
-        printf("Data:\n");
-        for (int i = 0; i < WARPS; ++i)
-            DisplayResults(h_in + (i * LOGICAL_WARP_THREADS), LOGICAL_WARP_THREADS);
-
-        printf("\nFlags:\n");
-        for (int i = 0; i < WARPS; ++i)
-            DisplayResults(h_flags + (i * LOGICAL_WARP_THREADS), LOGICAL_WARP_THREADS);
-    }
-
-    printf("\nGen-mode %d, head flag entropy reduction %d, %d warps, %d warp threads, %s (%d bytes) elements:\n",
-        gen_mode,
-        flag_entropy,
-        WARPS,
-        LOGICAL_WARP_THREADS,
-        typeid(T).name(),
-        (int) sizeof(T));
-    fflush(stdout);
-
-    // Run head-based kernel
-    WarpHeadSegmentedReduceKernel<WARPS, LOGICAL_WARP_THREADS><<<1, BLOCK_THREADS>>>(
-        d_in,
-        d_flags,
-        d_head_out,
-        reduction_op,
-        d_elapsed);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tHead-based segmented reduction results: ");
-    compare = CompareDeviceResults(h_head_out, d_head_out, BLOCK_THREADS, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    // Run tail-based kernel
-    WarpTailSegmentedReduceKernel<WARPS, LOGICAL_WARP_THREADS><<<1, BLOCK_THREADS>>>(
-        d_in,
-        d_flags,
-        d_tail_out,
-        reduction_op,
-        d_elapsed);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tTail-based segmented reduction results: ");
-    compare = CompareDeviceResults(h_tail_out, d_tail_out, BLOCK_THREADS, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_flags) delete[] h_flags;
-    if (h_head_out) delete[] h_head_out;
-    if (h_tail_out) delete[] h_tail_out;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_flags) CubDebugExit(g_allocator.DeviceFree(d_flags));
-    if (d_head_out) CubDebugExit(g_allocator.DeviceFree(d_head_out));
-    if (d_tail_out) CubDebugExit(g_allocator.DeviceFree(d_tail_out));
-    if (d_elapsed) CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-/**
- * Run battery of tests for different full and partial tile sizes
- */
-template <
-    int         WARPS,
-    int         LOGICAL_WARP_THREADS,
-    typename    T,
-    typename    ReductionOp>
-void Test(
-    GenMode     gen_mode,
-    ReductionOp reduction_op)
-{
-    // Partial tiles
-    for (
-        int valid_warp_threads = 1;
-        valid_warp_threads < LOGICAL_WARP_THREADS;
-        valid_warp_threads += CUB_MAX(1, LOGICAL_WARP_THREADS / 5))
-    {
-        // Without wrapper (to test non-excepting PTX POD-op specializations)
-        TestReduce<WARPS, LOGICAL_WARP_THREADS, T>(gen_mode, reduction_op, valid_warp_threads);
-
-        // With wrapper to ensure no ops called on OOB lanes
-        WrapperFunctor<ReductionOp, LOGICAL_WARP_THREADS> wrapped_op(reduction_op, valid_warp_threads);
-        TestReduce<WARPS, LOGICAL_WARP_THREADS, T>(gen_mode, wrapped_op, valid_warp_threads);
-    }
-
-    // Full tile
-    TestReduce<WARPS, LOGICAL_WARP_THREADS, T>(gen_mode, reduction_op, LOGICAL_WARP_THREADS);
-
-    // Segmented reduction with different head flags
-    for (int flag_entropy = 0; flag_entropy < 10; ++flag_entropy)
-    {
-        TestSegmentedReduce<WARPS, LOGICAL_WARP_THREADS, T>(gen_mode, flag_entropy, reduction_op);
-    }
-}
-
-
-/**
- * Run battery of tests for different data types and reduce ops
- */
-template <
-    int WARPS,
-    int LOGICAL_WARP_THREADS>
-void Test(GenMode gen_mode)
-{
-    // primitive
-    Test<WARPS, LOGICAL_WARP_THREADS, char>(                gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, short>(               gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, int>(                 gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, long long>(           gen_mode, Sum());
-
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned char>(       gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned short>(      gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned int>(        gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned long long>(  gen_mode, Sum());
-
-    if (gen_mode != RANDOM)
-    {
-        Test<WARPS, LOGICAL_WARP_THREADS, float>(           gen_mode, Sum());
-        Test<WARPS, LOGICAL_WARP_THREADS, double>(          gen_mode, Sum());
-    }
-
-    // primitive (alternative reduce op)
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned char>(       gen_mode, Max());
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned short>(      gen_mode, Max());
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned int>(        gen_mode, Max());
-    Test<WARPS, LOGICAL_WARP_THREADS, unsigned long long>(  gen_mode, Max());
-
-    // vec-1
-    Test<WARPS, LOGICAL_WARP_THREADS, uchar1>(              gen_mode, Sum());
-
-    // vec-2
-    Test<WARPS, LOGICAL_WARP_THREADS, uchar2>(              gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, ushort2>(             gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, uint2>(               gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, ulonglong2>(          gen_mode, Sum());
-
-    // vec-4
-    Test<WARPS, LOGICAL_WARP_THREADS, uchar4>(              gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, ushort4>(             gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, uint4>(               gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, ulonglong4>(          gen_mode, Sum());
-
-    // complex
-    Test<WARPS, LOGICAL_WARP_THREADS, TestFoo>(             gen_mode, Sum());
-    Test<WARPS, LOGICAL_WARP_THREADS, TestBar>(             gen_mode, Sum());
-}
-
-
-/**
- * Run battery of tests for different problem generation options
- */
-template <
-    int WARPS,
-    int LOGICAL_WARP_THREADS>
-void Test()
-{
-    Test<WARPS, LOGICAL_WARP_THREADS>(UNIFORM);
-    Test<WARPS, LOGICAL_WARP_THREADS>(INTEGER_SEED);
-    Test<WARPS, LOGICAL_WARP_THREADS>(RANDOM);
-}
-
-
-/**
- * Run battery of tests for different number of active warps
- */
-template <int LOGICAL_WARP_THREADS>
-void Test()
-{
-    Test<1, LOGICAL_WARP_THREADS>();
-    Test<2, LOGICAL_WARP_THREADS>();
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-#ifdef QUICK_TEST
-
-    // Compile/run quick tests
-    TestReduce<1, 32, int>(UNIFORM, Sum());
-
-    TestReduce<1, 32, double>(UNIFORM, Sum());
-    TestReduce<2, 16, TestBar>(UNIFORM, Sum());
-    TestSegmentedReduce<1, 32, int>(UNIFORM, 1, Sum());
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test logical warp sizes
-        Test<32>();
-        Test<16>();
-        Test<9>();
-        Test<7>();
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
-
diff --git a/ml-xgboost/cub/test/test_warp_scan.cu b/ml-xgboost/cub/test/test_warp_scan.cu
deleted file mode 100644
index 48ea768..0000000
--- a/ml-xgboost/cub/test/test_warp_scan.cu
+++ /dev/null
@@ -1,630 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Test of WarpScan utilities
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <stdio.h>
-#include <typeinfo>
-
-#include <cub/warp/warp_scan.cuh>
-#include <cub/util_allocator.cuh>
-
-#include "test_util.h"
-
-using namespace cub;
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-bool                    g_verbose       = false;
-int                     g_repeat        = 0;
-CachingDeviceAllocator  g_allocator(true);
-
-
-/**
- * Primitive variant to test
- */
-enum TestMode
-{
-    BASIC,
-    AGGREGATE,
-};
-
-
-
-/**
- * \brief WrapperFunctor (for precluding test-specialized dispatch to *Sum variants)
- */
-template<typename OpT>
-struct WrapperFunctor
-{
-    OpT op;
-
-    WrapperFunctor(OpT op) : op(op) {}
-
-    template <typename T>
-    __host__ __device__ __forceinline__ T operator()(const T &a, const T &b) const
-    {
-        return op(a, b);
-    }
-};
-
-//---------------------------------------------------------------------
-// Test kernels
-//---------------------------------------------------------------------
-
-/// Exclusive scan basic
-template <typename WarpScanT, typename T, typename ScanOpT, typename IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    T                               &initial_value,
-    ScanOpT                         &scan_op,
-    T                               &aggregate,
-    Int2Type<BASIC>                 test_mode,
-    IsPrimitiveT                    is_primitive)
-{
-    // Test basic warp scan
-    warp_scan.ExclusiveScan(data, data, initial_value, scan_op);
-}
-
-/// Exclusive scan aggregate
-template <
-    typename    WarpScanT,
-    typename    T,
-    typename    ScanOpT,
-    typename    IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    T                               &initial_value,
-    ScanOpT                         &scan_op,
-    T                               &aggregate,
-    Int2Type<AGGREGATE>             test_mode,
-    IsPrimitiveT                    is_primitive)
-{
-    // Test with cumulative aggregate
-    warp_scan.ExclusiveScan(data, data, initial_value, scan_op, aggregate);
-}
-
-
-/// Exclusive sum basic
-template <
-    typename    WarpScanT,
-    typename    T>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    T                               &initial_value,
-    Sum                             &scan_op,
-    T                               &aggregate,
-    Int2Type<BASIC>                 test_mode,
-    Int2Type<true>                  is_primitive)
-{
-    // Test basic warp scan
-    warp_scan.ExclusiveSum(data, data);
-}
-
-
-/// Exclusive sum aggregate
-template <
-    typename    WarpScanT,
-    typename    T>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    T                               &initial_value,
-    Sum                             &scan_op,
-    T                               &aggregate,
-    Int2Type<AGGREGATE>             test_mode,
-    Int2Type<true>                  is_primitive)
-{
-    // Test with cumulative aggregate
-    warp_scan.ExclusiveSum(data, data, aggregate);
-}
-
-
-/// Inclusive scan basic
-template <
-    typename    WarpScanT,
-    typename    T,
-    typename    ScanOpT,
-    typename    IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    NullType                        &initial_value,
-    ScanOpT                         &scan_op,
-    T                               &aggregate,
-    Int2Type<BASIC>                 test_mode,
-    IsPrimitiveT                    is_primitive)
-{
-    // Test basic warp scan
-    warp_scan.InclusiveScan(data, data, scan_op);
-}
-
-/// Inclusive scan aggregate
-template <
-    typename    WarpScanT,
-    typename    T,
-    typename    ScanOpT,
-    typename    IsPrimitiveT>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    NullType                        &initial_value,
-    ScanOpT                         &scan_op,
-    T                               &aggregate,
-    Int2Type<AGGREGATE>             test_mode,
-    IsPrimitiveT                    is_primitive)
-{
-    // Test with cumulative aggregate
-    warp_scan.InclusiveScan(data, data, scan_op, aggregate);
-}
-
-/// Inclusive sum basic
-template <
-    typename    WarpScanT,
-    typename    T,
-    typename    InitialValueT>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    NullType                        &initial_value,
-    Sum                             &scan_op,
-    T                               &aggregate,
-    Int2Type<BASIC>                 test_mode,
-    Int2Type<true>                  is_primitive)
-{
-    // Test basic warp scan
-    warp_scan.InclusiveSum(data, data);
-}
-
-/// Inclusive sum aggregate
-template <
-    typename    WarpScanT,
-    typename    T,
-    typename    InitialValueT>
-__device__ __forceinline__ void DeviceTest(
-    WarpScanT                       &warp_scan,
-    T                               &data,
-    NullType                        &initial_value,
-    Sum                             &scan_op,
-    T                               &aggregate,
-    Int2Type<AGGREGATE>             test_mode,
-    Int2Type<true>                  is_primitive)
-{
-    // Test with cumulative aggregate
-    warp_scan.InclusiveSum(data, data, aggregate);
-}
-
-
-/**
- * WarpScan test kernel
- */
-template <
-    int         LOGICAL_WARP_THREADS,
-    TestMode    TEST_MODE,
-    typename    T,
-    typename    ScanOpT,
-    typename    InitialValueT>
-__global__ void WarpScanKernel(
-    T               *d_in,
-    T               *d_out,
-    T               *d_aggregate,
-    ScanOpT         scan_op,
-    InitialValueT   initial_value,
-    clock_t         *d_elapsed)
-{
-    // Cooperative warp-scan utility type (1 warp)
-    typedef WarpScan<T, LOGICAL_WARP_THREADS> WarpScanT;
-
-    // Allocate temp storage in shared memory
-    __shared__ typename WarpScanT::TempStorage temp_storage;
-
-    // Per-thread tile data
-    T data = d_in[threadIdx.x];
-
-    // Start cycle timer
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t start = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    T aggregate;
-
-    // Test scan
-    WarpScanT warp_scan(temp_storage);
-    DeviceTest(
-        warp_scan,
-        data,
-        initial_value,
-        scan_op,
-        aggregate,
-        Int2Type<TEST_MODE>(),
-        Int2Type<Traits<T>::PRIMITIVE>());
-
-    // Stop cycle timer
-    __threadfence_block();      // workaround to prevent clock hoisting
-    clock_t stop = clock();
-    __threadfence_block();      // workaround to prevent clock hoisting
-
-    // Store data
-    d_out[threadIdx.x] = data;
-
-    if (TEST_MODE != BASIC)
-    {
-        // Store aggregate
-        d_aggregate[threadIdx.x] = aggregate;
-    }
-
-    // Store time
-    if (threadIdx.x == 0)
-    {
-        *d_elapsed = (start > stop) ? start - stop : stop - start;
-    }
-}
-
-
-//---------------------------------------------------------------------
-// Host utility subroutines
-//---------------------------------------------------------------------
-
-/**
- * Initialize exclusive-scan problem (and solution)
- */
-template <
-    typename        T,
-    typename        ScanOpT>
-T Initialize(
-    GenMode         gen_mode,
-    T               *h_in,
-    T               *h_reference,
-    int             num_items,
-    ScanOpT         scan_op,
-    T               initial_value)
-{
-    InitValue(gen_mode, h_in[0], 0);
-
-    T block_aggregate   = h_in[0];
-    h_reference[0]      = initial_value;
-    T inclusive         = scan_op(initial_value, h_in[0]);
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-        h_reference[i] = inclusive;
-        inclusive = scan_op(inclusive, h_in[i]);
-        block_aggregate = scan_op(block_aggregate, h_in[i]);
-    }
-
-    return block_aggregate;
-}
-
-
-/**
- * Initialize inclusive-scan problem (and solution)
- */
-template <
-    typename    T,
-    typename    ScanOpT>
-T Initialize(
-    GenMode     gen_mode,
-    T           *h_in,
-    T           *h_reference,
-    int         num_items,
-    ScanOpT     scan_op,
-    NullType)
-{
-    InitValue(gen_mode, h_in[0], 0);
-
-    T block_aggregate   = h_in[0];
-    T inclusive         = h_in[0];
-    h_reference[0]      = inclusive;
-
-    for (int i = 1; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-        inclusive = scan_op(inclusive, h_in[i]);
-        block_aggregate = scan_op(block_aggregate, h_in[i]);
-        h_reference[i] = inclusive;
-    }
-
-    return block_aggregate;
-}
-
-
-/**
- * Test warp scan
- */
-template <
-    int             LOGICAL_WARP_THREADS,
-    TestMode        TEST_MODE,
-    typename        T,
-    typename        ScanOpT,
-    typename        InitialValueT>        // NullType implies inclusive-scan, otherwise inclusive scan
-void Test(
-    GenMode         gen_mode,
-    ScanOpT         scan_op,
-    InitialValueT   initial_value)
-{
-    // Allocate host arrays
-    T *h_in = new T[LOGICAL_WARP_THREADS];
-    T *h_reference = new T[LOGICAL_WARP_THREADS];
-    T *h_aggregate = new T[LOGICAL_WARP_THREADS];
-
-    // Initialize problem
-    T aggregate = Initialize(
-        gen_mode,
-        h_in,
-        h_reference,
-        LOGICAL_WARP_THREADS,
-        scan_op,
-        initial_value);
-
-    if (g_verbose)
-    {
-        printf("Input: \n");
-        DisplayResults(h_in, LOGICAL_WARP_THREADS);
-        printf("\n");
-    }
-
-    for (int i = 0; i < LOGICAL_WARP_THREADS; ++i)
-    {
-        h_aggregate[i] = aggregate;
-    }
-
-    // Initialize/clear device arrays
-    T *d_in = NULL;
-    T *d_out = NULL;
-    T *d_aggregate = NULL;
-    clock_t *d_elapsed = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * LOGICAL_WARP_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * (LOGICAL_WARP_THREADS + 1)));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_aggregate, sizeof(T) * LOGICAL_WARP_THREADS));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_elapsed, sizeof(clock_t)));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * LOGICAL_WARP_THREADS, cudaMemcpyHostToDevice));
-    CubDebugExit(cudaMemset(d_out, 0, sizeof(T) * (LOGICAL_WARP_THREADS + 1)));
-    CubDebugExit(cudaMemset(d_aggregate, 0, sizeof(T) * LOGICAL_WARP_THREADS));
-
-    // Run kernel
-    printf("Test-mode %d (%s), gen-mode %d (%s), %s warpscan, %d warp threads, %s (%d bytes) elements:\n",
-        TEST_MODE, typeid(TEST_MODE).name(),
-        gen_mode, typeid(gen_mode).name(),
-        (Equals<InitialValueT, NullType>::VALUE) ? "Inclusive" : "Exclusive",
-        LOGICAL_WARP_THREADS,
-        typeid(T).name(),
-        (int) sizeof(T));
-    fflush(stdout);
-
-    // Run aggregate/prefix kernel
-    WarpScanKernel<LOGICAL_WARP_THREADS, TEST_MODE><<<1, LOGICAL_WARP_THREADS>>>(
-        d_in,
-        d_out,
-        d_aggregate,
-        scan_op,
-        initial_value,
-        d_elapsed);
-
-    printf("\tElapsed clocks: ");
-    DisplayDeviceResults(d_elapsed, 1);
-
-    CubDebugExit(cudaPeekAtLastError());
-    CubDebugExit(cudaDeviceSynchronize());
-
-    // Copy out and display results
-    printf("\tScan results: ");
-    int compare = CompareDeviceResults(h_reference, d_out, LOGICAL_WARP_THREADS, g_verbose, g_verbose);
-    printf("%s\n", compare ? "FAIL" : "PASS");
-    AssertEquals(0, compare);
-
-    // Copy out and display aggregate
-    if (TEST_MODE == AGGREGATE)
-    {
-        printf("\tScan aggregate: ");
-        compare = CompareDeviceResults(h_aggregate, d_aggregate, LOGICAL_WARP_THREADS, g_verbose, g_verbose);
-        printf("%s\n", compare ? "FAIL" : "PASS");
-        AssertEquals(0, compare);
-    }
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (h_reference) delete[] h_reference;
-    if (h_aggregate) delete[] h_aggregate;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-    if (d_aggregate) CubDebugExit(g_allocator.DeviceFree(d_aggregate));
-    if (d_elapsed) CubDebugExit(g_allocator.DeviceFree(d_elapsed));
-}
-
-
-/**
- * Run battery of tests for different primitive variants
- */
-template <
-    int         LOGICAL_WARP_THREADS,
-    typename    ScanOpT,
-    typename    T>
-void Test(
-    GenMode     gen_mode,
-    ScanOpT     scan_op,
-    T           initial_value)
-{
-    // Exclusive
-    Test<LOGICAL_WARP_THREADS, BASIC, T>(gen_mode, scan_op, T());
-    Test<LOGICAL_WARP_THREADS, AGGREGATE, T>(gen_mode, scan_op, T());
-
-    // Exclusive (non-specialized, so we can use initial-value)
-    Test<LOGICAL_WARP_THREADS, BASIC, T>(gen_mode, WrapperFunctor<ScanOpT>(scan_op), initial_value);
-    Test<LOGICAL_WARP_THREADS, AGGREGATE, T>(gen_mode, WrapperFunctor<ScanOpT>(scan_op), initial_value);
-
-    // Inclusive
-    Test<LOGICAL_WARP_THREADS, BASIC, T>(gen_mode, scan_op, NullType());
-    Test<LOGICAL_WARP_THREADS, AGGREGATE, T>(gen_mode, scan_op, NullType());
-}
-
-
-/**
- * Run battery of tests for different data types and scan ops
- */
-template <int LOGICAL_WARP_THREADS>
-void Test(GenMode gen_mode)
-{
-    // Get device ordinal
-    int device_ordinal;
-    CubDebugExit(cudaGetDevice(&device_ordinal));
-
-    // Get ptx version
-    int ptx_version;
-    CubDebugExit(PtxVersion(ptx_version));
-
-    // primitive
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (char) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (short) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (int) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (long) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (long long) 99);
-    if (gen_mode != RANDOM) {
-        // Only test numerically stable inputs
-        Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (float) 99);
-        if (ptx_version > 100)
-            Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), (double) 99);
-    }
-
-    // primitive (alternative scan op)
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Max(), (unsigned char) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Max(), (unsigned short) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Max(), (unsigned int) 99);
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Max(), (unsigned long long) 99);
-
-    // vec-2
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_uchar2(17, 21));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_ushort2(17, 21));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_uint2(17, 21));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_ulong2(17, 21));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_ulonglong2(17, 21));
-    if (gen_mode != RANDOM) {
-        // Only test numerically stable inputs
-        Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_float2(17, 21));
-        if (ptx_version > 100)
-            Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_double2(17, 21));
-    }
-
-    // vec-4
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_char4(17, 21, 32, 85));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_short4(17, 21, 32, 85));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_int4(17, 21, 32, 85));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_long4(17, 21, 32, 85));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_longlong4(17, 21, 32, 85));
-    if (gen_mode != RANDOM) {
-        // Only test numerically stable inputs
-        Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_float4(17, 21, 32, 85));
-        if (ptx_version > 100)
-            Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), make_double4(17, 21, 32, 85));
-    }
-
-    // complex
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), TestFoo::MakeTestFoo(17, 21, 32, 85));
-    Test<LOGICAL_WARP_THREADS>(gen_mode, Sum(), TestBar(17, 21));
-
-}
-
-
-/**
- * Run battery of tests for different problem generation options
- */
-template <int LOGICAL_WARP_THREADS>
-void Test()
-{
-    Test<LOGICAL_WARP_THREADS>(UNIFORM);
-    Test<LOGICAL_WARP_THREADS>(INTEGER_SEED);
-    Test<LOGICAL_WARP_THREADS>(RANDOM);
-}
-
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    g_verbose = args.CheckCmdLineFlag("v");
-    args.GetCmdLineArgument("repeat", g_repeat);
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--repeat=<repetitions of entire test suite>]"
-            "[--v] "
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-#ifdef QUICK_TEST
-
-    // Compile/run quick tests
-    Test<32, AGGREGATE, int>(UNIFORM, Sum(), (int) 0);
-    Test<32, AGGREGATE, float>(UNIFORM, Sum(), (float) 0);
-    Test<32, AGGREGATE, long long>(UNIFORM, Sum(), (long long) 0);
-    Test<32, AGGREGATE, double>(UNIFORM, Sum(), (double) 0);
-
-    typedef KeyValuePair<int, float> T;
-    cub::Sum sum_op;
-    Test<32, AGGREGATE, T>(UNIFORM, ReduceBySegmentOp<cub::Sum>(sum_op), T());
-
-#else
-
-    // Compile/run thorough tests
-    for (int i = 0; i <= g_repeat; ++i)
-    {
-        // Test logical warp sizes
-        Test<32>();
-        Test<16>();
-        Test<9>();
-        Test<7>();
-    }
-
-#endif
-
-    return 0;
-}
-
-
-
-
diff --git a/ml-xgboost/cub/tune/.gitignore b/ml-xgboost/cub/tune/.gitignore
deleted file mode 100644
index 5e56e04..0000000
--- a/ml-xgboost/cub/tune/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/bin
diff --git a/ml-xgboost/cub/tune/Makefile b/ml-xgboost/cub/tune/Makefile
deleted file mode 100644
index 331a356..0000000
--- a/ml-xgboost/cub/tune/Makefile
+++ /dev/null
@@ -1,192 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
- 
-#-------------------------------------------------------------------------------
-# Build script for project
-#-------------------------------------------------------------------------------
-
-NVCC = "$(shell which nvcc)"
-NVCC_VERSION = $(strip $(shell nvcc --version | grep release | sed 's/.*release //' |  sed 's/,.*//'))
-
-# detect OS
-OSUPPER = $(shell uname -s 2>/dev/null | tr [:lower:] [:upper:])
-
-#-------------------------------------------------------------------------------
-# Libs
-#-------------------------------------------------------------------------------
-
-
-#-------------------------------------------------------------------------------
-# Includes
-#-------------------------------------------------------------------------------
-
-INC = -I. -I.. -I../test
-
-#-------------------------------------------------------------------------------
-# Libs
-#-------------------------------------------------------------------------------
-
-LIBS += -lcudart 
-
-#-------------------------------------------------------------------------------
-# Defines
-#-------------------------------------------------------------------------------
-
-DEFINES = 
-
-#-------------------------------------------------------------------------------
-# SM Arch
-#-------------------------------------------------------------------------------
-
-ifdef sm
-	SM_ARCH = $(sm)
-else 
-    SM_ARCH = 200
-endif
-
-# Only one arch per tuning binary
-ifeq (350, $(findstring 350, $(SM_ARCH)))
-    SM_TARGETS = -arch=sm_35
-    SM_ARCH = 350
-endif
-ifeq (300, $(findstring 300, $(SM_ARCH)))
-    SM_TARGETS = -arch=sm_30
-    SM_ARCH = 300
-endif
-ifeq (200, $(findstring 200, $(SM_ARCH)))
-    SM_TARGETS = -arch=sm_20
-    SM_ARCH = 200
-endif
-ifeq (130, $(findstring 130, $(SM_ARCH)))
-    SM_TARGETS = -arch=sm_13
-    SM_ARCH = 130
-endif
-ifeq (110, $(findstring 110, $(SM_ARCH)))
-    SM_TARGETS = -arch=sm_11 
-    SM_ARCH = 110
-endif
-ifeq (100, $(findstring 100, $(SM_ARCH)))
-    SM_TARGETS = -arch=sm_10 
-    SM_ARCH = 100
-endif
-
-
-#-------------------------------------------------------------------------------
-# Compiler Flags
-#-------------------------------------------------------------------------------
-
-NVCCFLAGS = -Xptxas -v -Xcudafe -\#
-
-# Help the compiler/linker work with huge numbers of kernels on Windows
-ifeq (WIN_NT, $(findstring WIN_NT, $(OSUPPER)))
-	NVCCFLAGS += -Xcompiler /bigobj -Xcompiler /Zm500
-endif
-
-# 32/64-bit (32-bit device pointers by default) 
-ifeq ($(force32), 1)
-	CPU_ARCH = -m32
-	CPU_ARCH_SUFFIX = i386
-else
-	CPU_ARCH = -m64
-	CPU_ARCH_SUFFIX = x86_64
-endif
-
-# CUDA ABI enable/disable (enabled by default) 
-ifneq ($(abi), 0)
-	ABI_SUFFIX = abi
-else 
-	NVCCFLAGS += -Xptxas -abi=no
-	ABI_SUFFIX = noabi
-endif
-
-# NVVM/Open64 middle-end compiler (nvvm by default)
-ifeq ($(open64), 1)
-	NVCCFLAGS += -open64
-	PTX_SUFFIX = open64
-else 
-	PTX_SUFFIX = nvvm
-endif
-
-# Verbose toolchain output from nvcc
-ifeq ($(verbose), 1)
-	NVCCFLAGS += -v
-endif
-
-# Keep intermediate compilation artifacts
-ifeq ($(keep), 1)
-	NVCCFLAGS += -keep
-endif
-
-# Data type size to compile a schmoo binary for
-ifdef tunesize
-    TUNE_SIZE = $(tunesize)
-else 
-	TUNE_SIZE = 4
-endif
-
-
-SUFFIX = $(TUNE_SIZE)B_sm$(SM_ARCH)_$(PTX_SUFFIX)_$(NVCC_VERSION)_$(ABI_SUFFIX)_$(CPU_ARCH_SUFFIX)
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-DEPS =	 ./Makefile \
-		../test/test_util.h \
-		$(call rwildcard,../cub/,*.cuh)
-
-
-#-------------------------------------------------------------------------------
-# make default
-#-------------------------------------------------------------------------------
-
-default:
-
-
-#-------------------------------------------------------------------------------
-# make clean
-#-------------------------------------------------------------------------------
-
-clean :
-	rm -f bin/*$(CPU_ARCH_SUFFIX)* 
-	rm -f *.i* *.cubin *.cu.c *.cudafe* *.fatbin.c *.ptx *.hash *.cu.cpp *.o
-
-
-
-#-------------------------------------------------------------------------------
-# make tune_device_reduce
-#-------------------------------------------------------------------------------
-
-tune_device_reduce: bin/tune_device_reduce_$(SUFFIX)
-
-bin/tune_device_reduce_$(SUFFIX) : tune_device_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/tune_device_reduce_$(SUFFIX) tune_device_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3 -DTUNE_ARCH=$(SM_ARCH) -DTUNE_SIZE=$(TUNE_SIZE)
-
diff --git a/ml-xgboost/cub/tune/tune_device_reduce.cu b/ml-xgboost/cub/tune/tune_device_reduce.cu
deleted file mode 100644
index da20d00..0000000
--- a/ml-xgboost/cub/tune/tune_device_reduce.cu
+++ /dev/null
@@ -1,763 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011, Duane Merrill.  All rights reserved.
- * Copyright (c) 2011-2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-/******************************************************************************
- * Evaluates different tuning configurations of DeviceReduce.
- *
- * The best way to use this program:
- * (1) Find the best all-around single-block tune for a given arch.
- *     For example, 100 samples [1 ..512], 100 timing iterations per config per sample:
- *         ./bin/tune_device_reduce_sm200_nvvm_5.0_abi_i386 --i=100 --s=100 --n=512 --single --device=0
- * (2) Update the single tune in device_reduce.cuh
- * (3) Find the best all-around multi-block tune for a given arch.
- *     For example, 100 samples [single-block tile-size ..  50,331,648], 100 timing iterations per config per sample:
- *         ./bin/tune_device_reduce_sm200_nvvm_5.0_abi_i386 --i=100 --s=100 --device=0
- * (4) Update the multi-block tune in device_reduce.cuh
- *
- ******************************************************************************/
-
-// Ensure printing of CUDA runtime errors to console
-#define CUB_STDERR
-
-#include <vector>
-#include <algorithm>
-#include <stdio.h>
-#include <cub/cub.cuh>
-#include "../test/test_util.h"
-
-using namespace cub;
-using namespace std;
-
-
-//---------------------------------------------------------------------
-// Globals, constants and typedefs
-//---------------------------------------------------------------------
-
-#ifndef TUNE_ARCH
-#define TUNE_ARCH 100
-#endif
-
-int     g_max_items         = 48 * 1024 * 1024;
-int     g_samples           = 100;
-int     g_timing_iterations        = 2;
-bool    g_verbose           = false;
-bool    g_single            = false;
-bool    g_verify            = true;
-CachingDeviceAllocator  g_allocator;
-
-
-//---------------------------------------------------------------------
-// Host utility subroutines
-//---------------------------------------------------------------------
-
-/**
- * Initialize problem
- */
-template <typename T>
-void Initialize(
-    GenMode         gen_mode,
-    T               *h_in,
-    int             num_items)
-{
-    for (int i = 0; i < num_items; ++i)
-    {
-        InitValue(gen_mode, h_in[i], i);
-    }
-}
-
-/**
- * Sequential reduction
- */
-template <typename T, typename ReductionOp>
-T Reduce(
-    T               *h_in,
-    ReductionOp     reduction_op,
-    int             num_items)
-{
-    T retval = h_in[0];
-    for (int i = 1; i < num_items; ++i)
-        retval = reduction_op(retval, h_in[i]);
-
-    return retval;
-}
-
-
-
-//---------------------------------------------------------------------
-// Full tile test generation
-//---------------------------------------------------------------------
-
-
-
-/**
- * Wrapper structure for generating and running different tuning configurations
- */
-template <
-    typename T,
-    typename OffsetT,
-    typename ReductionOp>
-struct Schmoo
-{
-    //---------------------------------------------------------------------
-    // Types
-    //---------------------------------------------------------------------
-
-    /// Pairing of kernel function pointer and corresponding dispatch params
-    template <typename KernelPtr>
-    struct DispatchTuple
-    {
-        KernelPtr                           kernel_ptr;
-        DeviceReduce::KernelDispachParams   params;
-
-        float                               avg_throughput;
-        float                               best_avg_throughput;
-        OffsetT                              best_size;
-        float                               hmean_speedup;
-
-
-        DispatchTuple() :
-            kernel_ptr(0),
-            params(DeviceReduce::KernelDispachParams()),
-            avg_throughput(0.0),
-            best_avg_throughput(0.0),
-            hmean_speedup(0.0),
-            best_size(0)
-        {}
-    };
-
-    /**
-     * Comparison operator for DispatchTuple.avg_throughput
-     */
-    template <typename Tuple>
-    static bool MinSpeedup(const Tuple &a, const Tuple &b)
-    {
-        float delta = a.hmean_speedup - b.hmean_speedup;
-
-        return ((delta < 0.02) && (delta > -0.02)) ?
-            (a.best_avg_throughput < b.best_avg_throughput) :       // Negligible average performance differences: defer to best performance
-            (a.hmean_speedup < b.hmean_speedup);
-    }
-
-
-
-    /// Multi-block reduction kernel type and dispatch tuple type
-    typedef void (*MultiBlockDeviceReduceKernelPtr)(T*, T*, OffsetT, GridEvenShare<OffsetT>, GridQueue<OffsetT>, ReductionOp);
-    typedef DispatchTuple<MultiBlockDeviceReduceKernelPtr> MultiDispatchTuple;
-
-    /// Single-block reduction kernel type and dispatch tuple type
-    typedef void (*SingleBlockDeviceReduceKernelPtr)(T*, T*, OffsetT, ReductionOp);
-    typedef DispatchTuple<SingleBlockDeviceReduceKernelPtr> SingleDispatchTuple;
-
-
-    //---------------------------------------------------------------------
-    // Fields
-    //---------------------------------------------------------------------
-
-    vector<MultiDispatchTuple> multi_kernels;       // List of generated multi-block kernels
-    vector<SingleDispatchTuple> single_kernels;     // List of generated single-block kernels
-
-
-    //---------------------------------------------------------------------
-    // Kernel enumeration methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Must have smem that fits in the SM
-     * Must have vector load length that divides items per thread
-     */
-    template <typename TilesReducePolicy, typename ReductionOp>
-    struct SmemSize
-    {
-        enum
-        {
-            BYTES = sizeof(typename BlockReduceTiles<TilesReducePolicy, T*, OffsetT, ReductionOp>::TempStorage),
-            IS_OK = ((BYTES < ArchProps<TUNE_ARCH>::SMEM_BYTES) &&
-                     (TilesReducePolicy::ITEMS_PER_THREAD % TilesReducePolicy::VECTOR_LOAD_LENGTH == 0))
-        };
-    };
-
-
-    /**
-     * Specialization that allows kernel generation with the specified TilesReducePolicy
-     */
-    template <
-        typename    TilesReducePolicy,
-        bool        IsOk = SmemSize<TilesReducePolicy, ReductionOp>::IS_OK>
-    struct Ok
-    {
-        /// Enumerate multi-block kernel and add to the list
-        template <typename KernelsVector>
-        static void GenerateMulti(
-            KernelsVector &multi_kernels,
-            int subscription_factor)
-        {
-            MultiDispatchTuple tuple;
-            tuple.params.template Init<TilesReducePolicy>(subscription_factor);
-            tuple.kernel_ptr = ReducePrivatizedKernel<TilesReducePolicy, T*, T*, OffsetT, ReductionOp>;
-            multi_kernels.push_back(tuple);
-        }
-
-
-        /// Enumerate single-block kernel and add to the list
-        template <typename KernelsVector>
-        static void GenerateSingle(KernelsVector &single_kernels)
-        {
-            SingleDispatchTuple tuple;
-            tuple.params.template Init<TilesReducePolicy>();
-            tuple.kernel_ptr = ReduceSingleKernel<TilesReducePolicy, T*, T*, OffsetT, ReductionOp>;
-            single_kernels.push_back(tuple);
-        }
-    };
-
-    /**
-     * Specialization that rejects kernel generation with the specified TilesReducePolicy
-     */
-    template <typename TilesReducePolicy>
-    struct Ok<TilesReducePolicy, false>
-    {
-        template <typename KernelsVector>
-        static void GenerateMulti(KernelsVector &multi_kernels, int subscription_factor) {}
-
-        template <typename KernelsVector>
-        static void GenerateSingle(KernelsVector &single_kernels) {}
-    };
-
-
-    /// Enumerate block-scheduling variations
-    template <
-        int                     BLOCK_THREADS,
-        int                     ITEMS_PER_THREAD,
-        int                     VECTOR_LOAD_LENGTH,
-        BlockReduceAlgorithm    BLOCK_ALGORITHM,
-        CacheLoadModifier      LOAD_MODIFIER>
-    void Enumerate()
-    {
-        // Multi-block kernels
-        Ok<BlockReduceTilesPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_MODIFIER, GRID_MAPPING_EVEN_SHARE> >::GenerateMulti(multi_kernels, 1);
-        Ok<BlockReduceTilesPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_MODIFIER, GRID_MAPPING_EVEN_SHARE> >::GenerateMulti(multi_kernels, 2);
-        Ok<BlockReduceTilesPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_MODIFIER, GRID_MAPPING_EVEN_SHARE> >::GenerateMulti(multi_kernels, 4);
-        Ok<BlockReduceTilesPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_MODIFIER, GRID_MAPPING_EVEN_SHARE> >::GenerateMulti(multi_kernels, 8);
-#if TUNE_ARCH >= 200
-        Ok<BlockReduceTilesPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_MODIFIER, GRID_MAPPING_DYNAMIC> >::GenerateMulti(multi_kernels, 1);
-#endif
-
-        // Single-block kernels
-        Ok<BlockReduceTilesPolicy<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_MODIFIER, GRID_MAPPING_EVEN_SHARE> >::GenerateSingle(single_kernels);
-    }
-
-
-    /// Enumerate load modifier variations
-    template <
-        int                     BLOCK_THREADS,
-        int                     ITEMS_PER_THREAD,
-        int                     VECTOR_LOAD_LENGTH,
-        BlockReduceAlgorithm    BLOCK_ALGORITHM>
-    void Enumerate()
-    {
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_DEFAULT>();
-#if TUNE_ARCH >= 350
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_ALGORITHM, LOAD_LDG>();
-#endif
-    }
-
-
-    /// Enumerate block algorithms
-    template <
-        int BLOCK_THREADS,
-        int ITEMS_PER_THREAD,
-        int VECTOR_LOAD_LENGTH>
-    void Enumerate()
-    {
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_REDUCE_RAKING>();
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, VECTOR_LOAD_LENGTH, BLOCK_REDUCE_WARP_REDUCTIONS>();
-    }
-
-
-    /// Enumerate vectorization variations
-    template <
-        int BLOCK_THREADS,
-        int ITEMS_PER_THREAD>
-    void Enumerate()
-    {
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, 1>();
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, 2>();
-        Enumerate<BLOCK_THREADS, ITEMS_PER_THREAD, 4>();
-    }
-
-
-    /// Enumerate thread-granularity variations
-    template <int BLOCK_THREADS>
-    void Enumerate()
-    {
-        Enumerate<BLOCK_THREADS, 7>();
-        Enumerate<BLOCK_THREADS, 8>();
-        Enumerate<BLOCK_THREADS, 9>();
-
-        Enumerate<BLOCK_THREADS, 11>();
-        Enumerate<BLOCK_THREADS, 12>();
-        Enumerate<BLOCK_THREADS, 13>();
-
-        Enumerate<BLOCK_THREADS, 15>();
-        Enumerate<BLOCK_THREADS, 16>();
-        Enumerate<BLOCK_THREADS, 17>();
-
-        Enumerate<BLOCK_THREADS, 19>();
-        Enumerate<BLOCK_THREADS, 20>();
-        Enumerate<BLOCK_THREADS, 21>();
-
-        Enumerate<BLOCK_THREADS, 23>();
-        Enumerate<BLOCK_THREADS, 24>();
-        Enumerate<BLOCK_THREADS, 25>();
-    }
-
-
-    /// Enumerate block size variations
-    void Enumerate()
-    {
-        printf("\nEnumerating kernels\n"); fflush(stdout);
-
-        Enumerate<32>();
-        Enumerate<64>();
-        Enumerate<96>();
-        Enumerate<128>();
-        Enumerate<160>();
-        Enumerate<192>();
-        Enumerate<256>();
-        Enumerate<512>();
-    }
-
-
-    //---------------------------------------------------------------------
-    // Test methods
-    //---------------------------------------------------------------------
-
-    /**
-     * Test a configuration
-     */
-    void TestConfiguration(
-        MultiDispatchTuple      &multi_dispatch,
-        SingleDispatchTuple     &single_dispatch,
-        T*                      d_in,
-        T*                      d_out,
-        T*                      h_reference,
-        OffsetT                  num_items,
-        ReductionOp             reduction_op)
-    {
-        // Clear output
-        if (g_verify) CubDebugExit(cudaMemset(d_out, 0, sizeof(T)));
-
-        // Allocate temporary storage
-        void            *d_temp_storage = NULL;
-        size_t          temp_storage_bytes = 0;
-        CubDebugExit(DeviceReduce::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            multi_dispatch.kernel_ptr,
-            single_dispatch.kernel_ptr,
-            FillAndResetDrainKernel<OffsetT>,
-            multi_dispatch.params,
-            single_dispatch.params,
-            d_in,
-            d_out,
-            num_items,
-            reduction_op));
-        CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
-
-        // Warmup/correctness iteration
-        CubDebugExit(DeviceReduce::Dispatch(
-            d_temp_storage,
-            temp_storage_bytes,
-            multi_dispatch.kernel_ptr,
-            single_dispatch.kernel_ptr,
-            FillAndResetDrainKernel<OffsetT>,
-            multi_dispatch.params,
-            single_dispatch.params,
-            d_in,
-            d_out,
-            num_items,
-            reduction_op));
-
-        if (g_verify) CubDebugExit(cudaDeviceSynchronize());
-
-        // Copy out and display results
-        int compare = (g_verify) ?
-            CompareDeviceResults(h_reference, d_out, 1, true, false) :
-            0;
-
-        // Performance
-        GpuTimer gpu_timer;
-        float elapsed_millis = 0.0;
-        for (int i = 0; i < g_timing_iterations; i++)
-        {
-            gpu_timer.Start();
-
-            CubDebugExit(DeviceReduce::Dispatch(
-                d_temp_storage,
-                temp_storage_bytes,
-                multi_dispatch.kernel_ptr,
-                single_dispatch.kernel_ptr,
-                FillAndResetDrainKernel<OffsetT>,
-                multi_dispatch.params,
-                single_dispatch.params,
-                d_in,
-                d_out,
-                num_items,
-                reduction_op));
-
-            gpu_timer.Stop();
-            elapsed_millis += gpu_timer.ElapsedMillis();
-        }
-
-        // Mooch
-        CubDebugExit(cudaDeviceSynchronize());
-
-        float avg_elapsed = elapsed_millis / g_timing_iterations;
-        float avg_throughput = float(num_items) / avg_elapsed / 1000.0 / 1000.0;
-        float avg_bandwidth = avg_throughput * sizeof(T);
-
-        multi_dispatch.avg_throughput = CUB_MAX(avg_throughput, multi_dispatch.avg_throughput);
-        if (avg_throughput > multi_dispatch.best_avg_throughput)
-        {
-            multi_dispatch.best_avg_throughput = avg_throughput;
-            multi_dispatch.best_size = num_items;
-        }
-
-        single_dispatch.avg_throughput = CUB_MAX(avg_throughput, single_dispatch.avg_throughput);
-        if (avg_throughput > single_dispatch.best_avg_throughput)
-        {
-            single_dispatch.best_avg_throughput = avg_throughput;
-            single_dispatch.best_size = num_items;
-        }
-
-        if (g_verbose)
-        {
-            printf("\t%.2f GB/s, multi_dispatch( ", avg_bandwidth);
-            multi_dispatch.params.Print();
-            printf(" ), single_dispatch( ");
-            single_dispatch.params.Print();
-            printf(" )\n");
-            fflush(stdout);
-        }
-
-        AssertEquals(0, compare);
-
-        // Cleanup temporaries
-        if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
-    }
-
-
-    /**
-     * Evaluate multi-block configurations
-     */
-    void TestMulti(
-        T*                      h_in,
-        T*                      d_in,
-        T*                      d_out,
-        ReductionOp             reduction_op)
-    {
-        // Simple single kernel tuple for use with multi kernel sweep
-        typedef typename DeviceReduce::TunedPolicies<T, OffsetT, TUNE_ARCH>::SinglePolicy SimpleSinglePolicy;
-        SingleDispatchTuple simple_single_tuple;
-        simple_single_tuple.params.template Init<SimpleSinglePolicy>();
-        simple_single_tuple.kernel_ptr = ReduceSingleKernel<SimpleSinglePolicy, T*, T*, OffsetT, ReductionOp>;
-
-        double max_exponent      = log2(double(g_max_items));
-        double min_exponent      = log2(double(simple_single_tuple.params.tile_size));
-        unsigned int max_int     = (unsigned int) -1;
-
-        for (int sample = 0; sample < g_samples; ++sample)
-        {
-            printf("\nMulti-block sample %d, ", sample);
-
-            int num_items;
-            if (sample == 0)
-            {
-                // First sample: use max items
-                num_items = g_max_items;
-                printf("num_items: %d", num_items); fflush(stdout);
-            }
-            else
-            {
-                // Sample a problem size from [2^g_min_exponent, g_max_items].  First 2/3 of the samples are log-distributed, the other 1/3 are uniformly-distributed.
-                unsigned int bits;
-                RandomBits(bits);
-                double scale = double(bits) / max_int;
-
-                if (sample < g_samples / 2)
-                {
-                    // log bias
-                    double exponent = ((max_exponent - min_exponent) * scale) + min_exponent;
-                    num_items = pow(2.0, exponent);
-                    num_items = CUB_MIN(num_items, g_max_items);
-                    printf("num_items: %d (2^%.2f)", num_items, exponent); fflush(stdout);
-                }
-                else
-                {
-                    // uniform bias
-                    num_items = CUB_MAX(pow(2.0, min_exponent), scale * g_max_items);
-                    num_items = CUB_MIN(num_items, g_max_items);
-                    printf("num_items: %d (%.2f * %d)", num_items, scale, g_max_items); fflush(stdout);
-                }
-            }
-            if (g_verbose)
-                printf("\n");
-            else
-                printf(", ");
-
-            // Compute reference
-            T h_reference = Reduce(h_in, reduction_op, num_items);
-
-            // Run test on each multi-kernel configuration
-            float best_avg_throughput = 0.0;
-            for (int j = 0; j < multi_kernels.size(); ++j)
-            {
-                multi_kernels[j].avg_throughput = 0.0;
-
-                TestConfiguration(multi_kernels[j], simple_single_tuple, d_in, d_out, &h_reference, num_items, reduction_op);
-
-                best_avg_throughput = CUB_MAX(best_avg_throughput, multi_kernels[j].avg_throughput);
-            }
-
-            // Print best throughput for this problem size
-            printf("Best: %.2fe9 items/s (%.2f GB/s)\n", best_avg_throughput, best_avg_throughput * sizeof(T));
-
-            // Accumulate speedup (inverse for harmonic mean)
-            for (int j = 0; j < multi_kernels.size(); ++j)
-                multi_kernels[j].hmean_speedup += best_avg_throughput / multi_kernels[j].avg_throughput;
-        }
-
-        // Find max overall throughput and compute hmean speedups
-        float overall_max_throughput = 0.0;
-        for (int j = 0; j < multi_kernels.size(); ++j)
-        {
-            overall_max_throughput = CUB_MAX(overall_max_throughput, multi_kernels[j].best_avg_throughput);
-            multi_kernels[j].hmean_speedup = float(g_samples) / multi_kernels[j].hmean_speedup;
-        }
-
-        // Sort by cumulative speedup
-        sort(multi_kernels.begin(), multi_kernels.end(), MinSpeedup<MultiDispatchTuple>);
-
-        // Print ranked multi configurations
-        printf("\nRanked multi_kernels:\n");
-        for (int j = 0; j < multi_kernels.size(); ++j)
-        {
-            printf("\t (%d) params( ", multi_kernels.size() - j);
-            multi_kernels[j].params.Print();
-            printf(" ) hmean speedup: %.3f, best throughput %.2f @ %d elements (%.2f GB/s, %.2f%%)\n",
-                multi_kernels[j].hmean_speedup,
-                multi_kernels[j].best_avg_throughput,
-                (int) multi_kernels[j].best_size,
-                multi_kernels[j].best_avg_throughput * sizeof(T),
-                multi_kernels[j].best_avg_throughput / overall_max_throughput);
-        }
-
-        printf("\nMax multi-block throughput %.2f (%.2f GB/s)\n", overall_max_throughput, overall_max_throughput * sizeof(T));
-    }
-
-
-    /**
-     * Evaluate single-block configurations
-     */
-    void TestSingle(
-        T*                      h_in,
-        T*                      d_in,
-        T*                      d_out,
-        ReductionOp             reduction_op)
-     {
-        // Construct a NULL-ptr multi-kernel tuple that forces a single-kernel pass
-        MultiDispatchTuple multi_tuple;
-
-        double max_exponent     = log2(double(g_max_items));
-        unsigned int max_int    = (unsigned int) -1;
-
-        for (int sample = 0; sample < g_samples; ++sample)
-        {
-            printf("\nSingle-block sample %d, ", sample);
-
-            int num_items;
-            if (sample == 0)
-            {
-                // First sample: use max items
-                num_items = g_max_items;
-                printf("num_items: %d", num_items); fflush(stdout);
-            }
-            else
-            {
-                // Sample a problem size from [2, g_max_items], log-distributed
-                unsigned int bits;
-                RandomBits(bits);
-                double scale = double(bits) / max_int;
-                double exponent = ((max_exponent - 1) * scale) + 1;
-                num_items = pow(2.0, exponent);
-                printf("num_items: %d (2^%.2f)", num_items, exponent); fflush(stdout);
-            }
-
-            if (g_verbose)
-                printf("\n");
-            else
-                printf(", ");
-
-            // Compute reference
-            T h_reference = Reduce(h_in, reduction_op, num_items);
-
-            // Run test on each single-kernel configuration (pick first multi-config to use, which shouldn't be
-            float best_avg_throughput = 0.0;
-            for (int j = 0; j < single_kernels.size(); ++j)
-            {
-                single_kernels[j].avg_throughput = 0.0;
-
-                TestConfiguration(multi_tuple, single_kernels[j], d_in, d_out, &h_reference, num_items, reduction_op);
-
-                best_avg_throughput = CUB_MAX(best_avg_throughput, single_kernels[j].avg_throughput);
-            }
-
-            // Print best throughput for this problem size
-            printf("Best: %.2fe9 items/s (%.2f GB/s)\n", best_avg_throughput, best_avg_throughput * sizeof(T));
-
-            // Accumulate speedup (inverse for harmonic mean)
-            for (int j = 0; j < single_kernels.size(); ++j)
-                single_kernels[j].hmean_speedup += best_avg_throughput / single_kernels[j].avg_throughput;
-        }
-
-        // Find max overall throughput and compute hmean speedups
-        float overall_max_throughput = 0.0;
-        for (int j = 0; j < single_kernels.size(); ++j)
-        {
-            overall_max_throughput = CUB_MAX(overall_max_throughput, single_kernels[j].best_avg_throughput);
-            single_kernels[j].hmean_speedup = float(g_samples) / single_kernels[j].hmean_speedup;
-        }
-
-        // Sort by cumulative speedup
-        sort(single_kernels.begin(), single_kernels.end(), MinSpeedup<SingleDispatchTuple>);
-
-        // Print ranked single configurations
-        printf("\nRanked single_kernels:\n");
-        for (int j = 0; j < single_kernels.size(); ++j)
-        {
-            printf("\t (%d) params( ", single_kernels.size() - j);
-            single_kernels[j].params.Print();
-            printf(" ) hmean speedup: %.3f, best throughput %.2f @ %d elements (%.2f GB/s, %.2f%%)\n",
-                single_kernels[j].hmean_speedup,
-                single_kernels[j].best_avg_throughput,
-                (int) single_kernels[j].best_size,
-                single_kernels[j].best_avg_throughput * sizeof(T),
-                single_kernels[j].best_avg_throughput / overall_max_throughput);
-        }
-
-        printf("\nMax single-block throughput %.2f (%.2f GB/s)\n", overall_max_throughput, overall_max_throughput * sizeof(T));
-    }
-
-};
-
-
-
-//---------------------------------------------------------------------
-// Main
-//---------------------------------------------------------------------
-
-/**
- * Main
- */
-int main(int argc, char** argv)
-{
-    // Initialize command line
-    CommandLineArgs args(argc, argv);
-    args.GetCmdLineArgument("n", g_max_items);
-    args.GetCmdLineArgument("s", g_samples);
-    args.GetCmdLineArgument("i", g_timing_iterations);
-    g_verbose = args.CheckCmdLineFlag("v");
-    g_single = args.CheckCmdLineFlag("single");
-    g_verify = !args.CheckCmdLineFlag("noverify");
-
-    // Print usage
-    if (args.CheckCmdLineFlag("help"))
-    {
-        printf("%s "
-            "[--device=<device-id>] "
-            "[--n=<max items>]"
-            "[--s=<samples>]"
-            "[--i=<timing iterations>]"
-            "[--single]"
-            "[--v]"
-            "[--noverify]"
-            "\n", argv[0]);
-        exit(0);
-    }
-
-    // Initialize device
-    CubDebugExit(args.DeviceInit());
-
-#if (TUNE_SIZE == 1)
-    typedef unsigned char T;
-#elif (TUNE_SIZE == 2)
-    typedef unsigned short T;
-#elif (TUNE_SIZE == 4)
-    typedef unsigned int T;
-#elif (TUNE_SIZE == 8)
-    typedef unsigned long long T;
-#else
-    // Default
-    typedef unsigned int T;
-#endif
-
-    typedef unsigned int OffsetT;
-    Sum reduction_op;
-
-    // Enumerate kernels
-    Schmoo<T, OffsetT, Sum > schmoo;
-    schmoo.Enumerate();
-
-    // Allocate host arrays
-    T *h_in = new T[g_max_items];
-
-    // Initialize problem
-    Initialize(UNIFORM, h_in, g_max_items);
-
-    // Initialize device arrays
-    T *d_in = NULL;
-    T *d_out = NULL;
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_in, sizeof(T) * g_max_items));
-    CubDebugExit(g_allocator.DeviceAllocate((void**)&d_out, sizeof(T) * 1));
-    CubDebugExit(cudaMemcpy(d_in, h_in, sizeof(T) * g_max_items, cudaMemcpyHostToDevice));
-
-    // Test kernels
-    if (g_single)
-        schmoo.TestSingle(h_in, d_in, d_out, reduction_op);
-    else
-        schmoo.TestMulti(h_in, d_in, d_out, reduction_op);
-
-    // Cleanup
-    if (h_in) delete[] h_in;
-    if (d_in) CubDebugExit(g_allocator.DeviceFree(d_in));
-    if (d_out) CubDebugExit(g_allocator.DeviceFree(d_out));
-
-    return 0;
-}
-
-
-
diff --git a/ml-xgboost/demo/.gitignore b/ml-xgboost/demo/.gitignore
deleted file mode 100644
index ee79c70..0000000
--- a/ml-xgboost/demo/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-*.libsvm
-*.pkl
diff --git a/ml-xgboost/demo/README.md b/ml-xgboost/demo/README.md
deleted file mode 100644
index e11ef57..0000000
--- a/ml-xgboost/demo/README.md
+++ /dev/null
@@ -1,147 +0,0 @@
-Awesome XGBoost
-===============
-This page contains a curated list of examples, tutorials, blogs about XGBoost usecases.
-It is inspired by [awesome-MXNet](https://github.com/dmlc/mxnet/blob/master/example/README.md),
-[awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).
-
-Please send a pull request if you find things that belongs to here.
-
-Contents
---------
-- [Code Examples](#code-examples)
-  - [Features Walkthrough](#features-walkthrough)
-  - [Basic Examples by Tasks](#basic-examples-by-tasks)
-  - [Benchmarks](#benchmarks)
-- [Machine Learning Challenge Winning Solutions](#machine-learning-challenge-winning-solutions)
-- [Tutorials](#tutorials)
-- [Usecases](#usecases)
-- [Tools using XGBoost](#tools-using-xgboost)
-- [Integrations with 3rd party software](#integrations-with-3rd-party-software)
-- [Awards](#awards)
-- [Windows Binaries](#windows-binaries)
-
-Code Examples
--------------
-### Features Walkthrough
-
-This is a list of short codes introducing different functionalities of xgboost packages.
-
-* Basic walkthrough of packages
-  [python](guide-python/basic_walkthrough.py)
-  [R](../R-package/demo/basic_walkthrough.R)
-  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
-  [PHP](https://github.com/bpachev/xgboost-php/blob/master/demo/titanic_demo.php)
-* Customize loss function, and evaluation metric
-  [python](guide-python/custom_objective.py)
-  [R](../R-package/demo/custom_objective.R)
-  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl)
-* Boosting from existing prediction
-  [python](guide-python/boost_from_prediction.py)
-  [R](../R-package/demo/boost_from_prediction.R)
-  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
-* Predicting using first n trees
-  [python](guide-python/predict_first_ntree.py)
-  [R](../R-package/demo/predict_first_ntree.R)
-  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/predict_first_ntree.jl)
-* Generalized Linear Model
-  [python](guide-python/generalized_linear_model.py)
-  [R](../R-package/demo/generalized_linear_model.R)
-  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)
-* Cross validation
-  [python](guide-python/cross_validation.py)
-  [R](../R-package/demo/cross_validation.R)
-  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)
-* Predicting leaf indices
-  [python](guide-python/predict_leaf_indices.py)
-  [R](../R-package/demo/predict_leaf_indices.R)
-
-### Basic Examples by Tasks
-
-Most of examples in this section are based on CLI or python version.
-However, the parameter settings can be applied to all versions
-
-- [Binary classification](binary_classification)
-- [Multiclass classification](multiclass_classification)
-- [Regression](regression)
-- [Learning to Rank](rank)
-
-### Benchmarks
-
-- [Starter script for Kaggle Higgs Boson](kaggle-higgs)
-- [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
-- [Benchmarking the most commonly used open source tools for binary classification](https://github.com/szilard/benchm-ml#boosting-gradient-boosted-treesgradient-boosting-machines)
-
-
-## Machine Learning Challenge Winning Solutions
-
-XGBoost is extensively used by machine learning practitioners to create state of art data science solutions,
-this is a list of machine learning winning solutions with XGBoost.
-Please send pull requests if you find ones that are missing here.
-
-- Maksims Volkovs, Guangwei Yu and Tomi Poutanen, 1st place of the [2017 ACM RecSys challenge](http://2017.recsyschallenge.com/). Link to [paper](http://www.cs.toronto.edu/~mvolkovs/recsys2017_challenge.pdf).
-- Vlad Sandulescu, Mihai Chiru, 1st place of the [KDD Cup 2016 competition](https://kddcup2016.azurewebsites.net). Link to [the arxiv paper](http://arxiv.org/abs/1609.02728).
-- Marios Michailidis, Mathias Müller and HJ van Veen, 1st place of the [Dato Truely Native? competition](https://www.kaggle.com/c/dato-native). Link to [the Kaggle interview](http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/).
-- Vlad Mironov, Alexander Guschin, 1st place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/).
-- Josef Slavicek, 3rd place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/23/flavour-of-physics-winners-interview-3rd-place-josef-slavicek/).
-- Mario Filho, Josef Feigl, Lucas, Gilberto, 1st place of the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Link to [the Kaggle interview](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/).
-- Qingchen Wang, 1st place of the [Liberty Mutual Property Inspection](https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction). Link to [the Kaggle interview](http://blog.kaggle.com/2015/09/28/liberty-mutual-property-inspection-winners-interview-qingchen-wang/).
-- Chenglong Chen, 1st place of the [Crowdflower Search Results Relevance](https://www.kaggle.com/c/crowdflower-search-relevance). Link to [the winning solution](https://www.kaggle.com/c/crowdflower-search-relevance/forums/t/15186/1st-place-winner-solution-chenglong-chen/).
-- Alexandre Barachant (“Cat”) and Rafał Cycoń (“Dog”), 1st place of the [Grasp-and-Lift EEG Detection](https://www.kaggle.com/c/grasp-and-lift-eeg-detection). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/12/grasp-and-lift-eeg-winners-interview-1st-place-cat-dog/).
-- Halla Yang, 2nd place of the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/).
-- Owen Zhang, 1st place of the [Avito Context Ad Clicks competition](https://www.kaggle.com/c/avito-context-ad-clicks). Link to [the Kaggle interview](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/).
-- Keiichi Kuroyanagi, 2nd place of the [Airbnb New User Bookings](https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings). Link to [the Kaggle interview](http://blog.kaggle.com/2016/03/17/airbnb-new-user-bookings-winners-interview-2nd-place-keiichi-kuroyanagi-keiku/).
-- Marios Michailidis, Mathias Müller and Ning Situ, 1st place [Homesite Quote Conversion](https://www.kaggle.com/c/homesite-quote-conversion). Link to [the Kaggle interview](http://blog.kaggle.com/2016/04/08/homesite-quote-conversion-winners-write-up-1st-place-kazanova-faron-clobber/).
-
-## Talks
-- [XGBoost: A Scalable Tree Boosting System](http://datascience.la/xgboost-workshop-and-meetup-talk-with-tianqi-chen/) (video+slides) by Tianqi Chen at the Los Angeles Data Science meetup
-
-## Tutorials
-
-- [Machine Learning with XGBoost on Qubole Spark Cluster](https://www.qubole.com/blog/machine-learning-xgboost-qubole-spark-cluster/)
-- [XGBoost Official RMarkdown Tutorials](https://xgboost.readthedocs.org/en/latest/R-package/index.html#tutorials)
-- [An Introduction to XGBoost R Package](http://dmlc.ml/rstats/2016/03/10/xgboost.html) by Tong He
-- [Open Source Tools & Data Science Competitions](http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1) by Owen Zhang - XGBoost parameter tuning tips
-* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
-* [Winning solution of Kaggle Higgs competition: what a single model can do](http://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do/)
-- [XGBoost - eXtreme Gradient Boosting](http://www.slideshare.net/ShangxuanZhang/xgboost) by Tong He
-- [How to use XGBoost algorithm in R in easy steps](http://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/) by TAVISH SRIVASTAVA ([Chinese Translation 中文翻译](https://segmentfault.com/a/1190000004421821) by [HarryZhu](https://segmentfault.com/u/harryprince))
-- [Kaggle Solution: What’s Cooking ? (Text Mining Competition)](http://www.analyticsvidhya.com/blog/2015/12/kaggle-solution-cooking-text-mining-competition/) by MANISH SARASWAT
-- Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R) by Manuel Amunategui ([Youtube Link](https://www.youtube.com/watch?v=Og7CGAfSr_Y)) ([Github Link](https://github.com/amunategui/BetterCrossValidation))
-- [XGBoost Rossman Parameter Tuning](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/run/90168/notebook) by [Norbert Kozlowski](https://www.kaggle.com/khozzy)
-- [Featurizing log data before XGBoost](http://www.slideshare.net/DataRobot/featurizing-log-data-before-xgboost) by Xavier Conort, Owen Zhang etc
-- [West Nile Virus Competition Benchmarks & Tutorials](http://blog.kaggle.com/2015/07/21/west-nile-virus-competition-benchmarks-tutorials/) by [Anna Montoya](http://blog.kaggle.com/author/annamontoya/)
-- [Ensemble Decision Tree with XGBoost](https://www.kaggle.com/binghsu/predict-west-nile-virus/xgboost-starter-code-python-0-69) by [Bing Xu](https://www.kaggle.com/binghsu)
-- [Notes on eXtreme Gradient Boosting](http://startup.ml/blog/xgboost) by ARSHAK NAVRUZYAN ([iPython Notebook](https://github.com/startupml/koan/blob/master/eXtreme%20Gradient%20Boosting.ipynb))
-- [Complete Guide to Parameter Tuning in XGBoost](http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/) by Aarshay Jain
-- [Practical XGBoost in Python online course](http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python) by Parrot Prediction
-- [Spark and XGBoost using Scala](http://www.elenacuoco.com/2016/10/10/scala-spark-xgboost-classification/) by Elena Cuoco
-
-## Usecases
-If you have particular usecase of xgboost that you would like to highlight.
-Send a PR to add a one sentence description:)
-
-- XGBoost is used in [Kaggle Script](https://www.kaggle.com/scripts) to solve data science challenges.
-- Distribute XGBoost as Rest API server from Jupyter notebook with [BentoML](https://github.com/bentoml/bentoml). [Link to notebook](https://github.com/bentoml/BentoML/blob/master/examples/xgboost-predict-titanic-survival/XGBoost-titanic-survival-prediction.ipynb)
-- [Seldon predictive service powered by XGBoost](http://docs.seldon.io/iris-demo.html)
-- XGBoost Distributed is used in [ODPS Cloud Service by Alibaba](https://yq.aliyun.com/articles/6355) (in Chinese)
-- XGBoost is incoporated as part of [Graphlab Create](https://dato.com/products/create/) for scalable machine learning.
-- [Hanjing Su](https://www.52cs.org) from Tencent data platform team: "We use distributed XGBoost for click through prediction in wechat shopping and lookalikes. The problems involve hundreds millions of users and thousands of features. XGBoost is cleanly designed and can be easily integrated into our production environment, reducing our cost in developments."
-- [CNevd](https://github.com/CNevd) from autohome.com ad platform team: "Distributed XGBoost is used for click through rate prediction in our display advertising, XGBoost is highly efficient and flexible and can be easily used on our distributed platform, our ctr made a great improvement with hundred millions samples and millions features due to this awesome XGBoost"
-
-## Tools using XGBoost
-
-- [BayesBoost](https://github.com/mpearmain/BayesBoost) - Bayesian Optimization using xgboost and sklearn API
-- [gp_xgboost_gridsearch](https://github.com/vatsan/gp_xgboost_gridsearch) - In-database parallel grid-search for XGBoost on [Greenplum](https://github.com/greenplum-db/gpdb) using PL/Python
-- [tpot](https://github.com/rhiever/tpot) - A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming.
-
-## Integrations with 3rd party software
-Open source integrations with XGBoost:
-* [Neptune.ai](http://neptune.ai/) - Experiment management and collaboration tool for ML/DL/RL specialists. Integration has a form of the [XGBoost callback](https://docs.neptune.ai/integrations/xgboost.html) that automatically logs training and evaluation metrics, as well as saved model (booster), feature importance chart and visualized trees.
-* [Optuna](https://optuna.org/) - An open source hyperparameter optimization framework to automate hyperparameter search. Optuna integrates with XGBoost in the [XGBoostPruningCallback](https://optuna.readthedocs.io/en/stable/reference/integration.html#optuna.integration.XGBoostPruningCallback) that let users easily prune unpromising trials.
-
-## Awards
-- [John Chambers Award](http://stat-computing.org/awards/jmc/winners.html) - 2016 Winner: XGBoost R Package, by Tong He (Simon Fraser University) and Tianqi Chen (University of Washington)
-- [InfoWorld’s 2019 Technology of the Year Award](https://www.infoworld.com/article/3336072/application-development/infoworlds-2019-technology-of-the-year-award-winners.html)
-
-## Windows Binaries
-Unofficial windows binaries and instructions on how to use them are hosted on [Guido Tapia's blog](http://www.picnet.com.au/blogs/guido/post/2016/09/22/xgboost-windows-x64-binaries-for-download/)
diff --git a/ml-xgboost/demo/aft_survival/aft_survival_demo.py b/ml-xgboost/demo/aft_survival/aft_survival_demo.py
deleted file mode 100644
index 3cdccc1..0000000
--- a/ml-xgboost/demo/aft_survival/aft_survival_demo.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model
-"""
-from sklearn.model_selection import ShuffleSplit
-import pandas as pd
-import numpy as np
-import xgboost as xgb
-
-# The Veterans' Administration Lung Cancer Trial
-# The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
-df = pd.read_csv('../data/veterans_lung_cancer.csv')
-print('Training data:')
-print(df)
-
-# Split features and labels
-y_lower_bound = df['Survival_label_lower_bound']
-y_upper_bound = df['Survival_label_upper_bound']
-X = df.drop(['Survival_label_lower_bound', 'Survival_label_upper_bound'], axis=1)
-
-# Split data into training and validation sets
-rs = ShuffleSplit(n_splits=2, test_size=.7, random_state=0)
-train_index, valid_index = next(rs.split(X))
-dtrain = xgb.DMatrix(X.values[train_index, :])
-dtrain.set_float_info('label_lower_bound', y_lower_bound[train_index])
-dtrain.set_float_info('label_upper_bound', y_upper_bound[train_index])
-dvalid = xgb.DMatrix(X.values[valid_index, :])
-dvalid.set_float_info('label_lower_bound', y_lower_bound[valid_index])
-dvalid.set_float_info('label_upper_bound', y_upper_bound[valid_index])
-
-# Train gradient boosted trees using AFT loss and metric
-params = {'verbosity': 0,
-          'objective': 'survival:aft',
-          'eval_metric': 'aft-nloglik',
-          'tree_method': 'hist',
-          'learning_rate': 0.05,
-          'aft_loss_distribution': 'normal',
-          'aft_loss_distribution_scale': 1.20,
-          'max_depth': 6,
-          'lambda': 0.01,
-          'alpha': 0.02}
-bst = xgb.train(params, dtrain, num_boost_round=10000,
-                evals=[(dtrain, 'train'), (dvalid, 'valid')], 
-                early_stopping_rounds=50)
-
-# Run prediction on the validation set
-df = pd.DataFrame({'Label (lower bound)': y_lower_bound[valid_index],
-                   'Label (upper bound)': y_upper_bound[valid_index],
-                   'Predicted label': bst.predict(dvalid)})
-print(df)
-# Show only data points with right-censored labels
-print(df[np.isinf(df['Label (upper bound)'])])
-
-# Save trained model
-bst.save_model('aft_model.json')
diff --git a/ml-xgboost/demo/aft_survival/aft_survival_demo_with_optuna.py b/ml-xgboost/demo/aft_survival/aft_survival_demo_with_optuna.py
deleted file mode 100644
index 117be8b..0000000
--- a/ml-xgboost/demo/aft_survival/aft_survival_demo_with_optuna.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""
-Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model, using Optuna
-to tune hyperparameters
-"""
-from sklearn.model_selection import ShuffleSplit
-import pandas as pd
-import numpy as np
-import xgboost as xgb
-import optuna
-
-# The Veterans' Administration Lung Cancer Trial
-# The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
-df = pd.read_csv('../data/veterans_lung_cancer.csv')
-print('Training data:')
-print(df)
-
-# Split features and labels
-y_lower_bound = df['Survival_label_lower_bound']
-y_upper_bound = df['Survival_label_upper_bound']
-X = df.drop(['Survival_label_lower_bound', 'Survival_label_upper_bound'], axis=1)
-
-# Split data into training and validation sets
-rs = ShuffleSplit(n_splits=2, test_size=.7, random_state=0)
-train_index, valid_index = next(rs.split(X))
-dtrain = xgb.DMatrix(X.values[train_index, :])
-dtrain.set_float_info('label_lower_bound', y_lower_bound[train_index])
-dtrain.set_float_info('label_upper_bound', y_upper_bound[train_index])
-dvalid = xgb.DMatrix(X.values[valid_index, :])
-dvalid.set_float_info('label_lower_bound', y_lower_bound[valid_index])
-dvalid.set_float_info('label_upper_bound', y_upper_bound[valid_index])
-
-# Define hyperparameter search space
-base_params = {'verbosity': 0,
-              'objective': 'survival:aft',
-              'eval_metric': 'aft-nloglik',
-              'tree_method': 'hist'}  # Hyperparameters common to all trials
-def objective(trial):
-    params = {'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 1.0),
-              'aft_loss_distribution': trial.suggest_categorical('aft_loss_distribution',
-                                                                  ['normal', 'logistic', 'extreme']),
-              'aft_loss_distribution_scale': trial.suggest_loguniform('aft_loss_distribution_scale', 0.1, 10.0),
-              'max_depth': trial.suggest_int('max_depth', 3, 8),
-              'lambda': trial.suggest_loguniform('lambda', 1e-8, 1.0),
-              'alpha': trial.suggest_loguniform('alpha', 1e-8, 1.0)}  # Search space
-    params.update(base_params)
-    pruning_callback = optuna.integration.XGBoostPruningCallback(trial, 'valid-aft-nloglik')
-    bst = xgb.train(params, dtrain, num_boost_round=10000,
-                    evals=[(dtrain, 'train'), (dvalid, 'valid')], 
-                    early_stopping_rounds=50, verbose_eval=False, callbacks=[pruning_callback])
-    if bst.best_iteration >= 25:
-        return bst.best_score
-    else:
-        return np.inf  # Reject models with < 25 trees
-
-# Run hyperparameter search
-study = optuna.create_study(direction='minimize')
-study.optimize(objective, n_trials=200)
-print('Completed hyperparameter tuning with best aft-nloglik = {}.'.format(study.best_trial.value))
-params = {}
-params.update(base_params)
-params.update(study.best_trial.params)
-
-# Re-run training with the best hyperparameter combination
-print('Re-running the best trial... params = {}'.format(params))
-bst = xgb.train(params, dtrain, num_boost_round=10000,
-                evals=[(dtrain, 'train'), (dvalid, 'valid')], 
-                early_stopping_rounds=50)
-
-# Run prediction on the validation set
-df = pd.DataFrame({'Label (lower bound)': y_lower_bound[valid_index],
-                   'Label (upper bound)': y_upper_bound[valid_index],
-                   'Predicted label': bst.predict(dvalid)})
-print(df)
-# Show only data points with right-censored labels
-print(df[np.isinf(df['Label (upper bound)'])])
-
-# Save trained model
-bst.save_model('aft_best_model.json')
diff --git a/ml-xgboost/demo/aft_survival/aft_survival_viz_demo.py b/ml-xgboost/demo/aft_survival/aft_survival_viz_demo.py
deleted file mode 100644
index fe622f9..0000000
--- a/ml-xgboost/demo/aft_survival/aft_survival_viz_demo.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""
-Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
-
-This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble model
-starts out as a flat line and evolves into a step function in order to account for all ranged
-labels.
-"""
-import numpy as np
-import xgboost as xgb
-import matplotlib.pyplot as plt
-
-plt.rcParams.update({'font.size': 13})
-
-# Function to visualize censored labels
-def plot_censored_labels(X, y_lower, y_upper):
-    def replace_inf(x, target_value):
-        x[np.isinf(x)] = target_value
-        return x
-    plt.plot(X, y_lower, 'o', label='y_lower', color='blue')
-    plt.plot(X, y_upper, 'o', label='y_upper', color='fuchsia')
-    plt.vlines(X, ymin=replace_inf(y_lower, 0.01), ymax=replace_inf(y_upper, 1000),
-               label='Range for y', color='gray')
-
-# Toy data
-X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
-INF = np.inf
-y_lower = np.array([ 10,  15, -INF, 30, 100])
-y_upper = np.array([INF, INF,   20, 50, INF])
-
-# Visualize toy data
-plt.figure(figsize=(5, 4))
-plot_censored_labels(X, y_lower, y_upper)
-plt.ylim((6, 200))
-plt.legend(loc='lower right')
-plt.title('Toy data')
-plt.xlabel('Input feature')
-plt.ylabel('Label')
-plt.yscale('log')
-plt.tight_layout()
-plt.show(block=True)
-
-# Will be used to visualize XGBoost model
-grid_pts = np.linspace(0.8, 5.2, 1000).reshape((-1, 1))
-
-# Train AFT model using XGBoost
-dmat = xgb.DMatrix(X)
-dmat.set_float_info('label_lower_bound', y_lower)
-dmat.set_float_info('label_upper_bound', y_upper)
-params = {'max_depth': 3, 'objective':'survival:aft', 'min_child_weight': 0}
-
-accuracy_history = []
-def plot_intermediate_model_callback(env):
-    """Custom callback to plot intermediate models"""
-    # Compute y_pred = prediction using the intermediate model, at current boosting iteration
-    y_pred = env.model.predict(dmat)
-    # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
-    #              the corresponding predicted label (y_pred)
-    acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper)/len(X) * 100)
-    accuracy_history.append(acc)
-    
-    # Plot ranged labels as well as predictions by the model
-    plt.subplot(5, 3, env.iteration + 1)
-    plot_censored_labels(X, y_lower, y_upper)
-    y_pred_grid_pts = env.model.predict(xgb.DMatrix(grid_pts))
-    plt.plot(grid_pts, y_pred_grid_pts, 'r-', label='XGBoost AFT model', linewidth=4)
-    plt.title('Iteration {}'.format(env.iteration), x=0.5, y=0.8)
-    plt.xlim((0.8, 5.2))
-    plt.ylim((1 if np.min(y_pred) < 6 else 6, 200))
-    plt.yscale('log')
-
-res = {}
-plt.figure(figsize=(12,13))
-bst = xgb.train(params, dmat, 15, [(dmat, 'train')], evals_result=res,
-                callbacks=[plot_intermediate_model_callback])
-plt.tight_layout()
-plt.legend(loc='lower center', ncol=4,
-           bbox_to_anchor=(0.5, 0),
-           bbox_transform=plt.gcf().transFigure)
-plt.tight_layout()
-
-# Plot negative log likelihood over boosting iterations
-plt.figure(figsize=(8,3))
-plt.subplot(1, 2, 1)
-plt.plot(res['train']['aft-nloglik'], 'b-o', label='aft-nloglik')
-plt.xlabel('# Boosting Iterations')
-plt.legend(loc='best')
-
-# Plot "accuracy" over boosting iterations
-# "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
-#              the corresponding predicted label (y_pred)
-plt.subplot(1, 2, 2)
-plt.plot(accuracy_history, 'r-o', label='Accuracy (%)')
-plt.xlabel('# Boosting Iterations')
-plt.legend(loc='best')
-plt.tight_layout()
-
-plt.show()
diff --git a/ml-xgboost/demo/binary_classification/README.md b/ml-xgboost/demo/binary_classification/README.md
deleted file mode 100644
index 8947adf..0000000
--- a/ml-xgboost/demo/binary_classification/README.md
+++ /dev/null
@@ -1,164 +0,0 @@
-Binary Classification
-=====================
-This is the quick start tutorial for xgboost CLI version.
-Here we demonstrate how to use XGBoost for a binary classification task. Before getting started, make sure you compile xgboost in the root directory of the project by typing ```make```.
-The script 'runexp.sh' can be used to run the demo. Here we use [mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) from UCI machine learning repository.
-
-### Tutorial
-#### Generate Input Data
-XGBoost takes LibSVM format. An example of faked input data is below:
-```
-1 101:1.2 102:0.03
-0 1:2.1 10001:300 10002:400
-...
-```
-Each line represent a single instance, and in the first line '1' is the instance label,'101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instance being positive.
-
-
-First we will transform the dataset into classic LibSVM format and split the data into training set and test set by running:
-```
-python mapfeat.py
-python mknfold.py agaricus.txt 1
-```
-The two files, 'agaricus.txt.train' and 'agaricus.txt.test' will be used as training set and test set.
-
-#### Training
-Then we can run the training process:
-```
-../../xgboost mushroom.conf
-```
-
-mushroom.conf is the configuration for both training and testing. Each line containing the [attribute]=[value] configuration:
-
-```conf
-# General Parameters, see comment for each definition
-# can be gbtree or gblinear
-booster = gbtree
-# choose logistic regression loss function for binary classification
-objective = binary:logistic
-
-# Tree Booster Parameters
-# step size shrinkage
-eta = 1.0
-# minimum loss reduction required to make a further partition
-gamma = 1.0
-# minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1
-# maximum depth of a tree
-max_depth = 3
-
-# Task Parameters
-# the number of round to do boosting
-num_round = 2
-# 0 means do not save any model except the final round model
-save_period = 0
-# The path of training data
-data = "agaricus.txt.train"
-# The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "agaricus.txt.test"
-# The path of test data
-test:data = "agaricus.txt.test"
-```
-We use the tree booster and logistic regression objective in our setting. This indicates that we accomplish our task using classic gradient boosting regression tree(GBRT), which is a promising method for binary classification.
-
-The parameters shown in the example gives the most common ones that are needed to use xgboost.
-If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](../../doc/parameter.rst). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
-
-```
-../../xgboost mushroom.conf max_depth=6
-```
-This means that the parameter max_depth will be set as 6 rather than 3 in the conf file. When you use command line, make sure max_depth=6 is passed in as single argument, i.e. do not contain space in the argument. When a parameter setting is provided in both command line input and  the config file, the command line setting will override the setting in config file.
-
-In this example, we use tree booster for gradient boosting. If you would like to use linear booster for regression, you can keep all the parameters except booster and the tree booster parameters as below:
-```conf
-# General Parameters
-# choose the linear booster
-booster = gblinear
-...
-
-# Change Tree Booster Parameters into Linear Booster Parameters
-# L2 regularization term on weights, default 0
-lambda = 0.01
-# L1 regularization term on weights, default 0
-alpha = 0.01
-# L2 regularization term on bias, default 0
-lambda_bias = 0.01
-
-# Regression Parameters
-...
-```
-
-#### Get Predictions
-After training, we can use the output model to get the prediction of the test data:
-```
-../../xgboost mushroom.conf task=pred model_in=0002.model
-```
-For binary classification, the output predictions are probability confidence scores in [0,1], corresponds to the probability of the label to be positive.
-
-#### Dump Model
-This is a preliminary feature, so only tree models support text dump. XGBoost can display the tree models in text or JSON files, and we can scan the model in an easy way:
-```
-../../xgboost mushroom.conf task=dump model_in=0002.model name_dump=dump.raw.txt
-../../xgboost mushroom.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt
-```
-
-In this demo, the tree boosters obtained will be printed in dump.raw.txt and dump.nice.txt, and the latter one is easier to understand because of usage of feature mapping featmap.txt
-
-Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
-  - Feature id must be from 0 to number of features, in sorted order.
-  - i means this feature is binary indicator feature
-  - q means this feature is a quantitative value, such as age, time, can be missing
-  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)
-
-#### Monitoring Progress
-When you run training we can find there are messages displayed on screen
-```
-tree train end, 1 roots, 12 extra nodes, 0 pruned nodes ,max_depth=3
-[0]  test-error:0.016139
-boosting round 1, 0 sec elapsed
-
-tree train end, 1 roots, 10 extra nodes, 0 pruned nodes ,max_depth=3
-[1]  test-error:0.000000
-```
-The messages for evaluation are printed into stderr, so if you want only to log the evaluation progress, simply type
-```
-../../xgboost mushroom.conf 2>log.txt
-```
-Then you can find the following content in log.txt
-```
-[0]     test-error:0.016139
-[1]     test-error:0.000000
-```
-We can also monitor both training and test statistics, by adding following lines to configure
-```conf
-eval[test] = "agaricus.txt.test"
-eval[trainname] = "agaricus.txt.train"
-```
-Run the command again, we can find the log file becomes
-```
-[0]     test-error:0.016139     trainname-error:0.014433
-[1]     test-error:0.000000     trainname-error:0.001228
-```
-The rule is eval[name-printed-in-log] = filename, then the file will be added to monitoring process, and evaluated each round.
-
-xgboost also supports monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes
-```
-[0]     test-error:0.016139     test-negllik:0.029795   trainname-error:0.014433        trainname-negllik:0.027023
-[1]     test-error:0.000000     test-negllik:0.000000   trainname-error:0.001228        trainname-negllik:0.002457
-```
-### Saving Progress Models
-If you want to save model every two round, simply set save_period=2. You will find 0002.model in the current folder. If you want to change the output folder of models, add model_dir=foldername. By default xgboost saves the model of last round.
-
-#### Continue from Existing Model
-If you want to continue boosting from existing model, say 0002.model, use
-```
-../../xgboost mushroom.conf model_in=0002.model num_round=2 model_out=continue.model
-```
-xgboost will load from 0002.model continue boosting for 2 rounds, and save output to continue.model. However, beware that the training and evaluation data specified in mushroom.conf should not change when you use this function.
-#### Use Multi-Threading
-When you are working with a large dataset, you may want to take advantage of parallelism. If your compiler supports OpenMP, xgboost is naturally multi-threaded, to set number of parallel running add ```nthread``` parameter to your configuration.
-Eg. ```nthread=10```
-
-Set nthread to be the number of your real cpu (On Unix, this can be found using ```lscpu```)
-Some systems will have ```Thread(s) per core = 2```, for example, a 4 core cpu with 8 threads, in such case set ```nthread=4``` and not 8.
-
diff --git a/ml-xgboost/demo/binary_classification/agaricus-lepiota.data b/ml-xgboost/demo/binary_classification/agaricus-lepiota.data
deleted file mode 100644
index 14fe8bb..0000000
--- a/ml-xgboost/demo/binary_classification/agaricus-lepiota.data
+++ /dev/null
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-e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
-p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
-p,f,y,c,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
-e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
-p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
-p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
-p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
-e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
-e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
-e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
-p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
-e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
diff --git a/ml-xgboost/demo/binary_classification/agaricus-lepiota.fmap b/ml-xgboost/demo/binary_classification/agaricus-lepiota.fmap
deleted file mode 100644
index e1efc28..0000000
--- a/ml-xgboost/demo/binary_classification/agaricus-lepiota.fmap
+++ /dev/null
@@ -1,32 +0,0 @@
-     1. cap-shape:                bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s
-     2. cap-surface:              fibrous=f,grooves=g,scaly=y,smooth=s
-     3. cap-color:                brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y
-     4. bruises?:                 bruises=t,no=f
-     5. odor:                     almond=a,anise=l,creosote=c,fishy=y,foul=f,
-                                  musty=m,none=n,pungent=p,spicy=s
-     6. gill-attachment:          attached=a,descending=d,free=f,notched=n
-     7. gill-spacing:             close=c,crowded=w,distant=d
-     8. gill-size:                broad=b,narrow=n
-     9. gill-color:               black=k,brown=n,buff=b,chocolate=h,gray=g,
-                                  green=r,orange=o,pink=p,purple=u,red=e,
-                                  white=w,yellow=y
-    10. stalk-shape:              enlarging=e,tapering=t
-    11. stalk-root:               bulbous=b,club=c,cup=u,equal=e,
-                                  rhizomorphs=z,rooted=r,missing=?
-    12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
-    13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
-    14. stalk-color-above-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
-                                  pink=p,red=e,white=w,yellow=y
-    15. stalk-color-below-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
-                                  pink=p,red=e,white=w,yellow=y
-    16. veil-type:                partial=p,universal=u
-    17. veil-color:               brown=n,orange=o,white=w,yellow=y
-    18. ring-number:              none=n,one=o,two=t
-    19. ring-type:                cobwebby=c,evanescent=e,flaring=f,large=l,
-                                  none=n,pendant=p,sheathing=s,zone=z
-    20. spore-print-color:        black=k,brown=n,buff=b,chocolate=h,green=r,
-                                  orange=o,purple=u,white=w,yellow=y
-    21. population:               abundant=a,clustered=c,numerous=n,
-                                  scattered=s,several=v,solitary=y
-    22. habitat:                  grasses=g,leaves=l,meadows=m,paths=p,
-                                  urban=u,waste=w,woods=d
diff --git a/ml-xgboost/demo/binary_classification/agaricus-lepiota.names b/ml-xgboost/demo/binary_classification/agaricus-lepiota.names
deleted file mode 100644
index 4f1f3b5..0000000
--- a/ml-xgboost/demo/binary_classification/agaricus-lepiota.names
+++ /dev/null
@@ -1,148 +0,0 @@
-1. Title: Mushroom Database
-
-2. Sources: 
-    (a) Mushroom records drawn from The Audubon Society Field Guide to North
-        American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred
-        A. Knopf
-    (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
-    (c) Date: 27 April 1987
-
-3. Past Usage:
-    1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational
-       Adjustment (Technical Report 87-19).  Doctoral disseration, Department
-       of Information and Computer Science, University of California, Irvine.
-       --- STAGGER: asymptoted to 95% classification accuracy after reviewing
-           1000 instances.
-    2. Iba,W., Wogulis,J., & Langley,P. (1988).  Trading off Simplicity
-       and Coverage in Incremental Concept Learning. In Proceedings of 
-       the 5th International Conference on Machine Learning, 73-79.
-       Ann Arbor, Michigan: Morgan Kaufmann.  
-       -- approximately the same results with their HILLARY algorithm    
-    3. In the following references a set of rules (given below) were
-	learned for this data set which may serve as a point of
-	comparison for other researchers.
-
-	Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules
-	from training data using backpropagation networks, in: Proc. of the
-	The 1st Online Workshop on Soft Computing, 19-30.Aug.1996, pp. 25-30,
-	available on-line at: http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
-
-	Duch W, Adamczak R, Grabczewski K, Ishikawa M, Ueda H, Extraction of
-	crisp logical rules using constrained backpropagation networks -
-	comparison of two new approaches, in: Proc. of the European Symposium
-	on Artificial Neural Networks (ESANN'97), Bruge, Belgium 16-18.4.1997,
-	pp. xx-xx
-
-	Wlodzislaw Duch, Department of Computer Methods, Nicholas Copernicus
-	University, 87-100 Torun, Grudziadzka 5, Poland
-	e-mail: duch@phys.uni.torun.pl
-	WWW     http://www.phys.uni.torun.pl/kmk/
-	
-	Date: Mon, 17 Feb 1997 13:47:40 +0100
-	From: Wlodzislaw Duch <duch@phys.uni.torun.pl>
-	Organization: Dept. of Computer Methods, UMK
-
-	I have attached a file containing logical rules for mushrooms.
-	It should be helpful for other people since only in the last year I
-	have seen about 10 papers analyzing this dataset and obtaining quite
-	complex rules. We will try to contribute other results later.
-
-	With best regards, Wlodek Duch
-	________________________________________________________________
-
-	Logical rules for the mushroom data sets.
-
-	Logical rules given below seem to be the simplest possible for the
-	mushroom dataset and therefore should be treated as benchmark results.
-
-	Disjunctive rules for poisonous mushrooms, from most general
-	to most specific:
-
-	P_1) odor=NOT(almond.OR.anise.OR.none)
-	     120 poisonous cases missed, 98.52% accuracy
-
-	P_2) spore-print-color=green
-	     48 cases missed, 99.41% accuracy
-         
-	P_3) odor=none.AND.stalk-surface-below-ring=scaly.AND.
-	          (stalk-color-above-ring=NOT.brown) 
-	     8 cases missed, 99.90% accuracy
-         
-	P_4) habitat=leaves.AND.cap-color=white
-	         100% accuracy     
-
-	Rule P_4) may also be
-
-	P_4') population=clustered.AND.cap_color=white
-
-	These rule involve 6 attributes (out of 22). Rules for edible
-	mushrooms are obtained as negation of the rules given above, for
-	example the rule:
-
-	odor=(almond.OR.anise.OR.none).AND.spore-print-color=NOT.green
-
-	gives 48 errors, or 99.41% accuracy on the whole dataset.
-
-	Several slightly more complex variations on these rules exist,
-	involving other attributes, such as gill_size, gill_spacing,
-	stalk_surface_above_ring, but the rules given above are the simplest
-	we have found.
-
-
-4. Relevant Information:
-    This data set includes descriptions of hypothetical samples
-    corresponding to 23 species of gilled mushrooms in the Agaricus and
-    Lepiota Family (pp. 500-525).  Each species is identified as
-    definitely edible, definitely poisonous, or of unknown edibility and
-    not recommended.  This latter class was combined with the poisonous
-    one.  The Guide clearly states that there is no simple rule for
-    determining the edibility of a mushroom; no rule like ``leaflets
-    three, let it be'' for Poisonous Oak and Ivy.
-
-5. Number of Instances: 8124
-
-6. Number of Attributes: 22 (all nominally valued)
-
-7. Attribute Information: (classes: edible=e, poisonous=p)
-     1. cap-shape:                bell=b,conical=c,convex=x,flat=f,
-                                  knobbed=k,sunken=s
-     2. cap-surface:              fibrous=f,grooves=g,scaly=y,smooth=s
-     3. cap-color:                brown=n,buff=b,cinnamon=c,gray=g,green=r,
-                                  pink=p,purple=u,red=e,white=w,yellow=y
-     4. bruises?:                 bruises=t,no=f
-     5. odor:                     almond=a,anise=l,creosote=c,fishy=y,foul=f,
-                                  musty=m,none=n,pungent=p,spicy=s
-     6. gill-attachment:          attached=a,descending=d,free=f,notched=n
-     7. gill-spacing:             close=c,crowded=w,distant=d
-     8. gill-size:                broad=b,narrow=n
-     9. gill-color:               black=k,brown=n,buff=b,chocolate=h,gray=g,
-                                  green=r,orange=o,pink=p,purple=u,red=e,
-                                  white=w,yellow=y
-    10. stalk-shape:              enlarging=e,tapering=t
-    11. stalk-root:               bulbous=b,club=c,cup=u,equal=e,
-                                  rhizomorphs=z,rooted=r,missing=?
-    12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
-    13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
-    14. stalk-color-above-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
-                                  pink=p,red=e,white=w,yellow=y
-    15. stalk-color-below-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
-                                  pink=p,red=e,white=w,yellow=y
-    16. veil-type:                partial=p,universal=u
-    17. veil-color:               brown=n,orange=o,white=w,yellow=y
-    18. ring-number:              none=n,one=o,two=t
-    19. ring-type:                cobwebby=c,evanescent=e,flaring=f,large=l,
-                                  none=n,pendant=p,sheathing=s,zone=z
-    20. spore-print-color:        black=k,brown=n,buff=b,chocolate=h,green=r,
-                                  orange=o,purple=u,white=w,yellow=y
-    21. population:               abundant=a,clustered=c,numerous=n,
-                                  scattered=s,several=v,solitary=y
-    22. habitat:                  grasses=g,leaves=l,meadows=m,paths=p,
-                                  urban=u,waste=w,woods=d
-
-8. Missing Attribute Values: 2480 of them (denoted by "?"), all for
-   attribute #11.
-
-9. Class Distribution: 
-    --    edible: 4208 (51.8%)
-    -- poisonous: 3916 (48.2%)
-    --     total: 8124 instances
diff --git a/ml-xgboost/demo/binary_classification/mapfeat.py b/ml-xgboost/demo/binary_classification/mapfeat.py
deleted file mode 100644
index 4cb98f6..0000000
--- a/ml-xgboost/demo/binary_classification/mapfeat.py
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/usr/bin/python
-
-def loadfmap( fname ):
-    fmap = {}
-    nmap = {}
-
-    for l in open( fname ):
-        arr = l.split()
-        if arr[0].find('.') != -1:
-            idx = int( arr[0].strip('.') )
-            assert idx not in fmap
-            fmap[ idx ] = {}
-            ftype = arr[1].strip(':')
-            content = arr[2]
-        else:
-            content = arr[0]
-        for it in content.split(','):
-            if it.strip() == '':
-                continue
-            k , v = it.split('=')
-            fmap[ idx ][ v ] = len(nmap)
-            nmap[ len(nmap) ] = ftype+'='+k
-    return fmap, nmap
-
-def write_nmap( fo, nmap ):
-    for i in range( len(nmap) ):
-        fo.write('%d\t%s\ti\n' % (i, nmap[i]) )
-
-# start here
-fmap, nmap = loadfmap( 'agaricus-lepiota.fmap' )
-fo = open( 'featmap.txt', 'w' )
-write_nmap( fo, nmap )
-fo.close()
-
-fo = open( 'agaricus.txt', 'w' )
-for l in open( 'agaricus-lepiota.data' ):
-    arr = l.split(',')
-    if arr[0] == 'p':
-        fo.write('1')
-    else:
-        assert arr[0] == 'e'
-        fo.write('0')
-    for i in range( 1,len(arr) ):
-        fo.write( ' %d:1' % fmap[i][arr[i].strip()] )
-    fo.write('\n')
-
-fo.close()
diff --git a/ml-xgboost/demo/binary_classification/mknfold.py b/ml-xgboost/demo/binary_classification/mknfold.py
deleted file mode 100644
index a941f86..0000000
--- a/ml-xgboost/demo/binary_classification/mknfold.py
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/python
-import sys
-import random
-
-if len(sys.argv) < 2:
-    print ('Usage:<filename> <k> [nfold = 5]')
-    exit(0)
-
-random.seed( 10 )
-
-k = int( sys.argv[2] )
-if len(sys.argv) > 3:
-    nfold = int( sys.argv[3] )
-else:
-    nfold = 5
-
-fi = open( sys.argv[1], 'r' )
-ftr = open( sys.argv[1]+'.train', 'w' )
-fte = open( sys.argv[1]+'.test', 'w' )
-for l in fi:
-    if random.randint( 1 , nfold ) == k:
-        fte.write( l )
-    else:
-        ftr.write( l )
-
-fi.close()
-ftr.close()
-fte.close()
-
diff --git a/ml-xgboost/demo/binary_classification/mushroom.conf b/ml-xgboost/demo/binary_classification/mushroom.conf
deleted file mode 100644
index 435c9bf..0000000
--- a/ml-xgboost/demo/binary_classification/mushroom.conf
+++ /dev/null
@@ -1,29 +0,0 @@
-# General Parameters, see comment for each definition
-# choose the booster, can be gbtree or gblinear
-booster = gbtree
-# choose logistic regression loss function for binary classification
-objective = binary:logistic
-
-# Tree Booster Parameters
-# step size shrinkage
-eta = 1.0
-# minimum loss reduction required to make a further partition
-gamma = 1.0
-# minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1
-# maximum depth of a tree
-max_depth = 3
-
-# Task Parameters
-# the number of round to do boosting
-num_round = 2
-# 0 means do not save any model except the final round model
-save_period = 0
-# The path of training data
-data = "agaricus.txt.train"
-# The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "agaricus.txt.test"
-# evaluate on training data as well each round
-eval_train = 1
-# The path of test data
-test:data = "agaricus.txt.test"
diff --git a/ml-xgboost/demo/binary_classification/runexp.sh b/ml-xgboost/demo/binary_classification/runexp.sh
deleted file mode 100644
index 68c3e6f..0000000
--- a/ml-xgboost/demo/binary_classification/runexp.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-# map feature using indicator encoding, also produce featmap.txt
-python mapfeat.py
-# split train and test
-python mknfold.py agaricus.txt 1
-# training and output the models
-../../xgboost mushroom.conf
-# output prediction task=pred 
-../../xgboost mushroom.conf task=pred model_in=0002.model
-# print the boosters of 00002.model in dump.raw.txt
-../../xgboost mushroom.conf task=dump model_in=0002.model name_dump=dump.raw.txt 
-# use the feature map in printing for better visualization
-../../xgboost mushroom.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt
-cat dump.nice.txt
-
diff --git a/ml-xgboost/demo/c-api/CMakeLists.txt b/ml-xgboost/demo/c-api/CMakeLists.txt
deleted file mode 100644
index 20a8158..0000000
--- a/ml-xgboost/demo/c-api/CMakeLists.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-cmake_minimum_required(VERSION 3.12)
-find_package(xgboost REQUIRED)
-add_executable(api-demo c-api-demo.c)
-target_link_libraries(api-demo xgboost::xgboost)
diff --git a/ml-xgboost/demo/c-api/Makefile b/ml-xgboost/demo/c-api/Makefile
deleted file mode 100644
index 345079f..0000000
--- a/ml-xgboost/demo/c-api/Makefile
+++ /dev/null
@@ -1,19 +0,0 @@
-SRC=c-api-demo.c
-TGT=c-api-demo
-
-cc=cc
-CFLAGS ?=-O3
-XGBOOST_ROOT ?=../..
-INCLUDE_DIR=-I$(XGBOOST_ROOT)/include -I$(XGBOOST_ROOT)/dmlc-core/include -I$(XGBOOST_ROOT)/rabit/include
-LIB_DIR=-L$(XGBOOST_ROOT)/lib
-
-build: $(TGT)
-
-$(TGT): $(SRC) Makefile
-	$(cc) $(CFLAGS) $(INCLUDE_DIR) $(LIB_DIR) -o $(TGT) $(SRC) -lxgboost
-
-run: $(TGT)
-	LD_LIBRARY_PATH=$(XGBOOST_ROOT)/lib ./$(TGT)
-
-clean:
-	rm -f $(TGT)
diff --git a/ml-xgboost/demo/c-api/README.md b/ml-xgboost/demo/c-api/README.md
deleted file mode 100644
index d94f4ac..0000000
--- a/ml-xgboost/demo/c-api/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-C-APIs
-===
-
-**XGBoost** implements a C API originally designed for various language
-bindings.  For detailed reference, please check xgboost/c_api.h.  Here is a
-demonstration of using the API.
-
-# CMake
-If you use **CMake** for your project, you can either install **XGBoost**
-somewhere in your system and tell CMake to find it by calling
-`find_package(xgboost)`, or put **XGBoost** inside your project's source tree
-and call **CMake** command: `add_subdirectory(xgboost)`.  To use
-`find_package()`, put the following in your **CMakeLists.txt**:
-
-``` CMake
-find_package(xgboost REQUIRED)
-add_executable(api-demo c-api-demo.c)
-target_link_libraries(api-demo xgboost::xgboost)
-```
-
-If you want to put XGBoost inside your project (like git submodule), use this
-instead:
-``` CMake
-add_subdirectory(xgboost)
-add_executable(api-demo c-api-demo.c)
-target_link_libraries(api-demo xgboost)
-```
-
-# make
-You can start by modifying the makefile in this directory to fit your need.
\ No newline at end of file
diff --git a/ml-xgboost/demo/c-api/c-api-demo.c b/ml-xgboost/demo/c-api/c-api-demo.c
deleted file mode 100644
index 2fd212f..0000000
--- a/ml-xgboost/demo/c-api/c-api-demo.c
+++ /dev/null
@@ -1,88 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- *
- * \file c-api-demo.c
- * \brief A simple example of using xgboost C API.
- */
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <xgboost/c_api.h>
-
-#define safe_xgboost(call) {                                            \
-int err = (call);                                                       \
-if (err != 0) {                                                         \
-  fprintf(stderr, "%s:%d: error in %s: %s\n", __FILE__, __LINE__, #call, XGBGetLastError()); \
-  exit(1);                                                              \
-}                                                                       \
-}
-
-int main(int argc, char** argv) {
-  int silent = 0;
-  int use_gpu = 0;  // set to 1 to use the GPU for training
-  
-  // load the data
-  DMatrixHandle dtrain, dtest;
-  safe_xgboost(XGDMatrixCreateFromFile("../data/agaricus.txt.train", silent, &dtrain));
-  safe_xgboost(XGDMatrixCreateFromFile("../data/agaricus.txt.test", silent, &dtest));
-  
-  // create the booster
-  BoosterHandle booster;
-  DMatrixHandle eval_dmats[2] = {dtrain, dtest};
-  safe_xgboost(XGBoosterCreate(eval_dmats, 2, &booster));
-
-  // configure the training
-  // available parameters are described here:
-  //   https://xgboost.readthedocs.io/en/latest/parameter.html
-  safe_xgboost(XGBoosterSetParam(booster, "tree_method", use_gpu ? "gpu_hist" : "hist"));
-  if (use_gpu) {
-    // set the GPU to use;
-    // this is not necessary, but provided here as an illustration
-    safe_xgboost(XGBoosterSetParam(booster, "gpu_id", "0"));
-  } else {
-    // avoid evaluating objective and metric on a GPU
-    safe_xgboost(XGBoosterSetParam(booster, "gpu_id", "-1"));
-  }
-
-  safe_xgboost(XGBoosterSetParam(booster, "objective", "binary:logistic"));
-  safe_xgboost(XGBoosterSetParam(booster, "min_child_weight", "1"));
-  safe_xgboost(XGBoosterSetParam(booster, "gamma", "0.1"));
-  safe_xgboost(XGBoosterSetParam(booster, "max_depth", "3"));
-  safe_xgboost(XGBoosterSetParam(booster, "verbosity", silent ? "0" : "1"));
-  
-  // train and evaluate for 10 iterations
-  int n_trees = 10;
-  const char* eval_names[2] = {"train", "test"};
-  const char* eval_result = NULL;
-  for (int i = 0; i < n_trees; ++i) {
-    safe_xgboost(XGBoosterUpdateOneIter(booster, i, dtrain));
-    safe_xgboost(XGBoosterEvalOneIter(booster, i, eval_dmats, eval_names, 2, &eval_result));
-    printf("%s\n", eval_result);
-  }
-
-  // predict
-  bst_ulong out_len = 0;
-  const float* out_result = NULL;
-  int n_print = 10;
-
-  safe_xgboost(XGBoosterPredict(booster, dtest, 0, 0, 0, &out_len, &out_result));
-  printf("y_pred: ");
-  for (int i = 0; i < n_print; ++i) {
-    printf("%1.4f ", out_result[i]);
-  }
-  printf("\n");
-
-  // print true labels
-  safe_xgboost(XGDMatrixGetFloatInfo(dtest, "label", &out_len, &out_result));
-  printf("y_test: ");
-  for (int i = 0; i < n_print; ++i) {
-    printf("%1.4f ", out_result[i]);
-  }
-  printf("\n");
-
-  // free everything
-  safe_xgboost(XGBoosterFree(booster));
-  safe_xgboost(XGDMatrixFree(dtrain));
-  safe_xgboost(XGDMatrixFree(dtest));
-  return 0;
-}
diff --git a/ml-xgboost/demo/dask/README.md b/ml-xgboost/demo/dask/README.md
deleted file mode 100644
index b70248c..0000000
--- a/ml-xgboost/demo/dask/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Dask
-====
-
-This directory contains some demonstrations for using `dask` with `XGBoost`.
-For an overview, see
-https://xgboost.readthedocs.io/en/latest/tutorials/dask.html .
\ No newline at end of file
diff --git a/ml-xgboost/demo/dask/cpu_training.py b/ml-xgboost/demo/dask/cpu_training.py
deleted file mode 100644
index b86958e..0000000
--- a/ml-xgboost/demo/dask/cpu_training.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import xgboost as xgb
-from xgboost.dask import DaskDMatrix
-from dask.distributed import Client
-from dask.distributed import LocalCluster
-from dask import array as da
-
-
-def main(client):
-    # generate some random data for demonstration
-    m = 100000
-    n = 100
-    X = da.random.random(size=(m, n), chunks=100)
-    y = da.random.random(size=(m, ), chunks=100)
-
-    # DaskDMatrix acts like normal DMatrix, works as a proxy for local
-    # DMatrix scatter around workers.
-    dtrain = DaskDMatrix(client, X, y)
-
-    # Use train method from xgboost.dask instead of xgboost.  This
-    # distributed version of train returns a dictionary containing the
-    # resulting booster and evaluation history obtained from
-    # evaluation metrics.
-    output = xgb.dask.train(client,
-                            {'verbosity': 1,
-                             'tree_method': 'hist'},
-                            dtrain,
-                            num_boost_round=4, evals=[(dtrain, 'train')])
-    bst = output['booster']
-    history = output['history']
-
-    # you can pass output directly into `predict` too.
-    prediction = xgb.dask.predict(client, bst, dtrain)
-    print('Evaluation history:', history)
-    return prediction
-
-
-if __name__ == '__main__':
-    # or use other clusters for scaling
-    with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
-        with Client(cluster) as client:
-            main(client)
diff --git a/ml-xgboost/demo/dask/gpu_training.py b/ml-xgboost/demo/dask/gpu_training.py
deleted file mode 100644
index 3b3363f..0000000
--- a/ml-xgboost/demo/dask/gpu_training.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from dask_cuda import LocalCUDACluster
-from dask.distributed import Client
-from dask import array as da
-import xgboost as xgb
-from xgboost.dask import DaskDMatrix
-
-
-def main(client):
-    # generate some random data for demonstration
-    m = 100000
-    n = 100
-    X = da.random.random(size=(m, n), chunks=100)
-    y = da.random.random(size=(m, ), chunks=100)
-
-    # DaskDMatrix acts like normal DMatrix, works as a proxy for local
-    # DMatrix scatter around workers.
-    dtrain = DaskDMatrix(client, X, y)
-
-    # Use train method from xgboost.dask instead of xgboost.  This
-    # distributed version of train returns a dictionary containing the
-    # resulting booster and evaluation history obtained from
-    # evaluation metrics.
-    output = xgb.dask.train(client,
-                            {'verbosity': 2,
-                             # Golden line for GPU training
-                             'tree_method': 'gpu_hist'},
-                            dtrain,
-                            num_boost_round=4, evals=[(dtrain, 'train')])
-    bst = output['booster']
-    history = output['history']
-
-    # you can pass output directly into `predict` too.
-    prediction = xgb.dask.predict(client, bst, dtrain)
-    prediction = prediction.compute()
-    print('Evaluation history:', history)
-    return prediction
-
-
-if __name__ == '__main__':
-    # `LocalCUDACluster` is used for assigning GPU to XGBoost processes.  Here
-    # `n_workers` represents the number of GPUs since we use one GPU per worker
-    # process.
-    with LocalCUDACluster(n_workers=2, threads_per_worker=4) as cluster:
-        with Client(cluster) as client:
-            main(client)
diff --git a/ml-xgboost/demo/dask/sklearn_cpu_training.py b/ml-xgboost/demo/dask/sklearn_cpu_training.py
deleted file mode 100644
index 0549aa3..0000000
--- a/ml-xgboost/demo/dask/sklearn_cpu_training.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''Dask interface demo:
-
-Use scikit-learn regressor interface with CPU histogram tree method.'''
-from dask.distributed import Client
-from dask.distributed import LocalCluster
-from dask import array as da
-import xgboost
-
-
-def main(client):
-    # generate some random data for demonstration
-    n = 100
-    m = 10000
-    partition_size = 100
-    X = da.random.random((m, n), partition_size)
-    y = da.random.random(m, partition_size)
-
-    regressor = xgboost.dask.DaskXGBRegressor(verbosity=1, n_estimators=2)
-    regressor.set_params(tree_method='hist')
-    # assigning client here is optional
-    regressor.client = client
-
-    regressor.fit(X, y, eval_set=[(X, y)])
-    prediction = regressor.predict(X)
-
-    bst = regressor.get_booster()
-    history = regressor.evals_result()
-
-    print('Evaluation history:', history)
-    # returned prediction is always a dask array.
-    assert isinstance(prediction, da.Array)
-    return bst                  # returning the trained model
-
-
-if __name__ == '__main__':
-    # or use other clusters for scaling
-    with LocalCluster(n_workers=4, threads_per_worker=1) as cluster:
-        with Client(cluster) as client:
-            main(client)
diff --git a/ml-xgboost/demo/dask/sklearn_gpu_training.py b/ml-xgboost/demo/dask/sklearn_gpu_training.py
deleted file mode 100644
index afba215..0000000
--- a/ml-xgboost/demo/dask/sklearn_gpu_training.py
+++ /dev/null
@@ -1,42 +0,0 @@
-'''Dask interface demo:
-
-Use scikit-learn regressor interface with GPU histogram tree method.'''
-
-from dask.distributed import Client
-# It's recommended to use dask_cuda for GPU assignment
-from dask_cuda import LocalCUDACluster
-from dask import array as da
-import xgboost
-
-
-def main(client):
-    # generate some random data for demonstration
-    n = 100
-    m = 1000000
-    partition_size = 10000
-    X = da.random.random((m, n), partition_size)
-    y = da.random.random(m, partition_size)
-
-    regressor = xgboost.dask.DaskXGBRegressor(verbosity=1)
-    regressor.set_params(tree_method='gpu_hist')
-    # assigning client here is optional
-    regressor.client = client
-
-    regressor.fit(X, y, eval_set=[(X, y)])
-    prediction = regressor.predict(X)
-
-    bst = regressor.get_booster()
-    history = regressor.evals_result()
-
-    print('Evaluation history:', history)
-    # returned prediction is always a dask array.
-    assert isinstance(prediction, da.Array)
-    return bst                  # returning the trained model
-
-
-if __name__ == '__main__':
-    # With dask cuda, one can scale up XGBoost to arbitrary GPU clusters.
-    # `LocalCUDACluster` used here is only for demonstration purpose.
-    with LocalCUDACluster() as cluster:
-        with Client(cluster) as client:
-            main(client)
diff --git a/ml-xgboost/demo/data/README.md b/ml-xgboost/demo/data/README.md
deleted file mode 100644
index d2d63ec..0000000
--- a/ml-xgboost/demo/data/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-This folder contains processed example dataset used by the demos.
-Copyright of the dataset belongs to the original copyright holder
diff --git a/ml-xgboost/demo/data/agaricus.txt.test b/ml-xgboost/demo/data/agaricus.txt.test
deleted file mode 100644
index 83bdd26..0000000
--- a/ml-xgboost/demo/data/agaricus.txt.test
+++ /dev/null
@@ -1,1611 +0,0 @@
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diff --git a/ml-xgboost/demo/data/agaricus.txt.train b/ml-xgboost/demo/data/agaricus.txt.train
deleted file mode 100644
index 10c7902..0000000
--- a/ml-xgboost/demo/data/agaricus.txt.train
+++ /dev/null
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diff --git a/ml-xgboost/demo/data/dermatology.data.test b/ml-xgboost/demo/data/dermatology.data.test
deleted file mode 100644
index 7619d99..0000000
--- a/ml-xgboost/demo/data/dermatology.data.test
+++ /dev/null
@@ -1,110 +0,0 @@
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diff --git a/ml-xgboost/demo/data/dermatology.data.train b/ml-xgboost/demo/data/dermatology.data.train
deleted file mode 100644
index 846bcab..0000000
--- a/ml-xgboost/demo/data/dermatology.data.train
+++ /dev/null
@@ -1,256 +0,0 @@
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-2 0:2 1:1 2:2 3:2 4:0 5:2 6:0 7:0 8:0 9:1 10:0 11:1 12:1 13:0 14:0 15:2 16:3 17:1 18:2 19:0 20:0 21:0 22:0 23:0 24:2 25:0 26:2 27:2 28:2 29:0 30:0 31:2 32:2
diff --git a/ml-xgboost/demo/data/dermatology_process.py b/ml-xgboost/demo/data/dermatology_process.py
deleted file mode 100644
index 3fdb012..0000000
--- a/ml-xgboost/demo/data/dermatology_process.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/python
-
-import numpy as np
-
-# label need to be 0 to num_class -1
-data = np.loadtxt('./dermatology.data', delimiter=',',
-                  converters={33: lambda x:int(x == '?'), 34: lambda x:int(x) - 1})
-sz = data.shape
-
-train = data[:int(sz[0] * 0.7), :]
-test = data[int(sz[0] * 0.7):, :]
-
-train_X = train[:, :33]
-train_Y = train[:, 34]
-
-test_X = test[:, :33]
-test_Y = test[:, 34]
-
-def process(X, Y):
-    s = ""
-    for i in range(len(X)):
-        s += str(int(Y[i]))
-        for j in range(len(X[i])):
-            s += (" %d:%d" % (j, int(X[i][j])))
-        s += "\n"
-    return s
-
-with open("./dermatology.data.train", 'w') as fp:
-    fp.write(process(train_X, train_Y))
-
-with open("./dermatology.data.test", 'w') as fp:
-    fp.write(process(test_X, test_Y))
\ No newline at end of file
diff --git a/ml-xgboost/demo/data/featmap.txt b/ml-xgboost/demo/data/featmap.txt
deleted file mode 100644
index c9e6465..0000000
--- a/ml-xgboost/demo/data/featmap.txt
+++ /dev/null
@@ -1,126 +0,0 @@
-0	cap-shape=bell	i
-1	cap-shape=conical	i
-2	cap-shape=convex	i
-3	cap-shape=flat	i
-4	cap-shape=knobbed	i
-5	cap-shape=sunken	i
-6	cap-surface=fibrous	i
-7	cap-surface=grooves	i
-8	cap-surface=scaly	i
-9	cap-surface=smooth	i
-10	cap-color=brown	i
-11	cap-color=buff	i
-12	cap-color=cinnamon	i
-13	cap-color=gray	i
-14	cap-color=green	i
-15	cap-color=pink	i
-16	cap-color=purple	i
-17	cap-color=red	i
-18	cap-color=white	i
-19	cap-color=yellow	i
-20	bruises?=bruises	i
-21	bruises?=no	i
-22	odor=almond	i
-23	odor=anise	i
-24	odor=creosote	i
-25	odor=fishy	i
-26	odor=foul	i
-27	odor=musty	i
-28	odor=none	i
-29	odor=pungent	i
-30	odor=spicy	i
-31	gill-attachment=attached	i
-32	gill-attachment=descending	i
-33	gill-attachment=free	i
-34	gill-attachment=notched	i
-35	gill-spacing=close	i
-36	gill-spacing=crowded	i
-37	gill-spacing=distant	i
-38	gill-size=broad	i
-39	gill-size=narrow	i
-40	gill-color=black	i
-41	gill-color=brown	i
-42	gill-color=buff	i
-43	gill-color=chocolate	i
-44	gill-color=gray	i
-45	gill-color=green	i
-46	gill-color=orange	i
-47	gill-color=pink	i
-48	gill-color=purple	i
-49	gill-color=red	i
-50	gill-color=white	i
-51	gill-color=yellow	i
-52	stalk-shape=enlarging	i
-53	stalk-shape=tapering	i
-54	stalk-root=bulbous	i
-55	stalk-root=club	i
-56	stalk-root=cup	i
-57	stalk-root=equal	i
-58	stalk-root=rhizomorphs	i
-59	stalk-root=rooted	i
-60	stalk-root=missing	i
-61	stalk-surface-above-ring=fibrous	i
-62	stalk-surface-above-ring=scaly	i
-63	stalk-surface-above-ring=silky	i
-64	stalk-surface-above-ring=smooth	i
-65	stalk-surface-below-ring=fibrous	i
-66	stalk-surface-below-ring=scaly	i
-67	stalk-surface-below-ring=silky	i
-68	stalk-surface-below-ring=smooth	i
-69	stalk-color-above-ring=brown	i
-70	stalk-color-above-ring=buff	i
-71	stalk-color-above-ring=cinnamon	i
-72	stalk-color-above-ring=gray	i
-73	stalk-color-above-ring=orange	i
-74	stalk-color-above-ring=pink	i
-75	stalk-color-above-ring=red	i
-76	stalk-color-above-ring=white	i
-77	stalk-color-above-ring=yellow	i
-78	stalk-color-below-ring=brown	i
-79	stalk-color-below-ring=buff	i
-80	stalk-color-below-ring=cinnamon	i
-81	stalk-color-below-ring=gray	i
-82	stalk-color-below-ring=orange	i
-83	stalk-color-below-ring=pink	i
-84	stalk-color-below-ring=red	i
-85	stalk-color-below-ring=white	i
-86	stalk-color-below-ring=yellow	i
-87	veil-type=partial	i
-88	veil-type=universal	i
-89	veil-color=brown	i
-90	veil-color=orange	i
-91	veil-color=white	i
-92	veil-color=yellow	i
-93	ring-number=none	i
-94	ring-number=one	i
-95	ring-number=two	i
-96	ring-type=cobwebby	i
-97	ring-type=evanescent	i
-98	ring-type=flaring	i
-99	ring-type=large	i
-100	ring-type=none	i
-101	ring-type=pendant	i
-102	ring-type=sheathing	i
-103	ring-type=zone	i
-104	spore-print-color=black	i
-105	spore-print-color=brown	i
-106	spore-print-color=buff	i
-107	spore-print-color=chocolate	i
-108	spore-print-color=green	i
-109	spore-print-color=orange	i
-110	spore-print-color=purple	i
-111	spore-print-color=white	i
-112	spore-print-color=yellow	i
-113	population=abundant	i
-114	population=clustered	i
-115	population=numerous	i
-116	population=scattered	i
-117	population=several	i
-118	population=solitary	i
-119	habitat=grasses	i
-120	habitat=leaves	i
-121	habitat=meadows	i
-122	habitat=paths	i
-123	habitat=urban	i
-124	habitat=waste	i
-125	habitat=woods	i
diff --git a/ml-xgboost/demo/data/gen_autoclaims.R b/ml-xgboost/demo/data/gen_autoclaims.R
deleted file mode 100644
index 5465db0..0000000
--- a/ml-xgboost/demo/data/gen_autoclaims.R
+++ /dev/null
@@ -1,18 +0,0 @@
-site <- 'http://cran.r-project.org'
-if (!require('dummies'))
-    install.packages('dummies', repos=site)
-if (!require('insuranceData'))
-    install.packages('insuranceData', repos=site)
-
-library(dummies)
-library(insuranceData)
-
-data(AutoClaims)
-data = AutoClaims
-
-data$STATE = as.factor(data$STATE)
-data$CLASS = as.factor(data$CLASS)
-data$GENDER = as.factor(data$GENDER)
-
-data.dummy <- dummy.data.frame(data, dummy.class='factor', omit.constants=T);
-write.table(data.dummy, 'autoclaims.csv', sep=',', row.names=F, col.names=F, quote=F)
diff --git a/ml-xgboost/demo/data/veterans_lung_cancer.csv b/ml-xgboost/demo/data/veterans_lung_cancer.csv
deleted file mode 100644
index 24466b5..0000000
--- a/ml-xgboost/demo/data/veterans_lung_cancer.csv
+++ /dev/null
@@ -1,138 +0,0 @@
-Survival_label_lower_bound,Survival_label_upper_bound,Age_in_years,Karnofsky_score,Months_from_Diagnosis,Celltype=adeno,Celltype=large,Celltype=smallcell,Celltype=squamous,Prior_therapy=no,Prior_therapy=yes,Treatment=standard,Treatment=test
-72.0,72.0,69.0,60.0,7.0,0,0,0,1,1,0,1,0
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-20.0,20.0,65.0,30.0,5.0,0,0,1,0,1,0,1,0
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-18.0,18.0,60.0,30.0,4.0,0,0,1,0,1,0,1,0
-51.0,51.0,67.0,60.0,1.0,0,0,1,0,1,0,1,0
-122.0,122.0,53.0,80.0,28.0,0,0,1,0,1,0,1,0
-27.0,27.0,62.0,60.0,8.0,0,0,1,0,1,0,1,0
-54.0,54.0,67.0,70.0,1.0,0,0,1,0,1,0,1,0
-7.0,7.0,72.0,50.0,7.0,0,0,1,0,1,0,1,0
-63.0,63.0,48.0,50.0,11.0,0,0,1,0,1,0,1,0
-392.0,392.0,68.0,40.0,4.0,0,0,1,0,1,0,1,0
-10.0,10.0,67.0,40.0,23.0,0,0,1,0,0,1,1,0
-8.0,8.0,61.0,20.0,19.0,1,0,0,0,0,1,1,0
-92.0,92.0,60.0,70.0,10.0,1,0,0,0,1,0,1,0
-35.0,35.0,62.0,40.0,6.0,1,0,0,0,1,0,1,0
-117.0,117.0,38.0,80.0,2.0,1,0,0,0,1,0,1,0
-132.0,132.0,50.0,80.0,5.0,1,0,0,0,1,0,1,0
-12.0,12.0,63.0,50.0,4.0,1,0,0,0,0,1,1,0
-162.0,162.0,64.0,80.0,5.0,1,0,0,0,1,0,1,0
-3.0,3.0,43.0,30.0,3.0,1,0,0,0,1,0,1,0
-95.0,95.0,34.0,80.0,4.0,1,0,0,0,1,0,1,0
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-162.0,162.0,62.0,80.0,5.0,0,1,0,0,1,0,1,0
-216.0,216.0,52.0,50.0,15.0,0,1,0,0,1,0,1,0
-553.0,553.0,47.0,70.0,2.0,0,1,0,0,1,0,1,0
-278.0,278.0,63.0,60.0,12.0,0,1,0,0,1,0,1,0
-12.0,12.0,68.0,40.0,12.0,0,1,0,0,0,1,1,0
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-182.0,inf,62.0,90.0,2.0,0,1,0,0,1,0,1,0
-143.0,143.0,60.0,90.0,8.0,0,1,0,0,1,0,1,0
-105.0,105.0,66.0,80.0,11.0,0,1,0,0,1,0,1,0
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-999.0,999.0,54.0,90.0,12.0,0,0,0,1,0,1,0,1
-112.0,112.0,60.0,80.0,6.0,0,0,0,1,1,0,0,1
-87.0,inf,48.0,80.0,3.0,0,0,0,1,1,0,0,1
-231.0,inf,52.0,50.0,8.0,0,0,0,1,0,1,0,1
-242.0,242.0,70.0,50.0,1.0,0,0,0,1,1,0,0,1
-991.0,991.0,50.0,70.0,7.0,0,0,0,1,0,1,0,1
-111.0,111.0,62.0,70.0,3.0,0,0,0,1,1,0,0,1
-1.0,1.0,65.0,20.0,21.0,0,0,0,1,0,1,0,1
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-25.0,25.0,63.0,20.0,36.0,0,0,0,1,1,0,0,1
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-467.0,467.0,64.0,90.0,2.0,0,0,0,1,1,0,0,1
-201.0,201.0,52.0,80.0,28.0,0,0,0,1,0,1,0,1
-1.0,1.0,35.0,50.0,7.0,0,0,0,1,1,0,0,1
-30.0,30.0,63.0,70.0,11.0,0,0,0,1,1,0,0,1
-44.0,44.0,70.0,60.0,13.0,0,0,0,1,0,1,0,1
-283.0,283.0,51.0,90.0,2.0,0,0,0,1,1,0,0,1
-15.0,15.0,40.0,50.0,13.0,0,0,0,1,0,1,0,1
-25.0,25.0,69.0,30.0,2.0,0,0,1,0,1,0,0,1
-103.0,inf,36.0,70.0,22.0,0,0,1,0,0,1,0,1
-21.0,21.0,71.0,20.0,4.0,0,0,1,0,1,0,0,1
-13.0,13.0,62.0,30.0,2.0,0,0,1,0,1,0,0,1
-87.0,87.0,60.0,60.0,2.0,0,0,1,0,1,0,0,1
-2.0,2.0,44.0,40.0,36.0,0,0,1,0,0,1,0,1
-20.0,20.0,54.0,30.0,9.0,0,0,1,0,0,1,0,1
-7.0,7.0,66.0,20.0,11.0,0,0,1,0,1,0,0,1
-24.0,24.0,49.0,60.0,8.0,0,0,1,0,1,0,0,1
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-8.0,8.0,68.0,80.0,2.0,0,0,1,0,1,0,0,1
-99.0,99.0,62.0,85.0,4.0,0,0,1,0,1,0,0,1
-61.0,61.0,71.0,70.0,2.0,0,0,1,0,1,0,0,1
-25.0,25.0,70.0,70.0,2.0,0,0,1,0,1,0,0,1
-95.0,95.0,61.0,70.0,1.0,0,0,1,0,1,0,0,1
-80.0,80.0,71.0,50.0,17.0,0,0,1,0,1,0,0,1
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-29.0,29.0,67.0,40.0,8.0,0,0,1,0,1,0,0,1
-24.0,24.0,60.0,40.0,2.0,1,0,0,0,1,0,0,1
-18.0,18.0,69.0,40.0,5.0,1,0,0,0,0,1,0,1
-83.0,inf,57.0,99.0,3.0,1,0,0,0,1,0,0,1
-31.0,31.0,39.0,80.0,3.0,1,0,0,0,1,0,0,1
-51.0,51.0,62.0,60.0,5.0,1,0,0,0,1,0,0,1
-90.0,90.0,50.0,60.0,22.0,1,0,0,0,0,1,0,1
-52.0,52.0,43.0,60.0,3.0,1,0,0,0,1,0,0,1
-73.0,73.0,70.0,60.0,3.0,1,0,0,0,1,0,0,1
-8.0,8.0,66.0,50.0,5.0,1,0,0,0,1,0,0,1
-36.0,36.0,61.0,70.0,8.0,1,0,0,0,1,0,0,1
-48.0,48.0,81.0,10.0,4.0,1,0,0,0,1,0,0,1
-7.0,7.0,58.0,40.0,4.0,1,0,0,0,1,0,0,1
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-19.0,19.0,42.0,50.0,10.0,1,0,0,0,1,0,0,1
-45.0,45.0,69.0,40.0,3.0,1,0,0,0,1,0,0,1
-80.0,80.0,63.0,40.0,4.0,1,0,0,0,1,0,0,1
-52.0,52.0,45.0,60.0,4.0,0,1,0,0,1,0,0,1
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-19.0,19.0,39.0,30.0,4.0,0,1,0,0,0,1,0,1
-53.0,53.0,66.0,60.0,12.0,0,1,0,0,1,0,0,1
-15.0,15.0,63.0,30.0,5.0,0,1,0,0,1,0,0,1
-43.0,43.0,49.0,60.0,11.0,0,1,0,0,0,1,0,1
-340.0,340.0,64.0,80.0,10.0,0,1,0,0,0,1,0,1
-133.0,133.0,65.0,75.0,1.0,0,1,0,0,1,0,0,1
-111.0,111.0,64.0,60.0,5.0,0,1,0,0,1,0,0,1
-231.0,231.0,67.0,70.0,18.0,0,1,0,0,0,1,0,1
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-49.0,49.0,37.0,30.0,3.0,0,1,0,0,1,0,0,1
diff --git a/ml-xgboost/demo/distributed-training/README.md b/ml-xgboost/demo/distributed-training/README.md
deleted file mode 100644
index 7a7a019..0000000
--- a/ml-xgboost/demo/distributed-training/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
-Distributed XGBoost Training
-============================
-This is an tutorial of Distributed XGBoost Training.
-Currently xgboost supports distributed training via CLI program with the configuration file.
-There is also plan push distributed python and other language bindings, please open an issue
-if you are interested in contributing.
-
-Build XGBoost with Distributed Filesystem Support
--------------------------------------------------
-To use distributed xgboost, you only need to turn the options on to build
-with distributed filesystems(HDFS or S3) in cmake.
-
-```
-cmake <path/to/xgboost> -DUSE_HDFS=ON -DUSE_S3=ON -DUSE_AZURE=ON
-```
-
-
-Step by Step Tutorial on AWS
-----------------------------
-Checkout [this tutorial](https://xgboost.readthedocs.org/en/latest/tutorials/aws_yarn.html) for running distributed xgboost.
-
-
-Model Analysis
---------------
-XGBoost is exchangeable across all bindings and platforms.
-This means you can use python or R to analyze the learnt model and do prediction.
-For example, you can use the [plot_model.ipynb](plot_model.ipynb) to visualize the learnt model.
diff --git a/ml-xgboost/demo/distributed-training/mushroom.aws.conf b/ml-xgboost/demo/distributed-training/mushroom.aws.conf
deleted file mode 100644
index 0428376..0000000
--- a/ml-xgboost/demo/distributed-training/mushroom.aws.conf
+++ /dev/null
@@ -1,27 +0,0 @@
-# General Parameters, see comment for each definition
-# choose the booster, can be gbtree or gblinear
-booster = gbtree
-# choose logistic regression loss function for binary classification
-objective = binary:logistic
-
-# Tree Booster Parameters
-# step size shrinkage
-eta = 1.0
-# minimum loss reduction required to make a further partition
-gamma = 1.0
-# minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1
-# maximum depth of a tree
-max_depth = 3
-
-# Task Parameters
-# the number of round to do boosting
-num_round = 2
-# 0 means do not save any model except the final round model
-save_period = 0
-# The path of training data
-data = "s3://mybucket/xgb-demo/train"
-# The path of validation data, used to monitor training process, here [test] sets name of the validation set
-# evaluate on training data as well each round
-eval_train = 1
-
diff --git a/ml-xgboost/demo/distributed-training/plot_model.ipynb b/ml-xgboost/demo/distributed-training/plot_model.ipynb
deleted file mode 100644
index 227f960..0000000
--- a/ml-xgboost/demo/distributed-training/plot_model.ipynb
+++ /dev/null
@@ -1,107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# XGBoost Model Analysis\n",
-    "\n",
-    "This notebook can be used to load and analysis model learnt from all xgboost bindings, including distributed training. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "%matplotlib inline "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Please change the ```pkg_path``` and ```model_file``` to be correct path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "pkg_path = '../../python-package/'\n",
-    "model_file = 's3://my-bucket/xgb-demo/model/0002.model'\n",
-    "sys.path.insert(0, pkg_path)\n",
-    "import xgboost as xgb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plot the Feature Importance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# plot the first two trees.\n",
-    "bst = xgb.Booster(model_file=model_file)\n",
-    "xgb.plot_importance(bst)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plot the First Tree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "tree_id = 0\n",
-    "xgb.to_graphviz(bst, tree_id)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ml-xgboost/demo/distributed-training/run_aws.sh b/ml-xgboost/demo/distributed-training/run_aws.sh
deleted file mode 100644
index 0b7cb17..0000000
--- a/ml-xgboost/demo/distributed-training/run_aws.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-# This is the example script to run distributed xgboost on AWS.
-# Change the following two lines for configuration
-
-export BUCKET=mybucket
-
-# submit the job to YARN
-../../dmlc-core/tracker/dmlc-submit --cluster=yarn --num-workers=2 --worker-cores=2\
-    ../../xgboost mushroom.aws.conf nthread=2\
-    data=s3://${BUCKET}/xgb-demo/train\
-    eval[test]=s3://${BUCKET}/xgb-demo/test\
-    model_dir=s3://${BUCKET}/xgb-demo/model
diff --git a/ml-xgboost/demo/gpu_acceleration/README.md b/ml-xgboost/demo/gpu_acceleration/README.md
deleted file mode 100644
index f6b0539..0000000
--- a/ml-xgboost/demo/gpu_acceleration/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# GPU Acceleration Demo
-
-`cover_type.py` shows how to train a model on the [forest cover type](https://archive.ics.uci.edu/ml/datasets/covertype) dataset using GPU acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it time consuming to process. We compare the run-time and accuracy of the GPU and CPU histogram algorithms.
-
-`memory.py` shows how to repeatedly train xgboost models while freeing memory between iterations.
diff --git a/ml-xgboost/demo/gpu_acceleration/cover_type.py b/ml-xgboost/demo/gpu_acceleration/cover_type.py
deleted file mode 100644
index 5a073eb..0000000
--- a/ml-xgboost/demo/gpu_acceleration/cover_type.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import xgboost as xgb
-import numpy as np
-from sklearn.datasets import fetch_covtype
-from sklearn.model_selection import train_test_split
-import time
-
-# Fetch dataset using sklearn
-cov = fetch_covtype()
-X = cov.data
-y = cov.target
-
-# Create 0.75/0.25 train/test split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, train_size=0.75,
-                                                    random_state=42)
-
-# Specify sufficient boosting iterations to reach a minimum
-num_round = 3000
-
-# Leave most parameters as default
-param = {'objective': 'multi:softmax', # Specify multiclass classification
-         'num_class': 8, # Number of possible output classes
-         'tree_method': 'gpu_hist' # Use GPU accelerated algorithm
-         }
-
-# Convert input data from numpy to XGBoost format
-dtrain = xgb.DMatrix(X_train, label=y_train)
-dtest = xgb.DMatrix(X_test, label=y_test)
-
-gpu_res = {} # Store accuracy result
-tmp = time.time()
-# Train model
-xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=gpu_res)
-print("GPU Training Time: %s seconds" % (str(time.time() - tmp)))
-
-# Repeat for CPU algorithm
-tmp = time.time()
-param['tree_method'] = 'hist'
-cpu_res = {}
-xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=cpu_res)
-print("CPU Training Time: %s seconds" % (str(time.time() - tmp)))
diff --git a/ml-xgboost/demo/gpu_acceleration/memory.py b/ml-xgboost/demo/gpu_acceleration/memory.py
deleted file mode 100644
index f0f9555..0000000
--- a/ml-xgboost/demo/gpu_acceleration/memory.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import xgboost as xgb
-import numpy as np
-import time
-import pickle
-import GPUtil
-
-n = 10000
-m = 1000
-X = np.random.random((n, m))
-y = np.random.random(n)
-
-param = {'objective': 'binary:logistic',
-         'tree_method': 'gpu_hist'
-         }
-iterations = 5
-dtrain = xgb.DMatrix(X, label=y)
-
-# High memory usage
-# active bst objects with device memory persist across iterations
-boosters = []
-for i in range(iterations):
-    bst = xgb.train(param, dtrain)
-    boosters.append(bst)
-
-print("Example 1")
-GPUtil.showUtilization()
-del boosters
-
-# Better memory usage
-# The bst object can be destroyed by the python gc, freeing device memory
-# The gc may not immediately free the object, so more than one booster can be allocated at a time
-boosters = []
-for i in range(iterations):
-    bst = xgb.train(param, dtrain)
-    boosters.append(pickle.dumps(bst))
-
-print("Example 2")
-GPUtil.showUtilization()
-del boosters
-
-# Best memory usage
-# The gc explicitly frees the booster before starting the next iteration
-boosters = []
-for i in range(iterations):
-    bst = xgb.train(param, dtrain)
-    boosters.append(pickle.dumps(bst))
-    del bst
-
-print("Example 3")
-GPUtil.showUtilization()
-del boosters
diff --git a/ml-xgboost/demo/guide-python/README.md b/ml-xgboost/demo/guide-python/README.md
deleted file mode 100644
index 58110d6..0000000
--- a/ml-xgboost/demo/guide-python/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-XGBoost Python Feature Walkthrough
-==================================
-* [Basic walkthrough of wrappers](basic_walkthrough.py)
-* [Customize loss function, and evaluation metric](custom_objective.py)
-* [Re-implement RMSLE as customized metric and objective](custom_rmsle.py)
-* [Re-Implement `multi:softmax` objective as customized objective](custom_softmax.py)
-* [Boosting from existing prediction](boost_from_prediction.py)
-* [Predicting using first n trees](predict_first_ntree.py)
-* [Generalized Linear Model](generalized_linear_model.py)
-* [Cross validation](cross_validation.py)
-* [Predicting leaf indices](predict_leaf_indices.py)
-* [Sklearn Wrapper](sklearn_examples.py)
-* [Sklearn Parallel](sklearn_parallel.py)
-* [Sklearn access evals result](sklearn_evals_result.py)
-* [Access evals result](evals_result.py)
-* [External Memory](external_memory.py)
diff --git a/ml-xgboost/demo/guide-python/basic_walkthrough.py b/ml-xgboost/demo/guide-python/basic_walkthrough.py
deleted file mode 100644
index ec93968..0000000
--- a/ml-xgboost/demo/guide-python/basic_walkthrough.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-import numpy as np
-import scipy.sparse
-import pickle
-import xgboost as xgb
-import os
-
-# Make sure the demo knows where to load the data.
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
-DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, 'demo')
-
-# simple example
-# load file from text file, also binary buffer generated by xgboost
-dtrain = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train'))
-dtest = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.test'))
-
-# specify parameters via map, definition are same as c++ version
-param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
-
-# specify validations set to watch performance
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 2
-bst = xgb.train(param, dtrain, num_round, watchlist)
-
-# this is prediction
-preds = bst.predict(dtest)
-labels = dtest.get_label()
-print('error=%f' %
-      (sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) /
-       float(len(preds))))
-bst.save_model('0001.model')
-# dump model
-bst.dump_model('dump.raw.txt')
-# dump model with feature map
-bst.dump_model('dump.nice.txt', os.path.join(DEMO_DIR, 'data/featmap.txt'))
-
-# save dmatrix into binary buffer
-dtest.save_binary('dtest.buffer')
-# save model
-bst.save_model('xgb.model')
-# load model and data in
-bst2 = xgb.Booster(model_file='xgb.model')
-dtest2 = xgb.DMatrix('dtest.buffer')
-preds2 = bst2.predict(dtest2)
-# assert they are the same
-assert np.sum(np.abs(preds2 - preds)) == 0
-
-# alternatively, you can pickle the booster
-pks = pickle.dumps(bst2)
-# load model and data in
-bst3 = pickle.loads(pks)
-preds3 = bst3.predict(dtest2)
-# assert they are the same
-assert np.sum(np.abs(preds3 - preds)) == 0
-
-###
-# build dmatrix from scipy.sparse
-print('start running example of build DMatrix from scipy.sparse CSR Matrix')
-labels = []
-row = []
-col = []
-dat = []
-i = 0
-for l in open(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train')):
-    arr = l.split()
-    labels.append(int(arr[0]))
-    for it in arr[1:]:
-        k, v = it.split(':')
-        row.append(i)
-        col.append(int(k))
-        dat.append(float(v))
-    i += 1
-csr = scipy.sparse.csr_matrix((dat, (row, col)))
-dtrain = xgb.DMatrix(csr, label=labels)
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-bst = xgb.train(param, dtrain, num_round, watchlist)
-
-print('start running example of build DMatrix from scipy.sparse CSC Matrix')
-# we can also construct from csc matrix
-csc = scipy.sparse.csc_matrix((dat, (row, col)))
-dtrain = xgb.DMatrix(csc, label=labels)
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-bst = xgb.train(param, dtrain, num_round, watchlist)
-
-print('start running example of build DMatrix from numpy array')
-# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix
-# in internal implementation then convert to DMatrix
-npymat = csr.todense()
-dtrain = xgb.DMatrix(npymat, label=labels)
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-bst = xgb.train(param, dtrain, num_round, watchlist)
diff --git a/ml-xgboost/demo/guide-python/boost_from_prediction.py b/ml-xgboost/demo/guide-python/boost_from_prediction.py
deleted file mode 100644
index 948b47a..0000000
--- a/ml-xgboost/demo/guide-python/boost_from_prediction.py
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-###
-# advanced: start from a initial base prediction
-#
-print('start running example to start from a initial prediction')
-# specify parameters via map, definition are same as c++ version
-param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
-# train xgboost for 1 round
-bst = xgb.train(param, dtrain, 1, watchlist)
-# Note: we need the margin value instead of transformed prediction in
-# set_base_margin
-# do predict with output_margin=True, will always give you margin values
-# before logistic transformation
-ptrain = bst.predict(dtrain, output_margin=True)
-ptest = bst.predict(dtest, output_margin=True)
-dtrain.set_base_margin(ptrain)
-dtest.set_base_margin(ptest)
-
-print('this is result of running from initial prediction')
-bst = xgb.train(param, dtrain, 1, watchlist)
diff --git a/ml-xgboost/demo/guide-python/cross_validation.py b/ml-xgboost/demo/guide-python/cross_validation.py
deleted file mode 100644
index 948992c..0000000
--- a/ml-xgboost/demo/guide-python/cross_validation.py
+++ /dev/null
@@ -1,62 +0,0 @@
-#!/usr/bin/python
-import numpy as np
-import xgboost as xgb
-
-### load data in do training
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
-num_round = 2
-
-print('running cross validation')
-# do cross validation, this will print result out as
-# [iteration]  metric_name:mean_value+std_value
-# std_value is standard deviation of the metric
-xgb.cv(param, dtrain, num_round, nfold=5,
-       metrics={'error'}, seed=0,
-       callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
-
-print('running cross validation, disable standard deviation display')
-# do cross validation, this will print result out as
-# [iteration]  metric_name:mean_value
-res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
-             metrics={'error'}, seed=0,
-             callbacks=[xgb.callback.print_evaluation(show_stdv=False),
-                        xgb.callback.early_stop(3)])
-print(res)
-print('running cross validation, with preprocessing function')
-# define the preprocessing function
-# used to return the preprocessed training, test data, and parameter
-# we can use this to do weight rescale, etc.
-# as a example, we try to set scale_pos_weight
-def fpreproc(dtrain, dtest, param):
-    label = dtrain.get_label()
-    ratio = float(np.sum(label == 0)) / np.sum(label == 1)
-    param['scale_pos_weight'] = ratio
-    return (dtrain, dtest, param)
-
-# do cross validation, for each fold
-# the dtrain, dtest, param will be passed into fpreproc
-# then the return value of fpreproc will be used to generate
-# results of that fold
-xgb.cv(param, dtrain, num_round, nfold=5,
-       metrics={'auc'}, seed=0, fpreproc=fpreproc)
-
-###
-# you can also do cross validation with customized loss function
-# See custom_objective.py
-##
-print('running cross validation, with customized loss function')
-def logregobj(preds, dtrain):
-    labels = dtrain.get_label()
-    preds = 1.0 / (1.0 + np.exp(-preds))
-    grad = preds - labels
-    hess = preds * (1.0 - preds)
-    return grad, hess
-def evalerror(preds, dtrain):
-    labels = dtrain.get_label()
-    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
-
-param = {'max_depth':2, 'eta':1, 'silent':1}
-# train with customized objective
-xgb.cv(param, dtrain, num_round, nfold=5, seed=0,
-       obj=logregobj, feval=evalerror)
diff --git a/ml-xgboost/demo/guide-python/custom_objective.py b/ml-xgboost/demo/guide-python/custom_objective.py
deleted file mode 100644
index 5bbcecc..0000000
--- a/ml-xgboost/demo/guide-python/custom_objective.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/python
-import numpy as np
-import xgboost as xgb
-###
-# advanced: customized loss function
-#
-print('start running example to used customized objective function')
-
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-
-# note: for customized objective function, we leave objective as default
-# note: what we are getting is margin value in prediction
-# you must know what you are doing
-param = {'max_depth': 2, 'eta': 1, 'silent': 1}
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 2
-
-
-# user define objective function, given prediction, return gradient and second
-# order gradient this is log likelihood loss
-def logregobj(preds, dtrain):
-    labels = dtrain.get_label()
-    preds = 1.0 / (1.0 + np.exp(-preds))
-    grad = preds - labels
-    hess = preds * (1.0 - preds)
-    return grad, hess
-
-
-# user defined evaluation function, return a pair metric_name, result
-
-# NOTE: when you do customized loss function, the default prediction value is
-# margin. this may make builtin evaluation metric not function properly for
-# example, we are doing logistic loss, the prediction is score before logistic
-# transformation the builtin evaluation error assumes input is after logistic
-# transformation Take this in mind when you use the customization, and maybe
-# you need write customized evaluation function
-def evalerror(preds, dtrain):
-    labels = dtrain.get_label()
-    # return a pair metric_name, result. The metric name must not contain a
-    # colon (:) or a space since preds are margin(before logistic
-    # transformation, cutoff at 0)
-    return 'my-error', float(sum(labels != (preds > 0.0))) / len(labels)
-
-
-# training with customized objective, we can also do step by step training
-# simply look at xgboost.py's implementation of train
-bst = xgb.train(param, dtrain, num_round, watchlist, obj=logregobj,
-                feval=evalerror)
diff --git a/ml-xgboost/demo/guide-python/custom_rmsle.py b/ml-xgboost/demo/guide-python/custom_rmsle.py
deleted file mode 100644
index 6292636..0000000
--- a/ml-xgboost/demo/guide-python/custom_rmsle.py
+++ /dev/null
@@ -1,197 +0,0 @@
-'''Demo for defining customized metric and objective.  Notice that for
-simplicity reason weight is not used in following example. In this
-script, we implement the Squared Log Error (SLE) objective and RMSLE metric as customized
-functions, then compare it with native implementation in XGBoost.
-
-See doc/tutorials/custom_metric_obj.rst for a step by step
-walkthrough, with other details.
-
-The `SLE` objective reduces impact of outliers in training dataset,
-hence here we also compare its performance with standard squared
-error.
-
-'''
-import numpy as np
-import xgboost as xgb
-from typing import Tuple, Dict, List
-from time import time
-import argparse
-import matplotlib
-from matplotlib import pyplot as plt
-
-# shape of generated data.
-kRows = 4096
-kCols = 16
-
-kOutlier = 10000                # mean of generated outliers
-kNumberOfOutliers = 64
-
-kRatio = 0.7
-kSeed = 1994
-
-kBoostRound = 20
-
-np.random.seed(seed=kSeed)
-
-
-def generate_data() -> Tuple[xgb.DMatrix, xgb.DMatrix]:
-    '''Generate data containing outliers.'''
-    x = np.random.randn(kRows, kCols)
-    y = np.random.randn(kRows)
-    y += np.abs(np.min(y))
-
-    # Create outliers
-    for i in range(0, kNumberOfOutliers):
-        ind = np.random.randint(0, len(y)-1)
-        y[ind] += np.random.randint(0, kOutlier)
-
-    train_portion = int(kRows * kRatio)
-
-    # rmsle requires all label be greater than -1.
-    assert np.all(y > -1.0)
-
-    train_x: np.ndarray = x[: train_portion]
-    train_y: np.ndarray = y[: train_portion]
-    dtrain = xgb.DMatrix(train_x, label=train_y)
-
-    test_x = x[train_portion:]
-    test_y = y[train_portion:]
-    dtest = xgb.DMatrix(test_x, label=test_y)
-    return dtrain, dtest
-
-
-def native_rmse(dtrain: xgb.DMatrix,
-                dtest: xgb.DMatrix) -> Dict[str, Dict[str, List[float]]]:
-    '''Train using native implementation of Root Mean Squared Loss.'''
-    print('Squared Error')
-    squared_error = {
-        'objective': 'reg:squarederror',
-        'eval_metric': 'rmse',
-        'tree_method': 'hist',
-        'seed': kSeed
-    }
-    start = time()
-    results: Dict[str, Dict[str, List[float]]] = {}
-    xgb.train(squared_error,
-              dtrain=dtrain,
-              num_boost_round=kBoostRound,
-              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
-              evals_result=results)
-    print('Finished Squared Error in:', time() - start, '\n')
-    return results
-
-
-def native_rmsle(dtrain: xgb.DMatrix,
-                 dtest: xgb.DMatrix) -> Dict[str, Dict[str, List[float]]]:
-    '''Train using native implementation of Squared Log Error.'''
-    print('Squared Log Error')
-    results: Dict[str, Dict[str, List[float]]] = {}
-    squared_log_error = {
-        'objective': 'reg:squaredlogerror',
-        'eval_metric': 'rmsle',
-        'tree_method': 'hist',
-        'seed': kSeed
-    }
-    start = time()
-    xgb.train(squared_log_error,
-              dtrain=dtrain,
-              num_boost_round=kBoostRound,
-              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
-              evals_result=results)
-    print('Finished Squared Log Error in:', time() - start)
-    return results
-
-
-def py_rmsle(dtrain: xgb.DMatrix, dtest: xgb.DMatrix) -> Dict:
-    '''Train using Python implementation of Squared Log Error.'''
-    def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:
-        '''Compute the gradient squared log error.'''
-        y = dtrain.get_label()
-        return (np.log1p(predt) - np.log1p(y)) / (predt + 1)
-
-    def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:
-        '''Compute the hessian for squared log error.'''
-        y = dtrain.get_label()
-        return ((-np.log1p(predt) + np.log1p(y) + 1) /
-                np.power(predt + 1, 2))
-
-    def squared_log(predt: np.ndarray,
-                    dtrain: xgb.DMatrix) -> Tuple[np.ndarray, np.ndarray]:
-        '''Squared Log Error objective. A simplified version for RMSLE used as
-        objective function.
-
-        :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`
-
-        '''
-        predt[predt < -1] = -1 + 1e-6
-        grad = gradient(predt, dtrain)
-        hess = hessian(predt, dtrain)
-        return grad, hess
-
-    def rmsle(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
-        ''' Root mean squared log error metric.
-
-        :math:`\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}`
-        '''
-        y = dtrain.get_label()
-        predt[predt < -1] = -1 + 1e-6
-        elements = np.power(np.log1p(y) - np.log1p(predt), 2)
-        return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y)))
-
-    results: Dict[str, Dict[str, List[float]]] = {}
-    xgb.train({'tree_method': 'hist', 'seed': kSeed,
-               'disable_default_eval_metric': 1},
-              dtrain=dtrain,
-              num_boost_round=kBoostRound,
-              obj=squared_log,
-              feval=rmsle,
-              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
-              evals_result=results)
-
-    return results
-
-
-def plot_history(rmse_evals, rmsle_evals, py_rmsle_evals):
-    fig, axs = plt.subplots(3, 1)
-    ax0: matplotlib.axes.Axes = axs[0]
-    ax1: matplotlib.axes.Axes = axs[1]
-    ax2: matplotlib.axes.Axes = axs[2]
-
-    x = np.arange(0, kBoostRound, 1)
-
-    ax0.plot(x, rmse_evals['dtrain']['rmse'], label='train-RMSE')
-    ax0.plot(x, rmse_evals['dtest']['rmse'], label='test-RMSE')
-    ax0.legend()
-
-    ax1.plot(x, rmsle_evals['dtrain']['rmsle'], label='train-native-RMSLE')
-    ax1.plot(x, rmsle_evals['dtest']['rmsle'], label='test-native-RMSLE')
-    ax1.legend()
-
-    ax2.plot(x, py_rmsle_evals['dtrain']['PyRMSLE'], label='train-PyRMSLE')
-    ax2.plot(x, py_rmsle_evals['dtest']['PyRMSLE'], label='test-PyRMSLE')
-    ax2.legend()
-
-    plt.show()
-    plt.close()
-
-
-def main(args):
-    dtrain, dtest = generate_data()
-    rmse_evals = native_rmse(dtrain, dtest)
-    rmsle_evals = native_rmsle(dtrain, dtest)
-    py_rmsle_evals = py_rmsle(dtrain, dtest)
-
-    if args.plot != 0:
-        plot_history(rmse_evals, rmsle_evals, py_rmsle_evals)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='Arguments for custom RMSLE objective function demo.')
-    parser.add_argument(
-        '--plot',
-        type=int,
-        default=1,
-        help='Set to 0 to disable plotting the evaluation history.')
-    args = parser.parse_args()
-    main(args)
diff --git a/ml-xgboost/demo/guide-python/custom_softmax.py b/ml-xgboost/demo/guide-python/custom_softmax.py
deleted file mode 100644
index b38b23d..0000000
--- a/ml-xgboost/demo/guide-python/custom_softmax.py
+++ /dev/null
@@ -1,148 +0,0 @@
-'''Demo for creating customized multi-class objective function.  This demo is
-only applicable after (excluding) XGBoost 1.0.0, as before this version XGBoost
-returns transformed prediction for multi-class objective function.  More
-details in comments.
-
-'''
-
-import numpy as np
-import xgboost as xgb
-from matplotlib import pyplot as plt
-import argparse
-
-np.random.seed(1994)
-
-kRows = 100
-kCols = 10
-kClasses = 4                    # number of classes
-
-kRounds = 10                    # number of boosting rounds.
-
-# Generate some random data for demo.
-X = np.random.randn(kRows, kCols)
-y = np.random.randint(0, 4, size=kRows)
-
-m = xgb.DMatrix(X, y)
-
-
-def softmax(x):
-    '''Softmax function with x as input vector.'''
-    e = np.exp(x)
-    return e / np.sum(e)
-
-
-def softprob_obj(predt: np.ndarray, data: xgb.DMatrix):
-    '''Loss function.  Computing the gradient and approximated hessian (diagonal).
-    Reimplements the `multi:softprob` inside XGBoost.
-
-    '''
-    labels = data.get_label()
-    if data.get_weight().size == 0:
-        # Use 1 as weight if we don't have custom weight.
-        weights = np.ones((kRows, 1), dtype=float)
-    else:
-        weights = data.get_weight()
-
-    # The prediction is of shape (rows, classes), each element in a row
-    # represents a raw prediction (leaf weight, hasn't gone through softmax
-    # yet).  In XGBoost 1.0.0, the prediction is transformed by a softmax
-    # function, fixed in later versions.
-    assert predt.shape == (kRows, kClasses)
-
-    grad = np.zeros((kRows, kClasses), dtype=float)
-    hess = np.zeros((kRows, kClasses), dtype=float)
-
-    eps = 1e-6
-
-    # compute the gradient and hessian, slow iterations in Python, only
-    # suitable for demo.  Also the one in native XGBoost core is more robust to
-    # numeric overflow as we don't do anything to mitigate the `exp` in
-    # `softmax` here.
-    for r in range(predt.shape[0]):
-        target = labels[r]
-        p = softmax(predt[r, :])
-        for c in range(predt.shape[1]):
-            assert target >= 0 or target <= kClasses
-            g = p[c] - 1.0 if c == target else p[c]
-            g = g * weights[r]
-            h = max((2.0 * p[c] * (1.0 - p[c]) * weights[r]).item(), eps)
-            grad[r, c] = g
-            hess[r, c] = h
-
-    # Right now (XGBoost 1.0.0), reshaping is necessary
-    grad = grad.reshape((kRows * kClasses, 1))
-    hess = hess.reshape((kRows * kClasses, 1))
-    return grad, hess
-
-
-def predict(booster, X):
-    '''A customized prediction function that converts raw prediction to
-    target class.
-
-    '''
-    # Output margin means we want to obtain the raw prediction obtained from
-    # tree leaf weight.
-    predt = booster.predict(X, output_margin=True)
-    out = np.zeros(kRows)
-    for r in range(predt.shape[0]):
-        # the class with maximum prob (not strictly prob as it haven't gone
-        # through softmax yet so it doesn't sum to 1, but result is the same
-        # for argmax).
-        i = np.argmax(predt[r])
-        out[r] = i
-    return out
-
-
-def plot_history(custom_results, native_results):
-    fig, axs = plt.subplots(2, 1)
-    ax0 = axs[0]
-    ax1 = axs[1]
-
-    x = np.arange(0, kRounds, 1)
-    ax0.plot(x, custom_results['train']['merror'], label='Custom objective')
-    ax0.legend()
-    ax1.plot(x, native_results['train']['merror'], label='multi:softmax')
-    ax1.legend()
-
-    plt.show()
-
-
-def main(args):
-    custom_results = {}
-    # Use our custom objective function
-    booster_custom = xgb.train({'num_class': kClasses},
-                               m,
-                               num_boost_round=kRounds,
-                               obj=softprob_obj,
-                               evals_result=custom_results,
-                               evals=[(m, 'train')])
-
-    predt_custom = predict(booster_custom, m)
-
-    native_results = {}
-    # Use the same objective function defined in XGBoost.
-    booster_native = xgb.train({'num_class': kClasses},
-                               m,
-                               num_boost_round=kRounds,
-                               evals_result=native_results,
-                               evals=[(m, 'train')])
-    predt_native = booster_native.predict(m)
-
-    # We are reimplementing the loss function in XGBoost, so it should
-    # be the same for normal cases.
-    assert np.all(predt_custom == predt_native)
-
-    if args.plot != 0:
-        plot_history(custom_results, native_results)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='Arguments for custom softmax objective function demo.')
-    parser.add_argument(
-        '--plot',
-        type=int,
-        default=1,
-        help='Set to 0 to disable plotting the evaluation history.')
-    args = parser.parse_args()
-    main(args)
diff --git a/ml-xgboost/demo/guide-python/evals_result.py b/ml-xgboost/demo/guide-python/evals_result.py
deleted file mode 100644
index 8449b93..0000000
--- a/ml-xgboost/demo/guide-python/evals_result.py
+++ /dev/null
@@ -1,30 +0,0 @@
-##
-#  This script demonstrate how to access the eval metrics in xgboost
-##
-
-import xgboost as xgb
-dtrain = xgb.DMatrix('../data/agaricus.txt.train', silent=True)
-dtest = xgb.DMatrix('../data/agaricus.txt.test', silent=True)
-
-param = [('max_depth', 2), ('objective', 'binary:logistic'), ('eval_metric', 'logloss'), ('eval_metric', 'error')]
- 
-num_round = 2
-watchlist = [(dtest,'eval'), (dtrain,'train')]
-
-evals_result = {}
-bst = xgb.train(param, dtrain, num_round, watchlist, evals_result=evals_result)
-
-print('Access logloss metric directly from evals_result:')
-print(evals_result['eval']['logloss'])
-
-print('')
-print('Access metrics through a loop:')
-for e_name, e_mtrs in evals_result.items():
-    print('- {}'.format(e_name))
-    for e_mtr_name, e_mtr_vals in e_mtrs.items():
-        print('   - {}'.format(e_mtr_name))
-        print('      - {}'.format(e_mtr_vals))
-
-print('')
-print('Access complete dictionary:')
-print(evals_result)
diff --git a/ml-xgboost/demo/guide-python/external_memory.py b/ml-xgboost/demo/guide-python/external_memory.py
deleted file mode 100644
index 97a74b0..0000000
--- a/ml-xgboost/demo/guide-python/external_memory.py
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/python
-import numpy as np
-import scipy.sparse
-import xgboost as xgb
-
-### simple example for using external memory version
-
-# this is the only difference, add a # followed by a cache prefix name
-# several cache file with the prefix will be generated
-# currently only support convert from libsvm file
-dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
-dtest = xgb.DMatrix('../data/agaricus.txt.test#dtest.cache')
-
-# specify validations set to watch performance
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
-
-# performance notice: set nthread to be the number of your real cpu
-# some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case set nthread=4
-#param['nthread']=num_real_cpu
-
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 2
-bst = xgb.train(param, dtrain, num_round, watchlist)
-
-
diff --git a/ml-xgboost/demo/guide-python/gamma_regression.py b/ml-xgboost/demo/guide-python/gamma_regression.py
deleted file mode 100644
index af7103b..0000000
--- a/ml-xgboost/demo/guide-python/gamma_regression.py
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-import numpy as np
-
-#  this script demonstrates how to fit gamma regression model (with log link function)
-#  in xgboost, before running the demo you need to generate the autoclaims dataset
-#  by running gen_autoclaims.R located in xgboost/demo/data.
-
-data = np.genfromtxt('../data/autoclaims.csv', delimiter=',')
-dtrain = xgb.DMatrix(data[0:4741, 0:34], data[0:4741, 34])
-dtest = xgb.DMatrix(data[4741:6773, 0:34], data[4741:6773, 34])
-
-# for gamma regression, we need to set the objective to 'reg:gamma', it also suggests
-# to set the base_score to a value between 1 to 5 if the number of iteration is small
-param = {'silent':1, 'objective':'reg:gamma', 'booster':'gbtree', 'base_score':3}
-
-# the rest of settings are the same
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 30
-
-# training and evaluation
-bst = xgb.train(param, dtrain, num_round, watchlist)
-preds = bst.predict(dtest)
-labels = dtest.get_label()
-print('test deviance=%f' % (2 * np.sum((labels - preds) / preds - np.log(labels) + np.log(preds))))
diff --git a/ml-xgboost/demo/guide-python/generalized_linear_model.py b/ml-xgboost/demo/guide-python/generalized_linear_model.py
deleted file mode 100644
index c85c5ca..0000000
--- a/ml-xgboost/demo/guide-python/generalized_linear_model.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-##
-#  this script demonstrate how to fit generalized linear model in xgboost
-#  basically, we are using linear model, instead of tree for our boosters
-##
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-# change booster to gblinear, so that we are fitting a linear model
-# alpha is the L1 regularizer
-# lambda is the L2 regularizer
-# you can also set lambda_bias which is L2 regularizer on the bias term
-param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
-         'alpha': 0.0001, 'lambda': 1}
-
-# normally, you do not need to set eta (step_size)
-# XGBoost uses a parallel coordinate descent algorithm (shotgun),
-# there could be affection on convergence with parallelization on certain cases
-# setting eta to be smaller value, e.g 0.5 can make the optimization more stable
-# param['eta'] = 1
-
-##
-# the rest of settings are the same
-##
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 4
-bst = xgb.train(param, dtrain, num_round, watchlist)
-preds = bst.predict(dtest)
-labels = dtest.get_label()
-print('error=%f' % (sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))))
diff --git a/ml-xgboost/demo/guide-python/predict_first_ntree.py b/ml-xgboost/demo/guide-python/predict_first_ntree.py
deleted file mode 100644
index 3a8dbbb..0000000
--- a/ml-xgboost/demo/guide-python/predict_first_ntree.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/python
-import numpy as np
-import xgboost as xgb
-
-### load data in do training
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 3
-bst = xgb.train(param, dtrain, num_round, watchlist)
-
-print('start testing prediction from first n trees')
-### predict using first 1 tree
-label = dtest.get_label()
-ypred1 = bst.predict(dtest, ntree_limit=1)
-# by default, we predict using all the trees
-ypred2 = bst.predict(dtest)
-print('error of ypred1=%f' % (np.sum((ypred1 > 0.5) != label) / float(len(label))))
-print('error of ypred2=%f' % (np.sum((ypred2 > 0.5) != label) / float(len(label))))
diff --git a/ml-xgboost/demo/guide-python/predict_leaf_indices.py b/ml-xgboost/demo/guide-python/predict_leaf_indices.py
deleted file mode 100644
index 383e8d5..0000000
--- a/ml-xgboost/demo/guide-python/predict_leaf_indices.py
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-
-### load data in do training
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 3
-bst = xgb.train(param, dtrain, num_round, watchlist)
-
-print ('start testing predict the leaf indices')
-### predict using first 2 tree
-leafindex = bst.predict(dtest, ntree_limit=2, pred_leaf=True)
-print(leafindex.shape)
-print(leafindex)
-### predict all trees
-leafindex = bst.predict(dtest, pred_leaf=True)
-print(leafindex.shape)
diff --git a/ml-xgboost/demo/guide-python/runall.sh b/ml-xgboost/demo/guide-python/runall.sh
deleted file mode 100644
index 9eda92b..0000000
--- a/ml-xgboost/demo/guide-python/runall.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-export PYTHONPATH=$PYTHONPATH:../../python-package
-python basic_walkthrough.py
-python custom_objective.py
-python boost_from_prediction.py
-python predict_first_ntree.py
-python generalized_linear_model.py
-python cross_validation.py
-python predict_leaf_indices.py
-python sklearn_examples.py
-python sklearn_parallel.py
-python external_memory.py
-rm -rf *~ *.model *.buffer
diff --git a/ml-xgboost/demo/guide-python/sklearn_evals_result.py b/ml-xgboost/demo/guide-python/sklearn_evals_result.py
deleted file mode 100644
index a72cdfc..0000000
--- a/ml-xgboost/demo/guide-python/sklearn_evals_result.py
+++ /dev/null
@@ -1,43 +0,0 @@
-##
-#  This script demonstrate how to access the xgboost eval metrics by using sklearn
-##
-
-import xgboost as xgb
-import numpy as np
-from sklearn.datasets import make_hastie_10_2
-
-X, y = make_hastie_10_2(n_samples=2000, random_state=42)
-
-# Map labels from {-1, 1} to {0, 1}
-labels, y = np.unique(y, return_inverse=True)
-
-X_train, X_test = X[:1600], X[1600:]
-y_train, y_test = y[:1600], y[1600:]
-
-param_dist = {'objective':'binary:logistic', 'n_estimators':2}
-
-clf = xgb.XGBModel(**param_dist)
-# Or you can use: clf = xgb.XGBClassifier(**param_dist)
-
-clf.fit(X_train, y_train,
-        eval_set=[(X_train, y_train), (X_test, y_test)], 
-        eval_metric='logloss',
-        verbose=True)
-
-# Load evals result by calling the evals_result() function
-evals_result = clf.evals_result()
-
-print('Access logloss metric directly from validation_0:')
-print(evals_result['validation_0']['logloss'])
-
-print('')
-print('Access metrics through a loop:')
-for e_name, e_mtrs in evals_result.items():
-    print('- {}'.format(e_name))
-    for e_mtr_name, e_mtr_vals in e_mtrs.items():
-        print('   - {}'.format(e_mtr_name))
-        print('      - {}'.format(e_mtr_vals))
- 
-print('')
-print('Access complete dict:')
-print(evals_result)
diff --git a/ml-xgboost/demo/guide-python/sklearn_examples.py b/ml-xgboost/demo/guide-python/sklearn_examples.py
deleted file mode 100644
index d4f9924..0000000
--- a/ml-xgboost/demo/guide-python/sklearn_examples.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/usr/bin/python
-'''
-Created on 1 Apr 2015
-
-@author: Jamie Hall
-'''
-import pickle
-import xgboost as xgb
-
-import numpy as np
-from sklearn.model_selection import KFold, train_test_split, GridSearchCV
-from sklearn.metrics import confusion_matrix, mean_squared_error
-from sklearn.datasets import load_iris, load_digits, load_boston
-
-rng = np.random.RandomState(31337)
-
-print("Zeros and Ones from the Digits dataset: binary classification")
-digits = load_digits(2)
-y = digits['target']
-X = digits['data']
-kf = KFold(n_splits=2, shuffle=True, random_state=rng)
-for train_index, test_index in kf.split(X):
-    xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
-    predictions = xgb_model.predict(X[test_index])
-    actuals = y[test_index]
-    print(confusion_matrix(actuals, predictions))
-
-print("Iris: multiclass classification")
-iris = load_iris()
-y = iris['target']
-X = iris['data']
-kf = KFold(n_splits=2, shuffle=True, random_state=rng)
-for train_index, test_index in kf.split(X):
-    xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
-    predictions = xgb_model.predict(X[test_index])
-    actuals = y[test_index]
-    print(confusion_matrix(actuals, predictions))
-
-print("Boston Housing: regression")
-boston = load_boston()
-y = boston['target']
-X = boston['data']
-kf = KFold(n_splits=2, shuffle=True, random_state=rng)
-for train_index, test_index in kf.split(X):
-    xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index])
-    predictions = xgb_model.predict(X[test_index])
-    actuals = y[test_index]
-    print(mean_squared_error(actuals, predictions))
-
-print("Parameter optimization")
-y = boston['target']
-X = boston['data']
-xgb_model = xgb.XGBRegressor()
-clf = GridSearchCV(xgb_model,
-                   {'max_depth': [2,4,6],
-                    'n_estimators': [50,100,200]}, verbose=1)
-clf.fit(X,y)
-print(clf.best_score_)
-print(clf.best_params_)
-
-# The sklearn API models are picklable
-print("Pickling sklearn API models")
-# must open in binary format to pickle
-pickle.dump(clf, open("best_boston.pkl", "wb"))
-clf2 = pickle.load(open("best_boston.pkl", "rb"))
-print(np.allclose(clf.predict(X), clf2.predict(X)))
-
-# Early-stopping
-
-X = digits['data']
-y = digits['target']
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
-clf = xgb.XGBClassifier()
-clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
-        eval_set=[(X_test, y_test)])
-
diff --git a/ml-xgboost/demo/guide-python/sklearn_parallel.py b/ml-xgboost/demo/guide-python/sklearn_parallel.py
deleted file mode 100644
index 04f13f7..0000000
--- a/ml-xgboost/demo/guide-python/sklearn_parallel.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-
-if __name__ == "__main__":
-    # NOTE: on posix systems, this *has* to be here and in the
-    # `__name__ == "__main__"` clause to run XGBoost in parallel processes
-    # using fork, if XGBoost was built with OpenMP support. Otherwise, if you
-    # build XGBoost without OpenMP support, you can use fork, which is the
-    # default backend for joblib, and omit this.
-    try:
-        from multiprocessing import set_start_method
-    except ImportError:
-        raise ImportError("Unable to import multiprocessing.set_start_method."
-                          " This example only runs on Python 3.4")
-    set_start_method("forkserver")
-
-    import numpy as np
-    from sklearn.model_selection import GridSearchCV
-    from sklearn.datasets import load_boston
-    import xgboost as xgb
-
-    rng = np.random.RandomState(31337)
-
-    print("Parallel Parameter optimization")
-    boston = load_boston()
-
-    os.environ["OMP_NUM_THREADS"] = "2"  # or to whatever you want
-    y = boston['target']
-    X = boston['data']
-    xgb_model = xgb.XGBRegressor()
-    clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
-                                   'n_estimators': [50, 100, 200]}, verbose=1,
-                       n_jobs=2)
-    clf.fit(X, y)
-    print(clf.best_score_)
-    print(clf.best_params_)
diff --git a/ml-xgboost/demo/json-model/README.md b/ml-xgboost/demo/json-model/README.md
deleted file mode 100644
index 065d854..0000000
--- a/ml-xgboost/demo/json-model/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-We introduced initial support for saving XGBoost model in JSON format in 1.0.0.  Note that
-it's still experimental and under development, output schema is subject to change due to
-bug fixes or further refactoring.  For an overview, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html .
\ No newline at end of file
diff --git a/ml-xgboost/demo/json-model/json_parser.py b/ml-xgboost/demo/json-model/json_parser.py
deleted file mode 100644
index eedcbf9..0000000
--- a/ml-xgboost/demo/json-model/json_parser.py
+++ /dev/null
@@ -1,180 +0,0 @@
-'''Demonstration for parsing JSON tree model file generated by XGBoost.  The
-support is experimental, output schema is subject to change in the future.
-'''
-import json
-import argparse
-
-
-class Tree:
-    '''A tree built by XGBoost.'''
-    # Index into node array
-    _left = 0
-    _right = 1
-    _parent = 2
-    _ind = 3
-    _cond = 4
-    _default_left = 5
-    # Index into stat array
-    _loss_chg = 0
-    _sum_hess = 1
-    _base_weight = 2
-    _child_cnt = 3
-
-    def __init__(self, tree_id: int, nodes, stats):
-        self.tree_id = tree_id
-        self.nodes = nodes
-        self.stats = stats
-
-    def loss_change(self, node_id: int):
-        '''Loss gain of a node.'''
-        return self.stats[node_id][self._loss_chg]
-
-    def sum_hessian(self, node_id: int):
-        '''Sum Hessian of a node.'''
-        return self.stats[node_id][self._sum_hess]
-
-    def base_weight(self, node_id: int):
-        '''Base weight of a node.'''
-        return self.stats[node_id][self._base_weight]
-
-    def num_children(self, node_id: int):
-        '''Number of children of a node.'''
-        return self.stats[node_id][self._child_cnt]
-
-    def split_index(self, node_id: int):
-        '''Split feature index of node.'''
-        return self.nodes[node_id][self._ind]
-
-    def split_condition(self, node_id: int):
-        '''Split value of a node.'''
-        return self.nodes[node_id][self._cond]
-
-    def parent(self, node_id: int):
-        '''Parent ID of a node.'''
-        return self.nodes[node_id][self._parent]
-
-    def left_child(self, node_id: int):
-        '''Left child ID of a node.'''
-        return self.nodes[node_id][self._left]
-
-    def right_child(self, node_id: int):
-        '''Right child ID of a node.'''
-        return self.nodes[node_id][self._right]
-
-    def is_leaf(self, node_id: int):
-        '''Whether a node is leaf.'''
-        return self.nodes[node_id][self._left] == -1
-
-    def is_deleted(self, node_id: int):
-        '''Whether a node is deleted.'''
-        # std::numeric_limits<uint32_t>::max()
-        return self.nodes[node_id][self._ind] == 4294967295
-
-    def __str__(self):
-        stacks = [0]
-        nodes = []
-        while stacks:
-            node = {}
-            nid = stacks.pop()
-
-            node['node id'] = nid
-            node['gain'] = self.loss_change(nid)
-            node['cover'] = self.sum_hessian(nid)
-            nodes.append(node)
-
-            if not self.is_leaf(nid) and not self.is_deleted(nid):
-                left = self.left_child(nid)
-                right = self.right_child(nid)
-                stacks.append(left)
-                stacks.append(right)
-
-        string = '\n'.join(map(lambda x: '  ' + str(x), nodes))
-        return string
-
-
-class Model:
-    '''Gradient boosted tree model.'''
-    def __init__(self, m: dict):
-        '''Construct the Model from JSON object.
-
-         parameters
-         ----------
-          m: A dictionary loaded by json
-        '''
-        # Basic property of a model
-        self.learner_model_shape = model['learner']['learner_model_param']
-        self.num_output_group = int(self.learner_model_shape['num_class'])
-        self.num_feature = int(self.learner_model_shape['num_feature'])
-        self.base_score = float(self.learner_model_shape['base_score'])
-        # A field encoding which output group a tree belongs
-        self.tree_info = model['learner']['gradient_booster']['model'][
-            'tree_info']
-
-        model_shape = model['learner']['gradient_booster']['model'][
-            'gbtree_model_param']
-
-        # JSON representation of trees
-        j_trees = model['learner']['gradient_booster']['model']['trees']
-
-        # Load the trees
-        self.num_trees = int(model_shape['num_trees'])
-        self.leaf_size = int(model_shape['size_leaf_vector'])
-        # Right now XGBoost doesn't support vector leaf yet
-        assert self.leaf_size == 0, str(self.leaf_size)
-
-        trees = []
-        for i in range(self.num_trees):
-            tree = j_trees[i]
-            tree_id = int(tree['id'])
-            assert tree_id == i, (tree_id, i)
-            # properties
-            left_children = tree['left_children']
-            right_children = tree['right_children']
-            parents = tree['parents']
-            split_conditions = tree['split_conditions']
-            split_indices = tree['split_indices']
-            default_left = tree['default_left']
-            # stats
-            base_weights = tree['base_weights']
-            loss_changes = tree['loss_changes']
-            sum_hessian = tree['sum_hessian']
-            leaf_child_counts = tree['leaf_child_counts']
-
-            stats = []
-            nodes = []
-            # We resemble the structure used inside XGBoost, which is similar
-            # to adjacency list.
-            for node_id in range(len(left_children)):
-                nodes.append([
-                    left_children[node_id], right_children[node_id],
-                    parents[node_id], split_indices[node_id],
-                    split_conditions[node_id], default_left[node_id]
-                ])
-                stats.append([
-                    loss_changes[node_id], sum_hessian[node_id],
-                    base_weights[node_id], leaf_child_counts[node_id]
-                ])
-
-            tree = Tree(tree_id, nodes, stats)
-            trees.append(tree)
-
-        self.trees = trees
-
-    def print_model(self):
-        for i, tree in enumerate(self.trees):
-            print('tree_id:', i)
-            print(tree)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='Demonstration for loading and printing XGBoost model.')
-    parser.add_argument('--model',
-                        type=str,
-                        required=True,
-                        help='Path to JSON model file.')
-    args = parser.parse_args()
-    with open(args.model, 'r') as fd:
-        model = json.load(fd)
-    model = Model(model)
-    model.print_model()
diff --git a/ml-xgboost/demo/kaggle-higgs/README.md b/ml-xgboost/demo/kaggle-higgs/README.md
deleted file mode 100644
index d202a99..0000000
--- a/ml-xgboost/demo/kaggle-higgs/README.md
+++ /dev/null
@@ -1,31 +0,0 @@
-Highlights
-=====
-Higgs challenge ends recently, xgboost is being used by many users. This list highlights the xgboost solutions of players
-* Blogpost by phunther: [Winning solution of Kaggle Higgs competition: what a single model can do](http://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do/)
-* The solution by Tianqi Chen and Tong He [Link](https://github.com/hetong007/higgsml)
-
-Guide for Kaggle Higgs Challenge
-=====
-
-This is the folder giving example of how to use XGBoost Python Module  to run Kaggle Higgs competition
-
-This script will achieve about 3.600 AMS score in public leaderboard. To get start, you need do following step:
-
-1. Compile the XGBoost python lib
-```bash
-cd ../..
-make
-```
-
-2. Put training.csv test.csv on folder './data' (you can create a symbolic link)
-
-3. Run ./run.sh
-
-Speed
-=====
-speedtest.py compares xgboost's speed on this dataset with sklearn.GBM
-
-
-Using R module
-=====
-* Alternatively, you can run using R, higgs-train.R and higgs-pred.R.
diff --git a/ml-xgboost/demo/kaggle-higgs/higgs-cv.py b/ml-xgboost/demo/kaggle-higgs/higgs-cv.py
deleted file mode 100644
index d5bbc39..0000000
--- a/ml-xgboost/demo/kaggle-higgs/higgs-cv.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/python
-import numpy as np
-import xgboost as xgb
-
-### load data in do training
-train = np.loadtxt('./data/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
-label  = train[:,32]
-data   = train[:,1:31]
-weight = train[:,31]
-dtrain = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
-param = {'max_depth':6, 'eta':0.1, 'silent':1, 'objective':'binary:logitraw', 'nthread':4}
-num_round = 120
-
-print ('running cross validation, with preprocessing function')
-# define the preprocessing function
-# used to return the preprocessed training, test data, and parameter
-# we can use this to do weight rescale, etc.
-# as a example, we try to set scale_pos_weight
-def fpreproc(dtrain, dtest, param):
-    label = dtrain.get_label()
-    ratio = float(np.sum(label == 0)) / np.sum(label==1)
-    param['scale_pos_weight'] = ratio
-    wtrain = dtrain.get_weight()
-    wtest = dtest.get_weight()
-    sum_weight = sum(wtrain) + sum(wtest)
-    wtrain *= sum_weight / sum(wtrain)
-    wtest *= sum_weight / sum(wtest)
-    dtrain.set_weight(wtrain)
-    dtest.set_weight(wtest)
-    return (dtrain, dtest, param)
-
-# do cross validation, for each fold
-# the dtrain, dtest, param will be passed into fpreproc
-# then the return value of fpreproc will be used to generate
-# results of that fold
-xgb.cv(param, dtrain, num_round, nfold=5,
-       metrics={'ams@0.15', 'auc'}, seed = 0, fpreproc = fpreproc)
diff --git a/ml-xgboost/demo/kaggle-higgs/higgs-numpy.py b/ml-xgboost/demo/kaggle-higgs/higgs-numpy.py
deleted file mode 100644
index 004dd55..0000000
--- a/ml-xgboost/demo/kaggle-higgs/higgs-numpy.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/python
-# this is the example script to use xgboost to train
-import numpy as np
-
-import xgboost as xgb
-
-test_size = 550000
-
-# path to where the data lies
-dpath = 'data'
-
-# load in training data, directly use numpy
-dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
-print ('finish loading from csv ')
-
-label  = dtrain[:,32]
-data   = dtrain[:,1:31]
-# rescale weight to make it same as test set
-weight = dtrain[:,31] * float(test_size) / len(label)
-
-sum_wpos = sum( weight[i] for i in range(len(label)) if label[i] == 1.0  )
-sum_wneg = sum( weight[i] for i in range(len(label)) if label[i] == 0.0  )
-
-# print weight statistics
-print ('weight statistics: wpos=%g, wneg=%g, ratio=%g' % ( sum_wpos, sum_wneg, sum_wneg/sum_wpos ))
-
-# construct xgboost.DMatrix from numpy array, treat -999.0 as missing value
-xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
-
-# setup parameters for xgboost
-param = {}
-# use logistic regression loss, use raw prediction before logistic transformation
-# since we only need the rank
-param['objective'] = 'binary:logitraw'
-# scale weight of positive examples
-param['scale_pos_weight'] = sum_wneg/sum_wpos
-param['eta'] = 0.1
-param['max_depth'] = 6
-param['eval_metric'] = 'auc'
-param['silent'] = 1
-param['nthread'] = 16
-
-# you can directly throw param in, though we want to watch multiple metrics here
-plst = list(param.items())+[('eval_metric', 'ams@0.15')]
-
-watchlist = [ (xgmat,'train') ]
-# boost 120 trees
-num_round = 120
-print ('loading data end, start to boost trees')
-bst = xgb.train( plst, xgmat, num_round, watchlist );
-# save out model
-bst.save_model('higgs.model')
-
-print ('finish training')
diff --git a/ml-xgboost/demo/kaggle-higgs/higgs-pred.R b/ml-xgboost/demo/kaggle-higgs/higgs-pred.R
deleted file mode 100644
index 5136223..0000000
--- a/ml-xgboost/demo/kaggle-higgs/higgs-pred.R
+++ /dev/null
@@ -1,24 +0,0 @@
-# install xgboost package, see R-package in root folder
-require(xgboost)
-require(methods)
-
-modelfile <- "higgs.model"
-outfile <- "higgs.pred.csv"
-dtest <- read.csv("data/test.csv", header=TRUE)
-data <- as.matrix(dtest[2:31])
-idx <- dtest[[1]]
-
-xgmat <- xgb.DMatrix(data, missing = -999.0)
-bst <- xgb.load(modelfile=modelfile)
-ypred <- predict(bst, xgmat)
-
-rorder <- rank(ypred, ties.method="first")
-
-threshold <- 0.15
-# to be completed
-ntop <- length(rorder) - as.integer(threshold*length(rorder))
-plabel <- ifelse(rorder > ntop, "s", "b")
-outdata <- list("EventId" = idx,
-                "RankOrder" = rorder,
-                "Class" = plabel)
-write.csv(outdata, file = outfile, quote=FALSE, row.names=FALSE)
diff --git a/ml-xgboost/demo/kaggle-higgs/higgs-pred.py b/ml-xgboost/demo/kaggle-higgs/higgs-pred.py
deleted file mode 100644
index bc669f5..0000000
--- a/ml-xgboost/demo/kaggle-higgs/higgs-pred.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/python
-# make prediction
-import numpy as np
-import xgboost as xgb
-
-# path to where the data lies
-dpath = 'data'
-
-modelfile = 'higgs.model'
-outfile = 'higgs.pred.csv'
-# make top 15% as positive
-threshold_ratio = 0.15
-
-# load in training data, directly use numpy
-dtest = np.loadtxt( dpath+'/test.csv', delimiter=',', skiprows=1 )
-data   = dtest[:,1:31]
-idx = dtest[:,0]
-
-print ('finish loading from csv ')
-xgmat = xgb.DMatrix( data, missing = -999.0 )
-bst = xgb.Booster({'nthread':16}, model_file = modelfile)
-ypred = bst.predict( xgmat )
-
-res  = [ ( int(idx[i]), ypred[i] ) for i in range(len(ypred)) ]
-
-rorder = {}
-for k, v in sorted( res, key = lambda x:-x[1] ):
-    rorder[ k ] = len(rorder) + 1
-
-# write out predictions
-ntop = int( threshold_ratio * len(rorder ) )
-fo = open(outfile, 'w')
-nhit = 0
-ntot = 0
-fo.write('EventId,RankOrder,Class\n')
-for k, v in res:
-    if rorder[k] <= ntop:
-        lb = 's'
-        nhit += 1
-    else:
-        lb = 'b'
-    # change output rank order to follow Kaggle convention
-    fo.write('%s,%d,%s\n' % ( k,  len(rorder)+1-rorder[k], lb ) )
-    ntot += 1
-fo.close()
-
-print ('finished writing into prediction file')
-
-
-
diff --git a/ml-xgboost/demo/kaggle-higgs/higgs-train.R b/ml-xgboost/demo/kaggle-higgs/higgs-train.R
deleted file mode 100644
index 426d1f6..0000000
--- a/ml-xgboost/demo/kaggle-higgs/higgs-train.R
+++ /dev/null
@@ -1,33 +0,0 @@
-# install xgboost package, see R-package in root folder
-require(xgboost)
-require(methods)
-
-testsize <- 550000
-
-dtrain <- read.csv("data/training.csv", header=TRUE)
-dtrain[33] <- dtrain[33] == "s"
-label <- as.numeric(dtrain[[33]])
-data <- as.matrix(dtrain[2:31])
-weight <- as.numeric(dtrain[[32]]) * testsize / length(label)
-
-sumwpos <- sum(weight * (label==1.0))
-sumwneg <- sum(weight * (label==0.0))
-print(paste("weight statistics: wpos=", sumwpos, "wneg=", sumwneg, "ratio=", sumwneg / sumwpos))
-
-xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
-param <- list("objective" = "binary:logitraw",
-              "scale_pos_weight" = sumwneg / sumwpos,
-              "bst:eta" = 0.1,
-              "bst:max_depth" = 6,
-              "eval_metric" = "auc",
-              "eval_metric" = "ams@0.15",
-              "silent" = 1,
-              "nthread" = 16)
-watchlist <- list("train" = xgmat)
-nrounds = 120
-print ("loading data end, start to boost trees")
-bst = xgb.train(param, xgmat, nrounds, watchlist );
-# save out model
-xgb.save(bst, "higgs.model")
-print ('finish training')
-
diff --git a/ml-xgboost/demo/kaggle-higgs/run.sh b/ml-xgboost/demo/kaggle-higgs/run.sh
deleted file mode 100644
index 23cde39..0000000
--- a/ml-xgboost/demo/kaggle-higgs/run.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-python -u higgs-numpy.py
-ret=$?
-if [[ $ret != 0 ]]; then
-    echo "ERROR in higgs-numpy.py"
-    exit $ret
-fi
-python -u higgs-pred.py
-ret=$?
-if [[ $ret != 0 ]]; then
-    echo "ERROR in higgs-pred.py"
-    exit $ret
-fi
diff --git a/ml-xgboost/demo/kaggle-higgs/speedtest.R b/ml-xgboost/demo/kaggle-higgs/speedtest.R
deleted file mode 100644
index d17d8a1..0000000
--- a/ml-xgboost/demo/kaggle-higgs/speedtest.R
+++ /dev/null
@@ -1,71 +0,0 @@
-# install xgboost package, see R-package in root folder
-require(xgboost)
-require(gbm)
-require(methods)
-
-testsize <- 550000
-
-dtrain <- read.csv("data/training.csv", header=TRUE, nrows=350001)
-dtrain$Label = as.numeric(dtrain$Label=='s')
-# gbm.time = system.time({
-#   gbm.model <- gbm(Label ~ ., data = dtrain[, -c(1,32)], n.trees = 120, 
-#                    interaction.depth = 6, shrinkage = 0.1, bag.fraction = 1,
-#                    verbose = TRUE)
-# })
-# print(gbm.time)
-# Test result: 761.48 secs
-
-# dtrain[33] <- dtrain[33] == "s"
-# label <- as.numeric(dtrain[[33]])
-data <- as.matrix(dtrain[2:31])
-weight <- as.numeric(dtrain[[32]]) * testsize / length(label)
-
-sumwpos <- sum(weight * (label==1.0))
-sumwneg <- sum(weight * (label==0.0))
-print(paste("weight statistics: wpos=", sumwpos, "wneg=", sumwneg, "ratio=", sumwneg / sumwpos))
-
-xgboost.time = list()
-threads = c(1,2,4,8,16)
-for (i in 1:length(threads)){
-  thread = threads[i]
-  xgboost.time[[i]] = system.time({
-    xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
-    param <- list("objective" = "binary:logitraw",
-                  "scale_pos_weight" = sumwneg / sumwpos,
-                  "bst:eta" = 0.1,
-                  "bst:max_depth" = 6,
-                  "eval_metric" = "auc",
-                  "eval_metric" = "ams@0.15",
-                  "silent" = 1,
-                  "nthread" = thread)
-    watchlist <- list("train" = xgmat)
-    nrounds = 120
-    print ("loading data end, start to boost trees")
-    bst = xgb.train(param, xgmat, nrounds, watchlist );
-    # save out model
-    xgb.save(bst, "higgs.model")
-    print ('finish training')
-  })
-}
-
-xgboost.time
-# [[1]]
-# user  system elapsed 
-# 99.015   0.051  98.982 
-# 
-# [[2]]
-# user  system elapsed 
-# 100.268   0.317  55.473 
-# 
-# [[3]]
-# user  system elapsed 
-# 111.682   0.777  35.963 
-# 
-# [[4]]
-# user  system elapsed 
-# 149.396   1.851  32.661 
-# 
-# [[5]]
-# user  system elapsed 
-# 157.390   5.988  40.949 
-
diff --git a/ml-xgboost/demo/kaggle-higgs/speedtest.py b/ml-xgboost/demo/kaggle-higgs/speedtest.py
deleted file mode 100644
index 93672de..0000000
--- a/ml-xgboost/demo/kaggle-higgs/speedtest.py
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/usr/bin/python
-# this is the example script to use xgboost to train
-import numpy as np
-import xgboost as xgb
-from sklearn.ensemble import GradientBoostingClassifier
-import time
-test_size = 550000
-
-# path to where the data lies
-dpath = 'data'
-
-# load in training data, directly use numpy
-dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s') } )
-print ('finish loading from csv ')
-
-label  = dtrain[:,32]
-data   = dtrain[:,1:31]
-# rescale weight to make it same as test set
-weight = dtrain[:,31] * float(test_size) / len(label)
-
-sum_wpos = sum( weight[i] for i in range(len(label)) if label[i] == 1.0  )
-sum_wneg = sum( weight[i] for i in range(len(label)) if label[i] == 0.0  )
-
-# print weight statistics
-print ('weight statistics: wpos=%g, wneg=%g, ratio=%g' % ( sum_wpos, sum_wneg, sum_wneg/sum_wpos ))
-
-# construct xgboost.DMatrix from numpy array, treat -999.0 as missing value
-xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
-
-# setup parameters for xgboost
-param = {}
-# use logistic regression loss
-param['objective'] = 'binary:logitraw'
-# scale weight of positive examples
-param['scale_pos_weight'] = sum_wneg/sum_wpos
-param['bst:eta'] = 0.1
-param['bst:max_depth'] = 6
-param['eval_metric'] = 'auc'
-param['silent'] = 1
-param['nthread'] = 4
-
-plst = param.items()+[('eval_metric', 'ams@0.15')]
-
-watchlist = [ (xgmat,'train') ]
-# boost 10 trees
-num_round = 10
-print ('loading data end, start to boost trees')
-print ("training GBM from sklearn")
-tmp = time.time()
-gbm = GradientBoostingClassifier(n_estimators=num_round, max_depth=6, verbose=2)
-gbm.fit(data, label)
-print ("sklearn.GBM costs: %s seconds" % str(time.time() - tmp))
-#raw_input()
-print ("training xgboost")
-threads = [1, 2, 4, 16]
-for i in threads:
-    param['nthread'] = i
-    tmp = time.time()
-    plst = param.items()+[('eval_metric', 'ams@0.15')]
-    bst = xgb.train( plst, xgmat, num_round, watchlist );
-    print ("XGBoost with %d thread costs: %s seconds" % (i, str(time.time() - tmp)))
-
-print ('finish training')
diff --git a/ml-xgboost/demo/kaggle-otto/README.MD b/ml-xgboost/demo/kaggle-otto/README.MD
deleted file mode 100644
index bdb3b07..0000000
--- a/ml-xgboost/demo/kaggle-otto/README.MD
+++ /dev/null
@@ -1,22 +0,0 @@
-Benchmark for Otto Group Competition
-=========
-
-This is a folder containing the benchmark for the [Otto Group Competition on Kaggle](http://www.kaggle.com/c/otto-group-product-classification-challenge).
-
-## Getting started
-
-1. Put `train.csv` and `test.csv` under the `data` folder
-2. Run the script
-3. Submit the `submission.csv`
-
-The parameter `nthread` controls the number of cores to run on, please set it to suit your machine.
-
-## R-package
-
-To install the R-package of xgboost, please run
-
-```r
-devtools::install_github('tqchen/xgboost',subdir='R-package')
-```
-
-Windows users may need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
diff --git a/ml-xgboost/demo/kaggle-otto/otto_train_pred.R b/ml-xgboost/demo/kaggle-otto/otto_train_pred.R
deleted file mode 100644
index ec0f85e..0000000
--- a/ml-xgboost/demo/kaggle-otto/otto_train_pred.R
+++ /dev/null
@@ -1,43 +0,0 @@
-require(xgboost)
-require(methods)
-
-train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = F)
-test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = F)
-train = train[,-1]
-test = test[,-1]
-
-y = train[,ncol(train)]
-y = gsub('Class_','',y)
-y = as.integer(y)-1  # xgboost take features in [0,numOfClass)
-
-x = rbind(train[,-ncol(train)],test)
-x = as.matrix(x)
-x = matrix(as.numeric(x),nrow(x),ncol(x))
-trind = 1:length(y)
-teind = (nrow(train)+1):nrow(x)
-
-# Set necessary parameter
-param <- list("objective" = "multi:softprob",
-              "eval_metric" = "mlogloss",
-              "num_class" = 9,
-              "nthread" = 8)
-
-# Run Cross Validation
-cv.nrounds = 50
-bst.cv = xgb.cv(param=param, data = x[trind,], label = y, 
-                nfold = 3, nrounds=cv.nrounds)
-
-# Train the model
-nrounds = 50
-bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nrounds)
-
-# Make prediction
-pred = predict(bst,x[teind,])
-pred = matrix(pred,9,length(pred)/9)
-pred = t(pred)
-
-# Output submission
-pred = format(pred, digits=2,scientific=F) # shrink the size of submission
-pred = data.frame(1:nrow(pred),pred)
-names(pred) = c('id', paste0('Class_',1:9))
-write.csv(pred,file='submission.csv', quote=FALSE,row.names=FALSE)
diff --git a/ml-xgboost/demo/kaggle-otto/understandingXGBoostModel.Rmd b/ml-xgboost/demo/kaggle-otto/understandingXGBoostModel.Rmd
deleted file mode 100644
index b37c407..0000000
--- a/ml-xgboost/demo/kaggle-otto/understandingXGBoostModel.Rmd
+++ /dev/null
@@ -1,231 +0,0 @@
----
-title: "Understanding XGBoost Model on Otto Dataset"
-author: "Michaël Benesty"
-output:
-  rmarkdown::html_vignette:
-    css: ../../R-package/vignettes/vignette.css
-    number_sections: yes
-    toc: yes
----
-
-Introduction
-============
-
-**XGBoost** is an implementation of the famous gradient boosting algorithm. This model is often described as a *blackbox*, meaning it works well but it is not trivial to understand how. Indeed, the model is made of hundreds (thousands?) of decision trees. You may wonder how possible a human would be able to have a general view of the model?
-
-While XGBoost is known for its fast speed and accurate predictive power, it also comes with various functions to help you understand the model.
-The purpose of this RMarkdown document is to demonstrate how easily we can leverage the functions already implemented in **XGBoost R** package. Of course, everything showed below can be applied to the dataset you may have to manipulate at work or wherever!
-
-First we will prepare the **Otto** dataset and train a model, then we will generate two visualisations to get a clue of what is important to the model, finally, we will see how we can leverage these information.
-
-Preparation of the data
-=======================
-
-This part is based on the **R** tutorial example by [Tong He](https://github.com/dmlc/xgboost/blob/master/demo/kaggle-otto/otto_train_pred.R)
-
-First, let's load the packages and the dataset.
-
-```{r loading}
-require(xgboost)
-require(methods)
-require(data.table)
-require(magrittr)
-train <- fread('data/train.csv', header = T, stringsAsFactors = F)
-test <- fread('data/test.csv', header=TRUE, stringsAsFactors = F)
-```
-> `magrittr` and `data.table` are here to make the code cleaner and much more rapid.
-
-Let's explore the dataset.
-
-```{r explore}
-# Train dataset dimensions
-dim(train)
-
-# Training content
-train[1:6,1:5, with =F]
-
-# Test dataset dimensions
-dim(test)
-
-# Test content
-test[1:6,1:5, with =F]
-```
-> We only display the 6 first rows and 5 first columns for convenience
-
-Each *column* represents a feature measured by an `integer`. Each *row* is an **Otto** product.
-
-Obviously the first column (`ID`) doesn't contain any useful information.
-
-To let the algorithm focus on real stuff, we will delete it.
-
-```{r clean, results='hide'}
-# Delete ID column in training dataset
-train[, id := NULL]
-
-# Delete ID column in testing dataset
-test[, id := NULL]
-```
-
-According to its description, the **Otto** challenge is a multi class classification challenge. We need to extract the labels (here the name of the different classes) from the dataset. We only have two files (test and training), it seems logical that the training file contains the class we are looking for. Usually the labels is in the first or the last column. We already know what is in the first column, let's check the content of the last one.
-
-```{r searchLabel}
-# Check the content of the last column
-train[1:6, ncol(train), with  = F]
-# Save the name of the last column
-nameLastCol <- names(train)[ncol(train)]
-```
-
-The classes are provided as character string in the `r ncol(train)`th column called `r nameLastCol`. As you may know, **XGBoost** doesn't support anything else than numbers. So we will convert classes to `integer`. Moreover, according to the documentation, it should start at `0`.
-
-For that purpose, we will:
-
-* extract the target column
-* remove `Class_` from each class name
-* convert to `integer`
-* remove `1` to the new value
-
-```{r classToIntegers}
-# Convert from classes to numbers
-y <- train[, nameLastCol, with = F][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
-
-# Display the first 5 levels
-y[1:5]
-```
-
-We remove label column from training dataset, otherwise **XGBoost** would use it to guess the labels!
-
-```{r deleteCols, results='hide'}
-train[, nameLastCol:=NULL, with = F]
-```
-
-`data.table` is an awesome implementation of data.frame, unfortunately it is not a format supported natively by **XGBoost**. We need to convert both datasets (training and test) in `numeric` Matrix format.
-
-```{r convertToNumericMatrix}
-trainMatrix <- train[,lapply(.SD,as.numeric)] %>% as.matrix
-testMatrix <- test[,lapply(.SD,as.numeric)] %>% as.matrix
-```
-
-Model training
-==============
-
-Before the learning we will use the cross validation to evaluate the our error rate.
-
-Basically **XGBoost** will divide the training data in `nfold` parts, then **XGBoost** will retain the first part to use it as the test data and perform a training. Then it will reintegrate the first part and retain the second part, do a training and so on...
-
-You can look at the function documentation for more information.
-
-```{r crossValidation}
-numberOfClasses <- max(y) + 1
-
-param <- list("objective" = "multi:softprob",
-              "eval_metric" = "mlogloss",
-              "num_class" = numberOfClasses)
-
-cv.nrounds <- 5
-cv.nfold <- 3
-
-bst.cv = xgb.cv(param=param, data = trainMatrix, label = y,
-                nfold = cv.nfold, nrounds = cv.nrounds)
-```
-> As we can see the error rate is low on the test dataset (for a 5mn trained model).
-
-Finally, we are ready to train the real model!!!
-
-```{r modelTraining}
-nrounds = 50
-bst = xgboost(param=param, data = trainMatrix, label = y, nrounds=nrounds)
-```
-
-Model understanding
-===================
-
-Feature importance
-------------------
-
-So far, we have built a model made of **`r nrounds`** trees.
-
-To build a tree, the dataset is divided recursively several times. At the end of the process, you get groups of observations (here, these observations are properties regarding **Otto** products).
-
-Each division operation is called a *split*.
-
-Each group at each division level is called a branch and the deepest level is called a *leaf*.
-
-In the final model, these *leafs* are supposed to be as pure as possible for each tree, meaning in our case that each *leaf* should be made of one class of **Otto** product only (of course it is not true, but that's what we try to achieve in a minimum of splits).
-
-**Not all *splits* are equally important**. Basically the first *split* of a tree will have more impact on the purity that, for instance, the deepest *split*. Intuitively, we understand that the first *split* makes most of the work, and the following *splits* focus on smaller parts of the dataset which have been misclassified by the first *tree*.
-
-In the same way, in Boosting we try to optimize the misclassification at each round (it is called the *loss*). So the first *tree* will do the big work and the following trees will focus on the remaining, on the parts not correctly learned by the previous *trees*.
-
-The improvement brought by each *split* can be measured, it is the *gain*.
-
-Each *split* is done on one feature only at one value.
-
-Let's see what the model looks like.
-
-```{r modelDump}
-model <- xgb.dump(bst, with.stats = T)
-model[1:10]
-```
-> For convenience, we are displaying the first 10 lines of the model only.
-
-Clearly, it is not easy to understand what it means.
-
-Basically each line represents a *branch*, there is the *tree* ID, the feature ID, the point where it *splits*, and information regarding the next *branches* (left, right, when the row for this feature is N/A).
-
-Hopefully, **XGBoost** offers a better representation: **feature importance**.
-
-Feature importance is about averaging the *gain* of each feature for all *split* and all *trees*.
-
-Then we can use the function `xgb.plot.importance`.
-
-```{r importanceFeature, fig.align='center', fig.height=5, fig.width=10}
-# Get the feature real names
-names <- dimnames(trainMatrix)[[2]]
-
-# Compute feature importance matrix
-importance_matrix <- xgb.importance(names, model = bst)
-
-# Nice graph
-xgb.plot.importance(importance_matrix[1:10,])
-```
-
-> To make it understandable we first extract the column names from the `Matrix`.
-
-Interpretation
---------------
-
-In the feature importance above, we can see the first 10 most important features.
-
-This function gives a color to each bar. These colors represent groups of features. Basically a K-means clustering is  applied to group each feature by importance.
-
-From here you can take several actions. For instance you can remove the less important feature (feature selection process), or go deeper in the interaction between the most important features and labels.
-
-Or you can just reason about why these features are so important (in **Otto** challenge we can't go this way because there is not enough information).
-
-Tree graph
-----------
-
-Feature importance gives you feature weight information but not interaction between features.
-
-**XGBoost R** package have another useful function for that.
-
-Please, scroll on the right to see the tree.
-
-```{r treeGraph, dpi=1500, fig.align='left'}
-xgb.plot.tree(feature_names = names, model = bst, n_first_tree = 2)
-```
-
-We are just displaying the first two trees here.
-
-On simple models the first two trees may be enough. Here, it might not be the case. We can see from the size of the trees that the interaction between features is complicated.
-Besides, **XGBoost** generate `k` trees at each round for a `k`-classification problem. Therefore the two trees illustrated here are trying to classify data into different classes.
-
-Going deeper
-============
-
-There are 4 documents you may also be interested in:
-
-* [xgboostPresentation.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd): general presentation
-* [discoverYourData.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/discoverYourData.Rmd): explaining feature analysis
-* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit): use case
-* [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): very good book to have a good understanding of the model
diff --git a/ml-xgboost/demo/multiclass_classification/README.md b/ml-xgboost/demo/multiclass_classification/README.md
deleted file mode 100644
index 6554ee1..0000000
--- a/ml-xgboost/demo/multiclass_classification/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-Demonstrating how to use XGBoost accomplish Multi-Class classification task on [UCI Dermatology dataset](https://archive.ics.uci.edu/ml/datasets/Dermatology)
-
-Make sure you make xgboost python module in ../../python
-
-1. Run runexp.sh
-```bash
-./runexp.sh
-```
-
-**R version** please see the `train.R`.
diff --git a/ml-xgboost/demo/multiclass_classification/runexp.sh b/ml-xgboost/demo/multiclass_classification/runexp.sh
deleted file mode 100644
index 0af8147..0000000
--- a/ml-xgboost/demo/multiclass_classification/runexp.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-if [ -f dermatology.data ]
-then
-    echo "use existing data to run multi class classification"
-else
-    echo "getting data from uci, make sure you are connected to internet"
-    wget https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data
-fi
-python train.py
diff --git a/ml-xgboost/demo/multiclass_classification/train.R b/ml-xgboost/demo/multiclass_classification/train.R
deleted file mode 100644
index 4a07f27..0000000
--- a/ml-xgboost/demo/multiclass_classification/train.R
+++ /dev/null
@@ -1,64 +0,0 @@
-library(data.table)
-library(xgboost)
-
-if (!file.exists("./dermatology.data")) {
-  download.file(
-    "https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data",
-    "dermatology.data",
-    method = "curl"
-  )
-}
-
-df <- fread("dermatology.data", sep = ",", header = FALSE)
-
-df[, `:=`(V34 = as.integer(ifelse(V34 == "?", 0L, V34)),
-          V35 = V35 - 1L)]
-
-idx <- sample(nrow(df), size = round(0.7 * nrow(df)), replace = FALSE)
-
-train <- df[idx,]
-test <- df[-idx,]
-
-train_x <- train[, 1:34]
-train_y <- train[, V35]
-
-test_x <- test[, 1:34]
-test_y <- test[, V35]
-
-xg_train <- xgb.DMatrix(data = as.matrix(train_x), label = train_y)
-xg_test = xgb.DMatrix(as.matrix(test_x), label = test_y)
-
-params <- list(
-  objective = 'multi:softmax',
-  num_class = 6,
-  max_depth = 6,
-  nthread = 4,
-  eta = 0.1
-)
-
-watchlist = list(train = xg_train, test = xg_test)
-
-bst <- xgb.train(
-  params = params,
-  data = xg_train,
-  watchlist = watchlist,
-  nrounds = 5
-)
-
-pred <- predict(bst, xg_test)
-error_rate <- sum(pred != test_y) / length(test_y)
-print(paste("Test error using softmax =", error_rate))
-
-# do the same thing again, but output probabilities
-params$objective <- 'multi:softprob'
-bst <- xgb.train(params, xg_train, nrounds = 5, watchlist)
-
-pred_prob <- predict(bst, xg_test)
-
-pred_mat <- matrix(pred_prob, ncol = 6, byrow = TRUE)
-# validation
-# rowSums(pred_mat)
-
-pred_label <- apply(pred_mat, 1, which.max) - 1L
-error_rate = sum(pred_label != test_y) / length(test_y)
-print(paste("Test error using softprob =", error_rate))
diff --git a/ml-xgboost/demo/multiclass_classification/train.py b/ml-xgboost/demo/multiclass_classification/train.py
deleted file mode 100644
index 4dbce82..0000000
--- a/ml-xgboost/demo/multiclass_classification/train.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/python
-
-from __future__ import division
-
-import numpy as np
-import xgboost as xgb
-
-# label need to be 0 to num_class -1
-data = np.loadtxt('./dermatology.data', delimiter=',',
-        converters={33: lambda x:int(x == '?'), 34: lambda x:int(x) - 1})
-sz = data.shape
-
-train = data[:int(sz[0] * 0.7), :]
-test = data[int(sz[0] * 0.7):, :]
-
-train_X = train[:, :33]
-train_Y = train[:, 34]
-
-test_X = test[:, :33]
-test_Y = test[:, 34]
-
-xg_train = xgb.DMatrix(train_X, label=train_Y)
-xg_test = xgb.DMatrix(test_X, label=test_Y)
-# setup parameters for xgboost
-param = {}
-# use softmax multi-class classification
-param['objective'] = 'multi:softmax'
-# scale weight of positive examples
-param['eta'] = 0.1
-param['max_depth'] = 6
-param['silent'] = 1
-param['nthread'] = 4
-param['num_class'] = 6
-
-watchlist = [(xg_train, 'train'), (xg_test, 'test')]
-num_round = 5
-bst = xgb.train(param, xg_train, num_round, watchlist)
-# get prediction
-pred = bst.predict(xg_test)
-error_rate = np.sum(pred != test_Y) / test_Y.shape[0]
-print('Test error using softmax = {}'.format(error_rate))
-
-# do the same thing again, but output probabilities
-param['objective'] = 'multi:softprob'
-bst = xgb.train(param, xg_train, num_round, watchlist)
-# Note: this convention has been changed since xgboost-unity
-# get prediction, this is in 1D array, need reshape to (ndata, nclass)
-pred_prob = bst.predict(xg_test).reshape(test_Y.shape[0], 6)
-pred_label = np.argmax(pred_prob, axis=1)
-error_rate = np.sum(pred_label != test_Y) / test_Y.shape[0]
-print('Test error using softprob = {}'.format(error_rate))
diff --git a/ml-xgboost/demo/rank/README.md b/ml-xgboost/demo/rank/README.md
deleted file mode 100644
index 1f112b4..0000000
--- a/ml-xgboost/demo/rank/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-Learning to rank
-====
-XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/tutorials/input_format.rst#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank. See [parameters](../../doc/parameter.rst) for supported metrics.
-
-### Parameters
-The configuration setting is similar to the regression and binary classification setting, except user need to specify the objectives:
-
-```
-...
-objective="rank:pairwise"
-...
-```
-For more usage details please refer to the [binary classification demo](../binary_classification),
-
-Instructions
-====
-The dataset for ranking demo is from LETOR04 MQ2008 fold1.
-Before running the examples, you need to get the data by running:
-
-```
-./wgetdata.sh
-```
-
-### Command Line
-Run the example:
-```
-./runexp.sh
-```
-
-### Python
-There are two ways of doing ranking in python.
-
-Run the example using `xgboost.train`:
-```
-python rank.py
-```
-
-Run the example using `XGBRanker`:
-```
-python rank_sklearn.py
-```
diff --git a/ml-xgboost/demo/rank/mq2008.conf b/ml-xgboost/demo/rank/mq2008.conf
deleted file mode 100644
index a19758b..0000000
--- a/ml-xgboost/demo/rank/mq2008.conf
+++ /dev/null
@@ -1,28 +0,0 @@
-# General Parameters, see comment for each definition
-
-# specify objective
-objective="rank:pairwise"
-
-# Tree Booster Parameters
-# step size shrinkage
-eta = 0.1 
-# minimum loss reduction required to make a further partition
-gamma = 1.0 
-# minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 0.1
-# maximum depth of a tree
-max_depth = 6
-
-# Task parameters
-# the number of round to do boosting
-num_round = 4
-# 0 means do not save any model except the final round model
-save_period = 0 
-# The path of training data
-data = "mq2008.train" 
-# The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "mq2008.vali" 
-# The path of test data 
-test:data = "mq2008.test"      
-
-
diff --git a/ml-xgboost/demo/rank/rank.py b/ml-xgboost/demo/rank/rank.py
deleted file mode 100644
index d19b2c5..0000000
--- a/ml-xgboost/demo/rank/rank.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-from xgboost import DMatrix
-from sklearn.datasets import load_svmlight_file
-
-
-#  This script demonstrate how to do ranking with xgboost.train
-x_train, y_train = load_svmlight_file("mq2008.train")
-x_valid, y_valid = load_svmlight_file("mq2008.vali")
-x_test, y_test = load_svmlight_file("mq2008.test")
-
-group_train = []
-with open("mq2008.train.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_train.append(int(line.split("\n")[0]))
-
-group_valid = []
-with open("mq2008.vali.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_valid.append(int(line.split("\n")[0]))
-
-group_test = []
-with open("mq2008.test.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_test.append(int(line.split("\n")[0]))
-
-train_dmatrix = DMatrix(x_train, y_train)
-valid_dmatrix = DMatrix(x_valid, y_valid)
-test_dmatrix = DMatrix(x_test)
-
-train_dmatrix.set_group(group_train)
-valid_dmatrix.set_group(group_valid)
-
-params = {'objective': 'rank:ndcg', 'eta': 0.1, 'gamma': 1.0,
-          'min_child_weight': 0.1, 'max_depth': 6}
-xgb_model = xgb.train(params, train_dmatrix, num_boost_round=4,
-                      evals=[(valid_dmatrix, 'validation')])
-pred = xgb_model.predict(test_dmatrix)
diff --git a/ml-xgboost/demo/rank/rank_sklearn.py b/ml-xgboost/demo/rank/rank_sklearn.py
deleted file mode 100644
index 723b8c7..0000000
--- a/ml-xgboost/demo/rank/rank_sklearn.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-from sklearn.datasets import load_svmlight_file
-
-#  This script demonstrate how to do ranking with XGBRanker
-x_train, y_train = load_svmlight_file("mq2008.train")
-x_valid, y_valid = load_svmlight_file("mq2008.vali")
-x_test, y_test = load_svmlight_file("mq2008.test")
-
-group_train = []
-with open("mq2008.train.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_train.append(int(line.split("\n")[0]))
-
-group_valid = []
-with open("mq2008.vali.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_valid.append(int(line.split("\n")[0]))
-
-group_test = []
-with open("mq2008.test.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_test.append(int(line.split("\n")[0]))
-
-params = {'objective': 'rank:ndcg', 'learning_rate': 0.1,
-          'gamma': 1.0, 'min_child_weight': 0.1,
-          'max_depth': 6, 'n_estimators': 4}
-model = xgb.sklearn.XGBRanker(**params)
-model.fit(x_train, y_train, group_train, verbose=True,
-          eval_set=[(x_valid, y_valid)], eval_group=[group_valid])
-pred = model.predict(x_test)
diff --git a/ml-xgboost/demo/rank/runexp.sh b/ml-xgboost/demo/rank/runexp.sh
deleted file mode 100644
index 5b299f9..0000000
--- a/ml-xgboost/demo/rank/runexp.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-
-../../xgboost mq2008.conf
-
-../../xgboost mq2008.conf task=pred model_in=0004.model
diff --git a/ml-xgboost/demo/rank/trans_data.py b/ml-xgboost/demo/rank/trans_data.py
deleted file mode 100644
index 7282848..0000000
--- a/ml-xgboost/demo/rank/trans_data.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import sys
-
-def save_data(group_data,output_feature,output_group):
-    if len(group_data) == 0:
-        return
-
-    output_group.write(str(len(group_data))+"\n")
-    for data in group_data:
-        # only include nonzero features
-        feats = [ p for p in data[2:] if float(p.split(':')[1]) != 0.0 ]        
-        output_feature.write(data[0] + " " + " ".join(feats) + "\n")
-
-if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print ("Usage: python trans_data.py [Ranksvm Format Input] [Output Feature File] [Output Group File]")
-        sys.exit(0)
-
-    fi = open(sys.argv[1])
-    output_feature = open(sys.argv[2],"w")
-    output_group = open(sys.argv[3],"w")
-    
-    group_data = []
-    group = ""
-    for line in fi:
-        if not line:
-            break
-        if "#" in line:
-            line = line[:line.index("#")]
-        splits = line.strip().split(" ")
-        if splits[1] != group:
-            save_data(group_data,output_feature,output_group)
-            group_data = []
-        group = splits[1]
-        group_data.append(splits)
-
-    save_data(group_data,output_feature,output_group)
-
-    fi.close()
-    output_feature.close()
-    output_group.close()
-
diff --git a/ml-xgboost/demo/rank/wgetdata.sh b/ml-xgboost/demo/rank/wgetdata.sh
deleted file mode 100644
index 3bd5bd3..0000000
--- a/ml-xgboost/demo/rank/wgetdata.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-wget https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.rar
-unrar x MQ2008.rar
-mv -f MQ2008/Fold1/*.txt .
-
-python trans_data.py train.txt mq2008.train mq2008.train.group
-
-python trans_data.py test.txt mq2008.test mq2008.test.group
-
-python trans_data.py vali.txt mq2008.vali mq2008.vali.group
diff --git a/ml-xgboost/demo/regression/README.md b/ml-xgboost/demo/regression/README.md
deleted file mode 100644
index 0a87f37..0000000
--- a/ml-xgboost/demo/regression/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-Regression
-====
-Using XGBoost for regression is very similar to using it for binary classification. We suggest that you can refer to the [binary classification demo](../binary_classification) first. In XGBoost if we use negative log likelihood as the loss function for regression, the training procedure is same as training binary classifier of XGBoost. 
-
-### Tutorial
-The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
-```
-# General parameter
-# this is the only difference with classification, use reg:squarederror to do linear regression
-# when labels are in [0,1] we can also use reg:logistic
-objective = reg:squarederror
-...
-
-```
-
-The input format is same as binary classification, except that the label is now the target regression values. We use linear regression here, if we want use objective = reg:logistic logistic regression, the label needed to be pre-scaled into [0,1].
-
diff --git a/ml-xgboost/demo/regression/machine.conf b/ml-xgboost/demo/regression/machine.conf
deleted file mode 100644
index 14fc0f9..0000000
--- a/ml-xgboost/demo/regression/machine.conf
+++ /dev/null
@@ -1,30 +0,0 @@
-# General Parameters, see comment for each definition
-# choose the tree booster, can also change to gblinear
-booster = gbtree
-# this is the only difference with classification, use reg:squarederror to do linear classification
-# when labels are in [0,1] we can also use reg:logistic
-objective = reg:squarederror
-
-# Tree Booster Parameters
-# step size shrinkage
-eta = 1.0 
-# minimum loss reduction required to make a further partition
-gamma = 1.0 
-# minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1 
-# maximum depth of a tree
-max_depth = 3 
-
-# Task parameters
-# the number of round to do boosting
-num_round = 2
-# 0 means do not save any model except the final round model
-save_period = 0 
-# The path of training data
-data = "machine.txt.train" 
-# The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "machine.txt.test" 
-# The path of test data 
-test:data = "machine.txt.test"      
-
-
diff --git a/ml-xgboost/demo/regression/machine.data b/ml-xgboost/demo/regression/machine.data
deleted file mode 100644
index 656ed8c..0000000
--- a/ml-xgboost/demo/regression/machine.data
+++ /dev/null
@@ -1,209 +0,0 @@
-adviser,32/60,125,256,6000,256,16,128,198,199
-amdahl,470v/7,29,8000,32000,32,8,32,269,253
-amdahl,470v/7a,29,8000,32000,32,8,32,220,253
-amdahl,470v/7b,29,8000,32000,32,8,32,172,253
-amdahl,470v/7c,29,8000,16000,32,8,16,132,132
-amdahl,470v/b,26,8000,32000,64,8,32,318,290
-amdahl,580-5840,23,16000,32000,64,16,32,367,381
-amdahl,580-5850,23,16000,32000,64,16,32,489,381
-amdahl,580-5860,23,16000,64000,64,16,32,636,749
-amdahl,580-5880,23,32000,64000,128,32,64,1144,1238
-apollo,dn320,400,1000,3000,0,1,2,38,23
-apollo,dn420,400,512,3500,4,1,6,40,24
-basf,7/65,60,2000,8000,65,1,8,92,70
-basf,7/68,50,4000,16000,65,1,8,138,117
-bti,5000,350,64,64,0,1,4,10,15
-bti,8000,200,512,16000,0,4,32,35,64
-burroughs,b1955,167,524,2000,8,4,15,19,23
-burroughs,b2900,143,512,5000,0,7,32,28,29
-burroughs,b2925,143,1000,2000,0,5,16,31,22
-burroughs,b4955,110,5000,5000,142,8,64,120,124
-burroughs,b5900,143,1500,6300,0,5,32,30,35
-burroughs,b5920,143,3100,6200,0,5,20,33,39
-burroughs,b6900,143,2300,6200,0,6,64,61,40
-burroughs,b6925,110,3100,6200,0,6,64,76,45
-c.r.d,68/10-80,320,128,6000,0,1,12,23,28
-c.r.d,universe:2203t,320,512,2000,4,1,3,69,21
-c.r.d,universe:68,320,256,6000,0,1,6,33,28
-c.r.d,universe:68/05,320,256,3000,4,1,3,27,22
-c.r.d,universe:68/137,320,512,5000,4,1,5,77,28
-c.r.d,universe:68/37,320,256,5000,4,1,6,27,27
-cdc,cyber:170/750,25,1310,2620,131,12,24,274,102
-cdc,cyber:170/760,25,1310,2620,131,12,24,368,102
-cdc,cyber:170/815,50,2620,10480,30,12,24,32,74
-cdc,cyber:170/825,50,2620,10480,30,12,24,63,74
-cdc,cyber:170/835,56,5240,20970,30,12,24,106,138
-cdc,cyber:170/845,64,5240,20970,30,12,24,208,136
-cdc,omega:480-i,50,500,2000,8,1,4,20,23
-cdc,omega:480-ii,50,1000,4000,8,1,5,29,29
-cdc,omega:480-iii,50,2000,8000,8,1,5,71,44
-cambex,1636-1,50,1000,4000,8,3,5,26,30
-cambex,1636-10,50,1000,8000,8,3,5,36,41
-cambex,1641-1,50,2000,16000,8,3,5,40,74
-cambex,1641-11,50,2000,16000,8,3,6,52,74
-cambex,1651-1,50,2000,16000,8,3,6,60,74
-dec,decsys:10:1091,133,1000,12000,9,3,12,72,54
-dec,decsys:20:2060,133,1000,8000,9,3,12,72,41
-dec,microvax-1,810,512,512,8,1,1,18,18
-dec,vax:11/730,810,1000,5000,0,1,1,20,28
-dec,vax:11/750,320,512,8000,4,1,5,40,36
-dec,vax:11/780,200,512,8000,8,1,8,62,38
-dg,eclipse:c/350,700,384,8000,0,1,1,24,34
-dg,eclipse:m/600,700,256,2000,0,1,1,24,19
-dg,eclipse:mv/10000,140,1000,16000,16,1,3,138,72
-dg,eclipse:mv/4000,200,1000,8000,0,1,2,36,36
-dg,eclipse:mv/6000,110,1000,4000,16,1,2,26,30
-dg,eclipse:mv/8000,110,1000,12000,16,1,2,60,56
-dg,eclipse:mv/8000-ii,220,1000,8000,16,1,2,71,42
-formation,f4000/100,800,256,8000,0,1,4,12,34
-formation,f4000/200,800,256,8000,0,1,4,14,34
-formation,f4000/200ap,800,256,8000,0,1,4,20,34
-formation,f4000/300,800,256,8000,0,1,4,16,34
-formation,f4000/300ap,800,256,8000,0,1,4,22,34
-four-phase,2000/260,125,512,1000,0,8,20,36,19
-gould,concept:32/8705,75,2000,8000,64,1,38,144,75
-gould,concept:32/8750,75,2000,16000,64,1,38,144,113
-gould,concept:32/8780,75,2000,16000,128,1,38,259,157
-hp,3000/30,90,256,1000,0,3,10,17,18
-hp,3000/40,105,256,2000,0,3,10,26,20
-hp,3000/44,105,1000,4000,0,3,24,32,28
-hp,3000/48,105,2000,4000,8,3,19,32,33
-hp,3000/64,75,2000,8000,8,3,24,62,47
-hp,3000/88,75,3000,8000,8,3,48,64,54
-hp,3000/iii,175,256,2000,0,3,24,22,20
-harris,100,300,768,3000,0,6,24,36,23
-harris,300,300,768,3000,6,6,24,44,25
-harris,500,300,768,12000,6,6,24,50,52
-harris,600,300,768,4500,0,1,24,45,27
-harris,700,300,384,12000,6,1,24,53,50
-harris,80,300,192,768,6,6,24,36,18
-harris,800,180,768,12000,6,1,31,84,53
-honeywell,dps:6/35,330,1000,3000,0,2,4,16,23
-honeywell,dps:6/92,300,1000,4000,8,3,64,38,30
-honeywell,dps:6/96,300,1000,16000,8,2,112,38,73
-honeywell,dps:7/35,330,1000,2000,0,1,2,16,20
-honeywell,dps:7/45,330,1000,4000,0,3,6,22,25
-honeywell,dps:7/55,140,2000,4000,0,3,6,29,28
-honeywell,dps:7/65,140,2000,4000,0,4,8,40,29
-honeywell,dps:8/44,140,2000,4000,8,1,20,35,32
-honeywell,dps:8/49,140,2000,32000,32,1,20,134,175
-honeywell,dps:8/50,140,2000,8000,32,1,54,66,57
-honeywell,dps:8/52,140,2000,32000,32,1,54,141,181
-honeywell,dps:8/62,140,2000,32000,32,1,54,189,181
-honeywell,dps:8/20,140,2000,4000,8,1,20,22,32
-ibm,3033:s,57,4000,16000,1,6,12,132,82
-ibm,3033:u,57,4000,24000,64,12,16,237,171
-ibm,3081,26,16000,32000,64,16,24,465,361
-ibm,3081:d,26,16000,32000,64,8,24,465,350
-ibm,3083:b,26,8000,32000,0,8,24,277,220
-ibm,3083:e,26,8000,16000,0,8,16,185,113
-ibm,370/125-2,480,96,512,0,1,1,6,15
-ibm,370/148,203,1000,2000,0,1,5,24,21
-ibm,370/158-3,115,512,6000,16,1,6,45,35
-ibm,38/3,1100,512,1500,0,1,1,7,18
-ibm,38/4,1100,768,2000,0,1,1,13,20
-ibm,38/5,600,768,2000,0,1,1,16,20
-ibm,38/7,400,2000,4000,0,1,1,32,28
-ibm,38/8,400,4000,8000,0,1,1,32,45
-ibm,4321,900,1000,1000,0,1,2,11,18
-ibm,4331-1,900,512,1000,0,1,2,11,17
-ibm,4331-11,900,1000,4000,4,1,2,18,26
-ibm,4331-2,900,1000,4000,8,1,2,22,28
-ibm,4341,900,2000,4000,0,3,6,37,28
-ibm,4341-1,225,2000,4000,8,3,6,40,31
-ibm,4341-10,225,2000,4000,8,3,6,34,31
-ibm,4341-11,180,2000,8000,8,1,6,50,42
-ibm,4341-12,185,2000,16000,16,1,6,76,76
-ibm,4341-2,180,2000,16000,16,1,6,66,76
-ibm,4341-9,225,1000,4000,2,3,6,24,26
-ibm,4361-4,25,2000,12000,8,1,4,49,59
-ibm,4361-5,25,2000,12000,16,3,5,66,65
-ibm,4381-1,17,4000,16000,8,6,12,100,101
-ibm,4381-2,17,4000,16000,32,6,12,133,116
-ibm,8130-a,1500,768,1000,0,0,0,12,18
-ibm,8130-b,1500,768,2000,0,0,0,18,20
-ibm,8140,800,768,2000,0,0,0,20,20
-ipl,4436,50,2000,4000,0,3,6,27,30
-ipl,4443,50,2000,8000,8,3,6,45,44
-ipl,4445,50,2000,8000,8,1,6,56,44
-ipl,4446,50,2000,16000,24,1,6,70,82
-ipl,4460,50,2000,16000,24,1,6,80,82
-ipl,4480,50,8000,16000,48,1,10,136,128
-magnuson,m80/30,100,1000,8000,0,2,6,16,37
-magnuson,m80/31,100,1000,8000,24,2,6,26,46
-magnuson,m80/32,100,1000,8000,24,3,6,32,46
-magnuson,m80/42,50,2000,16000,12,3,16,45,80
-magnuson,m80/43,50,2000,16000,24,6,16,54,88
-magnuson,m80/44,50,2000,16000,24,6,16,65,88
-microdata,seq.ms/3200,150,512,4000,0,8,128,30,33
-nas,as/3000,115,2000,8000,16,1,3,50,46
-nas,as/3000-n,115,2000,4000,2,1,5,40,29
-nas,as/5000,92,2000,8000,32,1,6,62,53
-nas,as/5000-e,92,2000,8000,32,1,6,60,53
-nas,as/5000-n,92,2000,8000,4,1,6,50,41
-nas,as/6130,75,4000,16000,16,1,6,66,86
-nas,as/6150,60,4000,16000,32,1,6,86,95
-nas,as/6620,60,2000,16000,64,5,8,74,107
-nas,as/6630,60,4000,16000,64,5,8,93,117
-nas,as/6650,50,4000,16000,64,5,10,111,119
-nas,as/7000,72,4000,16000,64,8,16,143,120
-nas,as/7000-n,72,2000,8000,16,6,8,105,48
-nas,as/8040,40,8000,16000,32,8,16,214,126
-nas,as/8050,40,8000,32000,64,8,24,277,266
-nas,as/8060,35,8000,32000,64,8,24,370,270
-nas,as/9000-dpc,38,16000,32000,128,16,32,510,426
-nas,as/9000-n,48,4000,24000,32,8,24,214,151
-nas,as/9040,38,8000,32000,64,8,24,326,267
-nas,as/9060,30,16000,32000,256,16,24,510,603
-ncr,v8535:ii,112,1000,1000,0,1,4,8,19
-ncr,v8545:ii,84,1000,2000,0,1,6,12,21
-ncr,v8555:ii,56,1000,4000,0,1,6,17,26
-ncr,v8565:ii,56,2000,6000,0,1,8,21,35
-ncr,v8565:ii-e,56,2000,8000,0,1,8,24,41
-ncr,v8575:ii,56,4000,8000,0,1,8,34,47
-ncr,v8585:ii,56,4000,12000,0,1,8,42,62
-ncr,v8595:ii,56,4000,16000,0,1,8,46,78
-ncr,v8635,38,4000,8000,32,16,32,51,80
-ncr,v8650,38,4000,8000,32,16,32,116,80
-ncr,v8655,38,8000,16000,64,4,8,100,142
-ncr,v8665,38,8000,24000,160,4,8,140,281
-ncr,v8670,38,4000,16000,128,16,32,212,190
-nixdorf,8890/30,200,1000,2000,0,1,2,25,21
-nixdorf,8890/50,200,1000,4000,0,1,4,30,25
-nixdorf,8890/70,200,2000,8000,64,1,5,41,67
-perkin-elmer,3205,250,512,4000,0,1,7,25,24
-perkin-elmer,3210,250,512,4000,0,4,7,50,24
-perkin-elmer,3230,250,1000,16000,1,1,8,50,64
-prime,50-2250,160,512,4000,2,1,5,30,25
-prime,50-250-ii,160,512,2000,2,3,8,32,20
-prime,50-550-ii,160,1000,4000,8,1,14,38,29
-prime,50-750-ii,160,1000,8000,16,1,14,60,43
-prime,50-850-ii,160,2000,8000,32,1,13,109,53
-siemens,7.521,240,512,1000,8,1,3,6,19
-siemens,7.531,240,512,2000,8,1,5,11,22
-siemens,7.536,105,2000,4000,8,3,8,22,31
-siemens,7.541,105,2000,6000,16,6,16,33,41
-siemens,7.551,105,2000,8000,16,4,14,58,47
-siemens,7.561,52,4000,16000,32,4,12,130,99
-siemens,7.865-2,70,4000,12000,8,6,8,75,67
-siemens,7.870-2,59,4000,12000,32,6,12,113,81
-siemens,7.872-2,59,8000,16000,64,12,24,188,149
-siemens,7.875-2,26,8000,24000,32,8,16,173,183
-siemens,7.880-2,26,8000,32000,64,12,16,248,275
-siemens,7.881-2,26,8000,32000,128,24,32,405,382
-sperry,1100/61-h1,116,2000,8000,32,5,28,70,56
-sperry,1100/81,50,2000,32000,24,6,26,114,182
-sperry,1100/82,50,2000,32000,48,26,52,208,227
-sperry,1100/83,50,2000,32000,112,52,104,307,341
-sperry,1100/84,50,4000,32000,112,52,104,397,360
-sperry,1100/93,30,8000,64000,96,12,176,915,919
-sperry,1100/94,30,8000,64000,128,12,176,1150,978
-sperry,80/3,180,262,4000,0,1,3,12,24
-sperry,80/4,180,512,4000,0,1,3,14,24
-sperry,80/5,180,262,4000,0,1,3,18,24
-sperry,80/6,180,512,4000,0,1,3,21,24
-sperry,80/8,124,1000,8000,0,1,8,42,37
-sperry,90/80-model-3,98,1000,8000,32,2,8,46,50
-sratus,32,125,2000,8000,0,2,14,52,41
-wang,vs-100,480,512,8000,32,0,0,67,47
-wang,vs-90,480,1000,4000,0,0,0,45,25
diff --git a/ml-xgboost/demo/regression/machine.names b/ml-xgboost/demo/regression/machine.names
deleted file mode 100644
index f19a218..0000000
--- a/ml-xgboost/demo/regression/machine.names
+++ /dev/null
@@ -1,72 +0,0 @@
-1. Title: Relative CPU Performance Data 
-
-2. Source Information
-   -- Creators: Phillip Ein-Dor and Jacob Feldmesser
-     -- Ein-Dor: Faculty of Management; Tel Aviv University; Ramat-Aviv; 
-        Tel Aviv, 69978; Israel
-   -- Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779   
-   -- Date: October, 1987
- 
-3. Past Usage:
-    1. Ein-Dor and Feldmesser (CACM 4/87, pp 308-317)
-       -- Results: 
-          -- linear regression prediction of relative cpu performance
-          -- Recorded 34% average deviation from actual values 
-    2. Kibler,D. & Aha,D. (1988).  Instance-Based Prediction of
-       Real-Valued Attributes.  In Proceedings of the CSCSI (Canadian
-       AI) Conference.
-       -- Results:
-          -- instance-based prediction of relative cpu performance
-          -- similar results; no transformations required
-    - Predicted attribute: cpu relative performance (numeric)
-
-4. Relevant Information:
-   -- The estimated relative performance values were estimated by the authors
-      using a linear regression method.  See their article (pp 308-313) for
-      more details on how the relative performance values were set.
-
-5. Number of Instances: 209 
-
-6. Number of Attributes: 10 (6 predictive attributes, 2 non-predictive, 
-                             1 goal field, and the linear regression's guess)
-
-7. Attribute Information:
-   1. vendor name: 30 
-      (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, 
-       dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, 
-       microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, 
-       sratus, wang)
-   2. Model Name: many unique symbols
-   3. MYCT: machine cycle time in nanoseconds (integer)
-   4. MMIN: minimum main memory in kilobytes (integer)
-   5. MMAX: maximum main memory in kilobytes (integer)
-   6. CACH: cache memory in kilobytes (integer)
-   7. CHMIN: minimum channels in units (integer)
-   8. CHMAX: maximum channels in units (integer)
-   9. PRP: published relative performance (integer)
-  10. ERP: estimated relative performance from the original article (integer)
-
-8. Missing Attribute Values: None
-
-9. Class Distribution: the class value (PRP) is continuously valued.
-   PRP Value Range:   Number of Instances in Range:
-   0-20               31
-   21-100             121
-   101-200            27
-   201-300            13
-   301-400            7
-   401-500            4
-   501-600            2
-   above 600          4
-
-Summary Statistics:
-	   Min  Max   Mean    SD      PRP Correlation
-   MCYT:   17   1500  203.8   260.3   -0.3071
-   MMIN:   64   32000 2868.0  3878.7   0.7949
-   MMAX:   64   64000 11796.1 11726.6  0.8630
-   CACH:   0    256   25.2    40.6     0.6626
-   CHMIN:  0    52    4.7     6.8      0.6089
-   CHMAX:  0    176   18.2    26.0     0.6052
-   PRP:    6    1150  105.6   160.8    1.0000
-   ERP:   15    1238  99.3    154.8    0.9665
-
diff --git a/ml-xgboost/demo/regression/mapfeat.py b/ml-xgboost/demo/regression/mapfeat.py
deleted file mode 100644
index c747c7b..0000000
--- a/ml-xgboost/demo/regression/mapfeat.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/python
-
-fo = open( 'machine.txt', 'w' )
-cnt = 6
-fmap = {}
-for l in open( 'machine.data' ):
-    arr = l.split(',')
-    fo.write(arr[8])
-    for i in range( 0,6 ):
-        fo.write( ' %d:%s' %(i,arr[i+2]) )
-
-    if arr[0] not in fmap:
-        fmap[arr[0]] = cnt
-        cnt += 1
-
-    fo.write( ' %d:1' % fmap[arr[0]] )
-    fo.write('\n')
-
-fo.close()
-
-# create feature map for machine data
-fo = open('featmap.txt', 'w')
-# list from machine.names
-names = ['vendor','MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX', 'PRP', 'ERP' ];
-
-for i in range(0,6):
-    fo.write( '%d\t%s\tint\n' % (i, names[i+1]))
-
-for v, k in sorted( fmap.items(), key = lambda x:x[1] ):
-    fo.write( '%d\tvendor=%s\ti\n' % (k, v))
-fo.close()
diff --git a/ml-xgboost/demo/regression/mknfold.py b/ml-xgboost/demo/regression/mknfold.py
deleted file mode 100644
index a941f86..0000000
--- a/ml-xgboost/demo/regression/mknfold.py
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/python
-import sys
-import random
-
-if len(sys.argv) < 2:
-    print ('Usage:<filename> <k> [nfold = 5]')
-    exit(0)
-
-random.seed( 10 )
-
-k = int( sys.argv[2] )
-if len(sys.argv) > 3:
-    nfold = int( sys.argv[3] )
-else:
-    nfold = 5
-
-fi = open( sys.argv[1], 'r' )
-ftr = open( sys.argv[1]+'.train', 'w' )
-fte = open( sys.argv[1]+'.test', 'w' )
-for l in fi:
-    if random.randint( 1 , nfold ) == k:
-        fte.write( l )
-    else:
-        ftr.write( l )
-
-fi.close()
-ftr.close()
-fte.close()
-
diff --git a/ml-xgboost/demo/regression/runexp.sh b/ml-xgboost/demo/regression/runexp.sh
deleted file mode 100644
index 900a80c..0000000
--- a/ml-xgboost/demo/regression/runexp.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-# map the data to features. For convenience we only use 7 original attributes and encode them as features in a trivial way 
-python mapfeat.py
-# split train and test
-python mknfold.py machine.txt 1
-# training and output the models
-../../xgboost machine.conf
-# output predictions of test data
-../../xgboost machine.conf task=pred model_in=0002.model
-# print the boosters of 0002.model in dump.raw.txt
-../../xgboost machine.conf task=dump model_in=0002.model name_dump=dump.raw.txt
-# print the boosters of 0002.model in dump.nice.txt with feature map
-../../xgboost machine.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt 
-
-# cat the result
-cat dump.nice.txt
diff --git a/ml-xgboost/demo/yearpredMSD/README.md b/ml-xgboost/demo/yearpredMSD/README.md
deleted file mode 100644
index 3fe3505..0000000
--- a/ml-xgboost/demo/yearpredMSD/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-Demonstrating how to use XGBoost on [Year Prediction task of Million Song Dataset](https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD)
-
-1. Run runexp.sh
-```bash
-./runexp.sh
-```
-
-You can also use the script to prepare LIBSVM format, and run the [Distributed Version](../../multi-node).
-Note that though that normally you only need to use single machine for dataset at this scale, and use distributed version for larger scale dataset.
diff --git a/ml-xgboost/demo/yearpredMSD/csv2libsvm.py b/ml-xgboost/demo/yearpredMSD/csv2libsvm.py
deleted file mode 100644
index d7c1d15..0000000
--- a/ml-xgboost/demo/yearpredMSD/csv2libsvm.py
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/python
-import sys
-
-if len(sys.argv) < 3:
-    print 'Usage: <csv> <libsvm>'
-    print 'convert a all numerical csv to libsvm'
-
-fo = open(sys.argv[2], 'w')
-for l in open(sys.argv[1]):
-    arr = l.split(',')
-    fo.write('%s' % arr[0])
-    for i in xrange(len(arr) - 1):
-        fo.write(' %d:%s' % (i, arr[i+1]))
-fo.close()
diff --git a/ml-xgboost/demo/yearpredMSD/runexp.sh b/ml-xgboost/demo/yearpredMSD/runexp.sh
deleted file mode 100644
index 8853c3f..0000000
--- a/ml-xgboost/demo/yearpredMSD/runexp.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-if [ -f YearPredictionMSD.txt ]
-then
-    echo "use existing data to run experiment"
-else
-    echo "getting data from uci, make sure you are connected to internet"
-    wget https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip
-    unzip YearPredictionMSD.txt.zip
-fi
-echo "start making data.."
-# map feature using indicator encoding, also produce featmap.txt
-python csv2libsvm.py YearPredictionMSD.txt yearpredMSD.libsvm
-head -n 463715 yearpredMSD.libsvm > yearpredMSD.libsvm.train
-tail -n 51630 yearpredMSD.libsvm > yearpredMSD.libsvm.test
-echo "finish making the data"
-../../xgboost yearpredMSD.conf
diff --git a/ml-xgboost/demo/yearpredMSD/yearpredMSD.conf b/ml-xgboost/demo/yearpredMSD/yearpredMSD.conf
deleted file mode 100644
index 36cdf39..0000000
--- a/ml-xgboost/demo/yearpredMSD/yearpredMSD.conf
+++ /dev/null
@@ -1,29 +0,0 @@
-# General Parameters, see comment for each definition
-# choose the tree booster, can also change to gblinear
-booster = gbtree
-# this is the only difference with classification, use reg:squarederror to do linear classification
-# when labels are in [0,1] we can also use reg:logistic
-objective = reg:squarederror
-
-# Tree Booster Parameters
-# step size shrinkage
-eta = 1.0
-# minimum loss reduction required to make a further partition
-gamma = 1.0
-# minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1
-# maximum depth of a tree
-max_depth = 5
-
-base_score = 2001
-# Task parameters
-# the number of round to do boosting
-num_round = 100
-# 0 means do not save any model except the final round model
-save_period = 0
-# The path of training data
-data = "yearpredMSD.libsvm.train"
-# The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "yearpredMSD.libsvm.test"
-# The path of test data
-#test:data = "yearpredMSD.libsvm.test"
diff --git a/ml-xgboost/dev/query_contributors.py b/ml-xgboost/dev/query_contributors.py
deleted file mode 100644
index 6b7ee3c..0000000
--- a/ml-xgboost/dev/query_contributors.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Query list of all contributors and reviewers in a release"""
-
-from sh.contrib import git
-import sys
-import re
-import requests
-import json
-
-if len(sys.argv) != 5:
-    print(f'Usage: {sys.argv[0]} [starting commit/tag] [ending commit/tag] [GitHub username] [GitHub password]')
-    sys.exit(1)
-
-from_commit = sys.argv[1]
-to_commit = sys.argv[2]
-username = sys.argv[3]
-password = sys.argv[4]
-
-contributors = set()
-reviewers = set()
-
-for line in git.log(f'{from_commit}..{to_commit}', '--pretty=format:%s', '--reverse'):
-    m = re.search('\(#([0-9]+)\)$', line.rstrip())
-    if m:
-        pr_id = m.group(1)
-        print(f'PR #{pr_id}')
-
-        r = requests.get(f'https://api.github.com/repos/dmlc/xgboost/pulls/{pr_id}/commits', auth=(username, password))
-        assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-        commit_list = json.loads(r.text)
-        try:
-            contributors.update([commit['author']['login'] for commit in commit_list])
-        except TypeError:
-            contributors.update(str(input(f'Error fetching contributors for PR #{pr_id}. Enter it manually, as a space-separated list:')).split(' '))
-
-        r = requests.get(f'https://api.github.com/repos/dmlc/xgboost/pulls/{pr_id}/reviews', auth=(username, password))
-        assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-        review_list = json.loads(r.text)
-        reviewers.update([x['user']['login'] for x in review_list])
-
-        r = requests.get(f'https://api.github.com/repos/dmlc/xgboost/issues/{pr_id}/comments', auth=(username, password))
-        assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-        comment_list = json.loads(r.text)
-        reviewers.update([x['user']['login'] for x in comment_list])
-
-print('Contributors:', end='')
-for x in sorted(contributors):
-    r = requests.get(f'https://api.github.com/users/{x}', auth=(username, password))
-    assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-    user_info = json.loads(r.text)
-    if user_info['name'] is None:
-        print(f"@{x}, ", end='')
-    else:
-        print(f"{user_info['name']} (@{x}), ", end='')
-
-print('Reviewers:', end='')
-for x in sorted(reviewers):
-    r = requests.get(f'https://api.github.com/users/{x}', auth=(username, password))
-    assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-    user_info = json.loads(r.text)
-    if user_info['name'] is None:
-        print(f"@{x}, ", end='')
-    else:
-        print(f"{user_info['name']} (@{x}), ", end='')
diff --git a/ml-xgboost/dmlc-core/.editorconfig b/ml-xgboost/dmlc-core/.editorconfig
deleted file mode 100644
index 17fd3a0..0000000
--- a/ml-xgboost/dmlc-core/.editorconfig
+++ /dev/null
@@ -1,7 +0,0 @@
-root = true
-
-[*]
-charset=utf-8
-indent_style = space
-indent_size = 2
-insert_final_newline = true
\ No newline at end of file
diff --git a/ml-xgboost/dmlc-core/.gitignore b/ml-xgboost/dmlc-core/.gitignore
deleted file mode 100644
index 124d396..0000000
--- a/ml-xgboost/dmlc-core/.gitignore
+++ /dev/null
@@ -1,48 +0,0 @@
-# Compiled Object files
-*.slo
-*.lo
-*.o
-*.obj
-
-# Precompiled Headers
-*.gch
-*.pch
-
-# Compiled Dynamic libraries
-*.so
-*.dylib
-*.dll
-
-# Fortran module files
-*.mod
-
-# Compiled Static libraries
-*.lai
-*.la
-*.a
-*.lib
-
-# Executables
-*.exe
-*.out
-*.app
-*~
-config.mk
-*.pyc
-
-# Vim
-*.swp
-*.swo
-*.swn
-*.csv
-.vimrc
-
-# Emacs
-.clang_complete
-deps
-recommonmark
-build
-
-# CLion
-.idea
-cmake-build-*
diff --git a/ml-xgboost/dmlc-core/CMakeLists.txt b/ml-xgboost/dmlc-core/CMakeLists.txt
deleted file mode 100644
index 3a2855e..0000000
--- a/ml-xgboost/dmlc-core/CMakeLists.txt
+++ /dev/null
@@ -1,258 +0,0 @@
-cmake_minimum_required(VERSION 3.2)
-
-project(dmlc VERSION 0.3 LANGUAGES C CXX)
-
-if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/build/private/local_config.cmake)
-  include(${CMAKE_CURRENT_SOURCE_DIR}/build/private/local_config.cmake)
-endif()
-
-set(CMAKE_LOCAL "${PROJECT_SOURCE_DIR}/cmake")
-list(APPEND CMAKE_MODULE_PATH ${CMAKE_LOCAL}/Modules)
-
-include(CheckCXXSymbolExists)
-include(cmake/Utils.cmake)
-
-# Options
-dmlccore_option(USE_HDFS "Build with HDFS support" OFF)
-dmlccore_option(USE_AZURE "Build with AZURE support" OFF)
-dmlccore_option(USE_S3 "Build with S3 support" OFF)
-dmlccore_option(USE_OPENMP "Build with OpenMP" ON)
-dmlccore_option(USE_CXX14_IF_AVAILABLE "Build with C++14 if the compiler supports it" OFF)
-dmlccore_option(GOOGLE_TEST "Build google tests" OFF)
-dmlccore_option(INSTALL_DOCUMENTATION "Install documentation" OFF)
-dmlccore_option(DMLC_FORCE_SHARED_CRT "Build with dynamic CRT on Windows (/MD)" OFF)
-dmlccore_option(DMLC_USE_SANITIZER "Use santizer flags; to specify a custom path for sanitizers, set this variable a value that's not ON or OFF" OFF)
-set(DMLC_ENABLED_SANITIZERS "address" "leak" CACHE STRING
-  "Semicolon separated list of sanitizer names. E.g 'address;leak'. Supported sanitizers are
-  address, leak and thread.")
-
-include(CheckCXXCompilerFlag)
-if(USE_CXX14_IF_AVAILABLE)
-  check_cxx_compiler_flag("-std=c++14" SUPPORT_CXX14)
-endif()
-if(SUPPORT_CXX14)
-  set(CMAKE_CXX_STANDARD 14)
-else()
-  set(CMAKE_CXX_STANDARD 11)
-endif()
-set(CMAKE_CXX_EXTENSIONS OFF)
-
-FILE(GLOB SOURCE "src/*.cc")
-FILE(GLOB_RECURSE SOURCE_INCLUDE "include/*")
-list(APPEND SOURCE ${SOURCE_INCLUDE})
-list(APPEND SOURCE "src/io/line_split.cc")
-list(APPEND SOURCE "src/io/recordio_split.cc")
-list(APPEND SOURCE "src/io/indexed_recordio_split.cc")
-list(APPEND SOURCE "src/io/input_split_base.cc")
-list(APPEND SOURCE "src/io/filesys.cc")
-list(APPEND SOURCE "src/io/local_filesys.cc")
-if(USE_HDFS)
-  list(APPEND SOURCE "src/io/hdfs_filesys.cc")
-endif()
-if(USE_S3)
-  list(APPEND SOURCE "src/io/s3_filesys.cc")
-endif()
-if(USE_AZURE)
-  list(APPEND SOURCE "src/io/azure_filesys.cc")
-endif()
-
-add_library(dmlc ${SOURCE})
-
-# Sanitizer
-if (DMLC_USE_SANITIZER)
-  # Older CMake versions have had troubles with Sanitizer
-  cmake_minimum_required(VERSION 3.12)
-  include(cmake/Sanitizer.cmake)
-  enable_sanitizers("${DMLC_ENABLED_SANITIZERS}")
-endif (DMLC_USE_SANITIZER)
-
-# HDFS configurations
-if(USE_HDFS)
-  find_package(HDFS REQUIRED)
-  find_package(JNI REQUIRED)
-  target_include_directories(dmlc PRIVATE ${HDFS_INCLUDE_DIR})
-  target_link_libraries(dmlc PRIVATE ${HDFS_STATIC_LIB} ${JAVA_JVM_LIBRARY})
-  target_compile_definitions(dmlc PRIVATE -DDMLC_USE_HDFS=1)
-else()
-  target_compile_definitions(dmlc PRIVATE -DDMLC_USE_HDFS=0)
-endif()
-# S3 configurations
-if(USE_S3)
-  find_package(CURL REQUIRED)
-  target_include_directories(dmlc SYSTEM PRIVATE ${CURL_INCLUDE_DIR})
-  target_link_libraries(dmlc PRIVATE ${CURL_LIBRARY})
-
-  find_package(OpenSSL REQUIRED)
-  target_include_directories(dmlc SYSTEM PRIVATE ${OPENSSL_INCLUDE_DIR})
-  target_link_libraries(dmlc PRIVATE ${OPENSSL_LIBRARY} ${OPENSSL_LIBRARIES} ${OPENSSL_CRYPTO_LIBRARY})
-  target_compile_definitions(dmlc PRIVATE -DDMLC_USE_S3=1)
-else()
-  target_compile_definitions(dmlc PRIVATE -DDMLC_USE_S3=0)
-endif()
-# Azure configurations
-if(USE_AZURE)
-  target_compile_definitions(dmlc PRIVATE -DDMLC_USE_AZURE=1)
-else()
-  target_compile_definitions(dmlc PRIVATE -DDMLC_USE_AZURE=0)
-endif()
-
-# OpenMP
-if(USE_OPENMP)
-  if(APPLE AND (NOT CMAKE_COMPILER_IS_GNUCC))
-    # Require CMake 3.16+ for Mac to ensure that OpenMP can be located
-    # (Exception: it's okay if Homebrew GCC is used)
-    cmake_minimum_required(VERSION 3.16)
-  endif()
-
-  find_package(OpenMP REQUIRED)
-
-  # For CMake < 3.9, we need to make target OpenMP::OpenMP_CXX ourselves
-  if(NOT TARGET OpenMP::OpenMP_CXX)
-    find_package(Threads REQUIRED)
-    add_library(OpenMP::OpenMP_CXX IMPORTED INTERFACE)
-    set_property(TARGET OpenMP::OpenMP_CXX
-                 PROPERTY INTERFACE_COMPILE_OPTIONS ${OpenMP_CXX_FLAGS})
-    set_property(TARGET OpenMP::OpenMP_CXX
-                 PROPERTY INTERFACE_LINK_LIBRARIES ${OpenMP_CXX_FLAGS} Threads::Threads)
-  endif()
-  target_link_libraries(dmlc PRIVATE OpenMP::OpenMP_CXX)
-endif()
-
-if(WIN32 AND (NOT MSVC))  # On Windows, link Shlwapi.lib for non-MSVC compilers
-  target_link_libraries(dmlc PRIVATE Shlwapi)
-endif()
-
-# Check location of clock_gettime; if it's in librt, link it
-include(CheckLibraryExists)
-CHECK_LIBRARY_EXISTS(rt clock_gettime "time.h" HAVE_CLOCK_GETTIME_IN_LIBRT)
-if(HAVE_CLOCK_GETTIME_IN_LIBRT)
-  target_link_libraries(dmlc PRIVATE rt)
-endif()
-
-# Check headers and symbols
-include(CheckSymbolExists)
-include(CheckIncludeFile)
-include(CheckIncludeFileCXX)
-check_symbol_exists(fopen64 stdio.h DMLC_FOPEN_64_PRESENT)
-check_include_file_cxx(cxxabi.h DMLC_CXXABI_H_PRESENT)
-check_symbol_exists(nanosleep time.h DMLC_NANOSLEEP_PRESENT)
-
-# Check existence of backtrace(3)
-find_package(Backtrace)
-if(Backtrace_FOUND)
-  set(DMLC_EXECINFO_H_PRESENT 1)
-  set(DMLC_EXECINFO_H ${Backtrace_HEADER})
-  target_include_directories(dmlc SYSTEM PRIVATE ${Backtrace_INCLUDE_DIRS})
-  target_link_libraries(dmlc PRIVATE ${Backtrace_LIBRARIES})
-else()
-  set(DMLC_EXECINFO_H_PRESENT 0)
-endif()
-
-# Check endianness
-include(TestBigEndian)
-test_big_endian(BIG_ENDIAN)
-if(BIG_ENDIAN)
-  set(DMLC_CMAKE_LITTLE_ENDIAN 0)
-else()
-  set(DMLC_CMAKE_LITTLE_ENDIAN 1)
-endif()
-
-message(STATUS "${CMAKE_LOCAL}/build_config.h.in -> include/dmlc/build_config.h")
-configure_file("cmake/build_config.h.in" "include/dmlc/build_config.h")
-
-target_include_directories(dmlc PUBLIC
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>
-  $<INSTALL_INTERFACE:${CMAKE_INSTALL_PREFIX}/include>)
-target_compile_definitions(dmlc PRIVATE -D_XOPEN_SOURCE=700
-  -D_POSIX_SOURCE -D_POSIX_C_SOURCE=200809L -D_DARWIN_C_SOURCE)
-# Older stdc++ enable c++11 items
-target_compile_definitions(dmlc PUBLIC -D__USE_XOPEN2K8)
-# DMLC_CORE_USE_CMAKE macro constant indicates the use of CMake
-target_compile_definitions(dmlc PUBLIC -DDMLC_CORE_USE_CMAKE)
-
-# compiler flags
-if(MSVC)
-  target_compile_definitions(dmlc PUBLIC -DDMLC_USE_CXX11=1)
-  if(NOT BUILD_SHARED_LIBS AND NOT DMLC_FORCE_SHARED_CRT)
-    foreach(flag_var
-          CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
-          CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
-      if(${flag_var} MATCHES "/MD")
-        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
-      endif(${flag_var} MATCHES "/MD")
-    endforeach(flag_var)
-  endif()
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc")
-else()
-  set(CMAKE_POSITION_INDEPENDENT_CODE ON)
-  check_cxx_compiler_flag("-msse2" SUPPORT_MSSE2)
-  if(SUPPORT_MSSE2)
-    target_compile_options(dmlc PRIVATE -mavx)
-  endif()
-  target_compile_options(dmlc PRIVATE -Wall -Wno-unknown-pragmas -fPIC)
-  if(CMAKE_BUILD_TYPE STREQUAL "DEBUG" OR CMAKE_BUILD_TYPE STREQUAL "Debug")
-    target_compile_options(dmlc PRIVATE -g -O0)
-  else()
-    target_compile_options(dmlc PRIVATE -O3)
-  endif()
-
-  target_compile_definitions(dmlc PUBLIC -DDMLC_USE_CXX11=1)
-  if(SUPPORT_CXX14)
-    target_compile_definitions(dmlc PUBLIC -DDMLC_USE_CXX14=1)
-  endif()
-endif()
-
-
-include(GNUInstallDirs)
-# ---[ Install Includes
-install(DIRECTORY ${PROJECT_SOURCE_DIR}/include/dmlc
-        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
-install(FILES ${CMAKE_CURRENT_BINARY_DIR}/include/dmlc/build_config.h
-        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/dmlc)
-
-# ---[ Install the archive static lib and header files
-install(TARGETS dmlc
-  EXPORT DMLCTargets
-  ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR})
-install(EXPORT DMLCTargets
-  FILE DMLCTargets.cmake
-  NAMESPACE dmlc::
-  EXPORT_LINK_INTERFACE_LIBRARIES
-  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/dmlc)
-
-# ---[ Install documentation
-if(INSTALL_DOCUMENTATION)
-  install(DIRECTORY doc DESTINATION ${CMAKE_INSTALL_DATADIR})
-endif()
-
-# ---[ Package configurations
-include(CMakePackageConfigHelpers)
-configure_package_config_file(
-  ${CMAKE_LOCAL}/dmlc-config.cmake.in
-  ${CMAKE_BINARY_DIR}/cmake/dmlc-config.cmake
-  INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/dmlc)
-write_basic_package_version_file(
-  ${CMAKE_BINARY_DIR}/cmake/dmlc-config-version.cmake
-  VERSION ${DMLC_VERSION}
-  COMPATIBILITY AnyNewerVersion)
-install(
-  FILES
-  ${CMAKE_BINARY_DIR}/cmake/dmlc-config.cmake
-  ${CMAKE_BINARY_DIR}/cmake/dmlc-config-version.cmake
-  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/dmlc)
-
-# ---[ Linter target
-if(MSVC)
-  find_package(PythonInterp)
-  set(PYTHON_EXECUTABLE ${PYTHON_EXECUTABLE} CACHE FILEPATH "Path to the python 2.x executable")
-endif()
-set(LINT_DIRS include src scripts)
-add_custom_target(dmlc_lint COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE}  -DPROJECT_SOURCE_DIR=${PROJECT_SOURCE_DIR} -DLINT_DIRS=${LINT_DIRS} -DPROJECT_NAME=dmlc -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
-
-# Setup testing
-if(GOOGLE_TEST)
-  include(CTest)
-  add_subdirectory(test/unittest)
-endif()
diff --git a/ml-xgboost/dmlc-core/LICENSE b/ml-xgboost/dmlc-core/LICENSE
deleted file mode 100644
index 8dada3e..0000000
--- a/ml-xgboost/dmlc-core/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
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-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
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-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
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-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
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-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
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-      Work (including but not limited to damages for loss of goodwill,
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-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
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-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
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-   END OF TERMS AND CONDITIONS
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-   APPENDIX: How to apply the Apache License to your work.
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-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "{}"
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-      file or class name and description of purpose be included on the
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-   Copyright {yyyy} {name of copyright owner}
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-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
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-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/ml-xgboost/dmlc-core/Makefile b/ml-xgboost/dmlc-core/Makefile
deleted file mode 100644
index d920f58..0000000
--- a/ml-xgboost/dmlc-core/Makefile
+++ /dev/null
@@ -1,108 +0,0 @@
-ifndef config
-	ifneq ("$(wildcard ./config.mk)","")
-		config = config.mk
-	else
-		config = make/config.mk
-	endif
-endif
-# use customized config file
-include $(config)
-include make/dmlc.mk
-
-NOLINT_FILES = --exclude_path include/dmlc/concurrentqueue.h include/dmlc/blockingconcurrentqueue.h
-
-# this is the common build script for dmlc lib
-export LDFLAGS= -pthread -lm
-export CFLAGS = -O3 -Wall -Wno-unknown-pragmas -Iinclude
-ifeq ($(USE_GNU11), 1)
-	CFLAGS += -std=gnu++11
-else
-	CFLAGS += -std=c++0x
-endif
-LDFLAGS+= $(DMLC_LDFLAGS) $(ADD_LDFLAGS)
-CFLAGS+= $(DMLC_CFLAGS) $(ADD_CFLAGS)
-
-ifndef USE_SSE
-	USE_SSE = 1
-endif
-
-# ifeq ($(USE_SSE), 1)
-# 	CFLAGS += -mavx
-# endif
-
-
-ifdef DEPS_PATH
-CFLAGS+= -I$(DEPS_PATH)/include
-LDFLAGS+= -L$(DEPS_PATH)/lib
-endif
-
-.PHONY: clean all test lint doc example pylint
-
-OBJ=line_split.o indexed_recordio_split.o recordio_split.o input_split_base.o io.o filesys.o local_filesys.o data.o recordio.o config.o
-
-ifeq ($(USE_HDFS), 1)
-	OBJ += hdfs_filesys.o
-endif
-
-ifeq ($(USE_S3), 1)
-	OBJ += s3_filesys.o
-endif
-
-ifeq ($(USE_AZURE), 1)
-	OBJ += azure_filesys.o
-endif
-
-ifndef LINT_LANG
-	LINT_LANG="all"
-endif
-
-
-ALIB=libdmlc.a
-all: $(ALIB) test
-
-include test/dmlc_test.mk
-include example/dmlc_example.mk
-
-ifeq ($(BUILD_TEST), 1)
-test: $(ALL_TEST)
-endif
-
-example: $(ALL_EXAMPLE)
-
-line_split.o: src/io/line_split.cc
-recordio_split.o: src/io/recordio_split.cc
-indexed_recordio_split.o: src/io/indexed_recordio_split.cc
-input_split_base.o: src/io/input_split_base.cc
-filesys.o: src/io/filesys.cc
-hdfs_filesys.o: src/io/hdfs_filesys.cc
-s3_filesys.o: src/io/s3_filesys.cc
-azure_filesys.o: src/io/azure_filesys.cc
-local_filesys.o: src/io/local_filesys.cc
-io.o: src/io.cc
-data.o: src/data.cc
-recordio.o: src/recordio.cc
-config.o: src/config.cc
-
-libdmlc.a: $(OBJ)
-
-
-$(BIN) :
-	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a,  $^) $(LDFLAGS)
-
-$(OBJ) :
-	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c %.cc, $^) )
-
-$(ALIB):
-	$(AR) cr $@ $+
-
-lint:
-	scripts/lint.py dmlc ${LINT_LANG} include src scripts $(NOLINT_FILES)
-
-pylint:
-	scripts/lint.py dmlc ${LINT_LANG} tracker/dmlc_tracker
-
-doxygen:
-	doxygen doc/Doxyfile
-
-clean:
-	$(RM) $(OBJ) $(BIN) $(ALIB) $(ALL_TEST) $(ALL_TEST_OBJ) *~ src/*~ src/*/*~ include/dmlc/*~ test/*~
diff --git a/ml-xgboost/dmlc-core/README.md b/ml-xgboost/dmlc-core/README.md
deleted file mode 100644
index e073d75..0000000
--- a/ml-xgboost/dmlc-core/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-Distributed Machine Learning Common Codebase
-============================================
-
-[![Build Status](https://github.com/dmlc/dmlc-core/workflows/continuous%20build/badge.svg)](https://github.com/dmlc/dmlc-core/actions)
-[![Documentation Status](https://readthedocs.org/projects/dmlc-core/badge/?version=latest)](http://dmlc-core.readthedocs.org/en/latest/)
-[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
-
-
-DMLC-Core is the backbone library to support all DMLC projects, offers the bricks to build efficient and scalable distributed machine learning libraries.
-
-Developer Channel [![Join the chat at https://gitter.im/dmlc/dmlc-core](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/dmlc-core?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-
-
-What's New
-----------
-* [Note on Parameter Module for Machine Learning](http://dmlc-core.readthedocs.org/en/latest/parameter.html)
-
-
-Contents
---------
-* [Documentation and Tutorials](http://dmlc-core.readthedocs.org/en/latest/)
-* [Contributing](#contributing)
-
-Known Issues
-------------
-* RecordIO format is not portable across different processor endians. So it is not possible to save RecordIO file on a x86 machine and then load it on a SPARC machine, because x86 is little endian while SPARC is big endian.
-
-
-Contributing
-------------
-
-Contributing to dmlc-core is welcomed! dmlc-core follows google's C style guide. If you are interested in contributing, take a look at [feature wishlist](https://github.com/dmlc/dmlc-core/labels/feature%20wishlist) and open a new issue if you like to add something.
-
-* Use of c++11 is allowed, given that the code is macro guarded with ```DMLC_USE_CXX11```
-* Try to introduce minimum dependency when possible
-
-### CheckList before submit code
-* Type ```make lint``` and fix all the style problems.
-* Type ```make doc``` and fix all the warnings.
-
-NOTE
-----
-deps:
-
-libcurl4-openssl-dev
diff --git a/ml-xgboost/dmlc-core/appveyor.yml b/ml-xgboost/dmlc-core/appveyor.yml
deleted file mode 100644
index 54ba43f..0000000
--- a/ml-xgboost/dmlc-core/appveyor.yml
+++ /dev/null
@@ -1,134 +0,0 @@
-environment:
-    matrix:
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2019
-          target: msvc
-          ver: 2019
-          generator: "Visual Studio 16 2019"
-          configuration: Debug
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2019
-          target: msvc
-          ver: 2019
-          generator: "Visual Studio 16 2019"
-          configuration: Release
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2015
-          target: mingw32
-          generator: "MinGW Makefiles"
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2017
-          target: cygwin
-          generator: "Unix Makefiles"
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2015
-          target: mingw
-          generator: "MinGW Makefiles"
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2017
-          target: msys2
-          generator: "Unix Makefiles"
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2015
-          target: msvc
-          ver: 2013
-          generator: "Visual Studio 12 2013 Win64"
-          configuration: Release
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2015
-          target: msvc
-          ver: 2015
-          generator: "Visual Studio 14 2015 Win64"
-          configuration: Debug
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2015
-          target: msvc
-          ver: 2015
-          generator: "Visual Studio 14 2015 Win64"
-          configuration: Release
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2017
-          target: msvc
-          ver: 2017
-          generator: "Visual Studio 15 2017 Win64"
-          configuration: Debug
-        - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2017
-          target: msvc
-          ver: 2017
-          generator: "Visual Studio 15 2017 Win64"
-          configuration: Release
-
-matrix:
-    fast_finish: true
-
-platform:
-    - x64
-
-install:
-    - git submodule update --init --recursive
-    # Set PATH
-    - if /i "%target%" == "msys2" set PATH=C:\msys64\mingw64\bin;C:\msys64\usr\bin;%PATH%
-    - if /i "%target%" == "mingw32" set PATH=C:\mingw-w64\i686-6.3.0-posix-dwarf-rt_v5-rev1\mingw32\bin;%PATH:C:\Program Files\Git\usr\bin;=%
-    - if /i "%target%" == "mingw" set PATH=C:\MinGW-w64\x86_64-7.3.0-posix-seh-rt_v5-rev0\mingw64\bin;%PATH:C:\Program Files\Git\usr\bin;=%
-    - if /i "%target%" == "cygwin" set PATH=C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%
-    # Install packages and show information
-    - if /i "%target%" == "msys2" (
-        gcc -v
-      )
-    - if /i "%target%" == "mingw" (
-        gcc -v
-      )
-    - if /i "%target%" == "cygwin" (
-        gcc -v
-      )
-
-build_script:
-    - cd %APPVEYOR_BUILD_FOLDER%
-    - if /i "%target%" == "msvc" (
-        mkdir build_msvc%ver% &&
-        cd build_msvc%ver% &&
-        if /i "%generator%" == "Visual Studio 12 2013 Win64" (
-          cmake .. -G"%generator%" -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_CONFIGURATION_TYPES="Release;Debug;" &&
-          msbuild dmlc.sln
-        ) else if /i "%generator%" == "Visual Studio 16 2019" (
-          cmake .. -G"%generator%" -A x64 -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_CONFIGURATION_TYPES="Release;Debug;" -DGOOGLE_TEST=ON &&
-          msbuild dmlc.sln
-        ) else (
-          cmake .. -G"%generator%" -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_CONFIGURATION_TYPES="Release;Debug;" -DGOOGLE_TEST=ON &&
-          msbuild dmlc.sln
-        )
-      )
-    - if /i "%target%" == "msys2" (
-        mkdir build_msys2 &&
-        cd build_msys2 &&
-        cmake .. -G"%generator%" -DCMAKE_VERBOSE_MAKEFILE=ON -DGOOGLE_TEST=ON &&
-        cmake --build . -- -j2
-      )
-    - if /i "%target%" == "mingw32" (
-        mkdir build_mingw32 &&
-        cd build_mingw32 &&
-        cmake .. -G"%generator%" -DCMAKE_VERBOSE_MAKEFILE=ON -DGOOGLE_TEST=ON &&
-        cmake --build . -- -j2
-      )
-    - if /i "%target%" == "mingw" (
-        mkdir build_mingw &&
-        cd build_mingw &&
-        cmake .. -G"%generator%" -DCMAKE_VERBOSE_MAKEFILE=ON -DGOOGLE_TEST=ON &&
-        cmake --build . -- -j2
-      )
-    - if /i "%target%" == "cygwin" (
-        mkdir build_cygwin &&
-        cd build_cygwin &&
-        cmake .. -G"%generator%" -DCMAKE_VERBOSE_MAKEFILE=ON -DGOOGLE_TEST=ON -DOpenMP_gomp_LIBRARY:FILEPATH=-lgomp &&
-        cmake --build . -- -j2
-      )
-
-test_script:
-    - cd %APPVEYOR_BUILD_FOLDER%
-    - if /i "%target%" == "msvc" (
-        if /i not "%generator%" == "Visual Studio 12 2013 Win64" (
-          .\build_msvc%ver%\test\unittest\%configuration%\dmlc_unit_tests.exe
-        )
-      )
-    - if /i "%target%" == "msys2" (
-        .\build_msys2\test\unittest\dmlc_unit_tests.exe
-      )
-    - if /i "%target%" == "mingw32" (
-        .\build_mingw32\test\unittest\dmlc_unit_tests.exe
-      )
-    - if /i "%target%" == "mingw" (
-        .\build_mingw\test\unittest\dmlc_unit_tests.exe
-      )
-    - if /i "%target%" == "cygwin" (
-        .\build_cygwin\test\unittest\dmlc_unit_tests.exe
-      )
diff --git a/ml-xgboost/dmlc-core/cmake/Modules/FindASan.cmake b/ml-xgboost/dmlc-core/cmake/Modules/FindASan.cmake
deleted file mode 100644
index 18f5cde..0000000
--- a/ml-xgboost/dmlc-core/cmake/Modules/FindASan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(ASan_LIB_NAME ASan)
-
-find_library(ASan_LIBRARY
-  NAMES libasan.so libasan.so.4 libasan.so.3 libasan.so.2 libasan.so.1 libasan.so.0
-  PATHS ${DMLC_USE_SANITIZER} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(ASan DEFAULT_MSG
-  ASan_LIBRARY)
-
-mark_as_advanced(
-  ASan_LIBRARY
-  ASan_LIB_NAME)
diff --git a/ml-xgboost/dmlc-core/cmake/Modules/FindHDFS.cmake b/ml-xgboost/dmlc-core/cmake/Modules/FindHDFS.cmake
deleted file mode 100644
index dcc547f..0000000
--- a/ml-xgboost/dmlc-core/cmake/Modules/FindHDFS.cmake
+++ /dev/null
@@ -1,72 +0,0 @@
-# DerivedFrom: https://github.com/cloudera/Impala/blob/cdh5-trunk/cmake_modules/FindHDFS.cmake
-# - Find HDFS (hdfs.h and libhdfs.so)
-# This module defines
-#  Hadoop_VERSION, version string of ant if found
-#  HDFS_INCLUDE_DIR, directory containing hdfs.h
-#  HDFS_LIBRARIES, location of libhdfs.so
-#  HDFS_FOUND, whether HDFS is found.
-#  hdfs_static, imported static hdfs library.
-
-exec_program(hadoop ARGS version OUTPUT_VARIABLE Hadoop_VERSION
-             RETURN_VALUE Hadoop_RETURN)
-
-# currently only looking in HADOOP_HOME
-find_path(HDFS_INCLUDE_DIR hdfs.h PATHS
-  $ENV{HADOOP_HDFS_HOME}/include/
-  # make sure we don't accidentally pick up a different version
-  NO_DEFAULT_PATH
-)
-
-if ("${CMAKE_SIZEOF_VOID_P}" STREQUAL "8")
-  set(arch_hint "x64")
-elseif ("$ENV{LIB}" MATCHES "(amd64|ia64)")
-  set(arch_hint "x64")
-else ()
-  set(arch_hint "x86")
-endif()
-
-message(STATUS "Architecture: ${arch_hint}")
-
-if ("${arch_hint}" STREQUAL "x64")
-  set(HDFS_LIB_PATHS $ENV{HADOOP_HDFS_HOME}/lib/native)
-else ()
-  set(HDFS_LIB_PATHS $ENV{HADOOP_HDFS_HOME}/lib/native)
-endif ()
-
-message(STATUS "HDFS_LIB_PATHS: ${HDFS_LIB_PATHS}")
-
-find_library(HDFS_LIB NAMES hdfs PATHS
-  ${HDFS_LIB_PATHS}
-  # make sure we don't accidentally pick up a different version
-  NO_DEFAULT_PATH
-)
-
-if (HDFS_LIB)
-  set(HDFS_FOUND TRUE)
-  set(HDFS_LIBRARIES ${HDFS_LIB})
-  set(HDFS_STATIC_LIB ${HDFS_LIB_PATHS}/${CMAKE_STATIC_LIBRARY_PREFIX}hdfs${CMAKE_STATIC_LIBRARY_SUFFIX})
-
-  add_library(hdfs_static STATIC IMPORTED)
-  set_target_properties(hdfs_static PROPERTIES IMPORTED_LOCATION ${HDFS_STATIC_LIB})
-
-else ()
-  set(HDFS_FOUND FALSE)
-endif ()
-
-if (HDFS_FOUND)
-  if (NOT HDFS_FIND_QUIETLY)
-    message(STATUS "${Hadoop_VERSION}")
-    message(STATUS "HDFS_INCLUDE_DIR: ${HDFS_INCLUDE_DIR}")
-    message(STATUS "HDFS_LIBRARIES: ${HDFS_LIBRARIES}")
-    message(STATUS "hdfs_static: ${HDFS_STATIC_LIB}")
-  endif ()
-else ()
-  message(FATAL_ERROR "HDFS includes and libraries NOT found."
-    "(${HDFS_INCLUDE_DIR}, ${HDFS_LIB})")
-endif ()
-
-mark_as_advanced(
-  HDFS_LIBRARIES
-  HDFS_INCLUDE_DIR
-  hdfs_static
-)
diff --git a/ml-xgboost/dmlc-core/cmake/Modules/FindLSan.cmake b/ml-xgboost/dmlc-core/cmake/Modules/FindLSan.cmake
deleted file mode 100644
index 0b2e871..0000000
--- a/ml-xgboost/dmlc-core/cmake/Modules/FindLSan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(LSan_LIB_NAME lsan)
-
-find_library(LSan_LIBRARY
-  NAMES liblsan.so liblsan.so.0 liblsan.so.0.0.0
-  PATHS ${DMLC_USE_SANITIZER} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(LSan DEFAULT_MSG
-  LSan_LIBRARY)
-
-mark_as_advanced(
-  LSan_LIBRARY
-  LSan_LIB_NAME)
diff --git a/ml-xgboost/dmlc-core/cmake/Modules/FindTSan.cmake b/ml-xgboost/dmlc-core/cmake/Modules/FindTSan.cmake
deleted file mode 100644
index 2403e91..0000000
--- a/ml-xgboost/dmlc-core/cmake/Modules/FindTSan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(TSan_LIB_NAME tsan)
-
-find_library(TSan_LIBRARY
-  NAMES libtsan.so libtsan.so.0 libtsan.so.0.0.0
-  PATHS ${DMLC_USE_SANITIZER} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(TSan DEFAULT_MSG
-  TSan_LIBRARY)
-
-mark_as_advanced(
-  TSan_LIBRARY
-  TSan_LIB_NAME)
diff --git a/ml-xgboost/dmlc-core/cmake/Modules/FindUBSan.cmake b/ml-xgboost/dmlc-core/cmake/Modules/FindUBSan.cmake
deleted file mode 100644
index e1b72eb..0000000
--- a/ml-xgboost/dmlc-core/cmake/Modules/FindUBSan.cmake
+++ /dev/null
@@ -1,13 +0,0 @@
-set(UBSan_LIB_NAME UBSan)
-
-find_library(UBSan_LIBRARY
-  NAMES libubsan.so libubsan.so.5 libubsan.so.4 libubsan.so.3 libubsan.so.2 libubsan.so.1 libubsan.so.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(UBSan DEFAULT_MSG
-  UBSan_LIBRARY)
-
-mark_as_advanced(
-  UBSan_LIBRARY
-  UBSan_LIB_NAME)
diff --git a/ml-xgboost/dmlc-core/cmake/Sanitizer.cmake b/ml-xgboost/dmlc-core/cmake/Sanitizer.cmake
deleted file mode 100644
index c1afb14..0000000
--- a/ml-xgboost/dmlc-core/cmake/Sanitizer.cmake
+++ /dev/null
@@ -1,63 +0,0 @@
-# Set appropriate compiler and linker flags for sanitizers.
-#
-# Usage of this module:
-#  enable_sanitizers("address;leak")
-
-# Add flags
-macro(enable_sanitizer sanitizer)
-  if(${sanitizer} MATCHES "address")
-    find_package(ASan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=address")
-    link_libraries(${ASan_LIBRARY})
-
-  elseif(${sanitizer} MATCHES "thread")
-    find_package(TSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=thread")
-    link_libraries(${TSan_LIBRARY})
-
-  elseif(${sanitizer} MATCHES "leak")
-    find_package(LSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=leak")
-    link_libraries(${LSan_LIBRARY})
-
-  elseif(${sanitizer} MATCHES "undefined")
-    find_package(UBSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=undefined -fno-sanitize-recover=undefined")
-    link_libraries(${UBSan_LIBRARY})
-
-  else()
-    message(FATAL_ERROR "Santizer ${sanitizer} not supported.")
-  endif()
-endmacro()
-
-macro(enable_sanitizers SANITIZERS)
-  # Check sanitizers compatibility.
-  # Idealy, we should use if(san IN_LIST SANITIZERS) ... endif()
-  # But I haven't figure out how to make it work.
-  foreach ( _san ${SANITIZERS} )
-    string(TOLOWER ${_san} _san)
-    if (_san MATCHES "thread")
-      if (${_use_other_sanitizers})
-        message(FATAL_ERROR
-          "thread sanitizer is not compatible with ${_san} sanitizer.")
-      endif()
-      set(_use_thread_sanitizer 1)
-    else ()
-      if (${_use_thread_sanitizer})
-        message(FATAL_ERROR
-          "${_san} sanitizer is not compatible with thread sanitizer.")
-      endif()
-      set(_use_other_sanitizers 1)
-    endif()
-  endforeach()
-
-  message("Sanitizers: ${SANITIZERS}")
-
-  foreach( _san ${SANITIZERS} )
-    string(TOLOWER ${_san} _san)
-    enable_sanitizer(${_san})
-  endforeach()
-  message("Sanitizers compile flags: ${SAN_COMPILE_FLAGS}")
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SAN_COMPILE_FLAGS}")
-  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SAN_COMPILE_FLAGS}")
-endmacro()
diff --git a/ml-xgboost/dmlc-core/cmake/Utils.cmake b/ml-xgboost/dmlc-core/cmake/Utils.cmake
deleted file mode 100644
index 74c21a2..0000000
--- a/ml-xgboost/dmlc-core/cmake/Utils.cmake
+++ /dev/null
@@ -1,381 +0,0 @@
-################################################################################################
-# Command alias for debugging messages
-# Usage:
-#   dmsg(<message>)
-function(dmsg)
-  message(STATUS ${ARGN})
-endfunction()
-
-################################################################################################
-# Removes duplicates from list(s)
-# Usage:
-#   dmlccore_list_unique(<list_variable> [<list_variable>] [...])
-macro(dmlccore_list_unique)
-  foreach(__lst ${ARGN})
-    if(${__lst})
-      list(REMOVE_DUPLICATES ${__lst})
-    endif()
-  endforeach()
-endmacro()
-
-################################################################################################
-# Clears variables from list
-# Usage:
-#   dmlccore_clear_vars(<variables_list>)
-macro(dmlccore_clear_vars)
-  foreach(_var ${ARGN})
-    unset(${_var})
-  endforeach()
-endmacro()
-
-################################################################################################
-# Removes duplicates from string
-# Usage:
-#   dmlccore_string_unique(<string_variable>)
-function(dmlccore_string_unique __string)
-  if(${__string})
-    set(__list ${${__string}})
-    separate_arguments(__list)
-    list(REMOVE_DUPLICATES __list)
-    foreach(__e ${__list})
-      set(__str "${__str} ${__e}")
-    endforeach()
-    set(${__string} ${__str} PARENT_SCOPE)
-  endif()
-endfunction()
-
-################################################################################################
-# Prints list element per line
-# Usage:
-#   dmlccore_print_list(<list>)
-function(dmlccore_print_list)
-  foreach(e ${ARGN})
-    message(STATUS ${e})
-  endforeach()
-endfunction()
-
-################################################################################################
-# Function merging lists of compiler flags to single string.
-# Usage:
-#   dmlccore_merge_flag_lists(out_variable <list1> [<list2>] [<list3>] ...)
-function(dmlccore_merge_flag_lists out_var)
-  set(__result "")
-  foreach(__list ${ARGN})
-    foreach(__flag ${${__list}})
-      string(STRIP ${__flag} __flag)
-      set(__result "${__result} ${__flag}")
-    endforeach()
-  endforeach()
-  string(STRIP ${__result} __result)
-  set(${out_var} ${__result} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Converts all paths in list to absolute
-# Usage:
-#   dmlccore_convert_absolute_paths(<list_variable>)
-function(dmlccore_convert_absolute_paths variable)
-  set(__dlist "")
-  foreach(__s ${${variable}})
-    get_filename_component(__abspath ${__s} ABSOLUTE)
-    list(APPEND __list ${__abspath})
-  endforeach()
-  set(${variable} ${__list} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Reads set of version defines from the header file
-# Usage:
-#   dmlccore_parse_header(<file> <define1> <define2> <define3> ..)
-macro(dmlccore_parse_header FILENAME FILE_VAR)
-  set(vars_regex "")
-  set(__parnet_scope OFF)
-  set(__add_cache OFF)
-  foreach(name ${ARGN})
-    if("${name}" STREQUAL "PARENT_SCOPE")
-      set(__parnet_scope ON)
-    elseif("${name}" STREQUAL "CACHE")
-      set(__add_cache ON)
-    elseif(vars_regex)
-      set(vars_regex "${vars_regex}|${name}")
-    else()
-      set(vars_regex "${name}")
-    endif()
-  endforeach()
-  if(EXISTS "${FILENAME}")
-    file(STRINGS "${FILENAME}" ${FILE_VAR} REGEX "#define[ \t]+(${vars_regex})[ \t]+[0-9]+" )
-  else()
-    unset(${FILE_VAR})
-  endif()
-  foreach(name ${ARGN})
-    if(NOT "${name}" STREQUAL "PARENT_SCOPE" AND NOT "${name}" STREQUAL "CACHE")
-      if(${FILE_VAR})
-        if(${FILE_VAR} MATCHES ".+[ \t]${name}[ \t]+([0-9]+).*")
-          string(REGEX REPLACE ".+[ \t]${name}[ \t]+([0-9]+).*" "\\1" ${name} "${${FILE_VAR}}")
-        else()
-          set(${name} "")
-        endif()
-        if(__add_cache)
-          set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
-        elseif(__parnet_scope)
-          set(${name} "${${name}}" PARENT_SCOPE)
-        endif()
-      else()
-        unset(${name} CACHE)
-      endif()
-    endif()
-  endforeach()
-endmacro()
-
-################################################################################################
-# Reads single version define from the header file and parses it
-# Usage:
-#   dmlccore_parse_header_single_define(<library_name> <file> <define_name>)
-function(dmlccore_parse_header_single_define LIBNAME HDR_PATH VARNAME)
-  set(${LIBNAME}_H "")
-  if(EXISTS "${HDR_PATH}")
-    file(STRINGS "${HDR_PATH}" ${LIBNAME}_H REGEX "^#define[ \t]+${VARNAME}[ \t]+\"[^\"]*\".*$" LIMIT_COUNT 1)
-  endif()
-
-  if(${LIBNAME}_H)
-    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${${LIBNAME}_H}")
-    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${${LIBNAME}_H}")
-    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${${LIBNAME}_H}")
-    set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
-    set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
-    set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
-    set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
-
-    # append a TWEAK version if it exists:
-    set(${LIBNAME}_VERSION_TWEAK "")
-    if("${${LIBNAME}_H}" MATCHES "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.[0-9]+\\.([0-9]+).*$")
-      set(${LIBNAME}_VERSION_TWEAK "${CMAKE_MATCH_1}" ${ARGN} PARENT_SCOPE)
-    endif()
-    if(${LIBNAME}_VERSION_TWEAK)
-      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}.${${LIBNAME}_VERSION_TWEAK}" ${ARGN} PARENT_SCOPE)
-    else()
-      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}" ${ARGN} PARENT_SCOPE)
-    endif()
-  endif()
-endfunction()
-
-########################################################################################################
-# An option that the user can select. Can accept condition to control when option is available for user.
-# Usage:
-#   dmlccore_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
-function(dmlccore_option variable description value)
-  set(__value ${value})
-  set(__condition "")
-  set(__varname "__value")
-  foreach(arg ${ARGN})
-    if(arg STREQUAL "IF" OR arg STREQUAL "if")
-      set(__varname "__condition")
-    else()
-      list(APPEND ${__varname} ${arg})
-    endif()
-  endforeach()
-  unset(__varname)
-  if("${__condition}" STREQUAL "")
-    set(__condition 2 GREATER 1)
-  endif()
-
-  if(${__condition})
-    if("${__value}" MATCHES ";")
-      if(${__value})
-        option(${variable} "${description}" ON)
-      else()
-        option(${variable} "${description}" OFF)
-      endif()
-    elseif(DEFINED ${__value})
-      if(${__value})
-        option(${variable} "${description}" ON)
-      else()
-        option(${variable} "${description}" OFF)
-      endif()
-    else()
-      option(${variable} "${description}" ${__value})
-    endif()
-  else()
-    unset(${variable} CACHE)
-  endif()
-endfunction()
-
-################################################################################################
-# Utility macro for comparing two lists. Used for CMake debugging purposes
-# Usage:
-#   dmlccore_compare_lists(<list_variable> <list2_variable> [description])
-function(dmlccore_compare_lists list1 list2 desc)
-  set(__list1 ${${list1}})
-  set(__list2 ${${list2}})
-  list(SORT __list1)
-  list(SORT __list2)
-  list(LENGTH __list1 __len1)
-  list(LENGTH __list2 __len2)
-
-  if(NOT ${__len1} EQUAL ${__len2})
-    message(FATAL_ERROR "Lists are not equal. ${__len1} != ${__len2}. ${desc}")
-  endif()
-
-  foreach(__i RANGE 1 ${__len1})
-    math(EXPR __index "${__i}- 1")
-    list(GET __list1 ${__index} __item1)
-    list(GET __list2 ${__index} __item2)
-    if(NOT ${__item1} STREQUAL ${__item2})
-      message(FATAL_ERROR "Lists are not equal. Differ at element ${__index}. ${desc}")
-    endif()
-  endforeach()
-endfunction()
-
-################################################################################################
-# Command for disabling warnings for different platforms (see below for gcc and VisualStudio)
-# Usage:
-#   dmlccore_warnings_disable(<CMAKE_[C|CXX]_FLAGS[_CONFIGURATION]> -Wshadow /wd4996 ..,)
-macro(dmlccore_warnings_disable)
-  set(_flag_vars "")
-  set(_msvc_warnings "")
-  set(_gxx_warnings "")
-
-  foreach(arg ${ARGN})
-    if(arg MATCHES "^CMAKE_")
-      list(APPEND _flag_vars ${arg})
-    elseif(arg MATCHES "^/wd")
-      list(APPEND _msvc_warnings ${arg})
-    elseif(arg MATCHES "^-W")
-      list(APPEND _gxx_warnings ${arg})
-    endif()
-  endforeach()
-
-  if(NOT _flag_vars)
-    set(_flag_vars CMAKE_C_FLAGS CMAKE_CXX_FLAGS)
-  endif()
-
-  if(MSVC AND _msvc_warnings)
-    foreach(var ${_flag_vars})
-      foreach(warning ${_msvc_warnings})
-        set(${var} "${${var}} ${warning}")
-      endforeach()
-    endforeach()
-  elseif((CMAKE_COMPILER_IS_GNUCXX OR CMAKE_COMPILER_IS_CLANGXX) AND _gxx_warnings)
-    foreach(var ${_flag_vars})
-      foreach(warning ${_gxx_warnings})
-        if(NOT warning MATCHES "^-Wno-")
-          string(REPLACE "${warning}" "" ${var} "${${var}}")
-          string(REPLACE "-W" "-Wno-" warning "${warning}")
-        endif()
-        set(${var} "${${var}} ${warning}")
-      endforeach()
-    endforeach()
-  endif()
-  dmlccore_clear_vars(_flag_vars _msvc_warnings _gxx_warnings)
-endmacro()
-
-################################################################################################
-# Helper function get current definitions
-# Usage:
-#   dmlccore_get_current_definitions(<definitions_variable>)
-function(dmlccore_get_current_definitions definitions_var)
-  get_property(current_definitions DIRECTORY PROPERTY COMPILE_DEFINITIONS)
-  set(result "")
-
-  foreach(d ${current_definitions})
-    list(APPEND result -D${d})
-  endforeach()
-
-  dmlccore_list_unique(result)
-  set(${definitions_var} ${result} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Helper function get current includes/definitions
-# Usage:
-#   dmlccore_get_current_cflags(<cflagslist_variable>)
-function(dmlccore_get_current_cflags cflags_var)
-  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
-  dmlccore_convert_absolute_paths(current_includes)
-  dmlccore_get_current_definitions(cflags)
-
-  foreach(i ${current_includes})
-    list(APPEND cflags "-I${i}")
-  endforeach()
-
-  dmlccore_list_unique(cflags)
-  set(${cflags_var} ${cflags} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Helper function to parse current linker libs into link directories, libflags and osx frameworks
-# Usage:
-#   dmlccore_parse_linker_libs(<dmlccore_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
-function(dmlccore_parse_linker_libs dmlccore_LINKER_LIBS_variable folders_var flags_var frameworks_var)
-
-  set(__unspec "")
-  set(__debug "")
-  set(__optimized "")
-  set(__framework "")
-  set(__varname "__unspec")
-
-  # split libs into debug, optimized, unspecified and frameworks
-  foreach(list_elem ${${dmlccore_LINKER_LIBS_variable}})
-    if(list_elem STREQUAL "debug")
-      set(__varname "__debug")
-    elseif(list_elem STREQUAL "optimized")
-      set(__varname "__optimized")
-    elseif(list_elem MATCHES "^-framework[ \t]+([^ \t].*)")
-      list(APPEND __framework -framework ${CMAKE_MATCH_1})
-    else()
-      list(APPEND ${__varname} ${list_elem})
-      set(__varname "__unspec")
-    endif()
-  endforeach()
-
-  # attach debug or optimized libs to unspecified according to current configuration
-  if(CMAKE_BUILD_TYPE MATCHES "Debug")
-    set(__libs ${__unspec} ${__debug})
-  else()
-    set(__libs ${__unspec} ${__optimized})
-  endif()
-
-  set(libflags "")
-  set(folders "")
-
-  # convert linker libraries list to link flags
-  foreach(lib ${__libs})
-    if(TARGET ${lib})
-      list(APPEND folders $<TARGET_LINKER_FILE_DIR:${lib}>)
-      list(APPEND libflags -l${lib})
-    elseif(lib MATCHES "^-l.*")
-      list(APPEND libflags ${lib})
-    elseif(IS_ABSOLUTE ${lib})
-      get_filename_component(name_we ${lib} NAME_WE)
-      get_filename_component(folder  ${lib} PATH)
-
-      string(REGEX MATCH "^lib(.*)" __match ${name_we})
-      list(APPEND libflags -l${CMAKE_MATCH_1})
-      list(APPEND folders    ${folder})
-    else()
-      message(FATAL_ERROR "Logic error. Need to update cmake script")
-    endif()
-  endforeach()
-
-  dmlccore_list_unique(libflags folders)
-
-  set(${folders_var} ${folders} PARENT_SCOPE)
-  set(${flags_var} ${libflags} PARENT_SCOPE)
-  set(${frameworks_var} ${__framework} PARENT_SCOPE)
-endfunction()
-
-################################################################################################
-# Helper function to detect Darwin version, i.e. 10.8, 10.9, 10.10, ....
-# Usage:
-#   dmlccore_detect_darwin_version(<version_variable>)
-function(dmlccore_detect_darwin_version output_var)
-  if(APPLE)
-    execute_process(COMMAND /usr/bin/sw_vers -productVersion
-                    RESULT_VARIABLE __sw_vers OUTPUT_VARIABLE __sw_vers_out
-                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-    set(${output_var} ${__sw_vers_out} PARENT_SCOPE)
-  else()
-    set(${output_var} "" PARENT_SCOPE)
-  endif()
-endfunction()
diff --git a/ml-xgboost/dmlc-core/cmake/build_config.h.in b/ml-xgboost/dmlc-core/cmake/build_config.h.in
deleted file mode 100644
index d7fc4ea..0000000
--- a/ml-xgboost/dmlc-core/cmake/build_config.h.in
+++ /dev/null
@@ -1,27 +0,0 @@
-#ifndef DMLC_BUILD_CONFIG_H_
-#define DMLC_BUILD_CONFIG_H_
-
-#cmakedefine DMLC_FOPEN_64_PRESENT
-
-#if !defined(DMLC_FOPEN_64_PRESENT) && DMLC_USE_FOPEN64
-  #define fopen64 std::fopen
-#endif
-
-#cmakedefine DMLC_CXXABI_H_PRESENT
-#cmakedefine DMLC_EXECINFO_H_PRESENT
-
-#if (defined DMLC_CXXABI_H_PRESENT) && (defined DMLC_EXECINFO_H_PRESENT)
-  #ifndef DMLC_LOG_STACK_TRACE
-  #define DMLC_LOG_STACK_TRACE 1
-  #endif
-  #ifndef DMLC_LOG_STACK_TRACE_SIZE
-  #define DMLC_LOG_STACK_TRACE_SIZE 10
-  #endif
-  #cmakedefine DMLC_EXECINFO_H <${DMLC_EXECINFO_H}>
-#endif
-
-#cmakedefine DMLC_NANOSLEEP_PRESENT
-
-#define DMLC_CMAKE_LITTLE_ENDIAN ${DMLC_CMAKE_LITTLE_ENDIAN}
-
-#endif  // DMLC_BUILD_CONFIG_H_
diff --git a/ml-xgboost/dmlc-core/cmake/dmlc-config.cmake.in b/ml-xgboost/dmlc-core/cmake/dmlc-config.cmake.in
deleted file mode 100644
index a318d48..0000000
--- a/ml-xgboost/dmlc-core/cmake/dmlc-config.cmake.in
+++ /dev/null
@@ -1,5 +0,0 @@
-@PACKAGE_INIT@
-
-if(NOT TARGET dmlc::dmlc)
-  include(${CMAKE_CURRENT_LIST_DIR}/DMLCTargets.cmake)
-endif()
diff --git a/ml-xgboost/dmlc-core/cmake/gtest_cmake.in b/ml-xgboost/dmlc-core/cmake/gtest_cmake.in
deleted file mode 100644
index d7acc70..0000000
--- a/ml-xgboost/dmlc-core/cmake/gtest_cmake.in
+++ /dev/null
@@ -1,15 +0,0 @@
-cmake_minimum_required(VERSION 2.8.2)
-
-project(googletest-download NONE)
-
-include(ExternalProject)
-ExternalProject_Add(googletest
-    GIT_REPOSITORY https://github.com/google/googletest.git
-    GIT_TAG release-1.10.0
-    SOURCE_DIR "${CMAKE_BINARY_DIR}/googletest-src"
-    BINARY_DIR "${CMAKE_BINARY_DIR}/googletest-build"
-    CONFIGURE_COMMAND ""
-    BUILD_COMMAND ""
-    INSTALL_COMMAND ""
-    TEST_COMMAND ""
-)
diff --git a/ml-xgboost/dmlc-core/cmake/lint.cmake b/ml-xgboost/dmlc-core/cmake/lint.cmake
deleted file mode 100644
index cb0db2e..0000000
--- a/ml-xgboost/dmlc-core/cmake/lint.cmake
+++ /dev/null
@@ -1,21 +0,0 @@
-get_filename_component(CMAKE_SOURCE_DIR "${CMAKE_CURRENT_LIST_DIR}/.." ABSOLUTE)
-if(NOT MSVC)
-    set(LINT_COMMAND ${CMAKE_SOURCE_DIR}/scripts/lint.py)
-else()
-    if((NOT PYTHON_EXECUTABLE))
-         message(FATAL_ERROR "Cannot lint without python")
-    endif()
-    # format output so VS can bring us to the offending file/line
-	set(LINT_COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/scripts/lint.py)
-endif()
-
-cmake_policy(SET CMP0009 NEW)  # suppress cmake warning
-string(REPLACE " " ";" LINT_DIRS ${LINT_DIRS})
-string(REPLACE " " ";" EXCLUDE_PATH ${EXCLUDE_PATH})
-execute_process(
-    COMMAND ${LINT_COMMAND} ${PROJECT_NAME} all ${LINT_DIRS} --exclude_path=${EXCLUDE_PATH}
-	WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
-    ERROR_VARIABLE LINT_OUTPUT
-    ERROR_STRIP_TRAILING_WHITESPACE
-)
-message(STATUS ${LINT_OUTPUT})
\ No newline at end of file
diff --git a/ml-xgboost/dmlc-core/doc/.gitignore b/ml-xgboost/dmlc-core/doc/.gitignore
deleted file mode 100644
index 39742b9..0000000
--- a/ml-xgboost/dmlc-core/doc/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-
-_build
-doxygen
diff --git a/ml-xgboost/dmlc-core/doc/Doxyfile b/ml-xgboost/dmlc-core/doc/Doxyfile
deleted file mode 100644
index 4db428c..0000000
--- a/ml-xgboost/dmlc-core/doc/Doxyfile
+++ /dev/null
@@ -1,2406 +0,0 @@
-# Doxyfile 1.8.13
-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
-#
-# All text after a double hash (##) is considered a comment and is placed in
-# front of the TAG it is preceding.
-#
-# All text after a single hash (#) is considered a comment and will be ignored.
-# The format is:
-# TAG = value [value, ...]
-# For lists, items can also be appended using:
-# TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (\" \").
-
-#---------------------------------------------------------------------------
-# Project related configuration options
-#---------------------------------------------------------------------------
-
-# This tag specifies the encoding used for all characters in the config file
-# that follow. The default is UTF-8 which is also the encoding used for all text
-# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
-# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
-# for the list of possible encodings.
-# The default value is: UTF-8.
-
-DOXYFILE_ENCODING      = UTF-8
-
-# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
-# double-quotes, unless you are using Doxywizard) that should identify the
-# project for which the documentation is generated. This name is used in the
-# title of most generated pages and in a few other places.
-# The default value is: My Project.
-
-PROJECT_NAME           = "dmlc-core"
-
-# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
-# could be handy for archiving the generated documentation or if some version
-# control system is used.
-
-PROJECT_NUMBER         =
-
-# Using the PROJECT_BRIEF tag one can provide an optional one line description
-# for a project that appears at the top of each page and should give viewer a
-# quick idea about the purpose of the project. Keep the description short.
-
-PROJECT_BRIEF          =
-
-# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
-# in the documentation. The maximum height of the logo should not exceed 55
-# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
-# the logo to the output directory.
-
-PROJECT_LOGO           =
-
-# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
-# into which the generated documentation will be written. If a relative path is
-# entered, it will be relative to the location where doxygen was started. If
-# left blank the current directory will be used.
-
-OUTPUT_DIRECTORY       = doc/doxygen
-
-# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
-# directories (in 2 levels) under the output directory of each output format and
-# will distribute the generated files over these directories. Enabling this
-# option can be useful when feeding doxygen a huge amount of source files, where
-# putting all generated files in the same directory would otherwise causes
-# performance problems for the file system.
-# The default value is: NO.
-
-CREATE_SUBDIRS         = NO
-
-# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
-# characters to appear in the names of generated files. If set to NO, non-ASCII
-# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
-# U+3044.
-# The default value is: NO.
-
-ALLOW_UNICODE_NAMES    = NO
-
-# The OUTPUT_LANGUAGE tag is used to specify the language in which all
-# documentation generated by doxygen is written. Doxygen will use this
-# information to generate all constant output in the proper language.
-# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
-# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
-# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
-# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
-# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
-# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
-# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
-# Ukrainian and Vietnamese.
-# The default value is: English.
-
-OUTPUT_LANGUAGE        = English
-
-# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member
-# descriptions after the members that are listed in the file and class
-# documentation (similar to Javadoc). Set to NO to disable this.
-# The default value is: YES.
-
-BRIEF_MEMBER_DESC      = YES
-
-# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief
-# description of a member or function before the detailed description
-#
-# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
-# brief descriptions will be completely suppressed.
-# The default value is: YES.
-
-REPEAT_BRIEF           = YES
-
-# This tag implements a quasi-intelligent brief description abbreviator that is
-# used to form the text in various listings. Each string in this list, if found
-# as the leading text of the brief description, will be stripped from the text
-# and the result, after processing the whole list, is used as the annotated
-# text. Otherwise, the brief description is used as-is. If left blank, the
-# following values are used ($name is automatically replaced with the name of
-# the entity):The $name class, The $name widget, The $name file, is, provides,
-# specifies, contains, represents, a, an and the.
-
-ABBREVIATE_BRIEF       =
-
-# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
-# doxygen will generate a detailed section even if there is only a brief
-# description.
-# The default value is: NO.
-
-ALWAYS_DETAILED_SEC    = NO
-
-# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
-# inherited members of a class in the documentation of that class as if those
-# members were ordinary class members. Constructors, destructors and assignment
-# operators of the base classes will not be shown.
-# The default value is: NO.
-
-INLINE_INHERITED_MEMB  = NO
-
-# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
-# before files name in the file list and in the header files. If set to NO the
-# shortest path that makes the file name unique will be used
-# The default value is: YES.
-
-FULL_PATH_NAMES        = YES
-
-# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
-# Stripping is only done if one of the specified strings matches the left-hand
-# part of the path. The tag can be used to show relative paths in the file list.
-# If left blank the directory from which doxygen is run is used as the path to
-# strip.
-#
-# Note that you can specify absolute paths here, but also relative paths, which
-# will be relative from the directory where doxygen is started.
-# This tag requires that the tag FULL_PATH_NAMES is set to YES.
-
-STRIP_FROM_PATH        =
-
-# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
-# path mentioned in the documentation of a class, which tells the reader which
-# header file to include in order to use a class. If left blank only the name of
-# the header file containing the class definition is used. Otherwise one should
-# specify the list of include paths that are normally passed to the compiler
-# using the -I flag.
-
-STRIP_FROM_INC_PATH    =
-
-# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
-# less readable) file names. This can be useful is your file systems doesn't
-# support long names like on DOS, Mac, or CD-ROM.
-# The default value is: NO.
-
-SHORT_NAMES            = NO
-
-# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
-# first line (until the first dot) of a Javadoc-style comment as the brief
-# description. If set to NO, the Javadoc-style will behave just like regular Qt-
-# style comments (thus requiring an explicit @brief command for a brief
-# description.)
-# The default value is: NO.
-
-JAVADOC_AUTOBRIEF      = NO
-
-# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
-# line (until the first dot) of a Qt-style comment as the brief description. If
-# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
-# requiring an explicit \brief command for a brief description.)
-# The default value is: NO.
-
-QT_AUTOBRIEF           = NO
-
-# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
-# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
-# a brief description. This used to be the default behavior. The new default is
-# to treat a multi-line C++ comment block as a detailed description. Set this
-# tag to YES if you prefer the old behavior instead.
-#
-# Note that setting this tag to YES also means that rational rose comments are
-# not recognized any more.
-# The default value is: NO.
-
-MULTILINE_CPP_IS_BRIEF = NO
-
-# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
-# documentation from any documented member that it re-implements.
-# The default value is: YES.
-
-INHERIT_DOCS           = YES
-
-# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
-# page for each member. If set to NO, the documentation of a member will be part
-# of the file/class/namespace that contains it.
-# The default value is: NO.
-
-SEPARATE_MEMBER_PAGES  = NO
-
-# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
-# uses this value to replace tabs by spaces in code fragments.
-# Minimum value: 1, maximum value: 16, default value: 4.
-
-TAB_SIZE               = 8
-
-# This tag can be used to specify a number of aliases that act as commands in
-# the documentation. An alias has the form:
-# name=value
-# For example adding
-# "sideeffect=@par Side Effects:\n"
-# will allow you to put the command \sideeffect (or @sideeffect) in the
-# documentation, which will result in a user-defined paragraph with heading
-# "Side Effects:". You can put \n's in the value part of an alias to insert
-# newlines.
-
-ALIASES                =
-
-# This tag can be used to specify a number of word-keyword mappings (TCL only).
-# A mapping has the form "name=value". For example adding "class=itcl::class"
-# will allow you to use the command class in the itcl::class meaning.
-
-TCL_SUBST              =
-
-# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
-# only. Doxygen will then generate output that is more tailored for C. For
-# instance, some of the names that are used will be different. The list of all
-# members will be omitted, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_FOR_C  = NO
-
-# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
-# Python sources only. Doxygen will then generate output that is more tailored
-# for that language. For instance, namespaces will be presented as packages,
-# qualified scopes will look different, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_JAVA   = NO
-
-# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
-# sources. Doxygen will then generate output that is tailored for Fortran.
-# The default value is: NO.
-
-OPTIMIZE_FOR_FORTRAN   = NO
-
-# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
-# sources. Doxygen will then generate output that is tailored for VHDL.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_VHDL   = NO
-
-# Doxygen selects the parser to use depending on the extension of the files it
-# parses. With this tag you can assign which parser to use for a given
-# extension. Doxygen has a built-in mapping, but you can override or extend it
-# using this tag. The format is ext=language, where ext is a file extension, and
-# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
-# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
-# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
-# Fortran. In the later case the parser tries to guess whether the code is fixed
-# or free formatted code, this is the default for Fortran type files), VHDL. For
-# instance to make doxygen treat .inc files as Fortran files (default is PHP),
-# and .f files as C (default is Fortran), use: inc=Fortran f=C.
-#
-# Note: For files without extension you can use no_extension as a placeholder.
-#
-# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
-# the files are not read by doxygen.
-
-EXTENSION_MAPPING      =
-
-# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
-# according to the Markdown format, which allows for more readable
-# documentation. See http://daringfireball.net/projects/markdown/ for details.
-# The output of markdown processing is further processed by doxygen, so you can
-# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
-# case of backward compatibilities issues.
-# The default value is: YES.
-
-MARKDOWN_SUPPORT       = YES
-
-# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up
-# to that level are automatically included in the table of contents, even if
-# they do not have an id attribute.
-# Note: This feature currently applies only to Markdown headings.
-# Minimum value: 0, maximum value: 99, default value: 0.
-# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
-
-TOC_INCLUDE_HEADINGS   = 0
-
-# When enabled doxygen tries to link words that correspond to documented
-# classes, or namespaces to their corresponding documentation. Such a link can
-# be prevented in individual cases by putting a % sign in front of the word or
-# globally by setting AUTOLINK_SUPPORT to NO.
-# The default value is: YES.
-
-AUTOLINK_SUPPORT       = YES
-
-# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
-# to include (a tag file for) the STL sources as input, then you should set this
-# tag to YES in order to let doxygen match functions declarations and
-# definitions whose arguments contain STL classes (e.g. func(std::string);
-# versus func(std::string) {}). This also make the inheritance and collaboration
-# diagrams that involve STL classes more complete and accurate.
-# The default value is: NO.
-
-BUILTIN_STL_SUPPORT    = NO
-
-# If you use Microsoft's C++/CLI language, you should set this option to YES to
-# enable parsing support.
-# The default value is: NO.
-
-CPP_CLI_SUPPORT        = NO
-
-# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
-# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
-# will parse them like normal C++ but will assume all classes use public instead
-# of private inheritance when no explicit protection keyword is present.
-# The default value is: NO.
-
-SIP_SUPPORT            = NO
-
-# For Microsoft's IDL there are propget and propput attributes to indicate
-# getter and setter methods for a property. Setting this option to YES will make
-# doxygen to replace the get and set methods by a property in the documentation.
-# This will only work if the methods are indeed getting or setting a simple
-# type. If this is not the case, or you want to show the methods anyway, you
-# should set this option to NO.
-# The default value is: YES.
-
-IDL_PROPERTY_SUPPORT   = YES
-
-# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
-# tag is set to YES then doxygen will reuse the documentation of the first
-# member in the group (if any) for the other members of the group. By default
-# all members of a group must be documented explicitly.
-# The default value is: NO.
-
-DISTRIBUTE_GROUP_DOC   = NO
-
-# If one adds a struct or class to a group and this option is enabled, then also
-# any nested class or struct is added to the same group. By default this option
-# is disabled and one has to add nested compounds explicitly via \ingroup.
-# The default value is: NO.
-
-GROUP_NESTED_COMPOUNDS = NO
-
-# Set the SUBGROUPING tag to YES to allow class member groups of the same type
-# (for instance a group of public functions) to be put as a subgroup of that
-# type (e.g. under the Public Functions section). Set it to NO to prevent
-# subgrouping. Alternatively, this can be done per class using the
-# \nosubgrouping command.
-# The default value is: YES.
-
-SUBGROUPING            = YES
-
-# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
-# are shown inside the group in which they are included (e.g. using \ingroup)
-# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
-# and RTF).
-#
-# Note that this feature does not work in combination with
-# SEPARATE_MEMBER_PAGES.
-# The default value is: NO.
-
-INLINE_GROUPED_CLASSES = NO
-
-# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
-# with only public data fields or simple typedef fields will be shown inline in
-# the documentation of the scope in which they are defined (i.e. file,
-# namespace, or group documentation), provided this scope is documented. If set
-# to NO, structs, classes, and unions are shown on a separate page (for HTML and
-# Man pages) or section (for LaTeX and RTF).
-# The default value is: NO.
-
-INLINE_SIMPLE_STRUCTS  = NO
-
-# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
-# enum is documented as struct, union, or enum with the name of the typedef. So
-# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
-# with name TypeT. When disabled the typedef will appear as a member of a file,
-# namespace, or class. And the struct will be named TypeS. This can typically be
-# useful for C code in case the coding convention dictates that all compound
-# types are typedef'ed and only the typedef is referenced, never the tag name.
-# The default value is: NO.
-
-TYPEDEF_HIDES_STRUCT   = NO
-
-# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
-# cache is used to resolve symbols given their name and scope. Since this can be
-# an expensive process and often the same symbol appears multiple times in the
-# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
-# doxygen will become slower. If the cache is too large, memory is wasted. The
-# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
-# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
-# symbols. At the end of a run doxygen will report the cache usage and suggest
-# the optimal cache size from a speed point of view.
-# Minimum value: 0, maximum value: 9, default value: 0.
-
-LOOKUP_CACHE_SIZE      = 0
-
-#---------------------------------------------------------------------------
-# Build related configuration options
-#---------------------------------------------------------------------------
-
-# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
-# documentation are documented, even if no documentation was available. Private
-# class members and static file members will be hidden unless the
-# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
-# Note: This will also disable the warnings about undocumented members that are
-# normally produced when WARNINGS is set to YES.
-# The default value is: NO.
-
-EXTRACT_ALL            = NO
-
-# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
-# be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PRIVATE        = NO
-
-# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
-# scope will be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PACKAGE        = NO
-
-# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
-# included in the documentation.
-# The default value is: NO.
-
-EXTRACT_STATIC         = NO
-
-# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
-# locally in source files will be included in the documentation. If set to NO,
-# only classes defined in header files are included. Does not have any effect
-# for Java sources.
-# The default value is: YES.
-
-EXTRACT_LOCAL_CLASSES  = YES
-
-# This flag is only useful for Objective-C code. If set to YES, local methods,
-# which are defined in the implementation section but not in the interface are
-# included in the documentation. If set to NO, only methods in the interface are
-# included.
-# The default value is: NO.
-
-EXTRACT_LOCAL_METHODS  = NO
-
-# If this flag is set to YES, the members of anonymous namespaces will be
-# extracted and appear in the documentation as a namespace called
-# 'anonymous_namespace{file}', where file will be replaced with the base name of
-# the file that contains the anonymous namespace. By default anonymous namespace
-# are hidden.
-# The default value is: NO.
-
-EXTRACT_ANON_NSPACES   = NO
-
-# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
-# undocumented members inside documented classes or files. If set to NO these
-# members will be included in the various overviews, but no documentation
-# section is generated. This option has no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_MEMBERS     = NO
-
-# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
-# undocumented classes that are normally visible in the class hierarchy. If set
-# to NO, these classes will be included in the various overviews. This option
-# has no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_CLASSES     = NO
-
-# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
-# (class|struct|union) declarations. If set to NO, these declarations will be
-# included in the documentation.
-# The default value is: NO.
-
-HIDE_FRIEND_COMPOUNDS  = NO
-
-# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
-# documentation blocks found inside the body of a function. If set to NO, these
-# blocks will be appended to the function's detailed documentation block.
-# The default value is: NO.
-
-HIDE_IN_BODY_DOCS      = NO
-
-# The INTERNAL_DOCS tag determines if documentation that is typed after a
-# \internal command is included. If the tag is set to NO then the documentation
-# will be excluded. Set it to YES to include the internal documentation.
-# The default value is: NO.
-
-INTERNAL_DOCS          = NO
-
-# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
-# names in lower-case letters. If set to YES, upper-case letters are also
-# allowed. This is useful if you have classes or files whose names only differ
-# in case and if your file system supports case sensitive file names. Windows
-# and Mac users are advised to set this option to NO.
-# The default value is: system dependent.
-
-CASE_SENSE_NAMES       = YES
-
-# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
-# their full class and namespace scopes in the documentation. If set to YES, the
-# scope will be hidden.
-# The default value is: NO.
-
-HIDE_SCOPE_NAMES       = NO
-
-# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
-# append additional text to a page's title, such as Class Reference. If set to
-# YES the compound reference will be hidden.
-# The default value is: NO.
-
-HIDE_COMPOUND_REFERENCE= NO
-
-# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
-# the files that are included by a file in the documentation of that file.
-# The default value is: YES.
-
-SHOW_INCLUDE_FILES     = YES
-
-# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
-# grouped member an include statement to the documentation, telling the reader
-# which file to include in order to use the member.
-# The default value is: NO.
-
-SHOW_GROUPED_MEMB_INC  = NO
-
-# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
-# files with double quotes in the documentation rather than with sharp brackets.
-# The default value is: NO.
-
-FORCE_LOCAL_INCLUDES   = NO
-
-# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
-# documentation for inline members.
-# The default value is: YES.
-
-INLINE_INFO            = YES
-
-# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
-# (detailed) documentation of file and class members alphabetically by member
-# name. If set to NO, the members will appear in declaration order.
-# The default value is: YES.
-
-SORT_MEMBER_DOCS       = YES
-
-# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
-# descriptions of file, namespace and class members alphabetically by member
-# name. If set to NO, the members will appear in declaration order. Note that
-# this will also influence the order of the classes in the class list.
-# The default value is: NO.
-
-SORT_BRIEF_DOCS        = NO
-
-# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
-# (brief and detailed) documentation of class members so that constructors and
-# destructors are listed first. If set to NO the constructors will appear in the
-# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
-# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
-# member documentation.
-# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
-# detailed member documentation.
-# The default value is: NO.
-
-SORT_MEMBERS_CTORS_1ST = NO
-
-# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
-# of group names into alphabetical order. If set to NO the group names will
-# appear in their defined order.
-# The default value is: NO.
-
-SORT_GROUP_NAMES       = NO
-
-# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
-# fully-qualified names, including namespaces. If set to NO, the class list will
-# be sorted only by class name, not including the namespace part.
-# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
-# Note: This option applies only to the class list, not to the alphabetical
-# list.
-# The default value is: NO.
-
-SORT_BY_SCOPE_NAME     = NO
-
-# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
-# type resolution of all parameters of a function it will reject a match between
-# the prototype and the implementation of a member function even if there is
-# only one candidate or it is obvious which candidate to choose by doing a
-# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
-# accept a match between prototype and implementation in such cases.
-# The default value is: NO.
-
-STRICT_PROTO_MATCHING  = NO
-
-# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
-# list. This list is created by putting \todo commands in the documentation.
-# The default value is: YES.
-
-GENERATE_TODOLIST      = YES
-
-# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
-# list. This list is created by putting \test commands in the documentation.
-# The default value is: YES.
-
-GENERATE_TESTLIST      = YES
-
-# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
-# list. This list is created by putting \bug commands in the documentation.
-# The default value is: YES.
-
-GENERATE_BUGLIST       = YES
-
-# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
-# the deprecated list. This list is created by putting \deprecated commands in
-# the documentation.
-# The default value is: YES.
-
-GENERATE_DEPRECATEDLIST= YES
-
-# The ENABLED_SECTIONS tag can be used to enable conditional documentation
-# sections, marked by \if <section_label> ... \endif and \cond <section_label>
-# ... \endcond blocks.
-
-ENABLED_SECTIONS       =
-
-# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
-# initial value of a variable or macro / define can have for it to appear in the
-# documentation. If the initializer consists of more lines than specified here
-# it will be hidden. Use a value of 0 to hide initializers completely. The
-# appearance of the value of individual variables and macros / defines can be
-# controlled using \showinitializer or \hideinitializer command in the
-# documentation regardless of this setting.
-# Minimum value: 0, maximum value: 10000, default value: 30.
-
-MAX_INITIALIZER_LINES  = 30
-
-# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
-# the bottom of the documentation of classes and structs. If set to YES, the
-# list will mention the files that were used to generate the documentation.
-# The default value is: YES.
-
-SHOW_USED_FILES        = YES
-
-# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
-# will remove the Files entry from the Quick Index and from the Folder Tree View
-# (if specified).
-# The default value is: YES.
-
-SHOW_FILES             = YES
-
-# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
-# page. This will remove the Namespaces entry from the Quick Index and from the
-# Folder Tree View (if specified).
-# The default value is: YES.
-
-SHOW_NAMESPACES        = YES
-
-# The FILE_VERSION_FILTER tag can be used to specify a program or script that
-# doxygen should invoke to get the current version for each file (typically from
-# the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command command input-file, where command is the value of the
-# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
-# by doxygen. Whatever the program writes to standard output is used as the file
-# version. For an example see the documentation.
-
-FILE_VERSION_FILTER    =
-
-# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
-# by doxygen. The layout file controls the global structure of the generated
-# output files in an output format independent way. To create the layout file
-# that represents doxygen's defaults, run doxygen with the -l option. You can
-# optionally specify a file name after the option, if omitted DoxygenLayout.xml
-# will be used as the name of the layout file.
-#
-# Note that if you run doxygen from a directory containing a file called
-# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
-# tag is left empty.
-
-LAYOUT_FILE            =
-
-# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
-# the reference definitions. This must be a list of .bib files. The .bib
-# extension is automatically appended if omitted. This requires the bibtex tool
-# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
-# For LaTeX the style of the bibliography can be controlled using
-# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
-# search path. See also \cite for info how to create references.
-
-CITE_BIB_FILES         =
-
-#---------------------------------------------------------------------------
-# Configuration options related to warning and progress messages
-#---------------------------------------------------------------------------
-
-# The QUIET tag can be used to turn on/off the messages that are generated to
-# standard output by doxygen. If QUIET is set to YES this implies that the
-# messages are off.
-# The default value is: NO.
-
-QUIET                  = NO
-
-# The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
-# this implies that the warnings are on.
-#
-# Tip: Turn warnings on while writing the documentation.
-# The default value is: YES.
-
-WARNINGS               = YES
-
-# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
-# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
-# will automatically be disabled.
-# The default value is: YES.
-
-WARN_IF_UNDOCUMENTED   = YES
-
-# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as not documenting some parameters
-# in a documented function, or documenting parameters that don't exist or using
-# markup commands wrongly.
-# The default value is: YES.
-
-WARN_IF_DOC_ERROR      = YES
-
-# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
-# are documented, but have no documentation for their parameters or return
-# value. If set to NO, doxygen will only warn about wrong or incomplete
-# parameter documentation, but not about the absence of documentation.
-# The default value is: NO.
-
-WARN_NO_PARAMDOC       = NO
-
-# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
-# a warning is encountered.
-# The default value is: NO.
-
-WARN_AS_ERROR          = NO
-
-# The WARN_FORMAT tag determines the format of the warning messages that doxygen
-# can produce. The string should contain the $file, $line, and $text tags, which
-# will be replaced by the file and line number from which the warning originated
-# and the warning text. Optionally the format may contain $version, which will
-# be replaced by the version of the file (if it could be obtained via
-# FILE_VERSION_FILTER)
-# The default value is: $file:$line: $text.
-
-WARN_FORMAT            = "$file:$line: $text"
-
-# The WARN_LOGFILE tag can be used to specify a file to which warning and error
-# messages should be written. If left blank the output is written to standard
-# error (stderr).
-
-WARN_LOGFILE           =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the input files
-#---------------------------------------------------------------------------
-
-# The INPUT tag is used to specify the files and/or directories that contain
-# documented source files. You may enter file names like myfile.cpp or
-# directories like /usr/src/myproject. Separate the files or directories with
-# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
-# Note: If this tag is empty the current directory is searched.
-
-INPUT                  = include
-
-# This tag can be used to specify the character encoding of the source files
-# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
-# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see: http://www.gnu.org/software/libiconv) for the list of
-# possible encodings.
-# The default value is: UTF-8.
-
-INPUT_ENCODING         = UTF-8
-
-# If the value of the INPUT tag contains directories, you can use the
-# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
-# *.h) to filter out the source-files in the directories.
-#
-# Note that for custom extensions or not directly supported extensions you also
-# need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# read by doxygen.
-#
-# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
-# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
-# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
-# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,
-# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf.
-
-FILE_PATTERNS          =
-
-# The RECURSIVE tag can be used to specify whether or not subdirectories should
-# be searched for input files as well.
-# The default value is: NO.
-
-RECURSIVE              = YES
-
-# The EXCLUDE tag can be used to specify files and/or directories that should be
-# excluded from the INPUT source files. This way you can easily exclude a
-# subdirectory from a directory tree whose root is specified with the INPUT tag.
-#
-# Note that relative paths are relative to the directory from which doxygen is
-# run.
-
-EXCLUDE                =
-
-# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
-# directories that are symbolic links (a Unix file system feature) are excluded
-# from the input.
-# The default value is: NO.
-
-EXCLUDE_SYMLINKS       = NO
-
-# If the value of the INPUT tag contains directories, you can use the
-# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
-# certain files from those directories.
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories for example use the pattern */test/*
-
-EXCLUDE_PATTERNS       = */test/* \
-                         logging.h
-
-# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
-# (namespaces, classes, functions, etc.) that should be excluded from the
-# output. The symbol name can be a fully qualified name, a word, or if the
-# wildcard * is used, a substring. Examples: ANamespace, AClass,
-# AClass::ANamespace, ANamespace::*Test
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories use the pattern */test/*
-
-EXCLUDE_SYMBOLS        =
-
-# The EXAMPLE_PATH tag can be used to specify one or more files or directories
-# that contain example code fragments that are included (see the \include
-# command).
-
-EXAMPLE_PATH           =
-
-# If the value of the EXAMPLE_PATH tag contains directories, you can use the
-# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank all
-# files are included.
-
-EXAMPLE_PATTERNS       =
-
-# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
-# searched for input files to be used with the \include or \dontinclude commands
-# irrespective of the value of the RECURSIVE tag.
-# The default value is: NO.
-
-EXAMPLE_RECURSIVE      = NO
-
-# The IMAGE_PATH tag can be used to specify one or more files or directories
-# that contain images that are to be included in the documentation (see the
-# \image command).
-
-IMAGE_PATH             =
-
-# The INPUT_FILTER tag can be used to specify a program that doxygen should
-# invoke to filter for each input file. Doxygen will invoke the filter program
-# by executing (via popen()) the command:
-#
-# <filter> <input-file>
-#
-# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
-# name of an input file. Doxygen will then use the output that the filter
-# program writes to standard output. If FILTER_PATTERNS is specified, this tag
-# will be ignored.
-#
-# Note that the filter must not add or remove lines; it is applied before the
-# code is scanned, but not when the output code is generated. If lines are added
-# or removed, the anchors will not be placed correctly.
-#
-# Note that for custom extensions or not directly supported extensions you also
-# need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# properly processed by doxygen.
-
-INPUT_FILTER           =
-
-# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
-# basis. Doxygen will compare the file name with each pattern and apply the
-# filter if there is a match. The filters are a list of the form: pattern=filter
-# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
-# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
-# patterns match the file name, INPUT_FILTER is applied.
-#
-# Note that for custom extensions or not directly supported extensions you also
-# need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# properly processed by doxygen.
-
-FILTER_PATTERNS        =
-
-# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
-# INPUT_FILTER) will also be used to filter the input files that are used for
-# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
-# The default value is: NO.
-
-FILTER_SOURCE_FILES    = NO
-
-# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
-# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
-# it is also possible to disable source filtering for a specific pattern using
-# *.ext= (so without naming a filter).
-# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
-
-FILTER_SOURCE_PATTERNS =
-
-# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
-# is part of the input, its contents will be placed on the main page
-# (index.html). This can be useful if you have a project on for instance GitHub
-# and want to reuse the introduction page also for the doxygen output.
-
-USE_MDFILE_AS_MAINPAGE =
-
-#---------------------------------------------------------------------------
-# Configuration options related to source browsing
-#---------------------------------------------------------------------------
-
-# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
-# generated. Documented entities will be cross-referenced with these sources.
-#
-# Note: To get rid of all source code in the generated output, make sure that
-# also VERBATIM_HEADERS is set to NO.
-# The default value is: NO.
-
-SOURCE_BROWSER         = NO
-
-# Setting the INLINE_SOURCES tag to YES will include the body of functions,
-# classes and enums directly into the documentation.
-# The default value is: NO.
-
-INLINE_SOURCES         = NO
-
-# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
-# special comment blocks from generated source code fragments. Normal C, C++ and
-# Fortran comments will always remain visible.
-# The default value is: YES.
-
-STRIP_CODE_COMMENTS    = YES
-
-# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
-# function all documented functions referencing it will be listed.
-# The default value is: NO.
-
-REFERENCED_BY_RELATION = NO
-
-# If the REFERENCES_RELATION tag is set to YES then for each documented function
-# all documented entities called/used by that function will be listed.
-# The default value is: NO.
-
-REFERENCES_RELATION    = NO
-
-# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
-# to YES then the hyperlinks from functions in REFERENCES_RELATION and
-# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
-# link to the documentation.
-# The default value is: YES.
-
-REFERENCES_LINK_SOURCE = YES
-
-# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
-# source code will show a tooltip with additional information such as prototype,
-# brief description and links to the definition and documentation. Since this
-# will make the HTML file larger and loading of large files a bit slower, you
-# can opt to disable this feature.
-# The default value is: YES.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-SOURCE_TOOLTIPS        = YES
-
-# If the USE_HTAGS tag is set to YES then the references to source code will
-# point to the HTML generated by the htags(1) tool instead of doxygen built-in
-# source browser. The htags tool is part of GNU's global source tagging system
-# (see http://www.gnu.org/software/global/global.html). You will need version
-# 4.8.6 or higher.
-#
-# To use it do the following:
-# - Install the latest version of global
-# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
-# - Make sure the INPUT points to the root of the source tree
-# - Run doxygen as normal
-#
-# Doxygen will invoke htags (and that will in turn invoke gtags), so these
-# tools must be available from the command line (i.e. in the search path).
-#
-# The result: instead of the source browser generated by doxygen, the links to
-# source code will now point to the output of htags.
-# The default value is: NO.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-USE_HTAGS              = NO
-
-# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
-# verbatim copy of the header file for each class for which an include is
-# specified. Set to NO to disable this.
-# See also: Section \class.
-# The default value is: YES.
-
-VERBATIM_HEADERS       = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the alphabetical class index
-#---------------------------------------------------------------------------
-
-# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
-# compounds will be generated. Enable this if the project contains a lot of
-# classes, structs, unions or interfaces.
-# The default value is: YES.
-
-ALPHABETICAL_INDEX     = YES
-
-# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
-# which the alphabetical index list will be split.
-# Minimum value: 1, maximum value: 20, default value: 5.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-COLS_IN_ALPHA_INDEX    = 5
-
-# In case all classes in a project start with a common prefix, all classes will
-# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
-# can be used to specify a prefix (or a list of prefixes) that should be ignored
-# while generating the index headers.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-IGNORE_PREFIX          =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the HTML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
-# The default value is: YES.
-
-GENERATE_HTML          = YES
-
-# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_OUTPUT            = html
-
-# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
-# generated HTML page (for example: .htm, .php, .asp).
-# The default value is: .html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FILE_EXTENSION    = .html
-
-# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
-# each generated HTML page. If the tag is left blank doxygen will generate a
-# standard header.
-#
-# To get valid HTML the header file that includes any scripts and style sheets
-# that doxygen needs, which is dependent on the configuration options used (e.g.
-# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
-# default header using
-# doxygen -w html new_header.html new_footer.html new_stylesheet.css
-# YourConfigFile
-# and then modify the file new_header.html. See also section "Doxygen usage"
-# for information on how to generate the default header that doxygen normally
-# uses.
-# Note: The header is subject to change so you typically have to regenerate the
-# default header when upgrading to a newer version of doxygen. For a description
-# of the possible markers and block names see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_HEADER            =
-
-# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
-# generated HTML page. If the tag is left blank doxygen will generate a standard
-# footer. See HTML_HEADER for more information on how to generate a default
-# footer and what special commands can be used inside the footer. See also
-# section "Doxygen usage" for information on how to generate the default footer
-# that doxygen normally uses.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FOOTER            =
-
-# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
-# sheet that is used by each HTML page. It can be used to fine-tune the look of
-# the HTML output. If left blank doxygen will generate a default style sheet.
-# See also section "Doxygen usage" for information on how to generate the style
-# sheet that doxygen normally uses.
-# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
-# it is more robust and this tag (HTML_STYLESHEET) will in the future become
-# obsolete.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_STYLESHEET        =
-
-# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
-# cascading style sheets that are included after the standard style sheets
-# created by doxygen. Using this option one can overrule certain style aspects.
-# This is preferred over using HTML_STYLESHEET since it does not replace the
-# standard style sheet and is therefore more robust against future updates.
-# Doxygen will copy the style sheet files to the output directory.
-# Note: The order of the extra style sheet files is of importance (e.g. the last
-# style sheet in the list overrules the setting of the previous ones in the
-# list). For an example see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_EXTRA_STYLESHEET  =
-
-# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the HTML output directory. Note
-# that these files will be copied to the base HTML output directory. Use the
-# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
-# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
-# files will be copied as-is; there are no commands or markers available.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_EXTRA_FILES       =
-
-# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
-# will adjust the colors in the style sheet and background images according to
-# this color. Hue is specified as an angle on a colorwheel, see
-# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
-# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
-# purple, and 360 is red again.
-# Minimum value: 0, maximum value: 359, default value: 220.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_HUE    = 220
-
-# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
-# in the HTML output. For a value of 0 the output will use grayscales only. A
-# value of 255 will produce the most vivid colors.
-# Minimum value: 0, maximum value: 255, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_SAT    = 100
-
-# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
-# luminance component of the colors in the HTML output. Values below 100
-# gradually make the output lighter, whereas values above 100 make the output
-# darker. The value divided by 100 is the actual gamma applied, so 80 represents
-# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
-# change the gamma.
-# Minimum value: 40, maximum value: 240, default value: 80.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_GAMMA  = 80
-
-# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting this
-# to YES can help to show when doxygen was last run and thus if the
-# documentation is up to date.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_TIMESTAMP         = YES
-
-# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
-# documentation will contain sections that can be hidden and shown after the
-# page has loaded.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_DYNAMIC_SECTIONS  = NO
-
-# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
-# shown in the various tree structured indices initially; the user can expand
-# and collapse entries dynamically later on. Doxygen will expand the tree to
-# such a level that at most the specified number of entries are visible (unless
-# a fully collapsed tree already exceeds this amount). So setting the number of
-# entries 1 will produce a full collapsed tree by default. 0 is a special value
-# representing an infinite number of entries and will result in a full expanded
-# tree by default.
-# Minimum value: 0, maximum value: 9999, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_INDEX_NUM_ENTRIES = 100
-
-# If the GENERATE_DOCSET tag is set to YES, additional index files will be
-# generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see: http://developer.apple.com/tools/xcode/), introduced with
-# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
-# Makefile in the HTML output directory. Running make will produce the docset in
-# that directory and running make install will install the docset in
-# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
-# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
-# for more information.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_DOCSET        = NO
-
-# This tag determines the name of the docset feed. A documentation feed provides
-# an umbrella under which multiple documentation sets from a single provider
-# (such as a company or product suite) can be grouped.
-# The default value is: Doxygen generated docs.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_FEEDNAME        = "Doxygen generated docs"
-
-# This tag specifies a string that should uniquely identify the documentation
-# set bundle. This should be a reverse domain-name style string, e.g.
-# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_BUNDLE_ID       = org.doxygen.Project
-
-# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
-# the documentation publisher. This should be a reverse domain-name style
-# string, e.g. com.mycompany.MyDocSet.documentation.
-# The default value is: org.doxygen.Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
-
-# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
-# The default value is: Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_NAME  = Publisher
-
-# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
-# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
-# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
-# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
-# Windows.
-#
-# The HTML Help Workshop contains a compiler that can convert all HTML output
-# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
-# files are now used as the Windows 98 help format, and will replace the old
-# Windows help format (.hlp) on all Windows platforms in the future. Compressed
-# HTML files also contain an index, a table of contents, and you can search for
-# words in the documentation. The HTML workshop also contains a viewer for
-# compressed HTML files.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_HTMLHELP      = NO
-
-# The CHM_FILE tag can be used to specify the file name of the resulting .chm
-# file. You can add a path in front of the file if the result should not be
-# written to the html output directory.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_FILE               =
-
-# The HHC_LOCATION tag can be used to specify the location (absolute path
-# including file name) of the HTML help compiler (hhc.exe). If non-empty,
-# doxygen will try to run the HTML help compiler on the generated index.hhp.
-# The file has to be specified with full path.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-HHC_LOCATION           =
-
-# The GENERATE_CHI flag controls if a separate .chi index file is generated
-# (YES) or that it should be included in the master .chm file (NO).
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-GENERATE_CHI           = NO
-
-# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
-# and project file content.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_INDEX_ENCODING     =
-
-# The BINARY_TOC flag controls whether a binary table of contents is generated
-# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
-# enables the Previous and Next buttons.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-BINARY_TOC             = NO
-
-# The TOC_EXPAND flag can be set to YES to add extra items for group members to
-# the table of contents of the HTML help documentation and to the tree view.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-TOC_EXPAND             = NO
-
-# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
-# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
-# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
-# (.qch) of the generated HTML documentation.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_QHP           = NO
-
-# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
-# the file name of the resulting .qch file. The path specified is relative to
-# the HTML output folder.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QCH_FILE               =
-
-# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
-# Project output. For more information please see Qt Help Project / Namespace
-# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_NAMESPACE          = org.doxygen.Project
-
-# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
-# Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
-# folders).
-# The default value is: doc.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_VIRTUAL_FOLDER     = doc
-
-# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
-# filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_NAME   =
-
-# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
-# custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_ATTRS  =
-
-# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
-# project's filter section matches. Qt Help Project / Filter Attributes (see:
-# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_SECT_FILTER_ATTRS  =
-
-# The QHG_LOCATION tag can be used to specify the location of Qt's
-# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
-# generated .qhp file.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHG_LOCATION           =
-
-# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
-# generated, together with the HTML files, they form an Eclipse help plugin. To
-# install this plugin and make it available under the help contents menu in
-# Eclipse, the contents of the directory containing the HTML and XML files needs
-# to be copied into the plugins directory of eclipse. The name of the directory
-# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
-# After copying Eclipse needs to be restarted before the help appears.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_ECLIPSEHELP   = NO
-
-# A unique identifier for the Eclipse help plugin. When installing the plugin
-# the directory name containing the HTML and XML files should also have this
-# name. Each documentation set should have its own identifier.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
-
-ECLIPSE_DOC_ID         = org.doxygen.Project
-
-# If you want full control over the layout of the generated HTML pages it might
-# be necessary to disable the index and replace it with your own. The
-# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
-# of each HTML page. A value of NO enables the index and the value YES disables
-# it. Since the tabs in the index contain the same information as the navigation
-# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-DISABLE_INDEX          = NO
-
-# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
-# structure should be generated to display hierarchical information. If the tag
-# value is set to YES, a side panel will be generated containing a tree-like
-# index structure (just like the one that is generated for HTML Help). For this
-# to work a browser that supports JavaScript, DHTML, CSS and frames is required
-# (i.e. any modern browser). Windows users are probably better off using the
-# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
-# further fine-tune the look of the index. As an example, the default style
-# sheet generated by doxygen has an example that shows how to put an image at
-# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
-# the same information as the tab index, you could consider setting
-# DISABLE_INDEX to YES when enabling this option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_TREEVIEW      = NO
-
-# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
-# doxygen will group on one line in the generated HTML documentation.
-#
-# Note that a value of 0 will completely suppress the enum values from appearing
-# in the overview section.
-# Minimum value: 0, maximum value: 20, default value: 4.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-ENUM_VALUES_PER_LINE   = 4
-
-# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
-# to set the initial width (in pixels) of the frame in which the tree is shown.
-# Minimum value: 0, maximum value: 1500, default value: 250.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-TREEVIEW_WIDTH         = 250
-
-# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
-# external symbols imported via tag files in a separate window.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-EXT_LINKS_IN_WINDOW    = NO
-
-# Use this tag to change the font size of LaTeX formulas included as images in
-# the HTML documentation. When you change the font size after a successful
-# doxygen run you need to manually remove any form_*.png images from the HTML
-# output directory to force them to be regenerated.
-# Minimum value: 8, maximum value: 50, default value: 10.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_FONTSIZE       = 10
-
-# Use the FORMULA_TRANPARENT tag to determine whether or not the images
-# generated for formulas are transparent PNGs. Transparent PNGs are not
-# supported properly for IE 6.0, but are supported on all modern browsers.
-#
-# Note that when changing this option you need to delete any form_*.png files in
-# the HTML output directory before the changes have effect.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_TRANSPARENT    = YES
-
-# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
-# http://www.mathjax.org) which uses client side Javascript for the rendering
-# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
-# installed or if you want to formulas look prettier in the HTML output. When
-# enabled you may also need to install MathJax separately and configure the path
-# to it using the MATHJAX_RELPATH option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-USE_MATHJAX            = NO
-
-# When MathJax is enabled you can set the default output format to be used for
-# the MathJax output. See the MathJax site (see:
-# http://docs.mathjax.org/en/latest/output.html) for more details.
-# Possible values are: HTML-CSS (which is slower, but has the best
-# compatibility), NativeMML (i.e. MathML) and SVG.
-# The default value is: HTML-CSS.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_FORMAT         = HTML-CSS
-
-# When MathJax is enabled you need to specify the location relative to the HTML
-# output directory using the MATHJAX_RELPATH option. The destination directory
-# should contain the MathJax.js script. For instance, if the mathjax directory
-# is located at the same level as the HTML output directory, then
-# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
-# Content Delivery Network so you can quickly see the result without installing
-# MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from http://www.mathjax.org before deployment.
-# The default value is: http://cdn.mathjax.org/mathjax/latest.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
-
-# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
-# extension names that should be enabled during MathJax rendering. For example
-# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_EXTENSIONS     =
-
-# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
-# of code that will be used on startup of the MathJax code. See the MathJax site
-# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
-# example see the documentation.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_CODEFILE       =
-
-# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
-# the HTML output. The underlying search engine uses javascript and DHTML and
-# should work on any modern browser. Note that when using HTML help
-# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
-# there is already a search function so this one should typically be disabled.
-# For large projects the javascript based search engine can be slow, then
-# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
-# search using the keyboard; to jump to the search box use <access key> + S
-# (what the <access key> is depends on the OS and browser, but it is typically
-# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
-# key> to jump into the search results window, the results can be navigated
-# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
-# the search. The filter options can be selected when the cursor is inside the
-# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
-# to select a filter and <Enter> or <escape> to activate or cancel the filter
-# option.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-SEARCHENGINE           = YES
-
-# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
-# implemented using a web server instead of a web client using Javascript. There
-# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
-# setting. When disabled, doxygen will generate a PHP script for searching and
-# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
-# and searching needs to be provided by external tools. See the section
-# "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SERVER_BASED_SEARCH    = NO
-
-# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
-# script for searching. Instead the search results are written to an XML file
-# which needs to be processed by an external indexer. Doxygen will invoke an
-# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
-# search results.
-#
-# Doxygen ships with an example indexer (doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/).
-#
-# See the section "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-EXTERNAL_SEARCH        = NO
-
-# The SEARCHENGINE_URL should point to a search engine hosted by a web server
-# which will return the search results when EXTERNAL_SEARCH is enabled.
-#
-# Doxygen ships with an example indexer (doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/). See the section "External Indexing and
-# Searching" for details.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SEARCHENGINE_URL       =
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
-# search data is written to a file for indexing by an external tool. With the
-# SEARCHDATA_FILE tag the name of this file can be specified.
-# The default file is: searchdata.xml.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SEARCHDATA_FILE        = searchdata.xml
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
-# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
-# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
-# projects and redirect the results back to the right project.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-EXTERNAL_SEARCH_ID     =
-
-# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
-# projects other than the one defined by this configuration file, but that are
-# all added to the same external search index. Each project needs to have a
-# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
-# to a relative location where the documentation can be found. The format is:
-# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-EXTRA_SEARCH_MAPPINGS  =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the LaTeX output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
-# The default value is: YES.
-
-GENERATE_LATEX         = YES
-
-# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_OUTPUT           = latex
-
-# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
-# invoked.
-#
-# Note that when enabling USE_PDFLATEX this option is only used for generating
-# bitmaps for formulas in the HTML output, but not in the Makefile that is
-# written to the output directory.
-# The default file is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_CMD_NAME         = latex
-
-# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
-# index for LaTeX.
-# The default file is: makeindex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-MAKEINDEX_CMD_NAME     = makeindex
-
-# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-COMPACT_LATEX          = NO
-
-# The PAPER_TYPE tag can be used to set the paper type that is used by the
-# printer.
-# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
-# 14 inches) and executive (7.25 x 10.5 inches).
-# The default value is: a4.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PAPER_TYPE             = a4
-
-# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
-# that should be included in the LaTeX output. The package can be specified just
-# by its name or with the correct syntax as to be used with the LaTeX
-# \usepackage command. To get the times font for instance you can specify :
-# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
-# To use the option intlimits with the amsmath package you can specify:
-# EXTRA_PACKAGES=[intlimits]{amsmath}
-# If left blank no extra packages will be included.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-EXTRA_PACKAGES         =
-
-# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
-# generated LaTeX document. The header should contain everything until the first
-# chapter. If it is left blank doxygen will generate a standard header. See
-# section "Doxygen usage" for information on how to let doxygen write the
-# default header to a separate file.
-#
-# Note: Only use a user-defined header if you know what you are doing! The
-# following commands have a special meaning inside the header: $title,
-# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
-# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
-# string, for the replacement values of the other commands the user is referred
-# to HTML_HEADER.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HEADER           =
-
-# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
-# generated LaTeX document. The footer should contain everything after the last
-# chapter. If it is left blank doxygen will generate a standard footer. See
-# LATEX_HEADER for more information on how to generate a default footer and what
-# special commands can be used inside the footer.
-#
-# Note: Only use a user-defined footer if you know what you are doing!
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_FOOTER           =
-
-# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
-# LaTeX style sheets that are included after the standard style sheets created
-# by doxygen. Using this option one can overrule certain style aspects. Doxygen
-# will copy the style sheet files to the output directory.
-# Note: The order of the extra style sheet files is of importance (e.g. the last
-# style sheet in the list overrules the setting of the previous ones in the
-# list).
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_EXTRA_STYLESHEET =
-
-# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the LATEX_OUTPUT output
-# directory. Note that the files will be copied as-is; there are no commands or
-# markers available.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_EXTRA_FILES      =
-
-# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
-# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
-# contain links (just like the HTML output) instead of page references. This
-# makes the output suitable for online browsing using a PDF viewer.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PDF_HYPERLINKS         = YES
-
-# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
-# the PDF file directly from the LaTeX files. Set this option to YES, to get a
-# higher quality PDF documentation.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-USE_PDFLATEX           = YES
-
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
-# command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help. This option is also used
-# when generating formulas in HTML.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BATCHMODE        = NO
-
-# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
-# index chapters (such as File Index, Compound Index, etc.) in the output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HIDE_INDICES     = NO
-
-# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
-# code with syntax highlighting in the LaTeX output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_SOURCE_CODE      = NO
-
-# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
-# bibliography, e.g. plainnat, or ieeetr. See
-# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
-# The default value is: plain.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BIB_STYLE        = plain
-
-# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
-# page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_TIMESTAMP        = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the RTF output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
-# RTF output is optimized for Word 97 and may not look too pretty with other RTF
-# readers/editors.
-# The default value is: NO.
-
-GENERATE_RTF           = NO
-
-# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: rtf.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_OUTPUT             = rtf
-
-# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-COMPACT_RTF            = NO
-
-# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
-# contain hyperlink fields. The RTF file will contain links (just like the HTML
-# output) instead of page references. This makes the output suitable for online
-# browsing using Word or some other Word compatible readers that support those
-# fields.
-#
-# Note: WordPad (write) and others do not support links.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_HYPERLINKS         = NO
-
-# Load stylesheet definitions from file. Syntax is similar to doxygen's config
-# file, i.e. a series of assignments. You only have to provide replacements,
-# missing definitions are set to their default value.
-#
-# See also section "Doxygen usage" for information on how to generate the
-# default style sheet that doxygen normally uses.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_STYLESHEET_FILE    =
-
-# Set optional variables used in the generation of an RTF document. Syntax is
-# similar to doxygen's config file. A template extensions file can be generated
-# using doxygen -e rtf extensionFile.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_EXTENSIONS_FILE    =
-
-# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
-# with syntax highlighting in the RTF output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_SOURCE_CODE        = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the man page output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
-# classes and files.
-# The default value is: NO.
-
-GENERATE_MAN           = NO
-
-# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it. A directory man3 will be created inside the directory specified by
-# MAN_OUTPUT.
-# The default directory is: man.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_OUTPUT             = man
-
-# The MAN_EXTENSION tag determines the extension that is added to the generated
-# man pages. In case the manual section does not start with a number, the number
-# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
-# optional.
-# The default value is: .3.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_EXTENSION          = .3
-
-# The MAN_SUBDIR tag determines the name of the directory created within
-# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
-# MAN_EXTENSION with the initial . removed.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_SUBDIR             =
-
-# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
-# will generate one additional man file for each entity documented in the real
-# man page(s). These additional files only source the real man page, but without
-# them the man command would be unable to find the correct page.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_LINKS              = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the XML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
-# captures the structure of the code including all documentation.
-# The default value is: NO.
-
-GENERATE_XML           = YES
-
-# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: xml.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_OUTPUT             = xml
-
-# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
-# listings (including syntax highlighting and cross-referencing information) to
-# the XML output. Note that enabling this will significantly increase the size
-# of the XML output.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_PROGRAMLISTING     = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the DOCBOOK output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
-# that can be used to generate PDF.
-# The default value is: NO.
-
-GENERATE_DOCBOOK       = NO
-
-# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
-# front of it.
-# The default directory is: docbook.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-DOCBOOK_OUTPUT         = docbook
-
-# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
-# program listings (including syntax highlighting and cross-referencing
-# information) to the DOCBOOK output. Note that enabling this will significantly
-# increase the size of the DOCBOOK output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-DOCBOOK_PROGRAMLISTING = NO
-
-#---------------------------------------------------------------------------
-# Configuration options for the AutoGen Definitions output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
-# AutoGen Definitions (see http://autogen.sf.net) file that captures the
-# structure of the code including all documentation. Note that this feature is
-# still experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_AUTOGEN_DEF   = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the Perl module output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
-# file that captures the structure of the code including all documentation.
-#
-# Note that this feature is still experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_PERLMOD       = NO
-
-# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
-# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
-# output from the Perl module output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_LATEX          = NO
-
-# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
-# formatted so it can be parsed by a human reader. This is useful if you want to
-# understand what is going on. On the other hand, if this tag is set to NO, the
-# size of the Perl module output will be much smaller and Perl will parse it
-# just the same.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_PRETTY         = YES
-
-# The names of the make variables in the generated doxyrules.make file are
-# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
-# so different doxyrules.make files included by the same Makefile don't
-# overwrite each other's variables.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_MAKEVAR_PREFIX =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the preprocessor
-#---------------------------------------------------------------------------
-
-# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
-# C-preprocessor directives found in the sources and include files.
-# The default value is: YES.
-
-ENABLE_PREPROCESSING   = NO
-
-# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
-# in the source code. If set to NO, only conditional compilation will be
-# performed. Macro expansion can be done in a controlled way by setting
-# EXPAND_ONLY_PREDEF to YES.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-MACRO_EXPANSION        = NO
-
-# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
-# the macro expansion is limited to the macros specified with the PREDEFINED and
-# EXPAND_AS_DEFINED tags.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-EXPAND_ONLY_PREDEF     = NO
-
-# If the SEARCH_INCLUDES tag is set to YES, the include files in the
-# INCLUDE_PATH will be searched if a #include is found.
-# The default value is: YES.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-SEARCH_INCLUDES        = YES
-
-# The INCLUDE_PATH tag can be used to specify one or more directories that
-# contain include files that are not input files but should be processed by the
-# preprocessor.
-# This tag requires that the tag SEARCH_INCLUDES is set to YES.
-
-INCLUDE_PATH           =
-
-# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
-# patterns (like *.h and *.hpp) to filter out the header-files in the
-# directories. If left blank, the patterns specified with FILE_PATTERNS will be
-# used.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-INCLUDE_FILE_PATTERNS  =
-
-# The PREDEFINED tag can be used to specify one or more macro names that are
-# defined before the preprocessor is started (similar to the -D option of e.g.
-# gcc). The argument of the tag is a list of macros of the form: name or
-# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
-# is assumed. To prevent a macro definition from being undefined via #undef or
-# recursively expanded use the := operator instead of the = operator.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-PREDEFINED             =
-
-# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
-# tag can be used to specify a list of macro names that should be expanded. The
-# macro definition that is found in the sources will be used. Use the PREDEFINED
-# tag if you want to use a different macro definition that overrules the
-# definition found in the source code.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-EXPAND_AS_DEFINED      =
-
-# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
-# remove all references to function-like macros that are alone on a line, have
-# an all uppercase name, and do not end with a semicolon. Such function macros
-# are typically used for boiler-plate code, and will confuse the parser if not
-# removed.
-# The default value is: YES.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-SKIP_FUNCTION_MACROS   = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to external references
-#---------------------------------------------------------------------------
-
-# The TAGFILES tag can be used to specify one or more tag files. For each tag
-# file the location of the external documentation should be added. The format of
-# a tag file without this location is as follows:
-# TAGFILES = file1 file2 ...
-# Adding location for the tag files is done as follows:
-# TAGFILES = file1=loc1 "file2 = loc2" ...
-# where loc1 and loc2 can be relative or absolute paths or URLs. See the
-# section "Linking to external documentation" for more information about the use
-# of tag files.
-# Note: Each tag file must have a unique name (where the name does NOT include
-# the path). If a tag file is not located in the directory in which doxygen is
-# run, you must also specify the path to the tagfile here.
-
-TAGFILES               =
-
-# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
-# tag file that is based on the input files it reads. See section "Linking to
-# external documentation" for more information about the usage of tag files.
-
-GENERATE_TAGFILE       =
-
-# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
-# the class index. If set to NO, only the inherited external classes will be
-# listed.
-# The default value is: NO.
-
-ALLEXTERNALS           = NO
-
-# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
-# in the modules index. If set to NO, only the current project's groups will be
-# listed.
-# The default value is: YES.
-
-EXTERNAL_GROUPS        = YES
-
-# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
-# the related pages index. If set to NO, only the current project's pages will
-# be listed.
-# The default value is: YES.
-
-EXTERNAL_PAGES         = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the dot tool
-#---------------------------------------------------------------------------
-
-# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
-# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
-# NO turns the diagrams off. Note that this option also works with HAVE_DOT
-# disabled, but it is recommended to install and use dot, since it yields more
-# powerful graphs.
-# The default value is: YES.
-
-CLASS_DIAGRAMS         = YES
-
-# You can include diagrams made with dia in doxygen documentation. Doxygen will
-# then run dia to produce the diagram and insert it in the documentation. The
-# DIA_PATH tag allows you to specify the directory where the dia binary resides.
-# If left empty dia is assumed to be found in the default search path.
-
-DIA_PATH               =
-
-# If set to YES the inheritance and collaboration graphs will hide inheritance
-# and usage relations if the target is undocumented or is not a class.
-# The default value is: YES.
-
-HIDE_UNDOC_RELATIONS   = YES
-
-# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
-# available from the path. This tool is part of Graphviz (see:
-# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
-# Bell Labs. The other options in this section have no effect if this option is
-# set to NO
-# The default value is: NO.
-
-HAVE_DOT               = NO
-
-# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
-# to run in parallel. When set to 0 doxygen will base this on the number of
-# processors available in the system. You can set it explicitly to a value
-# larger than 0 to get control over the balance between CPU load and processing
-# speed.
-# Minimum value: 0, maximum value: 32, default value: 0.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_NUM_THREADS        = 0
-
-# When you want a differently looking font in the dot files that doxygen
-# generates you can specify the font name using DOT_FONTNAME. You need to make
-# sure dot is able to find the font, which can be done by putting it in a
-# standard location or by setting the DOTFONTPATH environment variable or by
-# setting DOT_FONTPATH to the directory containing the font.
-# The default value is: Helvetica.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTNAME           = Helvetica
-
-# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
-# dot graphs.
-# Minimum value: 4, maximum value: 24, default value: 10.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTSIZE           = 10
-
-# By default doxygen will tell dot to use the default font as specified with
-# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
-# the path where dot can find it using this tag.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_FONTPATH           =
-
-# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
-# each documented class showing the direct and indirect inheritance relations.
-# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CLASS_GRAPH            = YES
-
-# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
-# graph for each documented class showing the direct and indirect implementation
-# dependencies (inheritance, containment, and class references variables) of the
-# class with other documented classes.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-COLLABORATION_GRAPH    = YES
-
-# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
-# groups, showing the direct groups dependencies.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GROUP_GRAPHS           = YES
-
-# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
-# collaboration diagrams in a style similar to the OMG's Unified Modeling
-# Language.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-UML_LOOK               = NO
-
-# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
-# class node. If there are many fields or methods and many nodes the graph may
-# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
-# number of items for each type to make the size more manageable. Set this to 0
-# for no limit. Note that the threshold may be exceeded by 50% before the limit
-# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
-# but if the number exceeds 15, the total amount of fields shown is limited to
-# 10.
-# Minimum value: 0, maximum value: 100, default value: 10.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-UML_LIMIT_NUM_FIELDS   = 10
-
-# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
-# collaboration graphs will show the relations between templates and their
-# instances.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-TEMPLATE_RELATIONS     = NO
-
-# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
-# YES then doxygen will generate a graph for each documented file showing the
-# direct and indirect include dependencies of the file with other documented
-# files.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INCLUDE_GRAPH          = YES
-
-# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
-# set to YES then doxygen will generate a graph for each documented file showing
-# the direct and indirect include dependencies of the file with other documented
-# files.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-INCLUDED_BY_GRAPH      = YES
-
-# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
-# dependency graph for every global function or class method.
-#
-# Note that enabling this option will significantly increase the time of a run.
-# So in most cases it will be better to enable call graphs for selected
-# functions only using the \callgraph command. Disabling a call graph can be
-# accomplished by means of the command \hidecallgraph.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CALL_GRAPH             = NO
-
-# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
-# dependency graph for every global function or class method.
-#
-# Note that enabling this option will significantly increase the time of a run.
-# So in most cases it will be better to enable caller graphs for selected
-# functions only using the \callergraph command. Disabling a caller graph can be
-# accomplished by means of the command \hidecallergraph.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-CALLER_GRAPH           = NO
-
-# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
-# hierarchy of all classes instead of a textual one.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-GRAPHICAL_HIERARCHY    = YES
-
-# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
-# dependencies a directory has on other directories in a graphical way. The
-# dependency relations are determined by the #include relations between the
-# files in the directories.
-# The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DIRECTORY_GRAPH        = YES
-
-# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
-# generated by dot. For an explanation of the image formats see the section
-# output formats in the documentation of the dot tool (Graphviz (see:
-# http://www.graphviz.org/)).
-# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
-# to make the SVG files visible in IE 9+ (other browsers do not have this
-# requirement).
-# Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
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-#
-# Note that this requires a modern browser other than Internet Explorer. Tested
-# and working are Firefox, Chrome, Safari, and Opera.
-# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
-# the SVG files visible. Older versions of IE do not have SVG support.
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
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-INTERACTIVE_SVG        = NO
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-# The DOT_PATH tag can be used to specify the path where the dot tool can be
-# found. If left blank, it is assumed the dot tool can be found in the path.
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-
-DOT_PATH               =
-
-# The DOTFILE_DIRS tag can be used to specify one or more directories that
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-DOTFILE_DIRS           =
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-# The DIAFILE_DIRS tag can be used to specify one or more directories that
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-# that will be shown in the graph. If the number of nodes in a graph becomes
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-# children of the root node in a graph is already larger than
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diff --git a/ml-xgboost/dmlc-core/doc/Makefile b/ml-xgboost/dmlc-core/doc/Makefile
deleted file mode 100644
index 40bba2a..0000000
--- a/ml-xgboost/dmlc-core/doc/Makefile
+++ /dev/null
@@ -1,192 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = _build
-
-# User-friendly check for sphinx-build
-ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
-$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
-endif
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
-
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  applehelp  to make an Apple Help Book"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  xml        to make Docutils-native XML files"
-	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-	@echo "  coverage   to run coverage check of the documentation (if enabled)"
-
-clean:
-	rm -rf $(BUILDDIR)/*
-
-html:
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/rabit.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/rabit.qhc"
-
-applehelp:
-	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
-	@echo
-	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
-	@echo "N.B. You won't be able to view it unless you put it in" \
-	      "~/Library/Documentation/Help or install it in your application" \
-	      "bundle."
-
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/rabit"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/rabit"
-	@echo "# devhelp"
-
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-latexpdfja:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through platex and dvipdfmx..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
-
-coverage:
-	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
-	@echo "Testing of coverage in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/coverage/python.txt."
-
-xml:
-	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
-	@echo
-	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-pseudoxml:
-	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
-	@echo
-	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/ml-xgboost/dmlc-core/doc/README b/ml-xgboost/dmlc-core/doc/README
deleted file mode 100644
index 9f8bb53..0000000
--- a/ml-xgboost/dmlc-core/doc/README
+++ /dev/null
@@ -1,7 +0,0 @@
-This document is generated by sphinx.
-Make sure you cloned the following repos in the root.
-
-- https://github.com/tqchen/recommonmark
-
-Type make html in doc folder.
-
diff --git a/ml-xgboost/dmlc-core/doc/build.md b/ml-xgboost/dmlc-core/doc/build.md
deleted file mode 100644
index 71b7667..0000000
--- a/ml-xgboost/dmlc-core/doc/build.md
+++ /dev/null
@@ -1,16 +0,0 @@
-Using dmlc-core with CMake
-==========================
-dmlc defines a exported CMake target which can be used by `find_package` command.
-
-For example, if you have a simple C++ project that contains only a main.cc file,
-which uses dmlc-core as dependency, the CMakeLists.txt for your project can be
-defined as follow:
-
-``` cmake
-project(demo)
-cmake_minimum_required(VERSION 3.2)
-
-find_package(dmlc REQUIRED)
-add_executable(demo main.cc)
-target_link_libraries(demo dmlc::dmlc)
-```
\ No newline at end of file
diff --git a/ml-xgboost/dmlc-core/doc/conf.py b/ml-xgboost/dmlc-core/doc/conf.py
deleted file mode 100644
index c7ad06a..0000000
--- a/ml-xgboost/dmlc-core/doc/conf.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# documentation build configuration file, created by
-# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-import sys
-import os, subprocess
-import shlex
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.insert(0, curr_path)
-from sphinx_util import MarkdownParser, AutoStructify
-
-# -- General configuration ------------------------------------------------
-
-# General information about the project.
-project = u'dmlc-core'
-copyright = u'2015, dmlc-core developers'
-author = u'dmlc-core developers'
-github_doc_root = 'https://github.com/dmlc-core/dmlc-core/tree/master/doc/'
-
-# add markdown parser
-MarkdownParser.github_doc_root = github_doc_root
-source_parsers = {
-    '.md': MarkdownParser,
-}
-# Version information.
-
-version = "0.5.0"
-release = version
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.mathjax',
-    'breathe',
-]
-
-# Use breathe to include doxygen documents
-breathe_projects = {'dmlc-core' : 'doxygen/xml/'}
-breathe_default_project = 'dmlc-core'
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-# source_suffix = ['.rst', '.md']
-source_suffix = ['.rst', '.md']
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = ['_build']
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-#keep_warnings = False
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-# html_theme = 'alabaster'
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = project + 'doc'
-
-# -- Options for LaTeX output ---------------------------------------------
-latex_elements = {
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-  (master_doc, 'dmlc-core.tex', project,
-   author, 'manual'),
-]
-
-# hook for doxygen
-def run_doxygen(folder):
-    """Run the doxygen make command in the designated folder."""
-    try:
-        retcode = subprocess.call("cd %s; make doxygen" % folder, shell=True)
-        retcode = subprocess.call("rm -rf _build/html/doxygen", shell=True)
-        retcode = subprocess.call("mkdir _build", shell=True)
-        retcode = subprocess.call("mkdir _build/html", shell=True)
-        retcode = subprocess.call("cp -rf doxygen/html _build/html/doxygen", shell=True)
-        if retcode < 0:
-            sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
-    except OSError as e:
-        sys.stderr.write("doxygen execution failed: %s" % e)
-
-
-def generate_doxygen_xml(app):
-    """Run the doxygen make commands if we're on the ReadTheDocs server"""
-    run_doxygen('..')
-
-def setup(app):
-    # Add hook for building doxygen xml when needed
-    app.connect("builder-inited", generate_doxygen_xml)
-    app.add_config_value('recommonmark_config', {
-            'url_resolver': lambda url: github_doc_root + url,
-            }, True)
-    app.add_transform(AutoStructify)
diff --git a/ml-xgboost/dmlc-core/doc/index.md b/ml-xgboost/dmlc-core/doc/index.md
deleted file mode 100644
index 9f1f27c..0000000
--- a/ml-xgboost/dmlc-core/doc/index.md
+++ /dev/null
@@ -1,18 +0,0 @@
-DMLC-Core Documentation
-=======================
-DMLC Core contains common codebase to help us build machine learning toolkits easier.
-
-Contents
---------
-* [Using dmlc-core with CMake](build.md)
-* [Parameter Structure for Machine Learning](parameter.md)
-* [Doxygen C++ API Reference](https://dmlc-core.readthedocs.org/en/latest/doxygen)
-
-Indices and tables
-------------------
-
-```eval_rst
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-```
diff --git a/ml-xgboost/dmlc-core/doc/parameter.md b/ml-xgboost/dmlc-core/doc/parameter.md
deleted file mode 100644
index 187d358..0000000
--- a/ml-xgboost/dmlc-core/doc/parameter.md
+++ /dev/null
@@ -1,198 +0,0 @@
-Parameter Structure for Machine Learning
-========================================
-One of the most important ingredients of machine learning projects are the parameters.
-Parameters act as a way of communication between users and the library. In this article, we will introduce the parameter module of DMLC, a lightweight C++ module that is designed to support
-general machine learning libraries. It comes with the following nice properties:
-
-- Easy declaration of typed fields, default values and constraints.
-- Auto checking of constraints and throw exceptions when constraint is not met.
-- Auto generation of human readable docstrings on parameters.
-- Serialization and de-serialization into JSON and ```std::map<std::string, std::string>```.
-
-Use Parameter Module
---------------------
-### Declare the Parameter
-In the dmlc parameter module, every parameter can be declared as a structure. 
-This means you can easily access these fields as they normally are efficiently.
-For example, it is very common to write 
-```c++
-weight -= param.learning_rate * gradient;
-```
-
-The only difference between a normal structure is that we will need to declare
-all the fields, as well as their default value and constraints.
-The following code gives an example of declaring parameter structure ```MyParam```.
-
-```c++
-#include <dmlc/parameter.h>
-
-// declare the parameter, normally put it in header file.
-struct MyParam : public dmlc::Parameter<MyParam> {
-  float learning_rate;
-  int num_hidden;
-  int activation;
-  std::string name;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(MyParam) {
-    DMLC_DECLARE_FIELD(num_hidden).set_range(0, 1000)
-        .describe("Number of hidden unit in the fully connected layer.");
-    DMLC_DECLARE_FIELD(learning_rate).set_default(0.01f)
-        .describe("Learning rate of SGD optimization.");
-    DMLC_DECLARE_FIELD(activation).add_enum("relu", 1).add_enum("sigmoid", 2)
-        .describe("Activation function type.");
-    DMLC_DECLARE_FIELD(name).set_default("layer")
-        .describe("Name of the net.");
-  }
-};
-
-// register the parameter, this is normally in a cc file.
-DMLC_REGISTER_PARAMETER(MyParam);
-```
-
-We can find that the only difference is the lines after ```DMLC_DECLARE_PARAMETER(MyParam)```,
-where all the fields are declared. In this example, we have declared parameters of ```float,int,string``` types.
-Here are some highlights in this example:
-
-- For the numeric parameters, it is possible to set a range constraints via ```.set_range(begin, end)```.
-- It is possible to define enumeration types, in this case activation. 
-  User is only allowed to set ```sigmoid``` or ```relu``` into the activation field, and they will be mapped into 1 and 2 separately.
-- The ```describe``` function adds a description on the field, which is used to generate human readable docstring. 
-
-### Set the Parameters
-After we declared the parameters, we can declare this structure as normal structure.
-Except that the ```MyParam``` structure now comes with a few member functions 
-to make parameter manipulation easy.
-To set the parameters from external data source, we can use the ```Init``` function.
-```c++
-int main() {
-   MyParam param;
-   std::vector<std::pair<std::string, std::string> > param_data = {
-     {"num_hidden", "100"},
-	 {"activation", "relu"},
-	 {"name", "myname"}
-   };
-   // set the parameters
-   param.Init(param_data);
-   return 0;
-}
-```
-After the ```Init``` function is called, the ```param``` will be filled with the specified key values in ```param_data```.
-More importantly, the ```Init``` function will do automatic checking of parameter range and throw an ```dmlc::ParamError``` 
-with detailed error message if things went wrong.
-
-### Generate Human Readable Docstrings
-Another useful feature of the parameter module is to get an human readable docstring of the parameter.
-This is helpful when we are creating language binding such as python and R, and we can use it to generate docstring of 
-foreign language interface.
-
-The following code obtains the dostring of ```MyParam```.
-```c++
-std::string docstring = MyParam::__DOC__();
-```
-
-We also provide a more structured way to access the detail of the fields(name, default value, detailed description) via
-```c++
-std::vector<dmlc::ParamFieldInfo> fields = MyParam::__FIELDS__();
-```
-
-### Serialization of Parameters
-One of the most common way to serialize the parameter is to convert it back to representation of ```std::map<string, string>```
-by using the following code. 
-```c++
-std::map<string, string> dict = param.__DICT__();
-```
-The ```std::map<string, string>``` can further be serialized easily. This way of serialization is more device and platform(32/64 bit) agnostic.
-However, this is not very compact, and recommended only used to serialize the general parameters set by the user.
-
-Direct serialization and loading of JSON format is also support.
-
-### Play with an Example
-We provide an example program [parameter.cc](https://github.com/dmlc/dmlc-core/blob/master/example/parameter.cc), to 
-demonstrate the usage mentioned above, and allow you to play with it and get sense of what is going on.
-
-How does it work
-----------------
-Hope you like the parameter module so far. In this section, we will explain how does it work. Making such parameter module 
-in ```C++``` is not easy. Because this basically means some way of reflection -- getting the information of fields in a 
-structure out, which is not supported by ```C++```. 
-
-Consider the following program, how do the Init function know the location of ```num_hidden```, and set it correctly
-in ```Init``` function?
-
-```c++
-#include <vector>
-#include <string>
-#include <dmlc/parameter.h>
-
-// declare the parameter, normally put it in header file.
-struct MyParam : public dmlc::Parameter<MyParam> {
-  float learning_rate;
-  int num_hidden;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(MyParam) {
-    DMLC_DECLARE_FIELD(num_hidden);
-    DMLC_DECLARE_FIELD(learning_rate).set_default(0.01f);
-  }
-};
-
-// register the parameter, this is normally in a cc file.
-DMLC_REGISTER_PARAMETER(MyParam);
-
-int main(int argc, char *argv[]) {
-  MyParam param;
-  std::vector<std::pair<std::string, std::string> > param_data = {
-    {"num_hidden", "100"},
-  };
-  param.Init(param_data);
-  return 0;
-}
-```
-
-The secret lies in the function ```DMLC_DECLARE_PARAMETER(MyParam)```, this is a macro defined in the parameter module.
-If we expand the macro, the code roughly becomes the following code.
-
-```c++
-struct Parameter<MyParam> {
-  template<typename ValueType>
-  inline FieldEntry<ValueType>&
-  DECLARE(ParamManagerSingleton<MyParam> *manager,
-		  const std::string& key,
-		  ValueType& ref){
-	// offset gives a generic way to access the address of the field
-	// from beginning of the structure.
-	size_t offset = ((char*)&ref - (char*)this);
-	parameter::FieldEntry<ValueType> *e =
-        new parameter::FieldEntry<ValueType>(key, offset);
-	manager->AddEntry(key, e);
-	return *e;
-  }
-};
-
-struct MyParam : public dmlc::Parameter<MyParam> {
-  float learning_rate;
-  int num_hidden;
-  // declare parameters
-  inline void __DECLARE__(ParamManagerSingleton<MyParam> *manager) {
-    this->DECLARE(manager, "num_hidden", num_hidden);
-	this->DECLARE(manager, "learning_rate", learning_rate).set_default(0.01f);
-  }
-};
-
-// This code is only used to show the general idea.
-// This code will only run once, the real code is done via singleton declaration pattern.
-{
-  static ParamManagerSingleton<MyParam> manager;
-  MyParam tmp;
-  tmp->__DECLARE__(&manager);
-}
-```
-This is not the actual code that runs, but generally shows the idea on how it works. 
-The key is that the structure layout is fixed for all the instances of objects.
-To figure out how to access each of the field, we can
-- Create an instance of MyParam, call the ```__DECLARE__``` function.
-- The relative position of the field against the head of the structure is recorded into a global singleton.
-- When we call ```Init```, we can get the ```offset``` from the singleton, and access the address of the field via ```(ValueType*)((char*)this + offset)```.
-
-You are welcomed to check out the real details in [dmlc/parameter.h](https://github.com/dmlc/dmlc-core/blob/master/include/dmlc/parameter.h).
-By using the generic template programming in C++, we have created a simple and useful parameter module for machine learning libraries.
-This module is used extensively by DMLC projects. Hope you will find it useful as well :).
diff --git a/ml-xgboost/dmlc-core/doc/sphinx_util.py b/ml-xgboost/dmlc-core/doc/sphinx_util.py
deleted file mode 100644
index f6a33ff..0000000
--- a/ml-xgboost/dmlc-core/doc/sphinx_util.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Helper utilty function for customization."""
-import sys
-import os
-import docutils
-import subprocess
-
-if os.environ.get('READTHEDOCS', None) == 'True':
-    subprocess.call('cd ..; rm -rf recommonmark;' +
-                    'git clone https://github.com/tqchen/recommonmark', shell=True)
-
-sys.path.insert(0, os.path.abspath('../recommonmark/'))
-from recommonmark import parser, transform
-
-MarkdownParser = parser.CommonMarkParser
-AutoStructify = transform.AutoStructify
diff --git a/ml-xgboost/dmlc-core/example/dmlc_example.mk b/ml-xgboost/dmlc-core/example/dmlc_example.mk
deleted file mode 100644
index 3cd30f8..0000000
--- a/ml-xgboost/dmlc-core/example/dmlc_example.mk
+++ /dev/null
@@ -1,9 +0,0 @@
-ALL_EXAMPLE=example/parameter
-
-
-example/parameter: example/parameter.cc libdmlc.a
-
-$(ALL_EXAMPLE) :
-	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a,  $^) $(LDFLAGS)
-
-
diff --git a/ml-xgboost/dmlc-core/example/parameter.cc b/ml-xgboost/dmlc-core/example/parameter.cc
deleted file mode 100644
index 6d11b71..0000000
--- a/ml-xgboost/dmlc-core/example/parameter.cc
+++ /dev/null
@@ -1,64 +0,0 @@
-// This is an example program showing usage of parameter module
-// Build, on root folder, type
-//
-//   make example
-//
-// Example usage:
-//
-//   example/parameter num_hidden=100 name=aaa activation=relu
-//
-
-#include <dmlc/parameter.h>
-
-struct MyParam : public dmlc::Parameter<MyParam> {
-  float learning_rate;
-  int num_hidden;
-  int activation;
-  std::string name;
-  // declare parameters in header file
-  DMLC_DECLARE_PARAMETER(MyParam) {
-    DMLC_DECLARE_FIELD(num_hidden).set_range(0, 1000)
-        .describe("Number of hidden unit in the fully connected layer.");
-    DMLC_DECLARE_FIELD(learning_rate).set_default(0.01f)
-        .describe("Learning rate of SGD optimization.");
-    DMLC_DECLARE_FIELD(activation).add_enum("relu", 1).add_enum("sigmoid", 2)
-        .describe("Activation function type.");
-    DMLC_DECLARE_FIELD(name).set_default("mnet")
-        .describe("Name of the net.");
-
-    // user can also set nhidden besides num_hidden
-    DMLC_DECLARE_ALIAS(num_hidden, nhidden);
-    DMLC_DECLARE_ALIAS(activation, act);
-  }
-};
-
-// register it in cc file
-DMLC_REGISTER_PARAMETER(MyParam);
-
-
-int main(int argc, char *argv[]) {
-  if (argc == 1) {
-    printf("Usage: [key=value] ...\n");
-    return 0;
-  }
-
-  MyParam param;
-  std::map<std::string, std::string> kwargs;
-  for (int i = 0; i < argc; ++i) {
-    char name[256], val[256];
-    if (sscanf(argv[i], "%[^=]=%[^\n]", name, val) == 2) {
-      kwargs[name] = val;
-    }
-  }
-  printf("Docstring\n---------\n%s", MyParam::__DOC__().c_str());
-
-  printf("start to set parameters ...\n");
-  param.Init(kwargs);
-  printf("-----\n");
-  printf("param.num_hidden=%d\n", param.num_hidden);
-  printf("param.learning_rate=%f\n", param.learning_rate);
-  printf("param.name=%s\n", param.name.c_str());
-  printf("param.activation=%d\n", param.activation);
-  return 0;
-}
-
diff --git a/ml-xgboost/dmlc-core/include/dmlc/any.h b/ml-xgboost/dmlc-core/include/dmlc/any.h
deleted file mode 100644
index e8d8b99..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/any.h
+++ /dev/null
@@ -1,427 +0,0 @@
-/*!
- * Copyright (c) 2016 by Contributors
- * \file any.h
- * \brief Container to hold any data type.
- */
-#ifndef DMLC_ANY_H_
-#define DMLC_ANY_H_
-
-// This code need c++11 to compile
-#include <typeinfo>
-#include <type_traits>
-#include <utility>
-#include <algorithm>
-#include <cstring>
-
-#include "./base.h"
-#include "./logging.h"
-
-namespace dmlc {
-// forward declare any;
-class any;
-
-/*!
- * Get a reference to content stored in the any as type T.
- * This will cause an error if
- * T does not match the type stored.
- * This function is not part of std::any standard.
- *
- * \param src The source source any container.
- * \return The reference of content
- * \tparam T The type of the value to be fetched.
- */
-template<typename T>
-inline T& get(any& src);  // NOLINT(*)
-
-/*!
- * Get the const reference content stored in the any as type T.
- * This will cause an error if
- * T does not match the type stored.
- * This function is not part of std::any standard.
- *
- * \param src The source source any container.
- * \return The reference of content
- * \tparam T The type of the value to be fetched.
- */
-template<typename T>
-inline const T& get(const any& src);
-
-/*!
- * The "unsafe" versions of get. It is required when where we know
- * what type is stored in the any and can't use typeid() comparison,
- * e.g., when our types may travel across different shared libraries.
- * This function is not part of std::any standard.
- *
- * \param src The source source any container.
- * \return The reference of content
- * \tparam T The type of the value to be fetched.
- */
-template<typename T>
-inline const T& unsafe_get(const any& src);
-
-/*!
- * The "unsafe" versions of get. It is required when where we know
- * what type is stored in the any and can't use typeid() comparison,
- * e.g., when our types may travel across different shared libraries.
- * This function is not part of std::any standard.
- *
- * \param src The source source any container.
- * \return The reference of content
- * \tparam T The type of the value to be fetched.
- */
-template<typename T>
-inline T& unsafe_get(any& src);  // NOLINT(*)
-
-/*!
- * \brief An any class that is compatible to std::any in c++17.
- *
- * \code
- *   dmlc::any a = std::string("mydear"), b = 1;
- *   // get reference out and add it
- *   dmlc::get<int>(b) += 1;
- *   // a is now string
- *   LOG(INFO) << dmlc::get<std::string>(a);
- *   // a is now 2, the string stored will be properly destructed
- *   a = std::move(b);
- *   LOG(INFO) << dmlc::get<int>(a);
- * \endcode
- * \sa get
- */
-class any {
- public:
-  /*! \brief default constructor */
-  inline any() = default;
-  /*!
-   * \brief move constructor from another any
-   * \param other The other any to be moved
-   */
-  inline any(any&& other);  // NOLINT(*)
-  /*!
-   * \brief copy constructor
-   * \param other The other any to be copied
-   */
-  inline any(const any& other);  // NOLINT(*)
-  /*!
-   * \brief constructor from any types
-   * \param other The other types to be constructed into any.
-   * \tparam T The value type of other.
-   */
-  template<typename T>
-  inline any(T&& other);  // NOLINT(*)
-  /*! \brief destructor */
-  inline ~any();
-  /*!
-   * \brief assign operator from other
-   * \param other The other any to be copy or moved.
-   * \return self
-   */
-  inline any& operator=(any&& other);
-  /*!
-   * \brief assign operator from other
-   * \param other The other any to be copy or moved.
-   * \return self
-   */
-  inline any& operator=(const any& other);
-  /*!
-   * \brief assign operator from any type.
-   * \param other The other any to be copy or moved.
-   * \tparam T The value type of other.
-   * \return self
-   */
-  template<typename T>
-  inline any& operator=(T&& other);
-  /*!
-   * \return whether the container is empty.
-   */
-  inline bool empty() const;
-  /*!
-   * \brief clear the content of container
-   */
-  inline void clear();
-  /*!
-   * swap current content with other
-   * \param other The other data to be swapped.
-   */
-  inline void swap(any& other); // NOLINT(*)
-  /*!
-   * \return The type_info about the stored type.
-   */
-  inline const std::type_info& type() const;
-  /*! \brief Construct value of type T inplace */
-  template<typename T, typename... Args>
-  inline void construct(Args&&... args);
-
- private:
-  //! \cond Doxygen_Suppress
-  // declare of helper class
-  template<typename T>
-  class TypeOnHeap;
-  template<typename T>
-  class TypeOnStack;
-  template<typename T>
-  class TypeInfo;
-  // size of stack space, it takes 32 bytes for one any type.
-  static const size_t kStack = sizeof(void*) * 3;
-  static const size_t kAlign = sizeof(void*);
-  // container use dynamic storage only when space runs lager
-  union Data {
-    // stack space
-    std::aligned_storage<kStack, kAlign>::type stack;
-    // pointer to heap space
-    void* pheap;
-  };
-  // type specific information
-  struct Type {
-    // destructor function
-    void (*destroy)(Data* data);
-    // copy constructor
-    void (*create_from_data)(Data* dst, const Data& src);
-    // the type info function
-    const std::type_info* ptype_info;
-  };
-  // constant to check if data can be stored on heap.
-  template<typename T>
-  struct data_on_stack {
-    static const bool value = alignof(T) <= kAlign && sizeof(T) <= kStack;
-  };
-  // declare friend with
-  template<typename T>
-  friend T& get(any& src);  // NOLINT(*)
-  template<typename T>
-  friend const T& get(const any& src);
-  template<typename T>
-  friend T& unsafe_get(any& src);  // NOLINT(*)
-  template<typename T>
-  friend const T& unsafe_get(const any& src);
-  // internal construct function
-  inline void construct(any&& other);
-  // internal construct function
-  inline void construct(const any& other);
-  // internal function to check if type is correct.
-  template<typename T>
-  inline void check_type() const;
-  template<typename T>
-  inline void check_type_by_name() const;
-  // internal type specific information
-  const Type* type_{nullptr};
-  // internal data
-  Data data_;
-};
-
-template<typename T>
-inline any::any(T&& other) {
-  typedef typename std::decay<T>::type DT;
-  if (std::is_same<DT, any>::value) {
-    this->construct(std::forward<T>(other));
-  } else {
-    static_assert(std::is_copy_constructible<DT>::value,
-                  "Any can only hold value that is copy constructable");
-    type_ = TypeInfo<DT>::get_type();
-    if (data_on_stack<DT>::value) {
-#pragma GCC diagnostic push
-#if 6 <= __GNUC__
-#pragma GCC diagnostic ignored "-Wplacement-new"
-#endif
-      new (&(data_.stack)) DT(std::forward<T>(other));
-#pragma GCC diagnostic pop
-    } else {
-      data_.pheap = new DT(std::forward<T>(other));
-    }
-  }
-}
-
-inline any::any(any&& other) {
-  this->construct(std::move(other));
-}
-
-inline any::any(const any& other) {
-  this->construct(other);
-}
-
-inline void any::construct(any&& other) {
-  type_ = other.type_;
-  data_ = other.data_;
-  other.type_ = nullptr;
-}
-
-inline void any::construct(const any& other) {
-  type_ = other.type_;
-  if (type_ != nullptr) {
-    type_->create_from_data(&data_, other.data_);
-  }
-}
-
-template<typename T, typename... Args>
-inline void any::construct(Args&&... args) {
-  clear();
-  typedef typename std::decay<T>::type DT;
-  type_ = TypeInfo<DT>::get_type();
-  if (data_on_stack<DT>::value) {
-#pragma GCC diagnostic push
-#if 6 <= __GNUC__
-#pragma GCC diagnostic ignored "-Wplacement-new"
-#endif
-    new (&(data_.stack)) DT(std::forward<Args>(args)...);
-#pragma GCC diagnostic pop
-  } else {
-    data_.pheap = new DT(std::forward<Args>(args)...);
-  }
-}
-
-inline any::~any() {
-  this->clear();
-}
-
-inline any& any::operator=(any&& other) {
-  any(std::move(other)).swap(*this);
-  return *this;
-}
-
-inline any& any::operator=(const any& other) {
-  any(other).swap(*this);
-  return *this;
-}
-
-template<typename T>
-inline any& any::operator=(T&& other) {
-  any(std::forward<T>(other)).swap(*this);
-  return *this;
-}
-
-inline void any::swap(any& other) { // NOLINT(*)
-  std::swap(type_, other.type_);
-  std::swap(data_, other.data_);
-}
-
-inline void any::clear() {
-  if (type_ != nullptr) {
-    if (type_->destroy != nullptr) {
-      type_->destroy(&data_);
-    }
-    type_ = nullptr;
-  }
-}
-
-inline bool any::empty() const {
-  return type_ == nullptr;
-}
-
-inline const std::type_info& any::type() const {
-  if (type_ != nullptr) {
-    return *(type_->ptype_info);
-  } else {
-    return typeid(void);
-  }
-}
-
-template<typename T>
-inline void any::check_type() const {
-  CHECK(type_ != nullptr)
-      << "The any container is empty"
-      << " requested=" << typeid(T).name();
-  CHECK(*(type_->ptype_info) == typeid(T))
-      << "The stored type mismatch"
-      << " stored=" << type_->ptype_info->name()
-      << " requested=" << typeid(T).name();
-}
-
-template<typename T>
-inline void any::check_type_by_name() const {
-  CHECK(type_ != nullptr)
-      << "The any container is empty"
-      << " requested=" << typeid(T).name();
-  CHECK(strcmp(type_->ptype_info->name(), typeid(T).name()) == 0)
-      << "The stored type name mismatch"
-      << " stored=" << type_->ptype_info->name()
-      << " requested=" << typeid(T).name();
-}
-
-template<typename T>
-inline const T& get(const any& src) {
-  src.check_type<T>();
-  return *any::TypeInfo<T>::get_ptr(&(src.data_));
-}
-
-template<typename T>
-inline T& get(any& src) { // NOLINT(*)
-  src.check_type<T>();
-  return *any::TypeInfo<T>::get_ptr(&(src.data_));
-}
-
-template<typename T>
-inline const T& unsafe_get(const any& src) {
-  src.check_type_by_name<T>();
-  return *any::TypeInfo<T>::get_ptr(&(src.data_));
-}
-
-template<typename T>
-inline T& unsafe_get(any& src) { // NOLINT(*)
-  src.check_type_by_name<T>();
-  return *any::TypeInfo<T>::get_ptr(&(src.data_));
-}
-
-template<typename T>
-class any::TypeOnHeap {
- public:
-  inline static T* get_ptr(any::Data* data) {
-    return static_cast<T*>(data->pheap);
-  }
-  inline static const T* get_ptr(const any::Data* data) {
-    return static_cast<const T*>(data->pheap);
-  }
-  inline static void create_from_data(any::Data* dst, const any::Data& data) {
-    dst->pheap = new T(*get_ptr(&data));
-  }
-  inline static void destroy(Data* data) {
-    delete static_cast<T*>(data->pheap);
-  }
-};
-
-template<typename T>
-class any::TypeOnStack {
- public:
-  inline static T* get_ptr(any::Data* data) {
-    return reinterpret_cast<T*>(&(data->stack));
-  }
-  inline static const T* get_ptr(const any::Data* data) {
-    return reinterpret_cast<const T*>(&(data->stack));
-  }
-  inline static void create_from_data(any::Data* dst, const any::Data& data) {
-    new (&(dst->stack)) T(*get_ptr(&data));
-  }
-  inline static void destroy(Data* data) {
-    T* dptr = reinterpret_cast<T*>(&(data->stack));
-    dptr->~T();
-  }
-};
-
-template<typename T>
-class any::TypeInfo
-    : public std::conditional<any::data_on_stack<T>::value,
-                              any::TypeOnStack<T>,
-                              any::TypeOnHeap<T> >::type {
- public:
-  inline static const Type* get_type() {
-    static TypeInfo<T> tp;
-    return &(tp.type_);
-  }
-
- private:
-  // local type
-  Type type_;
-  // constructor
-  TypeInfo() {
-    if (std::is_pod<T>::value && data_on_stack<T>::value) {
-      type_.destroy = nullptr;
-    } else {
-      type_.destroy = TypeInfo<T>::destroy;
-    }
-    type_.create_from_data = TypeInfo<T>::create_from_data;
-    type_.ptype_info = &typeid(T);
-  }
-};
-//! \endcond
-
-}  // namespace dmlc
-
-#endif  // DMLC_ANY_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/array_view.h b/ml-xgboost/dmlc-core/include/dmlc/array_view.h
deleted file mode 100644
index 5e01a78..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/array_view.h
+++ /dev/null
@@ -1,128 +0,0 @@
-/*!
- *  Copyright (c) 2016 by Contributors
- * \file array_view.h
- * \brief Read only data structure to reference array
- */
-#ifndef DMLC_ARRAY_VIEW_H_
-#define DMLC_ARRAY_VIEW_H_
-
-#include <vector>
-#include <array>
-
-namespace dmlc {
-
-/*!
- * \brief Read only data structure to reference continuous memory region of array.
- * Provide unified view for vector, array and C style array.
- * This data structure do not guarantee aliveness of referenced array.
- *
- * Make sure do not use array_view to record data in async function closures.
- * Also do not use array_view to create reference to temporary data structure.
- *
- * \tparam ValueType The value
- *
- * \code
- *  std::vector<int> myvec{1,2,3};
- *  dmlc::array_view<int> view(myvec);
- *  // indexed visit to the view.
- *  LOG(INFO) << view[0];
- *
- *  for (int v : view) {
- *     // visit each element in the view
- *  }
- * \endcode
- */
-template<typename ValueType>
-class array_view {
- public:
-  /*! \brief default constructor */
-  array_view() = default;
-  /*!
-   * \brief default copy constructor
-   * \param other another array view.
-   */
-  array_view(const array_view<ValueType> &other) = default;  // NOLINT(*)
-#ifndef _MSC_VER
-  /*!
-   * \brief default move constructor
-   * \param other another array view.
-   */
-  array_view(array_view<ValueType>&& other) = default; // NOLINT(*)
-#else
-  /*!
-  * \brief default move constructor
-  * \param other another array view.
-  */
-  array_view(array_view<ValueType>&& other) { // NOLINT(*)
-    begin_ = other.begin_;
-    size_ = other.size_;
-    other.begin_ = nullptr;
-  }
-#endif
-  /*!
-   * \brief default assign constructor
-   * \param other another array view.
-   * \return self.
-   */
-  array_view<ValueType>& operator=(const array_view<ValueType>& other) = default; // NOLINT(*)
-  /*!
-   * \brief construct array view std::vector
-   * \param other vector container
-   */
-  array_view(const std::vector<ValueType>& other) {  // NOLINT(*)
-    if (other.size() != 0) {
-      begin_ = &other[0]; size_ = other.size();
-    }
-  }
-  /*!
-   * \brief construct array std::array
-   * \param other another array view.
-   */
-  template<std::size_t size>
-  array_view(const std::array<ValueType, size>& other) {  // NOLINT(*)
-    if (size != 0) {
-      begin_ = &other[0]; size_ = size;
-    }
-  }
-  /*!
-   * \brief construct array view from continuous segment
-   * \param begin beginning pointre
-   * \param end end pointer
-   */
-  array_view(const ValueType* begin, const ValueType* end) {
-    if (begin < end) {
-      begin_ = begin;
-      size_ = end - begin;
-    }
-  }
-  /*! \return size of the array */
-  inline size_t size() const {
-    return size_;
-  }
-  /*! \return begin of the array */
-  inline const ValueType* begin() const {
-    return begin_;
-  }
-  /*! \return end point of the array */
-  inline const ValueType* end() const {
-    return begin_ + size_;
-  }
-  /*!
-   * \brief get i-th element from the view
-   * \param i The index.
-   * \return const reference to i-th element.
-   */
-  inline const ValueType& operator[](size_t i) const {
-    return begin_[i];
-  }
-
- private:
-  /*! \brief the begin of the view */
-  const ValueType* begin_{nullptr};
-  /*! \brief The size of the view */
-  size_t size_{0};
-};
-
-}  // namespace dmlc
-
-#endif  // DMLC_ARRAY_VIEW_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/base.h b/ml-xgboost/dmlc-core/include/dmlc/base.h
deleted file mode 100644
index 9ff4726..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/base.h
+++ /dev/null
@@ -1,303 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file base.h
- * \brief defines configuration macros
- */
-#ifndef DMLC_BASE_H_
-#define DMLC_BASE_H_
-
-/*! \brief whether use glog for logging */
-#ifndef DMLC_USE_GLOG
-#define DMLC_USE_GLOG 0
-#endif
-
-/*!
- * \brief whether throw dmlc::Error instead of
- *  directly calling abort when FATAL error occured
- *  NOTE: this may still not be perfect.
- *  do not use FATAL and CHECK in destructors
- */
-#ifndef DMLC_LOG_FATAL_THROW
-#define DMLC_LOG_FATAL_THROW 1
-#endif
-
-/*!
- * \brief whether always log a message before throw
- * This can help identify the error that cannot be catched.
- */
-#ifndef DMLC_LOG_BEFORE_THROW
-#define DMLC_LOG_BEFORE_THROW 0
-#endif
-
-/*!
- * \brief Whether to use customized logger,
- * whose output can be decided by other libraries.
- */
-#ifndef DMLC_LOG_CUSTOMIZE
-#define DMLC_LOG_CUSTOMIZE 0
-#endif
-
-/*!
- * \brief Whether to enable debug logging feature.
- */
-#ifndef DMLC_LOG_DEBUG
-#ifdef NDEBUG
-#define DMLC_LOG_DEBUG 0
-#else
-#define DMLC_LOG_DEBUG 1
-#endif
-#endif
-
-/*! \brief whether compile with hdfs support */
-#ifndef DMLC_USE_HDFS
-#define DMLC_USE_HDFS 0
-#endif
-
-/*! \brief whether compile with s3 support */
-#ifndef DMLC_USE_S3
-#define DMLC_USE_S3 0
-#endif
-
-/*! \brief whether or not use parameter server */
-#ifndef DMLC_USE_PS
-#define DMLC_USE_PS 0
-#endif
-
-/*! \brief whether or not use c++11 support */
-#ifndef DMLC_USE_CXX11
-#if defined(__GXX_EXPERIMENTAL_CXX0X__) || defined(_MSC_VER)
-#define DMLC_USE_CXX11 1
-#else
-#define DMLC_USE_CXX11 (__cplusplus >= 201103L)
-#endif
-#endif
-
-/*! \brief strict CXX11 support */
-#ifndef DMLC_STRICT_CXX11
-#if defined(_MSC_VER)
-#define DMLC_STRICT_CXX11 1
-#else
-#define DMLC_STRICT_CXX11 (__cplusplus >= 201103L)
-#endif
-#endif
-
-/*! \brief Whether cxx11 thread local is supported */
-#ifndef DMLC_CXX11_THREAD_LOCAL
-#if defined(_MSC_VER)
-#define DMLC_CXX11_THREAD_LOCAL (_MSC_VER >= 1900)
-#elif defined(__clang__)
-#define DMLC_CXX11_THREAD_LOCAL (__has_feature(cxx_thread_local))
-#else
-#define DMLC_CXX11_THREAD_LOCAL (__cplusplus >= 201103L)
-#endif
-#endif
-
-/*! \brief Whether to use modern thread local construct */
-#ifndef DMLC_MODERN_THREAD_LOCAL
-#define DMLC_MODERN_THREAD_LOCAL 1
-#endif
-
-
-
-/*! \brief whether RTTI is enabled */
-#ifndef DMLC_ENABLE_RTTI
-#define DMLC_ENABLE_RTTI 1
-#endif
-
-/*! \brief whether use fopen64 */
-#ifndef DMLC_USE_FOPEN64
-#define DMLC_USE_FOPEN64 1
-#endif
-
-/// check if g++ is before 4.6
-#if DMLC_USE_CXX11 && defined(__GNUC__) && !defined(__clang_version__)
-#if __GNUC__ == 4 && __GNUC_MINOR__ < 6
-#pragma message("Will need g++-4.6 or higher to compile all"           \
-                "the features in dmlc-core, "                           \
-                "compile without c++0x, some features may be disabled")
-#undef DMLC_USE_CXX11
-#define DMLC_USE_CXX11 0
-#endif
-#endif
-
-/*!
- * \brief Use little endian for binary serialization
- *  if this is set to 0, use big endian.
- */
-#ifndef DMLC_IO_USE_LITTLE_ENDIAN
-#define DMLC_IO_USE_LITTLE_ENDIAN 1
-#endif
-
-/*!
- * \brief Enable std::thread related modules,
- *  Used to disable some module in mingw compile.
- */
-#ifndef DMLC_ENABLE_STD_THREAD
-#define DMLC_ENABLE_STD_THREAD DMLC_USE_CXX11
-#endif
-
-/*! \brief whether enable regex support, actually need g++-4.9 or higher*/
-#ifndef DMLC_USE_REGEX
-#define DMLC_USE_REGEX DMLC_STRICT_CXX11
-#endif
-
-/*! \brief helper macro to supress unused warning */
-#if defined(__GNUC__)
-#define DMLC_ATTRIBUTE_UNUSED __attribute__((unused))
-#else
-#define DMLC_ATTRIBUTE_UNUSED
-#endif
-
-/*! \brief helper macro to supress Undefined Behavior Sanitizer for a specific function */
-#if defined(__clang__)
-#define DMLC_SUPPRESS_UBSAN __attribute__((no_sanitize("undefined")))
-#elif defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)
-#define DMLC_SUPPRESS_UBSAN __attribute__((no_sanitize_undefined))
-#else
-#define DMLC_SUPPRESS_UBSAN
-#endif
-
-/*! \brief helper macro to generate string concat */
-#define DMLC_STR_CONCAT_(__x, __y) __x##__y
-#define DMLC_STR_CONCAT(__x, __y) DMLC_STR_CONCAT_(__x, __y)
-
-/*!
- * \brief Disable copy constructor and assignment operator.
- *
- * If C++11 is supported, both copy and move constructors and
- * assignment operators are deleted explicitly. Otherwise, they are
- * only declared but not implemented. Place this macro in private
- * section if C++11 is not available.
- */
-#ifndef DISALLOW_COPY_AND_ASSIGN
-#  if DMLC_USE_CXX11
-#    define DISALLOW_COPY_AND_ASSIGN(T) \
-       T(T const&) = delete; \
-       T(T&&) = delete; \
-       T& operator=(T const&) = delete; \
-       T& operator=(T&&) = delete
-#  else
-#    define DISALLOW_COPY_AND_ASSIGN(T) \
-       T(T const&); \
-       T& operator=(T const&)
-#  endif
-#endif
-
-#ifdef __APPLE__
-#  define off64_t off_t
-#endif
-
-#ifdef _MSC_VER
-#if _MSC_VER < 1900
-// NOTE: sprintf_s is not equivalent to snprintf,
-// they are equivalent when success, which is sufficient for our case
-#define snprintf sprintf_s
-#define vsnprintf vsprintf_s
-#endif
-#else
-#ifdef _FILE_OFFSET_BITS
-#if _FILE_OFFSET_BITS == 32
-#pragma message("Warning: FILE OFFSET BITS defined to be 32 bit")
-#endif
-#endif
-
-extern "C" {
-#include <sys/types.h>
-}
-#endif
-
-#ifdef _MSC_VER
-//! \cond Doxygen_Suppress
-typedef signed char int8_t;
-typedef __int16 int16_t;
-typedef __int32 int32_t;
-typedef __int64 int64_t;
-typedef unsigned char uint8_t;
-typedef unsigned __int16 uint16_t;
-typedef unsigned __int32 uint32_t;
-typedef unsigned __int64 uint64_t;
-//! \endcond
-#else
-#include <inttypes.h>
-#endif
-#include <string>
-#include <vector>
-
-#if defined(_MSC_VER) && _MSC_VER < 1900
-#define noexcept_true throw ()
-#define noexcept_false
-#define noexcept(a) noexcept_##a
-#endif
-
-#if DMLC_USE_CXX11
-#define DMLC_THROW_EXCEPTION noexcept(false)
-#define DMLC_NO_EXCEPTION  noexcept(true)
-#else
-#define DMLC_THROW_EXCEPTION
-#define DMLC_NO_EXCEPTION
-#endif
-
-/*! \brief namespace for dmlc */
-namespace dmlc {
-/*!
- * \brief safely get the beginning address of a vector
- * \param vec input vector
- * \return beginning address of a vector
- */
-template<typename T>
-inline T *BeginPtr(std::vector<T> &vec) {  // NOLINT(*)
-  if (vec.size() == 0) {
-    return NULL;
-  } else {
-    return &vec[0];
-  }
-}
-/*!
- * \brief get the beginning address of a const vector
- * \param vec input vector
- * \return beginning address of a vector
- */
-template<typename T>
-inline const T *BeginPtr(const std::vector<T> &vec) {
-  if (vec.size() == 0) {
-    return NULL;
-  } else {
-    return &vec[0];
-  }
-}
-/*!
- * \brief get the beginning address of a string
- * \param str input string
- * \return beginning address of a string
- */
-inline char* BeginPtr(std::string &str) {  // NOLINT(*)
-  if (str.length() == 0) return NULL;
-  return &str[0];
-}
-/*!
- * \brief get the beginning address of a const string
- * \param str input string
- * \return beginning address of a string
- */
-inline const char* BeginPtr(const std::string &str) {
-  if (str.length() == 0) return NULL;
-  return &str[0];
-}
-}  // namespace dmlc
-
-#if defined(_MSC_VER) && _MSC_VER < 1900
-#define constexpr const
-#define alignof __alignof
-#endif
-
-/* If fopen64 is not defined by current machine,
-   replace fopen64 with std::fopen. Also determine ability to print stack trace
-   for fatal error and define DMLC_LOG_STACK_TRACE if stack trace can be
-   produced. Always keep this include directive at the bottom of dmlc/base.h */
-#ifdef DMLC_CORE_USE_CMAKE
-#include <dmlc/build_config.h>
-#else
-#include <dmlc/build_config_default.h>
-#endif
-
-#endif  // DMLC_BASE_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/blockingconcurrentqueue.h b/ml-xgboost/dmlc-core/include/dmlc/blockingconcurrentqueue.h
deleted file mode 100644
index 9d24943..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/blockingconcurrentqueue.h
+++ /dev/null
@@ -1,991 +0,0 @@
-//! \cond Doxygen_Suppress
-// Provides an efficient blocking version of moodycamel::ConcurrentQueue.
-// ©2015-2016 Cameron Desrochers. Distributed under the terms of the simplified
-// BSD license, available at the top of concurrentqueue.h.
-// Uses Jeff Preshing's semaphore implementation (under the terms of its
-// separate zlib license, embedded below).
-
-#ifndef DMLC_BLOCKINGCONCURRENTQUEUE_H_
-#define DMLC_BLOCKINGCONCURRENTQUEUE_H_
-
-#pragma once
-
-#include "concurrentqueue.h"
-#include <type_traits>
-#include <cerrno>
-#include <memory>
-#include <chrono>
-#include <ctime>
-
-#if defined(_WIN32)
-// Avoid including windows.h in a header; we only need a handful of
-// items, so we'll redeclare them here (this is relatively safe since
-// the API generally has to remain stable between Windows versions).
-// I know this is an ugly hack but it still beats polluting the global
-// namespace with thousands of generic names or adding a .cpp for nothing.
-extern "C" {
-	struct _SECURITY_ATTRIBUTES;
-	__declspec(dllimport) void* __stdcall CreateSemaphoreW(_SECURITY_ATTRIBUTES* lpSemaphoreAttributes, long lInitialCount, long lMaximumCount, const wchar_t* lpName);
-	__declspec(dllimport) int __stdcall CloseHandle(void* hObject);
-	__declspec(dllimport) unsigned long __stdcall WaitForSingleObject(void* hHandle, unsigned long dwMilliseconds);
-	__declspec(dllimport) int __stdcall ReleaseSemaphore(void* hSemaphore, long lReleaseCount, long* lpPreviousCount);
-}
-#elif defined(__MACH__)
-#include <mach/mach.h>
-#elif defined(__unix__)
-#include <semaphore.h>
-#endif
-
-namespace dmlc {
-
-namespace moodycamel
-{
-namespace details
-{
-	// Code in the mpmc_sema namespace below is an adaptation of Jeff Preshing's
-	// portable + lightweight semaphore implementations, originally from
-	// https://github.com/preshing/cpp11-on-multicore/blob/master/common/sema.h
-	// LICENSE:
-	// Copyright (c) 2015 Jeff Preshing
-	//
-	// This software is provided 'as-is', without any express or implied
-	// warranty. In no event will the authors be held liable for any damages
-	// arising from the use of this software.
-	//
-	// Permission is granted to anyone to use this software for any purpose,
-	// including commercial applications, and to alter it and redistribute it
-	// freely, subject to the following restrictions:
-	//
-	// 1. The origin of this software must not be misrepresented; you must not
-	//	claim that you wrote the original software. If you use this software
-	//	in a product, an acknowledgement in the product documentation would be
-	//	appreciated but is not required.
-	// 2. Altered source versions must be plainly marked as such, and must not be
-	//	misrepresented as being the original software.
-	// 3. This notice may not be removed or altered from any source distribution.
-	namespace mpmc_sema
-	{
-#if defined(_WIN32)
-		class Semaphore
-		{
-		private:
-			void* m_hSema;
-
-			Semaphore(const Semaphore& other) MOODYCAMEL_DELETE_FUNCTION;
-			Semaphore& operator=(const Semaphore& other) MOODYCAMEL_DELETE_FUNCTION;
-
-		public:
-			Semaphore(int initialCount = 0)
-			{
-				assert(initialCount >= 0);
-				const long maxLong = 0x7fffffff;
-				m_hSema = CreateSemaphoreW(nullptr, initialCount, maxLong, nullptr);
-			}
-
-			~Semaphore()
-			{
-				CloseHandle(m_hSema);
-			}
-
-			void wait()
-			{
-				const unsigned long infinite = 0xffffffff;
-				WaitForSingleObject(m_hSema, infinite);
-			}
-
-			bool try_wait()
-			{
-				const unsigned long RC_WAIT_TIMEOUT = 0x00000102;
-				return WaitForSingleObject(m_hSema, 0) != RC_WAIT_TIMEOUT;
-			}
-
-			bool timed_wait(std::uint64_t usecs)
-			{
-				const unsigned long RC_WAIT_TIMEOUT = 0x00000102;
-				return WaitForSingleObject(m_hSema, (unsigned long)(usecs / 1000)) != RC_WAIT_TIMEOUT;
-			}
-
-			void signal(int count = 1)
-			{
-				ReleaseSemaphore(m_hSema, count, nullptr);
-			}
-		};
-#elif defined(__MACH__)
-		//---------------------------------------------------------
-		// Semaphore (Apple iOS and OSX)
-		// Can't use POSIX semaphores due to http://lists.apple.com/archives/darwin-kernel/2009/Apr/msg00010.html
-		//---------------------------------------------------------
-		class Semaphore
-		{
-		private:
-			semaphore_t m_sema;
-
-			Semaphore(const Semaphore& other) MOODYCAMEL_DELETE_FUNCTION;
-			Semaphore& operator=(const Semaphore& other) MOODYCAMEL_DELETE_FUNCTION;
-
-		public:
-			Semaphore(int initialCount = 0)
-			{
-				assert(initialCount >= 0);
-				semaphore_create(mach_task_self(), &m_sema, SYNC_POLICY_FIFO, initialCount);
-			}
-
-			~Semaphore()
-			{
-				semaphore_destroy(mach_task_self(), m_sema);
-			}
-
-			void wait()
-			{
-				semaphore_wait(m_sema);
-			}
-
-			bool try_wait()
-			{
-				return timed_wait(0);
-			}
-
-			bool timed_wait(std::uint64_t timeout_usecs)
-			{
-				mach_timespec_t ts;
-				ts.tv_sec = static_cast<unsigned int>(timeout_usecs / 1000000);
-				ts.tv_nsec = (timeout_usecs % 1000000) * 1000;
-
-				// added in OSX 10.10: https://developer.apple.com/library/prerelease/mac/documentation/General/Reference/APIDiffsMacOSX10_10SeedDiff/modules/Darwin.html
-				kern_return_t rc = semaphore_timedwait(m_sema, ts);
-
-				return rc != KERN_OPERATION_TIMED_OUT;
-			}
-
-			void signal()
-			{
-				semaphore_signal(m_sema);
-			}
-
-			void signal(int count)
-			{
-				while (count-- > 0)
-				{
-					semaphore_signal(m_sema);
-				}
-			}
-		};
-#elif defined(__unix__)
-		//---------------------------------------------------------
-		// Semaphore (POSIX, Linux)
-		//---------------------------------------------------------
-		class Semaphore
-		{
-		private:
-			sem_t m_sema;
-
-			Semaphore(const Semaphore& other) MOODYCAMEL_DELETE_FUNCTION;
-			Semaphore& operator=(const Semaphore& other) MOODYCAMEL_DELETE_FUNCTION;
-
-		public:
-			Semaphore(int initialCount = 0)
-			{
-				assert(initialCount >= 0);
-				sem_init(&m_sema, 0, initialCount);
-			}
-
-			~Semaphore()
-			{
-				sem_destroy(&m_sema);
-			}
-
-			void wait()
-			{
-				// http://stackoverflow.com/questions/2013181/gdb-causes-sem-wait-to-fail-with-eintr-error
-				int rc;
-				do {
-					rc = sem_wait(&m_sema);
-				} while (rc == -1 && errno == EINTR);
-			}
-
-			bool try_wait()
-			{
-				int rc;
-				do {
-					rc = sem_trywait(&m_sema);
-				} while (rc == -1 && errno == EINTR);
-				return !(rc == -1 && errno == EAGAIN);
-			}
-
-			bool timed_wait(std::uint64_t usecs)
-			{
-				struct timespec ts;
-				const int usecs_in_1_sec = 1000000;
-				const int nsecs_in_1_sec = 1000000000;
-				clock_gettime(CLOCK_REALTIME, &ts);
-				ts.tv_sec += usecs / usecs_in_1_sec;
-				ts.tv_nsec += (usecs % usecs_in_1_sec) * 1000;
-				// sem_timedwait bombs if you have more than 1e9 in tv_nsec
-				// so we have to clean things up before passing it in
-				if (ts.tv_nsec >= nsecs_in_1_sec) {
-					ts.tv_nsec -= nsecs_in_1_sec;
-					++ts.tv_sec;
-				}
-
-				int rc;
-				do {
-					rc = sem_timedwait(&m_sema, &ts);
-				} while (rc == -1 && errno == EINTR);
-				return !(rc == -1 && errno == ETIMEDOUT);
-			}
-
-			void signal()
-			{
-				sem_post(&m_sema);
-			}
-
-			void signal(int count)
-			{
-				while (count-- > 0)
-				{
-					sem_post(&m_sema);
-				}
-			}
-		};
-#else
-#error Unsupported platform! (No semaphore wrapper available)
-#endif
-
-		//---------------------------------------------------------
-		// LightweightSemaphore
-		//---------------------------------------------------------
-		class LightweightSemaphore
-		{
-		public:
-			typedef std::make_signed<std::size_t>::type ssize_t;
-
-		private:
-			std::atomic<ssize_t> m_count;
-			Semaphore m_sema;
-
-			bool waitWithPartialSpinning(std::int64_t timeout_usecs = -1)
-			{
-				ssize_t oldCount;
-				// Is there a better way to set the initial spin count?
-				// If we lower it to 1000, testBenaphore becomes 15x slower on my Core i7-5930K Windows PC,
-				// as threads start hitting the kernel semaphore.
-				int spin = 10000;
-				while (--spin >= 0)
-				{
-					oldCount = m_count.load(std::memory_order_relaxed);
-					if ((oldCount > 0) && m_count.compare_exchange_strong(oldCount, oldCount - 1, std::memory_order_acquire, std::memory_order_relaxed))
-						return true;
-					std::atomic_signal_fence(std::memory_order_acquire);	 // Prevent the compiler from collapsing the loop.
-				}
-				oldCount = m_count.fetch_sub(1, std::memory_order_acquire);
-				if (oldCount > 0)
-					return true;
-				if (timeout_usecs < 0)
-				{
-					m_sema.wait();
-					return true;
-				}
-				if (m_sema.timed_wait((std::uint64_t)timeout_usecs))
-					return true;
-				// At this point, we've timed out waiting for the semaphore, but the
-				// count is still decremented indicating we may still be waiting on
-				// it. So we have to re-adjust the count, but only if the semaphore
-				// wasn't signaled enough times for us too since then. If it was, we
-				// need to release the semaphore too.
-				while (true)
-				{
-					oldCount = m_count.load(std::memory_order_acquire);
-					if (oldCount >= 0 && m_sema.try_wait())
-						return true;
-					if (oldCount < 0 && m_count.compare_exchange_strong(oldCount, oldCount + 1, std::memory_order_relaxed, std::memory_order_relaxed))
-						return false;
-				}
-			}
-
-			ssize_t waitManyWithPartialSpinning(ssize_t max, std::int64_t timeout_usecs = -1)
-			{
-				assert(max > 0);
-				ssize_t oldCount;
-				int spin = 10000;
-				while (--spin >= 0)
-				{
-					oldCount = m_count.load(std::memory_order_relaxed);
-					if (oldCount > 0)
-					{
-						ssize_t newCount = oldCount > max ? oldCount - max : 0;
-						if (m_count.compare_exchange_strong(oldCount, newCount, std::memory_order_acquire, std::memory_order_relaxed))
-							return oldCount - newCount;
-					}
-					std::atomic_signal_fence(std::memory_order_acquire);
-				}
-				oldCount = m_count.fetch_sub(1, std::memory_order_acquire);
-				if (oldCount <= 0)
-				{
-					if (timeout_usecs < 0)
-						m_sema.wait();
-					else if (!m_sema.timed_wait((std::uint64_t)timeout_usecs))
-					{
-						while (true)
-						{
-							oldCount = m_count.load(std::memory_order_acquire);
-							if (oldCount >= 0 && m_sema.try_wait())
-								break;
-							if (oldCount < 0 && m_count.compare_exchange_strong(oldCount, oldCount + 1, std::memory_order_relaxed, std::memory_order_relaxed))
-								return 0;
-						}
-					}
-				}
-				if (max > 1)
-					return 1 + tryWaitMany(max - 1);
-				return 1;
-			}
-
-		public:
-			LightweightSemaphore(ssize_t initialCount = 0) : m_count(initialCount)
-			{
-				assert(initialCount >= 0);
-			}
-
-			bool tryWait()
-			{
-				ssize_t oldCount = m_count.load(std::memory_order_relaxed);
-				while (oldCount > 0)
-				{
-					if (m_count.compare_exchange_weak(oldCount, oldCount - 1, std::memory_order_acquire, std::memory_order_relaxed))
-						return true;
-				}
-				return false;
-			}
-
-			void wait()
-			{
-				if (!tryWait())
-					waitWithPartialSpinning();
-			}
-
-			bool wait(std::int64_t timeout_usecs)
-			{
-				return tryWait() || waitWithPartialSpinning(timeout_usecs);
-			}
-
-			// Acquires between 0 and (greedily) max, inclusive
-			ssize_t tryWaitMany(ssize_t max)
-			{
-				assert(max >= 0);
-				ssize_t oldCount = m_count.load(std::memory_order_relaxed);
-				while (oldCount > 0)
-				{
-					ssize_t newCount = oldCount > max ? oldCount - max : 0;
-					if (m_count.compare_exchange_weak(oldCount, newCount, std::memory_order_acquire, std::memory_order_relaxed))
-						return oldCount - newCount;
-				}
-				return 0;
-			}
-
-			// Acquires at least one, and (greedily) at most max
-			ssize_t waitMany(ssize_t max, std::int64_t timeout_usecs)
-			{
-				assert(max >= 0);
-				ssize_t result = tryWaitMany(max);
-				if (result == 0 && max > 0)
-					result = waitManyWithPartialSpinning(max, timeout_usecs);
-				return result;
-			}
-
-			ssize_t waitMany(ssize_t max)
-			{
-				ssize_t result = waitMany(max, -1);
-				assert(result > 0);
-				return result;
-			}
-
-			void signal(ssize_t count = 1)
-			{
-				assert(count >= 0);
-				ssize_t oldCount = m_count.fetch_add(count, std::memory_order_release);
-				ssize_t toRelease = -oldCount < count ? -oldCount : count;
-				if (toRelease > 0)
-				{
-					m_sema.signal((int)toRelease);
-				}
-			}
-
-			ssize_t availableApprox() const
-			{
-				ssize_t count = m_count.load(std::memory_order_relaxed);
-				return count > 0 ? count : 0;
-			}
-		};
-	}	// end namespace mpmc_sema
-}	// end namespace details
-
-
-// This is a blocking version of the queue. It has an almost identical interface to
-// the normal non-blocking version, with the addition of various wait_dequeue() methods
-// and the removal of producer-specific dequeue methods.
-template<typename T, typename Traits = ConcurrentQueueDefaultTraits>
-class BlockingConcurrentQueue
-{
-private:
-	typedef ::dmlc::moodycamel::ConcurrentQueue<T, Traits> ConcurrentQueue;
-	typedef details::mpmc_sema::LightweightSemaphore LightweightSemaphore;
-
-public:
-	typedef typename ConcurrentQueue::producer_token_t producer_token_t;
-	typedef typename ConcurrentQueue::consumer_token_t consumer_token_t;
-
-	typedef typename ConcurrentQueue::index_t index_t;
-	typedef typename ConcurrentQueue::size_t size_t;
-	typedef typename std::make_signed<size_t>::type ssize_t;
-
-	static const size_t BLOCK_SIZE = ConcurrentQueue::BLOCK_SIZE;
-	static const size_t EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD = ConcurrentQueue::EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD;
-	static const size_t EXPLICIT_INITIAL_INDEX_SIZE = ConcurrentQueue::EXPLICIT_INITIAL_INDEX_SIZE;
-	static const size_t IMPLICIT_INITIAL_INDEX_SIZE = ConcurrentQueue::IMPLICIT_INITIAL_INDEX_SIZE;
-	static const size_t INITIAL_IMPLICIT_PRODUCER_HASH_SIZE = ConcurrentQueue::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE;
-	static const std::uint32_t EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE = ConcurrentQueue::EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE;
-	static const size_t MAX_SUBQUEUE_SIZE = ConcurrentQueue::MAX_SUBQUEUE_SIZE;
-
-public:
-	// Creates a queue with at least `capacity` element slots; note that the
-	// actual number of elements that can be inserted without additional memory
-	// allocation depends on the number of producers and the block size (e.g. if
-	// the block size is equal to `capacity`, only a single block will be allocated
-	// up-front, which means only a single producer will be able to enqueue elements
-	// without an extra allocation -- blocks aren't shared between producers).
-	// This method is not thread safe -- it is up to the user to ensure that the
-	// queue is fully constructed before it starts being used by other threads (this
-	// includes making the memory effects of construction visible, possibly with a
-	// memory barrier).
-	explicit BlockingConcurrentQueue(size_t capacity = 6 * BLOCK_SIZE)
-		: inner(capacity), sema(create<LightweightSemaphore>(), &BlockingConcurrentQueue::template destroy<LightweightSemaphore>)
-	{
-		assert(reinterpret_cast<ConcurrentQueue*>((BlockingConcurrentQueue*)1) == &((BlockingConcurrentQueue*)1)->inner && "BlockingConcurrentQueue must have ConcurrentQueue as its first member");
-		if (!sema) {
-			MOODYCAMEL_THROW(std::bad_alloc());
-		}
-	}
-
-	BlockingConcurrentQueue(size_t minCapacity, size_t maxExplicitProducers, size_t maxImplicitProducers)
-		: inner(minCapacity, maxExplicitProducers, maxImplicitProducers), sema(create<LightweightSemaphore>(), &BlockingConcurrentQueue::template destroy<LightweightSemaphore>)
-	{
-		assert(reinterpret_cast<ConcurrentQueue*>((BlockingConcurrentQueue*)1) == &((BlockingConcurrentQueue*)1)->inner && "BlockingConcurrentQueue must have ConcurrentQueue as its first member");
-		if (!sema) {
-			MOODYCAMEL_THROW(std::bad_alloc());
-		}
-	}
-
-	// Disable copying and copy assignment
-	BlockingConcurrentQueue(BlockingConcurrentQueue const&) MOODYCAMEL_DELETE_FUNCTION;
-	BlockingConcurrentQueue& operator=(BlockingConcurrentQueue const&) MOODYCAMEL_DELETE_FUNCTION;
-
-	// Moving is supported, but note that it is *not* a thread-safe operation.
-	// Nobody can use the queue while it's being moved, and the memory effects
-	// of that move must be propagated to other threads before they can use it.
-	// Note: When a queue is moved, its tokens are still valid but can only be
-	// used with the destination queue (i.e. semantically they are moved along
-	// with the queue itself).
-	BlockingConcurrentQueue(BlockingConcurrentQueue&& other) MOODYCAMEL_NOEXCEPT
-		: inner(std::move(other.inner)), sema(std::move(other.sema))
-	{ }
-
-	inline BlockingConcurrentQueue& operator=(BlockingConcurrentQueue&& other) MOODYCAMEL_NOEXCEPT
-	{
-		return swap_internal(other);
-	}
-
-	// Swaps this queue's state with the other's. Not thread-safe.
-	// Swapping two queues does not invalidate their tokens, however
-	// the tokens that were created for one queue must be used with
-	// only the swapped queue (i.e. the tokens are tied to the
-	// queue's movable state, not the object itself).
-	inline void swap(BlockingConcurrentQueue& other) MOODYCAMEL_NOEXCEPT
-	{
-		swap_internal(other);
-	}
-
-private:
-	BlockingConcurrentQueue& swap_internal(BlockingConcurrentQueue& other)
-	{
-		if (this == &other) {
-			return *this;
-		}
-
-		inner.swap(other.inner);
-		sema.swap(other.sema);
-		return *this;
-	}
-
-public:
-	// Enqueues a single item (by copying it).
-	// Allocates memory if required. Only fails if memory allocation fails (or implicit
-	// production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0,
-	// or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-	// Thread-safe.
-	inline bool enqueue(T const& item)
-	{
-		if (details::likely(inner.enqueue(item))) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by moving it, if possible).
-	// Allocates memory if required. Only fails if memory allocation fails (or implicit
-	// production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0,
-	// or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-	// Thread-safe.
-	inline bool enqueue(T&& item)
-	{
-		if (details::likely(inner.enqueue(std::move(item)))) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by copying it) using an explicit producer token.
-	// Allocates memory if required. Only fails if memory allocation fails (or
-	// Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-	// Thread-safe.
-	inline bool enqueue(producer_token_t const& token, T const& item)
-	{
-		if (details::likely(inner.enqueue(token, item))) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by moving it, if possible) using an explicit producer token.
-	// Allocates memory if required. Only fails if memory allocation fails (or
-	// Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-	// Thread-safe.
-	inline bool enqueue(producer_token_t const& token, T&& item)
-	{
-		if (details::likely(inner.enqueue(token, std::move(item)))) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues several items.
-	// Allocates memory if required. Only fails if memory allocation fails (or
-	// implicit production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE
-	// is 0, or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-	// Note: Use std::make_move_iterator if the elements should be moved instead of copied.
-	// Thread-safe.
-	template<typename It>
-	inline bool enqueue_bulk(It itemFirst, size_t count)
-	{
-		if (details::likely(inner.enqueue_bulk(std::forward<It>(itemFirst), count))) {
-			sema->signal((LightweightSemaphore::ssize_t)(ssize_t)count);
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues several items using an explicit producer token.
-	// Allocates memory if required. Only fails if memory allocation fails
-	// (or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-	// Note: Use std::make_move_iterator if the elements should be moved
-	// instead of copied.
-	// Thread-safe.
-	template<typename It>
-	inline bool enqueue_bulk(producer_token_t const& token, It itemFirst, size_t count)
-	{
-		if (details::likely(inner.enqueue_bulk(token, std::forward<It>(itemFirst), count))) {
-			sema->signal((LightweightSemaphore::ssize_t)(ssize_t)count);
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by copying it).
-	// Does not allocate memory. Fails if not enough room to enqueue (or implicit
-	// production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE
-	// is 0).
-	// Thread-safe.
-	inline bool try_enqueue(T const& item)
-	{
-		if (inner.try_enqueue(item)) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by moving it, if possible).
-	// Does not allocate memory (except for one-time implicit producer).
-	// Fails if not enough room to enqueue (or implicit production is
-	// disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0).
-	// Thread-safe.
-	inline bool try_enqueue(T&& item)
-	{
-		if (inner.try_enqueue(std::move(item))) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by copying it) using an explicit producer token.
-	// Does not allocate memory. Fails if not enough room to enqueue.
-	// Thread-safe.
-	inline bool try_enqueue(producer_token_t const& token, T const& item)
-	{
-		if (inner.try_enqueue(token, item)) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues a single item (by moving it, if possible) using an explicit producer token.
-	// Does not allocate memory. Fails if not enough room to enqueue.
-	// Thread-safe.
-	inline bool try_enqueue(producer_token_t const& token, T&& item)
-	{
-		if (inner.try_enqueue(token, std::move(item))) {
-			sema->signal();
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues several items.
-	// Does not allocate memory (except for one-time implicit producer).
-	// Fails if not enough room to enqueue (or implicit production is
-	// disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0).
-	// Note: Use std::make_move_iterator if the elements should be moved
-	// instead of copied.
-	// Thread-safe.
-	template<typename It>
-	inline bool try_enqueue_bulk(It itemFirst, size_t count)
-	{
-		if (inner.try_enqueue_bulk(std::forward<It>(itemFirst), count)) {
-			sema->signal((LightweightSemaphore::ssize_t)(ssize_t)count);
-			return true;
-		}
-		return false;
-	}
-
-	// Enqueues several items using an explicit producer token.
-	// Does not allocate memory. Fails if not enough room to enqueue.
-	// Note: Use std::make_move_iterator if the elements should be moved
-	// instead of copied.
-	// Thread-safe.
-	template<typename It>
-	inline bool try_enqueue_bulk(producer_token_t const& token, It itemFirst, size_t count)
-	{
-		if (inner.try_enqueue_bulk(token, std::forward<It>(itemFirst), count)) {
-			sema->signal((LightweightSemaphore::ssize_t)(ssize_t)count);
-			return true;
-		}
-		return false;
-	}
-
-
-	// Attempts to dequeue from the queue.
-	// Returns false if all producer streams appeared empty at the time they
-	// were checked (so, the queue is likely but not guaranteed to be empty).
-	// Never allocates. Thread-safe.
-	template<typename U>
-	inline bool try_dequeue(U& item)
-	{
-		if (sema->tryWait()) {
-			while (!inner.try_dequeue(item)) {
-				continue;
-			}
-			return true;
-		}
-		return false;
-	}
-
-	// Attempts to dequeue from the queue using an explicit consumer token.
-	// Returns false if all producer streams appeared empty at the time they
-	// were checked (so, the queue is likely but not guaranteed to be empty).
-	// Never allocates. Thread-safe.
-	template<typename U>
-	inline bool try_dequeue(consumer_token_t& token, U& item)
-	{
-		if (sema->tryWait()) {
-			while (!inner.try_dequeue(token, item)) {
-				continue;
-			}
-			return true;
-		}
-		return false;
-	}
-
-	// Attempts to dequeue several elements from the queue.
-	// Returns the number of items actually dequeued.
-	// Returns 0 if all producer streams appeared empty at the time they
-	// were checked (so, the queue is likely but not guaranteed to be empty).
-	// Never allocates. Thread-safe.
-	template<typename It>
-	inline size_t try_dequeue_bulk(It itemFirst, size_t max)
-	{
-		size_t count = 0;
-		max = (size_t)sema->tryWaitMany((LightweightSemaphore::ssize_t)(ssize_t)max);
-		while (count != max) {
-			count += inner.template try_dequeue_bulk<It&>(itemFirst, max - count);
-		}
-		return count;
-	}
-
-	// Attempts to dequeue several elements from the queue using an explicit consumer token.
-	// Returns the number of items actually dequeued.
-	// Returns 0 if all producer streams appeared empty at the time they
-	// were checked (so, the queue is likely but not guaranteed to be empty).
-	// Never allocates. Thread-safe.
-	template<typename It>
-	inline size_t try_dequeue_bulk(consumer_token_t& token, It itemFirst, size_t max)
-	{
-		size_t count = 0;
-		max = (size_t)sema->tryWaitMany((LightweightSemaphore::ssize_t)(ssize_t)max);
-		while (count != max) {
-			count += inner.template try_dequeue_bulk<It&>(token, itemFirst, max - count);
-		}
-		return count;
-	}
-
-
-
-	// Blocks the current thread until there's something to dequeue, then
-	// dequeues it.
-	// Never allocates. Thread-safe.
-	template<typename U>
-	inline void wait_dequeue(U& item)
-	{
-		sema->wait();
-		while (!inner.try_dequeue(item)) {
-			continue;
-		}
-	}
-
-	// Blocks the current thread until either there's something to dequeue
-	// or the timeout (specified in microseconds) expires. Returns false
-	// without setting `item` if the timeout expires, otherwise assigns
-	// to `item` and returns true.
-	// Using a negative timeout indicates an indefinite timeout,
-	// and is thus functionally equivalent to calling wait_dequeue.
-	// Never allocates. Thread-safe.
-	template<typename U>
-	inline bool wait_dequeue_timed(U& item, std::int64_t timeout_usecs)
-	{
-		if (!sema->wait(timeout_usecs)) {
-			return false;
-		}
-		while (!inner.try_dequeue(item)) {
-			continue;
-		}
-		return true;
-	}
-
-    // Blocks the current thread until either there's something to dequeue
-	// or the timeout expires. Returns false without setting `item` if the
-    // timeout expires, otherwise assigns to `item` and returns true.
-	// Never allocates. Thread-safe.
-	template<typename U, typename Rep, typename Period>
-	inline bool wait_dequeue_timed(U& item, std::chrono::duration<Rep, Period> const& timeout)
-    {
-        return wait_dequeue_timed(item, std::chrono::duration_cast<std::chrono::microseconds>(timeout).count());
-    }
-
-	// Blocks the current thread until there's something to dequeue, then
-	// dequeues it using an explicit consumer token.
-	// Never allocates. Thread-safe.
-	template<typename U>
-	inline void wait_dequeue(consumer_token_t& token, U& item)
-	{
-		sema->wait();
-		while (!inner.try_dequeue(token, item)) {
-			continue;
-		}
-	}
-
-	// Blocks the current thread until either there's something to dequeue
-	// or the timeout (specified in microseconds) expires. Returns false
-	// without setting `item` if the timeout expires, otherwise assigns
-	// to `item` and returns true.
-	// Using a negative timeout indicates an indefinite timeout,
-	// and is thus functionally equivalent to calling wait_dequeue.
-	// Never allocates. Thread-safe.
-	template<typename U>
-	inline bool wait_dequeue_timed(consumer_token_t& token, U& item, std::int64_t timeout_usecs)
-	{
-		if (!sema->wait(timeout_usecs)) {
-			return false;
-		}
-		while (!inner.try_dequeue(token, item)) {
-			continue;
-		}
-		return true;
-	}
-
-    // Blocks the current thread until either there's something to dequeue
-	// or the timeout expires. Returns false without setting `item` if the
-    // timeout expires, otherwise assigns to `item` and returns true.
-	// Never allocates. Thread-safe.
-	template<typename U, typename Rep, typename Period>
-	inline bool wait_dequeue_timed(consumer_token_t& token, U& item, std::chrono::duration<Rep, Period> const& timeout)
-    {
-        return wait_dequeue_timed(token, item, std::chrono::duration_cast<std::chrono::microseconds>(timeout).count());
-    }
-
-	// Attempts to dequeue several elements from the queue.
-	// Returns the number of items actually dequeued, which will
-	// always be at least one (this method blocks until the queue
-	// is non-empty) and at most max.
-	// Never allocates. Thread-safe.
-	template<typename It>
-	inline size_t wait_dequeue_bulk(It itemFirst, size_t max)
-	{
-		size_t count = 0;
-		max = (size_t)sema->waitMany((LightweightSemaphore::ssize_t)(ssize_t)max);
-		while (count != max) {
-			count += inner.template try_dequeue_bulk<It&>(itemFirst, max - count);
-		}
-		return count;
-	}
-
-	// Attempts to dequeue several elements from the queue.
-	// Returns the number of items actually dequeued, which can
-	// be 0 if the timeout expires while waiting for elements,
-	// and at most max.
-	// Using a negative timeout indicates an indefinite timeout,
-	// and is thus functionally equivalent to calling wait_dequeue_bulk.
-	// Never allocates. Thread-safe.
-	template<typename It>
-	inline size_t wait_dequeue_bulk_timed(It itemFirst, size_t max, std::int64_t timeout_usecs)
-	{
-		size_t count = 0;
-		max = (size_t)sema->waitMany((LightweightSemaphore::ssize_t)(ssize_t)max, timeout_usecs);
-		while (count != max) {
-			count += inner.template try_dequeue_bulk<It&>(itemFirst, max - count);
-		}
-		return count;
-	}
-
-    // Attempts to dequeue several elements from the queue.
-	// Returns the number of items actually dequeued, which can
-	// be 0 if the timeout expires while waiting for elements,
-	// and at most max.
-	// Never allocates. Thread-safe.
-	template<typename It, typename Rep, typename Period>
-	inline size_t wait_dequeue_bulk_timed(It itemFirst, size_t max, std::chrono::duration<Rep, Period> const& timeout)
-    {
-        return wait_dequeue_bulk_timed<It&>(itemFirst, max, std::chrono::duration_cast<std::chrono::microseconds>(timeout).count());
-    }
-
-	// Attempts to dequeue several elements from the queue using an explicit consumer token.
-	// Returns the number of items actually dequeued, which will
-	// always be at least one (this method blocks until the queue
-	// is non-empty) and at most max.
-	// Never allocates. Thread-safe.
-	template<typename It>
-	inline size_t wait_dequeue_bulk(consumer_token_t& token, It itemFirst, size_t max)
-	{
-		size_t count = 0;
-		max = (size_t)sema->waitMany((LightweightSemaphore::ssize_t)(ssize_t)max);
-		while (count != max) {
-			count += inner.template try_dequeue_bulk<It&>(token, itemFirst, max - count);
-		}
-		return count;
-	}
-
-	// Attempts to dequeue several elements from the queue using an explicit consumer token.
-	// Returns the number of items actually dequeued, which can
-	// be 0 if the timeout expires while waiting for elements,
-	// and at most max.
-	// Using a negative timeout indicates an indefinite timeout,
-	// and is thus functionally equivalent to calling wait_dequeue_bulk.
-	// Never allocates. Thread-safe.
-	template<typename It>
-	inline size_t wait_dequeue_bulk_timed(consumer_token_t& token, It itemFirst, size_t max, std::int64_t timeout_usecs)
-	{
-		size_t count = 0;
-		max = (size_t)sema->waitMany((LightweightSemaphore::ssize_t)(ssize_t)max, timeout_usecs);
-		while (count != max) {
-			count += inner.template try_dequeue_bulk<It&>(token, itemFirst, max - count);
-		}
-		return count;
-	}
-
-	// Attempts to dequeue several elements from the queue using an explicit consumer token.
-	// Returns the number of items actually dequeued, which can
-	// be 0 if the timeout expires while waiting for elements,
-	// and at most max.
-	// Never allocates. Thread-safe.
-	template<typename It, typename Rep, typename Period>
-	inline size_t wait_dequeue_bulk_timed(consumer_token_t& token, It itemFirst, size_t max, std::chrono::duration<Rep, Period> const& timeout)
-    {
-        return wait_dequeue_bulk_timed<It&>(token, itemFirst, max, std::chrono::duration_cast<std::chrono::microseconds>(timeout).count());
-    }
-
-
-	// Returns an estimate of the total number of elements currently in the queue. This
-	// estimate is only accurate if the queue has completely stabilized before it is called
-	// (i.e. all enqueue and dequeue operations have completed and their memory effects are
-	// visible on the calling thread, and no further operations start while this method is
-	// being called).
-	// Thread-safe.
-	inline size_t size_approx() const
-	{
-		return (size_t)sema->availableApprox();
-	}
-
-
-	// Returns true if the underlying atomic variables used by
-	// the queue are lock-free (they should be on most platforms).
-	// Thread-safe.
-	static bool is_lock_free()
-	{
-		return ConcurrentQueue::is_lock_free();
-	}
-
-
-private:
-	template<typename U>
-	static inline U* create()
-	{
-		auto p = (Traits::malloc)(sizeof(U));
-		return p != nullptr ? new (p) U : nullptr;
-	}
-
-	template<typename U, typename A1>
-	static inline U* create(A1&& a1)
-	{
-		auto p = (Traits::malloc)(sizeof(U));
-		return p != nullptr ? new (p) U(std::forward<A1>(a1)) : nullptr;
-	}
-
-	template<typename U>
-	static inline void destroy(U* p)
-	{
-		if (p != nullptr) {
-			p->~U();
-		}
-		(Traits::free)(p);
-	}
-
-private:
-	ConcurrentQueue inner;
-	std::unique_ptr<LightweightSemaphore, void (*)(LightweightSemaphore*)> sema;
-};
-
-
-template<typename T, typename Traits>
-inline void swap(BlockingConcurrentQueue<T, Traits>& a, BlockingConcurrentQueue<T, Traits>& b) MOODYCAMEL_NOEXCEPT
-{
-	a.swap(b);
-}
-
-}	// end namespace moodycamel
-}  // namespace dmlc
-
-#endif  // DMLC_BLOCKINGCONCURRENTQUEUE_H_
-//! \endcond Doxygen_Suppress
diff --git a/ml-xgboost/dmlc-core/include/dmlc/build_config_default.h b/ml-xgboost/dmlc-core/include/dmlc/build_config_default.h
deleted file mode 100644
index e57367c..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/build_config_default.h
+++ /dev/null
@@ -1,39 +0,0 @@
-/*!
- * Copyright (c) 2018 by Contributors
- * \file build_config_default.h
- * \brief Default detection logic for fopen64 and other symbols.
- *        May be overriden by CMake
- * \author KOLANICH
- */
-#ifndef DMLC_BUILD_CONFIG_DEFAULT_H_
-#define DMLC_BUILD_CONFIG_DEFAULT_H_
-
-/* default logic for fopen64 */
-#if DMLC_USE_FOPEN64 && \
-  (!defined(__GNUC__) || (defined __ANDROID__) || (defined __FreeBSD__) \
-  || (defined __APPLE__) || ((defined __MINGW32__) && !(defined __MINGW64__)) \
-  || (defined __CYGWIN__) )
-  #define fopen64 std::fopen
-#endif
-
-/* default logic for stack trace */
-#if (defined(__GNUC__) && !defined(__MINGW32__)\
-     && !defined(__sun) && !defined(__SVR4)\
-     && !(defined __MINGW64__) && !(defined __ANDROID__))\
-     && !defined(__CYGWIN__) && !defined(__EMSCRIPTEN__)\
-     && !defined(__RISCV__) && !defined(__hexagon__)
-  #ifndef DMLC_LOG_STACK_TRACE
-  #define DMLC_LOG_STACK_TRACE 1
-  #endif
-  #ifndef DMLC_LOG_STACK_TRACE_SIZE
-  #define DMLC_LOG_STACK_TRACE_SIZE 10
-  #endif
-  #define DMLC_EXECINFO_H <execinfo.h>
-#endif
-
-/* default logic for detecting existence of nanosleep() */
-#if !(defined _WIN32) || (defined __CYGWIN__)
-  #define DMLC_NANOSLEEP_PRESENT
-#endif
-
-#endif  // DMLC_BUILD_CONFIG_DEFAULT_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/common.h b/ml-xgboost/dmlc-core/include/dmlc/common.h
deleted file mode 100644
index f2f0c73..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/common.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file common.h
- * \brief defines some common utility function.
- */
-#ifndef DMLC_COMMON_H_
-#define DMLC_COMMON_H_
-
-#include <vector>
-#include <string>
-#include <sstream>
-#include <mutex>
-#include <utility>
-#include "./logging.h"
-
-namespace dmlc {
-/*!
- * \brief Split a string by delimiter
- * \param s String to be splitted.
- * \param delim The delimiter.
- * \return a splitted vector of strings.
- */
-inline std::vector<std::string> Split(const std::string& s, char delim) {
-  std::string item;
-  std::istringstream is(s);
-  std::vector<std::string> ret;
-  while (std::getline(is, item, delim)) {
-    ret.push_back(item);
-  }
-  return ret;
-}
-
-/*!
- * \brief hash an object and combines the key with previous keys
- */
-template<typename T>
-inline size_t HashCombine(size_t key, const T& value) {
-  std::hash<T> hash_func;
-  return key ^ (hash_func(value) + 0x9e3779b9 + (key << 6) + (key >> 2));
-}
-
-/*!
- * \brief specialize for size_t
- */
-template<>
-inline size_t HashCombine<size_t>(size_t key, const size_t& value) {
-  return key ^ (value + 0x9e3779b9 + (key << 6) + (key >> 2));
-}
-
-/*!
- * \brief OMP Exception class catches, saves and rethrows exception from OMP blocks
- */
-class OMPException {
- private:
-  // exception_ptr member to store the exception
-  std::exception_ptr omp_exception_;
-  // mutex to be acquired during catch to set the exception_ptr
-  std::mutex mutex_;
-
- public:
-  /*!
-   * \brief Parallel OMP blocks should be placed within Run to save exception
-   */
-  template <typename Function, typename... Parameters>
-  void Run(Function f, Parameters... params) {
-    try {
-      f(params...);
-    } catch (dmlc::Error &ex) {
-      std::lock_guard<std::mutex> lock(mutex_);
-      if (!omp_exception_) {
-        omp_exception_ = std::current_exception();
-      }
-    } catch (std::exception &ex) {
-      std::lock_guard<std::mutex> lock(mutex_);
-      if (!omp_exception_) {
-        omp_exception_ = std::current_exception();
-      }
-    }
-  }
-
-  /*!
-   * \brief should be called from the main thread to rethrow the exception
-   */
-  void Rethrow() {
-    if (this->omp_exception_) std::rethrow_exception(this->omp_exception_);
-  }
-};
-
-}  // namespace dmlc
-
-#endif  // DMLC_COMMON_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/concurrency.h b/ml-xgboost/dmlc-core/include/dmlc/concurrency.h
deleted file mode 100644
index 105c576..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/concurrency.h
+++ /dev/null
@@ -1,263 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file concurrency.h
- * \brief thread-safe data structures.
- * \author Yutian Li
- */
-#ifndef DMLC_CONCURRENCY_H_
-#define DMLC_CONCURRENCY_H_
-// this code depends on c++11
-#if DMLC_USE_CXX11
-#include <atomic>
-#include <deque>
-#include <queue>
-#include <mutex>
-#include <vector>
-#include <utility>
-#include <condition_variable>
-#include "dmlc/base.h"
-
-namespace dmlc {
-
-/*!
- * \brief Simple userspace spinlock implementation.
- */
-class Spinlock {
- public:
-#ifdef _MSC_VER
-  Spinlock() {
-    lock_.clear();
-  }
-#else
-#if defined(__clang__)
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wbraced-scalar-init"
-#endif  // defined(__clang__)
-  Spinlock() : lock_(ATOMIC_FLAG_INIT) {
-  }
-#if defined(__clang__)
-#pragma clang diagnostic pop
-#endif  // defined(__clang__)
-#endif
-  ~Spinlock() = default;
-  /*!
-   * \brief Acquire lock.
-   */
-  inline void lock() noexcept(true);
-  /*!
-   * \brief Release lock.
-   */
-  inline void unlock() noexcept(true);
-
- private:
-  std::atomic_flag lock_;
-  /*!
-   * \brief Disable copy and move.
-   */
-  DISALLOW_COPY_AND_ASSIGN(Spinlock);
-};
-
-/*! \brief type of concurrent queue */
-enum class ConcurrentQueueType {
-  /*! \brief FIFO queue */
-  kFIFO,
-  /*! \brief queue with priority */
-  kPriority
-};
-
-/*!
- * \brief Cocurrent blocking queue.
- */
-template <typename T,
-          ConcurrentQueueType type = ConcurrentQueueType::kFIFO>
-class ConcurrentBlockingQueue {
- public:
-  ConcurrentBlockingQueue();
-  ~ConcurrentBlockingQueue() = default;
-  /*!
-   * \brief Push element to the end of the queue.
-   * \param e Element to push into.
-   * \param priority the priority of the element, only used for priority queue.
-   *            The higher the priority is, the better.
-   * \tparam E the element type
-   *
-   * It will copy or move the element into the queue, depending on the type of
-   * the parameter.
-   */
-  template <typename E>
-  void Push(E&& e, int priority = 0);
-
-  /*!
-   * \brief Push element to the front of the queue. Only works for FIFO queue.
-   *        For priority queue it is the same as Push.
-   * \param e Element to push into.
-   * \param priority the priority of the element, only used for priority queue.
-   *            The higher the priority is, the better.
-   * \tparam E the element type
-   *
-   * It will copy or move the element into the queue, depending on the type of
-   * the parameter.
-   */
-  template <typename E>
-  void PushFront(E&& e, int priority = 0);
-  /*!
-   * \brief Pop element from the queue.
-   * \param rv Element popped.
-   * \return On false, the queue is exiting.
-   *
-   * The element will be copied or moved into the object passed in.
-   */
-  bool Pop(T* rv);
-  /*!
-   * \brief Signal the queue for destruction.
-   *
-   * After calling this method, all blocking pop call to the queue will return
-   * false.
-   */
-  void SignalForKill();
-  /*!
-   * \brief Get the size of the queue.
-   * \return The size of the queue.
-   */
-  size_t Size();
-
- private:
-  struct Entry {
-    T data;
-    int priority;
-    inline bool operator<(const Entry &b) const {
-      return priority < b.priority;
-    }
-  };
-
-  std::mutex mutex_;
-  std::condition_variable cv_;
-  std::atomic<bool> exit_now_;
-  int nwait_consumer_;
-  // a priority queue
-  std::vector<Entry> priority_queue_;
-  // a FIFO queue
-  std::deque<T> fifo_queue_;
-  /*!
-   * \brief Disable copy and move.
-   */
-  DISALLOW_COPY_AND_ASSIGN(ConcurrentBlockingQueue);
-};
-
-inline void Spinlock::lock() noexcept(true) {
-  while (lock_.test_and_set(std::memory_order_acquire)) {
-  }
-}
-
-inline void Spinlock::unlock() noexcept(true) {
-  lock_.clear(std::memory_order_release);
-}
-
-template <typename T, ConcurrentQueueType type>
-ConcurrentBlockingQueue<T, type>::ConcurrentBlockingQueue()
-    : exit_now_{false}, nwait_consumer_{0} {}
-
-template <typename T, ConcurrentQueueType type>
-template <typename E>
-void ConcurrentBlockingQueue<T, type>::Push(E&& e, int priority) {
-  static_assert(std::is_same<typename std::remove_cv<
-                                 typename std::remove_reference<E>::type>::type,
-                             T>::value,
-                "Types must match.");
-  bool notify;
-  {
-    std::lock_guard<std::mutex> lock{mutex_};
-    if (type == ConcurrentQueueType::kFIFO) {
-      fifo_queue_.emplace_back(std::forward<E>(e));
-      notify = nwait_consumer_ != 0;
-    } else {
-      Entry entry;
-      entry.data = std::move(e);
-      entry.priority = priority;
-      priority_queue_.push_back(std::move(entry));
-      std::push_heap(priority_queue_.begin(), priority_queue_.end());
-      notify = nwait_consumer_ != 0;
-    }
-  }
-  if (notify) cv_.notify_one();
-}
-
-template <typename T, ConcurrentQueueType type>
-template <typename E>
-void ConcurrentBlockingQueue<T, type>::PushFront(E&& e, int priority) {
-  static_assert(std::is_same<typename std::remove_cv<
-                                 typename std::remove_reference<E>::type>::type,
-                             T>::value,
-                "Types must match.");
-  bool notify;
-  {
-    std::lock_guard<std::mutex> lock{mutex_};
-    if (type == ConcurrentQueueType::kFIFO) {
-      fifo_queue_.emplace_front(std::forward<E>(e));
-      notify = nwait_consumer_ != 0;
-    } else {
-      Entry entry;
-      entry.data = std::move(e);
-      entry.priority = priority;
-      priority_queue_.push_back(std::move(entry));
-      std::push_heap(priority_queue_.begin(), priority_queue_.end());
-      notify = nwait_consumer_ != 0;
-    }
-  }
-  if (notify) cv_.notify_one();
-}
-
-template <typename T, ConcurrentQueueType type>
-bool ConcurrentBlockingQueue<T, type>::Pop(T* rv) {
-  std::unique_lock<std::mutex> lock{mutex_};
-  if (type == ConcurrentQueueType::kFIFO) {
-    ++nwait_consumer_;
-    cv_.wait(lock, [this] {
-        return !fifo_queue_.empty() || exit_now_.load();
-      });
-    --nwait_consumer_;
-    if (!exit_now_.load()) {
-      *rv = std::move(fifo_queue_.front());
-      fifo_queue_.pop_front();
-      return true;
-    } else {
-      return false;
-    }
-  } else {
-    ++nwait_consumer_;
-    cv_.wait(lock, [this] {
-        return !priority_queue_.empty() || exit_now_.load();
-      });
-    --nwait_consumer_;
-    if (!exit_now_.load()) {
-      std::pop_heap(priority_queue_.begin(), priority_queue_.end());
-      *rv = std::move(priority_queue_.back().data);
-      priority_queue_.pop_back();
-      return true;
-    } else {
-      return false;
-    }
-  }
-}
-
-template <typename T, ConcurrentQueueType type>
-void ConcurrentBlockingQueue<T, type>::SignalForKill() {
-  {
-    std::lock_guard<std::mutex> lock{mutex_};
-    exit_now_.store(true);
-  }
-  cv_.notify_all();
-}
-
-template <typename T, ConcurrentQueueType type>
-size_t ConcurrentBlockingQueue<T, type>::Size() {
-  std::lock_guard<std::mutex> lock{mutex_};
-  if (type == ConcurrentQueueType::kFIFO) {
-    return fifo_queue_.size();
-  } else {
-    return priority_queue_.size();
-  }
-}
-}  // namespace dmlc
-#endif  // DMLC_USE_CXX11
-#endif  // DMLC_CONCURRENCY_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/concurrentqueue.h b/ml-xgboost/dmlc-core/include/dmlc/concurrentqueue.h
deleted file mode 100644
index e80e84a..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/concurrentqueue.h
+++ /dev/null
@@ -1,3719 +0,0 @@
-//! \cond Doxygen_Suppress
-// Provides a C++11 implementation of a multi-producer, multi-consumer lock-free queue.
-// An overview, including benchmark results, is provided here:
-//     http://moodycamel.com/blog/2014/a-fast-general-purpose-lock-free-queue-for-c++
-// The full design is also described in excruciating detail at:
-//    http://moodycamel.com/blog/2014/detailed-design-of-a-lock-free-queue
-
-// Simplified BSD license:
-// Copyright (c) 2013-2016, Cameron Desrochers.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-// - Redistributions of source code must retain the above copyright notice, this list of
-// conditions and the following disclaimer.
-// - Redistributions in binary form must reproduce the above copyright notice, this list of
-// conditions and the following disclaimer in the documentation and/or other materials
-// provided with the distribution.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
-// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
-// THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
-// OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
-// TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
-// EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#ifndef DMLC_CONCURRENTQUEUE_H_
-#define DMLC_CONCURRENTQUEUE_H_
-#pragma once
-
-#if defined(__GNUC__)
-// Disable -Wconversion warnings (spuriously triggered when Traits::size_t and
-// Traits::index_t are set to < 32 bits, causing integer promotion, causing warnings
-// upon assigning any computed values)
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-
-#ifdef MCDBGQ_USE_RELACY
-#pragma GCC diagnostic ignored "-Wint-to-pointer-cast"
-#endif
-#endif
-
-#if defined(_WIN32) || defined(__WINDOWS__) || defined(__WIN32__) || defined(_WIN64)
-#include <windows.h>  // for GetCurrentThreadId()
-#endif
-
-#if defined(__APPLE__)
-#include "TargetConditionals.h"
-#endif
-
-#ifdef MCDBGQ_USE_RELACY
-#include "relacy/relacy_std.hpp"
-#include "relacy_shims.h"
-// We only use malloc/free anyway, and the delete macro messes up `= delete` method declarations.
-// We'll override the default trait malloc ourselves without a macro.
-#undef new
-#undef delete
-#undef malloc
-#undef free
-#else
-#include <atomic>		// Requires C++11. Sorry VS2010.
-#include <cassert>
-#endif
-#include <cstddef>              // for max_align_t
-#include <cstdint>
-#include <cstdlib>
-#include <type_traits>
-#include <algorithm>
-#include <utility>
-#include <limits>
-#include <climits>		// for CHAR_BIT
-#include <array>
-#include <thread>		// partly for __WINPTHREADS_VERSION if on MinGW-w64 w/ POSIX threading
-
-namespace dmlc {
-
-// Platform-specific definitions of a numeric thread ID type and an invalid value
-namespace moodycamel { namespace details {
-template<typename thread_id_t> struct thread_id_converter {
-  typedef thread_id_t thread_id_numeric_size_t;
-  typedef thread_id_t thread_id_hash_t;
-  static thread_id_hash_t prehash(thread_id_t const& x) { return x; }
-};
-} }
-#if defined(MCDBGQ_USE_RELACY)
-namespace moodycamel { namespace details {
-  typedef std::uint32_t thread_id_t;
-  static const thread_id_t invalid_thread_id  = 0xFFFFFFFFU;
-  static const thread_id_t invalid_thread_id2 = 0xFFFFFFFEU;
-  static inline thread_id_t thread_id() { return rl::thread_index(); }
-} }
-#elif defined(_WIN32) || defined(__WINDOWS__) || defined(__WIN32__)
-// No sense pulling in windows.h in a header, we'll manually declare the function
-// we use and rely on backwards-compatibility for this not to break
-extern "C" __declspec(dllimport) unsigned long __stdcall GetCurrentThreadId(void);
-namespace moodycamel { namespace details {
-  static_assert(sizeof(unsigned long) == sizeof(std::uint32_t), "Expected size of unsigned long to be 32 bits on Windows");
-  typedef std::uint32_t thread_id_t;
-  static const thread_id_t invalid_thread_id  = 0;			// See http://blogs.msdn.com/b/oldnewthing/archive/2004/02/23/78395.aspx
-  static const thread_id_t invalid_thread_id2 = 0xFFFFFFFFU;	// Not technically guaranteed to be invalid, but is never used in practice. Note that all Win32 thread IDs are presently multiples of 4.
-  static inline thread_id_t thread_id() { return static_cast<thread_id_t>(::GetCurrentThreadId()); }
-} }
-#elif defined(__arm__) || defined(_M_ARM) || defined(__aarch64__) || (defined(__APPLE__) && TARGET_OS_IPHONE)
-namespace moodycamel { namespace details {
-  static_assert(sizeof(std::thread::id) == 4 || sizeof(std::thread::id) == 8, "std::thread::id is expected to be either 4 or 8 bytes");
-
-  typedef std::thread::id thread_id_t;
-  static const thread_id_t invalid_thread_id;         // Default ctor creates invalid ID
-
-  // Note we don't define a invalid_thread_id2 since std::thread::id doesn't have one; it's
-  // only used if MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED is defined anyway, which it won't
-  // be.
-  static inline thread_id_t thread_id() { return std::this_thread::get_id(); }
-
-  template<std::size_t> struct thread_id_size { };
-  template<> struct thread_id_size<4> { typedef std::uint32_t numeric_t; };
-  template<> struct thread_id_size<8> { typedef std::uint64_t numeric_t; };
-
-  template<> struct thread_id_converter<thread_id_t> {
-    typedef thread_id_size<sizeof(thread_id_t)>::numeric_t thread_id_numeric_size_t;
-#ifndef __APPLE__
-    typedef std::size_t thread_id_hash_t;
-#else
-    typedef thread_id_numeric_size_t thread_id_hash_t;
-#endif
-
-    static thread_id_hash_t prehash(thread_id_t const& x)
-    {
-#ifndef __APPLE__
-      return std::hash<std::thread::id>()(x);
-#else
-      return *reinterpret_cast<thread_id_hash_t const*>(&x);
-#endif
-    }
-  };
-} }
-#else
-// Use a nice trick from this answer: http://stackoverflow.com/a/8438730/21475
-// In order to get a numeric thread ID in a platform-independent way, we use a thread-local
-// static variable's address as a thread identifier :-)
-#if defined(__GNUC__) || defined(__INTEL_COMPILER)
-#define MOODYCAMEL_THREADLOCAL __thread
-#elif defined(_MSC_VER)
-#define MOODYCAMEL_THREADLOCAL __declspec(thread)
-#else
-// Assume C++11 compliant compiler
-#define MOODYCAMEL_THREADLOCAL thread_local
-#endif
-namespace moodycamel { namespace details {
-typedef std::uintptr_t thread_id_t;
-static const thread_id_t invalid_thread_id  = 0;		// Address can't be nullptr
-static const thread_id_t invalid_thread_id2 = 1;		// Member accesses off a null pointer are also generally invalid. Plus it's not aligned.
-static inline thread_id_t thread_id() { static MOODYCAMEL_THREADLOCAL int x; return reinterpret_cast<thread_id_t>(&x); }
-} }
-#endif
-
-// Exceptions
-#ifndef MOODYCAMEL_EXCEPTIONS_ENABLED
-#if (defined(_MSC_VER) && defined(_CPPUNWIND)) || (defined(__GNUC__) && defined(__EXCEPTIONS)) || (!defined(_MSC_VER) && !defined(__GNUC__))
-#define MOODYCAMEL_EXCEPTIONS_ENABLED
-#endif
-#endif
-#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED
-#define MOODYCAMEL_TRY try
-#define MOODYCAMEL_CATCH(...) catch(__VA_ARGS__)
-#define MOODYCAMEL_RETHROW throw
-#define MOODYCAMEL_THROW(expr) throw (expr)
-#else
-#define MOODYCAMEL_TRY if (true)
-#define MOODYCAMEL_CATCH(...) else if (false)
-#define MOODYCAMEL_RETHROW
-#define MOODYCAMEL_THROW(expr)
-#endif
-
-#ifndef MOODYCAMEL_NOEXCEPT
-#if !defined(MOODYCAMEL_EXCEPTIONS_ENABLED)
-#define MOODYCAMEL_NOEXCEPT
-#define MOODYCAMEL_NOEXCEPT_CTOR(type, valueType, expr) true
-#define MOODYCAMEL_NOEXCEPT_ASSIGN(type, valueType, expr) true
-#elif defined(_MSC_VER) && defined(_NOEXCEPT) && _MSC_VER < 1800
-// VS2012's std::is_nothrow_[move_]constructible is broken and returns true when it shouldn't :-(
-// We have to assume *all* non-trivial constructors may throw on VS2012!
-#define MOODYCAMEL_NOEXCEPT _NOEXCEPT
-#define MOODYCAMEL_NOEXCEPT_CTOR(type, valueType, expr) (std::is_rvalue_reference<valueType>::value && std::is_move_constructible<type>::value ? std::is_trivially_move_constructible<type>::value : std::is_trivially_copy_constructible<type>::value)
-#define MOODYCAMEL_NOEXCEPT_ASSIGN(type, valueType, expr) ((std::is_rvalue_reference<valueType>::value && std::is_move_assignable<type>::value ? std::is_trivially_move_assignable<type>::value || std::is_nothrow_move_assignable<type>::value : std::is_trivially_copy_assignable<type>::value || std::is_nothrow_copy_assignable<type>::value) && MOODYCAMEL_NOEXCEPT_CTOR(type, valueType, expr))
-#elif defined(_MSC_VER) && defined(_NOEXCEPT) && _MSC_VER < 1900
-#define MOODYCAMEL_NOEXCEPT _NOEXCEPT
-#define MOODYCAMEL_NOEXCEPT_CTOR(type, valueType, expr) (std::is_rvalue_reference<valueType>::value && std::is_move_constructible<type>::value ? std::is_trivially_move_constructible<type>::value || std::is_nothrow_move_constructible<type>::value : std::is_trivially_copy_constructible<type>::value || std::is_nothrow_copy_constructible<type>::value)
-#define MOODYCAMEL_NOEXCEPT_ASSIGN(type, valueType, expr) ((std::is_rvalue_reference<valueType>::value && std::is_move_assignable<type>::value ? std::is_trivially_move_assignable<type>::value || std::is_nothrow_move_assignable<type>::value : std::is_trivially_copy_assignable<type>::value || std::is_nothrow_copy_assignable<type>::value) && MOODYCAMEL_NOEXCEPT_CTOR(type, valueType, expr))
-#else
-#define MOODYCAMEL_NOEXCEPT noexcept
-#define MOODYCAMEL_NOEXCEPT_CTOR(type, valueType, expr) noexcept(expr)
-#define MOODYCAMEL_NOEXCEPT_ASSIGN(type, valueType, expr) noexcept(expr)
-#endif
-#endif
-
-#ifndef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-#ifdef MCDBGQ_USE_RELACY
-#define MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-#else
-// VS2013 doesn't support `thread_local`, and MinGW-w64 w/ POSIX threading has a crippling bug: http://sourceforge.net/p/mingw-w64/bugs/445
-// g++ <=4.7 doesn't support thread_local either.
-// Finally, iOS/ARM doesn't have support for it either, and g++/ARM allows it to compile but it's unconfirmed to actually work
-#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && (!defined(__MINGW32__) && !defined(__MINGW64__) || !defined(__WINPTHREADS_VERSION)) && (!defined(__GNUC__) || __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 8)) && (!defined(__APPLE__) || !TARGET_OS_IPHONE) && !defined(__arm__) && !defined(_M_ARM) && !defined(__aarch64__)
-// Assume `thread_local` is fully supported in all other C++11 compilers/platforms
-//#define MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED    // always disabled for now since several users report having problems with it on
-#endif
-#endif
-#endif
-
-// VS2012 doesn't support deleted functions.
-// In this case, we declare the function normally but don't define it. A link error will be generated if the function is called.
-#ifndef MOODYCAMEL_DELETE_FUNCTION
-#if defined(_MSC_VER) && _MSC_VER < 1800
-#define MOODYCAMEL_DELETE_FUNCTION
-#else
-#define MOODYCAMEL_DELETE_FUNCTION = delete
-#endif
-#endif
-
-// Compiler-specific likely/unlikely hints
-namespace moodycamel { namespace details {
-#if defined(__GNUC__)
-inline bool likely(bool x) { return __builtin_expect((x), true); }
-inline bool unlikely(bool x) { return __builtin_expect((x), false); }
-#else
-inline bool likely(bool x) { return x; }
-  inline bool unlikely(bool x) { return x; }
-#endif
-} }
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-#include "internal/concurrentqueue_internal_debug.h"
-#endif
-
-namespace moodycamel {
-namespace details {
-template<typename T>
-struct const_numeric_max {
-  static_assert(std::is_integral<T>::value, "const_numeric_max can only be used with integers");
-  static const T value = std::numeric_limits<T>::is_signed
-                         ? (static_cast<T>(1) << (sizeof(T) * CHAR_BIT - 1)) - static_cast<T>(1)
-                         : static_cast<T>(-1);
-};
-
-#if defined(__GLIBCXX__)
-typedef ::max_align_t std_max_align_t;      // libstdc++ forgot to add it to std:: for a while
-#else
-typedef std::max_align_t std_max_align_t;   // Others (e.g. MSVC) insist it can *only* be accessed via std::
-#endif
-
-// Some platforms have incorrectly set max_align_t to a type with <8 bytes alignment even while supporting
-// 8-byte aligned scalar values (*cough* 32-bit iOS). Work around this with our own union. See issue #64.
-typedef union {
-  std_max_align_t x;
-  long long y;
-  void* z;
-} max_align_t;
-}
-
-// Default traits for the ConcurrentQueue. To change some of the
-// traits without re-implementing all of them, inherit from this
-// struct and shadow the declarations you wish to be different;
-// since the traits are used as a template type parameter, the
-// shadowed declarations will be used where defined, and the defaults
-// otherwise.
-struct ConcurrentQueueDefaultTraits
-{
-  // General-purpose size type. std::size_t is strongly recommended.
-  typedef std::size_t size_t;
-
-  // The type used for the enqueue and dequeue indices. Must be at least as
-  // large as size_t. Should be significantly larger than the number of elements
-  // you expect to hold at once, especially if you have a high turnover rate;
-  // for example, on 32-bit x86, if you expect to have over a hundred million
-  // elements or pump several million elements through your queue in a very
-  // short space of time, using a 32-bit type *may* trigger a race condition.
-  // A 64-bit int type is recommended in that case, and in practice will
-  // prevent a race condition no matter the usage of the queue. Note that
-  // whether the queue is lock-free with a 64-int type depends on the whether
-  // std::atomic<std::uint64_t> is lock-free, which is platform-specific.
-  typedef std::size_t index_t;
-
-  // Internally, all elements are enqueued and dequeued from multi-element
-  // blocks; this is the smallest controllable unit. If you expect few elements
-  // but many producers, a smaller block size should be favoured. For few producers
-  // and/or many elements, a larger block size is preferred. A sane default
-  // is provided. Must be a power of 2.
-  static const size_t BLOCK_SIZE = 32;
-
-  // For explicit producers (i.e. when using a producer token), the block is
-  // checked for being empty by iterating through a list of flags, one per element.
-  // For large block sizes, this is too inefficient, and switching to an atomic
-  // counter-based approach is faster. The switch is made for block sizes strictly
-  // larger than this threshold.
-  static const size_t EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD = 32;
-
-  // How many full blocks can be expected for a single explicit producer? This should
-  // reflect that number's maximum for optimal performance. Must be a power of 2.
-  static const size_t EXPLICIT_INITIAL_INDEX_SIZE = 32;
-
-  // How many full blocks can be expected for a single implicit producer? This should
-  // reflect that number's maximum for optimal performance. Must be a power of 2.
-  static const size_t IMPLICIT_INITIAL_INDEX_SIZE = 32;
-
-  // The initial size of the hash table mapping thread IDs to implicit producers.
-  // Note that the hash is resized every time it becomes half full.
-  // Must be a power of two, and either 0 or at least 1. If 0, implicit production
-  // (using the enqueue methods without an explicit producer token) is disabled.
-  static const size_t INITIAL_IMPLICIT_PRODUCER_HASH_SIZE = 32;
-
-  // Controls the number of items that an explicit consumer (i.e. one with a token)
-  // must consume before it causes all consumers to rotate and move on to the next
-  // internal queue.
-  static const std::uint32_t EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE = 256;
-
-  // The maximum number of elements (inclusive) that can be enqueued to a sub-queue.
-  // Enqueue operations that would cause this limit to be surpassed will fail. Note
-  // that this limit is enforced at the block level (for performance reasons), i.e.
-  // it's rounded up to the nearest block size.
-  static const size_t MAX_SUBQUEUE_SIZE = details::const_numeric_max<size_t>::value;
-
-
-#ifndef MCDBGQ_USE_RELACY
-  // Memory allocation can be customized if needed.
-  // malloc should return nullptr on failure, and handle alignment like std::malloc.
-#if defined(malloc) || defined(free)
-  // Gah, this is 2015, stop defining macros that break standard code already!
-  // Work around malloc/free being special macros:
-  static inline void* WORKAROUND_malloc(size_t size) { return malloc(size); }
-  static inline void WORKAROUND_free(void* ptr) { return free(ptr); }
-  static inline void* (malloc)(size_t size) { return WORKAROUND_malloc(size); }
-  static inline void (free)(void* ptr) { return WORKAROUND_free(ptr); }
-#else
-  static inline void* malloc(size_t size) { return std::malloc(size); }
-  static inline void free(void* ptr) { return std::free(ptr); }
-#endif
-#else
-  // Debug versions when running under the Relacy race detector (ignore
-  // these in user code)
-  static inline void* malloc(size_t size) { return rl::rl_malloc(size, $); }
-  static inline void free(void* ptr) { return rl::rl_free(ptr, $); }
-#endif
-};
-
-
-// When producing or consuming many elements, the most efficient way is to:
-//    1) Use one of the bulk-operation methods of the queue with a token
-//    2) Failing that, use the bulk-operation methods without a token
-//    3) Failing that, create a token and use that with the single-item methods
-//    4) Failing that, use the single-parameter methods of the queue
-// Having said that, don't create tokens willy-nilly -- ideally there should be
-// a maximum of one token per thread (of each kind).
-struct ProducerToken;
-struct ConsumerToken;
-
-template<typename T, typename Traits> class ConcurrentQueue;
-template<typename T, typename Traits> class BlockingConcurrentQueue;
-class ConcurrentQueueTests;
-
-
-namespace details
-{
-struct ConcurrentQueueProducerTypelessBase
-{
-  ConcurrentQueueProducerTypelessBase* next;
-  std::atomic<bool> inactive;
-  ProducerToken* token;
-
-  ConcurrentQueueProducerTypelessBase()
-    : next(nullptr), inactive(false), token(nullptr)
-  {
-  }
-};
-
-template<bool use32> struct _hash_32_or_64 {
-  static inline std::uint32_t hash(std::uint32_t h)
-  {
-    // MurmurHash3 finalizer -- see https://code.google.com/p/smhasher/source/browse/trunk/MurmurHash3.cpp
-    // Since the thread ID is already unique, all we really want to do is propagate that
-    // uniqueness evenly across all the bits, so that we can use a subset of the bits while
-    // reducing collisions significantly
-    h ^= h >> 16;
-    h *= 0x85ebca6b;
-    h ^= h >> 13;
-    h *= 0xc2b2ae35;
-    return h ^ (h >> 16);
-  }
-};
-template<> struct _hash_32_or_64<1> {
-  static inline std::uint64_t hash(std::uint64_t h)
-  {
-    h ^= h >> 33;
-    h *= 0xff51afd7ed558ccd;
-    h ^= h >> 33;
-    h *= 0xc4ceb9fe1a85ec53;
-    return h ^ (h >> 33);
-  }
-};
-template<std::size_t size> struct hash_32_or_64 : public _hash_32_or_64<(size > 4)> {  };
-
-static inline size_t hash_thread_id(thread_id_t id)
-{
-  static_assert(sizeof(thread_id_t) <= 8, "Expected a platform where thread IDs are at most 64-bit values");
-  return static_cast<size_t>(hash_32_or_64<sizeof(thread_id_converter<thread_id_t>::thread_id_hash_t)>::hash(
-    thread_id_converter<thread_id_t>::prehash(id)));
-}
-
-template<typename T>
-static inline bool circular_less_than(T a, T b)
-{
-#ifdef _MSC_VER
-  #pragma warning(push)
-#pragma warning(disable: 4554)
-#endif
-  static_assert(std::is_integral<T>::value && !std::numeric_limits<T>::is_signed, "circular_less_than is intended to be used only with unsigned integer types");
-  return static_cast<T>(a - b) > static_cast<T>(static_cast<T>(1) << static_cast<T>(sizeof(T) * CHAR_BIT - 1));
-#ifdef _MSC_VER
-#pragma warning(pop)
-#endif
-}
-
-template<typename U>
-static inline char* align_for(char* ptr)
-{
-  const std::size_t alignment = std::alignment_of<U>::value;
-  return ptr + (alignment - (reinterpret_cast<std::uintptr_t>(ptr) % alignment)) % alignment;
-}
-
-template<typename T>
-static inline T ceil_to_pow_2(T x)
-{
-  static_assert(std::is_integral<T>::value && !std::numeric_limits<T>::is_signed, "ceil_to_pow_2 is intended to be used only with unsigned integer types");
-
-  // Adapted from http://graphics.stanford.edu/~seander/bithacks.html#RoundUpPowerOf2
-  --x;
-  x |= x >> 1;
-  x |= x >> 2;
-  x |= x >> 4;
-  for (std::size_t i = 1; i < sizeof(T); i <<= 1) {
-    x |= x >> (i << 3);
-  }
-  ++x;
-  return x;
-}
-
-template<typename T>
-static inline void swap_relaxed(std::atomic<T>& left, std::atomic<T>& right)
-{
-  T temp = std::move(left.load(std::memory_order_relaxed));
-  left.store(std::move(right.load(std::memory_order_relaxed)), std::memory_order_relaxed);
-  right.store(std::move(temp), std::memory_order_relaxed);
-}
-
-template<typename T>
-static inline T const& nomove(T const& x)
-{
-  return x;
-}
-
-template<bool Enable>
-struct nomove_if
-{
-  template<typename T>
-  static inline T const& eval(T const& x)
-  {
-    return x;
-  }
-};
-
-template<>
-struct nomove_if<false>
-{
-  template<typename U>
-  static inline auto eval(U&& x)
-  -> decltype(std::forward<U>(x))
-  {
-    return std::forward<U>(x);
-  }
-};
-
-template<typename It>
-static inline auto deref_noexcept(It& it) MOODYCAMEL_NOEXCEPT -> decltype(*it)
-{
-  return *it;
-}
-
-#if defined(__clang__) || !defined(__GNUC__) || __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 8)
-template<typename T> struct is_trivially_destructible : std::is_trivially_destructible<T> { };
-#else
-template<typename T> struct is_trivially_destructible : std::has_trivial_destructor<T> { };
-#endif
-
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-#ifdef MCDBGQ_USE_RELACY
-  typedef RelacyThreadExitListener ThreadExitListener;
-  typedef RelacyThreadExitNotifier ThreadExitNotifier;
-#else
-  struct ThreadExitListener
-  {
-    typedef void (*callback_t)(void*);
-    callback_t callback;
-    void* userData;
-
-    ThreadExitListener* next;		// reserved for use by the ThreadExitNotifier
-  };
-
-
-  class ThreadExitNotifier
-  {
-  public:
-    static void subscribe(ThreadExitListener* listener)
-    {
-      auto& tlsInst = instance();
-      listener->next = tlsInst.tail;
-      tlsInst.tail = listener;
-    }
-
-    static void unsubscribe(ThreadExitListener* listener)
-    {
-      auto& tlsInst = instance();
-      ThreadExitListener** prev = &tlsInst.tail;
-      for (auto ptr = tlsInst.tail; ptr != nullptr; ptr = ptr->next) {
-        if (ptr == listener) {
-          *prev = ptr->next;
-          break;
-        }
-        prev = &ptr->next;
-      }
-    }
-
-  private:
-    ThreadExitNotifier() : tail(nullptr) { }
-    ThreadExitNotifier(ThreadExitNotifier const&) MOODYCAMEL_DELETE_FUNCTION;
-    ThreadExitNotifier& operator=(ThreadExitNotifier const&) MOODYCAMEL_DELETE_FUNCTION;
-
-    ~ThreadExitNotifier()
-    {
-      // This thread is about to exit, let everyone know!
-      assert(this == &instance() && "If this assert fails, you likely have a buggy compiler! Change the preprocessor conditions such that MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED is no longer defined.");
-      for (auto ptr = tail; ptr != nullptr; ptr = ptr->next) {
-        ptr->callback(ptr->userData);
-      }
-    }
-
-    // Thread-local
-    static inline ThreadExitNotifier& instance()
-    {
-      static thread_local ThreadExitNotifier notifier;
-      return notifier;
-    }
-
-  private:
-    ThreadExitListener* tail;
-  };
-#endif
-#endif
-
-template<typename T> struct static_is_lock_free_num { enum { value = 0 }; };
-template<> struct static_is_lock_free_num<signed char> { enum { value = ATOMIC_CHAR_LOCK_FREE }; };
-template<> struct static_is_lock_free_num<short> { enum { value = ATOMIC_SHORT_LOCK_FREE }; };
-template<> struct static_is_lock_free_num<int> { enum { value = ATOMIC_INT_LOCK_FREE }; };
-template<> struct static_is_lock_free_num<long> { enum { value = ATOMIC_LONG_LOCK_FREE }; };
-template<> struct static_is_lock_free_num<long long> { enum { value = ATOMIC_LLONG_LOCK_FREE }; };
-template<typename T> struct static_is_lock_free : static_is_lock_free_num<typename std::make_signed<T>::type> {  };
-template<> struct static_is_lock_free<bool> { enum { value = ATOMIC_BOOL_LOCK_FREE }; };
-template<typename U> struct static_is_lock_free<U*> { enum { value = ATOMIC_POINTER_LOCK_FREE }; };
-}
-
-
-struct ProducerToken
-{
-  template<typename T, typename Traits>
-  explicit ProducerToken(ConcurrentQueue<T, Traits>& queue);
-
-  template<typename T, typename Traits>
-  explicit ProducerToken(BlockingConcurrentQueue<T, Traits>& queue);
-
-  ProducerToken(ProducerToken&& other) MOODYCAMEL_NOEXCEPT
-    : producer(other.producer)
-  {
-    other.producer = nullptr;
-    if (producer != nullptr) {
-      producer->token = this;
-    }
-  }
-
-  inline ProducerToken& operator=(ProducerToken&& other) MOODYCAMEL_NOEXCEPT
-  {
-    swap(other);
-    return *this;
-  }
-
-  void swap(ProducerToken& other) MOODYCAMEL_NOEXCEPT
-  {
-    std::swap(producer, other.producer);
-    if (producer != nullptr) {
-      producer->token = this;
-    }
-    if (other.producer != nullptr) {
-      other.producer->token = &other;
-    }
-  }
-
-  // A token is always valid unless:
-  //     1) Memory allocation failed during construction
-  //     2) It was moved via the move constructor
-  //        (Note: assignment does a swap, leaving both potentially valid)
-  //     3) The associated queue was destroyed
-  // Note that if valid() returns true, that only indicates
-  // that the token is valid for use with a specific queue,
-  // but not which one; that's up to the user to track.
-  inline bool valid() const { return producer != nullptr; }
-
-  ~ProducerToken()
-  {
-    if (producer != nullptr) {
-      producer->token = nullptr;
-      producer->inactive.store(true, std::memory_order_release);
-    }
-  }
-
-  // Disable copying and assignment
-  ProducerToken(ProducerToken const&) MOODYCAMEL_DELETE_FUNCTION;
-  ProducerToken& operator=(ProducerToken const&) MOODYCAMEL_DELETE_FUNCTION;
-
- private:
-  template<typename T, typename Traits> friend class ConcurrentQueue;
-  friend class ConcurrentQueueTests;
-
- protected:
-  details::ConcurrentQueueProducerTypelessBase* producer;
-};
-
-
-struct ConsumerToken
-{
-  template<typename T, typename Traits>
-  explicit ConsumerToken(ConcurrentQueue<T, Traits>& q);
-
-  template<typename T, typename Traits>
-  explicit ConsumerToken(BlockingConcurrentQueue<T, Traits>& q);
-
-  ConsumerToken(ConsumerToken&& other) MOODYCAMEL_NOEXCEPT
-    : initialOffset(other.initialOffset), lastKnownGlobalOffset(other.lastKnownGlobalOffset), itemsConsumedFromCurrent(other.itemsConsumedFromCurrent), currentProducer(other.currentProducer), desiredProducer(other.desiredProducer)
-  {
-  }
-
-  inline ConsumerToken& operator=(ConsumerToken&& other) MOODYCAMEL_NOEXCEPT
-  {
-    swap(other);
-    return *this;
-  }
-
-  void swap(ConsumerToken& other) MOODYCAMEL_NOEXCEPT
-  {
-    std::swap(initialOffset, other.initialOffset);
-    std::swap(lastKnownGlobalOffset, other.lastKnownGlobalOffset);
-    std::swap(itemsConsumedFromCurrent, other.itemsConsumedFromCurrent);
-    std::swap(currentProducer, other.currentProducer);
-    std::swap(desiredProducer, other.desiredProducer);
-  }
-
-  // Disable copying and assignment
-  ConsumerToken(ConsumerToken const&) MOODYCAMEL_DELETE_FUNCTION;
-  ConsumerToken& operator=(ConsumerToken const&) MOODYCAMEL_DELETE_FUNCTION;
-
- private:
-  template<typename T, typename Traits> friend class ConcurrentQueue;
-  friend class ConcurrentQueueTests;
-
- private: // but shared with ConcurrentQueue
-  std::uint32_t initialOffset;
-  std::uint32_t lastKnownGlobalOffset;
-  std::uint32_t itemsConsumedFromCurrent;
-  details::ConcurrentQueueProducerTypelessBase* currentProducer;
-  details::ConcurrentQueueProducerTypelessBase* desiredProducer;
-};
-
-// Need to forward-declare this swap because it's in a namespace.
-// See http://stackoverflow.com/questions/4492062/why-does-a-c-friend-class-need-a-forward-declaration-only-in-other-namespaces
-template<typename T, typename Traits>
-inline void swap(typename ConcurrentQueue<T, Traits>::ImplicitProducerKVP& a, typename ConcurrentQueue<T, Traits>::ImplicitProducerKVP& b) MOODYCAMEL_NOEXCEPT;
-
-
-template<typename T, typename Traits = ConcurrentQueueDefaultTraits>
-class ConcurrentQueue {
- public:
-  typedef ::dmlc::moodycamel::ProducerToken producer_token_t;
-  typedef ::dmlc::moodycamel::ConsumerToken consumer_token_t;
-
-  typedef typename Traits::index_t index_t;
-  typedef typename Traits::size_t size_t;
-
-  static const size_t BLOCK_SIZE = static_cast<size_t>(Traits::BLOCK_SIZE);
-  static const size_t EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD = static_cast<size_t>(Traits::EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD);
-  static const size_t EXPLICIT_INITIAL_INDEX_SIZE = static_cast<size_t>(Traits::EXPLICIT_INITIAL_INDEX_SIZE);
-  static const size_t IMPLICIT_INITIAL_INDEX_SIZE = static_cast<size_t>(Traits::IMPLICIT_INITIAL_INDEX_SIZE);
-  static const size_t INITIAL_IMPLICIT_PRODUCER_HASH_SIZE = static_cast<size_t>(Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE);
-  static const std::uint32_t EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE = static_cast<std::uint32_t>(Traits::EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE);
-#ifdef _MSC_VER
-  #pragma warning(push)
-#pragma warning(disable: 4307)		// + integral constant overflow (that's what the ternary expression is for!)
-#pragma warning(disable: 4309)		// static_cast: Truncation of constant value
-#endif
-  static const size_t MAX_SUBQUEUE_SIZE = (details::const_numeric_max<size_t>::value -
-                                           static_cast<size_t>(Traits::MAX_SUBQUEUE_SIZE) <
-                                           BLOCK_SIZE) ? details::const_numeric_max<size_t>::value
-                                                       : (
-                                            (static_cast<size_t>(Traits::MAX_SUBQUEUE_SIZE) +
-                                             (BLOCK_SIZE - 1)) / BLOCK_SIZE * BLOCK_SIZE);
-#ifdef _MSC_VER
-#pragma warning(pop)
-#endif
-
-  static_assert(!std::numeric_limits<size_t>::is_signed && std::is_integral<size_t>::value,
-                "Traits::size_t must be an unsigned integral type");
-  static_assert(!std::numeric_limits<index_t>::is_signed && std::is_integral<index_t>::value,
-                "Traits::index_t must be an unsigned integral type");
-  static_assert(sizeof(index_t) >= sizeof(size_t),
-                "Traits::index_t must be at least as wide as Traits::size_t");
-  static_assert((BLOCK_SIZE > 1) && !(BLOCK_SIZE & (BLOCK_SIZE - 1)),
-                "Traits::BLOCK_SIZE must be a power of 2 (and at least 2)");
-  static_assert((EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD > 1) &&
-                !(EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD &
-                  (EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD - 1)),
-                "Traits::EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD must be a power of 2 (and greater than 1)");
-  static_assert((EXPLICIT_INITIAL_INDEX_SIZE > 1) &&
-                !(EXPLICIT_INITIAL_INDEX_SIZE & (EXPLICIT_INITIAL_INDEX_SIZE - 1)),
-                "Traits::EXPLICIT_INITIAL_INDEX_SIZE must be a power of 2 (and greater than 1)");
-  static_assert((IMPLICIT_INITIAL_INDEX_SIZE > 1) &&
-                !(IMPLICIT_INITIAL_INDEX_SIZE & (IMPLICIT_INITIAL_INDEX_SIZE - 1)),
-                "Traits::IMPLICIT_INITIAL_INDEX_SIZE must be a power of 2 (and greater than 1)");
-  static_assert((INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) ||
-                !(INITIAL_IMPLICIT_PRODUCER_HASH_SIZE & (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE - 1)),
-                "Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE must be a power of 2");
-  static_assert(
-    INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0 || INITIAL_IMPLICIT_PRODUCER_HASH_SIZE >= 1,
-    "Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE must be at least 1 (or 0 to disable implicit enqueueing)");
-
- public:
-  // Creates a queue with at least `capacity` element slots; note that the
-  // actual number of elements that can be inserted without additional memory
-  // allocation depends on the number of producers and the block size (e.g. if
-  // the block size is equal to `capacity`, only a single block will be allocated
-  // up-front, which means only a single producer will be able to enqueue elements
-  // without an extra allocation -- blocks aren't shared between producers).
-  // This method is not thread safe -- it is up to the user to ensure that the
-  // queue is fully constructed before it starts being used by other threads (this
-  // includes making the memory effects of construction visible, possibly with a
-  // memory barrier).
-  explicit ConcurrentQueue(size_t capacity = 6 * BLOCK_SIZE)
-    : producerListTail(nullptr), producerCount(0), initialBlockPoolIndex(0), nextExplicitConsumerId(
-    0), globalExplicitConsumerOffset(0) {
-    implicitProducerHashResizeInProgress.clear(std::memory_order_relaxed);
-    populate_initial_implicit_producer_hash();
-    populate_initial_block_list(
-      capacity / BLOCK_SIZE + ((capacity & (BLOCK_SIZE - 1)) == 0 ? 0 : 1));
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    // Track all the producers using a fully-resolved typed list for
-    // each kind; this makes it possible to debug them starting from
-    // the root queue object (otherwise wacky casts are needed that
-    // don't compile in the debugger's expression evaluator).
-    explicitProducers.store(nullptr, std::memory_order_relaxed);
-    implicitProducers.store(nullptr, std::memory_order_relaxed);
-#endif
-  }
-
-  // Computes the correct amount of pre-allocated blocks for you based
-  // on the minimum number of elements you want available at any given
-  // time, and the maximum concurrent number of each type of producer.
-  ConcurrentQueue(size_t minCapacity, size_t maxExplicitProducers, size_t maxImplicitProducers)
-    : producerListTail(nullptr), producerCount(0), initialBlockPoolIndex(0), nextExplicitConsumerId(
-    0), globalExplicitConsumerOffset(0) {
-    implicitProducerHashResizeInProgress.clear(std::memory_order_relaxed);
-    populate_initial_implicit_producer_hash();
-    size_t blocks =
-      (((minCapacity + BLOCK_SIZE - 1) / BLOCK_SIZE) - 1) * (maxExplicitProducers + 1) +
-      2 * (maxExplicitProducers + maxImplicitProducers);
-    populate_initial_block_list(blocks);
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    explicitProducers.store(nullptr, std::memory_order_relaxed);
-    implicitProducers.store(nullptr, std::memory_order_relaxed);
-#endif
-  }
-
-  // Note: The queue should not be accessed concurrently while it's
-  // being deleted. It's up to the user to synchronize this.
-  // This method is not thread safe.
-  ~ConcurrentQueue() {
-    // Destroy producers
-    auto ptr = producerListTail.load(std::memory_order_relaxed);
-    while (ptr != nullptr) {
-      auto next = ptr->next_prod();
-      if (ptr->token != nullptr) {
-        ptr->token->producer = nullptr;
-      }
-      destroy(ptr);
-      ptr = next;
-    }
-
-    // Destroy implicit producer hash tables
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE != 0) {
-      auto hash = implicitProducerHash.load(std::memory_order_relaxed);
-      while (hash != nullptr) {
-        auto prev = hash->prev;
-        if (prev !=
-            nullptr) {    // The last hash is part of this object and was not allocated dynamically
-          for (size_t i = 0; i != hash->capacity; ++i) {
-            hash->entries[i].~ImplicitProducerKVP();
-          }
-          hash->~ImplicitProducerHash();
-          (Traits::free)(hash);
-        }
-        hash = prev;
-      }
-    }
-
-    // Destroy global free list
-    auto block = freeList.head_unsafe();
-    while (block != nullptr) {
-      auto next = block->freeListNext.load(std::memory_order_relaxed);
-      if (block->dynamicallyAllocated) {
-        destroy(block);
-      }
-      block = next;
-    }
-
-    // Destroy initial free list
-    destroy_array(initialBlockPool, initialBlockPoolSize);
-  }
-
-  // Disable copying and copy assignment
-  ConcurrentQueue(ConcurrentQueue const &) MOODYCAMEL_DELETE_FUNCTION;
-
-  ConcurrentQueue &operator=(ConcurrentQueue const &) MOODYCAMEL_DELETE_FUNCTION;
-
-  // Moving is supported, but note that it is *not* a thread-safe operation.
-  // Nobody can use the queue while it's being moved, and the memory effects
-  // of that move must be propagated to other threads before they can use it.
-  // Note: When a queue is moved, its tokens are still valid but can only be
-  // used with the destination queue (i.e. semantically they are moved along
-  // with the queue itself).
-  ConcurrentQueue(ConcurrentQueue &&other) MOODYCAMEL_NOEXCEPT
-    : producerListTail(other.producerListTail.load(std::memory_order_relaxed)), producerCount(
-    other.producerCount.load(std::memory_order_relaxed)), initialBlockPoolIndex(
-    other.initialBlockPoolIndex.load(std::memory_order_relaxed)), initialBlockPool(
-    other.initialBlockPool), initialBlockPoolSize(other.initialBlockPoolSize), freeList(
-    std::move(other.freeList)), nextExplicitConsumerId(
-    other.nextExplicitConsumerId.load(std::memory_order_relaxed)), globalExplicitConsumerOffset(
-    other.globalExplicitConsumerOffset.load(std::memory_order_relaxed)) {
-    // Move the other one into this, and leave the other one as an empty queue
-    implicitProducerHashResizeInProgress.clear(std::memory_order_relaxed);
-    populate_initial_implicit_producer_hash();
-    swap_implicit_producer_hashes(other);
-
-    other.producerListTail.store(nullptr, std::memory_order_relaxed);
-    other.producerCount.store(0, std::memory_order_relaxed);
-    other.nextExplicitConsumerId.store(0, std::memory_order_relaxed);
-    other.globalExplicitConsumerOffset.store(0, std::memory_order_relaxed);
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    explicitProducers.store(other.explicitProducers.load(std::memory_order_relaxed), std::memory_order_relaxed);
-    other.explicitProducers.store(nullptr, std::memory_order_relaxed);
-    implicitProducers.store(other.implicitProducers.load(std::memory_order_relaxed), std::memory_order_relaxed);
-    other.implicitProducers.store(nullptr, std::memory_order_relaxed);
-#endif
-
-    other.initialBlockPoolIndex.store(0, std::memory_order_relaxed);
-    other.initialBlockPoolSize = 0;
-    other.initialBlockPool = nullptr;
-
-    reown_producers();
-  }
-
-  inline ConcurrentQueue &operator=(ConcurrentQueue &&other) MOODYCAMEL_NOEXCEPT {
-    return swap_internal(other);
-  }
-
-  // Swaps this queue's state with the other's. Not thread-safe.
-  // Swapping two queues does not invalidate their tokens, however
-  // the tokens that were created for one queue must be used with
-  // only the swapped queue (i.e. the tokens are tied to the
-  // queue's movable state, not the object itself).
-  inline void swap(ConcurrentQueue &other) MOODYCAMEL_NOEXCEPT {
-    swap_internal(other);
-  }
-
- private:
-  ConcurrentQueue &swap_internal(ConcurrentQueue &other) {
-    if (this == &other) {
-      return *this;
-    }
-
-    details::swap_relaxed(producerListTail, other.producerListTail);
-    details::swap_relaxed(producerCount, other.producerCount);
-    details::swap_relaxed(initialBlockPoolIndex, other.initialBlockPoolIndex);
-    std::swap(initialBlockPool, other.initialBlockPool);
-    std::swap(initialBlockPoolSize, other.initialBlockPoolSize);
-    freeList.swap(other.freeList);
-    details::swap_relaxed(nextExplicitConsumerId, other.nextExplicitConsumerId);
-    details::swap_relaxed(globalExplicitConsumerOffset, other.globalExplicitConsumerOffset);
-
-    swap_implicit_producer_hashes(other);
-
-    reown_producers();
-    other.reown_producers();
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    details::swap_relaxed(explicitProducers, other.explicitProducers);
-    details::swap_relaxed(implicitProducers, other.implicitProducers);
-#endif
-
-    return *this;
-  }
-
- public:
-  // Enqueues a single item (by copying it).
-  // Allocates memory if required. Only fails if memory allocation fails (or implicit
-  // production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0,
-  // or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-  // Thread-safe.
-  inline bool enqueue(T const &item) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return false;
-    return inner_enqueue<CanAlloc>(item);
-  }
-
-  // Enqueues a single item (by moving it, if possible).
-  // Allocates memory if required. Only fails if memory allocation fails (or implicit
-  // production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0,
-  // or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-  // Thread-safe.
-  inline bool enqueue(T &&item) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return false;
-    return inner_enqueue<CanAlloc>(std::move(item));
-  }
-
-  // Enqueues a single item (by copying it) using an explicit producer token.
-  // Allocates memory if required. Only fails if memory allocation fails (or
-  // Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-  // Thread-safe.
-  inline bool enqueue(producer_token_t const &token, T const &item) {
-    return inner_enqueue<CanAlloc>(token, item);
-  }
-
-  // Enqueues a single item (by moving it, if possible) using an explicit producer token.
-  // Allocates memory if required. Only fails if memory allocation fails (or
-  // Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-  // Thread-safe.
-  inline bool enqueue(producer_token_t const &token, T &&item) {
-    return inner_enqueue<CanAlloc>(token, std::move(item));
-  }
-
-  // Enqueues several items.
-  // Allocates memory if required. Only fails if memory allocation fails (or
-  // implicit production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE
-  // is 0, or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-  // Note: Use std::make_move_iterator if the elements should be moved instead of copied.
-  // Thread-safe.
-  template<typename It>
-  bool enqueue_bulk(It itemFirst, size_t count) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return false;
-    return inner_enqueue_bulk<CanAlloc>(itemFirst, count);
-  }
-
-  // Enqueues several items using an explicit producer token.
-  // Allocates memory if required. Only fails if memory allocation fails
-  // (or Traits::MAX_SUBQUEUE_SIZE has been defined and would be surpassed).
-  // Note: Use std::make_move_iterator if the elements should be moved
-  // instead of copied.
-  // Thread-safe.
-  template<typename It>
-  bool enqueue_bulk(producer_token_t const &token, It itemFirst, size_t count) {
-    return inner_enqueue_bulk<CanAlloc>(token, itemFirst, count);
-  }
-
-  // Enqueues a single item (by copying it).
-  // Does not allocate memory. Fails if not enough room to enqueue (or implicit
-  // production is disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE
-  // is 0).
-  // Thread-safe.
-  inline bool try_enqueue(T const &item) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return false;
-    return inner_enqueue<CannotAlloc>(item);
-  }
-
-  // Enqueues a single item (by moving it, if possible).
-  // Does not allocate memory (except for one-time implicit producer).
-  // Fails if not enough room to enqueue (or implicit production is
-  // disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0).
-  // Thread-safe.
-  inline bool try_enqueue(T &&item) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return false;
-    return inner_enqueue<CannotAlloc>(std::move(item));
-  }
-
-  // Enqueues a single item (by copying it) using an explicit producer token.
-  // Does not allocate memory. Fails if not enough room to enqueue.
-  // Thread-safe.
-  inline bool try_enqueue(producer_token_t const &token, T const &item) {
-    return inner_enqueue<CannotAlloc>(token, item);
-  }
-
-  // Enqueues a single item (by moving it, if possible) using an explicit producer token.
-  // Does not allocate memory. Fails if not enough room to enqueue.
-  // Thread-safe.
-  inline bool try_enqueue(producer_token_t const &token, T &&item) {
-    return inner_enqueue<CannotAlloc>(token, std::move(item));
-  }
-
-  // Enqueues several items.
-  // Does not allocate memory (except for one-time implicit producer).
-  // Fails if not enough room to enqueue (or implicit production is
-  // disabled because Traits::INITIAL_IMPLICIT_PRODUCER_HASH_SIZE is 0).
-  // Note: Use std::make_move_iterator if the elements should be moved
-  // instead of copied.
-  // Thread-safe.
-  template<typename It>
-  bool try_enqueue_bulk(It itemFirst, size_t count) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return false;
-    return inner_enqueue_bulk<CannotAlloc>(itemFirst, count);
-  }
-
-  // Enqueues several items using an explicit producer token.
-  // Does not allocate memory. Fails if not enough room to enqueue.
-  // Note: Use std::make_move_iterator if the elements should be moved
-  // instead of copied.
-  // Thread-safe.
-  template<typename It>
-  bool try_enqueue_bulk(producer_token_t const &token, It itemFirst, size_t count) {
-    return inner_enqueue_bulk<CannotAlloc>(token, itemFirst, count);
-  }
-
-
-  // Attempts to dequeue from the queue.
-  // Returns false if all producer streams appeared empty at the time they
-  // were checked (so, the queue is likely but not guaranteed to be empty).
-  // Never allocates. Thread-safe.
-  template<typename U>
-  bool try_dequeue(U &item) {
-    // Instead of simply trying each producer in turn (which could cause needless contention on the first
-    // producer), we score them heuristically.
-    size_t nonEmptyCount = 0;
-    ProducerBase *best = nullptr;
-    size_t bestSize = 0;
-    for (auto ptr = producerListTail.load(std::memory_order_acquire);
-         nonEmptyCount < 3 && ptr != nullptr; ptr = ptr->next_prod()) {
-      auto size = ptr->size_approx();
-      if (size > 0) {
-        if (size > bestSize) {
-          bestSize = size;
-          best = ptr;
-        }
-        ++nonEmptyCount;
-      }
-    }
-
-    // If there was at least one non-empty queue but it appears empty at the time
-    // we try to dequeue from it, we need to make sure every queue's been tried
-    if (nonEmptyCount > 0) {
-      if (details::likely(best->dequeue(item))) {
-        return true;
-      }
-      for (auto ptr = producerListTail.load(std::memory_order_acquire);
-           ptr != nullptr; ptr = ptr->next_prod()) {
-        if (ptr != best && ptr->dequeue(item)) {
-          return true;
-        }
-      }
-    }
-    return false;
-  }
-
-  // Attempts to dequeue from the queue.
-  // Returns false if all producer streams appeared empty at the time they
-  // were checked (so, the queue is likely but not guaranteed to be empty).
-  // This differs from the try_dequeue(item) method in that this one does
-  // not attempt to reduce contention by interleaving the order that producer
-  // streams are dequeued from. So, using this method can reduce overall throughput
-  // under contention, but will give more predictable results in single-threaded
-  // consumer scenarios. This is mostly only useful for internal unit tests.
-  // Never allocates. Thread-safe.
-  template<typename U>
-  bool try_dequeue_non_interleaved(U &item) {
-    for (auto ptr = producerListTail.load(std::memory_order_acquire);
-         ptr != nullptr; ptr = ptr->next_prod()) {
-      if (ptr->dequeue(item)) {
-        return true;
-      }
-    }
-    return false;
-  }
-
-  // Attempts to dequeue from the queue using an explicit consumer token.
-  // Returns false if all producer streams appeared empty at the time they
-  // were checked (so, the queue is likely but not guaranteed to be empty).
-  // Never allocates. Thread-safe.
-  template<typename U>
-  bool try_dequeue(consumer_token_t &token, U &item) {
-    // The idea is roughly as follows:
-    // Every 256 items from one producer, make everyone rotate (increase the global offset) -> this means the highest efficiency consumer dictates the rotation speed of everyone else, more or less
-    // If you see that the global offset has changed, you must reset your consumption counter and move to your designated place
-    // If there's no items where you're supposed to be, keep moving until you find a producer with some items
-    // If the global offset has not changed but you've run out of items to consume, move over from your current position until you find an producer with something in it
-
-    if (token.desiredProducer == nullptr || token.lastKnownGlobalOffset !=
-                                            globalExplicitConsumerOffset.load(
-                                              std::memory_order_relaxed)) {
-      if (!update_current_producer_after_rotation(token)) {
-        return false;
-      }
-    }
-
-    // If there was at least one non-empty queue but it appears empty at the time
-    // we try to dequeue from it, we need to make sure every queue's been tried
-    if (static_cast<ProducerBase *>(token.currentProducer)->dequeue(item)) {
-      if (++token.itemsConsumedFromCurrent == EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE) {
-        globalExplicitConsumerOffset.fetch_add(1, std::memory_order_relaxed);
-      }
-      return true;
-    }
-
-    auto tail = producerListTail.load(std::memory_order_acquire);
-    auto ptr = static_cast<ProducerBase *>(token.currentProducer)->next_prod();
-    if (ptr == nullptr) {
-      ptr = tail;
-    }
-    while (ptr != static_cast<ProducerBase *>(token.currentProducer)) {
-      if (ptr->dequeue(item)) {
-        token.currentProducer = ptr;
-        token.itemsConsumedFromCurrent = 1;
-        return true;
-      }
-      ptr = ptr->next_prod();
-      if (ptr == nullptr) {
-        ptr = tail;
-      }
-    }
-    return false;
-  }
-
-  // Attempts to dequeue several elements from the queue.
-  // Returns the number of items actually dequeued.
-  // Returns 0 if all producer streams appeared empty at the time they
-  // were checked (so, the queue is likely but not guaranteed to be empty).
-  // Never allocates. Thread-safe.
-  template<typename It>
-  size_t try_dequeue_bulk(It itemFirst, size_t max) {
-    size_t count = 0;
-    for (auto ptr = producerListTail.load(std::memory_order_acquire);
-         ptr != nullptr; ptr = ptr->next_prod()) {
-      count += ptr->dequeue_bulk(itemFirst, max - count);
-      if (count == max) {
-        break;
-      }
-    }
-    return count;
-  }
-
-  // Attempts to dequeue several elements from the queue using an explicit consumer token.
-  // Returns the number of items actually dequeued.
-  // Returns 0 if all producer streams appeared empty at the time they
-  // were checked (so, the queue is likely but not guaranteed to be empty).
-  // Never allocates. Thread-safe.
-  template<typename It>
-  size_t try_dequeue_bulk(consumer_token_t &token, It itemFirst, size_t max) {
-    if (token.desiredProducer == nullptr || token.lastKnownGlobalOffset !=
-                                            globalExplicitConsumerOffset.load(
-                                              std::memory_order_relaxed)) {
-      if (!update_current_producer_after_rotation(token)) {
-        return 0;
-      }
-    }
-
-    size_t count = static_cast<ProducerBase *>(token.currentProducer)->dequeue_bulk(itemFirst, max);
-    if (count == max) {
-      if ((token.itemsConsumedFromCurrent += static_cast<std::uint32_t>(max)) >=
-          EXPLICIT_CONSUMER_CONSUMPTION_QUOTA_BEFORE_ROTATE) {
-        globalExplicitConsumerOffset.fetch_add(1, std::memory_order_relaxed);
-      }
-      return max;
-    }
-    token.itemsConsumedFromCurrent += static_cast<std::uint32_t>(count);
-    max -= count;
-
-    auto tail = producerListTail.load(std::memory_order_acquire);
-    auto ptr = static_cast<ProducerBase *>(token.currentProducer)->next_prod();
-    if (ptr == nullptr) {
-      ptr = tail;
-    }
-    while (ptr != static_cast<ProducerBase *>(token.currentProducer)) {
-      auto dequeued = ptr->dequeue_bulk(itemFirst, max);
-      count += dequeued;
-      if (dequeued != 0) {
-        token.currentProducer = ptr;
-        token.itemsConsumedFromCurrent = static_cast<std::uint32_t>(dequeued);
-      }
-      if (dequeued == max) {
-        break;
-      }
-      max -= dequeued;
-      ptr = ptr->next_prod();
-      if (ptr == nullptr) {
-        ptr = tail;
-      }
-    }
-    return count;
-  }
-
-
-  // Attempts to dequeue from a specific producer's inner queue.
-  // If you happen to know which producer you want to dequeue from, this
-  // is significantly faster than using the general-case try_dequeue methods.
-  // Returns false if the producer's queue appeared empty at the time it
-  // was checked (so, the queue is likely but not guaranteed to be empty).
-  // Never allocates. Thread-safe.
-  template<typename U>
-  inline bool try_dequeue_from_producer(producer_token_t const &producer, U &item) {
-    return static_cast<ExplicitProducer *>(producer.producer)->dequeue(item);
-  }
-
-  // Attempts to dequeue several elements from a specific producer's inner queue.
-  // Returns the number of items actually dequeued.
-  // If you happen to know which producer you want to dequeue from, this
-  // is significantly faster than using the general-case try_dequeue methods.
-  // Returns 0 if the producer's queue appeared empty at the time it
-  // was checked (so, the queue is likely but not guaranteed to be empty).
-  // Never allocates. Thread-safe.
-  template<typename It>
-  inline size_t
-  try_dequeue_bulk_from_producer(producer_token_t const &producer, It itemFirst, size_t max) {
-    return static_cast<ExplicitProducer *>(producer.producer)->dequeue_bulk(itemFirst, max);
-  }
-
-
-  // Returns an estimate of the total number of elements currently in the queue. This
-  // estimate is only accurate if the queue has completely stabilized before it is called
-  // (i.e. all enqueue and dequeue operations have completed and their memory effects are
-  // visible on the calling thread, and no further operations start while this method is
-  // being called).
-  // Thread-safe.
-  size_t size_approx() const {
-    size_t size = 0;
-    for (auto ptr = producerListTail.load(std::memory_order_acquire);
-         ptr != nullptr; ptr = ptr->next_prod()) {
-      size += ptr->size_approx();
-    }
-    return size;
-  }
-
-
-  // Returns true if the underlying atomic variables used by
-  // the queue are lock-free (they should be on most platforms).
-  // Thread-safe.
-  static bool is_lock_free() {
-    return
-      details::static_is_lock_free<bool>::value == 2 &&
-      details::static_is_lock_free<size_t>::value == 2 &&
-      details::static_is_lock_free<std::uint32_t>::value == 2 &&
-      details::static_is_lock_free<index_t>::value == 2 &&
-      details::static_is_lock_free<void *>::value == 2 &&
-      details::static_is_lock_free<typename details::thread_id_converter<details::thread_id_t>::thread_id_numeric_size_t>::value ==
-      2;
-  }
-
-
- private:
-  friend struct ProducerToken;
-  friend struct ConsumerToken;
-  friend struct ExplicitProducer;
-
-  friend class ConcurrentQueueTests;
-
-  enum AllocationMode {
-    CanAlloc, CannotAlloc
-  };
-
-
-  ///////////////////////////////
-  // Queue methods
-  ///////////////////////////////
-
-  template<AllocationMode canAlloc, typename U>
-  inline bool inner_enqueue(producer_token_t const &token, U &&element) {
-    return static_cast<ExplicitProducer *>(token.producer)->ConcurrentQueue::ExplicitProducer::template enqueue<canAlloc>(
-      std::forward<U>(element));
-  }
-
-  template<AllocationMode canAlloc, typename U>
-  inline bool inner_enqueue(U &&element) {
-    auto producer = get_or_add_implicit_producer();
-    return producer == nullptr ? false
-                               : producer->ConcurrentQueue::ImplicitProducer::template enqueue<canAlloc>(
-        std::forward<U>(element));
-  }
-
-  template<AllocationMode canAlloc, typename It>
-  inline bool inner_enqueue_bulk(producer_token_t const &token, It itemFirst, size_t count) {
-    return static_cast<ExplicitProducer *>(token.producer)->ConcurrentQueue::ExplicitProducer::template enqueue_bulk<canAlloc>(
-      itemFirst, count);
-  }
-
-  template<AllocationMode canAlloc, typename It>
-  inline bool inner_enqueue_bulk(It itemFirst, size_t count) {
-    auto producer = get_or_add_implicit_producer();
-    return producer == nullptr ? false
-                               : producer->ConcurrentQueue::ImplicitProducer::template enqueue_bulk<canAlloc>(
-        itemFirst, count);
-  }
-
-  inline bool update_current_producer_after_rotation(consumer_token_t &token) {
-    // Ah, there's been a rotation, figure out where we should be!
-    auto tail = producerListTail.load(std::memory_order_acquire);
-    if (token.desiredProducer == nullptr && tail == nullptr) {
-      return false;
-    }
-    auto prodCount = producerCount.load(std::memory_order_relaxed);
-    auto globalOffset = globalExplicitConsumerOffset.load(std::memory_order_relaxed);
-    if (details::unlikely(token.desiredProducer == nullptr)) {
-      // Aha, first time we're dequeueing anything.
-      // Figure out our local position
-      // Note: offset is from start, not end, but we're traversing from end -- subtract from count first
-      std::uint32_t offset = prodCount - 1 - (token.initialOffset % prodCount);
-      token.desiredProducer = tail;
-      for (std::uint32_t i = 0; i != offset; ++i) {
-        token.desiredProducer = static_cast<ProducerBase *>(token.desiredProducer)->next_prod();
-        if (token.desiredProducer == nullptr) {
-          token.desiredProducer = tail;
-        }
-      }
-    }
-
-    std::uint32_t delta = globalOffset - token.lastKnownGlobalOffset;
-    if (delta >= prodCount) {
-      delta = delta % prodCount;
-    }
-    for (std::uint32_t i = 0; i != delta; ++i) {
-      token.desiredProducer = static_cast<ProducerBase *>(token.desiredProducer)->next_prod();
-      if (token.desiredProducer == nullptr) {
-        token.desiredProducer = tail;
-      }
-    }
-
-    token.lastKnownGlobalOffset = globalOffset;
-    token.currentProducer = token.desiredProducer;
-    token.itemsConsumedFromCurrent = 0;
-    return true;
-  }
-
-
-  ///////////////////////////
-  // Free list
-  ///////////////////////////
-
-  template<typename N>
-  struct FreeListNode {
-    FreeListNode()
-      : freeListRefs(0), freeListNext(nullptr) {}
-
-    std::atomic<std::uint32_t> freeListRefs;
-    std::atomic<N *> freeListNext;
-  };
-
-  // A simple CAS-based lock-free free list. Not the fastest thing in the world under heavy contention, but
-  // simple and correct (assuming nodes are never freed until after the free list is destroyed), and fairly
-  // speedy under low contention.
-  template<typename N>    // N must inherit FreeListNode or have the same fields (and initialization of them)
-  struct FreeList {
-    FreeList()
-      : freeListHead(nullptr) {}
-
-    FreeList(FreeList &&other)
-      : freeListHead(other.freeListHead.load(std::memory_order_relaxed)) {
-      other.freeListHead.store(nullptr, std::memory_order_relaxed);
-    }
-
-    void swap(FreeList &other) { details::swap_relaxed(freeListHead, other.freeListHead); }
-
-    FreeList(FreeList const &) MOODYCAMEL_DELETE_FUNCTION;
-
-    FreeList &operator=(FreeList const &) MOODYCAMEL_DELETE_FUNCTION;
-
-    inline void add(N *node) {
-#if MCDBGQ_NOLOCKFREE_FREELIST
-      debug::DebugLock lock(mutex);
-#endif
-      // We know that the should-be-on-freelist bit is 0 at this point, so it's safe to
-      // set it using a fetch_add
-      if (node->freeListRefs.fetch_add(SHOULD_BE_ON_FREELIST, std::memory_order_acq_rel) == 0) {
-        // Oh look! We were the last ones referencing this node, and we know
-        // we want to add it to the free list, so let's do it!
-        add_knowing_refcount_is_zero(node);
-      }
-    }
-
-    inline N *try_get() {
-#if MCDBGQ_NOLOCKFREE_FREELIST
-      debug::DebugLock lock(mutex);
-#endif
-      auto head = freeListHead.load(std::memory_order_acquire);
-      while (head != nullptr) {
-        auto prevHead = head;
-        auto refs = head->freeListRefs.load(std::memory_order_relaxed);
-        if ((refs & REFS_MASK) == 0 ||
-            !head->freeListRefs.compare_exchange_strong(refs, refs + 1, std::memory_order_acquire,
-                                                        std::memory_order_relaxed)) {
-          head = freeListHead.load(std::memory_order_acquire);
-          continue;
-        }
-
-        // Good, reference count has been incremented (it wasn't at zero), which means we can read the
-        // next and not worry about it changing between now and the time we do the CAS
-        auto next = head->freeListNext.load(std::memory_order_relaxed);
-        if (freeListHead.compare_exchange_strong(head, next, std::memory_order_acquire,
-                                                 std::memory_order_relaxed)) {
-          // Yay, got the node. This means it was on the list, which means shouldBeOnFreeList must be false no
-          // matter the refcount (because nobody else knows it's been taken off yet, it can't have been put back on).
-          assert((head->freeListRefs.load(std::memory_order_relaxed) & SHOULD_BE_ON_FREELIST) == 0);
-
-          // Decrease refcount twice, once for our ref, and once for the list's ref
-          head->freeListRefs.fetch_sub(2, std::memory_order_release);
-          return head;
-        }
-
-        // OK, the head must have changed on us, but we still need to decrease the refcount we increased.
-        // Note that we don't need to release any memory effects, but we do need to ensure that the reference
-        // count decrement happens-after the CAS on the head.
-        refs = prevHead->freeListRefs.fetch_sub(1, std::memory_order_acq_rel);
-        if (refs == SHOULD_BE_ON_FREELIST + 1) {
-          add_knowing_refcount_is_zero(prevHead);
-        }
-      }
-
-      return nullptr;
-    }
-
-    // Useful for traversing the list when there's no contention (e.g. to destroy remaining nodes)
-    N *head_unsafe() const { return freeListHead.load(std::memory_order_relaxed); }
-
-   private:
-    inline void add_knowing_refcount_is_zero(N *node) {
-      // Since the refcount is zero, and nobody can increase it once it's zero (except us, and we run
-      // only one copy of this method per node at a time, i.e. the single thread case), then we know
-      // we can safely change the next pointer of the node; however, once the refcount is back above
-      // zero, then other threads could increase it (happens under heavy contention, when the refcount
-      // goes to zero in between a load and a refcount increment of a node in try_get, then back up to
-      // something non-zero, then the refcount increment is done by the other thread) -- so, if the CAS
-      // to add the node to the actual list fails, decrease the refcount and leave the add operation to
-      // the next thread who puts the refcount back at zero (which could be us, hence the loop).
-      auto head = freeListHead.load(std::memory_order_relaxed);
-      while (true) {
-        node->freeListNext.store(head, std::memory_order_relaxed);
-        node->freeListRefs.store(1, std::memory_order_release);
-        if (!freeListHead.compare_exchange_strong(head, node, std::memory_order_release,
-                                                  std::memory_order_relaxed)) {
-          // Hmm, the add failed, but we can only try again when the refcount goes back to zero
-          if (node->freeListRefs.fetch_add(SHOULD_BE_ON_FREELIST - 1, std::memory_order_release) ==
-              1) {
-            continue;
-          }
-        }
-        return;
-      }
-    }
-
-   private:
-    // Implemented like a stack, but where node order doesn't matter (nodes are inserted out of order under contention)
-    std::atomic<N *> freeListHead;
-
-    static const std::uint32_t REFS_MASK = 0x7FFFFFFF;
-    static const std::uint32_t SHOULD_BE_ON_FREELIST = 0x80000000;
-
-#if MCDBGQ_NOLOCKFREE_FREELIST
-    debug::DebugMutex mutex;
-#endif
-  };
-
-
-  ///////////////////////////
-  // Block
-  ///////////////////////////
-
-  enum InnerQueueContext {
-    implicit_context = 0, explicit_context = 1
-  };
-
-  struct Block {
-    Block()
-      : next(nullptr), elementsCompletelyDequeued(0), freeListRefs(0), freeListNext(nullptr)
-        , shouldBeOnFreeList(false), dynamicallyAllocated(true) {
-#if MCDBGQ_TRACKMEM
-      owner = nullptr;
-#endif
-    }
-
-    template<InnerQueueContext context>
-    inline bool is_empty() const {
-      if (context == explicit_context && BLOCK_SIZE <= EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD) {
-        // Check flags
-        for (size_t i = 0; i < BLOCK_SIZE; ++i) {
-          if (!emptyFlags[i].load(std::memory_order_relaxed)) {
-            return false;
-          }
-        }
-
-        // Aha, empty; make sure we have all other memory effects that happened before the empty flags were set
-        std::atomic_thread_fence(std::memory_order_acquire);
-        return true;
-      } else {
-        // Check counter
-        if (elementsCompletelyDequeued.load(std::memory_order_relaxed) == BLOCK_SIZE) {
-          std::atomic_thread_fence(std::memory_order_acquire);
-          return true;
-        }
-        assert(elementsCompletelyDequeued.load(std::memory_order_relaxed) <= BLOCK_SIZE);
-        return false;
-      }
-    }
-
-    // Returns true if the block is now empty (does not apply in explicit context)
-    template<InnerQueueContext context>
-    inline bool set_empty(index_t i) {
-      if (context == explicit_context && BLOCK_SIZE <= EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD) {
-        // Set flag
-        assert(!emptyFlags[BLOCK_SIZE - 1 -
-                           static_cast<size_t>(i & static_cast<index_t>(BLOCK_SIZE - 1))].load(
-          std::memory_order_relaxed));
-        emptyFlags[BLOCK_SIZE - 1 -
-                   static_cast<size_t>(i & static_cast<index_t>(BLOCK_SIZE - 1))].store(true,
-                                                                                        std::memory_order_release);
-        return false;
-      } else {
-        // Increment counter
-        auto prevVal = elementsCompletelyDequeued.fetch_add(1, std::memory_order_release);
-        assert(prevVal < BLOCK_SIZE);
-        return prevVal == BLOCK_SIZE - 1;
-      }
-    }
-
-    // Sets multiple contiguous item statuses to 'empty' (assumes no wrapping and count > 0).
-    // Returns true if the block is now empty (does not apply in explicit context).
-    template<InnerQueueContext context>
-    inline bool set_many_empty(index_t i, size_t count) {
-      if (context == explicit_context && BLOCK_SIZE <= EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD) {
-        // Set flags
-        std::atomic_thread_fence(std::memory_order_release);
-        i = BLOCK_SIZE - 1 - static_cast<size_t>(i & static_cast<index_t>(BLOCK_SIZE - 1)) - count +
-            1;
-        for (size_t j = 0; j != count; ++j) {
-          assert(!emptyFlags[i + j].load(std::memory_order_relaxed));
-          emptyFlags[i + j].store(true, std::memory_order_relaxed);
-        }
-        return false;
-      } else {
-        // Increment counter
-        auto prevVal = elementsCompletelyDequeued.fetch_add(count, std::memory_order_release);
-        assert(prevVal + count <= BLOCK_SIZE);
-        return prevVal + count == BLOCK_SIZE;
-      }
-    }
-
-    template<InnerQueueContext context>
-    inline void set_all_empty() {
-      if (context == explicit_context && BLOCK_SIZE <= EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD) {
-        // Set all flags
-        for (size_t i = 0; i != BLOCK_SIZE; ++i) {
-          emptyFlags[i].store(true, std::memory_order_relaxed);
-        }
-      } else {
-        // Reset counter
-        elementsCompletelyDequeued.store(BLOCK_SIZE, std::memory_order_relaxed);
-      }
-    }
-
-    template<InnerQueueContext context>
-    inline void reset_empty() {
-      if (context == explicit_context && BLOCK_SIZE <= EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD) {
-        // Reset flags
-        for (size_t i = 0; i != BLOCK_SIZE; ++i) {
-          emptyFlags[i].store(false, std::memory_order_relaxed);
-        }
-      } else {
-        // Reset counter
-        elementsCompletelyDequeued.store(0, std::memory_order_relaxed);
-      }
-    }
-
-    inline T *operator[](index_t idx) MOODYCAMEL_NOEXCEPT {
-      return static_cast<T *>(static_cast<void *>(elements)) +
-             static_cast<size_t>(idx & static_cast<index_t>(BLOCK_SIZE - 1));
-    }
-
-    inline T const *operator[](index_t idx) const MOODYCAMEL_NOEXCEPT {
-      return static_cast<T const *>(static_cast<void const *>(elements)) +
-             static_cast<size_t>(idx & static_cast<index_t>(BLOCK_SIZE - 1));
-    }
-
-   private:
-    // IMPORTANT: This must be the first member in Block, so that if T depends on the alignment of
-    // addresses returned by malloc, that alignment will be preserved. Apparently clang actually
-    // generates code that uses this assumption for AVX instructions in some cases. Ideally, we
-    // should also align Block to the alignment of T in case it's higher than malloc's 16-byte
-    // alignment, but this is hard to do in a cross-platform way. Assert for this case:
-    static_assert(std::alignment_of<T>::value <= std::alignment_of<details::max_align_t>::value,
-                  "The queue does not support super-aligned types at this time");
-    // Additionally, we need the alignment of Block itself to be a multiple of max_align_t since
-    // otherwise the appropriate padding will not be added at the end of Block in order to make
-    // arrays of Blocks all be properly aligned (not just the first one). We use a union to force
-    // this.
-    union {
-      char elements[sizeof(T) * BLOCK_SIZE];
-      details::max_align_t dummy;
-    };
-   public:
-    Block *next;
-    std::atomic<size_t> elementsCompletelyDequeued;
-    std::atomic<bool> emptyFlags[
-      BLOCK_SIZE <= EXPLICIT_BLOCK_EMPTY_COUNTER_THRESHOLD ? BLOCK_SIZE : 1];
-   public:
-    std::atomic<std::uint32_t> freeListRefs;
-    std::atomic<Block *> freeListNext;
-    std::atomic<bool> shouldBeOnFreeList;
-    bool dynamicallyAllocated;    // Perhaps a better name for this would be 'isNotPartOfInitialBlockPool'
-
-#if MCDBGQ_TRACKMEM
-    void* owner;
-#endif
-  };
-
-  static_assert(std::alignment_of<Block>::value >= std::alignment_of<details::max_align_t>::value,
-                "Internal error: Blocks must be at least as aligned as the type they are wrapping");
-
-
-#if MCDBGQ_TRACKMEM
-  public:
-    struct MemStats;
-  private:
-#endif
-
-  ///////////////////////////
-  // Producer base
-  ///////////////////////////
-
-  struct ProducerBase : public details::ConcurrentQueueProducerTypelessBase {
-    ProducerBase(ConcurrentQueue *parent_, bool isExplicit_)
-      :
-      tailIndex(0), headIndex(0), dequeueOptimisticCount(0), dequeueOvercommit(0), tailBlock(
-      nullptr), isExplicit(isExplicit_), parent(parent_) {
-    }
-
-    virtual ~ProducerBase() {};
-
-    template<typename U>
-    inline bool dequeue(U &element) {
-      if (isExplicit) {
-        return static_cast<ExplicitProducer *>(this)->dequeue(element);
-      } else {
-        return static_cast<ImplicitProducer *>(this)->dequeue(element);
-      }
-    }
-
-    template<typename It>
-    inline size_t dequeue_bulk(It &itemFirst, size_t max) {
-      if (isExplicit) {
-        return static_cast<ExplicitProducer *>(this)->dequeue_bulk(itemFirst, max);
-      } else {
-        return static_cast<ImplicitProducer *>(this)->dequeue_bulk(itemFirst, max);
-      }
-    }
-
-    inline ProducerBase *next_prod() const { return static_cast<ProducerBase *>(next); }
-
-    inline size_t size_approx() const {
-      auto tail = tailIndex.load(std::memory_order_relaxed);
-      auto head = headIndex.load(std::memory_order_relaxed);
-      return details::circular_less_than(head, tail) ? static_cast<size_t>(tail - head) : 0;
-    }
-
-    inline index_t getTail() const { return tailIndex.load(std::memory_order_relaxed); }
-
-   protected:
-    std::atomic<index_t> tailIndex;    // Where to enqueue to next
-    std::atomic<index_t> headIndex;    // Where to dequeue from next
-
-    std::atomic<index_t> dequeueOptimisticCount;
-    std::atomic<index_t> dequeueOvercommit;
-
-    Block *tailBlock;
-
-   public:
-    bool isExplicit;
-    ConcurrentQueue *parent;
-
-   protected:
-#if MCDBGQ_TRACKMEM
-    friend struct MemStats;
-#endif
-  };
-
-
-  ///////////////////////////
-  // Explicit queue
-  ///////////////////////////
-
-  struct ExplicitProducer : public ProducerBase {
-    explicit ExplicitProducer(ConcurrentQueue *parent)
-      :
-      ProducerBase(parent, true), blockIndex(nullptr), pr_blockIndexSlotsUsed(0), pr_blockIndexSize(
-      EXPLICIT_INITIAL_INDEX_SIZE >> 1), pr_blockIndexFront(0), pr_blockIndexEntries(nullptr)
-      , pr_blockIndexRaw(nullptr) {
-      size_t poolBasedIndexSize = details::ceil_to_pow_2(parent->initialBlockPoolSize) >> 1;
-      if (poolBasedIndexSize > pr_blockIndexSize) {
-        pr_blockIndexSize = poolBasedIndexSize;
-      }
-
-      new_block_index(
-        0);    // This creates an index with double the number of current entries, i.e. EXPLICIT_INITIAL_INDEX_SIZE
-    }
-
-    ~ExplicitProducer() {
-      // Destruct any elements not yet dequeued.
-      // Since we're in the destructor, we can assume all elements
-      // are either completely dequeued or completely not (no halfways).
-      if (this->tailBlock != nullptr) {    // Note this means there must be a block index too
-        // First find the block that's partially dequeued, if any
-        Block *halfDequeuedBlock = nullptr;
-        if ((this->headIndex.load(std::memory_order_relaxed) &
-             static_cast<index_t>(BLOCK_SIZE - 1)) != 0) {
-          // The head's not on a block boundary, meaning a block somewhere is partially dequeued
-          // (or the head block is the tail block and was fully dequeued, but the head/tail are still not on a boundary)
-          size_t i = (pr_blockIndexFront - pr_blockIndexSlotsUsed) & (pr_blockIndexSize - 1);
-          while (details::circular_less_than<index_t>(pr_blockIndexEntries[i].base + BLOCK_SIZE,
-                                                      this->headIndex.load(
-                                                        std::memory_order_relaxed))) {
-            i = (i + 1) & (pr_blockIndexSize - 1);
-          }
-          assert(details::circular_less_than<index_t>(pr_blockIndexEntries[i].base,
-                                                      this->headIndex.load(
-                                                        std::memory_order_relaxed)));
-          halfDequeuedBlock = pr_blockIndexEntries[i].block;
-        }
-
-        // Start at the head block (note the first line in the loop gives us the head from the tail on the first iteration)
-        auto block = this->tailBlock;
-        do {
-          block = block->next;
-          if (block->template is_empty<explicit_context>()) {
-            continue;
-          }
-
-          size_t i = 0;  // Offset into block
-          if (block == halfDequeuedBlock) {
-            i = static_cast<size_t>(this->headIndex.load(std::memory_order_relaxed) &
-                                    static_cast<index_t>(BLOCK_SIZE - 1));
-          }
-
-          // Walk through all the items in the block; if this is the tail block, we need to stop when we reach the tail index
-          auto lastValidIndex = (this->tailIndex.load(std::memory_order_relaxed) &
-                                 static_cast<index_t>(BLOCK_SIZE - 1)) == 0 ? BLOCK_SIZE
-                                                                            : static_cast<size_t>(
-                                  this->tailIndex.load(std::memory_order_relaxed) &
-                                  static_cast<index_t>(BLOCK_SIZE - 1));
-          while (i != BLOCK_SIZE && (block != this->tailBlock || i != lastValidIndex)) {
-            (*block)[i++]->~T();
-          }
-        } while (block != this->tailBlock);
-      }
-
-      // Destroy all blocks that we own
-      if (this->tailBlock != nullptr) {
-        auto block = this->tailBlock;
-        do {
-          auto nextBlock = block->next;
-          if (block->dynamicallyAllocated) {
-            destroy(block);
-          } else {
-            this->parent->add_block_to_free_list(block);
-          }
-          block = nextBlock;
-        } while (block != this->tailBlock);
-      }
-
-      // Destroy the block indices
-      auto header = static_cast<BlockIndexHeader *>(pr_blockIndexRaw);
-      while (header != nullptr) {
-        auto prev = static_cast<BlockIndexHeader *>(header->prev);
-        header->~BlockIndexHeader();
-        (Traits::free)(header);
-        header = prev;
-      }
-    }
-
-    template<AllocationMode allocMode, typename U>
-    inline bool enqueue(U &&element) {
-      index_t currentTailIndex = this->tailIndex.load(std::memory_order_relaxed);
-      index_t newTailIndex = 1 + currentTailIndex;
-      if ((currentTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) == 0) {
-        // We reached the end of a block, start a new one
-        auto startBlock = this->tailBlock;
-        auto originalBlockIndexSlotsUsed = pr_blockIndexSlotsUsed;
-        if (this->tailBlock != nullptr &&
-            this->tailBlock->next->template is_empty<explicit_context>()) {
-          // We can re-use the block ahead of us, it's empty!
-          this->tailBlock = this->tailBlock->next;
-          this->tailBlock->template reset_empty<explicit_context>();
-
-          // We'll put the block on the block index (guaranteed to be room since we're conceptually removing the
-          // last block from it first -- except instead of removing then adding, we can just overwrite).
-          // Note that there must be a valid block index here, since even if allocation failed in the ctor,
-          // it would have been re-attempted when adding the first block to the queue; since there is such
-          // a block, a block index must have been successfully allocated.
-        } else {
-          // Whatever head value we see here is >= the last value we saw here (relatively),
-          // and <= its current value. Since we have the most recent tail, the head must be
-          // <= to it.
-          auto head = this->headIndex.load(std::memory_order_relaxed);
-          assert(!details::circular_less_than<index_t>(currentTailIndex, head));
-          if (!details::circular_less_than<index_t>(head, currentTailIndex + BLOCK_SIZE)
-              || (MAX_SUBQUEUE_SIZE != details::const_numeric_max<size_t>::value &&
-                  (MAX_SUBQUEUE_SIZE == 0 ||
-                   MAX_SUBQUEUE_SIZE - BLOCK_SIZE < currentTailIndex - head))) {
-            // We can't enqueue in another block because there's not enough leeway -- the
-            // tail could surpass the head by the time the block fills up! (Or we'll exceed
-            // the size limit, if the second part of the condition was true.)
-            return false;
-          }
-          // We're going to need a new block; check that the block index has room
-          if (pr_blockIndexRaw == nullptr || pr_blockIndexSlotsUsed == pr_blockIndexSize) {
-            // Hmm, the circular block index is already full -- we'll need
-            // to allocate a new index. Note pr_blockIndexRaw can only be nullptr if
-            // the initial allocation failed in the constructor.
-
-            if (allocMode == CannotAlloc || !new_block_index(pr_blockIndexSlotsUsed)) {
-              return false;
-            }
-          }
-
-          // Insert a new block in the circular linked list
-          auto newBlock = this->parent->ConcurrentQueue::template requisition_block<allocMode>();
-          if (newBlock == nullptr) {
-            return false;
-          }
-#if MCDBGQ_TRACKMEM
-          newBlock->owner = this;
-#endif
-          newBlock->template reset_empty<explicit_context>();
-          if (this->tailBlock == nullptr) {
-            newBlock->next = newBlock;
-          } else {
-            newBlock->next = this->tailBlock->next;
-            this->tailBlock->next = newBlock;
-          }
-          this->tailBlock = newBlock;
-          ++pr_blockIndexSlotsUsed;
-        }
-
-        if (!MOODYCAMEL_NOEXCEPT_CTOR(T, U, new(nullptr) T(std::forward<U>(element)))) {
-          // The constructor may throw. We want the element not to appear in the queue in
-          // that case (without corrupting the queue):
-          MOODYCAMEL_TRY {
-            new((*this->tailBlock)[currentTailIndex]) T(std::forward<U>(element));
-          }
-          MOODYCAMEL_CATCH (...) {
-            // Revert change to the current block, but leave the new block available
-            // for next time
-            pr_blockIndexSlotsUsed = originalBlockIndexSlotsUsed;
-            this->tailBlock = startBlock == nullptr ? this->tailBlock : startBlock;
-            MOODYCAMEL_RETHROW;
-          }
-        } else {
-          (void) startBlock;
-          (void) originalBlockIndexSlotsUsed;
-        }
-
-        // Add block to block index
-        auto &entry = blockIndex.load(std::memory_order_relaxed)->entries[pr_blockIndexFront];
-        entry.base = currentTailIndex;
-        entry.block = this->tailBlock;
-        blockIndex.load(std::memory_order_relaxed)->front.store(pr_blockIndexFront,
-                                                                std::memory_order_release);
-        pr_blockIndexFront = (pr_blockIndexFront + 1) & (pr_blockIndexSize - 1);
-
-        if (!MOODYCAMEL_NOEXCEPT_CTOR(T, U, new(nullptr) T(std::forward<U>(element)))) {
-          this->tailIndex.store(newTailIndex, std::memory_order_release);
-          return true;
-        }
-      }
-
-      // Enqueue
-      new((*this->tailBlock)[currentTailIndex]) T(std::forward<U>(element));
-
-      this->tailIndex.store(newTailIndex, std::memory_order_release);
-      return true;
-    }
-
-    template<typename U>
-    bool dequeue(U &element) {
-      auto tail = this->tailIndex.load(std::memory_order_relaxed);
-      auto overcommit = this->dequeueOvercommit.load(std::memory_order_relaxed);
-      if (details::circular_less_than<index_t>(
-        this->dequeueOptimisticCount.load(std::memory_order_relaxed) - overcommit, tail)) {
-        // Might be something to dequeue, let's give it a try
-
-        // Note that this if is purely for performance purposes in the common case when the queue is
-        // empty and the values are eventually consistent -- we may enter here spuriously.
-
-        // Note that whatever the values of overcommit and tail are, they are not going to change (unless we
-        // change them) and must be the same value at this point (inside the if) as when the if condition was
-        // evaluated.
-
-        // We insert an acquire fence here to synchronize-with the release upon incrementing dequeueOvercommit below.
-        // This ensures that whatever the value we got loaded into overcommit, the load of dequeueOptisticCount in
-        // the fetch_add below will result in a value at least as recent as that (and therefore at least as large).
-        // Note that I believe a compiler (signal) fence here would be sufficient due to the nature of fetch_add (all
-        // read-modify-write operations are guaranteed to work on the latest value in the modification order), but
-        // unfortunately that can't be shown to be correct using only the C++11 standard.
-        // See http://stackoverflow.com/questions/18223161/what-are-the-c11-memory-ordering-guarantees-in-this-corner-case
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        // Increment optimistic counter, then check if it went over the boundary
-        auto myDequeueCount = this->dequeueOptimisticCount.fetch_add(1, std::memory_order_relaxed);
-
-        // Note that since dequeueOvercommit must be <= dequeueOptimisticCount (because dequeueOvercommit is only ever
-        // incremented after dequeueOptimisticCount -- this is enforced in the `else` block below), and since we now
-        // have a version of dequeueOptimisticCount that is at least as recent as overcommit (due to the release upon
-        // incrementing dequeueOvercommit and the acquire above that synchronizes with it), overcommit <= myDequeueCount.
-        assert(overcommit <= myDequeueCount);
-
-        // Note that we reload tail here in case it changed; it will be the same value as before or greater, since
-        // this load is sequenced after (happens after) the earlier load above. This is supported by read-read
-        // coherency (as defined in the standard), explained here: http://en.cppreference.com/w/cpp/atomic/memory_order
-        tail = this->tailIndex.load(std::memory_order_acquire);
-        if (details::likely(
-          details::circular_less_than<index_t>(myDequeueCount - overcommit, tail))) {
-          // Guaranteed to be at least one element to dequeue!
-
-          // Get the index. Note that since there's guaranteed to be at least one element, this
-          // will never exceed tail. We need to do an acquire-release fence here since it's possible
-          // that whatever condition got us to this point was for an earlier enqueued element (that
-          // we already see the memory effects for), but that by the time we increment somebody else
-          // has incremented it, and we need to see the memory effects for *that* element, which is
-          // in such a case is necessarily visible on the thread that incremented it in the first
-          // place with the more current condition (they must have acquired a tail that is at least
-          // as recent).
-          auto index = this->headIndex.fetch_add(1, std::memory_order_acq_rel);
-
-
-          // Determine which block the element is in
-
-          auto localBlockIndex = blockIndex.load(std::memory_order_acquire);
-          auto localBlockIndexHead = localBlockIndex->front.load(std::memory_order_acquire);
-
-          // We need to be careful here about subtracting and dividing because of index wrap-around.
-          // When an index wraps, we need to preserve the sign of the offset when dividing it by the
-          // block size (in order to get a correct signed block count offset in all cases):
-          auto headBase = localBlockIndex->entries[localBlockIndexHead].base;
-          auto blockBaseIndex = index & ~static_cast<index_t>(BLOCK_SIZE - 1);
-          auto offset = static_cast<size_t>(
-            static_cast<typename std::make_signed<index_t>::type>(blockBaseIndex - headBase) /
-            BLOCK_SIZE);
-          auto block = localBlockIndex->entries[(localBlockIndexHead + offset) &
-                                                (localBlockIndex->size - 1)].block;
-
-          // Dequeue
-          auto &el = *((*block)[index]);
-          if (!MOODYCAMEL_NOEXCEPT_ASSIGN(T, T &&, element = std::move(el))) {
-            // Make sure the element is still fully dequeued and destroyed even if the assignment
-            // throws
-            struct Guard {
-              Block *block;
-              index_t index;
-
-              ~Guard() {
-                (*block)[index]->~T();
-                block->template set_empty<explicit_context>(index);
-              }
-            } guard = {block, index};
-
-            element = std::move(el);
-          } else {
-            element = std::move(el);
-            el.~T();
-            block->template set_empty<explicit_context>(index);
-          }
-
-          return true;
-        } else {
-          // Wasn't anything to dequeue after all; make the effective dequeue count eventually consistent
-          this->dequeueOvercommit.fetch_add(1,
-                                            std::memory_order_release);    // Release so that the fetch_add on dequeueOptimisticCount is guaranteed to happen before this write
-        }
-      }
-
-      return false;
-    }
-
-    template<AllocationMode allocMode, typename It>
-    bool enqueue_bulk(It itemFirst, size_t count) {
-      // First, we need to make sure we have enough room to enqueue all of the elements;
-      // this means pre-allocating blocks and putting them in the block index (but only if
-      // all the allocations succeeded).
-      index_t startTailIndex = this->tailIndex.load(std::memory_order_relaxed);
-      auto startBlock = this->tailBlock;
-      auto originalBlockIndexFront = pr_blockIndexFront;
-      auto originalBlockIndexSlotsUsed = pr_blockIndexSlotsUsed;
-
-      Block *firstAllocatedBlock = nullptr;
-
-      // Figure out how many blocks we'll need to allocate, and do so
-      size_t blockBaseDiff =
-        ((startTailIndex + count - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1)) -
-        ((startTailIndex - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1));
-      index_t currentTailIndex = (startTailIndex - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1);
-      if (blockBaseDiff > 0) {
-        // Allocate as many blocks as possible from ahead
-        while (blockBaseDiff > 0 && this->tailBlock != nullptr &&
-               this->tailBlock->next != firstAllocatedBlock &&
-               this->tailBlock->next->template is_empty<explicit_context>()) {
-          blockBaseDiff -= static_cast<index_t>(BLOCK_SIZE);
-          currentTailIndex += static_cast<index_t>(BLOCK_SIZE);
-
-          this->tailBlock = this->tailBlock->next;
-          firstAllocatedBlock =
-            firstAllocatedBlock == nullptr ? this->tailBlock : firstAllocatedBlock;
-
-          auto &entry = blockIndex.load(std::memory_order_relaxed)->entries[pr_blockIndexFront];
-          entry.base = currentTailIndex;
-          entry.block = this->tailBlock;
-          pr_blockIndexFront = (pr_blockIndexFront + 1) & (pr_blockIndexSize - 1);
-        }
-
-        // Now allocate as many blocks as necessary from the block pool
-        while (blockBaseDiff > 0) {
-          blockBaseDiff -= static_cast<index_t>(BLOCK_SIZE);
-          currentTailIndex += static_cast<index_t>(BLOCK_SIZE);
-
-          auto head = this->headIndex.load(std::memory_order_relaxed);
-          assert(!details::circular_less_than<index_t>(currentTailIndex, head));
-          bool full = !details::circular_less_than<index_t>(head, currentTailIndex + BLOCK_SIZE) ||
-                      (MAX_SUBQUEUE_SIZE != details::const_numeric_max<size_t>::value &&
-                       (MAX_SUBQUEUE_SIZE == 0 ||
-                        MAX_SUBQUEUE_SIZE - BLOCK_SIZE < currentTailIndex - head));
-          if (pr_blockIndexRaw == nullptr || pr_blockIndexSlotsUsed == pr_blockIndexSize || full) {
-            if (allocMode == CannotAlloc || full || !new_block_index(originalBlockIndexSlotsUsed)) {
-              // Failed to allocate, undo changes (but keep injected blocks)
-              pr_blockIndexFront = originalBlockIndexFront;
-              pr_blockIndexSlotsUsed = originalBlockIndexSlotsUsed;
-              this->tailBlock = startBlock == nullptr ? firstAllocatedBlock : startBlock;
-              return false;
-            }
-
-            // pr_blockIndexFront is updated inside new_block_index, so we need to
-            // update our fallback value too (since we keep the new index even if we
-            // later fail)
-            originalBlockIndexFront = originalBlockIndexSlotsUsed;
-          }
-
-          // Insert a new block in the circular linked list
-          auto newBlock = this->parent->ConcurrentQueue::template requisition_block<allocMode>();
-          if (newBlock == nullptr) {
-            pr_blockIndexFront = originalBlockIndexFront;
-            pr_blockIndexSlotsUsed = originalBlockIndexSlotsUsed;
-            this->tailBlock = startBlock == nullptr ? firstAllocatedBlock : startBlock;
-            return false;
-          }
-
-#if MCDBGQ_TRACKMEM
-          newBlock->owner = this;
-#endif
-          newBlock->template set_all_empty<explicit_context>();
-          if (this->tailBlock == nullptr) {
-            newBlock->next = newBlock;
-          } else {
-            newBlock->next = this->tailBlock->next;
-            this->tailBlock->next = newBlock;
-          }
-          this->tailBlock = newBlock;
-          firstAllocatedBlock =
-            firstAllocatedBlock == nullptr ? this->tailBlock : firstAllocatedBlock;
-
-          ++pr_blockIndexSlotsUsed;
-
-          auto &entry = blockIndex.load(std::memory_order_relaxed)->entries[pr_blockIndexFront];
-          entry.base = currentTailIndex;
-          entry.block = this->tailBlock;
-          pr_blockIndexFront = (pr_blockIndexFront + 1) & (pr_blockIndexSize - 1);
-        }
-
-        // Excellent, all allocations succeeded. Reset each block's emptiness before we fill them up, and
-        // publish the new block index front
-        auto block = firstAllocatedBlock;
-        while (true) {
-          block->template reset_empty<explicit_context>();
-          if (block == this->tailBlock) {
-            break;
-          }
-          block = block->next;
-        }
-
-        if (MOODYCAMEL_NOEXCEPT_CTOR(T, decltype(*itemFirst),
-                                     new(nullptr) T(details::deref_noexcept(itemFirst)))) {
-          blockIndex.load(std::memory_order_relaxed)->front.store(
-            (pr_blockIndexFront - 1) & (pr_blockIndexSize - 1), std::memory_order_release);
-        }
-      }
-
-      // Enqueue, one block at a time
-      index_t newTailIndex = startTailIndex + static_cast<index_t>(count);
-      currentTailIndex = startTailIndex;
-      auto endBlock = this->tailBlock;
-      this->tailBlock = startBlock;
-      assert((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) != 0 ||
-             firstAllocatedBlock != nullptr || count == 0);
-      if ((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) == 0 &&
-          firstAllocatedBlock != nullptr) {
-        this->tailBlock = firstAllocatedBlock;
-      }
-      while (true) {
-        auto stopIndex = (currentTailIndex & ~static_cast<index_t>(BLOCK_SIZE - 1)) +
-                         static_cast<index_t>(BLOCK_SIZE);
-        if (details::circular_less_than<index_t>(newTailIndex, stopIndex)) {
-          stopIndex = newTailIndex;
-        }
-        if (MOODYCAMEL_NOEXCEPT_CTOR(T, decltype(*itemFirst),
-                                     new(nullptr) T(details::deref_noexcept(itemFirst)))) {
-          while (currentTailIndex != stopIndex) {
-            new((*this->tailBlock)[currentTailIndex++]) T(*itemFirst++);
-          }
-        } else {
-          MOODYCAMEL_TRY {
-            while (currentTailIndex != stopIndex) {
-              // Must use copy constructor even if move constructor is available
-              // because we may have to revert if there's an exception.
-              // Sorry about the horrible templated next line, but it was the only way
-              // to disable moving *at compile time*, which is important because a type
-              // may only define a (noexcept) move constructor, and so calls to the
-              // cctor will not compile, even if they are in an if branch that will never
-              // be executed
-              new((*this->tailBlock)[currentTailIndex]) T(
-                details::nomove_if<(bool) !MOODYCAMEL_NOEXCEPT_CTOR(T, decltype(*itemFirst),
-                                                                    new(nullptr) T(
-                                                                      details::deref_noexcept(
-                                                                        itemFirst)))>::eval(
-                  *itemFirst));
-              ++currentTailIndex;
-              ++itemFirst;
-            }
-          }
-          MOODYCAMEL_CATCH (...) {
-            // Oh dear, an exception's been thrown -- destroy the elements that
-            // were enqueued so far and revert the entire bulk operation (we'll keep
-            // any allocated blocks in our linked list for later, though).
-            auto constructedStopIndex = currentTailIndex;
-            auto lastBlockEnqueued = this->tailBlock;
-
-            pr_blockIndexFront = originalBlockIndexFront;
-            pr_blockIndexSlotsUsed = originalBlockIndexSlotsUsed;
-            this->tailBlock = startBlock == nullptr ? firstAllocatedBlock : startBlock;
-
-            if (!details::is_trivially_destructible<T>::value) {
-              auto block = startBlock;
-              if ((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) == 0) {
-                block = firstAllocatedBlock;
-              }
-              currentTailIndex = startTailIndex;
-              while (true) {
-                stopIndex = (currentTailIndex & ~static_cast<index_t>(BLOCK_SIZE - 1)) +
-                            static_cast<index_t>(BLOCK_SIZE);
-                if (details::circular_less_than<index_t>(constructedStopIndex, stopIndex)) {
-                  stopIndex = constructedStopIndex;
-                }
-                while (currentTailIndex != stopIndex) {
-                  (*block)[currentTailIndex++]->~T();
-                }
-                if (block == lastBlockEnqueued) {
-                  break;
-                }
-                block = block->next;
-              }
-            }
-            MOODYCAMEL_RETHROW;
-          }
-        }
-
-        if (this->tailBlock == endBlock) {
-          assert(currentTailIndex == newTailIndex);
-          break;
-        }
-        this->tailBlock = this->tailBlock->next;
-      }
-
-      if (!MOODYCAMEL_NOEXCEPT_CTOR(T, decltype(*itemFirst),
-                                    new(nullptr) T(details::deref_noexcept(itemFirst))) &&
-          firstAllocatedBlock != nullptr) {
-        blockIndex.load(std::memory_order_relaxed)->front.store(
-          (pr_blockIndexFront - 1) & (pr_blockIndexSize - 1), std::memory_order_release);
-      }
-
-      this->tailIndex.store(newTailIndex, std::memory_order_release);
-      return true;
-    }
-
-    template<typename It>
-    size_t dequeue_bulk(It &itemFirst, size_t max) {
-      auto tail = this->tailIndex.load(std::memory_order_relaxed);
-      auto overcommit = this->dequeueOvercommit.load(std::memory_order_relaxed);
-      auto desiredCount = static_cast<size_t>(tail - (this->dequeueOptimisticCount.load(
-        std::memory_order_relaxed) - overcommit));
-      if (details::circular_less_than<size_t>(0, desiredCount)) {
-        desiredCount = desiredCount < max ? desiredCount : max;
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        auto myDequeueCount = this->dequeueOptimisticCount.fetch_add(desiredCount,
-                                                                     std::memory_order_relaxed);
-        assert(overcommit <= myDequeueCount);
-
-        tail = this->tailIndex.load(std::memory_order_acquire);
-        auto actualCount = static_cast<size_t>(tail - (myDequeueCount - overcommit));
-        if (details::circular_less_than<size_t>(0, actualCount)) {
-          actualCount = desiredCount < actualCount ? desiredCount : actualCount;
-          if (actualCount < desiredCount) {
-            this->dequeueOvercommit.fetch_add(desiredCount - actualCount,
-                                              std::memory_order_release);
-          }
-
-          // Get the first index. Note that since there's guaranteed to be at least actualCount elements, this
-          // will never exceed tail.
-          auto firstIndex = this->headIndex.fetch_add(actualCount, std::memory_order_acq_rel);
-
-          // Determine which block the first element is in
-          auto localBlockIndex = blockIndex.load(std::memory_order_acquire);
-          auto localBlockIndexHead = localBlockIndex->front.load(std::memory_order_acquire);
-
-          auto headBase = localBlockIndex->entries[localBlockIndexHead].base;
-          auto firstBlockBaseIndex = firstIndex & ~static_cast<index_t>(BLOCK_SIZE - 1);
-          auto offset = static_cast<size_t>(
-            static_cast<typename std::make_signed<index_t>::type>(firstBlockBaseIndex - headBase) /
-            BLOCK_SIZE);
-          auto indexIndex = (localBlockIndexHead + offset) & (localBlockIndex->size - 1);
-
-          // Iterate the blocks and dequeue
-          auto index = firstIndex;
-          do {
-            auto firstIndexInBlock = index;
-            auto endIndex =
-              (index & ~static_cast<index_t>(BLOCK_SIZE - 1)) + static_cast<index_t>(BLOCK_SIZE);
-            endIndex = details::circular_less_than<index_t>(
-              firstIndex + static_cast<index_t>(actualCount), endIndex) ? firstIndex +
-                                                                          static_cast<index_t>(actualCount)
-                                                                        : endIndex;
-            auto block = localBlockIndex->entries[indexIndex].block;
-            if (MOODYCAMEL_NOEXCEPT_ASSIGN(T, T &&, details::deref_noexcept(itemFirst) = std::move(
-              (*(*block)[index])))) {
-              while (index != endIndex) {
-                auto &el = *((*block)[index]);
-                *itemFirst++ = std::move(el);
-                el.~T();
-                ++index;
-              }
-            } else {
-              MOODYCAMEL_TRY {
-                while (index != endIndex) {
-                  auto &el = *((*block)[index]);
-                  *itemFirst = std::move(el);
-                  ++itemFirst;
-                  el.~T();
-                  ++index;
-                }
-              }
-              MOODYCAMEL_CATCH (...) {
-                // It's too late to revert the dequeue, but we can make sure that all
-                // the dequeued objects are properly destroyed and the block index
-                // (and empty count) are properly updated before we propagate the exception
-                do {
-                  block = localBlockIndex->entries[indexIndex].block;
-                  while (index != endIndex) {
-                    (*block)[index++]->~T();
-                  }
-                  block->template set_many_empty<explicit_context>(
-                    firstIndexInBlock, static_cast<size_t>(endIndex - firstIndexInBlock));
-                  indexIndex = (indexIndex + 1) & (localBlockIndex->size - 1);
-
-                  firstIndexInBlock = index;
-                  endIndex = (index & ~static_cast<index_t>(BLOCK_SIZE - 1)) +
-                             static_cast<index_t>(BLOCK_SIZE);
-                  endIndex = details::circular_less_than<index_t>(
-                    firstIndex + static_cast<index_t>(actualCount), endIndex) ? firstIndex +
-                                                                                static_cast<index_t>(actualCount)
-                                                                              : endIndex;
-                } while (index != firstIndex + actualCount);
-
-                MOODYCAMEL_RETHROW;
-              }
-            }
-            block->template set_many_empty<explicit_context>(
-              firstIndexInBlock, static_cast<size_t>(endIndex - firstIndexInBlock));
-            indexIndex = (indexIndex + 1) & (localBlockIndex->size - 1);
-          } while (index != firstIndex + actualCount);
-
-          return actualCount;
-        } else {
-          // Wasn't anything to dequeue after all; make the effective dequeue count eventually consistent
-          this->dequeueOvercommit.fetch_add(desiredCount, std::memory_order_release);
-        }
-      }
-
-      return 0;
-    }
-
-   private:
-    struct BlockIndexEntry {
-      index_t base;
-      Block *block;
-    };
-
-    struct BlockIndexHeader {
-      size_t size;
-      std::atomic<size_t> front;    // Current slot (not next, like pr_blockIndexFront)
-      BlockIndexEntry *entries;
-      void *prev;
-    };
-
-
-    bool new_block_index(size_t numberOfFilledSlotsToExpose) {
-      auto prevBlockSizeMask = pr_blockIndexSize - 1;
-
-      // Create the new block
-      pr_blockIndexSize <<= 1;
-      auto newRawPtr = static_cast<char *>((Traits::malloc)(
-        sizeof(BlockIndexHeader) + std::alignment_of<BlockIndexEntry>::value - 1 +
-        sizeof(BlockIndexEntry) * pr_blockIndexSize));
-      if (newRawPtr == nullptr) {
-        pr_blockIndexSize >>= 1;    // Reset to allow graceful retry
-        return false;
-      }
-
-      auto newBlockIndexEntries = reinterpret_cast<BlockIndexEntry *>(details::align_for<BlockIndexEntry>(
-        newRawPtr + sizeof(BlockIndexHeader)));
-
-      // Copy in all the old indices, if any
-      size_t j = 0;
-      if (pr_blockIndexSlotsUsed != 0) {
-        auto i = (pr_blockIndexFront - pr_blockIndexSlotsUsed) & prevBlockSizeMask;
-        do {
-          newBlockIndexEntries[j++] = pr_blockIndexEntries[i];
-          i = (i + 1) & prevBlockSizeMask;
-        } while (i != pr_blockIndexFront);
-      }
-
-      // Update everything
-      auto header = new(newRawPtr) BlockIndexHeader;
-      header->size = pr_blockIndexSize;
-      header->front.store(numberOfFilledSlotsToExpose - 1, std::memory_order_relaxed);
-      header->entries = newBlockIndexEntries;
-      header->prev = pr_blockIndexRaw;    // we link the new block to the old one so we can free it later
-
-      pr_blockIndexFront = j;
-      pr_blockIndexEntries = newBlockIndexEntries;
-      pr_blockIndexRaw = newRawPtr;
-      blockIndex.store(header, std::memory_order_release);
-
-      return true;
-    }
-
-   private:
-    std::atomic<BlockIndexHeader *> blockIndex;
-
-    // To be used by producer only -- consumer must use the ones in referenced by blockIndex
-    size_t pr_blockIndexSlotsUsed;
-    size_t pr_blockIndexSize;
-    size_t pr_blockIndexFront;    // Next slot (not current)
-    BlockIndexEntry *pr_blockIndexEntries;
-    void *pr_blockIndexRaw;
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    public:
-      ExplicitProducer* nextExplicitProducer;
-    private:
-#endif
-
-#if MCDBGQ_TRACKMEM
-    friend struct MemStats;
-#endif
-  };
-
-
-  //////////////////////////////////
-  // Implicit queue
-  //////////////////////////////////
-
-  struct ImplicitProducer : public ProducerBase {
-    ImplicitProducer(ConcurrentQueue *parent)
-      :
-      ProducerBase(parent, false), nextBlockIndexCapacity(IMPLICIT_INITIAL_INDEX_SIZE), blockIndex(
-      nullptr) {
-      new_block_index();
-    }
-
-    ~ImplicitProducer() {
-      // Note that since we're in the destructor we can assume that all enqueue/dequeue operations
-      // completed already; this means that all undequeued elements are placed contiguously across
-      // contiguous blocks, and that only the first and last remaining blocks can be only partially
-      // empty (all other remaining blocks must be completely full).
-
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-      // Unregister ourselves for thread termination notification
-      if (!this->inactive.load(std::memory_order_relaxed)) {
-        details::ThreadExitNotifier::unsubscribe(&threadExitListener);
-      }
-#endif
-
-      // Destroy all remaining elements!
-      auto tail = this->tailIndex.load(std::memory_order_relaxed);
-      auto index = this->headIndex.load(std::memory_order_relaxed);
-      Block *block = nullptr;
-      assert(index == tail || details::circular_less_than(index, tail));
-      bool forceFreeLastBlock =
-        index != tail;    // If we enter the loop, then the last (tail) block will not be freed
-      while (index != tail) {
-        if ((index & static_cast<index_t>(BLOCK_SIZE - 1)) == 0 || block == nullptr) {
-          if (block != nullptr) {
-            // Free the old block
-            this->parent->add_block_to_free_list(block);
-          }
-
-          block = get_block_index_entry_for_index(index)->value.load(std::memory_order_relaxed);
-        }
-
-        ((*block)[index])->~T();
-        ++index;
-      }
-      // Even if the queue is empty, there's still one block that's not on the free list
-      // (unless the head index reached the end of it, in which case the tail will be poised
-      // to create a new block).
-      if (this->tailBlock != nullptr &&
-          (forceFreeLastBlock || (tail & static_cast<index_t>(BLOCK_SIZE - 1)) != 0)) {
-        this->parent->add_block_to_free_list(this->tailBlock);
-      }
-
-      // Destroy block index
-      auto localBlockIndex = blockIndex.load(std::memory_order_relaxed);
-      if (localBlockIndex != nullptr) {
-        for (size_t i = 0; i != localBlockIndex->capacity; ++i) {
-          localBlockIndex->index[i]->~BlockIndexEntry();
-        }
-        do {
-          auto prev = localBlockIndex->prev;
-          localBlockIndex->~BlockIndexHeader();
-          (Traits::free)(localBlockIndex);
-          localBlockIndex = prev;
-        } while (localBlockIndex != nullptr);
-      }
-    }
-
-    template<AllocationMode allocMode, typename U>
-    inline bool enqueue(U &&element) {
-      index_t currentTailIndex = this->tailIndex.load(std::memory_order_relaxed);
-      index_t newTailIndex = 1 + currentTailIndex;
-      if ((currentTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) == 0) {
-        // We reached the end of a block, start a new one
-        auto head = this->headIndex.load(std::memory_order_relaxed);
-        assert(!details::circular_less_than<index_t>(currentTailIndex, head));
-        if (!details::circular_less_than<index_t>(head, currentTailIndex + BLOCK_SIZE) ||
-            (MAX_SUBQUEUE_SIZE != details::const_numeric_max<size_t>::value &&
-             (MAX_SUBQUEUE_SIZE == 0 ||
-              MAX_SUBQUEUE_SIZE - BLOCK_SIZE < currentTailIndex - head))) {
-          return false;
-        }
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-        debug::DebugLock lock(mutex);
-#endif
-        // Find out where we'll be inserting this block in the block index
-        BlockIndexEntry *idxEntry;
-        if (!insert_block_index_entry<allocMode>(idxEntry, currentTailIndex)) {
-          return false;
-        }
-
-        // Get ahold of a new block
-        auto newBlock = this->parent->ConcurrentQueue::template requisition_block<allocMode>();
-        if (newBlock == nullptr) {
-          rewind_block_index_tail();
-          idxEntry->value.store(nullptr, std::memory_order_relaxed);
-          return false;
-        }
-#if MCDBGQ_TRACKMEM
-        newBlock->owner = this;
-#endif
-        newBlock->template reset_empty<implicit_context>();
-
-        if (!MOODYCAMEL_NOEXCEPT_CTOR(T, U, new(nullptr) T(std::forward<U>(element)))) {
-          // May throw, try to insert now before we publish the fact that we have this new block
-          MOODYCAMEL_TRY {
-            new((*newBlock)[currentTailIndex]) T(std::forward<U>(element));
-          }
-          MOODYCAMEL_CATCH (...) {
-            rewind_block_index_tail();
-            idxEntry->value.store(nullptr, std::memory_order_relaxed);
-            this->parent->add_block_to_free_list(newBlock);
-            MOODYCAMEL_RETHROW;
-          }
-        }
-
-        // Insert the new block into the index
-        idxEntry->value.store(newBlock, std::memory_order_relaxed);
-
-        this->tailBlock = newBlock;
-
-        if (!MOODYCAMEL_NOEXCEPT_CTOR(T, U, new(nullptr) T(std::forward<U>(element)))) {
-          this->tailIndex.store(newTailIndex, std::memory_order_release);
-          return true;
-        }
-      }
-
-      // Enqueue
-      new((*this->tailBlock)[currentTailIndex]) T(std::forward<U>(element));
-
-      this->tailIndex.store(newTailIndex, std::memory_order_release);
-      return true;
-    }
-
-    template<typename U>
-    bool dequeue(U &element) {
-      // See ExplicitProducer::dequeue for rationale and explanation
-      index_t tail = this->tailIndex.load(std::memory_order_relaxed);
-      index_t overcommit = this->dequeueOvercommit.load(std::memory_order_relaxed);
-      if (details::circular_less_than<index_t>(
-        this->dequeueOptimisticCount.load(std::memory_order_relaxed) - overcommit, tail)) {
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        index_t myDequeueCount = this->dequeueOptimisticCount.fetch_add(1,
-                                                                        std::memory_order_relaxed);
-        assert(overcommit <= myDequeueCount);
-        tail = this->tailIndex.load(std::memory_order_acquire);
-        if (details::likely(
-          details::circular_less_than<index_t>(myDequeueCount - overcommit, tail))) {
-          index_t index = this->headIndex.fetch_add(1, std::memory_order_acq_rel);
-
-          // Determine which block the element is in
-          auto entry = get_block_index_entry_for_index(index);
-
-          // Dequeue
-          auto block = entry->value.load(std::memory_order_relaxed);
-          auto &el = *((*block)[index]);
-
-          if (!MOODYCAMEL_NOEXCEPT_ASSIGN(T, T &&, element = std::move(el))) {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-            // Note: Acquiring the mutex with every dequeue instead of only when a block
-            // is released is very sub-optimal, but it is, after all, purely debug code.
-            debug::DebugLock lock(producer->mutex);
-#endif
-            struct Guard {
-              Block *block;
-              index_t index;
-              BlockIndexEntry *entry;
-              ConcurrentQueue *parent;
-
-              ~Guard() {
-                (*block)[index]->~T();
-                if (block->template set_empty<implicit_context>(index)) {
-                  entry->value.store(nullptr, std::memory_order_relaxed);
-                  parent->add_block_to_free_list(block);
-                }
-              }
-            } guard = {block, index, entry, this->parent};
-
-            element = std::move(el);
-          } else {
-            element = std::move(el);
-            el.~T();
-
-            if (block->template set_empty<implicit_context>(index)) {
-              {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-                debug::DebugLock lock(mutex);
-#endif
-                // Add the block back into the global free pool (and remove from block index)
-                entry->value.store(nullptr, std::memory_order_relaxed);
-              }
-              this->parent->add_block_to_free_list(block);    // releases the above store
-            }
-          }
-
-          return true;
-        } else {
-          this->dequeueOvercommit.fetch_add(1, std::memory_order_release);
-        }
-      }
-
-      return false;
-    }
-
-    template<AllocationMode allocMode, typename It>
-    bool enqueue_bulk(It itemFirst, size_t count) {
-      // First, we need to make sure we have enough room to enqueue all of the elements;
-      // this means pre-allocating blocks and putting them in the block index (but only if
-      // all the allocations succeeded).
-
-      // Note that the tailBlock we start off with may not be owned by us any more;
-      // this happens if it was filled up exactly to the top (setting tailIndex to
-      // the first index of the next block which is not yet allocated), then dequeued
-      // completely (putting it on the free list) before we enqueue again.
-
-      index_t startTailIndex = this->tailIndex.load(std::memory_order_relaxed);
-      auto startBlock = this->tailBlock;
-      Block *firstAllocatedBlock = nullptr;
-      auto endBlock = this->tailBlock;
-
-      // Figure out how many blocks we'll need to allocate, and do so
-      size_t blockBaseDiff =
-        ((startTailIndex + count - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1)) -
-        ((startTailIndex - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1));
-      index_t currentTailIndex = (startTailIndex - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1);
-      if (blockBaseDiff > 0) {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-        debug::DebugLock lock(mutex);
-#endif
-        do {
-          blockBaseDiff -= static_cast<index_t>(BLOCK_SIZE);
-          currentTailIndex += static_cast<index_t>(BLOCK_SIZE);
-
-          // Find out where we'll be inserting this block in the block index
-          BlockIndexEntry *idxEntry = nullptr;  // initialization here unnecessary but compiler can't always tell
-          Block *newBlock;
-          bool indexInserted = false;
-          auto head = this->headIndex.load(std::memory_order_relaxed);
-          assert(!details::circular_less_than<index_t>(currentTailIndex, head));
-          bool full = !details::circular_less_than<index_t>(head, currentTailIndex + BLOCK_SIZE) ||
-                      (MAX_SUBQUEUE_SIZE != details::const_numeric_max<size_t>::value &&
-                       (MAX_SUBQUEUE_SIZE == 0 ||
-                        MAX_SUBQUEUE_SIZE - BLOCK_SIZE < currentTailIndex - head));
-          if (full ||
-              !(indexInserted = insert_block_index_entry<allocMode>(idxEntry, currentTailIndex)) ||
-              (newBlock = this->parent->ConcurrentQueue::template requisition_block<allocMode>()) ==
-              nullptr) {
-            // Index allocation or block allocation failed; revert any other allocations
-            // and index insertions done so far for this operation
-            if (indexInserted) {
-              rewind_block_index_tail();
-              idxEntry->value.store(nullptr, std::memory_order_relaxed);
-            }
-            currentTailIndex = (startTailIndex - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1);
-            for (auto block = firstAllocatedBlock; block != nullptr; block = block->next) {
-              currentTailIndex += static_cast<index_t>(BLOCK_SIZE);
-              idxEntry = get_block_index_entry_for_index(currentTailIndex);
-              idxEntry->value.store(nullptr, std::memory_order_relaxed);
-              rewind_block_index_tail();
-            }
-            this->parent->add_blocks_to_free_list(firstAllocatedBlock);
-            this->tailBlock = startBlock;
-
-            return false;
-          }
-
-#if MCDBGQ_TRACKMEM
-          newBlock->owner = this;
-#endif
-          newBlock->template reset_empty<implicit_context>();
-          newBlock->next = nullptr;
-
-          // Insert the new block into the index
-          idxEntry->value.store(newBlock, std::memory_order_relaxed);
-
-          // Store the chain of blocks so that we can undo if later allocations fail,
-          // and so that we can find the blocks when we do the actual enqueueing
-          if ((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) != 0 ||
-              firstAllocatedBlock != nullptr) {
-            assert(this->tailBlock != nullptr);
-            this->tailBlock->next = newBlock;
-          }
-          this->tailBlock = newBlock;
-          endBlock = newBlock;
-          firstAllocatedBlock = firstAllocatedBlock == nullptr ? newBlock : firstAllocatedBlock;
-        } while (blockBaseDiff > 0);
-      }
-
-      // Enqueue, one block at a time
-      index_t newTailIndex = startTailIndex + static_cast<index_t>(count);
-      currentTailIndex = startTailIndex;
-      this->tailBlock = startBlock;
-      assert((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) != 0 ||
-             firstAllocatedBlock != nullptr || count == 0);
-      if ((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) == 0 &&
-          firstAllocatedBlock != nullptr) {
-        this->tailBlock = firstAllocatedBlock;
-      }
-      while (true) {
-        auto stopIndex = (currentTailIndex & ~static_cast<index_t>(BLOCK_SIZE - 1)) +
-                         static_cast<index_t>(BLOCK_SIZE);
-        if (details::circular_less_than<index_t>(newTailIndex, stopIndex)) {
-          stopIndex = newTailIndex;
-        }
-        if (MOODYCAMEL_NOEXCEPT_CTOR(T, decltype(*itemFirst),
-                                     new(nullptr) T(details::deref_noexcept(itemFirst)))) {
-          while (currentTailIndex != stopIndex) {
-            new((*this->tailBlock)[currentTailIndex++]) T(*itemFirst++);
-          }
-        } else {
-          MOODYCAMEL_TRY {
-            while (currentTailIndex != stopIndex) {
-              new((*this->tailBlock)[currentTailIndex]) T(
-                details::nomove_if<(bool) !MOODYCAMEL_NOEXCEPT_CTOR(T, decltype(*itemFirst),
-                                                                    new(nullptr) T(
-                                                                      details::deref_noexcept(
-                                                                        itemFirst)))>::eval(
-                  *itemFirst));
-              ++currentTailIndex;
-              ++itemFirst;
-            }
-          }
-          MOODYCAMEL_CATCH (...) {
-            auto constructedStopIndex = currentTailIndex;
-            auto lastBlockEnqueued = this->tailBlock;
-
-            if (!details::is_trivially_destructible<T>::value) {
-              auto block = startBlock;
-              if ((startTailIndex & static_cast<index_t>(BLOCK_SIZE - 1)) == 0) {
-                block = firstAllocatedBlock;
-              }
-              currentTailIndex = startTailIndex;
-              while (true) {
-                stopIndex = (currentTailIndex & ~static_cast<index_t>(BLOCK_SIZE - 1)) +
-                            static_cast<index_t>(BLOCK_SIZE);
-                if (details::circular_less_than<index_t>(constructedStopIndex, stopIndex)) {
-                  stopIndex = constructedStopIndex;
-                }
-                while (currentTailIndex != stopIndex) {
-                  (*block)[currentTailIndex++]->~T();
-                }
-                if (block == lastBlockEnqueued) {
-                  break;
-                }
-                block = block->next;
-              }
-            }
-
-            currentTailIndex = (startTailIndex - 1) & ~static_cast<index_t>(BLOCK_SIZE - 1);
-            for (auto block = firstAllocatedBlock; block != nullptr; block = block->next) {
-              currentTailIndex += static_cast<index_t>(BLOCK_SIZE);
-              auto idxEntry = get_block_index_entry_for_index(currentTailIndex);
-              idxEntry->value.store(nullptr, std::memory_order_relaxed);
-              rewind_block_index_tail();
-            }
-            this->parent->add_blocks_to_free_list(firstAllocatedBlock);
-            this->tailBlock = startBlock;
-            MOODYCAMEL_RETHROW;
-          }
-        }
-
-        if (this->tailBlock == endBlock) {
-          assert(currentTailIndex == newTailIndex);
-          break;
-        }
-        this->tailBlock = this->tailBlock->next;
-      }
-      this->tailIndex.store(newTailIndex, std::memory_order_release);
-      return true;
-    }
-
-    template<typename It>
-    size_t dequeue_bulk(It &itemFirst, size_t max) {
-      auto tail = this->tailIndex.load(std::memory_order_relaxed);
-      auto overcommit = this->dequeueOvercommit.load(std::memory_order_relaxed);
-      auto desiredCount = static_cast<size_t>(tail - (this->dequeueOptimisticCount.load(
-        std::memory_order_relaxed) - overcommit));
-      if (details::circular_less_than<size_t>(0, desiredCount)) {
-        desiredCount = desiredCount < max ? desiredCount : max;
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        auto myDequeueCount = this->dequeueOptimisticCount.fetch_add(desiredCount,
-                                                                     std::memory_order_relaxed);
-        assert(overcommit <= myDequeueCount);
-
-        tail = this->tailIndex.load(std::memory_order_acquire);
-        auto actualCount = static_cast<size_t>(tail - (myDequeueCount - overcommit));
-        if (details::circular_less_than<size_t>(0, actualCount)) {
-          actualCount = desiredCount < actualCount ? desiredCount : actualCount;
-          if (actualCount < desiredCount) {
-            this->dequeueOvercommit.fetch_add(desiredCount - actualCount,
-                                              std::memory_order_release);
-          }
-
-          // Get the first index. Note that since there's guaranteed to be at least actualCount elements, this
-          // will never exceed tail.
-          auto firstIndex = this->headIndex.fetch_add(actualCount, std::memory_order_acq_rel);
-
-          // Iterate the blocks and dequeue
-          auto index = firstIndex;
-          BlockIndexHeader *localBlockIndex;
-          auto indexIndex = get_block_index_index_for_index(index, localBlockIndex);
-          do {
-            auto blockStartIndex = index;
-            auto endIndex =
-              (index & ~static_cast<index_t>(BLOCK_SIZE - 1)) + static_cast<index_t>(BLOCK_SIZE);
-            endIndex = details::circular_less_than<index_t>(
-              firstIndex + static_cast<index_t>(actualCount), endIndex) ? firstIndex +
-                                                                          static_cast<index_t>(actualCount)
-                                                                        : endIndex;
-
-            auto entry = localBlockIndex->index[indexIndex];
-            auto block = entry->value.load(std::memory_order_relaxed);
-            if (MOODYCAMEL_NOEXCEPT_ASSIGN(T, T &&, details::deref_noexcept(itemFirst) = std::move(
-              (*(*block)[index])))) {
-              while (index != endIndex) {
-                auto &el = *((*block)[index]);
-                *itemFirst++ = std::move(el);
-                el.~T();
-                ++index;
-              }
-            } else {
-              MOODYCAMEL_TRY {
-                while (index != endIndex) {
-                  auto &el = *((*block)[index]);
-                  *itemFirst = std::move(el);
-                  ++itemFirst;
-                  el.~T();
-                  ++index;
-                }
-              }
-              MOODYCAMEL_CATCH (...) {
-                do {
-                  entry = localBlockIndex->index[indexIndex];
-                  block = entry->value.load(std::memory_order_relaxed);
-                  while (index != endIndex) {
-                    (*block)[index++]->~T();
-                  }
-
-                  if (block->template set_many_empty<implicit_context>(
-                    blockStartIndex, static_cast<size_t>(endIndex - blockStartIndex))) {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-                    debug::DebugLock lock(mutex);
-#endif
-                    entry->value.store(nullptr, std::memory_order_relaxed);
-                    this->parent->add_block_to_free_list(block);
-                  }
-                  indexIndex = (indexIndex + 1) & (localBlockIndex->capacity - 1);
-
-                  blockStartIndex = index;
-                  endIndex = (index & ~static_cast<index_t>(BLOCK_SIZE - 1)) +
-                             static_cast<index_t>(BLOCK_SIZE);
-                  endIndex = details::circular_less_than<index_t>(
-                    firstIndex + static_cast<index_t>(actualCount), endIndex) ? firstIndex +
-                                                                                static_cast<index_t>(actualCount)
-                                                                              : endIndex;
-                } while (index != firstIndex + actualCount);
-
-                MOODYCAMEL_RETHROW;
-              }
-            }
-            if (block->template set_many_empty<implicit_context>(
-              blockStartIndex, static_cast<size_t>(endIndex - blockStartIndex))) {
-              {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-                debug::DebugLock lock(mutex);
-#endif
-                // Note that the set_many_empty above did a release, meaning that anybody who acquires the block
-                // we're about to free can use it safely since our writes (and reads!) will have happened-before then.
-                entry->value.store(nullptr, std::memory_order_relaxed);
-              }
-              this->parent->add_block_to_free_list(block);    // releases the above store
-            }
-            indexIndex = (indexIndex + 1) & (localBlockIndex->capacity - 1);
-          } while (index != firstIndex + actualCount);
-
-          return actualCount;
-        } else {
-          this->dequeueOvercommit.fetch_add(desiredCount, std::memory_order_release);
-        }
-      }
-
-      return 0;
-    }
-
-   private:
-    // The block size must be > 1, so any number with the low bit set is an invalid block base index
-    static const index_t INVALID_BLOCK_BASE = 1;
-
-    struct BlockIndexEntry {
-      std::atomic<index_t> key;
-      std::atomic<Block *> value;
-    };
-
-    struct BlockIndexHeader {
-      size_t capacity;
-      std::atomic<size_t> tail;
-      BlockIndexEntry *entries;
-      BlockIndexEntry **index;
-      BlockIndexHeader *prev;
-    };
-
-    template<AllocationMode allocMode>
-    inline bool insert_block_index_entry(BlockIndexEntry *&idxEntry, index_t blockStartIndex) {
-      auto localBlockIndex = blockIndex.load(
-        std::memory_order_relaxed);    // We're the only writer thread, relaxed is OK
-      if (localBlockIndex == nullptr) {
-        return false;  // this can happen if new_block_index failed in the constructor
-      }
-      auto newTail = (localBlockIndex->tail.load(std::memory_order_relaxed) + 1) &
-                     (localBlockIndex->capacity - 1);
-      idxEntry = localBlockIndex->index[newTail];
-      if (idxEntry->key.load(std::memory_order_relaxed) == INVALID_BLOCK_BASE ||
-          idxEntry->value.load(std::memory_order_relaxed) == nullptr) {
-
-        idxEntry->key.store(blockStartIndex, std::memory_order_relaxed);
-        localBlockIndex->tail.store(newTail, std::memory_order_release);
-        return true;
-      }
-
-      // No room in the old block index, try to allocate another one!
-      if (allocMode == CannotAlloc || !new_block_index()) {
-        return false;
-      }
-      localBlockIndex = blockIndex.load(std::memory_order_relaxed);
-      newTail = (localBlockIndex->tail.load(std::memory_order_relaxed) + 1) &
-                (localBlockIndex->capacity - 1);
-      idxEntry = localBlockIndex->index[newTail];
-      assert(idxEntry->key.load(std::memory_order_relaxed) == INVALID_BLOCK_BASE);
-      idxEntry->key.store(blockStartIndex, std::memory_order_relaxed);
-      localBlockIndex->tail.store(newTail, std::memory_order_release);
-      return true;
-    }
-
-    inline void rewind_block_index_tail() {
-      auto localBlockIndex = blockIndex.load(std::memory_order_relaxed);
-      localBlockIndex->tail.store((localBlockIndex->tail.load(std::memory_order_relaxed) - 1) &
-                                  (localBlockIndex->capacity - 1), std::memory_order_relaxed);
-    }
-
-    inline BlockIndexEntry *get_block_index_entry_for_index(index_t index) const {
-      BlockIndexHeader *localBlockIndex;
-      auto idx = get_block_index_index_for_index(index, localBlockIndex);
-      return localBlockIndex->index[idx];
-    }
-
-    inline size_t
-    get_block_index_index_for_index(index_t index, BlockIndexHeader *&localBlockIndex) const {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-      debug::DebugLock lock(mutex);
-#endif
-      index &= ~static_cast<index_t>(BLOCK_SIZE - 1);
-      localBlockIndex = blockIndex.load(std::memory_order_acquire);
-      auto tail = localBlockIndex->tail.load(std::memory_order_acquire);
-      auto tailBase = localBlockIndex->index[tail]->key.load(std::memory_order_relaxed);
-      assert(tailBase != INVALID_BLOCK_BASE);
-      // Note: Must use division instead of shift because the index may wrap around, causing a negative
-      // offset, whose negativity we want to preserve
-      auto offset = static_cast<size_t>(
-        static_cast<typename std::make_signed<index_t>::type>(index - tailBase) / BLOCK_SIZE);
-      size_t idx = (tail + offset) & (localBlockIndex->capacity - 1);
-      assert(localBlockIndex->index[idx]->key.load(std::memory_order_relaxed) == index &&
-             localBlockIndex->index[idx]->value.load(std::memory_order_relaxed) != nullptr);
-      return idx;
-    }
-
-    bool new_block_index() {
-      auto prev = blockIndex.load(std::memory_order_relaxed);
-      size_t prevCapacity = prev == nullptr ? 0 : prev->capacity;
-      auto entryCount = prev == nullptr ? nextBlockIndexCapacity : prevCapacity;
-      auto raw = static_cast<char *>((Traits::malloc)(
-        sizeof(BlockIndexHeader) +
-        std::alignment_of<BlockIndexEntry>::value - 1 + sizeof(BlockIndexEntry) * entryCount +
-        std::alignment_of<BlockIndexEntry *>::value - 1 +
-        sizeof(BlockIndexEntry * ) * nextBlockIndexCapacity));
-      if (raw == nullptr) {
-        return false;
-      }
-
-      auto header = new(raw) BlockIndexHeader;
-      auto entries = reinterpret_cast<BlockIndexEntry *>(details::align_for<BlockIndexEntry>(
-        raw + sizeof(BlockIndexHeader)));
-      auto index = reinterpret_cast<BlockIndexEntry **>(details::align_for<BlockIndexEntry *>(
-        reinterpret_cast<char *>(entries) + sizeof(BlockIndexEntry) * entryCount));
-      if (prev != nullptr) {
-        auto prevTail = prev->tail.load(std::memory_order_relaxed);
-        auto prevPos = prevTail;
-        size_t i = 0;
-        do {
-          prevPos = (prevPos + 1) & (prev->capacity - 1);
-          index[i++] = prev->index[prevPos];
-        } while (prevPos != prevTail);
-        assert(i == prevCapacity);
-      }
-      for (size_t i = 0; i != entryCount; ++i) {
-        new(entries + i) BlockIndexEntry;
-        entries[i].key.store(INVALID_BLOCK_BASE, std::memory_order_relaxed);
-        index[prevCapacity + i] = entries + i;
-      }
-      header->prev = prev;
-      header->entries = entries;
-      header->index = index;
-      header->capacity = nextBlockIndexCapacity;
-      header->tail.store((prevCapacity - 1) & (nextBlockIndexCapacity - 1),
-                         std::memory_order_relaxed);
-
-      blockIndex.store(header, std::memory_order_release);
-
-      nextBlockIndexCapacity <<= 1;
-
-      return true;
-    }
-
-   private:
-    size_t nextBlockIndexCapacity;
-    std::atomic<BlockIndexHeader *> blockIndex;
-
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-    public:
-      details::ThreadExitListener threadExitListener;
-    private:
-#endif
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    public:
-      ImplicitProducer* nextImplicitProducer;
-    private:
-#endif
-
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODBLOCKINDEX
-    mutable debug::DebugMutex mutex;
-#endif
-#if MCDBGQ_TRACKMEM
-    friend struct MemStats;
-#endif
-  };
-
-
-  //////////////////////////////////
-  // Block pool manipulation
-  //////////////////////////////////
-
-  void populate_initial_block_list(size_t blockCount) {
-    initialBlockPoolSize = blockCount;
-    if (initialBlockPoolSize == 0) {
-      initialBlockPool = nullptr;
-      return;
-    }
-
-    initialBlockPool = create_array<Block>(blockCount);
-    if (initialBlockPool == nullptr) {
-      initialBlockPoolSize = 0;
-    }
-    for (size_t i = 0; i < initialBlockPoolSize; ++i) {
-      initialBlockPool[i].dynamicallyAllocated = false;
-    }
-  }
-
-  inline Block *try_get_block_from_initial_pool() {
-    if (initialBlockPoolIndex.load(std::memory_order_relaxed) >= initialBlockPoolSize) {
-      return nullptr;
-    }
-
-    auto index = initialBlockPoolIndex.fetch_add(1, std::memory_order_relaxed);
-
-    return index < initialBlockPoolSize ? (initialBlockPool + index) : nullptr;
-  }
-
-  inline void add_block_to_free_list(Block *block) {
-#if MCDBGQ_TRACKMEM
-    block->owner = nullptr;
-#endif
-    freeList.add(block);
-  }
-
-  inline void add_blocks_to_free_list(Block *block) {
-    while (block != nullptr) {
-      auto next = block->next;
-      add_block_to_free_list(block);
-      block = next;
-    }
-  }
-
-  inline Block *try_get_block_from_free_list() {
-    return freeList.try_get();
-  }
-
-  // Gets a free block from one of the memory pools, or allocates a new one (if applicable)
-  template<AllocationMode canAlloc>
-  Block *requisition_block() {
-    auto block = try_get_block_from_initial_pool();
-    if (block != nullptr) {
-      return block;
-    }
-
-    block = try_get_block_from_free_list();
-    if (block != nullptr) {
-      return block;
-    }
-
-    if (canAlloc == CanAlloc) {
-      return create<Block>();
-    }
-
-    return nullptr;
-  }
-
-
-#if MCDBGQ_TRACKMEM
-  public:
-    struct MemStats {
-      size_t allocatedBlocks;
-      size_t usedBlocks;
-      size_t freeBlocks;
-      size_t ownedBlocksExplicit;
-      size_t ownedBlocksImplicit;
-      size_t implicitProducers;
-      size_t explicitProducers;
-      size_t elementsEnqueued;
-      size_t blockClassBytes;
-      size_t queueClassBytes;
-      size_t implicitBlockIndexBytes;
-      size_t explicitBlockIndexBytes;
-
-      friend class ConcurrentQueue;
-
-    private:
-      static MemStats getFor(ConcurrentQueue* q)
-      {
-        MemStats stats = { 0 };
-
-        stats.elementsEnqueued = q->size_approx();
-
-        auto block = q->freeList.head_unsafe();
-        while (block != nullptr) {
-          ++stats.allocatedBlocks;
-          ++stats.freeBlocks;
-          block = block->freeListNext.load(std::memory_order_relaxed);
-        }
-
-        for (auto ptr = q->producerListTail.load(std::memory_order_acquire); ptr != nullptr; ptr = ptr->next_prod()) {
-          bool implicit = dynamic_cast<ImplicitProducer*>(ptr) != nullptr;
-          stats.implicitProducers += implicit ? 1 : 0;
-          stats.explicitProducers += implicit ? 0 : 1;
-
-          if (implicit) {
-            auto prod = static_cast<ImplicitProducer*>(ptr);
-            stats.queueClassBytes += sizeof(ImplicitProducer);
-            auto head = prod->headIndex.load(std::memory_order_relaxed);
-            auto tail = prod->tailIndex.load(std::memory_order_relaxed);
-            auto hash = prod->blockIndex.load(std::memory_order_relaxed);
-            if (hash != nullptr) {
-              for (size_t i = 0; i != hash->capacity; ++i) {
-                if (hash->index[i]->key.load(std::memory_order_relaxed) != ImplicitProducer::INVALID_BLOCK_BASE && hash->index[i]->value.load(std::memory_order_relaxed) != nullptr) {
-                  ++stats.allocatedBlocks;
-                  ++stats.ownedBlocksImplicit;
-                }
-              }
-              stats.implicitBlockIndexBytes += hash->capacity * sizeof(typename ImplicitProducer::BlockIndexEntry);
-              for (; hash != nullptr; hash = hash->prev) {
-                stats.implicitBlockIndexBytes += sizeof(typename ImplicitProducer::BlockIndexHeader) + hash->capacity * sizeof(typename ImplicitProducer::BlockIndexEntry*);
-              }
-            }
-            for (; details::circular_less_than<index_t>(head, tail); head += BLOCK_SIZE) {
-              //auto block = prod->get_block_index_entry_for_index(head);
-              ++stats.usedBlocks;
-            }
-          }
-          else {
-            auto prod = static_cast<ExplicitProducer*>(ptr);
-            stats.queueClassBytes += sizeof(ExplicitProducer);
-            auto tailBlock = prod->tailBlock;
-            bool wasNonEmpty = false;
-            if (tailBlock != nullptr) {
-              auto block = tailBlock;
-              do {
-                ++stats.allocatedBlocks;
-                if (!block->template is_empty<explicit_context>() || wasNonEmpty) {
-                  ++stats.usedBlocks;
-                  wasNonEmpty = wasNonEmpty || block != tailBlock;
-                }
-                ++stats.ownedBlocksExplicit;
-                block = block->next;
-              } while (block != tailBlock);
-            }
-            auto index = prod->blockIndex.load(std::memory_order_relaxed);
-            while (index != nullptr) {
-              stats.explicitBlockIndexBytes += sizeof(typename ExplicitProducer::BlockIndexHeader) + index->size * sizeof(typename ExplicitProducer::BlockIndexEntry);
-              index = static_cast<typename ExplicitProducer::BlockIndexHeader*>(index->prev);
-            }
-          }
-        }
-
-        auto freeOnInitialPool = q->initialBlockPoolIndex.load(std::memory_order_relaxed) >= q->initialBlockPoolSize ? 0 : q->initialBlockPoolSize - q->initialBlockPoolIndex.load(std::memory_order_relaxed);
-        stats.allocatedBlocks += freeOnInitialPool;
-        stats.freeBlocks += freeOnInitialPool;
-
-        stats.blockClassBytes = sizeof(Block) * stats.allocatedBlocks;
-        stats.queueClassBytes += sizeof(ConcurrentQueue);
-
-        return stats;
-      }
-    };
-
-    // For debugging only. Not thread-safe.
-    MemStats getMemStats()
-    {
-      return MemStats::getFor(this);
-    }
-  private:
-    friend struct MemStats;
-#endif
-
-
-  //////////////////////////////////
-  // Producer list manipulation
-  //////////////////////////////////
-
-  ProducerBase *recycle_or_create_producer(bool isExplicit) {
-    bool recycled;
-    return recycle_or_create_producer(isExplicit, recycled);
-  }
-
-  ProducerBase *recycle_or_create_producer(bool isExplicit, bool &recycled) {
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODHASH
-    debug::DebugLock lock(implicitProdMutex);
-#endif
-    // Try to re-use one first
-    for (auto ptr = producerListTail.load(std::memory_order_acquire);
-         ptr != nullptr; ptr = ptr->next_prod()) {
-      if (ptr->inactive.load(std::memory_order_relaxed) && ptr->isExplicit == isExplicit) {
-        bool expected = true;
-        if (ptr->inactive.compare_exchange_strong(expected, /* desired */ false,
-                                                  std::memory_order_acquire,
-                                                  std::memory_order_relaxed)) {
-          // We caught one! It's been marked as activated, the caller can have it
-          recycled = true;
-          return ptr;
-        }
-      }
-    }
-
-    recycled = false;
-    return add_producer(isExplicit ? static_cast<ProducerBase *>(create<ExplicitProducer>(this))
-                                   : create<ImplicitProducer>(this));
-  }
-
-  ProducerBase *add_producer(ProducerBase *producer) {
-    // Handle failed memory allocation
-    if (producer == nullptr) {
-      return nullptr;
-    }
-
-    producerCount.fetch_add(1, std::memory_order_relaxed);
-
-    // Add it to the lock-free list
-    auto prevTail = producerListTail.load(std::memory_order_relaxed);
-    do {
-      producer->next = prevTail;
-    } while (!producerListTail.compare_exchange_weak(prevTail, producer, std::memory_order_release,
-                                                     std::memory_order_relaxed));
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-    if (producer->isExplicit) {
-      auto prevTailExplicit = explicitProducers.load(std::memory_order_relaxed);
-      do {
-        static_cast<ExplicitProducer*>(producer)->nextExplicitProducer = prevTailExplicit;
-      } while (!explicitProducers.compare_exchange_weak(prevTailExplicit, static_cast<ExplicitProducer*>(producer), std::memory_order_release, std::memory_order_relaxed));
-    }
-    else {
-      auto prevTailImplicit = implicitProducers.load(std::memory_order_relaxed);
-      do {
-        static_cast<ImplicitProducer*>(producer)->nextImplicitProducer = prevTailImplicit;
-      } while (!implicitProducers.compare_exchange_weak(prevTailImplicit, static_cast<ImplicitProducer*>(producer), std::memory_order_release, std::memory_order_relaxed));
-    }
-#endif
-
-    return producer;
-  }
-
-  void reown_producers() {
-    // After another instance is moved-into/swapped-with this one, all the
-    // producers we stole still think their parents are the other queue.
-    // So fix them up!
-    for (auto ptr = producerListTail.load(std::memory_order_relaxed);
-         ptr != nullptr; ptr = ptr->next_prod()) {
-      ptr->parent = this;
-    }
-  }
-
-
-  //////////////////////////////////
-  // Implicit producer hash
-  //////////////////////////////////
-
-  struct ImplicitProducerKVP {
-    std::atomic<details::thread_id_t> key;
-    ImplicitProducer *value;    // No need for atomicity since it's only read by the thread that sets it in the first place
-
-    ImplicitProducerKVP()
-      : value(nullptr) {}
-
-    ImplicitProducerKVP(ImplicitProducerKVP &&other) MOODYCAMEL_NOEXCEPT {
-      key.store(other.key.load(std::memory_order_relaxed), std::memory_order_relaxed);
-      value = other.value;
-    }
-
-    inline ImplicitProducerKVP &operator=(ImplicitProducerKVP &&other) MOODYCAMEL_NOEXCEPT {
-      swap(other);
-      return *this;
-    }
-
-    inline void swap(ImplicitProducerKVP &other) MOODYCAMEL_NOEXCEPT {
-      if (this != &other) {
-        details::swap_relaxed(key, other.key);
-        std::swap(value, other.value);
-      }
-    }
-  };
-
-  template<typename XT, typename XTraits>
-  friend void moodycamel::swap(typename ConcurrentQueue<XT, XTraits>::ImplicitProducerKVP &,
-                               typename ConcurrentQueue<XT, XTraits>::ImplicitProducerKVP &) MOODYCAMEL_NOEXCEPT;
-
-  struct ImplicitProducerHash {
-    size_t capacity;
-    ImplicitProducerKVP *entries;
-    ImplicitProducerHash *prev;
-  };
-
-  inline void populate_initial_implicit_producer_hash() {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return;
-
-    implicitProducerHashCount.store(0, std::memory_order_relaxed);
-    auto hash = &initialImplicitProducerHash;
-    hash->capacity = INITIAL_IMPLICIT_PRODUCER_HASH_SIZE;
-    hash->entries = &initialImplicitProducerHashEntries[0];
-    for (size_t i = 0; i != INITIAL_IMPLICIT_PRODUCER_HASH_SIZE; ++i) {
-      initialImplicitProducerHashEntries[i].key.store(details::invalid_thread_id,
-                                                      std::memory_order_relaxed);
-    }
-    hash->prev = nullptr;
-    implicitProducerHash.store(hash, std::memory_order_relaxed);
-  }
-
-  void swap_implicit_producer_hashes(ConcurrentQueue &other) {
-    if (INITIAL_IMPLICIT_PRODUCER_HASH_SIZE == 0) return;
-
-    // Swap (assumes our implicit producer hash is initialized)
-    initialImplicitProducerHashEntries.swap(other.initialImplicitProducerHashEntries);
-    initialImplicitProducerHash.entries = &initialImplicitProducerHashEntries[0];
-    other.initialImplicitProducerHash.entries = &other.initialImplicitProducerHashEntries[0];
-
-    details::swap_relaxed(implicitProducerHashCount, other.implicitProducerHashCount);
-
-    details::swap_relaxed(implicitProducerHash, other.implicitProducerHash);
-    if (implicitProducerHash.load(std::memory_order_relaxed) ==
-        &other.initialImplicitProducerHash) {
-      implicitProducerHash.store(&initialImplicitProducerHash, std::memory_order_relaxed);
-    } else {
-      ImplicitProducerHash *hash;
-      for (hash = implicitProducerHash.load(std::memory_order_relaxed);
-           hash->prev != &other.initialImplicitProducerHash; hash = hash->prev) {
-        continue;
-      }
-      hash->prev = &initialImplicitProducerHash;
-    }
-    if (other.implicitProducerHash.load(std::memory_order_relaxed) ==
-        &initialImplicitProducerHash) {
-      other.implicitProducerHash.store(&other.initialImplicitProducerHash,
-                                       std::memory_order_relaxed);
-    } else {
-      ImplicitProducerHash *hash;
-      for (hash = other.implicitProducerHash.load(std::memory_order_relaxed);
-           hash->prev != &initialImplicitProducerHash; hash = hash->prev) {
-        continue;
-      }
-      hash->prev = &other.initialImplicitProducerHash;
-    }
-  }
-
-  // Only fails (returns nullptr) if memory allocation fails
-  ImplicitProducer *get_or_add_implicit_producer() {
-    // Note that since the data is essentially thread-local (key is thread ID),
-    // there's a reduced need for fences (memory ordering is already consistent
-    // for any individual thread), except for the current table itself.
-
-    // Start by looking for the thread ID in the current and all previous hash tables.
-    // If it's not found, it must not be in there yet, since this same thread would
-    // have added it previously to one of the tables that we traversed.
-
-    // Code and algorithm adapted from http://preshing.com/20130605/the-worlds-simplest-lock-free-hash-table
-
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODHASH
-    debug::DebugLock lock(implicitProdMutex);
-#endif
-
-    auto id = details::thread_id();
-    auto hashedId = details::hash_thread_id(id);
-
-    auto mainHash = implicitProducerHash.load(std::memory_order_acquire);
-    for (auto hash = mainHash; hash != nullptr; hash = hash->prev) {
-      // Look for the id in this hash
-      auto index = hashedId;
-      while (true) {    // Not an infinite loop because at least one slot is free in the hash table
-        index &= hash->capacity - 1;
-
-        auto probedKey = hash->entries[index].key.load(std::memory_order_relaxed);
-        if (probedKey == id) {
-          // Found it! If we had to search several hashes deep, though, we should lazily add it
-          // to the current main hash table to avoid the extended search next time.
-          // Note there's guaranteed to be room in the current hash table since every subsequent
-          // table implicitly reserves space for all previous tables (there's only one
-          // implicitProducerHashCount).
-          auto value = hash->entries[index].value;
-          if (hash != mainHash) {
-            index = hashedId;
-            while (true) {
-              index &= mainHash->capacity - 1;
-              probedKey = mainHash->entries[index].key.load(std::memory_order_relaxed);
-              auto empty = details::invalid_thread_id;
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-              auto reusable = details::invalid_thread_id2;
-              if ((probedKey == empty    && mainHash->entries[index].key.compare_exchange_strong(empty,    id, std::memory_order_relaxed, std::memory_order_relaxed)) ||
-                (probedKey == reusable && mainHash->entries[index].key.compare_exchange_strong(reusable, id, std::memory_order_acquire, std::memory_order_acquire))) {
-#else
-              if ((probedKey == empty &&
-                   mainHash->entries[index].key.compare_exchange_strong(empty, id,
-                                                                        std::memory_order_relaxed,
-                                                                        std::memory_order_relaxed))) {
-#endif
-                mainHash->entries[index].value = value;
-                break;
-              }
-              ++index;
-            }
-          }
-
-          return value;
-        }
-        if (probedKey == details::invalid_thread_id) {
-          break;    // Not in this hash table
-        }
-        ++index;
-      }
-    }
-
-    // Insert!
-    auto newCount = 1 + implicitProducerHashCount.fetch_add(1, std::memory_order_relaxed);
-    while (true) {
-      if (newCount >= (mainHash->capacity >> 1) &&
-          !implicitProducerHashResizeInProgress.test_and_set(std::memory_order_acquire)) {
-        // We've acquired the resize lock, try to allocate a bigger hash table.
-        // Note the acquire fence synchronizes with the release fence at the end of this block, and hence when
-        // we reload implicitProducerHash it must be the most recent version (it only gets changed within this
-        // locked block).
-        mainHash = implicitProducerHash.load(std::memory_order_acquire);
-        if (newCount >= (mainHash->capacity >> 1)) {
-          auto newCapacity = mainHash->capacity << 1;
-          while (newCount >= (newCapacity >> 1)) {
-            newCapacity <<= 1;
-          }
-          auto raw = static_cast<char *>((Traits::malloc)(
-            sizeof(ImplicitProducerHash) + std::alignment_of<ImplicitProducerKVP>::value - 1 +
-            sizeof(ImplicitProducerKVP) * newCapacity));
-          if (raw == nullptr) {
-            // Allocation failed
-            implicitProducerHashCount.fetch_sub(1, std::memory_order_relaxed);
-            implicitProducerHashResizeInProgress.clear(std::memory_order_relaxed);
-            return nullptr;
-          }
-
-          auto newHash = new(raw) ImplicitProducerHash;
-          newHash->capacity = newCapacity;
-          newHash->entries = reinterpret_cast<ImplicitProducerKVP *>(details::align_for<ImplicitProducerKVP>(
-            raw + sizeof(ImplicitProducerHash)));
-          for (size_t i = 0; i != newCapacity; ++i) {
-            new(newHash->entries + i) ImplicitProducerKVP;
-            newHash->entries[i].key.store(details::invalid_thread_id, std::memory_order_relaxed);
-          }
-          newHash->prev = mainHash;
-          implicitProducerHash.store(newHash, std::memory_order_release);
-          implicitProducerHashResizeInProgress.clear(std::memory_order_release);
-          mainHash = newHash;
-        } else {
-          implicitProducerHashResizeInProgress.clear(std::memory_order_release);
-        }
-      }
-
-      // If it's < three-quarters full, add to the old one anyway so that we don't have to wait for the next table
-      // to finish being allocated by another thread (and if we just finished allocating above, the condition will
-      // always be true)
-      if (newCount < (mainHash->capacity >> 1) + (mainHash->capacity >> 2)) {
-        bool recycled;
-        auto producer = static_cast<ImplicitProducer *>(recycle_or_create_producer(false,
-                                                                                   recycled));
-        if (producer == nullptr) {
-          implicitProducerHashCount.fetch_sub(1, std::memory_order_relaxed);
-          return nullptr;
-        }
-        if (recycled) {
-          implicitProducerHashCount.fetch_sub(1, std::memory_order_relaxed);
-        }
-
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-        producer->threadExitListener.callback = &ConcurrentQueue::implicit_producer_thread_exited_callback;
-        producer->threadExitListener.userData = producer;
-        details::ThreadExitNotifier::subscribe(&producer->threadExitListener);
-#endif
-
-        auto index = hashedId;
-        while (true) {
-          index &= mainHash->capacity - 1;
-          auto probedKey = mainHash->entries[index].key.load(std::memory_order_relaxed);
-
-          auto empty = details::invalid_thread_id;
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-          auto reusable = details::invalid_thread_id2;
-          if ((probedKey == empty    && mainHash->entries[index].key.compare_exchange_strong(empty,    id, std::memory_order_relaxed, std::memory_order_relaxed)) ||
-            (probedKey == reusable && mainHash->entries[index].key.compare_exchange_strong(reusable, id, std::memory_order_acquire, std::memory_order_acquire))) {
-#else
-          if ((probedKey == empty && mainHash->entries[index].key.compare_exchange_strong(empty, id,
-                                                                                          std::memory_order_relaxed,
-                                                                                          std::memory_order_relaxed))) {
-#endif
-            mainHash->entries[index].value = producer;
-            break;
-          }
-          ++index;
-        }
-        return producer;
-      }
-
-      // Hmm, the old hash is quite full and somebody else is busy allocating a new one.
-      // We need to wait for the allocating thread to finish (if it succeeds, we add, if not,
-      // we try to allocate ourselves).
-      mainHash = implicitProducerHash.load(std::memory_order_acquire);
-    }
-  }
-
-#ifdef MOODYCAMEL_CPP11_THREAD_LOCAL_SUPPORTED
-  void implicit_producer_thread_exited(ImplicitProducer* producer)
-  {
-    // Remove from thread exit listeners
-    details::ThreadExitNotifier::unsubscribe(&producer->threadExitListener);
-
-    // Remove from hash
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODHASH
-    debug::DebugLock lock(implicitProdMutex);
-#endif
-    auto hash = implicitProducerHash.load(std::memory_order_acquire);
-    assert(hash != nullptr);		// The thread exit listener is only registered if we were added to a hash in the first place
-    auto id = details::thread_id();
-    auto hashedId = details::hash_thread_id(id);
-    details::thread_id_t probedKey;
-
-    // We need to traverse all the hashes just in case other threads aren't on the current one yet and are
-    // trying to add an entry thinking there's a free slot (because they reused a producer)
-    for (; hash != nullptr; hash = hash->prev) {
-      auto index = hashedId;
-      do {
-        index &= hash->capacity - 1;
-        probedKey = hash->entries[index].key.load(std::memory_order_relaxed);
-        if (probedKey == id) {
-          hash->entries[index].key.store(details::invalid_thread_id2, std::memory_order_release);
-          break;
-        }
-        ++index;
-      } while (probedKey != details::invalid_thread_id);		// Can happen if the hash has changed but we weren't put back in it yet, or if we weren't added to this hash in the first place
-    }
-
-    // Mark the queue as being recyclable
-    producer->inactive.store(true, std::memory_order_release);
-  }
-
-  static void implicit_producer_thread_exited_callback(void* userData)
-  {
-    auto producer = static_cast<ImplicitProducer*>(userData);
-    auto queue = producer->parent;
-    queue->implicit_producer_thread_exited(producer);
-  }
-#endif
-
-  //////////////////////////////////
-  // Utility functions
-  //////////////////////////////////
-
-  template<typename U>
-  static inline U *create_array(size_t count) {
-    assert(count > 0);
-    auto p = static_cast<U *>((Traits::malloc)(sizeof(U) * count));
-    if (p == nullptr) {
-      return nullptr;
-    }
-
-    for (size_t i = 0; i != count; ++i) {
-      new(p + i) U();
-    }
-    return p;
-  }
-
-  template<typename U>
-  static inline void destroy_array(U *p, size_t count) {
-    if (p != nullptr) {
-      assert(count > 0);
-      for (size_t i = count; i != 0;) {
-        (p + --i)->~U();
-      }
-      (Traits::free)(p);
-    }
-  }
-
-  template<typename U>
-  static inline U *create() {
-    auto p = (Traits::malloc)(sizeof(U));
-    return p != nullptr ? new(p) U : nullptr;
-  }
-
-  template<typename U, typename A1>
-  static inline U *create(A1 &&a1) {
-    auto p = (Traits::malloc)(sizeof(U));
-    return p != nullptr ? new(p) U(std::forward<A1>(a1)) : nullptr;
-  }
-
-  template<typename U>
-  static inline void destroy(U *p) {
-    if (p != nullptr) {
-      p->~U();
-    }
-    (Traits::free)(p);
-  }
-
- private:
-  std::atomic<ProducerBase *> producerListTail;
-  std::atomic<std::uint32_t> producerCount;
-
-  std::atomic<size_t> initialBlockPoolIndex;
-  Block *initialBlockPool;
-  size_t initialBlockPoolSize;
-
-#if !MCDBGQ_USEDEBUGFREELIST
-  FreeList<Block> freeList;
-#else
-  debug::DebugFreeList<Block> freeList;
-#endif
-
-  std::atomic<ImplicitProducerHash *> implicitProducerHash;
-  std::atomic<size_t> implicitProducerHashCount;    // Number of slots logically used
-  ImplicitProducerHash initialImplicitProducerHash;
-  std::array<ImplicitProducerKVP, INITIAL_IMPLICIT_PRODUCER_HASH_SIZE> initialImplicitProducerHashEntries;
-  std::atomic_flag implicitProducerHashResizeInProgress;
-
-  std::atomic<std::uint32_t> nextExplicitConsumerId;
-  std::atomic<std::uint32_t> globalExplicitConsumerOffset;
-
-#if MCDBGQ_NOLOCKFREE_IMPLICITPRODHASH
-  debug::DebugMutex implicitProdMutex;
-#endif
-
-#ifdef MOODYCAMEL_QUEUE_INTERNAL_DEBUG
-  std::atomic<ExplicitProducer*> explicitProducers;
-  std::atomic<ImplicitProducer*> implicitProducers;
-#endif
-};
-
-
-template<typename T, typename Traits>
-ProducerToken::ProducerToken(ConcurrentQueue<T, Traits> &queue)
-  : producer(queue.recycle_or_create_producer(true)) {
-  if (producer != nullptr) {
-    producer->token = this;
-  }
-}
-
-template<typename T, typename Traits>
-ProducerToken::ProducerToken(BlockingConcurrentQueue<T, Traits> &queue)
-  : producer(
-  reinterpret_cast<ConcurrentQueue<T, Traits> *>(&queue)->recycle_or_create_producer(true)) {
-  if (producer != nullptr) {
-    producer->token = this;
-  }
-}
-
-template<typename T, typename Traits>
-ConsumerToken::ConsumerToken(ConcurrentQueue<T, Traits> &queue)
-  : itemsConsumedFromCurrent(0), currentProducer(nullptr), desiredProducer(nullptr) {
-  initialOffset = queue.nextExplicitConsumerId.fetch_add(1, std::memory_order_release);
-  lastKnownGlobalOffset = -1;
-}
-
-template<typename T, typename Traits>
-ConsumerToken::ConsumerToken(BlockingConcurrentQueue<T, Traits> &queue)
-  : itemsConsumedFromCurrent(0), currentProducer(nullptr), desiredProducer(nullptr) {
-  initialOffset = reinterpret_cast<ConcurrentQueue <T, Traits> *>(&queue)->nextExplicitConsumerId.fetch_add(
-    1, std::memory_order_release);
-  lastKnownGlobalOffset = -1;
-}
-
-template<typename T, typename Traits>
-inline void swap(ConcurrentQueue<T, Traits> &a, ConcurrentQueue<T, Traits> &b) MOODYCAMEL_NOEXCEPT {
-  a.swap(b);
-}
-
-inline void swap(ProducerToken &a, ProducerToken &b) MOODYCAMEL_NOEXCEPT {
-  a.swap(b);
-}
-
-inline void swap(ConsumerToken &a, ConsumerToken &b) MOODYCAMEL_NOEXCEPT {
-  a.swap(b);
-}
-
-template<typename T, typename Traits>
-inline void swap(typename ConcurrentQueue<T, Traits>::ImplicitProducerKVP &a,
-                 typename ConcurrentQueue<T, Traits>::ImplicitProducerKVP &b) MOODYCAMEL_NOEXCEPT {
-  a.swap(b);
-}
-
-}
-
-}  // namespace dmlc
-
-#if defined(__GNUC__)
-#pragma GCC diagnostic pop
-#endif
-
-#endif  // DMLC_CONCURRENTQUEUE_H_
-//! \endcond Doxygen_Suppress
diff --git a/ml-xgboost/dmlc-core/include/dmlc/config.h b/ml-xgboost/dmlc-core/include/dmlc/config.h
deleted file mode 100644
index a4c5b53..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/config.h
+++ /dev/null
@@ -1,186 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file config.h
- * \brief defines config parser class
- */
-#ifndef DMLC_CONFIG_H_
-#define DMLC_CONFIG_H_
-
-#include <cstring>
-#include <iostream>
-#include <iterator>
-#include <map>
-#include <vector>
-#include <utility>
-#include <string>
-#include <sstream>
-
-/*! \brief namespace for dmlc */
-namespace dmlc {
-
-/*!
- * \brief class for config parser
- *
- * Two modes are supported:
- * 1. non-multi value mode: if two same keys in the configure file, the later one will replace the
- *      ealier one; when using iterator, the order will be the "last effective insersion" order
- * 2. multi value mode: multiple values with the same key could co-exist; when using iterator, the
- *      order will be the insersion order.
- *
- * [Basic usage]
- *
- * Config cfg(file_input_stream);
- * for(Config::ConfigIterator iter = cfg.begin(); iter != cfg.end(); ++iter) {
- *     ConfigEntry ent = *iter;
- *     std::string key = ent.first;
- *     std::string value = ent.second;
- *     do_something_with(key, value);
- * }
- */
-class Config {
- public:
-  /*!
-   * \brief type when extracting from iterator
-   */
-  typedef std::pair<std::string, std::string> ConfigEntry;
-
-  /*!
-   * \brief iterator class
-   */
-  class ConfigIterator;
-
-  /*!
-   * \brief create empty config
-   * \param multi_value whether the config supports multi value
-   */
-  explicit Config(bool multi_value = false);
-  /*!
-   * \brief create config and load content from the given stream
-   * \param is input stream
-   * \param multi_value whether the config supports multi value
-   */
-  explicit Config(std::istream& is, bool multi_value = false);  // NOLINT(*)
-  /*!
-   * \brief clear all the values
-   */
-  void Clear(void);
-  /*!
-   * \brief load the contents from the stream
-   * \param is the stream as input
-   */
-  void LoadFromStream(std::istream& is);  // NOLINT(*)
-  /*!
-   * \brief set a key-value pair into the config; if the key already exists in the configure file,
-   *        it will either replace the old value with the given one (in non-multi value mode) or
-   *        store it directly (in multi-value mode);
-   * \param key key
-   * \param value value
-   * \param is_string whether the value should be wrapped by quotes in proto string
-   */
-  template<class T>
-  void SetParam(const std::string& key, const T& value, bool is_string = false);
-
-  /*!
-   * \brief get the config under the key; if multiple values exist for the same key,
-   *        return the last inserted one.
-   * \param key key
-   * \return config value
-   */
-  const std::string& GetParam(const std::string& key) const;
-
-  /*!
-   * \brief check whether the configure value given by the key should be wrapped by quotes
-   * \param key key
-   * \return whether the configure value is represented by string
-   */
-  bool IsGenuineString(const std::string& key) const;
-
-  /*!
-   * \brief transform all the configuration into string recognizable to protobuf
-   * \return string that could be parsed directly by protobuf
-   */
-  std::string ToProtoString(void) const;
-
-  /*!
-   * \brief get begin iterator
-   * \return begin iterator
-   */
-  ConfigIterator begin() const;
-
-  /*!
-   * \brief get end iterator
-   * \return end iterator
-   */
-  ConfigIterator end() const;
-
- public:
-  /*!
-   * \brief iterator class
-   */
-  class ConfigIterator : public std::iterator< std::input_iterator_tag, ConfigEntry > {
-    friend class Config;
-   public:
-    /*!
-     * \brief copy constructor
-     */
-    ConfigIterator(const ConfigIterator& other);
-    /*!
-     * \brief uni-increment operators
-     * \return the reference of current config
-     */
-    ConfigIterator& operator++();
-    /*!
-     * \brief uni-increment operators
-     * \return the reference of current config
-     */
-    ConfigIterator operator++(int);  // NOLINT(*)
-    /*!
-     * \brief compare operators
-     * \param rhs the other config to compare against
-     * \return the compared result
-     */
-    bool operator == (const ConfigIterator& rhs) const;
-    /*!
-     * \brief compare operators not equal
-     * \param rhs the other config to compare against
-     * \return the compared result
-     */
-    bool operator != (const ConfigIterator& rhs) const;
-    /*!
-     * \brief retrieve value from operator
-     */
-    ConfigEntry operator * () const;
-
-   private:
-    ConfigIterator(size_t index, const Config* config);
-    void FindNextIndex();
-
-   private:
-    size_t index_;
-    const Config* config_;
-  };
-
- private:
-  struct ConfigValue {
-    std::vector<std::string> val;
-    std::vector<size_t> insert_index;
-    bool is_string;
-  };
-  void Insert(const std::string& key, const std::string& value, bool is_string);
-
- private:
-  std::map<std::string, ConfigValue> config_map_;
-  std::vector<std::pair<std::string, size_t> > order_;
-  const bool multi_value_;
-};
-
-template<class T>
-void Config::SetParam(const std::string& key, const T& value, bool is_string) {
-  std::ostringstream oss;
-  oss << value;
-  Insert(key, oss.str(), is_string);
-}
-
-}  // namespace dmlc
-
-#endif  // DMLC_CONFIG_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/data.h b/ml-xgboost/dmlc-core/include/dmlc/data.h
deleted file mode 100644
index a6ac19c..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/data.h
+++ /dev/null
@@ -1,397 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file data.h
- * \brief defines common input data structure,
- *  and interface for handling the input data
- */
-#ifndef DMLC_DATA_H_
-#define DMLC_DATA_H_
-
-#include <string>
-#include <vector>
-#include <map>
-#include "./base.h"
-#include "./io.h"
-#include "./logging.h"
-#include "./registry.h"
-
-// To help C Preprocessor with processing c++ templated types
-#define __DMLC_COMMA ,
-
-namespace dmlc {
-/*!
- * \brief this defines the float point
- * that will be used to store feature values
- */
-typedef float real_t;
-
-/*!
- * \brief this defines the unsigned integer type
- * that can normally be used to store feature index
- */
-typedef unsigned index_t;
-
-// This file describes common data structure that can be used
-// for large-scale machine learning, this may not be a complete list
-// But we will keep the most common and useful ones, and keep adding new ones
-/*!
- * \brief data iterator interface
- *  this is not a C++ style iterator, but nice for data pulling:)
- *  This interface is used to pull in the data
- *  The system can do some useful tricks for you like pre-fetching
- *  from disk and pre-computation.
- *
- * Usage example:
- * \code
- *
- *   itr->BeforeFirst();
- *   while (itr->Next()) {
- *      const DType &batch = itr->Value();
- *      // some computations
- *   }
- * \endcode
- * \tparam DType the data type
- */
-template<typename DType>
-class DataIter {
- public:
-  /*! \brief destructor */
-  virtual ~DataIter(void) DMLC_THROW_EXCEPTION {}
-  /*! \brief set before first of the item */
-  virtual void BeforeFirst(void) = 0;
-  /*! \brief move to next item */
-  virtual bool Next(void) = 0;
-  /*! \brief get current data */
-  virtual const DType &Value(void) const = 0;
-};
-
-/*!
- * \brief one row of training instance
- * \tparam IndexType type of index
- * \tparam DType type of data (both label and value will be of DType
- */
-template<typename IndexType, typename DType = real_t>
-class Row {
- public:
-  /*! \brief label of the instance */
-  const DType *label;
-  /*! \brief weight of the instance */
-  const real_t *weight;
-  /*! \brief session-id of the instance */
-  const uint64_t *qid;
-  /*! \brief length of the sparse vector */
-  size_t length;
-  /*!
-   * \brief field of each instance
-   */
-  const IndexType *field;
-  /*!
-   * \brief index of each instance
-   */
-  const IndexType *index;
-  /*!
-   * \brief array value of each instance, this can be NULL
-   *  indicating every value is set to be 1
-   */
-  const DType *value;
-  /*!
-   * \param i the input index
-   * \return field for i-th feature
-   */
-  inline IndexType get_field(size_t i) const {
-    return field[i];
-  }
-  /*!
-   * \param i the input index
-   * \return i-th feature
-   */
-  inline IndexType get_index(size_t i) const {
-    return index[i];
-  }
-  /*!
-   * \param i the input index
-   * \return i-th feature value, this function is always
-   *  safe even when value == NULL
-   */
-  inline DType get_value(size_t i) const {
-    return value == NULL ? DType(1.0f) : value[i];
-  }
-  /*!
-   * \return the label of the instance
-   */
-  inline DType get_label() const {
-    return *label;
-  }
-  /*!
-   * \return the weight of the instance, this function is always
-   *  safe even when weight == NULL
-   */
-  inline real_t get_weight() const {
-    return weight == NULL ? 1.0f : *weight;
-  }
-  /*!
-   * \return the qid of the instance, this function is always
-   *  safe even when qid == NULL
-   */
-  inline uint64_t get_qid() const {
-    return qid == NULL ? 0 : *qid;
-  }
-  /*!
-   * \brief helper function to compute dot product of current
-   * \param weight the dense array of weight we want to product
-   * \param size the size of the weight vector
-   * \tparam V type of the weight vector
-   * \return the result of dot product
-   */
-  template<typename V>
-  inline V SDot(const V *weight, size_t size) const {
-    V sum = static_cast<V>(0);
-    if (value == NULL) {
-      for (size_t i = 0; i < length; ++i) {
-        CHECK(index[i] < size) << "feature index exceed bound";
-        sum += weight[index[i]];
-      }
-    } else {
-      for (size_t i = 0; i < length; ++i) {
-        CHECK(index[i] < size) << "feature index exceed bound";
-        sum += weight[index[i]] * value[i];
-      }
-    }
-    return sum;
-  }
-};
-
-/*!
- * \brief a block of data, containing several rows in sparse matrix
- *  This is useful for (streaming-sxtyle) algorithms that scans through rows of data
- *  examples include: SGD, GD, L-BFGS, kmeans
- *
- *  The size of batch is usually large enough so that parallelizing over the rows
- *  can give significant speedup
- * \tparam IndexType type to store the index used in row batch
- * \tparam DType type to store the label and value used in row batch
- */
-template<typename IndexType, typename DType = real_t>
-struct RowBlock {
-  /*! \brief batch size */
-  size_t size;
-  /*! \brief array[size+1], row pointer to beginning of each rows */
-  const size_t *offset;
-  /*! \brief array[size] label of each instance */
-  const DType *label;
-  /*! \brief With weight: array[size] label of each instance, otherwise nullptr */
-  const real_t *weight;
-  /*! \brief With qid: array[size] session id of each instance, otherwise nullptr */
-  const uint64_t *qid;
-  /*! \brief field id*/
-  const IndexType *field;
-  /*! \brief feature index */
-  const IndexType *index;
-  /*! \brief feature value, can be NULL, indicating all values are 1 */
-  const DType *value;
-  /*!
-   * \brief get specific rows in the batch
-   * \param rowid the rowid in that row
-   * \return the instance corresponding to the row
-   */
-  inline Row<IndexType, DType> operator[](size_t rowid) const;
-  /*! \return memory cost of the block in bytes */
-  inline size_t MemCostBytes(void) const {
-    size_t cost = size * (sizeof(size_t) + sizeof(DType));
-    if (weight != NULL) cost += size * sizeof(real_t);
-    if (qid != NULL) cost += size * sizeof(size_t);
-    size_t ndata = offset[size] - offset[0];
-    if (field != NULL) cost += ndata * sizeof(IndexType);
-    if (index != NULL) cost += ndata * sizeof(IndexType);
-    if (value != NULL) cost += ndata * sizeof(DType);
-    return cost;
-  }
-  /*!
-   * \brief slice a RowBlock to get rows in [begin, end)
-   * \param begin the begin row index
-   * \param end the end row index
-   * \return the sliced RowBlock
-   */
-  inline RowBlock Slice(size_t begin, size_t end) const {
-    CHECK(begin <= end && end <= size);
-    RowBlock ret;
-    ret.size = end - begin;
-    ret.label = label + begin;
-    if (weight != NULL) {
-      ret.weight = weight + begin;
-    } else {
-      ret.weight = NULL;
-    }
-    if (qid != NULL) {
-      ret.qid = qid + begin;
-    } else {
-      ret.qid = NULL;
-    }
-    ret.offset = offset + begin;
-    ret.field = field;
-    ret.index = index;
-    ret.value = value;
-    return ret;
-  }
-};
-
-/*!
- * \brief Data structure that holds the data
- * Row block iterator interface that gets RowBlocks
- * Difference between RowBlockIter and Parser:
- *     RowBlockIter caches the data internally that can be used
- *     to iterate the dataset multiple times,
- *     Parser holds very limited internal state and was usually
- *     used to read data only once
- *
- * \sa Parser
- * \tparam IndexType type of index in RowBlock
- * \tparam DType type of label and value in RowBlock
- *  Create function was only implemented for IndexType uint64_t and uint32_t
- *  and DType real_t and int
- */
-template<typename IndexType, typename DType = real_t>
-class RowBlockIter : public DataIter<RowBlock<IndexType, DType> > {
- public:
-  /*!
-   * \brief create a new instance of iterator that returns rowbatch
-   *  by default, a in-memory based iterator will be returned
-   *
-   * \param uri the uri of the input, can contain hdfs prefix
-   * \param part_index the part id of current input
-   * \param num_parts total number of splits
-   * \param type type of dataset can be: "libsvm", ...
-   *
-   * \return the created data iterator
-   */
-  static RowBlockIter<IndexType, DType> *
-  Create(const char *uri,
-         unsigned part_index,
-         unsigned num_parts,
-         const char *type);
-  /*! \return maximum feature dimension in the dataset */
-  virtual size_t NumCol() const = 0;
-};
-
-/*!
- * \brief parser interface that parses input data
- * used to load dmlc data format into your own data format
- * Difference between RowBlockIter and Parser:
- *     RowBlockIter caches the data internally that can be used
- *     to iterate the dataset multiple times,
- *     Parser holds very limited internal state and was usually
- *     used to read data only once
- *
- *
- * \sa RowBlockIter
- * \tparam IndexType type of index in RowBlock
- * \tparam DType type of label and value in RowBlock
- *  Create function was only implemented for IndexType uint64_t and uint32_t
- *  and DType real_t and int
- */
-template <typename IndexType, typename DType = real_t>
-class Parser : public DataIter<RowBlock<IndexType, DType> > {
- public:
-  /*!
-  * \brief create a new instance of parser based on the "type"
-  *
-  * \param uri_ the uri of the input, can contain hdfs prefix
-  * \param part_index the part id of current input
-  * \param num_parts total number of splits
-  * \param type type of dataset can be: "libsvm", "auto", ...
-  *
-  * When "auto" is passed, the type is decided by format argument string in URI.
-  *
-  * \return the created parser
-  */
-  static Parser<IndexType, DType> *
-  Create(const char *uri_,
-         unsigned part_index,
-         unsigned num_parts,
-         const char *type);
-  /*! \return size of bytes read so far */
-  virtual size_t BytesRead(void) const = 0;
-  /*! \brief Factory type of the parser*/
-  typedef Parser<IndexType, DType>* (*Factory)
-      (const std::string& path,
-       const std::map<std::string, std::string>& args,
-       unsigned part_index,
-       unsigned num_parts);
-};
-
-/*!
- * \brief registry entry of parser factory
- * \tparam IndexType The type of index
- * \tparam DType The type of label and value
- */
-template<typename IndexType, typename DType = real_t>
-struct ParserFactoryReg
-    : public FunctionRegEntryBase<ParserFactoryReg<IndexType, DType>,
-                                  typename Parser<IndexType, DType>::Factory> {};
-
-/*!
- * \brief Register a new distributed parser to dmlc-core.
- *
- * \param IndexType The type of Batch index, can be uint32_t or uint64_t
- * \param DataType The type of Batch label and value, can be real_t or int
- * \param TypeName The typename of of the data.
- * \param FactoryFunction The factory function that creates the parser.
- *
- * \begincode
- *
- *  // define the factory function
- *  template<typename IndexType, typename DType = real_t>
- *  Parser<IndexType, DType>*
- *  CreateLibSVMParser(const char* uri, unsigned part_index, unsigned num_parts) {
- *    return new LibSVMParser(uri, part_index, num_parts);
- *  }
- *
- *  // Register it to DMLC
- *  // Then we can use Parser<uint32_t>::Create(uri, part_index, num_parts, "libsvm");
- *  // to create the parser
- *
- *  DMLC_REGISTER_DATA_PARSER(uint32_t, real_t, libsvm, CreateLibSVMParser<uint32_t>);
- *  DMLC_REGISTER_DATA_PARSER(uint64_t, real_t, libsvm, CreateLibSVMParser<uint64_t>);
- *
- * \endcode
- */
-#define DMLC_REGISTER_DATA_PARSER(IndexType, DataType, TypeName, FactoryFunction) \
-  DMLC_REGISTRY_REGISTER(ParserFactoryReg<IndexType __DMLC_COMMA DataType>,           \
-                         ParserFactoryReg ## _ ## IndexType ## _ ## DataType, TypeName)  \
-  .set_body(FactoryFunction)
-
-
-// implementation of operator[]
-template<typename IndexType, typename DType>
-inline Row<IndexType, DType>
-RowBlock<IndexType, DType>::operator[](size_t rowid) const {
-  CHECK(rowid < size);
-  Row<IndexType, DType> inst;
-  inst.label = label + rowid;
-  if (weight != NULL) {
-    inst.weight = weight + rowid;
-  } else {
-    inst.weight = NULL;
-  }
-  if (qid != NULL) {
-    inst.qid = qid + rowid;
-  } else {
-    inst.qid = NULL;
-  }
-  inst.length = offset[rowid + 1] - offset[rowid];
-  if (field != NULL) {
-    inst.field = field + offset[rowid];
-  } else {
-    inst.field = NULL;
-  }
-  inst.index = index + offset[rowid];
-  if (value == NULL) {
-    inst.value = NULL;
-  } else {
-    inst.value = value + offset[rowid];
-  }
-  return inst;
-}
-
-}  // namespace dmlc
-#endif  // DMLC_DATA_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/endian.h b/ml-xgboost/dmlc-core/include/dmlc/endian.h
deleted file mode 100644
index c72739e..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/endian.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*!
- *  Copyright (c) 2017 by Contributors
- * \file endian.h
- * \brief Endian testing, need c++11
- */
-#ifndef DMLC_ENDIAN_H_
-#define DMLC_ENDIAN_H_
-
-#include "./base.h"
-
-#ifdef DMLC_CMAKE_LITTLE_ENDIAN
-  // If compiled with CMake, use CMake's endian detection logic
-  #define DMLC_LITTLE_ENDIAN DMLC_CMAKE_LITTLE_ENDIAN
-#else
-  #if defined(__APPLE__) || defined(_WIN32)
-    #define DMLC_LITTLE_ENDIAN 1
-  #elif defined(__GLIBC__) || defined(__GNU_LIBRARY__) \
-        || defined(__ANDROID__) || defined(__RISCV__)
-    #include <endian.h>
-    #define DMLC_LITTLE_ENDIAN (__BYTE_ORDER == __LITTLE_ENDIAN)
-  #elif defined(__FreeBSD__) || defined(__OpenBSD__)
-    #include <sys/endian.h>
-    #define DMLC_LITTLE_ENDIAN (_BYTE_ORDER == _LITTLE_ENDIAN)
-  #elif defined(__EMSCRIPTEN__) || defined(__hexagon__)
-    #define DMLC_LITTLE_ENDIAN 1
-  #elif defined(__sun) || defined(sun)
-    #include <sys/isa_defs.h>
-    #if defined(_LITTLE_ENDIAN)
-      #define DMLC_LITTLE_ENDIAN 1
-    #else
-      #define DMLC_LITTLE_ENDIAN 0
-    #endif
-  #else
-    #error "Unable to determine endianness of your machine; use CMake to compile"
-  #endif
-#endif
-
-/*! \brief whether serialize using little endian */
-#define DMLC_IO_NO_ENDIAN_SWAP (DMLC_LITTLE_ENDIAN == DMLC_IO_USE_LITTLE_ENDIAN)
-
-namespace dmlc {
-
-/*!
- * \brief A generic inplace byte swapping function.
- * \param data The data pointer.
- * \param elem_bytes The number of bytes of the data elements
- * \param num_elems Number of elements in the data.
- * \note Always try pass in constant elem_bytes to enable
- *       compiler optimization
- */
-inline void ByteSwap(void* data, size_t elem_bytes, size_t num_elems) {
-  for (size_t i = 0; i < num_elems; ++i) {
-    uint8_t* bptr = reinterpret_cast<uint8_t*>(data) + elem_bytes * i;
-    for (size_t j = 0; j < elem_bytes / 2; ++j) {
-      uint8_t v = bptr[elem_bytes - 1 - j];
-      bptr[elem_bytes - 1 - j] = bptr[j];
-      bptr[j] = v;
-    }
-  }
-}
-
-}  // namespace dmlc
-#endif  // DMLC_ENDIAN_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/filesystem.h b/ml-xgboost/dmlc-core/include/dmlc/filesystem.h
deleted file mode 100644
index 64d1073..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/filesystem.h
+++ /dev/null
@@ -1,158 +0,0 @@
-/*!
- *  Copyright (c) 2018 by Contributors
- * \file filesystem.h
- * \brief Utilities to manipulate files
- * \author Hyunsu Philip Cho
- */
-#ifndef DMLC_FILESYSTEM_H_
-#define DMLC_FILESYSTEM_H_
-
-#include <dmlc/logging.h>
-#include <dmlc/io.h>
-#include <algorithm>
-#include <string>
-#include <vector>
-#include <random>
-
-/* platform specific headers */
-#ifdef _WIN32
-#define NOMINMAX
-#include <windows.h>
-#include <Shlwapi.h>
-#pragma comment(lib, "Shlwapi.lib")
-#else  // _WIN32
-#include <unistd.h>
-#include <sys/stat.h>
-#include <sys/types.h>
-#endif  // _WIN32
-
-namespace dmlc {
-
-/*!
- * \brief Manager class for temporary directories. Whenever a new
- *        TemporaryDirectory object is constructed, a temporary directory is
- *        created. The directory is deleted when the object is deleted or goes
- *        out of scope. Note: no symbolic links are allowed inside the
- *        temporary directory.
- *
- * Usage example:
- * \code
- *
- *   void foo() {
- *     dmlc::TemporaryDirectory tempdir;
- *     // Create a file my_file.txt inside the temporary directory
- *     std::ofstream of(tempdir.path + "/my_file.txt");
- *     // ... write to my_file.txt ...
- *
- *     // ... use my_file.txt
- *
- *     // When tempdir goes out of scope, the temporary directory is deleted
- *   }
- *
- * \endcode
- */
-class TemporaryDirectory {
- public:
-  /*!
-   * \brief Default constructor.
-   *        Creates a new temporary directory with a unique name.
-   * \param verbose whether to emit extra messages
-   */
-  explicit TemporaryDirectory(bool verbose = false)
-    : verbose_(verbose) {
-#if _WIN32
-    /* locate the root directory of temporary area */
-    char tmproot[MAX_PATH] = {0};
-    const DWORD dw_retval = GetTempPathA(MAX_PATH, tmproot);
-    if (dw_retval > MAX_PATH || dw_retval == 0) {
-      LOG(FATAL) << "TemporaryDirectory(): "
-                 << "Could not create temporary directory";
-    }
-    /* generate a unique 8-letter alphanumeric string */
-    const std::string letters = "abcdefghijklmnopqrstuvwxyz0123456789_";
-    std::string uniqstr(8, '\0');
-    std::random_device rd;
-    std::mt19937 gen(rd());
-    std::uniform_int_distribution<int> dis(0, letters.length() - 1);
-    std::generate(uniqstr.begin(), uniqstr.end(),
-      [&dis, &gen, &letters]() -> char {
-        return letters[dis(gen)];
-      });
-    /* combine paths to get the name of the temporary directory */
-    char tmpdir[MAX_PATH] = {0};
-    PathCombineA(tmpdir, tmproot, uniqstr.c_str());
-    if (!CreateDirectoryA(tmpdir, NULL)) {
-      LOG(FATAL) << "TemporaryDirectory(): "
-                 << "Could not create temporary directory";
-    }
-    path = std::string(tmpdir);
-#else  // _WIN32
-    std::string tmproot; /* root directory of temporary area */
-    std::string dirtemplate; /* template for temporary directory name */
-    /* Get TMPDIR env variable or fall back to /tmp/ */
-    {
-      const char* tmpenv = getenv("TMPDIR");
-      if (tmpenv) {
-        tmproot = std::string(tmpenv);
-        // strip trailing forward slashes
-        while (tmproot.length() != 0 && tmproot[tmproot.length() - 1] == '/') {
-          tmproot.resize(tmproot.length() - 1);
-        }
-      } else {
-        tmproot = "/tmp";
-      }
-    }
-    dirtemplate = tmproot + "/tmpdir.XXXXXX";
-    std::vector<char> dirtemplate_buf(dirtemplate.begin(), dirtemplate.end());
-    dirtemplate_buf.push_back('\0');
-    char* tmpdir = mkdtemp(&dirtemplate_buf[0]);
-    if (!tmpdir) {
-      LOG(FATAL) << "TemporaryDirectory(): "
-                 << "Could not create temporary directory";
-    }
-    path = std::string(tmpdir);
-#endif  // _WIN32
-    if (verbose_) {
-      LOG(INFO) << "Created temporary directory " << path;
-    }
-  }
-
-  /*! \brief Destructor. Will perform recursive deletion via RecursiveDelete() */
-  ~TemporaryDirectory() {
-    RecursiveDelete(path);
-  }
-
-  /*! \brief Full path of the temporary directory */
-  std::string path;
-
- private:
-  /*! \brief Whether to emit extra messages */
-  bool verbose_;
-
-  /*!
-   * \brief Determine whether a given path is a symbolic link
-   * \param path String representation of path
-   */
-  inline bool IsSymlink(const std::string& path) {
-#ifdef _WIN32
-    DWORD attr = GetFileAttributesA(path.c_str());
-    CHECK_NE(attr, INVALID_FILE_ATTRIBUTES)
-      << "dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes";
-    return attr & FILE_ATTRIBUTE_REPARSE_POINT;
-#else  // _WIN32
-    struct stat sb;
-    CHECK_EQ(lstat(path.c_str(), &sb), 0)
-      << "dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes";
-    return S_ISLNK(sb.st_mode);
-#endif  // _WIN32
-  }
-
-  /*!
-   * \brief Delete a directory recursively, along with sub-directories and files.
-   * \param path String representation of path. It must refer to a directory.
-   */
-  void RecursiveDelete(const std::string& path);
-};
-
-}  // namespace dmlc
-#endif  // DMLC_FILESYSTEM_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/input_split_shuffle.h b/ml-xgboost/dmlc-core/include/dmlc/input_split_shuffle.h
deleted file mode 100644
index fc2c65e..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/input_split_shuffle.h
+++ /dev/null
@@ -1,168 +0,0 @@
-/*!
- *  Copyright (c) 2016 by Contributors
- * \file input_split_shuffle.h
- * \brief base class to construct input split with global shuffling
- * \author Yifeng Geng
- */
-#ifndef DMLC_INPUT_SPLIT_SHUFFLE_H_
-#define DMLC_INPUT_SPLIT_SHUFFLE_H_
-
-#include <cstdio>
-#include <cstring>
-#include <vector>
-#include <string>
-#include <algorithm>
-#include <memory>
-
-namespace dmlc {
-/*! \brief class to construct input split with global shuffling */
-class InputSplitShuffle : public InputSplit {
- public:
-  // destructor
-  virtual ~InputSplitShuffle(void) { source_.reset(); }
-  // implement BeforeFirst
-  virtual void BeforeFirst(void) {
-    if (num_shuffle_parts_ > 1) {
-      std::shuffle(shuffle_indexes_.begin(), shuffle_indexes_.end(), trnd_);
-      int idx = shuffle_indexes_[0] + part_index_ * num_shuffle_parts_;
-      source_->ResetPartition(idx, num_parts_ * num_shuffle_parts_);
-      cur_shuffle_idx_ = 0;
-    } else {
-      source_->BeforeFirst();
-    }
-  }
-  virtual void HintChunkSize(size_t chunk_size) {
-    source_->HintChunkSize(chunk_size);
-  }
-  virtual size_t GetTotalSize(void) {
-    return source_->GetTotalSize();
-  }
-  // implement next record
-  virtual bool NextRecord(Blob *out_rec) {
-    if (num_shuffle_parts_ > 1) {
-      if (!source_->NextRecord(out_rec)) {
-        if (cur_shuffle_idx_ == num_shuffle_parts_ - 1) {
-          return false;
-        }
-        ++cur_shuffle_idx_;
-        int idx =
-            shuffle_indexes_[cur_shuffle_idx_] + part_index_ * num_shuffle_parts_;
-        source_->ResetPartition(idx, num_parts_ * num_shuffle_parts_);
-        return NextRecord(out_rec);
-      } else {
-        return true;
-      }
-    } else {
-      return source_->NextRecord(out_rec);
-    }
-  }
-  // implement next chunk
-  virtual bool NextChunk(Blob* out_chunk) {
-    if (num_shuffle_parts_ > 1) {
-      if (!source_->NextChunk(out_chunk)) {
-        if (cur_shuffle_idx_ == num_shuffle_parts_ - 1) {
-          return false;
-        }
-        ++cur_shuffle_idx_;
-        int idx =
-            shuffle_indexes_[cur_shuffle_idx_] + part_index_ * num_shuffle_parts_;
-        source_->ResetPartition(idx, num_parts_ * num_shuffle_parts_);
-        return NextChunk(out_chunk);
-      } else {
-        return true;
-      }
-    } else {
-      return source_->NextChunk(out_chunk);
-    }
-  }
-  // implement ResetPartition.
-  virtual void ResetPartition(unsigned rank, unsigned nsplit) {
-    CHECK(nsplit == num_parts_) << "num_parts is not consistent!";
-    int idx = shuffle_indexes_[0] + rank * num_shuffle_parts_;
-    source_->ResetPartition(idx, nsplit * num_shuffle_parts_);
-    cur_shuffle_idx_ = 0;
-  }
-  /*!
-   * \brief constructor
-   * \param uri the uri of the input, can contain hdfs prefix
-   * \param part_index the part id of current input
-   * \param num_parts total number of splits
-   * \param type type of record
-   *   List of possible types: "text", "recordio"
-   *     - "text":
-   *         text file, each line is treated as a record
-   *         input split will split on '\\n' or '\\r'
-   *     - "recordio":
-   *         binary recordio file, see recordio.h
-   * \param num_shuffle_parts number of shuffle chunks for each split
-   * \param shuffle_seed shuffle seed for chunk shuffling
-   */
-  InputSplitShuffle(const char* uri,
-                    unsigned part_index,
-                    unsigned num_parts,
-                    const char* type,
-                    unsigned num_shuffle_parts,
-                    int shuffle_seed)
-      : part_index_(part_index),
-        num_parts_(num_parts),
-        num_shuffle_parts_(num_shuffle_parts),
-        cur_shuffle_idx_(0) {
-    for (unsigned i = 0; i < num_shuffle_parts_; i++) {
-      shuffle_indexes_.push_back(i);
-    }
-    trnd_.seed(kRandMagic_ + part_index_ + num_parts_ + num_shuffle_parts_ +
-               shuffle_seed);
-    std::shuffle(shuffle_indexes_.begin(), shuffle_indexes_.end(), trnd_);
-    int idx = shuffle_indexes_[cur_shuffle_idx_] + part_index_ * num_shuffle_parts_;
-    source_.reset(
-        InputSplit::Create(uri, idx , num_parts_ * num_shuffle_parts_, type));
-  }
-  /*!
-   * \brief factory function:
-   *  create input split with chunk shuffling given a uri
-   * \param uri the uri of the input, can contain hdfs prefix
-   * \param part_index the part id of current input
-   * \param num_parts total number of splits
-   * \param type type of record
-   *   List of possible types: "text", "recordio"
-   *     - "text":
-   *         text file, each line is treated as a record
-   *         input split will split on '\\n' or '\\r'
-   *     - "recordio":
-   *         binary recordio file, see recordio.h
-   * \param num_shuffle_parts number of shuffle chunks for each split
-   * \param shuffle_seed shuffle seed for chunk shuffling
-   * \return a new input split
-   * \sa InputSplit::Type
-   */
-  static InputSplit* Create(const char* uri,
-                            unsigned part_index,
-                            unsigned num_parts,
-                            const char* type,
-                            unsigned num_shuffle_parts,
-                            int shuffle_seed) {
-    CHECK(num_shuffle_parts > 0) << "number of shuffle parts should be greater than zero!";
-    return new InputSplitShuffle(
-        uri, part_index, num_parts, type, num_shuffle_parts, shuffle_seed);
-  }
-
- private:
-  // magic nyumber for seed
-  static const int kRandMagic_ = 666;
-  /*! \brief random engine */
-  std::mt19937 trnd_;
-  /*! \brief inner inputsplit */
-  std::unique_ptr<InputSplit> source_;
-  /*! \brief part index */
-  unsigned part_index_;
-  /*! \brief number of parts */
-  unsigned num_parts_;
-  /*! \brief the number of block for shuffling*/
-  unsigned num_shuffle_parts_;
-  /*! \brief current shuffle block index */
-  unsigned cur_shuffle_idx_;
-  /*! \brief shuffled indexes */
-  std::vector<int> shuffle_indexes_;
-};
-}  // namespace dmlc
-#endif  // DMLC_INPUT_SPLIT_SHUFFLE_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/io.h b/ml-xgboost/dmlc-core/include/dmlc/io.h
deleted file mode 100644
index c1bc75e..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/io.h
+++ /dev/null
@@ -1,635 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file io.h
- * \brief defines serializable interface of dmlc
- */
-#ifndef DMLC_IO_H_
-#define DMLC_IO_H_
-#include <cstdio>
-#include <string>
-#include <cstring>
-#include <vector>
-#include <istream>
-#include <ostream>
-#include <streambuf>
-#include "./logging.h"
-
-// include uint64_t only to make io standalone
-#ifdef _MSC_VER
-/*! \brief uint64 */
-typedef unsigned __int64 uint64_t;
-#else
-#include <inttypes.h>
-#endif
-
-/*! \brief namespace for dmlc */
-namespace dmlc {
-/*!
- * \brief interface of stream I/O for serialization
- */
-class Stream {  // NOLINT(*)
- public:
-  /*!
-   * \brief reads data from a stream
-   * \param ptr pointer to a memory buffer
-   * \param size block size
-   * \return the size of data read
-   */
-  virtual size_t Read(void *ptr, size_t size) = 0;
-  /*!
-   * \brief writes data to a stream
-   * \param ptr pointer to a memory buffer
-   * \param size block size
-   */
-  virtual void Write(const void *ptr, size_t size) = 0;
-  /*! \brief virtual destructor */
-  virtual ~Stream(void) {}
-  /*!
-   * \brief generic factory function
-   *  create an stream, the stream will close the underlying files upon deletion
-   *
-   * \param uri the uri of the input currently we support
-   *            hdfs://, s3://, and file:// by default file:// will be used
-   * \param flag can be "w", "r", "a"
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  static Stream *Create(const char *uri,
-                        const char* const flag,
-                        bool allow_null = false);
-  // helper functions to write/read different data structures
-  /*!
-   * \brief writes a data to stream.
-   *
-   * dmlc::Stream support Write/Read of most STL composites and base types.
-   * If the data type is not supported, a compile time error will be issued.
-   *
-   * This function is endian-aware,
-   * the output endian defined by DMLC_IO_USE_LITTLE_ENDIAN
-   *
-   * \param data data to be written
-   * \tparam T the data type to be written
-   */
-  template<typename T>
-  inline void Write(const T &data);
-  /*!
-   * \brief loads a data from stream.
-   *
-   * dmlc::Stream support Write/Read of most STL composites and base types.
-   * If the data type is not supported, a compile time error will be issued.
-   *
-   * This function is endian-aware,
-   * the input endian defined by DMLC_IO_USE_LITTLE_ENDIAN
-   *
-   * \param out_data place holder of data to be deserialized
-   * \return whether the load was successful
-   */
-  template<typename T>
-  inline bool Read(T *out_data);
-  /*!
-   * \brief Endian aware write array of data.
-   * \param data The data pointer
-   * \param num_elems Number of elements
-   * \tparam T the data type.
-   */
-  template<typename T>
-  inline void WriteArray(const T* data, size_t num_elems);
-  /*!
-   * \brief Endian aware read array of data.
-   * \param data The data pointer
-   * \param num_elems Number of elements
-   * \tparam T the data type.
-   * \return whether the load was successful
-   */
-  template<typename T>
-  inline bool ReadArray(T* data, size_t num_elems);
-};
-
-/*! \brief interface of i/o stream that support seek */
-class SeekStream: public Stream {
- public:
-  // virtual destructor
-  virtual ~SeekStream(void) {}
-  /*! \brief seek to certain position of the file */
-  virtual void Seek(size_t pos) = 0;
-  /*! \brief tell the position of the stream */
-  virtual size_t Tell(void) = 0;
-  /*!
-   * \brief generic factory function
-   *  create an SeekStream for read only,
-   *  the stream will close the underlying files upon deletion
-   *  error will be reported and the system will exit when create failed
-   * \param uri the uri of the input currently we support
-   *            hdfs://, s3://, and file:// by default file:// will be used
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  static SeekStream *CreateForRead(const char *uri,
-                                   bool allow_null = false);
-};
-
-/*! \brief interface for serializable objects */
-class Serializable {
- public:
-  /*! \brief virtual destructor */
-  virtual ~Serializable() {}
-  /*!
-  * \brief load the model from a stream
-  * \param fi stream where to load the model from
-  */
-  virtual void Load(Stream *fi) = 0;
-  /*!
-  * \brief saves the model to a stream
-  * \param fo stream where to save the model to
-  */
-  virtual void Save(Stream *fo) const = 0;
-};
-
-/*!
- * \brief input split creates that allows reading
- *  of records from split of data,
- *  independent part that covers all the dataset
- *
- *  see InputSplit::Create for definition of record
- */
-class InputSplit {
- public:
-  /*! \brief a blob of memory region */
-  struct Blob {
-    /*! \brief points to start of the memory region */
-    void *dptr;
-    /*! \brief size of the memory region */
-    size_t size;
-  };
-  /*!
-   * \brief hint the inputsplit how large the chunk size
-   *  it should return when implementing NextChunk
-   *  this is a hint so may not be enforced,
-   *  but InputSplit will try adjust its internal buffer
-   *  size to the hinted value
-   * \param chunk_size the chunk size
-   */
-  virtual void HintChunkSize(size_t chunk_size) {}
-  /*! \brief get the total size of the InputSplit */
-  virtual size_t GetTotalSize(void) = 0;
-  /*! \brief reset the position of InputSplit to beginning */
-  virtual void BeforeFirst(void) = 0;
-  /*!
-   * \brief get the next record, the returning value
-   *   is valid until next call to NextRecord, NextChunk or NextBatch
-   *   caller can modify the memory content of out_rec
-   *
-   *   For text, out_rec contains a single line
-   *   For recordio, out_rec contains one record content(with header striped)
-   *
-   * \param out_rec used to store the result
-   * \return true if we can successfully get next record
-   *     false if we reached end of split
-   * \sa InputSplit::Create for definition of record
-   */
-  virtual bool NextRecord(Blob *out_rec) = 0;
-  /*!
-   * \brief get a chunk of memory that can contain multiple records,
-   *  the caller needs to parse the content of the resulting chunk,
-   *  for text file, out_chunk can contain data of multiple lines
-   *  for recordio, out_chunk can contain multiple records(including headers)
-   *
-   *  This function ensures there won't be partial record in the chunk
-   *  caller can modify the memory content of out_chunk,
-   *  the memory is valid until next call to NextRecord, NextChunk or NextBatch
-   *
-   *  Usually NextRecord is sufficient, NextChunk can be used by some
-   *  multi-threaded parsers to parse the input content
-   *
-   * \param out_chunk used to store the result
-   * \return true if we can successfully get next record
-   *     false if we reached end of split
-   * \sa InputSplit::Create for definition of record
-   * \sa RecordIOChunkReader to parse recordio content from out_chunk
-   */
-  virtual bool NextChunk(Blob *out_chunk) = 0;
-  /*!
-   * \brief get a chunk of memory that can contain multiple records,
-   *  with hint for how many records is needed,
-   *  the caller needs to parse the content of the resulting chunk,
-   *  for text file, out_chunk can contain data of multiple lines
-   *  for recordio, out_chunk can contain multiple records(including headers)
-   *
-   *  This function ensures there won't be partial record in the chunk
-   *  caller can modify the memory content of out_chunk,
-   *  the memory is valid until next call to NextRecord, NextChunk or NextBatch
-   *
-   *
-   * \param out_chunk used to store the result
-   * \param n_records used as a hint for how many records should be returned, may be ignored
-   * \return true if we can successfully get next record
-   *     false if we reached end of split
-   * \sa InputSplit::Create for definition of record
-   * \sa RecordIOChunkReader to parse recordio content from out_chunk
-   */
-  virtual bool NextBatch(Blob *out_chunk, size_t n_records) {
-    return NextChunk(out_chunk);
-  }
-  /*! \brief destructor*/
-  virtual ~InputSplit(void) DMLC_THROW_EXCEPTION {}
-  /*!
-   * \brief reset the Input split to a certain part id,
-   *  The InputSplit will be pointed to the head of the new specified segment.
-   *  This feature may not be supported by every implementation of InputSplit.
-   * \param part_index The part id of the new input.
-   * \param num_parts The total number of parts.
-   */
-  virtual void ResetPartition(unsigned part_index, unsigned num_parts) = 0;
-  /*!
-   * \brief factory function:
-   *  create input split given a uri
-   * \param uri the uri of the input, can contain hdfs prefix
-   * \param part_index the part id of current input
-   * \param num_parts total number of splits
-   * \param type type of record
-   *   List of possible types: "text", "recordio", "indexed_recordio"
-   *     - "text":
-   *         text file, each line is treated as a record
-   *         input split will split on '\\n' or '\\r'
-   *     - "recordio":
-   *         binary recordio file, see recordio.h
-   *     - "indexed_recordio":
-   *         binary recordio file with index, see recordio.h
-   * \return a new input split
-   * \sa InputSplit::Type
-   */
-  static InputSplit* Create(const char *uri,
-                            unsigned part_index,
-                            unsigned num_parts,
-                            const char *type);
-  /*!
-   * \brief factory function:
-   *  create input split given a uri for input and index
-   * \param uri the uri of the input, can contain hdfs prefix
-   * \param index_uri the uri of the index, can contain hdfs prefix
-   * \param part_index the part id of current input
-   * \param num_parts total number of splits
-   * \param type type of record
-   *   List of possible types: "text", "recordio", "indexed_recordio"
-   *     - "text":
-   *         text file, each line is treated as a record
-   *         input split will split on '\\n' or '\\r'
-   *     - "recordio":
-   *         binary recordio file, see recordio.h
-   *     - "indexed_recordio":
-   *         binary recordio file with index, see recordio.h
-   * \param shuffle whether to shuffle the output from the InputSplit,
-   *                supported only by "indexed_recordio" type.
-   *                Defaults to "false"
-   * \param seed random seed to use in conjunction with the "shuffle"
-   *             option. Defaults to 0
-   * \param batch_size a hint to InputSplit what is the intended number
-   *                   of examples return per batch. Used only by
-   *                   "indexed_recordio" type
-   * \param recurse_directories whether to recursively traverse directories
-   * \return a new input split
-   * \sa InputSplit::Type
-   */
-  static InputSplit* Create(const char *uri,
-                            const char *index_uri,
-                            unsigned part_index,
-                            unsigned num_parts,
-                            const char *type,
-                            const bool shuffle = false,
-                            const int seed = 0,
-                            const size_t batch_size = 256,
-                            const bool recurse_directories = false);
-};
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-/*!
- * \brief a std::ostream class that can can wrap Stream objects,
- *  can use ostream with that output to underlying Stream
- *
- * Usage example:
- * \code
- *
- *   Stream *fs = Stream::Create("hdfs:///test.txt", "w");
- *   dmlc::ostream os(fs);
- *   os << "hello world" << std::endl;
- *   delete fs;
- * \endcode
- */
-class ostream : public std::basic_ostream<char> {
- public:
-  /*!
-   * \brief construct std::ostream type
-   * \param stream the Stream output to be used
-   * \param buffer_size internal streambuf size
-   */
-  explicit ostream(Stream *stream,
-                   size_t buffer_size = (1 << 10))
-      : std::basic_ostream<char>(NULL), buf_(buffer_size) {
-    this->set_stream(stream);
-  }
-  // explictly synchronize the buffer
-  virtual ~ostream() DMLC_NO_EXCEPTION {
-    buf_.pubsync();
-  }
-  /*!
-   * \brief set internal stream to be stream, reset states
-   * \param stream new stream as output
-   */
-  inline void set_stream(Stream *stream) {
-    buf_.set_stream(stream);
-    this->rdbuf(&buf_);
-  }
-
-  /*! \return how many bytes we written so far */
-  inline size_t bytes_written(void) const {
-    return buf_.bytes_out();
-  }
-
- private:
-  // internal streambuf
-  class OutBuf : public std::streambuf {
-   public:
-    explicit OutBuf(size_t buffer_size)
-        : stream_(NULL), buffer_(buffer_size), bytes_out_(0) {
-      if (buffer_size == 0) buffer_.resize(2);
-    }
-    // set stream to the buffer
-    inline void set_stream(Stream *stream);
-
-    inline size_t bytes_out() const { return bytes_out_; }
-   private:
-    /*! \brief internal stream by StreamBuf */
-    Stream *stream_;
-    /*! \brief internal buffer */
-    std::vector<char> buffer_;
-    /*! \brief number of bytes written so far */
-    size_t bytes_out_;
-    // override sync
-    inline int_type sync(void);
-    // override overflow
-    inline int_type overflow(int c);
-  };
-  /*! \brief buffer of the stream */
-  OutBuf buf_;
-};
-
-/*!
- * \brief a std::istream class that can can wrap Stream objects,
- *  can use istream with that output to underlying Stream
- *
- * Usage example:
- * \code
- *
- *   Stream *fs = Stream::Create("hdfs:///test.txt", "r");
- *   dmlc::istream is(fs);
- *   is >> mydata;
- *   delete fs;
- * \endcode
- */
-class istream : public std::basic_istream<char> {
- public:
-  /*!
-   * \brief construct std::ostream type
-   * \param stream the Stream output to be used
-   * \param buffer_size internal buffer size
-   */
-  explicit istream(Stream *stream,
-                   size_t buffer_size = (1 << 10))
-      : std::basic_istream<char>(NULL), buf_(buffer_size) {
-    this->set_stream(stream);
-  }
-  virtual ~istream() DMLC_NO_EXCEPTION {}
-  /*!
-   * \brief set internal stream to be stream, reset states
-   * \param stream new stream as output
-   */
-  inline void set_stream(Stream *stream) {
-    buf_.set_stream(stream);
-    this->rdbuf(&buf_);
-  }
-  /*! \return how many bytes we read so far */
-  inline size_t bytes_read(void) const {
-    return buf_.bytes_read();
-  }
-
- private:
-  // internal streambuf
-  class InBuf : public std::streambuf {
-   public:
-    explicit InBuf(size_t buffer_size)
-        : stream_(NULL), bytes_read_(0),
-          buffer_(buffer_size) {
-      if (buffer_size == 0) buffer_.resize(2);
-    }
-    // set stream to the buffer
-    inline void set_stream(Stream *stream);
-    // return how many bytes read so far
-    inline size_t bytes_read(void) const {
-      return bytes_read_;
-    }
-   private:
-    /*! \brief internal stream by StreamBuf */
-    Stream *stream_;
-    /*! \brief how many bytes we read so far */
-    size_t bytes_read_;
-    /*! \brief internal buffer */
-    std::vector<char> buffer_;
-    // override underflow
-    inline int_type underflow();
-  };
-  /*! \brief input buffer */
-  InBuf buf_;
-};
-#endif
-}  // namespace dmlc
-
-#include "./serializer.h"
-
-namespace dmlc {
-// implementations of inline functions
-template<typename T>
-inline void Stream::Write(const T &data) {
-  serializer::Handler<T>::Write(this, data);
-}
-template<typename T>
-inline bool Stream::Read(T *out_data) {
-  return serializer::Handler<T>::Read(this, out_data);
-}
-
-template<typename T>
-inline void Stream::WriteArray(const T* data, size_t num_elems) {
-  for (size_t i = 0; i < num_elems; ++i) {
-    this->Write<T>(data[i]);
-  }
-}
-
-template<typename T>
-inline bool Stream::ReadArray(T* data, size_t num_elems) {
-  for (size_t i = 0; i < num_elems; ++i) {
-    if (!this->Read<T>(data + i)) return false;
-  }
-  return true;
-}
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-// implementations for ostream
-inline void ostream::OutBuf::set_stream(Stream *stream) {
-  if (stream_ != NULL) this->pubsync();
-  this->stream_ = stream;
-  this->setp(&buffer_[0], &buffer_[0] + buffer_.size() - 1);
-}
-inline int ostream::OutBuf::sync(void) {
-  if (stream_ == NULL) return -1;
-  std::ptrdiff_t n = pptr() - pbase();
-  stream_->Write(pbase(), n);
-  this->pbump(-static_cast<int>(n));
-  bytes_out_ += n;
-  return 0;
-}
-inline int ostream::OutBuf::overflow(int c) {
-  *(this->pptr()) = c;
-  std::ptrdiff_t n = pptr() - pbase();
-  this->pbump(-static_cast<int>(n));
-  if (c == EOF) {
-    stream_->Write(pbase(), n);
-    bytes_out_ += n;
-  } else {
-    stream_->Write(pbase(), n + 1);
-    bytes_out_ += n + 1;
-  }
-  return c;
-}
-
-// implementations for istream
-inline void istream::InBuf::set_stream(Stream *stream) {
-  stream_ = stream;
-  this->setg(&buffer_[0], &buffer_[0], &buffer_[0]);
-}
-inline int istream::InBuf::underflow() {
-  char *bhead = &buffer_[0];
-  if (this->gptr() == this->egptr()) {
-    size_t sz = stream_->Read(bhead, buffer_.size());
-    this->setg(bhead, bhead, bhead + sz);
-    bytes_read_ += sz;
-  }
-  if (this->gptr() == this->egptr()) {
-    return traits_type::eof();
-  } else {
-    return traits_type::to_int_type(*gptr());
-  }
-}
-#endif
-
-namespace io {
-/*! \brief common data structure for URI */
-struct URI {
-  /*! \brief protocol */
-  std::string protocol;
-  /*!
-   * \brief host name, namenode for HDFS, bucket name for s3
-   */
-  std::string host;
-  /*! \brief name of the path */
-  std::string name;
-  /*! \brief enable default constructor */
-  URI(void) {}
-  /*!
-   * \brief construct from URI string
-   */
-  explicit URI(const char *uri) {
-    const char *p = std::strstr(uri, "://");
-    if (p == NULL) {
-      name = uri;
-    } else {
-      protocol = std::string(uri, p - uri + 3);
-      uri = p + 3;
-      p = std::strchr(uri, '/');
-      if (p == NULL) {
-        host = uri; name = '/';
-      } else {
-        host = std::string(uri, p - uri);
-        name = p;
-      }
-    }
-  }
-  /*! \brief string representation */
-  inline std::string str(void) const {
-    return protocol + host + name;
-  }
-};
-
-/*! \brief type of file */
-enum FileType {
-  /*! \brief the file is file */
-  kFile,
-  /*! \brief the file is directory */
-  kDirectory
-};
-
-/*! \brief use to store file information */
-struct FileInfo {
-  /*! \brief full path to the file */
-  URI path;
-  /*! \brief the size of the file */
-  size_t size;
-  /*! \brief the type of the file */
-  FileType type;
-  /*! \brief default constructor */
-  FileInfo() : size(0), type(kFile) {}
-};
-
-/*! \brief file system system interface */
-class FileSystem {
- public:
-  /*!
-   * \brief get singleton of filesystem instance according to URI
-   * \param path can be s3://..., hdfs://..., file://...,
-   *            empty string(will return local)
-   * \return a corresponding filesystem, report error if
-   *         we cannot find a matching system
-   */
-  static FileSystem *GetInstance(const URI &path);
-  /*! \brief virtual destructor */
-  virtual ~FileSystem() {}
-  /*!
-   * \brief get information about a path
-   * \param path the path to the file
-   * \return the information about the file
-   */
-  virtual FileInfo GetPathInfo(const URI &path) = 0;
-  /*!
-   * \brief list files in a directory
-   * \param path to the file
-   * \param out_list the output information about the files
-   */
-  virtual void ListDirectory(const URI &path, std::vector<FileInfo> *out_list) = 0;
-  /*!
-   * \brief list files in a directory recursively using ListDirectory
-   * \param path to the file
-   * \param out_list the output information about the files
-   */
-  virtual void ListDirectoryRecursive(const URI &path,
-                                      std::vector<FileInfo> *out_list);
-  /*!
-   * \brief open a stream
-   * \param path path to file
-   * \param flag can be "w", "r", "a
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual Stream *Open(const URI &path,
-                       const char* const flag,
-                       bool allow_null = false) = 0;
-  /*!
-   * \brief open a seekable stream for read
-   * \param path the path to the file
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual SeekStream *OpenForRead(const URI &path,
-                                  bool allow_null = false) = 0;
-};
-
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/json.h b/ml-xgboost/dmlc-core/include/dmlc/json.h
deleted file mode 100644
index fcc20bd..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/json.h
+++ /dev/null
@@ -1,983 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file json.h
- * \brief Lightweight JSON Reader/Writer that read save into C++ data structs.
- *  This includes STL composites and structures.
- */
-#ifndef DMLC_JSON_H_
-#define DMLC_JSON_H_
-
-// This code requires C++11 to compile
-#include <vector>
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-#include <iostream>
-#include <sstream>
-#endif
-#include <cctype>
-#include <string>
-#include <algorithm>
-#include <map>
-#include <list>
-#include <utility>
-
-#include "./base.h"
-#include "./logging.h"
-#include "./type_traits.h"
-
-#if DMLC_USE_CXX11
-#include <typeindex>
-#include <typeinfo>
-#include <unordered_map>
-#if DMLC_STRICT_CXX11
-#if DMLC_ENABLE_RTTI
-#include "./any.h"
-#endif  // DMLC_ENABLE_RTTI
-#endif  // DMLC_STRICT_CXX11
-#endif  // DMLC_USE_CXX11
-
-namespace dmlc {
-/*!
- * \brief Lightweight JSON Reader to read any STL compositions and structs.
- *  The user need to know the schema of the
- *
- */
-class JSONReader {
- public:
-  /*!
-   * \brief Constructor.
-   * \param is the input source.
-   */
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  explicit JSONReader(std::istream *is)
-#else
-  explicit JSONReader(std::string *is)
-#endif
-      : is_(is),
-        line_count_r_(0),
-        line_count_n_(0) {}
-  /*!
-   * \brief Parse next JSON string.
-   * \param out_str the output string.
-   * \throw dmlc::Error when next token is not string
-   */
-  inline void ReadString(std::string *out_str);
-  /*!
-   * \brief Read Number.
-   * \param out_value output value;
-   * \throw dmlc::Error when next token is not number of ValueType.
-   * \tparam ValueType type of the number
-   */
-  template<typename ValueType>
-  inline void ReadNumber(ValueType *out_value);
-  /*!
-   * \brief Begin parsing an object.
-   * \code
-   *  std::string key;
-   *  // value can be any type that is json serializable.
-   *  std::string value;
-   *  reader->BeginObject();
-   *  while (reader->NextObjectItem(&key)) {
-   *    // do somthing to key value
-   *    reader->Read(&value);
-   *  }
-   * \endcode
-   */
-  inline void BeginObject();
-  /*!
-   * \brief Begin parsing an array.
-   * \code
-   *  // value can be any type that is json serializable.
-   *  std::string value;
-   *  reader->BeginArray();
-   *  while (reader->NextArrayItem()) {
-   *    reader->Read(&value);
-   *    // do somthing to value
-   *  }
-   * \endcode
-   */
-  inline void BeginArray();
-  /*!
-   * \brief Try to move to next object item.
-   *  If this call is successful, user can proceed to call
-   *  reader->Read to read in the value.
-   * \param out_key the key to the next object.
-   * \return true if the read is successful, false if we are at end of the object.
-   */
-  inline bool NextObjectItem(std::string *out_key);
-  /*!
-   * \brief Try to read the next element in the array.
-   *  If this call is successful, user can proceed to call
-   *  reader->Read to read in the value.
-   * \return true if the read is successful, false if we are at end of the array.
-   */
-  inline bool NextArrayItem();
-  /*!
-   * \brief Read next ValueType.
-   * \param out_value any STL or json readable type to be read
-   * \throw dmlc::Error when the read of ValueType is not successful.
-   * \tparam ValueType the data type to be read.
-   */
-  template<typename ValueType>
-  inline void Read(ValueType *out_value);
-
-  /*! \return current line count */
-  inline std::string line_info() const {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-    char temp[64];
-    std::ostringstream os;
-    os << " Line " << std::max(line_count_r_, line_count_n_);
-    is_->getline(temp, 64);
-    os << ", around ^`" << temp << "`";
-    return os.str();
-#else
-    std::string info = " Line ";
-    info += std::to_string(std::max(line_count_r_, line_count_n_));
-
-    // string getline
-    size_t end_pos = is_->find('\n');
-    end_pos = std::min((size_t)64,
-        end_pos == std::string::npos ? is_->size() : end_pos);
-    std::string line = is_->substr(0, end_pos);
-    is_->erase(0, line.size() + 1);  // +1 for \n
-
-    info += ", around ^`" + line + "`";
-    return info;
-#endif
-  }
-
- private:
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  /*! \brief internal reader stream */
-  std::istream *is_;
-#else
-  /*! \brief internal reader string */
-  std::string *is_;
-#endif
-  /*! \brief "\\r" counter */
-  size_t line_count_r_;
-  /*! \brief "\\n" counter */
-  size_t line_count_n_;
-  /*!
-   * \brief record how many element processed in
-   *  current array/object scope.
-   */
-  std::vector<size_t> scope_counter_;
-  /*!
-   * \brief Read next nonspace character.
-   * \return the next nonspace character.
-   */
-  inline int NextNonSpace();
-  /*!
-   * \brief Read just before next nonspace but not read that.
-   * \return the next nonspace character.
-   */
-  inline int PeekNextNonSpace();
-  /*!
-   * \brief Takes the next char from the input source.
-   * \return the next character.
-   */
-  inline int NextChar();
-  /*!
-   * \brief Returns the next char from the input source.
-   * \return the next character.
-   */
-  inline int PeekNextChar();
-};
-
-/*!
- * \brief Lightweight json to write any STL compositions.
- */
-class JSONWriter {
- public:
-  /*!
-   * \brief Constructor.
-   * \param os the output reciever.
-   */
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  explicit JSONWriter(std::ostream *os)
-#else
-  explicit JSONWriter(std::string *os)
-#endif
-      : os_(os) {}
-  /*!
-   * \brief Write a string that do not contain escape characters.
-   * \param s the string to be written.
-   */
-  inline void WriteNoEscape(const std::string &s);
-  /*!
-   * \brief Write a string that can contain escape characters.
-   * \param s the string to be written.
-   */
-  inline void WriteString(const std::string &s);
-  /*!
-   * \brief Write a string that can contain escape characters.
-   * \param v the value to be written.
-   * \tparam ValueType The value type to be written.
-   */
-  template<typename ValueType>
-  inline void WriteNumber(const ValueType &v);
-  /*!
-   * \brief Start beginning of array.
-   * \param multi_line whether to start an multi_line array.
-   * \code
-   *  writer->BeginArray();
-   *  for (auto& v : vdata) {
-   *    writer->WriteArrayItem(v);
-   *  }
-   *  writer->EndArray();
-   * \endcode
-   */
-  inline void BeginArray(bool multi_line = true);
-  /*! \brief Finish writing an array. */
-  inline void EndArray();
-  /*!
-   * \brief Start beginning of array.
-   * \param multi_line whether to start an multi_line array.
-   * \code
-   *  writer->BeginObject();
-   *  for (auto& kv : vmap) {
-   *    writer->WriteObjectKeyValue(kv.first, kv.second);
-   *  }
-   *  writer->EndObject();
-   * \endcode
-   */
-  inline void BeginObject(bool multi_line = true);
-  /*! \brief Finish writing object. */
-  inline void EndObject();
-  /*!
-   * \brief Write key value pair in the object.
-   * \param key the key of the object.
-   * \param value the value of to be written.
-   * \tparam ValueType The value type to be written.
-   */
-  template<typename ValueType>
-  inline void WriteObjectKeyValue(const std::string &key,
-                                  const ValueType &value);
-  /*!
-   * \brief Write seperator of array, before writing next element.
-   * User can proceed to call writer->Write to write next item
-   */
-  inline void WriteArraySeperator();
-  /*!
-   * \brief Write value into array.
-   * \param value The value of to be written.
-   * \tparam ValueType The value type to be written.
-   */
-  template<typename ValueType>
-  inline void WriteArrayItem(const ValueType &value);
-  /*!
-   * \brief Write value to json.
-   * \param value any STL or json readable that can be written.
-   * \tparam ValueType the data type to be write.
-   */
-  template<typename ValueType>
-  inline void Write(const ValueType &value);
-
- private:
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  /*! \brief Output stream */
-  std::ostream *os_;
-#else
-  std::string *os_;
-#endif
-  /*!
-   * \brief record how many element processed in
-   *  current array/object scope.
-   */
-  std::vector<size_t> scope_counter_;
-  /*! \brief Record whether current is a multiline scope */
-  std::vector<bool> scope_multi_line_;
-  /*!
-   * \brief Write seperating space and newlines
-   */
-  inline void WriteSeperator();
-};
-
-/*!
- * \brief Helper class to read JSON into a class or struct object.
- * \code
- *  struct Param {
- *    std::string name;
- *    int value;
- *    // define load function from JSON
- *    inline void Load(dmlc::JSONReader *reader) {
- *      dmlc::JSONStructReadHelper helper;
- *      helper.DeclareField("name", &name);
- *      helper.DeclareField("value", &value);
- *      helper.ReadAllFields(reader);
- *    }
- *  };
- * \endcode
- */
-class JSONObjectReadHelper {
- public:
-  /*!
-   * \brief Declare field of type T
-   * \param key the key of the of field.
-   * \param addr address of the data type.
-   * \tparam T the data type to be read, must be STL composition of JSON serializable.
-   */
-  template<typename T>
-  inline void DeclareField(const std::string &key, T *addr) {
-    DeclareFieldInternal(key, addr, false);
-  }
-  /*!
-   * \brief Declare optional field of type T
-   * \param key the key of the of field.
-   * \param addr address of the data type.
-   * \tparam T the data type to be read, must be STL composition of JSON serializable.
-   */
-  template<typename T>
-  inline void DeclareOptionalField(const std::string &key, T *addr) {
-    DeclareFieldInternal(key, addr, true);
-  }
-  /*!
-   * \brief Read in all the declared fields.
-   * \param reader the JSONReader to read the json.
-   */
-  inline void ReadAllFields(JSONReader *reader);
-
- private:
-  /*!
-   * \brief Internal function to declare field.
-   * \param key the key of the of field.
-   * \param addr address of the data type.
-   * \param optional if set to true, no error will be reported if the key is not presented.
-   * \tparam T the data type to be read, must be STL composition of JSON serializable.
-   */
-  template<typename T>
-  inline void DeclareFieldInternal(const std::string &key, T *addr, bool optional);
-  /*!
-   * \brief The internal reader function.
-   * \param reader The reader to read.
-   * \param addr The memory address to read.
-   */
-  template<typename T>
-  inline static void ReaderFunction(JSONReader *reader, void *addr);
-  /*! \brief callback type to reader function */
-  typedef void (*ReadFunction)(JSONReader *reader, void *addr);
-  /*! \brief internal data entry */
-  struct Entry {
-    /*! \brief the reader function */
-    ReadFunction func;
-    /*! \brief the address to read */
-    void *addr;
-    /*! \brief whether it is optional */
-    bool optional;
-  };
-  /*! \brief the internal map of reader callbacks */
-  std::map<std::string, Entry> map_;
-};
-
-#define DMLC_JSON_ENABLE_ANY_VAR_DEF(KeyName)                  \
-  static DMLC_ATTRIBUTE_UNUSED ::dmlc::json::AnyJSONManager&   \
-  __make_AnyJSONType ## _ ## KeyName ## __
-
-/*!
- * \def DMLC_JSON_ENABLE_ANY
- * \brief Macro to enable save/load JSON of dmlc:: whose actual type is Type.
- * Any type will be saved as json array [KeyName, content]
- *
- * \param Type The type to be registered.
- * \param KeyName The Type key assigned to the type, must be same during load.
- */
-#define DMLC_JSON_ENABLE_ANY(Type, KeyName)                             \
-  DMLC_STR_CONCAT(DMLC_JSON_ENABLE_ANY_VAR_DEF(KeyName), __COUNTER__) = \
-    ::dmlc::json::AnyJSONManager::Global()->EnableType<Type>(#KeyName) \
-
-//! \cond Doxygen_Suppress
-namespace json {
-
-/*!
- * \brief generic serialization handler
- * \tparam T the type to be serialized
- */
-template<typename T>
-struct Handler;
-
-template<typename ValueType>
-struct NumericHandler {
-  inline static void Write(JSONWriter *writer, const ValueType &value) {
-    writer->WriteNumber<ValueType>(value);
-  }
-  inline static void Read(JSONReader *reader, ValueType *value) {
-    reader->ReadNumber<ValueType>(value);
-  }
-};
-
-template<typename ContainerType>
-struct ArrayHandler {
-  inline static void Write(JSONWriter *writer, const ContainerType &array) {
-    typedef typename ContainerType::value_type ElemType;
-    writer->BeginArray(array.size() > 10 || !dmlc::is_pod<ElemType>::value);
-    for (typename ContainerType::const_iterator it = array.begin();
-         it != array.end(); ++it) {
-      writer->WriteArrayItem(*it);
-    }
-    writer->EndArray();
-  }
-  inline static void Read(JSONReader *reader, ContainerType *array) {
-    typedef typename ContainerType::value_type ElemType;
-    array->clear();
-    reader->BeginArray();
-    while (reader->NextArrayItem()) {
-      ElemType value;
-      Handler<ElemType>::Read(reader, &value);
-      array->insert(array->end(), value);
-    }
-  }
-};
-
-template<typename ContainerType>
-struct MapHandler{
-  inline static void Write(JSONWriter *writer, const ContainerType &map) {
-    writer->BeginObject(map.size() > 1);
-    for (typename ContainerType::const_iterator it = map.begin(); it != map.end(); ++it) {
-      writer->WriteObjectKeyValue(it->first, it->second);
-    }
-    writer->EndObject();
-  }
-  inline static void Read(JSONReader *reader, ContainerType *map) {
-    typedef typename ContainerType::mapped_type ElemType;
-    map->clear();
-    reader->BeginObject();
-    std::string key;
-    while (reader->NextObjectItem(&key)) {
-      ElemType value;
-      reader->Read(&value);
-      (*map)[key] = value;
-    }
-  }
-};
-
-template<typename T>
-struct CommonJSONSerializer {
-  inline static void Write(JSONWriter *writer, const T &value) {
-    value.Save(writer);
-  }
-  inline static void Read(JSONReader *reader, T *value) {
-    value->Load(reader);
-  }
-};
-
-template<>
-struct Handler<std::string> {
-  inline static void Write(JSONWriter *writer, const std::string &value) {
-    writer->WriteString(value);
-  }
-  inline static void Read(JSONReader *reader, std::string *str) {
-    reader->ReadString(str);
-  }
-};
-
-template<typename T>
-struct Handler<std::vector<T> > : public ArrayHandler<std::vector<T> > {
-};
-
-template<typename K, typename V>
-struct Handler<std::pair<K, V> > {
-  inline static void Write(JSONWriter *writer, const std::pair<K, V> &kv) {
-    writer->BeginArray();
-    writer->WriteArrayItem(kv.first);
-    writer->WriteArrayItem(kv.second);
-    writer->EndArray();
-  }
-  inline static void Read(JSONReader *reader, std::pair<K, V> *kv) {
-    reader->BeginArray();
-    CHECK(reader->NextArrayItem())
-        << "Expect array of length 2";
-    Handler<K>::Read(reader, &(kv->first));
-    CHECK(reader->NextArrayItem())
-        << "Expect array of length 2";
-    Handler<V>::Read(reader, &(kv->second));
-    CHECK(!reader->NextArrayItem())
-        << "Expect array of length 2";
-  }
-};
-
-template<typename T>
-struct Handler<std::list<T> > : public ArrayHandler<std::list<T> > {
-};
-
-template<typename V>
-struct Handler<std::map<std::string, V> > : public MapHandler<std::map<std::string, V> > {
-};
-
-#if DMLC_USE_CXX11
-template<typename V>
-struct Handler<std::unordered_map<std::string, V> >
-    : public MapHandler<std::unordered_map<std::string, V> > {
-};
-#endif  // DMLC_USE_CXX11
-
-template<typename T>
-struct Handler {
-  inline static void Write(JSONWriter *writer, const T &data) {
-    typedef typename dmlc::IfThenElseType<dmlc::is_arithmetic<T>::value,
-                                          NumericHandler<T>,
-                                          CommonJSONSerializer<T> >::Type THandler;
-    THandler::Write(writer, data);
-  }
-  inline static void Read(JSONReader *reader, T *data) {
-    typedef typename dmlc::IfThenElseType<dmlc::is_arithmetic<T>::value,
-                                          NumericHandler<T>,
-                                          CommonJSONSerializer<T> >::Type THandler;
-    THandler::Read(reader, data);
-  }
-};
-
-#if DMLC_STRICT_CXX11
-#if DMLC_ENABLE_RTTI
-// Manager to store json serialization strategy.
-class AnyJSONManager {
- public:
-  template<typename T>
-  inline AnyJSONManager& EnableType(const std::string& type_name) {  // NOLINT(*)
-    std::type_index tp = std::type_index(typeid(T));
-    if (type_name_.count(tp) != 0) {
-      CHECK(type_name_.at(tp) == type_name)
-          << "Type has already been registered as another typename " << type_name_.at(tp);
-      return *this;
-    }
-    CHECK(type_map_.count(type_name) == 0)
-        << "Type name " << type_name << " already registered in registry";
-    Entry e;
-    e.read = ReadAny<T>;
-    e.write = WriteAny<T>;
-    type_name_[tp] = type_name;
-    type_map_[type_name] = e;
-    return *this;
-  }
-  // return global singleton
-  inline static AnyJSONManager* Global() {
-    static AnyJSONManager inst;
-    return &inst;
-  }
-
- private:
-  AnyJSONManager() {}
-
-  template<typename T>
-  inline static void WriteAny(JSONWriter *writer, const any &data) {
-    writer->Write(dmlc::unsafe_get<T>(data));
-  }
-  template<typename T>
-  inline static void ReadAny(JSONReader *reader, any* data) {
-    T temp;
-    reader->Read(&temp);
-    *data = std::move(temp);
-  }
-  // data entry to store vtable for any type
-  struct Entry {
-    void (*read)(JSONReader* reader, any *data);
-    void (*write)(JSONWriter* reader, const any& data);
-  };
-
-  template<typename T>
-  friend struct Handler;
-
-  std::unordered_map<std::type_index, std::string> type_name_;
-  std::unordered_map<std::string, Entry> type_map_;
-};
-
-template<>
-struct Handler<any> {
-  inline static void Write(JSONWriter *writer, const any &data) {
-    std::unordered_map<std::type_index, std::string>&
-        nmap = AnyJSONManager::Global()->type_name_;
-    std::type_index id = std::type_index(data.type());
-    auto it = nmap.find(id);
-    CHECK(it != nmap.end() && it->first == id)
-        << "Type " << id.name() << " has not been registered via DMLC_JSON_ENABLE_ANY";
-    std::string type_name = it->second;
-    AnyJSONManager::Entry e = AnyJSONManager::Global()->type_map_.at(type_name);
-    writer->BeginArray(false);
-    writer->WriteArrayItem(type_name);
-    writer->WriteArraySeperator();
-    e.write(writer, data);
-    writer->EndArray();
-  }
-  inline static void Read(JSONReader *reader, any *data) {
-    std::string type_name;
-    reader->BeginArray();
-    CHECK(reader->NextArrayItem()) << "invalid any json format";
-    Handler<std::string>::Read(reader, &type_name);
-    std::unordered_map<std::string, AnyJSONManager::Entry>&
-        tmap = AnyJSONManager::Global()->type_map_;
-    auto it = tmap.find(type_name);
-    CHECK(it != tmap.end() && it->first == type_name)
-        << "Typename " << type_name << " has not been registered via DMLC_JSON_ENABLE_ANY";
-    AnyJSONManager::Entry e = it->second;
-    CHECK(reader->NextArrayItem()) << "invalid any json format";
-    e.read(reader, data);
-    CHECK(!reader->NextArrayItem()) << "invalid any json format";
-  }
-};
-#endif  // DMLC_ENABLE_RTTI
-#endif  // DMLC_STRICT_CXX11
-
-}  // namespace json
-
-// implementations of JSONReader/Writer
-inline int JSONReader::NextChar() {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  return is_->get();
-#else
-  int ch = is_->at(0);
-  is_->erase(0, 1);
-  return ch;
-#endif
-}
-
-inline int JSONReader::PeekNextChar() {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  return is_->peek();
-#else
-  return is_->at(0);
-#endif
-}
-
-inline int JSONReader::NextNonSpace() {
-  int ch;
-  do {
-    ch = NextChar();
-    if (ch == '\n') ++line_count_n_;
-    if (ch == '\r') ++line_count_r_;
-  } while (isspace(ch));
-  return ch;
-}
-
-inline int JSONReader::PeekNextNonSpace() {
-  int ch;
-  while (true) {
-    ch = PeekNextChar();
-    if (ch == '\n') ++line_count_n_;
-    if (ch == '\r') ++line_count_r_;
-    if (!isspace(ch)) break;
-    NextChar();
-  }
-  return ch;
-}
-
-namespace {
-  template<typename T>
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  void Extend(std::ostream *os, T item) {
-    *os << item;
-  }
-#else
-  void Extend(std::string *ostr, T item) {
-    *ostr += item;
-  }
-#endif
-}  // namespace
-
-inline void JSONReader::ReadString(std::string *out_str) {
-  int ch = NextNonSpace();
-  CHECK_EQ(ch, '\"')
-      << "Error at" << line_info()
-      << ", Expect \'\"\' but get \'" << static_cast<char>(ch) << '\'';
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::ostringstream output;
-#else
-  std::string output = "";
-#endif
-  while (true) {
-    ch = NextChar();
-    if (ch == '\\') {
-      char sch = static_cast<char>(NextChar());
-      switch (sch) {
-        case 'r': Extend(&output, "\r"); break;
-        case 'n': Extend(&output, "\n"); break;
-        case '\\': Extend(&output, "\\"); break;
-        case 't': Extend(&output, "\t"); break;
-        case '\"': Extend(&output, "\""); break;
-        default: LOG(FATAL) << "unknown string escape \\" << sch;
-      }
-    } else {
-      if (ch == '\"') break;
-      Extend(&output, static_cast<char>(ch));
-    }
-    if (ch == EOF || ch == '\r' || ch == '\n') {
-      LOG(FATAL)
-          << "Error at" << line_info()
-          << ", Expect \'\"\' but reach end of line ";
-    }
-  }
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  *out_str = output.str();
-#else
-  *out_str = output;
-#endif
-}
-
-template<typename ValueType>
-inline void JSONReader::ReadNumber(ValueType *out_value) {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  *is_ >> *out_value;
-  CHECK(!is_->fail())
-      << "Error at" << line_info()
-      << ", Expect number";
-#else
-  char* endptr;
-  const char* icstr = is_->c_str();
-  unsigned number = strtol(icstr, &endptr, 10);
-  is_->erase(0, endptr - icstr);
-  *out_value = static_cast<ValueType>(number);
-#endif
-}
-
-inline void JSONReader::BeginObject() {
-  int ch = NextNonSpace();
-  CHECK_EQ(ch, '{')
-      << "Error at" << line_info()
-      << ", Expect \'{\' but get \'" << static_cast<char>(ch) << '\'';
-  scope_counter_.push_back(0);
-}
-
-inline void JSONReader::BeginArray() {
-  int ch = NextNonSpace();
-  CHECK_EQ(ch, '[')
-      << "Error at" << line_info()
-      << ", Expect \'[\' but get \'" << static_cast<char>(ch) << '\'';
-  scope_counter_.push_back(0);
-}
-
-inline bool JSONReader::NextObjectItem(std::string *out_key) {
-  bool next = true;
-  if (scope_counter_.back() != 0) {
-    int ch = NextNonSpace();
-    if (ch == EOF) {
-      next = false;
-    } else if (ch == '}') {
-      next = false;
-    } else {
-      CHECK_EQ(ch, ',')
-          << "Error at" << line_info()
-          << ", JSON object expect \'}\' or \',\' \'" << static_cast<char>(ch) << '\'';
-    }
-  } else {
-    int ch = PeekNextNonSpace();
-    if (ch == '}') {
-      NextChar();
-      next = false;
-    }
-  }
-  if (!next) {
-    scope_counter_.pop_back();
-    return false;
-  } else {
-    scope_counter_.back() += 1;
-    ReadString(out_key);
-    int ch = NextNonSpace();
-    CHECK_EQ(ch, ':')
-        << "Error at" << line_info()
-        << ", Expect \':\' but get \'" << static_cast<char>(ch) << '\'';
-    return true;
-  }
-}
-
-inline bool JSONReader::NextArrayItem() {
-  bool next = true;
-  if (scope_counter_.back() != 0) {
-    int ch = NextNonSpace();
-    if (ch == EOF) {
-      next = false;
-    } else if (ch == ']') {
-      next = false;
-    } else {
-      CHECK_EQ(ch, ',')
-          << "Error at" << line_info()
-          << ", JSON array expect \']\' or \',\'. Get \'" << static_cast<char>(ch) << "\' instead";
-    }
-  } else {
-    int ch = PeekNextNonSpace();
-    if (ch == ']') {
-      NextChar();
-      next = false;
-    }
-  }
-  if (!next) {
-    scope_counter_.pop_back();
-    return false;
-  } else {
-    scope_counter_.back() += 1;
-    return true;
-  }
-}
-
-template<typename ValueType>
-inline void JSONReader::Read(ValueType *out_value) {
-  json::Handler<ValueType>::Read(this, out_value);
-}
-
-inline void JSONWriter::WriteNoEscape(const std::string &s) {
-  Extend(os_, '\"');
-  Extend(os_, s);
-  Extend(os_, '\"');
-}
-
-inline void JSONWriter::WriteString(const std::string &s) {
-  Extend(os_, '\"');
-  for (size_t i = 0; i < s.length(); ++i) {
-    char ch = s[i];
-    switch (ch) {
-      case '\r': Extend(os_, "\\r"); break;
-      case '\n': Extend(os_, "\\n"); break;
-      case '\\': Extend(os_, "\\\\"); break;
-      case '\t': Extend(os_, "\\t"); break;
-      case '\"': Extend(os_, "\\\""); break;
-      default: Extend(os_, ch);
-    }
-  }
-  Extend(os_, '\"');
-}
-
-template<typename ValueType>
-inline void JSONWriter::WriteNumber(const ValueType &v) {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  Extend(os_, v);
-#else
-  Extend(os_, std::to_string(v));
-#endif
-}
-
-inline void JSONWriter::BeginArray(bool multi_line) {
-  Extend(os_, '[');
-  scope_multi_line_.push_back(multi_line);
-  scope_counter_.push_back(0);
-}
-
-inline void JSONWriter::EndArray() {
-  CHECK_NE(scope_multi_line_.size(), 0U);
-  CHECK_NE(scope_counter_.size(), 0U);
-  bool newline = scope_multi_line_.back();
-  size_t nelem = scope_counter_.back();
-  scope_multi_line_.pop_back();
-  scope_counter_.pop_back();
-  if (newline && nelem != 0) WriteSeperator();
-  Extend(os_, ']');
-}
-
-inline void JSONWriter::BeginObject(bool multi_line) {
-  Extend(os_, '{');
-  scope_multi_line_.push_back(multi_line);
-  scope_counter_.push_back(0);
-}
-
-inline void JSONWriter::EndObject() {
-  CHECK_NE(scope_multi_line_.size(), 0U);
-  CHECK_NE(scope_counter_.size(), 0U);
-  bool newline = scope_multi_line_.back();
-  size_t nelem = scope_counter_.back();
-  scope_multi_line_.pop_back();
-  scope_counter_.pop_back();
-  if (newline && nelem != 0) WriteSeperator();
-  Extend(os_, '}');
-}
-
-template<typename ValueType>
-inline void JSONWriter::WriteObjectKeyValue(const std::string &key,
-                                            const ValueType &value) {
-  if (scope_counter_.back() > 0) {
-    Extend(os_, ", ");
-  }
-  WriteSeperator();
-  Extend(os_, '\"');
-  Extend(os_, key);
-  Extend(os_, "\": ");
-  scope_counter_.back() += 1;
-  json::Handler<ValueType>::Write(this, value);
-}
-
-inline void JSONWriter::WriteArraySeperator() {
-  if (scope_counter_.back() != 0) {
-    Extend(os_, ", ");
-  }
-  scope_counter_.back() += 1;
-  WriteSeperator();
-}
-
-template<typename ValueType>
-inline void JSONWriter::WriteArrayItem(const ValueType &value) {
-  this->WriteArraySeperator();
-  json::Handler<ValueType>::Write(this, value);
-}
-
-template<typename ValueType>
-inline void JSONWriter::Write(const ValueType &value) {
-  size_t nscope = scope_multi_line_.size();
-  json::Handler<ValueType>::Write(this, value);
-  CHECK_EQ(nscope, scope_multi_line_.size())
-      << "Uneven scope, did you call EndArray/EndObject after each BeginObject/Array?";
-}
-
-inline void JSONWriter::WriteSeperator() {
-  if (scope_multi_line_.size() == 0 || scope_multi_line_.back()) {
-    Extend(os_, '\n');
-    Extend(os_, std::string(scope_multi_line_.size() * 2, ' '));
-  }
-}
-
-inline void JSONObjectReadHelper::ReadAllFields(JSONReader *reader) {
-  reader->BeginObject();
-  std::map<std::string, int> visited;
-  std::string key;
-  while (reader->NextObjectItem(&key)) {
-    if (map_.count(key) != 0) {
-      Entry e = map_[key];
-      (*e.func)(reader, e.addr);
-      visited[key] = 0;
-    } else {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-      std::ostringstream err;
-#else
-      std::string err("");
-#endif
-      Extend(&err, "JSONReader: Unknown field ");
-      Extend(&err, key);
-      Extend(&err, ", candidates are: \n");
-      for (std::map<std::string, Entry>::iterator
-               it = map_.begin(); it != map_.end(); ++it) {
-        Extend(&err, '\"');
-        Extend(&err, it->first);
-        Extend(&err, "\"\n");
-      }
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-      LOG(FATAL) << err.str();
-#else
-      LOG(FATAL) << err;
-#endif
-    }
-  }
-  if (visited.size() != map_.size()) {
-    for (std::map<std::string, Entry>::iterator
-             it = map_.begin(); it != map_.end(); ++it) {
-      if (it->second.optional) continue;
-      CHECK_NE(visited.count(it->first), 0U)
-          << "JSONReader: Missing field \"" << it->first << "\"\n At "
-          << reader->line_info();
-    }
-  }
-}
-
-template<typename T>
-inline void JSONObjectReadHelper::ReaderFunction(JSONReader *reader, void *addr) {
-  json::Handler<T>::Read(reader, static_cast<T*>(addr));
-}
-
-template<typename T>
-inline void JSONObjectReadHelper::
-DeclareFieldInternal(const std::string &key, T *addr, bool optional) {
-  CHECK_EQ(map_.count(key), 0U)
-      << "Adding duplicate field " << key;
-  Entry e;
-  e.func = ReaderFunction<T>;
-  e.addr = static_cast<void*>(addr);
-  e.optional = optional;
-  map_[key] = e;
-}
-
-//! \endcond
-}  // namespace dmlc
-#endif  // DMLC_JSON_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/logging.h b/ml-xgboost/dmlc-core/include/dmlc/logging.h
deleted file mode 100644
index 4c50184..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/logging.h
+++ /dev/null
@@ -1,470 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file logging.h
- * \brief defines logging macros of dmlc
- *  allows use of GLOG, fall back to internal
- *  implementation when disabled
- */
-#ifndef DMLC_LOGGING_H_
-#define DMLC_LOGGING_H_
-#include <cstdio>
-#include <cstdlib>
-#include <string>
-#include <vector>
-#include <stdexcept>
-#include <memory>
-#include "./base.h"
-
-#if DMLC_LOG_STACK_TRACE
-#include <cxxabi.h>
-#include <sstream>
-#include DMLC_EXECINFO_H
-#endif
-
-namespace dmlc {
-/*!
- * \brief exception class that will be thrown by
- *  default logger if DMLC_LOG_FATAL_THROW == 1
- */
-struct Error : public std::runtime_error {
-  /*!
-   * \brief constructor
-   * \param s the error message
-   */
-  explicit Error(const std::string &s) : std::runtime_error(s) {}
-};
-
-#if DMLC_LOG_STACK_TRACE
-// get stack trace logging depth from env variable.
-inline size_t LogStackTraceLevel() {
-  size_t level;
-  if (auto var = std::getenv("DMLC_LOG_STACK_TRACE_DEPTH")) {
-    if (1 == sscanf(var, "%zu", &level)) {
-      return level + 1;
-    }
-  }
-  return DMLC_LOG_STACK_TRACE_SIZE;
-}
-
-inline std::string Demangle(char const *msg_str) {
-  using std::string;
-  string msg(msg_str);
-  size_t symbol_start = string::npos;
-  size_t symbol_end = string::npos;
-  if ( ((symbol_start = msg.find("_Z")) != string::npos)
-       && (symbol_end = msg.find_first_of(" +", symbol_start)) ) {
-    string left_of_symbol(msg, 0, symbol_start);
-    string symbol(msg, symbol_start, symbol_end - symbol_start);
-    string right_of_symbol(msg, symbol_end);
-
-    int status = 0;
-    size_t length = string::npos;
-    std::unique_ptr<char, void (*)(void *__ptr)> demangled_symbol =
-        {abi::__cxa_demangle(symbol.c_str(), 0, &length, &status), &std::free};
-    if (demangled_symbol && status == 0 && length > 0) {
-      string symbol_str(demangled_symbol.get());
-      std::ostringstream os;
-      os << left_of_symbol << symbol_str << right_of_symbol;
-      return os.str();
-    }
-  }
-  return string(msg_str);
-}
-
-// By default skip the first frame because
-// that belongs to ~LogMessageFatal
-inline std::string StackTrace(
-    size_t start_frame = 1,
-    const size_t stack_size = DMLC_LOG_STACK_TRACE_SIZE) {
-  using std::string;
-  std::ostringstream stacktrace_os;
-  std::vector<void*> stack(stack_size);
-  int nframes = backtrace(stack.data(), static_cast<int>(stack_size));
-  if (start_frame < static_cast<size_t>(nframes)) {
-    stacktrace_os << "Stack trace:\n";
-  }
-  char **msgs = backtrace_symbols(stack.data(), nframes);
-  if (msgs != nullptr) {
-    for (int frameno = start_frame; frameno < nframes; ++frameno) {
-      string msg = dmlc::Demangle(msgs[frameno]);
-      stacktrace_os << "  [bt] (" << frameno - start_frame << ") " << msg << "\n";
-    }
-  }
-  free(msgs);
-  string stack_trace = stacktrace_os.str();
-  return stack_trace;
-}
-
-#else  // DMLC_LOG_STACK_TRACE is off
-
-inline size_t LogStackTraceLevel() {
-  return 0;
-}
-
-inline std::string demangle(char const* msg_str) {
-  return std::string();
-}
-
-inline std::string StackTrace(size_t start_frame = 1,
-                              const size_t stack_size = 0) {
-  return std::string("Stack trace not available when "
-  "DMLC_LOG_STACK_TRACE is disabled at compile time.");
-}
-
-#endif  // DMLC_LOG_STACK_TRACE
-}  // namespace dmlc
-
-#if DMLC_USE_GLOG
-#include <glog/logging.h>
-
-namespace dmlc {
-/*!
- * \brief optionally redirect to google's init log
- * \param argv0 The arguments.
- */
-inline void InitLogging(const char* argv0) {
-  google::InitGoogleLogging(argv0);
-}
-}  // namespace dmlc
-
-#else
-// use a light version of glog
-#include <assert.h>
-#include <iostream>
-#include <sstream>
-#include <ctime>
-
-#if defined(_MSC_VER)
-#pragma warning(disable : 4722)
-#pragma warning(disable : 4068)
-#endif
-
-namespace dmlc {
-inline void InitLogging(const char*) {
-  // DO NOTHING
-}
-
-// get debug option from env variable.
-inline bool DebugLoggingEnabled() {
-  static int state = 0;
-  if (state == 0) {
-    if (auto var = std::getenv("DMLC_LOG_DEBUG")) {
-      if (std::string(var) == "1") {
-        state = 1;
-      } else {
-        state = -1;
-      }
-    } else {
-      // by default hide debug logging.
-      state = -1;
-    }
-  }
-  return state == 1;
-}
-
-class LogCheckError {
- public:
-  LogCheckError() : str(nullptr) {}
-  explicit LogCheckError(const std::string& str_) : str(new std::string(str_)) {}
-  LogCheckError(const LogCheckError& other) = delete;
-  LogCheckError(LogCheckError&& other) : str(other.str) {
-    other.str = nullptr;
-  }
-  ~LogCheckError() { if (str != nullptr) delete str; }
-  operator bool() const { return str != nullptr; }
-  LogCheckError& operator=(const LogCheckError& other) = delete;
-  LogCheckError& operator=(LogCheckError&& other) = delete;
-  std::string* str;
-};
-
-#ifndef DMLC_GLOG_DEFINED
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-#define DEFINE_CHECK_FUNC(name, op)                               \
-  template <typename X, typename Y>                               \
-  inline LogCheckError LogCheck##name(const X& x, const Y& y) {   \
-    if (x op y) return LogCheckError();                           \
-    std::ostringstream os;                                        \
-    os << " (" << x << " vs. " << y << ") ";  /* CHECK_XX(x, y) requires x and y can be serialized to string. Use CHECK(x OP y) otherwise. NOLINT(*) */ \
-    return LogCheckError(os.str());                               \
-  }                                                               \
-  inline LogCheckError LogCheck##name(int x, int y) {             \
-    return LogCheck##name<int, int>(x, y);                        \
-  }
-#else
-#define DEFINE_CHECK_FUNC(name, op)                               \
-  template <typename X, typename Y>                               \
-  inline LogCheckError LogCheck##name(const X& x, const Y& y) {   \
-    if (x op y) return LogCheckError();                           \
-    return LogCheckError("Error.");                               \
-  }                                                               \
-  inline LogCheckError LogCheck##name(int x, int y) {             \
-    return LogCheck##name<int, int>(x, y);                        \
-  }
-#endif
-
-#define CHECK_BINARY_OP(name, op, x, y)                               \
-  if (dmlc::LogCheckError _check_err = dmlc::LogCheck##name(x, y))    \
-    dmlc::LogMessageFatal(__FILE__, __LINE__).stream()                \
-      << "Check failed: " << #x " " #op " " #y << *(_check_err.str) << ": "
-
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wsign-compare"
-DEFINE_CHECK_FUNC(_LT, <)
-DEFINE_CHECK_FUNC(_GT, >)
-DEFINE_CHECK_FUNC(_LE, <=)
-DEFINE_CHECK_FUNC(_GE, >=)
-DEFINE_CHECK_FUNC(_EQ, ==)
-DEFINE_CHECK_FUNC(_NE, !=)
-#pragma GCC diagnostic pop
-
-// Always-on checking
-#define CHECK(x)                                           \
-  if (!(x))                                                \
-    dmlc::LogMessageFatal(__FILE__, __LINE__).stream()     \
-      << "Check failed: " #x << ": "
-#define CHECK_LT(x, y) CHECK_BINARY_OP(_LT, <, x, y)
-#define CHECK_GT(x, y) CHECK_BINARY_OP(_GT, >, x, y)
-#define CHECK_LE(x, y) CHECK_BINARY_OP(_LE, <=, x, y)
-#define CHECK_GE(x, y) CHECK_BINARY_OP(_GE, >=, x, y)
-#define CHECK_EQ(x, y) CHECK_BINARY_OP(_EQ, ==, x, y)
-#define CHECK_NE(x, y) CHECK_BINARY_OP(_NE, !=, x, y)
-#define CHECK_NOTNULL(x) \
-  ((x) == NULL ? dmlc::LogMessageFatal(__FILE__, __LINE__).stream() << "Check  notnull: "  #x << ' ', (x) : (x)) // NOLINT(*)
-
-// Debug-only checking.
-#if DMLC_LOG_DEBUG
-#define DCHECK(x) \
-  while (false) CHECK(x)
-#define DCHECK_LT(x, y) \
-  while (false) CHECK((x) < (y))
-#define DCHECK_GT(x, y) \
-  while (false) CHECK((x) > (y))
-#define DCHECK_LE(x, y) \
-  while (false) CHECK((x) <= (y))
-#define DCHECK_GE(x, y) \
-  while (false) CHECK((x) >= (y))
-#define DCHECK_EQ(x, y) \
-  while (false) CHECK((x) == (y))
-#define DCHECK_NE(x, y) \
-  while (false) CHECK((x) != (y))
-#else
-#define DCHECK(x) CHECK(x)
-#define DCHECK_LT(x, y) CHECK((x) < (y))
-#define DCHECK_GT(x, y) CHECK((x) > (y))
-#define DCHECK_LE(x, y) CHECK((x) <= (y))
-#define DCHECK_GE(x, y) CHECK((x) >= (y))
-#define DCHECK_EQ(x, y) CHECK((x) == (y))
-#define DCHECK_NE(x, y) CHECK((x) != (y))
-#endif  // DMLC_LOG_DEBUG
-
-#if DMLC_LOG_CUSTOMIZE
-#define LOG_INFO dmlc::CustomLogMessage(__FILE__, __LINE__)
-#else
-#define LOG_INFO dmlc::LogMessage(__FILE__, __LINE__)
-#endif
-#define LOG_ERROR LOG_INFO
-#define LOG_WARNING LOG_INFO
-#define LOG_FATAL dmlc::LogMessageFatal(__FILE__, __LINE__)
-#define LOG_QFATAL LOG_FATAL
-
-// Poor man version of VLOG
-#define VLOG(x) LOG_INFO.stream()
-
-#define LOG(severity) LOG_##severity.stream()
-#define LG LOG_INFO.stream()
-#define LOG_IF(severity, condition) \
-  !(condition) ? (void)0 : dmlc::LogMessageVoidify() & LOG(severity)
-
-#if DMLC_LOG_DEBUG
-
-#define LOG_DFATAL LOG_FATAL
-#define DFATAL FATAL
-#define DLOG(severity) LOG_IF(severity, ::dmlc::DebugLoggingEnabled())
-#define DLOG_IF(severity, condition) LOG_IF(severity, ::dmlc::DebugLoggingEnabled() && (condition))
-
-#else
-
-#define LOG_DFATAL LOG_ERROR
-#define DFATAL ERROR
-#define DLOG(severity) true ? (void)0 : dmlc::LogMessageVoidify() & LOG(severity)
-#define DLOG_IF(severity, condition) \
-  (true || !(condition)) ? (void)0 : dmlc::LogMessageVoidify() & LOG(severity)
-#endif
-
-// Poor man version of LOG_EVERY_N
-#define LOG_EVERY_N(severity, n) LOG(severity)
-
-#endif  // DMLC_GLOG_DEFINED
-
-class DateLogger {
- public:
-  DateLogger() {
-#if defined(_MSC_VER)
-    _tzset();
-#endif
-  }
-  const char* HumanDate() {
-#ifndef _LIBCPP_SGX_CONFIG
-#if defined(_MSC_VER)
-    _strtime_s(buffer_, sizeof(buffer_));
-#else
-    time_t time_value = time(NULL);
-    struct tm *pnow;
-#if !defined(_WIN32)
-    struct tm now;
-    pnow = localtime_r(&time_value, &now);
-#else
-    pnow = localtime(&time_value);  // NOLINT(*)
-#endif
-    snprintf(buffer_, sizeof(buffer_), "%02d:%02d:%02d",
-             pnow->tm_hour, pnow->tm_min, pnow->tm_sec);
-#endif
-#endif  // _LIBCPP_SGX_CONFIG
-    return buffer_;
-  }
-
- private:
-  char buffer_[9];
-};
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-class LogMessage {
- public:
-  LogMessage(const char* file, int line)
-      :
-#ifdef __ANDROID__
-        log_stream_(std::cout)
-#else
-        log_stream_(std::cerr)
-#endif
-  {
-    log_stream_ << "[" << pretty_date_.HumanDate() << "] " << file << ":"
-                << line << ": ";
-  }
-  ~LogMessage() { log_stream_ << '\n'; }
-  std::ostream& stream() { return log_stream_; }
-
- protected:
-  std::ostream& log_stream_;
-
- private:
-  DateLogger pretty_date_;
-  LogMessage(const LogMessage&);
-  void operator=(const LogMessage&);
-};
-
-// customized logger that can allow user to define where to log the message.
-class CustomLogMessage {
- public:
-  CustomLogMessage(const char* file, int line) {
-    log_stream_ << "[" << DateLogger().HumanDate() << "] " << file << ":"
-                << line << ": ";
-  }
-  ~CustomLogMessage() {
-    Log(log_stream_.str());
-  }
-  std::ostream& stream() { return log_stream_; }
-  /*!
-   * \brief customized logging of the message.
-   * This function won't be implemented by libdmlc
-   * \param msg The message to be logged.
-   */
-  static void Log(const std::string& msg);
-
- private:
-  std::ostringstream log_stream_;
-};
-#else
-class DummyOStream {
- public:
-  template <typename T>
-  DummyOStream& operator<<(T _) { return *this; }
-  inline std::string str() { return ""; }
-};
-class LogMessage {
- public:
-  LogMessage(const char* file, int line) : log_stream_() {}
-  DummyOStream& stream() { return log_stream_; }
-
- protected:
-  DummyOStream log_stream_;
-
- private:
-  LogMessage(const LogMessage&);
-  void operator=(const LogMessage&);
-};
-#endif
-
-
-#if defined(_LIBCPP_SGX_NO_IOSTREAMS)
-class LogMessageFatal : public LogMessage {
- public:
-  LogMessageFatal(const char* file, int line) : LogMessage(file, line) {}
-  ~LogMessageFatal() {
-    abort();
-  }
- private:
-  LogMessageFatal(const LogMessageFatal&);
-  void operator=(const LogMessageFatal&);
-};
-#elif DMLC_LOG_FATAL_THROW == 0
-class LogMessageFatal : public LogMessage {
- public:
-  LogMessageFatal(const char* file, int line) : LogMessage(file, line) {}
-  ~LogMessageFatal() {
-    log_stream_ << "\n" << StackTrace(1, LogStackTraceLevel()) << "\n";
-    abort();
-  }
-
- private:
-  LogMessageFatal(const LogMessageFatal&);
-  void operator=(const LogMessageFatal&);
-};
-#else
-class LogMessageFatal {
- public:
-  LogMessageFatal(const char* file, int line) {
-    log_stream_ << "[" << pretty_date_.HumanDate() << "] " << file << ":"
-                << line << ": ";
-  }
-  std::ostringstream &stream() { return log_stream_; }
-  ~LogMessageFatal() DMLC_THROW_EXCEPTION {
-#if DMLC_LOG_STACK_TRACE
-    log_stream_ << "\n" << StackTrace(1, LogStackTraceLevel()) << "\n";
-#endif
-
-    // throwing out of destructor is evil
-    // hopefully we can do it here
-    // also log the message before throw
-#if DMLC_LOG_BEFORE_THROW
-    LOG(ERROR) << log_stream_.str();
-#endif
-    throw Error(log_stream_.str());
-  }
-
- private:
-  std::ostringstream log_stream_;
-  DateLogger pretty_date_;
-  LogMessageFatal(const LogMessageFatal&);
-  void operator=(const LogMessageFatal&);
-};
-#endif
-
-// This class is used to explicitly ignore values in the conditional
-// logging macros.  This avoids compiler warnings like "value computed
-// is not used" and "statement has no effect".
-class LogMessageVoidify {
- public:
-  LogMessageVoidify() {}
-  // This has to be an operator with a precedence lower than << but
-  // higher than "?:". See its usage.
-#if !defined(_LIBCPP_SGX_NO_IOSTREAMS)
-  void operator&(std::ostream&) {}
-#endif
-};
-
-}  // namespace dmlc
-
-#endif
-#endif  // DMLC_LOGGING_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/lua.h b/ml-xgboost/dmlc-core/include/dmlc/lua.h
deleted file mode 100644
index 13aa7b7..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/lua.h
+++ /dev/null
@@ -1,739 +0,0 @@
-/*!
- *  Copyright (c) 2016 by Contributors
- * \file lua.h
- * \brief C++11 header only interface to easily interact with Lua and Torch.
- *  This code is evolved from torch plugin code for MXNet.
- *
- *  This header will require Torch and Lua to be presented, do not include.
- *
- * \author Junyuan Xie, Min Lin, Tianqi Chen
- *
- * \code
- *
- * // Example code to use the lua module.
- * dmlc::LuaState* lua = dmlc::LuaState::ThreadLocalState();
- * // vectors converts automatically to lua table.
- * auto tbl = lua->Convert(std::vector<int>{1,2,3});
- * // use eval to get lua reference, this is a function
- * auto print = lua->Eval("return function(x) print(x) end");
- * // lua function can be directly called from c++, arguments are converted.
- * print(100);
- *
- * // set field in the table.
- * tbl.SetField("square", lua->Eval("return function(x) x*x end"));
- * // call the function, covert back to C++ values.
- * int x = tbl["square"](100).Get<int>();
- *
- * \endcode
- */
-#ifndef DMLC_LUA_H_
-#define DMLC_LUA_H_
-
-extern "C" {
-#include <lua.h>
-#include <luaT.h>
-#include <lualib.h>
-}
-
-#include <string>
-#include <stdexcept>
-#include <tuple>
-#include <mutex>
-#include <memory>
-#include <vector>
-#include <utility>
-#include <algorithm>
-#include <unordered_map>
-#include <type_traits>
-
-#include "./base.h"
-#include "./logging.h"
-#include "./thread_local.h"
-
-namespace dmlc {
-
-// forward declare torch state
-class LuaState;
-
-namespace lua_stack {
-template<typename T>
-struct Handler;
-};
-
-/*! \brief an reference to lua object */
-class LuaRef {
- public:
-  /*! \brief construct an nil ref */
-  LuaRef() = default;
-  /*!
-   * \brief move constructor from another LuaRef
-   * \param other The other LuaRef to be moved
-   */
-  inline LuaRef(LuaRef&& other);  // NOLINT(*)
-  /*!
-   * \brief copy constructor
-   * \param other The other LuaRef to be copied
-   */
-  inline LuaRef(const LuaRef& other);  // NOLINT(*)
-  /*!
-   * \brief assign operator from other
-   * \param other The other LuaRef to be copy or moved.
-   * \return self
-   */
-  inline LuaRef& operator=(LuaRef&& other);
-  /*!
-   * \brief assign operator from other
-   * \param other The other LuaRef to be copy or moved.
-   * \return self
-   */
-  inline LuaRef& operator=(const LuaRef& other);
-  /*! \brief destructor */
-  inline ~LuaRef();
-  /*!
-   * \brief swap content with another ref
-   * \param other another LuaRef to be swaped.
-   */
-  inline void swap(LuaRef& other); // NOLINT(*)
-  /*!
-   * \brief Get content out as type T.
-   *
-   * \tparam T the type to be fetched.
-   * \return the corresponding c type.
-   */
-  template<typename T>
-  inline T Get() const;
-  /*!
-   * \brief Get user data pointer from LuaRef
-   *
-   *  CAREFUL when getting userdata(e.g. pointer to Tensor's storage) from LuaRef.
-   *  Remember they are managed by Lua, and can get deleted when all the
-   *  LuaRef to the userdata destructs. A good practice is always use a LuaRef to keep
-   *  the userdata alive when you need them from C++ side.
-   *
-   * \tparam T the type of pointer to be fetched.
-   * \return the corresponding c type.
-   */
-  template<typename T>
-  inline T* GetUDataPtr() const;
-  /*! \return whether the value is nil */
-  inline bool is_nil() const;
-  /*!
-   * \brief invoke the LuaRef as function
-   * \param args Arguments to be passed.
-   * \tparam Args arguments to be passed.
-   * \return The first return value.
-   */
-  template<typename... Args>
-  inline LuaRef operator()(Args&& ...args) const;
-  /*!
-   * \brief Get field from the lua table.
-   *  The reference must be a table
-   * \param key The key to the table
-   * \return a new ref to the corresponding field.
-   */
-  inline LuaRef operator[](const std::string& key) const;
-  /*!
-   * \brief Get field from the lua array
-   *  The reference must be a array
-   * \param index The index to the array,
-   *  Note: the index convention follows lua table, starts from 1
-   * \return a new ref to the corresponding field.
-   */
-  inline LuaRef operator[](size_t index) const;
-  /*!
-   * \brief Set field of lua table.
-   *  The reference must be a table
-   * \param key The key to the table
-   * \param value Lua convertable value to be setted.
-   * \return self.
-   */
-  template<typename T>
-  inline LuaRef& SetField(const std::string& key, const T& value);  // NOLINT(*)
-  /*!
-   * \brief Set LuaRef to the value on top of the stack.
-   *  This state must be nil.
-   *  This is API used by developer.
-   *
-   * \param s the corresponding lua state.
-   */
-  inline void SetByPopStack_(LuaState* s);
-
- private:
-  // friend with luastate
-  friend struct lua_stack::Handler<LuaRef>;
-  friend class LuaState;
-  friend std::ostream &operator<<(std::ostream &os, const LuaRef &r);
-  /*! \brief pointer to the state */
-  LuaState* state_{nullptr};
-  /*! \brief reference index */
-  int ref_;
-};
-
-/*! \brief A Lua state */
-class LuaState {
- public:
-  /*! \brief options to be provided in lua state */
-  enum Option {
-    kNoThreadProtect,
-    kThreadLocal,
-    kLocking,
-  };
-  /*! \brief destructor */
-  inline ~LuaState();
-  /*!
-   * \brief evaluate a piece of lua code, return the first result.
-   * \param lua_code Lua code
-   * \return A LuaRef object of the first returned result,
-   *  Can be nil if the code did not return LuaRefthing.
-   */
-  inline LuaRef Eval(const char* lua_code);
-  /*!
-   * \brief evaluate a piece of lua code, return the first result.
-   * \param lua_code Lua code
-   * \return A LuaRef object of the first returned result,
-   *  Can be nil if the code did not return anything.
-   */
-  inline LuaRef Eval(const std::string& lua_code) {
-    return this->Eval(lua_code.c_str());
-  }
-  /*!
-   * \brief convert a C++ type to lua type
-   * \param value The data to be converted.
-   *  vector, map will be converted to table.
-   * \return a converted value.
-   * \tparam T the type to be converted.
-   */
-  template<typename T>
-  inline LuaRef Convert(const T& value);
-  /*!
-   * \brief get global field from the state
-   * \param key The key to the global field.
-   * \return The global field value.
-   */
-  inline LuaRef operator[](const std::string& key);
-  /*!
-   * \brief Set the value to the global table.
-   * \param key The key of the global field.
-   * \param value The value to the set.
-   */
-  inline void SetGlobalField(const std::string& key, const LuaRef& value);
-  /*!
-   *  Get a thread local version of lua state.
-   *  The LuaState runs in thread local mode,
-   *  all the LuaRef can only be run on the current thread.
-   *  This is the recommended behavior when invoking Lua.
-   *
-   * \return a threadlocal version of lua state.
-   */
-  static inline LuaState* ThreadLocalState();
-  /*!
-   * Create a new lua state.
-   * \note It is highly recommended to use ThreadLocalState instead.
-   *
-   *  Most Lua program assumes it only runs from the same thread.
-   *  Some Lua code that wraps C library(e.g. Torch) could rely
-   *  on thread_local storage to store global state such as random number generator.
-   *  This means if the code is invoked by another thread, the thread_local
-   *  might become inavailable, depending on the implementation.
-   *
-   *  If the global state is stored only in Lua's global table, then
-   *  it is safe to use kLocking mode and call the code from multiple thread.
-   *  Never-the-less, using ThreadLocalState removes the need to lock,
-   *  and is the desirable usecase in most times.
-   *
-   * \sa ThreadLocalState
-   * \param option The option to use the state.
-   * \return a newly created lua state
-   */
-  static inline LuaState* Create_(Option option);
-
-  /*!
-   * \brief protected run f, this is used by API developers.
-   *  always call this to access lua state
-   *  f must not destruct LuaRef, or access the mutex
-   *
-   * \param f the function to be called.
-   * \tparam F the function to be called, signiture (lua_State *L)
-   */
-  template<typename F>
-  inline void PRun_(F f);
-  /*!
-   * \param L the other lua state.
-   * \return if the internal lua state is same as L
-   */
-  inline bool SameLuaState(lua_State *L) const {
-    return L_ == L;
-  }
-
- protected:
-  struct StackReset;
-  friend class LuaRef;
-  friend struct ThreadLocalStore<LuaState>;
-  /*!
-   * \brief constructor
-   */
-  inline LuaState();
-
-  /*! \brief internal option, default to thread local */
-  Option option_{kThreadLocal};
-  /*! \brief internal lua state */
-  lua_State* L_;
-  /*! \brief internal lock about the state */
-  std::mutex mutex_;
-};
-
-// implementations after this line
-//! \cond Doxygen_Suppress
-/*! \brief macro to check error during lua call */
-#define LUA_CALL(x)                                                     \
-  if ((x)) {                                                            \
-    LOG(FATAL) << "Lua Call Error:" <<  lua_tostring(L, -1);            \
-  }
-
-/*!
- * \brief namespace to handle conversions between lua and c++
- *  User can provide an specialization of dmlc::lua_stack::Handler
- *  to allow customized c++ data types to interact with Lua.
- *
- *  By default basic data types, composition of vector, and unordered_map is supported.
- *  The conversion rules
- *  - basic types(string, int, float) to corresponding lua types.
- *  - unordered_map to Lua table.
- *  - vector to lua indexed table.
- */
-namespace lua_stack {
-inline int lua_abs_index(lua_State* L, int index) {
-  if (index > 0 || index <= LUA_REGISTRYINDEX) return index;
-  return lua_gettop(L) + index + 1;
-}
-
-template<typename T>
-struct Handler;
-
-template<typename T>
-struct NumberHandler {
-  static inline T Get(lua_State* L, int index, LuaState* s) {
-    CHECK_EQ(lua_type(L, index), LUA_TNUMBER)
-        << "Attempt to get number but type is \'"
-        << lua_typename(L, lua_type(L, index)) << '\'';
-    if (std::is_integral<T>::value) {
-      return static_cast<T>(lua_tointeger(L, index));
-    } else {
-      return static_cast<T>(lua_tonumber(L, index));
-    }
-  }
-  static inline void Push(lua_State* L, const T& v) {
-    if (std::is_integral<T>::value) {
-      lua_pushinteger(L, static_cast<lua_Integer>(v));
-    } else {
-      lua_pushnumber(L, static_cast<lua_Number>(v));
-    }
-  }
-};
-
-template<typename ContainerType>
-struct MapHandler {
-  using K = typename ContainerType::key_type;
-  using V = typename ContainerType::mapped_type;
-  static inline ContainerType Get(lua_State* L, int index, LuaState* s) {
-    ContainerType ret;
-    CHECK(lua_istable(L, index))
-        << "Expected a table but get "
-        << lua_typename(L, lua_type(L, index)) << '\'';
-    int tid = lua_abs_index(L, index);
-    lua_pushnil(L);
-    while (lua_next(L, -2)) {
-      ret[Handler<K>::Get(L, -2, s)] = Handler<V>::Pop(L, -1, s);
-      lua_pop(L, 1);
-    }
-    lua_settop(L, tid);
-    return ret;
-  }
-  static inline void Push(lua_State* L, const ContainerType& v) {
-    lua_createtable(L, v.size(), 0);
-    for (const auto& kv : v) {
-      Handler<K>::Push(L, kv.first);
-      Handler<V>::Push(L, kv.second);
-      lua_settable(L, -3);
-    }
-  }
-};
-
-struct UndefinedHandler {
-};
-
-template<typename T>
-struct Handler
-    : public std::conditional<std::is_arithmetic<T>::value,
-                              NumberHandler<T>,
-                              UndefinedHandler>::type {
-};
-
-template<>
-struct Handler<std::string> {
-  static inline std::string Get(lua_State* L, int index, LuaState* s) {
-    CHECK_EQ(lua_type(L, index), LUA_TSTRING);
-    return std::string(lua_tostring(L, index));
-  }
-  static inline void Push(lua_State* L, const std::string& v) {
-    lua_pushstring(L, v.c_str());
-  }
-};
-
-template<typename T>
-struct Handler<std::vector<T> > {
-  static inline std::vector<T> Get(lua_State* L, int index, LuaState* s) {
-    std::vector<T> ret;
-    CHECK(lua_istable(L, index))
-        << "Expected a table but get "
-        << lua_typename(L, lua_type(L, index)) << '\'';
-    int tid = lua_abs_index(L, index);
-    lua_pushnil(L);
-    while (lua_next(L, tid)) {
-      CHECK_EQ(Handler<size_t>::Get(L, -2, s), ret.size() + 1)
-          << "Target table is not an array";
-      ret.push_back(Handler<T>::Get(L, -1, s));
-      lua_pop(L, 1);
-    }
-    lua_settop(L, tid);
-    return ret;
-  }
-  static inline void Push(lua_State* L, const std::vector<T>& v) {
-    lua_createtable(L, v.size(), 0);
-    for (size_t i = 0; i < v.size(); ++i) {
-      Handler<T>::Push(L, v[i]);
-      lua_rawseti(L, -2, i + 1);
-    }
-  }
-};
-
-template<typename K, typename V>
-struct Handler<std::unordered_map<K, V> >
-    : public MapHandler<std::unordered_map<K, V> > {
-};
-
-template<>
-struct Handler<LuaRef> {
-  static inline LuaRef Get(lua_State* L, int index, LuaState* s) {
-    LuaRef ret;
-    lua_pushvalue(L, index);
-    ret.SetByPopStack_(s);
-    return ret;
-  }
-
-  static inline void Push(lua_State* L, const LuaRef& v) {
-    if (v.is_nil()) {
-      lua_pushnil(L);
-    } else {
-      CHECK(v.state_->SameLuaState(L))
-          << "Cannot pass LuaRef on a different LuaState's function";
-      lua_rawgeti(L, LUA_REGISTRYINDEX, v.ref_);
-    }
-  }
-};
-
-template<>
-struct Handler<std::nullptr_t> {
-  static inline LuaRef Get(lua_State* L, int index, LuaState* s) {
-    LOG(FATAL) << "not supported";
-    return LuaRef();
-  }
-  static inline void Push(lua_State* L, const std::nullptr_t& v) {
-    lua_pushnil(L);
-  }
-};
-
-// generic functor to call push the arguments.
-struct PushArg {
-  lua_State* L;
-  template<typename T>
-  inline void operator()(const T& v) const {
-    Handler<T>::Push(L, v);
-  }
-};
-
-}  // namespace lua_stack
-
-inline LuaState::LuaState() {
-  L_ = luaL_newstate();
-  CHECK(L_ != nullptr)
-      << "Failed to create new lua state";
-  luaL_openlibs(L_);
-}
-
-inline LuaState::~LuaState() {
-  if (option_ != kThreadLocal && L_ != nullptr) {
-    // never close threadlocal, for save destruction.
-    lua_close(L_);
-  }
-}
-
-inline LuaState* LuaState::Create_(Option opt) {
-  LuaState* s = new LuaState();
-  s->option_ = opt;
-  CHECK_NE(opt, kThreadLocal)
-      << "use LuaState::ThreadLocalState() to get the thread local state";
-  return s;
-}
-
-inline void LuaRef::SetByPopStack_(LuaState* s) {
-  CHECK(state_ == nullptr);
-  lua_State* L = s->L_;
-  if (!lua_isnil(L, -1)) {
-    ref_ = lua_ref(L, LUA_REGISTRYINDEX);
-    state_ = s;
-  } else {
-    lua_pop(L, 1);
-  }
-}
-
-// RAII guard to reset stack
-struct LuaState::StackReset {
-  lua_State* L;
-  int top;
-  ~StackReset() {
-    lua_settop(L, top);
-  }
-};
-
-template<typename F>
-inline void LuaState::PRun_(F f) {
-  if (option_ != kLocking) {
-    StackReset reset{L_, lua_gettop(L_)};
-    if (option_ == kThreadLocal) {
-      CHECK_EQ(ThreadLocalState(), this)
-          << "Invoke lua from a different thread in ThreadLocal mode.";
-    }
-    f(L_);
-    CHECK_EQ(reset.top, lua_gettop(L_));
-  } else {
-    std::lock_guard<std::mutex> lock(mutex_);
-    StackReset reset{L_, lua_gettop(L_)};
-    f(L_);
-    CHECK_EQ(reset.top, lua_gettop(L_));
-  }
-}
-
-inline LuaState* LuaState::ThreadLocalState() {
-  return ThreadLocalStore<LuaState>::Get();
-}
-
-inline LuaRef LuaState::Eval(const char* lua_code) {
-  LuaRef ret;
-  this->PRun_([this, lua_code, &ret](lua_State* L) {
-      luaL_loadstring(L, lua_code);
-      CHECK_EQ(lua_pcall(L, 0, 1, 0), 0)
-          << "Lua call error: " << lua_tostring(L, -1) << '\n'
-          << "---------\n"
-          << lua_code
-          << "\n----------";
-      ret.SetByPopStack_(this);
-    });
-  return ret;
-}
-
-template<typename T>
-inline LuaRef LuaState::Convert(const T& value) {
-  LuaRef ret;
-  this->PRun_([this, &value, &ret](lua_State* L) {
-      lua_stack::Handler<T>::Push(L, value);
-      ret.SetByPopStack_(this);
-    });
-  return ret;
-}
-
-inline LuaRef LuaState::operator[](const std::string& key) {
-  LuaRef ret;
-  this->PRun_([this, &key, &ret](lua_State* L) {
-      lua_getglobal(L, key.c_str());
-      ret.SetByPopStack_(this);
-    });
-  return ret;
-}
-
-inline void LuaState::SetGlobalField(
-    const std::string& key, const LuaRef& value) {
-  this->PRun_([this, &key, &value](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, value.ref_);
-      lua_setglobal(L, key.c_str());
-    });
-}
-
-inline LuaRef::LuaRef(const LuaRef& other) {
-  if (other.state_ != nullptr) {
-    state_ = other.state_;
-    state_->PRun_([this, &other](lua_State* L) {
-        lua_rawgeti(L, LUA_REGISTRYINDEX, other.ref_);
-        ref_ = luaL_ref(L, LUA_REGISTRYINDEX);
-      });
-  }
-}
-
-inline LuaRef::LuaRef(LuaRef&& other) {
-  ref_ = other.ref_;
-  state_ = other.state_;
-  other.state_ = nullptr;
-}
-
-inline LuaRef& LuaRef::operator=(LuaRef&& other) {
-  LuaRef(std::move(other)).swap(*this);
-  return *this;
-}
-
-inline LuaRef& LuaRef::operator=(const LuaRef& other) {
-  LuaRef(other).swap(*this);
-  return *this;
-}
-
-inline void LuaRef::swap(LuaRef& other) { // NOLINT(*)
-  std::swap(state_, other.state_);
-  std::swap(ref_, other.ref_);
-}
-
-inline LuaRef::~LuaRef() {
-  if (state_ != nullptr) {
-    state_->PRun_([this](lua_State* L) {
-        luaL_unref(L, LUA_REGISTRYINDEX, ref_);
-      });
-  }
-}
-
-inline bool LuaRef::is_nil() const {
-  return state_ == nullptr;
-}
-
-std::ostream &operator<<(std::ostream &os, const LuaRef &r) {
-  if (!r.is_nil()) {
-    r.state_->PRun_([&os, &r](lua_State* L) {
-        lua_rawgeti(L, LUA_REGISTRYINDEX, r.ref_);
-        int type = lua_type(L, -1);
-        switch (type) {
-          case LUA_TSTRING:
-            os << "lua_string:'" << lua_tostring(L, -1) << "'"; break;
-          case LUA_TBOOLEAN:
-            os << "lua_bool:" << (lua_toboolean(L, -1) ? "true" : "false"); break;
-          case LUA_TNUMBER:
-            os << "lua_number:" << lua_tonumber(L, -1); break;
-          default:
-            os << "lua[ref=" << r.ref_ << ']' << lua_typename(L, type); break;
-        }
-        lua_pop(L, 1);
-      });
-  } else {
-    os << "lua_nil";
-  }
-  return os;
-}
-
-template<typename T>
-inline T LuaRef::Get() const {
-  CHECK(state_ != nullptr) << "Get:: LuaRef is nil";
-  T ret;
-  state_->PRun_([&ret, this](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, ref_);
-      ret = lua_stack::Handler<T>::Get(L, -1, state_);
-      lua_pop(L, 1);
-    });
-  return ret;
-}
-
-template<typename T>
-inline T* LuaRef::GetUDataPtr() const {
-  CHECK(state_ != nullptr) << "Get:: LuaRef is nil";
-  T* ret;
-  state_->PRun_([&ret, this](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, ref_);
-      ret = reinterpret_cast<T*>(lua_touserdata(L, -1));
-      lua_pop(L, 1);
-    });
-  return ret;
-}
-
-// helper function to dispatch varg foreach
-template<bool stop, std::size_t I, typename F, typename ...Args>
-struct for_each_dispatcher_ {
-  static inline void run(const std::tuple<Args...>& args, F f) {
-    f(std::get<I>(args));
-    for_each_dispatcher_<(I + 1) == sizeof...(Args), (I+1), F, Args...>::run(args, f);
-  }
-};
-// helper function to run foreach
-template<std::size_t I, typename F, typename ...Args>
-struct for_each_dispatcher_<true, I, F, Args...>  {
-  static inline void run(const std::tuple<Args...>& args, F f) {
-  }
-};
-
-// template function to iterate over tuples
-template<typename F, typename ...Args>
-inline void for_each(const std::tuple<Args...>& args, F f) {
-  for_each_dispatcher_<sizeof...(Args) == 0, 0, F, Args...>::run(args, f);
-}
-
-template<typename... Args>
-inline LuaRef LuaRef::operator()(Args&& ...args) const {
-  CHECK(state_ != nullptr) << "LuaRef is nil";
-  auto targ = std::make_tuple(std::forward<Args>(args)...);
-  size_t nargs = sizeof...(Args);
-  LuaRef ret;
-  state_->PRun_([this, nargs, &targ, &ret](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, this->ref_);
-      CHECK(lua_isfunction(L, -1))
-          << "Expect to invoke a function but type='"
-          << lua_typename(L, lua_type(L, -1)) << '\'';
-      for_each(targ, lua_stack::PushArg{L});
-      LUA_CALL(lua_pcall(L, nargs, 1, 0));
-      ret.SetByPopStack_(state_);
-    });
-  return ret;
-}
-
-template<typename T>
-inline LuaRef& LuaRef::SetField(const std::string& key, const T& value) {  // NOLINT(*)
-  CHECK(state_ != nullptr) << "LuaRef is nil";
-  state_->PRun_([this, &key, &value](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, this->ref_);
-      CHECK(lua_istable(L, -1))
-          << "Expect a table but type='"
-          << lua_typename(L, lua_type(L, -1)) << '\'';
-      lua_stack::Handler<T>::Push(L, value);
-      lua_setfield(L, -2, key.c_str());
-      lua_pop(L, 1);
-    });
-  return *this;
-}
-
-inline LuaRef LuaRef::operator[](const std::string& key) const {
-  CHECK(state_ != nullptr) << "LuaRef is nil";
-  LuaRef ret;
-  state_->PRun_([this, &key, &ret](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, this->ref_);
-      CHECK(lua_istable(L, -1))
-          << "Expect a table but type='"
-          << lua_typename(L, lua_type(L, -1)) << '\'';
-      lua_getfield(L, -1, key.c_str());
-      ret.SetByPopStack_(state_);
-      lua_pop(L, 1);
-    });
-  return ret;
-}
-
-inline LuaRef LuaRef::operator[](size_t index) const {
-  CHECK(state_ != nullptr) << "LuaRef is nil";
-  LuaRef ret;
-  state_->PRun_([this, index, &ret](lua_State* L) {
-      lua_rawgeti(L, LUA_REGISTRYINDEX, this->ref_);
-      CHECK(lua_istable(L, -1))
-          << "Expect a table but type='"
-          << lua_typename(L, lua_type(L, -1)) << '\'';
-      lua_rawgeti(L, -1, index);
-      ret.SetByPopStack_(state_);
-      lua_pop(L, 1);
-    });
-  return ret;
-}
-
-//! \endcond
-}  // namespace dmlc
-
-#endif  // DMLC_LUA_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/memory.h b/ml-xgboost/dmlc-core/include/dmlc/memory.h
deleted file mode 100644
index 0080112..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/memory.h
+++ /dev/null
@@ -1,263 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file memory.h
- * \brief Additional memory hanlding utilities.
- */
-#ifndef DMLC_MEMORY_H_
-#define DMLC_MEMORY_H_
-
-#include <vector>
-#include <memory>
-#include <utility>
-#include "./base.h"
-#include "./logging.h"
-#include "./thread_local.h"
-
-namespace dmlc {
-
-/*!
- * \brief A memory pool that allocate memory of fixed size and alignment.
- * \tparam size The size of each piece.
- * \tparam align The alignment requirement of the memory.
- */
-template<size_t size, size_t align>
-class MemoryPool {
- public:
-  /*! \brief constructor */
-  MemoryPool() {
-    static_assert(align % alignof(LinkedList) == 0,
-                  "alignment requirement failed.");
-    curr_page_.reset(new Page());
-  }
-  /*! \brief allocate a new memory of size */
-  inline void* allocate() {
-    if (head_ != nullptr) {
-      LinkedList* ret = head_;
-      head_ = head_->next;
-      return ret;
-    } else {
-      if (page_ptr_ < kPageSize) {
-        return &(curr_page_->data[page_ptr_++]);
-      } else {
-        allocated_.push_back(std::move(curr_page_));
-        curr_page_.reset(new Page());
-        page_ptr_ = 1;
-        return &(curr_page_->data[0]);
-      }
-    }
-  }
-  /*!
-   * \brief deallocate a piece of memory
-   * \param p The pointer to the memory to be de-allocated.
-   */
-  inline void deallocate(void* p) {
-    LinkedList* ptr = static_cast<LinkedList*>(p);
-    ptr->next = head_;
-    head_ = ptr;
-  }
-
- private:
-  // page size of each member
-  static const int kPageSize = ((1 << 22) / size);
-  // page to be requested.
-  struct Page {
-    typename std::aligned_storage<size, align>::type data[kPageSize];
-  };
-  // internal linked list structure.
-  struct LinkedList {
-    LinkedList* next{nullptr};
-  };
-  // head of free list
-  LinkedList* head_{nullptr};
-  // current free page
-  std::unique_ptr<Page> curr_page_;
-  // pointer to the current free page position.
-  size_t page_ptr_{0};
-  // allocated pages.
-  std::vector<std::unique_ptr<Page> > allocated_;
-};
-
-
-/*!
- * \brief A thread local allocator that get memory from a threadlocal memory pool.
- * This is suitable to allocate objects that do not cross thread.
- * \tparam T the type of the data to be allocated.
- */
-template<typename T>
-class ThreadlocalAllocator {
- public:
-  /*! \brief pointer type */
-  typedef T* pointer;
-  /*! \brief const pointer type */
-  typedef const T* const_ptr;
-  /*! \brief value type */
-  typedef T value_type;
-  /*! \brief default constructor */
-  ThreadlocalAllocator() {}
-  /*!
-   * \brief constructor from another allocator
-   * \param other another allocator
-   * \tparam U another type
-   */
-  template<typename U>
-  ThreadlocalAllocator(const ThreadlocalAllocator<U>& other) {}
-  /*!
-   * \brief allocate memory
-   * \param n number of blocks
-   * \return an uninitialized memory of type T.
-   */
-  inline T* allocate(size_t n) {
-    CHECK_EQ(n, 1);
-    typedef ThreadLocalStore<MemoryPool<sizeof(T), alignof(T)> > Store;
-    return static_cast<T*>(Store::Get()->allocate());
-  }
-  /*!
-   * \brief deallocate memory
-   * \param p a memory to be returned.
-   * \param n number of blocks
-   */
-  inline void deallocate(T* p, size_t n) {
-    CHECK_EQ(n, 1);
-    typedef ThreadLocalStore<MemoryPool<sizeof(T), alignof(T)> > Store;
-    Store::Get()->deallocate(p);
-  }
-};
-
-
-/*!
- * \brief a shared pointer like type that allocate object
- *   from a threadlocal object pool. This object is not thread-safe
- *   but can be faster than shared_ptr in certain usecases.
- * \tparam T the data type.
- */
-template<typename T>
-struct ThreadlocalSharedPtr {
- public:
-  /*! \brief default constructor */
-  ThreadlocalSharedPtr() : block_(nullptr) {}
-  /*!
-   * \brief constructor from nullptr
-   * \param other the nullptr type
-   */
-  ThreadlocalSharedPtr(std::nullptr_t other) : block_(nullptr) {}  // NOLINT(*)
-  /*!
-   * \brief copy constructor
-   * \param other another pointer.
-   */
-  ThreadlocalSharedPtr(const ThreadlocalSharedPtr<T>& other)
-      : block_(other.block_) {
-    IncRef(block_);
-  }
-  /*!
-   * \brief move constructor
-   * \param other another pointer.
-   */
-  ThreadlocalSharedPtr(ThreadlocalSharedPtr<T>&& other)
-      : block_(other.block_) {
-    other.block_ = nullptr;
-  }
-  /*!
-   * \brief destructor
-   */
-  ~ThreadlocalSharedPtr() {
-    DecRef(block_);
-  }
-  /*!
-   * \brief move assignment
-   * \param other another object to be assigned.
-   * \return self.
-   */
-  inline ThreadlocalSharedPtr<T>& operator=(ThreadlocalSharedPtr<T>&& other) {
-    DecRef(block_);
-    block_ = other.block_;
-    other.block_ = nullptr;
-    return *this;
-  }
-  /*!
-   * \brief copy assignment
-   * \param other another object to be assigned.
-   * \return self.
-   */
-  inline ThreadlocalSharedPtr<T> &operator=(const ThreadlocalSharedPtr<T>& other) {
-    DecRef(block_);
-    block_ = other.block_;
-    IncRef(block_);
-    return *this;
-  }
-  /*! \brief check if nullptr */
-  inline bool operator==(std::nullptr_t other) const {
-    return block_ == nullptr;
-  }
-  /*!
-   * \return get the pointer content.
-   */
-  inline T* get() const {
-    if (block_ == nullptr) return nullptr;
-    return reinterpret_cast<T*>(&(block_->data));
-  }
-  /*!
-   * \brief reset the pointer to nullptr.
-   */
-  inline void reset() {
-    DecRef(block_);
-    block_ = nullptr;
-  }
-  /*! \return if use_count == 1*/
-  inline bool unique() const {
-    if (block_ == nullptr) return false;
-    return block_->use_count_ == 1;
-  }
-  /*! \return dereference pointer */
-  inline T* operator*() const {
-    return reinterpret_cast<T*>(&(block_->data));
-  }
-  /*! \return dereference pointer */
-  inline T* operator->() const {
-    return reinterpret_cast<T*>(&(block_->data));
-  }
-  /*!
-   * \brief create a new space from threadlocal storage and return it.
-   * \tparam Args the arguments.
-   * \param args The input argument
-   * \return the allocated pointer.
-   */
-  template <typename... Args>
-  inline static ThreadlocalSharedPtr<T> Create(Args&&... args) {
-    ThreadlocalAllocator<RefBlock> arena;
-    ThreadlocalSharedPtr<T> p;
-    p.block_ = arena.allocate(1);
-    p.block_->use_count_ = 1;
-    new (&(p.block_->data)) T(std::forward<Args>(args)...);
-    return p;
-  }
-
- private:
-  // internal reference block
-  struct RefBlock {
-    typename std::aligned_storage<sizeof(T), alignof(T)>::type data;
-    unsigned use_count_;
-  };
-  // decrease ref counter
-  inline static void DecRef(RefBlock* block) {
-    if (block != nullptr) {
-      if (--block->use_count_ == 0) {
-        ThreadlocalAllocator<RefBlock> arena;
-        T* dptr = reinterpret_cast<T*>(&(block->data));
-        dptr->~T();
-        arena.deallocate(block, 1);
-      }
-    }
-  }
-  // increase ref counter
-  inline static void IncRef(RefBlock* block) {
-    if (block != nullptr) {
-      ++block->use_count_;
-    }
-  }
-  // internal block
-  RefBlock *block_;
-};
-
-}  // namespace dmlc
-
-#endif  // DMLC_MEMORY_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/memory_io.h b/ml-xgboost/dmlc-core/include/dmlc/memory_io.h
deleted file mode 100644
index 4e80758..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/memory_io.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file memory_io.h
- * \brief defines binary serialization class to serialize things into/from memory region.
- */
-#ifndef DMLC_MEMORY_IO_H_
-#define DMLC_MEMORY_IO_H_
-
-#include <cstring>
-#include <string>
-#include <algorithm>
-#include "./base.h"
-#include "./io.h"
-#include "./logging.h"
-
-namespace dmlc {
-/*!
- * \brief A Stream that operates on fixed region of memory
- *  This class allows us to read/write from/to a fixed memory region.
- */
-struct MemoryFixedSizeStream : public SeekStream {
- public:
-  /*!
-   * \brief constructor
-   * \param p_buffer the head pointer of the memory region.
-   * \param buffer_size the size of the memorybuffer
-   */
-  MemoryFixedSizeStream(void *p_buffer, size_t buffer_size)
-      : p_buffer_(reinterpret_cast<char*>(p_buffer)),
-        buffer_size_(buffer_size) {
-    curr_ptr_ = 0;
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    CHECK(curr_ptr_ + size <= buffer_size_);
-    size_t nread = std::min(buffer_size_ - curr_ptr_, size);
-    if (nread != 0) std::memcpy(ptr, p_buffer_ + curr_ptr_, nread);
-    curr_ptr_ += nread;
-    return nread;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    if (size == 0) return;
-    CHECK(curr_ptr_ + size <=  buffer_size_);
-    std::memcpy(p_buffer_ + curr_ptr_, ptr, size);
-    curr_ptr_ += size;
-  }
-  virtual void Seek(size_t pos) {
-    curr_ptr_ = static_cast<size_t>(pos);
-  }
-  virtual size_t Tell(void) {
-    return curr_ptr_;
-  }
-
- private:
-  /*! \brief in memory buffer */
-  char *p_buffer_;
-  /*! \brief current pointer */
-  size_t buffer_size_;
-  /*! \brief current pointer */
-  size_t curr_ptr_;
-};  // class MemoryFixedSizeStream
-
-/*!
- * \brief A in memory stream that is backed by std::string.
- *  This class allows us to read/write from/to a std::string.
- */
-struct MemoryStringStream : public dmlc::SeekStream {
- public:
-  /*!
-   * \brief constructor
-   * \param p_buffer the pointer to the string.
-   */
-  explicit MemoryStringStream(std::string *p_buffer)
-      : p_buffer_(p_buffer) {
-    curr_ptr_ = 0;
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    CHECK(curr_ptr_ <= p_buffer_->length());
-    size_t nread = std::min(p_buffer_->length() - curr_ptr_, size);
-    if (nread != 0) std::memcpy(ptr, &(*p_buffer_)[0] + curr_ptr_, nread);
-    curr_ptr_ += nread;
-    return nread;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    if (size == 0) return;
-    if (curr_ptr_ + size > p_buffer_->length()) {
-      p_buffer_->resize(curr_ptr_+size);
-    }
-    std::memcpy(&(*p_buffer_)[0] + curr_ptr_, ptr, size);
-    curr_ptr_ += size;
-  }
-  virtual void Seek(size_t pos) {
-    curr_ptr_ = static_cast<size_t>(pos);
-  }
-  virtual size_t Tell(void) {
-    return curr_ptr_;
-  }
-
- private:
-  /*! \brief in memory buffer */
-  std::string *p_buffer_;
-  /*! \brief current pointer */
-  size_t curr_ptr_;
-};  // class MemoryStringStream
-}  // namespace dmlc
-#endif  // DMLC_MEMORY_IO_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/omp.h b/ml-xgboost/dmlc-core/include/dmlc/omp.h
deleted file mode 100644
index 3b1cbe2..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/omp.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file omp.h
- * \brief header to handle OpenMP compatibility issues
- */
-#ifndef DMLC_OMP_H_
-#define DMLC_OMP_H_
-
-
-#if defined(_OPENMP)
-#include <omp.h>
-#else
-
-#if defined(__clang__)
-#undef __GOMP_NOTHROW
-#define __GOMP_NOTHROW
-#elif defined(__cplusplus)
-#undef __GOMP_NOTHROW
-#define __GOMP_NOTHROW throw()
-#else
-#undef __GOMP_NOTHROW
-#define __GOMP_NOTHROW __attribute__((__nothrow__))
-#endif
-
-//! \cond Doxygen_Suppress
-#ifdef __cplusplus
-extern "C" {
-#endif
-inline int omp_get_thread_num() __GOMP_NOTHROW { return 0; }
-inline int omp_get_num_threads() __GOMP_NOTHROW { return 1; }
-inline int omp_get_max_threads() __GOMP_NOTHROW { return 1; }
-inline int omp_get_num_procs() __GOMP_NOTHROW { return 1; }
-inline void omp_set_num_threads(int nthread) __GOMP_NOTHROW {}
-#ifdef __cplusplus
-}
-#endif  // __cplusplus
-#endif  // _OPENMP
-
-// loop variable used in openmp
-namespace dmlc {
-#ifdef _MSC_VER
-typedef int omp_uint;
-typedef long omp_ulong;  // NOLINT(*)
-#else
-typedef unsigned omp_uint;
-typedef unsigned long omp_ulong; // NOLINT(*)
-#endif
-//! \endcond
-}  // namespace dmlc
-#endif  // DMLC_OMP_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/optional.h b/ml-xgboost/dmlc-core/include/dmlc/optional.h
deleted file mode 100644
index 7ed2e85..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/optional.h
+++ /dev/null
@@ -1,271 +0,0 @@
-/*!
- * Copyright (c) 2016 by Contributors
- * \file optional.h
- * \brief Container to hold optional data.
- */
-#ifndef DMLC_OPTIONAL_H_
-#define DMLC_OPTIONAL_H_
-
-#include <iostream>
-#include <string>
-#include <utility>
-#include <algorithm>
-
-#include "./base.h"
-#include "./common.h"
-#include "./logging.h"
-#include "./type_traits.h"
-
-namespace dmlc {
-
-/*! \brief dummy type for assign null to optional */
-struct nullopt_t {
-#if defined(_MSC_VER) && _MSC_VER < 1900
-  /*! \brief dummy constructor */
-  explicit nullopt_t(int a) {}
-#else
-  /*! \brief dummy constructor */
-  constexpr explicit nullopt_t(int a) {}
-#endif
-};
-
-/*! Assign null to optional: optional<T> x = nullopt; */
-constexpr const nullopt_t nullopt = nullopt_t(0);
-
-/*!
- * \brief c++17 compatible optional class.
- *
- * At any time an optional<T> instance either
- * hold no value (string representation "None")
- * or hold a value of type T.
- */
-template<typename T>
-class optional {
- public:
-  /*! \brief construct an optional object that contains no value */
-  optional() : is_none(true) {}
-  /*! \brief construct an optional object with value */
-  explicit optional(const T& value) {
-#pragma GCC diagnostic push
-#if __GNUC__ >= 6
-#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
-#endif
-    is_none = false;
-    new (&val) T(value);
-#pragma GCC diagnostic pop
-  }
-  /*! \brief construct an optional object with another optional object */
-  optional(const optional<T>& other) {
-#pragma GCC diagnostic push
-#if __GNUC__ >= 6
-#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
-#endif
-    is_none = other.is_none;
-    if (!is_none) {
-      new (&val) T(other.value());
-    }
-#pragma GCC diagnostic pop
-  }
-  /*! \brief deconstructor */
-  ~optional() {
-    if (!is_none) {
-      reinterpret_cast<T*>(&val)->~T();
-    }
-  }
-  /*! \brief swap two optional */
-  void swap(optional<T>& other) {
-    std::swap(val, other.val);
-    std::swap(is_none, other.is_none);
-  }
-  /*! \brief set this object to hold value
-   *  \param value the value to hold
-   *  \return return self to support chain assignment
-   */
-  optional<T>& operator=(const T& value) {
-    (optional<T>(value)).swap(*this);
-    return *this;
-  }
-  /*! \brief set this object to hold the same value with other
-   *  \param other the other object
-   *  \return return self to support chain assignment
-   */
-  optional<T>& operator=(const optional<T> &other) {
-    (optional<T>(other)).swap(*this);
-    return *this;
-  }
-  /*! \brief clear the value this object is holding.
-   *         optional<T> x = nullopt;
-   */
-  optional<T>& operator=(nullopt_t) {
-    (optional<T>()).swap(*this);
-    return *this;
-  }
-  /*! \brief non-const dereference operator */
-  T& operator*() {  // NOLINT(*)
-    return *reinterpret_cast<T*>(&val);
-  }
-  /*! \brief const dereference operator */
-  const T& operator*() const {
-    return *reinterpret_cast<const T*>(&val);
-  }
-  /*! \brief equal comparison */
-  bool operator==(const optional<T>& other) const {
-    return this->is_none == other.is_none &&
-           (this->is_none == true || this->value() == other.value());
-  }
-  /*! \brief return the holded value.
-   *         throws std::logic_error if holding no value
-   */
-  const T& value() const {
-    if (is_none) {
-      throw std::logic_error("bad optional access");
-    }
-    return *reinterpret_cast<const T*>(&val);
-  }
-  /*! \brief whether this object is holding a value */
-  explicit operator bool() const { return !is_none; }
-  /*! \brief whether this object is holding a value (alternate form). */
-  bool has_value() const { return operator bool(); }
-
- private:
-  // whether this is none
-  bool is_none;
-  // on stack storage of value
-  typename std::aligned_storage<sizeof(T), alignof(T)>::type val;
-};
-
-/*! \brief serialize an optional object to string.
- *
- *  \code
- *    dmlc::optional<int> x;
- *    std::cout << x;  // None
- *    x = 0;
- *    std::cout << x;  // 0
- *  \endcode
- *
- *  \param os output stream
- *  \param t source optional<T> object
- *  \return output stream
- */
-template<typename T>
-std::ostream &operator<<(std::ostream &os, const optional<T> &t) {
-  if (t) {
-    os << *t;
-  } else {
-    os << "None";
-  }
-  return os;
-}
-
-/*! \brief parse a string object into optional<T>
- *
- *  \code
- *    dmlc::optional<int> x;
- *    std::string s1 = "1";
- *    std::istringstream is1(s1);
- *    s1 >> x;  // x == optional<int>(1)
- *
- *    std::string s2 = "None";
- *    std::istringstream is2(s2);
- *    s2 >> x;  // x == optional<int>()
- *  \endcode
- *
- *  \param is input stream
- *  \param t target optional<T> object
- *  \return input stream
- */
-template<typename T>
-std::istream &operator>>(std::istream &is, optional<T> &t) {
-  char buf[4];
-  std::streampos origin = is.tellg();
-  is.read(buf, 4);
-  if (is.fail() || buf[0] != 'N' || buf[1] != 'o' ||
-      buf[2] != 'n' || buf[3] != 'e') {
-    is.clear();
-    is.seekg(origin);
-    T x;
-    is >> x;
-    t = x;
-    if (std::is_integral<T>::value && !is.eof() && is.peek() == 'L') is.get();
-  } else {
-    t = nullopt;
-  }
-  return is;
-}
-/*! \brief specialization of '>>' istream parsing for optional<bool>
- *
- * Permits use of generic parameter FieldEntry<DType> class to create
- * FieldEntry<optional<bool>> without explicit specialization.
- *
- *  \code
- *    dmlc::optional<bool> x;
- *    std::string s1 = "true";
- *    std::istringstream is1(s1);
- *    s1 >> x;  // x == optional<bool>(true)
- *
- *    std::string s2 = "None";
- *    std::istringstream is2(s2);
- *    s2 >> x;  // x == optional<bool>()
- *  \endcode
- *
- *  \param is input stream
- *  \param t target optional<bool> object
- *  \return input stream
- */
-inline std::istream &operator>>(std::istream &is, optional<bool> &t) {
-  // Discard initial whitespace
-  while (isspace(is.peek()))
-    is.get();
-  // Extract chars that might be valid into a separate string, stopping
-  // on whitespace or other non-alphanumerics such as ",)]".
-  std::string s;
-  while (isalnum(is.peek()))
-    s.push_back(is.get());
-
-  if (!is.fail()) {
-    std::transform(s.begin(), s.end(), s.begin(), ::tolower);
-    if (s == "1" || s == "true")
-      t = true;
-    else if (s == "0" || s == "false")
-      t = false;
-    else if (s == "none")
-      t = nullopt;
-    else
-      is.setstate(std::ios::failbit);
-  }
-
-  return is;
-}
-
-/*! \brief description for optional int */
-DMLC_DECLARE_TYPE_NAME(optional<int>, "int or None");
-/*! \brief description for optional bool */
-DMLC_DECLARE_TYPE_NAME(optional<bool>, "boolean or None");
-/*! \brief description for optional float */
-DMLC_DECLARE_TYPE_NAME(optional<float>, "float or None");
-/*! \brief description for optional double */
-DMLC_DECLARE_TYPE_NAME(optional<double>, "double or None");
-
-}  // namespace dmlc
-
-namespace std {
-/*! \brief std hash function for optional */
-template<typename T>
-struct hash<dmlc::optional<T> > {
-  /*!
-   * \brief returns hash of the optional value.
-   * \param val value.
-   * \return hash code.
-   */
-  size_t operator()(const dmlc::optional<T>& val) const {
-    std::hash<bool> hash_bool;
-    size_t res = hash_bool(val.has_value());
-    if (val.has_value()) {
-      res = dmlc::HashCombine(res, val.value());
-    }
-    return res;
-  }
-};
-}  // namespace std
-
-#endif  // DMLC_OPTIONAL_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/parameter.h b/ml-xgboost/dmlc-core/include/dmlc/parameter.h
deleted file mode 100644
index 61f696c..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/parameter.h
+++ /dev/null
@@ -1,1181 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file parameter.h
- * \brief Provide lightweight util to do parameter setup and checking.
- */
-#ifndef DMLC_PARAMETER_H_
-#define DMLC_PARAMETER_H_
-
-#include <cstddef>
-#include <cstdlib>
-#include <cmath>
-#include <sstream>
-#include <limits>
-#include <map>
-#include <set>
-#include <typeinfo>
-#include <string>
-#include <vector>
-#include <algorithm>
-#include <utility>
-#include <stdexcept>
-#include <iostream>
-#include <iomanip>
-#include <cerrno>
-#include "./base.h"
-#include "./json.h"
-#include "./logging.h"
-#include "./type_traits.h"
-#include "./optional.h"
-#include "./strtonum.h"
-
-namespace dmlc {
-// this file is backward compatible with non-c++11
-/*! \brief Error throwed by parameter checking */
-struct ParamError : public dmlc::Error {
-  /*!
-   * \brief constructor
-   * \param msg error message
-   */
-  explicit ParamError(const std::string &msg)
-      : dmlc::Error(msg) {}
-};
-
-/*!
- * \brief Get environment variable with default.
- * \param key the name of environment variable.
- * \param default_value the default value of environment vriable.
- * \return The value received
- */
-template<typename ValueType>
-inline ValueType GetEnv(const char *key,
-                        ValueType default_value);
-/*!
- * \brief Set environment variable.
- * \param key the name of environment variable.
- * \param value the new value for key.
- * \return The value received
- */
-template<typename ValueType>
-inline void SetEnv(const char *key,
-                   ValueType value);
-
-/*! \brief internal namespace for parameter manangement */
-namespace parameter {
-// forward declare ParamManager
-class ParamManager;
-// forward declare FieldAccessEntry
-class FieldAccessEntry;
-// forward declare FieldEntry
-template<typename DType>
-class FieldEntry;
-// forward declare ParamManagerSingleton
-template<typename PType>
-struct ParamManagerSingleton;
-
-/*! \brief option in parameter initialization */
-enum ParamInitOption {
-  /*! \brief allow unknown parameters */
-  kAllowUnknown,
-  /*! \brief need to match exact parameters */
-  kAllMatch,
-  /*! \brief allow unmatched hidden field with format __*__ */
-  kAllowHidden
-};
-}  // namespace parameter
-/*!
- * \brief Information about a parameter field in string representations.
- */
-struct ParamFieldInfo {
-  /*! \brief name of the field */
-  std::string name;
-  /*! \brief type of the field in string format */
-  std::string type;
-  /*!
-   * \brief detailed type information string
-   *  This include the default value, enum constran and typename.
-   */
-  std::string type_info_str;
-  /*! \brief detailed description of the type */
-  std::string description;
-};
-
-/*!
- * \brief Parameter is the base type every parameter struct should inherit from
- * The following code is a complete example to setup parameters.
- * \code
- *   struct Param : public dmlc::Parameter<Param> {
- *     float learning_rate;
- *     int num_hidden;
- *     std::string name;
- *     // declare parameters in header file
- *     DMLC_DECLARE_PARAMETER(Param) {
- *       DMLC_DECLARE_FIELD(num_hidden).set_range(0, 1000);
- *       DMLC_DECLARE_FIELD(learning_rate).set_default(0.01f);
- *       DMLC_DECLARE_FIELD(name).set_default("hello");
- *     }
- *   };
- *   // register it in cc file
- *   DMLC_REGISTER_PARAMETER(Param);
- * \endcode
- *
- *  After that, the Param struct will get all the functions defined in Parameter.
- * \tparam PType the type of parameter struct
- *
- * \sa DMLC_DECLARE_FIELD, DMLC_REGISTER_PARAMETER, DMLC_DECLARE_PARAMETER
- */
-template<typename PType>
-struct Parameter {
- public:
-  /*!
-   * \brief initialize the parameter by keyword arguments.
-   *  This function will initialize the parameter struct, check consistency
-   *  and throw error if something wrong happens.
-   *
-   * \param kwargs map of keyword arguments, or vector of pairs
-   * \parma option The option on initialization.
-   * \tparam Container container type
-   * \throw ParamError when something go wrong.
-   */
-  template<typename Container>
-  inline void Init(const Container &kwargs,
-                   parameter::ParamInitOption option = parameter::kAllowHidden) {
-    PType::__MANAGER__()->RunInit(static_cast<PType*>(this),
-                                  kwargs.begin(), kwargs.end(),
-                                  NULL,
-                                  option);
-  }
-  /*!
-   * \brief initialize the parameter by keyword arguments.
-   *  This is same as Init, but allow unknown arguments.
-   *
-   * \param kwargs map of keyword arguments, or vector of pairs
-   * \tparam Container container type
-   * \throw ParamError when something go wrong.
-   * \return vector of pairs of unknown arguments.
-   */
-  template<typename Container>
-  inline std::vector<std::pair<std::string, std::string> >
-  InitAllowUnknown(const Container &kwargs) {
-    std::vector<std::pair<std::string, std::string> > unknown;
-    PType::__MANAGER__()->RunInit(static_cast<PType*>(this),
-                                  kwargs.begin(), kwargs.end(),
-                                  &unknown, parameter::kAllowUnknown);
-    return unknown;
-  }
-
-  /*!
-   * \brief Update the parameter by keyword arguments.  This is same as
-   * `InitAllowUnknown', but without setting not provided parameters to their default.
-   *
-   * \tparam Container container type
-   *
-   * \param kwargs map of keyword arguments, or vector of pairs
-   * \param out_changed (optional) Output whether any parameter is changed during update.
-   *
-   * \throw ParamError when something go wrong.
-   * \return vector of pairs of unknown arguments.
-   */
-  template <typename Container>
-  std::vector<std::pair<std::string, std::string> >
-  UpdateAllowUnknown(Container const& kwargs, bool* out_changed = nullptr) {
-    std::vector<std::pair<std::string, std::string> > unknown;
-    bool changed {false};
-    changed = PType::__MANAGER__()->RunUpdate(static_cast<PType*>(this),
-                                              kwargs.begin(), kwargs.end(),
-                                              parameter::kAllowUnknown, &unknown, nullptr);
-    if (out_changed) { *out_changed = changed; }
-    return unknown;
-  }
-
-  /*!
-   * \brief Update the dict with values stored in parameter.
-   *
-   * \param dict The dictionary to be updated.
-   * \tparam Container container type
-   */
-  template<typename Container>
-  inline void UpdateDict(Container *dict) const {
-    PType::__MANAGER__()->UpdateDict(this->head(), dict);
-  }
-  /*!
-   * \brief Return a dictionary representation of the parameters
-   * \return A dictionary that maps key -> value
-   */
-  inline std::map<std::string, std::string> __DICT__() const {
-    std::vector<std::pair<std::string, std::string> > vec
-        = PType::__MANAGER__()->GetDict(this->head());
-    return std::map<std::string, std::string>(vec.begin(), vec.end());
-  }
-  /*!
-   * \brief Write the parameters in JSON format.
-   * \param writer JSONWriter used for writing.
-   */
-  inline void Save(dmlc::JSONWriter *writer) const {
-    writer->Write(this->__DICT__());
-  }
-  /*!
-   * \brief Load the parameters from JSON.
-   * \param reader JSONReader used for loading.
-   * \throw ParamError when something go wrong.
-   */
-  inline void Load(dmlc::JSONReader *reader) {
-    std::map<std::string, std::string> kwargs;
-    reader->Read(&kwargs);
-    this->Init(kwargs);
-  }
-  /*!
-   * \brief Get the fields of the parameters.
-   * \return List of ParamFieldInfo of each field.
-   */
-  inline static std::vector<ParamFieldInfo> __FIELDS__() {
-    return PType::__MANAGER__()->GetFieldInfo();
-  }
-  /*!
-   * \brief Print docstring of the parameter
-   * \return the printed docstring
-   */
-  inline static std::string __DOC__() {
-    std::ostringstream os;
-    PType::__MANAGER__()->PrintDocString(os);
-    return os.str();
-  }
-
- protected:
-  /*!
-   * \brief internal function to allow declare of a parameter memember
-   * \param manager the parameter manager
-   * \param key the key name of the parameter
-   * \param ref the reference to the parameter in the struct.
-   */
-  template<typename DType>
-  inline parameter::FieldEntry<DType>& DECLARE(
-      parameter::ParamManagerSingleton<PType> *manager,
-      const std::string &key, DType &ref) { // NOLINT(*)
-    parameter::FieldEntry<DType> *e =
-        new parameter::FieldEntry<DType>();
-    e->Init(key, this->head(), ref);
-    manager->manager.AddEntry(key, e);
-    return *e;
-  }
-
- private:
-  /*! \return Get head pointer of child structure */
-  inline PType *head() const {
-    return static_cast<PType*>(const_cast<Parameter<PType>*>(this));
-  }
-};
-
-//! \cond Doxygen_Suppress
-/*!
- * \brief macro used to declare parameter
- *
- * Example:
- * \code
- *   struct Param : public dmlc::Parameter<Param> {
- *     // declare parameters in header file
- *     DMLC_DECLARE_PARAMETER(Param) {
- *        // details of declarations
- *     }
- *   };
- * \endcode
- *
- * This macro need to be put in a source file so that registration only happens once.
- * Refer to example code in Parameter for details
- *
- * \param PType the name of parameter struct.
- * \sa Parameter
- */
-#define DMLC_DECLARE_PARAMETER(PType)                                   \
-  static ::dmlc::parameter::ParamManager *__MANAGER__();                \
-  inline void __DECLARE__(::dmlc::parameter::ParamManagerSingleton<PType> *manager) \
-
-/*!
- * \brief macro to declare fields
- * \param FieldName the name of the field.
- */
-#define DMLC_DECLARE_FIELD(FieldName)  this->DECLARE(manager, #FieldName, FieldName)
-
-/*!
- * \brief macro to declare alias of a fields
- * \param FieldName the name of the field.
- * \param AliasName the name of the alias, must be declared after the field is declared.
- */
-#define DMLC_DECLARE_ALIAS(FieldName, AliasName)  manager->manager.AddAlias(#FieldName, #AliasName)
-
-/*!
- * \brief Macro used to register parameter.
- *
- * This macro need to be put in a source file so that registeration only happens once.
- * Refer to example code in Parameter for details
- * \param PType the type of parameter struct.
- * \sa Parameter
- */
-#define DMLC_REGISTER_PARAMETER(PType)                                  \
-  ::dmlc::parameter::ParamManager *PType::__MANAGER__() {               \
-    static ::dmlc::parameter::ParamManagerSingleton<PType> inst(#PType); \
-    return &inst.manager;                                               \
-  }                                                                     \
-  static DMLC_ATTRIBUTE_UNUSED ::dmlc::parameter::ParamManager&         \
-  __make__ ## PType ## ParamManager__ =                                 \
-      (*PType::__MANAGER__())                                           \
-
-//! \endcond
-/*!
- * \brief internal namespace for parameter management
- * There is no need to use it directly in normal case
- */
-namespace parameter {
-/*!
- * \brief FieldAccessEntry interface to help manage the parameters
- *  Each entry can be used to access one parameter in the Parameter struct.
- *
- *  This is an internal interface used that is used to manage parameters
- */
-class FieldAccessEntry {
- public:
-  FieldAccessEntry()
-      : has_default_(false), index_(0) {}
-  /*! \brief destructor */
-  virtual ~FieldAccessEntry() {}
-  /*!
-   * \brief set the default value.
-   * \param head the pointer to the head of the struct
-   * \throw error if no default is presented
-   */
-  virtual void SetDefault(void *head) const = 0;
-  /*!
-   * \brief set the parameter by string value
-   * \param head the pointer to the head of the struct
-   * \param value the value to be set
-   */
-  virtual void Set(void *head, const std::string &value) const = 0;
-  /*!
-   * \brief See if new and old values are the same
-   * \param head the pointer to the head of the struct
-   * \param value the value to be set
-   */
-  virtual bool Same(void* head, const std::string& value) const = 0;
-  // check if value is OK
-  virtual void Check(void *head) const {}
-  /*!
-   * \brief get the string representation of value.
-   * \param head the pointer to the head of the struct
-   */
-  virtual std::string GetStringValue(void *head) const = 0;
-  /*!
-   * \brief Get field information
-   * \return the corresponding field information
-   */
-  virtual ParamFieldInfo GetFieldInfo() const = 0;
-
- protected:
-  /*! \brief whether this parameter have default value */
-  bool has_default_;
-  /*! \brief positional index of parameter in struct */
-  size_t index_;
-  /*! \brief parameter key name */
-  std::string key_;
-  /*! \brief parameter type */
-  std::string type_;
-  /*! \brief description of the parameter */
-  std::string description_;
-  // internal offset of the field
-  ptrdiff_t offset_;
-  /*! \brief get pointer to parameter */
-  char* GetRawPtr(void* head) const {
-    return reinterpret_cast<char*>(head) + offset_;
-  }
-  /*!
-   * \brief print string representation of default value
-   * \parma os the stream to print the docstring to.
-   */
-  virtual void PrintDefaultValueString(std::ostream &os) const = 0;  // NOLINT(*)
-  // allow ParamManager to modify self
-  friend class ParamManager;
-};
-
-/*!
- * \brief manager class to handle parameter structure for each type
- *  An manager will be created for each parameter structure.
- */
-class ParamManager {
- public:
-  /*! \brief destructor */
-  ~ParamManager() {
-    for (size_t i = 0; i < entry_.size(); ++i) {
-      delete entry_[i];
-    }
-  }
-  /*!
-   * \brief find the access entry by parameter key
-   * \param key the key of the parameter.
-   * \return pointer to FieldAccessEntry, NULL if nothing is found.
-   */
-  inline FieldAccessEntry *Find(const std::string &key) const {
-    std::map<std::string, FieldAccessEntry*>::const_iterator it =
-        entry_map_.find(key);
-    if (it == entry_map_.end()) return NULL;
-    return it->second;
-  }
-  /*!
-   * \brief Set parameter by keyword arguments and default values.
-   * \param head head to the parameter field.
-   * \param begin begin iterator of original kwargs
-   * \param end end iterator of original kwargs
-   * \param unknown_args optional, used to hold unknown arguments
-   *          When it is specified, unknown arguments will be stored into here, instead of raise an error
-   * \tparam RandomAccessIterator iterator type
-   * \throw ParamError when there is unknown argument and unknown_args == NULL, or required argument is missing.
-   */
-  template<typename RandomAccessIterator>
-  inline void RunInit(void *head,
-                      RandomAccessIterator begin,
-                      RandomAccessIterator end,
-                      std::vector<std::pair<std::string, std::string> > *unknown_args,
-                      parameter::ParamInitOption option) const {
-    std::set<FieldAccessEntry*> selected_args;
-    RunUpdate(head, begin, end, option, unknown_args, &selected_args);
-    for (auto const& kv : entry_map_) {
-      if (selected_args.find(kv.second) == selected_args.cend()) {
-        kv.second->SetDefault(head);
-      }
-    }
-    for (std::map<std::string, FieldAccessEntry*>::const_iterator it = entry_map_.begin();
-         it != entry_map_.end(); ++it) {
-      if (selected_args.count(it->second) == 0) {
-        it->second->SetDefault(head);
-      }
-    }
-  }
-  /*!
-   * \brief Update parameters by keyword arguments.
-   *
-   * \tparam RandomAccessIterator iterator type
-   * \param head head to the parameter field.
-   * \param begin begin iterator of original kwargs
-   * \param end end iterator of original kwargs
-   * \param unknown_args optional, used to hold unknown arguments
-   *          When it is specified, unknown arguments will be stored into here, instead of raise an error
-   * \param selected_args The arguments used in update will be pushed into it, defaullt to nullptr.
-   * \throw ParamError when there is unknown argument and unknown_args == NULL, or required argument is missing.
-   */
-  template <typename RandomAccessIterator>
-  bool RunUpdate(void *head,
-                 RandomAccessIterator begin,
-                 RandomAccessIterator end,
-                 parameter::ParamInitOption option,
-                 std::vector<std::pair<std::string, std::string> > *unknown_args,
-                 std::set<FieldAccessEntry*>* selected_args = nullptr) const {
-    bool changed {false};
-    for (RandomAccessIterator it = begin; it != end; ++it) {
-      if (FieldAccessEntry *e = Find(it->first)) {
-        if (!e->Same(head, it->second)) {
-          changed = true;
-        }
-        e->Set(head, it->second);
-        e->Check(head);
-        if (selected_args) {
-          selected_args->insert(e);
-        }
-      } else {
-        if (unknown_args != NULL) {
-          unknown_args->push_back(*it);
-        } else {
-          if (option != parameter::kAllowUnknown) {
-            if (option == parameter::kAllowHidden &&
-                it->first.length() > 4 &&
-                it->first.find("__") == 0 &&
-                it->first.rfind("__") == it->first.length()-2) {
-              continue;
-            }
-            std::ostringstream os;
-            os << "Cannot find argument \'" << it->first << "\', Possible Arguments:\n";
-            os << "----------------\n";
-            PrintDocString(os);
-            throw dmlc::ParamError(os.str());
-          }
-        }
-      }
-    }
-    return changed;
-  }
-  /*!
-   * \brief internal function to add entry to manager,
-   *  The manager will take ownership of the entry.
-   * \param key the key to the parameters
-   * \param e the pointer to the new entry.
-   */
-  inline void AddEntry(const std::string &key, FieldAccessEntry *e) {
-    e->index_ = entry_.size();
-    // TODO(bing) better error message
-    if (entry_map_.count(key) != 0) {
-      LOG(FATAL) << "key " << key << " has already been registered in " << name_;
-    }
-    entry_.push_back(e);
-    entry_map_[key] = e;
-  }
-  /*!
-   * \brief internal function to add entry to manager,
-   *  The manager will take ownership of the entry.
-   * \param key the key to the parameters
-   * \param e the pointer to the new entry.
-   */
-  inline void AddAlias(const std::string& field, const std::string& alias) {
-    if (entry_map_.count(field) == 0) {
-      LOG(FATAL) << "key " << field << " has not been registered in " << name_;
-    }
-    if (entry_map_.count(alias) != 0) {
-      LOG(FATAL) << "Alias " << alias << " has already been registered in " << name_;
-    }
-    entry_map_[alias] = entry_map_[field];
-  }
-  /*!
-   * \brief set the name of parameter manager
-   * \param name the name to set
-   */
-  inline void set_name(const std::string &name) {
-    name_ = name;
-  }
-  /*!
-   * \brief get field information of each field.
-   * \return field information
-   */
-  inline std::vector<ParamFieldInfo> GetFieldInfo() const {
-    std::vector<ParamFieldInfo> ret(entry_.size());
-    for (size_t i = 0; i < entry_.size(); ++i) {
-      ret[i] = entry_[i]->GetFieldInfo();
-    }
-    return ret;
-  }
-  /*!
-   * \brief Print readible docstring to ostream, add newline.
-   * \parma os the stream to print the docstring to.
-   */
-  inline void PrintDocString(std::ostream &os) const {  // NOLINT(*)
-    for (size_t i = 0; i < entry_.size(); ++i) {
-      ParamFieldInfo info = entry_[i]->GetFieldInfo();
-      os << info.name << " : " << info.type_info_str << '\n';
-      if (info.description.length() != 0) {
-        os << "    " << info.description << '\n';
-      }
-    }
-  }
-  /*!
-   * \brief Get internal parameters in vector of pairs.
-   * \param head the head of the struct.
-   * \param skip_default skip the values that equals default value.
-   * \return the parameter dictionary.
-   */
-  inline std::vector<std::pair<std::string, std::string> > GetDict(void * head) const {
-    std::vector<std::pair<std::string, std::string> > ret;
-    for (std::map<std::string, FieldAccessEntry*>::const_iterator
-            it = entry_map_.begin(); it != entry_map_.end(); ++it) {
-      ret.push_back(std::make_pair(it->first, it->second->GetStringValue(head)));
-    }
-    return ret;
-  }
-  /*!
-   * \brief Update the dictionary with values in parameter.
-   * \param head the head of the struct.
-   * \tparam Container The container type
-   * \return the parameter dictionary.
-   */
-  template<typename Container>
-  inline void UpdateDict(void * head, Container* dict) const {
-    for (std::map<std::string, FieldAccessEntry*>::const_iterator
-            it = entry_map_.begin(); it != entry_map_.end(); ++it) {
-      (*dict)[it->first] = it->second->GetStringValue(head);
-    }
-  }
-
- private:
-  /*! \brief parameter struct name */
-  std::string name_;
-  /*! \brief positional list of entries */
-  std::vector<FieldAccessEntry*> entry_;
-  /*! \brief map from key to entry */
-  std::map<std::string, FieldAccessEntry*> entry_map_;
-};
-
-//! \cond Doxygen_Suppress
-
-// The following piece of code will be template heavy and less documented
-// singleton parameter manager for certain type, used for initialization
-template<typename PType>
-struct ParamManagerSingleton {
-  ParamManager manager;
-  explicit ParamManagerSingleton(const std::string &param_name) {
-    PType param;
-    manager.set_name(param_name);
-    param.__DECLARE__(this);
-  }
-};
-
-// Base class of FieldEntry
-// implement set_default
-template<typename TEntry, typename DType>
-class FieldEntryBase : public FieldAccessEntry {
- public:
-  // entry type
-  typedef TEntry EntryType;
-  // implement set value
-  void Set(void *head, const std::string &value) const override {
-    std::istringstream is(value);
-    is >> this->Get(head);
-    if (!is.fail()) {
-      while (!is.eof()) {
-        int ch = is.get();
-        if (ch == EOF) {
-          is.clear(); break;
-        }
-        if (!isspace(ch)) {
-          is.setstate(std::ios::failbit); break;
-        }
-      }
-    }
-
-    if (is.fail()) {
-      std::ostringstream os;
-      os << "Invalid Parameter format for " << key_
-         << " expect " << type_ << " but value=\'" << value<< '\'';
-      throw dmlc::ParamError(os.str());
-    }
-  }
-
-  // Don't check this function for Undefined Behavior (UB), as the function
-  // reads from a possibly uninitialized field
-  DMLC_SUPPRESS_UBSAN
-  bool Same(void* head, std::string const& value) const override {
-    DType old = this->Get(head);
-    DType now;
-    std::istringstream is(value);
-    is >> now;
-    // don't require = operator
-    bool is_same = std::equal(
-        reinterpret_cast<char*>(&now), reinterpret_cast<char*>(&now) + sizeof(now),
-        reinterpret_cast<char*>(&old));
-    return is_same;
-  }
-  std::string GetStringValue(void *head) const override {
-    std::ostringstream os;
-    PrintValue(os, this->Get(head));
-    return os.str();
-  }
-  ParamFieldInfo GetFieldInfo() const override {
-    ParamFieldInfo info;
-    std::ostringstream os;
-    info.name = key_;
-    info.type = type_;
-    os << type_;
-    if (has_default_) {
-      os << ',' << " optional, default=";
-      PrintDefaultValueString(os);
-    } else {
-      os << ", required";
-    }
-    info.type_info_str = os.str();
-    info.description = description_;
-    return info;
-  }
-  // implement set head to default value
-  void SetDefault(void *head) const override {
-    if (!has_default_) {
-      std::ostringstream os;
-      os << "Required parameter " << key_
-         << " of " << type_ << " is not presented";
-      throw dmlc::ParamError(os.str());
-    } else {
-      this->Get(head) = default_value_;
-    }
-  }
-  // return reference of self as derived type
-  inline TEntry &self() {
-    return *(static_cast<TEntry*>(this));
-  }
-  // implement set_default
-  inline TEntry &set_default(const DType &default_value) {
-    default_value_ = default_value;
-    has_default_ = true;
-    // return self to allow chaining
-    return this->self();
-  }
-  // implement describe
-  inline TEntry &describe(const std::string &description) {
-    description_ = description;
-    // return self to allow chaining
-    return this->self();
-  }
-  // initialization function
-  inline void Init(const std::string &key,
-                   void *head, DType &ref) { // NOLINT(*)
-    this->key_ = key;
-    if (this->type_.length() == 0) {
-      this->type_ = dmlc::type_name<DType>();
-    }
-    this->offset_ = ((char*)&ref) - ((char*)head);  // NOLINT(*)
-  }
-
- protected:
-  // print the value
-  virtual void PrintValue(std::ostream &os, DType value) const { // NOLINT(*)
-    os << value;
-  }
-  void PrintDefaultValueString(std::ostream &os) const override {  // NOLINT(*)
-    PrintValue(os, default_value_);
-  }
-  // get the internal representation of parameter
-  // for example if this entry corresponds field param.learning_rate
-  // then Get(&param) will return reference to param.learning_rate
-  inline DType &Get(void *head) const {
-    return *(DType*)this->GetRawPtr(head);  // NOLINT(*)
-  }
-  // default value of field
-  DType default_value_;
-};
-
-// parameter base for numeric types that have range
-template<typename TEntry, typename DType>
-class FieldEntryNumeric
-    : public FieldEntryBase<TEntry, DType> {
- public:
-  FieldEntryNumeric()
-      : has_begin_(false), has_end_(false) {}
-  // implement set_range
-  virtual TEntry &set_range(DType begin, DType end) {
-    begin_ = begin; end_ = end;
-    has_begin_ = true; has_end_ = true;
-    return this->self();
-  }
-  // implement set_range
-  virtual TEntry &set_lower_bound(DType begin) {
-    begin_ = begin; has_begin_ = true;
-    return this->self();
-  }
-  // consistency check for numeric ranges
-  virtual void Check(void *head) const {
-    FieldEntryBase<TEntry, DType>::Check(head);
-    DType v = this->Get(head);
-    if (has_begin_ && has_end_) {
-      if (v < begin_ || v > end_) {
-        std::ostringstream os;
-        os << "value " << v << " for Parameter " << this->key_
-           << " exceed bound [" << begin_ << ',' << end_ <<']' << '\n';
-        os << this->key_ << ": " << this->description_;
-        throw dmlc::ParamError(os.str());
-      }
-    } else if (has_begin_ && v < begin_) {
-        std::ostringstream os;
-        os << "value " << v << " for Parameter " << this->key_
-           << " should be greater equal to " << begin_ << '\n';
-        os << this->key_ << ": " << this->description_;
-        throw dmlc::ParamError(os.str());
-    } else if (has_end_ && v > end_) {
-        std::ostringstream os;
-        os << "value " << v << " for Parameter " << this->key_
-           << " should be smaller equal to " << end_ << '\n';
-        os << this->key_ << ": " << this->description_;
-        throw dmlc::ParamError(os.str());
-    }
-  }
-
- protected:
-  // whether it have begin and end range
-  bool has_begin_, has_end_;
-  // data bound
-  DType begin_, end_;
-};
-
-/*!
- * \brief FieldEntry defines parsing and checking behavior of DType.
- * This class can be specialized to implement specific behavior of more settings.
- * \tparam DType the data type of the entry.
- */
-template<typename DType>
-class FieldEntry :
-      public IfThenElseType<dmlc::is_arithmetic<DType>::value,
-                            FieldEntryNumeric<FieldEntry<DType>, DType>,
-                            FieldEntryBase<FieldEntry<DType>, DType> >::Type {
-};
-
-// specialize define for int(enum)
-template<>
-class FieldEntry<int>
-    : public FieldEntryNumeric<FieldEntry<int>, int> {
- public:
-  // construct
-  FieldEntry<int>() : is_enum_(false) {}
-  // parent
-  typedef FieldEntryNumeric<FieldEntry<int>, int> Parent;
-  // override set
-  virtual void Set(void *head, const std::string &value) const {
-    if (is_enum_) {
-      std::map<std::string, int>::const_iterator it = enum_map_.find(value);
-      std::ostringstream os;
-      if (it == enum_map_.end()) {
-        os << "Invalid Input: \'" << value;
-        os << "\', valid values are: ";
-        PrintEnums(os);
-        throw dmlc::ParamError(os.str());
-      } else {
-        os << it->second;
-        Parent::Set(head, os.str());
-      }
-    } else {
-      Parent::Set(head, value);
-    }
-  }
-  virtual ParamFieldInfo GetFieldInfo() const {
-    if (is_enum_) {
-      ParamFieldInfo info;
-      std::ostringstream os;
-      info.name = key_;
-      info.type = type_;
-      PrintEnums(os);
-      if (has_default_) {
-        os << ',' << "optional, default=";
-        PrintDefaultValueString(os);
-      } else {
-        os << ", required";
-      }
-      info.type_info_str = os.str();
-      info.description = description_;
-      return info;
-    } else {
-      return Parent::GetFieldInfo();
-    }
-  }
-  // add enum
-  inline FieldEntry<int> &add_enum(const std::string &key, int value) {
-    if ((enum_map_.size() != 0 && enum_map_.count(key) != 0) || \
-        enum_back_map_.count(value) != 0) {
-      std::ostringstream os;
-      os << "Enum " << "(" << key << ": " << value << " exisit!" << ")\n";
-      os << "Enums: ";
-      for (std::map<std::string, int>::const_iterator it = enum_map_.begin();
-           it != enum_map_.end(); ++it) {
-        os << "(" << it->first << ": " << it->second << "), ";
-      }
-      throw dmlc::ParamError(os.str());
-    }
-    enum_map_[key] = value;
-    enum_back_map_[value] = key;
-    is_enum_ = true;
-    return this->self();
-  }
-
- protected:
-  // enum flag
-  bool is_enum_;
-  // enum map
-  std::map<std::string, int> enum_map_;
-  // enum map
-  std::map<int, std::string> enum_back_map_;
-  // override print behavior
-  virtual void PrintDefaultValueString(std::ostream &os) const { // NOLINT(*)
-    os << '\'';
-    PrintValue(os, default_value_);
-    os << '\'';
-  }
-  // override print default
-  virtual void PrintValue(std::ostream &os, int value) const {  // NOLINT(*)
-    if (is_enum_) {
-      CHECK_NE(enum_back_map_.count(value), 0U)
-          << "Value not found in enum declared";
-      os << enum_back_map_.at(value);
-    } else {
-      os << value;
-    }
-  }
-
-
- private:
-  inline void PrintEnums(std::ostream &os) const {  // NOLINT(*)
-    os << '{';
-    for (std::map<std::string, int>::const_iterator
-             it = enum_map_.begin(); it != enum_map_.end(); ++it) {
-      if (it != enum_map_.begin()) {
-        os << ", ";
-      }
-      os << "\'" << it->first << '\'';
-    }
-    os << '}';
-  }
-};
-
-
-// specialize define for optional<int>(enum)
-template<>
-class FieldEntry<optional<int> >
-    : public FieldEntryBase<FieldEntry<optional<int> >, optional<int> > {
- public:
-  // construct
-  FieldEntry<optional<int> >() : is_enum_(false) {}
-  // parent
-  typedef FieldEntryBase<FieldEntry<optional<int> >, optional<int> > Parent;
-  // override set
-  virtual void Set(void *head, const std::string &value) const {
-    if (is_enum_ && value != "None") {
-      std::map<std::string, int>::const_iterator it = enum_map_.find(value);
-      std::ostringstream os;
-      if (it == enum_map_.end()) {
-        os << "Invalid Input: \'" << value;
-        os << "\', valid values are: ";
-        PrintEnums(os);
-        throw dmlc::ParamError(os.str());
-      } else {
-        os << it->second;
-        Parent::Set(head, os.str());
-      }
-    } else {
-      Parent::Set(head, value);
-    }
-  }
-  virtual ParamFieldInfo GetFieldInfo() const {
-    if (is_enum_) {
-      ParamFieldInfo info;
-      std::ostringstream os;
-      info.name = key_;
-      info.type = type_;
-      PrintEnums(os);
-      if (has_default_) {
-        os << ',' << "optional, default=";
-        PrintDefaultValueString(os);
-      } else {
-        os << ", required";
-      }
-      info.type_info_str = os.str();
-      info.description = description_;
-      return info;
-    } else {
-      return Parent::GetFieldInfo();
-    }
-  }
-  // add enum
-  inline FieldEntry<optional<int> > &add_enum(const std::string &key, int value) {
-    CHECK_NE(key, "None") << "None is reserved for empty optional<int>";
-    if ((enum_map_.size() != 0 && enum_map_.count(key) != 0) || \
-        enum_back_map_.count(value) != 0) {
-      std::ostringstream os;
-      os << "Enum " << "(" << key << ": " << value << " exisit!" << ")\n";
-      os << "Enums: ";
-      for (std::map<std::string, int>::const_iterator it = enum_map_.begin();
-           it != enum_map_.end(); ++it) {
-        os << "(" << it->first << ": " << it->second << "), ";
-      }
-      throw dmlc::ParamError(os.str());
-    }
-    enum_map_[key] = value;
-    enum_back_map_[value] = key;
-    is_enum_ = true;
-    return this->self();
-  }
-
- protected:
-  // enum flag
-  bool is_enum_;
-  // enum map
-  std::map<std::string, int> enum_map_;
-  // enum map
-  std::map<int, std::string> enum_back_map_;
-  // override print behavior
-  virtual void PrintDefaultValueString(std::ostream &os) const { // NOLINT(*)
-    os << '\'';
-    PrintValue(os, default_value_);
-    os << '\'';
-  }
-  // override print default
-  virtual void PrintValue(std::ostream &os, optional<int> value) const {  // NOLINT(*)
-    if (is_enum_) {
-      if (!value) {
-        os << "None";
-      } else {
-        CHECK_NE(enum_back_map_.count(value.value()), 0U)
-            << "Value not found in enum declared";
-        os << enum_back_map_.at(value.value());
-      }
-    } else {
-      os << value;
-    }
-  }
-
-
- private:
-  inline void PrintEnums(std::ostream &os) const {  // NOLINT(*)
-    os << "{None";
-    for (std::map<std::string, int>::const_iterator
-             it = enum_map_.begin(); it != enum_map_.end(); ++it) {
-      os << ", ";
-      os << "\'" << it->first << '\'';
-    }
-    os << '}';
-  }
-};
-
-// specialize define for string
-template<>
-class FieldEntry<std::string>
-    : public FieldEntryBase<FieldEntry<std::string>, std::string> {
- public:
-  // parent class
-  typedef FieldEntryBase<FieldEntry<std::string>, std::string> Parent;
-  // override set
-  virtual void Set(void *head, const std::string &value) const {
-    this->Get(head) = value;
-  }
-  // override print default
-  virtual void PrintDefaultValueString(std::ostream &os) const {  // NOLINT(*)
-    os << '\'' << default_value_ << '\'';
-  }
-};
-
-// specialize define for bool
-template<>
-class FieldEntry<bool>
-    : public FieldEntryBase<FieldEntry<bool>, bool> {
- public:
-  // parent class
-  typedef FieldEntryBase<FieldEntry<bool>, bool> Parent;
-  // override set
-  virtual void Set(void *head, const std::string &value) const {
-    std::string lower_case; lower_case.resize(value.length());
-    std::transform(value.begin(), value.end(), lower_case.begin(), ::tolower);
-    bool &ref = this->Get(head);
-    if (lower_case == "true") {
-      ref = true;
-    } else if (lower_case == "false") {
-      ref = false;
-    } else if (lower_case == "1") {
-      ref = true;
-    } else if (lower_case == "0") {
-      ref = false;
-    } else {
-      std::ostringstream os;
-      os << "Invalid Parameter format for " << key_
-         << " expect " << type_ << " but value=\'" << value<< '\'';
-      throw dmlc::ParamError(os.str());
-    }
-  }
-
- protected:
-  // print default string
-  virtual void PrintValue(std::ostream &os, bool value) const {  // NOLINT(*)
-    os << static_cast<int>(value);
-  }
-};
-
-
-// specialize define for float. Uses stof for platform independent handling of
-// INF, -INF, NAN, etc.
-#if DMLC_USE_CXX11
-template <>
-class FieldEntry<float> : public FieldEntryNumeric<FieldEntry<float>, float> {
- public:
-  // parent
-  typedef FieldEntryNumeric<FieldEntry<float>, float> Parent;
-  // override set
-  virtual void Set(void *head, const std::string &value) const {
-    size_t pos = 0;  // number of characters processed by dmlc::stof()
-    try {
-      this->Get(head) = dmlc::stof(value, &pos);
-    } catch (const std::invalid_argument &) {
-      std::ostringstream os;
-      os << "Invalid Parameter format for " << key_ << " expect " << type_
-         << " but value=\'" << value << '\'';
-      throw dmlc::ParamError(os.str());
-    } catch (const std::out_of_range&) {
-      std::ostringstream os;
-      os << "Out of range value for " << key_ << ", value=\'" << value << '\'';
-      throw dmlc::ParamError(os.str());
-    }
-    CHECK_LE(pos, value.length());  // just in case
-    if (pos < value.length()) {
-      std::ostringstream os;
-      os << "Some trailing characters could not be parsed: \'"
-         << value.substr(pos) << "\'";
-      throw dmlc::ParamError(os.str());
-    }
-  }
-
- protected:
-  // print the value
-  virtual void PrintValue(std::ostream &os, float value) const {  // NOLINT(*)
-    os << std::setprecision(std::numeric_limits<float>::max_digits10) << value;
-  }
-};
-
-// specialize define for double. Uses stod for platform independent handling of
-// INF, -INF, NAN, etc.
-template <>
-class FieldEntry<double>
-    : public FieldEntryNumeric<FieldEntry<double>, double> {
- public:
-  // parent
-  typedef FieldEntryNumeric<FieldEntry<double>, double> Parent;
-  // override set
-  virtual void Set(void *head, const std::string &value) const {
-    size_t pos = 0;  // number of characters processed by dmlc::stod()
-    try {
-      this->Get(head) = dmlc::stod(value, &pos);
-    } catch (const std::invalid_argument &) {
-      std::ostringstream os;
-      os << "Invalid Parameter format for " << key_ << " expect " << type_
-         << " but value=\'" << value << '\'';
-      throw dmlc::ParamError(os.str());
-    } catch (const std::out_of_range&) {
-      std::ostringstream os;
-      os << "Out of range value for " << key_ << ", value=\'" << value << '\'';
-      throw dmlc::ParamError(os.str());
-    }
-    CHECK_LE(pos, value.length());  // just in case
-    if (pos < value.length()) {
-      std::ostringstream os;
-      os << "Some trailing characters could not be parsed: \'"
-         << value.substr(pos) << "\'";
-      throw dmlc::ParamError(os.str());
-    }
-  }
-
- protected:
-  // print the value
-  virtual void PrintValue(std::ostream &os, double value) const {  // NOLINT(*)
-    os << std::setprecision(std::numeric_limits<double>::max_digits10) << value;
-  }
-};
-#endif  // DMLC_USE_CXX11
-
-}  // namespace parameter
-//! \endcond
-
-// implement GetEnv
-template<typename ValueType>
-inline ValueType GetEnv(const char *key,
-                        ValueType default_value) {
-  const char *val = getenv(key);
-  // On some implementations, if the var is set to a blank string (i.e. "FOO="), then
-  // a blank string will be returned instead of NULL.  In order to be consistent, if
-  // the environment var is a blank string, then also behave as if a null was returned.
-  if (val == nullptr || !*val) {
-    return default_value;
-  }
-  ValueType ret;
-  parameter::FieldEntry<ValueType> e;
-  e.Init(key, &ret, ret);
-  e.Set(&ret, val);
-  return ret;
-}
-
-// implement SetEnv
-template<typename ValueType>
-inline void SetEnv(const char *key,
-                   ValueType value) {
-  parameter::FieldEntry<ValueType> e;
-  e.Init(key, &value, value);
-#ifdef _WIN32
-  _putenv_s(key, e.GetStringValue(&value).c_str());
-#else
-  setenv(key, e.GetStringValue(&value).c_str(), 1);
-#endif  // _WIN32
-}
-}  // namespace dmlc
-#endif  // DMLC_PARAMETER_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/recordio.h b/ml-xgboost/dmlc-core/include/dmlc/recordio.h
deleted file mode 100644
index 6220780..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/recordio.h
+++ /dev/null
@@ -1,196 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file recordio.h
- * \brief recordio that is able to pack binary data into a splittable
- *   format, useful to exchange data in binary serialization,
- *   such as binary raw data or protobuf
- */
-#ifndef DMLC_RECORDIO_H_
-#define DMLC_RECORDIO_H_
-#include <cstring>
-#include <string>
-#include "./io.h"
-#include "./logging.h"
-
-namespace dmlc {
-/*!
- * \brief writer of binary recordio
- *  binary format for recordio
- *  recordio format: magic lrecord data pad
- *
- *  - magic is magic number
- *  - pad is simply a padding space to make record align to 4 bytes
- *  - lrecord encodes length and continue bit
- *     - data.length() = (lrecord & (1U<<29U - 1));
- *     - cflag == (lrecord >> 29U) & 7;
- *
- *  cflag was used to handle (rare) special case when magic number
- *  occured in the data sequence.
- *
- *  In such case, the data is splitted into multiple records by
- *  the cells of magic number
- *
- *  (1) cflag == 0: this is a complete record;
- *  (2) cflag == 1: start of a multiple-rec;
- *      cflag == 2: middle of multiple-rec;
- *      cflag == 3: end of multiple-rec
- */
-class RecordIOWriter {
- public:
-  /*!
-   * \brief magic number of recordio
-   * note: (kMagic >> 29U) & 7 > 3
-   * this ensures lrec will not be kMagic
-   */
-  static const uint32_t kMagic = 0xced7230a;
-  /*!
-   * \brief encode the lrecord
-   * \param cflag cflag part of the lrecord
-   * \param length length part of lrecord
-   * \return the encoded data
-   */
-  inline static uint32_t EncodeLRec(uint32_t cflag, uint32_t length) {
-    return (cflag << 29U) | length;
-  }
-  /*!
-   * \brief decode the flag part of lrecord
-   * \param rec the lrecord
-   * \return the flag
-   */
-  inline static uint32_t DecodeFlag(uint32_t rec) {
-    return (rec >> 29U) & 7U;
-  }
-  /*!
-   * \brief decode the length part of lrecord
-   * \param rec the lrecord
-   * \return the length
-   */
-  inline static uint32_t DecodeLength(uint32_t rec) {
-    return rec & ((1U << 29U) - 1U);
-  }
-  /*!
-   * \brief constructor
-   * \param stream the stream to be constructed
-   */
-  explicit RecordIOWriter(Stream *stream)
-      : stream_(stream), seek_stream_(dynamic_cast<SeekStream*>(stream)),
-        except_counter_(0) {
-    CHECK(sizeof(uint32_t) == 4) << "uint32_t needs to be 4 bytes";
-  }
-  /*!
-   * \brief write record to the stream
-   * \param buf the buffer of memory region
-   * \param size the size of record to write out
-   */
-  void WriteRecord(const void *buf, size_t size);
-  /*!
-   * \brief write record to the stream
-   * \param data the data to write out
-   */
-  inline void WriteRecord(const std::string &data) {
-    this->WriteRecord(data.c_str(), data.length());
-  }
-  /*!
-   * \return number of exceptions(occurance of magic number)
-   *   during the writing process
-   */
-  inline size_t except_counter(void) const {
-    return except_counter_;
-  }
-
-  /*! \brief tell the current position of the input stream */
-  inline size_t Tell(void) {
-    CHECK(seek_stream_ != NULL) << "The input stream is not seekable";
-    return seek_stream_->Tell();
-  }
-
- private:
-  /*! \brief output stream */
-  Stream *stream_;
-  /*! \brief seekable stream */
-  SeekStream *seek_stream_;
-  /*! \brief counts the number of exceptions */
-  size_t except_counter_;
-};
-/*!
- * \brief reader of binary recordio to reads in record from stream
- * \sa RecordIOWriter
- */
-class RecordIOReader {
- public:
-  /*!
-   * \brief constructor
-   * \param stream the stream to be constructed
-   */
-  explicit RecordIOReader(Stream *stream)
-      : stream_(stream), seek_stream_(dynamic_cast<SeekStream*>(stream)),
-        end_of_stream_(false) {
-    CHECK(sizeof(uint32_t) == 4) << "uint32_t needs to be 4 bytes";
-  }
-  /*!
-   * \brief read next complete record from stream
-   * \param out_rec used to store output record in string
-   * \return true of read was successful, false if end of stream was reached
-   */
-  bool NextRecord(std::string *out_rec);
-
-  /*! \brief seek to certain position of the input stream */
-  inline void Seek(size_t pos) {
-    CHECK(seek_stream_ != NULL) << "The input stream is not seekable";
-    seek_stream_->Seek(pos);
-  }
-
-  /*! \brief tell the current position of the input stream */
-  inline size_t Tell(void) {
-    CHECK(seek_stream_ != NULL) << "The input stream is not seekable";
-    return seek_stream_->Tell();
-  }
-
- private:
-  /*! \brief output stream */
-  Stream *stream_;
-  SeekStream *seek_stream_;
-  /*! \brief whether we are at end of stream */
-  bool end_of_stream_;
-};
-
-/*!
- * \brief reader of binary recordio from Blob returned by InputSplit
- *  This class divides the blob into several independent parts specified by caller,
- *  and read from one segment.
- *  The part reading can be used together with InputSplit::NextChunk for
- *  multi-threaded parsing(each thread take a RecordIOChunkReader)
- *
- * \sa RecordIOWriter, InputSplit
- */
-class RecordIOChunkReader {
- public:
-  /*!
-   * \brief constructor
-   * \param chunk source data returned by InputSplit
-   * \param part_index which part we want to reado
-   * \param num_parts number of total segments
-   */
-  explicit RecordIOChunkReader(InputSplit::Blob chunk,
-                               unsigned part_index = 0,
-                               unsigned num_parts = 1);
-  /*!
-   * \brief read next complete record from stream
-   *   the blob contains the memory content
-   *   NOTE: this function is not threadsafe, use one
-   *   RecordIOChunkReader per thread
-   * \param out_rec used to store output blob, the header is already
-   *        removed and out_rec only contains the memory content
-   * \return true of read was successful, false if end was reached
-   */
-  bool NextRecord(InputSplit::Blob *out_rec);
-
- private:
-  /*! \brief internal temporal data */
-  std::string temp_;
-  /*! \brief internal data pointer */
-  char *pbegin_, *pend_;
-};
-
-}  // namespace dmlc
-#endif  // DMLC_RECORDIO_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/registry.h b/ml-xgboost/dmlc-core/include/dmlc/registry.h
deleted file mode 100644
index 9cda806..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/registry.h
+++ /dev/null
@@ -1,310 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file registry.h
- * \brief Registry utility that helps to build registry singletons.
- */
-#ifndef DMLC_REGISTRY_H_
-#define DMLC_REGISTRY_H_
-
-#include <map>
-#include <string>
-#include <vector>
-#include "./base.h"
-#include "./logging.h"
-#include "./parameter.h"
-#include "./type_traits.h"
-
-namespace dmlc {
-/*!
- * \brief Registry class.
- *  Registry can be used to register global singletons.
- *  The most commonly use case are factory functions.
- *
- * \tparam EntryType Type of Registry entries,
- *     EntryType need to name a name field.
- */
-template<typename EntryType>
-class Registry {
- public:
-  /*! \return list of entries in the registry(excluding alias) */
-  inline static const std::vector<const EntryType*>& List() {
-    return Get()->const_list_;
-  }
-  /*! \return list all names registered in the registry, including alias */
-  inline static std::vector<std::string> ListAllNames() {
-    const std::map<std::string, EntryType*> &fmap = Get()->fmap_;
-    typename std::map<std::string, EntryType*>::const_iterator p;
-    std::vector<std::string> names;
-    for (p = fmap.begin(); p !=fmap.end(); ++p) {
-      names.push_back(p->first);
-    }
-    return names;
-  }
-  /*!
-   * \brief Find the entry with corresponding name.
-   * \param name name of the function
-   * \return the corresponding function, can be NULL
-   */
-  inline static const EntryType *Find(const std::string &name) {
-    const std::map<std::string, EntryType*> &fmap = Get()->fmap_;
-    typename std::map<std::string, EntryType*>::const_iterator p = fmap.find(name);
-    if (p != fmap.end()) {
-      return p->second;
-    } else {
-      return NULL;
-    }
-  }
-  /*!
-   * \brief Add alias to the key_name
-   * \param key_name The original entry key
-   * \param alias The alias key.
-   */
-  inline void AddAlias(const std::string& key_name,
-                       const std::string& alias) {
-    EntryType* e = fmap_.at(key_name);
-    if (fmap_.count(alias)) {
-      CHECK_EQ(e, fmap_.at(alias))
-          << "Trying to register alias " << alias << " for key " << key_name
-          << " but " << alias << " is already taken";
-    } else {
-      fmap_[alias] = e;
-    }
-  }
-  /*!
-   * \brief Internal function to register a name function under name.
-   * \param name name of the function
-   * \return ref to the registered entry, used to set properties
-   */
-  inline EntryType &__REGISTER__(const std::string& name) {
-    std::lock_guard<std::mutex> guard(registering_mutex);
-    if (fmap_.count(name) > 0) {
-      return *fmap_[name];
-    }
-    EntryType *e = new EntryType();
-    e->name = name;
-    fmap_[name] = e;
-    const_list_.push_back(e);
-    entry_list_.push_back(e);
-    return *e;
-  }
-  /*!
-   * \brief Internal function to either register or get registered entry
-   * \param name name of the function
-   * \return ref to the registered entry, used to set properties
-   */
-  inline EntryType &__REGISTER_OR_GET__(const std::string& name) {
-    if (fmap_.count(name) == 0) {
-      return __REGISTER__(name);
-    } else {
-      return *fmap_.at(name);
-    }
-  }
-  /*!
-   * \brief get a singleton of the Registry.
-   *  This function can be defined by DMLC_REGISTRY_ENABLE.
-   * \return get a singleton
-   */
-  static Registry *Get();
-
- private:
-  /*! \brief list of entry types */
-  std::vector<EntryType*> entry_list_;
-  /*! \brief list of entry types */
-  std::vector<const EntryType*> const_list_;
-  /*! \brief map of name->function */
-  std::map<std::string, EntryType*> fmap_;
-  /*! \brief lock guarding the registering*/
-  std::mutex registering_mutex;
-  /*! \brief constructor */
-  Registry() {}
-  /*! \brief destructor */
-  ~Registry() {
-    for (size_t i = 0; i < entry_list_.size(); ++i) {
-      delete entry_list_[i];
-    }
-  }
-};
-
-/*!
- * \brief Common base class for function registry.
- *
- * \code
- *  // This example demonstrates how to use Registry to create a factory of trees.
- *  struct TreeFactory :
- *      public FunctionRegEntryBase<TreeFactory, std::function<Tree*()> > {
- *  };
- *
- *  // in a independent cc file
- *  namespace dmlc {
- *  DMLC_REGISTRY_ENABLE(TreeFactory);
- *  }
- *  // register binary tree constructor into the registry.
- *  DMLC_REGISTRY_REGISTER(TreeFactory, TreeFactory, BinaryTree)
- *      .describe("Constructor of BinaryTree")
- *      .set_body([]() { return new BinaryTree(); });
- * \endcode
- *
- * \tparam EntryType The type of subclass that inheritate the base.
- * \tparam FunctionType The function type this registry is registerd.
- */
-template<typename EntryType, typename FunctionType>
-class FunctionRegEntryBase {
- public:
-  /*! \brief name of the entry */
-  std::string name;
-  /*! \brief description of the entry */
-  std::string description;
-  /*! \brief additional arguments to the factory function */
-  std::vector<ParamFieldInfo> arguments;
-  /*! \brief Function body to create ProductType */
-  FunctionType body;
-  /*! \brief Return type of the function */
-  std::string return_type;
-
-  /*!
-   * \brief Set the function body.
-   * \param body Function body to set.
-   * \return reference to self.
-   */
-  inline EntryType &set_body(FunctionType body) {
-    this->body = body;
-    return this->self();
-  }
-  /*!
-   * \brief Describe the function.
-   * \param description The description of the factory function.
-   * \return reference to self.
-   */
-  inline EntryType &describe(const std::string &description) {
-    this->description = description;
-    return this->self();
-  }
-  /*!
-   * \brief Add argument information to the function.
-   * \param name Name of the argument.
-   * \param type Type of the argument.
-   * \param description Description of the argument.
-   * \return reference to self.
-   */
-  inline EntryType &add_argument(const std::string &name,
-                                 const std::string &type,
-                                 const std::string &description) {
-    ParamFieldInfo info;
-    info.name = name;
-    info.type = type;
-    info.type_info_str = info.type;
-    info.description = description;
-    arguments.push_back(info);
-    return this->self();
-  }
-  /*!
-   * \brief Append list if arguments to the end.
-   * \param args Additional list of arguments.
-   * \return reference to self.
-   */
-  inline EntryType &add_arguments(const std::vector<ParamFieldInfo> &args) {
-    arguments.insert(arguments.end(), args.begin(), args.end());
-    return this->self();
-  }
-  /*!
-  * \brief Set the return type.
-  * \param type Return type of the function, could be Symbol or Symbol[]
-  * \return reference to self.
-  */
-  inline EntryType &set_return_type(const std::string &type) {
-    return_type = type;
-    return this->self();
-  }
-
- protected:
-  /*!
-   * \return reference of self as derived type
-   */
-  inline EntryType &self() {
-    return *(static_cast<EntryType*>(this));
-  }
-};
-
-/*!
- * \def DMLC_REGISTRY_ENABLE
- * \brief Macro to enable the registry of EntryType.
- * This macro must be used under namespace dmlc, and only used once in cc file.
- * \param EntryType Type of registry entry
- */
-#define DMLC_REGISTRY_ENABLE(EntryType)                                 \
-  template<>                                                            \
-  Registry<EntryType > *Registry<EntryType >::Get() {                   \
-    static Registry<EntryType > inst;                                   \
-    return &inst;                                                       \
-  }                                                                     \
-
-/*!
- * \brief Generic macro to register an EntryType
- *  There is a complete example in FactoryRegistryEntryBase.
- *
- * \param EntryType The type of registry entry.
- * \param EntryTypeName The typename of EntryType, must do not contain namespace :: .
- * \param Name The name to be registered.
- * \sa FactoryRegistryEntryBase
- */
-#define DMLC_REGISTRY_REGISTER(EntryType, EntryTypeName, Name)          \
-  static DMLC_ATTRIBUTE_UNUSED EntryType & __make_ ## EntryTypeName ## _ ## Name ## __ = \
-      ::dmlc::Registry<EntryType>::Get()->__REGISTER__(#Name)           \
-
-/*!
- * \brief (Optional) Declare a file tag to current file that contains object registrations.
- *
- *  This will declare a dummy function that will be called by register file to
- *  incur a link dependency.
- *
- * \param UniqueTag The unique tag used to represent.
- * \sa DMLC_REGISTRY_LINK_TAG
- */
-#define DMLC_REGISTRY_FILE_TAG(UniqueTag)                                \
-  int __dmlc_registry_file_tag_ ## UniqueTag ## __() { return 0; }
-
-/*!
- * \brief (Optional) Force link to all the objects registered in file tag.
- *
- *  This macro must be used in the same file as DMLC_REGISTRY_ENABLE and
- *  in the same namespace as DMLC_REGISTRY_FILE_TAG
- *
- *  DMLC_REGISTRY_FILE_TAG and DMLC_REGISTRY_LINK_TAG are optional macros for registration.
- *  They are used to encforce link of certain file into during static linking.
- *
- *  This is mainly used to solve problem during statically link a library which contains backward registration.
- *  Specifically, this avoids the objects in these file tags to be ignored by compiler.
- *
- *  For dynamic linking, this problem won't occur as everything is loaded by default.
- *
- *  Use of this is optional as it will create an error when a file tag do not exist.
- *  An alternative solution is always ask user to enable --whole-archieve during static link.
- *
- * \begincode
- * // in file objective_registry.cc
- * DMLC_REGISTRY_ENABLE(MyObjective);
- * DMLC_REGISTRY_LINK_TAG(regression_op);
- * DMLC_REGISTRY_LINK_TAG(rank_op);
- *
- * // in file regression_op.cc
- * // declare tag of this file.
- * DMLC_REGISTRY_FILE_TAG(regression_op);
- * DMLC_REGISTRY_REGISTER(MyObjective, logistic_reg, logistic_reg);
- * // ...
- *
- * // in file rank_op.cc
- * // declare tag of this file.
- * DMLC_REGISTRY_FILE_TAG(rank_op);
- * DMLC_REGISTRY_REGISTER(MyObjective, pairwiserank, pairwiserank);
- *
- * \endcode
- *
- * \param UniqueTag The unique tag used to represent.
- * \sa DMLC_REGISTRY_ENABLE, DMLC_REGISTRY_FILE_TAG
- */
-#define DMLC_REGISTRY_LINK_TAG(UniqueTag)                                \
-  int __dmlc_registry_file_tag_ ## UniqueTag ## __();                   \
-  static int DMLC_ATTRIBUTE_UNUSED __reg_file_tag_ ## UniqueTag ## __ = \
-      __dmlc_registry_file_tag_ ## UniqueTag ## __();
-}  // namespace dmlc
-#endif  // DMLC_REGISTRY_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/serializer.h b/ml-xgboost/dmlc-core/include/dmlc/serializer.h
deleted file mode 100644
index 4bede4a..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/serializer.h
+++ /dev/null
@@ -1,410 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file serializer.h
- * \brief serializer template class that helps serialization.
- *  This file do not need to be directly used by most user.
- */
-#ifndef DMLC_SERIALIZER_H_
-#define DMLC_SERIALIZER_H_
-
-#include <vector>
-#include <string>
-#include <map>
-#include <set>
-#include <list>
-#include <deque>
-#include <utility>
-
-#include "./base.h"
-#include "./io.h"
-#include "./logging.h"
-#include "./type_traits.h"
-#include "./endian.h"
-
-#if DMLC_USE_CXX11
-#include <unordered_map>
-#include <unordered_set>
-#endif
-
-namespace dmlc {
-/*! \brief internal namespace for serializers */
-namespace serializer {
-/*!
- * \brief generic serialization handler
- * \tparam T the type to be serialized
- * \tparam need_endian_swap Whether use little endian
- */
-template<typename T>
-struct Handler;
-
-//! \cond Doxygen_Suppress
-/*!
- * \brief Serializer that redirect calls by condition
- * \tparam cond the condition
- * \tparam Then the serializer used for then condition
- * \tparam Else the serializer used for else condition
- * \tparam Return the type of data the serializer handles
- */
-template<bool cond, typename Then, typename Else, typename Return>
-struct IfThenElse;
-
-template<typename Then, typename Else, typename T>
-struct IfThenElse<true, Then, Else, T> {
-  inline static void Write(Stream *strm, const T &data) {
-    Then::Write(strm, data);
-  }
-  inline static bool Read(Stream *strm, T *data) {
-    return Then::Read(strm, data);
-  }
-};
-template<typename Then, typename Else, typename T>
-struct IfThenElse<false, Then, Else, T> {
-  inline static void Write(Stream *strm, const T &data) {
-    Else::Write(strm, data);
-  }
-  inline static bool Read(Stream *strm, T *data) {
-    return Else::Read(strm, data);
-  }
-};
-
-/*! \brief Serializer for POD(plain-old-data) data */
-template<typename T>
-struct NativePODHandler {
-  inline static void Write(Stream *strm, const T &data) {
-    strm->Write(&data, sizeof(T));
-  }
-  inline static bool Read(Stream *strm, T *dptr) {
-    return strm->Read((void*)dptr, sizeof(T)) == sizeof(T);  // NOLINT(*)
-  }
-};
-
-/*! \brief Serializer for arithmetic data, handle endianness */
-template<typename T>
-struct ArithmeticHandler {
-  inline static void Write(Stream *strm, const T &data) {
-    if (DMLC_IO_NO_ENDIAN_SWAP) {
-      strm->Write(&data, sizeof(T));
-    } else {
-      T copy = data;
-      ByteSwap(&copy, sizeof(T), 1);
-      strm->Write(&copy, sizeof(T));
-    }
-  }
-  inline static bool Read(Stream *strm, T *dptr) {
-    bool ret = strm->Read((void*)dptr, sizeof(T)) == sizeof(T);  // NOLINT(*)
-    if (!DMLC_IO_NO_ENDIAN_SWAP) {
-      ByteSwap(dptr, sizeof(T), 1);
-    }
-    return ret;
-  }
-};
-
-// serializer for class that have save/load function
-template<typename T>
-struct SaveLoadClassHandler {
-  inline static void Write(Stream *strm, const T &data) {
-    data.Save(strm);
-  }
-  inline static bool Read(Stream *strm, T *data) {
-    return data->Load(strm);
-  }
-};
-
-/*!
- * \brief dummy class for undefined serialization.
- *   This is used to generate error message when user tries to
- *   serialize something that is not supported.
- * \tparam T the type to be serialized
- */
-template<typename T>
-struct UndefinedSerializerFor {
-};
-
-/*!
- * \brief Serializer handler for std::vector<T> where T is POD type.
- * \tparam T element type
- */
-template<typename T>
-struct NativePODVectorHandler {
-  inline static void Write(Stream *strm, const std::vector<T> &vec) {
-    uint64_t sz = static_cast<uint64_t>(vec.size());
-    strm->Write<uint64_t>(sz);
-    if (sz != 0) {
-      strm->Write(&vec[0], sizeof(T) * vec.size());
-    }
-  }
-  inline static bool Read(Stream *strm, std::vector<T> *out_vec) {
-    uint64_t sz;
-    if (!strm->Read<uint64_t>(&sz)) return false;
-    size_t size = static_cast<size_t>(sz);
-    out_vec->resize(size);
-    if (sz != 0) {
-      size_t nbytes = sizeof(T) * size;
-      return strm->Read(&(*out_vec)[0], nbytes) == nbytes;
-    }
-    return true;
-  }
-};
-
-/*!
- * \brief Serializer handler for std::vector<T> where T can be composed type
- * \tparam T element type
- */
-template<typename T>
-struct ComposeVectorHandler {
-  inline static void Write(Stream *strm, const std::vector<T> &vec) {
-    uint64_t sz = static_cast<uint64_t>(vec.size());
-    strm->Write<uint64_t>(sz);
-    strm->WriteArray(dmlc::BeginPtr(vec), vec.size());
-  }
-  inline static bool Read(Stream *strm, std::vector<T> *out_vec) {
-    uint64_t sz;
-    if (!strm->Read<uint64_t>(&sz)) return false;
-    size_t size = static_cast<size_t>(sz);
-    out_vec->resize(size);
-    return strm->ReadArray(dmlc::BeginPtr(*out_vec), size);
-  }
-};
-
-/*!
- * \brief Serializer handler for std::basic_string<T> where T is POD type.
- * \tparam T element type
- */
-template<typename T>
-struct NativePODStringHandler {
-  inline static void Write(Stream *strm, const std::basic_string<T> &vec) {
-    uint64_t sz = static_cast<uint64_t>(vec.length());
-    strm->Write<uint64_t>(sz);
-    if (sz != 0) {
-      strm->Write(&vec[0], sizeof(T) * vec.length());
-    }
-  }
-  inline static bool Read(Stream *strm, std::basic_string<T> *out_vec) {
-    uint64_t sz;
-    if (!strm->Read<uint64_t>(&sz)) return false;
-    size_t size = static_cast<size_t>(sz);
-    out_vec->resize(size);
-    if (sz != 0) {
-      size_t nbytes = sizeof(T) * size;
-      return strm->Read(&(*out_vec)[0], nbytes) == nbytes;
-    }
-    return true;
-  }
-};
-
-/*! \brief Serializer for std::pair */
-template<typename TA, typename TB>
-struct PairHandler {
-  inline static void Write(Stream *strm, const std::pair<TA, TB> &data) {
-    Handler<TA>::Write(strm, data.first);
-    Handler<TB>::Write(strm, data.second);
-  }
-  inline static bool Read(Stream *strm, std::pair<TA, TB> *data) {
-    return Handler<TA>::Read(strm, &(data->first)) &&
-        Handler<TB>::Read(strm, &(data->second));
-  }
-};
-
-// set type handler that can handle most collection type case
-template<typename ContainerType, typename ElemType>
-struct CollectionHandler {
-  inline static void Write(Stream *strm, const ContainerType &data) {
-    // dump data to vector
-    std::vector<ElemType> vdata(data.begin(), data.end());
-    // serialize the vector
-    Handler<std::vector<ElemType> >::Write(strm, vdata);
-  }
-  inline static bool Read(Stream *strm, ContainerType *data) {
-    std::vector<ElemType> vdata;
-    if (!Handler<std::vector<ElemType> >::Read(strm, &vdata)) return false;
-    data->clear();
-    data->insert(vdata.begin(), vdata.end());
-    return true;
-  }
-};
-
-
-// handler that can handle most list type case
-// this type insert function takes additional iterator
-template<typename ListType>
-struct ListHandler {
-  inline static void Write(Stream *strm, const ListType &data) {
-    typedef typename ListType::value_type ElemType;
-    // dump data to vector
-    std::vector<ElemType> vdata(data.begin(), data.end());
-    // serialize the vector
-    Handler<std::vector<ElemType> >::Write(strm, vdata);
-  }
-  inline static bool Read(Stream *strm, ListType *data) {
-    typedef typename ListType::value_type ElemType;
-    std::vector<ElemType> vdata;
-    if (!Handler<std::vector<ElemType> >::Read(strm, &vdata)) return false;
-    data->clear();
-    data->insert(data->begin(), vdata.begin(), vdata.end());
-    return true;
-  }
-};
-
-//! \endcond
-
-/*!
- * \brief generic serialization handler for type T
- *
- *  User can define specialization of this class to support
- *  composite serialization of their own class.
- *
- * \tparam T the type to be serialized
- */
-template<typename T>
-struct Handler {
-  /*!
-   * \brief write data to stream
-   * \param strm the stream we write the data.
-   * \param data the data obeject to be serialized
-   */
-  inline static void Write(Stream *strm, const T &data) {
-    IfThenElse<dmlc::is_arithmetic<T>::value,
-               ArithmeticHandler<T>,
-               IfThenElse<dmlc::is_pod<T>::value && DMLC_IO_NO_ENDIAN_SWAP,
-                          NativePODHandler<T>,
-                          IfThenElse<dmlc::has_saveload<T>::value,
-                                     SaveLoadClassHandler<T>,
-                                     UndefinedSerializerFor<T>, T>,
-                          T>,
-               T>
-        ::Write(strm, data);
-  }
-  /*!
-   * \brief read data to stream
-   * \param strm the stream to read the data.
-   * \param data the pointer to the data obeject to read
-   * \return whether the read is successful
-   */
-  inline static bool Read(Stream *strm, T *data) {
-    return
-    IfThenElse<dmlc::is_arithmetic<T>::value,
-               ArithmeticHandler<T>,
-               IfThenElse<dmlc::is_pod<T>::value && DMLC_IO_NO_ENDIAN_SWAP,
-                          NativePODHandler<T>,
-                          IfThenElse<dmlc::has_saveload<T>::value,
-                                     SaveLoadClassHandler<T>,
-                                     UndefinedSerializerFor<T>, T>,
-                          T>,
-               T>
-    ::Read(strm, data);
-  }
-};
-
-//! \cond Doxygen_Suppress
-template<typename T>
-struct Handler<std::vector<T> > {
-  inline static void Write(Stream *strm, const std::vector<T> &data) {
-    IfThenElse<dmlc::is_pod<T>::value && DMLC_IO_NO_ENDIAN_SWAP,
-               NativePODVectorHandler<T>,
-               ComposeVectorHandler<T>, std::vector<T> >
-    ::Write(strm, data);
-  }
-  inline static bool Read(Stream *strm, std::vector<T> *data) {
-    return IfThenElse<dmlc::is_pod<T>::value && DMLC_IO_NO_ENDIAN_SWAP,
-                      NativePODVectorHandler<T>,
-                      ComposeVectorHandler<T>,
-                      std::vector<T> >
-    ::Read(strm, data);
-  }
-};
-
-template<typename T>
-struct Handler<std::basic_string<T> > {
-  inline static void Write(Stream *strm, const std::basic_string<T> &data) {
-    IfThenElse<dmlc::is_pod<T>::value && (DMLC_IO_NO_ENDIAN_SWAP || sizeof(T) == 1),
-               NativePODStringHandler<T>,
-               UndefinedSerializerFor<T>,
-               std::basic_string<T> >
-    ::Write(strm, data);
-  }
-  inline static bool Read(Stream *strm, std::basic_string<T> *data) {
-    return IfThenElse<dmlc::is_pod<T>::value && (DMLC_IO_NO_ENDIAN_SWAP || sizeof(T) == 1),
-                      NativePODStringHandler<T>,
-                      UndefinedSerializerFor<T>,
-                      std::basic_string<T> >
-    ::Read(strm, data);
-  }
-};
-
-template<typename TA, typename TB>
-struct Handler<std::pair<TA, TB> > {
-  inline static void Write(Stream *strm, const std::pair<TA, TB> &data) {
-    IfThenElse<dmlc::is_pod<TA>::value &&
-               dmlc::is_pod<TB>::value &&
-               DMLC_IO_NO_ENDIAN_SWAP,
-               NativePODHandler<std::pair<TA, TB> >,
-               PairHandler<TA, TB>,
-               std::pair<TA, TB> >
-    ::Write(strm, data);
-  }
-  inline static bool Read(Stream *strm, std::pair<TA, TB> *data) {
-    return IfThenElse<dmlc::is_pod<TA>::value &&
-                      dmlc::is_pod<TB>::value &&
-                      DMLC_IO_NO_ENDIAN_SWAP,
-                      NativePODHandler<std::pair<TA, TB> >,
-                      PairHandler<TA, TB>,
-                      std::pair<TA, TB> >
-    ::Read(strm, data);
-  }
-};
-
-template<typename K, typename V>
-struct Handler<std::map<K, V> >
-    : public CollectionHandler<std::map<K, V>, std::pair<K, V> > {
-};
-
-template<typename K, typename V>
-struct Handler<std::multimap<K, V> >
-    : public CollectionHandler<std::multimap<K, V>, std::pair<K, V> > {
-};
-
-template<typename T>
-struct Handler<std::set<T> >
-    : public CollectionHandler<std::set<T>, T> {
-};
-
-template<typename T>
-struct Handler<std::multiset<T> >
-    : public CollectionHandler<std::multiset<T>, T> {
-};
-
-template<typename T>
-struct Handler<std::list<T> >
-    : public ListHandler<std::list<T> > {
-};
-
-template<typename T>
-struct Handler<std::deque<T> >
-    : public ListHandler<std::deque<T> > {
-};
-
-#if DMLC_USE_CXX11
-template<typename K, typename V>
-struct Handler<std::unordered_map<K, V> >
-    : public CollectionHandler<std::unordered_map<K, V>, std::pair<K, V> > {
-};
-
-template<typename K, typename V>
-struct Handler<std::unordered_multimap<K, V> >
-    : public CollectionHandler<std::unordered_multimap<K, V>, std::pair<K, V> > {
-};
-
-template<typename T>
-struct Handler<std::unordered_set<T> >
-    : public CollectionHandler<std::unordered_set<T>, T> {
-};
-
-template<typename T>
-struct Handler<std::unordered_multiset<T> >
-    : public CollectionHandler<std::unordered_multiset<T>, T> {
-};
-#endif
-//! \endcond
-}  // namespace serializer
-}  // namespace dmlc
-#endif  // DMLC_SERIALIZER_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/strtonum.h b/ml-xgboost/dmlc-core/include/dmlc/strtonum.h
deleted file mode 100644
index 2ce10a8..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/strtonum.h
+++ /dev/null
@@ -1,737 +0,0 @@
-/*!
- * Copyright (c) 2015-2018 by Contributors
- * \file strtonum.h
- * \brief A faster implementation of strtof and strtod
- */
-#ifndef DMLC_STRTONUM_H_
-#define DMLC_STRTONUM_H_
-
-#if DMLC_USE_CXX11
-#include <type_traits>
-#endif
-
-#include <string>
-#include <limits>
-#include <cstdint>
-#include "./base.h"
-#include "./logging.h"
-
-namespace dmlc {
-/*!
- * \brief Inline implementation of isspace(). Tests whether the given character
- *        is a whitespace letter.
- * \param c Character to test
- * \return Result of the test
- */
-inline bool isspace(char c) {
-  return (c == ' ' || c == '\t' || c == '\r' || c == '\n' || c == '\f');
-}
-
-/*!
- * \brief Inline implementation of isblank(). Tests whether the given character
- *        is a space or tab character.
- * \param c Character to test
- * \return Result of the test
- */
-inline bool isblank(char c) {
-  return (c == ' ' || c == '\t');
-}
-
-/*!
- * \brief Inline implementation of isdigit(). Tests whether the given character
- *        is a decimal digit
- * \param c Character to test
- * \return Result of the test
- */
-inline bool isdigit(char c) {
-  return (c >= '0' && c <= '9');
-}
-
-/*!
- * \brief Inline implementation of isalpha(). Tests whether the given character
- *        is an alphabet letter
- * \param c Character to test
- * \return Result of the test
- */
-inline bool isalpha(char c) {
-  static_assert(
-    static_cast<int>('A') == 65 && static_cast<int>('Z' - 'A') == 25,
-    "Only system with ASCII character set is supported");
-  return (c >= 'a' && c <= 'z') || (c >= 'A' && c <= 'Z');
-}
-
-/*!
- * \brief Tests whether the given character is a valid letter in the string
- *        representation of a floating-point value, i.e. decimal digits,
- *        signs (+/-), decimal point (.), or exponent marker (e/E).
- * \param c Character to test
- * \return Result of the test
- */
-inline bool isdigitchars(char c) {
-  return (c >= '0' && c <= '9')
-    || c == '+' || c == '-'
-    || c == '.'
-    || c == 'e' || c == 'E';
-}
-
-/*!
- * \brief Maximum number of decimal digits dmlc::strtof() / dmlc::strtod()
- *        will process. Trailing digits will be ignored.
- */
-const int kStrtofMaxDigits = 19;
-
-/*!
- * \brief Common implementation for dmlc::strtof() and dmlc::strtod()
- * TODO: the current version does not support hex number
- * \param nptr Beginning of the string that's to be converted into a
- *             floating-point number
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \return Converted floating-point value, in FloatType
- * \tparam FloatType Type of floating-point number to be obtained. This must
- *                   be either float or double.
- * \tparam CheckRange Whether to check for overflow. If set to true, an out-
- *                    of-range value will cause errno to be set to ERANGE and
- *                    ParseFloat() to return HUGE_VAL / HUGE_VALF; otherwise,
- *                    all out-of-range vlaues will be silently clipped.
- */
-template <typename FloatType, bool CheckRange = false>
-inline FloatType ParseFloat(const char* nptr, char** endptr) {
-#if DMLC_USE_CXX11
-  static_assert(std::is_same<FloatType, double>::value
-                || std::is_same<FloatType, float>::value,
-               "ParseFloat is defined only for 'float' and 'double' types");
-  constexpr unsigned kMaxExponent
-    = (std::is_same<FloatType, double>::value ? 308U : 38U);
-  constexpr FloatType kMaxSignificandForMaxExponent
-    = static_cast<FloatType>(std::is_same<FloatType, double>::value
-                             ? 1.79769313486231570 : 3.402823466);
-    // If a floating-point value has kMaxExponent, what is
-    //   the largest possible significand value?
-  constexpr FloatType kMaxSignificandForNegMaxExponent
-    = static_cast<FloatType>(std::is_same<FloatType, double>::value
-                             ? 2.22507385850720139 : 1.175494351);
-    // If a floating-point value has -kMaxExponent, what is
-    //   the largest possible significand value?
-#else
-  const unsigned kMaxExponent
-    = (sizeof(FloatType) == sizeof(double) ? 308U : 38U);
-  const FloatType kMaxSignificandForMaxExponent
-    = static_cast<FloatType>(sizeof(FloatType) == sizeof(double)
-                             ? 1.79769313486231570 : 3.402823466);
-  const FloatType kMaxSignificandForNegMaxExponent
-    = static_cast<FloatType>(sizeof(FloatType) == sizeof(double)
-                             ? 2.22507385850720139 : 1.175494351);
-#endif
-
-  const char *p = nptr;
-  // Skip leading white space, if any. Not necessary
-  while (isspace(*p) ) ++p;
-
-  // Get sign, if any.
-  bool sign = true;
-  if (*p == '-') {
-    sign = false; ++p;
-  } else if (*p == '+') {
-    ++p;
-  }
-
-  // Handle INF and NAN
-  {
-    int i = 0;
-    // case-insensitive match for INF and INFINITY
-    while (i < 8 && static_cast<char>((*p) | 32) == "infinity"[i]) {
-      ++i; ++p;
-    }
-    if (i == 3 || i == 8) {
-      if (endptr) *endptr = (char*)p;  // NOLINT(*)
-      return sign ?  std::numeric_limits<FloatType>::infinity()
-                  : -std::numeric_limits<FloatType>::infinity();
-    } else {
-      p -= i;
-    }
-
-    // case-insensitive match for NAN
-    i = 0;
-    while (i < 3 && static_cast<char>((*p) | 32) == "nan"[i]) {
-      ++i; ++p;
-    }
-    if (i == 3) {
-      // Got NAN; check if the value is of form NAN(char_sequence)
-      if (*p == '(') {
-        ++p;
-        while (isdigit(*p) || isalpha(*p) || *p == '_') ++p;
-        CHECK_EQ(*p, ')') << "Invalid NAN literal";
-        ++p;
-      }
-      static_assert(std::numeric_limits<FloatType>::has_quiet_NaN,
-        "Only system with quiet NaN is supported");
-      if (endptr) *endptr = (char*)p;  // NOLINT(*)
-      return std::numeric_limits<FloatType>::quiet_NaN();
-    } else {
-      p -= i;
-    }
-  }
-
-  // Get digits before decimal point or exponent, if any.
-  uint64_t predec;  // to store digits before decimal point
-  for (predec = 0; isdigit(*p); ++p) {
-    predec = predec * 10ULL + static_cast<uint64_t>(*p - '0');
-  }
-  FloatType value = static_cast<FloatType>(predec);
-
-  // Get digits after decimal point, if any.
-  if (*p == '.') {
-    uint64_t pow10 = 1;
-    uint64_t val2 = 0;
-    int digit_cnt = 0;
-    ++p;
-    while (isdigit(*p)) {
-      if (digit_cnt < kStrtofMaxDigits) {
-        val2 = val2 * 10ULL + static_cast<uint64_t>(*p - '0');
-        pow10 *= 10ULL;
-      }  // when kStrtofMaxDigits is read, ignored following digits
-      ++p;
-      ++digit_cnt;
-    }
-    value += static_cast<FloatType>(
-        static_cast<double>(val2) / static_cast<double>(pow10));
-  }
-
-  // Handle exponent, if any.
-  if ((*p == 'e') || (*p == 'E')) {
-    ++p;
-    bool frac = false;
-    FloatType scale = static_cast<FloatType>(1.0f);
-    unsigned expon;
-    // Get sign of exponent, if any.
-    if (*p == '-') {
-      frac = true;
-      ++p;
-    } else if (*p == '+') {
-      ++p;
-    }
-    // Get digits of exponent, if any.
-    for (expon = 0; isdigit(*p); ++p) {
-      expon = expon * 10U + static_cast<unsigned>(*p - '0');
-    }
-    if (expon > kMaxExponent) {  // out of range, clip or raise error
-      if (CheckRange) {
-        errno = ERANGE;
-        if (endptr) *endptr = (char*)p;  // NOLINT(*)
-        return std::numeric_limits<FloatType>::infinity();
-      } else {
-        expon = kMaxExponent;
-      }
-    }
-    // handle edge case where exponent is exactly kMaxExponent
-    if (expon == kMaxExponent
-        && ((!frac && value > kMaxSignificandForMaxExponent)
-           || (frac && value < kMaxSignificandForNegMaxExponent))) {
-      if (CheckRange) {
-        errno = ERANGE;
-        if (endptr) *endptr = (char*)p;  // NOLINT(*)
-        return std::numeric_limits<FloatType>::infinity();
-      } else {
-        value = (frac ? kMaxSignificandForNegMaxExponent
-                 : kMaxSignificandForMaxExponent);
-      }
-    }
-    // Calculate scaling factor.
-    while (expon >= 8U) { scale *= static_cast<FloatType>(1E8f);  expon -= 8U; }
-    while (expon >  0U) { scale *= static_cast<FloatType>(10.0f); expon -= 1U; }
-    // Return signed and scaled floating point result.
-    value = frac ? (value / scale) : (value * scale);
-  }
-  // Consume 'f' suffix, if any
-  if (*p == 'f' || *p == 'F') {
-    ++p;
-  }
-
-  if (endptr) *endptr = (char*)p;  // NOLINT(*)
-  return sign ? value : - value;
-}
-
-/*!
- * \brief A faster implementation of strtof(). See documentation of
- *        std::strtof() for more information. Note that this function does not
- *        check for overflow. Use strtof_check_range() to check for overflow.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param nptr Beginning of the string that's to be converted into float
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \return Converted floating-point value, in float type
- */
-inline float strtof(const char* nptr, char** endptr) {
-  return ParseFloat<float>(nptr, endptr);
-}
-
-/*!
- * \brief A faster implementation of strtof(). See documentation of
- *        std::strtof() for more information. This function will check for
- *        overflow. If the converted value is outside the range for the float
- *        type, errno is set to ERANGE and HUGE_VALF is returned.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param nptr Beginning of the string that's to be converted into float
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \return Converted floating-point value, in float type
- */
-inline float strtof_check_range(const char* nptr, char** endptr) {
-  return ParseFloat<float, true>(nptr, endptr);
-}
-
-/*!
- * \brief A faster implementation of strtod(). See documentation of
- *        std::strtof() for more information. Note that this function does not
- *        check for overflow. Use strtod_check_range() to check for overflow.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param nptr Beginning of the string that's to be converted into double
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \return Converted floating-point value, in double type
- */
-inline double strtod(const char* nptr, char** endptr) {
-  return ParseFloat<double>(nptr, endptr);
-}
-
-/*!
- * \brief A faster implementation of strtod(). See documentation of
- *        std::strtod() for more information. This function will check for
- *        overflow. If the converted value is outside the range for the double
- *        type, errno is set to ERANGE and HUGE_VAL is returned.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param nptr Beginning of the string that's to be converted into double
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \return Converted floating-point value, in float type
- */
-inline double strtod_check_range(const char* nptr, char** endptr) {
-  return ParseFloat<double, true>(nptr, endptr);
-}
-
-/*!
- * \brief A fast string-to-integer convertor, for signed integers
- * TODO: the current version supports only base <= 10
- * \param nptr Beginning of the string that's to be converted into a signed
- *             integer
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \param base Base to use for integer conversion
- * \return Converted value, in SignedIntType
- * \tparam SignedIntType Type of signed integer to be obtained.
- */
-template <typename SignedIntType>
-inline SignedIntType ParseSignedInt(const char* nptr, char** endptr, int base) {
-#ifdef DMLC_USE_CXX11
-  static_assert(std::is_signed<SignedIntType>::value
-                && std::is_integral<SignedIntType>::value,
-                "ParseSignedInt is defined for signed integers only");
-#endif
-  CHECK(base <= 10 && base >= 2);
-  const char* p = nptr;
-  // Skip leading white space, if any. Not necessary
-  while (isspace(*p) ) ++p;
-
-  // Get sign if any
-  bool sign = true;
-  if (*p == '-') {
-    sign = false; ++p;
-  } else if (*p == '+') {
-    ++p;
-  }
-
-  SignedIntType value;
-  const SignedIntType base_val = static_cast<SignedIntType>(base);
-  for (value = 0; isdigit(*p); ++p) {
-    value = value * base_val + static_cast<SignedIntType>(*p - '0');
-  }
-
-  if (endptr) *endptr = (char*)p;  // NOLINT(*)
-  return sign ? value : - value;
-}
-
-/*!
- * \brief A fast string-to-integer convertor, for unsigned integers
- * TODO: the current version supports only base <= 10
- * \param nptr Beginning of the string that's to be converted into an unsigned
- *             integer
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \param base Base to use for integer conversion
- * \return Converted value, in UnsignedIntType
- * \tparam UnsignedIntType Type of unsigned integer to be obtained.
- */
-template <typename UnsignedIntType>
-inline UnsignedIntType ParseUnsignedInt(const char* nptr, char** endptr, int base) {
-#ifdef DMLC_USE_CXX11
-  static_assert(std::is_unsigned<UnsignedIntType>::value
-                && std::is_integral<UnsignedIntType>::value,
-                "ParseUnsignedInt is defined for unsigned integers only");
-#endif
-  CHECK(base <= 10 && base >= 2);
-  const char *p = nptr;
-  // Skip leading white space, if any. Not necessary
-  while (isspace(*p)) ++p;
-
-  // Get sign if any
-  bool sign = true;
-  if (*p == '-') {
-    sign = false; ++p;
-  } else if (*p == '+') {
-    ++p;
-  }
-
-  // we are parsing unsigned, so no minus sign should be found
-  CHECK_EQ(sign, true);
-
-  UnsignedIntType value;
-  const UnsignedIntType base_val = static_cast<UnsignedIntType>(base);
-  for (value = 0; isdigit(*p); ++p) {
-    value = value * base_val + static_cast<UnsignedIntType>(*p - '0');
-  }
-
-  if (endptr) *endptr = (char*)p; // NOLINT(*)
-  return value;
-}
-
-/*!
- * \brief A faster implementation of strtoull(). See documentation of
- *        std::strtoull() for more information. Note that this function does not
- *        check for overflow.
- * TODO: the current version supports only base <= 10
- * \param nptr Beginning of the string that's to be converted into integer of
- *             type unsigned long long
- * \param endptr After the conversion, this pointer will be set to point one
- *               past the last character used in the conversion.
- * \param base Base to use for integer conversion
- * \return Converted value, as unsigned 64-bit integer
- */
-inline uint64_t strtoull(const char* nptr, char **endptr, int base) {
-  return ParseUnsignedInt<uint64_t>(nptr, endptr, base);
-}
-
-/*!
- * \brief A faster implementation of atol(). See documentation of std::atol()
- *        for more information. This function will use base 10. Note that this
- *        function does not check for overflow.
- * \param p Beginning of the string that's to be converted into integer of
- *          type long
- * \return Converted value, as long integer (width is system-dependent)
- */
-inline long atol(const char* p) {  // NOLINT(*)
-  return ParseSignedInt<long>(p, 0, 10); // NOLINT(*)
-}
-
-/*!
- * \brief A faster implementation of atof(). Unlike std::atof(), this function
- *        returns float type. Note that this function does not check for overflow.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param nptr Beginning of the string that's to be converted into float
- * \return Converted value, in float type
- */
-inline float atof(const char* nptr) {
-  return strtof(nptr, 0);
-}
-
-/*!
- * \brief A faster implementation of stof(). See documentation of std::stof()
- *        for more information. This function will test for overflow and
- *        invalid arguments.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param value The string to convert into float
- * \param pos If not null, it will store the number of characters processed
- * \return Converted value, in float type
- * \throw std::out_of_range If the converted value would fall out of the range
- *                          of the double type
- * \throw std::invalid_argument If no conversion could be performed
- */
-inline float stof(const std::string& value, size_t* pos = nullptr) {
-  const char* str_source = value.c_str();
-  char* endptr;
-  const float parsed_value = dmlc::strtof_check_range(str_source, &endptr);
-  if (errno == ERANGE && parsed_value == std::numeric_limits<float>::infinity()) {
-    throw std::out_of_range("Out of range value");
-  } else if (const_cast<const char*>(endptr) == str_source) {
-    throw std::invalid_argument("No conversion could be performed");
-  }
-  if (pos) {
-    *pos = static_cast<size_t>(const_cast<const char*>(endptr) - str_source);
-  }
-  return parsed_value;
-}
-
-/*!
- * \brief A faster implementation of stod(). See documentation of std::stod()
- *        for more information. This function will test for overflow and
- *        invalid arguments.
- * TODO: the current version does not support hex number
- * TODO: the current version does not handle long decimals: you may only have
- *       up to 19 digits after the decimal point, and you cannot have too many
- *       digits before the decimal point either.
- * \param value The string to convert into double
- * \param pos If not null, it will store the number of characters processed
- * \return Converted value, in double type
- * \throw std::out_of_range If the converted value would fall out of the range
- *                          of the double type
- * \throw std::invalid_argument If no conversion could be performed
- */
-inline double stod(const std::string& value, size_t* pos = nullptr) {
-  const char* str_source = value.c_str();
-  char* endptr;
-  const double parsed_value = dmlc::strtod_check_range(str_source, &endptr);
-  if (errno == ERANGE && parsed_value == std::numeric_limits<double>::infinity()) {
-    throw std::out_of_range("Out of range value");
-  } else if (const_cast<const char*>(endptr) == str_source) {
-    throw std::invalid_argument("No conversion could be performed");
-  }
-  if (pos) {
-    *pos = static_cast<size_t>(const_cast<const char*>(endptr) - str_source);
-  }
-  return parsed_value;
-}
-
-/*!
- * \brief Interface class that defines a single method get() to convert
- *        a string into type T. Define template specialization of this class
- *        to define the conversion method for a particular type.
- * \tparam Type of converted value
- */
-template<typename T>
-class Str2T {
- public:
-  /*!
-   * \brief Convert a string into type T
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, in type T
-   */
-  static inline T get(const char * begin, const char * end);
-};
-
-/*!
- * \brief Convenience function for converting string into type T
- * \param begin Beginning of the string to convert
- * \param end End of the string to convert
- * \return Converted value, in type T
- * \tparam Type of converted value
- */
-template<typename T>
-inline T Str2Type(const char * begin, const char * end) {
-  return Str2T<T>::get(begin, end);
-}
-
-/*!
- * \brief Template specialization of Str2T<> interface for signed 32-bit integer
- */
-template<>
-class Str2T<int32_t> {
- public:
-  /*!
-   * \brief Convert a string into signed 32-bit integer
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, as signed 32-bit integer
-   */
-  static inline int32_t get(const char * begin, const char * end) {
-    return ParseSignedInt<int32_t>(begin, NULL, 10);
-  }
-};
-
-/*!
- * \brief Template specialization of Str2T<> interface for unsigned 32-bit integer
- */
-template<>
-class Str2T<uint32_t> {
- public:
-  /*!
-   * \brief Convert a string into unsigned 32-bit integer
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, as unsigned 32-bit integer
-   */
-  static inline uint32_t get(const char* begin, const char* end) {
-    return ParseUnsignedInt<uint32_t>(begin, NULL, 10);
-  }
-};
-
-/*!
- * \brief Template specialization of Str2T<> interface for signed 64-bit integer
- */
-template<>
-class Str2T<int64_t> {
- public:
-  /*!
-   * \brief Convert a string into signed 64-bit integer
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, as signed 64-bit integer
-   */
-  static inline int64_t get(const char * begin, const char * end) {
-    return ParseSignedInt<int64_t>(begin, NULL, 10);
-  }
-};
-
-/*!
- * \brief Template specialization of Str2T<> interface for unsigned 64-bit integer
- */
-template<>
-class Str2T<uint64_t> {
- public:
-  /*!
-   * \brief Convert a string into unsigned 64-bit integer
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, as unsigned 64-bit integer
-   */
-  static inline uint64_t get(const char * begin, const char * end) {
-    return ParseUnsignedInt<uint64_t>(begin, NULL, 10);
-  }
-};
-
-/*!
- * \brief Template specialization of Str2T<> interface for float type
- */
-template<>
-class Str2T<float> {
- public:
-  /*!
-   * \brief Convert a string into float
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, in float type
-   */
-  static inline float get(const char * begin, const char * end) {
-    return atof(begin);
-  }
-};
-
-/*!
- * \brief Template specialization of Str2T<> interface for double type
- */
-template<>
-class Str2T<double> {
- public:
-  /*!
-   * \brief Convert a string into double
-   * \param begin Beginning of the string to convert
-   * \param end End of the string to convert
-   * \return Converted value, in double type
-   */
-  static inline double get(const char * begin, const char * end) {
-    return strtod(begin, 0);
-  }
-};
-
-/*!
- * \brief Parse colon seperated pair v1[:v2]
- * \param begin pointer to string
- * \param end one past end of string
- * \param endptr After conversion, will be set to one past of parsed string
- * \param v1 first value in the pair
- * \param v2 second value in the pair
- * \return number of values parsed
- * \tparam T1 type of v1
- * \tparam T2 type of v2
- */
-template<typename T1, typename T2>
-inline int ParsePair(const char * begin, const char * end,
-                     const char ** endptr, T1 &v1, T2 &v2) { // NOLINT(*)
-  const char * p = begin;
-  while (p != end && !isdigitchars(*p)) ++p;
-  if (p == end) {
-    *endptr = end;
-    return 0;
-  }
-  const char * q = p;
-  while (q != end && isdigitchars(*q)) ++q;
-  v1 = Str2Type<T1>(p, q);
-  p = q;
-  while (p != end && isblank(*p)) ++p;
-  if (p == end || *p != ':') {
-    // only v1
-    *endptr = p;
-    return 1;
-  }
-  p++;
-  while (p != end && !isdigitchars(*p)) ++p;
-  q = p;
-  while (q != end && isdigitchars(*q)) ++q;
-  *endptr = q;
-  v2 = Str2Type<T2>(p, q);
-  return 2;
-}
-
-/*!
- * \brief Parse colon seperated triple v1:v2[:v3]
- * \param begin pointer to string
- * \param end one past end of string
- * \param endptr After conversion, will be set to one past of parsed string
- * \param v1 first value in the triple
- * \param v2 second value in the triple
- * \param v3 third value in the triple
- * \return number of values parsed
- * \tparam T1 type of v1
- * \tparam T2 type of v2
- * \tparam T3 type of v3
- */
-template<typename T1, typename T2, typename T3>
-inline int ParseTriple(const char * begin, const char * end,
-                       const char ** endptr, T1 &v1, T2 &v2, T3 &v3) { // NOLINT(*)
-  const char * p = begin;
-  while (p != end && !isdigitchars(*p)) ++p;
-  if (p == end) {
-    *endptr = end;
-    return 0;
-  }
-  const char * q = p;
-  while (q != end && isdigitchars(*q)) ++q;
-  v1 = Str2Type<T1>(p, q);
-  p = q;
-  while (p != end && isblank(*p)) ++p;
-  if (p == end || *p != ':') {
-    // only v1
-    *endptr = p;
-    return 1;
-  }
-  p++;
-  while (p != end && !isdigitchars(*p)) ++p;
-  q = p;
-  while (q != end && isdigitchars(*q)) ++q;
-  v2 = Str2Type<T2>(p, q);
-  p = q;
-  while (p != end && isblank(*p)) ++p;
-  if (p == end || *p != ':') {
-    // only v1:v2
-    *endptr = p;
-    return 2;
-  }
-  p++;
-  while (p != end && !isdigitchars(*p)) ++p;
-  q = p;
-  while (q != end && isdigitchars(*q)) ++q;
-  *endptr = q;
-  v3 = Str2Type<T3>(p, q);
-  return 3;
-}
-}  // namespace dmlc
-
-#endif  // DMLC_STRTONUM_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/thread_group.h b/ml-xgboost/dmlc-core/include/dmlc/thread_group.h
deleted file mode 100644
index ced3ae0..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/thread_group.h
+++ /dev/null
@@ -1,808 +0,0 @@
-/*!
- * Copyright (c) 2017 by Contributors
- * \file thread_group.h
- * \brief Thread and synchronization primitives and lifecycle management
- */
-#ifndef DMLC_THREAD_GROUP_H_
-#define DMLC_THREAD_GROUP_H_
-
-#include <dmlc/concurrentqueue.h>
-#include <dmlc/blockingconcurrentqueue.h>
-#include <dmlc/logging.h>
-#include <string>
-#include <mutex>
-#include <utility>
-#include <memory>
-#include <set>
-#include <thread>
-#include <unordered_set>
-#include <unordered_map>
-#if defined(DMLC_USE_CXX14) || __cplusplus > 201103L  /* C++14 */
-#include <shared_mutex>
-#endif
-#include <condition_variable>
-#ifdef __linux__
-#include <unistd.h>
-#include <sys/syscall.h>
-#endif
-
-namespace dmlc {
-
-/*!
- * \brief Simple manual-reset event gate which remains open after signalled
- */
-class ManualEvent {
- public:
-  ManualEvent() : signaled_(false) {}
-
-  /*!
-   * \brief Wait for the object to become signaled.  If the object
-   * is already in the signaled state and reset() has not been called, then no wait will occur
-   */
-  void wait() {
-    std::unique_lock<std::mutex> lock(mutex_);
-    if (!signaled_) {
-      condition_variable_.wait(lock);
-    }
-  }
-
-  /*!
-   * \brief Set this object's state to signaled (wait() will release or pass through)
-   */
-  void signal() {
-    signaled_ = true;
-    std::unique_lock<std::mutex> lk(mutex_);
-    condition_variable_.notify_all();
-  }
-
-  /*!
-   * \brief Manually reset this object's state to unsignaled (wait() will block)
-   */
-  void reset() {
-    std::unique_lock<std::mutex> lk(mutex_);
-    signaled_ = false;
-  }
-
- private:
-  /*! \brief Internal mutex to protect condition variable and signaled_ variable */
-  std::mutex mutex_;
-  /*! \brief Internal condition variable */
-  std::condition_variable condition_variable_;
-  /*! \brief lockfree signal state check */
-  std::atomic<bool> signaled_;
-};
-
-#if defined(DMLC_USE_CXX14) || __cplusplus > 201103L  /* C++14 */
-/*! \brief Mutex which can be read-locked and write-locked */
-using SharedMutex = std::shared_timed_mutex;
-/*! \brief Write lock, disallows both reads and writes */
-using WriteLock = std::unique_lock<SharedMutex>;
-/*! \brief Read lock, allows concurrent data reads */
-using ReadLock = std::shared_lock<SharedMutex>;
-#else
-/*! \brief Standard mutex for C++ < 14 */
-using SharedMutex = std::recursive_mutex;
-/*! \brief Standard unique lock for C++ < 14 */
-using WriteLock = std::unique_lock<SharedMutex>;
-/*! \brief Standard unique lock for C++ < 14 */
-using ReadLock = std::unique_lock<SharedMutex>;
-#endif
-
-/*!
- * \brief Thread lifecycle management group
- * \note See gtest unit tests Syc.* for a usage examples
- */
-class ThreadGroup {
- public:
-  /*!
-   * \brief Lifecycle-managed thread (used by ThreadGroup)
-   * \note See gtest unit tests Syc.* for a usage examples
-   */
-  class Thread {
-   public:
-    /*! \brief Shared pointer type for readability */
-    using SharedPtr = std::shared_ptr<Thread>;
-
-    /*!
-     * \brief Constructor
-     * \param threadName User-defined name of the thread. must be unique per ThreadGroup
-     * \param owner The ThreadGroup object managing the lifecycle of this thread
-     * \param thrd Optionally-assigned std::thread object associated with this Thread class
-     */
-    Thread(std::string threadName, ThreadGroup *owner, std::thread *thrd = nullptr)
-      : name_(std::move(threadName))
-        , thread_(thrd)
-        , ready_event_(std::make_shared<ManualEvent>())
-        , start_event_(std::make_shared<ManualEvent>())
-        , owner_(owner)
-        , shutdown_requested_(false)
-        , auto_remove_(false) {
-      CHECK_NOTNULL(owner);
-    }
-
-    /*!
-     * \brief Destructor with cleanup
-     */
-    virtual ~Thread() {
-      const bool self_delete = is_current_thread();
-      if (!self_delete) {
-        request_shutdown();
-        internal_join(true);
-      }
-      WriteLock guard(thread_mutex_);
-      if (thread_.load()) {
-        std::thread *thrd = thread_.load();
-        thread_ = nullptr;
-        if (self_delete) {
-          thrd->detach();
-        }
-        delete thrd;
-      }
-    }
-
-    /*!
-     * \brief Name of the thread
-     * \return Pointer to the thread name's string
-     * \note This shoul ndly be used as immediate for the sacope of the
-     *       shared pointer pointing to this object
-     */
-    const char *name() const {
-      return name_.c_str();
-    }
-
-    /*!
-     * \brief Launch the given Thread object
-     * \tparam StartFunction Function type for the thread 'main' function
-     * \tparam Args Arguments to pass to the thread 'main' function
-     * \param pThis Shared pointer for the managed thread to launch
-     * \param autoRemove if true, automatically remove this Thread object from the
-     *                   ThreadGroup owner upon exit
-     * \param start_function The Thread's 'main' function
-     * \param args Arguments to pass to the Thread's 'main' function
-     * \return true if the thread was successfully created and added to the ThreadGroup
-     *              If false is returned, the thread may have already been started, but if something
-     *              went wrong (ie duplicte thread name for the ThreadGroup), then request_shutdown()
-     *              will have been been called on the running thread
-     */
-    template<typename StartFunction, typename ...Args>
-    static bool launch(std::shared_ptr<Thread> pThis,
-                       bool autoRemove,
-                       StartFunction start_function,
-                       Args ...args);
-
-    /*!
-     * \brief Check if this class represents the currently running thread (self)
-     * \return true if the current running thread belongs to this class
-     */
-    bool is_current_thread() const {
-      ReadLock guard(thread_mutex_);
-      return thread_.load() ? (thread_.load()->get_id() == std::this_thread::get_id()) : false;
-    }
-
-    /*!
-     * \brief Signal to this thread that a thread shutdown/exit is requested.
-     * \note This is a candidate for overrise in a derived class which may trigger shutdown
-     *       by means other than a boolean (ie condition variable, SimpleManualkEvent, etc).
-     */
-    virtual void request_shutdown() {
-      shutdown_requested_ = true;
-    }
-
-    /*!
-     * \brief Check whether shutdown has been requested (request_shutdown() was called)
-     * \return true if shutdown was requested.
-     * \note This may be overriden to match an overriden to match an overriden 'request_shutdown()',
-     *       for instance.
-     */
-    virtual bool is_shutdown_requested() const {
-      return shutdown_requested_.load();
-    }
-
-    /*!
-     * \brief Check whether the thread is set to auto-remove itself from the ThreadGroup owner
-     *        when exiting
-     * \return true if the thread will auto-remove itself from the ThreadGroup owner
-     *        when exiting
-     */
-    bool is_auto_remove() const {
-      return auto_remove_;
-    }
-
-    /*!
-     * \brief Make the thread joinable (by removing the auto_remove flag)
-     * \warning Care should be taken not to cause a race condition between this call
-     *          and parallel execution of this thread auto-removing itself
-     */
-    void make_joinable() {
-      auto_remove_ = false;
-    }
-
-    /*!
-     * \brief Check whether the thread is joinable
-     * \return true if the thread is joinable
-     */
-    bool joinable() const {
-      if (thread_.load()) {
-        CHECK_EQ(auto_remove_, false);
-        // be checked by searching the group or exit event.
-        return thread_.load()->joinable();
-      }
-      return false;
-    }
-
-    /*!
-     * \brief Thread join
-     * \note join() may not be called on auto-remove threads
-     */
-    void join() {
-      internal_join(false);
-    }
-
-    /*!
-     * \brief Get this thread's id
-     * \return this thread's id
-     */
-    std::thread::id get_id() const {
-      return thread_.load()->get_id();
-    }
-
-   private:
-    /*!
-     * \brief Internal join function
-     * \param auto_remove_ok Whether to allow join on an auto-remove thread
-     */
-    void internal_join(bool auto_remove_ok) {
-      ReadLock guard(thread_mutex_);
-      // should be careful calling (or any function externally) this when in
-      // auto-remove mode
-      if (thread_.load() && thread_.load()->get_id() != std::thread::id()) {
-        std::thread::id someId;
-        if (!auto_remove_ok) {
-          CHECK_EQ(auto_remove_, false);
-        }
-        CHECK_NOTNULL(thread_.load());
-        if (thread_.load()->joinable()) {
-          thread_.load()->join();
-        } else {
-          LOG(WARNING) << "Thread " << name_ << " ( "
-                       << thread_.load()->get_id() << " ) not joinable";
-        }
-      }
-    }
-
-    /*!
-     * \brief Thread bootstrapping and teardown wrapper
-     * \tparam StartFunction Thread's "main" function
-     * \tparam Args Argument types to be passed to the start_function
-     * \param pThis Shared pointer to the Thread object to operate upon
-     * \param start_function Thread's "main" function (i.e. passed to launch())
-     * \param args Arguments to be passed to the start_function
-     * \return The thread's return code
-     */
-    template <typename StartFunction, typename ...Args>
-    static int entry_and_exit_f(std::shared_ptr<Thread> pThis,
-                                StartFunction start_function,
-                                Args... args);
-    /*! \brief Thread name */
-    std::string name_;
-    /*! \brief Shared mutex for some thread operations */
-    mutable SharedMutex thread_mutex_;
-    /*! \brief Pointer to the stl thread object */
-    std::atomic<std::thread *> thread_;
-    /*! \brief Signaled when the thread is started and ready to execute user code */
-    std::shared_ptr<ManualEvent> ready_event_;
-    /*! \brief Thread will block after setting ready_event_ until start_event_ is signaled */
-    std::shared_ptr<ManualEvent> start_event_;
-    /*! \brief The ThreadGroup ownber managing this thread's lifecycle */
-    ThreadGroup *owner_;
-    /*! \brief Flag to determine if shutdown was requested. */
-    std::atomic<bool> shutdown_requested_;
-    /*!
-     * \brief Whether to automatically remove this thread's object from the ThreadGroup when the
-     *        thread exists (perform its own cleanup)
-     */
-    std::atomic<bool> auto_remove_;
-  };
-
-  /*!
-   * \brief Constructor
-   */
-  inline ThreadGroup()
-    : evEmpty_(std::make_shared<ManualEvent>()) {
-    evEmpty_->signal();  // Starts out empty
-  }
-
-  /*!
-   * \brief Destructor, perform cleanup. All child threads will be exited when this
-   *        destructor completes
-   */
-  virtual ~ThreadGroup() {
-    request_shutdown_all();
-    join_all();
-  }
-
-  /*!
-   * \brief Check if the current thread a member if this ThreadGroup
-   * \return true if the current thread is a member of this thread group
-   * \note This lookup involved a linear search, so for a large number of threads,
-   *       is it not advised to call this function in a performance-sensitive area
-   */
-  inline bool is_this_thread_in() const {
-    std::thread::id id = std::this_thread::get_id();
-    ReadLock guard(m_);
-    for (auto it = threads_.begin(), end = threads_.end(); it != end; ++it) {
-      std::shared_ptr<Thread> thrd = *it;
-      if (thrd->get_id() == id)
-        return true;
-    }
-    return false;
-  }
-
-  /*!
-   * \brief Check if the current thread is a member of this ThreadGroup
-   * \param thrd The thread to search for
-   * \return true if the given thread is a member of this ThreadGroup
-   */
-  inline bool is_thread_in(std::shared_ptr<Thread> thrd) const {
-    if (thrd) {
-      std::thread::id id = thrd->get_id();
-      ReadLock guard(m_);
-      for (auto it = threads_.begin(), end = threads_.end(); it != end; ++it) {
-        std::shared_ptr<Thread> thrd = *it;
-        if (thrd->get_id() == id)
-          return true;
-      }
-      return false;
-    } else {
-      return false;
-    }
-  }
-
-  /*!
-   * \brief Add a Thread object to this thread group
-   * \param thrd The thread to add to this ThreadGroup object
-   * \return true if the given thread was added to this ThreadGroup
-   */
-  inline bool add_thread(std::shared_ptr<Thread> thrd) {
-    if (thrd) {
-      WriteLock guard(m_);
-      auto iter = name_to_thread_.find(thrd->name());
-      if (iter == name_to_thread_.end()) {
-        name_to_thread_.emplace(std::make_pair(thrd->name(), thrd));
-        CHECK_EQ(threads_.insert(thrd).second, true);
-        evEmpty_->reset();
-        return true;
-      }
-    }
-    return false;
-  }
-
-  /*!
-   * \brief Remove a Thread object from this thread group
-   * \param thrd The thread to remove from this ThreadGroup object
-   * \return true if the given thread was removed from this ThreadGroup
-   */
-  inline bool remove_thread(std::shared_ptr<Thread> thrd) {
-    if (thrd) {
-      WriteLock guard(m_);
-      auto iter = threads_.find(thrd);
-      if (iter != threads_.end()) {
-        name_to_thread_.erase(thrd->name());
-        threads_.erase(iter);
-        if (threads_.empty()) {
-          evEmpty_->signal();
-        }
-        return true;
-      }
-    }
-    return false;
-  }
-
-  /*!
-   * \brief Join all threads in this ThreadGroup
-   * \note While it is not valid to call 'join' on an auto-remove thread, this function will
-   *       wait for auto-remove threads to exit (waits for the ThreadGroup to become empty)
-   */
-  inline void join_all() {
-    CHECK_EQ(!is_this_thread_in(), true);
-    do {
-      std::unique_lock<std::mutex> lk(join_all_mtx_);
-      std::unordered_set<std::shared_ptr<Thread>> working_set;
-      {
-        ReadLock guard(m_);
-        for (auto iter = threads_.begin(), e_iter = threads_.end(); iter != e_iter; ++iter) {
-          if (!(*iter)->is_auto_remove()) {
-            working_set.emplace(*iter);
-          }
-        }
-      }
-      // Where possible, prefer to do a proper join rather than simply waiting for empty
-      // (easier to troubleshoot)
-      while (!working_set.empty()) {
-        std::shared_ptr<Thread> thrd;
-        thrd = *working_set.begin();
-        if (thrd->joinable()) {
-          thrd->join();
-        }
-        remove_thread(thrd);
-        working_set.erase(working_set.begin());
-        thrd.reset();
-      }
-      // Wait for auto-remove threads (if any) to complete
-    } while (0);
-    evEmpty_->wait();
-    CHECK_EQ(threads_.size(), 0);
-  }
-
-  /*!
-   * \brief Call request_shutdown() on all threads in this ThreadGroup
-   * \param make_all_joinable If true, remove all auto_remove flags from child threads
-   */
-  inline void request_shutdown_all(const bool make_all_joinable = true) {
-    std::unique_lock<std::mutex> lk(join_all_mtx_);
-    ReadLock guard(m_);
-    for (auto &thread : threads_) {
-      if (make_all_joinable) {
-        thread->make_joinable();
-      }
-      thread->request_shutdown();
-    }
-  }
-
-  /*!
-   * \brief Return the number of threads in this thread group
-   * \return Number of threads in this thread group
-   */
-  inline size_t size() const {
-    ReadLock guard(m_);
-    return threads_.size();
-  }
-
-  /*!
-   * \brief Check if the ThreadGroup is empty
-   * \return true if the ThreadGroup is empty
-   */
-  inline bool empty() const {
-    ReadLock guard(m_);
-    return threads_.size() == 0;
-  }
-
-  /*!
-   * \brief Create and launch a new Thread object which will be owned by this ThreadGroup
-   * \tparam StartFunction Function type for the thread 'main' function
-   * \tparam ThreadType managedThreadclass type (in case it's derived, for instance)
-   * \tparam Args Arguments to pass to the thread 'main' function
-   * \param threadName Name if the thread. Must be unique for a ThreadGroup object
-   * \param auto_remove If true, automatically remove this Thread object from the
-   *                    ThreadGroup owner upon exit
-   * \param start_function The Thread's 'main' function
-   * \param args Arguments to pass to the Thread's 'main' function
-   * \return true if the thread was successfully created and added to the ThreadGroup
-   *              If false is returned, the thread may have already been started, but if something
-   *              went wrong (ie duplicte thread name for the ThreadGroup), then request_shutdown()
-   *              will have been been called on the running thread
-   */
-  template<typename StartFunction, typename ThreadType = Thread, typename ...Args>
-  inline bool create(const std::string &threadName,
-                     bool auto_remove,
-                     StartFunction start_function,
-                     Args... args) {
-    typename ThreadType::SharedPtr newThread(new ThreadType(threadName, this));
-    return Thread::launch(newThread, auto_remove, start_function, args...);
-  }
-
-  /*!
-   * \brief Lookup Thread object by name
-   * \param name Name of the thread to look up
-   * \return A shared pointer to the Thread object
-   */
-  inline std::shared_ptr<Thread> thread_by_name(const std::string& name) {
-    ReadLock guard(m_);
-    auto iter = name_to_thread_.find(name);
-    if (iter != name_to_thread_.end()) {
-      return iter->second;
-    }
-    return nullptr;
-  }
-
- private:
-  /*! \brief ThreadGroup synchronization mutex */
-  mutable SharedMutex m_;
-  /*! \brief join_all/auto_remove synchronization mutex */
-  mutable std::mutex join_all_mtx_;
-  /*! \brief Set of threads owned and managed by this ThreadGroup object */
-  std::unordered_set<std::shared_ptr<Thread>> threads_;
-  /*! \brief Manual event which is signaled when the thread group is empty */
-  std::shared_ptr<ManualEvent> evEmpty_;
-  /*! \brief name->thread mapping */
-  std::unordered_map<std::string, std::shared_ptr<Thread>> name_to_thread_;
-};
-
-/*!
- * \brief Blocking queue thread class
- * \tparam ObjectType Object type to queue
- * \tparam quit_item Object value to signify queue shutdown (ie nullptr for pointer type is common)
- * \note See gtest unit test Syc.ManagedThreadLaunchQueueThread for a usage example
- */
-template<typename ObjectType, ObjectType quit_item>
-class BlockingQueueThread : public ThreadGroup::Thread {
-  using BQT = BlockingQueueThread<ObjectType, quit_item>;
-
- public:
-  /*!
-   * \brief Constructor
-   * \param name Name for the blockin g queue thread. Must be unique for a specific ThreadGroup
-   * \param owner ThreadGroup lifecycle manafger/owner
-   * \param thrd Optionally attach an existing stl thread object
-   */
-  BlockingQueueThread(const std::string& name,
-                      dmlc::ThreadGroup *owner,
-                      std::thread *thrd = nullptr)
-    : ThreadGroup::Thread(std::move(name), owner, thrd)
-      , shutdown_in_progress_(false) {
-  }
-
-
-  /*!
-   * \brief Destructor
-   */
-  ~BlockingQueueThread() override {
-    // Call to parent first because we don't want to wait for the queue to empty
-    ThreadGroup::Thread::request_shutdown();
-    request_shutdown();
-  }
-
-  /*!
-   * \brief Signal the thread that a shutdown is desired
-   * \note Since consumer doesn't necessarily get items in order, we must wait for
-   *       the queue to empty.
-   *       This is generally a shutdown procedure and should not be called from
-   *       a performance-sensitive area
-   */
-  void request_shutdown() override {
-    shutdown_in_progress_ = true;
-    while (queue_->size_approx() > 0 && !ThreadGroup::Thread::is_shutdown_requested()) {
-      std::this_thread::sleep_for(std::chrono::milliseconds(1));
-    }
-    ThreadGroup::Thread::request_shutdown();
-    queue_->enqueue(quit_item);
-  }
-
-  /*!
-   * \brief Enqueue and item
-   * \param item The item to enqueue
-   */
-  void enqueue(const ObjectType& item) {
-    if (!shutdown_in_progress_) {
-      queue_->enqueue(item);
-    }
-  }
-
-  /*!
-   * \brief Get the approximate size of the queue
-   * \return The approximate size of the queue
-   */
-  size_t size_approx() const { return queue_->size_approx(); }
-
-  /*!
-   * \brief Launch to the 'run' function which will, in turn, call the class'
-   *        'run' function, passing it the given 'secondary_function'
-   *        for it to call as needed
-   * \tparam SecondaryFunction Type of the secondary function for 'run' override
-   *         to call as needed
-   * \param pThis Pointer to the managed thread to launch
-   * \param secondary_function secondary function for 'run' override to call as needed
-   * \return true if thread is launched successfully and added to the ThreadGroup
-   */
-  template<typename SecondaryFunction>
-  static bool launch_run(std::shared_ptr<BQT> pThis,
-                         SecondaryFunction secondary_function) {
-    return ThreadGroup::Thread::launch(pThis, true, [](std::shared_ptr<BQT> pThis,
-                                                       SecondaryFunction secondary_function) {
-                                         return pThis->run(secondary_function);
-                                       },
-                                       pThis, secondary_function);
-  }
-
-  /*!
-   * \brief Thread's main queue processing function
-   * \tparam OnItemFunction Function type to call when an item is dequeued
-   * \param on_item_function Function to call when an item is dequeued
-   * \return 0 if completed through a `quit_item`, nonzero if on_item_function requested an exit
-   */
-  template<typename OnItemFunction>
-  inline int run(OnItemFunction on_item_function) {
-    int rc = 0;
-    do {
-      ObjectType item;
-      queue_->wait_dequeue(item);
-      if (item == quit_item) {
-        break;
-      }
-      rc = on_item_function(item);
-      if (rc) {
-        break;
-      }
-    } while (true);
-    return rc;
-  }
-
- private:
-  /*! \brief The blocking queue associated with this thread */
-  std::shared_ptr<dmlc::moodycamel::BlockingConcurrentQueue<ObjectType>> queue_ =
-    std::make_shared<dmlc::moodycamel::BlockingConcurrentQueue<ObjectType>>();
-  /*! \brief Whether shutdown request is in progress */
-  std::atomic<bool> shutdown_in_progress_;
-};
-
-/*!
- * \brief Managed timer thread
- * \tparam Duration Duration type (ie seconds, microseconds, etc)
- */
-template<typename Duration>
-class TimerThread : public ThreadGroup::Thread {
-  using ThreadGroup::Thread::is_shutdown_requested;
-
- public:
-  /*!
-   * \brief Constructor
-   * \param name Name of the timer thread
-   * \param owner ThreadGroup owner if the timer thread
-   */
-  TimerThread(const std::string& name, ThreadGroup *owner)
-    : Thread(name, owner) {
-  }
-
-  /*!
-   * \brief Destructor
-   */
-  ~TimerThread() override {
-    request_shutdown();
-  }
-
-  /*!
-   * \brief Launch to the 'run' function which will, in turn, call the class'
-   *        'run' function, passing it the given 'secondary_function'
-   *        for it to call as needed
-   * \tparam SecondaryFunction Type of the secondary function for 'run' override
-   *         to call as needed
-   * \param pThis Pointer to the managed thread to launch
-   * \param secondary_function secondary function for 'run' override to call as needed
-   * \return true if thread is launched successfully and added to the ThreadGroup
-   */
-  template<typename SecondaryFunction>
-  static bool launch_run(std::shared_ptr<TimerThread<Duration>> pThis,
-                         SecondaryFunction secondary_function) {
-    return ThreadGroup::Thread::launch(pThis, true, [](std::shared_ptr<TimerThread<Duration>> pThis,
-                                                       SecondaryFunction secondary_function) {
-                                         return pThis->run(secondary_function);
-                                       },
-                                       pThis, secondary_function);
-  }
-
-  /*!
-   * \brief Start a given timer thread
-   * \tparam Function Type of the timer function
-   * \param timer_thread Thread object to perform the timer events
-   * \param duration Duration between the end end of the timer function and the next timer event
-   * \param function Function to call when the timer expires
-   * \note Calling shutdown_requested() will cause the thread to exit the next time that the timer
-   *       expires.
-   */
-  template<typename Function>
-  static void start(std::shared_ptr<TimerThread> timer_thread,
-                    Duration duration,
-                    Function function) {
-    timer_thread->duration_ = duration;
-    launch_run(timer_thread, function);
-  }
-
-  /*!
-   * \brief Internal timer execution function
-   * \tparam OnTimerFunction Type of function to call each time the timer expires
-   * \param on_timer_function Function to call each time the timer expires
-   * \return Exit code of the thread
-   */
-  template<typename OnTimerFunction>
-  inline int run(OnTimerFunction on_timer_function) {
-    int rc = 0;
-    while (!is_shutdown_requested()) {
-      std::this_thread::sleep_for(duration_);
-      if (!is_shutdown_requested()) {
-        rc = on_timer_function();
-      }
-    }
-    return rc;
-  }
-
- private:
-  Duration duration_;
-};
-
-/*
- * Inline functions - see declarations for usage
- */
-template <typename StartFunction, typename ...Args>
-inline int ThreadGroup::Thread::entry_and_exit_f(std::shared_ptr<Thread> pThis,
-                                                 StartFunction start_function,
-                                                 Args... args) {
-  int rc;
-  if (pThis) {
-    // Signal launcher that we're up and running
-    pThis->ready_event_->signal();
-    // Wait for launcher to be ready for us to start
-    pThis->start_event_->wait();
-    // Reset start_event_ for possible reuse
-    pThis->start_event_->reset();  // Reset in case it needs to be reused
-    // If we haven't been requested to shut down prematurely, then run the desired function
-    if (!pThis->is_shutdown_requested()) {
-      rc = start_function(args...);
-    } else {
-      rc = -1;
-    }
-    // If we're set up as auto-remove, then remove this thread from the thread group
-    if (pThis->is_auto_remove()) {
-      pThis->owner_->remove_thread(pThis);
-    }
-    // Release this thread shared pinter. May or may not be the last reference.
-    pThis.reset();
-  } else {
-    LOG(ERROR) << "Null pThis thread pointer";
-    rc = EINVAL;
-  }
-  return rc;
-}
-
-template<typename StartFunction, typename ...Args>
-inline bool ThreadGroup::Thread::launch(std::shared_ptr<Thread> pThis,
-                                        bool autoRemove,
-                                        StartFunction start_function,
-                                        Args ...args) {
-  WriteLock guard(pThis->thread_mutex_);
-  CHECK_EQ(!pThis->thread_.load(), true);
-  CHECK_NOTNULL(pThis->owner_);
-  // Set auto remove
-  pThis->auto_remove_ = autoRemove;
-  // Create the actual stl thread object
-  pThis->thread_ = new std::thread(Thread::template entry_and_exit_f<
-                                     StartFunction, Args...>,
-                                   pThis,
-                                   start_function,
-                                   args...);
-  // Attempt to add the thread to the thread group (after started, since in case
-  // something goes wrong, there's not a zombie thread in the thread group)
-  if (!pThis->owner_->add_thread(pThis)) {
-    pThis->request_shutdown();
-    LOG(ERROR) << "Duplicate thread name within the same thread group is not allowed";
-  }
-  // Wait for the thread to spin up
-  pThis->ready_event_->wait();
-  // Signal the thgread to continue (it will check its shutdown status)
-  pThis->start_event_->signal();
-  // Return if successful
-  return pThis->thread_.load() != nullptr;
-}
-
-/*!
- * \brief Utility function to easily create a timer
- * \tparam Duration Duration type (i.e. std::chrono::milliseconds)
- * \tparam TimerFunction Function to call each time the timer expires
- * \param timer_name Name of the timer. Must be unique per ThreadGroup object
- * \param duration Duration of the timer between calls to timer_function
- * \param owner ThreadGroup owner of the timer
- * \param timer_function Function to call each time the timer expires
- * \return true if the timer was successfully created
- */
-template<typename Duration, typename TimerFunction>
-inline bool CreateTimer(const std::string& timer_name,
-                        const Duration& duration,
-                        ThreadGroup *owner,
-                        TimerFunction timer_function) {
-  std::shared_ptr<dmlc::TimerThread<Duration>> timer_thread =
-    std::make_shared<dmlc::TimerThread<Duration>>(timer_name, owner);
-  dmlc::TimerThread<Duration>::start(timer_thread, duration, timer_function);
-  return timer_thread != nullptr;
-}
-}  // namespace dmlc
-
-#endif  // DMLC_THREAD_GROUP_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/thread_local.h b/ml-xgboost/dmlc-core/include/dmlc/thread_local.h
deleted file mode 100644
index 5caea4a..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/thread_local.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file thread_local.h
- * \brief Portable thread local storage.
- */
-#ifndef DMLC_THREAD_LOCAL_H_
-#define DMLC_THREAD_LOCAL_H_
-
-#include <mutex>
-#include <memory>
-#include <vector>
-#include "./base.h"
-
-namespace dmlc {
-
-// macro hanlding for threadlocal variables
-#ifdef __GNUC__
-  #define MX_THREAD_LOCAL __thread
-#elif __STDC_VERSION__ >= 201112L
-  #define  MX_THREAD_LOCAL _Thread_local
-#elif defined(_MSC_VER)
-  #define MX_THREAD_LOCAL __declspec(thread)
-#endif
-
-#if DMLC_CXX11_THREAD_LOCAL == 0
-#pragma message("Warning: CXX11 thread_local is not formally supported")
-#endif
-
-/*!
- * \brief A threadlocal store to store threadlocal variables.
- *  Will return a thread local singleton of type T
- * \tparam T the type we like to store
- */
-template<typename T>
-class ThreadLocalStore {
- public:
-  /*! \return get a thread local singleton */
-  static T* Get() {
-#if DMLC_CXX11_THREAD_LOCAL && DMLC_MODERN_THREAD_LOCAL == 1
-    static thread_local T inst;
-    return &inst;
-#else
-    static MX_THREAD_LOCAL T* ptr = nullptr;
-    if (ptr == nullptr) {
-      ptr = new T();
-      // Syntactic work-around for the nvcc of the initial cuda v10.1 release,
-      // which fails to compile 'Singleton()->' below. Fixed in v10.1 update 1.
-      (*Singleton()).RegisterDelete(ptr);
-    }
-    return ptr;
-#endif
-  }
-
- private:
-  /*! \brief constructor */
-  ThreadLocalStore() {}
-  /*! \brief destructor */
-  ~ThreadLocalStore() {
-    for (size_t i = 0; i < data_.size(); ++i) {
-      delete data_[i];
-    }
-  }
-  /*! \return singleton of the store */
-  static ThreadLocalStore<T> *Singleton() {
-    static ThreadLocalStore<T> inst;
-    return &inst;
-  }
-  /*!
-   * \brief register str for internal deletion
-   * \param str the string pointer
-   */
-  void RegisterDelete(T *str) {
-    std::unique_lock<std::mutex> lock(mutex_);
-    data_.push_back(str);
-    lock.unlock();
-  }
-  /*! \brief internal mutex */
-  std::mutex mutex_;
-  /*!\brief internal data */
-  std::vector<T*> data_;
-};
-
-}  // namespace dmlc
-
-#endif  // DMLC_THREAD_LOCAL_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/threadediter.h b/ml-xgboost/dmlc-core/include/dmlc/threadediter.h
deleted file mode 100644
index 6849371..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/threadediter.h
+++ /dev/null
@@ -1,512 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file threadediter.h
- * \brief thread backed iterator that can be used to implement
- *   general thread-based pipeline such as prefetch and pre-computation
- * To use the functions in this header, C++11 is required
- * \author Tianqi Chen
- */
-#ifndef DMLC_THREADEDITER_H_
-#define DMLC_THREADEDITER_H_
-// defines DMLC_USE_CXX11
-#include "./base.h"
-// this code depends on c++11
-#if DMLC_ENABLE_STD_THREAD
-#include <condition_variable>
-#include <functional>
-#include <mutex>
-#include <queue>
-#include <atomic>
-#include <thread>
-#include <utility>
-#include <memory>
-#include "./data.h"
-#include "./logging.h"
-
-namespace dmlc {
-
-/*!
- * \brief Wrapper class to manage std::thread; uses RAII pattern to automatically
- *        join std::thread upon destruction
- */
-class ScopedThread {
- public:
-  /*!
-   * \brief constructor
-   * \param thread thread to manage
-   */
-  explicit ScopedThread(std::thread thread)
-      : thread_(std::move(thread)) {
-    if (!thread_.joinable()) {
-      throw std::logic_error("No thread");
-    }
-  }
-  // destructor: join upon destruction
-  virtual ~ScopedThread() {
-    thread_.join();
-  }
-  // copy assignment and construction are not allowed
-  ScopedThread(ScopedThread const&) = delete;
-  ScopedThread& operator=(ScopedThread const&) = delete;
-
- private:
-  std::thread thread_;
-};
-
-/*!
- * \brief a iterator that was backed by a thread
- *  to pull data eagerly from a single producer into a bounded buffer
- *  the consumer can pull the data at its own rate
- *
- * NOTE: thread concurrency cost time, make sure to store big blob of data in DType
- *
- * Usage example:
- * \code
- * ThreadedIter<DType> iter;
- * iter.Init(&producer);
- * // the following code can be in parallel
- * DType *dptr;
- * while (iter.Next(&dptr)) {
- *   // do something on dptr
- *   // recycle the space
- *   iter.Recycle(&dptr);
- * }
- * \endcode
- * \tparam DType the type of data blob we support
- */
-template<typename DType>
-class ThreadedIter : public DataIter<DType> {
- public:
-  /*!
-   * \brief producer class interface
-   *  that threaditer used as source to
-   *  preduce the content
-   */
-  class Producer {
-   public:
-    // virtual destructor
-    virtual ~Producer() = default;
-    /*! \brief reset the producer to beginning */
-    virtual void BeforeFirst(void) {
-      NotImplemented();
-    }
-    /*!
-     * \brief load the data content into DType,
-     * the caller can pass in NULL or an existing address
-     * when inout_dptr is NULL:
-     *    producer need to allocate a DType and fill the content
-     * when inout_dptr is specified
-     *    producer takes need to fill the content into address
-     *    specified inout_dptr, or delete the one and create a new one
-     *
-     * \param inout_dptr used to pass in the data holder cell
-     *        and return the address of the cell filled
-     * \return true if there is next record, false if we reach the end
-     */
-    virtual bool Next(DType **inout_dptr) = 0;
-  };
-  /*!
-   * \brief constructor
-   * \param max_capacity maximum capacity of the queue
-   */
-  explicit ThreadedIter(size_t max_capacity = 8)
-      : producer_(nullptr),
-        producer_thread_(nullptr),
-        max_capacity_(max_capacity),
-        nwait_consumer_(0),
-        nwait_producer_(0),
-        out_data_(NULL) {}
-  /*! \brief destructor */
-  virtual ~ThreadedIter(void) {
-    this->Destroy();
-  }
-  /*!
-   * \brief destroy all the related resources
-   *  this is equivalent to destructor, can be used
-   *  to destroy the threaditer when user think it is
-   *  appropriate, it is safe to call this multiple times
-   */
-  inline void Destroy(void);
-  /*!
-   * \brief set maximum capacity of the queue
-   * \param max_capacity maximum capacity of the queue
-   */
-  inline void set_max_capacity(size_t max_capacity) {
-    max_capacity_ = max_capacity;
-  }
-  /*!
-   * \brief initialize the producer and start the thread can only be
-   *   called once
-   * \param producer pointer to the producer
-   */
-  inline void Init(std::shared_ptr<Producer> producer);
-  /*!
-   * \brief initialize the producer and start the thread
-   *  pass in two function(closure) of producer to represent the producer
-   *  the beforefirst function is optional, and defaults to not implemented
-   *   NOTE: the closure must remain valid until the ThreadedIter destructs
-   * \param next the function called to get next element, see Producer.Next
-   * \param beforefirst the function to call to reset the producer, see Producer.BeforeFirst
-   */
-  inline void Init(std::function<bool(DType **)> next,
-                   std::function<void()> beforefirst = NotImplemented);
-  /*!
-   * \brief get the next data, this function is threadsafe
-   * \param out_dptr used to hold the pointer to the record
-   *  after the function call, the caller takes ownership of the pointer
-   *  the caller can call recycle to return ownership back to the threaditer
-   *  so that the pointer can be re-used
-   * \return true if there is next record, false if we reach the end
-   * \sa Recycle
-   */
-  inline bool Next(DType **out_dptr);
-  /*!
-   * \brief recycle the data cell, this function is threadsafe
-   * the threaditer can reuse the data cell for future data loading
-   * \param inout_dptr pointer to the dptr to recycle, after the function call
-   *        the content of inout_dptr will be set to NULL
-   */
-  inline void Recycle(DType **inout_dptr);
-
-  /*!
-   * \brief Rethrows exception which is set by the producer
-   */
-  inline void ThrowExceptionIfSet(void);
-
-  /*!
-   * \brief clears exception_ptr, called from Init
-   */
-  inline void ClearException(void);
-
-  /*!
-   * \brief adapt the iterator interface's Next
-   *  NOTE: the call to this function is not threadsafe
-   *  use the other Next instead
-   * \return true if there is next record, false if we reach the end
-   */
-  virtual bool Next(void) {
-    if (out_data_ != NULL) {
-      this->Recycle(&out_data_);
-    }
-    if (Next(&out_data_)) {
-      return true;
-    } else {
-      return false;
-    }
-  }
-  /*!
-   * \brief adapt the iterator interface's Value
-   *  NOTE: the call to this function is not threadsafe
-   *  use the other Next instead
-   */
-  virtual const DType &Value(void) const {
-    CHECK(out_data_ != NULL) << "Calling Value at beginning or end?";
-    return *out_data_;
-  }
-  /*! \brief set the iterator before first location */
-  virtual void BeforeFirst(void) {
-    ThrowExceptionIfSet();
-    std::unique_lock<std::mutex> lock(mutex_);
-    if (out_data_ != NULL) {
-      free_cells_.push(out_data_);
-      out_data_ = NULL;
-    }
-    if (producer_sig_.load(std::memory_order_acquire) == kDestroy)  return;
-
-    producer_sig_.store(kBeforeFirst, std::memory_order_release);
-    CHECK(!producer_sig_processed_.load(std::memory_order_acquire));
-    if (nwait_producer_ != 0) {
-      producer_cond_.notify_one();
-    }
-    CHECK(!producer_sig_processed_.load(std::memory_order_acquire));
-    // wait until the request has been processed
-    consumer_cond_.wait(lock, [this]() {
-        return producer_sig_processed_.load(std::memory_order_acquire);
-      });
-    producer_sig_processed_.store(false, std::memory_order_release);
-    bool notify = nwait_producer_ != 0 && !produce_end_;
-    lock.unlock();
-    // notify producer, in case they are waiting for the condition.
-    if (notify) producer_cond_.notify_one();
-    ThrowExceptionIfSet();
-  }
-
- private:
-  /*! \brief not support BeforeFirst */
-  inline static void NotImplemented(void) {
-    LOG(FATAL) << "BeforeFirst is not supported";
-  }
-  /*! \brief signals send to producer */
-  enum Signal {
-    kProduce,
-    kBeforeFirst,
-    kDestroy
-  };
-  /*! \brief producer class */
-  // Producer *producer_owned_;
-  std::shared_ptr<Producer> producer_;
-
-  /*! \brief signal to producer */
-  std::atomic<Signal> producer_sig_;
-  /*! \brief whether the special signal other than kProduce is procssed */
-  std::atomic<bool> producer_sig_processed_;
-  /*! \brief thread that runs the producer */
-  std::unique_ptr<ScopedThread> producer_thread_;
-  /*! \brief whether produce ends */
-  std::atomic<bool> produce_end_;
-  /*! \brief maximum queue size */
-  size_t max_capacity_;
-  /*! \brief internal mutex */
-  std::mutex mutex_;
-  /*! brief internal mutex for exceptions */
-  std::mutex mutex_exception_;
-  /*! \brief number of consumer waiting */
-  unsigned nwait_consumer_;
-  /*! \brief number of producer waiting */
-  unsigned nwait_producer_;
-  /*! \brief conditional variable for producer thread */
-  std::condition_variable producer_cond_;
-  /*! \brief conditional variable for consumer threads */
-  std::condition_variable consumer_cond_;
-  /*! \brief the current output cell */
-  DType *out_data_;
-  /*! \brief internal queue of producer */
-  std::queue<DType*> queue_;
-  /*! \brief free cells that can be used */
-  std::queue<DType*> free_cells_;
-  /*! \brief holds a reference to iterator exception thrown in spawned threads */
-  std::exception_ptr iter_exception_{nullptr};
-};
-
-// implementation of functions
-template <typename DType> inline void ThreadedIter<DType>::Destroy(void) {
-  if (producer_thread_) {
-    {
-      // lock the mutex
-      std::lock_guard<std::mutex> lock(mutex_);
-      // send destroy signal
-      producer_sig_.store(kDestroy, std::memory_order_release);
-      if (nwait_producer_ != 0) {
-        producer_cond_.notify_one();
-      }
-    }
-    producer_thread_.reset(nullptr);
-  }
-  // end of critical region
-  // now the slave thread should exit
-  while (free_cells_.size() != 0) {
-    delete free_cells_.front();
-    free_cells_.pop();
-  }
-  while (queue_.size() != 0) {
-    delete queue_.front();
-    queue_.pop();
-  }
-  if (producer_ != NULL) {
-    producer_.reset();
-  }
-  if (out_data_ != NULL) {
-    delete out_data_;
-    out_data_ = NULL;
-  }
-}
-
-template<typename DType>
-inline void ThreadedIter<DType>::
-Init(std::shared_ptr<Producer> producer) {
-  CHECK(producer_ == NULL) << "can only call Init once";
-  auto next = [producer](DType **dptr) {
-      return producer->Next(dptr);
-  };
-  auto beforefirst = [producer]() {
-    producer->BeforeFirst();
-  };
-  this->Init(next, beforefirst);
-}
-
-template <typename DType>
-inline void ThreadedIter<DType>::Init(std::function<bool(DType **)> next,
-                                      std::function<void()> beforefirst) {
-  producer_sig_.store(kProduce, std::memory_order_release);
-  producer_sig_processed_.store(false, std::memory_order_release);
-  produce_end_.store(false, std::memory_order_release);
-  ClearException();
-  // procedure running in prodcuer
-  // run producer thread
-  auto producer_fun = [this, next, beforefirst]() {
-    while (true) {
-      try {
-        DType *cell = NULL;
-        {
-          // lockscope
-          std::unique_lock<std::mutex> lock(mutex_);
-          ++this->nwait_producer_;
-          producer_cond_.wait(lock, [this]() {
-            if (producer_sig_.load(std::memory_order_acquire) == kProduce) {
-              bool ret = !produce_end_.load(std::memory_order_acquire)
-                         && (queue_.size() < max_capacity_ ||
-                             free_cells_.size() != 0);
-              return ret;
-            } else {
-              return true;
-            }
-          });
-          --this->nwait_producer_;
-          if (producer_sig_.load(std::memory_order_acquire) == kProduce) {
-            if (free_cells_.size() != 0) {
-              cell = free_cells_.front();
-              free_cells_.pop();
-            }
-          } else if (producer_sig_.load(std::memory_order_acquire) == kBeforeFirst) {
-            // reset the producer
-            beforefirst();
-            // cleanup the queue
-            while (queue_.size() != 0) {
-              free_cells_.push(queue_.front());
-              queue_.pop();
-            }
-            // reset the state
-            produce_end_.store(false, std::memory_order_release);
-            producer_sig_processed_.store(true, std::memory_order_release);
-            producer_sig_.store(kProduce, std::memory_order_release);
-            // notify consumer that all the process as been done.
-            lock.unlock();
-            consumer_cond_.notify_all();
-            continue;
-          } else {
-            // destroy the thread
-            DCHECK(producer_sig_.load(std::memory_order_acquire) == kDestroy);
-            producer_sig_processed_.store(true, std::memory_order_release);
-            produce_end_.store(true, std::memory_order_release);
-            lock.unlock();
-            consumer_cond_.notify_all();
-            return;
-          }
-        }  // end of lock scope
-        // now without lock
-        produce_end_.store(!next(&cell), std::memory_order_release);
-        DCHECK(cell != NULL || produce_end_.load(std::memory_order_acquire));
-        bool notify;
-        {
-          // lockscope
-          std::lock_guard<std::mutex> lock(mutex_);
-          if (!produce_end_.load(std::memory_order_acquire)) {
-            queue_.push(cell);
-          } else {
-            if (cell != NULL)
-              free_cells_.push(cell);
-          }
-          // put things into queue
-          notify = nwait_consumer_ != 0;
-        }
-        if (notify)
-          consumer_cond_.notify_all();
-      } catch (std::exception &e) {
-        // Shouldn't throw exception in destructor
-        DCHECK(producer_sig_.load(std::memory_order_acquire) != kDestroy);
-        {
-          std::lock_guard<std::mutex> lock(mutex_exception_);
-          if (!iter_exception_) {
-            iter_exception_ = std::current_exception();
-          }
-        }
-        bool next_notify = false;
-        {
-          std::unique_lock<std::mutex> lock(mutex_);
-          if (producer_sig_.load(std::memory_order_acquire) == kBeforeFirst) {
-            while (queue_.size() != 0) {
-              free_cells_.push(queue_.front());
-              queue_.pop();
-            }
-            produce_end_.store(true, std::memory_order_release);
-            producer_sig_processed_.store(true, std::memory_order_release);
-            lock.unlock();
-            consumer_cond_.notify_all();
-          } else if (producer_sig_.load(std::memory_order_acquire) == kProduce) {
-            produce_end_.store(true, std::memory_order_release);
-            next_notify = nwait_consumer_ != 0;
-            lock.unlock();
-            if (next_notify)
-              consumer_cond_.notify_all();
-          }
-        }
-        return;
-      }
-    }
-  };
-  producer_thread_.reset(new ScopedThread{std::thread(producer_fun)});
-}
-
-template <typename DType>
-inline bool ThreadedIter<DType>::Next(DType **out_dptr) {
-  if (producer_sig_.load(std::memory_order_acquire) == kDestroy)
-    return false;
-  ThrowExceptionIfSet();
-  std::unique_lock<std::mutex> lock(mutex_);
-  CHECK(producer_sig_.load(std::memory_order_acquire) == kProduce)
-      << "Make sure you call BeforeFirst not inconcurrent with Next!";
-  ++nwait_consumer_;
-  consumer_cond_.wait(lock,
-                      [this]() { return queue_.size() != 0
-                                 || produce_end_.load(std::memory_order_acquire); });
-  --nwait_consumer_;
-  if (queue_.size() != 0) {
-    *out_dptr = queue_.front();
-    queue_.pop();
-    bool notify = nwait_producer_ != 0
-                  && !produce_end_.load(std::memory_order_acquire);
-    lock.unlock();
-    if (notify)
-      producer_cond_.notify_one();
-
-    ThrowExceptionIfSet();
-    return true;
-  } else {
-    CHECK(produce_end_.load(std::memory_order_acquire));
-    lock.unlock();
-
-    ThrowExceptionIfSet();
-    return false;
-  }
-}
-
-template <typename DType>
-inline void ThreadedIter<DType>::Recycle(DType **inout_dptr) {
-  bool notify;
-  ThrowExceptionIfSet();
-  {
-    std::lock_guard<std::mutex> lock(mutex_);
-    free_cells_.push(*inout_dptr);
-    *inout_dptr = NULL;
-    notify = nwait_producer_ != 0 && !produce_end_.load(std::memory_order_acquire);
-  }
-  if (notify)
-    producer_cond_.notify_one();
-  ThrowExceptionIfSet();
-}
-
-template <typename DType> inline void ThreadedIter<DType>::ThrowExceptionIfSet(void) {
-  std::exception_ptr tmp_exception{nullptr};
-  {
-    std::lock_guard<std::mutex> lock(mutex_exception_);
-    if (iter_exception_) {
-      tmp_exception = iter_exception_;
-    }
-  }
-  if (tmp_exception) {
-    try {
-      std::rethrow_exception(tmp_exception);
-    } catch (std::exception& exc) {
-      LOG(FATAL) << exc.what();
-    }
-  }
-}
-
-template <typename DType> inline void ThreadedIter<DType>::ClearException(void) {
-  std::lock_guard<std::mutex> lock(mutex_exception_);
-  iter_exception_ = nullptr;
-}
-
-}  // namespace dmlc
-#endif  // DMLC_USE_CXX11
-#endif  // DMLC_THREADEDITER_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/timer.h b/ml-xgboost/dmlc-core/include/dmlc/timer.h
deleted file mode 100644
index c97059f..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/timer.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file timer.h
- * \brief cross platform timer for timing
- * \author Tianqi Chen
- */
-#ifndef DMLC_TIMER_H_
-#define DMLC_TIMER_H_
-
-#include "base.h"
-
-#if DMLC_USE_CXX11
-#include <chrono>
-#endif
-
-#include <time.h>
-#ifdef __MACH__
-#include <mach/clock.h>
-#include <mach/mach.h>
-#endif
-#include "./logging.h"
-
-namespace dmlc {
-/*!
- * \brief return time in seconds
- */
-inline double GetTime(void) {
-  #if DMLC_USE_CXX11
-  return std::chrono::duration<double>(
-      std::chrono::high_resolution_clock::now().time_since_epoch()).count();
-  #elif defined __MACH__
-  clock_serv_t cclock;
-  mach_timespec_t mts;
-  host_get_clock_service(mach_host_self(), CALENDAR_CLOCK, &cclock);
-  CHECK(clock_get_time(cclock, &mts) == 0) << "failed to get time";
-  mach_port_deallocate(mach_task_self(), cclock);
-  return static_cast<double>(mts.tv_sec) + static_cast<double>(mts.tv_nsec) * 1e-9;
-  #else
-  #if defined(__unix__) || defined(__linux__)
-  timespec ts;
-  CHECK(clock_gettime(CLOCK_REALTIME, &ts) == 0) << "failed to get time";
-  return static_cast<double>(ts.tv_sec) + static_cast<double>(ts.tv_nsec) * 1e-9;
-  #else
-  return static_cast<double>(time(NULL));
-  #endif
-  #endif
-}
-}  // namespace dmlc
-#endif  // DMLC_TIMER_H_
diff --git a/ml-xgboost/dmlc-core/include/dmlc/type_traits.h b/ml-xgboost/dmlc-core/include/dmlc/type_traits.h
deleted file mode 100644
index 8c19816..0000000
--- a/ml-xgboost/dmlc-core/include/dmlc/type_traits.h
+++ /dev/null
@@ -1,192 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file type_traits.h
- * \brief type traits information header
- */
-#ifndef DMLC_TYPE_TRAITS_H_
-#define DMLC_TYPE_TRAITS_H_
-
-#include "./base.h"
-#if DMLC_USE_CXX11
-#include <type_traits>
-#endif
-#include <string>
-
-namespace dmlc {
-/*!
- * \brief whether a type is pod type
- * \tparam T the type to query
- */
-template<typename T>
-struct is_pod {
-#if DMLC_USE_CXX11
-  /*! \brief the value of the traits */
-  static const bool value = std::is_pod<T>::value;
-#else
-  /*! \brief the value of the traits */
-  static const bool value = false;
-#endif
-};
-
-
-/*!
- * \brief whether a type is integer type
- * \tparam T the type to query
- */
-template<typename T>
-struct is_integral {
-#if DMLC_USE_CXX11
-  /*! \brief the value of the traits */
-  static const bool value = std::is_integral<T>::value;
-#else
-  /*! \brief the value of the traits */
-  static const bool value = false;
-#endif
-};
-
-/*!
- * \brief whether a type is floating point type
- * \tparam T the type to query
- */
-template<typename T>
-struct is_floating_point {
-#if DMLC_USE_CXX11
-  /*! \brief the value of the traits */
-  static const bool value = std::is_floating_point<T>::value;
-#else
-  /*! \brief the value of the traits */
-  static const bool value = false;
-#endif
-};
-
-/*!
- * \brief whether a type is arithemetic type
- * \tparam T the type to query
- */
-template<typename T>
-struct is_arithmetic {
-#if DMLC_USE_CXX11
-  /*! \brief the value of the traits */
-  static const bool value = std::is_arithmetic<T>::value;
-#else
-  /*! \brief the value of the traits */
-  static const bool value = (dmlc::is_integral<T>::value ||
-                             dmlc::is_floating_point<T>::value);
-#endif
-};
-
-/*!
- * \brief helper class to construct a string that represents type name
- *
- * Specialized this class to defined type name of custom types
- *
- * \tparam T the type to query
- */
-template<typename T>
-struct type_name_helper {
-  /*!
-   * \return a string of typename.
-   */
-  static inline std::string value() {
-    return "";
-  }
-};
-
-/*!
- * \brief the string representation of type name
- * \tparam T the type to query
- * \return a const string of typename.
- */
-template<typename T>
-inline std::string type_name() {
-  return type_name_helper<T>::value();
-}
-
-/*!
- * \brief whether a type have save/load function
- * \tparam T the type to query
- */
-template<typename T>
-struct has_saveload {
-  /*! \brief the value of the traits */
-  static const bool value = false;
-};
-
-/*!
- * \brief template to select type based on condition
- * For example, IfThenElseType<true, int, float>::Type will give int
- * \tparam cond the condition
- * \tparam Then the typename to be returned if cond is true
- * \tparam Else typename to be returned if cond is false
-*/
-template<bool cond, typename Then, typename Else>
-struct IfThenElseType;
-
-/*! \brief macro to quickly declare traits information */
-#define DMLC_DECLARE_TRAITS(Trait, Type, Value)       \
-  template<>                                          \
-  struct Trait<Type> {                                \
-    static const bool value = Value;                  \
-  }
-
-/*! \brief macro to quickly declare traits information */
-#define DMLC_DECLARE_TYPE_NAME(Type, Name)            \
-  template<>                                          \
-  struct type_name_helper<Type> {                     \
-    static inline std::string value() {               \
-      return Name;                                    \
-    }                                                 \
-  }
-
-//! \cond Doxygen_Suppress
-// declare special traits when C++11 is not available
-#if DMLC_USE_CXX11 == 0
-DMLC_DECLARE_TRAITS(is_pod, char, true);
-DMLC_DECLARE_TRAITS(is_pod, int8_t, true);
-DMLC_DECLARE_TRAITS(is_pod, int16_t, true);
-DMLC_DECLARE_TRAITS(is_pod, int32_t, true);
-DMLC_DECLARE_TRAITS(is_pod, int64_t, true);
-DMLC_DECLARE_TRAITS(is_pod, uint8_t, true);
-DMLC_DECLARE_TRAITS(is_pod, uint16_t, true);
-DMLC_DECLARE_TRAITS(is_pod, uint32_t, true);
-DMLC_DECLARE_TRAITS(is_pod, uint64_t, true);
-DMLC_DECLARE_TRAITS(is_pod, float, true);
-DMLC_DECLARE_TRAITS(is_pod, double, true);
-
-DMLC_DECLARE_TRAITS(is_integral, char, true);
-DMLC_DECLARE_TRAITS(is_integral, int8_t, true);
-DMLC_DECLARE_TRAITS(is_integral, int16_t, true);
-DMLC_DECLARE_TRAITS(is_integral, int32_t, true);
-DMLC_DECLARE_TRAITS(is_integral, int64_t, true);
-DMLC_DECLARE_TRAITS(is_integral, uint8_t, true);
-DMLC_DECLARE_TRAITS(is_integral, uint16_t, true);
-DMLC_DECLARE_TRAITS(is_integral, uint32_t, true);
-DMLC_DECLARE_TRAITS(is_integral, uint64_t, true);
-
-DMLC_DECLARE_TRAITS(is_floating_point, float, true);
-DMLC_DECLARE_TRAITS(is_floating_point, double, true);
-
-#endif
-
-DMLC_DECLARE_TYPE_NAME(float, "float");
-DMLC_DECLARE_TYPE_NAME(double, "double");
-DMLC_DECLARE_TYPE_NAME(int, "int");
-DMLC_DECLARE_TYPE_NAME(int64_t, "long");
-DMLC_DECLARE_TYPE_NAME(uint32_t, "int (non-negative)");
-DMLC_DECLARE_TYPE_NAME(uint64_t, "long (non-negative)");
-DMLC_DECLARE_TYPE_NAME(std::string, "string");
-DMLC_DECLARE_TYPE_NAME(bool, "boolean");
-DMLC_DECLARE_TYPE_NAME(void*, "ptr");
-
-template<typename Then, typename Else>
-struct IfThenElseType<true, Then, Else> {
-  typedef Then Type;
-};
-
-template<typename Then, typename Else>
-struct IfThenElseType<false, Then, Else> {
-  typedef Else Type;
-};
-//! \endcond
-}  // namespace dmlc
-#endif  // DMLC_TYPE_TRAITS_H_
diff --git a/ml-xgboost/dmlc-core/make/dmlc.mk b/ml-xgboost/dmlc-core/make/dmlc.mk
deleted file mode 100644
index eb951bb..0000000
--- a/ml-xgboost/dmlc-core/make/dmlc.mk
+++ /dev/null
@@ -1,95 +0,0 @@
-#---------------------------------------------------------------------------------------
-#  mshadow configuration script
-#
-#  include dmlc.mk after the variables are set
-#
-#  Add DMLC_CFLAGS to the compile flags
-#  Add DMLC_LDFLAGS to the linker flags
-#----------------------------------------------------------------------------------------
-ifndef LIBJVM
-	LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
-endif
-
-# Mac OS X does not support "-lrt" flag
-ifeq ($(OS), Windows_NT)
-	UNAME=Windows
-	MACHINE=Windows
-else
-	UNAME=$(shell uname)
-	MACHINE=$(shell $(CC) -dumpmachine)
-endif
-
-ifneq ($(USE_OPENMP), 0)
-	DMLC_CFLAGS += -fopenmp
-	DMLC_LDFLAGS += -fopenmp
-endif
-
-ifeq (-android, $(findstring -android,$(MACHINE)))
-#$(info $$MACHINE is [${MACHINE}])
-#$(info detected ANDROID)
-else
-ifeq (-linux, $(findstring -linux,$(MACHINE)))
-#$(info detected Linux)
-        DMLC_LDFLAGS += -lrt
-endif
-endif
-
-# handle fpic options
-ifndef WITH_FPIC
-	WITH_FPIC = 1
-endif
-
-ifeq ($(WITH_FPIC), 1)
-	DMLC_CFLAGS += -fPIC
-endif
-
-# Using default hadoop_home
-ifndef HADOOP_HDFS_HOME
-	HADOOP_HDFS_HOME=$(HADOOP_HOME)
-endif
-
-ifeq ($(USE_HDFS),1)
-	ifndef HDFS_INC_PATH
-		HDFS_INC_PATH=$(HADOOP_HDFS_HOME)/include
-	endif
-	ifndef HDFS_LIB_PATH
-		HDFS_LIB_PATH=$(HADOOP_HDFS_HOME)/lib/native
-	endif
-
-	DMLC_CFLAGS+= -DDMLC_USE_HDFS=1 -I$(HDFS_INC_PATH) -I$(JAVA_HOME)/include
-
-	ifneq ("$(wildcard $(HDFS_LIB_PATH)/libhdfs.so)","")
-		DMLC_LDFLAGS+= -L$(HDFS_LIB_PATH) -lhdfs
-	else
-		DMLC_LDFLAGS+= $(HDFS_LIB_PATH)/libhdfs.a
-	endif
-
-	DMLC_LDFLAGS += -L$(LIBJVM) -ljvm
-	ifeq ($(UNAME), Darwin)
-		DMLC_LDFLAGS += -Wl,-rpath,$(LIBJVM)
-	else
-		DMLC_LDFLAGS += -Wl,-rpath=$(LIBJVM)
-	endif
-else
-	DMLC_CFLAGS+= -DDMLC_USE_HDFS=0
-endif
-
-# setup S3
-ifeq ($(USE_S3),1)
-	DMLC_CFLAGS+= -DDMLC_USE_S3=1
-	DMLC_LDFLAGS+= -lcurl -lssl -lcrypto
-else
-	DMLC_CFLAGS+= -DDMLC_USE_S3=0
-endif
-
-ifeq ($(USE_GLOG), 1)
-	DMLC_CFLAGS += -DDMLC_USE_GLOG=1
-	DMLC_LDFLAGS += -lglog
-endif
-
-ifeq ($(USE_AZURE),1)
-	DMLC_CFLAGS+= -DDMLC_USE_AZURE=1
-	DMLC_LDFLAGS+= -lazurestorage
-else
-	DMLC_CFLAGS+= -DDMLC_USE_AZURE=0
-endif
diff --git a/ml-xgboost/dmlc-core/scripts/lint.py b/ml-xgboost/dmlc-core/scripts/lint.py
deleted file mode 100644
index 8e5059c..0000000
--- a/ml-xgboost/dmlc-core/scripts/lint.py
+++ /dev/null
@@ -1,208 +0,0 @@
-#!/usr/bin/env python3
-# pylint: disable=protected-access, unused-variable, locally-disabled, len-as-condition
-"""Lint helper to generate lint summary of source.
-
-Copyright by Contributors
-"""
-from __future__ import print_function
-import argparse
-import codecs
-import sys
-import re
-import os
-import cpplint
-from cpplint import _cpplint_state
-from pylint import epylint
-
-CXX_SUFFIX = set(['cc', 'c', 'cpp', 'h', 'cu', 'hpp'])
-PYTHON_SUFFIX = set(['py'])
-
-def filepath_enumerate(paths):
-    """Enumerate the file paths of all subfiles of the list of paths"""
-    out = []
-    for path in paths:
-        if os.path.isfile(path):
-            out.append(path)
-        else:
-            for root, dirs, files in os.walk(path):
-                for name in files:
-                    out.append(os.path.normpath(os.path.join(root, name)))
-    return out
-
-# pylint: disable=useless-object-inheritance
-class LintHelper(object):
-    """Class to help runing the lint and records summary"""
-
-    @staticmethod
-    def _print_summary_map(strm, result_map, ftype):
-        """Print summary of certain result map."""
-        if len(result_map) == 0:
-            return 0
-        npass = len([x for k, x in result_map.items() if len(x) == 0])
-        strm.write('=====%d/%d %s files passed check=====\n' % (npass, len(result_map), ftype))
-        for fname, emap in result_map.items():
-            if len(emap) == 0:
-                continue
-            strm.write('%s: %d Errors of %d Categories map=%s\n' % (
-                fname, sum(emap.values()), len(emap), str(emap)))
-        return len(result_map) - npass
-
-    def __init__(self):
-        self.project_name = None
-        self.cpp_header_map = {}
-        self.cpp_src_map = {}
-        self.python_map = {}
-        pylint_disable = ['superfluous-parens',
-                          'too-many-instance-attributes',
-                          'too-few-public-methods']
-        # setup pylint
-        self.pylint_opts = ['--extension-pkg-whitelist=numpy',
-                            '--disable=' + ','.join(pylint_disable)]
-
-        self.pylint_cats = set(['error', 'warning', 'convention', 'refactor'])
-        # setup cpp lint
-        cpplint_args = ['.', '--extensions=' + (','.join(CXX_SUFFIX))]
-        _ = cpplint.ParseArguments(cpplint_args)
-        cpplint._SetFilters(','.join(['-build/c++11',
-                                      '-build/namespaces',
-                                      '-build/include,',
-                                      '+build/include_what_you_use',
-                                      '+build/include_order']))
-        cpplint._SetCountingStyle('toplevel')
-        cpplint._line_length = 100
-
-    def process_cpp(self, path, suffix):
-        """Process a cpp file."""
-        _cpplint_state.ResetErrorCounts()
-        cpplint.ProcessFile(str(path), _cpplint_state.verbose_level)
-        _cpplint_state.PrintErrorCounts()
-        errors = _cpplint_state.errors_by_category.copy()
-
-        if suffix == 'h':
-            self.cpp_header_map[str(path)] = errors
-        else:
-            self.cpp_src_map[str(path)] = errors
-
-    def process_python(self, path):
-        """Process a python file."""
-        (pylint_stdout, pylint_stderr) = epylint.py_run(
-            ' '.join([str(path)] + self.pylint_opts), return_std=True)
-        emap = {}
-        err = pylint_stderr.read()
-        if len(err):
-            print(err)
-        for line in pylint_stdout:
-            sys.stderr.write(line)
-            key = line.split(':')[-1].split('(')[0].strip()
-            if key not in self.pylint_cats:
-                continue
-            if key not in emap:
-                emap[key] = 1
-            else:
-                emap[key] += 1
-        self.python_map[str(path)] = emap
-
-    def print_summary(self, strm):
-        """Print summary of lint."""
-        nerr = 0
-        nerr += LintHelper._print_summary_map(strm, self.cpp_header_map, 'cpp-header')
-        nerr += LintHelper._print_summary_map(strm, self.cpp_src_map, 'cpp-source')
-        nerr += LintHelper._print_summary_map(strm, self.python_map, 'python')
-        if nerr == 0:
-            strm.write('All passed!\n')
-        else:
-            strm.write('%d files failed lint\n' % nerr)
-        return nerr
-
-# singleton helper for lint check
-_HELPER = LintHelper()
-
-def get_header_guard_dmlc(filename):
-    """Get Header Guard Convention for DMLC Projects.
-
-    For headers in include, directly use the path
-    For headers in src, use project name plus path
-
-    Examples: with project-name = dmlc
-        include/dmlc/timer.h -> DMLC_TIMTER_H_
-        src/io/libsvm_parser.h -> DMLC_IO_LIBSVM_PARSER_H_
-    """
-    fileinfo = cpplint.FileInfo(filename)
-    file_path_from_root = fileinfo.RepositoryName()
-    inc_list = ['include', 'api', 'wrapper', 'contrib']
-    if os.name == 'nt':
-        inc_list.append("mshadow")
-
-    if file_path_from_root.find('src/') != -1 and _HELPER.project_name is not None:
-        idx = file_path_from_root.find('src/')
-        file_path_from_root = _HELPER.project_name +  file_path_from_root[idx + 3:]
-    else:
-        idx = file_path_from_root.find("include/")
-        if idx != -1:
-            file_path_from_root = file_path_from_root[idx + 8:]
-        for spath in inc_list:
-            prefix = spath + '/'
-            if file_path_from_root.startswith(prefix):
-                file_path_from_root = re.sub('^' + prefix, '', file_path_from_root)
-                break
-    return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_'
-
-cpplint.GetHeaderGuardCPPVariable = get_header_guard_dmlc
-
-def process(fname, allow_type):
-    """Process a file."""
-    fname = str(fname)
-    arr = fname.rsplit('.', 1)
-    if fname.find('#') != -1 or arr[-1] not in allow_type:
-        return
-    if arr[-1] in CXX_SUFFIX:
-        _HELPER.process_cpp(fname, arr[-1])
-    if arr[-1] in PYTHON_SUFFIX:
-        _HELPER.process_python(fname)
-
-def main():
-    """Main entry function."""
-    parser = argparse.ArgumentParser(description="lint source codes")
-    parser.add_argument('project', help='project name')
-    parser.add_argument('filetype', choices=['python', 'cpp', 'all'],
-                        help='source code type')
-    parser.add_argument('path', nargs='+', help='path to traverse')
-    parser.add_argument('--exclude_path', nargs='+', default=[],
-                        help='exclude this path, and all subfolders if path is a folder')
-    parser.add_argument('--pylint-rc', default=None,
-                        help='pylint rc file')
-    args = parser.parse_args()
-
-    _HELPER.project_name = args.project
-    if args.pylint_rc is not None:
-        _HELPER.pylint_opts = ['--rcfile='+args.pylint_rc,]
-    file_type = args.filetype
-    allow_type = []
-    if file_type in ('python', 'all'):
-        allow_type += PYTHON_SUFFIX
-    if file_type in ('cpp', 'all'):
-        allow_type += CXX_SUFFIX
-    allow_type = set(allow_type)
-    if sys.version_info.major == 2 and os.name != 'nt':
-        sys.stderr = codecs.StreamReaderWriter(sys.stderr,
-                                               codecs.getreader('utf8'),
-                                               codecs.getwriter('utf8'),
-                                               'replace')
-    # get excluded files
-    excluded_paths = filepath_enumerate(args.exclude_path)
-    for path in args.path:
-        if os.path.isfile(path):
-            normpath = os.path.normpath(path)
-            if normpath not in excluded_paths:
-                process(path, allow_type)
-        else:
-            for root, dirs, files in os.walk(path):
-                for name in files:
-                    file_path = os.path.normpath(os.path.join(root, name))
-                    if file_path not in excluded_paths:
-                        process(file_path, allow_type)
-    nerr = _HELPER.print_summary(sys.stderr)
-    sys.exit(nerr > 0)
-
-if __name__ == '__main__':
-    main()
diff --git a/ml-xgboost/dmlc-core/scripts/packages.mk b/ml-xgboost/dmlc-core/scripts/packages.mk
deleted file mode 100644
index 56c8ef9..0000000
--- a/ml-xgboost/dmlc-core/scripts/packages.mk
+++ /dev/null
@@ -1,19 +0,0 @@
-# Makfile for easily install dependencies
-
-# List of packages here
-.PHONY: gtest
-
-# rules for gtest
-/tmp/gtest/include/gtest:
-	rm -rf gtest release-1.10.0.zip
-	wget https://github.com/google/googletest/archive/release-1.10.0.zip
-	unzip release-1.10.0.zip
-	mv googletest-release-1.10.0 gtest
-	cd gtest; $(CXX) $(CXXFLAGS) -std=c++11 -Igoogletest -Igoogletest/include -pthread -c googletest/src/gtest-all.cc -o gtest-all.o; cd ..
-	$(AR) -rv libgtest.a gtest/gtest-all.o
-	mkdir -p /tmp/gtest/include /tmp/gtest/lib
-	cp -r gtest/googletest/include/gtest /tmp/gtest/include
-	mv libgtest.a /tmp/gtest/lib
-	rm -rf release-1.10.0.zip
-
-gtest: | /tmp/gtest/include/gtest
diff --git a/ml-xgboost/dmlc-core/scripts/s390x/Dockerfile b/ml-xgboost/dmlc-core/scripts/s390x/Dockerfile
deleted file mode 100644
index 3a52f9d..0000000
--- a/ml-xgboost/dmlc-core/scripts/s390x/Dockerfile
+++ /dev/null
@@ -1,28 +0,0 @@
-FROM multiarch/debian-debootstrap:s390x-jessie
-
-ENV DEBIAN_FRONTEND noninteractive
-ENV GOSU_VERSION 1.10
-
-RUN uname -a
-# Add repositories for Debian Jessie
-RUN printf 'deb http://deb.debian.org/debian/ oldstable main contrib non-free\ndeb-src http://deb.debian.org/debian/ oldstable main contrib non-free\ndeb http://deb.debian.org/debian/ oldstable-updates main contrib non-free\ndeb-src http://deb.debian.org/debian/ oldstable-updates main contrib non-free\ndeb http://deb.debian.org/debian-security oldstable/updates main' > /etc/apt/sources.list
-RUN apt-get update && \
-  apt-get install -y --no-install-suggests --no-install-recommends \
-  build-essential \
-  gcc \
-  make \
-  git \
-  cmake \
-  ca-certificates && \
-  update-ca-certificates --fresh
-
-# Install lightweight sudo (not bound to TTY)
-RUN set -ex; \
-    wget --no-check-certificate -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
-    chmod +x /usr/local/bin/gosu && \
-    gosu nobody true
-
-COPY entrypoint.sh /scripts/
-
-WORKDIR /workspace
-ENTRYPOINT ["/scripts/entrypoint.sh"]
diff --git a/ml-xgboost/dmlc-core/scripts/s390x/build_via_cmake.sh b/ml-xgboost/dmlc-core/scripts/s390x/build_via_cmake.sh
deleted file mode 100644
index d1f16f1..0000000
--- a/ml-xgboost/dmlc-core/scripts/s390x/build_via_cmake.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-rm -rf build
-mkdir -p build
-cd build
-cmake .. -DGOOGLE_TEST=ON -DCMAKE_VERBOSE_MAKEFILE=ON
-make -j$(nproc)
diff --git a/ml-xgboost/dmlc-core/scripts/s390x/ci_build.sh b/ml-xgboost/dmlc-core/scripts/s390x/ci_build.sh
deleted file mode 100644
index 40ab911..0000000
--- a/ml-xgboost/dmlc-core/scripts/s390x/ci_build.sh
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env bash
-#
-# Execute command within a docker container
-#
-# Usage: ci_build.sh <DOCKER_IMG_NAME> [-e ENV_VAR] [-it] <COMMAND>
-#
-# DOCKER_IMG_NAME: Docker image name
-# COMMAND: Command to be executed in the docker container
-#
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-# Validate command line arguments.
-if [ "$#" -lt 1 ]
-then
-    echo "Usage: $(basename $0) DOCKER_IMG_NAME COMMAND"
-    exit 1
-fi
-
-DOCKER_IMG_NAME="$1"
-shift 1
-
-while [[ "$1" == "-e" ]]; do
-    ENV_VAR="$2"
-    CI_DOCKER_EXTRA_PARAMS+=('-e' "${ENV_VAR}")
-    shift 2
-done
-
-if [[ "$1" == "-it" ]]; then
-    CI_DOCKER_EXTRA_PARAMS+=('-it')
-    shift 1
-fi
-
-COMMAND=("$@")
-
-DOCKER_BINARY="docker"
-DOCKER_CONTEXT_PATH="${SCRIPT_DIR}"
-WORKSPACE="${WORKSPACE:-${SCRIPT_DIR}/../../}"
-
-# Bash on Ubuntu on Windows
-UBUNTU_ON_WINDOWS=$([ -e /proc/version ] && grep -l Microsoft /proc/version || echo "")
-# MSYS, Git Bash, etc.
-MSYS=$([ -e /proc/version ] && grep -l MINGW /proc/version || echo "")
-
-if [[ -z "$UBUNTU_ON_WINDOWS" ]] && [[ -z "$MSYS" ]]; then
-    USER_IDS="-e CI_BUILD_UID=$( id -u ) -e CI_BUILD_GID=$( id -g ) -e CI_BUILD_USER=$( id -un ) -e CI_BUILD_GROUP=$( id -gn ) -e CI_BUILD_HOME=${WORKSPACE}"
-fi
-
-# Print arguments.
-cat <<EOF
-   WORKSPACE: ${WORKSPACE}
-   CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[*]}
-   COMMAND: ${COMMAND[*]}
-   DOCKER CONTAINER NAME: ${DOCKER_IMG_NAME}
-   USER_IDS: ${USER_IDS}
-EOF
-
-
-# Build the docker container.
-echo "Building container (${DOCKER_IMG_NAME})..."
-# --pull should be default
-docker build \
-    -t "${DOCKER_IMG_NAME}" \
-    "${DOCKER_CONTEXT_PATH}"
-
-# Check docker build status
-if [[ $? != "0" ]]; then
-    echo "ERROR: docker build failed."
-    exit 1
-fi
-
-
-# Run the command inside the container.
-echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..."
-
-# By default we cleanup - remove the container once it finish running (--rm)
-# and share the PID namespace (--pid=host) so the process inside does not have
-# pid 1 and SIGKILL is propagated to the process inside (jenkins can kill it).
-
-${DOCKER_BINARY} run --rm --pid=host \
-    -v "${WORKSPACE}":/workspace \
-    -w /workspace \
-    ${USER_IDS} \
-    "${CI_DOCKER_EXTRA_PARAMS[@]}" \
-    "${DOCKER_IMG_NAME}" \
-    "${COMMAND[@]}"
-
diff --git a/ml-xgboost/dmlc-core/scripts/s390x/entrypoint.sh b/ml-xgboost/dmlc-core/scripts/s390x/entrypoint.sh
deleted file mode 100644
index 8e83039..0000000
--- a/ml-xgboost/dmlc-core/scripts/s390x/entrypoint.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env bash
-
-# This script is a wrapper creating the same user inside container as the one
-# running the ci_build.sh outside the container. It also set the home directory
-# for the user inside container to match the same absolute path as the workspace
-# outside of container.  Do not run this manually. It does not make sense. It is
-# intended to be called by ci_build.sh only.
-
-set -e
-
-COMMAND=("$@")
-
-if ! touch /this_is_writable_file_system; then
-  echo "You can't write to your filesystem!"
-  echo "If you are in Docker you should check you do not have too many images" \
-      "with too many files in them. Docker has some issue with it."
-  exit 1
-else
-  rm /this_is_writable_file_system
-fi
-
-if [[ -n $CI_BUILD_UID ]] && [[ -n $CI_BUILD_GID ]]; then
-    groupadd -o -g "${CI_BUILD_GID}" "${CI_BUILD_GROUP}"
-    useradd -o -m -g "${CI_BUILD_GID}" -u "${CI_BUILD_UID}" \
-        "${CI_BUILD_USER}"
-    export HOME="/home/${CI_BUILD_USER}"
-    shopt -s dotglob
-    cp -r /root/* "$HOME/"
-    chown -R "${CI_BUILD_UID}:${CI_BUILD_GID}" "$HOME"
-
-    # Allows project-specific customization
-    if [[ -e "/workspace/.pre_entry.sh" ]]; then
-        gosu "${CI_BUILD_UID}:${CI_BUILD_GID}" /workspace/.pre_entry.sh
-    fi
-
-    # Enable passwordless sudo capabilities for the user
-    chown root:"${CI_BUILD_GID}" "$(which gosu)"
-    chmod +s "$(which gosu)"; sync
-
-    exec gosu "${CI_BUILD_UID}:${CI_BUILD_GID}" "${COMMAND[@]}"
-else
-    exec "${COMMAND[@]}"
-fi
diff --git a/ml-xgboost/dmlc-core/scripts/test_script.sh b/ml-xgboost/dmlc-core/scripts/test_script.sh
deleted file mode 100644
index 7e0449c..0000000
--- a/ml-xgboost/dmlc-core/scripts/test_script.sh
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/bin/bash
-
-set -e
-set -x
-
-if [[ ${TASK} == "lint" ]]; then
-    # stop the build if there are Python syntax errors or undefined names
-    python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
-    # exit-zero treats all errors as warnings.  The GitHub editor is 127 chars wide
-    python3 -m flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-
-    make lint
-    make doxygen 2>log.txt
-    (cat log.txt| grep -v ENABLE_PREPROCESSING |grep -v "unsupported tag" |grep warning) && exit 1
-    exit 0
-fi
-
-# For all tests other than s390x_test, expect little endian
-export DMLC_UNIT_TEST_LITTLE_ENDIAN=1
-
-if [[ ${TASK} == "unittest_gtest" ]]; then
-    cp make/config.mk .
-    if [[ $(uname) != "Darwin" ]]; then
-        echo "USE_S3=1" >> config.mk
-        echo "export CXX = g++-4.8" >> config.mk
-        export CXX=g++-4.8
-    else
-        echo "USE_S3=0" >> config.mk
-        echo "USE_OPENMP=1" >> config.mk
-        echo "export CXX=g++-8" >> config.mk
-        export CXX=g++-8
-    fi
-    make -f scripts/packages.mk gtest
-    echo "GTEST_PATH="/tmp/gtest >> config.mk
-    echo "BUILD_TEST=1" >> config.mk
-    make all
-    test/unittest/dmlc_unittest
-fi
-
-if [[ ${TASK} == "cmake_test" ]]; then
-    # Build dmlc-core with CMake, including unit tests
-    rm -rf build
-    mkdir build && cd build
-    cmake .. -DGOOGLE_TEST=ON
-    make
-    cd ..
-    ./build/test/unittest/dmlc_unit_tests
-fi
-
-if [[ ${TASK} == "sanitizer_test" ]]; then
-    rm -rf build
-    mkdir build && cd build
-    cmake .. -DGOOGLE_TEST=ON -DDMLC_USE_SANITIZER=ON \
-             -DDMLC_ENABLED_SANITIZERS="thread" -DCMAKE_BUILD_TYPE=Debug ..
-    make -j2
-    cd ..
-    ./build/test/unittest/dmlc_unit_tests || true   # For now just display sanitizer errors
-fi
-
-if [[ ${TASK} == "s390x_test" ]]; then
-    # Run unit tests inside emulated s390x Docker container (uses QEMU transparently).
-    # This should help us achieve compatibility with big endian targets.
-    scripts/s390x/ci_build.sh s390_container scripts/s390x/build_via_cmake.sh
-    scripts/s390x/ci_build.sh s390_container -e DMLC_UNIT_TEST_LITTLE_ENDIAN=0 build/test/unittest/dmlc_unit_tests
-fi
diff --git a/ml-xgboost/dmlc-core/src/config.cc b/ml-xgboost/dmlc-core/src/config.cc
deleted file mode 100644
index 4b25356..0000000
--- a/ml-xgboost/dmlc-core/src/config.cc
+++ /dev/null
@@ -1,279 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- */
-#include <sstream>
-#include <exception>
-
-#include "dmlc/config.h"
-#include "dmlc/logging.h"
-
-using namespace std;
-
-namespace dmlc {
-
-struct Token {
-  std::string buf;
-  bool is_string;
-};
-
-class TokenizeError : public exception {
- public:
-  explicit TokenizeError(const string& msg = "tokenize error"): msg_(msg) { }
-  ~TokenizeError() throw() {}
-  virtual const char* what() const throw() {
-    return msg_.c_str();
-  }
- private:
-  string msg_;
-};
-
-class Tokenizer {
- public:
-  explicit Tokenizer(istream& is): is_(is), state_(kNone) {}  // NOLINT(*)
-  bool GetNextToken(Token* tok) {
-    // token is defined as
-    // 1. [^\s=]+
-    // 2. "[(^"|\\")]*"
-    // 3. =
-    state_ = kNone;
-    tok->buf.clear();
-    tok->is_string = false;
-    int ch;
-    while ( (ch = PeekChar()) != EOF && state_ != kFinish ) {
-      switch (ch) {
-      case ' ': case '\t': case '\n': case '\r':
-        if (state_ == kToken) {
-          state_ = kFinish;
-        } else {
-          EatChar();  // ignore
-        }
-        break;
-      case '\"':
-        ParseString(&tok->buf);
-        state_ = kFinish;
-        tok->is_string = true;
-        break;
-      case '=':
-        if (state_ != kToken) {
-          tok->buf = '=';
-          EatChar();
-        }
-        state_ = kFinish;
-        break;
-      case '#':
-        ParseComments();
-        break;
-      default:
-        state_ = kToken;
-        tok->buf += ch;
-        EatChar();
-        break;
-      }
-    }
-    return PeekChar() != EOF;
-  }
-
-  void ParseString(string* tok) {
-    EatChar();  // eat the first quotation mark
-    int ch;
-    while ( (ch = PeekChar()) != '\"' ) {
-      switch (ch) {
-        case '\\':
-          EatChar();
-          ch = PeekChar();
-          if (ch == '\"') {
-            *tok += '\"';
-          } else {
-            throw TokenizeError("error parsing escape characters");
-          }
-          break;
-        case '\n': case '\r': case EOF:
-          throw TokenizeError("quotation mark is not closed");
-        default:
-          *tok += ch;
-          break;
-      }
-      EatChar();
-    }
-    EatChar();  // eat the last quotation mark
-  }
-
-  void ParseComments() {
-    int ch;
-    while ( (ch = PeekChar()) ) {
-      if (ch == '\n' || ch == '\r' || ch == EOF) {
-        break;  // end of comment
-      }
-      EatChar();  // ignore all others
-    }
-  }
-
- private:
-  int PeekChar() {
-    return is_.peek();
-  }
-  void EatChar() {
-    is_.get();
-  }
-
-  enum ParseState {
-    kNone = 0,
-    kToken,
-    kFinish,
-  };
-  istream& is_;
-  ParseState state_;
-};
-
-//////////////////////// Config /////////////////////////////
-Config::Config(bool m): multi_value_(m) {
-  Clear();
-}
-
-Config::Config(istream& is, bool m): multi_value_(m) {
-  Clear();
-  LoadFromStream(is);
-}
-
-void Config::Clear() {
-  config_map_.clear();
-  order_.clear();
-}
-
-void Config::LoadFromStream(istream& is) {
-  Tokenizer tokenizer(is);
-  Token key, eqop, value;
-  try {
-    while ( true ) {
-      tokenizer.GetNextToken(&key);
-      if (key.buf.length() == 0) {
-        break;  // no content left
-      }
-      tokenizer.GetNextToken(&eqop);
-      tokenizer.GetNextToken(&value);
-      if (eqop.buf != "=") {
-        LOG(ERROR) << "Parsing error: expect format \"k = v\"; but got \""
-          << key.buf << eqop.buf << value.buf << "\"";
-      }
-      Insert(key.buf, value.buf, value.is_string);
-    }
-  } catch(TokenizeError& err) {
-    LOG(ERROR) << "Tokenize error: " << err.what();
-  }
-}
-
-const string& Config::GetParam(const string& key) const {
-  CHECK(config_map_.find(key) != config_map_.end())
-      << "key \"" << key << "\" not found in configure";
-  const std::vector<std::string>& vals = config_map_.find(key)->second.val;
-  return vals[vals.size() - 1];  // return tne latest inserted one
-}
-
-bool Config::IsGenuineString(const std::string& key) const {
-  CHECK(config_map_.find(key) != config_map_.end())
-      << "key \"" << key << "\" not found in configure";
-  return config_map_.find(key)->second.is_string;
-}
-
-string MakeProtoStringValue(const std::string& str) {
-  string rst = "\"";
-  for (size_t i = 0; i < str.length(); ++i) {
-    if (str[i] != '\"') {
-      rst += str[i];
-    } else {
-      rst += "\\\"";
-    }
-  }
-  rst += "\"";
-  return rst;
-}
-
-string Config::ToProtoString(void) const {
-  ostringstream oss;
-  for (ConfigIterator iter = begin(); iter != end(); ++iter) {
-    const ConfigEntry& entry = *iter;
-    bool is_string = IsGenuineString(entry.first);
-    oss << entry.first << " : " <<
-      (is_string? MakeProtoStringValue(entry.second) : entry.second)
-      << "\n";
-  }
-  return oss.str();
-}
-
-Config::ConfigIterator Config::begin() const {
-  return ConfigIterator(0, this);
-}
-
-Config::ConfigIterator Config::end() const {
-  return ConfigIterator(order_.size(), this);
-}
-
-void Config::Insert(const std::string& key, const std::string& value, bool is_string) {
-  size_t insert_index = order_.size();
-  if (!multi_value_) {
-    config_map_[key] = ConfigValue();
-  }
-  ConfigValue& cv = config_map_[key];
-  size_t val_index = cv.val.size();
-  cv.val.push_back(value);
-  cv.insert_index.push_back(insert_index);
-  cv.is_string = is_string;
-
-  order_.push_back(make_pair(key, val_index));
-}
-
-////////////////////// ConfigIterator //////////////////////
-
-Config::ConfigIterator::ConfigIterator(size_t i, const Config* c)
-    : index_(i), config_(c) {
-  FindNextIndex();
-}
-
-Config::ConfigIterator::ConfigIterator(const Config::ConfigIterator& other)
-    : index_(other.index_), config_(other.config_) {
-}
-
-Config::ConfigIterator& Config::ConfigIterator::operator++() {
-  if (index_ < config_->order_.size()) {
-    ++index_;
-  }
-  FindNextIndex();
-  return *this;
-}
-
-Config::ConfigIterator Config::ConfigIterator::operator++(int any) {
-  ConfigIterator tmp(*this);
-  operator++();
-  return tmp;
-}
-
-bool Config::ConfigIterator::operator==(const Config::ConfigIterator& rhs) const {
-  return index_ == rhs.index_ && config_ == rhs.config_;
-}
-
-bool Config::ConfigIterator::operator!=(const Config::ConfigIterator& rhs) const {
-  return !(operator == (rhs));
-}
-
-Config::ConfigEntry Config::ConfigIterator::operator * () const {
-  const std::string& key = config_->order_[index_].first;
-  size_t val_index = config_->order_[index_].second;
-  const std::string& val = config_->config_map_.find(key)->second.val[val_index];
-  return make_pair(key, val);
-}
-
-void Config::ConfigIterator::FindNextIndex() {
-  bool found = false;
-  while (!found && index_ < config_->order_.size()) {
-    const std::string& key = config_->order_[index_].first;
-    size_t val_index = config_->order_[index_].second;
-    size_t val_insert_index = config_->config_map_.find(key)->second.insert_index[val_index];
-    if (val_insert_index == index_) {
-      found = true;
-    } else {
-      ++index_;
-    }
-  }
-}
-
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/data.cc b/ml-xgboost/dmlc-core/src/data.cc
deleted file mode 100644
index ed619cb..0000000
--- a/ml-xgboost/dmlc-core/src/data.cc
+++ /dev/null
@@ -1,258 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/base.h>
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <dmlc/data.h>
-#include <dmlc/registry.h>
-#include <cstring>
-#include <string>
-#include "io/uri_spec.h"
-#include "data/parser.h"
-#include "data/basic_row_iter.h"
-#include "data/disk_row_iter.h"
-#include "data/libsvm_parser.h"
-#include "data/libfm_parser.h"
-#include "data/csv_parser.h"
-
-namespace dmlc {
-/*! \brief namespace for useful input data structure */
-namespace data {
-
-template<typename IndexType, typename DType = real_t>
-Parser<IndexType> *
-CreateLibSVMParser(const std::string& path,
-                   const std::map<std::string, std::string>& args,
-                   unsigned part_index,
-                   unsigned num_parts) {
-  InputSplit* source = InputSplit::Create(
-      path.c_str(), part_index, num_parts, "text");
-  ParserImpl<IndexType> *parser = new LibSVMParser<IndexType>(source, args, 2);
-#if DMLC_ENABLE_STD_THREAD
-  parser = new ThreadedParser<IndexType>(parser);
-#endif
-  return parser;
-}
-
-template<typename IndexType, typename DType = real_t>
-Parser<IndexType> *
-CreateLibFMParser(const std::string& path,
-                  const std::map<std::string, std::string>& args,
-                  unsigned part_index,
-                  unsigned num_parts) {
-  InputSplit* source = InputSplit::Create(
-      path.c_str(), part_index, num_parts, "text");
-  ParserImpl<IndexType> *parser = new LibFMParser<IndexType>(source, args, 2);
-#if DMLC_ENABLE_STD_THREAD
-  parser = new ThreadedParser<IndexType>(parser);
-#endif
-  return parser;
-}
-
-template<typename IndexType, typename DType = real_t>
-Parser<IndexType, DType> *
-CreateCSVParser(const std::string& path,
-                const std::map<std::string, std::string>& args,
-                unsigned part_index,
-                unsigned num_parts) {
-  InputSplit* source = InputSplit::Create(
-      path.c_str(), part_index, num_parts, "text");
-  return new CSVParser<IndexType, DType>(source, args, 2);
-}
-
-template<typename IndexType, typename DType = real_t>
-inline Parser<IndexType, DType> *
-CreateParser_(const char *uri_,
-              unsigned part_index,
-              unsigned num_parts,
-              const char *type) {
-  std::string ptype = type;
-  io::URISpec spec(uri_, part_index, num_parts);
-  if (ptype == "auto") {
-    if (spec.args.count("format") != 0) {
-      ptype = spec.args.at("format");
-    } else {
-      ptype = "libsvm";
-    }
-  }
-
-  const ParserFactoryReg<IndexType, DType>* e =
-      Registry<ParserFactoryReg<IndexType, DType> >::Get()->Find(ptype);
-  if (e == NULL) {
-    LOG(FATAL) << "Unknown data type " << ptype;
-  }
-  // create parser
-  return (*e->body)(spec.uri, spec.args, part_index, num_parts);
-}
-
-template<typename IndexType, typename DType = real_t>
-inline RowBlockIter<IndexType, DType> *
-CreateIter_(const char *uri_,
-            unsigned part_index,
-            unsigned num_parts,
-            const char *type) {
-  using namespace std;
-  io::URISpec spec(uri_, part_index, num_parts);
-  Parser<IndexType, DType> *parser = CreateParser_<IndexType, DType>
-      (spec.uri.c_str(), part_index, num_parts, type);
-  if (spec.cache_file.length() != 0) {
-#if DMLC_ENABLE_STD_THREAD
-    return new DiskRowIter<IndexType, DType>(parser, spec.cache_file.c_str(), true);
-#else
-    LOG(FATAL) << "compile with c++0x or c++11 to enable cache file";
-    return NULL;
-#endif
-  } else {
-    return new BasicRowIter<IndexType, DType>(parser);
-  }
-}
-
-DMLC_REGISTER_PARAMETER(LibSVMParserParam);
-DMLC_REGISTER_PARAMETER(LibFMParserParam);
-DMLC_REGISTER_PARAMETER(CSVParserParam);
-}  // namespace data
-
-// template specialization
-template<>
-RowBlockIter<uint32_t, real_t> *
-RowBlockIter<uint32_t, real_t>::Create(const char *uri,
-                                       unsigned part_index,
-                                       unsigned num_parts,
-                                       const char *type) {
-  return data::CreateIter_<uint32_t, real_t>(uri, part_index, num_parts, type);
-}
-
-template<>
-RowBlockIter<uint64_t, real_t> *
-RowBlockIter<uint64_t, real_t>::Create(const char *uri,
-                                       unsigned part_index,
-                                       unsigned num_parts,
-                                       const char *type) {
-  return data::CreateIter_<uint64_t, real_t>(uri, part_index, num_parts, type);
-}
-
-template<>
-RowBlockIter<uint32_t, int32_t> *
-RowBlockIter<uint32_t, int32_t>::Create(const char *uri,
-                                    unsigned part_index,
-                                    unsigned num_parts,
-                                    const char *type) {
-  return data::CreateIter_<uint32_t, int32_t>(uri, part_index, num_parts, type);
-}
-
-template<>
-RowBlockIter<uint64_t, int32_t> *
-RowBlockIter<uint64_t, int32_t>::Create(const char *uri,
-                                    unsigned part_index,
-                                    unsigned num_parts,
-                                    const char *type) {
-  return data::CreateIter_<uint64_t, int32_t>(uri, part_index, num_parts, type);
-}
-
-template<>
-RowBlockIter<uint32_t, int64_t> *
-RowBlockIter<uint32_t, int64_t>::Create(const char *uri,
-                                        unsigned part_index,
-                                        unsigned num_parts,
-                                        const char *type) {
-  return data::CreateIter_<uint32_t, int64_t>(uri, part_index, num_parts, type);
-}
-
-template<>
-RowBlockIter<uint64_t, int64_t> *
-RowBlockIter<uint64_t, int64_t>::Create(const char *uri,
-                                        unsigned part_index,
-                                        unsigned num_parts,
-                                        const char *type) {
-  return data::CreateIter_<uint64_t, int64_t>(uri, part_index, num_parts, type);
-}
-
-template<>
-Parser<uint32_t, real_t> *
-Parser<uint32_t, real_t>::Create(const char *uri_,
-                                 unsigned part_index,
-                                 unsigned num_parts,
-                                 const char *type) {
-  return data::CreateParser_<uint32_t, real_t>(uri_, part_index, num_parts, type);
-}
-
-template<>
-Parser<uint64_t, real_t> *
-Parser<uint64_t, real_t>::Create(const char *uri_,
-                                 unsigned part_index,
-                                 unsigned num_parts,
-                                 const char *type) {
-  return data::CreateParser_<uint64_t, real_t>(uri_, part_index, num_parts, type);
-}
-
-template<>
-Parser<uint32_t, int32_t> *
-Parser<uint32_t, int32_t>::Create(const char *uri_,
-                              unsigned part_index,
-                              unsigned num_parts,
-                              const char *type) {
-  return data::CreateParser_<uint32_t, int32_t>(uri_, part_index, num_parts, type);
-}
-
-template<>
-Parser<uint64_t, int32_t> *
-Parser<uint64_t, int32_t>::Create(const char *uri_,
-                              unsigned part_index,
-                              unsigned num_parts,
-                              const char *type) {
-  return data::CreateParser_<uint64_t, int32_t>(uri_, part_index, num_parts, type);
-}
-
-template<>
-Parser<uint32_t, int64_t> *
-Parser<uint32_t, int64_t>::Create(const char *uri_,
-                                  unsigned part_index,
-                                  unsigned num_parts,
-                                  const char *type) {
-  return data::CreateParser_<uint32_t, int64_t>(uri_, part_index, num_parts, type);
-}
-
-template<>
-Parser<uint64_t, int64_t> *
-Parser<uint64_t, int64_t>::Create(const char *uri_,
-                                  unsigned part_index,
-                                  unsigned num_parts,
-                                  const char *type) {
-  return data::CreateParser_<uint64_t, int64_t>(uri_, part_index, num_parts, type);
-}
-
-// registry
-typedef ParserFactoryReg<uint32_t, real_t> Reg32flt;
-typedef ParserFactoryReg<uint32_t, int32_t> Reg32int32;
-typedef ParserFactoryReg<uint32_t, int64_t> Reg32int64;
-typedef ParserFactoryReg<uint64_t, real_t> Reg64flt;
-typedef ParserFactoryReg<uint64_t, int32_t> Reg64int32;
-typedef ParserFactoryReg<uint64_t, int64_t> Reg64int64;
-DMLC_REGISTRY_ENABLE(Reg32flt);
-DMLC_REGISTRY_ENABLE(Reg32int32);
-DMLC_REGISTRY_ENABLE(Reg32int64);
-DMLC_REGISTRY_ENABLE(Reg64flt);
-DMLC_REGISTRY_ENABLE(Reg64int32);
-DMLC_REGISTRY_ENABLE(Reg64int64);
-
-DMLC_REGISTER_DATA_PARSER(
-  uint32_t, real_t, libsvm, data::CreateLibSVMParser<uint32_t __DMLC_COMMA real_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint64_t, real_t, libsvm, data::CreateLibSVMParser<uint64_t __DMLC_COMMA real_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint32_t, real_t, libfm, data::CreateLibFMParser<uint32_t __DMLC_COMMA real_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint64_t, real_t, libfm, data::CreateLibFMParser<uint64_t __DMLC_COMMA real_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint32_t, real_t, csv, data::CreateCSVParser<uint32_t __DMLC_COMMA real_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint64_t, real_t, csv, data::CreateCSVParser<uint64_t __DMLC_COMMA real_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint32_t, int32_t, csv, data::CreateCSVParser<uint32_t __DMLC_COMMA int32_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint64_t, int32_t, csv, data::CreateCSVParser<uint64_t __DMLC_COMMA int32_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint32_t, int64_t, csv, data::CreateCSVParser<uint32_t __DMLC_COMMA int64_t>);
-DMLC_REGISTER_DATA_PARSER(
-  uint64_t, int64_t, csv, data::CreateCSVParser<uint64_t __DMLC_COMMA int64_t>);
-
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/data/basic_row_iter.h b/ml-xgboost/dmlc-core/src/data/basic_row_iter.h
deleted file mode 100644
index 7d7124b..0000000
--- a/ml-xgboost/dmlc-core/src/data/basic_row_iter.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file basic_row_iter.h
- * \brief row based iterator that
- *   loads in everything into memory and returns
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_BASIC_ROW_ITER_H_
-#define DMLC_DATA_BASIC_ROW_ITER_H_
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <dmlc/data.h>
-#include <dmlc/timer.h>
-#include "./row_block.h"
-#include "./parser.h"
-
-namespace dmlc {
-namespace data {
-/*!
- * \brief basic set of row iterators that provides
- * \tparam IndexType the type of index we are using
- */
-template<typename IndexType, typename DType = real_t>
-class BasicRowIter: public RowBlockIter<IndexType, DType> {
- public:
-  explicit BasicRowIter(Parser<IndexType, DType> *parser)
-      : at_head_(true) {
-    this->Init(parser);
-    delete parser;
-  }
-  virtual ~BasicRowIter() {}
-  virtual void BeforeFirst(void) {
-    at_head_ = true;
-  }
-  virtual bool Next(void) {
-    if (at_head_) {
-      at_head_ = false;
-      return true;
-    } else {
-      return false;
-    }
-  }
-  virtual const RowBlock<IndexType, DType> &Value(void) const {
-    return row_;
-  }
-  virtual size_t NumCol(void) const {
-    return static_cast<size_t>(data_.max_index) + 1;
-  }
-
- private:
-  // at head
-  bool at_head_;
-  // row block to store
-  RowBlock<IndexType, DType> row_;
-  // back end data
-  RowBlockContainer<IndexType, DType> data_;
-  // initialize
-  inline void Init(Parser<IndexType, DType> *parser);
-};
-
-template<typename IndexType, typename DType>
-inline void BasicRowIter<IndexType, DType>::Init(Parser<IndexType, DType> *parser) {
-  data_.Clear();
-  double tstart = GetTime();
-  size_t bytes_expect = 10UL << 20UL;
-  while (parser->Next()) {
-    data_.Push(parser->Value());
-    double tdiff = GetTime() - tstart;
-    size_t bytes_read  = parser->BytesRead();
-    if (bytes_read >= bytes_expect) {
-      bytes_read = bytes_read >> 20UL;
-      LOG(INFO) << bytes_read << "MB read,"
-                << bytes_read / tdiff << " MB/sec";
-      bytes_expect += 10UL << 20UL;
-    }
-  }
-  row_ = data_.GetBlock();
-  double tdiff = GetTime() - tstart;
-  LOG(INFO) << "finish reading at "
-            << (parser->BytesRead() >> 20UL) / tdiff
-            << " MB/sec";
-}
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_BASIC_ROW_ITER_H__
diff --git a/ml-xgboost/dmlc-core/src/data/csv_parser.h b/ml-xgboost/dmlc-core/src/data/csv_parser.h
deleted file mode 100644
index 9b945a6..0000000
--- a/ml-xgboost/dmlc-core/src/data/csv_parser.h
+++ /dev/null
@@ -1,150 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file csv_parser.h
- * \brief iterator parser to parse csv format
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_CSV_PARSER_H_
-#define DMLC_DATA_CSV_PARSER_H_
-
-#include <dmlc/data.h>
-#include <dmlc/strtonum.h>
-#include <dmlc/parameter.h>
-#include <cmath>
-#include <cstring>
-#include <map>
-#include <string>
-#include <limits>
-#include "./row_block.h"
-#include "./text_parser.h"
-
-namespace dmlc {
-namespace data {
-
-struct CSVParserParam : public Parameter<CSVParserParam> {
-  std::string format;
-  int label_column;
-  std::string delimiter;
-  int weight_column;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(CSVParserParam) {
-    DMLC_DECLARE_FIELD(format).set_default("csv")
-        .describe("File format.");
-    DMLC_DECLARE_FIELD(label_column).set_default(-1)
-        .describe("Column index (0-based) that will put into label.");
-    DMLC_DECLARE_FIELD(delimiter).set_default(",")
-      .describe("Delimiter used in the csv file.");
-    DMLC_DECLARE_FIELD(weight_column).set_default(-1)
-        .describe("Column index that will put into instance weights.");
-  }
-};
-
-
-/*!
- * \brief CSVParser, parses a dense csv format.
- *  Currently is a dummy implementation, when label column is not specified.
- *  All columns are treated as real dense data.
- *  label will be assigned to 0.
- *
- *  This should be extended in future to accept arguments of column types.
- */
-template <typename IndexType, typename DType = real_t>
-class CSVParser : public TextParserBase<IndexType, DType> {
- public:
-  explicit CSVParser(InputSplit *source,
-                     const std::map<std::string, std::string>& args,
-                     int nthread)
-      : TextParserBase<IndexType, DType>(source, nthread) {
-    param_.Init(args);
-    CHECK_EQ(param_.format, "csv");
-    CHECK(param_.label_column != param_.weight_column
-          || param_.label_column < 0)
-      << "Must have distinct columns for labels and instance weights";
-  }
-
- protected:
-  virtual void ParseBlock(const char *begin,
-                          const char *end,
-                          RowBlockContainer<IndexType, DType> *out);
-
- private:
-  CSVParserParam param_;
-};
-
-template <typename IndexType, typename DType>
-void CSVParser<IndexType, DType>::
-ParseBlock(const char *begin,
-           const char *end,
-           RowBlockContainer<IndexType, DType> *out) {
-  out->Clear();
-  const char * lbegin = begin;
-  const char * lend = lbegin;
-  // advance lbegin if it points to newlines
-  while ((lbegin != end) && (*lbegin == '\n' || *lbegin == '\r')) ++lbegin;
-  while (lbegin != end) {
-    // get line end
-    this->IgnoreUTF8BOM(&lbegin, &end);
-    lend = lbegin + 1;
-    while (lend != end && *lend != '\n' && *lend != '\r') ++lend;
-
-    const char* p = lbegin;
-    int column_index = 0;
-    IndexType idx = 0;
-    DType label = DType(0.0f);
-    real_t weight = std::numeric_limits<real_t>::quiet_NaN();
-
-    while (p != lend) {
-      char *endptr;
-      DType v;
-      // if DType is float32
-      if (std::is_same<DType, real_t>::value) {
-        v = strtof(p, &endptr);
-      // If DType is int32
-      } else if (std::is_same<DType, int32_t>::value) {
-        v = static_cast<int32_t>(strtoll(p, &endptr, 0));
-      // If DType is int64
-      } else if (std::is_same<DType, int64_t>::value) {
-        v = static_cast<int64_t>(strtoll(p, &endptr, 0));
-      // If DType is all other types
-      } else {
-        LOG(FATAL) << "Only float32, int32, and int64 are supported for the time being";
-      }
-
-      if (column_index == param_.label_column) {
-        label = v;
-      } else if (std::is_same<DType, real_t>::value
-                 && column_index == param_.weight_column) {
-        weight = v;
-      } else {
-        if (std::distance(p, static_cast<char const*>(endptr)) != 0) {
-          out->value.push_back(v);
-          out->index.push_back(idx++);
-        } else {
-          idx++;
-        }
-      }
-      p = (endptr >= lend) ? lend : endptr;
-      ++column_index;
-      while (*p != param_.delimiter[0] && p != lend) ++p;
-      if (p == lend && idx == 0) {
-        LOG(FATAL) << "Delimiter \'" << param_.delimiter << "\' is not found in the line. "
-                   << "Expected \'" << param_.delimiter
-                   << "\' as the delimiter to separate fields.";
-      }
-      if (p != lend) ++p;
-    }
-    // skip empty line
-    while ((*lend == '\n' || *lend == '\r') && lend != end) ++lend;
-    lbegin = lend;
-    out->label.push_back(label);
-    if (!std::isnan(weight)) {
-      out->weight.push_back(weight);
-    }
-    out->offset.push_back(out->index.size());
-  }
-  CHECK(out->label.size() + 1 == out->offset.size());
-  CHECK(out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size());
-}
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_CSV_PARSER_H_
diff --git a/ml-xgboost/dmlc-core/src/data/disk_row_iter.h b/ml-xgboost/dmlc-core/src/data/disk_row_iter.h
deleted file mode 100644
index 718d62a..0000000
--- a/ml-xgboost/dmlc-core/src/data/disk_row_iter.h
+++ /dev/null
@@ -1,145 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file basic_row_iter.h
- * \brief row based iterator that
- *   caches things into disk and then load segments
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_DISK_ROW_ITER_H_
-#define DMLC_DATA_DISK_ROW_ITER_H_
-
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <dmlc/data.h>
-#include <dmlc/timer.h>
-#include <dmlc/threadediter.h>
-#include <algorithm>
-#include <string>
-#include "./row_block.h"
-#include "./libsvm_parser.h"
-
-#if DMLC_ENABLE_STD_THREAD
-namespace dmlc {
-namespace data {
-/*!
- * \brief basic set of row iterators that provides
- * \tparam IndexType the type of index we are using
- */
-template<typename IndexType, typename DType = real_t>
-class DiskRowIter: public RowBlockIter<IndexType, DType> {
- public:
-  // page size 64MB
-  static const size_t kPageSize = 64UL << 20UL;
-  /*!
-   * \brief disk row iterator constructor
-   * \param parser parser used to generate this
-
-   */
-  explicit DiskRowIter(Parser<IndexType, DType> *parser,
-                       const char *cache_file,
-                       bool reuse_cache)
-      : cache_file_(cache_file), fi_(NULL) {
-    if (reuse_cache) {
-      if (!TryLoadCache()) {
-        this->BuildCache(parser);
-        CHECK(TryLoadCache())
-            << "failed to build cache file " << cache_file;
-      }
-    } else {
-      this->BuildCache(parser);
-      CHECK(TryLoadCache())
-          << "failed to build cache file " << cache_file;
-    }
-    delete parser;
-  }
-  virtual ~DiskRowIter(void) {
-    iter_.Destroy();
-    delete fi_;
-  }
-  virtual void BeforeFirst(void) {
-    iter_.BeforeFirst();
-  }
-  virtual bool Next(void) {
-    if (iter_.Next()) {
-      row_ = iter_.Value().GetBlock();
-      return true;
-    } else {
-      return false;
-    }
-  }
-  virtual const RowBlock<IndexType, DType> &Value(void) const {
-    return row_;
-  }
-  virtual size_t NumCol(void) const {
-    return num_col_;
-  }
-
- private:
-  // file place
-  std::string cache_file_;
-  // input stream
-  SeekStream *fi_;
-  // maximum feature dimension
-  size_t num_col_;
-  // row block to store
-  RowBlock<IndexType, DType> row_;
-  // iterator
-  ThreadedIter<RowBlockContainer<IndexType, DType> > iter_;
-  // load disk cache file
-  inline bool TryLoadCache(void);
-  // build disk cache
-  inline void BuildCache(Parser<IndexType, DType> *parser);
-};
-
-// build disk cache
-template<typename IndexType, typename DType>
-inline bool DiskRowIter<IndexType, DType>::TryLoadCache(void) {
-  SeekStream *fi = SeekStream::CreateForRead(cache_file_.c_str(), true);
-  if (fi == NULL) return false;
-  this->fi_ = fi;
-  iter_.Init([fi](RowBlockContainer<IndexType, DType> **dptr) {
-      if (*dptr ==NULL) {
-        *dptr = new RowBlockContainer<IndexType, DType>();
-      }
-      return (*dptr)->Load(fi);
-    },
-    [fi]() { fi->Seek(0); });
-  return true;
-}
-
-template<typename IndexType, typename DType>
-inline void DiskRowIter<IndexType, DType>::
-BuildCache(Parser<IndexType, DType> *parser) {
-  Stream *fo = Stream::Create(cache_file_.c_str(), "w");
-  // back end data
-  RowBlockContainer<IndexType, DType> data;
-  num_col_ = 0;
-  double tstart = GetTime();
-  while (parser->Next()) {
-    data.Push(parser->Value());
-    double tdiff = GetTime() - tstart;
-    if (data.MemCostBytes() >= kPageSize) {
-      size_t bytes_read = parser->BytesRead();
-      bytes_read = bytes_read >> 20UL;
-      LOG(INFO) << bytes_read << "MB read,"
-                << bytes_read / tdiff << " MB/sec";
-      num_col_ = std::max(num_col_,
-                          static_cast<size_t>(data.max_index) + 1);
-      data.Save(fo);
-      data.Clear();
-    }
-  }
-  if (data.Size() != 0) {
-    num_col_ = std::max(num_col_,
-                        static_cast<size_t>(data.max_index) + 1);
-    data.Save(fo);
-  }
-  delete fo;
-  double tdiff = GetTime() - tstart;
-  LOG(INFO) << "finish reading at %g MB/sec"
-            << (parser->BytesRead() >> 20UL) / tdiff;
-}
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_USE_CXX11
-#endif  // DMLC_DATA_DISK_ROW_ITER_H_
diff --git a/ml-xgboost/dmlc-core/src/data/libfm_parser.h b/ml-xgboost/dmlc-core/src/data/libfm_parser.h
deleted file mode 100644
index a897bd5..0000000
--- a/ml-xgboost/dmlc-core/src/data/libfm_parser.h
+++ /dev/null
@@ -1,148 +0,0 @@
-/*!
- *  Copyright (c) 2017 by Contributors
- * \file libfm_parser.h
- * \brief iterator parser to parse libfm format
- * \author formath
- */
-#ifndef DMLC_DATA_LIBFM_PARSER_H_
-#define DMLC_DATA_LIBFM_PARSER_H_
-
-#include <dmlc/data.h>
-#include <dmlc/strtonum.h>
-#include <dmlc/parameter.h>
-#include <map>
-#include <string>
-#include <limits>
-#include <algorithm>
-#include <cstring>
-#include "./row_block.h"
-#include "./text_parser.h"
-
-namespace dmlc {
-namespace data {
-
-struct LibFMParserParam : public Parameter<LibFMParserParam> {
-  std::string format;
-  int indexing_mode;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(LibFMParserParam) {
-    DMLC_DECLARE_FIELD(format).set_default("libfm")
-        .describe("File format");
-    DMLC_DECLARE_FIELD(indexing_mode).set_default(0)
-        .describe(
-          "If >0, treat all field and feature indices as 1-based. "
-          "If =0, treat all field and feature indices as 0-based. "
-          "If <0, use heuristic to automatically detect mode of indexing. "
-          "See https://en.wikipedia.org/wiki/Array_data_type#Index_origin "
-          "for more details on indexing modes.");
-  }
-};
-
-/*!
- * \brief Text parser that parses the input lines
- * and returns rows in input data
- */
-template <typename IndexType, typename DType = real_t>
-class LibFMParser : public TextParserBase<IndexType, DType> {
- public:
-  explicit LibFMParser(InputSplit *source, int nthread)
-      : LibFMParser(source, std::map<std::string, std::string>(), nthread) {}
-  explicit LibFMParser(InputSplit *source,
-                       const std::map<std::string, std::string>& args,
-                       int nthread)
-      : TextParserBase<IndexType>(source, nthread) {
-    param_.Init(args);
-    CHECK_EQ(param_.format, "libfm");
-  }
-
- protected:
-  virtual void ParseBlock(const char *begin,
-                          const char *end,
-                          RowBlockContainer<IndexType, DType> *out);
-
- private:
-  LibFMParserParam param_;
-};
-
-template <typename IndexType, typename DType>
-void LibFMParser<IndexType, DType>::
-ParseBlock(const char *begin,
-           const char *end,
-           RowBlockContainer<IndexType, DType> *out) {
-  out->Clear();
-  const char * lbegin = begin;
-  const char * lend = lbegin;
-  IndexType min_field_id = std::numeric_limits<IndexType>::max();
-  IndexType min_feat_id = std::numeric_limits<IndexType>::max();
-  while (lbegin != end) {
-    // get line end
-    lend = lbegin + 1;
-    while (lend != end && *lend != '\n' && *lend != '\r') ++lend;
-    // parse label[:weight]
-    const char * p = lbegin;
-    const char * q = NULL;
-    real_t label;
-    real_t weight;
-    int r = ParsePair<real_t, real_t>(p, lend, &q, label, weight);
-    if (r < 1) {
-      // empty line
-      lbegin = lend;
-      continue;
-    }
-    if (r == 2) {
-      // has weight
-      out->weight.push_back(weight);
-    }
-    if (out->label.size() != 0) {
-      out->offset.push_back(out->index.size());
-    }
-    out->label.push_back(label);
-    // parse fieldid:feature:value
-    p = q;
-    while (p != lend) {
-      IndexType fieldId;
-      IndexType featureId;
-      real_t value;
-      int r = ParseTriple<IndexType, IndexType, real_t>(p, lend, &q, fieldId, featureId, value);
-      if (r <= 1) {
-        p = q;
-        continue;
-      }
-      out->field.push_back(fieldId);
-      out->index.push_back(featureId);
-      min_field_id = std::min(fieldId, min_field_id);
-      min_feat_id = std::min(featureId, min_feat_id);
-      if (r == 3) {
-        // has value
-        out->value.push_back(value);
-      }
-      p = q;
-    }
-    // next line
-    lbegin = lend;
-  }
-  if (out->label.size() != 0) {
-    out->offset.push_back(out->index.size());
-  }
-  CHECK(out->field.size() == out->index.size());
-  CHECK(out->label.size() + 1 == out->offset.size());
-
-  // detect indexing mode
-  // heuristic adopted from sklearn.datasets.load_svmlight_file
-  // If all feature and field id's exceed 0, then detect 1-based indexing
-  if (param_.indexing_mode > 0
-      || (param_.indexing_mode < 0 && !out->index.empty() && min_feat_id > 0
-          && !out->field.empty() && min_field_id > 0) ) {
-    // convert from 1-based to 0-based indexing
-    for (IndexType& e : out->index) {
-      --e;
-    }
-    for (IndexType& e : out->field) {
-      --e;
-    }
-  }
-}
-
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_LIBFM_PARSER_H_
diff --git a/ml-xgboost/dmlc-core/src/data/libsvm_parser.h b/ml-xgboost/dmlc-core/src/data/libsvm_parser.h
deleted file mode 100644
index cc9d3ed..0000000
--- a/ml-xgboost/dmlc-core/src/data/libsvm_parser.h
+++ /dev/null
@@ -1,173 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file libsvm_parser.h
- * \brief iterator parser to parse libsvm format
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_LIBSVM_PARSER_H_
-#define DMLC_DATA_LIBSVM_PARSER_H_
-
-#include <dmlc/data.h>
-#include <dmlc/strtonum.h>
-#include <dmlc/parameter.h>
-#include <map>
-#include <string>
-#include <limits>
-#include <algorithm>
-#include <cstring>
-#include "./row_block.h"
-#include "./text_parser.h"
-
-namespace dmlc {
-namespace data {
-
-struct LibSVMParserParam : public Parameter<LibSVMParserParam> {
-  std::string format;
-  int indexing_mode;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(LibSVMParserParam) {
-    DMLC_DECLARE_FIELD(format).set_default("libsvm")
-        .describe("File format");
-    DMLC_DECLARE_FIELD(indexing_mode).set_default(0)
-        .describe(
-          "If >0, treat all feature indices as 1-based. "
-          "If =0, treat all feature indices as 0-based. "
-          "If <0, use heuristic to automatically detect mode of indexing. "
-          "See https://en.wikipedia.org/wiki/Array_data_type#Index_origin "
-          "for more details on indexing modes.");
-  }
-};
-
-/*!
- * \brief Text parser that parses the input lines
- * and returns rows in input data
- */
-template <typename IndexType, typename DType = real_t>
-class LibSVMParser : public TextParserBase<IndexType> {
- public:
-  explicit LibSVMParser(InputSplit *source, int nthread)
-      : LibSVMParser(source, std::map<std::string, std::string>(), nthread) {}
-  explicit LibSVMParser(InputSplit *source,
-                        const std::map<std::string, std::string>& args,
-                        int nthread)
-      : TextParserBase<IndexType>(source, nthread) {
-    param_.Init(args);
-    CHECK_EQ(param_.format, "libsvm");
-  }
-
- protected:
-  virtual void ParseBlock(const char *begin,
-                          const char *end,
-                          RowBlockContainer<IndexType, DType> *out);
-
- private:
-  LibSVMParserParam param_;
-};
-
-template <char kSymbol = '#'>
-std::ptrdiff_t IgnoreCommentAndBlank(char const* beg,
-                                     char const* line_end) {
-  char const* p = beg;
-  std::ptrdiff_t length = std::distance(beg, line_end);
-  while (p != line_end) {
-    if (*p == kSymbol) {
-      // advance to line end, `ParsePair' will return empty line.
-      return length;
-    }
-    if (!isblank(*p)) {
-      return std::distance(beg, p);  // advance to p
-    }
-    p++;
-  }
-  // advance to line end, `ParsePair' will return empty line.
-  return length;
-}
-
-template <typename IndexType, typename DType>
-void LibSVMParser<IndexType, DType>::
-ParseBlock(const char *begin,
-           const char *end,
-           RowBlockContainer<IndexType, DType> *out) {
-  out->Clear();
-  const char * lbegin = begin;
-  const char * lend = lbegin;
-  IndexType min_feat_id = std::numeric_limits<IndexType>::max();
-  while (lbegin != end) {
-    // get line end
-    lend = lbegin + 1;
-    while (lend != end && *lend != '\n' && *lend != '\r') ++lend;
-    // parse label[:weight]
-    const char * p = lbegin;
-    const char * q = NULL;
-    real_t label;
-    real_t weight;
-    std::ptrdiff_t advanced = IgnoreCommentAndBlank(p, lend);
-    p += advanced;
-    int r = ParsePair<real_t, real_t>(p, lend, &q, label, weight);
-    if (r < 1) {
-      // empty line
-      lbegin = lend;
-      continue;
-    }
-    if (r == 2) {
-      // has weight
-      out->weight.push_back(weight);
-    }
-    if (out->label.size() != 0) {
-      out->offset.push_back(out->index.size());
-    }
-    out->label.push_back(label);
-    // parse qid:id
-    uint64_t qid;
-    p = q;
-    while (p != end && *p == ' ') ++p;
-    if (p != lend && (strncmp(p, "qid:", 4) == 0)) {
-      p += 4;
-      qid = static_cast<uint64_t>(atoll(p));
-      while (p != lend && isdigitchars(*p)) ++p;
-      out->qid.push_back(qid);
-    }
-    // parse feature[:value]
-    while (p != lend) {
-      IndexType featureId;
-      real_t value;
-      std::ptrdiff_t advanced = IgnoreCommentAndBlank(p, lend);
-      p += advanced;
-      int r = ParsePair<IndexType, real_t>(p, lend, &q, featureId, value);
-      if (r < 1) {
-        // q is set to line end by `ParsePair', here is p. The latter terminates
-        // while loop of parsing features.
-        p = q;
-        continue;
-      }
-      out->index.push_back(featureId);
-      min_feat_id = std::min(featureId, min_feat_id);
-      if (r == 2) {
-        // has value
-        out->value.push_back(value);
-      }
-      p = q;
-    }
-    // next line
-    lbegin = lend;
-  }
-  if (out->label.size() != 0) {
-    out->offset.push_back(out->index.size());
-  }
-  CHECK(out->label.size() + 1 == out->offset.size());
-
-  // detect indexing mode
-  // heuristic adopted from sklearn.datasets.load_svmlight_file
-  // If all feature id's exceed 0, then detect 1-based indexing
-  if (param_.indexing_mode > 0
-      || (param_.indexing_mode < 0 && !out->index.empty() && min_feat_id > 0)) {
-    // convert from 1-based to 0-based indexing
-    for (IndexType& e : out->index) {
-      --e;
-    }
-  }
-}
-
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_LIBSVM_PARSER_H_
diff --git a/ml-xgboost/dmlc-core/src/data/parser.h b/ml-xgboost/dmlc-core/src/data/parser.h
deleted file mode 100644
index 1701c24..0000000
--- a/ml-xgboost/dmlc-core/src/data/parser.h
+++ /dev/null
@@ -1,130 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file libsvm_parser.h
- * \brief iterator parser to parse libsvm format
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_PARSER_H_
-#define DMLC_DATA_PARSER_H_
-
-#include <dmlc/base.h>
-#include <dmlc/logging.h>
-#include <dmlc/threadediter.h>
-#include <vector>
-#include "./row_block.h"
-
-namespace dmlc {
-namespace data {
-/*! \brief declare thread class */
-template <typename IndexType, typename DType>
-class ThreadedParser;
-/*! \brief base class for parser to parse data */
-
-template <typename IndexType, typename DType = real_t>
-class ParserImpl : public Parser<IndexType, DType> {
- public:
-  ParserImpl() : data_ptr_(0), data_end_(0) {}
-  // virtual destructor
-  virtual ~ParserImpl() {}
-  /*! \brief implement next */
-  virtual bool Next(void) {
-    while (true) {
-      while (data_ptr_ < data_end_) {
-        data_ptr_ += 1;
-        if (data_[data_ptr_ - 1].Size() != 0) {
-          block_ = data_[data_ptr_ - 1].GetBlock();
-          return true;
-        }
-      }
-      if (!ParseNext(&data_)) break;
-      data_ptr_ = 0;
-      data_end_ = static_cast<IndexType>(data_.size());
-    }
-    return false;
-  }
-  virtual const RowBlock<IndexType, DType> &Value(void) const {
-    return block_;
-  }
-  /*! \return size of bytes read so far */
-  virtual size_t BytesRead(void) const = 0;
-
- protected:
-  // allow ThreadedParser to see ParseNext
-  friend class ThreadedParser<IndexType, DType>;
-  /*!
-   * \brief read in next several blocks of data
-   * \param data vector of data to be returned
-   * \return true if the data is loaded, false if reach end
-   */
-  virtual bool ParseNext(std::vector<RowBlockContainer<IndexType, DType> > *data) = 0;
-  /*! \brief pointer to begin and end of data */
-  IndexType data_ptr_, data_end_;
-  /*! \brief internal data */
-  std::vector<RowBlockContainer<IndexType, DType> > data_;
-  /*! \brief internal row block */
-  RowBlock<IndexType, DType> block_;
-};
-
-#if DMLC_ENABLE_STD_THREAD
-
-template <typename IndexType, typename DType = real_t>
-class ThreadedParser : public ParserImpl<IndexType, DType> {
- public:
-  explicit ThreadedParser(ParserImpl<IndexType, DType> *base)
-      : base_(base), tmp_(NULL) {
-    iter_.set_max_capacity(8);
-    iter_.Init([base](std::vector<RowBlockContainer<IndexType, DType> > **dptr) {
-        if (*dptr == NULL) {
-          *dptr = new std::vector<RowBlockContainer<IndexType, DType> >();
-        }
-        return base->ParseNext(*dptr);
-      }, [base]() {base->BeforeFirst();});
-  }
-  virtual ~ThreadedParser(void) {
-    // stop things before base is deleted
-    iter_.Destroy();
-    delete base_;
-    delete tmp_;
-  }
-  virtual void BeforeFirst() {
-    iter_.BeforeFirst();
-  }
-  /*! \brief implement next */
-  using ParserImpl<IndexType, DType>::data_ptr_;
-  using ParserImpl<IndexType, DType>::data_end_;
-  virtual bool Next(void) {
-    while (true) {
-      while (data_ptr_ < data_end_) {
-        data_ptr_ += 1;
-        if ((*tmp_)[data_ptr_ - 1].Size() != 0) {
-          this->block_ = (*tmp_)[data_ptr_ - 1].GetBlock();
-          return true;
-        }
-      }
-      if (tmp_ != NULL) iter_.Recycle(&tmp_);
-      if (!iter_.Next(&tmp_)) break;
-      data_ptr_ = 0; data_end_ = tmp_->size();
-    }
-    return false;
-  }
-  virtual size_t BytesRead(void) const {
-    return base_->BytesRead();
-  }
-
- protected:
-  virtual bool ParseNext(std::vector<RowBlockContainer<IndexType, DType> > *data) {
-    LOG(FATAL) << "cannot call ParseNext"; return false;
-  }
-
- private:
-  /*! \brief the place where we get the data */
-  Parser<IndexType, DType> *base_;
-  /*! \brief backend threaded iterator */
-  ThreadedIter<std::vector<RowBlockContainer<IndexType, DType> > > iter_;
-  /*! \brief current chunk of data */
-  std::vector<RowBlockContainer<IndexType, DType> > *tmp_;
-};
-#endif  // DMLC_USE_CXX11
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_PARSER_H_
diff --git a/ml-xgboost/dmlc-core/src/data/row_block.h b/ml-xgboost/dmlc-core/src/data/row_block.h
deleted file mode 100644
index 7e0e328..0000000
--- a/ml-xgboost/dmlc-core/src/data/row_block.h
+++ /dev/null
@@ -1,218 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file row_block.h
- * \brief additional data structure to support
- *        RowBlock data structure
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_ROW_BLOCK_H_
-#define DMLC_DATA_ROW_BLOCK_H_
-
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <dmlc/data.h>
-#include <cstring>
-#include <vector>
-#include <limits>
-#include <algorithm>
-
-namespace dmlc {
-namespace data {
-/*!
- * \brief dynamic data structure that holds
- *        a row block of data
- * \tparam IndexType the type of index we are using
- */
-template<typename IndexType, typename DType = real_t>
-struct RowBlockContainer {
-  /*! \brief array[size+1], row pointer to beginning of each rows */
-  std::vector<size_t> offset;
-  /*! \brief array[size] label of each instance */
-  std::vector<DType> label;
-  /*! \brief array[size] weight of each instance */
-  std::vector<real_t> weight;
-  /*! \brief array[size] session-id of each instance */
-  std::vector<uint64_t> qid;
-  /*! \brief field index */
-  std::vector<IndexType> field;
-  /*! \brief feature index */
-  std::vector<IndexType> index;
-  /*! \brief feature value */
-  std::vector<DType> value;
-  /*! \brief maximum value of field */
-  IndexType max_field;
-  /*! \brief maximum value of index */
-  IndexType max_index;
-  // constructor
-  RowBlockContainer(void) {
-    this->Clear();
-  }
-  /*! \brief convert to a row block */
-  inline RowBlock<IndexType, DType> GetBlock(void) const;
-  /*!
-   * \brief write the row block to a binary stream
-   * \param fo output stream
-   */
-  inline void Save(Stream *fo) const;
-  /*!
-   * \brief load row block from a binary stream
-   * \param fi output stream
-   * \return false if at end of file
-   */
-  inline bool Load(Stream *fi);
-  /*! \brief clear the container */
-  inline void Clear(void) {
-    offset.clear(); offset.push_back(0);
-    label.clear(); field.clear(); index.clear(); value.clear(); weight.clear(); qid.clear();
-    max_field = 0;
-    max_index = 0;
-  }
-  /*! \brief size of the data */
-  inline size_t Size(void) const {
-    return offset.size() - 1;
-  }
-  /*! \return estimation of memory cost of this container */
-  inline size_t MemCostBytes(void) const {
-    return offset.size() * sizeof(size_t) +
-        label.size() * sizeof(real_t) +
-        weight.size() * sizeof(real_t) +
-        qid.size() * sizeof(size_t) +
-        field.size() * sizeof(IndexType) +
-        index.size() * sizeof(IndexType) +
-        value.size() * sizeof(DType);
-  }
-  /*!
-   * \brief push the row into container
-   * \param row the row to push back
-   * \tparam I the index type of the row
-   */
-  template<typename I>
-  inline void Push(Row<I, DType> row) {
-    label.push_back(row.get_label());
-    weight.push_back(row.get_weight());
-    qid.push_back(row.get_qid());
-    if (row.field != NULL) {
-      for (size_t i = 0; i < row.length; ++i) {
-        CHECK_LE(row.field[i], std::numeric_limits<IndexType>::max())
-            << "field exceed numeric bound of current type";
-        IndexType field_id = static_cast<IndexType>(row.field[i]);
-        field.push_back(field_id);
-        max_field = std::max(max_field, field_id);
-    }
-    }
-    for (size_t i = 0; i < row.length; ++i) {
-      CHECK_LE(row.index[i], std::numeric_limits<IndexType>::max())
-          << "index exceed numeric bound of current type";
-      IndexType findex = static_cast<IndexType>(row.index[i]);
-      index.push_back(findex);
-      max_index = std::max(max_index, findex);
-    }
-    if (row.value != NULL) {
-      for (size_t i = 0; i < row.length; ++i) {
-        value.push_back(row.value[i]);
-      }
-    }
-    offset.push_back(index.size());
-  }
-  /*!
-   * \brief push the row block into container
-   * \param row the row to push back
-   * \tparam I the index type of the row
-   */
-  template<typename I>
-  inline void Push(RowBlock<I, DType> batch) {
-    size_t size = label.size();
-    label.resize(label.size() + batch.size);
-    std::memcpy(BeginPtr(label) + size, batch.label,
-                batch.size * sizeof(DType));
-    if (batch.weight != NULL) {
-      weight.insert(weight.end(), batch.weight, batch.weight + batch.size);
-    }
-    if (batch.qid != NULL) {
-      qid.insert(qid.end(), batch.qid, batch.qid + batch.size);
-    }
-    size_t ndata = batch.offset[batch.size] - batch.offset[0];
-    if (batch.field != NULL) {
-      field.resize(field.size() + ndata);
-      IndexType *fhead = BeginPtr(field) + offset.back();
-      for (size_t i = 0; i < ndata; ++i) {
-        CHECK_LE(batch.field[i], std::numeric_limits<IndexType>::max())
-            << "field  exceed numeric bound of current type";
-        IndexType field_id = static_cast<IndexType>(batch.field[i]);
-        fhead[i] = field_id;
-        max_field = std::max(max_field, field_id);
-      }
-    }
-    index.resize(index.size() + ndata);
-    IndexType *ihead = BeginPtr(index) + offset.back();
-    for (size_t i = 0; i < ndata; ++i) {
-      CHECK_LE(batch.index[i], std::numeric_limits<IndexType>::max())
-          << "index  exceed numeric bound of current type";
-      IndexType findex = static_cast<IndexType>(batch.index[i]);
-      ihead[i] = findex;
-      max_index = std::max(max_index, findex);
-    }
-    if (batch.value != NULL) {
-      value.resize(value.size() + ndata);
-      std::memcpy(BeginPtr(value) + value.size() - ndata, batch.value,
-                  ndata * sizeof(DType));
-    }
-    size_t shift = offset[size];
-    offset.resize(offset.size() + batch.size);
-    size_t *ohead = BeginPtr(offset) + size + 1;
-    for (size_t i = 0; i < batch.size; ++i) {
-      ohead[i] = shift + batch.offset[i + 1] - batch.offset[0];
-    }
-  }
-};
-
-template<typename IndexType, typename DType>
-inline RowBlock<IndexType, DType>
-RowBlockContainer<IndexType, DType>::GetBlock(void) const {
-  // consistency check
-  if (label.size()) {
-    CHECK_EQ(label.size() + 1, offset.size());
-  }
-  CHECK_EQ(offset.back(), index.size());
-  CHECK(offset.back() == value.size() || value.size() == 0);
-  RowBlock<IndexType, DType> data;
-  data.size = offset.size() - 1;
-  data.offset = BeginPtr(offset);
-  data.label = BeginPtr(label);
-  data.weight = BeginPtr(weight);
-  data.qid = BeginPtr(qid);
-  data.field = BeginPtr(field);
-  data.index = BeginPtr(index);
-  data.value = BeginPtr(value);
-  return data;
-}
-template<typename IndexType, typename DType>
-inline void
-RowBlockContainer<IndexType, DType>::Save(Stream *fo) const {
-  fo->Write(offset);
-  fo->Write(label);
-  fo->Write(weight);
-  fo->Write(qid);
-  fo->Write(field);
-  fo->Write(index);
-  fo->Write(value);
-  fo->Write(&max_field, sizeof(IndexType));
-  fo->Write(&max_index, sizeof(IndexType));
-}
-template<typename IndexType, typename DType>
-inline bool
-RowBlockContainer<IndexType, DType>::Load(Stream *fi) {
-  if (!fi->Read(&offset)) return false;
-  CHECK(fi->Read(&label)) << "Bad RowBlock format";
-  CHECK(fi->Read(&weight)) << "Bad RowBlock format";
-  CHECK(fi->Read(&qid)) << "Bad RowBlock format";
-  CHECK(fi->Read(&field)) << "Bad RowBlock format";
-  CHECK(fi->Read(&index)) << "Bad RowBlock format";
-  CHECK(fi->Read(&value)) << "Bad RowBlock format";
-  CHECK(fi->Read(&max_field, sizeof(IndexType))) << "Bad RowBlock format";
-  CHECK(fi->Read(&max_index, sizeof(IndexType))) << "Bad RowBlock format";
-  return true;
-}
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_ROW_BLOCK_H_
diff --git a/ml-xgboost/dmlc-core/src/data/text_parser.h b/ml-xgboost/dmlc-core/src/data/text_parser.h
deleted file mode 100644
index 97524b7..0000000
--- a/ml-xgboost/dmlc-core/src/data/text_parser.h
+++ /dev/null
@@ -1,150 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file text_parser.h
- * \brief iterator parser to parse text format
- * \author Tianqi Chen
- */
-#ifndef DMLC_DATA_TEXT_PARSER_H_
-#define DMLC_DATA_TEXT_PARSER_H_
-
-#include <dmlc/data.h>
-#include <dmlc/omp.h>
-#include <dmlc/common.h>
-#include <thread>
-#include <mutex>
-#include <vector>
-#include <cstring>
-#include <algorithm>
-#include "./row_block.h"
-#include "./parser.h"
-
-namespace dmlc {
-namespace data {
-/*!
- * \brief Text parser that parses the input lines
- * and returns rows in input data
- */
-template <typename IndexType, typename DType = real_t>
-class TextParserBase : public ParserImpl<IndexType, DType> {
- public:
-  explicit TextParserBase(InputSplit *source,
-                          int nthread)
-      : bytes_read_(0), source_(source) {
-    int maxthread = std::max(omp_get_num_procs() / 2 - 4, 1);
-    nthread_ = std::min(maxthread, nthread);
-  }
-  virtual ~TextParserBase() {
-    delete source_;
-  }
-  virtual void BeforeFirst(void) {
-    source_->BeforeFirst();
-  }
-  virtual size_t BytesRead(void) const {
-    return bytes_read_;
-  }
-  virtual bool ParseNext(std::vector<RowBlockContainer<IndexType, DType> > *data) {
-    return FillData(data);
-  }
-
- protected:
-   /*!
-    * \brief parse data into out
-    * \param begin beginning of buffer
-    * \param end end of buffer
-    */
-  virtual void ParseBlock(const char *begin, const char *end,
-                          RowBlockContainer<IndexType, DType> *out) = 0;
-   /*!
-    * \brief read in next several blocks of data
-    * \param data vector of data to be returned
-    * \return true if the data is loaded, false if reach end
-    */
-  inline bool FillData(std::vector<RowBlockContainer<IndexType, DType>> *data);
-   /*!
-    * \brief start from bptr, go backward and find first endof line
-    * \param bptr end position to go backward
-    * \param begin the beginning position of buffer
-    * \return position of first endof line going backward, returns begin if not found
-    */
-  static inline const char *BackFindEndLine(const char *bptr, const char *begin) {
-     for (; bptr != begin; --bptr) {
-       if (*bptr == '\n' || *bptr == '\r')
-         return bptr;
-     }
-     return begin;
-  }
-  /*!
-   * \brief Ignore UTF-8 BOM if present
-   * \param begin reference to begin pointer
-   * \param end reference to end pointer
-   */
-  static inline void IgnoreUTF8BOM(const char **begin, const char **end) {
-    int count = 0;
-    for (count = 0; *begin != *end && count < 3; count++, ++*begin) {
-      if (!begin || !*begin)
-        break;
-      if (**begin != '\xEF' && count == 0)
-        break;
-      if (**begin != '\xBB' && count == 1)
-        break;
-      if (**begin != '\xBF' && count == 2)
-        break;
-    }
-    if (count < 3)
-      *begin -= count;
-  }
-
- private:
-  // nthread
-  int nthread_;
-  // number of bytes readed
-  size_t bytes_read_;
-  // source split that provides the data
-  InputSplit *source_;
-  // OMPException object to catch and rethrow exceptions in omp blocks
-  dmlc::OMPException omp_exc_;
-};
-
-// implementation
-template <typename IndexType, typename DType>
-inline bool TextParserBase<IndexType, DType>::FillData(
-    std::vector<RowBlockContainer<IndexType, DType> > *data) {
-  InputSplit::Blob chunk;
-  if (!source_->NextChunk(&chunk)) return false;
-  const int nthread = omp_get_max_threads();
-  // reserve space for data
-  data->resize(nthread);
-  bytes_read_ += chunk.size;
-  CHECK_NE(chunk.size, 0U);
-  const char *head = reinterpret_cast<char *>(chunk.dptr);
-
-  std::vector<std::thread> threads;
-  for (int tid = 0; tid < nthread; ++tid) {
-    threads.push_back(std::thread([&chunk, head, data, nthread, tid, this] {
-      this->omp_exc_.Run([&] {
-        size_t nstep = (chunk.size + nthread - 1) / nthread;
-        size_t sbegin = std::min(tid * nstep, chunk.size);
-        size_t send = std::min((tid + 1) * nstep, chunk.size);
-        const char *pbegin = BackFindEndLine(head + sbegin, head);
-        const char *pend;
-        if (tid + 1 == nthread) {
-          pend = head + send;
-        } else {
-          pend = BackFindEndLine(head + send, head);
-        }
-        ParseBlock(pbegin, pend, &(*data)[tid]);
-      });
-    }));
-  }
-  for (int i = 0; i < nthread; ++i) {
-    threads[i].join();
-  }
-  omp_exc_.Rethrow();
-
-  this->data_ptr_ = 0;
-  return true;
-}
-
-}  // namespace data
-}  // namespace dmlc
-#endif  // DMLC_DATA_TEXT_PARSER_H_
diff --git a/ml-xgboost/dmlc-core/src/io.cc b/ml-xgboost/dmlc-core/src/io.cc
deleted file mode 100644
index 732f304..0000000
--- a/ml-xgboost/dmlc-core/src/io.cc
+++ /dev/null
@@ -1,145 +0,0 @@
-// Copyright by Contributors
-
-#include <dmlc/base.h>
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <cstring>
-#include "io/uri_spec.h"
-#include "io/line_split.h"
-#include "io/recordio_split.h"
-#include "io/indexed_recordio_split.h"
-#include "io/single_file_split.h"
-#include "io/local_filesys.h"
-#include "io/cached_input_split.h"
-#include "io/threaded_input_split.h"
-
-#if DMLC_USE_HDFS
-#include "io/hdfs_filesys.h"
-#endif
-
-#if DMLC_USE_S3
-#include "io/s3_filesys.h"
-#endif
-
-#if DMLC_USE_AZURE
-#include "io/azure_filesys.h"
-#endif
-
-namespace dmlc {
-namespace io {
-FileSystem *FileSystem::GetInstance(const URI &path) {
-  if (path.protocol == "file://" || path.protocol.length() == 0) {
-    return LocalFileSystem::GetInstance();
-  }
-  if (path.protocol == "hdfs://" || path.protocol == "viewfs://") {
-#if DMLC_USE_HDFS
-    if (path.host.length() == 0) {
-      return HDFSFileSystem::GetInstance("default");
-    } else if (path.protocol == "viewfs://") {
-      char* defaultFS = nullptr;
-      hdfsConfGetStr("fs.defaultFS", &defaultFS);
-      if (path.host.length() != 0) {
-        CHECK("viewfs://" + path.host == defaultFS)
-            << "viewfs is only supported as a fs.defaultFS.";
-      }
-      return HDFSFileSystem::GetInstance("default");
-    } else {
-      return HDFSFileSystem::GetInstance(path.host);
-    }
-#else
-    LOG(FATAL) << "Please compile with DMLC_USE_HDFS=1 to use hdfs";
-#endif
-  }
-  if (path.protocol == "s3://" || path.protocol == "http://" || path.protocol == "https://") {
-#if DMLC_USE_S3
-    return S3FileSystem::GetInstance();
-#else
-    LOG(FATAL) << "Please compile with DMLC_USE_S3=1 to use S3";
-#endif
-  }
-
-  if (path.protocol == "azure://") {
-#if DMLC_USE_AZURE
-    return AzureFileSystem::GetInstance();
-#else
-    LOG(FATAL) << "Please compile with DMLC_USE_AZURE=1 to use Azure";
-#endif
-  }
-
-  LOG(FATAL) << "unknown filesystem protocol " + path.protocol;
-  return NULL;
-}
-}  // namespace io
-
-InputSplit* InputSplit::Create(const char *uri_,
-                               unsigned part,
-                               unsigned nsplit,
-                               const char *type) {
-    return Create(uri_, nullptr, part, nsplit, type);
-}
-
-InputSplit* InputSplit::Create(const char *uri_,
-                               const char *index_uri_,
-                               unsigned part,
-                               unsigned nsplit,
-                               const char *type,
-                               const bool shuffle,
-                               const int seed,
-                               const size_t batch_size,
-                               const bool recurse_directories) {
-  using namespace std;
-  using namespace dmlc::io;
-  // allow cachefile in format path#cachefile
-  io::URISpec spec(uri_, part, nsplit);
-  if (!strcmp(spec.uri.c_str(), "stdin")) {
-    return new SingleFileSplit(spec.uri.c_str());
-  }
-  CHECK(part < nsplit) << "invalid input parameter for InputSplit::Create";
-  URI path(spec.uri.c_str());
-  InputSplitBase *split = NULL;
-  if (!strcmp(type, "text")) {
-    split =  new LineSplitter(FileSystem::GetInstance(path),
-                              spec.uri.c_str(), part, nsplit);
-  } else if (!strcmp(type, "indexed_recordio")) {
-      if (index_uri_ != nullptr) {
-      io::URISpec index_spec(index_uri_, part, nsplit);
-    split =  new IndexedRecordIOSplitter(FileSystem::GetInstance(path),
-                                  spec.uri.c_str(), index_spec.uri.c_str(), part, nsplit,
-                                  batch_size, shuffle, seed);
-      } else {
-        LOG(FATAL) << "need to pass index file to use IndexedRecordIO";
-      }
-  } else if (!strcmp(type, "recordio")) {
-    split =  new RecordIOSplitter(FileSystem::GetInstance(path),
-                                  spec.uri.c_str(), part, nsplit,
-                                  recurse_directories);
-  } else {
-    LOG(FATAL) << "unknown input split type " << type;
-  }
-#if DMLC_ENABLE_STD_THREAD
-  if (spec.cache_file.length() == 0) {
-    return new ThreadedInputSplit(split, batch_size);
-  } else {
-    return new CachedInputSplit(split, spec.cache_file.c_str());
-  }
-#else
-  CHECK(spec.cache_file.length() == 0)
-      << "to enable cached file, compile with c++11";
-  return split;
-#endif
-}
-
-Stream *Stream::Create(const char *uri,
-                       const char * const flag,
-                       bool try_create) {
-  io::URI path(uri);
-  return io::FileSystem::
-      GetInstance(path)->Open(path, flag, try_create);
-}
-
-SeekStream *SeekStream::CreateForRead(const char *uri, bool try_create) {
-  io::URI path(uri);
-  return io::FileSystem::
-      GetInstance(path)->OpenForRead(path, try_create);
-}
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/azure_filesys.cc b/ml-xgboost/dmlc-core/src/io/azure_filesys.cc
deleted file mode 100644
index 564f017..0000000
--- a/ml-xgboost/dmlc-core/src/io/azure_filesys.cc
+++ /dev/null
@@ -1,92 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file azure_filesys.cc
- * \brief Azure access module
- * \author Mu Li
- */
-#include "./azure_filesys.h"
-#include "stdafx.h"
-
-#include "was/storage_account.h"
-#include "was/blob.h"
-#include "cpprest/filestream.h"
-#include "cpprest/containerstream.h"
-
-namespace dmlc {
-namespace io {
-
-namespace {
-std::vector<std::string> split(std::string str, char delimiter) {
-  std::vector<std::string> internal;
-  std::stringstream ss(str);
-  std::string tok;
-
-  while (std::getline(ss, tok, delimiter)) {
-    internal.push_back(tok);
-  }
-  return internal;
-}
-}  // namespace
-
-AzureFileSystem::AzureFileSystem() {
-  const char *name = getenv("AZURE_STORAGE_ACCOUNT");
-  const char* key = getenv("AZURE_STORAGE_ACCESS_KEY");
-  CHECK_NE(name, NULL)
-      << "Need to set enviroment variable AZURE_STORAGE_ACCOUNT to use Azure";
-  CHECK_NE(key, NULL)
-      << "Need to set enviroment variable AZURE_STORAGE_ACCESS_KEY to use Azure";
-  azure_account_ = name;
-  azure_key_ = key;
-}
-
-void AzureFileSystem::ListDirectory(
-    const URI &path, std::vector<FileInfo> *out_list) {
-  CHECK(path.host.length()) << "container name not specified in azure";
-  out_list->clear();
-
-  utility::string_t
-      storage_connection_string(U("DefaultEndpointsProtocol=https;AccountName="
-      + azure_account_ + ";AccountKey= " + azure_key_));
-
-  // Retrieve storage account from connection string.
-  azure::storage::cloud_storage_account storage_account
-      = azure::storage::cloud_storage_account::parse(storage_connection_string);
-
-  // Create the blob client.
-  azure::storage::cloud_blob_client blob_client
-      = storage_account.create_cloud_blob_client();
-
-  // Retrieve a reference to a previously created container.
-  azure::storage::cloud_blob_container container
-      = blob_client.get_container_reference(U("container"));
-
-  // Output URI of each item.
-  azure::storage::list_blob_item_iterator end_of_results;
-  for (auto it = container.list_blobs(); it != end_of_results; ++it) {
-    if (it->is_blob()) {
-      ucout << U("Blob: ") << it->as_blob().uri().primary_uri().to_string() << std::endl;
-      FileInfo info;
-      info.path = path;
-      size_t value = it->as_blob().properties().size();
-      info.size = static_cast<size_t>(value);
-      std::vector<std::string> splitVec
-          = split(it->as_blob().uri().primary_uri().to_string(), '/');
-      info.path.name = '/' + splitVec[splitVec.size()-1];
-      info.type = kFile;
-      out_list->push_back(info);
-    } else {
-      ucout << U("Directory: ") << it->as_directory().uri().primary_uri().to_string() << std::endl;
-      FileInfo info;
-      info.path = path;
-      info.size = 0;
-      std::vector<std::string> splitVec =
-          split(it->as_directory().uri().primary_uri().to_string(), '/');
-      info.path.name = '/' + splitVec[splitVec.size()-1];
-      info.type = kDirectory;
-      out_list->push_back(info);
-    }
-  }
-}
-
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/azure_filesys.h b/ml-xgboost/dmlc-core/src/io/azure_filesys.h
deleted file mode 100644
index 8f22d89..0000000
--- a/ml-xgboost/dmlc-core/src/io/azure_filesys.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file azure_filesys.h
- * \brief Azure access module
- * \author Mu Li
- */
-#ifndef DMLC_IO_AZURE_FILESYS_H_
-#define DMLC_IO_AZURE_FILESYS_H_
-
-#include <dmlc/filesystem.h>
-#include <vector>
-#include <string>
-
-namespace dmlc {
-namespace io {
-
-/*! \brief Microsoft Azure Blob filesystem */
-class AzureFileSystem : public FileSystem {
- public:
-  virtual ~AzureFileSystem() {}
-
-  virtual FileInfo GetPathInfo(const URI &path) { return FileInfo(); }
-
-  virtual void ListDirectory(const URI &path, std::vector<FileInfo> *out_list);
-
-  virtual Stream *Open(const URI &path, const char* const flag, bool allow_null) {
-    return NULL;
-  }
-
-  virtual SeekStream *OpenForRead(const URI &path, bool allow_null) {
-    return NULL;
-  }
-
-  /*!
-   * \brief get a singleton of AzureFileSystem when needed
-   * \return a singleton instance
-   */
-  inline static AzureFileSystem *GetInstance(void) {
-    static AzureFileSystem instance;
-    return &instance;
-  }
-
- private:
-  /*! \brief constructor */
-  AzureFileSystem();
-
-  /*! \brief Azure storage account name */
-  std::string azure_account_;
-
-  /*! \brief Azure storage account key */
-  std::string azure_key_;
-};
-
-}  // namespace io
-}  // namespace dmlc
-
-#endif  // DMLC_IO_AZURE_FILESYS_H_
diff --git a/ml-xgboost/dmlc-core/src/io/cached_input_split.h b/ml-xgboost/dmlc-core/src/io/cached_input_split.h
deleted file mode 100644
index 2a264ef..0000000
--- a/ml-xgboost/dmlc-core/src/io/cached_input_split.h
+++ /dev/null
@@ -1,193 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file cached_input_split.h
- * \brief InputSplit that reads from an existing InputSplit
- *  and cache the data into local disk, the second iteration
- *  will be reading from the local cached data
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_CACHED_INPUT_SPLIT_H_
-#define DMLC_IO_CACHED_INPUT_SPLIT_H_
-
-#include <dmlc/base.h>
-// this code depends on c++11
-
-#if DMLC_ENABLE_STD_THREAD
-#include <dmlc/threadediter.h>
-#include <string>
-#include <algorithm>
-#include "./input_split_base.h"
-
-namespace dmlc {
-namespace io {
-/*!
- * \brief InputSplit that reads from an existing InputSplit
- *  and cache the data into local disk, the second iteration
- *  will be reading from the local cached data
- */
-class CachedInputSplit : public InputSplit {
- public:
-  /*!
-   * \brief constructor
-   * \param base source input split
-   * \param cache_file the path to cache file
-   * \param reuse_exist_cache whether reuse existing cache file, if any
-   */
-  CachedInputSplit(InputSplitBase *base,
-                   const char *cache_file,
-                   bool reuse_exist_cache = true)
-      : buffer_size_(InputSplitBase::kBufferSize),
-        cache_file_(cache_file),
-        fo_(NULL), fi_(NULL),
-        base_(base), tmp_chunk_(NULL),
-        iter_preproc_(NULL) {
-    if (reuse_exist_cache) {
-      if (!this->InitCachedIter()) {
-        this->InitPreprocIter();
-      }
-    } else {
-      this->InitPreprocIter();
-    }
-  }
-  // destructor
-  virtual ~CachedInputSplit(void) {
-    // NOTE delete can handle NULL ptr
-    // deletion order matters
-    delete iter_preproc_;
-    delete fo_;
-    iter_cached_.Destroy();
-    delete tmp_chunk_;
-    delete base_;
-    delete fi_;
-  }
-  virtual void BeforeFirst(void) {
-    // if preprocessing did not end
-    // pull data from preprocessing module
-    if (iter_preproc_ != NULL) {
-      if (tmp_chunk_ != NULL) {
-        iter_preproc_->Recycle(&tmp_chunk_);
-      }
-      while (iter_preproc_->Next(&tmp_chunk_)) {
-        iter_preproc_->Recycle(&tmp_chunk_);
-      }
-      // finalize the push out process
-      delete iter_preproc_;
-      delete fo_;
-      iter_preproc_ = NULL;
-      fo_ = NULL;
-      CHECK(this->InitCachedIter())
-          << "Failed to initialize CachedIter";
-    } else {
-      iter_cached_.BeforeFirst();
-    }
-    if (tmp_chunk_ != NULL) {
-      iter_cached_.Recycle(&tmp_chunk_);
-    }
-  }
-  virtual void ResetPartition(unsigned part_index, unsigned num_parts) {
-    LOG(FATAL) << "ResetPartition is not supported in CachedInputSplit";
-  }
-  virtual void HintChunkSize(size_t chunk_size) {
-    buffer_size_ = std::max(chunk_size / sizeof(uint32_t), buffer_size_);
-  }
-  virtual size_t GetTotalSize(void) {
-    return base_->GetTotalSize();
-  }
-  // implement next record
-  virtual bool NextRecord(Blob *out_rec) {
-    auto *iter = iter_preproc_ != NULL ? iter_preproc_ : &iter_cached_;
-    if (tmp_chunk_ == NULL) {
-      if (!iter->Next(&tmp_chunk_)) return false;
-    }
-    while (!base_->ExtractNextRecord(out_rec, tmp_chunk_)) {
-      iter->Recycle(&tmp_chunk_);
-      if (!iter->Next(&tmp_chunk_)) return false;
-    }
-    return true;
-  }
-  // implement next chunk
-  virtual bool NextChunk(Blob *out_chunk) {
-    auto *iter = iter_preproc_ != NULL ? iter_preproc_ : &iter_cached_;
-    if (tmp_chunk_ == NULL) {
-      if (!iter->Next(&tmp_chunk_)) return false;
-    }
-    while (!base_->ExtractNextChunk(out_chunk, tmp_chunk_)) {
-      iter->Recycle(&tmp_chunk_);
-      if (!iter->Next(&tmp_chunk_)) return false;
-    }
-    return true;
-  }
-
- private:
-  /*! \brief internal buffer size */
-  size_t buffer_size_;
-  /*! \brief cache file path */
-  std::string cache_file_;
-  /*! \brief output stream to cache file*/
-  dmlc::Stream *fo_;
-  /*! \brief input stream from cache file */
-  dmlc::SeekStream *fi_;
-  /*! \brief the place where we get the data */
-  InputSplitBase *base_;
-  /*! \brief current chunk of data */
-  InputSplitBase::Chunk *tmp_chunk_;
-  /*! \brief backend thread iterator for preprocessing  */
-  ThreadedIter<InputSplitBase::Chunk> *iter_preproc_;
-  /*! \brief backend thread iterator for cache */
-  ThreadedIter<InputSplitBase::Chunk> iter_cached_;
-  /*! \brief initialize the cached iterator */
-  inline void InitPreprocIter(void);
-  /*!
-   * \brief initialize the cached iterator
-   * \return wheher the file exist and
-   *  initialization is successful
-   */
-  inline bool InitCachedIter(void);
-};
-
-inline void CachedInputSplit:: InitPreprocIter(void) {
-  fo_ = dmlc::Stream::Create(cache_file_.c_str(), "w");
-  iter_preproc_ = new ThreadedIter<InputSplitBase::Chunk>();
-  iter_preproc_->set_max_capacity(16);
-  iter_preproc_->Init([this](InputSplitBase::Chunk **dptr) {
-      if (*dptr == NULL) {
-        *dptr = new InputSplitBase::Chunk(buffer_size_);
-      }
-      auto *p = *dptr;
-      if (!base_->NextChunkEx(p)) return false;
-      // after loading, save to disk
-      size_t size = p->end - p->begin;
-      fo_->Write(&size, sizeof(size));
-      fo_->Write(p->begin, size);
-      return true;
-    });
-}
-
-inline bool CachedInputSplit::InitCachedIter(void) {
-  fi_ = dmlc::SeekStream::CreateForRead(cache_file_.c_str(), true);
-  if (fi_ == NULL) return false;
-  iter_cached_.Init([this](InputSplitBase::Chunk **dptr) {
-      if (*dptr == NULL) {
-        *dptr = new InputSplitBase::Chunk(buffer_size_);
-      }
-      auto *p = *dptr;
-      // read data from cache file
-      size_t size;
-      size_t nread = fi_->Read(&size, sizeof(size));
-      if (nread == 0) return false;
-      CHECK(nread == sizeof(size))
-          << cache_file_ << " has invalid cache file format";
-      p->data.resize(size / sizeof(size_t) + 1);
-      p->begin = reinterpret_cast<char*>(BeginPtr(p->data));
-      p->end = p->begin + size;
-      CHECK(fi_->Read(p->begin, size) == size)
-          << cache_file_ << " has invalid cache file format";
-      return true;
-    },
-    [this]() { fi_->Seek(0); });
-  return true;
-}
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_USE_CXX11
-#endif  // DMLC_IO_CACHED_INPUT_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/filesys.cc b/ml-xgboost/dmlc-core/src/io/filesys.cc
deleted file mode 100644
index 086bc8d..0000000
--- a/ml-xgboost/dmlc-core/src/io/filesys.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-// Copyright by Contributors
-
-#include <dmlc/filesystem.h>
-#include <queue>
-
-namespace dmlc {
-namespace io {
-
-void FileSystem::ListDirectoryRecursive(const URI &path,
-                                        std::vector<FileInfo> *out_list) {
-  std::queue<URI> queue;
-  queue.push(path);
-  while (!queue.empty()) {
-    std::vector<FileInfo> dfiles;
-    ListDirectory(queue.front(), &dfiles);
-    queue.pop();
-    for (auto dfile : dfiles) {
-      if (dfile.type == kDirectory) {
-        queue.push(dfile.path);
-      } else {
-        out_list->push_back(dfile);
-      }
-    }
-  }
-}
-
-}  // namespace io
-
-void TemporaryDirectory::RecursiveDelete(const std::string &path) {
-  io::URI uri(path.c_str());
-  io::FileSystem* fs = io::FileSystem::GetInstance(uri);
-  std::vector<io::FileInfo> file_list;
-  fs->ListDirectory(uri, &file_list);
-  for (io::FileInfo info : file_list) {
-    CHECK(!IsSymlink(info.path.name))
-        << "Symlink not supported in TemporaryDirectory";
-    if (info.type == io::FileType::kDirectory) {
-      RecursiveDelete(info.path.name);
-    } else {
-      if (std::remove(info.path.name.c_str()) != 0) {
-        LOG(INFO) << "Couldn't remove file " << info.path.name
-                  << "; you may want to remove it manually";
-      }
-    }
-  }
-#if _WIN32
-  const bool rmdir_success = (RemoveDirectoryA(path.c_str()) != 0);
-#else
-  const bool rmdir_success = (rmdir(path.c_str()) == 0);
-#endif
-  if (rmdir_success) {
-    if (verbose_) {
-      LOG(INFO) << "Successfully deleted temporary directory " << path;
-    }
-  } else {
-    LOG(INFO) << "~TemporaryDirectory(): "
-              << "Could not remove temporary directory " << path
-              << "; you may want to remove it manually";
-  }
-}
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/hdfs_filesys.cc b/ml-xgboost/dmlc-core/src/io/hdfs_filesys.cc
deleted file mode 100644
index 6d22750..0000000
--- a/ml-xgboost/dmlc-core/src/io/hdfs_filesys.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/logging.h>
-#include <algorithm>
-#include <limits>
-#include "./hdfs_filesys.h"
-
-namespace dmlc {
-namespace io {
-// implementation of HDFS stream
-class HDFSStream : public SeekStream {
- public:
-  HDFSStream(hdfsFS fs,
-             int *ref_counter,
-             hdfsFile fp)
-      : fs_(fs), ref_counter_(ref_counter),
-        fp_(fp) {
-  }
-
-  virtual ~HDFSStream(void) {
-    this->Close();
-    ref_counter_[0] -= 1;
-    if (ref_counter_[0] == 0) {
-      delete ref_counter_;
-      if (hdfsDisconnect(fs_) != 0) {
-        int errsv = errno;
-        LOG(FATAL) << "HDFSStream.hdfsDisconnect Error:" << strerror(errsv);
-      }
-    }
-  }
-
-  virtual size_t Read(void *ptr, size_t size) {
-    char *buf = static_cast<char*>(ptr);
-    size_t nleft = size;
-    while (nleft != 0) {
-      size_t nmax = static_cast<size_t>(std::numeric_limits<tSize>::max());
-      tSize ret = hdfsRead(fs_, fp_, buf, std::min(nleft, nmax));
-      if (ret > 0) {
-        size_t n = static_cast<size_t>(ret);
-        nleft -= n; buf += n;
-      } else if (ret == 0) {
-        break;
-      } else {
-        int errsv = errno;
-        if (errno == EINTR) continue;
-        LOG(FATAL) << "HDFSStream.hdfsRead Error:" << strerror(errsv);
-      }
-    }
-    return size - nleft;
-  }
-
-  virtual void Write(const void *ptr, size_t size) {
-    const char *buf = reinterpret_cast<const char*>(ptr);
-    while (size != 0) {
-      tSize nwrite = hdfsWrite(fs_, fp_, buf, size);
-      if (nwrite == -1) {
-        int errsv = errno;
-        LOG(FATAL) << "HDFSStream.hdfsWrite Error:" << strerror(errsv);
-      }
-      size_t sz = static_cast<size_t>(nwrite);
-      buf += sz; size -= sz;
-    }
-  }
-  virtual void Seek(size_t pos) {
-    if (hdfsSeek(fs_, fp_, pos) != 0) {
-      int errsv = errno;
-      LOG(FATAL) << "HDFSStream.hdfsSeek Error:" << strerror(errsv);
-    }
-  }
-  virtual size_t Tell(void) {
-    tOffset offset = hdfsTell(fs_, fp_);
-    if (offset == -1) {
-      int errsv = errno;
-      LOG(FATAL) << "HDFSStream.hdfsTell Error:" << strerror(errsv);
-    }
-    return static_cast<size_t>(offset);
-  }
-  inline void Close(void) {
-    if (fp_ != NULL) {
-      if (hdfsCloseFile(fs_, fp_) == -1) {
-        int errsv = errno;
-        LOG(FATAL) << "HDFSStream.hdfsClose Error:" << strerror(errsv);
-      }
-      fp_ = NULL;
-    }
-  }
-
- private:
-  hdfsFS fs_;
-  int *ref_counter_;
-  hdfsFile fp_;
-};
-
-HDFSFileSystem::HDFSFileSystem(const std::string &namenode): namenode_(namenode) {
-  fs_ = hdfsConnect(namenode_.c_str(), 0);
-  if (fs_ == NULL) {
-    LOG(FATAL) << "Failed to load HDFS-configuration:";
-  }
-  ref_counter_ = new int();
-  ref_counter_[0] = 1;
-}
-
-HDFSFileSystem::~HDFSFileSystem(void) {
-  ref_counter_[0] -= 1;
-  if (ref_counter_[0] == 0) {
-    delete ref_counter_;
-    if (hdfsDisconnect(fs_) != 0) {
-      int errsv = errno;
-      LOG(FATAL) << "HDFSStream.hdfsDisconnect Error:" << strerror(errsv);
-    }
-  }
-}
-
-void HDFSFileSystem::ResetNamenode(const std::string &namenode) {
-  if (hdfsDisconnect(fs_) != 0) {
-    int errsv = errno;
-    LOG(FATAL) << "HDFSStream.hdfsDisconnect Error: " << strerror(errsv);
-  }
-
-  namenode_ = namenode;
-  fs_ = hdfsConnect(namenode_.c_str(), 0);
-  if (fs_ == NULL) {
-    LOG(FATAL) << "Failed to load HDFS-configuration: " << namenode_.c_str();
-  }
-  ref_counter_[0] = 1;
-}
-
-inline FileInfo ConvertPathInfo(const URI &path, const hdfsFileInfo &info) {
-  FileInfo ret;
-  ret.size = info.mSize;
-  switch (info.mKind) {
-    case 'D': ret.type = kDirectory; break;
-    case 'F': ret.type = kFile; break;
-    default: LOG(FATAL) << "unknown file type" << info.mKind;
-  }
-  URI hpath(info.mName);
-  if (hpath.protocol == "hdfs://" || hpath.protocol == "viewfs://") {
-    ret.path = hpath;
-  } else {
-    ret.path = path;
-    ret.path.name = info.mName;
-  }
-  return ret;
-}
-
-FileInfo HDFSFileSystem::GetPathInfo(const URI &path) {
-  CHECK(path.protocol == "hdfs://" || path.protocol == "viewfs://")
-      << "HDFSFileSystem only works with hdfs and viewfs";
-  hdfsFileInfo *info = hdfsGetPathInfo(fs_, path.str().c_str());
-  CHECK(info != NULL) << "Path do not exist:" << path.str();
-  FileInfo ret = ConvertPathInfo(path, *info);
-  hdfsFreeFileInfo(info, 1);
-  return ret;
-}
-
-void HDFSFileSystem::ListDirectory(const URI &path, std::vector<FileInfo> *out_list) {
-  int nentry;
-  hdfsFileInfo *files = hdfsListDirectory(fs_, path.name.c_str(), &nentry);
-  CHECK(files != NULL) << "Error when ListDirectory " << path.str();
-  out_list->clear();
-  for (int i = 0; i < nentry; ++i) {
-    out_list->push_back(ConvertPathInfo(path, files[i]));
-  }
-  hdfsFreeFileInfo(files, nentry);
-}
-
-SeekStream *HDFSFileSystem::Open(const URI &path,
-                                 const char* const mode,
-                                 bool allow_null) {
-  using namespace std;
-  int flag = 0;
-  if (!strcmp(mode, "r")) {
-    flag = O_RDONLY;
-  } else if (!strcmp(mode, "w"))  {
-    flag = O_WRONLY;
-  } else if (!strcmp(mode, "a"))  {
-    flag = O_WRONLY | O_APPEND;
-  } else {
-    LOG(FATAL) << "HDFSStream: unknown flag %s" << mode;
-  }
-  hdfsFile fp_ = hdfsOpenFile(fs_, path.str().c_str(), flag, 0, 0, 0);
-  if (fp_ != NULL) {
-    ref_counter_[0] += 1;
-    return new HDFSStream(fs_, ref_counter_, fp_);
-  }
-  CHECK(allow_null) << " HDFSFileSystem: fail to open \"" << path.str() << '\"';
-  return NULL;
-}
-
-SeekStream *HDFSFileSystem::OpenForRead(const URI &path, bool allow_null) {
-  return Open(path, "r", allow_null);
-}
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/hdfs_filesys.h b/ml-xgboost/dmlc-core/src/io/hdfs_filesys.h
deleted file mode 100644
index ef057b0..0000000
--- a/ml-xgboost/dmlc-core/src/io/hdfs_filesys.h
+++ /dev/null
@@ -1,82 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file hdfs_filesys.h
- * \brief HDFS access module
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_HDFS_FILESYS_H_
-#define DMLC_IO_HDFS_FILESYS_H_
-extern "C" {
-#include <hdfs.h>
-}
-#include <dmlc/filesystem.h>
-
-#include <vector>
-#include <string>
-
-namespace dmlc {
-namespace io {
-/*! \brief HDFS file system */
-class HDFSFileSystem : public FileSystem {
- public:
-  /*! \brief destructor */
-  virtual ~HDFSFileSystem();
-  /*!
-   * \brief get information about a path
-   * \param path the path to the file
-   * \return the information about the file
-   */
-  virtual FileInfo GetPathInfo(const URI &path);
-  /*!
-   * \brief list files in a directory
-   * \param path to the file
-   * \param out_list the output information about the files
-   */
-  virtual void ListDirectory(const URI &path, std::vector<FileInfo> *out_list);
-  /*!
-   * \brief open a stream, will report error and exit if bad thing happens
-   * NOTE: the Stream can continue to work even when filesystem was destructed
-   * \param path path to file
-   * \param uri the uri of the input, can contain hdfs prefix
-   * \param flag can be "w", "r", "a"
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual SeekStream *Open(const URI &path,
-                           const char* const flag,
-                           bool allow_null);
-  /*!
-   * \brief open a seekable stream for read
-   * \param path the path to the file
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual SeekStream *OpenForRead(const URI &path, bool allow_null);
-  /*!
-   * \brief get a singleton of HDFSFileSystem when needed
-   * \return a singleton instance
-   */
-  inline static HDFSFileSystem *GetInstance(const std::string &namenode = "default") {
-    static HDFSFileSystem instance(namenode);
-    // switch to another hdfs
-    if (namenode != "default" && instance.namenode_ != namenode) {
-      instance.ResetNamenode(namenode);
-    }
-    return &instance;
-  }
-
- private:
-  /*! \brief constructor */
-  explicit HDFSFileSystem(const std::string &namenode);
-  /*! \brief switch to another hdfs cluster */
-  void ResetNamenode(const std::string &namenode);
-  /*! \brief namenode address */
-  std::string namenode_;
-  /*! \brief hdfs handle */
-  hdfsFS fs_;
-  /*! \brief reference counter of fs */
-  int *ref_counter_;
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_HDFS_FILESYS_H_
diff --git a/ml-xgboost/dmlc-core/src/io/indexed_recordio_split.cc b/ml-xgboost/dmlc-core/src/io/indexed_recordio_split.cc
deleted file mode 100644
index 8a32ad8..0000000
--- a/ml-xgboost/dmlc-core/src/io/indexed_recordio_split.cc
+++ /dev/null
@@ -1,235 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/recordio.h>
-#include <dmlc/logging.h>
-#include <dmlc/io.h>
-#include <algorithm>
-#include <fstream>
-#include "./indexed_recordio_split.h"
-
-namespace dmlc {
-namespace io {
-
-void IndexedRecordIOSplitter::ResetPartition(unsigned rank, unsigned nsplit) {
-  size_t ntotal = index_.size();
-  size_t ntotalbytes = file_offset_.back();
-  size_t nstep = (ntotal + nsplit - 1) / nsplit;
-  if (rank * nstep >= ntotal) return;
-  index_begin_ = rank * nstep;
-  offset_begin_ = index_[index_begin_].first;
-  if ((rank + 1) * nstep < ntotal) {
-    index_end_ = (rank + 1) * nstep;
-    offset_end_ = index_[index_end_].first;
-  } else {
-    offset_end_ = ntotalbytes;
-    index_end_ = index_.size();
-    index_.push_back(std::make_pair(offset_end_, 0));
-  }
-  offset_curr_ = offset_begin_;
-  file_ptr_ = std::upper_bound(file_offset_.begin(),
-                               file_offset_.end(),
-                               offset_begin_) - file_offset_.begin() - 1;
-  file_ptr_end_ = std::upper_bound(file_offset_.begin(),
-                                   file_offset_.end(),
-                                   offset_end_) - file_offset_.begin() - 1;
-  if (fs_ != NULL) {
-    delete fs_; fs_ = NULL;
-  }
-  fs_ = filesys_->OpenForRead(files_[file_ptr_].path);
-  current_index_ = index_begin_;
-  n_overflow_ = 0;
-  this->BeforeFirst();
-}
-
-void IndexedRecordIOSplitter::ReadIndexFile(FileSystem *fs, const std::string& index_uri) {
-  std::vector<URI> expanded_list = this->ConvertToURIs(index_uri);
-  CHECK_EQ(expanded_list.size(), 1ul)
-    << "IndexedRecordIOSplitter does not support multiple index files";
-  for (size_t i = 0; i < expanded_list.size(); ++i) {
-    const URI& path = expanded_list[i];
-    std::unique_ptr<dmlc::Stream> file_stream(fs->Open(path, "r", true));
-    dmlc::istream index_file(file_stream.get());
-    std::vector<size_t> temp;
-    size_t index, offset;
-    while (index_file >> index >> offset) {
-      temp.push_back(offset);
-    }
-    std::sort(temp.begin(), temp.end());
-    for (size_t j = 0; j < temp.size() - 1; ++j) {
-      index_.push_back(std::make_pair(temp[j], temp[j + 1] - temp[j]));
-    }
-    index_.push_back(std::make_pair(temp.back(), file_offset_.back() - temp.back()));
-  }
-}
-
-// Inefficient, but not used anywhere and optimization
-// would require change of the API, so I leave it as is
-size_t IndexedRecordIOSplitter::SeekRecordBegin(Stream *fi) {
-  size_t nstep = 0;
-  uint32_t v, lrec;
-  while (true) {
-    if (fi->Read(&v, sizeof(v)) == 0) return nstep;
-    nstep += sizeof(v);
-    if (v == RecordIOWriter::kMagic) {
-      CHECK(fi->Read(&lrec, sizeof(lrec)) != 0)
-            << "invalid record io format";
-      nstep += sizeof(lrec);
-      uint32_t cflag = RecordIOWriter::DecodeFlag(lrec);
-      if (cflag == 0 || cflag == 1) break;
-    }
-  }
-  // should point at head of record
-  return nstep - 2 * sizeof(uint32_t);
-}
-
-// Inefficient, but not used anywhere and optimization
-// would require change of the API, so I leave it as is
-const char* IndexedRecordIOSplitter::FindLastRecordBegin(const char *begin,
-                                                  const char *end) {
-  CHECK_EQ((reinterpret_cast<size_t>(begin) & 3UL), 0U);
-  CHECK_EQ((reinterpret_cast<size_t>(end) & 3UL), 0U);
-  const uint32_t *pbegin = reinterpret_cast<const uint32_t *>(begin);
-  const uint32_t *p = reinterpret_cast<const uint32_t *>(end);
-  CHECK(p >= pbegin + 2);
-  for (p = p - 2; p != pbegin; --p) {
-    if (p[0] == RecordIOWriter::kMagic) {
-      uint32_t cflag = RecordIOWriter::DecodeFlag(p[1]);
-      if (cflag == 0 || cflag == 1) {
-        return reinterpret_cast<const char*>(p);
-      }
-    }
-  }
-  return begin;
-}
-
-bool IndexedRecordIOSplitter::ExtractNextRecord(Blob *out_rec, Chunk *chunk) {
-  if (chunk->begin == chunk->end) return false;
-  CHECK(chunk->begin + 2 * sizeof(uint32_t) <= chunk->end)
-      << "Invalid RecordIO Format";
-  CHECK_EQ((reinterpret_cast<size_t>(chunk->begin) & 3UL), 0U);
-  CHECK_EQ((reinterpret_cast<size_t>(chunk->end) & 3UL), 0U);
-  uint32_t *p = reinterpret_cast<uint32_t *>(chunk->begin);
-  uint32_t cflag = RecordIOWriter::DecodeFlag(p[1]);
-  uint32_t clen = RecordIOWriter::DecodeLength(p[1]);
-  // skip header
-  out_rec->dptr = chunk->begin + 2 * sizeof(uint32_t);
-  // move pbegin
-  chunk->begin += 2 * sizeof(uint32_t) + (((clen + 3U) >> 2U) << 2U);
-  CHECK(chunk->begin <= chunk->end) << "Invalid RecordIO Format";
-  out_rec->size = clen;
-  if (cflag == 0) return true;
-  const uint32_t kMagic = RecordIOWriter::kMagic;
-  // abnormal path, move data around to make a full part
-  CHECK(cflag == 1U) << "Invalid RecordIO Format";
-  while (cflag != 3U) {
-    CHECK(chunk->begin + 2 * sizeof(uint32_t) <= chunk->end);
-    p = reinterpret_cast<uint32_t *>(chunk->begin);
-    CHECK(p[0] == RecordIOWriter::kMagic);
-    cflag = RecordIOWriter::DecodeFlag(p[1]);
-    clen = RecordIOWriter::DecodeLength(p[1]);
-    // pad kmagic in between
-    std::memcpy(reinterpret_cast<char*>(out_rec->dptr) + out_rec->size,
-                &kMagic, sizeof(kMagic));
-    out_rec->size += sizeof(kMagic);
-    // move the rest of the blobs
-    if (clen != 0) {
-      std::memmove(reinterpret_cast<char*>(out_rec->dptr) + out_rec->size,
-                   chunk->begin + 2 * sizeof(uint32_t), clen);
-      out_rec->size += clen;
-    }
-    chunk->begin += 2 * sizeof(uint32_t) + (((clen + 3U) >> 2U) << 2U);
-  }
-  return true;
-}
-
-bool IndexedRecordIOSplitter::ReadChunk(void *buf, size_t *size) {
-  size_t max_size = *size;
-  size_t nread = this->Read(reinterpret_cast<char*>(buf),
-                            max_size);
-  if (nread == 0) return false;
-  if (nread != max_size) {
-    *size = nread;
-  }
-  return true;
-}
-
-bool IndexedRecordIOSplitter::NextChunk(Blob *out_chunk) {
-  return this->NextBatch(out_chunk, batch_size_);
-}
-
-bool IndexedRecordIOSplitter::NextBatchEx(Chunk *chunk, size_t n_records) {
-    if (shuffle_) {
-      bool ret = true;
-      size_t n_read = 0;
-      size_t n = n_overflow_ == 0?n_records:n_overflow_;
-      while (n_read < n) {
-        if (current_index_ < permutation_.size()) {
-          offset_curr_ = index_[permutation_[current_index_]].first;
-          buffer_size_ = index_[permutation_[current_index_]].second/sizeof(uint32_t);
-          size_t new_file_ptr = std::upper_bound(file_offset_.begin(),
-                                 file_offset_.end(),
-                                 offset_curr_) - file_offset_.begin() - 1;
-          if (new_file_ptr != file_ptr_) {
-            delete fs_;
-            file_ptr_ = new_file_ptr;
-            fs_ = filesys_->OpenForRead(files_[file_ptr_].path);
-          }
-          fs_->Seek(offset_curr_ - file_offset_[file_ptr_]);
-          if (n_read == 0) {
-            ret = ret && chunk->Load(this, buffer_size_);
-          } else {
-            ret = ret && chunk->Append(this, buffer_size_);
-          }
-          if (ret) {
-            ++n_read;
-            ++current_index_;
-          } else {
-            break;
-          }
-        } else {
-          break;
-        }
-      }
-      if (n_read > 0) {
-        n_overflow_ = n - n_read;
-        return true;
-      } else {
-        return false;
-      }
-    } else {
-      size_t last;
-      if (n_overflow_ == 0) {
-        last = std::min(current_index_ + n_records, index_end_);
-        n_overflow_ = current_index_ + n_records - last;
-      } else {
-        last = std::min(current_index_ + n_overflow_, index_end_);
-        n_overflow_ = current_index_ + n_overflow_ - last;
-      }
-      buffer_size_ = (index_[last].first - index_[current_index_].first)/INDEXED_RECORDIO_ALIGN;
-      current_index_ = last;
-      return chunk->Load(this, buffer_size_);
-    }
-    return true;
-}
-
-bool IndexedRecordIOSplitter::NextBatch(Blob *out_chunk, size_t batch_size) {
-  while (!ExtractNextChunk(out_chunk, &tmp_chunk_)) {
-    if (!NextBatchEx(&tmp_chunk_, batch_size)) return false;
-  }
-  return true;
-}
-
-void IndexedRecordIOSplitter::BeforeFirst(void) {
-  if (shuffle_) {
-    permutation_.clear();
-    for (size_t i = index_begin_; i < index_end_; ++i) {
-      permutation_.push_back(i);
-    }
-    std::shuffle(permutation_.begin(), permutation_.end(), rnd_);
-    current_index_ = 0;
-  } else {
-    current_index_ = index_begin_;
-  }
-  InputSplitBase::BeforeFirst();
-}
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/indexed_recordio_split.h b/ml-xgboost/dmlc-core/src/io/indexed_recordio_split.h
deleted file mode 100644
index 8c34ed7..0000000
--- a/ml-xgboost/dmlc-core/src/io/indexed_recordio_split.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*!
- *  Copyright (c) 2017 by Contributors
- * \file indexed_recordio_split.h
- * \brief input split that splits indexed recordio files
- */
-#ifndef DMLC_IO_INDEXED_RECORDIO_SPLIT_H_
-#define DMLC_IO_INDEXED_RECORDIO_SPLIT_H_
-
-#include <dmlc/io.h>
-#include <dmlc/recordio.h>
-#include <vector>
-#include <cstdio>
-#include <string>
-#include <cstring>
-#include <utility>
-#include <random>
-#include "./input_split_base.h"
-
-namespace dmlc {
-namespace io {
-const unsigned INDEXED_RECORDIO_ALIGN = 4;
-/*! \brief class that splits the recordIO file by record */
-class IndexedRecordIOSplitter : public InputSplitBase {
- public:
-  IndexedRecordIOSplitter(FileSystem *fs,
-                          const char *uri,
-                          const char *index_uri,
-                          unsigned rank,
-                          unsigned nsplit,
-                          const size_t batch_size,
-                          const bool shuffle,
-                          const int seed = 0) {
-    this->shuffle_ = shuffle;
-    if (shuffle) SetRandomSeed(seed);
-    this->batch_size_ = batch_size;
-    this->Init(fs, uri, INDEXED_RECORDIO_ALIGN);
-    this->ReadIndexFile(fs, index_uri);
-    this->ResetPartition(rank, nsplit);
-  }
-
-  bool IsTextParser(void) override {
-    return false;
-  }
-  bool ExtractNextRecord(Blob *out_rec, Chunk *chunk) override;
-  bool ReadChunk(void *buf, size_t *size) override;
-  bool NextChunk(Blob *out_chunk) override;
-  void BeforeFirst(void) override;
-  bool NextBatch(Blob *out_chunk, size_t n_records) override;
-  bool NextRecord(Blob *out_rec) override {
-    while (!ExtractNextRecord(out_rec, &tmp_chunk_)) {
-      if (!tmp_chunk_.Load(this, buffer_size_)) return false;
-      ++current_index_;
-    }
-    return true;
-  }
-  void SetRandomSeed(size_t seed) {
-    rnd_.seed(kRandMagic + seed);
-  }
-  void SetBatchSize(int batch_size) {
-    this->batch_size_ = batch_size;
-  }
-  bool NextChunkEx(Chunk *out_chunk) override {
-    return NextBatchEx(out_chunk, batch_size_);
-  }
-  bool NextBatchEx(Chunk *out_chunk, size_t n_records) override;
-
- protected:
-  size_t SeekRecordBegin(Stream *fi) override;
-  const char*
-  FindLastRecordBegin(const char *begin, const char *end) override;
-  virtual void ReadIndexFile(FileSystem *fs, const std::string& index_uri);
-  void ResetPartition(unsigned rank, unsigned nsplit) override;
-
-  std::vector<std::pair<size_t, size_t> > index_;
-  std::vector<size_t> permutation_;
-  bool shuffle_;
-  size_t current_index_;
-  size_t index_begin_;
-  size_t index_end_;
-  size_t batch_size_;
-  size_t n_overflow_;
-  const int kRandMagic = 111;
-  std::mt19937 rnd_;
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_INDEXED_RECORDIO_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/input_split_base.cc b/ml-xgboost/dmlc-core/src/io/input_split_base.cc
deleted file mode 100644
index a826633..0000000
--- a/ml-xgboost/dmlc-core/src/io/input_split_base.cc
+++ /dev/null
@@ -1,308 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/logging.h>
-#include <dmlc/common.h>
-#include <algorithm>
-#include "./line_split.h"
-
-#if DMLC_USE_REGEX
-#include <regex>
-#endif
-
-namespace dmlc {
-namespace io {
-void InputSplitBase::Init(FileSystem *filesys,
-                          const char *uri,
-                          size_t align_bytes,
-                          const bool recurse_directories) {
-  this->filesys_ = filesys;
-  // initialize the path
-  this->InitInputFileInfo(uri, recurse_directories);
-  file_offset_.resize(files_.size() + 1);
-  file_offset_[0] = 0;
-  for (size_t i = 0; i < files_.size(); ++i) {
-    file_offset_[i + 1] = file_offset_[i] + files_[i].size;
-    CHECK(files_[i].size % align_bytes == 0)
-        << "file do not align by " << align_bytes << " bytes";
-  }
-  this->align_bytes_ = align_bytes;
-}
-
-void InputSplitBase::ResetPartition(unsigned rank,
-                                    unsigned nsplit) {
-  size_t ntotal = file_offset_.back();
-  size_t nstep = (ntotal + nsplit - 1) / nsplit;
-  // align the nstep to 4 bytes
-  nstep = ((nstep + align_bytes_ - 1) / align_bytes_) * align_bytes_;
-  offset_begin_ = std::min(nstep * rank, ntotal);
-  offset_end_ = std::min(nstep * (rank + 1), ntotal);
-  offset_curr_ = offset_begin_;
-  if (offset_begin_ == offset_end_) return;
-  file_ptr_ = std::upper_bound(file_offset_.begin(),
-                               file_offset_.end(),
-                               offset_begin_) - file_offset_.begin() - 1;
-  file_ptr_end_ = std::upper_bound(file_offset_.begin(),
-                                   file_offset_.end(),
-                                   offset_end_) - file_offset_.begin() - 1;
-  if (fs_ != NULL) {
-    delete fs_; fs_ = NULL;
-  }
-  // find the exact ending position
-  if (offset_end_ != file_offset_[file_ptr_end_]) {
-    CHECK(offset_end_ >file_offset_[file_ptr_end_]);
-    CHECK(file_ptr_end_ < files_.size());
-    fs_ = filesys_->OpenForRead(files_[file_ptr_end_].path);
-    fs_->Seek(offset_end_ - file_offset_[file_ptr_end_]);
-    offset_end_ += SeekRecordBegin(fs_);
-    delete fs_;
-  }
-  fs_ = filesys_->OpenForRead(files_[file_ptr_].path);
-  if (offset_begin_ != file_offset_[file_ptr_]) {
-    fs_->Seek(offset_begin_ - file_offset_[file_ptr_]);
-    offset_begin_ += SeekRecordBegin(fs_);
-  }
-  this->BeforeFirst();
-}
-
-void InputSplitBase::BeforeFirst(void) {
-  if (offset_begin_ >= offset_end_) return;
-  size_t fp = std::upper_bound(file_offset_.begin(),
-                               file_offset_.end(),
-                               offset_begin_) - file_offset_.begin() - 1;
-  if (file_ptr_ != fp) {
-    delete fs_;
-    file_ptr_ = fp;
-    fs_ = filesys_->OpenForRead(files_[file_ptr_].path);
-  }
-  // seek to beginning of stream
-  fs_->Seek(offset_begin_ - file_offset_[file_ptr_]);
-  offset_curr_ = offset_begin_;
-  tmp_chunk_.begin = tmp_chunk_.end = NULL;
-  // clear overflow buffer
-  overflow_.clear();
-}
-
-InputSplitBase::~InputSplitBase(void) {
-  delete fs_;
-  // no need to delete filesystem, it was singleton
-}
-
-std::string InputSplitBase::StripEnd(std::string str, char ch) {
-  while (str.length() != 0 && str[str.length() - 1] == ch) {
-    str.resize(str.length() - 1);
-  }
-  return str;
-}
-
-std::vector<URI> InputSplitBase::ConvertToURIs(const std::string& uri) {
-  // split by :
-  const char dlm = ';';
-  std::vector<std::string> file_list = Split(uri, dlm);
-  std::vector<URI> expanded_list;
-
-  // expand by match regex pattern.
-  for (size_t i = 0; i < file_list.size(); ++i) {
-    URI path(file_list[i].c_str());
-    size_t pos = path.name.rfind('/');
-    if (pos == std::string::npos || pos + 1 == path.name.length()) {
-      expanded_list.push_back(path);
-    } else {
-      URI dir = path;
-      dir.name = path.name.substr(0, pos);
-      std::vector<FileInfo> dfiles;
-      filesys_->ListDirectory(dir, &dfiles);
-      bool exact_match = false;
-      for (size_t i = 0; i < dfiles.size(); ++i) {
-        if (StripEnd(dfiles[i].path.name, '/') == StripEnd(path.name, '/')) {
-          expanded_list.push_back(dfiles[i].path);
-          exact_match = true;
-          break;
-        }
-      }
-#if DMLC_USE_REGEX
-      if (!exact_match) {
-        std::string spattern = path.name;
-        try {
-          std::regex pattern(spattern);
-          for (size_t i = 0; i < dfiles.size(); ++i) {
-            if (dfiles[i].type != kFile || dfiles[i].size == 0) continue;
-            std::string stripped = StripEnd(dfiles[i].path.name, '/');
-            std::smatch base_match;
-            if (std::regex_match(stripped, base_match, pattern)) {
-              for (size_t j = 0; j < base_match.size(); ++j) {
-                if (base_match[j].str() == stripped) {
-                  expanded_list.push_back(dfiles[i].path); break;
-                }
-              }
-            }
-          }
-        } catch (std::regex_error& e) {
-          LOG(FATAL) << e.what() << " bad regex " << spattern
-                     << "This could due to compiler version, g++-4.9 is needed";
-        }
-      }
-#endif  // DMLC_USE_REGEX
-    }
-  }
-  return expanded_list;
-}
-
-void InputSplitBase::InitInputFileInfo(const std::string& uri,
-                                       const bool recurse_directories) {
-  std::vector<URI> expanded_list = this->ConvertToURIs(uri);
-  for (size_t i = 0; i < expanded_list.size(); ++i) {
-    const URI& path = expanded_list[i];
-    FileInfo info = filesys_->GetPathInfo(path);
-    if (info.type == kDirectory) {
-      std::vector<FileInfo> dfiles;
-      if (!recurse_directories) {
-        filesys_->ListDirectory(info.path, &dfiles);
-      } else {
-        filesys_->ListDirectoryRecursive(info.path, &dfiles);
-      }
-      for (size_t i = 0; i < dfiles.size(); ++i) {
-        if (dfiles[i].size != 0 && dfiles[i].type == kFile) {
-          files_.push_back(dfiles[i]);
-        }
-      }
-    } else {
-      if (info.size != 0) {
-        files_.push_back(info);
-      }
-    }
-  }
-  CHECK_NE(files_.size(), 0U)
-      << "Cannot find any files that matches the URI pattern " << uri;
-}
-
-size_t InputSplitBase::Read(void *ptr, size_t size) {
-  const bool is_text_parser = this->IsTextParser();
-
-  if (fs_ == NULL) {
-    return 0;
-  }
-  if (offset_begin_ >= offset_end_) return 0;
-  if (offset_curr_ +  size > offset_end_) {
-    size = offset_end_ - offset_curr_;
-  }
-  if (size == 0) return 0;
-  size_t nleft = size;
-  char *buf = reinterpret_cast<char*>(ptr);
-  while (true) {
-    size_t n = fs_->Read(buf, nleft);
-    nleft -= n; buf += n;
-    offset_curr_ += n;
-    if (nleft == 0) break;
-    if (n == 0) {
-      if (is_text_parser) {
-        // Insert a newline between files to handle files with NOEOL.
-        // See https://github.com/dmlc/dmlc-core/pull/385 for explanation.
-        buf[0] = '\n'; ++buf; --nleft;
-      }
-      if (offset_curr_ != file_offset_[file_ptr_ + 1]) {
-        LOG(ERROR) << "curr=" << offset_curr_
-                   << ",begin=" << offset_begin_
-                   << ",end=" << offset_end_
-                   << ",fileptr=" << file_ptr_
-                   << ",fileoffset=" << file_offset_[file_ptr_ + 1];
-        for (size_t i = 0; i < file_ptr_; ++i) {
-          LOG(ERROR) << "offset[" << i << "]=" << file_offset_[i];
-        }
-        LOG(FATAL) << "file offset not calculated correctly";
-      }
-      if (file_ptr_ + 1 >= files_.size()) break;
-      file_ptr_ += 1;
-      delete fs_;
-      fs_ = filesys_->OpenForRead(files_[file_ptr_].path);
-    }
-  }
-  return size - nleft;
-}
-
-bool InputSplitBase::ReadChunk(void *buf, size_t *size) {
-  size_t max_size = *size;
-  if (max_size <= overflow_.length()) {
-    *size = 0; return true;
-  }
-  if (overflow_.length() != 0) {
-    std::memcpy(buf, BeginPtr(overflow_), overflow_.length());
-  }
-  size_t olen = overflow_.length();
-  overflow_.resize(0);
-  size_t nread = this->Read(reinterpret_cast<char*>(buf) + olen,
-                            max_size - olen);
-  nread += olen;
-  if (nread == 0) return false;
-  if (this->IsTextParser()) {
-    if (nread == olen) {
-      // Insert a newline between files to handle files with NOEOL.
-      // See https://github.com/dmlc/dmlc-core/pull/452 for explanation.
-      char *bufptr = reinterpret_cast<char*>(buf);
-      bufptr[nread] = '\n';
-      nread++;
-    }
-  } else {
-    if (nread != max_size) {
-      *size = nread;
-      return true;
-    }
-  }
-  const char *bptr = reinterpret_cast<const char*>(buf);
-  // return the last position where a record starts
-  const char *bend = this->FindLastRecordBegin(bptr, bptr + nread);
-  *size = bend - bptr;
-  overflow_.resize(nread - *size);
-  if (overflow_.length() != 0) {
-    std::memcpy(BeginPtr(overflow_), bend, overflow_.length());
-  }
-  return true;
-}
-
-bool InputSplitBase::Chunk::Load(InputSplitBase *split, size_t buffer_size) {
-  data.resize(buffer_size + 1);
-  while (true) {
-    // leave one tail chunk
-    size_t size = (data.size() - 1) * sizeof(uint32_t);
-    // set back to 0 for string safety
-    data.back() = 0;
-    if (!split->ReadChunk(BeginPtr(data), &size)) return false;
-    if (size == 0) {
-      data.resize(data.size() * 2);
-    } else {
-      begin = reinterpret_cast<char *>(BeginPtr(data));
-      end = begin + size;
-      break;
-    }
-  }
-  return true;
-}
-
-bool InputSplitBase::Chunk::Append(InputSplitBase *split, size_t buffer_size) {
-  size_t previous_size = end - begin;
-  data.resize(data.size() + buffer_size);
-  while (true) {
-    // leave one tail chunk
-    size_t size = buffer_size * sizeof(uint32_t);
-    // set back to 0 for string safety
-    data.back() = 0;
-    if (!split->ReadChunk(reinterpret_cast<char *>(BeginPtr(data)) + previous_size, &size))
-      return false;
-    if (size == 0) {
-      data.resize(data.size() * 2);
-    } else {
-      begin = reinterpret_cast<char *>(BeginPtr(data));
-      end = begin + previous_size + size;
-      break;
-    }
-  }
-  return true;
-}
-
-bool InputSplitBase::ExtractNextChunk(Blob *out_chunk, Chunk *chunk) {
-  if (chunk->begin == chunk->end) return false;
-  out_chunk->dptr = chunk->begin;
-  out_chunk->size = chunk->end - chunk->begin;
-  chunk->begin = chunk->end;
-  return true;
-}
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/input_split_base.h b/ml-xgboost/dmlc-core/src/io/input_split_base.h
deleted file mode 100644
index 3662788..0000000
--- a/ml-xgboost/dmlc-core/src/io/input_split_base.h
+++ /dev/null
@@ -1,197 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file input_split_base.h
- * \brief base class to construct input split from multiple files
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_INPUT_SPLIT_BASE_H_
-#define DMLC_IO_INPUT_SPLIT_BASE_H_
-
-#include <dmlc/io.h>
-#include <dmlc/filesystem.h>
-#include <cstdio>
-#include <cstring>
-#include <vector>
-#include <string>
-#include <algorithm>
-
-namespace dmlc {
-namespace io {
-/*! \brief class to construct input split from multiple files */
-class InputSplitBase : public InputSplit {
- public:
-  /*!
-   * \brief helper struct to hold chunk data
-   *  with internal pointer to move along the record
-   */
-  struct Chunk {
-    char *begin;
-    char *end;
-    std::vector<uint32_t> data;
-    explicit Chunk(size_t buffer_size)
-        : begin(NULL), end(NULL),
-          data(buffer_size + 1) {}
-    // load chunk from split
-    bool Load(InputSplitBase *split, size_t buffer_size);
-    // append to chunk
-    bool Append(InputSplitBase *split, size_t buffer_size);
-  };
-  // 16 MB
-  static const size_t kBufferSize = 2UL << 20UL;
-  // destructor
-  virtual ~InputSplitBase(void);
-  // implement BeforeFirst
-  virtual void BeforeFirst(void);
-  virtual void HintChunkSize(size_t chunk_size) {
-    buffer_size_ = std::max(chunk_size / sizeof(uint32_t), buffer_size_);
-  }
-  virtual size_t GetTotalSize(void) {
-    return file_offset_.back();
-  }
-  // implement next record
-  virtual bool NextRecord(Blob *out_rec) {
-    while (!ExtractNextRecord(out_rec, &tmp_chunk_)) {
-      if (!NextChunkEx(&tmp_chunk_)) return false;
-    }
-    return true;
-  }
-  // implement next chunk
-  virtual bool NextChunk(Blob *out_chunk) {
-    while (!ExtractNextChunk(out_chunk, &tmp_chunk_)) {
-      if (!NextChunkEx(&tmp_chunk_)) return false;
-    }
-    return true;
-  }
-  // implement ResetPartition.
-  virtual void ResetPartition(unsigned rank, unsigned nsplit);
-  /*!
-   * \brief read a chunk of data into buf
-   *   the data can span multiple records,
-   *   but cannot contain partial records
-   *
-   * \param buf the memory region of the buffer,
-   *        should be properly aligned to 64 bits
-   * \param size the maximum size of memory,
-   *   after the function returns, it stores the size of the chunk
-   * \return whether end of file was reached
-   */
-  virtual bool ReadChunk(void *buf, size_t *size);
-  /*!
-   * \brief extract next chunk from the chunk
-   * \param out_chunk the output record
-   * \param chunk the chunk information
-   * \return true if non-empty record is extracted
-   *    false if the chunk is already finishes its life
-   */
-  bool ExtractNextChunk(Blob *out_rchunk, Chunk *chunk);
-  /*!
-   * \brief extract next record from the chunk
-   * \param out_rec the output record
-   * \param chunk the chunk information
-   * \return true if non-empty record is extracted
-   *    false if the chunk is already finishes its life
-   */
-  virtual bool ExtractNextRecord(Blob *out_rec, Chunk *chunk) = 0;
-  /*!
-   * \brief query whether this object is a text parser
-   * \return true if this object represents a text parser; false if it represents
-   *         a binary parser
-   */
-  virtual bool IsTextParser(void) = 0;
-  /*!
-   * \brief fill the given
-   *  chunk with new data without using internal
-   *  temporary chunk
-   */
-  virtual bool NextChunkEx(Chunk *chunk) {
-    if (!chunk->Load(this, buffer_size_)) return false;
-    return true;
-  }
-  /*!
-   * \brief fill the given
-   *  chunk with new batch of data without using internal
-   *  temporary chunk
-   */
-  virtual bool NextBatchEx(Chunk *chunk, size_t n_records) {
-    return NextChunkEx(chunk);
-  }
-
- protected:
-  /*! \brief FileSystem */
-  FileSystem *filesys_;
-  /*! \brief byte-offset of each file */
-  std::vector<size_t> file_offset_;
-  /*! \brief get the current offset */
-  size_t offset_curr_;
-  /*! \brief beginning of offset */
-  size_t offset_begin_;
-  /*! \brief end of the offset */
-  size_t offset_end_;
-  /*! \brief information about files */
-  std::vector<FileInfo> files_;
-  /*! \brief current input stream */
-  SeekStream *fs_;
-  /*! \brief file pointer of which file to read on */
-  size_t file_ptr_;
-  /*! \brief file pointer where the end of file lies */
-  size_t file_ptr_end_;
-  /*! \brief temporal chunk */
-  Chunk tmp_chunk_;
-  /*! \brief buffer size */
-  size_t buffer_size_;
-  // constructor
-  InputSplitBase()
-      : fs_(NULL),
-        tmp_chunk_(kBufferSize),
-        buffer_size_(kBufferSize),
-        align_bytes_(8) {}
-  /*!
-   * \brief intialize the base before doing anything
-   * \param fs the filesystem ptr
-   * \param uri the uri of the files
-   * \param rank the rank of the split
-   * \param nsplit number of splits
-   * \param align_bytes the head split must be multiple of align_bytes
-   *   this also checks if file size are multiple of align_bytes
-   * \param recurse_directories recursively travese directories
-   */
-  void Init(FileSystem *fs,
-            const char *uri,
-            size_t align_bytes,
-            const bool recurse_directories = false);
-  // to be implemented by child class
-  /*!
-   * \brief seek to the beginning of the first record
-   *        in current file pointer
-   * \return how many bytes we read past
-   */
-  virtual size_t SeekRecordBegin(Stream *fi) = 0;
-  /*!
-   * \brief find the last occurance of record header
-   * \param begin beginning of the buffer
-   * \param end end of the buffer
-   * \return the pointer between [begin, end] indicating the
-   *         last record head
-   */
-  virtual const char*
-  FindLastRecordBegin(const char *begin, const char *end) = 0;
-
-  /*! \brief split string list of files into vector of URIs */
-  std::vector<URI> ConvertToURIs(const std::string& uri);
-  /*! \brief same as stream.Read */
-  size_t Read(void *ptr, size_t size);
-
- private:
-  /*! \brief bytes to be aligned */
-  size_t align_bytes_;
-  /*! \brief internal overflow buffer */
-  std::string overflow_;
-  /*! \brief initialize information in files */
-  void InitInputFileInfo(const std::string& uri,
-                         const bool recurse_directories);
-  /*! \brief strip continous chars in the end of str */
-  std::string StripEnd(std::string str, char ch);
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_INPUT_SPLIT_BASE_H_
diff --git a/ml-xgboost/dmlc-core/src/io/line_split.cc b/ml-xgboost/dmlc-core/src/io/line_split.cc
deleted file mode 100644
index 3096a13..0000000
--- a/ml-xgboost/dmlc-core/src/io/line_split.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <algorithm>
-#include "./line_split.h"
-
-namespace dmlc {
-namespace io {
-size_t LineSplitter::SeekRecordBegin(Stream *fi) {
-  char c = '\0';
-  size_t nstep = 0;
-  // search till fist end-of-line
-  while (true) {
-    if (fi->Read(&c, sizeof(c)) == 0) return nstep;
-    nstep += 1;
-    if (c == '\n' || c == '\r') break;
-  }
-  // search until first non-endofline
-  while (true) {
-    if (fi->Read(&c, sizeof(c)) == 0) return nstep;
-    if (c != '\n' && c != '\r') break;
-    // non-end-of-line should not count
-    nstep += 1;
-  }
-  return nstep;
-}
-const char* LineSplitter::FindLastRecordBegin(const char *begin,
-                                              const char *end) {
-  CHECK(begin != end);
-  for (const char *p = end - 1; p != begin; --p) {
-    if (*p == '\n' || *p == '\r') return p + 1;
-  }
-  return begin;
-}
-
-bool LineSplitter::ExtractNextRecord(Blob *out_rec, Chunk *chunk) {
-  if (chunk->begin == chunk->end) return false;
-  char *p;
-  for (p = chunk->begin; p != chunk->end; ++p) {
-    if (*p == '\n' || *p == '\r') break;
-  }
-  for (; p != chunk->end; ++p) {
-    if (*p != '\n' && *p != '\r') break;
-  }
-  // set the string end sign for safety
-  if (p == chunk->end) {
-    *p = '\0';
-  } else {
-    *(p - 1) = '\0';
-  }
-  out_rec->dptr = chunk->begin;
-  out_rec->size = p - chunk->begin;
-  chunk->begin = p;
-  return true;
-}
-
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/line_split.h b/ml-xgboost/dmlc-core/src/io/line_split.h
deleted file mode 100644
index d0d525e..0000000
--- a/ml-xgboost/dmlc-core/src/io/line_split.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file line_split.h
- * \brief base class implementation of input splitter
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_LINE_SPLIT_H_
-#define DMLC_IO_LINE_SPLIT_H_
-
-#include <dmlc/io.h>
-#include <vector>
-#include <cstdio>
-#include <string>
-#include <cstring>
-#include "./input_split_base.h"
-
-namespace dmlc {
-namespace io {
-/*! \brief class that split the files by line */
-class LineSplitter : public InputSplitBase {
- public:
-  LineSplitter(FileSystem *fs,
-               const char *uri,
-               unsigned rank,
-               unsigned nsplit) {
-    this->Init(fs, uri, 1);
-    this->ResetPartition(rank, nsplit);
-  }
-
-  bool IsTextParser(void) {
-    return true;
-  }
-  virtual bool ExtractNextRecord(Blob *out_rec, Chunk *chunk);
- protected:
-  virtual size_t SeekRecordBegin(Stream *fi);
-  virtual const char*
-  FindLastRecordBegin(const char *begin, const char *end);
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_LINE_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/local_filesys.cc b/ml-xgboost/dmlc-core/src/io/local_filesys.cc
deleted file mode 100644
index ec738e3..0000000
--- a/ml-xgboost/dmlc-core/src/io/local_filesys.cc
+++ /dev/null
@@ -1,217 +0,0 @@
-// Copyright by Contributors
-
-#include <dmlc/base.h>
-#include <dmlc/logging.h>
-#include <errno.h>
-extern "C" {
-#include <sys/stat.h>
-}
-#ifndef _WIN32
-extern "C" {
-#include <sys/types.h>
-#include <dirent.h>
-}
-#define stat_struct stat
-#else  // _WIN32
-#include <Windows.h>
-#define stat _stat64
-#define stat_struct __stat64
-#endif  // _WIN32
-
-#include "./local_filesys.h"
-
-
-namespace dmlc {
-namespace io {
-/*! \brief implementation of file i/o stream */
-class FileStream : public SeekStream {
- public:
-  explicit FileStream(FILE *fp, bool use_stdio)
-      : fp_(fp), use_stdio_(use_stdio) {}
-  virtual ~FileStream(void) {
-    this->Close();
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    return std::fread(ptr, 1, size, fp_);
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    CHECK(std::fwrite(ptr, 1, size, fp_) == size)
-        << "FileStream.Write incomplete";
-  }
-  virtual void Seek(size_t pos) {
-#ifndef _MSC_VER
-    CHECK(!std::fseek(fp_, static_cast<long>(pos), SEEK_SET));  // NOLINT(*)
-#else  // _MSC_VER
-    CHECK(!_fseeki64(fp_, pos, SEEK_SET));
-#endif  // _MSC_VER
-  }
-  virtual size_t Tell(void) {
-#ifndef _MSC_VER
-    return std::ftell(fp_);
-#else  // _MSC_VER
-    return _ftelli64(fp_);
-#endif  // _MSC_VER
-  }
-  virtual bool AtEnd(void) const {
-    return std::feof(fp_) != 0;
-  }
-  inline void Close(void) {
-    if (fp_ != NULL && !use_stdio_) {
-      std::fclose(fp_); fp_ = NULL;
-    }
-  }
-
- private:
-  std::FILE *fp_;
-  bool use_stdio_;
-};
-
-FileInfo LocalFileSystem::GetPathInfo(const URI &path) {
-  struct stat_struct sb;
-  FileInfo ret;
-  ret.path = path;
-  if (stat(path.name.c_str(), &sb) == -1) {
-    int errsv = errno;
-#ifndef _WIN32
-    // If lstat succeeds where stat failed, assume a problematic
-    // symlink and treat this as if it were a 0-length file.
-    if (lstat(path.name.c_str(), &sb) == 0) {
-      ret.size = 0;
-      ret.type = kFile;
-      LOG(INFO) << "LocalFileSystem.GetPathInfo: detected symlink "
-                << path.name << " error: " << strerror(errsv);
-      return ret;
-    }
-#endif  // _WIN32
-    LOG(FATAL) << "LocalFileSystem.GetPathInfo: "
-               << path.name << " error: " << strerror(errsv);
-  }
-  ret.size = sb.st_size;
-
-  if ((sb.st_mode & S_IFMT) == S_IFDIR) {
-    ret.type = kDirectory;
-  } else {
-    ret.type = kFile;
-  }
-  return ret;
-}
-
-void LocalFileSystem::ListDirectory(const URI &path, std::vector<FileInfo> *out_list) {
-#ifndef _WIN32
-  DIR *dir = opendir(path.name.c_str());
-  if (dir == NULL) {
-    int errsv = errno;
-    LOG(FATAL) << "LocalFileSystem.ListDirectory " << path.str()
-               <<" error: " << strerror(errsv);
-  }
-  out_list->clear();
-  struct dirent *ent;
-  /* print all the files and directories within directory */
-  while ((ent = readdir(dir)) != NULL) {
-    if (!strcmp(ent->d_name, ".")) continue;
-    if (!strcmp(ent->d_name, "..")) continue;
-    URI pp = path;
-    if (pp.name[pp.name.length() - 1] != '/') {
-      pp.name += '/';
-    }
-    pp.name += ent->d_name;
-    out_list->push_back(GetPathInfo(pp));
-  }
-  closedir(dir);
-#else  // _WIN32
-  WIN32_FIND_DATA fd;
-  std::string pattern = path.name + "/*";
-  HANDLE handle = FindFirstFile(pattern.c_str(), &fd);
-  if (handle == INVALID_HANDLE_VALUE) {
-    int errsv = GetLastError();
-    LOG(FATAL) << "LocalFileSystem.ListDirectory " << path.str()
-               << " error: " << strerror(errsv);
-  }
-  do {
-    if (strcmp(fd.cFileName, ".") && strcmp(fd.cFileName, "..")) {
-      URI pp = path;
-      char clast = pp.name[pp.name.length() - 1];
-      if (pp.name == ".") {
-        pp.name = fd.cFileName;
-      } else if (clast != '/' && clast != '\\') {
-        pp.name += '/';
-        pp.name += fd.cFileName;
-      }
-      out_list->push_back(GetPathInfo(pp));
-    }
-  }  while (FindNextFile(handle, &fd));
-  FindClose(handle);
-#endif  // _WIN32
-}
-
-SeekStream *LocalFileSystem::Open(const URI &path,
-                                  const char* const mode,
-                                  bool allow_null) {
-  bool use_stdio = false;
-  FILE *fp = NULL;
-#ifdef _WIN32
-  const int fname_length = MultiByteToWideChar(CP_UTF8, 0, path.name.c_str(), -1, nullptr, 0);
-  CHECK(fname_length > 0) << " LocalFileSystem::Open \"" << path.str()
-                          << "\": " << "Invalid character sequence.";
-  std::wstring fname(fname_length, 0);
-  MultiByteToWideChar(CP_UTF8, 0, path.name.c_str(), -1, &fname[0], fname_length);
-
-  const int mode_length = MultiByteToWideChar(CP_UTF8, 0, mode, -1, nullptr, 0);
-  std::wstring wmode(mode_length, 0);
-  MultiByteToWideChar(CP_UTF8, 0, mode, -1, &wmode[0], mode_length);
-
-  using namespace std;
-#ifndef DMLC_DISABLE_STDIN
-  if (!wcscmp(fname.c_str(), L"stdin")) {
-    use_stdio = true; fp = stdin;
-  }
-  if (!wcscmp(fname.c_str(), L"stdout")) {
-    use_stdio = true; fp = stdout;
-  }
-#endif  // DMLC_DISABLE_STDIN
-  if (!wcsncmp(fname.c_str(), L"file://", 7)) { fname = fname.substr(7); }
-  if (!use_stdio) {
-    std::wstring flag(wmode.c_str());
-    if (flag == L"w") flag = L"wb";
-    if (flag == L"r") flag = L"rb";
-#if DMLC_USE_FOPEN64
-    fp = _wfopen(fname.c_str(), flag.c_str());
-#else  // DMLC_USE_FOPEN64
-    fp = fopen(fname, flag.c_str());
-#endif  // DMLC_USE_FOPEN64
-  }
-#else  // _WIN32
-  const char *fname = path.name.c_str();
-  using namespace std;
-#ifndef DMLC_DISABLE_STDIN
-  if (!strcmp(fname, "stdin")) {
-    use_stdio = true; fp = stdin;
-  }
-  if (!strcmp(fname, "stdout")) {
-    use_stdio = true; fp = stdout;
-  }
-#endif  // DMLC_DISABLE_STDIN
-  if (!strncmp(fname, "file://", 7)) fname += 7;
-  if (!use_stdio) {
-    std::string flag = mode;
-    if (flag == "w") flag = "wb";
-    if (flag == "r") flag = "rb";
-#if DMLC_USE_FOPEN64
-    fp = fopen64(fname, flag.c_str());
-#else  // DMLC_USE_FOPEN64
-    fp = fopen(fname, flag.c_str());
-#endif  // DMLC_USE_FOPEN64
-  }
-#endif  // _WIN32
-  if (fp != NULL) {
-    return new FileStream(fp, use_stdio);
-  } else {
-    CHECK(allow_null) << " LocalFileSystem::Open \"" << path.str() << "\": " << strerror(errno);
-    return NULL;
-  }
-}
-SeekStream *LocalFileSystem::OpenForRead(const URI &path, bool allow_null) {
-  return Open(path, "r", allow_null);
-}
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/local_filesys.h b/ml-xgboost/dmlc-core/src/io/local_filesys.h
deleted file mode 100644
index a1b5f5c..0000000
--- a/ml-xgboost/dmlc-core/src/io/local_filesys.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file local_filesys.h
- * \brief local access module
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_LOCAL_FILESYS_H_
-#define DMLC_IO_LOCAL_FILESYS_H_
-
-#include <dmlc/filesystem.h>
-#include <vector>
-
-namespace dmlc {
-namespace io {
-/*! \brief local file system */
-class LocalFileSystem : public FileSystem {
- public:
-  /*! \brief destructor */
-  virtual ~LocalFileSystem() {}
-  /*!
-   * \brief get information about a path
-   * \param path the path to the file
-   * \return the information about the file
-   */
-  virtual FileInfo GetPathInfo(const URI &path);
-  /*!
-   * \brief list files in a directory
-   * \param path to the file
-   * \param out_list the output information about the files
-   */
-  virtual void ListDirectory(const URI &path, std::vector<FileInfo> *out_list);
-  /*!
-   * \brief open a stream, will report error and exit if bad thing happens
-   * NOTE: the IStream can continue to work even when filesystem was destructed
-   * \param path path to file
-   * \param uri the uri of the input
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual SeekStream *Open(const URI &path,
-                           const char* const flag,
-                           bool allow_null);
-  /*!
-   * \brief open a seekable stream for read
-   * \param path the path to the file
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual SeekStream *OpenForRead(const URI &path, bool allow_null);
-  /*!
-   * \brief get a singleton of LocalFileSystem when needed
-   * \return a singleton instance
-   */
-  inline static LocalFileSystem *GetInstance(void) {
-    static LocalFileSystem instance;
-    return &instance;
-  }
-
- private:
-  LocalFileSystem() {}
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_LOCAL_FILESYS_H_
diff --git a/ml-xgboost/dmlc-core/src/io/recordio_split.cc b/ml-xgboost/dmlc-core/src/io/recordio_split.cc
deleted file mode 100644
index 78c5408..0000000
--- a/ml-xgboost/dmlc-core/src/io/recordio_split.cc
+++ /dev/null
@@ -1,84 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/recordio.h>
-#include <dmlc/logging.h>
-#include <algorithm>
-#include "./recordio_split.h"
-
-namespace dmlc {
-namespace io {
-size_t RecordIOSplitter::SeekRecordBegin(Stream *fi) {
-  size_t nstep = 0;
-  uint32_t v, lrec;
-  while (true) {
-    if (fi->Read(&v, sizeof(v)) == 0) return nstep;
-    nstep += sizeof(v);
-    if (v == RecordIOWriter::kMagic) {
-      CHECK(fi->Read(&lrec, sizeof(lrec)) != 0)
-            << "invalid record io format";
-      nstep += sizeof(lrec);
-      uint32_t cflag = RecordIOWriter::DecodeFlag(lrec);
-      if (cflag == 0 || cflag == 1) break;
-    }
-  }
-  // should point at head of record
-  return nstep - 2 * sizeof(uint32_t);
-}
-const char* RecordIOSplitter::FindLastRecordBegin(const char *begin,
-                                                  const char *end) {
-  CHECK_EQ((reinterpret_cast<size_t>(begin) & 3UL), 0U);
-  CHECK_EQ((reinterpret_cast<size_t>(end) & 3UL), 0U);
-  const uint32_t *pbegin = reinterpret_cast<const uint32_t *>(begin);
-  const uint32_t *p = reinterpret_cast<const uint32_t *>(end);
-  CHECK(p >= pbegin + 2);
-  for (p = p - 2; p != pbegin; --p) {
-    if (p[0] == RecordIOWriter::kMagic) {
-      uint32_t cflag = RecordIOWriter::DecodeFlag(p[1]);
-      if (cflag == 0 || cflag == 1) {
-        return reinterpret_cast<const char*>(p);
-      }
-    }
-  }
-  return begin;
-}
-
-bool RecordIOSplitter::ExtractNextRecord(Blob *out_rec, Chunk *chunk) {
-  if (chunk->begin == chunk->end) return false;
-  CHECK(chunk->begin + 2 * sizeof(uint32_t) <= chunk->end)
-      << "Invalid RecordIO Format";
-  CHECK_EQ((reinterpret_cast<size_t>(chunk->begin) & 3UL), 0U);
-  CHECK_EQ((reinterpret_cast<size_t>(chunk->end) & 3UL), 0U);
-  uint32_t *p = reinterpret_cast<uint32_t *>(chunk->begin);
-  uint32_t cflag = RecordIOWriter::DecodeFlag(p[1]);
-  uint32_t clen = RecordIOWriter::DecodeLength(p[1]);
-  // skip header
-  out_rec->dptr = chunk->begin + 2 * sizeof(uint32_t);
-  // move pbegin
-  chunk->begin += 2 * sizeof(uint32_t) + (((clen + 3U) >> 2U) << 2U);
-  CHECK(chunk->begin <= chunk->end) << "Invalid RecordIO Format";
-  out_rec->size = clen;
-  if (cflag == 0) return true;
-  const uint32_t kMagic = RecordIOWriter::kMagic;
-  // abnormal path, move data around to make a full part
-  CHECK(cflag == 1U) << "Invalid RecordIO Format";
-  while (cflag != 3U) {
-    CHECK(chunk->begin + 2 * sizeof(uint32_t) <= chunk->end);
-    p = reinterpret_cast<uint32_t *>(chunk->begin);
-    CHECK(p[0] == RecordIOWriter::kMagic);
-    cflag = RecordIOWriter::DecodeFlag(p[1]);
-    clen = RecordIOWriter::DecodeLength(p[1]);
-    // pad kmagic in between
-    std::memcpy(reinterpret_cast<char*>(out_rec->dptr) + out_rec->size,
-                &kMagic, sizeof(kMagic));
-    out_rec->size += sizeof(kMagic);
-    // move the rest of the blobs
-    if (clen != 0) {
-      std::memmove(reinterpret_cast<char*>(out_rec->dptr) + out_rec->size,
-                   chunk->begin + 2 * sizeof(uint32_t), clen);
-      out_rec->size += clen;
-    }
-    chunk->begin += 2 * sizeof(uint32_t) + (((clen + 3U) >> 2U) << 2U);
-  }
-  return true;
-}
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/recordio_split.h b/ml-xgboost/dmlc-core/src/io/recordio_split.h
deleted file mode 100644
index 63386b8..0000000
--- a/ml-xgboost/dmlc-core/src/io/recordio_split.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file recordio_split.h
- * \brief input split that splits recordio files
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_RECORDIO_SPLIT_H_
-#define DMLC_IO_RECORDIO_SPLIT_H_
-
-#include <dmlc/io.h>
-#include <dmlc/recordio.h>
-#include <vector>
-#include <cstdio>
-#include <string>
-#include <cstring>
-#include "./input_split_base.h"
-
-namespace dmlc {
-namespace io {
-/*! \brief class that split the files by line */
-class RecordIOSplitter : public InputSplitBase {
- public:
-  RecordIOSplitter(FileSystem *fs,
-                   const char *uri,
-                   unsigned rank,
-                   unsigned nsplit,
-                   const bool recurse_directories) {
-    this->Init(fs, uri, 4, recurse_directories);
-    this->ResetPartition(rank, nsplit);
-  }
-
-  bool IsTextParser(void) {
-    return false;
-  }
-  virtual bool ExtractNextRecord(Blob *out_rec, Chunk *chunk);
-
- protected:
-  virtual size_t SeekRecordBegin(Stream *fi);
-  virtual const char*
-  FindLastRecordBegin(const char *begin, const char *end);
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_RECORDIO_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/s3_filesys.cc b/ml-xgboost/dmlc-core/src/io/s3_filesys.cc
deleted file mode 100644
index 7786130..0000000
--- a/ml-xgboost/dmlc-core/src/io/s3_filesys.cc
+++ /dev/null
@@ -1,1309 +0,0 @@
-// Copyright by Contributors
-extern "C" {
-#include <errno.h>
-#include <curl/curl.h>
-#include <openssl/hmac.h>
-#include <openssl/buffer.h>
-#include <openssl/sha.h>
-}
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <string>
-#include <algorithm>
-#include <ctime>
-#include <sstream>
-#include <iomanip>
-
-#include "./s3_filesys.h"
-
-namespace dmlc {
-namespace io {
-/*! \brief namespace for helper utils */
-namespace s3 {
-// simple XML parser
-struct XMLIter {
-  // content of xml
-  const char *content_;
-  // end of content
-  const char *cend_;
-  XMLIter()
-      : content_(NULL), cend_(NULL) {
-  }
-  // constructor
-  explicit XMLIter(const char *content)
-      : content_(content) {
-    cend_ = content_ + strlen(content_);
-  }
-  /*! \brief convert to string */
-  inline std::string str(void) const {
-    if (content_ >= cend_) return std::string("");
-    return std::string(content_, cend_ - content_);
-  }
-  /*!
-   * \brief get next value of corresponding key in xml string
-   * \param key the key in xml field
-   * \param value the return value if success
-   * \return if the get is success
-   */
-  inline bool GetNext(const char *key,
-                      XMLIter *value) {
-    std::string begin = std::string("<") + key +">";
-    std::string end = std::string("</") + key +">";
-    const char *pbegin = strstr(content_, begin.c_str());
-    if (pbegin == NULL || pbegin > cend_) return false;
-    content_ = pbegin + begin.size();
-    const char *pend = strstr(content_, end.c_str());
-    CHECK(pend != NULL) << "bad xml format";
-    value->content_ = content_;
-    value->cend_ = pend;
-    content_ = pend + end.size();
-    return true;
-  }
-};
-
-/*!
- * \brief Converts hash to hex representation
- * \param hash unsigned char array with hash
- * \param size size of hash
- * \return string in hex representation
- */
-static std::string SHA256HashToHex(unsigned char *hash, int size) {
-  CHECK_EQ(size, SHA256_DIGEST_LENGTH);
-  std::stringstream ss;
-  for (int i=0; i < SHA256_DIGEST_LENGTH; i++) {
-    ss << std::hex << std::setw(2) << std::setfill('0') << static_cast<int>(hash[i]);
-  }
-  return ss.str();
-}
-
-/*!
- * \brief Generates hash of input as per SHA256 algorithm and converts it to hex representation
- * \param str input to hash
- * \return string with hex representation of SHA256 Hash
- */
-static std::string SHA256Hex(const std::string &str) noexcept {
-  if (str.empty()) return "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855";
-  unsigned char hash[SHA256_DIGEST_LENGTH];
-  SHA256_CTX sha256;
-  SHA256_Init(&sha256);
-  SHA256_Update(&sha256, str.c_str(), str.size());
-  SHA256_Final(hash, &sha256);
-  return SHA256HashToHex(hash, SHA256_DIGEST_LENGTH);
-}
-
-/*!
- * \brief Returns datetime in ISO8601 format
- * Example: 20131222T043039Z
- * \param time
- * \return datetime in above format as string
- */
-static std::string GetDateISO8601(const std::time_t &t) noexcept {
-  char buf[sizeof "YYYYMMDDTHHMMSSZ"];
-  std::strftime(buf, sizeof buf, "%Y%m%dT%H%M%SZ", std::gmtime(&t));
-  return std::string{buf};
-}
-
-/*!
- * \brief Returns datetime in YYYYMMDD format
- * Example: 20131222T043039Z
- * \param time
- * \return datetime in above format as string
- */
-static std::string GetDateYYYYMMDD(const std::time_t &t) noexcept {
-  char buf[sizeof "YYYYMMDD"];
-  std::strftime(buf, sizeof buf, "%Y%m%d", std::gmtime(&t));
-  return std::string{buf};
-}
-
-static void AddDefaultCanonicalHeaders(std::map<std::string, std::string> *canonical_headers,
-                                       const time_t &curr_time,
-                                       const std::string &s3_session_token,
-                                       const std::string &data,
-                                       bool addDataHash = false) {
-  (*canonical_headers)["x-amz-date"] = GetDateISO8601(curr_time);
-  if (s3_session_token != "") {
-    (*canonical_headers)["x-amz-security-token"] = s3_session_token;
-  }
-  if (addDataHash) {
-    (*canonical_headers)["x-amz-content-sha256"] = SHA256Hex(data);
-  }
-}
-
-
-/*!
- * \brief Returns keys of canonical_headers separated with semicolon
- * as per AWS SIG4 authentication
- * \param canonical_headers
- * \return signedHeaders as a string
- */
-static std::string GetSignedHeaders(const std::map<std::string, std::string> &canonical_headers) {
-  std::ostringstream stream;
-  for (auto it = canonical_headers.begin(); it != canonical_headers.end(); ++it) {
-    if (it != canonical_headers.begin()) {
-      stream << ";";
-    }
-    stream << it->first;
-  }
-  return stream.str();
-}
-
-/*!
- * Encoding as required by SIG4
- * \param str string to encode
- * \param encodeSlash whether or not to encode slash (/) character
- * \return
- */
-std::string URIEncode(const std::string& str,
-                      bool encodeSlash = true) {
-  std::stringstream encoded_str;
-  encoded_str << std::hex << std::uppercase << std::setfill('0');
-  for (std::string::const_iterator it = str.begin(); it != str.end(); ++it) {
-    char c = *it;
-    if ((c >= 'a' && c <= 'z') ||
-        (c >= 'A' && c <= 'Z') ||
-        (c >= '0' && c <= '9') ||
-        c == '-' || c == '_' ||
-        c == '.' || c == '~') {
-      encoded_str << c;
-    } else if (c == '/') {
-      if (encodeSlash) {
-        encoded_str << "%2F";
-      } else {
-        encoded_str << c;
-      }
-    } else {
-      encoded_str << '%';
-      encoded_str << std::setw(2) << static_cast<unsigned>(c);
-    }
-  }
-  return encoded_str.str();
-}
-
-/*!
- * \brief creates query string from keys and values in params
- * \param params query keys and values
- * \param is_canonical whether or not to produce canonical query by URIEncoding
- * \return query as a string
- */
-static std::string GetQueryMultipart(const std::map<std::string, std::string> &params,
-                                     const bool is_canonical) {
-  bool init_request = (params.find("uploads") != params.end());
-  std::ostringstream stream;
-  for (auto it = params.begin(); it != params.end(); ++it) {
-    if (it != params.begin()) {
-      stream << "&";
-    }
-    if (is_canonical) {
-      stream << URIEncode(it->first) << "=" << URIEncode(it->second);
-    } else {
-      if (init_request) {
-        stream << it->first;
-      } else {
-        stream << it->first << "=" << it->second;
-      }
-    }
-  }
-  return stream.str();
-}
-
-/*!
- * \brief Returns credential scope as per AWS SIG4 authentication for S3 requests
- * \param time
- * \param region s3 region
- * \return credential scope
- */
-static std::string GetCredentialScope(const time_t &time, const std::string &region) {
-  return GetDateYYYYMMDD(time) + "/" + region  + "/s3/aws4_request";
-}
-
-/*!
- * \brief Calculates SIG4 Signature for an AWS request
- * \param request_date
- * \param secret s3_secret_key
- * \param region s3_region
- * \param service AWS service name
- * \param string_to_sign
- * \return signature
- */
-static std::string CalculateSig4Sign(const std::time_t &request_date,
-                                     const std::string &secret,
-                                     const std::string &region,
-                                     const std::string &service,
-                                     const std::string &string_to_sign) {
-  const std::string key1{"AWS4" + secret};
-  const std::string yyyymmdd = GetDateYYYYMMDD(request_date);
-
-  unsigned char* kDate;
-  unsigned int kDateLen;
-  kDate = HMAC(EVP_sha256(), key1.c_str(), key1.size(),
-               reinterpret_cast<const unsigned char*>(yyyymmdd.c_str()),
-               yyyymmdd.size(), NULL, &kDateLen);
-
-  unsigned char *kRegion;
-  unsigned int kRegionLen;
-  kRegion = HMAC(EVP_sha256(), kDate, kDateLen,
-                 reinterpret_cast<const unsigned char*>(region.c_str()),
-                 region.size(), NULL, &kRegionLen);
-
-  unsigned char *kService;
-  unsigned int kServiceLen;
-  kService = HMAC(EVP_sha256(), kRegion, kRegionLen,
-                  reinterpret_cast<const unsigned char*>(service.c_str()),
-                  service.size(), NULL, &kServiceLen);
-
-  const std::string AWS4_REQUEST{"aws4_request"};
-  unsigned char *kSigning;
-  unsigned int kSigningLen;
-  kSigning = HMAC(EVP_sha256(), kService, kServiceLen,
-                  reinterpret_cast<const unsigned char*>(AWS4_REQUEST.c_str()),
-                  AWS4_REQUEST.size(), NULL, &kSigningLen);
-
-  unsigned char *kSig;
-  unsigned int kSigLen;
-  kSig = HMAC(EVP_sha256(), kSigning, kSigningLen,
-              reinterpret_cast<const unsigned char*>(string_to_sign.c_str()),
-              string_to_sign.size(), NULL, &kSigLen);
-  return SHA256HashToHex(kSig, SHA256_DIGEST_LENGTH);
-}
-
-/*!
- * \brief Builds HTTP request headers for SIG4 auth requests to AWS
- * \param sauth stream for auth header
- * \param sdate stream for date
- * \param stoken stream for token
- * \param scontent stream for content related headers
- * \param time
- * \param s3_access_id
- * \param s3_region
- * \param s3_session_token
- * \param canonical_headers
- * \param signature SIG4 signature
- * \param payload data to send as payload
- */
-static void BuildRequestHeaders(std::ostringstream& sauth,
-                                std::ostringstream& sdate,
-                                std::ostringstream& stoken,
-                                std::ostringstream& scontent,
-                                const time_t& curr_time,
-                                const std::string& s3_access_id,
-                                const std::string& s3_region,
-                                const std::string& s3_session_token,
-                                const std::map<std::string, std::string>& canonical_headers,
-                                const std::string& signature,
-                                const std::string& payload) {
-  sauth << "Authorization: AWS4-HMAC-SHA256 ";
-  sauth << "Credential=" << s3_access_id << "/" << GetCredentialScope(curr_time, s3_region) << ",";
-  sauth << "SignedHeaders=" << GetSignedHeaders(canonical_headers) << ",";
-  sauth << "Signature=" << signature;
-  sdate << "x-amz-date: " << GetDateISO8601(curr_time);
-  stoken << "x-amz-security-token: " << s3_session_token;
-  scontent << "x-amz-content-sha256: " << SHA256Hex(payload);
-}
-
-/*!
- * \brief Signs the request as per SIG4 Auth scheme
- * https://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-auth-using-authorization-header.html
- * \param key s3_access_key
- * \param s3_region
- * \param method method of HTTP request
- * \param time
- * \param canonical_uri
- * \param canonical_query
- * \param canonical_headers
- * \param payload data to send as payload
- * return signature
- */
-static std::string SignSig4(const std::string &key,
-                            const std::string &s3_region,
-                            const std::string &method,
-                            const time_t &time,
-                            const std::string &canonical_uri,
-                            const std::string &canonical_query,
-                            const std::map<std::string, std::string> &canonical_headers,
-                            const std::string &payload) {
-  std::ostringstream can_req;
-  can_req << method << "\n";
-  can_req << canonical_uri << "\n";
-  can_req << canonical_query << "\n";
-  for (const auto & header : canonical_headers) {
-    can_req << header.first << ":" << header.second << "\n";
-  }
-  can_req << "\n";
-  can_req << GetSignedHeaders(canonical_headers);
-  can_req << "\n";
-  can_req << SHA256Hex(payload);
-
-  std::string canonical_request = can_req.str();
-  std::string hash_request = SHA256Hex(canonical_request);
-  std::ostringstream to_sign;
-  to_sign << "AWS4-HMAC-SHA256" << "\n";
-  to_sign << GetDateISO8601(time) << "\n";
-  to_sign << GetCredentialScope(time, s3_region) << "\n";
-  to_sign << hash_request;
-  return CalculateSig4Sign(time, key, s3_region, "s3", to_sign.str());
-}
-
-// remove the beginning slash at name
-inline const char *RemoveBeginSlash(const std::string &name) {
-  const char *s = name.c_str();
-  while (*s == '/') {
-    ++s;
-  }
-  return s;
-}
-// find the error field of the header
-inline bool FindHttpError(const std::string &header) {
-  std::string hd, ret;
-  int code;
-  std::istringstream is(header);
-  if (is >> hd >> code >> ret) {
-    if (code == 206 || ret == "OK") {
-      return false;
-    } else if (ret == "Continue") {
-      return false;
-    }
-  }
-  return true;
-}
-
-// curl callback to write sstream
-size_t WriteSStreamCallback(char *buf, size_t size, size_t count, void *fp) {
-  static_cast<std::ostringstream*>(fp)->write(buf, size * count);
-  return size * count;
-}
-
-// callback by curl to write to std::string
-size_t WriteStringCallback(char *buf, size_t size, size_t count, void *fp) {
-  size *= count;
-  std::string *str = static_cast<std::string*>(fp);
-  size_t len = str->length();
-  str->resize(len + size);
-  std::memcpy(BeginPtr(*str) + len, buf, size);
-  return size;
-}
-
-std::string getEndpoint(std::string region_name) {
-  // using if elseif chain switching region_name
-  if (region_name == "us-east-1") {
-    return "s3.amazonaws.com";
-  } else if (region_name == "cn-north-1" || region_name == "cn-northwest-1") {
-    return "s3."+ region_name + ".amazonaws.com.cn";
-  } else {
-    return "s3-" + region_name + ".amazonaws.com";
-  }
-}
-
-// useful callback for reading memory
-struct ReadStringStream {
-  const char *dptr;
-  size_t nleft;
-  // constructor
-  explicit ReadStringStream(const std::string &data) {
-    dptr = BeginPtr(data);
-    nleft = data.length();
-  }
-  // curl callback to write sstream
-  static size_t Callback(char *buf, size_t size, size_t count, void *fp) {
-    size *= count;
-    ReadStringStream *s = static_cast<ReadStringStream*>(fp);
-    size_t nread = std::min(size, s->nleft);
-    std::memcpy(buf, s->dptr, nread);
-    s->dptr += nread; s->nleft -= nread;
-    return nread;
-  }
-};
-
-/*!
- * \brief reader stream that can be used to read from CURL
- */
-class CURLReadStreamBase : public SeekStream {
- public:
-  virtual ~CURLReadStreamBase() {
-    this->Cleanup();
-  }
-  virtual size_t Tell(void) {
-    return curr_bytes_;
-  }
-  virtual bool AtEnd(void) const {
-    return at_end_;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    LOG(FATAL) << "CURL.ReadStream cannot be used for write";
-  }
-  // lazy seek function
-  virtual void Seek(size_t pos) {
-    if (curr_bytes_ != pos) {
-      this->Cleanup();
-      curr_bytes_ = pos;
-    }
-  }
-  virtual size_t Read(void *ptr, size_t size);
-
- protected:
-  CURLReadStreamBase()
-      : mcurl_(NULL), ecurl_(NULL), slist_(NULL),
-        read_ptr_(0), curr_bytes_(0), at_end_(false) {
-    expect_file_size_ = 0;
-  }
-  /*!
-   * \brief initialize the ecurl request,
-   * \param begin_bytes the beginning bytes of the stream
-   * \param ecurl a curl easy handle that can be used to set request
-   * \param slist a curl slist handle that can be used to set headers
-   */
-  virtual void InitRequest(size_t begin_bytes,
-                           CURL *ecurl,
-                           curl_slist **slist) = 0;
-
- protected:
-  // the total size of the file
-  size_t expect_file_size_;
-
- private:
-  /*!
-   * \brief called by child class to initialize read
-   * \param begin_bytes the beginning bytes of the stream
-   */
-  void Init(size_t begin_bytes);
-  /*!
-   * \brief cleanup the previous session for restart
-   */
-  void Cleanup(void);
-  /*!
-   * \brief try to fill the buffer with at least wanted bytes
-   * \param want_bytes number of bytes we want to fill
-   * \return number of remainning running curl handles
-   */
-  int FillBuffer(size_t want_bytes);
-  // multi and easy curl handle
-  CURL *mcurl_, *ecurl_;
-  // slist needed by the program
-  curl_slist *slist_;
-  // data buffer
-  std::string buffer_;
-  // header buffer
-  std::string header_;
-  // data pointer to read position
-  size_t read_ptr_;
-  // current position in the stream
-  size_t curr_bytes_;
-  // mark end of stream
-  bool at_end_;
-};
-
-// read data in
-size_t CURLReadStreamBase::Read(void *ptr, size_t size) {
-  // lazy initialize
-  if (mcurl_ == NULL) Init(curr_bytes_);
-  // check at end
-  if (at_end_) return 0;
-
-  size_t nleft = size;
-  char *buf = reinterpret_cast<char*>(ptr);
-  while (nleft != 0) {
-    if (read_ptr_ == buffer_.length()) {
-      read_ptr_ = 0; buffer_.clear();
-      if (this->FillBuffer(nleft) == 0 && buffer_.length() == 0) {
-        at_end_ = true;
-        break;
-      }
-    }
-    size_t nread = std::min(nleft, buffer_.length() - read_ptr_);
-    std::memcpy(buf, BeginPtr(buffer_) + read_ptr_, nread);
-    buf += nread; read_ptr_ += nread; nleft -= nread;
-  }
-  size_t read_bytes = size - nleft;
-  curr_bytes_ += read_bytes;
-
-  // safety check, re-establish connection if failure happens
-  if (at_end_ && expect_file_size_ != 0 &&
-      curr_bytes_ != expect_file_size_) {
-    int nretry = 0;
-    CHECK_EQ(buffer_.length(), 0U);
-    while (true) {
-      LOG(ERROR) << "Re-establishing connection to Amazon S3, retry " << nretry;
-      size_t rec_curr_bytes = curr_bytes_;
-      this->Cleanup();
-      this->Init(rec_curr_bytes);
-      if (this->FillBuffer(nleft) != 0) break;
-      ++nretry;
-      CHECK_LT(nretry, 50)
-          << "Unable to re-establish connection to read full file"
-          << " ,expect_file_size=" << expect_file_size_
-          << " ,curr_bytes=" << curr_bytes_;
-      // sleep 100ms
-#ifdef _WIN32
-      Sleep(100);
-#else
-      struct timeval wait = { 0, 100 * 1000 };
-      select(0, NULL, NULL, NULL, &wait);
-#endif
-    }
-  }
-  return read_bytes;
-}
-
-// cleanup the previous sessions for restart
-void CURLReadStreamBase::Cleanup() {
-  if (mcurl_ != NULL) {
-    curl_multi_remove_handle(mcurl_, ecurl_);
-    curl_easy_cleanup(ecurl_);
-    curl_multi_cleanup(mcurl_);
-    mcurl_ = NULL;
-    ecurl_ = NULL;
-  }
-  if (slist_ != NULL) {
-    curl_slist_free_all(slist_);
-    slist_ = NULL;
-  }
-  buffer_.clear(); header_.clear();
-  curr_bytes_ = 0; at_end_ = false;
-}
-
-void CURLReadStreamBase::Init(size_t begin_bytes) {
-  CHECK(mcurl_ == NULL && ecurl_ == NULL &&
-        slist_ == NULL) << "must call init in clean state";
-  // make request
-  ecurl_ = curl_easy_init();
-  this->InitRequest(begin_bytes, ecurl_, &slist_);
-  CHECK(curl_easy_setopt(ecurl_, CURLOPT_WRITEFUNCTION, WriteStringCallback) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl_, CURLOPT_WRITEDATA, &buffer_) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl_, CURLOPT_HEADERFUNCTION, WriteStringCallback) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl_, CURLOPT_HEADERDATA, &header_) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl_, CURLOPT_NOSIGNAL, 1) == CURLE_OK);
-  mcurl_ = curl_multi_init();
-  CHECK(curl_multi_add_handle(mcurl_, ecurl_) == CURLM_OK);
-  int nrun;
-  curl_multi_perform(mcurl_, &nrun);
-  CHECK(nrun != 0 || header_.length() != 0 || buffer_.length() != 0);
-  // start running and check header
-  this->FillBuffer(1);
-  if (FindHttpError(header_)) {
-    while (this->FillBuffer(buffer_.length() + 256) != 0) {}
-    LOG(FATAL) << "Request Error:\n" << header_ << buffer_;
-  }
-  // setup the variables
-  at_end_ = false;
-  curr_bytes_ = begin_bytes;
-  read_ptr_ = 0;
-}
-
-// fill the buffer with wanted bytes
-int CURLReadStreamBase::FillBuffer(size_t nwant) {
-  int nrun = 0;
-  while (buffer_.length() < nwant) {
-    // wait for the event of read ready
-    fd_set fdread;
-    fd_set fdwrite;
-    fd_set fdexcep;
-    FD_ZERO(&fdread);
-    FD_ZERO(&fdwrite);
-    FD_ZERO(&fdexcep);
-    int maxfd = -1;
-
-    timeval timeout;
-    long curl_timeo;  // NOLINT(*)
-    curl_multi_timeout(mcurl_, &curl_timeo);
-    if (curl_timeo < 0) curl_timeo = 980;
-    timeout.tv_sec = curl_timeo / 1000;
-    timeout.tv_usec = (curl_timeo % 1000) * 1000;
-    CHECK(curl_multi_fdset(mcurl_, &fdread, &fdwrite, &fdexcep, &maxfd) == CURLM_OK);
-    int rc;
-    if (maxfd == -1) {
-#ifdef _WIN32
-      Sleep(100);
-      rc = 0;
-#else
-      struct timeval wait = { 0, 100 * 1000 };
-      rc = select(0, NULL, NULL, NULL, &wait);
-#endif
-    } else {
-      rc = select(maxfd + 1, &fdread, &fdwrite, &fdexcep, &timeout);
-    }
-    if (rc != -1) {
-      CURLMcode ret = curl_multi_perform(mcurl_, &nrun);
-      if (ret ==  CURLM_CALL_MULTI_PERFORM) continue;
-      CHECK(ret == CURLM_OK);
-      if (nrun == 0) break;
-    }
-  }
-
-  // loop through all the subtasks in curl_multi_perform and look for errors
-  struct CURLMsg *m;
-  do {
-    int msgq = 0;
-    m = curl_multi_info_read(mcurl_, &msgq);
-    if (m && (m->msg == CURLMSG_DONE)) {
-      if (m->data.result != CURLE_OK) {
-        LOG(INFO) << "request failed with error "
-                  << curl_easy_strerror(m->data.result);
-      }
-    }
-  } while (m);
-
-  return nrun;
-}
-// End of CURLReadStreamBase functions
-
-// singleton class for global initialization
-struct CURLGlobal {
-  CURLGlobal() {
-    CHECK(curl_global_init(CURL_GLOBAL_DEFAULT) == CURLE_OK);
-  }
-  ~CURLGlobal() {
-    curl_global_cleanup();
-  }
-};
-
-// used for global initialization
-static CURLGlobal curl_global;
-
-/*! \brief reader stream that can be used to read */
-class ReadStream : public CURLReadStreamBase {
- public:
-  ReadStream(const URI &path,
-             const std::string &s3_id,
-             const std::string &s3_key,
-             const std::string &s3_session_token,
-             const std::string &s3_region,
-             const std::string &s3_endpoint,
-             const bool s3_verify_ssl,
-             const bool s3_is_aws,
-             size_t file_size)
-      : path_(path), s3_id_(s3_id), s3_key_(s3_key), s3_session_token_(s3_session_token),
-         s3_region_(s3_region), s3_endpoint_(s3_endpoint), s3_verify_ssl_(s3_verify_ssl),
-         s3_is_aws_(s3_is_aws) {
-    this->expect_file_size_ = file_size;
-  }
-  virtual ~ReadStream(void) {}
-
- protected:
-  // implement InitRequest
-  virtual void InitRequest(size_t begin_bytes,
-                           CURL *ecurl,
-                           curl_slist **slist);
-
- private:
-  // path we are reading
-  URI path_;
-  // s3 access key and id
-  std::string s3_id_, s3_key_, s3_session_token_, s3_region_, s3_endpoint_;
-  bool s3_verify_ssl_, s3_is_aws_;
-};
-
-// initialize the reader at begin bytes
-void ReadStream::InitRequest(size_t begin_bytes,
-                             CURL *ecurl,
-                             curl_slist **slist) {
-  std::string payload;
-  time_t curr_time = time(NULL);
-  std::map<std::string, std::string> canonical_headers;
-  AddDefaultCanonicalHeaders(&canonical_headers, curr_time, s3_session_token_, payload, true);
-  std::ostringstream sauth, sdate, stoken, surl, scontent, srange;
-  std::ostringstream result;
-  std::string canonical_querystring;
-  std::string canonical_uri;
-  CHECK_EQ(path_.name.front(), '/');
-  CHECK_NE(path_.host.front(), '/');
-  if (s3_is_aws_ && path_.host.find('.', 0) == std::string::npos) {
-    // use virtual host style if no period in host
-    canonical_uri = URIEncode(path_.name, false);
-    canonical_headers["host"] = path_.host + "." + s3::getEndpoint(s3_region_);
-    surl << "https://" << canonical_headers["host"]
-         << '/' << RemoveBeginSlash(path_.name);
-  } else {
-    canonical_uri = URIEncode("/" + path_.host + path_.name, false);
-    canonical_headers["host"] = s3_endpoint_;
-    surl << "https://" << s3_endpoint_ << '/' << path_.host << '/'
-         << RemoveBeginSlash(path_.name);
-  }
-  std::string signature = SignSig4(s3_key_, s3_region_, "GET", curr_time,
-                                   canonical_uri, canonical_querystring,
-                                   canonical_headers, payload);
-  BuildRequestHeaders(sauth, sdate, stoken, scontent,
-                      curr_time, s3_id_, s3_region_, s3_session_token_,
-                      canonical_headers, signature, payload);
-
-  srange << "Range: bytes=" << begin_bytes << "-";
-  *slist = curl_slist_append(*slist, sdate.str().c_str());
-  *slist = curl_slist_append(*slist, scontent.str().c_str());
-  *slist = curl_slist_append(*slist, srange.str().c_str());
-  *slist = curl_slist_append(*slist, sauth.str().c_str());
-  if (s3_session_token_ != "") {
-    *slist = curl_slist_append(*slist, stoken.str().c_str());
-  }
-  CHECK(curl_easy_setopt(ecurl, CURLOPT_HTTPHEADER, *slist) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl, CURLOPT_URL, surl.str().c_str()) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl, CURLOPT_HTTPGET, 1L) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl, CURLOPT_HEADER, 0L) == CURLE_OK);
-  CHECK(curl_easy_setopt(ecurl, CURLOPT_NOSIGNAL, 1) == CURLE_OK);
-  if (!s3_verify_ssl_) {
-    CHECK(curl_easy_setopt(ecurl, CURLOPT_SSL_VERIFYHOST, 0L) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl, CURLOPT_SSL_VERIFYPEER, 0L) == CURLE_OK);
-  }
-}
-
-/*! \brief simple http read stream to check */
-class HttpReadStream : public CURLReadStreamBase {
- public:
-  explicit HttpReadStream(const URI &path)
-      : path_(path) {}
-  // implement InitRequest
-  virtual void InitRequest(size_t begin_bytes,
-                           CURL *ecurl,
-                           curl_slist **slist) {
-    CHECK(begin_bytes == 0)
-        << " HttpReadStream: do not support Seek";
-    CHECK(curl_easy_setopt(ecurl, CURLOPT_URL, path_.str().c_str()) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl, CURLOPT_NOSIGNAL, 1) == CURLE_OK);
-  }
-
- private:
-  URI path_;
-};
-
-class WriteStream : public Stream {
- public:
-  WriteStream(const URI &path,
-              const std::string &s3_id,
-              const std::string &s3_key,
-              const std::string &s3_session_token,
-              const std::string &s3_region,
-              const std::string &s3_endpoint,
-              bool s3_verify_ssl,
-              bool s3_is_aws)
-      : path_(path), s3_id_(s3_id), s3_key_(s3_key), s3_session_token_(s3_session_token),
-         s3_region_(s3_region), s3_endpoint_(s3_endpoint), s3_verify_ssl_(s3_verify_ssl),
-         s3_is_aws_(s3_is_aws), closed_(false) {
-    const char *buz = getenv("DMLC_S3_WRITE_BUFFER_MB");
-    if (buz != NULL) {
-      max_buffer_size_ = static_cast<size_t>(atol(buz)) << 20UL;
-    } else {
-      // 64 MB
-      const size_t kDefaultBufferSize = 64 << 20UL;
-      max_buffer_size_ = kDefaultBufferSize;
-    }
-    max_error_retry_ = 3;
-    ecurl_ = curl_easy_init();
-    this->Init();
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    LOG(FATAL) << "S3.WriteStream cannot be used for read";
-    return 0;
-  }
-  virtual void Write(const void *ptr, size_t size);
-  // destructor
-  virtual ~WriteStream() {
-    this->Close();
-  }
-
-  /*! \brief Closes the write stream */
-  virtual void Close() {
-    if (!closed_) {
-      closed_ = true;
-      this->Upload(true);
-      this->Finish();
-      curl_easy_cleanup(ecurl_);
-    }
-  }
-
- private:
-  // internal maximum buffer size
-  size_t max_buffer_size_;
-  // maximum time of retry when error occurs
-  int max_error_retry_;
-  // path we are reading
-  URI path_;
-  // s3 access key and id
-  std::string s3_id_, s3_key_, s3_session_token_, s3_region_, s3_endpoint_;
-  bool s3_verify_ssl_, s3_is_aws_;
-  // easy curl handle used for the request
-  CURL *ecurl_;
-  // upload_id used by AWS
-  std::string upload_id_;
-  // write data buffer
-  std::string buffer_;
-  // etags of each part we uploaded
-  std::vector<std::string> etags_;
-  // part id of each part we uploaded
-  std::vector<size_t> part_ids_;
-  // whether the stream is closed
-  bool closed_;
-  /*!
-   * \brief helper function to do http post request
-   * \param method method to peform
-   * \param url_args additional arguments in URL
-   * \param url_args translated arguments to sign
-   * \param content_type content type of the data
-   * \param data data to post
-   * \param out_header holds output Header
-   * \param out_data holds output data
-   */
-  void Run(const std::string &method,
-           const std::map<std::string, std::string> &params,
-           const std::string &content_type,
-           const std::string &data,
-           std::string *out_header,
-           std::string *out_data);
-  /*!
-   * \brief initialize the upload request
-   */
-  void Init(void);
-  /*!
-   * \brief upload the buffer to S3, store the etag
-   * clear the buffer
-   */
-  void Upload(bool force_upload_even_if_zero_bytes = false);
-  /*!
-   * \brief commit the upload and finish the session
-   */
-  void Finish(void);
-};
-
-void WriteStream::Write(const void *ptr, size_t size) {
-  size_t rlen = buffer_.length();
-  buffer_.resize(rlen + size);
-  std::memcpy(BeginPtr(buffer_) + rlen, ptr, size);
-  if (buffer_.length() >= max_buffer_size_) {
-    this->Upload();
-  }
-}
-
-void WriteStream::Run(const std::string &method,
-                      const std::map<std::string, std::string> &params,
-                      const std::string &content_type,
-                      const std::string &data,
-                      std::string *out_header,
-                      std::string *out_data) {
-  CHECK(path_.host.length() != 0) << "bucket name not specified for s3 location";
-  CHECK(path_.name.length() != 0) << "key name not specified for s3 location";
-  time_t curr_time = time(NULL);
-  std::map<std::string, std::string> canonical_headers;
-  AddDefaultCanonicalHeaders(&canonical_headers, curr_time, s3_session_token_, data, true);
-  std::string canonical_query = GetQueryMultipart(params, true);
-  std::string canonical_uri;
-  std::ostringstream sauth, sdate, stoken, surl, scontent;
-  std::ostringstream rheader, rdata;
-  if (s3_is_aws_ && path_.host.find('.', 0) == std::string::npos) {
-    canonical_uri = URIEncode(path_.name, false);
-    canonical_headers["host"] = path_.host + "." + s3::getEndpoint(s3_region_);
-    surl << "https://" << canonical_headers["host"]
-         << path_.name << "?" << GetQueryMultipart(params, false);
-  } else {
-    canonical_uri = URIEncode("/" + path_.host + path_.name, false);
-    canonical_headers["host"] = s3_endpoint_;
-    surl << "https://" << s3_endpoint_ << "/" << path_.host
-         << path_.name << "?" << GetQueryMultipart(params, false);
-  }
-  std::string signature = SignSig4(s3_key_, s3_region_, method, curr_time,
-                                   canonical_uri, canonical_query,
-                                   canonical_headers, data);
-  BuildRequestHeaders(sauth, sdate, stoken, scontent,
-                      curr_time, s3_id_, s3_region_, s3_session_token_,
-                      canonical_headers, signature, data);
-  scontent << "\nContent-Type: "<< content_type;
-
-  // list
-  curl_slist *slist = NULL;
-  slist = curl_slist_append(slist, sdate.str().c_str());
-  slist = curl_slist_append(slist, scontent.str().c_str());
-  if (!s3_session_token_.empty()) {
-    slist = curl_slist_append(slist, stoken.str().c_str());
-  }
-  slist = curl_slist_append(slist, sauth.str().c_str());
-
-  int num_retry = 0;
-  while (true) {
-    // helper for read string
-    ReadStringStream ss(data);
-    curl_easy_reset(ecurl_);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_HTTPHEADER, slist) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_URL, surl.str().c_str()) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_HEADER, 0L) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_WRITEFUNCTION, WriteSStreamCallback) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_WRITEDATA, &rdata) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_WRITEHEADER, WriteSStreamCallback) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_HEADERDATA, &rheader) == CURLE_OK);
-    CHECK(curl_easy_setopt(ecurl_, CURLOPT_NOSIGNAL, 1) == CURLE_OK);
-    if (!s3_verify_ssl_) {
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_SSL_VERIFYHOST, 0L) == CURLE_OK);
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_SSL_VERIFYPEER, 0L) == CURLE_OK);
-    }
-    if (method == "POST") {
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_POST, 0L) == CURLE_OK);
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_POSTFIELDSIZE, data.length()) == CURLE_OK);
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_POSTFIELDS, BeginPtr(data)) == CURLE_OK);
-    } else if (method == "PUT") {
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_PUT, 1L) == CURLE_OK);
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_READDATA, &ss) == CURLE_OK);
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_INFILESIZE_LARGE, data.length()) == CURLE_OK);
-      CHECK(curl_easy_setopt(ecurl_, CURLOPT_READFUNCTION, ReadStringStream::Callback) == CURLE_OK);
-    }
-    CURLcode ret = curl_easy_perform(ecurl_);
-    if (ret != CURLE_OK) {
-      LOG(INFO) << "request " << surl.str() << "failed with error "
-                << curl_easy_strerror(ret) << " Progress "
-                << etags_.size() << " uploaded " << " retry=" << num_retry;
-      num_retry += 1;
-      CHECK(num_retry < max_error_retry_) << " maximum retry time reached";
-      curl_easy_cleanup(ecurl_);
-      ecurl_ = curl_easy_init();
-    } else {
-      break;
-    }
-  }
-  curl_slist_free_all(slist);
-  *out_header = rheader.str();
-  *out_data = rdata.str();
-  if (FindHttpError(*out_header) ||
-      out_data->find("<Error>") != std::string::npos) {
-    LOG(FATAL) << "AWS S3 Error:\n" << *out_header << *out_data;
-  }
-}
-
-void WriteStream::Init(void) {
-  std::string rheader, rdata;
-  std::map<std::string, std::string> params;
-  params["uploads"] = "";
-  Run("POST", params, "binary/octel-stream", "", &rheader, &rdata);
-  XMLIter xml(rdata.c_str());
-  XMLIter upid;
-  CHECK(xml.GetNext("UploadId", &upid)) << "missing UploadId";
-  upload_id_ = upid.str();
-}
-
-void WriteStream::Upload(bool force_upload_even_if_zero_bytes) {
-  if (buffer_.length() == 0 && !force_upload_even_if_zero_bytes) return;
-  std::string rheader, rdata;
-  size_t partno = etags_.size() + 1;
-  std::map<std::string, std::string> params;
-  params["partNumber"] = std::to_string(partno);
-  params["uploadId"] = upload_id_;
-  Run("PUT", params, "binary/octel-stream", buffer_, &rheader, &rdata);
-  const char *p = strstr(rheader.c_str(), "ETag: ");
-  CHECK(p != NULL) << "cannot find ETag in header";
-  p = strchr(p, '\"');
-  CHECK(p != NULL) << "cannot find ETag in header";
-  const char *end = strchr(p + 1, '\"');
-  CHECK(end != NULL) << "cannot find ETag in header";
-
-  etags_.push_back(std::string(p, end - p + 1));
-  part_ids_.push_back(partno);
-  buffer_.clear();
-}
-
-void WriteStream::Finish(void) {
-  std::string rheader, rdata;
-  std::map<std::string, std::string> params;
-  params["uploadId"] = upload_id_;
-
-  std::ostringstream sdata;
-  sdata << "<CompleteMultipartUpload>\n";
-  CHECK(etags_.size() == part_ids_.size());
-  for (size_t i = 0; i < etags_.size(); ++i) {
-    sdata << " <Part>\n"
-          << "  <PartNumber>" << part_ids_[i] << "</PartNumber>\n"
-          << "  <ETag>" << etags_[i] << "</ETag>\n"
-          << " </Part>\n";
-  }
-  sdata << "</CompleteMultipartUpload>\n";
-
-  Run("POST", params, "text/xml", sdata.str(), &rheader, &rdata);
-}
-}  // namespace s3
-
-void S3FileSystem::ListObjects(const URI &path, std::vector<FileInfo> *out_list) {
-  CHECK(path.host.length() != 0) << "bucket name not specified for s3 location";
-  out_list->clear();
-  using namespace s3;
-
-  std::string next_token = "";
-  std::string has_next_page = "false";
-
-  do {
-    time_t curr_time = time(NULL);
-    std::map<std::string, std::string> canonical_headers;
-    std::string payload;
-    std::ostringstream sauth, sdate, stoken, surl, scontent;
-    std::ostringstream result;
-    std::string canonical_uri;
-    std::string canonical_querystring;
-
-    AddDefaultCanonicalHeaders(&canonical_headers, curr_time, s3_session_token_, payload, true);
-    if (next_token == "") {
-        canonical_querystring = "delimiter=%2F&prefix=" +
-            URIEncode(std::string{RemoveBeginSlash(path.name)});
-    } else {
-        canonical_querystring = "delimiter=%2F&marker=" + URIEncode(std::string{next_token}) +
-            "&prefix=" + URIEncode(std::string{RemoveBeginSlash(path.name)});
-    }
-
-    if (s3_is_aws_ && path.host.find('.', 0) == std::string::npos) {
-      // use virtual host style if no period in host
-      canonical_uri = "/";
-      canonical_headers["host"] = path.host + "." + s3::getEndpoint(s3_region_);
-      surl << "https://" << canonical_headers["host"]
-           << "/?delimiter=/&prefix=" << RemoveBeginSlash(path.name);
-    } else {
-      canonical_uri = URIEncode("/" + path.host + "/", false);
-      canonical_headers["host"] = s3_endpoint_;
-      surl << "https://" << s3_endpoint_ << "/" << path.host << "/?delimiter=/&prefix="
-           << RemoveBeginSlash(path.name);
-    }
-
-    if (next_token != "") {
-        surl << "&marker=" << next_token;
-    }
-
-    std::string signature = SignSig4(s3_secret_key_, s3_region_, "GET", curr_time,
-                                     canonical_uri, canonical_querystring,
-                                     canonical_headers, payload);
-    BuildRequestHeaders(sauth, sdate, stoken, scontent,
-                        curr_time, s3_access_id_, s3_region_, s3_session_token_,
-                        canonical_headers, signature, payload);
-
-    // make request
-    CURL *curl = curl_easy_init();
-    curl_slist *slist = NULL;
-    slist = curl_slist_append(slist, sdate.str().c_str());
-    slist = curl_slist_append(slist, sauth.str().c_str());
-    slist = curl_slist_append(slist, scontent.str().c_str());
-    if (!s3_session_token_.empty()) {
-      slist = curl_slist_append(slist, stoken.str().c_str());
-    }
-    char errbuf[CURL_ERROR_SIZE];
-    CHECK(curl_easy_setopt(curl, CURLOPT_ERRORBUFFER, &errbuf) == CURLE_OK);
-    CHECK(curl_easy_setopt(curl, CURLOPT_HTTPHEADER, slist) == CURLE_OK);
-    CHECK(curl_easy_setopt(curl, CURLOPT_URL, surl.str().c_str()) == CURLE_OK);
-    CHECK(curl_easy_setopt(curl, CURLOPT_HTTPGET, 1L) == CURLE_OK);
-    CHECK(curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteSStreamCallback) == CURLE_OK);
-    CHECK(curl_easy_setopt(curl, CURLOPT_WRITEDATA, &result) == CURLE_OK);
-    CHECK(curl_easy_setopt(curl, CURLOPT_NOSIGNAL, 1) == CURLE_OK);
-    if (!s3_verify_ssl_) {
-      CHECK(curl_easy_setopt(curl, CURLOPT_SSL_VERIFYHOST, 0L) == CURLE_OK);
-      CHECK(curl_easy_setopt(curl, CURLOPT_SSL_VERIFYPEER, 0L) == CURLE_OK);
-    }
-    CHECK(curl_easy_perform(curl) == CURLE_OK) << "Error: " << errbuf;
-    curl_slist_free_all(slist);
-    curl_easy_cleanup(curl);
-
-    // parse xml
-    std::string ret = result.str();
-    if (ret.find("<Error>") != std::string::npos) {
-      LOG(FATAL) << ret;
-    }
-
-    {
-      // get files
-      XMLIter xml(ret.c_str());
-      XMLIter data;
-      CHECK(xml.GetNext("IsTruncated", &data)) << "missing IsTruncated";
-      has_next_page = data.str();
-    }
-
-    {
-      // get files
-      XMLIter xml(ret.c_str());
-      XMLIter data;
-
-      if (xml.GetNext("NextMarker", &data)) {
-        // If NextContinuationToken exists in the response, more requests needs
-        // to be made to get the full list of objects.
-        next_token = data.str();
-      }
-
-      while (xml.GetNext("Contents", &data)) {
-        FileInfo info;
-        info.path = path;
-        XMLIter value;
-        CHECK(data.GetNext("Key", &value));
-        // add root path to be consistent with other filesys convention
-        info.path.name = '/' + value.str();
-        CHECK(data.GetNext("Size", &value));
-        info.size = static_cast<size_t>(atol(value.str().c_str()));
-        info.type = kFile;
-        out_list->push_back(info);
-      }
-    }
-
-    {
-      // get directories
-      XMLIter xml(ret.c_str());
-      XMLIter data;
-      while (xml.GetNext("CommonPrefixes", &data)) {
-        FileInfo info;
-        info.path = path;
-        XMLIter value;
-        CHECK(data.GetNext("Prefix", &value));
-        // add root path to be consistent with other filesys convention
-        info.path.name = '/' + value.str();
-        info.size = 0; info.type = kDirectory;
-        out_list->push_back(info);
-      }
-    }
-  } while (has_next_page == "true");
-}
-
-S3FileSystem::S3FileSystem() {
-  const char *isAWS = getenv("S3_IS_AWS");
-  const char *keyid = getenv("S3_ACCESS_KEY_ID");
-  const char *seckey = getenv("S3_SECRET_ACCESS_KEY");
-  const char *token = getenv("S3_SESSION_TOKEN");
-  const char *region = getenv("S3_REGION");
-  const char *endpoint = getenv("S3_ENDPOINT");
-  const char *verify_ssl = getenv("S3_VERIFY_SSL");
-
-  if (keyid == NULL || (strcmp(keyid, "") == 0)) {
-    keyid = getenv("AWS_ACCESS_KEY_ID");
-  }
-  if (seckey == NULL || (strcmp(seckey, "") == 0)) {
-    seckey = getenv("AWS_SECRET_ACCESS_KEY");
-  }
-  if (token == NULL || (strcmp(token, "") == 0)) {
-    token = getenv("AWS_SESSION_TOKEN");
-  }
-  if (region == NULL || (strcmp(region, "") == 0)) {
-    region = getenv("AWS_REGION");
-  }
-
-  if (keyid == NULL) {
-    LOG(FATAL) << "Need to set enviroment variable S3_ACCESS_KEY_ID to use S3";
-  }
-  if (seckey == NULL) {
-    LOG(FATAL) << "Need to set enviroment variable S3_SECRET_ACCESS_KEY to use S3";
-  }
-
-  if (isAWS == NULL || (strcmp(isAWS, "1") == 0)) {
-    s3_is_aws_ = true;
-  } else {
-    s3_is_aws_ = false;
-  }
-  if (region == NULL) {
-    LOG(WARNING) << "No AWS Region set, using default region us-east-1.";
-    LOG(WARNING) << "Need to set enviroment variable S3_REGION to set region.";
-    s3_region_ = "us-east-1";
-  } else if (strcmp(region, "") == 0) {
-    LOG(WARNING) << "AWS Region was set to empty string, using default region us-east-1.";
-    LOG(WARNING) << "Need to set enviroment variable S3_REGION to set region.";
-    s3_region_ = "us-east-1";
-  } else {
-    s3_region_ = region;
-  }
-
-  s3_access_id_ = keyid;
-  s3_secret_key_ = seckey;
-
-  if (token != NULL) {
-    s3_session_token_ = token;
-  }
-  if (endpoint == NULL || (strcmp(endpoint, "") == 0)) {
-    s3_endpoint_ = s3::getEndpoint(s3_region_);
-  } else {
-    s3_endpoint_ = endpoint;
-  }
-
-  if (verify_ssl == NULL || (strcmp(verify_ssl, "1") == 0)) {
-    s3_verify_ssl_ = true;
-  } else {
-    s3_verify_ssl_ = false;
-  }
-}
-
-void S3FileSystem::SetCredentials(const std::string& s3_access_id,
-                                  const std::string& s3_secret_key) {
-  s3_access_id_ = s3_access_id;
-  s3_secret_key_ = s3_secret_key;
-}
-
-bool S3FileSystem::TryGetPathInfo(const URI &path_, FileInfo *out_info) {
-  URI path = path_;
-  while (path.name.length() > 1 &&
-         *path.name.rbegin() == '/') {
-    path.name.resize(path.name.length() - 1);
-  }
-  std::vector<FileInfo> files;
-  ListObjects(path,  &files);
-  std::string pdir = path.name + '/';
-  for (size_t i = 0; i < files.size(); ++i) {
-    if (files[i].path.name == path.name) {
-      *out_info = files[i]; return true;
-    }
-    if (files[i].path.name == pdir) {
-      *out_info = files[i]; return true;
-    }
-  }
-  return false;
-}
-
-FileInfo S3FileSystem::GetPathInfo(const URI &path) {
-  CHECK(path.protocol == "s3://")
-      << " S3FileSystem.ListDirectory";
-  FileInfo info;
-  CHECK(TryGetPathInfo(path, &info))
-      << "S3FileSytem.GetPathInfo cannot find information about " + path.str();
-  return info;
-}
-void S3FileSystem::ListDirectory(const URI &path, std::vector<FileInfo> *out_list) {
-  CHECK(path.protocol == "s3://")
-      << " S3FileSystem.ListDirectory";
-  if (path.name[path.name.length() - 1] == '/') {
-    ListObjects(path,  out_list);
-    return;
-  }
-  std::vector<FileInfo> files;
-  std::string pdir = path.name + '/';
-  out_list->clear();
-  ListObjects(path,  &files);
-  if (path.name.empty()) {
-    // then insert all files in the bucket
-    out_list->insert(out_list->end(), files.begin(), files.end());
-    return;
-  }
-  for (size_t i = 0; i < files.size(); ++i) {
-    if (files[i].path.name == path.name) {
-      CHECK(files[i].type == kFile);
-      out_list->push_back(files[i]);
-      return;
-    }
-    if (files[i].path.name == pdir) {
-      CHECK(files[i].type == kDirectory);
-      ListObjects(files[i].path, out_list);
-      return;
-    }
-  }
-}
-
-Stream *S3FileSystem::Open(const URI &path, const char* const flag, bool allow_null) {
-  using namespace std;
-  if (!strcmp(flag, "r") || !strcmp(flag, "rb")) {
-    return OpenForRead(path, allow_null);
-  } else if (!strcmp(flag, "w") || !strcmp(flag, "wb")) {
-    CHECK(path.protocol == "s3://") << " S3FileSystem.Open";
-    return new s3::WriteStream(path, s3_access_id_, s3_secret_key_, s3_session_token_,
-                               s3_region_, s3_endpoint_, s3_verify_ssl_, s3_is_aws_);
-  } else {
-    LOG(FATAL) << "S3FileSytem.Open do not support flag " << flag;
-    return NULL;
-  }
-}
-
-SeekStream *S3FileSystem::OpenForRead(const URI &path, bool allow_null) {
-  // simple http read stream
-  if (!allow_null && (path.protocol == "http://"|| path.protocol == "https://")) {
-    return new s3::HttpReadStream(path);
-  }
-  CHECK(path.protocol == "s3://") << " S3FileSystem.Open";
-  FileInfo info;
-  if (TryGetPathInfo(path, &info) && info.type == kFile) {
-    return new s3::ReadStream(path, s3_access_id_, s3_secret_key_, s3_session_token_,
-                              s3_region_, s3_endpoint_, s3_verify_ssl_, s3_is_aws_, info.size);
-  } else {
-    CHECK(allow_null) << " S3FileSystem: fail to open \"" << path.str() << "\"";
-    return NULL;
-  }
-}
-}  // namespace io
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/src/io/s3_filesys.h b/ml-xgboost/dmlc-core/src/io/s3_filesys.h
deleted file mode 100644
index 3cf6640..0000000
--- a/ml-xgboost/dmlc-core/src/io/s3_filesys.h
+++ /dev/null
@@ -1,103 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file s3_filesys.h
- * \brief S3 access module
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_S3_FILESYS_H_
-#define DMLC_IO_S3_FILESYS_H_
-
-#include <dmlc/filesystem.h>
-#include <vector>
-#include <string>
-
-namespace dmlc {
-namespace io {
-/*! \brief S3 filesystem */
-class S3FileSystem : public FileSystem {
- public:
-  /*! \brief destructor */
-  virtual ~S3FileSystem() {}
-
-  /*!
-   * \brief Sets S3 access credentials
-   * \param s3_access_id The S3 Access Key ID
-   * \param s3_secret_key The S3 Secret Key
-   * \return the information about the file
-   */
-  void SetCredentials(const std::string& s3_access_id,
-                      const std::string& s3_secret_key);
-
-  /*!
-   * \brief get information about a path
-   * \param path the path to the file
-   * \return the information about the file
-   */
-  virtual FileInfo GetPathInfo(const URI &path);
-  /*!
-   * \brief list files in a directory
-   * \param path to the file
-   * \param out_list the output information about the files
-   */
-  virtual void ListDirectory(const URI &path, std::vector<FileInfo> *out_list);
-  /*!
-   * \brief open a stream, will report error and exit if bad thing happens
-   * NOTE: the Stream can continue to work even when filesystem was destructed
-   * \param path path to file
-   * \param uri the uri of the input
-   * \param flag can be "w", "r", "a"
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual Stream *Open(const URI &path, const char* const flag, bool allow_null);
-  /*!
-   * \brief open a seekable stream for read
-   * \param path the path to the file
-   * \param allow_null whether NULL can be returned, or directly report error
-   * \return the created stream, can be NULL when allow_null == true and file do not exist
-   */
-  virtual SeekStream *OpenForRead(const URI &path, bool allow_null);
-  /*!
-   * \brief get a singleton of S3FileSystem when needed
-   * \return a singleton instance
-   */
-  inline static S3FileSystem *GetInstance(void) {
-    static S3FileSystem instance;
-    return &instance;
-  }
-
- private:
-  /*! \brief constructor */
-  S3FileSystem();
-  /*! \brief S3 access id */
-  std::string s3_access_id_;
-  /*! \brief S3 secret key */
-  std::string s3_secret_key_;
-  /*! \brief S3 session token */
-  std::string s3_session_token_;
-  /*! \brief S3 region*/
-  std::string s3_region_;
-  /*! \brief S3 endpoint*/
-  std::string s3_endpoint_;
-  /*! \brief S3 verify ssl*/
-  bool s3_verify_ssl_;
-  bool s3_is_aws_;
-
-  /*!
-   * \brief try to get information about a path
-   * \param path the path to the file
-   * \param out_info holds the path info
-   * \return return false when path do not exist
-   */
-  bool TryGetPathInfo(const URI &path, FileInfo *info);
-
-  /*!
-  * \brief list the objects in the bucket with prefix specified by path.name
-  * \param path the path to query
-  * \param out_list stores the output results which match given prefix
-  */
-  void ListObjects(const URI &path, std::vector<FileInfo> *out_list);
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_S3_FILESYS_H_
diff --git a/ml-xgboost/dmlc-core/src/io/single_file_split.h b/ml-xgboost/dmlc-core/src/io/single_file_split.h
deleted file mode 100644
index b786a74..0000000
--- a/ml-xgboost/dmlc-core/src/io/single_file_split.h
+++ /dev/null
@@ -1,182 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file single_file_split.h
- * \brief base implementation of line-spliter
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_SINGLE_FILE_SPLIT_H_
-#define DMLC_IO_SINGLE_FILE_SPLIT_H_
-
-#include <dmlc/base.h>
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <sys/stat.h>
-#include <cstdio>
-#include <string>
-#include <algorithm>
-
-#ifdef _WIN32
-#define stat_struct __stat64
-#define fstat _fstat64
-#define fileno _fileno
-#else  // _WIN32
-#define stat_struct stat
-#endif  // _WIN32
-
-namespace dmlc {
-namespace io {
-/*!
- * \brief line split implementation from single FILE
- * simply returns lines of files, used for stdin
- */
-class SingleFileSplit : public InputSplit {
- public:
-  explicit SingleFileSplit(const char *fname)
-      : use_stdin_(false), buffer_size_(kBufferSize),
-        chunk_begin_(NULL), chunk_end_(NULL) {
-    if (!std::strcmp(fname, "stdin")) {
-#ifndef DMLC_STRICT_CXX98_
-      use_stdin_ = true; fp_ = stdin;
-#endif
-    }
-    if (!use_stdin_) {
-#if DMLC_USE_FOPEN64
-      fp_ = fopen64(fname, "rb");
-#else
-      fp_ = fopen(fname, "rb");
-#endif
-      CHECK(fp_ != NULL) << "SingleFileSplit: fail to open " << fname;
-    }
-    buffer_.resize(kBufferSize);
-  }
-  virtual ~SingleFileSplit(void) {
-    if (!use_stdin_) std::fclose(fp_);
-  }
-  virtual void BeforeFirst(void) {
-    fseek(fp_, 0, SEEK_SET);
-  }
-  virtual void HintChunkSize(size_t chunk_size) {
-    buffer_size_ = std::max(chunk_size, buffer_size_);
-  }
-  virtual size_t GetTotalSize(void) {
-    struct stat_struct buf;
-    fstat(fileno(fp_), &buf);
-    return buf.st_size;
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    return std::fread(ptr, 1, size, fp_);
-  }
-  virtual void ResetPartition(unsigned part_index, unsigned num_parts) {
-    CHECK(part_index == 0 && num_parts == 1);
-    this->BeforeFirst();
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    LOG(FATAL) << "InputSplit do not support write";
-  }
-  virtual bool NextRecord(Blob *out_rec) {
-    if (chunk_begin_ == chunk_end_) {
-      if (!LoadChunk()) return false;
-    }
-    char *next = FindNextRecord(chunk_begin_,
-                                chunk_end_);
-    out_rec->dptr = chunk_begin_;
-    out_rec->size = next - chunk_begin_;
-    chunk_begin_ = next;
-    return true;
-  }
-  virtual bool NextChunk(Blob *out_chunk) {
-    if (chunk_begin_ == chunk_end_) {
-      if (!LoadChunk()) return false;
-    }
-    out_chunk->dptr = chunk_begin_;
-    out_chunk->size = chunk_end_ - chunk_begin_;
-    chunk_begin_ = chunk_end_;
-    return true;
-  }
-  inline bool ReadChunk(void *buf, size_t *size) {
-    size_t max_size = *size;
-    if (max_size <= overflow_.length()) {
-      *size = 0; return true;
-    }
-    if (overflow_.length() != 0) {
-      std::memcpy(buf, BeginPtr(overflow_), overflow_.length());
-    }
-    size_t olen = overflow_.length();
-    overflow_.resize(0);
-    size_t nread = this->Read(reinterpret_cast<char*>(buf) + olen,
-                              max_size - olen);
-    nread += olen;
-    if (nread == 0) return false;
-    if (nread != max_size) {
-      *size = nread;
-      return true;
-    } else {
-      const char *bptr = reinterpret_cast<const char*>(buf);
-      // return the last position where a record starts
-      const char *bend = this->FindLastRecordBegin(bptr, bptr + max_size);
-      *size = bend - bptr;
-      overflow_.resize(max_size - *size);
-      if (overflow_.length() != 0) {
-        std::memcpy(BeginPtr(overflow_), bend, overflow_.length());
-      }
-      return true;
-    }
-  }
-
- protected:
-  inline const char* FindLastRecordBegin(const char *begin,
-                                         const char *end) {
-    if (begin == end) return begin;
-    for (const char *p = end - 1; p != begin; --p) {
-      if (*p == '\n' || *p == '\r') return p + 1;
-    }
-    return begin;
-  }
-  inline char* FindNextRecord(char *begin, char *end) {
-    char *p;
-    for (p = begin; p != end; ++p) {
-      if (*p == '\n' || *p == '\r') break;
-    }
-    for (; p != end; ++p) {
-      if (*p != '\n' && *p != '\r') return p;
-    }
-    return end;
-  }
-  inline bool LoadChunk(void) {
-    if (buffer_.length() < buffer_size_) {
-      buffer_.resize(buffer_size_);
-    }
-    while (true) {
-      size_t size = buffer_.length();
-      if (!ReadChunk(BeginPtr(buffer_), &size)) return false;
-      if (size == 0) {
-        buffer_.resize(buffer_.length() * 2);
-      } else {
-        chunk_begin_ = reinterpret_cast<char *>(BeginPtr(buffer_));
-        chunk_end_ = chunk_begin_ + size;
-        break;
-      }
-    }
-    return true;
-  }
-
- private:
-  // buffer size
-  static const size_t kBufferSize = 1 << 18UL;
-  // file
-  std::FILE *fp_;
-  bool use_stdin_;
-  // internal overflow
-  std::string overflow_;
-  // internal buffer
-  std::string buffer_;
-  // internal buffer size
-  size_t buffer_size_;
-  // beginning of chunk
-  char *chunk_begin_;
-  // end of chunk
-  char *chunk_end_;
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_SINGLE_FILE_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/single_threaded_input_split.h b/ml-xgboost/dmlc-core/src/io/single_threaded_input_split.h
deleted file mode 100644
index 05dd471..0000000
--- a/ml-xgboost/dmlc-core/src/io/single_threaded_input_split.h
+++ /dev/null
@@ -1,87 +0,0 @@
-// Copyright by contributors
-#ifndef DMLC_IO_SINGLE_THREADED_INPUT_SPLIT_H_
-#define DMLC_IO_SINGLE_THREADED_INPUT_SPLIT_H_
-
-#include <dmlc/threadediter.h>
-#include <dmlc/base.h>
-#include <algorithm>
-#include "./input_split_base.h"
-
-namespace dmlc {
-namespace io {
-/*!
- * \brief provides a single threaded input split
- *  Useful for debugging purposes. Be cautious of use
- *  for production use cases, as this is much less performant
- *  compared to ThreadedInputSplit
- */
-class SingleThreadedInputSplit : public InputSplit {
- public:
-  explicit SingleThreadedInputSplit(InputSplitBase *base,
-                                    const size_t batch_size)
-      : buffer_size_(InputSplitBase::kBufferSize), batch_size_(batch_size),
-        base_(base), tmp_chunk_(NULL) {}
-  bool NextProducer(InputSplitBase::Chunk **dptr) {
-    if (*dptr == NULL) {
-      *dptr = new InputSplitBase::Chunk(buffer_size_);
-    }
-    return base_->NextBatchEx(*dptr, batch_size_);
-  }
-  void BeforeFirstProducer() { base_->BeforeFirst(); }
-  virtual ~SingleThreadedInputSplit(void) {
-    delete tmp_chunk_;
-    delete base_;
-  }
-  virtual void BeforeFirst() {
-    BeforeFirstProducer();
-    if (tmp_chunk_ != NULL) {
-      tmp_chunk_ = NULL;
-    }
-  }
-  virtual void HintChunkSize(size_t chunk_size) {
-    buffer_size_ = std::max(chunk_size / sizeof(uint32_t), buffer_size_);
-  }
-
-  virtual bool NextRecord(Blob *out_rec) {
-    if (tmp_chunk_ == NULL) {
-      if (!NextProducer(&tmp_chunk_))
-        return false;
-    }
-    while (!base_->ExtractNextRecord(out_rec, tmp_chunk_)) {
-      tmp_chunk_ = NULL;
-      if (!NextProducer(&tmp_chunk_))
-        return false;
-    }
-    return true;
-  }
-
-  virtual bool NextChunk(Blob *out_chunk) {
-    if (tmp_chunk_ == NULL) {
-      if (!NextProducer(&tmp_chunk_))
-        return false;
-    }
-    while (!base_->ExtractNextChunk(out_chunk, tmp_chunk_)) {
-      tmp_chunk_ = NULL;
-      if (!NextProducer(&tmp_chunk_))
-        return false;
-    }
-    return true;
-  }
-
-  virtual size_t GetTotalSize(void) { return base_->GetTotalSize(); }
-
-  virtual void ResetPartition(unsigned part_index, unsigned num_parts) {
-    base_->ResetPartition(part_index, num_parts);
-    this->BeforeFirst();
-  }
-
- private:
-  size_t buffer_size_;
-  size_t batch_size_;
-  InputSplitBase *base_;
-  InputSplitBase::Chunk *tmp_chunk_;
-};
-}  //  namespace io
-}  //  namespace dmlc
-
-#endif  // DMLC_IO_SINGLE_THREADED_INPUT_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/threaded_input_split.h b/ml-xgboost/dmlc-core/src/io/threaded_input_split.h
deleted file mode 100644
index 0dc2beb..0000000
--- a/ml-xgboost/dmlc-core/src/io/threaded_input_split.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file threaded_input_split.h
- * \brief a threaded version of InputSplit with a prefetch thread
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_THREADED_INPUT_SPLIT_H_
-#define DMLC_IO_THREADED_INPUT_SPLIT_H_
-
-#include <dmlc/base.h>
-// this code depends on c++11
-#if DMLC_ENABLE_STD_THREAD
-#include <dmlc/threadediter.h>
-#include <algorithm>
-#include "./input_split_base.h"
-
-namespace dmlc {
-namespace io {
-/*!
- * \brief a threaded version of InputSplit
- *  wraps an InputSplitBase to use an thread to prefetch the data
- */
-class ThreadedInputSplit : public InputSplit {
- public:
-  /*!
-   * \brief constructor
-   * \param base an base object to define how to read data
-   */
-  explicit ThreadedInputSplit(InputSplitBase *base, const size_t batch_size)
-      : buffer_size_(InputSplitBase::kBufferSize),
-        batch_size_(batch_size),
-        base_(base), tmp_chunk_(NULL) {
-    iter_.set_max_capacity(2);
-    // initalize the iterator
-    iter_.Init([this](InputSplitBase::Chunk **dptr) {
-        if (*dptr == NULL) {
-          *dptr = new InputSplitBase::Chunk(buffer_size_);
-        }
-        return base_->NextBatchEx(*dptr, batch_size_);
-      },
-      [base]() { base->BeforeFirst(); });
-  }
-  // destructor
-  virtual ~ThreadedInputSplit(void) {
-    iter_.Destroy();
-    delete tmp_chunk_;
-    delete base_;
-  }
-  virtual void BeforeFirst() {
-    iter_.BeforeFirst();
-    if (tmp_chunk_ != NULL) {
-      iter_.Recycle(&tmp_chunk_);
-    }
-  }
-  virtual void HintChunkSize(size_t chunk_size) {
-    buffer_size_ = std::max(chunk_size / sizeof(uint32_t), buffer_size_);
-  }
-  // implement next record
-  virtual bool NextRecord(Blob *out_rec) {
-    if (tmp_chunk_ == NULL) {
-      if (!iter_.Next(&tmp_chunk_)) return false;
-    }
-    while (!base_->ExtractNextRecord(out_rec, tmp_chunk_)) {
-      iter_.Recycle(&tmp_chunk_);
-      if (!iter_.Next(&tmp_chunk_)) return false;
-    }
-    return true;
-  }
-  // implement next chunk
-  virtual bool NextChunk(Blob *out_chunk) {
-    if (tmp_chunk_ == NULL) {
-      if (!iter_.Next(&tmp_chunk_)) return false;
-    }
-    while (!base_->ExtractNextChunk(out_chunk, tmp_chunk_)) {
-      iter_.Recycle(&tmp_chunk_);
-      if (!iter_.Next(&tmp_chunk_)) return false;
-    }
-    return true;
-  }
-
-  virtual size_t GetTotalSize(void) {
-    return base_->GetTotalSize();
-  }
-
-  virtual void ResetPartition(unsigned part_index, unsigned num_parts) {
-    base_->ResetPartition(part_index, num_parts);
-    this->BeforeFirst();
-  }
-
- private:
-  /*! \brief internal buffer size */
-  size_t buffer_size_;
-  /*! \brief batch size */
-  size_t batch_size_;
-  /*! \brief the place where we get the data */
-  InputSplitBase *base_;
-  /*! \brief backend thread iterator */
-  ThreadedIter<InputSplitBase::Chunk> iter_;
-  /*! \brief current chunk of data */
-  InputSplitBase::Chunk *tmp_chunk_;
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_USE_CXX11
-#endif  // DMLC_IO_THREADED_INPUT_SPLIT_H_
diff --git a/ml-xgboost/dmlc-core/src/io/uri_spec.h b/ml-xgboost/dmlc-core/src/io/uri_spec.h
deleted file mode 100644
index c9c4b38..0000000
--- a/ml-xgboost/dmlc-core/src/io/uri_spec.h
+++ /dev/null
@@ -1,79 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file uri_spec.h
- * \brief common specification of sugars in URI
- *    string passed to dmlc Create functions
- *    such as local file cache
- * \author Tianqi Chen
- */
-#ifndef DMLC_IO_URI_SPEC_H_
-#define DMLC_IO_URI_SPEC_H_
-
-#include <dmlc/common.h>
-#include <string>
-#include <sstream>
-#include <map>
-#include <vector>
-#include <utility>
-
-namespace dmlc {
-namespace io {
-/*!
- * \brief some super set of URI
- *  that allows sugars to be passed around
- *  Example:
- *
- *  hdfs:///mylibsvm/?format=libsvm&clabel=0#mycache-file.
- */
-class URISpec {
- public:
-  /*! \brief the real URI */
-  std::string uri;
-  /*! \brief arguments in the URL */
-  std::map<std::string, std::string> args;
-  /*! \brief the path to cache file */
-  std::string cache_file;
-  /*!
-   * \brief constructor.
-   * \param uri The raw uri string.
-   * \param part_index The parition index of the part.
-   * \param num_parts total number of parts.
-   */
-  explicit URISpec(const std::string& uri,
-                   unsigned part_index,
-                   unsigned num_parts) {
-    std::vector<std::string> name_cache = Split(uri, '#');
-
-    if (name_cache.size() == 2) {
-      std::ostringstream os;
-      os << name_cache[1];
-      if (num_parts != 1) {
-        os << ".split" << num_parts << ".part" << part_index;
-      }
-      this->cache_file = os.str();
-    } else {
-      CHECK_EQ(name_cache.size(), 1U)
-          << "only one `#` is allowed in file path for cachefile specification";
-    }
-    std::vector<std::string> name_args = Split(name_cache[0], '?');
-    if (name_args.size() == 2) {
-      std::vector<std::string> arg_list = Split(name_args[1], '&');
-      for (size_t i = 0; i < arg_list.size(); ++i) {
-        std::istringstream is(arg_list[i]);
-        std::pair<std::string, std::string> kv;
-        CHECK(std::getline(is, kv.first, '=')) << "Invalid uri argument format"
-          << " for key in arg " << i + 1;
-        CHECK(std::getline(is, kv.second)) << "Invalid uri argument format"
-          << " for value in arg " << i + 1;
-        this->args.insert(kv);
-      }
-    } else {
-      CHECK_EQ(name_args.size(), 1U)
-          << "only one `#` is allowed in file path for cachefile specification";
-    }
-    this->uri = name_args[0];
-  }
-};
-}  // namespace io
-}  // namespace dmlc
-#endif  // DMLC_IO_URI_SPEC_H_
diff --git a/ml-xgboost/dmlc-core/src/recordio.cc b/ml-xgboost/dmlc-core/src/recordio.cc
deleted file mode 100644
index 1bd7123..0000000
--- a/ml-xgboost/dmlc-core/src/recordio.cc
+++ /dev/null
@@ -1,157 +0,0 @@
-// Copyright by Contributors
-
-#include <dmlc/base.h>
-#include <dmlc/recordio.h>
-#include <dmlc/logging.h>
-#include <algorithm>
-
-
-namespace dmlc {
-// implementation
-void RecordIOWriter::WriteRecord(const void *buf, size_t size) {
-  CHECK(size < (1 << 29U))
-      << "RecordIO only accept record less than 2^29 bytes";
-  const uint32_t umagic = kMagic;
-  // initialize the magic number, in stack
-  const char *magic = reinterpret_cast<const char*>(&umagic);
-  const char *bhead = reinterpret_cast<const char*>(buf);
-  uint32_t len = static_cast<uint32_t>(size);
-  uint32_t lower_align = (len >> 2U) << 2U;
-  uint32_t upper_align = ((len + 3U) >> 2U) << 2U;
-  uint32_t dptr = 0;
-  for (uint32_t i = 0; i < lower_align ; i += 4) {
-    // use char check for alignment safety reason
-    if (bhead[i] == magic[0] &&
-        bhead[i + 1] == magic[1] &&
-        bhead[i + 2] == magic[2] &&
-        bhead[i + 3] == magic[3]) {
-      uint32_t lrec = EncodeLRec(dptr == 0 ? 1U : 2U,
-                                 i - dptr);
-      stream_->Write(magic, 4);
-      stream_->Write(&lrec, sizeof(lrec));
-      if (i != dptr) {
-        stream_->Write(bhead + dptr, i - dptr);
-      }
-      dptr = i + 4;
-      except_counter_ += 1;
-    }
-  }
-  uint32_t lrec = EncodeLRec(dptr != 0 ? 3U : 0U,
-                             len - dptr);
-  stream_->Write(magic, 4);
-  stream_->Write(&lrec, sizeof(lrec));
-  if (len != dptr) {
-    stream_->Write(bhead + dptr, len - dptr);
-  }
-  // write padded bytes
-  uint32_t zero = 0;
-  if (upper_align != len) {
-    stream_->Write(&zero, upper_align - len);
-  }
-}
-
-bool RecordIOReader::NextRecord(std::string *out_rec) {
-  if (end_of_stream_) return false;
-  const uint32_t kMagic = RecordIOWriter::kMagic;
-  out_rec->clear();
-  size_t size = 0;
-  while (true) {
-    uint32_t header[2];
-    size_t nread = stream_->Read(header, sizeof(header));
-    if (nread == 0) {
-      end_of_stream_ = true; return false;
-    }
-    CHECK(nread == sizeof(header)) << "Inavlid RecordIO File";
-    CHECK(header[0] == RecordIOWriter::kMagic) << "Invalid RecordIO File";
-    uint32_t cflag = RecordIOWriter::DecodeFlag(header[1]);
-    uint32_t len = RecordIOWriter::DecodeLength(header[1]);
-    uint32_t upper_align = ((len + 3U) >> 2U) << 2U;
-    out_rec->resize(size + upper_align);
-    if (upper_align != 0) {
-      CHECK(stream_->Read(BeginPtr(*out_rec) + size, upper_align) == upper_align)
-          << "Invalid RecordIO File upper_align=" << upper_align;
-    }
-    // squeeze back
-    size += len; out_rec->resize(size);
-    if (cflag == 0U || cflag == 3U) break;
-    out_rec->resize(size + sizeof(kMagic));
-    std::memcpy(BeginPtr(*out_rec) + size, &kMagic, sizeof(kMagic));
-    size += sizeof(kMagic);
-  }
-  return true;
-}
-
-// helper function to find next recordio head
-inline char *FindNextRecordIOHead(char *begin, char *end) {
-  CHECK_EQ((reinterpret_cast<size_t>(begin) & 3UL),  0U);
-  CHECK_EQ((reinterpret_cast<size_t>(end) & 3UL), 0U);
-  uint32_t *p = reinterpret_cast<uint32_t *>(begin);
-  uint32_t *pend = reinterpret_cast<uint32_t *>(end);
-  for (; p + 1 < pend; ++p) {
-    if (p[0] == RecordIOWriter::kMagic) {
-      uint32_t cflag = RecordIOWriter::DecodeFlag(p[1]);
-      if (cflag == 0 || cflag == 1) {
-        return reinterpret_cast<char*>(p);
-      }
-    }
-  }
-  return end;
-}
-
-RecordIOChunkReader::RecordIOChunkReader(InputSplit::Blob chunk,
-                                         unsigned part_index,
-                                         unsigned num_parts) {
-  size_t nstep = (chunk.size + num_parts - 1) / num_parts;
-  // align
-  nstep = ((nstep + 3UL) >> 2UL) << 2UL;
-  size_t begin = std::min(chunk.size, nstep * part_index);
-  size_t end = std::min(chunk.size, nstep * (part_index + 1));
-  char *head = reinterpret_cast<char*>(chunk.dptr);
-  pbegin_ = FindNextRecordIOHead(head + begin, head + chunk.size);
-  pend_ = FindNextRecordIOHead(head + end, head + chunk.size);
-}
-
-bool RecordIOChunkReader::NextRecord(InputSplit::Blob *out_rec) {
-  if (pbegin_ >= pend_) return false;
-  uint32_t *p = reinterpret_cast<uint32_t *>(pbegin_);
-  CHECK(p[0] == RecordIOWriter::kMagic);
-  uint32_t cflag = RecordIOWriter::DecodeFlag(p[1]);
-  uint32_t clen = RecordIOWriter::DecodeLength(p[1]);
-  if (cflag == 0) {
-    // skip header
-    out_rec->dptr = pbegin_ + 2 * sizeof(uint32_t);
-    // move pbegin
-    pbegin_ += 2 * sizeof(uint32_t) + (((clen + 3U) >> 2U) << 2U);
-    CHECK(pbegin_ <= pend_) << "Invalid RecordIO Format";
-    out_rec->size = clen;
-    return true;
-  } else {
-    const uint32_t kMagic = RecordIOWriter::kMagic;
-    // abnormal path, read into string
-    CHECK(cflag == 1U) << "Invalid RecordIO Format";
-    temp_.resize(0);
-    while (true) {
-      CHECK(pbegin_ + 2 * sizeof(uint32_t) <= pend_);
-      p = reinterpret_cast<uint32_t *>(pbegin_);
-      CHECK(p[0] == RecordIOWriter::kMagic);
-      cflag = RecordIOWriter::DecodeFlag(p[1]);
-      clen = RecordIOWriter::DecodeLength(p[1]);
-      size_t tsize = temp_.length();
-      temp_.resize(tsize + clen);
-      if (clen != 0) {
-        std::memcpy(BeginPtr(temp_) + tsize,
-                    pbegin_ + 2 * sizeof(uint32_t),
-                    clen);
-        tsize += clen;
-      }
-      pbegin_ += 2 * sizeof(uint32_t) + (((clen + 3U) >> 2U) << 2U);
-      if (cflag == 3U) break;
-      temp_.resize(tsize + sizeof(kMagic));
-      std::memcpy(BeginPtr(temp_) + tsize, &kMagic, sizeof(kMagic));
-    }
-    out_rec->dptr = BeginPtr(temp_);
-    out_rec->size = temp_.length();
-    return true;
-  }
-}
-}  // namespace dmlc
diff --git a/ml-xgboost/dmlc-core/test/.gitignore b/ml-xgboost/dmlc-core/test/.gitignore
deleted file mode 100644
index 4880b79..0000000
--- a/ml-xgboost/dmlc-core/test/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-*_test
-*.csv
\ No newline at end of file
diff --git a/ml-xgboost/dmlc-core/test/README.md b/ml-xgboost/dmlc-core/test/README.md
deleted file mode 100644
index 4600ade..0000000
--- a/ml-xgboost/dmlc-core/test/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-This folder contains testcases for the project
-
-test scripts for s3:
-
-`test.sh`
-
-```bash
-for r in {0..10}; do
-    file=data/${RANDOM}
-    start=`date +'%s.%N'`
-    ./filesys_test cat s3://dmlc/ilsvrc12/val.rec >$file
-    # ./filesys_test cat s3://dmlc/cifar10/train.rec >$file
-    end=`date +'%s.%N'`
-    res=$(echo "$end - $start" | bc -l)
-    md5=`md5sum $file`
-    rm $file
-    echo "job $1, rp $r, $md5, time $res"
-done
-echo "job $1 done"
-```
-
-`run.sh`
-
-```bash
-mkdir -p data
-rm -f data/*
-for i in {0..9}; do
-    bash test.sh $i &
-    sleep 1
-done
-wait
-```
diff --git a/ml-xgboost/dmlc-core/test/csv_parser_test.cc b/ml-xgboost/dmlc-core/test/csv_parser_test.cc
deleted file mode 100644
index 70348ee..0000000
--- a/ml-xgboost/dmlc-core/test/csv_parser_test.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-// test reading speed from a InputSplit
-#include <cstdlib>
-#include <cstdio>
-#include <dmlc/io.h>
-#include <dmlc/timer.h>
-#include "../src/data/csv_parser.h"
-
-int main(int argc, char *argv[]) {
-  if (argc < 5) {
-    printf("Usage: <libsvm> partid npart nthread [dump csv]\n");
-    return 0;
-  }
-  FILE *fo = NULL;
-  if (argc > 5) {
-    if (!strcmp(argv[5], "stdout")) {
-      fo = stdout;
-    } else {
-      fo = fopen(argv[5], "w");
-    }
-  }
-  using namespace dmlc;
-  std::unique_ptr<dmlc::Parser<unsigned, int> > parser(
-      dmlc::Parser<unsigned, int>::Create(argv[1],
-                                     atoi(argv[2]),
-                                     atoi(argv[3]),
-                                     "csv"));
-  double tstart = GetTime();
-  size_t bytes_read = 0;
-  size_t bytes_expect = 10UL << 20UL;
-  size_t num_ex = 0;
-  while (parser->Next());
-  parser->BeforeFirst();
-  while (parser->Next()) {
-    bytes_read  = parser->BytesRead();
-    num_ex += parser->Value().size;
-    if (fo != NULL){
-      const dmlc::RowBlock<unsigned, int>& batch = parser->Value();
-      for (size_t i = 0; i < batch.size; ++i) {
-        for (size_t j = 0; j < batch[i].length; ++j) {
-          fprintf(fo, "%d", batch[i].value[j]);
-          if (j + 1 == batch[i].length) {
-            fprintf(fo, "\n");
-          } else {
-            fprintf(fo, ",");
-          }
-        }
-      }
-    }
-    double tdiff = GetTime() - tstart;
-    if (bytes_read >= bytes_expect) {
-      printf("%lu examples, %lu MB read, %g MB/sec\n",
-             num_ex, bytes_read >> 20UL,
-             (bytes_read >> 20UL) / tdiff);
-      bytes_expect += 10UL << 20UL;
-    }
-  }
-  if (fo != NULL && fo != stdout) {
-    fclose(fo);
-  }
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/dataiter_test.cc b/ml-xgboost/dmlc-core/test/dataiter_test.cc
deleted file mode 100644
index 4d32e63..0000000
--- a/ml-xgboost/dmlc-core/test/dataiter_test.cc
+++ /dev/null
@@ -1,31 +0,0 @@
-#include <dmlc/data.h>
-#include <dmlc/timer.h>
-
-int main(int argc, char *argv[]) {
-  if (argc < 4) {
-    printf("Usage: filename partid npart [format]\n");
-    return 0;
-  }
-  char libsvm[10] = "libsvm";
-  char* format;
-  if (argc > 4) {
-    format = argv[4];
-  } else {
-    format = libsvm;
-  }
-
-  using namespace dmlc;
-  RowBlockIter<index_t> *iter =
-      RowBlockIter<index_t>::Create(
-          argv[1], atoi(argv[2]), atoi(argv[3]), format);
-  double tstart = GetTime();
-  size_t bytes_read = 0;
-  while (iter->Next()) {
-    const RowBlock<index_t> &batch = iter->Value();
-    bytes_read += batch.MemCostBytes();
-    double tdiff = GetTime() - tstart;
-    LOG(INFO) << (bytes_read >> 20UL) <<
-        " MB read " << ((bytes_read >> 20UL) / tdiff)<< " MB/sec";
-  }
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/dmlc_test.mk b/ml-xgboost/dmlc-core/test/dmlc_test.mk
deleted file mode 100644
index c27d49a..0000000
--- a/ml-xgboost/dmlc-core/test/dmlc_test.mk
+++ /dev/null
@@ -1,30 +0,0 @@
-TEST=test/filesys_test test/dataiter_test\
-	test/iostream_test test/recordio_test test/split_read_test\
-	test/stream_read_test test/split_test test/libsvm_parser_test\
-	test/libfm_parser_test test/split_repeat_read_test test/strtonum_test\
-	test/logging_test test/parameter_test test/registry_test\
-	test/csv_parser_test
-
-test/filesys_test: test/filesys_test.cc src/io/*.h libdmlc.a
-test/dataiter_test: test/dataiter_test.cc  libdmlc.a
-test/iostream_test: test/iostream_test.cc libdmlc.a
-test/recordio_test: test/recordio_test.cc libdmlc.a
-test/split_read_test: test/split_read_test.cc libdmlc.a
-test/split_repeat_read_test: test/split_repeat_read_test.cc libdmlc.a
-test/stream_read_test: test/stream_read_test.cc libdmlc.a
-test/split_test: test/split_test.cc libdmlc.a
-test/libsvm_parser_test: test/libsvm_parser_test.cc src/data/libsvm_parser.h libdmlc.a
-test/libfm_parser_test: test/libfm_parser_test.cc src/data/libfm_parser.h libdmlc.a
-test/csv_parser_test: test/csv_parser_test.cc src/data/csv_parser.h libdmlc.a
-test/strtonum_test: test/strtonum_test.cc
-test/logging_test: test/logging_test.cc
-test/parameter_test: test/parameter_test.cc
-test/registry_test: test/registry_test.cc
-
-$(TEST) :
-	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a,  $^) $(LDFLAGS)
-
-include test/unittest/dmlc_unittest.mk
-
-ALL_TEST=$(TEST) $(UNITTEST)
-ALL_TEST_OBJ=$(UNITTEST_OBJ)
diff --git a/ml-xgboost/dmlc-core/test/filesys_test.cc b/ml-xgboost/dmlc-core/test/filesys_test.cc
deleted file mode 100644
index 940baf6..0000000
--- a/ml-xgboost/dmlc-core/test/filesys_test.cc
+++ /dev/null
@@ -1,59 +0,0 @@
-#include <cstdio>
-#include <cstdlib>
-#include <dmlc/logging.h>
-#include <dmlc/io.h>
-#include <dmlc/endian.h>
-#include <dmlc/filesystem.h>
-
-int main(int argc, char *argv[]) {
-  if (argc < 3) {
-    printf("Usage: command files\n");
-    printf("Possible commands: all path can start with hdfs:// s3:// file:// or no protocol(file:// is used)\n");
-    printf("\tcat file\n");
-    printf("\tls path\n");
-    printf("\tcp file1 file2\n");
-    return 0;
-  }
-  using namespace dmlc;
-  using namespace dmlc::io;
-  if (!strcmp(argv[1], "ls")) {
-    URI path(argv[2]);
-    FileSystem *fs = FileSystem::GetInstance(path);
-    std::vector<FileInfo> info;
-    fs->ListDirectory(path, &info);
-    for (size_t i = 0; i < info.size(); ++i) {
-      printf("%s\t%lu\tis_dir=%d\n", info[i].path.name.c_str(), info[i].size,
-             info[i].type == kDirectory);
-    }
-    return 0;
-  }
-  if (!strcmp(argv[1], "cat")) {
-    URI path(argv[2]);
-    FileSystem *fs = FileSystem::GetInstance(path);
-    dmlc::Stream *fp = fs->OpenForRead(path);
-    char buf[32];
-    while (true) {
-      size_t nread = fp->Read(buf, 32);
-      if (nread == 0) break;
-      fprintf(stdout, "%s", std::string(buf, nread).c_str());
-    }
-    fflush(stdout);
-    delete fp;
-    return 0;
-  }
-  if (!strcmp(argv[1], "cp")) {
-    CHECK(argc >= 4) << "cp requres source and dest";
-    Stream *src = Stream::Create(argv[2], "r");
-    Stream *dst = Stream::Create(argv[3], "w");
-    char buf[32];
-    size_t nread;
-    while ((nread = src->Read(buf, 32)) != 0) {
-      dst->Write(buf, nread);
-    }
-    delete src; delete dst;
-    printf("copy %s to %s finished\n", argv[2], argv[3]);
-    return 0;
-  }
-  LOG(FATAL) << "unknown command " << argv[1];
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/iostream_test.cc b/ml-xgboost/dmlc-core/test/iostream_test.cc
deleted file mode 100644
index 8a44292..0000000
--- a/ml-xgboost/dmlc-core/test/iostream_test.cc
+++ /dev/null
@@ -1,25 +0,0 @@
-#include <iostream>
-#include <dmlc/io.h>
-
-int main(int argc, char *argv[]) {
-  if (argc < 2) {
-    printf("Usage: <filename>\n");
-    return 0;
-  }
-  {// output
-    dmlc::Stream *fs = dmlc::Stream::Create(argv[1], "w");
-    dmlc::ostream os(fs);
-    os << "hello-world " << 1e-10<< std::endl;
-    delete fs;
-  }
-  {// input
-    std::string name;
-    double data;
-    dmlc::Stream *fs = dmlc::Stream::Create(argv[1], "r");
-    dmlc::istream is(fs);
-    is >> name >> data;
-    std::cout << name << " " << data << std::endl;
-    delete fs;
-  }
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/libfm_parser_test.cc b/ml-xgboost/dmlc-core/test/libfm_parser_test.cc
deleted file mode 100644
index 7946a2e..0000000
--- a/ml-xgboost/dmlc-core/test/libfm_parser_test.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-#include <cstdlib>
-#include <cstdio>
-#include <dmlc/io.h>
-#include <dmlc/timer.h>
-#include "../src/data/libfm_parser.h"
-
-int main(int argc, char *argv[]) {
-  if (argc < 5) {
-    printf("Usage: <libfm> partid npart nthread\n");
-    return 0;
-  }
-  using namespace dmlc;
-  InputSplit *split = InputSplit::Create(argv[1],
-                                         atoi(argv[2]),
-                                         atoi(argv[3]),
-                                         "text");
-  int nthread = atoi(argv[4]);
-  data::LibFMParser<unsigned> parser(split, nthread);
-  double tstart = GetTime();
-  size_t bytes_read = 0;
-  size_t bytes_expect = 10UL << 20UL;
-  size_t num_ex = 0;
-  while (parser.Next()) {
-    bytes_read  = parser.BytesRead();
-    num_ex += parser.Value().size;
-    std::cout << "read bytes:" << bytes_read << " batch size:" << num_ex << std::endl;
-    double tdiff = GetTime() - tstart;
-    if (bytes_read >= bytes_expect) {
-      printf("%lu examples, %lu MB read, %g MB/sec\n",
-             num_ex, bytes_read >> 20UL,
-             (bytes_read >> 20UL) / tdiff);
-      bytes_expect += 10UL << 20UL;
-    }
-  }
-  return 0;
-}
\ No newline at end of file
diff --git a/ml-xgboost/dmlc-core/test/libsvm_parser_test.cc b/ml-xgboost/dmlc-core/test/libsvm_parser_test.cc
deleted file mode 100644
index 357bf26..0000000
--- a/ml-xgboost/dmlc-core/test/libsvm_parser_test.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-// test reading speed from a InputSplit
-#include <cstdlib>
-#include <cstdio>
-#include <dmlc/io.h>
-#include <dmlc/timer.h>
-#include "../src/data/libsvm_parser.h"
-
-int main(int argc, char *argv[]) {
-  if (argc < 5) {
-    printf("Usage: <libsvm> partid npart nthread\n");
-    return 0;
-  }
-  using namespace dmlc;
-  InputSplit *split = InputSplit::Create(argv[1],
-                                         atoi(argv[2]),
-                                         atoi(argv[3]),
-                                         "text");
-  int nthread = atoi(argv[4]);
-  data::LibSVMParser<unsigned> parser(split, nthread);
-  double tstart = GetTime();
-  size_t bytes_read = 0;
-  size_t bytes_expect = 10UL << 20UL;
-  size_t num_ex = 0;
-  while (parser.Next()) {
-    bytes_read  = parser.BytesRead();
-    num_ex += parser.Value().size;
-    double tdiff = GetTime() - tstart;
-    if (bytes_read >= bytes_expect) {
-      printf("%lu examples, %lu MB read, %g MB/sec\n",
-             num_ex, bytes_read >> 20UL,
-             (bytes_read >> 20UL) / tdiff);
-      bytes_expect += 10UL << 20UL;
-    }
-  }
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/logging_test.cc b/ml-xgboost/dmlc-core/test/logging_test.cc
deleted file mode 100644
index 5f0af76..0000000
--- a/ml-xgboost/dmlc-core/test/logging_test.cc
+++ /dev/null
@@ -1,14 +0,0 @@
-#include <dmlc/logging.h>
-
-int main(void) {
-  LOG(INFO) << "hello";
-  LOG(ERROR) << "error";
-  try {
-    LOG(FATAL)<<'a'<<11<<33;
-  } catch (dmlc::Error& e) {
-    LOG(INFO) << "catch " << e.what();
-  }
-  CHECK(2!=3) << "test";
-  CHECK(2==3) << "test";
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/parameter_test.cc b/ml-xgboost/dmlc-core/test/parameter_test.cc
deleted file mode 100644
index d791d7a..0000000
--- a/ml-xgboost/dmlc-core/test/parameter_test.cc
+++ /dev/null
@@ -1,81 +0,0 @@
-#include <dmlc/parameter.h>
-
-// this is actual pice of code
-struct Param : public dmlc::Parameter<Param> {
-  float learning_rate;
-  int num_hidden;
-  int act;
-  std::string name;
-  // declare parameters in header file
-  DMLC_DECLARE_PARAMETER(Param) {
-    DMLC_DECLARE_FIELD(num_hidden).set_range(0, 1000)
-        .describe("Number of hidden unit in the fully connected layer.");
-    DMLC_DECLARE_FIELD(learning_rate).set_default(0.01f)
-        .describe("Learning rate of SGD optimization.");
-    DMLC_DECLARE_FIELD(act).add_enum("relu", 1).add_enum("sigmoid", 2)
-        .describe("Activation function type.");
-    DMLC_DECLARE_FIELD(name).set_default("A")
-        .describe("Name of the net.");
-  }
-};
-
-// this is actual pice of code
-struct SecondParam : public dmlc::Parameter<SecondParam> {
-  int num_data;
-  // declare parameters in header file
-  DMLC_DECLARE_PARAMETER(SecondParam) {
-    DMLC_DECLARE_FIELD(num_data).set_range(0, 1000)
-        .describe("Number of data points");
-  }
-};
-// register it in cc file
-DMLC_REGISTER_PARAMETER(Param);
-DMLC_REGISTER_PARAMETER(SecondParam);
-
-int main(int argc, char *argv[]) {
-  Param param;
-  SecondParam param2;
-  std::map<std::string, std::string> kwargs;
-  for (int i = 0; i < argc; ++i) {
-    char name[256], val[256];
-    if (sscanf(argv[i], "%[^=]=%[^\n]", name, val) == 2) {
-      printf("call set %s=%s\n", name, val);
-      kwargs[name] = val;
-    }
-  }
-  printf("Parameters\n-----------\n%s", Param::__DOC__().c_str());
-  std::vector<std::pair<std::string, std::string> > unknown;
-  unknown = param.InitAllowUnknown(kwargs);
-  param2.Init(unknown);
-
-
-  printf("-----\n");
-  printf("param.num_hidden=%d\n", param.num_hidden);
-  printf("param.learning_rate=%f\n", param.learning_rate);
-  printf("param.name=%s\n", param.name.c_str());
-  printf("param.act=%d\n", param.act);
-  printf("param.size=%lu\n", sizeof(param));
-
-  printf("Unknown parameters:\n");
-  for (size_t i = 0; i < unknown.size(); ++i) {
-    printf("%s=%s\n", unknown[i].first.c_str(), unknown[i].second.c_str());
-  }
-  printf("------\n");
-  std::unordered_map<std::string, std::string> dict;
-  param.UpdateDict(&dict);
-  for (const auto &kv : dict) {
-    printf("dict.%s=%s\n", kv.first.c_str(), kv.second.c_str());
-  }
-
-  std::ostringstream os;
-  dmlc::JSONWriter writer(&os);
-  param.Save(&writer);
-  printf("JSON:\n%s\n", os.str().c_str());
-  printf("Environment variables\n");
-  int test_env = dmlc::GetEnv("TEST_ENV", 1);
-  std::string test_env2 = dmlc::GetEnv<std::string>("TEST_ENV2", "hello");
-  printf("TEST_ENV=%d\n", test_env);
-  printf("TEST_ENV2=%s\n", test_env2.c_str());
-
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/recordio_test.cc b/ml-xgboost/dmlc-core/test/recordio_test.cc
deleted file mode 100644
index e863687..0000000
--- a/ml-xgboost/dmlc-core/test/recordio_test.cc
+++ /dev/null
@@ -1,117 +0,0 @@
-#include <string>
-#include <cstdlib>
-#include <dmlc/io.h>
-#include <dmlc/recordio.h>
-
-int main(int argc, char *argv[]) {  
-  if (argc < 4) {
-    printf("Usage: <filename> ndata dlen [nsplit]\n");
-    return 0;
-  }
-  using namespace dmlc;
-  int nsplit = 4;
-  if (argc > 4) nsplit = atoi(argv[4]);
-  LOG(INFO) << "generate the test-cases into";
-  int ndata = atoi(argv[2]);
-  int dlen = atoi(argv[3]);
-  std::vector<std::string> data;  
-  const unsigned kMagic = dmlc::RecordIOWriter::kMagic;
-  for (int i = 0; i < ndata; ++i) {
-    std::string s;
-    s.resize(rand() % dlen);
-    // generate random string
-    for (size_t j = 0; j < s.length(); ++j) {
-      s[j] = static_cast<char>(rand() & 255);
-    }
-    int rnd = rand() % 4;
-    if (rnd == 4) {
-      size_t n = s.length();
-      s.resize(s.length() + 4);
-      std::memcpy(BeginPtr(s) + n, &kMagic, sizeof(kMagic));
-    } else if (rnd == 3) {
-      s.resize(std::max(s.length(), 4UL));
-      std::memcpy(BeginPtr(s), &kMagic, sizeof(kMagic));      
-    } else if (rnd == 2) {
-      for (size_t k = 0; k + 4 <= s.length(); k += 4) {
-        if (rand() % 2) {
-          std::memcpy(BeginPtr(s) + 4, &kMagic, sizeof(kMagic));
-        }
-      }
-    } else if (rnd == 1) {
-      for (size_t k = 0; k + 4 <= s.length(); k += 4) {
-        if (rand() % 10) {
-          std::memcpy(BeginPtr(s) + 4, &kMagic, sizeof(kMagic));
-        }
-      }
-    }
-    data.push_back(s);    
-  }
-  LOG(INFO) << "generate the test-cases into" << argv[1];  
-  {// output
-    dmlc::Stream *fs = dmlc::Stream::Create(argv[1], "wb");
-    dmlc::RecordIOWriter writer(fs);
-    for (size_t i = 0; i < data.size(); ++i) {
-      writer.WriteRecord(data[i]);
-    }
-    delete fs;
-    printf("finish writing with %lu exceptions\n", writer.except_counter());
-  }
-  {// input
-    LOG(INFO) << "Test RecordIOReader..";
-    dmlc::Stream *fi = dmlc::Stream::Create(argv[1], "r");
-    dmlc::RecordIOReader reader(fi);
-    std::string temp;
-    size_t lcnt = 0;
-    while (reader.NextRecord(&temp)) {
-      CHECK(lcnt < data.size());
-      CHECK(temp.length() == data[lcnt].length());
-      if (temp.length() != 0) {
-        CHECK(!memcmp(BeginPtr(temp), BeginPtr(data[lcnt]), temp.length()));
-      }
-      ++lcnt;
-    }
-    delete fi;
-    LOG(INFO) << "Test RecordIOReader.. Pass";
-  }
-  {// InputSplit::RecordiO
-    LOG(INFO) << "Test InputSplit for RecordIO..";
-    size_t lcnt = 0;
-    for (int i = 0; i < nsplit; ++i) {
-      InputSplit::Blob rec;
-      dmlc::InputSplit *split = InputSplit::Create(argv[1], i, nsplit, "recordio");
-      while (split->NextRecord(&rec)) {
-        CHECK(lcnt < data.size());
-        CHECK(rec.size == data[lcnt].length());
-        if (rec.size != 0) {
-          CHECK(!memcmp(rec.dptr, BeginPtr(data[lcnt]), rec.size));
-        }
-        ++lcnt;
-      }
-      delete split;
-    }
-    LOG(INFO) << "Test InputSplit for RecordIO.. Pass";
-  }
-  {// InputSplit::RecordIO Chunk Read
-    LOG(INFO) << "Test InputSplit for RecordIO.. ChunkReader";
-    size_t lcnt = 0;
-    InputSplit::Blob chunk;
-    dmlc::InputSplit *split = InputSplit::Create(argv[1], 0, 1, "recordio");
-    while (split->NextChunk(&chunk)) {
-      for (int i = 0; i < nsplit; ++i) {
-        InputSplit::Blob rec;
-        dmlc::RecordIOChunkReader reader(chunk, i, nsplit);
-        while (reader.NextRecord(&rec)) {
-          CHECK(lcnt < data.size());
-          CHECK(rec.size == data[lcnt].length());
-          if (rec.size != 0) {
-            CHECK(!memcmp(rec.dptr, BeginPtr(data[lcnt]), rec.size));
-          }
-          ++lcnt;
-        }
-      }
-    }
-    delete split;
-    LOG(INFO) << "Test InputSplit for RecordIO.. ChunkReader Pass";
-  }
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/registry_test.cc b/ml-xgboost/dmlc-core/test/registry_test.cc
deleted file mode 100644
index 8b5a969..0000000
--- a/ml-xgboost/dmlc-core/test/registry_test.cc
+++ /dev/null
@@ -1,60 +0,0 @@
-#include <cstdio>
-#include <functional>
-#include <dmlc/registry.h>
-
-namespace tree {
-struct Tree {
-  virtual void Print() = 0;
-  virtual ~Tree() {}
-};
-
-struct BinaryTree : public Tree {
-  virtual void Print() {
-    printf("I am binary tree\n");
-  }
-};
-
-struct AVLTree : public Tree {
-  virtual void Print() {
-    printf("I am AVL tree\n");
-  }
-};
-// registry to get the trees
-struct TreeFactory
-    : public dmlc::FunctionRegEntryBase<TreeFactory, std::function<Tree*()> > {
-};
-
-#define REGISTER_TREE(Name)                                             \
-  DMLC_REGISTRY_REGISTER(::tree::TreeFactory, TreeFactory, Name)        \
-  .set_body([]() { return new Name(); } )
-
-DMLC_REGISTRY_FILE_TAG(my_tree);
-
-}  // namespace tree
-
-
-// usually this sits on a seperate file
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(tree::TreeFactory);
-}
-
-namespace tree {
-// Register the trees, can be in seperate files
-REGISTER_TREE(BinaryTree)
-.describe("This is a binary tree.");
-
-REGISTER_TREE(AVLTree);
-
-DMLC_REGISTRY_LINK_TAG(my_tree);
-}
-
-int main(int argc, char *argv[]) {
-  // construct a binary tree
-  tree::Tree *binary = dmlc::Registry<tree::TreeFactory>::Find("BinaryTree")->body();
-  binary->Print();
-  // construct a binary tree
-  tree::Tree *avl = dmlc::Registry<tree::TreeFactory>::Find("AVLTree")->body();
-  avl->Print();
-  delete binary; delete avl;
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/split_read_test.cc b/ml-xgboost/dmlc-core/test/split_read_test.cc
deleted file mode 100644
index bd37945..0000000
--- a/ml-xgboost/dmlc-core/test/split_read_test.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-// test reading speed from a InputSplit
-#include <cstdlib>
-#include <cstdio>
-#include <dmlc/io.h>
-#include <dmlc/timer.h>
-
-int main(int argc, char *argv[]) {
-  if (argc < 4) {
-    printf("Usage: <libsvm> partid npart\n");
-    return 0;
-  }
-  using namespace dmlc;
-  InputSplit *split = InputSplit::Create(argv[1],
-                                         atoi(argv[2]),
-                                         atoi(argv[3]),
-                                         "text");
-  std::vector<std::string> data;
-  InputSplit::Blob blb;
-  double tstart = GetTime();
-  size_t bytes_read = 0;
-  size_t bytes_expect = 10UL << 20UL;
-  while (split->NextRecord(&blb)) {
-    std::string dat = std::string((char*)blb.dptr, 
-                                  blb.size);
-    data.push_back(dat);
-    bytes_read += blb.size;
-    double tdiff = GetTime() - tstart;
-    if (bytes_read >= bytes_expect) {
-      printf("%lu MB read, %g MB/sec\n",
-             bytes_read >> 20UL,
-             (bytes_read >> 20UL) / tdiff);
-      bytes_expect += 10UL << 20UL;
-    }
-  }
-  delete split;
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/split_repeat_read_test.cc b/ml-xgboost/dmlc-core/test/split_repeat_read_test.cc
deleted file mode 100644
index 68f9964..0000000
--- a/ml-xgboost/dmlc-core/test/split_repeat_read_test.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-#include <string>
-#include <vector>
-#include <cstdlib>
-#include <cstring>
-#include <dmlc/io.h>
-#include <dmlc/recordio.h>
-
-int main(int argc, char *argv[]) {  
-  if (argc < 5) {
-    printf("Usage: <filename> partid npart nmax\n");
-    return 0;
-  }
-  using namespace dmlc;
-  dmlc::InputSplit *in = dmlc::InputSplit::
-      Create(argv[1],
-             atoi(argv[2]),
-             atoi(argv[3]),
-             "text");
-  size_t nmax = static_cast<size_t>(atol(argv[4]));
-  size_t lcnt = 0;
-  InputSplit::Blob rec;
-  std::vector<std::string> data;
-  while (in->NextRecord(&rec)) {
-    data.push_back(std::string((char*)rec.dptr, rec.size));
-    ++lcnt;
-    if (lcnt == nmax) {
-      LOG(INFO) << "finish loading " << lcnt << " lines";
-      break;
-    }
-  }
-  LOG(INFO) << "Call BeforeFirst when lcnt="
-            << lcnt << " nmax=" << nmax;
-  in->BeforeFirst();
-  lcnt = 0;
-  while (in->NextRecord(&rec)) {
-    std::string dat = std::string((char*)rec.dptr, rec.size);
-    if (lcnt < nmax) {
-      CHECK(rec.size == data[lcnt].length());
-      CHECK(!memcmp(rec.dptr, BeginPtr(data[lcnt]), rec.size));
-    } else {
-      data.push_back(dat);
-    }
-    ++lcnt;
-  }
-  LOG(INFO) << "Call BeforeFirst again";
-  in->BeforeFirst();
-  lcnt = 0;
-  while (in->NextRecord(&rec)) {
-    std::string dat = std::string((char*)rec.dptr, rec.size);
-    CHECK(lcnt < data.size());
-    CHECK(rec.size == data[lcnt].length());
-    CHECK(!memcmp(rec.dptr, BeginPtr(data[lcnt]), rec.size));
-    ++lcnt;
-  }
-  delete in;
-  LOG(INFO) << "All tests passed";
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/split_test.cc b/ml-xgboost/dmlc-core/test/split_test.cc
deleted file mode 100644
index e5f2d3e..0000000
--- a/ml-xgboost/dmlc-core/test/split_test.cc
+++ /dev/null
@@ -1,25 +0,0 @@
-// test reading speed from a InputSplit
-#include <cstdlib>
-#include <cstdio>
-#include <iostream>
-#include <dmlc/io.h>
-#include <dmlc/base.h>
-
-int main(int argc, char *argv[]) {
-  if (argc < 5) {
-    printf("Usage: <libsvm> partid npart\n");
-    return 0;
-  }
-  using namespace dmlc;
-  InputSplit *split = InputSplit::Create(argv[1],
-                                         atoi(argv[2]),
-                                         atoi(argv[3]),
-                                         "text");
-  InputSplit::Blob blb;
-  while (split->NextChunk(&blb)) {
-    std::cout << std::string((char*)blb.dptr, blb.size);
-  }
-  delete split;
-  return 0;
-}
-
diff --git a/ml-xgboost/dmlc-core/test/stream_read_test.cc b/ml-xgboost/dmlc-core/test/stream_read_test.cc
deleted file mode 100644
index fde0c71..0000000
--- a/ml-xgboost/dmlc-core/test/stream_read_test.cc
+++ /dev/null
@@ -1,48 +0,0 @@
-// test reading speed from a Stream
-#include <cstdlib>
-#include <cstdio>
-#include <dmlc/io.h>
-#include <dmlc/timer.h>
-
-int main(int argc, char *argv[]) {
-  if (argc < 3) {
-    printf("Usage: uri buffersize [skip-proc]\n");
-    return 0;
-  }
-  int skip_proc = 0;
-  if (argc > 3) {
-    skip_proc = atoi(argv[3]);
-  }
-  size_t sz = atol(argv[2]);
-  std::string buffer; buffer.resize(sz);
-  using namespace dmlc;
-  Stream *fi = Stream::Create(argv[1], "r", true);
-  CHECK(fi != NULL) << "cannot open " << argv[1];
-  double tstart = GetTime();
-  size_t size;
-  size_t bytes_read = 0;
-  size_t bytes_expect = 10UL << 20UL;
-  while ((size = fi->Read(BeginPtr(buffer), sz)) != 0) {
-    int cnt = 0;
-    if (skip_proc == 0) {
-      //#pragma omp parallel for reduction(+:cnt)
-      for (size_t i = 0; i < size; ++i) {
-        if (buffer[i] == '\n' || buffer[i] == '\r') {
-          buffer[i] = '\0'; ++ cnt;
-        }
-      }    
-    }
-    bytes_read += size;
-    double tdiff = GetTime() - tstart;
-    if (bytes_read >= bytes_expect) {
-      printf("%lu MB read, %g MB/sec, cnt=%d\n",
-             bytes_read >> 20UL,
-             (bytes_read >> 20UL) / tdiff, cnt);
-      bytes_expect += 10UL << 20UL;
-    }
-  }
-  delete fi;
-  return 0;
-}
-
-
diff --git a/ml-xgboost/dmlc-core/test/strtonum_test.cc b/ml-xgboost/dmlc-core/test/strtonum_test.cc
deleted file mode 100644
index 35be4c8..0000000
--- a/ml-xgboost/dmlc-core/test/strtonum_test.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-#include <dmlc/strtonum.h>
-#include <dmlc/logging.h>
-#include <cstdlib>
-
-int main(int argc, char *argv[]) {
-  using namespace dmlc;
-
-  // float
-  std::vector<std::string> f = {
-    "1234567901234", "+12345.6789", "-0.00123", "+0123.234e-2",
-    "-234234.123123e20", "3.1029831e+38", "000.123e-28",
-    "17.065995780200002000000", "0.00017065995780200002"};
-  for (size_t i = 0; i < f.size(); ++i) {
-    float v1 = dmlc::atof(f[i].c_str());
-    float v2 = std::atof(f[i].c_str());
-    CHECK_EQ(v1, v2);
-  }
-
-  // long
-  std::vector<std::string> l = {
-    "2147483647", "+12345", "-123123", "-2147483648"
-  };
-  for (size_t i = 0; i < l.size(); ++i) {
-    long v1 = dmlc::atol(l[i].c_str());
-    long v2 = std::atol(l[i].c_str());
-    CHECK_EQ(v1, v2);
-  }
-
-  // uint64
-  std::vector<std::string> ull = {
-    "2147483647", "+12345", "18446744073709551615"
-  };
-  for (size_t i = 0; i < ull.size(); ++i) {
-    unsigned long long v1 = dmlc::strtoull(ull[i].c_str(), 0, 10);
-    unsigned long long v2 = std::strtoull(ull[i].c_str(), 0, 10);
-    CHECK_EQ(v1, v2);
-  }
-  return 0;
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/.gitignore b/ml-xgboost/dmlc-core/test/unittest/.gitignore
deleted file mode 100644
index 844bdf7..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-dmlc_unittest
-build_config.h
diff --git a/ml-xgboost/dmlc-core/test/unittest/CMakeLists.txt b/ml-xgboost/dmlc-core/test/unittest/CMakeLists.txt
deleted file mode 100644
index 7a29704..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/CMakeLists.txt
+++ /dev/null
@@ -1,68 +0,0 @@
-# ---[ Google Test
-if(MSVC)
-  if(MSVC_VERSION LESS 1900)
-    message(FATAL_ERROR "Need Visual Studio 2015 or newer to compile unit tests")
-  endif()
-endif()
-
-if (UNIX)
-  SET(CMAKE_EXE_LINKER_FLAGS "-pthread")
-endif(UNIX)
-
-# Compiler definitions needed to use GNU/POSIX extensions
-set(ENABLE_GNU_EXTENSION_FLAGS -D_XOPEN_SOURCE=700
-  -D_POSIX_SOURCE -D_POSIX_C_SOURCE=200809L -D_DARWIN_C_SOURCE)
-
-enable_testing()
-find_package(Threads REQUIRED)
-
-file(GLOB_RECURSE UNIT_TEST_SOURCE "*.cc")
-add_executable(dmlc_unit_tests ${UNIT_TEST_SOURCE})
-set_property(TARGET dmlc_unit_tests
-  PROPERTY RUNTIME_OUTPUT_DIRECTORY ${PRIVATE_RUNTIME_DIR})
-
-message(STATUS "${CMAKE_CURRENT_SOURCE_DIR}/build_config.h.in -> ${CMAKE_CURRENT_SOURCE_DIR}/build_config.h")
-configure_file("${CMAKE_CURRENT_SOURCE_DIR}/build_config.h.in" "${CMAKE_CURRENT_SOURCE_DIR}/build_config.h")
-
-target_compile_definitions(dmlc_unit_tests PRIVATE -DDMLC_UNIT_TESTS_USE_CMAKE -DDMLC_CORE_USE_CMAKE ${ENABLE_GNU_EXTENSION_FLAGS})
-target_include_directories(dmlc_unit_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
-
-find_package(GTest)
-if (NOT GTEST_FOUND)
-  message(STATUS "GTest not found, downloading GTest.")
-  # Download and unpack googletest at configure time
-  message("${CMAKE_LOCAL}/gtest_cmake.in -> ${CMAKE_BINARY_DIR}/googletest-download/CMakeLists.txt")
-  configure_file("${CMAKE_LOCAL}/gtest_cmake.in" "${CMAKE_BINARY_DIR}/googletest-download/CMakeLists.txt")
-  execute_process(COMMAND "${CMAKE_COMMAND}" -G "${CMAKE_GENERATOR}" .
-    WORKING_DIRECTORY "${CMAKE_BINARY_DIR}/googletest-download" )
-  execute_process(COMMAND "${CMAKE_COMMAND}" --build .
-    WORKING_DIRECTORY "${CMAKE_BINARY_DIR}/googletest-download" )
-  # Prevent GoogleTest from overriding our compiler/linker options
-  # when building with Visual Studio
-  set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
-
-  # Add googletest directly to our build. This adds
-  # the following targets: gtest, gtest_main, gmock
-  # and gmock_main
-  add_subdirectory("${CMAKE_BINARY_DIR}/googletest-src"
-    "${CMAKE_BINARY_DIR}/googletest-build")
-
-  target_compile_definitions(gtest PRIVATE ${ENABLE_GNU_EXTENSION_FLAGS})
-  target_compile_definitions(gmock PRIVATE ${ENABLE_GNU_EXTENSION_FLAGS})
-  target_compile_definitions(gtest_main PRIVATE ${ENABLE_GNU_EXTENSION_FLAGS})
-  target_compile_definitions(gmock_main PRIVATE ${ENABLE_GNU_EXTENSION_FLAGS})
-  target_include_directories(dmlc_unit_tests PRIVATE
-    "${gtest_SOURCE_DIR}/include" "${gmock_SOURCE_DIR}/include")
-  target_link_libraries(dmlc_unit_tests
-    gtest dmlc Threads::Threads)
-else()
-  target_include_directories(dmlc_unit_tests PRIVATE ${GTEST_INCLUDE_DIRS})
-  target_link_libraries(dmlc_unit_tests
-    ${GTEST_LIBRARIES} dmlc Threads::Threads)
-endif()
-
-if(USE_OPENMP)
-  target_link_libraries(dmlc_unit_tests OpenMP::OpenMP_CXX)
-endif()
-
-add_test(AllTestsInDMLCUnitTests ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/dmlc_unit_tests)
diff --git a/ml-xgboost/dmlc-core/test/unittest/build_config.h.in b/ml-xgboost/dmlc-core/test/unittest/build_config.h.in
deleted file mode 100644
index c31d345..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/build_config.h.in
+++ /dev/null
@@ -1 +0,0 @@
-#cmakedefine CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}"
diff --git a/ml-xgboost/dmlc-core/test/unittest/dmlc_unittest.mk b/ml-xgboost/dmlc-core/test/unittest/dmlc_unittest.mk
deleted file mode 100644
index 778b348..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/dmlc_unittest.mk
+++ /dev/null
@@ -1,13 +0,0 @@
-UTEST_ROOT=test/unittest
-UNITTEST=$(UTEST_ROOT)/dmlc_unittest
-UNITTEST_SRC=$(wildcard $(UTEST_ROOT)/*.cc)
-UNITTEST_OBJ=$(patsubst %.cc,%.o,$(UNITTEST_SRC))
-
-GTEST_LIB=$(GTEST_PATH)/lib/
-GTEST_INC=$(GTEST_PATH)/include/
-
-$(UTEST_ROOT)/%.o : $(UTEST_ROOT)/%.cc libdmlc.a
-	$(CXX) $(CFLAGS) -I$(GTEST_INC) -o $@ -c $<
-
-$(UNITTEST) : $(UNITTEST_OBJ)
-	$(CXX) $(CFLAGS) -L$(GTEST_LIB) -o $@ $^ libdmlc.a $(LDFLAGS) -lgtest
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_any.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_any.cc
deleted file mode 100644
index 1721989..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_any.cc
+++ /dev/null
@@ -1,78 +0,0 @@
-// Copyright by Contributors
-
-#include <unordered_map>
-#include <vector>
-#include <string>
-#include <memory>
-#include <dmlc/any.h>
-#include <dmlc/json.h>
-#include <gtest/gtest.h>
-
-
-TEST(Any, basics) {
-  std::unordered_map<std::string, dmlc::any> dict;
-  dict["1"] = 1;
-  dict["vec"] = std::vector<int>{1,2,3};
-  dict["shapex"] = std::string("xtyz");
-  std::unordered_map<std::string, dmlc::any> dict2(std::move(dict));
-  dmlc::get<int>(dict2["1"]) += 1;
-
-  CHECK_EQ(dmlc::get<int>(dict2["1"]), 2);
-  CHECK_EQ(dmlc::get<std::vector<int> >(dict2["vec"])[1], 2);
-}
-
-TEST(Any, cover) {
-  dmlc::any a = std::string("abc");
-  dmlc::any b = 1;
-
-  CHECK_EQ(dmlc::get<std::string>(a), "abc");
-  a = std::move(b);
-  CHECK(b.empty());
-  CHECK_EQ(dmlc::get<int>(a), 1);
-
-  std::shared_ptr<int> x = std::make_shared<int>(10);
-  {
-    dmlc::any aa(x);
-    CHECK_EQ(*dmlc::get<std::shared_ptr<int> >(aa), 10);
-  }
-  // aa must be destructed.
-  CHECK(x.unique());
-}
-
-DMLC_JSON_ENABLE_ANY(std::vector<int>, IntVector);
-DMLC_JSON_ENABLE_ANY(int, Int);
-
-TEST(Any, json) {
-  std::unordered_map<std::string, dmlc::any> x;
-  x["vec"] = std::vector<int>{1, 2, 3};
-  x["int"] = 300;
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::ostringstream os;
-#else
-  std::string os;
-#endif
-  {
-    std::unordered_map<std::string, dmlc::any> temp(x);
-    dmlc::JSONWriter writer(&os);
-    writer.Write(temp);
-    temp.clear();
-  }
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::string json = os.str();
-  std::istringstream is(json);
-#else
-  std::string json = os;
-  std::string is(json);
-#endif
-  LOG(INFO) << json;
-
-  dmlc::JSONReader reader(&is);
-  std::unordered_map<std::string, dmlc::any> copy_data;
-  reader.Read(&copy_data);
-
-  ASSERT_EQ(dmlc::get<std::vector<int> >(x["vec"]),
-            dmlc::get<std::vector<int> >(copy_data["vec"]));
-  ASSERT_EQ(dmlc::get<int>(x["int"]),
-            dmlc::get<int>(copy_data["int"]));
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_array_view.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_array_view.cc
deleted file mode 100644
index a743f02..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_array_view.cc
+++ /dev/null
@@ -1,20 +0,0 @@
-#include <dmlc/logging.h>
-#include <gtest/gtest.h>
-#include <dmlc/array_view.h>
-
-void ArrayViewTest(dmlc::array_view<int> view, int base) {
-  int cnt = base;
-  for (int v : view) {
-    CHECK_EQ(v, cnt);
-    ++cnt;
-  }
-}
-
-TEST(ArrayView, Basic) {
-  std::vector<int> vec{0, 1, 2};
-  ArrayViewTest(vec, 0);
-  int arr[] = {1, 2, 3};
-  ArrayViewTest(dmlc::array_view<int>(arr, arr + 3), 1);
-  dmlc::array_view<int> a = vec;
-  CHECK_EQ(a.size(), vec.size());
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_config.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_config.cc
deleted file mode 100644
index fbd1786..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_config.cc
+++ /dev/null
@@ -1,115 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/config.h>
-#include <gtest/gtest.h>
-#include <cstdio>
-#include <cstdlib>
-#include <sstream>
-#include <iostream>
-#include <fstream>
-
-
-using namespace std;
-
-TEST(Config, basics) {
-  string cfg_str =
-    "k1=1243\n"
-    "k2=0.5\n"
-    "k3=\"abc\"\n"
-    "k4=\"wmj\"\n"
-    "k5=\"x=1\"\n"
-    "k6=\"hello world\"\n"
-    "k7=\"quote\\\"quote\"\n"
-    "#i am comment\n"
-    "#i am evil comment x=1\n"
-    "k8=-1.2  #comment \n"
-    "k9=10\n"
-    "k10=\"#not comment\"\n"
-    ;
-  istringstream iss(cfg_str);
-  using namespace dmlc;
-  Config cfg(iss);
-  for(const auto& entry : cfg) {
-    cout << "k: " << entry.first << "\tv: " << entry.second << endl;
-  }
-  cout << "Proto string:" << endl;
-  cout << cfg.ToProtoString() << endl;
-}
-
-TEST(Config, multi_value) {
-  string cfg_str =
-    "k1 = 0.1\n"
-    "k1 = 0.2\n"
-    "k1 = 0.3\n"
-    "k2 = -0.1\n"
-    "k2 = -0.2\n"
-    "k3 = 0\n"
-    ;
-  {
-    cout << "<<<<<< No multi-value <<<<<<<" << endl;
-    istringstream iss(cfg_str);
-    using namespace dmlc;
-    Config cfg(iss);
-    for(const auto& entry : cfg) {
-      cout << "k: " << entry.first << "\tv: " << entry.second << endl;
-    }
-    cout << "Proto string:" << endl;
-    cout << cfg.ToProtoString() << endl;
-  }
-
-  {
-    cout << "<<<<<< With multi-value <<<<<<<" << endl;
-    istringstream iss(cfg_str);
-    using namespace dmlc;
-    Config cfg(iss, true);
-    for(const auto& entry : cfg) {
-      cout << "k: " << entry.first << "\tv: " << entry.second << endl;
-    }
-    cout << "Proto string:" << endl;
-    cout << cfg.ToProtoString() << endl;
-  }
-
-}
-
-TEST(Config, set_param) {
-  using namespace dmlc;
-  Config cfg;
-  cfg.SetParam("k1", 1);
-  cfg.SetParam("k2", "123", true);
-  cout << "Proto string:" << endl;
-  cout << cfg.ToProtoString() << endl;
-}
-
-TEST(Config, order) {
-  string cfg_str =
-    "k1 = 0.1\n"
-    "k2 = -0.1\n"
-    "k1 = 0.2\n"
-    "k2 = -0.2\n"
-    "k1 = 0.3\n"
-    "k3 = 0\n"
-    ;
-  {
-    cout << "<<<<<< No multi-value <<<<<<<" << endl;
-    istringstream iss(cfg_str);
-    using namespace dmlc;
-    Config cfg(iss);
-    for(const auto& entry : cfg) {
-      cout << "k: " << entry.first << "\tv: " << entry.second << endl;
-    }
-    cout << "Proto string:" << endl;
-    cout << cfg.ToProtoString() << endl;
-  }
-
-  {
-    cout << "<<<<<< With multi-value <<<<<<<" << endl;
-    istringstream iss(cfg_str);
-    using namespace dmlc;
-    Config cfg(iss, true);
-    for(const auto& entry : cfg) {
-      cout << "k: " << entry.first << "\tv: " << entry.second << endl;
-    }
-    cout << "Proto string:" << endl;
-    cout << cfg.ToProtoString() << endl;
-  }
-
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_env.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_env.cc
deleted file mode 100644
index d0e3a40..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_env.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-// Copyright by Contributors
-#include <dmlc/config.h>
-#include <gtest/gtest.h>
-#include <dmlc/parameter.h>
-
-#ifdef _WIN32
-static int setenv(const char* name, const char* value, int overwrite) {
-  return _putenv_s(name, value);
-}
-#define putenv _putenv
-#endif
-
-TEST(Env, Blank) {
-  const char *var_name = "test_environment_var__askjaposcjp";
-  setenv(var_name, "foo", 1);
-  std::string res = dmlc::GetEnv(var_name, std::string("not_food"));
-  GTEST_ASSERT_EQ(res, "foo");
-  setenv(var_name, "bar", 1);
-  res = dmlc::GetEnv(var_name, std::string("bar"));
-  GTEST_ASSERT_EQ(res, "bar");
-  auto assignment = (std::string{var_name} + "=");
-  putenv(const_cast<char *>(assignment.c_str()));
-  const char *s = ::getenv(var_name);  // On Mac, this may return an empty string
-  if (s) {
-    // Some implementations will return an empty string instead of null
-    res = dmlc::GetEnv(var_name, std::string("another_default"));
-    GTEST_ASSERT_EQ(res, "another_default");
-  }
-  setenv(var_name, "", 1);
-  s = getenv(var_name);  // On Linux, this may return an empty string
-  if (s) {
-    // Some implementations will return an empty string instead of null
-    res = dmlc::GetEnv(var_name, std::string("another_default"));
-    GTEST_ASSERT_EQ(res, "another_default");
-  }
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_inputsplit.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_inputsplit.cc
deleted file mode 100644
index b74d1a4..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_inputsplit.cc
+++ /dev/null
@@ -1,194 +0,0 @@
-#include <dmlc/data.h>
-#include <dmlc/filesystem.h>
-#include <string>
-#include <iostream>
-#include <fstream>
-#include <vector>
-#include <algorithm>
-#include <random>
-#include <future>
-#include <cstdlib>
-#include <gtest/gtest.h>
-
-namespace {
-
-inline void CountDimensions(dmlc::Parser<uint32_t>* parser,
-                            size_t* out_num_row, size_t* out_num_col) {
-  size_t num_row = 0;
-  size_t num_col = 0;
-  while (parser->Next()) {
-    const dmlc::RowBlock<uint32_t>& batch = parser->Value();
-    num_row += batch.size;
-    for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
-      const uint32_t index = batch.index[i];
-      num_col = std::max(num_col, static_cast<size_t>(index + 1));
-    }
-  }
-  *out_num_row = num_row;
-  *out_num_col = num_col;
-}
-
-struct RecordIOHeader {
-  uint32_t flag;
-  float label;
-  uint64_t image_id[2];
-};
-
-}  // namespace anonymous
-
-TEST(InputSplit, test_split_csv_noeol) {
-  size_t num_row, num_col;
-  {
-    /* Create a test case for partitioned csv with NOEOL */
-    dmlc::TemporaryDirectory tempdir;
-    {
-      std::ofstream of(tempdir.path + "/train_0.csv", std::ios::binary);
-      of << "0,1,1,1";  // NOEOL (no '\n' at end of file)
-    }
-    {
-      std::ofstream of(tempdir.path + "/train_1.csv", std::ios::binary);
-      of << "0,1,1,2\n";
-    }
-    {
-      std::ofstream of(tempdir.path + "/train_2.csv", std::ios::binary);
-      of << "0,1,1,2\n";
-    }
-    /* Load the test case with InputSplit and obtain matrix dimensions */
-    {
-      std::unique_ptr<dmlc::Parser<uint32_t> > parser(
-        dmlc::Parser<uint32_t>::Create(tempdir.path.c_str(), 0, 1, "csv"));
-      CountDimensions(parser.get(), &num_row, &num_col);
-    }
-  }
-  /* Check matrix dimensions: must be 3x4 */
-  ASSERT_EQ(num_row, 3U);
-  ASSERT_EQ(num_col, 4U);
-}
-
-TEST(InputSplit, test_split_libsvm_noeol) {
-  {
-    /* Create a test case for partitioned libsvm with NOEOL */
-    dmlc::TemporaryDirectory tempdir;
-    const char* line
-      = "1 3:1 10:1 11:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 "
-        "77:1 86:1 88:1 92:1 95:1 102:1 105:1 117:1 124:1";
-    {
-      std::ofstream of(tempdir.path + "/train_0.libsvm", std::ios::binary);
-      of << line << "\n";
-    }
-    {
-      std::ofstream of(tempdir.path + "/train_1.libsvm", std::ios::binary);
-      of << line;  // NOEOL (no '\n' at end of file)
-    }
-    std::unique_ptr<dmlc::Parser<uint32_t> > parser(
-      dmlc::Parser<uint32_t>::Create(tempdir.path.c_str(), 0, 1, "libsvm"));
-    size_t num_row, num_col;
-    CountDimensions(parser.get(), &num_row, &num_col);
-    ASSERT_EQ(num_row, 2);
-    ASSERT_EQ(num_col, 125);
-  }
-}
-
-TEST(InputSplit, test_split_libsvm) {
-  size_t num_row, num_col;
-  {
-    /* Create a test case for partitioned libsvm */
-    dmlc::TemporaryDirectory tempdir;
-    const int nfile = 5;
-    for (int file_id = 0; file_id < nfile; ++file_id) {
-      std::ofstream of(tempdir.path + "/test_" + std::to_string(file_id) + ".libsvm",
-                       std::ios::binary);
-      of << "1 3:1 10:1 11:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 "
-         << "77:1 86:1 88:1 92:1 95:1 102:1 105:1 117:1 124:1\n";
-    }
-    /* Load the test case with InputSplit and obtain matrix dimensions */
-    {
-      std::unique_ptr<dmlc::Parser<uint32_t> > parser(
-        dmlc::Parser<uint32_t>::Create(tempdir.path.c_str(), 0, 1, "libsvm"));
-      CountDimensions(parser.get(), &num_row, &num_col);
-    }
-  }
-  /* Check matrix dimensions: must be 5x125 */
-  ASSERT_EQ(num_row, 5U);
-  ASSERT_EQ(num_col, 125U);
-}
-
-TEST(InputSplit, test_split_libsvm_distributed) {
-  {
-    /* Create a test case for partitioned libsvm */
-    dmlc::TemporaryDirectory tempdir;
-    const char* line
-      = "1 3:1 10:1 11:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 "
-        "77:1 86:1 88:1 92:1 95:1 102:1 105:1 117:1 124:1\n";
-    const int nfile = 5;
-    for (int file_id = 0; file_id < nfile; ++file_id) {
-      std::ofstream of(tempdir.path + "/test_" + std::to_string(file_id) + ".libsvm",
-                       std::ios::binary);
-      const int nrepeat = (file_id == 0 ? 6 : 1);
-      for (int i = 0; i < nrepeat; ++i) {
-        of << line;
-      }
-    }
-
-    /* Load the test case with InputSplit and obtain matrix dimensions */
-    const int npart = 2;
-    const size_t expected_dims[npart][2] = { {6, 125}, {4, 125} };
-    for (int part_id = 0; part_id < npart; ++part_id) {
-      std::unique_ptr<dmlc::Parser<uint32_t> > parser(
-        dmlc::Parser<uint32_t>::Create(tempdir.path.c_str(), part_id, npart, "libsvm"));
-      size_t num_row, num_col;
-      CountDimensions(parser.get(), &num_row, &num_col);
-      ASSERT_EQ(num_row, expected_dims[part_id][0]);
-      ASSERT_EQ(num_col, expected_dims[part_id][1]);
-    }
-  }
-}
-
-#ifdef DMLC_UNIT_TESTS_USE_CMAKE
-/* Don't run the following when CMake is not used */
-
-#include "./build_config.h"
-#include <dmlc/build_config.h>
-
-#ifndef DMLC_CMAKE_LITTLE_ENDIAN
-  #error "DMLC_CMAKE_LITTLE_ENDIAN not defined"
-#endif // DMLC_CMAKE_LITTLE_ENDIAN
-
-#if DMLC_CMAKE_LITTLE_ENDIAN
-
-TEST(InputSplit, test_recordio) {
-  dmlc::TemporaryDirectory tempdir;
-
-  std::unique_ptr<dmlc::InputSplit> source(
-    dmlc::InputSplit::Create(CMAKE_CURRENT_SOURCE_DIR "/sample.rec", 0, 1, "recordio"));
-
-  source->BeforeFirst();
-  dmlc::InputSplit::Blob rec;
-  char* content;
-  RecordIOHeader header;
-  size_t content_size;
-
-  int idx = 1;
-
-  while (source->NextRecord(&rec)) {
-    ASSERT_GT(rec.size, sizeof(header));
-    std::memcpy(&header, rec.dptr, sizeof(header));
-    content = reinterpret_cast<char*>(rec.dptr) + sizeof(header);
-    content_size = rec.size - sizeof(header);
-
-    std::string expected;
-    for (int i = 0; i < 10; ++i) {
-      expected += std::to_string(idx) + "\n";
-    }
-
-    ASSERT_EQ(header.label, static_cast<float>(idx % 2));
-    ASSERT_EQ(header.image_id[0], idx);
-    ASSERT_EQ(std::string(content, content_size), expected);
-
-    ++idx;
-  }
-}
-
-#endif  // DMLC_CMAKE_LITTLE_ENDIAN
-
-#endif  // DMLC_UNIT_TESTS_USE_CMAKE
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_json.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_json.cc
deleted file mode 100644
index 4e13e41..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_json.cc
+++ /dev/null
@@ -1,130 +0,0 @@
-#include <dmlc/json.h>
-#include <dmlc/io.h>
-#include <dmlc/memory_io.h>
-#include <dmlc/logging.h>
-#include <gtest/gtest.h>
-#include <sstream>
-#include <cstring>
-#include <unordered_map>
-#include <iostream>
-
-using namespace std;
-namespace json {
-template<typename T>
-inline void TestSaveLoad(T data) {
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::ostringstream os;
-#else
-  std::string os;
-#endif
-  {
-    T temp(data);
-    dmlc::JSONWriter writer(&os);
-    writer.Write(temp);
-    temp.clear();
-  }
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::string json = os.str();
-  std::istringstream is(json);
-#else
-  std::string json = os;
-  std::string is(json);
-#endif
-  LOG(INFO) << json;
-  dmlc::JSONReader reader(&is);
-  T copy_data;
-  reader.Read(&copy_data);
-  ASSERT_EQ(data, copy_data);
-}
-
-class MyClass {
- public:
-  MyClass() {}
-  MyClass(std::string data) : data_{data}, value_(0) {}
-  inline void Save(dmlc::JSONWriter *writer) const {
-    writer->BeginObject();
-    writer->WriteObjectKeyValue("value", value_);
-    writer->WriteObjectKeyValue("data", data_);
-    writer->EndObject();
-  }
-  inline void Load(dmlc::JSONReader *reader) {
-    dmlc::JSONObjectReadHelper helper;
-    helper.DeclareField("data", &data_);
-    helper.DeclareOptionalField("value", &value_);
-    helper.ReadAllFields(reader);
-  }
-  inline bool operator==(const MyClass &other) const {
-    return value_ == other.value_;
-  }
-
- private:
-  std::vector<std::string> data_;
-  int value_;
-};
-}
-
-DMLC_JSON_ENABLE_ANY(std::vector<std::string>, StrVector);
-
-// test json module
-TEST(JSON, basics) {
-  using namespace json;
-  int n = 10;
-  std::vector<int> a;
-  for (int i = 0; i < n; ++i) {
-    a.push_back(i);
-  }
-  TestSaveLoad(a);
-
-  std::vector<std::string> b;
-  for (int i = 0; i < n; ++i) {
-    std::string ss(i, 'a' + (i % 26));
-    b.push_back(ss);
-  }
-  TestSaveLoad(b);
-
-  std::vector<std::vector<int> > temp {{1,2,3}, {1,2}, {1,2,3,4}};
-  TestSaveLoad(temp);
-
-  std::vector<std::vector<int> > temp2 {{}, {}, {1,2,3,4}};
-  TestSaveLoad(temp2);
-
-  TestSaveLoad(
-      std::map<std::string, int>  {{"hellkow", 1}, {"world", 2}});
-
-  TestSaveLoad(
-      std::unordered_map<std::string, int>  {{"hellkow", 1}, {"world", 2}});
-  TestSaveLoad(std::list<std::string>  {"hjhjm", "asasa"});
-  TestSaveLoad(std::list<int>(a.begin(), a.end()));
-  TestSaveLoad(std::list<MyClass> {MyClass("abc"), MyClass("def")});
-}
-
-
-TEST(JSON, any) {
-  dmlc::any x = std::vector<std::string>{"a", "b", "c"};
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::ostringstream os;
-#else
-  std::string os;
-#endif
-  {
-    dmlc::any temp(x);
-    dmlc::JSONWriter writer(&os);
-    writer.Write(temp);
-  }
-
-#ifndef _LIBCPP_SGX_NO_IOSTREAMS
-  std::string json = os.str();
-  std::istringstream is(json);
-#else
-  std::string json = os;
-  std::string is(json);
-#endif
-  LOG(INFO) << json;
-  dmlc::JSONReader reader(&is);
-  dmlc::any copy_data;
-  reader.Read(&copy_data);
-
-  ASSERT_EQ(dmlc::get<std::vector<std::string> >(x),
-            dmlc::get<std::vector<std::string> >(copy_data));
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_lockfree.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_lockfree.cc
deleted file mode 100644
index 266dfc4..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_lockfree.cc
+++ /dev/null
@@ -1,138 +0,0 @@
-#include <dmlc/json.h>
-#include <dmlc/io.h>
-#include <dmlc/memory_io.h>
-#include <dmlc/concurrentqueue.h>
-#include <dmlc/blockingconcurrentqueue.h>
-#include <dmlc/thread_group.h>
-#include <gtest/gtest.h>
-
-template<typename TQueue>
-struct LFQThreadData {
-  LFQThreadData() : count_(0) {}
-  std::atomic<size_t> count_;
-  std::shared_ptr<TQueue> q_ = std::make_shared<TQueue>();
-  std::shared_ptr<dmlc::ManualEvent> ready_ = std::make_shared<dmlc::ManualEvent>();
-  std::mutex cs_map_;
-  std::set<int> thread_map_;
-};
-
-template<typename TQueue>
-static int PushThread(const int id, std::shared_ptr<LFQThreadData<TQueue>> data) {
-  ++data->count_;
-  data->ready_->wait();
-  data->q_->enqueue(id);
-  std::unique_lock<std::mutex> lk(data->cs_map_);
-  data->thread_map_.erase(id);
-  return 0;
-}
-
-template<typename TQueue>
-static int PullThread(const int id, std::shared_ptr<LFQThreadData<TQueue>> data) {
-  ++data->count_;
-  data->ready_->wait();
-  int val;
-  CHECK_EQ(data->q_->try_dequeue(val), true);
-  std::unique_lock<std::mutex> lk(data->cs_map_);
-  data->thread_map_.erase(id);
-  return 0;
-}
-
-template<typename TQueue>
-static int BlockingPullThread(const int id, std::shared_ptr<LFQThreadData<TQueue>> data) {
-  ++data->count_;
-  data->ready_->wait();
-  int val;
-  data->q_->wait_dequeue(val);
-  std::unique_lock<std::mutex> lk(data->cs_map_);
-  data->thread_map_.erase(id);
-  return 0;
-}
-
-static inline std::string TName(const std::string& s, int x) { return s + "-" + std::to_string(x); }
-
-TEST(Lockfree, ConcurrentQueue) {
-  dmlc::ThreadGroup threads;
-  const size_t ITEM_COUNT = 100;
-  auto data = std::make_shared<LFQThreadData<dmlc::moodycamel::ConcurrentQueue<int>>>();
-  for(size_t x = 0; x < ITEM_COUNT; ++x) {
-    std::unique_lock<std::mutex> lk(data->cs_map_);
-    data->thread_map_.insert(x);
-    threads.create(TName("PushThread", x), true, PushThread<dmlc::moodycamel::ConcurrentQueue<int>>, x, data);
-  }
-  while(data->count_ < ITEM_COUNT) {
-    std::this_thread::sleep_for(std::chrono::milliseconds(1));
-  }
-  data->ready_->signal();
-  size_t remaining = ITEM_COUNT;
-  do {
-    std::this_thread::sleep_for(std::chrono::milliseconds(10));
-    std::unique_lock<std::mutex> lk(data->cs_map_);
-    remaining = data->thread_map_.size();
-  } while (remaining);
-
-  size_t count = data->q_->size_approx();
-  GTEST_ASSERT_EQ(count, ITEM_COUNT);
-
-  threads.join_all();
-  GTEST_ASSERT_EQ(threads.size(), 0U);
-
-  for(size_t x = 0; x < ITEM_COUNT; ++x) {
-    std::unique_lock<std::mutex> lk(data->cs_map_);
-    data->thread_map_.insert(x);
-    // Just to mix things up, don't auto-remove
-    threads.create(TName("PullThread", x), false, PullThread<dmlc::moodycamel::ConcurrentQueue<int>>, x, data);
-  }
-  data->ready_->signal();
-  threads.join_all();
-  GTEST_ASSERT_EQ(threads.size(), 0U);
-
-  count = data->q_->size_approx();
-  GTEST_ASSERT_EQ(count, 0UL);
-}
-
-TEST(Lockfree, BlockingConcurrentQueue) {
-  using BlockingQueue = dmlc::moodycamel::BlockingConcurrentQueue<
-    int, dmlc::moodycamel::ConcurrentQueueDefaultTraits>;
-
-  using BlockingQueue = dmlc::moodycamel::BlockingConcurrentQueue<
-    int, dmlc::moodycamel::ConcurrentQueueDefaultTraits>;
-
-  dmlc::ThreadGroup threads;
-  const size_t ITEM_COUNT = 100;
-  auto data = std::make_shared<LFQThreadData<BlockingQueue>>();
-  for(size_t x = 0; x < ITEM_COUNT; ++x) {
-    std::unique_lock<std::mutex> lk(data->cs_map_);
-    data->thread_map_.insert(x);
-    // Just to mix things up, don't auto-remove
-    threads.create(TName("PushThread", x), false, PushThread<BlockingQueue>, x, data);
-  }
-  while(data->count_ < ITEM_COUNT) {
-    std::this_thread::sleep_for(std::chrono::milliseconds(1));
-  }
-  data->ready_->signal();
-  size_t remaining = ITEM_COUNT;
-  do {
-    std::this_thread::sleep_for(std::chrono::milliseconds(10));
-    std::unique_lock<std::mutex> lk(data->cs_map_);
-    remaining = data->thread_map_.size();
-  } while (remaining);
-
-  size_t count = data->q_->size_approx();
-  GTEST_ASSERT_EQ(count, ITEM_COUNT);
-
-  threads.join_all();
-  GTEST_ASSERT_EQ(threads.size(), 0U);
-
-  for(size_t x = 0; x < ITEM_COUNT; ++x) {
-    std::unique_lock<std::mutex> lk(data->cs_map_);
-    data->thread_map_.insert(static_cast<int>(x));
-    threads.create(TName("BlockingPullThread", x), true, BlockingPullThread<BlockingQueue>, x, data);
-  }
-  data->ready_->signal();
-  threads.join_all();
-  GTEST_ASSERT_EQ(threads.size(), 0U);
-
-  count = data->q_->size_approx();
-  GTEST_ASSERT_EQ(count, 0UL);
-}
-
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_logging.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_logging.cc
deleted file mode 100644
index c0e81d6..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_logging.cc
+++ /dev/null
@@ -1,19 +0,0 @@
-// Copyright by Contributors
-#define DMLC_LOG_FATAL_THROW 0
-
-#include <dmlc/logging.h>
-#include <gtest/gtest.h>
-
-TEST(Logging, basics) {
-  LOG(INFO) << "hello";
-  LOG(ERROR) << "error";
-
-  int x = 1, y = 1;
-  CHECK_EQ(x, y);
-  CHECK_GE(x, y);
-
-  int *z = &x;
-  CHECK_EQ(*CHECK_NOTNULL(z), x);
-
-  ASSERT_DEATH(CHECK_NE(x, y), ".*");
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_main.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_main.cc
deleted file mode 100644
index 14525c7..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_main.cc
+++ /dev/null
@@ -1,8 +0,0 @@
-// Copyright by Contributors
-#include <gtest/gtest.h>
-
-int main(int argc, char ** argv) {
-  testing::InitGoogleTest(&argc, argv);
-  testing::FLAGS_gtest_death_test_style = "threadsafe";
-  return RUN_ALL_TESTS();
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_optional.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_optional.cc
deleted file mode 100644
index 035894a..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_optional.cc
+++ /dev/null
@@ -1,204 +0,0 @@
-// Copyright by Contributors
-
-#include <iostream>
-#include <vector>
-#include <dmlc/optional.h>
-#include <dmlc/parameter.h>
-#include <gtest/gtest.h>
-
-
-TEST(Optional, basics_int) {
-  dmlc::optional<int> x;
-  CHECK(!bool(x));
-  x = 1;
-  CHECK(bool(x));
-  CHECK_EQ(x.value(), 1);
-  x = dmlc::nullopt;
-  CHECK(!bool(x));
-  x = 1;
-  dmlc::optional<int> y;
-  y = x;
-  CHECK_EQ(y.value(), 1);
-}
-
-TEST(Optional, parsing_int) {
-  dmlc::optional<int> x;
-  {
-    std::ostringstream os;
-    os << x;
-    CHECK_EQ(os.str(), "None");
-  }
-
-  {
-    x = 1;
-    std::ostringstream os;
-    os << x;
-    CHECK_EQ(os.str(), "1");
-  }
-
-  {
-    std::string none("None");
-    std::istringstream is(none);
-    is >> x;
-    CHECK(!bool(x));
-  }
-
-  {
-    std::string one("1");
-    std::istringstream is(one);
-    is >> x;
-    CHECK_EQ(x.value(), 1);
-  }
-}
-
-struct OptionalParamInt : public dmlc::Parameter<OptionalParamInt> {
-  dmlc::optional<int> none;
-  dmlc::optional<int> one;
-  dmlc::optional<int> long_one;
-  dmlc::optional<int> def;
-
-  DMLC_DECLARE_PARAMETER(OptionalParamInt) {
-    DMLC_DECLARE_FIELD(none)
-    .add_enum("one", 1);
-    DMLC_DECLARE_FIELD(one)
-    .add_enum("one", 1);
-    DMLC_DECLARE_FIELD(long_one);
-    DMLC_DECLARE_FIELD(def)
-    .add_enum("one", 1)
-    .set_default(dmlc::optional<int>());
-  }
-};
-
-DMLC_REGISTER_PARAMETER(OptionalParamInt);
-
-TEST(Optional, add_enum_int) {
-  OptionalParamInt param;
-  std::map<std::string, std::string> kwargs;
-  kwargs["none"] = "None";
-  kwargs["one"] = "one";
-  kwargs["long_one"] = "1L";
-  param.Init(kwargs);
-  CHECK(!param.none);
-  CHECK_EQ(param.one.value(), 1);
-  CHECK_EQ(param.long_one.value(), 1);
-  CHECK(!param.def);
-}
-
-// Repeat above tests, but now testing optional<bool> rather than optional<int>
-
-TEST(Optional, basics_bool) {
-  dmlc::optional<bool> x;
-  CHECK(!bool(x));
-  x = true;
-  CHECK(bool(x));
-  CHECK_EQ(x.value(), true);
-  x = dmlc::nullopt;
-  CHECK(!bool(x));
-  x = true;
-  dmlc::optional<bool> y;
-  y = x;
-  CHECK_EQ(y.value(), true);
-  x = false;
-  y = x;
-  CHECK_EQ(y.value(), false);
-}
-
-TEST(Optional, parsing_bool) {
-  dmlc::optional<bool> x;
-  dmlc::optional<bool> y;
-  {
-    std::ostringstream os;
-    os << x;
-    CHECK_EQ(os.str(), "None");
-  }
-
-  {
-    x = true;
-    std::ostringstream os;
-    os << x;
-    CHECK_EQ(os.str(), "1");
-  }
-
-  {
-    x = false;
-    std::ostringstream os;
-    os << x;
-    CHECK_EQ(os.str(), "0");
-  }
-
-  {
-    std::string none("None");
-    std::istringstream is(none);
-    is >> x;
-    CHECK(!bool(x));
-  }
-
-  {
-    std::string one("1");
-    std::istringstream is(one);
-    is >> x;
-    CHECK_EQ(x.value(), true);
-  }
-
-  {
-    std::string zero("0");
-    std::istringstream is(zero);
-    is >> x;
-    CHECK_EQ(x.value(), false);
-  }
-
-  {
-    std::string one("true");
-    std::istringstream is(one);
-    is >> x;
-    CHECK_EQ(x.value(), true);
-  }
-
-  {
-    std::string zero("false");
-    std::istringstream is(zero);
-    is >> x;
-    CHECK_EQ(x.value(), false);
-  }
-
-  {
-    std::istringstream is("false true");
-    is >> x >> y;
-    CHECK_EQ(x.value(), false);
-    CHECK_EQ(y.value(), true);
-  }
-}
-
-struct OptionalParamBool : public dmlc::Parameter<OptionalParamBool> {
-  dmlc::optional<bool> none;
-  dmlc::optional<bool> none_with_default;
-  dmlc::optional<bool> set_to_none;
-
-  DMLC_DECLARE_PARAMETER(OptionalParamBool) {
-    DMLC_DECLARE_FIELD(none);
-    DMLC_DECLARE_FIELD(none_with_default)
-    .set_default(dmlc::optional<bool>());
-    DMLC_DECLARE_FIELD(set_to_none);
-  }
-};
-
-DMLC_REGISTER_PARAMETER(OptionalParamBool);
-
-TEST(Optional, bool_in_struct) {
-  OptionalParamBool param;
-  CHECK(!param.none);
-  // With optional<bool>, the following explicit approach avoids confusion.
-  CHECK(!param.none.has_value());
-  CHECK(!param.none_with_default);
-  std::map<std::string, std::string> kwargs;
-  // Assign new logical values, testing string assignment
-  kwargs["none"] = "0";
-  kwargs["none_with_default"] = "true";
-  kwargs["set_to_none"] = "None";
-  param.Init(kwargs);
-  CHECK(param.none);
-  CHECK(!param.none.value());
-  CHECK(param.none_with_default);
-  CHECK(param.none_with_default.value());
-  CHECK(!param.set_to_none.has_value());
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_param.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_param.cc
deleted file mode 100644
index 88450a2..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_param.cc
+++ /dev/null
@@ -1,189 +0,0 @@
-#include <gtest/gtest.h>
-#include <dmlc/parameter.h>
-#include <vector>
-#include <string>
-#include <utility>
-#include <cmath>
-
-struct LearningParam : public dmlc::Parameter<LearningParam> {
-  float float_param;
-  double double_param;
-  DMLC_DECLARE_PARAMETER(LearningParam) {
-      DMLC_DECLARE_FIELD(float_param).set_default(0.01f);
-      DMLC_DECLARE_FIELD(double_param).set_default(0.1);
-  }
-};
-
-DMLC_REGISTER_PARAMETER(LearningParam);
-
-TEST(Parameter, parsing_float) {
-  LearningParam param;
-  std::map<std::string, std::string> kwargs;
-
-  kwargs["float_param"] = "0";
-  param.Init(kwargs);
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "0.015625";  // can be represented exactly in IEEE 754
-  ASSERT_NO_THROW(param.Init(kwargs));
-  ASSERT_EQ(param.float_param, 0.015625f);
-  kwargs["float_param"] = "-0.015625";  // can be represented exactly in IEEE 754
-  ASSERT_NO_THROW(param.Init(kwargs));
-  ASSERT_EQ(param.float_param, -0.015625f);
-
-  kwargs["float_param"] = "1e-10";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "1e10";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "1.2f";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "1.2e-2f";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "3.4e+38";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "1.2e-38";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "16777216.01";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "4.920005e9";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["float_param"] = "4920000500.0";
-  ASSERT_NO_THROW(param.Init(kwargs));
-
-  // Range error should be caught
-  kwargs["float_param"] = "1e-100";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "1e100";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "3.5e+38";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "1.1e-38";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-
-  // Invalid inputs should be detected
-  kwargs["float_param"] = "foobar";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "foo1.2";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "1.2e10foo";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "1.2e-2 foo";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-
-  kwargs = std::map<std::string, std::string>();
-
-  kwargs["double_param"] = "0";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "0.00048828125";  // can be represented exactly in IEEE 754
-  ASSERT_NO_THROW(param.Init(kwargs));
-  ASSERT_EQ(param.double_param, 0.00048828125);
-  kwargs["double_param"] = "-0.00048828125";  // can be represented exactly in IEEE 754
-  ASSERT_NO_THROW(param.Init(kwargs));
-  ASSERT_EQ(param.double_param, -0.00048828125);
-
-  kwargs["double_param"] = "1e-10";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "1e10";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "1.2f";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "1.2e-2f";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "1e-100";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "1e100";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "1.7e+308";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "2.3e-308";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "16777217.01";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "100000000.01";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "9007199254740992.01";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "4.920005e9";
-  ASSERT_NO_THROW(param.Init(kwargs));
-  kwargs["double_param"] = "4920000500.0";
-  ASSERT_NO_THROW(param.Init(kwargs));
-
-  // Range error should be caught
-  kwargs["double_param"] = "1e-500";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["double_param"] = "1e500";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["double_param"] = "1.8e+308";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["double_param"] = "2.2e-308";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-
-  // Invalid inputs should be detected
-  kwargs["double_param"] = "foobar";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["double_param"] = "foo1.2";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["double_param"] = "1.2e10foo";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["double_param"] = "1.2e-2 foo";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-
-  // INF and NAN
-  kwargs = std::map<std::string, std::string>();
-  errno = 0;  // clear errno, to clear previous range error
-  for (const char* s : {
-      "inf", "+inf", "-inf", "INF", "+INF", "-INF", "infinity", "+infinity",
-      "-infinity", "INFINITY", "+INFINITY", "-INFINITY"}) {
-    kwargs["float_param"] = s;
-    ASSERT_NO_THROW(param.Init(kwargs));
-    ASSERT_TRUE(std::isinf(param.float_param));
-    kwargs["double_param"] = s;
-    ASSERT_NO_THROW(param.Init(kwargs));
-    ASSERT_TRUE(std::isinf(param.double_param));
-  }
-  for (const char* s : {
-      "nan", "NAN", "nan(foobar)", "NAN(FooBar)", "NaN", "NaN(foo_bar_12)",
-      "+nan", "+NAN", "+nan(foobar)", "+NAN(FooBar)", "+NaN", "+NaN(foo_bar_12)",
-      "-nan", "-NAN", "-nan(foobar)", "-NAN(FooBar)", "-NaN",
-      "-NaN(foo_bar_12)"}) {
-    kwargs["float_param"] = s;
-    ASSERT_NO_THROW(param.Init(kwargs));
-    ASSERT_TRUE(std::isnan(param.float_param));
-    kwargs["double_param"] = s;
-    ASSERT_NO_THROW(param.Init(kwargs));
-    ASSERT_TRUE(std::isnan(param.double_param));
-  }
-  kwargs["float_param"] = "infamous";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "infinity war";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-  kwargs["float_param"] = "Nanny";
-  ASSERT_THROW(param.Init(kwargs), dmlc::ParamError);
-}
-
-TEST(Parameter, Update) {
-  LearningParam param;
-  bool changed = false;
-  using Args = std::vector<std::pair<std::string, std::string> >;
-  auto unknown =
-      param.UpdateAllowUnknown(Args{{"float_param", "0.02"},
-                                    {"foo", "bar"}}, &changed);
-  ASSERT_EQ(unknown.size(), 1);
-  ASSERT_EQ(unknown[0].first, "foo");
-  ASSERT_EQ(unknown[0].second, "bar");
-  ASSERT_NEAR(param.float_param, 0.02f, 1e-6);
-  ASSERT_TRUE(changed);
-
-  param.float_param = 0.02;
-  param.UpdateAllowUnknown(Args{{"float_param", "0.02"},
-                                {"foo", "bar"}}, &changed);
-  ASSERT_FALSE(changed);
-
-  param.UpdateAllowUnknown(Args{{"foo", "bar"}}, &changed);
-  ASSERT_FALSE(changed);
-
-  param.UpdateAllowUnknown(Args{{"double_param", "0.13"},
-                                {"foo", "bar"}}, &changed);
-  ASSERT_TRUE(changed);
-  ASSERT_NEAR(param.float_param, 0.02f, 1e-6);  // stays the same
-  ASSERT_NEAR(param.double_param, 0.13, 1e-6);
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_parser.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_parser.cc
deleted file mode 100644
index 637f730..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_parser.cc
+++ /dev/null
@@ -1,533 +0,0 @@
-#include "../src/data/csv_parser.h"
-#include "../src/data/libsvm_parser.h"
-#include "../src/data/libfm_parser.h"
-#include <cstdio>
-#include <cstdlib>
-#include <dmlc/io.h>
-#include <gtest/gtest.h>
-
-using namespace dmlc;
-using namespace dmlc::data;
-
-namespace parser_test {
-template <typename IndexType, typename DType = real_t>
-class CSVParserTest : public CSVParser<IndexType, DType> {
-public:
-  explicit CSVParserTest(InputSplit *source,
-                         const std::map<std::string, std::string> &args,
-                         int nthread)
-      : CSVParser<IndexType, DType>(source, args, nthread) {}
-  void CallParseBlock(char *begin, char *end,
-                      RowBlockContainer<IndexType, DType> *out) {
-    CSVParser<IndexType, DType>::ParseBlock(begin, end, out);
-  }
-};
-
-template <typename IndexType, typename DType = real_t>
-class LibSVMParserTest : public LibSVMParser<IndexType, DType> {
-public:
-  explicit LibSVMParserTest(InputSplit *source,
-                            const std::map<std::string, std::string> &args,
-                            int nthread)
-      : LibSVMParser<IndexType, DType>(source, args, nthread) {}
-  void CallParseBlock(char *begin, char *end,
-                      RowBlockContainer<IndexType, DType> *out) {
-    LibSVMParser<IndexType, DType>::ParseBlock(begin, end, out);
-  }
-};
-
-template <typename IndexType, typename DType = real_t>
-class LibFMParserTest : public LibFMParser<IndexType, DType> {
-public:
-  explicit LibFMParserTest(InputSplit *source,
-                           const std::map<std::string, std::string> &args,
-                           int nthread)
-      : LibFMParser<IndexType, DType>(source, args, nthread) {}
-  void CallParseBlock(char *begin, char *end,
-                      RowBlockContainer<IndexType, DType> *out) {
-    LibFMParser<IndexType, DType>::ParseBlock(begin, end, out);
-  }
-};
-
-}  // namespace parser_test
-
-namespace {
-
-template <typename IndexType>
-static inline void CountDimensions(RowBlockContainer<IndexType>* rctr,
-                                   size_t* out_num_row, size_t* out_num_col) {
-  size_t num_row = rctr->label.size();
-  size_t num_col = 0;
-  for (size_t i = rctr->offset[0]; i < rctr->offset[num_row]; ++i) {
-    const IndexType index = rctr->index[i];
-    num_col = std::max(num_col, static_cast<size_t>(index + 1));
-  }
-  *out_num_row = num_row;
-  *out_num_col = num_col;
-}
-
-}  // namespace anonymous
-
-TEST(CSVParser, test_ignore_bom) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::string data = "\xEF\xBB\xBF\x31\n\xEF\xBB\x32\n";
-  char *out_data = const_cast<char *>(data.c_str());
-  std::unique_ptr<RowBlockContainer<unsigned> > rctr {new RowBlockContainer<unsigned>()};
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  CHECK(rctr->value.size() == 1);
-  CHECK(rctr->value.at(0) == 1);
-
-  data = "\xEF\xBB\xBF\x31\n\xEF\xBB\xBF\x32\n";
-  out_data = const_cast<char *>(data.c_str());
-  rctr.reset(new RowBlockContainer<unsigned>());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  CHECK(rctr->value.size() == 2);
-  CHECK(rctr->value.at(0) == 1);
-  CHECK(rctr->value.at(1) == 2);
-}
-
-TEST(CSVParser, test_standard_case) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr { new RowBlockContainer<unsigned>() };
-  std::string data = "0,1,2,3\n4,5,6,7\n8,9,10,11\n";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK(i == rctr->value[i]);
-  }
-}
-
-TEST(CSVParser, missing_values) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr { new RowBlockContainer<unsigned>() };
-  std::string data = "0,,,3\n4,5,6,7\n8,9,10,11\n";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  CHECK_EQ(rctr->value.size(), 10);
-  CHECK(rctr->value[0] == 0);
-  CHECK(rctr->index[0] == 0);
-  CHECK_EQ(rctr->value[1], 3);
-  CHECK(rctr->index[1] == 3);
-
-  for (size_t i = 2; i < rctr->value.size(); ++i) {
-    CHECK_EQ(rctr->value[i], i + 2);
-  }
-}
-
-TEST(CSVParser, test_int32_parse) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned, int32_t>> parser(
-      new CSVParserTest<unsigned, int32_t>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned, int32_t>> rctr {
-    new RowBlockContainer<unsigned, int32_t>()};
-  std::string data = "20000000,20000001,20000002,20000003\n"
-                     "20000004,20000005,20000006,20000007\n"
-                     "20000008,20000009,20000010,20000011\n";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK((i+20000000) == (size_t)rctr->value[i]);
-  }
-}
-
-TEST(CSVParser, test_int64_parse) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned, int64_t>> parser(
-    new CSVParserTest<unsigned, int64_t>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned, int64_t> > rctr {
-    new RowBlockContainer<unsigned, int64_t>()};
-  std::string data = "2147483648,2147483649,2147483650,2147483651\n"
-                     "2147483652,2147483653,2147483654,2147483655\n"
-                     "2147483656,2147483657,2147483658,2147483659\n";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK((i+2147483648) == (size_t)rctr->value[i]);
-  }
-}
-
-TEST(CSVParser, test_different_newlines) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned> > rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "0,1,2,3\r\n4,5,6,7\r\n8,9,10,11\r\n";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK(i == rctr->value[i]);
-  }
-}
-
-TEST(CSVParser, test_noeol) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned> > rctr {new RowBlockContainer<unsigned>()} ;
-  std::string data = "0,1,2,3\r\n4,5,6,7\r\n8,9,10,11";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK(i == rctr->value[i]);
-  }
-}
-
-TEST(CSVParser, test_delimiter) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{ {"delimiter", " "} };
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "0 1 2 3\n4 5 6 7\n8 9 10 11";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK(i == rctr->value[i]);
-  }
-}
-
-TEST(CSVParser, test_weight_column) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{ {"weight_column", "2"} };
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "0,1,2,3\n4,5,6,7\n8,9,10,11";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  CHECK_EQ(rctr->weight.size(), 3U);
-  for (size_t i = 0; i < rctr->weight.size(); i++) {
-    CHECK_EQ(rctr->weight[i], 2.0f + 4.0f * i);
-  }
-  const std::vector<real_t>
-    expected_values{0.0f, 1.0f, 3.0f, 4.0f, 5.0f, 7.0f, 8.0f, 9.0f, 11.0f};
-  CHECK_EQ(rctr->value.size(), expected_values.size());
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK_EQ(rctr->value[i], expected_values[i]);
-  }
-}
-
-TEST(CSVParser, test_weight_column_2) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<CSVParserTest<unsigned>> parser(
-      new CSVParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "0,1,2,3\n4,5,6,7\n8,9,10,11";
-  char *out_data = const_cast<char *>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  CHECK(rctr->weight.empty());
-  CHECK_EQ(rctr->value.size(), 12U);
-  for (size_t i = 0; i < rctr->value.size(); i++) {
-    CHECK(i == rctr->value[i]);
-  }
-}
-
-void test_qid(std::string data) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<LibSVMParserTest<unsigned>> parser(
-      new LibSVMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-  const std::vector<size_t> expected_offset{
-    0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60
-  };
-  const std::vector<real_t> expected_label{
-    3, 2, 1, 1, 1, 2, 1, 1, 2, 3, 4, 1
-  };
-  const std::vector<uint64_t> expected_qid{
-    1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3
-  };
-  const std::vector<unsigned> expected_index{
-    1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,
-    1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,
-    1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5
-  };
-  const std::vector<real_t> expected_value{
-    1.0f, 1.0f, 0.0f, 0.2f, 0.0f, 0.0f, 0.0f, 1.0f, 0.1f, 1.0f, 0.0f, 1.0f, 0.0f, 0.4f, 0.0f, 0.0f,
-    0.0f, 1.0f, 0.3f, 0.0f, 0.0f, 0.0f, 1.0f, 0.2f, 0.0f, 1.0f, 0.0f, 1.0f, 0.4f, 0.0f, 0.0f, 0.0f,
-    1.0f, 0.1f, 0.0f, 0.0f, 0.0f, 1.0f, 0.2f, 0.0f, 0.0f, 0.0f, 1.0f, 0.1f, 1.0f, 1.0f, 1.0f, 0.0f,
-    0.3f, 0.0f, 1.0f, 0.0f, 0.0f, 0.4f, 1.0f, 0.0f, 1.0f, 1.0f, 0.5f, 0.0f
-  };
-  CHECK(rctr->offset == expected_offset);
-  CHECK(rctr->label == expected_label);
-  CHECK(rctr->qid == expected_qid);
-  CHECK(rctr->index == expected_index);
-  CHECK(rctr->value == expected_value);
-}
-
-TEST(LibSVMParser, test_qid) {
-  std::string data = R"qid(3 qid:1 1:1 2:1 3:0 4:0.2 5:0
-                           2 qid:1 1:0 2:0 3:1 4:0.1 5:1
-                           1 qid:1 1:0 2:1 3:0 4:0.4 5:0
-                           1 qid:1 1:0 2:0 3:1 4:0.3 5:0
-                           1 qid:2 1:0 2:0 3:1 4:0.2 5:0
-                           2 qid:2 1:1 2:0 3:1 4:0.4 5:0
-                           1 qid:2 1:0 2:0 3:1 4:0.1 5:0
-                           1 qid:2 1:0 2:0 3:1 4:0.2 5:0
-                           2 qid:3 1:0 2:0 3:1 4:0.1 5:1
-                           3 qid:3 1:1 2:1 3:0 4:0.3 5:0
-                           4 qid:3 1:1 2:0 3:0 4:0.4 5:1
-                           1 qid:3 1:0 2:1 3:1 4:0.5 5:0)qid";
-  test_qid(data);
-}
-
-TEST(LibSVMParser, test_qid_with_comment) {
-  std::string data = R"qid(# what does foo bar mean anyway
-                           3 qid:1 1:1 2:1 3:0 4:0.2 5:0 # foo
-                           2 qid:1 1:0 2:0 3:1 4:0.1 5:1
-                           1 qid:1 1:0 2:1 3:0 4:0.4 5:0
-                           1 qid:1 1:0 2:0 3:1 4:0.3 5:0
-                           1 qid:2 1:0 2:0 3:1 4:0.2 5:0 # bar
-                           2 qid:2 1:1 2:0 3:1 4:0.4 5:0
-                           1 qid:2 1:0 2:0 3:1 4:0.1 5:0
-                           1 qid:2 1:0 2:0 3:1 4:0.2 5:0
-                           2 qid:3 1:0 2:0 3:1 4:0.1 5:1
-                           3 qid:3 1:1 2:1 3:0 4:0.3 5:0
-                           4 qid:3 1:1 2:0 3:0 4:0.4 5:1
-                           1 qid:3 1:0 2:1 3:1 4:0.5 5:0)qid";
-  test_qid(data);
-}
-
-TEST(LibSVMParser, test_excess_decimal_digits) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<LibSVMParserTest<unsigned>> parser(
-      new LibSVMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "0 1:17.065995780200002000000 4:17.0659957802 "
-                     "6:0.00017065995780200002 8:0.000170659957802\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 1U);
-  CHECK_EQ(num_col, 9U);
-
-  const std::vector<unsigned> expected_index{1, 4, 6, 8};
-  CHECK(rctr->index == expected_index);  // perform element-wise comparsion
-  CHECK_EQ(rctr->value[0], rctr->value[1]);
-  CHECK_EQ(rctr->value[2], rctr->value[3]);
-}
-
-TEST(LibSVMParser, test_indexing_mode_0_based) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<LibSVMParserTest<unsigned>> parser(
-      new LibSVMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "1 1:1 2:-1\n0 1:-1 2:1\n1 1:-1 2:-1\n0 1:1 2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 3U);
-
-  const std::vector<unsigned> expected_index{1, 2, 1, 2, 1, 2, 1, 2};
-  const std::vector<real_t> expected_value{1, -1, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->index == expected_index);  // perform element-wise comparsion
-  CHECK(rctr->value == expected_value);
-}
-
-TEST(LibSVMParser, test_indexing_mode_1_based) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{{"indexing_mode", "1"}};
-  std::unique_ptr<LibSVMParserTest<unsigned>> parser(
-      new LibSVMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "1 1:1 2:-1\n0 1:-1 2:1\n1 1:-1 2:-1\n0 1:1 2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 2U);
-
-  const std::vector<unsigned> expected_index{0, 1, 0, 1, 0, 1, 0, 1};
-    // with indexing_mode=1, parser will subtract 1 from each feature index
-  const std::vector<real_t> expected_value{1, -1, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->index == expected_index);  // perform element-wise comparsion
-  CHECK(rctr->value == expected_value);
-}
-
-TEST(LibSVMParser, test_indexing_mode_auto_detect) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{{"indexing_mode", "-1"}};
-  std::unique_ptr<LibSVMParserTest<unsigned>> parser(
-      new LibSVMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "1 1:1 2:-1\n0 1:-1 2:1\n1 1:-1 2:-1\n0 1:1 2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 2U);
-
-  const std::vector<unsigned> expected_index{0, 1, 0, 1, 0, 1, 0, 1};
-    // expect to detect 1-based indexing, since the least feature id is 1
-  const std::vector<real_t> expected_value{1, -1, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->index == expected_index);  // perform element-wise comparsion
-  CHECK(rctr->value == expected_value);
-}
-
-TEST(LibSVMParser, test_indexing_mode_auto_detect_2) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{{"indexing_mode", "-1"}};
-  std::unique_ptr<LibSVMParserTest<unsigned>> parser(
-      new LibSVMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data = "1 1:1 2:-1\n0 0:-2 1:-1 2:1\n1 1:-1 2:-1\n0 1:1 2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 3U);
-
-  const std::vector<unsigned> expected_index{1, 2, 0, 1, 2, 1, 2, 1, 2};
-    // expect to detect 0-based indexing, since the least feature id is 0
-  const std::vector<real_t> expected_value{1, -1, -2, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->index == expected_index);  // perform element-wise comparsion
-  CHECK(rctr->value == expected_value);
-}
-
-TEST(LibFMParser, test_indexing_mode_0_based) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args;
-  std::unique_ptr<LibFMParserTest<unsigned>> parser(
-      new LibFMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data
-    = "1 1:1:1 1:2:-1\n0 1:1:-1 2:2:1\n1 2:1:-1 1:2:-1\n0 2:1:1 2:2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 3U);
-
-  const std::vector<unsigned> expected_field{1, 1, 1, 2, 2, 1, 2, 2};
-  const std::vector<unsigned> expected_index{1, 2, 1, 2, 1, 2, 1, 2};
-  const std::vector<real_t> expected_value{1, -1, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->field == expected_field);
-  CHECK(rctr->index == expected_index);
-  CHECK(rctr->value == expected_value);  // perform element-wise comparsion
-}
-
-TEST(LibFMParser, test_indexing_mode_1_based) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{{"indexing_mode", "1"}};
-  std::unique_ptr<LibFMParserTest<unsigned>> parser(
-      new LibFMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data
-    = "1 1:1:1 1:2:-1\n0 1:1:-1 2:2:1\n1 2:1:-1 1:2:-1\n0 2:1:1 2:2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 2U);
-
-  const std::vector<unsigned> expected_field{0, 0, 0, 1, 1, 0, 1, 1};
-  const std::vector<unsigned> expected_index{0, 1, 0, 1, 0, 1, 0, 1};
-    // with indexing_mode=1, parser will subtract 1 from field/feature indices
-  const std::vector<real_t> expected_value{1, -1, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->field == expected_field);
-  CHECK(rctr->index == expected_index);
-  CHECK(rctr->value == expected_value);  // perform element-wise comparsion
-}
-
-TEST(LibFMParser, test_indexing_mode_auto_detect) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{{"indexing_mode", "-1"}};
-  std::unique_ptr<LibFMParserTest<unsigned>> parser(
-      new LibFMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data
-    = "1 1:1:1 1:2:-1\n0 1:1:-1 2:2:1\n1 2:1:-1 1:2:-1\n0 2:1:1 2:2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 2U);
-
-  const std::vector<unsigned> expected_field{0, 0, 0, 1, 1, 0, 1, 1};
-  const std::vector<unsigned> expected_index{0, 1, 0, 1, 0, 1, 0, 1};
-    // expect to detect 1-based indexing, since all field/feature id's exceed 0
-  const std::vector<real_t> expected_value{1, -1, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->field == expected_field);
-  CHECK(rctr->index == expected_index);
-  CHECK(rctr->value == expected_value);  // perform element-wise comparsion
-}
-
-TEST(LibFMParser, test_indexing_mode_auto_detect_2) {
-  using namespace parser_test;
-  InputSplit *source = nullptr;
-  const std::map<std::string, std::string> args{{"indexing_mode", "-1"}};
-  std::unique_ptr<LibFMParserTest<unsigned>> parser(
-      new LibFMParserTest<unsigned>(source, args, 1));
-  std::unique_ptr<RowBlockContainer<unsigned>> rctr {new RowBlockContainer<unsigned>()};
-  std::string data
-    = "1 1:1:1 1:2:-1\n0 0:0:-2 1:1:-1 2:2:1\n1 2:1:-1 1:2:-1\n0 2:1:1 2:2:1\n";
-  char* out_data = const_cast<char*>(data.c_str());
-  parser->CallParseBlock(out_data, out_data + data.size(), rctr.get());
-
-  size_t num_row, num_col;
-  CountDimensions(rctr.get(), &num_row, &num_col);
-  CHECK_EQ(num_row, 4U);
-  CHECK_EQ(num_col, 3U);
-
-  const std::vector<unsigned> expected_field{1, 1, 0, 1, 2, 2, 1, 2, 2};
-  const std::vector<unsigned> expected_index{1, 2, 0, 1, 2, 1, 2, 1, 2};
-    // expect to detect 0-based indexing, since second row has feature id 0
-  const std::vector<real_t> expected_value{1, -1, -2, -1, 1, -1, -1, 1, 1};
-  CHECK(rctr->field == expected_field);
-  CHECK(rctr->index == expected_index);
-  CHECK(rctr->value == expected_value);  // perform element-wise comparsion
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_serializer.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_serializer.cc
deleted file mode 100644
index 87e35c5..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_serializer.cc
+++ /dev/null
@@ -1,126 +0,0 @@
-// test for case where we use big endian serializer
-// this is a harder case
-#define DMLC_IO_USE_LITTLE_ENDIAN 0
-
-#include <dmlc/io.h>
-#include <dmlc/memory_io.h>
-#include <dmlc/parameter.h>
-#include <dmlc/logging.h>
-#include <gtest/gtest.h>
-#include <sstream>
-#include <cstring>
-#include <unordered_map>
-#include <iostream>
-
-using namespace std;
-
-template<typename T>
-inline void TestSaveLoad(T data) {
-  std::string blob;
-  dmlc::MemoryStringStream fs(&blob);
-  {
-    T temp(data);
-    static_cast<dmlc::Stream*>(&fs)->Write(temp);
-    temp.clear();
-  }
-  fs.Seek(0);
-  T copy_data;
-  CHECK(static_cast<dmlc::Stream*>(&fs)->Read(&copy_data));
-  ASSERT_EQ(data, copy_data);
-}
-
-class MyClass {
- public:
-  MyClass() {}
-  MyClass(std::string data) : data_(data) {}
-  inline void Save(dmlc::Stream *strm) const {
-    strm->Write(this->data_);
-  }
-  inline bool Load(dmlc::Stream *strm) {
-    return strm->Read(&data_);
-  }
-  inline bool operator==(const MyClass &other) const {
-    return data_ == other.data_;
-  }
-
- private:
-  std::string data_;
-};
-// need to declare the traits property of my class to dmlc
-namespace dmlc { DMLC_DECLARE_TRAITS(has_saveload, MyClass, true); }
-
-// test serializer
-TEST(Serializer, basics) {
-  int n = 10;
-  std::vector<int> a;
-  for (int i = 0; i < n; ++i) {
-    a.push_back(i);
-  }
-  TestSaveLoad(a);
-
-  std::vector<std::string> b;
-  for (int i = 0; i < n; ++i) {
-    std::string ss(i, 'a' + (i % 26));
-    b.push_back(ss);
-  }
-  TestSaveLoad(b);
-
-  std::vector<std::vector<int> > temp {{1,2,3}, {1,2}, {1,2,3,4}};
-  TestSaveLoad(temp);
-  TestSaveLoad(
-      std::map<int, std::string>  {{1, "hellkow"}, {2, "world"}});
-  TestSaveLoad(
-      std::unordered_map<int, std::string>  {{1, "hellkow"}, {2, "world"}});
-  TestSaveLoad(
-      std::unordered_multimap<int, std::string>  {{1, "hellkow"}, {1, "world"}, {2, "111"}});
-  TestSaveLoad(std::set<std::string>  {"hjhjm", "asasa"});
-  TestSaveLoad(std::unordered_set<std::string>  {"hjhjm", "asasa"});
-  LOG(INFO) << "jere";
-  TestSaveLoad(std::list<std::string>  {"hjhjm", "asasa"});
-  TestSaveLoad(std::list<int>(a.begin(), a.end()));
-  TestSaveLoad(std::list<MyClass> {MyClass("abc"), MyClass("def")});
-}
-
-
-// test serializer
-TEST(Serializer, endian) {
-  int n = 10;
-  std::string blob;
-  dmlc::MemoryStringStream fs(&blob);
-  dmlc::Stream* strm = &fs;
-  strm->Write(n);
-  // big endians
-  if (DMLC_IO_USE_LITTLE_ENDIAN == 0) {
-    ASSERT_EQ(blob[0], 0);
-    ASSERT_EQ(blob[1], 0);
-    ASSERT_EQ(blob[2], 0);
-    ASSERT_EQ(blob[3], 10);
-  } else {
-    ASSERT_EQ(blob[0], 10);
-    ASSERT_EQ(blob[1], 0);
-    ASSERT_EQ(blob[2], 0);
-    ASSERT_EQ(blob[3], 0);
-  }
-}
-
-#ifndef DMLC_LITTLE_ENDIAN
-  #error "DMLC_LITTLE_ENDIAN must be defined"
-#endif
-
-TEST(Serializer, endian_detection) {
-  std::string little_endian_flag
-    = dmlc::GetEnv("DMLC_UNIT_TEST_LITTLE_ENDIAN", std::string("NULL"));
-  if (little_endian_flag == "0" || little_endian_flag == "1") {
-    // DMLC_UNIT_TEST_LITTLE_ENDIAN env var needs to be set, so that
-    // CI can provide the correct endian value
-    if (little_endian_flag == "1") {
-      std::cout << "Assert that this machine is little endian..." << std::endl;
-      ASSERT_TRUE(DMLC_LITTLE_ENDIAN);
-    } else {
-      std::cout << "Assert that this machine is big endian..." << std::endl;
-      ASSERT_FALSE(DMLC_LITTLE_ENDIAN);
-    }
-  } else {
-    std::cout << "\x1B[33mWarning\u001B[0m: Skipping this test because DMLC_UNIT_TEST_LITTLE_ENDIAN is not set" << std::endl;
-  }
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_tempdir.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_tempdir.cc
deleted file mode 100644
index 9d704a5..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_tempdir.cc
+++ /dev/null
@@ -1,77 +0,0 @@
-#include <dmlc/filesystem.h>
-#include <gtest/gtest.h>
-#include <fstream>
-#include <string>
-#include <queue>
-#include <utility>
-
-#ifdef _WIN32
-#include <direct.h>
-#else  // _WIN32
-#include <sys/stat.h>
-#endif  // _WIN32
-
-static inline void MakeDirectory(const std::string& path) {
-#ifdef _WIN32
-  CHECK_EQ(_mkdir(path.c_str()), 0) << "Failed to make directory " << path;
-#else  // _WIN32
-  CHECK_EQ(mkdir(path.c_str(), 0777), 0) << "Failed to make directory " << path;
-#endif  // _WIN32
-}
-
-TEST(TemporaryDirectory, test_basic) {
-  std::string tempdir_path;
-  {
-    dmlc::TemporaryDirectory tempdir;
-    tempdir_path = tempdir.path;
-    const int num_file = 5;
-    for (int i = 0; i < num_file; ++i) {
-      std::ofstream fout(tempdir.path + "/" + std::to_string(i) + ".txt");
-      fout << "0,1,1," << (i + 1) << "\n";
-    }
-    // Check if each file can be read back
-    for (int i = 0; i < num_file; ++i) {
-      std::ifstream fin(tempdir.path + "/" + std::to_string(i) + ".txt");
-      std::string s;
-      ASSERT_TRUE(static_cast<bool>(std::getline(fin, s)));
-      ASSERT_EQ(s, std::string("0,1,1," + std::to_string(i + 1)));
-      ASSERT_FALSE(static_cast<bool>(std::getline(fin, s)));
-    }
-  }
-  // Test the directory is indeed deleted.
-  const dmlc::io::URI uri(tempdir_path.c_str());
-  ASSERT_ANY_THROW(dmlc::io::FileSystem::GetInstance(uri)->GetPathInfo(uri));
-}
-
-TEST(TemporaryDirectory, test_recursive) {
-  std::string tempdir_path;
-  {
-    dmlc::TemporaryDirectory tempdir;
-    tempdir_path = tempdir.path;
-    const int recurse_depth = 5;
-
-    std::queue<std::pair<int, std::string>> Q;  // (depth, directory)
-    Q.emplace(0, tempdir.path);
-    while (!Q.empty()) {
-      auto e = Q.front(); Q.pop();
-      const int current_depth = e.first;
-      const std::string current_directory = e.second;
-      if (current_depth < recurse_depth) {
-        {
-          std::ofstream of(current_directory + "/foobar.txt");
-          of << "hello world\n";
-        }
-        MakeDirectory(current_directory + "/1");
-        MakeDirectory(current_directory + "/2");
-        Q.emplace(current_depth + 1, current_directory + "/1");
-        Q.emplace(current_depth + 1, current_directory + "/2");
-      } else {
-        std::ofstream of(current_directory + "/foobar.txt");
-        of << "hello world\n";
-      }
-    }
-  }
-  // Test the directory is indeed deleted.
-  const dmlc::io::URI uri(tempdir_path.c_str());
-  ASSERT_ANY_THROW(dmlc::io::FileSystem::GetInstance(uri)->GetPathInfo(uri));
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_thread_group.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_thread_group.cc
deleted file mode 100644
index 75186df..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_thread_group.cc
+++ /dev/null
@@ -1,240 +0,0 @@
-#include <dmlc/io.h>
-#include <dmlc/memory_io.h>
-#include <dmlc/blockingconcurrentqueue.h>
-#include <dmlc/thread_group.h>
-#include <gtest/gtest.h>
-
-#if (defined _WIN32)
-
-#define NOMINMAX
-#include <Windows.h>
-static inline void dmlc_usleep(__int64 usec) {
-  HANDLE timer;
-  LARGE_INTEGER ft;
-
-  ft.QuadPart = -(10*usec); // Convert to 100 nanosecond interval, negative value indicates relative time
-
-  timer = CreateWaitableTimer(NULL, TRUE, NULL);
-  SetWaitableTimer(timer, &ft, 0, NULL, NULL, 0);
-  WaitForSingleObject(timer, INFINITE);
-  CloseHandle(timer);
-}
-
-#elif (defined DMLC_NANOSLEEP_PRESENT)
-
-#include <sys/types.h>  // for useconds_t, time_t
-#include <time.h>  // for timespec, nanosleep
-
-static inline int dmlc_usleep(useconds_t useconds) {
-  timespec ts;
-  ts.tv_sec = static_cast<time_t>(useconds / 1000000);
-  ts.tv_nsec = static_cast<long>(useconds % 1000000 * 1000ul);
-  return nanosleep(&ts, NULL);
-}
-
-#else
-
-#include <unistd.h>   // for usleep()
-
-static inline int dmlc_usleep(useconds_t useconds) {
-  return usleep(useconds);
-}
-
-#endif
-
-static std::atomic<int> thread_count(0);
-
-static int this_is_thread_func(std::string label, const bool with_delay) {
-  ++thread_count;
-  if(with_delay) {
-    dmlc_usleep(1e4);
-  }
-  --thread_count;
-  return 0;
-}
-
-/*!
- * \brief Generic Thread launch to standalone function, passing ThreadGroup owner
- */
-TEST(ThreadGroup, ThreadLaunchAutoRemove) {
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-  for(int x = 0; x < 200; ++x) {
-    dmlc::ThreadGroup::Thread::SharedPtr thread =
-      std::make_shared<dmlc::ThreadGroup::Thread>(std::string("test_thread_ar ")
-                                                         + std::to_string(x), thread_group.get());
-    dmlc::ThreadGroup::Thread::launch(thread, true, this_is_thread_func, "Runner", false);
-  }
-  thread_group.reset();
-  CHECK_EQ(thread_count, 0);
-}
-
-/*!
- * \brief Generic Thread launch to standalone function, passing ThreadGroup owner
- */
-TEST(ThreadGroup, ThreadLaunchAutoRemoveWithDelay) {
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-  for(int x = 0; x < 200; ++x) {
-    dmlc::ThreadGroup::Thread::SharedPtr thread =
-      std::make_shared<dmlc::ThreadGroup::Thread>(std::string("test_thread_rwd ")
-                                                         + std::to_string(x), thread_group.get());
-    dmlc::ThreadGroup::Thread::launch(thread, true, this_is_thread_func, "Runner", true);
-  }
-  thread_group.reset();
-  CHECK_EQ(thread_count, 0);
-}
-
-/*!
- * \brief Generic Thread launch to standalone function, passing ThreadGroup owner
- */
-TEST(ThreadGroup, ThreadLaunchNoAutoRemove) {
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-  for(int x = 0; x < 200; ++x) {
-    dmlc::ThreadGroup::Thread::SharedPtr thread =
-      std::make_shared<dmlc::ThreadGroup::Thread>(std::string("test_thread_nao ")
-                                                         + std::to_string(x), thread_group.get());
-    dmlc::ThreadGroup::Thread::launch(thread, false, this_is_thread_func, "Runner", false);
-  }
-  thread_group.reset();
-  CHECK_EQ(thread_count, 0);
-}
-
-/*!
- * \brief Generic Thread launch to standalone function, passing ThreadGroup owner
- */
-TEST(ThreadGroup, ThreadLaunchNoAutoRemoveWithDelay) {
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-  for(int x = 0; x < 200; ++x) {
-    dmlc::ThreadGroup::Thread::SharedPtr thread =
-      std::make_shared<dmlc::ThreadGroup::Thread>(std::string("test_thread_narwd ")
-                                                         + std::to_string(x), thread_group.get());
-    dmlc::ThreadGroup::Thread::launch(thread, false, this_is_thread_func, "Runner", true);
-  }
-  thread_group.reset();
-  CHECK_EQ(thread_count, 0);
-}
-
-/*!
- * \brief Test BlockingQueueThread
- */
-TEST(ThreadGroup, ThreadLaunchQueueThread) {
-  // Define the queue type for convenience
-  using BQ = dmlc::BlockingQueueThread<int, -1>;
-
-  // Create the thread group
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-
-  // Create the queue thread object
-  std::shared_ptr<BQ> queue_thread = std::make_shared<BQ>("BlockingQueueThread",
-                                                          thread_group.get());
-
-  // Queue some stuff before the thread starts
-  queue_thread->enqueue(1);
-  queue_thread->enqueue(2);
-  queue_thread->enqueue(3);
-  queue_thread->enqueue(4);
-  CHECK_EQ(queue_thread->size_approx(), 4U);
-  // Launch the queue thread, passing queue item handler as lambda
-  BQ::launch_run(queue_thread,
-                 // Queue item handler
-                 [queue_thread](int item) -> int {
-                   std::cout << "ITEM: " << item
-                             << std::endl << std::flush;
-                   if(item >= 2 && item <= 3) {
-                     // Queue some more while thread is running
-                     queue_thread->enqueue(100 + item);
-                   }
-                   return 0;  // return 0 means continue
-                 });
-  // Trigger the queues to exit
-  thread_group->request_shutdown_all(false);
-  // Wait for all of the queue threads to exit
-  thread_group->join_all();
-  // Check that the queue is empty
-  CHECK_EQ(queue_thread->size_approx(), 0);
-}
-
-using Tick = std::chrono::high_resolution_clock::time_point;
-static inline Tick Now() { return std::chrono::high_resolution_clock::now(); }
-static inline uint64_t GetDurationInNanoseconds(const Tick &t1, const Tick &t2) {
-  return static_cast<uint64_t>(
-    std::chrono::duration_cast<std::chrono::nanoseconds>(t2 - t1).count());
-}
-static inline uint64_t GetDurationInNanoseconds(const Tick &since) {
-  return GetDurationInNanoseconds(since, Now());
-}
-
-constexpr size_t SLEEP_DURATION = 500;
-constexpr size_t TIMER_PERIOD = 10;  // Ideal is 50 periods occur
-constexpr size_t MIN_COUNT_WHILE_SLEEPING = 10;
-constexpr size_t MAX_COUNT_WHILE_SLEEPING = 150;
-
-inline size_t GetDurationInMilliseconds(const Tick& start_time) {
-  return static_cast<size_t>(GetDurationInNanoseconds(start_time)/1000/1000);
-}
-
-/*!
- * \brief Test TimerThread
- */
-TEST(ThreadGroup, TimerThread) {
-  // Create the thread group
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-
-  using Duration = std::chrono::milliseconds;
-  // Create the queue thread object
-  std::shared_ptr<dmlc::TimerThread<Duration>> timer_thread =
-    std::make_shared<dmlc::TimerThread<Duration>>("TimerThread", thread_group.get());
-  Tick start_time = Now();
-  size_t count = 0;
-  // Launch the queue thread, passing queue item handler as lambda
-  dmlc::TimerThread<Duration>::start(
-    timer_thread, Duration(TIMER_PERIOD), [timer_thread, start_time, &count]() -> int {
-      if ((count + 1) % 5 == 0) {
-        // output slows it down a bit, so print fewer times
-        std::cout << "[" << (count + 1) << "] TIME: "
-                  << GetDurationInMilliseconds(start_time)
-                  << std::endl << std::flush;
-      }
-      ++count;
-      return 0;  // return 0 means continue
-    });
-  std::this_thread::sleep_for(Duration(SLEEP_DURATION));
-  // Trigger the queues to exit
-  thread_group->request_shutdown_all(true);
-  // Wait for all of the queue threads to exit
-  thread_group->join_all();
-  GTEST_ASSERT_GE(count, MIN_COUNT_WHILE_SLEEPING);  // Should have at least done three
-  GTEST_ASSERT_LE(count, MAX_COUNT_WHILE_SLEEPING); // Should not have had time to do 20 of them
-}
-
-/*!
- * \brief Test TimerThread Simple
- */
-TEST(ThreadGroup, TimerThreadSimple) {
-  // Create the thread group
-  std::shared_ptr<dmlc::ThreadGroup> thread_group = std::make_shared<dmlc::ThreadGroup>();
-
-  using Duration = std::chrono::milliseconds;
-  Tick start_time = Now();
-  size_t count = 0;
-  // Launch the queue thread, passing queue item handler as lambda
-  dmlc::CreateTimer("TimerThreadSimple",
-                    Duration(TIMER_PERIOD),
-                    thread_group.get(),
-                    [start_time, &count]() -> int {
-                      if ((count + 1) % 5 == 0) {
-                        // output slows it down a bit, so print fewer times
-                        std::cout << "[" << (count + 1) << "] TIME: "
-                                  << GetDurationInMilliseconds(start_time)
-                                  << std::endl << std::flush;
-                      }
-                      ++count;
-                      return 0;  // return 0 means continue
-                    });
-  std::this_thread::sleep_for(Duration(SLEEP_DURATION));
-  // Trigger the queues to exit
-  thread_group->request_shutdown_all();
-  // Wait for all of the queue threads to exit
-  thread_group->join_all();
-  GTEST_ASSERT_GE(count, MIN_COUNT_WHILE_SLEEPING);  // Should have at least done three
-  GTEST_ASSERT_LE(count, MAX_COUNT_WHILE_SLEEPING); // Should not have had time to do 20 of them
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_threaditer.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_threaditer.cc
deleted file mode 100644
index 61ee065..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_threaditer.cc
+++ /dev/null
@@ -1,75 +0,0 @@
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <chrono>
-#include <gtest/gtest.h>
-#include <dmlc/threadediter.h>
-
-using namespace dmlc;
-namespace producer_test {
-inline void delay(int sleep) {
-  if (sleep < 0) {
-    int d = rand() % (-sleep);
-    std::this_thread::sleep_for(std::chrono::milliseconds(d));
-  } else {
-    std::this_thread::sleep_for(std::chrono::milliseconds(sleep));
-  }
-}
-
-// int was only used as example, in real life
-// use big data blob
-struct IntProducer : public ThreadedIter<int>::Producer {
-  int counter;
-  int maxcap;
-  int sleep;
-  IntProducer(int maxcap, int sleep)
-      : counter(0), maxcap(maxcap), sleep(sleep) {}
-  virtual void BeforeFirst(void) {
-    counter = 0;
-  }
-  virtual bool Next(int **inout_dptr) {
-    if (counter == maxcap) return false;
-    // allocate space if not exist
-    if (*inout_dptr == NULL) {
-      *inout_dptr = new int();
-    }
-    delay(sleep);
-    **inout_dptr = counter++;
-    return true;
-  }
-};
-
-}
-
-TEST(ThreadedIter, basics) {
-  using namespace producer_test;
-  ThreadedIter<int> iter;
-  iter.set_max_capacity(1);
-  auto prod = std::make_shared<IntProducer>(10, 100);
-  int d = 100;
-  iter.Init(prod);
-  int counter = 0;
-  while (iter.Next()) {
-    CHECK(counter == iter.Value());
-    delay(d);
-    LOG(INFO)  << counter;
-    ++counter;
-  }
-  CHECK(!iter.Next());
-  iter.BeforeFirst();
-  iter.BeforeFirst();
-  iter.BeforeFirst();
-  iter.Next();
-  iter.BeforeFirst();
-  iter.BeforeFirst();
-  counter = 0;
-  int *value;
-  while (iter.Next(&value)) {
-    LOG(INFO)  << *value;
-    CHECK(counter == *value);
-    ++counter;
-    iter.Recycle(&value);
-    delay(d);
-    CHECK(value == NULL);
-  }
-  LOG(INFO) << "finish";
-}
diff --git a/ml-xgboost/dmlc-core/test/unittest/unittest_threaditer_exc_handling.cc b/ml-xgboost/dmlc-core/test/unittest/unittest_threaditer_exc_handling.cc
deleted file mode 100644
index 17210e4..0000000
--- a/ml-xgboost/dmlc-core/test/unittest/unittest_threaditer_exc_handling.cc
+++ /dev/null
@@ -1,175 +0,0 @@
-#include <chrono>
-#include <dmlc/io.h>
-#include <dmlc/logging.h>
-#include <dmlc/threadediter.h>
-#include <gtest/gtest.h>
-
-enum ExcType {
-  kDMLCException,
-  kStdException,
-};
-
-using namespace dmlc;
-namespace producer_test {
-inline void delay(int sleep) {
-  if (sleep < 0) {
-    int d = rand() % (-sleep);
-    std::this_thread::sleep_for(std::chrono::milliseconds(d));
-  } else {
-    std::this_thread::sleep_for(std::chrono::milliseconds(sleep));
-  }
-}
-
-// int was only used as example, in real life
-// use big data blob
-struct IntProducerNextExc : public ThreadedIter<int>::Producer {
-  int counter;
-  int maxcap;
-  int sleep;
-  ExcType exc_type;
-
-  IntProducerNextExc(int maxcap, int sleep, ExcType exc_type = ExcType::kDMLCException)
-      : counter(0), maxcap(maxcap), sleep(sleep), exc_type(exc_type) {}
-  virtual ~IntProducerNextExc() = default;
-  virtual void BeforeFirst(void) { counter = 0; }
-  virtual bool Next(int **inout_dptr) {
-    if (counter == maxcap)
-      return false;
-    if (counter == (maxcap - 1)) {
-      counter++;
-      if (exc_type == kDMLCException) {
-        LOG(FATAL) << "Test Throw exception";
-      } else {
-        LOG(WARNING) << "Throw std::exception";
-        throw std::exception();
-      }
-    }
-    // allocate space if not exist
-    if (*inout_dptr == NULL) {
-      *inout_dptr = new int();
-    }
-    delay(sleep);
-    **inout_dptr = counter++;
-    return true;
-  }
-};
-
-struct IntProducerBeforeFirst : public ThreadedIter<int>::Producer {
-  ExcType exc_type;
-  IntProducerBeforeFirst(ExcType exc_type = ExcType::kDMLCException)
-      : exc_type(exc_type) {}
-  virtual ~IntProducerBeforeFirst() = default;
-  virtual void BeforeFirst(void) {
-    if (exc_type == ExcType::kDMLCException) {
-      LOG(FATAL) << "Throw exception in before first";
-    } else {
-      throw std::exception();
-    }
-  }
-  virtual bool Next(int **inout_dptr) { return true; }
-};
-}
-
-TEST(ThreadedIter, dmlc_exception) {
-  using namespace producer_test;
-  int* value = nullptr;
-  ThreadedIter<int> iter2;
-  iter2.set_max_capacity(7);
-  auto prod = std::make_shared<IntProducerNextExc>(5, 100);
-  bool caught = false;
-  iter2.Init(prod);  // t1 is created in here, not passing ownership
-  iter2.BeforeFirst();
-  try {
-    delay(1000);
-    iter2.Recycle(&value);
-  } catch (dmlc::Error &e) {
-    caught = true;
-    LOG(INFO) << "recycle exception caught";
-  }
-  CHECK(caught);
-  iter2.Init(prod);
-  caught = false;
-  iter2.BeforeFirst();
-  try {
-    while (iter2.Next(&value)) {
-      iter2.Recycle(&value);
-    }
-  } catch (dmlc::Error &e) {
-    caught = true;
-    LOG(INFO) << "next exception caught";
-  }
-  CHECK(caught);
-  LOG(INFO) << "finish";
-  ThreadedIter<int> iter3;
-  iter3.set_max_capacity(1);
-  auto prod2 = std::make_shared<IntProducerBeforeFirst>();
-  iter3.Init(prod2);
-  caught = false;
-  try {
-    iter3.BeforeFirst();
-  } catch (dmlc::Error &e) {
-    caught = true;
-    LOG(INFO) << "beforefirst exception caught";
-  }
-  caught = false;
-  try {
-  iter3.BeforeFirst();
-  } catch (dmlc::Error &e) {
-    LOG(INFO) << "beforefirst exception thrown/caught";
-    caught = true;
-  }
-  CHECK(caught);
-  delete(value);
-}
-
-TEST(ThreadedIter, std_exception) {
-  using namespace producer_test;
-  int *value = nullptr;
-  ThreadedIter<int> iter2;
-  iter2.set_max_capacity(7);
-  auto prod =std::make_shared<IntProducerNextExc>(5, 100, ExcType::kStdException);
-  bool caught = false;
-  iter2.Init(prod);
-  iter2.BeforeFirst();
-  try {
-    delay(1000);
-    iter2.Recycle(&value);
-  } catch (dmlc::Error &e) {
-    caught = true;
-    LOG(INFO) << "recycle exception caught";
-  }
-  CHECK(caught);
-  iter2.Init(prod);
-  caught = false;
-  iter2.BeforeFirst();
-  try {
-    while (iter2.Next(&value)) {
-      iter2.Recycle(&value);
-    }
-  } catch (dmlc::Error &e) {
-    caught = true;
-    LOG(INFO) << "next exception caught";
-  }
-  CHECK(caught);
-  LOG(INFO) << "finish";
-  ThreadedIter<int> iter3;
-  iter3.set_max_capacity(1);
-  auto prod2 = std::make_shared<IntProducerBeforeFirst>(ExcType::kStdException);
-  iter3.Init(prod2);
-  caught = false;
-  try {
-    iter3.BeforeFirst();
-  } catch (dmlc::Error &e) {
-    caught = true;
-    LOG(INFO) << "beforefirst exception caught";
-  }
-  caught = false;
-  try {
-  iter3.BeforeFirst();
-  } catch (dmlc::Error &e) {
-    LOG(INFO) << "beforefirst exception thrown/caught";
-    caught = true;
-  }
-  CHECK(caught);
-  delete(value);
-}
diff --git a/ml-xgboost/dmlc-core/tracker/README.md b/ml-xgboost/dmlc-core/tracker/README.md
deleted file mode 100644
index 63a6706..0000000
--- a/ml-xgboost/dmlc-core/tracker/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-DMLC Tracker
-============
-Job submission and tracking script for DMLC. To submit your job to cluster.
-Use the following command
-
-```bash
-dmlc-submit --mode <cluster-mode> [arguments] [command]
-```
-
-DMLC job will start executors, each act as role of worker or server.
-It works for both parameter server based jobs as well as rabit allreduce jobs.
-
-Parameters
-----------
-The following is a list of frequently used arguments available in the dmlc-submit command.
-To get full list of arguments, you can run
-```bash
-dmlc-submit -h
-```
-
-- ```--cluster``` string, {'mpi', 'yarn', 'local',  'sge'}, default to ${DMLC_SUBMIT_CLUSTER}
-  - Job submission mode.
-- ```--num-workers``` integer, required
-  - Number of workers in the job.
-- ```--num-servers``` integer, default=0
-  - Number of servers in the job.
-- ```--worker-cores``` integer, default=1
-  - Number of cores needed to be allocated for worker job.
-- ```--server-cores``` integer, default=1
-  -  Number of cores needed to be allocated for server job.
-- ```--worker-memory``` string, default='1g'
-  - Memory needed for server job.
-- ```--server-memory``` string, default='1g'
-  - Memory needed for server job.
-- ```--jobname``` string, default=auto specify
-  - Name of the job.
-- ```--queue``` string, default='default'
-  - The submission queue we should submit the job to.
-- ```--log-level``` string, {INFO, DEBUG}
-  - The logging level.
-- ```--log-file``` string, default='None'
-  - Output log to the specific log file, the log is still printed on stderr.
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc-submit b/ml-xgboost/dmlc-core/tracker/dmlc-submit
deleted file mode 100644
index d70e1f3..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc-submit
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import os
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.insert(0, curr_path)
-
-from dmlc_tracker import submit
-
-submit.main()
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/__init__.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/__init__.py
deleted file mode 100644
index 303aef8..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""DMLC Tracker modules for running jobs on different platforms."""
-from __future__ import absolute_import
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/kubernetes.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/kubernetes.py
deleted file mode 100644
index ab7f979..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/kubernetes.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env python3
-"""
-DMLC submission script by kubernetes
-
-One need to make sure kubectl-able.
-"""
-from __future__ import absolute_import
-
-import os
-from os import path
-import sys
-import uuid
-import logging
-from kubernetes import client, config
-from . import tracker
-import yaml
-
-template_volume = {
-        "name":""
-        }
-template_volumemount = {
-        "mountPath":"",
-        "name":""
-        }
-template_resouce = {
-        "requests":{},
-        "limits":{}
-        }
-sched_port = 9091
-def create_svc_manifest(name, port, target_port):
-    spec = client.V1ServiceSpec(
-            selector={"app": name},
-            ports=[client.V1ServicePort(protocol="TCP", port=port, target_port=target_port)]
-            )
-    service = client.V1Service( metadata=client.V1ObjectMeta(name=name), spec=spec)
-    return service
-def create_sched_svc_manifest(name, port):
-    return create_svc_manifest(name, port, port)
-
-def create_job_manifest(envs, commands, name, image, template_file):
-    if template_file is not None:
-        with open( template_file ) as f:
-            job=yaml.safe_load(f)
-            job["metadata"]["name"]=name
-            job["spec"]["template"]["metadata"]["labels"]["app"]=name
-            job["spec"]["template"]["spec"]["containers"][0]["image"]=image
-            job["spec"]["template"]["spec"]["containers"][0]["command"]=commands
-            job["spec"]["template"]["spec"]["containers"][0]["name"]=name
-            job["spec"]["template"]["spec"]["containers"][0]["env"]=envs
-            job["spec"]["template"]["spec"]["containers"][0]["command"]=commands
-    else:
-        container=client.V1Container(image=image, command=commands, name=name, env=envs)
-        pod_temp=client.V1PodTemplateSpec(
-                spec=client.V1PodSpec(restart_policy="OnFailure", containers=[container]),
-                metadata=client.V1ObjectMeta(name=name, labels={"app":name})
-                )
-        job=client.V1Job(
-                api_version="batch/v1",
-                kind="Job",
-                spec=client.V1JobSpec(template=pod_temp),
-                metadata=client.V1ObjectMeta(name=name)
-                )
-    return job
-
-def create_ps_manifest( ps_id, ps_num, job_name, envs, image, commands, template_file ):
-    envs.append( client.V1EnvVar( name="DMLC_SERVER_ID", value=ps_id ))
-    envs.append( client.V1EnvVar( name="DMLC_ROLE", value="server" ))
-    if job_name is not None:
-        name = "mx-" + job_name + "-server-" + ps_id
-    else:
-        name = "mx-server-" + ps_id
-    return create_job_manifest(envs, commands, name, image, template_file )
-
-def create_wk_manifest( wk_id, wk_num, ps_num, job_name, envs, image, commands, template_file ):
-    envs.append( client.V1EnvVar( name="DMLC_WORKER_ID", value=wk_id ))
-    envs.append( client.V1EnvVar( name="DMLC_SERVER_ID", value="0" ))
-    envs.append( client.V1EnvVar( name="DMLC_ROLE", value="worker" ))
-    if job_name is not None:
-        name = "mx-" + job_name + "-worker-" + wk_id
-    else:
-        name = "mx-worker-" + wk_id
-    return create_job_manifest(envs, commands, name, image, template_file )
-
-def create_sched_job_manifest( wk_num, ps_num, envs, image,  commands):
-    envs.append( client.V1EnvVar( name="DMLC_ROLE", value="scheduler" ))
-    name = ""
-    for i in envs:
-        if i.name == "DMLC_PS_ROOT_URI":
-            name = i.value
-            break
-    return create_job_manifest(envs, commands, name, image, None )
-    
-def create_env(root_uri, root_port, sv_num, wk_num ):
-    envs = []
-    envs.append( client.V1EnvVar( name="DMLC_PS_ROOT_URI", value=root_uri))
-    envs.append( client.V1EnvVar( name="DMLC_PS_ROOT_PORT", value=str(root_port)))
-    envs.append( client.V1EnvVar( name="DMLC_NUM_SERVER", value=str(sv_num)))
-    envs.append( client.V1EnvVar( name="DMLC_NUM_WORKER", value=str(wk_num)))
-    return envs
- 
-
-def submit(args):
-    def kubernetes_submit(nworker, nserver, pass_envs):
-        sv_image = args.kube_server_image
-        wk_image = args.kube_worker_image
-        if args.jobname is not None:
-            r_uri = "mx-" + args.jobname + "-sched"
-        else:
-            r_uri = "mx-sched"
-        r_port = 9091
-        sd_envs = create_env( r_uri, r_port, nserver, nworker )
-        mn_jobs = []
-        mn_sh_job = create_sched_job_manifest( str(nworker), str(nserver), sd_envs, sv_image, args.command)
-        mn_sh_svc = create_sched_svc_manifest(r_uri, r_port)
-        
-        for i in range(nserver):
-            envs = create_env( r_uri, r_port, nserver, nworker )
-            mn_sv = create_ps_manifest( str(i), str(nserver), args.jobname, envs, sv_image, args.command, args.kube_server_template )
-            mn_jobs.append(mn_sv)
-
-        for i in range(nworker):
-            envs = create_env( r_uri, r_port, nserver, nworker )
-            mn_wk = create_wk_manifest( str(i), str(nworker), str(nserver), args.jobname, envs, wk_image, args.command, args.kube_worker_template )
-            mn_jobs.append(mn_wk)
-
-        config.load_kube_config()
-        k8s_coreapi = client.CoreV1Api()
-        k8s_batch = client.BatchV1Api()
-        resp = k8s_batch.create_namespaced_job(namespace=args.kube_namespace, body=mn_sh_job)
-        print( resp.kind + " " + resp.metadata.name +" is created." )
-        resp = k8s_coreapi.create_namespaced_service(namespace="default", body=mn_sh_svc)
-        print( resp.kind + " " + resp.metadata.name +" is created." )
-        for m in mn_jobs:
-            resp = k8s_batch.create_namespaced_job(
-                    body=m, namespace="default")
-            print( resp.kind + " " + resp.metadata.name +" is created." )
-
-
-        return kubernetes_submit
-
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=kubernetes_submit,
-                   pscmd="echo \"To check each log, try 'kubectl logs job/{{role}}-{{jobname}}-{{workerID}}'\"")
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/launcher.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/launcher.py
deleted file mode 100644
index 7152d58..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/launcher.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python3
-# pylint: disable=invalid-name
-"""The container launcher script that launches DMLC with the right env variable."""
-from __future__ import absolute_import
-
-import glob
-import sys
-import os
-import subprocess
-from .util import py_str
-
-def unzip_archives(ar_list, env):
-    for fname in ar_list:
-        if not os.path.exists(fname):
-            continue
-        if fname.endswith('.zip'):
-            subprocess.call(args=['unzip', fname], env=env)
-        elif fname.find('.tar') != -1:
-            subprocess.call(args=['tar', '-xf', fname], env=env)
-
-def main():
-    """Main moduke of the launcher."""
-    if len(sys.argv) < 2:
-        print('Usage: launcher.py your command')
-        sys.exit(0)
-
-    hadoop_home = os.getenv('HADOOP_HOME')
-    hdfs_home = os.getenv('HADOOP_HDFS_HOME')
-    java_home = os.getenv('JAVA_HOME')
-    hadoop_home = os.getenv('HADOOP_PREFIX') if hadoop_home is None else hadoop_home
-    cluster = os.getenv('DMLC_JOB_CLUSTER')
-
-    assert cluster is not None, 'need to have DMLC_JOB_CLUSTER'
-
-    env = os.environ.copy()
-    library_path = ['./']
-    class_path = []
-
-    if cluster == 'yarn':
-        assert hadoop_home is not None, 'need to set HADOOP_HOME'
-        assert hdfs_home is not None, 'need to set HADOOP_HDFS_HOME'
-        assert java_home is not None, 'need to set JAVA_HOME'
-
-    if cluster == 'sge':
-        num_worker = int(env['DMLC_NUM_WORKER'])
-        task_id = int(env['DMLC_TASK_ID'])
-        if task_id < num_worker:
-            env['DMLC_ROLE'] = 'worker'
-        else:
-            env['DMLC_ROLE'] = 'server'
-
-    if hadoop_home:
-        library_path.append('%s/lib/native' % hdfs_home)
-        library_path.append('%s/lib' % hdfs_home)
-        (classpath, _) = subprocess.Popen('%s/bin/hadoop classpath' % hadoop_home,
-                                          stdout=subprocess.PIPE, shell=True,
-                                          env=os.environ).communicate()
-        classpath = py_str(class_path)
-        for f in classpath.split(':'):
-            class_path += glob.glob(f)
-
-    if java_home:
-        library_path.append('%s/jre/lib/amd64/server' % java_home)
-
-    env['CLASSPATH'] = '${CLASSPATH}:' + (':'.join(class_path))
-
-    # setup hdfs options
-    if 'DMLC_HDFS_OPTS' in env:
-        env['LIBHDFS_OPTS'] = env['DMLC_HDFS_OPTS']
-    elif 'LIBHDFS_OPTS' not in env:
-        env['LIBHDFS_OPTS'] = '--Xmx128m'
-
-    LD_LIBRARY_PATH = env['LD_LIBRARY_PATH'] if 'LD_LIBRARY_PATH' in env else ''
-    env['LD_LIBRARY_PATH'] = LD_LIBRARY_PATH + ':' + ':'.join(library_path)
-
-    # unzip the archives.
-    if 'DMLC_JOB_ARCHIVES' in env:
-        unzip_archives(env['DMLC_JOB_ARCHIVES'].split(':'), env)
-
-    ret = subprocess.call(args=sys.argv[1:], env=env)
-    sys.exit(ret)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/local.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/local.py
deleted file mode 100644
index 6e4af12..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/local.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Submission job for local jobs."""
-# pylint: disable=invalid-name
-from __future__ import absolute_import
-
-import sys
-import os
-import subprocess
-import logging
-from threading import Thread
-from . import tracker
-
-def exec_cmd(cmd, num_attempt, role, taskid, pass_env):
-    """Execute the command line command."""
-    if cmd[0].find('/') == -1 and os.path.exists(cmd[0]) and os.name != 'nt':
-        cmd[0] = './' + cmd[0]
-    cmdline = ' '.join(cmd)
-    env = os.environ.copy()
-    for k, v in pass_env.items():
-        env[k] = str(v)
-
-    env['DMLC_TASK_ID'] = str(taskid)
-    env['DMLC_ROLE'] = role
-    env['DMLC_JOB_CLUSTER'] = 'local'
-
-    # backward compatibility
-    num_retry = env.get('DMLC_NUM_ATTEMPT', num_attempt)
-    num_trial = 0
-
-    logging.debug('num of retry %d',num_retry)
-
-    while True:
-        if os.name == 'nt':
-            ret = subprocess.call(cmdline, shell=True, env=env)
-        else:
-            ret = subprocess.call(cmdline, shell=True, executable='bash', env=env)
-        if ret == 0:
-            logging.debug('Thread %d exit with 0', taskid)
-            return
-        else:
-            num_trial += 1
-            num_retry -= 1
-
-            if num_retry >= 0:
-                cmdline = ' '.join(cmd + ['DMLC_NUM_ATTEMPT=' + str(num_trial)])
-                continue
-            if os.name == 'nt':
-                sys.exit(-1)
-            else:
-                raise RuntimeError('Get nonzero return code=%d on %s %s' % (ret, cmd, env))
-
-
-def submit(args):
-    """Submit function of local jobs."""
-    def mthread_submit(nworker, nserver, envs):
-        """
-        customized submit script, that submit nslave jobs, each must contain args as parameter
-        note this can be a lambda function containing additional parameters in input
-
-        Parameters
-        ----------
-        nworker: number of slave process to start up
-        nserver: number of server nodes to start up
-        envs: enviroment variables to be added to the starting programs
-        """
-        procs = {}
-        for i in range(nworker + nserver):
-            if i < nworker:
-                role = 'worker'
-            else:
-                role = 'server'
-            procs[i] = Thread(target=exec_cmd, args=(args.command, args.local_num_attempt, role, i, envs))
-            procs[i].setDaemon(True)
-            procs[i].start()
-
-    # call submit, with nslave, the commands to run each job and submit function
-    tracker.submit(args.num_workers, args.num_servers, fun_submit=mthread_submit,
-                   pscmd=(' '.join(args.command)))
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/mesos.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/mesos.py
deleted file mode 100644
index cfd346f..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/mesos.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python3
-"""
-DMLC submission script by mesos
-
-One need to make sure all slaves machines are ssh-able.
-"""
-from __future__ import absolute_import
-
-import os
-import sys
-import json
-import uuid
-import logging
-from threading import Thread
-from . import tracker
-try:
-    import pymesos.subprocess
-    logging.getLogger('pymesos').setLevel(logging.WARNING)
-
-    def _run(prog, env, resources):
-        cwd = os.getcwd()
-        pymesos.subprocess.check_call(
-            prog, shell=True, env=env, cwd=cwd,
-            cpus=resources['cpus'], mem=resources['mem']
-        )
-
-    _USE_PYMESOS = True
-
-except ImportError:
-    import subprocess
-    DEVNULL = open(os.devnull, 'w')
-
-    def _run(prog, env, resources):
-        master = os.environ['MESOS_MASTER']
-        if ':' not in master:
-            master += ':5050'
-
-        name = str(uuid.uuid4())
-        cwd = os.getcwd()
-        prog = "cd %s && %s" % (cwd, prog)
-
-        resources = ';'.join('%s:%s' % (k, v) for k, v in resources.items())
-        prog = prog.replace('\'', '\\\'')
-        env = json.dumps(env).replace('\'', '\\\'')
-        resources = resources.replace('\'', '\\\'')
-        cmd = (
-            'mesos-execute --master=%s --name=\'%s\''
-            ' --command=\'%s\' --env=\'%s\' --resources=\'%s\'' %
-            (master, name, prog, env, resources)
-        )
-
-        subprocess.check_call(
-            cmd,
-            shell=True,
-            stdout=DEVNULL,
-            stderr=subprocess.STDOUT)
-
-    _USE_PYMESOS = False
-
-def get_env():
-    # get system envs
-    keys = set(['OMP_NUM_THREADS', 'KMP_AFFINITY', 'LD_LIBRARY_PATH'])
-    return {k: v for k, v in os.environ.items() if k in keys}
-
-
-def submit(args):
-    def mesos_submit(nworker, nserver, pass_envs):
-        """
-        customized submit script
-        """
-        # launch jobs
-        for i in range(nworker + nserver):
-            resources = {}
-            pass_envs['DMLC_ROLE'] = 'server' if i < nserver else 'worker'
-            if i < nserver:
-                pass_envs['DMLC_SERVER_ID'] = i
-                resources['cpus'] = args.server_cores
-                resources['mem'] = args.server_memory_mb
-            else:
-                pass_envs['DMLC_WORKER_ID'] = i - nserver
-                resources['cpus'] = args.worker_cores
-                resources['mem'] = args.worker_memory_mb
-
-            env = {str(k): str(v) for k, v in pass_envs.items()}
-            env.update(get_env())
-            prog = ' '.join(args.command)
-            thread = Thread(target=_run, args=(prog, env, resources))
-            thread.setDaemon(True)
-            thread.start()
-
-        return mesos_submit
-
-    if not _USE_PYMESOS:
-        logging.warning('No PyMesos found, use mesos-execute instead,'
-                        ' no task output available')
-
-    if args.mesos_master:
-        os.environ['MESOS_MASTER'] = args.mesos_master
-
-    assert 'MESOS_MASTER' in os.environ, 'No mesos master configured!'
-
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=mesos_submit,
-                   pscmd=(' '.join(args.command)))
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/mpi.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/mpi.py
deleted file mode 100644
index 3a3d89c..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/mpi.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""
-DMLC submission script, MPI version
-"""
-# pylint: disable=invalid-name
-from __future__ import absolute_import
-
-import sys
-import subprocess, logging
-from threading import Thread
-from . import tracker
-
-def get_mpi_env(envs):
-    """get the mpirun command for setting the envornment
-    support both openmpi and mpich2
-    """
-
-    cmd = ''
-    # windows hack: we will use msmpi
-    if sys.platform == 'win32':
-        for k, v in envs.items():
-            cmd += ' -env %s %s' % (k, str(v))
-        return cmd
-
-    # decide MPI version.
-    (out, err) = subprocess.Popen(['mpirun', '--version'],
-                                  stdout=subprocess.PIPE,
-                                  stderr=subprocess.PIPE).communicate()
-    if b'Open MPI' in out:
-        for k, v in envs.items():
-            cmd += ' -x %s=%s' % (k, str(v))
-    elif b'mpich' in out:
-        for k, v in envs.items():
-            cmd += ' -env %s %s' % (k, str(v))
-    else:
-        raise RuntimeError('Unknown MPI Version')
-    return cmd
-
-
-def submit(args):
-    """Submission script with MPI."""
-    def mpi_submit(nworker, nserver, pass_envs):
-        """Internal closure for job submission."""
-        def run(prog):
-            """run the program"""
-            subprocess.check_call(prog, shell=True)
-
-        cmd = ''
-        if args.host_file is not None:
-            cmd = '--hostfile %s ' % (args.host_file)
-        cmd += ' ' + ' '.join(args.command)
-
-        pass_envs['DMLC_JOB_CLUSTER'] = 'mpi'
-
-        # start workers
-        if nworker > 0:
-            logging.info('Start %d workers by mpirun' % nworker)
-            pass_envs['DMLC_ROLE'] = 'worker'
-            if sys.platform == 'win32':
-                prog = 'mpiexec -n %d %s %s' % (nworker, get_mpi_env(pass_envs), cmd)
-            else:
-                prog = 'mpirun -n %d %s %s' % (nworker, get_mpi_env(pass_envs), cmd)
-            thread = Thread(target=run, args=(prog,))
-            thread.setDaemon(True)
-            thread.start()
-
-
-        # start servers
-        if nserver > 0:
-            logging.info('Start %d servers by mpirun' % nserver)
-            pass_envs['DMLC_ROLE'] = 'server'
-            if sys.platform == 'win32':
-                prog = 'mpiexec -n %d %s %s' % (nserver, get_mpi_env(pass_envs), cmd)
-            else:
-                prog = 'mpirun -n %d %s %s' % (nserver, get_mpi_env(pass_envs), cmd)
-            thread = Thread(target=run, args=(prog,))
-            thread.setDaemon(True)
-            thread.start()
-
-
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=mpi_submit,
-                   pscmd=(' '.join(args.command)))
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/opts.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/opts.py
deleted file mode 100644
index d642b00..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/opts.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# pylint: disable=invalid-name
-"""Command line options of job submission script."""
-import os
-import argparse
-
-def get_cache_file_set(args):
-    """Get the list of files to be cached.
-
-    Parameters
-    ----------
-    args: ArgumentParser.Argument
-        The arguments returned by the parser.
-
-    Returns
-    -------
-    cache_file_set: set of str
-        The set of files to be cached to local execution environment.
-
-    command: list of str
-        The commands that get rewritten after the file cache is used.
-    """
-    fset = set()
-    cmds = []
-    if args.auto_file_cache:
-        for i in range(len(args.command)):
-            fname = args.command[i]
-            if os.path.exists(fname):
-                fset.add(fname)
-                cmds.append('./' + fname.split('/')[-1])
-            else:
-                cmds.append(fname)
-
-    for fname in args.files:
-        if os.path.exists(fname):
-            fset.add(fname)
-    return fset, cmds
-
-
-def get_memory_mb(mem_str):
-    """Get the memory in MB from memory string.
-
-    mem_str: str
-        String representation of memory requirement.
-
-    Returns
-    -------
-    mem_mb: int
-        Memory requirement in MB.
-    """
-    mem_str = mem_str.lower()
-    if mem_str.endswith('g'):
-        return int(float(mem_str[:-1]) * 1024)
-    elif mem_str.endswith('m'):
-        return int(float(mem_str[:-1]))
-    else:
-        msg = 'Invalid memory specification %s, need to be a number follows g or m' % mem_str
-        raise RuntimeError(msg)
-
-
-def get_opts(args=None):
-    """Get options to launch the job.
-
-    Returns
-    -------
-    args: ArgumentParser.Argument
-        The arguments returned by the parser.
-
-    cache_file_set: set of str
-        The set of files to be cached to local execution environment.
-    """
-    parser = argparse.ArgumentParser(description='DMLC job submission.')
-    parser.add_argument('--cluster', type=str,
-                        choices=['yarn', 'slurm', 'mpi', 'sge', 'local', 'ssh', 'mesos', 'kubernetes'],
-                        help=('Cluster type of this submission,' +
-                              'default to env variable ${DMLC_SUBMIT_CLUSTER}.'))
-    parser.add_argument('--num-workers', required=True, type=int,
-                        help='Number of worker proccess to be launched.')
-    parser.add_argument('--worker-cores', default=1, type=int,
-                        help='Number of cores to be allocated for each worker process.')
-    parser.add_argument('--worker-memory', default='1g', type=str,
-                        help=('Memory need to be allocated for each worker,' +
-                              ' need to ends with g or m'))
-    parser.add_argument('--num-servers', default=0, type=int,
-                        help='Number of server process to be launched. Only used in PS jobs.')
-    parser.add_argument('--server-cores', default=1, type=int,
-                        help=('Number of cores to be allocated for each server process.' +
-                              'Only used in PS jobs.'))
-    parser.add_argument('--server-memory', default='1g', type=str,
-                        help=('Memory need to be allocated for each server, ' +
-                              'need to ends with g or m.'))
-    parser.add_argument('--jobname', default=None, type=str, help='Name of the job.')
-    parser.add_argument('--queue', default='default', type=str,
-                        help='The submission queue the job should goes to.')
-    parser.add_argument('--log-level', default='INFO', type=str,
-                        choices=['INFO', 'DEBUG'],
-                        help='Logging level of the logger.')
-    parser.add_argument('--log-file', default=None, type=str,
-                        help=('Output log to the specific log file, ' +
-                              'the log is still printed on stderr.'))
-    parser.add_argument('--host-ip', default=None, type=str,
-                        help=('Host IP addressed, this is only needed ' +
-                              'if the host IP cannot be automatically guessed.'))
-    parser.add_argument('--hdfs-tempdir', default='/tmp', type=str,
-                        help=('Temporary directory in HDFS, ' +
-                              ' only needed in YARN mode.'))
-    parser.add_argument('--host-file', default=None, type=str,
-                        help=('The file contains the list of hostnames, needed for MPI and ssh.'))
-    parser.add_argument('--sge-log-dir', default=None, type=str,
-                        help=('Log directory of SGD jobs, only needed in SGE mode.'))
-    parser.add_argument(
-        '--auto-file-cache', default=True, type=bool,
-        help=('Automatically cache files appeared in the command line' +
-              'to local executor folder.' +
-              ' This will also cause rewritten of all the file names in the command,' +
-              ' e.g. `../../kmeans ../kmeans.conf` will be rewritten to `./kmeans kmeans.conf`'))
-    parser.add_argument('--files', default=[], action='append',
-                        help=('The cached file list which will be copied to local environment,' +
-                              ' You may need this option to cache additional files.' +
-                              ' You  --auto-file-cache is off'))
-    parser.add_argument('--archives', default=[], action='append',
-                        help=('Same as cached files,' +
-                              ' but corresponds to archieve files that will be unziped locally,' +
-                              ' You can use this option to ship python libraries.' +
-                              ' Only valid in yarn jobs.'))
-    parser.add_argument('--env', action='append', default=[],
-                        help='Client and ApplicationMaster environment variables.')
-    parser.add_argument('--yarn-app-classpath', type=str,
-                        help=('Explicit YARN ApplicationMaster classpath.' +
-                              'Can be used to override defaults.'))
-    parser.add_argument('--yarn-app-dir', type=str,
-                        default=os.path.join(os.path.dirname(__file__), os.pardir, 'yarn'),
-                        help=('Directory to YARN appmaster. Only used in YARN mode.'))
-    parser.add_argument('--mesos-master', type=str,
-                        help=('Mesos master, default to ${MESOS_MASTER}')),
-    parser.add_argument('--ship-libcxx', default=None, type=str,
-                        help=('The path to the customized gcc lib folder.' +
-                              'You can use this option to ship customized libstdc++' +
-                              ' library to the workers.'))
-    parser.add_argument('--sync-dst-dir', type=str,
-                        help = 'if specificed, it will sync the current \
-                        directory into remote machines\'s SYNC_DST_DIR')
-    parser.add_argument('command', nargs='+',
-                        help='Command to be launched')
-    parser.add_argument('--slurm-worker-nodes', default=None, type=int,
-                        help=('Number of nodes on which workers are run. Used only in SLURM mode.' +
-                              'If not explicitly set, it defaults to number of workers.'))
-    parser.add_argument('--slurm-server-nodes', default=None, type=int,
-                        help=('Number of nodes on which parameter servers are run. Used only in SLURM mode.' +
-                              'If not explicitly set, it defaults to number of parameter servers.'))
-    parser.add_argument('--kube-namespace', default="default", type=str,
-                        help=('A namespace in whitch all tasks are run. Used only in Kubernetes mode.' +
-                              'If not explicitly set, it defaults to default.'))
-    parser.add_argument('--kube-worker-image', default="mxnet/python", type=str,
-                        help=('Container image of workers. Used only in Kubernetes mode.' +
-                              'If not explicitly set, it defaults to mxnet/python.'))
-    parser.add_argument('--kube-server-image', default="mxnet/python", type=str,
-                        help=('Container image of servers. Used only in Kubernetes mode.' +
-                              'If not explicitly set, it defaults to mxnet/python.'))
-    parser.add_argument('--kube-worker-template', default=None, type=str,
-                        help=('Manifest template for workers. Used only in Kubernetes mode.' +
-                              'Can be used to override defaults.'))
-    parser.add_argument('--kube-server-template', default=None, type=str,
-                        help=('Manifest template for servers. Used only in Kubernetes mode.' +
-                              'Can be used to override defaults.'))
-    parser.add_argument('--local-num-attempt', default=0, type=int,
-                        help=('Number of attempt local tracker can restart slave.'))
-    (args, unknown) = parser.parse_known_args(args)
-    args.command += unknown
-
-    if args.cluster is None:
-        args.cluster = os.getenv('DMLC_SUBMIT_CLUSTER', None)
-
-    if args.cluster is None:
-        raise RuntimeError('--cluster is not specified, ' +
-                           'you can also specify the default behavior via ' +
-                           'environment variable DMLC_SUBMIT_CLUSTER')
-
-    args.worker_memory_mb = get_memory_mb(args.worker_memory)
-    args.server_memory_mb = get_memory_mb(args.server_memory)
-    return args
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/sge.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/sge.py
deleted file mode 100644
index 348b5ab..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/sge.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Submit jobs to Sun Grid Engine."""
-# pylint: disable=invalid-name
-from __future__ import absolute_import
-
-import os
-import subprocess
-from . import tracker
-
-def submit(args):
-    """Job submission script for SGE."""
-    if args.jobname is None:
-        args.jobname = ('dmlc%d.' % args.num_workers) + args.command[0].split('/')[-1]
-    if args.sge_log_dir is None:
-        args.sge_log_dir = args.jobname + '.log'
-
-    if os.path.exists(args.sge_log_dir):
-        if not os.path.isdir(args.sge_log_dir):
-            raise RuntimeError('specified --sge-log-dir %s is not a dir' % args.sge_log_dir)
-    else:
-        os.mkdir(args.sge_log_dir)
-
-    runscript = '%s/rundmlc.sh' % args.logdir
-    fo = open(runscript, 'w')
-    fo.write('source ~/.bashrc\n')
-    fo.write('export DMLC_TASK_ID=${SGE_TASK_ID}\n')
-    fo.write('export DMLC_JOB_CLUSTER=sge\n')
-    fo.write('\"$@\"\n')
-    fo.close()
-
-    def sge_submit(nworker, nserver, pass_envs):
-        """Internal submission function."""
-        env_arg = ','.join(['%s=\"%s\"' % (k, str(v)) for k, v in pass_envs.items()])
-        cmd = 'qsub -cwd -t 1-%d -S /bin/bash' % (nworker + nserver)
-        if args.queue != 'default':
-            cmd += '-q %s' % args.queue
-        cmd += ' -N %s ' % args.jobname
-        cmd += ' -e %s -o %s' % (args.logdir, args.logdir)
-        cmd += ' -pe orte %d' % (args.vcores)
-        cmd += ' -v %s,PATH=${PATH}:.' % env_arg
-        cmd += ' %s %s' % (runscript, ' '.join(args.command))
-        print(cmd)
-        subprocess.check_call(cmd, shell=True)
-        print('Waiting for the jobs to get up...')
-
-    # call submit, with nslave, the commands to run each job and submit function
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=sge_submit,
-                   pscmd=' '.join(args.command))
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/slurm.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/slurm.py
deleted file mode 100644
index 12681b1..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/slurm.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""
-DMLC submission script, SLURM version
-"""
-# pylint: disable=invalid-name
-from __future__ import absolute_import
-
-import subprocess, logging
-from threading import Thread
-from . import tracker
-
-def get_mpi_env(envs):
-    """get the slurm command for setting the environment
-    """
-    cmd = ''
-    for k, v in envs.items():
-        cmd += '%s=%s ' % (k, str(v))
-    return cmd
-
-
-def submit(args):
-    """Submission script with SLURM."""
-    def mpi_submit(nworker, nserver, pass_envs):
-        """Internal closure for job submission."""
-        def run(prog):
-            """run the program"""
-            subprocess.check_call(prog, shell=True)
-
-        cmd = ' '.join(args.command)
-
-        pass_envs['DMLC_JOB_CLUSTER'] = 'slurm'
-
-        if args.slurm_worker_nodes is None:
-          nworker_nodes = nworker
-        else:
-          nworker_nodes=args.slurm_worker_nodes
-
-
-        # start workers
-        if nworker > 0:
-          logging.info('Start %d workers by srun' % nworker)
-          pass_envs['DMLC_ROLE'] = 'worker'
-          prog = '%s srun --share --exclusive=user -N %d -n %d %s' % (get_mpi_env(pass_envs), nworker_nodes, nworker, cmd)
-          thread = Thread(target=run, args=(prog,))
-          thread.setDaemon(True)
-          thread.start()
-
-
-        if args.slurm_server_nodes is None:
-          nserver_nodes = nserver
-        else:
-          nserver_nodes=args.slurm_server_nodes
-
-        # start servers
-        if nserver > 0:
-          logging.info('Start %d servers by srun' % nserver)
-          pass_envs['DMLC_ROLE'] = 'server'
-          prog = '%s srun --share --exclusive=user -N %d -n %d %s' % (get_mpi_env(pass_envs), nserver_nodes, nserver, cmd)
-          thread = Thread(target=run, args=(prog,))
-          thread.setDaemon(True)
-          thread.start()
-
-
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=mpi_submit,
-                   pscmd=(' '.join(args.command)))
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/ssh.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/ssh.py
deleted file mode 100644
index 5eecf12..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/ssh.py
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/env python3
-"""
-DMLC submission script by ssh
-
-One need to make sure all slaves machines are ssh-able.
-"""
-from __future__ import absolute_import
-
-from multiprocessing import Pool, Process
-import os, subprocess, logging
-from threading import Thread
-from . import tracker
-
-def sync_dir(local_dir, slave_node, slave_dir):
-    """
-    sync the working directory from root node into slave node
-    """
-    remote = slave_node[0] + ':' + slave_dir
-    logging.info('rsync %s -> %s', local_dir, remote)
-    prog = 'rsync -az --rsh="ssh -o StrictHostKeyChecking=no -p %s" %s %s' % (
-        slave_node[1], local_dir, remote)
-    subprocess.check_call([prog], shell = True)
-
-def get_env(pass_envs):
-    envs = []
-    # get system envs
-    keys = ['OMP_NUM_THREADS', 'KMP_AFFINITY', 'LD_LIBRARY_PATH', 'AWS_ACCESS_KEY_ID',
-            'AWS_SECRET_ACCESS_KEY', 'DMLC_INTERFACE']
-    for k in keys:
-        v = os.getenv(k)
-        if v is not None:
-            envs.append('export ' + k + '=' + v + ';')
-    # get ass_envs
-    for k, v in pass_envs.items():
-        envs.append('export ' + str(k) + '=' + str(v) + ';')
-    return (' '.join(envs))
-
-def submit(args):
-    assert args.host_file is not None
-    with open(args.host_file) as f:
-        tmp = f.readlines()
-    assert len(tmp) > 0
-    hosts=[]
-    for h in tmp:
-        if len(h.strip()) > 0:
-            # parse addresses of the form ip:port
-            h = h.strip()
-
-            # parse mpi host file form ip slots=??
-            # this is to create an unified api for mpi and ssh
-            i = h.find("slots=")
-            if i != -1:
-                h = h[:i].strip()
-
-            i = h.find(":")
-            p = "22"
-            if i != -1:
-                p = h[i+1:]
-                h = h[:i]
-            # hosts now contain the pair ip, port
-            hosts.append((h, p))
-
-    def ssh_submit(nworker, nserver, pass_envs):
-        """
-        customized submit script
-        """
-        # thread func to run the job
-        def run(prog):
-            subprocess.check_call(prog, shell = True)
-
-        # sync programs if necessary
-        local_dir = os.getcwd()+'/'
-        working_dir = local_dir
-        if args.sync_dst_dir is not None and args.sync_dst_dir != 'None':
-            working_dir = args.sync_dst_dir
-            pool = Pool(processes=len(hosts))
-            for h in hosts:
-                pool.apply_async(sync_dir, args=(local_dir, h, working_dir))
-            pool.close()
-            pool.join()
-            
-
-        # launch jobs
-        for i in range(nworker + nserver):
-            pass_envs['DMLC_ROLE'] = 'server' if i < nserver else 'worker'
-            (node, port) = hosts[i % len(hosts)]
-            pass_envs['DMLC_NODE_HOST'] = node
-            prog = get_env(pass_envs) + ' cd ' + working_dir + '; ' + (' '.join(args.command))
-            prog = 'ssh -o StrictHostKeyChecking=no ' + node + ' -p ' + port + ' \'' + prog + '\''
-            thread = Thread(target = run, args=(prog,))
-            thread.setDaemon(True)
-            thread.start()
-
-        return ssh_submit
-
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=ssh_submit,
-                   pscmd=(' '.join(args.command)),
-                   hostIP=args.host_ip)
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/submit.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/submit.py
deleted file mode 100644
index e8db4f2..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/submit.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""Job submission script"""
-from __future__ import absolute_import
-
-import logging
-from . import opts
-from . import local
-from . import mpi
-from . import sge
-from . import yarn
-from . import mesos
-from . import kubernetes
-
-def config_logger(args):
-    """Configure the logger according to the arguments
-
-    Parameters
-    ----------
-    args: argparser.Arguments
-       The arguments passed in by the user.
-    """
-    fmt = '%(asctime)s %(levelname)s %(message)s'
-    if args.log_level == 'INFO':
-        level = logging.INFO
-    elif args.log_level == 'DEBUG':
-        level = logging.DEBUG
-    else:
-        raise RuntimeError("Unknown logging level %s" % args.log_level)
-
-    if args.log_file is None:
-        logging.basicConfig(format=fmt, level=level)
-    else:
-        logging.basicConfig(format=fmt, level=level, filename=args.log_file)
-        console = logging.StreamHandler()
-        console.setFormatter(logging.Formatter(fmt))
-        console.setLevel(level)
-        logging.getLogger('').addHandler(console)
-
-def main():
-    """Main submission function."""
-    args = opts.get_opts()
-    config_logger(args)
-
-    if args.cluster == 'local':
-        local.submit(args)
-    elif args.cluster == 'sge':
-        sge.submit(args)
-    elif args.cluster == 'yarn':
-        yarn.submit(args)
-    elif args.cluster == 'mpi':
-        mpi.submit(args)
-    elif args.cluster == 'mesos':
-        mesos.submit(args)
-    elif args.cluster == 'kubernetes':
-        kubernetes.submit(args)
-    else:
-        raise RuntimeError('Unknown submission cluster type %s' % args.cluster)
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/tracker.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/tracker.py
deleted file mode 100644
index ad7217b..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/tracker.py
+++ /dev/null
@@ -1,487 +0,0 @@
-"""
-Tracker script for DMLC
-Implements the tracker control protocol
- - start dmlc jobs
- - start ps scheduler and rabit tracker
- - help nodes to establish links with each other
-
-Tianqi Chen
-"""
-# pylint: disable=invalid-name, missing-docstring, too-many-arguments, too-many-locals
-# pylint: disable=too-many-branches, too-many-statements
-from __future__ import absolute_import
-
-import os
-import sys
-import socket
-import struct
-import subprocess
-import argparse
-import time
-import logging
-from threading import Thread
-
-class ExSocket(object):
-    """
-    Extension of socket to handle recv and send of special data
-    """
-    def __init__(self, sock):
-        self.sock = sock
-    def recvall(self, nbytes):
-        res = []
-        nread = 0
-        while nread < nbytes:
-            chunk = self.sock.recv(min(nbytes - nread, 1024))
-            nread += len(chunk)
-            res.append(chunk)
-        return b''.join(res)
-    def recvint(self):
-        return struct.unpack('@i', self.recvall(4))[0]
-    def sendint(self, n):
-        self.sock.sendall(struct.pack('@i', n))
-    def sendstr(self, s):
-        self.sendint(len(s))
-        self.sock.sendall(s.encode())
-    def recvstr(self):
-        slen = self.recvint()
-        return self.recvall(slen).decode()
-
-# magic number used to verify existence of data
-kMagic = 0xff99
-
-def get_some_ip(host):
-    return socket.getaddrinfo(host, None)[0][4][0]
-
-def get_family(addr):
-    return socket.getaddrinfo(addr, None)[0][0]
-
-class SlaveEntry(object):
-    def __init__(self, sock, s_addr):
-        slave = ExSocket(sock)
-        self.sock = slave
-        self.host = get_some_ip(s_addr[0])
-        magic = slave.recvint()
-        assert magic == kMagic, 'invalid magic number=%d from %s' % (magic, self.host)
-        slave.sendint(kMagic)
-        self.rank = slave.recvint()
-        self.world_size = slave.recvint()
-        self.jobid = slave.recvstr()
-        self.cmd = slave.recvstr()
-        self.wait_accept = 0
-        self.port = None
-
-    def decide_rank(self, job_map):
-        if self.rank >= 0:
-            return self.rank
-        if self.jobid != 'NULL' and self.jobid in job_map:
-            return job_map[self.jobid]
-        return -1
-
-    def assign_rank(self, rank, wait_conn, tree_map, parent_map, ring_map):
-        self.rank = rank
-        nnset = set(tree_map[rank])
-        rprev, rnext = ring_map[rank]
-        self.sock.sendint(rank)
-        # send parent rank
-        self.sock.sendint(parent_map[rank])
-        # send world size
-        self.sock.sendint(len(tree_map))
-        self.sock.sendint(len(nnset))
-        # send the rprev and next link
-        for r in nnset:
-            self.sock.sendint(r)
-        # send prev link
-        if rprev != -1 and rprev != rank:
-            nnset.add(rprev)
-            self.sock.sendint(rprev)
-        else:
-            self.sock.sendint(-1)
-        # send next link
-        if rnext != -1 and rnext != rank:
-            nnset.add(rnext)
-            self.sock.sendint(rnext)
-        else:
-            self.sock.sendint(-1)
-        while True:
-            ngood = self.sock.recvint()
-            goodset = set([])
-            for _ in range(ngood):
-                goodset.add(self.sock.recvint())
-            assert goodset.issubset(nnset)
-            badset = nnset - goodset
-            conset = []
-            for r in badset:
-                if r in wait_conn:
-                    conset.append(r)
-            self.sock.sendint(len(conset))
-            self.sock.sendint(len(badset) - len(conset))
-            for r in conset:
-                self.sock.sendstr(wait_conn[r].host)
-                self.sock.sendint(wait_conn[r].port)
-                self.sock.sendint(r)
-            nerr = self.sock.recvint()
-            if nerr != 0:
-                continue
-            self.port = self.sock.recvint()
-            rmset = []
-            # all connection was successuly setup
-            for r in conset:
-                wait_conn[r].wait_accept -= 1
-                if wait_conn[r].wait_accept == 0:
-                    rmset.append(r)
-            for r in rmset:
-                wait_conn.pop(r, None)
-            self.wait_accept = len(badset) - len(conset)
-            return rmset
-
-class RabitTracker(object):
-    """
-    tracker for rabit
-    """
-    def __init__(self, hostIP, nslave, port=9091, port_end=9999):
-        sock = socket.socket(get_family(hostIP), socket.SOCK_STREAM)
-        for port in range(port, port_end):
-            try:
-                sock.bind((hostIP, port))
-                self.port = port
-                break
-            except socket.error as e:
-                if e.errno in [98, 48]:
-                    continue
-                else:
-                    raise
-        sock.listen(256)
-        self.sock = sock
-        self.hostIP = hostIP
-        self.thread = None
-        self.start_time = None
-        self.end_time = None
-        self.nslave = nslave
-        logging.info('start listen on %s:%d', hostIP, self.port)
-
-    def __del__(self):
-        self.sock.close()
-
-    @staticmethod
-    def get_neighbor(rank, nslave):
-        rank = rank + 1
-        ret = []
-        if rank > 1:
-            ret.append(rank // 2 - 1)
-        if rank * 2 - 1 < nslave:
-            ret.append(rank * 2 - 1)
-        if rank * 2 < nslave:
-            ret.append(rank * 2)
-        return ret
-
-    def slave_envs(self):
-        """
-        get enviroment variables for slaves
-        can be passed in as args or envs
-        """
-        return {'DMLC_TRACKER_URI': self.hostIP,
-                'DMLC_TRACKER_PORT': self.port}
-
-    def get_tree(self, nslave):
-        tree_map = {}
-        parent_map = {}
-        for r in range(nslave):
-            tree_map[r] = self.get_neighbor(r, nslave)
-            parent_map[r] = (r + 1) // 2 - 1
-        return tree_map, parent_map
-
-    def find_share_ring(self, tree_map, parent_map, r):
-        """
-        get a ring structure that tends to share nodes with the tree
-        return a list starting from r
-        """
-        nset = set(tree_map[r])
-        cset = nset - set([parent_map[r]])
-        if len(cset) == 0:
-            return [r]
-        rlst = [r]
-        cnt = 0
-        for v in cset:
-            vlst = self.find_share_ring(tree_map, parent_map, v)
-            cnt += 1
-            if cnt == len(cset):
-                vlst.reverse()
-            rlst += vlst
-        return rlst
-
-    def get_ring(self, tree_map, parent_map):
-        """
-        get a ring connection used to recover local data
-        """
-        assert parent_map[0] == -1
-        rlst = self.find_share_ring(tree_map, parent_map, 0)
-        assert len(rlst) == len(tree_map)
-        ring_map = {}
-        nslave = len(tree_map)
-        for r in range(nslave):
-            rprev = (r + nslave - 1) % nslave
-            rnext = (r + 1) % nslave
-            ring_map[rlst[r]] = (rlst[rprev], rlst[rnext])
-        return ring_map
-
-    def get_link_map(self, nslave):
-        """
-        get the link map, this is a bit hacky, call for better algorithm
-        to place similar nodes together
-        """
-        tree_map, parent_map = self.get_tree(nslave)
-        ring_map = self.get_ring(tree_map, parent_map)
-        rmap = {0 : 0}
-        k = 0
-        for i in range(nslave - 1):
-            k = ring_map[k][1]
-            rmap[k] = i + 1
-
-        ring_map_ = {}
-        tree_map_ = {}
-        parent_map_ = {}
-        for k, v in ring_map.items():
-            ring_map_[rmap[k]] = (rmap[v[0]], rmap[v[1]])
-        for k, v in tree_map.items():
-            tree_map_[rmap[k]] = [rmap[x] for x in v]
-        for k, v in parent_map.items():
-            if k != 0:
-                parent_map_[rmap[k]] = rmap[v]
-            else:
-                parent_map_[rmap[k]] = -1
-        return tree_map_, parent_map_, ring_map_
-
-    def accept_slaves(self, nslave):
-        # set of nodes that finishs the job
-        shutdown = {}
-        # set of nodes that is waiting for connections
-        wait_conn = {}
-        # maps job id to rank
-        job_map = {}
-        # list of workers that is pending to be assigned rank
-        pending = []
-        # lazy initialize tree_map
-        tree_map = None
-
-        while len(shutdown) != nslave:
-            fd, s_addr = self.sock.accept()
-            s = SlaveEntry(fd, s_addr)
-            if s.cmd == 'print':
-                msg = s.sock.recvstr()
-                logging.info(msg.strip())
-                continue
-            if s.cmd == 'shutdown':
-                assert s.rank >= 0 and s.rank not in shutdown
-                assert s.rank not in wait_conn
-                shutdown[s.rank] = s
-                logging.debug('Recieve %s signal from %d', s.cmd, s.rank)
-                continue
-            assert s.cmd == 'start' or s.cmd == 'recover'
-            # lazily initialize the slaves
-            if tree_map is None:
-                assert s.cmd == 'start'
-                if s.world_size > 0:
-                    nslave = s.world_size
-                tree_map, parent_map, ring_map = self.get_link_map(nslave)
-                # set of nodes that is pending for getting up
-                todo_nodes = list(range(nslave))
-            else:
-                assert s.world_size == -1 or s.world_size == nslave
-            if s.cmd == 'recover':
-                assert s.rank >= 0
-
-            rank = s.decide_rank(job_map)
-            # batch assignment of ranks
-            if rank == -1:
-                assert len(todo_nodes) != 0
-                pending.append(s)
-                if len(pending) == len(todo_nodes):
-                    pending.sort(key=lambda x: x.host)
-                    for s in pending:
-                        rank = todo_nodes.pop(0)
-                        if s.jobid != 'NULL':
-                            job_map[s.jobid] = rank
-                        s.assign_rank(rank, wait_conn, tree_map, parent_map, ring_map)
-                        if s.wait_accept > 0:
-                            wait_conn[rank] = s
-                        logging.debug('Recieve %s signal from %s; assign rank %d',
-                                      s.cmd, s.host, s.rank)
-                if len(todo_nodes) == 0:
-                    logging.info('@tracker All of %d nodes getting started', nslave)
-                    self.start_time = time.time()
-            else:
-                s.assign_rank(rank, wait_conn, tree_map, parent_map, ring_map)
-                logging.debug('Recieve %s signal from %d', s.cmd, s.rank)
-                if s.wait_accept > 0:
-                    wait_conn[rank] = s
-        logging.info('@tracker All nodes finishes job')
-        self.end_time = time.time()
-        logging.info('@tracker %s secs between node start and job finish',
-                     str(self.end_time - self.start_time))
-
-    def start(self, nslave):
-        def run():
-            self.accept_slaves(nslave)
-        self.thread = Thread(target=run, args=())
-        self.thread.setDaemon(True)
-        self.thread.start()
-
-    def join(self):
-        while self.thread.isAlive():
-            self.thread.join(100)
-
-    def alive(self):
-        return self.thread.isAlive()
-
-class PSTracker(object):
-    """
-    Tracker module for PS
-    """
-    def __init__(self, hostIP, cmd, port=9091, port_end=9999, envs=None):
-        """
-        Starts the PS scheduler
-        """
-        self.cmd = cmd
-        if cmd is None:
-            return
-        envs = {} if envs is None else envs
-        self.hostIP = hostIP
-        sock = socket.socket(get_family(hostIP), socket.SOCK_STREAM)
-        for port in range(port, port_end):
-            try:
-                sock.bind(('', port))
-                self.port = port
-                sock.close()
-                break
-            except socket.error:
-                continue
-        env = os.environ.copy()
-
-        env['DMLC_ROLE'] = 'scheduler'
-        env['DMLC_PS_ROOT_URI'] = str(self.hostIP)
-        env['DMLC_PS_ROOT_PORT'] = str(self.port)
-        for k, v in envs.items():
-            env[k] = str(v)
-        self.thread = Thread(
-            target=(lambda: subprocess.check_call(self.cmd, env=env, shell=True, executable='/bin/bash')), args=())
-        self.thread.setDaemon(True)
-        self.thread.start()
-
-    def join(self):
-        if self.cmd is not None:
-            while self.thread.isAlive():
-                self.thread.join(100)
-
-    def slave_envs(self):
-        if self.cmd is None:
-            return {}
-        else:
-            return {'DMLC_PS_ROOT_URI': self.hostIP,
-                    'DMLC_PS_ROOT_PORT': self.port}
-
-    def alive(self):
-        if self.cmd is not None:
-            return self.thread.isAlive()
-        else:
-            return False
-
-
-def get_host_ip(hostIP=None):
-    if hostIP is None or hostIP == 'auto':
-        hostIP = 'ip'
-
-    if hostIP == 'dns':
-        hostIP = socket.getfqdn()
-    elif hostIP == 'ip':
-        from socket import gaierror
-        try:
-            hostIP = socket.gethostbyname(socket.getfqdn())
-        except gaierror:
-            logging.warn('gethostbyname(socket.getfqdn()) failed... trying on hostname()')
-            hostIP = socket.gethostbyname(socket.gethostname())
-        if hostIP.startswith("127."):
-            s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-            # doesn't have to be reachable
-            s.connect(('10.255.255.255', 1))
-            hostIP = s.getsockname()[0]
-    return hostIP
-
-
-def submit(nworker, nserver, fun_submit, hostIP='auto', pscmd=None):
-    if nserver == 0:
-        pscmd = None
-
-    envs = {'DMLC_NUM_WORKER' : nworker,
-            'DMLC_NUM_SERVER' : nserver}
-    hostIP = get_host_ip(hostIP)
-
-    if nserver == 0:
-        rabit = RabitTracker(hostIP=hostIP, nslave=nworker)
-        envs.update(rabit.slave_envs())
-        rabit.start(nworker)
-        if rabit.alive():
-           fun_submit(nworker, nserver, envs)
-    else:
-        pserver = PSTracker(hostIP=hostIP, cmd=pscmd, envs=envs)
-        envs.update(pserver.slave_envs())
-        if pserver.alive():
-            fun_submit(nworker, nserver, envs)
-
-    if nserver == 0:
-        rabit.join()
-    else:
-        pserver.join()
-
-def start_rabit_tracker(args):
-    """Standalone function to start rabit tracker.
-
-    Parameters
-    ----------
-    args: arguments to start the rabit tracker.
-    """
-    envs = {'DMLC_NUM_WORKER' : args.num_workers,
-            'DMLC_NUM_SERVER' : args.num_servers}
-    rabit = RabitTracker(hostIP=get_host_ip(args.host_ip), nslave=args.num_workers)
-    envs.update(rabit.slave_envs())
-    rabit.start(args.num_workers)
-    sys.stdout.write('DMLC_TRACKER_ENV_START\n')
-    # simply write configuration to stdout
-    for k, v in envs.items():
-        sys.stdout.write('%s=%s\n' % (k, str(v)))
-    sys.stdout.write('DMLC_TRACKER_ENV_END\n')
-    sys.stdout.flush()
-    rabit.join()
-
-
-def main():
-    """Main function if tracker is executed in standalone mode."""
-    parser = argparse.ArgumentParser(description='Rabit Tracker start.')
-    parser.add_argument('--num-workers', required=True, type=int,
-                        help='Number of worker proccess to be launched.')
-    parser.add_argument('--num-servers', default=0, type=int,
-                        help='Number of server process to be launched. Only used in PS jobs.')
-    parser.add_argument('--host-ip', default=None, type=str,
-                        help=('Host IP addressed, this is only needed ' +
-                              'if the host IP cannot be automatically guessed.'))
-    parser.add_argument('--log-level', default='INFO', type=str,
-                        choices=['INFO', 'DEBUG'],
-                        help='Logging level of the logger.')
-    args = parser.parse_args()
-
-    fmt = '%(asctime)s %(levelname)s %(message)s'
-    if args.log_level == 'INFO':
-        level = logging.INFO
-    elif args.log_level == 'DEBUG':
-        level = logging.DEBUG
-    else:
-        raise RuntimeError("Unknown logging level %s" % args.log_level)
-
-    logging.basicConfig(format=fmt, level=level)
-
-    if args.num_servers == 0:
-        start_rabit_tracker(args)
-    else:
-        raise RuntimeError("Do not yet support start ps tracker in standalone mode.")
-
-if __name__ == "__main__":
-    main()
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/util.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/util.py
deleted file mode 100644
index 371e580..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/util.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import sys
-
-# Compatibility shim for handling strings
-PY3 = (sys.version_info[0] == 3)
-
-if PY3:
-    def py_str(x):
-        """convert c string back to python string"""
-        return x.decode('utf-8')
-else:
-    def py_str(x):
-        """convert c string back to python string"""
-        return x
diff --git a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/yarn.py b/ml-xgboost/dmlc-core/tracker/dmlc_tracker/yarn.py
deleted file mode 100644
index f11a089..0000000
--- a/ml-xgboost/dmlc-core/tracker/dmlc_tracker/yarn.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""
-This is a script to submit dmlc job via Yarn
-dmlc will run as a Yarn application
-"""
-# pylint: disable=invalid-name, too-many-locals, too-many-branches, missing-docstring
-from __future__ import absolute_import
-import os
-import sys
-import subprocess
-import warnings
-import logging
-import platform
-from threading import Thread
-from . import opts
-from . import tracker
-from .util import py_str
-
-def yarn_submit(args, nworker, nserver, pass_env):
-    """Submission function for YARN."""
-    is_windows = os.name == 'nt'
-    hadoop_home = os.getenv('HADOOP_HOME')
-    assert hadoop_home is not None, 'Need to set HADOOP_HOME for YARN submission.'
-    hadoop_binary = os.path.join(hadoop_home, 'bin', 'hadoop')
-    assert os.path.exists(hadoop_binary), "HADOOP_HOME does not contain the hadoop binary"
-
-    if args.jobname is None:
-        if args.num_servers == 0:
-            prefix = ('DMLC[nworker=%d]:' % args.num_workers)
-        else:
-            prefix = ('DMLC[nworker=%d,nsever=%d]:' % (args.num_workers, args.num_servers))
-        args.jobname = prefix + args.command[0].split('/')[-1]
-
-    # Determine path for Yarn helpers
-    YARN_JAR_PATH = os.path.join(args.yarn_app_dir, 'dmlc-yarn.jar')
-    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-    YARN_BOOT_PY = os.path.join(curr_path, 'launcher.py')
-
-    if not os.path.exists(YARN_JAR_PATH):
-        warnings.warn("cannot find \"%s\", I will try to run build" % YARN_JAR_PATH)
-        cmd = 'cd %s;./build.%s' % \
-              (os.path.join(os.path.dirname(__file__), os.pardir, 'yarn'),
-               'bat' if is_windows else 'sh')
-        print(cmd)
-        subprocess.check_call(cmd, shell=True, env=os.environ)
-        assert os.path.exists(YARN_JAR_PATH), "failed to build dmlc-yarn.jar, try it manually"
-
-    # detech hadoop version
-    (out, _) = subprocess.Popen('%s version' % hadoop_binary,
-                                shell=True, stdout=subprocess.PIPE).communicate()
-    out = py_str(out).split('\n')[0].split()
-    assert out[0] == 'Hadoop', 'cannot parse hadoop version string'
-    hadoop_version = int(out[1].split('.')[0])
-    (classpath, _) = subprocess.Popen('%s classpath' % hadoop_binary,
-                                      shell=True, stdout=subprocess.PIPE).communicate()
-    classpath = py_str(classpath).strip()
-
-    if hadoop_version < 2:
-        raise RuntimeError('Hadoop Version is %s, dmlc_yarn will need Yarn(Hadoop 2.0)' % out[1])
-
-    fset, new_command = opts.get_cache_file_set(args)
-    fset.add(YARN_JAR_PATH)
-    fset.add(YARN_BOOT_PY)
-    ar_list = []
-
-    for fname in args.archives:
-        fset.add(fname)
-        ar_list.append(os.path.basename(fname))
-
-    JAVA_HOME = os.getenv('JAVA_HOME')
-    if JAVA_HOME is None:
-        JAVA = 'java'
-    else:
-        JAVA = os.path.join(JAVA_HOME, 'bin', 'java')
-    cmd = '%s -cp %s%s%s org.apache.hadoop.yarn.dmlc.Client '\
-          % (JAVA, classpath, ';' if is_windows else ':', YARN_JAR_PATH)
-    env = os.environ.copy()
-    for k, v in pass_env.items():
-        env[k] = str(v)
-
-    # ship lib-stdc++.so
-    if args.ship_libcxx is not None:
-        if platform.architecture()[0] == '64bit':
-            libcxx = args.ship_libcxx + '/libstdc++.so.6'
-        else:
-            libcxx = args.ship_libcxx + '/libstdc++.so'
-        fset.add(libcxx)
-        # update local LD_LIBRARY_PATH
-        LD_LIBRARY_PATH = env['LD_LIBRARY_PATH'] if 'LD_LIBRARY_PATH' in env else ''
-        env['LD_LIBRARY_PATH'] = args.ship_libcxx + ':' + LD_LIBRARY_PATH
-
-    env['DMLC_JOB_CLUSTER'] = 'yarn'
-    env['DMLC_WORKER_CORES'] = str(args.worker_cores)
-    env['DMLC_WORKER_MEMORY_MB'] = str(args.worker_memory_mb)
-    env['DMLC_SERVER_CORES'] = str(args.server_cores)
-    env['DMLC_SERVER_MEMORY_MB'] = str(args.server_memory_mb)
-    env['DMLC_NUM_WORKER'] = str(args.num_workers)
-    env['DMLC_NUM_SERVER'] = str(args.num_servers)
-    env['DMLC_JOB_ARCHIVES'] = ':'.join(ar_list)
-
-    for f in fset:
-        cmd += ' -file %s' % f
-    cmd += ' -jobname %s ' % args.jobname
-    cmd += ' -tempdir %s ' % args.hdfs_tempdir
-    cmd += ' -queue %s ' % args.queue
-    if args.yarn_app_classpath:
-        cmd += ' -appcp %s ' % args.yarn_app_classpath
-    for entry in args.env:
-        cmd += ' -env %s ' % entry
-    cmd += (' '.join(['./launcher.py'] + new_command))
-
-    logging.debug("Submit job with %d workers and %d servers", nworker, nserver)
-    def run():
-        """internal running function."""
-        logging.debug(cmd)
-        subprocess.check_call(cmd, shell=True, env=env)
-
-    thread = Thread(target=run, args=())
-    thread.setDaemon(True)
-    thread.start()
-    return thread
-
-def submit(args):
-    submit_thread = []
-    def yarn_submit_pass(nworker, nserver, pass_env):
-        submit_thread.append(yarn_submit(args, nworker, nserver, pass_env))
-
-    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-    YARN_BOOT_PY = os.path.join(curr_path, 'launcher.py')
-    tracker.submit(args.num_workers, args.num_servers,
-                   fun_submit=yarn_submit_pass,
-                   pscmd=(' '.join([YARN_BOOT_PY] + args.command)))
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/.gitignore b/ml-xgboost/dmlc-core/tracker/yarn/.gitignore
deleted file mode 100644
index 1162c62..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/.gitignore
+++ /dev/null
@@ -1,4 +0,0 @@
-bin
-.classpath
-.project
-*.jar
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/README.md b/ml-xgboost/dmlc-core/tracker/yarn/README.md
deleted file mode 100644
index fa04efb..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-DMLC YARN AppMaster
-===================
-* This folder contains Application code to allow rabit run on Yarn.
-* See [tracker](../) for job submission.
-  - run ```./build.sh``` to build the jar, before using the script
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/build.bat b/ml-xgboost/dmlc-core/tracker/yarn/build.bat
deleted file mode 100644
index 95e8fec..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/build.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-mkdir bin
-
-for /f %%i in ('%HADOOP_HOME%\bin\hadoop classpath') do set CPATH=%%i
-%JAVA_HOME%/bin/javac -cp %CPATH% -d bin src/main/java/org/apache/hadoop/yarn/dmlc/*.java
-%JAVA_HOME%/bin/jar cf dmlc-yarn.jar -C bin .
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/build.sh b/ml-xgboost/dmlc-core/tracker/yarn/build.sh
deleted file mode 100644
index 616ebc4..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/build.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-if [ ! -d bin ]; then
-    mkdir bin
-fi
-
-CPATH=`${HADOOP_HOME}/bin/hadoop classpath`
-${JAVA_HOME}/bin/javac -cp $CPATH -d bin src/main/java/org/apache/hadoop/yarn/dmlc/*.java
-${JAVA_HOME}/bin/jar cf dmlc-yarn.jar -C bin .
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/pom.xml b/ml-xgboost/dmlc-core/tracker/yarn/pom.xml
deleted file mode 100644
index 3866f18..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/pom.xml
+++ /dev/null
@@ -1,168 +0,0 @@
-<?xml version="1.0"?>
-<!--
-  Licensed under the Apache License, Version 2.0 (the "License");
-  you may not use this file except in compliance with the License.
-  You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-  Unless required by applicable law or agreed to in writing, software
-  distributed under the License is distributed on an "AS IS" BASIS,
-  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  See the License for the specific language governing permissions and
-  limitations under the License. See accompanying LICENSE file.
--->
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
-                      http://maven.apache.org/xsd/maven-4.0.0.xsd">
-  <parent>
-    <artifactId>hadoop-yarn-applications</artifactId>
-    <groupId>org.apache.hadoop</groupId>
-    <version>2.6.0</version>
-  </parent>
-  <modelVersion>4.0.0</modelVersion>
-  <groupId>org.apache.hadoop.yarn.dmlc</groupId>
-  <artifactId>dmlc-core-yarn</artifactId>
-  <name>dmlc-core-yarn</name>
-
-  <dependencies>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-common</artifactId>
-      <scope>provided</scope>
-      <exclusions>
-        <exclusion>
-          <groupId>commons-el</groupId>
-          <artifactId>commons-el</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>tomcat</groupId>
-          <artifactId>jasper-runtime</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>tomcat</groupId>
-          <artifactId>jasper-compiler</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.mortbay.jetty</groupId>
-          <artifactId>jsp-2.1-jetty</artifactId>
-        </exclusion>
-      </exclusions>
-    </dependency>
-
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-
-    <dependency>
-      <groupId>log4j</groupId>
-      <artifactId>log4j</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>commons-lang</groupId>
-      <artifactId>commons-lang</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>com.google.guava</groupId>
-      <artifactId>guava</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>commons-logging</groupId>
-      <artifactId>commons-logging</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>commons-cli</groupId>
-      <artifactId>commons-cli</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>commons-io</groupId>
-      <artifactId>commons-io</artifactId>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-annotations</artifactId>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-common</artifactId>
-      <type>test-jar</type>
-      <scope>test</scope>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-yarn-api</artifactId>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-yarn-common</artifactId>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-yarn-client</artifactId>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-yarn-server-nodemanager</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-yarn-server-resourcemanager</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <!-- 'mvn dependency:analyze' fails to detect use of this dependency -->
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-yarn-server-tests</artifactId>
-      <type>test-jar</type>
-      <scope>test</scope>
-    </dependency>
-  </dependencies>
-
-  <build>
-    <plugins>
-      <plugin>
-        <artifactId>maven-jar-plugin</artifactId>
-        <executions>
-          <execution>
-            <goals>
-              <goal>jar</goal>
-            </goals>
-            <!-- strictly speaking, the unit test is really a regression test. It
-                 needs the main jar to be available to be able to run. -->
-            <phase>test-compile</phase>
-          </execution>
-        </executions>
-        <configuration>
-           <archive>
-             <manifest>
-               <mainClass>org.apache.hadoop.yarn.dmlc.Client</mainClass>
-             </manifest>
-           </archive>
-        </configuration>
-      </plugin>
-      <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
-        <artifactId>maven-surefire-plugin</artifactId>
-        <configuration>
-          <environmentVariables>
-            <JAVA_HOME>${java.home}</JAVA_HOME>
-          </environmentVariables>
-       </configuration>
-      </plugin>
-    </plugins>
-  </build>
-
-
-</project>
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/ApplicationMaster.java b/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/ApplicationMaster.java
deleted file mode 100644
index 83ab06b..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/ApplicationMaster.java
+++ /dev/null
@@ -1,689 +0,0 @@
-package org.apache.hadoop.yarn.dmlc;
-
-import java.io.File;
-import java.io.IOException;
-import java.nio.ByteBuffer;
-import java.util.List;
-import java.util.Map;
-import java.util.Queue;
-import java.util.Collection;
-import java.util.Collections;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-import org.apache.hadoop.conf.Configuration;
-import org.apache.hadoop.fs.FileStatus;
-import org.apache.hadoop.fs.FileSystem;
-import org.apache.hadoop.fs.Path;
-import org.apache.hadoop.io.DataOutputBuffer;
-import org.apache.hadoop.yarn.util.ConverterUtils;
-import org.apache.hadoop.yarn.util.Records;
-import org.apache.hadoop.yarn.conf.YarnConfiguration;
-import org.apache.hadoop.yarn.api.ApplicationConstants;
-import org.apache.hadoop.yarn.api.protocolrecords.RegisterApplicationMasterResponse;
-import org.apache.hadoop.yarn.api.records.Container;
-import org.apache.hadoop.yarn.api.records.ContainerExitStatus;
-import org.apache.hadoop.yarn.api.records.ContainerLaunchContext;
-import org.apache.hadoop.yarn.api.records.ContainerState;
-import org.apache.hadoop.yarn.api.records.FinalApplicationStatus;
-import org.apache.hadoop.yarn.api.records.LocalResource;
-import org.apache.hadoop.yarn.api.records.LocalResourceType;
-import org.apache.hadoop.yarn.api.records.LocalResourceVisibility;
-import org.apache.hadoop.yarn.api.records.Priority;
-import org.apache.hadoop.yarn.api.records.Resource;
-import org.apache.hadoop.yarn.api.records.ContainerId;
-import org.apache.hadoop.yarn.api.records.ContainerStatus;
-import org.apache.hadoop.yarn.api.records.NodeReport;
-import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest;
-import org.apache.hadoop.yarn.client.api.async.NMClientAsync;
-import org.apache.hadoop.yarn.client.api.async.AMRMClientAsync;
-import org.apache.hadoop.security.Credentials;
-import org.apache.hadoop.security.UserGroupInformation;
-import org.apache.hadoop.security.token.Token;
-
-/**
- * application master for allocating resources of dmlc client
- *
- * @author Tianqi Chen
- */
-public class ApplicationMaster {
-    // logger
-    private static final Log LOG = LogFactory.getLog(ApplicationMaster.class);
-    // configuration
-    private Configuration conf = new YarnConfiguration();
-    // hdfs handler
-    private FileSystem dfs;
-
-    // number of cores allocated for each worker task
-    private int workerCores = 1;
-    // number of cores allocated for each server task
-    private int serverCores = 1;
-    // memory needed requested for the worker task
-    private int workerMemoryMB = 10;
-    // memory needed requested for the server task
-    private int serverMemoryMB = 10;
-    // priority of the workers tasks
-    private int workerPriority = 1;
-    // priority of the server nodes
-    private int serverPriority = 2;
-    // total number of workers
-    private int numWorker = 1;
-    // total number of server
-    private int numServer = 0;
-    // total number of tasks
-    private int numTasks;
-    // maximum number of attempts to try in each task
-    private int maxNumAttempt = 3;
-    // command to launch
-    private String command = "";
-
-    // username
-    private String userName = "";
-    // user credentials
-    private Credentials credentials = null;
-    // application tracker hostname
-    private String appHostName = "";
-    // tracker URL to do
-    private String appTrackerUrl = "";
-    // tracker port
-    private int appTrackerPort = 0;
-
-    // whether we start to abort the application, due to whatever fatal reasons
-    private boolean startAbort = false;
-    // worker resources
-    private Map<String, LocalResource> workerResources = new java.util.HashMap<String, LocalResource>();
-    // record the aborting reason
-    private String abortDiagnosis = "";
-    // resource manager
-    private AMRMClientAsync<ContainerRequest> rmClient = null;
-    // node manager
-    private NMClientAsync nmClient = null;
-
-    // list of tasks that pending for resources to be allocated
-    private final Queue<TaskRecord> pendingTasks = new java.util.LinkedList<TaskRecord>();
-    // map containerId->task record of tasks that was running
-    private final Map<ContainerId, TaskRecord> runningTasks = new java.util.HashMap<ContainerId, TaskRecord>();
-    // collection of tasks
-    private final Collection<TaskRecord> finishedTasks = new java.util.LinkedList<TaskRecord>();
-    // collection of killed tasks
-    private final Collection<TaskRecord> killedTasks = new java.util.LinkedList<TaskRecord>();
-    // worker environment
-    private final Map<String, String> env = new java.util.HashMap<String, String>();
-
-    //add the blacklist
-    private Collection<String> blackList = new java.util.HashSet();
-
-    public static void main(String[] args) throws Exception {
-        new ApplicationMaster().run(args);
-    }
-
-    private ApplicationMaster() throws IOException {
-        dfs = FileSystem.get(conf);
-        userName = UserGroupInformation.getCurrentUser().getShortUserName();
-        credentials = UserGroupInformation.getCurrentUser().getCredentials();
-    }
-
-
-    /**
-     * setup security token given current user
-     * @return the ByeBuffer containing the security tokens
-     * @throws IOException
-     */
-    private ByteBuffer setupTokens() {
-        try {
-            DataOutputBuffer dob = new DataOutputBuffer();
-            credentials.writeTokenStorageToStream(dob);
-            return ByteBuffer.wrap(dob.getData(), 0, dob.getLength()).duplicate();
-        } catch (IOException e) {
-            throw new RuntimeException(e);  // TODO: FIXME
-        }
-    }
-
-
-    /**
-     * get integer argument from environment variable
-     *
-     * @param name
-     *            name of key
-     * @param required
-     *            whether this is required
-     * @param defv
-     *            default value
-     * @return the requested result
-     */
-    private int getEnvInteger(String name, boolean required, int defv)
-            throws IOException {
-        String value = System.getenv(name);
-        if (value == null) {
-            if (required) {
-                throw new IOException("environment variable " + name
-                        + " not set");
-            } else {
-                return defv;
-            }
-        }
-        return Integer.valueOf(value);
-    }
-
-    /**
-     * initialize from arguments and command lines
-     *
-     * @param args
-     */
-    private void initArgs(String args[]) throws IOException {
-        LOG.info("Start AM as user=" + this.userName);
-        // get user name
-        userName = UserGroupInformation.getCurrentUser().getShortUserName();
-        // cached maps
-        Map<String, Path> cacheFiles = new java.util.HashMap<String, Path>();
-        for (int i = 0; i < args.length; ++i) {
-            if (args[i].equals("-file")) {
-                String[] arr = args[++i].split("#");
-                Path path = new Path(arr[0]);
-                if (arr.length == 1) {
-                    cacheFiles.put(path.getName(), path);
-                } else {
-                    cacheFiles.put(arr[1], path);
-                }
-            } else if (args[i].equals("-env")) {
-                String[] pair = args[++i].split("=", 2);
-                env.put(pair[0], (pair.length == 1) ? "" : pair[1]);
-            } else {
-                this.command += args[i] + " ";
-            }
-        }
-        for (Map.Entry<String, Path> e : cacheFiles.entrySet()) {
-            LocalResource r = Records.newRecord(LocalResource.class);
-            FileStatus status = dfs.getFileStatus(e.getValue());
-            r.setResource(ConverterUtils.getYarnUrlFromPath(e.getValue()));
-            r.setSize(status.getLen());
-            r.setTimestamp(status.getModificationTime());
-            r.setType(LocalResourceType.FILE);
-            r.setVisibility(LocalResourceVisibility.APPLICATION);
-            workerResources.put(e.getKey(), r);
-        }
-        workerCores = this.getEnvInteger("DMLC_WORKER_CORES", true, workerCores);
-        serverCores = this.getEnvInteger("DMLC_SERVER_CORES", true, serverCores);
-        workerMemoryMB = this.getEnvInteger("DMLC_WORKER_MEMORY_MB", true, workerMemoryMB);
-        serverMemoryMB = this.getEnvInteger("DMLC_SERVER_MEMORY_MB", true, serverMemoryMB);
-        numWorker = this.getEnvInteger("DMLC_NUM_WORKER", true, numWorker);
-        numServer = this.getEnvInteger("DMLC_NUM_SERVER", true, numServer);
-        numTasks = numWorker + numServer;
-        maxNumAttempt = this.getEnvInteger("DMLC_MAX_ATTEMPT", false,
-                                           maxNumAttempt);
-        LOG.info("Try to start " + numServer + " Servers and " + numWorker + " Workers");
-    }
-
-    /**
-     * called to start the application
-     */
-    private void run(String args[]) throws Exception {
-        this.initArgs(args);
-        this.rmClient = AMRMClientAsync.createAMRMClientAsync(1000,
-                new RMCallbackHandler());
-        this.nmClient = NMClientAsync
-                .createNMClientAsync(new NMCallbackHandler());
-        this.rmClient.init(conf);
-        this.rmClient.start();
-        this.nmClient.init(conf);
-        this.nmClient.start();
-        RegisterApplicationMasterResponse response = this.rmClient
-                .registerApplicationMaster(this.appHostName,
-                        this.appTrackerPort, this.appTrackerUrl);
-
-        boolean success = false;
-        String diagnostics = "";
-        try {
-            // list of tasks that waits to be submit
-            java.util.Collection<TaskRecord> tasks = new java.util.LinkedList<TaskRecord>();
-            // add waiting tasks
-            for (int i = 0; i < this.numWorker; ++i) {
-                tasks.add(new TaskRecord(i, "worker"));
-            }
-            for (int i = 0; i < this.numServer; ++i) {
-                tasks.add(new TaskRecord(i, "server"));
-            }
-            Resource maxResource = response.getMaximumResourceCapability();
-
-            if (maxResource.getMemory() < this.serverMemoryMB) {
-              LOG.warn("[DMLC] memory requested exceed bound "
-                        + maxResource.getMemory());
-                this.serverMemoryMB = maxResource.getMemory();
-            }
-            if (maxResource.getMemory() < this.workerMemoryMB) {
-              LOG.warn("[DMLC] memory requested exceed bound "
-                        + maxResource.getMemory());
-                this.workerMemoryMB = maxResource.getMemory();
-            }
-            if (maxResource.getVirtualCores() < this.workerCores) {
-               LOG.warn("[DMLC] cores requested exceed bound "
-                        + maxResource.getVirtualCores());
-               this.workerCores = maxResource.getVirtualCores();
-            }
-            if (maxResource.getVirtualCores() < this.serverCores) {
-              LOG.warn("[DMLC] cores requested exceed bound "
-                        + maxResource.getVirtualCores());
-                this.serverCores = maxResource.getVirtualCores();
-            }
-            this.submitTasks(tasks);
-            LOG.info("[DMLC] ApplicationMaster started");
-            while (!this.doneAllJobs()) {
-                try {
-                    Thread.sleep(100);
-                } catch (InterruptedException e) {
-                }
-            }
-            assert (killedTasks.size() + finishedTasks.size() == numTasks);
-            success = finishedTasks.size() == numTasks;
-            LOG.info("Application completed. Stopping running containers");
-            diagnostics = "Diagnostics." + ", num_tasks" + this.numTasks
-                + ", finished=" + this.finishedTasks.size() + ", failed="
-                + this.killedTasks.size() + "\n" + this.abortDiagnosis;
-            LOG.info(diagnostics);
-        } catch (Exception e) {
-            diagnostics = e.toString();
-        }
-        rmClient.unregisterApplicationMaster(
-                success ? FinalApplicationStatus.SUCCEEDED
-                        : FinalApplicationStatus.FAILED, diagnostics,
-                appTrackerUrl);
-        if (!success)
-            throw new Exception("Application not successful");
-    }
-
-    /**
-     * check if the job finishes
-     *
-     * @return whether we finished all the jobs
-     */
-    private synchronized boolean doneAllJobs() {
-        return pendingTasks.size() == 0 && runningTasks.size() == 0;
-    }
-
-    /**
-     * submit tasks to request containers for the tasks
-     *
-     * @param tasks
-     *            a collection of tasks we want to ask container for
-     */
-    private synchronized void submitTasks(Collection<TaskRecord> tasks) {
-        for (TaskRecord r : tasks) {
-            Resource resource = Records.newRecord(Resource.class);
-            Priority priority = Records.newRecord(Priority.class);
-            if (r.taskRole == "server") {
-              resource.setMemory(serverMemoryMB);
-              resource.setVirtualCores(serverCores);
-              priority.setPriority(this.serverPriority);
-            } else {
-              resource.setMemory(workerMemoryMB);
-              resource.setVirtualCores(workerCores);
-              priority.setPriority(this.workerPriority);
-            }
-            r.containerRequest = new ContainerRequest(resource, null, null,
-                    priority);
-            rmClient.addContainerRequest(r.containerRequest);
-            pendingTasks.add(r);
-        }
-    }
-
-
-
-    private synchronized void launchDummyTask(Container container){
-        ContainerLaunchContext ctx = Records.newRecord(ContainerLaunchContext.class);
-        String new_command = "./launcher.py";
-        String cmd = new_command + " 1>"
-            + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout"
-            + " 2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR
-            + "/stderr";
-        ctx.setCommands(Collections.singletonList(cmd));
-        ctx.setTokens(setupTokens());
-        ctx.setLocalResources(this.workerResources);
-        synchronized (this){
-            this.nmClient.startContainerAsync(container, ctx);
-        }
-    }
-    /**
-     * launch the task on container
-     *
-     * @param container
-     *            container to run the task
-     * @param task
-     *            the task
-     */
-    private void launchTask(Container container, TaskRecord task) {
-        task.container = container;
-        task.containerRequest = null;
-        ContainerLaunchContext ctx = Records
-                .newRecord(ContainerLaunchContext.class);
-        String cmd =
-        // use this to setup CLASSPATH correctly for libhdfs
-             this.command + " 1>"
-            + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout"
-            + " 2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR
-            + "/stderr";
-        ctx.setCommands(Collections.singletonList(cmd));
-        // TODO: token was not right
-        ctx.setTokens(setupTokens());
-        LOG.info(workerResources);
-        ctx.setLocalResources(this.workerResources);
-        // setup environment variables
-
-        boolean isWindows = System.getProperty("os.name").startsWith("Windows");
-        // setup class path, this is kind of duplicated, ignoring
-        String classPathStr = isWindows? "%CLASSPATH%" : "${CLASSPATH}";
-        StringBuilder cpath = new StringBuilder(classPathStr
-                       + File.pathSeparatorChar
-                       + "./*");
-        for (String c : conf.getStrings(
-                YarnConfiguration.YARN_APPLICATION_CLASSPATH,
-                YarnConfiguration.DEFAULT_YARN_APPLICATION_CLASSPATH)) {
-            if (isWindows) c = c.replace('\\', '/');
-            String[] arrPath = c.split("" + File.pathSeparatorChar);
-            for (String ps : arrPath) {
-                if (ps.endsWith("*.jar")
-                        || ps.endsWith("*")
-                        || ps.endsWith("/")) {
-                    ps = ps.substring(0, ps.lastIndexOf('*'));
-                    if (ps.startsWith("$") || ps.startsWith("%")) {
-                        String[] arr =ps.split("/", 2);
-                        if (arr.length != 2) continue;
-                        try {
-                            String vname = isWindows ?
-                                           arr[0].substring(1, arr[0].length() - 1) :
-                                           arr[0].substring(1);
-                            String vv = System.getenv(vname);
-                            if (isWindows) vv = vv.replace('\\', '/');
-                                ps = vv + '/' + arr[1];
-                        } catch (Exception e){
-                            continue;
-                        }
-                    }
-                    File dir = new File(ps);
-                    if (dir.isDirectory()) {
-                        for (File f: dir.listFiles()) {
-                            if (f.isFile() && f.getPath().endsWith(".jar")) {
-                                cpath.append(File.pathSeparatorChar);
-                                cpath.append(ps + '/' + f.getName());
-                            }
-                        }
-                    }
-                    cpath.append(File.pathSeparatorChar);
-                    cpath.append(ps + '/');
-                } else {
-                    cpath.append(File.pathSeparatorChar);
-                    cpath.append(ps.trim());
-                }
-            }
-        }
-        // already use hadoop command to get class path in worker, maybe a
-        // better solution in future
-        env.put("CLASSPATH", cpath.toString());
-        // setup LD_LIBARY_PATH path for libhdfs
-        String oldLD_LIBRARY_PATH = System.getenv("LD_LIBRARY_PATH");
-        env.put("LD_LIBRARY_PATH",
-                oldLD_LIBRARY_PATH == null ? "" : oldLD_LIBRARY_PATH + ":$HADOOP_HDFS_HOME/lib/native:$JAVA_HOME/jre/lib/amd64/server");
-        env.put("PYTHONPATH", "${PYTHONPATH}:.");
-        // inherit all rabit variables
-        for (Map.Entry<String, String> e : System.getenv().entrySet()) {
-            if (e.getKey().startsWith("DMLC_")) {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey().startsWith("rabit_")) {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey().startsWith("AWS_")) {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey() == "LIBHDFS_OPTS") {
-                env.put(e.getKey(), e.getValue());
-            }
-        }
-        String nodeHost = container.getNodeId().getHost();
-        env.put("DMLC_NODE_HOST", nodeHost);
-        env.put("DMLC_TASK_ID", String.valueOf(task.taskId));
-        env.put("DMLC_ROLE", task.taskRole);
-        env.put("DMLC_NUM_ATTEMPT", String.valueOf(task.attemptCounter));
-        // ctx.setUser(userName);
-        ctx.setEnvironment(env);
-        LOG.info(env);
-        synchronized (this) {
-            assert (!this.runningTasks.containsKey(container.getId()));
-            this.runningTasks.put(container.getId(), task);
-            this.nmClient.startContainerAsync(container, ctx);
-        }
-    }
-    /**
-     * free the containers that have not yet been launched
-     *
-     * @param containers
-     */
-    private synchronized void onStartContainerError(ContainerId cid) {
-        ApplicationMaster.this.handleFailure(Collections.singletonList(cid));
-    }
-    /**
-     * free the containers that have not yet been launched
-     *
-     * @param containers
-     */
-    private synchronized void freeUnusedContainers(
-            Collection<Container> containers) {
-        if(containers.size() == 0) return;
-        for(Container c : containers){
-            launchDummyTask(c);
-        }
-    }
-
-    /**
-     * handle method for AMRMClientAsync.CallbackHandler container allocation
-     *
-     * @param containers
-     */
-    private synchronized void onContainersAllocated(List<Container> containers) {
-        if (this.startAbort) {
-            this.freeUnusedContainers(containers);
-            return;
-        }
-        Collection<Container> freelist = new java.util.LinkedList<Container>();
-        for (Container c : containers) {
-            if(blackList.contains(c.getNodeHttpAddress())){
-			    launchDummyTask(c);
-                continue;
-		    }
-
-            TaskRecord task;
-            task = pendingTasks.poll();
-            if (task == null) {
-                freelist.add(c);
-                continue;
-            }
-            this.launchTask(c, task);
-        }
-        this.freeUnusedContainers(freelist);
-    }
-
-    /**
-     * start aborting the job
-     *
-     * @param msg
-     *            the fatal message
-     */
-    private synchronized void abortJob(String msg) {
-        if (!this.startAbort)
-            this.abortDiagnosis = msg;
-        this.startAbort = true;
-        for (TaskRecord r : this.runningTasks.values()) {
-            if (!r.abortRequested) {
-                nmClient.stopContainerAsync(r.container.getId(),
-                        r.container.getNodeId());
-                r.abortRequested = true;
-
-                this.killedTasks.add(r);
-            }
-        }
-        this.killedTasks.addAll(this.pendingTasks);
-        for (TaskRecord r : this.pendingTasks) {
-            rmClient.removeContainerRequest(r.containerRequest);
-        }
-        this.pendingTasks.clear();
-        this.runningTasks.clear();
-        LOG.info(msg);
-    }
-
-    /**
-     * handle non fatal failures
-     *
-     * @param cid
-     */
-    private synchronized void handleFailure(Collection<ContainerId> failed) {
-        Collection<TaskRecord> tasks = new java.util.LinkedList<TaskRecord>();
-        for (ContainerId cid : failed) {
-            TaskRecord r = runningTasks.remove(cid);
-            if (r == null) {
-                continue;
-            }
-            LOG.info("Task "
-                    + r.taskId
-                    + " failed on "
-                    + r.container.getId()
-                    + ". See LOG at : "
-                    + String.format("http://%s/node/containerlogs/%s/"
-                            + userName, r.container.getNodeHttpAddress(),
-                            r.container.getId()));
-            r.attemptCounter += 1;
-
-            //stop the failed container and add it to blacklist
-            nmClient.stopContainerAsync(r.container.getId(), r.container.getNodeId());
-            blackList.add(r.container.getNodeHttpAddress());
-
-            r.container = null;
-            tasks.add(r);
-            if (r.attemptCounter >= this.maxNumAttempt) {
-                this.abortJob("[DMLC] Task " + r.taskId + " failed more than "
-                        + r.attemptCounter + "times");
-            }
-        }
-        if (this.startAbort) {
-            this.killedTasks.addAll(tasks);
-        } else {
-            this.submitTasks(tasks);
-        }
-    }
-
-    /**
-     * handle method for AMRMClientAsync.CallbackHandler container allocation
-     *
-     * @param status
-     *            list of status
-     */
-    private synchronized void onContainersCompleted(List<ContainerStatus> status) {
-        Collection<ContainerId> failed = new java.util.LinkedList<ContainerId>();
-        for (ContainerStatus s : status) {
-            assert (s.getState().equals(ContainerState.COMPLETE));
-            int exstatus = s.getExitStatus();
-            TaskRecord r = runningTasks.get(s.getContainerId());
-            if (r == null)
-                continue;
-            if (exstatus == ContainerExitStatus.SUCCESS) {
-                finishedTasks.add(r);
-                runningTasks.remove(s.getContainerId());
-            } else {
-                try {
-                    if (exstatus == ContainerExitStatus.class.getField(
-                            "KILLED_EXCEEDED_PMEM").getInt(null)) {
-                        this.abortJob("[DMLC] Task "
-                                + r.taskId
-                                + " killed because of exceeding allocated physical memory");
-                        return;
-                    }
-                    if (exstatus == ContainerExitStatus.class.getField(
-                            "KILLED_EXCEEDED_VMEM").getInt(null)) {
-                        this.abortJob("[DMLC] Task "
-                                + r.taskId
-                                + " killed because of exceeding allocated virtual memory");
-                        return;
-                    }
-                } catch (Exception e) {
-                    LOG.warn(e.getMessage());
-                }
-                LOG.info("[DMLC] Task " + r.taskId + " exited with status "
-                         + exstatus + " Diagnostics:"+ s.getDiagnostics());
-                failed.add(s.getContainerId());
-            }
-        }
-        this.handleFailure(failed);
-    }
-
-    /**
-     * callback handler for resource manager
-     */
-    private class RMCallbackHandler implements AMRMClientAsync.CallbackHandler {
-        @Override
-        public float getProgress() {
-            return 1.0f - (float) (pendingTasks.size()) / numTasks;
-        }
-
-        @Override
-        public void onContainersAllocated(List<Container> containers) {
-            ApplicationMaster.this.onContainersAllocated(containers);
-        }
-
-        @Override
-        public void onContainersCompleted(List<ContainerStatus> status) {
-            ApplicationMaster.this.onContainersCompleted(status);
-        }
-
-        @Override
-        public void onError(Throwable ex) {
-            ApplicationMaster.this.abortJob("[DMLC] Resource manager Error "
-                    + ex.toString());
-        }
-
-        @Override
-        public void onNodesUpdated(List<NodeReport> nodereport) {
-        }
-
-        @Override
-        public void onShutdownRequest() {
-            ApplicationMaster.this
-                    .abortJob("[DMLC] Get shutdown request, start to shutdown...");
-        }
-    }
-
-    private class NMCallbackHandler implements NMClientAsync.CallbackHandler {
-        @Override
-        public void onContainerStarted(ContainerId cid,
-                Map<String, ByteBuffer> services) {
-            LOG.info("onContainerStarted Invoked");
-        }
-
-        @Override
-        public void onContainerStatusReceived(ContainerId cid,
-                ContainerStatus status) {
-            LOG.info("onContainerStatusReceived Invoked");
-        }
-
-        @Override
-        public void onContainerStopped(ContainerId cid) {
-            LOG.info("onContainerStopped Invoked");
-        }
-
-        @Override
-        public void onGetContainerStatusError(ContainerId cid, Throwable ex) {
-            LOG.info("onGetContainerStatusError Invoked: " + ex.toString());
-            ApplicationMaster.this
-                    .handleFailure(Collections.singletonList(cid));
-        }
-
-        @Override
-        public void onStartContainerError(ContainerId cid, Throwable ex) {
-            LOG.info("onStartContainerError Invoked: " + ex.getMessage());
-            ApplicationMaster.this
-               .onStartContainerError(cid);
-        }
-
-        @Override
-        public void onStopContainerError(ContainerId cid, Throwable ex) {
-            LOG.info("onStopContainerError Invoked: " + ex.toString());
-        }
-    }
-}
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/Client.java b/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/Client.java
deleted file mode 100644
index 8686684..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/Client.java
+++ /dev/null
@@ -1,350 +0,0 @@
-package org.apache.hadoop.yarn.dmlc;
-import java.io.File;
-import java.io.IOException;
-import java.nio.ByteBuffer;
-import java.util.Collections;
-import java.util.Map;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-import org.apache.hadoop.fs.Path;
-import org.apache.hadoop.fs.FileStatus;
-import org.apache.hadoop.fs.FileSystem;
-import org.apache.hadoop.fs.permission.FsPermission;
-import org.apache.hadoop.io.DataOutputBuffer;
-import org.apache.hadoop.security.UserGroupInformation;
-import org.apache.hadoop.security.Credentials;
-import org.apache.hadoop.security.token.Token;
-import org.apache.hadoop.yarn.api.ApplicationConstants;
-import org.apache.hadoop.yarn.api.ApplicationConstants.Environment;
-import org.apache.hadoop.yarn.api.records.ApplicationId;
-import org.apache.hadoop.yarn.api.records.ApplicationReport;
-import org.apache.hadoop.yarn.api.records.ApplicationSubmissionContext;
-import org.apache.hadoop.yarn.api.records.ContainerLaunchContext;
-import org.apache.hadoop.yarn.api.records.FinalApplicationStatus;
-import org.apache.hadoop.yarn.api.records.LocalResource;
-import org.apache.hadoop.yarn.api.records.LocalResourceType;
-import org.apache.hadoop.yarn.api.records.LocalResourceVisibility;
-import org.apache.hadoop.yarn.api.records.Resource;
-import org.apache.hadoop.yarn.api.records.QueueInfo;
-import org.apache.hadoop.yarn.api.records.YarnApplicationState;
-import org.apache.hadoop.yarn.client.api.YarnClient;
-import org.apache.hadoop.yarn.client.api.YarnClientApplication;
-import org.apache.hadoop.yarn.conf.YarnConfiguration;
-import org.apache.hadoop.yarn.util.ConverterUtils;
-import org.apache.hadoop.yarn.util.Records;
-
-import sun.misc.Signal;
-import sun.misc.SignalHandler;
-
-public class Client {
-    // logger
-    private static final Log LOG = LogFactory.getLog(Client.class);
-    // permission for temp file
-    private static final FsPermission permTemp = new FsPermission("777");
-    // configuration
-    private YarnConfiguration conf = new YarnConfiguration();
-    // hdfs handler
-    private FileSystem dfs;
-    // cached maps
-    private Map<String, String> cacheFiles = new java.util.HashMap<String, String>();
-    // enviroment variable to setup cachefiles
-    private String cacheFileArg = "";
-    // args to pass to application master
-    private String appArgs = "";
-    // HDFS Path to store temporal result
-    private String tempdir = "/tmp";
-    // user name
-    private String userName = "";
-    // user credentials
-    private Credentials credentials = null;
-    // job name
-    private String jobName = "";
-    // queue
-    private String queue = "default";
-    // ApplicationMaster classpath
-    private String appCp = null;
-    // ApplicationMaster env
-    private Map<String, String> env = new java.util.HashMap<String, String>();
-
-    /**
-     * constructor
-     * @throws IOException
-     */
-    private Client() throws IOException {
-        conf.addResource(new Path(System.getenv("HADOOP_CONF_DIR") +"/core-site.xml"));
-        conf.addResource(new Path(System.getenv("HADOOP_CONF_DIR") +"/hdfs-site.xml"));
-        dfs = FileSystem.get(conf);
-        userName = UserGroupInformation.getCurrentUser().getShortUserName();
-        credentials = UserGroupInformation.getCurrentUser().getCredentials();
-    }
-
-    /**
-     * setup security token given current user
-     * @return the ByeBuffer containing the security tokens
-     * @throws IOException
-     */
-    private ByteBuffer setupTokens() throws IOException {
-        DataOutputBuffer buffer = new DataOutputBuffer();
-        String loc = System.getenv().get("HADOOP_TOKEN_FILE_LOCATION");
-        if ((loc != null && loc.trim().length() > 0)
-        ||  (!UserGroupInformation.isSecurityEnabled())) {
-            this.credentials.writeTokenStorageToStream(buffer);
-        } else {
-            // Note: Credentials class is marked as LimitedPrivate for HDFS and MapReduce
-            Credentials credentials = new Credentials();
-            String tokenRenewer = conf.get(YarnConfiguration.RM_PRINCIPAL);
-            if (tokenRenewer == null || tokenRenewer.length() == 0) {
-                throw new IOException(
-                "Can't get Master Kerberos principal for the RM to use as renewer");
-            }
-
-            // For now, only getting tokens for the default file-system.
-            final Token<?> tokens[] = dfs.addDelegationTokens(tokenRenewer, credentials);
-            if (tokens != null) {
-                for (Token<?> token : tokens) {
-                    LOG.info("Got dt for " + dfs.getUri() + "; " + token);
-                }
-            }
-            credentials.writeTokenStorageToStream(buffer);
-        }
-        return ByteBuffer.wrap(buffer.getData(), 0, buffer.getLength());
-    }
-
-    /**
-     * setup all the cached files
-     *
-     * @param fmaps
-     *            the file maps
-     * @return the resource map
-     * @throws IOException
-     */
-    private Map<String, LocalResource> setupCacheFiles(ApplicationId appId) throws IOException {
-        // create temporary dmlc directory
-        Path tmpPath = new Path(this.tempdir);
-        if (!dfs.exists(tmpPath)) {
-            dfs.mkdirs(tmpPath, permTemp);
-            LOG.info("HDFS temp directory do not exist, creating.. " + tmpPath);
-        }
-        tmpPath = new Path(tmpPath + "/temp-dmlc-yarn-" + appId);
-        if (dfs.exists(tmpPath)) {
-            dfs.delete(tmpPath, true);
-        }
-        // create temporary directory
-        FileSystem.mkdirs(dfs, tmpPath, permTemp);
-
-        StringBuilder cstr = new StringBuilder();
-        Map<String, LocalResource> rmap = new java.util.HashMap<String, LocalResource>();
-        for (Map.Entry<String, String> e : cacheFiles.entrySet()) {
-            LocalResource r = Records.newRecord(LocalResource.class);
-            Path path = new Path(e.getValue());
-            // copy local data to temporary folder in HDFS
-            if (!e.getValue().startsWith("hdfs://")) {
-                Path dst = new Path("hdfs://" + tmpPath + "/"+  path.getName());
-                dfs.copyFromLocalFile(false, true, path, dst);
-                dfs.setPermission(dst, permTemp);
-                dfs.deleteOnExit(dst);
-                path = dst;
-            }
-            FileStatus status = dfs.getFileStatus(path);
-            r.setResource(ConverterUtils.getYarnUrlFromPath(path));
-            r.setSize(status.getLen());
-            r.setTimestamp(status.getModificationTime());
-            r.setType(LocalResourceType.FILE);
-            r.setVisibility(LocalResourceVisibility.APPLICATION);
-            rmap.put(e.getKey(), r);
-            cstr.append(" -file \"");
-            cstr.append(path.toString());
-            cstr.append('#');
-            cstr.append(e.getKey());
-            cstr.append("\"");
-        }
-
-        dfs.deleteOnExit(tmpPath);
-        this.cacheFileArg = cstr.toString();
-        return rmap;
-    }
-
-    /**
-     * get the environment variables for container
-     *
-     * @return the env variable for child class
-     */
-    private Map<String, String> getEnvironment() {
-        // Setup environment variables
-
-        if (appCp != null) {
-            env.put("CLASSPATH", appCp);
-        } else {
-            StringBuilder cpath = new StringBuilder()
-                .append(Environment.CLASSPATH.$$())
-                .append(File.pathSeparatorChar)
-                .append("." + File.pathSeparator + "*");
-            for (String c : conf.getStrings(
-                        YarnConfiguration.YARN_APPLICATION_CLASSPATH,
-                        YarnConfiguration.DEFAULT_YARN_APPLICATION_CLASSPATH)) {
-                cpath.append(File.pathSeparatorChar)
-                     .append(c.trim());
-            }
-            env.put("CLASSPATH", cpath.toString());
-        }
-        for (Map.Entry<String, String> e : System.getenv().entrySet()) {
-            if (e.getKey().startsWith("DMLC_")) {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey().startsWith("AWS_")) {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey().startsWith("rabit_")) {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey() == "LIBHDFS_OPTS") {
-                env.put(e.getKey(), e.getValue());
-            }
-            if (e.getKey().equals("LD_LIBRARY_PATH")) {
-                env.put(e.getKey(), e.getValue());
-            }
-        }
-        LOG.debug(env);
-        return env;
-    }
-
-    /**
-     * initialize the settings
-     *
-     * @param args
-     */
-    private void initArgs(String[] args) {
-        // directly pass all arguments except args0
-        StringBuilder sargs = new StringBuilder("");
-        for (int i = 0; i < args.length; ++i) {
-            if (args[i].equals("-file")) {
-                String[] arr = args[++i].split("#");
-                if (arr.length == 1) {
-                    cacheFiles.put(new Path(arr[0]).getName(), arr[0]);
-                } else {
-                    cacheFiles.put(arr[1], arr[0]);
-                }
-            } else if(args[i].equals("-jobname")) {
-                this.jobName = args[++i];
-            } else if(args[i].equals("-tempdir")) {
-                this.tempdir = args[++i];
-            } else if(args[i].equals("-queue")) {
-                this.queue = args[++i];
-            } else if(args[i].equals("-appcp")) {
-                this.appCp = args[++i];
-            } else if(args[i].equals("-env")) {
-                sargs.append(" ");
-                sargs.append(args[i]);
-                sargs.append(" ");
-                sargs.append(args[i+1]);
-                String[] pair = args[++i].split("=", 2);
-                env.put(pair[0], (pair.length == 1) ? "" : pair[1]);
-            } else {
-                sargs.append(" ");
-                sargs.append(args[i]);
-            }
-        }
-        this.appArgs = sargs.toString();
-    }
-
-    private void run(String[] args) throws Exception {
-        if (args.length == 0) {
-            System.out.println("Usage: [options] [commands..]");
-            System.out.println("options: [-file filename] [-appcp appClasspath]");
-            return;
-        }
-        this.initArgs(args);
-        // Create yarnClient
-        YarnClient yarnClient = YarnClient.createYarnClient();
-        yarnClient.init(conf);
-        yarnClient.start();
-
-        // Create application via yarnClient
-        YarnClientApplication app = yarnClient.createApplication();
-
-        // Set up the container launch context for the application master
-        ContainerLaunchContext amContainer = Records
-                .newRecord(ContainerLaunchContext.class);
-        ApplicationSubmissionContext appContext = app
-                .getApplicationSubmissionContext();
-        // Submit application
-        ApplicationId appId = appContext.getApplicationId();
-
-        //add ctrl+c signal handler
-        CtrlCHandler handler = new CtrlCHandler(appId, yarnClient);
-        Signal intSignal = new Signal("INT");
-        Signal.handle(intSignal, handler);
-
-        // setup security token
-        amContainer.setTokens(this.setupTokens());
-        // setup cache-files and environment variables
-        amContainer.setLocalResources(this.setupCacheFiles(appId));
-        amContainer.setEnvironment(this.getEnvironment());
-        String cmd = Environment.JAVA_HOME.$$() + "/bin/java"
-                + " -Xmx900m"
-                + " org.apache.hadoop.yarn.dmlc.ApplicationMaster"
-                + this.cacheFileArg + ' ' + this.appArgs + " 1>"
-                + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout"
-                + " 2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr";
-
-        LOG.debug(cmd);
-        amContainer.setCommands(Collections.singletonList(cmd));
-
-        // Set up resource type requirements for ApplicationMaster
-        Resource capability = Records.newRecord(Resource.class);
-        capability.setMemory(1024);
-        capability.setVirtualCores(1);
-        LOG.info("jobname=" + this.jobName + ",username=" + this.userName);
-
-        appContext.setApplicationName(jobName + ":DMLC-YARN");
-        appContext.setAMContainerSpec(amContainer);
-        appContext.setResource(capability);
-        appContext.setQueue(queue);
-        //appContext.setUser(userName);
-        LOG.info("Submitting application " + appId);
-        yarnClient.submitApplication(appContext);
-
-        ApplicationReport appReport = yarnClient.getApplicationReport(appId);
-        YarnApplicationState appState = appReport.getYarnApplicationState();
-        while (appState != YarnApplicationState.FINISHED
-                && appState != YarnApplicationState.KILLED
-                && appState != YarnApplicationState.FAILED) {
-            Thread.sleep(100);
-            appReport = yarnClient.getApplicationReport(appId);
-            appState = appReport.getYarnApplicationState();
-        }
-
-        System.out.println("Application " + appId + " finished with"
-                + " state " + appState + " at " + appReport.getFinishTime());
-        if (!appReport.getFinalApplicationStatus().equals(
-                FinalApplicationStatus.SUCCEEDED)) {
-            System.err.println(appReport.getDiagnostics());
-            System.out.println("Available queues:");
-            for (QueueInfo q : yarnClient.getAllQueues()) {
-              System.out.println(q.getQueueName());
-            }
-
-            yarnClient.killApplication(appId);
-        }
-    }
-
-    class CtrlCHandler implements SignalHandler{
-        private ApplicationId appId;
-        private YarnClient yarnClient;
-        public CtrlCHandler(ApplicationId appId, YarnClient yarnClient){
-            this.appId = appId;
-            this.yarnClient = yarnClient;
-        }
-        public void handle(Signal signal){
-            try{
-                yarnClient.killApplication(appId);
-            }catch (Exception e){
-                System.out.println("yarn client exception");
-            }
-        }
-    }
-    public static void main(String[] args) throws Exception {
-        new Client().run(args);
-    }
-}
diff --git a/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/TaskRecord.java b/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/TaskRecord.java
deleted file mode 100644
index 0c979c0..0000000
--- a/ml-xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/TaskRecord.java
+++ /dev/null
@@ -1,27 +0,0 @@
-package org.apache.hadoop.yarn.dmlc;
-
-import org.apache.hadoop.yarn.api.records.Container;
-import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest;
-
-/**
- * data structure to hold the task information
- */
-public class TaskRecord {
-    // task id of the task
-    public int taskId = 0;
-    // role of current node 
-    public String taskRole = "worker";
-    // number of failed attempts to run the task
-    public int attemptCounter = 0;
-    // container request, can be null if task is already running
-    public ContainerRequest containerRequest = null;
-    // running container, can be null if the task is not launched
-    public Container container = null;
-    // whether we have requested abortion of this task
-    public boolean abortRequested = false;
-
-    public TaskRecord(int taskId, String role) {
-        this.taskId = taskId;
-        this.taskRole = role;
-    }
-}
diff --git a/ml-xgboost/dmlc-core/windows/.gitignore b/ml-xgboost/dmlc-core/windows/.gitignore
deleted file mode 100644
index 8293add..0000000
--- a/ml-xgboost/dmlc-core/windows/.gitignore
+++ /dev/null
@@ -1,11 +0,0 @@
-Debug
-*suo
-*.dll
-*i386
-*x64
-ipch
-*.filters
-*.user
-*sdf
-Release
-Debug
diff --git a/ml-xgboost/dmlc-core/windows/README.md b/ml-xgboost/dmlc-core/windows/README.md
deleted file mode 100644
index 58f0772..0000000
--- a/ml-xgboost/dmlc-core/windows/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-MSVC Project
-====
-The solution has been created with Visual Studio Express 2010.
-Preliminary project for testing windows compatibility.
-It do not come with a warranty.
diff --git a/ml-xgboost/dmlc-core/windows/dmlc.sln b/ml-xgboost/dmlc-core/windows/dmlc.sln
deleted file mode 100644
index ab86146..0000000
--- a/ml-xgboost/dmlc-core/windows/dmlc.sln
+++ /dev/null
@@ -1,54 +0,0 @@
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-EndProject
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-		{1028539A-D3A9-4DAB-BC07-CFFD432D58C1}.Release|Win32.ActiveCfg = Release|Win32
-		{1028539A-D3A9-4DAB-BC07-CFFD432D58C1}.Release|Win32.Build.0 = Release|Win32
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-		{1028539A-D3A9-4DAB-BC07-CFFD432D58C1}.Release|x64.Build.0 = Release|x64
-	EndGlobalSection
-	GlobalSection(SolutionProperties) = preSolution
-		HideSolutionNode = FALSE
-	EndGlobalSection
-	GlobalSection(Performance) = preSolution
-		HasPerformanceSessions = true
-	EndGlobalSection
-EndGlobal
diff --git a/ml-xgboost/dmlc-core/windows/dmlc/dmlc.vcxproj b/ml-xgboost/dmlc-core/windows/dmlc/dmlc.vcxproj
deleted file mode 100644
index 782fa52..0000000
--- a/ml-xgboost/dmlc-core/windows/dmlc/dmlc.vcxproj
+++ /dev/null
@@ -1,165 +0,0 @@
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-      <Platform>Win32</Platform>
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-    <ProjectConfiguration Include="Debug|x64">
-      <Configuration>Debug</Configuration>
-      <Platform>x64</Platform>
-    </ProjectConfiguration>
-    <ProjectConfiguration Include="Release|Win32">
-      <Configuration>Release</Configuration>
-      <Platform>Win32</Platform>
-    </ProjectConfiguration>
-    <ProjectConfiguration Include="Release|x64">
-      <Configuration>Release</Configuration>
-      <Platform>x64</Platform>
-    </ProjectConfiguration>
-  </ItemGroup>
-  <PropertyGroup Label="Globals">
-    <ProjectGuid>{6E6DDA36-69BA-4555-BA88-10166B11F2D2}</ProjectGuid>
-    <RootNamespace>dmlc</RootNamespace>
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-    <CharacterSet>MultiByte</CharacterSet>
-    <PlatformToolset>v120</PlatformToolset>
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-  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration">
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-    <CharacterSet>MultiByte</CharacterSet>
-    <PlatformToolset>v120</PlatformToolset>
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-  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
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-    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
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-      <Optimization>Disabled</Optimization>
-      <OpenMPSupport>true</OpenMPSupport>
-      <AdditionalIncludeDirectories>../../include;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
-      <RuntimeLibrary>MultiThreaded</RuntimeLibrary>
-      <PreprocessorDefinitions>DMLC_USE_CXX11;%(PreprocessorDefinitions)</PreprocessorDefinitions>
-    </ClCompile>
-    <Link>
-      <GenerateDebugInformation>true</GenerateDebugInformation>
-    </Link>
-  </ItemDefinitionGroup>
-  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
-    <ClCompile>
-      <WarningLevel>Level3</WarningLevel>
-      <Optimization>Disabled</Optimization>
-      <PreprocessorDefinitions>WIN32;_DEBUG;_CONSOLE;_LIB;DMLC_USE_CXX11;%(PreprocessorDefinitions)</PreprocessorDefinitions>
-      <AdditionalIncludeDirectories>../../include;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
-      <RuntimeLibrary>MultiThreadedDebugDLL</RuntimeLibrary>
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-    <Link>
-      <GenerateDebugInformation>true</GenerateDebugInformation>
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-  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
-    <ClCompile>
-      <WarningLevel>Level3</WarningLevel>
-      <Optimization>MaxSpeed</Optimization>
-      <FunctionLevelLinking>true</FunctionLevelLinking>
-      <IntrinsicFunctions>true</IntrinsicFunctions>
-      <OpenMPSupport>true</OpenMPSupport>
-      <AdditionalIncludeDirectories>../../include;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
-      <RuntimeLibrary>MultiThreaded</RuntimeLibrary>
-      <PreprocessorDefinitions>DMLC_USE_CXX11;%(PreprocessorDefinitions)</PreprocessorDefinitions>
-    </ClCompile>
-    <Link>
-      <GenerateDebugInformation>true</GenerateDebugInformation>
-      <EnableCOMDATFolding>true</EnableCOMDATFolding>
-      <OptimizeReferences>true</OptimizeReferences>
-    </Link>
-  </ItemDefinitionGroup>
-  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
-    <ClCompile>
-      <WarningLevel>Level3</WarningLevel>
-      <Optimization>MaxSpeed</Optimization>
-      <FunctionLevelLinking>true</FunctionLevelLinking>
-      <IntrinsicFunctions>true</IntrinsicFunctions>
-      <RuntimeLibrary>MultiThreadedDLL</RuntimeLibrary>
-      <AdditionalIncludeDirectories>../../include;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
-      <OpenMPSupport>true</OpenMPSupport>
-      <PreprocessorDefinitions>DMLC_USE_CXX11;%(PreprocessorDefinitions)</PreprocessorDefinitions>
-    </ClCompile>
-    <Link>
-      <GenerateDebugInformation>true</GenerateDebugInformation>
-      <EnableCOMDATFolding>true</EnableCOMDATFolding>
-      <OptimizeReferences>true</OptimizeReferences>
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-  </ItemDefinitionGroup>
-  <ItemGroup>
-    <ClCompile Include="..\..\src\data.cc" />
-    <ClCompile Include="..\..\src\io.cc" />
-    <ClCompile Include="..\..\src\io\input_split_base.cc" />
-    <ClCompile Include="..\..\src\io\line_split.cc" />
-    <ClCompile Include="..\..\src\io\local_filesys.cc" />
-    <ClCompile Include="..\..\src\io\recordio_split.cc" />
-    <ClCompile Include="..\..\src\recordio.cc" />
-  </ItemGroup>
-  <ItemGroup>
-    <ClInclude Include="..\..\include\dmlc\base.h" />
-    <ClInclude Include="..\..\include\dmlc\data.h" />
-    <ClInclude Include="..\..\include\dmlc\io.h" />
-    <ClInclude Include="..\..\include\dmlc\logging.h" />
-    <ClInclude Include="..\..\include\dmlc\omp.h" />
-    <ClInclude Include="..\..\include\dmlc\parser.h" />
-    <ClInclude Include="..\..\include\dmlc\recordio.h" />
-    <ClInclude Include="..\..\include\dmlc\threadediter.h" />
-    <ClInclude Include="..\..\include\dmlc\timer.h" />
-    <ClInclude Include="..\..\src\data\basic_row_iter.h" />
-    <ClInclude Include="..\..\src\data\disk_row_iter.h" />
-    <ClInclude Include="..\..\src\data\libsvm_parser.h" />
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\ No newline at end of file
diff --git a/ml-xgboost/doc/.gitignore b/ml-xgboost/doc/.gitignore
deleted file mode 100644
index 61e1516..0000000
--- a/ml-xgboost/doc/.gitignore
+++ /dev/null
@@ -1,8 +0,0 @@
-html
-latex
-*.sh
-_*
-doxygen
-parser.py
-*.pyc
-web-data
diff --git a/ml-xgboost/doc/Doxyfile.in b/ml-xgboost/doc/Doxyfile.in
deleted file mode 100644
index 766034e..0000000
--- a/ml-xgboost/doc/Doxyfile.in
+++ /dev/null
@@ -1,2353 +0,0 @@
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-
-# This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
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-# The default value is: My Project.
-
-PROJECT_NAME           = "xgboost"
-
-# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
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-# control system is used.
-
-PROJECT_NUMBER         = @XGBOOST_VERSION@
-
-# Using the PROJECT_BRIEF tag one can provide an optional one line description
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-PROJECT_LOGO           =
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-OUTPUT_DIRECTORY       = @PROJECT_BINARY_DIR@/doc_doxygen
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-# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
-# path mentioned in the documentation of a class, which tells the reader which
-# header file to include in order to use a class. If left blank only the name of
-# the header file containing the class definition is used. Otherwise one should
-# specify the list of include paths that are normally passed to the compiler
-# using the -I flag.
-
-STRIP_FROM_INC_PATH    =
-
-# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
-# less readable) file names. This can be useful is your file systems doesn't
-# support long names like on DOS, Mac, or CD-ROM.
-# The default value is: NO.
-
-SHORT_NAMES            = NO
-
-# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
-# first line (until the first dot) of a Javadoc-style comment as the brief
-# description. If set to NO, the Javadoc-style will behave just like regular Qt-
-# style comments (thus requiring an explicit @brief command for a brief
-# description.)
-# The default value is: NO.
-
-JAVADOC_AUTOBRIEF      = NO
-
-# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
-# line (until the first dot) of a Qt-style comment as the brief description. If
-# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
-# requiring an explicit \brief command for a brief description.)
-# The default value is: NO.
-
-QT_AUTOBRIEF           = NO
-
-# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
-# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
-# a brief description. This used to be the default behavior. The new default is
-# to treat a multi-line C++ comment block as a detailed description. Set this
-# tag to YES if you prefer the old behavior instead.
-#
-# Note that setting this tag to YES also means that rational rose comments are
-# not recognized any more.
-# The default value is: NO.
-
-MULTILINE_CPP_IS_BRIEF = NO
-
-# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
-# documentation from any documented member that it re-implements.
-# The default value is: YES.
-
-INHERIT_DOCS           = YES
-
-# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce a
-# new page for each member. If set to NO, the documentation of a member will be
-# part of the file/class/namespace that contains it.
-# The default value is: NO.
-
-SEPARATE_MEMBER_PAGES  = NO
-
-# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
-# uses this value to replace tabs by spaces in code fragments.
-# Minimum value: 1, maximum value: 16, default value: 4.
-
-TAB_SIZE               = 8
-
-# This tag can be used to specify a number of aliases that act as commands in
-# the documentation. An alias has the form:
-# name=value
-# For example adding
-# "sideeffect=@par Side Effects:\n"
-# will allow you to put the command \sideeffect (or @sideeffect) in the
-# documentation, which will result in a user-defined paragraph with heading
-# "Side Effects:". You can put \n's in the value part of an alias to insert
-# newlines.
-
-ALIASES                =
-
-# This tag can be used to specify a number of word-keyword mappings (TCL only).
-# A mapping has the form "name=value". For example adding "class=itcl::class"
-# will allow you to use the command class in the itcl::class meaning.
-
-TCL_SUBST              =
-
-# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
-# only. Doxygen will then generate output that is more tailored for C. For
-# instance, some of the names that are used will be different. The list of all
-# members will be omitted, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_FOR_C  = NO
-
-# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
-# Python sources only. Doxygen will then generate output that is more tailored
-# for that language. For instance, namespaces will be presented as packages,
-# qualified scopes will look different, etc.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_JAVA   = NO
-
-# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
-# sources. Doxygen will then generate output that is tailored for Fortran.
-# The default value is: NO.
-
-OPTIMIZE_FOR_FORTRAN   = NO
-
-# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
-# sources. Doxygen will then generate output that is tailored for VHDL.
-# The default value is: NO.
-
-OPTIMIZE_OUTPUT_VHDL   = NO
-
-# Doxygen selects the parser to use depending on the extension of the files it
-# parses. With this tag you can assign which parser to use for a given
-# extension. Doxygen has a built-in mapping, but you can override or extend it
-# using this tag. The format is ext=language, where ext is a file extension, and
-# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
-# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
-# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
-# Fortran. In the later case the parser tries to guess whether the code is fixed
-# or free formatted code, this is the default for Fortran type files), VHDL. For
-# instance to make doxygen treat .inc files as Fortran files (default is PHP),
-# and .f files as C (default is Fortran), use: inc=Fortran f=C.
-#
-# Note For files without extension you can use no_extension as a placeholder.
-#
-# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
-# the files are not read by doxygen.
-
-EXTENSION_MAPPING      =
-
-# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
-# according to the Markdown format, which allows for more readable
-# documentation. See http://daringfireball.net/projects/markdown/ for details.
-# The output of markdown processing is further processed by doxygen, so you can
-# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
-# case of backward compatibilities issues.
-# The default value is: YES.
-
-#MARKDOWN_SUPPORT       = YES
-
-# When enabled doxygen tries to link words that correspond to documented
-# classes, or namespaces to their corresponding documentation. Such a link can
-# be prevented in individual cases by by putting a % sign in front of the word
-# or globally by setting AUTOLINK_SUPPORT to NO.
-# The default value is: YES.
-
-#AUTOLINK_SUPPORT       = YES
-
-# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
-# to include (a tag file for) the STL sources as input, then you should set this
-# tag to YES in order to let doxygen match functions declarations and
-# definitions whose arguments contain STL classes (e.g. func(std::string);
-# versus func(std::string) {}). This also make the inheritance and collaboration
-# diagrams that involve STL classes more complete and accurate.
-# The default value is: NO.
-
-BUILTIN_STL_SUPPORT    = NO
-
-# If you use Microsoft's C++/CLI language, you should set this option to YES to
-# enable parsing support.
-# The default value is: NO.
-
-CPP_CLI_SUPPORT        = NO
-
-# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
-# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
-# will parse them like normal C++ but will assume all classes use public instead
-# of private inheritance when no explicit protection keyword is present.
-# The default value is: NO.
-
-SIP_SUPPORT            = NO
-
-# For Microsoft's IDL there are propget and propput attributes to indicate
-# getter and setter methods for a property. Setting this option to YES will make
-# doxygen to replace the get and set methods by a property in the documentation.
-# This will only work if the methods are indeed getting or setting a simple
-# type. If this is not the case, or you want to show the methods anyway, you
-# should set this option to NO.
-# The default value is: YES.
-
-IDL_PROPERTY_SUPPORT   = YES
-
-# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
-# tag is set to YES, then doxygen will reuse the documentation of the first
-# member in the group (if any) for the other members of the group. By default
-# all members of a group must be documented explicitly.
-# The default value is: NO.
-
-DISTRIBUTE_GROUP_DOC   = NO
-
-# Set the SUBGROUPING tag to YES to allow class member groups of the same type
-# (for instance a group of public functions) to be put as a subgroup of that
-# type (e.g. under the Public Functions section). Set it to NO to prevent
-# subgrouping. Alternatively, this can be done per class using the
-# \nosubgrouping command.
-# The default value is: YES.
-
-SUBGROUPING            = YES
-
-# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
-# are shown inside the group in which they are included (e.g. using \ingroup)
-# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
-# and RTF).
-#
-# Note that this feature does not work in combination with
-# SEPARATE_MEMBER_PAGES.
-# The default value is: NO.
-
-INLINE_GROUPED_CLASSES = NO
-
-# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
-# with only public data fields or simple typedef fields will be shown inline in
-# the documentation of the scope in which they are defined (i.e. file,
-# namespace, or group documentation), provided this scope is documented. If set
-# to NO, structs, classes, and unions are shown on a separate page (for HTML and
-# Man pages) or section (for LaTeX and RTF).
-# The default value is: NO.
-
-INLINE_SIMPLE_STRUCTS  = NO
-
-# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
-# enum is documented as struct, union, or enum with the name of the typedef. So
-# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
-# with name TypeT. When disabled the typedef will appear as a member of a file,
-# namespace, or class. And the struct will be named TypeS. This can typically be
-# useful for C code in case the coding convention dictates that all compound
-# types are typedef'ed and only the typedef is referenced, never the tag name.
-# The default value is: NO.
-
-TYPEDEF_HIDES_STRUCT   = NO
-
-# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
-# cache is used to resolve symbols given their name and scope. Since this can be
-# an expensive process and often the same symbol appears multiple times in the
-# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
-# doxygen will become slower. If the cache is too large, memory is wasted. The
-# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
-# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
-# symbols. At the end of a run doxygen will report the cache usage and suggest
-# the optimal cache size from a speed point of view.
-# Minimum value: 0, maximum value: 9, default value: 0.
-
-LOOKUP_CACHE_SIZE      = 0
-
-#---------------------------------------------------------------------------
-# Build related configuration options
-#---------------------------------------------------------------------------
-
-# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
-# documentation are documented, even if no documentation was available. Private
-# class members and static file members will be hidden unless the
-# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
-# Note: This will also disable the warnings about undocumented members that are
-# normally produced when WARNINGS is set to YES.
-# The default value is: NO.
-
-EXTRACT_ALL            = YES
-
-# If the EXTRACT_PRIVATE tag is set to YES all private members of a class will
-# be included in the documentation.
-# The default value is: NO.
-
-EXTRACT_PRIVATE        = NO
-
-# If the EXTRACT_PACKAGE tag is set to YES all members with package or internal
-# scope will be included in the documentation.
-# The default value is: NO.
-
-#EXTRACT_PACKAGE        = NO
-
-# If the EXTRACT_STATIC tag is set to YES all static members of a file will be
-# included in the documentation.
-# The default value is: NO.
-
-EXTRACT_STATIC         = NO
-
-# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) defined
-# locally in source files will be included in the documentation. If set to NO
-# only classes defined in header files are included. Does not have any effect
-# for Java sources.
-# The default value is: YES.
-
-EXTRACT_LOCAL_CLASSES  = YES
-
-# This flag is only useful for Objective-C code. When set to YES local methods,
-# which are defined in the implementation section but not in the interface are
-# included in the documentation. If set to NO only methods in the interface are
-# included.
-# The default value is: NO.
-
-EXTRACT_LOCAL_METHODS  = NO
-
-# If this flag is set to YES, the members of anonymous namespaces will be
-# extracted and appear in the documentation as a namespace called
-# 'anonymous_namespace{file}', where file will be replaced with the base name of
-# the file that contains the anonymous namespace. By default anonymous namespace
-# are hidden.
-# The default value is: NO.
-
-EXTRACT_ANON_NSPACES   = NO
-
-# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
-# undocumented members inside documented classes or files. If set to NO these
-# members will be included in the various overviews, but no documentation
-# section is generated. This option has no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_MEMBERS     = NO
-
-# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
-# undocumented classes that are normally visible in the class hierarchy. If set
-# to NO these classes will be included in the various overviews. This option has
-# no effect if EXTRACT_ALL is enabled.
-# The default value is: NO.
-
-HIDE_UNDOC_CLASSES     = NO
-
-# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
-# (class|struct|union) declarations. If set to NO these declarations will be
-# included in the documentation.
-# The default value is: NO.
-
-HIDE_FRIEND_COMPOUNDS  = NO
-
-# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
-# documentation blocks found inside the body of a function. If set to NO these
-# blocks will be appended to the function's detailed documentation block.
-# The default value is: NO.
-
-HIDE_IN_BODY_DOCS      = NO
-
-# The INTERNAL_DOCS tag determines if documentation that is typed after a
-# \internal command is included. If the tag is set to NO then the documentation
-# will be excluded. Set it to YES to include the internal documentation.
-# The default value is: NO.
-
-INTERNAL_DOCS          = NO
-
-# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
-# names in lower-case letters. If set to YES upper-case letters are also
-# allowed. This is useful if you have classes or files whose names only differ
-# in case and if your file system supports case sensitive file names. Windows
-# and Mac users are advised to set this option to NO.
-# The default value is: system dependent.
-
-CASE_SENSE_NAMES       = YES
-
-# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
-# their full class and namespace scopes in the documentation. If set to YES the
-# scope will be hidden.
-# The default value is: NO.
-
-HIDE_SCOPE_NAMES       = NO
-
-# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
-# the files that are included by a file in the documentation of that file.
-# The default value is: YES.
-
-SHOW_INCLUDE_FILES     = YES
-
-# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
-# grouped member an include statement to the documentation, telling the reader
-# which file to include in order to use the member.
-# The default value is: NO.
-
-#SHOW_GROUPED_MEMB_INC  = NO
-
-# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
-# files with double quotes in the documentation rather than with sharp brackets.
-# The default value is: NO.
-
-FORCE_LOCAL_INCLUDES   = NO
-
-# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
-# documentation for inline members.
-# The default value is: YES.
-
-INLINE_INFO            = YES
-
-# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
-# (detailed) documentation of file and class members alphabetically by member
-# name. If set to NO the members will appear in declaration order.
-# The default value is: YES.
-
-SORT_MEMBER_DOCS       = YES
-
-# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
-# descriptions of file, namespace and class members alphabetically by member
-# name. If set to NO the members will appear in declaration order. Note that
-# this will also influence the order of the classes in the class list.
-# The default value is: NO.
-
-SORT_BRIEF_DOCS        = NO
-
-# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
-# (brief and detailed) documentation of class members so that constructors and
-# destructors are listed first. If set to NO the constructors will appear in the
-# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
-# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
-# member documentation.
-# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
-# detailed member documentation.
-# The default value is: NO.
-
-SORT_MEMBERS_CTORS_1ST = NO
-
-# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
-# of group names into alphabetical order. If set to NO the group names will
-# appear in their defined order.
-# The default value is: NO.
-
-SORT_GROUP_NAMES       = NO
-
-# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
-# fully-qualified names, including namespaces. If set to NO, the class list will
-# be sorted only by class name, not including the namespace part.
-# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
-# Note: This option applies only to the class list, not to the alphabetical
-# list.
-# The default value is: NO.
-
-SORT_BY_SCOPE_NAME     = NO
-
-# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
-# type resolution of all parameters of a function it will reject a match between
-# the prototype and the implementation of a member function even if there is
-# only one candidate or it is obvious which candidate to choose by doing a
-# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
-# accept a match between prototype and implementation in such cases.
-# The default value is: NO.
-
-STRICT_PROTO_MATCHING  = NO
-
-# The GENERATE_TODOLIST tag can be used to enable ( YES) or disable ( NO) the
-# todo list. This list is created by putting \todo commands in the
-# documentation.
-# The default value is: YES.
-
-GENERATE_TODOLIST      = YES
-
-# The GENERATE_TESTLIST tag can be used to enable ( YES) or disable ( NO) the
-# test list. This list is created by putting \test commands in the
-# documentation.
-# The default value is: YES.
-
-GENERATE_TESTLIST      = YES
-
-# The GENERATE_BUGLIST tag can be used to enable ( YES) or disable ( NO) the bug
-# list. This list is created by putting \bug commands in the documentation.
-# The default value is: YES.
-
-GENERATE_BUGLIST       = YES
-
-# The GENERATE_DEPRECATEDLIST tag can be used to enable ( YES) or disable ( NO)
-# the deprecated list. This list is created by putting \deprecated commands in
-# the documentation.
-# The default value is: YES.
-
-GENERATE_DEPRECATEDLIST= YES
-
-# The ENABLED_SECTIONS tag can be used to enable conditional documentation
-# sections, marked by \if <section_label> ... \endif and \cond <section_label>
-# ... \endcond blocks.
-
-ENABLED_SECTIONS       =
-
-# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
-# initial value of a variable or macro / define can have for it to appear in the
-# documentation. If the initializer consists of more lines than specified here
-# it will be hidden. Use a value of 0 to hide initializers completely. The
-# appearance of the value of individual variables and macros / defines can be
-# controlled using \showinitializer or \hideinitializer command in the
-# documentation regardless of this setting.
-# Minimum value: 0, maximum value: 10000, default value: 30.
-
-MAX_INITIALIZER_LINES  = 30
-
-# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
-# the bottom of the documentation of classes and structs. If set to YES the list
-# will mention the files that were used to generate the documentation.
-# The default value is: YES.
-
-SHOW_USED_FILES        = YES
-
-# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
-# will remove the Files entry from the Quick Index and from the Folder Tree View
-# (if specified).
-# The default value is: YES.
-
-SHOW_FILES             = YES
-
-# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
-# page. This will remove the Namespaces entry from the Quick Index and from the
-# Folder Tree View (if specified).
-# The default value is: YES.
-
-SHOW_NAMESPACES        = YES
-
-# The FILE_VERSION_FILTER tag can be used to specify a program or script that
-# doxygen should invoke to get the current version for each file (typically from
-# the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command command input-file, where command is the value of the
-# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
-# by doxygen. Whatever the program writes to standard output is used as the file
-# version. For an example see the documentation.
-
-FILE_VERSION_FILTER    =
-
-# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
-# by doxygen. The layout file controls the global structure of the generated
-# output files in an output format independent way. To create the layout file
-# that represents doxygen's defaults, run doxygen with the -l option. You can
-# optionally specify a file name after the option, if omitted DoxygenLayout.xml
-# will be used as the name of the layout file.
-#
-# Note that if you run doxygen from a directory containing a file called
-# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
-# tag is left empty.
-
-LAYOUT_FILE            =
-
-# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
-# the reference definitions. This must be a list of .bib files. The .bib
-# extension is automatically appended if omitted. This requires the bibtex tool
-# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
-# For LaTeX the style of the bibliography can be controlled using
-# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
-# search path. See also \cite for info how to create references.
-
-CITE_BIB_FILES         =
-
-#---------------------------------------------------------------------------
-# Configuration options related to warning and progress messages
-#---------------------------------------------------------------------------
-
-# The QUIET tag can be used to turn on/off the messages that are generated to
-# standard output by doxygen. If QUIET is set to YES this implies that the
-# messages are off.
-# The default value is: NO.
-
-QUIET                  = NO
-
-# The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated to standard error ( stderr) by doxygen. If WARNINGS is set to YES
-# this implies that the warnings are on.
-#
-# Tip: Turn warnings on while writing the documentation.
-# The default value is: YES.
-
-WARNINGS               = YES
-
-# If the WARN_IF_UNDOCUMENTED tag is set to YES, then doxygen will generate
-# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
-# will automatically be disabled.
-# The default value is: YES.
-
-WARN_IF_UNDOCUMENTED   = YES
-
-# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as not documenting some parameters
-# in a documented function, or documenting parameters that don't exist or using
-# markup commands wrongly.
-# The default value is: YES.
-
-WARN_IF_DOC_ERROR      = YES
-
-# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
-# are documented, but have no documentation for their parameters or return
-# value. If set to NO doxygen will only warn about wrong or incomplete parameter
-# documentation, but not about the absence of documentation.
-# The default value is: NO.
-
-WARN_NO_PARAMDOC       = YES
-
-# The WARN_FORMAT tag determines the format of the warning messages that doxygen
-# can produce. The string should contain the $file, $line, and $text tags, which
-# will be replaced by the file and line number from which the warning originated
-# and the warning text. Optionally the format may contain $version, which will
-# be replaced by the version of the file (if it could be obtained via
-# FILE_VERSION_FILTER)
-# The default value is: $file:$line: $text.
-
-WARN_FORMAT            = "$file:$line: $text"
-
-# The WARN_LOGFILE tag can be used to specify a file to which warning and error
-# messages should be written. If left blank the output is written to standard
-# error (stderr).
-
-WARN_LOGFILE           =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the input files
-#---------------------------------------------------------------------------
-
-# The INPUT tag is used to specify the files and/or directories that contain
-# documented source files. You may enter file names like myfile.cpp or
-# directories like /usr/src/myproject. Separate the files or directories with
-# spaces.
-# Note: If this tag is empty the current directory is searched.
-
-INPUT                  = @PROJECT_SOURCE_DIR@/include @PROJECT_SOURCE_DIR@/src/common
-
-# This tag can be used to specify the character encoding of the source files
-# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
-# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see: http://www.gnu.org/software/libiconv) for the list of
-# possible encodings.
-# The default value is: UTF-8.
-
-INPUT_ENCODING         = UTF-8
-
-# If the value of the INPUT tag contains directories, you can use the
-# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank the
-# following patterns are tested:*.c, *.cc, *.cxx, *.cpp, *.c++, *.java, *.ii,
-# *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp,
-# *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown,
-# *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf,
-# *.qsf, *.as and *.js.
-
-FILE_PATTERNS          = *.h
-
-# The RECURSIVE tag can be used to specify whether or not subdirectories should
-# be searched for input files as well.
-# The default value is: NO.
-
-RECURSIVE              = YES
-
-# The EXCLUDE tag can be used to specify files and/or directories that should be
-# excluded from the INPUT source files. This way you can easily exclude a
-# subdirectory from a directory tree whose root is specified with the INPUT tag.
-#
-# Note that relative paths are relative to the directory from which doxygen is
-# run.
-
-EXCLUDE                =
-
-# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
-# directories that are symbolic links (a Unix file system feature) are excluded
-# from the input.
-# The default value is: NO.
-
-EXCLUDE_SYMLINKS       = NO
-
-# If the value of the INPUT tag contains directories, you can use the
-# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
-# certain files from those directories.
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories for example use the pattern */test/*
-
-EXCLUDE_PATTERNS       = */test/* \
-                         logging.h
-
-# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
-# (namespaces, classes, functions, etc.) that should be excluded from the
-# output. The symbol name can be a fully qualified name, a word, or if the
-# wildcard * is used, a substring. Examples: ANamespace, AClass,
-# AClass::ANamespace, ANamespace::*Test
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories use the pattern */test/*
-
-EXCLUDE_SYMBOLS        =
-
-# The EXAMPLE_PATH tag can be used to specify one or more files or directories
-# that contain example code fragments that are included (see the \include
-# command).
-
-EXAMPLE_PATH           =
-
-# If the value of the EXAMPLE_PATH tag contains directories, you can use the
-# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank all
-# files are included.
-
-EXAMPLE_PATTERNS       =
-
-# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
-# searched for input files to be used with the \include or \dontinclude commands
-# irrespective of the value of the RECURSIVE tag.
-# The default value is: NO.
-
-EXAMPLE_RECURSIVE      = NO
-
-# The IMAGE_PATH tag can be used to specify one or more files or directories
-# that contain images that are to be included in the documentation (see the
-# \image command).
-
-IMAGE_PATH             =
-
-# The INPUT_FILTER tag can be used to specify a program that doxygen should
-# invoke to filter for each input file. Doxygen will invoke the filter program
-# by executing (via popen()) the command:
-#
-# <filter> <input-file>
-#
-# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
-# name of an input file. Doxygen will then use the output that the filter
-# program writes to standard output. If FILTER_PATTERNS is specified, this tag
-# will be ignored.
-#
-# Note that the filter must not add or remove lines; it is applied before the
-# code is scanned, but not when the output code is generated. If lines are added
-# or removed, the anchors will not be placed correctly.
-
-INPUT_FILTER           =
-
-# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
-# basis. Doxygen will compare the file name with each pattern and apply the
-# filter if there is a match. The filters are a list of the form: pattern=filter
-# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
-# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
-# patterns match the file name, INPUT_FILTER is applied.
-
-FILTER_PATTERNS        =
-
-# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
-# INPUT_FILTER ) will also be used to filter the input files that are used for
-# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
-# The default value is: NO.
-
-FILTER_SOURCE_FILES    = NO
-
-# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
-# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
-# it is also possible to disable source filtering for a specific pattern using
-# *.ext= (so without naming a filter).
-# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
-
-FILTER_SOURCE_PATTERNS =
-
-# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
-# is part of the input, its contents will be placed on the main page
-# (index.html). This can be useful if you have a project on for instance GitHub
-# and want to reuse the introduction page also for the doxygen output.
-
-#USE_MDFILE_AS_MAINPAGE =
-
-#---------------------------------------------------------------------------
-# Configuration options related to source browsing
-#---------------------------------------------------------------------------
-
-# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
-# generated. Documented entities will be cross-referenced with these sources.
-#
-# Note: To get rid of all source code in the generated output, make sure that
-# also VERBATIM_HEADERS is set to NO.
-# The default value is: NO.
-
-SOURCE_BROWSER         = NO
-
-# Setting the INLINE_SOURCES tag to YES will include the body of functions,
-# classes and enums directly into the documentation.
-# The default value is: NO.
-
-INLINE_SOURCES         = NO
-
-# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
-# special comment blocks from generated source code fragments. Normal C, C++ and
-# Fortran comments will always remain visible.
-# The default value is: YES.
-
-STRIP_CODE_COMMENTS    = YES
-
-# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
-# function all documented functions referencing it will be listed.
-# The default value is: NO.
-
-REFERENCED_BY_RELATION = NO
-
-# If the REFERENCES_RELATION tag is set to YES then for each documented function
-# all documented entities called/used by that function will be listed.
-# The default value is: NO.
-
-REFERENCES_RELATION    = NO
-
-# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
-# to YES, then the hyperlinks from functions in REFERENCES_RELATION and
-# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
-# link to the documentation.
-# The default value is: YES.
-
-REFERENCES_LINK_SOURCE = YES
-
-# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
-# source code will show a tooltip with additional information such as prototype,
-# brief description and links to the definition and documentation. Since this
-# will make the HTML file larger and loading of large files a bit slower, you
-# can opt to disable this feature.
-# The default value is: YES.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-#SOURCE_TOOLTIPS        = YES
-
-# If the USE_HTAGS tag is set to YES then the references to source code will
-# point to the HTML generated by the htags(1) tool instead of doxygen built-in
-# source browser. The htags tool is part of GNU's global source tagging system
-# (see http://www.gnu.org/software/global/global.html). You will need version
-# 4.8.6 or higher.
-#
-# To use it do the following:
-# - Install the latest version of global
-# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
-# - Make sure the INPUT points to the root of the source tree
-# - Run doxygen as normal
-#
-# Doxygen will invoke htags (and that will in turn invoke gtags), so these
-# tools must be available from the command line (i.e. in the search path).
-#
-# The result: instead of the source browser generated by doxygen, the links to
-# source code will now point to the output of htags.
-# The default value is: NO.
-# This tag requires that the tag SOURCE_BROWSER is set to YES.
-
-USE_HTAGS              = NO
-
-# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
-# verbatim copy of the header file for each class for which an include is
-# specified. Set to NO to disable this.
-# See also: Section \class.
-# The default value is: YES.
-
-VERBATIM_HEADERS       = YES
-
-# If the CLANG_ASSISTED_PARSING tag is set to YES, then doxygen will use the
-# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
-# cost of reduced performance. This can be particularly helpful with template
-# rich C++ code for which doxygen's built-in parser lacks the necessary type
-# information.
-# Note: The availability of this option depends on whether or not doxygen was
-# compiled with the --with-libclang option.
-# The default value is: NO.
-
-#CLANG_ASSISTED_PARSING = NO
-
-# If clang assisted parsing is enabled you can provide the compiler with command
-# line options that you would normally use when invoking the compiler. Note that
-# the include paths will already be set by doxygen for the files and directories
-# specified with INPUT and INCLUDE_PATH.
-# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
-
-#CLANG_OPTIONS          =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the alphabetical class index
-#---------------------------------------------------------------------------
-
-# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
-# compounds will be generated. Enable this if the project contains a lot of
-# classes, structs, unions or interfaces.
-# The default value is: YES.
-
-ALPHABETICAL_INDEX     = YES
-
-# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
-# which the alphabetical index list will be split.
-# Minimum value: 1, maximum value: 20, default value: 5.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-COLS_IN_ALPHA_INDEX    = 5
-
-# In case all classes in a project start with a common prefix, all classes will
-# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
-# can be used to specify a prefix (or a list of prefixes) that should be ignored
-# while generating the index headers.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-IGNORE_PREFIX          =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the HTML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_HTML tag is set to YES doxygen will generate HTML output
-# The default value is: YES.
-
-GENERATE_HTML          = YES
-
-# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_OUTPUT            = html
-
-# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
-# generated HTML page (for example: .htm, .php, .asp).
-# The default value is: .html.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FILE_EXTENSION    = .html
-
-# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
-# each generated HTML page. If the tag is left blank doxygen will generate a
-# standard header.
-#
-# To get valid HTML the header file that includes any scripts and style sheets
-# that doxygen needs, which is dependent on the configuration options used (e.g.
-# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
-# default header using
-# doxygen -w html new_header.html new_footer.html new_stylesheet.css
-# YourConfigFile
-# and then modify the file new_header.html. See also section "Doxygen usage"
-# for information on how to generate the default header that doxygen normally
-# uses.
-# Note: The header is subject to change so you typically have to regenerate the
-# default header when upgrading to a newer version of doxygen. For a description
-# of the possible markers and block names see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_HEADER            =
-
-# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
-# generated HTML page. If the tag is left blank doxygen will generate a standard
-# footer. See HTML_HEADER for more information on how to generate a default
-# footer and what special commands can be used inside the footer. See also
-# section "Doxygen usage" for information on how to generate the default footer
-# that doxygen normally uses.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_FOOTER            =
-
-# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
-# sheet that is used by each HTML page. It can be used to fine-tune the look of
-# the HTML output. If left blank doxygen will generate a default style sheet.
-# See also section "Doxygen usage" for information on how to generate the style
-# sheet that doxygen normally uses.
-# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
-# it is more robust and this tag (HTML_STYLESHEET) will in the future become
-# obsolete.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_STYLESHEET        =
-
-# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
-# cascading style sheets that are included after the standard style sheets
-# created by doxygen. Using this option one can overrule certain style aspects.
-# This is preferred over using HTML_STYLESHEET since it does not replace the
-# standard style sheet and is therefor more robust against future updates.
-# Doxygen will copy the style sheet files to the output directory.
-# Note: The order of the extra stylesheet files is of importance (e.g. the last
-# stylesheet in the list overrules the setting of the previous ones in the
-# list). For an example see the documentation.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-#HTML_EXTRA_STYLESHEET  =
-
-# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the HTML output directory. Note
-# that these files will be copied to the base HTML output directory. Use the
-# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
-# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
-# files will be copied as-is; there are no commands or markers available.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_EXTRA_FILES       =
-
-# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
-# will adjust the colors in the stylesheet and background images according to
-# this color. Hue is specified as an angle on a colorwheel, see
-# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
-# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
-# purple, and 360 is red again.
-# Minimum value: 0, maximum value: 359, default value: 220.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_HUE    = 220
-
-# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
-# in the HTML output. For a value of 0 the output will use grayscales only. A
-# value of 255 will produce the most vivid colors.
-# Minimum value: 0, maximum value: 255, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_SAT    = 100
-
-# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
-# luminance component of the colors in the HTML output. Values below 100
-# gradually make the output lighter, whereas values above 100 make the output
-# darker. The value divided by 100 is the actual gamma applied, so 80 represents
-# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
-# change the gamma.
-# Minimum value: 40, maximum value: 240, default value: 80.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_COLORSTYLE_GAMMA  = 80
-
-# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_TIMESTAMP         = YES
-
-# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
-# documentation will contain sections that can be hidden and shown after the
-# page has loaded.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_DYNAMIC_SECTIONS  = NO
-
-# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
-# shown in the various tree structured indices initially; the user can expand
-# and collapse entries dynamically later on. Doxygen will expand the tree to
-# such a level that at most the specified number of entries are visible (unless
-# a fully collapsed tree already exceeds this amount). So setting the number of
-# entries 1 will produce a full collapsed tree by default. 0 is a special value
-# representing an infinite number of entries and will result in a full expanded
-# tree by default.
-# Minimum value: 0, maximum value: 9999, default value: 100.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-#HTML_INDEX_NUM_ENTRIES = 100
-
-# If the GENERATE_DOCSET tag is set to YES, additional index files will be
-# generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see: http://developer.apple.com/tools/xcode/), introduced with
-# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
-# Makefile in the HTML output directory. Running make will produce the docset in
-# that directory and running make install will install the docset in
-# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
-# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
-# for more information.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_DOCSET        = NO
-
-# This tag determines the name of the docset feed. A documentation feed provides
-# an umbrella under which multiple documentation sets from a single provider
-# (such as a company or product suite) can be grouped.
-# The default value is: Doxygen generated docs.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_FEEDNAME        = "Doxygen generated docs"
-
-# This tag specifies a string that should uniquely identify the documentation
-# set bundle. This should be a reverse domain-name style string, e.g.
-# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_BUNDLE_ID       = org.doxygen.Project
-
-# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
-# the documentation publisher. This should be a reverse domain-name style
-# string, e.g. com.mycompany.MyDocSet.documentation.
-# The default value is: org.doxygen.Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
-
-# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
-# The default value is: Publisher.
-# This tag requires that the tag GENERATE_DOCSET is set to YES.
-
-DOCSET_PUBLISHER_NAME  = Publisher
-
-# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
-# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
-# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
-# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
-# Windows.
-#
-# The HTML Help Workshop contains a compiler that can convert all HTML output
-# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
-# files are now used as the Windows 98 help format, and will replace the old
-# Windows help format (.hlp) on all Windows platforms in the future. Compressed
-# HTML files also contain an index, a table of contents, and you can search for
-# words in the documentation. The HTML workshop also contains a viewer for
-# compressed HTML files.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_HTMLHELP      = NO
-
-# The CHM_FILE tag can be used to specify the file name of the resulting .chm
-# file. You can add a path in front of the file if the result should not be
-# written to the html output directory.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_FILE               =
-
-# The HHC_LOCATION tag can be used to specify the location (absolute path
-# including file name) of the HTML help compiler ( hhc.exe). If non-empty
-# doxygen will try to run the HTML help compiler on the generated index.hhp.
-# The file has to be specified with full path.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-HHC_LOCATION           =
-
-# The GENERATE_CHI flag controls if a separate .chi index file is generated (
-# YES) or that it should be included in the master .chm file ( NO).
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-GENERATE_CHI           = NO
-
-# The CHM_INDEX_ENCODING is used to encode HtmlHelp index ( hhk), content ( hhc)
-# and project file content.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-CHM_INDEX_ENCODING     =
-
-# The BINARY_TOC flag controls whether a binary table of contents is generated (
-# YES) or a normal table of contents ( NO) in the .chm file. Furthermore it
-# enables the Previous and Next buttons.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-BINARY_TOC             = NO
-
-# The TOC_EXPAND flag can be set to YES to add extra items for group members to
-# the table of contents of the HTML help documentation and to the tree view.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
-
-TOC_EXPAND             = NO
-
-# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
-# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
-# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
-# (.qch) of the generated HTML documentation.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_QHP           = NO
-
-# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
-# the file name of the resulting .qch file. The path specified is relative to
-# the HTML output folder.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QCH_FILE               =
-
-# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
-# Project output. For more information please see Qt Help Project / Namespace
-# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_NAMESPACE          = org.doxygen.Project
-
-# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
-# Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
-# folders).
-# The default value is: doc.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_VIRTUAL_FOLDER     = doc
-
-# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
-# filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_NAME   =
-
-# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
-# custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_CUST_FILTER_ATTRS  =
-
-# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
-# project's filter section matches. Qt Help Project / Filter Attributes (see:
-# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHP_SECT_FILTER_ATTRS  =
-
-# The QHG_LOCATION tag can be used to specify the location of Qt's
-# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
-# generated .qhp file.
-# This tag requires that the tag GENERATE_QHP is set to YES.
-
-QHG_LOCATION           =
-
-# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
-# generated, together with the HTML files, they form an Eclipse help plugin. To
-# install this plugin and make it available under the help contents menu in
-# Eclipse, the contents of the directory containing the HTML and XML files needs
-# to be copied into the plugins directory of eclipse. The name of the directory
-# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
-# After copying Eclipse needs to be restarted before the help appears.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_ECLIPSEHELP   = NO
-
-# A unique identifier for the Eclipse help plugin. When installing the plugin
-# the directory name containing the HTML and XML files should also have this
-# name. Each documentation set should have its own identifier.
-# The default value is: org.doxygen.Project.
-# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
-
-ECLIPSE_DOC_ID         = org.doxygen.Project
-
-# If you want full control over the layout of the generated HTML pages it might
-# be necessary to disable the index and replace it with your own. The
-# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
-# of each HTML page. A value of NO enables the index and the value YES disables
-# it. Since the tabs in the index contain the same information as the navigation
-# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-DISABLE_INDEX          = NO
-
-# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
-# structure should be generated to display hierarchical information. If the tag
-# value is set to YES, a side panel will be generated containing a tree-like
-# index structure (just like the one that is generated for HTML Help). For this
-# to work a browser that supports JavaScript, DHTML, CSS and frames is required
-# (i.e. any modern browser). Windows users are probably better off using the
-# HTML help feature. Via custom stylesheets (see HTML_EXTRA_STYLESHEET) one can
-# further fine-tune the look of the index. As an example, the default style
-# sheet generated by doxygen has an example that shows how to put an image at
-# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
-# the same information as the tab index, you could consider setting
-# DISABLE_INDEX to YES when enabling this option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-GENERATE_TREEVIEW      = NO
-
-# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
-# doxygen will group on one line in the generated HTML documentation.
-#
-# Note that a value of 0 will completely suppress the enum values from appearing
-# in the overview section.
-# Minimum value: 0, maximum value: 20, default value: 4.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-ENUM_VALUES_PER_LINE   = 4
-
-# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
-# to set the initial width (in pixels) of the frame in which the tree is shown.
-# Minimum value: 0, maximum value: 1500, default value: 250.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-TREEVIEW_WIDTH         = 250
-
-# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open links to
-# external symbols imported via tag files in a separate window.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-EXT_LINKS_IN_WINDOW    = NO
-
-# Use this tag to change the font size of LaTeX formulas included as images in
-# the HTML documentation. When you change the font size after a successful
-# doxygen run you need to manually remove any form_*.png images from the HTML
-# output directory to force them to be regenerated.
-# Minimum value: 8, maximum value: 50, default value: 10.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_FONTSIZE       = 10
-
-# Use the FORMULA_TRANPARENT tag to determine whether or not the images
-# generated for formulas are transparent PNGs. Transparent PNGs are not
-# supported properly for IE 6.0, but are supported on all modern browsers.
-#
-# Note that when changing this option you need to delete any form_*.png files in
-# the HTML output directory before the changes have effect.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_TRANSPARENT    = YES
-
-# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
-# http://www.mathjax.org) which uses client side Javascript for the rendering
-# instead of using prerendered bitmaps. Use this if you do not have LaTeX
-# installed or if you want to formulas look prettier in the HTML output. When
-# enabled you may also need to install MathJax separately and configure the path
-# to it using the MATHJAX_RELPATH option.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-USE_MATHJAX            = NO
-
-# When MathJax is enabled you can set the default output format to be used for
-# the MathJax output. See the MathJax site (see:
-# http://docs.mathjax.org/en/latest/output.html) for more details.
-# Possible values are: HTML-CSS (which is slower, but has the best
-# compatibility), NativeMML (i.e. MathML) and SVG.
-# The default value is: HTML-CSS.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-#MATHJAX_FORMAT         = HTML-CSS
-
-# When MathJax is enabled you need to specify the location relative to the HTML
-# output directory using the MATHJAX_RELPATH option. The destination directory
-# should contain the MathJax.js script. For instance, if the mathjax directory
-# is located at the same level as the HTML output directory, then
-# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
-# Content Delivery Network so you can quickly see the result without installing
-# MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from http://www.mathjax.org before deployment.
-# The default value is: http://cdn.mathjax.org/mathjax/latest.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
-
-# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
-# extension names that should be enabled during MathJax rendering. For example
-# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-MATHJAX_EXTENSIONS     =
-
-# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
-# of code that will be used on startup of the MathJax code. See the MathJax site
-# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
-# example see the documentation.
-# This tag requires that the tag USE_MATHJAX is set to YES.
-
-#MATHJAX_CODEFILE       =
-
-# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
-# the HTML output. The underlying search engine uses javascript and DHTML and
-# should work on any modern browser. Note that when using HTML help
-# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
-# there is already a search function so this one should typically be disabled.
-# For large projects the javascript based search engine can be slow, then
-# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
-# search using the keyboard; to jump to the search box use <access key> + S
-# (what the <access key> is depends on the OS and browser, but it is typically
-# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
-# key> to jump into the search results window, the results can be navigated
-# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
-# the search. The filter options can be selected when the cursor is inside the
-# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
-# to select a filter and <Enter> or <escape> to activate or cancel the filter
-# option.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-SEARCHENGINE           = YES
-
-# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
-# implemented using a web server instead of a web client using Javascript. There
-# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
-# setting. When disabled, doxygen will generate a PHP script for searching and
-# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
-# and searching needs to be provided by external tools. See the section
-# "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-SERVER_BASED_SEARCH    = NO
-
-# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
-# script for searching. Instead the search results are written to an XML file
-# which needs to be processed by an external indexer. Doxygen will invoke an
-# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
-# search results.
-#
-# Doxygen ships with an example indexer ( doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/).
-#
-# See the section "External Indexing and Searching" for details.
-# The default value is: NO.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#EXTERNAL_SEARCH        = NO
-
-# The SEARCHENGINE_URL should point to a search engine hosted by a web server
-# which will return the search results when EXTERNAL_SEARCH is enabled.
-#
-# Doxygen ships with an example indexer ( doxyindexer) and search engine
-# (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/). See the section "External Indexing and
-# Searching" for details.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#SEARCHENGINE_URL       =
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
-# search data is written to a file for indexing by an external tool. With the
-# SEARCHDATA_FILE tag the name of this file can be specified.
-# The default file is: searchdata.xml.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#SEARCHDATA_FILE        = searchdata.xml
-
-# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
-# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
-# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
-# projects and redirect the results back to the right project.
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#EXTERNAL_SEARCH_ID     =
-
-# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
-# projects other than the one defined by this configuration file, but that are
-# all added to the same external search index. Each project needs to have a
-# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
-# to a relative location where the documentation can be found. The format is:
-# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
-# This tag requires that the tag SEARCHENGINE is set to YES.
-
-#EXTRA_SEARCH_MAPPINGS  =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the LaTeX output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_LATEX tag is set to YES doxygen will generate LaTeX output.
-# The default value is: YES.
-
-GENERATE_LATEX         = YES
-
-# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_OUTPUT           = latex
-
-# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
-# invoked.
-#
-# Note that when enabling USE_PDFLATEX this option is only used for generating
-# bitmaps for formulas in the HTML output, but not in the Makefile that is
-# written to the output directory.
-# The default file is: latex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_CMD_NAME         = latex
-
-# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
-# index for LaTeX.
-# The default file is: makeindex.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-MAKEINDEX_CMD_NAME     = makeindex
-
-# If the COMPACT_LATEX tag is set to YES doxygen generates more compact LaTeX
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-COMPACT_LATEX          = NO
-
-# The PAPER_TYPE tag can be used to set the paper type that is used by the
-# printer.
-# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
-# 14 inches) and executive (7.25 x 10.5 inches).
-# The default value is: a4.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PAPER_TYPE             = a4
-
-# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
-# that should be included in the LaTeX output. To get the times font for
-# instance you can specify
-# EXTRA_PACKAGES=times
-# If left blank no extra packages will be included.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-EXTRA_PACKAGES         =
-
-# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
-# generated LaTeX document. The header should contain everything until the first
-# chapter. If it is left blank doxygen will generate a standard header. See
-# section "Doxygen usage" for information on how to let doxygen write the
-# default header to a separate file.
-#
-# Note: Only use a user-defined header if you know what you are doing! The
-# following commands have a special meaning inside the header: $title,
-# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
-# $projectbrief, $projectlogo. Doxygen will replace $title with the empy string,
-# for the replacement values of the other commands the user is refered to
-# HTML_HEADER.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HEADER           =
-
-# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
-# generated LaTeX document. The footer should contain everything after the last
-# chapter. If it is left blank doxygen will generate a standard footer. See
-# LATEX_HEADER for more information on how to generate a default footer and what
-# special commands can be used inside the footer.
-#
-# Note: Only use a user-defined footer if you know what you are doing!
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_FOOTER           =
-
-# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
-# other source files which should be copied to the LATEX_OUTPUT output
-# directory. Note that the files will be copied as-is; there are no commands or
-# markers available.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-#LATEX_EXTRA_FILES      =
-
-# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
-# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
-# contain links (just like the HTML output) instead of page references. This
-# makes the output suitable for online browsing using a PDF viewer.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-PDF_HYPERLINKS         = YES
-
-# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
-# the PDF file directly from the LaTeX files. Set this option to YES to get a
-# higher quality PDF documentation.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-USE_PDFLATEX           = YES
-
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
-# command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help. This option is also used
-# when generating formulas in HTML.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BATCHMODE        = NO
-
-# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
-# index chapters (such as File Index, Compound Index, etc.) in the output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_HIDE_INDICES     = NO
-
-# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
-# code with syntax highlighting in the LaTeX output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_SOURCE_CODE      = NO
-
-# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
-# bibliography, e.g. plainnat, or ieeetr. See
-# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
-# The default value is: plain.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_BIB_STYLE        = plain
-
-#---------------------------------------------------------------------------
-# Configuration options related to the RTF output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_RTF tag is set to YES doxygen will generate RTF output. The
-# RTF output is optimized for Word 97 and may not look too pretty with other RTF
-# readers/editors.
-# The default value is: NO.
-
-GENERATE_RTF           = NO
-
-# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: rtf.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_OUTPUT             = rtf
-
-# If the COMPACT_RTF tag is set to YES doxygen generates more compact RTF
-# documents. This may be useful for small projects and may help to save some
-# trees in general.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-COMPACT_RTF            = NO
-
-# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
-# contain hyperlink fields. The RTF file will contain links (just like the HTML
-# output) instead of page references. This makes the output suitable for online
-# browsing using Word or some other Word compatible readers that support those
-# fields.
-#
-# Note: WordPad (write) and others do not support links.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_HYPERLINKS         = NO
-
-# Load stylesheet definitions from file. Syntax is similar to doxygen's config
-# file, i.e. a series of assignments. You only have to provide replacements,
-# missing definitions are set to their default value.
-#
-# See also section "Doxygen usage" for information on how to generate the
-# default style sheet that doxygen normally uses.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_STYLESHEET_FILE    =
-
-# Set optional variables used in the generation of an RTF document. Syntax is
-# similar to doxygen's config file. A template extensions file can be generated
-# using doxygen -e rtf extensionFile.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_EXTENSIONS_FILE    =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the man page output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_MAN tag is set to YES doxygen will generate man pages for
-# classes and files.
-# The default value is: NO.
-
-GENERATE_MAN           = NO
-
-# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it. A directory man3 will be created inside the directory specified by
-# MAN_OUTPUT.
-# The default directory is: man.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_OUTPUT             = man
-
-# The MAN_EXTENSION tag determines the extension that is added to the generated
-# man pages. In case the manual section does not start with a number, the number
-# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
-# optional.
-# The default value is: .3.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_EXTENSION          = .3
-
-# The MAN_SUBDIR tag determines the name of the directory created within
-# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
-# MAN_EXTENSION with the initial . removed.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-#MAN_SUBDIR             =
-
-# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
-# will generate one additional man file for each entity documented in the real
-# man page(s). These additional files only source the real man page, but without
-# them the man command would be unable to find the correct page.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_MAN is set to YES.
-
-MAN_LINKS              = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the XML output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_XML tag is set to YES doxygen will generate an XML file that
-# captures the structure of the code including all documentation.
-# The default value is: NO.
-
-GENERATE_XML           = YES
-
-# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
-# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
-# it.
-# The default directory is: xml.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_OUTPUT             = xml
-
-# If the XML_PROGRAMLISTING tag is set to YES doxygen will dump the program
-# listings (including syntax highlighting and cross-referencing information) to
-# the XML output. Note that enabling this will significantly increase the size
-# of the XML output.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_XML is set to YES.
-
-XML_PROGRAMLISTING     = YES
-
-#---------------------------------------------------------------------------
-# Configuration options related to the DOCBOOK output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_DOCBOOK tag is set to YES doxygen will generate Docbook files
-# that can be used to generate PDF.
-# The default value is: NO.
-
-#GENERATE_DOCBOOK       = NO
-
-# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
-# front of it.
-# The default directory is: docbook.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-#DOCBOOK_OUTPUT         = docbook
-
-# If the DOCBOOK_PROGRAMLISTING tag is set to YES doxygen will include the
-# program listings (including syntax highlighting and cross-referencing
-# information) to the DOCBOOK output. Note that enabling this will significantly
-# increase the size of the DOCBOOK output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-#DOCBOOK_PROGRAMLISTING = NO
-
-#---------------------------------------------------------------------------
-# Configuration options for the AutoGen Definitions output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_AUTOGEN_DEF tag is set to YES doxygen will generate an AutoGen
-# Definitions (see http://autogen.sf.net) file that captures the structure of
-# the code including all documentation. Note that this feature is still
-# experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_AUTOGEN_DEF   = NO
-
-#---------------------------------------------------------------------------
-# Configuration options related to the Perl module output
-#---------------------------------------------------------------------------
-
-# If the GENERATE_PERLMOD tag is set to YES doxygen will generate a Perl module
-# file that captures the structure of the code including all documentation.
-#
-# Note that this feature is still experimental and incomplete at the moment.
-# The default value is: NO.
-
-GENERATE_PERLMOD       = NO
-
-# If the PERLMOD_LATEX tag is set to YES doxygen will generate the necessary
-# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
-# output from the Perl module output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_LATEX          = NO
-
-# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be nicely
-# formatted so it can be parsed by a human reader. This is useful if you want to
-# understand what is going on. On the other hand, if this tag is set to NO the
-# size of the Perl module output will be much smaller and Perl will parse it
-# just the same.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_PRETTY         = YES
-
-# The names of the make variables in the generated doxyrules.make file are
-# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
-# so different doxyrules.make files included by the same Makefile don't
-# overwrite each other's variables.
-# This tag requires that the tag GENERATE_PERLMOD is set to YES.
-
-PERLMOD_MAKEVAR_PREFIX =
-
-#---------------------------------------------------------------------------
-# Configuration options related to the preprocessor
-#---------------------------------------------------------------------------
-
-# If the ENABLE_PREPROCESSING tag is set to YES doxygen will evaluate all
-# C-preprocessor directives found in the sources and include files.
-# The default value is: YES.
-
-ENABLE_PREPROCESSING   = YES
-
-# If the MACRO_EXPANSION tag is set to YES doxygen will expand all macro names
-# in the source code. If set to NO only conditional compilation will be
-# performed. Macro expansion can be done in a controlled way by setting
-# EXPAND_ONLY_PREDEF to YES.
-# The default value is: NO.
-# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
-
-MACRO_EXPANSION        = NO
-
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diff --git a/ml-xgboost/doc/Makefile b/ml-xgboost/doc/Makefile
deleted file mode 100644
index 40bba2a..0000000
--- a/ml-xgboost/doc/Makefile
+++ /dev/null
@@ -1,192 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = _build
-
-# User-friendly check for sphinx-build
-ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
-$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
-endif
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
-
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  applehelp  to make an Apple Help Book"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  xml        to make Docutils-native XML files"
-	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-	@echo "  coverage   to run coverage check of the documentation (if enabled)"
-
-clean:
-	rm -rf $(BUILDDIR)/*
-
-html:
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/rabit.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/rabit.qhc"
-
-applehelp:
-	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
-	@echo
-	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
-	@echo "N.B. You won't be able to view it unless you put it in" \
-	      "~/Library/Documentation/Help or install it in your application" \
-	      "bundle."
-
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/rabit"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/rabit"
-	@echo "# devhelp"
-
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-latexpdfja:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through platex and dvipdfmx..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
-
-coverage:
-	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
-	@echo "Testing of coverage in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/coverage/python.txt."
-
-xml:
-	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
-	@echo
-	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-pseudoxml:
-	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
-	@echo
-	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/ml-xgboost/doc/R-package/.gitignore b/ml-xgboost/doc/R-package/.gitignore
deleted file mode 100644
index b25c15b..0000000
--- a/ml-xgboost/doc/R-package/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-*~
diff --git a/ml-xgboost/doc/R-package/Makefile b/ml-xgboost/doc/R-package/Makefile
deleted file mode 100644
index 8b1d9a3..0000000
--- a/ml-xgboost/doc/R-package/Makefile
+++ /dev/null
@@ -1,15 +0,0 @@
-# This is the makefile for compiling Rmarkdown files into the md file with results.
-PKGROOT=../../R-package
-
-# ADD The Markdown to be built here, with suffix md
-discoverYourData.md: $(PKGROOT)/vignettes/discoverYourData.Rmd
-xgboostPresentation.md: $(PKGROOT)/vignettes/xgboostPresentation.Rmd
-
-# General Rules for build rmarkdowns, need knitr
-%.md:
-	Rscript -e \
-	"require(methods);"\
-	"require(knitr);"\
-	"knitr::opts_knit\$$set(root.dir=\".\");"\
-	"knitr::opts_chunk\$$set(fig.path=\"../web-data/xgboost/knitr/$(basename $@)-\");"\
-	"knitr::knit(\"$+\")"
diff --git a/ml-xgboost/doc/R-package/discoverYourData.md b/ml-xgboost/doc/R-package/discoverYourData.md
deleted file mode 100644
index bffdcd5..0000000
--- a/ml-xgboost/doc/R-package/discoverYourData.md
+++ /dev/null
@@ -1,471 +0,0 @@
-
-Understand your dataset with XGBoost
-====================================
-
-Introduction
-------------
-
-The purpose of this Vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
-
-This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
-
-Package loading:
-
-
-```r
-require(xgboost)
-require(Matrix)
-require(data.table)
-if (!require('vcd')) install.packages('vcd')
-```
-
-> **VCD** package is used for one of its embedded dataset only.
-
-Preparation of the dataset
---------------------------
-
-### Numeric VS categorical variables
-
-
-**Xgboost** manages only `numeric` vectors.
-
-What to do when you have *categorical* data?
-
-A *categorical* variable has a fixed number of different values. For instance, if a variable called *Colour* can have only one of these three values, *red*, *blue* or *green*, then *Colour* is a *categorical* variable.
-
-> In **R**, a *categorical* variable is called `factor`.
->
-> Type `?factor` in the console for more information.
-
-To answer the question above we will convert *categorical* variables to `numeric` one.
-
-### Conversion from categorical to numeric variables
-
-#### Looking at the raw data
-
-In this Vignette we will see how to transform a *dense* `data.frame` (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features.
-
-The method we are going to see is usually called [one-hot encoding](http://en.wikipedia.org/wiki/One-hot).
-
-The first step is to load `Arthritis` dataset in memory and wrap it with `data.table` package.
-
-
-```r
-data(Arthritis)
-df <- data.table(Arthritis, keep.rownames = F)
-```
-
-> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
-
-The first thing we want to do is to have a look to the first lines of the `data.table`:
-
-
-```r
-head(df)
-```
-
-```
-##    ID Treatment  Sex Age Improved
-## 1: 57   Treated Male  27     Some
-## 2: 46   Treated Male  29     None
-## 3: 77   Treated Male  30     None
-## 4: 17   Treated Male  32   Marked
-## 5: 36   Treated Male  46   Marked
-## 6: 23   Treated Male  58   Marked
-```
-
-Now we will check the format of each column.
-
-
-```r
-str(df)
-```
-
-```
-## Classes 'data.table' and 'data.frame':	84 obs. of  5 variables:
-##  $ ID       : int  57 46 77 17 36 23 75 39 33 55 ...
-##  $ Treatment: Factor w/ 2 levels "Placebo","Treated": 2 2 2 2 2 2 2 2 2 2 ...
-##  $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
-##  $ Age      : int  27 29 30 32 46 58 59 59 63 63 ...
-##  $ Improved : Ord.factor w/ 3 levels "None"<"Some"<..: 2 1 1 3 3 3 1 3 1 1 ...
-##  - attr(*, ".internal.selfref")=<externalptr>
-```
-
-2 columns have `factor` type, one has `ordinal` type.
-
-> `ordinal` variable :
->
-> * can take a limited number of values (like `factor`) ;
-> * these values are ordered (unlike `factor`). Here these ordered values are: `Marked > Some > None`
-
-#### Creation of new features based on old ones
-
-We will add some new *categorical* features to see if it helps.
-
-##### Grouping per 10 years
-
-For the first feature we create groups of age by rounding the real age.
-
-Note that we transform it to `factor` so the algorithm treat these age groups as independent values.
-
-Therefore, 20 is not closer to 30 than 60. To make it short, the distance between ages is lost in this transformation.
-
-
-```r
-head(df[,AgeDiscret := as.factor(round(Age/10,0))])
-```
-
-```
-##    ID Treatment  Sex Age Improved AgeDiscret
-## 1: 57   Treated Male  27     Some          3
-## 2: 46   Treated Male  29     None          3
-## 3: 77   Treated Male  30     None          3
-## 4: 17   Treated Male  32   Marked          3
-## 5: 36   Treated Male  46   Marked          5
-## 6: 23   Treated Male  58   Marked          6
-```
-
-##### Random split in two groups
-
-Following is an even stronger simplification of the real age with an arbitrary split at 30 years old. I choose this value **based on nothing**. We will see later if simplifying the information based on arbitrary values is a good strategy (you may already have an idea of how well it will work...).
-
-
-```r
-head(df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))])
-```
-
-```
-##    ID Treatment  Sex Age Improved AgeDiscret AgeCat
-## 1: 57   Treated Male  27     Some          3  Young
-## 2: 46   Treated Male  29     None          3  Young
-## 3: 77   Treated Male  30     None          3  Young
-## 4: 17   Treated Male  32   Marked          3    Old
-## 5: 36   Treated Male  46   Marked          5    Old
-## 6: 23   Treated Male  58   Marked          6    Old
-```
-
-##### Risks in adding correlated features
-
-These new features are highly correlated to the `Age` feature because they are simple transformations of this feature.
-
-For many machine learning algorithms, using correlated features is not a good idea. It may sometimes make prediction less accurate, and most of the time make interpretation of the model almost impossible. GLM, for instance, assumes that the features are uncorrelated.
-
-Fortunately, decision tree algorithms (including boosted trees) are very robust to these features. Therefore we have nothing to do to manage this situation.
-
-##### Cleaning data
-
-We remove ID as there is nothing to learn from this feature (it would just add some noise).
-
-
-```r
-df[,ID:=NULL]
-```
-
-We will list the different values for the column `Treatment`:
-
-
-```r
-levels(df[,Treatment])
-```
-
-```
-## [1] "Placebo" "Treated"
-```
-
-
-#### One-hot encoding
-
-Next step, we will transform the categorical data to dummy variables.
-This is the [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) step.
-
-The purpose is to transform each value of each *categorical* feature in a *binary* feature `{0, 1}`.
-
-For example, the column `Treatment` will be replaced by two columns, `Placebo`, and `Treated`. Each of them will be *binary*. Therefore, an observation which has the value `Placebo` in column `Treatment` before the transformation will have after the transformation the value `1` in the new column `Placebo` and the value `0` in the new column `Treated`. The column `Treatment` will disappear during the one-hot encoding.
-
-Column `Improved` is excluded because it will be our `label` column, the one we want to predict.
-
-
-```r
-sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
-head(sparse_matrix)
-```
-
-```
-## 6 x 10 sparse Matrix of class "dgCMatrix"
-##                       
-## 1 . 1 1 27 1 . . . . 1
-## 2 . 1 1 29 1 . . . . 1
-## 3 . 1 1 30 1 . . . . 1
-## 4 . 1 1 32 1 . . . . .
-## 5 . 1 1 46 . . 1 . . .
-## 6 . 1 1 58 . . . 1 . .
-```
-
-> Formulae `Improved~.-1` used above means transform all *categorical* features but column `Improved` to binary values. The `-1` is here to remove the first column which is full of `1` (this column is generated by the conversion). For more information, you can type `?sparse.model.matrix` in the console.
-
-Create the output `numeric` vector (not as a sparse `Matrix`):
-
-
-```r
-output_vector = df[,Improved] == "Marked"
-```
-
-1. set `Y` vector to `0`;
-2. set `Y` to `1` for rows where `Improved == Marked` is `TRUE` ;
-3. return `Y` vector.
-
-Build the model
----------------
-
-The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
-
-
-```r
-bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,
-               eta = 1, nthread = 2, nrounds = 10,objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.202381
-## [1]	train-error:0.166667
-## [2]	train-error:0.166667
-## [3]	train-error:0.166667
-## [4]	train-error:0.154762
-## [5]	train-error:0.154762
-## [6]	train-error:0.154762
-## [7]	train-error:0.166667
-## [8]	train-error:0.166667
-## [9]	train-error:0.166667
-```
-
-You can see some `train-error: 0.XXXXX` lines followed by a number. It decreases. Each line shows how well the model explains your data. Lower is better.
-
-A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
-
-> Here you can see the numbers decrease until line 7 and then increase.
->
-> It probably means we are overfitting. To fix that I should reduce the number of rounds to `nrounds = 4`. I will let things like that because I don't really care for the purpose of this example :-)
-
-Feature importance
-------------------
-
-## Measure feature importance
-
-
-### Build the feature importance data.table
-
-In the code below, `sparse_matrix@Dimnames[[2]]` represents the column names of the sparse matrix. These names are the original values of the features (remember, each binary column == one value of one *categorical* feature).
-
-
-```r
-importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
-head(importance)
-```
-
-```
-##             Feature        Gain      Cover  Frequency
-## 1:              Age 0.622031651 0.67251706 0.67241379
-## 2: TreatmentPlacebo 0.285750607 0.11916656 0.10344828
-## 3:          SexMale 0.048744054 0.04522027 0.08620690
-## 4:      AgeDiscret6 0.016604647 0.04784637 0.05172414
-## 5:      AgeDiscret3 0.016373791 0.08028939 0.05172414
-## 6:      AgeDiscret4 0.009270558 0.02858801 0.01724138
-```
-
-> The column `Gain` provide the information we are looking for.
->
-> As you can see, features are classified by `Gain`.
-
-`Gain` is the improvement in accuracy brought by a feature to the branches it is on. The idea is that before adding a new split on a feature X to the branch there was some wrongly classified elements, after adding the split on this feature, there are two new branches, and each of these branch is more accurate (one branch saying if your observation is on this branch then it should be classified as `1`, and the other branch saying the exact opposite).
-
-`Cover` measures the relative quantity of observations concerned by a feature.
-
-`Frequency` is a simpler way to measure the `Gain`. It just counts the number of times a feature is used in all generated trees. You should not use it (unless you know why you want to use it).
-
-#### Improvement in the interpretability of feature importance data.table
-
-We can go deeper in the analysis of the model. In the `data.table` above, we have discovered which features counts to predict if the illness will go or not. But we don't yet know the role of these features. For instance, one of the question we may want to answer would be: does receiving a placebo treatment helps to recover from the illness?
-
-One simple solution is to count the co-occurrences of a feature and a class of the classification.
-
-For that purpose we will execute the same function as above but using two more parameters, `data` and `label`.
-
-
-```r
-importanceRaw <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst, data = sparse_matrix, label = output_vector)
-
-# Cleaning for better display
-importanceClean <- importanceRaw[,`:=`(Cover=NULL, Frequency=NULL)]
-
-head(importanceClean)
-```
-
-```
-##             Feature        Split       Gain RealCover RealCover %
-## 1: TreatmentPlacebo -1.00136e-05 0.28575061         7   0.2500000
-## 2:              Age         61.5 0.16374034        12   0.4285714
-## 3:              Age           39 0.08705750         8   0.2857143
-## 4:              Age         57.5 0.06947553        11   0.3928571
-## 5:          SexMale -1.00136e-05 0.04874405         4   0.1428571
-## 6:              Age         53.5 0.04620627        10   0.3571429
-```
-
-> In the table above we have removed two not needed columns and select only the first lines.
-
-First thing you notice is the new column `Split`. It is the split applied to the feature on a branch of one of the tree. Each split is present, therefore a feature can appear several times in this table. Here we can see the feature `Age` is used several times with different splits.
-
-How the split is applied to count the co-occurrences? It is always `<`. For instance, in the second line, we measure the number of persons under 61.5 years with the illness gone after the treatment.
-
-The two other new columns are `RealCover` and `RealCover %`. In the first column it measures the number of observations in the dataset where the split is respected and the label marked as `1`. The second column is the percentage of the whole population that `RealCover` represents.
-
-Therefore, according to our findings, getting a placebo doesn't seem to help but being younger than 61 years may help (seems logic).
-
-> You may wonder how to interpret the `< 1.00001` on the first line. Basically, in a sparse `Matrix`, there is no `0`, therefore, looking for one hot-encoded categorical observations validating the rule `< 1.00001` is like just looking for `1` for this feature.
-
-### Plotting the feature importance
-
-
-All these things are nice, but it would be even better to plot the results.
-
-
-```r
-xgb.plot.importance(importance_matrix = importanceRaw)
-```
-
-```
-## Error in xgb.plot.importance(importance_matrix = importanceRaw): Importance matrix is not correct (column names issue)
-```
-
-Feature have automatically been divided in 2 clusters: the interesting features... and the others.
-
-> Depending of the dataset and the learning parameters you may have more than two clusters. Default value is to limit them to `10`, but you can increase this limit. Look at the function documentation for more information.
-
-According to the plot above, the most important features in this dataset to predict if the treatment will work are :
-
-* the Age ;
-* having received a placebo or not ;
-* the sex is third but already included in the not interesting features group ;
-* then we see our generated features (AgeDiscret). We can see that their contribution is very low.
-
-### Do these results make sense?
-
-
-Let's check some **Chi2** between each of these features and the label.
-
-Higher **Chi2** means better correlation.
-
-
-```r
-c2 <- chisq.test(df$Age, output_vector)
-print(c2)
-```
-
-```
-## 
-## 	Pearson's Chi-squared test
-## 
-## data:  df$Age and output_vector
-## X-squared = 35.475, df = 35, p-value = 0.4458
-```
-
-Pearson correlation between Age and illness disappearing is **35.48**.
-
-
-```r
-c2 <- chisq.test(df$AgeDiscret, output_vector)
-print(c2)
-```
-
-```
-## 
-## 	Pearson's Chi-squared test
-## 
-## data:  df$AgeDiscret and output_vector
-## X-squared = 8.2554, df = 5, p-value = 0.1427
-```
-
-Our first simplification of Age gives a Pearson correlation is **8.26**.
-
-
-```r
-c2 <- chisq.test(df$AgeCat, output_vector)
-print(c2)
-```
-
-```
-## 
-## 	Pearson's Chi-squared test with Yates' continuity correction
-## 
-## data:  df$AgeCat and output_vector
-## X-squared = 2.3571, df = 1, p-value = 0.1247
-```
-
-The perfectly random split I did between young and old at 30 years old have a low correlation of **2.36**. It's a result we may expect as may be in my mind > 30 years is being old (I am 32 and starting feeling old, this may explain that), but for the illness we are studying, the age to be vulnerable is not the same.
-
-Morality: don't let your *gut* lower the quality of your model.
-
-In *data science* expression, there is the word *science* :-)
-
-Conclusion
-----------
-
-As you can see, in general *destroying information by simplifying it won't improve your model*. **Chi2** just demonstrates that.
-
-But in more complex cases, creating a new feature based on existing one which makes link with the outcome more obvious may help the algorithm and improve the model.
-
-The case studied here is not enough complex to show that. Check [Kaggle website](http://www.kaggle.com/) for some challenging datasets. However it's almost always worse when you add some arbitrary rules.
-
-Moreover, you can notice that even if we have added some not useful new features highly correlated with other features, the boosting tree algorithm have been able to choose the best one, which in this case is the Age.
-
-Linear models may not be that smart in this scenario.
-
-Special Note: What about Random Forests™?
------------------------------------------
-
-As you may know, [Random Forests™](http://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](http://en.wikipedia.org/wiki/Ensemble_learning) family.
-
-Both train several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).
-
-This difference have an impact on a corner case in feature importance analysis: the *correlated features*.
-
-Imagine two features perfectly correlated, feature `A` and feature `B`. For one specific tree, if the algorithm needs one of them, it will choose randomly (true in both boosting and Random Forests™).
-
-However, in Random Forests™ this random choice will be done for each tree, because each tree is independent from the others. Therefore, approximatively, depending of your parameters, 50% of the trees will choose feature `A` and the other 50% will choose feature `B`. So the *importance* of the information contained in `A` and `B` (which is the same, because they are perfectly correlated) is diluted in `A` and `B`. So you won't easily know this information is important to predict what you want to predict! It is even worse when you have 10 correlated features...
-
-In boosting, when a specific link between feature and outcome have been learned by the algorithm, it will try to not refocus on it (in theory it is what happens, reality is not always that simple). Therefore, all the importance will be on feature `A` or on feature `B` (but not both). You will know that one feature have an important role in the link between the observations and the label. It is still up to you to search for the correlated features to the one detected as important if you need to know all of them.
-
-If you want to try Random Forests™ algorithm, you can tweak Xgboost parameters!
-
-**Warning**: this is still an experimental parameter.
-
-For instance, to compute a model with 1000 trees, with a 0.5 factor on sampling rows and columns:
-
-
-```r
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-
-#Random Forest™ - 1000 trees
-bst <- xgboost(data = train$data, label = train$label, max.depth = 4, num_parallel_tree = 1000, subsample = 0.5, colsample_bytree =0.5, nrounds = 1, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.002150
-```
-
-```r
-#Boosting - 3 rounds
-bst <- xgboost(data = train$data, label = train$label, max.depth = 4, nrounds = 3, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.006142
-## [1]	train-error:0.006756
-## [2]	train-error:0.001228
-```
-
-> Note that the parameter `round` is set to `1`.
-
-> [**Random Forests™**](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_papers.htm) is a trademark of Leo Breiman and Adele Cutler and is licensed exclusively to Salford Systems for the commercial release of the software.
diff --git a/ml-xgboost/doc/R-package/index.rst b/ml-xgboost/doc/R-package/index.rst
deleted file mode 100644
index 32dd790..0000000
--- a/ml-xgboost/doc/R-package/index.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-#################
-XGBoost R Package
-#################
-
-.. raw:: html
-
-  <a href="http://cran.r-project.org/web/packages/xgboost"><img alt="CRAN Status Badge" src="http://www.r-pkg.org/badges/version/xgboost"></a>
-  <a href="http://cran.rstudio.com/web/packages/xgboost/index.html"><img alt="CRAN Downloads" src="http://cranlogs.r-pkg.org/badges/xgboost"></a>
-
-You have found the XGBoost R Package!
-
-***********
-Get Started
-***********
-* Checkout the :doc:`Installation Guide </build>` contains instructions to install xgboost, and :doc:`Tutorials </tutorials/index>` for examples on how to use XGBoost for various tasks.
-* Read the `API documentation <https://cran.r-project.org/web/packages/xgboost/xgboost.pdf>`_.
-* Please visit `Walk-through Examples <https://github.com/dmlc/xgboost/tree/master/R-package/demo>`_.
-
-*********
-Tutorials
-*********
-
-.. toctree::
-  :maxdepth: 2
-  :titlesonly:
-  
-  Introduction to XGBoost in R <xgboostPresentation>
-  Understanding your dataset with XGBoost <discoverYourData>
diff --git a/ml-xgboost/doc/R-package/xgboostPresentation.md b/ml-xgboost/doc/R-package/xgboostPresentation.md
deleted file mode 100644
index b68507e..0000000
--- a/ml-xgboost/doc/R-package/xgboostPresentation.md
+++ /dev/null
@@ -1,589 +0,0 @@
-
-XGBoost R Tutorial
-==================
-
-## Introduction
-
-
-**Xgboost** is short for e**X**treme **G**radient **Boost**ing package.
-
-The purpose of this Vignette is to show you how to use **Xgboost** to build a model and make predictions.
-
-It is an efficient and scalable implementation of gradient boosting framework by @friedman2000additive and @friedman2001greedy. Two solvers are included:
-
-- *linear* model ;
-- *tree learning* algorithm.
-
-It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objective functions easily.
-
-It has been [used](https://github.com/dmlc/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions.
-
-It has several features:
-
-* Speed: it can automatically do parallel computation on *Windows* and *Linux*, with *OpenMP*. It is generally over 10 times faster than the classical `gbm`.
-* Input Type: it takes several types of input data:
-    * *Dense* Matrix: *R*'s *dense* matrix, i.e. `matrix` ;
-    * *Sparse* Matrix: *R*'s *sparse* matrix, i.e. `Matrix::dgCMatrix` ;
-    * Data File: local data files ;
-    * `xgb.DMatrix`: its own class (recommended).
-* Sparsity: it accepts *sparse* input for both *tree booster*  and *linear booster*, and is optimized for *sparse* input ;
-* Customization: it supports customized objective functions and evaluation functions.
-
-## Installation
-
-
-### Github version
-
-
-For weekly updated version (highly recommended), install from *Github*:
-
-
-```r
-install.packages("drat", repos="https://cran.rstudio.com")
-drat:::addRepo("dmlc")
-install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
-```
-
-> *Windows* users will need to install [Rtools](http://cran.r-project.org/bin/windows/Rtools/) first.
-
-### CRAN version
-
-
-The version 0.4-2 is on CRAN, and you can install it by:
-
-
-```r
-install.packages("xgboost")
-```
-
-Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost)
-
-## Learning
-
-
-For the purpose of this tutorial we will load **XGBoost** package.
-
-
-```r
-require(xgboost)
-```
-
-### Dataset presentation
-
-
-In this example, we are aiming to predict whether a mushroom can be eaten or not (like in many tutorials, example data are the same as you will use on in your every day life :-).
-
-Mushroom data is cited from UCI Machine Learning Repository. @Bache+Lichman:2013.
-
-### Dataset loading
-
-
-We will load the `agaricus` datasets embedded with the package and will link them to variables.
-
-The datasets are already split in:
-
-* `train`: will be used to build the model ;
-* `test`: will be used to assess the quality of our model.
-
-Why *split* the dataset in two parts?
-
-In the first part we will build our model. In the second part we will want to test it and assess its quality. Without dividing the dataset we would test the model on the data which the algorithm have already seen.
-
-
-```r
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
-train <- agaricus.train
-test <- agaricus.test
-```
-
-> In the real world, it would be up to you to make this division between `train` and `test` data. The way to do it is out of scope for this article, however `caret` package may [help](http://topepo.github.io/caret/data-splitting.html).
-
-Each variable is a `list` containing two things, `label` and `data`:
-
-
-```r
-str(train)
-```
-
-```
-## List of 2
-##  $ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
-##   .. ..@ i       : int [1:143286] 2 6 8 11 18 20 21 24 28 32 ...
-##   .. ..@ p       : int [1:127] 0 369 372 3306 5845 6489 6513 8380 8384 10991 ...
-##   .. ..@ Dim     : int [1:2] 6513 126
-##   .. ..@ Dimnames:List of 2
-##   .. .. ..$ : NULL
-##   .. .. ..$ : chr [1:126] "cap-shape=bell" "cap-shape=conical" "cap-shape=convex" "cap-shape=flat" ...
-##   .. ..@ x       : num [1:143286] 1 1 1 1 1 1 1 1 1 1 ...
-##   .. ..@ factors : list()
-##  $ label: num [1:6513] 1 0 0 1 0 0 0 1 0 0 ...
-```
-
-`label` is the outcome of our dataset meaning it is the binary *classification* we will try to predict.
-
-Let's discover the dimensionality of our datasets.
-
-
-```r
-dim(train$data)
-```
-
-```
-## [1] 6513  126
-```
-
-```r
-dim(test$data)
-```
-
-```
-## [1] 1611  126
-```
-
-This dataset is very small to not make the **R** package too heavy, however **XGBoost** is built to manage huge datasets very efficiently.
-
-As seen below, the `data` are stored in a `dgCMatrix` which is a *sparse* matrix and `label` vector is a `numeric` vector (`{0,1}`):
-
-
-```r
-class(train$data)[1]
-```
-
-```
-## [1] "dgCMatrix"
-```
-
-```r
-class(train$label)
-```
-
-```
-## [1] "numeric"
-```
-
-### Basic Training using XGBoost
-
-
-This step is the most critical part of the process for the quality of our model.
-
-#### Basic training
-
-We are using the `train` data. As explained above, both `data` and `label` are stored in a `list`.
-
-In a *sparse* matrix, cells containing `0` are not stored in memory. Therefore, in a dataset mainly made of `0`, memory size is reduced. It is very common to have such a dataset.
-
-We will train decision tree model using the following parameters:
-
-* `objective = "binary:logistic"`: we will train a binary classification model ;
-* `max.depth = 2`: the trees won't be deep, because our case is very simple ;
-* `nthread = 2`: the number of cpu threads we are going to use;
-* `nrounds = 2`: there will be two passes on the data, the second one will enhance the model by further reducing the difference between ground truth and prediction.
-
-
-```r
-bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.046522
-## [1]	train-error:0.022263
-```
-
-> The more complex the relationship between your features and your `label` is, the more passes you need.
-
-#### Parameter variations
-
-##### Dense matrix
-
-Alternatively, you can put your dataset in a *dense* matrix, i.e. a basic **R** matrix.
-
-
-```r
-bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.046522
-## [1]	train-error:0.022263
-```
-
-##### xgb.DMatrix
-
-**XGBoost** offers a way to group them in a `xgb.DMatrix`. You can even add other meta data in it. This will be useful for the most advanced features we will discover later.
-
-
-```r
-dtrain <- xgb.DMatrix(data = train$data, label = train$label)
-bstDMatrix <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.046522
-## [1]	train-error:0.022263
-```
-
-##### Verbose option
-
-**XGBoost** has several features to help you view the learning progress internally. The purpose is to help you to set the best parameters, which is the key of your model quality.
-
-One of the simplest way to see the training progress is to set the `verbose` option (see below for more advanced techniques).
-
-
-```r
-# verbose = 0, no message
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
-```
-
-
-```r
-# verbose = 1, print evaluation metric
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 1)
-```
-
-```
-## [0]	train-error:0.046522
-## [1]	train-error:0.022263
-```
-
-
-```r
-# verbose = 2, also print information about tree
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 2)
-```
-
-```
-## [11:41:01] amalgamation/../src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=2
-## [0]	train-error:0.046522
-## [11:41:01] amalgamation/../src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 0 pruned nodes, max_depth=2
-## [1]	train-error:0.022263
-```
-
-## Basic prediction using XGBoost
-
-
-## Perform the prediction
-
-
-The purpose of the model we have built is to classify new data. As explained before, we will use the `test` dataset for this step.
-
-
-```r
-pred <- predict(bst, test$data)
-
-# size of the prediction vector
-print(length(pred))
-```
-
-```
-## [1] 1611
-```
-
-```r
-# limit display of predictions to the first 10
-print(head(pred))
-```
-
-```
-## [1] 0.28583017 0.92392391 0.28583017 0.28583017 0.05169873 0.92392391
-```
-
-These numbers doesn't look like *binary classification* `{0,1}`. We need to perform a simple transformation before being able to use these results.
-
-## Transform the regression in a binary classification
-
-
-The only thing that **XGBoost** does is a *regression*. **XGBoost** is using `label` vector to build its *regression* model.
-
-How can we use a *regression* model to perform a binary classification?
-
-If we think about the meaning of a regression applied to our data, the numbers we get are probabilities that a datum will be classified as `1`. Therefore, we will set the rule that if this probability for a specific datum is `> 0.5` then the observation is classified as `1` (or `0` otherwise).
-
-
-```r
-prediction <- as.numeric(pred > 0.5)
-print(head(prediction))
-```
-
-```
-## [1] 0 1 0 0 0 1
-```
-
-## Measuring model performance
-
-
-To measure the model performance, we will compute a simple metric, the *average error*.
-
-
-```r
-err <- mean(as.numeric(pred > 0.5) != test$label)
-print(paste("test-error=", err))
-```
-
-```
-## [1] "test-error= 0.0217256362507759"
-```
-
-> Note that the algorithm has not seen the `test` data during the model construction.
-
-Steps explanation:
-
-1. `as.numeric(pred > 0.5)` applies our rule that when the probability (<=> regression <=> prediction) is `> 0.5` the observation is classified as `1` and `0` otherwise ;
-2. `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
-3. `mean(vectorOfErrors)` computes the *average error* itself.
-
-The most important thing to remember is that **to do a classification, you just do a regression to the** `label` **and then apply a threshold**.
-
-*Multiclass* classification works in a similar way.
-
-This metric is **0.02** and is pretty low: our yummly mushroom model works well!
-
-## Advanced features
-
-
-Most of the features below have been implemented to help you to improve your model by offering a better understanding of its content.
-
-
-### Dataset preparation
-
-
-For the following advanced features, we need to put data in `xgb.DMatrix` as explained above.
-
-
-```r
-dtrain <- xgb.DMatrix(data = train$data, label=train$label)
-dtest <- xgb.DMatrix(data = test$data, label=test$label)
-```
-
-### Measure learning progress with xgb.train
-
-
-Both `xgboost` (simple) and `xgb.train` (advanced) functions train models.
-
-One of the special features of `xgb.train` is the capacity to follow the progress of the learning after each round. Because of the way boosting works, there is a time when having too many rounds lead to overfitting. You can see this feature as a cousin of a cross-validation method. The following techniques will help you to avoid overfitting or optimizing the learning time in stopping it as soon as possible.
-
-One way to measure progress in the learning of a model is to provide to **XGBoost** a second dataset already classified. Therefore it can learn on the first dataset and test its model on the second one. Some metrics are measured after each round during the learning.
-
-> in some way it is similar to what we have done above with the average error. The main difference is that above it was after building the model, and now it is during the construction that we measure errors.
-
-For the purpose of this example, we use `watchlist` parameter. It is a list of `xgb.DMatrix`, each of them tagged with a name.
-
-
-```r
-watchlist <- list(train=dtrain, test=dtest)
-
-bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.046522	test-error:0.042831
-## [1]	train-error:0.022263	test-error:0.021726
-```
-
-**XGBoost** has computed at each round the same average error metric seen above (we set `nrounds` to 2, that is why we have two lines). Obviously, the `train-error` number is related to the training dataset (the one the algorithm learns from) and the `test-error` number to the test dataset.
-
-Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.
-
-If with your own dataset you do not have such results, you should think about how you divided your dataset in training and test. May be there is something to fix. Again, `caret` package may [help](http://topepo.github.io/caret/data-splitting.html).
-
-For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.
-
-
-```r
-bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.046522	train-logloss:0.233376	test-error:0.042831	test-logloss:0.226686
-## [1]	train-error:0.022263	train-logloss:0.136658	test-error:0.021726	test-logloss:0.137874
-```
-
-> `eval.metric` allows us to monitor two new metrics for each round, `logloss` and `error`.
-
-### Linear boosting
-
-
-Until now, all the learnings we have performed were based on boosting trees. **XGBoost** implements a second algorithm, based on linear boosting. The only difference with the previous command is `booster = "gblinear"` parameter (and removing `eta` parameter).
-
-
-```r
-bst <- xgb.train(data=dtrain, booster = "gblinear", max.depth=2, nthread = 2, nrounds=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.024720	train-logloss:0.184616	test-error:0.022967	test-logloss:0.184234
-## [1]	train-error:0.004146	train-logloss:0.069885	test-error:0.003724	test-logloss:0.068081
-```
-
-In this specific case, *linear boosting* gets slightly better performance metrics than a decision tree based algorithm.
-
-In simple cases, this will happen because there is nothing better than a linear algorithm to catch a linear link. However, decision trees are much better to catch a non linear link between predictors and outcome. Because there is no silver bullet, we advise you to check both algorithms with your own datasets to have an idea of what to use.
-
-### Manipulating xgb.DMatrix
-
-
-#### Save / Load
-
-Like saving models, `xgb.DMatrix` object (which groups both dataset and outcome) can also be saved using `xgb.DMatrix.save` function.
-
-
-```r
-xgb.DMatrix.save(dtrain, "dtrain.buffer")
-```
-
-```
-## [1] TRUE
-```
-
-```r
-# to load it in, simply call xgb.DMatrix
-dtrain2 <- xgb.DMatrix("dtrain.buffer")
-```
-
-```
-## [11:41:01] 6513x126 matrix with 143286 entries loaded from dtrain.buffer
-```
-
-```r
-bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
-```
-
-```
-## [0]	train-error:0.046522	test-error:0.042831
-## [1]	train-error:0.022263	test-error:0.021726
-```
-
-
-
-#### Information extraction
-
-Information can be extracted from an `xgb.DMatrix` using `getinfo` function. Hereafter we will extract `label` data.
-
-
-```r
-label = getinfo(dtest, "label")
-pred <- predict(bst, dtest)
-err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
-print(paste("test-error=", err))
-```
-
-```
-## [1] "test-error= 0.0217256362507759"
-```
-
-### View feature importance/influence from the learnt model
-
-
-Feature importance is similar to R gbm package's relative influence (rel.inf).
-
-```
-importance_matrix <- xgb.importance(model = bst)
-print(importance_matrix)
-xgb.plot.importance(importance_matrix = importance_matrix)
-```
-
-#### View the trees from a model
-
-
-You can dump the tree you learned using `xgb.dump` into a text file.
-
-
-```r
-xgb.dump(bst, with_stats = T)
-```
-
-```
-##  [1] "booster[0]"                                                          
-##  [2] "0:[f28<-1.00136e-05] yes=1,no=2,missing=1,gain=4000.53,cover=1628.25"
-##  [3] "1:[f55<-1.00136e-05] yes=3,no=4,missing=3,gain=1158.21,cover=924.5"  
-##  [4] "3:leaf=1.71218,cover=812"                                            
-##  [5] "4:leaf=-1.70044,cover=112.5"                                         
-##  [6] "2:[f108<-1.00136e-05] yes=5,no=6,missing=5,gain=198.174,cover=703.75"
-##  [7] "5:leaf=-1.94071,cover=690.5"                                         
-##  [8] "6:leaf=1.85965,cover=13.25"                                          
-##  [9] "booster[1]"                                                          
-## [10] "0:[f59<-1.00136e-05] yes=1,no=2,missing=1,gain=832.545,cover=788.852"
-## [11] "1:[f28<-1.00136e-05] yes=3,no=4,missing=3,gain=569.725,cover=768.39" 
-## [12] "3:leaf=0.784718,cover=458.937"                                       
-## [13] "4:leaf=-0.96853,cover=309.453"                                       
-## [14] "2:leaf=-6.23624,cover=20.4624"
-```
-
-You can plot the trees from your model using ```xgb.plot.tree``
-
-```
-xgb.plot.tree(model = bst)
-```
-
-> if you provide a path to `fname` parameter you can save the trees to your hard drive.
-
-#### Save and load models
-
-
-Maybe your dataset is big, and it takes time to train a model on it? May be you are not a big fan of losing time in redoing the same task again and again? In these very rare cases, you will want to save your model and load it when required.
-
-Helpfully for you, **XGBoost** implements such functions.
-
-
-```r
-# save model to binary local file
-xgb.save(bst, "xgboost.model")
-```
-
-```
-## [1] TRUE
-```
-
-> `xgb.save` function should return TRUE if everything goes well and crashes otherwise.
-
-An interesting test to see how identical our saved model is to the original one would be to compare the two predictions.
-
-
-```r
-# load binary model to R
-bst2 <- xgb.load("xgboost.model")
-pred2 <- predict(bst2, test$data)
-
-# And now the test
-print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
-```
-
-```
-## [1] "sum(abs(pred2-pred))= 0"
-```
-
-
-
-> result is `0`? We are good!
-
-In some very specific cases, like when you want to pilot **XGBoost** from `caret` package, you will want to save the model as a *R* binary vector. See below how to do it.
-
-
-```r
-# save model to R's raw vector
-rawVec <- xgb.save.raw(bst)
-
-# print class
-print(class(rawVec))
-```
-
-```
-## [1] "raw"
-```
-
-```r
-# load binary model to R
-bst3 <- xgb.load(rawVec)
-pred3 <- predict(bst3, test$data)
-
-# pred3 should be identical to pred
-print(paste("sum(abs(pred3-pred))=", sum(abs(pred3-pred))))
-```
-
-```
-## [1] "sum(abs(pred3-pred))= 0"
-```
-
-> Again `0`? It seems that `XGBoost` works pretty well!
-
-## References
diff --git a/ml-xgboost/doc/README b/ml-xgboost/doc/README
deleted file mode 100644
index fa7cf8a..0000000
--- a/ml-xgboost/doc/README
+++ /dev/null
@@ -1,5 +0,0 @@
-The documentation of xgboost is generated with recommonmark and sphinx.
-
-You can build it locally by typing "make html" in this folder.
-
-Checkout https://recommonmark.readthedocs.org for guide on how to write markdown with extensions used in this doc, such as math formulas and table of content.
diff --git a/ml-xgboost/doc/build.rst b/ml-xgboost/doc/build.rst
deleted file mode 100644
index ec07527..0000000
--- a/ml-xgboost/doc/build.rst
+++ /dev/null
@@ -1,466 +0,0 @@
-##################
-Installation Guide
-##################
-
-.. note:: Pre-built binary wheel for Python
-
-  If you are planning to use Python, consider installing XGBoost from a pre-built binary wheel, available from Python Package Index (PyPI). You may download and install it by running
-
-  .. code-block:: bash
-
-    # Ensure that you are downloading one of the following:
-    #   * xgboost-{version}-py2.py3-none-manylinux1_x86_64.whl
-    #   * xgboost-{version}-py2.py3-none-win_amd64.whl
-    pip3 install xgboost
-
-  * The binary wheel will support GPU algorithms (`gpu_hist`) on machines with NVIDIA GPUs. Please note that **training with multiple GPUs is only supported for Linux platform**. See :doc:`gpu/index`.
-  * Currently, we provide binary wheels for 64-bit Linux and Windows.
-  * Nightly builds are available. You can now run
-
-    .. code-block:: bash
-
-      pip install https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/xgboost-[version]+[commithash]-py2.py3-none-manylinux1_x86_64.whl
-
-    to install the nightly build with the given commit hash. See `this page
-    <https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/list.html>`_ to see the
-    list of all nightly builds.
-
-****************************
-Building XGBoost from source
-****************************
-This page gives instructions on how to build and install XGBoost from scratch on various systems.
-
-.. note:: Use of Git submodules
-
-  XGBoost uses Git submodules to manage dependencies. So when you clone the repo, remember to specify ``--recursive`` option:
-
-  .. code-block:: bash
-
-    git clone --recursive https://github.com/dmlc/xgboost
-
-For windows users who use github tools, you can open the git shell and type the following command:
-
-.. code-block:: batch
-
-  git submodule init
-  git submodule update
-
-Please refer to `Trouble Shooting`_ section first if you have any problem
-during installation. If the instructions do not work for you, please feel free
-to ask questions at `the user forum <https://discuss.xgboost.ai>`_.
-
-**Contents**
-
-* `Building the Shared Library`_
-
-  - `Building on Linux Distributions`_
-  - `Building on OSX`_
-  - `Building on Windows`_
-  - `Building with GPU support`_
-
-* `Python Package Installation`_
-* `R Package Installation`_
-* `Trouble Shooting`_
-* `Building the documentation`_
-
-.. _build_shared_lib:
-
-***************************
-Building the Shared Library
-***************************
-
-Our goal is to build the shared library:
-
-- On Linux/OSX the target library is ``libxgboost.so``
-- On Windows the target library is ``xgboost.dll``
-
-This shared library is used by different language bindings (with some additions depending
-on the binding you choose).  For building language specific package, see corresponding
-sections in this document.  The minimal building requirement is
-
-- A recent C++ compiler supporting C++11 (g++-5.0 or higher)
-- CMake 3.12 or higher.
-
-For a list of CMake options, see ``#-- Options`` in CMakeLists.txt on top level of source tree.
-
-Building on Linux distributions
-===============================
-
-On Ubuntu, one builds XGBoost by running CMake:
-
-.. code-block:: bash
-
-  git clone --recursive https://github.com/dmlc/xgboost
-  cd xgboost
-  mkdir build
-  cd build
-  cmake ..
-  make -j$(nproc)
-
-Building on OSX
-===============
-
-Install with pip: simple method
---------------------------------
-
-First, obtain the OpenMP library (``libomp``) with Homebrew (https://brew.sh/) to enable multi-threading (i.e. using multiple CPU threads for training):
-
-.. code-block:: bash
-
-  brew install libomp
-
-Then install XGBoost with ``pip``:
-
-.. code-block:: bash
-
-  pip3 install xgboost
-
-You might need to run the command with ``--user`` flag if you run into permission errors.
-
-Build from the source code - advanced method
---------------------------------------------
-
-Obtain ``libomp`` from Homebrew:
-
-.. code-block:: bash
-
-  brew install libomp
-
-
-Now clone the repository:
-
-.. code-block:: bash
-
-  git clone --recursive https://github.com/dmlc/xgboost
-
-Create the ``build/`` directory and invoke CMake. After invoking CMake, you can build XGBoost with ``make``:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake ..
-  make -j4
-
-You may now continue to `Python Package Installation`_.
-
-Building on Windows
-===================
-You need to first clone the XGBoost repo with ``--recursive`` option, to clone the submodules.
-We recommend you use `Git for Windows <https://git-for-windows.github.io/>`_, as it comes with a standard Bash shell. This will highly ease the installation process.
-
-.. code-block:: bash
-
-  git submodule init
-  git submodule update
-
-XGBoost support compilation with Microsoft Visual Studio and MinGW.
-
-Compile XGBoost with Microsoft Visual Studio
---------------------------------------------
-To build with Visual Studio, we will need CMake. Make sure to install a recent version of CMake. Then run the following from the root of the XGBoost directory:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake .. -G"Visual Studio 14 2015 Win64"
-  # for VS15: cmake .. -G"Visual Studio 15 2017" -A x64
-  # for VS16: cmake .. -G"Visual Studio 16 2019" -A x64
-  cmake --build . --config Release
-
-This specifies an out of source build using the Visual Studio 64 bit generator. (Change the ``-G`` option appropriately if you have a different version of Visual Studio installed.)
-
-After the build process successfully ends, you will find a ``xgboost.dll`` library file inside ``./lib/`` folder.
-
-.. _build_gpu_support:
-
-Building with GPU support
-=========================
-XGBoost can be built with GPU support for both Linux and Windows using CMake. GPU support works with the Python package as well as the CLI version. See `Installing R package with GPU support`_ for special instructions for R.
-
-An up-to-date version of the CUDA toolkit is required.
-
-From the command line on Linux starting from the XGBoost directory:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake .. -DUSE_CUDA=ON
-  make -j4
-
-.. note:: Enabling distributed GPU training
-
-  By default, distributed GPU training is disabled and only a single GPU will be used. To enable distributed GPU training, set the option ``USE_NCCL=ON``. Distributed GPU training depends on NCCL2, available at `this link <https://developer.nvidia.com/nccl>`_. Since NCCL2 is only available for Linux machines, **distributed GPU training is available only for Linux**.
-
-  .. code-block:: bash
-
-    mkdir build
-    cd build
-    cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
-    make -j4
-
-On Windows, run CMake as follows:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON
-
-(Change the ``-G`` option appropriately if you have a different version of Visual Studio installed.)
-
-.. note:: Visual Studio 2017 Win64 Generator may not work
-
-  Choosing the Visual Studio 2017 generator may cause compilation failure. When it happens, specify the 2015 compiler by adding the ``-T`` option:
-
-  .. code-block:: bash
-
-    cmake .. -G"Visual Studio 15 2017 Win64" -T v140,cuda=8.0 -DUSE_CUDA=ON
-
-To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DGPU_COMPUTE_VER=50``.
-The above cmake configuration run will create an ``xgboost.sln`` solution file in the build directory. Build this solution in release mode as a x64 build, either from Visual studio or from command line:
-
-.. code-block:: bash
-
-  cmake --build . --target xgboost --config Release
-
-To speed up compilation, run multiple jobs in parallel by appending option ``-- /MP``.
-
-Makefiles
-=========
-
-It's only used for creating shorthands for running linters, performing packaging tasks
-etc.  So the remaining makefiles are legacy.
-
-Python Package Installation
-===========================
-
-The Python package is located at ``python-package/``.  There are several ways to build and
-install the package from source:
-
-1. Use Python setuptools directly
-
-The XGBoost Python package supports most of the setuptools commands, here is a list of tested commands:
-
-.. code-block:: bash
-
-  python setup.py install  # Install the XGBoost to your current Python environment.
-  python setup.py build    # Build the Python package.
-  python setup.py build_ext # Build only the C++ core.
-  python setup.py sdist     # Create a source distribution
-  python setup.py bdist     # Create a binary distribution
-  python setup.py bdist_wheel # Create a binary distribution with wheel format
-
-Running ``python setup.py install`` will compile XGBoost using default CMake flags.  For
-passing additional compilation options, append the flags to the command.  For example, to
-enable CUDA acceleration and NCCL (distributed GPU) support:
-
-.. code-block:: bash
-
-  python setup.py install --use-cuda --use-nccl
-
-Please refer to ``setup.py`` for a complete list of avaiable options.  Some other options
-used for development are only available for using CMake directly.  See next section on
-how to use CMake with setuptools manually.
-
-You can install the created distribution packages using pip. For example, after running
-``sdist`` setuptools command, a tar ball similar to ``xgboost-1.0.0.tar.gz`` will be
-created under the ``dist`` directory.  Then you can install it by invoking the following
-command under ``dist`` directory:
-
-.. code-block:: bash
-
-  # under python-package directory
-  cd dist
-  pip install ./xgboost-1.0.0.tar.gz
-
-
-For details about these commands, please refer to the official document of `setuptools
-<https://setuptools.readthedocs.io/en/latest/>`_, or just Google "how to install Python
-package from source".  XGBoost Python package follows the general convention.  Setuptools
-is usually available with your Python distribution, if not you can install it via system
-command.  For example on Debian or Ubuntu:
-
-.. code-block:: bash
-
-  sudo apt-get install python-setuptools
-
-
-For cleaning up the directory after running above commands, ``python setup.py clean`` is
-not sufficient.  After copying out the build result, simply running ``git clean -xdf``
-under ``python-package`` is an efficient way to remove generated cache files.  If you find
-weird behaviors in Python build or running linter, it might be caused by those cached
-files.
-
-For using develop command (editable installation), see next section.
-
-.. code-block::
-
-  python setup.py develop   # Create a editable installation.
-  pip install -e .          # Same as above, but carried out by pip.
-
-
-2. Build C++ core with CMake first
-
-This is mostly for C++ developers who don't want to go through the hooks in Python
-setuptools.  You can build C++ library directly using CMake as described in above
-sections.  After compilation, a shared object (or called dynamic linked library, jargon
-depending on your platform) will appear in XGBoost's source tree under ``lib/`` directory.
-On Linux distributions it's ``lib/libxgboost.so``.  From there all Python setuptools
-commands will reuse that shared object instead of compiling it again.  This is especially
-convenient if you are using the editable installation, where the installed package is
-simply a link to the source tree.  We can perform rapid testing during development.  Here
-is a simple bash script does that:
-
-.. code-block:: bash
-
-  # Under xgboost source tree.
-  mkdir build
-  cd build
-  cmake ..
-  make -j$(nproc)
-  cd ../python-package
-  pip install -e .  # or equivalently python setup.py develop
-
-.. _mingw_python:
-
-Building XGBoost library for Python for Windows with MinGW-w64 (Advanced)
--------------------------------------------------------------------------
-
-Windows versions of Python are built with Microsoft Visual Studio. Usually Python binary modules are built with the same compiler the interpreter is built with. However, you may not be able to use Visual Studio, for following reasons:
-
-1. VS is proprietary and commercial software. Microsoft provides a freeware "Community" edition, but its licensing terms impose restrictions as to where and how it can be used.
-2. Visual Studio contains telemetry, as documented in `Microsoft Visual Studio Licensing Terms <https://visualstudio.microsoft.com/license-terms/mt736442/>`_. Running software with telemetry may be against the policy of your organization.
-
-So you may want to build XGBoost with GCC own your own risk. This presents some difficulties because MSVC uses Microsoft runtime and MinGW-w64 uses own runtime, and the runtimes have different incompatible memory allocators. But in fact this setup is usable if you know how to deal with it. Here is some experience.
-
-1. The Python interpreter will crash on exit if XGBoost was used. This is usually not a big issue.
-2. ``-O3`` is OK.
-3. ``-mtune=native`` is also OK.
-4. Don't use ``-march=native`` gcc flag. Using it causes the Python interpreter to crash if the DLL was actually used.
-5. You may need to provide the lib with the runtime libs. If ``mingw32/bin`` is not in ``PATH``, build a wheel (``python setup.py bdist_wheel``), open it with an archiver and put the needed dlls to the directory where ``xgboost.dll`` is situated. Then you can install the wheel with ``pip``.
-
-
-R Package Installation
-======================
-
-Installing pre-packaged version
--------------------------------
-
-You can install XGBoost from CRAN just like any other R package:
-
-.. code-block:: R
-
-   install.packages("xgboost")
-
-.. note:: Using all CPU cores (threads) on Mac OSX
-
-   If you are using Mac OSX, you should first install OpenMP library (``libomp``) by running
-
-   .. code-block:: bash
-
-      brew install libomp
-
-   and then run ``install.packages("xgboost")``. Without OpenMP, XGBoost will only use a single CPU core, leading to suboptimal training speed.
-
-Installing the development version
-----------------------------------
-
-Make sure you have installed git and a recent C++ compiler supporting C++11 (See above
-sections for requirements of building C++ core).  On Windows, Rtools must be installed,
-and its bin directory has to be added to ``PATH`` during the installation.
-
-Due to the use of git-submodules, ``devtools::install_github`` can no longer be used to install the latest version of R package.
-Thus, one has to run git to check out the code first:
-
-.. code-block:: bash
-
-  git clone --recursive https://github.com/dmlc/xgboost
-  cd xgboost
-  git submodule init
-  git submodule update
-  mkdir build
-  cd build
-  cmake .. -DR_LIB=ON
-  make -j$(nproc)
-  make install
-
-If all fails, try `Building the shared library`_ to see whether a problem is specific to R
-package or not.  Notice that the R package is installed by CMake directly.
-
-.. _r_gpu_support:
-
-Installing R package with GPU support
--------------------------------------
-
-The procedure and requirements are similar as in `Building with GPU support`_, so make sure to read it first.
-
-On Linux, starting from the XGBoost directory type:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake .. -DUSE_CUDA=ON -DR_LIB=ON
-  make install -j$(nproc)
-
-When default target is used, an R package shared library would be built in the ``build`` area.
-The ``install`` target, in addition, assembles the package files with this shared library under ``build/R-package`` and runs ``R CMD INSTALL``.
-
-On Windows, CMake with Visual C++ Build Tools (or Visual Studio) has to be used to build an R package with GPU support. Rtools must also be installed (perhaps, some other MinGW distributions with ``gendef.exe`` and ``dlltool.exe`` would work, but that was not tested).
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON -DR_LIB=ON
-  cmake --build . --target install --config Release
-
-When ``--target xgboost`` is used, an R package DLL would be built under ``build/Release``.
-The ``--target install``, in addition, assembles the package files with this dll under ``build/R-package`` and runs ``R CMD INSTALL``.
-
-If cmake can't find your R during the configuration step, you might provide the location of its executable to cmake like this: ``-DLIBR_EXECUTABLE="C:/Program Files/R/R-3.4.1/bin/x64/R.exe"``.
-
-If on Windows you get a "permission denied" error when trying to write to ...Program Files/R/... during the package installation, create a ``.Rprofile`` file in your personal home directory (if you don't already have one in there), and add a line to it which specifies the location of your R packages user library, like the following:
-
-.. code-block:: R
-
-  .libPaths( unique(c("C:/Users/USERNAME/Documents/R/win-library/3.4", .libPaths())))
-
-You might find the exact location by running ``.libPaths()`` in R GUI or RStudio.
-
-Trouble Shooting
-================
-
-1. Compile failed after ``git pull``
-
-   Please first update the submodules, clean all and recompile:
-
-   .. code-block:: bash
-
-     git submodule update && make clean_all && make -j4
-
-
-Building the Documentation
-==========================
-XGBoost uses `Sphinx <https://www.sphinx-doc.org/en/stable/>`_ for documentation.  To build it locally, you need a installed XGBoost with all its dependencies along with:
-
-* System dependencies
-
-  - git
-  - graphviz
-
-* Python dependencies
-
-  - sphinx
-  - breathe
-  - guzzle_sphinx_theme
-  - recommonmark
-  - mock
-  - sh
-  - graphviz
-  - matplotlib
-
-Under ``xgboost/doc`` directory, run ``make <format>`` with ``<format>`` replaced by the format you want.  For a list of supported formats, run ``make help`` under the same directory.
diff --git a/ml-xgboost/doc/c++.rst b/ml-xgboost/doc/c++.rst
deleted file mode 100644
index 4a045fc..0000000
--- a/ml-xgboost/doc/c++.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-###############
-XGBoost C++ API
-###############
-
-Starting from 1.0 release, CMake will generate installation rules to export all C++ headers. But
-the c++ interface is much closer to the internal of XGBoost than other language bindings.
-As a result it's changing quite often and we don't maintain its stability.  Along with the
-plugin system (see ``plugin/example`` in XGBoost's source tree), users can utilize some
-existing c++ headers for gaining more access to the internal of XGBoost.
-
-* `C++ interface documentation (latest master branch) <https://xgboost.readthedocs.io/en/latest/dev/files.html>`_
-* `C++ interface documentation (last stable release) <https://xgboost.readthedocs.io/en/stable/dev/files.html>`_
diff --git a/ml-xgboost/doc/c.rst b/ml-xgboost/doc/c.rst
deleted file mode 100644
index 2dc1526..0000000
--- a/ml-xgboost/doc/c.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-#################
-XGBoost C Package
-#################
-
-XGBoost implements a set of C API designed for various bindings, we maintain its
-stability and the CMake/make build interface.  See ``demo/c-api/README.md`` for an
-overview and related examples.  Also one can generate doxygen document by providing
-``-DBUILD_C_DOC=ON`` as parameter to ``CMake`` during build, or simply look at function
-comments in ``include/xgboost/c_api.h``.
-
-* `C API documentation (latest master branch) <https://xgboost.readthedocs.io/en/latest/dev/c__api_8h.html>`_
-* `C API documentation (last stable release) <https://xgboost.readthedocs.io/en/stable/dev/c__api_8h.html>`_
diff --git a/ml-xgboost/doc/cli.rst b/ml-xgboost/doc/cli.rst
deleted file mode 100644
index 0c49c8d..0000000
--- a/ml-xgboost/doc/cli.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-############################
-XGBoost Command Line version
-############################
-
-See `XGBoost Command Line walkthrough <https://github.com/dmlc/xgboost/blob/master/demo/binary_classification/README.md>`_.
diff --git a/ml-xgboost/doc/conf.py b/ml-xgboost/doc/conf.py
deleted file mode 100644
index f07678f..0000000
--- a/ml-xgboost/doc/conf.py
+++ /dev/null
@@ -1,236 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# documentation build configuration file, created by
-# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-from subprocess import call
-from sh.contrib import git
-import urllib.request
-from urllib.error import HTTPError
-from recommonmark.parser import CommonMarkParser
-import sys
-import re
-import os
-import subprocess
-import guzzle_sphinx_theme
-
-git_branch = os.getenv('SPHINX_GIT_BRANCH', default=None)
-if git_branch is None:
-    # If SPHINX_GIT_BRANCH environment variable is not given, run git
-    # to determine branch name
-    git_branch = [
-        re.sub(r'origin/', '', x.lstrip(' ')) for x in str(
-            git.branch('-r', '--contains', 'HEAD')).rstrip('\n').split('\n')
-    ]
-    git_branch = [x for x in git_branch if 'HEAD' not in x]
-print('git_branch = {}'.format(git_branch[0]))
-try:
-    filename, _ = urllib.request.urlretrieve(
-        'https://s3-us-west-2.amazonaws.com/xgboost-docs/{}.tar.bz2'.format(
-            git_branch[0]))
-    call(
-        'if [ -d tmp ]; then rm -rf tmp; fi; mkdir -p tmp/jvm; cd tmp/jvm; tar xvf {}'
-        .format(filename),
-        shell=True)
-except HTTPError:
-    print('JVM doc not found. Skipping...')
-try:
-    filename, _ = urllib.request.urlretrieve(
-        'https://s3-us-west-2.amazonaws.com/xgboost-docs/doxygen/{}.tar.bz2'.
-        format(git_branch[0]))
-    call(
-        'mkdir -p tmp/dev; cd tmp/dev; tar xvf {}; mv doc_doxygen/html/* .; rm -rf doc_doxygen'
-        .format(filename),
-        shell=True)
-except HTTPError:
-    print('C API doc not found. Skipping...')
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-libpath = os.path.join(curr_path, '../python-package/')
-sys.path.insert(0, libpath)
-sys.path.insert(0, curr_path)
-
-# -- mock out modules
-import mock                     # NOQA
-MOCK_MODULES = ['scipy', 'scipy.sparse', 'sklearn', 'pandas']
-for mod_name in MOCK_MODULES:
-    sys.modules[mod_name] = mock.Mock()
-
-# -- General configuration ------------------------------------------------
-
-# General information about the project.
-project = u'xgboost'
-author = u'%s developers' % project
-copyright = u'2020, %s' % author
-github_doc_root = 'https://github.com/dmlc/xgboost/tree/master/doc/'
-
-os.environ['XGBOOST_BUILD_DOC'] = '1'
-# Version information.
-import xgboost                  # NOQA
-version = xgboost.__version__
-release = xgboost.__version__
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
-extensions = [
-    'matplotlib.sphinxext.plot_directive',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.intersphinx',
-    'breathe'
-]
-
-graphviz_output_format = 'png'
-plot_formats = [('svg', 300), ('png', 100), ('hires.png', 300)]
-plot_html_show_source_link = False
-plot_html_show_formats = False
-
-# Breathe extension variables
-breathe_projects = {"xgboost": "doxyxml/"}
-breathe_default_project = "xgboost"
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-source_parsers = {
-  '.md': CommonMarkParser,
-}
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-source_suffix = ['.rst', '.md']
-
-# The encoding of source files.
-# source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-autoclass_content = 'both'
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-# today = ''
-# Else, today_fmt is used as the format for a strftime call.
-# today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = ['_build']
-html_extra_path = ['./tmp']
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-# default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-# add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-# add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-# show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-# modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-# keep_warnings = False
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-html_theme_path = guzzle_sphinx_theme.html_theme_path()
-html_theme = 'guzzle_sphinx_theme'
-
-# Register the theme as an extension to generate a sitemap.xml
-extensions.append("guzzle_sphinx_theme")
-
-# Guzzle theme options (see theme.conf for more information)
-html_theme_options = {
-    # Set the name of the project to appear in the sidebar
-    "project_nav_name": "XGBoost"
-}
-
-html_sidebars = {
-  '**': ['logo-text.html', 'globaltoc.html', 'searchbox.html']
-}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = project + 'doc'
-
-# -- Options for LaTeX output ---------------------------------------------
-latex_elements = {
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-  (master_doc, '%s.tex' % project, project,
-   author, 'manual'),
-]
-
-intersphinx_mapping = {
-    'python': ('https://docs.python.org/3.6', None),
-    'numpy': ('http://docs.scipy.org/doc/numpy/', None),
-    'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
-    'pandas': ('http://pandas-docs.github.io/pandas-docs-travis/', None),
-    'sklearn': ('http://scikit-learn.org/stable', None)
-}
-
-
-# hook for doxygen
-def run_doxygen(folder):
-    """Run the doxygen make command in the designated folder."""
-    try:
-        retcode = subprocess.call("cd %s; make doxygen" % folder, shell=True)
-        if retcode < 0:
-            sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
-    except OSError as e:
-        sys.stderr.write("doxygen execution failed: %s" % e)
-
-
-def generate_doxygen_xml(app):
-    """Run the doxygen make commands if we're on the ReadTheDocs server"""
-    read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True'
-    if read_the_docs_build:
-        run_doxygen('..')
-
-
-def setup(app):
-    app.add_stylesheet('custom.css')
diff --git a/ml-xgboost/doc/contrib/coding_guide.rst b/ml-xgboost/doc/contrib/coding_guide.rst
deleted file mode 100644
index 9eff456..0000000
--- a/ml-xgboost/doc/contrib/coding_guide.rst
+++ /dev/null
@@ -1,136 +0,0 @@
-################
-Coding Guideline
-################
-
-**Contents**
-
-.. contents::
-  :backlinks: none
-  :local:
-
-********************
-C++ Coding Guideline
-********************
-- Follow `Google style for C++ <https://google.github.io/styleguide/cppguide.html>`_, with two exceptions:
-
-  * Each line of text may contain up to 100 characters.
-  * The use of C++ exceptions is allowed.
-
-- Use C++11 features such as smart pointers, braced initializers, lambda functions, and ``std::thread``.
-- Use Doxygen to document all the interface code.
-- We have a series of automatic checks to ensure that all of our codebase complies with the Google style. Before submitting your pull request, you are encouraged to run the style checks on your machine. See :ref:`running_checks_locally`.
-
-***********************
-Python Coding Guideline
-***********************
-- Follow `PEP 8: Style Guide for Python Code <https://www.python.org/dev/peps/pep-0008/>`_. We use PyLint to automatically enforce PEP 8 style across our Python codebase. Before submitting your pull request, you are encouraged to run PyLint on your machine. See :ref:`running_checks_locally`.
-- Docstrings should be in `NumPy docstring format <https://numpydoc.readthedocs.io/en/latest/format.html>`_.
-
-.. _running_checks_locally:
-
-******************
-R Coding Guideline
-******************
-
-Code Style
-==========
-- We follow Google's C++ Style guide for C++ code.
-
-  - This is mainly to be consistent with the rest of the project.
-  - Another reason is we will be able to check style automatically with a linter.
-
-- You can check the style of the code by typing the following command at root folder.
-
-  .. code-block:: bash
-
-    make rcpplint
-
-- When needed, you can disable the linter warning of certain line with ``// NOLINT(*)`` comments.
-- We use `roxygen <https://cran.r-project.org/web/packages/roxygen2/vignettes/roxygen2.html>`_ for documenting the R package.
-
-Rmarkdown Vignettes
-===================
-Rmarkdown vignettes are placed in `R-package/vignettes <https://github.com/dmlc/xgboost/tree/master/R-package/vignettes>`_.
-These Rmarkdown files are not compiled. We host the compiled version on `doc/R-package <https://github.com/dmlc/xgboost/tree/master/doc/R-package>`_.
-
-The following steps are followed to add a new Rmarkdown vignettes:
-
-- Add the original rmarkdown to ``R-package/vignettes``.
-- Modify ``doc/R-package/Makefile`` to add the markdown files to be build.
-- Clone the `dmlc/web-data <https://github.com/dmlc/web-data>`_ repo to folder ``doc``.
-- Now type the following command on ``doc/R-package``:
-
-  .. code-block:: bash
-
-    make the-markdown-to-make.md
-
-- This will generate the markdown, as well as the figures in ``doc/web-data/xgboost/knitr``.
-- Modify the ``doc/R-package/index.md`` to point to the generated markdown.
-- Add the generated figure to the ``dmlc/web-data`` repo.
-
-  - If you already cloned the repo to doc, this means ``git add``
-
-- Create PR for both the markdown and ``dmlc/web-data``.
-- You can also build the document locally by typing the following command at the ``doc`` directory:
-
-  .. code-block:: bash
-
-    make html
-
-The reason we do this is to avoid exploded repo size due to generated images.
-
-R package versioning
-====================
-See :ref:`release`.
-
-Registering native routines in R
-================================
-According to `R extension manual <https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Registering-native-routines>`_,
-it is good practice to register native routines and to disable symbol search. When any changes or additions are made to the
-C++ interface of the R package, please make corresponding changes in ``src/init.c`` as well.
-
-*********************************
-Running Formatting Checks Locally
-*********************************
-
-Once you submit a pull request to `dmlc/xgboost <https://github.com/dmlc/xgboost>`_, we perform
-two automatic checks to enforce coding style conventions. To expedite the code review process, you are encouraged to run the checks locally on your machine prior to submitting your pull request.
-
-Linter
-======
-We use `pylint <https://github.com/PyCQA/pylint>`_ and `cpplint <https://github.com/cpplint/cpplint>`_ to enforce style convention and find potential errors. Linting is especially useful for Python, as we can catch many errors that would have otherwise occured at run-time.
-
-To run this check locally, run the following command from the top level source tree:
-
-.. code-block:: bash
-
-  cd /path/to/xgboost/
-  make lint
-
-This command requires the Python packages pylint and cpplint.
-
-Clang-tidy
-==========
-`Clang-tidy <https://clang.llvm.org/extra/clang-tidy/>`_ is an advance linter for C++ code, made by the LLVM team. We use it to conform our C++ codebase to modern C++ practices and conventions.
-
-To run this check locally, run the following command from the top level source tree:
-
-.. code-block:: bash
-
-  cd /path/to/xgboost/
-  python3 tests/ci_build/tidy.py
-
-Also, the script accepts two optional integer arguments, namely ``--cpp`` and ``--cuda``. By default they are both set to 1, meaning that both C++ and CUDA code will be checked. If the CUDA toolkit is not installed on your machine, you'll encounter an error. To exclude CUDA source from linting, use:
-
-.. code-block:: bash
-
-  cd /path/to/xgboost/
-  python3 tests/ci_build/tidy.py --cuda=0
-
-Similarly, if you want to exclude C++ source from linting:
-
-.. code-block:: bash
-
-  cd /path/to/xgboost/
-  python3 tests/ci_build/tidy.py --cpp=0
-
diff --git a/ml-xgboost/doc/contrib/community.rst b/ml-xgboost/doc/contrib/community.rst
deleted file mode 100644
index 9e96dec..0000000
--- a/ml-xgboost/doc/contrib/community.rst
+++ /dev/null
@@ -1,35 +0,0 @@
-.. _community_guide:
-
-XGBoost Community Guideline
-===========================
-
-XGBoost adopts the Apache style model and governs by merit. We believe that it is important to create an inclusive community where everyone can use, contribute to, and influence the direction of the project. See `CONTRIBUTORS.md <https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md>`_ for the current list of contributors.
-
-
-
-General Development Process
----------------------------
-Everyone in the community is welcomed to send patches, documents, and propose new directions to the project. The key guideline here is to enable everyone in the community to get involved and participate the decision and development.  When major changes are proposed, an RFC should be sent to allow discussion by the community. We encourage public discussion, archivable channels such as issues and discuss forum, so that everyone in the community can participate and review the process later.
-
-Code reviews are one of the key ways to ensure the quality of the code. High-quality code reviews prevent technical debt for long-term and are crucial to the success of the project. A pull request needs to be reviewed before it gets merged. A committer who has the expertise of the corresponding area would moderate the pull request and the merge the code when it is ready. The corresponding committer could request multiple reviewers who are familiar with the area of the code. We encourage contributors to request code reviews themselves and help review each other's code -- remember everyone is volunteering their time to the community, high-quality code review itself costs as much as the actual code contribution, you could get your code quickly reviewed if you do others the same favor.
-
-The community should strive to reach a consensus on technical decisions through discussion. We expect committers and PMCs to moderate technical discussions in a diplomatic way, and provide suggestions with clear technical reasoning when necessary.
-
-
-
-Committers
-----------
-Committers are individuals who are granted the write access to the project. A committer is usually responsible for a certain area or several areas of the code where they oversee the code review process. The area of contribution can take all forms, including code contributions and code reviews, documents, education, and outreach. Committers are essential for a high quality and healthy project. The community actively look for new committers from contributors. Here is a list of useful traits that help the community to recognize potential committers:
-
-- Sustained contribution to the project, demonstrated by discussion over RFCs, code reviews and proposals of new features, and other development activities. Being familiar with, and being able to take ownership on one or several areas of the project.
-- Quality of contributions: High-quality, readable code contributions indicated by pull requests that can be merged without a substantial code review.  History of creating clean, maintainable code and including good test cases. Informative code reviews to help other contributors that adhere to a good standard.
-- Community involvement: active participation in the discussion forum, promote the projects via tutorials, talks and outreach. We encourage committers to collaborate broadly, e.g. do code reviews and discuss designs with community members that they do not interact physically.
-
-The Project Management Committee(PMC) consists group of active committers that moderate the discussion, manage the project release, and proposes new committer/PMC members. Potential candidates are usually proposed via an internal discussion among PMCs, followed by a consensus approval, i.e. least 3 +1 votes, and no vetoes. Any veto must be accompanied by reasoning. PMCs should serve the community by upholding the community practices and guidelines XGBoost a better community for everyone. PMCs should strive to only nominate new candidates outside of their own organization.
-
-The PMC is in charge of the project's `continuous integration (CI) <https://en.wikipedia.org/wiki/Continuous_integration>`_ and testing infrastructure. Currently, we host our own Jenkins server at https://xgboost-ci.net. The PMC shall appoint committer(s) to manage the CI infrastructure. The PMC may accept 3rd-party donations and sponsorships that would defray the cost of the CI infrastructure. See :ref:`donation_policy`.
-
-
-Reviewers
----------
-Reviewers are individuals who actively contributed to the project and are willing to participate in the code review of new contributions. We identify reviewers from active contributors. The committers should explicitly solicit reviews from reviewers.  High-quality code reviews prevent technical debt for long-term and are crucial to the success of the project. A pull request to the project has to be reviewed by at least one reviewer in order to be merged.
diff --git a/ml-xgboost/doc/contrib/docs.rst b/ml-xgboost/doc/contrib/docs.rst
deleted file mode 100644
index 4990f0e..0000000
--- a/ml-xgboost/doc/contrib/docs.rst
+++ /dev/null
@@ -1,30 +0,0 @@
-##########################
-Documentation and Examples
-##########################
-
-**Contents**
-
-.. contents::
-  :backlinks: none
-  :local:
-
-*********
-Documents
-*********
-* Documentation is built using `Sphinx <http://www.sphinx-doc.org/en/master/>`_.
-* Each document is written in `reStructuredText <http://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_.
-* You can build document locally to see the effect, by running
-
-.. code-block:: bash
-
-  make html
-
-inside the ``doc/`` directory.
-
-********
-Examples
-********
-* Use cases and examples will be in `demo <https://github.com/dmlc/xgboost/tree/master/demo>`_.
-* We are super excited to hear about your story, if you have blogposts,
-  tutorials code solutions using XGBoost, please tell us and we will add
-  a link in the example pages.
diff --git a/ml-xgboost/doc/contrib/donate.rst b/ml-xgboost/doc/contrib/donate.rst
deleted file mode 100644
index ba86974..0000000
--- a/ml-xgboost/doc/contrib/donate.rst
+++ /dev/null
@@ -1,44 +0,0 @@
-.. _donation_policy:
-
-Donations
-=========
-
-.. raw:: html
-
-  <a href="https://opencollective.com/xgboost">Donate to dmlc/xgboost</a>
-
-Motivation
-----------
-DMLC/XGBoost has grown from a research project incubated in academia to one of the most widely used gradient boosting framework in production environment. On one side, with the growth of volume and variety of data in the production environment, users are putting accordingly growing expectation to XGBoost in terms of more functions, scalability and robustness. On the other side, as an open source project which develops in a fast pace, XGBoost has been receiving contributions from many individuals and organizations around the world. Given the high expectation from the users and the increasing channels of contribution to the project, delivering the high quality software presents a challenge to the project maintainers.
-
-A robust and efficient **continuous integration (CI)** infrastructure is one of the most critical solutions to address the above challenge. A CI service will monitor an open-source repository and run a suite of integration tests for every incoming contribution. This way, the CI ensures that every proposed change in the codebase is compatible with existing functionalities. Furthermore, XGBoost can enable more thorough tests with a powerful CI infrastructure to cover cases which are closer to the production environment.
-
-There are several CI services available free to open source projects, such as Travis CI and AppVeyor. The XGBoost project already utilizes Travis and AppVeyor. However, the XGBoost project has needs that these free services do not adequately address. In particular, the limited usage quota of resources such as CPU and memory leaves XGBoost developers unable to bring "too-intensive" tests. In addition, they do not offer test machines with GPUs for testing XGBoost-GPU code base which has been attracting more and more interest across many organizations. Consequently, the XGBoost project self-hosts a cloud server with Jenkins software installed: https://xgboost-ci.net/.
-
-The self-hosted Jenkins CI server has recurring operating expenses. It utilizes a leading cloud provider (AWS) to accommodate variable workload. The master node serving the web interface is available 24/7, to accomodate contributions from people around the globe. In addition, the master node launches slave nodes on demand, to run the test suite on incoming contributions. To save cost, the slave nodes are terminated when they are no longer needed.
-
-To help defray the hosting cost, the XGBoost project seeks donations from third parties.
-
-Donations and Sponsorships
---------------------------
-Donors may choose to make one-time donations or recurring donations on monthly or yearly basis. Donors who commit to the Sponsor tier will have their logo displayed on the front page of the XGBoost project.
-
-Fiscal host: Open Source Collective 501(c)(6)
----------------------------------------------
-The Project Management Committee (PMC) of the XGBoost project appointed `Open Source Collective <https://opencollective.com/opensource>`_ as their **fiscal host**. The platform is a 501(c)(6) registered entity and will manage the funds on the behalf of the PMC so that PMC members will not have to manage the funds directly. The platform currently hosts several well-known Javascript frameworks such as Babel, Vue, and Webpack.
-
-All expenses incurred for hosting CI will be submitted to the fiscal host with receipts. Only the expenses in the following categories will be approved for reimbursement:
-
-* Cloud exprenses for the Jenkins CI server (https://xgboost-ci.net)
-* Cost of domain https://xgboost-ci.net
-* Meetup.com account for XGBoost project
-* Hosting cost of the User Forum (https://discuss.xgboost.ai)
-
-Administration of Jenkins CI server
------------------------------------
-The PMC shall appoint committer(s) to administer the Jenkins CI server on their behalf. The current administrators are as follows:
-
-* Primary administrator: `Hyunsu Cho <https://github.com/hcho3>`_
-* Secondary administrator: `Jiaming Yuan <https://github.com/trivialfis>`_
-
-The administrators shall make good-faith effort to keep the CI expenses under control. The expenses shall not exceed the available funds. The administrators should post regular updates on CI expenses.
diff --git a/ml-xgboost/doc/contrib/git_guide.rst b/ml-xgboost/doc/contrib/git_guide.rst
deleted file mode 100644
index 5a2c8fa..0000000
--- a/ml-xgboost/doc/contrib/git_guide.rst
+++ /dev/null
@@ -1,76 +0,0 @@
-###################
-Git Workflow Howtos
-###################
-
-**Contents**
-
-.. contents::
-  :backlinks: none
-  :local:
-
-***********************************
-How to resolve conflict with master
-***********************************
-
-- First rebase to most recent master
-
-  .. code-block:: bash
-
-    # The first two steps can be skipped after you do it once.
-    git remote add upstream https://github.com/dmlc/xgboost
-    git fetch upstream
-    git rebase upstream/master
-
-- The git may show some conflicts it cannot merge, say ``conflicted.py``.
-
-  - Manually modify the file to resolve the conflict.
-  - After you resolved the conflict, mark it as resolved by
-
-    .. code-block:: bash
-
-      git add conflicted.py
-
-- Then you can continue rebase by
-
-  .. code-block:: bash
-
-    git rebase --continue
-
-- Finally push to your fork, you may need to force push here.
-
-  .. code-block:: bash
-
-    git push --force
-
-****************************************
-How to combine multiple commits into one
-****************************************
-Sometimes we want to combine multiple commits, especially when later commits are only fixes to previous ones,
-to create a PR with set of meaningful commits. You can do it by following steps.
-
-- Before doing so, configure the default editor of git if you haven't done so before.
-
-  .. code-block:: bash
-
-    git config core.editor the-editor-you-like
-
-- Assume we want to merge last 3 commits, type the following commands
-
-  .. code-block:: bash
-
-    git rebase -i HEAD~3
-
-- It will pop up an text editor. Set the first commit as ``pick``, and change later ones to ``squash``.
-- After you saved the file, it will pop up another text editor to ask you modify the combined commit message.
-- Push the changes to your fork, you need to force push.
-
-  .. code-block:: bash
-
-    git push --force
-
-*************************************
-What is the consequence of force push
-*************************************
-The previous two tips requires force push, this is because we altered the path of the commits.
-It is fine to force push to your own fork, as long as the commits changed are only yours.
-
diff --git a/ml-xgboost/doc/contrib/index.rst b/ml-xgboost/doc/contrib/index.rst
deleted file mode 100644
index afc6fff..0000000
--- a/ml-xgboost/doc/contrib/index.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-#####################
-Contribute to XGBoost
-#####################
-
-XGBoost has been developed by community members. Everyone is welcome to contribute. We value all forms of contributions, including, but not limited to:
-
-* Code reviews for pull requests
-* Documentation and usage examples
-* Community participation in forums and issues
-* Code readability and developer guide
-
-  - We welcome contributions that add code comments to improve readability.
-  - We also welcome contributions to docs to explain the design choices of the XGBoost internals.
-
-* Test cases to make the codebase more robust.
-* Tutorials, blog posts, talks that promote the project.
-
-Here are guidelines for contributing to various aspect of the XGBoost project:
-
-.. toctree::
-  :maxdepth: 2
-
-  Community Guideline <community>
-  donate
-  coding_guide
-  unit_tests
-  Docs and Examples <docs>
-  git_guide
-  release
diff --git a/ml-xgboost/doc/contrib/release.rst b/ml-xgboost/doc/contrib/release.rst
deleted file mode 100644
index 7980bcd..0000000
--- a/ml-xgboost/doc/contrib/release.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-.. _release:
-
-XGBoost Release Policy
-=======================
-
-Versioning Policy
----------------------------
-
-Starting from XGBoost 1.0.0, each XGBoost release will be versioned as [MAJOR].[FEATURE].[MAINTENANCE]
-
-* MAJOR: We gurantee the API compatibility across releases with the same major version number. We expect to have a 1+ years development period for a new MAJOR release version.
-* FEATURE: We ship new features, improvements and bug fixes through feature releases. The cycle length of a feature is decided by the size of feature roadmap. The roadmap is decided right after the previous release.
-* MAINTENANCE: Maintenance version only contains bug fixes. This type of release only occurs when we found significant correctness and/or performance bugs and barrier for users to upgrade to a new version of XGBoost smoothly.
diff --git a/ml-xgboost/doc/contrib/unit_tests.rst b/ml-xgboost/doc/contrib/unit_tests.rst
deleted file mode 100644
index 05c29d9..0000000
--- a/ml-xgboost/doc/contrib/unit_tests.rst
+++ /dev/null
@@ -1,179 +0,0 @@
-########################
-Adding and running tests
-########################
-
-A high-quality suite of tests is crucial in ensuring correctness and robustness of the codebase. Here, we provide instructions how to run unit tests, and also how to add a new one.
-
-**Contents**
-
-.. contents::
-  :backlinks: none
-  :local:
-
-**********************
-Adding a new unit test
-**********************
-
-Python package: pytest
-======================
-Add your test under the directory `tests/python/ <https://github.com/dmlc/xgboost/tree/master/tests/python>`_ or `tests/python-gpu/ <https://github.com/dmlc/xgboost/tree/master/tests/python-gpu>`_ (if you are testing GPU code). Refer to `the PyTest tutorial <https://docs.pytest.org/en/latest/getting-started.html>`_ to learn how to write tests for Python code.
-
-You may try running your test by following instructions in :ref:`this section <running_pytest>`.
-
-C++: Google Test
-================
-Add your test under the directory `tests/cpp/ <https://github.com/dmlc/xgboost/tree/master/tests/cpp>`_. Refer to `this excellent tutorial on using Google Test <https://developer.ibm.com/articles/au-googletestingframework/>`_.
-
-You may try running your test by following instructions in :ref:`this section <running_gtest>`. Note. Google Test version 1.8.1 or later is required.
-
-JVM packages: JUnit / scalatest
-===============================
-The JVM packages for XGBoost (XGBoost4J / XGBoost4J-Spark) use `the Maven Standard Directory Layout <https://maven.apache.org/guides/introduction/introduction-to-the-standard-directory-layout.html>`_. Specifically, the tests for the JVM packages are located in the following locations:
-
-* `jvm-packages/boostkit-xgboost4j/src/test/ <https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j/src/test>`_
-* `jvm-packages/boostkit-xgboost4j-spark/src/test/ <https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-spark/src/test>`_
-
-To write a test for Java code, see `JUnit 5 tutorial <https://junit.org/junit5/docs/current/user-guide/>`_.
-To write a test for Scala, see `Scalatest tutorial <http://www.scalatest.org/user_guide/writing_your_first_test>`_.
-
-You may try running your test by following instructions in :ref:`this section <running_jvm_tests>`.
-
-R package: testthat
-===================
-Add your test under the directory `R-package/tests/testthat <https://github.com/dmlc/xgboost/tree/master/R-package/tests/testthat>`_. Refer to `this excellent tutorial on testthat <https://kbroman.org/pkg_primer/pages/tests.html>`_.
-
-You may try running your test by following instructions in :ref:`this section <running_r_tests>`.
-
-**************************
-Running Unit Tests Locally
-**************************
-
-.. _running_r_tests:
-
-R package
-=========
-Run
-
-.. code-block:: bash
-
-  make Rcheck
-
-at the root of the project directory.
-
-.. _running_jvm_tests:
-
-JVM packages
-============
-As part of the building process, tests are run:
-
-.. code-block:: bash
-
-  mvn package
-
-.. _running_pytest:
-
-Python package: pytest
-======================
-
-To run Python unit tests, first install `pytest <https://docs.pytest.org/en/latest/contents.html>`_ package:
-
-.. code:: bash
-
-  pip3 install pytest
-
-Then compile XGBoost according to instructions in :ref:`build_shared_lib`. Finally, invoke pytest at the project root directory:
-
-.. code:: bash
-
-  # Tell Python where to find XGBoost module
-  export PYTHONPATH=./python-package
-  pytest -v -s --fulltrace tests/python
-
-In addition, to test CUDA code, run:
-
-.. code:: bash
-
-  # Tell Python where to find XGBoost module
-  export PYTHONPATH=./python-package
-  pytest -v -s --fulltrace tests/python-gpu
-
-(For this step, you should have compiled XGBoost with CUDA enabled.)
-
-.. _running_gtest:
-
-C++: Google Test
-================
-
-To build and run C++ unit tests enable tests while running CMake:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake -DGOOGLE_TEST=ON -DUSE_DMLC_GTEST=ON  ..
-  make
-  make test
-
-To enable tests for CUDA code, add ``-DUSE_CUDA=ON`` and ``-DUSE_NCCL=ON`` (CUDA toolkit required):
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake -DGOOGLE_TEST=ON -DUSE_DMLC_GTEST=ON -DUSE_CUDA=ON -DUSE_NCCL=ON ..
-  make
-  make test
-
-One can also run all unit test using ctest tool which provides higher flexibility. For example:
-
-.. code-block:: bash
-
-  ctest --verbose
-
-***********************************************
-Sanitizers: Detect memory errors and data races
-***********************************************
-
-By default, sanitizers are bundled in GCC and Clang/LLVM. One can enable
-sanitizers with GCC >= 4.8 or LLVM >= 3.1, But some distributions might package
-sanitizers separately.  Here is a list of supported sanitizers with
-corresponding library names:
-
-- Address sanitizer: libasan
-- Leak sanitizer:    liblsan
-- Thread sanitizer:  libtsan
-
-Memory sanitizer is exclusive to LLVM, hence not supported in XGBoost.
-
-How to build XGBoost with sanitizers
-====================================
-One can build XGBoost with sanitizer support by specifying -DUSE_SANITIZER=ON.
-By default, address sanitizer and leak sanitizer are used when you turn the
-USE_SANITIZER flag on.  You can always change the default by providing a
-semicolon separated list of sanitizers to ENABLED_SANITIZERS.  Note that thread
-sanitizer is not compatible with the other two sanitizers.
-
-.. code-block:: bash
-
-  cmake -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak" /path/to/xgboost
-
-By default, CMake will search regular system paths for sanitizers, you can also
-supply a specified SANITIZER_PATH.
-
-.. code-block:: bash
-
-  cmake -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak" \
-  -DSANITIZER_PATH=/path/to/sanitizers /path/to/xgboost
-
-How to use sanitizers with CUDA support
-=======================================
-Runing XGBoost on CUDA with address sanitizer (asan) will raise memory error.
-To use asan with CUDA correctly, you need to configure asan via ASAN_OPTIONS
-environment variable:
-
-.. code-block:: bash
-
-  ASAN_OPTIONS=protect_shadow_gap=0 ${BUILD_DIR}/testxgboost
-
-For details, please consult `official documentation <https://github.com/google/sanitizers/wiki>`_ for sanitizers.
-
diff --git a/ml-xgboost/doc/dump.schema b/ml-xgboost/doc/dump.schema
deleted file mode 100644
index cb2c61b..0000000
--- a/ml-xgboost/doc/dump.schema
+++ /dev/null
@@ -1,55 +0,0 @@
-{
-    "$schema": "http://json-schema.org/draft-07/schema#",
-    "definitions": {
-        "split_node": {
-            "type": "object",
-            "properties": {
-                "nodeid": {
-                    "type": "number",
-                    "minimum": 0
-                },
-                "depth": {
-                    "type": "number",
-                    "minimum": 0
-                },
-                "yes": {
-                    "type": "number",
-                    "minimum": 0
-                },
-                "no": {
-                    "type": "number",
-                    "minimum": 0
-                },
-                "split": {
-                    "type": "string"
-                },
-                "children": {
-                    "type": "array",
-                    "items": {
-                        "oneOf": [
-                            {"$ref": "#/definitions/split_node"},
-                            {"$ref": "#/definitions/leaf_node"}
-                        ]
-                    },
-                    "maxItems": 2
-                }
-            },
-            "required": ["nodeid", "depth", "yes", "no", "split", "children"]
-        },
-        "leaf_node": {
-            "type": "object",
-            "properties": {
-                "nodeid": {
-                    "type": "number",
-                    "minimum": 0
-                },
-                "leaf": {
-                    "type": "number"
-                }
-            },
-            "required": ["nodeid", "leaf"]
-        }
-    },
-    "type": "object",
-    "$ref": "#/definitions/split_node"
-}
diff --git a/ml-xgboost/doc/faq.rst b/ml-xgboost/doc/faq.rst
deleted file mode 100644
index 8015e1f..0000000
--- a/ml-xgboost/doc/faq.rst
+++ /dev/null
@@ -1,77 +0,0 @@
-##########################
-Frequently Asked Questions
-##########################
-
-This document contains frequently asked questions about XGBoost.
-
-**********************
-How to tune parameters
-**********************
-See :doc:`Parameter Tuning Guide </tutorials/param_tuning>`.
-
-************************
-Description on the model
-************************
-See :doc:`Introduction to Boosted Trees </tutorials/model>`.
-
-********************
-I have a big dataset
-********************
-XGBoost is designed to be memory efficient. Usually it can handle problems as long as the data fit into your memory.
-(This usually means millions of instances)
-If you are running out of memory, checkout :doc:`external memory version </tutorials/external_memory>` or
-:doc:`distributed version </tutorials/aws_yarn>` of XGBoost.
-
-**************************************************
-Running XGBoost on Platform X (Hadoop/Yarn, Mesos)
-**************************************************
-The distributed version of XGBoost is designed to be portable to various environment.
-Distributed XGBoost can be ported to any platform that supports `rabit <https://github.com/dmlc/rabit>`_.
-You can directly run XGBoost on Yarn. In theory Mesos and other resource allocation engines can be easily supported as well.
-
-*****************************************************************
-Why not implement distributed XGBoost on top of X (Spark, Hadoop)
-*****************************************************************
-The first fact we need to know is going distributed does not necessarily solve all the problems.
-Instead, it creates more problems such as more communication overhead and fault tolerance.
-The ultimate question will still come back to how to push the limit of each computation node
-and use less resources to complete the task (thus with less communication and chance of failure).
-
-To achieve these, we decide to reuse the optimizations in the single node XGBoost and build distributed version on top of it.
-The demand of communication in machine learning is rather simple, in the sense that we can depend on a limited set of API (in our case rabit).
-Such design allows us to reuse most of the code, while being portable to major platforms such as Hadoop/Yarn, MPI, SGE.
-Most importantly, it pushes the limit of the computation resources we can use.
-
-*****************************************
-How can I port the model to my own system
-*****************************************
-The model and data format of XGBoost is exchangeable,
-which means the model trained by one language can be loaded in another.
-This means you can train the model using R, while running prediction using
-Java or C++, which are more common in production systems.
-You can also train the model using distributed versions,
-and load them in from Python to do some interactive analysis.
-
-*************************
-Do you support LambdaMART
-*************************
-Yes, XGBoost implements LambdaMART. Checkout the objective section in :doc:`parameters </parameter>`.
-
-******************************
-How to deal with Missing Value
-******************************
-XGBoost supports missing value by default.
-In tree algorithms, branch directions for missing values are learned during training.
-Note that the gblinear booster treats missing values as zeros.
-
-**************************************
-Slightly different result between runs
-**************************************
-This could happen, due to non-determinism in floating point summation order and multi-threading.
-Though the general accuracy will usually remain the same.
-
-**********************************************************
-Why do I see different results with sparse and dense data?
-**********************************************************
-"Sparse" elements are treated as if they were "missing" by the tree booster, and as zeros by the linear booster.
-For tree models, it is important to use consistent data formats during training and scoring.
diff --git a/ml-xgboost/doc/get_started.rst b/ml-xgboost/doc/get_started.rst
deleted file mode 100644
index 08bee97..0000000
--- a/ml-xgboost/doc/get_started.rst
+++ /dev/null
@@ -1,94 +0,0 @@
-########################
-Get Started with XGBoost
-########################
-
-This is a quick start tutorial showing snippets for you to quickly try out XGBoost
-on the demo dataset on a binary classification task.
-
-********************************
-Links to Other Helpful Resources
-********************************
-- See :doc:`Installation Guide </build>` on how to install XGBoost.
-- See :doc:`Text Input Format </tutorials/input_format>` on using text format for specifying training/testing data.
-- See :doc:`Tutorials </tutorials/index>` for tips and tutorials.
-- See `Learning to use XGBoost by Examples <https://github.com/dmlc/xgboost/tree/master/demo>`_ for more code examples.
-
-******
-Python
-******
-
-.. code-block:: python
-
-  import xgboost as xgb
-  # read in data
-  dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
-  dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
-  # specify parameters via map
-  param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic' }
-  num_round = 2
-  bst = xgb.train(param, dtrain, num_round)
-  # make prediction
-  preds = bst.predict(dtest)
-
-***
-R
-***
-
-.. code-block:: R
-
-  # load data
-  data(agaricus.train, package='xgboost')
-  data(agaricus.test, package='xgboost')
-  train <- agaricus.train
-  test <- agaricus.test
-  # fit model
-  bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nrounds = 2,
-                 nthread = 2, objective = "binary:logistic")
-  # predict
-  pred <- predict(bst, test$data)
-
-*****
-Julia
-*****
-
-.. code-block:: julia
-
-  using XGBoost
-  # read data
-  train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
-  test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
-  # fit model
-  num_round = 2
-  bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
-  # predict
-  pred = predict(bst, test_X)
-
-*****
-Scala
-*****
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.DMatrix
-  import ml.dmlc.xgboost4j.scala.XGBoost
-  
-  object XGBoostScalaExample {
-    def main(args: Array[String]) {
-      // read trainining data, available at xgboost/demo/data
-      val trainData =
-        new DMatrix("/path/to/agaricus.txt.train")
-      // define parameters
-      val paramMap = List(
-        "eta" -> 0.1,
-        "max_depth" -> 2,
-        "objective" -> "binary:logistic").toMap
-      // number of iterations
-      val round = 2
-      // train the model
-      val model = XGBoost.train(trainData, paramMap, round)
-      // run prediction
-      val predTrain = model.predict(trainData)
-      // save model to the file.
-      model.saveModel("/local/path/to/model")
-    }
-  }
diff --git a/ml-xgboost/doc/gpu/index.rst b/ml-xgboost/doc/gpu/index.rst
deleted file mode 100644
index c9e8391..0000000
--- a/ml-xgboost/doc/gpu/index.rst
+++ /dev/null
@@ -1,245 +0,0 @@
-###################
-XGBoost GPU Support
-###################
-
-This page contains information about GPU algorithms supported in XGBoost.
-To install GPU support, checkout the :doc:`/build`.
-
-.. note:: CUDA 9.0, Compute Capability 3.5 required
-
-  The GPU algorithms in XGBoost require a graphics card with compute capability 3.5 or higher, with
-  CUDA toolkits 9.0 or later.
-  (See `this list <https://en.wikipedia.org/wiki/CUDA#GPUs_supported>`_ to look up compute capability of your GPU card.)
-
-*********************************************
-CUDA Accelerated Tree Construction Algorithms
-*********************************************
-Tree construction (training) and prediction can be accelerated with CUDA-capable GPUs.
-
-Usage
-=====
-Specify the ``tree_method`` parameter as one of the following algorithms.
-
-Algorithms
-----------
-
-+-----------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| tree_method           | Description                                                                                                                                                           |
-+=======================+=======================================================================================================================================================================+
-| gpu_hist              | Equivalent to the XGBoost fast histogram algorithm. Much faster and uses considerably less memory. NOTE: May run very slowly on GPUs older than Pascal architecture.  |
-+-----------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-Supported parameters
---------------------
-
-.. |tick| unicode:: U+2714
-.. |cross| unicode:: U+2718
-
-+--------------------------------+--------------+
-| parameter                      | ``gpu_hist`` |
-+================================+==============+
-| ``subsample``                  | |tick|       |
-+--------------------------------+--------------+
-| ``sampling_method``            | |tick|       |
-+--------------------------------+--------------+
-| ``colsample_bytree``           | |tick|       |
-+--------------------------------+--------------+
-| ``colsample_bylevel``          | |tick|       |
-+--------------------------------+--------------+
-| ``max_bin``                    | |tick|       |
-+--------------------------------+--------------+
-| ``gamma``                      | |tick|       |
-+--------------------------------+--------------+
-| ``gpu_id``                     | |tick|       |
-+--------------------------------+--------------+
-| ``predictor``                  | |tick|       |
-+--------------------------------+--------------+
-| ``grow_policy``                | |tick|       |
-+--------------------------------+--------------+
-| ``monotone_constraints``       | |tick|       |
-+--------------------------------+--------------+
-| ``interaction_constraints``    | |tick|       |
-+--------------------------------+--------------+
-| ``single_precision_histogram`` | |tick|       |
-+--------------------------------+--------------+
-
-GPU accelerated prediction is enabled by default for the above mentioned ``tree_method`` parameters but can be switched to CPU prediction by setting ``predictor`` to ``cpu_predictor``. This could be useful if you want to conserve GPU memory. Likewise when using CPU algorithms, GPU accelerated prediction can be enabled by setting ``predictor`` to ``gpu_predictor``.
-
-The experimental parameter ``single_precision_histogram`` can be set to True to enable building histograms using single precision. This may improve speed, in particular on older architectures.
-
-The device ordinal (which GPU to use if you have many of them) can be selected using the
-``gpu_id`` parameter, which defaults to 0 (the first device reported by CUDA runtime).
-
-
-The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/build` for details.
-
-.. code-block:: python
-  :caption: Python example
-
-  param['gpu_id'] = 0
-  param['tree_method'] = 'gpu_hist'
-
-.. code-block:: python
-  :caption: With Scikit-Learn interface
-
-  XGBRegressor(tree_method='gpu_hist', gpu_id=0)
-
-
-Multi-node Multi-GPU Training
-=============================
-XGBoost supports fully distributed GPU training using `Dask <https://dask.org/>`_. For
-getting started see our tutorial :doc:`/tutorials/dask` and worked examples `here
-<https://github.com/dmlc/xgboost/tree/master/demo/dask>`_, also Python documentation
-:ref:`dask_api` for complete reference.
-
-
-Objective functions
-===================
-Most of the objective functions implemented in XGBoost can be run on GPU.  Following table shows current support status.
-
-+--------------------+-------------+
-| Objectives         | GPU support |
-+--------------------+-------------+
-| reg:squarederror   | |tick|      |
-+--------------------+-------------+
-| reg:squaredlogerror| |tick|      |
-+--------------------+-------------+
-| reg:logistic       | |tick|      |
-+--------------------+-------------+
-| binary:logistic    | |tick|      |
-+--------------------+-------------+
-| binary:logitraw    | |tick|      |
-+--------------------+-------------+
-| binary:hinge       | |tick|      |
-+--------------------+-------------+
-| count:poisson      | |tick|      |
-+--------------------+-------------+
-| reg:gamma          | |tick|      |
-+--------------------+-------------+
-| reg:tweedie        | |tick|      |
-+--------------------+-------------+
-| multi:softmax      | |tick|      |
-+--------------------+-------------+
-| multi:softprob     | |tick|      |
-+--------------------+-------------+
-| survival:cox       | |cross|     |
-+--------------------+-------------+
-| rank:pairwise      | |tick|      |
-+--------------------+-------------+
-| rank:ndcg          | |tick|      |
-+--------------------+-------------+
-| rank:map           | |tick|      |
-+--------------------+-------------+
-
-Objective will run on GPU if GPU updater (``gpu_hist``), otherwise they will run on CPU by
-default.  For unsupported objectives XGBoost will fall back to using CPU implementation by
-default.  Note that when using GPU ranking objective, the result is not deterministic due
-to the non-associative aspect of floating point summation.
-
-Metric functions
-===================
-Following table shows current support status for evaluation metrics on the GPU.
-
-+-----------------+-------------+
-| Metric          | GPU Support |
-+=================+=============+
-| rmse            | |tick|      |
-+-----------------+-------------+
-| rmsle           | |tick|      |
-+-----------------+-------------+
-| mae             | |tick|      |
-+-----------------+-------------+
-| logloss         | |tick|      |
-+-----------------+-------------+
-| error           | |tick|      |
-+-----------------+-------------+
-| merror          | |tick|      |
-+-----------------+-------------+
-| mlogloss        | |tick|      |
-+-----------------+-------------+
-| auc             | |tick|      |
-+-----------------+-------------+
-| aucpr           | |cross|     |
-+-----------------+-------------+
-| ndcg            | |tick|      |
-+-----------------+-------------+
-| map             | |tick|      |
-+-----------------+-------------+
-| poisson-nloglik | |tick|      |
-+-----------------+-------------+
-| gamma-nloglik   | |tick|      |
-+-----------------+-------------+
-| cox-nloglik     | |cross|     |
-+-----------------+-------------+
-| gamma-deviance  | |tick|      |
-+-----------------+-------------+
-| tweedie-nloglik | |tick|      |
-+-----------------+-------------+
-
-Similar to objective functions, default device for metrics is selected based on tree
-updater and predictor (which is selected based on tree updater).
-
-Benchmarks
-==========
-You can run benchmarks on synthetic data for binary classification:
-
-.. code-block:: bash
-
-  python tests/benchmark/benchmark_tree.py --tree_method=gpu_hist
-  python tests/benchmark/benchmark_tree.py --tree_method=hist
-
-Training time on 1,000,000 rows x 50 columns of random data with 500 boosting iterations and 0.25/0.75 test/train split with AMD Ryzen 7 2700 8 core @3.20GHz and Nvidia 1080ti yields the following results:
-
-+--------------+----------+
-| tree_method  | Time (s) |
-+==============+==========+
-| gpu_hist     | 12.57    |
-+--------------+----------+
-| hist         | 36.01    |
-+--------------+----------+
-
-Memory usage
-============
-The following are some guidelines on the device memory usage of the `gpu_hist` updater.
-
-If you train xgboost in a loop you may notice xgboost is not freeing device memory after each training iteration. This is because memory is allocated over the lifetime of the booster object and does not get freed until the booster is freed. A workaround is to serialise the booster object after training. See `demo/gpu_acceleration/memory.py` for a simple example.
-
-Memory inside xgboost training is generally allocated for two reasons - storing the dataset and working memory.
-
-The dataset itself is stored on device in a compressed ELLPACK format. The ELLPACK format is a type of sparse matrix that stores elements with a constant row stride. This format is convenient for parallel computation when compared to CSR because the row index of each element is known directly from its address in memory. The disadvantage of the ELLPACK format is that it becomes less memory efficient if the maximum row length is significantly more than the average row length. Elements are quantised and stored as integers. These integers are compressed to a minimum bit length. Depending on the number of features, we usually don't need the full range of a 32 bit integer to store elements and so compress this down. The compressed, quantised ELLPACK format will commonly use 1/4 the space of a CSR matrix stored in floating point.
-
-In some cases the full CSR matrix stored in floating point needs to be allocated on the device. This currently occurs for prediction in multiclass classification. If this is a problem consider setting `'predictor'='cpu_predictor'`. This also occurs when the external data itself comes from a source on device e.g. a cudf DataFrame. These are known issues we hope to resolve.
-
-Working memory is allocated inside the algorithm proportional to the number of rows to keep track of gradients, tree positions and other per row statistics. Memory is allocated for histogram bins proportional to the number of bins, number of features and nodes in the tree. For performance reasons we keep histograms in memory from previous nodes in the tree, when a certain threshold of memory usage is passed we stop doing this to conserve memory at some performance loss.
-
-The quantile finding algorithm also uses some amount of working device memory. It is able to operate in batches, but is not currently well optimised for sparse data.
-
-If you are getting out-of-memory errors on a big dataset, try the `external memory version <../tutorials/external_memory.html>`_.
-
-Developer notes
-===============
-The application may be profiled with annotations by specifying USE_NTVX to cmake and providing the path to the stand-alone nvtx header via NVTX_HEADER_DIR. Regions covered by the 'Monitor' class in cuda code will automatically appear in the nsight profiler.
-
-**********
-References
-**********
-`Mitchell R, Frank E. (2017) Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science 3:e127 https://doi.org/10.7717/peerj-cs.127 <https://peerj.com/articles/cs-127/>`_
-
-`Nvidia Parallel Forall: Gradient Boosting, Decision Trees and XGBoost with CUDA <https://devblogs.nvidia.com/parallelforall/gradient-boosting-decision-trees-xgboost-cuda/>`_
-
-Contributors
-============
-Many thanks to the following contributors (alphabetical order):
-
-* Andrey Adinets
-* Jiaming Yuan
-* Jonathan C. McKinney
-* Matthew Jones
-* Philip Cho
-* Rong Ou
-* Rory Mitchell
-* Shankara Rao Thejaswi Nanditale
-* Sriram Chandramouli
-* Vinay Deshpande
-
-Please report bugs to the XGBoost issues list: https://github.com/dmlc/xgboost/issues.  For general questions please visit our user form: https://discuss.xgboost.ai/.
diff --git a/ml-xgboost/doc/index.rst b/ml-xgboost/doc/index.rst
deleted file mode 100644
index 223f9ab..0000000
--- a/ml-xgboost/doc/index.rst
+++ /dev/null
@@ -1,33 +0,0 @@
-#####################
-XGBoost Documentation
-#####################
-
-**XGBoost** is an optimized distributed gradient boosting library designed to be highly **efficient**, **flexible** and **portable**.
-It implements machine learning algorithms under the `Gradient Boosting <https://en.wikipedia.org/wiki/Gradient_boosting>`_ framework.
-XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
-The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
-
-********
-Contents
-********
-
-.. toctree::
-  :maxdepth: 2
-  :titlesonly:
-
-  build
-  get_started
-  tutorials/index
-  faq
-  XGBoost User Forum <https://discuss.xgboost.ai>
-  GPU support <gpu/index>
-  parameter
-  Python package <python/index>
-  R package <R-package/index>
-  JVM package <jvm/index>
-  Ruby package <https://github.com/ankane/xgb>
-  Julia package <julia>
-  C Package <c>
-  C++ Interface <c++>
-  CLI interface <cli>
-  contrib/index
diff --git a/ml-xgboost/doc/julia.rst b/ml-xgboost/doc/julia.rst
deleted file mode 100644
index 4dc147d..0000000
--- a/ml-xgboost/doc/julia.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-##########
-XGBoost.jl
-##########
-
-See `XGBoost.jl Project page <https://github.com/dmlc/XGBoost.jl>`_.
diff --git a/ml-xgboost/doc/jvm/index.rst b/ml-xgboost/doc/jvm/index.rst
deleted file mode 100644
index 69e4569..0000000
--- a/ml-xgboost/doc/jvm/index.rst
+++ /dev/null
@@ -1,218 +0,0 @@
-###################
-XGBoost JVM Package
-###################
-
-.. raw:: html
-
-  <a href="https://travis-ci.org/dmlc/xgboost">
-  <img alt="Build Status" src="https://travis-ci.org/dmlc/xgboost.svg?branch=master">
-  </a>
-  <a href="https://github.com/dmlc/xgboost/blob/master/LICENSE">
-  <img alt="GitHub license" src="https://dmlc.github.io/img/apache2.svg">
-  </a>
-
-You have found the XGBoost JVM Package!
-
-.. _install_jvm_packages:
-
-************
-Installation
-************
-
-.. contents::
-  :local:
-  :backlinks: none
-
-Installation from Maven repository
-==================================
-
-Access release version
-----------------------
-You can use XGBoost4J in your Java/Scala application by adding XGBoost4J as a dependency:
-
-.. code-block:: xml
-  :caption: Maven
-
-  <properties>
-    ...
-    <!-- Specify Scala version in package name -->
-    <scala.binary.version>2.12</scala.binary.version>
-  </properties>
-
-  <dependencies>
-    ...
-    <dependency>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-        <version>latest_version_num</version>
-    </dependency>
-    <dependency>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-        <version>latest_version_num</version>
-    </dependency>
-  </dependencies>
-
-.. code-block:: scala
-  :caption: sbt
-
-  libraryDependencies ++= Seq(
-    "ml.dmlc" %% "xgboost4j" % "latest_version_num",
-    "ml.dmlc" %% "xgboost4j-spark" % "latest_version_num"
-  )
-
-This will check out the latest stable version from the Maven Central.
-
-For the latest release version number, please check `here <https://github.com/dmlc/xgboost/releases>`_.
-
-.. note:: Using Maven repository hosted by the XGBoost project
-
-  There may be some delay until a new release becomes available to Maven Central. If you would like to access the latest release immediately, add the Maven repository hosted by the XGBoost project:
-
-  .. code-block:: xml
-    :caption: Maven
-
-    <repository>
-      <id>XGBoost4J Release Repo</id>
-      <name>XGBoost4J Release Repo</name>
-      <url>https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/release/</url>
-    </repository>
-
-  .. code-block:: scala
-    :caption: sbt
-
-    resolvers += "XGBoost4J Release Repo" at "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/release/"
-
-Access SNAPSHOT version
------------------------
-
-First add the following Maven repository hosted by the XGBoost project:
-
-.. code-block:: xml
-  :caption: Maven
-
-  <repository>
-    <id>XGBoost4J Snapshot Repo</id>
-    <name>XGBoost4J Snapshot Repo</name>
-    <url>https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/</url>
-  </repository>
-
-.. code-block:: scala
-  :caption: sbt
-
-  resolvers += "XGBoost4J Snapshot Repo" at "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/"
-
-Then add XGBoost4J as a dependency:
-
-.. code-block:: xml
-  :caption: maven
-
-  <properties>
-    ...
-    <!-- Specify Scala version in package name -->
-    <scala.binary.version>2.12</scala.binary.version>
-  </properties>
-
-  <dependencies>
-    ...
-    <dependency>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-        <version>latest_version_num-SNAPSHOT</version>
-    </dependency>
-    <dependency>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-        <version>latest_version_num-SNAPSHOT</version>
-    </dependency>
-  </dependencies>
-
-.. code-block:: scala
-  :caption: sbt
-
-  libraryDependencies ++= Seq(
-    "ml.dmlc" %% "xgboost4j" % "latest_version_num-SNAPSHOT",
-    "ml.dmlc" %% "xgboost4j-spark" % "latest_version_num-SNAPSHOT"
-  )
-
-Look up the ``version`` field in `pom.xml <https://github.com/dmlc/xgboost/blob/master/jvm-packages/pom.xml>`_ to get the correct version number.
-
-The SNAPSHOT JARs are hosted by the XGBoost project. Every commit in the ``master`` branch will automatically trigger generation of a new SNAPSHOT JAR. You can control how often Maven should upgrade your SNAPSHOT installation by specifying ``updatePolicy``. See `here <http://maven.apache.org/pom.html#Repositories>`_ for details.
-
-You can browse the file listing of the Maven repository at https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/list.html.
-
-.. note:: Windows not supported by published JARs
-
-  The published JARs from the Maven Central and GitHub currently only supports Linux and MacOS. Windows users should consider building XGBoost4J / XGBoost4J-Spark from the source. Alternatively, checkout pre-built JARs from `criteo-forks/xgboost-jars <https://github.com/criteo-forks/xgboost-jars>`_.
-
-Installation from source
-========================
-
-Building XGBoost4J using Maven requires Maven 3 or newer, Java 7+ and CMake 3.3+ for compiling the JNI bindings.
-
-Before you install XGBoost4J, you need to define environment variable ``JAVA_HOME`` as your JDK directory to ensure that your compiler can find ``jni.h`` correctly, since XGBoost4J relies on JNI to implement the interaction between the JVM and native libraries.
-
-After your ``JAVA_HOME`` is defined correctly, it is as simple as run ``mvn package`` under jvm-packages directory to install XGBoost4J. You can also skip the tests by running ``mvn -DskipTests=true package``, if you are sure about the correctness of your local setup.
-
-To publish the artifacts to your local maven repository, run
-
-.. code-block:: bash
-
-  mvn install
-
-Or, if you would like to skip tests, run
-
-.. code-block:: bash
-
-  mvn -DskipTests install
-
-This command will publish the xgboost binaries, the compiled java classes as well as the java sources to your local repository. Then you can use XGBoost4J in your Java projects by including the following dependency in ``pom.xml``:
-
-.. code-block:: xml
-
-  <dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j</artifactId>
-    <version>latest_source_version_num</version>
-  </dependency>
-
-For sbt, please add the repository and dependency in build.sbt as following:
-
-.. code-block:: scala
-
-  resolvers += "Local Maven Repository" at "file://"+Path.userHome.absolutePath+"/.m2/repository"
-
-  "ml.dmlc" % "xgboost4j" % "latest_source_version_num"
-
-If you want to use XGBoost4J-Spark, replace ``xgboost4j`` with ``xgboost4j-spark``.
-
-.. note:: XGBoost4J-Spark requires Apache Spark 2.3+
-
-  XGBoost4J-Spark now requires **Apache Spark 2.3+**. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
-
-  Also, make sure to install Spark directly from `Apache website <https://spark.apache.org/>`_. **Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark.** Consult appropriate third parties to obtain their distribution of XGBoost.
-
-Enabling OpenMP for Mac OS
---------------------------
-If you are on Mac OS and using a compiler that supports OpenMP, you need to go to the file ``xgboost/jvm-packages/create_jni.py`` and comment out the line
-
-.. code-block:: python
-
-  CONFIG["USE_OPENMP"] = "OFF"
-
-in order to get the benefit of multi-threading.
-
-********
-Contents
-********
-
-.. toctree::
-  :maxdepth: 2
-
-  java_intro
-  XGBoost4J-Spark Tutorial <xgboost4j_spark_tutorial>
-  Code Examples <https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example>
-  XGBoost4J Java API <javadocs/index>
-  XGBoost4J Scala API <scaladocs/xgboost4j/index>
-  XGBoost4J-Spark Scala API <scaladocs/xgboost4j-spark/index>
-  XGBoost4J-Flink Scala API <scaladocs/xgboost4j-flink/index>
diff --git a/ml-xgboost/doc/jvm/java_intro.rst b/ml-xgboost/doc/jvm/java_intro.rst
deleted file mode 100644
index 29fed96..0000000
--- a/ml-xgboost/doc/jvm/java_intro.rst
+++ /dev/null
@@ -1,158 +0,0 @@
-##############################
-Getting Started with XGBoost4J
-##############################
-This tutorial introduces Java API for XGBoost.
-
-**************
-Data Interface
-**************
-Like the XGBoost python module, XGBoost4J uses DMatrix to handle data.
-LIBSVM txt format file, sparse matrix in CSR/CSC format, and dense matrix are
-supported.
-
-* The first step is to import DMatrix:
-
-  .. code-block:: java
-
-    import ml.dmlc.xgboost4j.java.DMatrix;
-
-* Use DMatrix constructor to load data from a libsvm text format file:
-
-  .. code-block:: java
-
-    DMatrix dmat = new DMatrix("train.svm.txt");
-
-* Pass arrays to DMatrix constructor to load from sparse matrix.
-
-  Suppose we have a sparse matrix
-
-  .. code-block:: none
-
-    1 0 2 0
-    4 0 0 3
-    3 1 2 0
-
-  We can express the sparse matrix in `Compressed Sparse Row (CSR) <https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format)>`_ format:
-
-  .. code-block:: java
-
-    long[] rowHeaders = new long[] {0,2,4,7};
-    float[] data = new float[] {1f,2f,4f,3f,3f,1f,2f};
-    int[] colIndex = new int[] {0,2,0,3,0,1,2};
-    int numColumn = 4;
-    DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR, numColumn);
-
-  ... or in `Compressed Sparse Column (CSC) <https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS)>`_ format:
-
-  .. code-block:: java
-
-    long[] colHeaders = new long[] {0,3,4,6,7};
-    float[] data = new float[] {1f,4f,3f,1f,2f,2f,3f};
-    int[] rowIndex = new int[] {0,1,2,2,0,2,1};
-    int numRow = 3;
-    DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC, numRow);
-
-* You may also load your data from a dense matrix. Let's assume we have a matrix of form
-
-  .. code-block:: none
-
-    1    2
-    3    4
-    5    6
-
-  Using `row-major layout <https://en.wikipedia.org/wiki/Row-_and_column-major_order>`_, we specify the dense matrix as follows:
-
-  .. code-block:: java
-
-    float[] data = new float[] {1f,2f,3f,4f,5f,6f};
-    int nrow = 3;
-    int ncol = 2;
-    float missing = 0.0f;
-    DMatrix dmat = new DMatrix(data, nrow, ncol, missing);
-
-* To set weight:
-
-  .. code-block:: java
-
-    float[] weights = new float[] {1f,2f,1f};
-    dmat.setWeight(weights);
-
-******************
-Setting Parameters
-******************
-To set parameters, parameters are specified as a Map:
-
-.. code-block:: java
-
-  Map<String, Object> params = new HashMap<String, Object>() {
-    {
-      put("eta", 1.0);
-      put("max_depth", 2);
-      put("objective", "binary:logistic");
-      put("eval_metric", "logloss");
-    }
-  };
-
-**************
-Training Model
-**************
-With parameters and data, you are able to train a booster model.
-
-* Import Booster and XGBoost:
-
-  .. code-block:: java
-
-    import ml.dmlc.xgboost4j.java.Booster;
-    import ml.dmlc.xgboost4j.java.XGBoost;
-
-* Training
-
-  .. code-block:: java
-
-    DMatrix trainMat = new DMatrix("train.svm.txt");
-    DMatrix validMat = new DMatrix("valid.svm.txt");
-    // Specify a watch list to see model accuracy on data sets
-    Map<String, DMatrix> watches = new HashMap<String, DMatrix>() {
-      {
-        put("train", trainMat);
-        put("test", testMat);
-      }
-    };
-    int nround = 2;
-    Booster booster = XGBoost.train(trainMat, params, nround, watches, null, null);
-
-* Saving model
-
-  After training, you can save model and dump it out.
-
-  .. code-block:: java
-
-    booster.saveModel("model.bin");
-
-* Generaing model dump with feature map
-
-  .. code-block:: java
-
-    // dump without feature map
-    String[] model_dump = booster.getModelDump(null, false);
-    // dump with feature map
-    String[] model_dump_with_feature_map = booster.getModelDump("featureMap.txt", false);
-
-* Load a model
-
-  .. code-block:: java
-
-    Booster booster = XGBoost.loadModel("model.bin");
-
-**********
-Prediction
-**********
-After training and loading a model, you can use it to make prediction for other data. The result will be a two-dimension float array ``(nsample, nclass)``; for ``predictLeaf()``, the result would be of shape ``(nsample, nclass*ntrees)``.
-
-.. code-block:: java
-
-  DMatrix dtest = new DMatrix("test.svm.txt");
-  // predict
-  float[][] predicts = booster.predict(dtest);
-  // predict leaf
-  float[][] leafPredicts = booster.predictLeaf(dtest, 0);
diff --git a/ml-xgboost/doc/jvm/javadocs/index.rst b/ml-xgboost/doc/jvm/javadocs/index.rst
deleted file mode 100644
index 33bf528..0000000
--- a/ml-xgboost/doc/jvm/javadocs/index.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-==================
-XGBoost4J Java API
-==================
diff --git a/ml-xgboost/doc/jvm/scaladocs/xgboost4j-flink/index.rst b/ml-xgboost/doc/jvm/scaladocs/xgboost4j-flink/index.rst
deleted file mode 100644
index fe35703..0000000
--- a/ml-xgboost/doc/jvm/scaladocs/xgboost4j-flink/index.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-=========================
-XGBoost4J-Flink Scala API
-=========================
diff --git a/ml-xgboost/doc/jvm/scaladocs/xgboost4j-spark/index.rst b/ml-xgboost/doc/jvm/scaladocs/xgboost4j-spark/index.rst
deleted file mode 100644
index c702f6e..0000000
--- a/ml-xgboost/doc/jvm/scaladocs/xgboost4j-spark/index.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-=========================
-XGBoost4J-Spark Scala API
-=========================
diff --git a/ml-xgboost/doc/jvm/scaladocs/xgboost4j/index.rst b/ml-xgboost/doc/jvm/scaladocs/xgboost4j/index.rst
deleted file mode 100644
index 542dbc4..0000000
--- a/ml-xgboost/doc/jvm/scaladocs/xgboost4j/index.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-===================
-XGBoost4J Scala API
-===================
diff --git a/ml-xgboost/doc/jvm/xgboost4j_spark_tutorial.rst b/ml-xgboost/doc/jvm/xgboost4j_spark_tutorial.rst
deleted file mode 100644
index 6781761..0000000
--- a/ml-xgboost/doc/jvm/xgboost4j_spark_tutorial.rst
+++ /dev/null
@@ -1,559 +0,0 @@
-#######################################
-XGBoost4J-Spark Tutorial (version 0.9+)
-#######################################
-
-**XGBoost4J-Spark** is a project aiming to seamlessly integrate XGBoost and Apache Spark by fitting XGBoost to Apache Spark's MLLIB framework. With the integration, user can not only uses the high-performant algorithm implementation of XGBoost, but also leverages the powerful  data processing engine of Spark for:
-
-* Feature Engineering: feature extraction, transformation, dimensionality reduction, and selection, etc.
-* Pipelines: constructing, evaluating, and tuning ML Pipelines
-* Persistence: persist and load machine learning models and even whole Pipelines
-
-This tutorial is to cover the end-to-end process to build a machine learning pipeline with XGBoost4J-Spark. We will discuss
-
-* Using Spark to preprocess data to fit to XGBoost/XGBoost4J-Spark's data interface
-* Training a XGBoost model with XGBoost4J-Spark
-* Serving XGBoost model (prediction) with Spark
-* Building a Machine Learning Pipeline with XGBoost4J-Spark
-* Running XGBoost4J-Spark in Production
-
-.. contents::
-  :backlinks: none
-  :local:
-
-********************************************
-Build an ML Application with XGBoost4J-Spark
-********************************************
-
-Refer to XGBoost4J-Spark Dependency
-===================================
-
-Before we go into the tour of how to use XGBoost4J-Spark, you should first consult :ref:`Installation from Maven repository <install_jvm_packages>` in order to add XGBoost4J-Spark as a dependency for your project. We provide both stable releases and snapshots.
-
-.. note:: XGBoost4J-Spark requires Apache Spark 2.4+
-
-  XGBoost4J-Spark now requires **Apache Spark 2.4+**. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
-
-  Also, make sure to install Spark directly from `Apache website <https://spark.apache.org/>`_. **Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark.** Consult appropriate third parties to obtain their distribution of XGBoost.
-
-Installation from maven repo
-
-.. note:: Use of Python in XGBoost4J-Spark
-
-  By default, we use the tracker in `dmlc-core <https://github.com/dmlc/dmlc-core/tree/master/tracker>`_ to drive the training with XGBoost4J-Spark. It requires Python 2.7+. We also have an experimental Scala version of tracker which can be enabled by passing the parameter ``tracker_conf`` as ``scala``.
-
-Data Preparation
-================
-
-As aforementioned, XGBoost4J-Spark seamlessly integrates Spark and XGBoost. The integration enables
-users to apply various types of transformation over the training/test datasets with the convenient
-and powerful data processing framework, Spark.
-
-In this section, we use `Iris <https://archive.ics.uci.edu/ml/datasets/iris>`_ dataset as an example to
-showcase how we use Spark to transform raw dataset and make it fit to the data interface of XGBoost.
-
-Iris dataset is shipped in CSV format. Each instance contains 4 features, "sepal length", "sepal width",
-"petal length" and "petal width". In addition, it contains the "class" columnm, which is essentially the label with three possible values: "Iris Setosa", "Iris Versicolour" and "Iris Virginica".
-
-Read Dataset with Spark's Built-In Reader
------------------------------------------
-
-The first thing in data transformation is to load the dataset as Spark's structured data abstraction, DataFrame.
-
-.. code-block:: scala
-
-  import org.apache.spark.sql.SparkSession
-  import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
-
-  val spark = SparkSession.builder().getOrCreate()
-  val schema = new StructType(Array(
-    StructField("sepal length", DoubleType, true),
-    StructField("sepal width", DoubleType, true),
-    StructField("petal length", DoubleType, true),
-    StructField("petal width", DoubleType, true),
-    StructField("class", StringType, true)))
-  val rawInput = spark.read.schema(schema).csv("input_path")
-
-At the first line, we create a instance of `SparkSession <http://spark.apache.org/docs/latest/sql-programming-guide.html#starting-point-sparksession>`_ which is the entry of any Spark program working with DataFrame. The ``schema`` variable defines the schema of DataFrame wrapping Iris data. With this explicitly set schema, we can define the columns' name as well as their types; otherwise the column name would be the default ones derived by Spark, such as ``_col0``, etc. Finally, we can use Spark's built-in csv reader to load Iris csv file as a DataFrame named ``rawInput``.
-
-Spark also contains many built-in readers for other format. The latest version of Spark supports CSV, JSON, Parquet, and LIBSVM.
-
-Transform Raw Iris Dataset
---------------------------
-
-To make Iris dataset be recognizable to XGBoost, we need to
-
-1. Transform String-typed label, i.e. "class", to Double-typed label.
-2. Assemble the feature columns as a vector to fit to the data interface of Spark ML framework.
-
-To convert String-typed label to Double, we can use Spark's built-in feature transformer `StringIndexer <https://spark.apache.org/docs/2.3.1/api/scala/index.html#org.apache.spark.ml.feature.StringIndexer>`_.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.feature.StringIndexer
-  val stringIndexer = new StringIndexer().
-    setInputCol("class").
-    setOutputCol("classIndex").
-    fit(rawInput)
-  val labelTransformed = stringIndexer.transform(rawInput).drop("class")
-
-With a newly created StringIndexer instance:
-
-1. we set input column, i.e. the column containing String-typed label
-2. we set output column, i.e. the column to contain the Double-typed label.
-3. Then we ``fit`` StringIndex with our input DataFrame ``rawInput``, so that Spark internals can get information like total number of distinct values, etc.
-
-Now we have a StringIndexer which is ready to be applied to our input DataFrame. To execute the transformation logic of StringIndexer, we ``transform`` the input DataFrame ``rawInput`` and to keep a concise DataFrame,
-we drop the column "class" and only keeps the feature columns and the transformed Double-typed label column (in the last line of the above code snippet).
-
-The ``fit`` and ``transform`` are two key operations in MLLIB. Basically, ``fit`` produces a "transformer", e.g. StringIndexer, and each transformer applies ``transform`` method on DataFrame to add new column(s) containing transformed features/labels or prediction results, etc. To understand more about ``fit`` and ``transform``, You can find more details in `here <http://spark.apache.org/docs/latest/ml-pipeline.html#pipeline-components>`_.
-
-Similarly, we can use another transformer, `VectorAssembler <https://spark.apache.org/docs/2.4.0/api/java/org/apache/spark/ml/feature/VectorAssembler.html>`_, to assemble feature columns "sepal length", "sepal width", "petal length" and "petal width" as a vector.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.feature.VectorAssembler
-  val vectorAssembler = new VectorAssembler().
-    setInputCols(Array("sepal length", "sepal width", "petal length", "petal width")).
-    setOutputCol("features")
-  val xgbInput = vectorAssembler.transform(labelTransformed).select("features", "classIndex")
-
-Now, we have a DataFrame containing only two columns, "features" which contains vector-represented
-"sepal length", "sepal width", "petal length" and "petal width" and "classIndex" which has Double-typed
-labels. A DataFrame like this (containing vector-represented features and numeric labels) can be fed to XGBoost4J-Spark's training engine directly.
-
-Dealing with missing values
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-XGBoost supports missing values by default (`as desribed here <https://xgboost.readthedocs.io/en/latest/faq.html#how-to-deal-with-missing-value>`_).
-If given a SparseVector, XGBoost will treat any values absent from the SparseVector as missing. You are also able to
-specify to XGBoost to treat a specific value in your Dataset as if it was a missing value. By default XGBoost will treat NaN as the value representing missing.
-
-Example of setting a missing value (e.g. -999) to the "missing" parameter in XGBoostClassifier:
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
-  val xgbParam = Map("eta" -> 0.1f,
-        "missing" -> -999,
-        "objective" -> "multi:softprob",
-        "num_class" -> 3,
-        "num_round" -> 100,
-        "num_workers" -> 2)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-        setFeaturesCol("features").
-        setLabelCol("classIndex")
-
-.. note:: Missing values with Spark's VectorAssembler
-
-  If given a Dataset with enough features having a value of 0 Spark's VectorAssembler transformer class will return a
-  SparseVector where the absent values are meant to indicate a value of 0. This conflicts with XGBoost's default to
-  treat values absent from the SparseVector as missing. The model would effectively be
-  treating 0 as missing but not declaring that to be so which can lead to confusion when using the trained model on
-  other platforms. To avoid this, XGBoost will raise an exception if it receives a SparseVector and the "missing"
-  parameter has not been explicitly set to 0. To workaround this issue the user has three options:
-
-  1. Explicitly convert the Vector returned from VectorAssembler to a DenseVector to return the zeros to the dataset. If
-  doing this with missing values encoded as NaN, you will want to set ``setHandleInvalid = "keep"`` on VectorAssembler
-  in order to keep the NaN values in the dataset. You would then set the "missing" parameter to whatever you want to be
-  treated as missing. However this may cause a large amount of memory use if your dataset is very sparse.
-
-  2. Before calling VectorAssembler you can transform the values you want to represent missing into an irregular value
-  that is not 0, NaN, or Null and set the "missing" parameter to 0. The irregular value should ideally be chosen to be
-  outside the range of values that your features have.
-
-  3. Do not use the VectorAssembler class and instead use a custom way of constructing a SparseVector that allows for
-  specifying sparsity to indicate a non-zero value. You can then set the "missing" parameter to whatever sparsity
-  indicates in your Dataset. If this approach is taken you can pass the parameter
-  ``"allow_non_zero_for_missing_value" -> true`` to bypass XGBoost's assertion that "missing" must be zero when given a
-  SparseVector.
-
-  Option 1 is recommended if memory constraints are not an issue. Option 3 requires more work to get set up but is
-  guaranteed to give you correct results while option 2 will be quicker to set up but may be difficult to find a good
-  irregular value that does not conflict with your feature values.
-
-.. note:: Using a non-default missing value when using other bindings of XGBoost.
-
-  When XGBoost is saved in native format only the booster itself is saved, the value of the missing parameter is not
-  saved alongside the model. Thus, if a non-default missing parameter is used to train the model in Spark the user should
-  take care to use the same missing parameter when using the saved model in another binding.
-
-Training
-========
-
-XGBoost supports both regression and classification. While we use Iris dataset in this tutorial to show how we use XGBoost/XGBoost4J-Spark to resolve a multi-classes classification problem, the usage in Regression is very similar to classification.
-
-To train a XGBoost model for classification, we need to claim a XGBoostClassifier first:
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
-  val xgbParam = Map("eta" -> 0.1f,
-        "max_depth" -> 2,
-        "objective" -> "multi:softprob",
-        "num_class" -> 3,
-        "num_round" -> 100,
-        "num_workers" -> 2)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-        setFeaturesCol("features").
-        setLabelCol("classIndex")
-
-The available parameters for training a XGBoost model can be found in :doc:`here </parameter>`. In XGBoost4J-Spark, we support not only the default set of parameters but also the camel-case variant of these parameters to keep consistent with Spark's MLLIB parameters.
-
-Specifically, each parameter in :doc:`this page </parameter>` has its
-equivalent form in XGBoost4J-Spark with camel case. For example, to set ``max_depth`` for each tree, you can pass parameter just like what we did in the above code snippet (as ``max_depth`` wrapped in a Map), or you can do it through setters in XGBoostClassifer:
-
-.. code-block:: scala
-
-  val xgbClassifier = new XGBoostClassifier().
-    setFeaturesCol("features").
-    setLabelCol("classIndex")
-  xgbClassifier.setMaxDepth(2)
-
-After we set XGBoostClassifier parameters and feature/label column, we can build a transformer, XGBoostClassificationModel by fitting XGBoostClassifier with the input DataFrame. This ``fit`` operation is essentially the training process and the generated model can then be used in prediction.
-
-.. code-block:: scala
-
-  val xgbClassificationModel = xgbClassifier.fit(xgbInput)
-
-Early Stopping
-----------------
-
-Early stopping is a feature to prevent the unnecessary training iterations. By specifying ``num_early_stopping_rounds`` or directly call ``setNumEarlyStoppingRounds`` over a XGBoostClassifier or XGBoostRegressor, we can define number of rounds if the evaluation metric going away from the best iteration and early stop training iterations.
-
-When it comes to custom eval metrics, in additional to ``num_early_stopping_rounds``, you also need to define ``maximize_evaluation_metrics`` or call ``setMaximizeEvaluationMetrics`` to specify whether you want to maximize or minimize the metrics in training. For built-in eval metrics, XGBoost4J-Spark will automatically select the direction.
-
-For example, we need to maximize the evaluation metrics (set ``maximize_evaluation_metrics`` with true), and set ``num_early_stopping_rounds`` with 5. The evaluation metric of 10th iteration is the maximum one until now. In the following iterations, if there is no evaluation metric greater than the 10th iteration's (best one), the traning would be early stopped at 15th iteration.
-
-Training with Evaluation Sets
------------------------------
-
-You can also monitor the performance of the model during training with multiple evaluation datasets. By specifying ``eval_sets`` or call ``setEvalSets`` over a XGBoostClassifier or XGBoostRegressor, you can pass in multiple evaluation datasets typed as a Map from String to DataFrame.
-
-Prediction
-==========
-
-XGBoost4j-Spark supports two ways for model serving: batch prediction and single instance prediction.
-
-Batch Prediction
-----------------
-
-When we get a model, either XGBoostClassificationModel or XGBoostRegressionModel, it takes a DataFrame, read the column containing feature vectors, predict for each feature vector, and output a new DataFrame with the following columns by default:
-
-* XGBoostClassificationModel will output margins (``rawPredictionCol``), probabilities(``probabilityCol``) and the eventual prediction labels (``predictionCol``) for each possible label.
-* XGBoostRegressionModel will output prediction label(``predictionCol``).
-
-Batch prediction expects the user to pass the testset in the form of a DataFrame. XGBoost4J-Spark starts a XGBoost worker for each partition of DataFrame for parallel prediction and generates prediction results for the whole DataFrame in a batch.
-
-.. code-block:: scala
-
-  val xgbClassificationModel = xgbClassifier.fit(xgbInput)
-  val results = xgbClassificationModel.transform(testSet)
-
-With the above code snippet, we get a result DataFrame, result containing margin, probability for each class and the prediction for each instance
-
-.. code-block:: none
-
-  +-----------------+----------+--------------------+--------------------+----------+
-  |         features|classIndex|       rawPrediction|         probability|prediction|
-  +-----------------+----------+--------------------+--------------------+----------+
-  |[5.1,3.5,1.4,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[4.9,3.0,1.4,0.2]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[4.7,3.2,1.3,0.2]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
-  |[4.6,3.1,1.5,0.2]|       0.0|[3.45569849014282...|[0.99636095762252...|       0.0|
-  |[5.0,3.6,1.4,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[5.4,3.9,1.7,0.4]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
-  |[4.6,3.4,1.4,0.3]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
-  |[5.0,3.4,1.5,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[4.4,2.9,1.4,0.2]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[4.9,3.1,1.5,0.1]|       0.0|[3.45569849014282...|[0.99636095762252...|       0.0|
-  |[5.4,3.7,1.5,0.2]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
-  |[4.8,3.4,1.6,0.2]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
-  |[4.8,3.0,1.4,0.1]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[4.3,3.0,1.1,0.1]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[5.8,4.0,1.2,0.2]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
-  |[5.7,4.4,1.5,0.4]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
-  |[5.4,3.9,1.3,0.4]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
-  |[5.1,3.5,1.4,0.3]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[5.7,3.8,1.7,0.3]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
-  |[5.1,3.8,1.5,0.3]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  +-----------------+----------+--------------------+--------------------+----------+
-
-Single instance prediction
---------------------------
-
-XGBoostClassificationModel or XGBoostRegressionModel support make prediction on single instance as well.
-It accepts a single Vector as feature, and output the prediction label.
-
-However, the overhead of single-instance prediction is high due to the internal overhead of XGBoost, use it carefully!
-
-.. code-block:: scala
-
-  val features = xgbInput.head().getAs[Vector]("features")
-  val result = xgbClassificationModel.predict(features)
-
-Model Persistence
-=================
-
-Model and pipeline persistence
-------------------------------
-
-A data scientist produces an ML model and hands it over to an engineering team for deployment in a production environment. Reversely, a trained model may be used by data scientists, for example as a baseline, across the process of data exploration. So it's important to support model persistence to make the models available across usage scenarios and programming languages.
-
-XGBoost4j-Spark supports saving and loading XGBoostClassifier/XGBoostClassificationModel and XGBoostRegressor/XGBoostRegressionModel. It also supports saving and loading a ML pipeline which includes these estimators and models.
-
-We can save the XGBoostClassificationModel to file system:
-
-.. code-block:: scala
-
-  val xgbClassificationModelPath = "/tmp/xgbClassificationModel"
-  xgbClassificationModel.write.overwrite().save(xgbClassificationModelPath)
-
-and then loading the model in another session:
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel
-
-  val xgbClassificationModel2 = XGBoostClassificationModel.load(xgbClassificationModelPath)
-  xgbClassificationModel2.transform(xgbInput)
-
-With regards to ML pipeline save and load, please refer the next section.
-
-Interact with Other Bindings of XGBoost
----------------------------------------
-After we train a model with XGBoost4j-Spark on massive dataset, sometimes we want to do model serving in single machine or integrate it with other single node libraries for further processing. XGBoost4j-Spark supports export model to local by:
-
-.. code-block:: scala
-
-  val nativeModelPath = "/tmp/nativeModel"
-  xgbClassificationModel.nativeBooster.saveModel(nativeModelPath)
-
-Then we can load this model with single node Python XGBoost:
-
-.. code-block:: python
-
-  import xgboost as xgb
-  bst = xgb.Booster({'nthread': 4})
-  bst.load_model(nativeModelPath)
-
-.. note:: Using HDFS and S3 for exporting the models with nativeBooster.saveModel()
-
-  When interacting with other language bindings, XGBoost also supports saving-models-to and loading-models-from file systems other than the local one. You can use HDFS and S3 by prefixing the path with ``hdfs://`` and ``s3://`` respectively. However, for this capability, you must do **one** of the following:
-
-  1. Build XGBoost4J-Spark with the steps described in `here <https://xgboost.readthedocs.io/en/latest/jvm/index.html#installation-from-source>`_, but turning `USE_HDFS <https://github.com/dmlc/xgboost/blob/e939192978a0c152ad7b49b744630e99d54cffa8/jvm-packages/create_jni.py#L18>`_ (or USE_S3, etc. in the same place) switch on. With this approach, you can reuse the above code example by replacing "nativeModelPath" with a HDFS path.
-
-     - However, if you build with USE_HDFS, etc. you have to ensure that the involved shared object file, e.g. libhdfs.so, is put in the LIBRARY_PATH of your cluster. To avoid the complicated cluster environment configuration, choose the other option.
-
-  2. Use bindings of HDFS, S3, etc. to pass model files around. Here are the steps (taking HDFS as an example):
-
-     - Create a new file with
-
-       .. code-block:: scala
-
-         val outputStream = fs.create("hdfs_path")
-
-       where "fs" is an instance of `org.apache.hadoop.fs.FileSystem <https://hadoop.apache.org/docs/stable/api/org/apache/hadoop/fs/FileSystem.html>`_ class in Hadoop.
-
-     - Pass the returned OutputStream in the first step to nativeBooster.saveModel():
-
-       .. code-block:: scala
-
-         xgbClassificationModel.nativeBooster.saveModel(outputStream)
-
-     - Download file in other languages from HDFS and load with the pre-built (without the requirement of libhdfs.so) version of XGBoost. (The function "download_from_hdfs" is a helper function to be implemented by the user)
-
-       .. code-block:: python
-
-         import xgboost as xgb
-         bst = xgb.Booster({'nthread': 4})
-         local_path = download_from_hdfs("hdfs_path")
-         bst.load_model(local_path)
-
-.. note:: Consistency issue between XGBoost4J-Spark and other bindings
-
-  There is a consistency issue between XGBoost4J-Spark and other language bindings of XGBoost.
-
-  When users use Spark to load training/test data in LIBSVM format with the following code snippet:
-
-  .. code-block:: scala
-
-    spark.read.format("libsvm").load("trainingset_libsvm")
-
-  Spark assumes that the dataset is using 1-based indexing (feature indices staring with 1). However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is using 0-based indexing (feature indices starting with 0) by default. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost. The solution is to transform the dataset to 0-based indexing before you predict with, for example, Python API, or you append ``?indexing_mode=1`` to your file path when loading with DMatirx. For example in Python:
-
-  .. code-block:: python
-
-    xgb.DMatrix('test.libsvm?indexing_mode=1')
-
-*******************************************
-Building a ML Pipeline with XGBoost4J-Spark
-*******************************************
-
-Basic ML Pipeline
-=================
-
-Spark ML pipeline can combine multiple algorithms or functions into a single pipeline.
-It covers from feature extraction, transformation, selection to model training and prediction.
-XGBoost4j-Spark makes it feasible to embed XGBoost into such a pipeline seamlessly.
-The following example shows how to build such a pipeline consisting of Spark MLlib feature transformer
-and XGBoostClassifier estimator.
-
-We still use `Iris <https://archive.ics.uci.edu/ml/datasets/iris>`_ dataset and the ``rawInput`` DataFrame.
-First we need to split the dataset into training and test dataset.
-
-.. code-block:: scala
-
-  val Array(training, test) = rawInput.randomSplit(Array(0.8, 0.2), 123)
-
-The we build the ML pipeline which includes 4 stages:
-
-* Assemble all features into a single vector column.
-* From string label to indexed double label.
-* Use XGBoostClassifier to train classification model.
-* Convert indexed double label back to original string label.
-
-We have shown the first three steps in the earlier sections, and the last step is finished with a new transformer `IndexToString <https://spark.apache.org/docs/2.3.1/api/scala/index.html#org.apache.spark.ml.feature.IndexToString>`_:
-
-.. code-block:: scala
-
-	val labelConverter = new IndexToString()
-        .setInputCol("prediction")
-        .setOutputCol("realLabel")
-        .setLabels(stringIndexer.labels)
-
-We need to organize these steps as a Pipeline in Spark ML framework and evaluate the whole pipeline to get a PipelineModel:
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.feature._
-  import org.apache.spark.ml.Pipeline
-
-  val pipeline = new Pipeline()
-      .setStages(Array(assembler, stringIndexer, booster, labelConverter))
-  val model = pipeline.fit(training)
-
-After we get the PipelineModel, we can make prediction on the test dataset and evaluate the model accuracy.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
-  val prediction = model.transform(test)
-  val evaluator = new MulticlassClassificationEvaluator()
-  val accuracy = evaluator.evaluate(prediction)
-
-Pipeline with Hyper-parameter Tunning
-=====================================
-The most critical operation to maximize the power of XGBoost is to select the optimal parameters for the model. Tuning parameters manually is a tedious and labor-consuming process. With the latest version of XGBoost4J-Spark, we can utilize the Spark model selecting tool to automate this process.
-
-The following example shows the code snippet utilizing CrossValidation and MulticlassClassificationEvaluator
-to search the optimal combination of two XGBoost parameters, ``max_depth`` and ``eta``. (See :doc:`/parameter`.)
-The model producing the maximum accuracy defined by MulticlassClassificationEvaluator is selected and used to generate the prediction for the test set.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.tuning._
-  import org.apache.spark.ml.PipelineModel
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel
-
-  val paramGrid = new ParamGridBuilder()
-      .addGrid(booster.maxDepth, Array(3, 8))
-      .addGrid(booster.eta, Array(0.2, 0.6))
-      .build()
-  val cv = new CrossValidator()
-      .setEstimator(pipeline)
-      .setEvaluator(evaluator)
-      .setEstimatorParamMaps(paramGrid)
-      .setNumFolds(3)
-
-  val cvModel = cv.fit(training)
-
-  val bestModel = cvModel.bestModel.asInstanceOf[PipelineModel].stages(2)
-      .asInstanceOf[XGBoostClassificationModel]
-  bestModel.extractParamMap()
-
-*********************************
-Run XGBoost4J-Spark in Production
-*********************************
-
-XGBoost4J-Spark is one of the most important steps to bring XGBoost to production environment easier. In this section, we introduce three key features to run XGBoost4J-Spark in production.
-
-Parallel/Distributed Training
-=============================
-The massive size of training dataset is one of the most significant characteristics in production environment. To ensure that training in XGBoost scales with the data size, XGBoost4J-Spark bridges the distributed/parallel processing framework of Spark and the parallel/distributed training mechanism of XGBoost.
-
-In XGBoost4J-Spark, each XGBoost worker is wrapped by a Spark task and the training dataset in Spark's memory space is fed to XGBoost workers in a transparent approach to the user.
-
-In the code snippet where we build XGBoostClassifier, we set parameter ``num_workers`` (or ``numWorkers``).
-This parameter controls how many parallel workers we want to have when training a XGBoostClassificationModel.
-
-.. note:: Regarding OpenMP optimization
-
-  By default, we allocate a core per each XGBoost worker. Therefore, the OpenMP optimization within each XGBoost worker does not take effect and the parallelization of training is achieved
-  by running multiple workers (i.e. Spark tasks) at the same time.
-
-  If you do want OpenMP optimization, you have to
-
-  1. set ``nthread`` to a value larger than 1 when creating XGBoostClassifier/XGBoostRegressor
-  2. set ``spark.task.cpus`` in Spark to the same value as ``nthread``
-
-Gang Scheduling
-===============
-XGBoost uses `AllReduce <http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/>`_.
-algorithm to synchronize the stats, e.g. histogram values, of each worker during training. Therefore XGBoost4J-Spark requires that all of ``nthread * numWorkers`` cores should be available before the training runs.
-
-In the production environment where many users share the same cluster, it's hard to guarantee that your XGBoost4J-Spark application can get all requested resources for every run. By default, the communication layer in XGBoost will block the whole application when it requires more resources to be available. This process usually brings unnecessary resource waste as it keeps the ready resources and try to claim more. Additionally, this usually happens silently and does not bring the attention of users.
-
-XGBoost4J-Spark allows the user to setup a timeout threshold for claiming resources from the cluster. If the application cannot get enough resources within this time period, the application would fail instead of wasting resources for hanging long. To enable this feature, you can set with XGBoostClassifier/XGBoostRegressor:
-
-.. code-block:: scala
-
-  xgbClassifier.setTimeoutRequestWorkers(60000L)
-
-or pass in ``timeout_request_workers`` in ``xgbParamMap`` when building XGBoostClassifier:
-
-.. code-block:: scala
-
-  val xgbParam = Map("eta" -> 0.1f,
-     "max_depth" -> 2,
-     "objective" -> "multi:softprob",
-     "num_class" -> 3,
-     "num_round" -> 100,
-     "num_workers" -> 2,
-     "timeout_request_workers" -> 60000L)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-      setFeaturesCol("features").
-      setLabelCol("classIndex")
-
-If XGBoost4J-Spark cannot get enough resources for running two XGBoost workers, the application would fail. Users can have external mechanism to monitor the status of application and get notified for such case.
-
-Checkpoint During Training
-==========================
-
-Transient failures are also commonly seen in production environment. To simplify the design of XGBoost,
-we stop training if any of the distributed workers fail. However, if the training fails after having been through a long time, it would be a great waste of resources.
-
-We support creating checkpoint during training to facilitate more efficient recovery from failture. To enable this feature, you can set how many iterations we build each checkpoint with ``setCheckpointInterval`` and the location of checkpoints with ``setCheckpointPath``:
-
-.. code-block:: scala
-
-  xgbClassifier.setCheckpointInterval(2)
-  xgbClassifier.setCheckpointPath("/checkpoint_path")
-
-An equivalent way is to pass in parameters in XGBoostClassifier's constructor:
-
-.. code-block:: scala
-
-  val xgbParam = Map("eta" -> 0.1f,
-     "max_depth" -> 2,
-     "objective" -> "multi:softprob",
-     "num_class" -> 3,
-     "num_round" -> 100,
-     "num_workers" -> 2,
-     "checkpoint_path" -> "/checkpoints_path",
-     "checkpoint_interval" -> 2)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-      setFeaturesCol("features").
-      setLabelCol("classIndex")
-
-If the training failed during these 100 rounds, the next run of training would start by reading the latest checkpoint file in ``/checkpoints_path`` and start from the iteration when the checkpoint was built until to next failure or the specified 100 rounds.
diff --git a/ml-xgboost/doc/model.schema b/ml-xgboost/doc/model.schema
deleted file mode 100644
index 322e610..0000000
--- a/ml-xgboost/doc/model.schema
+++ /dev/null
@@ -1,446 +0,0 @@
-{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "definitions": {
-    "gbtree": {
-      "type": "object",
-      "properties": {
-        "name": {
-          "const": "gbtree"
-        },
-        "model": {
-          "type": "object",
-          "properties": {
-            "gbtree_model_param": {
-              "$ref": "#/definitions/gbtree_model_param"
-            },
-            "trees": {
-              "type": "array",
-              "items": {
-                "type": "object",
-                "properties": {
-                  "tree_param": {
-                    "type": "object",
-                    "properties": {
-                      "num_nodes": {
-                        "type": "string"
-                      },
-                      "size_leaf_vector": {
-                        "type": "string"
-                      },
-                      "num_feature": {
-                        "type": "string"
-                      }
-                    },
-                    "required": [
-                      "num_nodes",
-                      "num_feature",
-                      "size_leaf_vector"
-                    ]
-                  },
-                  "id": {
-                    "type": "integer"
-                  },
-                  "loss_changes": {
-                    "type": "array",
-                    "items": {
-                      "type": "number"
-                    }
-                  },
-                  "sum_hessian": {
-                    "type": "array",
-                    "items": {
-                      "type": "number"
-                    }
-                  },
-                  "base_weights": {
-                    "type": "array",
-                    "items": {
-                      "type": "number"
-                    }
-                  },
-                  "leaf_child_counts": {
-                    "type": "array",
-                    "items": {
-                      "type": "integer"
-                    }
-                  },
-                  "left_children": {
-                    "type": "array",
-                    "items": {
-                      "type": "integer"
-                    }
-                  },
-                  "right_children": {
-                    "type": "array",
-                    "items": {
-                      "type": "integer"
-                    }
-                  },
-                  "parents": {
-                    "type": "array",
-                    "items": {
-                      "type": "integer"
-                    }
-                  },
-                  "split_indices": {
-                    "type": "array",
-                    "items": {
-                      "type": "integer"
-                    }
-                  },
-                  "split_conditions": {
-                    "type": "array",
-                    "items": {
-                      "type": "number"
-                    }
-                  },
-                  "default_left": {
-                    "type": "array",
-                    "items": {
-                      "type": "boolean"
-                    }
-                  }
-                },
-                "required": [
-                  "tree_param",
-                  "loss_changes",
-                  "sum_hessian",
-                  "base_weights",
-                  "leaf_child_counts",
-                  "left_children",
-                  "right_children",
-                  "parents",
-                  "split_indices",
-                  "split_conditions",
-                  "default_left"
-                ]
-              }
-            },
-            "tree_info": {
-              "type": "array",
-              "items": {
-                "type": "integer"
-              }
-            }
-          },
-          "required": [
-            "gbtree_model_param",
-            "trees",
-            "tree_info"
-          ]
-        }
-      },
-      "required": [
-        "name",
-        "model"
-      ]
-    },
-    "gbtree_model_param": {
-      "type": "object",
-      "properties": {
-        "num_trees": {
-          "type": "string"
-        },
-        "size_leaf_vector": {
-          "type": "string"
-        }
-      },
-      "required": [
-        "num_trees",
-        "size_leaf_vector"
-      ]
-    },
-    "tree_param": {
-      "type": "object",
-      "properties": {
-        "num_nodes": {
-          "type": "string"
-        },
-        "size_leaf_vector": {
-          "type": "string"
-        },
-        "num_feature": {
-          "type": "string"
-        }
-      },
-      "required": [
-        "num_nodes",
-        "num_feature",
-        "size_leaf_vector"
-      ]
-    },
-    "reg_loss_param": {
-      "type": "object",
-      "properties": {
-        "scale_pos_weight": {
-          "type": "string"
-        }
-      }
-    },
-    "softmax_multiclass_param": {
-      "type": "object",
-      "properties": {
-        "num_class": { "type": "string" }
-      }
-    },
-    "lambda_rank_param": {
-      "type": "object",
-      "properties": {
-        "num_pairsample": { "type": "string" },
-        "fix_list_weight": { "type": "string" }
-      }
-    }
-  },
-  "type": "object",
-  "properties": {
-    "version": {
-      "type": "array",
-      "items": [
-        {
-          "type": "number",
-          "const": 1
-        },
-        {
-          "type": "number",
-          "minimum": 0
-        },
-        {
-          "type": "number",
-          "minimum": 0
-        }
-      ],
-      "minItems": 3,
-      "maxItems": 3
-    },
-    "learner": {
-      "type": "object",
-      "properties": {
-        "gradient_booster": {
-          "oneOf": [
-            {
-              "$ref": "#/definitions/gbtree"
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "gblinear" },
-                "model": {
-                  "type": "object",
-                  "properties": {
-                    "weights": {
-                      "type": "array",
-                      "items": {
-                        "type": "number"
-                      }
-                    }
-                  }
-                }
-              }
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "dart" },
-                "gbtree": {
-                  "$ref": "#/definitions/gbtree"
-                },
-                "weight_drop": {
-                  "type": "array",
-                  "items": {
-                    "type": "number"
-                  }
-                }
-              },
-              "required": [
-                "name",
-                "gbtree",
-                "weight_drop"
-              ]
-            }
-          ]
-        },
-
-        "objective": {
-          "oneOf": [
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "reg:squarederror" },
-                "reg_loss_param": { "$ref": "#/definitions/reg_loss_param"}
-              },
-              "required": [
-                "name",
-                "reg_loss_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "reg:squaredlogerror" },
-                "reg_loss_param": { "$ref": "#/definitions/reg_loss_param"}
-              },
-              "required": [
-                "name",
-                "reg_loss_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "reg:logistic" },
-                "reg_loss_param": { "$ref": "#/definitions/reg_loss_param"}
-              },
-              "required": [
-                "name",
-                "reg_loss_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "binary:logistic" },
-                "reg_loss_param": { "$ref": "#/definitions/reg_loss_param"}
-              },
-              "required": [
-                "name",
-                "reg_loss_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "binary:logitraw" },
-                "reg_loss_param": { "$ref": "#/definitions/reg_loss_param"}
-              },
-              "required": [
-                "name",
-                "reg_loss_param"
-              ]
-            },
-
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "count:poisson" },
-                "poisson_regression_param": {
-                  "type": "object",
-                  "properties": {
-                    "max_delta_step": { "type": "string" }
-                  }
-                }
-              },
-              "required": [
-                "name",
-                "poisson_regression_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "reg:tweedie" },
-                "tweedie_regression_param": {
-                  "type": "object",
-                  "properties": {
-                    "tweedie_variance_power": { "type": "string" }
-                  }
-                }
-              },
-              "required": [
-                "name",
-                "tweedie_regression_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "survival:cox" }
-              },
-              "required": [ "name" ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "reg:gamma" }
-              },
-              "required": [ "name" ]
-            },
-
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "multi:softprob" },
-                "softmax_multiclass_param": { "$ref": "#/definitions/softmax_multiclass_param"}
-              },
-              "required": [
-                "name",
-                "softmax_multiclass_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "multi:softmax" },
-                "softmax_multiclass_param": { "$ref": "#/definitions/softmax_multiclass_param"}
-              },
-              "required": [
-                "name",
-                "softmax_multiclass_param"
-              ]
-            },
-
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "rank:pairwise" },
-                "lambda_rank_param": { "$ref": "#/definitions/lambda_rank_param"}
-              },
-              "required": [
-                "name",
-                "lambda_rank_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "rank:ndcg" },
-                "lambda_rank_param": { "$ref": "#/definitions/lambda_rank_param"}
-              },
-              "required": [
-                "name",
-                "lambda_rank_param"
-              ]
-            },
-            {
-              "type": "object",
-              "properties": {
-                "name": { "const": "rank:map" },
-                "lambda_rank_param": { "$ref": "#/definitions/lambda_rank_param"}
-              },
-              "required": [
-                "name",
-                "lambda_rank_param"
-              ]
-            }
-          ]
-        },
-
-        "learner_model_param": {
-          "type": "object",
-          "properties": {
-            "base_score": { "type": "string" },
-            "num_class": { "type": "string" },
-            "num_feature": { "type": "string" }
-          }
-        }
-      },
-      "required": [
-        "gradient_booster",
-        "objective"
-      ]
-    }
-  },
-  "required": [
-    "version",
-    "learner"
-  ]
-}
diff --git a/ml-xgboost/doc/parameter.rst b/ml-xgboost/doc/parameter.rst
deleted file mode 100644
index 599ef7f..0000000
--- a/ml-xgboost/doc/parameter.rst
+++ /dev/null
@@ -1,461 +0,0 @@
-##################
-XGBoost Parameters
-##################
-Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters.
-
-- **General parameters** relate to which booster we are using to do boosting, commonly tree or linear model
-- **Booster parameters** depend on which booster you have chosen
-- **Learning task parameters** decide on the learning scenario. For example, regression tasks may use different parameters with ranking tasks.
-- **Command line parameters** relate to behavior of CLI version of XGBoost.
-
-.. note:: Parameters in R package
-
-  In R-package, you can use ``.`` (dot) to replace underscore in the parameters, for example, you can use ``max.depth`` to indicate ``max_depth``. The underscore parameters are also valid in R.
-
-.. contents::
-  :backlinks: none
-  :local:
-
-******************
-General Parameters
-******************
-* ``booster`` [default= ``gbtree`` ]
-
-  - Which booster to use. Can be ``gbtree``, ``gblinear`` or ``dart``; ``gbtree`` and ``dart`` use tree based models while ``gblinear`` uses linear functions.
-
-* ``verbosity`` [default=1]
-
-  - Verbosity of printing messages.  Valid values are 0 (silent), 1 (warning), 2 (info), 3
-    (debug).  Sometimes XGBoost tries to change configurations based on heuristics, which
-    is displayed as warning message.  If there's unexpected behaviour, please try to
-    increase value of verbosity.
-
-* ``validate_parameters`` [default to false, except for Python and R interface]
-
-  - When set to True, XGBoost will perform validation of input parameters to check whether
-    a parameter is used or not.  The feature is still experimental.  It's expected to have
-    some false positives, especially when used with Scikit-Learn interface.
-
-* ``nthread`` [default to maximum number of threads available if not set]
-
-  - Number of parallel threads used to run XGBoost
-
-* ``disable_default_eval_metric`` [default=0]
-
-  - Flag to disable default metric. Set to >0 to disable.
-
-* ``num_pbuffer`` [set automatically by XGBoost, no need to be set by user]
-
-  - Size of prediction buffer, normally set to number of training instances. The buffers are used to save the prediction results of last boosting step.
-
-* ``num_feature`` [set automatically by XGBoost, no need to be set by user]
-
-  - Feature dimension used in boosting, set to maximum dimension of the feature
-
-Parameters for Tree Booster
-===========================
-* ``eta`` [default=0.3, alias: ``learning_rate``]
-
-  - Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and ``eta`` shrinks the feature weights to make the boosting process more conservative.
-  - range: [0,1]
-
-* ``gamma`` [default=0, alias: ``min_split_loss``]
-
-  - Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger ``gamma`` is, the more conservative the algorithm will be.
-  - range: [0,∞]
-
-* ``max_depth`` [default=6]
-
-  - Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in ``lossguided`` growing policy when tree_method is set as ``hist`` and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.
-  - range: [0,∞] (0 is only accepted in ``lossguided`` growing policy when tree_method is set as ``hist``)
-
-* ``min_child_weight`` [default=1]
-
-  - Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than ``min_child_weight``, then the building process will give up further partitioning. In linear regression task, this simply corresponds to minimum number of instances needed to be in each node. The larger ``min_child_weight`` is, the more conservative the algorithm will be.
-  - range: [0,∞]
-
-* ``max_delta_step`` [default=0]
-
-  - Maximum delta step we allow each leaf output to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced. Set it to value of 1-10 might help control the update.
-  - range: [0,∞]
-
-* ``subsample`` [default=1]
-
-  - Subsample ratio of the training instances. Setting it to 0.5 means that XGBoost would randomly sample half of the training data prior to growing trees. and this will prevent overfitting. Subsampling will occur once in every boosting iteration.
-  - range: (0,1]
-
-* ``sampling_method`` [default= ``uniform``]
-
-  - The method to use to sample the training instances.
-  - ``uniform``: each training instance has an equal probability of being selected. Typically set
-    ``subsample`` >= 0.5 for good results.
-  - ``gradient_based``: the selection probability for each training instance is proportional to the
-    *regularized absolute value* of gradients (more specifically, :math:`\sqrt{g^2+\lambda h^2}`).
-    ``subsample`` may be set to as low as 0.1 without loss of model accuracy. Note that this
-    sampling method is only supported when ``tree_method`` is set to ``gpu_hist``; other tree
-    methods only support ``uniform`` sampling.
-
-* ``colsample_bytree``, ``colsample_bylevel``, ``colsample_bynode`` [default=1]
-
-  - This is a family of parameters for subsampling of columns.
-  - All ``colsample_by*`` parameters have a range of (0, 1], the default value of 1, and specify the fraction of columns to be subsampled.
-  - ``colsample_bytree`` is the subsample ratio of columns when constructing each tree. Subsampling occurs once for every tree constructed.
-  - ``colsample_bylevel`` is the subsample ratio of columns for each level. Subsampling occurs once for every new depth level reached in a tree. Columns are subsampled from the set of columns chosen for the current tree.
-  - ``colsample_bynode`` is the subsample ratio of columns for each node (split). Subsampling occurs once every time a new split is evaluated. Columns are subsampled from the set of columns chosen for the current level.
-  - ``colsample_by*`` parameters work cumulatively. For instance,
-    the combination ``{'colsample_bytree':0.5, 'colsample_bylevel':0.5,
-    'colsample_bynode':0.5}`` with 64 features will leave 8 features to choose from at
-    each split.
-
-* ``lambda`` [default=1, alias: ``reg_lambda``]
-
-  - L2 regularization term on weights. Increasing this value will make model more conservative.
-
-* ``alpha`` [default=0, alias: ``reg_alpha``]
-
-  - L1 regularization term on weights. Increasing this value will make model more conservative.
-
-* ``tree_method`` string [default= ``auto``]
-
-  - The tree construction algorithm used in XGBoost. See description in the `reference paper <http://arxiv.org/abs/1603.02754>`_.
-  - XGBoost supports  ``approx``, ``hist`` and ``gpu_hist`` for distributed training.  Experimental support for external memory is available for ``approx`` and ``gpu_hist``.
-
-  - Choices: ``auto``, ``exact``, ``approx``, ``hist``, ``gpu_hist``, this is a
-    combination of commonly used updaters.  For other updaters like ``refresh``, set the
-    parameter ``updater`` directly.
-
-    - ``auto``: Use heuristic to choose the fastest method.
-
-      - For small dataset, exact greedy (``exact``) will be used.
-      - For larger dataset, approximate algorithm (``approx``) will be chosen.  It's
-        recommended to try ``hist`` and ``gpu_hist`` for higher performance with large
-        dataset.
-        (``gpu_hist``)has support for ``external memory``.
-
-      - Because old behavior is always use exact greedy in single machine, user will get a
-        message when approximate algorithm is chosen to notify this choice.
-    - ``exact``: Exact greedy algorithm.  Enumerates all split candidates.
-    - ``approx``: Approximate greedy algorithm using quantile sketch and gradient histogram.
-    - ``hist``: Faster histogram optimized approximate greedy algorithm.
-    - ``gpu_hist``: GPU implementation of ``hist`` algorithm.
-
-* ``sketch_eps`` [default=0.03]
-
-  - Only used for ``tree_method=approx``.
-  - This roughly translates into ``O(1 / sketch_eps)`` number of bins.
-    Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy.
-  - Usually user does not have to tune this.
-    But consider setting to a lower number for more accurate enumeration of split candidates.
-  - range: (0, 1)
-
-* ``scale_pos_weight`` [default=1]
-
-  - Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: ``sum(negative instances) / sum(positive instances)``. See :doc:`Parameters Tuning </tutorials/param_tuning>` for more discussion. Also, see Higgs Kaggle competition demo for examples: `R <https://github.com/dmlc/xgboost/blob/master/demo/kaggle-higgs/higgs-train.R>`_, `py1 <https://github.com/dmlc/xgboost/blob/master/demo/kaggle-higgs/higgs-numpy.py>`_, `py2 <https://github.com/dmlc/xgboost/blob/master/demo/kaggle-higgs/higgs-cv.py>`_, `py3 <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py>`_.
-
-* ``updater`` [default= ``grow_colmaker,prune``]
-
-  - A comma separated string defining the sequence of tree updaters to run, providing a modular way to construct and to modify the trees. This is an advanced parameter that is usually set automatically, depending on some other parameters. However, it could be also set explicitly by a user. The following updaters exist:
-
-    - ``grow_colmaker``: non-distributed column-based construction of trees.
-    - ``grow_histmaker``: distributed tree construction with row-based data splitting based on global proposal of histogram counting.
-    - ``grow_local_histmaker``: based on local histogram counting.
-    - ``grow_skmaker``: uses the approximate sketching algorithm.
-    - ``grow_quantile_histmaker``: Grow tree using quantized histogram.
-    - ``grow_gpu_hist``: Grow tree with GPU.
-    - ``sync``: synchronizes trees in all distributed nodes.
-    - ``refresh``: refreshes tree's statistics and/or leaf values based on the current data. Note that no random subsampling of data rows is performed.
-    - ``prune``: prunes the splits where loss < min_split_loss (or gamma).
-
-  - In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune`` by default, and you can set ``tree_method`` as ``hist`` to use ``grow_histmaker``.
-
-* ``refresh_leaf`` [default=1]
-
-  - This is a parameter of the ``refresh`` updater. When this flag is 1, tree leafs as well as tree nodes' stats are updated. When it is 0, only node stats are updated.
-
-* ``process_type`` [default= ``default``]
-
-  - A type of boosting process to run.
-  - Choices: ``default``, ``update``
-
-    - ``default``: The normal boosting process which creates new trees.
-    - ``update``: Starts from an existing model and only updates its trees. In each boosting iteration, a tree from the initial model is taken, a specified sequence of updaters is run for that tree, and a modified tree is added to the new model. The new model would have either the same or smaller number of trees, depending on the number of boosting iteratons performed. Currently, the following built-in updaters could be meaningfully used with this process type: ``refresh``, ``prune``. With ``process_type=update``, one cannot use updaters that create new trees.
-
-* ``grow_policy`` [default= ``depthwise``]
-
-  - Controls a way new nodes are added to the tree.
-  - Currently supported only if ``tree_method`` is set to ``hist``.
-  - Choices: ``depthwise``, ``lossguide``, ``depthwiselossltd``
-
-    - ``depthwise``: split at nodes closest to the root.
-    - ``lossguide``: split at nodes with highest loss change.
-    - ``depthwiselossltd``: split at nodes closest to the root, stop splitting nodes with low loss improvement.
-
-* ``max_leaves`` [default=0]
-
-  - Maximum number of nodes to be added. Only relevant when ``grow_policy=lossguide`` is set.
-
-* ``max_bin``, [default=256]
-
-  - Only used if ``tree_method`` is set to ``hist``.
-  - Maximum number of discrete bins to bucket continuous features.
-  - Increasing this number improves the optimality of splits at the cost of higher computation time.
-
-* ``predictor``, [default=``auto``]
-
-  - The type of predictor algorithm to use. Provides the same results but allows the use of GPU or CPU.
-
-    - ``auto``: Configure predictor based on heuristics.
-    - ``cpu_predictor``: Multicore CPU prediction algorithm.
-    - ``gpu_predictor``: Prediction using GPU.  Used when ``tree_method`` is ``gpu_hist``.
-      When ``predictor`` is set to default value ``auto``, the ``gpu_hist`` tree method is
-      able to provide GPU based prediction without copying training data to GPU memory.
-      If ``gpu_predictor`` is explicitly specified, then all data is copied into GPU, only
-      recommended for performing prediction tasks.
-
-* ``num_parallel_tree``, [default=1]
-  - Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.
-
-* ``monotone_constraints``
-
-  - Constraint of variable monotonicity.  See tutorial for more information.
-
-* ``interaction_constraints``
-
-  - Constraints for interaction representing permitted interactions.  The constraints must
-    be specified in the form of a nest list, e.g. ``[[0, 1], [2, 3, 4]]``, where each inner
-    list is a group of indices of features that are allowed to interact with each other.
-    See tutorial for more information
-
-Additional parameters for `gpu_hist` tree method
-================================================
-
-* ``single_precision_histogram``, [default=``false``]
-
-  - Use single precision to build histograms.  See document for GPU support for more details.
-
-* ``deterministic_histogram``, [default=``true``]
-
-  - Build histogram on GPU deterministically.  Histogram building is not deterministic due
-    to the non-associative aspect of floating point summation.  We employ a pre-rounding
-    routine to mitigate the issue, which may lead to slightly lower accuracy.  Set to
-    ``false`` to disable it.
-
-Additional parameters for Dart Booster (``booster=dart``)
-=========================================================
-
-.. note:: Using ``predict()`` with DART booster
-
-  If the booster object is DART type, ``predict()`` will perform dropouts, i.e. only
-  some of the trees will be evaluated. This will produce incorrect results if ``data`` is
-  not the training data. To obtain correct results on test sets, set ``ntree_limit`` to
-  a nonzero value, e.g.
-
-  .. code-block:: python
-
-    preds = bst.predict(dtest, ntree_limit=num_round)
-
-* ``sample_type`` [default= ``uniform``]
-
-  - Type of sampling algorithm.
-
-    - ``uniform``: dropped trees are selected uniformly.
-    - ``weighted``: dropped trees are selected in proportion to weight.
-
-* ``normalize_type`` [default= ``tree``]
-
-  - Type of normalization algorithm.
-
-    - ``tree``: new trees have the same weight of each of dropped trees.
-
-      - Weight of new trees are ``1 / (k + learning_rate)``.
-      - Dropped trees are scaled by a factor of ``k / (k + learning_rate)``.
-
-    - ``forest``: new trees have the same weight of sum of dropped trees (forest).
-
-      - Weight of new trees are ``1 / (1 + learning_rate)``.
-      - Dropped trees are scaled by a factor of ``1 / (1 + learning_rate)``.
-
-* ``rate_drop`` [default=0.0]
-
-  - Dropout rate (a fraction of previous trees to drop during the dropout).
-  - range: [0.0, 1.0]
-
-* ``one_drop`` [default=0]
-
-  - When this flag is enabled, at least one tree is always dropped during the dropout (allows Binomial-plus-one or epsilon-dropout from the original DART paper).
-
-* ``skip_drop`` [default=0.0]
-
-  - Probability of skipping the dropout procedure during a boosting iteration.
-
-    - If a dropout is skipped, new trees are added in the same manner as ``gbtree``.
-    - Note that non-zero ``skip_drop`` has higher priority than ``rate_drop`` or ``one_drop``.
-
-  - range: [0.0, 1.0]
-
-Parameters for Linear Booster (``booster=gblinear``)
-====================================================
-* ``lambda`` [default=0, alias: ``reg_lambda``]
-
-  - L2 regularization term on weights. Increasing this value will make model more conservative. Normalised to number of training examples.
-
-* ``alpha`` [default=0, alias: ``reg_alpha``]
-
-  - L1 regularization term on weights. Increasing this value will make model more conservative. Normalised to number of training examples.
-
-* ``updater`` [default= ``shotgun``]
-
-  - Choice of algorithm to fit linear model
-
-    - ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run.
-    - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution.
-
-* ``feature_selector`` [default= ``cyclic``]
-
-  - Feature selection and ordering method
-
-    * ``cyclic``: Deterministic selection by cycling through features one at a time.
-    * ``shuffle``: Similar to ``cyclic`` but with random feature shuffling prior to each update.
-    * ``random``: A random (with replacement) coordinate selector.
-    * ``greedy``: Select coordinate with the greatest gradient magnitude.  It has ``O(num_feature^2)`` complexity. It is fully deterministic. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter. Doing so would reduce the complexity to ``O(num_feature*top_k)``.
-    * ``thrifty``: Thrifty, approximately-greedy feature selector. Prior to cyclic updates, reorders features in descending magnitude of their univariate weight changes. This operation is multithreaded and is a linear complexity approximation of the quadratic greedy selection. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter.
-
-* ``top_k`` [default=0]
-
-  - The number of top features to select in ``greedy`` and ``thrifty`` feature selector. The value of 0 means using all the features.
-
-Parameters for Tweedie Regression (``objective=reg:tweedie``)
-=============================================================
-* ``tweedie_variance_power`` [default=1.5]
-
-  - Parameter that controls the variance of the Tweedie distribution ``var(y) ~ E(y)^tweedie_variance_power``
-  - range: (1,2)
-  - Set closer to 2 to shift towards a gamma distribution
-  - Set closer to 1 to shift towards a Poisson distribution.
-
-************************
-Learning Task Parameters
-************************
-Specify the learning task and the corresponding learning objective. The objective options are below:
-
-* ``objective`` [default=reg:squarederror]
-
-  - ``reg:squarederror``: regression with squared loss.
-  - ``reg:squaredlogerror``: regression with squared log loss :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`.  All input labels are required to be greater than -1.  Also, see metric ``rmsle`` for possible issue  with this objective.
-  - ``reg:logistic``: logistic regression
-  - ``binary:logistic``: logistic regression for binary classification, output probability
-  - ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
-  - ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
-  - ``count:poisson`` --poisson regression for count data, output mean of poisson distribution
-
-    - ``max_delta_step`` is set to 0.7 by default in poisson regression (used to safeguard optimization)
-
-  - ``survival:cox``: Cox regression for right censored survival time data (negative values are considered right censored).
-    Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function ``h(t) = h0(t) * HR``).
-  - ``survival:aft``: Accelerated failure time model for censored survival time data.
-    See :doc:`/tutorials/aft_survival_analysis` for details.
-  - ``aft_loss_distribution``: Probabilty Density Function used by ``survival:aft`` and ``aft-nloglik`` metric.
-  - ``multi:softmax``: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)
-  - ``multi:softprob``: same as softmax, but output a vector of ``ndata * nclass``, which can be further reshaped to ``ndata * nclass`` matrix. The result contains predicted probability of each data point belonging to each class.
-  - ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
-  - ``rank:ndcg``: Use LambdaMART to perform list-wise ranking where `Normalized Discounted Cumulative Gain (NDCG) <http://en.wikipedia.org/wiki/NDCG>`_ is maximized
-  - ``rank:map``: Use LambdaMART to perform list-wise ranking where `Mean Average Precision (MAP) <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_ is maximized
-  - ``reg:gamma``: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Applications>`_.
-  - ``reg:tweedie``: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be `Tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Applications>`_.
-
-* ``base_score`` [default=0.5]
-
-  - The initial prediction score of all instances, global bias
-  - For sufficient number of iterations, changing this value will not have too much effect.
-
-* ``eval_metric`` [default according to objective]
-
-  - Evaluation metrics for validation data, a default metric will be assigned according to objective (rmse for regression, and error for classification, mean average precision for ranking)
-  - User can add multiple evaluation metrics. Python users: remember to pass the metrics in as list of parameters pairs instead of map, so that latter ``eval_metric`` won't override previous one
-  - The choices are listed below:
-
-    - ``rmse``: `root mean square error <http://en.wikipedia.org/wiki/Root_mean_square_error>`_
-    - ``rmsle``: root mean square log error: :math:`\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}`. Default metric of ``reg:squaredlogerror`` objective. This metric reduces errors generated by outliers in dataset.  But because ``log`` function is employed, ``rmsle`` might output ``nan`` when prediction value is less than -1.  See ``reg:squaredlogerror`` for other requirements.
-    - ``mae``: `mean absolute error <https://en.wikipedia.org/wiki/Mean_absolute_error>`_
-    - ``logloss``: `negative log-likelihood <http://en.wikipedia.org/wiki/Log-likelihood>`_
-    - ``error``: Binary classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
-    - ``error@t``: a different than 0.5 binary classification threshold value could be specified by providing a numerical value through 't'.
-    - ``merror``: Multiclass classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``.
-    - ``mlogloss``: `Multiclass logloss <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html>`_.
-    - ``auc``: `Area under the curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_
-    - ``aucpr``: `Area under the PR curve <https://en.wikipedia.org/wiki/Precision_and_recall>`_
-    - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
-    - ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
-    - ``ndcg@n``, ``map@n``: 'n' can be assigned as an integer to cut off the top positions in the lists for evaluation.
-    - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
-    - ``poisson-nloglik``: negative log-likelihood for Poisson regression
-    - ``gamma-nloglik``: negative log-likelihood for gamma regression
-    - ``cox-nloglik``: negative partial log-likelihood for Cox proportional hazards regression
-    - ``gamma-deviance``: residual deviance for gamma regression
-    - ``tweedie-nloglik``: negative log-likelihood for Tweedie regression (at a specified value of the ``tweedie_variance_power`` parameter)
-    - ``aft-nloglik``: Negative log likelihood of Accelerated Failure Time model.
-      See :doc:`/tutorials/aft_survival_analysis` for details.
-
-* ``seed`` [default=0]
-
-  - Random number seed.  This parameter is ignored in R package, use `set.seed()` instead.
-
-***********************
-Command Line Parameters
-***********************
-The following parameters are only used in the console version of XGBoost
-
-* ``num_round``
-
-  - The number of rounds for boosting
-
-* ``data``
-
-  - The path of training data
-
-* ``test:data``
-
-  - The path of test data to do prediction
-
-* ``save_period`` [default=0]
-
-  - The period to save the model. Setting ``save_period=10`` means that for every 10 rounds XGBoost will save the model. Setting it to 0 means not saving any model during the training.
-
-* ``task`` [default= ``train``] options: ``train``, ``pred``, ``eval``, ``dump``
-
-  - ``train``: training using data
-  - ``pred``: making prediction for test:data
-  - ``eval``: for evaluating statistics specified by ``eval[name]=filename``
-  - ``dump``: for dump the learned model into text format
-
-* ``model_in`` [default=NULL]
-
-  - Path to input model, needed for ``test``, ``eval``, ``dump`` tasks. If it is specified in training, XGBoost will continue training from the input model.
-
-* ``model_out`` [default=NULL]
-
-  - Path to output model after training finishes. If not specified, XGBoost will output files with such names as ``0003.model`` where ``0003`` is number of boosting rounds.
-
-* ``model_dir`` [default= ``models/``]
-
-  - The output directory of the saved models during training
-
-* ``fmap``
-
-  - Feature map, used for dumping model
-
-* ``dump_format`` [default= ``text``] options: ``text``, ``json``
-
-  - Format of model dump file
-
-* ``name_dump`` [default= ``dump.txt``]
-
-  - Name of model dump file
-
-* ``name_pred`` [default= ``pred.txt``]
-
-  - Name of prediction file, used in pred mode
-
-* ``pred_margin`` [default=0]
-
-  - Predict margin instead of transformed probability
diff --git a/ml-xgboost/doc/python/convert_090to100.py b/ml-xgboost/doc/python/convert_090to100.py
deleted file mode 100644
index 135489b..0000000
--- a/ml-xgboost/doc/python/convert_090to100.py
+++ /dev/null
@@ -1,79 +0,0 @@
-'''This is a simple script that converts a pickled XGBoost
-Scikit-Learn interface object from 0.90 to a native model.  Pickle
-format is not stable as it's a direct serialization of Python object.
-We advice not to use it when stability is needed.
-
-'''
-import pickle
-import json
-import os
-import argparse
-import numpy as np
-import xgboost
-import warnings
-
-
-def save_label_encoder(le):
-    '''Save the label encoder in XGBClassifier'''
-    meta = dict()
-    for k, v in le.__dict__.items():
-        if isinstance(v, np.ndarray):
-            meta[k] = v.tolist()
-        else:
-            meta[k] = v
-    return meta
-
-
-def xgboost_skl_90to100(skl_model):
-    '''Extract the model and related metadata in SKL model.'''
-    model = {}
-    with open(skl_model, 'rb') as fd:
-        old = pickle.load(fd)
-        if not isinstance(old, xgboost.XGBModel):
-            raise TypeError(
-                'The script only handes Scikit-Learn interface object')
-
-    # Save Scikit-Learn specific Python attributes into a JSON document.
-    for k, v in old.__dict__.items():
-        if k == '_le':
-            model[k] = save_label_encoder(v)
-        elif k == 'classes_':
-            model[k] = v.tolist()
-        elif k == '_Booster':
-            continue
-        else:
-            try:
-                json.dumps({k: v})
-                model[k] = v
-            except TypeError:
-                warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
-    booster = old.get_booster()
-    # Store the JSON serialization as an attribute
-    booster.set_attr(scikit_learn=json.dumps(model))
-
-    # Save it into a native model.
-    i = 0
-    while True:
-        path = 'xgboost_native_model_from_' + skl_model + '-' + str(i) + '.bin'
-        if os.path.exists(path):
-            i += 1
-            continue
-        booster.save_model(path)
-        break
-
-
-if __name__ == '__main__':
-    assert xgboost.__version__ != '1.0.0', ('Please use the XGBoost version'
-                                            ' that generates this pickle.')
-    parser = argparse.ArgumentParser(
-        description=('A simple script to convert pickle generated by'
-                     ' XGBoost 0.90 to XGBoost 1.0.0 model (not pickle).'))
-    parser.add_argument(
-        '--old-pickle',
-        type=str,
-        help='Path to old pickle file of Scikit-Learn interface object.  '
-        'Will output a native model converted from this pickle file',
-        required=True)
-    args = parser.parse_args()
-
-    xgboost_skl_90to100(args.old_pickle)
diff --git a/ml-xgboost/doc/python/index.rst b/ml-xgboost/doc/python/index.rst
deleted file mode 100644
index 1cbcc45..0000000
--- a/ml-xgboost/doc/python/index.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-######################
-XGBoost Python Package
-######################
-This page contains links to all the python related documents on python package.
-To install the package package, checkout :doc:`Installation Guide </build>`.
-
-********
-Contents
-********
-
-.. toctree::
-  python_intro
-  python_api
-  Python examples <https://github.com/dmlc/xgboost/tree/master/demo/guide-python>
diff --git a/ml-xgboost/doc/python/python_api.rst b/ml-xgboost/doc/python/python_api.rst
deleted file mode 100644
index 234b63d..0000000
--- a/ml-xgboost/doc/python/python_api.rst
+++ /dev/null
@@ -1,94 +0,0 @@
-Python API Reference
-====================
-This page gives the Python API reference of xgboost, please also refer to Python Package Introduction for more information about python package.
-
-.. contents::
-  :backlinks: none
-  :local:
-
-Core Data Structure
--------------------
-.. automodule:: xgboost.core
-
-.. autoclass:: xgboost.DMatrix
-    :members:
-    :show-inheritance:
-
-.. autoclass:: xgboost.DeviceQuantileDMatrix
-    :show-inheritance:
-
-.. autoclass:: xgboost.Booster
-    :members:
-    :show-inheritance:
-
-
-Learning API
-------------
-.. automodule:: xgboost.training
-
-.. autofunction:: xgboost.train
-
-.. autofunction:: xgboost.cv
-
-
-Scikit-Learn API
-----------------
-.. automodule:: xgboost.sklearn
-.. autoclass:: xgboost.XGBRegressor
-    :members:
-    :inherited-members:
-    :show-inheritance:
-.. autoclass:: xgboost.XGBClassifier
-    :members:
-    :inherited-members:
-    :show-inheritance:
-.. autoclass:: xgboost.XGBRanker
-    :members:
-    :inherited-members:
-    :show-inheritance:
-.. autoclass:: xgboost.XGBRFRegressor
-    :members:
-    :inherited-members:
-    :show-inheritance:
-.. autoclass:: xgboost.XGBRFClassifier
-    :members:
-    :inherited-members:
-    :show-inheritance:
-
-Plotting API
-------------
-.. automodule:: xgboost.plotting
-
-.. autofunction:: xgboost.plot_importance
-
-.. autofunction:: xgboost.plot_tree
-
-.. autofunction:: xgboost.to_graphviz
-
-.. _callback_api:
-
-Callback API
-------------
-.. autofunction:: xgboost.callback.print_evaluation
-
-.. autofunction:: xgboost.callback.record_evaluation
-
-.. autofunction:: xgboost.callback.reset_learning_rate
-
-.. autofunction:: xgboost.callback.early_stop
-
-.. _dask_api:
-
-Dask API
---------
-.. automodule:: xgboost.dask
-
-.. autofunction:: xgboost.dask.DaskDMatrix
-
-.. autofunction:: xgboost.dask.train
-
-.. autofunction:: xgboost.dask.predict
-
-.. autofunction:: xgboost.dask.DaskXGBClassifier
-
-.. autofunction:: xgboost.dask.DaskXGBRegressor
diff --git a/ml-xgboost/doc/python/python_intro.rst b/ml-xgboost/doc/python/python_intro.rst
deleted file mode 100644
index 6bb138a..0000000
--- a/ml-xgboost/doc/python/python_intro.rst
+++ /dev/null
@@ -1,228 +0,0 @@
-###########################
-Python Package Introduction
-###########################
-This document gives a basic walkthrough of xgboost python package.
-
-**List of other Helpful Links**
-
-* `Python walkthrough code collections <https://github.com/tqchen/xgboost/blob/master/demo/guide-python>`_
-* :doc:`Python API Reference <python_api>`
-
-Install XGBoost
----------------
-To install XGBoost, follow instructions in :doc:`/build`.
-
-To verify your installation, run the following in Python:
-
-.. code-block:: python
-
-  import xgboost as xgb
-
-.. _python_data_interface:
-
-Data Interface
---------------
-The XGBoost python module is able to load data from:
-
-- LibSVM text format file
-- Comma-separated values (CSV) file
-- NumPy 2D array
-- SciPy 2D sparse array
-- cuDF DataFrame
-- Pandas data frame, and
-- XGBoost binary buffer file.
-
-(See :doc:`/tutorials/input_format` for detailed description of text input format.)
-
-The data is stored in a :py:class:`DMatrix <xgboost.DMatrix>` object.
-
-* To load a libsvm text file or a XGBoost binary file into :py:class:`DMatrix <xgboost.DMatrix>`:
-
-  .. code-block:: python
-
-    dtrain = xgb.DMatrix('train.svm.txt')
-    dtest = xgb.DMatrix('test.svm.buffer')
-
-* To load a CSV file into :py:class:`DMatrix <xgboost.DMatrix>`:
-
-  .. code-block:: python
-
-    # label_column specifies the index of the column containing the true label
-    dtrain = xgb.DMatrix('train.csv?format=csv&label_column=0')
-    dtest = xgb.DMatrix('test.csv?format=csv&label_column=0')
-
-  .. note:: Categorical features not supported
-
-    Note that XGBoost does not provide specialization for categorical features; if your data contains
-    categorical features, load it as a NumPy array first and then perform corresponding preprocessing steps like
-    `one-hot encoding <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html>`_.
-
-  .. note:: Use Pandas to load CSV files with headers
-
-    Currently, the DMLC data parser cannot parse CSV files with headers. Use Pandas (see below) to read CSV files with headers.
-
-* To load a NumPy array into :py:class:`DMatrix <xgboost.DMatrix>`:
-
-  .. code-block:: python
-
-    data = np.random.rand(5, 10)  # 5 entities, each contains 10 features
-    label = np.random.randint(2, size=5)  # binary target
-    dtrain = xgb.DMatrix(data, label=label)
-
-* To load a :py:mod:`scipy.sparse` array into :py:class:`DMatrix <xgboost.DMatrix>`:
-
-  .. code-block:: python
-
-    csr = scipy.sparse.csr_matrix((dat, (row, col)))
-    dtrain = xgb.DMatrix(csr)
-
-* To load a Pandas data frame into :py:class:`DMatrix <xgboost.DMatrix>`:
-
-  .. code-block:: python
-
-    data = pandas.DataFrame(np.arange(12).reshape((4,3)), columns=['a', 'b', 'c'])
-    label = pandas.DataFrame(np.random.randint(2, size=4))
-    dtrain = xgb.DMatrix(data, label=label)
-
-* Saving :py:class:`DMatrix <xgboost.DMatrix>` into a XGBoost binary file will make loading faster:
-
-  .. code-block:: python
-
-    dtrain = xgb.DMatrix('train.svm.txt')
-    dtrain.save_binary('train.buffer')
-
-* Missing values can be replaced by a default value in the :py:class:`DMatrix <xgboost.DMatrix>` constructor:
-
-  .. code-block:: python
-
-    dtrain = xgb.DMatrix(data, label=label, missing=-999.0)
-
-* Weights can be set when needed:
-
-  .. code-block:: python
-
-    w = np.random.rand(5, 1)
-    dtrain = xgb.DMatrix(data, label=label, missing=-999.0, weight=w)
-
-When performing ranking tasks, the number of weights should be equal
-to number of groups.
-
-
-Setting Parameters
-------------------
-XGBoost can use either a list of pairs or a dictionary to set :doc:`parameters </parameter>`. For instance:
-
-* Booster parameters
-
-  .. code-block:: python
-
-    param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
-    param['nthread'] = 4
-    param['eval_metric'] = 'auc'
-
-* You can also specify multiple eval metrics:
-
-  .. code-block:: python
-
-    param['eval_metric'] = ['auc', 'ams@0']
-
-    # alternatively:
-    # plst = param.items()
-    # plst += [('eval_metric', 'ams@0')]
-
-* Specify validations set to watch performance
-
-  .. code-block:: python
-
-    evallist = [(dtest, 'eval'), (dtrain, 'train')]
-
-Training
---------
-
-Training a model requires a parameter list and data set.
-
-.. code-block:: python
-
-  num_round = 10
-  bst = xgb.train(param, dtrain, num_round, evallist)
-
-After training, the model can be saved.
-
-.. code-block:: python
-
-  bst.save_model('0001.model')
-
-The model and its feature map can also be dumped to a text file.
-
-.. code-block:: python
-
-  # dump model
-  bst.dump_model('dump.raw.txt')
-  # dump model with feature map
-  bst.dump_model('dump.raw.txt', 'featmap.txt')
-
-A saved model can be loaded as follows:
-
-.. code-block:: python
-
-  bst = xgb.Booster({'nthread': 4})  # init model
-  bst.load_model('model.bin')  # load data
-
-Methods including `update` and `boost` from `xgboost.Booster` are designed for
-internal usage only.  The wrapper function `xgboost.train` does some
-pre-configuration including setting up caches and some other parameters.
-
-Early Stopping
---------------
-If you have a validation set, you can use early stopping to find the optimal number of boosting rounds.
-Early stopping requires at least one set in ``evals``. If there's more than one, it will use the last.
-
-.. code-block:: python
-
-  train(..., evals=evals, early_stopping_rounds=10)
-
-The model will train until the validation score stops improving. Validation error needs to decrease at least every ``early_stopping_rounds`` to continue training.
-
-If early stopping occurs, the model will have three additional fields: ``bst.best_score``, ``bst.best_iteration`` and ``bst.best_ntree_limit``. Note that :py:meth:`xgboost.train` will return a model from the last iteration, not the best one.
-
-This works with both metrics to minimize (RMSE, log loss, etc.) and to maximize (MAP, NDCG, AUC). Note that if you specify more than one evaluation metric the last one in ``param['eval_metric']`` is used for early stopping.
-
-Prediction
-----------
-A model that has been trained or loaded can perform predictions on data sets.
-
-.. code-block:: python
-
-  # 7 entities, each contains 10 features
-  data = np.random.rand(7, 10)
-  dtest = xgb.DMatrix(data)
-  ypred = bst.predict(dtest)
-
-If early stopping is enabled during training, you can get predictions from the best iteration with ``bst.best_ntree_limit``:
-
-.. code-block:: python
-
-  ypred = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)
-
-Plotting
---------
-
-You can use plotting module to plot importance and output tree.
-
-To plot importance, use :py:meth:`xgboost.plot_importance`. This function requires ``matplotlib`` to be installed.
-
-.. code-block:: python
-
-  xgb.plot_importance(bst)
-
-To plot the output tree via ``matplotlib``, use :py:meth:`xgboost.plot_tree`, specifying the ordinal number of the target tree. This function requires ``graphviz`` and ``matplotlib``.
-
-.. code-block:: python
-
-  xgb.plot_tree(bst, num_trees=2)
-
-When you use ``IPython``, you can use the :py:meth:`xgboost.to_graphviz` function, which converts the target tree to a ``graphviz`` instance. The ``graphviz`` instance is automatically rendered in ``IPython``.
-
-.. code-block:: python
-
-  xgb.to_graphviz(bst, num_trees=2)
diff --git a/ml-xgboost/doc/requirements.txt b/ml-xgboost/doc/requirements.txt
deleted file mode 100644
index 7fb7038..0000000
--- a/ml-xgboost/doc/requirements.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-sphinx>=2.1
-mock
-guzzle_sphinx_theme
-breathe
-sh>=1.12.14
-matplotlib>=2.1
-graphviz
-numpy
diff --git a/ml-xgboost/doc/sphinx_util.py b/ml-xgboost/doc/sphinx_util.py
deleted file mode 100644
index 49de832..0000000
--- a/ml-xgboost/doc/sphinx_util.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Helper utility function for customization."""
-import sys
-import os
-import docutils
-import subprocess
-
-READTHEDOCS_BUILD = (os.environ.get('READTHEDOCS', None) is not None)
-
-if not os.path.exists('web-data'):
-  subprocess.call('rm -rf web-data;' +
-                  'git clone https://github.com/dmlc/web-data', shell = True)
-else:
-  subprocess.call('cd web-data; git pull', shell=True)
-
-sys.stderr.write('READTHEDOCS=%s\n' % (READTHEDOCS_BUILD))
diff --git a/ml-xgboost/doc/tutorials/aft_survival_analysis.rst b/ml-xgboost/doc/tutorials/aft_survival_analysis.rst
deleted file mode 100644
index 80bad9a..0000000
--- a/ml-xgboost/doc/tutorials/aft_survival_analysis.rst
+++ /dev/null
@@ -1,168 +0,0 @@
-###############################################
-Survival Analysis with Accelerated Failure Time
-###############################################
-
-.. contents::
-  :local:
-  :backlinks: none
-
-**************************
-What is survival analysis?
-**************************
-
-**Survival analysis (regression)** models **time to an event of interest**. Survival analysis is a special kind of regression and differs from the conventional regression task as follows:
-
-* The label is always positive, since you cannot wait a negative amount of time until the event occurs.
-* The label may not be fully known, or **censored**, because "it takes time to measure time."
-
-The second bullet point is crucial and we should dwell on it more. As you may have guessed from the name, one of the earliest applications of survival analysis is to model mortality of a given population. Let's take `NCCTG Lung Cancer Dataset <https://stat.ethz.ch/R-manual/R-devel/library/survival/html/lung.html>`_ as an example. The first 8 columns represent features and the last column, Time to death, represents the label.
-
-==== === === ======= ======== ========= ======== ======= ========================
-Inst Age Sex ph.ecog ph.karno pat.karno meal.cal wt.loss **Time to death (days)**
-==== === === ======= ======== ========= ======== ======= ========================
-3    74  1   1       90       100       1175     N/A     306
-3    68  1   0       90       90        1225     15      455
-3    56  1   0       90       90        N/A      15      :math:`[1010, +\infty)`
-5    57  1   1       90       60        1150     11      210
-1    60  1   0       100      90        N/A      0       883
-12   74  1   1       50       80        513      0       :math:`[1022, +\infty)`
-7    68  2   2       70       60        384      10      310
-==== === === ======= ======== ========= ======== ======= ========================
-
-Take a close look at the label for the third patient. **His label is a range, not a single number.** The third patient's label is said to be **censored**, because for some reason the experimenters could not get a complete measurement for that label. One possible scenario: the patient survived the first 1010 days and walked out of the clinic on the 1011th day, so his death was not directly observed. Another possibility: The experiment was cut short (since you cannot run it forever) before his death could be observed. In any case, his label is :math:`[1010, +\infty)`, meaning his time to death can be any number that's higher than 1010, e.g. 2000, 3000, or 10000.
-
-There are four kinds of censoring:
-
-* **Uncensored**: the label is not censored and given as a single number.
-* **Right-censored**: the label is of form :math:`[a, +\infty)`, where :math:`a` is the lower bound.
-* **Left-censored**: the label is of form :math:`(-\infty, b]`, where :math:`b` is the upper bound.
-* **Interval-censored**: the label is of form :math:`[a, b]`, where :math:`a` and :math:`b` are the lower and upper bounds, respectively.
-
-Right-censoring is the most commonly used.
-
-******************************
-Accelerated Failure Time model
-******************************
-**Accelerated Failure Time (AFT)** model is one of the most commonly used models in survival analysis. The model is of the following form:
-
-.. math::
-
-  \ln{Y} = \langle \mathbf{w}, \mathbf{x} \rangle + \sigma Z
-
-where
-
-* :math:`\mathbf{x}` is a vector in :math:`\mathbb{R}^d` representing the features.
-* :math:`\mathbf{w}` is a vector consisting of :math:`d` coefficients, each corresponding to a feature.
-* :math:`\langle \cdot, \cdot \rangle` is the usual dot product in :math:`\mathbb{R}^d`.
-* :math:`\ln{(\cdot)}` is the natural logarithm.
-* :math:`Y` and :math:`Z` are random variables.
-
-  - :math:`Y` is the output label.
-  - :math:`Z` is a random variable of a known probability distribution. Common choices are the normal distribution, the logistic distribution, and the extreme distribution. Intuitively, :math:`Z` represents the "noise" that pulls the prediction :math:`\langle \mathbf{w}, \mathbf{x} \rangle` away from the true log label :math:`\ln{Y}`.
-
-* :math:`\sigma` is a parameter that scales the size of :math:`Z`.
-
-Note that this model is a generalized form of a linear regression model :math:`Y = \langle \mathbf{w}, \mathbf{x} \rangle`. In order to make AFT work with gradient boosting, we revise the model as follows:
-
-.. math::
-
-  \ln{Y} = \mathcal{T}(\mathbf{x}) + \sigma Z
-
-where :math:`\mathcal{T}(\mathbf{x})` represents the output from a decision tree ensemble, given input :math:`\mathbf{x}`. Since :math:`Z` is a random variable, we have a likelihood defined for the expression :math:`\ln{Y} = \mathcal{T}(\mathbf{x}) + \sigma Z`. So the goal for XGBoost is to maximize the (log) likelihood by fitting a good tree ensemble :math:`\mathcal{T}(\mathbf{x})`.
-
-**********
-How to use
-**********
-The first step is to express the labels in the form of a range, so that **every data point has two numbers associated with it, namely the lower and upper bounds for the label.** For uncensored labels, use a degenerate interval of form :math:`[a, a]`.
-
-.. |tick| unicode:: U+2714
-.. |cross| unicode:: U+2718
-
-================= ==================== =================== ===================
-Censoring type    Interval form        Lower bound finite? Upper bound finite?
-================= ==================== =================== ===================
-Uncensored        :math:`[a, a]`       |tick|              |tick|
-Right-censored    :math:`[a, +\infty)` |tick|              |cross|
-Left-censored     :math:`(-\infty, b]` |cross|             |tick|
-Interval-censored :math:`[a, b]`       |tick|              |tick|
-================= ==================== =================== ===================
-
-Collect the lower bound numbers in one array (let's call it ``y_lower_bound``) and the upper bound number in another array (call it ``y_upper_bound``). The ranged labels are associated with a data matrix object via calls to :meth:`xgboost.DMatrix.set_float_info`:
-
-.. code-block:: python
-  :caption: Python
-
-  import numpy as np
-  import xgboost as xgb
-
-  # 4-by-2 Data matrix
-  X = np.array([[1, -1], [-1, 1], [0, 1], [1, 0]])
-  dtrain = xgb.DMatrix(X)
-  
-  # Associate ranged labels with the data matrix.
-  # This example shows each kind of censored labels.
-  #                         uncensored    right     left  interval
-  y_lower_bound = np.array([      2.0,     3.0, -np.inf,     4.0])
-  y_upper_bound = np.array([      2.0, +np.inf,     4.0,     5.0])
-  dtrain.set_float_info('label_lower_bound', y_lower_bound)
-  dtrain.set_float_info('label_upper_bound', y_upper_bound)
-
-.. code-block:: r
-  :caption: R
-  
-  library(xgboost)
-
-  # 4-by-2 Data matrix
-  X <- matrix(c(1., -1., -1., 1., 0., 1., 1., 0.),
-              nrow=4, ncol=2, byrow=TRUE)
-  dtrain <- xgb.DMatrix(X)
-
-  # Associate ranged labels with the data matrix.
-  # This example shows each kind of censored labels.
-  #                   uncensored  right  left  interval
-  y_lower_bound <- c(        2.,    3., -Inf,       4.)
-  y_upper_bound <- c(        2.,  +Inf,   4.,       5.)
-  setinfo(dtrain, 'label_lower_bound', y_lower_bound)
-  setinfo(dtrain, 'label_upper_bound', y_upper_bound)
-
-Now we are ready to invoke the training API:
-
-.. code-block:: python
-  :caption: Python
-
-  params = {'objective': 'survival:aft',
-            'eval_metric': 'aft-nloglik',
-            'aft_loss_distribution': 'normal',
-            'aft_loss_distribution_scale': 1.20,
-            'tree_method': 'hist', 'learning_rate': 0.05, 'max_depth': 2}
-  bst = xgb.train(params, dtrain, num_boost_round=5,
-                  evals=[(dtrain, 'train'), (dvalid, 'valid')])
-
-.. code-block:: r
-  :caption: R
-
-  params <- list(objective='survival:aft',
-                 eval_metric='aft-nloglik',
-                 aft_loss_distribution='normal',
-                 aft_loss_distribution_scale=1.20,
-                 tree_method='hist',
-                 learning_rate=0.05,
-                 max_depth=2)
-  watchlist <- list(train = dtrain)
-  bst <- xgb.train(params, dtrain, nrounds=5, watchlist)
-
-We set ``objective`` parameter to ``survival:aft`` and ``eval_metric`` to ``aft-nloglik``, so that the log likelihood for the AFT model would be maximized. (XGBoost will actually minimize the negative log likelihood, hence the name ``aft-nloglik``.)
-
-The parameter ``aft_loss_distribution`` corresponds to the distribution of the :math:`Z` term in the AFT model, and ``aft_loss_distribution_scale`` corresponds to the scaling factor :math:`\sigma`.
-
-Currently, you can choose from three probability distributions for ``aft_loss_distribution``:
-
-========================= ===========================================
-``aft_loss_distribution`` Probabilty Density Function (PDF)
-========================= ===========================================
-``normal``                :math:`\dfrac{\exp{(-z^2/2)}}{\sqrt{2\pi}}`
-``logistic``              :math:`\dfrac{e^z}{(1+e^z)^2}`
-``extreme``               :math:`e^z e^{-\exp{z}}`
-========================= ===========================================
-
-Note that it is not yet possible to set the ranged label using the scikit-learn interface (e.g. :class:`xgboost.XGBRegressor`). For now, you should use :class:`xgboost.train` with :class:`xgboost.DMatrix`.
\ No newline at end of file
diff --git a/ml-xgboost/doc/tutorials/aws_yarn.rst b/ml-xgboost/doc/tutorials/aws_yarn.rst
deleted file mode 100644
index aea7819..0000000
--- a/ml-xgboost/doc/tutorials/aws_yarn.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-###############################
-Distributed XGBoost YARN on AWS
-###############################
-[This page is under construction.]
-
-.. note:: XGBoost with Spark
-
-  If you are preprocessing training data with Spark, consider using :doc:`XGBoost4J-Spark </jvm/xgboost4j_spark_tutorial>`.
diff --git a/ml-xgboost/doc/tutorials/custom_metric_obj.rst b/ml-xgboost/doc/tutorials/custom_metric_obj.rst
deleted file mode 100644
index d9f559f..0000000
--- a/ml-xgboost/doc/tutorials/custom_metric_obj.rst
+++ /dev/null
@@ -1,147 +0,0 @@
-######################################
-Custom Objective and Evaluation Metric
-######################################
-
-XGBoost is designed to be an extensible library.  One way to extend it is by providing our
-own objective function for training and corresponding metric for performance monitoring.
-This document introduces implementing a customized elementwise evaluation metric and
-objective for XGBoost.  Although the introduction uses Python for demonstration, the
-concepts should be readily applicable to other language bindings.
-
-.. note::
-
-   * The ranking task does not support customized functions.
-   * The customized functions defined here are only applicable to single node training.
-     Distributed environment requires syncing with ``xgboost.rabit``, the interface is
-     subject to change hence beyond the scope of this tutorial.
-   * We also plan to improve the interface for multi-classes objective in the future.
-
-In the following sections, we will provide a step by step walk through of implementing
-``Squared Log Error(SLE)`` objective function:
-
-.. math::
-   \frac{1}{2}[log(pred + 1) - log(label + 1)]^2
-
-and its default metric ``Root Mean Squared Log Error(RMSLE)``:
-
-.. math::
-   \sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}
-
-Although XGBoost has native support for said functions, using it for demonstration
-provides us the opportunity of comparing the result from our own implementation and the
-one from XGBoost internal for learning purposes.  After finishing this tutorial, we should
-be able to provide our own functions for rapid experiments.
-
-*****************************
-Customized Objective Function
-*****************************
-
-During model training, the objective function plays an important role: provide gradient
-information, both first and second order gradient, based on model predictions and observed
-data labels (or targets).  Therefore, a valid objective function should accept two inputs,
-namely prediction and labels.  For implementing ``SLE``, we define:
-
-.. code-block:: python
-
-    import numpy as np
-    import xgboost as xgb
-
-    def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:
-        '''Compute the gradient squared log error.'''
-        y = dtrain.get_label()
-        return (np.log1p(predt) - np.log1p(y)) / (predt + 1)
-
-    def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:
-        '''Compute the hessian for squared log error.'''
-        y = dtrain.get_label()
-        return ((-np.log1p(predt) + np.log1p(y) + 1) /
-                np.power(predt + 1, 2))
-
-    def squared_log(predt: np.ndarray,
-                    dtrain: xgb.DMatrix) -> Tuple[np.ndarray, np.ndarray]:
-        '''Squared Log Error objective. A simplified version for RMSLE used as
-        objective function.
-        '''
-        predt[predt < -1] = -1 + 1e-6
-        grad = gradient(predt, dtrain)
-        hess = hessian(predt, dtrain)
-        return grad, hess
-
-
-In the above code snippet, ``squared_log`` is the objective function we want.  It accepts a
-numpy array ``predt`` as model prediction, and the training DMatrix for obtaining required
-information, including labels and weights (not used here).  This objective is then used as
-a callback function for XGBoost during training by passing it as an argument to
-``xgb.train``:
-
-.. code-block:: python
-
-   xgb.train({'tree_method': 'hist', 'seed': 1994},  # any other tree method is fine.
-              dtrain=dtrain,
-              num_boost_round=10,
-              obj=squared_log)
-
-Notice that in our definition of the objective, whether we subtract the labels from the
-prediction or the other way around is important.  If you find the training error goes up
-instead of down, this might be the reason.
-
-
-**************************
-Customized Metric Function
-**************************
-
-So after having a customized objective, we might also need a corresponding metric to
-monitor our model's performance.  As mentioned above, the default metric for ``SLE`` is
-``RMSLE``.  Similarly we define another callback like function as the new metric:
-
-.. code-block:: python
-
-    def rmsle(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
-        ''' Root mean squared log error metric.'''
-        y = dtrain.get_label()
-        predt[predt < -1] = -1 + 1e-6
-        elements = np.power(np.log1p(y) - np.log1p(predt), 2)
-        return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y)))
-
-Since we are demonstrating in Python, the metric or objective needs not be a function,
-any callable object should suffice.  Similarly to the objective function, our metric also
-accepts ``predt`` and ``dtrain`` as inputs, but returns the name of metric itself and a
-floating point value as result.  After passing it into XGBoost as argument of ``feval``
-parameter:
-
-.. code-block:: python
-
-    xgb.train({'tree_method': 'hist', 'seed': 1994,
-               'disable_default_eval_metric': 1},
-              dtrain=dtrain,
-              num_boost_round=10,
-              obj=squared_log,
-              feval=rmsle,
-              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
-              evals_result=results)
-
-We will be able to see XGBoost printing something like:
-
-.. code-block:: none
-
-    [0]	dtrain-PyRMSLE:1.37153	dtest-PyRMSLE:1.31487
-    [1]	dtrain-PyRMSLE:1.26619	dtest-PyRMSLE:1.20899
-    [2]	dtrain-PyRMSLE:1.17508	dtest-PyRMSLE:1.11629
-    [3]	dtrain-PyRMSLE:1.09836	dtest-PyRMSLE:1.03871
-    [4]	dtrain-PyRMSLE:1.03557	dtest-PyRMSLE:0.977186
-    [5]	dtrain-PyRMSLE:0.985783	dtest-PyRMSLE:0.93057
-    ...
-
-Notice that the parameter ``disable_default_eval_metric`` is used to suppress the default metric
-in XGBoost.
-
-For fully reproducible source code and comparison plots, see `custom_rmsle.py <https://github.com/dmlc/xgboost/tree/master/demo/guide-python/custom_rmsle.py>`_.
-
-
-******************************
-Multi-class objective function
-******************************
-
-A similiar demo for multi-class objective funtion is also available, see
-`demo/guide-python/custom_softmax.py <https://github.com/dmlc/xgboost/tree/master/demo/guide-python/custom_rmsle.py>`_
-for details.
diff --git a/ml-xgboost/doc/tutorials/dart.rst b/ml-xgboost/doc/tutorials/dart.rst
deleted file mode 100644
index a660e99..0000000
--- a/ml-xgboost/doc/tutorials/dart.rst
+++ /dev/null
@@ -1,111 +0,0 @@
-############
-DART booster
-############
-XGBoost mostly combines a huge number of regression trees with a small learning rate.
-In this situation, trees added early are significant and trees added late are unimportant.
-
-Vinayak and Gilad-Bachrach proposed a new method to add dropout techniques from the deep neural net community to boosted trees, and reported better results in some situations.
-
-This is a instruction of new tree booster ``dart``.
-
-**************
-Original paper
-**************
-Rashmi Korlakai Vinayak, Ran Gilad-Bachrach. "DART: Dropouts meet Multiple Additive Regression Trees." `JMLR <http://www.jmlr.org/proceedings/papers/v38/korlakaivinayak15.pdf>`_.
-
-********
-Features
-********
-- Drop trees in order to solve the over-fitting.
-
-  - Trivial trees (to correct trivial errors) may be prevented.
-
-Because of the randomness introduced in the training, expect the following few differences:
-
-- Training can be slower than ``gbtree`` because the random dropout prevents usage of the prediction buffer.
-- The early stop might not be stable, due to the randomness.
-
-************
-How it works
-************
-- In :math:`m`-th training round, suppose :math:`k` trees are selected to be dropped.
-- Let :math:`D = \sum_{i \in \mathbf{K}} F_i` be the leaf scores of dropped trees and :math:`F_m = \eta \tilde{F}_m` be the leaf scores of a new tree.
-- The objective function is as follows:
-
-.. math::
-
-  \mathrm{Obj}
-  = \sum_{j=1}^n L \left( y_j, \hat{y}_j^{m-1} - D_j + \tilde{F}_m \right)
-  + \Omega \left( \tilde{F}_m \right).
-
-- :math:`D` and :math:`F_m` are overshooting, so using scale factor
-
-.. math::
-
-  \hat{y}_j^m = \sum_{i \not\in \mathbf{K}} F_i + a \left( \sum_{i \in \mathbf{K}} F_i + b F_m \right) .
-
-**********
-Parameters
-**********
-
-The booster ``dart`` inherits ``gbtree`` booster, so it supports all parameters that ``gbtree`` does, such as ``eta``, ``gamma``, ``max_depth`` etc.
-
-Additional parameters are noted below:
-
-* ``sample_type``: type of sampling algorithm.
-
-  - ``uniform``: (default) dropped trees are selected uniformly.
-  - ``weighted``: dropped trees are selected in proportion to weight.
-
-* ``normalize_type``: type of normalization algorithm.
-
-  - ``tree``: (default) New trees have the same weight of each of dropped trees.
-
-  .. math::
-
-    a \left( \sum_{i \in \mathbf{K}} F_i + \frac{1}{k} F_m \right)
-    &= a \left( \sum_{i \in \mathbf{K}} F_i + \frac{\eta}{k} \tilde{F}_m \right) \\
-    &\sim a \left( 1 + \frac{\eta}{k} \right) D \\
-    &= a \frac{k + \eta}{k} D = D , \\
-    &\quad a = \frac{k}{k + \eta}
-
-  - ``forest``: New trees have the same weight of sum of dropped trees (forest).
-
-  .. math::
-
-    a \left( \sum_{i \in \mathbf{K}} F_i + F_m \right)
-    &= a \left( \sum_{i \in \mathbf{K}} F_i + \eta \tilde{F}_m \right) \\
-    &\sim a \left( 1 + \eta \right) D \\
-    &= a (1 + \eta) D = D , \\
-    &\quad a = \frac{1}{1 + \eta} .
-
-* ``rate_drop``: dropout rate.
-
-  - range: [0.0, 1.0]
-
-* ``skip_drop``: probability of skipping dropout.
-
-  - If a dropout is skipped, new trees are added in the same manner as gbtree.
-  - range: [0.0, 1.0]
-
-*************
-Sample Script
-*************
-
-.. code-block:: python
-
-  import xgboost as xgb
-  # read in data
-  dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
-  dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
-  # specify parameters via map
-  param = {'booster': 'dart',
-           'max_depth': 5, 'learning_rate': 0.1,
-           'objective': 'binary:logistic',
-           'sample_type': 'uniform',
-           'normalize_type': 'tree',
-           'rate_drop': 0.1,
-           'skip_drop': 0.5}
-  num_round = 50
-  bst = xgb.train(param, dtrain, num_round)
-  preds = bst.predict(dtest)
diff --git a/ml-xgboost/doc/tutorials/dask.rst b/ml-xgboost/doc/tutorials/dask.rst
deleted file mode 100644
index ae468d7..0000000
--- a/ml-xgboost/doc/tutorials/dask.rst
+++ /dev/null
@@ -1,144 +0,0 @@
-#############################
-Distributed XGBoost with Dask
-#############################
-
-`Dask <https://dask.org>`_ is a parallel computing library built on Python. Dask allows
-easy management of distributed workers and excels handling large distributed data science
-workflows.  The implementation in XGBoost originates from `dask-xgboost
-<https://github.com/dask/dask-xgboost>`_ with some extended functionalities and a
-different interface.  Right now it is still under construction and may change (with proper
-warnings) in the future.
-
-************
-Requirements
-************
-
-Dask is trivial to install using either pip or conda.  `See here for official install
-documentation <https://docs.dask.org/en/latest/install.html>`_.  For accelerating XGBoost
-with GPU, `dask-cuda <https://github.com/rapidsai/dask-cuda>`_ is recommended for creating
-GPU clusters.
-
-
-********
-Overview
-********
-
-There are 3 different components in dask from a user's perspective, namely a scheduler,
-bunch of workers and some clients connecting to the scheduler.  For using XGBoost with
-dask, one needs to call XGBoost dask interface from the client side.  A small example
-illustrates the basic usage:
-
-.. code-block:: python
-
-  cluster = LocalCluster(n_workers=4, threads_per_worker=1)
-  client = Client(cluster)
-
-  dtrain = xgb.dask.DaskDMatrix(client, X, y)  # X and y are dask dataframes or arrays
-
-  output = xgb.dask.train(client,
-                          {'verbosity': 2,
-                           'tree_method': 'hist'},
-                          dtrain,
-                          num_boost_round=4, evals=[(dtrain, 'train')])
-
-Here we first create a cluster in single-node mode wtih ``distributed.LocalCluster``, then
-connect a ``client`` to this cluster, setting up environment for later computation.
-Similar to non-distributed interface, we create a ``DMatrix`` object and pass it to
-``train`` along with some other parameters.  Except in dask interface, client is an extra
-argument for carrying out the computation, when set to ``None`` XGBoost will use the
-default client returned from dask.
-
-There are two sets of APIs implemented in XGBoost.  The first set is functional API
-illustrated in above example.  Given the data and a set of parameters, `train` function
-returns a model and the computation history as Python dictionary
-
-.. code-block:: python
-
-  {'booster': Booster,
-   'history': dict}
-
-For prediction, pass the ``output`` returned by ``train`` into ``xgb.dask.predict``
-
-.. code-block:: python
-
-  prediction = xgb.dask.predict(client, output, dtrain)
-
-Or equivalently, pass ``output['booster']``:
-
-.. code-block:: python
-
-  prediction = xgb.dask.predict(client, output['booster'], dtrain)
-
-Here ``prediction`` is a dask ``Array`` object containing predictions from model.
-
-Another set of API is a Scikit-Learn wrapper, which mimics the stateful Scikit-Learn
-interface with ``DaskXGBClassifier`` and ``DaskXGBRegressor``.  See ``xgboost/demo/dask``
-for more examples.
-
-*******
-Threads
-*******
-
-XGBoost has built in support for parallel computation through threads by the setting
-``nthread`` parameter (``n_jobs`` for scikit-learn).  If these parameters are set, they
-will override the configuration in Dask.  For example:
-
-.. code-block:: python
-
-  with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
-
-There are 4 threads allocated for each dask worker.  Then by default XGBoost will use 4
-threads in each process for both training and prediction.  But if ``nthread`` parameter is
-set:
-
-.. code-block:: python
-
-  output = xgb.dask.train(client,
-                          {'verbosity': 1,
-                           'nthread': 8,
-                           'tree_method': 'hist'},
-                          dtrain,
-                          num_boost_round=4, evals=[(dtrain, 'train')])
-
-XGBoost will use 8 threads in each training process.
-
-*****************************************************************************
-Why is the initialization of ``DaskDMatrix``  so slow and throws weird errors
-*****************************************************************************
-
-The dask API in XGBoost requires construction of ``DaskDMatrix``.  With ``Scikit-Learn``
-interface, ``DaskDMatrix`` is implicitly constructed for each input data during `fit` or
-`predict`.  You might have observed its construction is taking incredible amount of time,
-and sometimes throws error that doesn't seem to be relevant to `DaskDMatrix`.  Here is a
-brief explanation for why.  By default most of dask's computation is `lazy
-<https://docs.dask.org/en/latest/user-interfaces.html#laziness-and-computing>`_, which
-means the computation is not carried out until you explicitly ask for result, either by
-calling `compute()` or `wait()`.  See above link for details in dask, and `this wiki
-<https://en.wikipedia.org/wiki/Lazy_evaluation>`_ for general concept of lazy evaluation.
-The `DaskDMatrix` constructor forces all lazy computation to materialize, which means it's
-where all your earlier computation actually being carried out, including operations like
-`dd.read_csv()`.  To isolate the computation in `DaskDMatrix` from other lazy
-computations, one can explicitly wait for results of input data before calling constructor
-of `DaskDMatrix`.  Also dask's `web interface
-<https://distributed.dask.org/en/latest/web.html>`_ can be used to monitor what operations
-are currently being performed.
-
-***********
-Limitations
-***********
-
-Basic functionalities including training and generating predictions for regression and
-classification are implemented.  But there are still some other limitations we haven't
-addressed yet.
-
-- Label encoding for Scikit-Learn classifier may not be supported.  Meaning that user need
-  to encode their training labels into discrete values first.
-- Ranking is not supported right now.
-- Empty worker is not well supported by classifier.  If the training hangs for classifier
-  with a warning about empty DMatrix, please consider balancing your data first.  But
-  regressor works fine with empty DMatrix.
-- Callback functions are not tested.
-- Only ``GridSearchCV`` from ``scikit-learn`` is supported for dask interface.  Meaning
-  that we can distribute data among workers but have to train one model at a time.  If you
-  want to scale up grid searching with model parallelism by ``dask-ml``, please consider
-  using normal ``scikit-learn`` interface like `xgboost.XGBRegressor` for now.
diff --git a/ml-xgboost/doc/tutorials/external_memory.rst b/ml-xgboost/doc/tutorials/external_memory.rst
deleted file mode 100644
index 03a7c8c..0000000
--- a/ml-xgboost/doc/tutorials/external_memory.rst
+++ /dev/null
@@ -1,72 +0,0 @@
-#####################################
-Using XGBoost External Memory Version
-#####################################
-There is no big difference between using external memory version and in-memory version.
-The only difference is the filename format.
-
-The external memory version takes in the following `URI <https://en.wikipedia.org/wiki/Uniform_Resource_Identifier>`_ format:
-
-.. code-block:: none
-
-  filename#cacheprefix
-
-The ``filename`` is the normal path to libsvm format file you want to load in, and
-``cacheprefix`` is a path to a cache file that XGBoost will use for caching preprocessed
-data in binary form.
-
-To load from csv files, use the following syntax:
-
-.. code-block:: none
-
-  filename.csv?format=csv&label_column=0#cacheprefix
-
-where ``label_column`` should point to the csv column acting as the label.
-
-To provide a simple example for illustration, extracting the code from
-`demo/guide-python/external_memory.py <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/external_memory.py>`_. If
-you have a dataset stored in a file similar to ``agaricus.txt.train`` with libSVM format, the external memory support can be enabled by:
-
-.. code-block:: python
-
-  dtrain = DMatrix('../data/agaricus.txt.train#dtrain.cache')
-
-XGBoost will first load ``agaricus.txt.train`` in, preprocess it, then write to a new file named
-``dtrain.cache`` as an on disk cache for storing preprocessed data in an internal binary format.  For
-more notes about text input formats, see :doc:`/tutorials/input_format`.
-
-For CLI version, simply add the cache suffix, e.g. ``"../data/agaricus.txt.train#dtrain.cache"``.
-
-***********
-GPU Version
-***********
-External memory is fully supported in GPU algorithms (i.e. when ``tree_method`` is set to ``gpu_hist``).
-
-If you are still getting out-of-memory errors after enabling external memory, try subsampling the
-data to further reduce GPU memory usage:
-
-.. code-block:: python
-
-  param = {
-    ...
-    'subsample': 0.1,
-    'sampling_method': 'gradient_based',
-  }
-
-*******************
-Distributed Version
-*******************
-The external memory mode naturally works on distributed version, you can simply set path like
-
-.. code-block:: none
-
-  data = "hdfs://path-to-data/#dtrain.cache"
-
-XGBoost will cache the data to the local position. When you run on YARN, the current folder is temporary
-so that you can directly use ``dtrain.cache`` to cache to current folder.
-
-***********
-Limitations
-***********
-* The ``hist`` tree method hasn't been tested thoroughly with external memory support (see
-  `this issue <https://github.com/dmlc/xgboost/issues/4093>`_).
-* OSX is not tested.
diff --git a/ml-xgboost/doc/tutorials/feature_interaction_constraint.rst b/ml-xgboost/doc/tutorials/feature_interaction_constraint.rst
deleted file mode 100644
index 2055240..0000000
--- a/ml-xgboost/doc/tutorials/feature_interaction_constraint.rst
+++ /dev/null
@@ -1,277 +0,0 @@
-###############################
-Feature Interaction Constraints
-###############################
-
-The decision tree is a powerful tool to discover interaction among independent
-variables (features). Variables that appear together in a traversal path
-are interacting with one another, since the condition of a child node is
-predicated on the condition of the parent node. For example, the highlighted
-red path in the diagram below contains three variables: :math:`x_1`, :math:`x_7`,
-and :math:`x_{10}`, so the highlighted prediction (at the highlighted leaf node)
-is the product of interaction between :math:`x_1`, :math:`x_7`, and
-:math:`x_{10}`.
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration1 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">10</FONT></SUB> &lt; -1.5 ?>, shape=box, color=red, fontcolor=red];
-      1 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB> &lt; 2 ?>, shape=box];
-      2 [label=<x<SUB><FONT POINT-SIZE="11">7</FONT></SUB> &lt; 0.3 ?>, shape=box, color=red, fontcolor=red];
-      3 [label="...", shape=none];
-      4 [label="...", shape=none];
-      5 [label=<x<SUB><FONT POINT-SIZE="11">1</FONT></SUB> &lt; 0.5 ?>, shape=box, color=red, fontcolor=red];
-      6 [label="...", shape=none];
-      7 [label="...", shape=none];
-      8 [label="Predict +1.3", color=red, fontcolor=red];
-      0 -> 1 [labeldistance=2.0, labelangle=45, headlabel="Yes/Missing           "];
-      0 -> 2 [labeldistance=2.0, labelangle=-45,
-              headlabel="No", color=red, fontcolor=red];
-      1 -> 3 [labeldistance=2.0, labelangle=45, headlabel="Yes"];
-      1 -> 4 [labeldistance=2.0, labelangle=-45, headlabel="             No/Missing"];
-      2 -> 5 [labeldistance=2.0, labelangle=-45, headlabel="Yes",
-              color=red, fontcolor=red];
-      2 -> 6 [labeldistance=2.0, labelangle=-45, headlabel="           No/Missing"];
-      5 -> 7;
-      5 -> 8 [color=red];
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration1', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration1', view=False)
-
-.. raw:: html
-
-  <p>
-  <img src="../_static/feature_interaction_illustration1.svg"
-    onerror="this.src='../_static/feature_interaction_illustration1.png'; this.onerror=null;">
-  </p>
-
-When the tree depth is larger than one, many variables interact on
-the sole basis of minimizing training loss, and the resulting decision tree may
-capture a spurious relationship (noise) rather than a legitimate relationship
-that generalizes across different datasets. **Feature interaction constraints**
-allow users to decide which variables are allowed to interact and which are not.
-
-Potential benefits include:
-
-* Better predictive performance from focusing on interactions that work --
-  whether through domain specific knowledge or algorithms that rank interactions
-* Less noise in predictions; better generalization
-* More control to the user on what the model can fit. For example, the user may
-  want to exclude some interactions even if they perform well due to regulatory
-  constraints
-
-****************
-A Simple Example
-****************
-
-Feature interaction constraints are expressed in terms of groups of variables
-that are allowed to interact. For example, the constraint
-``[0, 1]`` indicates that variables :math:`x_0` and :math:`x_1` are allowed to
-interact with each other but with no other variable. Similarly, ``[2, 3, 4]``
-indicates that :math:`x_2`, :math:`x_3`, and :math:`x_4` are allowed to
-interact with one another but with no other variable. A set of feature
-interaction constraints is expressed as a nested list, e.g.
-``[[0, 1], [2, 3, 4]]``, where each inner list is a group of indices of features
-that are allowed to interact with each other.
-
-In the following diagram, the left decision tree is in violation of the first
-constraint (``[0, 1]``), whereas the right decision tree complies with both the
-first and second constraints (``[0, 1]``, ``[2, 3, 4]``).
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration2 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">0</FONT></SUB> &lt; 5.0 ?>, shape=box];
-      1 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB> &lt; -3.0 ?>, shape=box];
-      2 [label="+0.6"];
-      3 [label="-0.4"];
-      4 [label="+1.2"];
-      0 -> 1 [labeldistance=2.0, labelangle=45, headlabel="Yes/Missing           "];
-      0 -> 2 [labeldistance=2.0, labelangle=-45, headlabel="No"];
-      1 -> 3 [labeldistance=2.0, labelangle=45, headlabel="Yes"];
-      1 -> 4 [labeldistance=2.0, labelangle=-45, headlabel="           No/Missing"];
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration2', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration2', view=False)
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration3 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">3</FONT></SUB> &lt; 2.5 ?>, shape=box];
-      1 [label="+1.6"];
-      2 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB> &lt; -1.2 ?>, shape=box];
-      3 [label="+0.1"];
-      4 [label="-0.3"];
-      0 -> 1 [labeldistance=2.0, labelangle=45, headlabel="Yes"];
-      0 -> 2 [labeldistance=2.0, labelangle=-45, headlabel="           No/Missing"];
-      2 -> 3 [labeldistance=2.0, labelangle=45, headlabel="Yes/Missing           "];
-      2 -> 4 [labeldistance=2.0, labelangle=-45, headlabel="No"];
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration3', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration3', view=False)
-
-.. raw:: html
-
-  <p>
-  <img src="../_static/feature_interaction_illustration2.svg"
-       onerror="this.src='../_static/feature_interaction_illustration2.png'; this.onerror=null;">
-  <img src="../_static/feature_interaction_illustration3.svg"
-       onerror="this.src='../_static/feature_interaction_illustration3.png'; this.onerror=null;">
-  </p>
-
-****************************************************
-Enforcing Feature Interaction Constraints in XGBoost
-****************************************************
-
-It is very simple to enforce feature interaction constraints in XGBoost.  Here we will
-give an example using Python, but the same general idea generalizes to other
-platforms.
-
-Suppose the following code fits your model without feature interaction constraints:
-
-.. code-block:: python
-
-  model_no_constraints = xgb.train(params, dtrain,
-                                   num_boost_round = 1000, evals = evallist,
-                                   early_stopping_rounds = 10)
-
-Then fitting with feature interaction constraints only requires adding a single
-parameter:
-
-.. code-block:: python
-
-  params_constrained = params.copy()
-  # Use nested list to define feature interaction constraints
-  params_constrained['interaction_constraints'] = '[[0, 2], [1, 3, 4], [5, 6]]'
-  # Features 0 and 2 are allowed to interact with each other but with no other feature
-  # Features 1, 3, 4 are allowed to interact with one another but with no other feature
-  # Features 5 and 6 are allowed to interact with each other but with no other feature
-
-  model_with_constraints = xgb.train(params_constrained, dtrain,
-                                     num_boost_round = 1000, evals = evallist,
-                                     early_stopping_rounds = 10)
-
-**Choice of tree construction algorithm**. To use feature interaction constraints, be sure
-to set the ``tree_method`` parameter to one of the following: ``exact``, ``hist``,
-``approx`` or ``gpu_hist``.  Support for ``gpu_hist`` and ``approx`` is added only in
-1.0.0.
-
-**************
-Advanced topic
-**************
-
-The intuition behind interaction constraint is simple.  User have prior knowledge about
-relations between different features, and encode it as constraints during model
-construction.  But there are also some subtleties around specifying constraints.  Take
-constraint ``[[1, 2], [2, 3, 4]]`` as an example, the second feature appears in two
-different interaction sets ``[1, 2]`` and ``[2, 3, 4]``, so the union set of features
-allowed to interact with ``2`` is ``{1, 3, 4}``.  In following diagram, root splits at
-feature ``2``.  because all its descendants should be able to interact with it, so at the
-second layer all 4 features are legitimate split candidates for further splitting,
-disregarding specified constraint sets.
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration4 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB>>, shape=box, color=black, fontcolor=black];
-      1 [label=<x<SUB><FONT POINT-SIZE="11">{1, 2, 3, 4}</FONT></SUB>>, shape=box];
-      2 [label=<x<SUB><FONT POINT-SIZE="11">{1, 2, 3, 4}</FONT></SUB>>, shape=box, color=black, fontcolor=black];
-      3 [label="...", shape=none];
-      4 [label="...", shape=none];
-      5 [label="...", shape=none];
-      6 [label="...", shape=none];
-      0 -> 1;
-      0 -> 2;
-      1 -> 3;
-      1 -> 4;
-      2 -> 5;
-      2 -> 6;
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration4', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration5', view=False)
-
-.. figure:: ../_static/feature_interaction_illustration4.png
-   :align: center
-   :figwidth: 80 %
-
-   ``{1, 2, 3, 4}`` represents the sets of legitimate split features.
-
-This has lead to some interesting implications of feature interaction constraints.  Take
-``[[0, 1], [0, 1, 2], [1, 2]]`` as another example.  Assuming we have only 3 available
-features in our training datasets for presentation purpose, careful readers might have
-found out that the above constraint is same with ``[0, 1, 2]``.  Since no matter which
-feature is chosen for split in root node, all its descendants have to include every
-feature as legitimate split candidates to avoid violating interaction constraints.
-
-For one last example, we use ``[[0, 1], [1, 3, 4]]`` and choose feature ``0`` as split for
-root node.  At the second layer of built tree, ``1`` is the only legitimate split
-candidate except for ``0`` itself, since they belong to the same constraint set.
-Following the grow path of our example tree below, the node at second layer splits at
-feature ``1``.  But due to the fact that ``1`` also belongs to second constraint set ``[1,
-3, 4]``, at third layer, we need to include all features as candidates to comply with its
-ascendants.
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration5 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">0</FONT></SUB>>, shape=box, color=black, fontcolor=black];
-      1 [label="...", shape=none];
-      2 [label=<x<SUB><FONT POINT-SIZE="11">1</FONT></SUB>>, shape=box, color=black, fontcolor=black];
-      3 [label=<x<SUB><FONT POINT-SIZE="11">{0, 1, 3, 4}</FONT></SUB>>, shape=box, color=black, fontcolor=black];
-      4 [label=<x<SUB><FONT POINT-SIZE="11">{0, 1, 3, 4}</FONT></SUB>>, shape=box, color=black, fontcolor=black];
-      5 [label="...", shape=none];
-      6 [label="...", shape=none];
-      7 [label="...", shape=none];
-      8 [label="...", shape=none];
-      0 -> 1;
-      0 -> 2;
-      2 -> 3;
-      2 -> 4;
-      3 -> 5;
-      3 -> 6;
-      4 -> 7;
-      4 -> 8;
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration6', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration7', view=False)
-
-
-.. figure:: ../_static/feature_interaction_illustration6.png
-   :align: center
-   :figwidth: 80 %
-
-   ``{0, 1, 3, 4}`` represents the sets of legitimate split features.
diff --git a/ml-xgboost/doc/tutorials/index.rst b/ml-xgboost/doc/tutorials/index.rst
deleted file mode 100644
index bcc3428..0000000
--- a/ml-xgboost/doc/tutorials/index.rst
+++ /dev/null
@@ -1,26 +0,0 @@
-#################
-XGBoost Tutorials
-#################
-
-This section contains official tutorials inside XGBoost package.
-See `Awesome XGBoost <https://github.com/dmlc/xgboost/tree/master/demo>`_ for more resources.
-
-.. toctree::
-  :maxdepth: 1
-  :caption: Contents:
-
-  model
-  saving_model
-  Distributed XGBoost with AWS YARN <aws_yarn>
-  kubernetes
-  Distributed XGBoost with XGBoost4J-Spark <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html>
-  dart
-  monotonic
-  rf
-  feature_interaction_constraint
-  aft_survival_analysis
-  input_format
-  param_tuning
-  external_memory
-  custom_metric_obj
-  dask
diff --git a/ml-xgboost/doc/tutorials/input_format.rst b/ml-xgboost/doc/tutorials/input_format.rst
deleted file mode 100644
index f0cb69c..0000000
--- a/ml-xgboost/doc/tutorials/input_format.rst
+++ /dev/null
@@ -1,112 +0,0 @@
-############################
-Text Input Format of DMatrix
-############################
-
-******************
-Basic Input Format
-******************
-XGBoost currently supports two text formats for ingesting data: LibSVM and CSV. The rest of this document will describe the LibSVM format. (See `this Wikipedia article <https://en.wikipedia.org/wiki/Comma-separated_values>`_ for a description of the CSV format.).  Please be careful that, XGBoost does **not** understand file extensions, nor try to guess the file format, as there is no universal agreement upon file extension of LibSVM or CSV.  Instead it employs `URI <https://en.wikipedia.org/wiki/Uniform_Resource_Identifier>`_ format for specifying the precise input file type.  For example if you provide a `csv` file ``./data.train.csv`` as input, XGBoost will blindly use the default libsvm parser to digest it and generate a parser error.  Instead, users need to provide an uri in the form of ``train.csv?format=csv``.  For external memory input, the uri should of a form similar to ``train.csv?format=csv#dtrain.cache``.  See :ref:`python_data_interface` and :doc:`/tutorials/external_memory` also.
-
-For training or predicting, XGBoost takes an instance file with the format as below:
-
-.. code-block:: none
-  :caption: ``train.txt``
-
-  1 101:1.2 102:0.03
-  0 1:2.1 10001:300 10002:400
-  0 0:1.3 1:0.3
-  1 0:0.01 1:0.3
-  0 0:0.2 1:0.3
-
-Each line represent a single instance, and in the first line '1' is the instance label, '101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instance being positive.
-
-******************************************
-Auxiliary Files for Additional Information
-******************************************
-**Note: all information below is applicable only to single-node version of the package.** If you'd like to perform distributed training with multiple nodes, skip to the section `Embedding additional information inside LibSVM file`_.
-
-Group Input Format
-==================
-For `ranking task <https://github.com/dmlc/xgboost/tree/master/demo/rank>`_, XGBoost supports the group input format. In ranking task, instances are categorized into *query groups* in real world scenarios. For example, in the learning to rank web pages scenario, the web page instances are grouped by their queries. XGBoost requires an file that indicates the group information. For example, if the instance file is the ``train.txt`` shown above,  the group file should be named ``train.txt.group`` and be of the following format:
-
-.. code-block:: none
-  :caption: ``train.txt.group``
-
-  2
-  3
-
-This means that, the data set contains 5 instances, and the first two instances are in a group and the other three are in another group. The numbers in the group file are actually indicating the number of instances in each group in the instance file in order.
-At the time of configuration, you do not have to indicate the path of the group file. If the instance file name is ``xxx``, XGBoost will check whether there is a file named ``xxx.group`` in the same directory.
-
-Instance Weight File
-====================
-Instances in the training data may be assigned weights to differentiate relative importance among them. For example, if we provide an instance weight file for the ``train.txt`` file in the example as below:
-
-.. code-block:: none
-  :caption: ``train.txt.weight``
-
-  1
-  0.5
-  0.5
-  1
-  0.5
-
-It means that XGBoost will emphasize more on the first and fourth instance (i.e. the positive instances) while training.
-The configuration is similar to configuring the group information. If the instance file name is ``xxx``, XGBoost will look for a file named ``xxx.weight`` in the same directory. If the file exists, the instance weights will be extracted and used at the time of training.
-
-.. note:: Binary buffer format and instance weights
-
-  If you choose to save the training data as a binary buffer (using :py:meth:`save_binary() <xgboost.DMatrix.save_binary>`), keep in mind that the resulting binary buffer file will include the instance weights. To update the weights, use the :py:meth:`set_weight() <xgboost.DMatrix.set_weight>` function.
-
-Initial Margin File
-===================
-XGBoost supports providing each instance an initial margin prediction. For example, if we have a initial prediction using logistic regression for ``train.txt`` file, we can create the following file:
-
-.. code-block:: none
-  :caption: ``train.txt.base_margin``
-
-  -0.4
-  1.0
-  3.4
-
-XGBoost will take these values as initial margin prediction and boost from that. An important note about base_margin is that it should be margin prediction before transformation, so if you are doing logistic loss, you will need to put in value before logistic transformation. If you are using XGBoost predictor, use ``pred_margin=1`` to output margin values.
-
-***************************************************
-Embedding additional information inside LibSVM file
-***************************************************
-**This section is applicable to both single- and multiple-node settings.**
-
-Query ID Columns
-================
-This is most useful for `ranking task <https://github.com/dmlc/xgboost/tree/master/demo/rank>`_, where the instances are grouped into query groups. You may embed query group ID for each instance in the LibSVM file by adding a token of form ``qid:xx`` in each row:
-
-.. code-block:: none
-  :caption: ``train.txt``
-
-  1 qid:1 101:1.2 102:0.03
-  0 qid:1 1:2.1 10001:300 10002:400
-  0 qid:2 0:1.3 1:0.3
-  1 qid:2 0:0.01 1:0.3
-  0 qid:3 0:0.2 1:0.3
-  1 qid:3 3:-0.1 10:-0.3
-  0 qid:3 6:0.2 10:0.15
-
-Keep in mind the following restrictions:
-
-* You are not allowed to specify query ID's for some instances but not for others. Either every row is assigned query ID's or none at all.
-* The rows have to be sorted in ascending order by the query IDs. So, for instance, you may not have one row having large query ID than any of the following rows.
-
-Instance weights
-================
-You may specify instance weights in the LibSVM file by appending each instance label with the corresponding weight in the form of ``[label]:[weight]``, as shown by the following example:
-
-.. code-block:: none
-  :caption: ``train.txt``
-
-  1:1.0 101:1.2 102:0.03
-  0:0.5 1:2.1 10001:300 10002:400
-  0:0.5 0:1.3 1:0.3
-  1:1.0 0:0.01 1:0.3
-  0:0.5 0:0.2 1:0.3
-
-where the negative instances are assigned half weights compared to the positive instances.
diff --git a/ml-xgboost/doc/tutorials/kubernetes.rst b/ml-xgboost/doc/tutorials/kubernetes.rst
deleted file mode 100644
index d606858..0000000
--- a/ml-xgboost/doc/tutorials/kubernetes.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-###################################
-Distributed XGBoost on Kubernetes
-###################################
-
-Distributed XGBoost training and batch prediction on `Kubernetes <https://kubernetes.io/>`_ are supported via `Kubeflow XGBoost Operator <https://github.com/kubeflow/xgboost-operator>`_.
-
-************
-Instructions
-************
-In order to run a XGBoost job in a Kubernetes cluster, perform the following steps:
-
-1. Install XGBoost Operator on the Kubernetes cluster.
-
-   a. XGBoost Operator is designed to manage the scheduling and monitoring of XGBoost jobs. Follow `this installation guide <https://github.com/kubeflow/xgboost-operator#installing-xgboost-operator>`_ to install XGBoost Operator.
-
-2. Write application code that will be executed by the XGBoost Operator.
-
-   a. To use XGBoost Operator, you'll have to write a couple of Python scripts that implement the distributed training logic for XGBoost. Please refer to the `Iris classification example <https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/xgboost-dist>`_.
-   b. Data reader/writer: you need to implement the data reader and writer based on the specific requirements of your chosen data source. For example, if your dataset is stored in a Hive table, you have to write the code to read from or write to the Hive table based on the index of the worker.
-   c. Model persistence: in the `Iris classification example <https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/xgboost-dist>`_, the model is stored in `Alibaba OSS <https://www.alibabacloud.com/product/oss>`_. If you want to store your model in other storages such as Amazon S3 or Google NFS, you'll need to implement the model persistence logic based on the requirements of the chosen storage system.
-
-3. Configure the XGBoost job using a YAML file.
-
-   a. YAML file is used to configure the computational resources and environment for your XGBoost job to run, e.g. the number of workers/masters and the number of CPU/GPUs. Please refer to this `YAML template <https://github.com/kubeflow/xgboost-operator/blob/master/config/samples/xgboost-dist/xgboostjob_v1alpha1_iris_train.yaml>`_ for an example.
-
-4. Submit XGBoost job to a Kubernetes cluster.
-
-   a. Use `kubectl <https://kubernetes.io/docs/reference/kubectl/overview/>`_ to submit a distributed XGBoost job as illustrated `here <https://github.com/kubeflow/xgboost-operator#creating-a-xgboost-trainingprediction-job>`_.
-
-*******
-Support
-*******
-
-Please submit an issue on `XGBoost Operator repo <https://github.com/kubeflow/xgboost-operator>`_ for any feature requests or problems.
diff --git a/ml-xgboost/doc/tutorials/model.rst b/ml-xgboost/doc/tutorials/model.rst
deleted file mode 100644
index 4720e72..0000000
--- a/ml-xgboost/doc/tutorials/model.rst
+++ /dev/null
@@ -1,268 +0,0 @@
-#############################
-Introduction to Boosted Trees
-#############################
-XGBoost stands for "Extreme Gradient Boosting", where the term "Gradient Boosting" originates from the paper *Greedy Function Approximation: A Gradient Boosting Machine*, by Friedman.
-This is a tutorial on gradient boosted trees, and most of the content is based on `these slides <http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf>`_ by Tianqi Chen, the original author of XGBoost.
-
-The **gradient boosted trees** has been around for a while, and there are a lot of materials on the topic.
-This tutorial will explain boosted trees in a self-contained and principled way using the elements of supervised learning.
-We think this explanation is cleaner, more formal, and motivates the model formulation used in XGBoost.
-
-*******************************
-Elements of Supervised Learning
-*******************************
-XGBoost is used for supervised learning problems, where we use the training data (with multiple features) :math:`x_i` to predict a target variable :math:`y_i`.
-Before we learn about trees specifically, let us start by reviewing the basic elements in supervised learning.
-
-Model and Parameters
-====================
-The **model** in supervised learning usually refers to the mathematical structure of by which the prediction :math:`y_i` is made from the input :math:`x_i`.
-A common example is a *linear model*, where the prediction is given as :math:`\hat{y}_i = \sum_j \theta_j x_{ij}`, a linear combination of weighted input features.
-The prediction value can have different interpretations, depending on the task, i.e., regression or classification.
-For example, it can be logistic transformed to get the probability of positive class in logistic regression, and it can also be used as a ranking score when we want to rank the outputs.
-
-The **parameters** are the undetermined part that we need to learn from data. In linear regression problems, the parameters are the coefficients :math:`\theta`.
-Usually we will use :math:`\theta` to denote the parameters (there are many parameters in a model, our definition here is sloppy).
-
-Objective Function: Training Loss + Regularization
-==================================================
-With judicious choices for :math:`y_i`, we may express a variety of tasks, such as regression, classification, and ranking.
-The task of **training** the model amounts to finding the best parameters :math:`\theta` that best fit the training data :math:`x_i` and labels :math:`y_i`. In order to train the model, we need to define the **objective function**
-to measure how well the model fit the training data.
-
-A salient characteristic of objective functions is that they consist two parts: **training loss** and **regularization term**:
-
-.. math::
-
-  \text{obj}(\theta) = L(\theta) + \Omega(\theta)
-
-where :math:`L` is the training loss function, and :math:`\Omega` is the regularization term. The training loss measures how *predictive* our model is with respect to the training data.
-A common choice of :math:`L` is the *mean squared error*, which is given by
-
-.. math::
-
-  L(\theta) = \sum_i (y_i-\hat{y}_i)^2
-
-Another commonly used loss function is logistic loss, to be used for logistic regression:
-
-.. math::
-
-  L(\theta) = \sum_i[ y_i\ln (1+e^{-\hat{y}_i}) + (1-y_i)\ln (1+e^{\hat{y}_i})]
-
-The **regularization term** is what people usually forget to add. The regularization term controls the complexity of the model, which helps us to avoid overfitting.
-This sounds a bit abstract, so let us consider the following problem in the following picture. You are asked to *fit* visually a step function given the input data points
-on the upper left corner of the image.
-Which solution among the three do you think is the best fit?
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/step_fit.png
-  :alt: step functions to fit data points, illustrating bias-variance tradeoff
-
-The correct answer is marked in red. Please consider if this visually seems a reasonable fit to you. The general principle is we want both a *simple* and *predictive* model.
-The tradeoff between the two is also referred as **bias-variance tradeoff** in machine learning.
-
-Why introduce the general principle?
-====================================
-The elements introduced above form the basic elements of supervised learning, and they are natural building blocks of machine learning toolkits.
-For example, you should be able to describe the differences and commonalities between gradient boosted trees and random forests.
-Understanding the process in a formalized way also helps us to understand the objective that we are learning and the reason behind the heuristics such as
-pruning and smoothing.
-
-***********************
-Decision Tree Ensembles
-***********************
-Now that we have introduced the elements of supervised learning, let us get started with real trees.
-To begin with, let us first learn about the model choice of XGBoost: **decision tree ensembles**.
-The tree ensemble model consists of a set of classification and regression trees (CART). Here's a simple example of a CART that classifies whether someone will like a hypothetical computer game X.
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/cart.png
-  :width: 100%
-  :alt: a toy example for CART
-
-We classify the members of a family into different leaves, and assign them the score on the corresponding leaf.
-A CART is a bit different from decision trees, in which the leaf only contains decision values. In CART, a real score
-is associated with each of the leaves, which gives us richer interpretations that go beyond classification.
-This also allows for a principled, unified approach to optimization, as we will see in a later part of this tutorial.
-
-Usually, a single tree is not strong enough to be used in practice. What is actually used is the ensemble model,
-which sums the prediction of multiple trees together.
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/twocart.png
-  :width: 100%
-  :alt: a toy example for tree ensemble, consisting of two CARTs
-
-Here is an example of a tree ensemble of two trees. The prediction scores of each individual tree are summed up to get the final score.
-If you look at the example, an important fact is that the two trees try to *complement* each other.
-Mathematically, we can write our model in the form
-
-.. math::
-
-  \hat{y}_i = \sum_{k=1}^K f_k(x_i), f_k \in \mathcal{F}
-
-where :math:`K` is the number of trees, :math:`f` is a function in the functional space :math:`\mathcal{F}`, and :math:`\mathcal{F}` is the set of all possible CARTs. The objective function to be optimized is given by
-
-.. math::
-
-  \text{obj}(\theta) = \sum_i^n l(y_i, \hat{y}_i) + \sum_{k=1}^K \Omega(f_k)
-
-Now here comes a trick question: what is the *model* used in random forests? Tree ensembles! So random forests and boosted trees are really the same models; the
-difference arises from how we train them. This means that, if you write a predictive service for tree ensembles, you only need to write one and it should work
-for both random forests and gradient boosted trees. (See `Treelite <http://treelite.io>`_ for an actual example.) One example of why elements of supervised learning rock.
-
-*************
-Tree Boosting
-*************
-Now that we introduced the model, let us turn to training: How should we learn the trees?
-The answer is, as is always for all supervised learning models: *define an objective function and optimize it*!
-
-Let the following be the objective function (remember it always needs to contain training loss and regularization):
-
-.. math::
-
-  \text{obj} = \sum_{i=1}^n l(y_i, \hat{y}_i^{(t)}) + \sum_{i=1}^t\Omega(f_i)
-
-Additive Training
-=================
-
-The first question we want to ask: what are the **parameters** of trees?
-You can find that what we need to learn are those functions :math:`f_i`, each containing the structure
-of the tree and the leaf scores. Learning tree structure is much harder than traditional optimization problem where you can simply take the gradient.
-It is intractable to learn all the trees at once.
-Instead, we use an additive strategy: fix what we have learned, and add one new tree at a time.
-We write the prediction value at step :math:`t` as :math:`\hat{y}_i^{(t)}`. Then we have
-
-.. math::
-
-  \hat{y}_i^{(0)} &= 0\\
-  \hat{y}_i^{(1)} &= f_1(x_i) = \hat{y}_i^{(0)} + f_1(x_i)\\
-  \hat{y}_i^{(2)} &= f_1(x_i) + f_2(x_i)= \hat{y}_i^{(1)} + f_2(x_i)\\
-  &\dots\\
-  \hat{y}_i^{(t)} &= \sum_{k=1}^t f_k(x_i)= \hat{y}_i^{(t-1)} + f_t(x_i)
-
-It remains to ask: which tree do we want at each step?  A natural thing is to add the one that optimizes our objective.
-
-.. math::
-
-  \text{obj}^{(t)} & = \sum_{i=1}^n l(y_i, \hat{y}_i^{(t)}) + \sum_{i=1}^t\Omega(f_i) \\
-            & = \sum_{i=1}^n l(y_i, \hat{y}_i^{(t-1)} + f_t(x_i)) + \Omega(f_t) + \mathrm{constant}
-
-If we consider using mean squared error (MSE) as our loss function, the objective becomes
-
-.. math::
-
-  \text{obj}^{(t)} & = \sum_{i=1}^n (y_i - (\hat{y}_i^{(t-1)} + f_t(x_i)))^2 + \sum_{i=1}^t\Omega(f_i) \\
-            & = \sum_{i=1}^n [2(\hat{y}_i^{(t-1)} - y_i)f_t(x_i) + f_t(x_i)^2] + \Omega(f_t) + \mathrm{constant}
-
-The form of MSE is friendly, with a first order term (usually called the residual) and a quadratic term.
-For other losses of interest (for example, logistic loss), it is not so easy to get such a nice form.
-So in the general case, we take the *Taylor expansion of the loss function up to the second order*:
-
-.. math::
-
-  \text{obj}^{(t)} = \sum_{i=1}^n [l(y_i, \hat{y}_i^{(t-1)}) + g_i f_t(x_i) + \frac{1}{2} h_i f_t^2(x_i)] + \Omega(f_t) + \mathrm{constant}
-
-where the :math:`g_i` and :math:`h_i` are defined as
-
-.. math::
-
-  g_i &= \partial_{\hat{y}_i^{(t-1)}} l(y_i, \hat{y}_i^{(t-1)})\\
-  h_i &= \partial_{\hat{y}_i^{(t-1)}}^2 l(y_i, \hat{y}_i^{(t-1)})
-
-After we remove all the constants, the specific objective at step :math:`t` becomes
-
-.. math::
-
-  \sum_{i=1}^n [g_i f_t(x_i) + \frac{1}{2} h_i f_t^2(x_i)] + \Omega(f_t)
-
-This becomes our optimization goal for the new tree. One important advantage of this definition is that
-the value of the objective function only depends on :math:`g_i` and :math:`h_i`. This is how XGBoost supports custom loss functions.
-We can optimize every loss function, including logistic regression and pairwise ranking, using exactly
-the same solver that takes :math:`g_i` and :math:`h_i` as input!
-
-Model Complexity
-================
-We have introduced the training step, but wait, there is one important thing, the **regularization term**!
-We need to define the complexity of the tree :math:`\Omega(f)`. In order to do so, let us first refine the definition of the tree :math:`f(x)` as
-
-.. math::
-
-  f_t(x) = w_{q(x)}, w \in R^T, q:R^d\rightarrow \{1,2,\cdots,T\} .
-
-Here :math:`w` is the vector of scores on leaves, :math:`q` is a function assigning each data point to the corresponding leaf, and :math:`T` is the number of leaves.
-In XGBoost, we define the complexity as
-
-.. math::
-
-  \Omega(f) = \gamma T + \frac{1}{2}\lambda \sum_{j=1}^T w_j^2
-
-Of course, there is more than one way to define the complexity, but this one works well in practice. The regularization is one part most tree packages treat
-less carefully, or simply ignore. This was because the traditional treatment of tree learning only emphasized improving impurity, while the complexity control was left to heuristics.
-By defining it formally, we can get a better idea of what we are learning and obtain models that perform well in the wild.
-
-The Structure Score
-===================
-Here is the magical part of the derivation. After re-formulating the tree model, we can write the objective value with the :math:`t`-th tree as:
-
-.. math::
-
-  \text{obj}^{(t)} &\approx \sum_{i=1}^n [g_i w_{q(x_i)} + \frac{1}{2} h_i w_{q(x_i)}^2] + \gamma T + \frac{1}{2}\lambda \sum_{j=1}^T w_j^2\\
-  &= \sum^T_{j=1} [(\sum_{i\in I_j} g_i) w_j + \frac{1}{2} (\sum_{i\in I_j} h_i + \lambda) w_j^2 ] + \gamma T
-
-where :math:`I_j = \{i|q(x_i)=j\}` is the set of indices of data points assigned to the :math:`j`-th leaf.
-Notice that in the second line we have changed the index of the summation because all the data points on the same leaf get the same score.
-We could further compress the expression by defining :math:`G_j = \sum_{i\in I_j} g_i` and :math:`H_j = \sum_{i\in I_j} h_i`:
-
-.. math::
-
-  \text{obj}^{(t)} = \sum^T_{j=1} [G_jw_j + \frac{1}{2} (H_j+\lambda) w_j^2] +\gamma T
-
-In this equation, :math:`w_j` are independent with respect to each other, the form :math:`G_jw_j+\frac{1}{2}(H_j+\lambda)w_j^2` is quadratic and the best :math:`w_j` for a given structure :math:`q(x)` and the best objective reduction we can get is:
-
-.. math::
-
-  w_j^\ast &= -\frac{G_j}{H_j+\lambda}\\
-  \text{obj}^\ast &= -\frac{1}{2} \sum_{j=1}^T \frac{G_j^2}{H_j+\lambda} + \gamma T
-
-The last equation measures *how good* a tree structure :math:`q(x)` is.
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/struct_score.png
-  :width: 100%
-  :alt: illustration of structure score (fitness)
-
-If all this sounds a bit complicated, let's take a look at the picture, and see how the scores can be calculated.
-Basically, for a given tree structure, we push the statistics :math:`g_i` and :math:`h_i` to the leaves they belong to,
-sum the statistics together, and use the formula to calculate how good the tree is.
-This score is like the impurity measure in a decision tree, except that it also takes the model complexity into account.
-
-Learn the tree structure
-========================
-Now that we have a way to measure how good a tree is, ideally we would enumerate all possible trees and pick the best one.
-In practice this is intractable, so we will try to optimize one level of the tree at a time.
-Specifically we try to split a leaf into two leaves, and the score it gains is
-
-.. math::
-  Gain = \frac{1}{2} \left[\frac{G_L^2}{H_L+\lambda}+\frac{G_R^2}{H_R+\lambda}-\frac{(G_L+G_R)^2}{H_L+H_R+\lambda}\right] - \gamma
-
-This formula can be decomposed as 1) the score on the new left leaf 2) the score on the new right leaf 3) The score on the original leaf 4) regularization on the additional leaf.
-We can see an important fact here: if the gain is smaller than :math:`\gamma`, we would do better not to add that branch. This is exactly the **pruning** techniques in tree based
-models! By using the principles of supervised learning, we can naturally come up with the reason these techniques work :)
-
-For real valued data, we usually want to search for an optimal split. To efficiently do so, we place all the instances in sorted order, like the following picture.
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/split_find.png
-  :width: 100%
-  :alt: Schematic of choosing the best split
-
-A left to right scan is sufficient to calculate the structure score of all possible split solutions, and we can find the best split efficiently.
-
-.. note:: Limitation of additive tree learning
-
-  Since it is intractable to enumerate all possible tree structures, we add one split at a time. This approach works well most of the time, but there are some edge cases that fail due to this approach. For those edge cases, training results in a degenerate model because we consider only one feature dimension at a time. See `Can Gradient Boosting Learn Simple Arithmetic? <http://mariofilho.com/can-gradient-boosting-learn-simple-arithmetic/>`_ for an example.
-
-**********************
-Final words on XGBoost
-**********************
-Now that you understand what boosted trees are, you may ask, where is the introduction for XGBoost?
-XGBoost is exactly a tool motivated by the formal principle introduced in this tutorial!
-More importantly, it is developed with both deep consideration in terms of **systems optimization** and **principles in machine learning**.
-The goal of this library is to push the extreme of the computation limits of machines to provide a **scalable**, **portable** and **accurate** library.
-Make sure you try it out, and most importantly, contribute your piece of wisdom (code, examples, tutorials) to the community!
diff --git a/ml-xgboost/doc/tutorials/monotonic.rst b/ml-xgboost/doc/tutorials/monotonic.rst
deleted file mode 100644
index 1cecac5..0000000
--- a/ml-xgboost/doc/tutorials/monotonic.rst
+++ /dev/null
@@ -1,95 +0,0 @@
-#####################
-Monotonic Constraints
-#####################
-
-It is often the case in a modeling problem or project that the functional form of an acceptable model is constrained in some way. This may happen due to business considerations, or because of the type of scientific question being investigated.  In some cases, where there is a very strong prior belief that the true relationship has some quality, constraints can be used to improve the predictive performance of the model. 
-
-A common type of constraint in this situation is that certain features bear a **monotonic** relationship to the predicted response:
-
-.. math::
-
-  f(x_1, x_2, \ldots, x, \ldots, x_{n-1}, x_n) \leq f(x_1, x_2, \ldots, x', \ldots, x_{n-1}, x_n)
-
-whenever :math:`x \leq x'` is an **increasing constraint**; or 
-
-.. math::
-
-  f(x_1, x_2, \ldots, x, \ldots, x_{n-1}, x_n) \geq f(x_1, x_2, \ldots, x', \ldots, x_{n-1}, x_n)
-
-whenever :math:`x \leq x'` is a **decreasing constraint**.
-
-XGBoost has the ability to enforce monotonicity constraints on any features used in a boosted model. 
-
-****************
-A Simple Example
-****************
-
-To illustrate, let's create some simulated data with two features and a response according to the following scheme
-
-.. math::
-
-  y = 5 x_1 + \sin(10 \pi x_1) - 5 x_2 - \cos(10 \pi x_2) + N(0, 0.01)
-  x_1, x_2 \in [0, 1]
-
-The response generally increases with respect to the :math:`x_1` feature, but a sinusoidal variation has been superimposed, resulting in the true effect being non-monotonic.  For the :math:`x_2` feature the variation is decreasing with a sinusoidal variation.
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/monotonic/two.feature.sample.data.png
-  :alt: Data in sinusoidal fit
-
-Let's fit a boosted tree model to this data without imposing any monotonic constraints:
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/monotonic/two.feature.no.constraint.png
-  :alt: Fit of Model with No Constraint
-
-The black curve shows the trend inferred from the model for each feature.  To make these plots the distinguished feature :math:`x_i` is fed to the model over a one-dimensional grid of values, while all the other features (in this case only one other feature) are set to their average values.  We see that the model does a good job of capturing the general trend with the oscillatory wave superimposed.
-
-Here is the same model, but fit with monotonicity constraints:
-
-.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/monotonic/two.feature.with.constraint.png
-  :alt: Fit of Model with Constraint
-
-We see the effect of the constraint.  For each variable the general direction of the trend is still evident, but the oscillatory behaviour no longer remains as it would violate our imposed constraints.
-
-******************************************
-Enforcing Monotonic Constraints in XGBoost
-******************************************
-
-It is very simple to enforce monotonicity constraints in XGBoost.  Here we will give an example using Python, but the same general idea generalizes to other platforms.
-
-Suppose the following code fits your model without monotonicity constraints
-
-.. code-block:: python
-
-  model_no_constraints = xgb.train(params, dtrain, 
-                                   num_boost_round = 1000, evals = evallist, 
-                                   early_stopping_rounds = 10)
-
-Then fitting with monotonicity constraints only requires adding a single parameter
-
-.. code-block:: python
-
-  params_constrained = params.copy()
-  params_constrained['monotone_constraints'] = "(1,-1)"
-
-  model_with_constraints = xgb.train(params_constrained, dtrain, 
-                                     num_boost_round = 1000, evals = evallist, 
-                                     early_stopping_rounds = 10)
-
-In this example the training data ``X`` has two columns, and by using the parameter values ``(1,-1)`` we are telling XGBoost to impose an increasing constraint on the first predictor and a decreasing constraint on the second.
-
-Some other examples:
-
-- ``(1,0)``: An increasing constraint on the first predictor and no constraint on the second.
-- ``(0,-1)``: No constraint on the first predictor and a decreasing constraint on the second.
-
-**Choice of tree construction algorithm**. To use monotonic constraints, be
-sure to set the ``tree_method`` parameter to one of ``exact``, ``hist``, and
-``gpu_hist``.
-
-**Note for the 'hist' tree construction algorithm**.
-If ``tree_method`` is set to either ``hist`` or ``gpu_hist``, enabling monotonic
-constraints may produce unnecessarily shallow trees. This is because the
-``hist`` method reduces the number of candidate splits to be considered at each
-split. Monotonic constraints may wipe out all available split candidates, in
-which case no split is made. To reduce the effect, you may want to increase
-the ``max_bin`` parameter to consider more split candidates.
diff --git a/ml-xgboost/doc/tutorials/param_tuning.rst b/ml-xgboost/doc/tutorials/param_tuning.rst
deleted file mode 100644
index cce1454..0000000
--- a/ml-xgboost/doc/tutorials/param_tuning.rst
+++ /dev/null
@@ -1,60 +0,0 @@
-#########################
-Notes on Parameter Tuning
-#########################
-Parameter tuning is a dark art in machine learning, the optimal parameters
-of a model can depend on many scenarios. So it is impossible to create a
-comprehensive guide for doing so.
-
-This document tries to provide some guideline for parameters in XGBoost.
-
-************************************
-Understanding Bias-Variance Tradeoff
-************************************
-If you take a machine learning or statistics course, this is likely to be one
-of the most important concepts.
-When we allow the model to get more complicated (e.g. more depth), the model
-has better ability to fit the training data, resulting in a less biased model.
-However, such complicated model requires more data to fit.
-
-Most of parameters in XGBoost are about bias variance tradeoff. The best model
-should trade the model complexity with its predictive power carefully.
-:doc:`Parameters Documentation </parameter>` will tell you whether each parameter
-will make the model more conservative or not. This can be used to help you
-turn the knob between complicated model and simple model.
-
-*******************
-Control Overfitting
-*******************
-When you observe high training accuracy, but low test accuracy, it is likely that you encountered overfitting problem.
-
-There are in general two ways that you can control overfitting in XGBoost:
-
-* The first way is to directly control model complexity.
-
-  - This includes ``max_depth``, ``min_child_weight`` and ``gamma``.
-
-* The second way is to add randomness to make training robust to noise.
-
-  - This includes ``subsample`` and ``colsample_bytree``.
-  - You can also reduce stepsize ``eta``. Remember to increase ``num_round`` when you do so.
-
-***************************
-Faster training performance
-***************************
-There's a parameter called ``tree_method``, set it to ``hist`` or ``gpu_hist`` for faster computation.
-
-*************************
-Handle Imbalanced Dataset
-*************************
-For common cases such as ads clickthrough log, the dataset is extremely imbalanced.
-This can affect the training of XGBoost model, and there are two ways to improve it.
-
-* If you care only about the overall performance metric (AUC) of your prediction
-
-  - Balance the positive and negative weights via ``scale_pos_weight``
-  - Use AUC for evaluation
-
-* If you care about predicting the right probability
-
-  - In such a case, you cannot re-balance the dataset
-  - Set parameter ``max_delta_step`` to a finite number (say 1) to help convergence
diff --git a/ml-xgboost/doc/tutorials/rf.rst b/ml-xgboost/doc/tutorials/rf.rst
deleted file mode 100644
index 97dfb1e..0000000
--- a/ml-xgboost/doc/tutorials/rf.rst
+++ /dev/null
@@ -1,106 +0,0 @@
-#########################
-Random Forests in XGBoost
-#########################
-
-XGBoost is normally used to train gradient-boosted decision trees and other gradient
-boosted models. Random forests use the same model representation and inference, as
-gradient-boosted decision trees, but a different training algorithm.  One can use XGBoost
-to train a standalone random forest or use random forest as a base model for gradient
-boosting.  Here we focus on training standalone random forest.
-
-We have native APIs for training random forests since the early days, and a new
-Scikit-Learn wrapper after 0.82 (not included in 0.82).  Please note that the new
-Scikit-Learn wrapper is still **experimental**, which means we might change the interface
-whenever needed.
-
-*****************************************
-Standalone Random Forest With XGBoost API
-*****************************************
-
-The following parameters must be set to enable random forest training.
-
-* ``booster`` should be set to ``gbtree``, as we are training forests. Note that as this
-  is the default, this parameter needn't be set explicitly.
-* ``subsample`` must be set to a value less than 1 to enable random selection of training
-  cases (rows).
-* One of ``colsample_by*`` parameters must be set to a value less than 1 to enable random
-  selection of columns. Normally, ``colsample_bynode`` would be set to a value less than 1
-  to randomly sample columns at each tree split.
-* ``num_parallel_tree`` should be set to the size of the forest being trained.
-* ``num_boost_round`` should be set to 1 to prevent XGBoost from boosting multiple random
-  forests.  Note that this is a keyword argument to ``train()``, and is not part of the
-  parameter dictionary.
-* ``eta`` (alias: ``learning_rate``) must be set to 1 when training random forest
-  regression.
-* ``random_state`` can be used to seed the random number generator.
-
-
-Other parameters should be set in a similar way they are set for gradient boosting. For
-instance, ``objective`` will typically be ``reg:squarederror`` for regression and
-``binary:logistic`` for classification, ``lambda`` should be set according to a desired
-regularization weight, etc.
-
-If both ``num_parallel_tree`` and ``num_boost_round`` are greater than 1, training will
-use a combination of random forest and gradient boosting strategy. It will perform
-``num_boost_round`` rounds, boosting a random forest of ``num_parallel_tree`` trees at
-each round. If early stopping is not enabled, the final model will consist of
-``num_parallel_tree`` * ``num_boost_round`` trees.
-
-Here is a sample parameter dictionary for training a random forest on a GPU using
-xgboost::
-
-  params = {
-    'colsample_bynode': 0.8,
-    'learning_rate': 1,
-    'max_depth': 5,
-    'num_parallel_tree': 100,
-    'objective': 'binary:logistic',
-    'subsample': 0.8,
-    'tree_method': 'gpu_hist'
-  }
-
-A random forest model can then be trained as follows::
-
-  bst = train(params, dmatrix, num_boost_round=1)
-
-
-***************************************************
-Standalone Random Forest With Scikit-Learn-Like API
-***************************************************
-
-``XGBRFClassifier`` and ``XGBRFRegressor`` are SKL-like classes that provide random forest
-functionality. They are basically versions of ``XGBClassifier`` and ``XGBRegressor`` that
-train random forest instead of gradient boosting, and have default values and meaning of
-some of the parameters adjusted accordingly. In particular:
-
-* ``n_estimators`` specifies the size of the forest to be trained; it is converted to
-  ``num_parallel_tree``, instead of the number of boosting rounds
-* ``learning_rate`` is set to 1 by default
-* ``colsample_bynode`` and ``subsample`` are set to 0.8 by default
-* ``booster`` is always ``gbtree``
-
-For a simple example, you can train a random forest regressor with::
-
-    from sklearn.model_selection import KFold
-
-    # Your code ...
-
-    kf = KFold(n_splits=2)
-    for train_index, test_index in kf.split(X, y):
-        xgb_model = xgb.XGBRFRegressor(random_state=42).fit(
-	X[train_index], y[train_index])
-
-Note that these classes have a smaller selection of parameters compared to using
-``train()``. In particular, it is impossible to combine random forests with gradient
-boosting using this API.
-
-
-*******
-Caveats
-*******
-
-* XGBoost uses 2nd order approximation to the objective function. This can lead to results
-  that differ from a random forest implementation that uses the exact value of the
-  objective function.
-* XGBoost does not perform replacement when subsampling training cases. Each training case
-  can occur in a subsampled set either 0 or 1 time.
diff --git a/ml-xgboost/doc/tutorials/saving_model.rst b/ml-xgboost/doc/tutorials/saving_model.rst
deleted file mode 100644
index fc9a9fe..0000000
--- a/ml-xgboost/doc/tutorials/saving_model.rst
+++ /dev/null
@@ -1,231 +0,0 @@
-########################
-Introduction to Model IO
-########################
-
-In XGBoost 1.0.0, we introduced experimental support of using `JSON
-<https://www.json.org/json-en.html>`_ for saving/loading XGBoost models and related
-hyper-parameters for training, aiming to replace the old binary internal format with an
-open format that can be easily reused.  The support for binary format will be continued in
-the future until JSON format is no-longer experimental and has satisfying performance.
-This tutorial aims to share some basic insights into the JSON serialisation method used in
-XGBoost.  Without explicitly mentioned, the following sections assume you are using the
-experimental JSON format, which can be enabled by passing
-``enable_experimental_json_serialization=True`` as training parameter, or provide the file
-name with ``.json`` as file extension when saving/loading model:
-``booster.save_model('model.json')``.  More details below.
-
-Before we get started, XGBoost is a gradient boosting library with focus on tree model,
-which means inside XGBoost, there are 2 distinct parts: the model consisted of trees and
-algorithms used to build it.  If you come from Deep Learning community, then it should be
-clear to you that there are differences between the neural network structures composed of
-weights with fixed tensor operations, and the optimizers (like RMSprop) used to train
-them.
-
-So when one calls ``booster.save_model``, XGBoost saves the trees, some model parameters
-like number of input columns in trained trees, and the objective function, which combined
-to represent the concept of "model" in XGBoost.  As for why are we saving the objective as
-part of model, that's because objective controls transformation of global bias (called
-``base_score`` in XGBoost).  Users can share this model with others for prediction,
-evaluation or continue the training with a different set of hyper-parameters etc.
-However, this is not the end of story.  There are cases where we need to save something
-more than just the model itself.  For example, in distrbuted training, XGBoost performs
-checkpointing operation.  Or for some reasons, your favorite distributed computing
-framework decide to copy the model from one worker to another and continue the training in
-there.  In such cases, the serialisation output is required to contain enougth information
-to continue previous training without user providing any parameters again.  We consider
-such scenario as memory snapshot (or memory based serialisation method) and distinguish it
-with normal model IO operation.  In Python, this can be invoked by pickling the
-``Booster`` object.  Other language bindings are still working in progress.
-
-.. note::
-
-  The old binary format doesn't distinguish difference between model and raw memory
-  serialisation format, it's a mix of everything, which is part of the reason why we want
-  to replace it with a more robust serialisation method.  JVM Package has its own memory
-  based serialisation methods.
-
-To enable JSON format support for model IO (saving only the trees and objective), provide
-a filename with ``.json`` as file extension:
-
-.. code-block:: python
-
-  bst.save_model('model_file_name.json')
-
-While for enabling JSON as memory based serialisation format, pass
-``enable_experimental_json_serialization`` as a training parameter.  In Python this can be
-done by:
-
-.. code-block:: python
-
-  bst = xgboost.train({'enable_experimental_json_serialization': True}, dtrain)
-  with open('filename', 'wb') as fd:
-      pickle.dump(bst, fd)
-
-Notice the ``filename`` is for Python intrinsic function ``open``, not for XGBoost.  Hence
-parameter ``enable_experimental_json_serialization`` is required to enable JSON format.
-As the name suggested, memory based serialisation captures many stuffs internal to
-XGBoost, so it's only suitable to be used for checkpoints, which doesn't require stable
-output format.  That being said, loading pickled booster (memory snapshot) in a different
-XGBoost version may lead to errors or undefined behaviors.  But we promise the stable
-output format of binary model and JSON model (once it's no-longer experimental) as they
-are designed to be reusable.  This scheme fits as Python itself doesn't guarantee pickled
-bytecode can be used in different Python version.
-
-***************************
-Custom objective and metric
-***************************
-
-XGBoost accepts user provided objective and metric functions as an extension.  These
-functions are not saved in model file as they are language dependent feature.  With
-Python, user can pickle the model to include these functions in saved binary.  One
-drawback is, the output from pickle is not a stable serialization format and doesn't work
-on different Python version or XGBoost version, not to mention different language
-environment.  Another way to workaround this limitation is to provide these functions
-again after the model is loaded. If the customized function is useful, please consider
-making a PR for implementing it inside XGBoost, this way we can have your functions
-working with different language bindings.
-
-******************************************************
-Loading pickled file from different version of XGBoost
-******************************************************
-
-As noted, pickled model is neither portable nor stable, but in some cases the pickled
-models are valuable.  One way to restore it in the future is to load it back with that
-specific version of Python and XGBoost, export the model by calling `save_model`.  To help
-easing the mitigation, we created a simple script for converting pickled XGBoost 0.90
-Scikit-Learn interface object to XGBoost 1.0.0 native model.  Please note that the script
-suits simple use cases, and it's advised not to use pickle when stability is needed.
-It's located in ``xgboost/doc/python`` with the name ``convert_090to100.py``.  See
-comments in the script for more details.
-
-********************************************************
-Saving and Loading the internal parameters configuration
-********************************************************
-
-XGBoost's ``C API``, ``Python API`` and ``R API`` support saving and loading the internal
-configuration directly as a JSON string.  In Python package:
-
-.. code-block:: python
-
-  bst = xgboost.train(...)
-  config = bst.save_config()
-  print(config)
-
-
-or
-
-.. code-block:: R
-
-  config <- xgb.config(bst)
-  print(config)
-
-Will print out something similiar to (not actual output as it's too long for demonstration):
-
-.. code-block:: json
-
-    {
-      "Learner": {
-        "generic_parameter": {
-          "enable_experimental_json_serialization": "0",
-          "gpu_id": "0",
-          "gpu_page_size": "0",
-          "n_jobs": "0",
-          "random_state": "0",
-          "seed": "0",
-          "seed_per_iteration": "0"
-        },
-        "gradient_booster": {
-          "gbtree_train_param": {
-            "num_parallel_tree": "1",
-            "predictor": "gpu_predictor",
-            "process_type": "default",
-            "tree_method": "gpu_hist",
-            "updater": "grow_gpu_hist",
-            "updater_seq": "grow_gpu_hist"
-          },
-          "name": "gbtree",
-          "updater": {
-            "grow_gpu_hist": {
-              "gpu_hist_train_param": {
-                "debug_synchronize": "0",
-                "gpu_batch_nrows": "0",
-                "single_precision_histogram": "0"
-              },
-              "train_param": {
-                "alpha": "0",
-                "cache_opt": "1",
-                "colsample_bylevel": "1",
-                "colsample_bynode": "1",
-                "colsample_bytree": "1",
-                "default_direction": "learn",
-                "enable_feature_grouping": "0",
-                "eta": "0.300000012",
-                "gamma": "0",
-                "grow_policy": "depthwise",
-                "interaction_constraints": "",
-                "lambda": "1",
-                "learning_rate": "0.300000012",
-                "max_bin": "256",
-                "max_conflict_rate": "0",
-                "max_delta_step": "0",
-                "max_depth": "6",
-                "max_leaves": "0",
-                "max_search_group": "100",
-                "refresh_leaf": "1",
-                "sketch_eps": "0.0299999993",
-                "sketch_ratio": "2",
-                "subsample": "1"
-              }
-            }
-          }
-        },
-        "learner_train_param": {
-          "booster": "gbtree",
-          "disable_default_eval_metric": "0",
-          "dsplit": "auto",
-          "objective": "reg:squarederror"
-        },
-        "metrics": [],
-        "objective": {
-          "name": "reg:squarederror",
-          "reg_loss_param": {
-            "scale_pos_weight": "1"
-          }
-        }
-      },
-      "version": [1, 0, 0]
-    }
-
-
-You can load it back to the model generated by same version of XGBoost by:
-
-.. code-block:: python
-
-  bst.load_config(config)
-
-This way users can study the internal representation more closely.  Please note that some
-JSON generators make use of locale dependent floating point serialization methods, which
-is not supported by XGBoost.
-
-************
-Future Plans
-************
-
-Right now using the JSON format incurs longer serialisation time, we have been working on
-optimizing the JSON implementation to close the gap between binary format and JSON format.
-You can track the progress in `#5046 <https://github.com/dmlc/xgboost/pull/5046>`_.
-
-***********
-JSON Schema
-***********
-
-Another important feature of JSON format is a documented `Schema
-<https://json-schema.org/>`_, based on which one can easily reuse the output model from
-XGBoost.  Here is the initial draft of JSON schema for the output model (not
-serialization, which will not be stable as noted above).  It's subject to change due to
-the beta status.  For an example of parsing XGBoost tree model, see ``/demo/json-model``.
-Please notice the "weight_drop" field used in "dart" booster.  XGBoost does not scale tree
-leaf directly, instead it saves the weights as a separated array.
-
-.. include:: ../model.schema
-   :code: json
diff --git a/ml-xgboost/include/xgboost/base.h b/ml-xgboost/include/xgboost/base.h
deleted file mode 100644
index 32af107..0000000
--- a/ml-xgboost/include/xgboost/base.h
+++ /dev/null
@@ -1,269 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file base.h
- * \brief defines configuration macros of xgboost.
- */
-#ifndef XGBOOST_BASE_H_
-#define XGBOOST_BASE_H_
-
-#include <dmlc/base.h>
-#include <dmlc/omp.h>
-#include <cmath>
-#include <iostream>
-#include <vector>
-#include <string>
-#include <utility>
-
-/*!
- * \brief string flag for R library, to leave hooks when needed.
- */
-#ifndef XGBOOST_STRICT_R_MODE
-#define XGBOOST_STRICT_R_MODE 0
-#endif  // XGBOOST_STRICT_R_MODE
-
-/*!
- * \brief Whether always log console message with time.
- *  It will display like, with timestamp appended to head of the message.
- *  "[21:47:50] 6513x126 matrix with 143286 entries loaded from
- * ../data/agaricus.txt.train"
- */
-#ifndef XGBOOST_LOG_WITH_TIME
-#define XGBOOST_LOG_WITH_TIME 1
-#endif  // XGBOOST_LOG_WITH_TIME
-
-/*!
- * \brief Whether customize the logger outputs.
- */
-#ifndef XGBOOST_CUSTOMIZE_LOGGER
-#define XGBOOST_CUSTOMIZE_LOGGER XGBOOST_STRICT_R_MODE
-#endif  // XGBOOST_CUSTOMIZE_LOGGER
-
-/*!
- * \brief Whether to customize global PRNG.
- */
-#ifndef XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-#define XGBOOST_CUSTOMIZE_GLOBAL_PRNG XGBOOST_STRICT_R_MODE
-#endif  // XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-
-/*!
- * \brief Check if alignas(*) keyword is supported. (g++ 4.8 or higher)
- */
-#if defined(__GNUC__) && ((__GNUC__ == 4 && __GNUC_MINOR__ >= 8) || __GNUC__ > 4)
-#define XGBOOST_ALIGNAS(X) alignas(X)
-#else
-#define XGBOOST_ALIGNAS(X)
-#endif  // defined(__GNUC__) && ((__GNUC__ == 4 && __GNUC_MINOR__ >= 8) || __GNUC__ > 4)
-
-#if defined(__GNUC__) && ((__GNUC__ == 4 && __GNUC_MINOR__ >= 8) || __GNUC__ > 4) && \
-    !defined(__CUDACC__)
-#include <parallel/algorithm>
-#define XGBOOST_PARALLEL_SORT(X, Y, Z) __gnu_parallel::sort((X), (Y), (Z))
-#define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) \
-  __gnu_parallel::stable_sort((X), (Y), (Z))
-#elif defined(_MSC_VER) && (!__INTEL_COMPILER)
-#include <ppl.h>
-#define XGBOOST_PARALLEL_SORT(X, Y, Z) concurrency::parallel_sort((X), (Y), (Z))
-#define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) std::stable_sort((X), (Y), (Z))
-#else
-#define XGBOOST_PARALLEL_SORT(X, Y, Z) std::sort((X), (Y), (Z))
-#define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) std::stable_sort((X), (Y), (Z))
-#endif  // GLIBC VERSION
-
-#if defined(__GNUC__)
-#define XGBOOST_EXPECT(cond, ret)  __builtin_expect((cond), (ret))
-#else
-#define XGBOOST_EXPECT(cond, ret) (cond)
-#endif  // defined(__GNUC__)
-
-/*!
- * \brief Tag function as usable by device
- */
-#if defined (__CUDA__) || defined(__NVCC__)
-#define XGBOOST_DEVICE __host__ __device__
-#else
-#define XGBOOST_DEVICE
-#endif  // defined (__CUDA__) || defined(__NVCC__)
-
-// These check are for Makefile.
-#if !defined(XGBOOST_MM_PREFETCH_PRESENT) && !defined(XGBOOST_BUILTIN_PREFETCH_PRESENT)
-/* default logic for software pre-fetching */
-#if (defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_AMD64))) || defined(__INTEL_COMPILER)
-// Enable _mm_prefetch for Intel compiler and MSVC+x86
-  #define XGBOOST_MM_PREFETCH_PRESENT
-  #define XGBOOST_BUILTIN_PREFETCH_PRESENT
-#elif defined(__GNUC__)
-// Enable __builtin_prefetch for GCC
-#define XGBOOST_BUILTIN_PREFETCH_PRESENT
-#endif  // GUARDS
-
-#endif  // !defined(XGBOOST_MM_PREFETCH_PRESENT) && !defined()
-
-/*! \brief namespace of xgboost*/
-namespace xgboost {
-
-/*! \brief unsigned integer type used for feature index. */
-using bst_uint = uint32_t;  // NOLINT
-/*! \brief integer type. */
-using bst_int = int32_t;    // NOLINT
-/*! \brief unsigned long integers */
-using bst_ulong = uint64_t;  // NOLINT
-/*! \brief float type, used for storing statistics */
-using bst_float = float;  // NOLINT
-
-/*! \brief Type for data column (feature) index. */
-using bst_feature_t = uint32_t;  // NOLINT
-/*! \brief Type for data row index.
- *
- * Be careful `std::size_t' is implementation-defined.  Meaning that the binary
- * representation of DMatrix might not be portable across platform.  Booster model should
- * be portable as parameters are floating points.
- */
-using bst_row_t = std::size_t;   // NOLINT
-/*! \brief Type for tree node index. */
-using bst_node_t = int32_t;      // NOLINT
-/*! \brief Type for ranking group index. */
-using bst_group_t = uint32_t;    // NOLINT
-
-namespace detail {
-/*! \brief Implementation of gradient statistics pair. Template specialisation
- * may be used to overload different gradients types e.g. low precision, high
- * precision, integer, floating point. */
-template <typename T>
-class GradientPairInternal {
-  /*! \brief gradient statistics */
-  T grad_;
-  /*! \brief second order gradient statistics */
-  T hess_;
-
-  XGBOOST_DEVICE void SetGrad(T g) { grad_ = g; }
-  XGBOOST_DEVICE void SetHess(T h) { hess_ = h; }
-
- public:
-  using ValueT = T;
-
-  XGBOOST_DEVICE GradientPairInternal() : grad_(0), hess_(0) {}
-
-  XGBOOST_DEVICE GradientPairInternal(T grad, T hess) {
-    SetGrad(grad);
-    SetHess(hess);
-  }
-
-  // Copy constructor if of same value type
-  XGBOOST_DEVICE GradientPairInternal(const GradientPairInternal<T> &g)
-      : grad_(g.grad_), hess_(g.hess_) {}  // NOLINT
-
-  // Copy constructor if different value type - use getters and setters to
-  // perform conversion
-  template <typename T2>
-  XGBOOST_DEVICE explicit GradientPairInternal(const GradientPairInternal<T2> &g) {
-    SetGrad(g.GetGrad());
-    SetHess(g.GetHess());
-  }
-
-  XGBOOST_DEVICE T GetGrad() const { return grad_; }
-  XGBOOST_DEVICE T GetHess() const { return hess_; }
-
-  XGBOOST_DEVICE GradientPairInternal<T> &operator+=(
-      const GradientPairInternal<T> &rhs) {
-    grad_ += rhs.grad_;
-    hess_ += rhs.hess_;
-    return *this;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> operator+(
-      const GradientPairInternal<T> &rhs) const {
-    GradientPairInternal<T> g;
-    g.grad_ = grad_ + rhs.grad_;
-    g.hess_ = hess_ + rhs.hess_;
-    return g;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> &operator-=(
-      const GradientPairInternal<T> &rhs) {
-    grad_ -= rhs.grad_;
-    hess_ -= rhs.hess_;
-    return *this;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> operator-(
-      const GradientPairInternal<T> &rhs) const {
-    GradientPairInternal<T> g;
-    g.grad_ = grad_ - rhs.grad_;
-    g.hess_ = hess_ - rhs.hess_;
-    return g;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> &operator*=(float multiplier) {
-    grad_ *= multiplier;
-    hess_ *= multiplier;
-    return *this;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> operator*(float multiplier) const {
-    GradientPairInternal<T> g;
-    g.grad_ = grad_ * multiplier;
-    g.hess_ = hess_ * multiplier;
-    return g;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> &operator/=(float divisor) {
-    grad_ /= divisor;
-    hess_ /= divisor;
-    return *this;
-  }
-
-  XGBOOST_DEVICE GradientPairInternal<T> operator/(float divisor) const {
-    GradientPairInternal<T> g;
-    g.grad_ = grad_ / divisor;
-    g.hess_ = hess_ / divisor;
-    return g;
-  }
-
-  XGBOOST_DEVICE bool operator==(const GradientPairInternal<T> &rhs) const {
-    return grad_ == rhs.grad_ && hess_ == rhs.hess_;
-  }
-
-  XGBOOST_DEVICE explicit GradientPairInternal(int value) {
-    *this = GradientPairInternal<T>(static_cast<float>(value),
-                                  static_cast<float>(value));
-  }
-
-  friend std::ostream &operator<<(std::ostream &os,
-                                  const GradientPairInternal<T> &g) {
-    os << g.GetGrad() << "/" << g.GetHess();
-    return os;
-  }
-};
-}  // namespace detail
-
-/*! \brief gradient statistics pair usually needed in gradient boosting */
-using GradientPair = detail::GradientPairInternal<float>;
-
-/*! \brief High precision gradient statistics pair */
-using GradientPairPrecise = detail::GradientPairInternal<double>;
-
-using Args = std::vector<std::pair<std::string, std::string> >;
-
-/*! \brief small eps gap for minimum split decision. */
-constexpr bst_float kRtEps = 1e-6f;
-
-/*! \brief define unsigned long for openmp loop */
-using omp_ulong = dmlc::omp_ulong;  // NOLINT
-/*! \brief define unsigned int for openmp loop */
-using bst_omp_uint = dmlc::omp_uint;  // NOLINT
-/*! \brief Type used for representing version number in binary form.*/
-using XGBoostVersionT = int32_t;
-
-/*!
- * \brief define compatible keywords in g++
- *  Used to support g++-4.6 and g++4.7
- */
-#if DMLC_USE_CXX11 && defined(__GNUC__) && !defined(__clang_version__)
-#if __GNUC__ == 4 && __GNUC_MINOR__ < 8
-#define override
-#define final
-#endif  // __GNUC__ == 4 && __GNUC_MINOR__ < 8
-#endif  // DMLC_USE_CXX11 && defined(__GNUC__) && !defined(__clang_version__)
-}  // namespace xgboost
-
-#endif  // XGBOOST_BASE_H_
diff --git a/ml-xgboost/include/xgboost/c_api.h b/ml-xgboost/include/xgboost/c_api.h
deleted file mode 100644
index 67114ae..0000000
--- a/ml-xgboost/include/xgboost/c_api.h
+++ /dev/null
@@ -1,687 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright (c) 2015~2020 by Contributors
- * \file c_api.h
- * \author Tianqi Chen
- * \brief C API of XGBoost, used for interfacing to other languages.
- */
-#ifndef XGBOOST_C_API_H_
-#define XGBOOST_C_API_H_
-
-#ifdef __cplusplus
-#define XGB_EXTERN_C extern "C"
-#include <cstdio>
-#include <cstdint>
-#else
-#define XGB_EXTERN_C
-#include <stdio.h>
-#include <stdint.h>
-#endif  // __cplusplus
-
-#if defined(_MSC_VER) || defined(_WIN32)
-#define XGB_DLL XGB_EXTERN_C __declspec(dllexport)
-#else
-#define XGB_DLL XGB_EXTERN_C __attribute__ ((visibility ("default")))
-#endif  // defined(_MSC_VER) || defined(_WIN32)
-
-// manually define unsigned long
-typedef uint64_t bst_ulong;  // NOLINT(*)
-
-
-/*! \brief handle to DMatrix */
-typedef void *DMatrixHandle;  // NOLINT(*)
-/*! \brief handle to Booster */
-typedef void *BoosterHandle;  // NOLINT(*)
-/*! \brief handle to a data iterator */
-typedef void *DataIterHandle;  // NOLINT(*)
-/*! \brief handle to a internal data holder. */
-typedef void *DataHolderHandle;  // NOLINT(*)
-
-/*! \brief Mini batch used in XGBoost Data Iteration */
-typedef struct {  // NOLINT(*)
-  /*! \brief number of rows in the minibatch */
-  size_t size;
-  /* \brief number of columns in the minibatch. */
-  size_t columns;
-  /*! \brief row pointer to the rows in the data */
-#ifdef __APPLE__
-  /* Necessary as Java on MacOS defines jlong as long int
-   * and gcc defines int64_t as long long int. */
-  long* offset; // NOLINT(*)
-#else
-  int64_t* offset;  // NOLINT(*)
-#endif  // __APPLE__
-  /*! \brief labels of each instance */
-  float* label;
-  /*! \brief weight of each instance, can be NULL */
-  float* weight;
-  /*! \brief feature index */
-  int* index;
-  /*! \brief feature values */
-  float* value;
-} XGBoostBatchCSR;
-
-/*!
- * \brief Return the version of the XGBoost library being currently used.
- *
- *  The output variable is only written if it's not NULL.
- *
- * \param major Store the major version number
- * \param minor Store the minor version number
- * \param patch Store the patch (revision) number
- */
-XGB_DLL void XGBoostVersion(int* major, int* minor, int* patch);
-
-/*!
- * \brief Callback to set the data to handle,
- * \param handle The handle to the callback.
- * \param batch The data content to be set.
- */
-XGB_EXTERN_C typedef int XGBCallbackSetData(  // NOLINT(*)
-    DataHolderHandle handle, XGBoostBatchCSR batch);
-
-/*!
- * \brief The data reading callback function.
- *  The iterator will be able to give subset of batch in the data.
- *
- *  If there is data, the function will call set_function to set the data.
- *
- * \param data_handle The handle to the callback.
- * \param set_function The batch returned by the iterator
- * \param set_function_handle The handle to be passed to set function.
- * \return 0 if we are reaching the end and batch is not returned.
- */
-XGB_EXTERN_C typedef int XGBCallbackDataIterNext(  // NOLINT(*)
-    DataIterHandle data_handle, XGBCallbackSetData *set_function,
-    DataHolderHandle set_function_handle);
-
-/*!
- * \brief get string message of the last error
- *
- *  all function in this file will return 0 when success
- *  and -1 when an error occurred,
- *  XGBGetLastError can be called to retrieve the error
- *
- *  this function is thread safe and can be called by different thread
- * \return const char* error information
- */
-XGB_DLL const char *XGBGetLastError(void);
-
-/*!
- * \brief register callback function for LOG(INFO) messages -- helpful messages
- *        that are not errors.
- * Note: this function can be called by multiple threads. The callback function
- *       will run on the thread that registered it
- * \return 0 for success, -1 for failure
- */
-XGB_DLL int XGBRegisterLogCallback(void (*callback)(const char*));
-
-/*!
- * \brief load a data matrix
- * \param fname the name of the file
- * \param silent whether print messages during loading
- * \param out a loaded data matrix
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromFile(const char *fname,
-                                    int silent,
-                                    DMatrixHandle *out);
-
-/*!
- * \brief Create a DMatrix from a data iterator.
- * \param data_handle The handle to the data.
- * \param callback The callback to get the data.
- * \param cache_info Additional information about cache file, can be null.
- * \param out The created DMatrix
- * \return 0 when success, -1 when failure happens.
- */
-XGB_DLL int XGDMatrixCreateFromDataIter(
-    DataIterHandle data_handle,
-    XGBCallbackDataIterNext* callback,
-    const char* cache_info,
-    DMatrixHandle *out);
-
-/*!
- * \brief create a matrix content from CSR format
- * \param indptr pointer to row headers
- * \param indices findex
- * \param data fvalue
- * \param nindptr number of rows in the matrix + 1
- * \param nelem number of nonzero elements in the matrix
- * \param num_col number of columns; when it's set to 0, then guess from data
- * \param out created dmatrix
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
-                                     const unsigned* indices,
-                                     const float* data,
-                                     size_t nindptr,
-                                     size_t nelem,
-                                     size_t num_col,
-                                     DMatrixHandle* out);
-/*!
- * \brief create a matrix content from CSC format
- * \param col_ptr pointer to col headers
- * \param indices findex
- * \param data fvalue
- * \param nindptr number of rows in the matrix + 1
- * \param nelem number of nonzero elements in the matrix
- * \param num_row number of rows; when it's set to 0, then guess from data
- * \param out created dmatrix
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
-                                     const unsigned* indices,
-                                     const float* data,
-                                     size_t nindptr,
-                                     size_t nelem,
-                                     size_t num_row,
-                                     DMatrixHandle* out);
-
-/*!
- * \brief create matrix content from dense matrix
- * \param data pointer to the data space
- * \param nrow number of rows
- * \param ncol number columns
- * \param missing which value to represent missing value
- * \param out created dmatrix
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromMat(const float *data,
-                                   bst_ulong nrow,
-                                   bst_ulong ncol,
-                                   float missing,
-                                   DMatrixHandle *out);
-/*!
- * \brief create matrix content from dense matrix
- * \param data pointer to the data space
- * \param nrow number of rows
- * \param ncol number columns
- * \param missing which value to represent missing value
- * \param out created dmatrix
- * \param nthread number of threads (up to maximum cores available, if <=0 use all cores)
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromMat_omp(const float *data,  // NOLINT
-                                       bst_ulong nrow, bst_ulong ncol,
-                                       float missing, DMatrixHandle *out,
-                                       int nthread);
-/*!
- * \brief create matrix content from python data table
- * \param data pointer to pointer to column data
- * \param feature_stypes pointer to strings
- * \param nrow number of rows
- * \param ncol number columns
- * \param out created dmatrix
- * \param nthread number of threads (up to maximum cores available, if <=0 use all cores)
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromDT(void** data,
-                                  const char ** feature_stypes,
-                                  bst_ulong nrow,
-                                  bst_ulong ncol,
-                                  DMatrixHandle* out,
-                                  int nthread);
-/*!
- * \brief create a new dmatrix from sliced content of existing matrix
- * \param handle instance of data matrix to be sliced
- * \param idxset index set
- * \param len length of index set
- * \param out a sliced new matrix
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
-                                  const int *idxset,
-                                  bst_ulong len,
-                                  DMatrixHandle *out);
-/*!
- * \brief create a new dmatrix from sliced content of existing matrix
- * \param handle instance of data matrix to be sliced
- * \param idxset index set
- * \param len length of index set
- * \param out a sliced new matrix
- * \param allow_groups allow slicing of an array with groups
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixSliceDMatrixEx(DMatrixHandle handle,
-                                    const int *idxset,
-                                    bst_ulong len,
-                                    DMatrixHandle *out,
-                                    int allow_groups);
-/*!
- * \brief free space in data matrix
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixFree(DMatrixHandle handle);
-/*!
- * \brief load a data matrix into binary file
- * \param handle a instance of data matrix
- * \param fname file name
- * \param silent print statistics when saving
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixSaveBinary(DMatrixHandle handle,
-                                const char *fname, int silent);
-/*!
- * \brief set float vector to a content in info
- * \param handle a instance of data matrix
- * \param field field name, can be label, weight
- * \param array pointer to float vector
- * \param len length of array
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixSetFloatInfo(DMatrixHandle handle,
-                                  const char *field,
-                                  const float *array,
-                                  bst_ulong len);
-/*!
- * \brief set uint32 vector to a content in info
- * \param handle a instance of data matrix
- * \param field field name
- * \param array pointer to unsigned int vector
- * \param len length of array
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixSetUIntInfo(DMatrixHandle handle,
-                                 const char *field,
-                                 const unsigned *array,
-                                 bst_ulong len);
-
-/*!
- * \brief (deprecated) Use XGDMatrixSetUIntInfo instead. Set group of the training matrix
- * \param handle a instance of data matrix
- * \param group pointer to group size
- * \param len length of array
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixSetGroup(DMatrixHandle handle,
-                              const unsigned *group,
-                              bst_ulong len);
-
-/*!
- * \brief get float info vector from matrix.
- * \param handle a instance of data matrix
- * \param field field name
- * \param out_len used to set result length
- * \param out_dptr pointer to the result
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixGetFloatInfo(const DMatrixHandle handle,
-                                  const char *field,
-                                  bst_ulong* out_len,
-                                  const float **out_dptr);
-/*!
- * \brief get uint32 info vector from matrix
- * \param handle a instance of data matrix
- * \param field field name
- * \param out_len The length of the field.
- * \param out_dptr pointer to the result
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
-                                 const char *field,
-                                 bst_ulong* out_len,
-                                 const unsigned **out_dptr);
-/*!
- * \brief get number of rows.
- * \param handle the handle to the DMatrix
- * \param out The address to hold number of rows.
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixNumRow(DMatrixHandle handle,
-                            bst_ulong *out);
-/*!
- * \brief get number of columns
- * \param handle the handle to the DMatrix
- * \param out The output of number of columns
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixNumCol(DMatrixHandle handle,
-                            bst_ulong *out);
-// --- start XGBoost class
-/*!
- * \brief create xgboost learner
- * \param dmats matrices that are set to be cached
- * \param len length of dmats
- * \param out handle to the result booster
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterCreate(const DMatrixHandle dmats[],
-                            bst_ulong len,
-                            BoosterHandle *out);
-/*!
- * \brief free obj in handle
- * \param handle handle to be freed
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterFree(BoosterHandle handle);
-
-/*!
- * \brief set parameters
- * \param handle handle
- * \param name  parameter name
- * \param value value of parameter
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterSetParam(BoosterHandle handle,
-                              const char *name,
-                              const char *value);
-
-/*!
-* \brief update the model in one round using dtrain
-* \param handle handle
-* \param iter current iteration rounds
-* \param dtrain training data
-* \return 0 when success, -1 when failure happens
-*/
-XGB_DLL int XGBoosterUpdateOneIter(BoosterHandle handle,
-                                   int iter,
-                                   DMatrixHandle dtrain);
-
-/*!
- * \brief update the model in one round using dtrain
- * \param handle handle
- * \param iter current iteration rounds
- * \param dtrain training data
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterUpdateOneIterLtd(BoosterHandle handle,
-                                      int iter,
-                                      DMatrixHandle dtrain,
-                                      float* last_round_best_loss);
-/*!
- * \brief update the model, by directly specify gradient and second order gradient,
- *        this can be used to replace UpdateOneIter, to support customized loss function
- * \param handle handle
- * \param dtrain training data
- * \param grad gradient statistics
- * \param hess second order gradient statistics
- * \param len length of grad/hess array
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterBoostOneIter(BoosterHandle handle,
-                                  DMatrixHandle dtrain,
-                                  float *grad,
-                                  float *hess,
-                                  bst_ulong len);
-/*!
- * \brief get evaluation statistics for xgboost
- * \param handle handle
- * \param iter current iteration rounds
- * \param dmats pointers to data to be evaluated
- * \param evnames pointers to names of each data
- * \param len length of dmats
- * \param out_result the string containing evaluation statistics
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
-                                 int iter,
-                                 DMatrixHandle dmats[],
-                                 const char *evnames[],
-                                 bst_ulong len,
-                                 const char **out_result);
-/*!
- * \brief make prediction based on dmat
- * \param handle handle
- * \param dmat data matrix
- * \param option_mask bit-mask of options taken in prediction, possible values
- *          0:normal prediction
- *          1:output margin instead of transformed value
- *          2:output leaf index of trees instead of leaf value, note leaf index is unique per tree
- *          4:output feature contributions to individual predictions
- * \param ntree_limit limit number of trees used for prediction, this is only valid for boosted trees
- *    when the parameter is set to 0, we will use all the trees
- * \param training Whether the prediction function is used as part of a training loop.
- *    Prediction can be run in 2 scenarios:
- *    1. Given data matrix X, obtain prediction y_pred from the model.
- *    2. Obtain the prediction for computing gradients. For example, DART booster performs dropout
- *       during training, and the prediction result will be different from the one obtained by normal
- *       inference step due to dropped trees.
- *    Set training=false for the first scenario. Set training=true for the second scenario.
- *    The second scenario applies when you are defining a custom objective function.
- * \param out_len used to store length of returning result
- * \param out_result used to set a pointer to array
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterPredict(BoosterHandle handle,
-                             DMatrixHandle dmat,
-                             int option_mask,
-                             unsigned ntree_limit,
-                             int training,
-                             bst_ulong *out_len,
-                             const float **out_result);
-/*
- * Short note for serialization APIs.  There are 3 different sets of serialization API.
- *
- * - Functions with the term "Model" handles saving/loading XGBoost model like trees or
- *   linear weights.  Striping out parameters configuration like training algorithms or
- *   CUDA device ID helps user to reuse the trained model for different tasks, examples
- *   are prediction, training continuation or interpretation.
- *
- * - Functions with the term "Config" handles save/loading configuration.  It helps user
- *   to study the internal of XGBoost.  Also user can use the load method for specifying
- *   paramters in a structured way.  These functions are introduced in 1.0.0, and are not
- *   yet stable.
- *
- * - Functions with the term "Serialization" are combined of above two.  They are used in
- *   situations like check-pointing, or continuing training task in distributed
- *   environment.  In these cases the task must be carried out without any user
- *   intervention.
- */
-
-/*!
- * \brief Load model from existing file
- * \param handle handle
- * \param fname file name
-* \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterLoadModel(BoosterHandle handle,
-                               const char *fname);
-/*!
- * \brief Save model into existing file
- * \param handle handle
- * \param fname file name
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterSaveModel(BoosterHandle handle,
-                               const char *fname);
-/*!
- * \brief load model from in memory buffer
- * \param handle handle
- * \param buf pointer to the buffer
- * \param len the length of the buffer
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterLoadModelFromBuffer(BoosterHandle handle,
-                                         const void *buf,
-                                         bst_ulong len);
-/*!
- * \brief save model into binary raw bytes, return header of the array
- * user must copy the result out, before next xgboost call
- * \param handle handle
- * \param out_len the argument to hold the output length
- * \param out_dptr the argument to hold the output data pointer
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterGetModelRaw(BoosterHandle handle, bst_ulong *out_len,
-                                 const char **out_dptr);
-
-/*!
- * \brief Memory snapshot based serialization method.  Saves everything states
- * into buffer.
- *
- * \param handle handle
- * \param out_len the argument to hold the output length
- * \param out_dptr the argument to hold the output data pointer
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterSerializeToBuffer(BoosterHandle handle, bst_ulong *out_len,
-                                       const char **out_dptr);
-/*!
- * \brief Memory snapshot based serialization method.  Loads the buffer returned
- *        from `XGBoosterSerializeToBuffer'.
- *
- * \param handle handle
- * \param buf pointer to the buffer
- * \param len the length of the buffer
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterUnserializeFromBuffer(BoosterHandle handle,
-                                           const void *buf, bst_ulong len);
-
-/*!
- * \brief Initialize the booster from rabit checkpoint.
- *  This is used in distributed training API.
- * \param handle handle
- * \param version The output version of the model.
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterLoadRabitCheckpoint(BoosterHandle handle,
-                                         int* version);
-
-/*!
- * \brief Save the current checkpoint to rabit.
- * \param handle handle
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterSaveRabitCheckpoint(BoosterHandle handle);
-
-
-/*!
- * \brief Save XGBoost's internal configuration into a JSON document.  Currently the
- *        support is experimental, function signature may change in the future without
- *        notice.
- *
- * \param handle handle to Booster object.
- * \param out_len length of output string
- * \param out_str A valid pointer to array of characters.  The characters array is
- *                allocated and managed by XGBoost, while pointer to that array needs to
- *                be managed by caller.
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterSaveJsonConfig(BoosterHandle handle, bst_ulong *out_len,
-                                    char const **out_str);
-/*!
- * \brief Load XGBoost's internal configuration from a JSON document.  Currently the
- *        support is experimental, function signature may change in the future without
- *        notice.
- *
- * \param handle handle to Booster object.
- * \param json_parameters string representation of a JSON document.
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterLoadJsonConfig(BoosterHandle handle,
-                                    char const *json_parameters);
-
-/*!
- * \brief dump model, return array of strings representing model dump
- * \param handle handle
- * \param fmap  name to fmap can be empty string
- * \param with_stats whether to dump with statistics
- * \param out_len length of output array
- * \param out_dump_array pointer to hold representing dump of each model
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterDumpModel(BoosterHandle handle,
-                               const char *fmap,
-                               int with_stats,
-                               bst_ulong *out_len,
-                               const char ***out_dump_array);
-
-/*!
- * \brief dump model, return array of strings representing model dump
- * \param handle handle
- * \param fmap  name to fmap can be empty string
- * \param with_stats whether to dump with statistics
- * \param format the format to dump the model in
- * \param out_len length of output array
- * \param out_dump_array pointer to hold representing dump of each model
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterDumpModelEx(BoosterHandle handle,
-                                 const char *fmap,
-                                 int with_stats,
-                                 const char *format,
-                                 bst_ulong *out_len,
-                                 const char ***out_dump_array);
-
-/*!
- * \brief dump model, return array of strings representing model dump
- * \param handle handle
- * \param fnum number of features
- * \param fname names of features
- * \param ftype types of features
- * \param with_stats whether to dump with statistics
- * \param out_len length of output array
- * \param out_models pointer to hold representing dump of each model
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
-                                           int fnum,
-                                           const char **fname,
-                                           const char **ftype,
-                                           int with_stats,
-                                           bst_ulong *out_len,
-                                           const char ***out_models);
-
-/*!
- * \brief dump model, return array of strings representing model dump
- * \param handle handle
- * \param fnum number of features
- * \param fname names of features
- * \param ftype types of features
- * \param with_stats whether to dump with statistics
- * \param format the format to dump the model in
- * \param out_len length of output array
- * \param out_models pointer to hold representing dump of each model
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterDumpModelExWithFeatures(BoosterHandle handle,
-                                             int fnum,
-                                             const char **fname,
-                                             const char **ftype,
-                                             int with_stats,
-                                             const char *format,
-                                             bst_ulong *out_len,
-                                             const char ***out_models);
-
-/*!
- * \brief Get string attribute from Booster.
- * \param handle handle
- * \param key The key of the attribute.
- * \param out The result attribute, can be NULL if the attribute do not exist.
- * \param success Whether the result is contained in out.
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterGetAttr(BoosterHandle handle,
-                             const char* key,
-                             const char** out,
-                             int *success);
-/*!
- * \brief Set or delete string attribute.
- *
- * \param handle handle
- * \param key The key of the attribute.
- * \param value The value to be saved.
- *              If nullptr, the attribute would be deleted.
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterSetAttr(BoosterHandle handle,
-                             const char* key,
-                             const char* value);
-/*!
- * \brief Get the names of all attribute from Booster.
- * \param handle handle
- * \param out_len the argument to hold the output length
- * \param out pointer to hold the output attribute stings
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGBoosterGetAttrNames(BoosterHandle handle,
-                                  bst_ulong* out_len,
-                                  const char*** out);
-#endif  // XGBOOST_C_API_H_
diff --git a/ml-xgboost/include/xgboost/data.h b/ml-xgboost/include/xgboost/data.h
deleted file mode 100644
index e6754b9..0000000
--- a/ml-xgboost/include/xgboost/data.h
+++ /dev/null
@@ -1,575 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file data.h
- * \brief The input data structure of xgboost.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_DATA_H_
-#define XGBOOST_DATA_H_
-
-#include <dmlc/base.h>
-#include <dmlc/data.h>
-#include <dmlc/serializer.h>
-#include <rabit/rabit.h>
-#include <xgboost/base.h>
-#include <xgboost/span.h>
-#include <xgboost/host_device_vector.h>
-
-#include <memory>
-#include <numeric>
-#include <algorithm>
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace xgboost {
-// forward declare dmatrix.
-class DMatrix;
-
-/*! \brief data type accepted by xgboost interface */
-enum class DataType : uint8_t {
-  kFloat32 = 1,
-  kDouble = 2,
-  kUInt32 = 3,
-  kUInt64 = 4
-};
-
-/*!
- * \brief Meta information about dataset, always sit in memory.
- */
-class MetaInfo {
- public:
-  /*! \brief number of data fields in MetaInfo */
-  static constexpr uint64_t kNumField = 9;
-
-  /*! \brief number of rows in the data */
-  uint64_t num_row_{0};  // NOLINT
-  /*! \brief number of columns in the data */
-  uint64_t num_col_{0};  // NOLINT
-  /*! \brief number of nonzero entries in the data */
-  uint64_t num_nonzero_{0};  // NOLINT
-  /*! \brief label of each instance */
-  HostDeviceVector<bst_float> labels_;  // NOLINT
-  /*!
-   * \brief the index of begin and end of a group
-   *  needed when the learning task is ranking.
-   */
-  std::vector<bst_group_t> group_ptr_;  // NOLINT
-  /*! \brief weights of each instance, optional */
-  HostDeviceVector<bst_float> weights_;  // NOLINT
-  /*!
-   * \brief initialized margins,
-   * if specified, xgboost will start from this init margin
-   * can be used to specify initial prediction to boost from.
-   */
-  HostDeviceVector<bst_float> base_margin_;  // NOLINT
-  /*!
-   * \brief lower bound of the label, to be used for survival analysis (censored regression)
-   */
-  HostDeviceVector<bst_float> labels_lower_bound_;  // NOLINT
-  /*!
-   * \brief upper bound of the label, to be used for survival analysis (censored regression)
-   */
-  HostDeviceVector<bst_float> labels_upper_bound_;  // NOLINT
-
-  /*! \brief default constructor */
-  MetaInfo()  = default;
-  MetaInfo(MetaInfo&& that) = default;
-  MetaInfo& operator=(MetaInfo&& that) = default;
-  MetaInfo& operator=(MetaInfo const& that) {
-    this->num_row_ = that.num_row_;
-    this->num_col_ = that.num_col_;
-    this->num_nonzero_ = that.num_nonzero_;
-
-    this->labels_.Resize(that.labels_.Size());
-    this->labels_.Copy(that.labels_);
-
-    this->group_ptr_ = that.group_ptr_;
-
-    this->weights_.Resize(that.weights_.Size());
-    this->weights_.Copy(that.weights_);
-
-    this->base_margin_.Resize(that.base_margin_.Size());
-    this->base_margin_.Copy(that.base_margin_);
-
-    this->labels_lower_bound_.Resize(that.labels_lower_bound_.Size());
-    this->labels_lower_bound_.Copy(that.labels_lower_bound_);
-
-    this->labels_upper_bound_.Resize(that.labels_upper_bound_.Size());
-    this->labels_upper_bound_.Copy(that.labels_upper_bound_);
-    return *this;
-  }
-
-  /*!
-   * \brief Validate all metainfo.
-   */
-  void Validate(int32_t device) const;
-
-  MetaInfo Slice(common::Span<int32_t const> ridxs) const;
-  /*!
-   * \brief Get weight of each instances.
-   * \param i Instance index.
-   * \return The weight.
-   */
-  inline bst_float GetWeight(size_t i) const {
-    return weights_.Size() != 0 ?  weights_.HostVector()[i] : 1.0f;
-  }
-  /*! \brief get sorted indexes (argsort) of labels by absolute value (used by cox loss) */
-  inline const std::vector<size_t>& LabelAbsSort() const {
-    if (label_order_cache_.size() == labels_.Size()) {
-      return label_order_cache_;
-    }
-    label_order_cache_.resize(labels_.Size());
-    std::iota(label_order_cache_.begin(), label_order_cache_.end(), 0);
-    const auto& l = labels_.HostVector();
-    XGBOOST_PARALLEL_SORT(label_order_cache_.begin(), label_order_cache_.end(),
-              [&l](size_t i1, size_t i2) {return std::abs(l[i1]) < std::abs(l[i2]);});
-
-    return label_order_cache_;
-  }
-  /*! \brief clear all the information */
-  void Clear();
-  /*!
-   * \brief Load the Meta info from binary stream.
-   * \param fi The input stream
-   */
-  void LoadBinary(dmlc::Stream* fi);
-  /*!
-   * \brief Save the Meta info to binary stream
-   * \param fo The output stream.
-   */
-  void SaveBinary(dmlc::Stream* fo) const;
-  /*!
-   * \brief Set information in the meta info.
-   * \param key The key of the information.
-   * \param dptr The data pointer of the source array.
-   * \param dtype The type of the source data.
-   * \param num Number of elements in the source array.
-   */
-  void SetInfo(const char* key, const void* dptr, DataType dtype, size_t num);
-  /*!
-   * \brief Set information in the meta info with array interface.
-   * \param key The key of the information.
-   * \param interface_str String representation of json format array interface.
-   *
-   *          [ column_0, column_1, ... column_n ]
-   *
-   *        Right now only 1 column is permitted.
-   */
-  void SetInfo(const char* key, std::string const& interface_str);
-
- private:
-  /*! \brief argsort of labels */
-  mutable std::vector<size_t> label_order_cache_;
-};
-
-/*! \brief Element from a sparse vector */
-struct Entry {
-  /*! \brief feature index */
-  bst_feature_t index;
-  /*! \brief feature value */
-  bst_float fvalue;
-  /*! \brief default constructor */
-  Entry() = default;
-  /*!
-   * \brief constructor with index and value
-   * \param index The feature or row index.
-   * \param fvalue The feature value.
-   */
-  XGBOOST_DEVICE Entry(bst_feature_t index, bst_float fvalue) : index(index), fvalue(fvalue) {}
-  /*! \brief reversely compare feature values */
-  inline static bool CmpValue(const Entry& a, const Entry& b) {
-    return a.fvalue < b.fvalue;
-  }
-  inline bool operator==(const Entry& other) const {
-    return (this->index == other.index && this->fvalue == other.fvalue);
-  }
-};
-
-/*!
- * \brief Parameters for constructing batches.
- */
-struct BatchParam {
-  /*! \brief The GPU device to use. */
-  int gpu_id;
-  /*! \brief Maximum number of bins per feature for histograms. */
-  int max_bin{0};
-  /*! \brief Page size for external memory mode. */
-  size_t gpu_page_size;
-  BatchParam() = default;
-  BatchParam(int32_t device, int32_t max_bin, size_t gpu_page_size = 0)
-      : gpu_id{device}, max_bin{max_bin}, gpu_page_size{gpu_page_size} {}
-  inline bool operator!=(const BatchParam& other) const {
-    return gpu_id != other.gpu_id || max_bin != other.max_bin ||
-           gpu_page_size != other.gpu_page_size;
-  }
-};
-
-/*!
- * \brief In-memory storage unit of sparse batch, stored in CSR format.
- */
-class SparsePage {
- public:
-  // Offset for each row.
-  HostDeviceVector<bst_row_t> offset;
-  /*! \brief the data of the segments */
-  HostDeviceVector<Entry> data;
-
-  size_t base_rowid{};
-
-  /*! \brief an instance of sparse vector in the batch */
-  using Inst = common::Span<Entry const>;
-
-  /*! \brief get i-th row from the batch */
-  inline Inst operator[](size_t i) const {
-    const auto& data_vec = data.HostVector();
-    const auto& offset_vec = offset.HostVector();
-    size_t size;
-    // in distributed mode, some partitions may not get any instance for a feature. Therefore
-    // we should set the size as zero
-    if (rabit::IsDistributed() && i + 1 >= offset_vec.size()) {
-      size = 0;
-    } else {
-      size = offset_vec[i + 1] - offset_vec[i];
-    }
-    return {data_vec.data() + offset_vec[i],
-            static_cast<Inst::index_type>(size)};
-  }
-
-  /*! \brief constructor */
-  SparsePage() {
-    this->Clear();
-  }
-
-  /*! \return Number of instances in the page. */
-  inline size_t Size() const {
-    return offset.Size() == 0 ? 0 : offset.Size() - 1;
-  }
-
-  /*! \return estimation of memory cost of this page */
-  inline size_t MemCostBytes() const {
-    return offset.Size() * sizeof(size_t) + data.Size() * sizeof(Entry);
-  }
-
-  /*! \brief clear the page */
-  inline void Clear() {
-    base_rowid = 0;
-    auto& offset_vec = offset.HostVector();
-    offset_vec.clear();
-    offset_vec.push_back(0);
-    data.HostVector().clear();
-  }
-
-  /*! \brief Set the base row id for this page. */
-  inline void SetBaseRowId(size_t row_id) {
-    base_rowid = row_id;
-  }
-
-  SparsePage GetTranspose(int num_columns) const;
-
-  void SortRows() {
-    auto ncol = static_cast<bst_omp_uint>(this->Size());
-#pragma omp parallel for default(none) shared(ncol) schedule(dynamic, 1)
-    for (bst_omp_uint i = 0; i < ncol; ++i) {
-      if (this->offset.HostVector()[i] < this->offset.HostVector()[i + 1]) {
-        std::sort(
-            this->data.HostVector().begin() + this->offset.HostVector()[i],
-            this->data.HostVector().begin() + this->offset.HostVector()[i + 1],
-            Entry::CmpValue);
-      }
-    }
-  }
-
-  /*!
-   * \brief Push row block into the page.
-   * \param batch the row batch.
-   */
-  void Push(const dmlc::RowBlock<uint32_t>& batch);
-
-  /**
-   * \brief Pushes external data batch onto this page
-   *
-   * \tparam  AdapterBatchT
-   * \param batch
-   * \param missing
-   * \param nthread
-   *
-   * \return  The maximum number of columns encountered in this input batch. Useful when pushing many adapter batches to work out the total number of columns.
-   */
-  template <typename AdapterBatchT>
-  uint64_t Push(const AdapterBatchT& batch, float missing, int nthread);
-
-  /*!
-   * \brief Push a sparse page
-   * \param batch the row page
-   */
-  void Push(const SparsePage &batch);
-  /*!
-   * \brief Push a SparsePage stored in CSC format
-   * \param batch The row batch to be pushed
-   */
-  void PushCSC(const SparsePage& batch);
-};
-
-class CSCPage: public SparsePage {
- public:
-  CSCPage() : SparsePage() {}
-  explicit CSCPage(SparsePage page) : SparsePage(std::move(page)) {}
-};
-
-class SortedCSCPage : public SparsePage {
- public:
-  SortedCSCPage() : SparsePage() {}
-  explicit SortedCSCPage(SparsePage page) : SparsePage(std::move(page)) {}
-};
-
-class EllpackPageImpl;
-/*!
- * \brief A page stored in ELLPACK format.
- *
- * This class uses the PImpl idiom (https://en.cppreference.com/w/cpp/language/pimpl) to avoid
- * including CUDA-specific implementation details in the header.
- */
-class EllpackPage {
- public:
-  /*!
-   * \brief Default constructor.
-   *
-   * This is used in the external memory case. An empty ELLPACK page is constructed with its content
-   * set later by the reader.
-   */
-  EllpackPage();
-
-  /*!
-   * \brief Constructor from an existing DMatrix.
-   *
-   * This is used in the in-memory case. The ELLPACK page is constructed from an existing DMatrix
-   * in CSR format.
-   */
-  explicit EllpackPage(DMatrix* dmat, const BatchParam& param);
-
-  /*! \brief Destructor. */
-  ~EllpackPage();
-
-  /*! \return Number of instances in the page. */
-  size_t Size() const;
-
-  /*! \brief Set the base row id for this page. */
-  void SetBaseRowId(size_t row_id);
-
-  const EllpackPageImpl* Impl() const { return impl_.get(); }
-  EllpackPageImpl* Impl() { return impl_.get(); }
-
- private:
-  std::unique_ptr<EllpackPageImpl> impl_;
-};
-
-template<typename T>
-class BatchIteratorImpl {
- public:
-  virtual ~BatchIteratorImpl() = default;
-  virtual T& operator*() = 0;
-  virtual const T& operator*() const = 0;
-  virtual void operator++() = 0;
-  virtual bool AtEnd() const = 0;
-};
-
-template<typename T>
-class BatchIterator {
- public:
-  using iterator_category = std::forward_iterator_tag;  // NOLINT
-  explicit BatchIterator(BatchIteratorImpl<T>* impl) { impl_.reset(impl); }
-
-  void operator++() {
-    CHECK(impl_ != nullptr);
-    ++(*impl_);
-  }
-
-  T& operator*() {
-    CHECK(impl_ != nullptr);
-    return *(*impl_);
-  }
-
-  const T& operator*() const {
-    CHECK(impl_ != nullptr);
-    return *(*impl_);
-  }
-
-  bool operator!=(const BatchIterator& rhs) const {
-    CHECK(impl_ != nullptr);
-    return !impl_->AtEnd();
-  }
-
-  bool AtEnd() const {
-    CHECK(impl_ != nullptr);
-    return impl_->AtEnd();
-  }
-
- private:
-  std::shared_ptr<BatchIteratorImpl<T>> impl_;
-};
-
-template<typename T>
-class BatchSet {
- public:
-  explicit BatchSet(BatchIterator<T> begin_iter) : begin_iter_(std::move(begin_iter)) {}
-  BatchIterator<T> begin() { return begin_iter_; }  // NOLINT
-  BatchIterator<T> end() { return BatchIterator<T>(nullptr); }  // NOLINT
-
- private:
-  BatchIterator<T> begin_iter_;
-};
-
-/*!
- * \brief This is data structure that user can pass to DMatrix::Create
- *  to create a DMatrix for training, user can create this data structure
- *  for customized Data Loading on single machine.
- *
- *  On distributed setting, usually an customized dmlc::Parser is needed instead.
- */
-template<typename T>
-class DataSource : public dmlc::DataIter<T> {
- public:
-  /*!
-   * \brief Meta information about the dataset
-   * The subclass need to be able to load this correctly from data.
-   */
-  MetaInfo info;
-};
-
-/*!
- * \brief Internal data structured used by XGBoost during training.
- *  There are two ways to create a customized DMatrix that reads in user defined-format.
- *
- *  - Provide a dmlc::Parser and pass into the DMatrix::Create
- *  - Alternatively, if data can be represented by an URL, define a new dmlc::Parser and register by
- *    DMLC_REGISTER_DATA_PARSER;
- *      - This works best for user defined data input source, such as data-base, filesystem.
- *  - Provide a DataSource, that can be passed to DMatrix::Create
- *      This can be used to re-use inmemory data structure into DMatrix.
- */
-class DMatrix {
- public:
-  /*! \brief default constructor */
-  DMatrix()  = default;
-  /*! \brief meta information of the dataset */
-  virtual MetaInfo& Info() = 0;
-  /*! \brief meta information of the dataset */
-  virtual const MetaInfo& Info() const = 0;
-  /**
-   * \brief Gets batches. Use range based for loop over BatchSet to access individual batches.
-   */
-  template<typename T>
-  BatchSet<T> GetBatches(const BatchParam& param = {});
-  template <typename T>
-  bool PageExists() const;
-
-  // the following are column meta data, should be able to answer them fast.
-  /*! \return Whether the data columns single column block. */
-  virtual bool SingleColBlock() const = 0;
-  /*! \brief virtual destructor */
-  virtual ~DMatrix() = default;
-
-  /*! \brief Whether the matrix is dense. */
-  bool IsDense() const {
-    return Info().num_nonzero_ == Info().num_row_ * Info().num_col_;
-  }
-
-  /*!
-   * \brief Load DMatrix from URI.
-   * \param uri The URI of input.
-   * \param silent Whether print information during loading.
-   * \param load_row_split Flag to read in part of rows, divided among the workers in distributed mode.
-   * \param file_format The format type of the file, used for dmlc::Parser::Create.
-   *   By default "auto" will be able to load in both local binary file.
-   * \param page_size Page size for external memory.
-   * \return The created DMatrix.
-   */
-  static DMatrix* Load(const std::string& uri,
-                       bool silent,
-                       bool load_row_split,
-                       const std::string& file_format = "auto",
-                       size_t page_size = kPageSize);
-
-  /**
-   * \brief Creates a new DMatrix from an external data adapter.
-   *
-   * \tparam  AdapterT  Type of the adapter.
-   * \param [in,out]  adapter       View onto an external data.
-   * \param           missing       Values to count as missing.
-   * \param           nthread       Number of threads for construction.
-   * \param           cache_prefix  (Optional) The cache prefix for external memory.
-   * \param           page_size     (Optional) Size of the page.
-   *
-   * \return  a Created DMatrix.
-   */
-  template <typename AdapterT>
-  static DMatrix* Create(AdapterT* adapter, float missing, int nthread,
-                         const std::string& cache_prefix = "",
-                         size_t page_size = kPageSize);
-
-  virtual DMatrix* Slice(common::Span<int32_t const> ridxs) = 0;
-  /*! \brief page size 32 MB */
-  static const size_t kPageSize = 32UL << 20UL;
-
- protected:
-  virtual BatchSet<SparsePage> GetRowBatches() = 0;
-  virtual BatchSet<CSCPage> GetColumnBatches() = 0;
-  virtual BatchSet<SortedCSCPage> GetSortedColumnBatches() = 0;
-  virtual BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) = 0;
-
-  virtual bool EllpackExists() const = 0;
-  virtual bool SparsePageExists() const = 0;
-};
-
-template<>
-inline BatchSet<SparsePage> DMatrix::GetBatches(const BatchParam&) {
-  return GetRowBatches();
-}
-
-template<>
-inline bool DMatrix::PageExists<EllpackPage>() const {
-  return this->EllpackExists();
-}
-
-template<>
-inline bool DMatrix::PageExists<SparsePage>() const {
-  return this->SparsePageExists();
-}
-
-template<>
-inline BatchSet<CSCPage> DMatrix::GetBatches(const BatchParam&) {
-  return GetColumnBatches();
-}
-
-template<>
-inline BatchSet<SortedCSCPage> DMatrix::GetBatches(const BatchParam&) {
-  return GetSortedColumnBatches();
-}
-
-template<>
-inline BatchSet<EllpackPage> DMatrix::GetBatches(const BatchParam& param) {
-  return GetEllpackBatches(param);
-}
-}  // namespace xgboost
-
-namespace dmlc {
-DMLC_DECLARE_TRAITS(is_pod, xgboost::Entry, true);
-
-namespace serializer {
-
-template <>
-struct Handler<xgboost::Entry> {
-  inline static void Write(Stream* strm, const xgboost::Entry& data) {
-    strm->Write(data.index);
-    strm->Write(data.fvalue);
-  }
-
-  inline static bool Read(Stream* strm, xgboost::Entry* data) {
-    return strm->Read(&data->index) && strm->Read(&data->fvalue);
-  }
-};
-
-}  // namespace serializer
-}  // namespace dmlc
-#endif  // XGBOOST_DATA_H_
diff --git a/ml-xgboost/include/xgboost/feature_map.h b/ml-xgboost/include/xgboost/feature_map.h
deleted file mode 100644
index a48e28b..0000000
--- a/ml-xgboost/include/xgboost/feature_map.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/*!
- * Copyright 2014 by Contributors
- * \file feature_map.h
- * \brief Feature map data structure to help visualization and model dump.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_FEATURE_MAP_H_
-#define XGBOOST_FEATURE_MAP_H_
-
-#include <xgboost/logging.h>
-
-#include <vector>
-#include <string>
-#include <cstring>
-#include <iostream>
-
-namespace xgboost {
-/*!
- * \brief Feature map data structure to help text model dump.
- * TODO(tqchen) consider make it even more lightweight.
- */
-class FeatureMap {
- public:
-  /*! \brief type of feature maps */
-  enum Type {
-    kIndicator = 0,
-    kQuantitive = 1,
-    kInteger = 2,
-    kFloat = 3
-  };
-  /*!
-   * \brief load feature map from input stream
-   * \param is Input text stream
-   */
-  inline void LoadText(std::istream& is) { // NOLINT(*)
-    int fid;
-    std::string fname, ftype;
-    while (is >> fid >> fname >> ftype) {
-      this->PushBack(fid, fname.c_str(), ftype.c_str());
-    }
-  }
-  /*!
-   * \brief push back feature map.
-   * \param fid The feature index.
-   * \param fname The feature name.
-   * \param ftype The feature type.
-   */
-  inline void PushBack(int fid, const char *fname, const char *ftype) {
-    CHECK_EQ(fid, static_cast<int>(names_.size()));
-    names_.emplace_back(fname);
-    types_.push_back(GetType(ftype));
-  }
-  /*! \brief clear the feature map */
-  inline void Clear() {
-    names_.clear();
-    types_.clear();
-  }
-  /*! \return number of known features */
-  inline size_t Size() const {
-    return names_.size();
-  }
-  /*! \return name of specific feature */
-  inline const char* Name(size_t idx) const {
-    CHECK_LT(idx,  names_.size()) << "FeatureMap feature index exceed bound";
-    return names_[idx].c_str();
-  }
-  /*! \return type of specific feature */
-  Type TypeOf(size_t idx) const {
-    CHECK_LT(idx, names_.size()) << "FeatureMap feature index exceed bound";
-    return types_[idx];
-  }
-
- private:
-  /*!
-   * \return feature type enum given name.
-   * \param tname The type name.
-   * \return The translated type.
-   */
-  inline static Type GetType(const char* tname) {
-    using std::strcmp;
-    if (!strcmp("i", tname)) return kIndicator;
-    if (!strcmp("q", tname)) return kQuantitive;
-    if (!strcmp("int", tname)) return kInteger;
-    if (!strcmp("float", tname)) return kFloat;
-    LOG(FATAL) << "unknown feature type, use i for indicator and q for quantity";
-    return kIndicator;
-  }
-  /*! \brief name of the feature */
-  std::vector<std::string> names_;
-  /*! \brief type of the feature */
-  std::vector<Type> types_;
-};
-}  // namespace xgboost
-#endif  // XGBOOST_FEATURE_MAP_H_
diff --git a/ml-xgboost/include/xgboost/gbm.h b/ml-xgboost/include/xgboost/gbm.h
deleted file mode 100644
index a8a15c7..0000000
--- a/ml-xgboost/include/xgboost/gbm.h
+++ /dev/null
@@ -1,229 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file gbm.h
- * \brief Interface of gradient booster,
- *  that learns through gradient statistics.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_GBM_H_
-#define XGBOOST_GBM_H_
-
-#include <dmlc/registry.h>
-#include <dmlc/any.h>
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/host_device_vector.h>
-#include <xgboost/model.h>
-
-#include <vector>
-#include <utility>
-#include <string>
-#include <functional>
-#include <unordered_map>
-#include <memory>
-
-namespace xgboost {
-
-class Json;
-class FeatureMap;
-class ObjFunction;
-
-struct GenericParameter;
-struct LearnerModelParam;
-struct PredictionCacheEntry;
-class PredictionContainer;
-
-/*!
- * \brief interface of gradient boosting model.
- */
-class GradientBooster : public Model, public Configurable {
- protected:
-  GenericParameter const* generic_param_;
-
- public:
-  /*! \brief virtual destructor */
-  ~GradientBooster() override = default;
-  /*!
-   * \brief Set the configuration of gradient boosting.
-   *  User must call configure once before InitModel and Training.
-   *
-   * \param cfg configurations on both training and model parameters.
-   */
-  virtual void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) = 0;
-  /*!
-   * \brief load model from stream
-   * \param fi input stream.
-   */
-  virtual void Load(dmlc::Stream* fi) = 0;
-  /*!
-   * \brief save model to stream.
-   * \param fo output stream
-   */
-  virtual void Save(dmlc::Stream* fo) const = 0;
-  /*!
-   * \brief whether the model allow lazy checkpoint
-   * return true if model is only updated in DoBoost
-   * after all Allreduce calls
-   */
-  virtual bool AllowLazyCheckPoint() const {
-    return false;
-  }
-  /*!
-   * \brief perform update to the model(boosting)
-   * \param p_fmat feature matrix that provide access to features
-   * \param in_gpair address of the gradient pair statistics of the data
-   * \param prediction The output prediction cache entry that needs to be updated.
-   * \param iter defines number of iteration (used for iteration dependent boosting algorithms )
-   * \param _last_round_best_loss array of best losses achieved on previous round
-   */
-  virtual void DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
-                       PredictionCacheEntry* predt, int iter,
-                       float* _last_round_best_loss) = 0;
-
-  /*!
-   * \brief same as previous method but without iter and _last_round_best_loss parameters
-   */
-  void DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
-               PredictionCacheEntry* predt) {
-    this->DoBoost(p_fmat, in_gpair, predt, 0, NULL);
-  }
-
-  /*!
-   * \brief generate predictions for given feature matrix
-   * \param dmat feature matrix
-   * \param out_preds output vector to hold the predictions
-   * \param training Whether the prediction value is used for training.  For dart booster
-   *                 drop out is performed during training.
-   * \param ntree_limit limit the number of trees used in prediction,
-   *                    when it equals 0, this means we do not limit
-   *                    number of trees, this parameter is only valid
-   *                    for gbtree, but not for gblinear
-   */
-  virtual void PredictBatch(DMatrix* dmat,
-                            PredictionCacheEntry* out_preds,
-                            bool training,
-                            unsigned ntree_limit = 0) = 0;
-
-  /*!
-   * \brief Inplace prediction.
-   *
-   * \param           x                      A type erased data adapter.
-   * \param           missing                Missing value in the data.
-   * \param [in,out]  out_preds              The output preds.
-   * \param           layer_begin (Optional) Begining of boosted tree layer used for prediction.
-   * \param           layer_end   (Optional) End of booster layer. 0 means do not limit trees.
-   */
-  virtual void InplacePredict(dmlc::any const &x, float missing,
-                              PredictionCacheEntry *out_preds,
-                              uint32_t layer_begin = 0,
-                              uint32_t layer_end = 0) const {
-    LOG(FATAL) << "Inplace predict is not supported by current booster.";
-  }
-  /*!
-   * \brief online prediction function, predict score for one instance at a time
-   *  NOTE: use the batch prediction interface if possible, batch prediction is usually
-   *        more efficient than online prediction
-   *        This function is NOT threadsafe, make sure you only call from one thread
-   *
-   * \param inst the instance you want to predict
-   * \param out_preds output vector to hold the predictions
-   * \param ntree_limit limit the number of trees used in prediction
-   * \sa Predict
-   */
-  virtual void PredictInstance(const SparsePage::Inst& inst,
-                               std::vector<bst_float>* out_preds,
-                               unsigned ntree_limit = 0) = 0;
-  /*!
-   * \brief predict the leaf index of each tree, the output will be nsample * ntree vector
-   *        this is only valid in gbtree predictor
-   * \param dmat feature matrix
-   * \param out_preds output vector to hold the predictions
-   * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
-   *    we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear
-   */
-  virtual void PredictLeaf(DMatrix* dmat,
-                           std::vector<bst_float>* out_preds,
-                           unsigned ntree_limit = 0) = 0;
-
-  /*!
-   * \brief feature contributions to individual predictions; the output will be a vector
-   *         of length (nfeats + 1) * num_output_group * nsample, arranged in that order
-   * \param dmat feature matrix
-   * \param out_contribs output vector to hold the contributions
-   * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
-   *    we do not limit number of trees
-   * \param approximate use a faster (inconsistent) approximation of SHAP values
-   * \param condition condition on the condition_feature (0=no, -1=cond off, 1=cond on).
-   * \param condition_feature feature to condition on (i.e. fix) during calculations
-   */
-  virtual void PredictContribution(DMatrix* dmat,
-                                   std::vector<bst_float>* out_contribs,
-                                   unsigned ntree_limit = 0,
-                                   bool approximate = false, int condition = 0,
-                                   unsigned condition_feature = 0) = 0;
-
-  virtual void PredictInteractionContributions(DMatrix* dmat,
-                                               std::vector<bst_float>* out_contribs,
-                                               unsigned ntree_limit, bool approximate) = 0;
-
-  /*!
-   * \brief dump the model in the requested format
-   * \param fmap feature map that may help give interpretations of feature
-   * \param with_stats extra statistics while dumping model
-   * \param format the format to dump the model in
-   * \return a vector of dump for boosters.
-   */
-  virtual std::vector<std::string> DumpModel(const FeatureMap& fmap,
-                                             bool with_stats,
-                                             std::string format) const = 0;
-  /*!
-   * \brief Whether the current booster uses GPU.
-   */
-  virtual bool UseGPU() const = 0;
-  /*!
-   * \brief create a gradient booster from given name
-   * \param name name of gradient booster
-   * \param generic_param Pointer to runtime parameters
-   * \param learner_model_param pointer to global model parameters
-   * \return The created booster.
-   */
-  static GradientBooster* Create(
-      const std::string& name,
-      GenericParameter const* generic_param,
-      LearnerModelParam const* learner_model_param);
-};
-
-/*!
- * \brief Registry entry for tree updater.
- */
-struct GradientBoosterReg
-    : public dmlc::FunctionRegEntryBase<
-  GradientBoosterReg,
-  std::function<GradientBooster* (LearnerModelParam const* learner_model_param)> > {
-};
-
-/*!
- * \brief Macro to register gradient booster.
- *
- * \code
- * // example of registering a objective ndcg@k
- * XGBOOST_REGISTER_GBM(GBTree, "gbtree")
- * .describe("Boosting tree ensembles.")
- * .set_body([]() {
- *     return new GradientBooster<TStats>();
- *   });
- * \endcode
- */
-#define XGBOOST_REGISTER_GBM(UniqueId, Name)                            \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::GradientBoosterReg &          \
-  __make_ ## GradientBoosterReg ## _ ## UniqueId ## __ =                \
-      ::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->__REGISTER__(Name)
-
-}  // namespace xgboost
-#endif  // XGBOOST_GBM_H_
diff --git a/ml-xgboost/include/xgboost/generic_parameters.h b/ml-xgboost/include/xgboost/generic_parameters.h
deleted file mode 100644
index 7713d6b..0000000
--- a/ml-xgboost/include/xgboost/generic_parameters.h
+++ /dev/null
@@ -1,92 +0,0 @@
-/*!
- * Copyright 2014-2019 by Contributors
- * \file generic_parameters.h
- */
-#ifndef XGBOOST_GENERIC_PARAMETERS_H_
-#define XGBOOST_GENERIC_PARAMETERS_H_
-
-#include <xgboost/logging.h>
-#include <xgboost/parameter.h>
-
-#include <string>
-
-namespace xgboost {
-struct GenericParameter : public XGBoostParameter<GenericParameter> {
-  // Constant representing the device ID of CPU.
-  static int32_t constexpr kCpuId = -1;
-
- public:
-  // stored random seed
-  int seed;
-  // whether seed the PRNG each iteration
-  bool seed_per_iteration;
-  // number of threads to use if OpenMP is enabled
-  // if equals 0, use system default
-  int nthread;
-  // primary device, -1 means no gpu.
-  int gpu_id;
-  // gpu page size in external memory mode, 0 means using the default.
-  size_t gpu_page_size;
-  bool enable_experimental_json_serialization {false};
-  bool validate_parameters {false};
-
-  void CheckDeprecated() {
-    if (this->n_gpus != 0) {
-      LOG(WARNING)
-          << "\nn_gpus: "
-          << this->__MANAGER__()->Find("n_gpus")->GetFieldInfo().description;
-    }
-  }
-  /*!
-   * \brief Configure the parameter `gpu_id'.
-   *
-   * \param require_gpu  Whether GPU is explicitly required from user.
-   */
-  void ConfigureGpuId(bool require_gpu);
-
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(GenericParameter) {
-    DMLC_DECLARE_FIELD(seed).set_default(0).describe(
-        "Random number seed during training.");
-    DMLC_DECLARE_ALIAS(seed, random_state);
-    DMLC_DECLARE_FIELD(seed_per_iteration)
-        .set_default(false)
-        .describe(
-            "Seed PRNG determnisticly via iterator number, "
-            "this option will be switched on automatically on distributed "
-            "mode.");
-    DMLC_DECLARE_FIELD(nthread).set_default(0).describe(
-        "Number of threads to use.");
-    DMLC_DECLARE_ALIAS(nthread, n_jobs);
-
-    DMLC_DECLARE_FIELD(gpu_id)
-        .set_default(-1)
-        .set_lower_bound(-1)
-        .describe("The primary GPU device ordinal.");
-    DMLC_DECLARE_FIELD(gpu_page_size)
-        .set_default(0)
-        .set_lower_bound(0)
-        .describe("GPU page size when running in external memory mode.");
-    DMLC_DECLARE_FIELD(enable_experimental_json_serialization)
-        .set_default(false)
-        .describe("Enable using JSON for memory serialization (Python Pickle, "
-                  "rabit checkpoints etc.).");
-    DMLC_DECLARE_FIELD(validate_parameters)
-        .set_default(false)
-        .describe("Enable checking whether parameters are used or not.");
-    DMLC_DECLARE_FIELD(n_gpus)
-        .set_default(0)
-        .set_range(0, 1)
-        .describe(
-"\n\tDeprecated. Single process multi-GPU training is no longer supported."
-"\n\tPlease switch to distributed training with one process per GPU."
-"\n\tThis can be done using Dask or Spark.  See documentation for details.");
-  }
-
- private:
-  // number of devices to use (deprecated).
-  int n_gpus {0};  // NOLINT
-};
-}  // namespace xgboost
-
-#endif  // XGBOOST_GENERIC_PARAMETERS_H_
diff --git a/ml-xgboost/include/xgboost/host_device_vector.h b/ml-xgboost/include/xgboost/host_device_vector.h
deleted file mode 100644
index 7559518..0000000
--- a/ml-xgboost/include/xgboost/host_device_vector.h
+++ /dev/null
@@ -1,141 +0,0 @@
-/*!
- * Copyright 2017-2019 XGBoost contributors
- */
-
-/**
- * @file host_device_vector.h
- * @brief A device-and-host vector abstraction layer.
- *
- * Why HostDeviceVector?<br/>
- * With CUDA, one has to explicitly manage memory through 'cudaMemcpy' calls.
- * This wrapper class hides this management from the users, thereby making it
- * easy to integrate GPU/CPU usage under a single interface.
- *
- * Initialization/Allocation:<br/>
- * One can choose to initialize the vector on CPU or GPU during constructor.
- * (use the 'devices' argument) Or, can choose to use the 'Resize' method to
- * allocate/resize memory explicitly, and use the 'SetDevice' method
- * to specify the device.
- *
- * Accessing underlying data:<br/>
- * Use 'HostVector' method to explicitly query for the underlying std::vector.
- * If you need the raw device pointer, use the 'DevicePointer' method. For perf
- * implications of these calls, see below.
- *
- * Accessing underling data and their perf implications:<br/>
- * There are 4 scenarios to be considered here:
- * HostVector and data on CPU --> no problems, std::vector returned immediately
- * HostVector but data on GPU --> this causes a cudaMemcpy to be issued internally.
- *                        subsequent calls to HostVector, will NOT incur this penalty.
- *                        (assuming 'DevicePointer' is not called in between)
- * DevicePointer but data on CPU  --> this causes a cudaMemcpy to be issued internally.
- *                        subsequent calls to DevicePointer, will NOT incur this penalty.
- *                        (assuming 'HostVector' is not called in between)
- * DevicePointer and data on GPU  --> no problems, the device ptr
- *                        will be returned immediately.
- *
- * What if xgboost is compiled without CUDA?<br/>
- * In that case, there's a special implementation which always falls-back to
- * working with std::vector. This logic can be found in host_device_vector.cc
- *
- * Why not consider CUDA unified memory?<br/>
- * We did consider. However, it poses complications if we need to support both
- * compiling with and without CUDA toolkit. It was easier to have
- * 'HostDeviceVector' with a special-case implementation in host_device_vector.cc
- *
- * @note: Size and Devices methods are thread-safe.
- */
-
-#ifndef XGBOOST_HOST_DEVICE_VECTOR_H_
-#define XGBOOST_HOST_DEVICE_VECTOR_H_
-
-#include <initializer_list>
-#include <vector>
-
-#include "span.h"
-
-namespace xgboost {
-
-#ifdef __CUDACC__
-// Sets a function to call instead of cudaSetDevice();
-// only added for testing
-void SetCudaSetDeviceHandler(void (*handler)(int));
-#endif  // __CUDACC__
-
-template <typename T> struct HostDeviceVectorImpl;
-
-/*!
- * \brief Controls data access from the GPU.
- *
- * Since a `HostDeviceVector` can have data on both the host and device, access control needs to be
- * maintained to keep the data consistent.
- *
- * There are 3 scenarios supported:
- *   - Data is being manipulated on device. GPU has write access, host doesn't have access.
- *   - Data is read-only on both the host and device.
- *   - Data is being manipulated on the host. Host has write access, device doesn't have access.
- */
-enum GPUAccess {
-  kNone, kRead,
-  // write implies read
-  kWrite
-};
-
-template <typename T>
-class HostDeviceVector {
- public:
-  explicit HostDeviceVector(size_t size = 0, T v = T(), int device = -1);
-  HostDeviceVector(std::initializer_list<T> init, int device = -1);
-  explicit HostDeviceVector(const std::vector<T>& init, int device = -1);
-  ~HostDeviceVector();
-
-  HostDeviceVector(const HostDeviceVector<T>&) = delete;
-  HostDeviceVector(HostDeviceVector<T>&&);
-
-  HostDeviceVector<T>& operator=(const HostDeviceVector<T>&) = delete;
-  HostDeviceVector<T>& operator=(HostDeviceVector<T>&&);
-
-  size_t Size() const;
-  int DeviceIdx() const;
-  common::Span<T> DeviceSpan();
-  common::Span<const T> ConstDeviceSpan() const;
-  common::Span<const T> DeviceSpan() const { return ConstDeviceSpan(); }
-  T* DevicePointer();
-  const T* ConstDevicePointer() const;
-  const T* DevicePointer() const { return ConstDevicePointer(); }
-
-  T* HostPointer() { return HostVector().data(); }
-  common::Span<T> HostSpan() { return common::Span<T>{HostVector()}; }
-  common::Span<T const> HostSpan() const { return common::Span<T const>{HostVector()}; }
-  common::Span<T const> ConstHostSpan() const { return HostSpan(); }
-  const T* ConstHostPointer() const { return ConstHostVector().data(); }
-  const T* HostPointer() const { return ConstHostPointer(); }
-
-  void Fill(T v);
-  void Copy(const HostDeviceVector<T>& other);
-  void Copy(const std::vector<T>& other);
-  void Copy(std::initializer_list<T> other);
-
-  std::vector<T>& HostVector();
-  const std::vector<T>& ConstHostVector() const;
-  const std::vector<T>& HostVector() const {return ConstHostVector(); }
-
-  bool HostCanRead() const;
-  bool HostCanWrite() const;
-  bool DeviceCanRead() const;
-  bool DeviceCanWrite() const;
-  GPUAccess DeviceAccess() const;
-
-  void SetDevice(int device) const;
-
-  void Resize(size_t new_size, T v = T());
-
-  using value_type = T;  // NOLINT
-
- private:
-  HostDeviceVectorImpl<T>* impl_;
-};
-
-}  // namespace xgboost
-
-#endif  // XGBOOST_HOST_DEVICE_VECTOR_H_
diff --git a/ml-xgboost/include/xgboost/json.h b/ml-xgboost/include/xgboost/json.h
deleted file mode 100644
index c190505..0000000
--- a/ml-xgboost/include/xgboost/json.h
+++ /dev/null
@@ -1,567 +0,0 @@
-/*!
- * Copyright (c) by Contributors 2019
- */
-#ifndef XGBOOST_JSON_H_
-#define XGBOOST_JSON_H_
-
-#include <xgboost/logging.h>
-#include <xgboost/parameter.h>
-#include <string>
-
-#include <map>
-#include <memory>
-#include <vector>
-#include <functional>
-#include <utility>
-
-namespace xgboost {
-
-class Json;
-class JsonReader;
-class JsonWriter;
-
-class Value {
- public:
-  /*!\brief Simplified implementation of LLVM RTTI. */
-  enum class ValueKind {
-    kString,
-    kNumber,
-    kInteger,
-    kObject,  // std::map
-    kArray,   // std::vector
-    kBoolean,
-    kNull
-  };
-
-  explicit Value(ValueKind _kind) : kind_{_kind} {}
-
-  ValueKind Type() const { return kind_; }
-  virtual ~Value() = default;
-
-  virtual void Save(JsonWriter* writer) = 0;
-
-  virtual Json& operator[](std::string const & key) = 0;
-  virtual Json& operator[](int ind) = 0;
-
-  virtual bool operator==(Value const& rhs) const = 0;
-  virtual Value& operator=(Value const& rhs) = 0;
-
-  std::string TypeStr() const;
-
- private:
-  ValueKind kind_;
-};
-
-template <typename T>
-bool IsA(Value const* value) {
-  return T::IsClassOf(value);
-}
-
-template <typename T, typename U>
-T* Cast(U* value) {
-  if (IsA<T>(value)) {
-    return dynamic_cast<T*>(value);
-  } else {
-    LOG(FATAL) << "Invalid cast, from " + value->TypeStr() + " to " + T().TypeStr();
-  }
-  return dynamic_cast<T*>(value);  // supress compiler warning.
-}
-
-class JsonString : public Value {
-  std::string str_;
- public:
-  JsonString() : Value(ValueKind::kString) {}
-  JsonString(std::string const& str) :  // NOLINT
-      Value(ValueKind::kString), str_{str} {}
-  JsonString(std::string&& str) :  // NOLINT
-      Value(ValueKind::kString), str_{std::move(str)} {}
-
-  void Save(JsonWriter* writer) override;
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  std::string const& GetString() &&      { return str_; }
-  std::string const& GetString() const & { return str_; }
-  std::string&       GetString()       & { return str_; }
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kString;
-  }
-};
-
-class JsonArray : public Value {
-  std::vector<Json> vec_;
-
- public:
-  JsonArray() : Value(ValueKind::kArray) {}
-  JsonArray(std::vector<Json>&& arr) :  // NOLINT
-      Value(ValueKind::kArray), vec_{std::move(arr)} {}
-  JsonArray(std::vector<Json> const& arr) :  // NOLINT
-      Value(ValueKind::kArray), vec_{arr} {}
-  JsonArray(JsonArray const& that) = delete;
-  JsonArray(JsonArray && that);
-
-  void Save(JsonWriter* writer) override;
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  std::vector<Json> const& GetArray() &&      { return vec_; }
-  std::vector<Json> const& GetArray() const & { return vec_; }
-  std::vector<Json>&       GetArray()       & { return vec_; }
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kArray;
-  }
-};
-
-class JsonObject : public Value {
-  std::map<std::string, Json> object_;
-
- public:
-  JsonObject() : Value(ValueKind::kObject) {}
-  JsonObject(std::map<std::string, Json>&& object);  // NOLINT
-  JsonObject(JsonObject const& that) = delete;
-  JsonObject(JsonObject && that);
-
-  void Save(JsonWriter* writer) override;
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  std::map<std::string, Json> const& GetObject() &&      { return object_; }
-  std::map<std::string, Json> const& GetObject() const & { return object_; }
-  std::map<std::string, Json> &      GetObject() &       { return object_; }
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kObject;
-  }
-  ~JsonObject() override = default;
-};
-
-class JsonNumber : public Value {
- public:
-  using Float = float;
-
- private:
-  Float number_ { 0 };
-
- public:
-  JsonNumber() : Value(ValueKind::kNumber) {}
-  template <typename FloatT,
-            typename std::enable_if<std::is_same<FloatT, Float>::value>::type* = nullptr>
-  JsonNumber(FloatT value) : Value(ValueKind::kNumber) {  // NOLINT
-    number_ = value;
-  }
-  template <typename FloatT,
-            typename std::enable_if<std::is_same<FloatT, double>::value>::type* = nullptr>
-  JsonNumber(FloatT value) : Value{ValueKind::kNumber},  // NOLINT
-                             number_{static_cast<Float>(value)} {}
-
-  void Save(JsonWriter* writer) override;
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  Float const& GetNumber() &&      { return number_; }
-  Float const& GetNumber() const & { return number_; }
-  Float&       GetNumber()       & { return number_; }
-
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kNumber;
-  }
-};
-
-class JsonInteger : public Value {
- public:
-  using Int = int64_t;
-
- private:
-  Int integer_ {0};
-
- public:
-  JsonInteger() : Value(ValueKind::kInteger) {}  // NOLINT
-  template <typename IntT,
-            typename std::enable_if<std::is_same<IntT, Int>::value>::type* = nullptr>
-  JsonInteger(IntT value) : Value(ValueKind::kInteger), integer_{value} {} // NOLINT
-  template <typename IntT,
-            typename std::enable_if<std::is_same<IntT, size_t>::value>::type* = nullptr>
-  JsonInteger(IntT value) : Value(ValueKind::kInteger),  // NOLINT
-                            integer_{static_cast<Int>(value)} {}
-  template <typename IntT,
-            typename std::enable_if<std::is_same<IntT, int32_t>::value>::type* = nullptr>
-  JsonInteger(IntT value) : Value(ValueKind::kInteger),  // NOLINT
-                            integer_{static_cast<Int>(value)} {}
-  template <typename IntT,
-            typename std::enable_if<
-                std::is_same<IntT, uint32_t>::value &&
-                !std::is_same<std::size_t, uint32_t>::value>::type * = nullptr>
-  JsonInteger(IntT value)  // NOLINT
-      : Value(ValueKind::kInteger),
-        integer_{static_cast<Int>(value)} {}
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  Int const& GetInteger() &&      { return integer_; }
-  Int const& GetInteger() const & { return integer_; }
-  Int& GetInteger() &             { return integer_; }
-  void Save(JsonWriter* writer) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kInteger;
-  }
-};
-
-class JsonNull : public Value {
- public:
-  JsonNull() : Value(ValueKind::kNull) {}
-  JsonNull(std::nullptr_t) : Value(ValueKind::kNull) {}  // NOLINT
-
-  void Save(JsonWriter* writer) override;
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kNull;
-  }
-};
-
-/*! \brief Describes both true and false. */
-class JsonBoolean : public Value {
-  bool boolean_;
-
- public:
-  JsonBoolean() : Value(ValueKind::kBoolean) {}  // NOLINT
-  // Ambigious with JsonNumber.
-  template <typename Bool,
-            typename std::enable_if<
-              std::is_same<Bool, bool>::value ||
-              std::is_same<Bool, bool const>::value>::type* = nullptr>
-  JsonBoolean(Bool value) :  // NOLINT
-      Value(ValueKind::kBoolean), boolean_{value} {}
-
-  void Save(JsonWriter* writer) override;
-
-  Json& operator[](std::string const & key) override;
-  Json& operator[](int ind) override;
-
-  bool const& GetBoolean() &&      { return boolean_; }
-  bool const& GetBoolean() const & { return boolean_; }
-  bool&       GetBoolean()       & { return boolean_; }
-
-  bool operator==(Value const& rhs) const override;
-  Value& operator=(Value const& rhs) override;
-
-  static bool IsClassOf(Value const* value) {
-    return value->Type() == ValueKind::kBoolean;
-  }
-};
-
-struct StringView {
- private:
-  using CharT = char;  // unsigned char
-  CharT const* str_;
-  size_t size_;
-
- public:
-  StringView() = default;
-  StringView(CharT const* str, size_t size) : str_{str}, size_{size} {}
-
-  CharT const& operator[](size_t p) const { return str_[p]; }
-  CharT const& at(size_t p) const {  // NOLINT
-    CHECK_LT(p, size_);
-    return str_[p];
-  }
-  size_t size() const { return size_; }  // NOLINT
-  // Copies a portion of string.  Since we don't have std::from_chars and friends here, so
-  // copying substring is necessary for appending `\0`.  It's not too bad since string by
-  // default has small vector optimization, which is enabled by most if not all modern
-  // compilers for numeric values.
-  std::string substr(size_t beg, size_t n) const {  // NOLINT
-    CHECK_LE(beg, size_);
-    return std::string {str_ + beg, n < (size_ - beg) ? n : (size_ - beg)};
-  }
-  char const* c_str() const { return str_; }  // NOLINT
-};
-
-/*!
- * \brief Data structure representing JSON format.
- *
- * Limitation:  UTF-8 is not properly supported.  Code points above ASCII are
- *              invalid.
- *
- * Examples:
- *
- * \code
- *   // Create a JSON object.
- *   Json object { Object() };
- *   // Assign key "key" with a JSON string "Value";
- *   object["key"] = String("Value");
- *   // Assign key "arr" with a empty JSON Array;
- *   object["arr"] = Array();
- * \endcode
- */
-class Json {
-  friend JsonWriter;
-
- public:
-  /*! \brief Load a Json object from string. */
-  static Json Load(StringView str);
-  /*! \brief Pass your own JsonReader. */
-  static Json Load(JsonReader* reader);
-  /*! \brief Dump json into stream. */
-  static void Dump(Json json, std::ostream* stream,
-                   bool pretty = ConsoleLogger::ShouldLog(
-                       ConsoleLogger::LogVerbosity::kDebug));
-  static void Dump(Json json, std::string* out,
-                   bool pretty = ConsoleLogger::ShouldLog(
-                       ConsoleLogger::LogVerbosity::kDebug));
-
-  Json() : ptr_{new JsonNull} {}
-
-  // number
-  explicit Json(JsonNumber number) : ptr_{new JsonNumber(number)} {}
-  Json& operator=(JsonNumber number) {
-    ptr_.reset(new JsonNumber(std::move(number)));
-    return *this;
-  }
-
-  // integer
-  explicit Json(JsonInteger integer) : ptr_{new JsonInteger(integer)} {}
-  Json& operator=(JsonInteger integer) {
-    ptr_.reset(new JsonInteger(std::move(integer)));
-    return *this;
-  }
-
-  // array
-  explicit Json(JsonArray list) :
-      ptr_ {new JsonArray(std::move(list))} {}
-  Json& operator=(JsonArray array) {
-    ptr_.reset(new JsonArray(std::move(array)));
-    return *this;
-  }
-
-  // object
-  explicit Json(JsonObject object) :
-      ptr_{new JsonObject(std::move(object))} {}
-  Json& operator=(JsonObject object) {
-    ptr_.reset(new JsonObject(std::move(object)));
-    return *this;
-  }
-  // string
-  explicit Json(JsonString str) :
-      ptr_{new JsonString(std::move(str))} {}
-  Json& operator=(JsonString str) {
-    ptr_.reset(new JsonString(std::move(str)));
-    return *this;
-  }
-  // bool
-  explicit Json(JsonBoolean boolean) :
-      ptr_{new JsonBoolean(std::move(boolean))} {}
-  Json& operator=(JsonBoolean boolean) {
-    ptr_.reset(new JsonBoolean(std::move(boolean)));
-    return *this;
-  }
-  // null
-  explicit Json(JsonNull null) :
-      ptr_{new JsonNull(std::move(null))} {}
-  Json& operator=(JsonNull null) {
-    ptr_.reset(new JsonNull(std::move(null)));
-    return *this;
-  }
-
-  // copy
-  Json(Json const& other) = default;
-  Json& operator=(Json const& other);
-  // move
-  Json(Json&& other) : ptr_{std::move(other.ptr_)} {}
-  Json& operator=(Json&& other) {
-    ptr_ = std::move(other.ptr_);
-    return *this;
-  }
-
-  /*! \brief Index Json object with a std::string, used for Json Object. */
-  Json& operator[](std::string const & key) const { return (*ptr_)[key]; }
-  /*! \brief Index Json object with int, used for Json Array. */
-  Json& operator[](int ind)                 const { return (*ptr_)[ind]; }
-
-  /*! \brief Return the reference to stored Json value. */
-  Value const& GetValue() const & { return *ptr_; }
-  Value const& GetValue() &&      { return *ptr_; }
-  Value&       GetValue() &       { return *ptr_; }
-
-  bool operator==(Json const& rhs) const {
-    return *ptr_ == *(rhs.ptr_);
-  }
-
-  friend std::ostream& operator<<(std::ostream& os, Json const& j) {
-    std::string str;
-    Json::Dump(j, &str);
-    os << str;
-    return os;
-  }
-
- private:
-  std::shared_ptr<Value> ptr_;
-};
-
-template <typename T>
-bool IsA(Json const j) {
-  auto const& v = j.GetValue();
-  return IsA<T>(&v);
-}
-
-namespace detail {
-
-// Number
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonNumber>::value>::type* = nullptr>
-JsonNumber::Float& GetImpl(T& val) {  // NOLINT
-  return val.GetNumber();
-}
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonNumber const>::value>::type* = nullptr>
-JsonNumber::Float const& GetImpl(T& val) {  // NOLINT
-  return val.GetNumber();
-}
-
-// Integer
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonInteger>::value>::type* = nullptr>
-JsonInteger::Int& GetImpl(T& val) {  // NOLINT
-  return val.GetInteger();
-}
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonInteger const>::value>::type* = nullptr>
-JsonInteger::Int const& GetImpl(T& val) {  // NOLINT
-  return val.GetInteger();
-}
-
-// String
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonString>::value>::type* = nullptr>
-std::string& GetImpl(T& val) {  // NOLINT
-  return val.GetString();
-}
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonString const>::value>::type* = nullptr>
-std::string const& GetImpl(T& val) {  // NOLINT
-  return val.GetString();
-}
-
-// Boolean
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonBoolean>::value>::type* = nullptr>
-bool& GetImpl(T& val) {  // NOLINT
-  return val.GetBoolean();
-}
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonBoolean const>::value>::type* = nullptr>
-bool const& GetImpl(T& val) {  // NOLINT
-  return val.GetBoolean();
-}
-
-// Array
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonArray>::value>::type* = nullptr>
-std::vector<Json>& GetImpl(T& val) {  // NOLINT
-  return val.GetArray();
-}
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonArray const>::value>::type* = nullptr>
-std::vector<Json> const& GetImpl(T& val) {  // NOLINT
-  return val.GetArray();
-}
-
-// Object
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonObject>::value>::type* = nullptr>
-std::map<std::string, Json>& GetImpl(T& val) {  // NOLINT
-  return val.GetObject();
-}
-template <typename T,
-          typename std::enable_if<
-            std::is_same<T, JsonObject const>::value>::type* = nullptr>
-std::map<std::string, Json> const& GetImpl(T& val) {  // NOLINT
-  return val.GetObject();
-}
-
-}  // namespace detail
-
-/*!
- * \brief Get Json value.
- *
- * \tparam T One of the Json value type.
- *
- * \param json
- * \return Value contained in Json object of type T.
- */
-template <typename T, typename U>
-auto get(U& json) -> decltype(detail::GetImpl(*Cast<T>(&json.GetValue())))& { // NOLINT
-  auto& value = *Cast<T>(&json.GetValue());
-  return detail::GetImpl(value);
-}
-
-using Object  = JsonObject;
-using Array   = JsonArray;
-using Number  = JsonNumber;
-using Integer = JsonInteger;
-using Boolean = JsonBoolean;
-using String  = JsonString;
-using Null    = JsonNull;
-
-// Utils tailored for XGBoost.
-
-template <typename Parameter>
-Object ToJson(Parameter const& param) {
-  Object obj;
-  for (auto const& kv : param.__DICT__()) {
-    obj[kv.first] = kv.second;
-  }
-  return obj;
-}
-
-template <typename Parameter>
-void FromJson(Json const& obj, Parameter* param) {
-  auto const& j_param = get<Object const>(obj);
-  std::map<std::string, std::string> m;
-  for (auto const& kv : j_param) {
-    m[kv.first] = get<String const>(kv.second);
-  }
-  param->UpdateAllowUnknown(m);
-}
-}  // namespace xgboost
-#endif  // XGBOOST_JSON_H_
diff --git a/ml-xgboost/include/xgboost/json_io.h b/ml-xgboost/include/xgboost/json_io.h
deleted file mode 100644
index a58afe1..0000000
--- a/ml-xgboost/include/xgboost/json_io.h
+++ /dev/null
@@ -1,166 +0,0 @@
-/*!
- * Copyright (c) by Contributors 2019
- */
-#ifndef XGBOOST_JSON_IO_H_
-#define XGBOOST_JSON_IO_H_
-#include <xgboost/json.h>
-
-#include <memory>
-#include <string>
-#include <cinttypes>
-#include <utility>
-#include <map>
-#include <limits>
-#include <sstream>
-#include <locale>
-
-namespace xgboost {
-
-template <typename Allocator>
-class FixedPrecisionStreamContainer : public std::basic_stringstream<
-  char, std::char_traits<char>, Allocator> {
- public:
-  FixedPrecisionStreamContainer() {
-    this->precision(std::numeric_limits<double>::max_digits10);
-    this->imbue(std::locale("C"));
-    this->setf(std::ios::scientific);
-  }
-};
-
-using FixedPrecisionStream = FixedPrecisionStreamContainer<std::allocator<char>>;
-
-/*
- * \brief A json reader, currently error checking and utf-8 is not fully supported.
- */
-class JsonReader {
- protected:
-  size_t constexpr static kMaxNumLength =
-      std::numeric_limits<double>::max_digits10 + 1;
-
-  struct SourceLocation {
-   private:
-    size_t pos_ { 0 };  // current position in raw_str_
-
-   public:
-    SourceLocation() = default;
-    size_t  Pos()  const { return pos_; }
-
-    SourceLocation& Forward() {
-      pos_++;
-      return *this;
-    }
-    SourceLocation& Forward(uint32_t n) {
-      pos_ += n;
-      return *this;
-    }
-  } cursor_;
-
-  StringView raw_str_;
-
- protected:
-  void SkipSpaces();
-
-  char GetNextChar() {
-    if (cursor_.Pos() == raw_str_.size()) {
-      return -1;
-    }
-    char ch = raw_str_[cursor_.Pos()];
-    cursor_.Forward();
-    return ch;
-  }
-
-  char PeekNextChar() {
-    if (cursor_.Pos() == raw_str_.size()) {
-      return -1;
-    }
-    char ch = raw_str_[cursor_.Pos()];
-    return ch;
-  }
-
-  char GetNextNonSpaceChar() {
-    SkipSpaces();
-    return GetNextChar();
-  }
-
-  char GetChar(char c) {
-    char result = GetNextNonSpaceChar();
-    if (result != c) { Expect(c, result); }
-    return result;
-  }
-
-  void Error(std::string msg) const;
-
-  // Report expected character
-  void Expect(char c, char got) {
-    std::string msg = "Expecting: \"";
-    msg += c;
-    msg += "\", got: \"";
-    msg += std::string {got} + " \"";
-    Error(msg);
-  }
-
-  virtual Json ParseString();
-  virtual Json ParseObject();
-  virtual Json ParseArray();
-  virtual Json ParseNumber();
-  virtual Json ParseBoolean();
-  virtual Json ParseNull();
-
-  Json Parse();
-
- public:
-  explicit JsonReader(StringView str) :
-      raw_str_{str} {}
-
-  virtual ~JsonReader() = default;
-
-  Json Load();
-};
-
-class JsonWriter {
-  static constexpr size_t kIndentSize = 2;
-  FixedPrecisionStream convertor_;
-
-  size_t n_spaces_;
-  std::ostream* stream_;
-  bool pretty_;
-
- public:
-  JsonWriter(std::ostream* stream, bool pretty) :
-      n_spaces_{0}, stream_{stream}, pretty_{pretty} {}
-
-  virtual ~JsonWriter() = default;
-
-  void NewLine() {
-    if (pretty_) {
-      *stream_ << u8"\n" << std::string(n_spaces_, ' ');
-    }
-  }
-
-  void BeginIndent() {
-    n_spaces_ += kIndentSize;
-  }
-  void EndIndent() {
-    n_spaces_ -= kIndentSize;
-  }
-
-  void Write(std::string str) {
-    *stream_ << str;
-  }
-  void Write(StringView str) {
-    stream_->write(str.c_str(), str.size());
-  }
-
-  void Save(Json json);
-
-  virtual void Visit(JsonArray  const* arr);
-  virtual void Visit(JsonObject const* obj);
-  virtual void Visit(JsonNumber const* num);
-  virtual void Visit(JsonInteger const* num);
-  virtual void Visit(JsonNull   const* null);
-  virtual void Visit(JsonString const* str);
-  virtual void Visit(JsonBoolean const* boolean);
-};
-}      // namespace xgboost
-
-#endif  // XGBOOST_JSON_IO_H_
diff --git a/ml-xgboost/include/xgboost/learner.h b/ml-xgboost/include/xgboost/learner.h
deleted file mode 100644
index d64b938..0000000
--- a/ml-xgboost/include/xgboost/learner.h
+++ /dev/null
@@ -1,264 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2015-2020 by Contributors
- * \file learner.h
- * \brief Learner interface that integrates objective, gbm and evaluation together.
- *  This is the user facing XGBoost training module.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_LEARNER_H_
-#define XGBOOST_LEARNER_H_
-
-#include <dmlc/any.h>
-#include <rabit/rabit.h>
-#include <xgboost/base.h>
-#include <xgboost/feature_map.h>
-#include <xgboost/predictor.h>
-#include <xgboost/generic_parameters.h>
-#include <xgboost/host_device_vector.h>
-#include <xgboost/model.h>
-
-#include <utility>
-#include <map>
-#include <memory>
-#include <string>
-#include <vector>
-
-namespace xgboost {
-
-class Metric;
-class GradientBooster;
-class ObjFunction;
-class DMatrix;
-class Json;
-
-/*! \brief entry to to easily hold returning information */
-struct XGBAPIThreadLocalEntry {
-  /*! \brief result holder for returning string */
-  std::string ret_str;
-  /*! \brief result holder for returning strings */
-  std::vector<std::string> ret_vec_str;
-  /*! \brief result holder for returning string pointers */
-  std::vector<const char *> ret_vec_charp;
-  /*! \brief returning float vector. */
-  std::vector<bst_float> ret_vec_float;
-  /*! \brief temp variable of gradient pairs. */
-  std::vector<GradientPair> tmp_gpair;
-  PredictionCacheEntry prediction_entry;
-};
-
-
-/*!
- * \brief Learner class that does training and prediction.
- *  This is the user facing module of xgboost training.
- *  The Load/Save function corresponds to the model used in python/R.
- *  \code
- *
- *  std::unique_ptr<Learner> learner(new Learner::Create(cache_mats));
- *  learner.Configure(configs);
- *
- *  for (int iter = 0; iter < max_iter; ++iter) {
- *    learner->UpdateOneIter(iter, train_mat);
- *    LOG(INFO) << learner->EvalOneIter(iter, data_sets, data_names);
- *  }
- *
- *  \endcode
- */
-class Learner : public Model, public Configurable, public rabit::Serializable {
- public:
-  /*! \brief virtual destructor */
-  ~Learner() override;
-  /*!
-   * \brief Configure Learner based on set parameters.
-   */
-  virtual void Configure() = 0;
-  /*!
-   * \brief update the model for one iteration
-   *  With the specified objective function.
-   * \param iter current iteration number
-   * \param train reference to the data matrix.
-   */
-  virtual void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train, float* p_last_round_best_loss) = 0;
-
-  virtual void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) {
-    UpdateOneIter(iter, train, NULL);
-  };
-
-  /*!
-   * \brief Do customized gradient boosting with in_gpair.
-   *  in_gair can be mutated after this call.
-   * \param iter current iteration number
-   * \param train reference to the data matrix.
-   * \param in_gpair The input gradient statistics.
-   */
-  virtual void BoostOneIter(int iter,
-                            std::shared_ptr<DMatrix> train,
-                            HostDeviceVector<GradientPair>* in_gpair,
-                            float* p_last_round_best_loss = nullptr) = 0;
-  /*!
-   * \brief evaluate the model for specific iteration using the configured metrics.
-   * \param iter iteration number
-   * \param data_sets datasets to be evaluated.
-   * \param data_names name of each dataset
-   * \return a string corresponding to the evaluation result
-   */
-  virtual std::string EvalOneIter(int iter,
-                                  const std::vector<std::shared_ptr<DMatrix>>& data_sets,
-                                  const std::vector<std::string>& data_names) = 0;
-  /*!
-   * \brief get prediction given the model.
-   * \param data input data
-   * \param output_margin whether to only predict margin value instead of transformed prediction
-   * \param out_preds output vector that stores the prediction
-   * \param ntree_limit limit number of trees used for boosted tree
-   *   predictor, when it equals 0, this means we are using all the trees
-   * \param training Whether the prediction result is used for training
-   * \param pred_leaf whether to only predict the leaf index of each tree in a boosted tree predictor
-   * \param pred_contribs whether to only predict the feature contributions
-   * \param approx_contribs whether to approximate the feature contributions for speed
-   * \param pred_interactions whether to compute the feature pair contributions
-   */
-  virtual void Predict(std::shared_ptr<DMatrix> data,
-                       bool output_margin,
-                       HostDeviceVector<bst_float> *out_preds,
-                       unsigned ntree_limit = 0,
-                       bool training = false,
-                       bool pred_leaf = false,
-                       bool pred_contribs = false,
-                       bool approx_contribs = false,
-                       bool pred_interactions = false) = 0;
-
-  /*!
-   * \brief Inplace prediction.
-   *
-   * \param          x           A type erased data adapter.
-   * \param          type        Prediction type.
-   * \param          missing     Missing value in the data.
-   * \param [in,out] out_preds   Pointer to output prediction vector.
-   * \param          layer_begin (Optional) Begining of boosted tree layer used for prediction.
-   * \param          layer_end   (Optional) End of booster layer. 0 means do not limit trees.
-   */
-  virtual void InplacePredict(dmlc::any const& x, std::string const& type,
-                              float missing,
-                              HostDeviceVector<bst_float> **out_preds,
-                              uint32_t layer_begin = 0, uint32_t layer_end = 0) = 0;
-
-  void LoadModel(Json const& in) override = 0;
-  void SaveModel(Json* out) const override = 0;
-
-  virtual void LoadModel(dmlc::Stream* fi) = 0;
-  virtual void SaveModel(dmlc::Stream* fo) const = 0;
-
-  /*!
-   * \brief Set multiple parameters at once.
-   *
-   * \param args parameters.
-   */
-  virtual void SetParams(Args const& args) = 0;
-  /*!
-   * \brief Set parameter for booster
-   *
-   *  The property will NOT be saved along with booster
-   *
-   * \param key   The key of parameter
-   * \param value The value of parameter
-   */
-  virtual void SetParam(const std::string& key, const std::string& value) = 0;
-
-  /*!
-   * \brief Set additional attribute to the Booster.
-   *
-   *  The property will be saved along the booster.
-   *
-   * \param key The key of the property.
-   * \param value The value of the property.
-   */
-  virtual void SetAttr(const std::string& key, const std::string& value) = 0;
-  /*!
-   * \brief Get attribute from the booster.
-   *  The property will be saved along the booster.
-   * \param key The key of the attribute.
-   * \param out The output value.
-   * \return Whether the key exists among booster's attributes.
-   */
-  virtual bool GetAttr(const std::string& key, std::string* out) const = 0;
-  /*!
-   * \brief Delete an attribute from the booster.
-   * \param key The key of the attribute.
-   * \return Whether the key was found among booster's attributes.
-   */
-  virtual bool DelAttr(const std::string& key) = 0;
-  /*!
-   * \brief Get a vector of attribute names from the booster.
-   * \return vector of attribute name strings.
-   */
-  virtual std::vector<std::string> GetAttrNames() const = 0;
-  /*!
-   * \return whether the model allow lazy checkpoint in rabit.
-   */
-  bool AllowLazyCheckPoint() const;
-  /*!
-   * \brief dump the model in the requested format
-   * \param fmap feature map that may help give interpretations of feature
-   * \param with_stats extra statistics while dumping model
-   * \param format the format to dump the model in
-   * \return a vector of dump for boosters.
-   */
-  virtual std::vector<std::string> DumpModel(const FeatureMap& fmap,
-                                             bool with_stats,
-                                             std::string format) = 0;
-
-  virtual XGBAPIThreadLocalEntry& GetThreadLocal() const = 0;
-  /*!
-   * \brief Create a new instance of learner.
-   * \param cache_data The matrix to cache the prediction.
-   * \return Created learner.
-   */
-  static Learner* Create(const std::vector<std::shared_ptr<DMatrix> >& cache_data);
-
-  virtual GenericParameter const& GetGenericParameter() const = 0;
-  /*!
-   * \brief Get configuration arguments currently stored by the learner
-   * \return Key-value pairs representing configuration arguments
-   */
-  virtual const std::map<std::string, std::string>& GetConfigurationArguments() const = 0;
-
- protected:
-  /*! \brief objective function */
-  std::unique_ptr<ObjFunction> obj_;
-  /*! \brief The gradient booster used by the model*/
-  std::unique_ptr<GradientBooster> gbm_;
-  /*! \brief The evaluation metrics used to evaluate the model. */
-  std::vector<std::unique_ptr<Metric> > metrics_;
-  /*! \brief Training parameter. */
-  GenericParameter generic_parameters_;
-};
-
-struct LearnerModelParamLegacy;
-
-/*
- * \brief Basic Model Parameters, used to describe the booster.
- */
-struct LearnerModelParam {
-  /* \brief global bias */
-  bst_float base_score { 0.5f };
-  /* \brief number of features  */
-  uint32_t num_feature { 0 };
-  /* \brief number of classes, if it is multi-class classification  */
-  uint32_t num_output_group { 0 };
-
-  LearnerModelParam() = default;
-  // As the old `LearnerModelParamLegacy` is still used by binary IO, we keep
-  // this one as an immutable copy.
-  LearnerModelParam(LearnerModelParamLegacy const& user_param, float base_margin);
-  /* \brief Whether this parameter is initialized with LearnerModelParamLegacy. */
-  bool Initialized() const { return num_feature != 0; }
-};
-
-}  // namespace xgboost
-#endif  // XGBOOST_LEARNER_H_
diff --git a/ml-xgboost/include/xgboost/linear_updater.h b/ml-xgboost/include/xgboost/linear_updater.h
deleted file mode 100644
index 39a0c32..0000000
--- a/ml-xgboost/include/xgboost/linear_updater.h
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright 2018 by Contributors
- */
-#pragma once
-
-#include <dmlc/registry.h>
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/generic_parameters.h>
-#include <xgboost/host_device_vector.h>
-#include <xgboost/model.h>
-
-#include <functional>
-#include <string>
-#include <utility>
-#include <vector>
-
-
-namespace xgboost {
-
-class Json;
-
-namespace gbm {
-class GBLinearModel;
-}  // namespace gbm
-
-/*!
- * \brief interface of linear updater
- */
-class LinearUpdater : public Configurable {
- protected:
-  GenericParameter const* learner_param_;
-
- public:
-  /*! \brief virtual destructor */
-  ~LinearUpdater() override = default;
-  /*!
-   * \brief Initialize the updater with given arguments.
-   * \param args arguments to the objective function.
-   */
-  virtual void Configure(
-      const std::vector<std::pair<std::string, std::string> >& args) = 0;
-
-  /**
-   * \brief Updates linear model given gradients.
-   *
-   * \param in_gpair            The gradient pair statistics of the data.
-   * \param data                Input data matrix.
-   * \param model               Model to be updated.
-   * \param sum_instance_weight The sum instance weights, used to normalise l1/l2 penalty.
-   */
-  virtual void Update(HostDeviceVector<GradientPair>* in_gpair, DMatrix* data,
-                      gbm::GBLinearModel* model,
-                      double sum_instance_weight) = 0;
-
-  /*!
-   * \brief Create a linear updater given name
-   * \param name Name of the linear updater.
-   */
-  static LinearUpdater* Create(const std::string& name, GenericParameter const*);
-};
-
-/*!
- * \brief Registry entry for linear updater.
- */
-struct LinearUpdaterReg
-    : public dmlc::FunctionRegEntryBase<LinearUpdaterReg,
-                                        std::function<LinearUpdater*()> > {};
-
-/*!
- * \brief Macro to register linear updater.
- */
-#define XGBOOST_REGISTER_LINEAR_UPDATER(UniqueId, Name)                        \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::LinearUpdaterReg&                    \
-      __make_##LinearUpdaterReg##_##UniqueId##__ =                             \
-          ::dmlc::Registry< ::xgboost::LinearUpdaterReg>::Get()->__REGISTER__( \
-              Name)
-
-}  // namespace xgboost
diff --git a/ml-xgboost/include/xgboost/logging.h b/ml-xgboost/include/xgboost/logging.h
deleted file mode 100644
index 07a2e67..0000000
--- a/ml-xgboost/include/xgboost/logging.h
+++ /dev/null
@@ -1,165 +0,0 @@
-/*!
- * Copyright (c) 2015-2019 by Contributors
- * \file logging.h
- *
- * \brief defines console logging options for xgboost.  Use to enforce unified print
- *  behavior.
- */
-#ifndef XGBOOST_LOGGING_H_
-#define XGBOOST_LOGGING_H_
-
-#include <dmlc/logging.h>
-#include <dmlc/thread_local.h>
-
-#include <xgboost/base.h>
-#include <xgboost/parameter.h>
-
-#include <sstream>
-#include <map>
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace xgboost {
-
-class BaseLogger {
- public:
-  BaseLogger() {
-#if XGBOOST_LOG_WITH_TIME
-    log_stream_ << "[" << dmlc::DateLogger().HumanDate() << "] ";
-#endif  // XGBOOST_LOG_WITH_TIME
-  }
-  std::ostream& stream() { return log_stream_; }  // NOLINT
-
- protected:
-  std::ostringstream log_stream_;
-};
-
-// Parsing both silent and debug_verbose is to provide backward compatibility.
-struct ConsoleLoggerParam : public XGBoostParameter<ConsoleLoggerParam> {
-  int verbosity;
-
-  DMLC_DECLARE_PARAMETER(ConsoleLoggerParam) {
-    DMLC_DECLARE_FIELD(verbosity)
-        .set_range(0, 3)
-        .set_default(1)  // shows only warning
-        .describe("Flag to print out detailed breakdown of runtime.");
-    DMLC_DECLARE_ALIAS(verbosity, debug_verbose);
-  }
-};
-
-class ConsoleLogger : public BaseLogger {
- public:
-  enum class LogVerbosity {
-    kSilent = 0,
-    kWarning = 1,
-    kInfo = 2,   // information may interests users.
-    kDebug = 3,  // information only interesting to developers.
-    kIgnore = 4  // ignore global setting
-  };
-  using LV = LogVerbosity;
-
- private:
-  static LogVerbosity global_verbosity_;
-  static ConsoleLoggerParam param_;
-
-  LogVerbosity cur_verbosity_;
-
- public:
-  static void Configure(Args const& args);
-
-  static LogVerbosity GlobalVerbosity();
-  static LogVerbosity DefaultVerbosity();
-  static bool ShouldLog(LogVerbosity verbosity);
-
-  ConsoleLogger() = delete;
-  explicit ConsoleLogger(LogVerbosity cur_verb);
-  ConsoleLogger(const std::string& file, int line, LogVerbosity cur_verb);
-  ~ConsoleLogger();
-};
-
-class TrackerLogger : public BaseLogger {
- public:
-  ~TrackerLogger();
-};
-
-// custom logging callback; disabled for R wrapper
-#if !defined(XGBOOST_STRICT_R_MODE) || XGBOOST_STRICT_R_MODE == 0
-class LogCallbackRegistry {
- public:
-  using Callback = void (*)(const char*);
-  LogCallbackRegistry()
-    : log_callback_([] (const char* msg) { std::cerr << msg << std::endl; }) {}
-  inline void Register(Callback log_callback) {
-    this->log_callback_ = log_callback;
-  }
-  inline Callback Get() const {
-    return log_callback_;
-  }
- private:
-  Callback log_callback_;
-};
-#else
-class LogCallbackRegistry {
- public:
-  using Callback = void (*)(const char*);
-  LogCallbackRegistry() {}
-  inline void Register(Callback log_callback) {}
-  inline Callback Get() const {
-    return nullptr;
-  }
-};
-#endif  // !defined(XGBOOST_STRICT_R_MODE) || XGBOOST_STRICT_R_MODE == 0
-
-using LogCallbackRegistryStore = dmlc::ThreadLocalStore<LogCallbackRegistry>;
-
-// Redefines LOG_WARNING for controling verbosity
-#if defined(LOG_WARNING)
-#undef  LOG_WARNING
-#endif  // defined(LOG_WARNING)
-#define LOG_WARNING                                                            \
-  if (::xgboost::ConsoleLogger::ShouldLog(                                     \
-          ::xgboost::ConsoleLogger::LV::kWarning))                             \
-  ::xgboost::ConsoleLogger(__FILE__, __LINE__,                                 \
-                           ::xgboost::ConsoleLogger::LogVerbosity::kWarning)
-
-// Redefines LOG_INFO for controling verbosity
-#if defined(LOG_INFO)
-#undef  LOG_INFO
-#endif  // defined(LOG_INFO)
-#define LOG_INFO                                                               \
-  if (::xgboost::ConsoleLogger::ShouldLog(                                     \
-          ::xgboost::ConsoleLogger::LV::kInfo))                                \
-  ::xgboost::ConsoleLogger(__FILE__, __LINE__,                                 \
-                           ::xgboost::ConsoleLogger::LogVerbosity::kInfo)
-
-#if defined(LOG_DEBUG)
-#undef LOG_DEBUG
-#endif  // defined(LOG_DEBUG)
-#define LOG_DEBUG                                                              \
-  if (::xgboost::ConsoleLogger::ShouldLog(                                     \
-          ::xgboost::ConsoleLogger::LV::kDebug))                               \
-  ::xgboost::ConsoleLogger(__FILE__, __LINE__,                                 \
-                           ::xgboost::ConsoleLogger::LogVerbosity::kDebug)
-
-// redefines the logging macro if not existed
-#ifndef LOG
-#define LOG(severity) LOG_##severity.stream()
-#endif  // LOG
-
-// Enable LOG(CONSOLE) for print messages to console.
-#define LOG_CONSOLE ::xgboost::ConsoleLogger(           \
-    ::xgboost::ConsoleLogger::LogVerbosity::kIgnore)
-// Enable LOG(TRACKER) for print messages to tracker
-#define LOG_TRACKER ::xgboost::TrackerLogger()
-
-#if defined(CHECK)
-#undef CHECK
-#define CHECK(cond)                                     \
-  if (XGBOOST_EXPECT(!(cond), false))                   \
-    dmlc::LogMessageFatal(__FILE__, __LINE__).stream()  \
-        << "Check failed: " #cond << ": "
-#endif  // defined(CHECK)
-
-}  // namespace xgboost.
-#endif  // XGBOOST_LOGGING_H_
diff --git a/ml-xgboost/include/xgboost/metric.h b/ml-xgboost/include/xgboost/metric.h
deleted file mode 100644
index 8e9d2af..0000000
--- a/ml-xgboost/include/xgboost/metric.h
+++ /dev/null
@@ -1,106 +0,0 @@
-/*!
- * Copyright 2014 by Contributors
- * \file metric.h
- * \brief interface of evaluation metric function supported in xgboost.
- * \author Tianqi Chen, Kailong Chen
- */
-#ifndef XGBOOST_METRIC_H_
-#define XGBOOST_METRIC_H_
-
-#include <dmlc/registry.h>
-#include <xgboost/model.h>
-#include <xgboost/generic_parameters.h>
-#include <xgboost/data.h>
-#include <xgboost/base.h>
-#include <xgboost/host_device_vector.h>
-
-#include <vector>
-#include <string>
-#include <functional>
-#include <utility>
-
-namespace xgboost {
-/*!
- * \brief interface of evaluation metric used to evaluate model performance.
- *  This has nothing to do with training, but merely act as evaluation purpose.
- */
-class Metric : public Configurable {
- protected:
-  GenericParameter const* tparam_;
-
- public:
-  /*!
-   * \brief Configure the Metric with the specified parameters.
-   * \param args arguments to the objective function.
-   */
-  virtual void Configure(
-      const std::vector<std::pair<std::string, std::string> >& args) {}
-  /*!
-   * \brief Load configuration from JSON object
-   * By default, metric has no internal configuration;
-   * override this function to maintain internal configuration
-   * \param in JSON object containing the configuration
-   */
-  void LoadConfig(Json const& in) override {}
-  /*!
-   * \brief Save configuration to JSON object
-   * By default, metric has no internal configuration;
-   * override this function to maintain internal configuration
-   * \param out pointer to output JSON object
-   */
-  void SaveConfig(Json* out) const override {}
-
-  /*!
-   * \brief evaluate a specific metric
-   * \param preds prediction
-   * \param info information, including label etc.
-   * \param distributed whether a call to Allreduce is needed to gather
-   *        the average statistics across all the node,
-   *        this is only supported by some metrics
-   */
-  virtual bst_float Eval(const HostDeviceVector<bst_float>& preds,
-                         const MetaInfo& info,
-                         bool distributed) = 0;
-  /*! \return name of metric */
-  virtual const char* Name() const = 0;
-  /*! \brief virtual destructor */
-  ~Metric() override = default;
-  /*!
-   * \brief create a metric according to name.
-   * \param name name of the metric.
-   *        name can be in form metric[@]param and the name will be matched in the
-   *        registry.
-   * \param tparam A global generic parameter
-   * \return the created metric.
-   */
-  static Metric* Create(const std::string& name, GenericParameter const* tparam);
-};
-
-/*!
- * \brief Registry entry for Metric factory functions.
- *  The additional parameter const char* param gives the value after @, can be null.
- *  For example, metric map@3, then: param == "3".
- */
-struct MetricReg
-    : public dmlc::FunctionRegEntryBase<MetricReg,
-                                        std::function<Metric* (const char*)> > {
-};
-
-/*!
- * \brief Macro to register metric.
- *
- * \code
- * // example of registering a objective ndcg@k
- * XGBOOST_REGISTER_METRIC(RMSE, "ndcg")
- * .describe("Rooted mean square error.")
- * .set_body([](const char* param) {
- *     int at_k = atoi(param);
- *     return new NDCG(at_k);
- *   });
- * \endcode
- */
-#define XGBOOST_REGISTER_METRIC(UniqueId, Name)                         \
-  ::xgboost::MetricReg&  __make_ ## MetricReg ## _ ## UniqueId ## __ =  \
-      ::dmlc::Registry< ::xgboost::MetricReg>::Get()->__REGISTER__(Name)
-}  // namespace xgboost
-#endif  // XGBOOST_METRIC_H_
diff --git a/ml-xgboost/include/xgboost/model.h b/ml-xgboost/include/xgboost/model.h
deleted file mode 100644
index 3b661ae..0000000
--- a/ml-xgboost/include/xgboost/model.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*!
- * Copyright (c) 2019 by Contributors
- * \file model.h
- * \brief Defines the abstract interface for different components in XGBoost.
- */
-#ifndef XGBOOST_MODEL_H_
-#define XGBOOST_MODEL_H_
-
-namespace dmlc {
-class Stream;
-}  // namespace dmlc
-
-namespace xgboost {
-
-class Json;
-
-struct Model {
-  virtual ~Model() = default;
-  /*!
-   * \brief load the model from a json object
-   * \param in json object where to load the model from
-   */
-  virtual void LoadModel(Json const& in) = 0;
-  /*!
-   * \brief saves the model config to a json object
-   * \param out json container where to save the model to
-   */
-  virtual void SaveModel(Json* out) const = 0;
-};
-
-struct Configurable {
-  virtual ~Configurable() = default;
-  /*!
-   * \brief Load configuration from JSON object
-   * \param in JSON object containing the configuration
-   */
-  virtual void LoadConfig(Json const& in) = 0;
-  /*!
-   * \brief Save configuration to JSON object
-   * \param out pointer to output JSON object
-   */
-  virtual void SaveConfig(Json* out) const = 0;
-};
-}  // namespace xgboost
-
-#endif  // XGBOOST_MODEL_H_
diff --git a/ml-xgboost/include/xgboost/objective.h b/ml-xgboost/include/xgboost/objective.h
deleted file mode 100644
index 1c0942e..0000000
--- a/ml-xgboost/include/xgboost/objective.h
+++ /dev/null
@@ -1,108 +0,0 @@
-/*!
- * Copyright 2014-2019 by Contributors
- * \file objective.h
- * \brief interface of objective function used by xgboost.
- * \author Tianqi Chen, Kailong Chen
- */
-#ifndef XGBOOST_OBJECTIVE_H_
-#define XGBOOST_OBJECTIVE_H_
-
-#include <dmlc/registry.h>
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/model.h>
-#include <xgboost/generic_parameters.h>
-#include <xgboost/host_device_vector.h>
-
-#include <vector>
-#include <utility>
-#include <string>
-#include <functional>
-
-namespace xgboost {
-
-/*! \brief interface of objective function */
-class ObjFunction : public Configurable {
- protected:
-  GenericParameter const* tparam_;
-
- public:
-  /*! \brief virtual destructor */
-  ~ObjFunction() override = default;
-  /*!
-   * \brief Configure the objective with the specified parameters.
-   * \param args arguments to the objective function.
-   */
-  virtual void Configure(const std::vector<std::pair<std::string, std::string> >& args) = 0;
-  /*!
-   * \brief Get gradient over each of predictions, given existing information.
-   * \param preds prediction of current round
-   * \param info information about labels, weights, groups in rank
-   * \param iteration current iteration number.
-   * \param out_gpair output of get gradient, saves gradient and second order gradient in
-   */
-  virtual void GetGradient(const HostDeviceVector<bst_float>& preds,
-                           const MetaInfo& info,
-                           int iteration,
-                           HostDeviceVector<GradientPair>* out_gpair) = 0;
-
-  /*! \return the default evaluation metric for the objective */
-  virtual const char* DefaultEvalMetric() const = 0;
-  // the following functions are optional, most of time default implementation is good enough
-  /*!
-   * \brief transform prediction values, this is only called when Prediction is called
-   * \param io_preds prediction values, saves to this vector as well
-   */
-  virtual void PredTransform(HostDeviceVector<bst_float> *io_preds) {}
-
-  /*!
-   * \brief transform prediction values, this is only called when Eval is called,
-   *  usually it redirect to PredTransform
-   * \param io_preds prediction values, saves to this vector as well
-   */
-  virtual void EvalTransform(HostDeviceVector<bst_float> *io_preds) {
-    this->PredTransform(io_preds);
-  }
-  /*!
-   * \brief transform probability value back to margin
-   * this is used to transform user-set base_score back to margin
-   * used by gradient boosting
-   * \return transformed value
-   */
-  virtual bst_float ProbToMargin(bst_float base_score) const {
-    return base_score;
-  }
-  /*!
-   * \brief Create an objective function according to name.
-   * \param tparam Generic parameters.
-   * \param name Name of the objective.
-   */
-  static ObjFunction* Create(const std::string& name, GenericParameter const* tparam);
-};
-
-/*!
- * \brief Registry entry for objective factory functions.
- */
-struct ObjFunctionReg
-    : public dmlc::FunctionRegEntryBase<ObjFunctionReg,
-                                        std::function<ObjFunction* ()> > {
-};
-
-/*!
- * \brief Macro to register objective function.
- *
- * \code
- * // example of registering a objective
- * XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:squarederror")
- * .describe("Linear regression objective")
- * .set_body([]() {
- *     return new RegLossObj(LossType::kLinearSquare);
- *   });
- * \endcode
- */
-#define XGBOOST_REGISTER_OBJECTIVE(UniqueId, Name)                      \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::ObjFunctionReg &              \
-  __make_ ## ObjFunctionReg ## _ ## UniqueId ## __ =                    \
-      ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->__REGISTER__(Name)
-}  // namespace xgboost
-#endif  // XGBOOST_OBJECTIVE_H_
diff --git a/ml-xgboost/include/xgboost/parameter.h b/ml-xgboost/include/xgboost/parameter.h
deleted file mode 100644
index 3816161..0000000
--- a/ml-xgboost/include/xgboost/parameter.h
+++ /dev/null
@@ -1,131 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2018 by Contributors
- * \file parameter.h
- * \brief macro for using C++11 enum class as DMLC parameter
- * \author Hyunsu Philip Cho
- */
-
-#ifndef XGBOOST_PARAMETER_H_
-#define XGBOOST_PARAMETER_H_
-
-#include <dmlc/parameter.h>
-#include <xgboost/base.h>
-#include <string>
-#include <type_traits>
-
-/*!
- * \brief Specialization of FieldEntry for enum class (backed by int)
- *
- * Use this macro to use C++11 enum class as DMLC parameters
- *
- * Usage:
- *
- * \code{.cpp}
- *
- *   // enum class must inherit from int type
- *   enum class Foo : int {
- *     kBar = 0, kFrog = 1, kCat = 2, kDog = 3
- *   };
- *
- *   // This line is needed to prevent compilation error
- *   DECLARE_FIELD_ENUM_CLASS(Foo);
- *
- *   // Now define DMLC parameter as usual;
- *   //   enum classes can now be members.
- *   struct MyParam : dmlc::Parameter<MyParam> {
- *     Foo foo;
- *     DMLC_DECLARE_PARAMETER(MyParam) {
- *       DMLC_DECLARE_FIELD(foo)
- *         .set_default(Foo::kBar)
- *         .add_enum("bar", Foo::kBar)
- *         .add_enum("frog", Foo::kFrog)
- *         .add_enum("cat", Foo::kCat)
- *         .add_enum("dog", Foo::kDog);
- *     }
- *   };
- *
- *   DMLC_REGISTER_PARAMETER(MyParam);
- * \endcode
- */
-#define DECLARE_FIELD_ENUM_CLASS(EnumClass) \
-namespace dmlc {  \
-namespace parameter {  \
-template <>  \
-class FieldEntry<EnumClass> : public FieldEntry<int> {  \
- public:  \
-  FieldEntry<EnumClass>() {  \
-    static_assert(  \
-      std::is_same<int, typename std::underlying_type<EnumClass>::type>::value,  \
-      "enum class must be backed by int");  \
-    is_enum_ = true;  \
-  }  \
-  using Super = FieldEntry<int>;  \
-  void Set(void *head, const std::string &value) const override {  \
-    Super::Set(head, value);  \
-  }  \
-  inline FieldEntry<EnumClass>& add_enum(const std::string &key, EnumClass value) {  \
-    Super::add_enum(key, static_cast<int>(value));  \
-    return *this;  \
-  }  \
-  inline FieldEntry<EnumClass>& set_default(const EnumClass& default_value) {  \
-    default_value_ = static_cast<int>(default_value);  \
-    has_default_ = true;  \
-    return *this;  \
-  }  \
-  inline void Init(const std::string &key, void *head, EnumClass& ref) {  /* NOLINT */  \
-    Super::Init(key, head, *reinterpret_cast<int*>(&ref));  \
-  }  \
-};  \
-}  /* namespace parameter */  \
-}  /* namespace dmlc */
-
-namespace xgboost {
-template <typename Type>
-struct XGBoostParameter : public dmlc::Parameter<Type> {
- protected:
-  bool initialised_ {false};
-
- public:
-  template <typename Container>
-  Args UpdateAllowUnknown(Container const& kwargs, bool* out_changed = nullptr) {
-    if (initialised_) {
-      return dmlc::Parameter<Type>::UpdateAllowUnknown(kwargs, out_changed);
-    } else {
-      auto unknown = dmlc::Parameter<Type>::InitAllowUnknown(kwargs);
-      if (out_changed) {
-        *out_changed = true;
-      }
-      initialised_ = true;
-      return unknown;
-    }
-  }
-  bool GetInitialised() const { return static_cast<bool>(this->initialised_); }
-};
-}  // namespace xgboost
-
-namespace xgboost {
-namespace tree {
-struct TrainParamKernel {
-  enum SamplingStrategy {
-    eachTree = 0,
-    eachIteration = 1,
-    allIteration = 2,
-    multiIteration = 3,
-    gossStyle = 4
-  };
-  enum DefaultDirection {
-    learn = 0,
-    left = 1,
-    right = 2
-  };
-};
-}  // namespace tree
-}  // namespace xgboost
-
-#endif  // XGBOOST_PARAMETER_H_
diff --git a/ml-xgboost/include/xgboost/predictor.h b/ml-xgboost/include/xgboost/predictor.h
deleted file mode 100644
index 0e3f756..0000000
--- a/ml-xgboost/include/xgboost/predictor.h
+++ /dev/null
@@ -1,242 +0,0 @@
-/*!
- * Copyright 2017-2020 by Contributors
- * \file predictor.h
- * \brief Interface of predictor,
- *  performs predictions for a gradient booster.
- */
-#pragma once
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/generic_parameters.h>
-#include <xgboost/host_device_vector.h>
-
-#include <functional>
-#include <memory>
-#include <string>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-#include <mutex>
-
-// Forward declarations
-namespace xgboost {
-class TreeUpdater;
-namespace gbm {
-struct GBTreeModel;
-}  // namespace gbm
-}
-
-namespace xgboost {
-/**
- * \struct  PredictionCacheEntry
- *
- * \brief Contains pointer to input matrix and associated cached predictions.
- */
-struct PredictionCacheEntry {
-  // A storage for caching prediction values
-  HostDeviceVector<bst_float> predictions;
-  // The version of current cache, corresponding number of layers of trees
-  uint32_t version { 0 };
-  // A weak pointer for checking whether the DMatrix object has expired.
-  std::weak_ptr< DMatrix > ref;
-
-  PredictionCacheEntry() = default;
-  /* \brief Update the cache entry by number of versions.
-   *
-   * \param v Added versions.
-   */
-  void Update(uint32_t v) {
-    version += v;
-  }
-};
-
-/* \brief A container for managed prediction caches.
- */
-class PredictionContainer {
-  std::unordered_map<DMatrix *, PredictionCacheEntry> container_;
-  void ClearExpiredEntries();
-  std::mutex cache_lock_;
-
- public:
-  PredictionContainer() = default;
-  /* \brief Add a new DMatrix to the cache, at the same time this function will clear out
-   *        all expired caches by checking the `std::weak_ptr`.  Caching an existing
-   *        DMatrix won't renew it.
-   *
-   *  Passing in a `shared_ptr` is critical here.  First to create a `weak_ptr` inside the
-   *  entry this shared pointer is necessary.  More importantly, the life time of this
-   *  cache is tied to the shared pointer.
-   *
-   *  Another way to make a safe cache is create a proxy to this entry, with anther shared
-   *  pointer defined inside, and pass this proxy around instead of the real entry.  But
-   *  seems to be too messy.  In XGBoost, functions like `UpdateOneIter` will have
-   *  (memory) safe access to the DMatrix as long as it's passed in as a `shared_ptr`.
-   *
-   * \param m shared pointer to the DMatrix that needs to be cached.
-   * \param device Which device should the cache be allocated on.  Pass
-   *               GenericParameter::kCpuId for CPU or positive integer for GPU id.
-   *
-   * \return the cache entry for passed in DMatrix, either an existing cache or newly
-   *         created.
-   */
-  PredictionCacheEntry& Cache(std::shared_ptr<DMatrix> m, int32_t device);
-  /* \brief Get a prediction cache entry.  This entry must be already allocated by `Cache`
-   *        method.  Otherwise a dmlc::Error is thrown.
-   *
-   * \param m pointer to the DMatrix.
-   * \return The prediction cache for passed in DMatrix.
-   */
-  PredictionCacheEntry& Entry(DMatrix* m);
-  /* \brief Get a const reference to the underlying hash map.  Clear expired caches before
-   *        returning.
-   */
-  decltype(container_) const& Container();
-};
-
-/**
- * \class Predictor
- *
- * \brief Performs prediction on individual training instances or batches of instances for
- *        GBTree. Prediction functions all take a GBTreeModel and a DMatrix as input and
- *        output a vector of predictions. The predictor does not modify any state of the
- *        model itself.
- */
-class Predictor {
- protected:
-  /*
-   * \brief Runtime parameters.
-   */
-  GenericParameter const* generic_param_;
-
- public:
-  explicit Predictor(GenericParameter const* generic_param) :
-      generic_param_{generic_param} {}
-  virtual ~Predictor() = default;
-
-  /**
-   * \brief Configure and register input matrices in prediction cache.
-   *
-   * \param cfg   The configuration.
-   */
-  virtual void Configure(const std::vector<std::pair<std::string, std::string>>& cfg);
-
-  /**
-   * \brief Generate batch predictions for a given feature matrix. May use
-   * cached predictions if available instead of calculating from scratch.
-   *
-   * \param [in,out]  dmat        Feature matrix.
-   * \param [in,out]  out_preds   The output preds.
-   * \param           model       The model to predict from.
-   * \param           tree_begin  The tree begin index.
-   * \param           ntree_limit (Optional) The ntree limit. 0 means do not
-   *                              limit trees.
-   */
-  virtual void PredictBatch(DMatrix* dmat, PredictionCacheEntry* out_preds,
-                            const gbm::GBTreeModel& model, int tree_begin,
-                            uint32_t const ntree_limit = 0) = 0;
-
-  /**
-   * \brief Inplace prediction.
-   * \param           x                      Type erased data adapter.
-   * \param           model                  The model to predict from.
-   * \param           missing                Missing value in the data.
-   * \param [in,out]  out_preds              The output preds.
-   * \param           tree_begin (Optional) Begining of boosted trees used for prediction.
-   * \param           tree_end   (Optional) End of booster trees. 0 means do not limit trees.
-   */
-  virtual void InplacePredict(dmlc::any const &x, const gbm::GBTreeModel &model,
-                              float missing, PredictionCacheEntry *out_preds,
-                              uint32_t tree_begin = 0, uint32_t tree_end = 0) const = 0;
-  /**
-   * \brief online prediction function, predict score for one instance at a time
-   * NOTE: use the batch prediction interface if possible, batch prediction is
-   * usually more efficient than online prediction This function is NOT
-   * threadsafe, make sure you only call from one thread.
-   *
-   * \param           inst        The instance to predict.
-   * \param [in,out]  out_preds   The output preds.
-   * \param           model       The model to predict from
-   * \param           ntree_limit (Optional) The ntree limit.
-   */
-
-  virtual void PredictInstance(const SparsePage::Inst& inst,
-                               std::vector<bst_float>* out_preds,
-                               const gbm::GBTreeModel& model,
-                               unsigned ntree_limit = 0) = 0;
-
-  /**
-   * \fn  virtual void Predictor::PredictLeaf(DMatrix* dmat,
-   * std::vector<bst_float>* out_preds, const gbm::GBTreeModel& model, unsigned
-   * ntree_limit = 0) = 0;
-   *
-   * \brief predict the leaf index of each tree, the output will be nsample *
-   * ntree vector this is only valid in gbtree predictor.
-   *
-   * \param [in,out]  dmat        The input feature matrix.
-   * \param [in,out]  out_preds   The output preds.
-   * \param           model       Model to make predictions from.
-   * \param           ntree_limit (Optional) The ntree limit.
-   */
-
-  virtual void PredictLeaf(DMatrix* dmat, std::vector<bst_float>* out_preds,
-                           const gbm::GBTreeModel& model,
-                           unsigned ntree_limit = 0) = 0;
-
-  /**
-   * \fn  virtual void Predictor::PredictContribution( DMatrix* dmat,
-   * std::vector<bst_float>* out_contribs, const gbm::GBTreeModel& model,
-   * unsigned ntree_limit = 0) = 0;
-   *
-   * \brief feature contributions to individual predictions; the output will be
-   * a vector of length (nfeats + 1) * num_output_group * nsample, arranged in
-   * that order.
-   *
-   * \param [in,out]  dmat               The input feature matrix.
-   * \param [in,out]  out_contribs       The output feature contribs.
-   * \param           model              Model to make predictions from.
-   * \param           ntree_limit        (Optional) The ntree limit.
-   * \param           tree_weights       (Optional) Weights to multiply each tree by.
-   * \param           approximate        Use fast approximate algorithm.
-   * \param           condition          Condition on the condition_feature (0=no, -1=cond off, 1=cond on).
-   * \param           condition_feature  Feature to condition on (i.e. fix) during calculations.
-   */
-
-  virtual void PredictContribution(DMatrix* dmat,
-                                   std::vector<bst_float>* out_contribs,
-                                   const gbm::GBTreeModel& model,
-                                   unsigned ntree_limit = 0,
-                                   std::vector<bst_float>* tree_weights = nullptr,
-                                   bool approximate = false,
-                                   int condition = 0,
-                                   unsigned condition_feature = 0) = 0;
-
-  virtual void PredictInteractionContributions(DMatrix* dmat,
-                                               std::vector<bst_float>* out_contribs,
-                                               const gbm::GBTreeModel& model,
-                                               unsigned ntree_limit = 0,
-                                               std::vector<bst_float>* tree_weights = nullptr,
-                                               bool approximate = false) = 0;
-
-
-  /**
-   * \brief Creates a new Predictor*.
-   *
-   * \param name           Name of the predictor.
-   * \param generic_param  Pointer to runtime parameters.
-   */
-  static Predictor* Create(
-      std::string const& name, GenericParameter const* generic_param);
-};
-
-/*!
- * \brief Registry entry for predictor.
- */
-struct PredictorReg
-    : public dmlc::FunctionRegEntryBase<
-  PredictorReg, std::function<Predictor*(GenericParameter const*)>> {};
-
-#define XGBOOST_REGISTER_PREDICTOR(UniqueId, Name)      \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::PredictorReg& \
-      __make_##PredictorReg##_##UniqueId##__ =          \
-          ::dmlc::Registry<::xgboost::PredictorReg>::Get()->__REGISTER__(Name)
-}  // namespace xgboost
diff --git a/ml-xgboost/include/xgboost/span.h b/ml-xgboost/include/xgboost/span.h
deleted file mode 100644
index 29b0b9a..0000000
--- a/ml-xgboost/include/xgboost/span.h
+++ /dev/null
@@ -1,661 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- * \brief span class based on ISO++20 span
- *
- * About NOLINTs in this file:
- *
- *   If we want Span to work with std interface, like range for loop, the
- *   naming must be consistant with std, not XGBoost. Also, the interface also
- *   conflicts with XGBoost coding style, specifically, the use of `explicit'
- *   keyword.
- *
- *
- * Some of the code is copied from Guidelines Support Library, here is the
- * license:
- *
- * Copyright (c) 2015 Microsoft Corporation. All rights reserved.
- *
- * This code is licensed under the MIT License (MIT).
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-#ifndef XGBOOST_SPAN_H_
-#define XGBOOST_SPAN_H_
-
-#include <xgboost/base.h>
-#include <xgboost/logging.h>
-
-#include <cinttypes>          // size_t
-#include <limits>             // numeric_limits
-#include <iterator>
-#include <type_traits>
-#include <cstdio>
-
-/*!
- * The version number 1910 is picked up from GSL.
- *
- * We might want to use MOODYCAMEL_NOEXCEPT from dmlc/concurrentqueue.h. But
- * there are a lot more definitions in that file would cause warnings/troubles
- * in MSVC 2013. Currently we try to keep the closure of Span as minimal as
- * possible.
- *
- * There are other workarounds for MSVC, like _Unwrapped, _Verify_range ...
- * Some of these are hiden magics of MSVC and I tried to avoid them. Should any
- * of them become needed, please consult the source code of GSL, and possibily
- * some explanations from this thread:
- *
- *   https://github.com/Microsoft/GSL/pull/664
- *
- * TODO(trivialfis): Group these MSVC workarounds into a manageable place.
- */
-#if defined(_MSC_VER) && _MSC_VER < 1910
-
-#define __span_noexcept
-
-#pragma push_macro("constexpr")
-#define constexpr /*constexpr*/
-
-#else
-
-#define __span_noexcept noexcept
-
-#endif  // defined(_MSC_VER) && _MSC_VER < 1910
-
-namespace xgboost {
-namespace common {
-
-// Usual logging facility is not available inside device code.
-// assert is not supported in mac as of CUDA 10.0
-#define KERNEL_CHECK(cond)                                                     \
-  do {                                                                         \
-    if (!(cond)) {                                                             \
-      printf("\nKernel error:\n"                                               \
-             "In: %s: %d\n"                                                    \
-             "\t%s\n\tExpecting: %s\n"                                         \
-             "\tBlock: [%d, %d, %d], Thread: [%d, %d, %d]\n\n",                \
-             __FILE__, __LINE__, __PRETTY_FUNCTION__, #cond, blockIdx.x,       \
-             blockIdx.y, blockIdx.z, threadIdx.x, threadIdx.y, threadIdx.z);   \
-      asm("trap;");                                                            \
-    }                                                                          \
-  } while (0);
-
-#if defined(__CUDA_ARCH__)
-#define SPAN_CHECK KERNEL_CHECK
-#elif defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1  // R package
-#define SPAN_CHECK CHECK  // check from dmlc
-#else  // not CUDA, not R
-#define SPAN_CHECK(cond)                                                       \
-  do {                                                                         \
-    if (XGBOOST_EXPECT(!(cond), false)) {                                      \
-      fprintf(stderr, "[xgboost] Condition %s failed.\n", #cond);              \
-      fflush(stderr);  /* It seems stderr on Windows is beffered? */           \
-      std::terminate();                                                        \
-    }                                                                          \
-  } while (0);
-#endif  // __CUDA_ARCH__
-
-namespace detail {
-/*!
- * By default, XGBoost uses uint32_t for indexing data. int64_t covers all
- *   values uint32_t can represent. Also, On x86-64 Linux, GCC uses long int to
- *   represent ptrdiff_t, which is just int64_t. So we make it determinstic
- *   here.
- */
-using ptrdiff_t = typename std::conditional<  // NOLINT
-    std::is_same<std::ptrdiff_t, std::int64_t>::value,
-    std::ptrdiff_t, std::int64_t>::type;
-}  // namespace detail
-
-#if defined(_MSC_VER) && _MSC_VER < 1910
-constexpr const std::size_t
-dynamic_extent = std::numeric_limits<std::size_t>::max();  // NOLINT
-#else
-constexpr std::size_t dynamic_extent = std::numeric_limits<std::size_t>::max();  // NOLINT
-#endif  // defined(_MSC_VER) && _MSC_VER < 1910
-
-enum class byte : unsigned char {};  // NOLINT
-
-template <class ElementType, std::size_t Extent>
-class Span;
-
-namespace detail {
-
-template <typename SpanType, bool IsConst>
-class SpanIterator {
-  using ElementType = typename SpanType::element_type;
-
- public:
-  using iterator_category = std::random_access_iterator_tag;      // NOLINT
-  using value_type = typename SpanType::value_type;  // NOLINT
-  using difference_type = detail::ptrdiff_t;             // NOLINT
-
-  using reference = typename std::conditional<                    // NOLINT
-    IsConst, const ElementType, ElementType>::type&;
-  using pointer = typename std::add_pointer<reference>::type;     // NOLINT
-
-  constexpr SpanIterator() = default;
-
-  XGBOOST_DEVICE constexpr SpanIterator(
-      const SpanType* _span,
-      typename SpanType::index_type _idx) __span_noexcept :
-                                           span_(_span), index_(_idx) {}
-
-  friend SpanIterator<SpanType, true>;
-  template <bool B, typename std::enable_if<!B && IsConst>::type* = nullptr>
-  XGBOOST_DEVICE constexpr SpanIterator(                         // NOLINT
-      const SpanIterator<SpanType, B>& other_) __span_noexcept
-      : SpanIterator(other_.span_, other_.index_) {}
-
-  XGBOOST_DEVICE reference operator*() const {
-    SPAN_CHECK(index_ < span_->size());
-    return *(span_->data() + index_);
-  }
-  XGBOOST_DEVICE reference operator[](difference_type n) const {
-    return *(*this + n);
-  }
-
-  XGBOOST_DEVICE pointer operator->() const {
-    SPAN_CHECK(index_ != span_->size());
-    return span_->data() + index_;
-  }
-
-  XGBOOST_DEVICE SpanIterator& operator++() {
-    SPAN_CHECK(index_ != span_->size());
-    index_++;
-    return *this;
-  }
-
-  XGBOOST_DEVICE SpanIterator operator++(int) {
-    auto ret = *this;
-    ++(*this);
-    return ret;
-  }
-
-  XGBOOST_DEVICE SpanIterator& operator--() {
-    SPAN_CHECK(index_ != 0 && index_ <= span_->size());
-    index_--;
-    return *this;
-  }
-
-  XGBOOST_DEVICE SpanIterator operator--(int) {
-    auto ret = *this;
-    --(*this);
-    return ret;
-  }
-
-  XGBOOST_DEVICE SpanIterator operator+(difference_type n) const {
-    auto ret = *this;
-    return ret += n;
-  }
-
-  XGBOOST_DEVICE SpanIterator& operator+=(difference_type n) {
-    SPAN_CHECK((index_ + n) <= span_->size());
-    index_ += n;
-    return *this;
-  }
-
-  XGBOOST_DEVICE difference_type operator-(SpanIterator rhs) const {
-    SPAN_CHECK(span_ == rhs.span_);
-    return index_ - rhs.index_;
-  }
-
-  XGBOOST_DEVICE SpanIterator operator-(difference_type n) const {
-    auto ret = *this;
-    return ret -= n;
-  }
-
-  XGBOOST_DEVICE SpanIterator& operator-=(difference_type n) {
-    return *this += -n;
-  }
-
-  // friends
-  XGBOOST_DEVICE constexpr friend bool operator==(
-      SpanIterator _lhs, SpanIterator _rhs) __span_noexcept {
-    return _lhs.span_ == _rhs.span_ && _lhs.index_ == _rhs.index_;
-  }
-
-  XGBOOST_DEVICE constexpr friend bool operator!=(
-      SpanIterator _lhs, SpanIterator _rhs) __span_noexcept {
-    return !(_lhs == _rhs);
-  }
-
-  XGBOOST_DEVICE constexpr friend bool operator<(
-      SpanIterator _lhs, SpanIterator _rhs) __span_noexcept {
-    return _lhs.index_ < _rhs.index_;
-  }
-
-  XGBOOST_DEVICE constexpr friend bool operator<=(
-      SpanIterator _lhs, SpanIterator _rhs) __span_noexcept {
-    return !(_rhs < _lhs);
-  }
-
-  XGBOOST_DEVICE constexpr friend bool operator>(
-      SpanIterator _lhs, SpanIterator _rhs) __span_noexcept {
-    return _rhs < _lhs;
-  }
-
-  XGBOOST_DEVICE constexpr friend bool operator>=(
-      SpanIterator _lhs, SpanIterator _rhs) __span_noexcept {
-    return !(_rhs > _lhs);
-  }
-
- protected:
-  const SpanType *span_ { nullptr };
-  typename SpanType::index_type index_ { 0 };
-};
-
-
-// It's tempting to use constexpr instead of structs to do the following meta
-// programming. But remember that we are supporting MSVC 2013 here.
-
-/*!
- * The extent E of the span returned by subspan is determined as follows:
- *
- *   - If Count is not dynamic_extent, Count;
- *   - Otherwise, if Extent is not dynamic_extent, Extent - Offset;
- *   - Otherwise, dynamic_extent.
- */
-template <std::size_t Extent, std::size_t Offset, std::size_t Count>
-struct ExtentValue : public std::integral_constant<
-  std::size_t, Count != dynamic_extent ?
-  Count : (Extent != dynamic_extent ? Extent - Offset : Extent)> {};
-
-/*!
- * If N is dynamic_extent, the extent of the returned span E is also
- * dynamic_extent; otherwise it is std::size_t(sizeof(T)) * N.
- */
-template <typename T, std::size_t Extent>
-struct ExtentAsBytesValue : public std::integral_constant<
-  std::size_t,
-  Extent == dynamic_extent ?
-  Extent : sizeof(T) * Extent> {};
-
-template <std::size_t From, std::size_t To>
-struct IsAllowedExtentConversion : public std::integral_constant<
-  bool, From == To || From == dynamic_extent || To == dynamic_extent> {};
-
-template <class From, class To>
-struct IsAllowedElementTypeConversion : public std::integral_constant<
-  bool, std::is_convertible<From(*)[], To(*)[]>::value> {};
-
-template <class T>
-struct IsSpanOracle : std::false_type {};
-
-template <class T, std::size_t Extent>
-struct IsSpanOracle<Span<T, Extent>> : std::true_type {};
-
-template <class T>
-struct IsSpan : public IsSpanOracle<typename std::remove_cv<T>::type> {};
-
-// Re-implement std algorithms here to adopt CUDA.
-template <typename T>
-struct Less {
-  XGBOOST_DEVICE constexpr bool operator()(const T& _x, const T& _y) const {
-    return _x < _y;
-  }
-};
-
-template <typename T>
-struct Greater {
-  XGBOOST_DEVICE constexpr bool operator()(const T& _x, const T& _y) const {
-    return _x > _y;
-  }
-};
-
-template <class InputIt1, class InputIt2,
-          class Compare =
-          detail::Less<decltype(std::declval<InputIt1>().operator*())>>
-XGBOOST_DEVICE bool LexicographicalCompare(InputIt1 first1, InputIt1 last1,
-                                            InputIt2 first2, InputIt2 last2) {
-  Compare comp;
-  for (; first1 != last1 && first2 != last2; ++first1, ++first2) {
-    if (comp(*first1, *first2)) {
-      return true;
-    }
-    if (comp(*first2, *first1)) {
-      return false;
-    }
-  }
-  return first1 == last1 && first2 != last2;
-}
-
-}  // namespace detail
-
-
-/*!
- * \brief span class implementation, based on ISO++20 span<T>. The interface
- *      should be the same.
- *
- * What's different from span<T> in Guidelines Support Library (GSL)
- *
- *    Interface might be slightly different, we stick with ISO.
- *
- *    GSL uses C++14/17 features, which are not available here.
- *    GSL uses constexpr extensively, which is not possibile with limitation
- *      of C++11.
- *    GSL doesn't concern about CUDA.
- *
- *    GSL is more thoroughly implemented and tested.
- *    GSL is more optimized, especially for static extent.
- *
- *    GSL uses __buildin_unreachable() when error, Span<T> uses dmlc LOG and
- *      customized CUDA logging.
- *
- *
- * What's different from span<T> in ISO++20 (ISO)
- *
- *    ISO uses functions/structs from std library, which might be not available
- *      in CUDA.
- *    Initializing from std::array is not supported.
- *
- *    ISO uses constexpr extensively, which is not possibile with limitation
- *      of C++11.
- *    ISO uses C++14/17 features, which is not available here.
- *    ISO doesn't concern about CUDA.
- *
- *    ISO uses std::terminate(), Span<T> uses dmlc LOG and customized CUDA
- *      logging.
- *
- *
- * Limitations:
- *    With thrust:
- *       It's not adviced to initialize Span with host_vector directly, since
- *         host_vector::data() is a host function.
- *       It's not possible to initialize Span with device_vector directly, since
- *         device_vector::data() returns a wrapped pointer.
- *       It's unclear that what kind of thrust algorithm can be used without
- *         memory error. See the test case "GPUSpan.WithTrust"
- *
- *    Pass iterator to kernel:
- *       Not possible. Use subspan instead.
- *
- *       The underlying Span in SpanIterator is a pointer, but CUDA pass kernel
- *       parameter by value.  If we were to hold a Span value instead of a
- *       pointer, the following snippet will crash, violating the safety
- *       purpose of Span:
- *
- *       \code{.cpp}
- *       Span<float> span {arr_a};
- *       auto beg = span.begin();
- *
- *       Span<float> span_b = arr_b;
- *       span = span_b;
- *
- *       delete arr_a;
- *       beg++;                 // crash
- *       \endcode
- *
- *       While hoding a pointer or reference should avoid the problem, its a
- *       compromise. Since we have subspan, it's acceptable not to support
- *       passing iterator.
- */
-template <typename T,
-          std::size_t Extent = dynamic_extent>
-class Span {
- public:
-  using element_type = T;                               // NOLINT
-  using value_type = typename std::remove_cv<T>::type;  // NOLINT
-  using index_type = std::size_t;                       // NOLINT
-  using difference_type = detail::ptrdiff_t;            // NOLINT
-  using pointer = T*;                                   // NOLINT
-  using reference = T&;                                 // NOLINT
-
-  using iterator = detail::SpanIterator<Span<T, Extent>, false>;             // NOLINT
-  using const_iterator = const detail::SpanIterator<Span<T, Extent>, true>;  // NOLINT
-  using reverse_iterator = detail::SpanIterator<Span<T, Extent>, false>;     // NOLINT
-  using const_reverse_iterator = const detail::SpanIterator<Span<T, Extent>, true>;  // NOLINT
-
-  // constructors
-  constexpr Span() __span_noexcept = default;
-
-  XGBOOST_DEVICE Span(pointer _ptr, index_type _count) :
-      size_(_count), data_(_ptr) {
-    SPAN_CHECK(!(Extent != dynamic_extent && _count != Extent));
-    SPAN_CHECK(_ptr || _count == 0);
-  }
-
-  XGBOOST_DEVICE Span(pointer _first, pointer _last) :
-      size_(_last - _first), data_(_first) {
-    SPAN_CHECK(data_ || size_ == 0);
-  }
-
-  template <std::size_t N>
-  XGBOOST_DEVICE constexpr Span(element_type (&arr)[N])  // NOLINT
-      __span_noexcept : size_(N), data_(&arr[0]) {}
-
-  template <class Container,
-            class = typename std::enable_if<
-              !std::is_const<element_type>::value &&
-              !detail::IsSpan<Container>::value &&
-              std::is_convertible<typename Container::pointer, pointer>::value &&
-              std::is_convertible<typename Container::pointer,
-                                  decltype(std::declval<Container>().data())>::value>::type>
-  Span(Container& _cont) :  // NOLINT
-      size_(_cont.size()), data_(_cont.data()) {
-    static_assert(!detail::IsSpan<Container>::value, "Wrong constructor of Span is called.");
-  }
-
-  template <class Container,
-            class = typename std::enable_if<
-              std::is_const<element_type>::value &&
-              !detail::IsSpan<Container>::value &&
-              std::is_convertible<typename Container::pointer, pointer>::value &&
-              std::is_convertible<typename Container::pointer,
-                                  decltype(std::declval<Container>().data())>::value>::type>
-  Span(const Container& _cont) : size_(_cont.size()),  // NOLINT
-                                 data_(_cont.data()) {
-    static_assert(!detail::IsSpan<Container>::value, "Wrong constructor of Span is called.");
-  }
-
-  template <class U, std::size_t OtherExtent,
-            class = typename std::enable_if<
-              detail::IsAllowedElementTypeConversion<U, T>::value &&
-              detail::IsAllowedExtentConversion<OtherExtent, Extent>::value>>
-  XGBOOST_DEVICE constexpr Span(const Span<U, OtherExtent>& _other)   // NOLINT
-      __span_noexcept : size_(_other.size()), data_(_other.data()) {}
-
-  XGBOOST_DEVICE constexpr Span(const Span& _other)
-      __span_noexcept : size_(_other.size()), data_(_other.data()) {}
-
-  XGBOOST_DEVICE Span& operator=(const Span& _other) __span_noexcept {
-    size_ = _other.size();
-    data_ = _other.data();
-    return *this;
-  }
-
-  XGBOOST_DEVICE ~Span() __span_noexcept {};  // NOLINT
-
-  XGBOOST_DEVICE constexpr iterator begin() const __span_noexcept {  // NOLINT
-    return {this, 0};
-  }
-
-  XGBOOST_DEVICE constexpr iterator end() const __span_noexcept {    // NOLINT
-    return {this, size()};
-  }
-
-  XGBOOST_DEVICE constexpr const_iterator cbegin() const __span_noexcept {  // NOLINT
-    return {this, 0};
-  }
-
-  XGBOOST_DEVICE constexpr const_iterator cend() const __span_noexcept {    // NOLINT
-    return {this, size()};
-  }
-
-  XGBOOST_DEVICE constexpr reverse_iterator rbegin() const __span_noexcept {  // NOLINT
-    return reverse_iterator{end()};
-  }
-
-  XGBOOST_DEVICE constexpr reverse_iterator rend() const __span_noexcept {    // NOLINT
-    return reverse_iterator{begin()};
-  }
-
-  XGBOOST_DEVICE constexpr const_reverse_iterator crbegin() const __span_noexcept {  // NOLINT
-    return const_reverse_iterator{cend()};
-  }
-
-  XGBOOST_DEVICE constexpr const_reverse_iterator crend() const __span_noexcept {    // NOLINT
-    return const_reverse_iterator{cbegin()};
-  }
-
-  // element access
-
-  XGBOOST_DEVICE reference front() const {  // NOLINT
-    return (*this)[0];
-  }
-
-  XGBOOST_DEVICE reference back() const {  // NOLINT
-    return (*this)[size() - 1];
-  }
-
-  XGBOOST_DEVICE reference operator[](index_type _idx) const {
-    SPAN_CHECK(_idx < size());
-    return data()[_idx];
-  }
-
-  XGBOOST_DEVICE reference operator()(index_type _idx) const {
-    return this->operator[](_idx);
-  }
-
-  XGBOOST_DEVICE constexpr pointer data() const __span_noexcept {   // NOLINT
-    return data_;
-  }
-
-  // Observers
-  XGBOOST_DEVICE constexpr index_type size() const __span_noexcept {  // NOLINT
-    return size_;
-  }
-  XGBOOST_DEVICE constexpr index_type size_bytes() const __span_noexcept {  // NOLINT
-    return size() * sizeof(T);
-  }
-
-  XGBOOST_DEVICE constexpr bool empty() const __span_noexcept {  // NOLINT
-    return size() == 0;
-  }
-
-  // Subviews
-  template <std::size_t Count>
-  XGBOOST_DEVICE Span<element_type, Count> first() const {  // NOLINT
-    SPAN_CHECK(Count <= size());
-    return {data(), Count};
-  }
-
-  XGBOOST_DEVICE Span<element_type, dynamic_extent> first(  // NOLINT
-      std::size_t _count) const {
-    SPAN_CHECK(_count <= size());
-    return {data(), _count};
-  }
-
-  template <std::size_t Count>
-  XGBOOST_DEVICE Span<element_type, Count> last() const {  // NOLINT
-    SPAN_CHECK(Count <= size());
-    return {data() + size() - Count, Count};
-  }
-
-  XGBOOST_DEVICE Span<element_type, dynamic_extent> last(  // NOLINT
-      std::size_t _count) const {
-    SPAN_CHECK(_count <= size());
-    return subspan(size() - _count, _count);
-  }
-
-  /*!
-   * If Count is std::dynamic_extent, r.size() == this->size() - Offset;
-   * Otherwise r.size() == Count.
-   */
-  template <std::size_t Offset,
-            std::size_t Count = dynamic_extent>
-  XGBOOST_DEVICE auto subspan() const ->                   // NOLINT
-      Span<element_type,
-           detail::ExtentValue<Extent, Offset, Count>::value> {
-    SPAN_CHECK(Offset < size() || size() == 0);
-    SPAN_CHECK(Count == dynamic_extent || (Offset + Count <= size()));
-
-    return {data() + Offset, Count == dynamic_extent ? size() - Offset : Count};
-  }
-
-  XGBOOST_DEVICE Span<element_type, dynamic_extent> subspan(  // NOLINT
-      index_type _offset,
-      index_type _count = dynamic_extent) const {
-    SPAN_CHECK(_offset < size() || size() == 0);
-    SPAN_CHECK((_count == dynamic_extent) || (_offset + _count <= size()));
-
-    return {data() + _offset, _count ==
-            dynamic_extent ? size() - _offset : _count};
-  }
-
- private:
-  index_type size_ { 0 };
-  pointer data_ { nullptr };
-};
-
-template <class T, std::size_t X, class U, std::size_t Y>
-XGBOOST_DEVICE bool operator==(Span<T, X> l, Span<U, Y> r) {
-  if (l.size() != r.size()) {
-    return false;
-  }
-  for (auto l_beg = l.cbegin(), r_beg = r.cbegin(); l_beg != l.cend();
-       ++l_beg, ++r_beg) {
-    if (*l_beg != *r_beg) {
-      return false;
-    }
-  }
-  return true;
-}
-
-template <class T, std::size_t X, class U, std::size_t Y>
-XGBOOST_DEVICE constexpr bool operator!=(Span<T, X> l, Span<U, Y> r) {
-  return !(l == r);
-}
-
-template <class T, std::size_t X, class U, std::size_t Y>
-XGBOOST_DEVICE constexpr bool operator<(Span<T, X> l, Span<U, Y> r) {
-  return detail::LexicographicalCompare(l.begin(), l.end(),
-                                         r.begin(), r.end());
-}
-
-template <class T, std::size_t X, class U, std::size_t Y>
-XGBOOST_DEVICE constexpr bool operator<=(Span<T, X> l, Span<U, Y> r) {
-  return !(l > r);
-}
-
-template <class T, std::size_t X, class U, std::size_t Y>
-XGBOOST_DEVICE constexpr bool operator>(Span<T, X> l, Span<U, Y> r) {
-  return detail::LexicographicalCompare<
-    typename Span<T, X>::iterator, typename Span<U, Y>::iterator,
-    detail::Greater<typename Span<T, X>::element_type>>(l.begin(), l.end(),
-                                                        r.begin(), r.end());
-}
-
-template <class T, std::size_t X, class U, std::size_t Y>
-XGBOOST_DEVICE constexpr bool operator>=(Span<T, X> l, Span<U, Y> r) {
-  return !(l < r);
-}
-
-template <class T, std::size_t E>
-XGBOOST_DEVICE auto as_bytes(Span<T, E> s) __span_noexcept ->           // NOLINT
-    Span<const byte, detail::ExtentAsBytesValue<T, E>::value> {
-  return {reinterpret_cast<const byte*>(s.data()), s.size_bytes()};
-}
-
-template <class T, std::size_t E>
-XGBOOST_DEVICE auto as_writable_bytes(Span<T, E> s) __span_noexcept ->  // NOLINT
-    Span<byte, detail::ExtentAsBytesValue<T, E>::value> {
-  return {reinterpret_cast<byte*>(s.data()), s.size_bytes()};
-}
-
-}  // namespace common
-}  // namespace xgboost
-
-#if defined(_MSC_VER) &&_MSC_VER < 1910
-#undef constexpr
-#pragma pop_macro("constexpr")
-#undef __span_noexcept
-#endif  // _MSC_VER < 1910
-
-#endif  // XGBOOST_SPAN_H_
diff --git a/ml-xgboost/include/xgboost/tree_model.h b/ml-xgboost/include/xgboost/tree_model.h
deleted file mode 100644
index e7f6dc8..0000000
--- a/ml-xgboost/include/xgboost/tree_model.h
+++ /dev/null
@@ -1,658 +0,0 @@
-/*!
- * Copyright 2014-2019 by Contributors
- * \file tree_model.h
- * \brief model structure for tree
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_TREE_MODEL_H_
-#define XGBOOST_TREE_MODEL_H_
-
-#include <dmlc/io.h>
-#include <dmlc/parameter.h>
-
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/logging.h>
-#include <xgboost/feature_map.h>
-#include <xgboost/model.h>
-
-#include <limits>
-#include <vector>
-#include <string>
-#include <cstring>
-#include <algorithm>
-#include <tuple>
-#include <stack>
-
-namespace xgboost {
-
-struct PathElement;  // forward declaration
-
-class Json;
-// FIXME(trivialfis): Once binary IO is gone, make this parameter internal as it should
-// not be configured by users.
-/*! \brief meta parameters of the tree */
-struct TreeParam : public dmlc::Parameter<TreeParam> {
-  /*! \brief (Deprecated) number of start root */
-  int deprecated_num_roots;
-  /*! \brief total number of nodes */
-  int num_nodes;
-  /*!\brief number of deleted nodes */
-  int num_deleted;
-  /*! \brief maximum depth, this is a statistics of the tree */
-  int deprecated_max_depth;
-  /*! \brief number of features used for tree construction */
-  int num_feature;
-  /*!
-   * \brief leaf vector size, used for vector tree
-   * used to store more than one dimensional information in tree
-   */
-  int size_leaf_vector;
-  /*! \brief reserved part, make sure alignment works for 64bit */
-  int reserved[31];
-  /*! \brief constructor */
-  TreeParam() {
-    // assert compact alignment
-    static_assert(sizeof(TreeParam) == (31 + 6) * sizeof(int),
-                  "TreeParam: 64 bit align");
-    std::memset(this, 0, sizeof(TreeParam));
-    num_nodes = 1;
-    deprecated_num_roots = 1;
-  }
-  // declare the parameters
-  DMLC_DECLARE_PARAMETER(TreeParam) {
-    // only declare the parameters that can be set by the user.
-    // other arguments are set by the algorithm.
-    DMLC_DECLARE_FIELD(num_nodes).set_lower_bound(1).set_default(1);
-    DMLC_DECLARE_FIELD(num_feature)
-        .describe("Number of features used in tree construction.");
-    DMLC_DECLARE_FIELD(num_deleted);
-    DMLC_DECLARE_FIELD(size_leaf_vector).set_lower_bound(0).set_default(0)
-        .describe("Size of leaf vector, reserved for vector tree");
-  }
-
-  bool operator==(const TreeParam& b) const {
-    return num_nodes == b.num_nodes &&
-           num_deleted == b.num_deleted &&
-           num_feature == b.num_feature &&
-           size_leaf_vector == b.size_leaf_vector;
-  }
-};
-
-/*! \brief node statistics used in regression tree */
-struct RTreeNodeStat {
-  /*! \brief loss change caused by current split */
-  bst_float loss_chg;
-  /*! \brief sum of hessian values, used to measure coverage of data */
-  bst_float sum_hess;
-  /*! \brief weight of current node */
-  bst_float base_weight;
-  /*! \brief number of child that is leaf node known up to now */
-  int leaf_child_cnt {0};
-
-  RTreeNodeStat() = default;
-  RTreeNodeStat(float loss_chg, float sum_hess, float weight) :
-      loss_chg{loss_chg}, sum_hess{sum_hess}, base_weight{weight} {}
-  bool operator==(const RTreeNodeStat& b) const {
-    return loss_chg == b.loss_chg && sum_hess == b.sum_hess &&
-           base_weight == b.base_weight && leaf_child_cnt == b.leaf_child_cnt;
-  }
-};
-
-/*!
- * \brief define regression tree to be the most common tree model.
- *  This is the data structure used in xgboost's major tree models.
- */
-class RegTree : public Model {
- public:
-  using SplitCondT = bst_float;
-  static constexpr bst_node_t kInvalidNodeId {-1};
-  static constexpr uint32_t kDeletedNodeMarker = std::numeric_limits<uint32_t>::max();
-  static constexpr bst_node_t kRoot { 0 };
-
-  /*! \brief tree node */
-  class Node {
-   public:
-    XGBOOST_DEVICE Node()  {
-      // assert compact alignment
-      static_assert(sizeof(Node) == 4 * sizeof(int) + sizeof(Info),
-                    "Node: 64 bit align");
-    }
-    Node(int32_t cleft, int32_t cright, int32_t parent,
-         uint32_t split_ind, float split_cond, bool default_left) :
-        parent_{parent}, cleft_{cleft}, cright_{cright} {
-      this->SetParent(parent_);
-      this->SetSplit(split_ind, split_cond, default_left);
-    }
-
-    /*! \brief index of left child */
-    XGBOOST_DEVICE int LeftChild() const {
-      return this->cleft_;
-    }
-    /*! \brief index of right child */
-    XGBOOST_DEVICE int RightChild() const {
-      return this->cright_;
-    }
-    /*! \brief index of default child when feature is missing */
-    XGBOOST_DEVICE int DefaultChild() const {
-      return this->DefaultLeft() ? this->LeftChild() : this->RightChild();
-    }
-    /*! \brief feature index of split condition */
-    XGBOOST_DEVICE unsigned SplitIndex() const {
-      return sindex_ & ((1U << 31) - 1U);
-    }
-    /*! \brief when feature is unknown, whether goes to left child */
-    XGBOOST_DEVICE bool DefaultLeft() const {
-      return (sindex_ >> 31) != 0;
-    }
-    /*! \brief whether current node is leaf node */
-    XGBOOST_DEVICE bool IsLeaf() const {
-      return cleft_ == kInvalidNodeId;
-    }
-    /*! \return get leaf value of leaf node */
-    XGBOOST_DEVICE bst_float LeafValue() const {
-      return (this->info_).leaf_value;
-    }
-    /*! \return get split condition of the node */
-    XGBOOST_DEVICE SplitCondT SplitCond() const {
-      return (this->info_).split_cond;
-    }
-    /*! \brief get parent of the node */
-    XGBOOST_DEVICE int Parent() const {
-      return parent_ & ((1U << 31) - 1);
-    }
-    /*! \brief whether current node is left child */
-    XGBOOST_DEVICE bool IsLeftChild() const {
-      return (parent_ & (1U << 31)) != 0;
-    }
-    /*! \brief whether this node is deleted */
-    XGBOOST_DEVICE bool IsDeleted() const {
-      return sindex_ == kDeletedNodeMarker;
-    }
-    /*! \brief whether current node is root */
-    XGBOOST_DEVICE bool IsRoot() const { return parent_ == kInvalidNodeId; }
-    /*!
-     * \brief set the left child
-     * \param nid node id to right child
-     */
-    XGBOOST_DEVICE void SetLeftChild(int nid) {
-      this->cleft_ = nid;
-    }
-    /*!
-     * \brief set the right child
-     * \param nid node id to right child
-     */
-    XGBOOST_DEVICE void SetRightChild(int nid) {
-      this->cright_ = nid;
-    }
-    /*!
-     * \brief set split condition of current node
-     * \param split_index feature index to split
-     * \param split_cond  split condition
-     * \param default_left the default direction when feature is unknown
-     */
-    XGBOOST_DEVICE void SetSplit(unsigned split_index, SplitCondT split_cond,
-                          bool default_left = false) {
-      if (default_left) split_index |= (1U << 31);
-      this->sindex_ = split_index;
-      (this->info_).split_cond = split_cond;
-    }
-    /*!
-     * \brief set the leaf value of the node
-     * \param value leaf value
-     * \param right right index, could be used to store
-     *        additional information
-     */
-    XGBOOST_DEVICE void SetLeaf(bst_float value, int right = kInvalidNodeId) {
-      (this->info_).leaf_value = value;
-      this->cleft_ = kInvalidNodeId;
-      this->cright_ = right;
-    }
-    /*! \brief mark that this node is deleted */
-    XGBOOST_DEVICE void MarkDelete() {
-      this->sindex_ = kDeletedNodeMarker;
-    }
-    /*! \brief Reuse this deleted node. */
-    XGBOOST_DEVICE void Reuse() {
-      this->sindex_ = 0;
-    }
-    // set parent
-    XGBOOST_DEVICE void SetParent(int pidx, bool is_left_child = true) {
-      if (is_left_child) pidx |= (1U << 31);
-      this->parent_ = pidx;
-    }
-    bool operator==(const Node& b) const {
-      return parent_ == b.parent_ && cleft_ == b.cleft_ &&
-             cright_ == b.cright_ && sindex_ == b.sindex_ &&
-             info_.leaf_value == b.info_.leaf_value;
-    }
-
-   private:
-    /*!
-     * \brief in leaf node, we have weights, in non-leaf nodes,
-     *        we have split condition
-     */
-    union Info{
-      bst_float leaf_value;
-      SplitCondT split_cond;
-    };
-    // pointer to parent, highest bit is used to
-    // indicate whether it's a left child or not
-    int32_t parent_{kInvalidNodeId};
-    // pointer to left, right
-    int32_t cleft_{kInvalidNodeId}, cright_{kInvalidNodeId};
-    // split feature index, left split or right split depends on the highest bit
-    uint32_t sindex_{0};
-    // extra info
-    Info info_;
-  };
-
-  /*!
-   * \brief change a non leaf node to a leaf node, delete its children
-   * \param rid node id of the node
-   * \param value new leaf value
-   */
-  void ChangeToLeaf(int rid, bst_float value) {
-    CHECK(nodes_[nodes_[rid].LeftChild() ].IsLeaf());
-    CHECK(nodes_[nodes_[rid].RightChild()].IsLeaf());
-    this->DeleteNode(nodes_[rid].LeftChild());
-    this->DeleteNode(nodes_[rid].RightChild());
-    nodes_[rid].SetLeaf(value);
-  }
-  /*!
-   * \brief collapse a non leaf node to a leaf node, delete its children
-   * \param rid node id of the node
-   * \param value new leaf value
-   */
-  void CollapseToLeaf(int rid, bst_float value) {
-    if (nodes_[rid].IsLeaf()) return;
-    if (!nodes_[nodes_[rid].LeftChild() ].IsLeaf()) {
-      CollapseToLeaf(nodes_[rid].LeftChild(), 0.0f);
-    }
-    if (!nodes_[nodes_[rid].RightChild() ].IsLeaf()) {
-      CollapseToLeaf(nodes_[rid].RightChild(), 0.0f);
-    }
-    this->ChangeToLeaf(rid, value);
-  }
-
-  /*! \brief model parameter */
-  TreeParam param;
-  /*! \brief constructor */
-  RegTree() {
-    param.num_nodes = 1;
-    param.num_deleted = 0;
-    nodes_.resize(param.num_nodes);
-    stats_.resize(param.num_nodes);
-    for (int i = 0; i < param.num_nodes; i ++) {
-      nodes_[i].SetLeaf(0.0f);
-      nodes_[i].SetParent(kInvalidNodeId);
-    }
-  }
-  /*! \brief get node given nid */
-  Node& operator[](int nid) {
-    return nodes_[nid];
-  }
-  /*! \brief get node given nid */
-  const Node& operator[](int nid) const {
-    return nodes_[nid];
-  }
-
-  /*! \brief get const reference to nodes */
-  const std::vector<Node>& GetNodes() const { return nodes_; }
-
-  /*! \brief get node statistics given nid */
-  RTreeNodeStat& Stat(int nid) {
-    return stats_[nid];
-  }
-  /*! \brief get node statistics given nid */
-  const RTreeNodeStat& Stat(int nid) const {
-    return stats_[nid];
-  }
-
-  /*!
-   * \brief load model from stream
-   * \param fi input stream
-   */
-  void Load(dmlc::Stream* fi);
-  /*!
-   * \brief save model to stream
-   * \param fo output stream
-   */
-  void Save(dmlc::Stream* fo) const;
-
-  void LoadModel(Json const& in) override;
-  void SaveModel(Json* out) const override;
-
-  bool operator==(const RegTree& b) const {
-    return nodes_ == b.nodes_ && stats_ == b.stats_ &&
-           deleted_nodes_ == b.deleted_nodes_ && param == b.param;
-  }
-  /* \brief Iterate through all nodes in this tree.
-   *
-   * \param Function that accepts a node index, and returns false when iteration should
-   *        stop, otherwise returns true.
-   */
-  template <typename Func> void WalkTree(Func func) const {
-    std::stack<bst_node_t> nodes;
-    nodes.push(kRoot);
-    auto &self = *this;
-    while (!nodes.empty()) {
-      auto nidx = nodes.top();
-      nodes.pop();
-      if (!func(nidx)) {
-        return;
-      }
-      auto left = self[nidx].LeftChild();
-      auto right = self[nidx].RightChild();
-      if (left != RegTree::kInvalidNodeId) {
-        nodes.push(left);
-      }
-      if (right != RegTree::kInvalidNodeId) {
-        nodes.push(right);
-      }
-    }
-  }
-  /*!
-   * \brief Compares whether 2 trees are equal from a user's perspective.  The equality
-   *        compares only non-deleted nodes.
-   *
-   * \parm b The other tree.
-   */
-  bool Equal(const RegTree& b) const;
-
-  /**
-   * \brief Expands a leaf node into two additional leaf nodes.
-   *
-   * \param nid               The node index to expand.
-   * \param split_index       Feature index of the split.
-   * \param split_value       The split condition.
-   * \param default_left      True to default left.
-   * \param base_weight       The base weight, before learning rate.
-   * \param left_leaf_weight  The left leaf weight for prediction, modified by learning rate.
-   * \param right_leaf_weight The right leaf weight for prediction, modified by learning rate.
-   * \param loss_change       The loss change.
-   * \param sum_hess          The sum hess.
-   * \param left_sum          The sum hess of left leaf.
-   * \param right_sum         The sum hess of right leaf.
-   * \param leaf_right_child  The right child index of leaf, by default kInvalidNodeId,
-   *                          some updaters use the right child index of leaf as a marker
-   */
-  void ExpandNode(int nid, unsigned split_index, bst_float split_value,
-                  bool default_left, bst_float base_weight,
-                  bst_float left_leaf_weight, bst_float right_leaf_weight,
-                  bst_float loss_change, float sum_hess, float left_sum,
-                  float right_sum,
-                  bst_node_t leaf_right_child = kInvalidNodeId) {
-    int pleft = this->AllocNode();
-    int pright = this->AllocNode();
-    auto &node = nodes_[nid];
-    CHECK(node.IsLeaf());
-    node.SetLeftChild(pleft);
-    node.SetRightChild(pright);
-    nodes_[node.LeftChild()].SetParent(nid, true);
-    nodes_[node.RightChild()].SetParent(nid, false);
-    node.SetSplit(split_index, split_value,
-                  default_left);
-
-    nodes_[pleft].SetLeaf(left_leaf_weight, leaf_right_child);
-    nodes_[pright].SetLeaf(right_leaf_weight, leaf_right_child);
-
-    this->Stat(nid)    = {loss_change, sum_hess, base_weight};
-    this->Stat(pleft)  = {0.0f, left_sum, left_leaf_weight};
-    this->Stat(pright) = {0.0f, right_sum, right_leaf_weight};
-  }
-
-  /*!
-   * \brief get current depth
-   * \param nid node id
-   */
-  int GetDepth(int nid) const {
-    int depth = 0;
-    while (!nodes_[nid].IsRoot()) {
-      ++depth;
-      nid = nodes_[nid].Parent();
-    }
-    return depth;
-  }
-  /*!
-   * \brief get maximum depth
-   * \param nid node id
-   */
-  int MaxDepth(int nid) const {
-    if (nodes_[nid].IsLeaf()) return 0;
-    return std::max(MaxDepth(nodes_[nid].LeftChild())+1,
-                     MaxDepth(nodes_[nid].RightChild())+1);
-  }
-
-  /*!
-   * \brief get maximum depth
-   */
-  int MaxDepth() {
-    return MaxDepth(0);
-  }
-
-  /*! \brief number of extra nodes besides the root */
-  int NumExtraNodes() const {
-    return param.num_nodes - 1 - param.num_deleted;
-  }
-
-  /* \brief Count number of leaves in tree. */
-  bst_node_t GetNumLeaves() const;
-  bst_node_t GetNumSplitNodes() const;
-
-  /*!
-   * \brief dense feature vector that can be taken by RegTree
-   * and can be construct from sparse feature vector.
-   */
-  struct FVec {
-    /*!
-     * \brief initialize the vector with size vector
-     * \param size The size of the feature vector.
-     */
-    void Init(size_t size);
-    /*!
-     * \brief fill the vector with sparse vector
-     * \param inst The sparse instance to fill.
-     */
-    void Fill(const SparsePage::Inst& inst);
-    /*!
-     * \brief drop the trace after fill, must be called after fill.
-     * \param inst The sparse instance to drop.
-     */
-    void Drop(const SparsePage::Inst& inst);
-    /*!
-     * \brief returns the size of the feature vector
-     * \return the size of the feature vector
-     */
-    size_t Size() const;
-    /*!
-     * \brief get ith value
-     * \param i feature index.
-     * \return the i-th feature value
-     */
-    bst_float GetFvalue(size_t i) const;
-    /*!
-     * \brief check whether i-th entry is missing
-     * \param i feature index.
-     * \return whether i-th value is missing.
-     */
-    bool IsMissing(size_t i) const;
-
-   private:
-    /*!
-     * \brief a union value of value and flag
-     *  when flag == -1, this indicate the value is missing
-     */
-    union Entry {
-      bst_float fvalue;
-      int flag;
-    };
-    std::vector<Entry> data_;
-  };
-  /*!
-   * \brief get the leaf index
-   * \param feat dense feature vector, if the feature is missing the field is set to NaN
-   * \return the leaf index of the given feature
-   */
-  int GetLeafIndex(const FVec& feat) const;
-  /*!
-   * \brief calculate the feature contributions (https://arxiv.org/abs/1706.06060) for the tree
-   * \param feat dense feature vector, if the feature is missing the field is set to NaN
-   * \param out_contribs output vector to hold the contributions
-   * \param condition fix one feature to either off (-1) on (1) or not fixed (0 default)
-   * \param condition_feature the index of the feature to fix
-   */
-  void CalculateContributions(const RegTree::FVec& feat,
-                              bst_float* out_contribs, int condition = 0,
-                              unsigned condition_feature = 0) const;
-  /*!
-   * \brief Recursive function that computes the feature attributions for a single tree.
-   * \param feat dense feature vector, if the feature is missing the field is set to NaN
-   * \param phi dense output vector of feature attributions
-   * \param node_index the index of the current node in the tree
-   * \param unique_depth how many unique features are above the current node in the tree
-   * \param parent_unique_path a vector of statistics about our current path through the tree
-   * \param parent_zero_fraction what fraction of the parent path weight is coming as 0 (integrated)
-   * \param parent_one_fraction what fraction of the parent path weight is coming as 1 (fixed)
-   * \param parent_feature_index what feature the parent node used to split
-   * \param condition fix one feature to either off (-1) on (1) or not fixed (0 default)
-   * \param condition_feature the index of the feature to fix
-   * \param condition_fraction what fraction of the current weight matches our conditioning feature
-   */
-  void TreeShap(const RegTree::FVec& feat, bst_float* phi, unsigned node_index,
-                unsigned unique_depth, PathElement* parent_unique_path,
-                bst_float parent_zero_fraction, bst_float parent_one_fraction,
-                int parent_feature_index, int condition,
-                unsigned condition_feature, bst_float condition_fraction) const;
-
-  /*!
-   * \brief calculate the approximate feature contributions for the given root
-   * \param feat dense feature vector, if the feature is missing the field is set to NaN
-   * \param out_contribs output vector to hold the contributions
-   */
-  void CalculateContributionsApprox(const RegTree::FVec& feat,
-                                    bst_float* out_contribs) const;
-  /*!
-   * \brief get next position of the tree given current pid
-   * \param pid Current node id.
-   * \param fvalue feature value if not missing.
-   * \param is_unknown Whether current required feature is missing.
-   */
-  inline int GetNext(int pid, bst_float fvalue, bool is_unknown) const;
-  /*!
-   * \brief dump the model in the requested format as a text string
-   * \param fmap feature map that may help give interpretations of feature
-   * \param with_stats whether dump out statistics as well
-   * \param format the format to dump the model in
-   * \return the string of dumped model
-   */
-  std::string DumpModel(const FeatureMap& fmap,
-                        bool with_stats,
-                        std::string format) const;
-  /*!
-   * \brief calculate the mean value for each node, required for feature contributions
-   */
-  void FillNodeMeanValues();
-
- private:
-  // vector of nodes
-  std::vector<Node> nodes_;
-  // free node space, used during training process
-  std::vector<int>  deleted_nodes_;
-  // stats of nodes
-  std::vector<RTreeNodeStat> stats_;
-  std::vector<bst_float> node_mean_values_;
-  // allocate a new node,
-  // !!!!!! NOTE: may cause BUG here, nodes.resize
-  int AllocNode() {
-    if (param.num_deleted != 0) {
-      int nid = deleted_nodes_.back();
-      deleted_nodes_.pop_back();
-      nodes_[nid].Reuse();
-      --param.num_deleted;
-      return nid;
-    }
-    int nd = param.num_nodes++;
-    CHECK_LT(param.num_nodes, std::numeric_limits<int>::max())
-        << "number of nodes in the tree exceed 2^31";
-    nodes_.resize(param.num_nodes);
-    stats_.resize(param.num_nodes);
-    return nd;
-  }
-  // delete a tree node, keep the parent field to allow trace back
-  void DeleteNode(int nid) {
-    CHECK_GE(nid, 1);
-    auto pid = (*this)[nid].Parent();
-    if (nid == (*this)[pid].LeftChild()) {
-      (*this)[pid].SetLeftChild(kInvalidNodeId);
-    } else {
-      (*this)[pid].SetRightChild(kInvalidNodeId);
-    }
-
-    deleted_nodes_.push_back(nid);
-    nodes_[nid].MarkDelete();
-    ++param.num_deleted;
-  }
-  bst_float FillNodeMeanValue(int nid);
-};
-
-inline void RegTree::FVec::Init(size_t size) {
-  Entry e; e.flag = -1;
-  data_.resize(size);
-  std::fill(data_.begin(), data_.end(), e);
-}
-
-inline void RegTree::FVec::Fill(const SparsePage::Inst& inst) {
-  for (auto const& entry : inst) {
-    if (entry.index >= data_.size()) {
-      continue;
-    }
-    data_[entry.index].fvalue = entry.fvalue;
-  }
-}
-
-inline void RegTree::FVec::Drop(const SparsePage::Inst& inst) {
-  for (auto const& entry : inst) {
-    if (entry.index >= data_.size()) {
-      continue;
-    }
-    data_[entry.index].flag = -1;
-  }
-}
-
-inline size_t RegTree::FVec::Size() const {
-  return data_.size();
-}
-
-inline bst_float RegTree::FVec::GetFvalue(size_t i) const {
-  return data_[i].fvalue;
-}
-
-inline bool RegTree::FVec::IsMissing(size_t i) const {
-  return data_[i].flag == -1;
-}
-
-inline int RegTree::GetLeafIndex(const RegTree::FVec& feat) const {
-  bst_node_t nid = 0;
-  while (!(*this)[nid].IsLeaf()) {
-    unsigned split_index = (*this)[nid].SplitIndex();
-    nid = this->GetNext(nid, feat.GetFvalue(split_index), feat.IsMissing(split_index));
-  }
-  return nid;
-}
-
-/*! \brief get next position of the tree given current pid */
-inline int RegTree::GetNext(int pid, bst_float fvalue, bool is_unknown) const {
-  bst_float split_value = (*this)[pid].SplitCond();
-  if (is_unknown) {
-    return (*this)[pid].DefaultChild();
-  } else {
-    if (fvalue < split_value) {
-      return (*this)[pid].LeftChild();
-    } else {
-      return (*this)[pid].RightChild();
-    }
-  }
-}
-}  // namespace xgboost
-#endif  // XGBOOST_TREE_MODEL_H_
diff --git a/ml-xgboost/include/xgboost/tree_updater.h b/ml-xgboost/include/xgboost/tree_updater.h
deleted file mode 100644
index 9bafb88..0000000
--- a/ml-xgboost/include/xgboost/tree_updater.h
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2019 by Contributors
- * \file tree_updater.h
- * \brief General primitive for tree learning,
- *   Updating a collection of trees given the information.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_TREE_UPDATER_H_
-#define XGBOOST_TREE_UPDATER_H_
-
-#include <dmlc/registry.h>
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/tree_model.h>
-#include <xgboost/generic_parameters.h>
-#include <xgboost/host_device_vector.h>
-#include <xgboost/model.h>
-
-#include <functional>
-#include <vector>
-#include <utility>
-#include <string>
-
-namespace xgboost {
-
-class Json;
-
-/*!
- * \brief interface of tree update module, that performs update of a tree.
- */
-class TreeUpdater : public Configurable {
- protected:
-  GenericParameter const* tparam_;
-
- public:
-  /*! \brief virtual destructor */
-  ~TreeUpdater() override = default;
-  /*!
-   * \brief Initialize the updater with given arguments.
-   * \param args arguments to the objective function.
-   */
-  virtual void Configure(const Args& args) = 0;
-  /*! \brief Whether this updater can be used for updating existing trees.
-   *
-   *  Some updaters are used for building new trees (like `hist`), while some others are
-   *  used for modifying existing trees (like `prune`).  Return true if it can modify
-   *  existing trees.
-   */
-  virtual bool CanModifyTree() const { return false; }
-  /*!
-   * \brief perform update to the tree models
-   * \param gpair the gradient pair statistics of the data
-   * \param data The data matrix passed to the updater.
-   * \param trees references the trees to be updated, updater will change the content of trees
-   *   note: all the trees in the vector are updated, with the same statistics,
-   *         but maybe different random seeds, usually one tree is passed in at a time,
-   *         there can be multiple trees when we train random forest style model
-   * \param iter defines number of iteration (used for iteration dependent boosting algorithms )
-   * \param _last_round_best_loss array of best losses achieved on previous round
-   */
-  virtual void Update(HostDeviceVector<GradientPair>* gpair,
-                      DMatrix* data,
-                      const std::vector<RegTree*>& trees, int iter,
-                      float* p_last_round_best_loss) = 0;
-
-  void Update(HostDeviceVector<GradientPair>* gpair,
-              DMatrix* data,
-              const std::vector<RegTree*>& trees) {
-    this->Update(gpair, data, trees, 0, NULL);
-  }
-
-  /*!
-   * \brief determines whether updater has enough knowledge about a given dataset
-   *        to quickly update prediction cache its training data and performs the
-   *        update if possible.
-   * \param data: data matrix
-   * \param out_preds: prediction cache to be updated
-   * \return boolean indicating whether updater has capability to update
-   *         the prediction cache. If true, the prediction cache will have been
-   *         updated by the time this function returns.
-   */
-  virtual bool UpdatePredictionCache(const DMatrix* data,
-                                     HostDeviceVector<bst_float>* out_preds, int iter) {
-    return false;
-  }
-
-  virtual char const* Name() const = 0;
-
-  /*!
-   * \brief Create a tree updater given name
-   * \param name Name of the tree updater.
-   * \param tparam A global runtime parameter
-   */
-  static TreeUpdater* Create(const std::string& name, GenericParameter const* tparam);
-};
-
-/*!
- * \brief Registry entry for tree updater.
- */
-struct TreeUpdaterReg
-    : public dmlc::FunctionRegEntryBase<TreeUpdaterReg,
-                                        std::function<TreeUpdater* ()> > {
-};
-
-/*!
- * \brief Macro to register tree updater.
- *
- * \code
- * // example of registering a objective ndcg@k
- * XGBOOST_REGISTER_TREE_UPDATER(ColMaker, "colmaker")
- * .describe("Column based tree maker.")
- * .set_body([]() {
- *     return new ColMaker<TStats>();
- *   });
- * \endcode
- */
-#define XGBOOST_REGISTER_TREE_UPDATER(UniqueId, Name)                   \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::TreeUpdaterReg&               \
-  __make_ ## TreeUpdaterReg ## _ ## UniqueId ## __ =                    \
-      ::dmlc::Registry< ::xgboost::TreeUpdaterReg>::Get()->__REGISTER__(Name)
-
-}  // namespace xgboost
-
-namespace xgboost {
-namespace tree {
-void InitSampling(const std::vector<GradientPair>& gpair,
-                  const DMatrix& fmat,
-                  std::vector<size_t>* row_indices,
-                  const size_t nthread,
-                  const bool enable_bbgen,
-                  const float subsample,
-                  const bool goss);
-}  // namespace tree
-}  // namespace xgboost
-#endif  // XGBOOST_TREE_UPDATER_H_
diff --git a/ml-xgboost/include/xgboost/version_config.h b/ml-xgboost/include/xgboost/version_config.h
deleted file mode 100644
index 56492bc..0000000
--- a/ml-xgboost/include/xgboost/version_config.h
+++ /dev/null
@@ -1,11 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#ifndef XGBOOST_VERSION_CONFIG_H_
-#define XGBOOST_VERSION_CONFIG_H_
-
-#define XGBOOST_VER_MAJOR 1
-#define XGBOOST_VER_MINOR 1
-#define XGBOOST_VER_PATCH 0
-
-#endif  // XGBOOST_VERSION_CONFIG_H_
diff --git a/ml-xgboost/jvm-packages/.gitignore b/ml-xgboost/jvm-packages/.gitignore
deleted file mode 100644
index becd9e3..0000000
--- a/ml-xgboost/jvm-packages/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-tracker.py
-build.sh
-
diff --git a/ml-xgboost/jvm-packages/CMakeLists.txt b/ml-xgboost/jvm-packages/CMakeLists.txt
deleted file mode 100644
index 9ec7332..0000000
--- a/ml-xgboost/jvm-packages/CMakeLists.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-find_package(JNI REQUIRED)
-set(CMAKE_SKIP_RPATH TRUE)
-
-add_library(xgboost4j SHARED
-  ${PROJECT_SOURCE_DIR}/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.cpp
-  ${XGBOOST_OBJ_SOURCES})
-
-target_include_directories(xgboost4j
-  PRIVATE
-  ${JNI_INCLUDE_DIRS}
-  ${PROJECT_SOURCE_DIR}/jvm-packages/boostkit-xgboost4j/src/native
-  ${PROJECT_SOURCE_DIR}/include
-  ${PROJECT_SOURCE_DIR}/dmlc-core/include
-  ${PROJECT_SOURCE_DIR}/rabit/include
-  ${PROJECT_SOURCE_DIR}/kernel_include)
-
-set_output_directory(xgboost4j ${PROJECT_SOURCE_DIR}/lib)
-set_target_properties(
-  xgboost4j PROPERTIES
-  CXX_STANDARD 14
-  CXX_STANDARD_REQUIRED ON)
-target_link_libraries(xgboost4j
-  PRIVATE
-  ${LINKED_LIBRARIES_PRIVATE}
-  ${JAVA_JVM_LIBRARY})
-target_link_libraries(xgboost4j PRIVATE ${PROJECT_SOURCE_DIR}/lib/libboostkit_xgboost_kernel.so)
diff --git a/ml-xgboost/jvm-packages/README.md b/ml-xgboost/jvm-packages/README.md
deleted file mode 100644
index 5bc65e3..0000000
--- a/ml-xgboost/jvm-packages/README.md
+++ /dev/null
@@ -1,120 +0,0 @@
-# XGBoost4J: Distributed XGBoost for Scala/Java
-[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
-[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org/en/latest/jvm/index.html)
-[![GitHub license](http://dmlc.github.io/img/apache2.svg)](../LICENSE)
-
-[Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) |
-[Resources](../demo/README.md) |
-[Release Notes](../NEWS.md)
-
-XGBoost4J is the JVM package of xgboost. It brings all the optimizations
-and power xgboost into JVM ecosystem.
-
-- Train XGBoost models in scala and java with easy customizations.
-- Run distributed xgboost natively on jvm frameworks such as
-Apache Flink and Apache Spark.
-
-You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) and [Resource Page](../demo/README.md).
-
-## Add Maven Dependency
-
-XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g++-4.8.5  
-
-### Access release version
-
-<b>maven</b> 
-
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j_2.12</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-``` 
- 
-<b>sbt</b> 
-```sbt
- "ml.dmlc" %% "xgboost4j" % "latest_version_num"
-``` 
-
-For the latest release version number, please check [here](https://github.com/dmlc/xgboost/releases).
-
-if you want to use `xgboost4j-spark`, you just need to replace xgboost4j with `xgboost4j-spark`
-
-### Access SNAPSHOT version
-
-You need to add github as repo:
-
-<b>maven</b>:
-
-```xml
-<repository>
-  <id>GitHub Repo</id>
-  <name>GitHub Repo</name>
-  <url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
-</repository>
-```
-
-<b>sbt</b>:
- 
-```sbt 
-resolvers += "GitHub Repo" at "https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/"
-```
-
-the add dependency as following:
-
-<b>maven</b> 
-
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j_2.12</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-``` 
- 
-<b>sbt</b> 
-```sbt
- "ml.dmlc" %% "xgboost4j" % "latest_version_num"
-``` 
-
-For the latest release version number, please check [here](https://github.com/CodingCat/xgboost/tree/maven-repo/ml/dmlc/xgboost4j_2.12).
-
-if you want to use `xgboost4j-spark`, you just need to replace xgboost4j with `xgboost4j-spark`
-
-## Examples
-
-Full code examples for Scala, Java, Apache Spark, and Apache Flink can
-be found in the [examples package](https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example).
-
-**NOTE on LIBSVM Format**: 
-
-There is an inconsistent issue between XGBoost4J-Spark and other language bindings of XGBoost. 
-
-When users use Spark to load trainingset/testset in LibSVM format with the following code snippet:
-
-```scala
-spark.read.format("libsvm").load("trainingset_libsvm")
-```
-
-Spark assumes that the dataset is 1-based indexed. However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is 0-based indexed. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost.
-
-## Development
-
-You can build/package xgboost4j locally with the following steps:
-
-**Linux:**
-1. Ensure [Docker for Linux](https://docs.docker.com/install/) is installed.
-2. Clone this repo: `git clone --recursive https://github.com/dmlc/xgboost.git`
-3. Run the following command:
-  - With Tests: `./xgboost/jvm-packages/dev/build-linux.sh`
-  - Skip Tests: `./xgboost/jvm-packages/dev/build-linux.sh --skip-tests` 
-
-**Windows:**
-1. Ensure [Docker for Windows](https://docs.docker.com/docker-for-windows/install/) is installed.
-2. Clone this repo: `git clone --recursive https://github.com/dmlc/xgboost.git`
-3. Run the following command:
-  - With Tests: `.\xgboost\jvm-packages\dev\build-linux.cmd`
-  - Skip Tests: `.\xgboost\jvm-packages\dev\build-linux.cmd --skip-tests`
-
-*Note: this will create jars for deployment on Linux machines.*
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/LICENSE b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/LICENSE
deleted file mode 100644
index 9a1673b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/LICENSE
+++ /dev/null
@@ -1,15 +0,0 @@
-/*
-Copyright (c) 2014 by Contributors 
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-    
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
\ No newline at end of file
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/README.md b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/README.md
deleted file mode 100644
index 6c32f1a..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-XGBoost4J Code Examples
-=======================
-
-## Java API
-* [Basic walkthrough of wrappers](src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java)
-* [Customize loss function, and evaluation metric](src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java)
-* [Boosting from existing prediction](src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java)
-* [Predicting using first n trees](src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java)
-* [Generalized Linear Model](src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java)
-* [Cross validation](src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java)
-* [Predicting leaf indices](src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java)
-* [External Memory](src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java)
-
-## Scala API
-
-* [Basic walkthrough of wrappers](src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala)
-* [Customize loss function, and evaluation metric](src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala)
-* [Boosting from existing prediction](src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala)
-* [Predicting using first n trees](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala)
-* [Generalized Linear Model](src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala)
-* [Cross validation](src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala)
-* [Predicting leaf indices](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala)
-* [External Memory](src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala)
-
-## Spark API
-* [Distributed Training with Spark](src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala)
-
-## Flink API
-* [Distributed Training with Flink](src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala)
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/pom.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/pom.xml
deleted file mode 100644
index 956e255..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/pom.xml
+++ /dev/null
@@ -1,69 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-        <version>2.1.0</version>
-    </parent>
-    <artifactId>boostkit-xgboost4j-example_2.11</artifactId>
-    <version>2.1.0</version>
-    <packaging>jar</packaging>
-    <build>
-        <plugins>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-jar-plugin</artifactId>
-                <version>2.5</version>
-                <configuration>
-                    <archive>
-                       <addMavenDescriptor>false</addMavenDescriptor>
-                    </archive>
-                </configuration>
-                <executions>
-                    <execution>
-                        <goals>
-                            <goal>jar</goal>
-                        </goals>
-                    </execution>
-                    <execution>
-                        <id>default-jar</id>
-                        <phase>none</phase>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-assembly-plugin</artifactId>
-                <configuration>
-                    <skipAssembly>false</skipAssembly>
-                </configuration>
-            </plugin>
-        </plugins>
-    </build>
-    <dependencies>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j-spark2.3.2_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j-flink_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.commons</groupId>
-            <artifactId>commons-lang3</artifactId>
-            <version>3.4</version>
-        </dependency>
-    </dependencies>
-</project>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java
deleted file mode 100644
index 3852c75..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java
+++ /dev/null
@@ -1,131 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.io.File;
-import java.io.IOException;
-import java.io.PrintWriter;
-import java.util.Arrays;
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.Booster;
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-import ml.dmlc.xgboost4j.java.example.util.DataLoader;
-
-/**
- * a simple example of java wrapper for xgboost
- *
- * @author hzx
- */
-public class BasicWalkThrough {
-  public static boolean checkPredicts(float[][] fPredicts, float[][] sPredicts) {
-    if (fPredicts.length != sPredicts.length) {
-      return false;
-    }
-
-    for (int i = 0; i < fPredicts.length; i++) {
-      if (!Arrays.equals(fPredicts[i], sPredicts[i])) {
-        return false;
-      }
-    }
-
-    return true;
-  }
-
-  public static void saveDumpModel(String modelPath, String[] modelInfos) throws IOException {
-    try{
-      PrintWriter writer = new PrintWriter(modelPath, "UTF-8");
-      for(int i = 0; i < modelInfos.length; ++ i) {
-        writer.print("booster[" + i + "]:\n");
-        writer.print(modelInfos[i]);
-      }
-      writer.close();
-    } catch (Exception e) {
-      e.printStackTrace();
-    }
-  }
-
-  public static void main(String[] args) throws IOException, XGBoostError {
-    // load file from text file, also binary buffer generated by xgboost4j
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-
-    HashMap<String, Object> params = new HashMap<String, Object>();
-    params.put("eta", 1.0);
-    params.put("max_depth", 2);
-    params.put("silent", 1);
-    params.put("objective", "binary:logistic");
-
-
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-    //set round
-    int round = 2;
-
-    //train a boost model
-    Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
-
-    //predict
-    float[][] predicts = booster.predict(testMat);
-
-    //save model to modelPath
-    File file = new File("./model");
-    if (!file.exists()) {
-      file.mkdirs();
-    }
-
-    String modelPath = "./model/xgb.model";
-    booster.saveModel(modelPath);
-
-    //dump model with feature map
-    String[] modelInfos = booster.getModelDump("../../demo/data/featmap.txt", false);
-    saveDumpModel("./model/dump.raw.txt", modelInfos);
-
-    //save dmatrix into binary buffer
-    testMat.saveBinary("./model/dtest.buffer");
-
-    //reload model and data
-    Booster booster2 = XGBoost.loadModel("./model/xgb.model");
-    DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
-    float[][] predicts2 = booster2.predict(testMat2);
-
-
-    //check the two predicts
-    System.out.println(checkPredicts(predicts, predicts2));
-
-    System.out.println("start build dmatrix from csr sparse data ...");
-    //build dmatrix from CSR Sparse Matrix
-    DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
-
-    DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
-            DMatrix.SparseType.CSR);
-    trainMat2.setLabel(spData.labels);
-
-    //specify watchList
-    HashMap<String, DMatrix> watches2 = new HashMap<String, DMatrix>();
-    watches2.put("train", trainMat2);
-    watches2.put("test", testMat2);
-    Booster booster3 = XGBoost.train(trainMat2, params, round, watches2, null, null);
-    float[][] predicts3 = booster3.predict(testMat2);
-
-    //check predicts
-    System.out.println(checkPredicts(predicts, predicts3));
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java
deleted file mode 100644
index 7eb9e99..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.Booster;
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-
-/**
- * example for start from a initial base prediction
- *
- * @author hzx
- */
-public class BoostFromPrediction {
-  public static void main(String[] args) throws XGBoostError {
-    System.out.println("start running example to start from a initial prediction");
-
-    // load file from text file, also binary buffer generated by xgboost4j
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-
-    //specify parameters
-    HashMap<String, Object> params = new HashMap<String, Object>();
-    params.put("eta", 1.0);
-    params.put("max_depth", 2);
-    params.put("silent", 1);
-    params.put("objective", "binary:logistic");
-
-    //specify watchList
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-    //train xgboost for 1 round
-    Booster booster = XGBoost.train(trainMat, params, 1, watches, null, null);
-
-    float[][] trainPred = booster.predict(trainMat, true);
-    float[][] testPred = booster.predict(testMat, true);
-
-    trainMat.setBaseMargin(trainPred);
-    testMat.setBaseMargin(testPred);
-
-    System.out.println("result of running from initial prediction");
-    Booster booster2 = XGBoost.train(trainMat, params, 1, watches, null, null);
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java
deleted file mode 100644
index dbe5f36..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.io.IOException;
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-
-/**
- * an example of cross validation
- *
- * @author hzx
- */
-public class CrossValidation {
-  public static void main(String[] args) throws IOException, XGBoostError {
-    //load train mat
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-
-    //set params
-    HashMap<String, Object> params = new HashMap<String, Object>();
-
-    params.put("eta", 1.0);
-    params.put("max_depth", 3);
-    params.put("silent", 1);
-    params.put("nthread", 6);
-    params.put("objective", "binary:logistic");
-    params.put("gamma", 1.0);
-    params.put("eval_metric", "error");
-
-    //do 5-fold cross validation
-    int round = 2;
-    int nfold = 5;
-    //set additional eval_metrics
-    String[] metrics = null;
-
-    String[] evalHist = XGBoost.crossValidation(trainMat, params, round, nfold, metrics, null,
-            null);
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java
deleted file mode 100644
index 6d52997..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java
+++ /dev/null
@@ -1,168 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-import ml.dmlc.xgboost4j.java.*;
-
-/**
- * an example user define objective and eval
- * NOTE: when you do customized loss function, the default prediction value is margin
- * this may make buildin evalution metric not function properly
- * for example, we are doing logistic loss, the prediction is score before logistic transformation
- * he buildin evaluation error assumes input is after logistic transformation
- * Take this in mind when you use the customization, and maybe you need write customized evaluation
- * function
- *
- * @author hzx
- */
-public class CustomObjective {
-  /**
-   * loglikelihoode loss obj function
-   */
-  public static class LogRegObj implements IObjective {
-    private static final Log logger = LogFactory.getLog(LogRegObj.class);
-
-    /**
-     * simple sigmoid func
-     *
-     * @param input
-     * @return Note: this func is not concern about numerical stability, only used as example
-     */
-    public float sigmoid(float input) {
-      float val = (float) (1 / (1 + Math.exp(-input)));
-      return val;
-    }
-
-    public float[][] transform(float[][] predicts) {
-      int nrow = predicts.length;
-      float[][] transPredicts = new float[nrow][1];
-
-      for (int i = 0; i < nrow; i++) {
-        transPredicts[i][0] = sigmoid(predicts[i][0]);
-      }
-
-      return transPredicts;
-    }
-
-    @Override
-    public List<float[]> getGradient(float[][] predicts, DMatrix dtrain) {
-      int nrow = predicts.length;
-      List<float[]> gradients = new ArrayList<float[]>();
-      float[] labels;
-      try {
-        labels = dtrain.getLabel();
-      } catch (XGBoostError ex) {
-        logger.error(ex);
-        return null;
-      }
-      float[] grad = new float[nrow];
-      float[] hess = new float[nrow];
-
-      float[][] transPredicts = transform(predicts);
-
-      for (int i = 0; i < nrow; i++) {
-        float predict = transPredicts[i][0];
-        grad[i] = predict - labels[i];
-        hess[i] = predict * (1 - predict);
-      }
-
-      gradients.add(grad);
-      gradients.add(hess);
-      return gradients;
-    }
-  }
-
-  /**
-   * user defined eval function.
-   * NOTE: when you do customized loss function, the default prediction value is margin
-   * this may make buildin evalution metric not function properly
-   * for example, we are doing logistic loss, the prediction is score before logistic transformation
-   * the buildin evaluation error assumes input is after logistic transformation
-   * Take this in mind when you use the customization, and maybe you need write customized
-   * evaluation function
-   */
-  public static class EvalError implements IEvaluation {
-    private static final Log logger = LogFactory.getLog(EvalError.class);
-
-    String evalMetric = "custom_error";
-
-    public EvalError() {
-    }
-
-    @Override
-    public String getMetric() {
-      return evalMetric;
-    }
-
-    @Override
-    public float eval(float[][] predicts, DMatrix dmat) {
-      float error = 0f;
-      float[] labels;
-      try {
-        labels = dmat.getLabel();
-      } catch (XGBoostError ex) {
-        logger.error(ex);
-        return -1f;
-      }
-      int nrow = predicts.length;
-      for (int i = 0; i < nrow; i++) {
-        if (labels[i] == 0f && predicts[i][0] > 0) {
-          error++;
-        } else if (labels[i] == 1f && predicts[i][0] <= 0) {
-          error++;
-        }
-      }
-
-      return error / labels.length;
-    }
-  }
-
-  public static void main(String[] args) throws XGBoostError {
-    //load train mat (svmlight format)
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    //load valid mat (svmlight format)
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-
-    HashMap<String, Object> params = new HashMap<String, Object>();
-    params.put("eta", 1.0);
-    params.put("max_depth", 2);
-    params.put("silent", 1);
-
-
-    //set round
-    int round = 2;
-
-    //specify watchList
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-    //user define obj and eval
-    IObjective obj = new LogRegObj();
-    IEvaluation eval = new EvalError();
-
-    //train a booster
-    System.out.println("begin to train the booster model");
-    Booster booster = XGBoost.train(trainMat, params, round, watches, obj, eval);
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java
deleted file mode 100644
index 349098a..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.Booster;
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-
-/**
- * simple example for using external memory version
- *
- * @author hzx
- */
-public class ExternalMemory {
-  public static void main(String[] args) throws XGBoostError {
-    //this is the only difference, add a # followed by a cache prefix name
-    //several cache file with the prefix will be generated
-    //currently only support convert from libsvm file
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache");
-
-    //specify parameters
-    HashMap<String, Object> params = new HashMap<String, Object>();
-    params.put("eta", 1.0);
-    params.put("max_depth", 2);
-    params.put("silent", 1);
-    params.put("objective", "binary:logistic");
-
-    //performance notice: set nthread to be the number of your real cpu
-    //some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case
-    // set nthread=4
-    //param.put("nthread", num_real_cpu);
-
-    //specify watchList
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-    //set round
-    int round = 2;
-
-    //train a boost model
-    Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java
deleted file mode 100644
index 422cdea..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.Booster;
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-import ml.dmlc.xgboost4j.java.example.util.CustomEval;
-
-/**
- * this is an example of fit generalized linear model in xgboost
- * basically, we are using linear model, instead of tree for our boosters
- *
- * @author hzx
- */
-public class GeneralizedLinearModel {
-  public static void main(String[] args) throws XGBoostError {
-    // load file from text file, also binary buffer generated by xgboost4j
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-
-    //specify parameters
-    //change booster to gblinear, so that we are fitting a linear model
-    // alpha is the L1 regularizer
-    //lambda is the L2 regularizer
-    //you can also set lambda_bias which is L2 regularizer on the bias term
-    HashMap<String, Object> params = new HashMap<String, Object>();
-    params.put("alpha", 0.0001);
-    params.put("silent", 1);
-    params.put("objective", "binary:logistic");
-    params.put("booster", "gblinear");
-
-    //normally, you do not need to set eta (step_size)
-    //XGBoost uses a parallel coordinate descent algorithm (shotgun),
-    //there could be affection on convergence with parallelization on certain cases
-    //setting eta to be smaller value, e.g 0.5 can make the optimization more stable
-    //param.put("eta", "0.5");
-
-
-    //specify watchList
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-    //train a booster
-    int round = 4;
-    Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
-
-    float[][] predicts = booster.predict(testMat);
-
-    CustomEval eval = new CustomEval();
-    System.out.println("error=" + eval.eval(predicts, testMat));
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java
deleted file mode 100644
index c98534a..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.Booster;
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-import ml.dmlc.xgboost4j.java.example.util.CustomEval;
-
-/**
- * predict first ntree
- *
- * @author hzx
- */
-public class PredictFirstNtree {
-  public static void main(String[] args) throws XGBoostError {
-    // load file from text file, also binary buffer generated by xgboost4j
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-
-    //specify parameters
-    HashMap<String, Object> params = new HashMap<String, Object>();
-
-    params.put("eta", 1.0);
-    params.put("max_depth", 2);
-    params.put("silent", 1);
-    params.put("objective", "binary:logistic");
-
-
-    //specify watchList
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-
-    //train a booster
-    int round = 3;
-    Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
-
-    //predict use 1 tree
-    float[][] predicts1 = booster.predict(testMat, false, 1);
-    //by default all trees are used to do predict
-    float[][] predicts2 = booster.predict(testMat);
-
-    //use a simple evaluation class to check error result
-    CustomEval eval = new CustomEval();
-    System.out.println("error of predicts1: " + eval.eval(predicts1, testMat));
-    System.out.println("error of predicts2: " + eval.eval(predicts2, testMat));
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java
deleted file mode 100644
index 0fcfb39..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example;
-
-import java.util.Arrays;
-import java.util.HashMap;
-
-import ml.dmlc.xgboost4j.java.Booster;
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.XGBoost;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-
-/**
- * predict leaf indices
- *
- * @author hzx
- */
-public class PredictLeafIndices {
-  public static void main(String[] args) throws XGBoostError {
-    // load file from text file, also binary buffer generated by xgboost4j
-    DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-
-    //specify parameters
-    HashMap<String, Object> params = new HashMap<String, Object>();
-    params.put("eta", 1.0);
-    params.put("max_depth", 2);
-    params.put("silent", 1);
-    params.put("objective", "binary:logistic");
-
-    //specify watchList
-    HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
-    watches.put("train", trainMat);
-    watches.put("test", testMat);
-
-
-    //train a booster
-    int round = 3;
-    Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
-
-    //predict using first 2 tree
-    float[][] leafindex = booster.predictLeaf(testMat, 2);
-    for (float[] leafs : leafindex) {
-      System.out.println(Arrays.toString(leafs));
-    }
-
-    //predict all trees
-    leafindex = booster.predictLeaf(testMat, 0);
-    for (float[] leafs : leafindex) {
-      System.out.println(Arrays.toString(leafs));
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/CustomEval.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/CustomEval.java
deleted file mode 100644
index b34b97e..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/CustomEval.java
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example.util;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-import ml.dmlc.xgboost4j.java.DMatrix;
-import ml.dmlc.xgboost4j.java.IEvaluation;
-import ml.dmlc.xgboost4j.java.XGBoostError;
-
-/**
- * a util evaluation class for examples
- *
- * @author hzx
- */
-public class CustomEval implements IEvaluation {
-  private static final Log logger = LogFactory.getLog(CustomEval.class);
-
-  String evalMetric = "custom_error";
-
-  @Override
-  public String getMetric() {
-    return evalMetric;
-  }
-
-  @Override
-  public float eval(float[][] predicts, DMatrix dmat) {
-    float error = 0f;
-    float[] labels;
-    try {
-      labels = dmat.getLabel();
-    } catch (XGBoostError ex) {
-      logger.error(ex);
-      return -1f;
-    }
-    int nrow = predicts.length;
-    for (int i = 0; i < nrow; i++) {
-      if (labels[i] == 0f && predicts[i][0] > 0.5) {
-        error++;
-      } else if (labels[i] == 1f && predicts[i][0] <= 0.5) {
-        error++;
-      }
-    }
-
-    return error / labels.length;
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/DataLoader.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/DataLoader.java
deleted file mode 100644
index 5f1ac6e..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/DataLoader.java
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.example.util;
-
-import java.io.*;
-import java.util.ArrayList;
-import java.util.List;
-
-import org.apache.commons.lang3.ArrayUtils;
-
-/**
- * util class for loading data
- *
- * @author hzx
- */
-public class DataLoader {
-  public static class DenseData {
-    public float[] labels;
-    public float[] data;
-    public int nrow;
-    public int ncol;
-  }
-
-  public static class CSRSparseData {
-    public float[] labels;
-    public float[] data;
-    public long[] rowHeaders;
-    public int[] colIndex;
-  }
-
-  public static DenseData loadCSVFile(String filePath) throws IOException {
-    DenseData denseData = new DenseData();
-
-    File f = new File(filePath);
-    FileInputStream in = new FileInputStream(f);
-    BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
-
-    denseData.nrow = 0;
-    denseData.ncol = -1;
-    String line;
-    List<Float> tlabels = new ArrayList<>();
-    List<Float> tdata = new ArrayList<>();
-
-    while ((line = reader.readLine()) != null) {
-      String[] items = line.trim().split(",");
-      if (items.length == 0) {
-        continue;
-      }
-      denseData.nrow++;
-      if (denseData.ncol == -1) {
-        denseData.ncol = items.length - 1;
-      }
-
-      tlabels.add(Float.valueOf(items[items.length - 1]));
-      for (int i = 0; i < items.length - 1; i++) {
-        tdata.add(Float.valueOf(items[i]));
-      }
-    }
-
-    reader.close();
-    in.close();
-
-    denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
-    denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
-
-    return denseData;
-  }
-
-  public static CSRSparseData loadSVMFile(String filePath) throws IOException {
-    CSRSparseData spData = new CSRSparseData();
-
-    List<Float> tlabels = new ArrayList<>();
-    List<Float> tdata = new ArrayList<>();
-    List<Long> theaders = new ArrayList<>();
-    List<Integer> tindex = new ArrayList<>();
-
-    File f = new File(filePath);
-    FileInputStream in = new FileInputStream(f);
-    BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
-
-    String line;
-    long rowheader = 0;
-    theaders.add(rowheader);
-    while ((line = reader.readLine()) != null) {
-      String[] items = line.trim().split(" ");
-      if (items.length == 0) {
-        continue;
-      }
-
-      rowheader += items.length - 1;
-      theaders.add(rowheader);
-      tlabels.add(Float.valueOf(items[0]));
-
-      for (int i = 1; i < items.length; i++) {
-        String[] tup = items[i].split(":");
-        assert tup.length == 2;
-
-        tdata.add(Float.valueOf(tup[1]));
-        tindex.add(Integer.valueOf(tup[0]));
-      }
-    }
-
-    spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
-    spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
-    spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
-    spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));
-
-    return spData;
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala
deleted file mode 100644
index e8481b0..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.example
-
-import java.io.File
-import java.io.PrintWriter
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix}
-import ml.dmlc.xgboost4j.java.example.util.DataLoader
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-
-object BasicWalkThrough {
-  def saveDumpModel(modelPath: String, modelInfos: Array[String]): Unit = {
-    val writer = new PrintWriter(modelPath, "UTF-8")
-    for (i <- 0 until modelInfos.length) {
-      writer.print(s"booster[$i]:\n")
-      writer.print(modelInfos(i))
-    }
-    writer.close()
-  }
-
-  def main(args: Array[String]): Unit = {
-    val trainMax = new DMatrix("../../demo/data/agaricus.txt.train")
-    val testMax = new DMatrix("../../demo/data/agaricus.txt.test")
-
-    val params = new mutable.HashMap[String, Any]()
-    params += "eta" -> 1.0
-    params += "max_depth" -> 2
-    params += "silent" -> 1
-    params += "objective" -> "binary:logistic"
-
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMax
-    watches += "test" -> testMax
-
-    val round = 2
-    // train a model
-    val booster = XGBoost.train(trainMax, params.toMap, round, watches.toMap)
-    // predict
-    val predicts = booster.predict(testMax)
-    // save model to model path
-    val file = new File("./model")
-    if (!file.exists()) {
-      file.mkdirs()
-    }
-    booster.saveModel(file.getAbsolutePath + "/xgb.model")
-    // dump model with feature map
-    val modelInfos = booster.getModelDump(file.getAbsolutePath + "/featmap.txt", false)
-    saveDumpModel(file.getAbsolutePath + "/dump.raw.txt", modelInfos)
-    // save dmatrix into binary buffer
-    testMax.saveBinary(file.getAbsolutePath + "/dtest.buffer")
-
-    // reload model and data
-    val booster2 = XGBoost.loadModel(file.getAbsolutePath + "/xgb.model")
-    val testMax2 = new DMatrix(file.getAbsolutePath + "/dtest.buffer")
-    val predicts2 = booster2.predict(testMax2)
-
-    // check predicts
-    println(checkPredicts(predicts, predicts2))
-
-    // build dmatrix from CSR Sparse Matrix
-    println("start build dmatrix from csr sparse data ...")
-    val spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train")
-    val trainMax2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
-      JDMatrix.SparseType.CSR)
-    trainMax2.setLabel(spData.labels)
-
-    // specify watchList
-    val watches2 = new mutable.HashMap[String, DMatrix]
-    watches2 += "train" -> trainMax2
-    watches2 += "test" -> testMax2
-    val booster3 = XGBoost.train(trainMax2, params.toMap, round, watches2.toMap)
-    val predicts3 = booster3.predict(testMax2)
-    println(checkPredicts(predicts, predicts3))
-  }
-
-  def checkPredicts(fPredicts: Array[Array[Float]], sPredicts: Array[Array[Float]]): Boolean = {
-    require(fPredicts.length == sPredicts.length, "the comparing predicts must be with the same " +
-      "length")
-    for (i <- fPredicts.indices) {
-      if (!java.util.Arrays.equals(fPredicts(i), sPredicts(i))) {
-        return false
-      }
-    }
-    true
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala
deleted file mode 100644
index b894532..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.example
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-
-object BoostFromPrediction {
-  def main(args: Array[String]): Unit = {
-    println("start running example to start from a initial prediction")
-
-    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
-    val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
-
-    val params = new mutable.HashMap[String, Any]()
-    params += "eta" -> 1.0
-    params += "max_depth" -> 2
-    params += "silent" -> 1
-    params += "objective" -> "binary:logistic"
-
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMat
-    watches += "test" -> testMat
-
-    val round = 2
-    // train a model
-    val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
-
-    val trainPred = booster.predict(trainMat, true)
-    val testPred = booster.predict(testMat, true)
-
-    trainMat.setBaseMargin(trainPred)
-    testMat.setBaseMargin(testPred)
-
-    System.out.println("result of running from initial prediction")
-    val booster2 = XGBoost.train(trainMat, params.toMap, 1, watches.toMap, null, null)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala
deleted file mode 100644
index 62f8b46..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.scala.example
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-
-object CrossValidation {
-  def main(args: Array[String]): Unit = {
-    val trainMat: DMatrix = new DMatrix("../../demo/data/agaricus.txt.train")
-
-    // set params
-    val params = new mutable.HashMap[String, Any]
-
-    params.put("eta", 1.0)
-    params.put("max_depth", 3)
-    params.put("silent", 1)
-    params.put("nthread", 6)
-    params.put("objective", "binary:logistic")
-    params.put("gamma", 1.0)
-    params.put("eval_metric", "error")
-
-    // do 5-fold cross validation
-    val round: Int = 2
-    val nfold: Int = 5
-    // set additional eval_metrics
-    val metrics: Array[String] = null
-
-    val evalHist: Array[String] =
-      XGBoost.crossValidation(trainMat, params.toMap, round, nfold, metrics)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala
deleted file mode 100644
index fe88423..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala
+++ /dev/null
@@ -1,158 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.scala.example
-
-import scala.collection.mutable
-import scala.collection.mutable.ListBuffer
-
-import ml.dmlc.xgboost4j.java.XGBoostError
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix, EvalTrait, ObjectiveTrait}
-import org.apache.commons.logging.{LogFactory, Log}
-
-/**
- * an example user define objective and eval
- * NOTE: when you do customized loss function, the default prediction value is margin
- * this may make buildin evalution metric not function properly
- * for example, we are doing logistic loss, the prediction is score before logistic transformation
- * he buildin evaluation error assumes input is after logistic transformation
- * Take this in mind when you use the customization, and maybe you need write customized evaluation
- * function
- *
- */
-object CustomObjective {
-
-  /**
-   * loglikelihoode loss obj function
-   */
-  class LogRegObj extends ObjectiveTrait {
-    private val logger: Log = LogFactory.getLog(classOf[LogRegObj])
-    /**
-     * user define objective function, return gradient and second order gradient
-     *
-     * @param predicts untransformed margin predicts
-     * @param dtrain   training data
-     * @return List with two float array, correspond to first order grad and second order grad
-     */
-    override def getGradient(predicts: Array[Array[Float]], dtrain: DMatrix)
-        : List[Array[Float]] = {
-      val nrow = predicts.length
-      val gradients = new ListBuffer[Array[Float]]
-      var labels: Array[Float] = null
-      try {
-        labels = dtrain.getLabel
-      } catch {
-        case e: XGBoostError =>
-          logger.error(e)
-          null
-        case _: Throwable =>
-          null
-      }
-      val grad = new Array[Float](nrow)
-      val hess = new Array[Float](nrow)
-      val transPredicts = transform(predicts)
-
-      for (i <- 0 until nrow) {
-        val predict = transPredicts(i)(0)
-        grad(i) = predict - labels(i)
-        hess(i) = predict * (1 - predict)
-      }
-      gradients += grad
-      gradients += hess
-      gradients.toList
-    }
-
-    /**
-     * simple sigmoid func
-     *
-     * @param input
-     * @return Note: this func is not concern about numerical stability, only used as example
-     */
-    def sigmoid(input: Float): Float = {
-      (1 / (1 + Math.exp(-input))).toFloat
-    }
-
-    def transform(predicts: Array[Array[Float]]): Array[Array[Float]] = {
-      val nrow = predicts.length
-      val transPredicts = Array.fill[Float](nrow, 1)(0)
-      for (i <- 0 until nrow) {
-        transPredicts(i)(0) = sigmoid(predicts(i)(0))
-      }
-      transPredicts
-    }
-
-  }
-
-  class EvalError extends EvalTrait {
-
-    val logger = LogFactory.getLog(classOf[EvalError])
-
-    private[xgboost4j] var evalMetric: String = "custom_error"
-
-    /**
-     * get evaluate metric
-     *
-     * @return evalMetric
-     */
-    override def getMetric: String = evalMetric
-
-    /**
-     * evaluate with predicts and data
-     *
-     * @param predicts predictions as array
-     * @param dmat     data matrix to evaluate
-     * @return result of the metric
-     */
-    override def eval(predicts: Array[Array[Float]], dmat: DMatrix): Float = {
-      var error: Float = 0f
-      var labels: Array[Float] = null
-      try {
-        labels = dmat.getLabel
-      } catch {
-        case ex: XGBoostError =>
-          logger.error(ex)
-          return -1f
-      }
-      val nrow: Int = predicts.length
-      for (i <- 0 until nrow) {
-        if (labels(i) == 0.0 && predicts(i)(0) > 0) {
-          error += 1
-        } else if (labels(i) == 1.0 && predicts(i)(0) <= 0) {
-          error += 1
-        }
-      }
-      error / labels.length
-    }
-  }
-
-  def main(args: Array[String]): Unit = {
-    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
-    val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
-    val params = new mutable.HashMap[String, Any]()
-    params += "eta" -> 1.0
-    params += "max_depth" -> 2
-    params += "silent" -> 1
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMat
-    watches += "test" -> testMat
-
-    val round = 2
-    // train a model
-    val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
-    XGBoost.train(trainMat, params.toMap, round, watches.toMap,
-      obj = new LogRegObj, eval = new EvalError)
-  }
-
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala
deleted file mode 100644
index 447c982..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.example
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-
-object ExternalMemory {
-  def main(args: Array[String]): Unit = {
-    // this is the only difference, add a # followed by a cache prefix name
-    // several cache file with the prefix will be generated
-    // currently only support convert from libsvm file
-    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache")
-    val testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache")
-
-    val params = new mutable.HashMap[String, Any]()
-    params += "eta" -> 1.0
-    params += "max_depth" -> 2
-    params += "silent" -> 1
-    params += "objective" -> "binary:logistic"
-
-    // performance notice: set nthread to be the number of your real cpu
-    // some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case
-    // set nthread=4
-    // param.put("nthread", num_real_cpu);
-
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMat
-    watches += "test" -> testMat
-
-    val round = 2
-    // train a model
-    val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
-
-    val trainPred = booster.predict(trainMat, true)
-    val testPred = booster.predict(testMat, true)
-
-    trainMat.setBaseMargin(trainPred)
-    testMat.setBaseMargin(testPred)
-
-    System.out.println("result of running from initial prediction")
-    val booster2 = XGBoost.train(trainMat, params.toMap, 1, watches.toMap)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala
deleted file mode 100644
index 27ed98e..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.scala.example
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-import ml.dmlc.xgboost4j.scala.example.util.CustomEval
-
-
-/**
- * this is an example of fit generalized linear model in xgboost
- * basically, we are using linear model, instead of tree for our boosters
- */
-object GeneralizedLinearModel {
-  def main(args: Array[String]): Unit = {
-    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
-    val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
-
-    // specify parameters
-    // change booster to gblinear, so that we are fitting a linear model
-    // alpha is the L1 regularizer
-    // lambda is the L2 regularizer
-    // you can also set lambda_bias which is L2 regularizer on the bias term
-    val params = new mutable.HashMap[String, Any]()
-    params += "alpha" -> 0.0001
-    params += "boosterh" -> "gblinear"
-    params += "silent" -> 1
-    params += "objective" -> "binary:logistic"
-
-    // normally, you do not need to set eta (step_size)
-    // XGBoost uses a parallel coordinate descent algorithm (shotgun),
-    // there could be affection on convergence with parallelization on certain cases
-    // setting eta to be smaller value, e.g 0.5 can make the optimization more stable
-    // param.put("eta", "0.5");
-
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMat
-    watches += "test" -> testMat
-
-    val round = 4
-    val booster = XGBoost.train(trainMat, params.toMap, 1, watches.toMap)
-    val predicts = booster.predict(testMat)
-    val eval = new CustomEval
-    println(s"error=${eval.eval(predicts, testMat)}")
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala
deleted file mode 100644
index 5395e36..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.scala.example
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.scala.example.util.CustomEval
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-
-object PredictFirstNTree {
-
-  def main(args: Array[String]): Unit = {
-    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
-    val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
-
-    val params = new mutable.HashMap[String, Any]()
-    params += "eta" -> 1.0
-    params += "max_depth" -> 2
-    params += "silent" -> 1
-    params += "objective" -> "binary:logistic"
-
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMat
-    watches += "test" -> testMat
-
-    val round = 3
-    // train a model
-    val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
-
-    // predict use 1 tree
-    val predicts1 = booster.predict(testMat, false, 1)
-    // by default all trees are used to do predict
-    val predicts2 = booster.predict(testMat)
-
-    val eval = new CustomEval
-    println("error of predicts1: " + eval.eval(predicts1, testMat))
-    println("error of predicts2: " + eval.eval(predicts2, testMat))
-  }
-
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala
deleted file mode 100644
index f40a8aa..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.example
-
-import java.util
-
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
-
-object PredictLeafIndices {
-
-  def main(args: Array[String]): Unit = {
-    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
-    val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
-
-    val params = new mutable.HashMap[String, Any]()
-    params += "eta" -> 1.0
-    params += "max_depth" -> 2
-    params += "silent" -> 1
-    params += "objective" -> "binary:logistic"
-
-    val watches = new mutable.HashMap[String, DMatrix]
-    watches += "train" -> trainMat
-    watches += "test" -> testMat
-
-    val round = 3
-    val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
-
-    // predict using first 2 tree
-    val leafIndex = booster.predictLeaf(testMat, 2)
-    for (leafs <- leafIndex) {
-      println(java.util.Arrays.toString(leafs))
-    }
-
-    // predict all trees
-    val leafIndex2 = booster.predictLeaf(testMat, 0)
-    for (leafs <- leafIndex) {
-      println(java.util.Arrays.toString(leafs))
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala
deleted file mode 100644
index 74b24ac..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.scala.example.flink
-
-import ml.dmlc.xgboost4j.scala.flink.XGBoost
-import org.apache.flink.api.scala.{ExecutionEnvironment, _}
-import org.apache.flink.ml.MLUtils
-
-object DistTrainWithFlink {
-  def main(args: Array[String]) {
-    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
-    // read trainining data
-    val trainData =
-      MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
-    val testData = MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.test")
-    // define parameters
-    val paramMap = List(
-      "eta" -> 0.1,
-      "max_depth" -> 2,
-      "objective" -> "binary:logistic").toMap
-    // number of iterations
-    val round = 2
-    // train the model
-    val model = XGBoost.train(trainData, paramMap, round)
-    val predTest = model.predict(testData.map{x => x.vector})
-    model.saveModelAsHadoopFile("file:///path/to/xgboost.model")
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala
deleted file mode 100644
index 9e1b02a..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.example.spark
-
-import org.apache.spark.ml.{Pipeline, PipelineModel}
-import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.ml.feature._
-import org.apache.spark.ml.tuning._
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.types._
-
-import ml.dmlc.xgboost4j.scala.spark.{XGBoostClassifier, XGBoostClassificationModel}
-
-// this example works with Iris dataset (https://archive.ics.uci.edu/ml/datasets/iris)
-
-object SparkMLlibPipeline {
-
-  def main(args: Array[String]): Unit = {
-
-    if (args.length != 3) {
-      println("Usage: SparkMLlibPipeline input_path native_model_path pipeline_model_path")
-      sys.exit(1)
-    }
-
-    val inputPath = args(0)
-    val nativeModelPath = args(1)
-    val pipelineModelPath = args(2)
-
-    val spark = SparkSession
-      .builder()
-      .appName("XGBoost4J-Spark Pipeline Example")
-      .getOrCreate()
-
-    // Load dataset
-    val schema = new StructType(Array(
-      StructField("sepal length", DoubleType, true),
-      StructField("sepal width", DoubleType, true),
-      StructField("petal length", DoubleType, true),
-      StructField("petal width", DoubleType, true),
-      StructField("class", StringType, true)))
-
-    val rawInput = spark.read.schema(schema).csv(inputPath)
-
-    // Split training and test dataset
-    val Array(training, test) = rawInput.randomSplit(Array(0.8, 0.2), 123)
-
-    // Build ML pipeline, it includes 4 stages:
-    // 1, Assemble all features into a single vector column.
-    // 2, From string label to indexed double label.
-    // 3, Use XGBoostClassifier to train classification model.
-    // 4, Convert indexed double label back to original string label.
-    val assembler = new VectorAssembler()
-      .setInputCols(Array("sepal length", "sepal width", "petal length", "petal width"))
-      .setOutputCol("features")
-    val labelIndexer = new StringIndexer()
-      .setInputCol("class")
-      .setOutputCol("classIndex")
-      .fit(training)
-    val booster = new XGBoostClassifier(
-      Map("eta" -> 0.1f,
-        "max_depth" -> 2,
-        "objective" -> "multi:softprob",
-        "num_class" -> 3,
-        "num_round" -> 100,
-        "num_workers" -> 2
-      )
-    )
-    booster.setFeaturesCol("features")
-    booster.setLabelCol("classIndex")
-    val labelConverter = new IndexToString()
-      .setInputCol("prediction")
-      .setOutputCol("realLabel")
-      .setLabels(labelIndexer.labels)
-
-    val pipeline = new Pipeline()
-      .setStages(Array(assembler, labelIndexer, booster, labelConverter))
-    val model = pipeline.fit(training)
-
-    // Batch prediction
-    val prediction = model.transform(test)
-    prediction.show(false)
-
-    // Model evaluation
-    val evaluator = new MulticlassClassificationEvaluator()
-    evaluator.setLabelCol("classIndex")
-    evaluator.setPredictionCol("prediction")
-    val accuracy = evaluator.evaluate(prediction)
-    println("The model accuracy is : " + accuracy)
-
-    // Tune model using cross validation
-    val paramGrid = new ParamGridBuilder()
-      .addGrid(booster.maxDepth, Array(3, 8))
-      .addGrid(booster.eta, Array(0.2, 0.6))
-      .build()
-    val cv = new CrossValidator()
-      .setEstimator(pipeline)
-      .setEvaluator(evaluator)
-      .setEstimatorParamMaps(paramGrid)
-      .setNumFolds(3)
-
-    val cvModel = cv.fit(training)
-
-    val bestModel = cvModel.bestModel.asInstanceOf[PipelineModel].stages(2)
-      .asInstanceOf[XGBoostClassificationModel]
-    println("The params of best XGBoostClassification model : " +
-      bestModel.extractParamMap())
-    println("The training summary of best XGBoostClassificationModel : " +
-      bestModel.summary)
-
-    // Export the XGBoostClassificationModel as local XGBoost model,
-    // then you can load it back in local Python environment.
-    bestModel.nativeBooster.saveModel(nativeModelPath)
-
-    // ML pipeline persistence
-    model.write.overwrite().save(pipelineModelPath)
-
-    // Load a saved model and serving
-    val model2 = PipelineModel.load(pipelineModelPath)
-    model2.transform(test).show(false)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala
deleted file mode 100644
index d937536..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.example.spark
-
-import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
-
-import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
-
-// this example works with Iris dataset (https://archive.ics.uci.edu/ml/datasets/iris)
-object SparkTraining {
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 1) {
-      // scalastyle:off
-      println("Usage: program input_path")
-      sys.exit(1)
-    }
-    val spark = SparkSession.builder().getOrCreate()
-    val inputPath = args(0)
-    val schema = new StructType(Array(
-      StructField("sepal length", DoubleType, true),
-      StructField("sepal width", DoubleType, true),
-      StructField("petal length", DoubleType, true),
-      StructField("petal width", DoubleType, true),
-      StructField("class", StringType, true)))
-    val rawInput = spark.read.schema(schema).csv(inputPath)
-
-    // transform class to index to make xgboost happy
-    val stringIndexer = new StringIndexer()
-      .setInputCol("class")
-      .setOutputCol("classIndex")
-      .fit(rawInput)
-    val labelTransformed = stringIndexer.transform(rawInput).drop("class")
-    // compose all feature columns as vector
-    val vectorAssembler = new VectorAssembler().
-      setInputCols(Array("sepal length", "sepal width", "petal length", "petal width")).
-      setOutputCol("features")
-    val xgbInput = vectorAssembler.transform(labelTransformed).select("features",
-      "classIndex")
-
-    val Array(train, eval1, eval2, test) = xgbInput.randomSplit(Array(0.6, 0.2, 0.1, 0.1))
-
-    /**
-     * setup  "timeout_request_workers" -> 60000L to make this application if it cannot get enough resources
-     * to get 2 workers within 60000 ms
-     *
-     * setup "checkpoint_path" -> "/checkpoints" and "checkpoint_interval" -> 2 to save checkpoint for every
-     * two iterations
-     */
-    val xgbParam = Map("eta" -> 0.1f,
-      "max_depth" -> 2,
-      "objective" -> "multi:softprob",
-      "num_class" -> 3,
-      "num_round" -> 100,
-      "num_workers" -> 2,
-      "eval_sets" -> Map("eval1" -> eval1, "eval2" -> eval2))
-    val xgbClassifier = new XGBoostClassifier(xgbParam).
-      setFeaturesCol("features").
-      setLabelCol("classIndex")
-    val xgbClassificationModel = xgbClassifier.fit(train)
-    val results = xgbClassificationModel.transform(test)
-    results.show()
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/util/CustomEval.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/util/CustomEval.scala
deleted file mode 100644
index 6fb233c..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/util/CustomEval.scala
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.scala.example.util
-
-import ml.dmlc.xgboost4j.java.XGBoostError
-import ml.dmlc.xgboost4j.scala.{DMatrix, EvalTrait}
-import org.apache.commons.logging.{Log, LogFactory}
-
-class CustomEval extends EvalTrait {
-  private val logger: Log = LogFactory.getLog(classOf[CustomEval])
-  /**
-   * get evaluate metric
-   *
-   * @return evalMetric
-   */
-  override def getMetric: String = {
-    "custom_error"
-  }
-
-  /**
-   * evaluate with predicts and data
-   *
-   * @param predicts predictions as array
-   * @param dmat     data matrix to evaluate
-   * @return result of the metric
-   */
-  override def eval(predicts: Array[Array[Float]], dmat: DMatrix): Float = {
-    var error: Float = 0f
-    var labels: Array[Float] = null
-    try {
-      labels = dmat.getLabel
-    } catch {
-      case ex: XGBoostError =>
-        logger.error(ex)
-        return -1f
-    }
-    val nrow: Int = predicts.length
-    for (i <- 0 until nrow) {
-      if (labels(i) == 0.0 && predicts(i)(0) > 0.5) {
-        error += 1
-      } else if (labels(i) == 1.0 && predicts(i)(0) <= 0.5) {
-        error += 1
-      }
-    }
-    error / labels.length
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/pom.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/pom.xml
deleted file mode 100644
index 24347b5..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/pom.xml
+++ /dev/null
@@ -1,79 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-        <version>2.1.0</version>
-    </parent>
-    <artifactId>boostkit-xgboost4j-flink_2.11</artifactId>
-    <version>2.1.0</version>
-    <build>
-        <plugins>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-jar-plugin</artifactId>
-                <version>2.5</version>
-                <configuration>
-                    <archive>
-                       <addMavenDescriptor>false</addMavenDescriptor>
-                    </archive>
-                </configuration>
-                <executions>
-                    <execution>
-                        <goals>
-                            <goal>jar</goal>
-                        </goals>
-                    </execution>
-                    <execution>
-                        <id>default-jar</id>
-                        <phase>none</phase>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-assembly-plugin</artifactId>
-                <configuration>
-                    <skipAssembly>false</skipAssembly>
-                </configuration>
-            </plugin>
-        </plugins>
-    </build>
-    <packaging>jar</packaging>
-    <dependencies>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.commons</groupId>
-            <artifactId>commons-lang3</artifactId>
-            <version>3.4</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.flink</groupId>
-            <artifactId>flink-scala_${scala.binary.version}</artifactId>
-            <version>${flink.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.flink</groupId>
-            <artifactId>flink-clients_${scala.binary.version}</artifactId>
-            <version>${flink.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.flink</groupId>
-            <artifactId>flink-ml_${scala.binary.version}</artifactId>
-            <version>${flink.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.hadoop</groupId>
-            <artifactId>hadoop-common</artifactId>
-            <version>${hadoop.version}</version>
-        </dependency>
-    </dependencies>
-
-</project>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoost.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoost.scala
deleted file mode 100644
index c9aa163..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoost.scala
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.flink
-
-import scala.collection.JavaConverters.asScalaIteratorConverter
-
-import ml.dmlc.xgboost4j.LabeledPoint
-import ml.dmlc.xgboost4j.java.{Rabit, RabitTracker}
-import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost => XGBoostScala}
-
-import org.apache.commons.logging.LogFactory
-import org.apache.flink.api.common.functions.RichMapPartitionFunction
-import org.apache.flink.api.scala.{DataSet, _}
-import org.apache.flink.ml.common.LabeledVector
-import org.apache.flink.util.Collector
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
-
-object XGBoost {
-  /**
-    * Helper map function to start the job.
-    *
-    * @param workerEnvs
-    */
-  private class MapFunction(paramMap: Map[String, Any],
-                            round: Int,
-                            workerEnvs: java.util.Map[String, String])
-    extends RichMapPartitionFunction[LabeledVector, XGBoostModel] {
-    val logger = LogFactory.getLog(this.getClass)
-
-    def mapPartition(it: java.lang.Iterable[LabeledVector],
-                     collector: Collector[XGBoostModel]): Unit = {
-      workerEnvs.put("DMLC_TASK_ID", String.valueOf(this.getRuntimeContext.getIndexOfThisSubtask))
-      logger.info("start with env" + workerEnvs.toString)
-      Rabit.init(workerEnvs)
-      val mapper = (x: LabeledVector) => {
-        val (index, value) = x.vector.toSeq.unzip
-        LabeledPoint(x.label.toFloat, x.vector.size, index.toArray, value.map(_.toFloat).toArray)
-      }
-      val dataIter = for (x <- it.iterator().asScala) yield mapper(x)
-      val trainMat = new DMatrix(dataIter, null)
-      val watches = List("train" -> trainMat).toMap
-      val round = 2
-      val numEarlyStoppingRounds = paramMap.get("numEarlyStoppingRounds")
-          .map(_.toString.toInt).getOrElse(0)
-      val booster = XGBoostScala.train(trainMat, paramMap, round, watches,
-        earlyStoppingRound = numEarlyStoppingRounds)
-      Rabit.shutdown()
-      collector.collect(new XGBoostModel(booster))
-    }
-  }
-
-  val logger = LogFactory.getLog(this.getClass)
-
-  /**
-    * Load XGBoost model from path, using Hadoop Filesystem API.
-    *
-    * @param modelPath The path that is accessible by hadoop filesystem API.
-    * @return The loaded model
-    */
-  def loadModelFromHadoopFile(modelPath: String) : XGBoostModel = {
-    new XGBoostModel(
-      XGBoostScala.loadModel(FileSystem.get(new Configuration).open(new Path(modelPath))))
-  }
-
-  /**
-    * Train a xgboost model with link.
-    *
-    * @param dtrain The training data.
-    * @param params The parameters to XGBoost.
-    * @param round Number of rounds to train.
-    */
-  def train(dtrain: DataSet[LabeledVector], params: Map[String, Any], round: Int):
-      XGBoostModel = {
-    val tracker = new RabitTracker(dtrain.getExecutionEnvironment.getParallelism)
-    if (tracker.start(0L)) {
-      dtrain
-        .mapPartition(new MapFunction(params, round, tracker.getWorkerEnvs))
-        .reduce((x, y) => x).collect().head
-    } else {
-      throw new Error("Tracker cannot be started")
-      null
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoostModel.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoostModel.scala
deleted file mode 100644
index 71b3769..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoostModel.scala
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.flink
-
-import ml.dmlc.xgboost4j.LabeledPoint
-import ml.dmlc.xgboost4j.scala.{Booster, DMatrix}
-
-import org.apache.flink.api.scala.{DataSet, _}
-import org.apache.flink.ml.math.Vector
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
-
-class XGBoostModel (booster: Booster) extends Serializable {
-  /**
-    * Save the model as a Hadoop filesystem file.
-    *
-    * @param modelPath The model path as in Hadoop path.
-    */
-  def saveModelAsHadoopFile(modelPath: String): Unit = {
-    booster.saveModel(FileSystem
-      .get(new Configuration)
-      .create(new Path(modelPath)))
-  }
-
-  /**
-   * predict with the given DMatrix
-   * @param testSet the local test set represented as DMatrix
-   * @return prediction result
-   */
-  def predict(testSet: DMatrix): Array[Array[Float]] = {
-    booster.predict(testSet, true, 0)
-  }
-
-  /**
-    * Predict given vector dataset.
-    *
-    * @param data The dataset to be predicted.
-    * @return The prediction result.
-    */
-  def predict(data: DataSet[Vector]) : DataSet[Array[Float]] = {
-    val predictMap: Iterator[Vector] => Traversable[Array[Float]] =
-      (it: Iterator[Vector]) => {
-        val mapper = (x: Vector) => {
-          val (index, value) = x.toSeq.unzip
-          LabeledPoint(0.0f, x.size, index.toArray, value.map(_.toFloat).toArray)
-        }
-        val dataIter = for (x <- it) yield mapper(x)
-        val dmat = new DMatrix(dataIter, null)
-        this.booster.predict(dmat)
-      }
-    data.mapPartition(predictMap)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/pom.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/pom.xml
deleted file mode 100644
index bc2905c..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/pom.xml
+++ /dev/null
@@ -1,90 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-        <version>2.1.0</version>
-    </parent>
-    <artifactId>boostkit-xgboost4j-spark-client_2.11</artifactId>
-    <version>2.1.0</version>
-    <build>
-      <sourceDirectory>src/main/scala</sourceDirectory>
-        <plugins>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-jar-plugin</artifactId>
-                <version>2.5</version>
-                <configuration>
-                    <archive>
-                        <addMavenDescriptor>false</addMavenDescriptor>
-                    </archive>
-                </configuration>
-                <executions>
-                    <execution>
-                        <goals>
-                            <goal>jar</goal>
-                        </goals>
-                    </execution>
-                    <execution>
-                        <id>default-jar</id>
-                         <phase>none</phase>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-assembly-plugin</artifactId>
-                <configuration>
-                    <skipAssembly>false</skipAssembly>
-                </configuration>
-            </plugin>
-          <plugin>
-            <!-- see http://davidb.github.com/scala-maven-plugin -->
-            <groupId>net.alchim31.maven</groupId>
-            <artifactId>scala-maven-plugin</artifactId>
-            <version>3.2.0</version>
-            <executions>
-              <execution>
-                <goals>
-                  <goal>compile</goal>
-                </goals>
-                <configuration>
-                  <args>
-                    <arg>-dependencyfile</arg>
-                    <arg>${project.build.directory}/.scala_dependencies</arg>
-                  </args>
-                </configuration>
-              </execution>
-            </executions>
-          </plugin>
-        </plugins>
-    </build>
-    <dependencies>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-core_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-sql_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-    </dependencies>
-</project>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
deleted file mode 100644
index 6b52e3d..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
+++ /dev/null
@@ -1,240 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import ml.dmlc.xgboost4j.scala.{EvalTrait, ExternalCheckpointParams, ObjectiveTrait}
-import ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams
-import org.apache.commons.logging.LogFactory
-import org.apache.spark.SparkContext
-import org.apache.spark.sql.SparkSession
-
-
-/**
- * Rabit tracker configurations.
- *
- * @param workerConnectionTimeout The timeout for all workers to connect to the tracker.
- *                                Set timeout length to zero to disable timeout.
- *                                Use a finite, non-zero timeout value to prevent tracker from
- *                                hanging indefinitely (in milliseconds)
- *                                (supported by "scala" implementation only.)
- * @param trackerImpl Choice between "python" or "scala". The former utilizes the Java wrapper of
- *                    the Python Rabit tracker (in dmlc_core), whereas the latter is implemented
- *                    in Scala without Python components, and with full support of timeouts.
- *                    The Scala implementation is currently experimental, use at your own risk.
- */
-case class TrackerConf(workerConnectionTimeout: Long, trackerImpl: String)
-
-object TrackerConf {
-  def apply(): TrackerConf = TrackerConf(0L, "python")
-}
-
-private[this] case class XGBoostExecutionEarlyStoppingParams(numEarlyStoppingRounds: Int,
-                                                             maximizeEvalMetrics: Boolean)
-
-private[this] case class XGBoostExecutionInputParams(trainTestRatio: Double, seed: Long)
-
-private[spark] case class XGBoostExecutionParams(
-    numWorkers: Int,
-    var numRounds: Int,
-    useExternalMemory: Boolean,
-    obj: ObjectiveTrait,
-    eval: EvalTrait,
-    missing: Float,
-    allowNonZeroForMissing: Boolean,
-    trackerConf: TrackerConf,
-    timeoutRequestWorkers: Long,
-    checkpointParam: Option[ExternalCheckpointParams],
-    xgbInputParams: XGBoostExecutionInputParams,
-    earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
-    cacheTrainingSet: Boolean) {
-
-  private var rawParamMap: Map[String, Any] = _
-
-  def setRawParamMap(inputMap: Map[String, Any]): Unit = {
-    rawParamMap = inputMap
-  }
-
-  def toMap: Map[String, Any] = {
-    rawParamMap
-  }
-}
-
-private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], sc: SparkContext){
-
-  private val logger = LogFactory.getLog("XGBoostSpark")
-
-  private val overridedParams = overrideParams(rawParams, sc)
-
-  /**
-   * Check to see if Spark expects SSL encryption (`spark.ssl.enabled` set to true).
-   * If so, throw an exception unless this safety measure has been explicitly overridden
-   * via conf `xgboost.spark.ignoreSsl`.
-   */
-  private def validateSparkSslConf: Unit = {
-    val (sparkSslEnabled: Boolean, xgboostSparkIgnoreSsl: Boolean) =
-      SparkSession.getActiveSession match {
-        case Some(ss) =>
-          (ss.conf.getOption("spark.ssl.enabled").getOrElse("false").toBoolean,
-            ss.conf.getOption("xgboost.spark.ignoreSsl").getOrElse("false").toBoolean)
-        case None =>
-          (sc.getConf.getBoolean("spark.ssl.enabled", false),
-            sc.getConf.getBoolean("xgboost.spark.ignoreSsl", false))
-      }
-    if (sparkSslEnabled) {
-      if (xgboostSparkIgnoreSsl) {
-        logger.warn(s"spark-xgboost is being run without encrypting data in transit!  " +
-          s"Spark Conf spark.ssl.enabled=true was overridden with xgboost.spark.ignoreSsl=true.")
-      } else {
-        throw new Exception("xgboost-spark found spark.ssl.enabled=true to encrypt data " +
-          "in transit, but xgboost-spark sends non-encrypted data over the wire for efficiency. " +
-          "To override this protection and still use xgboost-spark at your own risk, " +
-          "you can set the SparkSession conf to use xgboost.spark.ignoreSsl=true.")
-      }
-    }
-  }
-
-  /**
-   * we should not include any nested structure in the output of this function as the map is
-   * eventually to be feed to xgboost4j layer
-   */
-  private def overrideParams(
-      params: Map[String, Any],
-      sc: SparkContext): Map[String, Any] = {
-    val coresPerTask = sc.getConf.getInt("spark.task.cpus", 1)
-    var overridedParams = params
-    if (overridedParams.contains("nthread")) {
-      val nThread = overridedParams("nthread").toString.toInt
-      require(nThread <= coresPerTask,
-        s"the nthread configuration ($nThread) must be no larger than " +
-          s"spark.task.cpus ($coresPerTask)")
-    } else {
-      overridedParams = overridedParams + ("nthread" -> coresPerTask)
-    }
-
-    val numEarlyStoppingRounds = overridedParams.getOrElse(
-      "num_early_stopping_rounds", 0).asInstanceOf[Int]
-    overridedParams += "num_early_stopping_rounds" -> numEarlyStoppingRounds
-    if (numEarlyStoppingRounds > 0 &&
-      !overridedParams.contains("maximize_evaluation_metrics")) {
-      if (overridedParams.contains("custom_eval")) {
-        throw new IllegalArgumentException("custom_eval does not support early stopping")
-      }
-      val eval_metric = overridedParams("eval_metric").toString
-      val maximize = LearningTaskParams.evalMetricsToMaximize contains eval_metric
-      logger.info("parameter \"maximize_evaluation_metrics\" is set to " + maximize)
-      overridedParams += ("maximize_evaluation_metrics" -> maximize)
-    }
-    overridedParams
-  }
-
-  def buildXGBRuntimeParams: XGBoostExecutionParams = {
-    val nWorkers = overridedParams("num_workers").asInstanceOf[Int]
-    val round = overridedParams("num_round").asInstanceOf[Int]
-    val useExternalMemory = overridedParams("use_external_memory").asInstanceOf[Boolean]
-    val obj = overridedParams.getOrElse("custom_obj", null).asInstanceOf[ObjectiveTrait]
-    val eval = overridedParams.getOrElse("custom_eval", null).asInstanceOf[EvalTrait]
-    val missing = overridedParams.getOrElse("missing", Float.NaN).asInstanceOf[Float]
-    val allowNonZeroForMissing = overridedParams
-      .getOrElse("allow_non_zero_for_missing", false)
-      .asInstanceOf[Boolean]
-    validateSparkSslConf
-    if (overridedParams.contains("tree_method")) {
-      require(overridedParams("tree_method") == "hist" ||
-        overridedParams("tree_method") == "approx" ||
-        overridedParams("tree_method") == "auto", "xgboost4j-spark only supports tree_method as" +
-        " 'hist', 'approx' and 'auto'")
-    }
-    if (overridedParams.contains("train_test_ratio")) {
-      logger.warn("train_test_ratio is deprecated since XGBoost 0.82, we recommend to explicitly" +
-        " pass a training and multiple evaluation datasets by passing 'eval_sets' and " +
-        "'eval_set_names'")
-    }
-    require(nWorkers > 0, "you must specify more than 0 workers")
-    if (obj != null) {
-      require(overridedParams.get("objective_type").isDefined, "parameter \"objective_type\" " +
-        "is not defined, you have to specify the objective type as classification or regression" +
-        " with a customized objective function")
-    }
-    val trackerConf = overridedParams.get("tracker_conf") match {
-      case None => TrackerConf()
-      case Some(conf: TrackerConf) => conf
-      case _ => throw new IllegalArgumentException("parameter \"tracker_conf\" must be an " +
-        "instance of TrackerConf.")
-    }
-    val timeoutRequestWorkers: Long = overridedParams.get("timeout_request_workers") match {
-      case None => 0L
-      case Some(interval: Long) => interval
-      case _ => throw new IllegalArgumentException("parameter \"timeout_request_workers\" must be" +
-        " an instance of Long.")
-    }
-    val checkpointParam =
-      ExternalCheckpointParams.extractParams(overridedParams)
-
-    val trainTestRatio = overridedParams.getOrElse("train_test_ratio", 1.0)
-      .asInstanceOf[Double]
-    val seed = overridedParams.getOrElse("seed", System.nanoTime()).asInstanceOf[Long]
-    val inputParams = XGBoostExecutionInputParams(trainTestRatio, seed)
-
-    val earlyStoppingRounds = overridedParams.getOrElse(
-      "num_early_stopping_rounds", 0).asInstanceOf[Int]
-    val maximizeEvalMetrics = overridedParams.getOrElse(
-      "maximize_evaluation_metrics", true).asInstanceOf[Boolean]
-    val xgbExecEarlyStoppingParams = XGBoostExecutionEarlyStoppingParams(earlyStoppingRounds,
-      maximizeEvalMetrics)
-
-    val cacheTrainingSet = overridedParams.getOrElse("cache_training_set", false)
-      .asInstanceOf[Boolean]
-
-    val xgbExecParam = XGBoostExecutionParams(nWorkers, round, useExternalMemory, obj, eval,
-      missing, allowNonZeroForMissing, trackerConf,
-      timeoutRequestWorkers,
-      checkpointParam,
-      inputParams,
-      xgbExecEarlyStoppingParams,
-      cacheTrainingSet)
-    xgbExecParam.setRawParamMap(overridedParams)
-    xgbExecParam
-  }
-
-  private[spark] def buildRabitParams : scala.collection.mutable.Map[String, String] =
-    scala.collection.mutable.Map(
-      "rabit_reduce_ring_mincount" ->
-        overridedParams.getOrElse("rabit_ring_reduce_threshold", 32 << 10).toString,
-      "rabit_debug" ->
-        (overridedParams.getOrElse("verbosity", 0).toString.toInt == 3).toString,
-      "rabit_timeout" ->
-        (overridedParams.getOrElse("rabit_timeout", -1).toString.toInt >= 0).toString,
-      "rabit_timeout_sec" -> {
-        if (overridedParams.getOrElse("rabit_timeout", -1).toString.toInt >= 0) {
-          overridedParams.get("rabit_timeout").toString
-        } else {
-          "1800"
-        }
-      },
-      "rabit_enable_tcp_no_delay" ->
-        overridedParams.getOrElse("rabit_enable_tcp_no_delay", false).toString,
-      "DMLC_WORKER_CONNECT_RETRY" ->
-        overridedParams.getOrElse("dmlc_worker_connect_retry", 5).toString
-    )
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
deleted file mode 100644
index 4f7b1cc..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
+++ /dev/null
@@ -1,127 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import scala.collection.immutable.HashSet
-
-import org.apache.spark.ml.param._
-
-private[spark] trait LearningTaskParams extends Params {
-
-  /**
-   * Specify the learning task and the corresponding learning objective.
-   * options: reg:squarederror, reg:squaredlogerror, reg:logistic, binary:logistic, binary:logitraw,
-   * count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma.
-   * default: reg:squarederror
-   */
-  final val objective = new Param[String](this, "objective",
-    "objective function used for training")
-
-  final def getObjective: String = $(objective)
-
-  /**
-   * The learning objective type of the specified custom objective and eval.
-   * Corresponding type will be assigned if custom objective is defined
-   * options: regression, classification. default: null
-   */
-  final val objectiveType = new Param[String](this, "objectiveType", "objective type used for " +
-    s"training, options: {${LearningTaskParams.supportedObjectiveType.mkString(",")}",
-    (value: String) => LearningTaskParams.supportedObjectiveType.contains(value))
-
-  final def getObjectiveType: String = $(objectiveType)
-
-
-  /**
-   * the initial prediction score of all instances, global bias. default=0.5
-   */
-  final val baseScore = new DoubleParam(this, "baseScore", "the initial prediction score of all" +
-    " instances, global bias")
-
-  final def getBaseScore: Double = $(baseScore)
-
-  /**
-   * evaluation metrics for validation data, a default metric will be assigned according to
-   * objective(rmse for regression, and error for classification, mean average precision for
-   * ranking). options: rmse, rmsle, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map,
-   * gamma-deviance
-   */
-  final val evalMetric = new Param[String](this, "evalMetric", "evaluation metrics for " +
-    "validation data, a default metric will be assigned according to objective " +
-    "(rmse for regression, and error for classification, mean average precision for ranking)")
-
-  final def getEvalMetric: String = $(evalMetric)
-
-  /**
-   * Fraction of training points to use for testing.
-   */
-  final val trainTestRatio = new DoubleParam(this, "trainTestRatio",
-    "fraction of training points to use for testing",
-    ParamValidators.inRange(0, 1))
-
-  final def getTrainTestRatio: Double = $(trainTestRatio)
-
-  /**
-   * whether caching training data
-   */
-  final val cacheTrainingSet = new BooleanParam(this, "cacheTrainingSet",
-    "whether caching training data")
-
-  /**
-   * whether cleaning checkpoint, always cleaning by default, having this parameter majorly for
-   * testing
-   */
-  final val skipCleanCheckpoint = new BooleanParam(this, "skipCleanCheckpoint",
-    "whether cleaning checkpoint data")
-
-  /**
-   * If non-zero, the training will be stopped after a specified number
-   * of consecutive increases in any evaluation metric.
-   */
-  final val numEarlyStoppingRounds = new IntParam(this, "numEarlyStoppingRounds",
-    "number of rounds of decreasing eval metric to tolerate before " +
-      "stopping the training",
-    (value: Int) => value == 0 || value > 1)
-
-  final def getNumEarlyStoppingRounds: Int = $(numEarlyStoppingRounds)
-
-
-  final val maximizeEvaluationMetrics = new BooleanParam(this, "maximizeEvaluationMetrics",
-    "define the expected optimization to the evaluation metrics, true to maximize otherwise" +
-      " minimize it")
-
-  final def getMaximizeEvaluationMetrics: Boolean = $(maximizeEvaluationMetrics)
-
-  setDefault(objective -> "reg:squarederror", baseScore -> 0.5,
-    trainTestRatio -> 1.0, numEarlyStoppingRounds -> 0, cacheTrainingSet -> false)
-}
-
-private[spark] object LearningTaskParams {
-
-  val supportedObjectiveType = HashSet("regression", "classification")
-
-  val evalMetricsToMaximize = HashSet("auc", "aucpr", "ndcg", "map")
-
-  val evalMetricsToMinimize = HashSet("rmse", "rmsle", "mae", "logloss", "error", "merror",
-    "mlogloss", "gamma-deviance")
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-kernel-client/pom.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-kernel-client/pom.xml
deleted file mode 100644
index 8aed1cc..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-kernel-client/pom.xml
+++ /dev/null
@@ -1,95 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-        <version>2.1.0</version>
-    </parent>
-    <artifactId>boostkit-xgboost4j-spark-kernel-client_2.11</artifactId>
-    <version>2.1.0</version>
-    <build>
-        <sourceDirectory>src/main/scala</sourceDirectory>
-        <plugins>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-jar-plugin</artifactId>
-                <version>2.5</version>
-                <configuration>
-                    <archive>
-                        <addMavenDescriptor>false</addMavenDescriptor>
-                    </archive>
-                </configuration>
-                <executions>
-                    <execution>
-                        <goals>
-                            <goal>jar</goal>
-                        </goals>
-                    </execution>
-                    <execution>
-                        <id>default-jar</id>
-                        <phase>none</phase>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-assembly-plugin</artifactId>
-                <configuration>
-                    <skipAssembly>false</skipAssembly>
-                </configuration>
-            </plugin>
-            <plugin>
-                <!-- see http://davidb.github.com/scala-maven-plugin -->
-                <groupId>net.alchim31.maven</groupId>
-                <artifactId>scala-maven-plugin</artifactId>
-                <version>3.2.0</version>
-                <executions>
-                    <execution>
-                    <goals>
-                        <goal>compile</goal>
-                    </goals>
-                    <configuration>
-                        <args>
-                            <arg>-dependencyfile</arg>
-                        <arg>${project.build.directory}/.scala_dependencies</arg>
-                        </args>
-                    </configuration>
-                </execution>
-                </executions>
-            </plugin>
-        </plugins>
-    </build>
-    <dependencies>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j-spark-client_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-core_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-sql_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-    </dependencies>
-</project>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-kernel-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostUtil.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-kernel-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostUtil.scala
deleted file mode 100644
index 5e5ada2..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark-kernel-client/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostUtil.scala
+++ /dev/null
@@ -1,39 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- * This file to You under the Apache License, Version 2.0;
- * you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *    http://www.apache.org/licenses/LICENSE-2.0
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import ml.dmlc.xgboost4j.scala.Booster
-import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkContext
-
-object XGBoostUtil {
-
-  def checkIfCacheData(cacheTrainingSet: Boolean, params: Map[String, Any]): Boolean = {
-    true
-  }
-
-  def trainDistributedKernel(
-      params: Map[String, Any],
-      xgbExecParams: XGBoostExecutionParams,
-      rabitParams: java.util.Map[String, String],
-      hasGroup: Boolean,
-      sc: SparkContext,
-      trainingData: Either[RDD[Array[XGBLabeledPoint]], RDD[XGBLabeledPoint]],
-      evalSetsMap: Map[String, RDD[XGBLabeledPoint]],
-      prevBooster: Booster): (Booster, Map[String, Array[Float]]) = {
-    null
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/checkstyle-suppressions.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/checkstyle-suppressions.xml
deleted file mode 100644
index 366f6a0..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/checkstyle-suppressions.xml
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-
-<!DOCTYPE suppressions PUBLIC
-"-//Puppy Crawl//DTD Suppressions 1.1//EN"
-"http://www.puppycrawl.com/dtds/suppressions_1_1.dtd">
-
-<!--
-
-    This file contains suppression rules for Checkstyle checks.
-    Ideally only files that cannot be modified (e.g. third-party code)
-    should be added here. All other violations should be fixed.
-
--->
-
-<suppressions>
-  <suppress checks=".*" files="XGBoostJNI.java"/>
-</suppressions>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/checkstyle.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/checkstyle.xml
deleted file mode 100644
index a926eb7..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/checkstyle.xml
+++ /dev/null
@@ -1,162 +0,0 @@
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-
-<!DOCTYPE module PUBLIC
-          "-//Puppy Crawl//DTD Check Configuration 1.3//EN"
-          "http://www.puppycrawl.com/dtds/configuration_1_3.dtd">
-
-<!--
-
-    Checkstyle configuration based on the Google coding conventions from:
-
-    -  Google Java Style
-       https://google-styleguide.googlecode.com/svn-history/r130/trunk/javaguide.html
-
-    with Spark-specific changes from:
-
-    https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
-
-    Checkstyle is very configurable. Be sure to read the documentation at
-    http://checkstyle.sf.net (or in your downloaded distribution).
-
-    Most Checks are configurable, be sure to consult the documentation.
-
-    To completely disable a check, just comment it out or delete it from the file.
-
-    Authors: Max Vetrenko, Ruslan Diachenko, Roman Ivanov.
-
- -->
-
-<module name = "Checker">
-    <property name="charset" value="UTF-8"/>
-
-    <property name="severity" value="error"/>
-
-    <property name="fileExtensions" value="java, properties, xml"/>
-
-		<module name="SuppressionFilter">
-			  <property name="file" value="checkstyle-suppressions.xml"/>
-		</module>
-
-    <!-- Checks for whitespace                               -->
-    <!-- See http://checkstyle.sf.net/config_whitespace.html -->
-    <module name="FileTabCharacter">
-        <property name="eachLine" value="true"/>
-    </module>
-
-    <module name="RegexpSingleline">
-        <!-- \s matches whitespace character, $ matches end of line. -->
-        <property name="format" value="\s+$"/>
-        <property name="message" value="No trailing whitespace allowed."/>
-    </module>
-
-    <module name="TreeWalker">
-        <module name="OuterTypeFilename"/>
-        <module name="IllegalTokenText">
-            <property name="tokens" value="STRING_LITERAL, CHAR_LITERAL"/>
-            <property name="format" value="\\u00(08|09|0(a|A)|0(c|C)|0(d|D)|22|27|5(C|c))|\\(0(10|11|12|14|15|42|47)|134)"/>
-            <property name="message" value="Avoid using corresponding octal or Unicode escape."/>
-        </module>
-        <module name="AvoidEscapedUnicodeCharacters">
-            <property name="allowEscapesForControlCharacters" value="true"/>
-            <property name="allowByTailComment" value="true"/>
-            <property name="allowNonPrintableEscapes" value="true"/>
-        </module>
-        
-        <module name="LineLength">
-            <property name="max" value="100"/>
-            <property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
-        </module>
-        
-        <module name="NoLineWrap"/>
-        <module name="EmptyBlock">
-            <property name="option" value="TEXT"/>
-            <property name="tokens" value="LITERAL_TRY, LITERAL_FINALLY, LITERAL_IF, LITERAL_ELSE, LITERAL_SWITCH"/>
-        </module>
-        <module name="NeedBraces">
-            <property name="allowSingleLineStatement" value="true"/>
-        </module>
-        <module name="OneStatementPerLine"/>
-        <module name="ArrayTypeStyle"/>
-        <module name="FallThrough"/>
-        <module name="UpperEll"/>
-        <module name="ModifierOrder"/>
-        <module name="SeparatorWrap">
-            <property name="tokens" value="DOT"/>
-            <property name="option" value="nl"/>
-        </module>
-        <module name="SeparatorWrap">
-            <property name="tokens" value="COMMA"/>
-            <property name="option" value="EOL"/>
-        </module>
-        <module name="PackageName">
-            <property name="format" value="^[a-z]+(\.[a-z][a-z0-9]*)*$"/>
-            <message key="name.invalidPattern"
-             value="Package name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="ClassTypeParameterName">
-            <property name="format" value="([A-Z][a-zA-Z0-9]*$)"/>
-            <message key="name.invalidPattern"
-             value="Class type name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="MethodTypeParameterName">
-            <property name="format" value="([A-Z][a-zA-Z0-9]*)"/>
-            <message key="name.invalidPattern"
-             value="Method type name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="GenericWhitespace">
-            <message key="ws.followed"
-             value="GenericWhitespace ''{0}'' is followed by whitespace."/>
-             <message key="ws.preceded"
-             value="GenericWhitespace ''{0}'' is preceded with whitespace."/>
-             <message key="ws.illegalFollow"
-             value="GenericWhitespace ''{0}'' should followed by whitespace."/>
-             <message key="ws.notPreceded"
-             value="GenericWhitespace ''{0}'' is not preceded with whitespace."/>
-        </module>
-        <module name="Indentation">
-            <property name="basicOffset" value="2"/>
-            <property name="braceAdjustment" value="0"/>
-            <property name="caseIndent" value="2"/>
-            <property name="throwsIndent" value="4"/>
-            <property name="lineWrappingIndentation" value="4"/>
-            <property name="arrayInitIndent" value="2"/>
-        </module>
-        <module name="ImportOrder">
-            <property name="separated" value="true"/>
-            <property name="ordered" value="true"/>
-            <property name="groups" value="/^javax?\./,scala,*,ml.dmlc.xgboost4j"/>
-        </module>
-        <module name="MethodParamPad"/>
-        <module name="AnnotationLocation">
-            <property name="tokens" value="CLASS_DEF, INTERFACE_DEF, ENUM_DEF, METHOD_DEF, CTOR_DEF"/>
-        </module>
-        <module name="AnnotationLocation">
-            <property name="tokens" value="VARIABLE_DEF"/>
-            <property name="allowSamelineMultipleAnnotations" value="true"/>
-        </module>
-        <module name="MethodName">
-            <property name="format" value="^[a-z][a-z0-9][a-zA-Z0-9_]*$"/>
-            <message key="name.invalidPattern"
-             value="Method name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="EmptyCatchBlock">
-            <property name="exceptionVariableName" value="expected"/>
-        </module>
-        <module name="CommentsIndentation"/>
-    </module>
-</module>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/pom.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/pom.xml
deleted file mode 100644
index eb71dc7..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/pom.xml
+++ /dev/null
@@ -1,83 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-        <version>2.1.0</version>
-    </parent>
-    <artifactId>boostkit-xgboost4j-spark2.3.2_2.11</artifactId>
-    <version>2.1.0</version>
-    <build>
-        <plugins>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-assembly-plugin</artifactId>
-                <configuration>
-                    <skipAssembly>false</skipAssembly>
-                </configuration>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-jar-plugin</artifactId>
-                <version>2.5</version>
-                <configuration>
-                    <archive>
-                        <addMavenDescriptor>false</addMavenDescriptor>
-                    </archive>
-                </configuration>
-                <executions>
-                    <execution>
-                        <goals>
-                            <goal>jar</goal>
-                        </goals>
-                    </execution>
-                    <execution>
-                        <id>default-jar</id>
-                        <phase>none</phase>
-                      </execution>
-                </executions>
-            </plugin>
-        </plugins>
-        <extensions>
-            <extension>
-                <groupId>kr.motd.maven</groupId>
-                <artifactId>os-maven-plugin</artifactId>
-                <version>1.6.1</version>
-            </extension>
-        </extensions>
-    </build>
-    <dependencies>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>boostkit-xgboost4j-spark-kernel-client_${scala.binary.version}</artifactId>
-            <version>${project.version}</version>
-            <scope>compile</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-core_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-sql_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
-            <version>${spark.version}</version>
-            <scope>provided</scope>
-        </dependency>
-    </dependencies>
-</project>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/scalastyle-config.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/scalastyle-config.xml
deleted file mode 100644
index fd4d22e..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/scalastyle-config.xml
+++ /dev/null
@@ -1,276 +0,0 @@
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-<!--
-
-If you wish to turn off checking for a section of code, you can put a comment in the source
-before and after the section, with the following syntax:
-
-  // scalastyle:off
-  ...  // stuff that breaks the styles
-  // scalastyle:on
-
-You can also disable only one rule, by specifying its rule id, as specified in:
-  http://www.scalastyle.org/rules-0.7.0.html
-
-  // scalastyle:off no.finalize
-  override def finalize(): Unit = ...
-  // scalastyle:on no.finalize
-
-This file is divided into 3 sections:
- (1) rules that we enforce.
- (2) rules that we would like to enforce, but haven't cleaned up the codebase to turn on yet
-     (or we need to make the scalastyle rule more configurable).
- (3) rules that we don't want to enforce.
--->
-
-<scalastyle>
-  <name>Scalastyle standard configuration</name>
-
-  <!-- ================================================================================ -->
-  <!--                               rules we enforce                                   -->
-  <!-- ================================================================================ -->
-
-  <check level="error" class="org.scalastyle.file.FileTabChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.HeaderMatchesChecker" enabled="true">
-    <parameters>
-       <parameter name="header"><![CDATA[/*
-				  Copyright (c) 2014 by Contributors
-
-					 Licensed under the Apache License, Version 2.0 (the "License");
-					  you may not use this file except in compliance with the License.
-						 You may obtain a copy of the License at
-
-						  http://www.apache.org/licenses/LICENSE-2.0
-
-							 Unless required by applicable law or agreed to in writing, software
-							  distributed under the License is distributed on an "AS IS" BASIS,
-								 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-								  See the License for the specific language governing permissions and
-									 limitations under the License.
-									  */]]></parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.SpacesAfterPlusChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.SpacesBeforePlusChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.WhitespaceEndOfLineChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.FileLineLengthChecker" enabled="true">
-    <parameters>
-      <parameter name="maxLineLength"><![CDATA[100]]></parameter>
-      <parameter name="tabSize"><![CDATA[2]]></parameter>
-      <parameter name="ignoreImports">true</parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ClassNamesChecker" enabled="true">
-    <parameters><parameter name="regex"><![CDATA[[A-Z][A-Za-z]*]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ObjectNamesChecker" enabled="true">
-    <parameters><parameter name="regex"><![CDATA[[A-Z][A-Za-z]*]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.PackageObjectNamesChecker" enabled="true">
-    <parameters><parameter name="regex"><![CDATA[^[a-z][A-Za-z]*$]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ParameterNumberChecker" enabled="true">
-    <parameters><parameter name="maxParameters"><![CDATA[10]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.NoFinalizeChecker" enabled="false"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.CovariantEqualsChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.StructuralTypeChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.UppercaseLChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.IfBraceChecker" enabled="true">
-    <parameters>
-      <parameter name="singleLineAllowed"><![CDATA[true]]></parameter>
-      <parameter name="doubleLineAllowed"><![CDATA[true]]></parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.PublicMethodsHaveTypeChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.NewLineAtEofChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.NonASCIICharacterChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.SpaceAfterCommentStartChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.EnsureSingleSpaceBeforeTokenChecker" enabled="true">
-   <parameters>
-     <parameter name="tokens">ARROW, EQUALS, ELSE, TRY, CATCH, FINALLY, LARROW, RARROW</parameter>
-   </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.EnsureSingleSpaceAfterTokenChecker" enabled="true">
-    <parameters>
-     <parameter name="tokens">ARROW, EQUALS, COMMA, COLON, IF, ELSE, DO, WHILE, FOR, MATCH, TRY, CATCH, FINALLY, LARROW, RARROW</parameter>
-    </parameters>
-  </check>
-
-  <!-- ??? usually shouldn't be checked into the code base. -->
-  <check level="error" class="org.scalastyle.scalariform.NotImplementedErrorUsage" enabled="true"></check>
-
-  <check customId="visiblefortesting" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">@VisibleForTesting</parameter></parameters>
-    <customMessage><![CDATA[
-      @VisibleForTesting causes classpath issues. Please note this in the java doc instead (SPARK-11615).
-    ]]></customMessage>
-  </check>
-
-  <check customId="runtimeaddshutdownhook" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">Runtime\.getRuntime\.addShutdownHook</parameter></parameters>
-    <customMessage><![CDATA[
-      Are you sure that you want to use Runtime.getRuntime.addShutdownHook? In most cases, you should use
-      ShutdownHookManager.addShutdownHook instead.
-      If you must use Runtime.getRuntime.addShutdownHook, wrap the code block with
-      // scalastyle:off runtimeaddshutdownhook
-      Runtime.getRuntime.addShutdownHook(...)
-      // scalastyle:on runtimeaddshutdownhook
-    ]]></customMessage>
-  </check>
-
-  <check customId="mutablesynchronizedbuffer" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">mutable\.SynchronizedBuffer</parameter></parameters>
-    <customMessage><![CDATA[
-      Are you sure that you want to use mutable.SynchronizedBuffer? In most cases, you should use
-      java.util.concurrent.ConcurrentLinkedQueue instead.
-      If you must use mutable.SynchronizedBuffer, wrap the code block with
-      // scalastyle:off mutablesynchronizedbuffer
-      mutable.SynchronizedBuffer[...]
-      // scalastyle:on mutablesynchronizedbuffer
-    ]]></customMessage>
-  </check>
-
-  <check customId="classforname" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">Class\.forName</parameter></parameters>
-    <customMessage><![CDATA[
-      Are you sure that you want to use Class.forName? In most cases, you should use Utils.classForName instead.
-      If you must use Class.forName, wrap the code block with
-      // scalastyle:off classforname
-      Class.forName(...)
-      // scalastyle:on classforname
-    ]]></customMessage>
-  </check>
-
-  <!-- As of SPARK-9613 JavaConversions should be replaced with JavaConverters -->
-  <check customId="javaconversions" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
-    <parameters><parameter name="regex">JavaConversions</parameter></parameters>
-    <customMessage>Instead of importing implicits in scala.collection.JavaConversions._, import
-    scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ImportOrderChecker" enabled="true">
-    <parameters>
-      <parameter name="groups">java,scala,3rdParty,spark</parameter>
-      <parameter name="group.java">javax?\..*</parameter>
-      <parameter name="group.scala">scala\..*</parameter>
-      <parameter name="group.3rdParty">(?!ml\.dmlc\.xgboost4j\.).*</parameter>
-      <parameter name="group.dmlc">ml.dmlc.xgboost4j.*</parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.DisallowSpaceBeforeTokenChecker" enabled="true">
-    <parameters>
-      <parameter name="tokens">COMMA</parameter>
-    </parameters>
-  </check>
-
-  <!-- ================================================================================ -->
-  <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
-  <!-- ================================================================================ -->
-
-  <!-- We cannot turn the following two on, because it'd fail a lot of string interpolation use cases. -->
-  <!-- Ideally the following two rules should be configurable to rule out string interpolation. -->
-  <check level="error" class="org.scalastyle.scalariform.NoWhitespaceBeforeLeftBracketChecker" enabled="false"></check>
-  <check level="error" class="org.scalastyle.scalariform.NoWhitespaceAfterLeftBracketChecker" enabled="false"></check>
-
-  <!-- This breaks symbolic method names so we don't turn it on. -->
-  <!-- Maybe we should update it to allow basic symbolic names, and then we are good to go. -->
-  <check level="error" class="org.scalastyle.scalariform.MethodNamesChecker" enabled="false">
-    <parameters>
-    <parameter name="regex"><![CDATA[^[a-z][A-Za-z0-9]*$]]></parameter>
-    </parameters>
-  </check>
-
-  <!-- Should turn this on, but we have a few places that need to be fixed first -->
-  <check level="error" class="org.scalastyle.scalariform.EqualsHashCodeChecker" enabled="false"></check>
-
-  <!-- ================================================================================ -->
-  <!--                               rules we don't want                                -->
-  <!-- ================================================================================ -->
-
-  <check level="error" class="org.scalastyle.scalariform.IllegalImportsChecker" enabled="false">
-    <parameters><parameter name="illegalImports"><![CDATA[sun._,java.awt._]]></parameter></parameters>
-  </check>
-
-  <!-- We want the opposite of this: NewLineAtEofChecker -->
-  <check level="error" class="org.scalastyle.file.NoNewLineAtEofChecker" enabled="false"></check>
-
-  <!-- This one complains about all kinds of random things. Disable. -->
-  <check level="error" class="org.scalastyle.scalariform.SimplifyBooleanExpressionChecker" enabled="false"></check>
-
-  <!-- We use return quite a bit for control flows and guards -->
-  <check level="error" class="org.scalastyle.scalariform.ReturnChecker" enabled="false"></check>
-
-  <!-- We use null a lot in low level code and to interface with 3rd party code -->
-  <check level="error" class="org.scalastyle.scalariform.NullChecker" enabled="false"></check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.NoCloneChecker" enabled="false"></check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.file.FileLengthChecker" enabled="false">
-    <parameters><parameter name="maxFileLength">800></parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.NumberOfTypesChecker" enabled="false">
-    <parameters><parameter name="maxTypes">30</parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.CyclomaticComplexityChecker" enabled="false">
-    <parameters><parameter name="maximum">10</parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.MethodLengthChecker" enabled="false">
-    <parameters><parameter name="maxLength">50</parameter></parameters>
-  </check>
-
-  <!-- Not exactly feasible to enforce this right now. -->
-  <!-- It is also infrequent that somebody introduces a new class with a lot of methods. -->
-  <check level="error" class="org.scalastyle.scalariform.NumberOfMethodsInTypeChecker" enabled="false">
-    <parameters><parameter name="maxMethods"><![CDATA[30]]></parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here, and we have a lot of magic numbers ... -->
-  <check level="error" class="org.scalastyle.scalariform.MagicNumberChecker" enabled="false">
-    <parameters><parameter name="ignore">-1,0,1,2,3</parameter></parameters>
-  </check>
-
-</scalastyle>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala
deleted file mode 100644
index df787d8..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala
+++ /dev/null
@@ -1,179 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-
-import org.apache.spark.HashPartitioner
-import org.apache.spark.ml.feature.{LabeledPoint => MLLabeledPoint}
-import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.ml.param.Param
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Column, DataFrame, Row}
-import org.apache.spark.sql.functions.col
-import org.apache.spark.sql.types.{FloatType, IntegerType}
-
-object DataUtils extends Serializable {
-  private[spark] implicit class XGBLabeledPointFeatures(
-      val labeledPoint: XGBLabeledPoint
-  ) extends AnyVal {
-    /** Converts the point to [[MLLabeledPoint]]. */
-    private[spark] def asML: MLLabeledPoint = {
-      MLLabeledPoint(labeledPoint.label, labeledPoint.features)
-    }
-
-    /**
-     * Returns feature of the point as [[org.apache.spark.ml.linalg.Vector]].
-     */
-    def features: Vector = if (labeledPoint.indices == null) {
-      Vectors.dense(labeledPoint.values.map(_.toDouble))
-    } else {
-      Vectors.sparse(labeledPoint.size, labeledPoint.indices, labeledPoint.values.map(_.toDouble))
-    }
-  }
-
-  private[spark] implicit class MLLabeledPointToXGBLabeledPoint(
-      val labeledPoint: MLLabeledPoint
-  ) extends AnyVal {
-    /** Converts an [[MLLabeledPoint]] to an [[XGBLabeledPoint]]. */
-    def asXGB: XGBLabeledPoint = {
-      labeledPoint.features.asXGB.copy(label = labeledPoint.label.toFloat)
-    }
-  }
-
-  private[spark] implicit class MLVectorToXGBLabeledPoint(val v: Vector) extends AnyVal {
-    /**
-     * Converts a [[Vector]] to a data point with a dummy label.
-     *
-     * This is needed for constructing a [[ml.dmlc.xgboost4j.scala.DMatrix]]
-     * for prediction.
-     */
-    def asXGB: XGBLabeledPoint = v match {
-      case v: DenseVector =>
-        XGBLabeledPoint(0.0f, v.size, null, v.values.map(_.toFloat))
-      case v: SparseVector =>
-        XGBLabeledPoint(0.0f, v.size, v.indices, v.values.map(_.toFloat))
-    }
-  }
-
-  private def featureValueOfDenseVector(rowHashCode: Int, features: DenseVector): Float = {
-    val featureId = {
-      if (rowHashCode > 0) {
-        rowHashCode % features.size
-      } else {
-        // prevent overflow
-        math.abs(rowHashCode + 1) % features.size
-      }
-    }
-    features.values(featureId).toFloat
-  }
-
-  private def featureValueOfSparseVector(rowHashCode: Int, features: SparseVector): Float = {
-    val featureId = {
-      if (rowHashCode > 0) {
-        rowHashCode % features.indices.length
-      } else {
-        // prevent overflow
-        math.abs(rowHashCode + 1) % features.indices.length
-      }
-    }
-    features.values(featureId).toFloat
-  }
-
-  private def calculatePartitionKey(row: Row, numPartitions: Int): Int = {
-    val Row(_, features: Vector, _, _) = row
-    val rowHashCode = row.hashCode()
-    val featureValue = features match {
-      case denseVector: DenseVector =>
-        featureValueOfDenseVector(rowHashCode, denseVector)
-      case sparseVector: SparseVector =>
-        featureValueOfSparseVector(rowHashCode, sparseVector)
-    }
-    math.abs((rowHashCode.toLong + featureValue).toString.hashCode % numPartitions)
-  }
-
-  private def attachPartitionKey(
-      row: Row,
-      deterministicPartition: Boolean,
-      numWorkers: Int,
-      xgbLp: XGBLabeledPoint): (Int, XGBLabeledPoint) = {
-    if (deterministicPartition) {
-      (calculatePartitionKey(row, numWorkers), xgbLp)
-    } else {
-      (1, xgbLp)
-    }
-  }
-
-  private def repartitionRDDs(
-      deterministicPartition: Boolean,
-      numWorkers: Int,
-      arrayOfRDDs: Array[RDD[(Int, XGBLabeledPoint)]]): Array[RDD[XGBLabeledPoint]] = {
-    if (deterministicPartition) {
-      arrayOfRDDs.map {rdd => rdd.partitionBy(new HashPartitioner(numWorkers))}.map {
-        rdd => rdd.map(_._2)
-      }
-    } else {
-      arrayOfRDDs.map(rdd => {
-        if (rdd.getNumPartitions != numWorkers) {
-          rdd.map(_._2).repartition(numWorkers)
-        } else {
-          rdd.map(_._2)
-        }
-      })
-    }
-  }
-
-  private[spark] def convertDataFrameToXGBLabeledPointRDDs(
-      labelCol: Column,
-      featuresCol: Column,
-      weight: Column,
-      baseMargin: Column,
-      group: Option[Column],
-      numWorkers: Int,
-      deterministicPartition: Boolean,
-      dataFrames: DataFrame*): Array[RDD[XGBLabeledPoint]] = {
-    val selectedColumns = group.map(groupCol => Seq(labelCol.cast(FloatType),
-      featuresCol,
-      weight.cast(FloatType),
-      groupCol.cast(IntegerType),
-      baseMargin.cast(FloatType))).getOrElse(Seq(labelCol.cast(FloatType),
-      featuresCol,
-      weight.cast(FloatType),
-      baseMargin.cast(FloatType)))
-    val arrayOfRDDs = dataFrames.toArray.map {
-      df => df.select(selectedColumns: _*).rdd.map {
-        case row @ Row(label: Float, features: Vector, weight: Float, group: Int,
-          baseMargin: Float) =>
-          val (size, indices, values) = features match {
-            case v: SparseVector => (v.size, v.indices, v.values.map(_.toFloat))
-            case v: DenseVector => (v.size, null, v.values.map(_.toFloat))
-          }
-          val xgbLp = XGBLabeledPoint(label, size, indices, values, weight, group, baseMargin)
-          attachPartitionKey(row, deterministicPartition, numWorkers, xgbLp)
-        case row @ Row(label: Float, features: Vector, weight: Float, baseMargin: Float) =>
-          val (size, indices, values) = features match {
-            case v: SparseVector => (v.size, v.indices, v.values.map(_.toFloat))
-            case v: DenseVector => (v.size, null, v.values.map(_.toFloat))
-          }
-          val xgbLp = XGBLabeledPoint(label, size, indices, values, weight, baseMargin = baseMargin)
-          attachPartitionKey(row, deterministicPartition, numWorkers, xgbLp)
-      }
-    }
-    repartitionRDDs(deterministicPartition, numWorkers, arrayOfRDDs)
-  }
-
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
deleted file mode 100644
index b7de24e..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
+++ /dev/null
@@ -1,1000 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import java.io.File
-import java.nio.file.Files
-import java.util.concurrent.{Callable, Executors, Future, TimeUnit}
-
-import scala.collection.{AbstractIterator, mutable}
-import scala.util.Random
-import scala.collection.JavaConverters._
-
-import ml.dmlc.xgboost4j.java.{IRabitTracker, Rabit, XGBoostError, RabitTracker => PyRabitTracker}
-import ml.dmlc.xgboost4j.scala.rabit.RabitTracker
-import ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams
-import ml.dmlc.xgboost4j.scala.ExternalCheckpointManager
-import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
-import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-import org.apache.commons.io.FileUtils
-import org.apache.commons.logging.LogFactory
-import org.apache.hadoop.fs.FileSystem
-import org.apache.spark.rdd.RDD
-import org.apache.spark.{SparkContext, SparkParallelismTracker, TaskContext, TaskFailedListener}
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.storage.StorageLevel
-
-
-/**
- * Rabit tracker configurations.
- *
- * @param workerConnectionTimeout The timeout for all workers to connect to the tracker.
- *                                Set timeout length to zero to disable timeout.
- *                                Use a finite, non-zero timeout value to prevent tracker from
- *                                hanging indefinitely (in milliseconds)
- *                                (supported by "scala" implementation only.)
- * @param trackerImpl Choice between "python" or "scala". The former utilizes the Java wrapper of
- *                    the Python Rabit tracker (in dmlc_core), whereas the latter is implemented
- *                    in Scala without Python components, and with full support of timeouts.
- *                    The Scala implementation is currently experimental, use at your own risk.
- */
-case class TrackerConf(workerConnectionTimeout: Long, trackerImpl: String)
-
-object TrackerConf {
-  def apply(): TrackerConf = TrackerConf(0L, "python")
-}
-
-private[this] case class XGBoostExecutionEarlyStoppingParams(numEarlyStoppingRounds: Int,
-                                                             maximizeEvalMetrics: Boolean)
-
-private[this] case class XGBoostExecutionInputParams(trainTestRatio: Double, seed: Long)
-
-private[spark] case class XGBoostExecutionParams(
-    numWorkers: Int,
-    var numRounds: Int,
-    useExternalMemory: Boolean,
-    obj: ObjectiveTrait,
-    eval: EvalTrait,
-    missing: Float,
-    allowNonZeroForMissing: Boolean,
-    trackerConf: TrackerConf,
-    timeoutRequestWorkers: Long,
-    checkpointParam: Option[ExternalCheckpointParams],
-    xgbInputParams: XGBoostExecutionInputParams,
-    earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
-    cacheTrainingSet: Boolean) {
-
-  private var rawParamMap: Map[String, Any] = _
-
-  def setRawParamMap(inputMap: Map[String, Any]): Unit = {
-    rawParamMap = inputMap
-  }
-
-  def toMap: Map[String, Any] = {
-    rawParamMap
-  }
-}
-
-private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], sc: SparkContext){
-
-  private val logger = LogFactory.getLog("XGBoostSpark")
-
-  private val overridedParams = overrideParams(rawParams, sc)
-
-  /**
-   * Check to see if Spark expects SSL encryption (`spark.ssl.enabled` set to true).
-   * If so, throw an exception unless this safety measure has been explicitly overridden
-   * via conf `xgboost.spark.ignoreSsl`.
-   */
-  private def validateSparkSslConf: Unit = {
-    val (sparkSslEnabled: Boolean, xgboostSparkIgnoreSsl: Boolean) =
-      SparkSession.getActiveSession match {
-        case Some(ss) =>
-          (ss.conf.getOption("spark.ssl.enabled").getOrElse("false").toBoolean,
-            ss.conf.getOption("xgboost.spark.ignoreSsl").getOrElse("false").toBoolean)
-        case None =>
-          (sc.getConf.getBoolean("spark.ssl.enabled", false),
-            sc.getConf.getBoolean("xgboost.spark.ignoreSsl", false))
-      }
-    if (sparkSslEnabled) {
-      if (xgboostSparkIgnoreSsl) {
-        logger.warn(s"spark-xgboost is being run without encrypting data in transit!  " +
-          s"Spark Conf spark.ssl.enabled=true was overridden with xgboost.spark.ignoreSsl=true.")
-      } else {
-        throw new Exception("xgboost-spark found spark.ssl.enabled=true to encrypt data " +
-          "in transit, but xgboost-spark sends non-encrypted data over the wire for efficiency. " +
-          "To override this protection and still use xgboost-spark at your own risk, " +
-          "you can set the SparkSession conf to use xgboost.spark.ignoreSsl=true.")
-      }
-    }
-  }
-
-  /**
-   * we should not include any nested structure in the output of this function as the map is
-   * eventually to be feed to xgboost4j layer
-   */
-  private def overrideParams(
-      params: Map[String, Any],
-      sc: SparkContext): Map[String, Any] = {
-    val coresPerTask = sc.getConf.getInt("spark.task.cpus", 1)
-    var overridedParams = params
-    if (overridedParams.contains("nthread")) {
-      val nThread = overridedParams("nthread").toString.toInt
-      require(nThread <= coresPerTask,
-        s"the nthread configuration ($nThread) must be no larger than " +
-          s"spark.task.cpus ($coresPerTask)")
-    } else {
-      overridedParams = overridedParams + ("nthread" -> coresPerTask)
-    }
-
-    val numEarlyStoppingRounds = overridedParams.getOrElse(
-      "num_early_stopping_rounds", 0).asInstanceOf[Int]
-    overridedParams += "num_early_stopping_rounds" -> numEarlyStoppingRounds
-    if (numEarlyStoppingRounds > 0 &&
-      !overridedParams.contains("maximize_evaluation_metrics")) {
-      if (overridedParams.contains("custom_eval")) {
-        throw new IllegalArgumentException("custom_eval does not support early stopping")
-      }
-      val eval_metric = overridedParams("eval_metric").toString
-      val maximize = LearningTaskParams.evalMetricsToMaximize contains eval_metric
-      logger.info("parameter \"maximize_evaluation_metrics\" is set to " + maximize)
-      overridedParams += ("maximize_evaluation_metrics" -> maximize)
-    }
-    overridedParams
-  }
-
-  def buildXGBRuntimeParams: XGBoostExecutionParams = {
-    val nWorkers = overridedParams("num_workers").asInstanceOf[Int]
-    val round = overridedParams("num_round").asInstanceOf[Int]
-    val useExternalMemory = overridedParams("use_external_memory").asInstanceOf[Boolean]
-    val obj = overridedParams.getOrElse("custom_obj", null).asInstanceOf[ObjectiveTrait]
-    val eval = overridedParams.getOrElse("custom_eval", null).asInstanceOf[EvalTrait]
-    val missing = overridedParams.getOrElse("missing", Float.NaN).asInstanceOf[Float]
-    val allowNonZeroForMissing = overridedParams
-                                 .getOrElse("allow_non_zero_for_missing", false)
-                                 .asInstanceOf[Boolean]
-    validateSparkSslConf
-    if (overridedParams.contains("tree_method")) {
-      require(overridedParams("tree_method") == "hist" ||
-        overridedParams("tree_method") == "approx" ||
-        overridedParams("tree_method") == "auto", "xgboost4j-spark only supports tree_method as" +
-        " 'hist', 'approx' and 'auto'")
-    }
-    if (overridedParams.contains("train_test_ratio")) {
-      logger.warn("train_test_ratio is deprecated since XGBoost 0.82, we recommend to explicitly" +
-        " pass a training and multiple evaluation datasets by passing 'eval_sets' and " +
-        "'eval_set_names'")
-    }
-    require(nWorkers > 0, "you must specify more than 0 workers")
-    if (obj != null) {
-      require(overridedParams.get("objective_type").isDefined, "parameter \"objective_type\" " +
-        "is not defined, you have to specify the objective type as classification or regression" +
-        " with a customized objective function")
-    }
-    val trackerConf = overridedParams.get("tracker_conf") match {
-      case None => TrackerConf()
-      case Some(conf: TrackerConf) => conf
-      case _ => throw new IllegalArgumentException("parameter \"tracker_conf\" must be an " +
-        "instance of TrackerConf.")
-    }
-    val timeoutRequestWorkers: Long = overridedParams.get("timeout_request_workers") match {
-      case None => 0L
-      case Some(interval: Long) => interval
-      case _ => throw new IllegalArgumentException("parameter \"timeout_request_workers\" must be" +
-        " an instance of Long.")
-    }
-    val checkpointParam =
-      ExternalCheckpointParams.extractParams(overridedParams)
-
-    val trainTestRatio = overridedParams.getOrElse("train_test_ratio", 1.0)
-      .asInstanceOf[Double]
-    val seed = overridedParams.getOrElse("seed", System.nanoTime()).asInstanceOf[Long]
-    val inputParams = XGBoostExecutionInputParams(trainTestRatio, seed)
-
-    val earlyStoppingRounds = overridedParams.getOrElse(
-      "num_early_stopping_rounds", 0).asInstanceOf[Int]
-    val maximizeEvalMetrics = overridedParams.getOrElse(
-      "maximize_evaluation_metrics", true).asInstanceOf[Boolean]
-    val xgbExecEarlyStoppingParams = XGBoostExecutionEarlyStoppingParams(earlyStoppingRounds,
-      maximizeEvalMetrics)
-
-    val cacheTrainingSet = overridedParams.getOrElse("cache_training_set", false)
-      .asInstanceOf[Boolean]
-
-    val xgbExecParam = XGBoostExecutionParams(nWorkers, round, useExternalMemory, obj, eval,
-      missing, allowNonZeroForMissing, trackerConf,
-      timeoutRequestWorkers,
-      checkpointParam,
-      inputParams,
-      xgbExecEarlyStoppingParams,
-      cacheTrainingSet)
-    xgbExecParam.setRawParamMap(overridedParams)
-    xgbExecParam
-  }
-
-  private[spark] def buildRabitParams : scala.collection.mutable.Map[String, String] =
-    scala.collection.mutable.Map(
-    "rabit_reduce_ring_mincount" ->
-      overridedParams.getOrElse("rabit_ring_reduce_threshold", 32 << 10).toString,
-    "rabit_debug" ->
-      (overridedParams.getOrElse("verbosity", 0).toString.toInt == 3).toString,
-    "rabit_timeout" ->
-      (overridedParams.getOrElse("rabit_timeout", -1).toString.toInt >= 0).toString,
-    "rabit_timeout_sec" -> {
-      if (overridedParams.getOrElse("rabit_timeout", -1).toString.toInt >= 0) {
-        overridedParams.get("rabit_timeout").toString
-      } else {
-        "1800"
-      }
-    },
-    "rabit_enable_tcp_no_delay" ->
-      overridedParams.getOrElse("rabit_enable_tcp_no_delay", false).toString,
-    "DMLC_WORKER_CONNECT_RETRY" ->
-      overridedParams.getOrElse("dmlc_worker_connect_retry", 5).toString
-  )
-}
-
-/**
- * Traing data group in a RDD partition.
- * @param groupId The group id
- * @param points Array of XGBLabeledPoint within the same group.
- * @param isEdgeGroup whether it is a frist or last group in a RDD partition.
- */
-private[spark] case class XGBLabeledPointGroup(
-    groupId: Int,
-    points: Array[XGBLabeledPoint],
-    isEdgeGroup: Boolean)
-
-object XGBoost extends Serializable {
-  private val logger = LogFactory.getLog("XGBoostSpark")
-
-  private def verifyMissingSetting(
-      xgbLabelPoints: Iterator[XGBLabeledPoint],
-      missing: Float,
-      allowNonZeroMissing: Boolean): Iterator[XGBLabeledPoint] = {
-    if (missing != 0.0f && !allowNonZeroMissing) {
-      xgbLabelPoints.map(labeledPoint => {
-        if (labeledPoint.indices != null) {
-            throw new RuntimeException(s"you can only specify missing value as 0.0 (the currently" +
-              s" set value $missing) when you have SparseVector or Empty vector as your feature" +
-              s" format. If you didn't use Spark's VectorAssembler class to build your feature " +
-              s"vector but instead did so in a way that preserves zeros in your feature vector " +
-              s"you can avoid this check by using the 'allow_non_zero_missing parameter'" +
-              s" (only use if you know what you are doing)")
-        }
-        labeledPoint
-      })
-    } else {
-      xgbLabelPoints
-    }
-  }
-
-  private def removeMissingValues(
-      xgbLabelPoints: Iterator[XGBLabeledPoint],
-      missing: Float,
-      keepCondition: Float => Boolean): Iterator[XGBLabeledPoint] = {
-    xgbLabelPoints.map { labeledPoint =>
-      val indicesBuilder = new mutable.ArrayBuilder.ofInt()
-      val valuesBuilder = new mutable.ArrayBuilder.ofFloat()
-      for ((value, i) <- labeledPoint.values.zipWithIndex if keepCondition(value)) {
-        indicesBuilder += (if (labeledPoint.indices == null) i else labeledPoint.indices(i))
-        valuesBuilder += value
-      }
-      labeledPoint.copy(indices = indicesBuilder.result(), values = valuesBuilder.result())
-    }
-  }
-
-  private[spark] def processMissingValues(
-      xgbLabelPoints: Iterator[XGBLabeledPoint],
-      missing: Float,
-      allowNonZeroMissing: Boolean): Iterator[XGBLabeledPoint] = {
-    if (!missing.isNaN) {
-      removeMissingValues(verifyMissingSetting(xgbLabelPoints, missing, allowNonZeroMissing),
-        missing, (v: Float) => v != missing)
-    } else {
-      removeMissingValues(verifyMissingSetting(xgbLabelPoints, missing, allowNonZeroMissing),
-        missing, (v: Float) => !v.isNaN)
-    }
-  }
-
-  private[spark] def processMissingValuesWithGroup(
-      xgbLabelPointGroups: Iterator[Array[XGBLabeledPoint]],
-      missing: Float,
-      allowNonZeroMissing: Boolean): Iterator[Array[XGBLabeledPoint]] = {
-    if (!missing.isNaN) {
-      xgbLabelPointGroups.map {
-        labeledPoints => XGBoost.processMissingValues(
-          labeledPoints.iterator,
-          missing,
-          allowNonZeroMissing
-        ).toArray
-      }
-    } else {
-      xgbLabelPointGroups
-    }
-  }
-
-  private[spark] def getCacheDirName(useExternalMemory: Boolean): Option[String] = {
-    val taskId = TaskContext.getPartitionId().toString
-    if (useExternalMemory) {
-      val dir = Files.createTempDirectory(s"${TaskContext.get().stageId()}-cache-$taskId")
-      Some(dir.toAbsolutePath.toString)
-    } else {
-      None
-    }
-  }
-
-  private[spark] def buildDistributedBooster(
-      watches: Watches,
-      xgbExecutionParam: XGBoostExecutionParams,
-      rabitEnv: java.util.Map[String, String],
-      obj: ObjectiveTrait,
-      eval: EvalTrait,
-      prevBooster: Booster): Iterator[(Booster, Map[String, Array[Float]])] = {
-    // to workaround the empty partitions in training dataset,
-    // this might not be the best efficient implementation, see
-    // (https://github.com/dmlc/xgboost/issues/1277)
-    if (watches.toMap("train").rowNum == 0) {
-      throw new XGBoostError(
-        s"detected an empty partition in the training data, partition ID:" +
-          s" ${TaskContext.getPartitionId()}")
-    }
-
-    val taskId = TaskContext.getPartitionId().toString
-    val attempt = TaskContext.get().attemptNumber.toString
-
-    rabitEnv.put("DMLC_TASK_ID", taskId)
-    rabitEnv.put("DMLC_NUM_ATTEMPT", attempt)
-    rabitEnv.put("DMLC_WORKER_STOP_PROCESS_ON_ERROR", "false")
-    val numRounds = xgbExecutionParam.numRounds
-    val makeCheckpoint = xgbExecutionParam.checkpointParam.isDefined && taskId.toInt == 0
-    try {
-      Rabit.init(rabitEnv)
-      val numEarlyStoppingRounds = xgbExecutionParam.earlyStoppingParams.numEarlyStoppingRounds
-      val metrics = Array.tabulate(watches.size)(_ => Array.ofDim[Float](numRounds))
-      val externalCheckpointParams = xgbExecutionParam.checkpointParam
-      val booster = if (makeCheckpoint) {
-        SXGBoost.trainAndSaveCheckpoint(
-          watches.toMap("train"), xgbExecutionParam.toMap, numRounds,
-          watches.toMap, metrics, obj, eval,
-          earlyStoppingRound = numEarlyStoppingRounds, prevBooster, externalCheckpointParams)
-      } else {
-        SXGBoost.train(watches.toMap("train"), xgbExecutionParam.toMap, numRounds,
-          watches.toMap, metrics, obj, eval,
-          earlyStoppingRound = numEarlyStoppingRounds, prevBooster)
-      }
-      Iterator(booster -> watches.toMap.keys.zip(metrics).toMap)
-    } catch {
-      case xgbException: XGBoostError =>
-        logger.error(s"XGBooster worker $taskId has failed $attempt times due to ", xgbException)
-        throw xgbException
-    } finally {
-      Rabit.shutdown()
-      watches.delete()
-    }
-  }
-
-  private[spark] def startTracker(nWorkers: Int, trackerConf: TrackerConf): IRabitTracker = {
-    val tracker: IRabitTracker = trackerConf.trackerImpl match {
-      case "scala" => new RabitTracker(nWorkers)
-      case "python" => new PyRabitTracker(nWorkers)
-      case _ => new PyRabitTracker(nWorkers)
-    }
-
-    require(tracker.start(trackerConf.workerConnectionTimeout), "FAULT: Failed to start tracker")
-    tracker
-  }
-
-  class IteratorWrapper[T](arrayOfXGBLabeledPoints: Array[(String, Iterator[T])])
-    extends Iterator[(String, Iterator[T])] {
-
-    private var currentIndex = 0
-
-    override def hasNext: Boolean = currentIndex <= arrayOfXGBLabeledPoints.length - 1
-
-    override def next(): (String, Iterator[T]) = {
-      currentIndex += 1
-      arrayOfXGBLabeledPoints(currentIndex - 1)
-    }
-  }
-
-  private def coPartitionNoGroupSets(
-      trainingData: RDD[XGBLabeledPoint],
-      evalSets: Map[String, RDD[XGBLabeledPoint]],
-      nWorkers: Int) = {
-    // eval_sets is supposed to be set by the caller of [[trainDistributed]]
-    val allDatasets = Map("train" -> trainingData) ++ evalSets
-    val repartitionedDatasets = allDatasets.map{case (name, rdd) =>
-      if (rdd.getNumPartitions != nWorkers) {
-        (name, rdd.repartition(nWorkers))
-      } else {
-        (name, rdd)
-      }
-    }
-    repartitionedDatasets.foldLeft(trainingData.sparkContext.parallelize(
-      Array.fill[(String, Iterator[XGBLabeledPoint])](nWorkers)(null), nWorkers)){
-      case (rddOfIterWrapper, (name, rddOfIter)) =>
-        rddOfIterWrapper.zipPartitions(rddOfIter){
-          (itrWrapper, itr) =>
-            if (!itr.hasNext) {
-              logger.error("when specifying eval sets as dataframes, you have to ensure that " +
-                "the number of elements in each dataframe is larger than the number of workers")
-              throw new Exception("too few elements in evaluation sets")
-            }
-            val itrArray = itrWrapper.toArray
-            if (itrArray.head != null) {
-              new IteratorWrapper(itrArray :+ (name -> itr))
-            } else {
-              new IteratorWrapper(Array(name -> itr))
-            }
-        }
-    }
-  }
-
-  private[spark] def trainForNonRanking(
-      trainingData: RDD[XGBLabeledPoint],
-      xgbExecutionParams: XGBoostExecutionParams,
-      rabitEnv: java.util.Map[String, String],
-      prevBooster: Booster,
-      evalSetsMap: Map[String, RDD[XGBLabeledPoint]]): RDD[(Booster, Map[String, Array[Float]])] = {
-    if (evalSetsMap.isEmpty) {
-      trainingData.mapPartitions(labeledPoints => {
-        val watches = Watches.buildWatches(xgbExecutionParams,
-          processMissingValues(labeledPoints, xgbExecutionParams.missing,
-            xgbExecutionParams.allowNonZeroForMissing),
-          getCacheDirName(xgbExecutionParams.useExternalMemory))
-        buildDistributedBooster(watches, xgbExecutionParams, rabitEnv, xgbExecutionParams.obj,
-          xgbExecutionParams.eval, prevBooster)
-      }).cache()
-    } else {
-      coPartitionNoGroupSets(trainingData,
-      evalSetsMap, xgbExecutionParams.numWorkers)
-        .mapPartitions {
-          nameAndLabeledPointSets =>
-            val watches = Watches.buildWatches(
-              nameAndLabeledPointSets.map {
-                case (name, iter) => (name, processMissingValues(iter,
-                  xgbExecutionParams.missing, xgbExecutionParams.allowNonZeroForMissing))
-              },
-              getCacheDirName(xgbExecutionParams.useExternalMemory))
-            buildDistributedBooster(watches, xgbExecutionParams, rabitEnv, xgbExecutionParams.obj,
-              xgbExecutionParams.eval, prevBooster)
-        }.cache()
-    }
-  }
-
-  private[spark] def trainForRanking(
-      trainingData: RDD[Array[XGBLabeledPoint]],
-      xgbExecutionParam: XGBoostExecutionParams,
-      rabitEnv: java.util.Map[String, String],
-      prevBooster: Booster,
-      evalSetsMap: Map[String, RDD[XGBLabeledPoint]]): RDD[(Booster, Map[String, Array[Float]])] = {
-    if (evalSetsMap.isEmpty) {
-      trainingData.mapPartitions(labeledPointGroups => {
-        val watches = Watches.buildWatchesWithGroup(xgbExecutionParam,
-          processMissingValuesWithGroup(labeledPointGroups, xgbExecutionParam.missing,
-            xgbExecutionParam.allowNonZeroForMissing),
-          getCacheDirName(xgbExecutionParam.useExternalMemory))
-        buildDistributedBooster(watches, xgbExecutionParam, rabitEnv,
-          xgbExecutionParam.obj, xgbExecutionParam.eval,
-          prevBooster)
-      }).cache()
-    } else {
-      coPartitionGroupSets(trainingData,
-      evalSetsMap, xgbExecutionParam.numWorkers).mapPartitions(
-        labeledPointGroupSets => {
-          val watches = Watches.buildWatchesWithGroup(
-            labeledPointGroupSets.map {
-              case (name, iter) => (name, processMissingValuesWithGroup(iter,
-                xgbExecutionParam.missing, xgbExecutionParam.allowNonZeroForMissing))
-            },
-            getCacheDirName(xgbExecutionParam.useExternalMemory))
-          buildDistributedBooster(watches, xgbExecutionParam, rabitEnv,
-            xgbExecutionParam.obj, xgbExecutionParam.eval,
-            prevBooster)
-        }).cache()
-    }
-  }
-
-  private[spark] def cacheData(ifCacheDataBoolean: Boolean, input: RDD[_]): RDD[_] = {
-    if (ifCacheDataBoolean) input.persist(StorageLevel.MEMORY_AND_DISK) else input
-  }
-
-  private def composeInputData(
-    trainingData: RDD[XGBLabeledPoint],
-    ifCacheDataBoolean: Boolean,
-    hasGroup: Boolean,
-    nWorkers: Int): Either[RDD[Array[XGBLabeledPoint]], RDD[XGBLabeledPoint]] = {
-    if (hasGroup) {
-      val repartitionedData = repartitionForTrainingGroup(trainingData, nWorkers)
-      Left(cacheData(ifCacheDataBoolean, repartitionedData).
-        asInstanceOf[RDD[Array[XGBLabeledPoint]]])
-    } else {
-      Right(cacheData(ifCacheDataBoolean, trainingData).asInstanceOf[RDD[XGBLabeledPoint]])
-    }
-  }
-
-  private[spark] def contextTrain(
-      xgbExecParams: XGBoostExecutionParams,
-      rabitParams: java.util.Map[String, String],
-      hasGroup: Boolean,
-      sc: SparkContext,
-      trainingData: Either[RDD[Array[XGBLabeledPoint]], RDD[XGBLabeledPoint]],
-      evalSetsMap: Map[String, RDD[XGBLabeledPoint]],
-      prevBooster: Booster): (Booster, Map[String, Array[Float]]) = {
-    val rabitTracker = XGBoost.startTracker(xgbExecParams.numWorkers,
-      xgbExecParams.trackerConf)
-    try {
-      val parallelismTracker = new SparkParallelismTracker(sc, xgbExecParams.timeoutRequestWorkers,
-        xgbExecParams.numWorkers)
-      val rabitEnv = rabitTracker.getWorkerEnvs
-      rabitEnv.putAll(rabitParams) // adding rabit params
-
-      val boostersAndMetrics = if (hasGroup) {
-        XGBoost.trainForRanking(trainingData.left.get, xgbExecParams, rabitEnv, prevBooster,
-          evalSetsMap)
-      } else {
-        XGBoost.trainForNonRanking(trainingData.right.get, xgbExecParams, rabitEnv,
-          prevBooster, evalSetsMap)
-      }
-
-      val sparkJobThread = new Thread() {
-        override def run() {
-          // force the job
-          boostersAndMetrics.foreachPartition(() => _)
-        }
-      }
-
-      sparkJobThread.setUncaughtExceptionHandler(rabitTracker)
-      sparkJobThread.start()
-
-      // wait for tracker
-      val trackerReturnVal = parallelismTracker.execute(rabitTracker.waitFor(0L))
-
-      // scalastyle:off field.name
-      val (booster, metrics) = postTrackerReturnProcessing(trackerReturnVal,
-        boostersAndMetrics, sparkJobThread)
-      // scalastyle:on field.name
-      (booster, metrics)
-    } finally {
-      rabitTracker.stop()
-    }
-  }
-
-  /**
-   * @return A tuple of the booster and the metrics used to build training summary
-   */
-  @throws(classOf[XGBoostError])
-  private[spark] def trainDistributed(
-      trainingData: RDD[XGBLabeledPoint],
-      params: Map[String, Any],
-      hasGroup: Boolean = false,
-      evalSetsMap: Map[String, RDD[XGBLabeledPoint]] = Map()):
-    (Booster, Map[String, Array[Float]]) = {
-    logger.info(s"Running XGBoost ${spark.VERSION} with parameters:\n${params.mkString("\n")}")
-    val sc = trainingData.sparkContext
-    val xgbParamsFactory = new XGBoostExecutionParamsFactory(params, sc)
-    val xgbExecParams = xgbParamsFactory.buildXGBRuntimeParams
-    val rabitParams = xgbParamsFactory.buildRabitParams.asJava
-
-    val ifCacheDataBoolean = XGBoostUtil.checkIfCacheData(xgbExecParams.cacheTrainingSet, params)
-
-    val transformedTrainingData = composeInputData(trainingData, ifCacheDataBoolean,
-      hasGroup, xgbExecParams.numWorkers)
-
-    val prevBooster = xgbExecParams.checkpointParam.map { checkpointParam =>
-      val checkpointManager = new ExternalCheckpointManager(
-        checkpointParam.checkpointPath,
-        FileSystem.get(sc.hadoopConfiguration))
-      checkpointManager.cleanUpHigherVersions(xgbExecParams.numRounds)
-      checkpointManager.loadCheckpointAsScalaBooster()
-    }.orNull
-    try {
-      val (booster, metrics) = XGBoostUtil.trainDistributedKernel(params,
-        xgbExecParams, rabitParams, hasGroup, sc, transformedTrainingData,
-        evalSetsMap, prevBooster)
-      // we should delete the checkpoint directory after a successful training
-      xgbExecParams.checkpointParam.foreach {
-        cpParam =>
-          if (!xgbExecParams.checkpointParam.get.skipCleanCheckpoint) {
-            val checkpointManager = new ExternalCheckpointManager(
-              cpParam.checkpointPath,
-              FileSystem.get(sc.hadoopConfiguration))
-            checkpointManager.cleanPath()
-          }
-      }
-      (booster, metrics)
-    } catch {
-      case t: Throwable =>
-        // if the job was aborted due to an exception
-        logger.error("the job was aborted due to ", t)
-        trainingData.sparkContext.stop()
-        throw t
-    } finally {
-      uncacheTrainingData(ifCacheDataBoolean, transformedTrainingData)
-    }
-  }
-
-  private[spark] def uncacheTrainingData(
-      ifCacheDataBoolean: Boolean,
-      transformedTrainingData: Either[RDD[Array[XGBLabeledPoint]], RDD[XGBLabeledPoint]]): Unit = {
-    if (ifCacheDataBoolean) {
-      if (transformedTrainingData.isLeft) {
-        transformedTrainingData.left.get.unpersist()
-      } else {
-        transformedTrainingData.right.get.unpersist()
-      }
-    }
-  }
-
-  private def aggByGroupInfo(trainingData: RDD[XGBLabeledPoint]) = {
-    val normalGroups: RDD[Array[XGBLabeledPoint]] = trainingData.mapPartitions(
-      // LabeledPointGroupIterator returns (Boolean, Array[XGBLabeledPoint])
-      new LabeledPointGroupIterator(_)).filter(!_.isEdgeGroup).map(_.points)
-
-    // edge groups with partition id.
-    val edgeGroups: RDD[(Int, XGBLabeledPointGroup)] = trainingData.mapPartitions(
-      new LabeledPointGroupIterator(_)).filter(_.isEdgeGroup).map(
-      group => (TaskContext.getPartitionId(), group))
-
-    // group chunks from different partitions together by group id in XGBLabeledPoint.
-    // use groupBy instead of aggregateBy since all groups within a partition have unique group ids.
-    val stitchedGroups: RDD[Array[XGBLabeledPoint]] = edgeGroups.groupBy(_._2.groupId).map(
-      groups => {
-        val it: Iterable[(Int, XGBLabeledPointGroup)] = groups._2
-        // sorted by partition id and merge list of Array[XGBLabeledPoint] into one array
-        it.toArray.sortBy(_._1).flatMap(_._2.points)
-      })
-    normalGroups.union(stitchedGroups)
-  }
-
-  private[spark] def repartitionForTrainingGroup(
-      trainingData: RDD[XGBLabeledPoint], nWorkers: Int): RDD[Array[XGBLabeledPoint]] = {
-    val allGroups = aggByGroupInfo(trainingData)
-    logger.info(s"repartitioning training group set to $nWorkers partitions")
-    allGroups.repartition(nWorkers)
-  }
-
-  private def coPartitionGroupSets(
-      aggedTrainingSet: RDD[Array[XGBLabeledPoint]],
-      evalSets: Map[String, RDD[XGBLabeledPoint]],
-      nWorkers: Int): RDD[(String, Iterator[Array[XGBLabeledPoint]])] = {
-    val repartitionedDatasets = Map("train" -> aggedTrainingSet) ++ evalSets.map {
-      case (name, rdd) => {
-        val aggedRdd = aggByGroupInfo(rdd)
-        if (aggedRdd.getNumPartitions != nWorkers) {
-          name -> aggedRdd.repartition(nWorkers)
-        } else {
-          name -> aggedRdd
-        }
-      }
-    }
-    repartitionedDatasets.foldLeft(aggedTrainingSet.sparkContext.parallelize(
-      Array.fill[(String, Iterator[Array[XGBLabeledPoint]])](nWorkers)(null), nWorkers)){
-      case (rddOfIterWrapper, (name, rddOfIter)) =>
-        rddOfIterWrapper.zipPartitions(rddOfIter){
-          (itrWrapper, itr) =>
-            if (!itr.hasNext) {
-              logger.error("when specifying eval sets as dataframes, you have to ensure that " +
-                "the number of elements in each dataframe is larger than the number of workers")
-              throw new Exception("too few elements in evaluation sets")
-            }
-            val itrArray = itrWrapper.toArray
-            if (itrArray.head != null) {
-              new IteratorWrapper(itrArray :+ (name -> itr))
-            } else {
-              new IteratorWrapper(Array(name -> itr))
-            }
-        }
-    }
-  }
-
-  private def postTrackerReturnProcessing(
-      trackerReturnVal: Int,
-      distributedBoostersAndMetrics: RDD[(Booster, Map[String, Array[Float]])],
-      sparkJobThread: Thread): (Booster, Map[String, Array[Float]]) = {
-    if (trackerReturnVal == 0) {
-      // Copies of the final booster and the corresponding metrics
-      // reside in each partition of the `distributedBoostersAndMetrics`.
-      // Any of them can be used to create the model.
-      // it's safe to block here forever, as the tracker has returned successfully, and the Spark
-      // job should have finished, there is no reason for the thread cannot return
-      sparkJobThread.join()
-      val (booster, metrics) = distributedBoostersAndMetrics.first()
-      distributedBoostersAndMetrics.unpersist(false)
-      (booster, metrics)
-    } else {
-      try {
-        if (sparkJobThread.isAlive) {
-          sparkJobThread.interrupt()
-        }
-      } catch {
-        case _: InterruptedException =>
-          logger.info("spark job thread is interrupted")
-      }
-      throw new XGBoostError("XGBoostModel training failed")
-    }
-  }
-
-}
-
-private class Watches private(
-    val datasets: Array[DMatrix],
-    val names: Array[String],
-    val cacheDirName: Option[String]) {
-
-  def toMap: Map[String, DMatrix] = {
-    names.zip(datasets).toMap.filter { case (_, matrix) => matrix.rowNum > 0 }
-  }
-
-  def size: Int = toMap.size
-
-  def delete(): Unit = {
-    toMap.values.foreach(_.delete())
-    cacheDirName.foreach { name =>
-      FileUtils.deleteDirectory(new File(name))
-    }
-  }
-
-  override def toString: String = toMap.toString
-}
-
-private object Watches {
-
-  private def fromBaseMarginsToArray(baseMargins: Iterator[Float]): Option[Array[Float]] = {
-    val builder = new mutable.ArrayBuilder.ofFloat()
-    var nTotal = 0
-    var nUndefined = 0
-    while (baseMargins.hasNext) {
-      nTotal += 1
-      val baseMargin = baseMargins.next()
-      if (baseMargin.isNaN) {
-        nUndefined += 1  // don't waste space for all-NaNs.
-      } else {
-        builder += baseMargin
-      }
-    }
-    if (nUndefined == nTotal) {
-      None
-    } else if (nUndefined == 0) {
-      Some(builder.result())
-    } else {
-      throw new IllegalArgumentException(
-        s"Encountered a partition with $nUndefined NaN base margin values. " +
-          s"If you want to specify base margin, ensure all values are non-NaN.")
-    }
-  }
-
-  def buildWatches(
-      nameAndLabeledPointSets: Iterator[(String, Iterator[XGBLabeledPoint])],
-      cachedDirName: Option[String]): Watches = {
-    val dms = nameAndLabeledPointSets.map {
-      case (name, labeledPoints) =>
-        val baseMargins = new mutable.ArrayBuilder.ofFloat
-        val duplicatedItr = labeledPoints.map(labeledPoint => {
-          baseMargins += labeledPoint.baseMargin
-          labeledPoint
-        })
-        val dMatrix = new DMatrix(duplicatedItr, cachedDirName.map(_ + s"/$name").orNull)
-        val baseMargin = fromBaseMarginsToArray(baseMargins.result().iterator)
-        if (baseMargin.isDefined) {
-          dMatrix.setBaseMargin(baseMargin.get)
-        }
-        (name, dMatrix)
-    }.toArray
-    new Watches(dms.map(_._2), dms.map(_._1), cachedDirName)
-  }
-
-  def buildWatches(
-      xgbExecutionParams: XGBoostExecutionParams,
-      labeledPoints: Iterator[XGBLabeledPoint],
-      cacheDirName: Option[String]): Watches = {
-    val trainTestRatio = xgbExecutionParams.xgbInputParams.trainTestRatio
-    val seed = xgbExecutionParams.xgbInputParams.seed
-    val r = new Random(seed)
-    val testPoints = mutable.ArrayBuffer.empty[XGBLabeledPoint]
-    val trainBaseMargins = new mutable.ArrayBuilder.ofFloat
-    val testBaseMargins = new mutable.ArrayBuilder.ofFloat
-    val trainPoints = labeledPoints.filter { labeledPoint =>
-      val accepted = r.nextDouble() <= trainTestRatio
-      if (!accepted) {
-        testPoints += labeledPoint
-        testBaseMargins += labeledPoint.baseMargin
-      } else {
-        trainBaseMargins += labeledPoint.baseMargin
-      }
-      accepted
-    }
-    val trainMatrix = new DMatrix(trainPoints, cacheDirName.map(_ + "/train").orNull)
-    val testMatrix = new DMatrix(testPoints.iterator, cacheDirName.map(_ + "/test").orNull)
-
-    val trainMargin = fromBaseMarginsToArray(trainBaseMargins.result().iterator)
-    val testMargin = fromBaseMarginsToArray(testBaseMargins.result().iterator)
-    if (trainMargin.isDefined) trainMatrix.setBaseMargin(trainMargin.get)
-    if (testMargin.isDefined) testMatrix.setBaseMargin(testMargin.get)
-
-    new Watches(Array(trainMatrix, testMatrix), Array("train", "test"), cacheDirName)
-  }
-
-  def buildWatchesWithGroup(
-      nameAndlabeledPointGroupSets: Iterator[(String, Iterator[Array[XGBLabeledPoint]])],
-      cachedDirName: Option[String]): Watches = {
-    val dms = nameAndlabeledPointGroupSets.map {
-      case (name, labeledPointsGroups) =>
-        val baseMargins = new mutable.ArrayBuilder.ofFloat
-        val groupsInfo = new mutable.ArrayBuilder.ofInt
-        val weights = new mutable.ArrayBuilder.ofFloat
-        val iter = labeledPointsGroups.filter(labeledPointGroup => {
-          var groupWeight = -1.0f
-          var groupSize = 0
-          labeledPointGroup.map { labeledPoint => {
-            if (groupWeight < 0) {
-              groupWeight = labeledPoint.weight
-            } else if (groupWeight != labeledPoint.weight) {
-              throw new IllegalArgumentException("the instances in the same group have to be" +
-                s" assigned with the same weight (unexpected weight ${labeledPoint.weight}")
-            }
-            baseMargins += labeledPoint.baseMargin
-            groupSize += 1
-            labeledPoint
-          }
-          }
-          weights += groupWeight
-          groupsInfo += groupSize
-          true
-        })
-        val dMatrix = new DMatrix(iter.flatMap(_.iterator), cachedDirName.map(_ + s"/$name").orNull)
-        val baseMargin = fromBaseMarginsToArray(baseMargins.result().iterator)
-        if (baseMargin.isDefined) {
-          dMatrix.setBaseMargin(baseMargin.get)
-        }
-        dMatrix.setGroup(groupsInfo.result())
-        dMatrix.setWeight(weights.result())
-        (name, dMatrix)
-    }.toArray
-    new Watches(dms.map(_._2), dms.map(_._1), cachedDirName)
-  }
-
-  def buildWatchesWithGroup(
-      xgbExecutionParams: XGBoostExecutionParams,
-      labeledPointGroups: Iterator[Array[XGBLabeledPoint]],
-      cacheDirName: Option[String]): Watches = {
-    val trainTestRatio = xgbExecutionParams.xgbInputParams.trainTestRatio
-    val seed = xgbExecutionParams.xgbInputParams.seed
-    val r = new Random(seed)
-    val testPoints = mutable.ArrayBuilder.make[XGBLabeledPoint]
-    val trainBaseMargins = new mutable.ArrayBuilder.ofFloat
-    val testBaseMargins = new mutable.ArrayBuilder.ofFloat
-
-    val trainGroups = new mutable.ArrayBuilder.ofInt
-    val testGroups = new mutable.ArrayBuilder.ofInt
-
-    val trainWeights = new mutable.ArrayBuilder.ofFloat
-    val testWeights = new mutable.ArrayBuilder.ofFloat
-
-    val trainLabelPointGroups = labeledPointGroups.filter { labeledPointGroup =>
-      val accepted = r.nextDouble() <= trainTestRatio
-      if (!accepted) {
-        var groupWeight = -1.0f
-        var groupSize = 0
-        labeledPointGroup.foreach(labeledPoint => {
-          testPoints += labeledPoint
-          testBaseMargins += labeledPoint.baseMargin
-          if (groupWeight < 0) {
-            groupWeight = labeledPoint.weight
-          } else if (labeledPoint.weight != groupWeight) {
-            throw new IllegalArgumentException("the instances in the same group have to be" +
-              s" assigned with the same weight (unexpected weight ${labeledPoint.weight}")
-          }
-          groupSize += 1
-        })
-        testWeights += groupWeight
-        testGroups += groupSize
-      } else {
-        var groupWeight = -1.0f
-        var groupSize = 0
-        labeledPointGroup.foreach { labeledPoint => {
-          if (groupWeight < 0) {
-            groupWeight = labeledPoint.weight
-          } else if (labeledPoint.weight != groupWeight) {
-            throw new IllegalArgumentException("the instances in the same group have to be" +
-              s" assigned with the same weight (unexpected weight ${labeledPoint.weight}")
-          }
-          trainBaseMargins += labeledPoint.baseMargin
-          groupSize += 1
-        }}
-        trainWeights += groupWeight
-        trainGroups += groupSize
-      }
-      accepted
-    }
-
-    val trainPoints = trainLabelPointGroups.flatMap(_.iterator)
-    val trainMatrix = new DMatrix(trainPoints, cacheDirName.map(_ + "/train").orNull)
-    trainMatrix.setGroup(trainGroups.result())
-    trainMatrix.setWeight(trainWeights.result())
-
-    val testMatrix = new DMatrix(testPoints.result().iterator, cacheDirName.map(_ + "/test").orNull)
-    if (trainTestRatio < 1.0) {
-      testMatrix.setGroup(testGroups.result())
-      testMatrix.setWeight(testWeights.result())
-    }
-
-    val trainMargin = fromBaseMarginsToArray(trainBaseMargins.result().iterator)
-    val testMargin = fromBaseMarginsToArray(testBaseMargins.result().iterator)
-    if (trainMargin.isDefined) trainMatrix.setBaseMargin(trainMargin.get)
-    if (testMargin.isDefined) testMatrix.setBaseMargin(testMargin.get)
-
-    new Watches(Array(trainMatrix, testMatrix), Array("train", "test"), cacheDirName)
-  }
-}
-
-/**
- * Within each RDD partition, group the <code>XGBLabeledPoint</code> by group id.</p>
- * And the first and the last groups may not have all the items due to the data partition.
- * <code>LabeledPointGroupIterator</code> orginaizes data in a tuple format:
- * (isFistGroup || isLastGroup, Array[XGBLabeledPoint]).</p>
- * The edge groups across partitions can be stitched together later.
- * @param base collection of <code>XGBLabeledPoint</code>
- */
-private[spark] class LabeledPointGroupIterator(base: Iterator[XGBLabeledPoint])
-  extends AbstractIterator[XGBLabeledPointGroup] {
-
-  private var firstPointOfNextGroup: XGBLabeledPoint = null
-  private var isNewGroup = false
-
-  override def hasNext: Boolean = {
-    base.hasNext || isNewGroup
-  }
-
-  override def next(): XGBLabeledPointGroup = {
-    val builder = mutable.ArrayBuilder.make[XGBLabeledPoint]
-    var isFirstGroup = true
-    if (firstPointOfNextGroup != null) {
-      builder += firstPointOfNextGroup
-      isFirstGroup = false
-    }
-
-    isNewGroup = false
-    while (!isNewGroup && base.hasNext) {
-      val point = base.next()
-      val groupId = if (firstPointOfNextGroup != null) firstPointOfNextGroup.group else point.group
-      firstPointOfNextGroup = point
-      if (point.group == groupId) {
-        // add to current group
-        builder += point
-      } else {
-        // start a new group
-        isNewGroup = true
-      }
-    }
-
-    val isLastGroup = !isNewGroup
-    val result = builder.result()
-    val group = XGBLabeledPointGroup(result(0).group, result, isFirstGroup || isLastGroup)
-
-    group
-  }
-}
-
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala
deleted file mode 100644
index 4ae41e8..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala
+++ /dev/null
@@ -1,548 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import ml.dmlc.xgboost4j.java.Rabit
-import ml.dmlc.xgboost4j.scala.spark.params._
-import ml.dmlc.xgboost4j.scala.{Booster, DMatrix, EvalTrait, ObjectiveTrait, XGBoost => SXGBoost}
-import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-import org.apache.hadoop.fs.Path
-import org.apache.spark.TaskContext
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.classification._
-import org.apache.spark.ml.linalg._
-import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.HasWeightCol
-import org.apache.spark.ml.util._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types._
-import org.json4s.DefaultFormats
-
-import scala.collection.JavaConverters._
-import scala.collection.{AbstractIterator, Iterator, mutable}
-
-class XGBoostClassifier (
-    override val uid: String,
-    private val xgboostParams: Map[String, Any])
-  extends ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel]
-    with XGBoostClassifierParams with DefaultParamsWritable {
-
-  def this() = this(Identifiable.randomUID("xgbc"), Map[String, Any]())
-
-  def this(uid: String) = this(uid, Map[String, Any]())
-
-  def this(xgboostParams: Map[String, Any]) = this(
-    Identifiable.randomUID("xgbc"), xgboostParams)
-
-  XGBoost2MLlibParams(xgboostParams)
-
-  def setWeightCol(value: String): this.type = set(weightCol, value)
-
-  def setBaseMarginCol(value: String): this.type = set(baseMarginCol, value)
-
-  def setNumClass(value: Int): this.type = set(numClass, value)
-
-  // setters for general params
-  def setNumRound(value: Int): this.type = set(numRound, value)
-
-  def setNumWorkers(value: Int): this.type = set(numWorkers, value)
-
-  def setNthread(value: Int): this.type = set(nthread, value)
-
-  def setUseExternalMemory(value: Boolean): this.type = set(useExternalMemory, value)
-
-  def setSilent(value: Int): this.type = set(silent, value)
-
-  def setMissing(value: Float): this.type = set(missing, value)
-
-  def setTimeoutRequestWorkers(value: Long): this.type = set(timeoutRequestWorkers, value)
-
-  def setCheckpointPath(value: String): this.type = set(checkpointPath, value)
-
-  def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  def setSeed(value: Long): this.type = set(seed, value)
-
-  def setEta(value: Double): this.type = set(eta, value)
-
-  def setGamma(value: Double): this.type = set(gamma, value)
-
-  def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  def setMinChildWeight(value: Double): this.type = set(minChildWeight, value)
-
-  def setMaxDeltaStep(value: Double): this.type = set(maxDeltaStep, value)
-
-  def setSubsample(value: Double): this.type = set(subsample, value)
-
-  def setColsampleBytree(value: Double): this.type = set(colsampleBytree, value)
-
-  def setColsampleBylevel(value: Double): this.type = set(colsampleBylevel, value)
-
-  def setLambda(value: Double): this.type = set(lambda, value)
-
-  def setAlpha(value: Double): this.type = set(alpha, value)
-
-  def setTreeMethod(value: String): this.type = set(treeMethod, value)
-
-  def setGrowPolicy(value: String): this.type = set(growPolicy, value)
-
-  def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  def setMaxLeaves(value: Int): this.type = set(maxLeaves, value)
-
-  def setSketchEps(value: Double): this.type = set(sketchEps, value)
-
-  def setScalePosWeight(value: Double): this.type = set(scalePosWeight, value)
-
-  def setSampleType(value: String): this.type = set(sampleType, value)
-
-  def setNormalizeType(value: String): this.type = set(normalizeType, value)
-
-  def setRateDrop(value: Double): this.type = set(rateDrop, value)
-
-  def setSkipDrop(value: Double): this.type = set(skipDrop, value)
-
-  def setLambdaBias(value: Double): this.type = set(lambdaBias, value)
-
-  // setters for learning params
-  def setObjective(value: String): this.type = set(objective, value)
-
-  def setObjectiveType(value: String): this.type = set(objectiveType, value)
-
-  def setBaseScore(value: Double): this.type = set(baseScore, value)
-
-  def setEvalMetric(value: String): this.type = set(evalMetric, value)
-
-  def setTrainTestRatio(value: Double): this.type = set(trainTestRatio, value)
-
-  def setNumEarlyStoppingRounds(value: Int): this.type = set(numEarlyStoppingRounds, value)
-
-  def setMaximizeEvaluationMetrics(value: Boolean): this.type =
-    set(maximizeEvaluationMetrics, value)
-
-  def setCustomObj(value: ObjectiveTrait): this.type = set(customObj, value)
-
-  def setCustomEval(value: EvalTrait): this.type = set(customEval, value)
-
-  // called at the start of fit/train when 'eval_metric' is not defined
-  private def setupDefaultEvalMetric(): String = {
-    require(isDefined(objective), "Users must set \'objective\' via xgboostParams.")
-    if ($(objective).startsWith("multi")) {
-      // multi
-      "merror"
-    } else {
-      // binary
-      "error"
-    }
-  }
-
-  override protected def train(dataset: Dataset[_]): XGBoostClassificationModel = {
-
-    if (!isDefined(evalMetric) || $(evalMetric).isEmpty) {
-      set(evalMetric, setupDefaultEvalMetric())
-    }
-
-    if (isDefined(customObj) && $(customObj) != null) {
-      set(objectiveType, "classification")
-    }
-
-    val _numClasses = getNumClasses(dataset)
-    if (isDefined(numClass) && $(numClass) != _numClasses) {
-      throw new Exception("The number of classes in dataset doesn't match " +
-        "\'num_class\' in xgboost params.")
-    }
-
-    val weight = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val baseMargin = if (!isDefined(baseMarginCol) || $(baseMarginCol).isEmpty) {
-      lit(Float.NaN)
-    } else {
-      col($(baseMarginCol))
-    }
-
-    val trainingSet: RDD[XGBLabeledPoint] = DataUtils.convertDataFrameToXGBLabeledPointRDDs(
-      col($(labelCol)), col($(featuresCol)), weight, baseMargin,
-      None, $(numWorkers), needDeterministicRepartitioning, dataset.asInstanceOf[DataFrame]).head
-    val evalRDDMap = getEvalSets(xgboostParams).map {
-      case (name, dataFrame) => (name,
-        DataUtils.convertDataFrameToXGBLabeledPointRDDs(col($(labelCol)), col($(featuresCol)),
-          weight, baseMargin, None, $(numWorkers), needDeterministicRepartitioning, dataFrame).head)
-    }
-    transformSchema(dataset.schema, logging = true)
-    val derivedXGBParamMap = MLlib2XGBoostParams
-    // All non-null param maps in XGBoostClassifier are in derivedXGBParamMap.
-    val (_booster, _metrics) = XGBoost.trainDistributed(trainingSet, derivedXGBParamMap,
-      hasGroup = false, evalRDDMap)
-    val model = new XGBoostClassificationModel(uid, _numClasses, _booster)
-    val summary = XGBoostTrainingSummary(_metrics)
-    model.setSummary(summary)
-    model
-  }
-
-  override def copy(extra: ParamMap): XGBoostClassifier = defaultCopy(extra)
-}
-
-object XGBoostClassifier extends DefaultParamsReadable[XGBoostClassifier] {
-
-  override def load(path: String): XGBoostClassifier = super.load(path)
-}
-
-class XGBoostClassificationModel private[ml](
-    override val uid: String,
-    override val numClasses: Int,
-    private[spark] val _booster: Booster)
-  extends ProbabilisticClassificationModel[Vector, XGBoostClassificationModel]
-    with XGBoostClassifierParams with InferenceParams
-    with MLWritable with Serializable {
-
-  import XGBoostClassificationModel._
-
-  // only called in copy()
-  def this(uid: String) = this(uid, 2, null)
-
-  /**
-   * Get the native booster instance of this model.
-   * This is used to call low-level APIs on native booster, such as "getFeatureScore".
-   */
-  def nativeBooster: Booster = _booster
-
-  private var trainingSummary: Option[XGBoostTrainingSummary] = None
-
-  /**
-   * Returns summary (e.g. train/test objective history) of model on the
-   * training set. An exception is thrown if no summary is available.
-   */
-  def summary: XGBoostTrainingSummary = trainingSummary.getOrElse {
-    throw new IllegalStateException("No training summary available for this XGBoostModel")
-  }
-
-  private[spark] def setSummary(summary: XGBoostTrainingSummary): this.type = {
-    trainingSummary = Some(summary)
-    this
-  }
-
-  def setLeafPredictionCol(value: String): this.type = set(leafPredictionCol, value)
-
-  def setContribPredictionCol(value: String): this.type = set(contribPredictionCol, value)
-
-  def setTreeLimit(value: Int): this.type = set(treeLimit, value)
-
-  def setMissing(value: Float): this.type = set(missing, value)
-
-  def setAllowZeroForMissingValue(value: Boolean): this.type = set(
-    allowNonZeroForMissing,
-    value
-  )
-
-  def setInferBatchSize(value: Int): this.type = set(inferBatchSize, value)
-
-  /**
-   * Single instance prediction.
-   * Note: The performance is not ideal, use it carefully!
-   */
-  override def predict(features: Vector): Double = {
-    import DataUtils._
-    val dm = new DMatrix(XGBoost.processMissingValues(
-      Iterator(features.asXGB),
-      $(missing),
-      $(allowNonZeroForMissing)
-    ))
-    val probability = _booster.predict(data = dm)(0).map(_.toDouble)
-    if (numClasses == 2) {
-      math.round(probability(0))
-    } else {
-      probability2prediction(Vectors.dense(probability))
-    }
-  }
-
-  // Actually we don't use this function at all, to make it pass compiler check.
-  override protected def predictRaw(features: Vector): Vector = {
-    throw new Exception("XGBoost-Spark does not support \'predictRaw\'")
-  }
-
-  // Actually we don't use this function at all, to make it pass compiler check.
-  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
-    throw new Exception("XGBoost-Spark does not support \'raw2probabilityInPlace\'")
-  }
-
-  // Generate raw prediction and probability prediction.
-  private def transformInternal(dataset: Dataset[_]): DataFrame = {
-
-    val schema = StructType(dataset.schema.fields ++
-      Seq(StructField(name = _rawPredictionCol, dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false)) ++
-      Seq(StructField(name = _probabilityCol, dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false)))
-
-    val bBooster = dataset.sparkSession.sparkContext.broadcast(_booster)
-    val appName = dataset.sparkSession.sparkContext.appName
-
-    val resultRDD = dataset.asInstanceOf[Dataset[Row]].rdd.mapPartitions { rowIterator =>
-      new AbstractIterator[Row] {
-        private var batchCnt = 0
-
-        private val batchIterImpl = rowIterator.grouped($(inferBatchSize)).flatMap { batchRow =>
-          if (batchCnt == 0) {
-            val rabitEnv = Array(
-              "DMLC_TASK_ID" -> TaskContext.getPartitionId().toString,
-              "DMLC_WORKER_STOP_PROCESS_ON_ERROR" -> "false").toMap
-            Rabit.init(rabitEnv.asJava)
-          }
-
-          val features = batchRow.iterator.map(row => row.getAs[Vector]($(featuresCol)))
-
-          import DataUtils._
-          val cacheInfo = {
-            if ($(useExternalMemory)) {
-              s"$appName-${TaskContext.get().stageId()}-dtest_cache-" +
-                  s"${TaskContext.getPartitionId()}-batch-$batchCnt"
-            } else {
-              null
-            }
-          }
-
-          val dm = new DMatrix(
-            XGBoost.processMissingValues(
-              features.map(_.asXGB),
-              $(missing),
-              $(allowNonZeroForMissing)
-            ),
-            cacheInfo)
-          try {
-            val Array(rawPredictionItr, probabilityItr, predLeafItr, predContribItr) =
-              producePredictionItrs(bBooster, dm)
-            produceResultIterator(batchRow.iterator,
-              rawPredictionItr, probabilityItr, predLeafItr, predContribItr)
-          } finally {
-            batchCnt += 1
-            dm.delete()
-          }
-        }
-
-        override def hasNext: Boolean = batchIterImpl.hasNext
-
-        override def next(): Row = {
-          val ret = batchIterImpl.next()
-          if (!batchIterImpl.hasNext) {
-            Rabit.shutdown()
-          }
-          ret
-        }
-      }
-    }
-
-    bBooster.unpersist(blocking = false)
-    dataset.sparkSession.createDataFrame(resultRDD, generateResultSchema(schema))
-  }
-
-  private def produceResultIterator(
-      originalRowItr: Iterator[Row],
-      rawPredictionItr: Iterator[Row],
-      probabilityItr: Iterator[Row],
-      predLeafItr: Iterator[Row],
-      predContribItr: Iterator[Row]): Iterator[Row] = {
-    // the following implementation is to be improved
-    if (isDefined(leafPredictionCol) && $(leafPredictionCol).nonEmpty &&
-      isDefined(contribPredictionCol) && $(contribPredictionCol).nonEmpty) {
-      originalRowItr.zip(rawPredictionItr).zip(probabilityItr).zip(predLeafItr).zip(predContribItr).
-        map { case ((((originals: Row, rawPrediction: Row), probability: Row), leaves: Row),
-        contribs: Row) =>
-          Row.fromSeq(originals.toSeq ++ rawPrediction.toSeq ++ probability.toSeq ++ leaves.toSeq ++
-            contribs.toSeq)
-      }
-    } else if (isDefined(leafPredictionCol) && $(leafPredictionCol).nonEmpty &&
-      (!isDefined(contribPredictionCol) || $(contribPredictionCol).isEmpty)) {
-      originalRowItr.zip(rawPredictionItr).zip(probabilityItr).zip(predLeafItr).
-        map { case (((originals: Row, rawPrediction: Row), probability: Row), leaves: Row) =>
-          Row.fromSeq(originals.toSeq ++ rawPrediction.toSeq ++ probability.toSeq ++ leaves.toSeq)
-        }
-    } else if ((!isDefined(leafPredictionCol) || $(leafPredictionCol).isEmpty) &&
-      isDefined(contribPredictionCol) && $(contribPredictionCol).nonEmpty) {
-      originalRowItr.zip(rawPredictionItr).zip(probabilityItr).zip(predContribItr).
-        map { case (((originals: Row, rawPrediction: Row), probability: Row), contribs: Row) =>
-          Row.fromSeq(originals.toSeq ++ rawPrediction.toSeq ++ probability.toSeq ++ contribs.toSeq)
-        }
-    } else {
-      originalRowItr.zip(rawPredictionItr).zip(probabilityItr).map {
-        case ((originals: Row, rawPrediction: Row), probability: Row) =>
-          Row.fromSeq(originals.toSeq ++ rawPrediction.toSeq ++ probability.toSeq)
-      }
-    }
-  }
-
-  private def generateResultSchema(fixedSchema: StructType): StructType = {
-    var resultSchema = fixedSchema
-    if (isDefined(leafPredictionCol)) {
-      resultSchema = resultSchema.add(StructField(name = $(leafPredictionCol), dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false))
-    }
-    if (isDefined(contribPredictionCol)) {
-      resultSchema = resultSchema.add(StructField(name = $(contribPredictionCol), dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false))
-    }
-    resultSchema
-  }
-
-  private def producePredictionItrs(broadcastBooster: Broadcast[Booster], dm: DMatrix):
-      Array[Iterator[Row]] = {
-    val rawPredictionItr = {
-      broadcastBooster.value.predict(dm, outPutMargin = true, $(treeLimit)).
-        map(Row(_)).iterator
-    }
-    val probabilityItr = {
-      broadcastBooster.value.predict(dm, outPutMargin = false, $(treeLimit)).
-        map(Row(_)).iterator
-    }
-    val predLeafItr = {
-      if (isDefined(leafPredictionCol)) {
-        broadcastBooster.value.predictLeaf(dm, $(treeLimit)).map(Row(_)).iterator
-      } else {
-        Iterator()
-      }
-    }
-    val predContribItr = {
-      if (isDefined(contribPredictionCol)) {
-        broadcastBooster.value.predictContrib(dm, $(treeLimit)).map(Row(_)).iterator
-      } else {
-        Iterator()
-      }
-    }
-    Array(rawPredictionItr, probabilityItr, predLeafItr, predContribItr)
-  }
-
-  override def transform(dataset: Dataset[_]): DataFrame = {
-    transformSchema(dataset.schema, logging = true)
-    if (isDefined(thresholds)) {
-      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
-        ".transform() called with non-matching numClasses and thresholds.length." +
-        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
-    }
-
-    // Output selected columns only.
-    // This is a bit complicated since it tries to avoid repeated computation.
-    var outputData = transformInternal(dataset)
-    var numColsOutput = 0
-
-    val rawPredictionUDF = udf { rawPrediction: mutable.WrappedArray[Float] =>
-      val raw = rawPrediction.map(_.toDouble).toArray
-      val rawPredictions = if (numClasses == 2) Array(-raw(0), raw(0)) else raw
-      Vectors.dense(rawPredictions)
-    }
-
-    val probabilityUDF = udf { probability: mutable.WrappedArray[Float] =>
-      val prob = probability.map(_.toDouble).toArray
-      val probabilities = if (numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
-      Vectors.dense(probabilities)
-    }
-
-    val predictUDF = udf { probability: mutable.WrappedArray[Float] =>
-      // From XGBoost probability to MLlib prediction
-      val prob = probability.map(_.toDouble).toArray
-      val probabilities = if (numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
-      probability2prediction(Vectors.dense(probabilities))
-    }
-
-    if ($(rawPredictionCol).nonEmpty) {
-      outputData = outputData
-        .withColumn(getRawPredictionCol, rawPredictionUDF(col(_rawPredictionCol)))
-      numColsOutput += 1
-    }
-
-    if ($(probabilityCol).nonEmpty) {
-      outputData = outputData
-        .withColumn(getProbabilityCol, probabilityUDF(col(_probabilityCol)))
-      numColsOutput += 1
-    }
-
-    if ($(predictionCol).nonEmpty) {
-      outputData = outputData
-        .withColumn($(predictionCol), predictUDF(col(_probabilityCol)))
-      numColsOutput += 1
-    }
-
-    if (numColsOutput == 0) {
-      this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" +
-        " since no output columns were set.")
-    }
-    outputData
-      .toDF
-      .drop(col(_rawPredictionCol))
-      .drop(col(_probabilityCol))
-  }
-
-  override def copy(extra: ParamMap): XGBoostClassificationModel = {
-    val newModel = copyValues(new XGBoostClassificationModel(uid, numClasses, _booster), extra)
-    newModel.setSummary(summary).setParent(parent)
-  }
-
-  override def write: MLWriter =
-    new XGBoostClassificationModel.XGBoostClassificationModelWriter(this)
-}
-
-object XGBoostClassificationModel extends MLReadable[XGBoostClassificationModel] {
-
-  private val _rawPredictionCol = "_rawPrediction"
-  private val _probabilityCol = "_probability"
-
-  override def read: MLReader[XGBoostClassificationModel] = new XGBoostClassificationModelReader
-
-  override def load(path: String): XGBoostClassificationModel = super.load(path)
-
-  private[XGBoostClassificationModel]
-  class XGBoostClassificationModelWriter(instance: XGBoostClassificationModel) extends MLWriter {
-
-    override protected def saveImpl(path: String): Unit = {
-      // Save metadata and Params
-      implicit val format = DefaultFormats
-      implicit val sc = super.sparkSession.sparkContext
-
-      DefaultXGBoostParamsWriter.saveMetadata(instance, path, sc)
-      // Save model data
-      val dataPath = new Path(path, "data").toString
-      val internalPath = new Path(dataPath, "XGBoostClassificationModel")
-      val outputStream = internalPath.getFileSystem(sc.hadoopConfiguration).create(internalPath)
-      outputStream.writeInt(instance.numClasses)
-      instance._booster.saveModel(outputStream)
-      outputStream.close()
-    }
-  }
-
-  private class XGBoostClassificationModelReader extends MLReader[XGBoostClassificationModel] {
-
-    /** Checked against metadata when loading model */
-    private val className = classOf[XGBoostClassificationModel].getName
-
-    override def load(path: String): XGBoostClassificationModel = {
-      implicit val sc = super.sparkSession.sparkContext
-
-
-      val metadata = DefaultXGBoostParamsReader.loadMetadata(path, sc, className)
-
-      val dataPath = new Path(path, "data").toString
-      val internalPath = new Path(dataPath, "XGBoostClassificationModel")
-      val dataInStream = internalPath.getFileSystem(sc.hadoopConfiguration).open(internalPath)
-      val numClasses = dataInStream.readInt()
-
-      val booster = SXGBoost.loadModel(dataInStream)
-      val model = new XGBoostClassificationModel(metadata.uid, numClasses, booster)
-      DefaultXGBoostParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostEstimatorCommon.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostEstimatorCommon.scala
deleted file mode 100644
index 1c8ddfb..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostEstimatorCommon.scala
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import ml.dmlc.xgboost4j.scala.spark.params._
-
-import org.apache.spark.ml.param.shared.HasWeightCol
-
-private[spark] sealed trait XGBoostEstimatorCommon extends GeneralParams with LearningTaskParams
-  with BoosterParams with RabitParams with ParamMapFuncs with NonParamVariables {
-
-  def needDeterministicRepartitioning: Boolean = {
-    getCheckpointPath != null && getCheckpointPath.nonEmpty && getCheckpointInterval > 0
-  }
-}
-
-private[spark] trait XGBoostClassifierParams extends HasWeightCol with HasBaseMarginCol
-  with HasNumClass with HasLeafPredictionCol with HasContribPredictionCol
-  with XGBoostEstimatorCommon
-
-private[spark] trait XGBoostRegressorParams extends HasBaseMarginCol with HasWeightCol
-  with HasGroupCol with HasLeafPredictionCol with HasContribPredictionCol
-  with XGBoostEstimatorCommon
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala
deleted file mode 100644
index 188b111..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala
+++ /dev/null
@@ -1,477 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-import scala.collection.{AbstractIterator, Iterator, mutable}
-import scala.collection.JavaConverters._
-
-import ml.dmlc.xgboost4j.java.{Rabit, XGBoost => JXGBoost}
-import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-import ml.dmlc.xgboost4j.scala.spark.params.{DefaultXGBoostParamsReader, _}
-import ml.dmlc.xgboost4j.scala.{Booster, DMatrix, XGBoost => SXGBoost}
-import ml.dmlc.xgboost4j.scala.{EvalTrait, ObjectiveTrait}
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark.TaskContext
-import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector}
-import org.apache.spark.ml.param.shared.HasWeightCol
-import org.apache.spark.ml.util._
-import org.apache.spark.ml._
-import org.apache.spark.ml.param._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types._
-import org.json4s.DefaultFormats
-import scala.collection.mutable.ListBuffer
-
-import org.apache.spark.broadcast.Broadcast
-
-class XGBoostRegressor (
-    override val uid: String,
-    private val xgboostParams: Map[String, Any])
-  extends Predictor[Vector, XGBoostRegressor, XGBoostRegressionModel]
-    with XGBoostRegressorParams with DefaultParamsWritable {
-
-  def this() = this(Identifiable.randomUID("xgbr"), Map[String, Any]())
-
-  def this(uid: String) = this(uid, Map[String, Any]())
-
-  def this(xgboostParams: Map[String, Any]) = this(
-    Identifiable.randomUID("xgbr"), xgboostParams)
-
-  XGBoost2MLlibParams(xgboostParams)
-
-  def setWeightCol(value: String): this.type = set(weightCol, value)
-
-  def setBaseMarginCol(value: String): this.type = set(baseMarginCol, value)
-
-  def setGroupCol(value: String): this.type = set(groupCol, value)
-
-  // setters for general params
-  def setNumRound(value: Int): this.type = set(numRound, value)
-
-  def setNumWorkers(value: Int): this.type = set(numWorkers, value)
-
-  def setNthread(value: Int): this.type = set(nthread, value)
-
-  def setUseExternalMemory(value: Boolean): this.type = set(useExternalMemory, value)
-
-  def setSilent(value: Int): this.type = set(silent, value)
-
-  def setMissing(value: Float): this.type = set(missing, value)
-
-  def setTimeoutRequestWorkers(value: Long): this.type = set(timeoutRequestWorkers, value)
-
-  def setCheckpointPath(value: String): this.type = set(checkpointPath, value)
-
-  def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
-
-  def setSeed(value: Long): this.type = set(seed, value)
-
-  def setEta(value: Double): this.type = set(eta, value)
-
-  def setGamma(value: Double): this.type = set(gamma, value)
-
-  def setMaxDepth(value: Int): this.type = set(maxDepth, value)
-
-  def setMinChildWeight(value: Double): this.type = set(minChildWeight, value)
-
-  def setMaxDeltaStep(value: Double): this.type = set(maxDeltaStep, value)
-
-  def setSubsample(value: Double): this.type = set(subsample, value)
-
-  def setColsampleBytree(value: Double): this.type = set(colsampleBytree, value)
-
-  def setColsampleBylevel(value: Double): this.type = set(colsampleBylevel, value)
-
-  def setLambda(value: Double): this.type = set(lambda, value)
-
-  def setAlpha(value: Double): this.type = set(alpha, value)
-
-  def setTreeMethod(value: String): this.type = set(treeMethod, value)
-
-  def setGrowPolicy(value: String): this.type = set(growPolicy, value)
-
-  def setMaxBins(value: Int): this.type = set(maxBins, value)
-
-  def setMaxLeaves(value: Int): this.type = set(maxLeaves, value)
-
-  def setSketchEps(value: Double): this.type = set(sketchEps, value)
-
-  def setScalePosWeight(value: Double): this.type = set(scalePosWeight, value)
-
-  def setSampleType(value: String): this.type = set(sampleType, value)
-
-  def setNormalizeType(value: String): this.type = set(normalizeType, value)
-
-  def setRateDrop(value: Double): this.type = set(rateDrop, value)
-
-  def setSkipDrop(value: Double): this.type = set(skipDrop, value)
-
-  def setLambdaBias(value: Double): this.type = set(lambdaBias, value)
-
-  // setters for learning params
-  def setObjective(value: String): this.type = set(objective, value)
-
-  def setObjectiveType(value: String): this.type = set(objectiveType, value)
-
-  def setBaseScore(value: Double): this.type = set(baseScore, value)
-
-  def setEvalMetric(value: String): this.type = set(evalMetric, value)
-
-  def setTrainTestRatio(value: Double): this.type = set(trainTestRatio, value)
-
-  def setNumEarlyStoppingRounds(value: Int): this.type = set(numEarlyStoppingRounds, value)
-
-  def setMaximizeEvaluationMetrics(value: Boolean): this.type =
-    set(maximizeEvaluationMetrics, value)
-
-  def setCustomObj(value: ObjectiveTrait): this.type = set(customObj, value)
-
-  def setCustomEval(value: EvalTrait): this.type = set(customEval, value)
-
-  // called at the start of fit/train when 'eval_metric' is not defined
-  private def setupDefaultEvalMetric(): String = {
-    require(isDefined(objective), "Users must set \'objective\' via xgboostParams.")
-    if ($(objective).startsWith("rank")) {
-      "map"
-    } else {
-      "rmse"
-    }
-  }
-
-  override protected def train(dataset: Dataset[_]): XGBoostRegressionModel = {
-
-    if (!isDefined(evalMetric) || $(evalMetric).isEmpty) {
-      set(evalMetric, setupDefaultEvalMetric())
-    }
-
-    if (isDefined(customObj) && $(customObj) != null) {
-      set(objectiveType, "regression")
-    }
-
-    val weight = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val baseMargin = if (!isDefined(baseMarginCol) || $(baseMarginCol).isEmpty) {
-      lit(Float.NaN)
-    } else {
-      col($(baseMarginCol))
-    }
-    val group = if (!isDefined(groupCol) || $(groupCol).isEmpty) lit(-1) else col($(groupCol))
-    val trainingSet: RDD[XGBLabeledPoint] = DataUtils.convertDataFrameToXGBLabeledPointRDDs(
-      col($(labelCol)), col($(featuresCol)), weight, baseMargin, Some(group),
-      $(numWorkers), needDeterministicRepartitioning, dataset.asInstanceOf[DataFrame]).head
-    val evalRDDMap = getEvalSets(xgboostParams).map {
-      case (name, dataFrame) => (name,
-        DataUtils.convertDataFrameToXGBLabeledPointRDDs(col($(labelCol)), col($(featuresCol)),
-          weight, baseMargin, Some(group), $(numWorkers), needDeterministicRepartitioning,
-          dataFrame).head)
-    }
-    transformSchema(dataset.schema, logging = true)
-    val derivedXGBParamMap = MLlib2XGBoostParams
-    // All non-null param maps in XGBoostRegressor are in derivedXGBParamMap.
-    val (_booster, _metrics) = XGBoost.trainDistributed(trainingSet, derivedXGBParamMap,
-      hasGroup = group != lit(-1), evalRDDMap)
-    val model = new XGBoostRegressionModel(uid, _booster)
-    val summary = XGBoostTrainingSummary(_metrics)
-    model.setSummary(summary)
-    model
-  }
-
-  override def copy(extra: ParamMap): XGBoostRegressor = defaultCopy(extra)
-}
-
-object XGBoostRegressor extends DefaultParamsReadable[XGBoostRegressor] {
-
-  override def load(path: String): XGBoostRegressor = super.load(path)
-}
-
-class XGBoostRegressionModel private[ml] (
-    override val uid: String,
-    private[spark] val _booster: Booster)
-  extends PredictionModel[Vector, XGBoostRegressionModel]
-    with XGBoostRegressorParams with InferenceParams
-    with MLWritable with Serializable {
-
-  import XGBoostRegressionModel._
-
-  // only called in copy()
-  def this(uid: String) = this(uid, null)
-
-  /**
-   * Get the native booster instance of this model.
-   * This is used to call low-level APIs on native booster, such as "getFeatureScore".
-   */
-  def nativeBooster: Booster = _booster
-
-  private var trainingSummary: Option[XGBoostTrainingSummary] = None
-
-  /**
-   * Returns summary (e.g. train/test objective history) of model on the
-   * training set. An exception is thrown if no summary is available.
-   */
-  def summary: XGBoostTrainingSummary = trainingSummary.getOrElse {
-    throw new IllegalStateException("No training summary available for this XGBoostModel")
-  }
-
-  private[spark] def setSummary(summary: XGBoostTrainingSummary): this.type = {
-    trainingSummary = Some(summary)
-    this
-  }
-
-  def setLeafPredictionCol(value: String): this.type = set(leafPredictionCol, value)
-
-  def setContribPredictionCol(value: String): this.type = set(contribPredictionCol, value)
-
-  def setTreeLimit(value: Int): this.type = set(treeLimit, value)
-
-  def setMissing(value: Float): this.type = set(missing, value)
-
-  def setAllowZeroForMissingValue(value: Boolean): this.type = set(
-    allowNonZeroForMissing,
-    value
-  )
-
-  def setInferBatchSize(value: Int): this.type = set(inferBatchSize, value)
-
-  /**
-   * Single instance prediction.
-   * Note: The performance is not ideal, use it carefully!
-   */
-  override def predict(features: Vector): Double = {
-    import DataUtils._
-    val dm = new DMatrix(XGBoost.processMissingValues(
-      Iterator(features.asXGB),
-      $(missing),
-      $(allowNonZeroForMissing)
-    ))
-    _booster.predict(data = dm)(0)(0)
-  }
-
-  private def transformInternal(dataset: Dataset[_]): DataFrame = {
-
-    val schema = StructType(dataset.schema.fields ++
-      Seq(StructField(name = _originalPredictionCol, dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false)))
-
-    val bBooster = dataset.sparkSession.sparkContext.broadcast(_booster)
-    val appName = dataset.sparkSession.sparkContext.appName
-
-    val resultRDD = dataset.asInstanceOf[Dataset[Row]].rdd.mapPartitions { rowIterator =>
-      new AbstractIterator[Row] {
-        private var batchCnt = 0
-
-        private val batchIterImpl = rowIterator.grouped($(inferBatchSize)).flatMap { batchRow =>
-          if (batchCnt == 0) {
-            val rabitEnv = Array(
-              "DMLC_TASK_ID" -> TaskContext.getPartitionId().toString,
-              "DMLC_WORKER_STOP_PROCESS_ON_ERROR" -> "false").toMap
-            Rabit.init(rabitEnv.asJava)
-          }
-
-          val features = batchRow.iterator.map(row => row.getAs[Vector]($(featuresCol)))
-
-          import DataUtils._
-          val cacheInfo = {
-            if ($(useExternalMemory)) {
-              s"$appName-${TaskContext.get().stageId()}-dtest_cache-" +
-                s"${TaskContext.getPartitionId()}-batch-$batchCnt"
-            } else {
-              null
-            }
-          }
-
-          val dm = new DMatrix(
-            XGBoost.processMissingValues(
-              features.map(_.asXGB),
-              $(missing),
-              $(allowNonZeroForMissing)
-            ),
-            cacheInfo)
-          try {
-            val Array(rawPredictionItr, predLeafItr, predContribItr) =
-              producePredictionItrs(bBooster, dm)
-            produceResultIterator(batchRow.iterator, rawPredictionItr, predLeafItr, predContribItr)
-          } finally {
-            batchCnt += 1
-            dm.delete()
-          }
-        }
-
-        override def hasNext: Boolean = batchIterImpl.hasNext
-
-        override def next(): Row = {
-          val ret = batchIterImpl.next()
-          if (!batchIterImpl.hasNext) {
-            Rabit.shutdown()
-          }
-          ret
-        }
-      }
-    }
-    bBooster.unpersist(blocking = false)
-    dataset.sparkSession.createDataFrame(resultRDD, generateResultSchema(schema))
-  }
-
-  private def produceResultIterator(
-      originalRowItr: Iterator[Row],
-      predictionItr: Iterator[Row],
-      predLeafItr: Iterator[Row],
-      predContribItr: Iterator[Row]): Iterator[Row] = {
-    // the following implementation is to be improved
-    if (isDefined(leafPredictionCol) && $(leafPredictionCol).nonEmpty &&
-      isDefined(contribPredictionCol) && $(contribPredictionCol).nonEmpty) {
-      originalRowItr.zip(predictionItr).zip(predLeafItr).zip(predContribItr).
-        map { case (((originals: Row, prediction: Row), leaves: Row), contribs: Row) =>
-          Row.fromSeq(originals.toSeq ++ prediction.toSeq ++ leaves.toSeq ++ contribs.toSeq)
-        }
-    } else if (isDefined(leafPredictionCol) && $(leafPredictionCol).nonEmpty &&
-      (!isDefined(contribPredictionCol) || $(contribPredictionCol).isEmpty)) {
-      originalRowItr.zip(predictionItr).zip(predLeafItr).
-        map { case ((originals: Row, prediction: Row), leaves: Row) =>
-          Row.fromSeq(originals.toSeq ++ prediction.toSeq ++ leaves.toSeq)
-        }
-    } else if ((!isDefined(leafPredictionCol) || $(leafPredictionCol).isEmpty) &&
-      isDefined(contribPredictionCol) && $(contribPredictionCol).nonEmpty) {
-      originalRowItr.zip(predictionItr).zip(predContribItr).
-        map { case ((originals: Row, prediction: Row), contribs: Row) =>
-          Row.fromSeq(originals.toSeq ++ prediction.toSeq ++ contribs.toSeq)
-        }
-    } else {
-      originalRowItr.zip(predictionItr).map {
-        case (originals: Row, originalPrediction: Row) =>
-          Row.fromSeq(originals.toSeq ++ originalPrediction.toSeq)
-      }
-    }
-  }
-
-  private def generateResultSchema(fixedSchema: StructType): StructType = {
-    var resultSchema = fixedSchema
-    if (isDefined(leafPredictionCol)) {
-      resultSchema = resultSchema.add(StructField(name = $(leafPredictionCol), dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false))
-    }
-    if (isDefined(contribPredictionCol)) {
-      resultSchema = resultSchema.add(StructField(name = $(contribPredictionCol), dataType =
-        ArrayType(FloatType, containsNull = false), nullable = false))
-    }
-    resultSchema
-  }
-
-  private def producePredictionItrs(booster: Broadcast[Booster], dm: DMatrix):
-      Array[Iterator[Row]] = {
-    val originalPredictionItr = {
-      booster.value.predict(dm, outPutMargin = false, $(treeLimit)).map(Row(_)).iterator
-    }
-    val predLeafItr = {
-      if (isDefined(leafPredictionCol)) {
-        booster.value.predictLeaf(dm, $(treeLimit)).
-          map(Row(_)).iterator
-      } else {
-        Iterator()
-      }
-    }
-    val predContribItr = {
-      if (isDefined(contribPredictionCol)) {
-        booster.value.predictContrib(dm, $(treeLimit)).
-          map(Row(_)).iterator
-      } else {
-        Iterator()
-      }
-    }
-    Array(originalPredictionItr, predLeafItr, predContribItr)
-  }
-
-  override def transform(dataset: Dataset[_]): DataFrame = {
-    transformSchema(dataset.schema, logging = true)
-    // Output selected columns only.
-    // This is a bit complicated since it tries to avoid repeated computation.
-    var outputData = transformInternal(dataset)
-    var numColsOutput = 0
-
-    val predictUDF = udf { (originalPrediction: mutable.WrappedArray[Float]) =>
-      originalPrediction(0).toDouble
-    }
-
-    if ($(predictionCol).nonEmpty) {
-      outputData = outputData
-        .withColumn($(predictionCol), predictUDF(col(_originalPredictionCol)))
-      numColsOutput += 1
-    }
-
-    if (numColsOutput == 0) {
-      this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" +
-        " since no output columns were set.")
-    }
-    outputData.toDF.drop(col(_originalPredictionCol))
-  }
-
-  override def copy(extra: ParamMap): XGBoostRegressionModel = {
-    val newModel = copyValues(new XGBoostRegressionModel(uid, _booster), extra)
-    newModel.setSummary(summary).setParent(parent)
-  }
-
-  override def write: MLWriter =
-    new XGBoostRegressionModel.XGBoostRegressionModelWriter(this)
-}
-
-object XGBoostRegressionModel extends MLReadable[XGBoostRegressionModel] {
-
-  private val _originalPredictionCol = "_originalPrediction"
-
-  override def read: MLReader[XGBoostRegressionModel] = new XGBoostRegressionModelReader
-
-  override def load(path: String): XGBoostRegressionModel = super.load(path)
-
-  private[XGBoostRegressionModel]
-  class XGBoostRegressionModelWriter(instance: XGBoostRegressionModel) extends MLWriter {
-
-    override protected def saveImpl(path: String): Unit = {
-      // Save metadata and Params
-      implicit val format = DefaultFormats
-      implicit val sc = super.sparkSession.sparkContext
-      DefaultXGBoostParamsWriter.saveMetadata(instance, path, sc)
-      // Save model data
-      val dataPath = new Path(path, "data").toString
-      val internalPath = new Path(dataPath, "XGBoostRegressionModel")
-      val outputStream = internalPath.getFileSystem(sc.hadoopConfiguration).create(internalPath)
-      instance._booster.saveModel(outputStream)
-      outputStream.close()
-    }
-  }
-
-  private class XGBoostRegressionModelReader extends MLReader[XGBoostRegressionModel] {
-
-    /** Checked against metadata when loading model */
-    private val className = classOf[XGBoostRegressionModel].getName
-
-    override def load(path: String): XGBoostRegressionModel = {
-      implicit val sc = super.sparkSession.sparkContext
-
-      val metadata = DefaultXGBoostParamsReader.loadMetadata(path, sc, className)
-
-      val dataPath = new Path(path, "data").toString
-      val internalPath = new Path(dataPath, "XGBoostRegressionModel")
-      val dataInStream = internalPath.getFileSystem(sc.hadoopConfiguration).open(internalPath)
-
-      val booster = SXGBoost.loadModel(dataInStream)
-      val model = new XGBoostRegressionModel(metadata.uid, booster)
-      DefaultXGBoostParamsReader.getAndSetParams(model, metadata)
-      model
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostTrainingSummary.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostTrainingSummary.scala
deleted file mode 100644
index 9454bef..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostTrainingSummary.scala
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark
-
-class XGBoostTrainingSummary private(
-    val trainObjectiveHistory: Array[Float],
-    val validationObjectiveHistory: (String, Array[Float])*) extends Serializable {
-
-  override def toString: String = {
-    val train = trainObjectiveHistory.mkString(",")
-    val vaidationObjectiveHistoryString = {
-      validationObjectiveHistory.map {
-        case (name, metrics) =>
-          s"${name}ObjectiveHistory=${metrics.mkString(",")}"
-      }.mkString(";")
-    }
-    s"XGBoostTrainingSummary(trainObjectiveHistory=$train; $vaidationObjectiveHistoryString)"
-  }
-}
-
-private[xgboost4j] object XGBoostTrainingSummary {
-  def apply(metrics: Map[String, Array[Float]]): XGBoostTrainingSummary = {
-    new XGBoostTrainingSummary(
-      trainObjectiveHistory = metrics("train"),
-      metrics.filter(_._1 != "train").toSeq: _*)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/package.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/package.scala
deleted file mode 100644
index df5e3bc..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/package.scala
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import java.util.Properties
-
-import org.apache.spark.SparkException
-
-package object spark {
-  private def loadVersionInfo(): String = {
-    val versionResourceFile = Thread.currentThread().getContextClassLoader.getResourceAsStream(
-      "xgboost4j-version.properties")
-    try {
-      val unknownProp = "<unknown>"
-      val props = new Properties()
-      props.load(versionResourceFile)
-      props.getProperty("version", unknownProp)
-    } catch {
-      case e: Exception =>
-        throw new SparkException("Error loading properties from xgboost4j-version.properties", e)
-    } finally {
-      if (versionResourceFile != null) {
-        try {
-          versionResourceFile.close()
-        } catch {
-          case e: Exception =>
-            throw new SparkException("Error closing xgboost4j version resource stream", e)
-        }
-      }
-    }
-  }
-
-  val VERSION: String = loadVersionInfo()
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala
deleted file mode 100644
index 0ba5410..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala
+++ /dev/null
@@ -1,303 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import scala.collection.immutable.HashSet
-
-import org.apache.spark.ml.param.{DoubleParam, IntParam, Param, Params}
-
-private[spark] trait BoosterParams extends Params {
-
-  /**
-   * step size shrinkage used in update to prevents overfitting. After each boosting step, we
-   * can directly get the weights of new features and eta actually shrinks the feature weights
-   * to make the boosting process more conservative. [default=0.3] range: [0,1]
-   */
-  final val eta = new DoubleParam(this, "eta", "step size shrinkage used in update to prevents" +
-    " overfitting. After each boosting step, we can directly get the weights of new features." +
-    " and eta actually shrinks the feature weights to make the boosting process more conservative.",
-    (value: Double) => value >= 0 && value <= 1)
-
-  final def getEta: Double = $(eta)
-
-  /**
-   * minimum loss reduction required to make a further partition on a leaf node of the tree.
-   * the larger, the more conservative the algorithm will be. [default=0] range: [0,
-   * Double.MaxValue]
-   */
-  final val gamma = new DoubleParam(this, "gamma", "minimum loss reduction required to make a " +
-    "further partition on a leaf node of the tree. the larger, the more conservative the " +
-    "algorithm will be.", (value: Double) => value >= 0)
-
-  final def getGamma: Double = $(gamma)
-
-  /**
-   * maximum depth of a tree, increase this value will make model more complex / likely to be
-   * overfitting. [default=6] range: [1, Int.MaxValue]
-   */
-  final val maxDepth = new IntParam(this, "maxDepth", "maximum depth of a tree, increase this " +
-    "value will make model more complex/likely to be overfitting.", (value: Int) => value >= 0)
-
-  final def getMaxDepth: Int = $(maxDepth)
-
-
-  /**
-   * Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.
-   */
-  final val maxLeaves = new IntParam(this, "maxLeaves",
-    "Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.",
-    (value: Int) => value >= 0)
-
-  final def getMaxLeaves: Int = $(maxLeaves)
-
-
-  /**
-   * minimum sum of instance weight(hessian) needed in a child. If the tree partition step results
-   * in a leaf node with the sum of instance weight less than min_child_weight, then the building
-   * process will give up further partitioning. In linear regression mode, this simply corresponds
-   * to minimum number of instances needed to be in each node. The larger, the more conservative
-   * the algorithm will be. [default=1] range: [0, Double.MaxValue]
-   */
-  final val minChildWeight = new DoubleParam(this, "minChildWeight", "minimum sum of instance" +
-    " weight(hessian) needed in a child. If the tree partition step results in a leaf node with" +
-    " the sum of instance weight less than min_child_weight, then the building process will" +
-    " give up further partitioning. In linear regression mode, this simply corresponds to minimum" +
-    " number of instances needed to be in each node. The larger, the more conservative" +
-    " the algorithm will be.", (value: Double) => value >= 0)
-
-  final def getMinChildWeight: Double = $(minChildWeight)
-
-  /**
-   * Maximum delta step we allow each tree's weight estimation to be. If the value is set to 0, it
-   * means there is no constraint. If it is set to a positive value, it can help making the update
-   * step more conservative. Usually this parameter is not needed, but it might help in logistic
-   * regression when class is extremely imbalanced. Set it to value of 1-10 might help control the
-   * update. [default=0] range: [0, Double.MaxValue]
-   */
-  final val maxDeltaStep = new DoubleParam(this, "maxDeltaStep", "Maximum delta step we allow " +
-    "each tree's weight" +
-    " estimation to be. If the value is set to 0, it means there is no constraint. If it is set" +
-    " to a positive value, it can help making the update step more conservative. Usually this" +
-    " parameter is not needed, but it might help in logistic regression when class is extremely" +
-    " imbalanced. Set it to value of 1-10 might help control the update",
-    (value: Double) => value >= 0)
-
-  final def getMaxDeltaStep: Double = $(maxDeltaStep)
-
-  /**
-   * subsample ratio of the training instance. Setting it to 0.5 means that XGBoost randomly
-   * collected half of the data instances to grow trees and this will prevent overfitting.
-   * [default=1] range:(0,1]
-   */
-  final val subsample = new DoubleParam(this, "subsample", "subsample ratio of the training " +
-    "instance. Setting it to 0.5 means that XGBoost randomly collected half of the data " +
-    "instances to grow trees and this will prevent overfitting.",
-    (value: Double) => value <= 1 && value > 0)
-
-  final def getSubsample: Double = $(subsample)
-
-  /**
-   * subsample ratio of columns when constructing each tree. [default=1] range: (0,1]
-   */
-  final val colsampleBytree = new DoubleParam(this, "colsampleBytree", "subsample ratio of " +
-    "columns when constructing each tree.", (value: Double) => value <= 1 && value > 0)
-
-  final def getColsampleBytree: Double = $(colsampleBytree)
-
-  /**
-   * subsample ratio of columns for each split, in each level. [default=1] range: (0,1]
-   */
-  final val colsampleBylevel = new DoubleParam(this, "colsampleBylevel", "subsample ratio of " +
-    "columns for each split, in each level.", (value: Double) => value <= 1 && value > 0)
-
-  final def getColsampleBylevel: Double = $(colsampleBylevel)
-
-  /**
-   * L2 regularization term on weights, increase this value will make model more conservative.
-   * [default=1]
-   */
-  final val lambda = new DoubleParam(this, "lambda", "L2 regularization term on weights, " +
-    "increase this value will make model more conservative.", (value: Double) => value >= 0)
-
-  final def getLambda: Double = $(lambda)
-
-  /**
-   * L1 regularization term on weights, increase this value will make model more conservative.
-   * [default=0]
-   */
-  final val alpha = new DoubleParam(this, "alpha", "L1 regularization term on weights, increase " +
-    "this value will make model more conservative.", (value: Double) => value >= 0)
-
-  final def getAlpha: Double = $(alpha)
-
-  /**
-   * The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx'}
-   *  [default='auto']
-   */
-  final val treeMethod = new Param[String](this, "treeMethod",
-    "The tree construction algorithm used in XGBoost, options: {'auto', 'exact', 'approx', 'hist'}",
-    (value: String) => BoosterParams.supportedTreeMethods.contains(value))
-
-  final def getTreeMethod: String = $(treeMethod)
-
-  /**
-   * growth policy for fast histogram algorithm
-   */
-  final val growPolicy = new Param[String](this, "growPolicy",
-    "Controls a way new nodes are added to the tree. Currently supported only if" +
-      " tree_method is set to hist. Choices: depthwise, lossguide, depthwiselossltd. " +
-      " depthwise: split at nodes closest to the root. " +
-      " lossguide: split at nodes with highest loss change." +
-      " depthwiselossltd: nodes that provide low loss change are prevented from split.",
-    (value: String) => BoosterParams.supportedGrowthPolicies.contains(value))
-
-  final def getGrowPolicy: String = $(growPolicy)
-
-  /**
-   * maximum number of bins in histogram
-   */
-  final val maxBins = new IntParam(this, "maxBin", "maximum number of bins in histogram",
-    (value: Int) => value > 0)
-
-  final def getMaxBins: Int = $(maxBins)
-
-  /**
-   * This is only used for approximate greedy algorithm.
-   * This roughly translated into O(1 / sketch_eps) number of bins. Compared to directly select
-   * number of bins, this comes with theoretical guarantee with sketch accuracy.
-   * [default=0.03] range: (0, 1)
-   */
-  final val sketchEps = new DoubleParam(this, "sketchEps",
-    "This is only used for approximate greedy algorithm. This roughly translated into" +
-      " O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes" +
-      " with theoretical guarantee with sketch accuracy.",
-    (value: Double) => value < 1 && value > 0)
-
-  final def getSketchEps: Double = $(sketchEps)
-
-  /**
-   * Control the balance of positive and negative weights, useful for unbalanced classes. A typical
-   * value to consider: sum(negative cases) / sum(positive cases).   [default=1]
-   */
-  final val scalePosWeight = new DoubleParam(this, "scalePosWeight", "Control the balance of " +
-    "positive and negative weights, useful for unbalanced classes. A typical value to consider:" +
-    " sum(negative cases) / sum(positive cases)")
-
-  final def getScalePosWeight: Double = $(scalePosWeight)
-
-  // Dart boosters
-
-  /**
-   * Parameter for Dart booster.
-   * Type of sampling algorithm. "uniform": dropped trees are selected uniformly.
-   * "weighted": dropped trees are selected in proportion to weight. [default="uniform"]
-   */
-  final val sampleType = new Param[String](this, "sampleType", "type of sampling algorithm, " +
-    "options: {'uniform', 'weighted'}",
-    (value: String) => BoosterParams.supportedSampleType.contains(value))
-
-  final def getSampleType: String = $(sampleType)
-
-  /**
-   * Parameter of Dart booster.
-   * type of normalization algorithm, options: {'tree', 'forest'}. [default="tree"]
-   */
-  final val normalizeType = new Param[String](this, "normalizeType", "type of normalization" +
-    " algorithm, options: {'tree', 'forest'}",
-    (value: String) => BoosterParams.supportedNormalizeType.contains(value))
-
-  final def getNormalizeType: String = $(normalizeType)
-
-  /**
-   * Parameter of Dart booster.
-   * dropout rate. [default=0.0] range: [0.0, 1.0]
-   */
-  final val rateDrop = new DoubleParam(this, "rateDrop", "dropout rate", (value: Double) =>
-    value >= 0 && value <= 1)
-
-  final def getRateDrop: Double = $(rateDrop)
-
-  /**
-   * Parameter of Dart booster.
-   * probability of skip dropout. If a dropout is skipped, new trees are added in the same manner
-   * as gbtree. [default=0.0] range: [0.0, 1.0]
-   */
-  final val skipDrop = new DoubleParam(this, "skipDrop", "probability of skip dropout. If" +
-    " a dropout is skipped, new trees are added in the same manner as gbtree.",
-    (value: Double) => value >= 0 && value <= 1)
-
-  final def getSkipDrop: Double = $(skipDrop)
-
-  // linear booster
-  /**
-   * Parameter of linear booster
-   * L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
-   */
-  final val lambdaBias = new DoubleParam(this, "lambdaBias", "L2 regularization term on bias, " +
-    "default 0 (no L1 reg on bias because it is not important)", (value: Double) => value >= 0)
-
-  final def getLambdaBias: Double = $(lambdaBias)
-
-  final val treeLimit = new IntParam(this, name = "treeLimit",
-    doc = "number of trees used in the prediction; defaults to 0 (use all trees).")
-
-  final def getTreeLimit: Int = $(treeLimit)
-
-  final val monotoneConstraints = new Param[String](this, name = "monotoneConstraints",
-    doc = "a list in length of number of features, 1 indicate monotonic increasing, - 1 means " +
-      "decreasing, 0 means no constraint. If it is shorter than number of features, 0 will be " +
-      "padded ")
-
-  final def getMonotoneConstraints: String = $(monotoneConstraints)
-
-  final val interactionConstraints = new Param[String](this,
-    name = "interactionConstraints",
-    doc = "Constraints for interaction representing permitted interactions. The constraints" +
-      " must be specified in the form of a nest list, e.g. [[0, 1], [2, 3, 4]]," +
-      " where each inner list is a group of indices of features that are allowed to interact" +
-      " with each other. See tutorial for more information")
-
-  final def getInteractionConstraints: String = $(interactionConstraints)
-
-  setDefault(eta -> 0.3, gamma -> 0, maxDepth -> 6,
-    minChildWeight -> 1, maxDeltaStep -> 0,
-    growPolicy -> "depthwise", maxBins -> 16,
-    subsample -> 1, colsampleBytree -> 1, colsampleBylevel -> 1,
-    lambda -> 1, alpha -> 0, treeMethod -> "auto", sketchEps -> 0.03,
-    scalePosWeight -> 1.0, sampleType -> "uniform", normalizeType -> "tree",
-    rateDrop -> 0.0, skipDrop -> 0.0, lambdaBias -> 0, treeLimit -> 0)
-}
-
-private[spark] object BoosterParams {
-
-  val supportedBoosters = HashSet("gbtree", "gblinear", "dart")
-
-  val supportedTreeMethods = HashSet("auto", "exact", "approx", "hist")
-
-  val supportedGrowthPolicies = HashSet("depthwise", "lossguide", "depthwiselossltd")
-
-  val supportedSampleType = HashSet("uniform", "weighted")
-
-  val supportedNormalizeType = HashSet("tree", "forest")
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/CustomParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/CustomParams.scala
deleted file mode 100644
index b7f696a..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/CustomParams.scala
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import ml.dmlc.xgboost4j.scala.{EvalTrait, ObjectiveTrait}
-import ml.dmlc.xgboost4j.scala.spark.TrackerConf
-import org.json4s.{DefaultFormats, Extraction, NoTypeHints}
-import org.json4s.jackson.JsonMethods.{compact, parse, render}
-
-import org.apache.spark.ml.param.{Param, ParamPair, Params}
-import org.apache.spark.sql.DataFrame
-
-class CustomEvalParam(
-    parent: Params,
-    name: String,
-    doc: String) extends Param[EvalTrait](parent, name, doc) {
-
-  /** Creates a param pair with the given value (for Java). */
-  override def w(value: EvalTrait): ParamPair[EvalTrait] = super.w(value)
-
-  override def jsonEncode(value: EvalTrait): String = {
-    import org.json4s.jackson.Serialization
-    implicit val formats = Serialization.formats(NoTypeHints)
-    compact(render(Extraction.decompose(value)))
-  }
-
-  override def jsonDecode(json: String): EvalTrait = {
-    implicit val formats = DefaultFormats
-    parse(json).extract[EvalTrait]
-  }
-}
-
-class CustomObjParam(
-    parent: Params,
-    name: String,
-    doc: String) extends Param[ObjectiveTrait](parent, name, doc) {
-
-  /** Creates a param pair with the given value (for Java). */
-  override def w(value: ObjectiveTrait): ParamPair[ObjectiveTrait] = super.w(value)
-
-  override def jsonEncode(value: ObjectiveTrait): String = {
-    import org.json4s.jackson.Serialization
-    implicit val formats = Serialization.formats(NoTypeHints)
-    compact(render(Extraction.decompose(value)))
-  }
-
-  override def jsonDecode(json: String): ObjectiveTrait = {
-    implicit val formats = DefaultFormats
-    parse(json).extract[ObjectiveTrait]
-  }
-}
-
-class TrackerConfParam(
-    parent: Params,
-    name: String,
-    doc: String) extends Param[TrackerConf](parent, name, doc) {
-
-  /** Creates a param pair with the given value (for Java). */
-  override def w(value: TrackerConf): ParamPair[TrackerConf] = super.w(value)
-
-  override def jsonEncode(value: TrackerConf): String = {
-    import org.json4s.jackson.Serialization
-    implicit val formats = Serialization.formats(NoTypeHints)
-    compact(render(Extraction.decompose(value)))
-  }
-
-  override def jsonDecode(json: String): TrackerConf = {
-    implicit val formats = DefaultFormats
-    val parsedValue = parse(json)
-    parsedValue.extract[TrackerConf]
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsReader.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsReader.scala
deleted file mode 100644
index bb75bb3..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsReader.scala
+++ /dev/null
@@ -1,149 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import org.apache.hadoop.fs.Path
-import org.json4s.{DefaultFormats, JValue}
-import org.json4s.JsonAST.JObject
-import org.json4s.jackson.JsonMethods.{compact, parse, render}
-
-import org.apache.spark.SparkContext
-import org.apache.spark.ml.param.{Param, Params}
-import org.apache.spark.ml.util.MLReader
-
-// This originates from apache-spark DefaultPramsReader copy paste
-private[spark] object DefaultXGBoostParamsReader {
-
-  private val paramNameCompatibilityMap: Map[String, String] = Map("silent" -> "verbosity")
-
-  private val paramValueCompatibilityMap: Map[String, Map[Any, Any]] =
-    Map("objective" -> Map("reg:linear" -> "reg:squarederror"))
-
-  /**
-   * All info from metadata file.
-   *
-   * @param params       paramMap, as a `JValue`
-   * @param metadata     All metadata, including the other fields
-   * @param metadataJson Full metadata file String (for debugging)
-   */
-  case class Metadata(
-      className: String,
-      uid: String,
-      timestamp: Long,
-      sparkVersion: String,
-      params: JValue,
-      metadata: JValue,
-      metadataJson: String) {
-
-    /**
-     * Get the JSON value of the [[org.apache.spark.ml.param.Param]] of the given name.
-     * This can be useful for getting a Param value before an instance of `Params`
-     * is available.
-     */
-    def getParamValue(paramName: String): JValue = {
-      implicit val format = DefaultFormats
-      params match {
-        case JObject(pairs) =>
-          val values = pairs.filter { case (pName, jsonValue) =>
-            pName == paramName
-          }.map(_._2)
-          assert(values.length == 1, s"Expected one instance of Param '$paramName' but found" +
-            s" ${values.length} in JSON Params: " + pairs.map(_.toString).mkString(", "))
-          values.head
-        case _ =>
-          throw new IllegalArgumentException(
-            s"Cannot recognize JSON metadata: $metadataJson.")
-      }
-    }
-  }
-
-  /**
-   * Load metadata saved using [[DefaultXGBoostParamsWriter.saveMetadata()]]
-   *
-   * @param expectedClassName If non empty, this is checked against the loaded metadata.
-   * @throws IllegalArgumentException if expectedClassName is specified and does not match metadata
-   */
-  def loadMetadata(path: String, sc: SparkContext, expectedClassName: String = ""): Metadata = {
-    val metadataPath = new Path(path, "metadata").toString
-    val metadataStr = sc.textFile(metadataPath, 1).first()
-    parseMetadata(metadataStr, expectedClassName)
-  }
-
-  /**
-   * Parse metadata JSON string produced by [[DefaultXGBoostParamsWriter.getMetadataToSave()]].
-   * This is a helper function for [[loadMetadata()]].
-   *
-   * @param metadataStr       JSON string of metadata
-   * @param expectedClassName If non empty, this is checked against the loaded metadata.
-   * @throws IllegalArgumentException if expectedClassName is specified and does not match metadata
-   */
-  def parseMetadata(metadataStr: String, expectedClassName: String = ""): Metadata = {
-    val metadata = parse(metadataStr)
-
-    implicit val format = DefaultFormats
-    val className = (metadata \ "class").extract[String]
-    val uid = (metadata \ "uid").extract[String]
-    val timestamp = (metadata \ "timestamp").extract[Long]
-    val sparkVersion = (metadata \ "sparkVersion").extract[String]
-    val params = metadata \ "paramMap"
-    if (expectedClassName.nonEmpty) {
-      require(className == expectedClassName, s"Error loading metadata: Expected class name" +
-        s" $expectedClassName but found class name $className")
-    }
-
-    Metadata(className, uid, timestamp, sparkVersion, params, metadata, metadataStr)
-  }
-
-  private def handleBrokenlyChangedValue[T](paramName: String, value: T): T = {
-    paramValueCompatibilityMap.getOrElse(paramName, Map()).getOrElse(value, value).asInstanceOf[T]
-  }
-
-  private def handleBrokenlyChangedName(paramName: String): String = {
-    paramNameCompatibilityMap.getOrElse(paramName, paramName)
-  }
-
-  /**
-   * Extract Params from metadata, and set them in the instance.
-   * This works if all Params implement [[org.apache.spark.ml.param.Param.jsonDecode()]].
-   * TODO: Move to [[Metadata]] method
-   */
-  def getAndSetParams(instance: Params, metadata: Metadata): Unit = {
-    implicit val format = DefaultFormats
-    metadata.params match {
-      case JObject(pairs) =>
-        pairs.foreach { case (paramName, jsonValue) =>
-          val param = instance.getParam(handleBrokenlyChangedName(paramName))
-          val value = param.jsonDecode(compact(render(jsonValue)))
-          instance.set(param, handleBrokenlyChangedValue(paramName, value))
-        }
-      case _ =>
-        throw new IllegalArgumentException(
-          s"Cannot recognize JSON metadata: ${metadata.metadataJson}.")
-    }
-  }
-
-  /**
-   * Load a `Params` instance from the given path, and return it.
-   * This assumes the instance implements [[org.apache.spark.ml.util.MLReadable]].
-   */
-  def loadParamsInstance[T](path: String, sc: SparkContext): T = {
-    val metadata = DefaultXGBoostParamsReader.loadMetadata(path, sc)
-    val cls = Utils.classForName(metadata.className)
-    cls.getMethod("read").invoke(null).asInstanceOf[MLReader[T]].load(path)
-  }
-}
-
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsWriter.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsWriter.scala
deleted file mode 100644
index acf6815..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsWriter.scala
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import org.apache.hadoop.fs.Path
-import org.apache.spark.SparkContext
-import org.apache.spark.ml.param.{ParamPair, Params}
-import org.json4s.JsonDSL._
-import org.json4s.jackson.JsonMethods._
-import org.json4s.{JObject, _}
-
-// This originates from apache-spark DefaultPramsWriter copy paste
-private[spark] object DefaultXGBoostParamsWriter {
-
-  /**
-   * Saves metadata + Params to: path + "/metadata"
-   *  - class
-   *  - timestamp
-   *  - sparkVersion
-   *  - uid
-   *  - paramMap
-   *  - (optionally, extra metadata)
-   *
-   * @param extraMetadata Extra metadata to be saved at same level as uid, paramMap, etc.
-   * @param paramMap      If given, this is saved in the "paramMap" field.
-   *                      Otherwise, all [[org.apache.spark.ml.param.Param]]s are encoded using
-   *                      [[org.apache.spark.ml.param.Param.jsonEncode()]].
-   */
-  def saveMetadata(
-    instance: Params,
-    path: String,
-    sc: SparkContext,
-    extraMetadata: Option[JObject] = None,
-    paramMap: Option[JValue] = None): Unit = {
-
-    val metadataPath = new Path(path, "metadata").toString
-    val metadataJson = getMetadataToSave(instance, sc, extraMetadata, paramMap)
-    sc.parallelize(Seq(metadataJson), 1).saveAsTextFile(metadataPath)
-  }
-
-  /**
-   * Helper for [[saveMetadata()]] which extracts the JSON to save.
-   * This is useful for ensemble models which need to save metadata for many sub-models.
-   *
-   * @see [[saveMetadata()]] for details on what this includes.
-   */
-  def getMetadataToSave(
-    instance: Params,
-    sc: SparkContext,
-    extraMetadata: Option[JObject] = None,
-    paramMap: Option[JValue] = None): String = {
-    val uid = instance.uid
-    val cls = instance.getClass.getName
-    val params = instance.extractParamMap().toSeq.asInstanceOf[Seq[ParamPair[Any]]]
-    val jsonParams = paramMap.getOrElse(render(params.filter{
-      case ParamPair(p, _) => p != null
-    }.map {
-      case ParamPair(p, v) =>
-        p.name -> parse(p.jsonEncode(v))
-    }.toList))
-    val basicMetadata = ("class" -> cls) ~
-      ("timestamp" -> System.currentTimeMillis()) ~
-      ("sparkVersion" -> sc.version) ~
-      ("uid" -> uid) ~
-      ("paramMap" -> jsonParams)
-    val metadata = extraMetadata match {
-      case Some(jObject) =>
-        basicMetadata ~ jObject
-      case None =>
-        basicMetadata
-    }
-    val metadataJson: String = compact(render(metadata))
-    metadataJson
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala
deleted file mode 100644
index 440481b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala
+++ /dev/null
@@ -1,314 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import com.google.common.base.CaseFormat
-import ml.dmlc.xgboost4j.scala.spark.TrackerConf
-
-import org.apache.spark.ml.param._
-import scala.collection.mutable
-
-import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-
-import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Column, DataFrame, Row}
-import org.apache.spark.sql.functions.col
-import org.apache.spark.sql.types.{FloatType, IntegerType}
-
-private[spark] trait GeneralParams extends Params {
-
-  /**
-   * The number of rounds for boosting
-   */
-  final val numRound = new IntParam(this, "numRound", "The number of rounds for boosting",
-    ParamValidators.gtEq(1))
-
-  final def getNumRound: Int = $(numRound)
-
-  /**
-   * number of workers used to train xgboost model. default: 1
-   */
-  final val numWorkers = new IntParam(this, "numWorkers", "number of workers used to run xgboost",
-    ParamValidators.gtEq(1))
-
-  final def getNumWorkers: Int = $(numWorkers)
-
-  /**
-   * number of threads used by per worker. default 1
-   */
-  final val nthread = new IntParam(this, "nthread", "number of threads used by per worker",
-    ParamValidators.gtEq(1))
-
-  final def getNthread: Int = $(nthread)
-
-  /**
-   * whether to use external memory as cache. default: false
-   */
-  final val useExternalMemory = new BooleanParam(this, "useExternalMemory",
-    "whether to use external memory as cache")
-
-  final def getUseExternalMemory: Boolean = $(useExternalMemory)
-
-  /**
-   * Deprecated. Please use verbosity instead.
-   * 0 means printing running messages, 1 means silent mode. default: 0
-   */
-  final val silent = new IntParam(this, "silent",
-    "Deprecated. Please use verbosity instead. " +
-    "0 means printing running messages, 1 means silent mode.",
-    (value: Int) => value >= 0 && value <= 1)
-
-  final def getSilent: Int = $(silent)
-
-  /**
-   * Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug).
-   * default: 1
-   */
-  final val verbosity = new IntParam(this, "verbosity",
-    "Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), " +
-    "3 (debug).",
-    (value: Int) => value >= 0 && value <= 3)
-
-  final def getVerbosity: Int = $(verbosity)
-
-  /**
-   * customized objective function provided by user. default: null
-   */
-  final val customObj = new CustomObjParam(this, "customObj", "customized objective function " +
-    "provided by user")
-
-  /**
-   * customized evaluation function provided by user. default: null
-   */
-  final val customEval = new CustomEvalParam(this, "customEval",
-    "customized evaluation function provided by user")
-
-  /**
-   * the value treated as missing. default: Float.NaN
-   */
-  final val missing = new FloatParam(this, "missing", "the value treated as missing")
-
-  final def getMissing: Float = $(missing)
-
-  /**
-    * Allows for having a non-zero value for missing when training on prediction
-    * on a Sparse or Empty vector.
-    */
-  final val allowNonZeroForMissing = new BooleanParam(
-    this,
-    "allowNonZeroForMissing",
-    "Allow to have a non-zero value for missing when training or " +
-      "predicting on a Sparse or Empty vector. Should only be used if did " +
-      "not use Spark's VectorAssembler class to construct the feature vector " +
-      "but instead used a method that preserves zeros in your vector."
-  )
-
-  final def getAllowNonZeroForMissingValue: Boolean = $(allowNonZeroForMissing)
-
-  /**
-    * the maximum time to wait for the job requesting new workers. default: 30 minutes
-    */
-  final val timeoutRequestWorkers = new LongParam(this, "timeoutRequestWorkers", "the maximum " +
-    "time to request new Workers if numCores are insufficient. The timeout will be disabled " +
-    "if this value is set smaller than or equal to 0.")
-
-  final def getTimeoutRequestWorkers: Long = $(timeoutRequestWorkers)
-
-  /**
-    * The hdfs folder to load and save checkpoint boosters. default: `empty_string`
-    */
-  final val checkpointPath = new Param[String](this, "checkpointPath", "the hdfs folder to load " +
-    "and save checkpoints. If there are existing checkpoints in checkpoint_path. The job will " +
-    "load the checkpoint with highest version as the starting point for training. If " +
-    "checkpoint_interval is also set, the job will save a checkpoint every a few rounds.")
-
-  final def getCheckpointPath: String = $(checkpointPath)
-
-  /**
-    * Param for set checkpoint interval (&gt;= 1) or disable checkpoint (-1). E.g. 10 means that
-    * the trained model will get checkpointed every 10 iterations. Note: `checkpoint_path` must
-    * also be set if the checkpoint interval is greater than 0.
-    */
-  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval",
-    "set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the trained " +
-      "model will get checkpointed every 10 iterations. Note: `checkpoint_path` must also be " +
-      "set if the checkpoint interval is greater than 0.",
-    (interval: Int) => interval == -1 || interval >= 1)
-
-  final def getCheckpointInterval: Int = $(checkpointInterval)
-
-  /**
-    * Rabit tracker configurations. The parameter must be provided as an instance of the
-    * TrackerConf class, which has the following definition:
-    *
-    *     case class TrackerConf(workerConnectionTimeout: Duration, trainingTimeout: Duration,
-    *                            trackerImpl: String)
-    *
-    * See below for detailed explanations.
-    *
-    *   - trackerImpl: Select the implementation of Rabit tracker.
-    *                  default: "python"
-    *
-    *        Choice between "python" or "scala". The former utilizes the Java wrapper of the
-    *        Python Rabit tracker (in dmlc_core), and does not support timeout settings.
-    *        The "scala" version removes Python components, and fully supports timeout settings.
-    *
-    *   - workerConnectionTimeout: the maximum wait time for all workers to connect to the tracker.
-    *                             default: 0 millisecond (no timeout)
-    *
-    *        The timeout value should take the time of data loading and pre-processing into account,
-    *        due to the lazy execution of Spark's operations. Alternatively, you may force Spark to
-    *        perform data transformation before calling XGBoost.train(), so that this timeout truly
-    *        reflects the connection delay. Set a reasonable timeout value to prevent model
-    *        training/testing from hanging indefinitely, possible due to network issues.
-    *        Note that zero timeout value means to wait indefinitely (equivalent to Duration.Inf).
-    *        Ignored if the tracker implementation is "python".
-    */
-  final val trackerConf = new TrackerConfParam(this, "trackerConf", "Rabit tracker configurations")
-
-  /** Random seed for the C++ part of XGBoost and train/test splitting. */
-  final val seed = new LongParam(this, "seed", "random seed")
-
-  final def getSeed: Long = $(seed)
-
-  setDefault(numRound -> 1, numWorkers -> 1, nthread -> 1,
-    useExternalMemory -> false, silent -> 0, verbosity -> 1,
-    customObj -> null, customEval -> null, missing -> Float.NaN,
-    trackerConf -> TrackerConf(), seed -> 0, timeoutRequestWorkers -> 30 * 60 * 1000L,
-    checkpointPath -> "", checkpointInterval -> -1,
-    allowNonZeroForMissing -> false)
-}
-
-trait HasLeafPredictionCol extends Params {
-  /**
-   * Param for leaf prediction column name.
-   * @group param
-   */
-  final val leafPredictionCol: Param[String] = new Param[String](this, "leafPredictionCol",
-    "name of the predictLeaf results")
-
-  /** @group getParam */
-  final def getLeafPredictionCol: String = $(leafPredictionCol)
-}
-
-trait HasContribPredictionCol extends Params {
-  /**
-   * Param for contribution prediction column name.
-   * @group param
-   */
-  final val contribPredictionCol: Param[String] = new Param[String](this, "contribPredictionCol",
-    "name of the predictContrib results")
-
-  /** @group getParam */
-  final def getContribPredictionCol: String = $(contribPredictionCol)
-}
-
-trait HasBaseMarginCol extends Params {
-
-  /**
-   * Param for initial prediction (aka base margin) column name.
-   * @group param
-   */
-  final val baseMarginCol: Param[String] = new Param[String](this, "baseMarginCol",
-    "Initial prediction (aka base margin) column name.")
-
-  /** @group getParam */
-  final def getBaseMarginCol: String = $(baseMarginCol)
-}
-
-trait HasGroupCol extends Params {
-
-  /**
-   * Param for group column name.
-   * @group param
-   */
-  final val groupCol: Param[String] = new Param[String](this, "groupCol", "group column name.")
-
-  /** @group getParam */
-  final def getGroupCol: String = $(groupCol)
-
-}
-
-trait HasNumClass extends Params {
-
-  /**
-   * number of classes
-   */
-  final val numClass = new IntParam(this, "numClass", "number of classes")
-
-  /** @group getParam */
-  final def getNumClass: Int = $(numClass)
-}
-
-private[spark] trait ParamMapFuncs extends Params {
-
-  // forwarding non-original params
-  var otherParams = Map[String, Any]()
-
-  def XGBoost2MLlibParams(xgboostParams: Map[String, Any]): Unit = {
-    for ((paramName, paramValue) <- xgboostParams) {
-      if ((paramName == "booster" && paramValue != "gbtree") ||
-        (paramName == "updater" && paramValue != "grow_histmaker,prune" &&
-          paramValue != "hist")) {
-        throw new IllegalArgumentException(s"you specified $paramName as $paramValue," +
-          s" XGBoost-Spark only supports gbtree as booster type" +
-          " and grow_histmaker,prune or hist as the updater type")
-      }
-      val name = CaseFormat.LOWER_UNDERSCORE.to(CaseFormat.LOWER_CAMEL, paramName)
-      val param = params.find(_.name == name)
-      param.foreach {
-        case _: DoubleParam =>
-          set(name, paramValue.toString.toDouble)
-        case _: BooleanParam =>
-          set(name, paramValue.toString.toBoolean)
-        case _: IntParam =>
-          set(name, paramValue.toString.toInt)
-        case _: FloatParam =>
-          set(name, paramValue.toString.toFloat)
-        case _: LongParam =>
-          set(name, paramValue.toString.toLong)
-        case _: Param[_] =>
-          set(name, paramValue)
-      }
-      if (param.isEmpty) {
-        otherParams += (paramName -> paramValue)
-      }
-    }
-  }
-
-  def MLlib2XGBoostParams: Map[String, Any] = {
-    val xgboostParams = new mutable.HashMap[String, Any]()
-    for (param <- params) {
-      if (isDefined(param)) {
-        val name = CaseFormat.LOWER_CAMEL.to(CaseFormat.LOWER_UNDERSCORE, param.name)
-        xgboostParams += name -> $(param)
-      }
-    }
-    for (param <- otherParams) {
-      xgboostParams += param
-    }
-    xgboostParams.toMap
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/InferenceParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/InferenceParams.scala
deleted file mode 100644
index abfe777..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/InferenceParams.scala
+++ /dev/null
@@ -1,32 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import org.apache.spark.ml.param.{IntParam, Params}
-
-private[spark] trait InferenceParams extends Params {
-
-  /**
-   * batch size of inference iteration
-   */
-  final val inferBatchSize = new IntParam(this, "batchSize", "batch size of inference iteration")
-
-  /** @group getParam */
-  final def getInferBatchSize: Int = ${inferBatchSize}
-
-  setDefault(inferBatchSize, 32 << 10)
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
deleted file mode 100644
index 1512c85..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import scala.collection.immutable.HashSet
-
-import org.apache.spark.ml.param._
-
-private[spark] trait LearningTaskParams extends Params {
-
-  /**
-   * Specify the learning task and the corresponding learning objective.
-   * options: reg:squarederror, reg:squaredlogerror, reg:logistic, binary:logistic, binary:logitraw,
-   * count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma.
-   * default: reg:squarederror
-   */
-  final val objective = new Param[String](this, "objective",
-    "objective function used for training")
-
-  final def getObjective: String = $(objective)
-
-  /**
-   * The learning objective type of the specified custom objective and eval.
-   * Corresponding type will be assigned if custom objective is defined
-   * options: regression, classification. default: null
-   */
-  final val objectiveType = new Param[String](this, "objectiveType", "objective type used for " +
-    s"training, options: {${LearningTaskParams.supportedObjectiveType.mkString(",")}",
-    (value: String) => LearningTaskParams.supportedObjectiveType.contains(value))
-
-  final def getObjectiveType: String = $(objectiveType)
-
-
-  /**
-   * the initial prediction score of all instances, global bias. default=0.5
-   */
-  final val baseScore = new DoubleParam(this, "baseScore", "the initial prediction score of all" +
-    " instances, global bias")
-
-  final def getBaseScore: Double = $(baseScore)
-
-  /**
-   * evaluation metrics for validation data, a default metric will be assigned according to
-   * objective(rmse for regression, and error for classification, mean average precision for
-   * ranking). options: rmse, rmsle, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map,
-   * gamma-deviance
-   */
-  final val evalMetric = new Param[String](this, "evalMetric", "evaluation metrics for " +
-    "validation data, a default metric will be assigned according to objective " +
-    "(rmse for regression, and error for classification, mean average precision for ranking)")
-
-  final def getEvalMetric: String = $(evalMetric)
-
-  /**
-   * Fraction of training points to use for testing.
-   */
-  final val trainTestRatio = new DoubleParam(this, "trainTestRatio",
-    "fraction of training points to use for testing",
-    ParamValidators.inRange(0, 1))
-
-  final def getTrainTestRatio: Double = $(trainTestRatio)
-
-  /**
-   * whether caching training data
-   */
-  final val cacheTrainingSet = new BooleanParam(this, "cacheTrainingSet",
-    "whether caching training data")
-
-  /**
-   * whether cleaning checkpoint, always cleaning by default, having this parameter majorly for
-   * testing
-   */
-  final val skipCleanCheckpoint = new BooleanParam(this, "skipCleanCheckpoint",
-    "whether cleaning checkpoint data")
-
-  /**
-   * If non-zero, the training will be stopped after a specified number
-   * of consecutive increases in any evaluation metric.
-   */
-  final val numEarlyStoppingRounds = new IntParam(this, "numEarlyStoppingRounds",
-    "number of rounds of decreasing eval metric to tolerate before " +
-    "stopping the training",
-    (value: Int) => value == 0 || value > 1)
-
-  final def getNumEarlyStoppingRounds: Int = $(numEarlyStoppingRounds)
-
-
-  final val maximizeEvaluationMetrics = new BooleanParam(this, "maximizeEvaluationMetrics",
-    "define the expected optimization to the evaluation metrics, true to maximize otherwise" +
-      " minimize it")
-
-  final def getMaximizeEvaluationMetrics: Boolean = $(maximizeEvaluationMetrics)
-
-  setDefault(objective -> "reg:squarederror", baseScore -> 0.5,
-    trainTestRatio -> 1.0, numEarlyStoppingRounds -> 0, cacheTrainingSet -> false)
-}
-
-private[spark] object LearningTaskParams {
-
-  val supportedObjectiveType = HashSet("regression", "classification")
-
-  val evalMetricsToMaximize = HashSet("auc", "aucpr", "ndcg", "map")
-
-  val evalMetricsToMinimize = HashSet("rmse", "rmsle", "mae", "logloss", "error", "merror",
-    "mlogloss", "gamma-deviance")
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/NonParamVariables.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/NonParamVariables.scala
deleted file mode 100644
index 276a938..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/NonParamVariables.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import org.apache.spark.sql.DataFrame
-
-trait NonParamVariables {
-  protected var evalSetsMap: Map[String, DataFrame] = Map.empty
-
-  def setEvalSets(evalSets: Map[String, DataFrame]): this.type = {
-    evalSetsMap = evalSets
-    this
-  }
-
-  def getEvalSets(params: Map[String, Any]): Map[String, DataFrame] = {
-    if (params.contains("eval_sets")) {
-      params("eval_sets").asInstanceOf[Map[String, DataFrame]]
-    } else {
-      evalSetsMap
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/RabitParams.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/RabitParams.scala
deleted file mode 100644
index 6b811e0..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/RabitParams.scala
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-import org.apache.spark.ml.param._
-
-private[spark] trait RabitParams extends Params {
-  /**
-   * Rabit parameters passed through Rabit.Init into native layer
-   * rabit_ring_reduce_threshold - minimal threshold to enable ring based allreduce operation
-   * rabit_timeout - wait interval before exit after rabit observed failures set -1 to disable
-   * dmlc_worker_connect_retry - number of retrys to tracker
-   * dmlc_worker_stop_process_on_error - exit process when rabit see assert/error
-   */
-  final val rabitRingReduceThreshold = new IntParam(this, "rabitRingReduceThreshold",
-    "threshold count to enable allreduce/broadcast with ring based topology",
-          ParamValidators.gtEq(1))
-
-  final def rabitTimeout: IntParam = new IntParam(this, "rabitTimeout",
-  "timeout threshold after rabit observed failures")
-
-  final def rabitConnectRetry: IntParam = new IntParam(this, "dmlcWorkerConnectRetry",
-    "number of retry worker do before fail", ParamValidators.gtEq(1))
-
-  setDefault(rabitRingReduceThreshold -> (32 << 10), rabitConnectRetry -> 5, rabitTimeout -> -1)
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/Utils.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/Utils.scala
deleted file mode 100644
index 7d6e7b9..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/Utils.scala
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.spark.params
-
-// based on org.apache.spark.util copy /paste
-private[spark] object Utils {
-
-  def getSparkClassLoader: ClassLoader = getClass.getClassLoader
-
-  def getContextOrSparkClassLoader: ClassLoader =
-    Option(Thread.currentThread().getContextClassLoader).getOrElse(getSparkClassLoader)
-
-  // scalastyle:off classforname
-  /** Preferred alternative to Class.forName(className) */
-  def classForName(className: String): Class[_] = {
-    Class.forName(className, true, getContextOrSparkClassLoader)
-    // scalastyle:on classforname
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/org/apache/spark/SparkParallelismTracker.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/org/apache/spark/SparkParallelismTracker.scala
deleted file mode 100644
index bd0ab4d..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j-spark/src/main/scala/org/apache/spark/SparkParallelismTracker.scala
+++ /dev/null
@@ -1,128 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package org.apache.spark
-
-import org.apache.commons.logging.LogFactory
-import org.apache.spark.scheduler._
-
-/**
- * A tracker that ensures enough number of executor cores are alive.
- * Throws an exception when the number of alive cores is less than nWorkers.
- *
- * @param sc The SparkContext object
- * @param timeout The maximum time to wait for enough number of workers.
- * @param numWorkers nWorkers used in an XGBoost Job
- */
-class SparkParallelismTracker(
-    val sc: SparkContext,
-    timeout: Long,
-    numWorkers: Int) {
-
-  private[this] val requestedCores = numWorkers * sc.conf.getInt("spark.task.cpus", 1)
-  private[this] val logger = LogFactory.getLog("XGBoostSpark")
-
-  private[this] def numAliveCores: Int = {
-    sc.statusStore.executorList(true).map(_.totalCores).sum
-  }
-
-  private[this] def waitForCondition(
-      condition: => Boolean,
-      timeout: Long,
-      checkInterval: Long = 100L) = {
-    val waitImpl = new ((Long, Boolean) => Boolean) {
-      override def apply(waitedTime: Long, status: Boolean): Boolean = status match {
-        case s if s => true
-        case _ => waitedTime match {
-          case t if t < timeout =>
-            Thread.sleep(checkInterval)
-            apply(t + checkInterval, status = condition)
-          case _ => false
-        }
-      }
-    }
-    waitImpl(0L, condition)
-  }
-
-  private[this] def safeExecute[T](body: => T): T = {
-    val listener = new TaskFailedListener
-    sc.addSparkListener(listener)
-    try {
-      body
-    } finally {
-      sc.removeSparkListener(listener)
-    }
-  }
-
-  /**
-   * Execute a blocking function call with two checks on enough nWorkers:
-   *  - Before the function starts, wait until there are enough executor cores.
-   *  - During the execution, throws an exception if there is any executor lost.
-   *
-   * @param body A blocking function call
-   * @tparam T Return type
-   * @return The return of body
-   */
-  def execute[T](body: => T): T = {
-    if (timeout <= 0) {
-      logger.info("starting training without setting timeout for waiting for resources")
-      body
-    } else {
-      logger.info(s"starting training with timeout set as $timeout ms for waiting for resources")
-      if (!waitForCondition(numAliveCores >= requestedCores, timeout)) {
-        throw new IllegalStateException(s"Unable to get $requestedCores cores for XGBoost training")
-      }
-      safeExecute(body)
-    }
-  }
-}
-
-private[spark] class TaskFailedListener extends SparkListener {
-
-  private[this] val logger = LogFactory.getLog("XGBoostTaskFailedListener")
-
-  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
-    taskEnd.reason match {
-      case taskEndReason: TaskFailedReason =>
-        logger.error(s"Training Task Failed during XGBoost Training: " +
-            s"$taskEndReason, stopping SparkContext")
-        TaskFailedListener.startedSparkContextKiller()
-      case _ =>
-    }
-  }
-}
-
-object TaskFailedListener {
-
-  var killerStarted = false
-
-  private def startedSparkContextKiller(): Unit = this.synchronized {
-    if (!killerStarted) {
-      // Spark does not allow ListenerThread to shutdown SparkContext so that we have to do it
-      // in a separate thread
-      val sparkContextKiller = new Thread() {
-        override def run(): Unit = {
-          LiveListenerBus.withinListenerThread.withValue(false) {
-            SparkContext.getOrCreate().stop()
-          }
-        }
-      }
-      sparkContextKiller.setDaemon(true)
-      sparkContextKiller.start()
-      killerStarted = true
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/LICENSE b/ml-xgboost/jvm-packages/boostkit-xgboost4j/LICENSE
deleted file mode 100644
index 9a1673b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/LICENSE
+++ /dev/null
@@ -1,15 +0,0 @@
-/*
-Copyright (c) 2014 by Contributors 
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-    
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
\ No newline at end of file
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/pom.xml b/ml-xgboost/jvm-packages/boostkit-xgboost4j/pom.xml
deleted file mode 100644
index 68472ff..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/pom.xml
+++ /dev/null
@@ -1,141 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <parent>
-        <groupId>ml.dmlc</groupId>
-        <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-        <version>2.1.0</version>
-    </parent>
-    <artifactId>boostkit-xgboost4j_2.11</artifactId>
-    <version>2.1.0</version>
-    <packaging>jar</packaging>
-    <dependencies>
-        <dependency>
-            <groupId>org.apache.hadoop</groupId>
-            <artifactId>hadoop-hdfs</artifactId>
-            <version>${hadoop.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.hadoop</groupId>
-            <artifactId>hadoop-common</artifactId>
-            <version>${hadoop.version}</version>
-            <scope>provided</scope>
-        </dependency>
-        <dependency>
-            <groupId>junit</groupId>
-            <artifactId>junit</artifactId>
-            <version>4.11</version>
-            <scope>test</scope>
-        </dependency>
-        <dependency>
-            <groupId>com.typesafe.akka</groupId>
-            <artifactId>akka-actor_${scala.binary.version}</artifactId>
-            <version>2.5.23</version>
-            <scope>compile</scope>
-        </dependency>
-        <dependency>
-            <groupId>com.typesafe.akka</groupId>
-            <artifactId>akka-testkit_${scala.binary.version}</artifactId>
-            <version>2.5.23</version>
-            <scope>test</scope>
-        </dependency>
-    </dependencies>
-
-    <build>
-      <plugins>
-          <plugin>
-              <groupId>org.apache.maven.plugins</groupId>
-              <artifactId>maven-javadoc-plugin</artifactId>
-              <version>2.10.3</version>
-              <configuration>
-                  <show>protected</show>
-                  <nohelp>true</nohelp>
-              </configuration>
-          </plugin>
-          <plugin>
-              <groupId>org.apache.maven.plugins</groupId>
-              <artifactId>maven-assembly-plugin</artifactId>
-              <configuration>
-                  <skipAssembly>false</skipAssembly>
-              </configuration>
-          </plugin>
-          <plugin>
-              <artifactId>exec-maven-plugin</artifactId>
-              <groupId>org.codehaus.mojo</groupId>
-              <version>1.6.0</version>
-              <executions>
-                  <execution>
-                      <id>native</id>
-                      <phase>generate-sources</phase>
-                      <goals>
-                          <goal>exec</goal>
-                      </goals>
-                      <configuration>
-                          <executable>python</executable>
-                          <arguments>
-                            <argument>create_jni.py</argument>
-                          </arguments>
-                          <workingDirectory>${user.dir}</workingDirectory>
-                      </configuration>
-                  </execution>
-              </executions>
-          </plugin>
-          <plugin>
-              <groupId>org.apache.maven.plugins</groupId>
-              <artifactId>maven-resources-plugin</artifactId>
-              <version>3.1.0</version>
-              <configuration>
-                  <nonFilteredFileExtensions>
-                      <nonFilteredFileExtension>dll</nonFilteredFileExtension>
-                      <nonFilteredFileExtension>dylib</nonFilteredFileExtension>
-                      <nonFilteredFileExtension>so</nonFilteredFileExtension>
-                  </nonFilteredFileExtensions>
-              </configuration>
-          </plugin>
-          <plugin>
-              <groupId>org.jacoco</groupId>
-              <artifactId>jacoco-maven-plugin</artifactId>
-              <version>0.8.2</version>
-              <executions>
-                  <execution>
-                    <goals>
-                      <goal>prepare-agent</goal>
-                    </goals>
-                  </execution>
-                  <!-- attached to Maven test phase -->
-                  <execution>
-                    <id>report</id>
-                    <phase>test</phase>
-                    <goals>
-                      <goal>report</goal>
-                    </goals>
-                  </execution>
-              </executions>
-          </plugin>
-          <plugin>
-              <groupId>org.apache.maven.plugins</groupId>
-              <artifactId>maven-jar-plugin</artifactId>
-              <version>2.5</version>
-              <configuration>
-                  <archive>
-                      <addMavenDescriptor>false</addMavenDescriptor>
-                  </archive>
-              </configuration>
-              <executions>
-                  <execution>
-                      <goals>
-                          <goal>jar</goal>
-                      </goals>
-                  </execution>
-                  <execution>
-                      <id>default-jar</id>
-                      <phase>none</phase>
-                  </execution>
-              </executions>
-        </plugin>
-      </plugins>
-    </build>
-</project>
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java
deleted file mode 100644
index 25e7d7f..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java
+++ /dev/null
@@ -1,792 +0,0 @@
-/*
- * Copyright (C) 2021. Huawei Technologies Co., Ltd.
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
- * */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.io.*;
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-
-import com.esotericsoftware.kryo.Kryo;
-import com.esotericsoftware.kryo.KryoSerializable;
-import com.esotericsoftware.kryo.io.Input;
-import com.esotericsoftware.kryo.io.Output;
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-/**
- * Booster for xgboost, this is a model API that support interactive build of a XGBoost Model
- */
-public class Booster implements Serializable, KryoSerializable {
-  private static final Log logger = LogFactory.getLog(Booster.class);
-  // handle to the booster.
-  private long handle = 0;
-  private int version = 0;
-
-  /**
-   * Create a new Booster with empty stage.
-   *
-   * @param params  Model parameters
-   * @param cacheMats Cached DMatrix entries,
-   *                  the prediction of these DMatrices will become faster than not-cached data.
-   * @throws XGBoostError native error
-   */
-  Booster(Map<String, Object> params, DMatrix[] cacheMats) throws XGBoostError {
-    init(cacheMats);
-    setParams(params);
-  }
-
-  /**
-   * Load a new Booster model from modelPath
-   * @param modelPath The path to the model.
-   * @return The created Booster.
-   * @throws XGBoostError
-   */
-  static Booster loadModel(String modelPath) throws XGBoostError {
-    if (modelPath == null) {
-      throw new NullPointerException("modelPath : null");
-    }
-    Booster ret = new Booster(new HashMap<String, Object>(), new DMatrix[0]);
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterLoadModel(ret.handle, modelPath));
-    return ret;
-  }
-
-  /**
-   * Load a new Booster model from a file opened as input stream.
-   * The assumption is the input stream only contains one XGBoost Model.
-   * This can be used to load existing booster models saved by other xgboost bindings.
-   *
-   * @param in The input stream of the file.
-   * @return The create boosted
-   * @throws XGBoostError
-   * @throws IOException
-   */
-  static Booster loadModel(InputStream in) throws XGBoostError, IOException {
-    int size;
-    byte[] buf = new byte[1<<20];
-    ByteArrayOutputStream os = new ByteArrayOutputStream();
-    while ((size = in.read(buf)) != -1) {
-      os.write(buf, 0, size);
-    }
-    in.close();
-    Booster ret = new Booster(new HashMap<String, Object>(), new DMatrix[0]);
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterLoadModelFromBuffer(ret.handle,os.toByteArray()));
-    return ret;
-  }
-
-  /**
-   * Set parameter to the Booster.
-   *
-   * @param key   param name
-   * @param value param value
-   * @throws XGBoostError native error
-   */
-  public final void setParam(String key, Object value) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterSetParam(handle, key, value.toString()));
-  }
-
-  /**
-   * Set parameters to the Booster.
-   *
-   * @param params parameters key-value map
-   * @throws XGBoostError native error
-   */
-  public void setParams(Map<String, Object> params) throws XGBoostError {
-    if (params != null) {
-      for (Map.Entry<String, Object> entry : params.entrySet()) {
-        setParam(entry.getKey(), entry.getValue().toString());
-      }
-    }
-  }
-
-  /**
-   * Get attributes stored in the Booster as a Map.
-   *
-   * @return A map contain attribute pairs.
-   * @throws XGBoostError native error
-   */
-  public final Map<String, String> getAttrs() throws XGBoostError {
-    String[][] attrNames = new String[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterGetAttrNames(handle, attrNames));
-    Map<String, String> attrMap = new HashMap<>();
-    for (String name: attrNames[0]) {
-      attrMap.put(name, this.getAttr(name));
-    }
-    return attrMap;
-  }
-
-  /**
-   * Get attribute from the Booster.
-   *
-   * @param key   attribute key
-   * @return attribute value
-   * @throws XGBoostError native error
-   */
-  public final String getAttr(String key) throws XGBoostError {
-    String[] attrValue = new String[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterGetAttr(handle, key, attrValue));
-    return attrValue[0];
-  }
-
-  /**
-   * Set attribute to the Booster.
-   *
-   * @param key   attribute key
-   * @param value attribute value
-   * @throws XGBoostError native error
-   */
-  public final void setAttr(String key, String value) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterSetAttr(handle, key, value));
-  }
-
-  /**
-   * Set attributes to the Booster.
-   *
-   * @param attrs attributes key-value map
-   * @throws XGBoostError native error
-   */
-  public void setAttrs(Map<String, String> attrs) throws XGBoostError {
-    if (attrs != null) {
-      for (Map.Entry<String, String> entry : attrs.entrySet()) {
-        setAttr(entry.getKey(), entry.getValue());
-      }
-    }
-  }
-
-  /**
-   * Update the booster for one iteration.
-   *
-   * @param dtrain training data
-   * @param iter   current iteration number
-   * @throws XGBoostError native error
-   */
-  public void update(DMatrix dtrain, int iter) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterUpdateOneIter(handle, iter, dtrain.getHandle()));
-  }
-
-  /**
-   * Update the booster for one iteration.
-   *
-   * @param dtrain training data
-   * @param iter   current iteration number
-   * @param last_round_best_loss last round best loss
-   * @throws XGBoostError native error
-   */
-  public void update(DMatrix dtrain, int iter, float[] last_round_best_loss) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterUpdateOneIterLtd(handle, iter, dtrain.getHandle(),
-            last_round_best_loss));
-  }
-
-  /**
-   * Update with customize obj func
-   *
-   * @param dtrain training data
-   * @param obj    customized objective class
-   * @throws XGBoostError native error
-   */
-  public void update(DMatrix dtrain, IObjective obj) throws XGBoostError {
-    float[][] predicts = this.predict(dtrain, true, 0, false, false);
-    List<float[]> gradients = obj.getGradient(predicts, dtrain);
-    boost(dtrain, gradients.get(0), gradients.get(1));
-  }
-
-  /**
-   * update with give grad and hess
-   *
-   * @param dtrain training data
-   * @param grad   first order of gradient
-   * @param hess   seconde order of gradient
-   * @throws XGBoostError native error
-   */
-  public void boost(DMatrix dtrain, float[] grad, float[] hess) throws XGBoostError {
-    if (grad.length != hess.length) {
-      throw new AssertionError(String.format("grad/hess length mismatch %s / %s", grad.length,
-              hess.length));
-    }
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterBoostOneIter(handle,
-            dtrain.getHandle(), grad, hess));
-  }
-
-  /**
-   * evaluate with given dmatrixs.
-   *
-   * @param evalMatrixs dmatrixs for evaluation
-   * @param evalNames   name for eval dmatrixs, used for check results
-   * @param iter        current eval iteration
-   * @return eval information
-   * @throws XGBoostError native error
-   */
-  public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter) throws XGBoostError {
-    long[] handles = dmatrixsToHandles(evalMatrixs);
-    String[] evalInfo = new String[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames,
-            evalInfo));
-    return evalInfo[0];
-  }
-
-  /**
-   * evaluate with given dmatrixs.
-   *
-   * @param evalMatrixs dmatrixs for evaluation
-   * @param evalNames   name for eval dmatrixs, used for check results
-   * @param iter        current eval iteration
-   * @param metricsOut  output array containing the evaluation metrics for each evalMatrix
-   * @return eval information
-   * @throws XGBoostError native error
-   */
-  public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter, float[] metricsOut)
-          throws XGBoostError {
-    String stringFormat = evalSet(evalMatrixs, evalNames, iter);
-    String[] metricPairs = stringFormat.split("\t");
-    for (int i = 1; i < metricPairs.length; i++) {
-      String value = metricPairs[i].split(":")[1];
-      if (value.equalsIgnoreCase("nan")) {
-        metricsOut[i - 1] = Float.NaN;
-      } else if (value.equalsIgnoreCase("-nan")) {
-        metricsOut[i - 1] = -Float.NaN;
-      } else {
-        metricsOut[i - 1] = Float.valueOf(value);
-      }
-    }
-    return stringFormat;
-  }
-
-  /**
-   * evaluate with given customized Evaluation class
-   *
-   * @param evalMatrixs evaluation matrix
-   * @param evalNames   evaluation names
-   * @param eval        custom evaluator
-   * @return eval information
-   * @throws XGBoostError native error
-   */
-  public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, IEvaluation eval)
-          throws XGBoostError {
-    // Hopefully, a tiny redundant allocation wouldn't hurt.
-    return evalSet(evalMatrixs, evalNames, eval, new float[evalNames.length]);
-  }
-
-  public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, IEvaluation eval,
-                        float[] metricsOut) throws XGBoostError {
-    String evalInfo = "";
-    for (int i = 0; i < evalNames.length; i++) {
-      String evalName = evalNames[i];
-      DMatrix evalMat = evalMatrixs[i];
-      float evalResult = eval.eval(predict(evalMat), evalMat);
-      String evalMetric = eval.getMetric();
-      evalInfo += String.format("\t%s-%s:%f", evalName, evalMetric, evalResult);
-      metricsOut[i] = evalResult;
-    }
-    return evalInfo;
-  }
-
-  /**
-   * Advanced predict function with all the options.
-   *
-   * @param data         data
-   * @param outputMargin output margin
-   * @param treeLimit    limit number of trees, 0 means all trees.
-   * @param predLeaf     prediction minimum to keep leafs
-   * @param predContribs prediction feature contributions
-   * @return predict results
-   */
-  private synchronized float[][] predict(DMatrix data,
-                                         boolean outputMargin,
-                                         int treeLimit,
-                                         boolean predLeaf,
-                                         boolean predContribs) throws XGBoostError {
-    int optionMask = 0;
-    if (outputMargin) {
-      optionMask = 1;
-    }
-    if (predLeaf) {
-      optionMask = 2;
-    }
-    if (predContribs) {
-      optionMask = 4;
-    }
-    float[][] rawPredicts = new float[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterPredict(handle, data.getHandle(), optionMask,
-            treeLimit, rawPredicts));
-    int row = (int) data.rowNum();
-    int col = rawPredicts[0].length / row;
-    float[][] predicts = new float[row][col];
-    int r, c;
-    for (int i = 0; i < rawPredicts[0].length; i++) {
-      r = i / col;
-      c = i % col;
-      predicts[r][c] = rawPredicts[0][i];
-    }
-    return predicts;
-  }
-
-  /**
-   * Predict leaf indices given the data
-   *
-   * @param data The input data.
-   * @param treeLimit Number of trees to include, 0 means all trees.
-   * @return The leaf indices of the instance.
-   * @throws XGBoostError
-   */
-  public float[][] predictLeaf(DMatrix data, int treeLimit) throws XGBoostError {
-    return this.predict(data, false, treeLimit, true, false);
-  }
-
-  /**
-   * Output feature contributions toward predictions of given data
-   *
-   * @param data The input data.
-   * @param treeLimit Number of trees to include, 0 means all trees.
-   * @return The feature contributions and bias.
-   * @throws XGBoostError
-   */
-  public float[][] predictContrib(DMatrix data, int treeLimit) throws XGBoostError {
-    return this.predict(data, false, treeLimit, true, true);
-  }
-
-  /**
-   * Predict with data
-   *
-   * @param data dmatrix storing the input
-   * @return predict result
-   * @throws XGBoostError native error
-   */
-  public float[][] predict(DMatrix data) throws XGBoostError {
-    return this.predict(data, false, 0, false, false);
-  }
-
-  /**
-   * Predict with data
-   *
-   * @param data  data
-   * @param outputMargin output margin
-   * @return predict results
-   */
-  public float[][] predict(DMatrix data, boolean outputMargin) throws XGBoostError {
-    return this.predict(data, outputMargin, 0, false, false);
-  }
-
-  /**
-   * Advanced predict function with all the options.
-   *
-   * @param data         data
-   * @param outputMargin output margin
-   * @param treeLimit    limit number of trees, 0 means all trees.
-   * @return predict results
-   */
-  public float[][] predict(DMatrix data, boolean outputMargin, int treeLimit) throws XGBoostError {
-    return this.predict(data, outputMargin, treeLimit, false, false);
-  }
-
-  /**
-   * Save model to modelPath
-   *
-   * @param modelPath model path
-   */
-  public void saveModel(String modelPath) throws XGBoostError{
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterSaveModel(handle, modelPath));
-  }
-
-  /**
-   * Save the model to file opened as output stream.
-   * The model format is compatible with other xgboost bindings.
-   * The output stream can only save one xgboost model.
-   * This function will close the OutputStream after the save.
-   *
-   * @param out The output stream
-   */
-  public void saveModel(OutputStream out) throws XGBoostError, IOException {
-    out.write(this.toByteArray());
-    out.close();
-  }
-
-  /**
-   * Get the dump of the model as a string array
-   *
-   * @param withStats Controls whether the split statistics are output.
-   * @return dumped model information
-   * @throws XGBoostError native error
-   */
-  public String[] getModelDump(String featureMap, boolean withStats) throws XGBoostError {
-    return getModelDump(featureMap, withStats, "text");
-  }
-
-  public String[] getModelDump(String featureMap, boolean withStats, String format)
-         throws XGBoostError {
-    int statsFlag = 0;
-    if (featureMap == null) {
-      featureMap = "";
-    }
-    if (withStats) {
-      statsFlag = 1;
-    }
-    if (format == null) {
-      format = "text";
-    }
-    String[][] modelInfos = new String[1][];
-    XGBoostJNI.checkCall(
-            XGBoostJNI.XGBoosterDumpModelEx(handle, featureMap, statsFlag, format, modelInfos));
-    return modelInfos[0];
-  }
-
-  /**
-   * Get the dump of the model as a string array with specified feature names.
-   *
-   * @param featureNames Names of the features.
-   * @return dumped model information
-   * @throws XGBoostError
-   */
-  public String[] getModelDump(String[] featureNames, boolean withStats) throws XGBoostError {
-    return getModelDump(featureNames, withStats, "text");
-  }
-
-  public String[] getModelDump(String[] featureNames, boolean withStats, String format)
-    throws XGBoostError {
-    int statsFlag = 0;
-    if (withStats) {
-      statsFlag = 1;
-    }
-    if (format == null) {
-      format = "text";
-    }
-    String[][] modelInfos = new String[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterDumpModelExWithFeatures(
-        handle, featureNames, statsFlag, format, modelInfos));
-    return modelInfos[0];
-  }
-
-  /**
-   * Supported feature importance types
-   *
-   * WEIGHT = Number of nodes that a feature was used to determine a split
-   * GAIN = Average information gain per split for a feature
-   * COVER = Average cover per split for a feature
-   * TOTAL_GAIN = Total information gain over all splits of a feature
-   * TOTAL_COVER = Total cover over all splits of a feature
-   */
-  public static class FeatureImportanceType {
-    public static final String WEIGHT = "weight";
-    public static final String GAIN = "gain";
-    public static final String COVER = "cover";
-    public static final String TOTAL_GAIN = "total_gain";
-    public static final String TOTAL_COVER = "total_cover";
-    public static final Set<String> ACCEPTED_TYPES = new HashSet<>(
-            Arrays.asList(WEIGHT, GAIN, COVER, TOTAL_GAIN, TOTAL_COVER));
-  }
-
-  /**
-   * Get importance of each feature with specified feature names.
-   *
-   * @return featureScoreMap  key: feature name, value: feature importance score, can be nill.
-   * @throws XGBoostError native error
-   */
-  public Map<String, Integer> getFeatureScore(String[] featureNames) throws XGBoostError {
-    String[] modelInfos = getModelDump(featureNames, false);
-    return getFeatureWeightsFromModel(modelInfos);
-  }
-
-  /**
-   * Get importance of each feature
-   *
-   * @return featureScoreMap  key: feature index, value: feature importance score, can be nill
-   * @throws XGBoostError native error
-   */
-  public Map<String, Integer> getFeatureScore(String featureMap) throws XGBoostError {
-    String[] modelInfos = getModelDump(featureMap, false);
-    return getFeatureWeightsFromModel(modelInfos);
-  }
-
-  /**
-   * Get the importance of each feature based purely on weights (number of splits)
-   *
-   * @return featureScoreMap key: feature index,
-   * value: feature importance score based on weight
-   * @throws XGBoostError native error
-   */
-  private Map<String, Integer> getFeatureWeightsFromModel(String[] modelInfos) throws XGBoostError {
-    Map<String, Integer> featureScore = new HashMap<>();
-    for (String tree : modelInfos) {
-      for (String node : tree.split("\n")) {
-        String[] array = node.split("\\[");
-        if (array.length == 1) {
-          continue;
-        }
-        String fid = array[1].split("\\]")[0];
-        fid = fid.split("<")[0];
-        if (featureScore.containsKey(fid)) {
-          featureScore.put(fid, 1 + featureScore.get(fid));
-        } else {
-          featureScore.put(fid, 1);
-        }
-      }
-    }
-    return featureScore;
-  }
-
-  /**
-   * Get the feature importances for gain or cover (average or total)
-   *
-   * @return featureImportanceMap key: feature index,
-   * values: feature importance score based on gain or cover
-   * @throws XGBoostError native error
-   */
-  public Map<String, Double> getScore(
-          String[] featureNames, String importanceType) throws XGBoostError {
-    String[] modelInfos = getModelDump(featureNames, true);
-    return getFeatureImportanceFromModel(modelInfos, importanceType);
-  }
-
-  /**
-   * Get the feature importances for gain or cover (average or total), with feature names
-   *
-   * @return featureImportanceMap key: feature name,
-   * values: feature importance score based on gain or cover
-   * @throws XGBoostError native error
-   */
-  public Map<String, Double> getScore(
-          String featureMap, String importanceType) throws XGBoostError {
-    String[] modelInfos = getModelDump(featureMap, true);
-    return getFeatureImportanceFromModel(modelInfos, importanceType);
-  }
-
-  /**
-   * Get the importance of each feature based on information gain or cover
-   *
-   * @return featureImportanceMap key: feature index, value: feature importance score
-   * based on information gain or cover
-   * @throws XGBoostError native error
-   */
-  private Map<String, Double> getFeatureImportanceFromModel(
-          String[] modelInfos, String importanceType) throws XGBoostError {
-    if (!FeatureImportanceType.ACCEPTED_TYPES.contains(importanceType)) {
-      throw new AssertionError(String.format("Importance type %s is not supported",
-              importanceType));
-    }
-    Map<String, Double> importanceMap = new HashMap<>();
-    Map<String, Double> weightMap = new HashMap<>();
-    if (importanceType.equals(FeatureImportanceType.WEIGHT)) {
-      Map<String, Integer> importanceWeights = getFeatureWeightsFromModel(modelInfos);
-      for (String feature: importanceWeights.keySet()) {
-        importanceMap.put(feature, new Double(importanceWeights.get(feature)));
-      }
-      return importanceMap;
-    }
-    /* Each split in the tree has this text form:
-    "0:[f28<-9.53674316e-07] yes=1,no=2,missing=1,gain=4000.53101,cover=1628.25"
-    So the line has to be split according to whether cover or gain is desired */
-    String splitter = "gain=";
-    if (importanceType.equals(FeatureImportanceType.COVER)
-        || importanceType.equals(FeatureImportanceType.TOTAL_COVER)) {
-      splitter = "cover=";
-    }
-    for (String tree: modelInfos) {
-      for (String node: tree.split("\n")) {
-        String[] array = node.split("\\[");
-        if (array.length == 1) {
-          continue;
-        }
-        String[] fidWithImportance = array[1].split("\\]");
-        // Extract gain or cover from string after closing bracket
-        Double importance = Double.parseDouble(
-            fidWithImportance[1].split(splitter)[1].split(",")[0]
-        );
-        String fid = fidWithImportance[0].split("<")[0];
-        if (importanceMap.containsKey(fid)) {
-          importanceMap.put(fid, importance + importanceMap.get(fid));
-          weightMap.put(fid, 1d + weightMap.get(fid));
-        } else {
-          importanceMap.put(fid, importance);
-          weightMap.put(fid, 1d);
-        }
-      }
-    }
-    /* By default we calculate total gain and total cover.
-    Divide by the number of nodes per feature to get gain / cover */
-    if (importanceType.equals(FeatureImportanceType.COVER)
-        || importanceType.equals(FeatureImportanceType.GAIN)) {
-      for (String fid: importanceMap.keySet()) {
-        importanceMap.put(fid, importanceMap.get(fid)/weightMap.get(fid));
-      }
-    }
-    return importanceMap;
-  }
-
-  /**
-   * Save the model as byte array representation.
-   * Write these bytes to a file will give compatible format with other xgboost bindings.
-   *
-   * If java natively support HDFS file API, use toByteArray and write the ByteArray
-   *
-   * @param withStats Controls whether the split statistics are output.
-   * @return dumped model information
-   * @throws XGBoostError native error
-   */
-  private String[] getDumpInfo(boolean withStats) throws XGBoostError {
-    int statsFlag = 0;
-    if (withStats) {
-      statsFlag = 1;
-    }
-    String[][] modelInfos = new String[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterDumpModelEx(handle, "", statsFlag, "text",
-            modelInfos));
-    return modelInfos[0];
-  }
-
-  public int getVersion() {
-    return this.version;
-  }
-
-  public void setVersion(int version) {
-    this.version = version;
-  }
-
-  /**
-   *
-   * @return the saved byte array.
-   * @throws XGBoostError native error
-   */
-  public byte[] toByteArray() throws XGBoostError {
-    byte[][] bytes = new byte[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterGetModelRaw(this.handle, bytes));
-    return bytes[0];
-  }
-
-  /**
-   * Load the booster model from thread-local rabit checkpoint.
-   * This is only used in distributed training.
-   * @return the stored version number of the checkpoint.
-   * @throws XGBoostError
-   */
-  int loadRabitCheckpoint() throws XGBoostError {
-    int[] out = new int[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterLoadRabitCheckpoint(this.handle, out));
-    version = out[0];
-    return version;
-  }
-
-  /**
-   * Save the booster model into thread-local rabit checkpoint and increment the version.
-   * This is only used in distributed training.
-   * @throws XGBoostError
-   */
-  void saveRabitCheckpoint() throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterSaveRabitCheckpoint(this.handle));
-    version += 1;
-  }
-
-  /**
-   * Internal initialization function.
-   * @param cacheMats The cached DMatrix.
-   * @throws XGBoostError
-   */
-  private void init(DMatrix[] cacheMats) throws XGBoostError {
-    long[] handles = null;
-    if (cacheMats != null) {
-      handles = dmatrixsToHandles(cacheMats);
-    }
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterCreate(handles, out));
-
-    handle = out[0];
-  }
-
-  /**
-   * transfer DMatrix array to handle array (used for native functions)
-   *
-   * @param dmatrixs
-   * @return handle array for input dmatrixs
-   */
-  private static long[] dmatrixsToHandles(DMatrix[] dmatrixs) {
-    long[] handles = new long[dmatrixs.length];
-    for (int i = 0; i < dmatrixs.length; i++) {
-      handles[i] = dmatrixs[i].getHandle();
-    }
-    return handles;
-  }
-
-  // making Booster serializable
-  private void writeObject(java.io.ObjectOutputStream out) throws IOException {
-    try {
-      out.writeInt(version);
-      out.writeObject(this.toByteArray());
-    } catch (XGBoostError ex) {
-      ex.printStackTrace();
-      logger.error(ex.getMessage());
-    }
-  }
-
-  private void readObject(java.io.ObjectInputStream in)
-          throws IOException, ClassNotFoundException {
-    try {
-      this.init(null);
-      this.version = in.readInt();
-      byte[] bytes = (byte[])in.readObject();
-      XGBoostJNI.checkCall(XGBoostJNI.XGBoosterLoadModelFromBuffer(this.handle, bytes));
-    } catch (XGBoostError ex) {
-      ex.printStackTrace();
-      logger.error(ex.getMessage());
-    }
-  }
-
-  @Override
-  protected void finalize() throws Throwable {
-    super.finalize();
-    dispose();
-  }
-
-  public synchronized void dispose() {
-    if (handle != 0L) {
-      XGBoostJNI.XGBoosterFree(handle);
-      handle = 0;
-    }
-  }
-
-  @Override
-  public void write(Kryo kryo, Output output) {
-    try {
-      byte[] serObj = this.toByteArray();
-      int serObjSize = serObj.length;
-      output.writeInt(serObjSize);
-      output.writeInt(version);
-      output.write(serObj);
-    } catch (XGBoostError ex) {
-      logger.error(ex.getMessage(), ex);
-    }
-  }
-
-  @Override
-  public void read(Kryo kryo, Input input) {
-    try {
-      this.init(null);
-      int serObjSize = input.readInt();
-      this.version = input.readInt();
-      byte[] bytes = new byte[serObjSize];
-      input.readBytes(bytes);
-      XGBoostJNI.checkCall(XGBoostJNI.XGBoosterLoadModelFromBuffer(this.handle, bytes));
-    } catch (XGBoostError ex) {
-      logger.error(ex.getMessage(), ex);
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DMatrix.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DMatrix.java
deleted file mode 100644
index 47a2807..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DMatrix.java
+++ /dev/null
@@ -1,347 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.util.Iterator;
-
-import ml.dmlc.xgboost4j.LabeledPoint;
-import ml.dmlc.xgboost4j.java.util.BigDenseMatrix;
-
-/**
- * DMatrix for xgboost.
- *
- * @author hzx
- */
-public class DMatrix {
-  protected long handle = 0;
-
-  /**
-   * sparse matrix type (CSR or CSC)
-   */
-  public static enum SparseType {
-    CSR,
-    CSC;
-  }
-
-  /**
-   * Create DMatrix from iterator.
-   *
-   * @param iter The data iterator of mini batch to provide the data.
-   * @param cacheInfo Cache path information, used for external memory setting, can be null.
-   * @throws XGBoostError
-   */
-  public DMatrix(Iterator<LabeledPoint> iter, String cacheInfo) throws XGBoostError {
-    if (iter == null) {
-      throw new NullPointerException("iter: null");
-    }
-    // 32k as batch size
-    int batchSize = 32 << 10;
-    Iterator<DataBatch> batchIter = new DataBatch.BatchIterator(iter, batchSize);
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromDataIter(batchIter, cacheInfo, out));
-    handle = out[0];
-  }
-
-  /**
-   * Create DMatrix by loading libsvm file from dataPath
-   *
-   * @param dataPath The path to the data.
-   * @throws XGBoostError
-   */
-  public DMatrix(String dataPath) throws XGBoostError {
-    if (dataPath == null) {
-      throw new NullPointerException("dataPath: null");
-    }
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromFile(dataPath, 1, out));
-    handle = out[0];
-  }
-
-  /**
-   * Create DMatrix from Sparse matrix in CSR/CSC format.
-   * @param headers The row index of the matrix.
-   * @param indices The indices of presenting entries.
-   * @param data The data content.
-   * @param st  Type of sparsity.
-   * @throws XGBoostError
-   */
-  @Deprecated
-  public DMatrix(long[] headers, int[] indices, float[] data, DMatrix.SparseType st)
-      throws XGBoostError {
-    long[] out = new long[1];
-    if (st == SparseType.CSR) {
-      XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromCSREx(headers, indices, data, 0, out));
-    } else if (st == SparseType.CSC) {
-      XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromCSCEx(headers, indices, data, 0, out));
-    } else {
-      throw new UnknownError("unknow sparsetype");
-    }
-    handle = out[0];
-  }
-
-  /**
-   * Create DMatrix from Sparse matrix in CSR/CSC format.
-   * @param headers The row index of the matrix.
-   * @param indices The indices of presenting entries.
-   * @param data The data content.
-   * @param st  Type of sparsity.
-   * @param shapeParam   when st is CSR, it specifies the column number, otherwise it is taken as
-   *                     row number
-   * @throws XGBoostError
-   */
-  public DMatrix(long[] headers, int[] indices, float[] data, DMatrix.SparseType st, int shapeParam)
-          throws XGBoostError {
-    long[] out = new long[1];
-    if (st == SparseType.CSR) {
-      XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromCSREx(headers, indices, data,
-              shapeParam, out));
-    } else if (st == SparseType.CSC) {
-      XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromCSCEx(headers, indices, data,
-              shapeParam, out));
-    } else {
-      throw new UnknownError("unknow sparsetype");
-    }
-    handle = out[0];
-  }
-
-  /**
-   * create DMatrix from dense matrix
-   *
-   * @param data data values
-   * @param nrow number of rows
-   * @param ncol number of columns
-   * @throws XGBoostError native error
-   */
-  public DMatrix(float[] data, int nrow, int ncol) throws XGBoostError {
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromMat(data, nrow, ncol, 0.0f, out));
-    handle = out[0];
-  }
-
-  /**
-   * create DMatrix from a BigDenseMatrix
-   *
-   * @param matrix instance of BigDenseMatrix
-   * @throws XGBoostError native error
-   */
-  public DMatrix(BigDenseMatrix matrix) throws XGBoostError {
-    this(matrix, 0.0f);
-  }
-
-  /**
-   * create DMatrix from dense matrix
-   * @param data data values
-   * @param nrow number of rows
-   * @param ncol number of columns
-   * @param missing the specified value to represent the missing value
-   */
-  public DMatrix(float[] data, int nrow, int ncol, float missing) throws XGBoostError {
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromMat(data, nrow, ncol, missing, out));
-    handle = out[0];
-  }
-
-  /**
-   * create DMatrix from dense matrix
-   * @param matrix instance of BigDenseMatrix
-   * @param missing the specified value to represent the missing value
-   */
-  public DMatrix(BigDenseMatrix matrix, float missing) throws XGBoostError {
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixCreateFromMatRef(matrix.address, matrix.nrow,
-        matrix.ncol, missing, out));
-    handle = out[0];
-  }
-
-  /**
-   * used for DMatrix slice
-   */
-  protected DMatrix(long handle) {
-    this.handle = handle;
-  }
-
-
-  /**
-   * set label of dmatrix
-   *
-   * @param labels labels
-   * @throws XGBoostError native error
-   */
-  public void setLabel(float[] labels) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixSetFloatInfo(handle, "label", labels));
-  }
-
-  /**
-   * set weight of each instance
-   *
-   * @param weights weights
-   * @throws XGBoostError native error
-   */
-  public void setWeight(float[] weights) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixSetFloatInfo(handle, "weight", weights));
-  }
-
-  /**
-   * Set base margin (initial prediction).
-   *
-   * The margin must have the same number of elements as the number of
-   * rows in this matrix.
-   */
-  public void setBaseMargin(float[] baseMargin) throws XGBoostError {
-    if (baseMargin.length != rowNum()) {
-      throw new IllegalArgumentException(String.format(
-              "base margin must have exactly %s elements, got %s",
-              rowNum(), baseMargin.length));
-    }
-
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixSetFloatInfo(handle, "base_margin", baseMargin));
-  }
-
-  /**
-   * Set base margin (initial prediction).
-   */
-  public void setBaseMargin(float[][] baseMargin) throws XGBoostError {
-    setBaseMargin(flatten(baseMargin));
-  }
-
-  /**
-   * Set group sizes of DMatrix (used for ranking)
-   *
-   * @param group group size as array
-   * @throws XGBoostError native error
-   */
-  public void setGroup(int[] group) throws XGBoostError {
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixSetUIntInfo(handle, "group", group));
-  }
-
-  /**
-   * Get group sizes of DMatrix
-   *
-   * @throws XGBoostError native error
-   * @return group size as array
-   */
-  public int[] getGroup() throws XGBoostError {
-    return getIntInfo("group_ptr");
-  }
-
-  private float[] getFloatInfo(String field) throws XGBoostError {
-    float[][] infos = new float[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixGetFloatInfo(handle, field, infos));
-    return infos[0];
-  }
-
-  private int[] getIntInfo(String field) throws XGBoostError {
-    int[][] infos = new int[1][];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixGetUIntInfo(handle, field, infos));
-    return infos[0];
-  }
-
-  /**
-   * get label values
-   *
-   * @return label
-   * @throws XGBoostError native error
-   */
-  public float[] getLabel() throws XGBoostError {
-    return getFloatInfo("label");
-  }
-
-  /**
-   * get weight of the DMatrix
-   *
-   * @return weights
-   * @throws XGBoostError native error
-   */
-  public float[] getWeight() throws XGBoostError {
-    return getFloatInfo("weight");
-  }
-
-  /**
-   * Get base margin of the DMatrix.
-   */
-  public float[] getBaseMargin() throws XGBoostError {
-    return getFloatInfo("base_margin");
-  }
-
-  /**
-   * Slice the DMatrix and return a new DMatrix that only contains `rowIndex`.
-   *
-   * @param rowIndex row index
-   * @return sliced new DMatrix
-   * @throws XGBoostError native error
-   */
-  public DMatrix slice(int[] rowIndex) throws XGBoostError {
-    long[] out = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixSliceDMatrix(handle, rowIndex, out));
-    long sHandle = out[0];
-    DMatrix sMatrix = new DMatrix(sHandle);
-    return sMatrix;
-  }
-
-  /**
-   * get the row number of DMatrix
-   *
-   * @return number of rows
-   * @throws XGBoostError native error
-   */
-  public long rowNum() throws XGBoostError {
-    long[] rowNum = new long[1];
-    XGBoostJNI.checkCall(XGBoostJNI.XGDMatrixNumRow(handle, rowNum));
-    return rowNum[0];
-  }
-
-  /**
-   * save DMatrix to filePath
-   */
-  public void saveBinary(String filePath) {
-    XGBoostJNI.XGDMatrixSaveBinary(handle, filePath, 1);
-  }
-
-  /**
-   * Get the handle
-   */
-  public long getHandle() {
-    return handle;
-  }
-
-  /**
-   * flatten a mat to array
-   */
-  private static float[] flatten(float[][] mat) {
-    int size = 0;
-    for (float[] array : mat) size += array.length;
-    float[] result = new float[size];
-    int pos = 0;
-    for (float[] ar : mat) {
-      System.arraycopy(ar, 0, result, pos, ar.length);
-      pos += ar.length;
-    }
-
-    return result;
-  }
-
-  @Override
-  protected void finalize() {
-    dispose();
-  }
-
-  public synchronized void dispose() {
-    if (handle != 0) {
-      XGBoostJNI.XGDMatrixFree(handle);
-      handle = 0;
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DataBatch.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DataBatch.java
deleted file mode 100644
index ad80d03..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DataBatch.java
+++ /dev/null
@@ -1,115 +0,0 @@
-package ml.dmlc.xgboost4j.java;
-
-import java.util.ArrayList;
-import java.util.Iterator;
-import java.util.List;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-import ml.dmlc.xgboost4j.LabeledPoint;
-
-/**
- * A mini-batch of data that can be converted to DMatrix.
- * The data is in sparse matrix CSR format.
- *
- * This class is used to support advanced creation of DMatrix from Iterator of DataBatch,
- */
-class DataBatch {
-  private static final Log logger = LogFactory.getLog(DataBatch.class);
-  /** The offset of each rows in the sparse matrix */
-  final long[] rowOffset;
-  /** weight of each data point, can be null */
-  final float[] weight;
-  /** label of each data point, can be null */
-  final float[] label;
-  /** index of each feature(column) in the sparse matrix */
-  final int[] featureIndex;
-  /** value of each non-missing entry in the sparse matrix */
-  final float[] featureValue ;
-  /** feature columns */
-  final int featureCols;
-
-  DataBatch(long[] rowOffset, float[] weight, float[] label, int[] featureIndex,
-            float[] featureValue, int featureCols) {
-    this.rowOffset = rowOffset;
-    this.weight = weight;
-    this.label = label;
-    this.featureIndex = featureIndex;
-    this.featureValue = featureValue;
-    this.featureCols = featureCols;
-  }
-
-  static class BatchIterator implements Iterator<DataBatch> {
-    private final Iterator<LabeledPoint> base;
-    private final int batchSize;
-
-    BatchIterator(Iterator<LabeledPoint> base, int batchSize) {
-      this.base = base;
-      this.batchSize = batchSize;
-    }
-
-    @Override
-    public boolean hasNext() {
-      return base.hasNext();
-    }
-
-    @Override
-    public DataBatch next() {
-      try {
-        int numRows = 0;
-        int numElem = 0;
-        int numCol  = -1;
-        List<LabeledPoint> batch = new ArrayList<>(batchSize);
-        while (base.hasNext() && batch.size() < batchSize) {
-          LabeledPoint labeledPoint = base.next();
-          if (numCol == -1) {
-            numCol = labeledPoint.size();
-          } else if (numCol != labeledPoint.size()) {
-            throw new RuntimeException("Feature size is not the same");
-          }
-          batch.add(labeledPoint);
-          numElem += labeledPoint.values().length;
-          numRows++;
-        }
-
-        long[] rowOffset = new long[numRows + 1];
-        float[] label = new float[numRows];
-        int[] featureIndex = new int[numElem];
-        float[] featureValue = new float[numElem];
-        float[] weight = new float[numRows];
-
-        int offset = 0;
-        for (int i = 0; i < batch.size(); i++) {
-          LabeledPoint labeledPoint = batch.get(i);
-          rowOffset[i] = offset;
-          label[i] = labeledPoint.label();
-          weight[i] = labeledPoint.weight();
-          if (labeledPoint.indices() != null) {
-            System.arraycopy(labeledPoint.indices(), 0, featureIndex, offset,
-                    labeledPoint.indices().length);
-          } else {
-            for (int j = 0; j < labeledPoint.values().length; j++) {
-              featureIndex[offset + j] = j;
-            }
-          }
-
-          System.arraycopy(labeledPoint.values(), 0, featureValue, offset,
-                  labeledPoint.values().length);
-          offset += labeledPoint.values().length;
-        }
-
-        rowOffset[batch.size()] = offset;
-        return new DataBatch(rowOffset, weight, label, featureIndex, featureValue, numCol);
-      } catch (RuntimeException runtimeError) {
-        logger.error(runtimeError);
-        return null;
-      }
-    }
-
-    @Override
-    public void remove() {
-      throw new UnsupportedOperationException("DataBatch.BatchIterator.remove");
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/ExternalCheckpointManager.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/ExternalCheckpointManager.java
deleted file mode 100644
index 655b990..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/ExternalCheckpointManager.java
+++ /dev/null
@@ -1,117 +0,0 @@
-package ml.dmlc.xgboost4j.java;
-
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import java.util.*;
-import java.util.stream.Collectors;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-import org.apache.hadoop.fs.FileSystem;
-import org.apache.hadoop.fs.Path;
-
-public class ExternalCheckpointManager {
-
-  private Log logger = LogFactory.getLog("ExternalCheckpointManager");
-  private String modelSuffix = ".model";
-  private Path checkpointPath;
-  private FileSystem fs;
-
-  public ExternalCheckpointManager(String checkpointPath, FileSystem fs) throws XGBoostError {
-    if (checkpointPath == null || checkpointPath.isEmpty()) {
-      throw new XGBoostError("cannot create ExternalCheckpointManager with null or" +
-              " empty checkpoint path");
-    }
-    this.checkpointPath = new Path(checkpointPath);
-    this.fs = fs;
-  }
-
-  private String getPath(int version) {
-    return checkpointPath.toUri().getPath() + "/" + version + modelSuffix;
-  }
-
-  private List<Integer> getExistingVersions() throws IOException {
-    if (!fs.exists(checkpointPath)) {
-      return new ArrayList<>();
-    } else {
-      return Arrays.stream(fs.listStatus(checkpointPath))
-              .map(path -> path.getPath().getName())
-              .filter(fileName -> fileName.endsWith(modelSuffix))
-              .map(fileName -> Integer.valueOf(
-                      fileName.substring(0, fileName.length() - modelSuffix.length())))
-              .collect(Collectors.toList());
-    }
-  }
-
-  public void cleanPath() throws IOException {
-    fs.delete(checkpointPath, true);
-  }
-
-  public Booster loadCheckpointAsBooster() throws IOException, XGBoostError {
-    List<Integer> versions = getExistingVersions();
-    if (versions.size() > 0) {
-      int latestVersion = versions.stream().max(Comparator.comparing(Integer::valueOf)).get();
-      String checkpointPath = getPath(latestVersion);
-      InputStream in = fs.open(new Path(checkpointPath));
-      logger.info("loaded checkpoint from " + checkpointPath);
-      Booster booster = XGBoost.loadModel(in);
-      booster.setVersion(latestVersion);
-      return booster;
-    } else {
-      return null;
-    }
-  }
-
-  public void updateCheckpoint(Booster boosterToCheckpoint) throws IOException, XGBoostError {
-    List<String> prevModelPaths = getExistingVersions().stream()
-            .map(this::getPath).collect(Collectors.toList());
-    String eventualPath = getPath(boosterToCheckpoint.getVersion());
-    String tempPath = eventualPath + "-" + UUID.randomUUID();
-    try (OutputStream out = fs.create(new Path(tempPath), true)) {
-      boosterToCheckpoint.saveModel(out);
-      fs.rename(new Path(tempPath), new Path(eventualPath));
-      logger.info("saving checkpoint with version " + boosterToCheckpoint.getVersion());
-      prevModelPaths.stream().forEach(path -> {
-        try {
-          fs.delete(new Path(path), true);
-        } catch (IOException e) {
-          logger.error("failed to delete outdated checkpoint at " + path, e);
-        }
-      });
-    }
-  }
-
-  public void cleanUpHigherVersions(int currentRound) throws IOException {
-    getExistingVersions().stream().filter(v -> v / 2 >= currentRound).forEach(v -> {
-      try {
-        fs.delete(new Path(getPath(v)), true);
-      } catch (IOException e) {
-        logger.error("failed to clean checkpoint from other training instance", e);
-      }
-    });
-  }
-
-  public List<Integer> getCheckpointRounds(int checkpointInterval, int numOfRounds)
-      throws IOException {
-    if (checkpointInterval > 0) {
-      List<Integer> prevRounds =
-              getExistingVersions().stream().map(v -> v / 2).collect(Collectors.toList());
-      prevRounds.add(0);
-      int firstCheckpointRound = prevRounds.stream()
-              .max(Comparator.comparing(Integer::valueOf)).get() + checkpointInterval;
-      List<Integer> arr = new ArrayList<>();
-      for (int i = firstCheckpointRound; i <= numOfRounds; i += checkpointInterval) {
-        arr.add(i);
-      }
-      arr.add(numOfRounds);
-      return arr;
-    } else if (checkpointInterval <= 0) {
-      List<Integer> l = new ArrayList<Integer>();
-      l.add(numOfRounds);
-      return l;
-    } else {
-      throw new IllegalArgumentException("parameters \"checkpoint_path\" should also be set.");
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IEvaluation.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IEvaluation.java
deleted file mode 100644
index 7f8abec..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IEvaluation.java
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.io.Serializable;
-
-/**
- * interface for customized evaluation
- *
- * @author hzx
- */
-public interface IEvaluation extends Serializable {
-  /**
-   * get evaluate metric
-   *
-   * @return evalMetric
-   */
-  String getMetric();
-
-  /**
-   * evaluate with predicts and data
-   *
-   * @param predicts predictions as array
-   * @param dmat     data matrix to evaluate
-   * @return result of the metric
-   */
-  float eval(float[][] predicts, DMatrix dmat);
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IObjective.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IObjective.java
deleted file mode 100644
index 3ee1d50..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IObjective.java
+++ /dev/null
@@ -1,35 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.io.Serializable;
-import java.util.List;
-
-/**
- * interface for customize Object function
- *
- * @author hzx
- */
-public interface IObjective extends Serializable {
-  /**
-   * user define objective function, return gradient and second order gradient
-   *
-   * @param predicts untransformed margin predicts
-   * @param dtrain   training data
-   * @return List with two float array, correspond to first order grad and second order grad
-   */
-  List<float[]> getGradient(float[][] predicts, DMatrix dtrain);
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IRabitTracker.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IRabitTracker.java
deleted file mode 100644
index 984fb80..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/IRabitTracker.java
+++ /dev/null
@@ -1,44 +0,0 @@
-package ml.dmlc.xgboost4j.java;
-
-import java.util.Map;
-import java.util.concurrent.TimeUnit;
-
-/**
- * Interface for Rabit tracker implementations with three public methods:
- *
- *  - start(timeout): Start the Rabit tracker awaiting for worker connections, with a given
- *  timeout value (in milliseconds.)
- *  - getWorkerEnvs(): Return the environment variables needed to initialize Rabit clients.
- *  - waitFor(timeout): Wait for the task execution by the worker nodes for at most `timeout`
- *  milliseconds.
- *
- * Each implementation is expected to implement a callback function
- *
- *    public void uncaughtException(Threat t, Throwable e) { ... }
- *
- * to interrupt waitFor() in order to prevent the tracker from hanging indefinitely.
- *
- * The Rabit tracker handles connections from distributed workers, assigns ranks to workers, and
- * brokers connections between workers.
- */
-public interface IRabitTracker extends Thread.UncaughtExceptionHandler {
-  enum TrackerStatus {
-    SUCCESS(0), INTERRUPTED(1), TIMEOUT(2), FAILURE(3);
-
-    private int statusCode;
-
-    TrackerStatus(int statusCode) {
-      this.statusCode = statusCode;
-    }
-
-    public int getStatusCode() {
-      return this.statusCode;
-    }
-  }
-
-  Map<String, String> getWorkerEnvs();
-  boolean start(long workerConnectionTimeout);
-  void stop();
-  // taskExecutionTimeout has no effect in current version of XGBoost.
-  int waitFor(long taskExecutionTimeout);
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/NativeLibLoader.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/NativeLibLoader.java
deleted file mode 100644
index 8e19c5b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/NativeLibLoader.java
+++ /dev/null
@@ -1,143 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.io.*;
-import java.lang.reflect.Field;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-/**
- * class to load native library
- *
- * @author hzx
- */
-class NativeLibLoader {
-  private static final Log logger = LogFactory.getLog(NativeLibLoader.class);
-
-  private static boolean initialized = false;
-  private static final String nativeResourcePath = "/lib/";
-  private static final String[] libNames = new String[]{"xgboost4j"};
-
-  static synchronized void initXGBoost() throws IOException {
-    if (!initialized) {
-      for (String libName : libNames) {
-        try {
-          String libraryFromJar = nativeResourcePath + System.mapLibraryName(libName);
-          loadLibraryFromJar(libraryFromJar);
-        } catch (IOException ioe) {
-          logger.error("failed to load " + libName + " library from jar");
-          throw ioe;
-        }
-      }
-      initialized = true;
-    }
-  }
-
-  /**
-   * Loads library from current JAR archive
-   * <p/>
-   * The file from JAR is copied into system temporary directory and then loaded.
-   * The temporary file is deleted after exiting.
-   * Method uses String as filename because the pathname is "abstract", not system-dependent.
-   * <p/>
-   * The restrictions of {@link File#createTempFile(java.lang.String, java.lang.String)} apply to
-   * {@code path}.
-   *
-   * @param path The filename inside JAR as absolute path (beginning with '/'),
-   *             e.g. /package/File.ext
-   * @throws IOException              If temporary file creation or read/write operation fails
-   * @throws IllegalArgumentException If source file (param path) does not exist
-   * @throws IllegalArgumentException If the path is not absolute or if the filename is shorter than
-   * three characters
-   */
-  private static void loadLibraryFromJar(String path) throws IOException, IllegalArgumentException{
-    String temp = createTempFileFromResource(path);
-    // Finally, load the library
-    System.load(temp);
-  }
-
-  /**
-   * Create a temp file that copies the resource from current JAR archive
-   * <p/>
-   * The file from JAR is copied into system temp file.
-   * The temporary file is deleted after exiting.
-   * Method uses String as filename because the pathname is "abstract", not system-dependent.
-   * <p/>
-   * The restrictions of {@link File#createTempFile(java.lang.String, java.lang.String)} apply to
-   * {@code path}.
-   * @param path Path to the resources in the jar
-   * @return The created temp file.
-   * @throws IOException
-   * @throws IllegalArgumentException
-   */
-  static String createTempFileFromResource(String path) throws
-          IOException, IllegalArgumentException {
-    // Obtain filename from path
-    if (!path.startsWith("/")) {
-      throw new IllegalArgumentException("The path has to be absolute (start with '/').");
-    }
-
-    String[] parts = path.split("/");
-    String filename = (parts.length > 1) ? parts[parts.length - 1] : null;
-
-    // Split filename to prexif and suffix (extension)
-    String prefix = "";
-    String suffix = null;
-    if (filename != null) {
-      parts = filename.split("\\.", 2);
-      prefix = parts[0];
-      suffix = (parts.length > 1) ? "." + parts[parts.length - 1] : null; // Thanks, davs! :-)
-    }
-
-    // Check if the filename is okay
-    if (filename == null || prefix.length() < 3) {
-      throw new IllegalArgumentException("The filename has to be at least 3 characters long.");
-    }
-    // Prepare temporary file
-    File temp = File.createTempFile(prefix, suffix);
-    temp.deleteOnExit();
-
-    if (!temp.exists()) {
-      throw new FileNotFoundException("File " + temp.getAbsolutePath() + " does not exist.");
-    }
-
-    // Prepare buffer for data copying
-    byte[] buffer = new byte[1024];
-    int readBytes;
-
-    // Open and check input stream
-    InputStream is = NativeLibLoader.class.getResourceAsStream(path);
-    if (is == null) {
-      throw new FileNotFoundException("File " + path + " was not found inside JAR.");
-    }
-
-    // Open output stream and copy data between source file in JAR and the temporary file
-    OutputStream os = new FileOutputStream(temp);
-    try {
-      while ((readBytes = is.read(buffer)) != -1) {
-        os.write(buffer, 0, readBytes);
-      }
-    } finally {
-      // If read/write fails, close streams safely before throwing an exception
-      os.close();
-      is.close();
-    }
-    return temp.getAbsolutePath();
-  }
-
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Rabit.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Rabit.java
deleted file mode 100644
index 7e019dc..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Rabit.java
+++ /dev/null
@@ -1,154 +0,0 @@
-package ml.dmlc.xgboost4j.java;
-
-import java.io.Serializable;
-import java.nio.ByteBuffer;
-import java.nio.ByteOrder;
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Map;
-
-/**
- * Rabit global class for synchronization.
- */
-public class Rabit {
-
-  public enum OpType implements Serializable {
-    MAX(0), MIN(1), SUM(2), BITWISE_OR(3);
-
-    private int op;
-
-    public int getOperand() {
-      return this.op;
-    }
-
-    OpType(int op) {
-      this.op = op;
-    }
-  }
-
-  public enum DataType implements Serializable {
-    CHAR(0, 1), UCHAR(1, 1), INT(2, 4), UNIT(3, 4),
-    LONG(4, 8), ULONG(5, 8), FLOAT(6, 4), DOUBLE(7, 8),
-    LONGLONG(8, 8), ULONGLONG(9, 8);
-
-    private int enumOp;
-    private int size;
-
-    public int getEnumOp() {
-      return this.enumOp;
-    }
-
-    public int getSize() {
-      return this.size;
-    }
-
-    DataType(int enumOp, int size) {
-      this.enumOp = enumOp;
-      this.size = size;
-    }
-  }
-
-  private static void checkCall(int ret) throws XGBoostError {
-    if (ret != 0) {
-      throw new XGBoostError(XGBoostJNI.XGBGetLastError());
-    }
-  }
-  // used as way to test/debug passed rabit init parameters
-  public static Map<String, String> rabitEnvs;
-  public static List<String> mockList = new LinkedList<>();
-  /**
-   * Initialize the rabit library on current working thread.
-   * @param envs The additional environment variables to pass to rabit.
-   * @throws XGBoostError
-   */
-  public static void init(Map<String, String> envs) throws XGBoostError {
-    rabitEnvs = envs;
-    String[] args = new String[envs.size() + mockList.size()];
-    int idx = 0;
-    for (java.util.Map.Entry<String, String> e : envs.entrySet()) {
-      args[idx++] = e.getKey() + '=' + e.getValue();
-    }
-    // pass list of rabit mock strings eg mock=0,1,0,0
-    for(String mock : mockList) {
-      args[idx++] =  "mock=" + mock;
-    }
-    checkCall(XGBoostJNI.RabitInit(args));
-  }
-
-  /**
-   * Shutdown the rabit engine in current working thread, equals to finalize.
-   * @throws XGBoostError
-   */
-  public static void shutdown() throws XGBoostError {
-    checkCall(XGBoostJNI.RabitFinalize());
-  }
-
-  /**
-   * Print the message on rabit tracker.
-   * @param msg
-   * @throws XGBoostError
-   */
-  public static void trackerPrint(String msg) throws XGBoostError {
-    checkCall(XGBoostJNI.RabitTrackerPrint(msg));
-  }
-
-  /**
-   * Get version number of current stored model in the thread.
-   * which means how many calls to CheckPoint we made so far.
-   * @return version Number.
-   * @throws XGBoostError
-   */
-  public static int versionNumber() throws XGBoostError {
-    int[] out = new int[1];
-    checkCall(XGBoostJNI.RabitVersionNumber(out));
-    return out[0];
-  }
-
-  /**
-   * get rank of current thread.
-   * @return the rank.
-   * @throws XGBoostError
-   */
-  public static int getRank() throws XGBoostError {
-    int[] out = new int[1];
-    checkCall(XGBoostJNI.RabitGetRank(out));
-    return out[0];
-  }
-
-  /**
-   * get world size of current job.
-   * @return the worldsize
-   * @throws XGBoostError
-   */
-  public static int getWorldSize() throws XGBoostError {
-    int[] out = new int[1];
-    checkCall(XGBoostJNI.RabitGetWorldSize(out));
-    return out[0];
-  }
-
-  /**
-   * perform Allreduce on distributed float vectors using operator op.
-   * This implementation of allReduce does not support customized prepare function callback in the
-   * native code, as this function is meant for testing purposes only (to test the Rabit tracker.)
-   *
-   * @param elements local elements on distributed workers.
-   * @param op operator used for Allreduce.
-   * @return All-reduced float elements according to the given operator.
-     */
-  public static float[] allReduce(float[] elements, OpType op) {
-    DataType dataType = DataType.FLOAT;
-    ByteBuffer buffer = ByteBuffer.allocateDirect(dataType.getSize() * elements.length)
-            .order(ByteOrder.nativeOrder());
-
-    for (float el : elements) {
-      buffer.putFloat(el);
-    }
-    buffer.flip();
-
-    XGBoostJNI.RabitAllreduce(buffer, elements.length, dataType.getEnumOp(), op.getOperand());
-    float[] results = new float[elements.length];
-    buffer.asFloatBuffer().get(results);
-
-    return results;
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/RabitTracker.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/RabitTracker.java
deleted file mode 100644
index 58b9b25..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/RabitTracker.java
+++ /dev/null
@@ -1,206 +0,0 @@
-package ml.dmlc.xgboost4j.java;
-
-
-
-import java.io.*;
-import java.util.HashMap;
-import java.util.Map;
-import java.util.concurrent.TimeUnit;
-import java.util.concurrent.atomic.AtomicReference;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-/**
- * Java implementation of the Rabit tracker to coordinate distributed workers.
- * As a wrapper of the Python Rabit tracker, this implementation does not handle timeout for both
- * start() and waitFor() methods (i.e., the timeout is infinite.)
- *
- * For systems lacking Python environment, or for timeout functionality, consider using the Scala
- * Rabit tracker (ml.dmlc.xgboost4j.scala.rabit.RabitTracker) which does not depend on Python, and
- * provides timeout support.
- *
- * The tracker must be started on driver node before running distributed jobs.
- */
-public class RabitTracker implements IRabitTracker {
-  // Maybe per tracker logger?
-  private static final Log logger = LogFactory.getLog(RabitTracker.class);
-  // tracker python file.
-  private static String tracker_py = null;
-  private static TrackerProperties trackerProperties = TrackerProperties.getInstance();
-  // environment variable to be pased.
-  private Map<String, String> envs = new HashMap<String, String>();
-  // number of workers to be submitted.
-  private int numWorkers;
-  private AtomicReference<Process> trackerProcess = new AtomicReference<Process>();
-
-  static {
-    try {
-      initTrackerPy();
-    } catch (IOException ex) {
-      logger.error("load tracker library failed.");
-      logger.error(ex);
-    }
-  }
-
-  /**
-   * Tracker logger that logs output from tracker.
-   */
-  private class TrackerProcessLogger implements Runnable {
-    public void run() {
-
-      Log trackerProcessLogger = LogFactory.getLog(TrackerProcessLogger.class);
-      BufferedReader reader = new BufferedReader(new InputStreamReader(
-              trackerProcess.get().getErrorStream()));
-      String line;
-      try {
-        while ((line = reader.readLine()) != null) {
-          trackerProcessLogger.info(line);
-        }
-        trackerProcess.get().waitFor();
-        trackerProcessLogger.info("Tracker Process ends with exit code " +
-                trackerProcess.get().exitValue());
-      } catch (IOException ex) {
-        trackerProcessLogger.error(ex.toString());
-      } catch (InterruptedException ie) {
-        // we should not get here as RabitTracker is accessed in the main thread
-        ie.printStackTrace();
-        logger.error("the RabitTracker thread is terminated unexpectedly");
-      }
-    }
-  }
-
-  private static void initTrackerPy() throws IOException {
-    try {
-      tracker_py = NativeLibLoader.createTempFileFromResource("/tracker.py");
-    } catch (IOException ioe) {
-      logger.trace("cannot access tracker python script");
-      throw ioe;
-    }
-  }
-
-  public RabitTracker(int numWorkers)
-    throws XGBoostError {
-    if (numWorkers < 1) {
-      throw new XGBoostError("numWorkers must be greater equal to one");
-    }
-    this.numWorkers = numWorkers;
-  }
-
-  public void uncaughtException(Thread t, Throwable e) {
-    logger.error("Uncaught exception thrown by worker:", e);
-    try {
-      Thread.sleep(5000L);
-    } catch (InterruptedException ex) {
-      logger.error(ex);
-    } finally {
-      trackerProcess.get().destroy();
-    }
-  }
-
-  /**
-   * Get environments that can be used to pass to worker.
-   * @return The environment settings.
-   */
-  public Map<String, String> getWorkerEnvs() {
-    return envs;
-  }
-
-  private void loadEnvs(InputStream ins) throws IOException {
-    try {
-      BufferedReader reader = new BufferedReader(new InputStreamReader(ins));
-      assert reader.readLine().trim().equals("DMLC_TRACKER_ENV_START");
-      String line;
-      while ((line = reader.readLine()) != null) {
-        if (line.trim().equals("DMLC_TRACKER_ENV_END")) {
-          break;
-        }
-        String[] sep = line.split("=");
-        if (sep.length == 2) {
-          envs.put(sep[0], sep[1]);
-        }
-      }
-      reader.close();
-    } catch (IOException ioe){
-      logger.error("cannot get runtime configuration from tracker process");
-      ioe.printStackTrace();
-      throw ioe;
-    }
-  }
-
-  private boolean startTrackerProcess() {
-    try {
-      String trackerExecString = this.addTrackerProperties("python " + tracker_py +
-          " --log-level=DEBUG --num-workers=" + String.valueOf(numWorkers));
-
-      trackerProcess.set(Runtime.getRuntime().exec(trackerExecString));
-      loadEnvs(trackerProcess.get().getInputStream());
-      return true;
-    } catch (IOException ioe) {
-      ioe.printStackTrace();
-      return false;
-    }
-  }
-
-  private String addTrackerProperties(String trackerExecString) {
-    StringBuilder sb = new StringBuilder(trackerExecString);
-    String hostIp = trackerProperties.getHostIp();
-
-    if(hostIp != null && !hostIp.isEmpty()){
-      logger.debug("Using provided host-ip: " + hostIp);
-      sb.append(" --host-ip=").append(hostIp);
-    }
-
-    return sb.toString();
-  }
-
-  public void stop() {
-    if (trackerProcess.get() != null) {
-      trackerProcess.get().destroy();
-    }
-  }
-
-  public boolean start(long timeout) {
-    if (timeout > 0L) {
-      logger.warn("Python RabitTracker does not support timeout. " +
-              "The tracker will wait for all workers to connect indefinitely, unless " +
-              "it is interrupted manually. Use the Scala RabitTracker for timeout support.");
-    }
-
-    if (startTrackerProcess()) {
-      logger.debug("Tracker started, with env=" + envs.toString());
-      System.out.println("Tracker started, with env=" + envs.toString());
-      // also start a tracker logger
-      Thread logger_thread = new Thread(new TrackerProcessLogger());
-      logger_thread.setDaemon(true);
-      logger_thread.start();
-      return true;
-    } else {
-      logger.error("FAULT: failed to start tracker process");
-      stop();
-      return false;
-    }
-  }
-
-  public int waitFor(long timeout) {
-    if (timeout > 0L) {
-      logger.warn("Python RabitTracker does not support timeout. " +
-              "The tracker will wait for either all workers to finish tasks and send " +
-              "shutdown signal, or manual interruptions. " +
-              "Use the Scala RabitTracker for timeout support.");
-    }
-
-    try {
-      trackerProcess.get().waitFor();
-      int returnVal = trackerProcess.get().exitValue();
-      logger.info("Tracker Process ends with exit code " + returnVal);
-      stop();
-      return returnVal;
-    } catch (InterruptedException e) {
-      // we should not get here as RabitTracker is accessed in the main thread
-      e.printStackTrace();
-      logger.error("the RabitTracker thread is terminated unexpectedly");
-      return TrackerStatus.INTERRUPTED.getStatusCode();
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/TrackerProperties.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/TrackerProperties.java
deleted file mode 100644
index 45a6b1e..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/TrackerProperties.java
+++ /dev/null
@@ -1,56 +0,0 @@
-package ml.dmlc.xgboost4j.java;
-
-import java.io.*;
-import java.net.URL;
-import java.util.Properties;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-public class TrackerProperties {
-  private static String PROPERTIES_FILENAME = "xgboost-tracker.properties";
-  private static String HOST_IP = "host-ip";
-
-  private static final Log logger = LogFactory.getLog(TrackerProperties.class);
-  private static TrackerProperties instance = new TrackerProperties();
-
-  private Properties properties;
-
-  private TrackerProperties() {
-    this.properties = new Properties();
-
-    InputStream inputStream = null;
-
-    try {
-      URL propertiesFileURL =
-          Thread.currentThread().getContextClassLoader().getResource(PROPERTIES_FILENAME);
-      if (propertiesFileURL != null){
-        inputStream = propertiesFileURL.openStream();
-      }
-    } catch (IOException e) {
-      logger.warn("Could not load " + PROPERTIES_FILENAME + " file. ", e);
-    }
-
-    if(inputStream != null){
-      try {
-        properties.load(inputStream);
-        logger.debug("Loaded properties from external source");
-      } catch (IOException e) {
-        logger.error("Error loading tracker properties file. Skipping and using defaults. ", e);
-      }
-      try {
-        inputStream.close();
-      } catch (IOException e) {
-        // ignore exception
-      }
-    }
-  }
-
-  public static TrackerProperties getInstance() {
-    return instance;
-  }
-
-  public String getHostIp(){
-    return this.properties.getProperty(HOST_IP);
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java
deleted file mode 100644
index cf4ef33..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java
+++ /dev/null
@@ -1,571 +0,0 @@
-/*
- * Copyright (C) 2021. Huawei Technologies Co., Ltd.
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
- * */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.io.File;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import java.util.*;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-import org.apache.hadoop.fs.FileSystem;
-import org.apache.hadoop.fs.Path;
-
-/**
- * trainer for xgboost
- *
- * @author hzx
- */
-public class XGBoost {
-  private static final Log logger = LogFactory.getLog(XGBoost.class);
-
-  /**
-   * load model from modelPath
-   *
-   * @param modelPath booster modelPath (model generated by booster.saveModel)
-   * @throws XGBoostError native error
-   */
-  public static Booster loadModel(String modelPath)
-          throws XGBoostError {
-    return Booster.loadModel(modelPath);
-  }
-
-  /**
-   * Load a new Booster model from a file opened as input stream.
-   * The assumption is the input stream only contains one XGBoost Model.
-   * This can be used to load existing booster models saved by other xgboost bindings.
-   *
-   * @param in The input stream of the file,
-   *           will be closed after this function call.
-   * @return The create boosted
-   * @throws XGBoostError
-   * @throws IOException
-   */
-  public static Booster loadModel(InputStream in)
-          throws XGBoostError, IOException {
-    return Booster.loadModel(in);
-  }
-
-  /**
-   * Train a booster given parameters.
-   *
-   * @param dtrain  Data to be trained.
-   * @param params  Parameters.
-   * @param round   Number of boosting iterations.
-   * @param watches a group of items to be evaluated during training, this allows user to watch
-   *                performance on the validation set.
-   * @param obj     customized objective
-   * @param eval    customized evaluation
-   * @return The trained booster.
-   */
-  public static Booster train(
-          DMatrix dtrain,
-          Map<String, Object> params,
-          int round,
-          Map<String, DMatrix> watches,
-          IObjective obj,
-          IEvaluation eval) throws XGBoostError {
-    return train(dtrain, params, round, watches, null, obj, eval, 0);
-  }
-
-  /**
-   * Train a booster given parameters.
-   *
-   * @param dtrain  Data to be trained.
-   * @param params  Parameters.
-   * @param round   Number of boosting iterations.
-   * @param watches a group of items to be evaluated during training, this allows user to watch
-   *                performance on the validation set.
-   * @param metrics array containing the evaluation metrics for each matrix in watches for each
-   *                iteration
-   * @param earlyStoppingRound if non-zero, training would be stopped
-   *                           after a specified number of consecutive
-   *                           increases in any evaluation metric.
-   * @param obj     customized objective
-   * @param eval    customized evaluation
-   * @return The trained booster.
-   */
-  public static Booster train(
-          DMatrix dtrain,
-          Map<String, Object> params,
-          int round,
-          Map<String, DMatrix> watches,
-          float[][] metrics,
-          IObjective obj,
-          IEvaluation eval,
-          int earlyStoppingRound) throws XGBoostError {
-    return train(dtrain, params, round, watches, metrics, obj, eval, earlyStoppingRound, null);
-  }
-
-  private static void saveCheckpoint(
-          Booster booster,
-          int iter,
-          Set<Integer> checkpointIterations,
-          ExternalCheckpointManager ecm) throws XGBoostError {
-    try {
-      if (checkpointIterations.contains(iter)) {
-        ecm.updateCheckpoint(booster);
-      }
-    } catch (Exception e) {
-      logger.error("failed to save checkpoint in XGBoost4J at iteration " + iter, e);
-      throw new XGBoostError("failed to save checkpoint in XGBoost4J at iteration" + iter, e);
-    }
-  }
-
-  public static Booster trainAndSaveCheckpoint(
-      DMatrix dtrain,
-      Map<String, Object> params,
-      int numRounds,
-      Map<String, DMatrix> watches,
-      float[][] metrics,
-      IObjective obj,
-      IEvaluation eval,
-      int earlyStoppingRounds,
-      Booster booster,
-      int checkpointInterval,
-      String checkpointPath,
-      FileSystem fs) throws XGBoostError, IOException {
-    //collect eval matrixs
-    String[] evalNames;
-    DMatrix[] evalMats;
-    float bestScore;
-    int bestIteration;
-    List<String> names = new ArrayList<String>();
-    List<DMatrix> mats = new ArrayList<DMatrix>();
-    Set<Integer> checkpointIterations = new HashSet<>();
-    ExternalCheckpointManager ecm = null;
-    if (checkpointPath != null) {
-      ecm = new ExternalCheckpointManager(checkpointPath, fs);
-    }
-
-    for (Map.Entry<String, DMatrix> evalEntry : watches.entrySet()) {
-      names.add(evalEntry.getKey());
-      mats.add(evalEntry.getValue());
-    }
-
-    evalNames = names.toArray(new String[names.size()]);
-    evalMats = mats.toArray(new DMatrix[mats.size()]);
-    if (isMaximizeEvaluation(params)) {
-      bestScore = -Float.MAX_VALUE;
-    } else {
-      bestScore = Float.MAX_VALUE;
-    }
-    bestIteration = 0;
-    metrics = metrics == null ? new float[evalNames.length][numRounds] : metrics;
-
-    //collect all data matrixs
-    DMatrix[] allMats;
-    if (evalMats.length > 0) {
-      allMats = new DMatrix[evalMats.length + 1];
-      allMats[0] = dtrain;
-      System.arraycopy(evalMats, 0, allMats, 1, evalMats.length);
-    } else {
-      allMats = new DMatrix[1];
-      allMats[0] = dtrain;
-    }
-
-    //initialize booster
-    if (booster == null) {
-      // Start training on a new booster
-      booster = new Booster(params, allMats);
-      booster.loadRabitCheckpoint();
-    } else {
-      // Start training on an existing booster
-      booster.setParams(params);
-    }
-
-    if (ecm != null) {
-      checkpointIterations = new HashSet<>(ecm.getCheckpointRounds(checkpointInterval, numRounds));
-    }
-
-    Integer num_class = tryGetIntFromObject(params.get("num_class"));
-    float[] last_round_best_loss = new float[num_class == null ? 1 : num_class];
-
-    // begin to train
-    for (int iter = booster.getVersion() / 2; iter < numRounds; iter++) {
-      if (booster.getVersion() % 2 == 0) {
-        if (obj != null) {
-          booster.update(dtrain, obj);
-        } else {
-          booster.update(dtrain, iter, last_round_best_loss);
-        }
-        saveCheckpoint(booster, iter, checkpointIterations, ecm);
-        booster.saveRabitCheckpoint();
-      }
-
-      //evaluation
-      if (evalMats.length > 0) {
-        float[] metricsOut = new float[evalMats.length];
-        String evalInfo;
-        if (eval != null) {
-          evalInfo = booster.evalSet(evalMats, evalNames, eval, metricsOut);
-        } else {
-          evalInfo = booster.evalSet(evalMats, evalNames, iter, metricsOut);
-        }
-        for (int i = 0; i < metricsOut.length; i++) {
-          metrics[i][iter] = metricsOut[i];
-        }
-
-        // If there is more than one evaluation datasets, the last one would be used
-        // to determinate early stop.
-        float score = metricsOut[metricsOut.length - 1];
-        if (isMaximizeEvaluation(params)) {
-          // Update best score if the current score is better (no update when equal)
-          if (score > bestScore) {
-            bestScore = score;
-            bestIteration = iter;
-          }
-        } else {
-          if (score < bestScore) {
-            bestScore = score;
-            bestIteration = iter;
-          }
-        }
-        if (earlyStoppingRounds > 0) {
-          if (shouldEarlyStop(earlyStoppingRounds, iter, bestIteration)) {
-            Rabit.trackerPrint(String.format(
-                    "early stopping after %d rounds away from the best iteration",
-                    earlyStoppingRounds));
-            break;
-          }
-        }
-        if (Rabit.getRank() == 0 && shouldPrint(params, iter)) {
-          if (shouldPrint(params, iter)){
-            Rabit.trackerPrint(evalInfo + '\n');
-          }
-        }
-      }
-      booster.saveRabitCheckpoint();
-    }
-    return booster;
-  }
-
-  /**
-   * Train a booster given parameters.
-   *
-   * @param dtrain  Data to be trained.
-   * @param params  Parameters.
-   * @param round   Number of boosting iterations.
-   * @param watches a group of items to be evaluated during training, this allows user to watch
-   *                performance on the validation set.
-   * @param metrics array containing the evaluation metrics for each matrix in watches for each
-   *                iteration
-   * @param earlyStoppingRounds if non-zero, training would be stopped
-   *                           after a specified number of consecutive
-   *                           goes to the unexpected direction in any evaluation metric.
-   * @param obj     customized objective
-   * @param eval    customized evaluation
-   * @param booster train from scratch if set to null; train from an existing booster if not null.
-   * @return The trained booster.
-   */
-  public static Booster train(
-          DMatrix dtrain,
-          Map<String, Object> params,
-          int round,
-          Map<String, DMatrix> watches,
-          float[][] metrics,
-          IObjective obj,
-          IEvaluation eval,
-          int earlyStoppingRounds,
-          Booster booster) throws XGBoostError {
-    try {
-      return trainAndSaveCheckpoint(dtrain, params, round, watches, metrics, obj, eval,
-              earlyStoppingRounds, booster,
-              -1, null, null);
-    } catch (IOException e) {
-      logger.error("training failed in xgboost4j", e);
-      throw new XGBoostError("training failed in xgboost4j ", e);
-    }
-  }
-
-  private static Integer tryGetIntFromObject(Object o) {
-    if (o instanceof Integer) {
-      return (int)o;
-    } else if (o instanceof String) {
-      try {
-        return Integer.parseInt((String)o);
-      } catch (NumberFormatException e) {
-        return null;
-      }
-    } else {
-      return null;
-    }
-  }
-
-  private static boolean shouldPrint(Map<String, Object> params, int iter) {
-    Object silent = params.get("silent");
-    Integer silentInt = tryGetIntFromObject(silent);
-    if (silent != null) {
-      if (silent.equals("true") || silent.equals("True")
-              || (silentInt != null && silentInt != 0)) {
-        return false;  // "silent" will stop printing, otherwise go look at "verbose_eval"
-      }
-    }
-
-    Object verboseEval = params.get("verbose_eval");
-    Integer verboseEvalInt = tryGetIntFromObject(verboseEval);
-    if (verboseEval == null) {
-      return true; // Default to printing evalInfo
-    } else if (verboseEval.equals("false") || verboseEval.equals("False")) {
-      return false;
-    } else if (verboseEvalInt != null) {
-      if (verboseEvalInt == 0) {
-        return false;
-      } else {
-        return iter % verboseEvalInt == 0;
-      }
-    } else {
-      return true; // Don't understand the option, default to printing
-    }
-  }
-
-  static boolean shouldEarlyStop(int earlyStoppingRounds, int iter, int bestIteration) {
-    return iter - bestIteration >= earlyStoppingRounds;
-  }
-
-  private static boolean isMaximizeEvaluation(Map<String, Object> params) {
-    try {
-      String maximize = String.valueOf(params.get("maximize_evaluation_metrics"));
-      assert(maximize != null);
-      return Boolean.valueOf(maximize);
-    } catch (Exception ex) {
-      logger.error("maximize_evaluation_metrics has to be specified for enabling early stop," +
-              " allowed value: true/false", ex);
-      throw ex;
-    }
-  }
-
-  /**
-   * Cross-validation with given parameters.
-   *
-   * @param data    Data to be trained.
-   * @param params  Booster params.
-   * @param round   Number of boosting iterations.
-   * @param nfold   Number of folds in CV.
-   * @param metrics Evaluation metrics to be watched in CV.
-   * @param obj     customized objective (set to null if not used)
-   * @param eval    customized evaluation (set to null if not used)
-   * @return evaluation history
-   * @throws XGBoostError native error
-   */
-  public static String[] crossValidation(
-      DMatrix data,
-      Map<String, Object> params,
-      int round,
-      int nfold,
-      String[] metrics,
-      IObjective obj,
-      IEvaluation eval) throws XGBoostError {
-    CVPack[] cvPacks = makeNFold(data, nfold, params, metrics);
-    String[] evalHist = new String[round];
-    String[] results = new String[cvPacks.length];
-    for (int i = 0; i < round; i++) {
-      for (CVPack cvPack : cvPacks) {
-        if (obj != null) {
-          cvPack.update(obj);
-        } else {
-          cvPack.update(i);
-        }
-      }
-
-      for (int j = 0; j < cvPacks.length; j++) {
-        if (eval != null) {
-          results[j] = cvPacks[j].eval(eval);
-        } else {
-          results[j] = cvPacks[j].eval(i);
-        }
-      }
-
-      evalHist[i] = aggCVResults(results);
-      logger.info(evalHist[i]);
-    }
-    return evalHist;
-  }
-
-  /**
-   * make an n-fold array of CVPack from random indices
-   *
-   * @param data        original data
-   * @param nfold       num of folds
-   * @param params      booster parameters
-   * @param evalMetrics Evaluation metrics
-   * @return CV package array
-   * @throws XGBoostError native error
-   */
-  private static CVPack[] makeNFold(DMatrix data, int nfold, Map<String, Object> params,
-                                    String[] evalMetrics) throws XGBoostError {
-    List<Integer> samples = genRandPermutationNums(0, (int) data.rowNum());
-    int step = samples.size() / nfold;
-    int[] testSlice = new int[step];
-    int[] trainSlice = new int[samples.size() - step];
-    int testid, trainid;
-    CVPack[] cvPacks = new CVPack[nfold];
-    for (int i = 0; i < nfold; i++) {
-      testid = 0;
-      trainid = 0;
-      for (int j = 0; j < samples.size(); j++) {
-        if (j > (i * step) && j < (i * step + step) && testid < step) {
-          testSlice[testid] = samples.get(j);
-          testid++;
-        } else {
-          if (trainid < samples.size() - step) {
-            trainSlice[trainid] = samples.get(j);
-            trainid++;
-          } else {
-            testSlice[testid] = samples.get(j);
-            testid++;
-          }
-        }
-      }
-
-      DMatrix dtrain = data.slice(trainSlice);
-      DMatrix dtest = data.slice(testSlice);
-      CVPack cvPack = new CVPack(dtrain, dtest, params);
-      //set eval types
-      if (evalMetrics != null) {
-        for (String type : evalMetrics) {
-          cvPack.booster.setParam("eval_metric", type);
-        }
-      }
-      cvPacks[i] = cvPack;
-    }
-
-    return cvPacks;
-  }
-
-  private static List<Integer> genRandPermutationNums(int start, int end) {
-    List<Integer> samples = new ArrayList<Integer>();
-    for (int i = start; i < end; i++) {
-      samples.add(i);
-    }
-    Collections.shuffle(samples);
-    return samples;
-  }
-
-  /**
-   * Aggregate cross-validation results.
-   *
-   * @param results eval info from each data sample
-   * @return cross-validation eval info
-   */
-  private static String aggCVResults(String[] results) {
-    Map<String, List<Float>> cvMap = new HashMap<String, List<Float>>();
-    String aggResult = results[0].split("\t")[0];
-    for (String result : results) {
-      String[] items = result.split("\t");
-      for (int i = 1; i < items.length; i++) {
-        String[] tup = items[i].split(":");
-        String key = tup[0];
-        Float value = Float.valueOf(tup[1]);
-        if (!cvMap.containsKey(key)) {
-          cvMap.put(key, new ArrayList<Float>());
-        }
-        cvMap.get(key).add(value);
-      }
-    }
-
-    for (String key : cvMap.keySet()) {
-      float value = 0f;
-      for (Float tvalue : cvMap.get(key)) {
-        value += tvalue;
-      }
-      value /= cvMap.get(key).size();
-      aggResult += String.format("\tcv-%s:%f", key, value);
-    }
-
-    return aggResult;
-  }
-
-  /**
-   * cross validation package for xgb
-   *
-   * @author hzx
-   */
-  private static class CVPack {
-    DMatrix dtrain;
-    DMatrix dtest;
-    DMatrix[] dmats;
-    String[] names;
-    Booster booster;
-
-    /**
-     * create an cross validation package
-     *
-     * @param dtrain train data
-     * @param dtest  test data
-     * @param params parameters
-     * @throws XGBoostError native error
-     */
-    public CVPack(DMatrix dtrain, DMatrix dtest, Map<String, Object> params)
-            throws XGBoostError {
-      dmats = new DMatrix[]{dtrain, dtest};
-      booster = new Booster(params, dmats);
-      names = new String[]{"train", "test"};
-      this.dtrain = dtrain;
-      this.dtest = dtest;
-    }
-
-    /**
-     * update one iteration
-     *
-     * @param iter iteration num
-     * @throws XGBoostError native error
-     */
-    public void update(int iter) throws XGBoostError {
-      booster.update(dtrain, iter);
-    }
-
-    /**
-     * update one iteration
-     *
-     * @param obj  customized objective
-     * @throws XGBoostError native error
-     */
-    public void update(IObjective obj) throws XGBoostError {
-      booster.update(dtrain, obj);
-    }
-
-    /**
-     * evaluation
-     *
-     * @param iter iteration num
-     * @return evaluation
-     * @throws XGBoostError native error
-     */
-    public String eval(int iter) throws XGBoostError {
-      return booster.evalSet(dmats, names, iter);
-    }
-
-    /**
-     * evaluation
-     *
-     * @param eval customized eval
-     * @return evaluation
-     * @throws XGBoostError native error
-     */
-    public String eval(IEvaluation eval) throws XGBoostError {
-      return booster.evalSet(dmats, names, eval);
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostError.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostError.java
deleted file mode 100644
index d113f5b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostError.java
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-/**
- * custom error class for xgboost
- *
- * @author hzx
- */
-public class XGBoostError extends Exception {
-  public XGBoostError(String message) {
-    super(message);
-  }
-
-  public XGBoostError(String message, Throwable cause) {
-    super(message, cause);
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostJNI.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostJNI.java
deleted file mode 100644
index a4d0a2b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostJNI.java
+++ /dev/null
@@ -1,145 +0,0 @@
-/*
- * Copyright (C) 2021. Huawei Technologies Co., Ltd.
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
- * */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java;
-
-import java.nio.ByteBuffer;
-
-import org.apache.commons.logging.Log;
-import org.apache.commons.logging.LogFactory;
-
-/**
- * xgboost JNI functions
- * change 2015-7-6: *use a long[] (length=1) as container of handle to get the output DMatrix or Booster
- *
- * @author hzx
- */
-class XGBoostJNI {
-  private static final Log logger = LogFactory.getLog(DMatrix.class);
-
-  static {
-    try {
-      NativeLibLoader.initXGBoost();
-    } catch (Exception ex) {
-      logger.error("Failed to load native library", ex);
-      throw new RuntimeException(ex);
-    }
-  }
-
-  /**
-   * Check the return code of the JNI call.
-   *
-   * @throws XGBoostError if the call failed.
-   */
-  static void checkCall(int ret) throws XGBoostError {
-    if (ret != 0) {
-      throw new XGBoostError(XGBGetLastError());
-    }
-  }
-
-  public final static native String XGBGetLastError();
-
-  public final static native int XGDMatrixCreateFromFile(String fname, int silent, long[] out);
-
-  final static native int XGDMatrixCreateFromDataIter(java.util.Iterator<DataBatch> iter,
-                                                             String cache_info, long[] out);
-
-  public final static native int XGDMatrixCreateFromCSREx(long[] indptr, int[] indices, float[] data,
-                                                        int shapeParam, long[] out);
-
-  public final static native int XGDMatrixCreateFromCSCEx(long[] colptr, int[] indices, float[] data,
-                                                          int shapeParam, long[] out);
-
-  public final static native int XGDMatrixCreateFromMat(float[] data, int nrow, int ncol,
-                                                        float missing, long[] out);
-
-  public final static native int XGDMatrixCreateFromMatRef(long dataRef, int nrow, int ncol,
-                                                           float missing, long[] out);
-
-  public final static native int XGDMatrixSliceDMatrix(long handle, int[] idxset, long[] out);
-
-  public final static native int XGDMatrixFree(long handle);
-
-  public final static native int XGDMatrixSaveBinary(long handle, String fname, int silent);
-
-  public final static native int XGDMatrixSetFloatInfo(long handle, String field, float[] array);
-
-  public final static native int XGDMatrixSetUIntInfo(long handle, String field, int[] array);
-
-  public final static native int XGDMatrixGetFloatInfo(long handle, String field, float[][] info);
-
-  public final static native int XGDMatrixGetUIntInfo(long handle, String filed, int[][] info);
-
-  public final static native int XGDMatrixNumRow(long handle, long[] row);
-
-  public final static native int XGBoosterCreate(long[] handles, long[] out);
-
-  public final static native int XGBoosterFree(long handle);
-
-  public final static native int XGBoosterSetParam(long handle, String name, String value);
-
-  public final static native int XGBoosterUpdateOneIter(long handle, int iter, long dtrain);
-
-  public final static native int XGBoosterUpdateOneIterLtd(long handle, int iter, long dtrain,
-                                                           float[] last_round_best_loss);
-
-  public final static native int XGBoosterBoostOneIter(long handle, long dtrain, float[] grad,
-                                                       float[] hess);
-
-  public final static native int XGBoosterEvalOneIter(long handle, int iter, long[] dmats,
-                                                      String[] evnames, String[] eval_info);
-
-  public final static native int XGBoosterPredict(long handle, long dmat, int option_mask,
-                                                  int ntree_limit, float[][] predicts);
-
-  public final static native int XGBoosterLoadModel(long handle, String fname);
-
-  public final static native int XGBoosterSaveModel(long handle, String fname);
-
-  public final static native int XGBoosterLoadModelFromBuffer(long handle, byte[] bytes);
-
-  public final static native int XGBoosterGetModelRaw(long handle, byte[][] out_bytes);
-
-  public final static native int XGBoosterDumpModelEx(long handle, String fmap, int with_stats,
-                                                      String format, String[][] out_strings);
-
-  public final static native int XGBoosterDumpModelExWithFeatures(
-    long handle, String[] feature_names, int with_stats, String format, String[][] out_strings);
-
-  public final static native int XGBoosterGetAttrNames(long handle, String[][] out_strings);
-  public final static native int XGBoosterGetAttr(long handle, String key, String[] out_string);
-  public final static native int XGBoosterSetAttr(long handle, String key, String value);
-  public final static native int XGBoosterLoadRabitCheckpoint(long handle, int[] out_version);
-  public final static native int XGBoosterSaveRabitCheckpoint(long handle);
-
-  // rabit functions
-  public final static native int RabitInit(String[] args);
-  public final static native int RabitFinalize();
-  public final static native int RabitTrackerPrint(String msg);
-  public final static native int RabitGetRank(int[] out);
-  public final static native int RabitGetWorldSize(int[] out);
-  public final static native int RabitVersionNumber(int[] out);
-
-  // Perform Allreduce operation on data in sendrecvbuf.
-  // This JNI function does not support the callback function for data preparation yet.
-  final static native int RabitAllreduce(ByteBuffer sendrecvbuf, int count,
-                                                int enum_dtype, int enum_op);
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/util/BigDenseMatrix.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/util/BigDenseMatrix.java
deleted file mode 100644
index 9dbebb5..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/util/BigDenseMatrix.java
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.util;
-
-/**
- * Off-heap implementation of a Dense Matrix, matrix size is only limited by the
- * amount of the available memory and the matrix dimension cannot exceed
- * Integer.MAX_VALUE (this is consistent with XGBoost API restrictions on maximum
- * length of a response).
- */
-public final class BigDenseMatrix {
-
-  private static final int FLOAT_BYTE_SIZE = 4;
-  public static final long MAX_MATRIX_SIZE = Long.MAX_VALUE / FLOAT_BYTE_SIZE;
-
-  public final int nrow;
-  public final int ncol;
-  public final long address;
-
-  public static void setDirect(long valAddress, float val) {
-    UtilUnsafe.UNSAFE.putFloat(valAddress, val);
-  }
-
-  public static float getDirect(long valAddress) {
-    return UtilUnsafe.UNSAFE.getFloat(valAddress);
-  }
-
-  public BigDenseMatrix(int nrow, int ncol) {
-    final long size = (long) nrow * ncol;
-    if (size > MAX_MATRIX_SIZE) {
-      throw new IllegalArgumentException("Matrix too large; matrix size cannot exceed " +
-          MAX_MATRIX_SIZE);
-    }
-    this.nrow = nrow;
-    this.ncol = ncol;
-    this.address = UtilUnsafe.UNSAFE.allocateMemory(size * FLOAT_BYTE_SIZE);
-  }
-
-  public final void set(long idx, float val) {
-    setDirect(address + idx * FLOAT_BYTE_SIZE, val);
-  }
-
-  public final void set(int i, int j, float val) {
-    set(index(i, j), val);
-  }
-
-  public final float get(long idx) {
-    return getDirect(address + idx * FLOAT_BYTE_SIZE);
-  }
-
-  public final float get(int i, int j) {
-    return get(index(i, j));
-  }
-
-  public final void dispose() {
-    UtilUnsafe.UNSAFE.freeMemory(address);
-  }
-
-  private long index(int i, int j) {
-    return (long) i * ncol + j;
-  }
-
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/util/UtilUnsafe.java b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/util/UtilUnsafe.java
deleted file mode 100644
index 501a9cf..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/util/UtilUnsafe.java
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-package ml.dmlc.xgboost4j.java.util;
-
-import java.lang.reflect.Field;
-
-import sun.misc.Unsafe;
-
-/**
- * Simple class to obtain access to the {@link Unsafe} object. Use responsibly :)
- */
-public final class UtilUnsafe {
-
-  static Unsafe UNSAFE = getUnsafe();
-
-  private UtilUnsafe() {
-  } // dummy private constructor
-
-  private static Unsafe getUnsafe() {
-    // Not on bootclasspath
-    if (UtilUnsafe.class.getClassLoader() == null) {
-      return Unsafe.getUnsafe();
-    }
-    try {
-      final Field fld = Unsafe.class.getDeclaredField("theUnsafe");
-      fld.setAccessible(true);
-      return (Unsafe) fld.get(UtilUnsafe.class);
-    } catch (Exception e) {
-      throw new RuntimeException("Could not obtain access to sun.misc.Unsafe", e);
-    }
-  }
-
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/resources/xgboost4j-version.properties b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/resources/xgboost4j-version.properties
deleted file mode 100644
index e5683df..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/resources/xgboost4j-version.properties
+++ /dev/null
@@ -1 +0,0 @@
-version=${project.version}
\ No newline at end of file
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/LabeledPoint.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/LabeledPoint.scala
deleted file mode 100644
index ccdedba..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/LabeledPoint.scala
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j
-
-/**
- * Labeled training data point.
- *
- * @param label Label of this point.
- * @param size Feature dimensionality
- * @param indices Feature indices of this point or `null` if the data is dense.
- * @param values Feature values of this point.
- * @param weight Weight of this point.
- * @param group Group of this point (used for ranking) or -1.
- * @param baseMargin Initial prediction on this point or `Float.NaN`
- */
-case class LabeledPoint(
-    label: Float,
-    size: Int,
-    indices: Array[Int],
-    values: Array[Float],
-    weight: Float = 1f,
-    group: Int = -1,
-    baseMargin: Float = Float.NaN) extends Serializable {
-  require(indices == null || indices.length == values.length,
-    "indices and values must have the same number of elements")
-
-  require(indices == null || size >= indices.length,
-    "feature dimensionality must be greater equal than size of indices")
-
-  def this(label: Float, size: Int, indices: Array[Int], values: Array[Float]) = {
-    // [[weight]] default duplicated to disambiguate the constructor call.
-    this(label, size, indices, values, 1.0f)
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/Booster.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/Booster.scala
deleted file mode 100644
index c0d5efe..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/Booster.scala
+++ /dev/null
@@ -1,337 +0,0 @@
-// scalastyle:off header.matches
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import com.esotericsoftware.kryo.io.{Output, Input}
-import com.esotericsoftware.kryo.{Kryo, KryoSerializable}
-import ml.dmlc.xgboost4j.java.{Booster => JBooster}
-import ml.dmlc.xgboost4j.java.XGBoostError
-import scala.collection.JavaConverters._
-import scala.collection.mutable
-
-/**
-  * Booster for xgboost, this is a model API that support interactive build of a XGBoost Model
-  *
-  * DEVELOPER WARNING: A Java Booster must not be shared by more than one Scala Booster
-  * @param booster the java booster object.
-  */
-class Booster private[xgboost4j](private[xgboost4j] var booster: JBooster)
-  extends Serializable  with KryoSerializable {
-
-  /**
-   * Get attributes stored in the Booster as a Map.
-   *
-   * @return A map contain attribute pairs.
-   */
-  @throws(classOf[XGBoostError])
-  def getAttrs: Map[String, String] = {
-    booster.getAttrs.asScala.toMap
-  }
-
-  /**
-   * Get attribute from the Booster.
-   *
-   * @param key   attr name
-   * @return attr value
-   */
-  @throws(classOf[XGBoostError])
-  def getAttr(key: String): String = {
-    booster.getAttr(key)
-  }
-
-  /**
-   * Set attribute to the Booster.
-   *
-   * @param key   attr name
-   * @param value attr value
-   */
-  @throws(classOf[XGBoostError])
-  def setAttr(key: String, value: String): Unit = {
-    booster.setAttr(key, value)
-  }
-
-  /**
-   * set attributes
-   *
-   * @param params attributes key-value map
-   */
-  @throws(classOf[XGBoostError])
-  def setAttrs(params: Map[String, String]): Unit = {
-    booster.setAttrs(params.asJava)
-  }
-
-  /**
-    * Set parameter to the Booster.
-    *
-    * @param key   param name
-    * @param value param value
-    */
-  @throws(classOf[XGBoostError])
-  def setParam(key: String, value: AnyRef): Unit = {
-    booster.setParam(key, value)
-  }
-
-  /**
-   * set parameters
-   *
-   * @param params parameters key-value map
-   */
-  @throws(classOf[XGBoostError])
-  def setParams(params: Map[String, AnyRef]): Unit = {
-    booster.setParams(params.asJava)
-  }
-
-  /**
-   * Update (one iteration)
-   *
-   * @param dtrain training data
-   * @param iter   current iteration number
-   */
-  @throws(classOf[XGBoostError])
-  def update(dtrain: DMatrix, iter: Int): Unit = {
-    booster.update(dtrain.jDMatrix, iter)
-  }
-
-  /**
-   * Update (one iteration)
-   *
-   * @param dtrain training data
-   * @param iter   current iteration number
-   */
-  @throws(classOf[XGBoostError])
-  def update(dtrain: DMatrix, iter: Int, last_round_best_loss: Array[Float]): Unit = {
-    booster.update(dtrain.jDMatrix, iter, last_round_best_loss)
-  }
-
-  /**
-   * update with customize obj func
-   *
-   * @param dtrain training data
-   * @param obj    customized objective class
-   */
-  @throws(classOf[XGBoostError])
-  def update(dtrain: DMatrix, obj: ObjectiveTrait): Unit = {
-    booster.update(dtrain.jDMatrix, obj)
-  }
-
-  /**
-   * update with give grad and hess
-   *
-   * @param dtrain training data
-   * @param grad   first order of gradient
-   * @param hess   seconde order of gradient
-   */
-  @throws(classOf[XGBoostError])
-  def boost(dtrain: DMatrix, grad: Array[Float], hess: Array[Float]): Unit = {
-    booster.boost(dtrain.jDMatrix, grad, hess)
-  }
-
-  /**
-   * evaluate with given dmatrixs.
-   *
-   * @param evalMatrixs dmatrixs for evaluation
-   * @param evalNames   name for eval dmatrixs, used for check results
-   * @param iter        current eval iteration
-   * @return eval information
-   */
-  @throws(classOf[XGBoostError])
-  def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int)
-    : String = {
-    booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, iter)
-  }
-
-  /**
-   * evaluate with given customized Evaluation class
-   *
-   * @param evalMatrixs evaluation matrix
-   * @param evalNames   evaluation names
-   * @param eval        custom evaluator
-   * @return eval information
-   */
-  @throws(classOf[XGBoostError])
-  def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: EvalTrait)
-    : String = {
-    booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, eval)
-  }
-
-
-  /**
-   * Predict with data
-   *
-   * @param data         dmatrix storing the input
-   * @param outPutMargin Whether to output the raw untransformed margin value.
-   * @param treeLimit    Limit number of trees in the prediction; defaults to 0 (use all trees).
-   * @return predict result
-   */
-  @throws(classOf[XGBoostError])
-  def predict(data: DMatrix, outPutMargin: Boolean = false, treeLimit: Int = 0):
-      Array[Array[Float]] = {
-    booster.predict(data.jDMatrix, outPutMargin, treeLimit)
-  }
-
-  /**
-   * Predict the leaf indices
-   *
-   * @param data      dmatrix storing the input
-   * @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
-   * @return predict result
-   * @throws XGBoostError native error
-   */
-  @throws(classOf[XGBoostError])
-  def predictLeaf(data: DMatrix, treeLimit: Int = 0): Array[Array[Float]] = {
-    booster.predictLeaf(data.jDMatrix, treeLimit)
-  }
-
-  /**
-    * Output feature contributions toward predictions of given data
-    *
-    * @param data      dmatrix storing the input
-    * @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
-    * @return The feature contributions and bias.
-    * @throws XGBoostError native error
-    */
-  @throws(classOf[XGBoostError])
-  def predictContrib(data: DMatrix, treeLimit: Int = 0) : Array[Array[Float]] = {
-    booster.predictContrib(data.jDMatrix, treeLimit)
-  }
-
-  /**
-   * save model to modelPath
-   *
-   * @param modelPath model path
-   */
-  @throws(classOf[XGBoostError])
-  def saveModel(modelPath: String): Unit = {
-    booster.saveModel(modelPath)
-  }
-  /**
-    * save model to Output stream
-    *
-    * @param out Output stream
-    */
-  @throws(classOf[XGBoostError])
-  def saveModel(out: java.io.OutputStream): Unit = {
-    booster.saveModel(out)
-  }
-  /**
-   * Dump model as Array of string
-   *
-   * @param featureMap featureMap file
-   * @param withStats  bool
-   *                   Controls whether the split statistics are output.
-   */
-  @throws(classOf[XGBoostError])
-  def getModelDump(featureMap: String = null, withStats: Boolean = false, format: String = "text")
-    : Array[String] = {
-    booster.getModelDump(featureMap, withStats, format)
-  }
-
-  /**
-    * Dump model as Array of string with specified feature names.
-    *
-    * @param featureNames Names of features.
-    */
-  @throws(classOf[XGBoostError])
-  def getModelDump(featureNames: Array[String]): Array[String] = {
-    booster.getModelDump(featureNames, false, "text")
-  }
-
-  def getModelDump(featureNames: Array[String], withStats: Boolean, format: String)
-    : Array[String] = {
-    booster.getModelDump(featureNames, withStats, format)
-  }
-
-
-  /**
-   * Get importance of each feature based on weight only (number of splits)
-   *
-   * @return featureScoreMap  key: feature index, value: feature importance score
-   */
-  @throws(classOf[XGBoostError])
-  def getFeatureScore(featureMap: String = null): mutable.Map[String, Integer] = {
-    booster.getFeatureScore(featureMap).asScala
-  }
-
-  /**
-    * Get importance of each feature based on weight only
-    * (number of splits), with specified feature names.
-    *
-    * @return featureScoreMap  key: feature name, value: feature importance score
-    */
-  @throws(classOf[XGBoostError])
-  def getFeatureScore(featureNames: Array[String]): mutable.Map[String, Integer] = {
-    booster.getFeatureScore(featureNames).asScala
-  }
-
-  /**
-    * Get importance of each feature based on information gain or cover
-    * Supported: ["gain, "cover", "total_gain", "total_cover"]
-    *
-    * @return featureScoreMap  key: feature index, value: feature importance score
-    */
-  @throws(classOf[XGBoostError])
-  def getScore(featureMap: String, importanceType: String): Map[String, Double] = {
-    Map(booster.getScore(featureMap, importanceType)
-        .asScala.mapValues(_.doubleValue).toSeq: _*)
-  }
-
-  /**
-    * Get importance of each feature based on information gain or cover
-    * , with specified feature names.
-    * Supported: ["gain, "cover", "total_gain", "total_cover"]
-    *
-    * @return featureScoreMap  key: feature name, value: feature importance score
-    */
-  @throws(classOf[XGBoostError])
-  def getScore(featureNames: Array[String], importanceType: String): Map[String, Double] = {
-    Map(booster.getScore(featureNames, importanceType)
-        .asScala.mapValues(_.doubleValue).toSeq: _*)
-  }
-
-  def getVersion: Int = booster.getVersion
-
-  def toByteArray: Array[Byte] = {
-    booster.toByteArray
-  }
-
-  /**
-    *  Dispose the booster when it is no longer needed
-    */
-  def dispose: Unit = {
-    booster.dispose()
-  }
-
-  override def finalize(): Unit = {
-    super.finalize()
-    dispose
-  }
-
-  override def write(kryo: Kryo, output: Output): Unit = {
-    kryo.writeObject(output, booster)
-  }
-
-  override def read(kryo: Kryo, input: Input): Unit = {
-    booster = kryo.readObject(input, classOf[JBooster])
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/DMatrix.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/DMatrix.scala
deleted file mode 100644
index 629a39d..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/DMatrix.scala
+++ /dev/null
@@ -1,227 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import _root_.scala.collection.JavaConverters._
-import ml.dmlc.xgboost4j.LabeledPoint
-import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix, DataBatch, XGBoostError}
-
-class DMatrix private[scala](private[scala] val jDMatrix: JDMatrix) {
-  /**
-   * init DMatrix from file (svmlight format)
-   *
-   * @param dataPath path of data file
-   * @throws XGBoostError native error
-   */
-  def this(dataPath: String) {
-    this(new JDMatrix(dataPath))
-  }
-
-  /**
-    *  init DMatrix from Iterator of LabeledPoint
-    *
-    * @param dataIter An iterator of LabeledPoint
-    * @param cacheInfo  Cache path information, used for external memory setting, null by default.
-    * @throws XGBoostError native error
-    */
-  def this(dataIter: Iterator[LabeledPoint], cacheInfo: String = null) {
-    this(new JDMatrix(dataIter.asJava, cacheInfo))
-  }
-
-  /**
-   * create DMatrix from sparse matrix
-   *
-   * @param headers index to headers (rowHeaders for CSR or colHeaders for CSC)
-   * @param indices Indices (colIndexs for CSR or rowIndexs for CSC)
-   * @param data    non zero values (sequence by row for CSR or by col for CSC)
-   * @param st      sparse matrix type (CSR or CSC)
-   */
-  @throws(classOf[XGBoostError])
-  @deprecated
-  def this(headers: Array[Long], indices: Array[Int], data: Array[Float], st: JDMatrix.SparseType) {
-    this(new JDMatrix(headers, indices, data, st))
-  }
-
-  /**
-   * create DMatrix from sparse matrix
-   *
-   * @param headers index to headers (rowHeaders for CSR or colHeaders for CSC)
-   * @param indices Indices (colIndexs for CSR or rowIndexs for CSC)
-   * @param data    non zero values (sequence by row for CSR or by col for CSC)
-   * @param st      sparse matrix type (CSR or CSC)
-   * @param shapeParam when st is CSR, it specifies the column number, otherwise it is taken as
-   *                     row number
-   */
-  @throws(classOf[XGBoostError])
-  def this(headers: Array[Long], indices: Array[Int], data: Array[Float], st: JDMatrix.SparseType,
-           shapeParam: Int) {
-    this(new JDMatrix(headers, indices, data, st, shapeParam))
-  }
-
-  /**
-   * create DMatrix from dense matrix
-   *
-   * @param data data values
-   * @param nrow number of rows
-   * @param ncol number of columns
-   */
-  @throws(classOf[XGBoostError])
-  def this(data: Array[Float], nrow: Int, ncol: Int) {
-    this(new JDMatrix(data, nrow, ncol))
-  }
-
-  /**
-   * create DMatrix from dense matrix
-   *
-   * @param data data values
-   * @param nrow number of rows
-   * @param ncol number of columns
-   * @param missing the specified value to represent the missing value
-   */
-  @throws(classOf[XGBoostError])
-  def this(data: Array[Float], nrow: Int, ncol: Int, missing: Float) {
-    this(new JDMatrix(data, nrow, ncol, missing))
-  }
-
-  /**
-   * set label of dmatrix
-   *
-   * @param labels labels
-   */
-  @throws(classOf[XGBoostError])
-  def setLabel(labels: Array[Float]): Unit = {
-    jDMatrix.setLabel(labels)
-  }
-
-  /**
-   * set weight of each instance
-   *
-   * @param weights weights
-   */
-  @throws(classOf[XGBoostError])
-  def setWeight(weights: Array[Float]): Unit = {
-    jDMatrix.setWeight(weights)
-  }
-
-  /**
-   * if specified, xgboost will start from this init margin
-   * can be used to specify initial prediction to boost from
-   *
-   * @param baseMargin base margin
-   */
-  @throws(classOf[XGBoostError])
-  def setBaseMargin(baseMargin: Array[Float]): Unit = {
-    jDMatrix.setBaseMargin(baseMargin)
-  }
-
-  /**
-   * if specified, xgboost will start from this init margin
-   * can be used to specify initial prediction to boost from
-   *
-   * @param baseMargin base margin
-   */
-  @throws(classOf[XGBoostError])
-  def setBaseMargin(baseMargin: Array[Array[Float]]): Unit = {
-    jDMatrix.setBaseMargin(baseMargin)
-  }
-
-  /**
-   * Set group sizes of DMatrix (used for ranking)
-   *
-   * @param group group size as array
-   */
-  @throws(classOf[XGBoostError])
-  def setGroup(group: Array[Int]): Unit = {
-    jDMatrix.setGroup(group)
-  }
-
-  /**
-   * Get group sizes of DMatrix (used for ranking)
-   */
-  @throws(classOf[XGBoostError])
-  def getGroup(): Array[Int] = {
-    jDMatrix.getGroup()
-  }
-
-  /**
-   * get label values
-   *
-   * @return label
-   */
-  @throws(classOf[XGBoostError])
-  def getLabel: Array[Float] = {
-    jDMatrix.getLabel
-  }
-
-  /**
-   * get weight of the DMatrix
-   *
-   * @return weights
-   */
-  @throws(classOf[XGBoostError])
-  def getWeight: Array[Float] = {
-    jDMatrix.getWeight
-  }
-
-  /**
-   * get base margin of the DMatrix
-   *
-   * @return base margin
-   */
-  @throws(classOf[XGBoostError])
-  def getBaseMargin: Array[Float] = {
-    jDMatrix.getBaseMargin
-  }
-
-  /**
-   * Slice the DMatrix and return a new DMatrix that only contains `rowIndex`.
-   *
-   * @param rowIndex row index
-   * @return sliced new DMatrix
-   */
-  @throws(classOf[XGBoostError])
-  def slice(rowIndex: Array[Int]): DMatrix = {
-    new DMatrix(jDMatrix.slice(rowIndex))
-  }
-
-  /**
-   * get the row number of DMatrix
-   *
-   * @return number of rows
-   */
-  @throws(classOf[XGBoostError])
-  def rowNum: Long = {
-    jDMatrix.rowNum
-  }
-
-  /**
-   * save DMatrix to filePath
-   *
-   * @param filePath file path
-   */
-  def saveBinary(filePath: String): Unit = {
-    jDMatrix.saveBinary(filePath)
-  }
-
-  def getHandle: Long = {
-    jDMatrix.getHandle
-  }
-
-  def delete(): Unit = {
-    jDMatrix.dispose()
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/EvalTrait.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/EvalTrait.scala
deleted file mode 100644
index 587ace3..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/EvalTrait.scala
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import ml.dmlc.xgboost4j.java
-import ml.dmlc.xgboost4j.java.IEvaluation
-
-trait EvalTrait extends IEvaluation {
-
-  /**
-   * get evaluate metric
-   *
-   * @return evalMetric
-   */
-  def getMetric: String
-
-  /**
-   * evaluate with predicts and data
-   *
-   * @param predicts predictions as array
-   * @param dmat     data matrix to evaluate
-   * @return result of the metric
-   */
-  def eval(predicts: Array[Array[Float]], dmat: DMatrix): Float
-
-  private[scala] def eval(predicts: Array[Array[Float]], jdmat: java.DMatrix): Float = {
-    require(predicts.length == jdmat.getLabel.length, "predicts size and label size must match " +
-      s" predicts size: ${predicts.length}, label size: ${jdmat.getLabel.length}")
-    eval(predicts, new DMatrix(jdmat))
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ExternalCheckpointManager.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ExternalCheckpointManager.scala
deleted file mode 100644
index 240c238..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ExternalCheckpointManager.scala
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import ml.dmlc.xgboost4j.java.{ExternalCheckpointManager => JavaECM}
-import org.apache.hadoop.fs.FileSystem
-
-class ExternalCheckpointManager(checkpointPath: String, fs: FileSystem)
-  extends JavaECM(checkpointPath, fs) {
-
-  def updateCheckpoint(booster: Booster): Unit = {
-    super.updateCheckpoint(booster.booster)
-  }
-
-  def loadCheckpointAsScalaBooster(): Booster = {
-    val loadedBooster = super.loadCheckpointAsBooster()
-    if (loadedBooster == null) {
-      null
-    } else {
-      new Booster(loadedBooster)
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ObjectiveTrait.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ObjectiveTrait.scala
deleted file mode 100644
index 24e6037..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ObjectiveTrait.scala
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import scala.collection.JavaConverters._
-
-import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix}
-import ml.dmlc.xgboost4j.java.IObjective
-
-trait ObjectiveTrait extends IObjective {
-  /**
-   * user define objective function, return gradient and second order gradient
-   *
-   * @param predicts untransformed margin predicts
-   * @param dtrain   training data
-   * @return List with two float array, correspond to grad and hess
-   */
-  def getGradient(predicts: Array[Array[Float]], dtrain: DMatrix): List[Array[Float]]
-
-  private[scala] def getGradient(predicts: Array[Array[Float]], dtrain: JDMatrix):
-    java.util.List[Array[Float]] = {
-    getGradient(predicts, new DMatrix(dtrain)).asJava
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala
deleted file mode 100644
index 791f06b..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala
-
-import java.io.InputStream
-
-import ml.dmlc.xgboost4j.java.{XGBoostError, Booster => JBooster, XGBoost => JXGBoost}
-import scala.collection.JavaConverters._
-
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
-
-/**
-  * XGBoost Scala Training function.
-  */
-object XGBoost {
-
-  private[scala] def trainAndSaveCheckpoint(
-      dtrain: DMatrix,
-      params: Map[String, Any],
-      numRounds: Int,
-      watches: Map[String, DMatrix] = Map(),
-      metrics: Array[Array[Float]] = null,
-      obj: ObjectiveTrait = null,
-      eval: EvalTrait = null,
-      earlyStoppingRound: Int = 0,
-      prevBooster: Booster,
-      checkpointParams: Option[ExternalCheckpointParams]): Booster = {
-    val jWatches = watches.mapValues(_.jDMatrix).asJava
-    val jBooster = if (prevBooster == null) {
-      null
-    } else {
-      prevBooster.booster
-    }
-    val xgboostInJava = checkpointParams.
-      map(cp => {
-          JXGBoost.trainAndSaveCheckpoint(
-            dtrain.jDMatrix,
-            // we have to filter null value for customized obj and eval
-            params.filter(_._2 != null).mapValues(_.toString.asInstanceOf[AnyRef]).asJava,
-            numRounds, jWatches, metrics, obj, eval, earlyStoppingRound, jBooster,
-            cp.checkpointInterval,
-            cp.checkpointPath,
-            new Path(cp.checkpointPath).getFileSystem(new Configuration()))
-        }).
-      getOrElse(
-        JXGBoost.train(
-          dtrain.jDMatrix,
-          // we have to filter null value for customized obj and eval
-          params.filter(_._2 != null).mapValues(_.toString.asInstanceOf[AnyRef]).asJava,
-          numRounds, jWatches, metrics, obj, eval, earlyStoppingRound, jBooster)
-      )
-    if (prevBooster == null) {
-      new Booster(xgboostInJava)
-    } else {
-      // Avoid creating a new SBooster with the same JBooster
-      prevBooster
-    }
-  }
-
-  /**
-    * Train a booster given parameters.
-    *
-    * @param dtrain  Data to be trained.
-    * @param params  Parameters.
-    * @param round   Number of boosting iterations.
-    * @param watches a group of items to be evaluated during training, this allows user to watch
-    *                performance on the validation set.
-    * @param metrics array containing the evaluation metrics for each matrix in watches for each
-    *                iteration
-    * @param earlyStoppingRound if non-zero, training would be stopped
-    *                           after a specified number of consecutive
-    *                           increases in any evaluation metric.
-    * @param obj     customized objective
-    * @param eval    customized evaluation
-    * @param booster train from scratch if set to null; train from an existing booster if not null.
-    * @return The trained booster.
-    */
-  @throws(classOf[XGBoostError])
-  def train(
-      dtrain: DMatrix,
-      params: Map[String, Any],
-      round: Int,
-      watches: Map[String, DMatrix] = Map(),
-      metrics: Array[Array[Float]] = null,
-      obj: ObjectiveTrait = null,
-      eval: EvalTrait = null,
-      earlyStoppingRound: Int = 0,
-      booster: Booster = null): Booster = {
-    trainAndSaveCheckpoint(dtrain, params, round, watches, metrics, obj, eval, earlyStoppingRound,
-      booster, None)
-  }
-
-  /**
-    * Cross-validation with given parameters.
-    *
-    * @param data    Data to be trained.
-    * @param params  Booster params.
-    * @param round   Number of boosting iterations.
-    * @param nfold   Number of folds in CV.
-    * @param metrics Evaluation metrics to be watched in CV.
-    * @param obj     customized objective
-    * @param eval    customized evaluation
-    * @return evaluation history
-    */
-  @throws(classOf[XGBoostError])
-  def crossValidation(
-      data: DMatrix,
-      params: Map[String, Any],
-      round: Int,
-      nfold: Int = 5,
-      metrics: Array[String] = null,
-      obj: ObjectiveTrait = null,
-      eval: EvalTrait = null): Array[String] = {
-    JXGBoost.crossValidation(
-      data.jDMatrix, params.map{ case (key: String, value) => (key, value.toString)}.
-        toMap[String, AnyRef].asJava,
-      round, nfold, metrics, obj, eval)
-  }
-
-  /**
-    * load model from modelPath
-    *
-    * @param modelPath booster modelPath
-    */
-  @throws(classOf[XGBoostError])
-  def loadModel(modelPath: String): Booster = {
-    val xgboostInJava = JXGBoost.loadModel(modelPath)
-    new Booster(xgboostInJava)
-  }
-
-  /**
-    * Load a new Booster model from a file opened as input stream.
-    * The assumption is the input stream only contains one XGBoost Model.
-    * This can be used to load existing booster models saved by other XGBoost bindings.
-    *
-    * @param in The input stream of the file.
-    * @return The create booster
-    */
-  @throws(classOf[XGBoostError])
-  def loadModel(in: InputStream): Booster = {
-    val xgboostInJava = JXGBoost.loadModel(in)
-    new Booster(xgboostInJava)
-  }
-}
-
-private[scala] case class ExternalCheckpointParams(
-    checkpointInterval: Int,
-    checkpointPath: String,
-    skipCleanCheckpoint: Boolean)
-
-private[scala] object ExternalCheckpointParams {
-
-  def extractParams(params: Map[String, Any]): Option[ExternalCheckpointParams] = {
-    val checkpointPath: String = params.get("checkpoint_path") match {
-      case None | Some(null) | Some("") => null
-      case Some(path: String) => path
-      case _ => throw new IllegalArgumentException("parameter \"checkpoint_path\" must be" +
-        s" an instance of String, but current value is ${params("checkpoint_path")}")
-    }
-
-    val checkpointInterval: Int = params.get("checkpoint_interval") match {
-      case None => 0
-      case Some(freq: Int) => freq
-      case _ => throw new IllegalArgumentException("parameter \"checkpoint_interval\" must be" +
-        " an instance of Int.")
-    }
-
-    val skipCleanCheckpointFile: Boolean = params.get("skip_clean_checkpoint") match {
-      case None => false
-      case Some(skipCleanCheckpoint: Boolean) => skipCleanCheckpoint
-      case _ => throw new IllegalArgumentException("parameter \"skip_clean_checkpoint\" must be" +
-        " an instance of Boolean")
-    }
-    if (checkpointPath == null || checkpointInterval == 0) {
-      None
-    } else {
-      Some(ExternalCheckpointParams(checkpointInterval, checkpointPath, skipCleanCheckpointFile))
-    }
-  }
-}
-
-
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/RabitTracker.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/RabitTracker.scala
deleted file mode 100644
index fb388d0..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/RabitTracker.scala
+++ /dev/null
@@ -1,195 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.rabit
-
-import java.net.{InetAddress, InetSocketAddress}
-
-import akka.actor.ActorSystem
-import akka.pattern.ask
-import ml.dmlc.xgboost4j.java.{IRabitTracker, TrackerProperties}
-import ml.dmlc.xgboost4j.scala.rabit.handler.RabitTrackerHandler
-
-import scala.concurrent.duration._
-import scala.concurrent.{Await, Future}
-import scala.util.{Failure, Success, Try}
-
-/**
-  * Scala implementation of the Rabit tracker interface without Python dependency.
-  * The Scala Rabit tracker fully implements the timeout logic, effectively preventing the tracker
-  * (and thus any distributed tasks) to hang indefinitely due to network issues or worker node
-  * failures.
-  *
-  * Note that this implementation is currently experimental, and should be used at your own risk.
-  *
-  * Example usage:
-  * {{{
-  *   import scala.concurrent.duration._
-  *
-  *   val tracker = new RabitTracker(32)
-  *   // allow up to 10 minutes for all workers to connect to the tracker.
-  *   tracker.start(10 minutes)
-  *
-  *   /* ...
-  *      launching workers in parallel
-  *      ...
-  *   */
-  *
-  *   // wait for worker execution up to 6 hours.
-  *   // providing a finite timeout prevents a long-running task from hanging forever in
-  *   // catastrophic events, like the loss of an executor during model training.
-  *   tracker.waitFor(6 hours)
-  * }}}
-  *
-  * @param numWorkers Number of distributed workers from which the tracker expects connections.
-  * @param port The minimum port number that the tracker binds to.
-  *             If port is omitted, or given as None, a random ephemeral port is chosen at runtime.
-  * @param maxPortTrials The maximum number of trials of socket binding, by sequentially
-  *                      increasing the port number.
-  */
-private[scala] class RabitTracker(numWorkers: Int, port: Option[Int] = None,
-                                  maxPortTrials: Int = 1000)
-  extends IRabitTracker {
-
-  import scala.collection.JavaConverters._
-
-  require(numWorkers >=1, "numWorkers must be greater than or equal to one (1).")
-
-  val system = ActorSystem.create("RabitTracker")
-  val handler = system.actorOf(RabitTrackerHandler.props(numWorkers), "Handler")
-  implicit val askTimeout: akka.util.Timeout = akka.util.Timeout(30 seconds)
-  private[this] val tcpBindingTimeout: Duration = 1 minute
-
-  var workerEnvs: Map[String, String] = Map.empty
-
-  override def uncaughtException(t: Thread, e: Throwable): Unit = {
-    handler ? RabitTrackerHandler.InterruptTracker(e)
-  }
-
-  /**
-    * Start the Rabit tracker.
-    *
-    * @param timeout The timeout for awaiting connections from worker nodes.
-    *      Note that when used in Spark applications, because all Spark transformations are
-    *      lazily executed, the I/O time for loading RDDs/DataFrames from external sources
-    *      (local dist, HDFS, S3 etc.) must be taken into account for the timeout value.
-    *      If the timeout value is too small, the Rabit tracker will likely timeout before workers
-    *      establishing connections to the tracker, due to the overhead of loading data.
-    *      Using a finite timeout is encouraged, as it prevents the tracker (thus the Spark driver
-    *      running it) from hanging indefinitely due to worker connection issues (e.g. firewall.)
-    * @return Boolean flag indicating if the Rabit tracker starts successfully.
-    */
-  private def start(timeout: Duration): Boolean = {
-    val hostAddress = Option(TrackerProperties.getInstance().getHostIp)
-      .map(InetAddress.getByName).getOrElse(InetAddress.getLocalHost)
-
-    handler ? RabitTrackerHandler.StartTracker(
-      new InetSocketAddress(hostAddress, port.getOrElse(0)), maxPortTrials, timeout)
-
-    // block by waiting for the actor to bind to a port
-    Try(Await.result(handler ? RabitTrackerHandler.RequestBoundFuture, askTimeout.duration)
-      .asInstanceOf[Future[Map[String, String]]]) match {
-      case Success(futurePortBound) =>
-        // The success of the Future is contingent on binding to an InetSocketAddress.
-        val isBound = Try(Await.ready(futurePortBound, tcpBindingTimeout)).isSuccess
-        if (isBound) {
-          workerEnvs = Await.result(futurePortBound, 0 nano)
-        }
-        isBound
-      case Failure(ex: Throwable) =>
-        false
-    }
-  }
-
-  /**
-    * Start the Rabit tracker.
-    *
-    * @param connectionTimeoutMillis Timeout, in milliseconds, for the tracker to wait for worker
-    *                                connections. If a non-positive value is provided, the tracker
-    *                                waits for incoming worker connections indefinitely.
-    * @return Boolean flag indicating if the Rabit tracker starts successfully.
-    */
-  def start(connectionTimeoutMillis: Long): Boolean = {
-    if (connectionTimeoutMillis <= 0) {
-      start(Duration.Inf)
-    } else {
-      start(Duration.fromNanos(connectionTimeoutMillis * 1e6))
-    }
-  }
-
-  def stop(): Unit = {
-    system.terminate()
-  }
-
-  /**
-    * Get a Map of necessary environment variables to initiate Rabit workers.
-    *
-    * @return HashMap containing tracker information.
-    */
-  def getWorkerEnvs: java.util.Map[String, String] = {
-    new java.util.HashMap((workerEnvs ++ Map(
-        "DMLC_NUM_WORKER" -> numWorkers.toString,
-        "DMLC_NUM_SERVER" -> "0"
-    )).asJava)
-  }
-
-  /**
-    * Await workers to complete assigned tasks for at most 'atMostMillis' milliseconds.
-    * This method blocks until timeout or task completion.
-    *
-    * @param atMost the maximum execution time for the workers. By default,
-    *     the tracker waits for the workers indefinitely.
-    * @return 0 if the tasks complete successfully, and non-zero otherwise.
-    */
-  private def waitFor(atMost: Duration): Int = {
-    // request the completion Future from the tracker actor
-    Try(Await.result(handler ? RabitTrackerHandler.RequestCompletionFuture, askTimeout.duration)
-      .asInstanceOf[Future[Int]]) match {
-      case Success(futureCompleted) =>
-        // wait for all workers to complete synchronously.
-        val statusCode = Try(Await.result(futureCompleted, atMost)) match {
-          case Success(n) if n == numWorkers =>
-            IRabitTracker.TrackerStatus.SUCCESS.getStatusCode
-          case Success(n) if n < numWorkers =>
-            IRabitTracker.TrackerStatus.TIMEOUT.getStatusCode
-          case Failure(e) =>
-            IRabitTracker.TrackerStatus.FAILURE.getStatusCode
-        }
-        system.terminate()
-        statusCode
-      case Failure(ex: Throwable) =>
-        system.terminate()
-        IRabitTracker.TrackerStatus.FAILURE.getStatusCode
-    }
-  }
-
-  /**
-    * Await workers to complete assigned tasks for at most 'atMostMillis' milliseconds.
-    * This method blocks until timeout or task completion.
-    *
-    * @param atMostMillis Number of milliseconds for the tracker to wait for workers. If a
-    *                     non-positive number is given, the tracker waits indefinitely.
-    * @return 0 if the tasks complete successfully, and non-zero otherwise
-    */
-  def waitFor(atMostMillis: Long): Int = {
-    if (atMostMillis <= 0) {
-      waitFor(Duration.Inf)
-    } else {
-      waitFor(Duration.fromNanos(atMostMillis * 1e6))
-    }
-  }
-}
-
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/handler/RabitTrackerHandler.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/handler/RabitTrackerHandler.scala
deleted file mode 100644
index f9de717..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/handler/RabitTrackerHandler.scala
+++ /dev/null
@@ -1,361 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.rabit.handler
-
-import java.net.InetSocketAddress
-import java.util.UUID
-
-import scala.concurrent.duration._
-import scala.collection.mutable
-import scala.concurrent.{Promise, TimeoutException}
-import akka.io.{IO, Tcp}
-import akka.actor._
-import ml.dmlc.xgboost4j.java.XGBoostError
-import ml.dmlc.xgboost4j.scala.rabit.util.{AssignedRank, LinkMap}
-
-import scala.util.{Failure, Random, Success, Try}
-
-/** The Akka actor for handling and coordinating Rabit worker connections.
-  * This is the main actor for handling socket connections, interacting with the synchronous
-  * tracker interface, and resolving tree/ring/parent dependencies between workers.
-  *
-  * @param numWorkers Number of workers to track.
-  */
-private[scala] class RabitTrackerHandler(numWorkers: Int)
-  extends Actor with ActorLogging {
-
-  import context.system
-  import RabitWorkerHandler._
-  import RabitTrackerHandler._
-
-  private[this] val promisedWorkerEnvs = Promise[Map[String, String]]()
-  private[this] val promisedShutdownWorkers = Promise[Int]()
-  private[this] val tcpManager = IO(Tcp)
-
-  // resolves worker connection dependency.
-  val resolver = context.actorOf(Props(classOf[WorkerDependencyResolver], self), "Resolver")
-
-  // workers that have sent "shutdown" signal
-  private[this] val shutdownWorkers = mutable.Set.empty[Int]
-  private[this] val jobToRankMap = mutable.HashMap.empty[String, Int]
-  private[this] val actorRefToHost = mutable.HashMap.empty[ActorRef, String]
-  private[this] val ranksToAssign = mutable.ListBuffer(0 until numWorkers: _*)
-  private[this] var maxPortTrials = 0
-  private[this] var workerConnectionTimeout: Duration = Duration.Inf
-  private[this] var portTrials = 0
-  private[this] val startedWorkers = mutable.Set.empty[Int]
-
-  val linkMap = new LinkMap(numWorkers)
-
-  def decideRank(rank: Int, jobId: String = "NULL"): Option[Int] = {
-    rank match {
-      case r if r >= 0 => Some(r)
-      case _ =>
-        jobId match {
-          case "NULL" => None
-          case jid => jobToRankMap.get(jid)
-        }
-    }
-  }
-
-  /**
-    * Handler for all Akka Tcp connection/binding events. Read/write over the socket is handled
-    * by the RabitWorkerHandler.
-    *
-    * @param event Generic Tcp.Event
-    */
-  private def handleTcpEvents(event: Tcp.Event): Unit = event match {
-    case Tcp.Bound(local) =>
-      // expect all workers to connect within timeout
-      log.info(s"Tracker listening @ ${local.getAddress.getHostAddress}:${local.getPort}")
-      log.info(s"Worker connection timeout is $workerConnectionTimeout.")
-
-      context.setReceiveTimeout(workerConnectionTimeout)
-      promisedWorkerEnvs.success(Map(
-        "DMLC_TRACKER_URI" -> local.getAddress.getHostAddress,
-        "DMLC_TRACKER_PORT" -> local.getPort.toString,
-        // not required because the world size will be communicated to the
-        // worker node after the rank is assigned.
-        "rabit_world_size" -> numWorkers.toString
-      ))
-
-    case Tcp.CommandFailed(cmd: Tcp.Bind) =>
-      if (portTrials < maxPortTrials) {
-        portTrials += 1
-        tcpManager ! Tcp.Bind(self,
-          new InetSocketAddress(cmd.localAddress.getAddress, cmd.localAddress.getPort + 1),
-          backlog = 256)
-      }
-
-    case Tcp.Connected(remote, local) =>
-      log.debug(s"Incoming connection from worker @ ${remote.getAddress.getHostAddress}")
-      // revoke timeout if all workers have connected.
-      val workerHandler = context.actorOf(RabitWorkerHandler.props(
-        remote.getAddress.getHostAddress, numWorkers, self, sender()
-      ), s"ConnectionHandler-${UUID.randomUUID().toString}")
-      val connection = sender()
-      connection ! Tcp.Register(workerHandler, keepOpenOnPeerClosed = true)
-
-      actorRefToHost.put(workerHandler, remote.getAddress.getHostName)
-  }
-
-  /**
-    * Handles external tracker control messages sent by RabitTracker (usually in ask patterns)
-    * to interact with the tracker interface.
-    *
-    * @param trackerMsg control messages sent by RabitTracker class.
-    */
-  private def handleTrackerControlMessage(trackerMsg: TrackerControlMessage): Unit =
-    trackerMsg match {
-
-    case msg: StartTracker =>
-      maxPortTrials = msg.maxPortTrials
-      workerConnectionTimeout = msg.connectionTimeout
-
-      // if the port number is missing, try binding to a random ephemeral port.
-      if (msg.addr.getPort == 0) {
-        tcpManager ! Tcp.Bind(self,
-          new InetSocketAddress(msg.addr.getAddress, new Random().nextInt(61000 - 32768) + 32768),
-          backlog = 256)
-      } else {
-        tcpManager ! Tcp.Bind(self, msg.addr, backlog = 256)
-      }
-      sender() ! true
-
-    case RequestBoundFuture =>
-      sender() ! promisedWorkerEnvs.future
-
-    case RequestCompletionFuture =>
-      sender() ! promisedShutdownWorkers.future
-
-    case InterruptTracker(e) =>
-      log.error(e, "Uncaught exception thrown by worker.")
-      // make sure that waitFor() does not hang indefinitely.
-      promisedShutdownWorkers.failure(e)
-      context.stop(self)
-  }
-
-  /**
-    * Handles messages sent by child actors representing connecting Rabit workers, by brokering
-    * messages to the dependency resolver, and processing worker commands.
-    *
-    * @param workerMsg Message sent by RabitWorkerHandler actors.
-    */
-  private def handleRabitWorkerMessage(workerMsg: RabitWorkerRequest): Unit = workerMsg match {
-    case req @ RequestAwaitConnWorkers(_, _) =>
-      // since the requester may request to connect to other workers
-      // that have not fully set up, delegate this request to the
-      // dependency resolver which handles the dependencies properly.
-      resolver forward req
-
-    // ---- Rabit worker commands: start/recover/shutdown/print ----
-    case WorkerTrackerPrint(_, _, _, msg) =>
-      log.info(msg.trim)
-
-    case WorkerShutdown(rank, _, _) =>
-      assert(rank >= 0, "Invalid rank.")
-      assert(!shutdownWorkers.contains(rank))
-      shutdownWorkers.add(rank)
-
-      log.info(s"Received shutdown signal from $rank")
-
-      if (shutdownWorkers.size == numWorkers) {
-        promisedShutdownWorkers.success(shutdownWorkers.size)
-      }
-
-    case WorkerRecover(prevRank, worldSize, jobId) =>
-      assert(prevRank >= 0)
-      sender() ! linkMap.assignRank(prevRank)
-
-    case WorkerStart(rank, worldSize, jobId) =>
-      assert(worldSize == numWorkers || worldSize == -1,
-        s"Purported worldSize ($worldSize) does not match worker count ($numWorkers)."
-      )
-
-      Try(decideRank(rank, jobId).getOrElse(ranksToAssign.remove(0))) match {
-        case Success(wkRank) =>
-          if (jobId != "NULL") {
-            jobToRankMap.put(jobId, wkRank)
-          }
-
-          val assignedRank = linkMap.assignRank(wkRank)
-          sender() ! assignedRank
-          resolver ! assignedRank
-
-          log.info("Received start signal from " +
-            s"${actorRefToHost.getOrElse(sender(), "")} [rank: $wkRank]")
-
-        case Failure(ex: IndexOutOfBoundsException) =>
-          // More than worldSize workers have connected, likely due to executor loss.
-          // Since Rabit currently does not support crash recovery (because the Allreduce results
-          // are not cached by the tracker, and because existing workers cannot reestablish
-          // connections to newly spawned executor/worker), the most reasonble action here is to
-          // interrupt the tracker immediate with failure state.
-          log.error("Received invalid start signal from " +
-            s"${actorRefToHost.getOrElse(sender(), "")}: all $worldSize workers have started."
-          )
-          promisedShutdownWorkers.failure(new XGBoostError("Invalid start signal" +
-            " received from worker, likely due to executor loss."))
-
-        case Failure(ex) =>
-          log.error(ex, "Unexpected error")
-          promisedShutdownWorkers.failure(ex)
-      }
-
-
-    // ---- Dependency resolving related messages ----
-    case msg @ WorkerStarted(host, rank, awaitingAcceptance) =>
-      log.info(s"Worker $host (rank: $rank) has started.")
-      resolver forward msg
-
-      startedWorkers.add(rank)
-      if (startedWorkers.size == numWorkers) {
-        log.info("All workers have started.")
-      }
-
-    case req @ DropFromWaitingList(_) =>
-      // all peer workers in dependency link map have connected;
-      // forward message to resolver to update dependencies.
-      resolver forward req
-
-    case _ =>
-  }
-
-  def receive: Actor.Receive = {
-    case tcpEvent: Tcp.Event => handleTcpEvents(tcpEvent)
-    case trackerMsg: TrackerControlMessage => handleTrackerControlMessage(trackerMsg)
-    case workerMsg: RabitWorkerRequest => handleRabitWorkerMessage(workerMsg)
-
-    case akka.actor.ReceiveTimeout =>
-      if (startedWorkers.size < numWorkers) {
-        promisedShutdownWorkers.failure(
-          new TimeoutException("Timed out waiting for workers to connect: " +
-            s"${numWorkers - startedWorkers.size} of $numWorkers did not start/connect.")
-        )
-        context.stop(self)
-      }
-
-      context.setReceiveTimeout(Duration.Undefined)
-  }
-}
-
-/**
-  * Resolve the dependency between nodes as they connect to the tracker.
-  * The dependency is enforced that a worker of rank K depends on its neighbors (from the treeMap
-  * and ringMap) whose ranks are smaller than K. Since ranks are assigned in the order of
-  * connections by workers, this dependency constraint assumes that a worker node connects first
-  * is likely to finish setup first.
-  */
-private[rabit] class WorkerDependencyResolver(handler: ActorRef) extends Actor with ActorLogging {
-  import RabitWorkerHandler._
-
-  context.watch(handler)
-
-  case class Fulfillment(toConnectSet: Set[Int], promise: Promise[AwaitingConnections])
-
-  // worker nodes that have connected, but have not send WorkerStarted message.
-  private val dependencyMap = mutable.Map.empty[Int, Set[Int]]
-  private val startedWorkers = mutable.Set.empty[Int]
-  // worker nodes that have started, and await for connections.
-  private val awaitConnWorkers = mutable.Map.empty[Int, ActorRef]
-  private val pendingFulfillment = mutable.Map.empty[Int, Fulfillment]
-
-  def awaitingWorkers(linkSet: Set[Int]): AwaitingConnections = {
-    val connSet = awaitConnWorkers.toMap
-      .filterKeys(k => linkSet.contains(k))
-    AwaitingConnections(connSet, linkSet.size - connSet.size)
-  }
-
-  def receive: Actor.Receive = {
-    // a copy of the AssignedRank message that is also sent to the worker
-    case AssignedRank(rank, tree_neighbors, ring, parent) =>
-      // the workers that the worker of given `rank` depends on:
-      // worker of rank K only depends on workers with rank smaller than K.
-      val dependentWorkers = (tree_neighbors.toSet ++ Set(ring._1, ring._2))
-        .filter{ r => r != -1 && r < rank}
-
-      log.debug(s"Rank $rank connected, dependencies: $dependentWorkers")
-      dependencyMap.put(rank, dependentWorkers)
-
-    case RequestAwaitConnWorkers(rank, toConnectSet) =>
-      val promise = Promise[AwaitingConnections]()
-
-      assert(dependencyMap.contains(rank))
-
-      val updatedDependency = dependencyMap(rank) diff startedWorkers
-      if (updatedDependency.isEmpty) {
-        // all dependencies are satisfied
-        log.debug(s"Rank $rank has all dependencies satisfied.")
-        promise.success(awaitingWorkers(toConnectSet))
-      } else {
-        log.debug(s"Rank $rank's request for AwaitConnWorkers is pending fulfillment.")
-        // promise is pending fulfillment due to unresolved dependency
-        pendingFulfillment.put(rank, Fulfillment(toConnectSet, promise))
-      }
-
-      sender() ! promise.future
-
-    case WorkerStarted(_, started, awaitingAcceptance) =>
-      startedWorkers.add(started)
-      if (awaitingAcceptance > 0) {
-        awaitConnWorkers.put(started, sender())
-      }
-
-      // remove the started rank from all dependencies.
-      dependencyMap.remove(started)
-      dependencyMap.foreach { case (r, dset) =>
-        val updatedDependency = dset diff startedWorkers
-        // fulfill the future if all dependencies are met (started.)
-        if (updatedDependency.isEmpty) {
-          log.debug(s"Rank $r has all dependencies satisfied.")
-          pendingFulfillment.remove(r).map{
-            case Fulfillment(toConnectSet, promise) =>
-              promise.success(awaitingWorkers(toConnectSet))
-          }
-        }
-
-        dependencyMap.update(r, updatedDependency)
-      }
-
-    case DropFromWaitingList(rank) =>
-      assert(awaitConnWorkers.remove(rank).isDefined)
-
-    case Terminated(ref) =>
-      if (ref.equals(handler)) {
-        context.stop(self)
-      }
-  }
-}
-
-private[scala] object RabitTrackerHandler {
-  // Messages sent by RabitTracker to this RabitTrackerHandler actor
-  trait TrackerControlMessage
-  case object RequestCompletionFuture extends TrackerControlMessage
-  case object RequestBoundFuture extends TrackerControlMessage
-  // Start the Rabit tracker at given socket address awaiting worker connections.
-  // All workers must connect to the tracker before connectionTimeout, otherwise the tracker will
-  // shut down due to timeout.
-  case class StartTracker(addr: InetSocketAddress,
-                          maxPortTrials: Int,
-                          connectionTimeout: Duration) extends TrackerControlMessage
-  // To interrupt the tracker handler due to uncaught exception thrown by the thread acting as
-  // driver for the distributed training.
-  case class InterruptTracker(e: Throwable) extends TrackerControlMessage
-
-  def props(numWorkers: Int): Props =
-    Props(new RabitTrackerHandler(numWorkers))
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/handler/RabitWorkerHandler.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/handler/RabitWorkerHandler.scala
deleted file mode 100644
index 234c4d2..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/handler/RabitWorkerHandler.scala
+++ /dev/null
@@ -1,467 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.rabit.handler
-
-import java.nio.{ByteBuffer, ByteOrder}
-
-import akka.io.Tcp
-import akka.actor._
-import akka.util.ByteString
-import ml.dmlc.xgboost4j.scala.rabit.util.{AssignedRank, RabitTrackerHelpers}
-
-import scala.concurrent.{Await, Future}
-import scala.concurrent.duration._
-import scala.util.Try
-
-/**
-  * Actor to handle socket communication from worker node.
-  * To handle fragmentation in received data, this class acts like a FSM
-  * (finite-state machine) to keep track of the internal states.
-  *
-  * @param host IP address of the remote worker
-  * @param worldSize number of total workers
-  * @param tracker the RabitTrackerHandler actor reference
-  */
-private[scala] class RabitWorkerHandler(host: String, worldSize: Int, tracker: ActorRef,
-                                        connection: ActorRef)
-  extends FSM[RabitWorkerHandler.State, RabitWorkerHandler.DataStruct]
-    with ActorLogging with Stash {
-
-  import RabitWorkerHandler._
-  import RabitTrackerHelpers._
-
-  private[this] var rank: Int = 0
-  private[this] var port: Int = 0
-
-  // indicate if the connection is transient (like "print" or "shutdown")
-  private[this] var transient: Boolean = false
-  private[this] var peerClosed: Boolean = false
-
-  // number of workers pending acceptance of current worker
-  private[this] var awaitingAcceptance: Int = 0
-  private[this] var neighboringWorkers = Set.empty[Int]
-
-  // TODO: use a single memory allocation to host all buffers,
-  // including the transient ones for writing.
-  private[this] val readBuffer = ByteBuffer.allocate(4096)
-    .order(ByteOrder.nativeOrder())
-  // in case the received message is longer than needed,
-  // stash the spilled over part in this buffer, and send
-  // to self when transition occurs.
-  private[this] val spillOverBuffer = ByteBuffer.allocate(4096)
-    .order(ByteOrder.nativeOrder())
-  // when setup is complete, need to notify peer handlers
-  // to reduce the awaiting-connection counter.
-  private[this] var pendingAcknowledgement: Option[AcknowledgeAcceptance] = None
-
-  private def resetBuffers(): Unit = {
-    readBuffer.clear()
-    if (spillOverBuffer.position() > 0) {
-      spillOverBuffer.flip()
-      self ! Tcp.Received(ByteString.fromByteBuffer(spillOverBuffer))
-      spillOverBuffer.clear()
-    }
-  }
-
-  private def stashSpillOver(buf: ByteBuffer): Unit = {
-    if (buf.remaining() > 0) spillOverBuffer.put(buf)
-  }
-
-  def getNeighboringWorkers: Set[Int] = neighboringWorkers
-
-  def decodeCommand(buffer: ByteBuffer): TrackerCommand = {
-    val readBuffer = buffer.duplicate().order(ByteOrder.nativeOrder())
-    readBuffer.flip()
-
-    val rank = readBuffer.getInt()
-    val worldSize = readBuffer.getInt()
-    val jobId = readBuffer.getString
-
-    val command = readBuffer.getString
-    val trackerCommand = command match {
-      case "start" => WorkerStart(rank, worldSize, jobId)
-      case "shutdown" =>
-        transient = true
-        WorkerShutdown(rank, worldSize, jobId)
-      case "recover" =>
-        require(rank >= 0, "Invalid rank for recovering worker.")
-        WorkerRecover(rank, worldSize, jobId)
-      case "print" =>
-        transient = true
-        WorkerTrackerPrint(rank, worldSize, jobId, readBuffer.getString)
-    }
-
-    stashSpillOver(readBuffer)
-    trackerCommand
-  }
-
-  startWith(AwaitingHandshake, DataStruct())
-
-  when(AwaitingHandshake) {
-    case Event(Tcp.Received(magic), _) =>
-      assert(magic.length == 4)
-      val purportedMagic = magic.asNativeOrderByteBuffer.getInt
-      assert(purportedMagic == MAGIC_NUMBER, s"invalid magic number $purportedMagic from $host")
-
-      // echo back the magic number
-      connection ! Tcp.Write(magic)
-      goto(AwaitingCommand) using StructTrackerCommand
-  }
-
-  when(AwaitingCommand) {
-    case Event(Tcp.Received(bytes), validator) =>
-      bytes.asByteBuffers.foreach { buf => readBuffer.put(buf) }
-      if (validator.verify(readBuffer)) {
-        Try(decodeCommand(readBuffer)) match {
-          case scala.util.Success(decodedCommand) =>
-            tracker ! decodedCommand
-          case scala.util.Failure(th: java.nio.BufferUnderflowException) =>
-            // BufferUnderflowException would occur if the message to print has not arrived yet.
-            // Do nothing, wait for next Tcp.Received event
-          case scala.util.Failure(th: Throwable) => throw th
-        }
-      }
-
-      stay
-    // when rank for a worker is assigned, send encoded rank information
-    // back to worker over Tcp socket.
-    case Event(aRank @ AssignedRank(assignedRank, neighbors, ring, parent), _) =>
-      log.debug(s"Assigned rank [$assignedRank] for $host, T: $neighbors, R: $ring, P: $parent")
-
-      rank = assignedRank
-      // ranks from the ring
-      val ringRanks = List(
-        // ringPrev
-        if (ring._1 != -1 && ring._1 != rank) ring._1 else -1,
-        // ringNext
-        if (ring._2 != -1 && ring._2 != rank) ring._2 else -1
-      )
-
-      // update the set of all linked workers to current worker.
-      neighboringWorkers = neighbors.toSet ++ ringRanks.filterNot(_ == -1).toSet
-
-      connection ! Tcp.Write(ByteString.fromByteBuffer(aRank.toByteBuffer(worldSize)))
-      // to prevent reading before state transition
-      connection ! Tcp.SuspendReading
-      goto(BuildingLinkMap) using StructNodes
-  }
-
-  when(BuildingLinkMap) {
-    case Event(Tcp.Received(bytes), validator) =>
-      bytes.asByteBuffers.foreach { buf =>
-        readBuffer.put(buf)
-      }
-
-      if (validator.verify(readBuffer)) {
-        readBuffer.flip()
-        // for a freshly started worker, numConnected should be 0.
-        val numConnected = readBuffer.getInt()
-        val toConnectSet = neighboringWorkers.diff(
-          (0 until numConnected).map { index => readBuffer.getInt() }.toSet)
-
-        // check which workers are currently awaiting connections
-        tracker ! RequestAwaitConnWorkers(rank, toConnectSet)
-      }
-      stay
-
-    // got a Future from the tracker (resolver) about workers that are
-    // currently awaiting connections (particularly from this node.)
-    case Event(future: Future[_], _) =>
-      // blocks execution until all dependencies for current worker is resolved.
-      Await.result(future, 1 minute).asInstanceOf[AwaitingConnections] match {
-        // numNotReachable is the number of workers that currently
-        // cannot be connected to (pending connection or setup). Instead, this worker will AWAIT
-        // connections from those currently non-reachable nodes in the future.
-        case AwaitingConnections(waitConnNodes, numNotReachable) =>
-          log.debug(s"Rank $rank needs to connect to: $waitConnNodes, # bad: $numNotReachable")
-          val buf = ByteBuffer.allocate(8).order(ByteOrder.nativeOrder())
-          buf.putInt(waitConnNodes.size).putInt(numNotReachable)
-          buf.flip()
-
-          // cache this message until the final state (SetupComplete)
-          pendingAcknowledgement = Some(AcknowledgeAcceptance(
-            waitConnNodes, numNotReachable))
-
-          connection ! Tcp.Write(ByteString.fromByteBuffer(buf))
-          if (waitConnNodes.isEmpty) {
-            connection ! Tcp.SuspendReading
-            goto(AwaitingErrorCount)
-          }
-          else {
-            waitConnNodes.foreach { case (peerRank, peerRef) =>
-              peerRef ! RequestWorkerHostPort
-            }
-
-            // a countdown for DivulgedHostPort messages.
-            stay using DataStruct(Seq.empty[DataField], waitConnNodes.size - 1)
-          }
-      }
-
-    case Event(DivulgedWorkerHostPort(peerRank, peerHost, peerPort), data) =>
-      val hostBytes = peerHost.getBytes()
-      val buffer = ByteBuffer.allocate(4 * 3 + hostBytes.length)
-        .order(ByteOrder.nativeOrder())
-      buffer.putInt(peerHost.length).put(hostBytes)
-        .putInt(peerPort).putInt(peerRank)
-
-      buffer.flip()
-      connection ! Tcp.Write(ByteString.fromByteBuffer(buffer))
-
-      if (data.counter == 0) {
-        // to prevent reading before state transition
-        connection ! Tcp.SuspendReading
-        goto(AwaitingErrorCount)
-      }
-      else {
-        stay using data.decrement()
-      }
-  }
-
-  when(AwaitingErrorCount) {
-    case Event(Tcp.Received(numErrors), _) =>
-      val buf = numErrors.asNativeOrderByteBuffer
-
-      buf.getInt match {
-        case 0 =>
-          stashSpillOver(buf)
-          goto(AwaitingPortNumber)
-        case _ =>
-          stashSpillOver(buf)
-          goto(BuildingLinkMap) using StructNodes
-      }
-  }
-
-  when(AwaitingPortNumber) {
-    case Event(Tcp.Received(assignedPort), _) =>
-      assert(assignedPort.length == 4)
-      port = assignedPort.asNativeOrderByteBuffer.getInt
-      log.debug(s"Rank $rank listening @ $host:$port")
-      // wait until the worker closes connection.
-      if (peerClosed) goto(SetupComplete) else stay
-
-    case Event(Tcp.PeerClosed, _) =>
-      peerClosed = true
-      if (port == 0) stay else goto(SetupComplete)
-  }
-
-  when(SetupComplete) {
-    case Event(ReduceWaitCount(count: Int), _) =>
-      awaitingAcceptance -= count
-      // check peerClosed to avoid prematurely stopping this actor (which sends RST to worker)
-      if (awaitingAcceptance == 0 && peerClosed) {
-        tracker ! DropFromWaitingList(rank)
-        // no longer needed.
-        context.stop(self)
-      }
-      stay
-
-    case Event(AcknowledgeAcceptance(peers, numBad), _) =>
-      awaitingAcceptance = numBad
-      tracker ! WorkerStarted(host, rank, awaitingAcceptance)
-      peers.values.foreach { peer =>
-        peer ! ReduceWaitCount(1)
-      }
-
-      if (awaitingAcceptance == 0 && peerClosed) self ! PoisonPill
-
-      stay
-
-    // can only divulge the complete host and port information
-    // when this worker is declared fully connected (otherwise
-    // port information is still missing.)
-    case Event(RequestWorkerHostPort, _) =>
-      sender() ! DivulgedWorkerHostPort(rank, host, port)
-      stay
-  }
-
-  onTransition {
-    // reset buffer when state transitions as data becomes stale
-    case _ -> SetupComplete =>
-      connection ! Tcp.ResumeReading
-      resetBuffers()
-      if (pendingAcknowledgement.isDefined) {
-        self ! pendingAcknowledgement.get
-      }
-    case _ =>
-      connection ! Tcp.ResumeReading
-      resetBuffers()
-  }
-
-  // default message handler
-  whenUnhandled {
-    case Event(Tcp.PeerClosed, _) =>
-      peerClosed = true
-      if (transient) context.stop(self)
-      stay
-  }
-}
-
-private[scala] object RabitWorkerHandler {
-  val MAGIC_NUMBER = 0xff99
-
-  // Finite states of this actor, which acts like a FSM.
-  // The following states are defined in order as the FSM progresses.
-  sealed trait State
-
-  // [1] Initial state, awaiting worker to send magic number per protocol.
-  case object AwaitingHandshake extends State
-  // [2] Awaiting worker to send command (start/print/recover/shutdown etc.)
-  case object AwaitingCommand extends State
-  // [3] Brokers connections between workers per ring/tree/parent link map.
-  case object BuildingLinkMap extends State
-  // [4] A transient state in which the worker reports the number of errors in establishing
-  // connections to other peer workers. If no errors, transition to next state.
-  case object AwaitingErrorCount extends State
-  // [5] Awaiting the worker to report its port number for accepting connections from peer workers.
-  // This port number information is later forwarded to linked workers.
-  case object AwaitingPortNumber extends State
-  // [6] Final state after completing the setup with the connecting worker. At this stage, the
-  // worker will have closed the Tcp connection. The actor remains alive to handle messages from
-  // peer actors representing workers with pending setups.
-  case object SetupComplete extends State
-
-  sealed trait DataField
-  case object IntField extends DataField
-  // an integer preceding the actual string
-  case object StringField extends DataField
-  case object IntSeqField extends DataField
-
-  object DataStruct {
-    def apply(): DataStruct = DataStruct(Seq.empty[DataField], 0)
-  }
-
-  // Internal data pertaining to individual state, used to verify the validity of packets sent by
-  // workers.
-  case class DataStruct(fields: Seq[DataField], counter: Int) {
-    /**
-      * Validate whether the provided buffer is complete (i.e., contains
-      * all data fields specified for this DataStruct.)
- *
-      * @param buf a byte buffer containing received data.
-      */
-    def verify(buf: ByteBuffer): Boolean = {
-      if (fields.isEmpty) return true
-
-      val dupBuf = buf.duplicate().order(ByteOrder.nativeOrder())
-      dupBuf.flip()
-
-      Try(fields.foldLeft(true) {
-        case (complete, field) =>
-          val remBytes = dupBuf.remaining()
-          complete && (remBytes > 0) && (remBytes >= (field match {
-            case IntField =>
-              dupBuf.position(dupBuf.position() + 4)
-              4
-            case StringField =>
-              val strLen = dupBuf.getInt
-              dupBuf.position(dupBuf.position() + strLen)
-              4 + strLen
-            case IntSeqField =>
-              val seqLen = dupBuf.getInt
-              dupBuf.position(dupBuf.position() + seqLen * 4)
-              4 + seqLen * 4
-          }))
-      }).getOrElse(false)
-    }
-
-    def increment(): DataStruct = DataStruct(fields, counter + 1)
-    def decrement(): DataStruct = DataStruct(fields, counter - 1)
-  }
-
-  val StructNodes = DataStruct(List(IntSeqField), 0)
-  val StructTrackerCommand = DataStruct(List(
-    IntField, IntField, StringField, StringField
-  ), 0)
-
-  // ---- Messages between RabitTrackerHandler and RabitTrackerConnectionHandler ----
-
-  // RabitWorkerHandler --> RabitTrackerHandler
-  sealed trait RabitWorkerRequest
-  // RabitWorkerHandler <-- RabitTrackerHandler
-  sealed trait RabitWorkerResponse
-
-  // Representations of decoded worker commands.
-  abstract class TrackerCommand(val command: String) extends RabitWorkerRequest {
-    def rank: Int
-    def worldSize: Int
-    def jobId: String
-
-    def encode: ByteString = {
-      val buf = ByteBuffer.allocate(4 * 4 + jobId.length + command.length)
-        .order(ByteOrder.nativeOrder())
-
-      buf.putInt(rank).putInt(worldSize).putInt(jobId.length).put(jobId.getBytes())
-        .putInt(command.length).put(command.getBytes()).flip()
-
-      ByteString.fromByteBuffer(buf)
-    }
-  }
-
-  case class WorkerStart(rank: Int, worldSize: Int, jobId: String)
-    extends TrackerCommand("start")
-  case class WorkerShutdown(rank: Int, worldSize: Int, jobId: String)
-    extends TrackerCommand("shutdown")
-  case class WorkerRecover(rank: Int, worldSize: Int, jobId: String)
-    extends TrackerCommand("recover")
-  case class WorkerTrackerPrint(rank: Int, worldSize: Int, jobId: String, msg: String)
-    extends TrackerCommand("print") {
-
-    override def encode: ByteString = {
-      val buf = ByteBuffer.allocate(4 * 5 + jobId.length + command.length + msg.length)
-        .order(ByteOrder.nativeOrder())
-
-      buf.putInt(rank).putInt(worldSize).putInt(jobId.length).put(jobId.getBytes())
-        .putInt(command.length).put(command.getBytes())
-        .putInt(msg.length).put(msg.getBytes()).flip()
-
-      ByteString.fromByteBuffer(buf)
-    }
-  }
-
-  // Request to remove the worker of given rank from the list of workers awaiting peer connections.
-  case class DropFromWaitingList(rank: Int) extends RabitWorkerRequest
-  // Notify the tracker that the worker of given rank has finished setup and started.
-  case class WorkerStarted(host: String, rank: Int, awaitingAcceptance: Int)
-    extends RabitWorkerRequest
-  // Request the set of workers to connect to, according to the LinkMap structure.
-  case class RequestAwaitConnWorkers(rank: Int, toConnectSet: Set[Int])
-    extends RabitWorkerRequest
-
-  // Request, from the tracker, the set of nodes to connect.
-  case class AwaitingConnections(workers: Map[Int, ActorRef], numBad: Int)
-    extends RabitWorkerResponse
-
-  // ---- Messages between ConnectionHandler actors ----
-  sealed trait IntraWorkerMessage
-
-  // Notify neighboring workers to decrease the counter of awaiting workers by `count`.
-  case class ReduceWaitCount(count: Int) extends IntraWorkerMessage
-  // Request host and port information from peer ConnectionHandler actors (acting on behave of
-  // connecting workers.) This message will be brokered by RabitTrackerHandler.
-  case object RequestWorkerHostPort extends IntraWorkerMessage
-  // Response to the above request
-  case class DivulgedWorkerHostPort(rank: Int, host: String, port: Int) extends IntraWorkerMessage
-  // A reminder to send ReduceWaitCount messages once the actor is in state "SetupComplete".
-  case class AcknowledgeAcceptance(peers: Map[Int, ActorRef], numBad: Int)
-    extends IntraWorkerMessage
-
-  // ---- End of message definitions ----
-
-  def props(host: String, worldSize: Int, tracker: ActorRef, connection: ActorRef): Props = {
-    Props(new RabitWorkerHandler(host, worldSize, tracker, connection))
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/util/LinkMap.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/util/LinkMap.scala
deleted file mode 100644
index edec493..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/util/LinkMap.scala
+++ /dev/null
@@ -1,136 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.rabit.util
-
-import java.nio.{ByteBuffer, ByteOrder}
-
-/**
-  * The assigned rank to a connecting Rabit worker, along with the information of the ranks of
-  * its linked peer workers, which are critical to perform Allreduce.
-  * When RabitWorkerHandler delegates "start" or "recover" commands from the connecting worker
-  * client, RabitTrackerHandler utilizes LinkMap to figure out linkage relationships, and respond
-  * with this class as a message, which is later encoded to byte string, and sent over socket
-  * connection to the worker client.
-  *
-  * @param rank assigned rank (ranked by worker connection order: first worker connecting to the
-  *             tracker is assigned rank 0, second with rank 1, etc.)
-  * @param neighbors ranks of neighboring workers in a tree map.
-  * @param ring ranks of neighboring workers in a ring map.
-  * @param parent rank of the parent worker.
-  */
-private[rabit] case class AssignedRank(rank: Int, neighbors: Seq[Int],
-                                       ring: (Int, Int), parent: Int) {
-  /**
-    * Encode the AssignedRank message into byte sequence for socket communication with Rabit worker
-    * client.
-    * @param worldSize the number of total distributed workers. Must match `numWorkers` used in
-    *                  LinkMap.
-    * @return a ByteBuffer containing encoded data.
-    */
-  def toByteBuffer(worldSize: Int): ByteBuffer = {
-    val buffer = ByteBuffer.allocate(4 * (neighbors.length + 6)).order(ByteOrder.nativeOrder())
-    buffer.putInt(rank).putInt(parent).putInt(worldSize).putInt(neighbors.length)
-    // neighbors in tree structure
-    neighbors.foreach { n => buffer.putInt(n) }
-    buffer.putInt(if (ring._1 != -1 && ring._1 != rank) ring._1 else -1)
-    buffer.putInt(if (ring._2 != -1 && ring._2 != rank) ring._2 else -1)
-
-    buffer.flip()
-    buffer
-  }
-}
-
-private[rabit] class LinkMap(numWorkers: Int) {
-  private def getNeighbors(rank: Int): Seq[Int] = {
-    val rank1 = rank + 1
-    Vector(rank1 / 2 - 1, rank1 * 2 - 1, rank1 * 2).filter { r =>
-      r >= 0 && r < numWorkers
-    }
-  }
-
-  /**
-    * Construct a ring structure that tends to share nodes with the tree.
-    *
-    * @param treeMap
-    * @param parentMap
-    * @param rank
-    * @return Seq[Int] instance starting from rank.
-    */
-  private def constructShareRing(treeMap: Map[Int, Seq[Int]],
-                                 parentMap: Map[Int, Int],
-                                 rank: Int = 0): Seq[Int] = {
-    treeMap(rank).toSet - parentMap(rank) match {
-      case emptySet if emptySet.isEmpty =>
-        List(rank)
-      case connectionSet =>
-        connectionSet.zipWithIndex.foldLeft(List(rank)) {
-          case (ringSeq, (v, cnt)) =>
-            val vConnSeq = constructShareRing(treeMap, parentMap, v)
-            vConnSeq match {
-              case vconn if vconn.size == cnt + 1 =>
-                ringSeq ++ vconn.reverse
-              case vconn =>
-                ringSeq ++ vconn
-            }
-        }
-    }
-  }
-  /**
-    * Construct a ring connection used to recover local data.
-    *
-    * @param treeMap
-    * @param parentMap
-    */
-  private def constructRingMap(treeMap: Map[Int, Seq[Int]], parentMap: Map[Int, Int]) = {
-    assert(parentMap(0) == -1)
-
-    val sharedRing = constructShareRing(treeMap, parentMap, 0).toVector
-    assert(sharedRing.length == treeMap.size)
-
-    (0 until numWorkers).map { r =>
-      val rPrev = (r + numWorkers - 1) % numWorkers
-      val rNext = (r + 1) % numWorkers
-      sharedRing(r) -> (sharedRing(rPrev), sharedRing(rNext))
-    }.toMap
-  }
-
-  private[this] val treeMap_ = (0 until numWorkers).map { r => r -> getNeighbors(r) }.toMap
-  private[this] val parentMap_ = (0 until numWorkers).map{ r => r -> ((r + 1) / 2 - 1) }.toMap
-  private[this] val ringMap_ = constructRingMap(treeMap_, parentMap_)
-  val rMap_ = (0 until (numWorkers - 1)).foldLeft((Map(0 -> 0), 0)) {
-    case ((rmap, k), i) =>
-      val kNext = ringMap_(k)._2
-      (rmap ++ Map(kNext -> (i + 1)), kNext)
-  }._1
-
-  val ringMap = ringMap_.map {
-    case (k, (v0, v1)) => rMap_(k) -> (rMap_(v0), rMap_(v1))
-  }
-  val treeMap = treeMap_.map {
-    case (k, vSeq) => rMap_(k) -> vSeq.map{ v => rMap_(v) }
-  }
-  val parentMap = parentMap_.map {
-    case (k, v) if k == 0 =>
-      rMap_(k) -> -1
-    case (k, v) =>
-      rMap_(k) -> rMap_(v)
-  }
-
-  def assignRank(rank: Int): AssignedRank = {
-    AssignedRank(rank, treeMap(rank), ringMap(rank), parentMap(rank))
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/util/RabitTrackerHelpers.scala b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/util/RabitTrackerHelpers.scala
deleted file mode 100644
index 3d7be61..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/rabit/util/RabitTrackerHelpers.scala
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-package ml.dmlc.xgboost4j.scala.rabit.util
-
-import java.nio.{ByteOrder, ByteBuffer}
-import akka.util.ByteString
-
-private[rabit] object RabitTrackerHelpers {
-  implicit class ByteStringHelplers(bs: ByteString) {
-    // Java by default uses big endian. Enforce native endian so that
-    // the byte order is consistent with the workers.
-    def asNativeOrderByteBuffer: ByteBuffer = {
-      bs.asByteBuffer.order(ByteOrder.nativeOrder())
-    }
-  }
-
-  implicit class ByteBufferHelpers(buf: ByteBuffer) {
-    def getString: String = {
-      val len = buf.getInt()
-      val stringBuffer = ByteBuffer.allocate(len).order(ByteOrder.nativeOrder())
-      buf.get(stringBuffer.array(), 0, len)
-      new String(stringBuffer.array(), "utf-8")
-    }
-  }
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.cpp b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.cpp
deleted file mode 100644
index 6c201ec..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.cpp
+++ /dev/null
@@ -1,979 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*
-  Copyright (c) 2014 by Contributors
-  Licensed under the Apache License, Version 2.0 (the "License");
-  you may not use this file except in compliance with the License.
-  You may obtain a copy of the License at
-
-  http://www.apache.org/licenses/LICENSE-2.0
-  Unless required by applicable law or agreed to in writing, software
-  distributed under the License is distributed on an "AS IS" BASIS,
-  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  See the License for the specific language governing permissions and
-  limitations under the License.
-*/
-
-#include <cstddef>
-#include <cstdint>
-#include <limits>
-#include <rabit/c_api.h>
-#include <xgboost/c_api.h>
-#include <xgboost/base.h>
-#include <xgboost/logging.h>
-#include "./xgboost4j.h"
-#include <cstring>
-#include <vector>
-#include <string>
-
-#define JVM_CHECK_CALL(__expr)                                                 \
-  {                                                                            \
-    int __errcode = (__expr);                                                  \
-    if (__errcode != 0) {                                                      \
-      return __errcode;                                                        \
-    }                                                                          \
-  }
-
-// helper functions
-// set handle
-void setHandle(JNIEnv *jenv, jlongArray jhandle, void* handle) {
-#ifdef __APPLE__
-  jlong out = (long) handle;
-#else
-  int64_t out = (int64_t) handle;
-#endif
-  jenv->SetLongArrayRegion(jhandle, 0, 1, &out);
-}
-
-// global JVM
-static JavaVM* global_jvm = nullptr;
-
-// overrides JNI on load
-jint JNI_OnLoad(JavaVM *vm, void *reserved) {
-  global_jvm = vm;
-  return JNI_VERSION_1_6;
-}
-
-XGB_EXTERN_C int XGBoost4jCallbackDataIterNext(
-    DataIterHandle data_handle,
-    XGBCallbackSetData* set_function,
-    DataHolderHandle set_function_handle) {
-  jobject jiter = static_cast<jobject>(data_handle);
-  JNIEnv* jenv;
-  int jni_status = global_jvm->GetEnv((void **)&jenv, JNI_VERSION_1_6);
-  if (jni_status == JNI_EDETACHED) {
-    global_jvm->AttachCurrentThread(reinterpret_cast<void **>(&jenv), nullptr);
-  } else {
-    CHECK(jni_status == JNI_OK);
-  }
-  try {
-    jclass iterClass = jenv->FindClass("java/util/Iterator");
-    jmethodID hasNext = jenv->GetMethodID(iterClass,
-                                          "hasNext", "()Z");
-    jmethodID next = jenv->GetMethodID(iterClass,
-                                       "next", "()Ljava/lang/Object;");
-    int ret_value;
-    if (jenv->CallBooleanMethod(jiter, hasNext)) {
-      ret_value = 1;
-      jobject batch = jenv->CallObjectMethod(jiter, next);
-      if (batch == nullptr) {
-        CHECK(jenv->ExceptionOccurred());
-        jenv->ExceptionDescribe();
-        return -1;
-      }
-
-      jclass batchClass = jenv->GetObjectClass(batch);
-      jlongArray joffset = (jlongArray)jenv->GetObjectField(
-          batch, jenv->GetFieldID(batchClass, "rowOffset", "[J"));
-      jfloatArray jlabel = (jfloatArray)jenv->GetObjectField(
-          batch, jenv->GetFieldID(batchClass, "label", "[F"));
-      jfloatArray jweight = (jfloatArray)jenv->GetObjectField(
-          batch, jenv->GetFieldID(batchClass, "weight", "[F"));
-      jintArray jindex = (jintArray)jenv->GetObjectField(
-          batch, jenv->GetFieldID(batchClass, "featureIndex", "[I"));
-      jfloatArray jvalue = (jfloatArray)jenv->GetObjectField(
-          batch, jenv->GetFieldID(batchClass, "featureValue", "[F"));
-      jint jcols = jenv->GetIntField(
-          batch, jenv->GetFieldID(batchClass, "featureCols", "I"));
-      XGBoostBatchCSR cbatch;
-      cbatch.size = jenv->GetArrayLength(joffset) - 1;
-      cbatch.columns = jcols;
-      cbatch.offset = reinterpret_cast<jlong *>(
-          jenv->GetLongArrayElements(joffset, 0));
-      if (jlabel != nullptr) {
-        cbatch.label = jenv->GetFloatArrayElements(jlabel, 0);
-        CHECK_EQ(jenv->GetArrayLength(jlabel), static_cast<long>(cbatch.size))
-            << "batch.label.length must equal batch.numRows()";
-      } else {
-        cbatch.label = nullptr;
-      }
-      if (jweight != nullptr) {
-        cbatch.weight = jenv->GetFloatArrayElements(jweight, 0);
-        CHECK_EQ(jenv->GetArrayLength(jweight), static_cast<long>(cbatch.size))
-            << "batch.weight.length must equal batch.numRows()";
-      } else {
-        cbatch.weight = nullptr;
-      }
-      long max_elem = cbatch.offset[cbatch.size];
-      cbatch.index = (int*) jenv->GetIntArrayElements(jindex, 0);
-      cbatch.value = jenv->GetFloatArrayElements(jvalue, 0);
-
-      CHECK_EQ(jenv->GetArrayLength(jindex), max_elem)
-          << "batch.index.length must equal batch.offset.back()";
-      CHECK_EQ(jenv->GetArrayLength(jvalue), max_elem)
-          << "batch.index.length must equal batch.offset.back()";
-      // cbatch is ready
-      CHECK_EQ((*set_function)(set_function_handle, cbatch), 0)
-          << XGBGetLastError();
-      // release the elements.
-      jenv->ReleaseLongArrayElements(
-          joffset, reinterpret_cast<jlong *>(cbatch.offset), 0);
-      jenv->DeleteLocalRef(joffset);
-      if (jlabel != nullptr) {
-        jenv->ReleaseFloatArrayElements(jlabel, cbatch.label, 0);
-        jenv->DeleteLocalRef(jlabel);
-      }
-      if (jweight != nullptr) {
-        jenv->ReleaseFloatArrayElements(jweight, cbatch.weight, 0);
-        jenv->DeleteLocalRef(jweight);
-      }
-      jenv->ReleaseIntArrayElements(jindex, (jint*) cbatch.index, 0);
-      jenv->DeleteLocalRef(jindex);
-      jenv->ReleaseFloatArrayElements(jvalue, cbatch.value, 0);
-      jenv->DeleteLocalRef(jvalue);
-      jenv->DeleteLocalRef(batch);
-      jenv->DeleteLocalRef(batchClass);
-      ret_value = 1;
-    } else {
-      ret_value = 0;
-    }
-    jenv->DeleteLocalRef(iterClass);
-    // only detach if it is a async call.
-    if (jni_status == JNI_EDETACHED) {
-      global_jvm->DetachCurrentThread();
-    }
-    return ret_value;
-  } catch(dmlc::Error e) {
-    // only detach if it is a async call.
-    if (jni_status == JNI_EDETACHED) {
-      global_jvm->DetachCurrentThread();
-    }
-    LOG(FATAL) << e.what();
-    return -1;
-  }
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBGetLastError
- * Signature: ()Ljava/lang/String;
- */
-JNIEXPORT jstring JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBGetLastError
-  (JNIEnv *jenv, jclass jcls) {
-  jstring jresult = 0;
-  const char* result = XGBGetLastError();
-  if (result != NULL) {
-    jresult = jenv->NewStringUTF(result);
-  }
-  return jresult;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromDataIter
- * Signature: (Ljava/util/Iterator;Ljava/lang/String;[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromDataIter
-  (JNIEnv *jenv, jclass jcls, jobject jiter, jstring jcache_info, jlongArray jout) {
-  DMatrixHandle result;
-  const char* cache_info = nullptr;
-  if (jcache_info != nullptr) {
-    cache_info = jenv->GetStringUTFChars(jcache_info, 0);
-  }
-  int ret = XGDMatrixCreateFromDataIter(
-      jiter, XGBoost4jCallbackDataIterNext, cache_info, &result);
-  JVM_CHECK_CALL(ret);
-  if (cache_info) {
-    jenv->ReleaseStringUTFChars(jcache_info, cache_info);
-  }
-  setHandle(jenv, jout, result);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromFile
- * Signature: (Ljava/lang/String;I[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromFile
-  (JNIEnv *jenv, jclass jcls, jstring jfname, jint jsilent, jlongArray jout) {
-  DMatrixHandle result;
-  const char* fname = jenv->GetStringUTFChars(jfname, 0);
-  int ret = XGDMatrixCreateFromFile(fname, jsilent, &result);
-  JVM_CHECK_CALL(ret);
-  if (fname) {
-    jenv->ReleaseStringUTFChars(jfname, fname);
-  }
-  setHandle(jenv, jout, result);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromCSREx
- * Signature: ([J[J[F)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromCSREx
-  (JNIEnv *jenv, jclass jcls, jlongArray jindptr, jintArray jindices, jfloatArray jdata, jint jcol, jlongArray jout) {
-  DMatrixHandle result;
-  jlong* indptr = jenv->GetLongArrayElements(jindptr, 0);
-  jint* indices = jenv->GetIntArrayElements(jindices, 0);
-  jfloat* data = jenv->GetFloatArrayElements(jdata, 0);
-  bst_ulong nindptr = (bst_ulong)jenv->GetArrayLength(jindptr);
-  bst_ulong nelem = (bst_ulong)jenv->GetArrayLength(jdata);
-  jint ret = (jint) XGDMatrixCreateFromCSREx((size_t const *)indptr,
-                                             (unsigned int const *)indices,
-                                             (float const *)data,
-                                             nindptr, nelem, jcol, &result);
-  JVM_CHECK_CALL(ret);
-  setHandle(jenv, jout, result);
-  //Release
-  jenv->ReleaseLongArrayElements(jindptr, indptr, 0);
-  jenv->ReleaseIntArrayElements(jindices, indices, 0);
-  jenv->ReleaseFloatArrayElements(jdata, data, 0);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromCSCEx
- * Signature: ([J[J[F)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromCSCEx
-  (JNIEnv *jenv, jclass jcls, jlongArray jindptr, jintArray jindices, jfloatArray jdata, jint jrow, jlongArray jout) {
-  DMatrixHandle result;
-  jlong* indptr = jenv->GetLongArrayElements(jindptr, NULL);
-  jint* indices = jenv->GetIntArrayElements(jindices, 0);
-  jfloat* data = jenv->GetFloatArrayElements(jdata, NULL);
-  bst_ulong nindptr = (bst_ulong)jenv->GetArrayLength(jindptr);
-  bst_ulong nelem = (bst_ulong)jenv->GetArrayLength(jdata);
-
-  jint ret = (jint) XGDMatrixCreateFromCSCEx((size_t const *)indptr,
-                                             (unsigned int const *)indices,
-                                             (float const *)data,
-                                             nindptr, nelem, jrow, &result);
-  JVM_CHECK_CALL(ret);
-  setHandle(jenv, jout, result);
-  //release
-  jenv->ReleaseLongArrayElements(jindptr, indptr, 0);
-  jenv->ReleaseIntArrayElements(jindices, indices, 0);
-  jenv->ReleaseFloatArrayElements(jdata, data, 0);
-
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromMatRef
- * Signature: (JIIF)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromMatRef
-  (JNIEnv *jenv, jclass jcls, jlong jdataRef, jint jnrow, jint jncol, jfloat jmiss, jlongArray jout) {
-  DMatrixHandle result;
-  bst_ulong nrow = (bst_ulong)jnrow;
-  bst_ulong ncol = (bst_ulong)jncol;
-  jint ret = (jint) XGDMatrixCreateFromMat((float const *)jdataRef, nrow, ncol, jmiss, &result);
-  JVM_CHECK_CALL(ret);
-  setHandle(jenv, jout, result);
-  return ret;
-}
-
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromMat
- * Signature: ([FIIF)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromMat
-  (JNIEnv *jenv, jclass jcls, jfloatArray jdata, jint jnrow, jint jncol, jfloat jmiss, jlongArray jout) {
-  DMatrixHandle result;
-  jfloat* data = jenv->GetFloatArrayElements(jdata, 0);
-  bst_ulong nrow = (bst_ulong)jnrow;
-  bst_ulong ncol = (bst_ulong)jncol;
-  jint ret = (jint) XGDMatrixCreateFromMat((float const *)data, nrow, ncol, jmiss, &result);
-  JVM_CHECK_CALL(ret);
-  setHandle(jenv, jout, result);
-  //release
-  jenv->ReleaseFloatArrayElements(jdata, data, 0);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSliceDMatrix
- * Signature: (J[I)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSliceDMatrix
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jintArray jindexset, jlongArray jout) {
-  DMatrixHandle result;
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-
-  jint* indexset = jenv->GetIntArrayElements(jindexset, 0);
-  bst_ulong len = (bst_ulong)jenv->GetArrayLength(jindexset);
-
-  // default to not allowing slicing with group ID specified -- feel free to add if necessary
-  jint ret = (jint) XGDMatrixSliceDMatrixEx(handle, (int const *)indexset, len, &result, 0);
-  JVM_CHECK_CALL(ret);
-  setHandle(jenv, jout, result);
-  //release
-  jenv->ReleaseIntArrayElements(jindexset, indexset, 0);
-
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixFree
- * Signature: (J)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixFree
-  (JNIEnv *jenv, jclass jcls, jlong jhandle) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  int ret = XGDMatrixFree(handle);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSaveBinary
- * Signature: (JLjava/lang/String;I)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSaveBinary
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfname, jint jsilent) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  const char* fname = jenv->GetStringUTFChars(jfname, 0);
-  int ret = XGDMatrixSaveBinary(handle, fname, jsilent);
-  JVM_CHECK_CALL(ret);
-  if (fname) jenv->ReleaseStringUTFChars(jfname, (const char *)fname);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSetFloatInfo
- * Signature: (JLjava/lang/String;[F)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSetFloatInfo
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jfloatArray jarray) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  const char*  field = jenv->GetStringUTFChars(jfield, 0);
-
-  jfloat* array = jenv->GetFloatArrayElements(jarray, NULL);
-  bst_ulong len = (bst_ulong)jenv->GetArrayLength(jarray);
-  int ret = XGDMatrixSetFloatInfo(handle, field, (float const *)array, len);
-  JVM_CHECK_CALL(ret);
-  //release
-  if (field) jenv->ReleaseStringUTFChars(jfield, field);
-  jenv->ReleaseFloatArrayElements(jarray, array, 0);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSetUIntInfo
- * Signature: (JLjava/lang/String;[I)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSetUIntInfo
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jintArray jarray) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  const char*  field = jenv->GetStringUTFChars(jfield, 0);
-  jint* array = jenv->GetIntArrayElements(jarray, NULL);
-  bst_ulong len = (bst_ulong)jenv->GetArrayLength(jarray);
-  int ret = XGDMatrixSetUIntInfo(handle, (char const *)field, (unsigned int const *)array, len);
-  JVM_CHECK_CALL(ret);
-  //release
-  if (field) jenv->ReleaseStringUTFChars(jfield, (const char *)field);
-  jenv->ReleaseIntArrayElements(jarray, array, 0);
-
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixGetFloatInfo
- * Signature: (JLjava/lang/String;)[F
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixGetFloatInfo
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jobjectArray jout) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  const char*  field = jenv->GetStringUTFChars(jfield, 0);
-  bst_ulong len;
-  float *result;
-  int ret = XGDMatrixGetFloatInfo(handle, field, &len, (const float**) &result);
-  JVM_CHECK_CALL(ret);
-  if (field) jenv->ReleaseStringUTFChars(jfield, field);
-
-  jsize jlen = (jsize) len;
-  jfloatArray jarray = jenv->NewFloatArray(jlen);
-  jenv->SetFloatArrayRegion(jarray, 0, jlen, (jfloat *) result);
-  jenv->SetObjectArrayElement(jout, 0, (jobject) jarray);
-
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixGetUIntInfo
- * Signature: (JLjava/lang/String;)[I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixGetUIntInfo
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jobjectArray jout) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  const char*  field = jenv->GetStringUTFChars(jfield, 0);
-  bst_ulong len;
-  unsigned int *result;
-  int ret = (jint) XGDMatrixGetUIntInfo(handle, field, &len, (const unsigned int **) &result);
-  JVM_CHECK_CALL(ret);
-  if (field) jenv->ReleaseStringUTFChars(jfield, field);
-
-  jsize jlen = (jsize) len;
-  jintArray jarray = jenv->NewIntArray(jlen);
-  jenv->SetIntArrayRegion(jarray, 0, jlen, (jint *) result);
-  jenv->SetObjectArrayElement(jout, 0, jarray);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixNumRow
- * Signature: (J)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixNumRow
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlongArray jout) {
-  DMatrixHandle handle = (DMatrixHandle) jhandle;
-  bst_ulong result[1];
-  int ret = (jint) XGDMatrixNumRow(handle, result);
-  JVM_CHECK_CALL(ret);
-  jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) result);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterCreate
- * Signature: ([J)J
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterCreate
-  (JNIEnv *jenv, jclass jcls, jlongArray jhandles, jlongArray jout) {
-  std::vector<DMatrixHandle> handles;
-  if (jhandles != nullptr) {
-    size_t len = jenv->GetArrayLength(jhandles);
-    jlong *cjhandles = jenv->GetLongArrayElements(jhandles, 0);
-    for (size_t i = 0; i < len; ++i) {
-      handles.push_back((DMatrixHandle) cjhandles[i]);
-    }
-    jenv->ReleaseLongArrayElements(jhandles, cjhandles, 0);
-  }
-  BoosterHandle result;
-  int ret = XGBoosterCreate(dmlc::BeginPtr(handles), handles.size(), &result);
-  JVM_CHECK_CALL(ret);
-  setHandle(jenv, jout, result);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterFree
- * Signature: (J)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterFree
-  (JNIEnv *jenv, jclass jcls, jlong jhandle) {
-    BoosterHandle handle = (BoosterHandle) jhandle;
-    return XGBoosterFree(handle);
-}
-
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSetParam
- * Signature: (JLjava/lang/String;Ljava/lang/String;)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSetParam
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jname, jstring jvalue) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  const char* name = jenv->GetStringUTFChars(jname, 0);
-  const char* value = jenv->GetStringUTFChars(jvalue, 0);
-  int ret = XGBoosterSetParam(handle, name, value);
-  JVM_CHECK_CALL(ret);
-  //release
-  if (name) jenv->ReleaseStringUTFChars(jname, name);
-  if (value) jenv->ReleaseStringUTFChars(jvalue, value);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterUpdateOneIter
- * Signature: (JIJ)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterUpdateOneIter
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jint jiter, jlong jdtrain) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  DMatrixHandle dtrain = (DMatrixHandle) jdtrain;
-  return XGBoosterUpdateOneIter(handle, jiter, dtrain);
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterUpdateOneIter
- * Signature: (JIJ)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterUpdateOneIterLtd
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jint jiter, jlong jdtrain,
-    jfloatArray j_last_round_best_loss) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  DMatrixHandle dtrain = (DMatrixHandle) jdtrain;
-
-  jfloat* last_round_best_loss = jenv->GetFloatArrayElements(j_last_round_best_loss, 0);
-  int ret = XGBoosterUpdateOneIterLtd(handle, jiter, dtrain, last_round_best_loss);
-  JVM_CHECK_CALL(ret);
-  jenv->ReleaseFloatArrayElements(j_last_round_best_loss, last_round_best_loss, 0); 
-   
-  return ret;  
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterBoostOneIter
- * Signature: (JJ[F[F)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterBoostOneIter
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlong jdtrain, jfloatArray jgrad, jfloatArray jhess) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  DMatrixHandle dtrain = (DMatrixHandle) jdtrain;
-  jfloat* grad = jenv->GetFloatArrayElements(jgrad, 0);
-  jfloat* hess = jenv->GetFloatArrayElements(jhess, 0);
-  bst_ulong len = (bst_ulong)jenv->GetArrayLength(jgrad);
-  int ret = XGBoosterBoostOneIter(handle, dtrain, grad, hess, len);
-  JVM_CHECK_CALL(ret);
-  //release
-  jenv->ReleaseFloatArrayElements(jgrad, grad, 0);
-  jenv->ReleaseFloatArrayElements(jhess, hess, 0);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterEvalOneIter
- * Signature: (JI[J[Ljava/lang/String;)Ljava/lang/String;
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterEvalOneIter
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jint jiter, jlongArray jdmats, jobjectArray jevnames, jobjectArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  std::vector<DMatrixHandle> dmats;
-  std::vector<std::string> evnames;
-  std::vector<const char*> evchars;
-
-  size_t len =  static_cast<size_t>(jenv->GetArrayLength(jdmats));
-  // put handle from jhandles to chandles
-  jlong* cjdmats = jenv->GetLongArrayElements(jdmats, 0);
-  for (size_t i = 0; i < len; ++i) {
-    dmats.push_back((DMatrixHandle) cjdmats[i]);
-    jstring jevname = (jstring)jenv->GetObjectArrayElement(jevnames, i);
-    const char *s =jenv->GetStringUTFChars(jevname, 0);
-    evnames.push_back(std::string(s, jenv->GetStringLength(jevname)));
-    if (s != nullptr) jenv->ReleaseStringUTFChars(jevname, s);
-  }
-  jenv->ReleaseLongArrayElements(jdmats, cjdmats, 0);
-  for (size_t i = 0; i < len; ++i) {
-    evchars.push_back(evnames[i].c_str());
-  }
-  const char* result;
-  int ret = XGBoosterEvalOneIter(handle, jiter,
-                                 dmlc::BeginPtr(dmats),
-                                 dmlc::BeginPtr(evchars),
-                                 len, &result);
-  JVM_CHECK_CALL(ret);
-  jstring jinfo = nullptr;
-  if (result != nullptr) {
-    jinfo = jenv->NewStringUTF(result);
-  }
-  jenv->SetObjectArrayElement(jout, 0, jinfo);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterPredict
- * Signature: (JJIJ)[F
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterPredict
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlong jdmat, jint joption_mask, jint jntree_limit, jobjectArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  DMatrixHandle dmat = (DMatrixHandle) jdmat;
-  bst_ulong len;
-  float *result;
-  int ret = XGBoosterPredict(handle, dmat, joption_mask, (unsigned int) jntree_limit,
-                             /* training = */ 0,  // Currently this parameter is not supported by JVM
-                             &len, (const float **) &result);
-  JVM_CHECK_CALL(ret);
-  if (len) {
-    jsize jlen = (jsize) len;
-    jfloatArray jarray = jenv->NewFloatArray(jlen);
-    jenv->SetFloatArrayRegion(jarray, 0, jlen, (jfloat *) result);
-    jenv->SetObjectArrayElement(jout, 0, jarray);
-  }
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterLoadModel
- * Signature: (JLjava/lang/String;)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterLoadModel
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfname) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  const char* fname = jenv->GetStringUTFChars(jfname, 0);
-
-  int ret = XGBoosterLoadModel(handle, fname);
-  JVM_CHECK_CALL(ret);
-  if (fname) {
-    jenv->ReleaseStringUTFChars(jfname,fname);
-  }
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSaveModel
- * Signature: (JLjava/lang/String;)V
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSaveModel
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfname) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  const char*  fname = jenv->GetStringUTFChars(jfname, 0);
-
-  int ret = XGBoosterSaveModel(handle, fname);
-  JVM_CHECK_CALL(ret);
-  if (fname) {
-    jenv->ReleaseStringUTFChars(jfname, fname);
-  }
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterLoadModelFromBuffer
- * Signature: (J[B)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterLoadModelFromBuffer
-    (JNIEnv *jenv, jclass jcls, jlong jhandle, jbyteArray jbytes) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  jbyte* buffer = jenv->GetByteArrayElements(jbytes, 0);
-  int ret = XGBoosterLoadModelFromBuffer(
-      handle, buffer, jenv->GetArrayLength(jbytes));
-  JVM_CHECK_CALL(ret);
-  jenv->ReleaseByteArrayElements(jbytes, buffer, 0);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterGetModelRaw
- * Signature: (J[[B)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetModelRaw
-  (JNIEnv * jenv, jclass jcls, jlong jhandle, jobjectArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  bst_ulong len = 0;
-  const char* result;
-  int ret = XGBoosterGetModelRaw(handle, &len, &result);
-  JVM_CHECK_CALL(ret);
-
-  if (result) {
-    jbyteArray jarray = jenv->NewByteArray(len);
-    jenv->SetByteArrayRegion(jarray, 0, len, (jbyte*)result);
-    jenv->SetObjectArrayElement(jout, 0, jarray);
-  }
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterDumpModelEx
- * Signature: (JLjava/lang/String;I)[Ljava/lang/String;
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterDumpModelEx
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfmap, jint jwith_stats, jstring jformat, jobjectArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  const char *fmap = jenv->GetStringUTFChars(jfmap, 0);
-  const char *format = jenv->GetStringUTFChars(jformat, 0);
-  bst_ulong len = 0;
-  char **result;
-
-  int ret = XGBoosterDumpModelEx(handle, fmap, jwith_stats, format, &len, (const char ***) &result);
-  JVM_CHECK_CALL(ret);
-
-  jsize jlen = (jsize) len;
-  jobjectArray jinfos = jenv->NewObjectArray(jlen, jenv->FindClass("java/lang/String"), jenv->NewStringUTF(""));
-  for(int i=0 ; i<jlen; i++) {
-    jenv->SetObjectArrayElement(jinfos, i, jenv->NewStringUTF((const char*) result[i]));
-  }
-  jenv->SetObjectArrayElement(jout, 0, jinfos);
-
-  if (fmap) jenv->ReleaseStringUTFChars(jfmap, (const char *)fmap);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterDumpModelExWithFeatures
- * Signature: (JLjava/lang/String;I[[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterDumpModelExWithFeatures
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jobjectArray jfeature_names, jint jwith_stats,
-    jstring jformat, jobjectArray jout) {
-
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  bst_ulong feature_num = (bst_ulong)jenv->GetArrayLength(jfeature_names);
-
-  std::vector<std::string> feature_names;
-  std::vector<char*> feature_names_char;
-
-  std::string feature_type_q = "q";
-  std::vector<char*> feature_types_char;
-
-  for (bst_ulong i = 0; i < feature_num; ++i) {
-    jstring jfeature_name = (jstring)jenv->GetObjectArrayElement(jfeature_names, i);
-    const char *s = jenv->GetStringUTFChars(jfeature_name, 0);
-    feature_names.push_back(std::string(s, jenv->GetStringLength(jfeature_name)));
-    if (s != nullptr) jenv->ReleaseStringUTFChars(jfeature_name, s);
-    if (feature_names.back().length() == 0) feature_names.pop_back();
-  }
-
-  for (size_t i = 0; i < feature_names.size(); ++i) {
-    feature_names_char.push_back(&feature_names[i][0]);
-    feature_types_char.push_back(&feature_type_q[0]);
-  }
-
-  const char *format = jenv->GetStringUTFChars(jformat, 0);
-  bst_ulong len = 0;
-  char **result;
-
-  int ret = XGBoosterDumpModelExWithFeatures(handle, feature_num,
-                                             (const char **) dmlc::BeginPtr(feature_names_char),
-                                             (const char **) dmlc::BeginPtr(feature_types_char),
-                                             jwith_stats, format, &len, (const char ***) &result);
-  JVM_CHECK_CALL(ret);
-
-  jsize jlen = (jsize) len;
-  jobjectArray jinfos = jenv->NewObjectArray(jlen, jenv->FindClass("java/lang/String"), jenv->NewStringUTF(""));
-  for(int i=0 ; i<jlen; i++) {
-    jenv->SetObjectArrayElement(jinfos, i, jenv->NewStringUTF((const char*) result[i]));
-  }
-  jenv->SetObjectArrayElement(jout, 0, jinfos);
-
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterGetAttrNames
- * Signature: (I[[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetAttrNames
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jobjectArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  bst_ulong len = 0;
-  char **result;
-  int ret = XGBoosterGetAttrNames(handle, &len, (const char ***) &result);
-  JVM_CHECK_CALL(ret);
-
-  jsize jlen = (jsize) len;
-  jobjectArray jinfos = jenv->NewObjectArray(jlen, jenv->FindClass("java/lang/String"), jenv->NewStringUTF(""));
-  for(int i=0 ; i<jlen; i++) {
-    jenv->SetObjectArrayElement(jinfos, i, jenv->NewStringUTF((const char*) result[i]));
-  }
-  jenv->SetObjectArrayElement(jout, 0, jinfos);
-
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterGetAttr
- * Signature: (JLjava/lang/String;[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetAttr
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jkey, jobjectArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  const char* key = jenv->GetStringUTFChars(jkey, 0);
-  const char* result;
-  int success;
-  int ret = XGBoosterGetAttr(handle, key, &result, &success);
-  JVM_CHECK_CALL(ret);
-  //release
-  if (key) jenv->ReleaseStringUTFChars(jkey, key);
-
-  if (success > 0) {
-    jstring jret = jenv->NewStringUTF(result);
-    jenv->SetObjectArrayElement(jout, 0, jret);
-  }
-
-  return ret;
-};
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSetAttr
- * Signature: (JLjava/lang/String;Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSetAttr
-  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jkey, jstring jvalue) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  const char* key = jenv->GetStringUTFChars(jkey, 0);
-  const char* value = jenv->GetStringUTFChars(jvalue, 0);
-  int ret = XGBoosterSetAttr(handle, key, value);
-  JVM_CHECK_CALL(ret);
-  //release
-  if (key) jenv->ReleaseStringUTFChars(jkey, key);
-  if (value) jenv->ReleaseStringUTFChars(jvalue, value);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterLoadRabitCheckpoint
- * Signature: (J[I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterLoadRabitCheckpoint
-  (JNIEnv *jenv , jclass jcls, jlong jhandle, jintArray jout) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  int version;
-  int ret = XGBoosterLoadRabitCheckpoint(handle, &version);
-  JVM_CHECK_CALL(ret);
-  jint jversion = version;
-  jenv->SetIntArrayRegion(jout, 0, 1, &jversion);
-  return ret;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSaveRabitCheckpoint
- * Signature: (J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSaveRabitCheckpoint
-  (JNIEnv *jenv, jclass jcls, jlong jhandle) {
-  BoosterHandle handle = (BoosterHandle) jhandle;
-  return XGBoosterSaveRabitCheckpoint(handle);
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitInit
- * Signature: ([Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitInit
-  (JNIEnv *jenv, jclass jcls, jobjectArray jargs) {
-  std::vector<std::string> args;
-  std::vector<char*> argv;
-  bst_ulong len = (bst_ulong)jenv->GetArrayLength(jargs);
-  for (bst_ulong i = 0; i < len; ++i) {
-    jstring arg = (jstring)jenv->GetObjectArrayElement(jargs, i);
-    const char *s = jenv->GetStringUTFChars(arg, 0);
-    args.push_back(std::string(s, jenv->GetStringLength(arg)));
-    if (s != nullptr) jenv->ReleaseStringUTFChars(arg, s);
-    if (args.back().length() == 0) args.pop_back();
-  }
-
-  for (size_t i = 0; i < args.size(); ++i) {
-    argv.push_back(&args[i][0]);
-  }
-
-  if (RabitInit(args.size(), dmlc::BeginPtr(argv))) {
-    return 0;
-  } else {
-    return 1;
-  }
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitFinalize
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitFinalize
-  (JNIEnv *jenv, jclass jcls) {
-  if (RabitFinalize()) {
-    return 0;
-  } else {
-    return 1;
-  }
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitTrackerPrint
- * Signature: (Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitTrackerPrint
-  (JNIEnv *jenv, jclass jcls, jstring jmsg) {
-  std::string str(jenv->GetStringUTFChars(jmsg, 0),
-                  jenv->GetStringLength(jmsg));
-  RabitTrackerPrint(str.c_str());
-  return 0;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitGetRank
- * Signature: ([I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitGetRank
-  (JNIEnv *jenv, jclass jcls, jintArray jout) {
-  jint rank = RabitGetRank();
-  jenv->SetIntArrayRegion(jout, 0, 1, &rank);
-  return 0;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitGetWorldSize
- * Signature: ([I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitGetWorldSize
-  (JNIEnv *jenv, jclass jcls, jintArray jout) {
-  jint out = RabitGetWorldSize();
-  jenv->SetIntArrayRegion(jout, 0, 1, &out);
-  return 0;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitVersionNumber
- * Signature: ([I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitVersionNumber
-  (JNIEnv *jenv, jclass jcls, jintArray jout) {
-  jint out = RabitVersionNumber();
-  jenv->SetIntArrayRegion(jout, 0, 1, &out);
-  return 0;
-}
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitAllreduce
- * Signature: (Ljava/nio/ByteBuffer;III)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitAllreduce
-  (JNIEnv *jenv, jclass jcls, jobject jsendrecvbuf, jint jcount, jint jenum_dtype, jint jenum_op) {
-  void *ptr_sendrecvbuf = jenv->GetDirectBufferAddress(jsendrecvbuf);
-  RabitAllreduce(ptr_sendrecvbuf, (size_t) jcount, jenum_dtype, jenum_op, NULL, NULL);
-
-  return 0;
-}
diff --git a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.h b/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.h
deleted file mode 100644
index 8d63867..0000000
--- a/ml-xgboost/jvm-packages/boostkit-xgboost4j/src/native/xgboost4j.h
+++ /dev/null
@@ -1,347 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/* DO NOT EDIT THIS FILE - it is machine generated */
-#include <jni.h>
-/* Header for class ml_dmlc_xgboost4j_java_XGBoostJNI */
-
-#ifndef _Included_ml_dmlc_xgboost4j_java_XGBoostJNI
-#define _Included_ml_dmlc_xgboost4j_java_XGBoostJNI
-#ifdef __cplusplus
-extern "C" {
-#endif
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBGetLastError
- * Signature: ()Ljava/lang/String;
- */
-JNIEXPORT jstring JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBGetLastError
-  (JNIEnv *, jclass);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromFile
- * Signature: (Ljava/lang/String;I[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromFile
-  (JNIEnv *, jclass, jstring, jint, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromDataIter
- * Signature: (Ljava/util/Iterator;Ljava/lang/String;[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromDataIter
-  (JNIEnv *, jclass, jobject, jstring, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromCSREx
- * Signature: ([J[I[F[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromCSREx
-  (JNIEnv *, jclass, jlongArray, jintArray, jfloatArray, jint, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromCSCEx
- * Signature: ([J[I[F[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromCSCEx
-  (JNIEnv *, jclass, jlongArray, jintArray, jfloatArray, jint, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromMat
- * Signature: ([FIIF[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromMat
-  (JNIEnv *, jclass, jfloatArray, jint, jint, jfloat, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixCreateFromMatRef
- * Signature: (JIIF[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromMatRef
-  (JNIEnv *jenv, jclass jcls, jlong jdataRef, jint jnrow, jint jncol, jfloat jmiss, jlongArray jout);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSliceDMatrix
- * Signature: (J[I[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSliceDMatrix
-  (JNIEnv *, jclass, jlong, jintArray, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixFree
- * Signature: (J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixFree
-  (JNIEnv *, jclass, jlong);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSaveBinary
- * Signature: (JLjava/lang/String;I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSaveBinary
-  (JNIEnv *, jclass, jlong, jstring, jint);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSetFloatInfo
- * Signature: (JLjava/lang/String;[F)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSetFloatInfo
-  (JNIEnv *, jclass, jlong, jstring, jfloatArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixSetUIntInfo
- * Signature: (JLjava/lang/String;[I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSetUIntInfo
-  (JNIEnv *, jclass, jlong, jstring, jintArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixGetFloatInfo
- * Signature: (JLjava/lang/String;[[F)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixGetFloatInfo
-  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixGetUIntInfo
- * Signature: (JLjava/lang/String;[[I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixGetUIntInfo
-  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGDMatrixNumRow
- * Signature: (J[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixNumRow
-  (JNIEnv *, jclass, jlong, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterCreate
- * Signature: ([J[J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterCreate
-  (JNIEnv *, jclass, jlongArray, jlongArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterFree
- * Signature: (J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterFree
-  (JNIEnv *, jclass, jlong);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSetParam
- * Signature: (JLjava/lang/String;Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSetParam
-  (JNIEnv *, jclass, jlong, jstring, jstring);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterUpdateOneIter
- * Signature: (JIJ)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterUpdateOneIter
-  (JNIEnv *, jclass, jlong, jint, jlong);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterUpdateOneIterLtd
- * Signature: (JIJ)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterUpdateOneIterLtd
-  (JNIEnv *, jclass, jlong, jint, jlong, jfloatArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterBoostOneIter
- * Signature: (JJ[F[F)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterBoostOneIter
-  (JNIEnv *, jclass, jlong, jlong, jfloatArray, jfloatArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterEvalOneIter
- * Signature: (JI[J[Ljava/lang/String;[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterEvalOneIter
-  (JNIEnv *, jclass, jlong, jint, jlongArray, jobjectArray, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterPredict
- * Signature: (JJII[[F)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterPredict
-  (JNIEnv *, jclass, jlong, jlong, jint, jint, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterLoadModel
- * Signature: (JLjava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterLoadModel
-  (JNIEnv *, jclass, jlong, jstring);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSaveModel
- * Signature: (JLjava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSaveModel
-  (JNIEnv *, jclass, jlong, jstring);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterLoadModelFromBuffer
- * Signature: (J[B)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterLoadModelFromBuffer
-  (JNIEnv *, jclass, jlong, jbyteArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterGetModelRaw
- * Signature: (J[[B)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetModelRaw
-  (JNIEnv *, jclass, jlong, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterDumpModelEx
- * Signature: (JLjava/lang/String;I[[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterDumpModelEx
-  (JNIEnv *, jclass, jlong, jstring, jint, jstring, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterDumpModelExWithFeatures
- * Signature: (JLjava/lang/String;I[[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterDumpModelExWithFeatures
-  (JNIEnv *, jclass, jlong, jobjectArray, jint, jstring, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterGetAttrNames
- * Signature: (I[[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetAttrNames
-  (JNIEnv *, jclass, jlong, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterGetAttr
- * Signature: (JLjava/lang/String;[Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetAttr
-  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSetAttr
- * Signature: (JLjava/lang/String;Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSetAttr
-  (JNIEnv *, jclass, jlong, jstring, jstring);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterLoadRabitCheckpoint
- * Signature: (J[I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterLoadRabitCheckpoint
-  (JNIEnv *, jclass, jlong, jintArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    XGBoosterSaveRabitCheckpoint
- * Signature: (J)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSaveRabitCheckpoint
-  (JNIEnv *, jclass, jlong);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitInit
- * Signature: ([Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitInit
-  (JNIEnv *, jclass, jobjectArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitFinalize
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitFinalize
-  (JNIEnv *, jclass);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitTrackerPrint
- * Signature: (Ljava/lang/String;)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitTrackerPrint
-  (JNIEnv *, jclass, jstring);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitGetRank
- * Signature: ([I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitGetRank
-  (JNIEnv *, jclass, jintArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitGetWorldSize
- * Signature: ([I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitGetWorldSize
-  (JNIEnv *, jclass, jintArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitVersionNumber
- * Signature: ([I)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitVersionNumber
-  (JNIEnv *, jclass, jintArray);
-
-/*
- * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
- * Method:    RabitAllreduce
- * Signature: (Ljava/nio/ByteBuffer;III)I
- */
-JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_RabitAllreduce
-  (JNIEnv *, jclass, jobject, jint, jint, jint);
-
-#ifdef __cplusplus
-}
-#endif
-#endif
diff --git a/ml-xgboost/jvm-packages/checkstyle-suppressions.xml b/ml-xgboost/jvm-packages/checkstyle-suppressions.xml
deleted file mode 100644
index 366f6a0..0000000
--- a/ml-xgboost/jvm-packages/checkstyle-suppressions.xml
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-
-<!DOCTYPE suppressions PUBLIC
-"-//Puppy Crawl//DTD Suppressions 1.1//EN"
-"http://www.puppycrawl.com/dtds/suppressions_1_1.dtd">
-
-<!--
-
-    This file contains suppression rules for Checkstyle checks.
-    Ideally only files that cannot be modified (e.g. third-party code)
-    should be added here. All other violations should be fixed.
-
--->
-
-<suppressions>
-  <suppress checks=".*" files="XGBoostJNI.java"/>
-</suppressions>
diff --git a/ml-xgboost/jvm-packages/checkstyle.xml b/ml-xgboost/jvm-packages/checkstyle.xml
deleted file mode 100644
index a926eb7..0000000
--- a/ml-xgboost/jvm-packages/checkstyle.xml
+++ /dev/null
@@ -1,162 +0,0 @@
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-
-<!DOCTYPE module PUBLIC
-          "-//Puppy Crawl//DTD Check Configuration 1.3//EN"
-          "http://www.puppycrawl.com/dtds/configuration_1_3.dtd">
-
-<!--
-
-    Checkstyle configuration based on the Google coding conventions from:
-
-    -  Google Java Style
-       https://google-styleguide.googlecode.com/svn-history/r130/trunk/javaguide.html
-
-    with Spark-specific changes from:
-
-    https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
-
-    Checkstyle is very configurable. Be sure to read the documentation at
-    http://checkstyle.sf.net (or in your downloaded distribution).
-
-    Most Checks are configurable, be sure to consult the documentation.
-
-    To completely disable a check, just comment it out or delete it from the file.
-
-    Authors: Max Vetrenko, Ruslan Diachenko, Roman Ivanov.
-
- -->
-
-<module name = "Checker">
-    <property name="charset" value="UTF-8"/>
-
-    <property name="severity" value="error"/>
-
-    <property name="fileExtensions" value="java, properties, xml"/>
-
-		<module name="SuppressionFilter">
-			  <property name="file" value="checkstyle-suppressions.xml"/>
-		</module>
-
-    <!-- Checks for whitespace                               -->
-    <!-- See http://checkstyle.sf.net/config_whitespace.html -->
-    <module name="FileTabCharacter">
-        <property name="eachLine" value="true"/>
-    </module>
-
-    <module name="RegexpSingleline">
-        <!-- \s matches whitespace character, $ matches end of line. -->
-        <property name="format" value="\s+$"/>
-        <property name="message" value="No trailing whitespace allowed."/>
-    </module>
-
-    <module name="TreeWalker">
-        <module name="OuterTypeFilename"/>
-        <module name="IllegalTokenText">
-            <property name="tokens" value="STRING_LITERAL, CHAR_LITERAL"/>
-            <property name="format" value="\\u00(08|09|0(a|A)|0(c|C)|0(d|D)|22|27|5(C|c))|\\(0(10|11|12|14|15|42|47)|134)"/>
-            <property name="message" value="Avoid using corresponding octal or Unicode escape."/>
-        </module>
-        <module name="AvoidEscapedUnicodeCharacters">
-            <property name="allowEscapesForControlCharacters" value="true"/>
-            <property name="allowByTailComment" value="true"/>
-            <property name="allowNonPrintableEscapes" value="true"/>
-        </module>
-        
-        <module name="LineLength">
-            <property name="max" value="100"/>
-            <property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
-        </module>
-        
-        <module name="NoLineWrap"/>
-        <module name="EmptyBlock">
-            <property name="option" value="TEXT"/>
-            <property name="tokens" value="LITERAL_TRY, LITERAL_FINALLY, LITERAL_IF, LITERAL_ELSE, LITERAL_SWITCH"/>
-        </module>
-        <module name="NeedBraces">
-            <property name="allowSingleLineStatement" value="true"/>
-        </module>
-        <module name="OneStatementPerLine"/>
-        <module name="ArrayTypeStyle"/>
-        <module name="FallThrough"/>
-        <module name="UpperEll"/>
-        <module name="ModifierOrder"/>
-        <module name="SeparatorWrap">
-            <property name="tokens" value="DOT"/>
-            <property name="option" value="nl"/>
-        </module>
-        <module name="SeparatorWrap">
-            <property name="tokens" value="COMMA"/>
-            <property name="option" value="EOL"/>
-        </module>
-        <module name="PackageName">
-            <property name="format" value="^[a-z]+(\.[a-z][a-z0-9]*)*$"/>
-            <message key="name.invalidPattern"
-             value="Package name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="ClassTypeParameterName">
-            <property name="format" value="([A-Z][a-zA-Z0-9]*$)"/>
-            <message key="name.invalidPattern"
-             value="Class type name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="MethodTypeParameterName">
-            <property name="format" value="([A-Z][a-zA-Z0-9]*)"/>
-            <message key="name.invalidPattern"
-             value="Method type name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="GenericWhitespace">
-            <message key="ws.followed"
-             value="GenericWhitespace ''{0}'' is followed by whitespace."/>
-             <message key="ws.preceded"
-             value="GenericWhitespace ''{0}'' is preceded with whitespace."/>
-             <message key="ws.illegalFollow"
-             value="GenericWhitespace ''{0}'' should followed by whitespace."/>
-             <message key="ws.notPreceded"
-             value="GenericWhitespace ''{0}'' is not preceded with whitespace."/>
-        </module>
-        <module name="Indentation">
-            <property name="basicOffset" value="2"/>
-            <property name="braceAdjustment" value="0"/>
-            <property name="caseIndent" value="2"/>
-            <property name="throwsIndent" value="4"/>
-            <property name="lineWrappingIndentation" value="4"/>
-            <property name="arrayInitIndent" value="2"/>
-        </module>
-        <module name="ImportOrder">
-            <property name="separated" value="true"/>
-            <property name="ordered" value="true"/>
-            <property name="groups" value="/^javax?\./,scala,*,ml.dmlc.xgboost4j"/>
-        </module>
-        <module name="MethodParamPad"/>
-        <module name="AnnotationLocation">
-            <property name="tokens" value="CLASS_DEF, INTERFACE_DEF, ENUM_DEF, METHOD_DEF, CTOR_DEF"/>
-        </module>
-        <module name="AnnotationLocation">
-            <property name="tokens" value="VARIABLE_DEF"/>
-            <property name="allowSamelineMultipleAnnotations" value="true"/>
-        </module>
-        <module name="MethodName">
-            <property name="format" value="^[a-z][a-z0-9][a-zA-Z0-9_]*$"/>
-            <message key="name.invalidPattern"
-             value="Method name ''{0}'' must match pattern ''{1}''."/>
-        </module>
-        <module name="EmptyCatchBlock">
-            <property name="exceptionVariableName" value="expected"/>
-        </module>
-        <module name="CommentsIndentation"/>
-    </module>
-</module>
diff --git a/ml-xgboost/jvm-packages/create_jni.py b/ml-xgboost/jvm-packages/create_jni.py
deleted file mode 100644
index 3e51d0b..0000000
--- a/ml-xgboost/jvm-packages/create_jni.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#!/usr/bin/env python
-import errno
-import glob
-import os
-import shutil
-import subprocess
-import sys
-from contextlib import contextmanager
-
-
-# Monkey-patch the API inconsistency between Python2.X and 3.X.
-if sys.platform.startswith("linux"):
-    sys.platform = "linux"
-
-
-CONFIG = {
-    "USE_OPENMP": "ON",
-    "USE_HDFS": "OFF",
-    "USE_AZURE": "OFF",
-    "USE_S3": "OFF",
-
-    "USE_CUDA": "OFF",
-    "JVM_BINDINGS": "ON"
-}
-
-
-@contextmanager
-def cd(path):
-    path = normpath(path)
-    cwd = os.getcwd()
-    os.chdir(path)
-    print("cd " + path)
-    try:
-        yield path
-    finally:
-        os.chdir(cwd)
-
-
-def maybe_makedirs(path):
-    path = normpath(path)
-    print("mkdir -p " + path)
-    try:
-        os.makedirs(path)
-    except OSError as e:
-        if e.errno != errno.EEXIST:
-            raise
-
-
-def run(command, **kwargs):
-    print(command)
-    subprocess.check_call(command, shell=True, **kwargs)
-
-
-def cp(source, target):
-    source = normpath(source)
-    target = normpath(target)
-    print("cp {0} {1}".format(source, target))
-    shutil.copy(source, target)
-
-
-def normpath(path):
-    """Normalize UNIX path to a native path."""
-    normalized = os.path.join(*path.split("/"))
-    if os.path.isabs(path):
-        return os.path.abspath("/") + normalized
-    else:
-        return normalized
-
-
-if __name__ == "__main__":
-    if sys.platform == "darwin":
-        # Enable of your compiler supports OpenMP.
-        CONFIG["USE_OPENMP"] = "OFF"
-        os.environ["JAVA_HOME"] = subprocess.check_output(
-            "/usr/libexec/java_home").strip().decode()
-
-    print("building Java wrapper")
-    with cd(".."):
-        maybe_makedirs("build")
-        with cd("build"):
-            if sys.platform == "win32":
-                # Force x64 build on Windows.
-                maybe_generator = ' -G"Visual Studio 14 Win64"'
-            else:
-                maybe_generator = ""
-            if sys.platform == "linux":
-                maybe_parallel_build = " -- -j $(nproc)"
-            else:
-                maybe_parallel_build = ""
-
-            args = ["-D{0}:BOOL={1}".format(k, v) for k, v in CONFIG.items()]
-
-            # if enviorment set rabit_mock
-            if os.getenv("RABIT_MOCK", None) is not None:
-                args.append("-DRABIT_MOCK:BOOL=ON")
-
-            run("cmake .. " + " ".join(args) + maybe_generator)
-            run("cmake --build . --config Release" + maybe_parallel_build)
-
-        with cd("demo/regression"):
-            run(sys.executable + " mapfeat.py")
-            run(sys.executable + " mknfold.py machine.txt 1")
-
-    print("copying native library")
-    library_name = {
-        "win32": "xgboost4j.dll",
-        "darwin": "libxgboost4j.dylib",
-        "linux": "libxgboost4j.so"
-    }[sys.platform]
-    maybe_makedirs("boostkit-xgboost4j/src/main/resources/lib")
-    cp("../lib/" + library_name, "boostkit-xgboost4j/src/main/resources/lib")
-
-    print("copying pure-Python tracker")
-    cp("../dmlc-core/tracker/dmlc_tracker/tracker.py",
-       "boostkit-xgboost4j/src/main/resources")
-
-    print("copying train/test files")
-    maybe_makedirs("boostkit-xgboost4j-spark/src/test/resources")
-    with cd("../demo/regression"):
-        run("{} mapfeat.py".format(sys.executable))
-        run("{} mknfold.py machine.txt 1".format(sys.executable))
-
-    for file in glob.glob("../demo/regression/machine.txt.t*"):
-        cp(file, "boostkit-xgboost4j-spark/src/test/resources")
-    for file in glob.glob("../demo/data/agaricus.*"):
-        cp(file, "boostkit-xgboost4j-spark/src/test/resources")
-
-    maybe_makedirs("boostkit-xgboost4j/src/test/resources")
-    for file in glob.glob("../demo/data/agaricus.*"):
-        cp(file, "boostkit-xgboost4j/src/test/resources")
diff --git a/ml-xgboost/jvm-packages/dev/.gitattributes b/ml-xgboost/jvm-packages/dev/.gitattributes
deleted file mode 100644
index ed670ec..0000000
--- a/ml-xgboost/jvm-packages/dev/.gitattributes
+++ /dev/null
@@ -1,3 +0,0 @@
-# Set line endings to LF, even on Windows. Otherwise, execution within Docker fails.
-# See https://help.github.com/articles/dealing-with-line-endings/
-*.sh text eol=lf
diff --git a/ml-xgboost/jvm-packages/dev/.gitignore b/ml-xgboost/jvm-packages/dev/.gitignore
deleted file mode 100644
index eb713db..0000000
--- a/ml-xgboost/jvm-packages/dev/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-.m2
diff --git a/ml-xgboost/jvm-packages/dev/Dockerfile b/ml-xgboost/jvm-packages/dev/Dockerfile
deleted file mode 100644
index 02df129..0000000
--- a/ml-xgboost/jvm-packages/dev/Dockerfile
+++ /dev/null
@@ -1,50 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-FROM centos:6
-
-# Install all basic requirements
-RUN \
-    yum -y update && \
-    yum install -y tar unzip wget xz git centos-release-scl yum-utils java-1.8.0-openjdk-devel && \
-    yum-config-manager --enable centos-sclo-rh-testing && \
-    yum -y update && \
-    yum install -y devtoolset-6-gcc devtoolset-6-binutils devtoolset-6-gcc-c++ && \
-    # Python
-    wget https://repo.continuum.io/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh && \
-    bash Miniconda3-4.5.12-Linux-x86_64.sh -b -p /opt/python && \
-    # CMake
-    wget -nv -nc https://cmake.org/files/v3.12/cmake-3.12.0-Linux-x86_64.sh --no-check-certificate && \
-    bash cmake-3.12.0-Linux-x86_64.sh --skip-license --prefix=/usr && \
-    # Maven
-    wget https://archive.apache.org/dist/maven/maven-3/3.6.1/binaries/apache-maven-3.6.1-bin.tar.gz && \
-    tar xvf apache-maven-3.6.1-bin.tar.gz -C /opt && \
-    ln -s /opt/apache-maven-3.6.1/ /opt/maven
-
-# Set the required environment variables
-ENV PATH=/opt/python/bin:/opt/maven/bin:$PATH
-ENV CC=/opt/rh/devtoolset-6/root/usr/bin/gcc
-ENV CXX=/opt/rh/devtoolset-6/root/usr/bin/c++
-ENV CPP=/opt/rh/devtoolset-6/root/usr/bin/cpp
-ENV JAVA_HOME=/usr/lib/jvm/java
-
-# Install Python packages
-RUN \
-    pip install numpy pytest scipy scikit-learn wheel kubernetes urllib3==1.22 awscli
-
-WORKDIR /xgboost
diff --git a/ml-xgboost/jvm-packages/dev/build-linux.cmd b/ml-xgboost/jvm-packages/dev/build-linux.cmd
deleted file mode 100644
index 6e16585..0000000
--- a/ml-xgboost/jvm-packages/dev/build-linux.cmd
+++ /dev/null
@@ -1,44 +0,0 @@
-@echo off
-
-rem
-rem Licensed to the Apache Software Foundation (ASF) under one
-rem or more contributor license agreements.  See the NOTICE file
-rem distributed with this work for additional information
-rem regarding copyright ownership.  The ASF licenses this file
-rem to you under the Apache License, Version 2.0 (the
-rem "License"); you may not use this file except in compliance
-rem with the License.  You may obtain a copy of the License at
-rem
-rem   http://www.apache.org/licenses/LICENSE-2.0
-rem
-rem Unless required by applicable law or agreed to in writing,
-rem software distributed under the License is distributed on an
-rem "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-rem KIND, either express or implied.  See the License for the
-rem specific language governing permissions and limitations
-rem under the License.
-rem
-
-rem The the local path of this file
-set "BASEDIR=%~dp0"
-
-rem The local path of .m2 directory for maven
-set "M2DIR=%BASEDIR%\.m2\"
-
-rem Create a local .m2 directory if needed
-if not exist "%M2DIR%" mkdir "%M2DIR%"
-
-rem Build and tag the Dockerfile
-docker build -t dmlc/xgboost4j-build %BASEDIR%
-
-docker run^
- -it^
- --rm^
- --memory 8g^
- --env JAVA_OPTS="-Xmx6g"^
- --env MAVEN_OPTS="-Xmx2g"^
- --ulimit core=-1^
- --volume %BASEDIR%\..\..:/xgboost^
- --volume %M2DIR%:/root/.m2^
- dmlc/xgboost4j-build^
- /xgboost/jvm-packages/dev/package-linux.sh "%*"
diff --git a/ml-xgboost/jvm-packages/dev/build-linux.sh b/ml-xgboost/jvm-packages/dev/build-linux.sh
deleted file mode 100644
index f14f10b..0000000
--- a/ml-xgboost/jvm-packages/dev/build-linux.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env bash
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-BASEDIR="$( cd "$( dirname "$0" )" && pwd )" # the directory of this file
-
-docker build -t dmlc/xgboost4j-build "${BASEDIR}" # build and tag the Dockerfile
-
-docker run \
-  -it \
-  --rm  \
-  --memory 8g \
-  --env JAVA_OPTS="-Xmx6g" \
-  --env MAVEN_OPTS="-Xmx2g" \
-  --ulimit core=-1 \
-  --volume "${BASEDIR}/../..":/xgboost \
-  --volume "${BASEDIR}/.m2":/root/.m2 \
-  dmlc/xgboost4j-build \
-  /xgboost/jvm-packages/dev/package-linux.sh "$@"
diff --git a/ml-xgboost/jvm-packages/dev/change_version.sh b/ml-xgboost/jvm-packages/dev/change_version.sh
deleted file mode 100644
index 8a51226..0000000
--- a/ml-xgboost/jvm-packages/dev/change_version.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env bash
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-# (Yizhi) This is mainly inspired by the script in apache/spark.
-# I did some modifications to get it with our project.
-# (Nan) Modified from MxNet
-#
-set -e
-
-if [[ ($# -ne 2) || ( $1 == "--help") ||  $1 == "-h" ]]; then
-  echo "Usage: $(basename $0) [-h|--help] <from_version> <to_version>" 1>&2
-  exit 1
-fi
-
-FROM_VERSION=$1
-TO_VERSION=$2
-
-sed_i() {
-  perl -p -000 -e "$1" "$2" > "$2.tmp" && mv "$2.tmp" "$2"
-}
-   
-export -f sed_i
- 
-BASEDIR=$(dirname $0)/..
-find "$BASEDIR" -name 'pom.xml' -not -path '*target*' -print \
-  -exec bash -c \
-  "sed_i 's/(<artifactId>(boostkit-xgboost-jvm.*|boostkit-xgboost4j.*)<\/artifactId>\s+<version)>'$FROM_VERSION'(<\/version>)/\1>'$TO_VERSION'\3/g' {}" \;
diff --git a/ml-xgboost/jvm-packages/dev/package-linux.sh b/ml-xgboost/jvm-packages/dev/package-linux.sh
deleted file mode 100644
index 1fd777d..0000000
--- a/ml-xgboost/jvm-packages/dev/package-linux.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-cd jvm-packages
-
-case "$1" in
-  --skip-tests) SKIP_TESTS=true ;;
-  "")           SKIP_TESTS=false ;;
-esac
-
-if [[ -n ${SKIP_TESTS} ]]; then
-  if [[ ${SKIP_TESTS} == "true" ]]; then
-    mvn --batch-mode clean package -DskipTests
-  elif [[ ${SKIP_TESTS} == "false" ]]; then
-    mvn --batch-mode clean package
-  fi
-else
-  echo "Usage: $0 [--skip-tests]"
-  exit 1
-fi
diff --git a/ml-xgboost/jvm-packages/pom.xml b/ml-xgboost/jvm-packages/pom.xml
deleted file mode 100644
index a96a4ee..0000000
--- a/ml-xgboost/jvm-packages/pom.xml
+++ /dev/null
@@ -1,444 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-
-    <groupId>ml.dmlc</groupId>
-    <artifactId>boostkit-xgboost-jvm_2.11</artifactId>
-    <version>2.1.0</version>
-    <packaging>pom</packaging>
-    <name> XGBoost JVM Package</name>
-    <description> JVM Package for XGBoost</description>
-    <licenses>
-        <license>
-            <name>The Apache License, Version 2.0</name>
-            <url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
-        </license>
-    </licenses>
-    <developers>
-        <developer>
-            <name>CodingCat</name>
-            <email>codingcat@apache.org</email>
-        </developer>
-    </developers>
-    <scm>
-        <connection>scm:git:git:/github.com/dmlc/xgboost.git</connection>
-        <developerConnection>scm:git:ssh://github.com/dmlc/xgboost.git</developerConnection>
-        <url>https://github.com/dmlc/xgboost</url>
-    </scm>
-    <properties>
-        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
-        <maven.compiler.source>1.8</maven.compiler.source>
-        <maven.compiler.target>1.8</maven.compiler.target>
-        <flink.version>1.7.2</flink.version>
-        <spark.version>2.3.2</spark.version>
-        <scala.version>2.11.8</scala.version>
-        <scala.binary.version>2.11</scala.binary.version>
-        <hadoop.version>3.1.1</hadoop.version>
-<!--        <module.version>test_version</module.version>-->
-    </properties>
-    <repositories>
-        <repository>
-            <id>central_maven</id>
-            <name>central maven</name>
-            <url>https://repo1.maven.org/maven2</url>
-        </repository>
-    </repositories>
-    <modules>
-        <module>boostkit-xgboost4j</module>
-        <module>boostkit-xgboost4j-example</module>
-        <module>boostkit-xgboost4j-spark</module>
-        <module>boostkit-xgboost4j-flink</module>
-        <module>boostkit-xgboost4j-spark-kernel-client</module>
-        <module>boostkit-xgboost4j-spark-client</module>
-    </modules>
-    <profiles>
-        <profile>
-            <id>release</id>
-            <build>
-                <plugins>
-                    <plugin>
-                        <groupId>org.apache.maven.plugins</groupId>
-                        <artifactId>maven-jar-plugin</artifactId>
-                        <version>2.5</version>
-                        <executions>
-                            <execution>
-                                <id>empty-javadoc-jar</id>
-                                <phase>package</phase>
-                                <goals>
-                                    <goal>jar</goal>
-                                </goals>
-                                <configuration>
-                                    <classifier>javadoc</classifier>
-                                    <classesDirectory>${basedir}/javadoc</classesDirectory>
-                                </configuration>
-                            </execution>
-                            <execution>
-                                <goals>
-                                    <goal>jar</goal>
-                                </goals>
-                                <configuration>
-                                    <classifier>${os.detected.arch}</classifier>
-                                </configuration>
-                          </execution>
-                        </executions>
-                    </plugin>
-                    <plugin>
-                        <groupId>org.apache.maven.plugins</groupId>
-                        <artifactId>maven-release-plugin</artifactId>
-                        <version>2.5.3</version>
-                        <configuration>
-                            <autoVersionSubmodules>true</autoVersionSubmodules>
-                            <useReleaseProfile>false</useReleaseProfile>
-                            <releaseProfiles>release</releaseProfiles>
-                            <goals>deploy</goals>
-                        </configuration>
-                    </plugin>
-                    <plugin>
-                        <groupId>org.apache.maven.plugins</groupId>
-                        <artifactId>maven-gpg-plugin</artifactId>
-                        <version>1.5</version>
-                        <executions>
-                            <execution>
-                                <id>sign-artifacts</id>
-                                <phase>verify</phase>
-                                <goals>
-                                    <goal>sign</goal>
-                                </goals>
-                            </execution>
-                        </executions>
-                    </plugin>
-                    <plugin>
-                        <groupId>org.apache.maven.plugins</groupId>
-                        <artifactId>maven-source-plugin</artifactId>
-                        <version>2.2.1</version>
-                        <executions>
-                            <execution>
-                                <id>attach-sources</id>
-                                <goals>
-                                    <goal>jar-no-fork</goal>
-                                </goals>
-                            </execution>
-                        </executions>
-                    </plugin>
-                    <plugin>
-                        <groupId>org.sonatype.plugins</groupId>
-                        <artifactId>nexus-staging-maven-plugin</artifactId>
-                        <version>1.6.7</version>
-                        <extensions>true</extensions>
-                        <configuration>
-                            <serverId>ossrh</serverId>
-                            <nexusUrl>https://oss.sonatype.org/</nexusUrl>
-                            <autoReleaseAfterClose>false</autoReleaseAfterClose>
-                        </configuration>
-                    </plugin>
-                </plugins>
-            </build>
-        </profile>
-        <profile>
-            <id>assembly</id>
-            <build>
-                <plugins>
-                    <plugin>
-                        <groupId>org.apache.maven.plugins</groupId>
-                        <artifactId>maven-assembly-plugin</artifactId>
-                        <version>2.6</version>
-                        <configuration>
-                            <descriptorRefs>
-                                <descriptorRef>jar-with-dependencies</descriptorRef>
-                            </descriptorRefs>
-                            <skipAssembly>true</skipAssembly>
-                        </configuration>
-                        <executions>
-                            <execution>
-                                <id>make-assembly</id>
-                                <phase>package</phase>
-                                <goals>
-                                    <goal>single</goal>
-                                </goals>
-                            </execution>
-                        </executions>
-                    </plugin>
-                </plugins>
-            </build>
-        </profile>
-        <profile>
-            <id>release-to-github</id>
-            <distributionManagement>
-                <repository>
-                    <id>github.repo</id>
-                    <name>Temporary Staging Repository</name>
-                    <url>file://${project.build.directory}/mvn-repo</url>
-                </repository>
-            </distributionManagement>
-            <properties>
-                <github.global.server>github</github.global.server>
-            </properties>
-            <build>
-                <plugins>
-                    <plugin>
-                        <groupId>com.github.github</groupId>
-                        <artifactId>site-maven-plugin</artifactId>
-                        <version>0.12</version>
-                        <configuration>
-                            <message>Maven artifacts for ${project.version}</message>
-                            <noJekyll>true</noJekyll>
-                            <outputDirectory>${project.build.directory}/mvn-repo</outputDirectory>
-                            <branch>refs/heads/maven-repo</branch>
-                            <excludes>
-                                <exclude>*-with-dependencies.jar</exclude>
-                            </excludes>
-                            <repositoryName>xgboost</repositoryName>
-                            <repositoryOwner>CodingCat</repositoryOwner>
-                            <merge>true</merge>
-                        </configuration>
-                        <executions>
-                            <!-- run site-maven-plugin's 'site' target as part of the build's normal 'deploy' phase -->
-                            <execution>
-                                <goals>
-                                    <goal>site</goal>
-                                </goals>
-                                <phase>deploy</phase>
-                            </execution>
-                        </executions>
-                    </plugin>
-                    <plugin>
-                        <groupId>org.apache.maven.plugins</groupId>
-                        <artifactId>maven-deploy-plugin</artifactId>
-                        <version>2.8.2</version>
-                        <configuration>
-                            <altDeploymentRepository>internal.repo::default::file://${project.build.directory}/mvn-repo</altDeploymentRepository>
-                        </configuration>
-                    </plugin>
-                </plugins>
-            </build>
-        </profile>
-        <profile>
-            <id>release-to-s3</id>
-            <distributionManagement>
-                <snapshotRepository>
-                    <id>maven-s3-snapshot-repo</id>
-                    <url>s3://xgboost-maven-repo/snapshot</url>
-                </snapshotRepository>
-                <repository>
-                    <id>maven-s3-release-repo</id>
-                    <url>s3://xgboost-maven-repo/release</url>
-                </repository>
-            </distributionManagement>
-            <repositories>
-                <repository>
-                    <id>maven-s3-snapshot-repo</id>
-                    <url>https://s3.amazonaws.com/xgboost-maven-repo/snapshot</url>
-                </repository>
-                <repository>
-                    <id>maven-s3-release-repo</id>
-                    <url>https://s3.amazonaws.com/xgboost-maven-repo/release</url>
-                </repository>
-            </repositories>
-        </profile>
-    </profiles>
-    <distributionManagement>
-        <snapshotRepository>
-            <id>ossrh</id>
-            <url>https://oss.sonatype.org/content/repositories/snapshots</url>
-        </snapshotRepository>
-    </distributionManagement>
-    <build>
-        <resources>
-            <resource>
-                <directory>src/main/resources</directory>
-                <filtering>true</filtering>
-            </resource>
-        </resources>
-        <plugins>
-            <plugin>
-                <groupId>org.scalastyle</groupId>
-                <artifactId>scalastyle-maven-plugin</artifactId>
-                <version>0.8.0</version>
-                <configuration>
-                    <verbose>false</verbose>
-                    <failOnViolation>true</failOnViolation>
-                    <includeTestSourceDirectory>true</includeTestSourceDirectory>
-                    <sourceDirectory>${basedir}/src/main/scala</sourceDirectory>
-                    <testSourceDirectory>${basedir}/src/test/scala</testSourceDirectory>
-                    <outputFile>${basedir}/target/scalastyle-output.xml</outputFile>
-                    <configLocation>scalastyle-config.xml</configLocation>
-                    <outputEncoding>UTF-8</outputEncoding>
-                </configuration>
-                <executions>
-                    <execution>
-                        <id>checkstyle</id>
-                        <phase>validate</phase>
-                        <goals>
-                            <goal>check</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-site-plugin</artifactId>
-                <version>3.0</version>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-checkstyle-plugin</artifactId>
-                <version>2.17</version>
-                <configuration>
-                    <configLocation>checkstyle.xml</configLocation>
-                    <failOnViolation>true</failOnViolation>
-                </configuration>
-                <executions>
-                    <execution>
-                        <id>checkstyle</id>
-                        <phase>validate</phase>
-                        <goals>
-                            <goal>check</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>net.alchim31.maven</groupId>
-                <artifactId>scala-maven-plugin</artifactId>
-                <version>3.2.2</version>
-                <executions>
-                    <execution>
-                        <id>compile</id>
-                        <goals>
-                            <goal>compile</goal>
-                        </goals>
-                        <phase>compile</phase>
-                    </execution>
-                    <execution>
-                        <id>test-compile</id>
-                        <goals>
-                            <goal>testCompile</goal>
-                        </goals>
-                        <phase>test-compile</phase>
-                    </execution>
-                    <execution>
-                        <phase>process-resources</phase>
-                        <goals>
-                            <goal>compile</goal>
-                        </goals>
-                    </execution>
-                    <execution>
-                        <id>scala-compile-first</id>
-                        <phase>process-resources</phase>
-                        <goals>
-                            <goal>compile</goal>
-                            <goal>add-source</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-surefire-plugin</artifactId>
-                <version>2.19.1</version>
-                <configuration>
-                    <skipTests>false</skipTests>
-                    <useSystemClassLoader>false</useSystemClassLoader>
-                </configuration>
-            </plugin>
-            <plugin>
-                <groupId>org.scalatest</groupId>
-                <artifactId>scalatest-maven-plugin</artifactId>
-                <version>1.0</version>
-                <configuration>
-                    <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
-                    <junitxml>.</junitxml>
-                    <filereports>TestSuite.txt</filereports>
-                    <htmlreporters>${project.build.directory}/site/scalatest</htmlreporters>
-              </configuration>
-                <executions>
-                    <execution>
-                        <id>test</id>
-                        <goals>
-                            <goal>test</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
-        </plugins>
-        <extensions>
-            <extension>
-                <groupId>org.kuali.maven.wagons</groupId>
-                <artifactId>maven-s3-wagon</artifactId>
-                <version>1.2.1</version>
-            </extension>
-            <extension>
-                <groupId>kr.motd.maven</groupId>
-                <artifactId>os-maven-plugin</artifactId>
-                <version>1.6.1</version>
-            </extension>
-        </extensions>
-    </build>
-    <reporting>
-        <plugins>
-            <plugin>
-                <artifactId>maven-project-info-reports-plugin</artifactId>
-                <version>2.2</version>
-            </plugin>
-            <plugin>
-                <groupId>net.alchim31.maven</groupId>
-                <artifactId>scala-maven-plugin</artifactId>
-                <version>3.2.1</version>
-                <configuration>
-                    <jvmArgs>
-                        <jvmArg>-Xms64m</jvmArg>
-                        <jvmArg>-Xmx1024m</jvmArg>
-                    </jvmArgs>
-                </configuration>
-            </plugin>
-        </plugins>
-    </reporting>
-    <dependencies>
-        <dependency>
-            <groupId>com.esotericsoftware</groupId>
-            <artifactId>kryo</artifactId>
-            <version>4.0.2</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scala-lang</groupId>
-            <artifactId>scala-compiler</artifactId>
-            <version>${scala.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scala-lang</groupId>
-            <artifactId>scala-reflect</artifactId>
-            <version>${scala.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scala-lang</groupId>
-            <artifactId>scala-library</artifactId>
-            <version>${scala.version}</version>
-        </dependency>
-        <dependency>
-            <groupId>commons-logging</groupId>
-            <artifactId>commons-logging</artifactId>
-            <version>1.2</version>
-        </dependency>
-        <dependency>
-            <groupId>org.scalatest</groupId>
-            <artifactId>scalatest_${scala.binary.version}</artifactId>
-            <version>3.0.3</version>
-            <scope>test</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.scalactic</groupId>
-            <artifactId>scalactic_${scala.binary.version}</artifactId>
-            <version>3.0.8</version>
-            <scope>test</scope>
-        </dependency>
-        <dependency>
-            <groupId>org.pegdown</groupId>
-            <artifactId>pegdown</artifactId>
-            <version>1.4.2</version>
-            <scope>test</scope>
-        </dependency>
-    </dependencies>
-</project>
diff --git a/ml-xgboost/jvm-packages/scalastyle-config.xml b/ml-xgboost/jvm-packages/scalastyle-config.xml
deleted file mode 100644
index fd4d22e..0000000
--- a/ml-xgboost/jvm-packages/scalastyle-config.xml
+++ /dev/null
@@ -1,276 +0,0 @@
-<!--
-  ~ Licensed to the Apache Software Foundation (ASF) under one or more
-  ~ contributor license agreements.  See the NOTICE file distributed with
-  ~ this work for additional information regarding copyright ownership.
-  ~ The ASF licenses this file to You under the Apache License, Version 2.0
-  ~ (the "License"); you may not use this file except in compliance with
-  ~ the License.  You may obtain a copy of the License at
-  ~
-  ~    http://www.apache.org/licenses/LICENSE-2.0
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS,
-  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  ~ See the License for the specific language governing permissions and
-  ~ limitations under the License.
-  -->
-<!--
-
-If you wish to turn off checking for a section of code, you can put a comment in the source
-before and after the section, with the following syntax:
-
-  // scalastyle:off
-  ...  // stuff that breaks the styles
-  // scalastyle:on
-
-You can also disable only one rule, by specifying its rule id, as specified in:
-  http://www.scalastyle.org/rules-0.7.0.html
-
-  // scalastyle:off no.finalize
-  override def finalize(): Unit = ...
-  // scalastyle:on no.finalize
-
-This file is divided into 3 sections:
- (1) rules that we enforce.
- (2) rules that we would like to enforce, but haven't cleaned up the codebase to turn on yet
-     (or we need to make the scalastyle rule more configurable).
- (3) rules that we don't want to enforce.
--->
-
-<scalastyle>
-  <name>Scalastyle standard configuration</name>
-
-  <!-- ================================================================================ -->
-  <!--                               rules we enforce                                   -->
-  <!-- ================================================================================ -->
-
-  <check level="error" class="org.scalastyle.file.FileTabChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.HeaderMatchesChecker" enabled="true">
-    <parameters>
-       <parameter name="header"><![CDATA[/*
-				  Copyright (c) 2014 by Contributors
-
-					 Licensed under the Apache License, Version 2.0 (the "License");
-					  you may not use this file except in compliance with the License.
-						 You may obtain a copy of the License at
-
-						  http://www.apache.org/licenses/LICENSE-2.0
-
-							 Unless required by applicable law or agreed to in writing, software
-							  distributed under the License is distributed on an "AS IS" BASIS,
-								 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-								  See the License for the specific language governing permissions and
-									 limitations under the License.
-									  */]]></parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.SpacesAfterPlusChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.SpacesBeforePlusChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.WhitespaceEndOfLineChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.FileLineLengthChecker" enabled="true">
-    <parameters>
-      <parameter name="maxLineLength"><![CDATA[100]]></parameter>
-      <parameter name="tabSize"><![CDATA[2]]></parameter>
-      <parameter name="ignoreImports">true</parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ClassNamesChecker" enabled="true">
-    <parameters><parameter name="regex"><![CDATA[[A-Z][A-Za-z]*]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ObjectNamesChecker" enabled="true">
-    <parameters><parameter name="regex"><![CDATA[[A-Z][A-Za-z]*]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.PackageObjectNamesChecker" enabled="true">
-    <parameters><parameter name="regex"><![CDATA[^[a-z][A-Za-z]*$]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ParameterNumberChecker" enabled="true">
-    <parameters><parameter name="maxParameters"><![CDATA[10]]></parameter></parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.NoFinalizeChecker" enabled="false"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.CovariantEqualsChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.StructuralTypeChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.UppercaseLChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.IfBraceChecker" enabled="true">
-    <parameters>
-      <parameter name="singleLineAllowed"><![CDATA[true]]></parameter>
-      <parameter name="doubleLineAllowed"><![CDATA[true]]></parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.PublicMethodsHaveTypeChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.file.NewLineAtEofChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.NonASCIICharacterChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.SpaceAfterCommentStartChecker" enabled="true"></check>
-
-  <check level="error" class="org.scalastyle.scalariform.EnsureSingleSpaceBeforeTokenChecker" enabled="true">
-   <parameters>
-     <parameter name="tokens">ARROW, EQUALS, ELSE, TRY, CATCH, FINALLY, LARROW, RARROW</parameter>
-   </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.EnsureSingleSpaceAfterTokenChecker" enabled="true">
-    <parameters>
-     <parameter name="tokens">ARROW, EQUALS, COMMA, COLON, IF, ELSE, DO, WHILE, FOR, MATCH, TRY, CATCH, FINALLY, LARROW, RARROW</parameter>
-    </parameters>
-  </check>
-
-  <!-- ??? usually shouldn't be checked into the code base. -->
-  <check level="error" class="org.scalastyle.scalariform.NotImplementedErrorUsage" enabled="true"></check>
-
-  <check customId="visiblefortesting" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">@VisibleForTesting</parameter></parameters>
-    <customMessage><![CDATA[
-      @VisibleForTesting causes classpath issues. Please note this in the java doc instead (SPARK-11615).
-    ]]></customMessage>
-  </check>
-
-  <check customId="runtimeaddshutdownhook" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">Runtime\.getRuntime\.addShutdownHook</parameter></parameters>
-    <customMessage><![CDATA[
-      Are you sure that you want to use Runtime.getRuntime.addShutdownHook? In most cases, you should use
-      ShutdownHookManager.addShutdownHook instead.
-      If you must use Runtime.getRuntime.addShutdownHook, wrap the code block with
-      // scalastyle:off runtimeaddshutdownhook
-      Runtime.getRuntime.addShutdownHook(...)
-      // scalastyle:on runtimeaddshutdownhook
-    ]]></customMessage>
-  </check>
-
-  <check customId="mutablesynchronizedbuffer" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">mutable\.SynchronizedBuffer</parameter></parameters>
-    <customMessage><![CDATA[
-      Are you sure that you want to use mutable.SynchronizedBuffer? In most cases, you should use
-      java.util.concurrent.ConcurrentLinkedQueue instead.
-      If you must use mutable.SynchronizedBuffer, wrap the code block with
-      // scalastyle:off mutablesynchronizedbuffer
-      mutable.SynchronizedBuffer[...]
-      // scalastyle:on mutablesynchronizedbuffer
-    ]]></customMessage>
-  </check>
-
-  <check customId="classforname" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
-    <parameters><parameter name="regex">Class\.forName</parameter></parameters>
-    <customMessage><![CDATA[
-      Are you sure that you want to use Class.forName? In most cases, you should use Utils.classForName instead.
-      If you must use Class.forName, wrap the code block with
-      // scalastyle:off classforname
-      Class.forName(...)
-      // scalastyle:on classforname
-    ]]></customMessage>
-  </check>
-
-  <!-- As of SPARK-9613 JavaConversions should be replaced with JavaConverters -->
-  <check customId="javaconversions" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
-    <parameters><parameter name="regex">JavaConversions</parameter></parameters>
-    <customMessage>Instead of importing implicits in scala.collection.JavaConversions._, import
-    scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.ImportOrderChecker" enabled="true">
-    <parameters>
-      <parameter name="groups">java,scala,3rdParty,spark</parameter>
-      <parameter name="group.java">javax?\..*</parameter>
-      <parameter name="group.scala">scala\..*</parameter>
-      <parameter name="group.3rdParty">(?!ml\.dmlc\.xgboost4j\.).*</parameter>
-      <parameter name="group.dmlc">ml.dmlc.xgboost4j.*</parameter>
-    </parameters>
-  </check>
-
-  <check level="error" class="org.scalastyle.scalariform.DisallowSpaceBeforeTokenChecker" enabled="true">
-    <parameters>
-      <parameter name="tokens">COMMA</parameter>
-    </parameters>
-  </check>
-
-  <!-- ================================================================================ -->
-  <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
-  <!-- ================================================================================ -->
-
-  <!-- We cannot turn the following two on, because it'd fail a lot of string interpolation use cases. -->
-  <!-- Ideally the following two rules should be configurable to rule out string interpolation. -->
-  <check level="error" class="org.scalastyle.scalariform.NoWhitespaceBeforeLeftBracketChecker" enabled="false"></check>
-  <check level="error" class="org.scalastyle.scalariform.NoWhitespaceAfterLeftBracketChecker" enabled="false"></check>
-
-  <!-- This breaks symbolic method names so we don't turn it on. -->
-  <!-- Maybe we should update it to allow basic symbolic names, and then we are good to go. -->
-  <check level="error" class="org.scalastyle.scalariform.MethodNamesChecker" enabled="false">
-    <parameters>
-    <parameter name="regex"><![CDATA[^[a-z][A-Za-z0-9]*$]]></parameter>
-    </parameters>
-  </check>
-
-  <!-- Should turn this on, but we have a few places that need to be fixed first -->
-  <check level="error" class="org.scalastyle.scalariform.EqualsHashCodeChecker" enabled="false"></check>
-
-  <!-- ================================================================================ -->
-  <!--                               rules we don't want                                -->
-  <!-- ================================================================================ -->
-
-  <check level="error" class="org.scalastyle.scalariform.IllegalImportsChecker" enabled="false">
-    <parameters><parameter name="illegalImports"><![CDATA[sun._,java.awt._]]></parameter></parameters>
-  </check>
-
-  <!-- We want the opposite of this: NewLineAtEofChecker -->
-  <check level="error" class="org.scalastyle.file.NoNewLineAtEofChecker" enabled="false"></check>
-
-  <!-- This one complains about all kinds of random things. Disable. -->
-  <check level="error" class="org.scalastyle.scalariform.SimplifyBooleanExpressionChecker" enabled="false"></check>
-
-  <!-- We use return quite a bit for control flows and guards -->
-  <check level="error" class="org.scalastyle.scalariform.ReturnChecker" enabled="false"></check>
-
-  <!-- We use null a lot in low level code and to interface with 3rd party code -->
-  <check level="error" class="org.scalastyle.scalariform.NullChecker" enabled="false"></check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.NoCloneChecker" enabled="false"></check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.file.FileLengthChecker" enabled="false">
-    <parameters><parameter name="maxFileLength">800></parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.NumberOfTypesChecker" enabled="false">
-    <parameters><parameter name="maxTypes">30</parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.CyclomaticComplexityChecker" enabled="false">
-    <parameters><parameter name="maximum">10</parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here ... -->
-  <check level="error" class="org.scalastyle.scalariform.MethodLengthChecker" enabled="false">
-    <parameters><parameter name="maxLength">50</parameter></parameters>
-  </check>
-
-  <!-- Not exactly feasible to enforce this right now. -->
-  <!-- It is also infrequent that somebody introduces a new class with a lot of methods. -->
-  <check level="error" class="org.scalastyle.scalariform.NumberOfMethodsInTypeChecker" enabled="false">
-    <parameters><parameter name="maxMethods"><![CDATA[30]]></parameter></parameters>
-  </check>
-
-  <!-- Doesn't seem super big deal here, and we have a lot of magic numbers ... -->
-  <check level="error" class="org.scalastyle.scalariform.MagicNumberChecker" enabled="false">
-    <parameters><parameter name="ignore">-1,0,1,2,3</parameter></parameters>
-  </check>
-
-</scalastyle>
diff --git a/ml-xgboost/kernel_include/README.md b/ml-xgboost/kernel_include/README.md
deleted file mode 100644
index 5de62e6..0000000
--- a/ml-xgboost/kernel_include/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-This directory contains header files from kernel source repositry and kernel source client for compilation:
-- include/boostkit_xgboost_kernel
-- include/boostkit_xgboost_kernel_client
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel/bbgen.h b/ml-xgboost/kernel_include/boostkit_xgboost_kernel/bbgen.h
deleted file mode 100644
index b42a02e..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel/bbgen.h
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
- * Description: XGBoost Kernel Bbgen header file
- */
-#ifndef XGBOOST_KERNEL_BBGEN_H
-#define XGBOOST_KERNEL_BBGEN_H
-
-#include <array>
-#include <climits>
-#include <memory>
-#include <random>
-#include <vector>
-#include <algorithm>
-
-namespace Bs {
-static const double BBGEN_TOL = 1.0 / 16.0;
-static const size_t KFORMULA_MAX_SIZE = 64;
-static const size_t KBLOCK_SIZE = 32;
-static const size_t BYTE_BIT = 8;
-static const size_t KBLOCK_SIZE_BIT = KBLOCK_SIZE * BYTE_BIT;
-static const double HALF = 0.5;
-
-enum LogFunction : uint8_t { K_NOT = 0, K_AND = 1 };
-using RngEngine = std::mt19937;
-
-struct Formula {
-    std::array<LogFunction, KFORMULA_MAX_SIZE> funcs {};
-    size_t funcsCnt = 0;
-    size_t bkCnt = 0;
-};
-
-class FormulaEvaluator {
-public:
-    constexpr explicit FormulaEvaluator(const Formula &fm): fm_(fm) {}
-    FormulaEvaluator(const FormulaEvaluator &) = delete;
-    FormulaEvaluator &operator = (const FormulaEvaluator &) = delete;
-    ~FormulaEvaluator() = default;
-    void Evaluate(const uint64_t &src, uint64_t &dst, size_t noblocks) const;
-    const Formula &GetFormula() const;
-
-protected:
-    Formula fm_;
-};
-
-using EvalPtr = std::unique_ptr<FormulaEvaluator>;
-EvalPtr CreateEvaluator(const double &prob, const double &tol);
-
-class Buffer {
-public:
-    Buffer()  = default;
-    explicit Buffer(size_t size);
-    Buffer(const Buffer &) = delete;
-    Buffer &operator = (const Buffer &) = delete;
-    ~Buffer() = default;
-    void * const Get() const;
-    size_t GetSize() const;
-
-private:
-    std::unique_ptr<void, decltype(&std::free)> ptr_ = {nullptr, &std::free};
-    std::size_t size_ = 0;
-};
-
-bool GetBit(const Buffer &ptr, size_t idx);
-
-class BatchBernGenerator {
-public:
-    explicit BatchBernGenerator(EvalPtr &executor): evaluator_(std::move(executor)) {}
-    BatchBernGenerator(const BatchBernGenerator &) = delete;
-    BatchBernGenerator &operator = (const BatchBernGenerator &) = delete;
-    ~BatchBernGenerator() = default;
-    size_t GetBufferSize(size_t nobits) const;
-    size_t GetBlockSize() const;
-    void Generate(RngEngine &rng, uint64_t &ptr, size_t size) const;
-
-private:
-    EvalPtr evaluator_ = nullptr;
-};
-
-class BGeneratorPtr {
-public:
-    BGeneratorPtr()  = default;
-    explicit BGeneratorPtr(std::unique_ptr<BatchBernGenerator> &gener): gen_(std::move(gener)) {}
-    BGeneratorPtr(const BGeneratorPtr &) = delete;
-    BGeneratorPtr &operator = (const BGeneratorPtr &) = delete;
-    ~BGeneratorPtr() = default;
-    inline void operator()(RngEngine &rng, const Buffer &ptr) const
-    {
-        return gen_->Generate(rng, *(static_cast<uint64_t *>(ptr.Get())), ptr.GetSize());
-    }
-
-    inline BatchBernGenerator *Get() const
-    {
-        return gen_.get();
-    }
-
-    inline std::unique_ptr<Buffer> CreateBuffer(size_t nobits) const
-    {
-        return std::make_unique<Buffer>(gen_->GetBufferSize(nobits));
-    }
-
-private:
-    std::unique_ptr<BatchBernGenerator> gen_ = {};
-};
-
-std::unique_ptr<BGeneratorPtr> CreateGenerator(double prob, double tol);
-}  // namespace Bs
-
-#endif
\ No newline at end of file
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel/rabit_intrinsics.h b/ml-xgboost/kernel_include/boostkit_xgboost_kernel/rabit_intrinsics.h
deleted file mode 100644
index dfb49e2..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel/rabit_intrinsics.h
+++ /dev/null
@@ -1,18 +0,0 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
- * Description: XGBoost Kernel Rabit_Intrinsics header file
- */
-
-#ifndef BOOSTKIT_XGBOOST_KERNEL_RABIT_INTRINSICS_H
-#define BOOSTKIT_XGBOOST_KERNEL_RABIT_INTRINSICS_H
-
-#include <rabit/internal/engine.h>
-
-namespace rabit {
-namespace op {
-void ReducerSumDouble(const double &srcRef, double &dstRef, int len, const MPI::Datatype &dtype);
-
-void ReducerSumFloat(const float &srcRef, float &dstRef, int len, const MPI::Datatype &dtype);
-}
-}
-#endif // BOOSTKIT_XGBOOST_KERNEL_RABIT_INTRINSICS_H
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel/update_quantile_hist_kernel.h b/ml-xgboost/kernel_include/boostkit_xgboost_kernel/update_quantile_hist_kernel.h
deleted file mode 100644
index a8d883d..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel/update_quantile_hist_kernel.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
- * Description: XGBoost Kernel Update_Quantile_Hist_Kernel header file
- */
-#ifndef XGBOOST_KERNEL_UPDATE_QUANTILE_HIST_KERNEL_H
-#define XGBOOST_KERNEL_UPDATE_QUANTILE_HIST_KERNEL_H
-
-#include <random>
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/tree_updater.h>
-#include "boostkit_xgboost_kernel/bbgen.h"
-
-namespace xgboost {
-namespace tree {
-static const size_t BITS_BATCH_SIZE_DEFAULT = 1024 * 32;
-static const size_t GOSS_TEST_SIZE = 4 * 1024;
-struct BbgenSamplingParam {
-    float subsample;
-    size_t tid;
-    size_t discardSize;
-    size_t ibegin;
-    size_t iend;
-    float thresholdGradVal;
-};
-
-struct InitSamplingKernelParam {
-    size_t nthread;
-    int iter;
-    int *lastIterSampling;
-    int samplingStrategy;
-    int samplingStep;
-    bool enableBbgen;
-    float subsample;
-};
-
-bool SaveCacheChecker(int samplingStrategy, float subsample, int iter, int samplingStep);
-bool KDepthWiseLossLtdLeafChecker(float minLossRatio, const float &pLastRoundBestLoss, bst_float lossChg);
-void UpdateBestSplitLossChange(float &pLastRoundBestLoss,
-                               size_t sizeT,
-                               const bst_float &nodesBestLossChg);
-void BbgenSampling(const struct BbgenSamplingParam &bbgenParam,
-                   std::vector<std::mt19937> &rnds,
-                   const std::vector<GradientPair> &gpair,
-                   size_t &pRowIndices,
-                   std::vector<size_t> &rowOffsets);
-float ThresholdGradientCal(const std::vector<GradientPair> &gpair, std::mt19937 &rnd, float subsample);
-void InitSamplingKernel(const std::vector<GradientPair> &gpair,
-                        const DMatrix &fmat,
-                        std::vector<size_t> &rowIndices,
-                        std::vector<size_t> &lastSampling,
-                        struct InitSamplingKernelParam initParam);
-int EffSplitDenomCal(int randomSplitDenom, int maxBin);
-bool SplitSkipChecker(int effSplitDenom, int32_t i, int procGridIdx);
-}  // namespace tree
-}  // namespace xgboost
-
-#endif // XGBOOST_KERNEL_UPDATE_QUANTILE_HIST_KERNEL_H
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/CMakeLists.txt b/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/CMakeLists.txt
deleted file mode 100644
index 19c26ab..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/CMakeLists.txt
+++ /dev/null
@@ -1,51 +0,0 @@
-include(${PROJECT_SOURCE_DIR}/cmake/Utils.cmake)
-
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC")
-
-option(USE_OPENMP "Build with OpenMP support." ON)
-## Force AVX
-option(USE_AVX "Use AVX" ON)
-
-include(CheckSymbolExists)
-include(CheckIncludeFile)
-include(CheckIncludeFileCXX)
-if(USE_AVX)
-    if(MSVC)
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
-    else()
-        EXECUTE_PROCESS(COMMAND uname -a OUTPUT_VARIABLE ARCH)
-        message(STATUS "Current architechture is ${ARCH}")
-
-        if (${ARCH} MATCHES ".*aarch64.*")
-            message(STATUS "Turn on ARM intrinsics")
-            add_compile_options(-DUSE_ARM_INTRINSICS)
-        endif()
-        if (${ARCH} MATCHES ".*x86.*")
-            message(STATUS "Turn on Intel intrinsics")
-            add_compile_options(-mavx -DUSE_INTEL_INTRINSICS)
-        endif()
-    endif()
-endif()
-
-add_library(boostkit_xgboost_kernel SHARED "bbgen.cpp;rabit_intrinsics.cpp;update_quantile_hist_kernel.cpp")
-target_include_directories(boostkit_xgboost_kernel PUBLIC ${PROJECT_SOURCE_DIR}/kernel_include)
-target_include_directories(boostkit_xgboost_kernel PRIVATE ${PROJECT_SOURCE_DIR}/include)
-target_include_directories(boostkit_xgboost_kernel PRIVATE ${PROJECT_SOURCE_DIR}/rabit/include)
-target_include_directories(boostkit_xgboost_kernel PRIVATE ${PROJECT_SOURCE_DIR}/dmlc-core/include)
-
-set_target_properties(
-    boostkit_xgboost_kernel PROPERTIES
-    CXX_STANDARD 14
-    CXX_STANDARD_REQUIRED ON)
-set_output_directory(boostkit_xgboost_kernel ${PROJECT_SOURCE_DIR}/lib)
-
-find_package(Threads REQUIRED)
-if (USE_OPENMP)
-    if (APPLE)
-        # Require CMake 3.16+ on Mac OSX, as previous versions of CMake had trouble locating
-        # OpenMP on Mac. See https://github.com/dmlc/xgboost/pull/5146#issuecomment-568312706
-        cmake_minimum_required(VERSION 3.16)
-    endif (APPLE)
-    find_package(OpenMP REQUIRED)
-    target_link_libraries(boostkit_xgboost_kernel PRIVATE OpenMP::OpenMP_CXX)
-endif(USE_OPENMP)
\ No newline at end of file
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/bbgen.cpp b/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/bbgen.cpp
deleted file mode 100644
index 93f3478..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/bbgen.cpp
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
- * Description: XGBoost Kernel Bbgen source file
- */
-#include "boostkit_xgboost_kernel/bbgen.h"
-
-namespace Bs {
-void FormulaEvaluator::Evaluate(const uint64_t &src, uint64_t &dst, size_t noblocks) const
-{
-}
-
-const Formula &FormulaEvaluator::GetFormula() const
-{
-    return fm_;
-}
-
-EvalPtr CreateEvaluator(const double &prob, const double &tol)
-{
-    return nullptr;
-}
-
-Buffer::Buffer(size_t size)
-{
-}
-
-void * const Buffer::Get() const
-{
-    return nullptr;
-}
-
-size_t Buffer::GetSize() const
-{
-    return 0;
-}
-
-bool GetBit(const Buffer &ptr, size_t idx)
-{
-    return false;
-}
-
-size_t BatchBernGenerator::GetBlockSize() const
-{
-    return 0;
-}
-
-size_t BatchBernGenerator::GetBufferSize(size_t bitsCnt) const
-{
-    return 0;
-}
-
-void BatchBernGenerator::Generate(RngEngine &rng, uint64_t &ptr, size_t size) const
-{
-}
-
-std::unique_ptr<BGeneratorPtr> CreateGenerator(double prob, double tol)
-{
-    return nullptr;
-}
-} // namespace Bs
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/rabit_intrinsics.cpp b/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/rabit_intrinsics.cpp
deleted file mode 100644
index 97d38a7..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/rabit_intrinsics.cpp
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
- * Description: XGBoost Kernel Rabit_Intrinsics source file
- */
-#include "boostkit_xgboost_kernel/rabit_intrinsics.h"
-
-#ifdef USE_INTEL_INTRINSICS
-#include <immintrin.h>
-#endif
-#ifdef USE_ARM_INTRINSICS
-#include <arm_neon.h>
-#endif
-
-namespace rabit {
-namespace op {
-#ifdef USE_INTEL_INTRINSICS
-void ReducerSumFloat(const float &srcRef, float &dstRef, const int len, const MPI::Datatype &dtype)
-{
-}
-
-void ReducerSumDouble(const double &srcRef, double &dstRef, const int len, const MPI::Datatype &dtype)
-{
-}
-#endif
-
-#ifdef USE_ARM_INTRINSICS
-void ReducerSumDouble(const double &srcRef, double &dstRef, int len, const MPI::Datatype &dtype)
-{
-}
-
-void ReducerSumFloat(const float &srcRef, float &dstRef, int len, const MPI::Datatype &dtype)
-{
-}
-#endif
-}  // namespace op
-}  // namespace rabit
\ No newline at end of file
diff --git a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/update_quantile_hist_kernel.cpp b/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/update_quantile_hist_kernel.cpp
deleted file mode 100644
index 8917668..0000000
--- a/ml-xgboost/kernel_include/boostkit_xgboost_kernel_client/update_quantile_hist_kernel.cpp
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
- * Description: XGBoost Kernel Update_Quantile_Hist_Kernel source file
- */
-#include "boostkit_xgboost_kernel/update_quantile_hist_kernel.h"
-
-namespace xgboost {
-namespace tree {
-bool SaveCacheChecker(int samplingStrategy, float subsample, int iter, int samplingStep)
-{
-    return false;
-}
-
-bool KDepthWiseLossLtdLeafChecker(float minLossRatio, const float &pLastRoundBestLoss, bst_float lossChg)
-{
-    return false;
-}
-
-void UpdateBestSplitLossChange(float &pLastRoundBestLoss,
-                               size_t sizeT,
-                               const bst_float &nodesBestLossChg)
-{
-}
-
-void BbgenSampling(const struct BbgenSamplingParam &bbgenParam,
-                   std::vector<std::mt19937> &rnds,
-                   const std::vector<GradientPair> &gpair,
-                   size_t &pRowIndices,
-                   std::vector<size_t> &rowOffsets)
-{
-}
-
-float ThresholdGradientCal(const std::vector<GradientPair> &gpair, std::mt19937 &rnd, float subsample)
-{
-    return 0.0;
-}
-
-int EffSplitDenomCal(int randomSplitDenom, int maxBin)
-{
-    return 0;
-}
-
-bool SplitSkipChecker(int effSplitDenom, int32_t i, int procGridIdx)
-{
-    return false;
-}
-
-void InitSamplingKernel(const std::vector<GradientPair> &gpair,
-                        const DMatrix &fmat,
-                        std::vector<size_t> &rowIndices,
-                        std::vector<size_t> &lastSampling,
-                        struct InitSamplingKernelParam initParam)
-{
-}
-}  // namespace tree
-}  // namespace xgboost
\ No newline at end of file
diff --git a/ml-xgboost/plugin/CMakeLists.txt b/ml-xgboost/plugin/CMakeLists.txt
deleted file mode 100644
index 0c44784..0000000
--- a/ml-xgboost/plugin/CMakeLists.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-if (PLUGIN_LZ4)
-  set(PLUGIN_SOURCES ${PLUGIN_SOURCES}
-    ${xgboost_SOURCE_DIR}/plugin/lz4/sparse_page_lz4_format.cc PARENT_SCOPE)
-endif (PLUGIN_LZ4)
-
-if (PLUGIN_DENSE_PARSER)
-  set(PLUGINS_SOURCES ${PLUGINS_SOURCES}
-    ${xgboost_SOURCE_DIR}/plugin/dense_parser/dense_libsvm.cc PARENT_SCOPE)
-endif (PLUGIN_DENSE_PARSER)
diff --git a/ml-xgboost/plugin/README.md b/ml-xgboost/plugin/README.md
deleted file mode 100644
index 6e115c4..0000000
--- a/ml-xgboost/plugin/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-XGBoost Plugins Modules
-=======================
-
-This folder contains plugin modules to xgboost that can be optionally installed.  The
-plugin system helps us to extend xgboost with additional features, and add experimental
-features that may not yet be ready to be included in the main project.
-
-To include a certain plugin, say ```plugin_a```, you only need to add the following line
-to `xgboost/plugin/CMakeLists.txt`
-``` cmake
-set(PLUGIN_SOURCES ${PLUGIN_SOURCES}
-    ${xgboost_SOURCE_DIR}/plugin/plugin_a.cc PARENT_SCOPE)
-```
-along with specified source file `plugin_a.cc`.
-
-Then rebuild XGBoost with CMake.
-
-Write Your Own Plugin
----------------------
-You can plugin your own modules to xgboost by adding code to this folder,
-without modification to the main code repo.
-The [example](example) folder provides an example to write a plugin.
-
-List of register functions
---------------------------
-A plugin has to register a new functionality to xgboost to be able to use it.
-The register macros available to plugin writers are:
-
- - XGBOOST_REGISTER_METRIC - Register an evaluation metric
- - XGBOOST_REGISTER_GBM - Register a new gradient booster that learns through
-   gradient statistics
- - XGBOOST_REGISTER_OBJECTIVE - Register a new objective function used by xgboost
- - XGBOOST_REGISTER_TREE_UPDATER - Register a new tree-updater which updates
-   the tree given the gradient information
-
-And from dmlc-core:
-
- - DMLC_REGISTER_PARAMETER - Register a set of parameter for a specific usecase
- - DMLC_REGISTER_DATA_PARSER - Register a data parser where the data can be
-   represented by a URL. This is used by DMatrix.
diff --git a/ml-xgboost/plugin/dense_parser/dense_libsvm.cc b/ml-xgboost/plugin/dense_parser/dense_libsvm.cc
deleted file mode 100644
index 0dd2d04..0000000
--- a/ml-xgboost/plugin/dense_parser/dense_libsvm.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file dense_libsvm.cc
- * \brief Plugin to load in libsvm, but fill all the missing entries with zeros.
- *  This plugin is mainly used for benchmark purposes and do not need to be included.
- */
-#include <xgboost/base.h>
-#include <dmlc/data.h>
-#include <memory>
-
-namespace dmlc {
-namespace data {
-
-template<typename IndexType>
-class DensifyParser : public dmlc::Parser<IndexType> {
- public:
-  DensifyParser(dmlc::Parser<IndexType>* parser, uint32_t num_col)
-      : parser_(parser), num_col_(num_col) {
-  }
-
-  void BeforeFirst() override {
-    parser_->BeforeFirst();
-  }
-
-  bool Next() override {
-    if (!parser_->Next()) return false;
-    const RowBlock<IndexType>& batch = parser_->Value();
-    LOG(INFO) << batch.size;
-    dense_index_.resize(num_col_ * batch.size);
-    dense_value_.resize(num_col_ * batch.size);
-    std::fill(dense_value_.begin(), dense_value_.end(), 0.0);
-    offset_.resize(batch.size + 1);
-    offset_[0] = 0;
-
-    for (size_t i = 0; i < batch.size; ++i) {
-      offset_[i + 1] = (i + 1) * num_col_;
-      Row<IndexType> row = batch[i];
-      for (uint32_t j = 0; j < num_col_; ++j) {
-        dense_index_[i * num_col_ + j] = j;
-      }
-      for (unsigned k = 0; k < row.length; ++k) {
-        uint32_t index = row.get_index(k);
-        CHECK_LT(index, num_col_)
-            << "Featuere index larger than num_col";
-        dense_value_[i * num_col_ + index]  = row.get_value(k);
-      }
-    }
-    out_ = batch;
-    out_.index = dmlc::BeginPtr(dense_index_);
-    out_.value = dmlc::BeginPtr(dense_value_);
-    out_.offset = dmlc::BeginPtr(offset_);
-    return true;
-  }
-
-  const dmlc::RowBlock<IndexType>& Value() const override {
-    return out_;
-  }
-
-  size_t BytesRead() const override {
-    return parser_->BytesRead();
-  }
-
- private:
-  RowBlock<IndexType> out_;
-  std::unique_ptr<Parser<IndexType> > parser_;
-  uint32_t num_col_;
-  std::vector<size_t> offset_;
-  std::vector<IndexType> dense_index_;
-  std::vector<xgboost::bst_float> dense_value_;
-};
-
-template<typename IndexType, typename DType = real_t>
-Parser<IndexType> *
-CreateDenseLibSVMParser(const std::string& path,
-                        const std::map<std::string, std::string>& args,
-                        unsigned part_index,
-                        unsigned num_parts) {
-  CHECK_NE(args.count("num_col"), 0) << "expect num_col in dense_libsvm";
-  return new DensifyParser<IndexType>(
-            Parser<IndexType>::Create(path.c_str(), part_index, num_parts, "libsvm"),
-           uint32_t(atoi(args.at("num_col").c_str())));
-}
-}  // namespace data
-
-DMLC_REGISTER_DATA_PARSER(uint32_t, real_t, dense_libsvm,
-  data::CreateDenseLibSVMParser<uint32_t __DMLC_COMMA real_t>);
-}  // namespace dmlc
diff --git a/ml-xgboost/plugin/example/README.md b/ml-xgboost/plugin/example/README.md
deleted file mode 100644
index 0d5ef15..0000000
--- a/ml-xgboost/plugin/example/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-XGBoost Plugin Example
-======================
-This folder provides an example of implementing xgboost plugin.
-
-There are three steps you need to do to add a plugin to xgboost
-- Create your source .cc file, implement a new extension
-  - In this example [custom_obj.cc](custom_obj.cc)
-- Register this extension to xgboost via a registration macro
-  - In this example ```XGBOOST_REGISTER_OBJECTIVE``` in [this line](custom_obj.cc#L78)
-- Add a line to `xgboost/plugin/CMakeLists.txt`:
-```
-set(PLUGINS_SOURCES ${PLUGINS_SOURCES}
-    ${xgboost_SOURCE_DIR}/plugin/example/custom_obj.cc PARENT_SCOPE)
-```
-
-Then you can test this plugin by using ```objective=mylogistic``` parameter.
-
-<!--  LocalWords:  XGBoost
- -->
diff --git a/ml-xgboost/plugin/example/custom_obj.cc b/ml-xgboost/plugin/example/custom_obj.cc
deleted file mode 100644
index 57dc2f4..0000000
--- a/ml-xgboost/plugin/example/custom_obj.cc
+++ /dev/null
@@ -1,94 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file custom_metric.cc
- * \brief This is an example to define plugin of xgboost.
- *  This plugin defines the additional metric function.
- */
-#include <xgboost/base.h>
-#include <xgboost/parameter.h>
-#include <xgboost/objective.h>
-#include <xgboost/json.h>
-
-namespace xgboost {
-namespace obj {
-
-// This is a helpful data structure to define parameters
-// You do not have to use it.
-// see http://dmlc-core.readthedocs.org/en/latest/parameter.html
-// for introduction of this module.
-struct MyLogisticParam : public XGBoostParameter<MyLogisticParam> {
-  float scale_neg_weight;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(MyLogisticParam) {
-    DMLC_DECLARE_FIELD(scale_neg_weight).set_default(1.0f).set_lower_bound(0.0f)
-        .describe("Scale the weight of negative examples by this factor");
-  }
-};
-
-DMLC_REGISTER_PARAMETER(MyLogisticParam);
-
-// Define a customized logistic regression objective in C++.
-// Implement the interface.
-class MyLogistic : public ObjFunction {
- public:
-  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-  void GetGradient(const HostDeviceVector<bst_float> &preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
-    out_gpair->Resize(preds.Size());
-    const std::vector<bst_float>& preds_h = preds.HostVector();
-    std::vector<GradientPair>& out_gpair_h = out_gpair->HostVector();
-    const std::vector<bst_float>& labels_h = info.labels_.HostVector();
-    for (size_t i = 0; i < preds_h.size(); ++i) {
-      bst_float w = info.GetWeight(i);
-      // scale the negative examples!
-      if (labels_h[i] == 0.0f) w *= param_.scale_neg_weight;
-      // logistic transformation
-      bst_float p = 1.0f / (1.0f + std::exp(-preds_h[i]));
-      // this is the gradient
-      bst_float grad = (p - labels_h[i]) * w;
-      // this is the second order gradient
-      bst_float hess = p * (1.0f - p) * w;
-      out_gpair_h.at(i) = GradientPair(grad, hess);
-    }
-  }
-  const char* DefaultEvalMetric() const override {
-    return "error";
-  }
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    // transform margin value to probability.
-    std::vector<bst_float> &preds = io_preds->HostVector();
-    for (auto& pred : preds) {
-      pred = 1.0f / (1.0f + std::exp(-pred));
-    }
-  }
-  bst_float ProbToMargin(bst_float base_score) const override {
-    // transform probability to margin value
-    return -std::log(1.0f / base_score - 1.0f);
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("my_logistic");
-    out["my_logistic_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["my_logistic_param"], &param_);
-  }
-
- private:
-  MyLogisticParam param_;
-};
-
-// Finally register the objective function.
-// After it succeeds you can try use xgboost with objective=mylogistic
-XGBOOST_REGISTER_OBJECTIVE(MyLogistic, "mylogistic")
-.describe("User defined logistic regression plugin")
-.set_body([]() { return new MyLogistic(); });
-
-}  // namespace obj
-}  // namespace xgboost
diff --git a/ml-xgboost/plugin/lz4/sparse_page_lz4_format.cc b/ml-xgboost/plugin/lz4/sparse_page_lz4_format.cc
deleted file mode 100644
index 7e9cce3..0000000
--- a/ml-xgboost/plugin/lz4/sparse_page_lz4_format.cc
+++ /dev/null
@@ -1,333 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file sparse_page_lz4_format.cc
- *  XGBoost Plugin to enable LZ4 compressed format on the external memory pages.
- */
-#include <dmlc/registry.h>
-
-#include <xgboost/data.h>
-#include <xgboost/logging.h>
-#include <xgboost/parameter.h>
-#include <lz4.h>
-#include <lz4hc.h>
-#include "../../src/data/sparse_page_writer.h"
-
-namespace xgboost {
-namespace data {
-
-DMLC_REGISTRY_FILE_TAG(sparse_page_lz4_format);
-
-// array to help compression of decompression.
-template<typename DType>
-class CompressArray {
- public:
-  // the data content.
-  std::vector<DType> data;
-  // Decompression helper
-  // number of chunks
-  inline int num_chunk() const {
-    CHECK_GT(raw_chunks_.size(), 1);
-    return static_cast<int>(raw_chunks_.size() - 1);
-  }
-  // raw bytes
-  inline size_t RawBytes() const {
-    return raw_chunks_.back() * sizeof(DType);
-  }
-  // encoded bytes
-  inline size_t EncodedBytes() const {
-    return encoded_chunks_.back() +
-        (encoded_chunks_.size() + raw_chunks_.size()) * sizeof(bst_uint);
-  }
-  // load the array from file.
-  inline void Read(dmlc::SeekStream* fi);
-  // run decode on chunk_id
-  inline void Decompress(int chunk_id);
-  // Compression helper
-  // initialize the compression chunks
-  inline void InitCompressChunks(const std::vector<bst_uint>& chunk_ptr);
-  // initialize the compression chunks
-  inline void InitCompressChunks(size_t chunk_size, size_t max_nchunk);
-  // run decode on chunk_id, level = -1 means default.
-  inline void Compress(int chunk_id, bool use_hc);
-  // save the output buffer into file.
-  inline void Write(dmlc::Stream* fo);
-
- private:
-  // the chunk split of the data, by number of elements
-  std::vector<bst_uint> raw_chunks_;
-  // the encoded chunk, by number of bytes
-  std::vector<bst_uint> encoded_chunks_;
-  // output buffer of compression.
-  std::vector<std::string> out_buffer_;
-  // input buffer of data.
-  std::string in_buffer_;
-};
-
-template<typename DType>
-inline void CompressArray<DType>::Read(dmlc::SeekStream* fi) {
-  CHECK(fi->Read(&raw_chunks_));
-  CHECK(fi->Read(&encoded_chunks_));
-  size_t buffer_size = encoded_chunks_.back();
-  in_buffer_.resize(buffer_size);
-  CHECK_EQ(fi->Read(dmlc::BeginPtr(in_buffer_), buffer_size), buffer_size);
-  data.resize(raw_chunks_.back());
-}
-
-template<typename DType>
-inline void CompressArray<DType>::Decompress(int chunk_id) {
-  int chunk_size = static_cast<int>(
-      raw_chunks_[chunk_id + 1] - raw_chunks_[chunk_id]) * sizeof(DType);
-  int encoded_size = static_cast<int>(
-      encoded_chunks_[chunk_id + 1] - encoded_chunks_[chunk_id]);
-  // decompress data
-  int src_size = LZ4_decompress_fast(
-      dmlc::BeginPtr(in_buffer_) + encoded_chunks_[chunk_id],
-      reinterpret_cast<char*>(dmlc::BeginPtr(data) + raw_chunks_[chunk_id]),
-      chunk_size);
-  CHECK_EQ(encoded_size, src_size);
-}
-
-template<typename DType>
-inline void CompressArray<DType>::InitCompressChunks(
-    const std::vector<bst_uint>& chunk_ptr) {
-  raw_chunks_ = chunk_ptr;
-  CHECK_GE(raw_chunks_.size(), 2);
-  out_buffer_.resize(raw_chunks_.size() - 1);
-  for (size_t i = 0; i < out_buffer_.size(); ++i) {
-    out_buffer_[i].resize(raw_chunks_[i + 1] - raw_chunks_[i]);
-  }
-}
-
-template<typename DType>
-inline void CompressArray<DType>::InitCompressChunks(size_t chunk_size, size_t max_nchunk) {
-  raw_chunks_.clear();
-  raw_chunks_.push_back(0);
-  size_t min_chunk_size = data.size() / max_nchunk;
-  chunk_size = std::max(min_chunk_size, chunk_size);
-  size_t nstep = data.size() / chunk_size;
-  for (size_t i = 0; i < nstep; ++i) {
-    raw_chunks_.push_back(raw_chunks_.back() + chunk_size);
-    CHECK_LE(raw_chunks_.back(), data.size());
-  }
-  if (nstep == 0) raw_chunks_.push_back(0);
-  raw_chunks_.back() = data.size();
-  CHECK_GE(raw_chunks_.size(), 2);
-  out_buffer_.resize(raw_chunks_.size() - 1);
-  for (size_t i = 0; i < out_buffer_.size(); ++i) {
-    out_buffer_[i].resize(raw_chunks_[i + 1] - raw_chunks_[i]);
-  }
-}
-
-template<typename DType>
-inline void CompressArray<DType>::Compress(int chunk_id, bool use_hc) {
-  CHECK_LT(static_cast<size_t>(chunk_id + 1), raw_chunks_.size());
-  std::string& buf = out_buffer_[chunk_id];
-  size_t raw_chunk_size = (raw_chunks_[chunk_id + 1] - raw_chunks_[chunk_id]) * sizeof(DType);
-  int bound = LZ4_compressBound(raw_chunk_size);
-  CHECK_NE(bound, 0);
-  buf.resize(bound);
-  int encoded_size;
-  if (use_hc) {
-    encoded_size = LZ4_compress_HC(
-        reinterpret_cast<char*>(dmlc::BeginPtr(data) + raw_chunks_[chunk_id]),
-        dmlc::BeginPtr(buf), raw_chunk_size, buf.length(), 9);
-  } else {
-    encoded_size = LZ4_compress_default(
-        reinterpret_cast<char*>(dmlc::BeginPtr(data) + raw_chunks_[chunk_id]),
-        dmlc::BeginPtr(buf), raw_chunk_size, buf.length());
-  }
-  CHECK_NE(encoded_size, 0);
-  CHECK_LE(static_cast<size_t>(encoded_size), buf.length());
-  buf.resize(encoded_size);
-}
-
-template<typename DType>
-inline void CompressArray<DType>::Write(dmlc::Stream* fo) {
-  encoded_chunks_.clear();
-  encoded_chunks_.push_back(0);
-  for (size_t i = 0; i < out_buffer_.size(); ++i) {
-    encoded_chunks_.push_back(encoded_chunks_.back() + out_buffer_[i].length());
-  }
-  fo->Write(raw_chunks_);
-  fo->Write(encoded_chunks_);
-  for (const std::string& buf : out_buffer_) {
-    fo->Write(dmlc::BeginPtr(buf), buf.length());
-  }
-}
-
-template<typename StorageIndex>
-class SparsePageLZ4Format : public SparsePageFormat {
- public:
-  explicit SparsePageLZ4Format(bool use_lz4_hc)
-      : use_lz4_hc_(use_lz4_hc) {
-    raw_bytes_ = raw_bytes_value_ = raw_bytes_index_ = 0;
-    encoded_bytes_value_ = encoded_bytes_index_ = 0;
-    nthread_ = dmlc::GetEnv("XGBOOST_LZ4_DECODE_NTHREAD", 4);
-    nthread_write_ = dmlc::GetEnv("XGBOOST_LZ4_COMPRESS_NTHREAD", 12);
-  }
-  virtual ~SparsePageLZ4Format() {
-    size_t encoded_bytes = raw_bytes_ +  encoded_bytes_value_ + encoded_bytes_index_;
-    raw_bytes_ += raw_bytes_value_ + raw_bytes_index_;
-    if (raw_bytes_ != 0) {
-      LOG(CONSOLE) << "raw_bytes=" << raw_bytes_
-                   << ", encoded_bytes=" << encoded_bytes
-                   << ", ratio=" << double(encoded_bytes) / raw_bytes_
-                   << ", ratio-index=" << double(encoded_bytes_index_) /raw_bytes_index_
-                   << ", ratio-value=" << double(encoded_bytes_value_) /raw_bytes_value_;
-    }
-  }
-
-  bool Read(SparsePage* page, dmlc::SeekStream* fi) override {
-    auto& offset_vec = page->offset.HostVector();
-    auto& data_vec = page->data.HostVector();
-    if (!fi->Read(&(offset_vec))) return false;
-    CHECK_NE(offset_vec.size(), 0) << "Invalid SparsePage file";
-    this->LoadIndexValue(fi);
-
-    data_vec.resize(offset_vec.back());
-    CHECK_EQ(index_.data.size(), value_.data.size());
-    CHECK_EQ(index_.data.size(), data_vec.size());
-    for (size_t i = 0; i < data_vec.size(); ++i) {
-      data_vec[i] = Entry(index_.data[i] + min_index_, value_.data[i]);
-    }
-    return true;
-  }
-
-  bool Read(SparsePage* page,
-            dmlc::SeekStream* fi,
-            const std::vector<bst_uint>& sorted_index_set) override {
-    if (!fi->Read(&disk_offset_)) return false;
-    this->LoadIndexValue(fi);
-    auto& offset_vec = page->offset.HostVector();
-    auto& data_vec = page->data.HostVector();
-    offset_vec.clear();
-    offset_vec.push_back(0);
-    for (bst_uint cid : sorted_index_set) {
-      offset_vec.push_back(
-          offset_vec.back() + disk_offset_[cid + 1] - disk_offset_[cid]);
-    }
-    data_vec.resize(offset_vec.back());
-    CHECK_EQ(index_.data.size(), value_.data.size());
-    CHECK_EQ(index_.data.size(), disk_offset_.back());
-
-    for (size_t i = 0; i < sorted_index_set.size(); ++i) {
-      bst_uint cid = sorted_index_set[i];
-      size_t dst_begin = offset_vec[i];
-      size_t src_begin = disk_offset_[cid];
-      size_t num = disk_offset_[cid + 1] - disk_offset_[cid];
-      for (size_t j = 0; j < num; ++j) {
-        data_vec[dst_begin + j] = Entry(
-            index_.data[src_begin + j] + min_index_, value_.data[src_begin + j]);
-      }
-    }
-    return true;
-  }
-
-  void Write(const SparsePage& page, dmlc::Stream* fo) override {
-    const auto& offset_vec = page.offset.HostVector();
-    const auto& data_vec = page.data.HostVector();
-    CHECK(offset_vec.size() != 0 && offset_vec[0] == 0);
-    CHECK_EQ(offset_vec.back(), data_vec.size());
-    fo->Write(offset_vec);
-    min_index_ = page.base_rowid;
-    fo->Write(&min_index_, sizeof(min_index_));
-    index_.data.resize(data_vec.size());
-    value_.data.resize(data_vec.size());
-
-    for (size_t i = 0; i < data_vec.size(); ++i) {
-      bst_uint idx = data_vec[i].index - min_index_;
-      CHECK_LE(idx, static_cast<bst_uint>(std::numeric_limits<StorageIndex>::max()))
-          << "The storage index is chosen to limited to smaller equal than "
-          << std::numeric_limits<StorageIndex>::max()
-          << "min_index=" << min_index_;
-      index_.data[i] = static_cast<StorageIndex>(idx);
-      value_.data[i] = data_vec[i].fvalue;
-    }
-
-    index_.InitCompressChunks(kChunkSize, kMaxChunk);
-    value_.InitCompressChunks(kChunkSize, kMaxChunk);
-
-    int nindex = index_.num_chunk();
-    int nvalue = value_.num_chunk();
-    int ntotal = nindex + nvalue;
-    #pragma omp parallel for schedule(dynamic, 1)  num_threads(nthread_write_)
-    for (int i = 0; i < ntotal; ++i) {
-      if (i < nindex) {
-        index_.Compress(i, use_lz4_hc_);
-      } else {
-        value_.Compress(i - nindex, use_lz4_hc_);
-      }
-    }
-    index_.Write(fo);
-    value_.Write(fo);
-    // statistics
-    raw_bytes_index_ += index_.RawBytes() * sizeof(bst_uint) / sizeof(StorageIndex);
-    raw_bytes_value_ += value_.RawBytes();
-    encoded_bytes_index_ += index_.EncodedBytes();
-    encoded_bytes_value_ += value_.EncodedBytes();
-    raw_bytes_ += offset_vec.size() * sizeof(size_t);
-  }
-
-  inline void LoadIndexValue(dmlc::SeekStream* fi) {
-    fi->Read(&min_index_, sizeof(min_index_));
-    index_.Read(fi);
-    value_.Read(fi);
-
-    int nindex = index_.num_chunk();
-    int nvalue = value_.num_chunk();
-    int ntotal = nindex + nvalue;
-    #pragma omp parallel for schedule(dynamic, 1) num_threads(nthread_)
-    for (int i = 0; i < ntotal; ++i) {
-      if (i < nindex) {
-        index_.Decompress(i);
-      } else {
-        value_.Decompress(i - nindex);
-      }
-    }
-  }
-
- private:
-  // default chunk size.
-  static const size_t kChunkSize = 64 << 10UL;
-  // maximum chunk size.
-  static const size_t kMaxChunk = 128;
-  // bool whether use hc
-  bool use_lz4_hc_;
-  // number of threads
-  int nthread_;
-  // number of writing threads
-  int nthread_write_;
-  // raw bytes
-  size_t raw_bytes_, raw_bytes_index_, raw_bytes_value_;
-  // encoded bytes
-  size_t encoded_bytes_index_, encoded_bytes_value_;
-  /*! \brief minimum index value */
-  uint32_t min_index_;
-  /*! \brief external memory column offset */
-  std::vector<size_t> disk_offset_;
-  // internal index
-  CompressArray<StorageIndex> index_;
-  // value set.
-  CompressArray<bst_float> value_;
-};
-
-XGBOOST_REGISTER_SPARSE_PAGE_FORMAT(lz4)
-.describe("Apply LZ4 binary data compression for ext memory.")
-.set_body([]() {
-    return new SparsePageLZ4Format<bst_uint>(false);
-  });
-
-XGBOOST_REGISTER_SPARSE_PAGE_FORMAT(lz4hc)
-.describe("Apply LZ4 binary data compression(high compression ratio) for ext memory.")
-.set_body([]() {
-    return new SparsePageLZ4Format<bst_uint>(true);
-  });
-
-XGBOOST_REGISTER_SPARSE_PAGE_FORMAT(lz4i16hc)
-.describe("Apply LZ4 binary data compression(16 bit index mode) for ext memory.")
-.set_body([]() {
-    return new SparsePageLZ4Format<uint16_t>(true);
-  });
-
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/plugin/updater_gpu/README.md b/ml-xgboost/plugin/updater_gpu/README.md
deleted file mode 100644
index 4001045..0000000
--- a/ml-xgboost/plugin/updater_gpu/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# XGBoost GPU algorithms
-
-GPU algorithms are no longer a plugin and are included in official releases. [See documentation for more details](https://xgboost.readthedocs.io/en/latest/gpu/).
diff --git a/ml-xgboost/python-package/.gitignore b/ml-xgboost/python-package/.gitignore
deleted file mode 100644
index d765c67..0000000
--- a/ml-xgboost/python-package/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-build
-dist
-*.egg*
\ No newline at end of file
diff --git a/ml-xgboost/python-package/.pylintrc b/ml-xgboost/python-package/.pylintrc
deleted file mode 100644
index 4c8f890..0000000
--- a/ml-xgboost/python-package/.pylintrc
+++ /dev/null
@@ -1,26 +0,0 @@
-[MASTER]
-
-ignore=tests
-
-extension-pkg-whitelist=numpy
-
-disable=unexpected-special-method-signature,too-many-nested-blocks,useless-object-inheritance,import-outside-toplevel,unsubscriptable-object,attribute-defined-outside-init
-
-dummy-variables-rgx=(unused|)_.*
-
-reports=no
-
-[BASIC]
-
-# Enforce naming convention
-const-naming-style=UPPER_CASE
-class-naming-style=PascalCase
-function-naming-style=snake_case
-method-naming-style=snake_case
-attr-naming-style=snake_case
-argument-naming-style=snake_case
-variable-naming-style=snake_case
-class-attribute-naming-style=snake_case
-
-# Allow single-letter variables
-variable-rgx=[a-zA-Z_][a-z0-9_]{0,30}$
diff --git a/ml-xgboost/python-package/MANIFEST.in b/ml-xgboost/python-package/MANIFEST.in
deleted file mode 100644
index 1bf9ad3..0000000
--- a/ml-xgboost/python-package/MANIFEST.in
+++ /dev/null
@@ -1,11 +0,0 @@
-include *.rst
-include xgboost/VERSION
-include xgboost/CMakeLists.txt
-
-recursive-include xgboost *
-recursive-include xgboost/include *
-recursive-include xgboost/src *
-recursive-include xgboost/rabit *
-recursive-include xgboost/dmlc-core *
-
-global-exclude *.py[oc]
\ No newline at end of file
diff --git a/ml-xgboost/python-package/README.rst b/ml-xgboost/python-package/README.rst
deleted file mode 100644
index 1fc0bb5..0000000
--- a/ml-xgboost/python-package/README.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-======================
-XGBoost Python Package
-======================
-
-|PyPI version|
-
-Installation
-============
-
-From `PyPI <https://pypi.python.org/pypi/xgboost>`_
----------------------------------------------------
-
-For a stable version, install using ``pip``::
-
-    pip install xgboost
-
-.. |PyPI version| image:: https://badge.fury.io/py/xgboost.svg
-   :target: http://badge.fury.io/py/xgboost
-
-For building from source, see `build <https://xgboost.readthedocs.io/en/latest/build.html>`_.
diff --git a/ml-xgboost/python-package/setup.cfg b/ml-xgboost/python-package/setup.cfg
deleted file mode 100644
index 5aef279..0000000
--- a/ml-xgboost/python-package/setup.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-[metadata]
-description-file = README.rst
diff --git a/ml-xgboost/python-package/setup.py b/ml-xgboost/python-package/setup.py
deleted file mode 100644
index 0e135b2..0000000
--- a/ml-xgboost/python-package/setup.py
+++ /dev/null
@@ -1,319 +0,0 @@
-"""Setup xgboost package."""
-import os
-import shutil
-import subprocess
-import logging
-import distutils
-import sys
-from platform import system
-from setuptools import setup, find_packages, Extension
-from setuptools.command import build_ext, sdist, install_lib, install
-
-# You can't use `pip install .` as pip copies setup.py to a temporary
-# directory, parent directory is no longer reachable (isolated build) .
-CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
-sys.path.insert(0, CURRENT_DIR)
-
-# Options only effect `python setup.py install`, building `bdist_wheel`
-# requires using CMake directly.
-USER_OPTIONS = {
-    'use-openmp': (None, 'Build with OpenMP support.', 1),
-    'use-cuda':   (None, 'Build with GPU acceleration.', 0),
-    'use-nccl':   (None, 'Build with NCCL to enable distributed GPU support.', 0),
-    'build-with-shared-nccl': (None, 'Build with shared NCCL library.', 0),
-    'hide-cxx-symbols':       (None, 'Hide all C++ symbols during build.', 1),
-    'use-hdfs':   (None, 'Build with HDFS support', 0),
-    'use-azure':  (None, 'Build with AZURE support.', 0),
-    'use-s3':     (None, 'Build with S3 support', 0),
-    'plugin-lz4': (None, 'Build lz4 plugin.', 0),
-    'plugin-dense-parser': (None, 'Build dense parser plugin.', 0)
-}
-
-NEED_CLEAN_TREE = set()
-NEED_CLEAN_FILE = set()
-BUILD_TEMP_DIR = None
-
-
-def lib_name():
-    '''Return platform dependent shared object name.'''
-    if system() == 'Linux' or system().upper().endswith('BSD'):
-        name = 'libxgboost.so'
-    elif system() == 'Darwin':
-        name = 'libxgboost.dylib'
-    elif system() == 'Windows':
-        name = 'xgboost.dll'
-    return name
-
-
-def copy_tree(src_dir, target_dir):
-    '''Copy source tree into build directory.'''
-    def clean_copy_tree(src, dst):
-        distutils.dir_util.copy_tree(src, dst)
-        NEED_CLEAN_TREE.add(os.path.abspath(dst))
-
-    def clean_copy_file(src, dst):
-        distutils.file_util.copy_file(src, dst)
-        NEED_CLEAN_FILE.add(os.path.abspath(dst))
-
-    src = os.path.join(src_dir, 'src')
-    inc = os.path.join(src_dir, 'include')
-    dmlc_core = os.path.join(src_dir, 'dmlc-core')
-    rabit = os.path.join(src_dir, 'rabit')
-    cmake = os.path.join(src_dir, 'cmake')
-    plugin = os.path.join(src_dir, 'plugin')
-
-    clean_copy_tree(src, os.path.join(target_dir, 'src'))
-    clean_copy_tree(inc, os.path.join(target_dir, 'include'))
-    clean_copy_tree(dmlc_core, os.path.join(target_dir, 'dmlc-core'))
-    clean_copy_tree(rabit, os.path.join(target_dir, 'rabit'))
-    clean_copy_tree(cmake, os.path.join(target_dir, 'cmake'))
-    clean_copy_tree(plugin, os.path.join(target_dir, 'plugin'))
-
-    cmake_list = os.path.join(src_dir, 'CMakeLists.txt')
-    clean_copy_file(cmake_list, os.path.join(target_dir, 'CMakeLists.txt'))
-    lic = os.path.join(src_dir, 'LICENSE')
-    clean_copy_file(lic, os.path.join(target_dir, 'LICENSE'))
-
-
-def clean_up():
-    '''Removed copied files.'''
-    for path in NEED_CLEAN_TREE:
-        shutil.rmtree(path)
-    for path in NEED_CLEAN_FILE:
-        os.remove(path)
-
-
-class CMakeExtension(Extension):  # pylint: disable=too-few-public-methods
-    '''Wrapper for extension'''
-    def __init__(self, name):
-        super().__init__(name=name, sources=[])
-
-
-class BuildExt(build_ext.build_ext):  # pylint: disable=too-many-ancestors
-    '''Custom build_ext command using CMake.'''
-
-    logger = logging.getLogger('XGBoost build_ext')
-
-    # pylint: disable=too-many-arguments,no-self-use
-    def build(self, src_dir, build_dir, generator, build_tool=None, use_omp=1):
-        '''Build the core library with CMake.'''
-        cmake_cmd = ['cmake', src_dir, generator]
-
-        for k, v in USER_OPTIONS.items():
-            arg = k.replace('-', '_').upper()
-            value = str(v[2])
-            cmake_cmd.append('-D' + arg + '=' + value)
-            if k == 'USE_OPENMP' and use_omp == 0:
-                continue
-
-        self.logger.info('Run CMake command: %s', str(cmake_cmd))
-        subprocess.check_call(cmake_cmd, cwd=build_dir)
-
-        if system() != 'Windows':
-            nproc = os.cpu_count()
-            subprocess.check_call([build_tool, '-j' + str(nproc)],
-                                  cwd=build_dir)
-        else:
-            subprocess.check_call(['cmake', '--build', '.',
-                                   '--config', 'Release'], cwd=build_dir)
-
-    def build_cmake_extension(self):
-        '''Configure and build using CMake'''
-        src_dir = 'xgboost'
-        try:
-            copy_tree(os.path.join(CURRENT_DIR, os.path.pardir),
-                      os.path.join(self.build_temp, src_dir))
-        except Exception:  # pylint: disable=broad-except
-            copy_tree(src_dir, os.path.join(self.build_temp, src_dir))
-        build_dir = self.build_temp
-        global BUILD_TEMP_DIR  # pylint: disable=global-statement
-        BUILD_TEMP_DIR = build_dir
-        libxgboost = os.path.abspath(
-            os.path.join(CURRENT_DIR, os.path.pardir, 'lib', lib_name()))
-
-        if os.path.exists(libxgboost):
-            self.logger.info('Found shared library, skipping build.')
-            return
-
-        self.logger.info('Building from source. %s', libxgboost)
-        if not os.path.exists(build_dir):
-            os.mkdir(build_dir)
-        if shutil.which('ninja'):
-            build_tool = 'ninja'
-        else:
-            build_tool = 'make'
-
-        if system() == 'Windows':
-            # Pick up from LGB, just test every possible tool chain.
-            for vs in ('-GVisual Studio 16 2019', '-GVisual Studio 15 2017',
-                       '-GVisual Studio 14 2015', '-GMinGW Makefiles'):
-                try:
-                    self.build(src_dir, build_dir, vs)
-                    self.logger.info(
-                        '%s is used for building Windows distribution.', vs)
-                    break
-                except subprocess.CalledProcessError:
-                    continue
-        else:
-            gen = '-GNinja' if build_tool == 'ninja' else '-GUnix Makefiles'
-            try:
-                self.build(src_dir, build_dir, gen, build_tool, use_omp=1)
-            except subprocess.CalledProcessError:
-                self.logger.warning('Disabling OpenMP support.')
-                self.build(src_dir, build_dir, gen, build_tool, use_omp=0)
-
-    def build_extension(self, ext):
-        '''Override the method for dispatching.'''
-        if isinstance(ext, CMakeExtension):
-            self.build_cmake_extension()
-        else:
-            super().build_extension(ext)
-
-    def copy_extensions_to_source(self):
-        '''Dummy override.  Invoked during editable installation.  Our binary
-        should available in `lib`.
-
-        '''
-        if not os.path.exists(
-                os.path.join(CURRENT_DIR, os.path.pardir, 'lib', lib_name())):
-            raise ValueError('For using editable installation, please ' +
-                             'build the shared object first with CMake.')
-
-
-class Sdist(sdist.sdist):       # pylint: disable=too-many-ancestors
-    '''Copy c++ source into Python directory.'''
-    logger = logging.getLogger('xgboost sdist')
-
-    def run(self):
-        copy_tree(os.path.join(CURRENT_DIR, os.path.pardir),
-                  os.path.join(CURRENT_DIR, 'xgboost'))
-        libxgboost = os.path.join(
-            CURRENT_DIR, os.path.pardir, 'lib', lib_name())
-        if os.path.exists(libxgboost):
-            self.logger.warning(
-                'Found shared library, removing to avoid being included in source distribution.'
-            )
-            os.remove(libxgboost)
-        super().run()
-
-
-class InstallLib(install_lib.install_lib):
-    '''Copy shared object into installation directory.'''
-    logger = logging.getLogger('xgboost install_lib')
-
-    def install(self):
-        outfiles = super().install()
-        lib_dir = os.path.join(self.install_dir, 'xgboost', 'lib')
-        if not os.path.exists(lib_dir):
-            os.mkdir(lib_dir)
-        dst = os.path.join(self.install_dir, 'xgboost', 'lib', lib_name())
-
-        global BUILD_TEMP_DIR   # pylint: disable=global-statement
-        libxgboost_path = lib_name()
-
-        dft_lib_dir = os.path.join(CURRENT_DIR, os.path.pardir, 'lib')
-        build_dir = os.path.join(BUILD_TEMP_DIR, 'xgboost', 'lib')
-
-        if os.path.exists(os.path.join(dft_lib_dir, libxgboost_path)):
-            # The library is built by CMake directly
-            src = os.path.join(dft_lib_dir, libxgboost_path)
-        else:
-            # The library is built by setup.py
-            src = os.path.join(build_dir, libxgboost_path)
-        self.logger.info('Installing shared library: %s', src)
-        dst, _ = self.copy_file(src, dst)
-        outfiles.append(dst)
-        return outfiles
-
-
-class Install(install.install):  # pylint: disable=too-many-instance-attributes
-    '''An interface to install command, accepting XGBoost specific
-    arguments.
-
-    '''
-    user_options = install.install.user_options + list(
-        (k, v[0], v[1]) for k, v in USER_OPTIONS.items())
-
-    def initialize_options(self):
-        super().initialize_options()
-        self.use_openmp = 1
-        self.use_cuda = 0
-        self.use_nccl = 0
-        self.build_with_shared_nccl = 0
-        self.hide_cxx_symbols = 1
-
-        self.use_hdfs = 0
-        self.use_azure = 0
-        self.use_s3 = 0
-
-        self.plugin_lz4 = 0
-        self.plugin_dense_parser = 0
-
-    def run(self):
-        for k, v in USER_OPTIONS.items():
-            arg = k.replace('-', '_')
-            if hasattr(self, arg):
-                USER_OPTIONS[k] = (v[0], v[1], getattr(self, arg))
-        super().run()
-
-
-if __name__ == '__main__':
-    # Supported commands:
-    # From internet:
-    # - pip install xgboost
-    # - pip install --no-binary :all: xgboost
-
-    # From source tree `xgboost/python-package`:
-    # - python setup.py build
-    # - python setup.py build_ext
-    # - python setup.py install
-    # - python setup.py sdist       && pip install <sdist-name>
-    # - python setup.py bdist_wheel && pip install <wheel-name>
-
-    # When XGBoost is compiled directly with CMake:
-    # - pip install . -e
-    # - python setup.py develop   # same as above
-    logging.basicConfig(level=logging.INFO)
-    setup(name='xgboost',
-          version=open(os.path.join(
-              CURRENT_DIR, 'xgboost/VERSION')).read().strip(),
-          description="XGBoost Python Package",
-          long_description=open(os.path.join(CURRENT_DIR, 'README.rst'),
-                                encoding='utf-8').read(),
-          install_requires=[
-              'numpy',
-              'scipy',
-          ],
-          ext_modules=[CMakeExtension('libxgboost')],
-          cmdclass={
-              'build_ext': BuildExt,
-              'sdist': Sdist,
-              'install_lib': InstallLib,
-              'install': Install
-          },
-          extras_require={
-              'pandas': ['pandas'],
-              'scikit-learn': ['scikit-learn'],
-              'dask': ['dask', 'pandas', 'distributed'],
-              'datatable': ['datatable'],
-              'plotting': ['graphviz', 'matplotlib']
-          },
-          maintainer='Hyunsu Cho',
-          maintainer_email='chohyu01@cs.washington.edu',
-          zip_safe=False,
-          packages=find_packages(),
-          include_package_data=True,
-          license='Apache-2.0',
-          classifiers=['License :: OSI Approved :: Apache Software License',
-                       'Development Status :: 5 - Production/Stable',
-                       'Operating System :: OS Independent',
-                       'Programming Language :: Python',
-                       'Programming Language :: Python :: 3',
-                       'Programming Language :: Python :: 3.5',
-                       'Programming Language :: Python :: 3.6',
-                       'Programming Language :: Python :: 3.7',
-                       'Programming Language :: Python :: 3.8'],
-          python_requires='>=3.5',
-          url='https://github.com/dmlc/xgboost')
-
-    clean_up()
diff --git a/ml-xgboost/python-package/xgboost/VERSION b/ml-xgboost/python-package/xgboost/VERSION
deleted file mode 100644
index 9084fa2..0000000
--- a/ml-xgboost/python-package/xgboost/VERSION
+++ /dev/null
@@ -1 +0,0 @@
-1.1.0
diff --git a/ml-xgboost/python-package/xgboost/__init__.py b/ml-xgboost/python-package/xgboost/__init__.py
deleted file mode 100644
index f4724e9..0000000
--- a/ml-xgboost/python-package/xgboost/__init__.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# coding: utf-8
-"""XGBoost: eXtreme Gradient Boosting library.
-
-Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
-"""
-
-import os
-import sys
-import warnings
-
-from .core import DMatrix, DeviceQuantileDMatrix, Booster
-from .training import train, cv
-from . import rabit  # noqa
-from . import tracker  # noqa
-from .tracker import RabitTracker  # noqa
-from . import dask
-
-try:
-    from .sklearn import XGBModel, XGBClassifier, XGBRegressor, XGBRanker
-    from .sklearn import XGBRFClassifier, XGBRFRegressor
-    from .plotting import plot_importance, plot_tree, to_graphviz
-except ImportError:
-    pass
-
-if sys.version_info[:2] == (3, 5):
-    warnings.warn(
-        'Python 3.5 support is deprecated; XGBoost will require Python 3.6+ in the near future. ' +
-        'Consider upgrading to Python 3.6+.',
-        FutureWarning)
-
-VERSION_FILE = os.path.join(os.path.dirname(__file__), 'VERSION')
-with open(VERSION_FILE) as f:
-    __version__ = f.read().strip()
-
-__all__ = ['DMatrix', 'DeviceQuantileDMatrix', 'Booster',
-           'train', 'cv',
-           'RabitTracker',
-           'XGBModel', 'XGBClassifier', 'XGBRegressor', 'XGBRanker',
-           'XGBRFClassifier', 'XGBRFRegressor',
-           'plot_importance', 'plot_tree', 'to_graphviz', 'dask']
diff --git a/ml-xgboost/python-package/xgboost/callback.py b/ml-xgboost/python-package/xgboost/callback.py
deleted file mode 100644
index 14b4aef..0000000
--- a/ml-xgboost/python-package/xgboost/callback.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# coding: utf-8
-# pylint: disable=invalid-name, too-many-statements
-"""Training Library containing training routines."""
-
-from . import rabit
-from .core import EarlyStopException
-
-
-def _get_callback_context(env):
-    """return whether the current callback context is cv or train"""
-    if env.model is not None and env.cvfolds is None:
-        context = 'train'
-    elif env.model is None and env.cvfolds is not None:
-        context = 'cv'
-    return context
-
-
-def _fmt_metric(value, show_stdv=True):
-    """format metric string"""
-    if len(value) == 2:
-        return '{0}:{1:.5f}'.format(value[0], value[1])
-    if len(value) == 3:
-        if show_stdv:
-            return  '{0}:{1:.5f}+{2:.5f}'.format(value[0], value[1], value[2])
-        return '{0}:{1:.5f}'.format(value[0], value[1])
-    raise ValueError("wrong metric value")
-
-
-def print_evaluation(period=1, show_stdv=True):
-    """Create a callback that print evaluation result.
-
-    We print the evaluation results every **period** iterations
-    and on the first and the last iterations.
-
-    Parameters
-    ----------
-    period : int
-        The period to log the evaluation results
-
-    show_stdv : bool, optional
-         Whether show stdv if provided
-
-    Returns
-    -------
-    callback : function
-        A callback that print evaluation every period iterations.
-    """
-    def callback(env):
-        """internal function"""
-        if env.rank != 0 or (not env.evaluation_result_list) or period is False or period == 0:
-            return
-        i = env.iteration
-        if i % period == 0 or i + 1 == env.begin_iteration or i + 1 == env.end_iteration:
-            msg = '\t'.join([_fmt_metric(x, show_stdv) for x in env.evaluation_result_list])
-            rabit.tracker_print('[%d]\t%s\n' % (i, msg))
-    return callback
-
-
-def record_evaluation(eval_result):
-    """Create a call back that records the evaluation history into **eval_result**.
-
-    Parameters
-    ----------
-    eval_result : dict
-       A dictionary to store the evaluation results.
-
-    Returns
-    -------
-    callback : function
-        The requested callback function.
-    """
-    if not isinstance(eval_result, dict):
-        raise TypeError('eval_result has to be a dictionary')
-    eval_result.clear()
-
-    def init(env):
-        """internal function"""
-        for k, _ in env.evaluation_result_list:
-            pos = k.index('-')
-            key = k[:pos]
-            metric = k[pos + 1:]
-            if key not in eval_result:
-                eval_result[key] = {}
-            if metric not in eval_result[key]:
-                eval_result[key][metric] = []
-
-    def callback(env):
-        """internal function"""
-        if not eval_result:
-            init(env)
-        for k, v in env.evaluation_result_list:
-            pos = k.index('-')
-            key = k[:pos]
-            metric = k[pos + 1:]
-            eval_result[key][metric].append(v)
-    return callback
-
-
-def reset_learning_rate(learning_rates):
-    """Reset learning rate after iteration 1
-
-    NOTE: the initial learning rate will still take in-effect on first iteration.
-
-    Parameters
-    ----------
-    learning_rates: list or function
-        List of learning rate for each boosting round
-        or a customized function that calculates eta in terms of
-        current number of round and the total number of boosting round (e.g.
-        yields learning rate decay)
-
-        * list ``l``: ``eta = l[boosting_round]``
-        * function ``f``: ``eta = f(boosting_round, num_boost_round)``
-
-    Returns
-    -------
-    callback : function
-        The requested callback function.
-    """
-    def get_learning_rate(i, n, learning_rates):
-        """helper providing the learning rate"""
-        if isinstance(learning_rates, list):
-            if len(learning_rates) != n:
-                raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
-            new_learning_rate = learning_rates[i]
-        else:
-            new_learning_rate = learning_rates(i, n)
-        return new_learning_rate
-
-    def callback(env):
-        """internal function"""
-        context = _get_callback_context(env)
-
-        if context == 'train':
-            bst, i, n = env.model, env.iteration, env.end_iteration
-            bst.set_param(
-                'learning_rate', get_learning_rate(i, n, learning_rates))
-        elif context == 'cv':
-            i, n = env.iteration, env.end_iteration
-            for cvpack in env.cvfolds:
-                bst = cvpack.bst
-                bst.set_param(
-                    'learning_rate', get_learning_rate(i, n, learning_rates))
-
-    callback.before_iteration = False
-    return callback
-
-
-def early_stop(stopping_rounds, maximize=False, verbose=True):
-    """Create a callback that activates early stoppping.
-
-    Validation error needs to decrease at least
-    every **stopping_rounds** round(s) to continue training.
-    Requires at least one item in **evals**.
-    If there's more than one, will use the last.
-    Returns the model from the last iteration (not the best one).
-    If early stopping occurs, the model will have three additional fields:
-    ``bst.best_score``, ``bst.best_iteration`` and ``bst.best_ntree_limit``.
-    (Use ``bst.best_ntree_limit`` to get the correct value if ``num_parallel_tree``
-    and/or ``num_class`` appears in the parameters)
-
-    Parameters
-    ----------
-    stopp_rounds : int
-       The stopping rounds before the trend occur.
-
-    maximize : bool
-        Whether to maximize evaluation metric.
-
-    verbose : optional, bool
-        Whether to print message about early stopping information.
-
-    Returns
-    -------
-    callback : function
-        The requested callback function.
-    """
-    state = {}
-
-    def init(env):
-        """internal function"""
-        bst = env.model
-
-        if not env.evaluation_result_list:
-            raise ValueError('For early stopping you need at least one set in evals.')
-        if len(env.evaluation_result_list) > 1 and verbose:
-            msg = ("Multiple eval metrics have been passed: "
-                   "'{0}' will be used for early stopping.\n\n")
-            rabit.tracker_print(msg.format(env.evaluation_result_list[-1][0]))
-        maximize_metrics = ('auc', 'aucpr', 'map', 'ndcg')
-        maximize_at_n_metrics = ('auc@', 'aucpr@', 'map@', 'ndcg@')
-        maximize_score = maximize
-        metric_label = env.evaluation_result_list[-1][0]
-        metric = metric_label.split('-', 1)[-1]
-
-        if any(metric.startswith(x) for x in maximize_at_n_metrics):
-            maximize_score = True
-
-        if any(metric.split(":")[0] == x for x in maximize_metrics):
-            maximize_score = True
-
-        if verbose and env.rank == 0:
-            msg = "Will train until {} hasn't improved in {} rounds.\n"
-            rabit.tracker_print(msg.format(metric_label, stopping_rounds))
-
-        state['maximize_score'] = maximize_score
-        state['best_iteration'] = 0
-        if maximize_score:
-            state['best_score'] = float('-inf')
-        else:
-            state['best_score'] = float('inf')
-        msg = '[%d]\t%s' % (
-            env.iteration,
-            '\t'.join([_fmt_metric(x) for x in env.evaluation_result_list]))
-        state['best_msg'] = msg
-
-        if bst is not None:
-            if bst.attr('best_score') is not None:
-                state['best_score'] = float(bst.attr('best_score'))
-                state['best_iteration'] = int(bst.attr('best_iteration'))
-                state['best_msg'] = bst.attr('best_msg')
-            else:
-                bst.set_attr(best_iteration=str(state['best_iteration']))
-                bst.set_attr(best_score=str(state['best_score']))
-        else:
-            assert env.cvfolds is not None
-
-    def callback(env):
-        """internal function"""
-        if not state:
-            init(env)
-        score = env.evaluation_result_list[-1][1]
-        best_score = state['best_score']
-        best_iteration = state['best_iteration']
-        maximize_score = state['maximize_score']
-        if (maximize_score and score > best_score) or \
-                (not maximize_score and score < best_score):
-            msg = '[%d]\t%s' % (
-                env.iteration,
-                '\t'.join([_fmt_metric(x) for x in env.evaluation_result_list]))
-            state['best_msg'] = msg
-            state['best_score'] = score
-            state['best_iteration'] = env.iteration
-            # save the property to attributes, so they will occur in checkpoint.
-            if env.model is not None:
-                env.model.set_attr(best_score=str(state['best_score']),
-                                   best_iteration=str(state['best_iteration']),
-                                   best_msg=state['best_msg'])
-        elif env.iteration - best_iteration >= stopping_rounds:
-            best_msg = state['best_msg']
-            if verbose and env.rank == 0:
-                msg = "Stopping. Best iteration:\n{}\n\n"
-                rabit.tracker_print(msg.format(best_msg))
-            raise EarlyStopException(best_iteration)
-    return callback
diff --git a/ml-xgboost/python-package/xgboost/compat.py b/ml-xgboost/python-package/xgboost/compat.py
deleted file mode 100644
index 23c36a4..0000000
--- a/ml-xgboost/python-package/xgboost/compat.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# coding: utf-8
-# pylint: disable= invalid-name,  unused-import
-"""For compatibility and optional dependencies."""
-import abc
-import os
-import sys
-from pathlib import PurePath
-
-import numpy as np
-
-assert (sys.version_info[0] == 3), 'Python 2 is no longer supported.'
-
-# pylint: disable=invalid-name, redefined-builtin
-STRING_TYPES = (str,)
-
-
-def py_str(x):
-    """convert c string back to python string"""
-    return x.decode('utf-8')
-
-
-###############################################################################
-# START NUMPY PATHLIB ATTRIBUTION
-###############################################################################
-# os.PathLike compatibility used in  Numpy:
-# https://github.com/numpy/numpy/tree/v1.17.0
-# Attribution:
-# https://github.com/numpy/numpy/blob/v1.17.0/numpy/compat/py3k.py#L188-L247
-# Backport os.fs_path, os.PathLike, and PurePath.__fspath__
-if sys.version_info[:2] >= (3, 6):
-    os_fspath = os.fspath
-    os_PathLike = os.PathLike
-else:
-    def _PurePath__fspath__(self):
-        return str(self)
-
-    class os_PathLike(abc.ABC):
-        """Abstract base class for implementing the file system path protocol."""
-
-        @abc.abstractmethod
-        def __fspath__(self):
-            """Return the file system path representation of the object."""
-            raise NotImplementedError
-
-        @classmethod
-        def __subclasshook__(cls, subclass):
-            if issubclass(subclass, PurePath):
-                return True
-            return hasattr(subclass, '__fspath__')
-
-    def os_fspath(path):
-        """Return the path representation of a path-like object.
-        If str or bytes is passed in, it is returned unchanged. Otherwise the
-        os.PathLike interface is used to get the path representation. If the
-        path representation is not str or bytes, TypeError is raised. If the
-        provided path is not str, bytes, or os.PathLike, TypeError is raised.
-        """
-        if isinstance(path, (str, bytes)):
-            return path
-
-        # Work from the object's type to match method resolution of other magic
-        # methods.
-        path_type = type(path)
-        try:
-            path_repr = path_type.__fspath__(path)
-        except AttributeError:
-            if hasattr(path_type, '__fspath__'):
-                raise
-            if issubclass(path_type, PurePath):
-                return _PurePath__fspath__(path)
-            raise TypeError("expected str, bytes or os.PathLike object, "
-                            "not " + path_type.__name__)
-        if isinstance(path_repr, (str, bytes)):
-            return path_repr
-        raise TypeError("expected {}.__fspath__() to return str or bytes, "
-                        "not {}".format(path_type.__name__,
-                                        type(path_repr).__name__))
-###############################################################################
-# END NUMPY PATHLIB ATTRIBUTION
-###############################################################################
-
-
-def lazy_isinstance(instance, module, name):
-    '''Use string representation to identify a type.'''
-    module = type(instance).__module__ == module
-    name = type(instance).__name__ == name
-    return module and name
-
-
-# pandas
-try:
-    from pandas import DataFrame, Series
-    from pandas import MultiIndex, Int64Index
-    from pandas import concat as pandas_concat
-
-    PANDAS_INSTALLED = True
-except ImportError:
-
-    MultiIndex = object
-    Int64Index = object
-    DataFrame = object
-    Series = object
-    pandas_concat = None
-    PANDAS_INSTALLED = False
-
-# cudf
-try:
-    from cudf import DataFrame as CUDF_DataFrame
-    from cudf import Series as CUDF_Series
-    from cudf import MultiIndex as CUDF_MultiIndex
-    from cudf import concat as CUDF_concat
-    CUDF_INSTALLED = True
-except ImportError:
-    CUDF_DataFrame = object
-    CUDF_Series = object
-    CUDF_MultiIndex = object
-    CUDF_INSTALLED = False
-    CUDF_concat = None
-
-# sklearn
-try:
-    from sklearn.base import BaseEstimator
-    from sklearn.base import RegressorMixin, ClassifierMixin
-    from sklearn.preprocessing import LabelEncoder
-
-    try:
-        from sklearn.model_selection import KFold, StratifiedKFold
-    except ImportError:
-        from sklearn.cross_validation import KFold, StratifiedKFold
-
-    SKLEARN_INSTALLED = True
-
-    XGBModelBase = BaseEstimator
-    XGBRegressorBase = RegressorMixin
-    XGBClassifierBase = ClassifierMixin
-
-    XGBKFold = KFold
-    XGBStratifiedKFold = StratifiedKFold
-
-    class XGBoostLabelEncoder(LabelEncoder):
-        '''Label encoder with JSON serialization methods.'''
-        def to_json(self):
-            '''Returns a JSON compatible dictionary'''
-            meta = dict()
-            for k, v in self.__dict__.items():
-                if isinstance(v, np.ndarray):
-                    meta[k] = v.tolist()
-                else:
-                    meta[k] = v
-            return meta
-
-        def from_json(self, doc):
-            # pylint: disable=attribute-defined-outside-init
-            '''Load the encoder back from a JSON compatible dict.'''
-            meta = dict()
-            for k, v in doc.items():
-                if k == 'classes_':
-                    self.classes_ = np.array(v)
-                    continue
-                meta[k] = v
-            self.__dict__.update(meta)
-except ImportError:
-    SKLEARN_INSTALLED = False
-
-    # used for compatibility without sklearn
-    XGBModelBase = object
-    XGBClassifierBase = object
-    XGBRegressorBase = object
-
-    XGBKFold = None
-    XGBStratifiedKFold = None
-    XGBoostLabelEncoder = None
-
-
-# dask
-try:
-    import dask
-    from dask import delayed
-    from dask import dataframe as dd
-    from dask import array as da
-    from dask.distributed import Client, get_client
-    from dask.distributed import comm as distributed_comm
-    from dask.distributed import wait as distributed_wait
-    from distributed import get_worker as distributed_get_worker
-
-    DASK_INSTALLED = True
-except ImportError:
-    dd = None
-    da = None
-    Client = None
-    delayed = None
-    get_client = None
-    distributed_comm = None
-    distributed_wait = None
-    distributed_get_worker = None
-    dask = None
-
-    DASK_INSTALLED = False
-
-
-try:
-    import sparse
-    import scipy.sparse as scipy_sparse
-    SCIPY_INSTALLED = True
-except ImportError:
-    sparse = False
-    scipy_sparse = False
-    SCIPY_INSTALLED = False
diff --git a/ml-xgboost/python-package/xgboost/core.py b/ml-xgboost/python-package/xgboost/core.py
deleted file mode 100644
index 6ea735a..0000000
--- a/ml-xgboost/python-package/xgboost/core.py
+++ /dev/null
@@ -1,2174 +0,0 @@
-# coding: utf-8
-# pylint: disable=too-many-arguments, too-many-branches, invalid-name
-# pylint: disable=too-many-branches, too-many-lines, too-many-locals
-# pylint: disable=too-many-public-methods
-"""Core XGBoost Library."""
-import collections
-# pylint: disable=no-name-in-module,import-error
-from collections.abc import Mapping  # Python 3
-# pylint: enable=no-name-in-module,import-error
-import ctypes
-import os
-import re
-import sys
-import warnings
-import json
-
-import numpy as np
-import scipy.sparse
-
-from .compat import (
-    STRING_TYPES, DataFrame, MultiIndex, Int64Index, py_str,
-    PANDAS_INSTALLED, CUDF_INSTALLED,
-    CUDF_DataFrame, CUDF_Series, CUDF_MultiIndex,
-    os_fspath, os_PathLike, lazy_isinstance)
-from .libpath import find_lib_path
-
-# c_bst_ulong corresponds to bst_ulong defined in xgboost/c_api.h
-c_bst_ulong = ctypes.c_uint64
-
-
-class XGBoostError(Exception):
-    """Error thrown by xgboost trainer."""
-
-
-class EarlyStopException(Exception):
-    """Exception to signal early stopping.
-
-    Parameters
-    ----------
-    best_iteration : int
-        The best iteration stopped.
-    """
-
-    def __init__(self, best_iteration):
-        super(EarlyStopException, self).__init__()
-        self.best_iteration = best_iteration
-
-
-# Callback environment used by callbacks
-CallbackEnv = collections.namedtuple(
-    "XGBoostCallbackEnv",
-    ["model",
-     "cvfolds",
-     "iteration",
-     "begin_iteration",
-     "end_iteration",
-     "rank",
-     "evaluation_result_list"])
-
-
-def from_pystr_to_cstr(data):
-    """Convert a list of Python str to C pointer
-
-    Parameters
-    ----------
-    data : list
-        list of str
-    """
-
-    if not isinstance(data, list):
-        raise NotImplementedError
-    pointers = (ctypes.c_char_p * len(data))()
-    data = [bytes(d, 'utf-8') for d in data]
-    pointers[:] = data
-    return pointers
-
-
-def from_cstr_to_pystr(data, length):
-    """Revert C pointer to Python str
-
-    Parameters
-    ----------
-    data : ctypes pointer
-        pointer to data
-    length : ctypes pointer
-        pointer to length of data
-    """
-    res = []
-    for i in range(length.value):
-        try:
-            res.append(str(data[i].decode('ascii')))
-        except UnicodeDecodeError:
-            res.append(str(data[i].decode('utf-8')))
-    return res
-
-
-def _expect(expectations, got):
-    """Translate input error into string.
-
-    Parameters
-    ----------
-    expectations: sequence
-        a list of expected value.
-    got:
-        actual input
-
-    Returns
-    -------
-    msg: str
-    """
-    msg = 'Expecting '
-    for t in range(len(expectations) - 1):
-        msg += str(expectations[t])
-        msg += ' or '
-    msg += str(expectations[-1])
-    msg += '.  Got ' + str(got)
-    return msg
-
-
-def _log_callback(msg):
-    """Redirect logs from native library into Python console"""
-    print("{0:s}".format(py_str(msg)))
-
-
-def _get_log_callback_func():
-    """Wrap log_callback() method in ctypes callback type"""
-    # pylint: disable=invalid-name
-    CALLBACK = ctypes.CFUNCTYPE(None, ctypes.c_char_p)
-    return CALLBACK(_log_callback)
-
-
-def _load_lib():
-    """Load xgboost Library."""
-    lib_paths = find_lib_path()
-    if not lib_paths:
-        return None
-    try:
-        pathBackup = os.environ['PATH'].split(os.pathsep)
-    except KeyError:
-        pathBackup = []
-    lib_success = False
-    os_error_list = []
-    for lib_path in lib_paths:
-        try:
-            # needed when the lib is linked with non-system-available
-            # dependencies
-            os.environ['PATH'] = os.pathsep.join(
-                pathBackup + [os.path.dirname(lib_path)])
-            lib = ctypes.cdll.LoadLibrary(lib_path)
-            lib_success = True
-        except OSError as e:
-            os_error_list.append(str(e))
-            continue
-        finally:
-            os.environ['PATH'] = os.pathsep.join(pathBackup)
-    if not lib_success:
-        libname = os.path.basename(lib_paths[0])
-        raise XGBoostError(
-            'XGBoost Library ({}) could not be loaded.\n'.format(libname) +
-            'Likely causes:\n' +
-            '  * OpenMP runtime is not installed ' +
-            '(vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, ' +
-            'libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run ' +
-            '`brew install libomp` to install OpenMP runtime.\n' +
-            '  * You are running 32-bit Python on a 64-bit OS\n' +
-            'Error message(s): {}\n'.format(os_error_list))
-    lib.XGBGetLastError.restype = ctypes.c_char_p
-    lib.callback = _get_log_callback_func()
-    if lib.XGBRegisterLogCallback(lib.callback) != 0:
-        raise XGBoostError(lib.XGBGetLastError())
-    return lib
-
-
-# load the XGBoost library globally
-_LIB = _load_lib()
-
-
-def _check_call(ret):
-    """Check the return value of C API call
-
-    This function will raise exception when error occurs.
-    Wrap every API call with this function
-
-    Parameters
-    ----------
-    ret : int
-        return value from API calls
-    """
-    if ret != 0:
-        raise XGBoostError(py_str(_LIB.XGBGetLastError()))
-
-
-def ctypes2numpy(cptr, length, dtype):
-    """Convert a ctypes pointer array to a numpy array."""
-    NUMPY_TO_CTYPES_MAPPING = {
-        np.float32: ctypes.c_float,
-        np.uint32: ctypes.c_uint,
-    }
-    if dtype not in NUMPY_TO_CTYPES_MAPPING:
-        raise RuntimeError('Supported types: {}'.format(
-            NUMPY_TO_CTYPES_MAPPING.keys()))
-    ctype = NUMPY_TO_CTYPES_MAPPING[dtype]
-    if not isinstance(cptr, ctypes.POINTER(ctype)):
-        raise RuntimeError('expected {} pointer'.format(ctype))
-    res = np.zeros(length, dtype=dtype)
-    if not ctypes.memmove(res.ctypes.data, cptr, length * res.strides[0]):
-        raise RuntimeError('memmove failed')
-    return res
-
-
-def ctypes2cupy(cptr, length, dtype):
-    """Convert a ctypes pointer array to a cupy array."""
-    # pylint: disable=import-error
-    import cupy
-    from cupy.cuda.memory import MemoryPointer
-    from cupy.cuda.memory import UnownedMemory
-    CUPY_TO_CTYPES_MAPPING = {
-        cupy.float32: ctypes.c_float,
-        cupy.uint32: ctypes.c_uint
-    }
-    if dtype not in CUPY_TO_CTYPES_MAPPING.keys():
-        raise RuntimeError('Supported types: {}'.format(
-            CUPY_TO_CTYPES_MAPPING.keys()
-        ))
-    addr = ctypes.cast(cptr, ctypes.c_void_p).value
-    # pylint: disable=c-extension-no-member,no-member
-    device = cupy.cuda.runtime.pointerGetAttributes(addr).device
-    # The owner field is just used to keep the memory alive with ref count.  As
-    # unowned's life time is scoped within this function we don't need that.
-    unownd = UnownedMemory(
-        addr, length.value * ctypes.sizeof(CUPY_TO_CTYPES_MAPPING[dtype]),
-        owner=None)
-    memptr = MemoryPointer(unownd, 0)
-    # pylint: disable=unexpected-keyword-arg
-    mem = cupy.ndarray((length.value, ), dtype=dtype, memptr=memptr)
-    assert mem.device.id == device
-    arr = cupy.array(mem, copy=True)
-    return arr
-
-
-def ctypes2buffer(cptr, length):
-    """Convert ctypes pointer to buffer type."""
-    if not isinstance(cptr, ctypes.POINTER(ctypes.c_char)):
-        raise RuntimeError('expected char pointer')
-    res = bytearray(length)
-    rptr = (ctypes.c_char * length).from_buffer(res)
-    if not ctypes.memmove(rptr, cptr, length):
-        raise RuntimeError('memmove failed')
-    return res
-
-
-def c_str(string):
-    """Convert a python string to cstring."""
-    return ctypes.c_char_p(string.encode('utf-8'))
-
-
-def c_array(ctype, values):
-    """Convert a python string to c array."""
-    if (isinstance(values, np.ndarray)
-            and values.dtype.itemsize == ctypes.sizeof(ctype)):
-        return (ctype * len(values)).from_buffer_copy(values)
-    return (ctype * len(values))(*values)
-
-
-PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64':
-                       'int', 'uint8': 'int', 'uint16': 'int', 'uint32': 'int',
-                       'uint64': 'int', 'float16': 'float', 'float32': 'float',
-                       'float64': 'float', 'bool': 'i'}
-
-
-# Either object has cuda array interface or contains columns with interfaces
-def _has_cuda_array_interface(data):
-    return hasattr(data, '__cuda_array_interface__') or (
-        CUDF_INSTALLED and isinstance(data, CUDF_DataFrame))
-
-
-def _maybe_pandas_data(data, feature_names, feature_types,
-                       meta=None, meta_type=None):
-    """Extract internal data from pd.DataFrame for DMatrix data"""
-    if not (PANDAS_INSTALLED and isinstance(data, DataFrame)):
-        return data, feature_names, feature_types
-    from pandas.api.types import is_sparse
-
-    data_dtypes = data.dtypes
-    if not all(dtype.name in PANDAS_DTYPE_MAPPER or is_sparse(dtype)
-               for dtype in data_dtypes):
-        bad_fields = [
-            str(data.columns[i]) for i, dtype in enumerate(data_dtypes)
-            if dtype.name not in PANDAS_DTYPE_MAPPER
-        ]
-
-        msg = """DataFrame.dtypes for data must be int, float or bool.
-                Did not expect the data types in fields """
-        raise ValueError(msg + ', '.join(bad_fields))
-
-    if feature_names is None and meta is None:
-        if isinstance(data.columns, MultiIndex):
-            feature_names = [
-                ' '.join([str(x) for x in i]) for i in data.columns
-            ]
-        elif isinstance(data.columns, Int64Index):
-            feature_names = list(map(str, data.columns))
-        else:
-            feature_names = data.columns.format()
-
-    if feature_types is None and meta is None:
-        feature_types = []
-        for dtype in data_dtypes:
-            if is_sparse(dtype):
-                feature_types.append(PANDAS_DTYPE_MAPPER[dtype.subtype.name])
-            else:
-                feature_types.append(PANDAS_DTYPE_MAPPER[dtype.name])
-
-    if meta and len(data.columns) > 1:
-        raise ValueError(
-            'DataFrame for {meta} cannot have multiple columns'.format(
-                meta=meta))
-
-    dtype = meta_type if meta_type else 'float'
-    data = data.values.astype(dtype)
-
-    return data, feature_names, feature_types
-
-
-def _cudf_array_interfaces(df):
-    '''Extract CuDF __cuda_array_interface__'''
-    interfaces = []
-    for col in df:
-        interface = df[col].__cuda_array_interface__
-        if 'mask' in interface:
-            interface['mask'] = interface['mask'].__cuda_array_interface__
-        interfaces.append(interface)
-    interfaces_str = bytes(json.dumps(interfaces, indent=2), 'utf-8')
-    return interfaces_str
-
-
-def _maybe_cudf_dataframe(data, feature_names, feature_types):
-    """Extract internal data from cudf.DataFrame for DMatrix data."""
-    if not (CUDF_INSTALLED and isinstance(data,
-                                          (CUDF_DataFrame, CUDF_Series))):
-        return data, feature_names, feature_types
-    if feature_names is None:
-        if isinstance(data, CUDF_Series):
-            feature_names = [data.name]
-        elif isinstance(data.columns, CUDF_MultiIndex):
-            feature_names = [
-                ' '.join([str(x) for x in i])
-                for i in data.columns
-            ]
-        else:
-            feature_names = data.columns.format()
-    if feature_types is None:
-        if isinstance(data, CUDF_Series):
-            dtypes = [data.dtype]
-        else:
-            dtypes = data.dtypes
-        feature_types = [PANDAS_DTYPE_MAPPER[d.name] for d in dtypes]
-    return data, feature_names, feature_types
-
-
-DT_TYPE_MAPPER = {'bool': 'bool', 'int': 'int', 'real': 'float'}
-
-DT_TYPE_MAPPER2 = {'bool': 'i', 'int': 'int', 'real': 'float'}
-
-
-def _maybe_dt_data(data, feature_names, feature_types,
-                   meta=None, meta_type=None):
-    """Validate feature names and types if data table"""
-    if (not lazy_isinstance(data, 'datatable', 'Frame') and
-            not lazy_isinstance(data, 'datatable', 'DataTable')):
-        return data, feature_names, feature_types
-
-    if meta and data.shape[1] > 1:
-        raise ValueError(
-            'DataTable for label or weight cannot have multiple columns')
-    if meta:
-        # below requires new dt version
-        # extract first column
-        data = data.to_numpy()[:, 0].astype(meta_type)
-        return data, None, None
-
-    data_types_names = tuple(lt.name for lt in data.ltypes)
-    bad_fields = [data.names[i]
-                  for i, type_name in enumerate(data_types_names)
-                  if type_name not in DT_TYPE_MAPPER]
-    if bad_fields:
-        msg = """DataFrame.types for data must be int, float or bool.
-                Did not expect the data types in fields """
-        raise ValueError(msg + ', '.join(bad_fields))
-
-    if feature_names is None and meta is None:
-        feature_names = data.names
-
-        # always return stypes for dt ingestion
-        if feature_types is not None:
-            raise ValueError(
-                'DataTable has own feature types, cannot pass them in.')
-        feature_types = np.vectorize(DT_TYPE_MAPPER2.get)(data_types_names)
-
-    return data, feature_names, feature_types
-
-def _is_dlpack(x):
-    return 'PyCapsule' in str(type(x)) and "dltensor" in str(x)
-
-# Just convert dlpack into cupy (zero copy)
-def _maybe_dlpack_data(data, feature_names, feature_types):
-    if not _is_dlpack(data):
-        return data, feature_names, feature_types
-    from cupy import fromDlpack # pylint: disable=E0401
-    data = fromDlpack(data)
-    return data, feature_names, feature_types
-
-
-def _convert_dataframes(data, feature_names, feature_types,
-                        meta=None, meta_type=None):
-    data, feature_names, feature_types = _maybe_pandas_data(data,
-                                                            feature_names,
-                                                            feature_types,
-                                                            meta,
-                                                            meta_type)
-
-    data, feature_names, feature_types = _maybe_dt_data(data,
-                                                        feature_names,
-                                                        feature_types,
-                                                        meta,
-                                                        meta_type)
-
-    data, feature_names, feature_types = _maybe_cudf_dataframe(
-        data, feature_names, feature_types)
-
-    data, feature_names, feature_types = _maybe_dlpack_data(
-        data, feature_names, feature_types)
-
-    return data, feature_names, feature_types
-
-
-def _maybe_np_slice(data, dtype=np.float32):
-    '''Handle numpy slice.  This can be removed if we use __array_interface__.
-    '''
-    try:
-        if not data.flags.c_contiguous:
-            warnings.warn(
-                "Use subset (sliced data) of np.ndarray is not recommended " +
-                "because it will generate extra copies and increase " +
-                "memory consumption")
-            data = np.array(data, copy=True, dtype=dtype)
-        else:
-            data = np.array(data, copy=False, dtype=dtype)
-    except AttributeError:
-        data = np.array(data, copy=False, dtype=dtype)
-    return data
-
-
-class DMatrix(object):
-    """Data Matrix used in XGBoost.
-
-    DMatrix is a internal data structure that used by XGBoost
-    which is optimized for both memory efficiency and training speed.
-    You can construct DMatrix from numpy.arrays
-    """
-
-    _feature_names = None  # for previous version's pickle
-    _feature_types = None
-
-    def __init__(self, data, label=None, weight=None, base_margin=None,
-                 missing=None,
-                 silent=False,
-                 feature_names=None,
-                 feature_types=None,
-                 nthread=None):
-        """Parameters
-        ----------
-        data : os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/
-               dt.Frame/cudf.DataFrame/cupy.array/dlpack
-            Data source of DMatrix.
-            When data is string or os.PathLike type, it represents the path
-            libsvm format txt file, csv file (by specifying uri parameter
-            'path_to_csv?format=csv'), or binary file that xgboost can read
-            from.
-        label : list, numpy 1-D array or cudf.DataFrame, optional
-            Label of the training data.
-        missing : float, optional
-            Value in the input data which needs to be present as a missing
-            value. If None, defaults to np.nan.
-        weight : list, numpy 1-D array or cudf.DataFrame , optional
-            Weight for each instance.
-
-            .. note:: For ranking task, weights are per-group.
-
-                In ranking task, one weight is assigned to each group (not each
-                data point). This is because we only care about the relative
-                ordering of data points within each group, so it doesn't make
-                sense to assign weights to individual data points.
-
-        silent : boolean, optional
-            Whether print messages during construction
-        feature_names : list, optional
-            Set names for features.
-        feature_types : list, optional
-            Set types for features.
-        nthread : integer, optional
-            Number of threads to use for loading data when parallelization is
-            applicable. If -1, uses maximum threads available on the system.
-
-        """
-        # force into void_p, mac need to pass things in as void_p
-        if data is None:
-            self.handle = None
-
-            if feature_names is not None:
-                self._feature_names = feature_names
-            if feature_types is not None:
-                self._feature_types = feature_types
-            return
-
-        if isinstance(data, list):
-            raise TypeError('Input data can not be a list.')
-
-        data, feature_names, feature_types = _convert_dataframes(
-            data, feature_names, feature_types
-        )
-        missing = missing if missing is not None else np.nan
-        nthread = nthread if nthread is not None else 1
-
-        if isinstance(data, (STRING_TYPES, os_PathLike)):
-            handle = ctypes.c_void_p()
-            _check_call(_LIB.XGDMatrixCreateFromFile(c_str(os_fspath(data)),
-                                                     ctypes.c_int(silent),
-                                                     ctypes.byref(handle)))
-            self.handle = handle
-        elif isinstance(data, scipy.sparse.csr_matrix):
-            self._init_from_csr(data)
-        elif isinstance(data, scipy.sparse.csc_matrix):
-            self._init_from_csc(data)
-        elif isinstance(data, np.ndarray):
-            self._init_from_npy2d(data, missing, nthread)
-        elif lazy_isinstance(data, 'datatable', 'Frame'):
-            self._init_from_dt(data, nthread)
-        elif hasattr(data, "__cuda_array_interface__"):
-            self._init_from_array_interface(data, missing, nthread)
-        elif CUDF_INSTALLED and isinstance(data, CUDF_DataFrame):
-            self._init_from_array_interface_columns(data, missing, nthread)
-        else:
-            try:
-                csr = scipy.sparse.csr_matrix(data)
-                self._init_from_csr(csr)
-            except Exception:
-                raise TypeError('can not initialize DMatrix from'
-                                ' {}'.format(type(data).__name__))
-
-        if label is not None:
-            self.set_label(label)
-        if weight is not None:
-            self.set_weight(weight)
-        if base_margin is not None:
-            self.set_base_margin(base_margin)
-
-        self.feature_names = feature_names
-        self.feature_types = feature_types
-
-    def _init_from_csr(self, csr):
-        """Initialize data from a CSR matrix."""
-        if len(csr.indices) != len(csr.data):
-            raise ValueError('length mismatch: {} vs {}'.format(
-                len(csr.indices), len(csr.data)))
-        handle = ctypes.c_void_p()
-        _check_call(_LIB.XGDMatrixCreateFromCSREx(
-            c_array(ctypes.c_size_t, csr.indptr),
-            c_array(ctypes.c_uint, csr.indices),
-            c_array(ctypes.c_float, csr.data),
-            ctypes.c_size_t(len(csr.indptr)),
-            ctypes.c_size_t(len(csr.data)),
-            ctypes.c_size_t(csr.shape[1]),
-            ctypes.byref(handle)))
-        self.handle = handle
-
-    def _init_from_csc(self, csc):
-        """Initialize data from a CSC matrix."""
-        if len(csc.indices) != len(csc.data):
-            raise ValueError('length mismatch: {} vs {}'.format(
-                len(csc.indices), len(csc.data)))
-        handle = ctypes.c_void_p()
-        _check_call(_LIB.XGDMatrixCreateFromCSCEx(
-            c_array(ctypes.c_size_t, csc.indptr),
-            c_array(ctypes.c_uint, csc.indices),
-            c_array(ctypes.c_float, csc.data),
-            ctypes.c_size_t(len(csc.indptr)),
-            ctypes.c_size_t(len(csc.data)),
-            ctypes.c_size_t(csc.shape[0]),
-            ctypes.byref(handle)))
-        self.handle = handle
-
-    def _init_from_npy2d(self, mat, missing, nthread):
-        """Initialize data from a 2-D numpy matrix.
-
-        If ``mat`` does not have ``order='C'`` (aka row-major) or is
-        not contiguous, a temporary copy will be made.
-
-        If ``mat`` does not have ``dtype=numpy.float32``, a temporary copy will
-        be made.
-
-        So there could be as many as two temporary data copies; be mindful of
-        input layout and type if memory use is a concern.
-
-        """
-        if len(mat.shape) != 2:
-            raise ValueError('Expecting 2 dimensional numpy.ndarray, got: ',
-                             mat.shape)
-        # flatten the array by rows and ensure it is float32.  we try to avoid
-        # data copies if possible (reshape returns a view when possible and we
-        # explicitly tell np.array to try and avoid copying)
-        data = np.array(mat.reshape(mat.size), copy=False, dtype=np.float32)
-        handle = ctypes.c_void_p()
-        _check_call(_LIB.XGDMatrixCreateFromMat_omp(
-            data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
-            c_bst_ulong(mat.shape[0]),
-            c_bst_ulong(mat.shape[1]),
-            ctypes.c_float(missing),
-            ctypes.byref(handle),
-            ctypes.c_int(nthread)))
-        self.handle = handle
-
-    def _init_from_dt(self, data, nthread):
-        """Initialize data from a datatable Frame."""
-        ptrs = (ctypes.c_void_p * data.ncols)()
-        if hasattr(data, "internal") and hasattr(data.internal, "column"):
-            # datatable>0.8.0
-            for icol in range(data.ncols):
-                col = data.internal.column(icol)
-                ptr = col.data_pointer
-                ptrs[icol] = ctypes.c_void_p(ptr)
-        else:
-            # datatable<=0.8.0
-            from datatable.internal import \
-                frame_column_data_r  # pylint: disable=no-name-in-module,import-error
-            for icol in range(data.ncols):
-                ptrs[icol] = frame_column_data_r(data, icol)
-
-        # always return stypes for dt ingestion
-        feature_type_strings = (ctypes.c_char_p * data.ncols)()
-        for icol in range(data.ncols):
-            feature_type_strings[icol] = ctypes.c_char_p(
-                data.stypes[icol].name.encode('utf-8'))
-
-        handle = ctypes.c_void_p()
-        _check_call(_LIB.XGDMatrixCreateFromDT(
-            ptrs, feature_type_strings,
-            c_bst_ulong(data.shape[0]),
-            c_bst_ulong(data.shape[1]),
-            ctypes.byref(handle),
-            ctypes.c_int(nthread)))
-        self.handle = handle
-
-    def _init_from_array_interface_columns(self, df, missing, nthread):
-        """Initialize DMatrix from columnar memory format."""
-        interfaces_str = _cudf_array_interfaces(df)
-        handle = ctypes.c_void_p()
-        _check_call(
-            _LIB.XGDMatrixCreateFromArrayInterfaceColumns(
-                interfaces_str,
-                ctypes.c_float(missing),
-                ctypes.c_int(nthread),
-                ctypes.byref(handle)))
-        self.handle = handle
-
-    def _init_from_array_interface(self, data, missing, nthread):
-        """Initialize DMatrix from cupy ndarray."""
-        interface = data.__cuda_array_interface__
-        if 'mask' in interface:
-            interface['mask'] = interface['mask'].__cuda_array_interface__
-        interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')
-
-        handle = ctypes.c_void_p()
-        _check_call(
-            _LIB.XGDMatrixCreateFromArrayInterface(
-                interface_str,
-                ctypes.c_float(missing),
-                ctypes.c_int(nthread),
-                ctypes.byref(handle)))
-        self.handle = handle
-
-    def __del__(self):
-        if hasattr(self, "handle") and self.handle:
-            _check_call(_LIB.XGDMatrixFree(self.handle))
-            self.handle = None
-
-    def get_float_info(self, field):
-        """Get float property from the DMatrix.
-
-        Parameters
-        ----------
-        field: str
-            The field name of the information
-
-        Returns
-        -------
-        info : array
-            a numpy array of float information of the data
-        """
-        length = c_bst_ulong()
-        ret = ctypes.POINTER(ctypes.c_float)()
-        _check_call(_LIB.XGDMatrixGetFloatInfo(self.handle,
-                                               c_str(field),
-                                               ctypes.byref(length),
-                                               ctypes.byref(ret)))
-        return ctypes2numpy(ret, length.value, np.float32)
-
-    def get_uint_info(self, field):
-        """Get unsigned integer property from the DMatrix.
-
-        Parameters
-        ----------
-        field: str
-            The field name of the information
-
-        Returns
-        -------
-        info : array
-            a numpy array of unsigned integer information of the data
-        """
-        length = c_bst_ulong()
-        ret = ctypes.POINTER(ctypes.c_uint)()
-        _check_call(_LIB.XGDMatrixGetUIntInfo(self.handle,
-                                              c_str(field),
-                                              ctypes.byref(length),
-                                              ctypes.byref(ret)))
-        return ctypes2numpy(ret, length.value, np.uint32)
-
-    def set_float_info(self, field, data):
-        """Set float type property into the DMatrix.
-
-        Parameters
-        ----------
-        field: str
-            The field name of the information
-
-        data: numpy array
-            The array of data to be set
-        """
-        data, _, _ = _convert_dataframes(data, None, None, field, 'float')
-        if isinstance(data, np.ndarray):
-            self.set_float_info_npy2d(field, data)
-            return
-        c_data = c_array(ctypes.c_float, data)
-        _check_call(_LIB.XGDMatrixSetFloatInfo(self.handle,
-                                               c_str(field),
-                                               c_data,
-                                               c_bst_ulong(len(data))))
-
-    def set_float_info_npy2d(self, field, data):
-        """Set float type property into the DMatrix
-           for numpy 2d array input
-
-        Parameters
-        ----------
-        field: str
-            The field name of the information
-
-        data: numpy array
-            The array of data to be set
-        """
-        data = _maybe_np_slice(data, np.float32)
-        c_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
-        _check_call(_LIB.XGDMatrixSetFloatInfo(self.handle,
-                                               c_str(field),
-                                               c_data,
-                                               c_bst_ulong(len(data))))
-
-    def set_uint_info(self, field, data):
-        """Set uint type property into the DMatrix.
-
-        Parameters
-        ----------
-        field: str
-            The field name of the information
-
-        data: numpy array
-            The array of data to be set
-        """
-        data = _maybe_np_slice(data, np.uint32)
-        data, _, _ = _convert_dataframes(data, None, None, field, 'uint32')
-        data = np.array(data, copy=False, dtype=ctypes.c_uint)
-        _check_call(_LIB.XGDMatrixSetUIntInfo(self.handle,
-                                              c_str(field),
-                                              c_array(ctypes.c_uint, data),
-                                              c_bst_ulong(len(data))))
-
-    def set_interface_info(self, field, data):
-        """Set info type property into DMatrix."""
-        # If we are passed a dataframe, extract the series
-        if CUDF_INSTALLED and isinstance(data, CUDF_DataFrame):
-            if len(data.columns) != 1:
-                raise ValueError(
-                    'Expecting meta-info to contain a single column')
-            data = data[data.columns[0]]
-
-        interface = bytes(json.dumps([data.__cuda_array_interface__],
-                                     indent=2), 'utf-8')
-        _check_call(_LIB.XGDMatrixSetInfoFromInterface(self.handle,
-                                                       c_str(field),
-                                                       interface))
-
-    def save_binary(self, fname, silent=True):
-        """Save DMatrix to an XGBoost buffer.  Saved binary can be later loaded
-        by providing the path to :py:func:`xgboost.DMatrix` as input.
-
-        Parameters
-        ----------
-        fname : string or os.PathLike
-            Name of the output buffer file.
-        silent : bool (optional; default: True)
-            If set, the output is suppressed.
-        """
-        _check_call(_LIB.XGDMatrixSaveBinary(self.handle,
-                                             c_str(os_fspath(fname)),
-                                             ctypes.c_int(silent)))
-
-    def set_label(self, label):
-        """Set label of dmatrix
-
-        Parameters
-        ----------
-        label: array like
-            The label information to be set into DMatrix
-        """
-        if _has_cuda_array_interface(label):
-            self.set_interface_info('label', label)
-        else:
-            self.set_float_info('label', label)
-
-    def set_weight(self, weight):
-        """Set weight of each instance.
-
-        Parameters
-        ----------
-        weight : array like
-            Weight for each data point
-
-            .. note:: For ranking task, weights are per-group.
-
-                In ranking task, one weight is assigned to each group (not each
-                data point). This is because we only care about the relative
-                ordering of data points within each group, so it doesn't make
-                sense to assign weights to individual data points.
-
-        """
-        if _has_cuda_array_interface(weight):
-            self.set_interface_info('weight', weight)
-        else:
-            self.set_float_info('weight', weight)
-
-    def set_base_margin(self, margin):
-        """Set base margin of booster to start from.
-
-        This can be used to specify a prediction value of existing model to be
-        base_margin However, remember margin is needed, instead of transformed
-        prediction e.g. for logistic regression: need to put in value before
-        logistic transformation see also example/demo.py
-
-        Parameters
-        ----------
-        margin: array like
-            Prediction margin of each datapoint
-
-        """
-        if _has_cuda_array_interface(margin):
-            self.set_interface_info('base_margin', margin)
-        else:
-            self.set_float_info('base_margin', margin)
-
-    def set_group(self, group):
-        """Set group size of DMatrix (used for ranking).
-
-        Parameters
-        ----------
-        group : array like
-            Group size of each group
-        """
-        if _has_cuda_array_interface(group):
-            self.set_interface_info('group', group)
-        else:
-            self.set_uint_info('group', group)
-
-    def get_label(self):
-        """Get the label of the DMatrix.
-
-        Returns
-        -------
-        label : array
-        """
-        return self.get_float_info('label')
-
-    def get_weight(self):
-        """Get the weight of the DMatrix.
-
-        Returns
-        -------
-        weight : array
-        """
-        return self.get_float_info('weight')
-
-    def get_base_margin(self):
-        """Get the base margin of the DMatrix.
-
-        Returns
-        -------
-        base_margin : float
-        """
-        return self.get_float_info('base_margin')
-
-    def num_row(self):
-        """Get the number of rows in the DMatrix.
-
-        Returns
-        -------
-        number of rows : int
-        """
-        ret = c_bst_ulong()
-        _check_call(_LIB.XGDMatrixNumRow(self.handle,
-                                         ctypes.byref(ret)))
-        return ret.value
-
-    def num_col(self):
-        """Get the number of columns (features) in the DMatrix.
-
-        Returns
-        -------
-        number of columns : int
-        """
-        ret = c_bst_ulong()
-        _check_call(_LIB.XGDMatrixNumCol(self.handle,
-                                         ctypes.byref(ret)))
-        return ret.value
-
-    def slice(self, rindex, allow_groups=False):
-        """Slice the DMatrix and return a new DMatrix that only contains `rindex`.
-
-        Parameters
-        ----------
-        rindex : list
-            List of indices to be selected.
-        allow_groups : boolean
-            Allow slicing of a matrix with a groups attribute
-
-        Returns
-        -------
-        res : DMatrix
-            A new DMatrix containing only selected indices.
-        """
-        res = DMatrix(None, feature_names=self.feature_names,
-                      feature_types=self.feature_types)
-        res.handle = ctypes.c_void_p()
-        _check_call(_LIB.XGDMatrixSliceDMatrixEx(self.handle,
-                                                 c_array(ctypes.c_int, rindex),
-                                                 c_bst_ulong(len(rindex)),
-                                                 ctypes.byref(res.handle),
-                                                 ctypes.c_int(1 if allow_groups else 0)))
-        return res
-
-    @property
-    def feature_names(self):
-        """Get feature names (column labels).
-
-        Returns
-        -------
-        feature_names : list or None
-        """
-        if self._feature_names is None:
-            self._feature_names = ['f{0}'.format(i)
-                                   for i in range(self.num_col())]
-        return self._feature_names
-
-    @property
-    def feature_types(self):
-        """Get feature types (column types).
-
-        Returns
-        -------
-        feature_types : list or None
-        """
-        return self._feature_types
-
-    @feature_names.setter
-    def feature_names(self, feature_names):
-        """Set feature names (column labels).
-
-        Parameters
-        ----------
-        feature_names : list or None
-            Labels for features. None will reset existing feature names
-        """
-        if feature_names is not None:
-            # validate feature name
-            try:
-                if not isinstance(feature_names, str):
-                    feature_names = list(feature_names)
-                else:
-                    feature_names = [feature_names]
-            except TypeError:
-                feature_names = [feature_names]
-
-            if len(feature_names) != len(set(feature_names)):
-                raise ValueError('feature_names must be unique')
-            if len(feature_names) != self.num_col():
-                msg = 'feature_names must have the same length as data'
-                raise ValueError(msg)
-            # prohibit to use symbols may affect to parse. e.g. []<
-            if not all(isinstance(f, STRING_TYPES) and
-                       not any(x in f for x in set(('[', ']', '<')))
-                       for f in feature_names):
-                raise ValueError('feature_names must be string, and may not contain [, ] or <')
-        else:
-            # reset feature_types also
-            self.feature_types = None
-        self._feature_names = feature_names
-
-    @feature_types.setter
-    def feature_types(self, feature_types):
-        """Set feature types (column types).
-
-        This is for displaying the results and unrelated
-        to the learning process.
-
-        Parameters
-        ----------
-        feature_types : list or None
-            Labels for features. None will reset existing feature names
-        """
-        if feature_types is not None:
-            if self._feature_names is None:
-                msg = 'Unable to set feature types before setting names'
-                raise ValueError(msg)
-
-            if isinstance(feature_types, STRING_TYPES):
-                # single string will be applied to all columns
-                feature_types = [feature_types] * self.num_col()
-
-            try:
-                if not isinstance(feature_types, str):
-                    feature_types = list(feature_types)
-                else:
-                    feature_types = [feature_types]
-            except TypeError:
-                feature_types = [feature_types]
-
-            if len(feature_types) != self.num_col():
-                msg = 'feature_types must have the same length as data'
-                raise ValueError(msg)
-
-            valid = ('int', 'float', 'i', 'q')
-            if not all(isinstance(f, STRING_TYPES) and f in valid
-                       for f in feature_types):
-                raise ValueError('All feature_names must be {int, float, i, q}')
-        self._feature_types = feature_types
-
-
-class DeviceQuantileDMatrix(DMatrix):
-    """Device memory Data Matrix used in XGBoost for training with tree_method='gpu_hist'. Do not
-    use this for test/validation tasks as some information may be lost in quantisation. This
-    DMatrix is primarily designed to save memory in training from device memory inputs by
-    avoiding intermediate storage. Implementation does not currently consider weights in
-    quantisation process(unlike DMatrix). Set max_bin to control the number of bins during
-    quantisation.
-
-    You can construct DeviceQuantileDMatrix from cupy/cudf/dlpack.
-
-    .. versionadded:: 1.1.0
-
-    """
-
-    def __init__(self, data, label=None, weight=None, base_margin=None,
-                 missing=None,
-                 silent=False,
-                 feature_names=None,
-                 feature_types=None,
-                 nthread=None, max_bin=256):
-        self.max_bin = max_bin
-        if not (hasattr(data, "__cuda_array_interface__") or (
-                CUDF_INSTALLED and isinstance(data, CUDF_DataFrame)) or _is_dlpack(data)):
-            raise ValueError('Only cupy/cudf/dlpack currently supported for DeviceQuantileDMatrix')
-
-        super().__init__(data, label=label, weight=weight, base_margin=base_margin,
-                         missing=missing,
-                         silent=silent,
-                         feature_names=feature_names,
-                         feature_types=feature_types,
-                         nthread=nthread)
-
-    def _init_from_array_interface_columns(self, df, missing, nthread):
-        """Initialize DMatrix from columnar memory format."""
-        interfaces_str = _cudf_array_interfaces(df)
-        handle = ctypes.c_void_p()
-        missing = missing if missing is not None else np.nan
-        nthread = nthread if nthread is not None else 1
-        _check_call(
-            _LIB.XGDeviceQuantileDMatrixCreateFromArrayInterfaceColumns(
-                interfaces_str,
-                ctypes.c_float(missing), ctypes.c_int(nthread),
-                ctypes.c_int(self.max_bin), ctypes.byref(handle)))
-        self.handle = handle
-
-    def _init_from_array_interface(self, data, missing, nthread):
-        """Initialize DMatrix from cupy ndarray."""
-        interface = data.__cuda_array_interface__
-        if 'mask' in interface:
-            interface['mask'] = interface['mask'].__cuda_array_interface__
-        interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')
-
-        handle = ctypes.c_void_p()
-        missing = missing if missing is not None else np.nan
-        nthread = nthread if nthread is not None else 1
-        _check_call(
-            _LIB.XGDeviceQuantileDMatrixCreateFromArrayInterface(
-                interface_str,
-                ctypes.c_float(missing), ctypes.c_int(nthread),
-                ctypes.c_int(self.max_bin), ctypes.byref(handle)))
-        self.handle = handle
-
-
-class Booster(object):
-    # pylint: disable=too-many-public-methods
-    """A Booster of XGBoost.
-
-    Booster is the model of xgboost, that contains low level routines for
-    training, prediction and evaluation.
-    """
-
-    feature_names = None
-
-    def __init__(self, params=None, cache=(), model_file=None):
-        # pylint: disable=invalid-name
-        """
-        Parameters
-        ----------
-        params : dict
-            Parameters for boosters.
-        cache : list
-            List of cache items.
-        model_file : string or os.PathLike
-            Path to the model file.
-        """
-        for d in cache:
-            if not isinstance(d, DMatrix):
-                raise TypeError('invalid cache item: {}'.format(type(d).__name__), cache)
-            self._validate_features(d)
-
-        dmats = c_array(ctypes.c_void_p, [d.handle for d in cache])
-        self.handle = ctypes.c_void_p()
-        _check_call(_LIB.XGBoosterCreate(dmats, c_bst_ulong(len(cache)),
-                                         ctypes.byref(self.handle)))
-        params = params or {}
-        if isinstance(params, list):
-            params.append(('validate_parameters', True))
-        else:
-            params['validate_parameters'] = True
-
-        self.set_param(params or {})
-        if (params is not None) and ('booster' in params):
-            self.booster = params['booster']
-        else:
-            self.booster = 'gbtree'
-        if isinstance(model_file, Booster):
-            assert self.handle is not None
-            # We use the pickle interface for getting memory snapshot from
-            # another model, and load the snapshot with this booster.
-            state = model_file.__getstate__()
-            handle = state['handle']
-            del state['handle']
-            ptr = (ctypes.c_char * len(handle)).from_buffer(handle)
-            length = c_bst_ulong(len(handle))
-            _check_call(
-                _LIB.XGBoosterUnserializeFromBuffer(self.handle, ptr, length))
-            self.__dict__.update(state)
-        elif isinstance(model_file, (STRING_TYPES, os_PathLike, bytearray)):
-            self.load_model(model_file)
-        elif model_file is None:
-            pass
-        else:
-            raise TypeError('Unknown type:', model_file)
-
-    def __del__(self):
-        if hasattr(self, 'handle') and self.handle is not None:
-            _check_call(_LIB.XGBoosterFree(self.handle))
-            self.handle = None
-
-    def __getstate__(self):
-        # can't pickle ctypes pointers, put model content in bytearray
-        this = self.__dict__.copy()
-        handle = this['handle']
-        if handle is not None:
-            length = c_bst_ulong()
-            cptr = ctypes.POINTER(ctypes.c_char)()
-            _check_call(_LIB.XGBoosterSerializeToBuffer(self.handle,
-                                                        ctypes.byref(length),
-                                                        ctypes.byref(cptr)))
-            buf = ctypes2buffer(cptr, length.value)
-            this["handle"] = buf
-        return this
-
-    def __setstate__(self, state):
-        # reconstruct handle from raw data
-        handle = state['handle']
-        if handle is not None:
-            buf = handle
-            dmats = c_array(ctypes.c_void_p, [])
-            handle = ctypes.c_void_p()
-            _check_call(_LIB.XGBoosterCreate(
-                dmats, c_bst_ulong(0), ctypes.byref(handle)))
-            length = c_bst_ulong(len(buf))
-            ptr = (ctypes.c_char * len(buf)).from_buffer(buf)
-            _check_call(
-                _LIB.XGBoosterUnserializeFromBuffer(handle, ptr, length))
-            state['handle'] = handle
-        self.__dict__.update(state)
-
-    def save_config(self):
-        '''Output internal parameter configuration of Booster as a JSON
-        string.
-
-        .. versionadded:: 1.0.0
-        '''
-        json_string = ctypes.c_char_p()
-        length = c_bst_ulong()
-        _check_call(_LIB.XGBoosterSaveJsonConfig(
-            self.handle,
-            ctypes.byref(length),
-            ctypes.byref(json_string)))
-        json_string = json_string.value.decode()
-        return json_string
-
-    def load_config(self, config):
-        '''Load configuration returned by `save_config`.
-
-        .. versionadded:: 1.0.0
-        '''
-        assert isinstance(config, str)
-        _check_call(_LIB.XGBoosterLoadJsonConfig(
-            self.handle,
-            c_str(config)))
-
-    def __copy__(self):
-        return self.__deepcopy__(None)
-
-    def __deepcopy__(self, _):
-        '''Return a copy of booster.'''
-        return Booster(model_file=self)
-
-    def copy(self):
-        """Copy the booster object.
-
-        Returns
-        -------
-        booster: `Booster`
-            a copied booster model
-        """
-        return self.__copy__()
-
-    def load_rabit_checkpoint(self):
-        """Initialize the model by load from rabit checkpoint.
-
-        Returns
-        -------
-        version: integer
-            The version number of the model.
-        """
-        version = ctypes.c_int()
-        _check_call(_LIB.XGBoosterLoadRabitCheckpoint(
-            self.handle, ctypes.byref(version)))
-        return version.value
-
-    def save_rabit_checkpoint(self):
-        """Save the current booster to rabit checkpoint."""
-        _check_call(_LIB.XGBoosterSaveRabitCheckpoint(self.handle))
-
-    def attr(self, key):
-        """Get attribute string from the Booster.
-
-        Parameters
-        ----------
-        key : str
-            The key to get attribute from.
-
-        Returns
-        -------
-        value : str
-            The attribute value of the key, returns None if attribute do not exist.
-        """
-        ret = ctypes.c_char_p()
-        success = ctypes.c_int()
-        _check_call(_LIB.XGBoosterGetAttr(
-            self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
-        if success.value != 0:
-            return py_str(ret.value)
-        return None
-
-    def attributes(self):
-        """Get attributes stored in the Booster as a dictionary.
-
-        Returns
-        -------
-        result : dictionary of  attribute_name: attribute_value pairs of strings.
-            Returns an empty dict if there's no attributes.
-        """
-        length = c_bst_ulong()
-        sarr = ctypes.POINTER(ctypes.c_char_p)()
-        _check_call(_LIB.XGBoosterGetAttrNames(self.handle,
-                                               ctypes.byref(length),
-                                               ctypes.byref(sarr)))
-        attr_names = from_cstr_to_pystr(sarr, length)
-        return {n: self.attr(n) for n in attr_names}
-
-    def set_attr(self, **kwargs):
-        """Set the attribute of the Booster.
-
-        Parameters
-        ----------
-        **kwargs
-            The attributes to set. Setting a value to None deletes an attribute.
-        """
-        for key, value in kwargs.items():
-            if value is not None:
-                if not isinstance(value, STRING_TYPES):
-                    raise ValueError("Set Attr only accepts string values")
-                value = c_str(str(value))
-            _check_call(_LIB.XGBoosterSetAttr(
-                self.handle, c_str(key), value))
-
-    def set_param(self, params, value=None):
-        """Set parameters into the Booster.
-
-        Parameters
-        ----------
-        params: dict/list/str
-           list of key,value pairs, dict of key to value or simply str key
-        value: optional
-           value of the specified parameter, when params is str key
-        """
-        if isinstance(params, Mapping):
-            params = params.items()
-        elif isinstance(params, STRING_TYPES) and value is not None:
-            params = [(params, value)]
-        for key, val in params:
-            if val is not None:
-                _check_call(_LIB.XGBoosterSetParam(self.handle, c_str(key),
-                                                   c_str(str(val))))
-
-    def update(self, dtrain, iteration, fobj=None):
-        """Update for one iteration, with objective function calculated
-        internally.  This function should not be called directly by users.
-
-        Parameters
-        ----------
-        dtrain : DMatrix
-            Training data.
-        iteration : int
-            Current iteration number.
-        fobj : function
-            Customized objective function.
-
-        """
-        if not isinstance(dtrain, DMatrix):
-            raise TypeError('invalid training matrix: {}'.format(
-                type(dtrain).__name__))
-        self._validate_features(dtrain)
-
-        if fobj is None:
-            _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,
-                                                    ctypes.c_int(iteration),
-                                                    dtrain.handle))
-        else:
-            pred = self.predict(dtrain, output_margin=True, training=True)
-            grad, hess = fobj(pred, dtrain)
-            self.boost(dtrain, grad, hess)
-
-    def boost(self, dtrain, grad, hess):
-        """Boost the booster for one iteration, with customized gradient
-        statistics.  Like :func:`xgboost.core.Booster.update`, this
-        function should not be called directly by users.
-
-        Parameters
-        ----------
-        dtrain : DMatrix
-            The training DMatrix.
-        grad : list
-            The first order of gradient.
-        hess : list
-            The second order of gradient.
-
-        """
-        if len(grad) != len(hess):
-            raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
-        if not isinstance(dtrain, DMatrix):
-            raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
-        self._validate_features(dtrain)
-
-        _check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
-                                               c_array(ctypes.c_float, grad),
-                                               c_array(ctypes.c_float, hess),
-                                               c_bst_ulong(len(grad))))
-
-    def eval_set(self, evals, iteration=0, feval=None):
-        # pylint: disable=invalid-name
-        """Evaluate a set of data.
-
-        Parameters
-        ----------
-        evals : list of tuples (DMatrix, string)
-            List of items to be evaluated.
-        iteration : int
-            Current iteration.
-        feval : function
-            Custom evaluation function.
-
-        Returns
-        -------
-        result: str
-            Evaluation result string.
-        """
-        for d in evals:
-            if not isinstance(d[0], DMatrix):
-                raise TypeError('expected DMatrix, got {}'.format(
-                    type(d[0]).__name__))
-            if not isinstance(d[1], STRING_TYPES):
-                raise TypeError('expected string, got {}'.format(
-                    type(d[1]).__name__))
-            self._validate_features(d[0])
-
-        dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
-        evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
-        msg = ctypes.c_char_p()
-        _check_call(_LIB.XGBoosterEvalOneIter(self.handle,
-                                              ctypes.c_int(iteration),
-                                              dmats, evnames,
-                                              c_bst_ulong(len(evals)),
-                                              ctypes.byref(msg)))
-        res = msg.value.decode()
-        if feval is not None:
-            for dmat, evname in evals:
-                feval_ret = feval(self.predict(dmat, training=False), dmat)
-                if isinstance(feval_ret, list):
-                    for name, val in feval_ret:
-                        res += '\t%s-%s:%f' % (evname, name, val)
-                else:
-                    name, val = feval_ret
-                    res += '\t%s-%s:%f' % (evname, name, val)
-        return res
-
-    def eval(self, data, name='eval', iteration=0):
-        """Evaluate the model on mat.
-
-        Parameters
-        ----------
-        data : DMatrix
-            The dmatrix storing the input.
-
-        name : str, optional
-            The name of the dataset.
-
-        iteration : int, optional
-            The current iteration number.
-
-        Returns
-        -------
-        result: str
-            Evaluation result string.
-        """
-        self._validate_features(data)
-        return self.eval_set([(data, name)], iteration)
-
-    def predict(self,
-                data,
-                output_margin=False,
-                ntree_limit=0,
-                pred_leaf=False,
-                pred_contribs=False,
-                approx_contribs=False,
-                pred_interactions=False,
-                validate_features=True,
-                training=False):
-        """Predict with data.
-
-        .. note:: This function is not thread safe except for ``gbtree``
-                  booster.
-
-          For ``gbtree`` booster, the thread safety is guaranteed by locks.
-          For lock free prediction use ``inplace_predict`` instead.  Also, the
-          safety does not hold when used in conjunction with other methods.
-
-          When using booster other than ``gbtree``, predict can only be called
-          from one thread.  If you want to run prediction using multiple
-          thread, call ``bst.copy()`` to make copies of model object and then
-          call ``predict()``.
-
-        Parameters
-        ----------
-        data : DMatrix
-            The dmatrix storing the input.
-
-        output_margin : bool
-            Whether to output the raw untransformed margin value.
-
-        ntree_limit : int
-            Limit number of trees in the prediction; defaults to 0 (use all
-            trees).
-
-        pred_leaf : bool
-            When this option is on, the output will be a matrix of (nsample,
-            ntrees) with each record indicating the predicted leaf index of
-            each sample in each tree.  Note that the leaf index of a tree is
-            unique per tree, so you may find leaf 1 in both tree 1 and tree 0.
-
-        pred_contribs : bool
-            When this is True the output will be a matrix of size (nsample,
-            nfeats + 1) with each record indicating the feature contributions
-            (SHAP values) for that prediction. The sum of all feature
-            contributions is equal to the raw untransformed margin value of the
-            prediction. Note the final column is the bias term.
-
-        approx_contribs : bool
-            Approximate the contributions of each feature
-
-        pred_interactions : bool
-            When this is True the output will be a matrix of size (nsample,
-            nfeats + 1, nfeats + 1) indicating the SHAP interaction values for
-            each pair of features. The sum of each row (or column) of the
-            interaction values equals the corresponding SHAP value (from
-            pred_contribs), and the sum of the entire matrix equals the raw
-            untransformed margin value of the prediction. Note the last row and
-            column correspond to the bias term.
-
-        validate_features : bool
-            When this is True, validate that the Booster's and data's
-            feature_names are identical.  Otherwise, it is assumed that the
-            feature_names are the same.
-
-        training : bool
-            Whether the prediction value is used for training.  This can effect
-            `dart` booster, which performs dropouts during training iterations.
-
-            .. versionadded:: 1.0.0
-
-        .. note:: Using ``predict()`` with DART booster
-
-          If the booster object is DART type, ``predict()`` will not perform
-          dropouts, i.e. all the trees will be evaluated.  If you want to
-          obtain result with dropouts, provide `training=True`.
-
-        Returns
-        -------
-        prediction : numpy array
-
-        """
-        option_mask = 0x00
-        if output_margin:
-            option_mask |= 0x01
-        if pred_leaf:
-            option_mask |= 0x02
-        if pred_contribs:
-            option_mask |= 0x04
-        if approx_contribs:
-            option_mask |= 0x08
-        if pred_interactions:
-            option_mask |= 0x10
-
-        if not isinstance(data, DMatrix):
-            raise TypeError('Expecting data to be a DMatrix object, got: ',
-                            type(data))
-
-        if validate_features:
-            self._validate_features(data)
-
-        length = c_bst_ulong()
-        preds = ctypes.POINTER(ctypes.c_float)()
-        _check_call(_LIB.XGBoosterPredict(self.handle, data.handle,
-                                          ctypes.c_int(option_mask),
-                                          ctypes.c_uint(ntree_limit),
-                                          ctypes.c_int(training),
-                                          ctypes.byref(length),
-                                          ctypes.byref(preds)))
-        preds = ctypes2numpy(preds, length.value, np.float32)
-        if pred_leaf:
-            preds = preds.astype(np.int32)
-        nrow = data.num_row()
-        if preds.size != nrow and preds.size % nrow == 0:
-            chunk_size = int(preds.size / nrow)
-
-            if pred_interactions:
-                ngroup = int(chunk_size / ((data.num_col() + 1) *
-                                           (data.num_col() + 1)))
-                if ngroup == 1:
-                    preds = preds.reshape(nrow,
-                                          data.num_col() + 1,
-                                          data.num_col() + 1)
-                else:
-                    preds = preds.reshape(nrow, ngroup,
-                                          data.num_col() + 1,
-                                          data.num_col() + 1)
-            elif pred_contribs:
-                ngroup = int(chunk_size / (data.num_col() + 1))
-                if ngroup == 1:
-                    preds = preds.reshape(nrow, data.num_col() + 1)
-                else:
-                    preds = preds.reshape(nrow, ngroup, data.num_col() + 1)
-            else:
-                preds = preds.reshape(nrow, chunk_size)
-        return preds
-
-    def inplace_predict(self, data, iteration_range=(0, 0),
-                        predict_type='value', missing=np.nan):
-        '''Run prediction in-place, Unlike ``predict`` method, inplace prediction does
-        not cache the prediction result.
-
-        Calling only ``inplace_predict`` in multiple threads is safe and lock
-        free.  But the safety does not hold when used in conjunction with other
-        methods. E.g. you can't train the booster in one thread and perform
-        prediction in the other.
-
-        .. code-block:: python
-
-            booster.set_param({'predictor': 'gpu_predictor'})
-            booster.inplace_predict(cupy_array)
-
-            booster.set_param({'predictor': 'cpu_predictor})
-            booster.inplace_predict(numpy_array)
-
-        .. versionadded:: 1.1.0
-
-        Parameters
-        ----------
-        data : numpy.ndarray/scipy.sparse.csr_matrix/cupy.ndarray/
-               cudf.DataFrame/pd.DataFrame
-            The input data, must not be a view for numpy array.  Set
-            ``predictor`` to ``gpu_predictor`` for running prediction on CuPy
-            array or CuDF DataFrame.
-        iteration_range : tuple
-            Specifies which layer of trees are used in prediction.  For
-            example, if a random forest is trained with 100 rounds.  Specifying
-            `iteration_range=(10, 20)`, then only the forests built during [10,
-            20) (open set) rounds are used in this prediction.
-        predict_type : str
-            * `value` Output model prediction values.
-            * `margin` Output the raw untransformed margin value.
-        missing : float
-            Value in the input data which needs to be present as a missing
-            value.
-
-        Returns
-        -------
-        prediction : numpy.ndarray/cupy.ndarray
-            The prediction result.  When input data is on GPU, prediction
-            result is stored in a cupy array.
-
-        '''
-
-        def reshape_output(predt, rows):
-            '''Reshape for multi-output prediction.'''
-            if predt.size != rows and predt.size % rows == 0:
-                cols = int(predt.size / rows)
-                predt = predt.reshape(rows, cols)
-                return predt
-            return predt
-
-        length = c_bst_ulong()
-        preds = ctypes.POINTER(ctypes.c_float)()
-        iteration_range = (ctypes.c_uint(iteration_range[0]),
-                           ctypes.c_uint(iteration_range[1]))
-
-        # once caching is supported, we can pass id(data) as cache id.
-        if isinstance(data, DataFrame):
-            data = data.values
-        if isinstance(data, np.ndarray):
-            assert data.flags.c_contiguous
-            arr = np.array(data.reshape(data.size), copy=False,
-                           dtype=np.float32)
-            _check_call(_LIB.XGBoosterPredictFromDense(
-                self.handle,
-                arr.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
-                c_bst_ulong(data.shape[0]),
-                c_bst_ulong(data.shape[1]),
-                ctypes.c_float(missing),
-                iteration_range[0],
-                iteration_range[1],
-                c_str(predict_type),
-                c_bst_ulong(0),
-                ctypes.byref(length),
-                ctypes.byref(preds)
-            ))
-            preds = ctypes2numpy(preds, length.value, np.float32)
-            rows = data.shape[0]
-            return reshape_output(preds, rows)
-        if isinstance(data, scipy.sparse.csr_matrix):
-            csr = data
-            _check_call(_LIB.XGBoosterPredictFromCSR(
-                self.handle,
-                c_array(ctypes.c_size_t, csr.indptr),
-                c_array(ctypes.c_uint, csr.indices),
-                c_array(ctypes.c_float, csr.data),
-                ctypes.c_size_t(len(csr.indptr)),
-                ctypes.c_size_t(len(csr.data)),
-                ctypes.c_size_t(csr.shape[1]),
-                ctypes.c_float(missing),
-                iteration_range[0],
-                iteration_range[1],
-                c_str(predict_type),
-                c_bst_ulong(0),
-                ctypes.byref(length),
-                ctypes.byref(preds)))
-            preds = ctypes2numpy(preds, length.value, np.float32)
-            rows = data.shape[0]
-            return reshape_output(preds, rows)
-        if lazy_isinstance(data, 'cupy.core.core', 'ndarray'):
-            assert data.flags.c_contiguous
-            interface = data.__cuda_array_interface__
-            if 'mask' in interface:
-                interface['mask'] = interface['mask'].__cuda_array_interface__
-            interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')
-            _check_call(_LIB.XGBoosterPredictFromArrayInterface(
-                self.handle,
-                interface_str,
-                ctypes.c_float(missing),
-                iteration_range[0],
-                iteration_range[1],
-                c_str(predict_type),
-                c_bst_ulong(0),
-                ctypes.byref(length),
-                ctypes.byref(preds)))
-            mem = ctypes2cupy(preds, length, np.float32)
-            rows = data.shape[0]
-            return reshape_output(mem, rows)
-        if lazy_isinstance(data, 'cudf.core.dataframe', 'DataFrame'):
-            interfaces_str = _cudf_array_interfaces(data)
-            _check_call(_LIB.XGBoosterPredictFromArrayInterfaceColumns(
-                self.handle,
-                interfaces_str,
-                ctypes.c_float(missing),
-                iteration_range[0],
-                iteration_range[1],
-                c_str(predict_type),
-                c_bst_ulong(0),
-                ctypes.byref(length),
-                ctypes.byref(preds)))
-            mem = ctypes2cupy(preds, length, np.float32)
-            rows = data.shape[0]
-            predt = reshape_output(mem, rows)
-            return predt
-
-        raise TypeError('Data type:' + str(type(data)) +
-                        ' not supported by inplace prediction.')
-
-    def save_model(self, fname):
-        """Save the model to a file.
-
-        The model is saved in an XGBoost internal format which is universal
-        among the various XGBoost interfaces. Auxiliary attributes of the
-        Python Booster object (such as feature_names) will not be saved.  To
-        preserve all attributes, pickle the Booster object.
-
-        Parameters
-        ----------
-        fname : string or os.PathLike
-            Output file name
-
-        """
-        if isinstance(fname, (STRING_TYPES, os_PathLike)):  # assume file name
-            _check_call(_LIB.XGBoosterSaveModel(
-                self.handle, c_str(os_fspath(fname))))
-        else:
-            raise TypeError("fname must be a string or os_PathLike")
-
-    def save_raw(self):
-        """Save the model to a in memory buffer representation
-
-        Returns
-        -------
-        a in memory buffer representation of the model
-        """
-        length = c_bst_ulong()
-        cptr = ctypes.POINTER(ctypes.c_char)()
-        _check_call(_LIB.XGBoosterGetModelRaw(self.handle,
-                                              ctypes.byref(length),
-                                              ctypes.byref(cptr)))
-        return ctypes2buffer(cptr, length.value)
-
-    def load_model(self, fname):
-        """Load the model from a file or bytearray. Path to file can be local
-        or as an URI.
-
-        The model is loaded from an XGBoost format which is universal among the
-        various XGBoost interfaces. Auxiliary attributes of the Python Booster
-        object (such as feature_names) will not be loaded.  To preserve all
-        attributes, pickle the Booster object.
-
-        Parameters
-        ----------
-        fname : string, os.PathLike, or a memory buffer
-            Input file name or memory buffer(see also save_raw)
-
-        """
-        if isinstance(fname, (STRING_TYPES, os_PathLike)):
-            # assume file name, cannot use os.path.exist to check, file can be
-            # from URL.
-            _check_call(_LIB.XGBoosterLoadModel(
-                self.handle, c_str(os_fspath(fname))))
-        elif isinstance(fname, bytearray):
-            buf = fname
-            length = c_bst_ulong(len(buf))
-            ptr = (ctypes.c_char * len(buf)).from_buffer(buf)
-            _check_call(_LIB.XGBoosterLoadModelFromBuffer(self.handle, ptr,
-                                                          length))
-        else:
-            raise TypeError('Unknown file type: ', fname)
-
-    def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"):
-        """Dump model into a text or JSON file.
-
-        Parameters
-        ----------
-        fout : string or os.PathLike
-            Output file name.
-        fmap : string or os.PathLike, optional
-            Name of the file containing feature map names.
-        with_stats : bool, optional
-            Controls whether the split statistics are output.
-        dump_format : string, optional
-            Format of model dump file. Can be 'text' or 'json'.
-        """
-        if isinstance(fout, (STRING_TYPES, os_PathLike)):
-            fout = open(os_fspath(fout), 'w')
-            need_close = True
-        else:
-            need_close = False
-        ret = self.get_dump(fmap, with_stats, dump_format)
-        if dump_format == 'json':
-            fout.write('[\n')
-            for i, _ in enumerate(ret):
-                fout.write(ret[i])
-                if i < len(ret) - 1:
-                    fout.write(",\n")
-            fout.write('\n]')
-        else:
-            for i, _ in enumerate(ret):
-                fout.write('booster[{}]:\n'.format(i))
-                fout.write(ret[i])
-        if need_close:
-            fout.close()
-
-    def get_dump(self, fmap='', with_stats=False, dump_format="text"):
-        """Returns the model dump as a list of strings.
-
-        Parameters
-        ----------
-        fmap : string or os.PathLike, optional
-            Name of the file containing feature map names.
-        with_stats : bool, optional
-            Controls whether the split statistics are output.
-        dump_format : string, optional
-            Format of model dump. Can be 'text', 'json' or 'dot'.
-        """
-        fmap = os_fspath(fmap)
-        length = c_bst_ulong()
-        sarr = ctypes.POINTER(ctypes.c_char_p)()
-        if self.feature_names is not None and fmap == '':
-            flen = len(self.feature_names)
-
-            fname = from_pystr_to_cstr(self.feature_names)
-
-            if self.feature_types is None:
-                # use quantitative as default
-                # {'q': quantitative, 'i': indicator}
-                ftype = from_pystr_to_cstr(['q'] * flen)
-            else:
-                ftype = from_pystr_to_cstr(self.feature_types)
-            _check_call(_LIB.XGBoosterDumpModelExWithFeatures(
-                self.handle,
-                ctypes.c_int(flen),
-                fname,
-                ftype,
-                ctypes.c_int(with_stats),
-                c_str(dump_format),
-                ctypes.byref(length),
-                ctypes.byref(sarr)))
-        else:
-            if fmap != '' and not os.path.exists(fmap):
-                raise ValueError("No such file: {0}".format(fmap))
-            _check_call(_LIB.XGBoosterDumpModelEx(self.handle,
-                                                  c_str(fmap),
-                                                  ctypes.c_int(with_stats),
-                                                  c_str(dump_format),
-                                                  ctypes.byref(length),
-                                                  ctypes.byref(sarr)))
-        res = from_cstr_to_pystr(sarr, length)
-        return res
-
-    def get_fscore(self, fmap=''):
-        """Get feature importance of each feature.
-
-        .. note:: Feature importance is defined only for tree boosters
-
-            Feature importance is only defined when the decision tree model is chosen as base
-            learner (`booster=gbtree`). It is not defined for other base learner types, such
-            as linear learners (`booster=gblinear`).
-
-        .. note:: Zero-importance features will not be included
-
-           Keep in mind that this function does not include zero-importance feature, i.e.
-           those features that have not been used in any split conditions.
-
-        Parameters
-        ----------
-        fmap: str or os.PathLike (optional)
-           The name of feature map file
-        """
-
-        return self.get_score(fmap, importance_type='weight')
-
-    def get_score(self, fmap='', importance_type='weight'):
-        """Get feature importance of each feature.
-        Importance type can be defined as:
-
-        * 'weight': the number of times a feature is used to split the data across all trees.
-        * 'gain': the average gain across all splits the feature is used in.
-        * 'cover': the average coverage across all splits the feature is used in.
-        * 'total_gain': the total gain across all splits the feature is used in.
-        * 'total_cover': the total coverage across all splits the feature is used in.
-
-        .. note:: Feature importance is defined only for tree boosters
-
-            Feature importance is only defined when the decision tree model is chosen as base
-            learner (`booster=gbtree`). It is not defined for other base learner types, such
-            as linear learners (`booster=gblinear`).
-
-        Parameters
-        ----------
-        fmap: str or os.PathLike (optional)
-           The name of feature map file.
-        importance_type: str, default 'weight'
-            One of the importance types defined above.
-        """
-        fmap = os_fspath(fmap)
-        if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
-            raise ValueError('Feature importance is not defined for Booster type {}'
-                             .format(self.booster))
-
-        allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover']
-        if importance_type not in allowed_importance_types:
-            msg = ("importance_type mismatch, got '{}', expected one of " +
-                   repr(allowed_importance_types))
-            raise ValueError(msg.format(importance_type))
-
-        # if it's weight, then omap stores the number of missing values
-        if importance_type == 'weight':
-            # do a simpler tree dump to save time
-            trees = self.get_dump(fmap, with_stats=False)
-            fmap = {}
-            for tree in trees:
-                for line in tree.split('\n'):
-                    # look for the opening square bracket
-                    arr = line.split('[')
-                    # if no opening bracket (leaf node), ignore this line
-                    if len(arr) == 1:
-                        continue
-
-                    # extract feature name from string between []
-                    fid = arr[1].split(']')[0].split('<')[0]
-
-                    if fid not in fmap:
-                        # if the feature hasn't been seen yet
-                        fmap[fid] = 1
-                    else:
-                        fmap[fid] += 1
-
-            return fmap
-
-        average_over_splits = True
-        if importance_type == 'total_gain':
-            importance_type = 'gain'
-            average_over_splits = False
-        elif importance_type == 'total_cover':
-            importance_type = 'cover'
-            average_over_splits = False
-
-        trees = self.get_dump(fmap, with_stats=True)
-
-        importance_type += '='
-        fmap = {}
-        gmap = {}
-        for tree in trees:
-            for line in tree.split('\n'):
-                # look for the opening square bracket
-                arr = line.split('[')
-                # if no opening bracket (leaf node), ignore this line
-                if len(arr) == 1:
-                    continue
-
-                # look for the closing bracket, extract only info within that bracket
-                fid = arr[1].split(']')
-
-                # extract gain or cover from string after closing bracket
-                g = float(fid[1].split(importance_type)[1].split(',')[0])
-
-                # extract feature name from string before closing bracket
-                fid = fid[0].split('<')[0]
-
-                if fid not in fmap:
-                    # if the feature hasn't been seen yet
-                    fmap[fid] = 1
-                    gmap[fid] = g
-                else:
-                    fmap[fid] += 1
-                    gmap[fid] += g
-
-        # calculate average value (gain/cover) for each feature
-        if average_over_splits:
-            for fid in gmap:
-                gmap[fid] = gmap[fid] / fmap[fid]
-
-        return gmap
-
-    def trees_to_dataframe(self, fmap=''):
-        """Parse a boosted tree model text dump into a pandas DataFrame structure.
-
-        This feature is only defined when the decision tree model is chosen as base
-        learner (`booster in {gbtree, dart}`). It is not defined for other base learner
-        types, such as linear learners (`booster=gblinear`).
-
-        Parameters
-        ----------
-        fmap: str or os.PathLike (optional)
-           The name of feature map file.
-        """
-        # pylint: disable=too-many-locals
-        fmap = os_fspath(fmap)
-        if not PANDAS_INSTALLED:
-            raise Exception(('pandas must be available to use this method.'
-                             'Install pandas before calling again.'))
-
-        if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
-            raise ValueError('This method is not defined for Booster type {}'
-                             .format(self.booster))
-
-        tree_ids = []
-        node_ids = []
-        fids = []
-        splits = []
-        y_directs = []
-        n_directs = []
-        missings = []
-        gains = []
-        covers = []
-
-        trees = self.get_dump(fmap, with_stats=True)
-        for i, tree in enumerate(trees):
-            for line in tree.split('\n'):
-                arr = line.split('[')
-                # Leaf node
-                if len(arr) == 1:
-                    # Last element of line.split is an empy string
-                    if arr == ['']:
-                        continue
-                    # parse string
-                    parse = arr[0].split(':')
-                    stats = re.split('=|,', parse[1])
-
-                    # append to lists
-                    tree_ids.append(i)
-                    node_ids.append(int(re.findall(r'\b\d+\b', parse[0])[0]))
-                    fids.append('Leaf')
-                    splits.append(float('NAN'))
-                    y_directs.append(float('NAN'))
-                    n_directs.append(float('NAN'))
-                    missings.append(float('NAN'))
-                    gains.append(float(stats[1]))
-                    covers.append(float(stats[3]))
-                # Not a Leaf Node
-                else:
-                    # parse string
-                    fid = arr[1].split(']')
-                    parse = fid[0].split('<')
-                    stats = re.split('=|,', fid[1])
-
-                    # append to lists
-                    tree_ids.append(i)
-                    node_ids.append(int(re.findall(r'\b\d+\b', arr[0])[0]))
-                    fids.append(parse[0])
-                    splits.append(float(parse[1]))
-                    str_i = str(i)
-                    y_directs.append(str_i + '-' + stats[1])
-                    n_directs.append(str_i + '-' + stats[3])
-                    missings.append(str_i + '-' + stats[5])
-                    gains.append(float(stats[7]))
-                    covers.append(float(stats[9]))
-
-        ids = [str(t_id) + '-' + str(n_id) for t_id, n_id in zip(tree_ids, node_ids)]
-        df = DataFrame({'Tree': tree_ids, 'Node': node_ids, 'ID': ids,
-                        'Feature': fids, 'Split': splits, 'Yes': y_directs,
-                        'No': n_directs, 'Missing': missings, 'Gain': gains,
-                        'Cover': covers})
-
-        if callable(getattr(df, 'sort_values', None)):
-            # pylint: disable=no-member
-            return df.sort_values(['Tree', 'Node']).reset_index(drop=True)
-        # pylint: disable=no-member
-        return df.sort(['Tree', 'Node']).reset_index(drop=True)
-
-    def _validate_features(self, data):
-        """
-        Validate Booster and data's feature_names are identical.
-        Set feature_names and feature_types from DMatrix
-        """
-        if self.feature_names is None:
-            self.feature_names = data.feature_names
-            self.feature_types = data.feature_types
-        else:
-            # Booster can't accept data with different feature names
-            if self.feature_names != data.feature_names:
-                dat_missing = set(self.feature_names) - set(data.feature_names)
-                my_missing = set(data.feature_names) - set(self.feature_names)
-
-                msg = 'feature_names mismatch: {0} {1}'
-
-                if dat_missing:
-                    msg += ('\nexpected ' + ', '.join(
-                        str(s) for s in dat_missing) + ' in input data')
-
-                if my_missing:
-                    msg += ('\ntraining data did not have the following fields: ' +
-                            ', '.join(str(s) for s in my_missing))
-
-                raise ValueError(msg.format(self.feature_names,
-                                            data.feature_names))
-
-    def get_split_value_histogram(self, feature, fmap='', bins=None,
-                                  as_pandas=True):
-        """Get split value histogram of a feature
-
-        Parameters
-        ----------
-        feature: str
-            The name of the feature.
-        fmap: str or os.PathLike (optional)
-            The name of feature map file.
-        bin: int, default None
-            The maximum number of bins.
-            Number of bins equals number of unique split values n_unique,
-            if bins == None or bins > n_unique.
-        as_pandas: bool, default True
-            Return pd.DataFrame when pandas is installed.
-            If False or pandas is not installed, return numpy ndarray.
-
-        Returns
-        -------
-        a histogram of used splitting values for the specified feature
-        either as numpy array or pandas DataFrame.
-        """
-        xgdump = self.get_dump(fmap=fmap)
-        values = []
-        regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature))
-        for i, _ in enumerate(xgdump):
-            m = re.findall(regexp, xgdump[i])
-            values.extend([float(x) for x in m])
-
-        n_unique = len(np.unique(values))
-        bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)
-
-        nph = np.histogram(values, bins=bins)
-        nph = np.column_stack((nph[1][1:], nph[0]))
-        nph = nph[nph[:, 1] > 0]
-
-        if as_pandas and PANDAS_INSTALLED:
-            return DataFrame(nph, columns=['SplitValue', 'Count'])
-        if as_pandas and not PANDAS_INSTALLED:
-            sys.stderr.write(
-                "Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
-        return nph
diff --git a/ml-xgboost/python-package/xgboost/dask.py b/ml-xgboost/python-package/xgboost/dask.py
deleted file mode 100644
index 1cc15d7..0000000
--- a/ml-xgboost/python-package/xgboost/dask.py
+++ /dev/null
@@ -1,840 +0,0 @@
-# pylint: disable=too-many-arguments, too-many-locals
-"""Dask extensions for distributed training. See
-https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for simple
-tutorial.  Also xgboost/demo/dask for some examples.
-
-There are two sets of APIs in this module, one is the functional API including
-``train`` and ``predict`` methods.  Another is stateful Scikit-Learner wrapper
-inherited from single-node Scikit-Learn interface.
-
-The implementation is heavily influenced by dask_xgboost:
-https://github.com/dask/dask-xgboost
-
-"""
-import platform
-import logging
-from collections import defaultdict
-from threading import Thread
-
-import numpy
-
-from . import rabit
-
-from .compat import DASK_INSTALLED
-from .compat import distributed_get_worker, distributed_wait, distributed_comm
-from .compat import da, dd, delayed, get_client
-from .compat import sparse, scipy_sparse
-from .compat import PANDAS_INSTALLED, DataFrame, Series, pandas_concat
-from .compat import CUDF_INSTALLED, CUDF_DataFrame, CUDF_Series, CUDF_concat
-from .compat import lazy_isinstance
-
-from .core import DMatrix, Booster, _expect
-from .training import train as worker_train
-from .tracker import RabitTracker
-from .sklearn import XGBModel, XGBRegressorBase, XGBClassifierBase
-from .sklearn import xgboost_model_doc
-
-# Current status is considered as initial support, many features are
-# not properly supported yet.
-#
-# TODOs:
-#   - Callback.
-#   - Label encoding.
-#   - CV
-#   - Ranking
-
-
-LOGGER = logging.getLogger('[xgboost.dask]')
-
-
-def _start_tracker(host, n_workers):
-    """Start Rabit tracker """
-    env = {'DMLC_NUM_WORKER': n_workers}
-    rabit_context = RabitTracker(hostIP=host, nslave=n_workers)
-    env.update(rabit_context.slave_envs())
-
-    rabit_context.start(n_workers)
-    thread = Thread(target=rabit_context.join)
-    thread.daemon = True
-    thread.start()
-    return env
-
-
-def _assert_dask_support():
-    if not DASK_INSTALLED:
-        raise ImportError(
-            'Dask needs to be installed in order to use this module')
-    if platform.system() == 'Windows':
-        msg = 'Windows is not officially supported for dask/xgboost,'
-        msg += ' contribution are welcomed.'
-        LOGGER.warning(msg)
-
-
-class RabitContext:
-    '''A context controling rabit initialization and finalization.'''
-    def __init__(self, args):
-        self.args = args
-        worker = distributed_get_worker()
-        self.args.append(
-            ('DMLC_TASK_ID=[xgboost.dask]:' + str(worker.address)).encode())
-
-    def __enter__(self):
-        rabit.init(self.args)
-        LOGGER.debug('-------------- rabit say hello ------------------')
-
-    def __exit__(self, *args):
-        rabit.finalize()
-        LOGGER.debug('--------------- rabit say bye ------------------')
-
-
-def concat(value):              # pylint: disable=too-many-return-statements
-    '''To be replaced with dask builtin.'''
-    if isinstance(value[0], numpy.ndarray):
-        return numpy.concatenate(value, axis=0)
-    if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
-        return scipy_sparse.vstack(value, format='csr')
-    if sparse and isinstance(value[0], sparse.SparseArray):
-        return sparse.concatenate(value, axis=0)
-    if PANDAS_INSTALLED and isinstance(value[0], (DataFrame, Series)):
-        return pandas_concat(value, axis=0)
-    if CUDF_INSTALLED and isinstance(value[0], (CUDF_DataFrame, CUDF_Series)):
-        return CUDF_concat(value, axis=0)
-    if lazy_isinstance(value[0], 'cupy.core.core', 'ndarray'):
-        import cupy             # pylint: disable=import-error
-        # pylint: disable=c-extension-no-member,no-member
-        d = cupy.cuda.runtime.getDevice()
-        for v in value:
-            d_v = v.device.id
-            assert d_v == d, 'Concatenating arrays on different devices.'
-        return cupy.concatenate(value, axis=0)
-    return dd.multi.concat(list(value), axis=0)
-
-
-def _xgb_get_client(client):
-    '''Simple wrapper around testing None.'''
-    if not isinstance(client, (type(get_client()), type(None))):
-        raise TypeError(
-            _expect([type(get_client()), type(None)], type(client)))
-    ret = get_client() if client is None else client
-    return ret
-
-
-def _get_client_workers(client):
-    workers = client.scheduler_info()['workers']
-    return workers
-
-
-class DaskDMatrix:
-    # pylint: disable=missing-docstring, too-many-instance-attributes
-    '''DMatrix holding on references to Dask DataFrame or Dask Array.  Constructing
-    a `DaskDMatrix` forces all lazy computation to be carried out.  Wait for
-    the input data explicitly if you want to see actual computation of
-    constructing `DaskDMatrix`.
-
-    .. versionadded:: 1.0.0
-
-    Parameters
-    ----------
-    client: dask.distributed.Client
-        Specify the dask client used for training.  Use default client
-        returned from dask if it's set to None.
-    data : dask.array.Array/dask.dataframe.DataFrame
-        data source of DMatrix.
-    label: dask.array.Array/dask.dataframe.DataFrame
-        label used for trainin.
-    missing : float, optional
-        Value in the  input data (e.g. `numpy.ndarray`) which needs
-        to be present as a missing value. If None, defaults to np.nan.
-    weight : dask.array.Array/dask.dataframe.DataFrame
-        Weight for each instance.
-    feature_names : list, optional
-        Set names for features.
-    feature_types : list, optional
-        Set types for features
-
-    '''
-
-    def __init__(self,
-                 client,
-                 data,
-                 label=None,
-                 missing=None,
-                 weight=None,
-                 feature_names=None,
-                 feature_types=None):
-        _assert_dask_support()
-        client = _xgb_get_client(client)
-
-        self.feature_names = feature_names
-        self.feature_types = feature_types
-        self.missing = missing
-
-        if len(data.shape) != 2:
-            raise ValueError(
-                'Expecting 2 dimensional input, got: {shape}'.format(
-                    shape=data.shape))
-
-        if not isinstance(data, (dd.DataFrame, da.Array)):
-            raise TypeError(_expect((dd.DataFrame, da.Array), type(data)))
-        if not isinstance(label, (dd.DataFrame, da.Array, dd.Series,
-                                  type(None))):
-            raise TypeError(
-                _expect((dd.DataFrame, da.Array, dd.Series), type(label)))
-
-        self.worker_map = None
-        self.has_label = label is not None
-        self.has_weights = weight is not None
-
-        client.sync(self.map_local_data, client, data, label, weight)
-
-    async def map_local_data(self, client, data, label=None, weights=None):
-        '''Obtain references to local data.'''
-
-        def inconsistent(left, left_name, right, right_name):
-            msg = 'Partitions between {a_name} and {b_name} are not ' \
-                'consistent: {a_len} != {b_len}.  ' \
-                'Please try to repartition/rechunk your data.'.format(
-                    a_name=left_name, b_name=right_name, a_len=len(left),
-                    b_len=len(right)
-                )
-            return msg
-
-        def check_columns(parts):
-            # x is required to be 2 dim in __init__
-            assert parts.ndim == 1 or parts.shape[1], 'Data should be' \
-                ' partitioned by row. To avoid this specify the number' \
-                ' of columns for your dask Array explicitly. e.g.' \
-                ' chunks=(partition_size, X.shape[1])'
-
-        data = data.persist()
-        if label is not None:
-            label = label.persist()
-        if weights is not None:
-            weights = weights.persist()
-        # Breaking data into partitions, a trick borrowed from dask_xgboost.
-
-        # `to_delayed` downgrades high-level objects into numpy or pandas
-        # equivalents.
-        X_parts = data.to_delayed()
-        if isinstance(X_parts, numpy.ndarray):
-            check_columns(X_parts)
-            X_parts = X_parts.flatten().tolist()
-
-        if label is not None:
-            y_parts = label.to_delayed()
-            if isinstance(y_parts, numpy.ndarray):
-                check_columns(y_parts)
-                y_parts = y_parts.flatten().tolist()
-        if weights is not None:
-            w_parts = weights.to_delayed()
-            if isinstance(w_parts, numpy.ndarray):
-                check_columns(w_parts)
-                w_parts = w_parts.flatten().tolist()
-
-        parts = [X_parts]
-        if label is not None:
-            assert len(X_parts) == len(
-                y_parts), inconsistent(X_parts, 'X', y_parts, 'labels')
-            parts.append(y_parts)
-        if weights is not None:
-            assert len(X_parts) == len(
-                w_parts), inconsistent(X_parts, 'X', w_parts, 'weights')
-            parts.append(w_parts)
-        parts = list(map(delayed, zip(*parts)))
-
-        parts = client.compute(parts)
-        await distributed_wait(parts)  # async wait for parts to be computed
-
-        for part in parts:
-            assert part.status == 'finished'
-
-        self.partition_order = {}
-        for i, part in enumerate(parts):
-            self.partition_order[part.key] = i
-
-        key_to_partition = {part.key: part for part in parts}
-        who_has = await client.scheduler.who_has(
-            keys=[part.key for part in parts])
-
-        worker_map = defaultdict(list)
-        for key, workers in who_has.items():
-            worker_map[next(iter(workers))].append(key_to_partition[key])
-
-        self.worker_map = worker_map
-
-    def get_worker_x_ordered(self, worker):
-        list_of_parts = self.worker_map[worker.address]
-        client = get_client()
-        list_of_parts_value = client.gather(list_of_parts)
-        result = []
-        for i, part in enumerate(list_of_parts):
-            result.append((list_of_parts_value[i][0],
-                           self.partition_order[part.key]))
-        return result
-
-    def get_worker_parts(self, worker):
-        '''Get mapped parts of data in each worker.'''
-        list_of_parts = self.worker_map[worker.address]
-        assert list_of_parts, 'data in ' + worker.address + ' was moved.'
-        assert isinstance(list_of_parts, list)
-
-        # `get_worker_parts` is launched inside worker.  In dask side
-        # this should be equal to `worker._get_client`.
-        client = get_client()
-        list_of_parts = client.gather(list_of_parts)
-
-        if self.has_label:
-            if self.has_weights:
-                data, labels, weights = zip(*list_of_parts)
-            else:
-                data, labels = zip(*list_of_parts)
-                weights = None
-        else:
-            data = [d[0] for d in list_of_parts]
-            labels = None
-            weights = None
-        return data, labels, weights
-
-    def get_worker_data(self, worker):
-        '''Get data that local to worker.
-
-          Parameters
-          ----------
-          worker: The worker used as key to data.
-
-          Returns
-          -------
-          A DMatrix object.
-
-        '''
-        if worker.address not in set(self.worker_map.keys()):
-            msg = 'worker {address} has an empty DMatrix.  ' \
-                'All workers associated with this DMatrix: {workers}'.format(
-                    address=worker.address,
-                    workers=set(self.worker_map.keys()))
-            LOGGER.warning(msg)
-            d = DMatrix(numpy.empty((0, 0)),
-                        feature_names=self.feature_names,
-                        feature_types=self.feature_types)
-            return d
-
-        data, labels, weights = self.get_worker_parts(worker)
-
-        data = concat(data)
-
-        if self.has_label:
-            labels = concat(labels)
-        else:
-            labels = None
-        if self.has_weights:
-            weights = concat(weights)
-        else:
-            weights = None
-        dmatrix = DMatrix(data,
-                          labels,
-                          weight=weights,
-                          missing=self.missing,
-                          feature_names=self.feature_names,
-                          feature_types=self.feature_types,
-                          nthread=worker.nthreads)
-        return dmatrix
-
-    def get_worker_data_shape(self, worker):
-        '''Get the shape of data X in each worker.'''
-        data, _, _ = self.get_worker_parts(worker)
-
-        shapes = [d.shape for d in data]
-        rows = 0
-        cols = 0
-        for shape in shapes:
-            rows += shape[0]
-
-            c = shape[1]
-            assert cols in (0, c), 'Shape between partitions are not the' \
-                ' same. Got: {left} and {right}'.format(left=c, right=cols)
-            cols = c
-        return (rows, cols)
-
-
-def _get_rabit_args(worker_map, client):
-    '''Get rabit context arguments from data distribution in DaskDMatrix.'''
-    host = distributed_comm.get_address_host(client.scheduler.address)
-
-    env = client.run_on_scheduler(_start_tracker, host.strip('/:'),
-                                  len(worker_map))
-    rabit_args = [('%s=%s' % item).encode() for item in env.items()]
-    return rabit_args
-
-# train and predict methods are supposed to be "functional", which meets the
-# dask paradigm.  But as a side effect, the `evals_result` in single-node API
-# is no longer supported since it mutates the input parameter, and it's not
-# intuitive to sync the mutation result.  Therefore, a dictionary containing
-# evaluation history is instead returned.
-
-
-def train(client, params, dtrain, *args, evals=(), **kwargs):
-    '''Train XGBoost model.
-
-    .. versionadded:: 1.0.0
-
-    Parameters
-    ----------
-    client: dask.distributed.Client
-        Specify the dask client used for training.  Use default client
-        returned from dask if it's set to None.
-    \\*\\*kwargs:
-        Other parameters are the same as `xgboost.train` except for
-        `evals_result`, which is returned as part of function return value
-        instead of argument.
-
-    Returns
-    -------
-    results: dict
-        A dictionary containing trained booster and evaluation history.
-        `history` field is the same as `eval_result` from `xgboost.train`.
-
-        .. code-block:: python
-
-            {'booster': xgboost.Booster,
-             'history': {'train': {'logloss': ['0.48253', '0.35953']},
-                         'eval': {'logloss': ['0.480385', '0.357756']}}}
-
-    '''
-    _assert_dask_support()
-    client = _xgb_get_client(client)
-    if 'evals_result' in kwargs.keys():
-        raise ValueError(
-            'evals_result is not supported in dask interface.',
-            'The evaluation history is returned as result of training.')
-
-    workers = list(_get_client_workers(client).keys())
-
-    rabit_args = _get_rabit_args(workers, client)
-
-    def dispatched_train(worker_addr):
-        '''Perform training on a single worker.'''
-        LOGGER.info('Training on %s', str(worker_addr))
-        worker = distributed_get_worker()
-        with RabitContext(rabit_args):
-            local_dtrain = dtrain.get_worker_data(worker)
-
-            local_evals = []
-            if evals:
-                for mat, name in evals:
-                    if mat is dtrain:
-                        local_evals.append((local_dtrain, name))
-                        continue
-                    local_mat = mat.get_worker_data(worker)
-                    local_evals.append((local_mat, name))
-
-            local_history = {}
-            local_param = params.copy()  # just to be consistent
-            msg = 'Overriding `nthreads` defined in dask worker.'
-            if 'nthread' in local_param.keys() and \
-               local_param['nthread'] is not None and \
-               local_param['nthread'] != worker.nthreads:
-                msg += '`nthread` is specified.  ' + msg
-                LOGGER.warning(msg)
-            elif 'n_jobs' in local_param.keys() and \
-                 local_param['n_jobs'] is not None and \
-                 local_param['n_jobs'] != worker.nthreads:
-                msg = '`n_jobs` is specified.  ' + msg
-                LOGGER.warning(msg)
-            else:
-                local_param['nthread'] = worker.nthreads
-            bst = worker_train(params=local_param,
-                               dtrain=local_dtrain,
-                               *args,
-                               evals_result=local_history,
-                               evals=local_evals,
-                               **kwargs)
-            ret = {'booster': bst, 'history': local_history}
-            if local_dtrain.num_row() == 0:
-                ret = None
-            return ret
-
-    futures = client.map(dispatched_train,
-                         workers,
-                         pure=False,
-                         workers=workers)
-    results = client.gather(futures)
-    return list(filter(lambda ret: ret is not None, results))[0]
-
-
-def predict(client, model, data, *args, missing=numpy.nan):
-    '''Run prediction with a trained booster.
-
-    .. note::
-
-        Only default prediction mode is supported right now.
-
-    .. versionadded:: 1.0.0
-
-    Parameters
-    ----------
-    client: dask.distributed.Client
-        Specify the dask client used for training.  Use default client
-        returned from dask if it's set to None.
-    model: A Booster or a dictionary returned by `xgboost.dask.train`.
-        The trained model.
-    data: DaskDMatrix/dask.dataframe.DataFrame/dask.array.Array
-        Input data used for prediction.
-    missing: float
-        Used when input data is not DaskDMatrix.  Specify the value
-        considered as missing.
-
-    Returns
-    -------
-    prediction: dask.array.Array/dask.dataframe.Series
-
-    '''
-    _assert_dask_support()
-    client = _xgb_get_client(client)
-    if isinstance(model, Booster):
-        booster = model
-    elif isinstance(model, dict):
-        booster = model['booster']
-    else:
-        raise TypeError(_expect([Booster, dict], type(model)))
-    if not isinstance(data, (DaskDMatrix, da.Array, dd.DataFrame)):
-        raise TypeError(_expect([DaskDMatrix, da.Array, dd.DataFrame],
-                                type(data)))
-
-    def mapped_predict(partition, is_df):
-        worker = distributed_get_worker()
-        m = DMatrix(partition, missing=missing, nthread=worker.nthreads)
-        predt = booster.predict(m, *args, validate_features=False)
-        if is_df:
-            predt = DataFrame(predt, columns=['prediction'])
-        return predt
-
-    if isinstance(data, da.Array):
-        predictions = client.submit(
-            da.map_blocks,
-            mapped_predict, data, False, drop_axis=1,
-            dtype=numpy.float32
-        ).result()
-        return predictions
-    if isinstance(data, dd.DataFrame):
-        predictions = client.submit(
-            dd.map_partitions,
-            mapped_predict, data, True,
-            meta=dd.utils.make_meta({'prediction': 'f4'})
-        ).result()
-        return predictions.iloc[:, 0]
-
-    # Prediction on dask DMatrix.
-    worker_map = data.worker_map
-
-    def dispatched_predict(worker_id):
-        '''Perform prediction on each worker.'''
-        LOGGER.info('Predicting on %d', worker_id)
-        worker = distributed_get_worker()
-        list_of_parts = data.get_worker_x_ordered(worker)
-        predictions = []
-        booster.set_param({'nthread': worker.nthreads})
-        for part, order in list_of_parts:
-            local_x = DMatrix(part,
-                              feature_names=data.feature_names,
-                              feature_types=data.feature_types,
-                              missing=data.missing,
-                              nthread=worker.nthreads)
-            predt = booster.predict(data=local_x,
-                                    validate_features=local_x.num_row() != 0,
-                                    *args)
-            ret = (delayed(predt), order)
-            predictions.append(ret)
-        return predictions
-
-    def dispatched_get_shape(worker_id):
-        '''Get shape of data in each worker.'''
-        LOGGER.info('Trying to get data shape on %d', worker_id)
-        worker = distributed_get_worker()
-        list_of_parts = data.get_worker_x_ordered(worker)
-        shapes = []
-        for part, order in list_of_parts:
-            shapes.append((part.shape, order))
-        return shapes
-
-    def map_function(func):
-        '''Run function for each part of the data.'''
-        futures = []
-        for wid in range(len(worker_map)):
-            list_of_workers = [list(worker_map.keys())[wid]]
-            f = client.submit(func, wid,
-                              pure=False,
-                              workers=list_of_workers)
-            futures.append(f)
-
-        # Get delayed objects
-        results = client.gather(futures)
-        results = [t for l in results for t in l]     # flatten into 1 dim list
-        # sort by order, l[0] is the delayed object, l[1] is its order
-        results = sorted(results, key=lambda l: l[1])
-        results = [predt for predt, order in results]  # remove order
-        return results
-
-    results = map_function(dispatched_predict)
-    shapes = map_function(dispatched_get_shape)
-
-    # Constructing a dask array from list of numpy arrays
-    # See https://docs.dask.org/en/latest/array-creation.html
-    arrays = []
-    for i, shape in enumerate(shapes):
-        arrays.append(da.from_delayed(results[i], shape=(shape[0], ),
-                                      dtype=numpy.float32))
-    predictions = da.concatenate(arrays, axis=0)
-    return predictions
-
-
-def inplace_predict(client, model, data,
-                    iteration_range=(0, 0),
-                    predict_type='value',
-                    missing=numpy.nan):
-    '''Inplace prediction.
-
-    .. versionadded:: 1.1.0
-
-    Parameters
-    ----------
-    client: dask.distributed.Client
-        Specify the dask client used for training.  Use default client
-        returned from dask if it's set to None.
-    model: Booster/dict
-        The trained model.
-    iteration_range: tuple
-        Specify the range of trees used for prediction.
-    predict_type: str
-        * 'value': Normal prediction result.
-        * 'margin': Output the raw untransformed margin value.
-    missing: float
-        Value in the input data which needs to be present as a missing
-        value. If None, defaults to np.nan.
-    Returns
-    -------
-    prediction: dask.array.Array
-    '''
-    _assert_dask_support()
-    client = _xgb_get_client(client)
-    if isinstance(model, Booster):
-        booster = model
-    elif isinstance(model, dict):
-        booster = model['booster']
-    else:
-        raise TypeError(_expect([Booster, dict], type(model)))
-    if not isinstance(data, (da.Array, dd.DataFrame)):
-        raise TypeError(_expect([da.Array, dd.DataFrame], type(data)))
-
-    def mapped_predict(data, is_df):
-        worker = distributed_get_worker()
-        booster.set_param({'nthread': worker.nthreads})
-        prediction = booster.inplace_predict(
-            data,
-            iteration_range=iteration_range,
-            predict_type=predict_type,
-            missing=missing)
-        if is_df:
-            if lazy_isinstance(data, 'cudf.core.dataframe', 'DataFrame'):
-                import cudf     # pylint: disable=import-error
-                prediction = cudf.DataFrame({'prediction': prediction},
-                                            dtype=numpy.float32)
-            else:
-                # If it's  from pandas, the partition is a numpy array
-                prediction = DataFrame(prediction, columns=['prediction'],
-                                       dtype=numpy.float32)
-        return prediction
-
-    if isinstance(data, da.Array):
-        predictions = client.submit(
-            da.map_blocks,
-            mapped_predict, data, False, drop_axis=1,
-            dtype=numpy.float32
-        ).result()
-        return predictions
-    if isinstance(data, dd.DataFrame):
-        predictions = client.submit(
-            dd.map_partitions,
-            mapped_predict, data, True,
-            meta=dd.utils.make_meta({'prediction': 'f4'})
-        ).result()
-        return predictions.iloc[:, 0]
-
-
-def _evaluation_matrices(client, validation_set, sample_weights, missing):
-    '''
-    Parameters
-    ----------
-    validation_set: list of tuples
-        Each tuple contains a validation dataset including input X and label y.
-        E.g.:
-
-        .. code-block:: python
-
-          [(X_0, y_0), (X_1, y_1), ... ]
-
-    sample_weights: list of arrays
-        The weight vector for validation data.
-
-    Returns
-    -------
-    evals: list of validation DMatrix
-    '''
-    evals = []
-    if validation_set is not None:
-        assert isinstance(validation_set, list)
-        for i, e in enumerate(validation_set):
-            w = (sample_weights[i]
-                 if sample_weights is not None else None)
-            dmat = DaskDMatrix(client=client, data=e[0], label=e[1], weight=w,
-                               missing=missing)
-            evals.append((dmat, 'validation_{}'.format(i)))
-    else:
-        evals = None
-    return evals
-
-
-class DaskScikitLearnBase(XGBModel):
-    '''Base class for implementing scikit-learn interface with Dask'''
-
-    _client = None
-
-    # pylint: disable=arguments-differ
-    def fit(self,
-            X,
-            y,
-            sample_weights=None,
-            eval_set=None,
-            sample_weight_eval_set=None,
-            verbose=True):
-        '''Fit the regressor.
-
-        Parameters
-        ----------
-        X : array_like
-            Feature matrix
-        y : array_like
-            Labels
-        sample_weight : array_like
-            instance weights
-        eval_set : list, optional
-            A list of (X, y) tuple pairs to use as validation sets, for which
-            metrics will be computed.
-            Validation metrics will help us track the performance of the model.
-        sample_weight_eval_set : list, optional
-            A list of the form [L_1, L_2, ..., L_n], where each L_i is a list
-            of group weights on the i-th validation set.
-        verbose : bool
-            If `verbose` and an evaluation set is used, writes the evaluation
-            metric measured on the validation set to stderr.'''
-        raise NotImplementedError
-
-    def predict(self, data):  # pylint: disable=arguments-differ
-        '''Predict with `data`.
-        Parameters
-        ----------
-          data: data that can be used to construct a DaskDMatrix
-        Returns
-        -------
-        prediction : dask.array.Array'''
-        raise NotImplementedError
-
-    @property
-    def client(self):
-        '''The dask client used in this model.'''
-        client = _xgb_get_client(self._client)
-        return client
-
-    @client.setter
-    def client(self, clt):
-        self._client = clt
-
-@xgboost_model_doc("""Implementation of the Scikit-Learn API for XGBoost.""",
-                   ['estimators', 'model'])
-class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
-    # pylint: disable=missing-docstring
-    def fit(self,
-            X,
-            y,
-            sample_weights=None,
-            eval_set=None,
-            sample_weight_eval_set=None,
-            verbose=True):
-        _assert_dask_support()
-        dtrain = DaskDMatrix(client=self.client,
-                             data=X, label=y, weight=sample_weights,
-                             missing=self.missing)
-        params = self.get_xgb_params()
-        evals = _evaluation_matrices(self.client,
-                                     eval_set, sample_weight_eval_set,
-                                     self.missing)
-
-        results = train(self.client, params, dtrain,
-                        num_boost_round=self.get_num_boosting_rounds(),
-                        evals=evals, verbose_eval=verbose)
-        # pylint: disable=attribute-defined-outside-init
-        self._Booster = results['booster']
-        # pylint: disable=attribute-defined-outside-init
-        self.evals_result_ = results['history']
-        return self
-
-    def predict(self, data):  # pylint: disable=arguments-differ
-        _assert_dask_support()
-        test_dmatrix = DaskDMatrix(client=self.client, data=data,
-                                   missing=self.missing)
-        pred_probs = predict(client=self.client,
-                             model=self.get_booster(), data=test_dmatrix)
-        return pred_probs
-
-
-@xgboost_model_doc(
-    'Implementation of the scikit-learn API for XGBoost classification.',
-    ['estimators', 'model']
-)
-class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
-    # pylint: disable=missing-docstring
-    _client = None
-
-    def fit(self,
-            X,
-            y,
-            sample_weights=None,
-            eval_set=None,
-            sample_weight_eval_set=None,
-            verbose=True):
-        _assert_dask_support()
-        dtrain = DaskDMatrix(client=self.client,
-                             data=X, label=y, weight=sample_weights,
-                             missing=self.missing)
-        params = self.get_xgb_params()
-
-        # pylint: disable=attribute-defined-outside-init
-        if isinstance(y, (da.Array)):
-            self.classes_ = da.unique(y).compute()
-        else:
-            self.classes_ = y.drop_duplicates().compute()
-        self.n_classes_ = len(self.classes_)
-
-        if self.n_classes_ > 2:
-            params["objective"] = "multi:softprob"
-            params['num_class'] = self.n_classes_
-        else:
-            params["objective"] = "binary:logistic"
-
-        evals = _evaluation_matrices(self.client,
-                                     eval_set, sample_weight_eval_set,
-                                     self.missing)
-        results = train(self.client, params, dtrain,
-                        num_boost_round=self.get_num_boosting_rounds(),
-                        evals=evals, verbose_eval=verbose)
-        self._Booster = results['booster']
-        # pylint: disable=attribute-defined-outside-init
-        self.evals_result_ = results['history']
-        return self
-
-    def predict(self, data):  # pylint: disable=arguments-differ
-        _assert_dask_support()
-        test_dmatrix = DaskDMatrix(client=self.client, data=data,
-                                   missing=self.missing)
-        pred_probs = predict(client=self.client,
-                             model=self.get_booster(), data=test_dmatrix)
-        return pred_probs
diff --git a/ml-xgboost/python-package/xgboost/libpath.py b/ml-xgboost/python-package/xgboost/libpath.py
deleted file mode 100644
index 65f7403..0000000
--- a/ml-xgboost/python-package/xgboost/libpath.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# coding: utf-8
-"""Find the path to xgboost dynamic library files."""
-
-import os
-import platform
-import sys
-
-
-class XGBoostLibraryNotFound(Exception):
-    """Error thrown by when xgboost is not found"""
-
-
-def find_lib_path():
-    """Find the path to xgboost dynamic library files.
-
-    Returns
-    -------
-    lib_path: list(string)
-       List of all found library path to xgboost
-    """
-    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-    dll_path = [
-        # normal, after installation `lib` is copied into Python package tree.
-        os.path.join(curr_path, 'lib'),
-        # editable installation, no copying is performed.
-        os.path.join(curr_path, os.path.pardir, os.path.pardir, 'lib'),
-    ]
-    if sys.platform == 'win32':
-        if platform.architecture()[0] == '64bit':
-            dll_path.append(
-                os.path.join(curr_path, '../../windows/x64/Release/'))
-            # hack for pip installation when copy all parent source
-            # directory here
-            dll_path.append(os.path.join(curr_path, './windows/x64/Release/'))
-        else:
-            dll_path.append(os.path.join(curr_path, '../../windows/Release/'))
-            # hack for pip installation when copy all parent source
-            # directory here
-            dll_path.append(os.path.join(curr_path, './windows/Release/'))
-        dll_path = [os.path.join(p, 'xgboost.dll') for p in dll_path]
-    elif sys.platform.startswith('linux') or sys.platform.startswith(
-            'freebsd'):
-        dll_path = [os.path.join(p, 'libxgboost.so') for p in dll_path]
-    elif sys.platform == 'darwin':
-        dll_path = [os.path.join(p, 'libxgboost.dylib') for p in dll_path]
-    elif sys.platform == 'cygwin':
-        dll_path = [os.path.join(p, 'cygxgboost.dll') for p in dll_path]
-
-    lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
-
-    # XGBOOST_BUILD_DOC is defined by sphinx conf.
-    if not lib_path and not os.environ.get('XGBOOST_BUILD_DOC', False):
-        link = 'https://xgboost.readthedocs.io/en/latest/build.html'
-        msg = 'Cannot find XGBoost Library in the candidate path.  ' + \
-            'List of candidates:\n- ' + ('\n- '.join(dll_path)) + \
-            '\nXGBoost Python package path: ' + curr_path + \
-            '\nsys.prefix: ' + sys.prefix + \
-            '\nSee: ' + link + ' for installing XGBoost.'
-        raise XGBoostLibraryNotFound(msg)
-    return lib_path
diff --git a/ml-xgboost/python-package/xgboost/plotting.py b/ml-xgboost/python-package/xgboost/plotting.py
deleted file mode 100644
index 8fa306f..0000000
--- a/ml-xgboost/python-package/xgboost/plotting.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# pylint: disable=too-many-locals, too-many-arguments, invalid-name,
-# pylint: disable=too-many-branches
-# coding: utf-8
-"""Plotting Library."""
-from io import BytesIO
-import numpy as np
-from .core import Booster
-from .sklearn import XGBModel
-
-
-def plot_importance(booster, ax=None, height=0.2,
-                    xlim=None, ylim=None, title='Feature importance',
-                    xlabel='F score', ylabel='Features',
-                    importance_type='weight', max_num_features=None,
-                    grid=True, show_values=True, **kwargs):
-    """Plot importance based on fitted trees.
-
-    Parameters
-    ----------
-    booster : Booster, XGBModel or dict
-        Booster or XGBModel instance, or dict taken by Booster.get_fscore()
-    ax : matplotlib Axes, default None
-        Target axes instance. If None, new figure and axes will be created.
-    grid : bool, Turn the axes grids on or off.  Default is True (On).
-    importance_type : str, default "weight"
-        How the importance is calculated: either "weight", "gain", or "cover"
-
-        * "weight" is the number of times a feature appears in a tree
-        * "gain" is the average gain of splits which use the feature
-        * "cover" is the average coverage of splits which use the feature
-          where coverage is defined as the number of samples affected by the split
-    max_num_features : int, default None
-        Maximum number of top features displayed on plot. If None, all features will be displayed.
-    height : float, default 0.2
-        Bar height, passed to ax.barh()
-    xlim : tuple, default None
-        Tuple passed to axes.xlim()
-    ylim : tuple, default None
-        Tuple passed to axes.ylim()
-    title : str, default "Feature importance"
-        Axes title. To disable, pass None.
-    xlabel : str, default "F score"
-        X axis title label. To disable, pass None.
-    ylabel : str, default "Features"
-        Y axis title label. To disable, pass None.
-    show_values : bool, default True
-        Show values on plot. To disable, pass False.
-    kwargs :
-        Other keywords passed to ax.barh()
-
-    Returns
-    -------
-    ax : matplotlib Axes
-    """
-    try:
-        import matplotlib.pyplot as plt
-    except ImportError:
-        raise ImportError('You must install matplotlib to plot importance')
-
-    if isinstance(booster, XGBModel):
-        importance = booster.get_booster().get_score(
-            importance_type=importance_type)
-    elif isinstance(booster, Booster):
-        importance = booster.get_score(importance_type=importance_type)
-    elif isinstance(booster, dict):
-        importance = booster
-    else:
-        raise ValueError('tree must be Booster, XGBModel or dict instance')
-
-    if not importance:
-        raise ValueError(
-            'Booster.get_score() results in empty.  ' +
-            'This maybe caused by having all trees as decision dumps.')
-
-    tuples = [(k, importance[k]) for k in importance]
-    if max_num_features is not None:
-        # pylint: disable=invalid-unary-operand-type
-        tuples = sorted(tuples, key=lambda x: x[1])[-max_num_features:]
-    else:
-        tuples = sorted(tuples, key=lambda x: x[1])
-    labels, values = zip(*tuples)
-
-    if ax is None:
-        _, ax = plt.subplots(1, 1)
-
-    ylocs = np.arange(len(values))
-    ax.barh(ylocs, values, align='center', height=height, **kwargs)
-
-    if show_values is True:
-        for x, y in zip(values, ylocs):
-            ax.text(x + 1, y, x, va='center')
-
-    ax.set_yticks(ylocs)
-    ax.set_yticklabels(labels)
-
-    if xlim is not None:
-        if not isinstance(xlim, tuple) or len(xlim) != 2:
-            raise ValueError('xlim must be a tuple of 2 elements')
-    else:
-        xlim = (0, max(values) * 1.1)
-    ax.set_xlim(xlim)
-
-    if ylim is not None:
-        if not isinstance(ylim, tuple) or len(ylim) != 2:
-            raise ValueError('ylim must be a tuple of 2 elements')
-    else:
-        ylim = (-1, len(values))
-    ax.set_ylim(ylim)
-
-    if title is not None:
-        ax.set_title(title)
-    if xlabel is not None:
-        ax.set_xlabel(xlabel)
-    if ylabel is not None:
-        ax.set_ylabel(ylabel)
-    ax.grid(grid)
-    return ax
-
-
-def to_graphviz(booster, fmap='', num_trees=0, rankdir=None,
-                yes_color=None, no_color=None,
-                condition_node_params=None, leaf_node_params=None, **kwargs):
-    """Convert specified tree to graphviz instance. IPython can automatically plot
-    the returned graphiz instance. Otherwise, you should call .render() method
-    of the returned graphiz instance.
-
-    Parameters
-    ----------
-    booster : Booster, XGBModel
-        Booster or XGBModel instance
-    fmap: str (optional)
-       The name of feature map file
-    num_trees : int, default 0
-        Specify the ordinal number of target tree
-    rankdir : str, default "UT"
-        Passed to graphiz via graph_attr
-    yes_color : str, default '#0000FF'
-        Edge color when meets the node condition.
-    no_color : str, default '#FF0000'
-        Edge color when doesn't meet the node condition.
-    condition_node_params : dict, optional
-        Condition node configuration for for graphviz.  Example:
-
-        .. code-block:: python
-
-            {'shape': 'box',
-             'style': 'filled,rounded',
-             'fillcolor': '#78bceb'}
-
-    leaf_node_params : dict, optional
-        Leaf node configuration for graphviz. Example:
-
-        .. code-block:: python
-
-            {'shape': 'box',
-             'style': 'filled',
-             'fillcolor': '#e48038'}
-
-    \\*\\*kwargs: dict, optional
-        Other keywords passed to graphviz graph_attr, e.g. ``graph [ {key} = {value} ]``
-
-    Returns
-    -------
-    graph: graphviz.Source
-
-    """
-    try:
-        from graphviz import Source
-    except ImportError:
-        raise ImportError('You must install graphviz to plot tree')
-    if isinstance(booster, XGBModel):
-        booster = booster.get_booster()
-
-    # squash everything back into kwargs again for compatibility
-    parameters = 'dot'
-    extra = {}
-    for key, value in kwargs.items():
-        extra[key] = value
-
-    if rankdir is not None:
-        kwargs['graph_attrs'] = {}
-        kwargs['graph_attrs']['rankdir'] = rankdir
-    for key, value in extra.items():
-        if 'graph_attrs' in kwargs.keys():
-            kwargs['graph_attrs'][key] = value
-        else:
-            kwargs['graph_attrs'] = {}
-        del kwargs[key]
-
-    if yes_color is not None or no_color is not None:
-        kwargs['edge'] = {}
-    if yes_color is not None:
-        kwargs['edge']['yes_color'] = yes_color
-    if no_color is not None:
-        kwargs['edge']['no_color'] = no_color
-
-    if condition_node_params is not None:
-        kwargs['condition_node_params'] = condition_node_params
-    if leaf_node_params is not None:
-        kwargs['leaf_node_params'] = leaf_node_params
-
-    if kwargs:
-        parameters += ':'
-        parameters += str(kwargs)
-    tree = booster.get_dump(
-        fmap=fmap,
-        dump_format=parameters)[num_trees]
-    g = Source(tree)
-    return g
-
-
-def plot_tree(booster, fmap='', num_trees=0, rankdir=None, ax=None, **kwargs):
-    """Plot specified tree.
-
-    Parameters
-    ----------
-    booster : Booster, XGBModel
-        Booster or XGBModel instance
-    fmap: str (optional)
-       The name of feature map file
-    num_trees : int, default 0
-        Specify the ordinal number of target tree
-    rankdir : str, default "TB"
-        Passed to graphiz via graph_attr
-    ax : matplotlib Axes, default None
-        Target axes instance. If None, new figure and axes will be created.
-    kwargs :
-        Other keywords passed to to_graphviz
-
-    Returns
-    -------
-    ax : matplotlib Axes
-
-    """
-    try:
-        from matplotlib import pyplot as plt
-        from matplotlib import image
-    except ImportError:
-        raise ImportError('You must install matplotlib to plot tree')
-
-    if ax is None:
-        _, ax = plt.subplots(1, 1)
-
-    g = to_graphviz(booster, fmap=fmap, num_trees=num_trees, rankdir=rankdir,
-                    **kwargs)
-
-    s = BytesIO()
-    s.write(g.pipe(format='png'))
-    s.seek(0)
-    img = image.imread(s)
-
-    ax.imshow(img)
-    ax.axis('off')
-    return ax
diff --git a/ml-xgboost/python-package/xgboost/rabit.py b/ml-xgboost/python-package/xgboost/rabit.py
deleted file mode 100644
index 9b39279..0000000
--- a/ml-xgboost/python-package/xgboost/rabit.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# coding: utf-8
-# pylint: disable= invalid-name
-"""Distributed XGBoost Rabit related API."""
-import sys
-import ctypes
-import pickle
-import numpy as np
-
-from .core import _LIB, c_str, STRING_TYPES
-
-
-def _init_rabit():
-    """internal library initializer."""
-    if _LIB is not None:
-        _LIB.RabitGetRank.restype = ctypes.c_int
-        _LIB.RabitGetWorldSize.restype = ctypes.c_int
-        _LIB.RabitIsDistributed.restype = ctypes.c_int
-        _LIB.RabitVersionNumber.restype = ctypes.c_int
-
-
-def init(args=None):
-    """Initialize the rabit library with arguments"""
-    if args is None:
-        args = []
-    arr = (ctypes.c_char_p * len(args))()
-    arr[:] = args
-    _LIB.RabitInit(len(arr), arr)
-
-
-def finalize():
-    """Finalize the process, notify tracker everything is done."""
-    _LIB.RabitFinalize()
-
-
-def get_rank():
-    """Get rank of current process.
-
-    Returns
-    -------
-    rank : int
-        Rank of current process.
-    """
-    ret = _LIB.RabitGetRank()
-    return ret
-
-
-def get_world_size():
-    """Get total number workers.
-
-    Returns
-    -------
-    n : int
-        Total number of process.
-    """
-    ret = _LIB.RabitGetWorldSize()
-    return ret
-
-
-def tracker_print(msg):
-    """Print message to the tracker.
-
-    This function can be used to communicate the information of
-    the progress to the tracker
-
-    Parameters
-    ----------
-    msg : str
-        The message to be printed to tracker.
-    """
-    if not isinstance(msg, STRING_TYPES):
-        msg = str(msg)
-    is_dist = _LIB.RabitIsDistributed()
-    if is_dist != 0:
-        _LIB.RabitTrackerPrint(c_str(msg))
-    else:
-        sys.stdout.write(msg)
-        sys.stdout.flush()
-
-
-def get_processor_name():
-    """Get the processor name.
-
-    Returns
-    -------
-    name : str
-        the name of processor(host)
-    """
-    mxlen = 256
-    length = ctypes.c_ulong()
-    buf = ctypes.create_string_buffer(mxlen)
-    _LIB.RabitGetProcessorName(buf, ctypes.byref(length), mxlen)
-    return buf.value
-
-
-def broadcast(data, root):
-    """Broadcast object from one node to all other nodes.
-
-    Parameters
-    ----------
-    data : any type that can be pickled
-        Input data, if current rank does not equal root, this can be None
-    root : int
-        Rank of the node to broadcast data from.
-
-    Returns
-    -------
-    object : int
-        the result of broadcast.
-    """
-    rank = get_rank()
-    length = ctypes.c_ulong()
-    if root == rank:
-        assert data is not None, 'need to pass in data when broadcasting'
-        s = pickle.dumps(data, protocol=pickle.HIGHEST_PROTOCOL)
-        length.value = len(s)
-    # run first broadcast
-    _LIB.RabitBroadcast(ctypes.byref(length),
-                        ctypes.sizeof(ctypes.c_ulong), root)
-    if root != rank:
-        dptr = (ctypes.c_char * length.value)()
-        # run second
-        _LIB.RabitBroadcast(ctypes.cast(dptr, ctypes.c_void_p),
-                            length.value, root)
-        data = pickle.loads(dptr.raw)
-        del dptr
-    else:
-        _LIB.RabitBroadcast(ctypes.cast(ctypes.c_char_p(s), ctypes.c_void_p),
-                            length.value, root)
-        del s
-    return data
-
-
-# enumeration of dtypes
-DTYPE_ENUM__ = {
-    np.dtype('int8'): 0,
-    np.dtype('uint8'): 1,
-    np.dtype('int32'): 2,
-    np.dtype('uint32'): 3,
-    np.dtype('int64'): 4,
-    np.dtype('uint64'): 5,
-    np.dtype('float32'): 6,
-    np.dtype('float64'): 7
-}
-
-
-def allreduce(data, op, prepare_fun=None):
-    """Perform allreduce, return the result.
-
-    Parameters
-    ----------
-    data: numpy array
-        Input data.
-    op: int
-        Reduction operators, can be MIN, MAX, SUM, BITOR
-    prepare_fun: function
-        Lazy preprocessing function, if it is not None, prepare_fun(data)
-        will be called by the function before performing allreduce, to initialize the data
-        If the result of Allreduce can be recovered directly,
-        then prepare_fun will NOT be called
-
-    Returns
-    -------
-    result : array_like
-        The result of allreduce, have same shape as data
-
-    Notes
-    -----
-    This function is not thread-safe.
-    """
-    if not isinstance(data, np.ndarray):
-        raise Exception('allreduce only takes in numpy.ndarray')
-    buf = data.ravel()
-    if buf.base is data.base:
-        buf = buf.copy()
-    if buf.dtype not in DTYPE_ENUM__:
-        raise Exception('data type %s not supported' % str(buf.dtype))
-    if prepare_fun is None:
-        _LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
-                            buf.size, DTYPE_ENUM__[buf.dtype],
-                            op, None, None)
-    else:
-        func_ptr = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
-
-        def pfunc(_):
-            """prepare function."""
-            prepare_fun(data)
-        _LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
-                            buf.size, DTYPE_ENUM__[buf.dtype],
-                            op, func_ptr(pfunc), None)
-    return buf
-
-
-def version_number():
-    """Returns version number of current stored model.
-
-    This means how many calls to CheckPoint we made so far.
-
-    Returns
-    -------
-    version : int
-        Version number of currently stored model
-    """
-    ret = _LIB.RabitVersionNumber()
-    return ret
-
-
-# intialization script
-_init_rabit()
diff --git a/ml-xgboost/python-package/xgboost/sklearn.py b/ml-xgboost/python-package/xgboost/sklearn.py
deleted file mode 100644
index 2b85ffe..0000000
--- a/ml-xgboost/python-package/xgboost/sklearn.py
+++ /dev/null
@@ -1,1264 +0,0 @@
-# coding: utf-8
-# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912, C0302
-"""Scikit-Learn Wrapper interface for XGBoost."""
-import copy
-import warnings
-import json
-import numpy as np
-from .core import Booster, DMatrix, XGBoostError
-from .training import train
-
-# Do not use class names on scikit-learn directly.  Re-define the classes on
-# .compat to guarantee the behavior without scikit-learn
-from .compat import (SKLEARN_INSTALLED, XGBModelBase,
-                     XGBClassifierBase, XGBRegressorBase, XGBoostLabelEncoder)
-
-
-def _objective_decorator(func):
-    """Decorate an objective function
-
-    Converts an objective function using the typical sklearn metrics
-    signature so that it is usable with ``xgboost.training.train``
-
-    Parameters
-    ----------
-    func: callable
-        Expects a callable with signature ``func(y_true, y_pred)``:
-
-        y_true: array_like of shape [n_samples]
-            The target values
-        y_pred: array_like of shape [n_samples]
-            The predicted values
-
-    Returns
-    -------
-    new_func: callable
-        The new objective function as expected by ``xgboost.training.train``.
-        The signature is ``new_func(preds, dmatrix)``:
-
-        preds: array_like, shape [n_samples]
-            The predicted values
-        dmatrix: ``DMatrix``
-            The training set from which the labels will be extracted using
-            ``dmatrix.get_label()``
-    """
-    def inner(preds, dmatrix):
-        """internal function"""
-        labels = dmatrix.get_label()
-        return func(labels, preds)
-    return inner
-
-
-__estimator_doc = '''
-    n_estimators : int
-        Number of gradient boosted trees.  Equivalent to number of boosting
-        rounds.
-'''
-
-__model_doc = '''
-    max_depth : int
-        Maximum tree depth for base learners.
-    learning_rate : float
-        Boosting learning rate (xgb's "eta")
-    verbosity : int
-        The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
-    objective : string or callable
-        Specify the learning task and the corresponding learning objective or
-        a custom objective function to be used (see note below).
-    booster: string
-        Specify which booster to use: gbtree, gblinear or dart.
-    tree_method: string
-        Specify which tree method to use.  Default to auto.  If this parameter
-        is set to default, XGBoost will choose the most conservative option
-        available.  It's recommended to study this option from parameters
-        document.
-    n_jobs : int
-        Number of parallel threads used to run xgboost.
-    gamma : float
-        Minimum loss reduction required to make a further partition on a leaf
-        node of the tree.
-    min_child_weight : int
-        Minimum sum of instance weight(hessian) needed in a child.
-    max_delta_step : int
-        Maximum delta step we allow each tree's weight estimation to be.
-    subsample : float
-        Subsample ratio of the training instance.
-    colsample_bytree : float
-        Subsample ratio of columns when constructing each tree.
-    colsample_bylevel : float
-        Subsample ratio of columns for each level.
-    colsample_bynode : float
-        Subsample ratio of columns for each split.
-    reg_alpha : float (xgb's alpha)
-        L1 regularization term on weights
-    reg_lambda : float (xgb's lambda)
-        L2 regularization term on weights
-    scale_pos_weight : float
-        Balancing of positive and negative weights.
-    base_score:
-        The initial prediction score of all instances, global bias.
-    random_state : int
-        Random number seed.
-
-        .. note::
-
-           Using gblinear booster with shotgun updater is nondeterministic as
-           it uses Hogwild algorithm.
-
-    missing : float, default np.nan
-        Value in the data which needs to be present as a missing value.
-    num_parallel_tree: int
-        Used for boosting random forest.
-    monotone_constraints : str
-        Constraint of variable monotonicity.  See tutorial for more
-        information.
-    interaction_constraints : str
-        Constraints for interaction representing permitted interactions.  The
-        constraints must be specified in the form of a nest list, e.g. [[0, 1],
-        [2, 3, 4]], where each inner list is a group of indices of features
-        that are allowed to interact with each other.  See tutorial for more
-        information
-    importance_type: string, default "gain"
-        The feature importance type for the feature_importances\\_ property:
-        either "gain", "weight", "cover", "total_gain" or "total_cover".
-
-    \\*\\*kwargs : dict, optional
-        Keyword arguments for XGBoost Booster object.  Full documentation of
-        parameters can be found here:
-        https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.
-        Attempting to set a parameter via the constructor args and \\*\\*kwargs
-        dict simultaneously will result in a TypeError.
-
-        .. note:: \\*\\*kwargs unsupported by scikit-learn
-
-            \\*\\*kwargs is unsupported by scikit-learn.  We do not guarantee
-            that parameters passed via this argument will interact properly
-            with scikit-learn.
-'''
-
-__custom_obj_note = '''
-        .. note::  Custom objective function
-
-            A custom objective function can be provided for the ``objective``
-            parameter. In this case, it should have the signature
-            ``objective(y_true, y_pred) -> grad, hess``:
-
-            y_true: array_like of shape [n_samples]
-                The target values
-            y_pred: array_like of shape [n_samples]
-                The predicted values
-
-            grad: array_like of shape [n_samples]
-                The value of the gradient for each sample point.
-            hess: array_like of shape [n_samples]
-                The value of the second derivative for each sample point
-'''
-
-
-def xgboost_model_doc(header, items, extra_parameters=None, end_note=None):
-    '''Obtain documentation for Scikit-Learn wrappers
-
-    Parameters
-    ----------
-    header: str
-       An introducion to the class.
-    items : list
-       A list of commom doc items.  Available items are:
-         - estimators: the meaning of n_estimators
-         - model: All the other parameters
-         - objective: note for customized objective
-    extra_parameters: str
-       Document for class specific parameters, placed at the head.
-    end_note: str
-       Extra notes put to the end.
-'''
-    def get_doc(item):
-        '''Return selected item'''
-        __doc = {'estimators': __estimator_doc,
-                 'model': __model_doc,
-                 'objective': __custom_obj_note}
-        return __doc[item]
-
-    def adddoc(cls):
-        doc = ['''
-Parameters
-----------
-''']
-        if extra_parameters:
-            doc.append(extra_parameters)
-        doc.extend([get_doc(i) for i in items])
-        if end_note:
-            doc.append(end_note)
-        full_doc = [header + '\n\n']
-        full_doc.extend(doc)
-        cls.__doc__ = ''.join(full_doc)
-        return cls
-    return adddoc
-
-
-@xgboost_model_doc("""Implementation of the Scikit-Learn API for XGBoost.""",
-                   ['estimators', 'model', 'objective'])
-class XGBModel(XGBModelBase):
-    # pylint: disable=too-many-arguments, too-many-instance-attributes, missing-docstring
-    def __init__(self, max_depth=None, learning_rate=None, n_estimators=100,
-                 verbosity=None, objective=None, booster=None,
-                 tree_method=None, n_jobs=None, gamma=None,
-                 min_child_weight=None, max_delta_step=None, subsample=None,
-                 colsample_bytree=None, colsample_bylevel=None,
-                 colsample_bynode=None, reg_alpha=None, reg_lambda=None,
-                 scale_pos_weight=None, base_score=None, random_state=None,
-                 missing=np.nan, num_parallel_tree=None,
-                 monotone_constraints=None, interaction_constraints=None,
-                 importance_type="gain", gpu_id=None,
-                 validate_parameters=None, **kwargs):
-        if not SKLEARN_INSTALLED:
-            raise XGBoostError(
-                'sklearn needs to be installed in order to use this module')
-        self.n_estimators = n_estimators
-        self.objective = objective
-
-        self.max_depth = max_depth
-        self.learning_rate = learning_rate
-        self.verbosity = verbosity
-        self.booster = booster
-        self.tree_method = tree_method
-        self.gamma = gamma
-        self.min_child_weight = min_child_weight
-        self.max_delta_step = max_delta_step
-        self.subsample = subsample
-        self.colsample_bytree = colsample_bytree
-        self.colsample_bylevel = colsample_bylevel
-        self.colsample_bynode = colsample_bynode
-        self.reg_alpha = reg_alpha
-        self.reg_lambda = reg_lambda
-        self.scale_pos_weight = scale_pos_weight
-        self.base_score = base_score
-        self.missing = missing
-        self.num_parallel_tree = num_parallel_tree
-        self.kwargs = kwargs
-        self.random_state = random_state
-        self.n_jobs = n_jobs
-        self.monotone_constraints = monotone_constraints
-        self.interaction_constraints = interaction_constraints
-        self.importance_type = importance_type
-        self.gpu_id = gpu_id
-        self.validate_parameters = validate_parameters
-
-    def _more_tags(self):
-        '''Tags used for scikit-learn data validation.'''
-        return {'allow_nan': True}
-
-    def get_booster(self):
-        """Get the underlying xgboost Booster of this model.
-
-        This will raise an exception when fit was not called
-
-        Returns
-        -------
-        booster : a xgboost booster of underlying model
-        """
-        if not hasattr(self, '_Booster'):
-            raise XGBoostError('need to call fit or load_model beforehand')
-        return self._Booster
-
-    def set_params(self, **params):
-        """Set the parameters of this estimator.  Modification of the sklearn method to
-        allow unknown kwargs. This allows using the full range of xgboost
-        parameters that are not defined as member variables in sklearn grid
-        search.
-
-        Returns
-        -------
-        self
-
-        """
-        if not params:
-            # Simple optimization to gain speed (inspect is slow)
-            return self
-
-        # this concatenates kwargs into paraemters, enabling `get_params` for
-        # obtaining parameters from keyword paraemters.
-        for key, value in params.items():
-            if hasattr(self, key):
-                setattr(self, key, value)
-            else:
-                self.kwargs[key] = value
-
-        return self
-
-    def get_params(self, deep=True):
-        # pylint: disable=attribute-defined-outside-init
-        """Get parameters."""
-        # Based on: https://stackoverflow.com/questions/59248211
-        # The basic flow in `get_params` is:
-        # 0. Return parameters in subclass first, by using inspect.
-        # 1. Return parameters in `XGBModel` (the base class).
-        # 2. Return whatever in `**kwargs`.
-        # 3. Merge them.
-        params = super().get_params(deep)
-        cp = copy.copy(self)
-        cp.__class__ = cp.__class__.__bases__[0]
-        params.update(cp.__class__.get_params(cp, deep))
-        # if kwargs is a dict, update params accordingly
-        if isinstance(self.kwargs, dict):
-            params.update(self.kwargs)
-        if isinstance(params['random_state'], np.random.RandomState):
-            params['random_state'] = params['random_state'].randint(
-                np.iinfo(np.int32).max)
-
-        def parse_parameter(value):
-            for t in (int, float, str):
-                try:
-                    ret = t(value)
-                    return ret
-                except ValueError:
-                    continue
-            return None
-
-        # Get internal parameter values
-        try:
-            config = json.loads(self.get_booster().save_config())
-            stack = [config]
-            internal = {}
-            while stack:
-                obj = stack.pop()
-                for k, v in obj.items():
-                    if k.endswith('_param'):
-                        for p_k, p_v in v.items():
-                            internal[p_k] = p_v
-                    elif isinstance(v, dict):
-                        stack.append(v)
-
-            for k, v in internal.items():
-                if k in params.keys() and params[k] is None:
-                    params[k] = parse_parameter(v)
-        except XGBoostError:
-            pass
-        return params
-
-    def get_xgb_params(self):
-        """Get xgboost specific parameters."""
-        params = self.get_params()
-        # Parameters that should not go into native learner.
-        wrapper_specific = {
-            'importance_type', 'kwargs', 'missing', 'n_estimators'}
-        filtered = dict()
-        for k, v in params.items():
-            if k not in wrapper_specific:
-                filtered[k] = v
-        return filtered
-
-    def get_num_boosting_rounds(self):
-        """Gets the number of xgboost boosting rounds."""
-        return self.n_estimators
-
-    def save_model(self, fname: str):
-        """Save the model to a file.
-
-        The model is saved in an XGBoost internal format which is universal
-        among the various XGBoost interfaces. Auxiliary attributes of the
-        Python Booster object (such as feature names) will not be saved.
-
-          .. note::
-
-            See:
-
-            https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
-
-        Parameters
-        ----------
-        fname : string
-            Output file name
-
-        """
-        meta = dict()
-        for k, v in self.__dict__.items():
-            if k == '_le':
-                meta['_le'] = self._le.to_json()
-                continue
-            if k == '_Booster':
-                continue
-            if k == 'classes_':
-                # numpy array is not JSON serializable
-                meta['classes_'] = self.classes_.tolist()
-                continue
-            try:
-                json.dumps({k: v})
-                meta[k] = v
-            except TypeError:
-                warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
-        meta['type'] = type(self).__name__
-        meta = json.dumps(meta)
-        self.get_booster().set_attr(scikit_learn=meta)
-        self.get_booster().save_model(fname)
-        # Delete the attribute after save
-        self.get_booster().set_attr(scikit_learn=None)
-
-    def load_model(self, fname):
-        # pylint: disable=attribute-defined-outside-init
-        """Load the model from a file.
-
-        The model is loaded from an XGBoost internal format which is universal
-        among the various XGBoost interfaces. Auxiliary attributes of the
-        Python Booster object (such as feature names) will not be loaded.
-
-        Parameters
-        ----------
-        fname : string
-            Input file name.
-
-        """
-        if not hasattr(self, '_Booster'):
-            self._Booster = Booster({'n_jobs': self.n_jobs})
-        self._Booster.load_model(fname)
-        meta = self._Booster.attr('scikit_learn')
-        if meta is None:
-            warnings.warn(
-                'Loading a native XGBoost model with Scikit-Learn interface.')
-            return
-        meta = json.loads(meta)
-        states = dict()
-        for k, v in meta.items():
-            if k == '_le':
-                self._le = XGBoostLabelEncoder()
-                self._le.from_json(v)
-                continue
-            if k == 'classes_':
-                self.classes_ = np.array(v)
-                continue
-            if k == 'type' and type(self).__name__ != v:
-                msg = 'Current model type: {}, '.format(type(self).__name__) + \
-                      'type of model in file: {}'.format(v)
-                raise TypeError(msg)
-            if k == 'type':
-                continue
-            states[k] = v
-        self.__dict__.update(states)
-        # Delete the attribute after load
-        self.get_booster().set_attr(scikit_learn=None)
-
-    def fit(self, X, y, sample_weight=None, base_margin=None,
-            eval_set=None, eval_metric=None, early_stopping_rounds=None,
-            verbose=True, xgb_model=None, sample_weight_eval_set=None,
-            callbacks=None):
-        # pylint: disable=invalid-name,attribute-defined-outside-init
-        """Fit gradient boosting model
-
-        Parameters
-        ----------
-        X : array_like
-            Feature matrix
-        y : array_like
-            Labels
-        sample_weight : array_like
-            instance weights
-        base_margin : array_like
-            global bias for each instance.
-        eval_set : list, optional
-            A list of (X, y) tuple pairs to use as validation sets, for which
-            metrics will be computed.
-            Validation metrics will help us track the performance of the model.
-        sample_weight_eval_set : list, optional
-            A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
-            instance weights on the i-th validation set.
-        eval_metric : str, list of str, or callable, optional
-            If a str, should be a built-in evaluation metric to use. See
-            doc/parameter.rst.
-            If a list of str, should be the list of multiple built-in evaluation metrics
-            to use.
-            If callable, a custom evaluation metric. The call
-            signature is ``func(y_predicted, y_true)`` where ``y_true`` will be a
-            DMatrix object such that you may need to call the ``get_label``
-            method. It must return a str, value pair where the str is a name
-            for the evaluation and value is the value of the evaluation
-            function. The callable custom objective is always minimized.
-        early_stopping_rounds : int
-            Activates early stopping. Validation metric needs to improve at least once in
-            every **early_stopping_rounds** round(s) to continue training.
-            Requires at least one item in **eval_set**.
-            The method returns the model from the last iteration (not the best one).
-            If there's more than one item in **eval_set**, the last entry will be used
-            for early stopping.
-            If there's more than one metric in **eval_metric**, the last metric will be
-            used for early stopping.
-            If early stopping occurs, the model will have three additional fields:
-            ``clf.best_score``, ``clf.best_iteration`` and ``clf.best_ntree_limit``.
-        verbose : bool
-            If `verbose` and an evaluation set is used, writes the evaluation
-            metric measured on the validation set to stderr.
-        xgb_model : str
-            file name of stored XGBoost model or 'Booster' instance XGBoost model to be
-            loaded before training (allows training continuation).
-        callbacks : list of callback functions
-            List of callback functions that are applied at end of each iteration.
-            It is possible to use predefined callbacks by using :ref:`callback_api`.
-            Example:
-
-            .. code-block:: python
-
-                [xgb.callback.reset_learning_rate(custom_rates)]
-        """
-        train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
-                                base_margin=base_margin,
-                                missing=self.missing,
-                                nthread=self.n_jobs)
-
-        evals_result = {}
-
-        if eval_set is not None:
-            if not isinstance(eval_set[0], (list, tuple)):
-                raise TypeError('Unexpected input type for `eval_set`')
-            if sample_weight_eval_set is None:
-                sample_weight_eval_set = [None] * len(eval_set)
-            else:
-                assert len(eval_set) == len(sample_weight_eval_set)
-            evals = list(
-                DMatrix(eval_set[i][0], label=eval_set[i][1], missing=self.missing,
-                        weight=sample_weight_eval_set[i], nthread=self.n_jobs)
-                for i in range(len(eval_set)))
-            evals = list(zip(evals, ["validation_{}".format(i) for i in
-                                     range(len(evals))]))
-        else:
-            evals = ()
-
-        params = self.get_xgb_params()
-
-        if callable(self.objective):
-            obj = _objective_decorator(self.objective)
-            params["objective"] = "reg:squarederror"
-        else:
-            obj = None
-
-        feval = eval_metric if callable(eval_metric) else None
-        if eval_metric is not None:
-            if callable(eval_metric):
-                eval_metric = None
-            else:
-                params.update({'eval_metric': eval_metric})
-
-        self._Booster = train(params, train_dmatrix,
-                              self.get_num_boosting_rounds(), evals=evals,
-                              early_stopping_rounds=early_stopping_rounds,
-                              evals_result=evals_result, obj=obj, feval=feval,
-                              verbose_eval=verbose, xgb_model=xgb_model,
-                              callbacks=callbacks)
-
-        if evals_result:
-            for val in evals_result.items():
-                evals_result_key = list(val[1].keys())[0]
-                evals_result[val[0]][evals_result_key] = val[1][
-                    evals_result_key]
-            self.evals_result_ = evals_result
-
-        if early_stopping_rounds is not None:
-            self.best_score = self._Booster.best_score
-            self.best_iteration = self._Booster.best_iteration
-            self.best_ntree_limit = self._Booster.best_ntree_limit
-        return self
-
-    def predict(self, data, output_margin=False, ntree_limit=None,
-                validate_features=True, base_margin=None):
-        """
-        Predict with `data`.
-
-        .. note:: This function is not thread safe.
-
-          For each booster object, predict can only be called from one thread.
-          If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
-          of model object and then call ``predict()``.
-
-          .. code-block:: python
-
-            preds = bst.predict(dtest, ntree_limit=num_round)
-
-        Parameters
-        ----------
-        data : numpy.array/scipy.sparse
-            Data to predict with
-        output_margin : bool
-            Whether to output the raw untransformed margin value.
-        ntree_limit : int
-            Limit number of trees in the prediction; defaults to best_ntree_limit if defined
-            (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
-        validate_features : bool
-            When this is True, validate that the Booster's and data's feature_names are identical.
-            Otherwise, it is assumed that the feature_names are the same.
-        Returns
-        -------
-        prediction : numpy array
-        """
-        # pylint: disable=missing-docstring,invalid-name
-        test_dmatrix = DMatrix(data, base_margin=base_margin,
-                               missing=self.missing, nthread=self.n_jobs)
-        # get ntree_limit to use - if none specified, default to
-        # best_ntree_limit if defined, otherwise 0.
-        if ntree_limit is None:
-            ntree_limit = getattr(self, "best_ntree_limit", 0)
-        return self.get_booster().predict(test_dmatrix,
-                                          output_margin=output_margin,
-                                          ntree_limit=ntree_limit,
-                                          validate_features=validate_features)
-
-    def apply(self, X, ntree_limit=0):
-        """Return the predicted leaf every tree for each sample.
-
-        Parameters
-        ----------
-        X : array_like, shape=[n_samples, n_features]
-            Input features matrix.
-
-        ntree_limit : int
-            Limit number of trees in the prediction; defaults to 0 (use all trees).
-
-        Returns
-        -------
-        X_leaves : array_like, shape=[n_samples, n_trees]
-            For each datapoint x in X and for each tree, return the index of the
-            leaf x ends up in. Leaves are numbered within
-            ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering.
-        """
-        test_dmatrix = DMatrix(X, missing=self.missing, nthread=self.n_jobs)
-        return self.get_booster().predict(test_dmatrix,
-                                          pred_leaf=True,
-                                          ntree_limit=ntree_limit)
-
-    def evals_result(self):
-        """Return the evaluation results.
-
-        If **eval_set** is passed to the `fit` function, you can call
-        ``evals_result()`` to get evaluation results for all passed **eval_sets**.
-        When **eval_metric** is also passed to the `fit` function, the
-        **evals_result** will contain the **eval_metrics** passed to the `fit` function.
-
-        Returns
-        -------
-        evals_result : dictionary
-
-        Example
-        -------
-
-        .. code-block:: python
-
-            param_dist = {'objective':'binary:logistic', 'n_estimators':2}
-
-            clf = xgb.XGBModel(**param_dist)
-
-            clf.fit(X_train, y_train,
-                    eval_set=[(X_train, y_train), (X_test, y_test)],
-                    eval_metric='logloss',
-                    verbose=True)
-
-            evals_result = clf.evals_result()
-
-        The variable **evals_result** will contain:
-
-        .. code-block:: python
-
-            {'validation_0': {'logloss': ['0.604835', '0.531479']},
-            'validation_1': {'logloss': ['0.41965', '0.17686']}}
-        """
-        if self.evals_result_:
-            evals_result = self.evals_result_
-        else:
-            raise XGBoostError('No results.')
-
-        return evals_result
-
-    @property
-    def feature_importances_(self):
-        """
-        Feature importances property
-
-        .. note:: Feature importance is defined only for tree boosters
-
-            Feature importance is only defined when the decision tree model is chosen as base
-            learner (`booster=gbtree`). It is not defined for other base learner types, such
-            as linear learners (`booster=gblinear`).
-
-        Returns
-        -------
-        feature_importances_ : array of shape ``[n_features]``
-
-        """
-        if self.get_params()['booster'] not in {'gbtree', 'dart'}:
-            raise AttributeError(
-                'Feature importance is not defined for Booster type {}'
-                .format(self.booster))
-        b = self.get_booster()
-        score = b.get_score(importance_type=self.importance_type)
-        all_features = [score.get(f, 0.) for f in b.feature_names]
-        all_features = np.array(all_features, dtype=np.float32)
-        return all_features / all_features.sum()
-
-    @property
-    def coef_(self):
-        """
-        Coefficients property
-
-        .. note:: Coefficients are defined only for linear learners
-
-            Coefficients are only defined when the linear model is chosen as
-            base learner (`booster=gblinear`). It is not defined for other base
-            learner types, such as tree learners (`booster=gbtree`).
-
-        Returns
-        -------
-        coef_ : array of shape ``[n_features]`` or ``[n_classes, n_features]``
-        """
-        if self.get_params()['booster'] != 'gblinear':
-            raise AttributeError(
-                'Coefficients are not defined for Booster type {}'
-                .format(self.booster))
-        b = self.get_booster()
-        coef = np.array(json.loads(
-            b.get_dump(dump_format='json')[0])['weight'])
-        # Logic for multiclass classification
-        n_classes = getattr(self, 'n_classes_', None)
-        if n_classes is not None:
-            if n_classes > 2:
-                assert len(coef.shape) == 1
-                assert coef.shape[0] % n_classes == 0
-                coef = coef.reshape((n_classes, -1))
-        return coef
-
-    @property
-    def intercept_(self):
-        """
-        Intercept (bias) property
-
-        .. note:: Intercept is defined only for linear learners
-
-            Intercept (bias) is only defined when the linear model is chosen as base
-            learner (`booster=gblinear`). It is not defined for other base learner types, such
-            as tree learners (`booster=gbtree`).
-
-        Returns
-        -------
-        intercept_ : array of shape ``(1,)`` or ``[n_classes]``
-        """
-        if self.get_params()['booster'] != 'gblinear':
-            raise AttributeError(
-                'Intercept (bias) is not defined for Booster type {}'
-                .format(self.booster))
-        b = self.get_booster()
-        return np.array(json.loads(b.get_dump(dump_format='json')[0])['bias'])
-
-
-@xgboost_model_doc(
-    "Implementation of the scikit-learn API for XGBoost classification.",
-    ['model', 'objective'])
-class XGBClassifier(XGBModel, XGBClassifierBase):
-    # pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
-    def __init__(self, objective="binary:logistic", **kwargs):
-        super().__init__(objective=objective, **kwargs)
-
-    def fit(self, X, y, sample_weight=None, base_margin=None,
-            eval_set=None, eval_metric=None,
-            early_stopping_rounds=None, verbose=True, xgb_model=None,
-            sample_weight_eval_set=None, callbacks=None):
-        # pylint: disable = attribute-defined-outside-init,arguments-differ
-
-        evals_result = {}
-        self.classes_ = np.unique(y)
-        self.n_classes_ = len(self.classes_)
-
-        xgb_options = self.get_xgb_params()
-
-        if callable(self.objective):
-            obj = _objective_decorator(self.objective)
-            # Use default value. Is it really not used ?
-            xgb_options["objective"] = "binary:logistic"
-        else:
-            obj = None
-
-        if self.n_classes_ > 2:
-            # Switch to using a multiclass objective in the underlying
-            # XGB instance
-            xgb_options['objective'] = 'multi:softprob'
-            xgb_options['num_class'] = self.n_classes_
-
-        feval = eval_metric if callable(eval_metric) else None
-        if eval_metric is not None:
-            if callable(eval_metric):
-                eval_metric = None
-            else:
-                xgb_options.update({"eval_metric": eval_metric})
-
-        self._le = XGBoostLabelEncoder().fit(y)
-        training_labels = self._le.transform(y)
-
-        if eval_set is not None:
-            if sample_weight_eval_set is None:
-                sample_weight_eval_set = [None] * len(eval_set)
-            else:
-                assert len(sample_weight_eval_set) == len(eval_set)
-            evals = list(
-                DMatrix(eval_set[i][0],
-                        label=self._le.transform(eval_set[i][1]),
-                        missing=self.missing, weight=sample_weight_eval_set[i],
-                        nthread=self.n_jobs)
-                for i in range(len(eval_set))
-            )
-            nevals = len(evals)
-            eval_names = ["validation_{}".format(i) for i in range(nevals)]
-            evals = list(zip(evals, eval_names))
-        else:
-            evals = ()
-
-        if len(X.shape) != 2:
-            # Simply raise an error here since there might be many
-            # different ways of reshaping
-            raise ValueError(
-                'Please reshape the input data X into 2-dimensional matrix.')
-        self._features_count = X.shape[1]
-        train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
-                                base_margin=base_margin,
-                                missing=self.missing, nthread=self.n_jobs)
-
-        self._Booster = train(xgb_options, train_dmatrix,
-                              self.get_num_boosting_rounds(),
-                              evals=evals,
-                              early_stopping_rounds=early_stopping_rounds,
-                              evals_result=evals_result, obj=obj, feval=feval,
-                              verbose_eval=verbose, xgb_model=xgb_model,
-                              callbacks=callbacks)
-
-        self.objective = xgb_options["objective"]
-        if evals_result:
-            for val in evals_result.items():
-                evals_result_key = list(val[1].keys())[0]
-                evals_result[val[0]][
-                    evals_result_key] = val[1][evals_result_key]
-            self.evals_result_ = evals_result
-
-        if early_stopping_rounds is not None:
-            self.best_score = self._Booster.best_score
-            self.best_iteration = self._Booster.best_iteration
-            self.best_ntree_limit = self._Booster.best_ntree_limit
-
-        return self
-
-    fit.__doc__ = XGBModel.fit.__doc__.replace(
-        'Fit gradient boosting model',
-        'Fit gradient boosting classifier', 1)
-
-    def predict(self, data, output_margin=False, ntree_limit=None,
-                validate_features=True, base_margin=None):
-        """
-        Predict with `data`.
-
-        .. note:: This function is not thread safe.
-
-          For each booster object, predict can only be called from one thread.
-          If you want to run prediction using multiple thread, call
-          ``xgb.copy()`` to make copies of model object and then call
-          ``predict()``.
-
-          .. code-block:: python
-
-            preds = bst.predict(dtest, ntree_limit=num_round)
-
-        Parameters
-        ----------
-        data : array_like
-            The dmatrix storing the input.
-        output_margin : bool
-            Whether to output the raw untransformed margin value.
-        ntree_limit : int
-            Limit number of trees in the prediction; defaults to
-            best_ntree_limit if defined (i.e. it has been trained with early
-            stopping), otherwise 0 (use all trees).
-        validate_features : bool
-            When this is True, validate that the Booster's and data's
-            feature_names are identical.  Otherwise, it is assumed that the
-            feature_names are the same.
-
-        Returns
-        -------
-        prediction : numpy array
-        """
-        test_dmatrix = DMatrix(data, base_margin=base_margin,
-                               missing=self.missing, nthread=self.n_jobs)
-        if ntree_limit is None:
-            ntree_limit = getattr(self, "best_ntree_limit", 0)
-        class_probs = self.get_booster().predict(
-            test_dmatrix,
-            output_margin=output_margin,
-            ntree_limit=ntree_limit,
-            validate_features=validate_features)
-        if output_margin:
-            # If output_margin is active, simply return the scores
-            return class_probs
-
-        if len(class_probs.shape) > 1:
-            column_indexes = np.argmax(class_probs, axis=1)
-        else:
-            column_indexes = np.repeat(0, class_probs.shape[0])
-            column_indexes[class_probs > 0.5] = 1
-
-        if hasattr(self, '_le'):
-            return self._le.inverse_transform(column_indexes)
-        warnings.warn(
-            'Label encoder is not defined.  Returning class probability.')
-        return class_probs
-
-    def predict_proba(self, data, ntree_limit=None, validate_features=True,
-                      base_margin=None):
-        """
-        Predict the probability of each `data` example being of a given class.
-
-        .. note:: This function is not thread safe
-
-            For each booster object, predict can only be called from one
-            thread.  If you want to run prediction using multiple thread, call
-            ``xgb.copy()`` to make copies of model object and then call predict
-
-        Parameters
-        ----------
-        data : DMatrix
-            The dmatrix storing the input.
-        ntree_limit : int
-            Limit number of trees in the prediction; defaults to best_ntree_limit if defined
-            (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
-        validate_features : bool
-            When this is True, validate that the Booster's and data's feature_names are identical.
-            Otherwise, it is assumed that the feature_names are the same.
-
-        Returns
-        -------
-        prediction : numpy array
-            a numpy array with the probability of each data example being of a given class.
-        """
-        test_dmatrix = DMatrix(data, base_margin=base_margin,
-                               missing=self.missing, nthread=self.n_jobs)
-        if ntree_limit is None:
-            ntree_limit = getattr(self, "best_ntree_limit", 0)
-        class_probs = self.get_booster().predict(test_dmatrix,
-                                                 ntree_limit=ntree_limit,
-                                                 validate_features=validate_features)
-        if self.objective == "multi:softprob":
-            return class_probs
-        classone_probs = class_probs
-        classzero_probs = 1.0 - classone_probs
-        return np.vstack((classzero_probs, classone_probs)).transpose()
-
-    def evals_result(self):
-        """Return the evaluation results.
-
-        If **eval_set** is passed to the `fit` function, you can call
-        ``evals_result()`` to get evaluation results for all passed **eval_sets**.
-        When **eval_metric** is also passed to the `fit` function, the
-        **evals_result** will contain the **eval_metrics** passed to the `fit` function.
-
-        Returns
-        -------
-        evals_result : dictionary
-
-        Example
-        -------
-
-        .. code-block:: python
-
-            param_dist = {'objective':'binary:logistic', 'n_estimators':2}
-
-            clf = xgb.XGBClassifier(**param_dist)
-
-            clf.fit(X_train, y_train,
-                    eval_set=[(X_train, y_train), (X_test, y_test)],
-                    eval_metric='logloss',
-                    verbose=True)
-
-            evals_result = clf.evals_result()
-
-        The variable **evals_result** will contain
-
-        .. code-block:: python
-
-            {'validation_0': {'logloss': ['0.604835', '0.531479']},
-            'validation_1': {'logloss': ['0.41965', '0.17686']}}
-        """
-        if self.evals_result_:
-            evals_result = self.evals_result_
-        else:
-            raise XGBoostError('No results.')
-
-        return evals_result
-
-
-@xgboost_model_doc(
-    "scikit-learn API for XGBoost random forest classification.",
-    ['model', 'objective'],
-    extra_parameters='''
-    n_estimators : int
-        Number of trees in random forest to fit.
-''')
-class XGBRFClassifier(XGBClassifier):
-    # pylint: disable=missing-docstring
-    def __init__(self,
-                 learning_rate=1,
-                 subsample=0.8,
-                 colsample_bynode=0.8,
-                 reg_lambda=1e-5,
-                 **kwargs):
-        super().__init__(learning_rate=learning_rate,
-                         subsample=subsample,
-                         colsample_bynode=colsample_bynode,
-                         reg_lambda=reg_lambda,
-                         **kwargs)
-
-    def get_xgb_params(self):
-        params = super(XGBRFClassifier, self).get_xgb_params()
-        params['num_parallel_tree'] = self.n_estimators
-        return params
-
-    def get_num_boosting_rounds(self):
-        return 1
-
-
-@xgboost_model_doc(
-    "Implementation of the scikit-learn API for XGBoost regression.",
-    ['estimators', 'model', 'objective'])
-class XGBRegressor(XGBModel, XGBRegressorBase):
-    # pylint: disable=missing-docstring
-    def __init__(self, objective="reg:squarederror", **kwargs):
-        super().__init__(objective=objective, **kwargs)
-
-
-@xgboost_model_doc(
-    "scikit-learn API for XGBoost random forest regression.",
-    ['model', 'objective'])
-class XGBRFRegressor(XGBRegressor):
-    # pylint: disable=missing-docstring
-    def __init__(self, learning_rate=1, subsample=0.8, colsample_bynode=0.8,
-                 reg_lambda=1e-5, **kwargs):
-        super().__init__(learning_rate=learning_rate, subsample=subsample,
-                         colsample_bynode=colsample_bynode,
-                         reg_lambda=reg_lambda, **kwargs)
-
-    def get_xgb_params(self):
-        params = super(XGBRFRegressor, self).get_xgb_params()
-        params['num_parallel_tree'] = self.n_estimators
-        return params
-
-    def get_num_boosting_rounds(self):
-        return 1
-
-
-@xgboost_model_doc(
-    'Implementation of the Scikit-Learn API for XGBoost Ranking.',
-    ['estimators', 'model'],
-    end_note='''
-        Note
-        ----
-        A custom objective function is currently not supported by XGBRanker.
-        Likewise, a custom metric function is not supported either.
-
-        Note
-        ----
-        Query group information is required for ranking tasks.
-
-        Before fitting the model, your data need to be sorted by query
-        group. When fitting the model, you need to provide an additional array
-        that contains the size of each query group.
-
-        For example, if your original data look like:
-
-        +-------+-----------+---------------+
-        |   qid |   label   |   features    |
-        +-------+-----------+---------------+
-        |   1   |   0       |   x_1         |
-        +-------+-----------+---------------+
-        |   1   |   1       |   x_2         |
-        +-------+-----------+---------------+
-        |   1   |   0       |   x_3         |
-        +-------+-----------+---------------+
-        |   2   |   0       |   x_4         |
-        +-------+-----------+---------------+
-        |   2   |   1       |   x_5         |
-        +-------+-----------+---------------+
-        |   2   |   1       |   x_6         |
-        +-------+-----------+---------------+
-        |   2   |   1       |   x_7         |
-        +-------+-----------+---------------+
-
-        then your group array should be ``[3, 4]``.
-''')
-class XGBRanker(XGBModel):
-    # pylint: disable=missing-docstring,too-many-arguments,invalid-name
-    def __init__(self, objective='rank:pairwise', **kwargs):
-        super().__init__(objective=objective, **kwargs)
-        if callable(self.objective):
-            raise ValueError(
-                "custom objective function not supported by XGBRanker")
-        if "rank:" not in self.objective:
-            raise ValueError("please use XGBRanker for ranking task")
-
-    def fit(self, X, y, group, sample_weight=None, base_margin=None,
-            eval_set=None,
-            sample_weight_eval_set=None, eval_group=None, eval_metric=None,
-            early_stopping_rounds=None, verbose=False, xgb_model=None,
-            callbacks=None):
-        # pylint: disable = attribute-defined-outside-init,arguments-differ
-        """Fit gradient boosting ranker
-
-        Parameters
-        ----------
-        X : array_like
-            Feature matrix
-        y : array_like
-            Labels
-        group : array_like
-            Size of each query group of training data. Should have as many
-            elements as the query groups in the training data
-        sample_weight : array_like
-            Query group weights
-
-            .. note:: Weights are per-group for ranking tasks
-
-                In ranking task, one weight is assigned to each query group
-                (not each data point). This is because we only care about the
-                relative ordering of data points within each group, so it
-                doesn't make sense to assign weights to individual data points.
-
-        base_margin : array_like
-            Global bias for each instance.
-        eval_set : list, optional
-            A list of (X, y) tuple pairs to use as validation sets, for which
-            metrics will be computed.
-            Validation metrics will help us track the performance of the model.
-        sample_weight_eval_set : list, optional
-            A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
-            group weights on the i-th validation set.
-
-            .. note:: Weights are per-group for ranking tasks
-
-                In ranking task, one weight is assigned to each query group (not each
-                data point). This is because we only care about the relative ordering of
-                data points within each group, so it doesn't make sense to assign
-                weights to individual data points.
-
-        eval_group : list of arrays, optional
-            A list in which ``eval_group[i]`` is the list containing the sizes of all
-            query groups in the ``i``-th pair in **eval_set**.
-        eval_metric : str, list of str, optional
-            If a str, should be a built-in evaluation metric to use. See
-            doc/parameter.rst.
-            If a list of str, should be the list of multiple built-in evaluation metrics
-            to use. The custom evaluation metric is not yet supported for the ranker.
-        early_stopping_rounds : int
-            Activates early stopping. Validation metric needs to improve at least once in
-            every **early_stopping_rounds** round(s) to continue training.
-            Requires at least one item in **eval_set**.
-            The method returns the model from the last iteration (not the best one).
-            If there's more than one item in **eval_set**, the last entry will be used
-            for early stopping.
-            If there's more than one metric in **eval_metric**, the last metric
-            will be used for early stopping.
-            If early stopping occurs, the model will have three additional
-            fields: ``clf.best_score``, ``clf.best_iteration`` and
-            ``clf.best_ntree_limit``.
-        verbose : bool
-            If `verbose` and an evaluation set is used, writes the evaluation
-            metric measured on the validation set to stderr.
-        xgb_model : str
-            file name of stored XGBoost model or 'Booster' instance XGBoost
-            model to be loaded before training (allows training continuation).
-        callbacks : list of callback functions
-            List of callback functions that are applied at end of each
-            iteration.  It is possible to use predefined callbacks by using
-            :ref:`callback_api`.  Example:
-
-            .. code-block:: python
-
-                [xgb.callback.reset_learning_rate(custom_rates)]
-
-        """
-        # check if group information is provided
-        if group is None:
-            raise ValueError("group is required for ranking task")
-
-        if eval_set is not None:
-            if eval_group is None:
-                raise ValueError(
-                    "eval_group is required if eval_set is not None")
-            if len(eval_group) != len(eval_set):
-                raise ValueError(
-                    "length of eval_group should match that of eval_set")
-            if any(group is None for group in eval_group):
-                raise ValueError(
-                    "group is required for all eval datasets for ranking task")
-
-        def _dmat_init(group, **params):
-            ret = DMatrix(**params)
-            ret.set_group(group)
-            return ret
-
-        train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
-                                base_margin=base_margin,
-                                missing=self.missing, nthread=self.n_jobs)
-        train_dmatrix.set_group(group)
-
-        evals_result = {}
-
-        if eval_set is not None:
-            if sample_weight_eval_set is None:
-                sample_weight_eval_set = [None] * len(eval_set)
-            evals = [_dmat_init(eval_group[i],
-                                data=eval_set[i][0],
-                                label=eval_set[i][1],
-                                missing=self.missing,
-                                weight=sample_weight_eval_set[i],
-                                nthread=self.n_jobs)
-                     for i in range(len(eval_set))]
-            nevals = len(evals)
-            eval_names = ["eval_{}".format(i) for i in range(nevals)]
-            evals = list(zip(evals, eval_names))
-        else:
-            evals = ()
-
-        params = self.get_xgb_params()
-
-        feval = eval_metric if callable(eval_metric) else None
-        if eval_metric is not None:
-            if callable(eval_metric):
-                raise ValueError(
-                    'Custom evaluation metric is not yet supported for XGBRanker.')
-            params.update({'eval_metric': eval_metric})
-
-        self._Booster = train(params, train_dmatrix,
-                              self.n_estimators,
-                              early_stopping_rounds=early_stopping_rounds,
-                              evals=evals,
-                              evals_result=evals_result, feval=feval,
-                              verbose_eval=verbose, xgb_model=xgb_model,
-                              callbacks=callbacks)
-
-        self.objective = params["objective"]
-
-        if evals_result:
-            for val in evals_result.items():
-                evals_result_key = list(val[1].keys())[0]
-                evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
-            self.evals_result = evals_result
-
-        if early_stopping_rounds is not None:
-            self.best_score = self._Booster.best_score
-            self.best_iteration = self._Booster.best_iteration
-            self.best_ntree_limit = self._Booster.best_ntree_limit
-
-        return self
-
-    def predict(self, data, output_margin=False,
-                ntree_limit=0, validate_features=True, base_margin=None):
-
-        test_dmatrix = DMatrix(data, base_margin=base_margin,
-                               missing=self.missing)
-        if ntree_limit is None:
-            ntree_limit = getattr(self, "best_ntree_limit", 0)
-
-        return self.get_booster().predict(test_dmatrix,
-                                          output_margin=output_margin,
-                                          ntree_limit=ntree_limit,
-                                          validate_features=validate_features)
-
-    predict.__doc__ = XGBModel.predict.__doc__
diff --git a/ml-xgboost/python-package/xgboost/tracker.py b/ml-xgboost/python-package/xgboost/tracker.py
deleted file mode 100644
index 8debf05..0000000
--- a/ml-xgboost/python-package/xgboost/tracker.py
+++ /dev/null
@@ -1,337 +0,0 @@
-"""
-This script is a variant of dmlc-core/dmlc_tracker/tracker.py,
-which is a specialized version for xgboost tasks.
-"""
-
-# pylint: disable=invalid-name, missing-docstring, too-many-arguments, too-many-locals
-# pylint: disable=too-many-branches, too-many-statements, too-many-instance-attributes
-import socket
-import struct
-import time
-import logging
-from threading import Thread
-
-
-class ExSocket(object):
-    """
-    Extension of socket to handle recv and send of special data
-    """
-
-    def __init__(self, sock):
-        self.sock = sock
-
-    def recvall(self, nbytes):
-        res = []
-        nread = 0
-        while nread < nbytes:
-            chunk = self.sock.recv(min(nbytes - nread, 1024))
-            nread += len(chunk)
-            res.append(chunk)
-        return b''.join(res)
-
-    def recvint(self):
-        return struct.unpack('@i', self.recvall(4))[0]
-
-    def sendint(self, n):
-        self.sock.sendall(struct.pack('@i', n))
-
-    def sendstr(self, s):
-        self.sendint(len(s))
-        self.sock.sendall(s.encode())
-
-    def recvstr(self):
-        slen = self.recvint()
-        return self.recvall(slen).decode()
-
-
-# magic number used to verify existence of data
-kMagic = 0xff99
-
-
-def get_some_ip(host):
-    return socket.getaddrinfo(host, None)[0][4][0]
-
-
-def get_family(addr):
-    return socket.getaddrinfo(addr, None)[0][0]
-
-
-class SlaveEntry(object):
-    def __init__(self, sock, s_addr):
-        slave = ExSocket(sock)
-        self.sock = slave
-        self.host = get_some_ip(s_addr[0])
-        magic = slave.recvint()
-        assert magic == kMagic, 'invalid magic number=%d from %s' % (magic, self.host)
-        slave.sendint(kMagic)
-        self.rank = slave.recvint()
-        self.world_size = slave.recvint()
-        self.jobid = slave.recvstr()
-        self.cmd = slave.recvstr()
-        self.wait_accept = 0
-        self.port = None
-
-    def decide_rank(self, job_map):
-        if self.rank >= 0:
-            return self.rank
-        if self.jobid != 'NULL' and self.jobid in job_map:
-            return job_map[self.jobid]
-        return -1
-
-    def assign_rank(self, rank, wait_conn, tree_map, parent_map, ring_map):
-        self.rank = rank
-        nnset = set(tree_map[rank])
-        rprev, rnext = ring_map[rank]
-        self.sock.sendint(rank)
-        # send parent rank
-        self.sock.sendint(parent_map[rank])
-        # send world size
-        self.sock.sendint(len(tree_map))
-        self.sock.sendint(len(nnset))
-        # send the rprev and next link
-        for r in nnset:
-            self.sock.sendint(r)
-        # send prev link
-        if rprev not in (-1, rank):
-            nnset.add(rprev)
-            self.sock.sendint(rprev)
-        else:
-            self.sock.sendint(-1)
-        # send next link
-        if rnext not in (-1, rank):
-            nnset.add(rnext)
-            self.sock.sendint(rnext)
-        else:
-            self.sock.sendint(-1)
-        while True:
-            ngood = self.sock.recvint()
-            goodset = set([])
-            for _ in range(ngood):
-                goodset.add(self.sock.recvint())
-            assert goodset.issubset(nnset)
-            badset = nnset - goodset
-            conset = []
-            for r in badset:
-                if r in wait_conn:
-                    conset.append(r)
-            self.sock.sendint(len(conset))
-            self.sock.sendint(len(badset) - len(conset))
-            for r in conset:
-                self.sock.sendstr(wait_conn[r].host)
-                self.sock.sendint(wait_conn[r].port)
-                self.sock.sendint(r)
-            nerr = self.sock.recvint()
-            if nerr != 0:
-                continue
-            self.port = self.sock.recvint()
-            rmset = []
-            # all connection was successuly setup
-            for r in conset:
-                wait_conn[r].wait_accept -= 1
-                if wait_conn[r].wait_accept == 0:
-                    rmset.append(r)
-            for r in rmset:
-                wait_conn.pop(r, None)
-            self.wait_accept = len(badset) - len(conset)
-            return rmset
-
-
-class RabitTracker(object):
-    """
-    tracker for rabit
-    """
-
-    def __init__(self, hostIP, nslave, port=9091, port_end=9999):
-        sock = socket.socket(get_family(hostIP), socket.SOCK_STREAM)
-        for _port in range(port, port_end):
-            try:
-                sock.bind((hostIP, _port))
-                self.port = _port
-                break
-            except socket.error as e:
-                if e.errno in [98, 48]:
-                    continue
-                raise
-        sock.listen(256)
-        self.sock = sock
-        self.hostIP = hostIP
-        self.thread = None
-        self.start_time = None
-        self.end_time = None
-        self.nslave = nslave
-        logging.info('start listen on %s:%d', hostIP, self.port)
-
-    def __del__(self):
-        self.sock.close()
-
-    @staticmethod
-    def get_neighbor(rank, nslave):
-        rank = rank + 1
-        ret = []
-        if rank > 1:
-            ret.append(rank // 2 - 1)
-        if rank * 2 - 1 < nslave:
-            ret.append(rank * 2 - 1)
-        if rank * 2 < nslave:
-            ret.append(rank * 2)
-        return ret
-
-    def slave_envs(self):
-        """
-        get enviroment variables for slaves
-        can be passed in as args or envs
-        """
-        return {'DMLC_TRACKER_URI': self.hostIP,
-                'DMLC_TRACKER_PORT': self.port}
-
-    def get_tree(self, nslave):
-        tree_map = {}
-        parent_map = {}
-        for r in range(nslave):
-            tree_map[r] = self.get_neighbor(r, nslave)
-            parent_map[r] = (r + 1) // 2 - 1
-        return tree_map, parent_map
-
-    def find_share_ring(self, tree_map, parent_map, r):
-        """
-        get a ring structure that tends to share nodes with the tree
-        return a list starting from r
-        """
-        nset = set(tree_map[r])
-        cset = nset - set([parent_map[r]])
-        if not cset:
-            return [r]
-        rlst = [r]
-        cnt = 0
-        for v in cset:
-            vlst = self.find_share_ring(tree_map, parent_map, v)
-            cnt += 1
-            if cnt == len(cset):
-                vlst.reverse()
-            rlst += vlst
-        return rlst
-
-    def get_ring(self, tree_map, parent_map):
-        """
-        get a ring connection used to recover local data
-        """
-        assert parent_map[0] == -1
-        rlst = self.find_share_ring(tree_map, parent_map, 0)
-        assert len(rlst) == len(tree_map)
-        ring_map = {}
-        nslave = len(tree_map)
-        for r in range(nslave):
-            rprev = (r + nslave - 1) % nslave
-            rnext = (r + 1) % nslave
-            ring_map[rlst[r]] = (rlst[rprev], rlst[rnext])
-        return ring_map
-
-    def get_link_map(self, nslave):
-        """
-        get the link map, this is a bit hacky, call for better algorithm
-        to place similar nodes together
-        """
-        tree_map, parent_map = self.get_tree(nslave)
-        ring_map = self.get_ring(tree_map, parent_map)
-        rmap = {0: 0}
-        k = 0
-        for i in range(nslave - 1):
-            k = ring_map[k][1]
-            rmap[k] = i + 1
-
-        ring_map_ = {}
-        tree_map_ = {}
-        parent_map_ = {}
-        for k, v in ring_map.items():
-            ring_map_[rmap[k]] = (rmap[v[0]], rmap[v[1]])
-        for k, v in tree_map.items():
-            tree_map_[rmap[k]] = [rmap[x] for x in v]
-        for k, v in parent_map.items():
-            if k != 0:
-                parent_map_[rmap[k]] = rmap[v]
-            else:
-                parent_map_[rmap[k]] = -1
-        return tree_map_, parent_map_, ring_map_
-
-    def accept_slaves(self, nslave):
-        # set of nodes that finishs the job
-        shutdown = {}
-        # set of nodes that is waiting for connections
-        wait_conn = {}
-        # maps job id to rank
-        job_map = {}
-        # list of workers that is pending to be assigned rank
-        pending = []
-        # lazy initialize tree_map
-        tree_map = None
-
-        while len(shutdown) != nslave:
-            fd, s_addr = self.sock.accept()
-            s = SlaveEntry(fd, s_addr)
-            if s.cmd == 'print':
-                msg = s.sock.recvstr()
-                logging.info(msg.strip())
-                continue
-            if s.cmd == 'shutdown':
-                assert s.rank >= 0 and s.rank not in shutdown
-                assert s.rank not in wait_conn
-                shutdown[s.rank] = s
-                logging.debug('Recieve %s signal from %d', s.cmd, s.rank)
-                continue
-            assert s.cmd == 'start' or s.cmd == 'recover'
-            # lazily initialize the slaves
-            if tree_map is None:
-                assert s.cmd == 'start'
-                if s.world_size > 0:
-                    nslave = s.world_size
-                tree_map, parent_map, ring_map = self.get_link_map(nslave)
-                # set of nodes that is pending for getting up
-                todo_nodes = list(range(nslave))
-            else:
-                assert s.world_size == -1 or s.world_size == nslave
-            if s.cmd == 'recover':
-                assert s.rank >= 0
-
-            rank = s.decide_rank(job_map)
-            # batch assignment of ranks
-            if rank == -1:
-                assert todo_nodes
-                pending.append(s)
-                if len(pending) == len(todo_nodes):
-                    pending.sort(key=lambda x: x.host)
-                    for s in pending:
-                        rank = todo_nodes.pop(0)
-                        if s.jobid != 'NULL':
-                            job_map[s.jobid] = rank
-                        s.assign_rank(rank, wait_conn, tree_map, parent_map, ring_map)
-                        if s.wait_accept > 0:
-                            wait_conn[rank] = s
-                        logging.debug('Recieve %s signal from %s; assign rank %d',
-                                      s.cmd, s.host, s.rank)
-                if not todo_nodes:
-                    logging.info('@tracker All of %d nodes getting started', nslave)
-                    self.start_time = time.time()
-            else:
-                s.assign_rank(rank, wait_conn, tree_map, parent_map, ring_map)
-                logging.debug('Recieve %s signal from %d', s.cmd, s.rank)
-                if s.wait_accept > 0:
-                    wait_conn[rank] = s
-        logging.info('@tracker All nodes finishes job')
-        self.end_time = time.time()
-        logging.info('@tracker %s secs between node start and job finish',
-                     str(self.end_time - self.start_time))
-
-    def start(self, nslave):
-        def run():
-            self.accept_slaves(nslave)
-
-        self.thread = Thread(target=run, args=())
-        self.thread.setDaemon(True)
-        self.thread.start()
-
-    def join(self):
-        while self.thread.is_alive():
-            self.thread.join(100)
-
-    def alive(self):
-        return self.thread.is_alive()
diff --git a/ml-xgboost/python-package/xgboost/training.py b/ml-xgboost/python-package/xgboost/training.py
deleted file mode 100644
index 7a29d39..0000000
--- a/ml-xgboost/python-package/xgboost/training.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# coding: utf-8
-# pylint: disable=too-many-locals, too-many-arguments, invalid-name
-# pylint: disable=too-many-branches, too-many-statements
-"""Training Library containing training routines."""
-import numpy as np
-from .core import Booster, STRING_TYPES, XGBoostError, CallbackEnv
-from .core import EarlyStopException
-from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold)
-from . import rabit
-from . import callback
-
-
-def _train_internal(params, dtrain,
-                    num_boost_round=10, evals=(),
-                    obj=None, feval=None,
-                    xgb_model=None, callbacks=None):
-    """internal training function"""
-    callbacks = [] if callbacks is None else callbacks
-    evals = list(evals)
-    params = params.copy()
-    if isinstance(params, dict) \
-            and 'eval_metric' in params \
-            and isinstance(params['eval_metric'], list):
-        params = dict((k, v) for k, v in params.items())
-        eval_metrics = params['eval_metric']
-        params.pop("eval_metric", None)
-        params = list(params.items())
-        for eval_metric in eval_metrics:
-            params += [('eval_metric', eval_metric)]
-
-    bst = Booster(params, [dtrain] + [d[0] for d in evals])
-    nboost = 0
-    num_parallel_tree = 1
-
-    if xgb_model is not None:
-        bst = Booster(params, [dtrain] + [d[0] for d in evals],
-                      model_file=xgb_model)
-        nboost = len(bst.get_dump())
-
-    _params = dict(params) if isinstance(params, list) else params
-
-    if 'num_parallel_tree' in _params and _params[
-            'num_parallel_tree'] is not None:
-        num_parallel_tree = _params['num_parallel_tree']
-        nboost //= num_parallel_tree
-    if 'num_class' in _params and _params['num_class'] is not None:
-        nboost //= _params['num_class']
-
-    # Distributed code: Load the checkpoint from rabit.
-    version = bst.load_rabit_checkpoint()
-    assert rabit.get_world_size() != 1 or version == 0
-    rank = rabit.get_rank()
-    start_iteration = int(version / 2)
-    nboost += start_iteration
-
-    callbacks_before_iter = [
-        cb for cb in callbacks
-        if cb.__dict__.get('before_iteration', False)]
-    callbacks_after_iter = [
-        cb for cb in callbacks
-        if not cb.__dict__.get('before_iteration', False)]
-
-    for i in range(start_iteration, num_boost_round):
-        for cb in callbacks_before_iter:
-            cb(CallbackEnv(model=bst,
-                           cvfolds=None,
-                           iteration=i,
-                           begin_iteration=start_iteration,
-                           end_iteration=num_boost_round,
-                           rank=rank,
-                           evaluation_result_list=None))
-        # Distributed code: need to resume to this point.
-        # Skip the first update if it is a recovery step.
-        if version % 2 == 0:
-            bst.update(dtrain, i, obj)
-            bst.save_rabit_checkpoint()
-            version += 1
-
-        assert rabit.get_world_size() == 1 or version == rabit.version_number()
-
-        nboost += 1
-        evaluation_result_list = []
-        # check evaluation result.
-        if evals:
-            bst_eval_set = bst.eval_set(evals, i, feval)
-            if isinstance(bst_eval_set, STRING_TYPES):
-                msg = bst_eval_set
-            else:
-                msg = bst_eval_set.decode()
-            res = [x.split(':') for x in msg.split()]
-            evaluation_result_list = [(k, float(v)) for k, v in res[1:]]
-        try:
-            for cb in callbacks_after_iter:
-                cb(CallbackEnv(model=bst,
-                               cvfolds=None,
-                               iteration=i,
-                               begin_iteration=start_iteration,
-                               end_iteration=num_boost_round,
-                               rank=rank,
-                               evaluation_result_list=evaluation_result_list))
-        except EarlyStopException:
-            break
-        # do checkpoint after evaluation, in case evaluation also updates booster.
-        bst.save_rabit_checkpoint()
-        version += 1
-
-    if bst.attr('best_score') is not None:
-        bst.best_score = float(bst.attr('best_score'))
-        bst.best_iteration = int(bst.attr('best_iteration'))
-    else:
-        bst.best_iteration = nboost - 1
-    bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
-
-    # Copy to serialise and unserialise booster to reset state and free training memory
-    return bst.copy()
-
-
-def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
-          maximize=False, early_stopping_rounds=None, evals_result=None,
-          verbose_eval=True, xgb_model=None, callbacks=None):
-    # pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
-    """Train a booster with given parameters.
-
-    Parameters
-    ----------
-    params : dict
-        Booster params.
-    dtrain : DMatrix
-        Data to be trained.
-    num_boost_round: int
-        Number of boosting iterations.
-    evals: list of pairs (DMatrix, string)
-        List of validation sets for which metrics will evaluated during training.
-        Validation metrics will help us track the performance of the model.
-    obj : function
-        Customized objective function.
-    feval : function
-        Customized evaluation function.
-    maximize : bool
-        Whether to maximize feval.
-    early_stopping_rounds: int
-        Activates early stopping. Validation metric needs to improve at least once in
-        every **early_stopping_rounds** round(s) to continue training.
-        Requires at least one item in **evals**.
-        The method returns the model from the last iteration (not the best one).
-        If there's more than one item in **evals**, the last entry will be used
-        for early stopping.
-        If there's more than one metric in the **eval_metric** parameter given in
-        **params**, the last metric will be used for early stopping.
-        If early stopping occurs, the model will have three additional fields:
-        ``bst.best_score``, ``bst.best_iteration`` and ``bst.best_ntree_limit``.
-        (Use ``bst.best_ntree_limit`` to get the correct value if
-        ``num_parallel_tree`` and/or ``num_class`` appears in the parameters)
-    evals_result: dict
-        This dictionary stores the evaluation results of all the items in watchlist.
-
-        Example: with a watchlist containing
-        ``[(dtest,'eval'), (dtrain,'train')]`` and
-        a parameter containing ``('eval_metric': 'logloss')``,
-        the **evals_result** returns
-
-        .. code-block:: python
-
-            {'train': {'logloss': ['0.48253', '0.35953']},
-             'eval': {'logloss': ['0.480385', '0.357756']}}
-
-    verbose_eval : bool or int
-        Requires at least one item in **evals**.
-        If **verbose_eval** is True then the evaluation metric on the validation set is
-        printed at each boosting stage.
-        If **verbose_eval** is an integer then the evaluation metric on the validation set
-        is printed at every given **verbose_eval** boosting stage. The last boosting stage
-        / the boosting stage found by using **early_stopping_rounds** is also printed.
-        Example: with ``verbose_eval=4`` and at least one item in **evals**, an evaluation metric
-        is printed every 4 boosting stages, instead of every boosting stage.
-    xgb_model : file name of stored xgb model or 'Booster' instance
-        Xgb model to be loaded before training (allows training continuation).
-    callbacks : list of callback functions
-        List of callback functions that are applied at end of each iteration.
-        It is possible to use predefined callbacks by using
-        :ref:`Callback API <callback_api>`.
-        Example:
-
-        .. code-block:: python
-
-            [xgb.callback.reset_learning_rate(custom_rates)]
-
-    Returns
-    -------
-    Booster : a trained booster model
-    """
-    callbacks = [] if callbacks is None else callbacks
-
-    # Most of legacy advanced options becomes callbacks
-    if isinstance(verbose_eval, bool) and verbose_eval:
-        callbacks.append(callback.print_evaluation())
-    else:
-        if isinstance(verbose_eval, int):
-            callbacks.append(callback.print_evaluation(verbose_eval))
-
-    if early_stopping_rounds is not None:
-        callbacks.append(callback.early_stop(early_stopping_rounds,
-                                             maximize=maximize,
-                                             verbose=bool(verbose_eval)))
-    if evals_result is not None:
-        callbacks.append(callback.record_evaluation(evals_result))
-
-    return _train_internal(params, dtrain,
-                           num_boost_round=num_boost_round,
-                           evals=evals,
-                           obj=obj, feval=feval,
-                           xgb_model=xgb_model, callbacks=callbacks)
-
-
-class CVPack(object):
-    """"Auxiliary datastruct to hold one fold of CV."""
-    def __init__(self, dtrain, dtest, param):
-        """"Initialize the CVPack"""
-        self.dtrain = dtrain
-        self.dtest = dtest
-        self.watchlist = [(dtrain, 'train'), (dtest, 'test')]
-        self.bst = Booster(param, [dtrain, dtest])
-
-    def update(self, iteration, fobj):
-        """"Update the boosters for one iteration"""
-        self.bst.update(self.dtrain, iteration, fobj)
-
-    def eval(self, iteration, feval):
-        """"Evaluate the CVPack for one iteration."""
-        return self.bst.eval_set(self.watchlist, iteration, feval)
-
-
-def groups_to_rows(groups, boundaries):
-    """
-    Given group row boundaries, convert ground indexes to row indexes
-    :param groups: list of groups for testing
-    :param boundaries: rows index limits of each group
-    :return: row in group
-    """
-    return np.concatenate([np.arange(boundaries[g], boundaries[g+1]) for g in groups])
-
-
-def mkgroupfold(dall, nfold, param, evals=(), fpreproc=None, shuffle=True):
-    """
-    Make n folds for cross-validation maintaining groups
-    :return: cross-validation folds
-    """
-    # we have groups for pairwise ranking... get a list of the group indexes
-    group_boundaries = dall.get_uint_info('group_ptr')
-    group_sizes = np.diff(group_boundaries)
-
-    if shuffle is True:
-        idx = np.random.permutation(len(group_sizes))
-    else:
-        idx = np.arange(len(group_sizes))
-    # list by fold of test group indexes
-    out_group_idset = np.array_split(idx, nfold)
-    # list by fold of train group indexes
-    in_group_idset = [np.concatenate([out_group_idset[i] for i in range(nfold) if k != i])
-                      for k in range(nfold)]
-    # from the group indexes, convert them to row indexes
-    in_idset = [groups_to_rows(in_groups, group_boundaries) for in_groups in in_group_idset]
-    out_idset = [groups_to_rows(out_groups, group_boundaries) for out_groups in out_group_idset]
-
-    # build the folds by taking the appropriate slices
-    ret = []
-    for k in range(nfold):
-        # perform the slicing using the indexes determined by the above methods
-        dtrain = dall.slice(in_idset[k], allow_groups=True)
-        dtrain.set_group(group_sizes[in_group_idset[k]])
-        dtest = dall.slice(out_idset[k], allow_groups=True)
-        dtest.set_group(group_sizes[out_group_idset[k]])
-        # run preprocessing on the data set if needed
-        if fpreproc is not None:
-            dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy())
-        else:
-            tparam = param
-        plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals]
-        ret.append(CVPack(dtrain, dtest, plst))
-    return ret
-
-
-def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
-            folds=None, shuffle=True):
-    """
-    Make an n-fold list of CVPack from random indices.
-    """
-    evals = list(evals)
-    np.random.seed(seed)
-
-    if stratified is False and folds is None:
-        # Do standard k-fold cross validation. Automatically determine the folds.
-        if len(dall.get_uint_info('group_ptr')) > 1:
-            return mkgroupfold(dall, nfold, param, evals=evals, fpreproc=fpreproc, shuffle=shuffle)
-
-        if shuffle is True:
-            idx = np.random.permutation(dall.num_row())
-        else:
-            idx = np.arange(dall.num_row())
-        out_idset = np.array_split(idx, nfold)
-        in_idset = [np.concatenate([out_idset[i] for i in range(nfold) if k != i])
-                    for k in range(nfold)]
-    elif folds is not None:
-        # Use user specified custom split using indices
-        try:
-            in_idset = [x[0] for x in folds]
-            out_idset = [x[1] for x in folds]
-        except TypeError:
-            # Custom stratification using Sklearn KFoldSplit object
-            splits = list(folds.split(X=dall.get_label(), y=dall.get_label()))
-            in_idset = [x[0] for x in splits]
-            out_idset = [x[1] for x in splits]
-        nfold = len(out_idset)
-    else:
-        # Do standard stratefied shuffle k-fold split
-        sfk = XGBStratifiedKFold(n_splits=nfold, shuffle=True, random_state=seed)
-        splits = list(sfk.split(X=dall.get_label(), y=dall.get_label()))
-        in_idset = [x[0] for x in splits]
-        out_idset = [x[1] for x in splits]
-        nfold = len(out_idset)
-
-    ret = []
-    for k in range(nfold):
-        # perform the slicing using the indexes determined by the above methods
-        dtrain = dall.slice(in_idset[k])
-        dtest = dall.slice(out_idset[k])
-        # run preprocessing on the data set if needed
-        if fpreproc is not None:
-            dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy())
-        else:
-            tparam = param
-        plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals]
-        ret.append(CVPack(dtrain, dtest, plst))
-    return ret
-
-
-def aggcv(rlist):
-    # pylint: disable=invalid-name
-    """
-    Aggregate cross-validation results.
-
-    If verbose_eval is true, progress is displayed in every call. If
-    verbose_eval is an integer, progress will only be displayed every
-    `verbose_eval` trees, tracked via trial.
-    """
-    cvmap = {}
-    idx = rlist[0].split()[0]
-    for line in rlist:
-        arr = line.split()
-        assert idx == arr[0]
-        for metric_idx, it in enumerate(arr[1:]):
-            if not isinstance(it, STRING_TYPES):
-                it = it.decode()
-            k, v = it.split(':')
-            if (metric_idx, k) not in cvmap:
-                cvmap[(metric_idx, k)] = []
-            cvmap[(metric_idx, k)].append(float(v))
-    msg = idx
-    results = []
-    for (metric_idx, k), v in sorted(cvmap.items(), key=lambda x: x[0][0]):
-        v = np.array(v)
-        if not isinstance(msg, STRING_TYPES):
-            msg = msg.decode()
-        mean, std = np.mean(v), np.std(v)
-        results.extend([(k, mean, std)])
-    return results
-
-
-def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None,
-       metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None,
-       fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True,
-       seed=0, callbacks=None, shuffle=True):
-    # pylint: disable = invalid-name
-    """Cross-validation with given parameters.
-
-    Parameters
-    ----------
-    params : dict
-        Booster params.
-    dtrain : DMatrix
-        Data to be trained.
-    num_boost_round : int
-        Number of boosting iterations.
-    nfold : int
-        Number of folds in CV.
-    stratified : bool
-        Perform stratified sampling.
-    folds : a KFold or StratifiedKFold instance or list of fold indices
-        Sklearn KFolds or StratifiedKFolds object.
-        Alternatively may explicitly pass sample indices for each fold.
-        For ``n`` folds, **folds** should be a length ``n`` list of tuples.
-        Each tuple is ``(in,out)`` where ``in`` is a list of indices to be used
-        as the training samples for the ``n`` th fold and ``out`` is a list of
-        indices to be used as the testing samples for the ``n`` th fold.
-    metrics : string or list of strings
-        Evaluation metrics to be watched in CV.
-    obj : function
-        Custom objective function.
-    feval : function
-        Custom evaluation function.
-    maximize : bool
-        Whether to maximize feval.
-    early_stopping_rounds: int
-        Activates early stopping. Cross-Validation metric (average of validation
-        metric computed over CV folds) needs to improve at least once in
-        every **early_stopping_rounds** round(s) to continue training.
-        The last entry in the evaluation history will represent the best iteration.
-        If there's more than one metric in the **eval_metric** parameter given in
-        **params**, the last metric will be used for early stopping.
-    fpreproc : function
-        Preprocessing function that takes (dtrain, dtest, param) and returns
-        transformed versions of those.
-    as_pandas : bool, default True
-        Return pd.DataFrame when pandas is installed.
-        If False or pandas is not installed, return np.ndarray
-    verbose_eval : bool, int, or None, default None
-        Whether to display the progress. If None, progress will be displayed
-        when np.ndarray is returned. If True, progress will be displayed at
-        boosting stage. If an integer is given, progress will be displayed
-        at every given `verbose_eval` boosting stage.
-    show_stdv : bool, default True
-        Whether to display the standard deviation in progress.
-        Results are not affected, and always contains std.
-    seed : int
-        Seed used to generate the folds (passed to numpy.random.seed).
-    callbacks : list of callback functions
-        List of callback functions that are applied at end of each iteration.
-        It is possible to use predefined callbacks by using
-        :ref:`Callback API <callback_api>`.
-        Example:
-
-        .. code-block:: python
-
-            [xgb.callback.reset_learning_rate(custom_rates)]
-    shuffle : bool
-        Shuffle data before creating folds.
-
-    Returns
-    -------
-    evaluation history : list(string)
-    """
-    if stratified is True and not SKLEARN_INSTALLED:
-        raise XGBoostError('sklearn needs to be installed in order to use stratified cv')
-
-    if isinstance(metrics, str):
-        metrics = [metrics]
-
-    if isinstance(params, list):
-        _metrics = [x[1] for x in params if x[0] == 'eval_metric']
-        params = dict(params)
-        if 'eval_metric' in params:
-            params['eval_metric'] = _metrics
-    else:
-        params = dict((k, v) for k, v in params.items())
-
-    if (not metrics) and 'eval_metric' in params:
-        if isinstance(params['eval_metric'], list):
-            metrics = params['eval_metric']
-        else:
-            metrics = [params['eval_metric']]
-
-    params.pop("eval_metric", None)
-
-    results = {}
-    cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc,
-                      stratified, folds, shuffle)
-
-    # setup callbacks
-    callbacks = [] if callbacks is None else callbacks
-    if early_stopping_rounds is not None:
-        callbacks.append(callback.early_stop(early_stopping_rounds,
-                                             maximize=maximize,
-                                             verbose=False))
-
-    if isinstance(verbose_eval, bool) and verbose_eval:
-        callbacks.append(callback.print_evaluation(show_stdv=show_stdv))
-    else:
-        if isinstance(verbose_eval, int):
-            callbacks.append(callback.print_evaluation(verbose_eval, show_stdv=show_stdv))
-
-    callbacks_before_iter = [
-        cb for cb in callbacks if
-        cb.__dict__.get('before_iteration', False)]
-    callbacks_after_iter = [
-        cb for cb in callbacks if
-        not cb.__dict__.get('before_iteration', False)]
-
-    for i in range(num_boost_round):
-        for cb in callbacks_before_iter:
-            cb(CallbackEnv(model=None,
-                           cvfolds=cvfolds,
-                           iteration=i,
-                           begin_iteration=0,
-                           end_iteration=num_boost_round,
-                           rank=0,
-                           evaluation_result_list=None))
-        for fold in cvfolds:
-            fold.update(i, obj)
-        res = aggcv([f.eval(i, feval) for f in cvfolds])
-
-        for key, mean, std in res:
-            if key + '-mean' not in results:
-                results[key + '-mean'] = []
-            if key + '-std' not in results:
-                results[key + '-std'] = []
-            results[key + '-mean'].append(mean)
-            results[key + '-std'].append(std)
-        try:
-            for cb in callbacks_after_iter:
-                cb(CallbackEnv(model=None,
-                               cvfolds=cvfolds,
-                               iteration=i,
-                               begin_iteration=0,
-                               end_iteration=num_boost_round,
-                               rank=0,
-                               evaluation_result_list=res))
-        except EarlyStopException as e:
-            for k in results:
-                results[k] = results[k][:(e.best_iteration + 1)]
-            break
-    if as_pandas:
-        try:
-            import pandas as pd
-            results = pd.DataFrame.from_dict(results)
-        except ImportError:
-            pass
-    return results
diff --git a/ml-xgboost/rabit/.gitignore b/ml-xgboost/rabit/.gitignore
deleted file mode 100644
index ad9fedf..0000000
--- a/ml-xgboost/rabit/.gitignore
+++ /dev/null
@@ -1,52 +0,0 @@
-# Compiled Object files
-*.slo
-*.lo
-*.o
-*.obj
-
-# Precompiled Headers
-*.gch
-*.pch
-*.lnk
-# Compiled Dynamic libraries
-*.so
-*.dylib
-*.dll
-
-# Fortran module files
-*.mod
-
-# Compiled Static libraries
-*.lai
-*.la
-*.a
-*.lib
-
-# Executables
-*.miss
-*.exe
-*.out
-*.app
-*~
-*.pyc
-*.mpi
-*.exe
-*tmp*
-*.rabit
-*.mock
-recommonmark
-recom
-_*
-
-#mpi lib
-mpich/
-mpich-3.2/
-
-# Jetbrain
-.idea
-cmake-build-debug/
-.vscode/
-
-# cmake
-build/
-compile_commands.json
\ No newline at end of file
diff --git a/ml-xgboost/rabit/.travis.yml b/ml-xgboost/rabit/.travis.yml
deleted file mode 100644
index f0a7a99..0000000
--- a/ml-xgboost/rabit/.travis.yml
+++ /dev/null
@@ -1,90 +0,0 @@
-sudo: true
-
-os:
-  - linux
-  - osx
-
-osx_image: xcode10.2
-
-dist: xenial
-
-language: cpp
-
-# Use Build Matrix to do lint and build seperately
-env:
-  matrix:
-    - TASK=lint LINT_LANG=cpp
-    - TASK=lint LINT_LANG=python
-    - TASK=doc
-    # - TASK=build
-    - TASK=mpi-build
-    - TASK=cmake-test
-
-matrix:
-  exclude:
-    - os: osx
-      env: TASK=lint LINT_LANG=cpp
-    - os: osx
-      env: TASK=lint LINT_LANG=python
-    - os: osx
-      env: TASK=doc
-    - os: osx
-      env: TASK=build
-
-# dependent apt packages
-addons:
-  apt:
-    sources:
-      - llvm-toolchain-trusty-5.0
-      - ubuntu-toolchain-r-test
-      - george-edison55-precise-backports
-    packages:
-      - doxygen
-      - wget
-      - git
-      - libcurl4-openssl-dev
-      - unzip
-      - python-numpy
-      - gcc-4.8
-      - g++-4.8
-      - openssh-client
-      - openssh-server
-      - python3
-      - python3-setuptools
-      - python3-pip
-      - tree
-  homebrew:
-    packages:
-      - gcc49
-      - openssl
-      - libgit2
-      - python3
-    update: true
-
-before_install:
-  - git clone https://github.com/dmlc/dmlc-core
-  - export TRAVIS=dmlc-core/scripts/travis/
-  - source ${TRAVIS}/travis_setup_env.sh
-  - ${TRAVIS}/travis_osx_install.sh
-  - source ./scripts/travis_setup.sh
-
-script: scripts/travis_script.sh
-
-cache:
-  directories:
-    - ${HOME}/.cache/usr
-    - ${HOME}/.cache/pip
-    - mpich
-
-before_cache:
-  - ${TRAVIS}/travis_before_cache.sh
-
-after_success:
-  - tree build
-  - bash <(curl -s https://codecov.io/bash) -a '-o src/ src/*.c'
-
-notifications:
-# Emails are sent to the committer's git-configured email address by default,
-  email:
-    on_success: change
-    on_failure: always
diff --git a/ml-xgboost/rabit/CMakeLists.txt b/ml-xgboost/rabit/CMakeLists.txt
deleted file mode 100644
index 3b7f47c..0000000
--- a/ml-xgboost/rabit/CMakeLists.txt
+++ /dev/null
@@ -1,185 +0,0 @@
-cmake_minimum_required(VERSION 3.3)
-
-project(rabit VERSION 0.3.0 LANGUAGES CXX)
-
-if ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
-  # This allows user to specify `RABIT_BUILD_DMLC` and others as CMake variable.
-  cmake_policy(SET CMP0077 NEW)
-endif ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
-
-option(RABIT_BUILD_TESTS "Build rabit tests" OFF)
-option(RABIT_BUILD_MPI "Build MPI" OFF)
-option(RABIT_BUILD_DMLC "Include DMLC_CORE in build" OFF)
-option(RABIT_WITH_R_LIB "Fit the strict environment of R" OFF)
-
-option(DMLC_ROOT "Specify root of external dmlc core.")
-# by default point to xgboost/dmlc-core
-set(DMLC_ROOT ${CMAKE_CURRENT_LIST_DIR}/../dmlc-core)
-
-# moved from xgboost build
-if(R_LIB OR MINGW OR WIN32)
-  add_library(rabit src/engine_empty.cc src/c_api.cc)
-  set(rabit_libs rabit)
-  set_target_properties(rabit
-          PROPERTIES CXX_STANDARD 14
-          CXX_STANDARD_REQUIRED ON
-          POSITION_INDEPENDENT_CODE ON)
-else()
-  find_package(Threads REQUIRED)
-  add_library(rabit_empty src/engine_empty.cc src/c_api.cc)
-  add_library(rabit_base src/allreduce_base.cc src/engine_base.cc src/c_api.cc)
-
-  add_library(rabit src/allreduce_base.cc src/allreduce_robust.cc src/engine.cc src/c_api.cc)
-  add_library(rabit_mock_static src/allreduce_base.cc src/allreduce_robust.cc src/engine_mock.cc src/c_api.cc)
-  add_library(rabit_mock SHARED src/allreduce_base.cc src/allreduce_robust.cc src/engine_mock.cc src/c_api.cc)
-  target_link_libraries(rabit Threads::Threads)
-  target_link_libraries(rabit_mock_static Threads::Threads)
-  target_link_libraries(rabit_mock Threads::Threads)
-
-  set(rabit_libs rabit rabit_base rabit_empty rabit_mock rabit_mock_static)
-  set_target_properties(rabit rabit_base rabit_empty rabit_mock rabit_mock_static
-    PROPERTIES CXX_STANDARD 14
-    CXX_STANDARD_REQUIRED ON
-    POSITION_INDEPENDENT_CODE ON)
-ENDIF(R_LIB OR MINGW OR WIN32)
-
-if(RABIT_BUILD_MPI)
-  find_package(MPI REQUIRED)
-  if (NOT MPI_CXX_FOUND)
-    message(FATAL_ERROR "CXX Interface for MPI is required for building MPI backend.")
-  endif (NOT MPI_CXX_FOUND)
-  add_library(rabit_mpi src/engine_mpi.cc ${MPI_INCLUDE_PATH})
-  target_link_libraries(rabit_mpi ${MPI_CXX_LIBRARIES})
-  list(APPEND rabit_libs rabit_mpi)
-endif()
-
-# place binaries and libraries according to GNU standards
-include(GNUInstallDirs)
-set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/${CMAKE_INSTALL_LIBDIR})
-set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/${CMAKE_INSTALL_LIBDIR})
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/${CMAKE_INSTALL_BINDIR})
-
-# we use this to get code coverage
-if ((CMAKE_CONFIGURATION_TYPES STREQUAL "Debug") AND (CMAKE_CXX_COMPILER_ID MATCHES GNU))
-  foreach(lib ${rabit_libs})
-    target_compile_options(${lib}
-      -fprofile-arcs
-      -ftest-coverage)
-    endforeach()
-endif((CMAKE_CONFIGURATION_TYPES STREQUAL "Debug") AND (CMAKE_CXX_COMPILER_ID MATCHES GNU))
-
-if(RABIT_BUILD_DMLC)
-  set(DMLC_ROOT ${CMAKE_CURRENT_LIST_DIR}/dmlc-core)
-endif()
-
-if(DMLC_ROOT)
-  message("DMLC_ROOT point to " ${DMLC_ROOT})
-endif(DMLC_ROOT)
-
-foreach(lib ${rabit_libs})
-  target_include_directories(${lib} PUBLIC
-          "$<BUILD_INTERFACE:${rabit_SOURCE_DIR}/include>"
-          "$<BUILD_INTERFACE:${DMLC_ROOT}/include>")
-  target_include_directories(${lib}
-          PRIVATE
-          ${rabit_SOURCE_DIR}/../kernel_include)
-endforeach()
-
-if (RABIT_BUILD_TESTS)
-  enable_testing()
-  add_subdirectory(${rabit_SOURCE_DIR}/test/cpp)
-
-  # rabit mock based integration tests
-  list(REMOVE_ITEM rabit_libs "rabit_mock_static") # remove here to avoid installing it
-  set(tests lazy_recover local_recover model_recover)
-
-  foreach(test ${tests})
-    add_executable(${test} test/${test}.cc)
-    target_link_libraries(${test} rabit_mock_static)
-    set_target_properties(${test}  PROPERTIES CXX_STANDARD 14 CXX_STANDARD_REQUIRED ON)
-    install(TARGETS ${test} DESTINATION test) # Why are we installing these??
-  endforeach()
-
-  if(RABIT_BUILD_MPI)
-    add_executable(speed_test_mpi test/speed_test.cc)
-    target_link_libraries(speed_test_mpi rabit_mpi)
-    install(TARGETS speed_test_mpi DESTINATION test)
-  endif()
-endif (RABIT_BUILD_TESTS)
-
-# Installation (https://github.com/forexample/package-example) {
-
-# Layout. This works for all platforms:
-#   * <prefix>/lib/cmake/<PROJECT-NAME>
-#   * <prefix>/lib/
-#   * <prefix>/include/
-set(CMAKE_INSTALL_PREFIX "${rabit_SOURCE_DIR}")
-set(config_install_dir "lib/cmake/${PROJECT_NAME}")
-set(include_install_dir "include")
-
-set(generated_dir "${CMAKE_CURRENT_BINARY_DIR}/generated")
-
-# Configuration
-set(version_config "${generated_dir}/${PROJECT_NAME}ConfigVersion.cmake")
-set(project_config "${generated_dir}/${PROJECT_NAME}Config.cmake")
-set(TARGETS_EXPORT_NAME "${PROJECT_NAME}Targets")
-set(namespace "${PROJECT_NAME}::")
-
-# Include module with fuction 'write_basic_package_version_file'
-include(CMakePackageConfigHelpers)
-
-# Configure '<PROJECT-NAME>ConfigVersion.cmake'
-# Use:
-#   * PROJECT_VERSION
-write_basic_package_version_file(
-    "${version_config}" COMPATIBILITY SameMajorVersion
-)
-
-# Configure '<PROJECT-NAME>Config.cmake'
-# Use variables:
-#   * TARGETS_EXPORT_NAME
-#   * PROJECT_NAME
-configure_package_config_file(
-    "cmake/Config.cmake.in"
-    "${project_config}"
-    INSTALL_DESTINATION "${config_install_dir}"
-)
-
-# Targets:
-#   * <prefix>/lib/librabit.a
-#   * <prefix>/lib/librabit_base
-#   * <prefix>/lib/librabit_empty
-#   * header location after install: <prefix>/include/rabit/rabit.h
-#   * headers can be included by C++ code `#include <rabit/rabit.h>`
-install(
-    TARGETS ${rabit_libs}
-    EXPORT "${TARGETS_EXPORT_NAME}"
-    LIBRARY DESTINATION "lib"
-    ARCHIVE DESTINATION "lib"
-    RUNTIME DESTINATION "bin"
-    INCLUDES DESTINATION "${include_install_dir}"
-)
-
-# Headers:
-install(
-    DIRECTORY "include/"
-    DESTINATION "${include_install_dir}"
-    FILES_MATCHING PATTERN "*.h"
-)
-
-# Config
-#   * <prefix>/lib/cmake/rabit/rabitConfig.cmake
-#   * <prefix>/lib/cmake/rabit/rabitConfigVersion.cmake
-install(
-    FILES "${project_config}" "${version_config}"
-    DESTINATION "${config_install_dir}"
-)
-
-# Config
-#   * <prefix>/lib/cmake/Foo/FooTargets.cmake
-install(
-    EXPORT "${TARGETS_EXPORT_NAME}"
-    NAMESPACE "${namespace}"
-    DESTINATION "${config_install_dir}"
-)
-# }
diff --git a/ml-xgboost/rabit/LICENSE b/ml-xgboost/rabit/LICENSE
deleted file mode 100644
index 2485f4e..0000000
--- a/ml-xgboost/rabit/LICENSE
+++ /dev/null
@@ -1,28 +0,0 @@
-Copyright (c) 2014 by Contributors
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this
-  list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice,
-  this list of conditions and the following disclaimer in the documentation
-  and/or other materials provided with the distribution.
-
-* Neither the name of rabit nor the names of its
-  contributors may be used to endorse or promote products derived from
-  this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
diff --git a/ml-xgboost/rabit/Makefile b/ml-xgboost/rabit/Makefile
deleted file mode 100644
index a5df97e..0000000
--- a/ml-xgboost/rabit/Makefile
+++ /dev/null
@@ -1,102 +0,0 @@
-OS := $(shell uname)
-
-RABIT_BUILD_DMLC = 0
-
-export WARNFLAGS= -Wall -Wextra -Wno-unused-parameter -Wno-unknown-pragmas -std=c++11
-export CFLAGS = -O3 $(WARNFLAGS)
-export LDFLAGS =-Llib
-
-#download mpi
-#echo $(shell scripts/mpi.sh)
-
-MPICXX=./mpich/bin/mpicxx
-
-export CXX = g++
-
-
-#----------------------------
-# Settings for power and arm arch
-#----------------------------
-ARCH := $(shell uname -a)
-ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
-	CFLAGS += -march=native
-endif
-
-ifndef WITH_FPIC
-	WITH_FPIC = 1
-endif
-ifeq ($(WITH_FPIC), 1)
-	CFLAGS += -fPIC
-endif
-
-ifndef LINT_LANG
-	LINT_LANG="all"
-endif
-
-ifeq ($(RABIT_BUILD_DMLC),1)
-    DMLC=dmlc-core
-else
-    DMLC=../dmlc-core
-endif
-
-CFLAGS += -I $(DMLC)/include -I include/
-
-# build path
-BPATH=.
-# objectives that makes up rabit library
-MPIOBJ= $(BPATH)/engine_mpi.o
-OBJ= $(BPATH)/allreduce_base.o $(BPATH)/allreduce_robust.o $(BPATH)/engine.o $(BPATH)/engine_empty.o $(BPATH)/engine_mock.o\
-	$(BPATH)/c_api.o $(BPATH)/engine_base.o
-SLIB= lib/librabit.so lib/librabit_mock.so lib/librabit_base.so
-ALIB= lib/librabit.a lib/librabit_empty.a lib/librabit_mock.a lib/librabit_base.a
-MPISLIB= lib/librabit_mpi.so
-MPIALIB= lib/librabit_mpi.a
-HEADERS=src/*.h include/rabit/*.h include/rabit/internal/*.h
-
-.PHONY: clean all install mpi python lint doc doxygen
-
-all: lib/librabit.a lib/librabit_mock.a  lib/librabit.so lib/librabit_base.a lib/librabit_mock.so
-mpi: lib/librabit_mpi.a lib/librabit_mpi.so
-
-$(BPATH)/allreduce_base.o: src/allreduce_base.cc $(HEADERS)
-$(BPATH)/engine.o: src/engine.cc $(HEADERS)
-$(BPATH)/allreduce_robust.o: src/allreduce_robust.cc $(HEADERS)
-$(BPATH)/engine_mpi.o: src/engine_mpi.cc $(HEADERS)
-$(BPATH)/engine_empty.o: src/engine_empty.cc $(HEADERS)
-$(BPATH)/engine_mock.o: src/engine_mock.cc $(HEADERS)
-$(BPATH)/engine_base.o: src/engine_base.cc $(HEADERS)
-$(BPATH)/c_api.o: src/c_api.cc $(HEADERS)
-
-lib/librabit.a lib/librabit.so: $(BPATH)/allreduce_base.o $(BPATH)/allreduce_robust.o $(BPATH)/engine.o $(BPATH)/c_api.o
-lib/librabit_base.a lib/librabit_base.so: $(BPATH)/allreduce_base.o $(BPATH)/engine_base.o $(BPATH)/c_api.o
-lib/librabit_mock.a lib/librabit_mock.so: $(BPATH)/allreduce_base.o $(BPATH)/allreduce_robust.o $(BPATH)/engine_mock.o $(BPATH)/c_api.o
-lib/librabit_empty.a: $(BPATH)/engine_empty.o $(BPATH)/c_api.o
-lib/librabit_mpi.a lib/librabit_mpi.so: $(MPIOBJ)
-
-$(OBJ) :
-	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c %.cc, $^) )
-
-$(ALIB):
-	ar cr $@ $+
-
-$(SLIB) :
-	$(CXX) $(CFLAGS) -shared -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS)
-
-$(MPIOBJ) :
-	$(MPICXX) -c $(CFLAGS) -I./mpich/include -o $@ $(firstword $(filter %.cpp %.c %.cc, $^) )
-
-$(MPIALIB):
-	ar cr $@ $+
-
-$(MPISLIB) :
-	$(MPICXX) $(CFLAGS) -I./mpich/include -shared -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) \
-	$(LDFLAGS) -L./mpich/lib -Wl,-rpath,./mpich/lib -lmpi
-
-lint:
-	$(DMLC)/scripts/lint.py rabit $(LINT_LANG) src include
-
-doc doxygen:
-	cd include; doxygen ../doc/Doxyfile; cd -
-
-clean:
-	$(RM)  $(OBJ) $(MPIOBJ) $(ALIB) $(MPIALIB) $(SLIB) *~ src/*~ include/*~ include/*/*~
diff --git a/ml-xgboost/rabit/README.md b/ml-xgboost/rabit/README.md
deleted file mode 100644
index eb0ce71..0000000
--- a/ml-xgboost/rabit/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Rabit: Reliable Allreduce and Broadcast Interface
-[![Build Status](https://travis-ci.org/dmlc/rabit.svg?branch=master)](https://travis-ci.org/dmlc/rabit)
-[![Documentation Status](https://readthedocs.org/projects/rabit/badge/?version=latest)](http://rabit.readthedocs.org/)
-
-rabit is a light weight library that provides a fault tolerant interface of Allreduce and Broadcast. It is designed to support easy implementations of distributed machine learning programs, many of which fall naturally under the Allreduce abstraction. The goal of rabit is to support ***portable*** , ***scalable*** and ***reliable*** distributed machine learning programs.
-
-* [Tutorial](guide)
-* [API Documentation](http://homes.cs.washington.edu/~tqchen/rabit/doc)
-* You can also directly read the [interface header](include/rabit.h)
-* [XGBoost](https://github.com/dmlc/xgboost)
-  - Rabit is one of the backbone library to support distributed XGBoost
-
-## Features
-All these features comes from the facts about small rabbit:)
-* Portable: rabit is light weight and runs everywhere
-  - Rabit is a library instead of a framework, a program only needs to link the library to run
-  - Rabit only replies on a mechanism to start program, which was provided by most framework
-  - You can run rabit programs on many platforms, including Yarn(Hadoop), MPI using the same code
-* Scalable and Flexible: rabit runs fast
-  * Rabit program use Allreduce to communicate, and do not suffer the cost between iterations of MapReduce abstraction.
-  - Programs can call rabit functions in any order, as opposed to frameworks where callbacks are offered and called by the framework, i.e. inversion of control principle.
-  - Programs persist over all the iterations, unless they fail and recover.
-* Reliable: rabit dig burrows to avoid disasters
-  - Rabit programs can recover the model and results using synchronous function calls.
-  - Rabit programs can set rabit_boostrap_cache=1 to support allreduce/broadcast operations before loadcheckpoint
-  `
-    rabit::Init(); -> rabit::AllReduce(); -> rabit::loadCheckpoint(); -> for () { rabit::AllReduce(); rabit::Checkpoint();} -> rabit::Shutdown();
-  `
-
-## Use Rabit
-* Type make in the root folder will compile the rabit library in lib folder
-* Add lib to the library path and include to the include path of compiler
-* Languages: You can use rabit in C++ and python
-  - It is also possible to port the library to other languages
-
-## Contributing
-Rabit is an open-source library, contributions are welcomed, including:
-* The rabit core library.
-* Customized tracker script for new platforms and interface of new languages.
-* Tutorial and examples about the library.
diff --git a/ml-xgboost/rabit/cmake/Config.cmake.in b/ml-xgboost/rabit/cmake/Config.cmake.in
deleted file mode 100644
index 38bbde7..0000000
--- a/ml-xgboost/rabit/cmake/Config.cmake.in
+++ /dev/null
@@ -1,4 +0,0 @@
-@PACKAGE_INIT@
-
-include("${CMAKE_CURRENT_LIST_DIR}/@TARGETS_EXPORT_NAME@.cmake")
-check_required_components("@PROJECT_NAME@")
diff --git a/ml-xgboost/rabit/cmake/googletest-download.cmake b/ml-xgboost/rabit/cmake/googletest-download.cmake
deleted file mode 100644
index 7cf367c..0000000
--- a/ml-xgboost/rabit/cmake/googletest-download.cmake
+++ /dev/null
@@ -1,20 +0,0 @@
-# code copied from https://crascit.com/2015/07/25/cmake-gtest/
-cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
-
-project(googletest-download NONE)
-
-include(ExternalProject)
-
-ExternalProject_Add(
-        googletest
-        SOURCE_DIR "@GOOGLETEST_DOWNLOAD_ROOT@/googletest-src"
-        BINARY_DIR "@GOOGLETEST_DOWNLOAD_ROOT@/googletest-build"
-        GIT_REPOSITORY
-        https://github.com/google/googletest.git
-        GIT_TAG
-        release-1.8.0
-        CONFIGURE_COMMAND ""
-        BUILD_COMMAND ""
-        INSTALL_COMMAND ""
-        TEST_COMMAND ""
-)
\ No newline at end of file
diff --git a/ml-xgboost/rabit/cmake/googletest.cmake b/ml-xgboost/rabit/cmake/googletest.cmake
deleted file mode 100644
index 0a0b1a5..0000000
--- a/ml-xgboost/rabit/cmake/googletest.cmake
+++ /dev/null
@@ -1,32 +0,0 @@
-# the following code to fetch googletest
-# is inspired by and adapted after https://crascit.com/2015/07/25/cmake-gtest/
-# download and unpack googletest at configure time
-
-macro(fetch_googletest _download_module_path _download_root)
-    set(GOOGLETEST_DOWNLOAD_ROOT ${_download_root})
-    configure_file(
-            ${_download_module_path}/googletest-download.cmake
-            ${_download_root}/CMakeLists.txt
-            @ONLY
-    )
-    unset(GOOGLETEST_DOWNLOAD_ROOT)
-
-    execute_process(
-            COMMAND
-            "${CMAKE_COMMAND}" -G "${CMAKE_GENERATOR}" .
-            WORKING_DIRECTORY
-            ${_download_root}
-    )
-    execute_process(
-            COMMAND
-            "${CMAKE_COMMAND}" --build .
-            WORKING_DIRECTORY
-            ${_download_root}
-    )
-
-    # adds the targers: gtest, gtest_main, gmock, gmock_main
-    add_subdirectory(
-            ${_download_root}/googletest-src
-            ${_download_root}/googletest-build
-    )
-endmacro()
\ No newline at end of file
diff --git a/ml-xgboost/rabit/doc/.gitignore b/ml-xgboost/rabit/doc/.gitignore
deleted file mode 100644
index 95f88be..0000000
--- a/ml-xgboost/rabit/doc/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-html
-latex
-*.sh
-_*
-doxygen
diff --git a/ml-xgboost/rabit/doc/Doxyfile b/ml-xgboost/rabit/doc/Doxyfile
deleted file mode 100644
index 3e64641..0000000
--- a/ml-xgboost/rabit/doc/Doxyfile
+++ /dev/null
@@ -1,281 +0,0 @@
-# Doxyfile 1.7.6.1
-
-#---------------------------------------------------------------------------
-# Project related configuration options
-#---------------------------------------------------------------------------
-DOXYFILE_ENCODING      = UTF-8
-PROJECT_NAME           = "rabit"
-PROJECT_NUMBER         =
-PROJECT_BRIEF          =
-PROJECT_LOGO           =
-OUTPUT_DIRECTORY       = ../doc/doxygen
-CREATE_SUBDIRS         = NO
-OUTPUT_LANGUAGE        = English
-BRIEF_MEMBER_DESC      = YES
-REPEAT_BRIEF           = YES
-ABBREVIATE_BRIEF       =
-ALWAYS_DETAILED_SEC    = NO
-INLINE_INHERITED_MEMB  = NO
-FULL_PATH_NAMES        = YES
-STRIP_FROM_PATH        =
-STRIP_FROM_INC_PATH    =
-SHORT_NAMES            = NO
-JAVADOC_AUTOBRIEF      = NO
-QT_AUTOBRIEF           = NO
-MULTILINE_CPP_IS_BRIEF = NO
-INHERIT_DOCS           = YES
-SEPARATE_MEMBER_PAGES  = NO
-TAB_SIZE               = 8
-ALIASES                =
-TCL_SUBST              =
-OPTIMIZE_OUTPUT_FOR_C  = YES
-OPTIMIZE_OUTPUT_JAVA   = NO
-OPTIMIZE_FOR_FORTRAN   = NO
-OPTIMIZE_OUTPUT_VHDL   = NO
-EXTENSION_MAPPING      =
-BUILTIN_STL_SUPPORT    = NO
-CPP_CLI_SUPPORT        = NO
-SIP_SUPPORT            = NO
-IDL_PROPERTY_SUPPORT   = YES
-DISTRIBUTE_GROUP_DOC   = NO
-SUBGROUPING            = YES
-INLINE_GROUPED_CLASSES = NO
-INLINE_SIMPLE_STRUCTS  = NO
-TYPEDEF_HIDES_STRUCT   = NO
-LOOKUP_CACHE_SIZE      = 0
-#---------------------------------------------------------------------------
-# Build related configuration options
-#---------------------------------------------------------------------------
-EXTRACT_ALL            = NO
-EXTRACT_PRIVATE        = NO
-EXTRACT_STATIC         = NO
-EXTRACT_LOCAL_CLASSES  = YES
-EXTRACT_LOCAL_METHODS  = NO
-EXTRACT_ANON_NSPACES   = NO
-HIDE_UNDOC_MEMBERS     = NO
-HIDE_UNDOC_CLASSES     = YES
-HIDE_FRIEND_COMPOUNDS  = NO
-HIDE_IN_BODY_DOCS      = NO
-INTERNAL_DOCS          = NO
-CASE_SENSE_NAMES       = YES
-HIDE_SCOPE_NAMES       = NO
-SHOW_INCLUDE_FILES     = YES
-FORCE_LOCAL_INCLUDES   = NO
-INLINE_INFO            = YES
-SORT_MEMBER_DOCS       = YES
-SORT_BRIEF_DOCS        = NO
-SORT_MEMBERS_CTORS_1ST = NO
-SORT_GROUP_NAMES       = NO
-SORT_BY_SCOPE_NAME     = NO
-STRICT_PROTO_MATCHING  = NO
-GENERATE_TODOLIST      = YES
-GENERATE_TESTLIST      = YES
-GENERATE_BUGLIST       = YES
-GENERATE_DEPRECATEDLIST= YES
-ENABLED_SECTIONS       =
-MAX_INITIALIZER_LINES  = 30
-SHOW_USED_FILES        = YES
-SHOW_FILES             = YES
-SHOW_NAMESPACES        = YES
-FILE_VERSION_FILTER    =
-LAYOUT_FILE            =
-CITE_BIB_FILES         =
-#---------------------------------------------------------------------------
-# configuration options related to warning and progress messages
-#---------------------------------------------------------------------------
-QUIET                  = NO
-WARNINGS               = YES
-WARN_IF_UNDOCUMENTED   = YES
-WARN_IF_DOC_ERROR      = YES
-WARN_NO_PARAMDOC       = YES
-WARN_FORMAT            = "$file:$line: $text"
-WARN_LOGFILE           =
-#---------------------------------------------------------------------------
-# configuration options related to the input files
-#---------------------------------------------------------------------------
-INPUT                  = rabit
-INPUT_ENCODING         = UTF-8
-FILE_PATTERNS          =
-RECURSIVE              = NO
-EXCLUDE                =
-EXCLUDE_SYMLINKS       = NO
-EXCLUDE_PATTERNS       = *-inl.hpp
-EXCLUDE_SYMBOLS        =
-EXAMPLE_PATH           =
-EXAMPLE_PATTERNS       =
-EXAMPLE_RECURSIVE      = NO
-IMAGE_PATH             =
-INPUT_FILTER           =
-FILTER_PATTERNS        =
-FILTER_SOURCE_FILES    = NO
-FILTER_SOURCE_PATTERNS =
-#---------------------------------------------------------------------------
-# configuration options related to source browsing
-#---------------------------------------------------------------------------
-SOURCE_BROWSER         = NO
-INLINE_SOURCES         = NO
-STRIP_CODE_COMMENTS    = YES
-REFERENCED_BY_RELATION = NO
-REFERENCES_RELATION    = NO
-REFERENCES_LINK_SOURCE = YES
-USE_HTAGS              = NO
-VERBATIM_HEADERS       = YES
-#---------------------------------------------------------------------------
-# configuration options related to the alphabetical class index
-#---------------------------------------------------------------------------
-ALPHABETICAL_INDEX     = YES
-COLS_IN_ALPHA_INDEX    = 5
-IGNORE_PREFIX          =
-#---------------------------------------------------------------------------
-# configuration options related to the HTML output
-#---------------------------------------------------------------------------
-GENERATE_HTML          = YES
-HTML_OUTPUT            = html
-HTML_FILE_EXTENSION    = .html
-HTML_HEADER            =
-HTML_FOOTER            =
-HTML_STYLESHEET        =
-HTML_EXTRA_FILES       =
-HTML_COLORSTYLE_HUE    = 220
-HTML_COLORSTYLE_SAT    = 100
-HTML_COLORSTYLE_GAMMA  = 80
-HTML_TIMESTAMP         = YES
-HTML_DYNAMIC_SECTIONS  = NO
-GENERATE_DOCSET        = NO
-DOCSET_FEEDNAME        = "Doxygen generated docs"
-DOCSET_BUNDLE_ID       = org.doxygen.Project
-DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
-DOCSET_PUBLISHER_NAME  = Publisher
-GENERATE_HTMLHELP      = NO
-CHM_FILE               =
-HHC_LOCATION           =
-GENERATE_CHI           = NO
-CHM_INDEX_ENCODING     =
-BINARY_TOC             = NO
-TOC_EXPAND             = NO
-GENERATE_QHP           = NO
-QCH_FILE               =
-QHP_NAMESPACE          = org.doxygen.Project
-QHP_VIRTUAL_FOLDER     = doc
-QHP_CUST_FILTER_NAME   =
-QHP_CUST_FILTER_ATTRS  =
-QHP_SECT_FILTER_ATTRS  =
-QHG_LOCATION           =
-GENERATE_ECLIPSEHELP   = NO
-ECLIPSE_DOC_ID         = org.doxygen.Project
-DISABLE_INDEX          = NO
-GENERATE_TREEVIEW      = NO
-ENUM_VALUES_PER_LINE   = 4
-TREEVIEW_WIDTH         = 250
-EXT_LINKS_IN_WINDOW    = NO
-FORMULA_FONTSIZE       = 10
-FORMULA_TRANSPARENT    = YES
-USE_MATHJAX            = NO
-MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
-MATHJAX_EXTENSIONS     =
-SEARCHENGINE           = YES
-SERVER_BASED_SEARCH    = NO
-#---------------------------------------------------------------------------
-# configuration options related to the LaTeX output
-#---------------------------------------------------------------------------
-GENERATE_LATEX         = YES
-LATEX_OUTPUT           = latex
-LATEX_CMD_NAME         = latex
-MAKEINDEX_CMD_NAME     = makeindex
-COMPACT_LATEX          = NO
-PAPER_TYPE             = a4
-EXTRA_PACKAGES         =
-LATEX_HEADER           =
-LATEX_FOOTER           =
-PDF_HYPERLINKS         = YES
-USE_PDFLATEX           = YES
-LATEX_BATCHMODE        = NO
-LATEX_HIDE_INDICES     = NO
-LATEX_SOURCE_CODE      = NO
-LATEX_BIB_STYLE        = plain
-#---------------------------------------------------------------------------
-# configuration options related to the RTF output
-#---------------------------------------------------------------------------
-GENERATE_RTF           = NO
-RTF_OUTPUT             = rtf
-COMPACT_RTF            = NO
-RTF_HYPERLINKS         = NO
-RTF_STYLESHEET_FILE    =
-RTF_EXTENSIONS_FILE    =
-#---------------------------------------------------------------------------
-# configuration options related to the man page output
-#---------------------------------------------------------------------------
-GENERATE_MAN           = NO
-MAN_OUTPUT             = man
-MAN_EXTENSION          = .3
-MAN_LINKS              = NO
-#---------------------------------------------------------------------------
-# configuration options related to the XML output
-#---------------------------------------------------------------------------
-GENERATE_XML           = YES
-XML_OUTPUT             = xml
-XML_PROGRAMLISTING     = YES
-#---------------------------------------------------------------------------
-# configuration options for the AutoGen Definitions output
-#---------------------------------------------------------------------------
-GENERATE_AUTOGEN_DEF   = NO
-#---------------------------------------------------------------------------
-# configuration options related to the Perl module output
-#---------------------------------------------------------------------------
-GENERATE_PERLMOD       = NO
-PERLMOD_LATEX          = NO
-PERLMOD_PRETTY         = YES
-PERLMOD_MAKEVAR_PREFIX =
-#---------------------------------------------------------------------------
-# Configuration options related to the preprocessor
-#---------------------------------------------------------------------------
-ENABLE_PREPROCESSING   = NO
-MACRO_EXPANSION        = NO
-EXPAND_ONLY_PREDEF     = NO
-SEARCH_INCLUDES        = YES
-INCLUDE_PATH           =
-INCLUDE_FILE_PATTERNS  =
-PREDEFINED             =
-EXPAND_AS_DEFINED      =
-SKIP_FUNCTION_MACROS   = YES
-#---------------------------------------------------------------------------
-# Configuration::additions related to external references
-#---------------------------------------------------------------------------
-TAGFILES               =
-GENERATE_TAGFILE       =
-ALLEXTERNALS           = NO
-EXTERNAL_GROUPS        = YES
-PERL_PATH              = /usr/bin/perl
-#---------------------------------------------------------------------------
-# Configuration options related to the dot tool
-#---------------------------------------------------------------------------
-CLASS_DIAGRAMS         = YES
-MSCGEN_PATH            =
-HIDE_UNDOC_RELATIONS   = YES
-HAVE_DOT               = NO
-DOT_NUM_THREADS        = 0
-DOT_FONTNAME           = Helvetica
-DOT_FONTSIZE           = 10
-DOT_FONTPATH           =
-CLASS_GRAPH            = YES
-COLLABORATION_GRAPH    = YES
-GROUP_GRAPHS           = YES
-UML_LOOK               = NO
-TEMPLATE_RELATIONS     = NO
-INCLUDE_GRAPH          = YES
-INCLUDED_BY_GRAPH      = YES
-CALL_GRAPH             = NO
-CALLER_GRAPH           = NO
-GRAPHICAL_HIERARCHY    = YES
-DIRECTORY_GRAPH        = YES
-DOT_IMAGE_FORMAT       = png
-INTERACTIVE_SVG        = NO
-DOT_PATH               =
-DOTFILE_DIRS           =
-MSCFILE_DIRS           =
-DOT_GRAPH_MAX_NODES    = 50
-MAX_DOT_GRAPH_DEPTH    = 0
-DOT_TRANSPARENT        = NO
-DOT_MULTI_TARGETS      = YES
-GENERATE_LEGEND        = YES
-DOT_CLEANUP            = YES
diff --git a/ml-xgboost/rabit/doc/Makefile b/ml-xgboost/rabit/doc/Makefile
deleted file mode 100644
index 40bba2a..0000000
--- a/ml-xgboost/rabit/doc/Makefile
+++ /dev/null
@@ -1,192 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = _build
-
-# User-friendly check for sphinx-build
-ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
-$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
-endif
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
-
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  applehelp  to make an Apple Help Book"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  xml        to make Docutils-native XML files"
-	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-	@echo "  coverage   to run coverage check of the documentation (if enabled)"
-
-clean:
-	rm -rf $(BUILDDIR)/*
-
-html:
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/rabit.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/rabit.qhc"
-
-applehelp:
-	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
-	@echo
-	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
-	@echo "N.B. You won't be able to view it unless you put it in" \
-	      "~/Library/Documentation/Help or install it in your application" \
-	      "bundle."
-
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/rabit"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/rabit"
-	@echo "# devhelp"
-
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-latexpdfja:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through platex and dvipdfmx..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
-
-coverage:
-	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
-	@echo "Testing of coverage in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/coverage/python.txt."
-
-xml:
-	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
-	@echo
-	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-pseudoxml:
-	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
-	@echo
-	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/ml-xgboost/rabit/doc/conf.py b/ml-xgboost/rabit/doc/conf.py
deleted file mode 100644
index ef89de4..0000000
--- a/ml-xgboost/rabit/doc/conf.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# documentation build configuration file, created by
-# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-import sys
-import os, subprocess
-import shlex
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-libpath = os.path.join(curr_path, '../wrapper/')
-sys.path.insert(0, os.path.join(curr_path, '../wrapper/'))
-sys.path.insert(0, curr_path)
-from sphinx_util import MarkdownParser, AutoStructify
-
-# -- General configuration ------------------------------------------------
-
-# General information about the project.
-project = u'rabit'
-copyright = u'2015, rabit developers'
-author = u'rabit developers'
-github_doc_root = 'https://github.com/dmlc/rabit/tree/master/doc/'
-
-# add markdown parser
-MarkdownParser.github_doc_root = github_doc_root
-source_parsers = {
-    '.md': MarkdownParser,
-}
-# Version information.
-import rabit
-
-version = rabit.__version__
-release = rabit.__version__
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.mathjax',
-    'breathe',
-]
-
-# Use breathe to include doxygen documents
-breathe_projects = {'rabit' : 'doxygen/xml/'}
-breathe_default_project = 'rabit'
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-# source_suffix = ['.rst', '.md']
-source_suffix = ['.rst', '.md']
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = ['_build']
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-#keep_warnings = False
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-# html_theme = 'alabaster'
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = project + 'doc'
-
-# -- Options for LaTeX output ---------------------------------------------
-latex_elements = {
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-  (master_doc, 'rabit.tex', project,
-   author, 'manual'),
-]
-
-# hook for doxygen
-def run_doxygen(folder):
-    """Run the doxygen make command in the designated folder."""
-    try:
-        retcode = subprocess.call("cd %s; make doxygen" % folder, shell=True)
-        if retcode < 0:
-            sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
-    except OSError as e:
-        sys.stderr.write("doxygen execution failed: %s" % e)
-
-
-def run_build_lib(folder):
-    """Run the doxygen make command in the designated folder."""
-    try:
-        retcode = subprocess.call("cd %s; make" % folder, shell=True)
-        retcode = subprocess.call("rm -rf _build/html/doxygen", shell=True)
-        retcode = subprocess.call("mkdir _build", shell=True)
-        retcode = subprocess.call("mkdir _build/html", shell=True)
-        retcode = subprocess.call("cp -rf doxygen/html _build/html/doxygen", shell=True)
-        if retcode < 0:
-            sys.stderr.write("build terminated by signal %s" % (-retcode))
-    except OSError as e:
-        sys.stderr.write("build execution failed: %s" % e)
-
-
-def generate_doxygen_xml(app):
-    """Run the doxygen make commands if we're on the ReadTheDocs server"""
-    read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True'
-    if read_the_docs_build:
-        run_doxygen('..')
-        sys.stderr.write('Check if shared lib exists\n')
-        run_build_lib('..')
-    sys.stderr.write('The wrapper path: %s\n' % str(os.listdir('../wrapper')))
-    rabit._loadlib()
-
-
-def setup(app):
-    # Add hook for building doxygen xml when needed
-    app.connect("builder-inited", generate_doxygen_xml)
-    app.add_config_value('recommonmark_config', {
-            'url_resolver': lambda url: github_doc_root + url,
-            }, True)
-    app.add_transform(AutoStructify)
diff --git a/ml-xgboost/rabit/doc/cpp_api.md b/ml-xgboost/rabit/doc/cpp_api.md
deleted file mode 100644
index c6184aa..0000000
--- a/ml-xgboost/rabit/doc/cpp_api.md
+++ /dev/null
@@ -1,9 +0,0 @@
-C++ Library API of Rabit
-========================
-This page contains document of Library API of rabit.
-
-```eval_rst
-.. toctree::
-
-.. doxygennamespace:: rabit
-```
diff --git a/ml-xgboost/rabit/doc/guide.md b/ml-xgboost/rabit/doc/guide.md
deleted file mode 100644
index 7bf50b0..0000000
--- a/ml-xgboost/rabit/doc/guide.md
+++ /dev/null
@@ -1,383 +0,0 @@
-Tutorial
-========
-This is rabit's tutorial, a ***Reliable Allreduce and Broadcast Interface***.
-All the example codes are in the [guide](https://github.com/dmlc/rabit/blob/master/guide/) folder of the project.
-To run the examples locally, you will need to build them with ```make```.
-
-**List of Topics**
-* [What is Allreduce](#what-is-allreduce)
-* [Common Use Case](#common-use-case)
-* [Use Rabit API](#use-rabit-api)
-  - [Structure of a Rabit Program](#structure-of-a-rabit-program)
-  - [Allreduce and Lazy Preparation](#allreduce-and-lazy-preparation)
-  - [Checkpoint and LazyCheckpoint](#checkpoint-and-lazycheckpoint)
-* [Compile Programs with Rabit](#compile-programs-with-rabit)
-* [Running Rabit Jobs](#running-rabit-jobs)
-* [Fault Tolerance](#fault-tolerance)
-
-What is Allreduce
------------------
-The main methods provided by rabit are Allreduce and Broadcast. Allreduce performs reduction across different computation nodes,
-and returns the result to every node. To understand the behavior of the function, consider the following example in [basic.cc](../guide/basic.cc) (there is a python example right after this if you are more familiar with python).
-```c++
-#include <rabit.h>
-using namespace rabit;
-const int N = 3;
-int main(int argc, char *argv[]) {
-  int a[N];
-  rabit::Init(argc, argv);
-  for (int i = 0; i < N; ++i) {
-    a[i] = rabit::GetRank() + i;
-  }
-  printf("@node[%d] before-allreduce: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // allreduce take max of each elements in all processes
-  Allreduce<op::Max>(&a[0], N);
-  printf("@node[%d] after-allreduce-max: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // second allreduce that sums everything up
-  Allreduce<op::Sum>(&a[0], N);
-  printf("@node[%d] after-allreduce-sum: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  rabit::Finalize();
-  return 0;
-}
-```
-You can run the example using the rabit_demo.py script. The following command
-starts the rabit program with two worker processes.
-```bash
-../tracker/rabit_demo.py -n 2 basic.rabit
-```
-This will start two processes, one process with rank 0 and the other with rank 1, both processes run the same code.
-The ```rabit::GetRank()``` function returns the rank of current process.
-
-Before the call to Allreduce, process 0 contains the array ```a = {0, 1, 2}```, while process 1 has the array
-```a = {1, 2, 3}```. After the call to Allreduce, the array contents in all processes are replaced by the
-reduction result (in this case, the maximum value in each position across all the processes). So, after the
-Allreduce call, the result will become ```a = {1, 2, 3}```.
-Rabit provides different reduction operators, for example,  if you change ```op::Max``` to ```op::Sum```,
-the reduction operation will be a summation, and the result will become ```a = {1, 3, 5}```.
-You can also run the example with different processes by setting -n to different values.
-
-If you are more familiar with python, you can also use rabit in python. The same example as before can be found in [basic.py](../guide/basic.py):
-
-```python
-import numpy as np
-import rabit
-
-rabit.init()
-n = 3
-rank = rabit.get_rank()
-a = np.zeros(n)
-for i in xrange(n):
-    a[i] = rank + i
-
-print '@node[%d] before-allreduce: a=%s' % (rank, str(a))
-a = rabit.allreduce(a, rabit.MAX)
-print '@node[%d] after-allreduce-max: a=%s' % (rank, str(a))
-a = rabit.allreduce(a, rabit.SUM)
-print '@node[%d] after-allreduce-sum: a=%s' % (rank, str(a))
-rabit.finalize()
-```
-You can run the program using the following command
-```bash
-../tracker/rabit_demo.py -n 2 basic.py
-```
-
-Broadcast is another method provided by rabit besides Allreduce. This function allows one node to broadcast its
-local data to all other nodes. The following code in [broadcast.cc](../guide/broadcast.cc) broadcasts a string from
-node 0 to all other nodes.
-```c++
-#include <rabit.h>
-using namespace rabit;
-const int N = 3;
-int main(int argc, char *argv[]) {
-  rabit::Init(argc, argv);
-  std::string s;
-  if (rabit::GetRank() == 0) s = "hello world";
-  printf("@node[%d] before-broadcast: s=\"%s\"\n",
-         rabit::GetRank(), s.c_str());
-  // broadcast s from node 0 to all other nodes
-  rabit::Broadcast(&s, 0);
-  printf("@node[%d] after-broadcast: s=\"%s\"\n",
-         rabit::GetRank(), s.c_str());
-  rabit::Finalize();
-  return 0;
-}
-```
-The following command starts the program with three worker processes.
-```bash
-../tracker/rabit_demo.py -n 3 broadcast.rabit
-```
-Besides strings, rabit also allows to broadcast constant size array and vectors.
-
-The counterpart in python can be found in [broadcast.py](../guide/broadcast.py). Here is a snippet so that you can get a better sense of how simple is to use the python library:
-
-```python
-import rabit
-rabit.init()
-n = 3
-rank = rabit.get_rank()
-s = None
-if rank == 0:
-    s = {'hello world':100, 2:3}
-print '@node[%d] before-broadcast: s=\"%s\"' % (rank, str(s))
-s = rabit.broadcast(s, 0)
-print '@node[%d] after-broadcast: s=\"%s\"' % (rank, str(s))
-rabit.finalize()
-```
-
-Common Use Case
----------------
-Many distributed machine learning algorithms involve splitting the data into different nodes,
-computing statistics locally, and finally aggregating them. Such workflow is usually done repetitively through many iterations before the algorithm converges. Allreduce naturally meets the structure of such programs,
-common use cases include:
-
-* Aggregation of gradient values, which can be used in optimization methods such as L-BFGS.
-* Aggregation of other statistics, which can be used in KMeans and Gaussian Mixture Models.
-* Find the best split candidate and aggregation of split statistics, used for tree based models.
-
-Rabit is a reliable and portable library for distributed machine learning programs, that allow programs to run reliably on different platforms.
-
-Use Rabit API
--------------
-This section introduces topics about how to use rabit API.
-You can always refer to [API Documentation](http://homes.cs.washington.edu/~tqchen/rabit/doc) for definition of each functions.
-This section trys to gives examples of different aspectes of rabit API.
-
-#### Structure of a Rabit Program
-The following code illustrates the common structure of a rabit program. This is an abstract example,
-you can also refer to [wormhole](https://github.com/dmlc/wormhole/blob/master/learn/kmeans/kmeans.cc) for an example implementation of kmeans algorithm.
-
-```c++
-#include <rabit.h>
-int main(int argc, char *argv[]) {
-  ...
-  rabit::Init(argc, argv);
-  // sync on expected model size before load checkpoint, if we pass rabit_bootstrap_cache=true
-  rabit::Allreduce<rabit::op::Max>(&model.size(), 1);
-  // load the latest checked model
-  int version = rabit::LoadCheckPoint(&model);
-  // initialize the model if it is the first version
-  if (version == 0) model.InitModel();
-  // the version number marks the iteration to resume
-  for (int iter = version; iter < max_iter; ++iter) {
-    // at this point, the model object should allow us to recover the program state
-    ...
-    // each iteration can contain multiple calls of allreduce/broadcast
-    rabit::Allreduce<rabit::op::Max>(&data[0], n);
-    ...
-    // checkpoint model after one iteration finishes
-    rabit::CheckPoint(&model);
-  }
-  rabit::Finalize();
-  return 0;
-}
-```
-
-Besides the common Allreduce and Broadcast functions, there are two additional functions: ```LoadCheckPoint```
-and ```CheckPoint```. These two functions are used for fault-tolerance purposes.
-As mentioned before, traditional machine learning programs involve several iterations. In each iteration, we start with a model, make some calls
-to Allreduce or Broadcast and update the model. The calling sequence in each iteration does not need to be the same.
-
-* When the nodes start from the beginning (i.e. iteration 0), ```LoadCheckPoint``` returns 0, so we can initialize the model.
-* ```CheckPoint``` saves the model after each iteration.
-  - Efficiency Note: the model is only kept in local memory and no save to disk is performed when calling Checkpoint
-* When a node goes down and restarts, ```LoadCheckPoint``` will recover the latest saved model, and
-* When a node goes down, the rest of the nodes will block in the call of Allreduce/Broadcast and wait for
-  the recovery of the failed node until it catches up.
-
-Please see the [Fault Tolerance](#fault-tolerance) section to understand the recovery procedure executed by rabit.
-
-#### Allreduce and Lazy Preparation
-Allreduce is one of the most important function provided by rabit. You can call allreduce by specifying the
-reduction operator, pointer to the data and size of the buffer, as follows
-```c++
-Allreduce<operator>(pointer_of_data, size_of_data);
-```
-This is the basic use case of Allreduce function. It is common that user writes the code to prepare the data needed
-into the data buffer, pass the data to Allreduce function, and get the reduced result. However, when a node restarts
-from failure, we can directly recover the result from other nodes(see also [Fault Tolerance](#fault-tolerance)) and
-the data preparation procedure no longer necessary. Rabit Allreduce add an optional parameter preparation function
-to support such scenario. User can pass in a function that corresponds to the data preparation procedure to Allreduce
-calls, and the data preparation function will only be called when necessary. We use [lazy_allreduce.cc](../guide/lazy_allreduce.cc)
-as an example to demonstrate this feature. It is modified from [basic.cc](../guide/basic.cc), and you can compare the two codes.
-```c++
-#include <rabit.h>
-using namespace rabit;
-const int N = 3;
-int main(int argc, char *argv[]) {
-  int a[N] = {0};
-  rabit::Init(argc, argv);
-  // lazy preparation function
-  auto prepare = [&]() {
-    printf("@node[%d] run prepare function\n", rabit::GetRank());
-    for (int i = 0; i < N; ++i) {
-      a[i] = rabit::GetRank() + i;
-    }
-  };
-  printf("@node[%d] before-allreduce: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // allreduce take max of each elements in all processes
-  Allreduce<op::Max>(&a[0], N, prepare);
-  printf("@node[%d] after-allreduce-sum: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // rum second allreduce
-  Allreduce<op::Sum>(&a[0], N);
-  printf("@node[%d] after-allreduce-max: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  rabit::Finalize();
-  return 0;
-}
-```
-Here we use features of C++11 because the lambda function makes things much shorter.
-There is also C++ compatible callback interface provided in the [API](http://homes.cs.washington.edu/~tqchen/rabit/doc).
-You can compile the program by typing ```make lazy_allreduce.mock```. We link against the mock library so that we can see
-the effect when a process goes down. You can run the program using the following command
-```bash
-../tracker/rabit_demo.py -n 2 lazy_allreduce.mock mock=0,0,1,0
-```
-The additional arguments ```mock=0,0,1,0``` will cause node 0 to kill itself before second call of Allreduce (see also [mock test](#link-against-mock-test-rabit-library)).
-You will find that the prepare function's print is only executed once and node 0 will no longer execute the preparation function when it restarts from failure.
-
-You can also find python version of the example in [lazy_allreduce.py](../guide/lazy_allreduce.py), and run it using the followin command
-```bash
-../tracker/rabit_demo.py -n 2 lazy_allreduce.py mock=0,0,1,0
-
-```
-
-Since lazy preparation function may not be called during execution. User should be careful when using this feature. For example, a possible mistake
-could be putting some memory allocation code in the lazy preparation function, and the computing memory was not allocated when lazy preparation function is not called.
-The example in [lazy_allreduce.cc](../guide/lazy_allreduce.cc) provides a simple way to migrate normal prepration code([basic.cc](../guide/basic.cc)) to lazy version: wrap the preparation
-code with a lambda function, and pass it to allreduce.
-
-#### Checkpoint and LazyCheckpoint
-Common machine learning algorithms usually involves iterative computation. As mentioned in the section ([Structure of a Rabit Program](#structure-of-a-rabit-program)),
-user can and should use Checkpoint to ```save``` the progress so far, so that when a node fails, the latest checkpointed model can be loaded.
-
-There are two model arguments you can pass to Checkpoint and LoadCheckpoint: ```global_model``` and ```local_model```:
-* ```global_model``` refers to the model that is commonly shared across all the nodes
-  - For example, the centriods of clusters in kmeans is shared across all nodes
-* ```local_model``` refers to the model that is specifically tied to the current node
-  - For example, in topic modeling, the topic assignments of subset of documents in current node is local model
-
-Because the different nature of the two types of models, different strategy will be used for them.
-```global_model``` is simply saved in local memory of each node, while ```local_model``` will replicated to some other
-nodes (selected using a ring replication strategy). The checkpoint is only saved in the memory without touching the disk which makes rabit programs more efficient.
-User is encouraged to use ```global_model``` only when is sufficient for better efficiency.
-
-To enable a model class to be checked pointed, user can implement a [serialization interface](../include/rabit_serialization.h). The serialization interface already
-provide serialization functions of STL vector and string. For python API, user can checkpoint any python object that can be pickled.
-
-There is a special Checkpoint function called [LazyCheckpoint](http://homes.cs.washington.edu/~tqchen/rabit/doc/namespacerabit.html#a99f74c357afa5fba2c80cc0363e4e459),
-which can be used for ```global_model``` only cases under certain condition.
-When LazyCheckpoint is called, no action is taken and the rabit engine only remembers the pointer to the model.
-The serialization will only happen when another node fails and the recovery starts. So user basically pays no extra cost calling LazyCheckpoint.
-To use this function, the user need to ensure the model remain unchanged until the last call of Allreduce/Broadcast in the current version finishes.
-So that when recovery procedure happens in these function calls, the serialized model will be the same.
-
-For example, consider the following calling sequence
-```
-LazyCheckPoint, code1, Allreduce, code2, Broadcast, code3, LazyCheckPoint
-```
-The user must only change the model in code3. Such condition can usually be satiesfied in many scenarios, and user can use LazyCheckpoint to further
-improve the efficiency of the program.
-
-
-Compile Programs with Rabit
----------------------------
-Rabit is a portable library, to use it, you only need to include the rabit header file.
-* You will need to add the path to [../include](../include) to the header search path of the compiler
-  - Solution 1: add ```-I/path/to/rabit/include``` to the compiler flag in gcc or clang
-  - Solution 2: add the path to the environment variable CPLUS_INCLUDE_PATH
-* You will need to add the path to [../lib](../lib) to the library search path of the compiler
-  - Solution 1: add ```-L/path/to/rabit/lib``` to the linker flag
-  - Solution 2: add the path to environment variable LIBRARY_PATH AND LD_LIBRARY_PATH
-* Link against lib/rabit.a
-  - Add ```-lrabit``` to the linker flag
-
-The procedure above allows you to compile a program with rabit. The following two sections contain additional
-options you can use to link against different backends other than the normal one.
-
-#### Link against MPI Allreduce
-You can link against ```rabit_mpi.a``` instead of using MPI Allreduce, however, the resulting program is backed by MPI and
-is not fault tolerant anymore.
-* Simply change the linker flag from ```-lrabit``` to ```-lrabit_mpi```
-* The final linking needs to be done by mpi wrapper compiler ```mpicxx```
-
-#### Link against Mock Test Rabit Library
-If you want to use a mock to test the program in order to see the behavior of the code when some nodes go down, you can link against ```rabit_mock.a``` .
-* Simply change the linker flag from ```-lrabit``` to ```-lrabit_mock```
-
-The resulting rabit mock program can take in additional arguments in the following format
-```
-mock=rank,version,seq,ndeath
-```
-
-The four integers specify an event that will cause the program to ```commit suicide```(exit with -2)
-* rank specifies the rank of the node to kill
-* version specifies the version (iteration) of the model where you want the process to die
-* seq specifies the sequence number of the Allreduce/Broadcast call since last checkpoint, where the process will be killed
-* ndeath specifies how many times this node died already
-
-For example, consider the following script in the test case
-```bash
-../tracker/rabit_demo.py -n 10 test_model_recover 10000\
-                         mock=0,0,1,0 mock=1,1,1,0 mock=1,1,1,1
-```
-* The first mock will cause node 0 to exit when calling the second Allreduce/Broadcast (seq = 1) in iteration 0
-* The second mock will cause node 1 to exit when calling the second Allreduce/Broadcast (seq = 1) in iteration 1
-* The third mock will cause node 1 to exit again when calling second Allreduce/Broadcast (seq = 1) in iteration 1
-  - Note that ndeath = 1 means this will happen only if node 1 died once, which is our case
-
-Running Rabit Jobs
-------------------
-Rabit is a portable library that can run on multiple platforms.
-All the rabit jobs can be submitted using [dmlc-tracker](https://github.com/dmlc/dmlc-core/tree/master/tracker)
-
-Fault Tolerance
----------------
-This section introduces how fault tolerance works in rabit.
-The following figure shows how rabit deals with failures.
-
-![](http://homes.cs.washington.edu/~tqchen/rabit/fig/fault-tol.png)
-
-The scenario is as follows:
-* Node 1 fails between the first and second call of Allreduce after the second checkpoint
-* The other nodes wait in the call of the second Allreduce in order to help node 1 to recover.
-* When node 1 restarts, it will call ```LoadCheckPoint```, and get the latest checkpoint from one of the existing nodes.
-* Then node 1 can start from the latest checkpoint and continue running.
-* When node 1 calls the first Allreduce again, as the other nodes already know the result, node 1 can get it from one of them.
-* When node 1 reaches the second Allreduce, the other nodes find out that node 1 has catched up and they can continue the program normally.
-
-This fault tolerance model is based on a key property of Allreduce and
-Broadcast: All the nodes get the same result after calling Allreduce/Broadcast.
-Because of this property, any node can record the results of history
-Allreduce/Broadcast calls.  When a node is recovered, it can fetch the lost
-results from some alive nodes and rebuild its model.
-
-The checkpoint is introduced so that we can discard the history results of
-Allreduce/Broadcast calls before the latest checkpoint. This saves memory
-consumption used for backup.  The checkpoint of each node is a model defined by
-users and can be split into 2 parts: a global model and a local model. The
-global model is shared by all nodes and can be backed up by any nodes. The
-local model of a node is replicated to some other nodes (selected using a ring
-replication strategy).  The checkpoint is only saved in the memory without
-touching the disk which makes rabit programs more efficient.  The strategy of
-rabit is different from the fail-restart strategy where all the nodes restart
-from the same checkpoint when any of them fail.  In rabit, all the alive nodes
-will block in the Allreduce call and help the recovery.  To catch up, the
-recovered node fetches its latest checkpoint and the results of
-Allreduce/Broadcast calls after the checkpoint from some alive nodes.
-
-This is just a conceptual introduction to rabit's fault tolerance model. The actual implementation is more sophisticated,
-and can deal with more complicated cases such as multiple nodes failure and node failure during recovery phase.
-
-Rabit Timeout
----------------
-
-In certain cases, rabit cluster may suffer lack of resources to retry failed workers.
-Thanks to fault tolerant assumption with infinite retry, it might cause entire cluster hang infinitely.
-We introduce sidecar thread which runs when rabit fault tolerant runtime observed allreduce/broadcast errors.
-By default, it will wait for 30 mins before all workers program exit. 
-User can opt-in this feature and change treshold by passing rabit_timeout=true and rabit_timeout_sec=x (in seconds).
diff --git a/ml-xgboost/rabit/doc/index.md b/ml-xgboost/rabit/doc/index.md
deleted file mode 100644
index d209d95..0000000
--- a/ml-xgboost/rabit/doc/index.md
+++ /dev/null
@@ -1,24 +0,0 @@
-Rabit Documentation
-=====================
-rabit is a light weight library that provides a fault tolerant interface of Allreduce and Broadcast. It is designed to support easy implementations of distributed machine learning programs, many of which fall naturally under the Allreduce abstraction. The goal of rabit is to support **portable** , **scalable** and **reliable** distributed machine learning programs.
-
-API Documents
--------------
-```eval_rst
-
-.. toctree::
-   :maxdepth: 2
-
-   python_api.md
-   cpp_api.md
-   parameters.md
-   guide.md
-```
-Indices and tables
-------------------
-
-```eval_rst
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-```
\ No newline at end of file
diff --git a/ml-xgboost/rabit/doc/parameters.md b/ml-xgboost/rabit/doc/parameters.md
deleted file mode 100644
index 37580d5..0000000
--- a/ml-xgboost/rabit/doc/parameters.md
+++ /dev/null
@@ -1,21 +0,0 @@
-Parameters
-==========
-This section list all the parameters that can be passed to rabit::Init function as argv.
-All the parameters are passed in as string in format of ``parameter-name=parameter-value``.
-In most setting these parameters have default value or will be automatically detected,
-and do not need to be manually configured.
-
-* rabit_tracker_uri [passed in automatically by tracker]
-  - The uri/ip of rabit tracker
-* rabit_tracker_port [passed in automatically by tracker]
-  - The port of rabit tracker
-* rabit_task_id [automatically detected]
-  - The unique identifier of computing process
-  - When running on hadoop, this is automatically extracted from enviroment variable
-* rabit_reduce_buffer [default = 256MB]
-  - The memory buffer used to store intermediate result of reduction
-  - Format "digits + unit", can be 128M, 1G
-* rabit_global_replica [default = 5]
-  - Number of replication copies of result kept for each Allreduce/Broadcast call
-* rabit_local_replica [default = 2]
-  - Number of replication of local model in check point
diff --git a/ml-xgboost/rabit/doc/python-requirements.txt b/ml-xgboost/rabit/doc/python-requirements.txt
deleted file mode 100644
index 5970c43..0000000
--- a/ml-xgboost/rabit/doc/python-requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-numpy
-breathe
-commonmark
-
diff --git a/ml-xgboost/rabit/doc/python_api.md b/ml-xgboost/rabit/doc/python_api.md
deleted file mode 100644
index 8a0eda9..0000000
--- a/ml-xgboost/rabit/doc/python_api.md
+++ /dev/null
@@ -1,11 +0,0 @@
-Python API of Rabit
-===================
-This page contains document of python API of rabit.
-
-```eval_rst
-.. toctree::
-
-.. automodule:: rabit
-    :members:
-    :show-inheritance:
-```
diff --git a/ml-xgboost/rabit/doc/sphinx_util.py b/ml-xgboost/rabit/doc/sphinx_util.py
deleted file mode 100644
index f6a33ff..0000000
--- a/ml-xgboost/rabit/doc/sphinx_util.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Helper utilty function for customization."""
-import sys
-import os
-import docutils
-import subprocess
-
-if os.environ.get('READTHEDOCS', None) == 'True':
-    subprocess.call('cd ..; rm -rf recommonmark;' +
-                    'git clone https://github.com/tqchen/recommonmark', shell=True)
-
-sys.path.insert(0, os.path.abspath('../recommonmark/'))
-from recommonmark import parser, transform
-
-MarkdownParser = parser.CommonMarkParser
-AutoStructify = transform.AutoStructify
diff --git a/ml-xgboost/rabit/guide/Makefile b/ml-xgboost/rabit/guide/Makefile
deleted file mode 100644
index e882056..0000000
--- a/ml-xgboost/rabit/guide/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-export CC  = gcc
-export CXX = g++
-export MPICXX = mpicxx
-export LDFLAGS= -pthread -lm -L../lib
-export CFLAGS = -Wall -O3 -mavx -std=c++11 -Wno-unknown-pragmas -fPIC -fopenmp -I../include
-
-.PHONY: clean all lib libmpi
-BIN = basic.rabit broadcast.rabit
-MOCKBIN= lazy_allreduce.mock
-
-all: $(BIN)
-basic.rabit: basic.cc lib ../lib/librabit.a
-broadcast.rabit: broadcast.cc lib ../lib/librabit.a
-lazy_allreduce.mock: lazy_allreduce.cc lib ../lib/librabit.a
-
-$(BIN) :
-	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a,  $^) $(LDFLAGS)
-
-$(MOCKBIN) :
-	$(CXX) $(CFLAGS) -std=c++11 -o $@ $(filter %.cpp %.o %.c %.cc,  $^) $(LDFLAGS) -lrabit_mock
-
-$(OBJ) :
-	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c %.cc, $^) )
-
-clean:
-	$(RM) $(OBJ) $(BIN) $(MOCKBIN) *~ ../src/*~
diff --git a/ml-xgboost/rabit/guide/README b/ml-xgboost/rabit/guide/README
deleted file mode 100644
index 2483d68..0000000
--- a/ml-xgboost/rabit/guide/README
+++ /dev/null
@@ -1 +0,0 @@
-See tutorial at ../doc/guide.md
\ No newline at end of file
diff --git a/ml-xgboost/rabit/guide/basic.cc b/ml-xgboost/rabit/guide/basic.cc
deleted file mode 100644
index d08397b..0000000
--- a/ml-xgboost/rabit/guide/basic.cc
+++ /dev/null
@@ -1,35 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file basic.cc
- * \brief This is an example demonstrating what is Allreduce
- *
- * \author Tianqi Chen
- */
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#include <vector>
-#include <rabit/rabit.h>
-using namespace rabit;
-int main(int argc, char *argv[]) {
-  int N = 3;
-  if (argc > 1) {
-    N = atoi(argv[1]);
-  }
-  std::vector<int> a(N);
-  rabit::Init(argc, argv);
-  for (int i = 0; i < N; ++i) {
-    a[i] = rabit::GetRank() + i;
-  }
-  printf("@node[%d] before-allreduce: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // allreduce take max of each elements in all processes
-  Allreduce<op::Max>(&a[0], N);
-  printf("@node[%d] after-allreduce-max: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // second allreduce that sums everything up
-  Allreduce<op::Sum>(&a[0], N);
-  printf("@node[%d] after-allreduce-sum: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  rabit::Finalize();
-  return 0;
-}
diff --git a/ml-xgboost/rabit/guide/basic.py b/ml-xgboost/rabit/guide/basic.py
deleted file mode 100644
index 363150b..0000000
--- a/ml-xgboost/rabit/guide/basic.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/python
-"""
-demo python script of rabit
-"""
-from __future__ import print_function
-from builtins import range
-import os
-import sys
-import numpy as np
-# import rabit, the tracker script will setup the lib path correctly
-# for normal run without tracker script, add following line
-# sys.path.append(os.path.dirname(__file__) + '/../python')
-import rabit
-
-rabit.init()
-n = 3
-rank = rabit.get_rank()
-a = np.zeros(n)
-for i in range(n):
-    a[i] = rank + i
-
-print('@node[%d] before-allreduce: a=%s' % (rank, str(a)))
-a = rabit.allreduce(a, rabit.MAX)
-print('@node[%d] after-allreduce-max: a=%s' % (rank, str(a)))
-a = rabit.allreduce(a, rabit.SUM)
-print('@node[%d] after-allreduce-sum: a=%s' % (rank, str(a)))
-rabit.finalize()
diff --git a/ml-xgboost/rabit/guide/broadcast.cc b/ml-xgboost/rabit/guide/broadcast.cc
deleted file mode 100644
index 9e360d8..0000000
--- a/ml-xgboost/rabit/guide/broadcast.cc
+++ /dev/null
@@ -1,16 +0,0 @@
-#include <rabit/rabit.h>
-using namespace rabit;
-const int N = 3;
-int main(int argc, char *argv[]) {
-  rabit::Init(argc, argv);
-  std::string s;
-  if (rabit::GetRank() == 0) s = "hello world";
-  printf("@node[%d] before-broadcast: s=\"%s\"\n",
-         rabit::GetRank(), s.c_str());
-  // broadcast s from node 0 to all other nodes
-  rabit::Broadcast(&s, 0);
-  printf("@node[%d] after-broadcast: s=\"%s\"\n",
-         rabit::GetRank(), s.c_str());
-  rabit::Finalize();
-  return 0;
-}
diff --git a/ml-xgboost/rabit/guide/broadcast.py b/ml-xgboost/rabit/guide/broadcast.py
deleted file mode 100644
index 8b81692..0000000
--- a/ml-xgboost/rabit/guide/broadcast.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/python
-"""
-demo python script of rabit
-"""
-from __future__ import print_function
-import os
-import sys
-# add path to wrapper
-# for normal run without tracker script, add following line
-# sys.path.append(os.path.dirname(__file__) + '/../wrapper')
-import rabit
-
-rabit.init()
-n = 3
-rank = rabit.get_rank()
-s = None
-if rank == 0:
-    s = {'hello world':100, 2:3}
-print('@node[%d] before-broadcast: s=\"%s\"' % (rank, str(s)))
-s = rabit.broadcast(s, 0)
-
-print('@node[%d] after-broadcast: s=\"%s\"' % (rank, str(s)))
-rabit.finalize()
diff --git a/ml-xgboost/rabit/guide/lazy_allreduce.cc b/ml-xgboost/rabit/guide/lazy_allreduce.cc
deleted file mode 100644
index b4b816f..0000000
--- a/ml-xgboost/rabit/guide/lazy_allreduce.cc
+++ /dev/null
@@ -1,34 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file basic.cc
- * \brief This is an example demonstrating what is Allreduce
- *
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-
-using namespace rabit;
-const int N = 3;
-int main(int argc, char *argv[]) {
-  int a[N] = {0};
-  rabit::Init(argc, argv);
-  // lazy preparation function
-  auto prepare = [&]() {
-    printf("@node[%d] run prepare function\n", rabit::GetRank());
-    for (int i = 0; i < N; ++i) {
-      a[i] = rabit::GetRank() + i;
-    }
-  };
-  printf("@node[%d] before-allreduce: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // allreduce take max of each elements in all processes
-  Allreduce<op::Max>(&a[0], N, prepare);
-  printf("@node[%d] after-allreduce-sum: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  // rum second allreduce
-  Allreduce<op::Sum>(&a[0], N);
-  printf("@node[%d] after-allreduce-max: a={%d, %d, %d}\n",
-         rabit::GetRank(), a[0], a[1], a[2]);
-  rabit::Finalize();
-  return 0;
-}
diff --git a/ml-xgboost/rabit/guide/lazy_allreduce.py b/ml-xgboost/rabit/guide/lazy_allreduce.py
deleted file mode 100644
index 2b60a8c..0000000
--- a/ml-xgboost/rabit/guide/lazy_allreduce.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/python
-"""
-demo python script of rabit: Lazy preparation function
-"""
-import os
-import sys
-import numpy as np
-# import rabit, the tracker script will setup the lib path correctly
-# for normal run without tracker script, add following line
-# sys.path.append(os.path.dirname(__file__) + '/../wrapper')
-import rabit
-
-
-# use mock library so that we can run failure test
-rabit.init(lib = 'mock')
-n = 3
-rank = rabit.get_rank()
-a = np.zeros(n)
-
-def prepare(a):
-    print('@node[%d] run prepare function' % rank)
-    # must take in reference and modify the reference
-    for i in xrange(n):
-        a[i] = rank + i
-
-print('@node[%d] before-allreduce: a=%s' % (rank, str(a)))
-a = rabit.allreduce(a, rabit.MAX, prepare_fun = prepare)
-print('@node[%d] after-allreduce-max: a=%s' % (rank, str(a)))
-a = rabit.allreduce(a, rabit.SUM)
-print('@node[%d] after-allreduce-sum: a=%s' % (rank, str(a)))
-rabit.finalize()
diff --git a/ml-xgboost/rabit/include/rabit/c_api.h b/ml-xgboost/rabit/include/rabit/c_api.h
deleted file mode 100644
index 47c5735..0000000
--- a/ml-xgboost/rabit/include/rabit/c_api.h
+++ /dev/null
@@ -1,196 +0,0 @@
-/*!
- * Copyright by Contributors
- * \file c_api.h
- * \author Tianqi Chen
- * \brief a C style API of rabit.
- */
-#ifndef RABIT_C_API_H_
-#define RABIT_C_API_H_
-
-#ifdef __cplusplus
-#define RABIT_EXTERN_C extern "C"
-#include <cstdio>
-#else
-#define RABIT_EXTERN_C
-#include <stdio.h>
-#endif  // __cplusplus
-
-#if defined(_MSC_VER) || defined(_WIN32)
-#define RABIT_DLL RABIT_EXTERN_C __declspec(dllexport)
-#else
-#define RABIT_DLL RABIT_EXTERN_C __attribute__ ((visibility ("default")))
-#endif  // defined(_MSC_VER) || defined(_WIN32)
-
-/*! \brief rabit unsigned long type */
-typedef unsigned long rbt_ulong;  // NOLINT(*)
-
-/*!
- * \brief intialize the rabit module,
- *  call this once before using anything
- *  The additional arguments is not necessary.
- *  Usually rabit will detect settings
- *  from environment variables.
- * \param argc number of arguments in argv
- * \param argv the array of input arguments
- * \return true if rabit is initialized successfully otherwise false
- */
-RABIT_DLL bool RabitInit(int argc, char *argv[]);
-
-/*!
- * \brief finalize the rabit engine,
- * call this function after you finished all jobs.
- * \return true if rabit is initialized successfully otherwise false
- */
-RABIT_DLL bool RabitFinalize(void);
-
-/*!
- * \brief get rank of previous process in ring topology
- * \return rank number of worker
- * */
-RABIT_DLL int RabitGetRingPrevRank(void);
-
-/*!
- * \brief get rank of current process
- * \return rank number of worker
- * */
-RABIT_DLL int RabitGetRank(void);
-
-/*!
- * \brief get total number of process
- * \return total world size
- * */
-RABIT_DLL int RabitGetWorldSize(void);
-
-/*!
- * \brief get rank of current process
- * \return if rabit is distributed
- * */
-RABIT_DLL int RabitIsDistributed(void);
-
-/*!
- * \brief print the msg to the tracker,
- *    this function can be used to communicate the information of the progress to
- *    the user who monitors the tracker
- * \param msg the message to be printed
- */
-RABIT_DLL void RabitTrackerPrint(const char *msg);
-/*!
- * \brief get name of processor
- * \param out_name hold output string
- * \param out_len hold length of output string
- * \param max_len maximum buffer length of input
-   */
-RABIT_DLL void RabitGetProcessorName(char *out_name,
-                                     rbt_ulong *out_len,
-                                     rbt_ulong max_len);
-/*!
- * \brief broadcast an memory region to all others from root
- *
- *     Example: int a = 1; Broadcast(&a, sizeof(a), root);
- * \param sendrecv_data the pointer to send or recive buffer,
- * \param size the size of the data
- * \param root the root of process
- */
-RABIT_DLL void RabitBroadcast(void *sendrecv_data,
-                              rbt_ulong size, int root);
-
-/*!
- * \brief Allgather function, each node have a segment of data in the ring of sendrecvbuf,
- *  the data provided by current node k is [slice_begin, slice_end),
- *  the next node's segment must start with slice_end
- *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
- *  use a ring based algorithm
- *
- * \param sendrecvbuf buffer for both sending and receiving data, it is a ring conceptually
- * \param total_size total size of data to be gathered
- * \param beginIndex beginning of the current slice in sendrecvbuf of type enum_dtype
- * \param size_node_slice size of the current node slice
- * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
- * \param enum_dtype the enumeration of data type, see rabit::engine::mpi::DataType in engine.h of rabit include
- * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-RABIT_DLL void RabitAllgather(void *sendrecvbuf,
-                                  size_t total_size,
-                                  size_t beginIndex,
-                                  size_t size_node_slice,
-                                  size_t size_prev_slice,
-                                  int enum_dtype);
-
-/*!
- * \brief perform in-place allreduce, on sendrecvbuf
- *        this function is NOT thread-safe
- *
- * Example Usage: the following code gives sum of the result
- *     vector<int> data(10);
- *     ...
- *     Allreduce<op::Sum>(&data[0], data.size());
- *     ...
- * \param sendrecvbuf buffer for both sending and recving data
- * \param count number of elements to be reduced
- * \param enum_dtype the enumeration of data type, see rabit::engine::mpi::DataType in engine.h of rabit include
- * \param enum_op the enumeration of operation type, see rabit::engine::mpi::OpType in engine.h of rabit
- * \param prepare_fun Lazy preprocessing function, if it is not NULL, prepare_fun(prepare_arg)
- *                    will be called by the function before performing Allreduce, to intialize the data in sendrecvbuf_.
- *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
-   * \param prepare_arg argument used to passed into the lazy preprocessing function
-   */
-RABIT_DLL void RabitAllreduce(void *sendrecvbuf,
-                              size_t count,
-                              int enum_dtype,
-                              int enum_op,
-                              void (*prepare_fun)(void *arg),
-                              void *prepare_arg);
-
-/*!
- * \brief load latest check point
- * \param out_global_model hold output of serialized global_model
- * \param out_global_len the output length of serialized global model
- * \param out_local_model hold output of serialized local_model, can be NULL
- * \param out_local_len the output length of serialized local model, can be NULL
- *
- * \return the version number of check point loaded
- *     if returned version == 0, this means no model has been CheckPointed
- *     nothing will be touched
- */
-RABIT_DLL int RabitLoadCheckPoint(char **out_global_model,
-                                  rbt_ulong *out_global_len,
-                                  char **out_local_model,
-                                  rbt_ulong *out_local_len);
-/*!
- * \brief checkpoint the model, meaning we finished a stage of execution
- *  every time we call check point, there is a version number which will increase by one
- *
- * \param global_model hold content of serialized global_model
- * \param global_len the content length of serialized global model
- * \param local_model hold content of serialized local_model, can be NULL
- * \param local_len the content length of serialized local model, can be NULL
- *
- * NOTE: local_model requires explicit replication of the model for fault-tolerance, which will
- *       bring replication cost in CheckPoint function. global_model do not need explicit replication.
- *       So only CheckPoint with global_model if possible
- */
-RABIT_DLL void RabitCheckPoint(const char *global_model,
-                               rbt_ulong global_len,
-                               const char *local_model,
-                               rbt_ulong local_len);
-/*!
- * \return version number of current stored model,
- * which means how many calls to CheckPoint we made so far
- * \return rabit version number
- */
-RABIT_DLL int RabitVersionNumber(void);
-
-
-/*!
- * \brief a Dummy function,
- *  used to cause force link of C API  into the  DLL.
- * \code
- * \/\/force link rabit C API library.
- * static int must_link_rabit_ = RabitLinkTag();
- * \endcode
- * \return a dummy integer.
- */
-RABIT_DLL int RabitLinkTag(void);
-
-#endif  // RABIT_C_API_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/engine.h b/ml-xgboost/rabit/include/rabit/internal/engine.h
deleted file mode 100644
index 0db10e7..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/engine.h
+++ /dev/null
@@ -1,346 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file engine.h
- * \brief This file defines the core interface of rabit library
- * \author Tianqi Chen, Nacho, Tianyi
- */
-#ifndef RABIT_INTERNAL_ENGINE_H_
-#define RABIT_INTERNAL_ENGINE_H_
-#include <string>
-#include "rabit/serializable.h"
-
-#if (defined(__GNUC__) && !defined(__clang__))
-#define _FILE  __builtin_FILE()
-#define _LINE  __builtin_LINE()
-#define _CALLER  __builtin_FUNCTION()
-#else
-#define _FILE  "N/A"
-#define _LINE  -1
-#define _CALLER  "N/A"
-#endif  // (defined(__GNUC__) && !defined(__clang__))
-
-namespace MPI {
-/*! \brief MPI data type just to be compatible with MPI reduce function*/
-class Datatype;
-}
-
-/*! \brief namespace of rabit */
-namespace rabit {
-/*! \brief core interface of the engine */
-namespace engine {
-/*! \brief interface of core Allreduce engine */
-class IEngine {
- public:
-  /*!
-   * \brief Preprocessing function, that is called before AllReduce,
-   *        used to prepare the data used by AllReduce
-   * \param arg additional possible argument used to invoke the preprocessor
-   */
-  typedef void (PreprocFunction) (void *arg);
-  /*!
-   * \brief reduce function, the same form of MPI reduce function is used,
-   *        to be compatible with MPI interface
-   *        In all the functions, the memory is ensured to aligned to 64-bit
-   *        which means it is OK to cast src,dst to double* int* etc
-   * \param src pointer to source space
-   * \param dst pointer to destination reduction
-   * \param count total number of elements to be reduced (note this is total number of elements instead of bytes)
-   *              the definition of the reduce function should be type aware
-   * \param dtype the data type object, to be compatible with MPI reduce
-   */
-  typedef void (ReduceFunction) (const void *src,
-                                 void *dst, int count,
-                                 const MPI::Datatype &dtype);
-  /*! \brief virtual destructor */
-  virtual ~IEngine() {}
-  /*!
-   * \brief Allgather function, each node have a segment of data in the ring of sendrecvbuf,
-   *  the data provided by current node k is [slice_begin, slice_end),
-   *  the next node's segment must start with slice_end
-   *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
-   *  use a ring based algorithm
-   *
-   * \param sendrecvbuf_ buffer for both sending and receiving data, it is a ring conceptually
-   * \param total_size total size of data to be gathered
-   * \param slice_begin beginning of the current slice
-   * \param slice_end end of the current slice
-   * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */ 
-  virtual void Allgather(void *sendrecvbuf,
-                         size_t total_size,
-                         size_t slice_begin,
-                         size_t slice_end,
-                         size_t size_prev_slice,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER) = 0;
-  /*!
-   * \brief performs in-place Allreduce, on sendrecvbuf
-   *        this function is NOT thread-safe
-   * \param sendrecvbuf_ buffer for both sending and receiving data
-   * \param type_nbytes the number of bytes the type has
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \param prepare_func Lazy preprocessing function, if it is not NULL, prepare_fun(prepare_arg)
-   *                     will be called by the function before performing Allreduce in order to initialize the data in sendrecvbuf.
-   *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
-   * \param prepare_arg argument used to pass into the lazy preprocessing function
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Allreduce(void *sendrecvbuf_,
-                         size_t type_nbytes,
-                         size_t count,
-                         ReduceFunction reducer,
-                         PreprocFunction prepare_fun = NULL,
-                         void *prepare_arg = NULL,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER) = 0;
-  /*!
-   * \brief broadcasts data from root to every other node
-   * \param sendrecvbuf_ buffer for both sending and receiving data
-   * \param size the size of the data to be broadcasted
-   * \param root the root worker id to broadcast the data
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Broadcast(void *sendrecvbuf_, size_t size, int root,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER) = 0;
-  /*!
-   * \brief explicitly re-initialize everything before calling LoadCheckPoint
-   *    call this function when IEngine throws an exception,
-   *    this function should only be used for test purposes
-   */
-  virtual void InitAfterException(void) = 0;
-  /*!
-   * \brief loads the latest check point
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller needs to guarantee that the global_model
-   *   is the same in all nodes
-   * \param local_model pointer to the local model that is specific to current node/rank
-   *   this can be NULL when no local model is needed
-   *
-   * \return the version number of the model loaded
-   *     if returned version == 0, this means no model has been CheckPointed
-   *     the p_model is not touched, users should do necessary initialization by themselves
-   *
-   *   Common usage example:
-   *      int iter = rabit::LoadCheckPoint(&model);
-   *      if (iter == 0) model.InitParameters();
-   *      for (i = iter; i < max_iter; ++i) {
-   *        do many things, include allreduce
-   *        rabit::CheckPoint(model);
-   *      }
-   *
-   * \sa CheckPoint, VersionNumber
-   */
-  virtual int LoadCheckPoint(Serializable *global_model,
-                             Serializable *local_model = NULL) = 0;
-  /*!
-   * \brief checkpoints the model, meaning a stage of execution was finished
-   *  every time we call check point, a version number increases by ones
-   *
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller needs to guarantee that the global_model
-   *   is the same in every node
-   * \param local_model pointer to the local model that is specific to current node/rank
-   *   this can be NULL when no local state is needed
-   *
-   * NOTE: local_model requires explicit replication of the model for fault-tolerance, which will
-   *       bring replication cost in CheckPoint function. global_model does not need explicit replication.
-   *       So, only CheckPoint with global_model if possible
-   *
-   * \sa LoadCheckPoint, VersionNumber
-   */
-  virtual void CheckPoint(const Serializable *global_model,
-                          const Serializable *local_model = NULL) = 0;
-  /*!
-   * \brief This function can be used to replace CheckPoint for global_model only,
-   *   when certain condition is met (see detailed explanation).
-   *
-   *   This is a "lazy" checkpoint such that only the pointer to global_model is
-   *   remembered and no memory copy is taken. To use this function, the user MUST ensure that:
-   *   The global_model must remain unchanged until the last call of Allreduce/Broadcast in the current version finishes.
-   *   In other words, global_model can be changed only between the last call of
-   *   Allreduce/Broadcast and LazyCheckPoint in the current version
-   *
-   *   For example, suppose the calling sequence is:
-   *   LazyCheckPoint, code1, Allreduce, code2, Broadcast, code3, LazyCheckPoint
-   *
-   *   If the user can only change global_model in code3, then LazyCheckPoint can be used to
-   *   improve the efficiency of the program.
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller needs to guarantee that global_model
-   *   is the same in every node
-   * \sa LoadCheckPoint, CheckPoint, VersionNumber
-   */
-  virtual void LazyCheckPoint(const Serializable *global_model) = 0;
-  /*!
-   * \return version number of the current stored model,
-   *         which means how many calls to CheckPoint we made so far
-   * \sa LoadCheckPoint, CheckPoint
-   */
-  virtual int VersionNumber(void) const = 0;
-  /*! \brief gets rank of previous node in ring topology */
-  virtual int GetRingPrevRank(void) const = 0;
-  /*! \brief gets rank of current node */
-  virtual int GetRank(void) const = 0;
-  /*! \brief gets total number of nodes */
-  virtual int GetWorldSize(void) const = 0;
-  /*! \brief whether we run in distribted mode */
-  virtual bool IsDistributed(void) const = 0;
-  /*! \brief gets the host name of the current node */
-  virtual std::string GetHost(void) const = 0;
-  /*!
-   * \brief prints the msg in the tracker,
-   *    this function can be used to communicate progress information to
-   *    the user who monitors the tracker
-   * \param msg message to be printed in the tracker
-   */
-  virtual void TrackerPrint(const std::string &msg) = 0;
-};
-
-/*! \brief initializes the engine module */
-bool Init(int argc, char *argv[]);
-/*! \brief finalizes the engine module */
-bool Finalize(void);
-/*! \brief singleton method to get engine */
-IEngine *GetEngine(void);
-
-/*! \brief namespace that contains stubs to be compatible with MPI */
-namespace mpi {
-/*!\brief enum of all operators */
-enum OpType {
-  kMax = 0,
-  kMin = 1,
-  kSum = 2,
-  kBitwiseOR = 3
-};
-/*!\brief enum of supported data types */
-enum DataType {
-  kChar = 0,
-  kUChar = 1,
-  kInt = 2,
-  kUInt = 3,
-  kLong = 4,
-  kULong = 5,
-  kFloat = 6,
-  kDouble = 7,
-  kLongLong = 8,
-  kULongLong = 9
-};
-}  // namespace mpi
-/*!
- * \brief Allgather function, each node have a segment of data in the ring of sendrecvbuf,
- *  the data provided by current node k is [slice_begin, slice_end),
- *  the next node's segment must start with slice_end
- *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
- *  use a ring based algorithm
- *
- * \param sendrecvbuf buffer for both sending and receiving data, it is a ring conceptually
- * \param total_size total size of data to be gathered
- * \param slice_begin beginning of the current slice
- * \param slice_end end of the current slice
- * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- */
-void Allgather(void* sendrecvbuf,
-                   size_t total_size,
-                   size_t slice_begin,
-                   size_t slice_end,
-                   size_t size_prev_slice,
-                   const char* _file = _FILE,
-                   const int _line = _LINE,
-                   const char* _caller = _CALLER);
-/*!
- * \brief perform in-place Allreduce, on sendrecvbuf
- *   this is an internal function used by rabit to be able to compile with MPI
- *   do not use this function directly
- * \param sendrecvbuf buffer for both sending and receiving data
- * \param type_nbytes the number of bytes the type has
- * \param count number of elements to be reduced
- * \param reducer reduce function
- * \param dtype the data type
- * \param op the reduce operator type
- * \param prepare_func Lazy preprocessing function, lazy prepare_fun(prepare_arg)
- *                     will be called by the function before performing Allreduce, to initialize the data in sendrecvbuf_.
- *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
- * \param prepare_arg argument used to pass into the lazy preprocessing function.
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- */
-void Allreduce_(void *sendrecvbuf,
-                size_t type_nbytes,
-                size_t count,
-                IEngine::ReduceFunction red,
-                mpi::DataType dtype,
-                mpi::OpType op,
-                IEngine::PreprocFunction prepare_fun = NULL,
-                void *prepare_arg = NULL,
-                const char* _file = _FILE,
-                const int _line = _LINE,
-                const char* _caller = _CALLER);
-/*!
- * \brief handle for customized reducer, used to handle customized reduce
- *  this class is mainly created for compatiblity issues with MPI's customized reduce
- */
-class ReduceHandle {
- public:
-  // constructor
-  ReduceHandle(void);
-  // destructor
-  ~ReduceHandle(void);
-  /*!
-   * \brief initialize the reduce function,
-   *   with the type the reduce function needs to deal with
-   *   the reduce function MUST be communicative
-   */
-  void Init(IEngine::ReduceFunction redfunc, size_t type_nbytes);
-  /*!
-   * \brief customized in-place all reduce operation
-   * \param sendrecvbuf the in place send-recv buffer
-   * \param type_n4bytes size of the type, in terms of 4bytes
-   * \param count number of elements to send
-   * \param prepare_func Lazy preprocessing function, lazy prepare_fun(prepare_arg)
-   *                     will be called by the function before performing Allreduce in order to initialize the data in sendrecvbuf_.
-   *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
-   * \param prepare_arg argument used to pass into the lazy preprocessing function
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  void Allreduce(void *sendrecvbuf,
-                 size_t type_nbytes,
-                 size_t count,
-                 IEngine::PreprocFunction prepare_fun = NULL,
-                 void *prepare_arg = NULL,
-                 const char* _file = _FILE,
-                 const int _line = _LINE,
-                 const char* _caller = _CALLER);
-  /*! \return the number of bytes occupied by the type */
-  static int TypeSize(const MPI::Datatype &dtype);
-
- protected:
-  // handle function field
-  void *handle_;
-  // reduce function of the reducer
-  IEngine::ReduceFunction *redfunc_;
-  // handle to the type field
-  void *htype_;
-  // the created type in 4 bytes
-  size_t created_type_nbytes_;
-};
-}  // namespace engine
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_ENGINE_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/io.h b/ml-xgboost/rabit/include/rabit/internal/io.h
deleted file mode 100644
index f7e255b..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/io.h
+++ /dev/null
@@ -1,114 +0,0 @@
-/*!
- *  Copyright (c) 2014-2019 by Contributors
- * \file io.h
- * \brief utilities with different serializable implementations
- * \author Tianqi Chen
- */
-#ifndef RABIT_INTERNAL_IO_H_
-#define RABIT_INTERNAL_IO_H_
-#include <cstdio>
-#include <vector>
-#include <cstring>
-#include <string>
-#include <algorithm>
-#include <numeric>
-#include <limits>
-#include "rabit/internal/utils.h"
-#include "rabit/serializable.h"
-
-namespace rabit {
-namespace utils {
-/*! \brief re-use definition of dmlc::SeekStream */
-typedef dmlc::SeekStream SeekStream;
-/*! \brief fixed size memory buffer */
-struct MemoryFixSizeBuffer : public SeekStream {
- public:
-  // similar to SEEK_END in libc
-  static size_t constexpr SeekEnd = std::numeric_limits<size_t>::max();
-
- public:
-  MemoryFixSizeBuffer(void *p_buffer, size_t buffer_size)
-      : p_buffer_(reinterpret_cast<char*>(p_buffer)),
-        buffer_size_(buffer_size) {
-    curr_ptr_ = 0;
-  }
-  virtual ~MemoryFixSizeBuffer(void) {}
-  virtual size_t Read(void *ptr, size_t size) {
-    size_t nread = std::min(buffer_size_ - curr_ptr_, size);
-    if (nread != 0) std::memcpy(ptr, p_buffer_ + curr_ptr_, nread);
-    curr_ptr_ += nread;
-    return nread;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    if (size == 0) return;
-    utils::Assert(curr_ptr_ + size <=  buffer_size_,
-                  "write position exceed fixed buffer size");
-    std::memcpy(p_buffer_ + curr_ptr_, ptr, size);
-    curr_ptr_ += size;
-  }
-  virtual void Seek(size_t pos) {
-    if (pos == SeekEnd) {
-      curr_ptr_ = buffer_size_;
-    } else {
-      curr_ptr_ = static_cast<size_t>(pos);
-    }
-  }
-  virtual size_t Tell(void) {
-    return curr_ptr_;
-  }
-  virtual bool AtEnd(void) const {
-    return curr_ptr_ == buffer_size_;
-  }
-
- private:
-  /*! \brief in memory buffer */
-  char *p_buffer_;
-  /*! \brief current pointer */
-  size_t buffer_size_;
-  /*! \brief current pointer */
-  size_t curr_ptr_;
-};  // class MemoryFixSizeBuffer
-
-/*! \brief a in memory buffer that can be read and write as stream interface */
-struct MemoryBufferStream : public SeekStream {
- public:
-  explicit MemoryBufferStream(std::string *p_buffer)
-      : p_buffer_(p_buffer) {
-    curr_ptr_ = 0;
-  }
-  virtual ~MemoryBufferStream(void) {}
-  virtual size_t Read(void *ptr, size_t size) {
-    utils::Assert(curr_ptr_ <= p_buffer_->length(),
-                  "read can not have position excceed buffer length");
-    size_t nread = std::min(p_buffer_->length() - curr_ptr_, size);
-    if (nread != 0) std::memcpy(ptr, &(*p_buffer_)[0] + curr_ptr_, nread);
-    curr_ptr_ += nread;
-    return nread;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    if (size == 0) return;
-    if (curr_ptr_ + size > p_buffer_->length()) {
-      p_buffer_->resize(curr_ptr_+size);
-    }
-    std::memcpy(&(*p_buffer_)[0] + curr_ptr_, ptr, size);
-    curr_ptr_ += size;
-  }
-  virtual void Seek(size_t pos) {
-    curr_ptr_ = static_cast<size_t>(pos);
-  }
-  virtual size_t Tell(void) {
-    return curr_ptr_;
-  }
-  virtual bool AtEnd(void) const {
-    return curr_ptr_ == p_buffer_->length();
-  }
-
- private:
-  /*! \brief in memory buffer */
-  std::string *p_buffer_;
-  /*! \brief current pointer */
-  size_t curr_ptr_;
-};  // class MemoryBufferStream
-}  // namespace utils
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_IO_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/rabit-inl.h b/ml-xgboost/rabit/include/rabit/internal/rabit-inl.h
deleted file mode 100644
index 82d0f44..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/rabit-inl.h
+++ /dev/null
@@ -1,401 +0,0 @@
-/*!
- * Copyright (c) 2014-2019 by Contributors
- * \file rabit-inl.h
- * \brief implementation of inline template function for rabit interface
- *
- * \author Tianqi Chen
- */
-#ifndef RABIT_INTERNAL_RABIT_INL_H_
-#define RABIT_INTERNAL_RABIT_INL_H_
-// use engine for implementation
-#include <vector>
-#include <string>
-#include <boostkit_xgboost_kernel/rabit_intrinsics.h>
-#include "rabit/internal/io.h"
-#include "rabit/internal/utils.h"
-#include "rabit/rabit.h"
-
-namespace rabit {
-namespace engine {
-namespace mpi {
-// template function to translate type to enum indicator
-template<typename DType>
-inline DataType GetType(void);
-template<>
-inline DataType GetType<char>(void) {
-  return kChar;
-}
-template<>
-inline DataType GetType<unsigned char>(void) {
-  return kUChar;
-}
-template<>
-inline DataType GetType<int>(void) {
-  return kInt;
-}
-template<>
-inline DataType GetType<unsigned int>(void) { // NOLINT(*)
-  return kUInt;
-}
-template<>
-inline DataType GetType<long>(void) {  // NOLINT(*)
-  return kLong;
-}
-template<>
-inline DataType GetType<unsigned long>(void) { // NOLINT(*)
-  return kULong;
-}
-template<>
-inline DataType GetType<float>(void) {
-  return kFloat;
-}
-template<>
-inline DataType GetType<double>(void) {
-  return kDouble;
-}
-template<>
-inline DataType GetType<long long>(void) { // NOLINT(*)
-  return kLongLong;
-}
-template<>
-inline DataType GetType<unsigned long long>(void) { // NOLINT(*)
-  return kULongLong;
-}
-}  // namespace mpi
-}  // namespace engine
-
-namespace op {
-struct Max {
-  static const engine::mpi::OpType kType = engine::mpi::kMax;
-  template<typename DType>
-  inline static void Reduce(DType &dst, const DType &src) { // NOLINT(*)
-    if (dst < src) dst = src;
-  }
-};
-struct Min {
-  static const engine::mpi::OpType kType = engine::mpi::kMin;
-  template<typename DType>
-  inline static void Reduce(DType &dst, const DType &src) { // NOLINT(*)
-    if (dst > src) dst = src;
-  }
-};
-struct Sum {
-  static const engine::mpi::OpType kType = engine::mpi::kSum;
-  template<typename DType>
-  inline static void Reduce(DType &dst, const DType &src) { // NOLINT(*)
-    dst += src;
-  }
-};
-struct BitOR {
-  static const engine::mpi::OpType kType = engine::mpi::kBitwiseOR;
-  template<typename DType>
-  inline static void Reduce(DType &dst, const DType &src) { // NOLINT(*)
-    dst |= src;
-  }
-};
-template<typename OP, typename DType>
-inline void Reducer(const void *src_, void *dst_, int len, const MPI::Datatype &dtype) {
-  const DType* src = (const DType*)src_;
-  DType* dst = (DType*)dst_;  // NOLINT(*)
-  for (int i = 0; i < len; i++) {
-    OP::Reduce(dst[i], src[i]);
-  }
-}
-
-template<>
-inline void Reducer<Sum, float>(const void *src_, void *dst_, int len, const MPI::Datatype &dtype) {
-    const auto *src = (const float *)src_;
-    auto *dst = (float *)dst_;
-    ReducerSumFloat(*src, *dst, len, dtype);
-}
-
-template<>
-inline void Reducer<Sum, double>(const void *src_, void *dst_, int len, const MPI::Datatype &dtype) {
-    const auto *src = (const double *)src_;
-    auto *dst = (double *)dst_;
-    ReducerSumDouble(*src, *dst, len, dtype);
-}
-}  // namespace op
-
-// intialize the rabit engine
-inline bool Init(int argc, char *argv[]) {
-  return engine::Init(argc, argv);
-}
-// finalize the rabit engine
-inline bool Finalize(void) {
-  return engine::Finalize();
-}
-// get the rank of the previous worker in ring topology
-inline int GetRingPrevRank(void) {
-  return engine::GetEngine()->GetRingPrevRank();
-}
-// get the rank of current process
-inline int GetRank(void) {
-  return engine::GetEngine()->GetRank();
-}
-// the the size of the world
-inline int GetWorldSize(void) {
-  return engine::GetEngine()->GetWorldSize();
-}
-// whether rabit is distributed
-inline bool IsDistributed(void) {
-  return engine::GetEngine()->IsDistributed();
-}
-// get the name of current processor
-inline std::string GetProcessorName(void) {
-  return engine::GetEngine()->GetHost();
-}
-// broadcast data to all other nodes from root
-inline void Broadcast(void *sendrecv_data, size_t size, int root,
-                      const char* _file,
-                      const int _line,
-                      const char* _caller) {
-  engine::GetEngine()->Broadcast(sendrecv_data, size, root,
-    _file, _line, _caller);
-}
-template<typename DType>
-inline void Broadcast(std::vector<DType> *sendrecv_data, int root,
-                      const char* _file,
-                      const int _line,
-                      const char* _caller) {
-  size_t size = sendrecv_data->size();
-  Broadcast(&size, sizeof(size), root, _file, _line, _caller);
-  if (sendrecv_data->size() != size) {
-    sendrecv_data->resize(size);
-  }
-  if (size != 0) {
-    Broadcast(&(*sendrecv_data)[0], size * sizeof(DType), root,
-      _file, _line, _caller);
-  }
-}
-inline void Broadcast(std::string *sendrecv_data, int root,
-                      const char* _file,
-                      const int _line,
-                      const char* _caller) {
-  size_t size = sendrecv_data->length();
-  Broadcast(&size, sizeof(size), root, _file, _line, _caller);
-  if (sendrecv_data->length() != size) {
-    sendrecv_data->resize(size);
-  }
-  if (size != 0) {
-    Broadcast(&(*sendrecv_data)[0], size * sizeof(char), root,
-    _file, _line, _caller);
-  }
-}
-
-// perform inplace Allreduce
-template<typename OP, typename DType>
-inline void Allreduce(DType *sendrecvbuf, size_t count,
-                      void (*prepare_fun)(void *arg),
-                      void *prepare_arg,
-                      const char* _file,
-                      const int _line,
-                      const char* _caller) {
-  engine::Allreduce_(sendrecvbuf, sizeof(DType), count, op::Reducer<OP, DType>,
-                     engine::mpi::GetType<DType>(), OP::kType, prepare_fun, prepare_arg,
-                     _file, _line, _caller);
-}
-
-// C++11 support for lambda prepare function
-#if DMLC_USE_CXX11
-inline void InvokeLambda_(void *fun) {
-  (*static_cast<std::function<void()>*>(fun))();
-}
-template<typename OP, typename DType>
-inline void Allreduce(DType *sendrecvbuf, size_t count,
-                      std::function<void()> prepare_fun,
-                      const char* _file,
-                      const int _line,
-                      const char* _caller) {
-  engine::Allreduce_(sendrecvbuf, sizeof(DType), count, op::Reducer<OP, DType>,
-                     engine::mpi::GetType<DType>(), OP::kType, InvokeLambda_, &prepare_fun,
-                     _file, _line, _caller);
-}
-
-// Performs inplace Allgather
-template<typename DType>
-inline void Allgather(DType *sendrecvbuf,
-                      size_t totalSize,
-                      size_t beginIndex,
-                      size_t sizeNodeSlice,
-                      size_t sizePrevSlice,
-                      const char* _file,
-                      const int _line,
-                      const char* _caller) {
-  engine::GetEngine()->Allgather(sendrecvbuf, totalSize * sizeof(DType), beginIndex * sizeof(DType),
-                        (beginIndex + sizeNodeSlice) * sizeof(DType),
-                        sizePrevSlice * sizeof(DType), _file, _line, _caller);
-}
-#endif  // C++11
-
-// print message to the tracker
-inline void TrackerPrint(const std::string &msg) {
-  engine::GetEngine()->TrackerPrint(msg);
-}
-#ifndef RABIT_STRICT_CXX98_
-inline void TrackerPrintf(const char *fmt, ...) {
-  const int kPrintBuffer = 1 << 10;
-  std::string msg(kPrintBuffer, '\0');
-  va_list args;
-  va_start(args, fmt);
-  vsnprintf(&msg[0], kPrintBuffer, fmt, args);
-  va_end(args);
-  msg.resize(strlen(msg.c_str()));
-  TrackerPrint(msg);
-}
-
-#endif  // RABIT_STRICT_CXX98_
-// load latest check point
-inline int LoadCheckPoint(Serializable *global_model,
-                          Serializable *local_model) {
-  return engine::GetEngine()->LoadCheckPoint(global_model, local_model);
-}
-// checkpoint the model, meaning we finished a stage of execution
-inline void CheckPoint(const Serializable *global_model,
-                       const Serializable *local_model) {
-  engine::GetEngine()->CheckPoint(global_model, local_model);
-}
-// lazy checkpoint the model, only remember the pointer to global_model
-inline void LazyCheckPoint(const Serializable *global_model) {
-  engine::GetEngine()->LazyCheckPoint(global_model);
-}
-// return the version number of currently stored model
-inline int VersionNumber(void) {
-  return engine::GetEngine()->VersionNumber();
-}
-// ---------------------------------
-// Code to handle customized Reduce
-// ---------------------------------
-// function to perform reduction for Reducer
-template<typename DType, void (*freduce)(DType &dst, const DType &src)>
-inline void ReducerSafe_(const void *src_, void *dst_, int len_, const MPI::Datatype &dtype) {
-  const size_t kUnit = sizeof(DType);
-  const char *psrc = reinterpret_cast<const char*>(src_);
-  char *pdst = reinterpret_cast<char*>(dst_);
-
-  for (int i = 0; i < len_; ++i) {
-    DType tdst, tsrc;
-    // use memcpy to avoid alignment issue
-    std::memcpy(&tdst, pdst + (i * kUnit), sizeof(tdst));
-    std::memcpy(&tsrc, psrc + (i * kUnit), sizeof(tsrc));
-    freduce(tdst, tsrc);
-    std::memcpy(pdst + i * kUnit, &tdst, sizeof(tdst));
-  }
-}
-// function to perform reduction for Reducer
-template<typename DType, void (*freduce)(DType &dst, const DType &src)> // NOLINT(*)
-inline void ReducerAlign_(const void *src_, void *dst_,
-                          int len_, const MPI::Datatype &dtype) {
-  const DType *psrc = reinterpret_cast<const DType*>(src_);
-  DType *pdst = reinterpret_cast<DType*>(dst_);
-  for (int i = 0; i < len_; ++i) {
-    freduce(pdst[i], psrc[i]);
-  }
-}
-template<typename DType, void (*freduce)(DType &dst, const DType &src)>  // NOLINT(*)
-inline Reducer<DType, freduce>::Reducer(void) {
-  // it is safe to directly use handle for aligned data types
-  if (sizeof(DType) == 8 || sizeof(DType) == 4 || sizeof(DType) == 1) {
-    this->handle_.Init(ReducerAlign_<DType, freduce>, sizeof(DType));
-  } else {
-    this->handle_.Init(ReducerSafe_<DType, freduce>, sizeof(DType));
-  }
-}
-template<typename DType, void (*freduce)(DType &dst, const DType &src)> // NOLINT(*)
-inline void Reducer<DType, freduce>::Allreduce(DType *sendrecvbuf, size_t count,
-                                               void (*prepare_fun)(void *arg),
-                                               void *prepare_arg,
-                                               const char* _file,
-                                               const int _line,
-                                               const char* _caller) {
-  handle_.Allreduce(sendrecvbuf, sizeof(DType), count, prepare_fun,
-    prepare_arg, _file, _line, _caller);
-}
-// function to perform reduction for SerializeReducer
-template<typename DType>
-inline void SerializeReducerFunc_(const void *src_, void *dst_,
-                                  int len_, const MPI::Datatype &dtype) {
-  int nbytes = engine::ReduceHandle::TypeSize(dtype);
-  // temp space
-  for (int i = 0; i < len_; ++i) {
-    DType tsrc, tdst;
-    utils::MemoryFixSizeBuffer fsrc((char*)(src_) + i * nbytes, nbytes); // NOLINT(*)
-    utils::MemoryFixSizeBuffer fdst((char*)(dst_) + i * nbytes, nbytes); // NOLINT(*)
-    tsrc.Load(fsrc);
-    tdst.Load(fdst);
-    // govern const check
-    tdst.Reduce(static_cast<const DType &>(tsrc), nbytes);
-    fdst.Seek(0);
-    tdst.Save(fdst);
-  }
-}
-template<typename DType>
-inline SerializeReducer<DType>::SerializeReducer(void) {
-  handle_.Init(SerializeReducerFunc_<DType>, sizeof(DType));
-}
-// closure to call Allreduce
-template<typename DType>
-struct SerializeReduceClosure {
-  DType *sendrecvobj;
-  size_t max_nbyte, count;
-  void (*prepare_fun)(void *arg);
-  void *prepare_arg;
-  std::string *p_buffer;
-  // invoke the closure
-  inline void Run(void) {
-    if (prepare_fun != NULL) prepare_fun(prepare_arg);
-    for (size_t i = 0; i < count; ++i) {
-      utils::MemoryFixSizeBuffer fs(BeginPtr(*p_buffer) + i * max_nbyte, max_nbyte);
-      sendrecvobj[i].Save(fs);
-    }
-  }
-  inline static void Invoke(void *c) {
-    static_cast<SerializeReduceClosure<DType>*>(c)->Run();
-  }
-};
-template<typename DType>
-inline void SerializeReducer<DType>::Allreduce(DType *sendrecvobj,
-                                               size_t max_nbyte, size_t count,
-                                               void (*prepare_fun)(void *arg),
-                                               void *prepare_arg,
-                                               const char* _file,
-                                               const int _line,
-                                               const char* _caller) {
-  buffer_.resize(max_nbyte * count);
-  // setup closure
-  SerializeReduceClosure<DType> c;
-  c.sendrecvobj = sendrecvobj; c.max_nbyte = max_nbyte; c.count = count;
-  c.prepare_fun = prepare_fun; c.prepare_arg = prepare_arg; c.p_buffer = &buffer_;
-  // invoke here
-  handle_.Allreduce(BeginPtr(buffer_), max_nbyte, count,
-                    SerializeReduceClosure<DType>::Invoke, &c,
-                    _file, _line, _caller);
-  for (size_t i = 0; i < count; ++i) {
-    utils::MemoryFixSizeBuffer fs(BeginPtr(buffer_) + i * max_nbyte, max_nbyte);
-    sendrecvobj[i].Load(fs);
-  }
-}
-
-#if DMLC_USE_CXX11
-template<typename DType, void (*freduce)(DType &dst, const DType &src)>  // NOLINT(*)g
-inline void Reducer<DType, freduce>::Allreduce(DType *sendrecvbuf, size_t count,
-                                               std::function<void()> prepare_fun,
-                                               const char* _file,
-                                               const int _line,
-                                               const char* _caller) {
-  this->Allreduce(sendrecvbuf, count, InvokeLambda_, &prepare_fun,
-    _file, _line, _caller);
-}
-template<typename DType>
-inline void SerializeReducer<DType>::Allreduce(DType *sendrecvobj,
-                                               size_t max_nbytes, size_t count,
-                                               std::function<void()> prepare_fun,
-                                               const char* _file,
-                                               const int _line,
-                                               const char* _caller) {
-  this->Allreduce(sendrecvobj, max_nbytes, count, InvokeLambda_, &prepare_fun,
-    _file, _line, _caller);
-}
-#endif  // DMLC_USE_CXX11
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_RABIT_INL_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/socket.h b/ml-xgboost/rabit/include/rabit/internal/socket.h
deleted file mode 100644
index a80c4bc..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/socket.h
+++ /dev/null
@@ -1,536 +0,0 @@
-/*!
- *  Copyright (c) 2014-2019 by Contributors
- * \file socket.h
- * \brief this file aims to provide a wrapper of sockets
- * \author Tianqi Chen
- */
-#ifndef RABIT_INTERNAL_SOCKET_H_
-#define RABIT_INTERNAL_SOCKET_H_
-#if defined(_WIN32)
-#include <winsock2.h>
-#include <ws2tcpip.h>
-#ifdef _MSC_VER
-#pragma comment(lib, "Ws2_32.lib")
-#endif  // _MSC_VER
-#else
-#include <fcntl.h>
-#include <netdb.h>
-#include <errno.h>
-#include <unistd.h>
-#include <arpa/inet.h>
-#include <netinet/in.h>
-#include <sys/socket.h>
-#include <sys/ioctl.h>
-#endif  // defined(_WIN32)
-#include <string>
-#include <cstring>
-#include <vector>
-#include <unordered_map>
-#include "utils.h"
-
-#if defined(_WIN32) || defined(__MINGW32__)
-typedef int ssize_t;
-#endif  // defined(_WIN32) || defined(__MINGW32__)
-
-#if defined(_WIN32)
-typedef int sock_size_t;
-
-static inline int poll(struct pollfd *pfd, int nfds,
-                       int timeout) { return WSAPoll ( pfd, nfds, timeout ); }
-#else
-#include <sys/poll.h>
-typedef int SOCKET;
-typedef size_t sock_size_t;
-const int INVALID_SOCKET = -1;
-#endif  // defined(_WIN32)
-
-namespace rabit {
-namespace utils {
-/*! \brief data structure for network address */
-struct SockAddr {
-  sockaddr_in addr;
-  // constructor
-  SockAddr(void) {}
-  SockAddr(const char *url, int port) {
-    this->Set(url, port);
-  }
-  inline static std::string GetHostName(void) {
-    std::string buf; buf.resize(256);
-    utils::Check(gethostname(&buf[0], 256) != -1, "fail to get host name");
-    return std::string(buf.c_str());
-  }
-  /*!
-   * \brief set the address
-   * \param url the url of the address
-   * \param port the port of address
-   */
-  inline void Set(const char *host, int port) {
-    addrinfo hints;
-    memset(&hints, 0, sizeof(hints));
-    hints.ai_family = AF_INET;
-    hints.ai_protocol = SOCK_STREAM;
-    addrinfo *res = NULL;
-    int sig = getaddrinfo(host, NULL, &hints, &res);
-    Check(sig == 0 && res != NULL, "cannot obtain address of %s", host);
-    Check(res->ai_family == AF_INET, "Does not support IPv6");
-    memcpy(&addr, res->ai_addr, res->ai_addrlen);
-    addr.sin_port = htons(port);
-    freeaddrinfo(res);
-  }
-  /*! \brief return port of the address*/
-  inline int port(void) const {
-    return ntohs(addr.sin_port);
-  }
-  /*! \return a string representation of the address */
-  inline std::string AddrStr(void) const {
-    std::string buf; buf.resize(256);
-#ifdef _WIN32
-    const char *s = inet_ntop(AF_INET, (PVOID)&addr.sin_addr,
-                    &buf[0], buf.length());
-#else
-    const char *s = inet_ntop(AF_INET, &addr.sin_addr,
-                              &buf[0], buf.length());
-#endif  // _WIN32
-    Assert(s != NULL, "cannot decode address");
-    return std::string(s);
-  }
-};
-
-/*!
- * \brief base class containing common operations of TCP and UDP sockets
- */
-class Socket {
- public:
-  /*! \brief the file descriptor of socket */
-  SOCKET sockfd;
-  // default conversion to int
-  inline operator SOCKET() const {
-    return sockfd;
-  }
-  /*!
-   * \return last error of socket operation
-   */
-  inline static int GetLastError(void) {
-#ifdef _WIN32
-    return WSAGetLastError();
-#else
-    return errno;
-#endif  // _WIN32
-  }
-  /*! \return whether last error was would block */
-  inline static bool LastErrorWouldBlock(void) {
-    int errsv = GetLastError();
-#ifdef _WIN32
-    return errsv == WSAEWOULDBLOCK;
-#else
-    return errsv == EAGAIN || errsv == EWOULDBLOCK;
-#endif  // _WIN32
-  }
-  /*!
-   * \brief start up the socket module
-   *   call this before using the sockets
-   */
-  inline static void Startup(void) {
-#ifdef _WIN32
-    WSADATA wsa_data;
-    if (WSAStartup(MAKEWORD(2, 2), &wsa_data) == -1) {
-    Socket::Error("Startup");
-    }
-    if (LOBYTE(wsa_data.wVersion) != 2 || HIBYTE(wsa_data.wVersion) != 2) {
-    WSACleanup();
-    utils::Error("Could not find a usable version of Winsock.dll\n");
-    }
-#endif  // _WIN32
-  }
-  /*!
-   * \brief shutdown the socket module after use, all sockets need to be closed
-   */
-  inline static void Finalize(void) {
-#ifdef _WIN32
-    WSACleanup();
-#endif  // _WIN32
-  }
-  /*!
-   * \brief set this socket to use non-blocking mode
-   * \param non_block whether set it to be non-block, if it is false
-   *        it will set it back to block mode
-   */
-  inline void SetNonBlock(bool non_block) {
-#ifdef _WIN32
-    u_long mode = non_block ? 1 : 0;
-    if (ioctlsocket(sockfd, FIONBIO, &mode) != NO_ERROR) {
-      Socket::Error("SetNonBlock");
-    }
-#else
-    int flag = fcntl(sockfd, F_GETFL, 0);
-    if (flag == -1) {
-      Socket::Error("SetNonBlock-1");
-    }
-    if (non_block) {
-      flag |= O_NONBLOCK;
-    } else {
-      flag &= ~O_NONBLOCK;
-    }
-    if (fcntl(sockfd, F_SETFL, flag) == -1) {
-      Socket::Error("SetNonBlock-2");
-    }
-#endif  // _WIN32
-  }
-  /*!
-   * \brief bind the socket to an address
-   * \param addr
-   */
-  inline void Bind(const SockAddr &addr) {
-    if (bind(sockfd, reinterpret_cast<const sockaddr*>(&addr.addr),
-             sizeof(addr.addr)) == -1) {
-      Socket::Error("Bind");
-    }
-  }
-  /*!
-   * \brief try bind the socket to host, from start_port to end_port
-   * \param start_port starting port number to try
-   * \param end_port ending port number to try
-   * \return the port successfully bind to, return -1 if failed to bind any port
-   */
-  inline int TryBindHost(int start_port, int end_port) {
-    // TODO(tqchen) add prefix check
-    for (int port = start_port; port < end_port; ++port) {
-      SockAddr addr("0.0.0.0", port);
-      if (bind(sockfd, reinterpret_cast<sockaddr*>(&addr.addr),
-               sizeof(addr.addr)) == 0) {
-        return port;
-      }
-#if defined(_WIN32)
-      if (WSAGetLastError() != WSAEADDRINUSE) {
-        Socket::Error("TryBindHost");
-      }
-#else
-      if (errno != EADDRINUSE) {
-        Socket::Error("TryBindHost");
-      }
-#endif  // defined(_WIN32)
-    }
-
-    return -1;
-  }
-  /*! \brief get last error code if any */
-  inline int GetSockError(void) const {
-    int error = 0;
-    socklen_t len = sizeof(error);
-    if (getsockopt(sockfd,  SOL_SOCKET, SO_ERROR,
-            reinterpret_cast<char*>(&error), &len) != 0) {
-      Error("GetSockError");
-    }
-    return error;
-  }
-  /*! \brief check if anything bad happens */
-  inline bool BadSocket(void) const {
-    if (IsClosed()) return true;
-    int err = GetSockError();
-    if (err == EBADF || err == EINTR) return true;
-    return false;
-  }
-  /*! \brief check if socket is already closed */
-  inline bool IsClosed(void) const {
-    return sockfd == INVALID_SOCKET;
-  }
-  /*! \brief close the socket */
-  inline void Close(void) {
-    if (sockfd != INVALID_SOCKET) {
-#ifdef _WIN32
-      closesocket(sockfd);
-#else
-      close(sockfd);
-#endif
-      sockfd = INVALID_SOCKET;
-    } else {
-      Error("Socket::Close double close the socket or close without create");
-    }
-  }
-  // report an socket error
-  inline static void Error(const char *msg) {
-    int errsv = GetLastError();
-#ifdef _WIN32
-    utils::Error("Socket %s Error:WSAError-code=%d", msg, errsv);
-#else
-    utils::Error("Socket %s Error:%s", msg, strerror(errsv));
-#endif
-  }
-
- protected:
-  explicit Socket(SOCKET sockfd) : sockfd(sockfd) {
-  }
-};
-
-/*!
- * \brief a wrapper of TCP socket that hopefully be cross platform
- */
-class TCPSocket : public Socket{
- public:
-  // constructor
-  TCPSocket(void) : Socket(INVALID_SOCKET) {
-  }
-  explicit TCPSocket(SOCKET sockfd) : Socket(sockfd) {
-  }
-  /*!
-   * \brief enable/disable TCP keepalive
-   * \param keepalive whether to set the keep alive option on
-   */
-  void SetKeepAlive(bool keepalive) {
-    int opt = static_cast<int>(keepalive);
-    if (setsockopt(sockfd, SOL_SOCKET, SO_KEEPALIVE,
-                   reinterpret_cast<char*>(&opt), sizeof(opt)) < 0) {
-      Socket::Error("SetKeepAlive");
-    }
-  }
-  inline void SetLinger(int timeout = 0) {
-    struct linger sl;
-    sl.l_onoff = 1;    /* non-zero value enables linger option in kernel */
-    sl.l_linger = timeout;    /* timeout interval in seconds */
-    if (setsockopt(sockfd, SOL_SOCKET, SO_LINGER, reinterpret_cast<char*>(&sl), sizeof(sl)) == -1) {
-      Socket::Error("SO_LINGER");
-    }
-  }
-  /*!
-   * \brief create the socket, call this before using socket
-   * \param af domain
-   */
-  inline void Create(int af = PF_INET) {
-    sockfd = socket(PF_INET, SOCK_STREAM, 0);
-    if (sockfd == INVALID_SOCKET) {
-      Socket::Error("Create");
-    }
-  }
-  /*!
-   * \brief perform listen of the socket
-   * \param backlog backlog parameter
-   */
-  inline void Listen(int backlog = 16) {
-    listen(sockfd, backlog);
-  }
-  /*! \brief get a new connection */
-  TCPSocket Accept(void) {
-    SOCKET newfd = accept(sockfd, NULL, NULL);
-    if (newfd == INVALID_SOCKET) {
-      Socket::Error("Accept");
-    }
-    return TCPSocket(newfd);
-  }
-  /*!
-   * \brief decide whether the socket is at OOB mark
-   * \return 1 if at mark, 0 if not, -1 if an error occured
-   */
-  inline int AtMark(void) const {
-#ifdef _WIN32
-    unsigned long atmark;  // NOLINT(*)
-    if (ioctlsocket(sockfd, SIOCATMARK, &atmark) != NO_ERROR) return -1;
-#else
-    int atmark;
-    if (ioctl(sockfd, SIOCATMARK, &atmark) == -1) return -1;
-#endif  // _WIN32
-    return static_cast<int>(atmark);
-  }
-  /*!
-   * \brief connect to an address
-   * \param addr the address to connect to
-   * \return whether connect is successful
-   */
-  inline bool Connect(const SockAddr &addr) {
-    return connect(sockfd, reinterpret_cast<const sockaddr*>(&addr.addr),
-                   sizeof(addr.addr)) == 0;
-  }
-  /*!
-   * \brief send data using the socket
-   * \param buf the pointer to the buffer
-   * \param len the size of the buffer
-   * \param flags extra flags
-   * \return size of data actually sent
-   *         return -1 if error occurs
-   */
-  inline ssize_t Send(const void *buf_, size_t len, int flag = 0) {
-    const char *buf = reinterpret_cast<const char*>(buf_);
-    return send(sockfd, buf, static_cast<sock_size_t>(len), flag);
-  }
-  /*!
-   * \brief receive data using the socket
-   * \param buf_ the pointer to the buffer
-   * \param len the size of the buffer
-   * \param flags extra flags
-   * \return size of data actually received
-   *         return -1 if error occurs
-   */
-  inline ssize_t Recv(void *buf_, size_t len, int flags = 0) {
-    char *buf = reinterpret_cast<char*>(buf_);
-    return recv(sockfd, buf, static_cast<sock_size_t>(len), flags);
-  }
-  /*!
-   * \brief peform block write that will attempt to send all data out
-   *    can still return smaller than request when error occurs
-   * \param buf the pointer to the buffer
-   * \param len the size of the buffer
-   * \return size of data actually sent
-   */
-  inline size_t SendAll(const void *buf_, size_t len) {
-    const char *buf = reinterpret_cast<const char*>(buf_);
-    size_t ndone = 0;
-    while (ndone <  len) {
-      ssize_t ret = send(sockfd, buf, static_cast<ssize_t>(len - ndone), 0);
-      if (ret == -1) {
-        if (LastErrorWouldBlock()) return ndone;
-        Socket::Error("SendAll");
-      }
-      buf += ret;
-      ndone += ret;
-    }
-    return ndone;
-  }
-  /*!
-   * \brief peforma block read that will attempt to read all data
-   *    can still return smaller than request when error occurs
-   * \param buf_ the buffer pointer
-   * \param len length of data to recv
-   * \return size of data actually sent
-   */
-  inline size_t RecvAll(void *buf_, size_t len) {
-    char *buf = reinterpret_cast<char*>(buf_);
-    size_t ndone = 0;
-    while (ndone <  len) {
-      ssize_t ret = recv(sockfd, buf,
-                         static_cast<sock_size_t>(len - ndone), MSG_WAITALL);
-      if (ret == -1) {
-        if (LastErrorWouldBlock()) return ndone;
-        Socket::Error("RecvAll");
-      }
-      if (ret == 0) return ndone;
-      buf += ret;
-      ndone += ret;
-    }
-    return ndone;
-  }
-  /*!
-   * \brief send a string over network
-   * \param str the string to be sent
-   */
-  inline void SendStr(const std::string &str) {
-    int len = static_cast<int>(str.length());
-    utils::Assert(this->SendAll(&len, sizeof(len)) == sizeof(len),
-                  "error during send SendStr");
-    if (len != 0) {
-      utils::Assert(this->SendAll(str.c_str(), str.length()) == str.length(),
-                    "error during send SendStr");
-    }
-  }
-  /*!
-   * \brief recv a string from network
-   * \param out_str the string to receive
-   */
-  inline void RecvStr(std::string *out_str) {
-    int len;
-    utils::Assert(this->RecvAll(&len, sizeof(len)) == sizeof(len),
-                  "error during send RecvStr");
-    out_str->resize(len);
-    if (len != 0) {
-      utils::Assert(this->RecvAll(&(*out_str)[0], len) == out_str->length(),
-                    "error during send SendStr");
-    }
-  }
-};
-
-/*! \brief helper data structure to perform poll */
-struct PollHelper {
- public:
-  /*!
-   * \brief add file descriptor to watch for read
-   * \param fd file descriptor to be watched
-   */
-  inline void WatchRead(SOCKET fd) {
-    auto& pfd = fds[fd];
-    pfd.fd = fd;
-    pfd.events |= POLLIN;
-  }
-  /*!
-   * \brief add file descriptor to watch for write
-   * \param fd file descriptor to be watched
-   */
-  inline void WatchWrite(SOCKET fd) {
-    auto& pfd = fds[fd];
-    pfd.fd = fd;
-    pfd.events |= POLLOUT;
-  }
-  /*!
-   * \brief add file descriptor to watch for exception
-   * \param fd file descriptor to be watched
-   */
-  inline void WatchException(SOCKET fd) {
-    auto& pfd = fds[fd];
-    pfd.fd = fd;
-    pfd.events |= POLLPRI;
-  }
-  /*!
-   * \brief Check if the descriptor is ready for read
-   * \param fd file descriptor to check status
-   */
-  inline bool CheckRead(SOCKET fd) const {
-    const auto& pfd = fds.find(fd);
-    return pfd != fds.end() && ((pfd->second.events & POLLIN) != 0);
-  }
-  /*!
-   * \brief Check if the descriptor is ready for write
-   * \param fd file descriptor to check status
-   */
-  inline bool CheckWrite(SOCKET fd) const {
-    const auto& pfd = fds.find(fd);
-    return pfd != fds.end() && ((pfd->second.events & POLLOUT) != 0);
-  }
-  /*!
-   * \brief Check if the descriptor has any exception
-   * \param fd file descriptor to check status
-   */
-  inline bool CheckExcept(SOCKET fd) const {
-    const auto& pfd = fds.find(fd);
-    return pfd != fds.end() && ((pfd->second.events & POLLPRI) != 0);
-  }
-  /*!
-   * \brief wait for exception event on a single descriptor
-   * \param fd the file descriptor to wait the event for
-   * \param timeout the timeout counter, can be negative, which means wait until the event happen
-   * \return 1 if success, 0 if timeout, and -1 if error occurs
-   */
-  inline static int WaitExcept(SOCKET fd, long timeout = -1) { // NOLINT(*)
-    pollfd pfd;
-    pfd.fd = fd;
-    pfd.events = POLLPRI;
-    return poll(&pfd, 1, timeout);
-  }
-
-  /*!
-   * \brief peform poll on the set defined, read, write, exception
-   * \param timeout specify timeout in milliseconds(ms) if negative, means poll will block
-   * \return
-   */
-  inline void Poll(long timeout = -1) {  // NOLINT(*)
-    std::vector<pollfd> fdset;
-    fdset.reserve(fds.size());
-    for (auto kv : fds) {
-      fdset.push_back(kv.second);
-    }
-    int ret = poll(fdset.data(), fdset.size(), timeout);
-    if (ret == -1) {
-      Socket::Error("Poll");
-    } else {
-      for (auto& pfd : fdset) {
-        auto revents = pfd.revents & pfd.events;
-        if (!revents) {
-          fds.erase(pfd.fd);
-        } else {
-          fds[pfd.fd].events = revents;
-        }
-      }
-    }
-  }
-
-  std::unordered_map<SOCKET, pollfd> fds;
-};
-}  // namespace utils
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_SOCKET_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/thread_local.h b/ml-xgboost/rabit/include/rabit/internal/thread_local.h
deleted file mode 100644
index 4eebd64..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/thread_local.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file thread_local.h
- * \brief Common utility for thread local storage.
- */
-#ifndef RABIT_INTERNAL_THREAD_LOCAL_H_
-#define RABIT_INTERNAL_THREAD_LOCAL_H_
-
-#include "../include/dmlc/base.h"
-
-#if DMLC_ENABLE_STD_THREAD
-#include <mutex>
-#endif  // DMLC_ENABLE_STD_THREAD
-
-#include <memory>
-#include <vector>
-
-namespace rabit {
-
-// macro hanlding for threadlocal variables
-#ifdef __GNUC__
-  #define MX_TREAD_LOCAL __thread
-#elif __STDC_VERSION__ >= 201112L
-  #define  MX_TREAD_LOCAL _Thread_local
-#elif defined(_MSC_VER)
-  #define MX_TREAD_LOCAL __declspec(thread)
-#endif  // __GNUC__
-
-#ifndef MX_TREAD_LOCAL
-#message("Warning: Threadlocal is not enabled");
-#endif  // MX_TREAD_LOCAL
-
-/*!
- * \brief A threadlocal store to store threadlocal variables.
- *  Will return a thread local singleton of type T
- * \tparam T the type we like to store
- */
-template<typename T>
-class ThreadLocalStore {
- public:
-  /*! \return get a thread local singleton */
-  static T* Get() {
-    static MX_TREAD_LOCAL T* ptr = nullptr;
-    if (ptr == nullptr) {
-      ptr = new T();
-      Singleton()->RegisterDelete(ptr);
-    }
-    return ptr;
-  }
-
- private:
-  /*! \brief constructor */
-  ThreadLocalStore() {}
-  /*! \brief destructor */
-  ~ThreadLocalStore() {
-    for (size_t i = 0; i < data_.size(); ++i) {
-      delete data_[i];
-    }
-  }
-  /*! \return singleton of the store */
-  static ThreadLocalStore<T> *Singleton() {
-    static ThreadLocalStore<T> inst;
-    return &inst;
-  }
-  /*!
-   * \brief register str for internal deletion
-   * \param str the string pointer
-   */
-  void RegisterDelete(T *str) {
-#if DMLC_ENABLE_STD_THREAD
-    std::unique_lock<std::mutex> lock(mutex_);
-    data_.push_back(str);
-    lock.unlock();
-#else
-    data_.push_back(str);
-#endif  // DMLC_ENABLE_STD_THREAD
-  }
-
-#if DMLC_ENABLE_STD_THREAD
-  /*! \brief internal mutex */
-  std::mutex mutex_;
-#endif  // DMLC_ENABLE_STD_THREAD
-  /*!\brief internal data */
-  std::vector<T*> data_;
-};
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_THREAD_LOCAL_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/timer.h b/ml-xgboost/rabit/include/rabit/internal/timer.h
deleted file mode 100644
index 3ce1bf8..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/timer.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*!
- * Copyright (c) 2014-2019 by Contributors
- * \file timer.h
- * \brief This file defines the utils for timing
- * \author Tianqi Chen, Nacho, Tianyi
- */
-#ifndef RABIT_INTERNAL_TIMER_H_
-#define RABIT_INTERNAL_TIMER_H_
-#include <time.h>
-#ifdef __MACH__
-#include <mach/clock.h>
-#include <mach/mach.h>
-#endif  // __MACH__
-#include "./utils.h"
-
-namespace rabit {
-namespace utils {
-/*!
- * \brief return time in seconds, not cross platform, avoid to use this in most places
- */
-inline double GetTime(void) {
-#ifdef __MACH__
-  clock_serv_t cclock;
-  mach_timespec_t mts;
-  host_get_clock_service(mach_host_self(), CALENDAR_CLOCK, &cclock);
-  utils::Check(clock_get_time(cclock, &mts) == 0, "failed to get time");
-  mach_port_deallocate(mach_task_self(), cclock);
-  return static_cast<double>(mts.tv_sec) + static_cast<double>(mts.tv_nsec) * 1e-9;
-#else
-#if defined(__unix__) || defined(__linux__)
-  timespec ts;
-  utils::Check(clock_gettime(CLOCK_REALTIME, &ts) == 0, "failed to get time");
-  return static_cast<double>(ts.tv_sec) + static_cast<double>(ts.tv_nsec) * 1e-9;
-#else
-  return static_cast<double>(time(NULL));
-#endif  // defined(__unix__) || defined(__linux__)
-#endif  // __MACH__
-}
-}  // namespace utils
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_TIMER_H_
diff --git a/ml-xgboost/rabit/include/rabit/internal/utils.h b/ml-xgboost/rabit/include/rabit/internal/utils.h
deleted file mode 100644
index 05f3555..0000000
--- a/ml-xgboost/rabit/include/rabit/internal/utils.h
+++ /dev/null
@@ -1,231 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file utils.h
- * \brief simple utils to support the code
- * \author Tianqi Chen
- */
-#ifndef RABIT_INTERNAL_UTILS_H_
-#define RABIT_INTERNAL_UTILS_H_
-#define _CRT_SECURE_NO_WARNINGS
-#include <string.h>
-#include <cstdio>
-#include <string>
-#include <cstdlib>
-#include <stdexcept>
-#include <vector>
-#include "dmlc/io.h"
-
-#ifndef RABIT_STRICT_CXX98_
-#include <cstdarg>
-#endif  // RABIT_STRICT_CXX98_
-
-#if !defined(__GNUC__) || defined(__FreeBSD__)
-#define fopen64 std::fopen
-#endif  // !defined(__GNUC__) || defined(__FreeBSD__)
-
-#ifdef _MSC_VER
-// NOTE: sprintf_s is not equivalent to snprintf,
-// they are equivalent when success, which is sufficient for our case
-#define snprintf sprintf_s
-#define vsnprintf vsprintf_s
-
-#else
-
-#ifdef _FILE_OFFSET_BITS
-#if _FILE_OFFSET_BITS == 32
-#pragma message("Warning: FILE OFFSET BITS defined to be 32 bit")
-#endif  // _FILE_OFFSET_BITS == 32
-#endif  // _FILE_OFFSET_BITS
-
-#ifdef __APPLE__
-#define off64_t off_t
-#define fopen64 std::fopen
-#endif  // __APPLE__
-
-extern "C" {
-#include <sys/types.h>
-}
-#endif  // _MSC_VER
-
-#ifdef _MSC_VER
-typedef unsigned char uint8_t;
-typedef unsigned __int16 uint16_t;
-typedef unsigned __int32 uint32_t;
-typedef unsigned __int64 uint64_t;
-typedef __int64 int64_t;
-#else
-#include <inttypes.h>
-#endif  // _MSC_VER
-
-namespace rabit {
-/*! \brief namespace for helper utils of the project */
-namespace utils {
-
-/*! \brief error message buffer length */
-const int kPrintBuffer = 1 << 12;
-
-/*! \brief we may want to keep the process alive when there are multiple workers
- * co-locate in the same process */
-extern bool STOP_PROCESS_ON_ERROR;
-
-/* \brief Case-insensitive string comparison */
-inline int CompareStringsCaseInsensitive(const char* s1, const char* s2) {
-#ifdef _MSC_VER
-  return _stricmp(s1, s2);
-#else  // _MSC_VER
-  return strcasecmp(s1, s2);
-#endif  // _MSC_VER
-}
-
-/* \brief parse config string too bool*/
-inline bool StringToBool(const char* s) {
-  return CompareStringsCaseInsensitive(s, "true") == 0 || atoi(s) != 0;
-}
-
-#ifndef RABIT_CUSTOMIZE_MSG_
-/*!
- * \brief handling of Assert error, caused by inappropriate input
- * \param msg error message
- */
-inline void HandleAssertError(const char *msg) {
-  if (STOP_PROCESS_ON_ERROR) {
-    fprintf(stderr, "AssertError:%s, shutting down process\n", msg);
-    exit(-1);
-  } else {
-    fprintf(stderr, "AssertError:%s, rabit is configured to keep process running\n", msg);
-    throw dmlc::Error(msg);
-  }
-}
-/*!
- * \brief handling of Check error, caused by inappropriate input
- * \param msg error message
- */
-inline void HandleCheckError(const char *msg) {
-  if (STOP_PROCESS_ON_ERROR) {
-    fprintf(stderr, "%s, shutting down process\n", msg);
-    exit(-1);
-  } else {
-    fprintf(stderr, "%s, rabit is configured to keep process running\n", msg);
-    throw dmlc::Error(msg);
-  }
-}
-inline void HandlePrint(const char *msg) {
-  printf("%s", msg);
-}
-
-inline void HandleLogInfo(const char *fmt, ...) {
-  std::string msg(kPrintBuffer, '\0');
-  va_list args;
-  va_start(args, fmt);
-  vsnprintf(&msg[0], kPrintBuffer, fmt, args);
-  va_end(args);
-  fprintf(stdout, "%s", msg.c_str());
-  fflush(stdout);
-}
-#else
-#ifndef RABIT_STRICT_CXX98_
-// include declarations, some one must implement this
-void HandleAssertError(const char *msg);
-void HandleCheckError(const char *msg);
-void HandlePrint(const char *msg);
-#endif  // RABIT_STRICT_CXX98_
-#endif  // RABIT_CUSTOMIZE_MSG_
-#ifdef RABIT_STRICT_CXX98_
-// these function pointers are to be assigned
-extern "C" void (*Printf)(const char *fmt, ...);
-extern "C" int (*SPrintf)(char *buf, size_t size, const char *fmt, ...);
-extern "C" void (*Assert)(int exp, const char *fmt, ...);
-extern "C" void (*Check)(int exp, const char *fmt, ...);
-extern "C" void (*Error)(const char *fmt, ...);
-#else
-/*! \brief printf, prints messages to the console */
-inline void Printf(const char *fmt, ...) {
-  std::string msg(kPrintBuffer, '\0');
-  va_list args;
-  va_start(args, fmt);
-  vsnprintf(&msg[0], kPrintBuffer, fmt, args);
-  va_end(args);
-  HandlePrint(msg.c_str());
-}
-/*! \brief portable version of snprintf */
-inline int SPrintf(char *buf, size_t size, const char *fmt, ...) {
-  va_list args;
-  va_start(args, fmt);
-  int ret = vsnprintf(buf, size, fmt, args);
-  va_end(args);
-  return ret;
-}
-
-/*! \brief assert a condition is true, use this to handle debug information */
-inline void Assert(bool exp, const char *fmt, ...) {
-  if (!exp) {
-    std::string msg(kPrintBuffer, '\0');
-    va_list args;
-    va_start(args, fmt);
-    vsnprintf(&msg[0], kPrintBuffer, fmt, args);
-    va_end(args);
-    HandleAssertError(msg.c_str());
-  }
-}
-
-/*!\brief same as assert, but this is intended to be used as a message for users */
-inline void Check(bool exp, const char *fmt, ...) {
-  if (!exp) {
-    std::string msg(kPrintBuffer, '\0');
-    va_list args;
-    va_start(args, fmt);
-    vsnprintf(&msg[0], kPrintBuffer, fmt, args);
-    va_end(args);
-    HandleCheckError(msg.c_str());
-  }
-}
-
-/*! \brief report error message, same as check */
-inline void Error(const char *fmt, ...) {
-  {
-    std::string msg(kPrintBuffer, '\0');
-    va_list args;
-    va_start(args, fmt);
-    vsnprintf(&msg[0], kPrintBuffer, fmt, args);
-    va_end(args);
-    HandleCheckError(msg.c_str());
-  }
-}
-#endif  // RABIT_STRICT_CXX98_
-
-/*! \brief replace fopen, report error when the file open fails */
-inline std::FILE *FopenCheck(const char *fname, const char *flag) {
-  std::FILE *fp = fopen64(fname, flag);
-  Check(fp != NULL, "can not open file \"%s\"\n", fname);
-  return fp;
-}
-}  // namespace utils
-// easy utils that can be directly accessed in xgboost
-/*! \brief get the beginning address of a vector */
-template<typename T>
-inline T *BeginPtr(std::vector<T> &vec) {  // NOLINT(*)
-  if (vec.size() == 0) {
-    return NULL;
-  } else {
-    return &vec[0];
-  }
-}
-/*! \brief get the beginning address of a vector */
-template<typename T>
-inline const T *BeginPtr(const std::vector<T> &vec) {  // NOLINT(*)
-  if (vec.size() == 0) {
-    return NULL;
-  } else {
-    return &vec[0];
-  }
-}
-inline char* BeginPtr(std::string &str) {  // NOLINT(*)
-  if (str.length() == 0) return NULL;
-  return &str[0];
-}
-inline const char* BeginPtr(const std::string &str) {
-  if (str.length() == 0) return NULL;
-  return &str[0];
-}
-}  // namespace rabit
-#endif  // RABIT_INTERNAL_UTILS_H_
diff --git a/ml-xgboost/rabit/include/rabit/rabit.h b/ml-xgboost/rabit/include/rabit/rabit.h
deleted file mode 100644
index 396354e..0000000
--- a/ml-xgboost/rabit/include/rabit/rabit.h
+++ /dev/null
@@ -1,460 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file rabit.h
- * \brief This file defines rabit's Allreduce/Broadcast interface
- *   The rabit engine contains the actual implementation
- *   Code that only uses this header can also be compiled with MPI Allreduce (non fault-tolerant),
- *
- *   rabit.h and serializable.h is all what the user needs to use the rabit interface
- * \author Tianqi Chen, Ignacio Cano, Tianyi Zhou
- */
-#ifndef RABIT_RABIT_H_  // NOLINT(*)
-#define RABIT_RABIT_H_  // NOLINT(*)
-#include <string>
-#include <vector>
-
-// whether or not use c++11 support
-#ifndef DMLC_USE_CXX11
-#if defined(__GXX_EXPERIMENTAL_CXX0X__) || defined(_MSC_VER)
-#define DMLC_USE_CXX11 1
-#else
-#define DMLC_USE_CXX11 (__cplusplus >= 201103L)
-#endif  // defined(__GXX_EXPERIMENTAL_CXX0X__) || defined(_MSC_VER)
-#endif  // DMLC_USE_CXX11
-
-// keeps rabit api caller signature
-#ifndef RABIT_API_CALLER_SIGNATURE
-#define RABIT_API_CALLER_SIGNATURE
-
-#if (defined(__GNUC__) && !defined(__clang__))
-#define _FILE  __builtin_FILE()
-#define _LINE  __builtin_LINE()
-#define _CALLER  __builtin_FUNCTION()
-#else
-#define _FILE  "N/A"
-#define _LINE  -1
-#define _CALLER  "N/A"
-#endif  // (defined(__GNUC__) && !defined(__clang__))
-
-#endif  // RABIT_API_CALLER_SIGNATURE
-
-// optionally support of lambda functions in C++11, if available
-#if DMLC_USE_CXX11
-#include <functional>
-#endif  // C++11
-// engine definition of rabit, defines internal implementation
-// to use rabit interface, there is no need to read engine.h
-// rabit.h and serializable.h are enough to use the interface
-#include "./internal/engine.h"
-
-/*! \brief rabit namespace */
-namespace rabit {
-/*!
- * \brief defines stream used in rabit
- * see definition of Stream in dmlc/io.h
- */
-typedef dmlc::Stream Stream;
-/*!
- * \brief defines serializable objects used in rabit
- * see definition of Serializable in dmlc/io.h
- */
-typedef dmlc::Serializable Serializable;
-
-/*!
- * \brief reduction operators namespace
- */
-namespace op {
-/*!
- * \class rabit::op::Max
- * \brief maximum reduction operator
- */
-struct Max;
-/*!
- * \class rabit::op::Min
- * \brief minimum reduction operator
- */
-struct Min;
-/*!
- * \class rabit::op::Sum
- * \brief sum reduction operator
- */
-struct Sum;
-/*!
- * \class rabit::op::BitOR
- * \brief bitwise OR reduction operator
- */
-struct BitOR;
-}  // namespace op
-/*!
- * \brief initializes rabit, call this once at the beginning of your program
- * \param argc number of arguments in argv
- * \param argv the array of input arguments
- * \return true if initialized successfully, otherwise false
- */
-inline bool Init(int argc, char *argv[]);
-/*!
- * \brief finalizes the rabit engine, call this function after you finished with all the jobs
- * \return true if finalized successfully, otherwise false
- */
-inline bool Finalize();
-/*! \brief gets rank of the current process
- * \return rank number of worker*/
-inline int GetRank();
-/*! \brief gets total number of processes
- * \return total world size*/
-inline int GetWorldSize();
-/*! \brief whether rabit env is in distributed mode
- * \return is distributed*/
-inline bool IsDistributed();
-
-/*! \brief gets processor's name
- * \return processor name*/
-inline std::string GetProcessorName();
-/*!
- * \brief prints the msg to the tracker,
- *    this function can be used to communicate progress information to
- *    the user who monitors the tracker
- * \param msg the message to be printed
- */
-inline void TrackerPrint(const std::string &msg);
-
-#ifndef RABIT_STRICT_CXX98_
-/*!
- * \brief prints the msg to the tracker, this function may not be available
- *    in very strict c++98 compilers, though it usually is.
- *    this function can be used to communicate progress information to
- *    the user who monitors the tracker
- * \param fmt the format string
- */
-inline void TrackerPrintf(const char *fmt, ...);
-#endif  // RABIT_STRICT_CXX98_
-/*!
- * \brief broadcasts a memory region to every node from the root
- *
- *     Example: int a = 1; Broadcast(&a, sizeof(a), root);
- * \param sendrecv_data the pointer to the send/receive buffer,
- * \param size the data size
- * \param root the process root
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- */
-inline void Broadcast(void *sendrecv_data, size_t size, int root,
-                      const char* _file = _FILE,
-                      const int _line = _LINE,
-                      const char* _caller = _CALLER);
-
-/*!
- * \brief broadcasts an std::vector<DType> to every node from root
- * \param sendrecv_data the pointer to send/receive vector,
- *        for the receiver, the vector does not need to be pre-allocated
- * \param root the process root
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- * \tparam DType the data type stored in the vector, has to be a simple data type
- *               that can be directly transmitted by sending the sizeof(DType)
- */
-template<typename DType>
-inline void Broadcast(std::vector<DType> *sendrecv_data, int root,
-                      const char* _file = _FILE,
-                      const int _line = _LINE,
-                      const char* _caller = _CALLER);
-/*!
- * \brief broadcasts a std::string to every node from the root
- * \param sendrecv_data the pointer to the send/receive buffer,
- *        for the receiver, the vector does not need to be pre-allocated
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- * \param root the process root
- */
-inline void Broadcast(std::string *sendrecv_data, int root,
-                      const char* _file = _FILE,
-                      const int _line = _LINE,
-                      const char* _caller = _CALLER);
-/*!
- * \brief performs in-place Allreduce on sendrecvbuf
- *        this function is NOT thread-safe
- *
- * Example Usage: the following code does an Allreduce and outputs the sum as the result
- * \code{.cpp}
- * vector<int> data(10);
- * ...
- * Allreduce<op::Sum>(&data[0], data.size());
- * ...
- * \endcode
- *
- * \param sendrecvbuf buffer for both sending and receiving data
- * \param count number of elements to be reduced
- * \param prepare_fun Lazy preprocessing function, if it is not NULL, prepare_fun(prepare_arg)
- *                    will be called by the function before performing Allreduce in order to initialize the data in sendrecvbuf.
- *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
- * \param prepare_arg argument used to pass into the lazy preprocessing function
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- * \tparam OP see namespace op, reduce operator
- * \tparam DType data type
- */
-template<typename OP, typename DType>
-inline void Allreduce(DType *sendrecvbuf, size_t count,
-                      void (*prepare_fun)(void *) = NULL,
-                      void *prepare_arg = NULL,
-                      const char* _file = _FILE,
-                      const int _line = _LINE,
-                      const char* _caller = _CALLER);
-
-/*!
-* \brief Allgather function, each node have a segment of data in the ring of sendrecvbuf,
-*  the data provided by current node k is [slice_begin, slice_end),
-*  the next node's segment must start with slice_end
-*  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
-*  use a ring based algorithm
-*
-* \param sendrecvbuf_ buffer for both sending and receiving data, it is a ring conceptually
-* \param total_size total size of data to be gathered
-* \param slice_begin beginning of the current slice
-* \param slice_end end of the current slice
-* \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
-* \param _file caller file name used to generate unique cache key
-* \param _line caller line number used to generate unique cache key
-* \param _caller caller function name used to generate unique cache key
-*/ 
-template<typename DType>
-inline void Allgather(DType *sendrecvbuf_,
-                  size_t total_size,
-                  size_t slice_begin,
-                  size_t slice_end,
-                  size_t size_prev_slice,
-                  const char* _file = _FILE,
-                  const int _line = _LINE,
-                  const char* _caller = _CALLER);
-
-// C++11 support for lambda prepare function
-#if DMLC_USE_CXX11
-/*!
- * \brief performs in-place Allreduce, on sendrecvbuf
- *        with a prepare function specified by a lambda function
- *
- * Example Usage:
- * \code{.cpp}
- * // the following code does an Allreduce and outputs the sum as the result
- * vector<int> data(10);
- * ...
- * Allreduce<op::Sum>(&data[0], data.size(), [&]() {
- *                     for (int i = 0; i < 10; ++i) {
- *                       data[i] = i;
- *                     }
- *                    });
- *     ...
- * \endcode
- * \param sendrecvbuf buffer for both sending and receiving data
- * \param count number of elements to be reduced
- * \param prepare_fun  Lazy lambda preprocessing function, prepare_fun() will be invoked
- *                     by the function before performing Allreduce in order to initialize the data in sendrecvbuf.
- *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- * \tparam OP see namespace op, reduce operator
- * \tparam DType data type
- */
-template<typename OP, typename DType>
-inline void Allreduce(DType *sendrecvbuf, size_t count,
-                      std::function<void()> prepare_fun,
-                      const char* _file = _FILE,
-                      const int _line = _LINE,
-                      const char* _caller = _CALLER);
-#endif  // C++11
-/*!
- * \brief loads the latest check point
- * \param global_model pointer to the globally shared model/state
- *   when calling this function, the caller needs to guarantee that the global_model
- *   is the same in every node
- * \param local_model pointer to the local model that is specific to the current node/rank
- *   this can be NULL when no local model is needed
- *
- * \return the version number of the check point loaded
- *     if returned version == 0, this means no model has been CheckPointed
- *     the p_model is not touched, users should do the necessary initialization by themselves
- *
- * \code{.cpp}
- * // Example usage code of LoadCheckPoint
- * int iter = rabit::LoadCheckPoint(&model);
- * if (iter == 0) model.InitParameters();
- * for (i = iter; i < max_iter; ++i) {
- *   // do many things, include allreduce
- *   rabit::CheckPoint(model);
- * }
- * \endcode
- * \sa CheckPoint, VersionNumber
- */
-inline int LoadCheckPoint(Serializable *global_model,
-                          Serializable *local_model = NULL);
-/*!
- * \brief checkpoints the model, meaning a stage of execution has finished.
- *  every time we call check point, a version number will be increased by one
- *
- * \param global_model pointer to the globally shared model/state
- *   when calling this function, the caller needs to guarantee that the global_model
- *   is the same in every node
- * \param local_model pointer to the local model that is specific to the current node/rank
- *   this can be NULL when no local state is needed
-   * NOTE: local_model requires explicit replication of the model for fault-tolerance, which will
-   *       bring replication cost in the CheckPoint function. global_model does not need explicit replication.
-   *       So, only CheckPoint with the global_model if possible
-   * \sa LoadCheckPoint, VersionNumber
-   */
-inline void CheckPoint(const Serializable *global_model,
-                       const Serializable *local_model = NULL);
-/*!
- * \brief This function can be used to replace CheckPoint for global_model only,
- *   when certain condition is met (see detailed explanation).
- *
- *   This is a "lazy" checkpoint such that only the pointer to the global_model is
- *   remembered and no memory copy is taken. To use this function, the user MUST ensure that:
- *   The global_model must remain unchanged until the last call of Allreduce/Broadcast in the current version finishes.
- *   In other words, the global_model model can be changed only between the last call of
- *   Allreduce/Broadcast and LazyCheckPoint, both in the same version
- *
- *   For example, suppose the calling sequence is:
- *   LazyCheckPoint, code1, Allreduce, code2, Broadcast, code3, LazyCheckPoint/(or can be CheckPoint)
- *
- *   Then the user MUST only change the global_model in code3.
- *
- *   The use of LazyCheckPoint instead of CheckPoint will improve the efficiency of the program.
- * \param global_model pointer to the globally shared model/state
- *   when calling this function, the caller needs to guarantee that the global_model
- *   is the same in every node
- * \sa LoadCheckPoint, CheckPoint, VersionNumber
- */
-inline void LazyCheckPoint(const Serializable *global_model);
-/*!
- * \return version number of the current stored model,
- *         which means how many calls to CheckPoint we made so far
- * \sa LoadCheckPoint, CheckPoint
- */
-inline int VersionNumber();
-// ----- extensions that allow customized reducer ------
-// helper class to do customized reduce, user do not need to know the type
-namespace engine {
-class ReduceHandle;
-}  // namespace engine
-/*!
- * \brief template class to make customized reduce and all reduce easy
- *  Do not use reducer directly in the function you call Finalize,
- *   because the destructor can execute after Finalize
- * \tparam DType data type that to be reduced
- * \tparam freduce the customized reduction function
- *  DType must be a struct, with no pointer
- */
-template<typename DType, void (*freduce)(DType &dst, const DType &src)>  // NOLINT(*)
-class Reducer {
- public:
-  Reducer();
-  /*!
-   * \brief customized in-place all reduce operation
-   * \param sendrecvbuf the in place send-recv buffer
-   * \param count number of elements to be reduced
-   * \param prepare_fun Lazy preprocessing function, if it is not NULL, prepare_fun(prepare_arg)
-   *                     will be called by the function before performing Allreduce, to initialize the data in sendrecvbuf.
-   *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
-   * \param prepare_arg argument used to pass into the lazy preprocessing function
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  inline void Allreduce(DType *sendrecvbuf, size_t count,
-                        void (*prepare_fun)(void *) = NULL,
-                        void *prepare_arg = NULL,
-                        const char* _file = _FILE,
-                        const int _line = _LINE,
-                        const char* _caller = _CALLER);
-#if DMLC_USE_CXX11
-  /*!
-   * \brief customized in-place all reduce operation, with lambda function as preprocessor
-   * \param sendrecvbuf pointer to the array of objects to be reduced
-   * \param count number of elements to be reduced
-   * \param prepare_fun lambda function executed to prepare the data, if necessary
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  inline void Allreduce(DType *sendrecvbuf, size_t count,
-                        std::function<void()> prepare_fun,
-                        const char* _file = _FILE,
-                        const int _line = _LINE,
-                        const char* _caller = _CALLER);
-#endif  // DMLC_USE_CXX11
-
- private:
-  /*! \brief function handle to do reduce */
-  engine::ReduceHandle handle_;
-};
-/*!
- * \brief template class to make customized reduce,
- *  this class defines complex reducer handles all the data structure that can be
- *  serialized/deserialized into fixed size buffer
- *  Do not use reducer directly in the function you call Finalize, because the destructor can execute after Finalize
- *
- * \tparam DType data type that to be reduced, DType must contain the following functions:
- * \tparam freduce the customized reduction function
- *   (1) Save(IStream &fs)  (2) Load(IStream &fs) (3) Reduce(const DType &src, size_t max_nbyte)
- */
-template<typename DType>
-class SerializeReducer {
- public:
-  SerializeReducer();
-  /*!
-   * \brief customized in-place all reduce operation
-   * \param sendrecvobj pointer to the array of objects to be reduced
-   * \param max_nbyte maximum amount of memory needed to serialize each object
-   *        this includes budget limit for intermediate and final result
-   * \param count number of elements to be reduced
-   * \param prepare_fun Lazy preprocessing function, if it is not NULL, prepare_fun(prepare_arg)
-   *                     will be called by the function before performing Allreduce, to initialize the data in sendrecvbuf.
-   *                     If the result of Allreduce can be recovered directly, then the prepare_func will NOT be called
-   * \param prepare_arg argument used to pass into the lazy preprocessing function
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  inline void Allreduce(DType *sendrecvobj,
-                        size_t max_nbyte, size_t count,
-                        void (*prepare_fun)(void *) = NULL,
-                        void *prepare_arg = NULL,
-                        const char* _file = _FILE,
-                        const int _line = _LINE,
-                        const char* _caller = _CALLER);
-// C++11 support for lambda prepare function
-#if DMLC_USE_CXX11
-  /*!
-   * \brief customized in-place all reduce operation, with lambda function as preprocessor
-   * \param sendrecvobj pointer to the array of objects to be reduced
-   * \param max_nbyte maximum amount of memory needed to serialize each object
-   *        this includes budget limit for intermediate and final result
-   * \param count number of elements to be reduced
-   * \param prepare_fun lambda function executed to prepare the data, if necessary
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  inline void Allreduce(DType *sendrecvobj,
-                        size_t max_nbyte, size_t count,
-                        std::function<void()> prepare_fun,
-                        const char* _file = _FILE,
-                        const int _line = _LINE,
-                        const char* _caller = _CALLER);
-#endif  // DMLC_USE_CXX11
-
- private:
-  /*! \brief function handle to do reduce */
-  engine::ReduceHandle handle_;
-  /*! \brief temporal buffer used to do reduce*/
-  std::string buffer_;
-};
-}  // namespace rabit
-// implementation of template functions
-#include "./internal/rabit-inl.h"
-#endif  // RABIT_RABIT_H_ // NOLINT(*)
diff --git a/ml-xgboost/rabit/include/rabit/serializable.h b/ml-xgboost/rabit/include/rabit/serializable.h
deleted file mode 100644
index 581262f..0000000
--- a/ml-xgboost/rabit/include/rabit/serializable.h
+++ /dev/null
@@ -1,26 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file serializable.h
- * \brief defines serializable interface of rabit
- * \author Tianqi Chen
- */
-#ifndef RABIT_SERIALIZABLE_H_
-#define RABIT_SERIALIZABLE_H_
-#include <vector>
-#include <string>
-#include "rabit/internal/utils.h"
-
-namespace rabit {
-/*!
- * \brief defines stream used in rabit
- * see definition of Stream in dmlc/io.h
- */
-typedef dmlc::Stream Stream;
-/*!
- * \brief defines serializable objects used in rabit
- * see definition of Serializable in dmlc/io.h
- */
-typedef dmlc::Serializable Serializable;
-
-}  // namespace rabit
-#endif  // RABIT_SERIALIZABLE_H_
diff --git a/ml-xgboost/rabit/python/rabit.py b/ml-xgboost/rabit/python/rabit.py
deleted file mode 100644
index a56cfcc..0000000
--- a/ml-xgboost/rabit/python/rabit.py
+++ /dev/null
@@ -1,364 +0,0 @@
-"""
-Reliable Allreduce and Broadcast Library.
-
-Author: Tianqi Chen
-"""
-# pylint: disable=unused-argument,invalid-name,global-statement,dangerous-default-value,
-import pickle
-import ctypes
-import os
-import platform
-import sys
-import warnings
-import numpy as np
-
-# version information about the doc
-__version__ = '1.0'
-
-_LIB = None
-
-def _find_lib_path(dll_name):
-    """Find the rabit dynamic library files.
-
-    Returns
-    -------
-    lib_path: list(string)
-       List of all found library path to rabit
-    """
-    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-    # make pythonpack hack: copy this directory one level upper for setup.py
-    dll_path = [curr_path,
-                os.path.join(curr_path, '../lib/'),
-                os.path.join(curr_path, './lib/')]
-    if os.name == 'nt':
-        dll_path = [os.path.join(p, dll_name) for p in dll_path]
-    else:
-        dll_path = [os.path.join(p, dll_name) for p in dll_path]
-    lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
-    #From github issues, most of installation errors come from machines w/o compilers
-    if len(lib_path) == 0 and not os.environ.get('XGBOOST_BUILD_DOC', False):
-        raise RuntimeError(
-            'Cannot find Rabit Libarary in the candicate path, ' +
-            'did you install compilers and run build.sh in root path?\n'
-            'List of candidates:\n' + ('\n'.join(dll_path)))
-    return lib_path
-
-# load in xgboost library
-def _loadlib(lib='standard', lib_dll=None):
-    """Load rabit library."""
-    global _LIB
-    if _LIB is not None:
-        warnings.warn('rabit.int call was ignored because it has'\
-                          ' already been initialized', level=2)
-        return
-
-    if lib_dll is not None:
-        _LIB = lib_dll
-        return
-
-    if lib == 'standard':
-        dll_name = 'librabit'
-    else:
-        dll_name = 'librabit_' + lib
-
-    if os.name == 'nt':
-        dll_name += '.dll'
-    elif platform.system() == 'Darwin':
-        dll_name += '.dylib'
-    else:
-        dll_name += '.so'
-
-    _LIB = ctypes.cdll.LoadLibrary(_find_lib_path(dll_name)[0])
-    _LIB.RabitGetRank.restype = ctypes.c_int
-    _LIB.RabitGetWorldSize.restype = ctypes.c_int
-    _LIB.RabitVersionNumber.restype = ctypes.c_int
-
-def _unloadlib():
-    """Unload rabit library."""
-    global _LIB
-    del _LIB
-    _LIB = None
-
-# reduction operators
-MAX = 0
-MIN = 1
-SUM = 2
-BITOR = 3
-
-def init(args=None, lib='standard', lib_dll=None):
-    """Intialize the rabit module, call this once before using anything.
-
-    Parameters
-    ----------
-    args: list of str, optional
-        The list of arguments used to initialized the rabit
-        usually you need to pass in sys.argv.
-        Defaults to sys.argv when it is None.
-    lib: {'standard', 'mock', 'mpi'}, optional
-        Type of library we want to load
-        When cdll is specified
-    lib_dll: ctypes.DLL, optional
-        The DLL object used as lib.
-        When this is presented argument lib will be ignored.
-    """
-    if args is None:
-        args = []
-    _loadlib(lib, lib_dll)
-    arr = (ctypes.c_char_p * len(args))()
-
-    arr[:] = args
-    _LIB.RabitInit(len(args), arr)
-
-def finalize():
-    """Finalize the rabit engine.
-
-    Call this function after you finished all jobs.
-    """
-    _LIB.RabitFinalize()
-    _unloadlib()
-
-def get_rank():
-    """Get rank of current process.
-
-    Returns
-    -------
-    rank : int
-        Rank of current process.
-    """
-    ret = _LIB.RabitGetRank()
-    return ret
-
-def get_world_size():
-    """Get total number workers.
-
-    Returns
-    -------
-    n : int
-        Total number of process.
-    """
-    ret = _LIB.RabitGetWorldSize()
-    return ret
-
-def tracker_print(msg):
-    """Print message to the tracker.
-
-    This function can be used to communicate the information of
-    the progress to the tracker
-
-    Parameters
-    ----------
-    msg : str
-        The message to be printed to tracker.
-    """
-    if not isinstance(msg, str):
-        msg = str(msg)
-    _LIB.RabitTrackerPrint(ctypes.c_char_p(msg).encode('utf-8'))
-
-def get_processor_name():
-    """Get the processor name.
-
-    Returns
-    -------
-    name : str
-        the name of processor(host)
-    """
-    mxlen = 256
-    length = ctypes.c_ulong()
-    buf = ctypes.create_string_buffer(mxlen)
-    _LIB.RabitGetProcessorName(buf, ctypes.byref(length), mxlen)
-    return buf.value
-
-def broadcast(data, root):
-    """Broadcast object from one node to all other nodes.
-
-    Parameters
-    ----------
-    data : any type that can be pickled
-        Input data, if current rank does not equal root, this can be None
-    root : int
-        Rank of the node to broadcast data from.
-
-    Returns
-    -------
-    object : int
-        the result of broadcast.
-    """
-    rank = get_rank()
-    length = ctypes.c_ulong()
-    if root == rank:
-        assert data is not None, 'need to pass in data when broadcasting'
-        s = pickle.dumps(data, protocol=pickle.HIGHEST_PROTOCOL)
-        length.value = len(s)
-    # run first broadcast
-    _LIB.RabitBroadcast(ctypes.byref(length),
-                        ctypes.sizeof(ctypes.c_ulong), root)
-    if root != rank:
-        dptr = (ctypes.c_char * length.value)()
-        # run second
-        _LIB.RabitBroadcast(ctypes.cast(dptr, ctypes.c_void_p),
-                            length.value, root)
-        data = pickle.loads(dptr.raw)
-        del dptr
-    else:
-        _LIB.RabitBroadcast(ctypes.cast(ctypes.c_char_p(s), ctypes.c_void_p),
-                            length.value, root)
-        del s
-    return data
-
-# enumeration of dtypes
-DTYPE_ENUM__ = {
-    np.dtype('int8') : 0,
-    np.dtype('uint8') : 1,
-    np.dtype('int32') : 2,
-    np.dtype('uint32') : 3,
-    np.dtype('int64') : 4,
-    np.dtype('uint64') : 5,
-    np.dtype('float32') : 6,
-    np.dtype('float64') : 7
-}
-
-def allreduce(data, op, prepare_fun=None):
-    """Perform allreduce, return the result.
-
-    Parameters
-    ----------
-    data: numpy array
-        Input data.
-    op: int
-        Reduction operators, can be MIN, MAX, SUM, BITOR
-    prepare_fun: function
-        Lazy preprocessing function, if it is not None, prepare_fun(data)
-        will be called by the function before performing allreduce, to intialize the data
-        If the result of Allreduce can be recovered directly,
-        then prepare_fun will NOT be called
-
-    Returns
-    -------
-    result : array_like
-        The result of allreduce, have same shape as data
-
-    Notes
-    -----
-    This function is not thread-safe.
-    """
-    if not isinstance(data, np.ndarray):
-        raise Exception('allreduce only takes in numpy.ndarray')
-    buf = data.ravel()
-    if buf.base is data.base:
-        buf = buf.copy()
-    if buf.dtype not in DTYPE_ENUM__:
-        raise Exception('data type %s not supported' % str(buf.dtype))
-    if prepare_fun is None:
-        _LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
-                            buf.size, DTYPE_ENUM__[buf.dtype],
-                            op, None, None)
-    else:
-        func_ptr = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
-        def pfunc(args):
-            """prepare function."""
-            prepare_fun(data)
-        _LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
-                            buf.size, DTYPE_ENUM__[buf.dtype],
-                            op, func_ptr(pfunc), None)
-    return buf
-
-
-def _load_model(ptr, length):
-    """
-    Internal function used by the module,
-    unpickle a model from a buffer specified by ptr, length
-    Arguments:
-        ptr: ctypes.POINTER(ctypes._char)
-            pointer to the memory region of buffer
-        length: int
-            the length of buffer
-    """
-    data = (ctypes.c_char * length).from_address(ctypes.addressof(ptr.contents))
-    return pickle.loads(data.raw)
-
-def load_checkpoint(with_local=False):
-    """Load latest check point.
-
-    Parameters
-    ----------
-    with_local: bool, optional
-        whether the checkpoint contains local model
-
-    Returns
-    -------
-    tuple : tuple
-        if with_local: return (version, gobal_model, local_model)
-        else return (version, gobal_model)
-        if returned version == 0, this means no model has been CheckPointed
-        and global_model, local_model returned will be None
-    """
-    gptr = ctypes.POINTER(ctypes.c_char)()
-    global_len = ctypes.c_ulong()
-    if with_local:
-        lptr = ctypes.POINTER(ctypes.c_char)()
-        local_len = ctypes.c_ulong()
-        version = _LIB.RabitLoadCheckPoint(
-            ctypes.byref(gptr),
-            ctypes.byref(global_len),
-            ctypes.byref(lptr),
-            ctypes.byref(local_len))
-        if version == 0:
-            return (version, None, None)
-        return (version,
-                _load_model(gptr, global_len.value),
-                _load_model(lptr, local_len.value))
-    else:
-        version = _LIB.RabitLoadCheckPoint(
-            ctypes.byref(gptr),
-            ctypes.byref(global_len),
-            None, None)
-        if version == 0:
-            return (version, None)
-        return (version,
-                _load_model(gptr, global_len.value))
-
-def checkpoint(global_model, local_model=None):
-    """Checkpoint the model.
-
-    This means we finished a stage of execution.
-    Every time we call check point, there is a version number which will increase by one.
-
-    Parameters
-    ----------
-    global_model: anytype that can be pickled
-        globally shared model/state when calling this function,
-        the caller need to gauranttees that global_model is the same in all nodes
-
-    local_model: anytype that can be pickled
-       Local model, that is specific to current node/rank.
-       This can be None when no local state is needed.
-
-    Notes
-    -----
-    local_model requires explicit replication of the model for fault-tolerance.
-    This will bring replication cost in checkpoint function.
-    while global_model do not need explicit replication.
-    It is recommended to use global_model if possible.
-    """
-    sglobal = pickle.dumps(global_model)
-    if local_model is None:
-        _LIB.RabitCheckPoint(sglobal, len(sglobal), None, 0)
-        del sglobal
-    else:
-        slocal = pickle.dumps(local_model)
-        _LIB.RabitCheckPoint(sglobal, len(sglobal), slocal, len(slocal))
-        del slocal
-        del sglobal
-
-def version_number():
-    """Returns version number of current stored model.
-
-    This means how many calls to CheckPoint we made so far.
-
-    Returns
-    -------
-    version : int
-        Version number of currently stored model
-    """
-    ret = _LIB.RabitVersionNumber()
-    return ret
diff --git a/ml-xgboost/rabit/scripts/mpi_build.sh b/ml-xgboost/rabit/scripts/mpi_build.sh
deleted file mode 100644
index b1e70be..0000000
--- a/ml-xgboost/rabit/scripts/mpi_build.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env bash
-
-if [ -f mpich/lib/libmpich.so ]; then
-  echo "libmpich.so found -- nothing to build."
-else
-  echo "Downloading mpich source."
-  wget http://www.mpich.org/static/downloads/3.2/mpich-3.2.tar.gz
-  tar xfz mpich-3.2.tar.gz
-  rm mpich-3.2.tar.gz*
-  echo "configuring and building mpich."
-  cd mpich-3.2
-  #CC=gcc CXX=g++ CFLAGS=-m64 CXXFLAGS=-m64 FFLAGS=-m64
-  ./configure \
-          --prefix=`pwd`/../mpich \
-          --enable-static=false \
-          --enable-alloca=true \
-          --disable-long-double \
-          --enable-threads=single \
-          --enable-fortran=no \
-          --enable-fast=all \
-          --enable-g=none \
-          --enable-timing=none \
-          --enable-cxx
-  make -j4
-  make install
-  cd -
-fi
\ No newline at end of file
diff --git a/ml-xgboost/rabit/scripts/travis_runtest.sh b/ml-xgboost/rabit/scripts/travis_runtest.sh
deleted file mode 100644
index 1ec04bb..0000000
--- a/ml-xgboost/rabit/scripts/travis_runtest.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-
-make -f test.mk RABIT_BUILD_DMLC=1 model_recover_10_10k || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 model_recover_10_10k_die_same  || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 model_recover_10_10k_die_hard || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 local_recover_10_10k || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 lazy_recover_10_10k_die_hard || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 lazy_recover_10_10k_die_same || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 ringallreduce_10_10k || exit -1
-make -f test.mk RABIT_BUILD_DMLC=1 pylocal_recover_10_10k || exit -1
diff --git a/ml-xgboost/rabit/scripts/travis_script.sh b/ml-xgboost/rabit/scripts/travis_script.sh
deleted file mode 100644
index 1912822..0000000
--- a/ml-xgboost/rabit/scripts/travis_script.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-
-# main script of travis
-if [ ${TASK} == "lint" ]; then
-    make lint RABIT_BUILD_DMLC=1 || exit -1
-fi
-
-if [ ${TASK} == "doc" ]; then
-    make doc 2>log.txt
-    (cat log.txt| grep -v ENABLE_PREPROCESSING |grep -v "unsupported tag" |grep warning) && exit -1
-fi
-
-# we should depreciate Makefile based build
-if [ ${TASK} == "build" ]; then
-    make all RABIT_BUILD_DMLC=1 || exit -1
-fi
-
-if [ ${TASK} == "mpi-build" ]; then
-    ./scripts/mpi_build.sh
-    cd test
-    make mpi RABIT_BUILD_DMLC=1 && make speed_test.mpi RABIT_BUILD_DMLC=1 || exit -1
-fi
-#
-if [ ${TASK} == "cmake-test" ]; then
-    mkdir build
-    cd build
-    cmake -DRABIT_BUILD_TESTS=ON -DRABIT_BUILD_DMLC=ON -DGTEST_ROOT=${HOME}/.local ..
-    # known osx gtest 1.8 issue
-    cp ${HOME}/.local/lib/*.dylib .
-    make -j$(nproc)
-    make test
-    make install || exit -1
-    cd ../test
-    ../scripts/travis_runtest.sh || exit -1
-    rm -rf ../build
-fi
diff --git a/ml-xgboost/rabit/scripts/travis_setup.sh b/ml-xgboost/rabit/scripts/travis_setup.sh
deleted file mode 100644
index d0b82be..0000000
--- a/ml-xgboost/rabit/scripts/travis_setup.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-
-echo "Testing on: ${TRAVIS_OS_NAME}, Home directory: ${HOME}"
-
-pip3 install cpplint pylint urllib3 numpy cpplint
-pip3 install websocket-client kubernetes
-
-
-# Install googletest under home directory
-GTEST_VERSION=1.8.1
-GTEST_RELEASE=release-${GTEST_VERSION}.tar.gz
-GTEST_TAR_BALL=googletest_${GTEST_RELEASE}
-
-wget https://github.com/google/googletest/archive/${GTEST_RELEASE} -O ${GTEST_TAR_BALL}
-echo "152b849610d91a9dfa1401293f43230c2e0c33f8 ${GTEST_TAR_BALL}" | sha1sum -c
-tar -xf ${GTEST_TAR_BALL}
-pushd .
-
-cd googletest-release-${GTEST_VERSION}
-mkdir build
-cd build
-echo "Installing to ${HOME}/.local"
-cmake .. -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=${HOME}/.local
-make -j$(nproc)
-make install
-
-popd
-
-if [ ${TRAVIS_OS_NAME} == "linux" ]; then
-    sudo apt-get install python3-pip tree
-fi
-
-if [ ${TRAVIS_OS_NAME} == "osx" ]; then
-    brew install python3
-fi
diff --git a/ml-xgboost/rabit/src/CMakeLists.txt b/ml-xgboost/rabit/src/CMakeLists.txt
deleted file mode 100644
index a7aa5d0..0000000
--- a/ml-xgboost/rabit/src/CMakeLists.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-option(DMLC_ROOT "Specify root of external dmlc core.")
-
-add_library(allreduce_base "")
-add_library(allreduce_mock "")
-
-target_sources(
-        allreduce_base
-        PRIVATE
-        allreduce_base.cc
-        PUBLIC
-        ${CMAKE_CURRENT_LIST_DIR}/allreduce_base.h
-)
-target_sources(
-	allreduce_mock
-	PRIVATE
-	allreduce_robust.cc
-	PUBLIC
-	${CMAKE_CURRENT_LIST_DIR}/allreduce_mock.h
-)
-
-target_include_directories(
-        allreduce_base
-        PUBLIC
-	${DMLC_ROOT}/include
-        ${CMAKE_CURRENT_LIST_DIR}/../../include)
-
-target_include_directories(
-	allreduce_mock
-	PUBLIC
-	${DMLC_ROOT}/include
-	${CMAKE_CURRENT_LIST_DIR}/../../include)
diff --git a/ml-xgboost/rabit/src/README.md b/ml-xgboost/rabit/src/README.md
deleted file mode 100644
index 5e55d92..0000000
--- a/ml-xgboost/rabit/src/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Source Files of Rabit
-====
-* This folder contains the source files of rabit library
-* The library headers are in folder [include](../include)
-* The .h files in this folder are internal header files that are only used by rabit and will not be seen by users
-
diff --git a/ml-xgboost/rabit/src/allreduce_base.cc b/ml-xgboost/rabit/src/allreduce_base.cc
deleted file mode 100644
index 20f9318..0000000
--- a/ml-xgboost/rabit/src/allreduce_base.cc
+++ /dev/null
@@ -1,965 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file allreduce_base.cc
- * \brief Basic implementation of AllReduce
- *
- * \author Tianqi Chen, Ignacio Cano, Tianyi Zhou
- */
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-#include <netinet/tcp.h>
-#include <cstring>
-#include <map>
-#include "allreduce_base.h"
-
-namespace rabit {
-
-namespace utils {
-  bool STOP_PROCESS_ON_ERROR = true;
-}
-
-namespace engine {
-// constructor
-AllreduceBase::AllreduceBase(void) {
-  tracker_uri = "NULL";
-  tracker_port = 9000;
-  host_uri = "";
-  slave_port = 9010;
-  nport_trial = 1000;
-  rank = 0;
-  world_size = -1;
-  connect_retry = 5;
-  hadoop_mode = 0;
-  version_number = 0;
-  // 32 K items
-  reduce_ring_mincount = 32 << 10;
-  // tracker URL
-  task_id = "NULL";
-  err_link = NULL;
-  dmlc_role = "worker";
-  this->SetParam("rabit_reduce_buffer", "256MB");
-  // setup possible enviroment variable of interest
-  // include dmlc support direct variables
-  env_vars.push_back("DMLC_TASK_ID");
-  env_vars.push_back("DMLC_ROLE");
-  env_vars.push_back("DMLC_NUM_ATTEMPT");
-  env_vars.push_back("DMLC_TRACKER_URI");
-  env_vars.push_back("DMLC_TRACKER_PORT");
-  env_vars.push_back("DMLC_WORKER_CONNECT_RETRY");
-  env_vars.push_back("DMLC_WORKER_STOP_PROCESS_ON_ERROR");
-}
-
-// initialization function
-bool AllreduceBase::Init(int argc, char* argv[]) {
-  // setup from enviroment variables
-  // handler to get variables from env
-  for (size_t i = 0; i < env_vars.size(); ++i) {
-    const char *value = getenv(env_vars[i].c_str());
-    if (value != NULL) {
-      this->SetParam(env_vars[i].c_str(), value);
-    }
-  }
-  // pass in arguments override env variable.
-  for (int i = 0; i < argc; ++i) {
-    char name[256], val[256];
-    if (sscanf(argv[i], "%[^=]=%s", name, val) == 2) {
-      this->SetParam(name, val);
-    }
-  }
-
-  {
-    // handling for hadoop
-    const char *task_id = getenv("mapred_tip_id");
-    if (task_id == NULL) {
-      task_id = getenv("mapreduce_task_id");
-    }
-    if (hadoop_mode) {
-      utils::Check(task_id != NULL,
-                   "hadoop_mode is set but cannot find mapred_task_id");
-    }
-    if (task_id != NULL) {
-      this->SetParam("rabit_task_id", task_id);
-      this->SetParam("rabit_hadoop_mode", "1");
-    }
-    const char *attempt_id = getenv("mapred_task_id");
-    if (attempt_id != 0) {
-      const char *att = strrchr(attempt_id, '_');
-      int num_trial;
-      if (att != NULL && sscanf(att + 1, "%d", &num_trial) == 1) {
-        this->SetParam("rabit_num_trial", att + 1);
-      }
-    }
-    // handling for hadoop
-    const char *num_task = getenv("mapred_map_tasks");
-    if (num_task == NULL) {
-      num_task = getenv("mapreduce_job_maps");
-    }
-    if (hadoop_mode) {
-      utils::Check(num_task != NULL,
-                   "hadoop_mode is set but cannot find mapred_map_tasks");
-    }
-    if (num_task != NULL) {
-      this->SetParam("rabit_world_size", num_task);
-    }
-  }
-  if (dmlc_role != "worker") {
-    fprintf(stderr, "Rabit Module currently only work with dmlc worker"\
-            ", quit this program by exit 0\n");
-    exit(0);
-  }
-
-  // clear the setting before start reconnection
-  this->rank = -1;
-  //---------------------
-  // start socket
-  utils::Socket::Startup();
-  utils::Assert(all_links.size() == 0, "can only call Init once");
-  this->host_uri = utils::SockAddr::GetHostName();
-  // get information from tracker
-  return this->ReConnectLinks();
-}
-
-bool AllreduceBase::Shutdown(void) {
-  try {
-    for (size_t i = 0; i < all_links.size(); ++i) {
-      all_links[i].sock.Close();
-    }
-    all_links.clear();
-    tree_links.plinks.clear();
-
-    if (tracker_uri == "NULL") return true;
-    // notify tracker rank i have shutdown
-    utils::TCPSocket tracker = this->ConnectTracker();
-    tracker.SendStr(std::string("shutdown"));
-    tracker.Close();
-    utils::TCPSocket::Finalize();
-    return true;
-  } catch (const std::exception& e) {
-    fprintf(stderr, "failed to shutdown due to %s\n", e.what());
-    return false;
-  }
-}
-
-void AllreduceBase::TrackerPrint(const std::string &msg) {
-  if (tracker_uri == "NULL") {
-    utils::Printf("%s", msg.c_str()); return;
-  }
-  utils::TCPSocket tracker = this->ConnectTracker();
-  tracker.SendStr(std::string("print"));
-  tracker.SendStr(msg);
-  tracker.Close();
-}
-
-// util to parse data with unit suffix
-inline size_t ParseUnit(const char *name, const char *val) {
-  char unit;
-  unsigned long amt;  // NOLINT(*)
-  int n = sscanf(val, "%lu%c", &amt, &unit);
-  size_t amount = amt;
-  if (n == 2) {
-    switch (unit) {
-      case 'B': return amount;
-      case 'K': return amount << 10UL;
-      case 'M': return amount << 20UL;
-      case 'G': return amount << 30UL;
-      default: utils::Error("invalid format for %s", name); return 0;
-    }
-  } else if (n == 1) {
-    return amount;
-  } else {
-    utils::Error("invalid format for %s,"                               \
-                 "shhould be {integer}{unit}, unit can be {B, KB, MB, GB}", name);
-    return 0;
-  }
-}
-/*!
- * \brief set parameters to the engine
- * \param name parameter name
- * \param val parameter value
- */
-void AllreduceBase::SetParam(const char *name, const char *val) {
-  if (!strcmp(name, "rabit_tracker_uri")) tracker_uri = val;
-  if (!strcmp(name, "rabit_tracker_port")) tracker_port = atoi(val);
-  if (!strcmp(name, "rabit_task_id")) task_id = val;
-  if (!strcmp(name, "DMLC_TRACKER_URI")) tracker_uri = val;
-  if (!strcmp(name, "DMLC_TRACKER_PORT")) tracker_port = atoi(val);
-  if (!strcmp(name, "DMLC_TASK_ID")) task_id = val;
-  if (!strcmp(name, "DMLC_ROLE")) dmlc_role = val;
-  if (!strcmp(name, "rabit_world_size")) world_size = atoi(val);
-  if (!strcmp(name, "rabit_hadoop_mode")) hadoop_mode = utils::StringToBool(val);
-  if (!strcmp(name, "rabit_reduce_ring_mincount")) {
-    reduce_ring_mincount = atoi(val);
-    utils::Assert(reduce_ring_mincount > 0, "rabit_reduce_ring_mincount should be greater than 0");
-  }
-  if (!strcmp(name, "rabit_reduce_buffer")) {
-    reduce_buffer_size = (ParseUnit(name, val) + 7) >> 3;
-  }
-  if (!strcmp(name, "DMLC_WORKER_CONNECT_RETRY")) {
-    connect_retry = atoi(val);
-  }
-  if (!strcmp(name, "DMLC_WORKER_STOP_PROCESS_ON_ERROR")) {
-    if (!strcmp(val, "true")) {
-      rabit::utils::STOP_PROCESS_ON_ERROR = true;
-    } else if (!strcmp(val, "false")) {
-      rabit::utils::STOP_PROCESS_ON_ERROR = false;
-    } else {
-      throw std::runtime_error("invalid value of DMLC_WORKER_STOP_PROCESS_ON_ERROR");
-    }
-  }
-  if (!strcmp(name, "rabit_bootstrap_cache")) {
-    rabit_bootstrap_cache = utils::StringToBool(val);
-  }
-  if (!strcmp(name, "rabit_debug")) {
-    rabit_debug = utils::StringToBool(val);
-  }
-  if (!strcmp(name, "rabit_timeout")) {
-    rabit_timeout = utils::StringToBool(val);
-  }
-  if (!strcmp(name, "rabit_timeout_sec")) {
-    timeout_sec = atoi(val);
-    utils::Assert(timeout_sec >= 0, "rabit_timeout_sec should be non negative second");
-  }
-  if (!strcmp(name, "rabit_enable_tcp_no_delay")) {
-    if (!strcmp(val, "true"))
-      rabit_enable_tcp_no_delay = true;
-    else
-      rabit_enable_tcp_no_delay = false;
-  }
-}
-/*!
- * \brief initialize connection to the tracker
- * \return a socket that initializes the connection
- */
-utils::TCPSocket AllreduceBase::ConnectTracker(void) const {
-  int magic = kMagic;
-  // get information from tracker
-  utils::TCPSocket tracker;
-  tracker.Create();
-
-  int retry = 0;
-  do {
-    if (!tracker.Connect(utils::SockAddr(tracker_uri.c_str(), tracker_port))) {
-      if (++retry >= connect_retry) {
-        fprintf(stderr, "connect to (failed): [%s]\n", tracker_uri.c_str());
-        utils::Socket::Error("Connect");
-      } else {
-        fprintf(stderr, "retry connect to ip(retry time %d): [%s]\n", retry, tracker_uri.c_str());
-#if defined(_MSC_VER) || defined (__MINGW32__)
-        Sleep(retry << 1);
-#else
-        sleep(retry << 1);
-#endif
-        continue;
-      }
-    }
-    break;
-  } while (1);
-
-  using utils::Assert;
-  Assert(tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic),
-         "ReConnectLink failure 1");
-  Assert(tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic),
-         "ReConnectLink failure 2");
-  utils::Check(magic == kMagic, "sync::Invalid tracker message, init failure");
-  Assert(tracker.SendAll(&rank, sizeof(rank)) == sizeof(rank),
-                "ReConnectLink failure 3");
-  Assert(tracker.SendAll(&world_size, sizeof(world_size)) == sizeof(world_size),
-         "ReConnectLink failure 3");
-  tracker.SendStr(task_id);
-  return tracker;
-}
-/*!
- * \brief connect to the tracker to fix the the missing links
- *   this function is also used when the engine start up
- */
-bool AllreduceBase::ReConnectLinks(const char *cmd) {
-  // single node mode
-  if (tracker_uri == "NULL") {
-    rank = 0; world_size = 1; return true;
-  }
-  try {
-    utils::TCPSocket tracker = this->ConnectTracker();
-    fprintf(stdout, "task %s connected to the tracker\n", task_id.c_str());
-    tracker.SendStr(std::string(cmd));
-
-    // the rank of previous link, next link in ring
-    int prev_rank, next_rank;
-    // the rank of neighbors
-    std::map<int, int> tree_neighbors;
-    using utils::Assert;
-    // get new ranks
-    int newrank, num_neighbors;
-    Assert(tracker.RecvAll(&newrank, sizeof(newrank)) == sizeof(newrank),
-           "ReConnectLink failure 4");
-    Assert(tracker.RecvAll(&parent_rank, sizeof(parent_rank)) == \
-         sizeof(parent_rank), "ReConnectLink failure 4");
-    Assert(tracker.RecvAll(&world_size, sizeof(world_size)) == sizeof(world_size),
-           "ReConnectLink failure 4");
-    Assert(rank == -1 || newrank == rank,
-           "must keep rank to same if the node already have one");
-    rank = newrank;
-
-    // tracker got overwhelemed and not able to assign correct rank
-    if (rank == -1) exit(-1);
-
-    fprintf(stdout, "task %s got new rank %d\n", task_id.c_str(), rank);
-
-    Assert(tracker.RecvAll(&num_neighbors, sizeof(num_neighbors)) == \
-         sizeof(num_neighbors), "ReConnectLink failure 4");
-    for (int i = 0; i < num_neighbors; ++i) {
-      int nrank;
-      Assert(tracker.RecvAll(&nrank, sizeof(nrank)) == sizeof(nrank),
-             "ReConnectLink failure 4");
-      tree_neighbors[nrank] = 1;
-    }
-    Assert(tracker.RecvAll(&prev_rank, sizeof(prev_rank)) == sizeof(prev_rank),
-           "ReConnectLink failure 4");
-    Assert(tracker.RecvAll(&next_rank, sizeof(next_rank)) == sizeof(next_rank),
-           "ReConnectLink failure 4");
-
-    utils::TCPSocket sock_listen;
-    if (!sock_listen.IsClosed()) {
-      sock_listen.Close();
-    }
-    // create listening socket
-    sock_listen.Create();
-    int port = sock_listen.TryBindHost(slave_port, slave_port + nport_trial);
-    utils::Check(port != -1, "ReConnectLink fail to bind the ports specified");
-    sock_listen.Listen();
-
-    // get number of to connect and number of to accept nodes from tracker
-    int num_conn, num_accept, num_error = 1;
-    do {
-      // send over good links
-      std::vector<int> good_link;
-      for (size_t i = 0; i < all_links.size(); ++i) {
-        if (!all_links[i].sock.BadSocket()) {
-          good_link.push_back(static_cast<int>(all_links[i].rank));
-        } else {
-          if (!all_links[i].sock.IsClosed()) all_links[i].sock.Close();
-        }
-      }
-      int ngood = static_cast<int>(good_link.size());
-      Assert(tracker.SendAll(&ngood, sizeof(ngood)) == sizeof(ngood),
-             "ReConnectLink failure 5");
-      for (size_t i = 0; i < good_link.size(); ++i) {
-        Assert(tracker.SendAll(&good_link[i], sizeof(good_link[i])) == \
-             sizeof(good_link[i]), "ReConnectLink failure 6");
-      }
-      Assert(tracker.RecvAll(&num_conn, sizeof(num_conn)) == sizeof(num_conn),
-             "ReConnectLink failure 7");
-      Assert(tracker.RecvAll(&num_accept, sizeof(num_accept)) == \
-           sizeof(num_accept), "ReConnectLink failure 8");
-      num_error = 0;
-      for (int i = 0; i < num_conn; ++i) {
-        LinkRecord r;
-        int hport, hrank;
-        std::string hname;
-        tracker.RecvStr(&hname);
-        Assert(tracker.RecvAll(&hport, sizeof(hport)) == sizeof(hport),
-               "ReConnectLink failure 9");
-        Assert(tracker.RecvAll(&hrank, sizeof(hrank)) == sizeof(hrank),
-               "ReConnectLink failure 10");
-
-        r.sock.Create();
-        if (!r.sock.Connect(utils::SockAddr(hname.c_str(), hport))) {
-          num_error += 1;
-          r.sock.Close();
-          continue;
-        }
-        Assert(r.sock.SendAll(&rank, sizeof(rank)) == sizeof(rank),
-               "ReConnectLink failure 12");
-        Assert(r.sock.RecvAll(&r.rank, sizeof(r.rank)) == sizeof(r.rank),
-               "ReConnectLink failure 13");
-        utils::Check(hrank == r.rank,
-                     "ReConnectLink failure, link rank inconsistent");
-        bool match = false;
-        for (size_t i = 0; i < all_links.size(); ++i) {
-          if (all_links[i].rank == hrank) {
-            Assert(all_links[i].sock.IsClosed(),
-                   "Override a link that is active");
-            all_links[i].sock = r.sock;
-            match = true;
-            break;
-          }
-        }
-        if (!match) all_links.push_back(r);
-      }
-      Assert(tracker.SendAll(&num_error, sizeof(num_error)) == sizeof(num_error),
-             "ReConnectLink failure 14");
-    } while (num_error != 0);
-    // send back socket listening port to tracker
-    Assert(tracker.SendAll(&port, sizeof(port)) == sizeof(port),
-           "ReConnectLink failure 14");
-    // close connection to tracker
-    tracker.Close();
-    // listen to incoming links
-    for (int i = 0; i < num_accept; ++i) {
-      LinkRecord r;
-      r.sock = sock_listen.Accept();
-      Assert(r.sock.SendAll(&rank, sizeof(rank)) == sizeof(rank),
-             "ReConnectLink failure 15");
-      Assert(r.sock.RecvAll(&r.rank, sizeof(r.rank)) == sizeof(r.rank),
-             "ReConnectLink failure 15");
-      bool match = false;
-      for (size_t i = 0; i < all_links.size(); ++i) {
-        if (all_links[i].rank == r.rank) {
-          utils::Assert(all_links[i].sock.IsClosed(),
-                        "Override a link that is active");
-          all_links[i].sock = r.sock;
-          match = true;
-          break;
-        }
-      }
-      if (!match) all_links.push_back(r);
-    }
-    sock_listen.Close();
-    this->parent_index = -1;
-    // setup tree links and ring structure
-    tree_links.plinks.clear();
-    int tcpNoDelay = 1;
-    for (size_t i = 0; i < all_links.size(); ++i) {
-      utils::Assert(!all_links[i].sock.BadSocket(), "ReConnectLink: bad socket");
-      // set the socket to non-blocking mode, enable TCP keepalive
-      all_links[i].sock.SetNonBlock(true);
-      all_links[i].sock.SetKeepAlive(true);
-      if (rabit_enable_tcp_no_delay) {
-        setsockopt(all_links[i].sock, IPPROTO_TCP,
-                   TCP_NODELAY, reinterpret_cast<void *>(&tcpNoDelay), sizeof(tcpNoDelay));
-      }
-      if (tree_neighbors.count(all_links[i].rank) != 0) {
-        if (all_links[i].rank == parent_rank) {
-          parent_index = static_cast<int>(tree_links.plinks.size());
-        }
-        tree_links.plinks.push_back(&all_links[i]);
-      }
-      if (all_links[i].rank == prev_rank) ring_prev = &all_links[i];
-      if (all_links[i].rank == next_rank) ring_next = &all_links[i];
-    }
-    Assert(parent_rank == -1 || parent_index != -1,
-           "cannot find parent in the link");
-    Assert(prev_rank == -1 || ring_prev != NULL,
-           "cannot find prev ring in the link");
-    Assert(next_rank == -1 || ring_next != NULL,
-           "cannot find next ring in the link");
-    return true;
-  } catch (const std::exception& e) {
-    fprintf(stderr, "failed in ReconnectLink %s\n", e.what());
-    return false;
-  }
-}
-/*!
- * \brief perform in-place allreduce, on sendrecvbuf, this function can fail, and will return the cause of failure
- *
- * NOTE on Allreduce:
- *    The kSuccess TryAllreduce does NOT mean every node have successfully finishes TryAllreduce.
- *    It only means the current node get the correct result of Allreduce.
- *    However, it means every node finishes LAST call(instead of this one) of Allreduce/Bcast
- *
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param type_nbytes the unit number of bytes the type have
- * \param count number of elements to be reduced
- * \param reducer reduce function
- * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceBase::ReturnType
-AllreduceBase::TryAllreduce(void *sendrecvbuf_,
-                            size_t type_nbytes,
-                            size_t count,
-                            ReduceFunction reducer) {
-  if (count > reduce_ring_mincount) {
-    return this->TryAllreduceRing(sendrecvbuf_, type_nbytes, count, reducer);
-  } else {
-    return this->TryAllreduceTree(sendrecvbuf_, type_nbytes, count, reducer);
-  }
-}
-/*!
- * \brief perform in-place allreduce, on sendrecvbuf,
- * this function implements tree-shape reduction
- *
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param type_nbytes the unit number of bytes the type have
- * \param count number of elements to be reduced
- * \param reducer reduce function
- * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceBase::ReturnType
-AllreduceBase::TryAllreduceTree(void *sendrecvbuf_,
-                                size_t type_nbytes,
-                                size_t count,
-                                ReduceFunction reducer) {
-  RefLinkVector &links = tree_links;
-  if (links.size() == 0 || count == 0) return kSuccess;
-  // total size of message
-  const size_t total_size = type_nbytes * count;
-  // number of links
-  const int nlink = static_cast<int>(links.size());
-  // send recv buffer
-  char *sendrecvbuf = reinterpret_cast<char*>(sendrecvbuf_);
-  // size of space that we already performs reduce in up pass
-  size_t size_up_reduce = 0;
-  // size of space that we have already passed to parent
-  size_t size_up_out = 0;
-  // size of message we received, and send in the down pass
-  size_t size_down_in = 0;
-  // initialize the link ring-buffer and pointer
-  for (int i = 0; i < nlink; ++i) {
-    if (i != parent_index) {
-      links[i].InitBuffer(type_nbytes, count, reduce_buffer_size);
-    }
-    links[i].ResetSize();
-  }
-  // if no childs, no need to reduce
-  if (nlink == static_cast<int>(parent_index != -1)) {
-    size_up_reduce = total_size;
-  }
-  // while we have not passed the messages out
-  while (true) {
-    // select helper
-    bool finished = true;
-    utils::PollHelper watcher;
-    for (int i = 0; i < nlink; ++i) {
-      if (i == parent_index) {
-        if (size_down_in != total_size) {
-          watcher.WatchRead(links[i].sock);
-          // only watch for exception in live channels
-          watcher.WatchException(links[i].sock);
-          finished = false;
-        }
-        if (size_up_out != total_size && size_up_out < size_up_reduce) {
-          watcher.WatchWrite(links[i].sock);
-        }
-      } else {
-        if (links[i].size_read != total_size) {
-          watcher.WatchRead(links[i].sock);
-        }
-        // size_write <= size_read
-        if (links[i].size_write != total_size) {
-          if (links[i].size_write < size_down_in) {
-            watcher.WatchWrite(links[i].sock);
-          }
-          // only watch for exception in live channels
-          watcher.WatchException(links[i].sock);
-          finished = false;
-        }
-      }
-    }
-    // finish runing allreduce
-    if (finished) break;
-    // select must return
-    watcher.Poll();
-    // exception handling
-    for (int i = 0; i < nlink; ++i) {
-      // recive OOB message from some link
-      if (watcher.CheckExcept(links[i].sock)) {
-        return ReportError(&links[i], kGetExcept);
-      }
-    }
-    // read data from childs
-    for (int i = 0; i < nlink; ++i) {
-      if (i != parent_index && watcher.CheckRead(links[i].sock)) {
-        ReturnType ret = links[i].ReadToRingBuffer(size_up_out, total_size);
-        if (ret != kSuccess) {
-          return ReportError(&links[i], ret);
-        }
-      }
-    }
-    // this node have childs, peform reduce
-    if (nlink > static_cast<int>(parent_index != -1)) {
-      size_t buffer_size = 0;
-      // do upstream reduce
-      size_t max_reduce = total_size;
-      for (int i = 0; i < nlink; ++i) {
-        if (i != parent_index) {
-          max_reduce = std::min(max_reduce, links[i].size_read);
-          utils::Assert(buffer_size == 0 || buffer_size == links[i].buffer_size,
-                        "buffer size inconsistent");
-          buffer_size = links[i].buffer_size;
-        }
-      }
-      utils::Assert(buffer_size != 0, "must assign buffer_size");
-      // round to type_n4bytes
-      max_reduce = (max_reduce / type_nbytes * type_nbytes);
-      // peform reduce, can be at most two rounds
-      while (size_up_reduce < max_reduce) {
-        // start position
-        size_t start = size_up_reduce % buffer_size;
-        // peform read till end of buffer
-        size_t nread = std::min(buffer_size - start,
-                                max_reduce - size_up_reduce);
-        utils::Assert(nread % type_nbytes == 0, "Allreduce: size check");
-        for (int i = 0; i < nlink; ++i) {
-          if (i != parent_index) {
-            reducer(links[i].buffer_head + start,
-                    sendrecvbuf + size_up_reduce,
-                    static_cast<int>(nread / type_nbytes),
-                    MPI::Datatype(type_nbytes));
-          }
-        }
-        size_up_reduce += nread;
-      }
-    }
-    if (parent_index != -1) {
-      // pass message up to parent, can pass data that are already been reduced
-      if (size_up_out < size_up_reduce) {
-        ssize_t len = links[parent_index].sock.
-            Send(sendrecvbuf + size_up_out, size_up_reduce - size_up_out);
-        if (len != -1) {
-          size_up_out += static_cast<size_t>(len);
-        } else {
-          ReturnType ret = Errno2Return();
-          if (ret != kSuccess) {
-            return ReportError(&links[parent_index], ret);
-          }
-        }
-      }
-      // read data from parent
-      if (watcher.CheckRead(links[parent_index].sock) &&
-          total_size > size_down_in) {
-        ssize_t len = links[parent_index].sock.
-            Recv(sendrecvbuf + size_down_in, total_size - size_down_in);
-        if (len == 0) {
-          links[parent_index].sock.Close();
-          return ReportError(&links[parent_index], kRecvZeroLen);
-        }
-        if (len != -1) {
-          size_down_in += static_cast<size_t>(len);
-          utils::Assert(size_down_in <= size_up_out,
-                        "Allreduce: boundary error");
-        } else {
-          ReturnType ret = Errno2Return();
-          if (ret != kSuccess) {
-            return ReportError(&links[parent_index], ret);
-          }
-        }
-      }
-    } else {
-      // this is root, can use reduce as most recent point
-      size_down_in = size_up_out = size_up_reduce;
-    }
-    // can pass message down to childs
-    for (int i = 0; i < nlink; ++i) {
-      if (i != parent_index && links[i].size_write < size_down_in) {
-        ReturnType ret = links[i].WriteFromArray(sendrecvbuf, size_down_in);
-        if (ret != kSuccess) {
-          return ReportError(&links[i], ret);
-        }
-      }
-    }
-  }
-  return kSuccess;
-}
-/*!
- * \brief broadcast data from root to all nodes, this function can fail,and will return the cause of failure
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param total_size the size of the data to be broadcasted
- * \param root the root worker id to broadcast the data
- * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceBase::ReturnType
-AllreduceBase::TryBroadcast(void *sendrecvbuf_, size_t total_size, int root) {
-  RefLinkVector &links = tree_links;
-  if (links.size() == 0 || total_size == 0) return kSuccess;
-  utils::Check(root < world_size,
-               "Broadcast: root should be smaller than world size");
-  // number of links
-  const int nlink = static_cast<int>(links.size());
-  // size of space already read from data
-  size_t size_in = 0;
-  // input link, -2 means unknown yet, -1 means this is root
-  int in_link = -2;
-
-  // initialize the link statistics
-  for (int i = 0; i < nlink; ++i) {
-    links[i].ResetSize();
-  }
-  // root have all the data
-  if (this->rank == root) {
-    size_in = total_size;
-    in_link = -1;
-  }
-  // while we have not passed the messages out
-  while (true) {
-    bool finished = true;
-    // select helper
-    utils::PollHelper watcher;
-    for (int i = 0; i < nlink; ++i) {
-      if (in_link == -2) {
-        watcher.WatchRead(links[i].sock); finished = false;
-      }
-      if (i == in_link && links[i].size_read != total_size) {
-        watcher.WatchRead(links[i].sock); finished = false;
-      }
-      if (in_link != -2 && i != in_link && links[i].size_write != total_size) {
-        if (links[i].size_write < size_in) {
-          watcher.WatchWrite(links[i].sock);
-        }
-        finished = false;
-      }
-      watcher.WatchException(links[i].sock);
-    }
-    // finish running
-    if (finished) break;
-    // select
-    watcher.Poll();
-    // exception handling
-    for (int i = 0; i < nlink; ++i) {
-      // recive OOB message from some link
-      if (watcher.CheckExcept(links[i].sock)) {
-        return ReportError(&links[i], kGetExcept);
-      }
-    }
-    if (in_link == -2) {
-      // probe in-link
-      for (int i = 0; i < nlink; ++i) {
-        if (watcher.CheckRead(links[i].sock)) {
-          ReturnType ret = links[i].ReadToArray(sendrecvbuf_, total_size);
-          if (ret != kSuccess) {
-            return ReportError(&links[i], ret);
-          }
-          size_in = links[i].size_read;
-          if (size_in != 0) {
-            in_link = i; break;
-          }
-        }
-      }
-    } else {
-      // read from in link
-      if (in_link >= 0 && watcher.CheckRead(links[in_link].sock)) {
-        ReturnType ret = links[in_link].ReadToArray(sendrecvbuf_, total_size);
-        if (ret != kSuccess) {
-          return ReportError(&links[in_link], ret);
-        }
-        size_in = links[in_link].size_read;
-      }
-    }
-    // send data to all out-link
-    for (int i = 0; i < nlink; ++i) {
-      if (i != in_link && links[i].size_write < size_in) {
-        ReturnType ret = links[i].WriteFromArray(sendrecvbuf_, size_in);
-        if (ret != kSuccess) {
-          return ReportError(&links[i], ret);
-        }
-      }
-    }
-  }
-  return kSuccess;
-}
-/*!
- * \brief internal Allgather function, each node have a segment of data in the ring of sendrecvbuf,
- *  the data provided by current node k is [slice_begin, slice_end),
- *  the next node's segment must start with slice_end
- *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
- *  use a ring based algorithm
- *
- * \param sendrecvbuf_ buffer for both sending and receiving data, it is a ring conceptually
- * \param total_size total size of data to be gathered
- * \param slice_begin beginning of the current slice
- * \param slice_end end of the current slice
- * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
- */
-AllreduceBase::ReturnType
-AllreduceBase::TryAllgatherRing(void *sendrecvbuf_, size_t total_size,
-                                size_t slice_begin,
-                                size_t slice_end,
-                                size_t size_prev_slice) {
-  // read from next link and send to prev one
-  LinkRecord &prev = *ring_prev, &next = *ring_next;
-  // need to reply on special rank structure
-  utils::Assert(next.rank == (rank + 1) % world_size &&
-                rank == (prev.rank + 1) % world_size,
-                "need to assume rank structure");
-  // send recv buffer
-  char *sendrecvbuf = reinterpret_cast<char*>(sendrecvbuf_);
-  const size_t stop_read = total_size + slice_begin;
-  const size_t stop_write = total_size + slice_begin - size_prev_slice;
-  size_t write_ptr = slice_begin;
-  size_t read_ptr = slice_end;
-
-  while (true) {
-    // select helper
-    bool finished = true;
-    utils::PollHelper watcher;
-    if (read_ptr != stop_read) {
-      watcher.WatchRead(next.sock);
-      finished = false;
-    }
-    if (write_ptr != stop_write) {
-      if (write_ptr < read_ptr) {
-        watcher.WatchWrite(prev.sock);
-      }
-      finished  = false;
-    }
-    if (finished) break;
-    watcher.Poll();
-    if (read_ptr != stop_read && watcher.CheckRead(next.sock)) {
-      size_t size = stop_read - read_ptr;
-      size_t start = read_ptr % total_size;
-      if (start + size > total_size) {
-        size = total_size - start;
-      }
-      ssize_t len = next.sock.Recv(sendrecvbuf + start, size);
-      if (len != -1) {
-        read_ptr += static_cast<size_t>(len);
-      } else {
-        ReturnType ret = Errno2Return();
-        if (ret != kSuccess) return ReportError(&next, ret);
-      }
-    }
-    if (write_ptr < read_ptr && write_ptr != stop_write) {
-      size_t size = std::min(read_ptr, stop_write) - write_ptr;
-      size_t start = write_ptr % total_size;
-      if (start + size > total_size) {
-        size = total_size - start;
-      }
-      ssize_t len = prev.sock.Send(sendrecvbuf + start, size);
-      if (len != -1) {
-        write_ptr += static_cast<size_t>(len);
-      } else {
-        ReturnType ret = Errno2Return();
-        if (ret != kSuccess) return ReportError(&prev, ret);
-      }
-    }
-  }
-  return kSuccess;
-}
-/*!
- * \brief perform in-place allreduce, on sendrecvbuf, this function can fail,
- *  and will return the cause of failure
- *
- *  Ring-based algorithm
- *
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param type_nbytes the unit number of bytes the type have
- * \param count number of elements to be reduced
- * \param reducer reduce function
- * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
- * \sa ReturnType, TryAllreduce
- */
-AllreduceBase::ReturnType
-AllreduceBase::TryReduceScatterRing(void *sendrecvbuf_,
-                                    size_t type_nbytes,
-                                    size_t count,
-                                    ReduceFunction reducer) {
-  // read from next link and send to prev one
-  LinkRecord &prev = *ring_prev, &next = *ring_next;
-  // need to reply on special rank structure
-  utils::Assert(next.rank == (rank + 1) % world_size &&
-                rank == (prev.rank + 1) % world_size,
-                "need to assume rank structure");
-  // total size of message
-  const size_t total_size = type_nbytes * count;
-  size_t n = static_cast<size_t>(world_size);
-  size_t step = (count + n - 1) / n;
-  size_t r = static_cast<size_t>(next.rank);
-  size_t write_ptr = std::min(r * step, count) * type_nbytes;
-  size_t read_ptr = std::min((r + 1) * step, count) * type_nbytes;
-  size_t reduce_ptr = read_ptr;
-  // send recv buffer
-  char *sendrecvbuf = reinterpret_cast<char*>(sendrecvbuf_);
-  // position to stop reading
-  const size_t stop_read = total_size + write_ptr;
-  // position to stop writing
-  size_t stop_write = total_size + std::min(rank * step, count) * type_nbytes;
-  if (stop_write > stop_read) {
-    stop_write -= total_size;
-    utils::Assert(write_ptr <= stop_write, "write ptr boundary check");
-  }
-  // use ring buffer in next position
-  next.InitBuffer(type_nbytes, step, reduce_buffer_size);
-  // set size_read to read pointer for ring buffer to work properly
-  next.size_read = read_ptr;
-
-  while (true) {
-    // select helper
-    bool finished = true;
-    utils::PollHelper watcher;
-    if (read_ptr != stop_read) {
-      watcher.WatchRead(next.sock);
-      finished = false;
-    }
-    if (write_ptr != stop_write) {
-      if (write_ptr < reduce_ptr) {
-        watcher.WatchWrite(prev.sock);
-      }
-      finished = false;
-    }
-    if (finished) break;
-    watcher.Poll();
-    if (read_ptr != stop_read && watcher.CheckRead(next.sock)) {
-      ReturnType ret = next.ReadToRingBuffer(reduce_ptr, stop_read);
-      if (ret != kSuccess) {
-        return ReportError(&next, ret);
-      }
-      // sync the rate
-      read_ptr = next.size_read;
-      utils::Assert(read_ptr <= stop_read, "[%d] read_ptr boundary check", rank);
-      const size_t buffer_size = next.buffer_size;
-      size_t max_reduce = (read_ptr  / type_nbytes) * type_nbytes;
-      while (reduce_ptr < max_reduce) {
-        size_t bstart = reduce_ptr % buffer_size;
-        size_t nread = std::min(buffer_size - bstart,
-                                max_reduce - reduce_ptr);
-        size_t rstart = reduce_ptr % total_size;
-        nread = std::min(nread, total_size - rstart);
-        reducer(next.buffer_head + bstart,
-                sendrecvbuf + rstart,
-                static_cast<int>(nread / type_nbytes),
-                MPI::Datatype(type_nbytes));
-        reduce_ptr += nread;
-      }
-    }
-    if (write_ptr < reduce_ptr && write_ptr != stop_write) {
-      size_t size = std::min(reduce_ptr, stop_write) - write_ptr;
-      size_t start = write_ptr % total_size;
-      if (start + size > total_size) {
-        size = total_size - start;
-      }
-      ssize_t len = prev.sock.Send(sendrecvbuf + start, size);
-      if (len != -1) {
-        write_ptr += static_cast<size_t>(len);
-      } else {
-        ReturnType ret = Errno2Return();
-        if (ret != kSuccess) return ReportError(&prev, ret);
-      }
-    }
-  }
-  return kSuccess;
-}
-/*!
- * \brief perform in-place allreduce, on sendrecvbuf
- *  use a ring based algorithm
- *
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param type_nbytes the unit number of bytes the type have
- * \param count number of elements to be reduced
- * \param reducer reduce function
- * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceBase::ReturnType
-AllreduceBase::TryAllreduceRing(void *sendrecvbuf_,
-                                size_t type_nbytes,
-                                size_t count,
-                                ReduceFunction reducer) {
-  ReturnType ret = TryReduceScatterRing(sendrecvbuf_, type_nbytes, count, reducer);
-  if (ret != kSuccess) return ret;
-  size_t n = static_cast<size_t>(world_size);
-  size_t step = (count + n - 1) / n;
-  size_t begin = std::min(rank * step, count) * type_nbytes;
-  size_t end = std::min((rank + 1) * step, count) * type_nbytes;
-  // previous rank
-  int prank = ring_prev->rank;
-  // get rank of previous
-  return TryAllgatherRing
-      (sendrecvbuf_, type_nbytes * count,
-       begin, end,
-       (std::min((prank + 1) * step, count) -
-        std::min(prank * step, count)) * type_nbytes);
-}
-}  // namespace engine
-}  // namespace rabit
diff --git a/ml-xgboost/rabit/src/allreduce_base.h b/ml-xgboost/rabit/src/allreduce_base.h
deleted file mode 100644
index d1d3333..0000000
--- a/ml-xgboost/rabit/src/allreduce_base.h
+++ /dev/null
@@ -1,587 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file allreduce_base.h
- * \brief Basic implementation of AllReduce
- *   using TCP non-block socket and tree-shape reduction.
- *
- *   This implementation provides basic utility of AllReduce and Broadcast
- *   without considering node failure
- *
- * \author Tianqi Chen, Ignacio Cano, Tianyi Zhou
- */
-#ifndef RABIT_ALLREDUCE_BASE_H_
-#define RABIT_ALLREDUCE_BASE_H_
-
-#include <vector>
-#include <string>
-#include <algorithm>
-#include "rabit/internal/utils.h"
-#include "rabit/internal/engine.h"
-#include "rabit/internal/socket.h"
-
-#ifdef RABIT_CXXTESTDEFS_H
-#define private   public
-#define protected public
-#endif  // RABIT_CXXTESTDEFS_H
-
-
-namespace MPI {
-// MPI data type to be compatible with existing MPI interface
-class Datatype {
- public:
-  size_t type_size;
-  explicit Datatype(size_t type_size) : type_size(type_size) {}
-};
-}
-namespace rabit {
-namespace engine {
-/*! \brief implementation of basic Allreduce engine */
-class AllreduceBase : public IEngine {
- public:
-  // magic number to verify server
-  static const int kMagic = 0xff99;
-  // constant one byte out of band message to indicate error happening
-  AllreduceBase(void);
-  virtual ~AllreduceBase(void) {}
-  // initialize the manager
-  virtual bool Init(int argc, char* argv[]);
-  // shutdown the engine
-  virtual bool Shutdown(void);
-  /*!
-   * \brief set parameters to the engine
-   * \param name parameter name
-   * \param val parameter value
-   */
-  virtual void SetParam(const char *name, const char *val);
-  /*!
-   * \brief print the msg in the tracker,
-   *    this function can be used to communicate the information of the progress to
-   *    the user who monitors the tracker
-   * \param msg message to be printed in the tracker
-   */
-  virtual void TrackerPrint(const std::string &msg);
-
-  /*! \brief get rank of previous node in ring topology*/
-  virtual int GetRingPrevRank(void) const {
-    return ring_prev->rank;
-  }
-  /*! \brief get rank */
-  virtual int GetRank(void) const {
-    return rank;
-  }
-  /*! \brief get rank */
-  virtual int GetWorldSize(void) const {
-    if (world_size == -1) return 1;
-    return world_size;
-  }
-  /*! \brief whether is distributed or not */
-  virtual bool IsDistributed(void) const {
-    return tracker_uri != "NULL";
-  }
-  /*! \brief get rank */
-  virtual std::string GetHost(void) const {
-    return host_uri;
-  }
-
-  /*!
-  * \brief internal Allgather function, each node have a segment of data in the ring of sendrecvbuf,
-  *  the data provided by current node k is [slice_begin, slice_end),
-  *  the next node's segment must start with slice_end
-  *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
-  *  use a ring based algorithm
-  *
-  * \param sendrecvbuf_ buffer for both sending and receiving data, it is a ring conceptually
-  * \param total_size total size of data to be gathered
-  * \param slice_begin beginning of the current slice
-  * \param slice_end end of the current slice
-  * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
-  * \param _file caller file name used to generate unique cache key
-  * \param _line caller line number used to generate unique cache key
-  * \param _caller caller function name used to generate unique cache key
-  */
-  virtual void Allgather(void *sendrecvbuf_, size_t total_size,
-                             size_t slice_begin,
-                             size_t slice_end,
-                             size_t size_prev_slice,
-                             const char* _file = _FILE,
-                             const int _line = _LINE,
-                             const char* _caller = _CALLER) {
-    if (world_size == 1 || world_size == -1) return;
-    utils::Assert(TryAllgatherRing(sendrecvbuf_, total_size,
-                                   slice_begin, slice_end, size_prev_slice) == kSuccess,
-                  "AllgatherRing failed");
-  }
-  /*!
-   * \brief perform in-place allreduce, on sendrecvbuf
-   *        this function is NOT thread-safe
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param type_nbytes the unit number of bytes the type have
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \param prepare_func Lazy preprocessing function, lazy prepare_fun(prepare_arg)
-   *                     will be called by the function before performing Allreduce, to intialize the data in sendrecvbuf_.
-   *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
-   * \param prepare_arg argument used to passed into the lazy preprocessing function
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Allreduce(void *sendrecvbuf_,
-                         size_t type_nbytes,
-                         size_t count,
-                         ReduceFunction reducer,
-                         PreprocFunction prepare_fun = NULL,
-                         void *prepare_arg = NULL,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER) {
-    if (prepare_fun != NULL) prepare_fun(prepare_arg);
-    if (world_size == 1 || world_size == -1) return;
-    utils::Assert(TryAllreduce(sendrecvbuf_,
-                               type_nbytes, count, reducer) == kSuccess,
-                  "Allreduce failed");
-  }
-  /*!
-   * \brief broadcast data from root to all nodes
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param size the size of the data to be broadcasted
-   * \param root the root worker id to broadcast the data
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Broadcast(void *sendrecvbuf_, size_t total_size, int root,
-    const char* _file = _FILE, const int _line = _LINE, const char* _caller = _CALLER) {
-    if (world_size == 1 || world_size == -1) return;
-    utils::Assert(TryBroadcast(sendrecvbuf_, total_size, root) == kSuccess,
-                  "Broadcast failed");
-  }
-  /*!
-   * \brief load latest check point
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \param local_model pointer to local model, that is specific to current node/rank
-   *   this can be NULL when no local model is needed
-   *
-   * \return the version number of check point loaded
-   *     if returned version == 0, this means no model has been CheckPointed
-   *     the p_model is not touched, user should do necessary initialization by themselves
-   *
-   *   Common usage example:
-   *      int iter = rabit::LoadCheckPoint(&model);
-   *      if (iter == 0) model.InitParameters();
-   *      for (i = iter; i < max_iter; ++i) {
-   *        do many things, include allreduce
-   *        rabit::CheckPoint(model);
-   *      }
-   *
-   * \sa CheckPoint, VersionNumber
-   */
-  virtual int LoadCheckPoint(Serializable *global_model,
-                             Serializable *local_model = NULL) {
-    return 0;
-  }
-  /*!
-   * \brief checkpoint the model, meaning we finished a stage of execution
-   *  every time we call check point, there is a version number which will increase by one
-   *
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \param local_model pointer to local model, that is specific to current node/rank
-   *   this can be NULL when no local state is needed
-   *
-   * NOTE: local_model requires explicit replication of the model for fault-tolerance, which will
-   *       bring replication cost in CheckPoint function. global_model do not need explicit replication.
-   *       So only CheckPoint with global_model if possible
-   *
-   * \sa LoadCheckPoint, VersionNumber
-   */
-  virtual void CheckPoint(const Serializable *global_model,
-                          const Serializable *local_model = NULL) {
-    version_number += 1;
-  }
-  /*!
-   * \brief This function can be used to replace CheckPoint for global_model only,
-   *   when certain condition is met(see detailed expplaination).
-   *
-   *   This is a "lazy" checkpoint such that only the pointer to global_model is
-   *   remembered and no memory copy is taken. To use this function, the user MUST ensure that:
-   *   The global_model must remain unchanged util last call of Allreduce/Broadcast in current version finishs.
-   *   In another words, global_model model can be changed only between last call of
-   *   Allreduce/Broadcast and LazyCheckPoint in current version
-   *
-   *   For example, suppose the calling sequence is:
-   *   LazyCheckPoint, code1, Allreduce, code2, Broadcast, code3, LazyCheckPoint
-   *
-   *   If user can only changes global_model in code3, then LazyCheckPoint can be used to
-   *   improve efficiency of the program.
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \sa LoadCheckPoint, CheckPoint, VersionNumber
-   */
-  virtual void LazyCheckPoint(const Serializable *global_model) {
-    version_number += 1;
-  }
-  /*!
-   * \return version number of current stored model,
-   *         which means how many calls to CheckPoint we made so far
-   * \sa LoadCheckPoint, CheckPoint
-   */
-  virtual int VersionNumber(void) const {
-    return version_number;
-  }
-  /*!
-   * \brief explicitly re-init everything before calling LoadCheckPoint
-   *    call this function when IEngine throw an exception out,
-   *    this function is only used for test purpose
-   */
-  virtual void InitAfterException(void) {
-    utils::Error("InitAfterException: not implemented");
-  }
-  /*!
-   * \brief report current status to the job tracker
-   * depending on the job tracker we are in
-   */
-  inline void ReportStatus(void) const {
-    if (hadoop_mode != 0) {
-      fprintf(stderr, "reporter:status:Rabit Phase[%03d] Operation %03d\n",
-              version_number, seq_counter);
-    }
-  }
-
- protected:
-  /*! \brief enumeration of possible returning results from Try functions */
-  enum ReturnTypeEnum {
-    /*! \brief execution is successful */
-    kSuccess,
-    /*! \brief a link was reset by peer */
-    kConnReset,
-    /*! \brief received a zero length message */
-    kRecvZeroLen,
-    /*! \brief a neighbor node go down, the connection is dropped */
-    kSockError,
-    /*!
-     * \brief another node which is not my neighbor go down,
-     *   get Out-of-Band exception notification from my neighbor
-     */
-    kGetExcept
-  };
-  /*! \brief struct return type to avoid implicit conversion to int/bool */
-  struct ReturnType {
-    /*! \brief internal return type */
-    ReturnTypeEnum value;
-    // constructor
-    ReturnType() {}
-    ReturnType(ReturnTypeEnum value) : value(value) {}  // NOLINT(*)
-    inline bool operator==(const ReturnTypeEnum &v) const {
-      return value == v;
-    }
-    inline bool operator!=(const ReturnTypeEnum &v) const {
-      return value != v;
-    }
-  };
-  /*! \brief translate errno to return type */
-  inline static ReturnType Errno2Return() {
-    int errsv = utils::Socket::GetLastError();
-    if (errsv == EAGAIN || errsv == EWOULDBLOCK || errsv == 0) return kSuccess;
-#ifdef _WIN32
-    if (errsv == WSAEWOULDBLOCK) return kSuccess;
-    if (errsv == WSAECONNRESET) return kConnReset;
-#endif  // _WIN32
-    if (errsv == ECONNRESET) return kConnReset;
-    return kSockError;
-  }
-  // link record to a neighbor
-  struct LinkRecord {
-   public:
-    // socket to get data from/to link
-    utils::TCPSocket sock;
-    // rank of the node in this link
-    int rank;
-    // size of data readed from link
-    size_t size_read;
-    // size of data sent to the link
-    size_t size_write;
-    // pointer to buffer head
-    char *buffer_head;
-    // buffer size, in bytes
-    size_t buffer_size;
-    // constructor
-    LinkRecord(void)
-        : buffer_head(NULL), buffer_size(0) {
-    }
-    // initialize buffer
-    inline void InitBuffer(size_t type_nbytes, size_t count,
-                           size_t reduce_buffer_size) {
-      size_t n = (type_nbytes * count + 7)/ 8;
-      buffer_.resize(std::min(reduce_buffer_size, n));
-      // make sure align to type_nbytes
-      buffer_size =
-          buffer_.size() * sizeof(uint64_t) / type_nbytes * type_nbytes;
-      utils::Assert(type_nbytes <= buffer_size,
-                    "too large type_nbytes=%lu, buffer_size=%lu",
-                    type_nbytes, buffer_size);
-      // set buffer head
-      buffer_head = reinterpret_cast<char*>(BeginPtr(buffer_));
-    }
-    // reset the recv and sent size
-    inline void ResetSize(void) {
-      size_write = size_read = 0;
-    }
-    /*!
-     * \brief read data into ring-buffer, with care not to existing useful override data
-     *  position after protect_start
-     * \param protect_start all data start from protect_start is still needed in buffer
-     *                      read shall not override this
-     * \param max_size_read maximum logical amount we can read, size_read cannot exceed this value
-     * \return the type of reading
-     */
-    inline ReturnType ReadToRingBuffer(size_t protect_start, size_t max_size_read) {
-      utils::Assert(buffer_head != NULL, "ReadToRingBuffer: buffer not allocated");
-      utils::Assert(size_read <= max_size_read, "ReadToRingBuffer: max_size_read check");
-      size_t ngap = size_read - protect_start;
-      utils::Assert(ngap <= buffer_size, "Allreduce: boundary check");
-      size_t offset = size_read % buffer_size;
-      size_t nmax = max_size_read - size_read;
-      nmax = std::min(nmax, buffer_size - ngap);
-      nmax = std::min(nmax, buffer_size - offset);
-      if (nmax == 0) return kSuccess;
-      ssize_t len = sock.Recv(buffer_head + offset, nmax);
-      // length equals 0, remote disconnected
-      if (len == 0) {
-        sock.Close(); return kRecvZeroLen;
-      }
-      if (len == -1) return Errno2Return();
-      size_read += static_cast<size_t>(len);
-      return kSuccess;
-    }
-    /*!
-     * \brief read data into array,
-     * this function can not be used together with ReadToRingBuffer
-     * a link can either read into the ring buffer, or existing array
-     * \param max_size maximum size of array
-     * \return true if it is an successful read, false if there is some error happens, check errno
-     */
-    inline ReturnType ReadToArray(void *recvbuf_, size_t max_size) {
-      if (max_size == size_read) return kSuccess;
-      char *p = static_cast<char*>(recvbuf_);
-      ssize_t len = sock.Recv(p + size_read, max_size - size_read);
-      // length equals 0, remote disconnected
-      if (len == 0) {
-        sock.Close(); return kRecvZeroLen;
-      }
-      if (len == -1) return Errno2Return();
-      size_read += static_cast<size_t>(len);
-      return kSuccess;
-    }
-    /*!
-     * \brief write data in array to sock
-     * \param sendbuf_ head of array
-     * \param max_size maximum size of array
-     * \return true if it is an successful write, false if there is some error happens, check errno
-     */
-    inline ReturnType WriteFromArray(const void *sendbuf_, size_t max_size) {
-      const char *p = static_cast<const char*>(sendbuf_);
-      ssize_t len = sock.Send(p + size_write, max_size - size_write);
-      if (len == -1) return Errno2Return();
-      size_write += static_cast<size_t>(len);
-      return kSuccess;
-    }
-
-   private:
-    // recv buffer to get data from child
-    // aligned with 64 bits, will be able to perform 64 bits operations freely
-    std::vector<uint64_t> buffer_;
-  };
-  /*!
-   * \brief simple data structure that works like a vector
-   *  but takes reference instead of space
-   */
-  struct RefLinkVector {
-    std::vector<LinkRecord*> plinks;
-    inline LinkRecord &operator[](size_t i) {
-      return *plinks[i];
-    }
-    inline size_t size(void) const {
-      return plinks.size();
-    }
-  };
-  /*!
-   * \brief initialize connection to the tracker
-   * \return a socket that initializes the connection
-   */
-  utils::TCPSocket ConnectTracker(void) const;
-  /*!
-   * \brief connect to the tracker to fix the the missing links
-   *   this function is also used when the engine start up
-   * \param cmd possible command to sent to tracker
-   */
-  bool ReConnectLinks(const char *cmd = "start");
-  /*!
-   * \brief perform in-place allreduce, on sendrecvbuf, this function can fail, and will return the cause of failure
-   *
-   * NOTE on Allreduce:
-   *    The kSuccess TryAllreduce does NOT mean every node have successfully finishes TryAllreduce.
-   *    It only means the current node get the correct result of Allreduce.
-   *    However, it means every node finishes LAST call(instead of this one) of Allreduce/Bcast
-   *
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param type_nbytes the unit number of bytes the type have
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryAllreduce(void *sendrecvbuf_,
-                          size_t type_nbytes,
-                          size_t count,
-                          ReduceFunction reducer);
-  /*!
-   * \brief broadcast data from root to all nodes, this function can fail,and will return the cause of failure
-   * \param sendrecvbuf_ buffer for both sending and receiving data
-   * \param size the size of the data to be broadcasted
-   * \param root the root worker id to broadcast the data
-   * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryBroadcast(void *sendrecvbuf_, size_t size, int root);
-  /*!
-   * \brief perform in-place allreduce, on sendrecvbuf,
-   * this function implements tree-shape reduction
-   *
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param type_nbytes the unit number of bytes the type have
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryAllreduceTree(void *sendrecvbuf_,
-                              size_t type_nbytes,
-                              size_t count,
-                              ReduceFunction reducer);
-  /*!
-   * \brief internal Allgather function, each node have a segment of data in the ring of sendrecvbuf,
-   *  the data provided by current node k is [slice_begin, slice_end),
-   *  the next node's segment must start with slice_end
-   *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
-   *  use a ring based algorithm
-   *
-   * \param sendrecvbuf_ buffer for both sending and receiving data, it is a ring conceptually
-   * \param total_size total size of data to be gathered
-   * \param slice_begin beginning of the current slice
-   * \param slice_end end of the current slice
-   * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
-   * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryAllgatherRing(void *sendrecvbuf_, size_t total_size,
-                              size_t slice_begin, size_t slice_end,
-                              size_t size_prev_slice);
-  /*!
-   * \brief perform in-place allreduce, reduce on the sendrecvbuf,
-   *
-   *  after the function, node k get k-th segment of the reduction result
-   *  the k-th segment is defined by [k * step, min((k + 1) * step,count) )
-   *  where step = ceil(count / world_size)
-   *
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param type_nbytes the unit number of bytes the type have
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
-   * \sa ReturnType, TryAllreduce
-   */
-  ReturnType TryReduceScatterRing(void *sendrecvbuf_,
-                                  size_t type_nbytes,
-                                  size_t count,
-                                  ReduceFunction reducer);
-  /*!
-   * \brief perform in-place allreduce, on sendrecvbuf
-   *  use a ring based algorithm, reduce-scatter + allgather
-   *
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param type_nbytes the unit number of bytes the type have
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \return this function can return kSuccess, kSockError, kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryAllreduceRing(void *sendrecvbuf_,
-                              size_t type_nbytes,
-                              size_t count,
-                              ReduceFunction reducer);
-  /*!
-   * \brief function used to report error when a link goes wrong
-   * \param link the pointer to the link who causes the error
-   * \param err the error type
-   */
-  inline ReturnType ReportError(LinkRecord *link, ReturnType err) {
-    err_link = link; return err;
-  }
-  //---- data structure related to model ----
-  // call sequence counter, records how many calls we made so far
-  // from last call to CheckPoint, LoadCheckPoint
-  int seq_counter;
-  // version number of model
-  int version_number;
-  // whether the job is running in hadoop
-  bool hadoop_mode;
-  //---- local data related to link ----
-  // index of parent link, can be -1, meaning this is root of the tree
-  int parent_index;
-  // rank of parent node, can be -1
-  int parent_rank;
-  // sockets of all links this connects to
-  std::vector<LinkRecord> all_links;
-  // used to record the link where things goes wrong
-  LinkRecord *err_link;
-  // all the links in the reduction tree connection
-  RefLinkVector tree_links;
-  // pointer to links in the ring
-  LinkRecord *ring_prev, *ring_next;
-  //----- meta information-----
-  // list of enviroment variables that are of possible interest
-  std::vector<std::string> env_vars;
-  // unique identifier of the possible job this process is doing
-  // used to assign ranks, optional, default to NULL
-  std::string task_id;
-  // uri of current host, to be set by Init
-  std::string host_uri;
-  // uri of tracker
-  std::string tracker_uri;
-  // role in dmlc jobs
-  std::string dmlc_role;
-  // port of tracker address
-  int tracker_port;
-  // port of slave process
-  int slave_port, nport_trial;
-  // reduce buffer size
-  size_t reduce_buffer_size;
-  // reduction method
-  int reduce_method;
-  // mininum count of cells to use ring based method
-  size_t reduce_ring_mincount;
-  // current rank
-  int rank;
-  // world size
-  int world_size;
-  // connect retry time
-  int connect_retry;
-  // enable bootstrap cache 0 false 1 true
-  bool rabit_bootstrap_cache = false;
-  // enable detailed logging
-  bool rabit_debug = false;
-  // by default, if rabit worker not recover in half an hour exit
-  int timeout_sec = 1800;
-  // flag to enable rabit_timeout
-  bool rabit_timeout = false;
-  // Enable TCP node delay
-  bool rabit_enable_tcp_no_delay = false;
-};
-}  // namespace engine
-}  // namespace rabit
-#endif  // RABIT_ALLREDUCE_BASE_H_
diff --git a/ml-xgboost/rabit/src/allreduce_mock.h b/ml-xgboost/rabit/src/allreduce_mock.h
deleted file mode 100644
index ab9f0e0..0000000
--- a/ml-xgboost/rabit/src/allreduce_mock.h
+++ /dev/null
@@ -1,206 +0,0 @@
-/*!
- * Copyright by Contributors
- * \file allreduce_mock.h
- * \brief Mock test module of AllReduce engine,
- * insert failures in certain call point, to test if the engine is robust to failure
- *
- * \author Ignacio Cano, Tianqi Chen
- */
-#ifndef RABIT_ALLREDUCE_MOCK_H_
-#define RABIT_ALLREDUCE_MOCK_H_
-#include <vector>
-#include <map>
-#include <sstream>
-#include "rabit/internal/engine.h"
-#include "rabit/internal/timer.h"
-#include "allreduce_robust.h"
-
-namespace rabit {
-namespace engine {
-class AllreduceMock : public AllreduceRobust {
- public:
-  // constructor
-  AllreduceMock(void) {
-    num_trial = 0;
-    force_local = 0;
-    report_stats = 0;
-    tsum_allreduce = 0.0;
-    tsum_allgather = 0.0;
-  }
-  // destructor
-  virtual ~AllreduceMock(void) {}
-  virtual void SetParam(const char *name, const char *val) {
-    AllreduceRobust::SetParam(name, val);
-    // additional parameters
-    if (!strcmp(name, "rabit_num_trial")) num_trial = atoi(val);
-    if (!strcmp(name, "DMLC_NUM_ATTEMPT")) num_trial = atoi(val);
-    if (!strcmp(name, "report_stats")) report_stats = atoi(val);
-    if (!strcmp(name, "force_local")) force_local = atoi(val);
-    if (!strcmp(name, "mock")) {
-      MockKey k;
-      utils::Check(sscanf(val, "%d,%d,%d,%d",
-                          &k.rank, &k.version, &k.seqno, &k.ntrial) == 4,
-                   "invalid mock parameter");
-      mock_map[k] = 1;
-    }
-  }
-  virtual void Allreduce(void *sendrecvbuf_,
-                         size_t type_nbytes,
-                         size_t count,
-                         ReduceFunction reducer,
-                         PreprocFunction prepare_fun,
-                         void *prepare_arg,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER) {
-    this->Verify(MockKey(rank, version_number, seq_counter, num_trial), "AllReduce");
-    double tstart = utils::GetTime();
-    AllreduceRobust::Allreduce(sendrecvbuf_, type_nbytes,
-                               count, reducer, prepare_fun, prepare_arg,
-                               _file, _line, _caller);
-    tsum_allreduce += utils::GetTime() - tstart;
-  }
-  virtual void Allgather(void *sendrecvbuf,
-                             size_t total_size,
-                             size_t slice_begin,
-                             size_t slice_end,
-                             size_t size_prev_slice,
-                             const char* _file = _FILE,
-                             const int _line = _LINE,
-                             const char* _caller = _CALLER) {
-    this->Verify(MockKey(rank, version_number, seq_counter, num_trial), "Allgather");
-    double tstart = utils::GetTime();
-    AllreduceRobust::Allgather(sendrecvbuf, total_size,
-                                   slice_begin, slice_end,
-                                   size_prev_slice, _file, _line, _caller);
-    tsum_allgather += utils::GetTime() - tstart;
-  }
-  virtual void Broadcast(void *sendrecvbuf_, size_t total_size, int root,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER) {
-    this->Verify(MockKey(rank, version_number, seq_counter, num_trial), "Broadcast");
-    AllreduceRobust::Broadcast(sendrecvbuf_, total_size, root, _file, _line, _caller);
-  }
-  virtual int LoadCheckPoint(Serializable *global_model,
-                             Serializable *local_model) {
-    tsum_allreduce = 0.0;
-    tsum_allgather = 0.0;
-    time_checkpoint = utils::GetTime();
-    if (force_local == 0) {
-      return AllreduceRobust::LoadCheckPoint(global_model, local_model);
-    } else {
-      DummySerializer dum;
-      ComboSerializer com(global_model, local_model);
-      return AllreduceRobust::LoadCheckPoint(&dum, &com);
-    }
-  }
-  virtual void CheckPoint(const Serializable *global_model,
-                          const Serializable *local_model) {
-    this->Verify(MockKey(rank, version_number, seq_counter, num_trial), "CheckPoint");
-    double tstart = utils::GetTime();
-    double tbet_chkpt = tstart - time_checkpoint;
-    if (force_local == 0) {
-      AllreduceRobust::CheckPoint(global_model, local_model);
-    } else {
-      DummySerializer dum;
-      ComboSerializer com(global_model, local_model);
-      AllreduceRobust::CheckPoint(&dum, &com);
-    }
-    time_checkpoint = utils::GetTime();
-    double tcost = utils::GetTime() - tstart;
-    if (report_stats != 0 && rank == 0) {
-      std::stringstream ss;
-      ss << "[v" << version_number << "] global_size=" << global_checkpoint.length()
-         << ",local_size=" << (local_chkpt[0].length() + local_chkpt[1].length())
-         << ",check_tcost="<< tcost <<" sec"
-         << ",allreduce_tcost=" << tsum_allreduce << " sec"
-         << ",allgather_tcost=" << tsum_allgather << " sec"
-         << ",between_chpt=" << tbet_chkpt << "sec\n";
-      this->TrackerPrint(ss.str());
-    }
-    tsum_allreduce = 0.0;
-    tsum_allgather = 0.0;
-  }
-
-  virtual void LazyCheckPoint(const Serializable *global_model) {
-    this->Verify(MockKey(rank, version_number, seq_counter, num_trial), "LazyCheckPoint");
-    AllreduceRobust::LazyCheckPoint(global_model);
-  }
-
- protected:
-  // force checkpoint to local
-  int force_local;
-  // whether report statistics
-  int report_stats;
-  // sum of allreduce
-  double tsum_allreduce;
-  // sum of allgather
-  double tsum_allgather;
-  double time_checkpoint;
-
- private:
-  struct DummySerializer : public Serializable {
-    virtual void Load(Stream *fi) {
-    }
-    virtual void Save(Stream *fo) const {
-    }
-  };
-  struct ComboSerializer : public Serializable {
-    Serializable *lhs;
-    Serializable *rhs;
-    const Serializable *c_lhs;
-    const Serializable *c_rhs;
-    ComboSerializer(Serializable *lhs, Serializable *rhs)
-        : lhs(lhs), rhs(rhs), c_lhs(lhs), c_rhs(rhs) {
-    }
-    ComboSerializer(const Serializable *lhs, const Serializable *rhs)
-        : lhs(NULL), rhs(NULL), c_lhs(lhs), c_rhs(rhs) {
-    }
-    virtual void Load(Stream *fi) {
-      if (lhs != NULL) lhs->Load(fi);
-      if (rhs != NULL) rhs->Load(fi);
-    }
-    virtual void Save(Stream *fo) const {
-      if (c_lhs != NULL) c_lhs->Save(fo);
-      if (c_rhs != NULL) c_rhs->Save(fo);
-    }
-  };
-  // key to identify the mock stage
-  struct MockKey {
-    int rank;
-    int version;
-    int seqno;
-    int ntrial;
-    MockKey(void) {}
-    MockKey(int rank, int version, int seqno, int ntrial)
-        : rank(rank), version(version), seqno(seqno), ntrial(ntrial) {}
-    inline bool operator==(const MockKey &b) const {
-      return rank == b.rank &&
-          version == b.version &&
-          seqno == b.seqno &&
-          ntrial == b.ntrial;
-    }
-    inline bool operator<(const MockKey &b) const {
-      if (rank != b.rank) return rank < b.rank;
-      if (version != b.version) return version < b.version;
-      if (seqno != b.seqno) return seqno < b.seqno;
-      return ntrial < b.ntrial;
-    }
-  };
-  // number of failure trials
-  int num_trial;
-  // record all mock actions
-  std::map<MockKey, int> mock_map;
-  // used to generate all kinds of exceptions
-  inline void Verify(const MockKey &key, const char *name) {
-    if (mock_map.count(key) != 0) {
-      num_trial += 1;
-      // data processing frameworks runs on shared process
-      _error("[%d]@@@Hit Mock Error:%s ", rank, name);
-    }
-  }
-};
-}  // namespace engine
-}  // namespace rabit
-#endif  // RABIT_ALLREDUCE_MOCK_H_
diff --git a/ml-xgboost/rabit/src/allreduce_robust-inl.h b/ml-xgboost/rabit/src/allreduce_robust-inl.h
deleted file mode 100644
index 7baa14b..0000000
--- a/ml-xgboost/rabit/src/allreduce_robust-inl.h
+++ /dev/null
@@ -1,169 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file allreduce_robust-inl.h
- * \brief implementation of inline template function in AllreduceRobust
- *
- * \author Tianqi Chen
- */
-#ifndef RABIT_ALLREDUCE_ROBUST_INL_H_
-#define RABIT_ALLREDUCE_ROBUST_INL_H_
-#include <vector>
-
-namespace rabit {
-namespace engine {
-/*!
- * \brief run message passing algorithm on the allreduce tree
- *        the result is edge message stored in p_edge_in and p_edge_out
- * \param node_value the value associated with current node
- * \param p_edge_in used to store input message from each of the edge
- * \param p_edge_out used to store output message from each of the edge
- * \param func a function that defines the message passing rule
- *        Parameters of func:
- *           - node_value same as node_value in the main function
- *           - edge_in the array of input messages from each edge,
- *                     this includes the output edge, which should be excluded
- *           - out_index array the index of output edge, the function should
- *                       exclude the output edge when compute the message passing value
- *        Return of func:
- *           the function returns the output message based on the input message and node_value
- *
- * \tparam EdgeType type of edge message, must be simple struct
- * \tparam NodeType type of node value
- */
-template<typename NodeType, typename EdgeType>
-inline AllreduceRobust::ReturnType
-AllreduceRobust::MsgPassing(const NodeType &node_value,
-                            std::vector<EdgeType> *p_edge_in,
-                            std::vector<EdgeType> *p_edge_out,
-                            EdgeType(*func)
-                            (const NodeType &node_value,
-                             const std::vector<EdgeType> &edge_in,
-                             size_t out_index)) {
-  RefLinkVector &links = tree_links;
-  if (links.size() == 0) return kSuccess;
-  // number of links
-  const int nlink = static_cast<int>(links.size());
-  // initialize the pointers
-  for (int i = 0; i < nlink; ++i) {
-    links[i].ResetSize();
-  }
-  std::vector<EdgeType> &edge_in = *p_edge_in;
-  std::vector<EdgeType> &edge_out = *p_edge_out;
-  edge_in.resize(nlink);
-  edge_out.resize(nlink);
-  // stages in the process
-  // 0: recv messages from childs
-  // 1: send message to parent
-  // 2: recv message from parent
-  // 3: send message to childs
-  int stage = 0;
-  // if no childs, no need to, directly start passing message
-  if (nlink == static_cast<int>(parent_index != -1)) {
-    utils::Assert(parent_index == 0, "parent must be 0");
-    edge_out[parent_index] = func(node_value, edge_in, parent_index);
-    stage = 1;
-  }
-  // while we have not passed the messages out
-  while (true) {
-    // for node with no parent, directly do stage 3
-    if (parent_index == -1) {
-      utils::Assert(stage != 2 && stage != 1, "invalie stage id");
-    }
-    // poll helper
-    utils::PollHelper watcher;
-    bool done = (stage == 3);
-    for (int i = 0; i < nlink; ++i) {
-      watcher.WatchException(links[i].sock);
-      switch (stage) {
-        case 0:
-          if (i != parent_index && links[i].size_read != sizeof(EdgeType)) {
-            watcher.WatchRead(links[i].sock);
-          }
-          break;
-        case 1:
-          if (i == parent_index) {
-            watcher.WatchWrite(links[i].sock);
-          }
-          break;
-        case 2:
-          if (i == parent_index) {
-            watcher.WatchRead(links[i].sock);
-          }
-          break;
-        case 3:
-          if (i != parent_index && links[i].size_write != sizeof(EdgeType)) {
-            watcher.WatchWrite(links[i].sock);
-            done = false;
-          }
-          break;
-        default: utils::Error("invalid stage");
-      }
-    }
-    // finish all the stages, and write out message
-    if (done) break;
-    watcher.Poll();
-    // exception handling
-    for (int i = 0; i < nlink; ++i) {
-      // recive OOB message from some link
-      if (watcher.CheckExcept(links[i].sock)) {
-        return ReportError(&links[i], kGetExcept);
-      }
-    }
-    if (stage == 0) {
-      bool finished = true;
-      // read data from childs
-      for (int i = 0; i < nlink; ++i) {
-        if (i != parent_index) {
-          if (watcher.CheckRead(links[i].sock)) {
-            ReturnType ret = links[i].ReadToArray(&edge_in[i], sizeof(EdgeType));
-            if (ret != kSuccess) return ReportError(&links[i], ret);
-          }
-          if (links[i].size_read != sizeof(EdgeType)) finished = false;
-        }
-      }
-      // if no parent, jump to stage 3, otherwise do stage 1
-      if (finished) {
-        if (parent_index != -1) {
-          edge_out[parent_index] = func(node_value, edge_in, parent_index);
-          stage = 1;
-        } else {
-          for (int i = 0; i < nlink; ++i) {
-            edge_out[i] = func(node_value, edge_in, i);
-          }
-          stage = 3;
-        }
-      }
-    }
-    if (stage == 1) {
-      const int pid = this->parent_index;
-      utils::Assert(pid != -1, "MsgPassing invalid stage");
-      ReturnType ret = links[pid].WriteFromArray(&edge_out[pid], sizeof(EdgeType));
-      if (ret != kSuccess) return ReportError(&links[pid], ret);
-      if (links[pid].size_write == sizeof(EdgeType)) stage = 2;
-    }
-    if (stage == 2) {
-      const int pid = this->parent_index;
-      utils::Assert(pid != -1, "MsgPassing invalid stage");
-      ReturnType ret = links[pid].ReadToArray(&edge_in[pid], sizeof(EdgeType));
-      if (ret != kSuccess) return ReportError(&links[pid], ret);
-      if (links[pid].size_read == sizeof(EdgeType)) {
-        for (int i = 0; i < nlink; ++i) {
-          if (i != pid) edge_out[i] = func(node_value, edge_in, i);
-        }
-        stage = 3;
-      }
-    }
-    if (stage == 3) {
-      for (int i = 0; i < nlink; ++i) {
-        if (i != parent_index && links[i].size_write != sizeof(EdgeType)) {
-          ReturnType ret = links[i].WriteFromArray(&edge_out[i], sizeof(EdgeType));
-          if (ret != kSuccess) return ReportError(&links[i], ret);
-        }
-      }
-    }
-  }
-  return kSuccess;
-}
-}  // namespace engine
-}  // namespace rabit
-#endif  // RABIT_ALLREDUCE_ROBUST_INL_H_
diff --git a/ml-xgboost/rabit/src/allreduce_robust.cc b/ml-xgboost/rabit/src/allreduce_robust.cc
deleted file mode 100644
index 8ae1acc..0000000
--- a/ml-xgboost/rabit/src/allreduce_robust.cc
+++ /dev/null
@@ -1,1589 +0,0 @@
-/*!
- *  Copyright (c) 2014-2019 by Contributors
- * \file allreduce_robust.cc
- * \brief Robust implementation of Allreduce
- *
- * \author Tianqi Chen, Ignacio Cano, Tianyi Zhou
- */
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-#include <chrono>
-#include <thread>
-#include <limits>
-#include <utility>
-#include "rabit/internal/io.h"
-#include "rabit/internal/timer.h"
-#include "rabit/internal/utils.h"
-#include "rabit/internal/engine.h"
-#include "rabit/internal/rabit-inl.h"
-#include "allreduce_robust.h"
-
-#undef _assert
-
-namespace rabit {
-namespace engine {
-
-AllreduceRobust::AllreduceRobust(void) {
-  num_local_replica = 0;
-  num_global_replica = 5;
-  default_local_replica = 2;
-  seq_counter = 0;
-  cur_cache_seq = 0;
-  local_chkpt_version = 0;
-  result_buffer_round = 1;
-  global_lazycheck = NULL;
-  use_local_model = -1;
-  recover_counter = 0;
-  checkpoint_loaded = false;
-  env_vars.push_back("rabit_global_replica");
-  env_vars.push_back("rabit_local_replica");
-}
-bool AllreduceRobust::Init(int argc, char* argv[]) {
-  if (AllreduceBase::Init(argc, argv)) {
-    // chenqin: alert user opted in experimental feature.
-    if (rabit_bootstrap_cache) utils::HandleLogInfo(
-      "[EXPERIMENTAL] bootstrap cache has been enabled\n");
-    checkpoint_loaded = false;
-    if (num_global_replica == 0) {
-      result_buffer_round = -1;
-    } else {
-      result_buffer_round = std::max(world_size / num_global_replica, 1);
-    }
-    return true;
-  } else {
-    return false;
-  }
-}
-/*! \brief shutdown the engine */
-bool AllreduceRobust::Shutdown(void) {
-  try {
-    // need to sync the exec before we shutdown, do a pesudo check point
-    // execute checkpoint, note: when checkpoint existing, load will not happen
-    _assert(RecoverExec(NULL, 0, ActionSummary::kCheckPoint, ActionSummary::kSpecialOp,
-      cur_cache_seq), "Shutdown: check point must return true");
-    // reset result buffer
-    resbuf.Clear(); seq_counter = 0;
-    cachebuf.Clear(); cur_cache_seq = 0;
-    lookupbuf.Clear();
-    // execute check ack step, load happens here
-    _assert(RecoverExec(NULL, 0, ActionSummary::kCheckAck,
-      ActionSummary::kSpecialOp, cur_cache_seq), "Shutdown: check ack must return true");
-// travis ci only osx test hang
-#if defined (__APPLE__)
-    sleep(1);
-#endif
-    shutdown_timeout = true;
-    if (rabit_timeout_task.valid()) {
-      rabit_timeout_task.wait();
-      _assert(rabit_timeout_task.get(), "expect timeout task return\n");
-    }
-    return AllreduceBase::Shutdown();
-  } catch (const std::exception& e) {
-    fprintf(stderr, "%s\n", e.what());
-    return false;
-  }
-}
-
-/*!
- * \brief set parameters to the engine
- * \param name parameter name
- * \param val parameter value
- */
-void AllreduceRobust::SetParam(const char *name, const char *val) {
-  AllreduceBase::SetParam(name, val);
-  if (!strcmp(name, "rabit_global_replica")) num_global_replica = atoi(val);
-  if (!strcmp(name, "rabit_local_replica")) {
-    num_local_replica = atoi(val);
-  }
-}
-
-int AllreduceRobust::SetBootstrapCache(const std::string &key, const void *buf,
-  const size_t type_nbytes, const size_t count) {
-  int index = -1;
-  for (int i = 0 ; i < cur_cache_seq; i++) {
-    size_t nsize = 0;
-    void* name = lookupbuf.Query(i, &nsize);
-    if (nsize == key.length() + 1
-      && strcmp(static_cast<const char*>(name), key.c_str()) == 0) {
-      index = i;
-      break;
-    }
-  }
-  // we should consider way to support duplicated signatures
-  // https://github.com/dmlc/xgboost/issues/5012
-  // _assert(index == -1, "immutable cache key already exists");
-  _assert(type_nbytes*count > 0, "can't set empty cache");
-  void* temp = cachebuf.AllocTemp(type_nbytes, count);
-  cachebuf.PushTemp(cur_cache_seq, type_nbytes, count);
-  std::memcpy(temp, buf, type_nbytes*count);
-
-  std::string k(key);
-  void* name = lookupbuf.AllocTemp(strlen(k.c_str()) + 1, 1);
-  lookupbuf.PushTemp(cur_cache_seq, strlen(k.c_str()) + 1, 1);
-  std::memcpy(name, key.c_str(), strlen(k.c_str()) + 1);
-  cur_cache_seq += 1;
-  return 0;
-}
-
-int AllreduceRobust::GetBootstrapCache(const std::string &key, void* buf,
-  const size_t type_nbytes, const size_t count) {
-  // as requester sync with rest of nodes on latest cache content
-  if (!RecoverExec(NULL, 0, ActionSummary::kLoadBootstrapCache,
-    seq_counter, cur_cache_seq)) return -1;
-
-  int index = -1;
-  for (int i = 0 ; i < cur_cache_seq; i++) {
-    size_t nsize = 0;
-    void* name = lookupbuf.Query(i, &nsize);
-    if (nsize == strlen(key.c_str()) + 1
-      && strcmp(reinterpret_cast<char*>(name), key.c_str()) == 0) {
-      index = i;
-      break;
-    }
-  }
-  // cache doesn't exists
-  if (index == -1) return -1;
-
-  size_t siz = 0;
-  void* temp = cachebuf.Query(index, &siz);
-  utils::Assert(cur_cache_seq > index, "cur_cache_seq is smaller than lookup cache seq index");
-  utils::Assert(siz == type_nbytes*count, "cache size stored expected to be same as requested");
-  utils::Assert(siz > 0, "cache size should be greater than 0");
-  std::memcpy(buf, temp, type_nbytes*count);
-  return 0;
-}
-
-/*!
- * \brief Allgather function, each node have a segment of data in the ring of sendrecvbuf,
- *  the data provided by current node k is [slice_begin, slice_end),
- *  the next node's segment must start with slice_end
- *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
- *  use a ring based algorithm
- *
- * \param sendrecvbuf buffer for both sending and receiving data, it is a ring conceptually
- * \param total_size total size of data to be gathered
- * \param slice_begin beginning of the current slice
- * \param slice_end end of the current slice
- * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key 
- */ 
-void AllreduceRobust::Allgather(void *sendrecvbuf,
-                                    size_t total_size,
-                                    size_t slice_begin,
-                                    size_t slice_end,
-                                    size_t size_prev_slice,
-                                    const char* _file,
-                                    const int _line,
-                                    const char* _caller) {
-  if (world_size == 1 || world_size == -1) return;
-  // genreate unique allgather signature
-  std::string key = std::string(_file) + "::" + std::to_string(_line) + "::"
-    + std::string(_caller) + "#" +std::to_string(total_size);
-
-  // try fetch bootstrap allgather results from cache
-  if (!checkpoint_loaded && rabit_bootstrap_cache &&
-    GetBootstrapCache(key, sendrecvbuf, total_size, 1) != -1) return;
-
-  double start = utils::GetTime();
-  bool recovered = RecoverExec(sendrecvbuf, total_size, 0, seq_counter, cur_cache_seq);
-
-  if (resbuf.LastSeqNo() != -1 &&
-    (result_buffer_round == -1 ||
-      resbuf.LastSeqNo() % result_buffer_round != rank % result_buffer_round)) {
-    resbuf.DropLast();
-  }
-
-  void *temp = resbuf.AllocTemp(total_size, 1);
-  while (true) {
-    if (recovered) {
-      std::memcpy(temp, sendrecvbuf, total_size); break;
-    } else {
-      std::memcpy(temp, sendrecvbuf, total_size);
-      if (CheckAndRecover(TryAllgatherRing(temp, total_size,
-                                           slice_begin, slice_end, size_prev_slice))) {
-        std::memcpy(sendrecvbuf, temp, total_size); break;
-      } else {
-        recovered = RecoverExec(sendrecvbuf, total_size, 0, seq_counter, cur_cache_seq);
-      }
-    }
-  }
-  double delta = utils::GetTime() - start;
-  // log allgather latency
-  if (rabit_debug) {
-    utils::HandleLogInfo("[%d] allgather (%s) finished version %d, seq %d, take %f seconds\n",
-      rank, key.c_str(), version_number, seq_counter, delta);
-  }
-
-  // if bootstrap allgather, store and fetch through cache
-  if (checkpoint_loaded || !rabit_bootstrap_cache) {
-    resbuf.PushTemp(seq_counter, total_size, 1);
-    seq_counter += 1;
-  } else {
-    SetBootstrapCache(key, sendrecvbuf, total_size, 1);
-  }
-}
-
-/*!
- * \brief perform in-place allreduce, on sendrecvbuf
- *        this function is NOT thread-safe
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param type_nbytes the unit number of bytes the type have
- * \param count number of elements to be reduced
- * \param reducer reduce function
- * \param prepare_func Lazy preprocessing function, lazy prepare_fun(prepare_arg)
- *                     will be called by the function before performing Allreduce, to intialize the data in sendrecvbuf_.
- *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
- * \param prepare_arg argument used to passed into the lazy preprocessing function
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- */
-void AllreduceRobust::Allreduce(void *sendrecvbuf_,
-                                size_t type_nbytes,
-                                size_t count,
-                                ReduceFunction reducer,
-                                PreprocFunction prepare_fun,
-                                void *prepare_arg,
-                                const char* _file,
-                                const int _line,
-                                const char* _caller) {
-  // skip action in single node
-  if (world_size == 1 || world_size == -1) {
-    if (prepare_fun != NULL) prepare_fun(prepare_arg);
-    return;
-  }
-
-  // genreate unique allreduce signature
-  std::string key = std::string(_file) + "::" + std::to_string(_line) + "::"
-    + std::string(_caller) + "#" +std::to_string(type_nbytes) + "x" + std::to_string(count);
-
-  // try fetch bootstrap allreduce results from cache
-  if (!checkpoint_loaded && rabit_bootstrap_cache &&
-    GetBootstrapCache(key, sendrecvbuf_, type_nbytes, count) != -1) return;
-
-  double start = utils::GetTime();
-  bool recovered = RecoverExec(sendrecvbuf_, type_nbytes * count, 0, seq_counter, cur_cache_seq);
-
-  if (resbuf.LastSeqNo() != -1 &&
-    (result_buffer_round == -1 ||
-      resbuf.LastSeqNo() % result_buffer_round != rank % result_buffer_round)) {
-    resbuf.DropLast();
-  }
-
-  if (!recovered && prepare_fun != NULL) prepare_fun(prepare_arg);
-  void *temp = resbuf.AllocTemp(type_nbytes, count);
-  while (true) {
-    if (recovered) {
-      std::memcpy(temp, sendrecvbuf_, type_nbytes * count); break;
-    } else {
-      std::memcpy(temp, sendrecvbuf_, type_nbytes * count);
-      if (CheckAndRecover(TryAllreduce(temp, type_nbytes, count, reducer))) {
-        std::memcpy(sendrecvbuf_, temp, type_nbytes * count); break;
-      } else {
-        recovered = RecoverExec(sendrecvbuf_, type_nbytes * count, 0, seq_counter, cur_cache_seq);
-      }
-    }
-  }
-  double delta = utils::GetTime() - start;
-  // log allreduce latency
-  if (rabit_debug) {
-    utils::HandleLogInfo("[%d] allreduce (%s) finished version %d, seq %d, take %f seconds\n",
-      rank, key.c_str(), version_number, seq_counter, delta);
-  }
-
-  // if bootstrap allreduce, store and fetch through cache
-  if (checkpoint_loaded || !rabit_bootstrap_cache) {
-    resbuf.PushTemp(seq_counter, type_nbytes, count);
-    seq_counter += 1;
-  } else {
-    SetBootstrapCache(key, sendrecvbuf_, type_nbytes, count);
-  }
-}
-/*!
- * \brief broadcast data from root to all nodes
- * \param sendrecvbuf_ buffer for both sending and recving data
- * \param size the size of the data to be broadcasted
- * \param root the root worker id to broadcast the data
- * \param _file caller file name used to generate unique cache key
- * \param _line caller line number used to generate unique cache key
- * \param _caller caller function name used to generate unique cache key
- */
-void AllreduceRobust::Broadcast(void *sendrecvbuf_, size_t total_size, int root,
-                                const char* _file,
-                                const int _line,
-                                const char* _caller) {
-  // skip action in single node
-  if (world_size == 1 || world_size == -1) return;
-  // genreate unique cache signature
-  std::string key = std::string(_file) + "::" + std::to_string(_line) + "::"
-    + std::string(_caller) + "#" +std::to_string(total_size) + "@" + std::to_string(root);
-  // try fetch bootstrap allreduce results from cache
-  if (!checkpoint_loaded && rabit_bootstrap_cache &&
-  GetBootstrapCache(key, sendrecvbuf_, total_size, 1) != -1) return;
-  double start = utils::GetTime();
-  bool recovered = RecoverExec(sendrecvbuf_, total_size, 0, seq_counter, cur_cache_seq);
-  // now we are free to remove the last result, if any
-  if (resbuf.LastSeqNo() != -1 &&
-      (result_buffer_round == -1 ||
-       resbuf.LastSeqNo() % result_buffer_round != rank % result_buffer_round)) {
-    resbuf.DropLast();
-  }
-  void *temp = resbuf.AllocTemp(1, total_size);
-  while (true) {
-    if (recovered) {
-      std::memcpy(temp, sendrecvbuf_, total_size); break;
-    } else {
-      if (CheckAndRecover(TryBroadcast(sendrecvbuf_, total_size, root))) {
-        std::memcpy(temp, sendrecvbuf_, total_size); break;
-      } else {
-        recovered = RecoverExec(sendrecvbuf_, total_size, 0, seq_counter, cur_cache_seq);
-      }
-    }
-  }
-
-  double delta = utils::GetTime() - start;
-  // log broadcast latency
-  if (rabit_debug) {
-    utils::HandleLogInfo(
-      "[%d] broadcast (%s) root %d finished version %d,seq %d, take %f seconds\n",
-      rank, key.c_str(), root, version_number, seq_counter, delta);
-  }
-  // if bootstrap broadcast, store and fetch through cache
-  if (checkpoint_loaded || !rabit_bootstrap_cache) {
-    resbuf.PushTemp(seq_counter, 1, total_size);
-    seq_counter += 1;
-  } else {
-    SetBootstrapCache(key, sendrecvbuf_, total_size, 1);
-  }
-}
-/*!
- * \brief load latest check point
- * \param global_model pointer to the globally shared model/state
- *   when calling this function, the caller need to gauranttees that global_model
- *   is the same in all nodes
- * \param local_model pointer to local model, that is specific to current node/rank
- *   this can be NULL when no local model is needed
- *
- * \return the version number of check point loaded
- *     if returned version == 0, this means no model has been CheckPointed
- *     the p_model is not touched, user should do necessary initialization by themselves
- *
- *   Common usage example:
- *      int iter = rabit::LoadCheckPoint(&model);
- *      if (iter == 0) model.InitParameters();
- *      for (i = iter; i < max_iter; ++i) {
- *        do many things, include allreduce
- *        rabit::CheckPoint(model);
- *      }
- *
- * \sa CheckPoint, VersionNumber
- */
-int AllreduceRobust::LoadCheckPoint(Serializable *global_model,
-                                    Serializable *local_model) {
-  checkpoint_loaded = true;
-  // skip action in single node
-  if (world_size == 1) return 0;
-  this->LocalModelCheck(local_model != NULL);
-  if (num_local_replica == 0) {
-    utils::Check(local_model == NULL,
-                 "need to set rabit_local_replica larger than 1 to checkpoint local_model");
-  }
-  double start = utils::GetTime();
-  // check if we succeed
-  if (RecoverExec(NULL, 0, ActionSummary::kLoadCheck, ActionSummary::kSpecialOp, cur_cache_seq)) {
-    int nlocal = std::max(static_cast<int>(local_rptr[local_chkpt_version].size()) - 1, 0);
-    if (local_model != NULL) {
-      if (nlocal == num_local_replica + 1) {
-        // load in local model
-        utils::MemoryFixSizeBuffer fs(BeginPtr(local_chkpt[local_chkpt_version]),
-                                      local_rptr[local_chkpt_version][1]);
-        local_model->Load(&fs);
-      } else {
-        _assert(nlocal == 0, "[%d] local model inconsistent, nlocal=%d", rank, nlocal);
-      }
-    }
-    // reset result buffer
-    resbuf.Clear(); seq_counter = 0;
-    // load from buffer
-    utils::MemoryBufferStream fs(&global_checkpoint);
-    if (global_checkpoint.length() == 0) {
-      version_number = 0;
-    } else {
-      _assert(fs.Read(&version_number, sizeof(version_number)) != 0,
-                    "read in version number");
-      global_model->Load(&fs);
-      _assert(local_model == NULL || nlocal == num_local_replica + 1,
-                    "local model inconsistent, nlocal=%d", nlocal);
-    }
-    // run another phase of check ack, if recovered from data
-    _assert(RecoverExec(NULL, 0, ActionSummary::kCheckAck,
-      ActionSummary::kSpecialOp, cur_cache_seq), "check ack must return true");
-
-    if (!RecoverExec(NULL, 0, ActionSummary::kLoadBootstrapCache, seq_counter, cur_cache_seq)) {
-      utils::Printf("no need to load cache\n");
-    }
-    double delta = utils::GetTime() - start;
-
-    // log broadcast latency
-    if (rabit_debug) {
-      utils::HandleLogInfo("[%d] loadcheckpoint size %ld finished version %d, "
-                         "seq %d, take %f seconds\n",
-                         rank, global_checkpoint.length(),
-                         version_number, seq_counter, delta);
-    }
-    return version_number;
-  } else {
-    // log job fresh start
-    if (rabit_debug) utils::HandleLogInfo("[%d] loadcheckpoint reset\n", rank);
-
-    // reset result buffer
-    resbuf.Clear(); seq_counter = 0; version_number = 0;
-    // nothing loaded, a fresh start, everyone init model
-    return version_number;
-  }
-}
-/*!
- * \brief internal consistency check function,
- *  use check to ensure user always call CheckPoint/LoadCheckPoint
- *  with or without local but not both, this function will set the approperiate settings
- *  in the first call of LoadCheckPoint/CheckPoint
- *
- * \param with_local whether the user calls CheckPoint with local model
- */
-void AllreduceRobust::LocalModelCheck(bool with_local) {
-  if (use_local_model == -1) {
-    if (with_local) {
-      use_local_model = 1;
-      if (num_local_replica == 0) {
-        num_local_replica = default_local_replica;
-      }
-    } else {
-      use_local_model = 0;
-      num_local_replica = 0;
-    }
-  } else {
-    utils::Check(use_local_model == static_cast<int>(with_local),
-                 "Can only call Checkpoint/LoadCheckPoint always with"\
-                 "or without local_model, but not mixed case");
-  }
-}
-/*!
- * \brief internal implementation of checkpoint, support both lazy and normal way
- *
- * \param global_model pointer to the globally shared model/state
- *   when calling this function, the caller need to gauranttees that global_model
- *   is the same in all nodes
- * \param local_model pointer to local model, that is specific to current node/rank
- *   this can be NULL when no local state is needed
- * \param lazy_checkpt whether the action is lazy checkpoint
- *
- * \sa CheckPoint, LazyCheckPoint
- */
-void AllreduceRobust::CheckPoint_(const Serializable *global_model,
-                                  const Serializable *local_model,
-                                  bool lazy_checkpt) {
-  // never do check point in single machine mode
-  if (world_size == 1) {
-    version_number += 1; return;
-  }
-  double start = utils::GetTime();
-  this->LocalModelCheck(local_model != NULL);
-  if (num_local_replica == 0) {
-    utils::Check(local_model == NULL,
-                 "need to set rabit_local_replica larger than 1 to checkpoint local_model");
-  }
-  if (num_local_replica != 0) {
-    while (true) {
-      if (RecoverExec(NULL, 0, 0, ActionSummary::kLocalCheckPoint)) break;
-      // save model to new version place
-      int new_version = !local_chkpt_version;
-
-      local_chkpt[new_version].clear();
-      utils::MemoryBufferStream fs(&local_chkpt[new_version]);
-      if (local_model != NULL) {
-        local_model->Save(&fs);
-      }
-      local_rptr[new_version].clear();
-      local_rptr[new_version].push_back(0);
-      local_rptr[new_version].push_back(local_chkpt[new_version].length());
-      if (CheckAndRecover(TryCheckinLocalState(&local_rptr[new_version],
-                                               &local_chkpt[new_version]))) break;
-    }
-    // run the ack phase, can be true or false
-    RecoverExec(NULL, 0, 0, ActionSummary::kLocalCheckAck);
-    // switch pointer to new version
-    local_chkpt_version = !local_chkpt_version;
-  }
-  // execute checkpoint, note: when checkpoint existing, load will not happen
-  _assert(RecoverExec(NULL, 0, ActionSummary::kCheckPoint,
-    ActionSummary::kSpecialOp, cur_cache_seq),
-                "check point must return true");
-  // this is the critical region where we will change all the stored models
-  // increase version number
-  version_number += 1;
-  // save model
-  if (lazy_checkpt) {
-    global_lazycheck = global_model;
-  } else {
-    global_checkpoint.resize(0);
-    utils::MemoryBufferStream fs(&global_checkpoint);
-    fs.Write(&version_number, sizeof(version_number));
-    global_model->Save(&fs);
-    global_lazycheck = NULL;
-  }
-  double delta = utils::GetTime() - start;
-  // log checkpoint latency
-  if (rabit_debug) {
-    utils::HandleLogInfo(
-      "[%d] checkpoint finished version %d,seq %d, take %f seconds\n",
-      rank, version_number, seq_counter, delta);
-  }
-  start = utils::GetTime();
-  // reset result buffer, mark boostrap phase complete
-  resbuf.Clear(); seq_counter = 0;
-  // execute check ack step, load happens here
-  _assert(RecoverExec(NULL, 0, ActionSummary::kCheckAck,
-    ActionSummary::kSpecialOp, cur_cache_seq), "check ack must return true");
-
-  delta = utils::GetTime() - start;
-  // log checkpoint ack latency
-  if (rabit_debug) {
-    utils::HandleLogInfo("[%d] checkpoint ack finished version %d, take %f seconds\n",
-    rank, version_number, delta);
-  }
-}
-/*!
- * \brief reset the all the existing links by sending Out-of-Band message marker
- *  after this function finishes, all the messages received and sent before in all live links are discarded,
- *  This allows us to get a fresh start after error has happened
- *
- * \return this function can return kSuccess or kSockError
- *         when kSockError is returned, it simply means there are bad sockets in the links,
- *         and some link recovery proceduer is needed
- */
-AllreduceRobust::ReturnType AllreduceRobust::TryResetLinks(void) {
-  // number of links
-  const int nlink = static_cast<int>(all_links.size());
-  for (int i = 0; i < nlink; ++i) {
-    all_links[i].InitBuffer(sizeof(int), 1 << 10, reduce_buffer_size);
-    all_links[i].ResetSize();
-  }
-  // read and discard data from all channels until pass mark
-  while (true) {
-    for (int i = 0; i < nlink; ++i) {
-      if (all_links[i].sock.BadSocket()) continue;
-      if (all_links[i].size_write == 0) {
-        char sig = kOOBReset;
-        ssize_t len = all_links[i].sock.Send(&sig, sizeof(sig), MSG_OOB);
-        // error will be filtered in next loop
-        if (len == sizeof(sig)) all_links[i].size_write = 1;
-      }
-      if (all_links[i].size_write == 1) {
-        char sig = kResetMark;
-        ssize_t len = all_links[i].sock.Send(&sig, sizeof(sig));
-        if (len == sizeof(sig)) all_links[i].size_write = 2;
-      }
-    }
-    utils::PollHelper rsel;
-    bool finished = true;
-    for (int i = 0; i < nlink; ++i) {
-      if (all_links[i].size_write != 2 && !all_links[i].sock.BadSocket()) {
-        rsel.WatchWrite(all_links[i].sock); finished = false;
-      }
-    }
-    if (finished) break;
-    // wait to read from the channels to discard data
-    rsel.Poll();
-  }
-  for (int i = 0; i < nlink; ++i) {
-    if (!all_links[i].sock.BadSocket()) {
-      utils::PollHelper::WaitExcept(all_links[i].sock);
-    }
-  }
-  while (true) {
-    utils::PollHelper rsel;
-    bool finished = true;
-    for (int i = 0; i < nlink; ++i) {
-      if (all_links[i].size_read == 0 && !all_links[i].sock.BadSocket()) {
-        rsel.WatchRead(all_links[i].sock); finished = false;
-      }
-    }
-    if (finished) break;
-    rsel.Poll();
-    for (int i = 0; i < nlink; ++i) {
-      if (all_links[i].sock.BadSocket()) continue;
-      if (all_links[i].size_read == 0) {
-        int atmark = all_links[i].sock.AtMark();
-        if (atmark < 0) {
-          _assert(all_links[i].sock.BadSocket(), "must already gone bad");
-        } else if (atmark > 0) {
-          all_links[i].size_read = 1;
-        } else {
-          // no at mark, read and discard data
-          ssize_t len = all_links[i].sock.Recv(all_links[i].buffer_head, all_links[i].buffer_size);
-          if (all_links[i].sock.AtMark()) all_links[i].size_read = 1;
-          // zero length, remote closed the connection, close socket
-          if (len == 0) all_links[i].sock.Close();
-        }
-      }
-    }
-  }
-  // start synchronization, use blocking I/O to avoid select
-  for (int i = 0; i < nlink; ++i) {
-    if (!all_links[i].sock.BadSocket()) {
-      char oob_mark;
-      all_links[i].sock.SetNonBlock(false);
-      ssize_t len = all_links[i].sock.Recv(&oob_mark, sizeof(oob_mark), MSG_WAITALL);
-      if (len == 0) {
-        all_links[i].sock.Close(); continue;
-      } else if (len > 0) {
-        _assert(oob_mark == kResetMark, "wrong oob msg");
-        _assert(all_links[i].sock.AtMark() != 1, "should already read past mark");
-      } else {
-        _assert(errno != EAGAIN|| errno != EWOULDBLOCK, "BUG");
-      }
-      // send out ack
-      char ack = kResetAck;
-      while (true) {
-        len = all_links[i].sock.Send(&ack, sizeof(ack));
-        if (len == sizeof(ack)) break;
-        if (len == -1) {
-          if (errno != EAGAIN && errno != EWOULDBLOCK) break;
-        }
-      }
-    }
-  }
-  // wait all ack
-  for (int i = 0; i < nlink; ++i) {
-    if (!all_links[i].sock.BadSocket()) {
-      char ack;
-      ssize_t len = all_links[i].sock.Recv(&ack, sizeof(ack), MSG_WAITALL);
-      if (len == 0) {
-        all_links[i].sock.Close(); continue;
-      } else if (len > 0) {
-        _assert(ack == kResetAck, "wrong Ack MSG");
-      } else {
-        _assert(errno != EAGAIN|| errno != EWOULDBLOCK, "BUG");
-      }
-      // set back to nonblock mode
-      all_links[i].sock.SetNonBlock(true);
-    }
-  }
-  for (int i = 0; i < nlink; ++i) {
-    if (all_links[i].sock.BadSocket()) return kSockError;
-  }
-  return kSuccess;
-}
-/*!
- * \brief if err_type indicates an error
- *         recover links according to the error type reported
- *        if there is no error, return true
- * \param err_type the type of error happening in the system
- * \return true if err_type is kSuccess, false otherwise
- */
-bool AllreduceRobust::CheckAndRecover(ReturnType err_type) {
-  shutdown_timeout = err_type == kSuccess;
-  if (err_type == kSuccess) return true;
-
-  _assert(err_link != NULL, "must know the error link");
-  recover_counter += 1;
-  // async launch timeout task if enable_rabit_timeout is set
-  if (rabit_timeout && !rabit_timeout_task.valid()) {
-    utils::Printf("[EXPERIMENTAL] timeout thread expires in %d second(s)\n", timeout_sec);
-    rabit_timeout_task = std::async(std::launch::async, [=]() {
-      if (rabit_debug) {
-        utils::Printf("[%d] timeout thread %ld starts\n", rank,
-                      std::this_thread::get_id());
-      }
-      int time = 0;
-      // check if rabit recovered every 100ms
-      while (time++ < 10 * timeout_sec) {
-        std::this_thread::sleep_for(std::chrono::milliseconds(100));
-        if (shutdown_timeout.load()) {
-          if (rabit_debug) {
-            utils::Printf("[%d] timeout task thread %ld exits\n",
-              rank, std::this_thread::get_id());
-          }
-          return true;
-        }
-      }
-      _error("[%d] exit due to time out %d s\n", rank, timeout_sec);
-      return false;
-    });
-  }
-  // simple way, shutdown all links
-  for (size_t i = 0; i < all_links.size(); ++i) {
-    if (!all_links[i].sock.BadSocket()) all_links[i].sock.Close();
-  }
-  // smooth out traffic to tracker
-  std::this_thread::sleep_for(std::chrono::milliseconds(10*rank));
-  ReConnectLinks("recover");
-  return false;
-}
-/*!
- * \brief message passing function, used to decide the
- *        shortest distance to the possible source of data
- * \param node_value a pair of have_data and size
- *           have_data whether current node have data
- *           size gives the size of data, if current node is kHaveData
- * \param dist_in the shorest to any data source distance in each direction
- * \param out_index the edge index of output link
- * \return the shorest distance result of out edge specified by out_index
- */
-inline std::pair<int, size_t>
-ShortestDist(const std::pair<bool, size_t> &node_value,
-             const std::vector< std::pair<int, size_t> > &dist_in,
-             size_t out_index) {
-  if (node_value.first) {
-    return std::make_pair(1, node_value.second);
-  }
-  size_t size = 0;
-  int res = std::numeric_limits<int>::max();
-  for (size_t i = 0; i < dist_in.size(); ++i) {
-    if (i == out_index) continue;
-    if (dist_in[i].first == std::numeric_limits<int>::max()) continue;
-    if (dist_in[i].first + 1 < res) {
-      res = dist_in[i].first + 1;
-      size = dist_in[i].second;
-    }
-  }
-  // add one hop
-
-  return std::make_pair(res, size);
-}
-/*!
- * \brief message passing function, used to decide the
- *    data request from each edge, whether need to request data from certain edge
- * \param node_value a pair of request_data and best_link
- *           request_data stores whether current node need to request data
- *           best_link gives the best edge index to fetch the data
- * \param req_in the data request from incoming edges
- * \param out_index the edge index of output link
- * \return the request to the output edge
- */
-inline char DataRequest(const std::pair<bool, int> &node_value,
-                        const std::vector<char> &req_in,
-                        size_t out_index) {
-  // whether current node need to request data
-  bool request_data = node_value.first;
-  // which edge index is the best link to request data
-  // can be -1, which means current node contains data
-  const int best_link = node_value.second;
-  if (static_cast<int>(out_index) == best_link) {
-    if (request_data) return 1;
-    for (size_t i = 0; i < req_in.size(); ++i) {
-      if (i == out_index) continue;
-      if (req_in[i] != 0) return 1;
-    }
-  }
-  return 0;
-}
-/*!
- * \brief try to decide the recovery message passing request
- * \param role the current role of the node
- * \param p_size used to store the size of the message, for node in state kHaveData,
- *               this size must be set correctly before calling the function
- *               for others, this surves as output parameter
- *
- * \param p_recvlink used to store the link current node should recv data from, if necessary
- *          this can be -1, which means current node have the data
- * \param p_req_in used to store the resulting vector, indicating which link we should send the data to
- *
- * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceRobust::ReturnType
-AllreduceRobust::TryDecideRouting(AllreduceRobust::RecoverType role,
-                                  size_t *p_size,
-                                  int *p_recvlink,
-                                  std::vector<bool> *p_req_in) {
-  int best_link = -2;
-  {
-    // get the shortest distance to the request point
-    std::vector<std::pair<int, size_t> > dist_in, dist_out;
-
-    ReturnType succ = MsgPassing(std::make_pair(role == kHaveData, *p_size),
-                                 &dist_in, &dist_out, ShortestDist);
-    if (succ != kSuccess) return succ;
-    if (role != kHaveData) {
-      for (size_t i = 0; i < dist_in.size(); ++i) {
-        if (dist_in[i].first != std::numeric_limits<int>::max()) {
-          utils::Check(best_link == -2 || *p_size == dist_in[i].second,
-                       "[%d] Allreduce size inconsistent, distin=%lu, size=%lu, reporting=%lu\n",
-                       rank, dist_in[i].first, *p_size, dist_in[i].second);
-          if (best_link == -2 || dist_in[i].first < dist_in[best_link].first) {
-            best_link = static_cast<int>(i);
-            *p_size = dist_in[i].second;
-          }
-        }
-      }
-      utils::Check(best_link != -2, "Too many nodes went down and we cannot recover..");
-    } else {
-      best_link = -1;
-    }
-  }
-  // get the node request
-  std::vector<char> req_in, req_out;
-  ReturnType succ = MsgPassing(std::make_pair(role == kRequestData, best_link),
-                               &req_in, &req_out, DataRequest);
-  if (succ != kSuccess) return succ;
-  // set p_req_in
-  p_req_in->resize(req_in.size());
-  for (size_t i = 0; i < req_in.size(); ++i) {
-    // set p_req_in
-    (*p_req_in)[i] = (req_in[i] != 0);
-    if (req_out[i] != 0) {
-      _assert(req_in[i] == 0, "cannot get and receive request");
-      _assert(static_cast<int>(i) == best_link, "request result inconsistent");
-    }
-  }
-  *p_recvlink = best_link;
-  return kSuccess;
-}
-/*!
- * \brief try to finish the data recovery request,
- *        this function is used together with TryDecideRouting
- * \param role the current role of the node
- * \param sendrecvbuf_ the buffer to store the data to be sent/recived
- *          - if the role is kHaveData, this stores the data to be sent
- *          - if the role is kRequestData, this is the buffer to store the result
- *          - if the role is kPassData, this will not be used, and can be NULL
- * \param size the size of the data, obtained from TryDecideRouting
- * \param recv_link the link index to receive data, if necessary, obtained from TryDecideRouting
- * \param req_in the request of each link to send data, obtained from TryDecideRouting
- *
- * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
- * \sa ReturnType, TryDecideRouting
- */
-AllreduceRobust::ReturnType
-AllreduceRobust::TryRecoverData(RecoverType role,
-                                void *sendrecvbuf_,
-                                size_t size,
-                                int recv_link,
-                                const std::vector<bool> &req_in) {
-  RefLinkVector &links = tree_links;
-  // no need to run recovery for zero size messages
-  if (links.size() == 0 || size == 0) return kSuccess;
-  _assert(req_in.size() == links.size(), "TryRecoverData");
-  const int nlink = static_cast<int>(links.size());
-  {
-    bool req_data = role == kRequestData;
-    for (int i = 0; i < nlink; ++i) {
-      if (req_in[i]) {
-        _assert(i != recv_link, "TryDecideRouting");
-        req_data = true;
-      }
-    }
-    // do not need to provide data or receive data, directly exit
-    if (!req_data) return kSuccess;
-  }
-  _assert(recv_link >= 0 || role == kHaveData, "recv_link must be active");
-  if (role == kPassData) {
-    links[recv_link].InitBuffer(1, size, reduce_buffer_size);
-  }
-  for (int i = 0; i < nlink; ++i) {
-    links[i].ResetSize();
-  }
-  while (true) {
-    bool finished = true;
-    utils::PollHelper watcher;
-    for (int i = 0; i < nlink; ++i) {
-      if (i == recv_link && links[i].size_read != size) {
-        watcher.WatchRead(links[i].sock);
-        finished = false;
-      }
-      if (req_in[i] && links[i].size_write != size) {
-        if (role == kHaveData ||
-            (links[recv_link].size_read != links[i].size_write)) {
-          watcher.WatchWrite(links[i].sock);
-        }
-        finished = false;
-      }
-      watcher.WatchException(links[i].sock);
-    }
-    if (finished) break;
-    watcher.Poll();
-    // exception handling
-    for (int i = 0; i < nlink; ++i) {
-      if (watcher.CheckExcept(links[i].sock)) {
-        return ReportError(&links[i], kGetExcept);
-      }
-    }
-    if (role == kRequestData) {
-      const int pid = recv_link;
-      if (watcher.CheckRead(links[pid].sock)) {
-        ReturnType ret = links[pid].ReadToArray(sendrecvbuf_, size);
-        if (ret != kSuccess) {
-          return ReportError(&links[pid], ret);
-        }
-      }
-      for (int i = 0; i < nlink; ++i) {
-        if (req_in[i] && links[i].size_write != links[pid].size_read) {
-          ReturnType ret = links[i].WriteFromArray(sendrecvbuf_, links[pid].size_read);
-          if (ret != kSuccess) {
-            return ReportError(&links[i], ret);
-          }
-        }
-      }
-    }
-    if (role == kHaveData) {
-      for (int i = 0; i < nlink; ++i) {
-        if (req_in[i] && links[i].size_write != size) {
-          ReturnType ret = links[i].WriteFromArray(sendrecvbuf_, size);
-          if (ret != kSuccess) {
-            return ReportError(&links[i], ret);
-          }
-        }
-      }
-    }
-    if (role == kPassData) {
-      const int pid = recv_link;
-      const size_t buffer_size = links[pid].buffer_size;
-      if (watcher.CheckRead(links[pid].sock)) {
-        size_t min_write = size;
-        for (int i = 0; i < nlink; ++i) {
-          if (req_in[i]) min_write = std::min(links[i].size_write, min_write);
-        }
-        _assert(min_write <= links[pid].size_read, "boundary check");
-        ReturnType ret = links[pid].ReadToRingBuffer(min_write, size);
-        if (ret != kSuccess) {
-          return ReportError(&links[pid], ret);
-        }
-      }
-      for (int i = 0; i < nlink; ++i) {
-        if (req_in[i] && links[pid].size_read != links[i].size_write) {
-          size_t start = links[i].size_write % buffer_size;
-          // send out data from ring buffer
-          size_t nwrite = std::min(buffer_size - start, links[pid].size_read - links[i].size_write);
-          ssize_t len = links[i].sock.Send(links[pid].buffer_head + start, nwrite);
-          if (len != -1) {
-            links[i].size_write += len;
-          } else {
-            ReturnType ret = Errno2Return();
-            if (ret != kSuccess) return ReportError(&links[i], ret);
-          }
-        }
-      }
-    }
-  }
-  return kSuccess;
-}
-/*!
- * \brief try to fetch allreduce/broadcast results from rest of nodes
- *  as collaberative function called by all nodes, only requester node
- *  will pass seqno to rest of nodes and reconstruct/backfill sendrecvbuf_
- *  of specific seqno from other nodes.
- */
-AllreduceRobust::ReturnType AllreduceRobust::TryRestoreCache(bool requester,
-  const int min_seq, const int max_seq) {
-  // clear requester and rebuild from those with most cache entries
-  if (requester) {
-    _assert(cur_cache_seq <= max_seq, "requester is expected to have fewer cache entries");
-    cachebuf.Clear();
-    lookupbuf.Clear();
-    cur_cache_seq = 0;
-  }
-  RecoverType role = requester ? kRequestData : kHaveData;
-  size_t size = 1;
-  int recv_link;
-  std::vector<bool> req_in;
-  ReturnType ret = TryDecideRouting(role, &size, &recv_link, &req_in);
-  if (ret != kSuccess) return ret;
-  // only recover missing cache entries in requester
-  // as tryrecoverdata is collective call, need to go through entire cache
-  // and only work on those missing
-  for (int i = 0; i < max_seq; i++) {
-    // restore lookup map
-    size_t cache_size = 0;
-    void* key = lookupbuf.Query(i, &cache_size);
-    ret = TryRecoverData(role, &cache_size, sizeof(size_t), recv_link, req_in);
-    if (ret != kSuccess) return ret;
-    if (requester) {
-      key = lookupbuf.AllocTemp(cache_size, 1);
-      lookupbuf.PushTemp(i, cache_size, 1);
-    }
-    ret = TryRecoverData(role, key, cache_size, recv_link, req_in);
-    if (ret != kSuccess) return ret;
-    // restore cache content
-    cache_size = 0;
-    void* buf = cachebuf.Query(i, &cache_size);
-    ret = TryRecoverData(role, &cache_size, sizeof(size_t), recv_link, req_in);
-    if (requester) {
-      buf = cachebuf.AllocTemp(cache_size, 1);
-      cachebuf.PushTemp(i, cache_size, 1);
-      cur_cache_seq +=1;
-    }
-    ret = TryRecoverData(role, buf, cache_size, recv_link, req_in);
-    if (ret != kSuccess) return ret;
-  }
-
-  return kSuccess;
-}
-
-/*!
- * \brief try to load check point
- *
- *        This is a collaborative function called by all nodes
- *        only the nodes with requester set to true really needs to load the check point
- *        other nodes acts as collaborative roles to complete this request
- *
- * \param requester whether current node is the requester
- * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceRobust::ReturnType AllreduceRobust::TryLoadCheckPoint(bool requester) {
-  // check in local data
-  RecoverType role =  requester ? kRequestData : kHaveData;
-  ReturnType succ;
-  if (num_local_replica != 0) {
-    if (requester) {
-      // clear existing history, if any, before load
-      local_rptr[local_chkpt_version].clear();
-      local_chkpt[local_chkpt_version].clear();
-    }
-    // recover local checkpoint
-    succ = TryRecoverLocalState(&local_rptr[local_chkpt_version],
-                                &local_chkpt[local_chkpt_version]);
-    if (succ != kSuccess) return succ;
-    int nlocal = std::max(static_cast<int>(local_rptr[local_chkpt_version].size()) - 1, 0);
-    // check if everyone is OK
-    unsigned state = 0;
-    if (nlocal == num_local_replica + 1) {
-      // complete recovery
-      state = 1;
-    } else if (nlocal == 0) {
-      // get nothing
-      state = 2;
-    } else {
-      // partially complete state
-      state = 4;
-    }
-    succ = TryAllreduce(&state, sizeof(state), 1, op::Reducer<op::BitOR, unsigned>);
-    if (succ != kSuccess) return succ;
-    utils::Check(state == 1 || state == 2,
-                 "LoadCheckPoint: too many nodes fails, cannot recover local state");
-  }
-  // do call save model if the checkpoint was lazy
-  if (role == kHaveData && global_lazycheck != NULL) {
-    global_checkpoint.resize(0);
-    utils::MemoryBufferStream fs(&global_checkpoint);
-    fs.Write(&version_number, sizeof(version_number));
-    global_lazycheck->Save(&fs);
-    global_lazycheck = NULL;
-  }
-  // recover global checkpoint
-  size_t size = this->global_checkpoint.length();
-  int recv_link;
-  std::vector<bool> req_in;
-  succ = TryDecideRouting(role, &size, &recv_link, &req_in);
-  if (succ != kSuccess) return succ;
-  if (role == kRequestData) {
-    global_checkpoint.resize(size);
-  }
-  if (size == 0) return kSuccess;
-  return TryRecoverData(role, BeginPtr(global_checkpoint), size, recv_link, req_in);
-}
-/*!
- * \brief try to get the result of operation specified by seqno
- *
- *        This is a collaborative function called by all nodes
- *        only the nodes with requester set to true really needs to get the result
- *        other nodes acts as collaborative roles to complete this request
- *
- * \param buf the buffer to store the result, this parameter is only used when current node is requester
- * \param size the total size of the buffer, this parameter is only used when current node is requester
- * \param seqno sequence number of the operation, this is unique index of a operation in current iteration
- * \param requester whether current node is the requester
- * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceRobust::ReturnType
-AllreduceRobust::TryGetResult(void *sendrecvbuf, size_t size, int seqno, bool requester) {
-  // if minimum sequence requested is local check point ack,
-  // this means all nodes have finished local check point, directly return
-  if (seqno == ActionSummary::kLocalCheckAck) return kSuccess;
-  if (seqno == ActionSummary::kLocalCheckPoint) {
-    // new version of local model
-    int new_version = !local_chkpt_version;
-    int nlocal = std::max(static_cast<int>(local_rptr[new_version].size()) - 1, 0);
-    // if we goes to this place, use must have already setup the state once
-    _assert(nlocal == 1 || nlocal == num_local_replica + 1,
-                  "TryGetResult::Checkpoint");
-    return TryRecoverLocalState(&local_rptr[new_version], &local_chkpt[new_version]);
-  }
-
-  // handles normal data recovery
-  RecoverType role;
-  if (!requester) {
-    sendrecvbuf = resbuf.Query(seqno, &size);
-    role = sendrecvbuf != NULL ? kHaveData : kPassData;
-  } else {
-    role = kRequestData;
-  }
-  int recv_link;
-  std::vector<bool> req_in;
-  // size of data
-  size_t data_size = size;
-  ReturnType succ = TryDecideRouting(role, &data_size, &recv_link, &req_in);
-  if (succ != kSuccess) return succ;
-  utils::Check(data_size != 0, "zero size check point is not allowed");
-  if (role == kRequestData || role == kHaveData) {
-    utils::Check(data_size == size,
-                 "Allreduce Recovered data size do not match the specification of function call.\n"\
-                 "Please check if calling sequence of recovered program is the " \
-                 "same the original one in current VersionNumber");
-  }
-  return TryRecoverData(role, sendrecvbuf, data_size, recv_link, req_in);
-}
-/*!
- * \brief try to run recover execution for a request action described by flag and seqno,
- *        the function will keep blocking to run possible recovery operations before the specified action,
- *        until the requested result is received by a recovering procedure,
- *        or the function discovers that the requested action is not yet executed, and return false
- *
- * \param buf the buffer to store the result
- * \param size the total size of the buffer
- * \param flag flag information about the action \sa ActionSummary
- * \param seqno sequence number of the action, if it is special action with flag set,
- *              seqno needs to be set to ActionSummary::kSpecialOp
- *
- * \return if this function can return true or false
- *    - true means buf already set to the
- *           result by recovering procedure, the action is complete, no further action is needed
- *    - false means this is the lastest action that has not yet been executed, need to execute the action
- */
-bool AllreduceRobust::RecoverExec(void *buf, size_t size, int flag, int seqno,
-                                  int cache_seqno, const char* caller) {
-  // kLoadBootstrapCache should be treated similar as allreduce
-  // when loadcheck/check/checkack runs in other nodes
-  if (flag != 0 && flag != ActionSummary::kLoadBootstrapCache) {
-    _assert(seqno == ActionSummary::kSpecialOp, "must only set seqno for normal operations");
-  }
-
-  std::string msg = std::string(caller) + " pass negative seqno "
-    + std::to_string(seqno) + " flag " + std::to_string(flag)
-    + " version " + std::to_string(version_number);
-  _assert(seqno >=0, msg.c_str());
-
-  ActionSummary req(flag, flag, seqno, cache_seqno);
-
-  while (true) {
-    this->ReportStatus();
-    // copy to action and send to allreduce with other nodes
-    ActionSummary act = req;
-    // get the reduced action
-    if (!CheckAndRecover(TryAllreduce(&act, sizeof(act), 1, ActionSummary::Reducer))) continue;
-
-    if (act.check_ack()) {
-      if (act.check_point()) {
-        // if we also have check_point, do check point first
-        _assert(!act.diff_seq(),
-                      "check ack & check pt  cannot occur together with normal ops");
-        // if we requested checkpoint, we are free to go
-        if (req.check_point()) return true;
-      } else if (act.load_check()) {
-        // if there is only check_ack and load_check, do load_check
-        if (!CheckAndRecover(TryLoadCheckPoint(req.load_check()))) continue;
-        // if requested load check, then misson complete
-        if (req.load_check()) return true;
-      } else {
-        // there is no check point and no load check, execute check ack
-        if (req.check_ack()) return true;
-      }
-      // if execute to this point
-      // this means the action requested has not been completed
-      // try next round
-    } else {
-      if (act.check_point()) {
-        if (act.diff_seq()) {
-          _assert(act.seqno() != ActionSummary::kSpecialOp, "min seq bug");
-          // print checkpoint consensus flag if user turn on debug
-          if (rabit_debug) {
-            req.print_flags(rank, "checkpoint req");
-            act.print_flags(rank, "checkpoint act");
-          }
-          /*
-           * Chen Qin
-           * at least one hit checkpoint_ code & at least one not hitting
-           * compare with version_number of req.check_point() set true with rest
-           * expect to be equal, means rest fall behind in sequence
-           * use resbuf resbuf to recover
-           * worker-0           worker-1
-           * checkpoint(n-1)    checkpoint(n-1)
-           * allreduce          allreduce (requester) |
-           * broadcast                                V
-           * checkpoint(n req)
-           * after catch up to checkpoint n, diff_seq will be false
-           * */
-          // assume requester is falling behind
-          bool requester = req.seqno() == act.seqno();
-          // if not load cache
-          if (!act.load_cache()) {
-            if (act.seqno() > 0) {
-              if (!requester) {
-                _assert(req.check_point(), "checkpoint node should be KHaveData role");
-                buf = resbuf.Query(act.seqno(), &size);
-                _assert(buf != NULL, "buf should have data from resbuf");
-                _assert(size > 0, "buf size should be greater than 0");
-              }
-              if (!CheckAndRecover(TryGetResult(buf, size, act.seqno(), requester))) continue;
-            }
-          } else {
-            // cache seq no should be smaller than kSpecialOp
-            _assert(act.seqno(SeqType::kCache) != ActionSummary::kSpecialOp,
-              "checkpoint with kSpecialOp");
-            int max_cache_seq = cur_cache_seq;
-            if (TryAllreduce(&max_cache_seq, sizeof(max_cache_seq), 1,
-              op::Reducer<op::Max, unsigned>) != kSuccess) continue;
-
-            if (TryRestoreCache(req.load_cache(), act.seqno(), max_cache_seq)
-              != kSuccess) continue;
-          }
-          if (requester) return true;
-        } else  {
-          // no difference in seq no, means we are free to check point
-          if (req.check_point()) return true;
-        }
-      } else {
-        // no check point
-        if (act.load_check()) {
-          // all the nodes called load_check, this is an incomplete action
-          if (!act.diff_seq()) return false;
-          // load check have higher priority, do load_check
-          if (!CheckAndRecover(TryLoadCheckPoint(req.load_check()))) continue;
-          // if requested load check, then misson complete
-          if (req.load_check()) return true;
-        } else {
-          // run all nodes in a isolated cache restore logic
-          if (act.load_cache()) {
-            // print checkpoint consensus flag if user turn on debug
-            if (rabit_debug) {
-              req.print_flags(rank, "loadcache req");
-              act.print_flags(rank, "loadcache act");
-            }
-            // load cache should not running in parralel with other states
-            _assert(!act.load_check(),
-              "load cache state expect no nodes doing load checkpoint");
-            _assert(!act.check_point() ,
-              "load cache state expect no nodes doing checkpoint");
-            _assert(!act.check_ack(),
-              "load cache state expect no nodes doing checkpoint ack");
-
-            // if all nodes are requester in load cache, skip
-            if (act.load_cache(SeqType::kCache)) return false;
-
-            // bootstrap cache always restore before loadcheckpoint
-            // requester always have seq diff with non requester
-            if (act.diff_seq()) {
-              // restore cache failed, retry from what's left
-              if (TryRestoreCache(req.load_cache(), act.seqno(), act.seqno(SeqType::kCache))
-                != kSuccess) continue;
-            }
-            // if requested load cache, then mission complete
-            if (req.load_cache()) return true;
-            continue;
-          }
-
-          // assert no req with load cache set goes into seq catch up
-          _assert(!req.load_cache(), "load cache not interacte with rest states");
-
-          // no special flags, no checkpoint, check ack, load_check
-          _assert(act.seqno() != ActionSummary::kSpecialOp, "min seq bug");
-          if (act.diff_seq()) {
-            bool requester = req.seqno() == act.seqno();
-            if (!CheckAndRecover(TryGetResult(buf, size, act.seqno(), requester))) continue;
-            if (requester) return true;
-          } else {
-            // all the request is same,
-            // this is most recent command that is yet to be executed
-            return false;
-          }
-        }
-      }
-      // something is still incomplete try next round
-    }
-  }
-  _assert(false, "RecoverExec: should not reach here");
-  return true;
-}
-/*!
- * \brief try to recover the local state, making each local state to be the result of itself
- *        plus replication of states in previous num_local_replica hops in the ring
- *
- * The input parameters must contain the valid local states available in current nodes,
- * This function try ist best to "complete" the missing parts of local_rptr and local_chkpt
- * If there is sufficient information in the ring, when the function returns, local_chkpt will
- * contain num_local_replica + 1 checkpoints (including the chkpt of this node)
- * If there is no sufficient information in the ring, this function the number of checkpoints
- * will be less than the specified value
- *
- * \param p_local_rptr the pointer to the segment pointers in the states array
- * \param p_local_chkpt the pointer to the storage of local check points
- * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
- * \sa ReturnType
- */
-AllreduceRobust::ReturnType
-AllreduceRobust::TryRecoverLocalState(std::vector<size_t> *p_local_rptr,
-                                      std::string *p_local_chkpt) {
-  // if there is no local replica, we can do nothing
-  if (num_local_replica == 0) return kSuccess;
-  std::vector<size_t> &rptr = *p_local_rptr;
-  std::string &chkpt = *p_local_chkpt;
-  if (rptr.size() == 0) {
-    rptr.push_back(0);
-    _assert(chkpt.length() == 0, "local chkpt space inconsistent");
-  }
-  const int n = num_local_replica;
-  {
-    // backward passing, passing state in backward direction of the ring
-    const int nlocal = static_cast<int>(rptr.size() - 1);
-    _assert(nlocal <= n + 1, "invalid local replica");
-    std::vector<int> msg_back(n + 1);
-    msg_back[0] = nlocal;
-    // backward passing one hop the request
-    ReturnType succ;
-    succ = RingPassing(BeginPtr(msg_back),
-                       1 * sizeof(int), (n+1) * sizeof(int),
-                       0 * sizeof(int), n * sizeof(int),
-                       ring_next, ring_prev);
-    if (succ != kSuccess) return succ;
-    int msg_forward[2];
-    msg_forward[0] = nlocal;
-    succ = RingPassing(msg_forward,
-                       1 * sizeof(int), 2 * sizeof(int),
-                       0 * sizeof(int), 1 * sizeof(int),
-                       ring_prev, ring_next);
-    if (succ != kSuccess) return succ;
-    // calculate the number of things we can read from next link
-    int nread_end = nlocal;
-    for (int i = 1; i <= n; ++i) {
-      nread_end = std::max(nread_end, msg_back[i] - i);
-    }
-    // gives the size of forward
-    int nwrite_start = std::min(msg_forward[1] + 1, nread_end);
-    // get the size of each segments
-    std::vector<size_t> sizes(nread_end);
-    for (int i = 0; i < nlocal; ++i) {
-      sizes[i] = rptr[i + 1] - rptr[i];
-    }
-    // pass size through the link
-    succ = RingPassing(BeginPtr(sizes),
-                       nlocal * sizeof(size_t),
-                       nread_end * sizeof(size_t),
-                       nwrite_start * sizeof(size_t),
-                       nread_end * sizeof(size_t),
-                       ring_next, ring_prev);
-    if (succ != kSuccess) return succ;
-    // update rptr
-    rptr.resize(nread_end + 1);
-    for (int i = nlocal; i < nread_end; ++i) {
-      rptr[i + 1] = rptr[i] + sizes[i];
-    }
-    chkpt.resize(rptr.back());
-    // pass data through the link
-    succ = RingPassing(BeginPtr(chkpt), rptr[nlocal], rptr[nread_end],
-                       rptr[nwrite_start], rptr[nread_end],
-                       ring_next, ring_prev);
-    if (succ != kSuccess) {
-      rptr.resize(nlocal + 1); chkpt.resize(rptr.back()); return succ;
-    }
-  }
-  {
-    // forward passing, passing state in forward direction of the ring
-    const int nlocal = static_cast<int>(rptr.size() - 1);
-    _assert(nlocal <= n + 1, "invalid local replica");
-    std::vector<int> msg_forward(n + 1);
-    msg_forward[0] = nlocal;
-    // backward passing one hop the request
-    ReturnType succ;
-    succ = RingPassing(BeginPtr(msg_forward),
-                       1 * sizeof(int), (n+1) * sizeof(int),
-                       0 * sizeof(int), n * sizeof(int),
-                       ring_prev, ring_next);
-    if (succ != kSuccess) return succ;
-    int msg_back[2];
-    msg_back[0] = nlocal;
-    succ = RingPassing(msg_back,
-                       1 * sizeof(int), 2 * sizeof(int),
-                       0 * sizeof(int), 1 * sizeof(int),
-                       ring_next, ring_prev);
-    if (succ != kSuccess) return succ;
-    // calculate the number of things we can read from next link
-    int nread_end = nlocal, nwrite_end = 1;
-    // have to have itself in order to get other data from prev link
-    if (nlocal != 0) {
-      for (int i = 1; i <= n; ++i) {
-        if (msg_forward[i] == 0) break;
-        nread_end = std::max(nread_end, i + 1);
-        nwrite_end = i + 1;
-      }
-      if (nwrite_end > n) nwrite_end = n;
-    } else  {
-      nread_end = 0; nwrite_end = 0;
-    }
-    // gives the size of forward
-    int nwrite_start = std::min(msg_back[1] - 1, nwrite_end);
-    // next node miss the state of itself, cannot recover
-    if (nwrite_start < 0) nwrite_start = nwrite_end = 0;
-    // get the size of each segments
-    std::vector<size_t> sizes(nread_end);
-    for (int i = 0; i < nlocal; ++i) {
-      sizes[i] = rptr[i + 1] - rptr[i];
-    }
-    // pass size through the link, check consistency
-    succ = RingPassing(BeginPtr(sizes),
-                       nlocal * sizeof(size_t),
-                       nread_end * sizeof(size_t),
-                       nwrite_start * sizeof(size_t),
-                       nwrite_end * sizeof(size_t),
-                       ring_prev, ring_next);
-    if (succ != kSuccess) return succ;
-    // update rptr
-    rptr.resize(nread_end + 1);
-    for (int i = nlocal; i < nread_end; ++i) {
-      rptr[i + 1] = rptr[i] + sizes[i];
-    }
-    chkpt.resize(rptr.back());
-    // pass data through the link
-    succ = RingPassing(BeginPtr(chkpt), rptr[nlocal], rptr[nread_end],
-                       rptr[nwrite_start], rptr[nwrite_end],
-                       ring_prev, ring_next);
-    if (succ != kSuccess) {
-      rptr.resize(nlocal + 1); chkpt.resize(rptr.back()); return succ;
-    }
-  }
-  return kSuccess;
-}
-/*!
- * \brief try to checkpoint local state, this function is called in normal executation phase
- *    of checkpoint that contains local state
- *  the input state must exactly one saved state(local state of current node),
- *  after complete, this function will get local state from previous num_local_replica nodes and put them
- *  into local_chkpt and local_rptr
- *
- *  It is also OK to call TryRecoverLocalState instead,
- *  TryRecoverLocalState makes less assumption about the input, and requires more communications
- *
- * \param p_local_rptr the pointer to the segment pointers in the states array
- * \param p_local_chkpt the pointer to the storage of local check points
- * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
- * \sa ReturnType, TryRecoverLocalState
- */
-AllreduceRobust::ReturnType
-AllreduceRobust::TryCheckinLocalState(std::vector<size_t> *p_local_rptr,
-                                      std::string *p_local_chkpt) {
-  // if there is no local replica, we can do nothing
-  if (num_local_replica == 0) return kSuccess;
-  std::vector<size_t> &rptr = *p_local_rptr;
-  std::string &chkpt = *p_local_chkpt;
-  _assert(rptr.size() == 2,
-                "TryCheckinLocalState must have exactly 1 state");
-  const int n = num_local_replica;
-  std::vector<size_t> sizes(n + 1);
-  sizes[0] = rptr[1] - rptr[0];
-  ReturnType succ;
-  // pass size through the link
-  succ = RingPassing(BeginPtr(sizes),
-                     1 * sizeof(size_t),
-                     (n + 1) * sizeof(size_t),
-                     0 * sizeof(size_t),
-                     n * sizeof(size_t),
-                     ring_prev, ring_next);
-  if (succ != kSuccess) return succ;
-  // update rptr
-  rptr.resize(n + 2);
-  for (int i = 1; i <= n; ++i) {
-    rptr[i + 1] = rptr[i] + sizes[i];
-  }
-  chkpt.resize(rptr.back());
-  // pass data through the link
-  succ = RingPassing(BeginPtr(chkpt),
-                     rptr[1], rptr[n + 1],
-                     rptr[0], rptr[n],
-                     ring_prev, ring_next);
-  if (succ != kSuccess) {
-    rptr.resize(2); chkpt.resize(rptr.back()); return succ;
-  }
-  return kSuccess;
-}
-/*!
- * \brief perform a ring passing to receive data from prev link, and sent data to next link
- *  this allows data to stream over a ring structure
- *  sendrecvbuf[0:read_ptr] are already provided by current node
- *  current node will recv sendrecvbuf[read_ptr:read_end] from prev link
- *  current node will send sendrecvbuf[write_ptr:write_end] to next link
- *  write_ptr will wait till the data is readed before sending the data
- *  this function requires read_end >= write_end
- *
- * \param sendrecvbuf_ the place to hold the incoming and outgoing data
- * \param read_ptr the initial read pointer
- * \param read_end the ending position to read
- * \param write_ptr the initial write pointer
- * \param write_end the ending position to write
- * \param read_link pointer to link to previous position in ring
- * \param write_link pointer to link of next position in ring
- */
-AllreduceRobust::ReturnType
-AllreduceRobust::RingPassing(void *sendrecvbuf_,
-                             size_t read_ptr,
-                             size_t read_end,
-                             size_t write_ptr,
-                             size_t write_end,
-                             LinkRecord *read_link,
-                             LinkRecord *write_link) {
-  if (read_link == NULL || write_link == NULL || read_end == 0) return kSuccess;
-  _assert(write_end <= read_end,
-                "RingPassing: boundary check1");
-  _assert(read_ptr <= read_end, "RingPassing: boundary check2");
-  _assert(write_ptr <= write_end, "RingPassing: boundary check3");
-  // take reference
-  LinkRecord &prev = *read_link, &next = *write_link;
-  // send recv buffer
-  char *buf = reinterpret_cast<char*>(sendrecvbuf_);
-  while (true) {
-    bool finished = true;
-    utils::PollHelper watcher;
-    if (read_ptr != read_end) {
-      watcher.WatchRead(prev.sock);
-      finished = false;
-    }
-    if (write_ptr < read_ptr && write_ptr != write_end) {
-      watcher.WatchWrite(next.sock);
-      finished = false;
-    }
-    watcher.WatchException(prev.sock);
-    watcher.WatchException(next.sock);
-    if (finished) break;
-    watcher.Poll();
-    if (watcher.CheckExcept(prev.sock)) return ReportError(&prev, kGetExcept);
-    if (watcher.CheckExcept(next.sock)) return ReportError(&next, kGetExcept);
-    if (read_ptr != read_end && watcher.CheckRead(prev.sock)) {
-      ssize_t len = prev.sock.Recv(buf + read_ptr, read_end - read_ptr);
-      if (len == 0) {
-        prev.sock.Close(); return ReportError(&prev, kRecvZeroLen);
-      }
-      if (len != -1) {
-        read_ptr += static_cast<size_t>(len);
-      } else {
-        ReturnType ret = Errno2Return();
-        if (ret != kSuccess) return ReportError(&prev, ret);
-      }
-    }
-    if (write_ptr != write_end && write_ptr < read_ptr) {
-      size_t nsend = std::min(write_end - write_ptr, read_ptr - write_ptr);
-      ssize_t len = next.sock.Send(buf + write_ptr, nsend);
-      if (len != -1) {
-        write_ptr += static_cast<size_t>(len);
-      } else {
-        ReturnType ret = Errno2Return();
-        if (ret != kSuccess) return ReportError(&prev, ret);
-      }
-    }
-  }
-  return kSuccess;
-}
-}  // namespace engine
-}  // namespace rabit
diff --git a/ml-xgboost/rabit/src/allreduce_robust.h b/ml-xgboost/rabit/src/allreduce_robust.h
deleted file mode 100644
index a4bee7c..0000000
--- a/ml-xgboost/rabit/src/allreduce_robust.h
+++ /dev/null
@@ -1,672 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file allreduce_robust.h
- * \brief Robust implementation of Allreduce
- *   using TCP non-block socket and tree-shape reduction.
- *
- *   This implementation considers the failure of nodes
- *
- * \author Tianqi Chen, Ignacio Cano, Tianyi Zhou
- */
-#ifndef RABIT_ALLREDUCE_ROBUST_H_
-#define RABIT_ALLREDUCE_ROBUST_H_
-#include <future>
-#include <vector>
-#include <string>
-#include <algorithm>
-#include "rabit/internal/engine.h"
-#include "allreduce_base.h"
-
-namespace rabit {
-namespace engine {
-/*! \brief implementation of fault tolerant all reduce engine */
-class AllreduceRobust : public AllreduceBase {
- public:
-  AllreduceRobust(void);
-  virtual ~AllreduceRobust(void) {}
-  // initialize the manager
-  virtual bool Init(int argc, char* argv[]);
-  /*! \brief shutdown the engine */
-  virtual bool Shutdown(void);
-  /*!
-   * \brief set parameters to the engine
-   * \param name parameter name
-   * \param val parameter value
-   */
-  virtual void SetParam(const char *name, const char *val);
-  /*!
-   * \brief perform immutable local bootstrap cache insertion
-   * \param key unique cache key
-   * \param buf buffer of allreduce/robust payload to copy
-   * \param buflen total number of bytes
-   * \return -1 if no recovery cache fetched otherwise 0
-   */
-  int SetBootstrapCache(const std::string &key, const void *buf,
-    const size_t type_nbytes, const size_t count);
-  /*!
-   * \brief perform bootstrap cache lookup if nodes in fault recovery
-   * \param key unique cache key
-   * \param buf buffer for recv allreduce/robust payload
-   * \param buflen total number of bytes
-   */
-  int GetBootstrapCache(const std::string &key, void *buf, const size_t type_nbytes,
-    const size_t count);
-  /*!
-   * \brief internal Allgather function, each node have a segment of data in the ring of sendrecvbuf,
-   *  the data provided by current node k is [slice_begin, slice_end),
-   *  the next node's segment must start with slice_end
-   *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
-   *  use a ring based algorithm
-   *
-   * \param sendrecvbuf_ buffer for both sending and receiving data, it is a ring conceptually
-   * \param total_size total size of data to be gathered
-   * \param slice_begin beginning of the current slice
-   * \param slice_end end of the current slice
-   * \param size_prev_slice size of the previous slice i.e. slice of node (rank - 1) % world_size
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Allgather(void *sendrecvbuf_, size_t total_size,
-                              size_t slice_begin,
-                              size_t slice_end,
-                              size_t size_prev_slice,
-                              const char* _file = _FILE,
-                              const int _line = _LINE,
-                              const char* _caller = _CALLER);
-  /*!
-   * \brief perform in-place allreduce, on sendrecvbuf
-   *        this function is NOT thread-safe
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param type_nbytes the unit number of bytes the type have
-   * \param count number of elements to be reduced
-   * \param reducer reduce function
-   * \param prepare_func Lazy preprocessing function, lazy prepare_fun(prepare_arg)
-   *                     will be called by the function before performing Allreduce, to intialize the data in sendrecvbuf_.
-   *                     If the result of Allreduce can be recovered directly, then prepare_func will NOT be called
-   * \param prepare_arg argument used to passed into the lazy preprocessing function
-   * \param prepare_arg argument used to passed into the lazy preprocessing function
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Allreduce(void *sendrecvbuf_,
-                         size_t type_nbytes,
-                         size_t count,
-                         ReduceFunction reducer,
-                         PreprocFunction prepare_fun = NULL,
-                         void *prepare_arg = NULL,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER);
-  /*!
-   * \brief broadcast data from root to all nodes
-   * \param sendrecvbuf_ buffer for both sending and recving data
-   * \param size the size of the data to be broadcasted
-   * \param root the root worker id to broadcast the data
-   * \param _file caller file name used to generate unique cache key
-   * \param _line caller line number used to generate unique cache key
-   * \param _caller caller function name used to generate unique cache key
-   */
-  virtual void Broadcast(void *sendrecvbuf_, size_t total_size, int root,
-                         const char* _file = _FILE,
-                         const int _line = _LINE,
-                         const char* _caller = _CALLER);
-  /*!
-   * \brief load latest check point
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \param local_model pointer to local model, that is specific to current node/rank
-   *   this can be NULL when no local model is needed
-   *
-   * \return the version number of check point loaded
-   *     if returned version == 0, this means no model has been CheckPointed
-   *     the p_model is not touched, user should do necessary initialization by themselves
-   *
-   *   Common usage example:
-   *      int iter = rabit::LoadCheckPoint(&model);
-   *      if (iter == 0) model.InitParameters();
-   *      for (i = iter; i < max_iter; ++i) {
-   *        do many things, include allreduce
-   *        rabit::CheckPoint(model);
-   *      }
-   *
-   * \sa CheckPoint, VersionNumber
-   */
-  virtual int LoadCheckPoint(Serializable *global_model,
-                             Serializable *local_model = NULL);
-  /*!
-   * \brief checkpoint the model, meaning we finished a stage of execution
-   *  every time we call check point, there is a version number which will increase by one
-   *
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \param local_model pointer to local model, that is specific to current node/rank
-   *   this can be NULL when no local state is needed
-   *
-   * NOTE: local_model requires explicit replication of the model for fault-tolerance, which will
-   *       bring replication cost in CheckPoint function. global_model do not need explicit replication.
-   *       So only CheckPoint with global_model if possible
-   *
-   * \sa LoadCheckPoint, VersionNumber
-   */
-  virtual void CheckPoint(const Serializable *global_model,
-                          const Serializable *local_model = NULL) {
-    this->CheckPoint_(global_model, local_model, false);
-  }
-  /*!
-   * \brief This function can be used to replace CheckPoint for global_model only,
-   *   when certain condition is met(see detailed expplaination).
-   *
-   *   This is a "lazy" checkpoint such that only the pointer to global_model is
-   *   remembered and no memory copy is taken. To use this function, the user MUST ensure that:
-   *   The global_model must remain unchanged util last call of Allreduce/Broadcast in current version finishs.
-   *   In another words, global_model model can be changed only between last call of
-   *   Allreduce/Broadcast and LazyCheckPoint in current version
-   *
-   *   For example, suppose the calling sequence is:
-   *   LazyCheckPoint, code1, Allreduce, code2, Broadcast, code3, LazyCheckPoint
-   *
-   *   If user can only changes global_model in code3, then LazyCheckPoint can be used to
-   *   improve efficiency of the program.
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \sa LoadCheckPoint, CheckPoint, VersionNumber
-   */
-  virtual void LazyCheckPoint(const Serializable *global_model) {
-    this->CheckPoint_(global_model, NULL, true);
-  }
-  /*!
-   * \brief explicitly re-init everything before calling LoadCheckPoint
-   *    call this function when IEngine throw an exception out,
-   *    this function is only used for test purpose
-   */
-  virtual void InitAfterException(void) {
-    // simple way, shutdown all links
-    for (size_t i = 0; i < all_links.size(); ++i) {
-      if (!all_links[i].sock.BadSocket()) all_links[i].sock.Close();
-    }
-    ReConnectLinks("recover");
-  }
-
- protected:
-  // constant one byte out of band message to indicate error happening
-  // and mark for channel cleanup
-  static const char kOOBReset = 95;
-  // and mark for channel cleanup, after OOB signal
-  static const char kResetMark = 97;
-  // and mark for channel cleanup
-  static const char kResetAck = 97;
-  /*! \brief type of roles each node can play during recovery */
-  enum RecoverType {
-    /*! \brief current node have data */
-    kHaveData = 0,
-    /*! \brief current node request data */
-    kRequestData = 1,
-    /*! \brief current node only helps to pass data around */
-    kPassData = 2
-  };
-
-  enum SeqType {
-    /*! \brief apply to rabit seq code */
-    kSeq = 0,
-    /*! \brief apply to rabit cache seq code */
-    kCache = 1
-  };
-  /*!
-   * \brief summary of actions proposed in all nodes
-   *  this data structure is used to make consensus decision
-   *  about next action to take in the recovery mode
-   */
-  struct ActionSummary {
-    // maximumly allowed sequence id
-    static const u_int32_t kSpecialOp = (1 << 26);
-    // special sequence number for local state checkpoint
-    static const u_int32_t kLocalCheckPoint = (1 << 26) - 2;
-    // special sequnce number for local state checkpoint ack signal
-    static const u_int32_t kLocalCheckAck = (1 << 26) - 1;
-    //---------------------------------------------
-    // The following are bit mask of flag used in
-    //----------------------------------------------
-    // some node want to load check point
-    static const int kLoadCheck = 1;
-    // some node want to do check point
-    static const int kCheckPoint = 2;
-    // check point Ack, we use a two phase message in check point,
-    // this is the second phase of check pointing
-    static const int kCheckAck = 4;
-    // there are difference sequence number the nodes proposed
-    // this means we want to do recover execution of the lower sequence
-    // action instead of normal execution
-    static const int kDiffSeq = 8;
-    // there are nodes request load cache
-    static const int kLoadBootstrapCache = 16;
-    // constructor
-    ActionSummary(void) {}
-    // constructor of action
-    explicit ActionSummary(int seqno_flag, int cache_flag = 0,
-      u_int32_t minseqno = kSpecialOp, u_int32_t maxseqno = kSpecialOp) {
-      seqcode = (minseqno << 5) | seqno_flag;
-      maxseqcode = (maxseqno << 5) | cache_flag;
-    }
-    // minimum number of all operations by default
-    // maximum number of all cache operations otherwise
-    inline u_int32_t seqno(SeqType t = SeqType::kSeq) const {
-      int code = t == SeqType::kSeq ? seqcode : maxseqcode;
-      return code >> 5;
-    }
-    // whether the operation set contains a load_check
-    inline bool load_check(SeqType t = SeqType::kSeq) const {
-      int code = t == SeqType::kSeq ? seqcode : maxseqcode;
-      return (code & kLoadCheck) != 0;
-    }
-    // whether the operation set contains a load_cache
-    inline bool load_cache(SeqType t = SeqType::kSeq) const {
-      int code = t == SeqType::kSeq ? seqcode : maxseqcode;
-      return (code & kLoadBootstrapCache) != 0;
-    }
-    // whether the operation set contains a check point
-    inline bool check_point(SeqType t = SeqType::kSeq) const {
-      int code = t == SeqType::kSeq ? seqcode : maxseqcode;
-      return (code & kCheckPoint) != 0;
-    }
-    // whether the operation set contains a check ack
-    inline bool check_ack(SeqType t = SeqType::kSeq) const {
-      int code = t == SeqType::kSeq ? seqcode : maxseqcode;
-      return (code & kCheckAck) != 0;
-    }
-    // whether the operation set contains different sequence number
-    inline bool diff_seq() const {
-      return (seqcode & kDiffSeq) != 0;
-    }
-    // returns the operation flag of the result
-    inline int flag(SeqType t = SeqType::kSeq) const {
-      int code = t == SeqType::kSeq ? seqcode : maxseqcode;
-      return code & 31;
-    }
-    // print flags in user friendly way
-    inline void print_flags(int rank, std::string prefix ) {
-      utils::HandleLogInfo("[%d] %s - |%lu|%d|%d|%d|%d| - |%lu|%d|\n",
-                    rank, prefix.c_str(),
-                    seqno(), check_point(), check_ack(), load_cache(),
-                    diff_seq(), seqno(SeqType::kCache), load_cache(SeqType::kCache));
-    }
-    // reducer for Allreduce, get the result ActionSummary from all nodes
-    inline static void Reducer(const void *src_, void *dst_,
-                               int len, const MPI::Datatype &dtype) {
-      const ActionSummary *src = (const ActionSummary*)src_;
-      ActionSummary *dst = reinterpret_cast<ActionSummary*>(dst_);
-      for (int i = 0; i < len; ++i) {
-        u_int32_t min_seqno = std::min(src[i].seqno(), dst[i].seqno());
-        u_int32_t max_seqno = std::max(src[i].seqno(SeqType::kCache),
-          dst[i].seqno(SeqType::kCache));
-        int action_flag = src[i].flag() | dst[i].flag();
-        // if any node is not requester set to 0 otherwise 1
-        int role_flag = src[i].flag(SeqType::kCache) & dst[i].flag(SeqType::kCache);
-        // if seqno is different in src and destination
-        int seq_diff_flag = src[i].seqno() != dst[i].seqno() ? kDiffSeq : 0;
-        // apply or to both seq diff flag as well as cache seq diff flag
-        dst[i] = ActionSummary(action_flag | seq_diff_flag,
-          role_flag, min_seqno, max_seqno);
-      }
-    }
-
-   private:
-    // internel sequence code min of rabit seqno
-    u_int32_t seqcode;
-    // internal sequence code max of cache seqno
-    u_int32_t maxseqcode;
-  };
-  /*! \brief data structure to remember result of Bcast and Allreduce calls*/
-  class ResultBuffer{
-   public:
-    // constructor
-    ResultBuffer(void) {
-      this->Clear();
-    }
-    // clear the existing record
-    inline void Clear(void) {
-      seqno_.clear(); size_.clear();
-      rptr_.clear(); rptr_.push_back(0);
-      data_.clear();
-    }
-    // allocate temporal space
-    inline void *AllocTemp(size_t type_nbytes, size_t count) {
-      size_t size = type_nbytes * count;
-      size_t nhop = (size + sizeof(uint64_t) - 1) / sizeof(uint64_t);
-      utils::Assert(nhop != 0, "cannot allocate 0 size memory");
-      // allocate addational nhop buffer size
-      data_.resize(rptr_.back() + nhop);
-      return BeginPtr(data_) + rptr_.back();
-    }
-    // push the result in temp to the
-    inline void PushTemp(int seqid, size_t type_nbytes, size_t count) {
-      size_t size = type_nbytes * count;
-      size_t nhop = (size + sizeof(uint64_t) - 1) / sizeof(uint64_t);
-      if (seqno_.size() != 0) {
-        utils::Assert(seqno_.back() < seqid, "PushTemp seqid inconsistent");
-      }
-      seqno_.push_back(seqid);
-      rptr_.push_back(rptr_.back() + nhop);
-      size_.push_back(size);
-      utils::Assert(data_.size() == rptr_.back(), "PushTemp inconsistent");
-    }
-    // return the stored result of seqid, if any
-    inline void* Query(int seqid, size_t *p_size) {
-      size_t idx = std::lower_bound(seqno_.begin(),
-                                    seqno_.end(), seqid) - seqno_.begin();
-      if (idx == seqno_.size() || seqno_[idx] != seqid) return NULL;
-      *p_size = size_[idx];
-      return BeginPtr(data_) + rptr_[idx];
-    }
-    // drop last stored result
-    inline void DropLast(void) {
-      utils::Assert(seqno_.size() != 0, "there is nothing to be dropped");
-      seqno_.pop_back();
-      rptr_.pop_back();
-      size_.pop_back();
-      data_.resize(rptr_.back());
-    }
-    // the sequence number of last stored result
-    inline int LastSeqNo(void) const {
-      if (seqno_.size() == 0) return -1;
-      return seqno_.back();
-    }
-
-   private:
-    // sequence number of each
-    std::vector<int> seqno_;
-    // pointer to the positions
-    std::vector<size_t> rptr_;
-    // actual size of each buffer
-    std::vector<size_t> size_;
-    // content of the buffer
-    std::vector<uint64_t> data_;
-  };
-  /*!
-   * \brief internal consistency check function,
-   *  use check to ensure user always call CheckPoint/LoadCheckPoint
-   *  with or without local but not both, this function will set the approperiate settings
-   *  in the first call of LoadCheckPoint/CheckPoint
-   *
-   * \param with_local whether the user calls CheckPoint with local model
-   */
-  void LocalModelCheck(bool with_local);
-  /*!
-   * \brief internal implementation of checkpoint, support both lazy and normal way
-   *
-   * \param global_model pointer to the globally shared model/state
-   *   when calling this function, the caller need to gauranttees that global_model
-   *   is the same in all nodes
-   * \param local_model pointer to local model, that is specific to current node/rank
-   *   this can be NULL when no local state is needed
-   * \param lazy_checkpt whether the action is lazy checkpoint
-   *
-   * \sa CheckPoint, LazyCheckPoint
-   */
-  void CheckPoint_(const Serializable *global_model,
-                   const Serializable *local_model,
-                   bool lazy_checkpt);
-  /*!
-   * \brief reset the all the existing links by sending Out-of-Band message marker
-   *  after this function finishes, all the messages received and sent
-   *  before in all live links are discarded,
-   *  This allows us to get a fresh start after error has happened
-   *
-   *  TODO(tqchen): this function is not yet functioning was not used by engine,
-   *   simple resetlink and reconnect strategy is used
-   *
-   * \return this function can return kSuccess or kSockError
-   *         when kSockError is returned, it simply means there are bad sockets in the links,
-   *         and some link recovery proceduer is needed
-   */
-  ReturnType TryResetLinks(void);
-  /*!
-   * \brief if err_type indicates an error
-   *         recover links according to the error type reported
-   *        if there is no error, return true
-   * \param err_type the type of error happening in the system
-   * \return true if err_type is kSuccess, false otherwise
-   */
-  bool CheckAndRecover(ReturnType err_type);
-  /*!
-   * \brief try to run recover execution for a request action described by flag and seqno,
-   *        the function will keep blocking to run possible recovery operations before the specified action,
-   *        until the requested result is received by a recovering procedure,
-   *        or the function discovers that the requested action is not yet executed, and return false
-   *
-   * \param buf the buffer to store the result
-   * \param size the total size of the buffer
-   * \param flag flag information about the action \sa ActionSummary
-   * \param seqno sequence number of the action, if it is special action with flag set,
-   *        seqno needs to be set to ActionSummary::kSpecialOp
-   *
-   * \return if this function can return true or false
-   *    - true means buf already set to the
-   *           result by recovering procedure, the action is complete, no further action is needed
-   *    - false means this is the lastest action that has not yet been executed, need to execute the action
-   */
-  bool RecoverExec(void *buf, size_t size, int flag,
-    int seqno = ActionSummary::kSpecialOp,
-    int cacheseqno = ActionSummary::kSpecialOp,
-    const char* caller = _CALLER);
-  /*!
-   * \brief try to load check point
-   *
-   *        This is a collaborative function called by all nodes
-   *        only the nodes with requester set to true really needs to load the check point
-   *        other nodes acts as collaborative roles to complete this request
-   *
-   * \param requester whether current node is the requester
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryLoadCheckPoint(bool requester);
-
-  /*!
-   * \brief try to load cache
-   *
-   *        This is a collaborative function called by all nodes
-   *        only the nodes with requester set to true really needs to load the check point
-   *        other nodes acts as collaborative roles to complete this request
-   * \param requester whether current node is the requester
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryRestoreCache(bool requester, const int min_seq = ActionSummary::kSpecialOp,
-    const int max_seq = ActionSummary::kSpecialOp);
-  /*!
-   * \brief try to get the result of operation specified by seqno
-   *
-   *        This is a collaborative function called by all nodes
-   *        only the nodes with requester set to true really needs to get the result
-   *        other nodes acts as collaborative roles to complete this request
-   *
-   * \param buf the buffer to store the result, this parameter is only used when current node is requester
-   * \param size the total size of the buffer, this parameter is only used when current node is requester
-   * \param seqno sequence number of the operation, this is unique index of a operation in current iteration
-   * \param requester whether current node is the requester
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryGetResult(void *buf, size_t size, int seqno, bool requester);
-  /*!
-   * \brief try to decide the routing strategy for recovery
-   * \param role the current role of the node
-   * \param p_size used to store the size of the message, for node in state kHaveData,
-   *               this size must be set correctly before calling the function
-   *               for others, this surves as output parameter
-
-   * \param p_recvlink used to store the link current node should recv data from, if necessary
-   *          this can be -1, which means current node have the data
-   * \param p_req_in used to store the resulting vector, indicating which link we should send the data to
-   *
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType, TryRecoverData
-   */
-  ReturnType TryDecideRouting(RecoverType role,
-                              size_t *p_size,
-                              int *p_recvlink,
-                              std::vector<bool> *p_req_in);
-  /*!
-   * \brief try to finish the data recovery request,
-   *        this function is used together with TryDecideRouting
-   * \param role the current role of the node
-   * \param sendrecvbuf_ the buffer to store the data to be sent/recived
-   *          - if the role is kHaveData, this stores the data to be sent
-   *          - if the role is kRequestData, this is the buffer to store the result
-   *          - if the role is kPassData, this will not be used, and can be NULL
-   * \param size the size of the data, obtained from TryDecideRouting
-   * \param recv_link the link index to receive data, if necessary, obtained from TryDecideRouting
-   * \param req_in the request of each link to send data, obtained from TryDecideRouting
-   *
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType, TryDecideRouting
-   */
-  ReturnType TryRecoverData(RecoverType role,
-                            void *sendrecvbuf_,
-                            size_t size,
-                            int recv_link,
-                            const std::vector<bool> &req_in);
-  /*!
-   * \brief try to recover the local state, making each local state to be the result of itself
-   *        plus replication of states in previous num_local_replica hops in the ring
-   *
-   * The input parameters must contain the valid local states available in current nodes,
-   * This function try ist best to "complete" the missing parts of local_rptr and local_chkpt
-   * If there is sufficient information in the ring, when the function returns, local_chkpt will
-   * contain num_local_replica + 1 checkpoints (including the chkpt of this node)
-   * If there is no sufficient information in the ring, this function the number of checkpoints
-   * will be less than the specified value
-   *
-   * \param p_local_rptr the pointer to the segment pointers in the states array
-   * \param p_local_chkpt the pointer to the storage of local check points
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType
-   */
-  ReturnType TryRecoverLocalState(std::vector<size_t> *p_local_rptr,
-                                  std::string *p_local_chkpt);
-  /*!
-   * \brief try to checkpoint local state, this function is called in normal executation phase
-   *    of checkpoint that contains local state
-o   *  the input state must exactly one saved state(local state of current node),
-   *  after complete, this function will get local state from previous num_local_replica nodes and put them
-   *  into local_chkpt and local_rptr
-   *
-   *  It is also OK to call TryRecoverLocalState instead,
-   *  TryRecoverLocalState makes less assumption about the input, and requires more communications
-   *
-   * \param p_local_rptr the pointer to the segment pointers in the states array
-   * \param p_local_chkpt the pointer to the storage of local check points
-   * \return this function can return kSuccess/kSockError/kGetExcept, see ReturnType for details
-   * \sa ReturnType, TryRecoverLocalState
-   */
-  ReturnType TryCheckinLocalState(std::vector<size_t> *p_local_rptr,
-                                  std::string *p_local_chkpt);
-  /*!
-   * \brief perform a ring passing to receive data from prev link, and sent data to next link
-   *  this allows data to stream over a ring structure
-   *  sendrecvbuf[0:read_ptr] are already provided by current node
-   *  current node will recv sendrecvbuf[read_ptr:read_end] from prev link
-   *  current node will send sendrecvbuf[write_ptr:write_end] to next link
-   *  write_ptr will wait till the data is readed before sending the data
-   *  this function requires read_end >= write_end
-   *
-   * \param sendrecvbuf_ the place to hold the incoming and outgoing data
-   * \param read_ptr the initial read pointer
-   * \param read_end the ending position to read
-   * \param write_ptr the initial write pointer
-   * \param write_end the ending position to write
-   * \param read_link pointer to link to previous position in ring
-   * \param write_link pointer to link of next position in ring
-   */
-  ReturnType RingPassing(void *senrecvbuf_,
-                         size_t read_ptr,
-                         size_t read_end,
-                         size_t write_ptr,
-                         size_t write_end,
-                         LinkRecord *read_link,
-                         LinkRecord *write_link);
-  /*!
-   * \brief run message passing algorithm on the allreduce tree
-   *        the result is edge message stored in p_edge_in and p_edge_out
-   * \param node_value the value associated with current node
-   * \param p_edge_in used to store input message from each of the edge
-   * \param p_edge_out used to store output message from each of the edge
-   * \param func a function that defines the message passing rule
-   *        Parameters of func:
-   *           - node_value same as node_value in the main function
-   *           - edge_in the array of input messages from each edge,
-   *                     this includes the output edge, which should be excluded
-   *           - out_index array the index of output edge, the function should
-   *                       exclude the output edge when compute the message passing value
-   *        Return of func:
-   *           the function returns the output message based on the input message and node_value
-   *
-   * \tparam EdgeType type of edge message, must be simple struct
-   * \tparam NodeType type of node value
-   */
-  template<typename NodeType, typename EdgeType>
-  inline ReturnType MsgPassing(const NodeType &node_value,
-                               std::vector<EdgeType> *p_edge_in,
-                               std::vector<EdgeType> *p_edge_out,
-                               EdgeType(*func)
-                               (const NodeType &node_value,
-                                const std::vector<EdgeType> &edge_in,
-                                size_t out_index));
-  //---- recovery data structure ----
-  // the round of result buffer, used to mode the result
-  int result_buffer_round;
-  // result buffer of all reduce
-  ResultBuffer resbuf;
-  // current cached allreduce/braodcast sequence number
-  int cur_cache_seq;
-  // result buffer of cached all reduce
-  ResultBuffer cachebuf;
-  // key of each cache entry
-  ResultBuffer lookupbuf;
-  // last check point global model
-  std::string global_checkpoint;
-  // lazy checkpoint of global model
-  const Serializable *global_lazycheck;
-  // number of replica for local state/model
-  int num_local_replica;
-  // number of default local replica
-  int default_local_replica;
-  // flag to decide whether local model is used, -1: unknown, 0: no, 1:yes
-  int use_local_model;
-  // number of replica for global state/model
-  int num_global_replica;
-  // number of times recovery happens
-  int recover_counter;
-  // --- recovery data structure for local checkpoint
-  // there is two version of the data structure,
-  // at one time one version is valid and another is used as temp memory
-  // pointer to memory position in the local model
-  // local model is stored in CSR format(like a sparse matrices)
-  // local_model[rptr[0]:rptr[1]] stores the model of current node
-  // local_model[rptr[k]:rptr[k+1]] stores the model of node in previous k hops
-  std::vector<size_t> local_rptr[2];
-  // storage for local model replicas
-  std::string local_chkpt[2];
-  // version of local checkpoint can be 1 or 0
-  int local_chkpt_version;
-  // if checkpoint were loaded, used to distinguish results boostrap cache from seqno cache
-  bool checkpoint_loaded;
-  // sidecar executing timeout task
-  std::future<bool> rabit_timeout_task;
-  // flag to shutdown rabit_timeout_task before timeout
-  std::atomic<bool> shutdown_timeout{false};
-  // error handler
-  void (* _error)(const char *fmt, ...) = utils::Error;
-  // assert handler
-  void (* _assert)(bool exp, const char *fmt, ...) = utils::Assert;
-};
-}  // namespace engine
-}  // namespace rabit
-// implementation of inline template function
-#include "./allreduce_robust-inl.h"
-#endif  // RABIT_ALLREDUCE_ROBUST_H_
diff --git a/ml-xgboost/rabit/src/c_api.cc b/ml-xgboost/rabit/src/c_api.cc
deleted file mode 100644
index d853319..0000000
--- a/ml-xgboost/rabit/src/c_api.cc
+++ /dev/null
@@ -1,344 +0,0 @@
-// Copyright by Contributors
-// implementations in ctypes
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-
-#include <cstring>
-#include <string>
-#include "rabit/rabit.h"
-#include "rabit/c_api.h"
-
-namespace rabit {
-namespace c_api {
-// helper use to avoid BitOR operator
-template<typename OP, typename DType>
-struct FHelper {
-  static void
-  Allreduce(DType *senrecvbuf_,
-            size_t count,
-            void (*prepare_fun)(void *arg),
-            void *prepare_arg) {
-    rabit::Allreduce<OP>(senrecvbuf_, count,
-                         prepare_fun, prepare_arg);
-  }
-};
-
-template<typename DType>
-struct FHelper<op::BitOR, DType> {
-  static void
-  Allreduce(DType *senrecvbuf_,
-            size_t count,
-            void (*prepare_fun)(void *arg),
-            void *prepare_arg) {
-    utils::Error("DataType does not support bitwise or operation");
-  }
-};
-
-template<typename OP>
-void Allreduce_(void *sendrecvbuf_,
-                size_t count,
-                engine::mpi::DataType enum_dtype,
-                void (*prepare_fun)(void *arg),
-                void *prepare_arg) {
-  using namespace engine::mpi;
-  switch (enum_dtype) {
-    case kChar:
-      rabit::Allreduce<OP>
-          (static_cast<char*>(sendrecvbuf_),
-           count, prepare_fun, prepare_arg);
-      return;
-    case kUChar:
-      rabit::Allreduce<OP>
-          (static_cast<unsigned char*>(sendrecvbuf_),
-           count, prepare_fun, prepare_arg);
-      return;
-    case kInt:
-      rabit::Allreduce<OP>
-          (static_cast<int*>(sendrecvbuf_),
-           count, prepare_fun, prepare_arg);
-      return;
-    case kUInt:
-      rabit::Allreduce<OP>
-          (static_cast<unsigned*>(sendrecvbuf_),
-           count, prepare_fun, prepare_arg);
-      return;
-    case kLong:
-      rabit::Allreduce<OP>
-          (static_cast<long*>(sendrecvbuf_),  // NOLINT(*)
-           count, prepare_fun, prepare_arg);
-      return;
-    case kULong:
-      rabit::Allreduce<OP>
-          (static_cast<unsigned long*>(sendrecvbuf_),  // NOLINT(*)
-           count, prepare_fun, prepare_arg);
-      return;
-    case kFloat:
-      FHelper<OP, float>::Allreduce
-          (static_cast<float*>(sendrecvbuf_),
-           count, prepare_fun, prepare_arg);
-      return;
-    case kDouble:
-      FHelper<OP, double>::Allreduce
-          (static_cast<double*>(sendrecvbuf_),
-           count, prepare_fun, prepare_arg);
-      return;
-    default: utils::Error("unknown data_type");
-  }
-}
-void Allreduce(void *sendrecvbuf,
-               size_t count,
-               engine::mpi::DataType enum_dtype,
-               engine::mpi::OpType enum_op,
-               void (*prepare_fun)(void *arg),
-               void *prepare_arg) {
-  using namespace engine::mpi;
-  switch (enum_op) {
-    case kMax:
-      Allreduce_<op::Max>
-          (sendrecvbuf,
-           count, enum_dtype,
-           prepare_fun, prepare_arg);
-      return;
-    case kMin:
-      Allreduce_<op::Min>
-          (sendrecvbuf,
-           count, enum_dtype,
-           prepare_fun, prepare_arg);
-      return;
-    case kSum:
-      Allreduce_<op::Sum>
-          (sendrecvbuf,
-           count, enum_dtype,
-           prepare_fun, prepare_arg);
-      return;
-    case kBitwiseOR:
-      Allreduce_<op::BitOR>
-          (sendrecvbuf,
-           count, enum_dtype,
-           prepare_fun, prepare_arg);
-      return;
-    default: utils::Error("unknown enum_op");
-  }
-}
-void Allgather(void *sendrecvbuf_,
-               size_t total_size,
-               size_t beginIndex,
-               size_t size_node_slice,
-               size_t size_prev_slice,
-               int enum_dtype) {
-  using namespace engine::mpi;
-  size_t type_size = 0;
-  switch (enum_dtype) {
-  case kChar:
-    type_size = sizeof(char);
-    rabit::Allgather(static_cast<char*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kUChar:
-    type_size = sizeof(unsigned char);
-    rabit::Allgather(static_cast<unsigned char*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kInt:
-    type_size = sizeof(int);
-    rabit::Allgather(static_cast<int*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kUInt:
-    type_size = sizeof(unsigned);
-    rabit::Allgather(static_cast<unsigned*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kLong:
-    type_size = sizeof(int64_t);
-    rabit::Allgather(static_cast<int64_t*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kULong:
-    type_size = sizeof(uint64_t);
-    rabit::Allgather(static_cast<uint64_t*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kFloat:
-    type_size = sizeof(float);
-    rabit::Allgather(static_cast<float*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  case kDouble:
-    type_size = sizeof(double);
-    rabit::Allgather(static_cast<double*>(sendrecvbuf_), total_size * type_size,
-      beginIndex * type_size, (beginIndex + size_node_slice) * type_size,
-      size_prev_slice * type_size);
-    break;
-  default: utils::Error("unknown data_type");
-  }
-}
-
-// wrapper for serialization
-struct ReadWrapper : public Serializable {
-  std::string *p_str;
-  explicit ReadWrapper(std::string *p_str)
-      : p_str(p_str) {}
-  virtual void Load(Stream *fi) {
-    uint64_t sz;
-    utils::Assert(fi->Read(&sz, sizeof(sz)) != 0,
-                 "Read pickle string");
-    p_str->resize(sz);
-    if (sz != 0) {
-      utils::Assert(fi->Read(&(*p_str)[0], sizeof(char) * sz) != 0,
-                    "Read pickle string");
-    }
-  }
-  virtual void Save(Stream *fo) const {
-    utils::Error("not implemented");
-  }
-};
-
-struct WriteWrapper : public Serializable {
-  const char *data;
-  size_t length;
-  explicit WriteWrapper(const char *data,
-                        size_t length)
-      : data(data), length(length) {
-  }
-  virtual void Load(Stream *fi) {
-    utils::Error("not implemented");
-  }
-  virtual void Save(Stream *fo) const {
-    uint64_t sz = static_cast<uint16_t>(length);
-    fo->Write(&sz, sizeof(sz));
-    fo->Write(data, length * sizeof(char));
-  }
-};
-}  // namespace c_api
-}  // namespace rabit
-
-bool RabitInit(int argc, char *argv[]) {
-  return rabit::Init(argc, argv);
-}
-
-bool RabitFinalize() {
-  return rabit::Finalize();
-}
-
-int RabitGetRingPrevRank() {
-  return rabit::GetRingPrevRank();
-}
-
-int RabitGetRank() {
-  return rabit::GetRank();
-}
-
-int RabitGetWorldSize() {
-  return rabit::GetWorldSize();
-}
-
-int RabitIsDistributed() {
-  return rabit::IsDistributed();
-}
-
-void RabitTrackerPrint(const char *msg) {
-  std::string m(msg);
-  rabit::TrackerPrint(m);
-}
-
-void RabitGetProcessorName(char *out_name,
-                           rbt_ulong *out_len,
-                           rbt_ulong max_len) {
-  std::string s = rabit::GetProcessorName();
-  if (s.length() > max_len) {
-    s.resize(max_len - 1);
-  }
-  strcpy(out_name, s.c_str()); // NOLINT(*)
-  *out_len = static_cast<rbt_ulong>(s.length());
-}
-
-void RabitBroadcast(void *sendrecv_data,
-                    rbt_ulong size, int root) {
-  rabit::Broadcast(sendrecv_data, size, root);
-}
-
-void RabitAllgather(void *sendrecvbuf_,
-                        size_t total_size,
-                        size_t beginIndex,
-                        size_t size_node_slice,
-                        size_t size_prev_slice,
-                        int enum_dtype) {
-  rabit::c_api::Allgather(sendrecvbuf_,
-                          total_size,
-                          beginIndex,
-                          size_node_slice,
-                          size_prev_slice,
-                          static_cast<rabit::engine::mpi::DataType>(enum_dtype));
-}
-
-
-void RabitAllreduce(void *sendrecvbuf,
-                    size_t count,
-                    int enum_dtype,
-                    int enum_op,
-                    void (*prepare_fun)(void *arg),
-                    void *prepare_arg) {
-  rabit::c_api::Allreduce
-      (sendrecvbuf, count,
-       static_cast<rabit::engine::mpi::DataType>(enum_dtype),
-       static_cast<rabit::engine::mpi::OpType>(enum_op),
-       prepare_fun, prepare_arg);
-}
-
-int RabitLoadCheckPoint(char **out_global_model,
-                        rbt_ulong *out_global_len,
-                        char **out_local_model,
-                        rbt_ulong *out_local_len) {
-  // NOTE: this function is not thread-safe
-  using rabit::BeginPtr;
-  using namespace rabit::c_api; // NOLINT(*)
-  static std::string global_buffer;
-  static std::string local_buffer;
-
-  ReadWrapper sg(&global_buffer);
-  ReadWrapper sl(&local_buffer);
-  int version;
-
-  if (out_local_model == NULL) {
-    version = rabit::LoadCheckPoint(&sg, NULL);
-    *out_global_model = BeginPtr(global_buffer);
-    *out_global_len = static_cast<rbt_ulong>(global_buffer.length());
-  } else {
-    version = rabit::LoadCheckPoint(&sg, &sl);
-    *out_global_model = BeginPtr(global_buffer);
-    *out_global_len = static_cast<rbt_ulong>(global_buffer.length());
-    *out_local_model = BeginPtr(local_buffer);
-    *out_local_len = static_cast<rbt_ulong>(local_buffer.length());
-  }
-  return version;
-}
-
-void RabitCheckPoint(const char *global_model,
-                     rbt_ulong global_len,
-                     const char *local_model,
-                     rbt_ulong local_len) {
-  using namespace rabit::c_api; // NOLINT(*)
-  WriteWrapper sg(global_model, global_len);
-  WriteWrapper sl(local_model, local_len);
-  if (local_model == NULL) {
-    rabit::CheckPoint(&sg, NULL);
-  } else {
-    rabit::CheckPoint(&sg, &sl);
-  }
-}
-
-int RabitVersionNumber() {
-  return rabit::VersionNumber();
-}
-
-int RabitLinkTag() {
-  return 0;
-}
diff --git a/ml-xgboost/rabit/src/engine.cc b/ml-xgboost/rabit/src/engine.cc
deleted file mode 100644
index 4701d2f..0000000
--- a/ml-xgboost/rabit/src/engine.cc
+++ /dev/null
@@ -1,145 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file engine.cc
- * \brief this file governs which implementation of engine we are actually using
- *  provides an singleton of engine interface
- *
- * \author Tianqi Chen, Ignacio Cano, Tianyi Zhou
- */
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-
-#include <memory>
-#include "rabit/internal/engine.h"
-#include "allreduce_base.h"
-#include "allreduce_robust.h"
-#include "rabit/internal/thread_local.h"
-
-namespace rabit {
-namespace engine {
-// singleton sync manager
-#ifndef RABIT_USE_BASE
-#ifndef RABIT_USE_MOCK
-typedef AllreduceRobust Manager;
-#else
-typedef AllreduceMock Manager;
-#endif  // RABIT_USE_MOCK
-#else
-typedef AllreduceBase Manager;
-#endif  // RABIT_USE_BASE
-
-/*! \brief entry to to easily hold returning information */
-struct ThreadLocalEntry {
-  /*! \brief stores the current engine */
-  std::unique_ptr<Manager> engine;
-  /*! \brief whether init has been called */
-  bool initialized;
-  /*! \brief constructor */
-  ThreadLocalEntry() : initialized(false) {}
-};
-
-// define the threadlocal store.
-typedef ThreadLocalStore<ThreadLocalEntry> EngineThreadLocal;
-
-/*! \brief intiialize the synchronization module */
-bool Init(int argc, char *argv[]) {
-  ThreadLocalEntry* e = EngineThreadLocal::Get();
-  if (e->engine.get() == nullptr) {
-    e->initialized = true;
-    e->engine.reset(new Manager());
-    return e->engine->Init(argc, argv);
-  } else {
-    return true;
-  }
-}
-
-/*! \brief finalize syncrhonization module */
-bool Finalize() {
-  ThreadLocalEntry* e = EngineThreadLocal::Get();
-  if (e->engine.get() != nullptr) {
-    if (e->engine->Shutdown()) {
-      e->engine.reset(nullptr);
-      e->initialized = false;
-      return true;
-    } else {
-      return false;
-    }
-  } else {
-    return true;
-  }
-}
-
-/*! \brief singleton method to get engine */
-IEngine *GetEngine() {
-  // un-initialized default manager.
-  static AllreduceBase default_manager;
-  ThreadLocalEntry* e = EngineThreadLocal::Get();
-  IEngine* ptr = e->engine.get();
-  if (ptr == nullptr) {
-    utils::Check(!e->initialized, "the rabit has not been initialized");
-    return &default_manager;
-  } else {
-    return ptr;
-  }
-}
-
-// perform in-place allgather, on sendrecvbuf
-void Allgather(void *sendrecvbuf_, size_t total_size,
-                   size_t slice_begin,
-                   size_t slice_end,
-                   size_t size_prev_slice,
-                   const char* _file,
-                   const int _line,
-                   const char* _caller) {
-  GetEngine()->Allgather(sendrecvbuf_, total_size, slice_begin,
-    slice_end, size_prev_slice, _file, _line, _caller);
-}
-
-
-// perform in-place allreduce, on sendrecvbuf
-void Allreduce_(void *sendrecvbuf,
-                size_t type_nbytes,
-                size_t count,
-                IEngine::ReduceFunction red,
-                mpi::DataType dtype,
-                mpi::OpType op,
-                IEngine::PreprocFunction prepare_fun,
-                void *prepare_arg,
-                const char* _file,
-                const int _line,
-                const char* _caller) {
-  GetEngine()->Allreduce(sendrecvbuf, type_nbytes, count, red, prepare_fun,
-    prepare_arg, _file, _line, _caller);
-}
-
-// code for reduce handle
-ReduceHandle::ReduceHandle(void)
-  : handle_(NULL), redfunc_(NULL), htype_(NULL) {
-}
-
-ReduceHandle::~ReduceHandle(void) {}
-
-int ReduceHandle::TypeSize(const MPI::Datatype &dtype) {
-  return static_cast<int>(dtype.type_size);
-}
-
-void ReduceHandle::Init(IEngine::ReduceFunction redfunc, size_t type_nbytes) {
-  utils::Assert(redfunc_ == NULL, "cannot initialize reduce handle twice");
-  redfunc_ = redfunc;
-}
-
-void ReduceHandle::Allreduce(void *sendrecvbuf,
-                             size_t type_nbytes, size_t count,
-                             IEngine::PreprocFunction prepare_fun,
-                             void *prepare_arg,
-                             const char* _file,
-                             const int _line,
-                             const char* _caller) {
-  utils::Assert(redfunc_ != NULL, "must intialize handle to call AllReduce");
-  GetEngine()->Allreduce(sendrecvbuf, type_nbytes, count,
-                         redfunc_, prepare_fun, prepare_arg,
-                         _file, _line, _caller);
-}
-}  // namespace engine
-}  // namespace rabit
diff --git a/ml-xgboost/rabit/src/engine_base.cc b/ml-xgboost/rabit/src/engine_base.cc
deleted file mode 100644
index 39da566..0000000
--- a/ml-xgboost/rabit/src/engine_base.cc
+++ /dev/null
@@ -1,15 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file engine_mock.cc
- * \brief this is an engine implementation that will 
- * insert failures in certain call point, to test if the engine is robust to failure
- * \author Tianqi Chen
- */
-// define use MOCK, os we will use mock Manager
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-// switch engine to AllreduceMock
-#define RABIT_USE_BASE
-#include "engine.cc"
-
diff --git a/ml-xgboost/rabit/src/engine_empty.cc b/ml-xgboost/rabit/src/engine_empty.cc
deleted file mode 100644
index 5cecc03..0000000
--- a/ml-xgboost/rabit/src/engine_empty.cc
+++ /dev/null
@@ -1,149 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file engine_empty.cc
- * \brief this file provides a dummy implementation of engine that does nothing
- *  this file provides a way to fall back to single node program without causing too many dependencies
- *  This is usually NOT needed, use engine_mpi or engine for real distributed version
- * \author Tianqi Chen
- */
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-
-#include "rabit/internal/engine.h"
-
-namespace rabit {
-
-namespace utils {
-  bool STOP_PROCESS_ON_ERROR = true;
-}
-
-namespace engine {
-/*! \brief EmptyEngine */
-class EmptyEngine : public IEngine {
- public:
-  EmptyEngine(void) {
-    version_number = 0;
-  }
-  virtual void Allgather(void *sendrecvbuf_,
-                         size_t total_size,
-                         size_t slice_begin,
-                         size_t slice_end,
-                         size_t size_prev_slice,
-                         const char* _file,
-                         const int _line,
-                         const char* _caller) {
-    utils::Error("EmptyEngine:: Allgather is not supported");
-  }
-  virtual int GetRingPrevRank(void) const {
-    utils::Error("EmptyEngine:: GetRingPrevRank is not supported");
-    return -1;
-  }
-  virtual void Allreduce(void *sendrecvbuf_,
-                         size_t type_nbytes,
-                         size_t count,
-                         ReduceFunction reducer,
-                         PreprocFunction prepare_fun,
-                         void *prepare_arg,
-                         const char* _file,
-                         const int _line,
-                         const char* _caller) {
-    utils::Error("EmptyEngine:: Allreduce is not supported,"\
-                 "use Allreduce_ instead");
-  }
-  virtual void Broadcast(void *sendrecvbuf_, size_t size, int root,
-                          const char* _file, const int _line, const char* _caller) {
-  }
-  virtual void InitAfterException(void) {
-    utils::Error("EmptyEngine is not fault tolerant");
-  }
-  virtual int LoadCheckPoint(Serializable *global_model,
-                             Serializable *local_model = NULL) {
-    return 0;
-  }
-  virtual void CheckPoint(const Serializable *global_model,
-                          const Serializable *local_model = NULL) {
-    version_number += 1;
-  }
-  virtual void LazyCheckPoint(const Serializable *global_model) {
-    version_number += 1;
-  }
-  virtual int VersionNumber(void) const {
-    return version_number;
-  }
-  /*! \brief get rank of current node */
-  virtual int GetRank(void) const {
-    return 0;
-  }
-  /*! \brief get total number of */
-  virtual int GetWorldSize(void) const {
-    return 1;
-  }
-  /*! \brief whether it is distributed */
-  virtual bool IsDistributed(void) const {
-    return false;
-  }
-  /*! \brief get the host name of current node */
-  virtual std::string GetHost(void) const {
-    return std::string("");
-  }
-  virtual void TrackerPrint(const std::string &msg) {
-    // simply print information into the tracker
-    utils::Printf("%s", msg.c_str());
-  }
-
- private:
-  int version_number;
-};
-
-// singleton sync manager
-EmptyEngine manager;
-
-/*! \brief intiialize the synchronization module */
-bool Init(int argc, char *argv[]) {
-  return true;
-}
-/*! \brief finalize syncrhonization module */
-bool Finalize(void) {
-  return true;
-}
-
-/*! \brief singleton method to get engine */
-IEngine *GetEngine(void) {
-  return &manager;
-}
-// perform in-place allreduce, on sendrecvbuf
-void Allreduce_(void *sendrecvbuf,
-                size_t type_nbytes,
-                size_t count,
-                IEngine::ReduceFunction red,
-                mpi::DataType dtype,
-                mpi::OpType op,
-                IEngine::PreprocFunction prepare_fun,
-                void *prepare_arg,
-                const char* _file,
-                const int _line,
-                const char* _caller) {
-  if (prepare_fun != NULL) prepare_fun(prepare_arg);
-}
-
-// code for reduce handle
-ReduceHandle::ReduceHandle(void) : handle_(NULL), htype_(NULL) {
-}
-ReduceHandle::~ReduceHandle(void) {}
-
-int ReduceHandle::TypeSize(const MPI::Datatype &dtype) {
-  return 0;
-}
-void ReduceHandle::Init(IEngine::ReduceFunction redfunc, size_t type_nbytes) {}
-void ReduceHandle::Allreduce(void *sendrecvbuf,
-                             size_t type_nbytes, size_t count,
-                             IEngine::PreprocFunction prepare_fun,
-                             void *prepare_arg,
-                             const char* _file,
-                             const int _line,
-                             const char* _caller) {
-  if (prepare_fun != NULL) prepare_fun(prepare_arg);
-}
-}  // namespace engine
-}  // namespace rabit
diff --git a/ml-xgboost/rabit/src/engine_mock.cc b/ml-xgboost/rabit/src/engine_mock.cc
deleted file mode 100644
index aab012d..0000000
--- a/ml-xgboost/rabit/src/engine_mock.cc
+++ /dev/null
@@ -1,16 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file engine_mock.cc
- * \brief this is an engine implementation that will 
- * insert failures in certain call point, to test if the engine is robust to failure
- * \author Tianqi Chen
- */
-// define use MOCK, os we will use mock Manager
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-// switch engine to AllreduceMock
-#define RABIT_USE_MOCK
-#include "allreduce_mock.h"
-#include "engine.cc"
-
diff --git a/ml-xgboost/rabit/src/engine_mpi.cc b/ml-xgboost/rabit/src/engine_mpi.cc
deleted file mode 100644
index f617708..0000000
--- a/ml-xgboost/rabit/src/engine_mpi.cc
+++ /dev/null
@@ -1,252 +0,0 @@
-/*!
- *  Copyright (c) 2014 by Contributors
- * \file engine_mpi.cc
- * \brief this file gives an implementation of engine interface using MPI,
- *   this will allow rabit program to run with MPI, but do not comes with fault tolerant
- *
- * \author Tianqi Chen
- */
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-#include <mpi.h>
-#include <cstdio>
-#include "rabit/internal/engine.h"
-#include "rabit/internal/utils.h"
-
-namespace rabit {
-
-namespace utils {
-    bool STOP_PROCESS_ON_ERROR = true;
-}
-
-namespace engine {
-/*! \brief implementation of engine using MPI */
-class MPIEngine : public IEngine {
- public:
-  MPIEngine(void) {
-    version_number = 0;
-  }
-  virtual void Allgather(void *sendrecvbuf_,
-                             size_t total_size,
-                             size_t slice_begin,
-                             size_t slice_end,
-                             size_t size_prev_slice,
-                             const char* _file,
-                             const int _line,
-                             const char* _caller) {
-    utils::Error("MPIEngine:: Allgather is not supported");
-  }
-  virtual void Allreduce(void *sendrecvbuf_,
-                         size_t type_nbytes,
-                         size_t count,
-                         ReduceFunction reducer,
-                         PreprocFunction prepare_fun,
-                         void *prepare_arg,
-                         const char* _file,
-                         const int _line,
-                         const char* _caller) {
-    utils::Error("MPIEngine:: Allreduce is not supported,"\
-                 "use Allreduce_ instead");
-  }
-  virtual int GetRingPrevRank(void) const {
-    utils::Error("MPIEngine:: GetRingPrevRank is not supported");
-  }
-  virtual void Broadcast(void *sendrecvbuf_, size_t size, int root,
-    const char* _file, const int _line,
-    const char* _caller) {
-    MPI::COMM_WORLD.Bcast(sendrecvbuf_, size, MPI::CHAR, root);
-  }
-  virtual void InitAfterException(void) {
-    utils::Error("MPI is not fault tolerant");
-  }
-  virtual int LoadCheckPoint(Serializable *global_model,
-                             Serializable *local_model = NULL) {
-    return 0;
-  }
-  virtual void CheckPoint(const Serializable *global_model,
-                          const Serializable *local_model = NULL) {
-    version_number += 1;
-  }
-  virtual void LazyCheckPoint(const Serializable *global_model) {
-    version_number += 1;
-  }
-  virtual int VersionNumber(void) const {
-    return version_number;
-  }
-  /*! \brief get rank of current node */
-  virtual int GetRank(void) const {
-    return MPI::COMM_WORLD.Get_rank();
-  }
-  /*! \brief get total number of */
-  virtual int GetWorldSize(void) const {
-    return MPI::COMM_WORLD.Get_size();
-  }
-  /*! \brief whether it is distributed */
-  virtual bool IsDistributed(void) const {
-    return true;
-  }
-  /*! \brief get the host name of current node */
-  virtual std::string GetHost(void) const {
-    int len;
-    char name[MPI_MAX_PROCESSOR_NAME];
-    MPI::Get_processor_name(name, len);
-    name[len] = '\0';
-    return std::string(name);
-  }
-  virtual void TrackerPrint(const std::string &msg) {
-    // simply print information into the tracker
-    if (GetRank() == 0) {
-      utils::Printf("%s", msg.c_str());
-    }
-  }
-
- private:
-  int version_number;
-};
-
-// singleton sync manager
-MPIEngine manager;
-
-/*! \brief initialize the synchronization module */
-bool Init(int argc, char *argv[]) {
-  try {
-    MPI::Init(argc, argv);
-    return true;
-  } catch (const std::exception& e) {
-    fprintf(stderr, " failed in MPI Init %s\n", e.what());
-    return false;
-  }
-}
-/*! \brief finalize syncrhonization module */
-bool Finalize(void) {
-  try {
-    MPI::Finalize();
-    return true;
-  } catch (const std::exception& e) {
-    fprintf(stderr, "failed in MPI shutdown %s\n", e.what());
-    return false;
-  }
-}
-
-/*! \brief singleton method to get engine */
-IEngine *GetEngine(void) {
-  return &manager;
-}
-// transform enum to MPI data type
-inline MPI::Datatype GetType(mpi::DataType dtype) {
-  using namespace mpi;
-  switch (dtype) {
-    case kChar: return MPI::CHAR;
-    case kUChar: return MPI::BYTE;
-    case kInt: return MPI::INT;
-    case kUInt: return MPI::UNSIGNED;
-    case kLong: return MPI::LONG;
-    case kULong: return MPI::UNSIGNED_LONG;
-    case kFloat: return MPI::FLOAT;
-    case kDouble: return MPI::DOUBLE;
-    case kLongLong: return MPI::LONG_LONG;
-    case kULongLong: return MPI::UNSIGNED_LONG_LONG;
-  }
-  utils::Error("unknown mpi::DataType");
-  return MPI::CHAR;
-}
-// transform enum to MPI OP
-inline MPI::Op GetOp(mpi::OpType otype) {
-  using namespace mpi;
-  switch (otype) {
-    case kMax: return MPI::MAX;
-    case kMin: return MPI::MIN;
-    case kSum: return MPI::SUM;
-    case kBitwiseOR: return MPI::BOR;
-  }
-  utils::Error("unknown mpi::OpType");
-  return MPI::MAX;
-}
-// perform in-place allreduce, on sendrecvbuf
-void Allreduce_(void *sendrecvbuf,
-                size_t type_nbytes,
-                size_t count,
-                IEngine::ReduceFunction red,
-                mpi::DataType dtype,
-                mpi::OpType op,
-                IEngine::PreprocFunction prepare_fun,
-                void *prepare_arg,
-                const char* _file,
-                const int _line,
-                const char* _caller) {
-  if (prepare_fun != NULL) prepare_fun(prepare_arg);
-  MPI::COMM_WORLD.Allreduce(MPI_IN_PLACE, sendrecvbuf,
-                            count, GetType(dtype), GetOp(op));
-}
-
-// code for reduce handle
-ReduceHandle::ReduceHandle(void)
-    : handle_(NULL), redfunc_(NULL), htype_(NULL) {
-}
-ReduceHandle::~ReduceHandle(void) {
-  if (handle_ != NULL) {
-    MPI::Op *op = reinterpret_cast<MPI::Op*>(handle_);
-    op->Free();
-    delete op;
-  }
-  if (htype_ != NULL) {
-    MPI::Datatype *dtype = reinterpret_cast<MPI::Datatype*>(htype_);
-    dtype->Free();
-    delete dtype;
-  }
-}
-int ReduceHandle::TypeSize(const MPI::Datatype &dtype) {
-  return dtype.Get_size();
-}
-void ReduceHandle::Init(IEngine::ReduceFunction redfunc, size_t type_nbytes) {
-  utils::Assert(handle_ == NULL, "cannot initialize reduce handle twice");
-  if (type_nbytes != 0) {
-    MPI::Datatype *dtype = new MPI::Datatype();
-    if (type_nbytes % 8 == 0) {
-      *dtype = MPI::LONG.Create_contiguous(type_nbytes / sizeof(long));  // NOLINT(*)
-    } else if (type_nbytes % 4 == 0) {
-      *dtype = MPI::INT.Create_contiguous(type_nbytes / sizeof(int));
-    } else {
-      *dtype = MPI::CHAR.Create_contiguous(type_nbytes);
-    }
-    dtype->Commit();
-    created_type_nbytes_ = type_nbytes;
-    htype_ = dtype;
-  }
-  MPI::Op *op = new MPI::Op();
-  MPI::User_function *pf = redfunc;
-  op->Init(pf, true);
-  handle_ = op;
-}
-void ReduceHandle::Allreduce(void *sendrecvbuf,
-                             size_t type_nbytes, size_t count,
-                             IEngine::PreprocFunction prepare_fun,
-                             void *prepare_arg,
-                             const char* _file,
-                             const int _line,
-                             const char* _caller) {
-  utils::Assert(handle_ != NULL, "must intialize handle to call AllReduce");
-  MPI::Op *op = reinterpret_cast<MPI::Op*>(handle_);
-  MPI::Datatype *dtype = reinterpret_cast<MPI::Datatype*>(htype_);
-  if (created_type_nbytes_ != type_nbytes || dtype == NULL) {
-    if (dtype == NULL) {
-      dtype = new MPI::Datatype();
-    } else {
-      dtype->Free();
-    }
-    if (type_nbytes % 8 == 0) {
-      *dtype = MPI::LONG.Create_contiguous(type_nbytes / sizeof(long));  // NOLINT(*)
-    } else if (type_nbytes % 4 == 0) {
-      *dtype = MPI::INT.Create_contiguous(type_nbytes / sizeof(int));
-    } else {
-      *dtype = MPI::CHAR.Create_contiguous(type_nbytes);
-    }
-    dtype->Commit();
-    created_type_nbytes_ = type_nbytes;
-  }
-  if (prepare_fun != NULL) prepare_fun(prepare_arg);
-  MPI::COMM_WORLD.Allreduce(MPI_IN_PLACE, sendrecvbuf, count, *dtype, *op);
-}
-}  // namespace engine
-}  // namespace rabit
diff --git a/ml-xgboost/rabit/test/.gitignore b/ml-xgboost/rabit/test/.gitignore
deleted file mode 100644
index 8e1ff37..0000000
--- a/ml-xgboost/rabit/test/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-*.mpi
-*_test
-*_recover
diff --git a/ml-xgboost/rabit/test/Makefile b/ml-xgboost/rabit/test/Makefile
deleted file mode 100644
index 3cd921a..0000000
--- a/ml-xgboost/rabit/test/Makefile
+++ /dev/null
@@ -1,77 +0,0 @@
-RABIT_BUILD_DMLC = 0
-
-ifeq ($(RABIT_BUILD_DMLC),1)
-    DMLC=../dmlc-core
-else
-    DMLC=../../dmlc-core
-endif
-
-MPICXX=../mpich/bin/mpicxx
-export LDFLAGS=  -L../lib -pthread -lm
-export CFLAGS = -Wall -O3 -Wno-unknown-pragmas
-
-export CC = gcc
-export CXX = g++
-
-
-#----------------------------
-# Settings for power and arm arch
-#----------------------------
-ARCH := $(shell uname -a)
-ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
-	CFLAGS += -march=native
-else
-	CFLAGS += -mavx
-endif
-
-ifndef WITH_FPIC
-	WITH_FPIC = 1
-endif
-ifeq ($(WITH_FPIC), 1)
-	CFLAGS += -fPIC
-endif
-
-CFLAGS += -I../include -I $(DMLC)/include -std=c++11
-
-# specify tensor path
-BIN = speed_test model_recover local_recover lazy_recover
-OBJ = $(RABIT_OBJ) speed_test.o model_recover.o local_recover.o lazy_recover.o
-MPIBIN = speed_test.mpi
-.PHONY: clean all lib mpi
-
-.PHONY: lib all
-
-all: $(BIN)
-
-lib:
-	cd ..;make clean;make;cd -
-
-.PHONY: mpi
-mpi:
-	cd ..;make mpi;cd -
-
-# programs
-speed_test.o: speed_test.cc ../include/rabit/*.h lib mpi
-model_recover.o: model_recover.cc ../include/rabit/*.h lib
-local_recover.o: local_recover.cc ../include/rabit/*.h lib
-lazy_recover.o: lazy_recover.cc ../include/rabit/*.h lib
-
-# we can link against MPI version to get use MPI
-speed_test: speed_test.o  $(RABIT_OBJ)
-speed_test.mpi: speed_test.o $(MPIOBJ)
-model_recover: model_recover.o  $(RABIT_OBJ)
-local_recover: local_recover.o  $(RABIT_OBJ)
-lazy_recover: lazy_recover.o  $(RABIT_OBJ)
-
-$(BIN) :
-	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc, $^) ../lib/librabit_mock.a $(LDFLAGS)
-
-$(OBJ) :
-	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c %.cc, $^) )
-
-$(MPIBIN) :
-	$(MPICXX) $(CFLAGS) -I../mpich/include -shared -o $@ $(filter %.cpp %.o %.c %.cc, $^) \
-    ../lib/librabit_mpi.so $(LDFLAGS) -L../mpich/lib -Wl,-rpath,../mpich/lib -lmpi
-
-clean:
-	$(RM) $(OBJ) $(BIN) $(MPIBIN) $(MPIOBJ) *~ ../src/*~
diff --git a/ml-xgboost/rabit/test/README.md b/ml-xgboost/rabit/test/README.md
deleted file mode 100644
index fb68112..0000000
--- a/ml-xgboost/rabit/test/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-Testcases of Rabit
-====
-This folder contains internal testcases to test correctness and efficiency of rabit API
-
-The example running scripts for testcases are given by test.mk
-* type ```make -f test.mk testcasename``` to run certain testcase
-
-
-Helper Scripts
-====
-* test.mk contains Makefile documentation of all testcases
-* keepalive.sh helper bash to restart a program when it dies abnormally
-
-List of Programs
-====
-* speed_test: test the running speed of rabit API
-* test_local_recover: test recovery of local state when error happens
-* test_model_recover: test recovery of global state when error happens
diff --git a/ml-xgboost/rabit/test/cpp/CMakeLists.txt b/ml-xgboost/rabit/test/cpp/CMakeLists.txt
deleted file mode 100644
index 11bc6a6..0000000
--- a/ml-xgboost/rabit/test/cpp/CMakeLists.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-find_package(GTest REQUIRED)
-
-add_executable(
-  unit_tests
-  test_io.cc
-  allreduce_robust_test.cc
-  allreduce_base_test.cc
-  allreduce_mock_test.cc
-  test_main.cpp)
-
-target_link_libraries(
-  unit_tests PRIVATE
-  GTest::GTest GTest::Main
-  rabit_base rabit_mock rabit)
-
-target_include_directories(unit_tests PUBLIC
-  "$<BUILD_INTERFACE:${rabit_SOURCE_DIR}/include>"
-  "$<BUILD_INTERFACE:${DMLC_ROOT}/include>")
-
-set_target_properties(unit_tests
-  PROPERTIES
-  CXX_STANDARD 14
-  CXX_STANDARD_REQUIRED ON
-  RUNTIME_OUTPUT_DIRECTORY ${rabit_BINARY_DIR}
-  RUNTIME_OUTPUT_DIRECTORY_DEBUG ${rabit_BINARY_DIR}
-  RUNTIME_OUTPUT_DIRECTORY_RELEASE ${rabit_BINARY_DIR})
-
-add_test(
-  NAME TestRabitLib
-  COMMAND unit_tests
-  WORKING_DIRECTORY ${rabit_BINARY_DIR})
diff --git a/ml-xgboost/rabit/test/cpp/README.md b/ml-xgboost/rabit/test/cpp/README.md
deleted file mode 100644
index 9962980..0000000
--- a/ml-xgboost/rabit/test/cpp/README.md
+++ /dev/null
@@ -1 +0,0 @@
-Unittests for Rabit
diff --git a/ml-xgboost/rabit/test/cpp/allreduce_base_test.cc b/ml-xgboost/rabit/test/cpp/allreduce_base_test.cc
deleted file mode 100644
index 65a3dd5..0000000
--- a/ml-xgboost/rabit/test/cpp/allreduce_base_test.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-#define RABIT_CXXTESTDEFS_H
-#include <gtest/gtest.h>
-
-#include <string>
-#include <iostream>
-#include "../../src/allreduce_base.h"
-
-TEST(allreduce_base, init_task)
-{
-  rabit::engine::AllreduceBase base;
-
-  std::string rabit_task_id = "rabit_task_id=1";
-  char cmd[rabit_task_id.size()+1];
-  std::copy(rabit_task_id.begin(), rabit_task_id.end(), cmd);
-  cmd[rabit_task_id.size()] = '\0';
-
-  char* argv[] = {cmd};
-  base.Init(1, argv);
-  EXPECT_EQ(base.task_id, "1");
-}
-
-TEST(allreduce_base, init_with_cache_on)
-{
-  rabit::engine::AllreduceBase base;
-
-  std::string rabit_task_id = "rabit_task_id=1";
-  char cmd[rabit_task_id.size()+1];
-  std::copy(rabit_task_id.begin(), rabit_task_id.end(), cmd);
-  cmd[rabit_task_id.size()] = '\0';
-
-  std::string rabit_bootstrap_cache = "rabit_bootstrap_cache=1";
-  char cmd2[rabit_bootstrap_cache.size()+1];
-  std::copy(rabit_bootstrap_cache.begin(), rabit_bootstrap_cache.end(), cmd2);
-  cmd2[rabit_bootstrap_cache.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd3[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd3);
-  cmd3[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd2, cmd3};
-  base.Init(3, argv);
-  EXPECT_EQ(base.task_id, "1");
-  EXPECT_EQ(base.rabit_bootstrap_cache, 1);
-  EXPECT_EQ(base.rabit_debug, 1);
-}
-
-TEST(allreduce_base, init_with_ring_reduce)
-{
-  rabit::engine::AllreduceBase base;
-
-  std::string rabit_task_id = "rabit_task_id=1";
-  char cmd[rabit_task_id.size()+1];
-  std::copy(rabit_task_id.begin(), rabit_task_id.end(), cmd);
-  cmd[rabit_task_id.size()] = '\0';
-
-  std::string rabit_reduce_ring_mincount = "rabit_reduce_ring_mincount=1";
-  char cmd2[rabit_reduce_ring_mincount.size()+1];
-  std::copy(rabit_reduce_ring_mincount.begin(), rabit_reduce_ring_mincount.end(), cmd2);
-  cmd2[rabit_reduce_ring_mincount.size()] = '\0';
-
-  char* argv[] = {cmd, cmd2};
-  base.Init(2, argv);
-  EXPECT_EQ(base.task_id, "1");
-  EXPECT_EQ(base.reduce_ring_mincount, 1);
-}
diff --git a/ml-xgboost/rabit/test/cpp/allreduce_base_test.cpp b/ml-xgboost/rabit/test/cpp/allreduce_base_test.cpp
deleted file mode 100644
index 65a3dd5..0000000
--- a/ml-xgboost/rabit/test/cpp/allreduce_base_test.cpp
+++ /dev/null
@@ -1,66 +0,0 @@
-#define RABIT_CXXTESTDEFS_H
-#include <gtest/gtest.h>
-
-#include <string>
-#include <iostream>
-#include "../../src/allreduce_base.h"
-
-TEST(allreduce_base, init_task)
-{
-  rabit::engine::AllreduceBase base;
-
-  std::string rabit_task_id = "rabit_task_id=1";
-  char cmd[rabit_task_id.size()+1];
-  std::copy(rabit_task_id.begin(), rabit_task_id.end(), cmd);
-  cmd[rabit_task_id.size()] = '\0';
-
-  char* argv[] = {cmd};
-  base.Init(1, argv);
-  EXPECT_EQ(base.task_id, "1");
-}
-
-TEST(allreduce_base, init_with_cache_on)
-{
-  rabit::engine::AllreduceBase base;
-
-  std::string rabit_task_id = "rabit_task_id=1";
-  char cmd[rabit_task_id.size()+1];
-  std::copy(rabit_task_id.begin(), rabit_task_id.end(), cmd);
-  cmd[rabit_task_id.size()] = '\0';
-
-  std::string rabit_bootstrap_cache = "rabit_bootstrap_cache=1";
-  char cmd2[rabit_bootstrap_cache.size()+1];
-  std::copy(rabit_bootstrap_cache.begin(), rabit_bootstrap_cache.end(), cmd2);
-  cmd2[rabit_bootstrap_cache.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd3[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd3);
-  cmd3[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd2, cmd3};
-  base.Init(3, argv);
-  EXPECT_EQ(base.task_id, "1");
-  EXPECT_EQ(base.rabit_bootstrap_cache, 1);
-  EXPECT_EQ(base.rabit_debug, 1);
-}
-
-TEST(allreduce_base, init_with_ring_reduce)
-{
-  rabit::engine::AllreduceBase base;
-
-  std::string rabit_task_id = "rabit_task_id=1";
-  char cmd[rabit_task_id.size()+1];
-  std::copy(rabit_task_id.begin(), rabit_task_id.end(), cmd);
-  cmd[rabit_task_id.size()] = '\0';
-
-  std::string rabit_reduce_ring_mincount = "rabit_reduce_ring_mincount=1";
-  char cmd2[rabit_reduce_ring_mincount.size()+1];
-  std::copy(rabit_reduce_ring_mincount.begin(), rabit_reduce_ring_mincount.end(), cmd2);
-  cmd2[rabit_reduce_ring_mincount.size()] = '\0';
-
-  char* argv[] = {cmd, cmd2};
-  base.Init(2, argv);
-  EXPECT_EQ(base.task_id, "1");
-  EXPECT_EQ(base.reduce_ring_mincount, 1);
-}
diff --git a/ml-xgboost/rabit/test/cpp/allreduce_mock_test.cc b/ml-xgboost/rabit/test/cpp/allreduce_mock_test.cc
deleted file mode 100644
index e659d8e..0000000
--- a/ml-xgboost/rabit/test/cpp/allreduce_mock_test.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-#define RABIT_CXXTESTDEFS_H
-#include <gtest/gtest.h>
-
-#include <string>
-#include <iostream>
-#include "../../src/allreduce_mock.h"
-
-TEST(allreduce_mock, mock_allreduce)
-{
-  rabit::engine::AllreduceMock m;
-
-  std::string mock_str = "mock=0,0,0,0";
-  char cmd[mock_str.size()+1];
-  std::copy(mock_str.begin(), mock_str.end(), cmd);
-  cmd[mock_str.size()] = '\0';
-
-  char* argv[] = {cmd};
-  m.Init(1, argv);
-  m.rank = 0;
-  EXPECT_EXIT(m.Allreduce(nullptr,0,0,nullptr,nullptr,nullptr), ::testing::ExitedWithCode(255), "");
-}
-
-TEST(allreduce_mock, mock_broadcast)
-{
-  rabit::engine::AllreduceMock m;
-  std::string mock_str = "mock=0,1,2,0";
-  char cmd[mock_str.size()+1];
-  std::copy(mock_str.begin(), mock_str.end(), cmd);
-  cmd[mock_str.size()] = '\0';
-  char* argv[] = {cmd};
-  m.Init(1, argv);
-  m.rank = 0;
-  m.version_number=1;
-  m.seq_counter=2;
-  EXPECT_EXIT(m.Broadcast(nullptr,0,0), ::testing::ExitedWithCode(255), "");
-}
diff --git a/ml-xgboost/rabit/test/cpp/allreduce_mock_test.cpp b/ml-xgboost/rabit/test/cpp/allreduce_mock_test.cpp
deleted file mode 100644
index ec3190c..0000000
--- a/ml-xgboost/rabit/test/cpp/allreduce_mock_test.cpp
+++ /dev/null
@@ -1,51 +0,0 @@
-#define RABIT_CXXTESTDEFS_H
-#include <gtest/gtest.h>
-
-#include <string>
-#include <iostream>
-#include "../../src/allreduce_mock.h"
-
-TEST(allreduce_mock, mock_allreduce)
-{
-  rabit::engine::AllreduceMock m;
-
-  std::string mock_str = "mock=0,0,0,0";
-  char cmd[mock_str.size()+1];
-  std::copy(mock_str.begin(), mock_str.end(), cmd);
-  cmd[mock_str.size()] = '\0';
-
-  char* argv[] = {cmd};
-  m.Init(1, argv);
-  m.rank = 0;
-  EXPECT_EXIT(m.Allreduce(nullptr,0,0,nullptr,nullptr,nullptr), ::testing::ExitedWithCode(255), "");
-}
-
-TEST(allreduce_mock, mock_broadcast)
-{
-  rabit::engine::AllreduceMock m;
-  std::string mock_str = "mock=0,1,2,0";
-  char cmd[mock_str.size()+1];
-  std::copy(mock_str.begin(), mock_str.end(), cmd);
-  cmd[mock_str.size()] = '\0';
-  char* argv[] = {cmd};
-  m.Init(1, argv);
-  m.rank = 0;
-  m.version_number=1;
-  m.seq_counter=2;
-  EXPECT_EXIT(m.Broadcast(nullptr,0,0), ::testing::ExitedWithCode(255), "");
-}
-
-TEST(allreduce_mock, mock_gather)
-{
-  rabit::engine::AllreduceMock m;
-  std::string mock_str = "mock=3,13,22,0";
-  char cmd[mock_str.size()+1];
-  std::copy(mock_str.begin(), mock_str.end(), cmd);
-  cmd[mock_str.size()] = '\0';
-  char* argv[] = {cmd};
-  m.Init(1, argv);
-  m.rank = 3;
-  m.version_number=13;
-  m.seq_counter=22;
-  EXPECT_EXIT(m.Allgather(nullptr,0,0,0,0), ::testing::ExitedWithCode(255), "");
-}
diff --git a/ml-xgboost/rabit/test/cpp/allreduce_robust_test.cc b/ml-xgboost/rabit/test/cpp/allreduce_robust_test.cc
deleted file mode 100644
index c86f762..0000000
--- a/ml-xgboost/rabit/test/cpp/allreduce_robust_test.cc
+++ /dev/null
@@ -1,233 +0,0 @@
-#define RABIT_CXXTESTDEFS_H
-#include <gtest/gtest.h>
-
-#include <chrono>
-#include <string>
-#include <iostream>
-#include "../../src/allreduce_robust.h"
-
-inline void mockerr(const char *fmt, ...) {EXPECT_STRCASEEQ(fmt, "[%d] exit due to time out %d s\n");}
-inline void mockassert(bool val, const char *fmt, ...) {}
-rabit::engine::AllreduceRobust::ReturnType err_type(rabit::engine::AllreduceRobust::ReturnTypeEnum::kSockError);
-rabit::engine::AllreduceRobust::ReturnType succ_type(rabit::engine::AllreduceRobust::ReturnTypeEnum::kSuccess);
-
-TEST(allreduce_robust, sync_error_timeout)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  char* argv[] = {cmd,cmd1};
-  m.Init(2, argv);
-  m.rank = 0;
-  m.rabit_bootstrap_cache = 1;
-  m._error = mockerr;
-  m._assert = mockassert;
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-  std::this_thread::sleep_for(std::chrono::milliseconds(1500));
-  EXPECT_EQ(m.rabit_timeout_task.get(), false);
-}
-
-TEST(allreduce_robust, sync_error_reset)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd2[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd2);
-  cmd2[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd1,cmd2};
-  m.Init(3, argv);
-  m.rank = 0;
-  m._assert = mockassert;
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-  std::this_thread::sleep_for(std::chrono::milliseconds(100));
-  EXPECT_EQ(m.CheckAndRecover(succ_type), true);
-  EXPECT_EQ(m.rabit_timeout_task.get(), true);
-  m.Shutdown();
-}
-
-TEST(allreduce_robust, sync_success_error_timeout)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd2[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd2);
-  cmd2[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd1,cmd2};
-  m.Init(3, argv);
-  m.rank = 0;
-  m.rabit_bootstrap_cache = 1;
-  m._assert = mockassert;
-  m._error = mockerr;
-  EXPECT_EQ(m.CheckAndRecover(succ_type), true);
-  std::this_thread::sleep_for(std::chrono::milliseconds(100));
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-  std::this_thread::sleep_for(std::chrono::milliseconds(1500));
-  EXPECT_EQ(m.rabit_timeout_task.get(), false);
-}
-
-TEST(allreduce_robust, sync_success_error_success)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd2[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd2);
-  cmd2[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd1,cmd2};
-  m.Init(3, argv);
-  m.rank = 0;
-  m.rabit_bootstrap_cache = 1;
-  m._assert = mockassert;
-  EXPECT_EQ(m.CheckAndRecover(succ_type), true);
-  std::this_thread::sleep_for(std::chrono::milliseconds(10));
-
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-  std::this_thread::sleep_for(std::chrono::milliseconds(10));
-  EXPECT_EQ(m.CheckAndRecover(succ_type), true);
-  std::this_thread::sleep_for(std::chrono::milliseconds(1100));
-  EXPECT_EQ(m.rabit_timeout_task.get(), true);
-  m.Shutdown();
-}
-
-TEST(allreduce_robust, sync_error_no_reset_timeout)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd2[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd2);
-  cmd2[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd1,cmd2};
-  m.Init(3, argv);
-  m.rank = 0;
-  m.rabit_bootstrap_cache = 1;
-  m._assert = mockassert;
-  m._error = mockerr;
-  auto start = std::chrono::system_clock::now();
-
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-  std::this_thread::sleep_for(std::chrono::milliseconds(1100));
-
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-
-  m.rabit_timeout_task.wait();
-  auto end = std::chrono::system_clock::now();
-  std::chrono::duration<double> diff = end-start;
-
-  EXPECT_EQ(m.rabit_timeout_task.get(), false);
-  // expect second error don't overwrite/reset timeout task
-  EXPECT_LT(diff.count(), 2);
-}
-
-TEST(allreduce_robust, no_timeout_shut_down)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd2[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd2);
-  cmd2[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd1,cmd2};
-  m.Init(3, argv);
-  m.rank = 0;
-
-  EXPECT_EQ(m.CheckAndRecover(succ_type), true);
-  std::this_thread::sleep_for(std::chrono::milliseconds(10));
-  m.Shutdown();
-}
-
-TEST(allreduce_robust, shut_down_before_timeout)
-{
-  rabit::engine::AllreduceRobust m;
-
-  std::string rabit_timeout = "rabit_timeout=1";
-  char cmd[rabit_timeout.size()+1];
-  std::copy(rabit_timeout.begin(), rabit_timeout.end(), cmd);
-  cmd[rabit_timeout.size()] = '\0';
-
-  std::string rabit_timeout_sec = "rabit_timeout_sec=1";
-  char cmd1[rabit_timeout_sec.size()+1];
-  std::copy(rabit_timeout_sec.begin(), rabit_timeout_sec.end(), cmd1);
-  cmd1[rabit_timeout_sec.size()] = '\0';
-
-  std::string rabit_debug = "rabit_debug=1";
-  char cmd2[rabit_debug.size()+1];
-  std::copy(rabit_debug.begin(), rabit_debug.end(), cmd2);
-  cmd2[rabit_debug.size()] = '\0';
-
-  char* argv[] = {cmd, cmd1,cmd2};
-  m.Init(3, argv);
-  m.rank = 0;
-  rabit::engine::AllreduceRobust::LinkRecord a;
-  m.err_link = &a;
-  
-  EXPECT_EQ(m.CheckAndRecover(err_type), false);
-  std::this_thread::sleep_for(std::chrono::milliseconds(10));
-  m.Shutdown();
-}
\ No newline at end of file
diff --git a/ml-xgboost/rabit/test/cpp/test_io.cc b/ml-xgboost/rabit/test/cpp/test_io.cc
deleted file mode 100644
index 0e4b70b..0000000
--- a/ml-xgboost/rabit/test/cpp/test_io.cc
+++ /dev/null
@@ -1,18 +0,0 @@
-/*!
- *  Copyright (c) 2019 by Contributors
- */
-#include <gtest/gtest.h>
-#include <rabit/internal/io.h>
-
-#include <vector>
-
-namespace rabit {
-TEST(MemoryFixSizeBuffer, Seek) {
-  size_t constexpr kSize { 64 };
-  std::vector<int32_t> memory( kSize );
-  utils::MemoryFixSizeBuffer buf(memory.data(), memory.size());
-  buf.Seek(utils::MemoryFixSizeBuffer::SeekEnd);
-  size_t end = buf.Tell();
-  ASSERT_EQ(end, kSize);
-}
-}  // namespace rabit
diff --git a/ml-xgboost/rabit/test/cpp/test_main.cpp b/ml-xgboost/rabit/test/cpp/test_main.cpp
deleted file mode 100644
index 6eb025a..0000000
--- a/ml-xgboost/rabit/test/cpp/test_main.cpp
+++ /dev/null
@@ -1,8 +0,0 @@
-#include "gtest/gtest.h"
-
-int main(int argc, char** argv)
-{
-    ::testing::InitGoogleTest(&argc, argv);
-    ::testing::FLAGS_gtest_death_test_style = "threadsafe";
-    return RUN_ALL_TESTS();
-}
diff --git a/ml-xgboost/rabit/test/lazy_recover.cc b/ml-xgboost/rabit/test/lazy_recover.cc
deleted file mode 100644
index 180e2e4..0000000
--- a/ml-xgboost/rabit/test/lazy_recover.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-// this is a test case to test whether rabit can recover model when
-// facing an exception
-#include <rabit/rabit.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-using namespace rabit;
-
-// dummy model
-class Model : public rabit::Serializable {
- public:
-  // iterations
-  std::vector<float> data;
-  // load from stream
-  virtual void Load(rabit::Stream *fi) {
-    fi->Read(&data);
-  }
-  /*! \brief save the model to the stream */
-  virtual void Save(rabit::Stream *fo) const {
-    fo->Write(data);
-  }
-  virtual void InitModel(size_t n) {
-    data.clear();
-    data.resize(n, 1.0f);
-  }
-};
-
-inline void TestMax(Model *model, int ntrial, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = iter + 111;
-
-  std::vector<float> ndata(model->data.size());
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % z  + model->data[i];
-  }
-  rabit::Allreduce<op::Max>(&ndata[0], ndata.size());
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    float rmax = (i * 1) % z + model->data[i];
-    for (int r = 0; r < nproc; ++r) {
-      rmax = std::max(rmax, (float)((i * (r+1)) % z) + model->data[i]);
-    }
-    utils::Check(rmax == ndata[i], "[%d] TestMax check failurem i=%lu, rmax=%f, ndata=%f", rank, i, rmax, ndata[i]);
-  }
-}
-
-inline void TestSum(Model *model, int ntrial, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = 131 + iter;
-
-  std::vector<float> ndata(model->data.size());
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % z + model->data[i];
-  }
-  Allreduce<op::Sum>(&ndata[0], ndata.size());
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    float rsum = model->data[i] * nproc;
-    for (int r = 0; r < nproc; ++r) {
-      rsum += (float)((i * (r+1)) % z);
-    }
-    utils::Check(fabsf(rsum - ndata[i]) < 1e-5 ,
-                 "[%d] TestSum check failure, local=%g, allreduce=%g", rank, rsum, ndata[i]);
-  }
-  model->data = ndata;
-}
-
-inline void TestBcast(size_t n, int root, int ntrial, int iter) {
-  int rank = rabit::GetRank();
-  std::string s; s.resize(n);
-  for (size_t i = 0; i < n; ++i) {
-    s[i] = char(i % 126 + 1);
-  }
-  std::string res;
-  if (root == rank) {
-    res = s;
-    rabit::Broadcast(&res, root);
-  } else {
-    rabit::Broadcast(&res, root);
-  }
-  utils::Check(res == s, "[%d] TestBcast fail", rank);
-}
-
-int main(int argc, char *argv[]) {
-  if (argc < 3) {
-    printf("Usage: <ndata> <config>\n");
-    return 0;
-  }
-  int n = atoi(argv[1]);
-  rabit::Init(argc, argv);
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  std::string name = rabit::GetProcessorName();
-  Model model;
-  srand(0);
-  int ntrial = 0;
-  for (int i = 1; i < argc; ++i) {
-    int n;
-    if (sscanf(argv[i], "rabit_num_trial=%d", &n) == 1) ntrial = n;
-  }
-  int iter = rabit::LoadCheckPoint(&model);
-  if (iter == 0) {
-    model.InitModel(n);
-    printf("[%d] reload-trail=%d, init iter=%d\n", rank, ntrial, iter);
-  } else {
-    printf("[%d] reload-trail=%d, init iter=%d\n", rank, ntrial, iter);
-  }
-  for (int r = iter; r < 3; ++r) {
-    TestMax(&model, ntrial, r);
-    printf("[%d] !!!TestMax pass, iter=%d\n",  rank, r);
-    int step = std::max(nproc / 3, 1);
-    for (int i = 0; i < nproc; i += step) {
-      TestBcast(n, i, ntrial, r);
-    }
-    printf("[%d] !!!TestBcast pass, iter=%d\n", rank, r);
-    TestSum(&model, ntrial, r);
-    printf("[%d] !!!TestSum pass, iter=%d\n", rank, r);
-    rabit::LazyCheckPoint(&model);
-    printf("[%d] !!!CheckPoint pass, iter=%d\n", rank, r);
-  }
-  rabit::Finalize();
-  return 0;
-}
diff --git a/ml-xgboost/rabit/test/local_recover.cc b/ml-xgboost/rabit/test/local_recover.cc
deleted file mode 100644
index 1f0b28b..0000000
--- a/ml-xgboost/rabit/test/local_recover.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-// this is a test case to test whether rabit can recover model when
-// facing an exception
-#include <rabit/rabit.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-
-using namespace rabit;
-
-// dummy model
-class Model : public rabit::Serializable {
- public:
-  // iterations
-  std::vector<float> data;
-  // load from stream
-  virtual void Load(rabit::Stream *fi) {
-    fi->Read(&data);
-  }
-  /*! \brief save the model to the stream */
-  virtual void Save(rabit::Stream *fo) const {
-    fo->Write(data);
-  }
-  virtual void InitModel(size_t n, float v) {
-    data.clear();
-    data.resize(n, v);
-  }
-};
-
-inline void TestMax(Model *model, Model *local, int ntrial, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = iter + 111;
-  std::vector<float> ndata(model->data.size());
-  rabit::Allreduce<op::Max>(&ndata[0], ndata.size(),
-                            [&]() {
-                              // use lambda expression to prepare the data
-                              for (size_t i = 0; i < ndata.size(); ++i) {
-                                ndata[i] = (i * (rank+1)) % z  + local->data[i];
-                              }
-                            });
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    float rmax = (i * 1) % z + model->data[i];
-    for (int r = 0; r < nproc; ++r) {
-      rmax = std::max(rmax, (float)((i * (r+1)) % z) + model->data[i] + r);
-    }
-    utils::Check(rmax == ndata[i], "[%d] TestMax check failure", rank);
-  }
-  model->data = ndata;
-  local->data = ndata;
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    local->data[i] = ndata[i] + rank;
-  }
-}
-
-inline void TestSum(Model *model, Model *local, int ntrial, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = 131 + iter;
-
-  std::vector<float> ndata(model->data.size());
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % z + local->data[i];
-  }
-  Allreduce<op::Sum>(&ndata[0], ndata.size());
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    float rsum = 0.0f;
-    for (int r = 0; r < nproc; ++r) {
-      rsum += (float)((i * (r+1)) % z) + model->data[i] + r;
-    }
-    utils::Check(fabsf(rsum - ndata[i]) < 1e-5 ,
-                 "[%d] TestSum check failure, local=%g, allreduce=%g", rank, rsum, ndata[i]);
-  }
-  model->data = ndata;
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    local->data[i] = ndata[i] + rank;
-  }
-}
-
-inline void TestBcast(size_t n, int root, int ntrial, int iter) {
-  int rank = rabit::GetRank();
-  std::string s; s.resize(n);
-  for (size_t i = 0; i < n; ++i) {
-    s[i] = char(i % 126 + 1);
-  }
-  std::string res;
-  if (root == rank) {
-    res = s;
-    rabit::Broadcast(&res, root);
-  } else {
-    rabit::Broadcast(&res, root);
-  }
-  utils::Check(res == s, "[%d] TestBcast fail", rank);
-}
-
-int main(int argc, char *argv[]) {
-  if (argc < 3) {
-    printf("Usage: <ndata>\n");
-    return 0;
-  }
-  int n = atoi(argv[1]);
-  rabit::Init(argc, argv);
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  std::string name = rabit::GetProcessorName();
-  Model model, local;
-  srand(0);
-  int ntrial = 0;
-  for (int i = 1; i < argc; ++i) {
-    int n;
-    if (sscanf(argv[i], "repeat=%d", &n) == 1) ntrial = n;
-  }
-  int iter = rabit::LoadCheckPoint(&model, &local);
-  if (iter == 0) {
-    model.InitModel(n, 1.0f);
-    local.InitModel(n, 1.0f + rank);
-    printf("[%d] reload-trail=%d, init iter=%d\n", rank, ntrial, iter);
-  } else {
-    printf("[%d] reload-trail=%d, init iter=%d\n", rank, ntrial, iter);
-  }
-  for (int r = iter; r < 3; ++r) {
-    TestMax(&model, &local, ntrial, r);
-    printf("[%d] !!!TestMax pass, iter=%d\n",  rank, r);
-    int step = std::max(nproc / 3, 1);
-    for (int i = 0; i < nproc; i += step) {
-      TestBcast(n, i, ntrial, r);
-    }
-    printf("[%d] !!!TestBcast pass, iter=%d\n", rank, r);
-    TestSum(&model, &local, ntrial, r);
-    printf("[%d] !!!TestSum pass, iter=%d\n", rank, r);
-    rabit::CheckPoint(&model, &local);
-    printf("[%d] !!!CheckPoint pass, iter=%d\n", rank, r);
-  }
-  rabit::Finalize();
-  return 0;
-}
diff --git a/ml-xgboost/rabit/test/local_recover.py b/ml-xgboost/rabit/test/local_recover.py
deleted file mode 100644
index 6f7fae8..0000000
--- a/ml-xgboost/rabit/test/local_recover.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/env python3
-
-from __future__ import print_function
-from builtins import range
-
-import sys
-sys.path.append('../python')
-
-import rabit
-import numpy as np
-
-rabit.init(lib='mock')
-rank = rabit.get_rank()
-n = 10
-nround = 3
-data = np.ones(n) * rank
-
-version, model, local = rabit.load_checkpoint(True)
-if version == 0:
-    model = np.zeros(n)
-    local = np.ones(n)
-else:
-    print('[%d] restart from version %d' % (rank, version))
-
-for i in range(version, nround):
-    res = rabit.allreduce(data + model+local, rabit.SUM)
-    print('[%d] iter=%d: %s' % (rank, i, str(res)))
-    model = res
-    local[:] = i
-    rabit.checkpoint(model, local)
-
-rabit.finalize()
diff --git a/ml-xgboost/rabit/test/model_recover.cc b/ml-xgboost/rabit/test/model_recover.cc
deleted file mode 100644
index 181638c..0000000
--- a/ml-xgboost/rabit/test/model_recover.cc
+++ /dev/null
@@ -1,157 +0,0 @@
-// this is a test case to test whether rabit can recover model when
-// facing an exception
-#include <rabit/rabit.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-
-using namespace rabit;
-
-// dummy model
-class Model : public rabit::Serializable {
- public:
-  // iterations
-  std::vector<float> data;
-  // load from stream
-  virtual void Load(rabit::Stream *fi) {
-    fi->Read(&data);
-  }
-  /*! \brief save the model to the stream */
-  virtual void Save(rabit::Stream *fo) const {
-    fo->Write(data);
-  }
-  virtual void InitModel(size_t n) {
-    data.clear();
-    data.resize(n, 1.0f);
-  }
-};
-
-inline void TestMax(Model *model, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = iter + 111;
-
-  std::vector<float> ndata(model->data.size());
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % z  + model->data[i];
-  }
-  rabit::Allreduce<op::Max>(&ndata[0], ndata.size());
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    float rmax = (i * 1) % z + model->data[i];
-    for (int r = 0; r < nproc; ++r) {
-      rmax = std::max(rmax, (float)((i * (r+1)) % z) + model->data[i]);
-    }
-    utils::Check(rmax == ndata[i], "[%d] TestMax check failurem i=%lu, rmax=%f, ndata=%f", rank, i, rmax, ndata[i]);
-  }
-  model->data = ndata;
-}
-
-inline void TestSum(Model *model, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = 131 + iter;
-
-  std::vector<float> ndata(model->data.size());
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % z + model->data[i];
-  }
-  Allreduce<op::Sum>(&ndata[0], ndata.size());
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    float rsum = model->data[i] * nproc;
-    for (int r = 0; r < nproc; ++r) {
-      rsum += (float)((i * (r+1)) % z);
-    }
-    utils::Check(fabsf(rsum - ndata[i]) < 1e-5 ,
-                 "[%d] TestSum check failure, local=%g, allreduce=%g", rank, rsum, ndata[i]);
-  }
-  model->data = ndata;
-}
-
-inline void TestAllgather(Model *model, int iter) {
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  const int z = 131 + iter;
-
-  std::vector<float> ndata(model->data.size() * nproc);
-  size_t beginSlice = rank * model->data.size();
-  for (size_t i = 0; i < model->data.size(); ++i) {
-    ndata[beginSlice + i] = (i * (rank+1)) % z + model->data[i];
-  }
-  Allgather(&ndata[0], ndata.size(), beginSlice,
-  model->data.size(), model->data.size());
-
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    int curRank = i / model->data.size();
-    int remainder = i % model->data.size();
-    float data = (remainder * (curRank+1)) % z + model->data[remainder];
-    utils::Check(fabsf(data - ndata[i]) < 1e-5 ,
-                 "[%d] TestAllgather check failure, local=%g, allgatherring=%g", rank, data, ndata[i]);
-  }
-  model->data = ndata;
-}
-
-inline void TestBcast(size_t n, int root) {
-  int rank = rabit::GetRank();
-  std::string s; s.resize(n);
-  for (size_t i = 0; i < n; ++i) {
-    s[i] = char(i % 126 + 1);
-  }
-  std::string res;
-  if (root == rank) {
-    res = s;
-  }
-  rabit::Broadcast(&res, root);
-
-  utils::Check(res == s, "[%d] TestBcast fail", rank);
-}
-
-int main(int argc, char *argv[]) {
-  if (argc < 3) {
-    printf("Usage: <ndata> <config>\n");
-    return 0;
-  }
-  int n = atoi(argv[1]);
-  rabit::Init(argc, argv);
-  int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  std::string name = rabit::GetProcessorName();
-
-  int max_rank = rank;
-  rabit::Allreduce<op::Max>(&max_rank, 1);
-  utils::Check(max_rank == nproc - 1, "max rank is world size-1");
-
-  Model model;
-  srand(0);
-  int ntrial = 0;
-  for (int i = 1; i < argc; ++i) {
-    int n;
-    if (sscanf(argv[i], "rabit_num_trial=%d", &n) == 1) ntrial = n;
-  }
-  int iter = rabit::LoadCheckPoint(&model);
-  if (iter == 0) {
-    model.InitModel(n);
-  }
-  printf("[%d] reload-trail=%d, init iter=%d\n", rank, ntrial, iter);
-
-  for (int r = iter; r < 3; ++r) {
-    TestMax(&model, r);
-    printf("[%d] !!!TestMax pass, iter=%d\n",  rank, r);
-    int step = std::max(nproc / 3, 1);
-    for (int i = 0; i < nproc; i += step) {
-      TestBcast(n, i);
-    }
-    printf("[%d] !!!TestBcast pass, iter=%d\n", rank, r);
-
-    TestSum(&model, r);
-    printf("[%d] !!!TestSum pass, iter=%d\n", rank, r);
-    TestAllgather(&model, r);
-    printf("[%d] !!!TestAllgather pass, iter=%d\n", rank, r);
-    rabit::CheckPoint(&model);
-    printf("[%d] !!!Checkpoint pass, iter=%d\n", rank, r);
-  }
-  rabit::Finalize();
-  return 0;
-}
-
diff --git a/ml-xgboost/rabit/test/speed_runner.py b/ml-xgboost/rabit/test/speed_runner.py
deleted file mode 100644
index 1644bfe..0000000
--- a/ml-xgboost/rabit/test/speed_runner.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import argparse
-import sys
-
-def main():
-  parser = argparse.ArgumentParser(description='TODO')
-  parser.add_argument('-ho', '--host_dir', required=True)
-  parser.add_argument('-s', '--submit_script', required=True)
-  parser.add_argument('-rex', '--rabit_exec', required=True)
-  parser.add_argument('-mpi', '--mpi_exec', required=True)
-  args = parser.parse_args()
-
-  ndata = [10**4, 10**5, 10**6, 10**7]
-  nrepeat = [10**4, 10**3, 10**2, 10]
-
-  machines = [2,4,8,16,31]
-
-  executables = [args.rabit_exec, args.mpi_exec]
-
-  for executable in executables:
-    sys.stderr.write('Executable %s' % executable)
-    sys.stderr.flush()
-    for i, data in enumerate(ndata):
-      for machine in machines:
-        host_file = os.path.join(args.host_dir, 'hosts%d' % machine)
-        cmd = 'python %s %d %s %s %d %d' % (args.submit_script, machine, host_file, executable, data, nrepeat[i])
-        sys.stderr.write('data=%d, repeat=%d, machine=%d\n' % (data, nrepeat[i], machine))
-        sys.stderr.flush()
-        os.system(cmd)
-    sys.stderr.write('\n')
-    sys.stderr.flush()
-
-if __name__ == "__main__":
-  main()
diff --git a/ml-xgboost/rabit/test/speed_test.cc b/ml-xgboost/rabit/test/speed_test.cc
deleted file mode 100644
index 8eb543d..0000000
--- a/ml-xgboost/rabit/test/speed_test.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-// This program is used to test the speed of rabit API
-#include <rabit/rabit.h>
-#include <rabit/internal/timer.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-#include <time.h>
-
-using namespace rabit;
-
-double max_tdiff, sum_tdiff, bcast_tdiff, tot_tdiff;
-
-inline void TestMax(size_t n) {
-  int rank = rabit::GetRank();
-  std::vector<float> ndata(n);
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % 111;
-  }
-  double tstart = utils::GetTime();
-  rabit::Allreduce<op::Max>(&ndata[0], ndata.size());
-  max_tdiff += utils::GetTime() - tstart;
-}
-
-inline void TestSum(size_t n) {
-  int rank = rabit::GetRank();
-  const int z = 131;
-  std::vector<float> ndata(n);
-  for (size_t i = 0; i < ndata.size(); ++i) {
-    ndata[i] = (i * (rank+1)) % z;
-  }
-  double tstart = utils::GetTime();
-  rabit::Allreduce<op::Sum>(&ndata[0], ndata.size());
-  sum_tdiff += utils::GetTime() - tstart;
-}
-
-inline void TestBcast(size_t n, int root) {
-  int rank = rabit::GetRank();
-  std::string s; s.resize(n);
-  for (size_t i = 0; i < n; ++i) {
-    s[i] = char(i % 126 + 1);
-  }
-  std::string res;
-  res.resize(n);
-  if (root == rank) {
-    res = s;
-  }
-  double tstart = utils::GetTime();
-  rabit::Broadcast(&res[0], res.length(), root);
-  bcast_tdiff += utils::GetTime() - tstart;
-}
-
-inline void PrintStats(const char *name, double tdiff, int n, int nrep, size_t size) {
-  int nproc = rabit::GetWorldSize();
-  double tsum = tdiff;
-  rabit::Allreduce<op::Sum>(&tsum, 1);
-  double tavg = tsum / nproc;
-  double tsqr = tdiff - tavg;
-  tsqr *= tsqr;
-  rabit::Allreduce<op::Sum>(&tsqr, 1);
-  double tstd = sqrt(tsqr / nproc);
-  if (rabit::GetRank() == 0) {
-    rabit::TrackerPrintf("%s: mean=%g, std=%g sec\n", name, tavg, tstd);
-    double ndata = n;
-    ndata *= nrep * size;
-    if (n != 0) {
-      rabit::TrackerPrintf("%s-speed: %g MB/sec\n", name, (ndata / tavg) / 1024 / 1024 );
-    }
-  }
-}
-
-int main(int argc, char *argv[]) {
-  if (argc < 3) {
-    printf("Usage: <ndata> <nrepeat>\n");
-    return 0;
-  }
-  srand(0);
-  int n = atoi(argv[1]);
-  int nrep = atoi(argv[2]);
-  utils::Check(nrep >= 1, "need to at least repeat running once");
-  rabit::Init(argc, argv);
-  //int rank = rabit::GetRank();
-  int nproc = rabit::GetWorldSize();
-  std::string name = rabit::GetProcessorName();
-  max_tdiff = sum_tdiff = bcast_tdiff = 0;
-  double tstart = utils::GetTime();
-  for (int i = 0; i < nrep; ++i) {
-    TestMax(n);
-    TestSum(n);
-    TestBcast(n, rand() % nproc);
-  }
-  tot_tdiff = utils::GetTime() - tstart;
-  // use allreduce to get the sum and std of time
-  PrintStats("max_tdiff", max_tdiff, n, nrep, sizeof(float));
-  PrintStats("sum_tdiff", sum_tdiff, n, nrep, sizeof(float));
-  PrintStats("bcast_tdiff", bcast_tdiff, n, nrep, sizeof(char));
-  PrintStats("tot_tdiff", tot_tdiff, 0, nrep, sizeof(float));
-  rabit::Finalize();
-  return 0;
-}
diff --git a/ml-xgboost/rabit/test/test.mk b/ml-xgboost/rabit/test/test.mk
deleted file mode 100644
index 1028ec5..0000000
--- a/ml-xgboost/rabit/test/test.mk
+++ /dev/null
@@ -1,37 +0,0 @@
-RABIT_BUILD_DMLC = 0
-
-ifeq ($(RABIT_BUILD_DMLC),1)
-    DMLC=../dmlc-core
-else
-    DMLC=../../dmlc-core
-endif
-
-# this is a makefile used to show testcases of rabit
-.PHONY: all
-
-all: model_recover_10_10k  model_recover_10_10k_die_same model_recover_10_10k_die_hard local_recover_10_10k lazy_recover_10_10k_die_hard lazy_recover_10_10k_die_same ringallreduce_10_10k pylocal_recover_10_10k
-
-# this experiment test recovery with actually process exit, use keepalive to keep program alive
-model_recover_10_10k:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 model_recover 10000 mock=0,0,1,0 mock=1,1,1,0 rabit_bootstrap_cache=true rabit_debug=true rabit_reduce_ring_mincount=1 rabit_timeout=true rabit_timeout_sec=5
-
-model_recover_10_10k_die_same:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 model_recover 10000 mock=0,0,1,0 mock=1,1,1,0 mock=0,1,1,0 mock=4,1,1,0 mock=9,1,1,0 rabit_bootstrap_cache=1
-
-model_recover_10_10k_die_hard:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 model_recover 10000 mock=0,0,1,0 mock=1,1,1,0 mock=1,1,1,1 mock=0,1,1,0 mock=4,1,1,0 mock=9,1,1,0 mock=8,1,2,0 mock=4,1,3,0 rabit_bootstrap_cache=1
-
-local_recover_10_10k:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 local_recover 10000 mock=0,0,1,0 mock=1,1,1,0 mock=0,1,1,0 mock=4,1,1,0 mock=9,1,1,0 mock=1,1,1,1
-
-pylocal_recover_10_10k:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 local_recover.py 10000 mock=0,0,1,0 mock=1,1,1,0 mock=0,1,1,0 mock=4,1,1,0 mock=9,1,1,0 mock=1,1,1,1
-
-lazy_recover_10_10k_die_hard:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 lazy_recover 10000 mock=0,0,1,0 mock=1,1,1,0 mock=1,1,1,1 mock=0,1,1,0 mock=4,1,1,0 mock=9,1,1,0 mock=8,1,2,0 mock=4,1,3,0
-
-lazy_recover_10_10k_die_same:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 --local-num-attempt=20 lazy_recover 10000 mock=0,0,1,0 mock=1,1,1,0 mock=0,1,1,0 mock=4,1,1,0 mock=9,1,1,0
-
-ringallreduce_10_10k:
-	$(DMLC)/tracker/dmlc-submit --cluster local --num-workers=10 model_recover 10000 rabit_reduce_ring_mincount=10
diff --git a/ml-xgboost/src/CMakeLists.txt b/ml-xgboost/src/CMakeLists.txt
deleted file mode 100644
index 2e806f6..0000000
--- a/ml-xgboost/src/CMakeLists.txt
+++ /dev/null
@@ -1,117 +0,0 @@
-file(GLOB_RECURSE CPU_SOURCES *.cc *.h)
-list(REMOVE_ITEM CPU_SOURCES ${xgboost_SOURCE_DIR}/src/cli_main.cc)
-
-#-- Object library
-# Object library is necessary for jvm-package, which creates its own shared
-# library.
-if (USE_CUDA)
-  file(GLOB_RECURSE CUDA_SOURCES *.cu *.cuh)
-  add_library(objxgboost OBJECT ${CPU_SOURCES} ${CUDA_SOURCES} ${PLUGINS_SOURCES})
-  target_compile_definitions(objxgboost
-    PRIVATE -DXGBOOST_USE_CUDA=1)
-  target_include_directories(objxgboost PRIVATE ${xgboost_SOURCE_DIR}/cub/)
-  target_compile_options(objxgboost PRIVATE
-    $<$<COMPILE_LANGUAGE:CUDA>:--expt-extended-lambda>
-    $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
-    $<$<COMPILE_LANGUAGE:CUDA>:-lineinfo>
-    $<$<AND:$<NOT:$<CXX_COMPILER_ID:MSVC>>,$<COMPILE_LANGUAGE:CUDA>>:--std=c++11>
-    $<$<COMPILE_LANGUAGE:CUDA>:${GEN_CODE}>)
-
-  if (USE_NCCL)
-    find_package(Nccl REQUIRED)
-    target_include_directories(objxgboost PRIVATE ${NCCL_INCLUDE_DIR})
-    target_compile_definitions(objxgboost PRIVATE -DXGBOOST_USE_NCCL=1)
-    list(APPEND SRC_LIBS ${NCCL_LIBRARY})
-  endif (USE_NCCL)
-
-  if (USE_NVTX)
-    target_include_directories(objxgboost PRIVATE "${NVTX_HEADER_DIR}")
-    target_compile_definitions(objxgboost PRIVATE -DXGBOOST_USE_NVTX=1)
-  endif (USE_NVTX)
-
-  target_compile_options(objxgboost PRIVATE
-    $<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=${OpenMP_CXX_FLAGS}>
-  )
-
-  if (MSVC)
-    target_compile_options(objxgboost PRIVATE
-      $<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=/utf-8>
-    )
-  endif (MSVC)
-
-  if (HIDE_CXX_SYMBOLS)
-    target_compile_options(objxgboost PRIVATE
-      $<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=-fvisibility=hidden>
-    )
-  endif (HIDE_CXX_SYMBOLS)
-
-  set_target_properties(objxgboost PROPERTIES
-    CUDA_SEPARABLE_COMPILATION OFF)
-else (USE_CUDA)
-  add_library(objxgboost OBJECT ${CPU_SOURCES} ${PLUGINS_SOURCES})
-endif (USE_CUDA)
-
-target_include_directories(objxgboost
-  PRIVATE
-  ${xgboost_SOURCE_DIR}/include
-  ${xgboost_SOURCE_DIR}/dmlc-core/include
-  ${xgboost_SOURCE_DIR}/rabit/include
-  ${xgboost_SOURCE_DIR}/kernel_include)
-target_compile_options(objxgboost
-  PRIVATE
-  $<$<AND:$<CXX_COMPILER_ID:MSVC>,$<COMPILE_LANGUAGE:CXX>>:/MP>
-  $<$<AND:$<NOT:$<CXX_COMPILER_ID:MSVC>>,$<COMPILE_LANGUAGE:CXX>>:-funroll-loops>)
-if (WIN32 AND MINGW)
-  target_compile_options(objxgboost PUBLIC -static-libstdc++)
-endif (WIN32 AND MINGW)
-
-if (MSVC)
-  target_compile_options(objxgboost PRIVATE
-    $<$<NOT:$<COMPILE_LANGUAGE:CUDA>>:/utf-8>
-  )
-endif (MSVC)
-
-set_target_properties(objxgboost PROPERTIES
-  POSITION_INDEPENDENT_CODE ON
-  CXX_STANDARD 14
-  CXX_STANDARD_REQUIRED ON)
-target_compile_definitions(objxgboost
-  PRIVATE
-  -DDMLC_LOG_CUSTOMIZE=1  # enable custom logging
-  $<$<NOT:$<CXX_COMPILER_ID:MSVC>>:_MWAITXINTRIN_H_INCLUDED>
-  ${XGBOOST_DEFINITIONS})
-if (USE_DEBUG_OUTPUT)
-  target_compile_definitions(objxgboost PRIVATE -DXGBOOST_USE_DEBUG_OUTPUT=1)
-endif (USE_DEBUG_OUTPUT)
-
-if (XGBOOST_MM_PREFETCH_PRESENT)
-  target_compile_definitions(objxgboost
-    PRIVATE
-    -DXGBOOST_MM_PREFETCH_PRESENT=1)
-endif(XGBOOST_MM_PREFETCH_PRESENT)
-if (XGBOOST_BUILTIN_PREFETCH_PRESENT)
-  target_compile_definitions(objxgboost
-    PRIVATE
-    -DXGBOOST_BUILTIN_PREFETCH_PRESENT=1)
-endif (XGBOOST_BUILTIN_PREFETCH_PRESENT)
-
-find_package(Threads REQUIRED)
-list(APPEND SRC_LIBS Threads::Threads ${CMAKE_THREAD_LIBS_INIT})
-
-if (USE_OPENMP OR USE_CUDA)  # CUDA requires OpenMP
-  find_package(OpenMP REQUIRED)
-  list(APPEND SRC_LIBS OpenMP::OpenMP_CXX)
-  target_link_libraries(objxgboost PRIVATE OpenMP::OpenMP_CXX)
-endif (USE_OPENMP OR USE_CUDA)
-set(LINKED_LIBRARIES_PRIVATE "${LINKED_LIBRARIES_PRIVATE};${SRC_LIBS}" PARENT_SCOPE)
-
-# For MSVC: Call msvc_use_static_runtime() once again to completely
-# replace /MD with /MT. See https://github.com/dmlc/xgboost/issues/4462
-# for issues caused by mixing of /MD and /MT flags
-msvc_use_static_runtime()
-
-# This grouping organises source files nicely in visual studio
-auto_source_group("${CUDA_SOURCES}")
-auto_source_group("${CPU_SOURCES}")
-
-#-- End object library
diff --git a/ml-xgboost/src/c_api/c_api.cc b/ml-xgboost/src/c_api/c_api.cc
deleted file mode 100644
index 395de51..0000000
--- a/ml-xgboost/src/c_api/c_api.cc
+++ /dev/null
@@ -1,800 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-// Copyright (c) 2014-2020 by Contributors
-#include <rabit/rabit.h>
-#include <rabit/c_api.h>
-
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <algorithm>
-#include <vector>
-#include <string>
-#include <memory>
-
-#include "xgboost/base.h"
-#include "xgboost/data.h"
-#include "xgboost/host_device_vector.h"
-#include "xgboost/learner.h"
-#include "xgboost/c_api.h"
-#include "xgboost/logging.h"
-#include "xgboost/version_config.h"
-#include "xgboost/json.h"
-
-#include "c_api_error.h"
-#include "../common/io.h"
-#include "../data/adapter.h"
-#include "../data/simple_dmatrix.h"
-
-using namespace xgboost; // NOLINT(*);
-
-XGB_DLL void XGBoostVersion(int* major, int* minor, int* patch) {
-  if (major) {
-    *major = XGBOOST_VER_MAJOR;
-  }
-  if (minor) {
-    *minor = XGBOOST_VER_MINOR;
-  }
-  if (patch) {
-    *patch = XGBOOST_VER_PATCH;
-  }
-}
-
-int XGBRegisterLogCallback(void (*callback)(const char*)) {
-  API_BEGIN();
-  LogCallbackRegistry* registry = LogCallbackRegistryStore::Get();
-  registry->Register(callback);
-  API_END();
-}
-
-int XGDMatrixCreateFromFile(const char *fname,
-                            int silent,
-                            DMatrixHandle *out) {
-  API_BEGIN();
-  bool load_row_split = false;
-  if (rabit::IsDistributed()) {
-    LOG(CONSOLE) << "XGBoost distributed mode detected, "
-                 << "will split data among workers";
-    load_row_split = true;
-  }
-  *out = new std::shared_ptr<DMatrix>(DMatrix::Load(fname, silent != 0, load_row_split));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromDataIter(
-    void *data_handle,                  // a Java interator
-    XGBCallbackDataIterNext *callback,  // C++ callback defined in xgboost4j.cpp
-    const char *cache_info, DMatrixHandle *out) {
-  API_BEGIN();
-
-  std::string scache;
-  if (cache_info != nullptr) {
-    scache = cache_info;
-  }
-  xgboost::data::IteratorAdapter adapter(data_handle, callback);
-  *out = new std::shared_ptr<DMatrix> {
-    DMatrix::Create(
-        &adapter, std::numeric_limits<float>::quiet_NaN(),
-        1, scache
-    )
-  };
-  API_END();
-}
-
-#ifndef XGBOOST_USE_CUDA
-XGB_DLL int XGDMatrixCreateFromArrayInterfaceColumns(char const* c_json_strs,
-                                                     bst_float missing,
-                                                     int nthread,
-                                                     DMatrixHandle* out) {
-  API_BEGIN();
-  common::AssertGPUSupport();
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromArrayInterface(char const* c_json_strs,
-                                              bst_float missing,
-                                              int nthread,
-                                              DMatrixHandle* out) {
-  API_BEGIN();
-  common::AssertGPUSupport();
-  API_END();
-}
-
-#endif
-
-XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
-                                     const unsigned* indices,
-                                     const bst_float* data,
-                                     size_t nindptr,
-                                     size_t nelem,
-                                     size_t num_col,
-                                     DMatrixHandle* out) {
-  API_BEGIN();
-  data::CSRAdapter adapter(indptr, indices, data, nindptr - 1, nelem, num_col);
-  *out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
-                                     const unsigned* indices,
-                                     const bst_float* data,
-                                     size_t nindptr,
-                                     size_t nelem,
-                                     size_t num_row,
-                                     DMatrixHandle* out) {
-  API_BEGIN();
-  data::CSCAdapter adapter(col_ptr, indices, data, nindptr - 1, num_row);
-  *out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
-                                   xgboost::bst_ulong nrow,
-                                   xgboost::bst_ulong ncol, bst_float missing,
-                                   DMatrixHandle* out) {
-  API_BEGIN();
-  data::DenseAdapter adapter(data, nrow, ncol);
-  *out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, 1));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data,  // NOLINT
-                                       xgboost::bst_ulong nrow,
-                                       xgboost::bst_ulong ncol,
-                                       bst_float missing, DMatrixHandle* out,
-                                       int nthread) {
-  API_BEGIN();
-  data::DenseAdapter adapter(data, nrow, ncol);
-  *out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
-                                  xgboost::bst_ulong nrow,
-                                  xgboost::bst_ulong ncol, DMatrixHandle* out,
-                                  int nthread) {
-  API_BEGIN();
-  data::DataTableAdapter adapter(data, feature_stypes, nrow, ncol);
-  *out = new std::shared_ptr<DMatrix>(
-      DMatrix::Create(&adapter, std::nan(""), nthread));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
-                                  const int* idxset,
-                                  xgboost::bst_ulong len,
-                                  DMatrixHandle* out) {
-  return XGDMatrixSliceDMatrixEx(handle, idxset, len, out, 0);
-}
-
-XGB_DLL int XGDMatrixSliceDMatrixEx(DMatrixHandle handle,
-                                    const int* idxset,
-                                    xgboost::bst_ulong len,
-                                    DMatrixHandle* out,
-                                    int allow_groups) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  if (!allow_groups) {
-    CHECK_EQ(static_cast<std::shared_ptr<DMatrix>*>(handle)
-                 ->get()
-                 ->Info()
-                 .group_ptr_.size(),
-             0U)
-        << "slice does not support group structure";
-  }
-  DMatrix* dmat = static_cast<std::shared_ptr<DMatrix>*>(handle)->get();
-  *out = new std::shared_ptr<DMatrix>(dmat->Slice({idxset, len}));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixFree(DMatrixHandle handle) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  delete static_cast<std::shared_ptr<DMatrix>*>(handle);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixSaveBinary(DMatrixHandle handle, const char* fname,
-                                int silent) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto dmat = static_cast<std::shared_ptr<DMatrix>*>(handle)->get();
-  if (data::SimpleDMatrix* derived = dynamic_cast<data::SimpleDMatrix*>(dmat)) {
-    derived->SaveToLocalFile(fname);
-  } else {
-    LOG(FATAL) << "binary saving only supported by SimpleDMatrix";
-  }
-  API_END();
-}
-
-XGB_DLL int XGDMatrixSetFloatInfo(DMatrixHandle handle,
-                                  const char* field,
-                                  const bst_float* info,
-                                  xgboost::bst_ulong len) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  static_cast<std::shared_ptr<DMatrix>*>(handle)
-      ->get()->Info().SetInfo(field, info, xgboost::DataType::kFloat32, len);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixSetInfoFromInterface(DMatrixHandle handle,
-                                          char const* field,
-                                          char const* interface_c_str) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  static_cast<std::shared_ptr<DMatrix>*>(handle)
-      ->get()->Info().SetInfo(field, interface_c_str);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixSetUIntInfo(DMatrixHandle handle,
-                                 const char* field,
-                                 const unsigned* info,
-                                 xgboost::bst_ulong len) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  static_cast<std::shared_ptr<DMatrix>*>(handle)
-      ->get()->Info().SetInfo(field, info, xgboost::DataType::kUInt32, len);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixSetGroup(DMatrixHandle handle,
-                              const unsigned* group,
-                              xgboost::bst_ulong len) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  LOG(WARNING) << "XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.";
-  static_cast<std::shared_ptr<DMatrix>*>(handle)
-      ->get()->Info().SetInfo("group", group, xgboost::DataType::kUInt32, len);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixGetFloatInfo(const DMatrixHandle handle,
-                                  const char* field,
-                                  xgboost::bst_ulong* out_len,
-                                  const bst_float** out_dptr) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info();
-  const std::vector<bst_float>* vec = nullptr;
-  if (!std::strcmp(field, "label")) {
-    vec = &info.labels_.HostVector();
-  } else if (!std::strcmp(field, "weight")) {
-    vec = &info.weights_.HostVector();
-  } else if (!std::strcmp(field, "base_margin")) {
-    vec = &info.base_margin_.HostVector();
-  } else if (!std::strcmp(field, "label_lower_bound")) {
-    vec = &info.labels_lower_bound_.HostVector();
-  } else if (!std::strcmp(field, "label_upper_bound")) {
-    vec = &info.labels_upper_bound_.HostVector();
-  } else {
-    LOG(FATAL) << "Unknown float field name " << field;
-  }
-  *out_len = static_cast<xgboost::bst_ulong>(vec->size());  // NOLINT
-  *out_dptr = dmlc::BeginPtr(*vec);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
-                                 const char *field,
-                                 xgboost::bst_ulong *out_len,
-                                 const unsigned **out_dptr) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info();
-  const std::vector<unsigned>* vec = nullptr;
-  if (!std::strcmp(field, "group_ptr")) {
-    vec = &info.group_ptr_;
-  } else {
-    LOG(FATAL) << "Unknown uint field name " << field;
-  }
-  *out_len = static_cast<xgboost::bst_ulong>(vec->size());
-  *out_dptr = dmlc::BeginPtr(*vec);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixNumRow(const DMatrixHandle handle,
-                            xgboost::bst_ulong *out) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  *out = static_cast<xgboost::bst_ulong>(
-      static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info().num_row_);
-  API_END();
-}
-
-XGB_DLL int XGDMatrixNumCol(const DMatrixHandle handle,
-                            xgboost::bst_ulong *out) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  *out = static_cast<xgboost::bst_ulong>(
-      static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info().num_col_);
-  API_END();
-}
-
-// xgboost implementation
-XGB_DLL int XGBoosterCreate(const DMatrixHandle dmats[],
-                            xgboost::bst_ulong len,
-                            BoosterHandle *out) {
-  API_BEGIN();
-  std::vector<std::shared_ptr<DMatrix> > mats;
-  for (xgboost::bst_ulong i = 0; i < len; ++i) {
-    mats.push_back(*static_cast<std::shared_ptr<DMatrix>*>(dmats[i]));
-  }
-  *out = Learner::Create(mats);
-  API_END();
-}
-
-XGB_DLL int XGBoosterFree(BoosterHandle handle) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  delete static_cast<Learner*>(handle);
-  API_END();
-}
-
-XGB_DLL int XGBoosterSetParam(BoosterHandle handle,
-                              const char *name,
-                              const char *value) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  static_cast<Learner*>(handle)->SetParam(name, value);
-  API_END();
-}
-
-XGB_DLL int XGBoosterLoadJsonConfig(BoosterHandle handle, char const* json_parameters) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  std::string str {json_parameters};
-  Json config { Json::Load(StringView{str.c_str(), str.size()}) };
-  static_cast<Learner*>(handle)->LoadConfig(config);
-  API_END();
-}
-
-XGB_DLL int XGBoosterSaveJsonConfig(BoosterHandle handle,
-                                    xgboost::bst_ulong *out_len,
-                                    char const** out_str) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  Json config { Object() };
-  auto* learner = static_cast<Learner*>(handle);
-  learner->Configure();
-  learner->SaveConfig(&config);
-  std::string& raw_str = learner->GetThreadLocal().ret_str;
-  Json::Dump(config, &raw_str);
-  *out_str = raw_str.c_str();
-  *out_len = static_cast<xgboost::bst_ulong>(raw_str.length());
-  API_END();
-}
-
-XGB_DLL int XGBoosterUpdateOneIter(BoosterHandle handle,
-                                   int iter,
-                                   DMatrixHandle dtrain) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* bst = static_cast<Learner*>(handle);
-  auto *dtr = static_cast<std::shared_ptr<DMatrix>*>(dtrain);
-
-  bst->UpdateOneIter(iter, *dtr, NULL);
-  API_END();
-}
-
-XGB_DLL int XGBoosterUpdateOneIterLtd(BoosterHandle handle,
-                                      int iter,
-                                      DMatrixHandle dtrain,
-                                      bst_float* last_round_best_loss) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* bst = static_cast<Learner*>(handle);
-  auto *dtr = static_cast<std::shared_ptr<DMatrix>*>(dtrain);
-
-  bst->UpdateOneIter(iter, *dtr, last_round_best_loss);
-  API_END();
-}
-
-XGB_DLL int XGBoosterBoostOneIter(BoosterHandle handle,
-                                  DMatrixHandle dtrain,
-                                  bst_float *grad,
-                                  bst_float *hess,
-                                  xgboost::bst_ulong len) {
-  HostDeviceVector<GradientPair> tmp_gpair;
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* bst = static_cast<Learner*>(handle);
-  auto* dtr =
-      static_cast<std::shared_ptr<DMatrix>*>(dtrain);
-  tmp_gpair.Resize(len);
-  std::vector<GradientPair>& tmp_gpair_h = tmp_gpair.HostVector();
-  for (xgboost::bst_ulong i = 0; i < len; ++i) {
-    tmp_gpair_h[i] = GradientPair(grad[i], hess[i]);
-  }
-
-  bst->BoostOneIter(0, *dtr, &tmp_gpair);
-  API_END();
-}
-
-XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
-                                 int iter,
-                                 DMatrixHandle dmats[],
-                                 const char* evnames[],
-                                 xgboost::bst_ulong len,
-                                 const char** out_str) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* bst = static_cast<Learner*>(handle);
-  std::string& eval_str = bst->GetThreadLocal().ret_str;
-
-  std::vector<std::shared_ptr<DMatrix>> data_sets;
-  std::vector<std::string> data_names;
-
-  for (xgboost::bst_ulong i = 0; i < len; ++i) {
-    data_sets.push_back(*static_cast<std::shared_ptr<DMatrix>*>(dmats[i]));
-    data_names.emplace_back(evnames[i]);
-  }
-
-  eval_str = bst->EvalOneIter(iter, data_sets, data_names);
-  *out_str = eval_str.c_str();
-  API_END();
-}
-
-XGB_DLL int XGBoosterPredict(BoosterHandle handle,
-                             DMatrixHandle dmat,
-                             int option_mask,
-                             unsigned ntree_limit,
-                             int training,
-                             xgboost::bst_ulong *len,
-                             const bst_float **out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto *learner = static_cast<Learner*>(handle);
-  auto& entry = learner->GetThreadLocal().prediction_entry;
-  learner->Predict(
-      *static_cast<std::shared_ptr<DMatrix>*>(dmat),
-      (option_mask & 1) != 0,
-      &entry.predictions, ntree_limit,
-      static_cast<bool>(training),
-      (option_mask & 2) != 0,
-      (option_mask & 4) != 0,
-      (option_mask & 8) != 0,
-      (option_mask & 16) != 0);
-  *out_result = dmlc::BeginPtr(entry.predictions.ConstHostVector());
-  *len = static_cast<xgboost::bst_ulong>(entry.predictions.Size());
-  API_END();
-}
-
-// A hidden API as cache id is not being supported yet.
-XGB_DLL int XGBoosterPredictFromDense(BoosterHandle handle, float *values,
-                                      xgboost::bst_ulong n_rows,
-                                      xgboost::bst_ulong n_cols,
-                                      float missing,
-                                      unsigned iteration_begin,
-                                      unsigned iteration_end,
-                                      char const* c_type,
-                                      xgboost::bst_ulong cache_id,
-                                      xgboost::bst_ulong *out_len,
-                                      const float **out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
-  auto *learner = static_cast<xgboost::Learner *>(handle);
-
-  auto x = xgboost::data::DenseAdapter(values, n_rows, n_cols);
-  HostDeviceVector<float>* p_predt { nullptr };
-  std::string type { c_type };
-  learner->InplacePredict(x, type, missing, &p_predt);
-  CHECK(p_predt);
-
-  *out_result = dmlc::BeginPtr(p_predt->HostVector());
-  *out_len = static_cast<xgboost::bst_ulong>(p_predt->Size());
-  API_END();
-}
-
-// A hidden API as cache id is not being supported yet.
-XGB_DLL int XGBoosterPredictFromCSR(BoosterHandle handle,
-                                    const size_t* indptr,
-                                    const unsigned* indices,
-                                    const bst_float* data,
-                                    size_t nindptr,
-                                    size_t nelem,
-                                    size_t num_col,
-                                    float missing,
-                                    unsigned iteration_begin,
-                                    unsigned iteration_end,
-                                    char const *c_type,
-                                    xgboost::bst_ulong cache_id,
-                                    xgboost::bst_ulong *out_len,
-                                    const float **out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
-  auto *learner = static_cast<xgboost::Learner *>(handle);
-
-  auto x = data::CSRAdapter(indptr, indices, data, nindptr - 1, nelem, num_col);
-  HostDeviceVector<float>* p_predt { nullptr };
-  std::string type { c_type };
-  learner->InplacePredict(x, type, missing, &p_predt);
-  CHECK(p_predt);
-
-  *out_result = dmlc::BeginPtr(p_predt->HostVector());
-  *out_len = static_cast<xgboost::bst_ulong>(p_predt->Size());
-  API_END();
-}
-
-#if !defined(XGBOOST_USE_CUDA)
-XGB_DLL int XGBoosterPredictFromArrayInterfaceColumns(BoosterHandle handle,
-                                                      char const* c_json_strs,
-                                                      float missing,
-                                                      unsigned iteration_begin,
-                                                      unsigned iteration_end,
-                                                      char const* c_type,
-                                                      xgboost::bst_ulong cache_id,
-                                                      xgboost::bst_ulong *out_len,
-                                                      float const** out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  common::AssertGPUSupport();
-  API_END();
-}
-XGB_DLL int XGBoosterPredictFromArrayInterface(BoosterHandle handle,
-                                               char const* c_json_strs,
-                                               float missing,
-                                               unsigned iteration_begin,
-                                               unsigned iteration_end,
-                                               char const* c_type,
-                                               xgboost::bst_ulong cache_id,
-                                               xgboost::bst_ulong *out_len,
-                                               const float **out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  common::AssertGPUSupport();
-  API_END();
-}
-#endif  // !defined(XGBOOST_USE_CUDA)
-
-XGB_DLL int XGBoosterLoadModel(BoosterHandle handle, const char* fname) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  if (common::FileExtension(fname) == "json") {
-    auto str = common::LoadSequentialFile(fname);
-    CHECK_GT(str.size(), 2);
-    CHECK_EQ(str[0], '{');
-    Json in { Json::Load({str.c_str(), str.size()}) };
-    static_cast<Learner*>(handle)->LoadModel(in);
-  } else {
-    std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname, "r"));
-    static_cast<Learner*>(handle)->LoadModel(fi.get());
-  }
-  API_END();
-}
-
-XGB_DLL int XGBoosterSaveModel(BoosterHandle handle, const char* c_fname) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(c_fname, "w"));
-  auto *learner = static_cast<Learner *>(handle);
-  learner->Configure();
-  if (common::FileExtension(c_fname) == "json") {
-    Json out { Object() };
-    learner->SaveModel(&out);
-    std::string str;
-    Json::Dump(out, &str);
-    fo->Write(str.c_str(), str.size());
-  } else {
-    auto *bst = static_cast<Learner*>(handle);
-    bst->SaveModel(fo.get());
-  }
-  API_END();
-}
-
-XGB_DLL int XGBoosterLoadModelFromBuffer(BoosterHandle handle,
-                                         const void* buf,
-                                         xgboost::bst_ulong len) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  common::MemoryFixSizeBuffer fs((void*)buf, len);  // NOLINT(*)
-  static_cast<Learner*>(handle)->LoadModel(&fs);
-  API_END();
-}
-
-XGB_DLL int XGBoosterGetModelRaw(BoosterHandle handle,
-                                 xgboost::bst_ulong* out_len,
-                                 const char** out_dptr) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto *learner = static_cast<Learner*>(handle);
-  std::string& raw_str = learner->GetThreadLocal().ret_str;
-  raw_str.resize(0);
-
-  common::MemoryBufferStream fo(&raw_str);
-
-  learner->Configure();
-  learner->SaveModel(&fo);
-  *out_dptr = dmlc::BeginPtr(raw_str);
-  *out_len = static_cast<xgboost::bst_ulong>(raw_str.length());
-  API_END();
-}
-
-// The following two functions are `Load` and `Save` for memory based
-// serialization methods. E.g. Python pickle.
-XGB_DLL int XGBoosterSerializeToBuffer(BoosterHandle handle,
-                                       xgboost::bst_ulong *out_len,
-                                       const char **out_dptr) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto *learner = static_cast<Learner*>(handle);
-  std::string &raw_str = learner->GetThreadLocal().ret_str;
-  raw_str.resize(0);
-  common::MemoryBufferStream fo(&raw_str);
-  learner->Configure();
-  learner->Save(&fo);
-  *out_dptr = dmlc::BeginPtr(raw_str);
-  *out_len = static_cast<xgboost::bst_ulong>(raw_str.length());
-  API_END();
-}
-
-XGB_DLL int XGBoosterUnserializeFromBuffer(BoosterHandle handle,
-                                           const void *buf,
-                                           xgboost::bst_ulong len) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  common::MemoryFixSizeBuffer fs((void*)buf, len);  // NOLINT(*)
-  static_cast<Learner*>(handle)->Load(&fs);
-  API_END();
-}
-
-XGB_DLL int XGBoosterLoadRabitCheckpoint(BoosterHandle handle,
-                                         int* version) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* bst = static_cast<Learner*>(handle);
-  *version = rabit::LoadCheckPoint(bst);
-  if (*version != 0) {
-    bst->Configure();
-  }
-  API_END();
-}
-
-XGB_DLL int XGBoosterSaveRabitCheckpoint(BoosterHandle handle) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* learner = static_cast<Learner*>(handle);
-  learner->Configure();
-  if (learner->AllowLazyCheckPoint()) {
-    rabit::LazyCheckPoint(learner);
-  } else {
-    rabit::CheckPoint(learner);
-  }
-  API_END();
-}
-
-inline void XGBoostDumpModelImpl(BoosterHandle handle, const FeatureMap &fmap,
-                                 int with_stats, const char *format,
-                                 xgboost::bst_ulong *len,
-                                 const char ***out_models) {
-  auto *bst = static_cast<Learner*>(handle);
-  std::vector<std::string>& str_vecs = bst->GetThreadLocal().ret_vec_str;
-  std::vector<const char*>& charp_vecs = bst->GetThreadLocal().ret_vec_charp;
-  str_vecs = bst->DumpModel(fmap, with_stats != 0, format);
-  charp_vecs.resize(str_vecs.size());
-  for (size_t i = 0; i < str_vecs.size(); ++i) {
-    charp_vecs[i] = str_vecs[i].c_str();
-  }
-  *out_models = dmlc::BeginPtr(charp_vecs);
-  *len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
-}
-
-XGB_DLL int XGBoosterDumpModel(BoosterHandle handle,
-                               const char* fmap,
-                               int with_stats,
-                               xgboost::bst_ulong* len,
-                               const char*** out_models) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  return XGBoosterDumpModelEx(handle, fmap, with_stats, "text", len, out_models);
-  API_END();
-}
-
-XGB_DLL int XGBoosterDumpModelEx(BoosterHandle handle,
-                                 const char* fmap,
-                                 int with_stats,
-                                 const char *format,
-                                 xgboost::bst_ulong* len,
-                                 const char*** out_models) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  FeatureMap featmap;
-  if (strlen(fmap) != 0) {
-    std::unique_ptr<dmlc::Stream> fs(
-        dmlc::Stream::Create(fmap, "r"));
-    dmlc::istream is(fs.get());
-    featmap.LoadText(is);
-  }
-  XGBoostDumpModelImpl(handle, featmap, with_stats, format, len, out_models);
-  API_END();
-}
-
-XGB_DLL int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
-                                           int fnum,
-                                           const char** fname,
-                                           const char** ftype,
-                                           int with_stats,
-                                           xgboost::bst_ulong* len,
-                                           const char*** out_models) {
-  return XGBoosterDumpModelExWithFeatures(handle, fnum, fname, ftype, with_stats,
-                                          "text", len, out_models);
-}
-
-XGB_DLL int XGBoosterDumpModelExWithFeatures(BoosterHandle handle,
-                                             int fnum,
-                                             const char** fname,
-                                             const char** ftype,
-                                             int with_stats,
-                                             const char *format,
-                                             xgboost::bst_ulong* len,
-                                             const char*** out_models) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  FeatureMap featmap;
-  for (int i = 0; i < fnum; ++i) {
-    featmap.PushBack(i, fname[i], ftype[i]);
-  }
-  XGBoostDumpModelImpl(handle, featmap, with_stats, format, len, out_models);
-  API_END();
-}
-
-XGB_DLL int XGBoosterGetAttr(BoosterHandle handle,
-                     const char* key,
-                     const char** out,
-                     int* success) {
-  auto* bst = static_cast<Learner*>(handle);
-  std::string& ret_str = bst->GetThreadLocal().ret_str;
-  API_BEGIN();
-  CHECK_HANDLE();
-  if (bst->GetAttr(key, &ret_str)) {
-    *out = ret_str.c_str();
-    *success = 1;
-  } else {
-    *out = nullptr;
-    *success = 0;
-  }
-  API_END();
-}
-
-XGB_DLL int XGBoosterSetAttr(BoosterHandle handle,
-                             const char* key,
-                             const char* value) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto* bst = static_cast<Learner*>(handle);
-  if (value == nullptr) {
-    bst->DelAttr(key);
-  } else {
-    bst->SetAttr(key, value);
-  }
-  API_END();
-}
-
-XGB_DLL int XGBoosterGetAttrNames(BoosterHandle handle,
-                                  xgboost::bst_ulong* out_len,
-                                  const char*** out) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  auto *learner = static_cast<Learner *>(handle);
-  std::vector<std::string> &str_vecs = learner->GetThreadLocal().ret_vec_str;
-  std::vector<const char *> &charp_vecs =
-      learner->GetThreadLocal().ret_vec_charp;
-  str_vecs = learner->GetAttrNames();
-  charp_vecs.resize(str_vecs.size());
-  for (size_t i = 0; i < str_vecs.size(); ++i) {
-    charp_vecs[i] = str_vecs[i].c_str();
-  }
-  *out = dmlc::BeginPtr(charp_vecs);
-  *out_len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
-  API_END();
-}
-
-// force link rabit
-static DMLC_ATTRIBUTE_UNUSED int XGBOOST_LINK_RABIT_C_API_ = RabitLinkTag();
diff --git a/ml-xgboost/src/c_api/c_api.cu b/ml-xgboost/src/c_api/c_api.cu
deleted file mode 100644
index 7fc49b4..0000000
--- a/ml-xgboost/src/c_api/c_api.cu
+++ /dev/null
@@ -1,111 +0,0 @@
-// Copyright (c) 2019-2020 by Contributors
-#include "xgboost/data.h"
-#include "xgboost/c_api.h"
-#include "xgboost/learner.h"
-#include "c_api_error.h"
-#include "../data/device_adapter.cuh"
-#include "../data/device_dmatrix.h"
-
-using namespace xgboost;  // NOLINT
-
-XGB_DLL int XGDMatrixCreateFromArrayInterfaceColumns(char const* c_json_strs,
-                                                     bst_float missing,
-                                                     int nthread,
-                                                     DMatrixHandle* out) {
-  API_BEGIN();
-  std::string json_str{c_json_strs};
-  data::CudfAdapter adapter(json_str);
-  *out =
-      new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
-  API_END();
-}
-
-XGB_DLL int XGDMatrixCreateFromArrayInterface(char const* c_json_strs,
-                                              bst_float missing, int nthread,
-                                              DMatrixHandle* out) {
-  API_BEGIN();
-  std::string json_str{c_json_strs};
-  data::CupyAdapter adapter(json_str);
-  *out =
-      new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
-  API_END();
-}
-
-XGB_DLL int XGDeviceQuantileDMatrixCreateFromArrayInterfaceColumns(char const* c_json_strs,
-  bst_float missing, int nthread, int max_bin,
-  DMatrixHandle* out) {
-  API_BEGIN();
-  std::string json_str{c_json_strs};
-  data::CudfAdapter adapter(json_str);
-  *out =
-    new std::shared_ptr<DMatrix>(new data::DeviceDMatrix(&adapter, missing, nthread, max_bin));
-  API_END();
-}
-
-XGB_DLL int XGDeviceQuantileDMatrixCreateFromArrayInterface(char const* c_json_strs,
-  bst_float missing, int nthread, int max_bin,
-  DMatrixHandle* out) {
-  API_BEGIN();
-  std::string json_str{c_json_strs};
-  data::CupyAdapter adapter(json_str);
-  *out =
-    new std::shared_ptr<DMatrix>(new data::DeviceDMatrix(&adapter, missing, nthread, max_bin));
-  API_END();
-}
-
-// A hidden API as cache id is not being supported yet.
-XGB_DLL int XGBoosterPredictFromArrayInterfaceColumns(BoosterHandle handle,
-                                                      char const* c_json_strs,
-                                                      float missing,
-                                                      unsigned iteration_begin,
-                                                      unsigned iteration_end,
-                                                      char const* c_type,
-                                                      xgboost::bst_ulong cache_id,
-                                                      xgboost::bst_ulong *out_len,
-                                                      float const** out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
-  auto *learner = static_cast<Learner*>(handle);
-
-  std::string json_str{c_json_strs};
-  auto x = data::CudfAdapter(json_str);
-  HostDeviceVector<float>* p_predt { nullptr };
-  std::string type { c_type };
-  learner->InplacePredict(x, type, missing, &p_predt);
-  CHECK(p_predt);
-  CHECK(p_predt->DeviceCanRead());
-
-  *out_result = p_predt->ConstDevicePointer();
-  *out_len = static_cast<xgboost::bst_ulong>(p_predt->Size());
-
-  API_END();
-}
-// A hidden API as cache id is not being supported yet.
-XGB_DLL int XGBoosterPredictFromArrayInterface(BoosterHandle handle,
-                                               char const* c_json_strs,
-                                               float missing,
-                                               unsigned iteration_begin,
-                                               unsigned iteration_end,
-                                               char const* c_type,
-                                               xgboost::bst_ulong cache_id,
-                                               xgboost::bst_ulong *out_len,
-                                               float const** out_result) {
-  API_BEGIN();
-  CHECK_HANDLE();
-  CHECK_EQ(cache_id, 0) << "Cache ID is not supported yet";
-  auto *learner = static_cast<Learner*>(handle);
-
-  std::string json_str{c_json_strs};
-  auto x = data::CupyAdapter(json_str);
-  HostDeviceVector<float>* p_predt { nullptr };
-  std::string type { c_type };
-  learner->InplacePredict(x, type, missing, &p_predt);
-  CHECK(p_predt);
-  CHECK(p_predt->DeviceCanRead());
-
-  *out_result = p_predt->ConstDevicePointer();
-  *out_len = static_cast<xgboost::bst_ulong>(p_predt->Size());
-
-  API_END();
-}
diff --git a/ml-xgboost/src/c_api/c_api_error.cc b/ml-xgboost/src/c_api/c_api_error.cc
deleted file mode 100644
index 5e6a2c6..0000000
--- a/ml-xgboost/src/c_api/c_api_error.cc
+++ /dev/null
@@ -1,21 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file c_api_error.cc
- * \brief C error handling
- */
-#include <dmlc/thread_local.h>
-#include "./c_api_error.h"
-
-struct XGBAPIErrorEntry {
-  std::string last_error;
-};
-
-using XGBAPIErrorStore = dmlc::ThreadLocalStore<XGBAPIErrorEntry>;
-
-const char *XGBGetLastError() {
-  return XGBAPIErrorStore::Get()->last_error.c_str();
-}
-
-void XGBAPISetLastError(const char* msg) {
-  XGBAPIErrorStore::Get()->last_error = msg;
-}
diff --git a/ml-xgboost/src/c_api/c_api_error.h b/ml-xgboost/src/c_api/c_api_error.h
deleted file mode 100644
index 648a4b9..0000000
--- a/ml-xgboost/src/c_api/c_api_error.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*!
- *  Copyright (c) 2015 by Contributors
- * \file c_api_error.h
- * \brief Error handling for C API.
- */
-#ifndef XGBOOST_C_API_C_API_ERROR_H_
-#define XGBOOST_C_API_C_API_ERROR_H_
-
-#include <dmlc/base.h>
-#include <dmlc/logging.h>
-#include <xgboost/c_api.h>
-
-/*! \brief  macro to guard beginning and end section of all functions */
-#define API_BEGIN() try {
-/*! \brief every function starts with API_BEGIN();
-     and finishes with API_END() or API_END_HANDLE_ERROR */
-#define API_END() } catch(dmlc::Error &_except_) { return XGBAPIHandleException(_except_); } return 0;  // NOLINT(*)
-#define CHECK_HANDLE() if (handle == nullptr) \
-  LOG(FATAL) << "DMatrix/Booster has not been intialized or has already been disposed.";
-/*!
- * \brief every function starts with API_BEGIN();
- *   and finishes with API_END() or API_END_HANDLE_ERROR
- *   The finally clause contains procedure to cleanup states when an error happens.
- */
-#define API_END_HANDLE_ERROR(Finalize) } catch(dmlc::Error &_except_) { Finalize; return XGBAPIHandleException(_except_); } return 0; // NOLINT(*)
-
-/*!
- * \brief Set the last error message needed by C API
- * \param msg The error message to set.
- */
-void XGBAPISetLastError(const char* msg);
-/*!
- * \brief handle exception thrown out
- * \param e the exception
- * \return the return value of API after exception is handled
- */
-inline int XGBAPIHandleException(const dmlc::Error &e) {
-  XGBAPISetLastError(e.what());
-  return -1;
-}
-#endif  // XGBOOST_C_API_C_API_ERROR_H_
diff --git a/ml-xgboost/src/cli_main.cc b/ml-xgboost/src/cli_main.cc
deleted file mode 100644
index fa3edf6..0000000
--- a/ml-xgboost/src/cli_main.cc
+++ /dev/null
@@ -1,386 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file cli_main.cc
- * \brief The command line interface program of xgboost.
- *  This file is not included in dynamic library.
- */
-// Copyright 2014 by Contributors
-#define _CRT_SECURE_NO_WARNINGS
-#define _CRT_SECURE_NO_DEPRECATE
-#define NOMINMAX
-
-#include <xgboost/learner.h>
-#include <xgboost/data.h>
-#include <xgboost/logging.h>
-#include <xgboost/parameter.h>
-
-#include <dmlc/timer.h>
-#include <iomanip>
-#include <ctime>
-#include <string>
-#include <cstdio>
-#include <cstring>
-#include <vector>
-#include "./common/common.h"
-#include "./common/config.h"
-
-namespace xgboost {
-
-enum CLITask {
-  kTrain = 0,
-  kDumpModel = 1,
-  kPredict = 2
-};
-
-/*! buffer size used for storing best loss reductions 
- (used for depthwiselossltd grow policy) */
-#define BUFF_SIZE (4*1024)  
-
-struct CLIParam : public XGBoostParameter<CLIParam> {
-  /*! \brief the task name */
-  int task;
-  /*! \brief whether evaluate training statistics */
-  bool eval_train;
-  /*! \brief number of boosting iterations */
-  int num_round;
-  /*! \brief the period to save the model, 0 means only save the final round model */
-  int save_period;
-  /*! \brief the path of training set */
-  std::string train_path;
-  /*! \brief path of test dataset */
-  std::string test_path;
-  /*! \brief the path of test model file, or file to restart training */
-  std::string model_in;
-  /*! \brief the path of final model file, to be saved */
-  std::string model_out;
-  /*! \brief the path of directory containing the saved models */
-  std::string model_dir;
-  /*! \brief name of predict file */
-  std::string name_pred;
-  /*! \brief data split mode */
-  int dsplit;
-  /*!\brief limit number of trees in prediction */
-  int ntree_limit;
-  /*!\brief whether to directly output margin value */
-  bool pred_margin;
-  /*! \brief whether dump statistics along with model */
-  int dump_stats;
-  /*! \brief what format to dump the model in */
-  std::string dump_format;
-  /*! \brief name of feature map */
-  std::string name_fmap;
-  /*! \brief name of dump file */
-  std::string name_dump;
-  /*! \brief the paths of validation data sets */
-  std::vector<std::string> eval_data_paths;
-  /*! \brief the names of the evaluation data used in output log */
-  std::vector<std::string> eval_data_names;
-  /*! \brief all the configurations */
-  std::vector<std::pair<std::string, std::string> > cfg;
-
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(CLIParam) {
-    // NOTE: declare everything except eval_data_paths.
-    DMLC_DECLARE_FIELD(task).set_default(kTrain)
-        .add_enum("train", kTrain)
-        .add_enum("dump", kDumpModel)
-        .add_enum("pred", kPredict)
-        .describe("Task to be performed by the CLI program.");
-    DMLC_DECLARE_FIELD(eval_train).set_default(false)
-        .describe("Whether evaluate on training data during training.");
-    DMLC_DECLARE_FIELD(num_round).set_default(10).set_lower_bound(1)
-        .describe("Number of boosting iterations");
-    DMLC_DECLARE_FIELD(save_period).set_default(0).set_lower_bound(0)
-        .describe("The period to save the model, 0 means only save final model.");
-    DMLC_DECLARE_FIELD(train_path).set_default("NULL")
-        .describe("Training data path.");
-    DMLC_DECLARE_FIELD(test_path).set_default("NULL")
-        .describe("Test data path.");
-    DMLC_DECLARE_FIELD(model_in).set_default("NULL")
-        .describe("Input model path, if any.");
-    DMLC_DECLARE_FIELD(model_out).set_default("NULL")
-        .describe("Output model path, if any.");
-    DMLC_DECLARE_FIELD(model_dir).set_default("./")
-        .describe("Output directory of period checkpoint.");
-    DMLC_DECLARE_FIELD(name_pred).set_default("pred.txt")
-        .describe("Name of the prediction file.");
-    DMLC_DECLARE_FIELD(dsplit).set_default(0)
-        .add_enum("auto", 0)
-        .add_enum("col", 1)
-        .add_enum("row", 2)
-        .describe("Data split mode.");
-    DMLC_DECLARE_FIELD(ntree_limit).set_default(0).set_lower_bound(0)
-        .describe("Number of trees used for prediction, 0 means use all trees.");
-    DMLC_DECLARE_FIELD(pred_margin).set_default(false)
-        .describe("Whether to predict margin value instead of probability.");
-    DMLC_DECLARE_FIELD(dump_stats).set_default(false)
-        .describe("Whether dump the model statistics.");
-    DMLC_DECLARE_FIELD(dump_format).set_default("text")
-        .describe("What format to dump the model in.");
-    DMLC_DECLARE_FIELD(name_fmap).set_default("NULL")
-        .describe("Name of the feature map file.");
-    DMLC_DECLARE_FIELD(name_dump).set_default("dump.txt")
-        .describe("Name of the output dump text file.");
-    // alias
-    DMLC_DECLARE_ALIAS(train_path, data);
-    DMLC_DECLARE_ALIAS(test_path, test:data);
-    DMLC_DECLARE_ALIAS(name_fmap, fmap);
-  }
-  // customized configure function of CLIParam
-  inline void Configure(const std::vector<std::pair<std::string, std::string> >& _cfg) {
-    this->cfg = _cfg;
-    this->UpdateAllowUnknown(_cfg);
-    for (const auto& kv : _cfg) {
-      if (!strncmp("eval[", kv.first.c_str(), 5)) {
-        char evname[256];
-        CHECK_EQ(sscanf(kv.first.c_str(), "eval[%[^]]", evname), 1)
-            << "must specify evaluation name for display";
-        eval_data_names.emplace_back(evname);
-        eval_data_paths.push_back(kv.second);
-      }
-    }
-    // constraint.
-    if (name_pred == "stdout") {
-      save_period = 0;
-    }
-    if (dsplit == 0 && rabit::IsDistributed()) {
-      dsplit = 2;
-    }
-  }
-};
-
-DMLC_REGISTER_PARAMETER(CLIParam);
-
-void CLITrain(const CLIParam& param) {
-  const double tstart_data_load = dmlc::GetTime();
-  if (rabit::IsDistributed()) {
-    std::string pname = rabit::GetProcessorName();
-    LOG(CONSOLE) << "start " << pname << ":" << rabit::GetRank();
-  }
-  // load in data.
-  std::shared_ptr<DMatrix> dtrain(
-      DMatrix::Load(
-          param.train_path,
-          ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
-          param.dsplit == 2));
-  std::vector<std::shared_ptr<DMatrix> > deval;
-  std::vector<std::shared_ptr<DMatrix> > cache_mats;
-  std::vector<std::shared_ptr<DMatrix>> eval_datasets;
-  cache_mats.push_back(dtrain);
-  for (size_t i = 0; i < param.eval_data_names.size(); ++i) {
-    deval.emplace_back(
-        std::shared_ptr<DMatrix>(DMatrix::Load(
-            param.eval_data_paths[i],
-            ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
-            param.dsplit == 2)));
-    eval_datasets.push_back(deval.back());
-    cache_mats.push_back(deval.back());
-  }
-  std::vector<std::string> eval_data_names = param.eval_data_names;
-  if (param.eval_train) {
-    eval_datasets.push_back(dtrain);
-    eval_data_names.emplace_back("train");
-  }
-  // initialize the learner.
-  std::unique_ptr<Learner> learner(Learner::Create(cache_mats));
-  int version = rabit::LoadCheckPoint(learner.get());
-  if (version == 0) {
-    // initialize the model if needed.
-    if (param.model_in != "NULL") {
-      std::unique_ptr<dmlc::Stream> fi(
-          dmlc::Stream::Create(param.model_in.c_str(), "r"));
-      learner->LoadModel(fi.get());
-      learner->SetParams(param.cfg);
-    } else {
-      learner->SetParams(param.cfg);
-    }
-  }
-  LOG(INFO) << "Loading data: " << dmlc::GetTime() - tstart_data_load << " sec";
-
-  // start training.
-  const double start = dmlc::GetTime();    
-  float last_round_best_loss[BUFF_SIZE];
-  int nclass = 1;  // for binary classification nclass should be equal to 1
-  for (int i = 0; i < param.cfg.size(); i++) {
-    if (param.cfg[i].first == "num_class") {
-      nclass = std::stoi(param.cfg[i].second);
-    }
-  }
-  for (int i = 0; i < nclass; i++) {
-    last_round_best_loss[i] = 0;
-  }
-
-  for (int i = version / 2; i < param.num_round; ++i) {
-    double elapsed = dmlc::GetTime() - start;
-    if (version % 2 == 0) {
-      LOG(INFO) << "boosting round " << i << ", " << elapsed << " sec elapsed";
-      learner->UpdateOneIter(i, dtrain, last_round_best_loss);
-      if (learner->AllowLazyCheckPoint()) {
-        rabit::LazyCheckPoint(learner.get());
-      } else {
-        rabit::CheckPoint(learner.get());
-      }
-      version += 1;
-    }
-    CHECK_EQ(version, rabit::VersionNumber());
-    std::string res = learner->EvalOneIter(i, eval_datasets, eval_data_names);
-    if (rabit::IsDistributed()) {
-      if (rabit::GetRank() == 0) {
-        LOG(TRACKER) << res;
-      }
-    } else {
-      LOG(CONSOLE) << res;
-    }
-    if (param.save_period != 0 &&
-        (i + 1) % param.save_period == 0 &&
-        rabit::GetRank() == 0) {
-      std::ostringstream os;
-      os << param.model_dir << '/'
-         << std::setfill('0') << std::setw(4)
-         << i + 1 << ".model";
-      std::unique_ptr<dmlc::Stream> fo(
-          dmlc::Stream::Create(os.str().c_str(), "w"));
-      learner->SaveModel(fo.get());
-    }
-
-    if (learner->AllowLazyCheckPoint()) {
-      rabit::LazyCheckPoint(learner.get());
-    } else {
-      rabit::CheckPoint(learner.get());
-    }
-    version += 1;
-    CHECK_EQ(version, rabit::VersionNumber());
-  }
-  LOG(INFO) << "Complete Training loop time: " << dmlc::GetTime() - start << " sec";
-  // always save final round
-  if ((param.save_period == 0 || param.num_round % param.save_period != 0) &&
-      param.model_out != "NONE" &&
-      rabit::GetRank() == 0) {
-    std::ostringstream os;
-    if (param.model_out == "NULL") {
-      os << param.model_dir << '/'
-         << std::setfill('0') << std::setw(4)
-         << param.num_round << ".model";
-    } else {
-      os << param.model_out;
-    }
-    std::unique_ptr<dmlc::Stream> fo(
-        dmlc::Stream::Create(os.str().c_str(), "w"));
-    learner->SaveModel(fo.get());
-  }
-
-  double elapsed = dmlc::GetTime() - start;
-  LOG(INFO) << "update end, " << elapsed << " sec in all";
-}
-
-void CLIDumpModel(const CLIParam& param) {
-  FeatureMap fmap;
-  if (param.name_fmap != "NULL") {
-    std::unique_ptr<dmlc::Stream> fs(
-        dmlc::Stream::Create(param.name_fmap.c_str(), "r"));
-    dmlc::istream is(fs.get());
-    fmap.LoadText(is);
-  }
-  // load model
-  CHECK_NE(param.model_in, "NULL")
-      << "Must specify model_in for dump";
-  std::unique_ptr<Learner> learner(Learner::Create({}));
-  std::unique_ptr<dmlc::Stream> fi(
-      dmlc::Stream::Create(param.model_in.c_str(), "r"));
-  learner->SetParams(param.cfg);
-  learner->LoadModel(fi.get());
-  // dump data
-  std::vector<std::string> dump = learner->DumpModel(
-      fmap, param.dump_stats, param.dump_format);
-  std::unique_ptr<dmlc::Stream> fo(
-      dmlc::Stream::Create(param.name_dump.c_str(), "w"));
-  dmlc::ostream os(fo.get());
-  if (param.dump_format == "json") {
-    os << "[" << std::endl;
-    for (size_t i = 0; i < dump.size(); ++i) {
-      if (i != 0) os << "," << std::endl;
-      os << dump[i];  // Dump the previously generated JSON here
-    }
-    os << std::endl << "]" << std::endl;
-  } else {
-    for (size_t i = 0; i < dump.size(); ++i) {
-      os << "booster[" << i << "]:\n";
-      os << dump[i];
-    }
-  }
-  // force flush before fo destruct.
-  os.set_stream(nullptr);
-}
-
-void CLIPredict(const CLIParam& param) {
-  CHECK_NE(param.test_path, "NULL")
-      << "Test dataset parameter test:data must be specified.";
-  // load data
-  std::shared_ptr<DMatrix> dtest(
-      DMatrix::Load(
-          param.test_path,
-          ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
-          param.dsplit == 2));
-  // load model
-  CHECK_NE(param.model_in, "NULL")
-      << "Must specify model_in for predict";
-  std::unique_ptr<Learner> learner(Learner::Create({}));
-  std::unique_ptr<dmlc::Stream> fi(
-      dmlc::Stream::Create(param.model_in.c_str(), "r"));
-  learner->LoadModel(fi.get());
-  learner->SetParams(param.cfg);
-
-  LOG(INFO) << "start prediction...";
-  HostDeviceVector<bst_float> preds;
-  learner->Predict(dtest, param.pred_margin, &preds, param.ntree_limit);
-  LOG(CONSOLE) << "writing prediction to " << param.name_pred;
-
-  std::unique_ptr<dmlc::Stream> fo(
-      dmlc::Stream::Create(param.name_pred.c_str(), "w"));
-  dmlc::ostream os(fo.get());
-  for (bst_float p : preds.ConstHostVector()) {
-    os << std::setprecision(std::numeric_limits<bst_float>::max_digits10)
-       << p << '\n';
-  }
-  // force flush before fo destruct.
-  os.set_stream(nullptr);
-}
-
-int CLIRunTask(int argc, char *argv[]) {
-  if (argc < 2) {
-    printf("Usage: <config>\n");
-    return 0;
-  }
-  rabit::Init(argc, argv);
-
-  common::ConfigParser cp(argv[1]);
-  auto cfg = cp.Parse();
-
-  for (int i = 2; i < argc; ++i) {
-    char name[256], val[256];
-    if (sscanf(argv[i], "%[^=]=%s", name, val) == 2) {
-      cfg.emplace_back(std::string(name), std::string(val));
-    }
-  }
-  CLIParam param;
-  param.Configure(cfg);
-
-  switch (param.task) {
-    case kTrain: CLITrain(param); break;
-    case kDumpModel: CLIDumpModel(param); break;
-    case kPredict: CLIPredict(param); break;
-  }
-  rabit::Finalize();
-  return 0;
-}
-}  // namespace xgboost
-
-int main(int argc, char *argv[]) {
-  return xgboost::CLIRunTask(argc, argv);
-}
diff --git a/ml-xgboost/src/common/base64.h b/ml-xgboost/src/common/base64.h
deleted file mode 100644
index 4c876b5..0000000
--- a/ml-xgboost/src/common/base64.h
+++ /dev/null
@@ -1,272 +0,0 @@
-/*!
- * Copyright 2014 by Contributors
- * \file base64.h
- * \brief data stream support to input and output from/to base64 stream
- * base64 is easier to store and pass as text format in mapreduce
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_BASE64_H_
-#define XGBOOST_COMMON_BASE64_H_
-
-#include <xgboost/logging.h>
-#include <cctype>
-#include <cstdio>
-#include <string>
-#include "./io.h"
-
-namespace xgboost {
-namespace common {
-/*! \brief buffer reader of the stream that allows you to get */
-class StreamBufferReader {
- public:
-  explicit StreamBufferReader(size_t buffer_size)
-      :stream_(NULL),
-       read_len_(1), read_ptr_(1) {
-    buffer_.resize(buffer_size);
-  }
-  /*!
-   * \brief set input stream
-   */
-  inline void set_stream(dmlc::Stream *stream) {
-    stream_ = stream;
-    read_len_ = read_ptr_ = 1;
-  }
-  /*!
-   * \brief allows quick read using get char
-   */
-  inline char GetChar(void) {
-    while (true) {
-      if (read_ptr_ < read_len_) {
-        return buffer_[read_ptr_++];
-      } else {
-        read_len_ = stream_->Read(&buffer_[0], buffer_.length());
-        if (read_len_ == 0) return EOF;
-        read_ptr_ = 0;
-      }
-    }
-  }
-  /*! \brief whether we are reaching the end of file */
-  inline bool AtEnd(void) const {
-    return read_len_ == 0;
-  }
-
- private:
-  /*! \brief the underlying stream */
-  dmlc::Stream *stream_;
-  /*! \brief buffer to hold data */
-  std::string buffer_;
-  /*! \brief length of valid data in buffer */
-  size_t read_len_;
-  /*! \brief pointer in the buffer */
-  size_t read_ptr_;
-};
-
-/*! \brief namespace of base64 decoding and encoding table */
-namespace base64 {
-const char DecodeTable[] = {
-  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-  62,  // '+'
-  0, 0, 0,
-  63,  // '/'
-  52, 53, 54, 55, 56, 57, 58, 59, 60, 61,  // '0'-'9'
-  0, 0, 0, 0, 0, 0, 0,
-  0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
-  13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,  // 'A'-'Z'
-  0, 0, 0, 0, 0, 0,
-  26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
-  39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,  // 'a'-'z'
-};
-static const char EncodeTable[] =
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
-}  // namespace base64
-/*! \brief the stream that reads from base64, note we take from file pointers */
-class Base64InStream: public dmlc::Stream {
- public:
-  explicit Base64InStream(dmlc::Stream *fs) : reader_(256) {
-    reader_.set_stream(fs);
-    num_prev = 0; tmp_ch = 0;
-  }
-  /*!
-   * \brief initialize the stream position to beginning of next base64 stream
-   * call this function before actually start read
-   */
-  inline void InitPosition(void) {
-    // get a character
-    do {
-      tmp_ch = reader_.GetChar();
-    } while (isspace(tmp_ch));
-  }
-  /*! \brief whether current position is end of a base64 stream */
-  inline bool IsEOF(void) const {
-    return num_prev == 0 && (tmp_ch == EOF || isspace(tmp_ch));
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    using base64::DecodeTable;
-    if (size == 0) return 0;
-    // use tlen to record left size
-    size_t tlen = size;
-    unsigned char *cptr = static_cast<unsigned char*>(ptr);
-    // if anything left, load from previous buffered result
-    if (num_prev != 0) {
-      if (num_prev == 2) {
-        if (tlen >= 2) {
-          *cptr++ = buf_prev[0];
-          *cptr++ = buf_prev[1];
-          tlen -= 2;
-          num_prev = 0;
-        } else {
-          // assert tlen == 1
-          *cptr++ = buf_prev[0]; --tlen;
-          buf_prev[0] = buf_prev[1];
-          num_prev = 1;
-        }
-      } else {
-        // assert num_prev == 1
-        *cptr++ = buf_prev[0]; --tlen; num_prev = 0;
-      }
-    }
-    if (tlen == 0) return size;
-    int nvalue;
-    // note: everything goes with 4 bytes in Base64
-    // so we process 4 bytes a unit
-    while (tlen && tmp_ch != EOF && !isspace(tmp_ch)) {
-      // first byte
-      nvalue = DecodeTable[tmp_ch] << 18;
-      {
-        // second byte
-        tmp_ch = reader_.GetChar();
-        CHECK(tmp_ch != EOF && !isspace(tmp_ch)) << "invalid base64 format";
-        nvalue |= DecodeTable[tmp_ch] << 12;
-        *cptr++ = (nvalue >> 16) & 0xFF; --tlen;
-        }
-      {
-        // third byte
-        tmp_ch = reader_.GetChar();
-        CHECK(tmp_ch != EOF && !isspace(tmp_ch)) << "invalid base64 format";
-        // handle termination
-        if (tmp_ch == '=') {
-          tmp_ch = reader_.GetChar();
-          CHECK(tmp_ch == '=') << "invalid base64 format";
-          tmp_ch = reader_.GetChar();
-          CHECK(tmp_ch == EOF || isspace(tmp_ch))
-              << "invalid base64 format";
-          break;
-        }
-        nvalue |= DecodeTable[tmp_ch] << 6;
-        if (tlen) {
-          *cptr++ = (nvalue >> 8) & 0xFF; --tlen;
-        } else {
-          buf_prev[num_prev++] = (nvalue >> 8) & 0xFF;
-        }
-      }
-      {
-        // fourth byte
-        tmp_ch = reader_.GetChar();
-        CHECK(tmp_ch != EOF && !isspace(tmp_ch))
-            << "invalid base64 format";
-        if (tmp_ch == '=') {
-          tmp_ch = reader_.GetChar();
-          CHECK(tmp_ch == EOF || isspace(tmp_ch))
-              << "invalid base64 format";
-          break;
-        }
-        nvalue |= DecodeTable[tmp_ch];
-        if (tlen) {
-          *cptr++ = nvalue & 0xFF; --tlen;
-        } else {
-          buf_prev[num_prev ++] = nvalue & 0xFF;
-        }
-      }
-      // get next char
-      tmp_ch = reader_.GetChar();
-    }
-    if (kStrictCheck) {
-      CHECK_EQ(tlen, 0) << "Base64InStream: read incomplete";
-    }
-    return size - tlen;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    LOG(FATAL) << "Base64InStream do not support write";
-  }
-
- private:
-  StreamBufferReader reader_;
-  int tmp_ch;
-  int num_prev;
-  unsigned char buf_prev[2];
-  // whether we need to do strict check
-  static const bool kStrictCheck = false;
-};
-/*! \brief the stream that write to base64, note we take from file pointers */
-class Base64OutStream: public dmlc::Stream {
- public:
-  explicit Base64OutStream(dmlc::Stream *fp) : fp(fp) {
-    buf_top = 0;
-  }
-  virtual void Write(const void *ptr, size_t size) {
-    using base64::EncodeTable;
-    size_t tlen = size;
-    const unsigned char *cptr = static_cast<const unsigned char*>(ptr);
-    while (tlen) {
-      while (buf_top < 3  && tlen != 0) {
-        buf[++buf_top] = *cptr++; --tlen;
-      }
-      if (buf_top == 3) {
-        // flush 4 bytes out
-        PutChar(EncodeTable[buf[1] >> 2]);
-        PutChar(EncodeTable[((buf[1] << 4) | (buf[2] >> 4)) & 0x3F]);
-        PutChar(EncodeTable[((buf[2] << 2) | (buf[3] >> 6)) & 0x3F]);
-        PutChar(EncodeTable[buf[3] & 0x3F]);
-        buf_top = 0;
-      }
-    }
-  }
-  virtual size_t Read(void *ptr, size_t size) {
-    LOG(FATAL) << "Base64OutStream do not support read";
-    return 0;
-  }
-  /*!
-   * \brief finish writing of all current base64 stream, do some post processing
-   * \param endch character to put to end of stream, if it is EOF, then nothing will be done
-   */
-  inline void Finish(char endch = EOF) {
-    using base64::EncodeTable;
-    if (buf_top == 1) {
-      PutChar(EncodeTable[buf[1] >> 2]);
-      PutChar(EncodeTable[(buf[1] << 4) & 0x3F]);
-      PutChar('=');
-      PutChar('=');
-    }
-    if (buf_top == 2) {
-      PutChar(EncodeTable[buf[1] >> 2]);
-      PutChar(EncodeTable[((buf[1] << 4) | (buf[2] >> 4)) & 0x3F]);
-      PutChar(EncodeTable[(buf[2] << 2) & 0x3F]);
-      PutChar('=');
-    }
-    buf_top = 0;
-    if (endch != EOF) PutChar(endch);
-    this->Flush();
-  }
-
- private:
-  dmlc::Stream *fp;
-  int buf_top;
-  unsigned char buf[4];
-  std::string out_buf;
-  static const size_t kBufferSize = 256;
-
-  inline void PutChar(char ch) {
-    out_buf += ch;
-    if (out_buf.length() >= kBufferSize) Flush();
-  }
-  inline void Flush(void) {
-    if (out_buf.length() != 0) {
-      fp->Write(&out_buf[0], out_buf.length());
-      out_buf.clear();
-    }
-  }
-};
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_BASE64_H_
diff --git a/ml-xgboost/src/common/bitfield.h b/ml-xgboost/src/common/bitfield.h
deleted file mode 100644
index 4353a52..0000000
--- a/ml-xgboost/src/common/bitfield.h
+++ /dev/null
@@ -1,252 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- * \file bitfield.h
- */
-#ifndef XGBOOST_COMMON_BITFIELD_H_
-#define XGBOOST_COMMON_BITFIELD_H_
-
-#include <algorithm>
-#include <bitset>
-#include <cinttypes>
-#include <iostream>
-#include <sstream>
-#include <string>
-#include <vector>
-
-#if defined(__CUDACC__)
-#include <thrust/copy.h>
-#include <thrust/device_ptr.h>
-#endif  // defined(__CUDACC__)
-
-#include "xgboost/span.h"
-#include "common.h"
-
-namespace xgboost {
-
-#if defined(__CUDACC__)
-using BitFieldAtomicType = unsigned long long;  // NOLINT
-
-__forceinline__ __device__ BitFieldAtomicType AtomicOr(BitFieldAtomicType* address,
-                                                       BitFieldAtomicType val) {
-  BitFieldAtomicType old = *address, assumed;  // NOLINT
-  do {
-    assumed = old;
-    old = atomicCAS(address, assumed, val | assumed);
-  } while (assumed != old);
-
-  return old;
-}
-
-__forceinline__ __device__ BitFieldAtomicType AtomicAnd(BitFieldAtomicType* address,
-                                                        BitFieldAtomicType val) {
-  BitFieldAtomicType old = *address, assumed;  // NOLINT
-  do {
-    assumed = old;
-    old = atomicCAS(address, assumed, val & assumed);
-  } while (assumed != old);
-
-  return old;
-}
-#endif  // defined(__CUDACC__)
-
-/*!
- * \brief A non-owning type with auxiliary methods defined for manipulating bits.
- *
- * \tparam Direction Whether the bits start from left or from right.
- */
-template <typename VT, typename Direction>
-struct BitFieldContainer {
-  using value_type = VT;  // NOLINT
-  using pointer = value_type*;  // NOLINT
-
-  static value_type constexpr kValueSize = sizeof(value_type) * 8;
-  static value_type constexpr kOne = 1;  // force correct type.
-
-  struct Pos {
-    value_type int_pos {0};
-    value_type bit_pos {0};
-  };
-
- private:
-  common::Span<value_type> bits_;
-  static_assert(!std::is_signed<VT>::value, "Must use unsiged type as underlying storage.");
-
-  XGBOOST_DEVICE static Pos ToBitPos(value_type pos) {
-    Pos pos_v;
-    if (pos == 0) {
-      return pos_v;
-    }
-    pos_v.int_pos =  pos / kValueSize;
-    pos_v.bit_pos =  pos % kValueSize;
-    return pos_v;
-  }
-
- public:
-  BitFieldContainer() = default;
-  XGBOOST_DEVICE explicit BitFieldContainer(common::Span<value_type> bits) : bits_{bits} {}
-  XGBOOST_DEVICE BitFieldContainer(BitFieldContainer const& other) : bits_{other.bits_} {}
-
-  common::Span<value_type>       Bits()       { return bits_; }
-  common::Span<value_type const> Bits() const { return bits_; }
-
-  /*\brief Compute the size of needed memory allocation.  The returned value is in terms
-   *       of number of elements with `BitFieldContainer::value_type'.
-   */
-  static size_t ComputeStorageSize(size_t size) {
-    return common::DivRoundUp(size, kValueSize);
-  }
-#if defined(__CUDA_ARCH__)
-  __device__ BitFieldContainer& operator|=(BitFieldContainer const& rhs) {
-    auto tid = blockIdx.x * blockDim.x + threadIdx.x;
-    size_t min_size = min(bits_.size(), rhs.bits_.size());
-    if (tid < min_size) {
-      bits_[tid] |= rhs.bits_[tid];
-    }
-    return *this;
-  }
-#else
-  BitFieldContainer& operator|=(BitFieldContainer const& rhs) {
-    size_t min_size = std::min(bits_.size(), rhs.bits_.size());
-    for (size_t i = 0; i < min_size; ++i) {
-      bits_[i] |= rhs.bits_[i];
-    }
-    return *this;
-  }
-#endif  // #if defined(__CUDA_ARCH__)
-
-#if defined(__CUDA_ARCH__)
-  __device__ BitFieldContainer& operator&=(BitFieldContainer const& rhs) {
-    size_t min_size = min(bits_.size(), rhs.bits_.size());
-    auto tid = blockIdx.x * blockDim.x + threadIdx.x;
-    if (tid < min_size) {
-      bits_[tid] &= rhs.bits_[tid];
-    }
-    return *this;
-  }
-#else
-  BitFieldContainer& operator&=(BitFieldContainer const& rhs) {
-    size_t min_size = std::min(bits_.size(), rhs.bits_.size());
-    for (size_t i = 0; i < min_size; ++i) {
-      bits_[i] &= rhs.bits_[i];
-    }
-    return *this;
-  }
-#endif  // defined(__CUDA_ARCH__)
-
-#if defined(__CUDA_ARCH__)
-  __device__ void Set(value_type pos) {
-    Pos pos_v = Direction::Shift(ToBitPos(pos));
-    value_type& value = bits_[pos_v.int_pos];
-    value_type set_bit = kOne << pos_v.bit_pos;
-    static_assert(sizeof(BitFieldAtomicType) == sizeof(value_type), "");
-    AtomicOr(reinterpret_cast<BitFieldAtomicType*>(&value), set_bit);
-  }
-  __device__ void Clear(value_type pos) {
-    Pos pos_v = Direction::Shift(ToBitPos(pos));
-    value_type& value = bits_[pos_v.int_pos];
-    value_type clear_bit = ~(kOne << pos_v.bit_pos);
-    static_assert(sizeof(BitFieldAtomicType) == sizeof(value_type), "");
-    AtomicAnd(reinterpret_cast<BitFieldAtomicType*>(&value), clear_bit);
-  }
-#else
-  void Set(value_type pos) {
-    Pos pos_v = Direction::Shift(ToBitPos(pos));
-    value_type& value = bits_[pos_v.int_pos];
-    value_type set_bit = kOne << pos_v.bit_pos;
-    value |= set_bit;
-  }
-  void Clear(value_type pos) {
-    Pos pos_v = Direction::Shift(ToBitPos(pos));
-    value_type& value = bits_[pos_v.int_pos];
-    value_type clear_bit = ~(kOne << pos_v.bit_pos);
-    value &= clear_bit;
-  }
-#endif  // defined(__CUDA_ARCH__)
-
-  XGBOOST_DEVICE bool Check(Pos pos_v) const {
-    pos_v = Direction::Shift(pos_v);
-    value_type const value = bits_[pos_v.int_pos];
-    value_type const test_bit = kOne << pos_v.bit_pos;
-    value_type result = test_bit & value;
-    return static_cast<bool>(result);
-  }
-  XGBOOST_DEVICE bool Check(value_type pos) const {
-    Pos pos_v = ToBitPos(pos);
-    return Check(pos_v);
-  }
-
-  XGBOOST_DEVICE size_t Size() const { return kValueSize * bits_.size(); }
-
-  XGBOOST_DEVICE pointer Data() const { return bits_.data(); }
-
-  friend std::ostream& operator<<(std::ostream& os, BitFieldContainer<VT, Direction> field) {
-    os << "Bits " << "storage size: " << field.bits_.size() << "\n";
-    for (typename common::Span<value_type>::index_type i = 0; i < field.bits_.size(); ++i) {
-      std::bitset<BitFieldContainer<VT, Direction>::kValueSize> bset(field.bits_[i]);
-      os << bset << "\n";
-    }
-    return os;
-  }
-};
-
-// Bits start from left most bits (most significant bit).
-template <typename VT>
-struct LBitsPolicy : public BitFieldContainer<VT, LBitsPolicy<VT>> {
-  using Container = BitFieldContainer<VT, LBitsPolicy<VT>>;
-  using Pos = typename Container::Pos;
-  using value_type = typename Container::value_type;  // NOLINT
-
-  XGBOOST_DEVICE static Pos Shift(Pos pos) {
-    pos.bit_pos = Container::kValueSize - pos.bit_pos - Container::kOne;
-    return pos;
-  }
-};
-
-// Bits start from right most bit (least significant bit) of each entry, but integer index
-// is from left to right.
-template <typename VT>
-struct RBitsPolicy : public BitFieldContainer<VT, RBitsPolicy<VT>> {
-  using Container = BitFieldContainer<VT, RBitsPolicy<VT>>;
-  using Pos = typename Container::Pos;
-  using value_type = typename Container::value_type;  // NOLINT
-
-  XGBOOST_DEVICE static Pos Shift(Pos pos) {
-    return pos;
-  }
-};
-
-// Format: <Direction>BitField<size of underlying type in bits>, underlying type must be unsigned.
-using LBitField64 = BitFieldContainer<uint64_t, LBitsPolicy<uint64_t>>;
-using RBitField8 = BitFieldContainer<uint8_t, RBitsPolicy<unsigned char>>;
-
-#if defined(__CUDACC__)
-
-template <typename V, typename D>
-inline void PrintDeviceBits(std::string name, BitFieldContainer<V, D> field) {
-  std::cout << "Bits: " << name << std::endl;
-  std::vector<typename BitFieldContainer<V, D>::value_type> h_field_bits(field.bits_.size());
-  thrust::copy(thrust::device_ptr<typename BitFieldContainer<V, D>::value_type>(field.bits_.data()),
-               thrust::device_ptr<typename BitFieldContainer<V, D>::value_type>(
-                   field.bits_.data() + field.bits_.size()),
-               h_field_bits.data());
-  BitFieldContainer<V, D> h_field;
-  h_field.bits_ = {h_field_bits.data(), h_field_bits.data() + h_field_bits.size()};
-  std::cout << h_field;
-}
-
-inline void PrintDeviceStorage(std::string name, common::Span<int32_t> list) {
-  std::cout << name << std::endl;
-  std::vector<int32_t> h_list(list.size());
-  thrust::copy(thrust::device_ptr<int32_t>(list.data()),
-               thrust::device_ptr<int32_t>(list.data() + list.size()),
-               h_list.data());
-  for (auto v : h_list) {
-    std::cout << v << ", ";
-  }
-  std::cout << std::endl;
-}
-
-#endif  // defined(__CUDACC__)
-}       // namespace xgboost
-
-#endif  // XGBOOST_COMMON_BITFIELD_H_
diff --git a/ml-xgboost/src/common/column_matrix.h b/ml-xgboost/src/common/column_matrix.h
deleted file mode 100644
index 861cfae..0000000
--- a/ml-xgboost/src/common/column_matrix.h
+++ /dev/null
@@ -1,336 +0,0 @@
-/*!
- * Copyright 2017 by Contributors
- * \file column_matrix.h
- * \brief Utility for fast column-wise access
- * \author Philip Cho
- */
-
-#ifndef XGBOOST_COMMON_COLUMN_MATRIX_H_
-#define XGBOOST_COMMON_COLUMN_MATRIX_H_
-
-#include <limits>
-#include <vector>
-#include <memory>
-#include "hist_util.h"
-
-namespace xgboost {
-namespace common {
-
-class ColumnMatrix;
-/*! \brief column type */
-enum ColumnType {
-  kDenseColumn,
-  kSparseColumn
-};
-
-/*! \brief a column storage, to be used with ApplySplit. Note that each
-    bin id is stored as index[i] + index_base.
-    Different types of column index for each column allow
-    to reduce the memory usage. */
-template <typename BinIdxType>
-class Column {
- public:
-  Column(ColumnType type, common::Span<const BinIdxType> index, const uint32_t index_base)
-      : type_(type),
-        index_(index),
-        index_base_(index_base) {}
-
-  uint32_t GetGlobalBinIdx(size_t idx) const {
-    return index_base_ + static_cast<uint32_t>(index_[idx]);
-  }
-
-  BinIdxType GetFeatureBinIdx(size_t idx) const { return index_[idx]; }
-
-  const uint32_t GetBaseIdx() const { return index_base_; }
-
-  common::Span<const BinIdxType> GetFeatureBinIdxPtr() const { return index_; }
-
-  ColumnType GetType() const { return type_; }
-
-  /* returns number of elements in column */
-  size_t Size() const { return index_.size(); }
-
- private:
-  /* type of column */
-  ColumnType type_;
-  /* bin indexes in range [0, max_bins - 1] */
-  common::Span<const BinIdxType> index_;
-  /* bin index offset for specific feature */
-  const uint32_t index_base_;
-};
-
-template <typename BinIdxType>
-class SparseColumn: public Column<BinIdxType> {
- public:
-  SparseColumn(ColumnType type, common::Span<const BinIdxType> index,
-              uint32_t index_base, common::Span<const size_t> row_ind)
-      : Column<BinIdxType>(type, index, index_base),
-        row_ind_(row_ind) {}
-
-  const size_t* GetRowData() const { return row_ind_.data(); }
-
-  size_t GetRowIdx(size_t idx) const {
-    return row_ind_.data()[idx];
-  }
-
- private:
-  /* indexes of rows */
-  common::Span<const size_t> row_ind_;
-};
-
-template <typename BinIdxType>
-class DenseColumn: public Column<BinIdxType> {
- public:
-  DenseColumn(ColumnType type, common::Span<const BinIdxType> index,
-              uint32_t index_base, const std::vector<bool>& missing_flags,
-              size_t feature_offset)
-      : Column<BinIdxType>(type, index, index_base),
-        missing_flags_(missing_flags),
-        feature_offset_(feature_offset) {}
-  bool IsMissing(size_t idx) const { return missing_flags_[feature_offset_ + idx]; }
- private:
-  /* flags for missing values in dense columns */
-  const std::vector<bool>& missing_flags_;
-  size_t feature_offset_;
-};
-
-/*! \brief a collection of columns, with support for construction from
-    GHistIndexMatrix. */
-class ColumnMatrix {
- public:
-  // get number of features
-  inline bst_uint GetNumFeature() const {
-    return static_cast<bst_uint>(type_.size());
-  }
-
-  // construct column matrix from GHistIndexMatrix
-  inline void Init(const GHistIndexMatrix& gmat,
-                   double  sparse_threshold) {
-    const int32_t nfeature = static_cast<int32_t>(gmat.cut.Ptrs().size() - 1);
-    const size_t nrow = gmat.row_ptr.size() - 1;
-    // identify type of each column
-    feature_counts_.resize(nfeature);
-    type_.resize(nfeature);
-    std::fill(feature_counts_.begin(), feature_counts_.end(), 0);
-    uint32_t max_val = std::numeric_limits<uint32_t>::max();
-    for (int32_t fid = 0; fid < nfeature; ++fid) {
-      CHECK_LE(gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid], max_val);
-    }
-    bool all_dense = gmat.IsDense();
-    gmat.GetFeatureCounts(&feature_counts_[0]);
-    // classify features
-    for (int32_t fid = 0; fid < nfeature; ++fid) {
-      if (static_cast<double>(feature_counts_[fid])
-                 < sparse_threshold * nrow) {
-        type_[fid] = kSparseColumn;
-        all_dense = false;
-      } else {
-        type_[fid] = kDenseColumn;
-      }
-    }
-
-    // want to compute storage boundary for each feature
-    // using variants of prefix sum scan
-    feature_offsets_.resize(nfeature + 1);
-    size_t accum_index_ = 0;
-    feature_offsets_[0] = accum_index_;
-    for (int32_t fid = 1; fid < nfeature + 1; ++fid) {
-      if (type_[fid - 1] == kDenseColumn) {
-        accum_index_ += static_cast<size_t>(nrow);
-      } else {
-        accum_index_ += feature_counts_[fid - 1];
-      }
-      feature_offsets_[fid] = accum_index_;
-    }
-
-    SetTypeSize(gmat.max_num_bins);
-
-    index_.resize(feature_offsets_[nfeature] * bins_type_size_, 0);
-    if (!all_dense) {
-      row_ind_.resize(feature_offsets_[nfeature]);
-    }
-
-    // store least bin id for each feature
-    index_base_ = const_cast<uint32_t*>(gmat.cut.Ptrs().data());
-
-    const bool noMissingValues = NoMissingValues(gmat.row_ptr[nrow], nrow, nfeature);
-
-    if (noMissingValues) {
-      missing_flags_.resize(feature_offsets_[nfeature], false);
-    } else {
-      missing_flags_.resize(feature_offsets_[nfeature], true);
-    }
-
-    // pre-fill index_ for dense columns
-    if (all_dense) {
-      BinTypeSize gmat_bin_size = gmat.index.GetBinTypeSize();
-      if (gmat_bin_size == kUint8BinsTypeSize) {
-          SetIndexAllDense(gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues);
-      } else if (gmat_bin_size == kUint16BinsTypeSize) {
-          SetIndexAllDense(gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues);
-      } else {
-          CHECK_EQ(gmat_bin_size, kUint32BinsTypeSize);
-          SetIndexAllDense(gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues);
-      }
-    /* For sparse DMatrix gmat.index.getBinTypeSize() returns always kUint32BinsTypeSize
-       but for ColumnMatrix we still have a chance to reduce the memory consumption */
-    } else {
-      if (bins_type_size_ == kUint8BinsTypeSize) {
-          SetIndex<uint8_t>(gmat.index.data<uint32_t>(), gmat, nrow, nfeature);
-      } else if (bins_type_size_ == kUint16BinsTypeSize) {
-          SetIndex<uint16_t>(gmat.index.data<uint32_t>(), gmat, nrow, nfeature);
-      } else {
-          CHECK_EQ(bins_type_size_, kUint32BinsTypeSize);
-          SetIndex<uint32_t>(gmat.index.data<uint32_t>(), gmat, nrow, nfeature);
-      }
-    }
-  }
-
-  /* Set the number of bytes based on numeric limit of maximum number of bins provided by user */
-  void SetTypeSize(size_t max_num_bins) {
-    if ( (max_num_bins - 1) <= static_cast<int>(std::numeric_limits<uint8_t>::max()) ) {
-      bins_type_size_ = kUint8BinsTypeSize;
-    } else if ((max_num_bins - 1) <= static_cast<int>(std::numeric_limits<uint16_t>::max())) {
-      bins_type_size_ = kUint16BinsTypeSize;
-    } else {
-      bins_type_size_ = kUint32BinsTypeSize;
-    }
-  }
-
-  /* Fetch an individual column. This code should be used with type swith
-     to determine type of bin id's */
-  template <typename BinIdxType>
-  std::unique_ptr<const Column<BinIdxType> > GetColumn(unsigned fid) const {
-    CHECK_EQ(sizeof(BinIdxType), bins_type_size_);
-
-    const size_t feature_offset = feature_offsets_[fid];  // to get right place for certain feature
-    const size_t column_size = feature_offsets_[fid + 1] - feature_offset;
-    common::Span<const BinIdxType> bin_index = { reinterpret_cast<const BinIdxType*>(
-                                                 &index_[feature_offset * bins_type_size_]),
-                                                 column_size };
-    std::unique_ptr<const Column<BinIdxType> > res;
-    if (type_[fid] == ColumnType::kDenseColumn) {
-      res.reset(new DenseColumn<BinIdxType>(type_[fid], bin_index, index_base_[fid],
-                                             missing_flags_, feature_offset));
-    } else {
-      res.reset(new SparseColumn<BinIdxType>(type_[fid], bin_index, index_base_[fid],
-                                       {&row_ind_[feature_offset], column_size}));
-    }
-    return res;
-  }
-
-  template<typename T>
-  inline void SetIndexAllDense(T* index, const GHistIndexMatrix& gmat,  const size_t nrow,
-                               const size_t nfeature,  const bool noMissingValues) {
-    T* local_index = reinterpret_cast<T*>(&index_[0]);
-
-    /* missing values make sense only for column with type kDenseColumn,
-       and if no missing values were observed it could be handled much faster. */
-    if (noMissingValues) {
-#pragma omp parallel for num_threads(omp_get_max_threads())
-      for (omp_ulong rid = 0; rid < nrow; ++rid) {
-        const size_t ibegin = rid*nfeature;
-        const size_t iend = (rid+1)*nfeature;
-        size_t j = 0;
-        for (size_t i = ibegin; i < iend; ++i, ++j) {
-            const size_t idx = feature_offsets_[j];
-            local_index[idx + rid] = index[i];
-        }
-      }
-    } else {
-      /* to handle rows in all batches, sum of all batch sizes equal to gmat.row_ptr.size() - 1 */
-      size_t rbegin = 0;
-      for (const auto &batch : gmat.p_fmat->GetBatches<SparsePage>()) {
-        const xgboost::Entry* data_ptr = batch.data.HostVector().data();
-        const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
-        const size_t batch_size = batch.Size();
-        CHECK_LT(batch_size, offset_vec.size());
-        for (size_t rid = 0; rid < batch_size; ++rid) {
-          const size_t size = offset_vec[rid + 1] - offset_vec[rid];
-          SparsePage::Inst inst = {data_ptr + offset_vec[rid], size};
-          const size_t ibegin = gmat.row_ptr[rbegin + rid];
-          const size_t iend = gmat.row_ptr[rbegin + rid + 1];
-          CHECK_EQ(ibegin + inst.size(), iend);
-          size_t j = 0;
-          size_t fid = 0;
-          for (size_t i = ibegin; i < iend; ++i, ++j) {
-              fid = inst[j].index;
-              const size_t idx = feature_offsets_[fid];
-              /* rbegin allows to store indexes from specific SparsePage batch */
-              local_index[idx + rbegin + rid] = index[i];
-              missing_flags_[idx + rbegin + rid] = false;
-          }
-        }
-        rbegin += batch.Size();
-      }
-    }
-  }
-
-  template<typename T>
-  inline void SetIndex(uint32_t* index, const GHistIndexMatrix& gmat,
-                       const size_t nrow, const size_t nfeature) {
-    std::vector<size_t> num_nonzeros;
-    num_nonzeros.resize(nfeature);
-    std::fill(num_nonzeros.begin(), num_nonzeros.end(), 0);
-
-    T* local_index = reinterpret_cast<T*>(&index_[0]);
-    size_t rbegin = 0;
-    for (const auto &batch : gmat.p_fmat->GetBatches<SparsePage>()) {
-      const xgboost::Entry* data_ptr = batch.data.HostVector().data();
-      const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
-      const size_t batch_size = batch.Size();
-      CHECK_LT(batch_size, offset_vec.size());
-      for (size_t rid = 0; rid < batch_size; ++rid) {
-        const size_t ibegin = gmat.row_ptr[rbegin + rid];
-        const size_t iend = gmat.row_ptr[rbegin + rid + 1];
-        size_t fid = 0;
-        const size_t size = offset_vec[rid + 1] - offset_vec[rid];
-        SparsePage::Inst inst = {data_ptr + offset_vec[rid], size};
-
-        CHECK_EQ(ibegin + inst.size(), iend);
-        size_t j = 0;
-        for (size_t i = ibegin; i < iend; ++i, ++j) {
-          const uint32_t bin_id = index[i];
-
-          fid = inst[j].index;
-          if (type_[fid] == kDenseColumn) {
-            T* begin = &local_index[feature_offsets_[fid]];
-            begin[rid + rbegin] = bin_id - index_base_[fid];
-            missing_flags_[feature_offsets_[fid] + rid + rbegin] = false;
-          } else {
-            T* begin = &local_index[feature_offsets_[fid]];
-            begin[num_nonzeros[fid]] = bin_id - index_base_[fid];
-            row_ind_[feature_offsets_[fid] + num_nonzeros[fid]] = rid + rbegin;
-            ++num_nonzeros[fid];
-          }
-        }
-      }
-      rbegin += batch.Size();
-    }
-  }
-  const BinTypeSize GetTypeSize() const {
-    return bins_type_size_;
-  }
-  const bool NoMissingValues(const size_t n_elements,
-                             const size_t n_row, const size_t n_features) {
-    return n_elements == n_features * n_row;
-  }
-
- private:
-  std::vector<uint8_t> index_;
-
-  std::vector<size_t> feature_counts_;
-  std::vector<ColumnType> type_;
-  std::vector<size_t> row_ind_;
-  /* indicate where each column's index and row_ind is stored. */
-  std::vector<size_t> feature_offsets_;
-
-  // index_base_[fid]: least bin id for feature fid
-  uint32_t* index_base_;
-  std::vector<bool> missing_flags_;
-  BinTypeSize bins_type_size_;
-};
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_COLUMN_MATRIX_H_
diff --git a/ml-xgboost/src/common/common.cc b/ml-xgboost/src/common/common.cc
deleted file mode 100644
index 8f4f4b5..0000000
--- a/ml-xgboost/src/common/common.cc
+++ /dev/null
@@ -1,33 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file common.cc
- * \brief Enable all kinds of global variables in common.
- */
-#include <dmlc/thread_local.h>
-#include <xgboost/logging.h>
-
-#include "common.h"
-#include "./random.h"
-
-namespace xgboost {
-namespace common {
-/*! \brief thread local entry for random. */
-struct RandomThreadLocalEntry {
-  /*! \brief the random engine instance. */
-  GlobalRandomEngine engine;
-};
-
-using RandomThreadLocalStore = dmlc::ThreadLocalStore<RandomThreadLocalEntry>;
-
-GlobalRandomEngine& GlobalRandom() {
-  return RandomThreadLocalStore::Get()->engine;
-}
-
-#if !defined(XGBOOST_USE_CUDA)
-int AllVisibleGPUs() {
-  return 0;
-}
-#endif  // !defined(XGBOOST_USE_CUDA)
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/common.cu b/ml-xgboost/src/common/common.cu
deleted file mode 100644
index d30fbc0..0000000
--- a/ml-xgboost/src/common/common.cu
+++ /dev/null
@@ -1,22 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-#include "common.h"
-
-namespace xgboost {
-namespace common {
-
-int AllVisibleGPUs() {
-  int n_visgpus = 0;
-  try {
-    // When compiled with CUDA but running on CPU only device,
-    // cudaGetDeviceCount will fail.
-    dh::safe_cuda(cudaGetDeviceCount(&n_visgpus));
-  } catch(const dmlc::Error &except) {
-    return 0;
-  }
-  return n_visgpus;
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/common.h b/ml-xgboost/src/common/common.h
deleted file mode 100644
index f9300f3..0000000
--- a/ml-xgboost/src/common/common.h
+++ /dev/null
@@ -1,159 +0,0 @@
-/*!
- * Copyright 2015-2018 by Contributors
- * \file common.h
- * \brief Common utilities
- */
-#ifndef XGBOOST_COMMON_COMMON_H_
-#define XGBOOST_COMMON_COMMON_H_
-
-#include <xgboost/base.h>
-#include <xgboost/logging.h>
-
-#include <exception>
-#include <limits>
-#include <type_traits>
-#include <vector>
-#include <string>
-#include <sstream>
-
-#if defined(__CUDACC__)
-#include <thrust/system/cuda/error.h>
-#include <thrust/system_error.h>
-
-#define WITH_CUDA() true
-
-#else
-
-#define WITH_CUDA() false
-
-#endif  // defined(__CUDACC__)
-
-namespace dh {
-#if defined(__CUDACC__)
-/*
- * Error handling  functions
- */
-#define safe_cuda(ans) ThrowOnCudaError((ans), __FILE__, __LINE__)
-
-inline cudaError_t ThrowOnCudaError(cudaError_t code, const char *file,
-                                    int line) {
-  if (code != cudaSuccess) {
-    LOG(FATAL) << thrust::system_error(code, thrust::cuda_category(),
-                                       std::string{file} + ": " +  // NOLINT
-                                       std::to_string(line)).what();
-  }
-  return code;
-}
-#endif  // defined(__CUDACC__)
-}  // namespace dh
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief Split a string by delimiter
- * \param s String to be splitted.
- * \param delim The delimiter.
- */
-inline std::vector<std::string> Split(const std::string& s, char delim) {
-  std::string item;
-  std::istringstream is(s);
-  std::vector<std::string> ret;
-  while (std::getline(is, item, delim)) {
-    ret.push_back(item);
-  }
-  return ret;
-}
-
-template <typename T>
-XGBOOST_DEVICE T Max(T a, T b) {
-  return a < b ? b : a;
-}
-
-// simple routine to convert any data to string
-template<typename T>
-inline std::string ToString(const T& data) {
-  std::ostringstream os;
-  os << data;
-  return os.str();
-}
-
-template <typename T1, typename T2>
-XGBOOST_DEVICE T1 DivRoundUp(const T1 a, const T2 b) {
-  return static_cast<T1>(std::ceil(static_cast<double>(a) / b));
-}
-
-/*
- * Range iterator
- */
-class Range {
- public:
-  using DifferenceType = int64_t;
-
-  class Iterator {
-    friend class Range;
-
-   public:
-    XGBOOST_DEVICE DifferenceType operator*() const { return i_; }
-    XGBOOST_DEVICE const Iterator &operator++() {
-      i_ += step_;
-      return *this;
-    }
-    XGBOOST_DEVICE Iterator operator++(int) {
-      Iterator res {*this};
-      i_ += step_;
-      return res;
-    }
-
-    XGBOOST_DEVICE bool operator==(const Iterator &other) const {
-      return i_ >= other.i_;
-    }
-    XGBOOST_DEVICE bool operator!=(const Iterator &other) const {
-      return i_ < other.i_;
-    }
-
-    XGBOOST_DEVICE void Step(DifferenceType s) { step_ = s; }
-
-   protected:
-    XGBOOST_DEVICE explicit Iterator(DifferenceType start) : i_(start) {}
-    XGBOOST_DEVICE explicit Iterator(DifferenceType start, DifferenceType step) :
-        i_{start}, step_{step} {}
-
-   private:
-    int64_t i_;
-    DifferenceType step_ = 1;
-  };
-
-  XGBOOST_DEVICE Iterator begin() const { return begin_; }  // NOLINT
-  XGBOOST_DEVICE Iterator end() const { return end_; }      // NOLINT
-
-  XGBOOST_DEVICE Range(DifferenceType begin, DifferenceType end)
-      : begin_(begin), end_(end) {}
-  XGBOOST_DEVICE Range(DifferenceType begin, DifferenceType end,
-                       DifferenceType step)
-      : begin_(begin, step), end_(end) {}
-
-  XGBOOST_DEVICE bool operator==(const Range& other) const {
-    return *begin_ == *other.begin_ && *end_ == *other.end_;
-  }
-  XGBOOST_DEVICE bool operator!=(const Range& other) const {
-    return !(*this == other);
-  }
-
-  XGBOOST_DEVICE void Step(DifferenceType s) { begin_.Step(s); }
-
- private:
-  Iterator begin_;
-  Iterator end_;
-};
-
-int AllVisibleGPUs();
-
-inline void AssertGPUSupport() {
-#ifndef XGBOOST_USE_CUDA
-    LOG(FATAL) << "XGBoost version not compiled with GPU support.";
-#endif  // XGBOOST_USE_CUDA
-}
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_COMMON_H_
diff --git a/ml-xgboost/src/common/compressed_iterator.h b/ml-xgboost/src/common/compressed_iterator.h
deleted file mode 100644
index 9f60722..0000000
--- a/ml-xgboost/src/common/compressed_iterator.h
+++ /dev/null
@@ -1,223 +0,0 @@
-/*!
- * Copyright 2017 by Contributors
- * \file compressed_iterator.h
- */
-#pragma once
-#include <xgboost/base.h>
-#include <cmath>
-#include <cstddef>
-#include <algorithm>
-
-#include "common.h"
-
-#ifdef __CUDACC__
-#include "device_helpers.cuh"
-#endif  // __CUDACC__
-
-namespace xgboost {
-namespace common {
-
-using CompressedByteT = unsigned char;
-
-namespace detail {
-inline void SetBit(CompressedByteT *byte, int bit_idx) {
-  *byte |= 1 << bit_idx;
-}
-template <typename T>
-inline T CheckBit(const T &byte, int bit_idx) {
-  return byte & (1 << bit_idx);
-}
-inline void ClearBit(CompressedByteT *byte, int bit_idx) {
-  *byte &= ~(1 << bit_idx);
-}
-static const int kPadding = 4;  // Assign padding so we can read slightly off
-                                // the beginning of the array
-
-// The number of bits required to represent a given unsigned range
-inline XGBOOST_DEVICE size_t SymbolBits(size_t num_symbols) {
-  auto bits = std::ceil(log2(static_cast<double>(num_symbols)));
-  return common::Max(static_cast<size_t>(bits), size_t(1));
-}
-}  // namespace detail
-
-/**
- * \class CompressedBufferWriter
- *
- * \brief Writes bit compressed symbols to a memory buffer. Use
- * CompressedIterator to read symbols back from buffer. Currently limited to a
- * maximum symbol size of 28 bits.
- *
- * \author  Rory
- * \date  7/9/2017
- */
-
-class CompressedBufferWriter {
-  size_t symbol_bits_;
-
- public:
-  XGBOOST_DEVICE explicit CompressedBufferWriter(size_t num_symbols)
-      : symbol_bits_(detail::SymbolBits(num_symbols)) {}
-
-  /**
-   * \fn  static size_t CompressedBufferWriter::CalculateBufferSize(int
-   * num_elements, int num_symbols)
-   *
-   * \brief Calculates number of bytes required for a given number of elements
-   * and a symbol range.
-   *
-   * \author  Rory
-   * \date  7/9/2017
-   *
-   * \param num_elements  Number of elements.
-   * \param num_symbols   Max number of symbols (alphabet size)
-   *
-   * \return  The calculated buffer size.
-   */
-  static size_t CalculateBufferSize(size_t num_elements, size_t num_symbols) {
-    constexpr int kBitsPerByte = 8;
-    size_t compressed_size = static_cast<size_t>(std::ceil(
-        static_cast<double>(detail::SymbolBits(num_symbols) * num_elements) /
-        kBitsPerByte));
-    // Handle atomicOr where input must be unsigned int, hence 4 bytes aligned.
-    size_t ret =
-        std::ceil(static_cast<double>(compressed_size + detail::kPadding) /
-                  static_cast<double>(sizeof(unsigned int))) *
-        sizeof(unsigned int);
-    return ret;
-  }
-
-  template <typename T>
-  void WriteSymbol(CompressedByteT *buffer, T symbol, size_t offset) {
-    const int bits_per_byte = 8;
-
-    for (size_t i = 0; i < symbol_bits_; i++) {
-      size_t byte_idx = ((offset + 1) * symbol_bits_ - (i + 1)) / bits_per_byte;
-      byte_idx += detail::kPadding;
-      size_t bit_idx =
-          ((bits_per_byte + i) - ((offset + 1) * symbol_bits_)) % bits_per_byte;
-
-      if (detail::CheckBit(symbol, i)) {
-        detail::SetBit(&buffer[byte_idx], bit_idx);
-      } else {
-        detail::ClearBit(&buffer[byte_idx], bit_idx);
-      }
-    }
-  }
-
-#ifdef __CUDACC__
-  __device__ void AtomicWriteSymbol
-    (CompressedByteT* buffer, uint64_t symbol, size_t offset) {
-    size_t ibit_start = offset * symbol_bits_;
-    size_t ibit_end = (offset + 1) * symbol_bits_ - 1;
-    size_t ibyte_start = ibit_start / 8, ibyte_end = ibit_end / 8;
-
-    symbol <<= 7 - ibit_end % 8;
-    for (ptrdiff_t ibyte = ibyte_end; ibyte >= static_cast<ptrdiff_t>(ibyte_start); --ibyte) {
-      dh::AtomicOrByte(reinterpret_cast<unsigned int*>(buffer + detail::kPadding),
-                       ibyte, symbol & 0xff);
-      symbol >>= 8;
-    }
-  }
-#endif  // __CUDACC__
-
-  template <typename IterT>
-  void Write(CompressedByteT *buffer, IterT input_begin, IterT input_end) {
-    uint64_t tmp = 0;
-    size_t stored_bits = 0;
-    const size_t max_stored_bits = 64 - symbol_bits_;
-    size_t buffer_position = detail::kPadding;
-    const size_t num_symbols = input_end - input_begin;
-    for (size_t i = 0; i < num_symbols; i++) {
-      typename std::iterator_traits<IterT>::value_type symbol = input_begin[i];
-      if (stored_bits > max_stored_bits) {
-        // Eject only full bytes
-        size_t tmp_bytes = stored_bits / 8;
-        for (size_t j = 0; j < tmp_bytes; j++) {
-          buffer[buffer_position] = static_cast<CompressedByteT>(
-              tmp >> (stored_bits - (j + 1) * 8));
-          buffer_position++;
-        }
-        stored_bits -= tmp_bytes * 8;
-        tmp &= (1 << stored_bits) - 1;
-      }
-      // Store symbol
-      tmp <<= symbol_bits_;
-      tmp |= symbol;
-      stored_bits += symbol_bits_;
-    }
-
-    // Eject all bytes
-    int tmp_bytes =
-        static_cast<int>(std::ceil(static_cast<float>(stored_bits) / 8));
-    for (int j = 0; j < tmp_bytes; j++) {
-      int shift_bits = static_cast<int>(stored_bits) - (j + 1) * 8;
-      if (shift_bits >= 0) {
-        buffer[buffer_position] =
-            static_cast<CompressedByteT>(tmp >> shift_bits);
-      } else {
-        buffer[buffer_position] =
-            static_cast<CompressedByteT>(tmp << std::abs(shift_bits));
-      }
-      buffer_position++;
-    }
-  }
-};
-
-/**
- * \brief Read symbols from a bit compressed memory buffer. Usable on device and host.
- *
- * \author  Rory
- * \date  7/9/2017
- *
- * \tparam  T Generic type parameter.
- */
-template <typename T>
-class CompressedIterator {
- public:
-  // Type definitions for thrust
-  typedef CompressedIterator<T> self_type;  // NOLINT
-  typedef ptrdiff_t difference_type;        // NOLINT
-  typedef T value_type;                     // NOLINT
-  typedef value_type *pointer;              // NOLINT
-  typedef value_type reference;             // NOLINT
-
- private:
-  const CompressedByteT *buffer_ {nullptr};
-  size_t symbol_bits_ {0};
-  size_t offset_ {0};
-
- public:
-  CompressedIterator() = default;
-  CompressedIterator(const CompressedByteT *buffer, size_t num_symbols)
-      : buffer_(buffer) {
-    symbol_bits_ = detail::SymbolBits(num_symbols);
-  }
-
-  XGBOOST_DEVICE reference operator*() const {
-    const int bits_per_byte = 8;
-    size_t start_bit_idx = ((offset_ + 1) * symbol_bits_ - 1);
-    size_t start_byte_idx = start_bit_idx / bits_per_byte;
-    start_byte_idx += detail::kPadding;
-
-    // Read 5 bytes - the maximum we will need
-    uint64_t tmp = static_cast<uint64_t>(buffer_[start_byte_idx - 4]) << 32 |
-                   static_cast<uint64_t>(buffer_[start_byte_idx - 3]) << 24 |
-                   static_cast<uint64_t>(buffer_[start_byte_idx - 2]) << 16 |
-                   static_cast<uint64_t>(buffer_[start_byte_idx - 1]) << 8 |
-                   buffer_[start_byte_idx];
-    int bit_shift =
-        (bits_per_byte - ((offset_ + 1) * symbol_bits_)) % bits_per_byte;
-    tmp >>= bit_shift;
-    // Mask off unneeded bits
-    uint64_t mask = (static_cast<uint64_t>(1) << symbol_bits_) - 1;
-    return static_cast<T>(tmp & mask);
-  }
-
-  XGBOOST_DEVICE reference operator[](size_t idx) const {
-    self_type offset = (*this);
-    offset.offset_ += idx;
-    return *offset;
-  }
-};
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/config.h b/ml-xgboost/src/common/config.h
deleted file mode 100644
index f10d865..0000000
--- a/ml-xgboost/src/common/config.h
+++ /dev/null
@@ -1,168 +0,0 @@
-/*!
- * Copyright 2014-2019 by Contributors
- * \file config.h
- * \brief helper class to load in configures from file
- * \author Haoda Fu, Hyunsu Cho
- */
-#ifndef XGBOOST_COMMON_CONFIG_H_
-#define XGBOOST_COMMON_CONFIG_H_
-
-#include <xgboost/logging.h>
-#include <cstdio>
-#include <string>
-#include <fstream>
-#include <istream>
-#include <sstream>
-#include <vector>
-#include <regex>
-#include <iterator>
-#include <utility>
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief Implementation of config reader
- */
-class ConfigParser {
- public:
-  /*!
-   * \brief Constructor for INI-style configuration parser
-   * \param path path to configuration file
-   */
-  explicit ConfigParser(const std::string path)
-      : path_(std::move(path)),
-      line_comment_regex_("^#"),
-      key_regex_(R"rx(^([^#"'=\r\n\t ]+)[\t ]*=)rx"),
-      key_regex_escaped_(R"rx(^(["'])([^"'=\r\n]+)\1[\t ]*=)rx"),
-      value_regex_(R"rx(^([^#"'\r\n\t ]+)[\t ]*(?:#.*){0,1}$)rx"),
-      value_regex_escaped_(R"rx(^(["'])([^"'\r\n]+)\1[\t ]*(?:#.*){0,1}$)rx")
-  {}
-
-  std::string LoadConfigFile(const std::string& path) {
-    std::ifstream fin(path, std::ios_base::in | std::ios_base::binary);
-    CHECK(fin) << "Failed to open: " << path;
-    std::string content{std::istreambuf_iterator<char>(fin),
-                        std::istreambuf_iterator<char>()};
-    return content;
-  }
-
-  /*!
-   * \brief Normalize end-of-line in a file so that it uses LF for all
-   *        line endings.
-   *
-   * This is needed because some OSes use CR or CR LF instead.  So we
-   * replace all CR with LF.
-   *
-   * \param p_config_str pointer to configuration
-   */
-  std::string NormalizeConfigEOL(std::string const& config_str) {
-    std::string result;
-    std::stringstream ss(config_str);
-    for (auto c : config_str) {
-      if (c == '\r') {
-        result.push_back('\n');
-        continue;
-      }
-      result.push_back(c);
-    }
-    return result;
-  }
-
-  /*!
-   * \brief Parse configuration file into key-value pairs.
-   * \param path path to configuration file
-   * \return list of key-value pairs
-   */
-  std::vector<std::pair<std::string, std::string>> Parse() {
-    std::string content { LoadConfigFile(path_) };
-    content = NormalizeConfigEOL(content);
-    std::stringstream ss { content };
-    std::vector<std::pair<std::string, std::string>> results;
-    std::string line;
-    std::string key, value;
-    // Loop over every line of the configuration file
-    while (std::getline(ss, line)) {
-      if (ParseKeyValuePair(line, &key, &value)) {
-        results.emplace_back(key, value);
-      }
-    }
-    return results;
-  }
-
- private:
-  std::string path_;
-  const std::regex line_comment_regex_, key_regex_, key_regex_escaped_,
-    value_regex_, value_regex_escaped_;
-
- public:
-  /*!
-   * \brief Remove leading and trailing whitespaces from a given string
-   * \param str string
-   * \return Copy of str with leading and trailing whitespaces removed
-   */
-  static std::string TrimWhitespace(const std::string& str) {
-    const auto first_char = str.find_first_not_of(" \t\n\r");
-    const auto last_char = str.find_last_not_of(" \t\n\r");
-    if (first_char == std::string::npos) {
-      // Every character in str is a whitespace
-      return std::string();
-    }
-    CHECK_NE(last_char, std::string::npos);
-    const auto substr_len = last_char + 1 - first_char;
-    return str.substr(first_char, substr_len);
-  }
-
-  /*!
-   * \brief Parse a key-value pair from a string representing a line
-   * \param str string (cannot be multi-line)
-   * \param key place to store the key, if parsing is successful
-   * \param value place to store the value, if parsing is successful
-   * \return Whether the parsing was successful
-   */
-  bool ParseKeyValuePair(const std::string& str, std::string* key,
-                         std::string* value) {
-    std::string buf = TrimWhitespace(str);
-    if (buf.empty()) {
-      return false;
-    }
-
-    /* Match key */
-    std::smatch m;
-    if (std::regex_search(buf, m, line_comment_regex_)) {
-      // This line is a comment
-      return false;
-    } else if (std::regex_search(buf, m, key_regex_)) {
-      // Key doesn't have whitespace or #
-      CHECK_EQ(m.size(), 2);
-      *key = m[1].str();
-    } else if (std::regex_search(buf, m, key_regex_escaped_)) {
-      // Key has a whitespace and/or #; it has to be wrapped around a pair of
-      // single or double quotes. Example: "foo bar"  'foo#bar'
-      CHECK_EQ(m.size(), 3);
-      *key = m[2].str();
-    } else {
-      LOG(FATAL) << "This line is not a valid key-value pair: " << str;
-    }
-
-    /* Match value */
-    buf = m.suffix().str();
-    buf = TrimWhitespace(buf);
-    if (std::regex_search(buf, m, value_regex_)) {
-      // Value doesn't have whitespace or #
-      CHECK_EQ(m.size(), 2);
-      *value = m[1].str();
-    } else if (std::regex_search(buf, m, value_regex_escaped_)) {
-      // Value has a whitespace and/or #; it has to be wrapped around a pair of
-      // single or double quotes. Example: "foo bar"  'foo#bar'
-      CHECK_EQ(m.size(), 3);
-      *value = m[2].str();
-    } else {
-      LOG(FATAL) << "This line is not a valid key-value pair: " << str;
-    }
-    return true;
-  }
-};
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_CONFIG_H_
diff --git a/ml-xgboost/src/common/device_helpers.cu b/ml-xgboost/src/common/device_helpers.cu
deleted file mode 100644
index c005c5a..0000000
--- a/ml-xgboost/src/common/device_helpers.cu
+++ /dev/null
@@ -1,95 +0,0 @@
-/*!
- * Copyright 2017-2019 XGBoost contributors
- *
- * \brief Utilities for CUDA.
- */
-#ifdef XGBOOST_USE_NCCL
-#include <nccl.h>
-#endif  // #ifdef XGBOOST_USE_NCCL
-#include <sstream>
-
-#include "device_helpers.cuh"
-
-namespace dh {
-
-#if __CUDACC_VER_MAJOR__ > 9
-constexpr std::size_t kUuidLength =
-    sizeof(std::declval<cudaDeviceProp>().uuid) / sizeof(uint64_t);
-
-void GetCudaUUID(int world_size, int rank, int device_ord,
-                 xgboost::common::Span<uint64_t, kUuidLength> uuid) {
-  cudaDeviceProp prob;
-  safe_cuda(cudaGetDeviceProperties(&prob, device_ord));
-  std::memcpy(uuid.data(), static_cast<void*>(&(prob.uuid)), sizeof(prob.uuid));
-}
-
-std::string PrintUUID(xgboost::common::Span<uint64_t, kUuidLength> uuid) {
-  std::stringstream ss;
-  for (auto v : uuid) {
-    ss << std::hex << v;
-  }
-  return ss.str();
-}
-
-#endif  // __CUDACC_VER_MAJOR__ > 9
-
-void AllReducer::Init(int _device_ordinal) {
-#ifdef XGBOOST_USE_NCCL
-  LOG(DEBUG) << "Running nccl init on: " << __CUDACC_VER_MAJOR__ << "." << __CUDACC_VER_MINOR__;
-
-  device_ordinal_ = _device_ordinal;
-  int32_t const rank = rabit::GetRank();
-
-#if __CUDACC_VER_MAJOR__ > 9
-  int32_t const world = rabit::GetWorldSize();
-
-  std::vector<uint64_t> uuids(world * kUuidLength, 0);
-  auto s_uuid = xgboost::common::Span<uint64_t>{uuids.data(), uuids.size()};
-  auto s_this_uuid = s_uuid.subspan(rank * kUuidLength, kUuidLength);
-  GetCudaUUID(world, rank, device_ordinal_, s_this_uuid);
-
-  // No allgather yet.
-  rabit::Allreduce<rabit::op::Sum, uint64_t>(uuids.data(), uuids.size());
-
-  std::vector<xgboost::common::Span<uint64_t, kUuidLength>> converted(world);;
-  size_t j = 0;
-  for (size_t i = 0; i < uuids.size(); i += kUuidLength) {
-    converted[j] =
-        xgboost::common::Span<uint64_t, kUuidLength>{uuids.data() + i, kUuidLength};
-    j++;
-  }
-
-  auto iter = std::unique(converted.begin(), converted.end());
-  auto n_uniques = std::distance(converted.begin(), iter);
-  CHECK_EQ(n_uniques, world)
-      << "Multiple processes within communication group running on same CUDA "
-      << "device is not supported";
-#endif  // __CUDACC_VER_MAJOR__ > 9
-
-  id_ = GetUniqueId();
-  dh::safe_cuda(cudaSetDevice(device_ordinal_));
-  dh::safe_nccl(ncclCommInitRank(&comm_, rabit::GetWorldSize(), id_, rank));
-  safe_cuda(cudaStreamCreate(&stream_));
-  initialised_ = true;
-#else
-  if (rabit::IsDistributed()) {
-    LOG(FATAL) << "XGBoost is not compiled with NCCL.";
-  }
-#endif  // XGBOOST_USE_NCCL
-}
-
-AllReducer::~AllReducer() {
-#ifdef XGBOOST_USE_NCCL
-  if (initialised_) {
-    dh::safe_cuda(cudaStreamDestroy(stream_));
-    ncclCommDestroy(comm_);
-  }
-  if (xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
-    LOG(CONSOLE) << "======== NCCL Statistics========";
-    LOG(CONSOLE) << "AllReduce calls: " << allreduce_calls_;
-    LOG(CONSOLE) << "AllReduce total MiB communicated: " << allreduce_bytes_/1048576;
-  }
-#endif  // XGBOOST_USE_NCCL
-}
-
-}  // namespace dh
diff --git a/ml-xgboost/src/common/device_helpers.cuh b/ml-xgboost/src/common/device_helpers.cuh
deleted file mode 100644
index daa546b..0000000
--- a/ml-xgboost/src/common/device_helpers.cuh
+++ /dev/null
@@ -1,916 +0,0 @@
-/*!
- * Copyright 2017-2020 XGBoost contributors
- */
-#pragma once
-#include <thrust/device_ptr.h>
-#include <thrust/device_vector.h>
-#include <thrust/device_malloc_allocator.h>
-#include <thrust/system/cuda/error.h>
-#include <thrust/system_error.h>
-#include <thrust/logical.h>
-#include <thrust/gather.h>
-#include <thrust/binary_search.h>
-
-#include <rabit/rabit.h>
-#include <cub/cub.cuh>
-#include <cub/util_allocator.cuh>
-
-#include <algorithm>
-#include <chrono>
-#include <numeric>
-#include <sstream>
-#include <string>
-#include <vector>
-
-#include "xgboost/logging.h"
-#include "xgboost/host_device_vector.h"
-#include "xgboost/span.h"
-
-#include "common.h"
-
-#ifdef XGBOOST_USE_NCCL
-#include "nccl.h"
-#include "../common/io.h"
-#endif
-
-#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600 || defined(__clang__)
-
-#else  // In device code and CUDA < 600
-__device__ __forceinline__ double atomicAdd(double* address, double val) {  // NOLINT
-  unsigned long long int* address_as_ull =
-      (unsigned long long int*)address;                   // NOLINT
-  unsigned long long int old = *address_as_ull, assumed;  // NOLINT
-
-  do {
-    assumed = old;
-    old = atomicCAS(address_as_ull, assumed,
-                    __double_as_longlong(val + __longlong_as_double(assumed)));
-
-    // Note: uses integer comparison to avoid hang in case of NaN (since NaN !=
-    // NaN)
-  } while (assumed != old);
-
-  return __longlong_as_double(old);
-}
-#endif
-
-namespace dh {
-
-#define HOST_DEV_INLINE XGBOOST_DEVICE __forceinline__
-#define DEV_INLINE __device__ __forceinline__
-
-#ifdef XGBOOST_USE_NCCL
-#define safe_nccl(ans) ThrowOnNcclError((ans), __FILE__, __LINE__)
-
-inline ncclResult_t ThrowOnNcclError(ncclResult_t code, const char *file,
-                                        int line) {
-  if (code != ncclSuccess) {
-    std::stringstream ss;
-    ss << "NCCL failure :" << ncclGetErrorString(code) << " ";
-    ss << file << "(" << line << ")";
-    throw std::runtime_error(ss.str());
-  }
-
-  return code;
-}
-#endif
-
-inline int32_t CudaGetPointerDevice(void* ptr) {
-  int32_t device = -1;
-  cudaPointerAttributes attr;
-  dh::safe_cuda(cudaPointerGetAttributes(&attr, ptr));
-  device = attr.device;
-  return device;
-}
-
-inline size_t AvailableMemory(int device_idx) {
-  size_t device_free = 0;
-  size_t device_total = 0;
-  safe_cuda(cudaSetDevice(device_idx));
-  dh::safe_cuda(cudaMemGetInfo(&device_free, &device_total));
-  return device_free;
-}
-
-inline size_t TotalMemory(int device_idx) {
-  size_t device_free = 0;
-  size_t device_total = 0;
-  safe_cuda(cudaSetDevice(device_idx));
-  dh::safe_cuda(cudaMemGetInfo(&device_free, &device_total));
-  return device_total;
-}
-
-/**
- * \fn  inline int MaxSharedMemory(int device_idx)
- *
- * \brief Maximum shared memory per block on this device.
- *
- * \param device_idx  Zero-based index of the device.
- */
-
-inline size_t MaxSharedMemory(int device_idx) {
-  int max_shared_memory = 0;
-  dh::safe_cuda(cudaDeviceGetAttribute
-                (&max_shared_memory, cudaDevAttrMaxSharedMemoryPerBlock,
-                 device_idx));
-  return size_t(max_shared_memory);
-}
-
-/**
- * \fn  inline int MaxSharedMemoryOptin(int device_idx)
- *
- * \brief Maximum dynamic shared memory per thread block on this device
-     that can be opted into when using cudaFuncSetAttribute().
- *
- * \param device_idx  Zero-based index of the device.
- */
-
-inline size_t MaxSharedMemoryOptin(int device_idx) {
-  int max_shared_memory = 0;
-  dh::safe_cuda(cudaDeviceGetAttribute
-                (&max_shared_memory, cudaDevAttrMaxSharedMemoryPerBlockOptin,
-                 device_idx));
-  return size_t(max_shared_memory);
-}
-
-inline void CheckComputeCapability() {
-  for (int d_idx = 0; d_idx < xgboost::common::AllVisibleGPUs(); ++d_idx) {
-    cudaDeviceProp prop;
-    safe_cuda(cudaGetDeviceProperties(&prop, d_idx));
-    std::ostringstream oss;
-    oss << "CUDA Capability Major/Minor version number: " << prop.major << "."
-        << prop.minor << " is insufficient.  Need >=3.5";
-    int failed = prop.major < 3 || (prop.major == 3 && prop.minor < 5);
-    if (failed) LOG(WARNING) << oss.str() << " for device: " << d_idx;
-  }
-}
-
-DEV_INLINE void AtomicOrByte(unsigned int* __restrict__ buffer, size_t ibyte, unsigned char b) {
-  atomicOr(&buffer[ibyte / sizeof(unsigned int)],
-           static_cast<unsigned int>(b) << (ibyte % (sizeof(unsigned int)) * 8));
-}
-
-template <typename T>
-__device__ xgboost::common::Range GridStrideRange(T begin, T end) {
-  begin += blockDim.x * blockIdx.x + threadIdx.x;
-  xgboost::common::Range r(begin, end);
-  r.Step(gridDim.x * blockDim.x);
-  return r;
-}
-
-template <typename T>
-__device__ xgboost::common::Range BlockStrideRange(T begin, T end) {
-  begin += threadIdx.x;
-  xgboost::common::Range r(begin, end);
-  r.Step(blockDim.x);
-  return r;
-}
-
-// Threadblock iterates over range, filling with value. Requires all threads in
-// block to be active.
-template <typename IterT, typename ValueT>
-__device__ void BlockFill(IterT begin, size_t n, ValueT value) {
-  for (auto i : BlockStrideRange(static_cast<size_t>(0), n)) {
-    begin[i] = value;
-  }
-}
-
-/*
- * Kernel launcher
- */
-
-template <typename L>
-__global__ void LaunchNKernel(size_t begin, size_t end, L lambda) {
-  for (auto i : GridStrideRange(begin, end)) {
-    lambda(i);
-  }
-}
-template <typename L>
-__global__ void LaunchNKernel(int device_idx, size_t begin, size_t end,
-                              L lambda) {
-  for (auto i : GridStrideRange(begin, end)) {
-    lambda(i, device_idx);
-  }
-}
-
-/* \brief A wrapper around kernel launching syntax, used to guard against empty input.
- *
- * - nvcc fails to deduce template argument when kernel is a template accepting __device__
- *   function as argument.  Hence functions like `LaunchN` cannot use this wrapper.
- *
- * - With c++ initialization list `{}` syntax, you are forced to comply with the CUDA type
- *   spcification.
- */
-class LaunchKernel {
-  size_t shmem_size_;
-  cudaStream_t stream_;
-
-  dim3 grids_;
-  dim3 blocks_;
-
- public:
-  LaunchKernel(uint32_t _grids, uint32_t _blk, size_t _shmem=0, cudaStream_t _s=nullptr) :
-      grids_{_grids, 1, 1}, blocks_{_blk, 1, 1}, shmem_size_{_shmem}, stream_{_s} {}
-  LaunchKernel(dim3 _grids, dim3 _blk, size_t _shmem=0, cudaStream_t _s=nullptr) :
-      grids_{_grids}, blocks_{_blk}, shmem_size_{_shmem}, stream_{_s} {}
-
-  template <typename K, typename... Args>
-  void operator()(K kernel, Args... args) {
-    if (XGBOOST_EXPECT(grids_.x * grids_.y * grids_.z == 0, false)) {
-      LOG(DEBUG) << "Skipping empty CUDA kernel.";
-      return;
-    }
-    kernel<<<grids_, blocks_, shmem_size_, stream_>>>(args...);  // NOLINT
-  }
-};
-
-template <int ITEMS_PER_THREAD = 8, int BLOCK_THREADS = 256, typename L>
-inline void LaunchN(int device_idx, size_t n, cudaStream_t stream, L lambda) {
-  if (n == 0) {
-    return;
-  }
-  const int GRID_SIZE =
-      static_cast<int>(xgboost::common::DivRoundUp(n, ITEMS_PER_THREAD * BLOCK_THREADS));
-  LaunchNKernel<<<GRID_SIZE, BLOCK_THREADS, 0, stream>>>(  // NOLINT
-      static_cast<size_t>(0), n, lambda);
-}
-
-// Default stream version
-template <int ITEMS_PER_THREAD = 8, int BLOCK_THREADS = 256, typename L>
-inline void LaunchN(int device_idx, size_t n, L lambda) {
-  LaunchN<ITEMS_PER_THREAD, BLOCK_THREADS>(device_idx, n, nullptr, lambda);
-}
-
-namespace detail {
-/** \brief Keeps track of global device memory allocations. Thread safe.*/
-class MemoryLogger {
-  // Information for a single device
-  struct DeviceStats {
-    size_t currently_allocated_bytes{ 0 };
-    size_t peak_allocated_bytes{ 0 };
-    size_t num_allocations{ 0 };
-    size_t num_deallocations{ 0 };
-    std::map<void *, size_t> device_allocations;
-    void RegisterAllocation(void *ptr, size_t n) {
-      device_allocations[ptr] = n;
-      currently_allocated_bytes += n;
-      peak_allocated_bytes =
-        std::max(peak_allocated_bytes, currently_allocated_bytes);
-      num_allocations++;
-      CHECK_GT(num_allocations, num_deallocations);
-    }
-    void RegisterDeallocation(void *ptr, size_t n, int current_device) {
-      auto itr = device_allocations.find(ptr);
-      if (itr == device_allocations.end()) {
-        LOG(FATAL) << "Attempting to deallocate " << n << " bytes on device "
-                   << current_device << " that was never allocated ";
-      }
-      num_deallocations++;
-      CHECK_LE(num_deallocations, num_allocations);
-      currently_allocated_bytes -= itr->second;
-      device_allocations.erase(itr);
-    }
-  };
-  DeviceStats stats_;
-  std::mutex mutex_;
-
-public:
-  void RegisterAllocation(void *ptr, size_t n) {
-    if (!xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
-      return;
-    }
-    std::lock_guard<std::mutex> guard(mutex_);
-    int current_device;
-    safe_cuda(cudaGetDevice(&current_device));
-    stats_.RegisterAllocation(ptr, n);
-  }
-  void RegisterDeallocation(void *ptr, size_t n) {
-    if (!xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
-      return;
-    }
-    std::lock_guard<std::mutex> guard(mutex_);
-    int current_device;
-    safe_cuda(cudaGetDevice(&current_device));
-    stats_.RegisterDeallocation(ptr, n, current_device);
-  }
-  size_t PeakMemory()
-  {
-    return stats_.peak_allocated_bytes;
-  }
-  void Clear()
-  {
-    stats_ = DeviceStats();
-  }
-
-  void Log() {
-    if (!xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
-      return;
-    }
-    std::lock_guard<std::mutex> guard(mutex_);
-    int current_device;
-    safe_cuda(cudaGetDevice(&current_device));
-    LOG(CONSOLE) << "======== Device " << current_device << " Memory Allocations: "
-      << " ========";
-    LOG(CONSOLE) << "Peak memory usage: "
-      << stats_.peak_allocated_bytes / 1048576 << "MiB";
-    LOG(CONSOLE) << "Number of allocations: " << stats_.num_allocations;
-  }
-};
-}  // namespace detail
-
-inline detail::MemoryLogger &GlobalMemoryLogger() {
-  static detail::MemoryLogger memory_logger;
-  return memory_logger;
-}
-
-// dh::DebugSyncDevice(__FILE__, __LINE__);
-inline void DebugSyncDevice(std::string file="", int32_t line = -1) {
-  if (file != "" && line != -1) {
-    auto rank = rabit::GetRank();
-    LOG(DEBUG) << "R:" << rank << ": " << file << ":" << line;
-  }
-  safe_cuda(cudaDeviceSynchronize());
-  safe_cuda(cudaGetLastError());
-}
-
-namespace detail {
-/**
- * \brief Default memory allocator, uses cudaMalloc/Free and logs allocations if verbose.
- */
-template <class T>
-struct XGBDefaultDeviceAllocatorImpl : thrust::device_malloc_allocator<T> {
-  using SuperT = thrust::device_malloc_allocator<T>;
-  using pointer = thrust::device_ptr<T>;  // NOLINT
-  template<typename U>
-  struct rebind  // NOLINT
-  {
-    using other = XGBDefaultDeviceAllocatorImpl<U>;  // NOLINT
-  };
-  pointer allocate(size_t n) {  // NOLINT
-    pointer ptr = SuperT::allocate(n);
-    GlobalMemoryLogger().RegisterAllocation(ptr.get(), n * sizeof(T));
-    return ptr;
-  }
-  void deallocate(pointer ptr, size_t n) {  // NOLINT
-    GlobalMemoryLogger().RegisterDeallocation(ptr.get(), n * sizeof(T));
-    return SuperT::deallocate(ptr, n);
-  }
-};
-
-/**
- * \brief Caching memory allocator, uses cub::CachingDeviceAllocator as a back-end and logs allocations if verbose. Does not initialise memory on construction.
- */
-template <class T>
-struct XGBCachingDeviceAllocatorImpl : thrust::device_malloc_allocator<T> {
-  using pointer = thrust::device_ptr<T>;  // NOLINT
-  template<typename U>
-  struct rebind  // NOLINT
-  {
-    using other = XGBCachingDeviceAllocatorImpl<U>;  // NOLINT
-  };
-   cub::CachingDeviceAllocator& GetGlobalCachingAllocator ()
-   {
-     // Configure allocator with maximum cached bin size of ~1GB and no limit on
-     // maximum cached bytes
-     static cub::CachingDeviceAllocator *allocator = new cub::CachingDeviceAllocator(2, 9, 29);
-     return *allocator;
-   }
-   pointer allocate(size_t n) {  // NOLINT
-     T *ptr;
-     GetGlobalCachingAllocator().DeviceAllocate(reinterpret_cast<void **>(&ptr),
-                                                n * sizeof(T));
-     pointer thrust_ptr(ptr);
-     GlobalMemoryLogger().RegisterAllocation(thrust_ptr.get(), n * sizeof(T));
-     return thrust_ptr;
-   }
-   void deallocate(pointer ptr, size_t n) {  // NOLINT
-     GlobalMemoryLogger().RegisterDeallocation(ptr.get(), n * sizeof(T));
-     GetGlobalCachingAllocator().DeviceFree(ptr.get());
-   }
-
-  __host__ __device__
-    void construct(T *)  // NOLINT
-  {
-    // no-op
-  }
-};
-}  // namespace detail
-
-// Declare xgboost allocators
-// Replacement of allocator with custom backend should occur here
-template <typename T>
-using XGBDeviceAllocator = detail::XGBDefaultDeviceAllocatorImpl<T>;
-/*! Be careful that the initialization constructor is a no-op, which means calling
- *  `vec.resize(n)` won't initialize the memory region to 0. Instead use
- * `vec.resize(n, 0)`*/
-template <typename T>
-using XGBCachingDeviceAllocator = detail::XGBCachingDeviceAllocatorImpl<T>;
-/** \brief Specialisation of thrust device vector using custom allocator. */
-template <typename T>
-using device_vector = thrust::device_vector<T,  XGBDeviceAllocator<T>>;  // NOLINT
-template <typename T>
-using caching_device_vector = thrust::device_vector<T,  XGBCachingDeviceAllocator<T>>;  // NOLINT
-
-// Faster to instantiate than caching_device_vector and invokes no synchronisation
-// Use this where vector functionality (e.g. resize) is not required
-template <typename T>
-class TemporaryArray {
- public:
-  using AllocT = XGBCachingDeviceAllocator<T>;
-  using value_type = T;  // NOLINT
-  explicit TemporaryArray(size_t n) : size_(n) { ptr_ = AllocT().allocate(n); }
-  ~TemporaryArray() { AllocT().deallocate(ptr_, this->size()); }
-
-  thrust::device_ptr<T> data() { return ptr_; }  // NOLINT
-  size_t size() { return size_; }  // NOLINT
-
- private:
-  thrust::device_ptr<T> ptr_;
-  size_t size_;
-};
-
-/**
- * \brief A double buffer, useful for algorithms like sort.
- */
-template <typename T>
-class DoubleBuffer {
- public:
-  cub::DoubleBuffer<T> buff;
-  xgboost::common::Span<T> a, b;
-  DoubleBuffer() = default;
-  template <typename VectorT>
-  DoubleBuffer(VectorT *v1, VectorT *v2) {
-    a = xgboost::common::Span<T>(v1->data().get(), v1->size());
-    b = xgboost::common::Span<T>(v2->data().get(), v2->size());
-    buff = cub::DoubleBuffer<T>(a.data(), b.data());
-  }
-
-  size_t Size() const {
-    CHECK_EQ(a.size(), b.size());
-    return a.size();
-  }
-  cub::DoubleBuffer<T> &CubBuffer() { return buff; }
-
-  T *Current() { return buff.Current(); }
-  xgboost::common::Span<T> CurrentSpan() {
-    return xgboost::common::Span<T>{buff.Current(), Size()};
-  }
-
-  T *Other() { return buff.Alternate(); }
-};
-
-/**
- * \brief Copies device span to std::vector.
- *
- * \tparam  T Generic type parameter.
- * \param [in,out]  dst Copy destination.
- * \param           src Copy source. Must be device memory.
- */
-template <typename T>
-void CopyDeviceSpanToVector(std::vector<T> *dst, xgboost::common::Span<T> src) {
-  CHECK_EQ(dst->size(), src.size());
-  dh::safe_cuda(cudaMemcpyAsync(dst->data(), src.data(), dst->size() * sizeof(T),
-                                cudaMemcpyDeviceToHost));
-}
-
-/**
- * \brief Copies const device span to std::vector.
- *
- * \tparam  T Generic type parameter.
- * \param [in,out]  dst Copy destination.
- * \param           src Copy source. Must be device memory.
- */
-template <typename T>
-void CopyDeviceSpanToVector(std::vector<T> *dst, xgboost::common::Span<const T> src) {
-  CHECK_EQ(dst->size(), src.size());
-  dh::safe_cuda(cudaMemcpyAsync(dst->data(), src.data(), dst->size() * sizeof(T),
-                                cudaMemcpyDeviceToHost));
-}
-
-// Keep track of pinned memory allocation
-struct PinnedMemory {
-  void *temp_storage{nullptr};
-  size_t temp_storage_bytes{0};
-
-  ~PinnedMemory() { Free(); }
-
-  template <typename T>
-  xgboost::common::Span<T> GetSpan(size_t size) {
-    size_t num_bytes = size * sizeof(T);
-    if (num_bytes > temp_storage_bytes) {
-      Free();
-      safe_cuda(cudaMallocHost(&temp_storage, num_bytes));
-      temp_storage_bytes = num_bytes;
-    }
-    return xgboost::common::Span<T>(static_cast<T *>(temp_storage), size);
-  }
-
-  void Free() {
-    if (temp_storage != nullptr) {
-      safe_cuda(cudaFreeHost(temp_storage));
-    }
-  }
-};
-
-/*
- *  Utility functions
- */
-
-/**
-* @brief Helper function to perform device-wide sum-reduction, returns to the
-* host
-* @param in the input array to be reduced
-* @param nVals number of elements in the input array
-*/
-template <typename T>
-typename std::iterator_traits<T>::value_type SumReduction(T in, int nVals) {
-  using ValueT = typename std::iterator_traits<T>::value_type;
-  size_t tmpSize {0};
-  ValueT *dummy_out = nullptr;
-  dh::safe_cuda(cub::DeviceReduce::Sum(nullptr, tmpSize, in, dummy_out, nVals));
-
-  TemporaryArray<char> temp(tmpSize + sizeof(ValueT));
-  auto ptr = reinterpret_cast<ValueT *>(temp.data().get()) + 1;
-  dh::safe_cuda(cub::DeviceReduce::Sum(
-      reinterpret_cast<void *>(ptr), tmpSize, in,
-      reinterpret_cast<ValueT *>(temp.data().get()),
-      nVals));
-  ValueT sum;
-  dh::safe_cuda(cudaMemcpy(&sum, temp.data().get(), sizeof(ValueT),
-                           cudaMemcpyDeviceToHost));
-  return sum;
-}
-
-/**
- * \class AllReducer
- *
- * \brief All reducer class that manages its own communication group and
- * streams. Must be initialised before use. If XGBoost is compiled without NCCL
- * this is a dummy class that will error if used with more than one GPU.
- */
-class AllReducer {
-  bool initialised_ {false};
-  size_t allreduce_bytes_ {0};  // Keep statistics of the number of bytes communicated
-  size_t allreduce_calls_ {0};  // Keep statistics of the number of reduce calls
-  std::vector<size_t> host_data_;  // Used for all reduce on host
-#ifdef XGBOOST_USE_NCCL
-  ncclComm_t comm_;
-  cudaStream_t stream_;
-  int device_ordinal_;
-  ncclUniqueId id_;
-#endif
-
- public:
-  AllReducer() = default;
-
-  /**
-   * \brief Initialise with the desired device ordinal for this communication
-   * group.
-   *
-   * \param device_ordinal The device ordinal.
-   */
-  void Init(int _device_ordinal);
-
-  ~AllReducer();
-
-  /**
-   * \brief Allreduce. Use in exactly the same way as NCCL but without needing
-   * streams or comms.
-   *
-   * \param sendbuff                The sendbuff.
-   * \param recvbuff                The recvbuff.
-   * \param count                   Number of elements.
-   */
-
-  void AllReduceSum(const double *sendbuff, double *recvbuff, int count) {
-#ifdef XGBOOST_USE_NCCL
-    CHECK(initialised_);
-    dh::safe_cuda(cudaSetDevice(device_ordinal_));
-    dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclDouble, ncclSum, comm_, stream_));
-    allreduce_bytes_ += count * sizeof(double);
-    allreduce_calls_ += 1;
-#endif
-  }
-
-  /**
-   * \brief Allreduce. Use in exactly the same way as NCCL but without needing
-   * streams or comms.
-   *
-   * \param sendbuff                The sendbuff.
-   * \param recvbuff                The recvbuff.
-   * \param count                   Number of elements.
-   */
-
-  void AllReduceSum(const float *sendbuff, float *recvbuff, int count) {
-#ifdef XGBOOST_USE_NCCL
-    CHECK(initialised_);
-    dh::safe_cuda(cudaSetDevice(device_ordinal_));
-    dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclFloat, ncclSum, comm_, stream_));
-    allreduce_bytes_ += count * sizeof(float);
-    allreduce_calls_ += 1;
-#endif
-  }
-
-  /**
-   * \brief Allreduce. Use in exactly the same way as NCCL but without needing streams or comms.
-   *
-   * \param count Number of.
-   *
-   * \param sendbuff                The sendbuff.
-   * \param recvbuff                The recvbuff.
-   * \param count                   Number of.
-   */
-
-  void AllReduceSum(const int64_t *sendbuff, int64_t *recvbuff, int count) {
-#ifdef XGBOOST_USE_NCCL
-    CHECK(initialised_);
-
-    dh::safe_cuda(cudaSetDevice(device_ordinal_));
-    dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclInt64, ncclSum, comm_, stream_));
-#endif
-  }
-
-  /**
-   * \fn  void Synchronize()
-   *
-   * \brief Synchronizes the entire communication group.
-   */
-  void Synchronize() {
-#ifdef XGBOOST_USE_NCCL
-    dh::safe_cuda(cudaSetDevice(device_ordinal_));
-    dh::safe_cuda(cudaStreamSynchronize(stream_));
-#endif
-  };
-
-#ifdef XGBOOST_USE_NCCL
-  /**
-   * \fn  ncclUniqueId GetUniqueId()
-   *
-   * \brief Gets the Unique ID from NCCL to be used in setting up interprocess
-   * communication
-   *
-   * \return the Unique ID
-   */
-  ncclUniqueId GetUniqueId() {
-    static const int kRootRank = 0;
-    ncclUniqueId id;
-    if (rabit::GetRank() == kRootRank) {
-      dh::safe_nccl(ncclGetUniqueId(&id));
-    }
-    rabit::Broadcast(
-        static_cast<void*>(&id),
-        sizeof(ncclUniqueId),
-        static_cast<int>(kRootRank));
-    return id;
-  }
-#endif
-};
-
-template <typename VectorT, typename T = typename VectorT::value_type,
-  typename IndexT = typename xgboost::common::Span<T>::index_type>
-xgboost::common::Span<T> ToSpan(
-    VectorT &vec,
-    IndexT offset = 0,
-    IndexT size = std::numeric_limits<size_t>::max()) {
-  size = size == std::numeric_limits<size_t>::max() ? vec.size() : size;
-  CHECK_LE(offset + size, vec.size());
-  return {vec.data().get() + offset, size};
-}
-
-template <typename T>
-xgboost::common::Span<T> ToSpan(thrust::device_vector<T>& vec,
-                                size_t offset, size_t size) {
-  return ToSpan(vec, offset, size);
-}
-
-// thrust begin, similiar to std::begin
-template <typename T>
-thrust::device_ptr<T> tbegin(xgboost::HostDeviceVector<T>& vector) {  // NOLINT
-  return thrust::device_ptr<T>(vector.DevicePointer());
-}
-
-template <typename T>
-thrust::device_ptr<T> tend(xgboost::HostDeviceVector<T>& vector) {  // // NOLINT
-  return tbegin(vector) + vector.Size();
-}
-
-template <typename T>
-thrust::device_ptr<T const> tcbegin(xgboost::HostDeviceVector<T> const& vector) {  // NOLINT
-  return thrust::device_ptr<T const>(vector.ConstDevicePointer());
-}
-
-template <typename T>
-thrust::device_ptr<T const> tcend(xgboost::HostDeviceVector<T> const& vector) {  // NOLINT
-  return tcbegin(vector) + vector.Size();
-}
-
-template <typename T>
-thrust::device_ptr<T> tbegin(xgboost::common::Span<T>& span) {  // NOLINT
-  return thrust::device_ptr<T>(span.data());
-}
-
-template <typename T>
-thrust::device_ptr<T> tend(xgboost::common::Span<T>& span) {  // NOLINT
-  return tbegin(span) + span.size();
-}
-
-template <typename T>
-thrust::device_ptr<T const> tcbegin(xgboost::common::Span<T> const& span) {  // NOLINT
-  return thrust::device_ptr<T const>(span.data());
-}
-
-template <typename T>
-thrust::device_ptr<T const> tcend(xgboost::common::Span<T> const& span) {  // NOLINT
-  return tcbegin(span) + span.size();
-}
-
-// This type sorts an array which is divided into multiple groups. The sorting is influenced
-// by the function object 'Comparator'
-template <typename T>
-class SegmentSorter {
- private:
-  // Items sorted within the group
-  caching_device_vector<T> ditems_;
-
-  // Original position of the items before they are sorted descendingly within its groups
-  caching_device_vector<uint32_t> doriginal_pos_;
-
-  // Segments within the original list that delineates the different groups
-  caching_device_vector<uint32_t> group_segments_;
-
-  // Need this on the device as it is used in the kernels
-  caching_device_vector<uint32_t> dgroups_;       // Group information on device
-
-  // Where did the item that was originally present at position 'x' move to after they are sorted
-  caching_device_vector<uint32_t> dindexable_sorted_pos_;
-
-  // Initialize everything but the segments
-  void Init(uint32_t num_elems) {
-    ditems_.resize(num_elems);
-
-    doriginal_pos_.resize(num_elems);
-    thrust::sequence(doriginal_pos_.begin(), doriginal_pos_.end());
-  }
-
-  // Initialize all with group info
-  void Init(const std::vector<uint32_t> &groups) {
-    uint32_t num_elems = groups.back();
-    this->Init(num_elems);
-    this->CreateGroupSegments(groups);
-  }
-
- public:
-  // This needs to be public due to device lambda
-  void CreateGroupSegments(const std::vector<uint32_t> &groups) {
-    uint32_t num_elems = groups.back();
-    group_segments_.resize(num_elems, 0);
-
-    dgroups_ = groups;
-
-    if (GetNumGroups() == 1) return;  // There are no segments; hence, no need to compute them
-
-    // Define the segments by assigning a group ID to each element
-    const uint32_t *dgroups = dgroups_.data().get();
-    uint32_t ngroups = dgroups_.size();
-    auto ComputeGroupIDLambda = [=] __device__(uint32_t idx) {
-      return thrust::upper_bound(thrust::seq, dgroups, dgroups + ngroups, idx) -
-             dgroups - 1;
-    };  // NOLINT
-
-    thrust::transform(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                      thrust::make_counting_iterator(num_elems),
-                      group_segments_.begin(),
-                      ComputeGroupIDLambda);
-  }
-
-  // Accessors that returns device pointer
-  inline uint32_t GetNumItems() const { return ditems_.size(); }
-  inline const xgboost::common::Span<const T> GetItemsSpan() const {
-    return { ditems_.data().get(), ditems_.size() };
-  }
-
-  inline const xgboost::common::Span<const uint32_t> GetOriginalPositionsSpan() const {
-    return { doriginal_pos_.data().get(), doriginal_pos_.size() };
-  }
-
-  inline const xgboost::common::Span<const uint32_t> GetGroupSegmentsSpan() const {
-    return { group_segments_.data().get(), group_segments_.size() };
-  }
-
-  inline uint32_t GetNumGroups() const { return dgroups_.size() - 1; }
-  inline const xgboost::common::Span<const uint32_t> GetGroupsSpan() const {
-    return { dgroups_.data().get(), dgroups_.size() };
-  }
-
-  inline const xgboost::common::Span<const uint32_t> GetIndexableSortedPositionsSpan() const {
-    return { dindexable_sorted_pos_.data().get(), dindexable_sorted_pos_.size() };
-  }
-
-  // Sort an array that is divided into multiple groups. The array is sorted within each group.
-  // This version provides the group information that is on the host.
-  // The array is sorted based on an adaptable binary predicate. By default a stateless predicate
-  // is used.
-  template <typename Comparator = thrust::greater<T>>
-  void SortItems(const T *ditems, uint32_t item_size, const std::vector<uint32_t> &groups,
-                 const Comparator &comp = Comparator()) {
-    this->Init(groups);
-    this->SortItems(ditems, item_size, this->GetGroupSegmentsSpan(), comp);
-  }
-
-  // Sort an array that is divided into multiple groups. The array is sorted within each group.
-  // This version provides the group information that is on the device.
-  // The array is sorted based on an adaptable binary predicate. By default a stateless predicate
-  // is used.
-  template <typename Comparator = thrust::greater<T>>
-  void SortItems(const T *ditems, uint32_t item_size,
-                 const xgboost::common::Span<const uint32_t> &group_segments,
-                 const Comparator &comp = Comparator()) {
-    this->Init(item_size);
-
-    // Sort the items that are grouped. We would like to avoid using predicates to perform the sort,
-    // as thrust resorts to using a merge sort as opposed to a much much faster radix sort
-    // when comparators are used. Hence, the following algorithm is used. This is done so that
-    // we can grab the appropriate related values from the original list later, after the
-    // items are sorted.
-    //
-    // Here is the internal representation:
-    // dgroups_:          [ 0, 3, 5, 8, 10 ]
-    // group_segments_:   0 0 0 | 1 1 | 2 2 2 | 3 3
-    // doriginal_pos_:    0 1 2 | 3 4 | 5 6 7 | 8 9
-    // ditems_:           1 0 1 | 2 1 | 1 3 3 | 4 4 (from original items)
-    //
-    // Sort the items first and make a note of the original positions in doriginal_pos_
-    // based on the sort
-    // ditems_:           4 4 3 3 2 1 1 1 1 0
-    // doriginal_pos_:    8 9 6 7 3 0 2 4 5 1
-    // NOTE: This consumes space, but is much faster than some of the other approaches - sorting
-    //       in kernel, sorting using predicates etc.
-
-    ditems_.assign(thrust::device_ptr<const T>(ditems),
-                   thrust::device_ptr<const T>(ditems) + item_size);
-
-    // Allocator to be used by sort for managing space overhead while sorting
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    thrust::stable_sort_by_key(thrust::cuda::par(alloc),
-                               ditems_.begin(), ditems_.end(),
-                               doriginal_pos_.begin(), comp);
-
-    if (GetNumGroups() == 1) return;  // The entire array is sorted, as it isn't segmented
-
-    // Next, gather the segments based on the doriginal_pos_. This is to reflect the
-    // holisitic item sort order on the segments
-    // group_segments_c_:   3 3 2 2 1 0 0 1 2 0
-    // doriginal_pos_:      8 9 6 7 3 0 2 4 5 1 (stays the same)
-    caching_device_vector<uint32_t> group_segments_c(item_size);
-    thrust::gather(doriginal_pos_.begin(), doriginal_pos_.end(),
-                   dh::tcbegin(group_segments), group_segments_c.begin());
-
-    // Now, sort the group segments so that you may bring the items within the group together,
-    // in the process also noting the relative changes to the doriginal_pos_ while that happens
-    // group_segments_c_:   0 0 0 1 1 2 2 2 3 3
-    // doriginal_pos_:      0 2 1 3 4 6 7 5 8 9
-    thrust::stable_sort_by_key(thrust::cuda::par(alloc),
-                               group_segments_c.begin(), group_segments_c.end(),
-                               doriginal_pos_.begin(), thrust::less<uint32_t>());
-
-    // Finally, gather the original items based on doriginal_pos_ to sort the input and
-    // to store them in ditems_
-    // doriginal_pos_:      0 2 1 3 4 6 7 5 8 9  (stays the same)
-    // ditems_:             1 1 0 2 1 3 3 1 4 4  (from unsorted items - ditems)
-    thrust::gather(doriginal_pos_.begin(), doriginal_pos_.end(),
-                   thrust::device_ptr<const T>(ditems), ditems_.begin());
-  }
-
-  // Determine where an item that was originally present at position 'x' has been relocated to
-  // after a sort. Creation of such an index has to be explicitly requested after a sort
-  void CreateIndexableSortedPositions() {
-    dindexable_sorted_pos_.resize(GetNumItems());
-    thrust::scatter(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                    thrust::make_counting_iterator(GetNumItems()),  // Rearrange indices...
-                    // ...based on this map
-                    dh::tcbegin(GetOriginalPositionsSpan()),
-                    dindexable_sorted_pos_.begin());  // Write results into this
-  }
-};
-
-// Atomic add function for gradients
-template <typename OutputGradientT, typename InputGradientT>
-DEV_INLINE void AtomicAddGpair(OutputGradientT* dest,
-                               const InputGradientT& gpair) {
-  auto dst_ptr = reinterpret_cast<typename OutputGradientT::ValueT*>(dest);
-
-  atomicAdd(dst_ptr,
-            static_cast<typename OutputGradientT::ValueT>(gpair.GetGrad()));
-  atomicAdd(dst_ptr + 1,
-            static_cast<typename OutputGradientT::ValueT>(gpair.GetHess()));
-}
-
-
-// Thrust version of this function causes error on Windows
-template <typename ReturnT, typename IterT, typename FuncT>
-thrust::transform_iterator<FuncT, IterT, ReturnT> MakeTransformIterator(
-  IterT iter, FuncT func) {
-  return thrust::transform_iterator<FuncT, IterT, ReturnT>(iter, func);
-}
-
-}  // namespace dh
diff --git a/ml-xgboost/src/common/group_data.h b/ml-xgboost/src/common/group_data.h
deleted file mode 100644
index 0144d80..0000000
--- a/ml-xgboost/src/common/group_data.h
+++ /dev/null
@@ -1,130 +0,0 @@
-/*!
- * Copyright 2014-2020 by Contributors
- * \file group_data.h
- * \brief this file defines utils to group data by integer keys
- *     Input: given input sequence (key,value), (k1,v1), (k2,v2)
- *     Ouptupt: an array of values data = [v1,v2,v3 .. vn]
- *              and a group pointer ptr,
- *              data[ptr[k]:ptr[k+1]] contains values that corresponds to key k
- *
- * This can be used to construct CSR/CSC matrix from un-ordered input
- * The major algorithm is a two pass linear scan algorithm that requires two pass scan over the data
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_GROUP_DATA_H_
-#define XGBOOST_COMMON_GROUP_DATA_H_
-
-#include <cstddef>
-#include <vector>
-#include <algorithm>
-
-#include "xgboost/base.h"
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief multi-thread version of group builder
- * \tparam ValueType type of entries in the sparse matrix
- * \tparam SizeType type of the index range holder
- */
-template<typename ValueType, typename SizeType = bst_ulong>
-class ParallelGroupBuilder {
- public:
-  /**
-   * \brief parallel group builder of data.
-   *
-   * \param [in,out]  p_rptr          Row offsets for CSR matrix.
-   * \param [in,out]  p_data          Data vector to populate
-   * \param           base_row_offset (Optional) If the matrix we are building
-   * is already partially populated, use this to indicate the row index we are
-   * starting from. This saves considerable amounts of time/memory when
-   * incrementaly building.
-   */
-  ParallelGroupBuilder(std::vector<SizeType> *p_rptr,
-                       std::vector<ValueType> *p_data,
-                       size_t base_row_offset = 0)
-      : rptr_(*p_rptr),
-        data_(*p_data),
-        base_row_offset_(base_row_offset) {}
-
-  /*!
-   * \brief step 1: initialize the helper, with hint of number keys
-   *                and thread used in the construction
-   * \param max_key number of keys in the matrix, can be smaller than expected
-   * \param nthread number of thread that will be used in construction
-   */
-  void InitBudget(std::size_t max_key, int nthread) {
-    thread_rptr_.resize(nthread);
-    for (std::size_t i = 0; i < thread_rptr_.size(); ++i) {
-      thread_rptr_[i].resize(max_key - std::min(base_row_offset_, max_key));
-      std::fill(thread_rptr_[i].begin(), thread_rptr_[i].end(), 0);
-    }
-  }
-  /*!
-   * \brief step 2: add budget to each key
-   * \param key the key
-   * \param threadid the id of thread that calls this function
-   * \param nelem number of element budget add to this row
-   */
-  void AddBudget(std::size_t key, int threadid, SizeType nelem = 1) {
-    std::vector<SizeType> &trptr = thread_rptr_[threadid];
-    size_t offset_key = key - base_row_offset_;
-    if (trptr.size() < offset_key + 1) {
-      trptr.resize(offset_key + 1, 0);
-    }
-    trptr[offset_key] += nelem;
-  }
-  /*! \brief step 3: initialize the necessary storage */
-  inline void InitStorage() {
-    // set rptr to correct size
-    SizeType rptr_fill_value = rptr_.empty() ? 0 : rptr_.back();
-    for (std::size_t tid = 0; tid < thread_rptr_.size(); ++tid) {
-      if (rptr_.size() <= thread_rptr_[tid].size() + base_row_offset_) {
-        rptr_.resize(thread_rptr_[tid].size() + base_row_offset_ + 1,
-                     rptr_fill_value);  // key + 1
-      }
-    }
-    // initialize rptr to be beginning of each segment
-    std::size_t count = 0;
-    for (std::size_t i = base_row_offset_; i + 1 < rptr_.size(); ++i) {
-      for (std::size_t tid = 0; tid < thread_rptr_.size(); ++tid) {
-        std::vector<SizeType> &trptr = thread_rptr_[tid];
-        if (i < trptr.size() +
-                    base_row_offset_) {  // i^th row is assigned for this thread
-          std::size_t thread_count =
-              trptr[i - base_row_offset_];  // how many entries in this row
-          trptr[i - base_row_offset_] = count + rptr_.back();
-          count += thread_count;
-        }
-      }
-      rptr_[i + 1] += count;  // pointer accumulated from all thread
-    }
-    data_.resize(rptr_.back());
-  }
-  /*!
-   * \brief step 4: add data to the allocated space,
-   *   the calls to this function should be exactly match previous call to AddBudget
-   *
-   * \param key the key of group.
-   * \param value The value to be pushed to the group.
-   * \param threadid the id of thread that calls this function
-   */
-  void Push(std::size_t key, ValueType value, int threadid) {
-    size_t offset_key = key - base_row_offset_;
-    SizeType &rp = thread_rptr_[threadid][offset_key];
-    data_[rp++] = value;
-  }
-
- private:
-  /*! \brief pointer to the beginning and end of each continuous key */
-  std::vector<SizeType> &rptr_;
-  /*! \brief index of nonzero entries in each row */
-  std::vector<ValueType> &data_;
-  /*! \brief thread local data structure */
-  std::vector<std::vector<SizeType> > thread_rptr_;
-  /** \brief Used when rows being pushed into the builder are strictly above some number. */
-  size_t base_row_offset_;
-};
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_GROUP_DATA_H_
diff --git a/ml-xgboost/src/common/hist_util.cc b/ml-xgboost/src/common/hist_util.cc
deleted file mode 100644
index 8b27ecf..0000000
--- a/ml-xgboost/src/common/hist_util.cc
+++ /dev/null
@@ -1,1336 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2017-2020 by Contributors
- * \file hist_util.cc
- */
-#include <dmlc/timer.h>
-#include <dmlc/omp.h>
-
-#include <rabit/rabit.h>
-#include <numeric>
-#include <vector>
-
-#include "xgboost/base.h"
-#include "../common/common.h"
-#include "hist_util.h"
-#include "random.h"
-#include "column_matrix.h"
-#include "quantile.h"
-#include "./../tree/updater_quantile_hist.h"
-
-#define ALIGNAS_NUM 16
-
-#if defined(XGBOOST_MM_PREFETCH_PRESENT)
-  #include <xmmintrin.h>
-  #define PREFETCH_READ_T0(addr) _mm_prefetch(reinterpret_cast<const char*>(addr), _MM_HINT_T0)
-#elif defined(XGBOOST_BUILTIN_PREFETCH_PRESENT)
-  #define PREFETCH_READ_T0(addr) __builtin_prefetch(reinterpret_cast<const char*>(addr), 0, 3)
-#else  // no SW pre-fetching available; PREFETCH_READ_T0 is no-op
-  #define PREFETCH_READ_T0(addr) do {} while (0)
-#endif  // defined(XGBOOST_MM_PREFETCH_PRESENT)
-
-#ifdef USE_INTEL_INTRINSICS
-  #include <xmmintrin.h>
-#endif
-#ifdef USE_ARM_INTRINSICS
-  #include <arm_neon.h>
-#endif
-
-namespace xgboost {
-namespace common {
-
-template<typename BinIdxType>
-void GHistIndexMatrix::SetIndexDataForDense(common::Span<BinIdxType> index_data_span,
-                                    size_t batch_threads, const SparsePage& batch,
-                                    size_t rbegin, common::Span<const uint32_t> offsets_span,
-                                    size_t nbins) {
-  const xgboost::Entry* data_ptr = batch.data.HostVector().data();
-  const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
-  const size_t batch_size = batch.Size();
-  CHECK_LT(batch_size, offset_vec.size());
-  BinIdxType* index_data = index_data_span.data();
-  const uint32_t* offsets = offsets_span.data();
-  #pragma omp parallel for num_threads(batch_threads) schedule(static)
-    for (omp_ulong i = 0; i < batch_size; ++i) {
-      const int tid = omp_get_thread_num();
-      size_t ibegin = row_ptr[rbegin + i];
-      size_t iend = row_ptr[rbegin + i + 1];
-      const size_t size = offset_vec[i + 1] - offset_vec[i];
-      SparsePage::Inst inst = {data_ptr + offset_vec[i], size};
-      CHECK_EQ(ibegin + inst.size(), iend);
-      for (bst_uint j = 0; j < inst.size(); ++j) {
-        uint32_t idx = cut.SearchBin(inst[j]);
-        index_data[ibegin + j] = static_cast<BinIdxType>(idx - offsets[j]);
-        ++hit_count_tloc_[tid * nbins + idx];
-      }
-    }
-}
-template void GHistIndexMatrix::SetIndexDataForDense(common::Span<uint8_t> index_data_span,
-                                             size_t batch_threads, const SparsePage& batch,
-                                             size_t rbegin,
-                                             common::Span<const uint32_t> offsets_span,
-                                             size_t nbins);
-template void GHistIndexMatrix::SetIndexDataForDense(common::Span<uint16_t> index_data_span,
-                                             size_t batch_threads, const SparsePage& batch,
-                                             size_t rbegin,
-                                             common::Span<const uint32_t> offsets_span,
-                                             size_t nbins);
-template void GHistIndexMatrix::SetIndexDataForDense(common::Span<uint32_t> index_data_span,
-                                             size_t batch_threads, const SparsePage& batch,
-                                             size_t rbegin,
-                                             common::Span<const uint32_t> offsets_span,
-                                             size_t nbins);
-
-void GHistIndexMatrix::SetIndexDataForSparse(common::Span<uint32_t> index_data_span,
-                                                 size_t batch_threads,
-                                                 const SparsePage& batch, size_t rbegin,
-                                                 size_t nbins) {
-  const xgboost::Entry* data_ptr = batch.data.HostVector().data();
-  const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
-  const size_t batch_size = batch.Size();
-  CHECK_LT(batch_size, offset_vec.size());
-  uint32_t* index_data = index_data_span.data();
-  #pragma omp parallel for num_threads(batch_threads) schedule(static)
-    for (omp_ulong i = 0; i < batch_size; ++i) {
-      const int tid = omp_get_thread_num();
-      size_t ibegin = row_ptr[rbegin + i];
-      size_t iend = row_ptr[rbegin + i + 1];
-      const size_t size = offset_vec[i + 1] - offset_vec[i];
-      SparsePage::Inst inst = {data_ptr + offset_vec[i], size};
-      CHECK_EQ(ibegin + inst.size(), iend);
-      for (bst_uint j = 0; j < inst.size(); ++j) {
-        uint32_t idx = cut.SearchBin(inst[j]);
-        index_data[ibegin + j] = idx;
-        ++hit_count_tloc_[tid * nbins + idx];
-      }
-    }
-}
-
-void GHistIndexMatrix::ResizeIndex(const size_t rbegin, const SparsePage& batch,
-                                   const size_t n_offsets, const size_t n_index,
-                                   const bool isDense) {
-  if ((max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint8_t>::max())) && isDense) {
-    index.SetBinTypeSize(kUint8BinsTypeSize);
-    index.Resize((sizeof(uint8_t)) * n_index);
-  } else if ((max_num_bins - 1 > static_cast<int>(std::numeric_limits<uint8_t>::max())  &&
-    max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint16_t>::max())) && isDense) {
-    index.SetBinTypeSize(kUint16BinsTypeSize);
-    index.Resize((sizeof(uint16_t)) * n_index);
-  } else {
-    index.SetBinTypeSize(kUint32BinsTypeSize);
-    index.Resize((sizeof(uint32_t)) * n_index);
-  }
-}
-
-HistogramCuts::HistogramCuts() {
-  monitor_.Init(__FUNCTION__);
-  cut_ptrs_.HostVector().emplace_back(0);
-}
-
-// Dispatch to specific builder.
-void HistogramCuts::Build(DMatrix* dmat, uint32_t const max_num_bins) {
-  auto const& info = dmat->Info();
-  size_t const total = info.num_row_ * info.num_col_;
-  size_t const nnz = info.num_nonzero_;
-  float const sparsity = static_cast<float>(nnz) / static_cast<float>(total);
-  // Use a small number to avoid calling `dmat->GetColumnBatches'.
-  float constexpr kSparsityThreshold = 0.0005;
-  // FIXME(trivialfis): Distributed environment is not supported.
-  if (sparsity < kSparsityThreshold && (!rabit::IsDistributed())) {
-    LOG(INFO) << "Building quantile cut on a sparse dataset.";
-    SparseCuts cuts(this);
-    cuts.Build(dmat, max_num_bins);
-  } else {
-    LOG(INFO) << "Building quantile cut on a dense dataset or distributed environment.";
-    DenseCuts cuts(this);
-    cuts.Build(dmat, max_num_bins);
-  }
-  LOG(INFO) << "Total number of hist bins: " << cut_ptrs_.HostVector().back();
-}
-
-bool CutsBuilder::UseGroup(DMatrix* dmat) {
-  auto& info = dmat->Info();
-  size_t const num_groups = info.group_ptr_.size() == 0 ?
-                            0 : info.group_ptr_.size() - 1;
-  // Use group index for weights?
-  bool const use_group_ind = num_groups != 0 &&
-                             (info.weights_.Size() != info.num_row_);
-  return use_group_ind;
-}
-
-void SparseCuts::SingleThreadBuild(SparsePage const& page, MetaInfo const& info,
-                                   uint32_t max_num_bins,
-                                   bool const use_group_ind,
-                                   uint32_t beg_col, uint32_t end_col,
-                                   uint32_t thread_id) {
-  CHECK_GE(end_col, beg_col);
-  constexpr float kFactor = 8;
-
-  // Data groups, used in ranking.
-  std::vector<bst_uint> const& group_ptr = info.group_ptr_;
-  auto &local_min_vals = p_cuts_->min_vals_.HostVector();
-  auto &local_cuts = p_cuts_->cut_values_.HostVector();
-  auto &local_ptrs = p_cuts_->cut_ptrs_.HostVector();
-  local_min_vals.resize(end_col - beg_col, 0);
-
-  for (uint32_t col_id = beg_col; col_id < page.Size() && col_id < end_col; ++col_id) {
-    // Using a local variable makes things easier, but at the cost of memory trashing.
-    WQSketch sketch;
-    common::Span<xgboost::Entry const> const column = page[col_id];
-    uint32_t const n_bins = std::min(static_cast<uint32_t>(column.size()),
-                                     max_num_bins);
-    if (n_bins == 0) {
-      // cut_ptrs_ is initialized with a zero, so there's always an element at the back
-      local_ptrs.emplace_back(local_ptrs.back());
-      continue;
-    }
-
-    sketch.Init(info.num_row_, 1.0 / (n_bins * kFactor));
-    for (auto const& entry : column) {
-      uint32_t weight_ind = 0;
-      if (use_group_ind) {
-        auto row_idx = entry.index;
-        uint32_t group_ind =
-            this->SearchGroupIndFromRow(group_ptr, page.base_rowid + row_idx);
-        weight_ind = group_ind;
-      } else {
-        weight_ind = entry.index;
-      }
-      sketch.Push(entry.fvalue, info.GetWeight(weight_ind));
-    }
-
-    WQSketch::SummaryContainer out_summary;
-    sketch.GetSummary(&out_summary);
-    WQSketch::SummaryContainer summary;
-    summary.Reserve(n_bins + 1);
-    summary.SetPrune(out_summary, n_bins + 1);
-
-    // Can be use data[1] as the min values so that we don't need to
-    // store another array?
-    float mval = summary.data[0].value;
-    local_min_vals[col_id - beg_col]  = mval - (fabs(mval) + 1e-5);
-
-    this->AddCutPoint(summary, max_num_bins);
-
-    bst_float cpt = (summary.size > 0) ?
-                    summary.data[summary.size - 1].value :
-                    local_min_vals[col_id - beg_col];
-    cpt += fabs(cpt) + 1e-5;
-    local_cuts.emplace_back(cpt);
-
-    local_ptrs.emplace_back(local_cuts.size());
-  }
-}
-
-std::vector<size_t> SparseCuts::LoadBalance(SparsePage const& page,
-                                            size_t const nthreads) {
-  /* Some sparse datasets have their mass concentrating on small
-   * number of features.  To avoid wating for a few threads running
-   * forever, we here distirbute different number of columns to
-   * different threads according to number of entries. */
-  size_t const total_entries = page.data.Size();
-  size_t const entries_per_thread = common::DivRoundUp(total_entries, nthreads);
-
-  std::vector<size_t> cols_ptr(nthreads+1, 0);
-  size_t count {0};
-  size_t current_thread {1};
-
-  for (size_t col_id = 0; col_id < page.Size(); ++col_id) {
-    auto const column = page[col_id];
-    cols_ptr[current_thread]++;  // add one column to thread
-    count += column.size();
-    if (count > entries_per_thread + 1) {
-      current_thread++;
-      count = 0;
-      cols_ptr[current_thread] = cols_ptr[current_thread-1];
-    }
-  }
-  // Idle threads.
-  for (; current_thread < cols_ptr.size() - 1; ++current_thread) {
-    cols_ptr[current_thread+1] = cols_ptr[current_thread];
-  }
-
-  return cols_ptr;
-}
-
-void SparseCuts::Build(DMatrix* dmat, uint32_t const max_num_bins) {
-  monitor_.Start(__FUNCTION__);
-  // Use group index for weights?
-  auto use_group = UseGroup(dmat);
-  uint32_t nthreads = omp_get_max_threads();
-  CHECK_GT(nthreads, 0);
-  std::vector<HistogramCuts> cuts_containers(nthreads);
-  std::vector<std::unique_ptr<SparseCuts>> sparse_cuts(nthreads);
-  for (size_t i = 0; i < nthreads; ++i) {
-    sparse_cuts[i].reset(new SparseCuts(&cuts_containers[i]));
-  }
-
-  for (auto const& page : dmat->GetBatches<CSCPage>()) {
-    CHECK_LE(page.Size(), dmat->Info().num_col_);
-    monitor_.Start("Load balance");
-    std::vector<size_t> col_ptr = LoadBalance(page, nthreads);
-    monitor_.Stop("Load balance");
-    // We here decouples the logic between build and parallelization
-    // to simplify things a bit.
-#pragma omp parallel for num_threads(nthreads) schedule(static)
-    for (omp_ulong i = 0; i < nthreads; ++i) {
-      common::Monitor t_monitor;
-      t_monitor.Init("SingleThreadBuild: " + std::to_string(i));
-      t_monitor.Start(std::to_string(i));
-      sparse_cuts[i]->SingleThreadBuild(page, dmat->Info(), max_num_bins, use_group,
-                                        col_ptr[i], col_ptr[i+1], i);
-      t_monitor.Stop(std::to_string(i));
-    }
-
-    this->Concat(sparse_cuts, dmat->Info().num_col_);
-  }
-
-  monitor_.Stop(__FUNCTION__);
-}
-
-void SparseCuts::Concat(
-    std::vector<std::unique_ptr<SparseCuts>> const& cuts, uint32_t n_cols) {
-  monitor_.Start(__FUNCTION__);
-  uint32_t nthreads = omp_get_max_threads();
-  auto &local_min_vals = p_cuts_->min_vals_.HostVector();
-  auto &local_cuts = p_cuts_->cut_values_.HostVector();
-  auto &local_ptrs = p_cuts_->cut_ptrs_.HostVector();
-  local_min_vals.resize(n_cols, std::numeric_limits<float>::max());
-  size_t min_vals_tail = 0;
-
-  for (uint32_t t = 0; t < nthreads; ++t) {
-    auto& thread_min_vals = cuts[t]->p_cuts_->min_vals_.HostVector();
-    auto& thread_cuts = cuts[t]->p_cuts_->cut_values_.HostVector();
-    auto& thread_ptrs = cuts[t]->p_cuts_->cut_ptrs_.HostVector();
-
-    // concat csc pointers.
-    size_t const old_ptr_size = local_ptrs.size();
-    local_ptrs.resize(
-        thread_ptrs.size() + local_ptrs.size() - 1);
-    size_t const new_icp_size = local_ptrs.size();
-    auto tail = local_ptrs[old_ptr_size-1];
-    for (size_t j = old_ptr_size; j < new_icp_size; ++j) {
-      local_ptrs[j] = tail + thread_ptrs[j-old_ptr_size+1];
-    }
-    // concat csc values
-    size_t const old_iv_size = local_cuts.size();
-    local_cuts.resize(
-        thread_cuts.size() + local_cuts.size());
-    size_t const new_iv_size = local_cuts.size();
-    for (size_t j = old_iv_size; j < new_iv_size; ++j) {
-      local_cuts[j] = thread_cuts[j-old_iv_size];
-    }
-    // merge min values
-    for (size_t j = 0; j < thread_min_vals.size(); ++j) {
-       local_min_vals.at(min_vals_tail + j) =
-          std::min(local_min_vals.at(min_vals_tail + j), thread_min_vals.at(j));
-    }
-    min_vals_tail += thread_min_vals.size();
-  }
-  monitor_.Stop(__FUNCTION__);
-}
-
-void DenseCuts::Build(DMatrix* p_fmat, uint32_t max_num_bins) {
-  monitor_.Start(__FUNCTION__);
-  const MetaInfo& info = p_fmat->Info();
-
-  // safe factor for better accuracy
-  constexpr int kFactor = 8;
-  std::vector<WQSketch> sketchs;
-
-  const int nthread = omp_get_max_threads();
-
-  unsigned const nstep =
-      static_cast<unsigned>((info.num_col_ + nthread - 1) / nthread);
-  unsigned const ncol = static_cast<unsigned>(info.num_col_);
-  sketchs.resize(info.num_col_);
-  for (auto& s : sketchs) {
-    s.Init(info.num_row_, 1.0 / (max_num_bins * kFactor));
-  }
-
-  // Data groups, used in ranking.
-  std::vector<bst_uint> const& group_ptr = info.group_ptr_;
-  size_t const num_groups = group_ptr.size() == 0 ? 0 : group_ptr.size() - 1;
-  // Use group index for weights?
-  bool const use_group = UseGroup(p_fmat);
-  const bool isDense = p_fmat->IsDense();
-  for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-    size_t group_ind = 0;
-    if (use_group) {
-      group_ind = this->SearchGroupIndFromRow(group_ptr, batch.base_rowid);
-    }
-#pragma omp parallel num_threads(nthread) firstprivate(group_ind, use_group)
-    {
-      CHECK_EQ(nthread, omp_get_num_threads());
-      auto tid = static_cast<unsigned>(omp_get_thread_num());
-      unsigned begin = std::min(nstep * tid, ncol);
-      unsigned end = std::min(nstep * (tid + 1), ncol);
-
-      // do not iterate if no columns are assigned to the thread
-      if (begin < end && end <= ncol) {
-        for (size_t i = 0; i < batch.Size(); ++i) { // NOLINT(*)
-          size_t const ridx = batch.base_rowid + i;
-          SparsePage::Inst const inst = batch[i];
-          if (use_group &&
-              group_ptr[group_ind] == ridx &&
-              // maximum equals to weights.size() - 1
-              group_ind < num_groups - 1) {
-            // move to next group
-            group_ind++;
-          }
-          size_t w_idx = use_group ? group_ind : ridx;
-          auto w = info.GetWeight(w_idx);
-          if (isDense) {
-            auto data = inst.data();
-            for (size_t ii = begin; ii < end; ii++) {
-              sketchs[ii].Push(data[ii].fvalue, w);
-            }
-          } else {
-            for (auto const& entry : inst) {
-              if (entry.index >= begin && entry.index < end) {
-                sketchs[entry.index].Push(entry.fvalue, w);
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-
-  Init(&sketchs, max_num_bins, info.num_row_);
-  monitor_.Stop(__FUNCTION__);
-}
-
-/**
- * \param [in,out]  in_sketchs
- * \param           max_num_bins  The maximum number bins.
- * \param           max_rows      Number of rows in this DMatrix.
- */
-void DenseCuts::Init
-(std::vector<WQSketch>* in_sketchs, uint32_t max_num_bins, size_t max_rows) {
-  monitor_.Start(__func__);
-  std::vector<WQSketch>& sketchs = *in_sketchs;
-
-  // Compute how many cuts samples we need at each node
-  // Do not require more than the number of total rows  in training data
-  // This allows efficient training on wide data
-  size_t global_max_rows = max_rows;
-  rabit::Allreduce<rabit::op::Sum>(&global_max_rows, 1);
-  constexpr int kFactor = 8;
-  size_t intermediate_num_cuts =
-      std::min(global_max_rows, static_cast<size_t>(max_num_bins * kFactor));
-  // gather the histogram data
-  rabit::SerializeReducer<WQSketch::SummaryContainer> sreducer;
-  std::vector<WQSketch::SummaryContainer> summary_array;
-  summary_array.resize(sketchs.size());
-  for (size_t i = 0; i < sketchs.size(); ++i) {
-    WQSketch::SummaryContainer out;
-    sketchs[i].GetSummary(&out);
-    summary_array[i].Reserve(intermediate_num_cuts);
-    summary_array[i].SetPrune(out, intermediate_num_cuts);
-  }
-  CHECK_EQ(summary_array.size(), in_sketchs->size());
-  size_t nbytes = WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts);
-  // TODO(chenqin): rabit failure recovery assumes no boostrap onetime call after loadcheckpoint
-  // we need to move this allreduce before loadcheckpoint call in future
-  sreducer.Allreduce(dmlc::BeginPtr(summary_array), nbytes, summary_array.size());
-  p_cuts_->min_vals_.HostVector().resize(sketchs.size());
-
-  for (size_t fid = 0; fid < summary_array.size(); ++fid) {
-    WQSketch::SummaryContainer a;
-    a.Reserve(max_num_bins + 1);
-    a.SetPrune(summary_array[fid], max_num_bins + 1);
-    const bst_float mval = a.data[0].value;
-    p_cuts_->min_vals_.HostVector()[fid] = mval - (fabs(mval) + 1e-5);
-    AddCutPoint(a, max_num_bins);
-    // push a value that is greater than anything
-    const bst_float cpt
-      = (a.size > 0) ? a.data[a.size - 1].value : p_cuts_->min_vals_.HostVector()[fid];
-    // this must be bigger than last value in a scale
-    const bst_float last = cpt + (fabs(cpt) + 1e-5);
-    p_cuts_->cut_values_.HostVector().push_back(last);
-
-    // Ensure that every feature gets at least one quantile point
-    CHECK_LE(p_cuts_->cut_values_.HostVector().size(), std::numeric_limits<uint32_t>::max());
-    auto cut_size = static_cast<uint32_t>(p_cuts_->cut_values_.HostVector().size());
-    CHECK_GT(cut_size, p_cuts_->cut_ptrs_.HostVector().back());
-    p_cuts_->cut_ptrs_.HostVector().push_back(cut_size);
-  }
-  monitor_.Stop(__func__);
-}
-
-void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_bins) {
-  cut.Build(p_fmat, max_bins);
-  max_num_bins = max_bins;
-  const int32_t nthread = omp_get_max_threads();
-  const uint32_t nbins = cut.Ptrs().back();
-  hit_count.resize(nbins, 0);
-  hit_count_tloc_.resize(nthread * nbins, 0);
-
-  this->p_fmat = p_fmat;
-  size_t new_size = 1;
-  for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-    new_size += batch.Size();
-  }
-
-  row_ptr.resize(new_size);
-  row_ptr[0] = 0;
-
-  size_t rbegin = 0;
-  size_t prev_sum = 0;
-  const bool isDense = p_fmat->IsDense();
-  this->isDense_ = isDense;
-
-  for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-    // The number of threads is pegged to the batch size. If the OMP
-    // block is parallelized on anything other than the batch/block size,
-    // it should be reassigned
-    const size_t batch_threads = std::max(
-        size_t(1),
-        std::min(batch.Size(), static_cast<size_t>(omp_get_max_threads())));
-    MemStackAllocator<size_t, 128> partial_sums(batch_threads);
-    size_t* p_part = partial_sums.Get();
-
-    size_t block_size =  batch.Size() / batch_threads;
-
-    #pragma omp parallel num_threads(batch_threads)
-    {
-      #pragma omp for
-      for (omp_ulong tid = 0; tid < batch_threads; ++tid) {
-        size_t ibegin = block_size * tid;
-        size_t iend = (tid == (batch_threads-1) ? batch.Size() : (block_size * (tid+1)));
-
-        size_t sum = 0;
-        for (size_t i = ibegin; i < iend; ++i) {
-          sum += batch[i].size();
-          row_ptr[rbegin + 1 + i] = sum;
-        }
-      }
-
-      #pragma omp single
-      {
-        p_part[0] = prev_sum;
-        for (size_t i = 1; i < batch_threads; ++i) {
-          p_part[i] = p_part[i - 1] + row_ptr[rbegin + i*block_size];
-        }
-      }
-
-      #pragma omp for
-      for (omp_ulong tid = 0; tid < batch_threads; ++tid) {
-        size_t ibegin = block_size * tid;
-        size_t iend = (tid == (batch_threads-1) ? batch.Size() : (block_size * (tid+1)));
-
-        for (size_t i = ibegin; i < iend; ++i) {
-          row_ptr[rbegin + 1 + i] += p_part[tid];
-        }
-      }
-    }
-
-    const size_t n_offsets = cut.Ptrs().size() - 1;
-    const size_t n_index = row_ptr[rbegin + batch.Size()];
-    ResizeIndex(rbegin, batch, n_offsets, n_index, isDense);
-
-    CHECK_GT(cut.Values().size(), 0U);
-
-    uint32_t* offsets = nullptr;
-    if (isDense) {
-      index.ResizeOffset(n_offsets);
-      offsets = index.Offset();
-      for (size_t i = 0; i < n_offsets; ++i) {
-        offsets[i] = cut.Ptrs()[i];
-      }
-    }
-
-    if (isDense) {
-      BinTypeSize curent_bin_size = index.GetBinTypeSize();
-      common::Span<const uint32_t> offsets_span = {offsets, n_offsets};
-      if (curent_bin_size == kUint8BinsTypeSize) {
-          common::Span<uint8_t> index_data_span = {index.data<uint8_t>(), n_index};
-          SetIndexDataForDense(index_data_span, batch_threads, batch, rbegin, offsets_span, nbins);
-      } else if (curent_bin_size == kUint16BinsTypeSize) {
-          common::Span<uint16_t> index_data_span = {index.data<uint16_t>(), n_index};
-          SetIndexDataForDense(index_data_span, batch_threads, batch, rbegin, offsets_span, nbins);
-      } else {
-          CHECK_EQ(curent_bin_size, kUint32BinsTypeSize);
-          common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
-          SetIndexDataForDense(index_data_span, batch_threads, batch, rbegin, offsets_span, nbins);
-      }
-
-    /* For sparse DMatrix we have to store index of feature for each bin
-       in index field to chose right offset. So offset is nullptr and index is not reduced */
-    } else {
-      common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
-      SetIndexDataForSparse(index_data_span, batch_threads, batch, rbegin, nbins);
-    }
-
-    #pragma omp parallel for num_threads(nthread) schedule(static)
-    for (bst_omp_uint idx = 0; idx < bst_omp_uint(nbins); ++idx) {
-      for (int32_t tid = 0; tid < nthread; ++tid) {
-        hit_count[idx] += hit_count_tloc_[tid * nbins + idx];
-        hit_count_tloc_[tid * nbins + idx] = 0;  // reset for next batch
-      }
-    }
-
-    prev_sum = row_ptr[rbegin + batch.Size()];
-    rbegin += batch.Size();
-  }
-}
-
-template <typename BinIdxType>
-static size_t GetConflictCount(const std::vector<bool>& mark,
-                               const Column<BinIdxType>& column_input,
-                               size_t max_cnt) {
-  size_t ret = 0;
-  if (column_input.GetType() == xgboost::common::kDenseColumn) {
-    const DenseColumn<BinIdxType>& column
-      = static_cast<const DenseColumn<BinIdxType>& >(column_input);
-    for (size_t i = 0; i < column.Size(); ++i) {
-      if ((!column.IsMissing(i)) && mark[i]) {
-        ++ret;
-        if (ret > max_cnt) {
-          return max_cnt + 1;
-        }
-      }
-    }
-  } else {
-    const SparseColumn<BinIdxType>& column
-      = static_cast<const SparseColumn<BinIdxType>& >(column_input);
-    for (size_t i = 0; i < column.Size(); ++i) {
-      if (mark[column.GetRowIdx(i)]) {
-        ++ret;
-        if (ret > max_cnt) {
-          return max_cnt + 1;
-        }
-      }
-    }
-  }
-  return ret;
-}
-
-template <typename BinIdxType>
-inline void
-MarkUsed(std::vector<bool>* p_mark, const Column<BinIdxType>& column_input) {
-  std::vector<bool>& mark = *p_mark;
-  if (column_input.GetType() == xgboost::common::kDenseColumn) {
-    const DenseColumn<BinIdxType>& column
-      = static_cast<const DenseColumn<BinIdxType>& >(column_input);
-    for (size_t i = 0; i < column.Size(); ++i) {
-      if (!column.IsMissing(i)) {
-        mark[i] = true;
-      }
-    }
-  } else {
-    const SparseColumn<BinIdxType>& column
-      = static_cast<const SparseColumn<BinIdxType>& >(column_input);
-    for (size_t i = 0; i < column.Size(); ++i) {
-      mark[column.GetRowIdx(i)] = true;
-    }
-  }
-}
-
-template <typename BinIdxType>
-inline void SetGroup(const unsigned fid, const Column<BinIdxType>& column,
-  const size_t max_conflict_cnt, const std::vector<size_t>& search_groups,
-  std::vector<size_t>* p_group_conflict_cnt,
-  std::vector<std::vector<bool>>* p_conflict_marks,
-  std::vector<std::vector<unsigned>>* p_groups,
-  std::vector<size_t>* p_group_nnz, const size_t cur_fid_nnz, const size_t nrow) {
-  bool need_new_group = true;
-  std::vector<size_t>& group_conflict_cnt = *p_group_conflict_cnt;
-  std::vector<std::vector<bool>>& conflict_marks = *p_conflict_marks;
-  std::vector<std::vector<unsigned>>& groups = *p_groups;
-  std::vector<size_t>& group_nnz = *p_group_nnz;
-
-  // examine each candidate group: is it okay to insert fid?
-  for (auto gid : search_groups) {
-    const size_t rest_max_cnt = max_conflict_cnt - group_conflict_cnt[gid];
-    const size_t cnt = GetConflictCount(conflict_marks[gid], column, rest_max_cnt);
-    if (cnt <= rest_max_cnt) {
-      need_new_group = false;
-      groups[gid].push_back(fid);
-      group_conflict_cnt[gid] += cnt;
-      group_nnz[gid] += cur_fid_nnz - cnt;
-      MarkUsed(&conflict_marks[gid], column);
-      break;
-    }
-  }
-  // create new group if necessary
-  if (need_new_group) {
-    groups.emplace_back();
-    groups.back().push_back(fid);
-    group_conflict_cnt.push_back(0);
-    conflict_marks.emplace_back(nrow, false);
-    MarkUsed(&conflict_marks.back(), column);
-    group_nnz.emplace_back(cur_fid_nnz);
-  }
-}
-
-inline std::vector<std::vector<unsigned>>
-FindGroups(const std::vector<unsigned>& feature_list,
-           const std::vector<size_t>& feature_nnz,
-           const ColumnMatrix& colmat,
-           size_t nrow,
-           const tree::TrainParam& param) {
-  /* Goal: Bundle features together that has little or no "overlap", i.e.
-           only a few data points should have nonzero values for
-           member features.
-           Note that one-hot encoded features will be grouped together. */
-
-  std::vector<std::vector<unsigned>> groups;
-  std::vector<std::vector<bool>> conflict_marks;
-  std::vector<size_t> group_nnz;
-  std::vector<size_t> group_conflict_cnt;
-  const auto max_conflict_cnt
-    = static_cast<size_t>(param.max_conflict_rate * nrow);
-
-  for (auto fid : feature_list) {
-    const size_t cur_fid_nnz = feature_nnz[fid];
-
-    // randomly choose some of existing groups as candidates
-    std::vector<size_t> search_groups;
-    for (size_t gid = 0; gid < groups.size(); ++gid) {
-      if (group_nnz[gid] + cur_fid_nnz <= nrow + max_conflict_cnt) {
-        search_groups.push_back(gid);
-      }
-    }
-    std::shuffle(search_groups.begin(), search_groups.end(), common::GlobalRandom());
-    if (param.max_search_group > 0 && search_groups.size() > param.max_search_group) {
-      search_groups.resize(param.max_search_group);
-    }
-
-    BinTypeSize bins_type_size = colmat.GetTypeSize();
-    if (bins_type_size == kUint8BinsTypeSize) {
-        const auto column = colmat.GetColumn<uint8_t>(fid);
-        SetGroup(fid, *(column.get()), max_conflict_cnt, search_groups,
-                 &group_conflict_cnt, &conflict_marks, &groups, &group_nnz, cur_fid_nnz, nrow);
-    } else if (bins_type_size == kUint16BinsTypeSize) {
-        const auto column = colmat.GetColumn<uint16_t>(fid);
-        SetGroup(fid, *(column.get()), max_conflict_cnt, search_groups,
-                 &group_conflict_cnt, &conflict_marks, &groups, &group_nnz, cur_fid_nnz, nrow);
-    } else {
-        CHECK_EQ(bins_type_size, kUint32BinsTypeSize);
-        const auto column = colmat.GetColumn<uint32_t>(fid);
-        SetGroup(fid, *(column.get()), max_conflict_cnt, search_groups,
-                 &group_conflict_cnt, &conflict_marks, &groups, &group_nnz, cur_fid_nnz, nrow);
-    }
-  }
-  return groups;
-}
-
-inline std::vector<std::vector<unsigned>>
-FastFeatureGrouping(const GHistIndexMatrix& gmat,
-                    const ColumnMatrix& colmat,
-                    const tree::TrainParam& param) {
-  const size_t nrow = gmat.row_ptr.size() - 1;
-  const size_t nfeature = gmat.cut.Ptrs().size() - 1;
-
-  std::vector<unsigned> feature_list(nfeature);
-  std::iota(feature_list.begin(), feature_list.end(), 0);
-
-  // sort features by nonzero counts, descending order
-  std::vector<size_t> feature_nnz(nfeature);
-  std::vector<unsigned> features_by_nnz(feature_list);
-  gmat.GetFeatureCounts(&feature_nnz[0]);
-  std::sort(features_by_nnz.begin(), features_by_nnz.end(),
-            [&feature_nnz](unsigned a, unsigned b) {
-    return feature_nnz[a] > feature_nnz[b];
-  });
-
-  auto groups_alt1 = FindGroups(feature_list, feature_nnz, colmat, nrow, param);
-  auto groups_alt2 = FindGroups(features_by_nnz, feature_nnz, colmat, nrow, param);
-  auto& groups = (groups_alt1.size() > groups_alt2.size()) ? groups_alt2 : groups_alt1;
-
-  // take apart small, sparse groups, as it won't help speed
-  {
-    std::vector<std::vector<unsigned>> ret;
-    for (const auto& group : groups) {
-      if (group.size() <= 1 || group.size() >= 5) {
-        ret.push_back(group);  // keep singleton groups and large (5+) groups
-      } else {
-        size_t nnz = 0;
-        for (auto fid : group) {
-          nnz += feature_nnz[fid];
-        }
-        double nnz_rate = static_cast<double>(nnz) / nrow;
-        // take apart small sparse group, due it will not gain on speed
-        if (nnz_rate <= param.sparse_threshold) {
-          for (auto fid : group) {
-            ret.emplace_back();
-            ret.back().push_back(fid);
-          }
-        } else {
-          ret.push_back(group);
-        }
-      }
-    }
-    groups = std::move(ret);
-  }
-
-  // shuffle groups
-  std::shuffle(groups.begin(), groups.end(), common::GlobalRandom());
-
-  return groups;
-}
-
-void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
-                                 const ColumnMatrix& colmat,
-                                 const tree::TrainParam& param) {
-  cut_ = &gmat.cut;
-
-  const size_t nrow = gmat.row_ptr.size() - 1;
-  const uint32_t nbins = gmat.cut.Ptrs().back();
-
-  /* step 1: form feature groups */
-  auto groups = FastFeatureGrouping(gmat, colmat, param);
-  const auto nblock = static_cast<uint32_t>(groups.size());
-
-  /* step 2: build a new CSR matrix for each feature group */
-  std::vector<uint32_t> bin2block(nbins);  // lookup table [bin id] => [block id]
-  for (uint32_t group_id = 0; group_id < nblock; ++group_id) {
-    for (auto& fid : groups[group_id]) {
-      const uint32_t bin_begin = gmat.cut.Ptrs()[fid];
-      const uint32_t bin_end = gmat.cut.Ptrs()[fid + 1];
-      for (uint32_t bin_id = bin_begin; bin_id < bin_end; ++bin_id) {
-        bin2block[bin_id] = group_id;
-      }
-    }
-  }
-
-  std::vector<std::vector<uint32_t>> index_temp(nblock);
-  std::vector<std::vector<size_t>> row_ptr_temp(nblock);
-  for (uint32_t block_id = 0; block_id < nblock; ++block_id) {
-    row_ptr_temp[block_id].push_back(0);
-  }
-  for (size_t rid = 0; rid < nrow; ++rid) {
-    const size_t ibegin = gmat.row_ptr[rid];
-    const size_t iend = gmat.row_ptr[rid + 1];
-    for (size_t j = ibegin; j < iend; ++j) {
-      const uint32_t bin_id = gmat.index[j];
-      const uint32_t block_id = bin2block[bin_id];
-      index_temp[block_id].push_back(bin_id);
-    }
-    for (uint32_t block_id = 0; block_id < nblock; ++block_id) {
-      row_ptr_temp[block_id].push_back(index_temp[block_id].size());
-    }
-  }
-
-  /* step 3: concatenate CSR matrices into one (index, row_ptr) pair */
-  std::vector<size_t> index_blk_ptr;
-  std::vector<size_t> row_ptr_blk_ptr;
-  index_blk_ptr.push_back(0);
-  row_ptr_blk_ptr.push_back(0);
-  for (uint32_t block_id = 0; block_id < nblock; ++block_id) {
-    index_.insert(index_.end(), index_temp[block_id].begin(), index_temp[block_id].end());
-    row_ptr_.insert(row_ptr_.end(), row_ptr_temp[block_id].begin(), row_ptr_temp[block_id].end());
-    index_blk_ptr.push_back(index_.size());
-    row_ptr_blk_ptr.push_back(row_ptr_.size());
-  }
-
-  // save shortcut for each block
-  for (uint32_t block_id = 0; block_id < nblock; ++block_id) {
-    Block blk;
-    blk.index_begin = &index_[index_blk_ptr[block_id]];
-    blk.row_ptr_begin = &row_ptr_[row_ptr_blk_ptr[block_id]];
-    blk.index_end = &index_[index_blk_ptr[block_id + 1]];
-    blk.row_ptr_end = &row_ptr_[row_ptr_blk_ptr[block_id + 1]];
-    blocks_.push_back(blk);
-  }
-}
-
-/*!
- * \brief fill a histogram by zeros in range [begin, end)
- */
-void InitilizeHistByZeroes(GHistRow hist, size_t begin, size_t end) {
-#if defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
-  std::fill(hist.begin() + begin, hist.begin() + end, tree::GradStats());
-#else  // defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
-  memset(hist.data() + begin, '\0', (end-begin)*sizeof(tree::GradStats));
-#endif  // defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
-}
-
-/*!
- * \brief Increment hist as dst += add in range [begin, end)
- */
-void IncrementHist(GHistRow dst, const GHistRow add, size_t begin, size_t end) {
-  using FPType = decltype(tree::GradStats::sum_grad);
-  FPType* pdst = reinterpret_cast<FPType*>(dst.data());
-  const FPType* padd = reinterpret_cast<const FPType*>(add.data());
-
-  for (size_t i = 2 * begin; i < 2 * end; ++i) {
-    pdst[i] += padd[i];
-  }
-}
-
-/*!
- * \brief Copy hist from src to dst in range [begin, end)
- */
-void CopyHist(GHistRow dst, const GHistRow src, size_t begin, size_t end) {
-  using FPType = decltype(tree::GradStats::sum_grad);
-  FPType* pdst = reinterpret_cast<FPType*>(dst.data());
-  const FPType* psrc = reinterpret_cast<const FPType*>(src.data());
-
-  for (size_t i = 2 * begin; i < 2 * end; ++i) {
-    pdst[i] = psrc[i];
-  }
-}
-
-/*!
- * \brief Compute Subtraction: dst = src1 - src2 in range [begin, end)
- */
-void SubtractionHist(GHistRow dst, const GHistRow src1, const GHistRow src2,
-                     size_t begin, size_t end) {
-  using FPType = decltype(tree::GradStats::sum_grad);
-  FPType* pdst = reinterpret_cast<FPType*>(dst.data());
-  const FPType* psrc1 = reinterpret_cast<const FPType*>(src1.data());
-  const FPType* psrc2 = reinterpret_cast<const FPType*>(src2.data());
-
-  for (size_t i = 2 * begin; i < 2 * end; ++i) {
-    pdst[i] = psrc1[i] - psrc2[i];
-  }
-}
-
-struct Prefetch {
- public:
-  static constexpr size_t kCacheLineSize = 64;
-  static constexpr size_t kPrefetchOffset = 10;
-
- private:
-  static constexpr size_t kNoPrefetchSize =
-      kPrefetchOffset + kCacheLineSize /
-      sizeof(decltype(GHistIndexMatrix::row_ptr)::value_type);
-
- public:
-  static size_t NoPrefetchSize(size_t rows) {
-    return std::min(rows, kNoPrefetchSize);
-  }
-
-  template <typename T>
-  static constexpr size_t GetPrefetchStep() {
-    return Prefetch::kCacheLineSize / sizeof(T);
-  }
-};
-
-constexpr size_t Prefetch::kNoPrefetchSize;
-
-
-template<typename FPType, bool do_prefetch, typename BinIdxType>
-void BuildHistDenseKernel(const std::vector<GradientPair>& gpair,
-                          const RowSetCollection::Elem row_indices,
-                          const GHistIndexMatrix& gmat,
-                          const size_t n_features,
-                          GHistRow hist) {
-  const size_t size = row_indices.Size();
-  const size_t* rid = row_indices.begin;
-  const float* pgh = reinterpret_cast<const float*>(gpair.data());
-  const BinIdxType* gradient_index = gmat.index.data<BinIdxType>();
-  const uint32_t* offsets = gmat.index.Offset();
-  FPType* hist_data = reinterpret_cast<FPType*>(hist.data());
-  const uint32_t two {2};  // Each element from 'gpair' and 'hist' contains
-                           // 2 FP values: gradient and hessian.
-                           // So we need to multiply each row-index/bin-index by 2
-                           // to work with gradient pairs as a singe row FP array
-
-  for (size_t i = 0; i < size; ++i) {
-    const size_t icol_start = rid[i] * n_features;
-    const size_t idx_gh = two * rid[i];
-
-    if (do_prefetch) {
-      const size_t icol_start_prefetch = rid[i + Prefetch::kPrefetchOffset] * n_features;
-
-      PREFETCH_READ_T0(pgh + two * rid[i + Prefetch::kPrefetchOffset]);
-      for (size_t j = icol_start_prefetch; j < icol_start_prefetch + n_features;
-           j += Prefetch::GetPrefetchStep<BinIdxType>()) {
-        PREFETCH_READ_T0(gradient_index + j);
-      }
-    }
-    const BinIdxType* gr_index_local = gradient_index + icol_start;
-    for (size_t j = 0; j < n_features; ++j) {
-      const uint32_t idx_bin = two * (static_cast<uint32_t>(gr_index_local[j]) +
-                                      offsets[j]);
-
-      hist_data[idx_bin]   += pgh[idx_gh];
-      hist_data[idx_bin+1] += pgh[idx_gh+1];
-    }
-  }
-}
-
-template<>
-void BuildHistDenseKernel<double, false, uint8_t>(const std::vector<GradientPair>& gpair,
-                                                  const RowSetCollection::Elem row_indices,
-                                                  const GHistIndexMatrix& gmat,
-                                                  const size_t n_features,
-                                                  GHistRow hist) {
-  const size_t size_dense_false = row_indices.Size();
-  const size_t* rid_dense_false = row_indices.begin;
-  const float* pgh_dense_false = reinterpret_cast<const float*>(gpair.data());
-  const uint8_t* gradient_index_dense_false = gmat.index.data<uint8_t>();
-  const uint32_t* offsets_dense_false = gmat.index.Offset();
-  double* hist_data_dense_false = reinterpret_cast<double*>(hist.data());
-  const uint32_t two{ 2 };  
-  alignas(ALIGNAS_NUM) double tmp[2];
-
-#ifdef USE_INTEL_INTRINSICS
-  for (size_t i = 0; i < size_dense_false; ++i) {
-    const size_t icol_start = rid_dense_false[i] * n_features;
-    const size_t idx_gh = two * rid_dense_false[i];
-    tmp[0] = pgh_dense_false[idx_gh];
-    tmp[1] = pgh_dense_false[idx_gh + 1];
-    __m128d v = _mm_load_pd(tmp);
-
-    const uint8_t* gr_index_local = gradient_index_dense_false + icol_start;
-    for (size_t j = 0; j < n_features; ++j) {
-      const uint32_t idx_bin = two * (static_cast<uint32_t>(gr_index_local[j]) +
-        offsets_dense_false[j]);
-      __m128d u = _mm_loadu_pd(&(hist_data_dense_false[idx_bin]));
-      __m128d res = _mm_add_pd(u, v);
-      _mm_storeu_pd(&(hist_data_dense_false[idx_bin]), res);
-    }
-  }
-#endif
-#ifdef USE_ARM_INTRINSICS
-  for (size_t i = 0; i < size_dense_false; ++i) {
-    const size_t icol_start = rid_dense_false[i] * n_features;
-    const size_t idx_gh = two * rid_dense_false[i];
-    tmp[0] = pgh_dense_false[idx_gh];
-    tmp[1] = pgh_dense_false[idx_gh + 1];
-    float64x2_t v = vld1q_f64(tmp);
-
-    const uint8_t* gr_index_local = gradient_index_dense_false + icol_start;
-    for (size_t j = 0; j < n_features; ++j) {
-      const uint32_t idx_bin = two * (static_cast<uint32_t>(gr_index_local[j]) +
-        offsets_dense_false[j]);
-      float64x2_t u = vld1q_f64(&(hist_data_dense_false[idx_bin]));
-      float64x2_t res = vaddq_f64(u, v);
-      vst1q_f64(&(hist_data_dense_false[idx_bin]), res);
-    }
-  }
-#endif
-}
-
-template<>
-void BuildHistDenseKernel<double, true, uint8_t>(const std::vector<GradientPair>& gpair,
-                                                 const RowSetCollection::Elem row_indices,
-                                                 const GHistIndexMatrix& gmat,
-                                                 const size_t n_features,
-                                                 GHistRow hist) {
-  const size_t size_dense_true = row_indices.Size();
-  const size_t* rid_dense_true = row_indices.begin;
-  const float* pgh_dense_true = reinterpret_cast<const float*>(gpair.data());
-  const uint8_t* gradient_index_dense_true = gmat.index.data<uint8_t>();
-  const uint32_t* offsets_dense_true = gmat.index.Offset();
-  double* hist_data_dense_true = reinterpret_cast<double*>(hist.data());
-  const uint32_t two{ 2 }; 
-  alignas(ALIGNAS_NUM) double tmp[2];
-
-  for (size_t i = 0; i < size_dense_true; ++i) {
-    const size_t icol_start = rid_dense_true[i] * n_features;
-    const size_t idx_gh = two * rid_dense_true[i];
-    tmp[0] = pgh_dense_true[idx_gh];
-    tmp[1] = pgh_dense_true[idx_gh + 1];
-#ifdef USE_INTEL_INTRINSICS
-    __m128d v = _mm_load_pd(tmp);
-#endif
-#ifdef USE_ARM_INTRINSICS
-    float64x2_t v = vld1q_f64(tmp);
-#endif
-
-    const size_t icol_start_prefetch = rid_dense_true[i + Prefetch::kPrefetchOffset] * n_features;
-    PREFETCH_READ_T0(pgh_dense_true + two * rid_dense_true[i + Prefetch::kPrefetchOffset]);
-    for (size_t j = icol_start_prefetch; j < icol_start_prefetch + n_features;
-      j += Prefetch::GetPrefetchStep<uint8_t>()) {
-      PREFETCH_READ_T0(gradient_index_dense_true + j);
-    }
-
-    const uint8_t* gr_index_local = gradient_index_dense_true + icol_start;
-    for (size_t j = 0; j < n_features; ++j) {
-      const uint32_t idx_bin = two * (static_cast<uint32_t>(gr_index_local[j]) +
-        offsets_dense_true[j]);
-#ifdef USE_INTEL_INTRINSICS
-      __m128d u = _mm_loadu_pd(&(hist_data_dense_true[idx_bin]));
-      __m128d res = _mm_add_pd(u, v);
-      _mm_storeu_pd(&(hist_data_dense_true[idx_bin]), res);
-#endif
-#ifdef USE_ARM_INTRINSICS
-      float64x2_t u = vld1q_f64(&(hist_data_dense_true[idx_bin]));
-      float64x2_t res = vaddq_f64(u, v);
-      vst1q_f64(&(hist_data_dense_true[idx_bin]), res);
-#endif
-    }
-  }
-}
-
-template<typename FPType, bool do_prefetch>
-void BuildHistSparseKernel(const std::vector<GradientPair>& gpair,
-                           const RowSetCollection::Elem row_indices,
-                           const GHistIndexMatrix& gmat,
-                           GHistRow hist) {
-  const size_t size = row_indices.Size();
-  const size_t* rid = row_indices.begin;
-  const float* pgh = reinterpret_cast<const float*>(gpair.data());
-  const uint32_t* gradient_index = gmat.index.data<uint32_t>();
-  const size_t* row_ptr =  gmat.row_ptr.data();
-  FPType* hist_data = reinterpret_cast<FPType*>(hist.data());
-  const uint32_t two {2};  // Each element from 'gpair' and 'hist' contains
-                           // 2 FP values: gradient and hessian.
-                           // So we need to multiply each row-index/bin-index by 2
-                           // to work with gradient pairs as a singe row FP array
-
-  for (size_t i = 0; i < size; ++i) {
-    const size_t icol_start = row_ptr[rid[i]];
-    const size_t icol_end = row_ptr[rid[i]+1];
-    const size_t idx_gh = two * rid[i];
-
-    if (do_prefetch) {
-      const size_t icol_start_prftch = row_ptr[rid[i+Prefetch::kPrefetchOffset]];
-      const size_t icol_end_prefect = row_ptr[rid[i+Prefetch::kPrefetchOffset]+1];
-
-      PREFETCH_READ_T0(pgh + two * rid[i + Prefetch::kPrefetchOffset]);
-      for (size_t j = icol_start_prftch; j < icol_end_prefect;
-        j+=Prefetch::GetPrefetchStep<uint32_t>()) {
-        PREFETCH_READ_T0(gradient_index + j);
-      }
-    }
-    for (size_t j = icol_start; j < icol_end; ++j) {
-      const uint32_t idx_bin = two * gradient_index[j];
-      hist_data[idx_bin]   += pgh[idx_gh];
-      hist_data[idx_bin+1] += pgh[idx_gh+1];
-    }
-  }
-}
-
-template<>
-void BuildHistSparseKernel<double, false>(const std::vector<GradientPair>& gpair,
-                                          const RowSetCollection::Elem row_indices,
-                                          const GHistIndexMatrix& gmat,
-                                          GHistRow hist) {
-  const size_t size_sparse_false = row_indices.Size();
-  const size_t* rid_sparse_false = row_indices.begin;
-  const float* pgh_sparse_false = reinterpret_cast<const float*>(gpair.data());
-  const uint32_t* gradient_index_sparse_false = gmat.index.data<uint32_t>();
-  const size_t* row_ptr_sparse_false = gmat.row_ptr.data();
-  double* hist_data_sparse_false = reinterpret_cast<double*>(hist.data());
-  const uint32_t two{ 2 };
-  alignas(ALIGNAS_NUM) double tmp[2];
-
-#ifdef USE_INTEL_INTRINSICS
-  for (size_t i = 0; i < size_sparse_false; ++i) {
-    const size_t icol_start = row_ptr_sparse_false[rid_sparse_false[i]];
-    const size_t icol_end = row_ptr_sparse_false[rid_sparse_false[i] + 1];
-    const size_t idx_gh = two * rid_sparse_false[i];
-    tmp[0] = pgh_sparse_false[idx_gh];
-    tmp[1] = pgh_sparse_false[idx_gh + 1];
-    __m128d v = _mm_load_pd(tmp);
-
-    for (size_t j = icol_start; j < icol_end; ++j) {
-      const uint32_t idx_bin = two * gradient_index_sparse_false[j];
-      __m128d u = _mm_loadu_pd(&(hist_data_sparse_false[idx_bin]));
-      __m128d res = _mm_add_pd(u, v);
-      _mm_storeu_pd(&(hist_data_sparse_false[idx_bin]), res);
-    }
-  }
-#endif
-#ifdef USE_ARM_INTRINSICS
-  for (size_t i = 0; i < size_sparse_false; ++i) {
-    const size_t icol_start = row_ptr_sparse_false[rid_sparse_false[i]];
-    const size_t icol_end = row_ptr_sparse_false[rid_sparse_false[i] + 1];
-    const size_t idx_gh = two * rid_sparse_false[i];
-    tmp[0] = pgh_sparse_false[idx_gh];
-    tmp[1] = pgh_sparse_false[idx_gh + 1];
-    float64x2_t v = vld1q_f64(tmp);
-
-    for (size_t j = icol_start; j < icol_end; ++j) {
-      const uint32_t idx_bin = two * gradient_index_sparse_false[j];
-      float64x2_t u = vld1q_f64(&(hist_data_sparse_false[idx_bin]));
-      float64x2_t res = vaddq_f64(u, v);
-      vst1q_f64(&(hist_data_sparse_false[idx_bin]), res);
-    }
-  }
-#endif
-}
-
-template<>
-void BuildHistSparseKernel<double, true>(const std::vector<GradientPair>& gpair,
-                                         const RowSetCollection::Elem row_indices,
-                                         const GHistIndexMatrix& gmat,
-                                         GHistRow hist) {
-  const size_t size_sparse_true = row_indices.Size();
-  const size_t* rid_sparse_true = row_indices.begin;
-  const float* pgh_sparse_true = reinterpret_cast<const float*>(gpair.data());
-  const uint32_t* gradient_index_sparse_true = gmat.index.data<uint32_t>();
-  const size_t* row_ptr_sparse_true = gmat.row_ptr.data();
-  double* hist_data_sparse_true = reinterpret_cast<double*>(hist.data());
-  const uint32_t two{ 2 };
-  alignas(ALIGNAS_NUM) double tmp[2];
-
-  for (size_t i = 0; i < size_sparse_true; ++i) {
-    const size_t icol_start = row_ptr_sparse_true[rid_sparse_true[i]];
-    const size_t icol_end = row_ptr_sparse_true[rid_sparse_true[i] + 1];
-    const size_t idx_gh = two * rid_sparse_true[i];
-    tmp[0] = pgh_sparse_true[idx_gh];
-    tmp[1] = pgh_sparse_true[idx_gh + 1];
-#ifdef USE_INTEL_INTRINSICS
-    __m128d v = _mm_load_pd(tmp);
-#endif
-#ifdef USE_ARM_INTRINSICS
-    float64x2_t v = vld1q_f64(tmp);
-#endif
-
-    const size_t icol_start_prftch = row_ptr_sparse_true[rid_sparse_true[i + Prefetch::kPrefetchOffset]];
-    const size_t icol_end_prefect = row_ptr_sparse_true[rid_sparse_true[i + Prefetch::kPrefetchOffset] + 1];
-    PREFETCH_READ_T0(pgh_sparse_true + two * rid_sparse_true[i + Prefetch::kPrefetchOffset]);
-    for (size_t j = icol_start_prftch; j < icol_end_prefect;
-      j += Prefetch::GetPrefetchStep<uint32_t>()) {
-      PREFETCH_READ_T0(gradient_index_sparse_true + j);
-    }
-
-    for (size_t j = icol_start; j < icol_end; ++j) {
-      const uint32_t idx_bin = two * gradient_index_sparse_true[j];
-#ifdef USE_INTEL_INTRINSICS
-      __m128d u = _mm_loadu_pd(&(hist_data_sparse_true[idx_bin]));
-      __m128d res = _mm_add_pd(u, v);
-      _mm_storeu_pd(&(hist_data_sparse_true[idx_bin]), res);
-#endif
-#ifdef USE_ARM_INTRINSICS
-      float64x2_t u = vld1q_f64(&(hist_data_sparse_true[idx_bin]));
-      float64x2_t res = vaddq_f64(u, v);
-      vst1q_f64(&(hist_data_sparse_true[idx_bin]), res);
-#endif
-    }
-  }
-}
-
-template<typename FPType, bool do_prefetch, typename BinIdxType>
-void BuildHistDispatchKernel(const std::vector<GradientPair>& gpair,
-                     const RowSetCollection::Elem row_indices,
-                     const GHistIndexMatrix& gmat, GHistRow hist, bool isDense) {
-  if (isDense) {
-    const size_t* row_ptr =  gmat.row_ptr.data();
-    const size_t n_features = row_ptr[row_indices.begin[0]+1] - row_ptr[row_indices.begin[0]];
-    BuildHistDenseKernel<FPType, do_prefetch, BinIdxType>(gpair, row_indices,
-                                                       gmat, n_features, hist);
-  } else {
-    BuildHistSparseKernel<FPType, do_prefetch>(gpair, row_indices,
-                                                        gmat, hist);
-  }
-}
-
-template<typename FPType, bool do_prefetch>
-void BuildHistKernel(const std::vector<GradientPair>& gpair,
-                     const RowSetCollection::Elem row_indices,
-                     const GHistIndexMatrix& gmat, const bool isDense, GHistRow hist) {
-  const bool is_dense = row_indices.Size() && isDense;
-  switch (gmat.index.GetBinTypeSize()) {
-    case kUint8BinsTypeSize:
-      BuildHistDispatchKernel<FPType, do_prefetch, uint8_t>(gpair, row_indices,
-                                                            gmat, hist, is_dense);
-      break;
-    case kUint16BinsTypeSize:
-      BuildHistDispatchKernel<FPType, do_prefetch, uint16_t>(gpair, row_indices,
-                                                             gmat, hist, is_dense);
-      break;
-    case kUint32BinsTypeSize:
-      BuildHistDispatchKernel<FPType, do_prefetch, uint32_t>(gpair, row_indices,
-                                                             gmat, hist, is_dense);
-      break;
-    default:
-      CHECK(false);  // no default behavior
-  }
-}
-
-void GHistBuilder::BuildHist(const std::vector<GradientPair>& gpair,
-                             const RowSetCollection::Elem row_indices,
-                             const GHistIndexMatrix& gmat,
-                             GHistRow hist,
-                             bool isDense) {
-  using FPType = decltype(tree::GradStats::sum_grad);
-  const size_t nrows = row_indices.Size();
-  const size_t no_prefetch_size = Prefetch::NoPrefetchSize(nrows);
-
-  // if need to work with all rows from bin-matrix (e.g. root node)
-  const bool contiguousBlock = (row_indices.begin[nrows - 1] - row_indices.begin[0]) == (nrows - 1);
-
-  if (contiguousBlock) {
-    // contiguous memory access, built-in HW prefetching is enough
-    BuildHistKernel<FPType, false>(gpair, row_indices, gmat, isDense, hist);
-  } else {
-    const RowSetCollection::Elem span1(row_indices.begin, row_indices.end - no_prefetch_size);
-    const RowSetCollection::Elem span2(row_indices.end - no_prefetch_size, row_indices.end);
-
-    BuildHistKernel<FPType, true>(gpair, span1, gmat, isDense, hist);
-    // no prefetching to avoid loading extra memory
-    BuildHistKernel<FPType, false>(gpair, span2, gmat, isDense, hist);
-  }
-}
-
-void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
-                                  const RowSetCollection::Elem row_indices,
-                                  const GHistIndexBlockMatrix& gmatb,
-                                  GHistRow hist) {
-  constexpr int kUnroll = 8;  // loop unrolling factor
-  const size_t nblock = gmatb.GetNumBlock();
-  const size_t nrows = row_indices.end - row_indices.begin;
-  const size_t rest = nrows % kUnroll;
-#if defined(_OPENMP)
-  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);  // NOLINT
-#endif  // defined(_OPENMP)
-  tree::GradStats* p_hist = hist.data();
-
-#pragma omp parallel for num_threads(nthread) schedule(guided)
-  for (bst_omp_uint bid = 0; bid < nblock; ++bid) {
-    auto gmat = gmatb[bid];
-
-    for (size_t i = 0; i < nrows - rest; i += kUnroll) {
-      size_t rid[kUnroll];
-      size_t ibegin[kUnroll];
-      size_t iend[kUnroll];
-      GradientPair stat[kUnroll];
-
-      for (int k = 0; k < kUnroll; ++k) {
-        rid[k] = row_indices.begin[i + k];
-        ibegin[k] = gmat.row_ptr[rid[k]];
-        iend[k] = gmat.row_ptr[rid[k] + 1];
-        stat[k] = gpair[rid[k]];
-      }
-      for (int k = 0; k < kUnroll; ++k) {
-        for (size_t j = ibegin[k]; j < iend[k]; ++j) {
-          const uint32_t bin = gmat.index[j];
-          p_hist[bin].Add(stat[k]);
-        }
-      }
-    }
-    for (size_t i = nrows - rest; i < nrows; ++i) {
-      const size_t rid = row_indices.begin[i];
-      const size_t ibegin = gmat.row_ptr[rid];
-      const size_t iend = gmat.row_ptr[rid + 1];
-      const GradientPair stat = gpair[rid];
-      for (size_t j = ibegin; j < iend; ++j) {
-        const uint32_t bin = gmat.index[j];
-        p_hist[bin].Add(stat);
-      }
-    }
-  }
-}
-
-void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
-  const size_t size = self.size();
-  CHECK_EQ(sibling.size(), size);
-  CHECK_EQ(parent.size(), size);
-
-  const size_t block_size = 1024;  // aproximatly 1024 values per block
-  size_t n_blocks = size/block_size + !!(size%block_size);
-
-#pragma omp parallel for
-  for (omp_ulong iblock = 0; iblock < n_blocks; ++iblock) {
-    const size_t ibegin = iblock*block_size;
-    const size_t iend = (((iblock+1)*block_size > size) ? size : ibegin + block_size);
-    SubtractionHist(self, parent, sibling, ibegin, iend);
-  }
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/hist_util.cu b/ml-xgboost/src/common/hist_util.cu
deleted file mode 100644
index ca07f83..0000000
--- a/ml-xgboost/src/common/hist_util.cu
+++ /dev/null
@@ -1,476 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-
-#include <xgboost/logging.h>
-
-#include <thrust/copy.h>
-#include <thrust/functional.h>
-#include <thrust/iterator/counting_iterator.h>
-#include <thrust/iterator/transform_iterator.h>
-#include <thrust/reduce.h>
-#include <thrust/sort.h>
-#include <thrust/binary_search.h>
-#include <thrust/execution_policy.h>
-
-#include <memory>
-#include <mutex>
-#include <utility>
-#include <vector>
-
-#include "../data/adapter.h"
-#include "../data/device_adapter.cuh"
-#include "device_helpers.cuh"
-#include "hist_util.h"
-#include "math.h"  // NOLINT
-#include "quantile.h"
-#include "xgboost/host_device_vector.h"
-
-
-namespace xgboost {
-namespace common {
-
-using WQSketch = DenseCuts::WQSketch;
-using SketchEntry = WQSketch::Entry;
-
-/*!
- * \brief A container that holds the device sketches across all
- *  sparse page batches which are distributed to different devices.
- *  As sketches are aggregated by column, the mutex guards
- *  multiple devices pushing sketch summary for the same column
- *  across distinct rows.
- */
-struct SketchContainer {
-  std::vector<DenseCuts::WQSketch> sketches_;  // NOLINT
-  static constexpr int kOmpNumColsParallelizeLimit = 1000;
-
-  SketchContainer(int max_bin, size_t num_columns, size_t num_rows) {
-    // Initialize Sketches for this dmatrix
-    sketches_.resize(num_columns);
-#pragma omp parallel for schedule(static) if (num_columns > kOmpNumColsParallelizeLimit)  // NOLINT
-    for (int icol = 0; icol < num_columns; ++icol) {                 // NOLINT
-      sketches_[icol].Init(num_rows, 1.0 / (8 * max_bin));
-    }
-  }
-
-  /**
-   * \brief Pushes cuts to the sketches.
-   *
-   * \param entries_per_column  The entries per column.
-   * \param entries             Vector of cuts from all columns, length
-   * entries_per_column * num_columns. \param column_scan         Exclusive scan
-   * of column sizes. Used to detect cases where there are fewer entries than we
-   * have storage for.
-   */
-  void Push(size_t entries_per_column,
-            const thrust::host_vector<SketchEntry>& entries,
-            const thrust::host_vector<size_t>& column_scan) {
-#pragma omp parallel for schedule(static) if (sketches_.size() > SketchContainer::kOmpNumColsParallelizeLimit)  // NOLINT
-    for (int icol = 0; icol < sketches_.size(); ++icol) {
-      size_t column_size = column_scan[icol + 1] - column_scan[icol];
-      if (column_size == 0) continue;
-      WQuantileSketch<bst_float, bst_float>::SummaryContainer summary;
-      size_t num_available_cuts =
-          std::min(size_t(entries_per_column), column_size);
-      summary.Reserve(num_available_cuts);
-      summary.MakeFromSorted(&entries[entries_per_column * icol],
-                             num_available_cuts);
-
-      sketches_[icol].PushSummary(summary);
-    }
-  }
-
-  // Prevent copying/assigning/moving this as its internals can't be
-  // assigned/copied/moved
-  SketchContainer(const SketchContainer&) = delete;
-  SketchContainer(const SketchContainer&&) = delete;
-  SketchContainer& operator=(const SketchContainer&) = delete;
-  SketchContainer& operator=(const SketchContainer&&) = delete;
-};
-
-struct EntryCompareOp {
-  __device__ bool operator()(const Entry& a, const Entry& b) {
-    if (a.index == b.index) {
-      return a.fvalue < b.fvalue;
-    }
-    return a.index < b.index;
-  }
-};
-
-// Compute number of sample cuts needed on local node to maintain accuracy
-// We take more cuts than needed and then reduce them later
-size_t RequiredSampleCuts(int max_bins, size_t num_rows) {
-  constexpr int kFactor = 8;
-  double eps = 1.0 / (kFactor * max_bins);
-  size_t dummy_nlevel;
-  size_t num_cuts;
-  WQuantileSketch<bst_float, bst_float>::LimitSizeLevel(
-      num_rows, eps, &dummy_nlevel, &num_cuts);
-  return std::min(num_cuts, num_rows);
-}
-
-
-// Count the entries in each column and exclusive scan
-void GetColumnSizesScan(int device,
-                        dh::caching_device_vector<size_t>* column_sizes_scan,
-                        Span<const Entry> entries, size_t num_columns) {
-  column_sizes_scan->resize(num_columns + 1, 0);
-  auto d_column_sizes_scan = column_sizes_scan->data().get();
-  auto d_entries = entries.data();
-  dh::LaunchN(device, entries.size(), [=] __device__(size_t idx) {
-    auto& e = d_entries[idx];
-    atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                  &d_column_sizes_scan[e.index]),
-              static_cast<unsigned long long>(1));  // NOLINT
-  });
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  thrust::exclusive_scan(thrust::cuda::par(alloc), column_sizes_scan->begin(),
-                         column_sizes_scan->end(), column_sizes_scan->begin());
-}
-
-/**
- * \brief Extracts the cuts from sorted data.
- *
- * \param device                The device.
- * \param cuts                  Output cuts
- * \param num_cuts_per_feature  Number of cuts per feature.
- * \param sorted_data           Sorted entries in segments of columns
- * \param column_sizes_scan     Describes the boundaries of column segments in
- * sorted data
- */
-void ExtractCuts(int device,
-                 size_t num_cuts_per_feature,
-                 Span<Entry const> sorted_data,
-                 Span<size_t const> column_sizes_scan,
-                 Span<SketchEntry> out_cuts) {
-  dh::LaunchN(device, out_cuts.size(), [=] __device__(size_t idx) {
-    // Each thread is responsible for obtaining one cut from the sorted input
-    size_t column_idx = idx / num_cuts_per_feature;
-    size_t column_size =
-        column_sizes_scan[column_idx + 1] - column_sizes_scan[column_idx];
-    size_t num_available_cuts =
-        min(static_cast<size_t>(num_cuts_per_feature), column_size);
-    size_t cut_idx = idx % num_cuts_per_feature;
-    if (cut_idx >= num_available_cuts) return;
-    Span<Entry const> column_entries =
-        sorted_data.subspan(column_sizes_scan[column_idx], column_size);
-    size_t rank = (column_entries.size() * cut_idx) /
-                  static_cast<float>(num_available_cuts);
-    out_cuts[idx] = WQSketch::Entry(rank, rank + 1, 1,
-                                    column_entries[rank].fvalue);
-  });
-}
-
-/**
- * \brief Extracts the cuts from sorted data, considering weights.
- *
- * \param device                The device.
- * \param cuts                  Output cuts.
- * \param num_cuts_per_feature  Number of cuts per feature.
- * \param sorted_data           Sorted entries in segments of columns.
- * \param weights_scan          Inclusive scan of weights for each entry in sorted_data.
- * \param column_sizes_scan     Describes the boundaries of column segments in sorted data.
- */
-void ExtractWeightedCuts(int device,
-                         size_t num_cuts_per_feature,
-                         Span<Entry> sorted_data,
-                         Span<float> weights_scan,
-                         Span<size_t> column_sizes_scan,
-                         Span<SketchEntry> cuts) {
-  dh::LaunchN(device, cuts.size(), [=] __device__(size_t idx) {
-    // Each thread is responsible for obtaining one cut from the sorted input
-    size_t column_idx = idx / num_cuts_per_feature;
-    size_t column_size =
-        column_sizes_scan[column_idx + 1] - column_sizes_scan[column_idx];
-    size_t num_available_cuts =
-        min(static_cast<size_t>(num_cuts_per_feature), column_size);
-    size_t cut_idx = idx % num_cuts_per_feature;
-    if (cut_idx >= num_available_cuts) return;
-    Span<Entry> column_entries =
-        sorted_data.subspan(column_sizes_scan[column_idx], column_size);
-
-    Span<float> column_weights_scan =
-        weights_scan.subspan(column_sizes_scan[column_idx], column_size);
-    float total_column_weight = column_weights_scan.back();
-    size_t sample_idx = 0;
-    if (cut_idx == 0) {
-      // First cut
-      sample_idx = 0;
-    } else if (cut_idx == num_available_cuts) {
-      // Last cut
-      sample_idx = column_entries.size() - 1;
-    } else if (num_available_cuts == column_size) {
-      // There are less samples available than our buffer
-      // Take every available sample
-      sample_idx = cut_idx;
-    } else {
-      bst_float rank = (total_column_weight * cut_idx) /
-                       static_cast<float>(num_available_cuts);
-      sample_idx = thrust::upper_bound(thrust::seq,
-                                       column_weights_scan.begin(),
-                                       column_weights_scan.end(),
-                                       rank) -
-                   column_weights_scan.begin();
-      sample_idx =
-          max(static_cast<size_t>(0),
-              min(sample_idx, column_entries.size() - 1));
-    }
-    // repeated values will be filtered out on the CPU
-    bst_float rmin = sample_idx > 0 ? column_weights_scan[sample_idx - 1] : 0.0f;
-    bst_float rmax = column_weights_scan[sample_idx];
-    cuts[idx] = WQSketch::Entry(rmin, rmax, rmax - rmin,
-                                column_entries[sample_idx].fvalue);
-  });
-}
-
-void ProcessBatch(int device, const SparsePage& page, size_t begin, size_t end,
-                  SketchContainer* sketch_container, int num_cuts,
-                  size_t num_columns) {
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  const auto& host_data = page.data.ConstHostVector();
-  dh::caching_device_vector<Entry> sorted_entries(host_data.begin() + begin,
-                                                  host_data.begin() + end);
-  thrust::sort(thrust::cuda::par(alloc), sorted_entries.begin(),
-               sorted_entries.end(), EntryCompareOp());
-
-  dh::caching_device_vector<size_t> column_sizes_scan;
-  GetColumnSizesScan(device, &column_sizes_scan,
-                     {sorted_entries.data().get(), sorted_entries.size()},
-                     num_columns);
-  thrust::host_vector<size_t> host_column_sizes_scan(column_sizes_scan);
-
-  dh::caching_device_vector<SketchEntry> cuts(num_columns * num_cuts);
-  ExtractCuts(device, num_cuts,
-              dh::ToSpan(sorted_entries),
-              dh::ToSpan(column_sizes_scan),
-              dh::ToSpan(cuts));
-
-  // add cuts into sketches
-  thrust::host_vector<SketchEntry> host_cuts(cuts);
-  sketch_container->Push(num_cuts, host_cuts, host_column_sizes_scan);
-}
-
-void ProcessWeightedBatch(int device, const SparsePage& page,
-                          Span<const float> weights, size_t begin, size_t end,
-                          SketchContainer* sketch_container, int num_cuts_per_feature,
-                          size_t num_columns,
-                          bool is_ranking, Span<bst_group_t const> d_group_ptr) {
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  const auto& host_data = page.data.ConstHostVector();
-  dh::caching_device_vector<Entry> sorted_entries(host_data.begin() + begin,
-                                                  host_data.begin() + end);
-
-  // Binary search to assign weights to each element
-  dh::caching_device_vector<float> temp_weights(sorted_entries.size());
-  auto d_temp_weights = temp_weights.data().get();
-  page.offset.SetDevice(device);
-  auto row_ptrs = page.offset.ConstDeviceSpan();
-  size_t base_rowid = page.base_rowid;
-  if (is_ranking) {
-    CHECK_GE(d_group_ptr.size(), 2)
-        << "Must have at least 1 group for ranking.";
-    CHECK_EQ(weights.size(), d_group_ptr.size() - 1)
-        << "Weight size should equal to number of groups.";
-    dh::LaunchN(device, temp_weights.size(), [=] __device__(size_t idx) {
-        size_t element_idx = idx + begin;
-        size_t ridx = thrust::upper_bound(thrust::seq, row_ptrs.begin(),
-                                          row_ptrs.end(), element_idx) -
-                      row_ptrs.begin() - 1;
-        auto it =
-            thrust::upper_bound(thrust::seq,
-                                d_group_ptr.cbegin(), d_group_ptr.cend(),
-                                ridx + base_rowid) - 1;
-        bst_group_t group = thrust::distance(d_group_ptr.cbegin(), it);
-        d_temp_weights[idx] = weights[group];
-      });
-  } else {
-    CHECK_EQ(weights.size(), page.offset.Size() - 1);
-    dh::LaunchN(device, temp_weights.size(), [=] __device__(size_t idx) {
-        size_t element_idx = idx + begin;
-        size_t ridx = thrust::upper_bound(thrust::seq, row_ptrs.begin(),
-                                          row_ptrs.end(), element_idx) -
-                      row_ptrs.begin() - 1;
-        d_temp_weights[idx] = weights[ridx + base_rowid];
-      });
-  }
-
-  // Sort both entries and wegihts.
-  thrust::sort_by_key(thrust::cuda::par(alloc), sorted_entries.begin(),
-                      sorted_entries.end(), temp_weights.begin(),
-                      EntryCompareOp());
-
-  // Scan weights
-  thrust::inclusive_scan_by_key(thrust::cuda::par(alloc),
-                                sorted_entries.begin(), sorted_entries.end(),
-                                temp_weights.begin(), temp_weights.begin(),
-                                [=] __device__(const Entry& a, const Entry& b) {
-                                  return a.index == b.index;
-                                });
-
-  dh::caching_device_vector<size_t> column_sizes_scan;
-  GetColumnSizesScan(device, &column_sizes_scan,
-                     {sorted_entries.data().get(), sorted_entries.size()},
-                     num_columns);
-  thrust::host_vector<size_t> host_column_sizes_scan(column_sizes_scan);
-
-  // Extract cuts
-  dh::caching_device_vector<SketchEntry> cuts(num_columns * num_cuts_per_feature);
-  ExtractWeightedCuts(device, num_cuts_per_feature,
-                      dh::ToSpan(sorted_entries),
-                      dh::ToSpan(temp_weights),
-                      dh::ToSpan(column_sizes_scan),
-                      dh::ToSpan(cuts));
-
-  // add cuts into sketches
-  thrust::host_vector<SketchEntry> host_cuts(cuts);
-  sketch_container->Push(num_cuts_per_feature, host_cuts, host_column_sizes_scan);
-}
-
-HistogramCuts DeviceSketch(int device, DMatrix* dmat, int max_bins,
-                           size_t sketch_batch_num_elements) {
-  // Configure batch size based on available memory
-  bool has_weights = dmat->Info().weights_.Size() > 0;
-  size_t num_cuts_per_feature = RequiredSampleCuts(max_bins, dmat->Info().num_row_);
-  if (sketch_batch_num_elements == 0) {
-    int bytes_per_element = has_weights ? 24 : 16;
-    size_t bytes_cuts = num_cuts_per_feature * dmat->Info().num_col_ * sizeof(SketchEntry);
-    // use up to 80% of available space
-    sketch_batch_num_elements =
-        (dh::AvailableMemory(device) - bytes_cuts) * 0.8 / bytes_per_element;
-  }
-
-  HistogramCuts cuts;
-  DenseCuts dense_cuts(&cuts);
-  SketchContainer sketch_container(max_bins, dmat->Info().num_col_,
-                                   dmat->Info().num_row_);
-
-  dmat->Info().weights_.SetDevice(device);
-  for (const auto& batch : dmat->GetBatches<SparsePage>()) {
-    size_t batch_nnz = batch.data.Size();
-    auto const& info = dmat->Info();
-    dh::caching_device_vector<uint32_t> groups(info.group_ptr_.cbegin(),
-                                               info.group_ptr_.cend());
-    for (auto begin = 0ull; begin < batch_nnz; begin += sketch_batch_num_elements) {
-      size_t end = std::min(batch_nnz, size_t(begin + sketch_batch_num_elements));
-      if (has_weights) {
-        bool is_ranking = CutsBuilder::UseGroup(dmat);
-        ProcessWeightedBatch(
-            device, batch, dmat->Info().weights_.ConstDeviceSpan(), begin, end,
-            &sketch_container,
-            num_cuts_per_feature,
-            dmat->Info().num_col_,
-            is_ranking, dh::ToSpan(groups));
-      } else {
-        ProcessBatch(device, batch, begin, end, &sketch_container, num_cuts_per_feature,
-                     dmat->Info().num_col_);
-      }
-    }
-  }
-
-  dense_cuts.Init(&sketch_container.sketches_, max_bins, dmat->Info().num_row_);
-  return cuts;
-}
-
-template <typename AdapterT>
-void ProcessBatch(AdapterT* adapter, size_t begin, size_t end, float missing,
-                  SketchContainer* sketch_container, int num_cuts) {
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  adapter->BeforeFirst();
-  adapter->Next();
-  auto &batch = adapter->Value();
-  // Enforce single batch
-  CHECK(!adapter->Next());
-  auto batch_iter = dh::MakeTransformIterator<data::COOTuple>(
-    thrust::make_counting_iterator(0llu),
-    [=] __device__(size_t idx) { return batch.GetElement(idx); });
-  auto entry_iter = dh::MakeTransformIterator<Entry>(
-      thrust::make_counting_iterator(0llu), [=] __device__(size_t idx) {
-        return Entry(batch.GetElement(idx).column_idx,
-                     batch.GetElement(idx).value);
-      });
-  // Work out how many valid entries we have in each column
-  dh::caching_device_vector<size_t> column_sizes_scan(adapter->NumColumns() + 1,
-                                                      0);
-
-  auto d_column_sizes_scan = column_sizes_scan.data().get();
-  data::IsValidFunctor is_valid(missing);
-  dh::LaunchN(adapter->DeviceIdx(), end - begin, [=] __device__(size_t idx) {
-    auto e = batch_iter[begin + idx];
-    if (is_valid(e)) {
-      atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                    &d_column_sizes_scan[e.column_idx]),
-                static_cast<unsigned long long>(1));  // NOLINT
-    }
-  });
-  thrust::exclusive_scan(thrust::cuda::par(alloc), column_sizes_scan.begin(),
-                         column_sizes_scan.end(), column_sizes_scan.begin());
-  thrust::host_vector<size_t> host_column_sizes_scan(column_sizes_scan);
-  size_t num_valid = host_column_sizes_scan.back();
-
-  // Copy current subset of valid elements into temporary storage and sort
-  dh::caching_device_vector<Entry> sorted_entries(num_valid);
-  thrust::copy_if(thrust::cuda::par(alloc), entry_iter + begin,
-                  entry_iter + end, sorted_entries.begin(), is_valid);
-  thrust::sort(thrust::cuda::par(alloc), sorted_entries.begin(),
-               sorted_entries.end(), EntryCompareOp());
-
-  // Extract the cuts from all columns concurrently
-  dh::caching_device_vector<SketchEntry> cuts(adapter->NumColumns() * num_cuts);
-  ExtractCuts(adapter->DeviceIdx(), num_cuts,
-              dh::ToSpan(sorted_entries),
-              dh::ToSpan(column_sizes_scan),
-              dh::ToSpan(cuts));
-
-  // Push cuts into sketches stored in host memory
-  thrust::host_vector<SketchEntry> host_cuts(cuts);
-  sketch_container->Push(num_cuts, host_cuts, host_column_sizes_scan);
-}
-
-template <typename AdapterT>
-HistogramCuts AdapterDeviceSketch(AdapterT* adapter, int num_bins,
-                                  float missing,
-                                  size_t sketch_batch_num_elements) {
-  size_t num_cuts = RequiredSampleCuts(num_bins, adapter->NumRows());
-  if (sketch_batch_num_elements == 0) {
-    int bytes_per_element = 16;
-    size_t bytes_cuts = num_cuts * adapter->NumColumns() * sizeof(SketchEntry);
-    size_t bytes_num_columns = (adapter->NumColumns() + 1) * sizeof(size_t);
-    // use up to 80% of available space
-    sketch_batch_num_elements = (dh::AvailableMemory(adapter->DeviceIdx()) -
-                                 bytes_cuts - bytes_num_columns) *
-                                0.8 / bytes_per_element;
-  }
-
-  CHECK(adapter->NumRows() != data::kAdapterUnknownSize);
-  CHECK(adapter->NumColumns() != data::kAdapterUnknownSize);
-
-  adapter->BeforeFirst();
-  adapter->Next();
-  auto& batch = adapter->Value();
-
-  // Enforce single batch
-  CHECK(!adapter->Next());
-
-  HistogramCuts cuts;
-  DenseCuts dense_cuts(&cuts);
-  SketchContainer sketch_container(num_bins, adapter->NumColumns(),
-                                   adapter->NumRows());
-
-  for (auto begin = 0ull; begin < batch.Size();
-       begin += sketch_batch_num_elements) {
-    size_t end = std::min(batch.Size(), size_t(begin + sketch_batch_num_elements));
-    ProcessBatch(adapter, begin, end, missing, &sketch_container, num_cuts);
-  }
-
-  dense_cuts.Init(&sketch_container.sketches_, num_bins, adapter->NumRows());
-  return cuts;
-}
-
-template HistogramCuts AdapterDeviceSketch(data::CudfAdapter* adapter,
-                                           int num_bins, float missing,
-                                           size_t sketch_batch_size);
-template HistogramCuts AdapterDeviceSketch(data::CupyAdapter* adapter,
-                                           int num_bins, float missing,
-                                           size_t sketch_batch_size);
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/hist_util.h b/ml-xgboost/src/common/hist_util.h
deleted file mode 100644
index 5e47976..0000000
--- a/ml-xgboost/src/common/hist_util.h
+++ /dev/null
@@ -1,703 +0,0 @@
-/*!
- * Copyright 2017-2020 by Contributors
- * \file hist_util.h
- * \brief Utility for fast histogram aggregation
- * \author Philip Cho, Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_HIST_UTIL_H_
-#define XGBOOST_COMMON_HIST_UTIL_H_
-
-#include <xgboost/data.h>
-#include <xgboost/generic_parameters.h>
-#include <limits>
-#include <vector>
-#include <algorithm>
-#include <memory>
-#include <utility>
-#include <map>
-
-#include "row_set.h"
-#include "threading_utils.h"
-#include "../tree/param.h"
-#include "./quantile.h"
-#include "./timer.h"
-#include "../include/rabit/rabit.h"
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief A single row in global histogram index.
- *  Directly represent the global index in the histogram entry.
- */
-using GHistIndexRow = Span<uint32_t const>;
-
-// A CSC matrix representing histogram cuts, used in CPU quantile hist.
-// The cut values represent upper bounds of bins containing approximately equal numbers of elements
-class HistogramCuts {
-  // Using friends to avoid creating a virtual class, since HistogramCuts is used as value
-  // object in many places.
-  friend class SparseCuts;
-  friend class DenseCuts;
-  friend class CutsBuilder;
-
- protected:
-  using BinIdx = uint32_t;
-  common::Monitor monitor_;
-
- public:
-  HostDeviceVector<bst_float> cut_values_;  // NOLINT
-  HostDeviceVector<uint32_t> cut_ptrs_;     // NOLINT
-  // storing minimum value in a sketch set.
-  HostDeviceVector<float> min_vals_;  // NOLINT
-
-  HistogramCuts();
-  HistogramCuts(HistogramCuts const& that) {
-    cut_values_.Resize(that.cut_values_.Size());
-    cut_ptrs_.Resize(that.cut_ptrs_.Size());
-    min_vals_.Resize(that.min_vals_.Size());
-    cut_values_.Copy(that.cut_values_);
-    cut_ptrs_.Copy(that.cut_ptrs_);
-    min_vals_.Copy(that.min_vals_);
-  }
-
-  HistogramCuts(HistogramCuts&& that) noexcept(true) {
-    *this = std::forward<HistogramCuts&&>(that);
-  }
-
-  HistogramCuts& operator=(HistogramCuts const& that) {
-    cut_values_.Resize(that.cut_values_.Size());
-    cut_ptrs_.Resize(that.cut_ptrs_.Size());
-    min_vals_.Resize(that.min_vals_.Size());
-    cut_values_.Copy(that.cut_values_);
-    cut_ptrs_.Copy(that.cut_ptrs_);
-    min_vals_.Copy(that.min_vals_);
-    return *this;
-  }
-
-  HistogramCuts& operator=(HistogramCuts&& that) noexcept(true) {
-    monitor_ = std::move(that.monitor_);
-    cut_ptrs_ = std::move(that.cut_ptrs_);
-    cut_values_ = std::move(that.cut_values_);
-    min_vals_ = std::move(that.min_vals_);
-    return *this;
-  }
-
-  /* \brief Build histogram cuts. */
-  void Build(DMatrix* dmat, uint32_t const max_num_bins);
-  /* \brief How many bins a feature has. */
-  uint32_t FeatureBins(uint32_t feature) const {
-    return cut_ptrs_.ConstHostVector().at(feature + 1) -
-           cut_ptrs_.ConstHostVector()[feature];
-  }
-
-  // Getters.  Cuts should be of no use after building histogram indices, but currently
-  // it's deeply linked with quantile_hist, gpu sketcher and gpu_hist.  So we preserve
-  // these for now.
-  std::vector<uint32_t> const& Ptrs()      const { return cut_ptrs_.ConstHostVector();   }
-  std::vector<float>    const& Values()    const { return cut_values_.ConstHostVector(); }
-  std::vector<float>    const& MinValues() const { return min_vals_.ConstHostVector();   }
-
-  size_t TotalBins() const { return cut_ptrs_.ConstHostVector().back(); }
-
-  // Return the index of a cut point that is strictly greater than the input
-  // value, or the last available index if none exists
-  BinIdx SearchBin(float value, uint32_t column_id) const {
-    auto beg = cut_ptrs_.ConstHostVector().at(column_id);
-    auto end = cut_ptrs_.ConstHostVector().at(column_id + 1);
-    const auto &values = cut_values_.ConstHostVector();
-    auto it = std::upper_bound(values.cbegin() + beg, values.cbegin() + end, value);
-    BinIdx idx = it - values.cbegin();
-    if (idx == end) {
-      idx -= 1;
-    }
-    return idx;
-  }
-
-  BinIdx SearchBin(Entry const& e) const {
-    return SearchBin(e.fvalue, e.index);
-  }
-};
-
-/* \brief An interface for building quantile cuts.
- *
- * `DenseCuts' always assumes there are `max_bins` for each feature, which makes it not
- * suitable for sparse dataset.  On the other hand `SparseCuts' uses `GetColumnBatches',
- * which doubles the memory usage, hence can not be applied to dense dataset.
- */
-class CutsBuilder {
- public:
-  using WQSketch = common::WQuantileSketch<bst_float, bst_float>;
-  /* \brief return whether group for ranking is used. */
-  static bool UseGroup(DMatrix* dmat);
-
- protected:
-  HistogramCuts* p_cuts_;
-
- public:
-  explicit CutsBuilder(HistogramCuts* p_cuts) : p_cuts_{p_cuts} {}
-  virtual ~CutsBuilder() = default;
-
-  static uint32_t SearchGroupIndFromRow(
-      std::vector<bst_uint> const& group_ptr, size_t const base_rowid) {
-    using KIt = std::vector<bst_uint>::const_iterator;
-    KIt res = std::lower_bound(group_ptr.cbegin(), group_ptr.cend() - 1, base_rowid);
-    // Cannot use CHECK_NE because it will try to print the iterator.
-    bool const found = res != group_ptr.cend() - 1;
-    if (!found) {
-      LOG(FATAL) << "Row " << base_rowid << " does not lie in any group!";
-    }
-    uint32_t group_ind = std::distance(group_ptr.cbegin(), res);
-    return group_ind;
-  }
-
-  void AddCutPoint(WQSketch::SummaryContainer const& summary, int max_bin) {
-    size_t required_cuts = std::min(summary.size, static_cast<size_t>(max_bin));
-    for (size_t i = 1; i < required_cuts; ++i) {
-      bst_float cpt = summary.data[i].value;
-      if (i == 1 || cpt > p_cuts_->cut_values_.ConstHostVector().back()) {
-        p_cuts_->cut_values_.HostVector().push_back(cpt);
-      }
-    }
-  }
-
-  /* \brief Build histogram indices. */
-  virtual void Build(DMatrix* dmat, uint32_t const max_num_bins) = 0;
-};
-
-/*! \brief Cut configuration for sparse dataset. */
-class SparseCuts : public CutsBuilder {
-  /* \brief Distrbute columns to each thread according to number of entries. */
-  static std::vector<size_t> LoadBalance(SparsePage const& page, size_t const nthreads);
-  Monitor monitor_;
-
- public:
-  explicit SparseCuts(HistogramCuts* container) :
-      CutsBuilder(container) {
-    monitor_.Init(__FUNCTION__);
-  }
-
-  /* \brief Concatonate the built cuts in each thread. */
-  void Concat(std::vector<std::unique_ptr<SparseCuts>> const& cuts, uint32_t n_cols);
-  /* \brief Build histogram indices in single thread. */
-  void SingleThreadBuild(SparsePage const& page, MetaInfo const& info,
-                         uint32_t max_num_bins,
-                         bool const use_group_ind,
-                         uint32_t beg, uint32_t end, uint32_t thread_id);
-  void Build(DMatrix* dmat, uint32_t const max_num_bins) override;
-};
-
-/*! \brief Cut configuration for dense dataset. */
-class DenseCuts  : public CutsBuilder {
- protected:
-  Monitor monitor_;
-
- public:
-  explicit DenseCuts(HistogramCuts* container) :
-      CutsBuilder(container) {
-    monitor_.Init(__FUNCTION__);
-  }
-  void Init(std::vector<WQSketch>* sketchs, uint32_t max_num_bins, size_t max_rows);
-  void Build(DMatrix* p_fmat, uint32_t max_num_bins) override;
-};
-
-// sketch_batch_num_elements 0 means autodetect. Only modify this for testing.
-HistogramCuts DeviceSketch(int device, DMatrix* dmat, int max_bins,
-                           size_t sketch_batch_num_elements = 0);
-
-// sketch_batch_num_elements 0 means autodetect. Only modify this for testing.
-template <typename AdapterT>
-HistogramCuts AdapterDeviceSketch(AdapterT* adapter, int num_bins,
-                                  float missing,
-                                  size_t sketch_batch_num_elements = 0);
-
-
-enum BinTypeSize {
-  kUint8BinsTypeSize  = 1,
-  kUint16BinsTypeSize = 2,
-  kUint32BinsTypeSize = 4
-};
-
-struct Index {
-  Index() {
-    SetBinTypeSize(binTypeSize_);
-  }
-  Index(const Index& i) = delete;
-  Index& operator=(Index i) = delete;
-  Index(Index&& i) = delete;
-  Index& operator=(Index&& i) = delete;
-  uint32_t operator[](size_t i) const {
-    if (offset_ptr_ != nullptr) {
-      return func_(data_ptr_, i) + offset_ptr_[i%p_];
-    } else {
-      return func_(data_ptr_, i);
-    }
-  }
-  void SetBinTypeSize(BinTypeSize binTypeSize) {
-    binTypeSize_ = binTypeSize;
-    switch (binTypeSize) {
-      case kUint8BinsTypeSize:
-        func_ = &GetValueFromUint8;
-        break;
-      case kUint16BinsTypeSize:
-        func_ = &GetValueFromUint16;
-        break;
-      case kUint32BinsTypeSize:
-        func_ = &GetValueFromUint32;
-        break;
-      default:
-        CHECK(binTypeSize == kUint8BinsTypeSize  ||
-              binTypeSize == kUint16BinsTypeSize ||
-              binTypeSize == kUint32BinsTypeSize);
-    }
-  }
-  BinTypeSize GetBinTypeSize() const {
-    return binTypeSize_;
-  }
-  template<typename T>
-  T* data() const {  // NOLINT
-    return static_cast<T*>(data_ptr_);
-  }
-  uint32_t* Offset() const {
-    return offset_ptr_;
-  }
-  size_t OffsetSize() const {
-    return offset_.size();
-  }
-  size_t Size() const {
-    return data_.size() / (binTypeSize_);
-  }
-  void Resize(const size_t nBytesData) {
-    data_.resize(nBytesData);
-    data_ptr_ = reinterpret_cast<void*>(data_.data());
-  }
-  void ResizeOffset(const size_t nDisps) {
-    offset_.resize(nDisps);
-    offset_ptr_ = offset_.data();
-    p_ = nDisps;
-  }
-  std::vector<uint8_t>::const_iterator begin() const {  // NOLINT
-    return data_.begin();
-  }
-  std::vector<uint8_t>::const_iterator end() const {  // NOLINT
-    return data_.end();
-  }
-
- private:
-  static uint32_t GetValueFromUint8(void *t, size_t i) {
-    return reinterpret_cast<uint8_t*>(t)[i];
-  }
-  static uint32_t GetValueFromUint16(void* t, size_t i) {
-    return reinterpret_cast<uint16_t*>(t)[i];
-  }
-  static uint32_t GetValueFromUint32(void* t, size_t i) {
-    return reinterpret_cast<uint32_t*>(t)[i];
-  }
-
-  using Func = uint32_t (*)(void*, size_t);
-
-  std::vector<uint8_t> data_;
-  std::vector<uint32_t> offset_;  // size of this field is equal to number of features
-  void* data_ptr_;
-  BinTypeSize binTypeSize_ {kUint8BinsTypeSize};
-  size_t p_ {1};
-  uint32_t* offset_ptr_ {nullptr};
-  Func func_;
-};
-
-
-/*!
- * \brief preprocessed global index matrix, in CSR format
- *
- *  Transform floating values to integer index in histogram This is a global histogram
- *  index for CPU histogram.  On GPU ellpack page is used.
- */
-struct GHistIndexMatrix {
-  /*! \brief row pointer to rows by element position */
-  std::vector<size_t> row_ptr;
-  /*! \brief The index data */
-  Index index;
-  /*! \brief hit count of each index */
-  std::vector<size_t> hit_count;
-  /*! \brief The corresponding cuts */
-  HistogramCuts cut;
-  DMatrix* p_fmat;
-  size_t max_num_bins;
-  // Create a global histogram matrix, given cut
-  void Init(DMatrix* p_fmat, int max_num_bins);
-
-  template<typename BinIdxType>
-  void SetIndexDataForDense(common::Span<BinIdxType> index_data_span,
-                    size_t batch_threads, const SparsePage& batch,
-                    size_t rbegin, common::Span<const uint32_t> offsets_span,
-                    size_t nbins);
-
-  // specific method for sparse data as no posibility to reduce allocated memory
-  void SetIndexDataForSparse(common::Span<uint32_t> index_data_span,
-                             size_t batch_threads, const SparsePage& batch,
-                             size_t rbegin, size_t nbins);
-
-  void ResizeIndex(const size_t rbegin, const SparsePage& batch,
-                   const size_t n_offsets, const size_t n_index,
-                   const bool isDense);
-
-  inline void GetFeatureCounts(size_t* counts) const {
-    auto nfeature = cut.Ptrs().size() - 1;
-    for (unsigned fid = 0; fid < nfeature; ++fid) {
-      auto ibegin = cut.Ptrs()[fid];
-      auto iend = cut.Ptrs()[fid + 1];
-      for (auto i = ibegin; i < iend; ++i) {
-        counts[fid] += hit_count[i];
-      }
-    }
-  }
-  inline bool IsDense() const {
-    return isDense_;
-  }
-
- private:
-  std::vector<size_t> hit_count_tloc_;
-  bool isDense_;
-};
-
-struct GHistIndexBlock {
-  const size_t* row_ptr;
-  const uint32_t* index;
-
-  inline GHistIndexBlock(const size_t* row_ptr, const uint32_t* index)
-    : row_ptr(row_ptr), index(index) {}
-
-  // get i-th row
-  inline GHistIndexRow operator[](size_t i) const {
-    return {&index[0] + row_ptr[i], row_ptr[i + 1] - row_ptr[i]};
-  }
-};
-
-class ColumnMatrix;
-
-class GHistIndexBlockMatrix {
- public:
-  void Init(const GHistIndexMatrix& gmat,
-            const ColumnMatrix& colmat,
-            const tree::TrainParam& param);
-
-  inline GHistIndexBlock operator[](size_t i) const {
-    return {blocks_[i].row_ptr_begin, blocks_[i].index_begin};
-  }
-
-  inline size_t GetNumBlock() const {
-    return blocks_.size();
-  }
-
- private:
-  std::vector<size_t> row_ptr_;
-  std::vector<uint32_t> index_;
-  const HistogramCuts* cut_;
-  struct Block {
-    const size_t* row_ptr_begin;
-    const size_t* row_ptr_end;
-    const uint32_t* index_begin;
-    const uint32_t* index_end;
-  };
-  std::vector<Block> blocks_;
-};
-
-/*!
- * \brief histogram of gradient statistics for a single node.
- *  Consists of multiple GradStats, each entry showing total gradient statistics
- *     for that particular bin
- *  Uses global bin id so as to represent all features simultaneously
- */
-using GHistRow = Span<tree::GradStats>;
-
-/*!
- * \brief fill a histogram by zeros
- */
-void InitilizeHistByZeroes(GHistRow hist, size_t begin, size_t end);
-
-/*!
- * \brief Increment hist as dst += add in range [begin, end)
- */
-void IncrementHist(GHistRow dst, const GHistRow add, size_t begin, size_t end);
-
-/*!
- * \brief Copy hist from src to dst in range [begin, end)
- */
-void CopyHist(GHistRow dst, const GHistRow src, size_t begin, size_t end);
-
-/*!
- * \brief Compute Subtraction: dst = src1 - src2 in range [begin, end)
- */
-void SubtractionHist(GHistRow dst, const GHistRow src1, const GHistRow src2,
-                     size_t begin, size_t end);
-
-/*!
- * \brief histogram of gradient statistics for multiple nodes
- */
-class HistCollection {
- public:
-  // access histogram for i-th node
-  GHistRow operator[](bst_uint nid) const {
-    constexpr uint32_t kMax = std::numeric_limits<uint32_t>::max();
-    CHECK_NE(row_ptr_[nid], kMax);
-    tree::GradStats* ptr =
-        const_cast<tree::GradStats*>(dmlc::BeginPtr(data_) + row_ptr_[nid]);
-    return {ptr, nbins_};
-  }
-
-  // have we computed a histogram for i-th node?
-  bool RowExists(bst_uint nid) const {
-    const uint32_t k_max = std::numeric_limits<uint32_t>::max();
-    return (nid < row_ptr_.size() && row_ptr_[nid] != k_max);
-  }
-
-  // initialize histogram collection
-  void Init(uint32_t nbins) {
-    if (nbins_ != nbins) {
-      nbins_ = nbins;
-      // quite expensive operation, so let's do this only once
-      data_.clear();
-    }
-    row_ptr_.clear();
-    n_nodes_added_ = 0;
-  }
-
-  // create an empty histogram for i-th node
-  void AddHistRow(bst_uint nid) {
-    constexpr uint32_t kMax = std::numeric_limits<uint32_t>::max();
-    if (nid >= row_ptr_.size()) {
-      row_ptr_.resize(nid + 1, kMax);
-    }
-    CHECK_EQ(row_ptr_[nid], kMax);
-
-    if (data_.size() < nbins_ * (nid + 1)) {
-      data_.resize(nbins_ * (nid + 1));
-    }
-
-    row_ptr_[nid] = nbins_ * n_nodes_added_;
-    n_nodes_added_++;
-  }
-
- private:
-  /*! \brief number of all bins over all features */
-  uint32_t nbins_ = 0;
-  /*! \brief amount of active nodes in hist collection */
-  uint32_t n_nodes_added_ = 0;
-
-  std::vector<tree::GradStats> data_;
-
-  /*! \brief row_ptr_[nid] locates bin for histogram of node nid */
-  std::vector<size_t> row_ptr_;
-};
-
-/*!
- * \brief Stores temporary histograms to compute them in parallel
- * Supports processing multiple tree-nodes for nested parallelism
- * Able to reduce histograms across threads in efficient way
- */
-class ParallelGHistBuilder {
- public:
-  void Init(size_t nbins) {
-    if (nbins != nbins_) {
-      hist_buffer_.Init(nbins);
-      nbins_ = nbins;
-    }
-  }
-
-  // Add new elements if needed, mark all hists as unused
-  // targeted_hists - already allocated hists which should contain final results after Reduce() call
-  void Reset(size_t nthreads, size_t nodes, const BlockedSpace2d& space,
-             const std::vector<GHistRow>& targeted_hists) {
-    hist_buffer_.Init(nbins_);
-    tid_nid_to_hist_.clear();
-    hist_memory_.clear();
-    threads_to_nids_map_.clear();
-
-    targeted_hists_ = targeted_hists;
-
-    CHECK_EQ(nodes, targeted_hists.size());
-
-    nodes_    = nodes;
-    nthreads_ = nthreads;
-
-    MatchThreadsToNodes(space);
-    AllocateAdditionalHistograms();
-    MatchNodeNidPairToHist();
-
-    hist_was_used_.resize(nthreads * nodes_);
-    std::fill(hist_was_used_.begin(), hist_was_used_.end(), static_cast<int>(false));
-  }
-
-  // Get specified hist, initialize hist by zeros if it wasn't used before
-  GHistRow GetInitializedHist(size_t tid, size_t nid) {
-    CHECK_LT(nid, nodes_);
-    CHECK_LT(tid, nthreads_);
-
-    size_t idx = tid_nid_to_hist_.at({tid, nid});
-    GHistRow hist = hist_memory_[idx];
-
-    if (!hist_was_used_[tid * nodes_ + nid]) {
-      InitilizeHistByZeroes(hist, 0, hist.size());
-      hist_was_used_[tid * nodes_ + nid] = static_cast<int>(true);
-    }
-
-    return hist;
-  }
-
-  // Reduce following bins (begin, end] for nid-node in dst across threads
-  void ReduceHist(size_t nid, size_t begin, size_t end) {
-    CHECK_GT(end, begin);
-    CHECK_LT(nid, nodes_);
-
-    GHistRow dst = targeted_hists_[nid];
-
-    bool is_updated = false;
-    for (size_t tid = 0; tid < nthreads_; ++tid) {
-      if (hist_was_used_[tid * nodes_ + nid]) {
-        is_updated = true;
-        const size_t idx = tid_nid_to_hist_.at({tid, nid});
-        GHistRow src = hist_memory_[idx];
-
-        if (dst.data() != src.data()) {
-          IncrementHist(dst, src, begin, end);
-        }
-      }
-    }
-    if (!is_updated) {
-      // In distributed mode - some tree nodes can be empty on local machines,
-      // So we need just set local hist by zeros in this case
-      InitilizeHistByZeroes(dst, begin, end);
-    }
-  }
-
- protected:
-  void MatchThreadsToNodes(const BlockedSpace2d& space) {
-    const size_t space_size = space.Size();
-    const size_t chunck_size = space_size / nthreads_ + !!(space_size % nthreads_);
-
-    threads_to_nids_map_.resize(nthreads_ * nodes_, false);
-
-    for (size_t tid = 0; tid < nthreads_; ++tid) {
-      size_t begin = chunck_size * tid;
-      size_t end   = std::min(begin + chunck_size, space_size);
-
-      if (begin < space_size) {
-        size_t nid_begin = space.GetFirstDimension(begin);
-        size_t nid_end   = space.GetFirstDimension(end-1);
-
-        for (size_t nid = nid_begin; nid <= nid_end; ++nid) {
-          // true - means thread 'tid' will work to compute partial hist for node 'nid'
-          threads_to_nids_map_[tid * nodes_ + nid] = true;
-        }
-      }
-    }
-  }
-
-  void AllocateAdditionalHistograms() {
-    size_t hist_allocated_additionally = 0;
-
-    for (size_t nid = 0; nid < nodes_; ++nid) {
-      int nthreads_for_nid = 0;
-
-      for (size_t tid = 0; tid < nthreads_; ++tid) {
-        if (threads_to_nids_map_[tid * nodes_ + nid]) {
-          nthreads_for_nid++;
-        }
-      }
-
-      // In distributed mode - some tree nodes can be empty on local machines,
-      // set nthreads_for_nid to 0 in this case.
-      // In another case - allocate additional (nthreads_for_nid - 1) histograms,
-      // because one is already allocated externally (will store final result for the node).
-      hist_allocated_additionally += std::max<int>(0, nthreads_for_nid - 1);
-    }
-
-    for (size_t i = 0; i < hist_allocated_additionally; ++i) {
-      hist_buffer_.AddHistRow(i);
-    }
-  }
-
-  void MatchNodeNidPairToHist() {
-    size_t hist_total = 0;
-    size_t hist_allocated_additionally = 0;
-
-    for (size_t nid = 0; nid < nodes_; ++nid) {
-      bool first_hist = true;
-      for (size_t tid = 0; tid < nthreads_; ++tid) {
-        if (threads_to_nids_map_[tid * nodes_ + nid]) {
-          if (first_hist) {
-            hist_memory_.push_back(targeted_hists_[nid]);
-            first_hist = false;
-          } else {
-            hist_memory_.push_back(hist_buffer_[hist_allocated_additionally]);
-            hist_allocated_additionally++;
-          }
-          // map pair {tid, nid} to index of allocated histogram from hist_memory_
-          tid_nid_to_hist_[{tid, nid}] = hist_total++;
-          CHECK_EQ(hist_total, hist_memory_.size());
-        }
-      }
-    }
-  }
-
-  /*! \brief number of bins in each histogram */
-  size_t nbins_ = 0;
-  /*! \brief number of threads for parallel computation */
-  size_t nthreads_ = 0;
-  /*! \brief number of nodes which will be processed in parallel  */
-  size_t nodes_ = 0;
-  /*! \brief Buffer for additional histograms for Parallel processing  */
-  HistCollection hist_buffer_;
-  /*!
-   * \brief Marks which hists were used, it means that they should be merged.
-   * Contains only {true or false} values
-   * but 'int' is used instead of 'bool', because std::vector<bool> isn't thread safe
-   */
-  std::vector<int> hist_was_used_;
-
-  /*! \brief Buffer for additional histograms for Parallel processing  */
-  std::vector<bool> threads_to_nids_map_;
-  /*! \brief Contains histograms for final results  */
-  std::vector<GHistRow> targeted_hists_;
-  /*! \brief Allocated memory for histograms used for construction  */
-  std::vector<GHistRow> hist_memory_;
-  /*! \brief map pair {tid, nid} to index of allocated histogram from hist_memory_  */
-  std::map<std::pair<size_t, size_t>, size_t> tid_nid_to_hist_;
-};
-
-/*!
- * \brief builder for histograms of gradient statistics
- */
-class GHistBuilder {
- public:
-  GHistBuilder() = default;
-  GHistBuilder(size_t nthread, uint32_t nbins) : nthread_{nthread}, nbins_{nbins} {}
-
-  // construct a histogram via histogram aggregation
-  void BuildHist(const std::vector<GradientPair>& gpair,
-                 const RowSetCollection::Elem row_indices,
-                 const GHistIndexMatrix& gmat,
-                 GHistRow hist,
-                 bool isDense);
-  // same, with feature grouping
-  void BuildBlockHist(const std::vector<GradientPair>& gpair,
-                      const RowSetCollection::Elem row_indices,
-                      const GHistIndexBlockMatrix& gmatb,
-                      GHistRow hist);
-  // construct a histogram via subtraction trick
-  void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent);
-
-  uint32_t GetNumBins() const {
-      return nbins_;
-  }
-
- private:
-  /*! \brief number of threads for parallel computation */
-  size_t nthread_ { 0 };
-  /*! \brief number of all bins over all features */
-  uint32_t nbins_ { 0 };
-};
-
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_HIST_UTIL_H_
diff --git a/ml-xgboost/src/common/host_device_vector.cc b/ml-xgboost/src/common/host_device_vector.cc
deleted file mode 100644
index 1dd9997..0000000
--- a/ml-xgboost/src/common/host_device_vector.cc
+++ /dev/null
@@ -1,183 +0,0 @@
-/*!
- * Copyright 2017 XGBoost contributors
- */
-#ifndef XGBOOST_USE_CUDA
-
-// dummy implementation of HostDeviceVector in case CUDA is not used
-
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <cstdint>
-#include <memory>
-#include <utility>
-#include "xgboost/host_device_vector.h"
-
-namespace xgboost {
-
-template <typename T>
-struct HostDeviceVectorImpl {
-  explicit HostDeviceVectorImpl(size_t size, T v) : data_h_(size, v) {}
-  HostDeviceVectorImpl(std::initializer_list<T> init) : data_h_(init) {}
-  explicit HostDeviceVectorImpl(std::vector<T>  init) : data_h_(std::move(init)) {}
-  HostDeviceVectorImpl(HostDeviceVectorImpl&& that) : data_h_(std::move(that.data_h_)) {}
-
-  void Swap(HostDeviceVectorImpl &other) {
-     data_h_.swap(other.data_h_);
-  }
-
-  std::vector<T>& Vec() { return data_h_; }
-
- private:
-  std::vector<T> data_h_;
-};
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(size_t size, T v, int device)
-  : impl_(nullptr) {
-  impl_ = new HostDeviceVectorImpl<T>(size, v);
-}
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, int device)
-  : impl_(nullptr) {
-  impl_ = new HostDeviceVectorImpl<T>(init);
-}
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, int device)
-  : impl_(nullptr) {
-  impl_ = new HostDeviceVectorImpl<T>(init);
-}
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(HostDeviceVector<T>&& that) {
-  impl_ = new HostDeviceVectorImpl<T>(std::move(*that.impl_));
-}
-
-template <typename T>
-HostDeviceVector<T>& HostDeviceVector<T>::operator=(HostDeviceVector<T>&& that) {
-  if (this == &that) { return *this; }
-
-  std::unique_ptr<HostDeviceVectorImpl<T>> new_impl(
-      new HostDeviceVectorImpl<T>(std::move(*that.impl_)));
-  delete impl_;
-  impl_ = new_impl.release();
-  return *this;
-}
-
-template <typename T>
-HostDeviceVector<T>::~HostDeviceVector() {
-  delete impl_;
-  impl_ = nullptr;
-}
-
-template <typename T>
-GPUAccess HostDeviceVector<T>::DeviceAccess() const {
-  return kNone;
-}
-
-template <typename T>
-size_t HostDeviceVector<T>::Size() const { return impl_->Vec().size(); }
-
-template <typename T>
-int HostDeviceVector<T>::DeviceIdx() const { return -1; }
-
-template <typename T>
-T* HostDeviceVector<T>::DevicePointer() { return nullptr; }
-
-template <typename T>
-const T* HostDeviceVector<T>::ConstDevicePointer() const {
-  return nullptr;
-}
-
-template <typename T>
-common::Span<T> HostDeviceVector<T>::DeviceSpan() {
-  return common::Span<T>();
-}
-
-template <typename T>
-common::Span<const T> HostDeviceVector<T>::ConstDeviceSpan() const {
-  return common::Span<const T>();
-}
-
-template <typename T>
-std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->Vec(); }
-
-template <typename T>
-const std::vector<T>& HostDeviceVector<T>::ConstHostVector() const {
-  return impl_->Vec();
-}
-
-template <typename T>
-void HostDeviceVector<T>::Resize(size_t new_size, T v) {
-  impl_->Vec().resize(new_size, v);
-}
-
-template <typename T>
-void HostDeviceVector<T>::Fill(T v) {
-  std::fill(HostVector().begin(), HostVector().end(), v);
-}
-
-template <typename T>
-void HostDeviceVector<T>::Copy(const HostDeviceVector<T>& other) {
-  CHECK_EQ(Size(), other.Size());
-  std::copy(other.HostVector().begin(), other.HostVector().end(), HostVector().begin());
-}
-
-template <typename T>
-void HostDeviceVector<T>::Copy(const std::vector<T>& other) {
-  CHECK_EQ(Size(), other.size());
-  std::copy(other.begin(), other.end(), HostVector().begin());
-}
-
-template <typename T>
-void HostDeviceVector<T>::Copy(std::initializer_list<T> other) {
-  CHECK_EQ(Size(), other.size());
-  std::copy(other.begin(), other.end(), HostVector().begin());
-}
-
-template <typename T>
-bool HostDeviceVector<T>::HostCanRead() const {
-  return true;
-}
-
-template <typename T>
-bool HostDeviceVector<T>::HostCanWrite() const {
-  return true;
-}
-
-template <typename T>
-bool HostDeviceVector<T>::DeviceCanRead() const {
-  return false;
-}
-
-template <typename T>
-bool HostDeviceVector<T>::DeviceCanWrite() const {
-  return false;
-}
-
-template <typename T>
-void HostDeviceVector<T>::SetDevice(int device) const {}
-
-// explicit instantiations are required, as HostDeviceVector isn't header-only
-template class HostDeviceVector<bst_float>;
-template class HostDeviceVector<GradientPair>;
-template class HostDeviceVector<int32_t>;   // bst_node_t
-template class HostDeviceVector<Entry>;
-template class HostDeviceVector<uint64_t>;  // bst_row_t
-template class HostDeviceVector<uint32_t>;  // bst_feature_t
-
-#if defined(__APPLE__)
-/*
- * On OSX:
- *
- * typedef unsigned int         uint32_t;
- * typedef unsigned long long   uint64_t;
- * typedef unsigned long       __darwin_size_t;
- */
-template class HostDeviceVector<std::size_t>;
-#endif  // defined(__APPLE__)
-
-}  // namespace xgboost
-
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/common/host_device_vector.cu b/ml-xgboost/src/common/host_device_vector.cu
deleted file mode 100644
index 3ffcf75..0000000
--- a/ml-xgboost/src/common/host_device_vector.cu
+++ /dev/null
@@ -1,390 +0,0 @@
-/*!
- * Copyright 2017 XGBoost contributors
- */
-
-#include <thrust/fill.h>
-#include <thrust/device_ptr.h>
-
-#include <algorithm>
-#include <cstdint>
-#include <mutex>
-
-#include "xgboost/data.h"
-#include "xgboost/host_device_vector.h"
-#include "device_helpers.cuh"
-
-namespace xgboost {
-
-// the handler to call instead of cudaSetDevice; only used for testing
-static void (*cudaSetDeviceHandler)(int) = nullptr;  // NOLINT
-
-void SetCudaSetDeviceHandler(void (*handler)(int)) {
-  cudaSetDeviceHandler = handler;
-}
-
-template <typename T>
-class HostDeviceVectorImpl {
- public:
-  HostDeviceVectorImpl(size_t size, T v, int device) : device_(device) {
-    if (device >= 0) {
-      gpu_access_ = GPUAccess::kWrite;
-      SetDevice();
-      data_d_->resize(size, v);
-    } else {
-      data_h_.resize(size, v);
-    }
-  }
-
-  // Initializer can be std::vector<T> or std::initializer_list<T>
-  template <class Initializer>
-  HostDeviceVectorImpl(const Initializer& init, int device) : device_(device) {
-    if (device >= 0) {
-      gpu_access_ = GPUAccess::kWrite;
-      LazyResizeDevice(init.size());
-      Copy(init);
-    } else {
-      data_h_ = init;
-    }
-  }
-
-  HostDeviceVectorImpl(HostDeviceVectorImpl<T>&& that) :
-    device_{that.device_},
-    data_h_{std::move(that.data_h_)},
-    data_d_{std::move(that.data_d_)},
-    gpu_access_{that.gpu_access_} {}
-
-  ~HostDeviceVectorImpl() {
-    if (device_ >= 0) {
-      SetDevice();
-    }
-  }
-
-  size_t Size() const { return HostCanRead() ? data_h_.size() : data_d_->size(); }
-
-  int DeviceIdx() const { return device_; }
-
-  T* DevicePointer() {
-    LazySyncDevice(GPUAccess::kWrite);
-    return data_d_->data().get();
-  }
-
-  const T* ConstDevicePointer() {
-    LazySyncDevice(GPUAccess::kRead);
-    return data_d_->data().get();
-  }
-
-  common::Span<T> DeviceSpan() {
-    LazySyncDevice(GPUAccess::kWrite);
-    return {data_d_->data().get(), Size()};
-  }
-
-  common::Span<const T> ConstDeviceSpan() {
-    LazySyncDevice(GPUAccess::kRead);
-    return {data_d_->data().get(), Size()};
-  }
-
-  void Fill(T v) {  // NOLINT
-    if (HostCanWrite()) {
-      std::fill(data_h_.begin(), data_h_.end(), v);
-    } else {
-      gpu_access_ = GPUAccess::kWrite;
-      SetDevice();
-      thrust::fill(data_d_->begin(), data_d_->end(), v);
-    }
-  }
-
-  void Copy(HostDeviceVectorImpl<T>* other) {
-    CHECK_EQ(Size(), other->Size());
-    SetDevice(other->device_);
-    // Data is on host.
-    if (HostCanWrite() && other->HostCanWrite()) {
-      std::copy(other->data_h_.begin(), other->data_h_.end(), data_h_.begin());
-      return;
-    }
-    SetDevice();
-    CopyToDevice(other);
-  }
-
-  void Copy(const std::vector<T>& other) {
-    CHECK_EQ(Size(), other.size());
-    if (HostCanWrite()) {
-      std::copy(other.begin(), other.end(), data_h_.begin());
-    } else {
-      CopyToDevice(other.data());
-    }
-  }
-
-  void Copy(std::initializer_list<T> other) {
-    CHECK_EQ(Size(), other.size());
-    if (HostCanWrite()) {
-      std::copy(other.begin(), other.end(), data_h_.begin());
-    } else {
-      CopyToDevice(other.begin());
-    }
-  }
-
-  std::vector<T>& HostVector() {
-    LazySyncHost(GPUAccess::kNone);
-    return data_h_;
-  }
-
-  const std::vector<T>& ConstHostVector() {
-    LazySyncHost(GPUAccess::kRead);
-    return data_h_;
-  }
-
-  void SetDevice(int device) {
-    if (device_ == device) { return; }
-    if (device_ >= 0) {
-      LazySyncHost(GPUAccess::kNone);
-    }
-    device_ = device;
-    if (device_ >= 0) {
-      LazyResizeDevice(data_h_.size());
-    }
-  }
-
-  void Resize(size_t new_size, T v) {
-    if (new_size == Size()) { return; }
-    if ((Size() == 0 && device_ >= 0) || (DeviceCanWrite() && device_ >= 0)) {
-      // fast on-device resize
-      gpu_access_ = GPUAccess::kWrite;
-      SetDevice();
-      data_d_->resize(new_size, v);
-    } else {
-      // resize on host
-      LazySyncHost(GPUAccess::kNone);
-      data_h_.resize(new_size, v);
-    }
-  }
-
-  void LazySyncHost(GPUAccess access) {
-    if (HostCanAccess(access)) { return; }
-    if (HostCanRead()) {
-      // data is present, just need to deny access to the device
-      gpu_access_ = access;
-      return;
-    }
-    gpu_access_ = access;
-    if (data_h_.size() != data_d_->size()) { data_h_.resize(data_d_->size()); }
-    SetDevice();
-    dh::safe_cuda(cudaMemcpy(data_h_.data(),
-                             data_d_->data().get(),
-                             data_d_->size() * sizeof(T),
-                             cudaMemcpyDeviceToHost));
-  }
-
-  void LazySyncDevice(GPUAccess access) {
-    if (DeviceCanAccess(access)) { return; }
-    if (DeviceCanRead()) {
-      // deny read to the host
-      gpu_access_ = access;
-      return;
-    }
-    // data is on the host
-    LazyResizeDevice(data_h_.size());
-    SetDevice();
-    dh::safe_cuda(cudaMemcpy(data_d_->data().get(),
-                             data_h_.data(),
-                             data_d_->size() * sizeof(T),
-                             cudaMemcpyHostToDevice));
-    gpu_access_ = access;
-  }
-
-  bool HostCanAccess(GPUAccess access) const { return gpu_access_ <= access; }
-  bool HostCanRead() const { return HostCanAccess(GPUAccess::kRead); }
-  bool HostCanWrite() const { return HostCanAccess(GPUAccess::kNone); }
-  bool DeviceCanAccess(GPUAccess access) const { return gpu_access_ >= access; }
-  bool DeviceCanRead() const { return DeviceCanAccess(GPUAccess::kRead); }
-  bool DeviceCanWrite() const { return DeviceCanAccess(GPUAccess::kWrite); }
-  GPUAccess Access() const { return gpu_access_; }
-
- private:
-  int device_{-1};
-  std::vector<T> data_h_{};
-  std::unique_ptr<dh::device_vector<T>> data_d_{};
-  GPUAccess gpu_access_{GPUAccess::kNone};
-
-  void CopyToDevice(HostDeviceVectorImpl* other) {
-    if (other->HostCanWrite()) {
-      CopyToDevice(other->data_h_.data());
-    } else {
-      LazyResizeDevice(Size());
-      gpu_access_ = GPUAccess::kWrite;
-      SetDevice();
-      dh::safe_cuda(cudaMemcpyAsync(data_d_->data().get(), other->data_d_->data().get(),
-                                    data_d_->size() * sizeof(T), cudaMemcpyDefault));
-    }
-  }
-
-  void CopyToDevice(const T* begin) {
-    LazyResizeDevice(Size());
-    gpu_access_ = GPUAccess::kWrite;
-    SetDevice();
-    dh::safe_cuda(cudaMemcpyAsync(data_d_->data().get(), begin,
-                                  data_d_->size() * sizeof(T), cudaMemcpyDefault));
-  }
-
-  void LazyResizeDevice(size_t new_size) {
-    if (data_d_ && new_size == data_d_->size()) { return; }
-    SetDevice();
-    data_d_->resize(new_size);
-  }
-
-  void SetDevice() {
-    CHECK_GE(device_, 0);
-    if (cudaSetDeviceHandler == nullptr) {
-      dh::safe_cuda(cudaSetDevice(device_));
-    } else {
-      (*cudaSetDeviceHandler)(device_);
-    }
-
-    if (!data_d_) {
-      data_d_.reset(new dh::device_vector<T>);
-    }
-  }
-};
-
-template<typename T>
-HostDeviceVector<T>::HostDeviceVector(size_t size, T v, int device)
-    : impl_(new HostDeviceVectorImpl<T>(size, v, device)) {}
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, int device)
-    : impl_(new HostDeviceVectorImpl<T>(init, device)) {}
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, int device)
-    : impl_(new HostDeviceVectorImpl<T>(init, device)) {}
-
-template <typename T>
-HostDeviceVector<T>::HostDeviceVector(HostDeviceVector<T>&& other)
-    : impl_(new HostDeviceVectorImpl<T>(std::move(*other.impl_))) {}
-
-template <typename T>
-HostDeviceVector<T>& HostDeviceVector<T>::operator=(HostDeviceVector<T>&& other) {
-  if (this == &other) { return *this; }
-
-  std::unique_ptr<HostDeviceVectorImpl<T>> new_impl(
-      new HostDeviceVectorImpl<T>(std::move(*other.impl_)));
-  delete impl_;
-  impl_ = new_impl.release();
-  return *this;
-}
-
-template <typename T>
-HostDeviceVector<T>::~HostDeviceVector() {
-  delete impl_;
-  impl_ = nullptr;
-}
-
-template <typename T>
-size_t HostDeviceVector<T>::Size() const { return impl_->Size(); }
-
-template <typename T>
-int HostDeviceVector<T>::DeviceIdx() const { return impl_->DeviceIdx(); }
-
-template <typename T>
-T* HostDeviceVector<T>::DevicePointer() {
-  return impl_->DevicePointer();
-}
-
-template <typename T>
-const T* HostDeviceVector<T>::ConstDevicePointer() const {
-  return impl_->ConstDevicePointer();
-}
-
-template <typename T>
-common::Span<T> HostDeviceVector<T>::DeviceSpan() {
-  return impl_->DeviceSpan();
-}
-
-template <typename T>
-common::Span<const T> HostDeviceVector<T>::ConstDeviceSpan() const {
-  return impl_->ConstDeviceSpan();
-}
-
-template <typename T>
-void HostDeviceVector<T>::Fill(T v) {
-  impl_->Fill(v);
-}
-
-template <typename T>
-void HostDeviceVector<T>::Copy(const HostDeviceVector<T>& other) {
-  impl_->Copy(other.impl_);
-}
-
-template <typename T>
-void HostDeviceVector<T>::Copy(const std::vector<T>& other) {
-  impl_->Copy(other);
-}
-
-template <typename T>
-void HostDeviceVector<T>::Copy(std::initializer_list<T> other) {
-  impl_->Copy(other);
-}
-
-template <typename T>
-std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->HostVector(); }
-
-template <typename T>
-const std::vector<T>& HostDeviceVector<T>::ConstHostVector() const {
-  return impl_->ConstHostVector();
-}
-
-template <typename T>
-bool HostDeviceVector<T>::HostCanRead() const {
-  return impl_->HostCanRead();
-}
-
-template <typename T>
-bool HostDeviceVector<T>::HostCanWrite() const {
-  return impl_->HostCanWrite();
-}
-
-template <typename T>
-bool HostDeviceVector<T>::DeviceCanRead() const {
-  return impl_->DeviceCanRead();
-}
-
-template <typename T>
-bool HostDeviceVector<T>::DeviceCanWrite() const {
-  return impl_->DeviceCanWrite();
-}
-
-template <typename T>
-GPUAccess HostDeviceVector<T>::DeviceAccess() const {
-  return impl_->Access();
-}
-
-template <typename T>
-void HostDeviceVector<T>::SetDevice(int device) const {
-  impl_->SetDevice(device);
-}
-
-template <typename T>
-void HostDeviceVector<T>::Resize(size_t new_size, T v) {
-  impl_->Resize(new_size, v);
-}
-
-// explicit instantiations are required, as HostDeviceVector isn't header-only
-template class HostDeviceVector<bst_float>;
-template class HostDeviceVector<GradientPair>;
-template class HostDeviceVector<int32_t>;   // bst_node_t
-template class HostDeviceVector<uint8_t>;
-template class HostDeviceVector<Entry>;
-template class HostDeviceVector<uint64_t>;  // bst_row_t
-template class HostDeviceVector<uint32_t>;  // bst_feature_t
-
-#if defined(__APPLE__)
-/*
- * On OSX:
- *
- * typedef unsigned int         uint32_t;
- * typedef unsigned long long   uint64_t;
- * typedef unsigned long       __darwin_size_t;
- */
-template class HostDeviceVector<std::size_t>;
-#endif  // defined(__APPLE__)
-
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/io.cc b/ml-xgboost/src/common/io.cc
deleted file mode 100644
index a8df164..0000000
--- a/ml-xgboost/src/common/io.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-/*!
- * Copyright (c) by XGBoost Contributors 2019
- */
-#if defined(__unix__)
-#include <sys/stat.h>
-#include <fcntl.h>
-#include <unistd.h>
-#endif  // defined(__unix__)
-#include <algorithm>
-#include <cstdio>
-#include <string>
-#include <utility>
-
-#include "xgboost/logging.h"
-#include "io.h"
-
-namespace xgboost {
-namespace common {
-
-size_t PeekableInStream::Read(void* dptr, size_t size) {
-  size_t nbuffer = buffer_.length() - buffer_ptr_;
-  if (nbuffer == 0) return strm_->Read(dptr, size);
-  if (nbuffer < size) {
-    std::memcpy(dptr, dmlc::BeginPtr(buffer_) + buffer_ptr_, nbuffer);
-    buffer_ptr_ += nbuffer;
-    return nbuffer + strm_->Read(reinterpret_cast<char*>(dptr) + nbuffer,
-                                 size - nbuffer);
-  } else {
-    std::memcpy(dptr, dmlc::BeginPtr(buffer_) + buffer_ptr_, size);
-    buffer_ptr_ += size;
-    return size;
-  }
-}
-
-size_t PeekableInStream::PeekRead(void* dptr, size_t size) {
-  size_t nbuffer = buffer_.length() - buffer_ptr_;
-  if (nbuffer < size) {
-    buffer_ = buffer_.substr(buffer_ptr_, buffer_.length());
-    buffer_ptr_ = 0;
-    buffer_.resize(size);
-    size_t nadd = strm_->Read(dmlc::BeginPtr(buffer_) + nbuffer, size - nbuffer);
-    buffer_.resize(nbuffer + nadd);
-    std::memcpy(dptr, dmlc::BeginPtr(buffer_), buffer_.length());
-    return buffer_.size();
-  } else {
-    std::memcpy(dptr, dmlc::BeginPtr(buffer_) + buffer_ptr_, size);
-    return size;
-  }
-}
-
-FixedSizeStream::FixedSizeStream(PeekableInStream* stream) : PeekableInStream(stream), pointer_{0} {
-  size_t constexpr kInitialSize = 4096;
-  size_t size {kInitialSize}, total {0};
-  buffer_.clear();
-  while (true) {
-    buffer_.resize(size);
-    size_t read = stream->PeekRead(&buffer_[0], size);
-    total = read;
-    if (read < size) {
-      break;
-    }
-    size *= 2;
-  }
-  buffer_.resize(total);
-}
-
-size_t FixedSizeStream::Read(void* dptr, size_t size) {
-  auto read = this->PeekRead(dptr, size);
-  pointer_ += read;
-  return read;
-}
-
-size_t FixedSizeStream::PeekRead(void* dptr, size_t size) {
-  if (size >= buffer_.size() - pointer_)  {
-    std::copy(buffer_.cbegin() + pointer_, buffer_.cend(), reinterpret_cast<char*>(dptr));
-    return std::distance(buffer_.cbegin() + pointer_, buffer_.cend());
-  } else {
-    auto const beg = buffer_.cbegin() + pointer_;
-    auto const end = beg + size;
-    std::copy(beg, end, reinterpret_cast<char*>(dptr));
-    return std::distance(beg, end);
-  }
-}
-
-void FixedSizeStream::Seek(size_t pos) {
-  pointer_ = pos;
-  CHECK_LE(pointer_, buffer_.size());
-}
-
-void FixedSizeStream::Take(std::string* out) {
-  CHECK(out);
-  *out = std::move(buffer_);
-}
-
-std::string LoadSequentialFile(std::string fname) {
-  auto OpenErr = [&fname]() {
-                   std::string msg;
-                   msg = "Opening " + fname + " failed: ";
-                   msg += strerror(errno);
-                   LOG(FATAL) << msg;
-                 };
-  auto ReadErr = [&fname]() {
-                   std::string msg {"Error in reading file: "};
-                   msg += fname;
-                   msg += ": ";
-                   msg += strerror(errno);
-                   LOG(FATAL) << msg;
-                 };
-
-  std::string buffer;
-#if defined(__unix__)
-  struct stat fs;
-  if (stat(fname.c_str(), &fs) != 0) {
-    OpenErr();
-  }
-
-  size_t f_size_bytes = fs.st_size;
-  buffer.resize(f_size_bytes + 1);
-  int32_t fd = open(fname.c_str(), O_RDONLY);
-#if defined(__linux__)
-  posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL);
-#endif  // defined(__linux__)
-  ssize_t bytes_read = read(fd, &buffer[0], f_size_bytes);
-  if (bytes_read < 0) {
-    close(fd);
-    ReadErr();
-  }
-  close(fd);
-#else  // defined(__unix__)
-  FILE *f = fopen(fname.c_str(), "r");
-  if (f == NULL) {
-    std::string msg;
-    OpenErr();
-  }
-  fseek(f, 0, SEEK_END);
-  auto fsize = ftell(f);
-  fseek(f, 0, SEEK_SET);
-
-  buffer.resize(fsize + 1);
-  fread(&buffer[0], 1, fsize, f);
-  fclose(f);
-#endif  // defined(__unix__)
-  buffer.back() = '\0';
-  return buffer;
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/io.h b/ml-xgboost/src/common/io.h
deleted file mode 100644
index 528296d..0000000
--- a/ml-xgboost/src/common/io.h
+++ /dev/null
@@ -1,92 +0,0 @@
-/*!
- * Copyright 2014 by Contributors
- * \file io.h
- * \brief general stream interface for serialization, I/O
- * \author Tianqi Chen
- */
-
-#ifndef XGBOOST_COMMON_IO_H_
-#define XGBOOST_COMMON_IO_H_
-
-#include <dmlc/io.h>
-#include <rabit/rabit.h>
-#include <string>
-#include <cstring>
-
-#include "common.h"
-
-namespace xgboost {
-namespace common {
-using MemoryFixSizeBuffer = rabit::utils::MemoryFixSizeBuffer;
-using MemoryBufferStream = rabit::utils::MemoryBufferStream;
-
-/*!
- * \brief Input stream that support additional PeekRead operation,
- *  besides read.
- */
-class PeekableInStream : public dmlc::Stream {
- public:
-  explicit PeekableInStream(dmlc::Stream* strm)
-      : strm_(strm), buffer_ptr_(0) {}
-
-  size_t Read(void* dptr, size_t size) override;
-  virtual size_t PeekRead(void* dptr, size_t size);
-
-  void Write(const void* dptr, size_t size) override {
-    LOG(FATAL) << "Not implemented";
-  }
-
- private:
-  /*! \brief input stream */
-  dmlc::Stream *strm_;
-  /*! \brief current buffer pointer */
-  size_t buffer_ptr_;
-  /*! \brief internal buffer */
-  std::string buffer_;
-};
-/*!
- * \brief A simple class used to consume `dmlc::Stream' all at once.
- *
- * With it one can load the rabit checkpoint into a known size string buffer.
- */
-class FixedSizeStream : public PeekableInStream {
- public:
-  explicit FixedSizeStream(PeekableInStream* stream);
-  ~FixedSizeStream() override = default;
-
-  size_t Read(void* dptr, size_t size) override;
-  size_t PeekRead(void* dptr, size_t size) override;
-  size_t Size() const { return buffer_.size(); }
-  size_t Tell() const { return pointer_; }
-  void Seek(size_t pos);
-
-  void Write(const void* dptr, size_t size) override {
-    LOG(FATAL) << "Not implemented";
-  }
-
-  /*!
-   *  \brief Take the buffer from `FixedSizeStream'.  The one in `FixedSizeStream' will be
-   *  cleared out.
-   */
-  void Take(std::string* out);
-
- private:
-  size_t pointer_;
-  std::string buffer_;
-};
-
-// Optimized for consecutive file loading in unix like systime.
-std::string LoadSequentialFile(std::string fname);
-
-inline std::string FileExtension(std::string const& fname) {
-  auto splited = Split(fname, '.');
-  if (splited.size() > 1) {
-    return splited.back();
-  } else {
-    return "";
-  }
-}
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_IO_H_
diff --git a/ml-xgboost/src/common/json.cc b/ml-xgboost/src/common/json.cc
deleted file mode 100644
index ca46623..0000000
--- a/ml-xgboost/src/common/json.cc
+++ /dev/null
@@ -1,720 +0,0 @@
-/*!
- * Copyright (c) by Contributors 2019
- */
-#include <cctype>
-#include <locale>
-#include <sstream>
-#include <limits>
-#include <cmath>
-
-#include "xgboost/base.h"
-#include "xgboost/logging.h"
-#include "xgboost/json.h"
-#include "xgboost/json_io.h"
-
-namespace xgboost {
-
-void JsonWriter::Save(Json json) {
-  json.ptr_->Save(this);
-}
-
-void JsonWriter::Visit(JsonArray const* arr) {
-  this->Write("[");
-  auto const& vec = arr->GetArray();
-  size_t size = vec.size();
-  for (size_t i = 0; i < size; ++i) {
-    auto const& value = vec[i];
-    this->Save(value);
-    if (i != size-1) { Write(","); }
-  }
-  this->Write("]");
-}
-
-void JsonWriter::Visit(JsonObject const* obj) {
-  this->Write("{");
-  this->BeginIndent();
-  this->NewLine();
-
-  size_t i = 0;
-  size_t size = obj->GetObject().size();
-
-  for (auto& value : obj->GetObject()) {
-    this->Write("\"" + value.first + "\":");
-    this->Save(value.second);
-
-    if (i != size-1) {
-      this->Write(",");
-      this->NewLine();
-    }
-    i++;
-  }
-  this->EndIndent();
-  this->NewLine();
-  this->Write("}");
-}
-
-void JsonWriter::Visit(JsonNumber const* num) {
-  convertor_ << num->GetNumber();
-  auto const& str = convertor_.str();
-  this->Write(StringView{str.c_str(), str.size()});
-  convertor_.str("");
-}
-
-void JsonWriter::Visit(JsonInteger const* num) {
-  convertor_ << num->GetInteger();
-  auto const& str = convertor_.str();
-  this->Write(StringView{str.c_str(), str.size()});
-  convertor_.str("");
-}
-
-void JsonWriter::Visit(JsonNull const* null) {
-  this->Write("null");
-}
-
-void JsonWriter::Visit(JsonString const* str) {
-  std::string buffer;
-  buffer += '"';
-  auto const& string = str->GetString();
-  for (size_t i = 0; i < string.length(); i++) {
-    const char ch = string[i];
-    if (ch == '\\') {
-      if (i < string.size() && string[i+1] == 'u') {
-        buffer += "\\";
-      } else {
-        buffer += "\\\\";
-      }
-    } else if (ch == '"') {
-      buffer += "\\\"";
-    } else if (ch == '\b') {
-      buffer += "\\b";
-    } else if (ch == '\f') {
-      buffer += "\\f";
-    } else if (ch == '\n') {
-      buffer += "\\n";
-    } else if (ch == '\r') {
-      buffer += "\\r";
-    } else if (ch == '\t') {
-      buffer += "\\t";
-    } else if (static_cast<uint8_t>(ch) <= 0x1f) {
-      // Unit separator
-      char buf[8];
-      snprintf(buf, sizeof buf, "\\u%04x", ch);
-      buffer += buf;
-    } else {
-      buffer += ch;
-    }
-  }
-  buffer += '"';
-  this->Write(buffer);
-}
-
-void JsonWriter::Visit(JsonBoolean const* boolean) {
-  bool val = boolean->GetBoolean();
-  if (val) {
-    this->Write(u8"true");
-  } else {
-    this->Write(u8"false");
-  }
-}
-
-// Value
-std::string Value::TypeStr() const {
-  switch (kind_) {
-    case ValueKind::kString:  return "String";  break;
-    case ValueKind::kNumber:  return "Number";  break;
-    case ValueKind::kObject:  return "Object";  break;
-    case ValueKind::kArray:   return "Array";   break;
-    case ValueKind::kBoolean: return "Boolean"; break;
-    case ValueKind::kNull:    return "Null";    break;
-    case ValueKind::kInteger: return "Integer"; break;
-  }
-  return "";
-}
-
-// Only used for keeping old compilers happy about non-reaching return
-// statement.
-Json& DummyJsonObject() {
-  static Json obj;
-  return obj;
-}
-
-// Json Object
-JsonObject::JsonObject(JsonObject && that) :
-    Value(ValueKind::kObject), object_{std::move(that.object_)} {}
-
-JsonObject::JsonObject(std::map<std::string, Json>&& object)
-    : Value(ValueKind::kObject), object_{std::move(object)} {}
-
-Json& JsonObject::operator[](std::string const & key) {
-  return object_[key];
-}
-
-Json& JsonObject::operator[](int ind) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by Integer.";
-  return DummyJsonObject();
-}
-
-bool JsonObject::operator==(Value const& rhs) const {
-  if (!IsA<JsonObject>(&rhs)) { return false; }
-  return object_ == Cast<JsonObject const>(&rhs)->GetObject();
-}
-
-Value& JsonObject::operator=(Value const &rhs) {
-  JsonObject const* casted = Cast<JsonObject const>(&rhs);
-  object_ = casted->GetObject();
-  return *this;
-}
-
-void JsonObject::Save(JsonWriter* writer) {
-  writer->Visit(this);
-}
-
-// Json String
-Json& JsonString::operator[](std::string const & key) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by string.";
-  return DummyJsonObject();
-}
-
-Json& JsonString::operator[](int ind) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by Integer."
-             << "  Please try obtaining std::string first.";
-  return DummyJsonObject();
-}
-
-bool JsonString::operator==(Value const& rhs) const {
-  if (!IsA<JsonString>(&rhs)) { return false; }
-  return Cast<JsonString const>(&rhs)->GetString() == str_;
-}
-
-Value & JsonString::operator=(Value const &rhs) {
-  JsonString const* casted = Cast<JsonString const>(&rhs);
-  str_ = casted->GetString();
-  return *this;
-}
-
-// FIXME: UTF-8 parsing support.
-void JsonString::Save(JsonWriter* writer) {
-  writer->Visit(this);
-}
-
-// Json Array
-JsonArray::JsonArray(JsonArray && that) :
-    Value(ValueKind::kArray), vec_{std::move(that.vec_)} {}
-
-Json& JsonArray::operator[](std::string const & key) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by string.";
-  return DummyJsonObject();
-}
-
-Json& JsonArray::operator[](int ind) {
-  return vec_.at(ind);
-}
-
-bool JsonArray::operator==(Value const& rhs) const {
-  if (!IsA<JsonArray>(&rhs)) { return false; }
-  auto& arr = Cast<JsonArray const>(&rhs)->GetArray();
-  return std::equal(arr.cbegin(), arr.cend(), vec_.cbegin());
-}
-
-Value & JsonArray::operator=(Value const &rhs) {
-  JsonArray const* casted = Cast<JsonArray const>(&rhs);
-  vec_ = casted->GetArray();
-  return *this;
-}
-
-void JsonArray::Save(JsonWriter* writer) {
-  writer->Visit(this);
-}
-
-// Json Number
-Json& JsonNumber::operator[](std::string const & key) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by string.";
-  return DummyJsonObject();
-}
-
-Json& JsonNumber::operator[](int ind) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by Integer.";
-  return DummyJsonObject();
-}
-
-bool JsonNumber::operator==(Value const& rhs) const {
-  if (!IsA<JsonNumber>(&rhs)) { return false; }
-  return std::abs(number_ - Cast<JsonNumber const>(&rhs)->GetNumber()) < kRtEps;
-}
-
-Value & JsonNumber::operator=(Value const &rhs) {
-  JsonNumber const* casted = Cast<JsonNumber const>(&rhs);
-  number_ = casted->GetNumber();
-  return *this;
-}
-
-void JsonNumber::Save(JsonWriter* writer) {
-  writer->Visit(this);
-}
-
-// Json Integer
-Json& JsonInteger::operator[](std::string const& key) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by string.";
-  return DummyJsonObject();
-}
-
-Json& JsonInteger::operator[](int ind) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by Integer.";
-  return DummyJsonObject();
-}
-
-bool JsonInteger::operator==(Value const& rhs) const {
-  if (!IsA<JsonInteger>(&rhs)) { return false; }
-  return integer_ == Cast<JsonInteger const>(&rhs)->GetInteger();
-}
-
-Value & JsonInteger::operator=(Value const &rhs) {
-  JsonInteger const* casted = Cast<JsonInteger const>(&rhs);
-  integer_ = casted->GetInteger();
-  return *this;
-}
-
-void JsonInteger::Save(JsonWriter* writer) {
-  writer->Visit(this);
-}
-
-// Json Null
-Json& JsonNull::operator[](std::string const & key) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by string.";
-  return DummyJsonObject();
-}
-
-Json& JsonNull::operator[](int ind) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by Integer.";
-  return DummyJsonObject();
-}
-
-bool JsonNull::operator==(Value const& rhs) const {
-  if (!IsA<JsonNull>(&rhs)) { return false; }
-  return true;
-}
-
-Value & JsonNull::operator=(Value const &rhs) {
-  Cast<JsonNull const>(&rhs);  // Checking only.
-  return *this;
-}
-
-void JsonNull::Save(JsonWriter* writer) {
-  writer->Write("null");
-}
-
-// Json Boolean
-Json& JsonBoolean::operator[](std::string const & key) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by string.";
-  return DummyJsonObject();
-}
-
-Json& JsonBoolean::operator[](int ind) {
-  LOG(FATAL) << "Object of type "
-             << Value::TypeStr() << " can not be indexed by Integer.";
-  return DummyJsonObject();
-}
-
-bool JsonBoolean::operator==(Value const& rhs) const {
-  if (!IsA<JsonBoolean>(&rhs)) { return false; }
-  return boolean_ == Cast<JsonBoolean const>(&rhs)->GetBoolean();
-}
-
-Value & JsonBoolean::operator=(Value const &rhs) {
-  JsonBoolean const* casted = Cast<JsonBoolean const>(&rhs);
-  boolean_ = casted->GetBoolean();
-  return *this;
-}
-
-void JsonBoolean::Save(JsonWriter *writer) {
-  writer->Visit(this);
-}
-
-size_t constexpr JsonReader::kMaxNumLength;
-
-Json JsonReader::Parse() {
-  while (true) {
-    SkipSpaces();
-    char c = PeekNextChar();
-    if (c == -1) { break; }
-
-    if (c == '{') {
-      return ParseObject();
-    } else if ( c == '[' ) {
-      return ParseArray();
-    } else if ( c == '-' || std::isdigit(c) ||
-                c == 'N' ) {
-      // For now we only accept `NaN`, not `nan` as the later violiates LR(1) with `null`.
-      return ParseNumber();
-    } else if ( c == '\"' ) {
-      return ParseString();
-    } else if ( c == 't' || c == 'f' ) {
-      return ParseBoolean();
-    } else if (c == 'n') {
-      return ParseNull();
-    } else {
-      Error("Unknown construct");
-    }
-  }
-  return Json();
-}
-
-Json JsonReader::Load() {
-  Json result = Parse();
-  return result;
-}
-
-void JsonReader::Error(std::string msg) const {
-  // just copy it.
-  std::istringstream str_s(raw_str_.substr(0, raw_str_.size()));
-
-  msg += ", around character: " + std::to_string(cursor_.Pos());
-  msg += '\n';
-
-  constexpr size_t kExtend = 8;
-  auto beg = static_cast<int64_t>(cursor_.Pos()) -
-             static_cast<int64_t>(kExtend) < 0 ? 0 : cursor_.Pos() - kExtend;
-  auto end = cursor_.Pos() + kExtend >= raw_str_.size() ?
-             raw_str_.size() : cursor_.Pos() + kExtend;
-
-  std::string const& raw_portion = raw_str_.substr(beg, end - beg);
-  std::string portion;
-  for (auto c : raw_portion) {
-    if (c == '\n') {
-      portion += "\\n";
-    } else {
-      portion += c;
-    }
-  }
-
-  msg += "    ";
-  msg += portion;
-  msg += '\n';
-
-  msg += "    ";
-  for (size_t i = beg; i < cursor_.Pos() - 1; ++i) {
-    msg += '~';
-  }
-  msg += '^';
-  for (size_t i = cursor_.Pos(); i < end; ++i) {
-    msg += '~';
-  }
-  LOG(FATAL) << msg;
-}
-
-// Json class
-void JsonReader::SkipSpaces() {
-  while (cursor_.Pos() < raw_str_.size()) {
-    char c = raw_str_[cursor_.Pos()];
-    if (std::isspace(c)) {
-      cursor_.Forward();
-    } else {
-      break;
-    }
-  }
-}
-
-void ParseStr(std::string const& str) {
-  size_t end = 0;
-  for (size_t i = 0; i < str.size(); ++i) {
-    if (str[i] == '"' && i > 0 && str[i-1] != '\\') {
-      end = i;
-      break;
-    }
-  }
-  std::string result;
-  result.resize(end);
-}
-
-Json JsonReader::ParseString() {
-  char ch { GetChar('\"') };  // NOLINT
-  std::ostringstream output;
-  std::string str;
-  while (true) {
-    ch = GetNextChar();
-    if (ch == '\\') {
-      char next = static_cast<char>(GetNextChar());
-      switch (next) {
-        case 'r':  str += u8"\r"; break;
-        case 'n':  str += u8"\n"; break;
-        case '\\': str += u8"\\"; break;
-        case 't':  str += u8"\t"; break;
-        case '\"': str += u8"\""; break;
-        case 'u':
-          str += ch;
-          str += 'u';
-          break;
-        default: Error("Unknown escape");
-      }
-    } else {
-      if (ch == '\"') break;
-      str += ch;
-    }
-    if (ch == EOF || ch == '\r' || ch == '\n') {
-      Expect('\"', ch);
-    }
-  }
-  return Json(std::move(str));
-}
-
-Json JsonReader::ParseNull() {
-  char ch = GetNextNonSpaceChar();
-  std::string buffer{ch};
-  for (size_t i = 0; i < 3; ++i) {
-    buffer.push_back(GetNextChar());
-  }
-  if (buffer != "null") {
-    Error("Expecting null value \"null\"");
-  }
-  return Json{JsonNull()};
-}
-
-Json JsonReader::ParseArray() {
-  std::vector<Json> data;
-
-  char ch { GetChar('[') };  // NOLINT
-  while (true) {
-    if (PeekNextChar() == ']') {
-      GetChar(']');
-      return Json(std::move(data));
-    }
-    auto obj = Parse();
-    data.push_back(obj);
-    ch = GetNextNonSpaceChar();
-    if (ch == ']') break;
-    if (ch != ',') {
-      Expect(',', ch);
-    }
-  }
-
-  return Json(std::move(data));
-}
-
-Json JsonReader::ParseObject() {
-  GetChar('{');
-
-  std::map<std::string, Json> data;
-  SkipSpaces();
-  char ch = PeekNextChar();
-
-  if (ch == '}') {
-    GetChar('}');
-    return Json(std::move(data));
-  }
-
-  while (true) {
-    SkipSpaces();
-    ch = PeekNextChar();
-    CHECK_NE(ch, -1) << "cursor_.Pos(): " << cursor_.Pos() << ", "
-                     << "raw_str_.size():" << raw_str_.size();
-    if (ch != '"') {
-      Expect('"', ch);
-    }
-    Json key = ParseString();
-
-    ch = GetNextNonSpaceChar();
-
-    if (ch != ':') {
-      Expect(':', ch);
-    }
-
-    Json value { Parse() };
-
-    data[get<String>(key)] = std::move(value);
-
-    ch = GetNextNonSpaceChar();
-
-    if (ch == '}') break;
-    if (ch != ',') {
-      Expect(',', ch);
-    }
-  }
-
-  return Json(std::move(data));
-}
-
-Json JsonReader::ParseNumber() {
-  // Adopted from sajson with some simplifications and small optimizations.
-  char const* p = raw_str_.c_str() + cursor_.Pos();
-  char const* const beg = p;  // keep track of current pointer
-
-  // TODO(trivialfis): Add back all the checks for number
-  bool negative = false;
-  if (XGBOOST_EXPECT(*p == 'N', false)) {
-    GetChar('N');
-    GetChar('a');
-    GetChar('N');
-    return Json(static_cast<Number::Float>(std::numeric_limits<float>::quiet_NaN()));
-  }
-
-  if ('-' == *p) {
-    ++p;
-    negative = true;
-  }
-
-  bool is_float = false;
-
-  using ExpInt = std::remove_const<
-    decltype(std::numeric_limits<Number::Float>::max_exponent)>::type;
-  constexpr auto kExpMax = std::numeric_limits<ExpInt>::max();
-  constexpr auto kExpMin = std::numeric_limits<ExpInt>::min();
-
-  JsonInteger::Int i = 0;
-  double f = 0.0;  // Use double to maintain accuracy
-
-  if (*p == '0') {
-    ++p;
-  } else {
-    char c = *p;
-    do {
-      ++p;
-      char digit = c - '0';
-      i = 10 * i + digit;
-      c = *p;
-    } while (std::isdigit(c));
-  }
-
-  ExpInt exponent = 0;
-  const char *const dot_position = p;
-  if ('.' == *p) {
-    is_float = true;
-    f = i;
-    ++p;
-    char c = *p;
-
-    do {
-      ++p;
-      f = f * 10 + (c - '0');
-      c = *p;
-    } while (std::isdigit(c));
-  }
-  if (is_float) {
-    exponent = dot_position - p + 1;
-  }
-
-  char e = *p;
-  if ('e' == e || 'E' == e) {
-    if (!is_float) {
-      is_float = true;
-      f = i;
-    }
-    ++p;
-
-    bool negative_exponent = false;
-    if ('-' == *p) {
-      negative_exponent = true;
-      ++p;
-    } else if ('+' == *p) {
-      ++p;
-    }
-
-    ExpInt exp = 0;
-
-    char c = *p;
-    while (std::isdigit(c)) {
-      unsigned char digit = c - '0';
-      if (XGBOOST_EXPECT(exp > (kExpMax - digit) / 10, false)) {
-        CHECK_GT(exp, (kExpMax - digit) / 10) << "Overflow";
-      }
-      exp = 10 * exp + digit;
-      ++p;
-      c = *p;
-    }
-    static_assert(-kExpMax >= kExpMin, "exp can be negated without loss or UB");
-    exponent += (negative_exponent ? -exp : exp);
-  }
-
-  if (exponent) {
-    CHECK(is_float);
-    // If d is zero but the exponent is huge, don't
-    // multiply zero by inf which gives nan.
-    if (f != 0.0) {
-      // Only use exp10 from libc on gcc+linux
-#if !defined(__GNUC__) || defined(_WIN32) || defined(__APPLE__) || !defined(__linux__)
-#define exp10(val) std::pow(10, (val))
-#endif  // !defined(__GNUC__) || defined(_WIN32) || defined(__APPLE__) || !defined(__linux__)
-      f *= exp10(exponent);
-#if !defined(__GNUC__) || defined(_WIN32) || defined(__APPLE__) || !defined(__linux__)
-#undef exp10
-#endif  // !defined(__GNUC__) || defined(_WIN32) || defined(__APPLE__) || !defined(__linux__)
-    }
-  }
-
-  if (negative) {
-      f = -f;
-      i = -i;
-  }
-
-  auto moved = std::distance(beg, p);
-  this->cursor_.Forward(moved);
-
-  if (is_float) {
-    return Json(static_cast<Number::Float>(f));
-  } else {
-    return Json(JsonInteger(i));
-  }
-}
-
-Json JsonReader::ParseBoolean() {
-  bool result = false;
-  char ch = GetNextNonSpaceChar();
-  std::string const t_value = u8"true";
-  std::string const f_value = u8"false";
-  std::string buffer;
-
-  if (ch == 't') {
-    for (size_t i = 0; i < 3; ++i) {
-      buffer.push_back(GetNextNonSpaceChar());
-    }
-    if (buffer != u8"rue") {
-      Error("Expecting boolean value \"true\".");
-    }
-    result = true;
-  } else {
-    for (size_t i = 0; i < 4; ++i) {
-      buffer.push_back(GetNextNonSpaceChar());
-    }
-    if (buffer != u8"alse") {
-      Error("Expecting boolean value \"false\".");
-    }
-    result = false;
-  }
-  return Json{JsonBoolean{result}};
-}
-
-Json Json::Load(StringView str) {
-  JsonReader reader(str);
-  Json json{reader.Load()};
-  return json;
-}
-
-Json Json::Load(JsonReader* reader) {
-  Json json{reader->Load()};
-  return json;
-}
-
-void Json::Dump(Json json, std::ostream *stream, bool pretty) {
-  JsonWriter writer(stream, pretty);
-  writer.Save(json);
-}
-
-void Json::Dump(Json json, std::string* str, bool pretty) {
-  std::stringstream ss;
-  JsonWriter writer(&ss, pretty);
-  writer.Save(json);
-  *str = ss.str();
-}
-
-Json& Json::operator=(Json const &other) = default;
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/math.h b/ml-xgboost/src/common/math.h
deleted file mode 100644
index 4190531..0000000
--- a/ml-xgboost/src/common/math.h
+++ /dev/null
@@ -1,196 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file math.h
- * \brief additional math utils
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_MATH_H_
-#define XGBOOST_COMMON_MATH_H_
-
-#include <xgboost/base.h>
-
-#include <algorithm>
-#include <cmath>
-#include <limits>
-#include <utility>
-#include <vector>
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief calculate the sigmoid of the input.
- * \param x input parameter
- * \return the transformed value.
- */
-XGBOOST_DEVICE inline float Sigmoid(float x) {
-  return 1.0f / (1.0f + expf(-x));
-}
-
-/*!
- * \brief Equality test for both integer and floating point.
- */
-template <typename T, typename U>
-XGBOOST_DEVICE constexpr bool CloseTo(T a, U b) {
-  using Casted =
-      typename std::conditional<
-        std::is_floating_point<T>::value || std::is_floating_point<U>::value,
-          double,
-          typename std::conditional<
-            std::is_signed<T>::value || std::is_signed<U>::value,
-            int64_t,
-            uint64_t>::type>::type;
-  return std::is_floating_point<Casted>::value ?
-      std::abs(static_cast<Casted>(a) -static_cast<Casted>(b)) < 1e-6 : a == b;
-}
-
-/*!
- * \brief Do inplace softmax transformaton on start to end
- *
- * \tparam Iterator Input iterator type
- *
- * \param start Start iterator of input
- * \param end end iterator of input
- */
-template <typename Iterator>
-XGBOOST_DEVICE inline void Softmax(Iterator start, Iterator end) {
-  static_assert(std::is_same<bst_float,
-                typename std::remove_reference<
-                  decltype(std::declval<Iterator>().operator*())>::type
-                >::value,
-                "Values should be of type bst_float");
-  bst_float wmax = *start;
-  for (Iterator i = start+1; i != end; ++i) {
-    wmax = fmaxf(*i, wmax);
-  }
-  double wsum = 0.0f;
-  for (Iterator i = start; i != end; ++i) {
-    *i = expf(*i - wmax);
-    wsum += *i;
-  }
-  for (Iterator i = start; i != end; ++i) {
-    *i /= static_cast<float>(wsum);
-  }
-}
-
-/*!
- * \brief Find the maximum iterator within the iterators
- * \param begin The begining iterator.
- * \param end The end iterator.
- * \return the iterator point to the maximum value.
- * \tparam Iterator The type of the iterator.
- */
-template<typename Iterator>
-XGBOOST_DEVICE inline Iterator FindMaxIndex(Iterator begin, Iterator end) {
-  Iterator maxit = begin;
-  for (Iterator it = begin; it != end; ++it) {
-    if (*it > *maxit) maxit = it;
-  }
-  return maxit;
-}
-
-/*!
- * \brief perform numerically safe logsum
- * \param x left input operand
- * \param y right input operand
- * \return  log(exp(x) + exp(y))
- */
-inline float LogSum(float x, float y) {
-  if (x < y) {
-    return y + std::log(std::exp(x - y) + 1.0f);
-  } else {
-    return x + std::log(std::exp(y - x) + 1.0f);
-  }
-}
-
-/*!
- * \brief perform numerically safe logsum
- * \param begin The begining iterator.
- * \param end The end iterator.
- * \return the iterator point to the maximum value.
- * \tparam Iterator The type of the iterator.
- */
-template<typename Iterator>
-inline float LogSum(Iterator begin, Iterator end) {
-  float mx = *begin;
-  for (Iterator it = begin; it != end; ++it) {
-    mx = std::max(mx, *it);
-  }
-  float sum = 0.0f;
-  for (Iterator it = begin; it != end; ++it) {
-    sum += std::exp(*it - mx);
-  }
-  return mx + std::log(sum);
-}
-
-// comparator functions for sorting pairs in descending order
-inline static bool CmpFirst(const std::pair<float, unsigned> &a,
-                            const std::pair<float, unsigned> &b) {
-  return a.first > b.first;
-}
-inline static bool CmpSecond(const std::pair<float, unsigned> &a,
-                             const std::pair<float, unsigned> &b) {
-  return a.second > b.second;
-}
-
-// Redefined here to workaround a VC bug that doesn't support overloadng for integer
-// types.
-template <typename T>
-XGBOOST_DEVICE typename std::enable_if<
-  std::numeric_limits<T>::is_integer, bool>::type
-CheckNAN(T) {
-  return false;
-}
-
-#if XGBOOST_STRICT_R_MODE && !defined(__CUDA_ARCH__)
-
-bool CheckNAN(double v);
-
-#else
-
-XGBOOST_DEVICE bool inline CheckNAN(float x) {
-#if defined(__CUDA_ARCH__)
-  return isnan(x);
-#else
-  return std::isnan(x);
-#endif  // defined(__CUDA_ARCH__)
-}
-
-XGBOOST_DEVICE bool inline CheckNAN(double x) {
-#if defined(__CUDA_ARCH__)
-  return isnan(x);
-#else
-  return std::isnan(x);
-#endif  // defined(__CUDA_ARCH__)
-}
-
-#endif  // XGBOOST_STRICT_R_MODE && !defined(__CUDA_ARCH__)
-// GPU version is not uploaded in CRAN anyway.
-// Specialize only when using R with CPU.
-#if XGBOOST_STRICT_R_MODE && !defined(XGBOOST_USE_CUDA)
-double LogGamma(double v);
-
-#else  // Not R or R with GPU.
-
-template<typename T>
-XGBOOST_DEVICE inline T LogGamma(T v) {
-#ifdef _MSC_VER
-
-#if _MSC_VER >= 1800
-  return lgamma(v);
-#else
-#pragma message("Warning: lgamma function was not available until VS2013"\
-                ", poisson regression will be disabled")
-  utils::Error("lgamma function was not available until VS2013");
-  return static_cast<T>(1.0);
-#endif  // _MSC_VER >= 1800
-
-#else
-  return lgamma(v);
-#endif  // _MSC_VER
-}
-
-#endif  // XGBOOST_STRICT_R_MODE && !defined(XGBOOST_USE_CUDA)
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_MATH_H_
diff --git a/ml-xgboost/src/common/observer.h b/ml-xgboost/src/common/observer.h
deleted file mode 100644
index e83c6c8..0000000
--- a/ml-xgboost/src/common/observer.h
+++ /dev/null
@@ -1,125 +0,0 @@
-/*!
- * Copyright 2019-2020 XGBoost contributors
- * \file observer.h
- */
-#ifndef XGBOOST_COMMON_OBSERVER_H_
-#define XGBOOST_COMMON_OBSERVER_H_
-
-#include <iostream>
-#include <limits>
-#include <string>
-#include <vector>
-
-#include "xgboost/host_device_vector.h"
-#include "xgboost/parameter.h"
-#include "xgboost/json.h"
-#include "xgboost/base.h"
-#include "xgboost/tree_model.h"
-
-#if defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
-#define OBSERVER_PRINT LOG(INFO)
-#define OBSERVER_ENDL ""
-#define OBSERVER_NEWLINE ""
-#else  // defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
-#define OBSERVER_PRINT std::cout << std::setprecision(17)
-#define OBSERVER_ENDL std::endl
-#define OBSERVER_NEWLINE "\n"
-#endif  // defined(XGBOOST_STRICT_R_MODE) && XGBOOST_STRICT_R_MODE == 1
-
-namespace xgboost {
-/*\brief  An observer for logging internal data structures.
- *
- *  This class is designed to be `diff` tool friendly, which means it uses plain
- *  `std::cout` for printing to avoid the time information emitted by `LOG(DEBUG)` or
- *  similiar facilities. Exception: use `LOG(INFO)` for the R package, to comply
- *  with CRAN policy.
- */
-class TrainingObserver {
-#if defined(XGBOOST_USE_DEBUG_OUTPUT)
-  bool constexpr static kObserve {true};
-#else
-  bool constexpr static kObserve {false};
-#endif  // defined(XGBOOST_USE_DEBUG_OUTPUT)
-
- public:
-  void Update(int32_t iter) const {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-    OBSERVER_PRINT << "Iter: " << iter << OBSERVER_ENDL;
-  }
-  /*\brief Observe tree. */
-  void Observe(RegTree const& tree) {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-    OBSERVER_PRINT << "Tree:" << OBSERVER_ENDL;
-    Json j_tree {Object()};
-    tree.SaveModel(&j_tree);
-    std::string str;
-    Json::Dump(j_tree, &str, true);
-    OBSERVER_PRINT << str << OBSERVER_ENDL;
-  }
-  /*\brief Observe tree. */
-  void Observe(RegTree const* p_tree) {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-    auto const& tree = *p_tree;
-    this->Observe(tree);
-  }
-  /*\brief Observe data hosted by `std::vector'. */
-  template <typename T>
-  void Observe(std::vector<T> const& h_vec, std::string name,
-               size_t n = std::numeric_limits<std::size_t>::max()) const {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-    OBSERVER_PRINT << "Procedure: " << name << OBSERVER_ENDL;
-
-    for (size_t i = 0; i < h_vec.size(); ++i) {
-      OBSERVER_PRINT << h_vec[i] << ", ";
-      if (i % 8 == 0) {
-        OBSERVER_PRINT << OBSERVER_NEWLINE;
-      }
-      if ((i + 1) == n) {
-        break;
-      }
-    }
-    OBSERVER_PRINT << OBSERVER_ENDL;
-  }
-  /*\brief Observe data hosted by `HostDeviceVector'. */
-  template <typename T>
-  void Observe(HostDeviceVector<T> const& vec, std::string name,
-               size_t n = std::numeric_limits<std::size_t>::max()) const {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-    auto const& h_vec = vec.HostVector();
-    this->Observe(h_vec, name, n);
-  }
-  template <typename T>
-  void Observe(HostDeviceVector<T>* vec, std::string name,
-               size_t n = std::numeric_limits<std::size_t>::max()) const {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-    this->Observe(*vec, name, n);
-  }
-
-  /*\brief Observe objects with `XGBoostParamer' type. */
-  template <typename Parameter,
-            typename std::enable_if<
-              std::is_base_of<XGBoostParameter<Parameter>, Parameter>::value>::type* = nullptr>
-  void Observe(const Parameter &p, std::string name) const {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-
-    Json obj {toJson(p)};
-    OBSERVER_PRINT << "Parameter: " << name << ":\n" << obj << OBSERVER_ENDL;
-  }
-  /*\brief Observe parameters provided by users. */
-  void Observe(Args const& args) const {
-    if (XGBOOST_EXPECT(!kObserve, true)) { return; }
-
-    for (auto kv : args) {
-      OBSERVER_PRINT << kv.first << ": " << kv.second << OBSERVER_NEWLINE;
-    }
-    OBSERVER_PRINT << OBSERVER_ENDL;
-  }
-
-  /*\brief Get a global instance. */
-  static TrainingObserver& Instance() {
-    static TrainingObserver observer;
-    return observer;
-  }
-};
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_OBSERVER_H_
diff --git a/ml-xgboost/src/common/probability_distribution.cc b/ml-xgboost/src/common/probability_distribution.cc
deleted file mode 100644
index 51bcc49..0000000
--- a/ml-xgboost/src/common/probability_distribution.cc
+++ /dev/null
@@ -1,107 +0,0 @@
-/*!
- * Copyright 2020 by Contributors
- * \file probability_distribution.cc
- * \brief Implementation of a few useful probability distributions
- * \author Avinash Barnwal and Hyunsu Cho
- */
-
-#include <xgboost/logging.h>
-#include <cmath>
-#include "probability_distribution.h"
-
-namespace xgboost {
-namespace common {
-
-ProbabilityDistribution* ProbabilityDistribution::Create(ProbabilityDistributionType dist) {
-  switch (dist) {
-    case ProbabilityDistributionType::kNormal:
-      return new NormalDist;
-    case ProbabilityDistributionType::kLogistic:
-      return new LogisticDist;
-    case ProbabilityDistributionType::kExtreme:
-      return new ExtremeDist;
-    default:
-      LOG(FATAL) << "Unknown distribution";
-  }
-  return nullptr;
-}
-
-double NormalDist::PDF(double z) {
-  const double pdf = std::exp(-z * z / 2) / std::sqrt(2 * probability_constant::kPI);
-  return pdf;
-}
-
-double NormalDist::CDF(double z) {
-  const double cdf = 0.5 * (1 + std::erf(z / std::sqrt(2)));
-  return cdf;
-}
-
-double NormalDist::GradPDF(double z) {
-  const double pdf = this->PDF(z);
-  const double grad = -1 * z * pdf;
-  return grad;
-}
-
-double NormalDist::HessPDF(double z) {
-  const double pdf = this->PDF(z);
-  const double hess = (z * z - 1) * pdf;
-  return hess;
-}
-
-double LogisticDist::PDF(double z) {
-  const double w = std::exp(z);
-  const double sqrt_denominator = 1 + w;
-  const double pdf
-    = (std::isinf(w) || std::isinf(w * w)) ? 0.0 : (w / (sqrt_denominator * sqrt_denominator));
-  return pdf;
-}
-
-double LogisticDist::CDF(double z) {
-  const double w = std::exp(z);
-  const double cdf = std::isinf(w) ? 1.0 : (w / (1 + w));
-  return cdf;
-}
-
-double LogisticDist::GradPDF(double z) {
-  const double pdf = this->PDF(z);
-  const double w = std::exp(z);
-  const double grad = std::isinf(w) ? 0.0 : pdf * (1 - w) / (1 + w);
-  return grad;
-}
-
-double LogisticDist::HessPDF(double z) {
-  const double pdf = this->PDF(z);
-  const double w = std::exp(z);
-  const double hess
-    = (std::isinf(w) || std::isinf(w * w)) ? 0.0 : pdf * (w * w - 4 * w + 1) / ((1 + w) * (1 + w));
-  return hess;
-}
-
-double ExtremeDist::PDF(double z) {
-  const double w = std::exp(z);
-  const double pdf = std::isinf(w) ? 0.0 : (w * std::exp(-w));
-  return pdf;
-}
-
-double ExtremeDist::CDF(double z) {
-  const double w = std::exp(z);
-  const double cdf = 1 - std::exp(-w);
-  return cdf;
-}
-
-double ExtremeDist::GradPDF(double z) {
-  const double pdf = this->PDF(z);
-  const double w = std::exp(z);
-  const double grad = std::isinf(w) ? 0.0 : ((1 - w) * pdf);
-  return grad;
-}
-
-double ExtremeDist::HessPDF(double z) {
-  const double pdf = this->PDF(z);
-  const double w = std::exp(z);
-  const double hess = (std::isinf(w) || std::isinf(w * w)) ? 0.0 : ((w * w - 3 * w + 1) * pdf);
-  return hess;
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/probability_distribution.h b/ml-xgboost/src/common/probability_distribution.h
deleted file mode 100644
index 66d6a3d..0000000
--- a/ml-xgboost/src/common/probability_distribution.h
+++ /dev/null
@@ -1,95 +0,0 @@
-/*!
- * Copyright 2020 by Contributors
- * \file probability_distribution.h
- * \brief Implementation of a few useful probability distributions
- * \author Avinash Barnwal and Hyunsu Cho
- */
-
-#ifndef XGBOOST_COMMON_PROBABILITY_DISTRIBUTION_H_
-#define XGBOOST_COMMON_PROBABILITY_DISTRIBUTION_H_
-
-namespace xgboost {
-namespace common {
-
-namespace probability_constant {
-
-/*! \brief Constant PI */
-const double kPI = 3.14159265358979323846;
-/*! \brief The Euler-Mascheroni_constant */
-const double kEulerMascheroni = 0.57721566490153286060651209008240243104215933593992;
-
-}  // namespace probability_constant
-
-/*! \brief Enum encoding possible choices of probability distribution */
-enum class ProbabilityDistributionType : int {
-  kNormal = 0, kLogistic = 1, kExtreme = 2
-};
-
-/*! \brief Interface for a probability distribution */
-class ProbabilityDistribution {
- public:
-  /*!
-   * \brief Evaluate Probability Density Function (PDF) at a particular point
-   * \param z point at which to evaluate PDF
-   * \return Value of PDF evaluated
-   */
-  virtual double PDF(double z) = 0;
-  /*!
-   * \brief Evaluate Cumulative Distribution Function (CDF) at a particular point
-   * \param z point at which to evaluate CDF
-   * \return Value of CDF evaluated
-   */
-  virtual double CDF(double z) = 0;
-  /*!
-   * \brief Evaluate first derivative of PDF at a particular point
-   * \param z point at which to evaluate first derivative of PDF
-   * \return Value of first derivative of PDF evaluated
-   */
-  virtual double GradPDF(double z) = 0;
-  /*!
-   * \brief Evaluate second derivative of PDF at a particular point
-   * \param z point at which to evaluate second derivative of PDF
-   * \return Value of second derivative of PDF evaluated
-   */
-  virtual double HessPDF(double z) = 0;
-
-  /*!
-   * \brief Factory function to instantiate a new probability distribution object
-   * \param dist kind of probability distribution
-   * \return Reference to the newly created probability distribution object
-   */
-  static ProbabilityDistribution* Create(ProbabilityDistributionType dist);
-  virtual ~ProbabilityDistribution() = default;
-};
-
-/*! \brief The (standard) normal distribution */
-class NormalDist : public ProbabilityDistribution {
- public:
-  double PDF(double z) override;
-  double CDF(double z) override;
-  double GradPDF(double z) override;
-  double HessPDF(double z) override;
-};
-
-/*! \brief The (standard) logistic distribution */
-class LogisticDist : public ProbabilityDistribution {
- public:
-  double PDF(double z) override;
-  double CDF(double z) override;
-  double GradPDF(double z) override;
-  double HessPDF(double z) override;
-};
-
-/*! \brief The extreme distribution, also known as the Gumbel (minimum) distribution */
-class ExtremeDist : public ProbabilityDistribution {
- public:
-  double PDF(double z) override;
-  double CDF(double z) override;
-  double GradPDF(double z) override;
-  double HessPDF(double z) override;
-};
-
-}  // namespace common
-}  // namespace xgboost
-
-#endif  // XGBOOST_COMMON_PROBABILITY_DISTRIBUTION_H_
diff --git a/ml-xgboost/src/common/quantile.h b/ml-xgboost/src/common/quantile.h
deleted file mode 100644
index 067b041..0000000
--- a/ml-xgboost/src/common/quantile.h
+++ /dev/null
@@ -1,855 +0,0 @@
-/*!
- * Copyright 2014 by Contributors
- * \file quantile.h
- * \brief util to compute quantiles
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_QUANTILE_H_
-#define XGBOOST_COMMON_QUANTILE_H_
-
-#include <dmlc/base.h>
-#include <xgboost/logging.h>
-#include <cmath>
-#include <vector>
-#include <cstring>
-#include <algorithm>
-#include <iostream>
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief experimental wsummary
- * \tparam DType type of data content
- * \tparam RType type of rank
- */
-template<typename DType, typename RType>
-struct WQSummary {
-  /*! \brief an entry in the sketch summary */
-  struct Entry {
-    /*! \brief minimum rank */
-    RType rmin;
-    /*! \brief maximum rank */
-    RType rmax;
-    /*! \brief maximum weight */
-    RType wmin;
-    /*! \brief the value of data */
-    DType value;
-    // constructor
-    XGBOOST_DEVICE Entry() {}  // NOLINT
-    // constructor
-    XGBOOST_DEVICE Entry(RType rmin, RType rmax, RType wmin, DType value)
-        : rmin(rmin), rmax(rmax), wmin(wmin), value(value) {}
-    /*!
-     * \brief debug function,  check Valid
-     * \param eps the tolerate level for violating the relation
-     */
-    inline void CheckValid(RType eps = 0) const {
-      CHECK(rmin >= 0 && rmax >= 0 && wmin >= 0) << "nonneg constraint";
-      CHECK(rmax- rmin - wmin > -eps) <<  "relation constraint: min/max";
-    }
-    /*! \return rmin estimation for v strictly bigger than value */
-    XGBOOST_DEVICE inline RType RMinNext() const {
-      return rmin + wmin;
-    }
-    /*! \return rmax estimation for v strictly smaller than value */
-    XGBOOST_DEVICE inline RType RMaxPrev() const {
-      return rmax - wmin;
-    }
-  };
-  /*! \brief input data queue before entering the summary */
-  struct Queue {
-    // entry in the queue
-    struct QEntry {
-      // value of the instance
-      DType value;
-      // weight of instance
-      RType weight;
-      // default constructor
-      QEntry() = default;
-      // constructor
-      QEntry(DType value, RType weight)
-          : value(value), weight(weight) {}
-      // comparator on value
-      inline bool operator<(const QEntry &b) const {
-        return value < b.value;
-      }
-    };
-    // the input queue
-    std::vector<QEntry> queue;
-    // end of the queue
-    size_t qtail;
-    // push data to the queue
-    inline void Push(DType x, RType w) {
-      if (qtail == 0 || queue[qtail - 1].value != x) {
-        queue[qtail++] = QEntry(x, w);
-      } else {
-        queue[qtail - 1].weight += w;
-      }
-    }
-    inline void MakeSummary(WQSummary *out) {
-      std::sort(queue.begin(), queue.begin() + qtail);
-      out->size = 0;
-      // start update sketch
-      RType wsum = 0;
-      // construct data with unique weights
-      for (size_t i = 0; i < qtail;) {
-        size_t j = i + 1;
-        RType w = queue[i].weight;
-        while (j < qtail && queue[j].value == queue[i].value) {
-          w += queue[j].weight; ++j;
-        }
-        out->data[out->size++] = Entry(wsum, wsum + w, w, queue[i].value);
-        wsum += w; i = j;
-      }
-    }
-  };
-  /*! \brief data field */
-  Entry *data;
-  /*! \brief number of elements in the summary */
-  size_t size;
-  // constructor
-  WQSummary(Entry *data, size_t size)
-      : data(data), size(size) {}
-  /*!
-   * \return the maximum error of the Summary
-   */
-  inline RType MaxError() const {
-    RType res = data[0].rmax - data[0].rmin - data[0].wmin;
-    for (size_t i = 1; i < size; ++i) {
-      res = std::max(data[i].RMaxPrev() - data[i - 1].RMinNext(), res);
-      res = std::max(data[i].rmax - data[i].rmin - data[i].wmin, res);
-    }
-    return res;
-  }
-  /*!
-   * \brief query qvalue, start from istart
-   * \param qvalue the value we query for
-   * \param istart starting position
-   */
-  inline Entry Query(DType qvalue, size_t &istart) const { // NOLINT(*)
-    while (istart < size && qvalue > data[istart].value) {
-      ++istart;
-    }
-    if (istart == size) {
-      RType rmax = data[size - 1].rmax;
-      return Entry(rmax, rmax, 0.0f, qvalue);
-    }
-    if (qvalue == data[istart].value) {
-      return data[istart];
-    } else {
-      if (istart == 0) {
-        return Entry(0.0f, 0.0f, 0.0f, qvalue);
-      } else {
-        return Entry(data[istart - 1].RMinNext(),
-                     data[istart].RMaxPrev(),
-                     0.0f, qvalue);
-      }
-    }
-  }
-  /*! \return maximum rank in the summary */
-  inline RType MaxRank() const {
-    return data[size - 1].rmax;
-  }
-  /*!
-   * \brief copy content from src
-   * \param src source sketch
-   */
-  inline void CopyFrom(const WQSummary &src) {
-    size = src.size;
-    std::memcpy(data, src.data, sizeof(Entry) * size);
-  }
-  inline void MakeFromSorted(const Entry* entries, size_t n) {
-    size = 0;
-    for (size_t i = 0; i < n;) {
-      size_t j = i + 1;
-      // ignore repeated values
-      for (; j < n && entries[j].value == entries[i].value; ++j) {}
-      data[size++] = Entry(entries[i].rmin, entries[i].rmax, entries[i].wmin,
-                           entries[i].value);
-      i = j;
-    }
-  }
-  /*!
-   * \brief debug function, validate whether the summary
-   *  run consistency check to check if it is a valid summary
-   * \param eps the tolerate error level, used when RType is floating point and
-   *        some inconsistency could occur due to rounding error
-   */
-  inline void CheckValid(RType eps) const {
-    for (size_t i = 0; i < size; ++i) {
-      data[i].CheckValid(eps);
-      if (i != 0) {
-        CHECK(data[i].rmin >= data[i - 1].rmin + data[i - 1].wmin) << "rmin range constraint";
-        CHECK(data[i].rmax >= data[i - 1].rmax + data[i].wmin) << "rmax range constraint";
-      }
-    }
-  }
-  /*!
-   * \brief set current summary to be pruned summary of src
-   *        assume data field is already allocated to be at least maxsize
-   * \param src source summary
-   * \param maxsize size we can afford in the pruned sketch
-   */
-
-  inline void SetPrune(const WQSummary &src, size_t maxsize) {
-    if (src.size <= maxsize) {
-      this->CopyFrom(src); return;
-    }
-    const RType begin = src.data[0].rmax;
-    const RType range = src.data[src.size - 1].rmin - src.data[0].rmax;
-    const size_t n = maxsize - 1;
-    data[0] = src.data[0];
-    this->size = 1;
-    // lastidx is used to avoid duplicated records
-    size_t i = 1, lastidx = 0;
-    for (size_t k = 1; k < n; ++k) {
-      RType dx2 =  2 * ((k * range) / n + begin);
-      // find first i such that  d < (rmax[i+1] + rmin[i+1]) / 2
-      while (i < src.size - 1
-             && dx2 >= src.data[i + 1].rmax + src.data[i + 1].rmin) ++i;
-      if (i == src.size - 1) break;
-      if (dx2 < src.data[i].RMinNext() + src.data[i + 1].RMaxPrev()) {
-        if (i != lastidx) {
-          data[size++] = src.data[i]; lastidx = i;
-        }
-      } else {
-        if (i + 1 != lastidx) {
-          data[size++] = src.data[i + 1]; lastidx = i + 1;
-        }
-      }
-    }
-    if (lastidx != src.size - 1) {
-      data[size++] = src.data[src.size - 1];
-    }
-  }
-  /*!
-   * \brief set current summary to be merged summary of sa and sb
-   * \param sa first input summary to be merged
-   * \param sb second input summary to be merged
-   */
-  inline void SetCombine(const WQSummary &sa,
-                         const WQSummary &sb) {
-    if (sa.size == 0) {
-      this->CopyFrom(sb); return;
-    }
-    if (sb.size == 0) {
-      this->CopyFrom(sa); return;
-    }
-    CHECK(sa.size > 0 && sb.size > 0);
-    const Entry *a = sa.data, *a_end = sa.data + sa.size;
-    const Entry *b = sb.data, *b_end = sb.data + sb.size;
-    // extended rmin value
-    RType aprev_rmin = 0, bprev_rmin = 0;
-    Entry *dst = this->data;
-    while (a != a_end && b != b_end) {
-      // duplicated value entry
-      if (a->value == b->value) {
-        *dst = Entry(a->rmin + b->rmin,
-                     a->rmax + b->rmax,
-                     a->wmin + b->wmin, a->value);
-        aprev_rmin = a->RMinNext();
-        bprev_rmin = b->RMinNext();
-        ++dst; ++a; ++b;
-      } else if (a->value < b->value) {
-        *dst = Entry(a->rmin + bprev_rmin,
-                     a->rmax + b->RMaxPrev(),
-                     a->wmin, a->value);
-        aprev_rmin = a->RMinNext();
-        ++dst; ++a;
-      } else {
-        *dst = Entry(b->rmin + aprev_rmin,
-                     b->rmax + a->RMaxPrev(),
-                     b->wmin, b->value);
-        bprev_rmin = b->RMinNext();
-        ++dst; ++b;
-      }
-    }
-    if (a != a_end) {
-      RType brmax = (b_end - 1)->rmax;
-      do {
-        *dst = Entry(a->rmin + bprev_rmin, a->rmax + brmax, a->wmin, a->value);
-        ++dst; ++a;
-      } while (a != a_end);
-    }
-    if (b != b_end) {
-      RType armax = (a_end - 1)->rmax;
-      do {
-        *dst = Entry(b->rmin + aprev_rmin, b->rmax + armax, b->wmin, b->value);
-        ++dst; ++b;
-      } while (b != b_end);
-    }
-    this->size = dst - data;
-    const RType tol = 10;
-    RType err_mingap, err_maxgap, err_wgap;
-    this->FixError(&err_mingap, &err_maxgap, &err_wgap);
-    if (err_mingap > tol || err_maxgap > tol || err_wgap > tol) {
-      LOG(INFO) << "mingap=" << err_mingap
-                << ", maxgap=" << err_maxgap
-                << ", wgap=" << err_wgap;
-    }
-    CHECK(size <= sa.size + sb.size) << "bug in combine";
-  }
-  // helper function to print the current content of sketch
-  inline void Print() const {
-    for (size_t i = 0; i < this->size; ++i) {
-      LOG(CONSOLE) << "[" << i << "] rmin=" << data[i].rmin
-                   << ", rmax=" << data[i].rmax
-                   << ", wmin=" << data[i].wmin
-                   << ", v=" << data[i].value;
-    }
-  }
-  // try to fix rounding error
-  // and re-establish invariance
-  inline void FixError(RType *err_mingap,
-                       RType *err_maxgap,
-                       RType *err_wgap) const {
-    *err_mingap = 0;
-    *err_maxgap = 0;
-    *err_wgap = 0;
-    RType prev_rmin = 0, prev_rmax = 0;
-    for (size_t i = 0; i < this->size; ++i) {
-      if (data[i].rmin < prev_rmin) {
-        data[i].rmin = prev_rmin;
-        *err_mingap = std::max(*err_mingap, prev_rmin - data[i].rmin);
-      } else {
-        prev_rmin = data[i].rmin;
-      }
-      if (data[i].rmax < prev_rmax) {
-        data[i].rmax = prev_rmax;
-        *err_maxgap = std::max(*err_maxgap, prev_rmax - data[i].rmax);
-      }
-      RType rmin_next = data[i].RMinNext();
-      if (data[i].rmax < rmin_next) {
-        data[i].rmax = rmin_next;
-        *err_wgap = std::max(*err_wgap, data[i].rmax - rmin_next);
-      }
-      prev_rmax = data[i].rmax;
-    }
-  }
-  // check consistency of the summary
-  inline bool Check(const char *msg) const {
-    const float tol = 10.0f;
-    for (size_t i = 0; i < this->size; ++i) {
-      if (data[i].rmin + data[i].wmin > data[i].rmax + tol ||
-          data[i].rmin < -1e-6f || data[i].rmax < -1e-6f) {
-        LOG(INFO) << "---------- WQSummary::Check did not pass ----------";
-        this->Print();
-        return false;
-      }
-    }
-    return true;
-  }
-};
-
-/*! \brief try to do efficient pruning */
-template<typename DType, typename RType>
-struct WXQSummary : public WQSummary<DType, RType> {
-  // redefine entry type
-  using Entry = typename WQSummary<DType, RType>::Entry;
-  // constructor
-  WXQSummary(Entry *data, size_t size)
-      : WQSummary<DType, RType>(data, size) {}
-  // check if the block is large chunk
-  inline static bool CheckLarge(const Entry &e, RType chunk) {
-    return  e.RMinNext() > e.RMaxPrev() + chunk;
-  }
-  // set prune
-  inline void SetPrune(const WQSummary<DType, RType> &src, size_t maxsize) {
-    if (src.size <= maxsize) {
-      this->CopyFrom(src); return;
-    }
-    RType begin = src.data[0].rmax;
-    // n is number of points exclude the min/max points
-    size_t n = maxsize - 2, nbig = 0;
-    // these is the range of data exclude the min/max point
-    RType range = src.data[src.size - 1].rmin - begin;
-    // prune off zero weights
-    if (range == 0.0f || maxsize <= 2) {
-      // special case, contain only two effective data pts
-      this->data[0] = src.data[0];
-      this->data[1] = src.data[src.size - 1];
-      this->size = 2;
-      return;
-    } else {
-      range = std::max(range, static_cast<RType>(1e-3f));
-    }
-    // Get a big enough chunk size, bigger than range / n
-    // (multiply by 2 is a safe factor)
-    const RType chunk = 2 * range / n;
-    // minimized range
-    RType mrange = 0;
-    {
-      // first scan, grab all the big chunk
-      // moving block index, exclude the two ends.
-      size_t bid = 0;
-      for (size_t i = 1; i < src.size - 1; ++i) {
-        // detect big chunk data point in the middle
-        // always save these data points.
-        if (CheckLarge(src.data[i], chunk)) {
-          if (bid != i - 1) {
-            // accumulate the range of the rest points
-            mrange += src.data[i].RMaxPrev() - src.data[bid].RMinNext();
-          }
-          bid = i; ++nbig;
-        }
-      }
-      if (bid != src.size - 2) {
-        mrange += src.data[src.size-1].RMaxPrev() - src.data[bid].RMinNext();
-      }
-    }
-    // assert: there cannot be more than n big data points
-    if (nbig >= n) {
-      // see what was the case
-      LOG(INFO) << " check quantile stats, nbig=" << nbig << ", n=" << n;
-      LOG(INFO) << " srcsize=" << src.size << ", maxsize=" << maxsize
-                << ", range=" << range << ", chunk=" << chunk;
-      src.Print();
-      CHECK(nbig < n) << "quantile: too many large chunk";
-    }
-    this->data[0] = src.data[0];
-    this->size = 1;
-    // The counter on the rest of points, to be selected equally from small chunks.
-    n = n - nbig;
-    // find the rest of point
-    size_t bid = 0, k = 1, lastidx = 0;
-    for (size_t end = 1; end < src.size; ++end) {
-      if (end == src.size - 1 || CheckLarge(src.data[end], chunk)) {
-        if (bid != end - 1) {
-          size_t i = bid;
-          RType maxdx2 = src.data[end].RMaxPrev() * 2;
-          for (; k < n; ++k) {
-            RType dx2 =  2 * ((k * mrange) / n + begin);
-            if (dx2 >= maxdx2) break;
-            while (i < end &&
-                   dx2 >= src.data[i + 1].rmax + src.data[i + 1].rmin) ++i;
-            if (i == end) break;
-            if (dx2 < src.data[i].RMinNext() + src.data[i + 1].RMaxPrev()) {
-              if (i != lastidx) {
-                this->data[this->size++] = src.data[i]; lastidx = i;
-              }
-            } else {
-              if (i + 1 != lastidx) {
-                this->data[this->size++] = src.data[i + 1]; lastidx = i + 1;
-              }
-            }
-          }
-        }
-        if (lastidx != end) {
-          this->data[this->size++] = src.data[end];
-          lastidx = end;
-        }
-        bid = end;
-        // shift base by the gap
-        begin += src.data[bid].RMinNext() - src.data[bid].RMaxPrev();
-      }
-    }
-  }
-};
-/*!
- * \brief traditional GK summary
- */
-template<typename DType, typename RType>
-struct GKSummary {
-  /*! \brief an entry in the sketch summary */
-  struct Entry {
-    /*! \brief minimum rank */
-    RType rmin;
-    /*! \brief maximum rank */
-    RType rmax;
-    /*! \brief the value of data */
-    DType value;
-    // constructor
-    Entry() = default;
-    // constructor
-    Entry(RType rmin, RType rmax, DType value)
-        : rmin(rmin), rmax(rmax), value(value) {}
-  };
-  /*! \brief input data queue before entering the summary */
-  struct Queue {
-    // the input queue
-    std::vector<DType> queue;
-    // end of the queue
-    size_t qtail;
-    // push data to the queue
-    inline void Push(DType x, RType w) {
-      queue[qtail++] = x;
-    }
-    inline void MakeSummary(GKSummary *out) {
-      std::sort(queue.begin(), queue.begin() + qtail);
-      out->size = qtail;
-      for (size_t i = 0; i < qtail; ++i) {
-        out->data[i] = Entry(i + 1, i + 1, queue[i]);
-      }
-    }
-  };
-  /*! \brief data field */
-  Entry *data;
-  /*! \brief number of elements in the summary */
-  size_t size;
-  GKSummary(Entry *data, size_t size)
-      : data(data), size(size) {}
-  /*! \brief the maximum error of the summary */
-  inline RType MaxError() const {
-    RType res = 0;
-    for (size_t i = 1; i < size; ++i) {
-      res = std::max(data[i].rmax - data[i-1].rmin, res);
-    }
-    return res;
-  }
-  /*! \return maximum rank in the summary */
-  inline RType MaxRank() const {
-    return data[size - 1].rmax;
-  }
-  /*!
-   * \brief copy content from src
-   * \param src source sketch
-   */
-  inline void CopyFrom(const GKSummary &src) {
-    size = src.size;
-    std::memcpy(data, src.data, sizeof(Entry) * size);
-  }
-  inline void CheckValid(RType eps) const {
-    // assume always valid
-  }
-  /*! \brief used for debug purpose, print the summary */
-  inline void Print() const {
-    for (size_t i = 0; i < size; ++i) {
-      LOG(CONSOLE) << "x=" << data[i].value << "\t"
-                   << "[" << data[i].rmin << "," << data[i].rmax << "]";
-    }
-  }
-  /*!
-   * \brief set current summary to be pruned summary of src
-   *        assume data field is already allocated to be at least maxsize
-   * \param src source summary
-   * \param maxsize size we can afford in the pruned sketch
-   */
-  inline void SetPrune(const GKSummary &src, size_t maxsize) {
-    if (src.size <= maxsize) {
-      this->CopyFrom(src); return;
-    }
-    const RType max_rank = src.MaxRank();
-    this->size = maxsize;
-    data[0] = src.data[0];
-    size_t n = maxsize - 1;
-    RType top = 1;
-    for (size_t i = 1; i < n; ++i) {
-      RType k = (i * max_rank) / n;
-      while (k > src.data[top + 1].rmax) ++top;
-      // assert src.data[top].rmin <= k
-      // because k > src.data[top].rmax >= src.data[top].rmin
-      if ((k - src.data[top].rmin) < (src.data[top+1].rmax - k)) {
-        data[i] = src.data[top];
-      } else {
-        data[i] = src.data[top + 1];
-      }
-    }
-    data[n] = src.data[src.size - 1];
-  }
-  inline void SetCombine(const GKSummary &sa,
-                         const GKSummary &sb) {
-    if (sa.size == 0) {
-      this->CopyFrom(sb); return;
-    }
-    if (sb.size == 0) {
-      this->CopyFrom(sa); return;
-    }
-    CHECK(sa.size > 0 && sb.size > 0) << "invalid input for merge";
-    const Entry *a = sa.data, *a_end = sa.data + sa.size;
-    const Entry *b = sb.data, *b_end = sb.data + sb.size;
-    this->size = sa.size + sb.size;
-    RType aprev_rmin = 0, bprev_rmin = 0;
-    Entry *dst = this->data;
-    while (a != a_end && b != b_end) {
-      if (a->value < b->value) {
-        *dst = Entry(bprev_rmin + a->rmin,
-                     a->rmax + b->rmax - 1, a->value);
-        aprev_rmin = a->rmin;
-        ++dst; ++a;
-      } else {
-        *dst = Entry(aprev_rmin + b->rmin,
-                     b->rmax + a->rmax - 1, b->value);
-        bprev_rmin = b->rmin;
-        ++dst; ++b;
-      }
-    }
-    if (a != a_end) {
-      RType bprev_rmax = (b_end - 1)->rmax;
-      do {
-        *dst = Entry(bprev_rmin + a->rmin, bprev_rmax + a->rmax, a->value);
-        ++dst; ++a;
-      } while (a != a_end);
-    }
-    if (b != b_end) {
-      RType aprev_rmax = (a_end - 1)->rmax;
-      do {
-        *dst = Entry(aprev_rmin + b->rmin, aprev_rmax + b->rmax, b->value);
-        ++dst; ++b;
-      } while (b != b_end);
-    }
-    CHECK(dst == data + size) << "bug in combine";
-  }
-};
-
-/*!
- * \brief template for all quantile sketch algorithm
- *        that uses merge/prune scheme
- * \tparam DType type of data content
- * \tparam RType type of rank
- * \tparam TSummary actual summary data structure it uses
- */
-template<typename DType, typename RType, class TSummary>
-class QuantileSketchTemplate {
- public:
-  /*! \brief type of summary type */
-  using Summary = TSummary;
-  /*! \brief the entry type */
-  using Entry = typename Summary::Entry;
-  /*! \brief same as summary, but use STL to backup the space */
-  struct SummaryContainer : public Summary {
-    std::vector<Entry> space;
-    SummaryContainer(const SummaryContainer &src) : Summary(nullptr, src.size) {
-      this->space = src.space;
-      this->data = dmlc::BeginPtr(this->space);
-    }
-    SummaryContainer() : Summary(nullptr, 0) {
-    }
-    /*! \brief reserve space for summary */
-    inline void Reserve(size_t size) {
-      if (size > space.size()) {
-        space.resize(size);
-        this->data = dmlc::BeginPtr(space);
-      }
-    }
-    /*!
-     * \brief set the space to be merge of all Summary arrays
-     * \param begin beginning position in the summary array
-     * \param end ending position in the Summary array
-     */
-    inline void SetMerge(const Summary *begin,
-                         const Summary *end) {
-      CHECK(begin < end) << "can not set combine to empty instance";
-      size_t len = end - begin;
-      if (len == 1) {
-        this->Reserve(begin[0].size);
-        this->CopyFrom(begin[0]);
-      } else if (len == 2) {
-        this->Reserve(begin[0].size + begin[1].size);
-        this->SetMerge(begin[0], begin[1]);
-      } else {
-        // recursive merge
-        SummaryContainer lhs, rhs;
-        lhs.SetCombine(begin, begin + len / 2);
-        rhs.SetCombine(begin + len / 2, end);
-        this->Reserve(lhs.size + rhs.size);
-        this->SetCombine(lhs, rhs);
-      }
-    }
-    /*!
-     * \brief do elementwise combination of summary array
-     *        this[i] = combine(this[i], src[i]) for each i
-     * \param src the source summary
-     * \param max_nbyte maximum number of byte allowed in here
-     */
-    inline void Reduce(const Summary &src, size_t max_nbyte) {
-      this->Reserve((max_nbyte - sizeof(this->size)) / sizeof(Entry));
-      SummaryContainer temp;
-      temp.Reserve(this->size + src.size);
-      temp.SetCombine(*this, src);
-      this->SetPrune(temp, space.size());
-    }
-    /*! \brief return the number of bytes this data structure cost in serialization */
-    inline static size_t CalcMemCost(size_t nentry) {
-      return sizeof(size_t) + sizeof(Entry) * nentry;
-    }
-    /*! \brief save the data structure into stream */
-    template<typename TStream>
-    inline void Save(TStream &fo) const {  // NOLINT(*)
-      fo.Write(&(this->size), sizeof(this->size));
-      if (this->size != 0) {
-        fo.Write(this->data, this->size * sizeof(Entry));
-      }
-    }
-    /*! \brief load data structure from input stream */
-    template<typename TStream>
-    inline void Load(TStream &fi) {  // NOLINT(*)
-      CHECK_EQ(fi.Read(&this->size, sizeof(this->size)), sizeof(this->size));
-      this->Reserve(this->size);
-      if (this->size != 0) {
-        CHECK_EQ(fi.Read(this->data, this->size * sizeof(Entry)),
-                 this->size * sizeof(Entry));
-      }
-    }
-  };
-  /*!
-   * \brief initialize the quantile sketch, given the performance specification
-   * \param maxn maximum number of data points can be feed into sketch
-   * \param eps accuracy level of summary
-   */
-  inline void Init(size_t maxn, double eps) {
-    LimitSizeLevel(maxn, eps, &nlevel, &limit_size);
-    // lazy reserve the space, if there is only one value, no need to allocate space
-    inqueue.queue.resize(1);
-    inqueue.qtail = 0;
-    data.clear();
-    level.clear();
-  }
-
-  inline static void LimitSizeLevel
-    (size_t maxn, double eps, size_t* out_nlevel, size_t* out_limit_size) {
-    size_t& nlevel = *out_nlevel;
-    size_t& limit_size = *out_limit_size;
-    nlevel = 1;
-    while (true) {
-      limit_size = static_cast<size_t>(ceil(nlevel / eps)) + 1;
-      limit_size = std::min(maxn, limit_size);
-      size_t n = (1ULL << nlevel);
-      if (n * limit_size >= maxn) break;
-      ++nlevel;
-    }
-    // check invariant
-    size_t n = (1ULL << nlevel);
-    CHECK(n * limit_size >= maxn) << "invalid init parameter";
-    CHECK(nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)))
-        << "invalid init parameter";
-  }
-
-  /*!
-   * \brief add an element to a sketch
-   * \param x The element added to the sketch
-   * \param w The weight of the element.
-   */
-  inline void Push(DType x, RType w = 1) {
-    if (w == static_cast<RType>(0)) return;
-    if (inqueue.qtail == inqueue.queue.size()) {
-      // jump from lazy one value to limit_size * 2
-      if (inqueue.queue.size() == 1) {
-        inqueue.queue.resize(limit_size * 2);
-      } else {
-        temp.Reserve(limit_size * 2);
-        inqueue.MakeSummary(&temp);
-        // cleanup queue
-        inqueue.qtail = 0;
-        this->PushTemp();
-      }
-    }
-    inqueue.Push(x, w);
-  }
-
-  inline void PushSummary(const Summary& summary) {
-    temp.Reserve(limit_size * 2);
-    temp.SetPrune(summary, limit_size * 2);
-    PushTemp();
-  }
-
-  /*! \brief push up temp */
-  inline void PushTemp() {
-    temp.Reserve(limit_size * 2);
-    for (size_t l = 1; true; ++l) {
-      this->InitLevel(l + 1);
-      // check if level l is empty
-      if (level[l].size == 0) {
-        level[l].SetPrune(temp, limit_size);
-        break;
-      } else {
-        // level 0 is actually temp space
-        level[0].SetPrune(temp, limit_size);
-        temp.SetCombine(level[0], level[l]);
-        if (temp.size > limit_size) {
-          // try next level
-          level[l].size = 0;
-        } else {
-          // if merged record is still smaller, no need to send to next level
-          level[l].CopyFrom(temp); break;
-        }
-      }
-    }
-  }
-  /*! \brief get the summary after finalize */
-  inline void GetSummary(SummaryContainer *out) {
-    if (level.size() != 0) {
-      out->Reserve(limit_size * 2);
-    } else {
-      out->Reserve(inqueue.queue.size());
-    }
-    inqueue.MakeSummary(out);
-    if (level.size() != 0) {
-      level[0].SetPrune(*out, limit_size);
-      for (size_t l = 1; l < level.size(); ++l) {
-        if (level[l].size == 0) continue;
-        if (level[0].size == 0) {
-          level[0].CopyFrom(level[l]);
-        } else {
-          out->SetCombine(level[0], level[l]);
-          level[0].SetPrune(*out, limit_size);
-        }
-      }
-      out->CopyFrom(level[0]);
-    } else {
-      if (out->size > limit_size) {
-        temp.Reserve(limit_size);
-        temp.SetPrune(*out, limit_size);
-        out->CopyFrom(temp);
-      }
-    }
-  }
-  // used for debug, check if the sketch is valid
-  inline void CheckValid(RType eps) const {
-    for (size_t l = 1; l < level.size(); ++l) {
-      level[l].CheckValid(eps);
-    }
-  }
-  // initialize level space to at least nlevel
-  inline void InitLevel(size_t nlevel) {
-    if (level.size() >= nlevel) return;
-    data.resize(limit_size * nlevel);
-    level.resize(nlevel, Summary(nullptr, 0));
-    for (size_t l = 0; l < level.size(); ++l) {
-      level[l].data = dmlc::BeginPtr(data) + l * limit_size;
-    }
-  }
-  // input data queue
-  typename Summary::Queue inqueue;
-  // number of levels
-  size_t nlevel;
-  // size of summary in each level
-  size_t limit_size;
-  // the level of each summaries
-  std::vector<Summary> level;
-  // content of the summary
-  std::vector<Entry> data;
-  // temporal summary, used for temp-merge
-  SummaryContainer temp;
-};
-
-/*!
- * \brief Quantile sketch use WQSummary
- * \tparam DType type of data content
- * \tparam RType type of rank
- */
-template<typename DType, typename RType = unsigned>
-class WQuantileSketch :
-      public QuantileSketchTemplate<DType, RType, WQSummary<DType, RType> > {
-};
-
-/*!
- * \brief Quantile sketch use WXQSummary
- * \tparam DType type of data content
- * \tparam RType type of rank
- */
-template<typename DType, typename RType = unsigned>
-class WXQuantileSketch :
-      public QuantileSketchTemplate<DType, RType, WXQSummary<DType, RType> > {
-};
-/*!
- * \brief Quantile sketch use WQSummary
- * \tparam DType type of data content
- * \tparam RType type of rank
- */
-template<typename DType, typename RType = unsigned>
-class GKQuantileSketch :
-      public QuantileSketchTemplate<DType, RType, GKSummary<DType, RType> > {
-};
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_QUANTILE_H_
diff --git a/ml-xgboost/src/common/random.h b/ml-xgboost/src/common/random.h
deleted file mode 100644
index 0d1c08b..0000000
--- a/ml-xgboost/src/common/random.h
+++ /dev/null
@@ -1,201 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file random.h
- * \brief Utility related to random.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_RANDOM_H_
-#define XGBOOST_COMMON_RANDOM_H_
-
-#include <rabit/rabit.h>
-#include <xgboost/logging.h>
-#include <algorithm>
-#include <vector>
-#include <limits>
-#include <map>
-#include <memory>
-#include <numeric>
-#include <random>
-
-#include "xgboost/host_device_vector.h"
-#include "io.h"
-
-namespace xgboost {
-namespace common {
-/*!
- * \brief Define mt19937 as default type Random Engine.
- */
-using RandomEngine = std::mt19937;
-
-#if XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-/*!
- * \brief An customized random engine, used to be plugged in PRNG from other systems.
- *  The implementation of this library is not provided by xgboost core library.
- *  Instead the other library can implement this class, which will be used as GlobalRandomEngine
- *  If XGBOOST_RANDOM_CUSTOMIZE = 1, by default this is switched off.
- */
-class CustomGlobalRandomEngine {
- public:
-  /*! \brief The result type */
-  using result_type = uint32_t;
-  /*! \brief The minimum of random numbers generated */
-  inline static constexpr result_type min() {
-    return 0;
-  }
-  /*! \brief The maximum random numbers generated */
-  inline static constexpr result_type max() {
-    return std::numeric_limits<result_type>::max();
-  }
-  /*!
-   * \brief seed function, to be implemented
-   * \param val The value of the seed.
-   */
-  void seed(result_type val);
-  /*!
-   * \return next random number.
-   */
-  result_type operator()();
-};
-
-/*!
- * \brief global random engine
- */
-typedef CustomGlobalRandomEngine GlobalRandomEngine;
-
-#else
-/*!
- * \brief global random engine
- */
-using GlobalRandomEngine = RandomEngine;
-#endif  // XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-
-/*!
- * \brief global singleton of a random engine.
- *  This random engine is thread-local and
- *  only visible to current thread.
- */
-GlobalRandomEngine& GlobalRandom(); // NOLINT(*)
-
-/**
- * \class ColumnSampler
- *
- * \brief Handles selection of columns due to colsample_bytree, colsample_bylevel and
- * colsample_bynode parameters. Should be initialised before tree construction and to
- * reset when tree construction is completed.
- */
-
-class ColumnSampler {
-  std::shared_ptr<HostDeviceVector<bst_feature_t>> feature_set_tree_;
-  std::map<int, std::shared_ptr<HostDeviceVector<bst_feature_t>>> feature_set_level_;
-  float colsample_bylevel_{1.0f};
-  float colsample_bytree_{1.0f};
-  float colsample_bynode_{1.0f};
-  GlobalRandomEngine rng_;
-
-  std::shared_ptr<HostDeviceVector<bst_feature_t>> ColSample(
-      std::shared_ptr<HostDeviceVector<bst_feature_t>> p_features, float colsample) {
-    if (colsample == 1.0f) return p_features;
-    const auto& features = p_features->HostVector();
-    CHECK_GT(features.size(), 0);
-    int n = std::max(1, static_cast<int>(colsample * features.size()));
-    auto p_new_features = std::make_shared<HostDeviceVector<bst_feature_t>>();
-    auto& new_features = *p_new_features;
-    new_features.Resize(features.size());
-    std::copy(features.begin(), features.end(),
-              new_features.HostVector().begin());
-    std::shuffle(new_features.HostVector().begin(),
-                 new_features.HostVector().end(), rng_);
-    new_features.Resize(n);
-    std::sort(new_features.HostVector().begin(),
-              new_features.HostVector().end());
-
-    return p_new_features;
-  }
-
- public:
-  /**
-   * \brief Column sampler constructor.
-   * \note This constructor manually sets the rng seed
-   */
-  explicit ColumnSampler(uint32_t seed) {
-    rng_.seed(seed);
-  }
-
-  /**
-  * \brief Column sampler constructor.
-  * \note This constructor synchronizes the RNG seed across processes.
-  */
-  ColumnSampler() {
-    uint32_t seed = common::GlobalRandom()();
-    rabit::Broadcast(&seed, sizeof(seed), 0, "seed");
-    rng_.seed(seed);
-  }
-
-  /**
-   * \brief Initialise this object before use.
-   *
-   * \param num_col
-   * \param colsample_bynode
-   * \param colsample_bylevel
-   * \param colsample_bytree
-   * \param skip_index_0      (Optional) True to skip index 0.
-   */
-  void Init(int64_t num_col, float colsample_bynode, float colsample_bylevel,
-            float colsample_bytree, bool skip_index_0 = false) {
-    colsample_bylevel_ = colsample_bylevel;
-    colsample_bytree_ = colsample_bytree;
-    colsample_bynode_ = colsample_bynode;
-
-    if (feature_set_tree_ == nullptr) {
-      feature_set_tree_ = std::make_shared<HostDeviceVector<bst_feature_t>>();
-    }
-    Reset();
-
-    int begin_idx = skip_index_0 ? 1 : 0;
-    feature_set_tree_->Resize(num_col - begin_idx);
-    std::iota(feature_set_tree_->HostVector().begin(),
-              feature_set_tree_->HostVector().end(), begin_idx);
-
-    feature_set_tree_ = ColSample(feature_set_tree_, colsample_bytree_);
-  }
-
-  /**
-   * \brief Resets this object.
-   */
-  void Reset() {
-    feature_set_tree_->Resize(0);
-    feature_set_level_.clear();
-  }
-
-  /**
-   * \brief Samples a feature set.
-   *
-   * \param depth The tree depth of the node at which to sample.
-   * \return The sampled feature set.
-   * \note If colsample_bynode_ < 1.0, this method creates a new feature set each time it
-   * is called. Therefore, it should be called only once per node.
-   * \note With distributed xgboost, this function must be called exactly once for the
-   * construction of each tree node, and must be called the same number of times in each
-   * process and with the same parameters to return the same feature set across processes.
-   */
-  std::shared_ptr<HostDeviceVector<bst_feature_t>> GetFeatureSet(int depth) {
-    if (colsample_bylevel_ == 1.0f && colsample_bynode_ == 1.0f) {
-      return feature_set_tree_;
-    }
-
-    if (feature_set_level_.count(depth) == 0) {
-      // Level sampling, level does not yet exist so generate it
-      feature_set_level_[depth] = ColSample(feature_set_tree_, colsample_bylevel_);
-    }
-    if (colsample_bynode_ == 1.0f) {
-      // Level sampling
-      return feature_set_level_[depth];
-    }
-    // Need to sample for the node individually
-    return ColSample(feature_set_level_[depth], colsample_bynode_);
-  }
-};
-
-}  // namespace common
-}  // namespace xgboost
-#endif  // XGBOOST_COMMON_RANDOM_H_
diff --git a/ml-xgboost/src/common/row_set.h b/ml-xgboost/src/common/row_set.h
deleted file mode 100644
index 25f7c73..0000000
--- a/ml-xgboost/src/common/row_set.h
+++ /dev/null
@@ -1,246 +0,0 @@
-/*!
- * Copyright 2017 by Contributors
- * \file row_set.h
- * \brief Quick Utility to compute subset of rows
- * \author Philip Cho, Tianqi Chen
- */
-#ifndef XGBOOST_COMMON_ROW_SET_H_
-#define XGBOOST_COMMON_ROW_SET_H_
-
-#include <xgboost/data.h>
-#include <algorithm>
-#include <vector>
-#include <utility>
-
-namespace xgboost {
-namespace common {
-
-/*! \brief collection of rowset */
-class RowSetCollection {
- public:
-  /*! \brief data structure to store an instance set, a subset of
-   *  rows (instances) associated with a particular node in a decision
-   *  tree. */
-  struct Elem {
-    const size_t* begin{nullptr};
-    const size_t* end{nullptr};
-    int node_id{-1};
-      // id of node associated with this instance set; -1 means uninitialized
-    Elem()
-         = default;
-    Elem(const size_t* begin,
-         const size_t* end,
-         int node_id = -1)
-        : begin(begin), end(end), node_id(node_id) {}
-
-    inline size_t Size() const {
-      return end - begin;
-    }
-  };
-  /* \brief specifies how to split a rowset into two */
-  struct Split {
-    std::vector<size_t> left;
-    std::vector<size_t> right;
-  };
-
-  inline std::vector<Elem>::const_iterator begin() const {  // NOLINT
-    return elem_of_each_node_.begin();
-  }
-
-  inline std::vector<Elem>::const_iterator end() const {  // NOLINT
-    return elem_of_each_node_.end();
-  }
-
-  /*! \brief return corresponding element set given the node_id */
-  inline const Elem& operator[](unsigned node_id) const {
-    const Elem& e = elem_of_each_node_[node_id];
-    CHECK(e.begin != nullptr)
-        << "access element that is not in the set";
-    return e;
-  }
-
-  /*! \brief return corresponding element set given the node_id */
-  inline Elem& operator[](unsigned node_id) {
-    Elem& e = elem_of_each_node_[node_id];
-    return e;
-  }
-
-  // clear up things
-  inline void Clear() {
-    elem_of_each_node_.clear();
-  }
-  // initialize node id 0->everything
-  inline void Init() {
-    CHECK_EQ(elem_of_each_node_.size(), 0U);
-
-    if (row_indices_.empty()) {  // edge case: empty instance set
-      // assign arbitrary address here, to bypass nullptr check
-      // (nullptr usually indicates a nonexistent rowset, but we want to
-      //  indicate a valid rowset that happens to have zero length and occupies
-      //  the whole instance set)
-      // this is okay, as BuildHist will compute (end-begin) as the set size
-      const size_t* begin = reinterpret_cast<size_t*>(20);
-      const size_t* end = begin;
-      elem_of_each_node_.emplace_back(Elem(begin, end, 0));
-      return;
-    }
-
-    const size_t* begin = dmlc::BeginPtr(row_indices_);
-    const size_t* end = dmlc::BeginPtr(row_indices_) + row_indices_.size();
-    elem_of_each_node_.emplace_back(Elem(begin, end, 0));
-  }
-
-  std::vector<size_t>* Data() { return &row_indices_; }
-  // split rowset into two
-  inline void AddSplit(unsigned node_id,
-                       unsigned left_node_id,
-                       unsigned right_node_id,
-                       size_t n_left,
-                       size_t n_right) {
-    const Elem e = elem_of_each_node_[node_id];
-    CHECK(e.begin != nullptr);
-    size_t* all_begin = dmlc::BeginPtr(row_indices_);
-    size_t* begin = all_begin + (e.begin - all_begin);
-
-    CHECK_EQ(n_left + n_right, e.Size());
-    CHECK_LE(begin + n_left, e.end);
-    CHECK_EQ(begin + n_left + n_right, e.end);
-
-    if (left_node_id >= elem_of_each_node_.size()) {
-      elem_of_each_node_.resize(left_node_id + 1, Elem(nullptr, nullptr, -1));
-    }
-    if (right_node_id >= elem_of_each_node_.size()) {
-      elem_of_each_node_.resize(right_node_id + 1, Elem(nullptr, nullptr, -1));
-    }
-
-    elem_of_each_node_[left_node_id] = Elem(begin, begin + n_left, left_node_id);
-    elem_of_each_node_[right_node_id] = Elem(begin + n_left, e.end, right_node_id);
-    elem_of_each_node_[node_id] = Elem(nullptr, nullptr, -1);
-  }
-
- private:
-  // stores the row indexes in the set
-  std::vector<size_t> row_indices_;
-  // vector: node_id -> elements
-  std::vector<Elem> elem_of_each_node_;
-};
-
-
-// The builder is required for samples partition to left and rights children for set of nodes
-// Responsible for:
-// 1) Effective memory allocation for intermediate results for multi-thread work
-// 2) Merging partial results produced by threads into original row set (row_set_collection_)
-// BlockSize is template to enable memory alignment easily with C++11 'alignas()' feature
-template<size_t BlockSize>
-class PartitionBuilder {
- public:
-  template<typename Func>
-  void Init(const size_t n_tasks, size_t n_nodes, Func funcNTaks) {
-    left_right_nodes_sizes_.resize(n_nodes);
-    blocks_offsets_.resize(n_nodes+1);
-
-    blocks_offsets_[0] = 0;
-    for (size_t i = 1; i < n_nodes+1; ++i) {
-      blocks_offsets_[i] = blocks_offsets_[i-1] + funcNTaks(i-1);
-    }
-
-    if (n_tasks > max_n_tasks_) {
-      mem_blocks_.resize(n_tasks);
-      max_n_tasks_ = n_tasks;
-    }
-  }
-
-  common::Span<size_t> GetLeftBuffer(int nid, size_t begin, size_t end) {
-    const size_t task_idx = GetTaskIdx(nid, begin);
-    return { mem_blocks_.at(task_idx).Left(), end - begin };
-  }
-
-  common::Span<size_t> GetRightBuffer(int nid, size_t begin, size_t end) {
-    const size_t task_idx = GetTaskIdx(nid, begin);
-    return { mem_blocks_.at(task_idx).Right(), end - begin };
-  }
-
-  void SetNLeftElems(int nid, size_t begin, size_t end, size_t n_left) {
-    size_t task_idx = GetTaskIdx(nid, begin);
-    mem_blocks_.at(task_idx).n_left = n_left;
-  }
-
-  void SetNRightElems(int nid, size_t begin, size_t end, size_t n_right) {
-    size_t task_idx = GetTaskIdx(nid, begin);
-    mem_blocks_.at(task_idx).n_right = n_right;
-  }
-
-
-  size_t GetNLeftElems(int nid) const {
-    return left_right_nodes_sizes_[nid].first;
-  }
-
-  size_t GetNRightElems(int nid) const {
-    return left_right_nodes_sizes_[nid].second;
-  }
-
-  // Each thread has partial results for some set of tree-nodes
-  // The function decides order of merging partial results into final row set
-  void CalculateRowOffsets() {
-    for (size_t i = 0; i < blocks_offsets_.size()-1; ++i) {
-      size_t n_left = 0;
-      for (size_t j = blocks_offsets_[i]; j < blocks_offsets_[i+1]; ++j) {
-        mem_blocks_[j].n_offset_left = n_left;
-        n_left += mem_blocks_[j].n_left;
-      }
-      size_t n_right = 0;
-      for (size_t j = blocks_offsets_[i]; j < blocks_offsets_[i+1]; ++j) {
-        mem_blocks_[j].n_offset_right = n_left + n_right;
-        n_right += mem_blocks_[j].n_right;
-      }
-      left_right_nodes_sizes_[i] = {n_left, n_right};
-    }
-  }
-
-  void MergeToArray(int nid, size_t begin, size_t* rows_indexes) {
-    size_t task_idx = GetTaskIdx(nid, begin);
-
-    size_t* left_result  = rows_indexes + mem_blocks_[task_idx].n_offset_left;
-    size_t* right_result = rows_indexes + mem_blocks_[task_idx].n_offset_right;
-
-    const size_t* left = mem_blocks_[task_idx].Left();
-    const size_t* right = mem_blocks_[task_idx].Right();
-
-    std::copy_n(left, mem_blocks_[task_idx].n_left, left_result);
-    std::copy_n(right, mem_blocks_[task_idx].n_right, right_result);
-  }
-
- protected:
-  size_t GetTaskIdx(int nid, size_t begin) {
-    return blocks_offsets_[nid] + begin / BlockSize;
-  }
-
-  struct BlockInfo{
-    size_t n_left;
-    size_t n_right;
-
-    size_t n_offset_left;
-    size_t n_offset_right;
-
-    size_t* Left() {
-      return &left_data_[0];
-    }
-
-    size_t* Right() {
-      return &right_data_[0];
-    }
-   private:
-    alignas(128) size_t left_data_[BlockSize];
-    alignas(128) size_t right_data_[BlockSize];
-  };
-  std::vector<std::pair<size_t, size_t>> left_right_nodes_sizes_;
-  std::vector<size_t> blocks_offsets_;
-  std::vector<BlockInfo> mem_blocks_;
-  size_t max_n_tasks_ = 0;
-};
-
-
-}  // namespace common
-}  // namespace xgboost
-
-#endif  // XGBOOST_COMMON_ROW_SET_H_
diff --git a/ml-xgboost/src/common/survival_util.cc b/ml-xgboost/src/common/survival_util.cc
deleted file mode 100644
index 5630da3..0000000
--- a/ml-xgboost/src/common/survival_util.cc
+++ /dev/null
@@ -1,264 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- * \file survival_util.cc
- * \brief Utility functions, useful for implementing objective and metric functions for survival
- *        analysis
- * \author Avinash Barnwal, Hyunsu Cho and Toby Hocking
- */
-
-#include <dmlc/registry.h>
-#include <algorithm>
-#include <cmath>
-#include "survival_util.h"
-
-/*
-- Formulas are motivated from document -
-  http://members.cbio.mines-paristech.fr/~thocking/survival.pdf
-- Detailed Derivation of Loss/Gradient/Hessian -
-  https://github.com/avinashbarnwal/GSOC-2019/blob/master/doc/Accelerated_Failure_Time.pdf
-*/
-
-namespace {
-
-// Allowable range for gradient and hessian. Used for regularization
-constexpr double kMinGradient = -15.0;
-constexpr double kMaxGradient = 15.0;
-constexpr double kMinHessian = 1e-16;  // Ensure that no data point gets zero hessian
-constexpr double kMaxHessian = 15.0;
-
-constexpr double kEps = 1e-12;  // A denomitor in a fraction should not be too small
-
-// Clip (limit) x to fit range [x_min, x_max].
-// If x < x_min, return x_min; if x > x_max, return x_max; if x_min <= x <= x_max, return x.
-// This function assumes x_min < x_max; behavior is undefined if this assumption does not hold.
-inline double Clip(double x, double x_min, double x_max) {
-  if (x < x_min) {
-    return x_min;
-  }
-  if (x > x_max) {
-    return x_max;
-  }
-  return x;
-}
-
-using xgboost::common::ProbabilityDistributionType;
-
-enum class CensoringType : uint8_t {
-  kUncensored, kRightCensored, kLeftCensored, kIntervalCensored
-};
-
-using xgboost::GradientPairPrecise;
-
-inline GradientPairPrecise GetLimitAtInfPred(ProbabilityDistributionType dist_type,
-                                             CensoringType censor_type,
-                                             double sign, double sigma) {
-  switch (censor_type) {
-  case CensoringType::kUncensored:
-    switch (dist_type) {
-    case ProbabilityDistributionType::kNormal:
-      return sign ? GradientPairPrecise{ kMinGradient, 1.0 / (sigma * sigma) }
-                  : GradientPairPrecise{ kMaxGradient, 1.0 / (sigma * sigma) };
-    case ProbabilityDistributionType::kLogistic:
-      return sign ? GradientPairPrecise{ -1.0 / sigma, kMinHessian }
-                  : GradientPairPrecise{ 1.0 / sigma, kMinHessian };
-    case ProbabilityDistributionType::kExtreme:
-      return sign ? GradientPairPrecise{ kMinGradient, kMaxHessian }
-                  : GradientPairPrecise{ 1.0 / sigma, kMinHessian };
-    default:
-      LOG(FATAL) << "Unknown distribution type";
-    }
-  case CensoringType::kRightCensored:
-    switch (dist_type) {
-    case ProbabilityDistributionType::kNormal:
-      return sign ? GradientPairPrecise{ kMinGradient, 1.0 / (sigma * sigma) }
-                  : GradientPairPrecise{ 0.0, kMinHessian };
-    case ProbabilityDistributionType::kLogistic:
-      return sign ? GradientPairPrecise{ -1.0 / sigma, kMinHessian }
-                  : GradientPairPrecise{ 0.0, kMinHessian };
-    case ProbabilityDistributionType::kExtreme:
-      return sign ? GradientPairPrecise{ kMinGradient, kMaxHessian }
-                  : GradientPairPrecise{ 0.0, kMinHessian };
-    default:
-      LOG(FATAL) << "Unknown distribution type";
-    }
-  case CensoringType::kLeftCensored:
-    switch (dist_type) {
-    case ProbabilityDistributionType::kNormal:
-      return sign ? GradientPairPrecise{ 0.0, kMinHessian }
-                  : GradientPairPrecise{ kMaxGradient, 1.0 / (sigma * sigma) };
-    case ProbabilityDistributionType::kLogistic:
-      return sign ? GradientPairPrecise{ 0.0, kMinHessian }
-                  : GradientPairPrecise{ 1.0 / sigma, kMinHessian };
-    case ProbabilityDistributionType::kExtreme:
-      return sign ? GradientPairPrecise{ 0.0, kMinHessian }
-                  : GradientPairPrecise{ 1.0 / sigma, kMinHessian };
-    default:
-      LOG(FATAL) << "Unknown distribution type";
-    }
-  case CensoringType::kIntervalCensored:
-    switch (dist_type) {
-    case ProbabilityDistributionType::kNormal:
-      return sign ? GradientPairPrecise{ kMinGradient, 1.0 / (sigma * sigma) }
-                  : GradientPairPrecise{ kMaxGradient, 1.0 / (sigma * sigma) };
-    case ProbabilityDistributionType::kLogistic:
-      return sign ? GradientPairPrecise{ -1.0 / sigma, kMinHessian }
-                  : GradientPairPrecise{ 1.0 / sigma, kMinHessian };
-    case ProbabilityDistributionType::kExtreme:
-      return sign ? GradientPairPrecise{ kMinGradient, kMaxHessian }
-                  : GradientPairPrecise{ 1.0 / sigma, kMinHessian };
-    default:
-      LOG(FATAL) << "Unknown distribution type";
-    }
-  default:
-    LOG(FATAL) << "Unknown censoring type";
-  }
-
-  return { 0.0, 0.0 };
-}
-
-}  // anonymous namespace
-
-namespace xgboost {
-namespace common {
-
-DMLC_REGISTER_PARAMETER(AFTParam);
-
-double AFTLoss::Loss(double y_lower, double y_upper, double y_pred, double sigma) {
-  const double log_y_lower = std::log(y_lower);
-  const double log_y_upper = std::log(y_upper);
-
-  double cost;
-
-  if (y_lower == y_upper) {  // uncensored
-    const double z = (log_y_lower - y_pred) / sigma;
-    const double pdf = dist_->PDF(z);
-    // Regularize the denominator with eps, to avoid INF or NAN
-    cost = -std::log(std::max(pdf / (sigma * y_lower), kEps));
-  } else {  // censored; now check what type of censorship we have
-    double z_u, z_l, cdf_u, cdf_l;
-    if (std::isinf(y_upper)) {  // right-censored
-      cdf_u = 1;
-    } else {  // left-censored or interval-censored
-      z_u = (log_y_upper - y_pred) / sigma;
-      cdf_u = dist_->CDF(z_u);
-    }
-    if (std::isinf(y_lower)) {  // left-censored
-      cdf_l = 0;
-    } else {  // right-censored or interval-censored
-      z_l = (log_y_lower - y_pred) / sigma;
-      cdf_l = dist_->CDF(z_l);
-    }
-    // Regularize the denominator with eps, to avoid INF or NAN
-    cost = -std::log(std::max(cdf_u - cdf_l, kEps));
-  }
-
-  return cost;
-}
-
-double AFTLoss::Gradient(double y_lower, double y_upper, double y_pred, double sigma) {
-  const double log_y_lower = std::log(y_lower);
-  const double log_y_upper = std::log(y_upper);
-  double numerator, denominator, gradient;  // numerator and denominator of gradient
-  CensoringType censor_type;
-  bool z_sign;  // sign of z-score
-
-  if (y_lower == y_upper) {  // uncensored
-    const double z = (log_y_lower - y_pred) / sigma;
-    const double pdf = dist_->PDF(z);
-    const double grad_pdf = dist_->GradPDF(z);
-    censor_type = CensoringType::kUncensored;
-    numerator = grad_pdf;
-    denominator = sigma * pdf;
-    z_sign = (z > 0);
-  } else {  // censored; now check what type of censorship we have
-    double z_u = 0.0, z_l = 0.0, pdf_u, pdf_l, cdf_u, cdf_l;
-    censor_type = CensoringType::kIntervalCensored;
-    if (std::isinf(y_upper)) {  // right-censored
-      pdf_u = 0;
-      cdf_u = 1;
-      censor_type = CensoringType::kRightCensored;
-    } else {  // interval-censored or left-censored
-      z_u = (log_y_upper - y_pred) / sigma;
-      pdf_u = dist_->PDF(z_u);
-      cdf_u = dist_->CDF(z_u);
-    }
-    if (std::isinf(y_lower)) {  // left-censored
-      pdf_l = 0;
-      cdf_l = 0;
-      censor_type = CensoringType::kLeftCensored;
-    } else {  // interval-censored or right-censored
-      z_l = (log_y_lower - y_pred) / sigma;
-      pdf_l = dist_->PDF(z_l);
-      cdf_l = dist_->CDF(z_l);
-    }
-    z_sign = (z_u > 0 || z_l > 0);
-    numerator = pdf_u - pdf_l;
-    denominator = sigma * (cdf_u - cdf_l);
-  }
-  gradient = numerator / denominator;
-  if (denominator < kEps && (std::isnan(gradient) || std::isinf(gradient))) {
-    gradient = GetLimitAtInfPred(dist_type_, censor_type, z_sign, sigma).GetGrad();
-  }
-
-  return Clip(gradient, kMinGradient, kMaxGradient);
-}
-
-double AFTLoss::Hessian(double y_lower, double y_upper, double y_pred, double sigma) {
-  const double log_y_lower = std::log(y_lower);
-  const double log_y_upper = std::log(y_upper);
-  double numerator, denominator, hessian;  // numerator and denominator of hessian
-  CensoringType censor_type;
-  bool z_sign;  // sign of z-score
-
-  if (y_lower == y_upper) {  // uncensored
-    const double z = (log_y_lower - y_pred) / sigma;
-    const double pdf = dist_->PDF(z);
-    const double grad_pdf = dist_->GradPDF(z);
-    const double hess_pdf = dist_->HessPDF(z);
-    censor_type = CensoringType::kUncensored;
-    numerator = -(pdf * hess_pdf - grad_pdf * grad_pdf);
-    denominator = sigma * sigma * pdf * pdf;
-    z_sign = (z > 0);
-  } else {  // censored; now check what type of censorship we have
-    double z_u = 0.0, z_l = 0.0, grad_pdf_u, grad_pdf_l, pdf_u, pdf_l, cdf_u, cdf_l;
-    censor_type = CensoringType::kIntervalCensored;
-    if (std::isinf(y_upper)) {  // right-censored
-      pdf_u = 0;
-      cdf_u = 1;
-      grad_pdf_u = 0;
-      censor_type = CensoringType::kRightCensored;
-    } else {  // interval-censored or left-censored
-      z_u = (log_y_upper - y_pred) / sigma;
-      pdf_u = dist_->PDF(z_u);
-      cdf_u = dist_->CDF(z_u);
-      grad_pdf_u = dist_->GradPDF(z_u);
-    }
-    if (std::isinf(y_lower)) {  // left-censored
-      pdf_l = 0;
-      cdf_l = 0;
-      grad_pdf_l = 0;
-      censor_type = CensoringType::kLeftCensored;
-    } else {  // interval-censored or right-censored
-      z_l = (log_y_lower - y_pred) / sigma;
-      pdf_l = dist_->PDF(z_l);
-      cdf_l = dist_->CDF(z_l);
-      grad_pdf_l = dist_->GradPDF(z_l);
-    }
-    const double cdf_diff = cdf_u - cdf_l;
-    const double pdf_diff = pdf_u - pdf_l;
-    const double grad_diff = grad_pdf_u - grad_pdf_l;
-    const double sqrt_denominator = sigma * cdf_diff;
-    z_sign = (z_u > 0 || z_l > 0);
-    numerator = -(cdf_diff * grad_diff - pdf_diff * pdf_diff);
-    denominator = sqrt_denominator * sqrt_denominator;
-  }
-  hessian = numerator / denominator;
-  if (denominator < kEps && (std::isnan(hessian) || std::isinf(hessian))) {
-    hessian = GetLimitAtInfPred(dist_type_, censor_type, z_sign, sigma).GetHess();
-  }
-
-  return Clip(hessian, kMinHessian, kMaxHessian);
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/survival_util.h b/ml-xgboost/src/common/survival_util.h
deleted file mode 100644
index 50c6aab..0000000
--- a/ml-xgboost/src/common/survival_util.h
+++ /dev/null
@@ -1,86 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- * \file survival_util.h
- * \brief Utility functions, useful for implementing objective and metric functions for survival
- *        analysis
- * \author Avinash Barnwal, Hyunsu Cho and Toby Hocking
- */
-#ifndef XGBOOST_COMMON_SURVIVAL_UTIL_H_
-#define XGBOOST_COMMON_SURVIVAL_UTIL_H_
-
-#include <xgboost/parameter.h>
-#include <memory>
-#include "probability_distribution.h"
-
-DECLARE_FIELD_ENUM_CLASS(xgboost::common::ProbabilityDistributionType);
-
-namespace xgboost {
-namespace common {
-
-/*! \brief Parameter structure for AFT loss and metric */
-struct AFTParam : public XGBoostParameter<AFTParam> {
-  /*! \brief Choice of probability distribution for the noise term in AFT */
-  ProbabilityDistributionType aft_loss_distribution;
-  /*! \brief Scaling factor to be applied to the distribution */
-  float aft_loss_distribution_scale;
-  DMLC_DECLARE_PARAMETER(AFTParam) {
-    DMLC_DECLARE_FIELD(aft_loss_distribution)
-        .set_default(ProbabilityDistributionType::kNormal)
-        .add_enum("normal", ProbabilityDistributionType::kNormal)
-        .add_enum("logistic", ProbabilityDistributionType::kLogistic)
-        .add_enum("extreme", ProbabilityDistributionType::kExtreme)
-        .describe("Choice of distribution for the noise term in "
-                  "Accelerated Failure Time model");
-    DMLC_DECLARE_FIELD(aft_loss_distribution_scale)
-        .set_default(1.0f)
-        .describe("Scaling factor used to scale the distribution in "
-                  "Accelerated Failure Time model");
-  }
-};
-
-/*! \brief The AFT loss function */
-class AFTLoss {
- private:
-  std::unique_ptr<ProbabilityDistribution> dist_;
-  ProbabilityDistributionType dist_type_;
-
- public:
-  /*!
-   * \brief Constructor for AFT loss function
-   * \param dist_type Choice of probability distribution for the noise term in AFT
-   */
-  explicit AFTLoss(ProbabilityDistributionType dist_type)
-    : dist_(ProbabilityDistribution::Create(dist_type)),
-      dist_type_(dist_type) {}
-
- public:
-  /*!
-   * \brief Compute the AFT loss
-   * \param y_lower Lower bound for the true label
-   * \param y_upper Upper bound for the true label
-   * \param y_pred Predicted label
-   * \param sigma Scaling factor to be applied to the distribution of the noise term
-   */
-  double Loss(double y_lower, double y_upper, double y_pred, double sigma);
-  /*!
-   * \brief Compute the gradient of the AFT loss
-   * \param y_lower Lower bound for the true label
-   * \param y_upper Upper bound for the true label
-   * \param y_pred Predicted label
-   * \param sigma Scaling factor to be applied to the distribution of the noise term
-   */
-  double Gradient(double y_lower, double y_upper, double y_pred, double sigma);
-  /*!
-   * \brief Compute the hessian of the AFT loss
-   * \param y_lower Lower bound for the true label
-   * \param y_upper Upper bound for the true label
-   * \param y_pred Predicted label
-   * \param sigma Scaling factor to be applied to the distribution of the noise term
-   */
-  double Hessian(double y_lower, double y_upper, double y_pred, double sigma);
-};
-
-}  // namespace common
-}  // namespace xgboost
-
-#endif  // XGBOOST_COMMON_SURVIVAL_UTIL_H_
diff --git a/ml-xgboost/src/common/threading_utils.h b/ml-xgboost/src/common/threading_utils.h
deleted file mode 100644
index 3f0519b..0000000
--- a/ml-xgboost/src/common/threading_utils.h
+++ /dev/null
@@ -1,134 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file common.h
- * \brief Threading utilities
- */
-#ifndef XGBOOST_COMMON_THREADING_UTILS_H_
-#define XGBOOST_COMMON_THREADING_UTILS_H_
-
-#include <vector>
-#include <algorithm>
-
-#include "xgboost/logging.h"
-
-namespace xgboost {
-namespace common {
-
-// Represent simple range of indexes [begin, end)
-// Inspired by tbb::blocked_range
-class Range1d {
- public:
-  Range1d(size_t begin, size_t end): begin_(begin), end_(end) {
-    CHECK_LT(begin, end);
-  }
-
-  size_t begin() const {  // NOLINT
-    return begin_;
-  }
-
-  size_t end() const {  // NOLINT
-    return end_;
-  }
-
- private:
-  size_t begin_;
-  size_t end_;
-};
-
-
-// Split 2d space to balanced blocks
-// Implementation of the class is inspired by tbb::blocked_range2d
-// However, TBB provides only (n x m) 2d range (matrix) separated by blocks. Example:
-// [ 1,2,3 ]
-// [ 4,5,6 ]
-// [ 7,8,9 ]
-// But the class is able to work with different sizes in each 'row'. Example:
-// [ 1,2 ]
-// [ 3,4,5,6 ]
-// [ 7,8,9]
-// If grain_size is 2: It produces following blocks:
-// [1,2], [3,4], [5,6], [7,8], [9]
-// The class helps to process data in several tree nodes (non-balanced usually) in parallel
-// Using nested parallelism (by nodes and by data in each node)
-// it helps  to improve CPU resources utilization
-class BlockedSpace2d {
- public:
-  // Example of space:
-  // [ 1,2 ]
-  // [ 3,4,5,6 ]
-  // [ 7,8,9]
-  // BlockedSpace2d will create following blocks (tasks) if grain_size=2:
-  // 1-block: first_dimension = 0, range of indexes in a 'row' = [0,2) (includes [1,2] values)
-  // 2-block: first_dimension = 1, range of indexes in a 'row' = [0,2) (includes [3,4] values)
-  // 3-block: first_dimension = 1, range of indexes in a 'row' = [2,4) (includes [5,6] values)
-  // 4-block: first_dimension = 2, range of indexes in a 'row' = [0,2) (includes [7,8] values)
-  // 5-block: first_dimension = 2, range of indexes in a 'row' = [2,3) (includes [9] values)
-  // Arguments:
-  // dim1 - size of the first dimension in the space
-  // getter_size_dim2 - functor to get the second dimensions for each 'row' by row-index
-  // grain_size - max size of produced blocks
-  template<typename Func>
-  BlockedSpace2d(size_t dim1, Func getter_size_dim2, size_t grain_size) {
-    for (size_t i = 0; i < dim1; ++i) {
-      const size_t size = getter_size_dim2(i);
-      const size_t n_blocks = size/grain_size + !!(size % grain_size);
-      for (size_t iblock = 0; iblock < n_blocks; ++iblock) {
-        const size_t begin = iblock * grain_size;
-        const size_t end   = std::min(begin + grain_size, size);
-        AddBlock(i, begin, end);
-      }
-    }
-  }
-
-  // Amount of blocks(tasks) in a space
-  size_t Size() const {
-    return ranges_.size();
-  }
-
-  // get index of the first dimension of i-th block(task)
-  size_t GetFirstDimension(size_t i) const {
-    CHECK_LT(i, first_dimension_.size());
-    return first_dimension_[i];
-  }
-
-  // get a range of indexes for the second dimension of i-th block(task)
-  Range1d GetRange(size_t i) const {
-    CHECK_LT(i, ranges_.size());
-    return ranges_[i];
-  }
-
- private:
-  void AddBlock(size_t first_dimension, size_t begin, size_t end) {
-    first_dimension_.push_back(first_dimension);
-    ranges_.emplace_back(begin, end);
-  }
-
-  std::vector<Range1d> ranges_;
-  std::vector<size_t> first_dimension_;
-};
-
-
-// Wrapper to implement nested parallelism with simple omp parallel for
-template<typename Func>
-void ParallelFor2d(const BlockedSpace2d& space, int nthreads, Func func) {
-  const size_t num_blocks_in_space = space.Size();
-  nthreads = std::min(nthreads, omp_get_max_threads());
-  nthreads = std::max(nthreads, 1);
-
-#pragma omp parallel num_threads(nthreads)
-  {
-    size_t tid = omp_get_thread_num();
-    size_t chunck_size = num_blocks_in_space / nthreads + !!(num_blocks_in_space % nthreads);
-
-    size_t begin = chunck_size * tid;
-    size_t end   = std::min(begin + chunck_size, num_blocks_in_space);
-    for (auto i = begin; i < end; i++) {
-      func(space.GetFirstDimension(i), space.GetRange(i));
-    }
-  }
-}
-
-}  // namespace common
-}  // namespace xgboost
-
-#endif  // XGBOOST_COMMON_THREADING_UTILS_H_
diff --git a/ml-xgboost/src/common/timer.cc b/ml-xgboost/src/common/timer.cc
deleted file mode 100644
index 2cc1404..0000000
--- a/ml-xgboost/src/common/timer.cc
+++ /dev/null
@@ -1,128 +0,0 @@
-/*!
- * Copyright by Contributors 2019
- */
-#include <rabit/rabit.h>
-#include <algorithm>
-#include <type_traits>
-#include <utility>
-#include <vector>
-#include <sstream>
-#include "timer.h"
-#include "xgboost/json.h"
-
-namespace xgboost {
-namespace common {
-
-void Monitor::Start(std::string const &name) {
-  if (ConsoleLogger::ShouldLog(ConsoleLogger::LV::kDebug)) {
-    statistics_map_[name].timer.Start();
-  }
-}
-
-void Monitor::Stop(const std::string &name) {
-  if (ConsoleLogger::ShouldLog(ConsoleLogger::LV::kDebug)) {
-    auto &stats = statistics_map_[name];
-    stats.timer.Stop();
-    stats.count++;
-  }
-}
-
-std::vector<Monitor::StatMap> Monitor::CollectFromOtherRanks() const {
-  // Since other nodes might have started timers that this one haven't, so
-  // we can't simply call all reduce.
-  size_t const world_size = rabit::GetWorldSize();
-  size_t const rank = rabit::GetRank();
-
-  // It's much easier to work with rabit if we have a string serialization.  So we go with
-  // json.
-  Json j_statistic { Object() };
-  j_statistic["rank"] = Integer(rank);
-  j_statistic["statistic"] = Object();
-
-  auto& statistic = j_statistic["statistic"];
-  for (auto const& kv : statistics_map_) {
-    statistic[kv.first] = Object();
-    auto& j_pair = statistic[kv.first];
-    j_pair["count"] = Integer(kv.second.count);
-    j_pair["elapsed"] = Integer(static_cast<int64_t>(
-        std::chrono::duration_cast<std::chrono::microseconds>(
-        kv.second.timer.elapsed).count()));
-  }
-
-  std::stringstream ss;
-  Json::Dump(j_statistic, &ss);
-  std::string const str { ss.str() };
-
-  size_t str_size = str.size();
-  rabit::Allreduce<rabit::op::Max>(&str_size, 1);
-  std::string buffer;
-  buffer.resize(str_size);
-
-  // vector storing stat from all workers
-  std::vector<StatMap> world(world_size);
-
-  // Actually only rank 0 is printing.
-  for (size_t i = 0; i < world_size; ++i) {
-    std::copy(str.cbegin(), str.cend(), buffer.begin());
-    rabit::Broadcast(&buffer, i);
-    auto j_other = Json::Load(StringView{buffer.c_str(), buffer.size()});
-    auto& other = world[i];
-
-    auto const& j_statistic = get<Object>(j_other["statistic"]);
-
-    for (auto const& kv : j_statistic) {
-      std::string const& timer_name = kv.first;
-      auto const& pair = kv.second;
-      other[timer_name] = {get<Integer>(pair["count"]), get<Integer>(pair["elapsed"])};
-    }
-
-    // FIXME(trivialfis): How to ask rabit to block here?
-  }
-
-  return world;
-}
-
-void Monitor::PrintStatistics(StatMap const& statistics) const {
-  for (auto &kv : statistics) {
-    if (kv.second.first == 0) {
-      LOG(WARNING) <<
-          "Timer for " << kv.first << " did not get stopped properly.";
-      continue;
-    }
-    LOG(CONSOLE) << kv.first << ": " << static_cast<double>(kv.second.second) / 1e+6
-                 << "s, " << kv.second.first << " calls @ "
-                 << kv.second.second
-                 << "us" << std::endl;
-  }
-}
-
-void Monitor::Print() const {
-  if (!ConsoleLogger::ShouldLog(ConsoleLogger::LV::kDebug)) { return; }
-
-  bool is_distributed = rabit::IsDistributed();
-
-  if (is_distributed) {
-    auto world = this->CollectFromOtherRanks();
-    // rank zero is in charge of printing
-    if (rabit::GetRank() == 0) {
-      LOG(CONSOLE) << "======== Monitor: " << label_ << " ========";
-      for (size_t i = 0; i < world.size(); ++i) {
-        LOG(CONSOLE) << "From rank: " << i << ": " << std::endl;
-        auto const& statistic = world[i];
-        this->PrintStatistics(statistic);
-      }
-    }
-  } else {
-    StatMap stat_map;
-    for (auto const& kv : statistics_map_) {
-      stat_map[kv.first] = std::make_pair(
-          kv.second.count, std::chrono::duration_cast<std::chrono::microseconds>(
-              kv.second.timer.elapsed).count());
-    }
-    LOG(CONSOLE) << "======== Monitor: " << label_ << " ========";
-    this->PrintStatistics(stat_map);
-  }
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/timer.cu b/ml-xgboost/src/common/timer.cu
deleted file mode 100644
index 8b8e54b..0000000
--- a/ml-xgboost/src/common/timer.cu
+++ /dev/null
@@ -1,38 +0,0 @@
-/*!
- * Copyright by Contributors 2019
- */
-#if defined(XGBOOST_USE_NVTX)
-#include <nvToolsExt.h>
-#endif  // defined(XGBOOST_USE_NVTX)
-
-#include <string>
-
-#include "xgboost/logging.h"
-#include "device_helpers.cuh"
-#include "timer.h"
-
-namespace xgboost {
-namespace common {
-
-void Monitor::StartCuda(const std::string& name) {
-  if (ConsoleLogger::ShouldLog(ConsoleLogger::LV::kDebug)) {
-    auto &stats = statistics_map_[name];
-    stats.timer.Start();
-#if defined(XGBOOST_USE_NVTX)
-    stats.nvtx_id = nvtxRangeStartA(name.c_str());
-#endif  // defined(XGBOOST_USE_NVTX)
-  }
-}
-
-void Monitor::StopCuda(const std::string& name) {
-  if (ConsoleLogger::ShouldLog(ConsoleLogger::LV::kDebug)) {
-    auto &stats = statistics_map_[name];
-    stats.timer.Stop();
-    stats.count++;
-#if defined(XGBOOST_USE_NVTX)
-    nvtxRangeEnd(stats.nvtx_id);
-#endif  // defined(XGBOOST_USE_NVTX)
-  }
-}
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/timer.h b/ml-xgboost/src/common/timer.h
deleted file mode 100644
index bc71dca..0000000
--- a/ml-xgboost/src/common/timer.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*!
- * Copyright by Contributors 2017-2019
- */
-#pragma once
-#include <xgboost/logging.h>
-#include <chrono>
-#include <iostream>
-#include <map>
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace xgboost {
-namespace common {
-
-struct Timer {
-  using ClockT = std::chrono::high_resolution_clock;
-  using TimePointT = std::chrono::high_resolution_clock::time_point;
-  using DurationT = std::chrono::high_resolution_clock::duration;
-  using SecondsT = std::chrono::duration<double>;
-
-  TimePointT start;
-  DurationT elapsed;
-  Timer() { Reset(); }
-  void Reset() {
-    elapsed = DurationT::zero();
-    Start();
-  }
-  void Start() { start = ClockT::now(); }
-  void Stop() { elapsed += ClockT::now() - start; }
-  double ElapsedSeconds() const { return SecondsT(elapsed).count(); }
-  void PrintElapsed(std::string label) {
-    char buffer[255];
-    snprintf(buffer, sizeof(buffer), "%s:\t %fs", label.c_str(),
-             SecondsT(elapsed).count());
-    LOG(CONSOLE) << buffer;
-    Reset();
-  }
-};
-
-/**
- * \struct  Monitor
- *
- * \brief Timing utility used to measure total method execution time over the
- * lifetime of the containing object.
- */
-struct Monitor {
- private:
-  struct Statistics {
-    Timer timer;
-    size_t count{0};
-    uint64_t nvtx_id;
-  };
-
-  // from left to right, <name <count, elapsed>>
-  using StatMap = std::map<std::string, std::pair<size_t, size_t>>;
-
-  std::string label_ = "";
-  std::map<std::string, Statistics> statistics_map_;
-  Timer self_timer_;
-
-  /*! \brief Collect time statistics across all workers. */
-  std::vector<StatMap> CollectFromOtherRanks() const;
-  void PrintStatistics(StatMap const& statistics) const;
-
- public:
-  Monitor() { self_timer_.Start(); }
-  /*\brief Print statistics info during destruction.
-   *
-   * Please note that this may not work, as with distributed frameworks like Dask, the
-   * model is pickled to other workers, and the global parameters like `global_verbosity_`
-   * are not included in the pickle.
-   */
-  ~Monitor() {
-    this->Print();
-    self_timer_.Stop();
-  }
-
-  /*! \brief Print all the statistics. */
-  void Print() const;
-
-  void Init(std::string label) { this->label_ = label; }
-  void Start(const std::string &name);
-  void Stop(const std::string &name);
-  void StartCuda(const std::string &name);
-  void StopCuda(const std::string &name);
-};
-}  // namespace common
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/transform.h b/ml-xgboost/src/common/transform.h
deleted file mode 100644
index fa2d0d3..0000000
--- a/ml-xgboost/src/common/transform.h
+++ /dev/null
@@ -1,211 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-#ifndef XGBOOST_COMMON_TRANSFORM_H_
-#define XGBOOST_COMMON_TRANSFORM_H_
-
-#include <dmlc/omp.h>
-#include <dmlc/common.h>
-
-#include <xgboost/data.h>
-#include <utility>
-#include <vector>
-#include <type_traits>  // enable_if
-
-#include "xgboost/host_device_vector.h"
-#include "xgboost/span.h"
-
-#include "common.h"
-
-#if defined (__CUDACC__)
-#include "device_helpers.cuh"
-#endif  // defined (__CUDACC__)
-
-namespace xgboost {
-namespace common {
-
-constexpr size_t kBlockThreads = 256;
-
-namespace detail {
-
-#if defined(__CUDACC__)
-template <typename Functor, typename... SpanType>
-__global__ void LaunchCUDAKernel(Functor _func, Range _range,
-                                 SpanType... _spans) {
-  for (auto i : dh::GridStrideRange(*_range.begin(), *_range.end())) {
-    _func(i, _spans...);
-  }
-}
-#endif  // defined(__CUDACC__)
-
-}  // namespace detail
-
-/*! \brief Do Transformation on HostDeviceVectors.
- *
- *  \tparam CompiledWithCuda A bool parameter used to distinguish compilation
- *         trajectories, users do not need to use it.
- *
- *  Note: Using Transform is a VERY tricky thing to do. Transform uses template
- *   argument to duplicate itself into two different types, one for CPU,
- *   another for CUDA.  The trick is not without its flaw:
- *
- *     If you use it in a function that can be compiled by both nvcc and host
- *     compiler, the behaviour is un-defined!  Because your function is NOT
- *     duplicated by `CompiledWithCuda`. At link time, cuda compiler resolution
- *     will merge functions with same signature.
- */
-template <bool CompiledWithCuda = WITH_CUDA()>
-class Transform {
- private:
-  template <typename Functor>
-  struct Evaluator {
-   public:
-    Evaluator(Functor func, Range range, int device, bool shard) :
-        func_(func), range_{std::move(range)},
-        shard_{shard},
-        device_{device} {}
-
-    /*!
-     * \brief Evaluate the functor with input pointers to HostDeviceVector.
-     *
-     * \tparam HDV...  HostDeviceVectors type.
-     * \param  vectors Pointers to HostDeviceVector.
-     */
-    template <typename... HDV>
-    void Eval(HDV... vectors) const {
-      bool on_device = device_ >= 0;
-
-      if (on_device) {
-        LaunchCUDA(func_, vectors...);
-      } else {
-        LaunchCPU(func_, vectors...);
-      }
-    }
-
-   private:
-    // CUDA UnpackHDV
-    template <typename T>
-    Span<T> UnpackHDVOnDevice(HostDeviceVector<T>* _vec) const {
-      auto span = _vec->DeviceSpan();
-      return span;
-    }
-    template <typename T>
-    Span<T const> UnpackHDVOnDevice(const HostDeviceVector<T>* _vec) const {
-      auto span = _vec->ConstDeviceSpan();
-      return span;
-    }
-    // CPU UnpackHDV
-    template <typename T>
-    Span<T> UnpackHDV(HostDeviceVector<T>* _vec) const {
-      return Span<T> {_vec->HostPointer(),
-            static_cast<typename Span<T>::index_type>(_vec->Size())};
-    }
-    template <typename T>
-    Span<T const> UnpackHDV(const HostDeviceVector<T>* _vec) const {
-      return Span<T const> {_vec->ConstHostPointer(),
-            static_cast<typename Span<T>::index_type>(_vec->Size())};
-    }
-    // Recursive sync host
-    template <typename T>
-    void SyncHost(const HostDeviceVector<T> *_vector) const {
-      _vector->ConstHostPointer();
-    }
-    template <typename Head, typename... Rest>
-    void SyncHost(const HostDeviceVector<Head> *_vector,
-                  const HostDeviceVector<Rest> *... _vectors) const {
-      _vector->ConstHostPointer();
-      SyncHost(_vectors...);
-    }
-    // Recursive unpack for Shard.
-    template <typename T>
-    void UnpackShard(int device, const HostDeviceVector<T> *vector) const {
-      vector->SetDevice(device);
-    }
-    template <typename Head, typename... Rest>
-    void UnpackShard(int device,
-                     const HostDeviceVector<Head> *_vector,
-                     const HostDeviceVector<Rest> *... _vectors) const {
-      _vector->SetDevice(device);
-      UnpackShard(device, _vectors...);
-    }
-
-#if defined(__CUDACC__)
-    template <typename std::enable_if<CompiledWithCuda>::type* = nullptr,
-              typename... HDV>
-    void LaunchCUDA(Functor _func, HDV*... _vectors) const {
-      if (shard_) {
-        UnpackShard(device_, _vectors...);
-      }
-
-      size_t range_size = *range_.end() - *range_.begin();
-
-      // Extract index to deal with possible old OpenMP.
-      // This deals with situation like multi-class setting where
-      // granularity is used in data vector.
-      size_t shard_size = range_size;
-      Range shard_range {0, static_cast<Range::DifferenceType>(shard_size)};
-      dh::safe_cuda(cudaSetDevice(device_));
-      const int kGrids =
-          static_cast<int>(DivRoundUp(*(range_.end()), kBlockThreads));
-      if (kGrids == 0) {
-        return;
-      }
-      detail::LaunchCUDAKernel<<<kGrids, kBlockThreads>>>(  // NOLINT
-          _func, shard_range, UnpackHDVOnDevice(_vectors)...);
-    }
-#else
-    /*! \brief Dummy funtion defined when compiling for CPU.  */
-    template <typename std::enable_if<!CompiledWithCuda>::type* = nullptr,
-              typename... HDV>
-    void LaunchCUDA(Functor _func, HDV*... _vectors) const {
-      LOG(FATAL) << "Not part of device code. WITH_CUDA: " << WITH_CUDA();
-    }
-#endif  // defined(__CUDACC__)
-
-    template <typename... HDV>
-    void LaunchCPU(Functor func, HDV*... vectors) const {
-      omp_ulong end = static_cast<omp_ulong>(*(range_.end()));
-      dmlc::OMPException omp_exc;
-      SyncHost(vectors...);
-#pragma omp parallel for schedule(static)
-      for (omp_ulong idx = 0; idx < end; ++idx) {
-        omp_exc.Run(func, idx, UnpackHDV(vectors)...);
-      }
-      omp_exc.Rethrow();
-    }
-
-   private:
-    /*! \brief Callable object. */
-    Functor func_;
-    /*! \brief Range object specifying parallel threads index range. */
-    Range range_;
-    /*! \brief Whether sharding for vectors is required. */
-    bool shard_;
-    int device_;
-  };
-
- public:
-  /*!
-   * \brief Initialize a Transform object.
-   *
-   * \tparam Functor  A callable object type.
-   * \return A Evaluator having one method Eval.
-   *
-   * \param func    A callable object, accepting a size_t thread index,
-   *                  followed by a set of Span classes.
-   * \param range   Range object specifying parallel threads index range.
-   * \param device  Specify GPU to use.
-   * \param shard Whether Shard for HostDeviceVector is needed.
-   */
-  template <typename Functor>
-  static Evaluator<Functor> Init(Functor func, Range const range,
-                                 int device,
-                                 bool const shard = true) {
-    return Evaluator<Functor> {func, std::move(range), device, shard};
-  }
-};
-
-}  // namespace common
-}  // namespace xgboost
-
-#endif  // XGBOOST_COMMON_TRANSFORM_H_
diff --git a/ml-xgboost/src/common/version.cc b/ml-xgboost/src/common/version.cc
deleted file mode 100644
index 3fb2e5c..0000000
--- a/ml-xgboost/src/common/version.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#include <dmlc/io.h>
-
-#include <string>
-#include <tuple>
-#include <vector>
-
-#include "xgboost/logging.h"
-#include "xgboost/json.h"
-#include "xgboost/version_config.h"
-#include "version.h"
-
-namespace xgboost {
-
-const Version::TripletT Version::kInvalid {-1, -1, -1};
-
-Version::TripletT Version::Load(Json const& in, bool check) {
-  if (get<Object const>(in).find("version") == get<Object const>(in).cend()) {
-    return kInvalid;
-  }
-  Integer::Int major {0}, minor {0}, patch {0};
-  try {
-    auto const& j_version = get<Array const>(in["version"]);
-    std::tie(major, minor, patch) = std::make_tuple(
-        get<Integer const>(j_version.at(0)),
-        get<Integer const>(j_version.at(1)),
-        get<Integer const>(j_version.at(2)));
-  } catch (dmlc::Error const& e) {
-    LOG(FATAL) << "Invaid version format in loaded JSON object: " << in;
-  }
-
-  return std::make_tuple(major, minor, patch);
-}
-
-Version::TripletT Version::Load(dmlc::Stream* fi) {
-  XGBoostVersionT major{0}, minor{0}, patch{0};
-  // This is only used in DMatrix serialization, so doesn't break model compability.
-  std::string msg { "Incorrect version format found in binary file.  "
-                    "Binary file from XGBoost < 1.0.0 is no longer supported. "
-                    "Please generate it again." };
-  std::string verstr { u8"version:" }, read;
-  read.resize(verstr.size(), 0);
-
-  CHECK_EQ(fi->Read(&read[0], verstr.size()), verstr.size()) << msg;
-  if (verstr != read) {
-    // read might contain `\0` that terminates the string.
-    LOG(FATAL) << msg;
-  }
-
-  CHECK_EQ(fi->Read(&major, sizeof(major)), sizeof(major)) << msg;
-  CHECK_EQ(fi->Read(&minor, sizeof(major)), sizeof(minor)) << msg;
-  CHECK_EQ(fi->Read(&patch, sizeof(major)), sizeof(patch)) << msg;
-
-  return std::make_tuple(major, minor, patch);
-}
-
-void Version::Save(Json* out) {
-  Integer::Int major, minor, patch;
-  std::tie(major, minor, patch)= Self();
-  (*out)["version"] = std::vector<Json>{Json(Integer{major}),
-                                        Json(Integer{minor}),
-                                        Json(Integer{patch})};
-}
-
-void Version::Save(dmlc::Stream* fo) {
-  XGBoostVersionT major, minor, patch;
-  std::tie(major, minor, patch) = Self();
-  std::string verstr { u8"version:" };
-  fo->Write(&verstr[0], verstr.size());
-  fo->Write(&major, sizeof(major));
-  fo->Write(&minor, sizeof(minor));
-  fo->Write(&patch, sizeof(patch));
-}
-
-std::string Version::String(TripletT const& version) {
-  std::stringstream ss;
-  ss << std::get<0>(version) << "." << get<1>(version) << "." << get<2>(version);
-  return ss.str();
-}
-
-Version::TripletT Version::Self() {
-  return std::make_tuple(XGBOOST_VER_MAJOR, XGBOOST_VER_MINOR, XGBOOST_VER_PATCH);
-}
-
-bool Version::Same(TripletT const& triplet) {
-  return triplet == Self();
-}
-
-}  // namespace xgboost
diff --git a/ml-xgboost/src/common/version.h b/ml-xgboost/src/common/version.h
deleted file mode 100644
index 96885f6..0000000
--- a/ml-xgboost/src/common/version.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#ifndef XGBOOST_COMMON_VERSION_H_
-#define XGBOOST_COMMON_VERSION_H_
-
-#include <dmlc/io.h>
-#include <string>
-#include <tuple>
-
-#include "xgboost/base.h"
-
-namespace xgboost {
-class Json;
-// a static class for handling version info
-struct Version {
-  using TripletT = std::tuple<XGBoostVersionT, XGBoostVersionT, XGBoostVersionT>;
-  static const TripletT kInvalid;
-
-  // Save/Load version info to Json document
-  static TripletT Load(Json const& in, bool check = false);
-  static void Save(Json* out);
-
-  // Save/Load version info to dmlc::Stream
-  static Version::TripletT Load(dmlc::Stream* fi);
-  static void Save(dmlc::Stream* fo);
-
-  static std::string String(TripletT const& version);
-  static TripletT Self();
-
-  static bool Same(TripletT const& triplet);
-};
-
-}      // namespace xgboost
-#endif  // XGBOOST_COMMON_VERSION_H_
diff --git a/ml-xgboost/src/data/adapter.h b/ml-xgboost/src/data/adapter.h
deleted file mode 100644
index e252e1d..0000000
--- a/ml-xgboost/src/data/adapter.h
+++ /dev/null
@@ -1,604 +0,0 @@
-/*!
- *  Copyright (c) 2019~2020 by Contributors
- * \file adapter.h
- */
-#ifndef XGBOOST_DATA_ADAPTER_H_
-#define XGBOOST_DATA_ADAPTER_H_
-#include <dmlc/data.h>
-
-#include <cstddef>
-#include <functional>
-#include <limits>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "xgboost/logging.h"
-#include "xgboost/base.h"
-#include "xgboost/data.h"
-#include "xgboost/span.h"
-#include "xgboost/c_api.h"
-
-#include "../c_api/c_api_error.h"
-
-namespace xgboost {
-namespace data {
-
-/**  External data formats should implement an adapter as below. The
- * adapter provides a uniform access to data outside xgboost, allowing
- * construction of DMatrix objects from a range of sources without duplicating
- * code.
- *
- * The adapter object is an iterator that returns batches of data. Each batch
- * contains a number of "lines". A line represents a set of elements from a
- * sparse input matrix, normally a row in the case of a CSR matrix or a column
- * for a CSC matrix. Typically in sparse matrix formats we can efficiently
- * access subsets of elements at a time, but cannot efficiently lookups elements
- * by random access, hence the "line" abstraction, allowing the sparse matrix to
- * return subsets of elements efficiently. Individual elements are described by
- * a COO tuple (row index, column index, value).
- *
- * This abstraction allows us to read through different sparse matrix formats
- * using the same interface. In particular we can write a DMatrix constructor
- * that uses the same code to construct itself from a CSR matrix, CSC matrix,
- * dense matrix, csv, libsvm file, or potentially other formats. To see why this
- * is necessary, imagine we have 5 external matrix formats and 5 internal
- * DMatrix types where each DMatrix needs a custom constructor for each possible
- * input. The number of constructors is 5*5=25. Using an abstraction over the
- * input data types the number of constructors is reduced to 5, as each DMatrix
- * is oblivious to the external data format. Adding a new input source is simply
- * a case of implementing an adapter.
- *
- * Most of the below adapters do not need more than one batch as the data
- * originates from an in memory source. The file adapter does require batches to
- * avoid loading the entire file in memory.
- *
- * An important detail is empty row/column handling. Files loaded from disk do
- * not provide meta information about the number of rows/columns to expect, this
- * needs to be inferred during construction. Other sparse formats may specify a
- * number of rows/columns, but we can encounter entirely sparse rows or columns,
- * leading to disagreement between the inferred number and the meta-info
- * provided. To resolve this, adapters have methods specifying the number of
- * rows/columns expected, these methods may return zero where these values must
- * be inferred from data. A constructed DMatrix should agree with the input
- * source on numbers of rows/columns, appending empty rows if necessary.
- *  */
-
-/** \brief An adapter can return this value for number of rows or columns
- * indicating that this value is currently unknown and should be inferred while
- * passing over the data. */
-constexpr size_t kAdapterUnknownSize = std::numeric_limits<size_t >::max();
-
-struct COOTuple {
-  COOTuple() = default;
-  XGBOOST_DEVICE COOTuple(size_t row_idx, size_t column_idx, float value)
-      : row_idx(row_idx), column_idx(column_idx), value(value) {}
-
-  size_t row_idx{0};
-  size_t column_idx{0};
-  float value{0};
-};
-
-namespace detail {
-
-/**
- * \brief Simplifies the use of DataIter when there is only one batch.
- */
-template <typename DType>
-class SingleBatchDataIter : dmlc::DataIter<DType> {
- public:
-  void BeforeFirst() override { counter_ = 0; }
-  bool Next() override {
-    if (counter_ == 0) {
-      counter_++;
-      return true;
-    }
-    return false;
-  }
-
- private:
-  int counter_{0};
-};
-
-/** \brief Indicates this data source cannot contain meta-info such as labels,
- * weights or qid. */
-class NoMetaInfo {
- public:
-  const float* Labels() const { return nullptr; }
-  const float* Weights() const { return nullptr; }
-  const uint64_t* Qid() const { return nullptr; }
-  const float* BaseMargin() const { return nullptr; }
-};
-
-};  // namespace detail
-
-class CSRAdapterBatch : public detail::NoMetaInfo {
- public:
-  class Line {
-   public:
-    Line(size_t row_idx, size_t size, const unsigned* feature_idx,
-         const float* values)
-        : row_idx_(row_idx),
-          size_(size),
-          feature_idx_(feature_idx),
-          values_(values) {}
-
-    size_t Size() const { return size_; }
-    COOTuple GetElement(size_t idx) const {
-      return COOTuple{row_idx_, feature_idx_[idx], values_[idx]};
-    }
-
-   private:
-    size_t row_idx_;
-    size_t size_;
-    const unsigned* feature_idx_;
-    const float* values_;
-  };
-  CSRAdapterBatch(const size_t* row_ptr, const unsigned* feature_idx,
-                  const float* values, size_t num_rows, size_t num_elements,
-                  size_t num_features)
-      : row_ptr_(row_ptr),
-        feature_idx_(feature_idx),
-        values_(values),
-        num_rows_(num_rows) {}
-  const Line GetLine(size_t idx) const {
-    size_t begin_offset = row_ptr_[idx];
-    size_t end_offset = row_ptr_[idx + 1];
-    return Line(idx, end_offset - begin_offset, &feature_idx_[begin_offset],
-                &values_[begin_offset]);
-  }
-  size_t Size() const { return num_rows_; }
-
- private:
-  const size_t* row_ptr_;
-  const unsigned* feature_idx_;
-  const float* values_;
-  size_t num_rows_;
-};
-
-class CSRAdapter : public detail::SingleBatchDataIter<CSRAdapterBatch> {
- public:
-  CSRAdapter(const size_t* row_ptr, const unsigned* feature_idx,
-             const float* values, size_t num_rows, size_t num_elements,
-             size_t num_features)
-      : batch_(row_ptr, feature_idx, values, num_rows, num_elements,
-               num_features),
-        num_rows_(num_rows),
-        num_columns_(num_features) {}
-  const CSRAdapterBatch& Value() const override { return batch_; }
-  size_t NumRows() const { return num_rows_; }
-  size_t NumColumns() const { return num_columns_; }
-
- private:
-  CSRAdapterBatch batch_;
-  size_t num_rows_;
-  size_t num_columns_;
-};
-
-class DenseAdapterBatch : public detail::NoMetaInfo {
- public:
-  DenseAdapterBatch(const float* values, size_t num_rows, size_t num_features)
-      : values_(values),
-        num_rows_(num_rows),
-        num_features_(num_features) {}
-
- private:
-  class Line {
-   public:
-    Line(const float* values, size_t size, size_t row_idx)
-        : row_idx_(row_idx), size_(size), values_(values) {}
-
-    size_t Size() const { return size_; }
-    COOTuple GetElement(size_t idx) const {
-      return COOTuple{row_idx_, idx, values_[idx]};
-    }
-
-   private:
-    size_t row_idx_;
-    size_t size_;
-    const float* values_;
-  };
-
- public:
-  size_t Size() const { return num_rows_; }
-  const Line GetLine(size_t idx) const {
-    return Line(values_ + idx * num_features_, num_features_, idx);
-  }
-
- private:
-  const float* values_;
-  size_t num_rows_;
-  size_t num_features_;
-};
-
-class DenseAdapter : public detail::SingleBatchDataIter<DenseAdapterBatch> {
- public:
-  DenseAdapter(const float* values, size_t num_rows, size_t num_features)
-      : batch_(values, num_rows, num_features),
-        num_rows_(num_rows),
-        num_columns_(num_features) {}
-  const DenseAdapterBatch& Value() const override { return batch_; }
-
-  size_t NumRows() const { return num_rows_; }
-  size_t NumColumns() const { return num_columns_; }
-
- private:
-  DenseAdapterBatch batch_;
-  size_t num_rows_;
-  size_t num_columns_;
-};
-
-class CSCAdapterBatch : public detail::NoMetaInfo {
- public:
-  CSCAdapterBatch(const size_t* col_ptr, const unsigned* row_idx,
-                  const float* values, size_t num_features)
-      : col_ptr_(col_ptr),
-        row_idx_(row_idx),
-        values_(values),
-        num_features_(num_features) {}
-
- private:
-  class Line {
-   public:
-    Line(size_t col_idx, size_t size, const unsigned* row_idx,
-         const float* values)
-        : col_idx_(col_idx), size_(size), row_idx_(row_idx), values_(values) {}
-
-    size_t Size() const { return size_; }
-    COOTuple GetElement(size_t idx) const {
-      return COOTuple{row_idx_[idx], col_idx_, values_[idx]};
-    }
-
-   private:
-    size_t col_idx_;
-    size_t size_;
-    const unsigned* row_idx_;
-    const float* values_;
-  };
-
- public:
-  size_t Size() const { return num_features_; }
-  const Line GetLine(size_t idx) const {
-    size_t begin_offset = col_ptr_[idx];
-    size_t end_offset = col_ptr_[idx + 1];
-    return Line(idx, end_offset - begin_offset, &row_idx_[begin_offset],
-                &values_[begin_offset]);
-  }
-
- private:
-  const size_t* col_ptr_;
-  const unsigned* row_idx_;
-  const float* values_;
-  size_t num_features_;
-};
-
-class CSCAdapter : public detail::SingleBatchDataIter<CSCAdapterBatch> {
- public:
-  CSCAdapter(const size_t* col_ptr, const unsigned* row_idx,
-             const float* values, size_t num_features, size_t num_rows)
-      : batch_(col_ptr, row_idx, values, num_features),
-        num_rows_(num_rows),
-        num_columns_(num_features) {}
-  const CSCAdapterBatch& Value() const override { return batch_; }
-
-  // JVM package sends 0 as unknown
-  size_t NumRows() const {
-    return num_rows_ == 0 ? kAdapterUnknownSize : num_rows_;
-  }
-  size_t NumColumns() const { return num_columns_; }
-
- private:
-  CSCAdapterBatch batch_;
-  size_t num_rows_;
-  size_t num_columns_;
-};
-
-class DataTableAdapterBatch : public detail::NoMetaInfo {
- public:
-  DataTableAdapterBatch(void** data, const char** feature_stypes,
-                        size_t num_rows, size_t num_features)
-      : data_(data),
-        feature_stypes_(feature_stypes),
-        num_features_(num_features),
-        num_rows_(num_rows) {}
-
- private:
-  enum class DTType : uint8_t {
-    kFloat32 = 0,
-    kFloat64 = 1,
-    kBool8 = 2,
-    kInt32 = 3,
-    kInt8 = 4,
-    kInt16 = 5,
-    kInt64 = 6,
-    kUnknown = 7
-  };
-
-  DTType DTGetType(std::string type_string) const {
-    if (type_string == "float32") {
-      return DTType::kFloat32;
-    } else if (type_string == "float64") {
-      return DTType::kFloat64;
-    } else if (type_string == "bool8") {
-      return DTType::kBool8;
-    } else if (type_string == "int32") {
-      return DTType::kInt32;
-    } else if (type_string == "int8") {
-      return DTType::kInt8;
-    } else if (type_string == "int16") {
-      return DTType::kInt16;
-    } else if (type_string == "int64") {
-      return DTType::kInt64;
-    } else {
-      LOG(FATAL) << "Unknown data table type.";
-      return DTType::kUnknown;
-    }
-  }
-
-  class Line {
-    float DTGetValue(const void* column, DTType dt_type, size_t ridx) const {
-      float missing = std::numeric_limits<float>::quiet_NaN();
-      switch (dt_type) {
-        case DTType::kFloat32: {
-          float val = reinterpret_cast<const float*>(column)[ridx];
-          return std::isfinite(val) ? val : missing;
-        }
-        case DTType::kFloat64: {
-          double val = reinterpret_cast<const double*>(column)[ridx];
-          return std::isfinite(val) ? static_cast<float>(val) : missing;
-        }
-        case DTType::kBool8: {
-          bool val = reinterpret_cast<const bool*>(column)[ridx];
-          return static_cast<float>(val);
-        }
-        case DTType::kInt32: {
-          int32_t val = reinterpret_cast<const int32_t*>(column)[ridx];
-          return val != (-2147483647 - 1) ? static_cast<float>(val) : missing;
-        }
-        case DTType::kInt8: {
-          int8_t val = reinterpret_cast<const int8_t*>(column)[ridx];
-          return val != -128 ? static_cast<float>(val) : missing;
-        }
-        case DTType::kInt16: {
-          int16_t val = reinterpret_cast<const int16_t*>(column)[ridx];
-          return val != -32768 ? static_cast<float>(val) : missing;
-        }
-        case DTType::kInt64: {
-          int64_t val = reinterpret_cast<const int64_t*>(column)[ridx];
-          return val != -9223372036854775807 - 1 ? static_cast<float>(val)
-                                                 : missing;
-        }
-        default: {
-          LOG(FATAL) << "Unknown data table type.";
-          return 0.0f;
-        }
-      }
-    }
-
-   public:
-    Line(DTType type, size_t size, size_t column_idx, const void* column)
-        : type_(type), size_(size), column_idx_(column_idx), column_(column) {}
-
-    size_t Size() const { return size_; }
-    COOTuple GetElement(size_t idx) const {
-      return COOTuple{idx, column_idx_, DTGetValue(column_, type_, idx)};
-    }
-
-   private:
-    DTType type_;
-    size_t size_;
-    size_t column_idx_;
-    const void* column_;
-  };
-
- public:
-  size_t Size() const { return num_features_; }
-  const Line GetLine(size_t idx) const {
-    return Line(DTGetType(feature_stypes_[idx]), num_rows_, idx, data_[idx]);
-  }
-
- private:
-  void** data_;
-  const char** feature_stypes_;
-  size_t num_features_;
-  size_t num_rows_;
-};
-
-class DataTableAdapter
-    : public detail::SingleBatchDataIter<DataTableAdapterBatch> {
- public:
-  DataTableAdapter(void** data, const char** feature_stypes, size_t num_rows,
-                   size_t num_features)
-      : batch_(data, feature_stypes, num_rows, num_features),
-        num_rows_(num_rows),
-        num_columns_(num_features) {}
-  const DataTableAdapterBatch& Value() const override { return batch_; }
-  size_t NumRows() const { return num_rows_; }
-  size_t NumColumns() const { return num_columns_; }
-
- private:
-  DataTableAdapterBatch batch_;
-  size_t num_rows_;
-  size_t num_columns_;
-};
-
-class FileAdapterBatch {
- public:
-  class Line {
-   public:
-    Line(size_t row_idx, const uint32_t *feature_idx, const float *value,
-         size_t size)
-        : row_idx_(row_idx),
-          feature_idx_(feature_idx),
-          value_(value),
-          size_(size) {}
-
-    size_t Size() { return size_; }
-    COOTuple GetElement(size_t idx) {
-      float fvalue = value_ == nullptr ? 1.0f : value_[idx];
-      return COOTuple{row_idx_, feature_idx_[idx], fvalue};
-    }
-
-   private:
-    size_t row_idx_;
-    const uint32_t* feature_idx_;
-    const float* value_;
-    size_t size_;
-  };
-  FileAdapterBatch(const dmlc::RowBlock<uint32_t>* block, size_t row_offset)
-      : block_(block), row_offset_(row_offset) {}
-  Line GetLine(size_t idx) const {
-    auto begin = block_->offset[idx];
-    auto end = block_->offset[idx + 1];
-    return Line{idx + row_offset_, &block_->index[begin], &block_->value[begin],
-                end - begin};
-  }
-  const float* Labels() const { return block_->label; }
-  const float* Weights() const { return block_->weight; }
-  const uint64_t* Qid() const { return block_->qid; }
-  const float* BaseMargin() const { return nullptr; }
-
-  size_t Size() const { return block_->size; }
-
- private:
-  const dmlc::RowBlock<uint32_t>* block_;
-  size_t row_offset_;
-};
-
-/** \brief FileAdapter wraps dmlc::parser to read files and provide access in a
- * common interface. */
-class FileAdapter : dmlc::DataIter<FileAdapterBatch> {
- public:
-  explicit FileAdapter(dmlc::Parser<uint32_t>* parser) : parser_(parser) {}
-
-  const FileAdapterBatch& Value() const override { return *batch_.get(); }
-  void BeforeFirst() override {
-    batch_.reset();
-    parser_->BeforeFirst();
-    row_offset_ = 0;
-  }
-  bool Next() override {
-    bool next = parser_->Next();
-    batch_.reset(new FileAdapterBatch(&parser_->Value(), row_offset_));
-    row_offset_ += parser_->Value().size;
-    return next;
-  }
-  // Indicates a number of rows/columns must be inferred
-  size_t NumRows() const { return kAdapterUnknownSize; }
-  size_t NumColumns() const { return kAdapterUnknownSize; }
-
- private:
-  size_t row_offset_{0};
-  std::unique_ptr<FileAdapterBatch> batch_;
-  dmlc::Parser<uint32_t>* parser_;
-};
-
-/*! \brief Data iterator that takes callback to return data, used in JVM package for
- *  accepting data iterator. */
-class IteratorAdapter : public dmlc::DataIter<FileAdapterBatch> {
- public:
-  IteratorAdapter(DataIterHandle data_handle,
-                  XGBCallbackDataIterNext* next_callback)
-      :  columns_{data::kAdapterUnknownSize}, row_offset_{0},
-         at_first_(true),
-         data_handle_(data_handle), next_callback_(next_callback) {}
-
-  // override functions
-  void BeforeFirst() override {
-    CHECK(at_first_) << "Cannot reset IteratorAdapter";
-  }
-
-  bool Next() override {
-    if ((*next_callback_)(
-            data_handle_,
-            [](void *handle, XGBoostBatchCSR batch) -> int {
-              API_BEGIN();
-              static_cast<IteratorAdapter *>(handle)->SetData(batch);
-              API_END();
-            },
-            this) != 0) {
-      at_first_ = false;
-      return true;
-    } else {
-      return false;
-    }
-  }
-
-  FileAdapterBatch const& Value() const override {
-    return *batch_.get();
-  }
-
-  // callback to set the data
-  void SetData(const XGBoostBatchCSR& batch) {
-    offset_.clear();
-    label_.clear();
-    weight_.clear();
-    index_.clear();
-    value_.clear();
-    offset_.insert(offset_.end(), batch.offset, batch.offset + batch.size + 1);
-
-    if (batch.label != nullptr) {
-      label_.insert(label_.end(), batch.label, batch.label + batch.size);
-    }
-    if (batch.weight != nullptr) {
-      weight_.insert(weight_.end(), batch.weight, batch.weight + batch.size);
-    }
-    if (batch.index != nullptr) {
-      index_.insert(index_.end(), batch.index + offset_[0],
-                    batch.index + offset_.back());
-    }
-    if (batch.value != nullptr) {
-      value_.insert(value_.end(), batch.value + offset_[0],
-                    batch.value + offset_.back());
-    }
-    if (offset_[0] != 0) {
-      size_t base = offset_[0];
-      for (size_t &item : offset_) {
-        item -= base;
-      }
-    }
-    CHECK(columns_ == data::kAdapterUnknownSize || columns_ == batch.columns)
-        << "Number of columns between batches changed from " << columns_
-        << " to " << batch.columns;
-
-    columns_ = batch.columns;
-    block_.size = batch.size;
-
-    block_.offset = dmlc::BeginPtr(offset_);
-    block_.label = dmlc::BeginPtr(label_);
-    block_.weight = dmlc::BeginPtr(weight_);
-    block_.qid = nullptr;
-    block_.field = nullptr;
-    block_.index = dmlc::BeginPtr(index_);
-    block_.value = dmlc::BeginPtr(value_);
-
-    batch_.reset(new FileAdapterBatch(&block_, row_offset_));
-    row_offset_ += offset_.size() - 1;
-  }
-
-  size_t NumColumns() const { return columns_; }
-  size_t NumRows() const { return kAdapterUnknownSize; }
-
- private:
-  std::vector<size_t> offset_;
-  std::vector<dmlc::real_t> label_;
-  std::vector<dmlc::real_t> weight_;
-  std::vector<uint32_t> index_;
-  std::vector<dmlc::real_t> value_;
-
-  size_t columns_;
-  size_t row_offset_;
-  // at the beinning.
-  bool at_first_;
-  // handle to the iterator,
-  DataIterHandle data_handle_;
-  // call back to get the data.
-  XGBCallbackDataIterNext *next_callback_;
-  // internal Rowblock
-  dmlc::RowBlock<uint32_t> block_;
-  std::unique_ptr<FileAdapterBatch> batch_;
-};
-};  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_ADAPTER_H_
diff --git a/ml-xgboost/src/data/array_interface.h b/ml-xgboost/src/data/array_interface.h
deleted file mode 100644
index 1c07f2b..0000000
--- a/ml-xgboost/src/data/array_interface.h
+++ /dev/null
@@ -1,321 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- * \file array_interface.h
- * \brief Basic structure holding a reference to arrow columnar data format.
- */
-#ifndef XGBOOST_DATA_ARRAY_INTERFACE_H_
-#define XGBOOST_DATA_ARRAY_INTERFACE_H_
-
-#include <cinttypes>
-#include <map>
-#include <string>
-#include <utility>
-
-#include "xgboost/data.h"
-#include "xgboost/json.h"
-#include "xgboost/logging.h"
-#include "xgboost/span.h"
-#include "../common/bitfield.h"
-
-namespace xgboost {
-// Common errors in parsing columnar format.
-struct ArrayInterfaceErrors {
-  static char const* Contigious() {
-    return "Memory should be contigious.";
-  }
-  static char const* TypestrFormat() {
-    return "`typestr' should be of format <endian><type><size of type in bytes>.";
-  }
-  // Not supported in Apache Arrow.
-  static char const* BigEndian() {
-    return "Big endian is not supported.";
-  }
-  static char const* Dimension(int32_t d) {
-    static std::string str;
-    str.clear();
-    str += "Only ";
-    str += std::to_string(d);
-    str += " dimensional array is valid.";
-    return str.c_str();
-  }
-  static char const* Version() {
-    return "Only version 1 of `__cuda_array_interface__' is supported.";
-  }
-  static char const* OfType(std::string const& type) {
-    static std::string str;
-    str.clear();
-    str += " should be of ";
-    str += type;
-    str += " type.";
-    return str.c_str();
-  }
-
-  static std::string TypeStr(char c) {
-    switch (c) {
-      case 't':
-        return "Bit field";
-      case 'b':
-        return "Boolean";
-      case 'i':
-        return "Integer";
-      case 'u':
-        return "Unsigned integer";
-      case 'f':
-        return "Floating point";
-      case 'c':
-        return "Complex floating point";
-      case 'm':
-        return "Timedelta";
-      case 'M':
-        return "Datetime";
-      case 'O':
-        return "Object";
-      case 'S':
-        return "String";
-      case 'U':
-        return "Unicode";
-      case 'V':
-        return "Other";
-      default:
-        LOG(FATAL) << "Invalid type code: " << c << " in `typestr' of input array."
-                   << "\nPlease verify the `__cuda_array_interface__' "
-                   << "of your input data complies to: "
-                   << "https://docs.scipy.org/doc/numpy/reference/arrays.interface.html"
-                   << "\nOr open an issue.";
-        return "";
-    }
-  }
-
-  static std::string UnSupportedType(const char (&typestr)[3]) {
-    return TypeStr(typestr[1]) + " is not supported.";
-  }
-};
-
-// TODO(trivialfis): Abstract this into a class that accept a json
-// object and turn it into an array (for cupy and numba).
-class ArrayInterfaceHandler {
- public:
-  template <typename T>
-  static constexpr char TypeChar() {
-    return
-        (std::is_floating_point<T>::value ? 'f' :
-         (std::is_integral<T>::value ?
-          (std::is_signed<T>::value ? 'i' : 'u') : '\0'));
-  }
-
-  template <typename PtrType>
-  static PtrType GetPtrFromArrayData(std::map<std::string, Json> const& obj) {
-    if (obj.find("data") == obj.cend()) {
-      LOG(FATAL) << "Empty data passed in.";
-    }
-    auto p_data = reinterpret_cast<PtrType>(static_cast<size_t>(
-        get<Integer const>(
-            get<Array const>(
-                obj.at("data"))
-            .at(0))));
-    return p_data;
-  }
-
-  static void Validate(std::map<std::string, Json> const& array) {
-    if (array.find("version") == array.cend()) {
-      LOG(FATAL) << "Missing `version' field for array interface";
-    }
-    if (array.find("typestr") == array.cend()) {
-      LOG(FATAL) << "Missing `typestr' field for array interface";
-    }
-    auto typestr = get<String const>(array.at("typestr"));
-    CHECK_EQ(typestr.size(),    3) << ArrayInterfaceErrors::TypestrFormat();
-    CHECK_NE(typestr.front(), '>') << ArrayInterfaceErrors::BigEndian();
-
-    if (array.find("shape") == array.cend()) {
-      LOG(FATAL) << "Missing `shape' field for array interface";
-    }
-    if (array.find("data") == array.cend()) {
-      LOG(FATAL) << "Missing `data' field for array interface";
-    }
-  }
-
-  // Find null mask (validity mask) field
-  // Mask object is also an array interface, but with different requirements.
-  static size_t ExtractMask(std::map<std::string, Json> const &column,
-                            common::Span<RBitField8::value_type> *p_out) {
-    auto& s_mask = *p_out;
-    if (column.find("mask") != column.cend()) {
-      auto const& j_mask = get<Object const>(column.at("mask"));
-      Validate(j_mask);
-
-      auto p_mask = GetPtrFromArrayData<RBitField8::value_type*>(j_mask);
-
-      auto j_shape = get<Array const>(j_mask.at("shape"));
-      CHECK_EQ(j_shape.size(), 1) << ArrayInterfaceErrors::Dimension(1);
-      auto typestr = get<String const>(j_mask.at("typestr"));
-      // For now this is just 1, we can support different size of interger in mask.
-      int64_t const type_length = typestr.at(2) - 48;
-
-      if (typestr.at(1) == 't') {
-        CHECK_EQ(type_length, 1) << "mask with bitfield type should be of 1 byte per bitfield.";
-      } else if (typestr.at(1) == 'i') {
-        CHECK_EQ(type_length, 1) << "mask with integer type should be of 1 byte per integer.";
-      } else {
-        LOG(FATAL) << "mask must be of integer type or bit field type.";
-      }
-      /*
-       * shape represents how many bits is in the mask. (This is a grey area, don't be
-       * suprised if it suddently represents something else when supporting a new
-       * implementation).  Quoting from numpy array interface:
-       *
-       *   The shape of this object should be "broadcastable" to the shape of the original
-       *   array.
-       *
-       * And that's the only requirement.
-       */
-      size_t const n_bits = static_cast<size_t>(get<Integer>(j_shape.at(0)));
-      // The size of span required to cover all bits.  Here with 8 bits bitfield, we
-      // assume 1 byte alignment.
-      size_t const span_size = RBitField8::ComputeStorageSize(n_bits);
-
-      if (j_mask.find("strides") != j_mask.cend()) {
-        auto strides = get<Array const>(column.at("strides"));
-        CHECK_EQ(strides.size(),                        1) << ArrayInterfaceErrors::Dimension(1);
-        CHECK_EQ(get<Integer>(strides.at(0)), type_length) << ArrayInterfaceErrors::Contigious();
-      }
-
-      s_mask = {p_mask, span_size};
-      return n_bits;
-    }
-    return 0;
-  }
-
-  static std::pair<size_t, size_t> ExtractShape(
-      std::map<std::string, Json> const& column) {
-    auto j_shape = get<Array const>(column.at("shape"));
-    auto typestr = get<String const>(column.at("typestr"));
-    if (column.find("strides") != column.cend()) {
-      if (!IsA<Null>(column.at("strides"))) {
-        auto strides = get<Array const>(column.at("strides"));
-        CHECK_EQ(strides.size(), j_shape.size())
-            << ArrayInterfaceErrors::Dimension(1);
-        CHECK_EQ(get<Integer>(strides.at(0)), typestr.at(2) - '0')
-            << ArrayInterfaceErrors::Contigious();
-      }
-    }
-
-    if (j_shape.size() == 1) {
-      return {static_cast<size_t>(get<Integer const>(j_shape.at(0))), 1};
-    } else {
-      CHECK_EQ(j_shape.size(), 2)
-          << "Only 1D or 2-D arrays currently supported.";
-      return {static_cast<size_t>(get<Integer const>(j_shape.at(0))),
-              static_cast<size_t>(get<Integer const>(j_shape.at(1)))};
-    }
-  }
-  template <typename T>
-  static common::Span<T> ExtractData(std::map<std::string, Json> const& column) {
-    Validate(column);
-
-    auto typestr = get<String const>(column.at("typestr"));
-    CHECK_EQ(typestr.at(1),   TypeChar<T>())
-        << "Input data type and typestr mismatch. typestr: " << typestr;
-    CHECK_EQ(typestr.at(2),   static_cast<char>(sizeof(T) + 48))
-        << "Input data type and typestr mismatch. typestr: " << typestr;
-
-
-    auto shape = ExtractShape(column);
-
-    T* p_data = ArrayInterfaceHandler::GetPtrFromArrayData<T*>(column);
-    return common::Span<T>{p_data, shape.first * shape.second};
-  }
-};
-
-// A view over __array_interface__
-class ArrayInterface {
- public:
-  ArrayInterface() = default;
-  explicit ArrayInterface(std::map<std::string, Json> const& column) {
-    ArrayInterfaceHandler::Validate(column);
-    data = ArrayInterfaceHandler::GetPtrFromArrayData<void*>(column);
-    CHECK(data) << "Column is null";
-    auto shape = ArrayInterfaceHandler::ExtractShape(column);
-    num_rows = shape.first;
-    num_cols = shape.second;
-
-    common::Span<RBitField8::value_type> s_mask;
-    size_t n_bits = ArrayInterfaceHandler::ExtractMask(column, &s_mask);
-
-    valid = RBitField8(s_mask);
-
-    if (s_mask.data()) {
-      CHECK_EQ(n_bits, num_rows)
-          << "Shape of bit mask doesn't match data shape. "
-          << "XGBoost doesn't support internal broadcasting.";
-    }
-    auto typestr = get<String const>(column.at("typestr"));
-    type[0] = typestr.at(0);
-    type[1] = typestr.at(1);
-    type[2] = typestr.at(2);
-    this->CheckType();
-  }
-
-  void CheckType() const {
-    if (type[1] == 'f' && type[2] == '4') {
-      return;
-    } else if (type[1] == 'f' && type[2] == '8') {
-      return;
-    } else if (type[1] == 'i' && type[2] == '1') {
-      return;
-    } else if (type[1] == 'i' && type[2] == '2') {
-      return;
-    } else if (type[1] == 'i' && type[2] == '4') {
-      return;
-    } else if (type[1] == 'i' && type[2] == '8') {
-      return;
-    } else if (type[1] == 'u' && type[2] == '1') {
-      return;
-    } else if (type[1] == 'u' && type[2] == '2') {
-      return;
-    } else if (type[1] == 'u' && type[2] == '4') {
-      return;
-    } else if (type[1] == 'u' && type[2] == '8') {
-      return;
-    } else {
-      LOG(FATAL) << ArrayInterfaceErrors::UnSupportedType(type);
-      return;
-    }
-  }
-
-  XGBOOST_DEVICE float GetElement(size_t idx) const {
-    if (type[1] == 'f' && type[2] == '4') {
-      return reinterpret_cast<float*>(data)[idx];
-    } else if (type[1] == 'f' && type[2] == '8') {
-      return reinterpret_cast<double*>(data)[idx];
-    } else if (type[1] == 'i' && type[2] == '1') {
-      return reinterpret_cast<int8_t*>(data)[idx];
-    } else if (type[1] == 'i' && type[2] == '2') {
-      return reinterpret_cast<int16_t*>(data)[idx];
-    } else if (type[1] == 'i' && type[2] == '4') {
-      return reinterpret_cast<int32_t*>(data)[idx];
-    } else if (type[1] == 'i' && type[2] == '8') {
-      return reinterpret_cast<int64_t*>(data)[idx];
-    } else if (type[1] == 'u' && type[2] == '1') {
-      return reinterpret_cast<uint8_t*>(data)[idx];
-    } else if (type[1] == 'u' && type[2] == '2') {
-      return reinterpret_cast<uint16_t*>(data)[idx];
-    } else if (type[1] == 'u' && type[2] == '4') {
-      return reinterpret_cast<uint32_t*>(data)[idx];
-    } else if (type[1] == 'u' && type[2] == '8') {
-      return reinterpret_cast<uint64_t*>(data)[idx];
-    } else {
-      SPAN_CHECK(false);
-      return 0;
-    }
-  }
-
-  RBitField8 valid;
-  int32_t num_rows;
-  int32_t num_cols;
-  void* data;
-  char type[3];
-};
-
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_ARRAY_INTERFACE_H_
diff --git a/ml-xgboost/src/data/data.cc b/ml-xgboost/src/data/data.cc
deleted file mode 100644
index 2a620b7..0000000
--- a/ml-xgboost/src/data/data.cc
+++ /dev/null
@@ -1,747 +0,0 @@
-/*!
- * Copyright 2015-2020 by Contributors
- * \file data.cc
- */
-#include <dmlc/registry.h>
-#include <cstring>
-
-#include "dmlc/io.h"
-#include "xgboost/data.h"
-#include "xgboost/host_device_vector.h"
-#include "xgboost/logging.h"
-#include "xgboost/version_config.h"
-#include "sparse_page_writer.h"
-#include "simple_dmatrix.h"
-
-#include "../common/io.h"
-#include "../common/math.h"
-#include "../common/version.h"
-#include "../common/group_data.h"
-#include "../data/adapter.h"
-
-#if DMLC_ENABLE_STD_THREAD
-#include "./sparse_page_source.h"
-#include "./sparse_page_dmatrix.h"
-#endif  // DMLC_ENABLE_STD_THREAD
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::SparsePage>);
-DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::CSCPage>);
-DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::SortedCSCPage>);
-DMLC_REGISTRY_ENABLE(::xgboost::data::SparsePageFormatReg<::xgboost::EllpackPage>);
-}  // namespace dmlc
-
-namespace {
-
-template <typename T>
-void SaveScalarField(dmlc::Stream *strm, const std::string &name,
-                     xgboost::DataType type, const T &field) {
-  strm->Write(name);
-  strm->Write(static_cast<uint8_t>(type));
-  strm->Write(true);  // is_scalar=True
-  strm->Write(field);
-}
-
-template <typename T>
-void SaveVectorField(dmlc::Stream *strm, const std::string &name,
-                     xgboost::DataType type, std::pair<uint64_t, uint64_t> shape,
-                     const std::vector<T>& field) {
-  strm->Write(name);
-  strm->Write(static_cast<uint8_t>(type));
-  strm->Write(false);  // is_scalar=False
-  strm->Write(shape.first);
-  strm->Write(shape.second);
-  strm->Write(field);
-}
-
-template <typename T>
-void SaveVectorField(dmlc::Stream* strm, const std::string& name,
-                     xgboost::DataType type, std::pair<uint64_t, uint64_t> shape,
-                     const xgboost::HostDeviceVector<T>& field) {
-  SaveVectorField(strm, name, type, shape, field.ConstHostVector());
-}
-
-template <typename T>
-void LoadScalarField(dmlc::Stream* strm, const std::string& expected_name,
-                     xgboost::DataType expected_type, T* field) {
-  const std::string invalid {"MetaInfo: Invalid format. "};
-  std::string name;
-  xgboost::DataType type;
-  bool is_scalar;
-  CHECK(strm->Read(&name)) << invalid;
-  CHECK_EQ(name, expected_name)
-      << invalid << " Expected field: " << expected_name << ", got: " << name;
-  uint8_t type_val;
-  CHECK(strm->Read(&type_val)) << invalid;
-  type = static_cast<xgboost::DataType>(type_val);
-  CHECK(type == expected_type)
-      << invalid << "Expected field of type: " << static_cast<int>(expected_type) << ", "
-      << "got field type: " << static_cast<int>(type);
-  CHECK(strm->Read(&is_scalar)) << invalid;
-  CHECK(is_scalar)
-    << invalid << "Expected field " << expected_name << " to be a scalar; got a vector";
-  CHECK(strm->Read(field, sizeof(T))) << invalid;
-}
-
-template <typename T>
-void LoadVectorField(dmlc::Stream* strm, const std::string& expected_name,
-                     xgboost::DataType expected_type, std::vector<T>* field) {
-  const std::string invalid {"MetaInfo: Invalid format. "};
-  std::string name;
-  xgboost::DataType type;
-  bool is_scalar;
-  CHECK(strm->Read(&name)) << invalid;
-  CHECK_EQ(name, expected_name)
-    << invalid << " Expected field: " << expected_name << ", got: " << name;
-  uint8_t type_val;
-  CHECK(strm->Read(&type_val)) << invalid;
-  type = static_cast<xgboost::DataType>(type_val);
-  CHECK(type == expected_type)
-    << invalid << "Expected field of type: " << static_cast<int>(expected_type) << ", "
-    << "got field type: " << static_cast<int>(type);
-  CHECK(strm->Read(&is_scalar)) << invalid;
-  CHECK(!is_scalar)
-    << invalid << "Expected field " << expected_name << " to be a vector; got a scalar";
-  std::pair<uint64_t, uint64_t> shape;
-
-  CHECK(strm->Read(&shape.first));
-  CHECK(strm->Read(&shape.second));
-  // TODO(hcho3): this restriction may be lifted, once we add a field with more than 1 column.
-  CHECK_EQ(shape.second, 1) << invalid << "Number of columns is expected to be 1.";
-
-  CHECK(strm->Read(field)) << invalid;
-}
-
-template <typename T>
-void LoadVectorField(dmlc::Stream* strm, const std::string& expected_name,
-                     xgboost::DataType expected_type,
-                     xgboost::HostDeviceVector<T>* field) {
-  LoadVectorField(strm, expected_name, expected_type, &field->HostVector());
-}
-
-}  // anonymous namespace
-
-namespace xgboost {
-
-uint64_t constexpr MetaInfo::kNumField;
-
-// implementation of inline functions
-void MetaInfo::Clear() {
-  num_row_ = num_col_ = num_nonzero_ = 0;
-  labels_.HostVector().clear();
-  group_ptr_.clear();
-  weights_.HostVector().clear();
-  base_margin_.HostVector().clear();
-}
-
-/*
- * Binary serialization format for MetaInfo:
- *
- * | name               | type     | is_scalar | num_row | num_col | value                   |
- * |--------------------+----------+-----------+---------+---------+-------------------------|
- * | num_row            | kUInt64  | True      | NA      |      NA | ${num_row_}             |
- * | num_col            | kUInt64  | True      | NA      |      NA | ${num_col_}             |
- * | num_nonzero        | kUInt64  | True      | NA      |      NA | ${num_nonzero_}         |
- * | labels             | kFloat32 | False     | ${size} |       1 | ${labels_}              |
- * | group_ptr          | kUInt32  | False     | ${size} |       1 | ${group_ptr_}           |
- * | weights            | kFloat32 | False     | ${size} |       1 | ${weights_}             |
- * | base_margin        | kFloat32 | False     | ${size} |       1 | ${base_margin_}         |
- * | labels_lower_bound | kFloat32 | False     | ${size} |       1 | ${labels_lower_bound__} |
- * | labels_upper_bound | kFloat32 | False     | ${size} |       1 | ${labels_upper_bound__} |
- *
- * Note that the scalar fields (is_scalar=True) will have num_row and num_col missing.
- * Also notice the difference between the saved name and the name used in `SetInfo':
- * the former uses the plural form.
- */
-
-void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
-  Version::Save(fo);
-  fo->Write(kNumField);
-  int field_cnt = 0;  // make sure we are actually writing kNumField fields
-
-  SaveScalarField(fo, u8"num_row", DataType::kUInt64, num_row_); ++field_cnt;
-  SaveScalarField(fo, u8"num_col", DataType::kUInt64, num_col_); ++field_cnt;
-  SaveScalarField(fo, u8"num_nonzero", DataType::kUInt64, num_nonzero_); ++field_cnt;
-  SaveVectorField(fo, u8"labels", DataType::kFloat32,
-                  {labels_.Size(), 1}, labels_); ++field_cnt;
-  SaveVectorField(fo, u8"group_ptr", DataType::kUInt32,
-                  {group_ptr_.size(), 1}, group_ptr_); ++field_cnt;
-  SaveVectorField(fo, u8"weights", DataType::kFloat32,
-                  {weights_.Size(), 1}, weights_); ++field_cnt;
-  SaveVectorField(fo, u8"base_margin", DataType::kFloat32,
-                  {base_margin_.Size(), 1}, base_margin_); ++field_cnt;
-  SaveVectorField(fo, u8"labels_lower_bound", DataType::kFloat32,
-                  {labels_lower_bound_.Size(), 1}, labels_lower_bound_); ++field_cnt;
-  SaveVectorField(fo, u8"labels_upper_bound", DataType::kFloat32,
-                  {labels_upper_bound_.Size(), 1}, labels_upper_bound_); ++field_cnt;
-
-  CHECK_EQ(field_cnt, kNumField) << "Wrong number of fields";
-}
-
-void MetaInfo::LoadBinary(dmlc::Stream *fi) {
-  auto version = Version::Load(fi);
-  auto major = std::get<0>(version);
-  // MetaInfo is saved in `SparsePageSource'.  So the version in MetaInfo represents the
-  // version of DMatrix.
-  CHECK_EQ(major, 1) << "Binary DMatrix generated by XGBoost: "
-                     << Version::String(version) << " is no longer supported. "
-                     << "Please process and save your data in current version: "
-                     << Version::String(Version::Self()) << " again.";
-
-  const uint64_t expected_num_field = kNumField;
-  uint64_t num_field { 0 };
-  CHECK(fi->Read(&num_field)) << "MetaInfo: invalid format";
-  CHECK_GE(num_field, expected_num_field)
-    << "MetaInfo: insufficient number of fields (expected at least " << expected_num_field
-    << " fields, but the binary file only contains " << num_field << "fields.)";
-  if (num_field > expected_num_field) {
-    LOG(WARNING) << "MetaInfo: the given binary file contains extra fields which will be ignored.";
-  }
-
-  LoadScalarField(fi, u8"num_row", DataType::kUInt64, &num_row_);
-  LoadScalarField(fi, u8"num_col", DataType::kUInt64, &num_col_);
-  LoadScalarField(fi, u8"num_nonzero", DataType::kUInt64, &num_nonzero_);
-  LoadVectorField(fi, u8"labels", DataType::kFloat32, &labels_);
-  LoadVectorField(fi, u8"group_ptr", DataType::kUInt32, &group_ptr_);
-  LoadVectorField(fi, u8"weights", DataType::kFloat32, &weights_);
-  LoadVectorField(fi, u8"base_margin", DataType::kFloat32, &base_margin_);
-  LoadVectorField(fi, u8"labels_lower_bound", DataType::kFloat32, &labels_lower_bound_);
-  LoadVectorField(fi, u8"labels_upper_bound", DataType::kFloat32, &labels_upper_bound_);
-}
-
-template <typename T>
-std::vector<T> Gather(const std::vector<T> &in, common::Span<int const> ridxs, size_t stride = 1) {
-  if (in.empty()) {
-    return {};
-  }
-  auto size = ridxs.size();
-  std::vector<T> out(size * stride);
-  for (auto i = 0ull; i < size; i++) {
-    auto ridx = ridxs[i];
-    for (size_t j = 0; j < stride; ++j) {
-      out[i * stride +j] = in[ridx * stride + j];
-    }
-  }
-  return out;
-}
-
-MetaInfo MetaInfo::Slice(common::Span<int32_t const> ridxs) const {
-  MetaInfo out;
-  out.num_row_ = ridxs.size();
-  out.num_col_ = this->num_col_;
-  // Groups is maintained by a higher level Python function.  We should aim at deprecating
-  // the slice function.
-  out.labels_.HostVector() = Gather(this->labels_.HostVector(), ridxs);
-  out.labels_upper_bound_.HostVector() =
-      Gather(this->labels_upper_bound_.HostVector(), ridxs);
-  out.labels_lower_bound_.HostVector() =
-      Gather(this->labels_lower_bound_.HostVector(), ridxs);
-  // weights
-  if (this->weights_.Size() + 1 == this->group_ptr_.size()) {
-    auto& h_weights =  out.weights_.HostVector();
-    // Assuming all groups are available.
-    out.weights_.HostVector() = h_weights;
-  } else {
-    out.weights_.HostVector() = Gather(this->weights_.HostVector(), ridxs);
-  }
-
-  if (this->base_margin_.Size() != this->num_row_) {
-    CHECK_EQ(this->base_margin_.Size() % this->num_row_, 0)
-        << "Incorrect size of base margin vector.";
-    size_t stride = this->base_margin_.Size() / this->num_row_;
-    out.base_margin_.HostVector() = Gather(this->base_margin_.HostVector(), ridxs, stride);
-  } else {
-    out.base_margin_.HostVector() = Gather(this->base_margin_.HostVector(), ridxs);
-  }
-  return out;
-}
-
-// try to load group information from file, if exists
-inline bool MetaTryLoadGroup(const std::string& fname,
-                             std::vector<unsigned>* group) {
-  std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
-  if (fi == nullptr) return false;
-  dmlc::istream is(fi.get());
-  group->clear();
-  group->push_back(0);
-  unsigned nline = 0;
-  while (is >> nline) {
-    group->push_back(group->back() + nline);
-  }
-  return true;
-}
-
-// try to load weight information from file, if exists
-inline bool MetaTryLoadFloatInfo(const std::string& fname,
-                                 std::vector<bst_float>* data) {
-  std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
-  if (fi == nullptr) return false;
-  dmlc::istream is(fi.get());
-  data->clear();
-  bst_float value;
-  while (is >> value) {
-    data->push_back(value);
-  }
-  return true;
-}
-
-// macro to dispatch according to specified pointer types
-#define DISPATCH_CONST_PTR(dtype, old_ptr, cast_ptr, proc)              \
-  switch (dtype) {                                                      \
-    case xgboost::DataType::kFloat32: {                                 \
-      auto cast_ptr = reinterpret_cast<const float*>(old_ptr); proc; break; \
-    }                                                                   \
-    case xgboost::DataType::kDouble: {                                  \
-      auto cast_ptr = reinterpret_cast<const double*>(old_ptr); proc; break; \
-    }                                                                   \
-    case xgboost::DataType::kUInt32: {                                  \
-      auto cast_ptr = reinterpret_cast<const uint32_t*>(old_ptr); proc; break; \
-    }                                                                   \
-    case xgboost::DataType::kUInt64: {                                  \
-      auto cast_ptr = reinterpret_cast<const uint64_t*>(old_ptr); proc; break; \
-    }                                                                   \
-    default: LOG(FATAL) << "Unknown data type" << static_cast<uint8_t>(dtype); \
-  }                                                                     \
-
-void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t num) {
-  if (!std::strcmp(key, "label")) {
-    auto& labels = labels_.HostVector();
-    labels.resize(num);
-    DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
-                       std::copy(cast_dptr, cast_dptr + num, labels.begin()));
-  } else if (!std::strcmp(key, "weight")) {
-    auto& weights = weights_.HostVector();
-    weights.resize(num);
-    DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
-                       std::copy(cast_dptr, cast_dptr + num, weights.begin()));
-  } else if (!std::strcmp(key, "base_margin")) {
-    auto& base_margin = base_margin_.HostVector();
-    base_margin.resize(num);
-    DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
-                       std::copy(cast_dptr, cast_dptr + num, base_margin.begin()));
-  } else if (!std::strcmp(key, "group")) {
-    group_ptr_.resize(num + 1);
-    DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
-                       std::copy(cast_dptr, cast_dptr + num, group_ptr_.begin() + 1));
-    group_ptr_[0] = 0;
-    for (size_t i = 1; i < group_ptr_.size(); ++i) {
-      group_ptr_[i] = group_ptr_[i - 1] + group_ptr_[i];
-    }
-  } else if (!std::strcmp(key, "label_lower_bound")) {
-    auto& labels = labels_lower_bound_.HostVector();
-    labels.resize(num);
-    DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
-                       std::copy(cast_dptr, cast_dptr + num, labels.begin()));
-  } else if (!std::strcmp(key, "label_upper_bound")) {
-    auto& labels = labels_upper_bound_.HostVector();
-    labels.resize(num);
-    DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
-                       std::copy(cast_dptr, cast_dptr + num, labels.begin()));
-  } else {
-    LOG(FATAL) << "Unknown key for MetaInfo: " << key;
-  }
-}
-
-void MetaInfo::Validate(int32_t device) const {
-  if (group_ptr_.size() != 0 && weights_.Size() != 0) {
-    CHECK_EQ(group_ptr_.size(), weights_.Size() + 1)
-        << "Size of weights must equal to number of groups when ranking "
-           "group is used.";
-    return;
-  }
-  if (group_ptr_.size() != 0) {
-    CHECK_EQ(group_ptr_.back(), num_row_)
-        << "Invalid group structure.  Number of rows obtained from groups "
-           "doesn't equal to actual number of rows given by data.";
-  }
-  auto check_device = [device](HostDeviceVector<float> const &v) {
-    CHECK(v.DeviceIdx() == GenericParameter::kCpuId ||
-          device  == GenericParameter::kCpuId ||
-          v.DeviceIdx() == device)
-        << "Data is resided on a different device than `gpu_id`. "
-        << "Device that data is on: " << v.DeviceIdx() << ", "
-        << "`gpu_id` for XGBoost: " << device;
-  };
-
-  if (weights_.Size() != 0) {
-    CHECK_EQ(weights_.Size(), num_row_)
-        << "Size of weights must equal to number of rows.";
-    check_device(weights_);
-    return;
-  }
-  if (labels_.Size() != 0) {
-    CHECK_EQ(labels_.Size(), num_row_)
-        << "Size of labels must equal to number of rows.";
-    check_device(labels_);
-    return;
-  }
-  if (labels_lower_bound_.Size() != 0) {
-    CHECK_EQ(labels_lower_bound_.Size(), num_row_)
-        << "Size of label_lower_bound must equal to number of rows.";
-    check_device(labels_lower_bound_);
-    return;
-  }
-  if (labels_upper_bound_.Size() != 0) {
-    CHECK_EQ(labels_upper_bound_.Size(), num_row_)
-        << "Size of label_upper_bound must equal to number of rows.";
-    check_device(labels_upper_bound_);
-    return;
-  }
-  CHECK_LE(num_nonzero_, num_col_ * num_row_);
-  if (base_margin_.Size() != 0) {
-    CHECK_EQ(base_margin_.Size() % num_row_, 0)
-        << "Size of base margin must be a multiple of number of rows.";
-    check_device(base_margin_);
-  }
-}
-
-#if !defined(XGBOOST_USE_CUDA)
-void MetaInfo::SetInfo(const char * c_key, std::string const& interface_str) {
-  common::AssertGPUSupport();
-}
-#endif  // !defined(XGBOOST_USE_CUDA)
-
-DMatrix* DMatrix::Load(const std::string& uri,
-                       bool silent,
-                       bool load_row_split,
-                       const std::string& file_format,
-                       const size_t page_size) {
-  std::string fname, cache_file;
-  size_t dlm_pos = uri.find('#');
-  if (dlm_pos != std::string::npos) {
-    cache_file = uri.substr(dlm_pos + 1, uri.length());
-    fname = uri.substr(0, dlm_pos);
-    CHECK_EQ(cache_file.find('#'), std::string::npos)
-        << "Only one `#` is allowed in file path for cache file specification.";
-    if (load_row_split) {
-      std::ostringstream os;
-      std::vector<std::string> cache_shards = common::Split(cache_file, ':');
-      for (size_t i = 0; i < cache_shards.size(); ++i) {
-        size_t pos = cache_shards[i].rfind('.');
-        if (pos == std::string::npos) {
-          os << cache_shards[i]
-             << ".r" << rabit::GetRank()
-             << "-" <<  rabit::GetWorldSize();
-        } else {
-          os << cache_shards[i].substr(0, pos)
-             << ".r" << rabit::GetRank()
-             << "-" <<  rabit::GetWorldSize()
-             << cache_shards[i].substr(pos, cache_shards[i].length());
-        }
-        if (i + 1 != cache_shards.size()) {
-          os << ':';
-        }
-      }
-      cache_file = os.str();
-    }
-  } else {
-    fname = uri;
-  }
-  int partid = 0, npart = 1;
-  if (load_row_split) {
-    partid = rabit::GetRank();
-    npart = rabit::GetWorldSize();
-  } else {
-    // test option to load in part
-    npart = dmlc::GetEnv("XGBOOST_TEST_NPART", 1);
-  }
-
-  if (npart != 1) {
-    LOG(CONSOLE) << "Load part of data " << partid
-                 << " of " << npart << " parts";
-  }
-
-  // legacy handling of binary data loading
-  if (file_format == "auto" && npart == 1) {
-    int magic;
-    std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
-    if (fi != nullptr) {
-      common::PeekableInStream is(fi.get());
-      if (is.PeekRead(&magic, sizeof(magic)) == sizeof(magic) &&
-        magic == data::SimpleDMatrix::kMagic) {
-        DMatrix* dmat = new data::SimpleDMatrix(&is);
-        if (!silent) {
-          LOG(CONSOLE) << dmat->Info().num_row_ << 'x' << dmat->Info().num_col_ << " matrix with "
-            << dmat->Info().num_nonzero_ << " entries loaded from " << uri;
-        }
-        return dmat;
-      }
-    }
-  }
-
-  std::unique_ptr<dmlc::Parser<uint32_t> > parser(
-      dmlc::Parser<uint32_t>::Create(fname.c_str(), partid, npart, file_format.c_str()));
-  data::FileAdapter adapter(parser.get());
-  DMatrix* dmat {nullptr};
-
-  try {
-    dmat = DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1,
-                           cache_file, page_size);
-  } catch (dmlc::Error& e) {
-    std::vector<std::string> splited = common::Split(fname, '#');
-    std::vector<std::string> args = common::Split(splited.front(), '?');
-    std::string format {file_format};
-    if (args.size() == 1 && file_format == "auto") {
-      auto extension = common::Split(args.front(), '.').back();
-      if (extension == "csv" || extension == "libsvm") {
-        format = extension;
-      }
-      if (format == extension) {
-        LOG(WARNING)
-            << "No format parameter is provided in input uri, but found file extension: "
-            << format << " .  "
-            << "Consider providing a uri parameter: filename?format=" << format;
-      } else {
-        LOG(WARNING)
-            << "No format parameter is provided in input uri.  "
-            << "Choosing default parser in dmlc-core.  "
-            << "Consider providing a uri parameter like: filename?format=csv";
-      }
-    }
-    LOG(FATAL) << "Encountered parser error:\n" << e.what();
-  }
-
-  if (!silent) {
-    LOG(CONSOLE) << dmat->Info().num_row_ << 'x' << dmat->Info().num_col_ << " matrix with "
-                 << dmat->Info().num_nonzero_ << " entries loaded from " << uri;
-  }
-  /* sync up number of features after matrix loaded.
-   * partitioned data will fail the train/val validation check
-   * since partitioned data not knowing the real number of features. */
-  rabit::Allreduce<rabit::op::Max>(&dmat->Info().num_col_, 1, nullptr,
-    nullptr, fname.c_str());
-  // backward compatiblity code.
-  if (!load_row_split) {
-    MetaInfo& info = dmat->Info();
-    if (MetaTryLoadGroup(fname + ".group", &info.group_ptr_) && !silent) {
-      LOG(CONSOLE) << info.group_ptr_.size() - 1
-                   << " groups are loaded from " << fname << ".group";
-    }
-    if (MetaTryLoadFloatInfo
-        (fname + ".base_margin", &info.base_margin_.HostVector()) && !silent) {
-      LOG(CONSOLE) << info.base_margin_.Size()
-                   << " base_margin are loaded from " << fname << ".base_margin";
-    }
-    if (MetaTryLoadFloatInfo
-        (fname + ".weight", &info.weights_.HostVector()) && !silent) {
-      LOG(CONSOLE) << info.weights_.Size()
-                   << " weights are loaded from " << fname << ".weight";
-    }
-  }
-  return dmat;
-}
-
-template <typename AdapterT>
-DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread,
-                         const std::string& cache_prefix,  size_t page_size ) {
-  if (cache_prefix.length() == 0) {
-    // Data split mode is fixed to be row right now.
-    return new data::SimpleDMatrix(adapter, missing, nthread);
-  } else {
-#if DMLC_ENABLE_STD_THREAD
-    return new data::SparsePageDMatrix(adapter, missing, nthread, cache_prefix,
-                                       page_size);
-#else
-    LOG(FATAL) << "External memory is not enabled in mingw";
-    return nullptr;
-#endif  // DMLC_ENABLE_STD_THREAD
-  }
-}
-
-template DMatrix* DMatrix::Create<data::DenseAdapter>(
-    data::DenseAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-template DMatrix* DMatrix::Create<data::CSRAdapter>(
-    data::CSRAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-template DMatrix* DMatrix::Create<data::CSCAdapter>(
-    data::CSCAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-template DMatrix* DMatrix::Create<data::DataTableAdapter>(
-    data::DataTableAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-template DMatrix* DMatrix::Create<data::FileAdapter>(
-    data::FileAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-template DMatrix* DMatrix::Create<data::IteratorAdapter>(
-    data::IteratorAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-
-SparsePage SparsePage::GetTranspose(int num_columns) const {
-  SparsePage transpose;
-  common::ParallelGroupBuilder<Entry, bst_row_t> builder(&transpose.offset.HostVector(),
-                                                         &transpose.data.HostVector());
-  const int nthread = omp_get_max_threads();
-  builder.InitBudget(num_columns, nthread);
-  long batch_size = static_cast<long>(this->Size());  // NOLINT(*)
-#pragma omp parallel for default(none) shared(batch_size, builder) schedule(static)
-  for (long i = 0; i < batch_size; ++i) {  // NOLINT(*)
-    int tid = omp_get_thread_num();
-    auto inst = (*this)[i];
-    for (const auto& entry : inst) {
-      builder.AddBudget(entry.index, tid);
-    }
-  }
-  builder.InitStorage();
-#pragma omp parallel for default(none) shared(batch_size, builder) schedule(static)
-  for (long i = 0; i < batch_size; ++i) {  // NOLINT(*)
-    int tid = omp_get_thread_num();
-    auto inst = (*this)[i];
-    for (const auto& entry : inst) {
-      builder.Push(
-          entry.index,
-          Entry(static_cast<bst_uint>(this->base_rowid + i), entry.fvalue),
-          tid);
-    }
-  }
-  return transpose;
-}
-void SparsePage::Push(const SparsePage &batch) {
-  auto& data_vec = data.HostVector();
-  auto& offset_vec = offset.HostVector();
-  const auto& batch_offset_vec = batch.offset.HostVector();
-  const auto& batch_data_vec = batch.data.HostVector();
-  size_t top = offset_vec.back();
-  data_vec.resize(top + batch.data.Size());
-  std::memcpy(dmlc::BeginPtr(data_vec) + top,
-              dmlc::BeginPtr(batch_data_vec),
-              sizeof(Entry) * batch.data.Size());
-  size_t begin = offset.Size();
-  offset_vec.resize(begin + batch.Size());
-  for (size_t i = 0; i < batch.Size(); ++i) {
-    offset_vec[i + begin] = top + batch_offset_vec[i + 1];
-  }
-}
-
-template <typename AdapterBatchT>
-uint64_t SparsePage::Push(const AdapterBatchT& batch, float missing, int nthread) {
-  // Set number of threads but keep old value so we can reset it after
-  const int nthreadmax = omp_get_max_threads();
-  if (nthread <= 0) nthread = nthreadmax;
-  int nthread_original = omp_get_max_threads();
-  omp_set_num_threads(nthread);
-  auto& offset_vec = offset.HostVector();
-  auto& data_vec = data.HostVector();
-  size_t builder_base_row_offset = this->Size();
-  common::ParallelGroupBuilder<
-      Entry, std::remove_reference<decltype(offset_vec)>::type::value_type>
-      builder(&offset_vec, &data_vec, builder_base_row_offset);
-  // Estimate expected number of rows by using last element in batch
-  // This is not required to be exact but prevents unnecessary resizing
-  size_t expected_rows = 0;
-  if (batch.Size() > 0) {
-    auto last_line = batch.GetLine(batch.Size() - 1);
-    if (last_line.Size() > 0) {
-      expected_rows =
-          last_line.GetElement(last_line.Size() - 1).row_idx - base_rowid;
-    }
-  }
-  builder.InitBudget(expected_rows, nthread);
-  uint64_t max_columns = 0;
-
-  // First-pass over the batch counting valid elements
-  size_t num_lines = batch.Size();
-#pragma omp parallel for schedule(static)
-  for (omp_ulong i = 0; i < static_cast<omp_ulong>(num_lines);
-       ++i) {  // NOLINT(*)
-    int tid = omp_get_thread_num();
-    auto line = batch.GetLine(i);
-    for (auto j = 0ull; j < line.Size(); j++) {
-      data::COOTuple element = line.GetElement(j);
-      max_columns =
-          std::max(max_columns, static_cast<uint64_t>(element.column_idx + 1));
-      if (!common::CheckNAN(element.value) && element.value != missing) {
-        size_t key = element.row_idx - base_rowid;
-        // Adapter row index is absolute, here we want it relative to
-        // current page
-        CHECK_GE(key,  builder_base_row_offset);
-        builder.AddBudget(key, tid);
-      }
-    }
-  }
-  builder.InitStorage();
-
-  // Second pass over batch, placing elements in correct position
-#pragma omp parallel for schedule(static)
-  for (omp_ulong i = 0; i < static_cast<omp_ulong>(num_lines);
-       ++i) {  // NOLINT(*)
-    int tid = omp_get_thread_num();
-    auto line = batch.GetLine(i);
-    for (auto j = 0ull; j < line.Size(); j++) {
-      auto element = line.GetElement(j);
-      if (!common::CheckNAN(element.value) && element.value != missing) {
-        size_t key = element.row_idx -
-                     base_rowid;  // Adapter row index is absolute, here we want
-                                  // it relative to current page
-        builder.Push(key, Entry(element.column_idx, element.value), tid);
-      }
-    }
-  }
-  omp_set_num_threads(nthread_original);
-  return max_columns;
-}
-
-void SparsePage::PushCSC(const SparsePage &batch) {
-  std::vector<xgboost::Entry>& self_data = data.HostVector();
-  std::vector<bst_row_t>& self_offset = offset.HostVector();
-
-  auto const& other_data = batch.data.ConstHostVector();
-  auto const& other_offset = batch.offset.ConstHostVector();
-
-  if (other_data.empty()) {
-    return;
-  }
-  if (!self_data.empty()) {
-    CHECK_EQ(self_offset.size(), other_offset.size())
-        << "self_data.size(): " << this->data.Size() << ", "
-        << "other_data.size(): " << other_data.size() << std::flush;
-  } else {
-    self_data = other_data;
-    self_offset = other_offset;
-    return;
-  }
-
-  std::vector<bst_row_t> offset(other_offset.size());
-  offset[0] = 0;
-
-  std::vector<xgboost::Entry> data(self_data.size() + other_data.size());
-
-  // n_cols in original csr data matrix, here in csc is n_rows
-  size_t const n_features = other_offset.size() - 1;
-  size_t beg = 0;
-  size_t ptr = 1;
-  for (size_t i = 0; i < n_features; ++i) {
-    size_t const self_beg = self_offset.at(i);
-    size_t const self_length = self_offset.at(i+1) - self_beg;
-    // It is possible that the current feature and further features aren't referenced
-    // in any rows accumulated thus far. It is also possible for this to happen
-    // in the current sparse page row batch as well.
-    // Hence, the incremental number of rows may stay constant thus equaling the data size
-    CHECK_LE(beg, data.size());
-    std::memcpy(dmlc::BeginPtr(data)+beg,
-                dmlc::BeginPtr(self_data) + self_beg,
-                sizeof(Entry) * self_length);
-    beg += self_length;
-
-    size_t const other_beg = other_offset.at(i);
-    size_t const other_length = other_offset.at(i+1) - other_beg;
-    CHECK_LE(beg, data.size());
-    std::memcpy(dmlc::BeginPtr(data)+beg,
-                dmlc::BeginPtr(other_data) + other_beg,
-                sizeof(Entry) * other_length);
-    beg += other_length;
-
-    CHECK_LT(ptr, offset.size());
-    offset.at(ptr) = beg;
-    ptr++;
-  }
-
-  self_data = std::move(data);
-  self_offset = std::move(offset);
-}
-
-namespace data {
-// List of files that will be force linked in static links.
-DMLC_REGISTRY_LINK_TAG(sparse_page_raw_format);
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/data.cu b/ml-xgboost/src/data/data.cu
deleted file mode 100644
index 322f1f6..0000000
--- a/ml-xgboost/src/data/data.cu
+++ /dev/null
@@ -1,87 +0,0 @@
-/*!
- * Copyright 2019 by XGBoost Contributors
- *
- * \file data.cu
- * \brief Handles setting metainfo from array interface.
- */
-#include "xgboost/data.h"
-#include "xgboost/logging.h"
-#include "xgboost/json.h"
-#include "array_interface.h"
-#include "../common/device_helpers.cuh"
-#include "device_adapter.cuh"
-#include "device_dmatrix.h"
-
-namespace xgboost {
-
-void CopyInfoImpl(ArrayInterface column, HostDeviceVector<float>* out) {
-  auto SetDeviceToPtr = [](void* ptr) {
-    cudaPointerAttributes attr;
-    dh::safe_cuda(cudaPointerGetAttributes(&attr, ptr));
-    int32_t ptr_device = attr.device;
-    dh::safe_cuda(cudaSetDevice(ptr_device));
-    return ptr_device;
-  };
-  auto ptr_device = SetDeviceToPtr(column.data);
-
-  out->SetDevice(ptr_device);
-  out->Resize(column.num_rows);
-
-  auto p_dst = thrust::device_pointer_cast(out->DevicePointer());
-
-  dh::LaunchN(ptr_device, column.num_rows, [=] __device__(size_t idx) {
-    p_dst[idx] = column.GetElement(idx);
-  });
-}
-
-void MetaInfo::SetInfo(const char * c_key, std::string const& interface_str) {
-  Json j_interface = Json::Load({interface_str.c_str(), interface_str.size()});
-  auto const& j_arr = get<Array>(j_interface);
-  CHECK_EQ(j_arr.size(), 1)
-      << "MetaInfo: " << c_key << ". " << ArrayInterfaceErrors::Dimension(1);
-  ArrayInterface array_interface(get<Object const>(j_arr[0]));
-  std::string key{c_key};
-  CHECK(!array_interface.valid.Data())
-      << "Meta info " << key << " should be dense, found validity mask";
-  CHECK_EQ(array_interface.num_cols, 1)
-      << "Meta info should be a single column.";
-
-  if (key == "label") {
-    CopyInfoImpl(array_interface, &labels_);
-  } else if (key == "weight") {
-    CopyInfoImpl(array_interface, &weights_);
-  } else if (key == "base_margin") {
-    CopyInfoImpl(array_interface, &base_margin_);
-  } else if (key == "group") {
-    // Ranking is not performed on device.
-    thrust::device_ptr<uint32_t> p_src{
-        reinterpret_cast<uint32_t*>(array_interface.data)};
-
-    auto length = array_interface.num_rows;
-    group_ptr_.resize(length + 1);
-    group_ptr_[0] = 0;
-    thrust::copy(p_src, p_src + length, group_ptr_.begin() + 1);
-    std::partial_sum(group_ptr_.begin(), group_ptr_.end(), group_ptr_.begin());
-
-    return;
-  } else {
-    LOG(FATAL) << "Unknown metainfo: " << key;
-  }
-}
-
-template <typename AdapterT>
-DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread,
-                         const std::string& cache_prefix, size_t page_size) {
-  CHECK_EQ(cache_prefix.size(), 0)
-      << "Device memory construction is not currently supported with external "
-         "memory.";
-  return new data::SimpleDMatrix(adapter, missing, nthread);
-}
-
-template DMatrix* DMatrix::Create<data::CudfAdapter>(
-    data::CudfAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-template DMatrix* DMatrix::Create<data::CupyAdapter>(
-    data::CupyAdapter* adapter, float missing, int nthread,
-    const std::string& cache_prefix, size_t page_size);
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/device_adapter.cuh b/ml-xgboost/src/data/device_adapter.cuh
deleted file mode 100644
index ca25cba..0000000
--- a/ml-xgboost/src/data/device_adapter.cuh
+++ /dev/null
@@ -1,217 +0,0 @@
-/*!
- *  Copyright (c) 2019 by Contributors
- * \file device_adapter.cuh
- */
-#ifndef XGBOOST_DATA_DEVICE_ADAPTER_H_
-#define XGBOOST_DATA_DEVICE_ADAPTER_H_
-#include <limits>
-#include <memory>
-#include <string>
-#include "../common/device_helpers.cuh"
-#include "../common/math.h"
-#include "adapter.h"
-#include "array_interface.h"
-
-namespace xgboost {
-namespace data {
-
-struct IsValidFunctor : public thrust::unary_function<Entry, bool> {
-  explicit IsValidFunctor(float missing) : missing(missing) {}
-
-  float missing;
-  __device__ bool operator()(const data::COOTuple& e) const {
-    if (common::CheckNAN(e.value) || e.value == missing) {
-      return false;
-    }
-    return true;
-  }
-  __device__ bool operator()(const Entry& e) const {
-    if (common::CheckNAN(e.fvalue) || e.fvalue == missing) {
-      return false;
-    }
-    return true;
-  }
-};
-
-class CudfAdapterBatch : public detail::NoMetaInfo {
- public:
-  CudfAdapterBatch() = default;
-  CudfAdapterBatch(common::Span<ArrayInterface> columns,
-                   common::Span<size_t> column_ptr, size_t num_elements)
-      : columns_(columns),
-        column_ptr_(column_ptr),
-        num_elements_(num_elements) {}
-  size_t Size() const { return num_elements_; }
-  __device__ COOTuple GetElement(size_t idx) const {
-    size_t column_idx =
-        thrust::upper_bound(thrust::seq,column_ptr_.begin(), column_ptr_.end(), idx) - column_ptr_.begin() - 1;
-    auto& column = columns_[column_idx];
-    size_t row_idx = idx - column_ptr_[column_idx];
-    float value = column.valid.Data() == nullptr || column.valid.Check(row_idx)
-                      ? column.GetElement(row_idx)
-                      : std::numeric_limits<float>::quiet_NaN();
-    return {row_idx, column_idx, value};
-  }
-  __device__ float GetValue(size_t ridx, bst_feature_t fidx) const {
-    auto const& column = columns_[fidx];
-    float value = column.valid.Data() == nullptr || column.valid.Check(ridx)
-                      ? column.GetElement(ridx)
-                      : std::numeric_limits<float>::quiet_NaN();
-    return value;
-  }
-
- private:
-  common::Span<ArrayInterface> columns_;
-  common::Span<size_t> column_ptr_;
-  size_t num_elements_;
-};
-
-/*!
- * Please be careful that, in official specification, the only three required
- * fields are `shape', `version' and `typestr'.  Any other is optional,
- * including `data'.  But here we have one additional requirements for input
- * data:
- *
- * - `data' field is required, passing in an empty dataset is not accepted, as
- * most (if not all) of our algorithms don't have test for empty dataset.  An
- * error is better than a crash.
- *
- * What if invalid value from dataframe is 0 but I specify missing=NaN in
- * XGBoost?  Since validity mask is ignored, all 0s are preserved in XGBoost.
- *
- * FIXME(trivialfis): Put above into document after we have a consistent way for
- * processing input data.
- *
- * Sample input:
- * [
- *   {
- *     "shape": [
- *       10
- *     ],
- *     "strides": [
- *       4
- *     ],
- *     "data": [
- *       30074864128,
- *       false
- *     ],
- *     "typestr": "<f4",
- *     "version": 1,
- *     "mask": {
- *       "shape": [
- *         64
- *       ],
- *       "strides": [
- *         1
- *       ],
- *       "data": [
- *         30074864640,
- *         false
- *       ],
- *       "typestr": "|i1",
- *       "version": 1
- *     }
- *   }
- * ]
- */
-class CudfAdapter : public detail::SingleBatchDataIter<CudfAdapterBatch> {
- public:
-  explicit CudfAdapter(std::string cuda_interfaces_str) {
-    Json interfaces =
-        Json::Load({cuda_interfaces_str.c_str(), cuda_interfaces_str.size()});
-    std::vector<Json> const& json_columns = get<Array>(interfaces);
-    size_t n_columns = json_columns.size();
-    CHECK_GT(n_columns, 0) << "Number of columns must not equal to 0.";
-
-    auto const& typestr = get<String const>(json_columns[0]["typestr"]);
-    CHECK_EQ(typestr.size(), 3) << ArrayInterfaceErrors::TypestrFormat();
-    CHECK_NE(typestr.front(), '>') << ArrayInterfaceErrors::BigEndian();
-    std::vector<ArrayInterface> columns;
-    std::vector<size_t> column_ptr({0});
-    auto first_column = ArrayInterface(get<Object const>(json_columns[0]));
-    device_idx_ = dh::CudaGetPointerDevice(first_column.data);
-    CHECK_NE(device_idx_, -1);
-    dh::safe_cuda(cudaSetDevice(device_idx_));
-    num_rows_ = first_column.num_rows;
-    for (auto& json_col : json_columns) {
-      auto column = ArrayInterface(get<Object const>(json_col));
-      columns.push_back(column);
-      CHECK_EQ(column.num_cols, 1);
-      column_ptr.emplace_back(column_ptr.back() + column.num_rows);
-      num_rows_ = std::max(num_rows_, size_t(column.num_rows));
-      CHECK_EQ(device_idx_, dh::CudaGetPointerDevice(column.data))
-          << "All columns should use the same device.";
-      CHECK_EQ(num_rows_, column.num_rows)
-          << "All columns should have same number of rows.";
-    }
-    columns_ = columns;
-    column_ptr_ = column_ptr;
-    batch_ = CudfAdapterBatch(dh::ToSpan(columns_), dh::ToSpan(column_ptr_),
-                             column_ptr.back());
-  }
-  const CudfAdapterBatch& Value() const override { return batch_; }
-
-  size_t NumRows() const { return num_rows_; }
-  size_t NumColumns() const { return columns_.size(); }
-  size_t DeviceIdx() const { return device_idx_; }
-
-  // Cudf is column major
-  bool IsRowMajor() { return false; }
-
- private:
-  CudfAdapterBatch batch_;
-  dh::device_vector<ArrayInterface> columns_;
-  dh::device_vector<size_t> column_ptr_;  // Exclusive scan of column sizes
-  size_t num_rows_{0};
-  int device_idx_;
-};
-
-class CupyAdapterBatch : public detail::NoMetaInfo {
- public:
-  CupyAdapterBatch() = default;
-  explicit CupyAdapterBatch(ArrayInterface array_interface)
-    : array_interface_(std::move(array_interface)) {}
-  size_t Size() const {
-    return array_interface_.num_rows * array_interface_.num_cols;
-  }
-  __device__ COOTuple GetElement(size_t idx) const {
-    size_t column_idx = idx % array_interface_.num_cols;
-    size_t row_idx = idx / array_interface_.num_cols;
-    float value = array_interface_.valid.Data() == nullptr ||
-                          array_interface_.valid.Check(row_idx)
-                      ? array_interface_.GetElement(idx)
-                      : std::numeric_limits<float>::quiet_NaN();
-    return {row_idx, column_idx, value};
-  }
-
- private:
-  ArrayInterface array_interface_;
-};
-
-class CupyAdapter : public detail::SingleBatchDataIter<CupyAdapterBatch> {
- public:
-  explicit CupyAdapter(std::string cuda_interface_str) {
-    Json json_array_interface =
-        Json::Load({cuda_interface_str.c_str(), cuda_interface_str.size()});
-    array_interface_ = ArrayInterface(get<Object const>(json_array_interface));
-    device_idx_ = dh::CudaGetPointerDevice(array_interface_.data);
-    CHECK_NE(device_idx_, -1);
-    batch_ = CupyAdapterBatch(array_interface_);
-  }
-  const CupyAdapterBatch& Value() const override { return batch_; }
-
-  size_t NumRows() const { return array_interface_.num_rows; }
-  size_t NumColumns() const { return array_interface_.num_cols; }
-  size_t DeviceIdx() const { return device_idx_; }
-
-  bool IsRowMajor() { return true; }
-
- private:
-  ArrayInterface array_interface_;
-  CupyAdapterBatch batch_;
-  int device_idx_;
-};
-
-};  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_DEVICE_ADAPTER_H_
diff --git a/ml-xgboost/src/data/device_dmatrix.cu b/ml-xgboost/src/data/device_dmatrix.cu
deleted file mode 100644
index 5dbc76d..0000000
--- a/ml-xgboost/src/data/device_dmatrix.cu
+++ /dev/null
@@ -1,239 +0,0 @@
-/*!
- * Copyright 2020 by Contributors
- * \file device_dmatrix.cu
- * \brief Device-memory version of DMatrix.
- */
-
-#include <thrust/execution_policy.h>
-#include <thrust/iterator/discard_iterator.h>
-#include <thrust/iterator/transform_output_iterator.h>
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <memory>
-#include <utility>
-#include "../common/hist_util.h"
-#include "adapter.h"
-#include "device_adapter.cuh"
-#include "ellpack_page.cuh"
-#include "device_dmatrix.h"
-
-namespace xgboost {
-namespace data {
-
-// Returns maximum row length
-template <typename AdapterBatchT>
-size_t GetRowCounts(const AdapterBatchT& batch, common::Span<size_t> offset,
-                    int device_idx, float missing) {
-  IsValidFunctor is_valid(missing);
-  // Count elements per row
-  dh::LaunchN(device_idx, batch.Size(), [=] __device__(size_t idx) {
-    auto element = batch.GetElement(idx);
-    if (is_valid(element)) {
-      atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                    &offset[element.row_idx]),
-                static_cast<unsigned long long>(1));  // NOLINT
-    }
-  });
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  size_t row_stride = thrust::reduce(
-      thrust::cuda::par(alloc), thrust::device_pointer_cast(offset.data()),
-      thrust::device_pointer_cast(offset.data()) + offset.size(), size_t(0),
-      thrust::maximum<size_t>());
-  return row_stride;
-}
-
-template <typename AdapterBatchT>
-struct WriteCompressedEllpackFunctor {
-  WriteCompressedEllpackFunctor(common::CompressedByteT* buffer,
-                                const common::CompressedBufferWriter& writer,
-                                AdapterBatchT batch,
-                                EllpackDeviceAccessor accessor,
-                                const IsValidFunctor& is_valid)
-      : d_buffer(buffer),
-        writer(writer),
-        batch(std::move(batch)),
-        accessor(std::move(accessor)),
-        is_valid(is_valid) {}
-
-  common::CompressedByteT* d_buffer;
-  common::CompressedBufferWriter writer;
-  AdapterBatchT batch;
-  EllpackDeviceAccessor accessor;
-  IsValidFunctor is_valid;
-
-  using Tuple = thrust::tuple<size_t, size_t, size_t>;
-  __device__ size_t operator()(Tuple out) {
-    auto e = batch.GetElement(out.get<2>());
-    if (is_valid(e)) {
-      // -1 because the scan is inclusive
-      size_t output_position =
-          accessor.row_stride * e.row_idx + out.get<1>() - 1;
-      auto bin_idx = accessor.SearchBin(e.value, e.column_idx);
-      writer.AtomicWriteSymbol(d_buffer, bin_idx, output_position);
-    }
-    return 0;
-  }
-};
-
-// Here the data is already correctly ordered and simply needs to be compacted
-// to remove missing data
-template <typename AdapterBatchT>
-void CopyDataRowMajor(const AdapterBatchT& batch, EllpackPageImpl* dst,
-                      int device_idx, float missing) {
-  // Some witchcraft happens here
-  // The goal is to copy valid elements out of the input to an ellpack matrix
-  // with a given row stride, using no extra working memory Standard stream
-  // compaction needs to be modified to do this, so we manually define a
-  // segmented stream compaction via operators on an inclusive scan. The output
-  // of this inclusive scan is fed to a custom function which works out the
-  // correct output position
-  auto counting = thrust::make_counting_iterator(0llu);
-  IsValidFunctor is_valid(missing);
-  auto key_iter = dh::MakeTransformIterator<size_t>(
-      counting,
-      [=] __device__(size_t idx) { return batch.GetElement(idx).row_idx; });
-  auto value_iter = dh::MakeTransformIterator<size_t>(
-      counting, [=] __device__(size_t idx) -> size_t {
-        return is_valid(batch.GetElement(idx));
-      });
-
-  auto key_value_index_iter = thrust::make_zip_iterator(
-      thrust::make_tuple(key_iter, value_iter, counting));
-
-  // Tuple[0] = The row index of the input, used as a key to define segments
-  // Tuple[1] = Scanned flags of valid elements for each row
-  // Tuple[2] = The index in the input data
-  using Tuple = thrust::tuple<size_t, size_t, size_t>;
-
-  auto device_accessor = dst->GetDeviceAccessor(device_idx);
-  common::CompressedBufferWriter writer(device_accessor.NumSymbols());
-  auto d_compressed_buffer = dst->gidx_buffer.DevicePointer();
-
-  // We redirect the scan output into this functor to do the actual writing
-  WriteCompressedEllpackFunctor<AdapterBatchT> functor(
-      d_compressed_buffer, writer, batch, device_accessor, is_valid);
-  thrust::discard_iterator<size_t> discard;
-  thrust::transform_output_iterator<
-      WriteCompressedEllpackFunctor<AdapterBatchT>, decltype(discard)>
-      out(discard, functor);
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  thrust::inclusive_scan(thrust::cuda::par(alloc), key_value_index_iter,
-                         key_value_index_iter + batch.Size(), out,
-                         [=] __device__(Tuple a, Tuple b) {
-                           // Key equal
-                           if (a.get<0>() == b.get<0>()) {
-                             b.get<1>() += a.get<1>();
-                             return b;
-                           }
-                           // Not equal
-                           return b;
-                         });
-}
-
-template <typename AdapterT, typename AdapterBatchT>
-void CopyDataColumnMajor(AdapterT* adapter, const AdapterBatchT& batch,
-                         EllpackPageImpl* dst, float missing) {
-  // Step 1: Get the sizes of the input columns
-  dh::caching_device_vector<size_t> column_sizes(adapter->NumColumns(), 0);
-  auto d_column_sizes = column_sizes.data().get();
-  // Populate column sizes
-  dh::LaunchN(adapter->DeviceIdx(), batch.Size(), [=] __device__(size_t idx) {
-    const auto& e = batch.GetElement(idx);
-    atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                  &d_column_sizes[e.column_idx]),
-              static_cast<unsigned long long>(1));  // NOLINT
-  });
-
-  thrust::host_vector<size_t> host_column_sizes = column_sizes;
-
-  // Step 2: Iterate over columns, place elements in correct row, increment
-  // temporary row pointers
-  dh::caching_device_vector<size_t> temp_row_ptr(adapter->NumRows(), 0);
-  auto d_temp_row_ptr = temp_row_ptr.data().get();
-  auto row_stride = dst->row_stride;
-  size_t begin = 0;
-  auto device_accessor = dst->GetDeviceAccessor(adapter->DeviceIdx());
-  common::CompressedBufferWriter writer(device_accessor.NumSymbols());
-  auto d_compressed_buffer = dst->gidx_buffer.DevicePointer();
-  IsValidFunctor is_valid(missing);
-  for (auto size : host_column_sizes) {
-    size_t end = begin + size;
-    dh::LaunchN(adapter->DeviceIdx(), end - begin, [=] __device__(size_t idx) {
-      auto writer_non_const =
-          writer;  // For some reason this variable gets captured as const
-      const auto& e = batch.GetElement(idx + begin);
-      if (!is_valid(e)) return;
-      size_t output_position =
-          e.row_idx * row_stride + d_temp_row_ptr[e.row_idx];
-      auto bin_idx = device_accessor.SearchBin(e.value, e.column_idx);
-      writer_non_const.AtomicWriteSymbol(d_compressed_buffer, bin_idx,
-                                         output_position);
-      d_temp_row_ptr[e.row_idx] += 1;
-    });
-
-    begin = end;
-  }
-}
-
-void WriteNullValues(EllpackPageImpl* dst, int device_idx,
-                     common::Span<size_t> row_counts) {
-  // Write the null values
-  auto device_accessor = dst->GetDeviceAccessor(device_idx);
-  common::CompressedBufferWriter writer(device_accessor.NumSymbols());
-  auto d_compressed_buffer = dst->gidx_buffer.DevicePointer();
-  auto row_stride = dst->row_stride;
-  dh::LaunchN(device_idx, row_stride * dst->n_rows, [=] __device__(size_t idx) {
-    auto writer_non_const =
-        writer;  // For some reason this variable gets captured as const
-    size_t row_idx = idx / row_stride;
-    size_t row_offset = idx % row_stride;
-    if (row_offset >= row_counts[row_idx]) {
-      writer_non_const.AtomicWriteSymbol(d_compressed_buffer,
-                                         device_accessor.NullValue(), idx);
-    }
-  });
-}
-
-// Does not currently support metainfo as no on-device data source contains this
-// Current implementation assumes a single batch. More batches can
-// be supported in future. Does not currently support inferring row/column size
-template <typename AdapterT>
-DeviceDMatrix::DeviceDMatrix(AdapterT* adapter, float missing, int nthread, int max_bin) {
-  common::HistogramCuts cuts =
-      common::AdapterDeviceSketch(adapter, max_bin, missing);
-  dh::safe_cuda(cudaSetDevice(adapter->DeviceIdx()));
-  auto& batch = adapter->Value();
-  // Work out how many valid entries we have in each row
-  dh::caching_device_vector<size_t> row_counts(adapter->NumRows() + 1, 0);
-  common::Span<size_t> row_counts_span(row_counts.data().get(),
-                                       row_counts.size());
-  size_t row_stride =
-      GetRowCounts(batch, row_counts_span, adapter->DeviceIdx(), missing);
-
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  info_.num_nonzero_ = thrust::reduce(thrust::cuda::par(alloc),
-                                     row_counts.begin(), row_counts.end());
-  info_.num_col_ = adapter->NumColumns();
-  info_.num_row_ = adapter->NumRows();
-  ellpack_page_.reset(new EllpackPage());
-  *ellpack_page_->Impl() =
-      EllpackPageImpl(adapter->DeviceIdx(), cuts, this->IsDense(), row_stride,
-                      adapter->NumRows());
-  if (adapter->IsRowMajor()) {
-    CopyDataRowMajor(batch, ellpack_page_->Impl(), adapter->DeviceIdx(),
-                     missing);
-  } else {
-    CopyDataColumnMajor(adapter, batch, ellpack_page_->Impl(), missing);
-  }
-
-  WriteNullValues(ellpack_page_->Impl(), adapter->DeviceIdx(), row_counts_span);
-
-  // Synchronise worker columns
-  rabit::Allreduce<rabit::op::Max>(&info_.num_col_, 1);
-}
-template DeviceDMatrix::DeviceDMatrix(CudfAdapter* adapter, float missing,
-                                      int nthread, int max_bin);
-template DeviceDMatrix::DeviceDMatrix(CupyAdapter* adapter, float missing,
-                                      int nthread, int max_bin);
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/device_dmatrix.h b/ml-xgboost/src/data/device_dmatrix.h
deleted file mode 100644
index 781461b..0000000
--- a/ml-xgboost/src/data/device_dmatrix.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*!
- * Copyright 2020 by Contributors
- * \file device_dmatrix.h
- * \brief Device-memory version of DMatrix.
- */
-#ifndef XGBOOST_DATA_DEVICE_DMATRIX_H_
-#define XGBOOST_DATA_DEVICE_DMATRIX_H_
-
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-
-#include <memory>
-
-#include "adapter.h"
-#include "simple_batch_iterator.h"
-#include "simple_dmatrix.h"
-
-namespace xgboost {
-namespace data {
-
-class DeviceDMatrix : public DMatrix {
- public:
-  template <typename AdapterT>
-  explicit DeviceDMatrix(AdapterT* adapter, float missing, int nthread, int max_bin);
-
-  MetaInfo& Info() override { return info_; }
-
-  const MetaInfo& Info() const override { return info_; }
-
-  bool SingleColBlock() const override { return true; }
-
-  bool EllpackExists() const override { return true; }
-  bool SparsePageExists() const override { return false; }
-  DMatrix *Slice(common::Span<int32_t const> ridxs) override {
-    LOG(FATAL) << "Slicing DMatrix is not supported for Device DMatrix.";
-    return nullptr;
-  }
-
- private:
-  BatchSet<SparsePage> GetRowBatches() override {
-    LOG(FATAL) << "Not implemented.";
-    return BatchSet<SparsePage>(BatchIterator<SparsePage>(nullptr));
-  }
-  BatchSet<CSCPage> GetColumnBatches() override {
-    LOG(FATAL) << "Not implemented.";
-    return BatchSet<CSCPage>(BatchIterator<CSCPage>(nullptr));
-  }
-  BatchSet<SortedCSCPage> GetSortedColumnBatches() override {
-    LOG(FATAL) << "Not implemented.";
-    return BatchSet<SortedCSCPage>(BatchIterator<SortedCSCPage>(nullptr));
-  }
-  BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) override {
-    auto begin_iter = BatchIterator<EllpackPage>(
-        new SimpleBatchIteratorImpl<EllpackPage>(ellpack_page_.get()));
-    return BatchSet<EllpackPage>(begin_iter);
-  }
-
-  MetaInfo info_;
-  // source data pointer.
-  std::unique_ptr<EllpackPage> ellpack_page_;
-};
-}  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_DEVICE_DMATRIX_H_
diff --git a/ml-xgboost/src/data/ellpack_page.cc b/ml-xgboost/src/data/ellpack_page.cc
deleted file mode 100644
index 2784dde..0000000
--- a/ml-xgboost/src/data/ellpack_page.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#ifndef XGBOOST_USE_CUDA
-
-#include <xgboost/data.h>
-
-// dummy implementation of EllpackPage in case CUDA is not used
-namespace xgboost {
-
-class EllpackPageImpl {};
-
-EllpackPage::EllpackPage() = default;
-
-EllpackPage::EllpackPage(DMatrix* dmat, const BatchParam& param) {
-  LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but "
-                "EllpackPage is required";
-}
-
-EllpackPage::~EllpackPage() {
-  LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but "
-                "EllpackPage is required";
-}
-
-void EllpackPage::SetBaseRowId(size_t row_id) {
-  LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but "
-                "EllpackPage is required";
-}
-size_t EllpackPage::Size() const {
-  LOG(FATAL) << "Internal Error: XGBoost is not compiled with CUDA but "
-                "EllpackPage is required";
-  return 0;
-}
-
-}  // namespace xgboost
-
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/data/ellpack_page.cu b/ml-xgboost/src/data/ellpack_page.cu
deleted file mode 100644
index 91f25b7..0000000
--- a/ml-xgboost/src/data/ellpack_page.cu
+++ /dev/null
@@ -1,309 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-
-#include <xgboost/data.h>
-
-#include "../common/hist_util.h"
-#include "../common/random.h"
-#include "./ellpack_page.cuh"
-
-namespace xgboost {
-
-EllpackPage::EllpackPage() : impl_{new EllpackPageImpl()} {}
-
-EllpackPage::EllpackPage(DMatrix* dmat, const BatchParam& param)
-    : impl_{new EllpackPageImpl(dmat, param)} {}
-
-EllpackPage::~EllpackPage() = default;
-
-size_t EllpackPage::Size() const { return impl_->Size(); }
-
-void EllpackPage::SetBaseRowId(size_t row_id) { impl_->SetBaseRowId(row_id); }
-
-// Bin each input data entry, store the bin indices in compressed form.
-__global__ void CompressBinEllpackKernel(
-    common::CompressedBufferWriter wr,
-    common::CompressedByteT* __restrict__ buffer,  // gidx_buffer
-    const size_t* __restrict__ row_ptrs,           // row offset of input data
-    const Entry* __restrict__ entries,      // One batch of input data
-    const float* __restrict__ cuts,         // HistogramCuts::cut_values_
-    const uint32_t* __restrict__ cut_rows,  // HistogramCuts::cut_ptrs_
-    size_t base_row,                        // batch_row_begin
-    size_t n_rows,
-    size_t row_stride,
-    unsigned int null_gidx_value) {
-  size_t irow = threadIdx.x + blockIdx.x * blockDim.x;
-  int ifeature = threadIdx.y + blockIdx.y * blockDim.y;
-  if (irow >= n_rows || ifeature >= row_stride) {
-    return;
-  }
-  int row_length = static_cast<int>(row_ptrs[irow + 1] - row_ptrs[irow]);
-  unsigned int bin = null_gidx_value;
-  if (ifeature < row_length) {
-    Entry entry = entries[row_ptrs[irow] - row_ptrs[0] + ifeature];
-    int feature = entry.index;
-    float fvalue = entry.fvalue;
-    // {feature_cuts, ncuts} forms the array of cuts of `feature'.
-    const float* feature_cuts = &cuts[cut_rows[feature]];
-    int ncuts = cut_rows[feature + 1] - cut_rows[feature];
-    // Assigning the bin in current entry.
-    // S.t.: fvalue < feature_cuts[bin]
-    bin = thrust::upper_bound(thrust::seq, feature_cuts, feature_cuts + ncuts,
-                              fvalue) -
-          feature_cuts;
-    if (bin >= ncuts) {
-      bin = ncuts - 1;
-    }
-    // Add the number of bins in previous features.
-    bin += cut_rows[feature];
-  }
-  // Write to gidx buffer.
-  wr.AtomicWriteSymbol(buffer, bin, (irow + base_row) * row_stride + ifeature);
-}
-
-// Construct an ELLPACK matrix with the given number of empty rows.
-EllpackPageImpl::EllpackPageImpl(int device, common::HistogramCuts cuts,
-                                 bool is_dense, size_t row_stride,
-                                 size_t n_rows)
-    : is_dense(is_dense),
-      cuts_(std::move(cuts)),
-      row_stride(row_stride),
-      n_rows(n_rows) {
-  monitor_.Init("ellpack_page");
-  dh::safe_cuda(cudaSetDevice(device));
-
-  monitor_.StartCuda("InitCompressedData");
-  InitCompressedData(device);
-  monitor_.StopCuda("InitCompressedData");
-}
-
-size_t GetRowStride(DMatrix* dmat) {
-  if (dmat->IsDense()) return dmat->Info().num_col_;
-
-  size_t row_stride = 0;
-  for (const auto& batch : dmat->GetBatches<SparsePage>()) {
-    const auto& row_offset = batch.offset.ConstHostVector();
-    for (auto i = 1ull; i < row_offset.size(); i++) {
-      row_stride = std::max(
-        row_stride, static_cast<size_t>(row_offset[i] - row_offset[i - 1]));
-    }
-  }
-  return row_stride;
-}
-
-// Construct an ELLPACK matrix in memory.
-EllpackPageImpl::EllpackPageImpl(DMatrix* dmat, const BatchParam& param)
-    : is_dense(dmat->IsDense()) {
-  monitor_.Init("ellpack_page");
-  dh::safe_cuda(cudaSetDevice(param.gpu_id));
-
-  n_rows = dmat->Info().num_row_;
-
-  monitor_.StartCuda("Quantiles");
-  // Create the quantile sketches for the dmatrix and initialize HistogramCuts.
-  row_stride = GetRowStride(dmat);
-  cuts_ = common::DeviceSketch(param.gpu_id, dmat, param.max_bin);
-  monitor_.StopCuda("Quantiles");
-
-  monitor_.StartCuda("InitCompressedData");
-  InitCompressedData(param.gpu_id);
-  monitor_.StopCuda("InitCompressedData");
-
-  monitor_.StartCuda("BinningCompression");
-  for (const auto& batch : dmat->GetBatches<SparsePage>()) {
-    CreateHistIndices(param.gpu_id, batch);
-  }
-  monitor_.StopCuda("BinningCompression");
-}
-
-// A functor that copies the data from one EllpackPage to another.
-struct CopyPage {
-  common::CompressedBufferWriter cbw;
-  common::CompressedByteT* dst_data_d;
-  common::CompressedIterator<uint32_t> src_iterator_d;
-  // The number of elements to skip.
-  size_t offset;
-
-  CopyPage(EllpackPageImpl* dst, EllpackPageImpl* src, size_t offset)
-      : cbw{dst->NumSymbols()},
-        dst_data_d{dst->gidx_buffer.DevicePointer()},
-        src_iterator_d{src->gidx_buffer.DevicePointer(), src->NumSymbols()},
-        offset(offset) {}
-
-  __device__ void operator()(size_t element_id) {
-    cbw.AtomicWriteSymbol(dst_data_d, src_iterator_d[element_id],
-                          element_id + offset);
-  }
-};
-
-// Copy the data from the given EllpackPage to the current page.
-size_t EllpackPageImpl::Copy(int device, EllpackPageImpl* page, size_t offset) {
-  monitor_.StartCuda("Copy");
-  size_t num_elements = page->n_rows * page->row_stride;
-  CHECK_EQ(row_stride, page->row_stride);
-  CHECK_EQ(NumSymbols(), page->NumSymbols());
-  CHECK_GE(n_rows * row_stride, offset + num_elements);
-  gidx_buffer.SetDevice(device);
-  page->gidx_buffer.SetDevice(device);
-  dh::LaunchN(device, num_elements, CopyPage(this, page, offset));
-  monitor_.StopCuda("Copy");
-  return num_elements;
-}
-
-// A functor that compacts the rows from one EllpackPage into another.
-struct CompactPage {
-  common::CompressedBufferWriter cbw;
-  common::CompressedByteT* dst_data_d;
-  common::CompressedIterator<uint32_t> src_iterator_d;
-  /*! \brief An array that maps the rows from the full DMatrix to the compacted
-   * page.
-   *
-   * The total size is the number of rows in the original, uncompacted DMatrix.
-   * Elements are the row ids in the compacted page. Rows not needed are set to
-   * SIZE_MAX.
-   *
-   * An example compacting 16 rows to 8 rows:
-   * [SIZE_MAX, 0, 1, SIZE_MAX, SIZE_MAX, 2, SIZE_MAX, 3, 4, 5, SIZE_MAX, 6,
-   * SIZE_MAX, 7, SIZE_MAX, SIZE_MAX]
-   */
-  common::Span<size_t> row_indexes;
-  size_t base_rowid;
-  size_t row_stride;
-
-  CompactPage(EllpackPageImpl* dst, EllpackPageImpl* src,
-              common::Span<size_t> row_indexes)
-      : cbw{dst->NumSymbols()},
-        dst_data_d{dst->gidx_buffer.DevicePointer()},
-        src_iterator_d{src->gidx_buffer.DevicePointer(), src->NumSymbols()},
-        row_indexes(row_indexes),
-        base_rowid{src->base_rowid},
-        row_stride{src->row_stride} {}
-
-  __device__ void operator()(size_t row_id) {
-    size_t src_row = base_rowid + row_id;
-    size_t dst_row = row_indexes[src_row];
-    if (dst_row == SIZE_MAX) return;
-    size_t dst_offset = dst_row * row_stride;
-    size_t src_offset = row_id * row_stride;
-    for (size_t j = 0; j < row_stride; j++) {
-      cbw.AtomicWriteSymbol(dst_data_d, src_iterator_d[src_offset + j],
-                            dst_offset + j);
-    }
-  }
-};
-
-// Compacts the data from the given EllpackPage into the current page.
-void EllpackPageImpl::Compact(int device, EllpackPageImpl* page,
-                              common::Span<size_t> row_indexes) {
-  monitor_.StartCuda("Compact");
-  CHECK_EQ(row_stride, page->row_stride);
-  CHECK_EQ(NumSymbols(), page->NumSymbols());
-  CHECK_LE(page->base_rowid + page->n_rows, row_indexes.size());
-  gidx_buffer.SetDevice(device);
-  page->gidx_buffer.SetDevice(device);
-  dh::LaunchN(device, page->n_rows, CompactPage(this, page, row_indexes));
-  monitor_.StopCuda("Compact");
-}
-
-// Initialize the buffer to stored compressed features.
-void EllpackPageImpl::InitCompressedData(int device) {
-  size_t num_symbols = NumSymbols();
-
-  // Required buffer size for storing data matrix in ELLPack format.
-  size_t compressed_size_bytes =
-    common::CompressedBufferWriter::CalculateBufferSize(row_stride * n_rows,
-      num_symbols);
-  gidx_buffer.SetDevice(device);
-  // Don't call fill unnecessarily
-  if (gidx_buffer.Size() == 0) {
-    gidx_buffer.Resize(compressed_size_bytes, 0);
-  } else {
-    gidx_buffer.Resize(compressed_size_bytes, 0);
-    thrust::fill(dh::tbegin(gidx_buffer), dh::tend(gidx_buffer), 0);
-  }
-}
-
-// Compress a CSR page into ELLPACK.
-void EllpackPageImpl::CreateHistIndices(int device,
-                                        const SparsePage& row_batch) {
-  if (row_batch.Size() == 0) return;
-  unsigned int null_gidx_value = NumSymbols() - 1;
-
-  const auto& offset_vec = row_batch.offset.ConstHostVector();
-
-  // bin and compress entries in batches of rows
-  size_t gpu_batch_nrows =
-      std::min(dh::TotalMemory(device) / (16 * row_stride * sizeof(Entry)),
-               static_cast<size_t>(row_batch.Size()));
-  const std::vector<Entry>& data_vec = row_batch.data.ConstHostVector();
-
-  size_t gpu_nbatches = common::DivRoundUp(row_batch.Size(), gpu_batch_nrows);
-
-  for (size_t gpu_batch = 0; gpu_batch < gpu_nbatches; ++gpu_batch) {
-    size_t batch_row_begin = gpu_batch * gpu_batch_nrows;
-    size_t batch_row_end =
-        std::min((gpu_batch + 1) * gpu_batch_nrows, row_batch.Size());
-    size_t batch_nrows = batch_row_end - batch_row_begin;
-
-    const auto ent_cnt_begin = offset_vec[batch_row_begin];
-    const auto ent_cnt_end = offset_vec[batch_row_end];
-
-    /*! \brief row offset in SparsePage (the input data). */
-    dh::device_vector<size_t> row_ptrs(batch_nrows + 1);
-    thrust::copy(offset_vec.data() + batch_row_begin,
-                 offset_vec.data() + batch_row_end + 1, row_ptrs.begin());
-
-    // number of entries in this batch.
-    size_t n_entries = ent_cnt_end - ent_cnt_begin;
-    dh::device_vector<Entry> entries_d(n_entries);
-    // copy data entries to device.
-    dh::safe_cuda(cudaMemcpy(entries_d.data().get(),
-                             data_vec.data() + ent_cnt_begin,
-                             n_entries * sizeof(Entry), cudaMemcpyDefault));
-    const dim3 block3(32, 8, 1);  // 256 threads
-    const dim3 grid3(common::DivRoundUp(batch_nrows, block3.x),
-                     common::DivRoundUp(row_stride, block3.y), 1);
-    auto device_accessor = GetDeviceAccessor(device);
-    dh::LaunchKernel {grid3, block3}(
-        CompressBinEllpackKernel, common::CompressedBufferWriter(NumSymbols()),
-        gidx_buffer.DevicePointer(), row_ptrs.data().get(),
-        entries_d.data().get(), device_accessor.gidx_fvalue_map.data(),
-        device_accessor.feature_segments.data(),
-        row_batch.base_rowid + batch_row_begin, batch_nrows, row_stride,
-        null_gidx_value);
-  }
-}
-
-// Return the number of rows contained in this page.
-size_t EllpackPageImpl::Size() const { return n_rows; }
-
-// Return the memory cost for storing the compressed features.
-size_t EllpackPageImpl::MemCostBytes(size_t num_rows, size_t row_stride,
-                                     const common::HistogramCuts& cuts) {
-  // Required buffer size for storing data matrix in EtoLLPack format.
-  size_t compressed_size_bytes =
-      common::CompressedBufferWriter::CalculateBufferSize(row_stride * num_rows,
-                                                          cuts.TotalBins() + 1);
-  return compressed_size_bytes;
-}
-
-EllpackDeviceAccessor EllpackPageImpl::GetDeviceAccessor(int device) const {
-  gidx_buffer.SetDevice(device);
-  return EllpackDeviceAccessor(
-      device, cuts_, is_dense, row_stride, base_rowid, n_rows,
-      common::CompressedIterator<uint32_t>(gidx_buffer.ConstDevicePointer(),
-                                           NumSymbols()));
-}
-
-EllpackPageImpl::EllpackPageImpl(int device, common::HistogramCuts cuts,
-                                 const SparsePage& page, bool is_dense,
-                                 size_t row_stride)
-    : cuts_(std::move(cuts)),
-      is_dense(is_dense),
-      n_rows(page.Size()),
-      row_stride(row_stride) {
-  this->InitCompressedData(device);
-  this->CreateHistIndices(device, page);
-}
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/ellpack_page.cuh b/ml-xgboost/src/data/ellpack_page.cuh
deleted file mode 100644
index 98ed358..0000000
--- a/ml-xgboost/src/data/ellpack_page.cuh
+++ /dev/null
@@ -1,234 +0,0 @@
-/*!
- * Copyright 2019 by XGBoost Contributors
- */
-
-#ifndef XGBOOST_DATA_ELLPACK_PAGE_H_
-#define XGBOOST_DATA_ELLPACK_PAGE_H_
-
-#include <xgboost/data.h>
-
-#include "../common/compressed_iterator.h"
-#include "../common/device_helpers.cuh"
-#include "../common/hist_util.h"
-#include <thrust/binary_search.h>
-
-namespace xgboost {
-
-// Find a gidx value for a given feature otherwise return -1 if not found
-__forceinline__ __device__ int BinarySearchRow(
-    bst_uint begin, bst_uint end,
-    common::CompressedIterator<uint32_t> data,
-    int const fidx_begin, int const fidx_end) {
-  bst_uint previous_middle = UINT32_MAX;
-  while (end != begin) {
-    auto middle = begin + (end - begin) / 2;
-    if (middle == previous_middle) {
-      break;
-    }
-    previous_middle = middle;
-
-    auto gidx = data[middle];
-
-    if (gidx >= fidx_begin && gidx < fidx_end) {
-      return gidx;
-    } else if (gidx < fidx_begin) {
-      begin = middle;
-    } else {
-      end = middle;
-    }
-  }
-  // Value is missing
-  return -1;
-}
-
-/** \brief Struct for accessing and manipulating an ellpack matrix on the
- * device. Does not own underlying memory and may be trivially copied into
- * kernels.*/
-struct EllpackDeviceAccessor {
-  /*! \brief Whether or not if the matrix is dense. */
-  bool is_dense;
-  /*! \brief Row length for ELLPack, equal to number of features. */
-  size_t row_stride;
-  size_t base_rowid{};
-  size_t n_rows{};
-  common::CompressedIterator<uint32_t> gidx_iter;
-  /*! \brief Minimum value for each feature. Size equals to number of features. */
-  common::Span<const bst_float> min_fvalue;
-  /*! \brief Histogram cut pointers. Size equals to (number of features + 1). */
-  common::Span<const uint32_t> feature_segments;
-  /*! \brief Histogram cut values. Size equals to (bins per feature * number of features). */
-  common::Span<const bst_float> gidx_fvalue_map;
-
-  EllpackDeviceAccessor(int device, const common::HistogramCuts& cuts,
-                        bool is_dense, size_t row_stride, size_t base_rowid,
-                        size_t n_rows,common::CompressedIterator<uint32_t> gidx_iter)
-      : is_dense(is_dense),
-        row_stride(row_stride),
-        base_rowid(base_rowid),
-        n_rows(n_rows) ,gidx_iter(gidx_iter){
-    cuts.cut_values_.SetDevice(device);
-    cuts.cut_ptrs_.SetDevice(device);
-    cuts.min_vals_.SetDevice(device);
-    gidx_fvalue_map = cuts.cut_values_.ConstDeviceSpan();
-    feature_segments = cuts.cut_ptrs_.ConstDeviceSpan();
-    min_fvalue = cuts.min_vals_.ConstDeviceSpan();
-  }
-  // Get a matrix element, uses binary search for look up Return NaN if missing
-  // Given a row index and a feature index, returns the corresponding cut value
-  __device__ int32_t GetBinIndex(size_t ridx, size_t fidx) const {
-    ridx -= base_rowid;
-    auto row_begin = row_stride * ridx;
-    auto row_end = row_begin + row_stride;
-    auto gidx = -1;
-    if (is_dense) {
-      gidx = gidx_iter[row_begin + fidx];
-    } else {
-      gidx = BinarySearchRow(row_begin,
-                             row_end,
-                             gidx_iter,
-                             feature_segments[fidx],
-                             feature_segments[fidx + 1]);
-    }
-    return gidx;
-  }
-
-  __device__ uint32_t SearchBin(float value, size_t column_id) const {
-    auto beg = feature_segments[column_id];
-    auto end = feature_segments[column_id + 1];
-    auto it =
-        thrust::upper_bound(thrust::seq, gidx_fvalue_map.cbegin()+ beg, gidx_fvalue_map.cbegin() + end, value);
-    uint32_t idx = it - gidx_fvalue_map.cbegin();
-    if (idx == end) {
-      idx -= 1;
-    }
-    return idx;
-  }
-
-  __device__ bst_float GetFvalue(size_t ridx, size_t fidx) const {
-    auto gidx = GetBinIndex(ridx, fidx);
-    if (gidx == -1) {
-      return nan("");
-    }
-    return gidx_fvalue_map[gidx];
-  }
-
-  // Check if the row id is withing range of the current batch.
-  __device__ bool IsInRange(size_t row_id) const {
-    return row_id >= base_rowid && row_id < base_rowid + n_rows;
-  }
-  /*! \brief Return the total number of symbols (total number of bins plus 1 for
-   * not found). */
-  XGBOOST_DEVICE size_t NumSymbols() const { return gidx_fvalue_map.size() + 1; }
-
-  XGBOOST_DEVICE size_t NullValue() const { return gidx_fvalue_map.size(); }
-
-  XGBOOST_DEVICE size_t NumBins() const { return gidx_fvalue_map.size(); }
-
-  XGBOOST_DEVICE size_t NumFeatures() const { return min_fvalue.size(); }
-};
-
-
-class EllpackPageImpl {
- public:
-  /*!
-   * \brief Default constructor.
-   *
-   * This is used in the external memory case. An empty ELLPACK page is constructed with its content
-   * set later by the reader.
-   */
-  EllpackPageImpl() = default;
-
-  /*!
-   * \brief Constructor from an existing EllpackInfo.
-   *
-   * This is used in the sampling case. The ELLPACK page is constructed from an existing EllpackInfo
-   * and the given number of rows.
-   */
-  EllpackPageImpl(int device, common::HistogramCuts cuts, bool is_dense,
-                  size_t row_stride, size_t n_rows);
-
-  EllpackPageImpl(int device, common::HistogramCuts cuts,
-                  const SparsePage& page,
-                  bool is_dense,size_t row_stride);
-
-  /*!
-   * \brief Constructor from an existing DMatrix.
-   *
-   * This is used in the in-memory case. The ELLPACK page is constructed from an existing DMatrix
-   * in CSR format.
-   */
-  explicit EllpackPageImpl(DMatrix* dmat, const BatchParam& parm);
-
-  /*! \brief Copy the elements of the given ELLPACK page into this page.
-   *
-   * @param device The GPU device to use.
-   * @param page The ELLPACK page to copy from.
-   * @param offset The number of elements to skip before copying.
-   * @returns The number of elements copied.
-   */
-  size_t Copy(int device, EllpackPageImpl* page, size_t offset);
-
-  /*! \brief Compact the given ELLPACK page into the current page.
-   *
-   * @param device The GPU device to use.
-   * @param page The ELLPACK page to compact from.
-   * @param row_indexes Row indexes for the compacted page.
-   */
-  void Compact(int device, EllpackPageImpl* page, common::Span<size_t> row_indexes);
-
-
-  /*! \return Number of instances in the page. */
-  size_t Size() const;
-
-  /*! \brief Set the base row id for this page. */
-  void SetBaseRowId(size_t row_id) {
-    base_rowid = row_id;
-  }
-
-  common::HistogramCuts& Cuts() { return cuts_; }
-  common::HistogramCuts const& Cuts() const { return cuts_; }
-
-  /*! \return Estimation of memory cost of this page. */
-  static size_t MemCostBytes(size_t num_rows, size_t row_stride, const common::HistogramCuts&cuts) ;
-
-
-  /*! \brief Return the total number of symbols (total number of bins plus 1 for
-   * not found). */
-  size_t NumSymbols() const { return cuts_.TotalBins() + 1; }
-
-  EllpackDeviceAccessor GetDeviceAccessor(int device) const;
-
- private:
-  /*!
-   * \brief Compress a single page of CSR data into ELLPACK.
-   *
-   * @param device The GPU device to use.
-   * @param row_batch The CSR page.
-   */
-  void CreateHistIndices(int device,
-                         const SparsePage& row_batch
-                         );
-  /*!
-   * \brief Initialize the buffer to store compressed features.
-   */
-  void InitCompressedData(int device);
-
-
-public:
-  /*! \brief Whether or not if the matrix is dense. */
-  bool is_dense;
-  /*! \brief Row length for ELLPack. */
-  size_t row_stride;
-  size_t base_rowid{0};
-  size_t n_rows{};
-  /*! \brief global index of histogram, which is stored in ELLPack format. */
-  HostDeviceVector<common::CompressedByteT> gidx_buffer;
-
- private:
-  common::HistogramCuts cuts_;
-  common::Monitor monitor_;
-};
-
-}  // namespace xgboost
-
-#endif  // XGBOOST_DATA_ELLPACK_PAGE_H_
diff --git a/ml-xgboost/src/data/ellpack_page_raw_format.cu b/ml-xgboost/src/data/ellpack_page_raw_format.cu
deleted file mode 100644
index d4caf37..0000000
--- a/ml-xgboost/src/data/ellpack_page_raw_format.cu
+++ /dev/null
@@ -1,59 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-
-#include <xgboost/data.h>
-#include <dmlc/registry.h>
-
-#include "./ellpack_page.cuh"
-#include "./sparse_page_writer.h"
-
-namespace xgboost {
-namespace data {
-
-DMLC_REGISTRY_FILE_TAG(ellpack_page_raw_format);
-
-class EllpackPageRawFormat : public SparsePageFormat<EllpackPage> {
- public:
-  bool Read(EllpackPage* page, dmlc::SeekStream* fi) override {
-    auto* impl = page->Impl();
-    fi->Read(&impl->Cuts().cut_values_.HostVector());
-    fi->Read(&impl->Cuts().cut_ptrs_.HostVector());
-    fi->Read(&impl->Cuts().min_vals_.HostVector());
-    fi->Read(&impl->n_rows);
-    fi->Read(&impl->is_dense);
-    fi->Read(&impl->row_stride);
-    if (!fi->Read(&impl->gidx_buffer.HostVector())) {
-      return false;
-    }
-    return true;
-  }
-
-  bool Read(EllpackPage* page,
-            dmlc::SeekStream* fi,
-            const std::vector<bst_uint>& sorted_index_set) override {
-    LOG(FATAL) << "Not implemented";
-    return false;
-  }
-
-  void Write(const EllpackPage& page, dmlc::Stream* fo) override {
-    auto* impl = page.Impl();
-    fo->Write(impl->Cuts().cut_values_.ConstHostVector());
-    fo->Write(impl->Cuts().cut_ptrs_.ConstHostVector());
-    fo->Write(impl->Cuts().min_vals_.ConstHostVector());
-    fo->Write(impl->n_rows);
-    fo->Write(impl->is_dense);
-    fo->Write(impl->row_stride);
-    CHECK(!impl->gidx_buffer.ConstHostVector().empty());
-    fo->Write(impl->gidx_buffer.HostVector());
-  }
-};
-
-XGBOOST_REGISTER_ELLPACK_PAGE_FORMAT(raw)
-    .describe("Raw ELLPACK binary data format.")
-    .set_body([]() {
-      return new EllpackPageRawFormat();
-    });
-
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/ellpack_page_source.cc b/ml-xgboost/src/data/ellpack_page_source.cc
deleted file mode 100644
index 2838dc4..0000000
--- a/ml-xgboost/src/data/ellpack_page_source.cc
+++ /dev/null
@@ -1,24 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#ifndef XGBOOST_USE_CUDA
-#include <dmlc/base.h>
-#if DMLC_ENABLE_STD_THREAD
-
-#include "ellpack_page_source.h"
-#include <xgboost/data.h>
-namespace xgboost {
-namespace data {
-
-EllpackPageSource::EllpackPageSource(DMatrix* dmat,
-                                     const std::string& cache_info,
-                                     const BatchParam& param) noexcept(false) {
-  LOG(FATAL)
-      << "Internal Error: "
-         "XGBoost is not compiled with CUDA but EllpackPageSource is required";
-}
-
-}  // namespace data
-}  // namespace xgboost
-#endif  // DMLC_ENABLE_STD_THREAD
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/data/ellpack_page_source.cu b/ml-xgboost/src/data/ellpack_page_source.cu
deleted file mode 100644
index 485df9d..0000000
--- a/ml-xgboost/src/data/ellpack_page_source.cu
+++ /dev/null
@@ -1,102 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#include <memory>
-#include <utility>
-
-#include "../common/hist_util.h"
-
-#include "ellpack_page.cuh"
-#include "ellpack_page_source.h"
-#include "sparse_page_source.h"
-
-namespace xgboost {
-namespace data {
-
-size_t GetRowStride(DMatrix* dmat) {
-  if (dmat->IsDense()) return dmat->Info().num_col_;
-
-  size_t row_stride = 0;
-  for (const auto& batch : dmat->GetBatches<SparsePage>()) {
-    const auto& row_offset = batch.offset.ConstHostVector();
-    for (auto i = 1ull; i < row_offset.size(); i++) {
-      row_stride = std::max(
-          row_stride, static_cast<size_t>(row_offset[i] - row_offset[i - 1]));
-    }
-  }
-  return row_stride;
-}
-
-// Build the quantile sketch across the whole input data, then use the histogram cuts to compress
-// each CSR page, and write the accumulated ELLPACK pages to disk.
-EllpackPageSource::EllpackPageSource(DMatrix* dmat,
-                                     const std::string& cache_info,
-                                     const BatchParam& param) noexcept(false) {
-  cache_info_ = ParseCacheInfo(cache_info, kPageType_);
-  for (auto file : cache_info_.name_shards) {
-    CheckCacheFileExists(file);
-  }
-  if (param.gpu_page_size > 0) {
-    page_size_ = param.gpu_page_size;
-  }
-
-  monitor_.Init("ellpack_page_source");
-  dh::safe_cuda(cudaSetDevice(param.gpu_id));
-
-  monitor_.StartCuda("Quantiles");
-  size_t row_stride = GetRowStride(dmat);
-  auto cuts = common::DeviceSketch(param.gpu_id, dmat, param.max_bin);
-  monitor_.StopCuda("Quantiles");
-
-  monitor_.StartCuda("WriteEllpackPages");
-  WriteEllpackPages(param.gpu_id, dmat, cuts, cache_info, row_stride);
-  monitor_.StopCuda("WriteEllpackPages");
-
-  external_prefetcher_.reset(
-      new ExternalMemoryPrefetcher<EllpackPage>(cache_info_));
-}
-
-// Compress each CSR page to ELLPACK, and write the accumulated pages to disk.
-void EllpackPageSource::WriteEllpackPages(int device, DMatrix* dmat,
-                                          const common::HistogramCuts& cuts,
-                                          const std::string& cache_info,
-                                          size_t row_stride) const {
-  auto cinfo = ParseCacheInfo(cache_info, kPageType_);
-  const size_t extra_buffer_capacity = 6;
-  SparsePageWriter<EllpackPage> writer(cinfo.name_shards, cinfo.format_shards,
-                                       extra_buffer_capacity);
-  std::shared_ptr<EllpackPage> page;
-  SparsePage temp_host_page;
-  writer.Alloc(&page);
-  auto* impl = page->Impl();
-
-  size_t bytes_write = 0;
-  double tstart = dmlc::GetTime();
-  for (const auto& batch : dmat->GetBatches<SparsePage>()) {
-    temp_host_page.Push(batch);
-
-    size_t mem_cost_bytes =
-        EllpackPageImpl::MemCostBytes(temp_host_page.Size(), row_stride, cuts);
-    if (mem_cost_bytes >= page_size_) {
-      bytes_write += mem_cost_bytes;
-      *impl = EllpackPageImpl(device, cuts, temp_host_page, dmat->IsDense(),
-                              row_stride);
-      writer.PushWrite(std::move(page));
-      writer.Alloc(&page);
-      impl = page->Impl();
-      temp_host_page.Clear();
-      double tdiff = dmlc::GetTime() - tstart;
-      LOG(INFO) << "Writing " << kPageType_ << " to " << cache_info << " in "
-                << ((bytes_write >> 20UL) / tdiff) << " MB/s, "
-                << (bytes_write >> 20UL) << " written";
-    }
-  }
-  if (temp_host_page.Size() != 0) {
-    *impl = EllpackPageImpl(device, cuts, temp_host_page, dmat->IsDense(),
-                            row_stride);
-    writer.PushWrite(std::move(page));
-  }
-}
-
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/ellpack_page_source.h b/ml-xgboost/src/data/ellpack_page_source.h
deleted file mode 100644
index a1ce587..0000000
--- a/ml-xgboost/src/data/ellpack_page_source.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*!
- * Copyright 2019 by XGBoost Contributors
- */
-
-#ifndef XGBOOST_DATA_ELLPACK_PAGE_SOURCE_H_
-#define XGBOOST_DATA_ELLPACK_PAGE_SOURCE_H_
-
-#include <xgboost/data.h>
-#include <memory>
-#include <string>
-
-#include "../common/timer.h"
-#include "../common/hist_util.h"
-#include "sparse_page_source.h"
-
-namespace xgboost {
-namespace data {
-
-/*!
- * \brief External memory data source for ELLPACK format.
- *
- */
-class EllpackPageSource {
- public:
-  /*!
-   * \brief Create source from cache files the cache_prefix.
-   * \param cache_prefix The prefix of cache we want to solve.
-   */
-  explicit EllpackPageSource(DMatrix* dmat,
-                             const std::string& cache_info,
-                             const BatchParam& param) noexcept(false);
-
-  BatchSet<EllpackPage> GetBatchSet() {
-    auto begin_iter = BatchIterator<EllpackPage>(
-        new SparseBatchIteratorImpl<ExternalMemoryPrefetcher<EllpackPage>,
-                                    EllpackPage>(external_prefetcher_.get()));
-    return BatchSet<EllpackPage>(begin_iter);
-  }
-
-  ~EllpackPageSource() {
-    external_prefetcher_.reset();
-    for (auto file : cache_info_.name_shards) {
-      TryDeleteCacheFile(file);
-    }
-  }
-
- private:
-  void WriteEllpackPages(int device, DMatrix* dmat,
-                         const common::HistogramCuts& cuts,
-                         const std::string& cache_info,
-                         size_t row_stride) const;
-
-  /*! \brief The page type string for ELLPACK. */
-  const std::string kPageType_{".ellpack.page"};
-
-  size_t page_size_{DMatrix::kPageSize};
-  common::Monitor monitor_;
-  std::unique_ptr<ExternalMemoryPrefetcher<EllpackPage>> external_prefetcher_;
-  CacheInfo cache_info_;
-};
-
-}  // namespace data
-}  // namespace xgboost
-
-#endif  // XGBOOST_DATA_ELLPACK_PAGE_SOURCE_H_
diff --git a/ml-xgboost/src/data/simple_batch_iterator.h b/ml-xgboost/src/data/simple_batch_iterator.h
deleted file mode 100644
index 53464c6..0000000
--- a/ml-xgboost/src/data/simple_batch_iterator.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#ifndef XGBOOST_DATA_SIMPLE_BATCH_ITERATOR_H_
-#define XGBOOST_DATA_SIMPLE_BATCH_ITERATOR_H_
-
-#include <xgboost/data.h>
-
-namespace xgboost {
-namespace data {
-
-template<typename T>
-class SimpleBatchIteratorImpl : public BatchIteratorImpl<T> {
- public:
-  explicit SimpleBatchIteratorImpl(T* page) : page_(page) {}
-  T& operator*() override {
-    CHECK(page_ != nullptr);
-    return *page_;
-  }
-  const T& operator*() const override {
-    CHECK(page_ != nullptr);
-    return *page_;
-  }
-  void operator++() override { page_ = nullptr; }
-  bool AtEnd() const override { return page_ == nullptr; }
-
- private:
-  T* page_{nullptr};
-};
-
-}  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_SIMPLE_BATCH_ITERATOR_H_
diff --git a/ml-xgboost/src/data/simple_dmatrix.cc b/ml-xgboost/src/data/simple_dmatrix.cc
deleted file mode 100644
index ca65eb4..0000000
--- a/ml-xgboost/src/data/simple_dmatrix.cc
+++ /dev/null
@@ -1,201 +0,0 @@
-/*!
- * Copyright 2014~2020 by Contributors
- * \file simple_dmatrix.cc
- * \brief the input data structure for gradient boosting
- * \author Tianqi Chen
- */
-#include "./simple_dmatrix.h"
-#include <xgboost/data.h>
-#include "./simple_batch_iterator.h"
-#include "../common/random.h"
-#include "adapter.h"
-
-namespace xgboost {
-namespace data {
-MetaInfo& SimpleDMatrix::Info() { return info_; }
-
-const MetaInfo& SimpleDMatrix::Info() const { return info_; }
-
-DMatrix* SimpleDMatrix::Slice(common::Span<int32_t const> ridxs) {
-  auto out = new SimpleDMatrix;
-  SparsePage& out_page = out->sparse_page_;
-  for (auto const &page : this->GetBatches<SparsePage>()) {
-    page.data.HostVector();
-    page.offset.HostVector();
-    auto& h_data = out_page.data.HostVector();
-    auto& h_offset = out_page.offset.HostVector();
-    size_t rptr{0};
-    for (auto ridx : ridxs) {
-      auto inst = page[ridx];
-      rptr += inst.size();
-      std::copy(inst.begin(), inst.end(), std::back_inserter(h_data));
-      h_offset.emplace_back(rptr);
-    }
-    out->Info() = this->Info().Slice(ridxs);
-    out->Info().num_nonzero_ = h_offset.back();
-  }
-  return out;
-}
-
-BatchSet<SparsePage> SimpleDMatrix::GetRowBatches() {
-  // since csr is the default data structure so `source_` is always available.
-  auto begin_iter = BatchIterator<SparsePage>(
-      new SimpleBatchIteratorImpl<SparsePage>(&sparse_page_));
-  return BatchSet<SparsePage>(begin_iter);
-}
-
-BatchSet<CSCPage> SimpleDMatrix::GetColumnBatches() {
-  // column page doesn't exist, generate it
-  if (!column_page_) {
-    column_page_.reset(new CSCPage(sparse_page_.GetTranspose(info_.num_col_)));
-  }
-  auto begin_iter =
-      BatchIterator<CSCPage>(new SimpleBatchIteratorImpl<CSCPage>(column_page_.get()));
-  return BatchSet<CSCPage>(begin_iter);
-}
-
-BatchSet<SortedCSCPage> SimpleDMatrix::GetSortedColumnBatches() {
-  // Sorted column page doesn't exist, generate it
-  if (!sorted_column_page_) {
-    sorted_column_page_.reset(
-        new SortedCSCPage(sparse_page_.GetTranspose(info_.num_col_)));
-    sorted_column_page_->SortRows();
-  }
-  auto begin_iter = BatchIterator<SortedCSCPage>(
-      new SimpleBatchIteratorImpl<SortedCSCPage>(sorted_column_page_.get()));
-  return BatchSet<SortedCSCPage>(begin_iter);
-}
-
-BatchSet<EllpackPage> SimpleDMatrix::GetEllpackBatches(const BatchParam& param) {
-  // ELLPACK page doesn't exist, generate it
-  if (!(batch_param_ != BatchParam{})) {
-    CHECK(param != BatchParam{}) << "Batch parameter is not initialized.";
-  }
-  if (!ellpack_page_  || (batch_param_ != param && param != BatchParam{})) {
-    CHECK_GE(param.gpu_id, 0);
-    CHECK_GE(param.max_bin, 2);
-    ellpack_page_.reset(new EllpackPage(this, param));
-    batch_param_ = param;
-  }
-  auto begin_iter =
-      BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(ellpack_page_.get()));
-  return BatchSet<EllpackPage>(begin_iter);
-}
-
-template <typename AdapterT>
-SimpleDMatrix::SimpleDMatrix(AdapterT* adapter, float missing, int nthread) {
-  // Set number of threads but keep old value so we can reset it after
-  const int nthreadmax = omp_get_max_threads();
-  if (nthread <= 0) nthread = nthreadmax;
-  int nthread_original = omp_get_max_threads();
-  omp_set_num_threads(nthread);
-
-  std::vector<uint64_t> qids;
-  uint64_t default_max = std::numeric_limits<uint64_t>::max();
-  uint64_t last_group_id = default_max;
-  bst_uint group_size = 0;
-  auto& offset_vec = sparse_page_.offset.HostVector();
-  auto& data_vec = sparse_page_.data.HostVector();
-  uint64_t inferred_num_columns = 0;
-
-  adapter->BeforeFirst();
-  // Iterate over batches of input data
-  while (adapter->Next()) {
-    auto& batch = adapter->Value();
-    auto batch_max_columns = sparse_page_.Push(batch, missing, nthread);
-    inferred_num_columns = std::max(batch_max_columns, inferred_num_columns);
-    // Append meta information if available
-    if (batch.Labels() != nullptr) {
-      auto& labels = info_.labels_.HostVector();
-      labels.insert(labels.end(), batch.Labels(),
-                    batch.Labels() + batch.Size());
-    }
-    if (batch.Weights() != nullptr) {
-      auto& weights = info_.weights_.HostVector();
-      weights.insert(weights.end(), batch.Weights(),
-                     batch.Weights() + batch.Size());
-    }
-    if (batch.BaseMargin() != nullptr) {
-      auto& base_margin = info_.base_margin_.HostVector();
-      base_margin.insert(base_margin.end(), batch.BaseMargin(),
-                     batch.BaseMargin() + batch.Size());
-    }
-    if (batch.Qid() != nullptr) {
-      qids.insert(qids.end(), batch.Qid(), batch.Qid() + batch.Size());
-      // get group
-      for (size_t i = 0; i < batch.Size(); ++i) {
-        const uint64_t cur_group_id = batch.Qid()[i];
-        if (last_group_id == default_max || last_group_id != cur_group_id) {
-          info_.group_ptr_.push_back(group_size);
-        }
-        last_group_id = cur_group_id;
-        ++group_size;
-      }
-    }
-  }
-
-  if (last_group_id != default_max) {
-    if (group_size > info_.group_ptr_.back()) {
-      info_.group_ptr_.push_back(group_size);
-    }
-  }
-
-  // Deal with empty rows/columns if necessary
-  if (adapter->NumColumns() == kAdapterUnknownSize) {
-    info_.num_col_ = inferred_num_columns;
-  } else {
-    info_.num_col_ = adapter->NumColumns();
-  }
-
-  // Synchronise worker columns
-  rabit::Allreduce<rabit::op::Max>(&info_.num_col_, 1);
-
-  if (adapter->NumRows() == kAdapterUnknownSize) {
-    info_.num_row_ = offset_vec.size() - 1;
-  } else {
-    if (offset_vec.empty()) {
-      offset_vec.emplace_back(0);
-    }
-
-    while (offset_vec.size() - 1 < adapter->NumRows()) {
-      offset_vec.emplace_back(offset_vec.back());
-    }
-    info_.num_row_ = adapter->NumRows();
-  }
-  info_.num_nonzero_ = data_vec.size();
-  omp_set_num_threads(nthread_original);
-}
-
-SimpleDMatrix::SimpleDMatrix(dmlc::Stream* in_stream) {
-  int tmagic;
-  CHECK(in_stream->Read(&tmagic, sizeof(tmagic)) == sizeof(tmagic))
-      << "invalid input file format";
-  CHECK_EQ(tmagic, kMagic) << "invalid format, magic number mismatch";
-  info_.LoadBinary(in_stream);
-  in_stream->Read(&sparse_page_.offset.HostVector());
-  in_stream->Read(&sparse_page_.data.HostVector());
-}
-
-void SimpleDMatrix::SaveToLocalFile(const std::string& fname) {
-    std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
-    int tmagic = kMagic;
-    fo->Write(&tmagic, sizeof(tmagic));
-    info_.SaveBinary(fo.get());
-    fo->Write(sparse_page_.offset.HostVector());
-    fo->Write(sparse_page_.data.HostVector());
-}
-
-template SimpleDMatrix::SimpleDMatrix(DenseAdapter* adapter, float missing,
-                                     int nthread);
-template SimpleDMatrix::SimpleDMatrix(CSRAdapter* adapter, float missing,
-                                     int nthread);
-template SimpleDMatrix::SimpleDMatrix(CSCAdapter* adapter, float missing,
-                                     int nthread);
-template SimpleDMatrix::SimpleDMatrix(DataTableAdapter* adapter, float missing,
-                                     int nthread);
-template SimpleDMatrix::SimpleDMatrix(FileAdapter* adapter, float missing,
-                                     int nthread);
-template SimpleDMatrix::SimpleDMatrix(IteratorAdapter* adapter, float missing,
-                                     int nthread);
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/simple_dmatrix.cu b/ml-xgboost/src/data/simple_dmatrix.cu
deleted file mode 100644
index f7faeca..0000000
--- a/ml-xgboost/src/data/simple_dmatrix.cu
+++ /dev/null
@@ -1,138 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- * \file simple_dmatrix.cu
- */
-#include <thrust/copy.h>
-#include <thrust/execution_policy.h>
-#include <thrust/sort.h>
-#include <xgboost/data.h>
-#include "../common/random.h"
-#include "./simple_dmatrix.h"
-#include "device_adapter.cuh"
-
-namespace xgboost {
-namespace data {
-
-
-template <typename AdapterBatchT>
-void CountRowOffsets(const AdapterBatchT& batch, common::Span<bst_row_t> offset,
-                     int device_idx, float missing) {
-  IsValidFunctor is_valid(missing);
-  // Count elements per row
-  dh::LaunchN(device_idx, batch.Size(), [=] __device__(size_t idx) {
-    auto element = batch.GetElement(idx);
-    if (is_valid(element)) {
-      atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                    &offset[element.row_idx]),
-                static_cast<unsigned long long>(1));  // NOLINT
-    }
-  });
-
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  thrust::exclusive_scan(thrust::cuda::par(alloc),
-      thrust::device_pointer_cast(offset.data()),
-      thrust::device_pointer_cast(offset.data() + offset.size()),
-      thrust::device_pointer_cast(offset.data()));
-}
-
-template <typename AdapterT>
-void CopyDataColumnMajor(AdapterT* adapter, common::Span<Entry> data,
-                         int device_idx, float missing,
-                         common::Span<size_t> row_ptr) {
-  // Step 1: Get the sizes of the input columns
-  dh::device_vector<size_t> column_sizes(adapter->NumColumns());
-  auto d_column_sizes = column_sizes.data().get();
-  auto& batch = adapter->Value();
-  // Populate column sizes
-  dh::LaunchN(device_idx, batch.Size(), [=] __device__(size_t idx) {
-    const auto& e = batch.GetElement(idx);
-    atomicAdd(reinterpret_cast<unsigned long long*>(  // NOLINT
-                  &d_column_sizes[e.column_idx]),
-              static_cast<unsigned long long>(1));  // NOLINT
-  });
-
-  thrust::host_vector<size_t> host_column_sizes = column_sizes;
-
-  // Step 2: Iterate over columns, place elements in correct row, increment
-  // temporary row pointers
-  dh::device_vector<size_t> temp_row_ptr(
-      thrust::device_pointer_cast(row_ptr.data()),
-      thrust::device_pointer_cast(row_ptr.data() + row_ptr.size()));
-  auto d_temp_row_ptr = temp_row_ptr.data().get();
-  size_t begin = 0;
-  IsValidFunctor is_valid(missing);
-  for (auto size : host_column_sizes) {
-    size_t end = begin + size;
-    dh::LaunchN(device_idx, end - begin, [=] __device__(size_t idx) {
-      const auto& e = batch.GetElement(idx + begin);
-      if (!is_valid(e)) return;
-      data[d_temp_row_ptr[e.row_idx]] = Entry(e.column_idx, e.value);
-      d_temp_row_ptr[e.row_idx] += 1;
-    });
-
-    begin = end;
-  }
-}
-
-// Here the data is already correctly ordered and simply needs to be compacted
-// to remove missing data
-template <typename AdapterT>
-void CopyDataRowMajor(AdapterT* adapter, common::Span<Entry> data,
-                      int device_idx, float missing,
-                      common::Span<size_t> row_ptr) {
-  auto& batch = adapter->Value();
-  auto transform_f = [=] __device__(size_t idx) {
-    const auto& e = batch.GetElement(idx);
-    return Entry(e.column_idx, e.value);
-  };  // NOLINT
-  auto counting = thrust::make_counting_iterator(0llu);
-  thrust::transform_iterator<decltype(transform_f), decltype(counting), Entry>
-      transform_iter(counting, transform_f);
-  dh::XGBCachingDeviceAllocator<char> alloc;
-  thrust::copy_if(
-      thrust::cuda::par(alloc), transform_iter, transform_iter + batch.Size(),
-      thrust::device_pointer_cast(data.data()), IsValidFunctor(missing));
-}
-
-// Does not currently support metainfo as no on-device data source contains this
-// Current implementation assumes a single batch. More batches can
-// be supported in future. Does not currently support inferring row/column size
-template <typename AdapterT>
-SimpleDMatrix::SimpleDMatrix(AdapterT* adapter, float missing, int nthread) {
-  dh::safe_cuda(cudaSetDevice(adapter->DeviceIdx()));
-  CHECK(adapter->NumRows() != kAdapterUnknownSize);
-  CHECK(adapter->NumColumns() != kAdapterUnknownSize);
-
-  adapter->BeforeFirst();
-  adapter->Next();
-  auto& batch = adapter->Value();
-  sparse_page_.offset.SetDevice(adapter->DeviceIdx());
-  sparse_page_.data.SetDevice(adapter->DeviceIdx());
-
-  // Enforce single batch
-  CHECK(!adapter->Next());
-  sparse_page_.offset.Resize(adapter->NumRows() + 1);
-  auto s_offset = sparse_page_.offset.DeviceSpan();
-  CountRowOffsets(batch, s_offset, adapter->DeviceIdx(), missing);
-  info_.num_nonzero_ = sparse_page_.offset.HostVector().back();
-  sparse_page_.data.Resize(info_.num_nonzero_);
-  if (adapter->IsRowMajor()) {
-    CopyDataRowMajor(adapter, sparse_page_.data.DeviceSpan(),
-                        adapter->DeviceIdx(), missing, s_offset);
-  } else {
-    CopyDataColumnMajor(adapter, sparse_page_.data.DeviceSpan(),
-                        adapter->DeviceIdx(), missing, s_offset);
-  }
-
-  info_.num_col_ = adapter->NumColumns();
-  info_.num_row_ = adapter->NumRows();
-  // Synchronise worker columns
-  rabit::Allreduce<rabit::op::Max>(&info_.num_col_, 1);
-}
-
-template SimpleDMatrix::SimpleDMatrix(CudfAdapter* adapter, float missing,
-                                      int nthread);
-template SimpleDMatrix::SimpleDMatrix(CupyAdapter* adapter, float missing,
-                                      int nthread);
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/simple_dmatrix.h b/ml-xgboost/src/data/simple_dmatrix.h
deleted file mode 100644
index 9d2130b..0000000
--- a/ml-xgboost/src/data/simple_dmatrix.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file simple_dmatrix.h
- * \brief In-memory version of DMatrix.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_DATA_SIMPLE_DMATRIX_H_
-#define XGBOOST_DATA_SIMPLE_DMATRIX_H_
-
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-
-#include <memory>
-#include <string>
-
-
-namespace xgboost {
-namespace data {
-// Used for single batch data.
-class SimpleDMatrix : public DMatrix {
- public:
-  SimpleDMatrix() = default;
-  template <typename AdapterT>
-  explicit SimpleDMatrix(AdapterT* adapter, float missing, int nthread);
-
-  explicit SimpleDMatrix(dmlc::Stream* in_stream);
-  ~SimpleDMatrix() override = default;
-
-  void SaveToLocalFile(const std::string& fname);
-
-  MetaInfo& Info() override;
-
-  const MetaInfo& Info() const override;
-
-  bool SingleColBlock() const override { return true; }
-  DMatrix* Slice(common::Span<int32_t const> ridxs) override;
-
-  /*! \brief magic number used to identify SimpleDMatrix binary files */
-  static const int kMagic = 0xffffab01;
-
- private:
-  BatchSet<SparsePage> GetRowBatches() override;
-  BatchSet<CSCPage> GetColumnBatches() override;
-  BatchSet<SortedCSCPage> GetSortedColumnBatches() override;
-  BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) override;
-
-  MetaInfo info_;
-  SparsePage sparse_page_;  // Primary storage type
-  std::unique_ptr<CSCPage> column_page_;
-  std::unique_ptr<SortedCSCPage> sorted_column_page_;
-  std::unique_ptr<EllpackPage> ellpack_page_;
-  BatchParam batch_param_;
-
-  bool EllpackExists() const override {
-    return static_cast<bool>(ellpack_page_);
-  }
-  bool SparsePageExists() const override {
-    return true;
-  }
-};
-}  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_SIMPLE_DMATRIX_H_
diff --git a/ml-xgboost/src/data/sparse_page_dmatrix.cc b/ml-xgboost/src/data/sparse_page_dmatrix.cc
deleted file mode 100644
index 4d903f8..0000000
--- a/ml-xgboost/src/data/sparse_page_dmatrix.cc
+++ /dev/null
@@ -1,59 +0,0 @@
-/*!
- * Copyright 2014-2020 by Contributors
- * \file sparse_page_dmatrix.cc
- * \brief The external memory version of Page Iterator.
- * \author Tianqi Chen
- */
-#include <dmlc/base.h>
-#include <dmlc/timer.h>
-
-#if DMLC_ENABLE_STD_THREAD
-#include "./sparse_page_dmatrix.h"
-
-#include "./simple_batch_iterator.h"
-
-namespace xgboost {
-namespace data {
-
-MetaInfo& SparsePageDMatrix::Info() {
-  return row_source_->info;
-}
-
-const MetaInfo& SparsePageDMatrix::Info() const {
-  return row_source_->info;
-}
-
-BatchSet<SparsePage> SparsePageDMatrix::GetRowBatches() {
-  return row_source_->GetBatchSet();
-}
-
-BatchSet<CSCPage> SparsePageDMatrix::GetColumnBatches() {
-  // Lazily instantiate
-  if (!column_source_) {
-    column_source_.reset(new CSCPageSource(this, cache_info_));
-  }
-  return column_source_->GetBatchSet();
-}
-
-BatchSet<SortedCSCPage> SparsePageDMatrix::GetSortedColumnBatches() {
-  // Lazily instantiate
-  if (!sorted_column_source_) {
-    sorted_column_source_.reset(new SortedCSCPageSource(this, cache_info_));
-  }
-  return sorted_column_source_->GetBatchSet();
-}
-
-BatchSet<EllpackPage> SparsePageDMatrix::GetEllpackBatches(const BatchParam& param) {
-  CHECK_GE(param.gpu_id, 0);
-  CHECK_GE(param.max_bin, 2);
-  // Lazily instantiate
-  if (!ellpack_source_ || (batch_param_ != param && param != BatchParam{})) {
-    ellpack_source_.reset(new EllpackPageSource(this, cache_info_, param));
-    batch_param_ = param;
-  }
-  return ellpack_source_->GetBatchSet();
-}
-
-}  // namespace data
-}  // namespace xgboost
-#endif  // DMLC_ENABLE_STD_THREAD
diff --git a/ml-xgboost/src/data/sparse_page_dmatrix.h b/ml-xgboost/src/data/sparse_page_dmatrix.h
deleted file mode 100644
index 3931726..0000000
--- a/ml-xgboost/src/data/sparse_page_dmatrix.h
+++ /dev/null
@@ -1,72 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file sparse_page_dmatrix.h
- * \brief External-memory version of DMatrix.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_DATA_SPARSE_PAGE_DMATRIX_H_
-#define XGBOOST_DATA_SPARSE_PAGE_DMATRIX_H_
-
-#include <xgboost/data.h>
-#include <algorithm>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "ellpack_page_source.h"
-#include "sparse_page_source.h"
-
-namespace xgboost {
-namespace data {
-// Used for external memory.
-class SparsePageDMatrix : public DMatrix {
- public:
-  template <typename AdapterT>
-  explicit SparsePageDMatrix(AdapterT* adapter, float missing, int nthread,
-                             const std::string& cache_prefix,
-                             size_t page_size = kPageSize)
-      : cache_info_(std::move(cache_prefix)) {
-    row_source_.reset(new data::SparsePageSource(adapter, missing, nthread,
-                                                 cache_prefix, page_size));
-  }
-  ~SparsePageDMatrix() override = default;
-
-  MetaInfo& Info() override;
-
-  const MetaInfo& Info() const override;
-
-  bool SingleColBlock() const override { return false; }
-  DMatrix *Slice(common::Span<int32_t const> ridxs) override {
-    LOG(FATAL) << "Slicing DMatrix is not supported for external memory.";
-    return nullptr;
-  }
-
- private:
-  BatchSet<SparsePage> GetRowBatches() override;
-  BatchSet<CSCPage> GetColumnBatches() override;
-  BatchSet<SortedCSCPage> GetSortedColumnBatches() override;
-  BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) override;
-
-  // source data pointers.
-  std::unique_ptr<SparsePageSource> row_source_;
-  std::unique_ptr<CSCPageSource> column_source_;
-  std::unique_ptr<SortedCSCPageSource> sorted_column_source_;
-  std::unique_ptr<EllpackPageSource> ellpack_source_;
-  // saved batch param
-  BatchParam batch_param_;
-  // the cache prefix
-  std::string cache_info_;
-  // Store column densities to avoid recalculating
-  std::vector<float> col_density_;
-
-  bool EllpackExists() const override {
-    return static_cast<bool>(ellpack_source_);
-  }
-  bool SparsePageExists() const override {
-    return static_cast<bool>(row_source_);
-  }
-};
-}  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_SPARSE_PAGE_DMATRIX_H_
diff --git a/ml-xgboost/src/data/sparse_page_raw_format.cc b/ml-xgboost/src/data/sparse_page_raw_format.cc
deleted file mode 100644
index b9a82bb..0000000
--- a/ml-xgboost/src/data/sparse_page_raw_format.cc
+++ /dev/null
@@ -1,118 +0,0 @@
-/*!
- * Copyright (c) 2015 by Contributors
- * \file sparse_page_raw_format.cc
- *  Raw binary format of sparse page.
- */
-#include <xgboost/data.h>
-#include <dmlc/registry.h>
-#include "./sparse_page_writer.h"
-
-namespace xgboost {
-namespace data {
-
-DMLC_REGISTRY_FILE_TAG(sparse_page_raw_format);
-
-template<typename T>
-class SparsePageRawFormat : public SparsePageFormat<T> {
- public:
-  bool Read(T* page, dmlc::SeekStream* fi) override {
-    auto& offset_vec = page->offset.HostVector();
-    if (!fi->Read(&offset_vec)) return false;
-    auto& data_vec = page->data.HostVector();
-    CHECK_NE(page->offset.Size(), 0U) << "Invalid SparsePage file";
-    data_vec.resize(offset_vec.back());
-    if (page->data.Size() != 0) {
-      CHECK_EQ(fi->Read(dmlc::BeginPtr(data_vec),
-                        (page->data).Size() * sizeof(Entry)),
-               (page->data).Size() * sizeof(Entry))
-          << "Invalid SparsePage file";
-    }
-    return true;
-  }
-
-  bool Read(T* page,
-            dmlc::SeekStream* fi,
-            const std::vector<bst_uint>& sorted_index_set) override {
-    if (!fi->Read(&disk_offset_)) return false;
-    auto& offset_vec = page->offset.HostVector();
-    auto& data_vec = page->data.HostVector();
-    // setup the offset
-    offset_vec.clear();
-    offset_vec.push_back(0);
-    for (unsigned int fid : sorted_index_set) {
-      CHECK_LT(fid + 1, disk_offset_.size());
-      size_t size = disk_offset_[fid + 1] - disk_offset_[fid];
-      offset_vec.push_back(offset_vec.back() + size);
-    }
-    data_vec.resize(offset_vec.back());
-    // read in the data
-    size_t begin = fi->Tell();
-    size_t curr_offset = 0;
-    for (size_t i = 0; i < sorted_index_set.size();) {
-      bst_uint fid = sorted_index_set[i];
-      if (disk_offset_[fid] != curr_offset) {
-        CHECK_GT(disk_offset_[fid], curr_offset);
-        fi->Seek(begin + disk_offset_[fid] * sizeof(Entry));
-        curr_offset = disk_offset_[fid];
-      }
-      size_t j, size_to_read = 0;
-      for (j = i; j < sorted_index_set.size(); ++j) {
-        if (disk_offset_[sorted_index_set[j]] == disk_offset_[fid] + size_to_read) {
-          size_to_read += offset_vec[j + 1] - offset_vec[j];
-        } else {
-          break;
-        }
-      }
-
-      if (size_to_read != 0) {
-        CHECK_EQ(fi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i],
-                          size_to_read * sizeof(Entry)),
-                 size_to_read * sizeof(Entry))
-            << "Invalid SparsePage file";
-        curr_offset += size_to_read;
-      }
-      i = j;
-    }
-    // seek to end of record
-    if (curr_offset != disk_offset_.back()) {
-      fi->Seek(begin + disk_offset_.back() * sizeof(Entry));
-    }
-    return true;
-  }
-
-  void Write(const T& page, dmlc::Stream* fo) override {
-    const auto& offset_vec = page.offset.HostVector();
-    const auto& data_vec = page.data.HostVector();
-    CHECK(page.offset.Size() != 0 && offset_vec[0] == 0);
-    CHECK_EQ(offset_vec.back(), page.data.Size());
-    fo->Write(offset_vec);
-    if (page.data.Size() != 0) {
-      fo->Write(dmlc::BeginPtr(data_vec), page.data.Size() * sizeof(Entry));
-    }
-  }
-
- private:
-  /*! \brief external memory column offset */
-  std::vector<size_t> disk_offset_;
-};
-
-XGBOOST_REGISTER_SPARSE_PAGE_FORMAT(raw)
-.describe("Raw binary data format.")
-.set_body([]() {
-    return new SparsePageRawFormat<SparsePage>();
-  });
-
-XGBOOST_REGISTER_CSC_PAGE_FORMAT(raw)
-.describe("Raw binary data format.")
-.set_body([]() {
-    return new SparsePageRawFormat<CSCPage>();
-  });
-
-XGBOOST_REGISTER_SORTED_CSC_PAGE_FORMAT(raw)
-.describe("Raw binary data format.")
-.set_body([]() {
-    return new SparsePageRawFormat<SortedCSCPage>();
-  });
-
-}  // namespace data
-}  // namespace xgboost
diff --git a/ml-xgboost/src/data/sparse_page_source.h b/ml-xgboost/src/data/sparse_page_source.h
deleted file mode 100644
index c7074c0..0000000
--- a/ml-xgboost/src/data/sparse_page_source.h
+++ /dev/null
@@ -1,519 +0,0 @@
-/*!
- *  Copyright (c) 2014-2019 by Contributors
- * \file page_csr_source.h
- *  External memory data source, saved with sparse_batch_page binary format.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_DATA_SPARSE_PAGE_SOURCE_H_
-#define XGBOOST_DATA_SPARSE_PAGE_SOURCE_H_
-
-#include <dmlc/threadediter.h>
-#include <dmlc/timer.h>
-
-#include <algorithm>
-#include <limits>
-#include <locale>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-#include <fstream>
-
-#include "xgboost/base.h"
-#include "xgboost/data.h"
-
-#include "adapter.h"
-#include "sparse_page_writer.h"
-#include "../common/common.h"
-#include <xgboost/data.h>
-
-namespace detail {
-
-// Split a cache info string with delimiter ':'
-// If cache info string contains drive letter (e.g. C:), exclude it before splitting
-inline std::vector<std::string>
-GetCacheShards(const std::string& cache_info) {
-#if (defined _WIN32) || (defined __CYGWIN__)
-  if (cache_info.length() >= 2
-      && std::isalpha(cache_info[0], std::locale::classic())
-      && cache_info[1] == ':') {
-    std::vector<std::string> cache_shards
-      = xgboost::common::Split(cache_info.substr(2), ':');
-    cache_shards[0] = cache_info.substr(0, 2) + cache_shards[0];
-    return cache_shards;
-  }
-#endif  // (defined _WIN32) || (defined __CYGWIN__)
-  return xgboost::common::Split(cache_info, ':');
-}
-
-}  // namespace detail
-
-namespace xgboost {
-namespace data {
-
-template<typename S, typename T>
-class SparseBatchIteratorImpl : public BatchIteratorImpl<T> {
- public:
-  explicit SparseBatchIteratorImpl(S* source) : source_(source) {
-    CHECK(source_ != nullptr);
-    source_->BeforeFirst();
-    source_->Next();
-  }
-  T& operator*() override { return source_->Value(); }
-  const T& operator*() const override { return source_->Value(); }
-  void operator++() override { at_end_ = !source_->Next(); }
-  bool AtEnd() const override { return at_end_; }
-
- private:
-  S* source_{nullptr};
-  bool at_end_{ false };
-};
-
-  /*! \brief magic number used to identify Page */
-  static const int kMagic = 0xffffab02;
-/*!
- * \brief decide the format from cache prefix.
- * \return pair of row format, column format type of the cache prefix.
- */
-inline std::pair<std::string, std::string> DecideFormat(const std::string& cache_prefix) {
-  size_t pos = cache_prefix.rfind(".fmt-");
-
-  if (pos != std::string::npos) {
-    std::string fmt = cache_prefix.substr(pos + 5, cache_prefix.length());
-    size_t cpos = fmt.rfind('-');
-    if (cpos != std::string::npos) {
-      return std::make_pair(fmt.substr(0, cpos), fmt.substr(cpos + 1, fmt.length()));
-    } else {
-      return std::make_pair(fmt, fmt);
-    }
-  } else {
-    std::string raw = "raw";
-    return std::make_pair(raw, raw);
-  }
-}
-
-struct CacheInfo {
-  std::string name_info;
-  std::vector<std::string> format_shards;
-  std::vector<std::string> name_shards;
-};
-
-inline CacheInfo ParseCacheInfo(const std::string& cache_info, const std::string& page_type) {
-  CacheInfo info;
-  std::vector<std::string> cache_shards = ::detail::GetCacheShards(cache_info);
-  CHECK_NE(cache_shards.size(), 0U);
-  // read in the info files.
-  info.name_info = cache_shards[0];
-  for (const std::string& prefix : cache_shards) {
-    info.name_shards.push_back(prefix + page_type);
-    info.format_shards.push_back(DecideFormat(prefix).first);
-  }
-  return info;
-}
-
-inline void TryDeleteCacheFile(const std::string& file) {
-  if (std::remove(file.c_str()) != 0) {
-    LOG(WARNING) << "Couldn't remove external memory cache file " << file
-              << "; you may want to remove it manually";
-  }
-}
-
-inline void CheckCacheFileExists(const std::string& file) {
-  std::ifstream f(file.c_str());
-  if (f.good()) {
-    LOG(FATAL) << "Cache file " << file
-               << " exists already; Is there another DMatrix with the same "
-                  "cache prefix? Otherwise please remove it manually.";
-  }
-}
-
-  /**
-   * \brief Given a set of cache files and page type, this object iterates over batches using prefetching for improved performance. Not thread safe.
-   *
-   * \tparam  PageT Type of the page t.
-   */
-  template <typename PageT>
-class ExternalMemoryPrefetcher : dmlc::DataIter<PageT> {
- public:
-    explicit ExternalMemoryPrefetcher(const CacheInfo& info) noexcept(false)
-      : base_rowid_(0), page_(nullptr), clock_ptr_(0) {
-    // read in the info files
-    CHECK_NE(info.name_shards.size(), 0U);
-    {
-      std::unique_ptr<dmlc::Stream> finfo(
-          dmlc::Stream::Create(info.name_info.c_str(), "r"));
-      int tmagic;
-      CHECK_EQ(finfo->Read(&tmagic, sizeof(tmagic)), sizeof(tmagic));
-      CHECK_EQ(tmagic, kMagic) << "invalid format, magic number mismatch";
-    }
-    files_.resize(info.name_shards.size());
-    formats_.resize(info.name_shards.size());
-    prefetchers_.resize(info.name_shards.size());
-
-    // read in the cache files.
-    for (size_t i = 0; i < info.name_shards.size(); ++i) {
-      std::string name_row = info.name_shards.at(i);
-      files_[i].reset(dmlc::SeekStream::CreateForRead(name_row.c_str()));
-      std::unique_ptr<dmlc::SeekStream>& fi = files_[i];
-      std::string format;
-      CHECK(fi->Read(&format)) << "Invalid page format";
-      formats_[i].reset(CreatePageFormat<PageT>(format));
-      std::unique_ptr<SparsePageFormat<PageT>>& fmt = formats_[i];
-      size_t fbegin = fi->Tell();
-      prefetchers_[i].reset(new dmlc::ThreadedIter<PageT>(4));
-      prefetchers_[i]->Init(
-          [&fi, &fmt](PageT** dptr) {
-            if (*dptr == nullptr) {
-              *dptr = new PageT();
-            }
-            return fmt->Read(*dptr, fi.get());
-          },
-          [&fi, fbegin]() { fi->Seek(fbegin); });
-    }
-  }
-  /*! \brief destructor */
-  ~ExternalMemoryPrefetcher() override {
-    delete page_;
-  }
-
-  // implement Next
-  bool Next() override {
-    CHECK(mutex_.try_lock()) << "Multiple threads attempting to use prefetcher";
-    // doing clock rotation over shards.
-    if (page_ != nullptr) {
-      size_t n = prefetchers_.size();
-      prefetchers_[(clock_ptr_ + n - 1) % n]->Recycle(&page_);
-    }
-
-    if (prefetchers_[clock_ptr_]->Next(&page_)) {
-      page_->SetBaseRowId(base_rowid_);
-      base_rowid_ += page_->Size();
-      // advance clock
-      clock_ptr_ = (clock_ptr_ + 1) % prefetchers_.size();
-      mutex_.unlock();
-      return true;
-    } else {
-      mutex_.unlock();
-      return false;
-    }
-  }
-
-  // implement BeforeFirst
-  void BeforeFirst() override {
-    CHECK(mutex_.try_lock()) << "Multiple threads attempting to use prefetcher";
-    base_rowid_ = 0;
-    clock_ptr_ = 0;
-    for (auto& p : prefetchers_) {
-      p->BeforeFirst();
-    }
-    mutex_.unlock();
-  }
-
-  // implement Value
-  PageT& Value() { return *page_; }
-
-  const PageT& Value() const override { return *page_; }
-
- private:
-  std::mutex mutex_;
-  /*! \brief number of rows */
-  size_t base_rowid_;
-  /*! \brief page currently on hold. */
-  PageT* page_;
-  /*! \brief internal clock ptr */
-  size_t clock_ptr_;
-  /*! \brief file pointer to the row blob file. */
-  std::vector<std::unique_ptr<dmlc::SeekStream>> files_;
-  /*! \brief Sparse page format file. */
-  std::vector<std::unique_ptr<SparsePageFormat<PageT>>> formats_;
-  /*! \brief internal prefetcher. */
-  std::vector<std::unique_ptr<dmlc::ThreadedIter<PageT>>> prefetchers_;
-};
-
-class SparsePageSource {
- public:
-  template <typename AdapterT>
-  SparsePageSource(AdapterT* adapter, float missing, int nthread,
-                   const std::string& cache_info,
-                   const size_t page_size = DMatrix::kPageSize) {
-    const std::string page_type = ".row.page";
-    cache_info_ = ParseCacheInfo(cache_info, page_type);
-
-    // Warn user if old cache files
-    CheckCacheFileExists(cache_info_.name_info);
-    for (auto file : cache_info_.name_shards) {
-      CheckCacheFileExists(file);
-    }
-
-    {
-      SparsePageWriter<SparsePage> writer(cache_info_.name_shards,
-                                          cache_info_.format_shards, 6);
-      std::shared_ptr<SparsePage> page;
-      writer.Alloc(&page);
-      page->Clear();
-
-      uint64_t inferred_num_columns = 0;
-      uint64_t inferred_num_rows = 0;
-      size_t bytes_write = 0;
-      double tstart = dmlc::GetTime();
-      // print every 4 sec.
-      constexpr double kStep = 4.0;
-      size_t tick_expected = static_cast<double>(kStep);
-
-      const uint64_t default_max = std::numeric_limits<uint64_t>::max();
-      uint64_t last_group_id = default_max;
-      bst_uint group_size = 0;
-      std::vector<uint64_t> qids;
-      adapter->BeforeFirst();
-      while (adapter->Next()) {
-        auto& batch = adapter->Value();
-        if (batch.Labels() != nullptr) {
-          auto& labels = info.labels_.HostVector();
-          labels.insert(labels.end(), batch.Labels(),
-                        batch.Labels() + batch.Size());
-        }
-        if (batch.Weights() != nullptr) {
-          auto& weights = info.weights_.HostVector();
-          weights.insert(weights.end(), batch.Weights(),
-                         batch.Weights() + batch.Size());
-        }
-        if (batch.BaseMargin() != nullptr) {
-          auto& base_margin = info.base_margin_.HostVector();
-          base_margin.insert(base_margin.end(), batch.BaseMargin(),
-                             batch.BaseMargin() + batch.Size());
-        }
-        if (batch.Qid() != nullptr) {
-          qids.insert(qids.end(), batch.Qid(), batch.Qid() + batch.Size());
-          // get group
-          for (size_t i = 0; i < batch.Size(); ++i) {
-            const uint64_t cur_group_id = batch.Qid()[i];
-            if (last_group_id == default_max ||
-                last_group_id != cur_group_id) {
-              info.group_ptr_.push_back(group_size);
-            }
-            last_group_id = cur_group_id;
-            ++group_size;
-          }
-        }
-        auto batch_max_columns = page->Push(batch, missing, nthread);
-        inferred_num_columns =
-            std::max(batch_max_columns, inferred_num_columns);
-        if (page->MemCostBytes() >= page_size) {
-          inferred_num_rows += page->Size();
-          info.num_nonzero_ += page->offset.HostVector().back();
-          bytes_write += page->MemCostBytes();
-          writer.PushWrite(std::move(page));
-          writer.Alloc(&page);
-          page->Clear();
-          page->SetBaseRowId(inferred_num_rows);
-
-          double tdiff = dmlc::GetTime() - tstart;
-          if (tdiff >= tick_expected) {
-            LOG(CONSOLE) << "Writing " << page_type << " to " << cache_info
-                         << " in " << ((bytes_write >> 20UL) / tdiff)
-                         << " MB/s, " << (bytes_write >> 20UL) << " written";
-            tick_expected += static_cast<size_t>(kStep);
-          }
-        }
-      }
-
-      if (last_group_id != default_max) {
-        if (group_size > info.group_ptr_.back()) {
-          info.group_ptr_.push_back(group_size);
-        }
-      }
-      inferred_num_rows += page->Size();
-      if (!page->offset.HostVector().empty()) {
-        info.num_nonzero_ += page->offset.HostVector().back();
-      }
-
-      // Deal with empty rows/columns if necessary
-      if (adapter->NumColumns() == kAdapterUnknownSize) {
-        info.num_col_ = inferred_num_columns;
-      } else {
-        info.num_col_ = adapter->NumColumns();
-      }
-      // Synchronise worker columns
-      rabit::Allreduce<rabit::op::Max>(&info.num_col_, 1);
-
-      if (adapter->NumRows() == kAdapterUnknownSize) {
-        info.num_row_ = inferred_num_rows;
-      } else {
-        if (page->offset.HostVector().empty()) {
-          page->offset.HostVector().emplace_back(0);
-        }
-
-        while (inferred_num_rows < adapter->NumRows()) {
-          page->offset.HostVector().emplace_back(
-              page->offset.HostVector().back());
-          inferred_num_rows++;
-        }
-        info.num_row_ = adapter->NumRows();
-      }
-
-      // Make sure we have at least one page if the dataset is empty
-      if (page->data.Size() > 0 || info.num_row_ == 0) {
-        writer.PushWrite(std::move(page));
-      }
-      std::unique_ptr<dmlc::Stream> fo(
-          dmlc::Stream::Create(cache_info_.name_info.c_str(), "w"));
-      int tmagic = kMagic;
-      fo->Write(&tmagic, sizeof(tmagic));
-      // Either every row has query ID or none at all
-      CHECK(qids.empty() || qids.size() == info.num_row_);
-      info.SaveBinary(fo.get());
-    }
-    LOG(INFO) << "SparsePageSource Finished writing to "
-              << cache_info_.name_info;
-
-    external_prefetcher_.reset(
-        new ExternalMemoryPrefetcher<SparsePage>(cache_info_));
-  }
-
-  ~SparsePageSource() {
-    external_prefetcher_.reset();
-    TryDeleteCacheFile(cache_info_.name_info);
-    for (auto file : cache_info_.name_shards) {
-      TryDeleteCacheFile(file);
-    }
-  }
-
-  BatchSet<SparsePage> GetBatchSet() {
-    auto begin_iter = BatchIterator<SparsePage>(
-        new SparseBatchIteratorImpl<ExternalMemoryPrefetcher<SparsePage>,
-                                    SparsePage>(external_prefetcher_.get()));
-    return BatchSet<SparsePage>(begin_iter);
-  }
-  MetaInfo info;
-
- private:
-  std::unique_ptr<ExternalMemoryPrefetcher<SparsePage>> external_prefetcher_;
-  CacheInfo cache_info_;
-};
-
-class CSCPageSource {
- public:
-  CSCPageSource(DMatrix* src, const std::string& cache_info,
-                const size_t page_size = DMatrix::kPageSize) {
-    std::string page_type = ".col.page";
-    cache_info_ = ParseCacheInfo(cache_info, page_type);
-    for (auto file : cache_info_.name_shards) {
-      CheckCacheFileExists(file);
-    }
-    {
-      SparsePageWriter<SparsePage> writer(cache_info_.name_shards,
-                                          cache_info_.format_shards, 6);
-      std::shared_ptr<SparsePage> page;
-      writer.Alloc(&page);
-      page->Clear();
-
-      size_t bytes_write = 0;
-      double tstart = dmlc::GetTime();
-      for (auto& batch : src->GetBatches<SparsePage>()) {
-        page->PushCSC(batch.GetTranspose(src->Info().num_col_));
-
-        if (page->MemCostBytes() >= page_size) {
-          bytes_write += page->MemCostBytes();
-          writer.PushWrite(std::move(page));
-          writer.Alloc(&page);
-          page->Clear();
-          double tdiff = dmlc::GetTime() - tstart;
-          LOG(INFO) << "Writing to " << cache_info << " in "
-                    << ((bytes_write >> 20UL) / tdiff) << " MB/s, "
-                    << (bytes_write >> 20UL) << " written";
-        }
-      }
-      if (page->data.Size() != 0) {
-        writer.PushWrite(std::move(page));
-      }
-      LOG(INFO) << "CSCPageSource: Finished writing to "
-                << cache_info_.name_info;
-    }
-    external_prefetcher_.reset(
-        new ExternalMemoryPrefetcher<CSCPage>(cache_info_));
-  }
-
-  ~CSCPageSource() {
-    external_prefetcher_.reset();
-    for (auto file : cache_info_.name_shards) {
-      TryDeleteCacheFile(file);
-    }
-  }
-
-  BatchSet<CSCPage> GetBatchSet() {
-    auto begin_iter = BatchIterator<CSCPage>(
-        new SparseBatchIteratorImpl<ExternalMemoryPrefetcher<CSCPage>, CSCPage>(
-            external_prefetcher_.get()));
-    return BatchSet<CSCPage>(begin_iter);
-  }
-
- private:
-  std::unique_ptr<ExternalMemoryPrefetcher<CSCPage>> external_prefetcher_;
-  CacheInfo cache_info_;
-};
-
-class SortedCSCPageSource {
- public:
-  SortedCSCPageSource(DMatrix* src, const std::string& cache_info,
-                      const size_t page_size = DMatrix::kPageSize) {
-    std::string page_type = ".sorted.col.page";
-    cache_info_ = ParseCacheInfo(cache_info, page_type);
-    for (auto file : cache_info_.name_shards) {
-      CheckCacheFileExists(file);
-    }
-    {
-      SparsePageWriter<SparsePage> writer(cache_info_.name_shards,
-                                          cache_info_.format_shards, 6);
-      std::shared_ptr<SparsePage> page;
-      writer.Alloc(&page);
-      page->Clear();
-
-      size_t bytes_write = 0;
-      double tstart = dmlc::GetTime();
-      for (auto& batch : src->GetBatches<SparsePage>()) {
-        SparsePage tmp = batch.GetTranspose(src->Info().num_col_);
-        page->PushCSC(tmp);
-        page->SortRows();
-
-        if (page->MemCostBytes() >= page_size) {
-          bytes_write += page->MemCostBytes();
-          writer.PushWrite(std::move(page));
-          writer.Alloc(&page);
-          page->Clear();
-          double tdiff = dmlc::GetTime() - tstart;
-          LOG(INFO) << "Writing to " << cache_info << " in "
-                    << ((bytes_write >> 20UL) / tdiff) << " MB/s, "
-                    << (bytes_write >> 20UL) << " written";
-        }
-      }
-      if (page->data.Size() != 0) {
-        writer.PushWrite(std::move(page));
-      }
-      LOG(INFO) << "SortedCSCPageSource: Finished writing to "
-                << cache_info_.name_info;
-    }
-    external_prefetcher_.reset(
-        new ExternalMemoryPrefetcher<SortedCSCPage>(cache_info_));
-  }
-  ~SortedCSCPageSource() {
-    external_prefetcher_.reset();
-    for (auto file : cache_info_.name_shards) {
-      TryDeleteCacheFile(file);
-    }
-  }
-
-  BatchSet<SortedCSCPage> GetBatchSet() {
-    auto begin_iter = BatchIterator<SortedCSCPage>(
-        new SparseBatchIteratorImpl<ExternalMemoryPrefetcher<SortedCSCPage>,
-                                    SortedCSCPage>(external_prefetcher_.get()));
-    return BatchSet<SortedCSCPage>(begin_iter);
-  }
-
- private:
-  std::unique_ptr<ExternalMemoryPrefetcher<SortedCSCPage>> external_prefetcher_;
-  CacheInfo cache_info_;
-};
-
-}  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_SPARSE_PAGE_SOURCE_H_
diff --git a/ml-xgboost/src/data/sparse_page_writer.h b/ml-xgboost/src/data/sparse_page_writer.h
deleted file mode 100644
index f63fcf0..0000000
--- a/ml-xgboost/src/data/sparse_page_writer.h
+++ /dev/null
@@ -1,213 +0,0 @@
-/*!
- * Copyright (c) 2014-2019 by Contributors
- * \file sparse_page_writer.h
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_DATA_SPARSE_PAGE_WRITER_H_
-#define XGBOOST_DATA_SPARSE_PAGE_WRITER_H_
-
-#include <xgboost/data.h>
-#include <dmlc/io.h>
-#include <vector>
-#include <algorithm>
-#include <cstring>
-#include <string>
-#include <utility>
-#include <memory>
-#include <functional>
-
-#if DMLC_ENABLE_STD_THREAD
-#include <dmlc/concurrency.h>
-#include <thread>
-#endif  // DMLC_ENABLE_STD_THREAD
-
-namespace xgboost {
-namespace data {
-
-template<typename T>
-struct SparsePageFormatReg;
-
-/*!
- * \brief Format specification of SparsePage.
- */
-template<typename T>
-class SparsePageFormat {
- public:
-  /*! \brief virtual destructor */
-  virtual ~SparsePageFormat() = default;
-  /*!
-   * \brief Load all the segments into page, advance fi to end of the block.
-   * \param page The data to read page into.
-   * \param fi the input stream of the file
-   * \return true of the loading as successful, false if end of file was reached
-   */
-  virtual bool Read(T* page, dmlc::SeekStream* fi) = 0;
-
-  /*!
-   * \brief read only the segments we are interested in, advance fi to end of the block.
-   * \param page The page to load the data into.
-   * \param fi the input stream of the file
-   * \param sorted_index_set sorted index of segments we are interested in
-   * \return true of the loading as successful, false if end of file was reached
-   */
-  virtual bool Read(T* page,
-                    dmlc::SeekStream* fi,
-                    const std::vector<bst_uint>& sorted_index_set) = 0;
-  /*!
-   * \brief save the data to fo, when a page was written.
-   * \param fo output stream
-   */
-  virtual void Write(const T& page, dmlc::Stream* fo) = 0;
-};
-
-/*!
- * \brief Create sparse page of format.
- * \return The created format functors.
- */
-template<typename T>
-inline SparsePageFormat<T>* CreatePageFormat(const std::string& name) {
-  auto *e = ::dmlc::Registry<SparsePageFormatReg<T>>::Get()->Find(name);
-  if (e == nullptr) {
-    LOG(FATAL) << "Unknown format type " << name;
-    return nullptr;
-  }
-  return (e->body)();
-}
-
-#if DMLC_ENABLE_STD_THREAD
-/*!
- * \brief A threaded writer to write sparse batch page to sharded files.
- * @tparam T Type of the page.
- */
-template<typename T>
-class SparsePageWriter {
- public:
-  /*!
-   * \brief constructor
-   * \param name_shards name of shard files.
-   * \param format_shards format of each shard.
-   * \param extra_buffer_capacity Extra buffer capacity before block.
-   */
-  explicit SparsePageWriter(const std::vector<std::string>& name_shards,
-                            const std::vector<std::string>& format_shards,
-                            size_t extra_buffer_capacity)
-      : num_free_buffer_(extra_buffer_capacity + name_shards.size()),
-        clock_ptr_(0),
-        workers_(name_shards.size()),
-        qworkers_(name_shards.size()) {
-    CHECK_EQ(name_shards.size(), format_shards.size());
-    // start writer threads
-    for (size_t i = 0; i < name_shards.size(); ++i) {
-      std::string name_shard = name_shards[i];
-      std::string format_shard = format_shards[i];
-      auto* wqueue = &qworkers_[i];
-      workers_[i].reset(new std::thread(
-          [this, name_shard, format_shard, wqueue]() {
-            std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(name_shard.c_str(), "w"));
-            std::unique_ptr<SparsePageFormat<T>> fmt(CreatePageFormat<T>(format_shard));
-            fo->Write(format_shard);
-            std::shared_ptr<T> page;
-            while (wqueue->Pop(&page)) {
-              if (page == nullptr) break;
-              fmt->Write(*page, fo.get());
-              qrecycle_.Push(std::move(page));
-            }
-            fo.reset(nullptr);
-            LOG(INFO) << "SparsePageWriter Finished writing to " << name_shard;
-          }));
-    }
-  }
-
-  /*! \brief destructor, will close the files automatically */
-  ~SparsePageWriter() {
-    for (auto& queue : qworkers_) {
-      // use nullptr to signal termination.
-      std::shared_ptr<T> sig(nullptr);
-      queue.Push(std::move(sig));
-    }
-    for (auto& thread : workers_) {
-      thread->join();
-    }
-  }
-
-  /*!
-   * \brief Push a write job to the writer.
-   * This function won't block,
-   * writing is done by another thread inside writer.
-   * \param page The page to be written
-   */
-  void PushWrite(std::shared_ptr<T>&& page) {
-    qworkers_[clock_ptr_].Push(std::move(page));
-    clock_ptr_ = (clock_ptr_ + 1) % workers_.size();
-  }
-
-  /*!
-   * \brief Allocate a page to store results.
-   *  This function can block when the writer is too slow and buffer pages
-   *  have not yet been recycled.
-   * \param out_page Used to store the allocated pages.
-   */
-  void Alloc(std::shared_ptr<T>* out_page) {
-    CHECK(*out_page == nullptr);
-    if (num_free_buffer_ != 0) {
-      out_page->reset(new T());
-      --num_free_buffer_;
-    } else {
-      CHECK(qrecycle_.Pop(out_page));
-    }
-  }
-
- private:
-  /*! \brief number of allocated pages */
-  size_t num_free_buffer_;
-  /*! \brief clock_pointer */
-  size_t clock_ptr_;
-  /*! \brief writer threads */
-  std::vector<std::unique_ptr<std::thread>> workers_;
-  /*! \brief recycler queue */
-  dmlc::ConcurrentBlockingQueue<std::shared_ptr<T>> qrecycle_;
-  /*! \brief worker threads */
-  std::vector<dmlc::ConcurrentBlockingQueue<std::shared_ptr<T>>> qworkers_;
-};
-#endif  // DMLC_ENABLE_STD_THREAD
-
-/*!
- * \brief Registry entry for sparse page format.
- */
-template<typename T>
-struct SparsePageFormatReg
-    : public dmlc::FunctionRegEntryBase<SparsePageFormatReg<T>,
-                                        std::function<SparsePageFormat<T>* ()>> {
-};
-
-/*!
- * \brief Macro to register sparse page format.
- *
- * \code
- * // example of registering a objective
- * XGBOOST_REGISTER_SPARSE_PAGE_FORMAT(raw)
- * .describe("Raw binary data format.")
- * .set_body([]() {
- *     return new RawFormat();
- *   });
- * \endcode
- */
-#define SparsePageFmt SparsePageFormat<SparsePage>
-#define XGBOOST_REGISTER_SPARSE_PAGE_FORMAT(Name)                       \
-  DMLC_REGISTRY_REGISTER(SparsePageFormatReg<SparsePage>, SparsePageFmt, Name)
-
-#define CSCPageFmt SparsePageFormat<CSCPage>
-#define XGBOOST_REGISTER_CSC_PAGE_FORMAT(Name)                       \
-  DMLC_REGISTRY_REGISTER(SparsePageFormatReg<CSCPage>, CSCPageFmt, Name)
-
-#define SortedCSCPageFmt SparsePageFormat<SortedCSCPage>
-#define XGBOOST_REGISTER_SORTED_CSC_PAGE_FORMAT(Name)                       \
-  DMLC_REGISTRY_REGISTER(SparsePageFormatReg<SortedCSCPage>, SortedCSCPageFmt, Name)
-
-#define EllpackPageFmt SparsePageFormat<EllpackPage>
-#define XGBOOST_REGISTER_ELLPACK_PAGE_FORMAT(Name)                       \
-  DMLC_REGISTRY_REGISTER(SparsePageFormatReg<EllpackPage>, EllpackPageFm, Name)
-
-}  // namespace data
-}  // namespace xgboost
-#endif  // XGBOOST_DATA_SPARSE_PAGE_WRITER_H_
diff --git a/ml-xgboost/src/gbm/gblinear.cc b/ml-xgboost/src/gbm/gblinear.cc
deleted file mode 100644
index 51943fe..0000000
--- a/ml-xgboost/src/gbm/gblinear.cc
+++ /dev/null
@@ -1,324 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file gblinear.cc
- * \brief Implementation of Linear booster, with L1/L2 regularization: Elastic Net
- *        the update rule is parallel coordinate descent (shotgun)
- * \author Tianqi Chen
- */
-#include <dmlc/omp.h>
-#include <dmlc/parameter.h>
-
-#include <vector>
-#include <string>
-#include <sstream>
-#include <algorithm>
-
-#include "xgboost/gbm.h"
-#include "xgboost/json.h"
-#include "xgboost/predictor.h"
-#include "xgboost/linear_updater.h"
-#include "xgboost/logging.h"
-#include "xgboost/learner.h"
-
-#include "gblinear_model.h"
-#include "../common/timer.h"
-#include "../common/common.h"
-
-namespace xgboost {
-namespace gbm {
-
-DMLC_REGISTRY_FILE_TAG(gblinear);
-
-// training parameters
-struct GBLinearTrainParam : public XGBoostParameter<GBLinearTrainParam> {
-  std::string updater;
-  float tolerance;
-  size_t max_row_perbatch;
-  DMLC_DECLARE_PARAMETER(GBLinearTrainParam) {
-    DMLC_DECLARE_FIELD(updater)
-        .set_default("shotgun")
-        .describe("Update algorithm for linear model. One of shotgun/coord_descent");
-    DMLC_DECLARE_FIELD(tolerance)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe("Stop if largest weight update is smaller than this number.");
-    DMLC_DECLARE_FIELD(max_row_perbatch)
-        .set_default(std::numeric_limits<size_t>::max())
-        .describe("Maximum rows per batch.");
-  }
-};
-/*!
- * \brief gradient boosted linear model
- */
-class GBLinear : public GradientBooster {
- public:
-  explicit GBLinear(LearnerModelParam const* learner_model_param)
-      : learner_model_param_{learner_model_param},
-        model_{learner_model_param},
-        previous_model_{learner_model_param},
-        sum_instance_weight_(0),
-        sum_weight_complete_(false),
-        is_converged_(false) {}
-
-  void Configure(const Args& cfg) override {
-    if (model_.weight.size() == 0) {
-      model_.Configure(cfg);
-    }
-    param_.UpdateAllowUnknown(cfg);
-    updater_.reset(LinearUpdater::Create(param_.updater, generic_param_));
-    updater_->Configure(cfg);
-    monitor_.Init("GBLinear");
-    if (param_.updater == "gpu_coord_descent") {
-      common::AssertGPUSupport();
-    }
-  }
-
-  void Load(dmlc::Stream* fi) override {
-    model_.Load(fi);
-  }
-  void Save(dmlc::Stream* fo) const override {
-    model_.Save(fo);
-  }
-
-  void SaveModel(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String{"gblinear"};
-
-    out["model"] = Object();
-    auto& model = out["model"];
-    model_.SaveModel(&model);
-  }
-  void LoadModel(Json const& in) override {
-    CHECK_EQ(get<String>(in["name"]), "gblinear");
-    auto const& model = in["model"];
-    model_.LoadModel(model);
-  }
-
-  void LoadConfig(Json const& in) override {
-    CHECK_EQ(get<String>(in["name"]), "gblinear");
-    FromJson(in["gblinear_train_param"], &param_);
-    updater_.reset(LinearUpdater::Create(param_.updater, generic_param_));
-    this->updater_->LoadConfig(in["updater"]);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String{"gblinear"};
-    out["gblinear_train_param"] = ToJson(param_);
-
-    out["updater"] = Object();
-    auto& j_updater = out["updater"];
-    CHECK(this->updater_);
-    this->updater_->SaveConfig(&j_updater);
-  }
-
-  void DoBoost(DMatrix *p_fmat,
-               HostDeviceVector<GradientPair> *in_gpair,
-               PredictionCacheEntry* predt, int iter,
-               float* _last_round_best_loss) override {
-    monitor_.Start("DoBoost");
-
-    model_.LazyInitModel();
-    this->LazySumWeights(p_fmat);
-
-    if (!this->CheckConvergence()) {
-      updater_->Update(in_gpair, p_fmat, &model_, sum_instance_weight_);
-    }
-
-    monitor_.Stop("DoBoost");
-  }
-
-  void PredictBatch(DMatrix *p_fmat,
-                    PredictionCacheEntry *predts,
-                    bool training,
-                    unsigned ntree_limit) override {
-    monitor_.Start("PredictBatch");
-    auto* out_preds = &predts->predictions;
-    CHECK_EQ(ntree_limit, 0U)
-        << "GBLinear::Predict ntrees is only valid for gbtree predictor";
-    this->PredictBatchInternal(p_fmat, &out_preds->HostVector());
-    monitor_.Stop("PredictBatch");
-  }
-  // add base margin
-  void PredictInstance(const SparsePage::Inst &inst,
-                       std::vector<bst_float> *out_preds,
-                       unsigned ntree_limit) override {
-    const int ngroup = model_.learner_model_param->num_output_group;
-    for (int gid = 0; gid < ngroup; ++gid) {
-      this->Pred(inst, dmlc::BeginPtr(*out_preds), gid,
-                 learner_model_param_->base_score);
-    }
-  }
-
-  void PredictLeaf(DMatrix *p_fmat,
-                   std::vector<bst_float> *out_preds,
-                   unsigned ntree_limit) override {
-    LOG(FATAL) << "gblinear does not support prediction of leaf index";
-  }
-
-  void PredictContribution(DMatrix* p_fmat,
-                           std::vector<bst_float>* out_contribs,
-                           unsigned ntree_limit, bool approximate, int condition = 0,
-                           unsigned condition_feature = 0) override {
-    model_.LazyInitModel();
-    CHECK_EQ(ntree_limit, 0U)
-        << "GBLinear::PredictContribution: ntrees is only valid for gbtree predictor";
-    const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
-    const int ngroup = model_.learner_model_param->num_output_group;
-    const size_t ncolumns = model_.learner_model_param->num_feature + 1;
-    // allocate space for (#features + bias) times #groups times #rows
-    std::vector<bst_float>& contribs = *out_contribs;
-    contribs.resize(p_fmat->Info().num_row_ * ncolumns * ngroup);
-    // make sure contributions is zeroed, we could be reusing a previously allocated one
-    std::fill(contribs.begin(), contribs.end(), 0);
-    // start collecting the contributions
-    for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-      // parallel over local batch
-      const auto nsize = static_cast<bst_omp_uint>(batch.Size());
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nsize; ++i) {
-        auto inst = batch[i];
-        auto row_idx = static_cast<size_t>(batch.base_rowid + i);
-        // loop over output groups
-        for (int gid = 0; gid < ngroup; ++gid) {
-          bst_float *p_contribs = &contribs[(row_idx * ngroup + gid) * ncolumns];
-          // calculate linear terms' contributions
-          for (auto& ins : inst) {
-            if (ins.index >= model_.learner_model_param->num_feature) continue;
-            p_contribs[ins.index] = ins.fvalue * model_[ins.index][gid];
-          }
-          // add base margin to BIAS
-          p_contribs[ncolumns - 1] = model_.Bias()[gid] +
-            ((base_margin.size() != 0) ? base_margin[row_idx * ngroup + gid] :
-                                         learner_model_param_->base_score);
-        }
-      }
-    }
-  }
-
-  void PredictInteractionContributions(DMatrix* p_fmat,
-                                       std::vector<bst_float>* out_contribs,
-                                       unsigned ntree_limit, bool approximate) override {
-    std::vector<bst_float>& contribs = *out_contribs;
-
-    // linear models have no interaction effects
-    const size_t nelements = model_.learner_model_param->num_feature *
-                             model_.learner_model_param->num_feature;
-    contribs.resize(p_fmat->Info().num_row_ * nelements *
-                    model_.learner_model_param->num_output_group);
-    std::fill(contribs.begin(), contribs.end(), 0);
-  }
-
-  std::vector<std::string> DumpModel(const FeatureMap& fmap,
-                                     bool with_stats,
-                                     std::string format) const override {
-    return model_.DumpModel(fmap, with_stats, format);
-  }
-
-  bool UseGPU() const override {
-    if (param_.updater == "gpu_coord_descent") {
-      return true;
-    } else {
-      return false;
-    }
-  }
-
- protected:
-  void PredictBatchInternal(DMatrix *p_fmat,
-                            std::vector<bst_float> *out_preds) {
-    monitor_.Start("PredictBatchInternal");
-    model_.LazyInitModel();
-    std::vector<bst_float> &preds = *out_preds;
-    const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
-    // start collecting the prediction
-    const int ngroup = model_.learner_model_param->num_output_group;
-    preds.resize(p_fmat->Info().num_row_ * ngroup);
-    for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-      // output convention: nrow * k, where nrow is number of rows
-      // k is number of group
-      // parallel over local batch
-      const auto nsize = static_cast<omp_ulong>(batch.Size());
-      if (base_margin.size() != 0) {
-        CHECK_EQ(base_margin.size(), nsize * ngroup);
-      }
-#pragma omp parallel for schedule(static)
-      for (omp_ulong i = 0; i < nsize; ++i) {
-        const size_t ridx = batch.base_rowid + i;
-        // loop over output groups
-        for (int gid = 0; gid < ngroup; ++gid) {
-          bst_float margin =
-              (base_margin.size() != 0) ?
-              base_margin[ridx * ngroup + gid] : learner_model_param_->base_score;
-          this->Pred(batch[i], &preds[ridx * ngroup], gid, margin);
-        }
-      }
-    }
-    monitor_.Stop("PredictBatchInternal");
-  }
-
-  bool CheckConvergence() {
-    if (param_.tolerance == 0.0f) return false;
-    if (is_converged_) return true;
-    if (previous_model_.weight.size() != model_.weight.size()) {
-      previous_model_ = model_;
-      return false;
-    }
-    float largest_dw = 0.0;
-    for (size_t i = 0; i < model_.weight.size(); i++) {
-      largest_dw = std::max(
-          largest_dw, std::abs(model_.weight[i] - previous_model_.weight[i]));
-    }
-    previous_model_ = model_;
-
-    is_converged_ = largest_dw <= param_.tolerance;
-    return is_converged_;
-  }
-
-  void LazySumWeights(DMatrix *p_fmat) {
-    if (!sum_weight_complete_) {
-      auto &info = p_fmat->Info();
-      for (size_t i = 0; i < info.num_row_; i++) {
-        sum_instance_weight_ += info.GetWeight(i);
-      }
-      sum_weight_complete_ = true;
-    }
-  }
-
-  void Pred(const SparsePage::Inst &inst, bst_float *preds, int gid,
-            bst_float base) {
-    bst_float psum = model_.Bias()[gid] + base;
-    for (const auto& ins : inst) {
-      if (ins.index >= model_.learner_model_param->num_feature) continue;
-      psum += ins.fvalue * model_[ins.index][gid];
-    }
-    preds[gid] = psum;
-  }
-
-  // biase margin score
-  LearnerModelParam const* learner_model_param_;
-  // model field
-  GBLinearModel model_;
-  GBLinearModel previous_model_;
-  GBLinearTrainParam param_;
-  std::unique_ptr<LinearUpdater> updater_;
-  double sum_instance_weight_;
-  bool sum_weight_complete_;
-  common::Monitor monitor_;
-  bool is_converged_;
-};
-
-// register the objective functions
-DMLC_REGISTER_PARAMETER(GBLinearTrainParam);
-
-XGBOOST_REGISTER_GBM(GBLinear, "gblinear")
-    .describe("Linear booster, implement generalized linear model.")
-    .set_body([](LearnerModelParam const* booster_config) {
-      return new GBLinear(booster_config);
-    });
-}  // namespace gbm
-}  // namespace xgboost
diff --git a/ml-xgboost/src/gbm/gblinear_model.cc b/ml-xgboost/src/gbm/gblinear_model.cc
deleted file mode 100644
index a1c63b6..0000000
--- a/ml-xgboost/src/gbm/gblinear_model.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- */
-#include <utility>
-#include <limits>
-#include "xgboost/json.h"
-#include "gblinear_model.h"
-
-namespace xgboost {
-namespace gbm {
-
-void GBLinearModel::SaveModel(Json* p_out) const {
-  using WeightType = std::remove_reference<decltype(std::declval<decltype(weight)>().back())>::type;
-  using JsonFloat = Number::Float;
-  static_assert(std::is_same<WeightType, JsonFloat>::value,
-                "Weight type should be of the same type with JSON float");
-  auto& out = *p_out;
-
-  size_t const n_weights = weight.size();
-  std::vector<Json> j_weights(n_weights);
-  for (size_t i = 0; i < n_weights; ++i) {
-    j_weights[i] = weight[i];
-  }
-  out["weights"] = std::move(j_weights);
-}
-
-void GBLinearModel::LoadModel(Json const& in) {
-  auto const& j_weights = get<Array const>(in["weights"]);
-  auto n_weights = j_weights.size();
-  weight.resize(n_weights);
-  for (size_t i = 0; i < n_weights; ++i) {
-    weight[i] = get<Number const>(j_weights[i]);
-  }
-}
-
-DMLC_REGISTER_PARAMETER(DeprecatedGBLinearModelParam);
-}  // namespace gbm
-}  // namespace xgboost
diff --git a/ml-xgboost/src/gbm/gblinear_model.h b/ml-xgboost/src/gbm/gblinear_model.h
deleted file mode 100644
index f2d0d9a..0000000
--- a/ml-xgboost/src/gbm/gblinear_model.h
+++ /dev/null
@@ -1,143 +0,0 @@
-/*!
- * Copyright 2018-2019 by Contributors
- */
-#pragma once
-#include <dmlc/io.h>
-#include <dmlc/parameter.h>
-#include <xgboost/learner.h>
-
-#include <vector>
-#include <string>
-#include <cstring>
-
-#include "xgboost/base.h"
-#include "xgboost/feature_map.h"
-#include "xgboost/model.h"
-#include "xgboost/json.h"
-#include "xgboost/parameter.h"
-
-namespace xgboost {
-class Json;
-namespace gbm {
-// Deprecated in 1.0.0. model parameter.  Only staying here for compatible binary model IO.
-struct DeprecatedGBLinearModelParam : public dmlc::Parameter<DeprecatedGBLinearModelParam> {
-  // number of feature dimension
-  uint32_t deprecated_num_feature;
-  // deprecated. use learner_model_param_->num_output_group.
-  int32_t deprecated_num_output_group;
-  // reserved field
-  int32_t reserved[32];
-  // constructor
-  DeprecatedGBLinearModelParam() {
-    static_assert(sizeof(*this) == sizeof(int32_t) * 34,
-                  "Model parameter size can not be changed.");
-    std::memset(this, 0, sizeof(DeprecatedGBLinearModelParam));
-  }
-
-  DMLC_DECLARE_PARAMETER(DeprecatedGBLinearModelParam) {}
-};
-
-// model for linear booster
-class GBLinearModel : public Model {
- private:
-  // Deprecated in 1.0.0
-  DeprecatedGBLinearModelParam param_;
-
- public:
-  LearnerModelParam const* learner_model_param;
-
- public:
-  explicit GBLinearModel(LearnerModelParam const* learner_model_param) :
-      learner_model_param {learner_model_param} {}
-  void Configure(Args const &cfg) { }
-
-  // weight for each of feature, bias is the last one
-  std::vector<bst_float> weight;
-  // initialize the model parameter
-  inline void LazyInitModel() {
-    if (!weight.empty()) {
-      return;
-    }
-    // bias is the last weight
-    weight.resize((learner_model_param->num_feature + 1) *
-                  learner_model_param->num_output_group);
-    std::fill(weight.begin(), weight.end(), 0.0f);
-  }
-
-  void SaveModel(Json *p_out) const override;
-  void LoadModel(Json const &in) override;
-
-  // save the model to file
-  void Save(dmlc::Stream *fo) const {
-    fo->Write(&param_, sizeof(param_));
-    fo->Write(weight);
-  }
-  // load model from file
-  void Load(dmlc::Stream *fi) {
-    CHECK_EQ(fi->Read(&param_, sizeof(param_)), sizeof(param_));
-    fi->Read(&weight);
-  }
-
-  // model bias
-  inline bst_float *Bias() {
-    return &weight[learner_model_param->num_feature *
-                   learner_model_param->num_output_group];
-  }
-  inline const bst_float *Bias() const {
-    return &weight[learner_model_param->num_feature *
-                   learner_model_param->num_output_group];
-  }
-  // get i-th weight
-  inline bst_float *operator[](size_t i) {
-    return &weight[i * learner_model_param->num_output_group];
-  }
-  inline const bst_float *operator[](size_t i) const {
-    return &weight[i * learner_model_param->num_output_group];
-  }
-
-  std::vector<std::string> DumpModel(const FeatureMap &fmap, bool with_stats,
-                                     std::string format) const {
-    const int ngroup = learner_model_param->num_output_group;
-    const unsigned nfeature = learner_model_param->num_feature;
-
-    std::stringstream fo("");
-    if (format == "json") {
-      fo << "  { \"bias\": [" << std::endl;
-      for (int gid = 0; gid < ngroup; ++gid) {
-        if (gid != 0) {
-          fo << "," << std::endl;
-        }
-        fo << "      " << this->Bias()[gid];
-      }
-      fo << std::endl
-         << "    ]," << std::endl
-         << "    \"weight\": [" << std::endl;
-      for (unsigned i = 0; i < nfeature; ++i) {
-        for (int gid = 0; gid < ngroup; ++gid) {
-          if (i != 0 || gid != 0) {
-            fo << "," << std::endl;
-          }
-          fo << "      " << (*this)[i][gid];
-        }
-      }
-      fo << std::endl << "    ]" << std::endl << "  }";
-    } else {
-      fo << "bias:\n";
-      for (int gid = 0; gid < ngroup; ++gid) {
-        fo << this->Bias()[gid] << std::endl;
-      }
-      fo << "weight:\n";
-      for (unsigned i = 0; i < nfeature; ++i) {
-        for (int gid = 0; gid < ngroup; ++gid) {
-          fo << (*this)[i][gid] << std::endl;
-        }
-      }
-    }
-    std::vector<std::string> v;
-    v.push_back(fo.str());
-    return v;
-  }
-};
-
-}  // namespace gbm
-}  // namespace xgboost
diff --git a/ml-xgboost/src/gbm/gbm.cc b/ml-xgboost/src/gbm/gbm.cc
deleted file mode 100644
index 87a6ded..0000000
--- a/ml-xgboost/src/gbm/gbm.cc
+++ /dev/null
@@ -1,40 +0,0 @@
-/*!
- * Copyright 2015-2020 by Contributors
- * \file gbm.cc
- * \brief Registry of gradient boosters.
- */
-#include <dmlc/registry.h>
-#include <string>
-#include <vector>
-#include <memory>
-
-#include "xgboost/gbm.h"
-#include "xgboost/learner.h"
-#include "xgboost/generic_parameters.h"
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::GradientBoosterReg);
-}  // namespace dmlc
-
-namespace xgboost {
-GradientBooster* GradientBooster::Create(
-    const std::string& name,
-    GenericParameter const* generic_param,
-    LearnerModelParam const* learner_model_param) {
-  auto *e = ::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->Find(name);
-  if (e == nullptr) {
-    LOG(FATAL) << "Unknown gbm type " << name;
-  }
-  auto p_bst =  (e->body)(learner_model_param);
-  p_bst->generic_param_ = generic_param;
-  return p_bst;
-}
-}  // namespace xgboost
-
-namespace xgboost {
-namespace gbm {
-// List of files that will be force linked in static links.
-DMLC_REGISTRY_LINK_TAG(gblinear);
-DMLC_REGISTRY_LINK_TAG(gbtree);
-}  // namespace gbm
-}  // namespace xgboost
diff --git a/ml-xgboost/src/gbm/gbtree.cc b/ml-xgboost/src/gbm/gbtree.cc
deleted file mode 100644
index e0983aa..0000000
--- a/ml-xgboost/src/gbm/gbtree.cc
+++ /dev/null
@@ -1,829 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file gbtree.cc
- * \brief gradient boosted tree implementation.
- * \author Tianqi Chen
- */
-#include <dmlc/omp.h>
-#include <dmlc/parameter.h>
-
-#include <vector>
-#include <memory>
-#include <utility>
-#include <string>
-#include <limits>
-#include <algorithm>
-
-#include "xgboost/data.h"
-#include "xgboost/gbm.h"
-#include "xgboost/logging.h"
-#include "xgboost/json.h"
-#include "xgboost/predictor.h"
-#include "xgboost/tree_updater.h"
-#include "xgboost/host_device_vector.h"
-
-#include "gbtree.h"
-#include "gbtree_model.h"
-#include "../common/common.h"
-#include "../common/random.h"
-#include "../common/timer.h"
-
-namespace xgboost {
-namespace gbm {
-
-DMLC_REGISTRY_FILE_TAG(gbtree);
-
-void GBTree::Configure(const Args& cfg) {
-  this->cfg_ = cfg;
-  std::string updater_seq = tparam_.updater_seq;
-  tparam_.UpdateAllowUnknown(cfg);
-
-  model_.Configure(cfg);
-
-  // for the 'update' process_type, move trees into trees_to_update
-  if (tparam_.process_type == TreeProcessType::kUpdate) {
-    model_.InitTreesToUpdate();
-  }
-
-  // configure predictors
-  if (!cpu_predictor_) {
-    cpu_predictor_ = std::unique_ptr<Predictor>(
-        Predictor::Create("cpu_predictor", this->generic_param_));
-  }
-  cpu_predictor_->Configure(cfg);
-#if defined(XGBOOST_USE_CUDA)
-  auto n_gpus = common::AllVisibleGPUs();
-  if (!gpu_predictor_ && n_gpus != 0) {
-    gpu_predictor_ = std::unique_ptr<Predictor>(
-        Predictor::Create("gpu_predictor", this->generic_param_));
-  }
-  if (n_gpus != 0) {
-    gpu_predictor_->Configure(cfg);
-  }
-#endif  // defined(XGBOOST_USE_CUDA)
-
-  monitor_.Init("GBTree");
-
-  specified_updater_ = std::any_of(cfg.cbegin(), cfg.cend(),
-                   [](std::pair<std::string, std::string> const& arg) {
-                     return arg.first == "updater";
-                   });
-
-  if (specified_updater_ && !showed_updater_warning_) {
-    LOG(WARNING) << "DANGER AHEAD: You have manually specified `updater` "
-        "parameter. The `tree_method` parameter will be ignored. "
-        "Incorrect sequence of updaters will produce undefined "
-        "behavior. For common uses, we recommend using"
-        "`tree_method` parameter instead.";
-    // Don't drive users to silent XGBOost.
-    showed_updater_warning_ = true;
-  }
-
-  this->ConfigureUpdaters();
-  if (updater_seq != tparam_.updater_seq) {
-    updaters_.clear();
-    this->InitUpdater(cfg);
-  } else {
-    for (auto &up : updaters_) {
-      up->Configure(cfg);
-    }
-  }
-
-  configured_ = true;
-}
-
-// FIXME(trivialfis): This handles updaters.  Because the choice of updaters depends on
-// whether external memory is used and how large is dataset.  We can remove the dependency
-// on DMatrix once `hist` tree method can handle external memory so that we can make it
-// default.
-void GBTree::ConfigureWithKnownData(Args const& cfg, DMatrix* fmat) {
-  CHECK(this->configured_);
-  std::string updater_seq = tparam_.updater_seq;
-  CHECK(tparam_.GetInitialised());
-
-  tparam_.UpdateAllowUnknown(cfg);
-
-  this->PerformTreeMethodHeuristic(fmat);
-  this->ConfigureUpdaters();
-
-  // initialize the updaters only when needed.
-  if (updater_seq != tparam_.updater_seq) {
-    LOG(DEBUG) << "Using updaters: " << tparam_.updater_seq;
-    this->updaters_.clear();
-    this->InitUpdater(cfg);
-  }
-}
-
-void GBTree::PerformTreeMethodHeuristic(DMatrix* fmat) {
-  if (specified_updater_) {
-    // This method is disabled when `updater` parameter is explicitly
-    // set, since only experts are expected to do so.
-    return;
-  }
-  // tparam_ is set before calling this function.
-  if (tparam_.tree_method != TreeMethod::kAuto) {
-    return;
-  }
-
-  if (rabit::IsDistributed()) {
-    LOG(WARNING) <<
-      "Tree method is automatically selected to be 'approx' "
-      "for distributed training.";
-    tparam_.tree_method = TreeMethod::kApprox;
-  } else if (!fmat->SingleColBlock()) {
-    LOG(WARNING) << "Tree method is automatically set to 'approx' "
-                    "since external-memory data matrix is used.";
-    tparam_.tree_method = TreeMethod::kApprox;
-  } else if (fmat->Info().num_row_ >= (4UL << 20UL)) {
-    /* Choose tree_method='approx' automatically for large data matrix */
-    LOG(WARNING) << "Tree method is automatically selected to be "
-        "'approx' for faster speed. To use old behavior "
-        "(exact greedy algorithm on single machine), "
-        "set tree_method to 'exact'.";
-    tparam_.tree_method = TreeMethod::kApprox;
-  } else {
-    tparam_.tree_method = TreeMethod::kExact;
-  }
-  LOG(DEBUG) << "Using tree method: " << static_cast<int>(tparam_.tree_method);
-}
-
-void GBTree::ConfigureUpdaters() {
-  if (specified_updater_) {
-    return;
-  }
-  // `updater` parameter was manually specified
-  /* Choose updaters according to tree_method parameters */
-  switch (tparam_.tree_method) {
-    case TreeMethod::kAuto:
-      // Use heuristic to choose between 'exact' and 'approx' This
-      // choice is carried out in PerformTreeMethodHeuristic() before
-      // calling this function.
-      break;
-    case TreeMethod::kApprox:
-      tparam_.updater_seq = "grow_histmaker,prune";
-      break;
-    case TreeMethod::kExact:
-      tparam_.updater_seq = "grow_colmaker,prune";
-      break;
-    case TreeMethod::kHist:
-      LOG(INFO) <<
-          "Tree method is selected to be 'hist', which uses a "
-          "single updater grow_quantile_histmaker.";
-      tparam_.updater_seq = "grow_quantile_histmaker";
-      break;
-    case TreeMethod::kGPUHist: {
-      common::AssertGPUSupport();
-      tparam_.updater_seq = "grow_gpu_hist";
-      break;
-    }
-    default:
-      LOG(FATAL) << "Unknown tree_method ("
-                 << static_cast<int>(tparam_.tree_method) << ") detected";
-  }
-}
-
-void GBTree::DoBoost(DMatrix* p_fmat,
-                     HostDeviceVector<GradientPair>* in_gpair,
-                     PredictionCacheEntry* predt, int iter,
-                     float* _last_round_best_loss) {
-  std::vector<std::vector<std::unique_ptr<RegTree> > > new_trees;
-  const int ngroup = model_.learner_model_param->num_output_group;
-  ConfigureWithKnownData(this->cfg_, p_fmat);
-  monitor_.Start("BoostNewTrees");
-  CHECK_NE(ngroup, 0);
-  if (ngroup == 1) {
-    std::vector<std::unique_ptr<RegTree> > ret;
-    float* p_last_round_best_loss = nullptr;
-    if (_last_round_best_loss != nullptr) {
-      p_last_round_best_loss = _last_round_best_loss;
-    }
-    BoostNewTrees(in_gpair, p_fmat, 0, &ret, iter, p_last_round_best_loss);
-    new_trees.push_back(std::move(ret));
-  } else {
-    CHECK_EQ(in_gpair->Size() % ngroup, 0U)
-        << "must have exactly ngroup * nrow gpairs";
-    // TODO(canonizer): perform this on GPU if HostDeviceVector has device set.
-    HostDeviceVector<GradientPair> tmp(in_gpair->Size() / ngroup,
-                                       GradientPair(),
-                                       in_gpair->DeviceIdx());
-    const auto& gpair_h = in_gpair->ConstHostVector();
-    auto nsize = static_cast<bst_omp_uint>(tmp.Size());
-    for (int gid = 0; gid < ngroup; ++gid) {
-      std::vector<GradientPair>& tmp_h = tmp.HostVector();
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nsize; ++i) {
-        tmp_h[i] = gpair_h[i * ngroup + gid];
-      }
-      std::vector<std::unique_ptr<RegTree> > ret;
-      float* p_last_round_best_loss = nullptr;
-      if (_last_round_best_loss != nullptr) {
-        p_last_round_best_loss = _last_round_best_loss + gid;
-      }
-      BoostNewTrees(&tmp, p_fmat, gid, &ret, iter, p_last_round_best_loss);
-      new_trees.push_back(std::move(ret));
-    }
-  }
-  monitor_.Stop("BoostNewTrees");
-  this->CommitModel(std::move(new_trees), p_fmat, predt, iter);
-}
-
-void GBTree::InitUpdater(Args const& cfg) {
-  std::string tval = tparam_.updater_seq;
-  std::vector<std::string> ups = common::Split(tval, ',');
-
-  if (updaters_.size() != 0) {
-    // Assert we have a valid set of updaters.
-    CHECK_EQ(ups.size(), updaters_.size());
-    for (auto const& up : updaters_) {
-      bool contains = std::any_of(ups.cbegin(), ups.cend(),
-                        [&up](std::string const& name) {
-                          return name == up->Name();
-                        });
-      if (!contains) {
-        std::stringstream ss;
-        ss << "Internal Error: " << " mismatched updater sequence.\n";
-        ss << "Specified updaters: ";
-        std::for_each(ups.cbegin(), ups.cend(),
-                      [&ss](std::string const& name){
-                        ss << name << " ";
-                      });
-        ss << "\n" << "Actual updaters: ";
-        std::for_each(updaters_.cbegin(), updaters_.cend(),
-                      [&ss](std::unique_ptr<TreeUpdater> const& updater){
-                        ss << updater->Name() << " ";
-                      });
-        LOG(FATAL) << ss.str();
-      }
-    }
-    // Do not push new updater in.
-    return;
-  }
-
-  // create new updaters
-  for (const std::string& pstr : ups) {
-    std::unique_ptr<TreeUpdater> up(TreeUpdater::Create(pstr.c_str(), generic_param_));
-    up->Configure(cfg);
-    updaters_.push_back(std::move(up));
-  }
-}
-
-void GBTree::BoostNewTrees(HostDeviceVector<GradientPair>* gpair,
-                           DMatrix *p_fmat,
-                           int bst_group,
-                           std::vector<std::unique_ptr<RegTree> >* ret, 
-                           int iter,
-                           float* p_last_round_best_loss) {
-  std::vector<RegTree*> new_trees;
-  ret->clear();
-  // create the trees
-  for (int i = 0; i < tparam_.num_parallel_tree; ++i) {
-    if (tparam_.process_type == TreeProcessType::kDefault) {
-      CHECK(!updaters_.front()->CanModifyTree())
-          << "Updater: `" << updaters_.front()->Name() << "` "
-          << "can not be used to create new trees. "
-          << "Set `process_type` to `update` if you want to update existing "
-             "trees.";
-      // create new tree
-      std::unique_ptr<RegTree> ptr(new RegTree());
-      ptr->param.UpdateAllowUnknown(this->cfg_);
-      new_trees.push_back(ptr.get());
-      ret->push_back(std::move(ptr));
-    } else if (tparam_.process_type == TreeProcessType::kUpdate) {
-      for (auto const& up : updaters_) {
-        CHECK(up->CanModifyTree())
-          << "Updater: `" << up->Name() << "` "
-          << "can not be used to modify existing trees. "
-          << "Set `process_type` to `default` if you want to build new trees.";
-      }
-      CHECK_LT(model_.trees.size(), model_.trees_to_update.size())
-          << "No more tree left for updating.  For updating existing trees, "
-          << "boosting rounds can not exceed previous training rounds";
-      // move an existing tree from trees_to_update
-      auto t = std::move(model_.trees_to_update[model_.trees.size() +
-                                                bst_group * tparam_.num_parallel_tree + i]);
-      new_trees.push_back(t.get());
-      ret->push_back(std::move(t));
-    }
-  }
-  // update the trees
-  CHECK_EQ(gpair->Size(), p_fmat->Info().num_row_)
-      << "Mismatching size between number of rows from input data and size of "
-         "gradient vector.";
-  for (auto& up : updaters_) {
-    up->Update(gpair, p_fmat, new_trees, iter, p_last_round_best_loss);
-  }
-}
-
-void GBTree::CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-                         DMatrix* m,
-                         PredictionCacheEntry* predts, int iter) {
-  monitor_.Start("CommitModel");
-  int num_new_trees = 0;
-  for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
-    num_new_trees += new_trees[gid].size();
-    model_.CommitModel(std::move(new_trees[gid]), gid);
-  }
-  auto* out = &predts->predictions;
-  if (model_.learner_model_param->num_output_group == 1 &&
-      updaters_.size() > 0 &&
-      num_new_trees == 1 &&
-      out->Size() > 0 &&
-      updaters_.back()->UpdatePredictionCache(m, out, iter)) {
-    auto delta = num_new_trees / model_.learner_model_param->num_output_group;
-    predts->Update(delta);
-  }
-  monitor_.Stop("CommitModel");
-}
-
-void GBTree::LoadConfig(Json const& in) {
-  CHECK_EQ(get<String>(in["name"]), "gbtree");
-  FromJson(in["gbtree_train_param"], &tparam_);
-  // Process type cannot be kUpdate from loaded model
-  // This would cause all trees to be pushed to trees_to_update
-  // e.g. updating a model, then saving and loading it would result in an empty model
-  tparam_.process_type = TreeProcessType::kDefault;
-  int32_t const n_gpus = xgboost::common::AllVisibleGPUs();
-  if (n_gpus == 0 && tparam_.predictor == PredictorType::kGPUPredictor) {
-    LOG(WARNING)
-        << "Loading from a raw memory buffer on CPU only machine.  "
-           "Changing predictor to auto.";
-    tparam_.UpdateAllowUnknown(Args{{"predictor", "auto"}});
-  }
-  if (n_gpus == 0 && tparam_.tree_method == TreeMethod::kGPUHist) {
-    tparam_.UpdateAllowUnknown(Args{{"tree_method", "hist"}});
-    LOG(WARNING)
-        << "Loading from a raw memory buffer on CPU only machine.  "
-           "Changing tree_method to hist.";
-  }
-
-  auto const& j_updaters = get<Object const>(in["updater"]);
-  updaters_.clear();
-  for (auto const& kv : j_updaters) {
-    std::unique_ptr<TreeUpdater> up(TreeUpdater::Create(kv.first, generic_param_));
-    up->LoadConfig(kv.second);
-    updaters_.push_back(std::move(up));
-  }
-
-  specified_updater_ = get<Boolean>(in["specified_updater"]);
-}
-
-void GBTree::SaveConfig(Json* p_out) const {
-  auto& out = *p_out;
-  out["name"] = String("gbtree");
-  out["gbtree_train_param"] = ToJson(tparam_);
-
-  // Process type cannot be kUpdate from loaded model
-  // This would cause all trees to be pushed to trees_to_update
-  // e.g. updating a model, then saving and loading it would result in an empty
-  // model
-  out["gbtree_train_param"]["process_type"] = String("default");
-
-  out["updater"] = Object();
-
-  auto& j_updaters = out["updater"];
-  for (auto const& up : updaters_) {
-    j_updaters[up->Name()] = Object();
-    auto& j_up = j_updaters[up->Name()];
-    up->SaveConfig(&j_up);
-  }
-  out["specified_updater"] = Boolean{specified_updater_};
-}
-
-void GBTree::LoadModel(Json const& in) {
-  CHECK_EQ(get<String>(in["name"]), "gbtree");
-  model_.LoadModel(in["model"]);
-}
-
-void GBTree::SaveModel(Json* p_out) const {
-  auto& out = *p_out;
-  out["name"] = String("gbtree");
-  out["model"] = Object();
-  auto& model = out["model"];
-  model_.SaveModel(&model);
-}
-
-void GBTree::PredictBatch(DMatrix* p_fmat,
-                          PredictionCacheEntry* out_preds,
-                          bool training,
-                          unsigned ntree_limit) {
-  CHECK(configured_);
-  GetPredictor(&out_preds->predictions, p_fmat)
-      ->PredictBatch(p_fmat, out_preds, model_, 0, ntree_limit);
-}
-
-std::unique_ptr<Predictor> const &
-GBTree::GetPredictor(HostDeviceVector<float> const *out_pred,
-                     DMatrix *f_dmat) const {
-  CHECK(configured_);
-  if (tparam_.predictor != PredictorType::kAuto) {
-    if (tparam_.predictor == PredictorType::kGPUPredictor) {
-#if defined(XGBOOST_USE_CUDA)
-      CHECK(gpu_predictor_);
-      return gpu_predictor_;
-#else
-      common::AssertGPUSupport();
-#endif  // defined(XGBOOST_USE_CUDA)
-    }
-    CHECK(cpu_predictor_);
-    return cpu_predictor_;
-  }
-
-  // Data comes from Device DMatrix.
-  auto is_ellpack = f_dmat && f_dmat->PageExists<EllpackPage>() &&
-                    !f_dmat->PageExists<SparsePage>();
-  // Data comes from device memory, like CuDF or CuPy.
-  auto is_from_device =
-      f_dmat && f_dmat->PageExists<SparsePage>() &&
-      (*(f_dmat->GetBatches<SparsePage>().begin())).data.DeviceCanRead();
-  auto on_device = is_ellpack || is_from_device;
-
-  // Use GPU Predictor if data is already on device and gpu_id is set.
-  if (on_device && generic_param_->gpu_id >= 0) {
-#if defined(XGBOOST_USE_CUDA)
-    CHECK(gpu_predictor_);
-    return gpu_predictor_;
-#else
-    LOG(FATAL) << "Data is on CUDA device, but XGBoost is not compiled with "
-                  "CUDA support.";
-    return cpu_predictor_;
-#endif  // defined(XGBOOST_USE_CUDA)
-  }
-
-  // GPU_Hist by default has prediction cache calculated from quantile values,
-  // so GPU Predictor is not used for training dataset.  But when XGBoost
-  // performs continue training with an existing model, the prediction cache is
-  // not availbale and number of trees doesn't equal zero, the whole training
-  // dataset got copied into GPU for precise prediction.  This condition tries
-  // to avoid such copy by calling CPU Predictor instead.
-  if ((out_pred && out_pred->Size() == 0) && (model_.param.num_trees != 0) &&
-      // FIXME(trivialfis): Implement a better method for testing whether data
-      // is on device after DMatrix refactoring is done.
-      !on_device) {
-    CHECK(cpu_predictor_);
-    return cpu_predictor_;
-  }
-
-  if (tparam_.tree_method == TreeMethod::kGPUHist) {
-#if defined(XGBOOST_USE_CUDA)
-    CHECK(gpu_predictor_);
-    return gpu_predictor_;
-#else
-    common::AssertGPUSupport();
-    return cpu_predictor_;
-#endif  // defined(XGBOOST_USE_CUDA)
-  }
-
-  CHECK(cpu_predictor_);
-  return cpu_predictor_;
-}
-
-class Dart : public GBTree {
- public:
-  explicit Dart(LearnerModelParam const* booster_config) :
-      GBTree(booster_config) {}
-
-  void Configure(const Args& cfg) override {
-    GBTree::Configure(cfg);
-    dparam_.UpdateAllowUnknown(cfg);
-  }
-
-  void SaveModel(Json *p_out) const override {
-    auto &out = *p_out;
-    out["name"] = String("dart");
-    out["gbtree"] = Object();
-    GBTree::SaveModel(&(out["gbtree"]));
-
-    std::vector<Json> j_weight_drop(weight_drop_.size());
-    for (size_t i = 0; i < weight_drop_.size(); ++i) {
-      j_weight_drop[i] = Number(weight_drop_[i]);
-    }
-    out["weight_drop"] = Array(std::move(j_weight_drop));
-  }
-  void LoadModel(Json const& in) override {
-    CHECK_EQ(get<String>(in["name"]), "dart");
-    auto const& gbtree = in["gbtree"];
-    GBTree::LoadModel(gbtree);
-
-    auto const& j_weight_drop = get<Array>(in["weight_drop"]);
-    weight_drop_.resize(j_weight_drop.size());
-    for (size_t i = 0; i < weight_drop_.size(); ++i) {
-      weight_drop_[i] = get<Number const>(j_weight_drop[i]);
-    }
-  }
-
-  void Load(dmlc::Stream* fi) override {
-    GBTree::Load(fi);
-    weight_drop_.resize(model_.param.num_trees);
-    if (model_.param.num_trees != 0) {
-      fi->Read(&weight_drop_);
-    }
-  }
-  void Save(dmlc::Stream* fo) const override {
-    GBTree::Save(fo);
-    if (weight_drop_.size() != 0) {
-      fo->Write(weight_drop_);
-    }
-  }
-
-  void LoadConfig(Json const& in) override {
-    CHECK_EQ(get<String>(in["name"]), "dart");
-    auto const& gbtree = in["gbtree"];
-    GBTree::LoadConfig(gbtree);
-    FromJson(in["dart_train_param"], &dparam_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("dart");
-    out["gbtree"] = Object();
-    auto& gbtree = out["gbtree"];
-    GBTree::SaveConfig(&gbtree);
-    out["dart_train_param"] = ToJson(dparam_);
-  }
-
-  void PredictBatch(DMatrix* p_fmat,
-                    PredictionCacheEntry* p_out_preds,
-                    bool training,
-                    unsigned ntree_limit) override {
-    DropTrees(training);
-    int num_group = model_.learner_model_param->num_output_group;
-    ntree_limit *= num_group;
-    if (ntree_limit == 0 || ntree_limit > model_.trees.size()) {
-      ntree_limit = static_cast<unsigned>(model_.trees.size());
-    }
-    size_t n = num_group * p_fmat->Info().num_row_;
-    const auto &base_margin = p_fmat->Info().base_margin_.ConstHostVector();
-    auto& out_preds = p_out_preds->predictions.HostVector();
-    out_preds.resize(n);
-    if (base_margin.size() != 0) {
-      CHECK_EQ(out_preds.size(), n);
-      std::copy(base_margin.begin(), base_margin.end(), out_preds.begin());
-    } else {
-      std::fill(out_preds.begin(), out_preds.end(),
-                model_.learner_model_param->base_score);
-    }
-    const int nthread = omp_get_max_threads();
-    InitThreadTemp(nthread);
-    PredLoopSpecalize(p_fmat, &out_preds, num_group, 0, ntree_limit);
-  }
-
-  void PredictInstance(const SparsePage::Inst &inst,
-                       std::vector<bst_float> *out_preds,
-                       unsigned ntree_limit) override {
-    DropTrees(false);
-    if (thread_temp_.size() == 0) {
-      thread_temp_.resize(1, RegTree::FVec());
-      thread_temp_[0].Init(model_.learner_model_param->num_feature);
-    }
-    out_preds->resize(model_.learner_model_param->num_output_group);
-    ntree_limit *= model_.learner_model_param->num_output_group;
-    if (ntree_limit == 0 || ntree_limit > model_.trees.size()) {
-      ntree_limit = static_cast<unsigned>(model_.trees.size());
-    }
-    // loop over output groups
-    for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
-      (*out_preds)[gid] =
-          PredValue(inst, gid, &thread_temp_[0], 0, ntree_limit) +
-          model_.learner_model_param->base_score;
-    }
-  }
-
-  bool UseGPU() const override {
-    return GBTree::UseGPU();
-  }
-
-  void PredictContribution(DMatrix* p_fmat,
-                           std::vector<bst_float>* out_contribs,
-                           unsigned ntree_limit, bool approximate, int condition,
-                           unsigned condition_feature) override {
-    CHECK(configured_);
-    cpu_predictor_->PredictContribution(p_fmat, out_contribs, model_,
-                                        ntree_limit, &weight_drop_, approximate);
-  }
-
-  void PredictInteractionContributions(DMatrix* p_fmat,
-                                       std::vector<bst_float>* out_contribs,
-                                       unsigned ntree_limit, bool approximate) override {
-    CHECK(configured_);
-    cpu_predictor_->PredictInteractionContributions(p_fmat, out_contribs, model_,
-                                                    ntree_limit, &weight_drop_, approximate);
-  }
-
-
- protected:
-  inline void PredLoopSpecalize(
-      DMatrix* p_fmat,
-      std::vector<bst_float>* out_preds,
-      int num_group,
-      unsigned tree_begin,
-      unsigned tree_end) {
-    CHECK_EQ(num_group, model_.learner_model_param->num_output_group);
-    std::vector<bst_float>& preds = *out_preds;
-    CHECK_EQ(model_.param.size_leaf_vector, 0)
-        << "size_leaf_vector is enforced to 0 so far";
-    CHECK_EQ(preds.size(), p_fmat->Info().num_row_ * num_group);
-    // start collecting the prediction
-    for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-      constexpr int kUnroll = 8;
-      const auto nsize = static_cast<bst_omp_uint>(batch.Size());
-      const bst_omp_uint rest = nsize % kUnroll;
-      if (nsize >= kUnroll) {
-#pragma omp parallel for schedule(static)
-        for (bst_omp_uint i = 0; i < nsize - rest; i += kUnroll) {
-          const int tid = omp_get_thread_num();
-          RegTree::FVec& feats = thread_temp_[tid];
-          int64_t ridx[kUnroll];
-          SparsePage::Inst inst[kUnroll];
-          for (int k = 0; k < kUnroll; ++k) {
-            ridx[k] = static_cast<int64_t>(batch.base_rowid + i + k);
-          }
-          for (int k = 0; k < kUnroll; ++k) {
-            inst[k] = batch[i + k];
-          }
-          for (int k = 0; k < kUnroll; ++k) {
-            for (int gid = 0; gid < num_group; ++gid) {
-              const size_t offset = ridx[k] * num_group + gid;
-              preds[offset] +=
-                  this->PredValue(inst[k], gid, &feats, tree_begin, tree_end);
-            }
-          }
-        }
-      }
-
-      for (bst_omp_uint i = nsize - rest; i < nsize; ++i) {
-        RegTree::FVec& feats = thread_temp_[0];
-        const auto ridx = static_cast<int64_t>(batch.base_rowid + i);
-        const SparsePage::Inst inst = batch[i];
-        for (int gid = 0; gid < num_group; ++gid) {
-          const size_t offset = ridx * num_group + gid;
-          preds[offset] +=
-              this->PredValue(inst, gid,
-                              &feats, tree_begin, tree_end);
-        }
-      }
-    }
-  }
-
-  // commit new trees all at once
-  void
-  CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-              DMatrix* m,
-              PredictionCacheEntry* predts, int iter) override {
-    int num_new_trees = 0;
-    for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
-      num_new_trees += new_trees[gid].size();
-      model_.CommitModel(std::move(new_trees[gid]), gid);
-    }
-    size_t num_drop = NormalizeTrees(num_new_trees);
-    LOG(INFO) << "drop " << num_drop << " trees, "
-              << "weight = " << weight_drop_.back();
-  }
-
-  // predict the leaf scores without dropped trees
-  bst_float PredValue(const SparsePage::Inst &inst, int bst_group,
-                      RegTree::FVec *p_feats, unsigned tree_begin,
-                      unsigned tree_end) const {
-    bst_float psum = 0.0f;
-    p_feats->Fill(inst);
-    for (size_t i = tree_begin; i < tree_end; ++i) {
-      if (model_.tree_info[i] == bst_group) {
-        bool drop = std::binary_search(idx_drop_.begin(), idx_drop_.end(), i);
-        if (!drop) {
-          int tid = model_.trees[i]->GetLeafIndex(*p_feats);
-          psum += weight_drop_[i] * (*model_.trees[i])[tid].LeafValue();
-        }
-      }
-    }
-    p_feats->Drop(inst);
-    return psum;
-  }
-
-  // select which trees to drop
-  // passing clear=True will clear selection
-  inline void DropTrees(bool is_training) {
-    idx_drop_.clear();
-    if (!is_training) {
-      return;
-    }
-
-    std::uniform_real_distribution<> runif(0.0, 1.0);
-    auto& rnd = common::GlobalRandom();
-    bool skip = false;
-    if (dparam_.skip_drop > 0.0) skip = (runif(rnd) < dparam_.skip_drop);
-    // sample some trees to drop
-    if (!skip) {
-      if (dparam_.sample_type == 1) {
-        bst_float sum_weight = 0.0;
-        for (auto elem : weight_drop_) {
-          sum_weight += elem;
-        }
-        for (size_t i = 0; i < weight_drop_.size(); ++i) {
-          if (runif(rnd) < dparam_.rate_drop * weight_drop_.size() * weight_drop_[i] / sum_weight) {
-            idx_drop_.push_back(i);
-          }
-        }
-        if (dparam_.one_drop && idx_drop_.empty() && !weight_drop_.empty()) {
-          // the expression below is an ugly but MSVC2013-friendly equivalent of
-          // size_t i = std::discrete_distribution<size_t>(weight_drop.begin(),
-          //                                               weight_drop.end())(rnd);
-          size_t i = std::discrete_distribution<size_t>(
-            weight_drop_.size(), 0., static_cast<double>(weight_drop_.size()),
-            [this](double x) -> double {
-              return weight_drop_[static_cast<size_t>(x)];
-            })(rnd);
-          idx_drop_.push_back(i);
-        }
-      } else {
-        for (size_t i = 0; i < weight_drop_.size(); ++i) {
-          if (runif(rnd) < dparam_.rate_drop) {
-            idx_drop_.push_back(i);
-          }
-        }
-        if (dparam_.one_drop && idx_drop_.empty() && !weight_drop_.empty()) {
-          size_t i = std::uniform_int_distribution<size_t>(0, weight_drop_.size() - 1)(rnd);
-          idx_drop_.push_back(i);
-        }
-      }
-    }
-  }
-
-  // set normalization factors
-  inline size_t NormalizeTrees(size_t size_new_trees) {
-    float lr = 1.0 * dparam_.learning_rate / size_new_trees;
-    size_t num_drop = idx_drop_.size();
-    if (num_drop == 0) {
-      for (size_t i = 0; i < size_new_trees; ++i) {
-        weight_drop_.push_back(1.0);
-      }
-    } else {
-      if (dparam_.normalize_type == 1) {
-        // normalize_type 1
-        float factor = 1.0 / (1.0 + lr);
-        for (auto i : idx_drop_) {
-          weight_drop_[i] *= factor;
-        }
-        for (size_t i = 0; i < size_new_trees; ++i) {
-          weight_drop_.push_back(factor);
-        }
-      } else {
-        // normalize_type 0
-        float factor = 1.0 * num_drop / (num_drop + lr);
-        for (auto i : idx_drop_) {
-          weight_drop_[i] *= factor;
-        }
-        for (size_t i = 0; i < size_new_trees; ++i) {
-          weight_drop_.push_back(1.0 / (num_drop + lr));
-        }
-      }
-    }
-    // reset
-    idx_drop_.clear();
-    return num_drop;
-  }
-
-  // init thread buffers
-  inline void InitThreadTemp(int nthread) {
-    int prev_thread_temp_size = thread_temp_.size();
-    if (prev_thread_temp_size < nthread) {
-      thread_temp_.resize(nthread, RegTree::FVec());
-      for (int i = prev_thread_temp_size; i < nthread; ++i) {
-        thread_temp_[i].Init(model_.learner_model_param->num_feature);
-      }
-    }
-  }
-
-  // --- data structure ---
-  // training parameter
-  DartTrainParam dparam_;
-  /*! \brief prediction buffer */
-  std::vector<bst_float> weight_drop_;
-  // indexes of dropped trees
-  std::vector<size_t> idx_drop_;
-  // temporal storage for per thread
-  std::vector<RegTree::FVec> thread_temp_;
-};
-
-// register the objective functions
-DMLC_REGISTER_PARAMETER(GBTreeModelParam);
-DMLC_REGISTER_PARAMETER(GBTreeTrainParam);
-DMLC_REGISTER_PARAMETER(DartTrainParam);
-
-XGBOOST_REGISTER_GBM(GBTree, "gbtree")
-.describe("Tree booster, gradient boosted trees.")
-.set_body([](LearnerModelParam const* booster_config) {
-    auto* p = new GBTree(booster_config);
-    return p;
-  });
-XGBOOST_REGISTER_GBM(Dart, "dart")
-.describe("Tree booster, dart.")
-.set_body([](LearnerModelParam const* booster_config) {
-    GBTree* p = new Dart(booster_config);
-    return p;
-  });
-}  // namespace gbm
-}  // namespace xgboost
diff --git a/ml-xgboost/src/gbm/gbtree.h b/ml-xgboost/src/gbm/gbtree.h
deleted file mode 100644
index b40cee2..0000000
--- a/ml-xgboost/src/gbm/gbtree.h
+++ /dev/null
@@ -1,309 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file gbtree.cc
- * \brief gradient boosted tree implementation.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_GBM_GBTREE_H_
-#define XGBOOST_GBM_GBTREE_H_
-
-#include <dmlc/omp.h>
-
-#include <vector>
-#include <map>
-#include <memory>
-#include <utility>
-#include <string>
-#include <unordered_map>
-
-#include "xgboost/base.h"
-#include "xgboost/data.h"
-#include "xgboost/logging.h"
-#include "xgboost/gbm.h"
-#include "xgboost/predictor.h"
-#include "xgboost/tree_updater.h"
-#include "xgboost/parameter.h"
-#include "xgboost/json.h"
-#include "xgboost/host_device_vector.h"
-
-#include "gbtree_model.h"
-#include "../common/common.h"
-#include "../common/timer.h"
-
-namespace xgboost {
-enum class TreeMethod : int {
-  kAuto = 0, kApprox = 1, kExact = 2, kHist = 3,
-  kGPUHist = 5
-};
-
-// boosting process types
-enum class TreeProcessType : int {
-  kDefault = 0,
-  kUpdate = 1
-};
-
-enum class PredictorType : int {
-  kAuto = 0,
-  kCPUPredictor,
-  kGPUPredictor
-};
-}  // namespace xgboost
-
-DECLARE_FIELD_ENUM_CLASS(xgboost::TreeMethod);
-DECLARE_FIELD_ENUM_CLASS(xgboost::TreeProcessType);
-DECLARE_FIELD_ENUM_CLASS(xgboost::PredictorType);
-
-namespace xgboost {
-namespace gbm {
-
-/*! \brief training parameters */
-struct GBTreeTrainParam : public XGBoostParameter<GBTreeTrainParam> {
-  /*!
-   * \brief number of parallel trees constructed each iteration
-   *  use this option to support boosted random forest
-   */
-  int num_parallel_tree;
-  /*! \brief tree updater sequence */
-  std::string updater_seq;
-  /*! \brief type of boosting process to run */
-  TreeProcessType process_type;
-  // predictor type
-  PredictorType predictor;
-  // tree construction method
-  TreeMethod tree_method;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(GBTreeTrainParam) {
-    DMLC_DECLARE_FIELD(num_parallel_tree)
-        .set_default(1)
-        .set_lower_bound(1)
-        .describe("Number of parallel trees constructed during each iteration."\
-                  " This option is used to support boosted random forest.");
-    DMLC_DECLARE_FIELD(updater_seq)
-        .set_default("grow_colmaker,prune")
-        .describe("Tree updater sequence.");
-    DMLC_DECLARE_FIELD(process_type)
-        .set_default(TreeProcessType::kDefault)
-        .add_enum("default", TreeProcessType::kDefault)
-        .add_enum("update", TreeProcessType::kUpdate)
-        .describe("Whether to run the normal boosting process that creates new trees,"\
-                  " or to update the trees in an existing model.");
-    DMLC_DECLARE_ALIAS(updater_seq, updater);
-    DMLC_DECLARE_FIELD(predictor)
-        .set_default(PredictorType::kAuto)
-        .add_enum("auto", PredictorType::kAuto)
-        .add_enum("cpu_predictor", PredictorType::kCPUPredictor)
-        .add_enum("gpu_predictor", PredictorType::kGPUPredictor)
-        .describe("Predictor algorithm type");
-    DMLC_DECLARE_FIELD(tree_method)
-        .set_default(TreeMethod::kAuto)
-        .add_enum("auto",      TreeMethod::kAuto)
-        .add_enum("approx",    TreeMethod::kApprox)
-        .add_enum("exact",     TreeMethod::kExact)
-        .add_enum("hist",      TreeMethod::kHist)
-        .add_enum("gpu_hist",  TreeMethod::kGPUHist)
-        .describe("Choice of tree construction method.");
-  }
-};
-
-/*! \brief training parameters */
-struct DartTrainParam : public XGBoostParameter<DartTrainParam> {
-  /*! \brief type of sampling algorithm */
-  int sample_type;
-  /*! \brief type of normalization algorithm */
-  int normalize_type;
-  /*! \brief fraction of trees to drop during the dropout */
-  float rate_drop;
-  /*! \brief whether at least one tree should always be dropped during the dropout */
-  bool one_drop;
-  /*! \brief probability of skipping the dropout during an iteration */
-  float skip_drop;
-  /*! \brief learning step size for a time */
-  float learning_rate;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(DartTrainParam) {
-    DMLC_DECLARE_FIELD(sample_type)
-        .set_default(0)
-        .add_enum("uniform", 0)
-        .add_enum("weighted", 1)
-        .describe("Different types of sampling algorithm.");
-    DMLC_DECLARE_FIELD(normalize_type)
-        .set_default(0)
-        .add_enum("tree", 0)
-        .add_enum("forest", 1)
-        .describe("Different types of normalization algorithm.");
-    DMLC_DECLARE_FIELD(rate_drop)
-        .set_range(0.0f, 1.0f)
-        .set_default(0.0f)
-        .describe("Fraction of trees to drop during the dropout.");
-    DMLC_DECLARE_FIELD(one_drop)
-        .set_default(false)
-        .describe("Whether at least one tree should always be dropped during the dropout.");
-    DMLC_DECLARE_FIELD(skip_drop)
-        .set_range(0.0f, 1.0f)
-        .set_default(0.0f)
-        .describe("Probability of skipping the dropout during a boosting iteration.");
-    DMLC_DECLARE_FIELD(learning_rate)
-        .set_lower_bound(0.0f)
-        .set_default(0.3f)
-        .describe("Learning rate(step size) of update.");
-    DMLC_DECLARE_ALIAS(learning_rate, eta);
-  }
-};
-
-// gradient boosted trees
-class GBTree : public GradientBooster {
- public:
-  explicit GBTree(LearnerModelParam const* booster_config) :
-      model_(booster_config) {}
-
-  void Configure(const Args& cfg) override;
-  // Revise `tree_method` and `updater` parameters after seeing the training
-  // data matrix, only useful when tree_method is auto.
-  void PerformTreeMethodHeuristic(DMatrix* fmat);
-  /*! \brief Map `tree_method` parameter to `updater` parameter */
-  void ConfigureUpdaters();
-  void ConfigureWithKnownData(Args const& cfg, DMatrix* fmat);
-
-  /*! \brief Carry out one iteration of boosting */
-  void DoBoost(DMatrix* p_fmat,
-               HostDeviceVector<GradientPair>* in_gpair,
-               PredictionCacheEntry* predt, int iter,
-               float* _last_round_best_loss) override;
-
-  bool UseGPU() const override {
-    return
-        tparam_.predictor == PredictorType::kGPUPredictor ||
-        tparam_.tree_method == TreeMethod::kGPUHist;
-  }
-
-  GBTreeTrainParam const& GetTrainParam() const {
-    return tparam_;
-  }
-
-  void Load(dmlc::Stream* fi) override {
-    model_.Load(fi);
-    this->cfg_.clear();
-  }
-
-  void Save(dmlc::Stream* fo) const override {
-    model_.Save(fo);
-  }
-
-  void LoadConfig(Json const& in) override;
-  void SaveConfig(Json* p_out) const override;
-
-  void SaveModel(Json* p_out) const override;
-  void LoadModel(Json const& in) override;
-
-  bool AllowLazyCheckPoint() const override {
-    return model_.learner_model_param->num_output_group == 1;
-  }
-
-  void PredictBatch(DMatrix* p_fmat,
-                    PredictionCacheEntry* out_preds,
-                    bool training,
-                    unsigned ntree_limit) override;
-
-  void InplacePredict(dmlc::any const &x, float missing,
-                      PredictionCacheEntry *out_preds,
-                      uint32_t layer_begin = 0,
-                      unsigned layer_end = 0) const override {
-    CHECK(configured_);
-    // From here on, layer becomes concrete trees.
-    bst_group_t groups = model_.learner_model_param->num_output_group;
-    uint32_t tree_begin = layer_begin * groups * tparam_.num_parallel_tree;
-    uint32_t tree_end = layer_end * groups * tparam_.num_parallel_tree;
-    if (tree_end == 0 || tree_end > model_.trees.size()) {
-      tree_end = static_cast<uint32_t>(model_.trees.size());
-    }
-    this->GetPredictor()->InplacePredict(x, model_, missing, out_preds,
-                                         tree_begin, tree_end);
-  }
-
-  void PredictInstance(const SparsePage::Inst& inst,
-                       std::vector<bst_float>* out_preds,
-                       unsigned ntree_limit) override {
-    CHECK(configured_);
-    cpu_predictor_->PredictInstance(inst, out_preds, model_,
-                                    ntree_limit);
-  }
-
-  void PredictLeaf(DMatrix* p_fmat,
-                   std::vector<bst_float>* out_preds,
-                   unsigned ntree_limit) override {
-    CHECK(configured_);
-    cpu_predictor_->PredictLeaf(p_fmat, out_preds, model_, ntree_limit);
-  }
-
-  void PredictContribution(DMatrix* p_fmat,
-                           std::vector<bst_float>* out_contribs,
-                           unsigned ntree_limit, bool approximate, int condition,
-                           unsigned condition_feature) override {
-    CHECK(configured_);
-    cpu_predictor_->PredictContribution(p_fmat, out_contribs, model_,
-                                        ntree_limit, nullptr, approximate);
-  }
-
-  void PredictInteractionContributions(DMatrix* p_fmat,
-                                       std::vector<bst_float>* out_contribs,
-                                       unsigned ntree_limit, bool approximate) override {
-    CHECK(configured_);
-    cpu_predictor_->PredictInteractionContributions(p_fmat, out_contribs, model_,
-                                                    ntree_limit, nullptr, approximate);
-  }
-
-  std::vector<std::string> DumpModel(const FeatureMap& fmap,
-                                     bool with_stats,
-                                     std::string format) const override {
-    return model_.DumpModel(fmap, with_stats, format);
-  }
-
- protected:
-  // initialize updater before using them
-  void InitUpdater(Args const& cfg);
-
-  // do group specific group
-  void BoostNewTrees(HostDeviceVector<GradientPair>* gpair,
-                     DMatrix *p_fmat,
-                     int bst_group,
-                     std::vector<std::unique_ptr<RegTree> >* ret, int iter,
-                     float* p_ret_best_losses);
-
-  std::unique_ptr<Predictor> const& GetPredictor(HostDeviceVector<float> const* out_pred = nullptr,
-                                                 DMatrix* f_dmat = nullptr) const;
-
-  // commit new trees all at once
-  virtual void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-                           DMatrix* m,
-                           PredictionCacheEntry* predts, int iter);
-
-  // --- data structure ---
-  GBTreeModel model_;
-  // training parameter
-  GBTreeTrainParam tparam_;
-  // ----training fields----
-  bool showed_updater_warning_ {false};
-  bool specified_updater_   {false};
-  bool configured_ {false};
-  // configurations for tree
-  Args cfg_;
-  // the updaters that can be applied to each of tree
-  std::vector<std::unique_ptr<TreeUpdater>> updaters_;
-  // Predictors
-  std::unique_ptr<Predictor> cpu_predictor_;
-#if defined(XGBOOST_USE_CUDA)
-  std::unique_ptr<Predictor> gpu_predictor_;
-#endif  // defined(XGBOOST_USE_CUDA)
-  common::Monitor monitor_;
-};
-
-}  // namespace gbm
-}  // namespace xgboost
-
-#endif  // XGBOOST_GBM_GBTREE_H_
diff --git a/ml-xgboost/src/gbm/gbtree_model.cc b/ml-xgboost/src/gbm/gbtree_model.cc
deleted file mode 100644
index a533467..0000000
--- a/ml-xgboost/src/gbm/gbtree_model.cc
+++ /dev/null
@@ -1,86 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- */
-#include "xgboost/json.h"
-#include "xgboost/logging.h"
-#include "gbtree_model.h"
-
-namespace xgboost {
-namespace gbm {
-
-void GBTreeModel::Save(dmlc::Stream* fo) const {
-  CHECK_EQ(param.num_trees, static_cast<int32_t>(trees.size()));
-  fo->Write(&param, sizeof(param));
-  for (const auto & tree : trees) {
-    tree->Save(fo);
-  }
-  if (tree_info.size() != 0) {
-    fo->Write(dmlc::BeginPtr(tree_info), sizeof(int32_t) * tree_info.size());
-  }
-}
-
-void GBTreeModel::Load(dmlc::Stream* fi) {
-  CHECK_EQ(fi->Read(&param, sizeof(param)), sizeof(param))
-      << "GBTree: invalid model file";
-  trees.clear();
-  trees_to_update.clear();
-  for (int32_t i = 0; i < param.num_trees; ++i) {
-    std::unique_ptr<RegTree> ptr(new RegTree());
-    ptr->Load(fi);
-    trees.push_back(std::move(ptr));
-  }
-  tree_info.resize(param.num_trees);
-  if (param.num_trees != 0) {
-    CHECK_EQ(
-        fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees),
-        sizeof(int32_t) * param.num_trees);
-  }
-}
-
-void GBTreeModel::SaveModel(Json* p_out) const {
-  auto& out = *p_out;
-  CHECK_EQ(param.num_trees, static_cast<int>(trees.size()));
-  out["gbtree_model_param"] = ToJson(param);
-  std::vector<Json> trees_json;
-  size_t t = 0;
-  for (auto const& tree : trees) {
-    Json tree_json{Object()};
-    tree->SaveModel(&tree_json);
-    // The field is not used in XGBoost, but might be useful for external project.
-    tree_json["id"] = Integer(t);
-    trees_json.emplace_back(tree_json);
-    t++;
-  }
-
-  std::vector<Json> tree_info_json(tree_info.size());
-  for (size_t i = 0; i < tree_info.size(); ++i) {
-    tree_info_json[i] = Integer(tree_info[i]);
-  }
-
-  out["trees"] = Array(std::move(trees_json));
-  out["tree_info"] = Array(std::move(tree_info_json));
-}
-
-void GBTreeModel::LoadModel(Json const& in) {
-  FromJson(in["gbtree_model_param"], &param);
-
-  trees.clear();
-  trees_to_update.clear();
-
-  auto const& trees_json = get<Array const>(in["trees"]);
-  trees.resize(trees_json.size());
-
-  for (size_t t = 0; t < trees.size(); ++t) {
-    trees[t].reset( new RegTree() );
-    trees[t]->LoadModel(trees_json[t]);
-  }
-
-  tree_info.resize(param.num_trees);
-  auto const& tree_info_json = get<Array const>(in["tree_info"]);
-  for (int32_t i = 0; i < param.num_trees; ++i) {
-    tree_info[i] = get<Integer const>(tree_info_json[i]);
-  }
-}
-
-}  // namespace gbm
-}  // namespace xgboost
diff --git a/ml-xgboost/src/gbm/gbtree_model.h b/ml-xgboost/src/gbm/gbtree_model.h
deleted file mode 100644
index 7ac7d8f..0000000
--- a/ml-xgboost/src/gbm/gbtree_model.h
+++ /dev/null
@@ -1,125 +0,0 @@
-/*!
- * Copyright 2017-2019 by Contributors
- * \file gbtree_model.h
- */
-#ifndef XGBOOST_GBM_GBTREE_MODEL_H_
-#define XGBOOST_GBM_GBTREE_MODEL_H_
-
-#include <dmlc/parameter.h>
-#include <dmlc/io.h>
-#include <xgboost/model.h>
-#include <xgboost/tree_model.h>
-#include <xgboost/parameter.h>
-#include <xgboost/learner.h>
-
-#include <memory>
-#include <utility>
-#include <string>
-#include <vector>
-
-namespace xgboost {
-
-class Json;
-
-namespace gbm {
-/*! \brief model parameters */
-struct GBTreeModelParam : public dmlc::Parameter<GBTreeModelParam> {
- public:
-  /*! \brief number of trees */
-  int32_t num_trees;
-  /*! \brief (Deprecated) number of roots */
-  int32_t deprecated_num_roots;
-  /*! \brief number of features to be used by trees */
-  int32_t deprecated_num_feature;
-  /*! \brief pad this space, for backward compatibility reason.*/
-  int32_t pad_32bit;
-  /*! \brief deprecated padding space. */
-  int64_t deprecated_num_pbuffer;
-  // deprecated. use learner_model_param_->num_output_group.
-  int32_t deprecated_num_output_group;
-  /*! \brief size of leaf vector needed in tree */
-  int32_t size_leaf_vector;
-  /*! \brief reserved parameters */
-  int32_t reserved[32];
-
-  /*! \brief constructor */
-  GBTreeModelParam() {
-    std::memset(this, 0, sizeof(GBTreeModelParam));  // FIXME(trivialfis): Why?
-    static_assert(sizeof(GBTreeModelParam) == (4 + 2 + 2 + 32) * sizeof(int32_t),
-                  "64/32 bit compatibility issue");
-    deprecated_num_roots = 1;
-  }
-
-  // declare parameters, only declare those that need to be set.
-  DMLC_DECLARE_PARAMETER(GBTreeModelParam) {
-    DMLC_DECLARE_FIELD(num_trees)
-        .set_lower_bound(0)
-        .set_default(0)
-        .describe("Number of features used for training and prediction.");
-    DMLC_DECLARE_FIELD(size_leaf_vector)
-        .set_lower_bound(0)
-        .set_default(0)
-        .describe("Reserved option for vector tree.");
-  }
-};
-
-struct GBTreeModel : public Model {
- public:
-  explicit GBTreeModel(LearnerModelParam const* learner_model) :
-      learner_model_param{learner_model} {}
-  void Configure(const Args& cfg) {
-    // initialize model parameters if not yet been initialized.
-    if (trees.size() == 0) {
-      param.UpdateAllowUnknown(cfg);
-    }
-  }
-
-  void InitTreesToUpdate() {
-    if (trees_to_update.size() == 0u) {
-      for (auto & tree : trees) {
-        trees_to_update.push_back(std::move(tree));
-      }
-      trees.clear();
-      param.num_trees = 0;
-      tree_info.clear();
-    }
-  }
-
-  void Load(dmlc::Stream* fi);
-  void Save(dmlc::Stream* fo) const;
-
-  void SaveModel(Json* p_out) const override;
-  void LoadModel(Json const& p_out) override;
-
-  std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
-                                     std::string format) const {
-    std::vector<std::string> dump;
-    for (const auto & tree : trees) {
-      dump.push_back(tree->DumpModel(fmap, with_stats, format));
-    }
-    return dump;
-  }
-  void CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
-                   int bst_group) {
-    for (auto & new_tree : new_trees) {
-      trees.push_back(std::move(new_tree));
-      tree_info.push_back(bst_group);
-    }
-    param.num_trees += static_cast<int>(new_trees.size());
-  }
-
-  // base margin
-  LearnerModelParam const* learner_model_param;
-  // model parameter
-  GBTreeModelParam param;
-  /*! \brief vector of trees stored in the model */
-  std::vector<std::unique_ptr<RegTree> > trees;
-  /*! \brief for the update process, a place to keep the initial trees */
-  std::vector<std::unique_ptr<RegTree> > trees_to_update;
-  /*! \brief some information indicator of the tree, reserved */
-  std::vector<int> tree_info;
-};
-}  // namespace gbm
-}  // namespace xgboost
-
-#endif  // XGBOOST_GBM_GBTREE_MODEL_H_
diff --git a/ml-xgboost/src/learner.cc b/ml-xgboost/src/learner.cc
deleted file mode 100644
index fddc279..0000000
--- a/ml-xgboost/src/learner.cc
+++ /dev/null
@@ -1,1091 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file learner.cc
- * \brief Implementation of learning algorithm.
- * \author Tianqi Chen
- */
-#include <dmlc/io.h>
-#include <dmlc/parameter.h>
-#include <dmlc/thread_local.h>
-
-#include <atomic>
-#include <mutex>
-#include <algorithm>
-#include <iomanip>
-#include <limits>
-#include <memory>
-#include <sstream>
-#include <string>
-#include <stack>
-#include <utility>
-#include <vector>
-
-#include "dmlc/any.h"
-#include "xgboost/base.h"
-#include "xgboost/data.h"
-#include "xgboost/model.h"
-#include "xgboost/predictor.h"
-#include "xgboost/feature_map.h"
-#include "xgboost/gbm.h"
-#include "xgboost/generic_parameters.h"
-#include "xgboost/host_device_vector.h"
-#include "xgboost/json.h"
-#include "xgboost/learner.h"
-#include "xgboost/logging.h"
-#include "xgboost/metric.h"
-#include "xgboost/objective.h"
-#include "xgboost/parameter.h"
-
-#include "common/common.h"
-#include "common/io.h"
-#include "common/observer.h"
-#include "common/random.h"
-#include "common/timer.h"
-#include "common/version.h"
-
-namespace {
-
-const char* kMaxDeltaStepDefaultValue = "0.7";
-}  // anonymous namespace
-
-namespace xgboost {
-
-enum class DataSplitMode : int {
-  kAuto = 0, kCol = 1, kRow = 2
-};
-}  // namespace xgboost
-
-DECLARE_FIELD_ENUM_CLASS(xgboost::DataSplitMode);
-
-namespace xgboost {
-// implementation of base learner.
-bool Learner::AllowLazyCheckPoint() const {
-  return gbm_->AllowLazyCheckPoint();
-}
-
-Learner::~Learner() = default;
-
-/*! \brief training parameter for regression
- *
- * Should be deprecated, but still used for being compatible with binary IO.
- * Once it's gone, `LearnerModelParam` should handle transforming `base_margin`
- * with objective by itself.
- */
-struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy> {
-  /* \brief global bias */
-  bst_float base_score;
-  /* \brief number of features  */
-  uint32_t num_feature;
-  /* \brief number of classes, if it is multi-class classification  */
-  int32_t num_class;
-  /*! \brief Model contain additional properties */
-  int32_t contain_extra_attrs;
-  /*! \brief Model contain eval metrics */
-  int32_t contain_eval_metrics;
-  /*! \brief the version of XGBoost. */
-  uint32_t major_version;
-  uint32_t minor_version;
-  /*! \brief reserved field */
-  int reserved[27];
-  /*! \brief constructor */
-  LearnerModelParamLegacy() {
-    std::memset(this, 0, sizeof(LearnerModelParamLegacy));
-    base_score = 0.5f;
-    major_version = std::get<0>(Version::Self());
-    minor_version = std::get<1>(Version::Self());
-    static_assert(sizeof(LearnerModelParamLegacy) == 136,
-                  "Do not change the size of this struct, as it will break binary IO.");
-  }
-  // Skip other legacy fields.
-  Json ToJson() const {
-    Object obj;
-    obj["base_score"] = std::to_string(base_score);
-    obj["num_feature"] = std::to_string(num_feature);
-    obj["num_class"] = std::to_string(num_class);
-    return Json(std::move(obj));
-  }
-  void FromJson(Json const& obj) {
-    auto const& j_param = get<Object const>(obj);
-    std::map<std::string, std::string> m;
-    m["base_score"] = get<String const>(j_param.at("base_score"));
-    m["num_feature"] = get<String const>(j_param.at("num_feature"));
-    m["num_class"] = get<String const>(j_param.at("num_class"));
-    this->Init(m);
-  }
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(LearnerModelParamLegacy) {
-    DMLC_DECLARE_FIELD(base_score)
-        .set_default(0.5f)
-        .describe("Global bias of the model.");
-    DMLC_DECLARE_FIELD(num_feature)
-        .set_default(0)
-        .describe(
-            "Number of features in training data,"
-            " this parameter will be automatically detected by learner.");
-    DMLC_DECLARE_FIELD(num_class).set_default(0).set_lower_bound(0).describe(
-        "Number of class option for multi-class classifier. "
-        " By default equals 0 and corresponds to binary classifier.");
-  }
-};
-
-LearnerModelParam::LearnerModelParam(
-    LearnerModelParamLegacy const &user_param, float base_margin)
-    : base_score{base_margin}, num_feature{user_param.num_feature},
-      num_output_group{user_param.num_class == 0
-                       ? 1
-                       : static_cast<uint32_t>(user_param.num_class)}
-{}
-
-struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
-  // data split mode, can be row, col, or none.
-  DataSplitMode dsplit;
-  // flag to disable default metric
-  int disable_default_eval_metric;
-  // FIXME(trivialfis): The following parameters belong to model itself, but can be
-  // specified by users.  Move them to model parameter once we can get rid of binary IO.
-  std::string booster;
-  std::string objective;
-
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
-    DMLC_DECLARE_FIELD(dsplit)
-        .set_default(DataSplitMode::kAuto)
-        .add_enum("auto", DataSplitMode::kAuto)
-        .add_enum("col", DataSplitMode::kCol)
-        .add_enum("row", DataSplitMode::kRow)
-        .describe("Data split mode for distributed training.");
-    DMLC_DECLARE_FIELD(disable_default_eval_metric)
-        .set_default(0)
-        .describe("Flag to disable default metric. Set to >0 to disable");
-    DMLC_DECLARE_FIELD(booster)
-        .set_default("gbtree")
-        .describe("Gradient booster used for training.");
-    DMLC_DECLARE_FIELD(objective)
-        .set_default("reg:squarederror")
-        .describe("Objective function used for obtaining gradient.");
-  }
-};
-
-
-DMLC_REGISTER_PARAMETER(LearnerModelParamLegacy);
-DMLC_REGISTER_PARAMETER(LearnerTrainParam);
-DMLC_REGISTER_PARAMETER(GenericParameter);
-
-int constexpr GenericParameter::kCpuId;
-
-void GenericParameter::ConfigureGpuId(bool require_gpu) {
-#if defined(XGBOOST_USE_CUDA)
-  if (gpu_id == kCpuId) {  // 0. User didn't specify the `gpu_id'
-    if (require_gpu) {     // 1. `tree_method' or `predictor' or both are using
-                           // GPU.
-      // 2. Use device 0 as default.
-      this->UpdateAllowUnknown(Args{{"gpu_id", "0"}});
-    }
-  }
-
-  // 3. When booster is loaded from a memory image (Python pickle or R
-  // raw model), number of available GPUs could be different.  Wrap around it.
-  int32_t n_gpus = common::AllVisibleGPUs();
-  if (n_gpus == 0) {
-    this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
-  } else if (gpu_id != kCpuId && gpu_id >= n_gpus) {
-    this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(gpu_id % n_gpus)}});
-  }
-#else
-  // Just set it to CPU, don't think about it.
-  this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
-#endif  // defined(XGBOOST_USE_CUDA)
-}
-
-using XGBAPIThreadLocalStore =
-    dmlc::ThreadLocalStore<std::map<Learner const *, XGBAPIThreadLocalEntry>>;
-
-class LearnerConfiguration : public Learner {
- protected:
-  static std::string const kEvalMetric;  // NOLINT
-
- protected:
-  PredictionContainer cache_;
-
- protected:
-  std::atomic<bool> need_configuration_;
-  std::map<std::string, std::string> cfg_;
-  // Stores information like best-iteration for early stopping.
-  std::map<std::string, std::string> attributes_;
-  common::Monitor monitor_;
-  LearnerModelParamLegacy mparam_;
-  LearnerModelParam learner_model_param_;
-  LearnerTrainParam tparam_;
-  std::vector<std::string> metric_names_;
-  std::mutex config_lock_;
-
- public:
-  explicit LearnerConfiguration(std::vector<std::shared_ptr<DMatrix> > cache)
-      : need_configuration_{true} {
-    monitor_.Init("Learner");
-    for (std::shared_ptr<DMatrix> const& d : cache) {
-      cache_.Cache(d, GenericParameter::kCpuId);
-    }
-  }
-  // Configuration before data is known.
-
-  void Configure() override {
-    // Varient of double checked lock
-    if (!this->need_configuration_) { return; }
-    std::lock_guard<std::mutex> gard(config_lock_);
-    if (!this->need_configuration_) { return; }
-
-    monitor_.Start("Configure");
-    auto old_tparam = tparam_;
-    Args args = {cfg_.cbegin(), cfg_.cend()};
-
-    tparam_.UpdateAllowUnknown(args);
-    auto mparam_backup = mparam_;
-    mparam_.UpdateAllowUnknown(args);
-    generic_parameters_.UpdateAllowUnknown(args);
-    generic_parameters_.CheckDeprecated();
-
-    ConsoleLogger::Configure(args);
-    if (generic_parameters_.nthread != 0) {
-      omp_set_num_threads(generic_parameters_.nthread);
-    }
-
-    // add additional parameters
-    // These are cosntraints that need to be satisfied.
-    if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
-      tparam_.dsplit = DataSplitMode::kRow;
-    }
-
-    // set seed only before the model is initialized
-    common::GlobalRandom().seed(generic_parameters_.seed);
-
-    // must precede configure gbm since num_features is required for gbm
-    this->ConfigureNumFeatures();
-    args = {cfg_.cbegin(), cfg_.cend()};  // renew
-    this->ConfigureObjective(old_tparam, &args);
-
-    // Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
-    // After 1.0.0 we save the value provided by user and keep it immutable instead.  To
-    // keep the stability, we initialize it in binary LoadModel instead of configuration.
-    // Under what condition should we omit the transformation:
-    //
-    // - base_score is loaded from old binary model.
-    //
-    // What are the other possible conditions:
-    //
-    // - model loaded from new binary or JSON.
-    // - model is created from scratch.
-    // - model is configured second time due to change of parameter
-    if (!learner_model_param_.Initialized() || mparam_.base_score != mparam_backup.base_score) {
-      learner_model_param_ = LearnerModelParam(mparam_,
-                                               obj_->ProbToMargin(mparam_.base_score));
-    }
-
-    this->ConfigureGBM(old_tparam, args);
-    generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
-
-    this->ConfigureMetrics(args);
-
-    this->need_configuration_ = false;
-    if (generic_parameters_.validate_parameters) {
-      this->ValidateParameters();
-    }
-
-    // FIXME(trivialfis): Clear the cache once binary IO is gone.
-    monitor_.Stop("Configure");
-  }
-
-  void LoadConfig(Json const& in) override {
-    CHECK(IsA<Object>(in));
-    Version::Load(in, true);
-
-    auto const& learner_parameters = get<Object>(in["learner"]);
-    FromJson(learner_parameters.at("learner_train_param"), &tparam_);
-
-    auto const& gradient_booster = learner_parameters.at("gradient_booster");
-
-    auto const& objective_fn = learner_parameters.at("objective");
-    if (!obj_) {
-      obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
-    }
-    obj_->LoadConfig(objective_fn);
-
-    tparam_.booster = get<String>(gradient_booster["name"]);
-    if (!gbm_) {
-      gbm_.reset(GradientBooster::Create(tparam_.booster,
-                                         &generic_parameters_, &learner_model_param_));
-    }
-    gbm_->LoadConfig(gradient_booster);
-
-    auto const& j_metrics = learner_parameters.at("metrics");
-    auto n_metrics = get<Array const>(j_metrics).size();
-    metric_names_.resize(n_metrics);
-    metrics_.resize(n_metrics);
-    for (size_t i = 0; i < n_metrics; ++i) {
-      metric_names_[i]= get<String>(j_metrics[i]);
-      metrics_[i] = std::unique_ptr<Metric>(
-          Metric::Create(metric_names_[i], &generic_parameters_));
-    }
-
-    FromJson(learner_parameters.at("generic_param"), &generic_parameters_);
-    // make sure the GPU ID is valid in new environment before start running configure.
-    generic_parameters_.ConfigureGpuId(false);
-
-    this->need_configuration_ = true;
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    CHECK(!this->need_configuration_) << "Call Configure before saving model.";
-    Version::Save(p_out);
-    Json& out { *p_out };
-    // parameters
-    out["learner"] = Object();
-    auto& learner_parameters = out["learner"];
-
-    learner_parameters["learner_train_param"] = ToJson(tparam_);
-    learner_parameters["learner_model_param"] = mparam_.ToJson();
-    learner_parameters["gradient_booster"] = Object();
-    auto& gradient_booster = learner_parameters["gradient_booster"];
-    gbm_->SaveConfig(&gradient_booster);
-
-    learner_parameters["objective"] = Object();
-    auto& objective_fn = learner_parameters["objective"];
-    obj_->SaveConfig(&objective_fn);
-
-    std::vector<Json> metrics(metrics_.size());
-    for (size_t i = 0; i < metrics_.size(); ++i) {
-      metrics[i] = String(metrics_[i]->Name());
-    }
-    learner_parameters["metrics"] = Array(std::move(metrics));
-
-    learner_parameters["generic_param"] = ToJson(generic_parameters_);
-  }
-
-  void SetParam(const std::string& key, const std::string& value) override {
-    this->need_configuration_ = true;
-    if (key == kEvalMetric) {
-      if (std::find(metric_names_.cbegin(), metric_names_.cend(),
-                    value) == metric_names_.cend()) {
-        metric_names_.emplace_back(value);
-      }
-    } else {
-      cfg_[key] = value;
-    }
-  }
-  // Short hand for setting multiple parameters
-  void SetParams(std::vector<std::pair<std::string, std::string>> const& args) override {
-    for (auto const& kv : args) {
-      this->SetParam(kv.first, kv.second);
-    }
-  }
-
-  void SetAttr(const std::string& key, const std::string& value) override {
-    attributes_[key] = value;
-    mparam_.contain_extra_attrs = 1;
-  }
-
-  bool GetAttr(const std::string& key, std::string* out) const override {
-    auto it = attributes_.find(key);
-    if (it == attributes_.end()) return false;
-    *out = it->second;
-    return true;
-  }
-
-  bool DelAttr(const std::string& key) override {
-    auto it = attributes_.find(key);
-    if (it == attributes_.end()) { return false; }
-    attributes_.erase(it);
-    return true;
-  }
-
-  std::vector<std::string> GetAttrNames() const override {
-    std::vector<std::string> out;
-    for (auto const& kv : attributes_) {
-      out.emplace_back(kv.first);
-    }
-    return out;
-  }
-
-  const std::map<std::string, std::string>& GetConfigurationArguments() const override {
-    return cfg_;
-  }
-
-  GenericParameter const& GetGenericParameter() const override {
-    return generic_parameters_;
-  }
-
- private:
-  void ValidateParameters() {
-    Json config { Object() };
-    this->SaveConfig(&config);
-    std::stack<Json> stack;
-    stack.push(config);
-    std::string const postfix{"_param"};
-
-    auto is_parameter = [&postfix](std::string const &key) {
-      return key.size() > postfix.size() &&
-             std::equal(postfix.rbegin(), postfix.rend(), key.rbegin());
-    };
-
-    // Extract all parameters
-    std::vector<std::string> keys;
-    while (!stack.empty()) {
-      auto j_obj = stack.top();
-      stack.pop();
-      auto const &obj = get<Object const>(j_obj);
-
-      for (auto const &kv : obj) {
-        if (is_parameter(kv.first)) {
-          auto parameter = get<Object const>(kv.second);
-          std::transform(parameter.begin(), parameter.end(), std::back_inserter(keys),
-                         [](std::pair<std::string const&, Json const&> const& kv) {
-                           return kv.first;
-                         });
-        } else if (IsA<Object>(kv.second)) {
-          stack.push(kv.second);
-        }
-      }
-    }
-
-    keys.emplace_back(kEvalMetric);
-    keys.emplace_back("verbosity");
-    keys.emplace_back("num_output_group");
-
-    std::sort(keys.begin(), keys.end());
-
-    std::vector<std::string> provided;
-    for (auto const &kv : cfg_) {
-      // FIXME(trivialfis): Make eval_metric a training parameter.
-      provided.push_back(kv.first);
-    }
-    std::sort(provided.begin(), provided.end());
-
-    std::vector<std::string> diff;
-    std::set_difference(provided.begin(), provided.end(), keys.begin(),
-                        keys.end(), std::back_inserter(diff));
-    if (diff.size() != 0) {
-      std::stringstream ss;
-      ss << "\nParameters: { ";
-      for (size_t i = 0; i < diff.size() - 1; ++i) {
-        ss << diff[i] << ", ";
-      }
-      ss << diff.back();
-      ss << R"W( } might not be used.
-
-  This may not be accurate due to some parameters are only used in language bindings but
-  passed down to XGBoost core.  Or some parameters are not used but slip through this
-  verification. Please open an issue if you find above cases.
-
-)W";
-      LOG(WARNING) << ss.str();
-    }
-  }
-
-  void ConfigureNumFeatures() {
-    // Compute number of global features if parameter not already set
-    if (mparam_.num_feature == 0) {
-      // TODO(hcho3): Change num_feature to 64-bit integer
-      unsigned num_feature = 0;
-      for (auto& matrix : cache_.Container()) {
-        CHECK(matrix.first);
-        CHECK(!matrix.second.ref.expired());
-        const uint64_t num_col = matrix.first->Info().num_col_;
-        CHECK_LE(num_col,
-                 static_cast<uint64_t>(std::numeric_limits<unsigned>::max()))
-            << "Unfortunately, XGBoost does not support data matrices with "
-            << std::numeric_limits<unsigned>::max() << " features or greater";
-        num_feature = std::max(num_feature, static_cast<uint32_t>(num_col));
-      }
-
-      rabit::Allreduce<rabit::op::Max>(&num_feature, 1, nullptr, nullptr,
-                                       "num_feature");
-      if (num_feature > mparam_.num_feature) {
-        mparam_.num_feature = num_feature;
-      }
-    }
-    CHECK_NE(mparam_.num_feature, 0)
-        << "0 feature is supplied.  Are you using raw Booster interface?";
-    // Remove these once binary IO is gone.
-    cfg_["num_feature"] = common::ToString(mparam_.num_feature);
-    cfg_["num_class"] = common::ToString(mparam_.num_class);
-  }
-
-  void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
-    if (gbm_ == nullptr || old.booster != tparam_.booster) {
-      gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
-                                         &learner_model_param_));
-    }
-    gbm_->Configure(args);
-  }
-
-  void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {
-    // Once binary IO is gone, NONE of these config is useful.
-    if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0" &&
-        tparam_.objective != "multi:softprob") {
-      cfg_["num_output_group"] = cfg_["num_class"];
-      if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
-        tparam_.objective = "multi:softmax";
-      }
-    }
-
-    if (cfg_.find("max_delta_step") == cfg_.cend() &&
-        cfg_.find("objective") != cfg_.cend() &&
-        tparam_.objective == "count:poisson") {
-      // max_delta_step is a duplicated parameter in Poisson regression and tree param.
-      // Rename one of them once binary IO is gone.
-      cfg_["max_delta_step"] = kMaxDeltaStepDefaultValue;
-    }
-    if (obj_ == nullptr || tparam_.objective != old.objective) {
-      obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
-    }
-    auto& args = *p_args;
-    args = {cfg_.cbegin(), cfg_.cend()};  // renew
-    obj_->Configure(args);
-  }
-
-  void ConfigureMetrics(Args const& args) {
-    for (auto const& name : metric_names_) {
-      auto DupCheck = [&name](std::unique_ptr<Metric> const& m) {
-                        return m->Name() != name;
-                      };
-      if (std::all_of(metrics_.begin(), metrics_.end(), DupCheck)) {
-        metrics_.emplace_back(std::unique_ptr<Metric>(Metric::Create(name, &generic_parameters_)));
-        mparam_.contain_eval_metrics = 1;
-      }
-    }
-    for (auto& p_metric : metrics_) {
-      p_metric->Configure(args);
-    }
-  }
-};
-
-std::string const LearnerConfiguration::kEvalMetric {"eval_metric"};  // NOLINT
-
-class LearnerIO : public LearnerConfiguration {
- private:
-  std::set<std::string> saved_configs_ = {"num_round"};
-  // Used to identify the offset of JSON string when
-  // `enable_experimental_json_serialization' is set to false.  Will be removed once JSON
-  // takes over.
-  std::string const serialisation_header_ { u8"CONFIG-offset:" };
-
- public:
-  explicit LearnerIO(std::vector<std::shared_ptr<DMatrix> > cache) :
-      LearnerConfiguration{cache} {}
-
-  void LoadModel(Json const& in) override {
-    CHECK(IsA<Object>(in));
-    Version::Load(in, false);
-    auto const& learner = get<Object>(in["learner"]);
-    mparam_.FromJson(learner.at("learner_model_param"));
-
-    auto const& objective_fn = learner.at("objective");
-
-    std::string name = get<String>(objective_fn["name"]);
-    tparam_.UpdateAllowUnknown(Args{{"objective", name}});
-    obj_.reset(ObjFunction::Create(name, &generic_parameters_));
-    obj_->LoadConfig(objective_fn);
-
-    auto const& gradient_booster = learner.at("gradient_booster");
-    name = get<String>(gradient_booster["name"]);
-    tparam_.UpdateAllowUnknown(Args{{"booster", name}});
-    gbm_.reset(GradientBooster::Create(tparam_.booster,
-                                       &generic_parameters_, &learner_model_param_));
-    gbm_->LoadModel(gradient_booster);
-
-    auto const& j_attributes = get<Object const>(learner.at("attributes"));
-    attributes_.clear();
-    for (auto const& kv : j_attributes) {
-      attributes_[kv.first] = get<String const>(kv.second);
-    }
-
-    this->need_configuration_ = true;
-  }
-
-  void SaveModel(Json* p_out) const override {
-    CHECK(!this->need_configuration_) << "Call Configure before saving model.";
-
-    Version::Save(p_out);
-    Json& out { *p_out };
-
-    out["learner"] = Object();
-    auto& learner = out["learner"];
-
-    learner["learner_model_param"] = mparam_.ToJson();
-    learner["gradient_booster"] = Object();
-    auto& gradient_booster = learner["gradient_booster"];
-    gbm_->SaveModel(&gradient_booster);
-
-    learner["objective"] = Object();
-    auto& objective_fn = learner["objective"];
-    obj_->SaveConfig(&objective_fn);
-
-    learner["attributes"] = Object();
-    for (auto const& kv : attributes_) {
-      learner["attributes"][kv.first] = String(kv.second);
-    }
-  }
-  // About to be deprecated by JSON format
-  void LoadModel(dmlc::Stream* fi) override {
-    generic_parameters_.UpdateAllowUnknown(Args{});
-    tparam_.Init(std::vector<std::pair<std::string, std::string>>{});
-    // TODO(tqchen) mark deprecation of old format.
-    common::PeekableInStream fp(fi);
-
-    // backward compatible header check.
-    std::string header;
-    header.resize(4);
-    if (fp.PeekRead(&header[0], 4) == 4) {
-      CHECK_NE(header, "bs64")
-          << "Base64 format is no longer supported in brick.";
-      if (header == "binf") {
-        CHECK_EQ(fp.Read(&header[0], 4), 4U);
-      }
-    }
-
-    if (header[0] == '{') {
-      // Dispatch to JSON
-      auto json_stream = common::FixedSizeStream(&fp);
-      std::string buffer;
-      json_stream.Take(&buffer);
-      auto model = Json::Load({buffer.c_str(), buffer.size()});
-      this->LoadModel(model);
-      return;
-    }
-    // use the peekable reader.
-    fi = &fp;
-    // read parameter
-    CHECK_EQ(fi->Read(&mparam_, sizeof(mparam_)), sizeof(mparam_))
-        << "BoostLearner: wrong model format";
-
-    CHECK(fi->Read(&tparam_.objective)) << "BoostLearner: wrong model format";
-    CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";
-
-    obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
-    gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
-                                       &learner_model_param_));
-    gbm_->Load(fi);
-    if (mparam_.contain_extra_attrs != 0) {
-      std::vector<std::pair<std::string, std::string> > attr;
-      fi->Read(&attr);
-      for (auto& kv : attr) {
-        const std::string prefix = "SAVED_PARAM_";
-        if (kv.first.find(prefix) == 0) {
-          const std::string saved_param = kv.first.substr(prefix.length());
-          if (saved_configs_.find(saved_param) != saved_configs_.end()) {
-            cfg_[saved_param] = kv.second;
-          }
-        }
-      }
-      attributes_ = std::map<std::string, std::string>(attr.begin(), attr.end());
-    }
-    bool warn_old_model { false };
-    if (attributes_.find("count_poisson_max_delta_step") != attributes_.cend()) {
-      // Loading model from < 1.0.0, objective is not saved.
-      cfg_["max_delta_step"] = attributes_.at("count_poisson_max_delta_step");
-      attributes_.erase("count_poisson_max_delta_step");
-      warn_old_model = true;
-    } else {
-      warn_old_model = false;
-    }
-
-    if (mparam_.major_version >= 1) {
-      learner_model_param_ = LearnerModelParam(mparam_,
-                                               obj_->ProbToMargin(mparam_.base_score));
-    } else {
-      // Before 1.0.0, base_score is saved as a transformed value, and there's no version
-      // attribute in the saved model.
-      learner_model_param_ = LearnerModelParam(mparam_, mparam_.base_score);
-      warn_old_model = true;
-    }
-    if (attributes_.find("objective") != attributes_.cend()) {
-      auto obj_str = attributes_.at("objective");
-      auto j_obj = Json::Load({obj_str.c_str(), obj_str.size()});
-      obj_->LoadConfig(j_obj);
-      attributes_.erase("objective");
-    } else {
-      warn_old_model = true;
-    }
-    if (attributes_.find("metrics") != attributes_.cend()) {
-      auto metrics_str = attributes_.at("metrics");
-      std::vector<std::string> names { common::Split(metrics_str, ';') };
-      attributes_.erase("metrics");
-      for (auto const& n : names) {
-        this->SetParam(kEvalMetric, n);
-      }
-    }
-
-    if (warn_old_model) {
-      LOG(WARNING) << "Loading model from XGBoost < 1.0.0, consider saving it "
-                      "again for improved compatibility";
-    }
-
-    // Renew the version.
-    mparam_.major_version = std::get<0>(Version::Self());
-    mparam_.minor_version = std::get<1>(Version::Self());
-
-    cfg_["num_class"] = common::ToString(mparam_.num_class);
-    cfg_["num_feature"] = common::ToString(mparam_.num_feature);
-
-    auto n = tparam_.__DICT__();
-    cfg_.insert(n.cbegin(), n.cend());
-
-    // copy dsplit from config since it will not run again during restore
-    if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
-      tparam_.dsplit = DataSplitMode::kRow;
-    }
-
-    this->need_configuration_ = true;
-  }
-
-  // Save model into binary format.  The code is about to be deprecated by more robust
-  // JSON serialization format.  This function is uneffected by
-  // `enable_experimental_json_serialization` as user might enable this flag for pickle
-  // while still want a binary output.  As we are progressing at replacing the binary
-  // format, there's no need to put too much effort on it.
-  void SaveModel(dmlc::Stream* fo) const override {
-    LearnerModelParamLegacy mparam = mparam_;  // make a copy to potentially modify
-    std::vector<std::pair<std::string, std::string> > extra_attr;
-    mparam.contain_extra_attrs = 1;
-
-    {
-      std::vector<std::string> saved_params;
-      // check if rabit_bootstrap_cache were set to non zero before adding to checkpoint
-      if (cfg_.find("rabit_bootstrap_cache") != cfg_.end() &&
-        (cfg_.find("rabit_bootstrap_cache"))->second != "0") {
-        std::copy(saved_configs_.begin(), saved_configs_.end(),
-                  std::back_inserter(saved_params));
-      }
-      for (const auto& key : saved_params) {
-        auto it = cfg_.find(key);
-        if (it != cfg_.end()) {
-          mparam.contain_extra_attrs = 1;
-          extra_attr.emplace_back("SAVED_PARAM_" + key, it->second);
-        }
-      }
-    }
-    {
-      // Similar to JSON model IO, we save the objective.
-      Json j_obj { Object() };
-      obj_->SaveConfig(&j_obj);
-      std::string obj_doc;
-      Json::Dump(j_obj, &obj_doc);
-      extra_attr.emplace_back("objective", obj_doc);
-    }
-    // As of 1.0.0, JVM Package and R Package uses Save/Load model for serialization.
-    // Remove this part once they are ported to use actual serialization methods.
-    if (mparam.contain_eval_metrics != 0) {
-      std::stringstream os;
-      for (auto& ev : metrics_) {
-        os << ev->Name() << ";";
-      }
-      extra_attr.emplace_back("metrics", os.str());
-    }
-    std::string header {"binf"};
-    fo->Write(header.data(), 4);
-    fo->Write(&mparam, sizeof(LearnerModelParamLegacy));
-    fo->Write(tparam_.objective);
-    fo->Write(tparam_.booster);
-    gbm_->Save(fo);
-    if (mparam.contain_extra_attrs != 0) {
-      std::map<std::string, std::string> attr(attributes_);
-      for (const auto& kv : extra_attr) {
-        attr[kv.first] = kv.second;
-      }
-      fo->Write(std::vector<std::pair<std::string, std::string>>(
-          attr.begin(), attr.end()));
-    }
-  }
-
-  void Save(dmlc::Stream* fo) const override {
-    if (generic_parameters_.enable_experimental_json_serialization) {
-      Json memory_snapshot{Object()};
-      memory_snapshot["Model"] = Object();
-      auto &model = memory_snapshot["Model"];
-      this->SaveModel(&model);
-      memory_snapshot["Config"] = Object();
-      auto &config = memory_snapshot["Config"];
-      this->SaveConfig(&config);
-      std::string out_str;
-      Json::Dump(memory_snapshot, &out_str);
-      fo->Write(out_str.c_str(), out_str.size());
-    } else {
-      std::string binary_buf;
-      common::MemoryBufferStream s(&binary_buf);
-      this->SaveModel(&s);
-      Json config{ Object() };
-      // Do not use std::size_t as it's not portable.
-      int64_t const json_offset = binary_buf.size();
-      this->SaveConfig(&config);
-      std::string config_str;
-      Json::Dump(config, &config_str);
-      // concatonate the model and config at final output, it's a temporary solution for
-      // continuing support for binary model format
-      fo->Write(&serialisation_header_[0], serialisation_header_.size());
-      fo->Write(&json_offset, sizeof(json_offset));
-      fo->Write(&binary_buf[0], binary_buf.size());
-      fo->Write(&config_str[0], config_str.size());
-    }
-  }
-
-  void Load(dmlc::Stream* fi) override {
-    common::PeekableInStream fp(fi);
-    char c {0};
-    fp.PeekRead(&c, 1);
-    if (c == '{') {
-      std::string buffer;
-      common::FixedSizeStream{&fp}.Take(&buffer);
-      auto memory_snapshot = Json::Load({buffer.c_str(), buffer.size()});
-      this->LoadModel(memory_snapshot["Model"]);
-      this->LoadConfig(memory_snapshot["Config"]);
-    } else {
-      std::string header;
-      header.resize(serialisation_header_.size());
-      CHECK_EQ(fp.Read(&header[0], header.size()), serialisation_header_.size());
-      // Avoid printing the content in loaded header, which might be random binary code.
-      CHECK(header == serialisation_header_)  // NOLINT
-          << R"doc(
-
-  If you are loading a serialized model (like pickle in Python) generated by older
-  XGBoost, please export the model by calling `Booster.save_model` from that version
-  first, then load it back in current version.  There's a simple script for helping
-  the process. See:
-
-    https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
-
-  for reference to the script, and more details about differences between saving model and
-  serializing.
-
-)doc";
-      int64_t sz {-1};
-      CHECK_EQ(fp.Read(&sz, sizeof(sz)), sizeof(sz));
-      CHECK_GT(sz, 0);
-      size_t json_offset = static_cast<size_t>(sz);
-      std::string buffer;
-      common::FixedSizeStream{&fp}.Take(&buffer);
-
-      common::MemoryFixSizeBuffer binary_buf(&buffer[0], json_offset);
-      this->LoadModel(&binary_buf);
-
-      auto config = Json::Load({buffer.c_str() + json_offset, buffer.size() - json_offset});
-      this->LoadConfig(config);
-    }
-  }
-};
-
-/*!
- * \brief learner that performs gradient boosting for a specific objective
- * function. It does training and prediction.
- */
-class LearnerImpl : public LearnerIO {
- public:
-  explicit LearnerImpl(std::vector<std::shared_ptr<DMatrix> > cache)
-      : LearnerIO{cache} {}
-  ~LearnerImpl() override {
-    auto local_map = XGBAPIThreadLocalStore::Get();
-    if (local_map->find(this) != local_map->cend()) {
-      local_map->erase(this);
-    }
-  }
-  // Configuration before data is known.
-  void CheckDataSplitMode() {
-    if (rabit::IsDistributed()) {
-      CHECK(tparam_.dsplit != DataSplitMode::kAuto)
-        << "Precondition violated; dsplit cannot be 'auto' in distributed mode";
-      if (tparam_.dsplit == DataSplitMode::kCol) {
-        LOG(FATAL) << "Column-wise data split is currently not supported.";
-      }
-    }
-  }
-
-  std::vector<std::string> DumpModel(const FeatureMap& fmap,
-                                     bool with_stats,
-                                     std::string format) override {
-    this->Configure();
-    return gbm_->DumpModel(fmap, with_stats, format);
-  }
-
-  void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train, float* p_last_round_best_loss) override {
-    monitor_.Start("UpdateOneIter");
-    TrainingObserver::Instance().Update(iter);
-    this->Configure();
-    if (generic_parameters_.seed_per_iteration || rabit::IsDistributed()) {
-      common::GlobalRandom().seed(generic_parameters_.seed * kRandSeedMagic + iter);
-    }
-    this->CheckDataSplitMode();
-    this->ValidateDMatrix(train.get());
-
-    auto& predt = this->cache_.Cache(train, generic_parameters_.gpu_id);
-
-    monitor_.Start("PredictRaw");
-    this->PredictRaw(train.get(), &predt, true);
-    TrainingObserver::Instance().Observe(predt.predictions, "Predictions");
-    monitor_.Stop("PredictRaw");
-
-    monitor_.Start("GetGradient");
-    obj_->GetGradient(predt.predictions, train->Info(), iter, &gpair_);
-    monitor_.Stop("GetGradient");
-    TrainingObserver::Instance().Observe(gpair_, "Gradients");
-
-    gbm_->DoBoost(train.get(), &gpair_, &predt, iter, p_last_round_best_loss);
-    monitor_.Stop("UpdateOneIter");
-  }
-
-
-  void BoostOneIter(int iter, std::shared_ptr<DMatrix> train,
-                    HostDeviceVector<GradientPair>* in_gpair, 
-                    float* p_last_round_best_loss) override {
-    monitor_.Start("BoostOneIter");
-    this->Configure();
-    if (generic_parameters_.seed_per_iteration || rabit::IsDistributed()) {
-      common::GlobalRandom().seed(generic_parameters_.seed * kRandSeedMagic + iter);
-    }
-    this->CheckDataSplitMode();
-    this->ValidateDMatrix(train.get());
-    this->cache_.Cache(train, generic_parameters_.gpu_id);
-
-    gbm_->DoBoost(train.get(), in_gpair, &cache_.Entry(train.get()), iter, p_last_round_best_loss);
-    monitor_.Stop("BoostOneIter");
-  }
-
-  std::string EvalOneIter(int iter,
-                          const std::vector<std::shared_ptr<DMatrix>>& data_sets,
-                          const std::vector<std::string>& data_names) override {
-    monitor_.Start("EvalOneIter");
-    this->Configure();
-
-    std::ostringstream os;
-    os << '[' << iter << ']' << std::setiosflags(std::ios::fixed);
-    if (metrics_.size() == 0 && tparam_.disable_default_eval_metric <= 0) {
-      metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric(), &generic_parameters_));
-      metrics_.back()->Configure({cfg_.begin(), cfg_.end()});
-    }
-    for (size_t i = 0; i < data_sets.size(); ++i) {
-      std::shared_ptr<DMatrix> m = data_sets[i];
-      auto &predt = this->cache_.Cache(m, generic_parameters_.gpu_id);
-      this->ValidateDMatrix(m.get());
-      this->PredictRaw(m.get(), &predt, false);
-
-      auto &out = output_predictions_.Cache(m, generic_parameters_.gpu_id).predictions;
-      out.Resize(predt.predictions.Size());
-      out.Copy(predt.predictions);
-
-      obj_->EvalTransform(&out);
-      for (auto& ev : metrics_) {
-        os << '\t' << data_names[i] << '-' << ev->Name() << ':'
-           << ev->Eval(out, m->Info(), tparam_.dsplit == DataSplitMode::kRow);
-      }
-    }
-
-    monitor_.Stop("EvalOneIter");
-    return os.str();
-  }
-
-  void Predict(std::shared_ptr<DMatrix> data, bool output_margin,
-               HostDeviceVector<bst_float>* out_preds, unsigned ntree_limit,
-               bool training,
-               bool pred_leaf, bool pred_contribs, bool approx_contribs,
-               bool pred_interactions) override {
-    int multiple_predictions = static_cast<int>(pred_leaf) +
-                               static_cast<int>(pred_interactions) +
-                               static_cast<int>(pred_contribs);
-    this->Configure();
-    CHECK_LE(multiple_predictions, 1) << "Perform one kind of prediction at a time.";
-    if (pred_contribs) {
-      gbm_->PredictContribution(data.get(), &out_preds->HostVector(), ntree_limit, approx_contribs);
-    } else if (pred_interactions) {
-      gbm_->PredictInteractionContributions(data.get(), &out_preds->HostVector(), ntree_limit,
-                                            approx_contribs);
-    } else if (pred_leaf) {
-      gbm_->PredictLeaf(data.get(), &out_preds->HostVector(), ntree_limit);
-    } else {
-      auto& prediction = cache_.Cache(data, generic_parameters_.gpu_id);
-      this->PredictRaw(data.get(), &prediction, training, ntree_limit);
-      // Copy the prediction cache to output prediction. out_preds comes from C API
-      out_preds->SetDevice(generic_parameters_.gpu_id);
-      out_preds->Resize(prediction.predictions.Size());
-      out_preds->Copy(prediction.predictions);
-      if (!output_margin) {
-        obj_->PredTransform(out_preds);
-      }
-    }
-  }
-
-  XGBAPIThreadLocalEntry& GetThreadLocal() const override {
-    return (*XGBAPIThreadLocalStore::Get())[this];
-  }
-
-  void InplacePredict(dmlc::any const &x, std::string const &type,
-                      float missing, HostDeviceVector<bst_float> **out_preds,
-                      uint32_t layer_begin = 0, uint32_t layer_end = 0) override {
-    this->Configure();
-    auto& out_predictions = this->GetThreadLocal().prediction_entry;
-    this->gbm_->InplacePredict(x, missing, &out_predictions, layer_begin,
-                               layer_end);
-    if (type == "value") {
-      obj_->PredTransform(&out_predictions.predictions);
-    } else if (type == "margin") {
-    } else {
-      LOG(FATAL) << "Unsupported prediction type:" << type;
-    }
-    *out_preds = &out_predictions.predictions;
-  }
-
-  const std::map<std::string, std::string>& GetConfigurationArguments() const override {
-    return cfg_;
-  }
-
- protected:
-  /*!
-   * \brief get un-transformed prediction
-   * \param data training data matrix
-   * \param out_preds output vector that stores the prediction
-   * \param ntree_limit limit number of trees used for boosted tree
-   *   predictor, when it equals 0, this means we are using all the trees
-   * \param training allow dropout when the DART booster is being used
-   */
-  void PredictRaw(DMatrix* data, PredictionCacheEntry* out_preds,
-                  bool training,
-                  unsigned ntree_limit = 0) const {
-    CHECK(gbm_ != nullptr) << "Predict must happen after Load or configuration";
-    this->ValidateDMatrix(data);
-    gbm_->PredictBatch(data, out_preds, training, ntree_limit);
-  }
-
-  void ValidateDMatrix(DMatrix* p_fmat) const {
-    MetaInfo const& info = p_fmat->Info();
-    info.Validate(generic_parameters_.gpu_id);
-
-    auto const row_based_split = [this]() {
-      return tparam_.dsplit == DataSplitMode::kRow ||
-             tparam_.dsplit == DataSplitMode::kAuto;
-    };
-    if (row_based_split()) {
-      CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
-          << "Number of columns does not match number of features in booster.";
-    }
-  }
-
- private:
-  /*! \brief random number transformation seed. */
-  static int32_t constexpr kRandSeedMagic = 127;
-  // gradient pairs
-  HostDeviceVector<GradientPair> gpair_;
-  /*! \brief Temporary storage to prediction.  Useful for storing data transformed by
-   *  objective function */
-  PredictionContainer output_predictions_;
-};
-
-constexpr int32_t LearnerImpl::kRandSeedMagic;
-
-Learner* Learner::Create(
-    const std::vector<std::shared_ptr<DMatrix> >& cache_data) {
-  return new LearnerImpl(cache_data);
-}
-}  // namespace xgboost
diff --git a/ml-xgboost/src/linear/coordinate_common.h b/ml-xgboost/src/linear/coordinate_common.h
deleted file mode 100644
index 45c7a2b..0000000
--- a/ml-xgboost/src/linear/coordinate_common.h
+++ /dev/null
@@ -1,480 +0,0 @@
-/*!
- * Copyright 2018 by Contributors
- * \author Rory Mitchell
- */
-#pragma once
-#include <algorithm>
-#include <string>
-#include <utility>
-#include <vector>
-#include <limits>
-
-#include "xgboost/data.h"
-#include "xgboost/parameter.h"
-#include "./param.h"
-#include "../gbm/gblinear_model.h"
-#include "../common/random.h"
-
-namespace xgboost {
-namespace linear {
-
-struct CoordinateParam : public XGBoostParameter<CoordinateParam> {
-  int top_k;
-  DMLC_DECLARE_PARAMETER(CoordinateParam) {
-    DMLC_DECLARE_FIELD(top_k)
-        .set_lower_bound(0)
-        .set_default(0)
-        .describe("The number of top features to select in 'thrifty' feature_selector. "
-                  "The value of zero means using all the features.");
-  }
-};
-
-/**
- * \brief Calculate change in weight for a given feature. Applies l1/l2 penalty normalised by the
- *        number of training instances.
- *
- * \param sum_grad            The sum gradient.
- * \param sum_hess            The sum hess.
- * \param w                   The weight.
- * \param reg_alpha           Unnormalised L1 penalty.
- * \param reg_lambda          Unnormalised L2 penalty.
- *
- * \return  The weight update.
- */
-inline double CoordinateDelta(double sum_grad, double sum_hess, double w,
-                              double reg_alpha, double reg_lambda) {
-  if (sum_hess < 1e-5f) return 0.0f;
-  const double sum_grad_l2 = sum_grad + reg_lambda * w;
-  const double sum_hess_l2 = sum_hess + reg_lambda;
-  const double tmp = w - sum_grad_l2 / sum_hess_l2;
-  if (tmp >= 0) {
-    return std::max(-(sum_grad_l2 + reg_alpha) / sum_hess_l2, -w);
-  } else {
-    return std::min(-(sum_grad_l2 - reg_alpha) / sum_hess_l2, -w);
-  }
-}
-
-/**
- * \brief Calculate update to bias.
- *
- * \param sum_grad  The sum gradient.
- * \param sum_hess  The sum hess.
- *
- * \return  The weight update.
- */
-inline double CoordinateDeltaBias(double sum_grad, double sum_hess) {
-  return -sum_grad / sum_hess;
-}
-
-/**
- * \brief Get the gradient with respect to a single feature.
- *
- * \param group_idx Zero-based index of the group.
- * \param num_group Number of groups.
- * \param fidx      The target feature.
- * \param gpair     Gradients.
- * \param p_fmat    The feature matrix.
- *
- * \return  The gradient and diagonal Hessian entry for a given feature.
- */
-inline std::pair<double, double> GetGradient(int group_idx, int num_group, int fidx,
-                                             const std::vector<GradientPair> &gpair,
-                                             DMatrix *p_fmat) {
-  double sum_grad = 0.0, sum_hess = 0.0;
-  for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
-    auto col = batch[fidx];
-    const auto ndata = static_cast<bst_omp_uint>(col.size());
-    for (bst_omp_uint j = 0; j < ndata; ++j) {
-      const bst_float v = col[j].fvalue;
-      auto &p = gpair[col[j].index * num_group + group_idx];
-      if (p.GetHess() < 0.0f) continue;
-      sum_grad += p.GetGrad() * v;
-      sum_hess += p.GetHess() * v * v;
-    }
-  }
-  return std::make_pair(sum_grad, sum_hess);
-}
-
-/**
- * \brief Get the gradient with respect to a single feature. Row-wise multithreaded.
- *
- * \param group_idx Zero-based index of the group.
- * \param num_group Number of groups.
- * \param fidx      The target feature.
- * \param gpair     Gradients.
- * \param p_fmat    The feature matrix.
- *
- * \return  The gradient and diagonal Hessian entry for a given feature.
- */
-inline std::pair<double, double> GetGradientParallel(int group_idx, int num_group, int fidx,
-                                                     const std::vector<GradientPair> &gpair,
-                                                     DMatrix *p_fmat) {
-  double sum_grad = 0.0, sum_hess = 0.0;
-  for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
-    auto col = batch[fidx];
-    const auto ndata = static_cast<bst_omp_uint>(col.size());
-#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
-    for (bst_omp_uint j = 0; j < ndata; ++j) {
-      const bst_float v = col[j].fvalue;
-      auto &p = gpair[col[j].index * num_group + group_idx];
-      if (p.GetHess() < 0.0f) continue;
-      sum_grad += p.GetGrad() * v;
-      sum_hess += p.GetHess() * v * v;
-    }
-  }
-  return std::make_pair(sum_grad, sum_hess);
-}
-
-/**
- * \brief Get the gradient with respect to the bias. Row-wise multithreaded.
- *
- * \param group_idx Zero-based index of the group.
- * \param num_group Number of groups.
- * \param gpair     Gradients.
- * \param p_fmat    The feature matrix.
- *
- * \return  The gradient and diagonal Hessian entry for the bias.
- */
-inline std::pair<double, double> GetBiasGradientParallel(int group_idx, int num_group,
-                                                         const std::vector<GradientPair> &gpair,
-                                                         DMatrix *p_fmat) {
-  double sum_grad = 0.0, sum_hess = 0.0;
-  const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
-#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
-  for (bst_omp_uint i = 0; i < ndata; ++i) {
-    auto &p = gpair[i * num_group + group_idx];
-    if (p.GetHess() >= 0.0f) {
-      sum_grad += p.GetGrad();
-      sum_hess += p.GetHess();
-    }
-  }
-  return std::make_pair(sum_grad, sum_hess);
-}
-
-/**
- * \brief Updates the gradient vector with respect to a change in weight.
- *
- * \param fidx      The feature index.
- * \param group_idx Zero-based index of the group.
- * \param num_group Number of groups.
- * \param dw        The change in weight.
- * \param in_gpair  The gradient vector to be updated.
- * \param p_fmat    The input feature matrix.
- */
-inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
-                                   float dw, std::vector<GradientPair> *in_gpair,
-                                   DMatrix *p_fmat) {
-  if (dw == 0.0f) return;
-  for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
-    auto col = batch[fidx];
-    // update grad value
-    const auto num_row = static_cast<bst_omp_uint>(col.size());
-#pragma omp parallel for schedule(static)
-    for (bst_omp_uint j = 0; j < num_row; ++j) {
-      GradientPair &p = (*in_gpair)[col[j].index * num_group + group_idx];
-      if (p.GetHess() < 0.0f) continue;
-      p += GradientPair(p.GetHess() * col[j].fvalue * dw, 0);
-    }
-  }
-}
-
-/**
- * \brief Updates the gradient vector based on a change in the bias.
- *
- * \param group_idx Zero-based index of the group.
- * \param num_group Number of groups.
- * \param dbias     The change in bias.
- * \param in_gpair  The gradient vector to be updated.
- * \param p_fmat    The input feature matrix.
- */
-inline void UpdateBiasResidualParallel(int group_idx, int num_group, float dbias,
-                                       std::vector<GradientPair> *in_gpair,
-                                       DMatrix *p_fmat) {
-  if (dbias == 0.0f) return;
-  const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
-#pragma omp parallel for schedule(static)
-  for (bst_omp_uint i = 0; i < ndata; ++i) {
-    GradientPair &g = (*in_gpair)[i * num_group + group_idx];
-    if (g.GetHess() < 0.0f) continue;
-    g += GradientPair(g.GetHess() * dbias, 0);
-  }
-}
-
-/**
- * \brief Abstract class for stateful feature selection or ordering
- *        in coordinate descent algorithms.
- */
-class FeatureSelector {
- public:
-  /*! \brief factory method */
-  static FeatureSelector *Create(int choice);
-  /*! \brief virtual destructor */
-  virtual ~FeatureSelector() = default;
-  /**
-   * \brief Setting up the selector state prior to looping through features.
-   *
-   * \param model  The model.
-   * \param gpair  The gpair.
-   * \param p_fmat The feature matrix.
-   * \param alpha  Regularisation alpha.
-   * \param lambda Regularisation lambda.
-   * \param param  A parameter with algorithm-dependent use.
-   */
-  virtual void Setup(const gbm::GBLinearModel &model,
-                     const std::vector<GradientPair> &gpair,
-                     DMatrix *p_fmat,
-                     float alpha, float lambda, int param) {}
-  /**
-   * \brief Select next coordinate to update.
-   *
-   * \param iteration The iteration in a loop through features
-   * \param model     The model.
-   * \param group_idx Zero-based index of the group.
-   * \param gpair     The gpair.
-   * \param p_fmat    The feature matrix.
-   * \param alpha     Regularisation alpha.
-   * \param lambda    Regularisation lambda.
-   *
-   * \return  The index of the selected feature. -1 indicates none selected.
-   */
-  virtual int NextFeature(int iteration,
-                          const gbm::GBLinearModel &model,
-                          int group_idx,
-                          const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat, float alpha, float lambda) = 0;
-};
-
-/**
- * \brief Deterministic selection by cycling through features one at a time.
- */
-class CyclicFeatureSelector : public FeatureSelector {
- public:
-  int NextFeature(int iteration, const gbm::GBLinearModel &model,
-                  int group_idx, const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat, float alpha, float lambda) override {
-    return iteration % model.learner_model_param->num_feature;
-  }
-};
-
-/**
- * \brief Similar to Cyclic but with random feature shuffling prior to each update.
- * \note Its randomness is controllable by setting a random seed.
- */
-class ShuffleFeatureSelector : public FeatureSelector {
- public:
-  void Setup(const gbm::GBLinearModel &model,
-             const std::vector<GradientPair> &gpair,
-             DMatrix *p_fmat, float alpha, float lambda, int param) override {
-    if (feat_index_.size() == 0) {
-      feat_index_.resize(model.learner_model_param->num_feature);
-      std::iota(feat_index_.begin(), feat_index_.end(), 0);
-    }
-    std::shuffle(feat_index_.begin(), feat_index_.end(), common::GlobalRandom());
-  }
-
-  int NextFeature(int iteration, const gbm::GBLinearModel &model,
-                  int group_idx, const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat, float alpha, float lambda) override {
-    return feat_index_[iteration % model.learner_model_param->num_feature];
-  }
-
- protected:
-  std::vector<bst_uint> feat_index_;
-};
-
-/**
- * \brief A random (with replacement) coordinate selector.
- * \note Its randomness is controllable by setting a random seed.
- */
-class RandomFeatureSelector : public FeatureSelector {
- public:
-  int NextFeature(int iteration, const gbm::GBLinearModel &model,
-                  int group_idx, const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat, float alpha, float lambda) override {
-    return common::GlobalRandom()() % model.learner_model_param->num_feature;
-  }
-};
-
-/**
- * \brief Select coordinate with the greatest gradient magnitude.
- * \note It has O(num_feature^2) complexity. It is fully deterministic.
- *
- * \note It allows restricting the selection to top_k features per group with
- * the largest magnitude of univariate weight change, by passing the top_k value
- * through the `param` argument of Setup(). That would reduce the complexity to
- * O(num_feature*top_k).
- */
-class GreedyFeatureSelector : public FeatureSelector {
- public:
-  void Setup(const gbm::GBLinearModel &model,
-             const std::vector<GradientPair> &gpair,
-             DMatrix *p_fmat, float alpha, float lambda, int param) override {
-    top_k_ = static_cast<bst_uint>(param);
-    const bst_uint ngroup = model.learner_model_param->num_output_group;
-    if (param <= 0) top_k_ = std::numeric_limits<bst_uint>::max();
-    if (counter_.size() == 0) {
-      counter_.resize(ngroup);
-      gpair_sums_.resize(model.learner_model_param->num_feature * ngroup);
-    }
-    for (bst_uint gid = 0u; gid < ngroup; ++gid) {
-      counter_[gid] = 0u;
-    }
-  }
-
-  int NextFeature(int iteration, const gbm::GBLinearModel &model,
-                  int group_idx, const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat, float alpha, float lambda) override {
-    // k-th selected feature for a group
-    auto k = counter_[group_idx]++;
-    // stop after either reaching top-K or going through all the features in a group
-    if (k >= top_k_ || counter_[group_idx] == model.learner_model_param->num_feature) return -1;
-
-    const int ngroup = model.learner_model_param->num_output_group;
-    const bst_omp_uint nfeat = model.learner_model_param->num_feature;
-    // Calculate univariate gradient sums
-    std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
-  for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
-      #pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nfeat; ++i) {
-        const auto col = batch[i];
-        const bst_uint ndata = col.size();
-        auto &sums = gpair_sums_[group_idx * nfeat + i];
-        for (bst_uint j = 0u; j < ndata; ++j) {
-          const bst_float v = col[j].fvalue;
-          auto &p = gpair[col[j].index * ngroup + group_idx];
-          if (p.GetHess() < 0.f) continue;
-          sums.first += p.GetGrad() * v;
-          sums.second += p.GetHess() * v * v;
-        }
-      }
-    }
-    // Find a feature with the largest magnitude of weight change
-    int best_fidx = 0;
-    double best_weight_update = 0.0f;
-    for (bst_omp_uint fidx = 0; fidx < nfeat; ++fidx) {
-      auto &s = gpair_sums_[group_idx * nfeat + fidx];
-      float dw = std::abs(static_cast<bst_float>(
-                 CoordinateDelta(s.first, s.second, model[fidx][group_idx], alpha, lambda)));
-      if (dw > best_weight_update) {
-        best_weight_update = dw;
-        best_fidx = fidx;
-      }
-    }
-    return best_fidx;
-  }
-
- protected:
-  bst_uint top_k_;
-  std::vector<bst_uint> counter_;
-  std::vector<std::pair<double, double>> gpair_sums_;
-};
-
-/**
- * \brief Thrifty, approximately-greedy feature selector.
- *
- * \note Prior to cyclic updates, reorders features in descending magnitude of
- * their univariate weight changes. This operation is multithreaded and is a
- * linear complexity approximation of the quadratic greedy selection.
- *
- * \note It allows restricting the selection to top_k features per group with
- * the largest magnitude of univariate weight change, by passing the top_k value
- * through the `param` argument of Setup().
- */
-class ThriftyFeatureSelector : public FeatureSelector {
- public:
-  void Setup(const gbm::GBLinearModel &model,
-             const std::vector<GradientPair> &gpair,
-             DMatrix *p_fmat, float alpha, float lambda, int param) override {
-    top_k_ = static_cast<bst_uint>(param);
-    if (param <= 0) top_k_ = std::numeric_limits<bst_uint>::max();
-    const bst_uint ngroup = model.learner_model_param->num_output_group;
-    const bst_omp_uint nfeat = model.learner_model_param->num_feature;
-
-    if (deltaw_.size() == 0) {
-      deltaw_.resize(nfeat * ngroup);
-      sorted_idx_.resize(nfeat * ngroup);
-      counter_.resize(ngroup);
-      gpair_sums_.resize(nfeat * ngroup);
-    }
-    // Calculate univariate gradient sums
-    std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
-    for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
-// column-parallel is usually faster than row-parallel
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nfeat; ++i) {
-        const auto col = batch[i];
-        const bst_uint ndata = col.size();
-        for (bst_uint gid = 0u; gid < ngroup; ++gid) {
-          auto &sums = gpair_sums_[gid * nfeat + i];
-          for (bst_uint j = 0u; j < ndata; ++j) {
-            const bst_float v = col[j].fvalue;
-            auto &p = gpair[col[j].index * ngroup + gid];
-            if (p.GetHess() < 0.f) continue;
-            sums.first += p.GetGrad() * v;
-            sums.second += p.GetHess() * v * v;
-          }
-        }
-      }
-    }
-    // rank by descending weight magnitude within the groups
-    std::fill(deltaw_.begin(), deltaw_.end(), 0.f);
-    std::iota(sorted_idx_.begin(), sorted_idx_.end(), 0);
-    bst_float *pdeltaw = &deltaw_[0];
-    for (bst_uint gid = 0u; gid < ngroup; ++gid) {
-      // Calculate univariate weight changes
-      for (bst_omp_uint i = 0; i < nfeat; ++i) {
-        auto ii = gid * nfeat + i;
-        auto &s = gpair_sums_[ii];
-        deltaw_[ii] = static_cast<bst_float>(CoordinateDelta(
-                       s.first, s.second, model[i][gid], alpha, lambda));
-      }
-      // sort in descending order of deltaw abs values
-      auto start = sorted_idx_.begin() + gid * nfeat;
-      std::sort(start, start + nfeat,
-                [pdeltaw](size_t i, size_t j) {
-                  return std::abs(*(pdeltaw + i)) > std::abs(*(pdeltaw + j));
-                });
-      counter_[gid] = 0u;
-    }
-  }
-
-  int NextFeature(int iteration, const gbm::GBLinearModel &model,
-                  int group_idx, const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat, float alpha, float lambda) override {
-    // k-th selected feature for a group
-    auto k = counter_[group_idx]++;
-    // stop after either reaching top-N or going through all the features in a group
-    if (k >= top_k_ || counter_[group_idx] == model.learner_model_param->num_feature) return -1;
-    // note that sorted_idx stores the "long" indices
-    const size_t grp_offset = group_idx * model.learner_model_param->num_feature;
-    return static_cast<int>(sorted_idx_[grp_offset + k] - grp_offset);
-  }
-
- protected:
-  bst_uint top_k_;
-  std::vector<bst_float> deltaw_;
-  std::vector<size_t> sorted_idx_;
-  std::vector<bst_uint> counter_;
-  std::vector<std::pair<double, double>> gpair_sums_;
-};
-
-inline FeatureSelector *FeatureSelector::Create(int choice) {
-  switch (choice) {
-    case kCyclic:
-      return new CyclicFeatureSelector();
-    case kShuffle:
-      return new ShuffleFeatureSelector();
-    case kThrifty:
-      return new ThriftyFeatureSelector();
-    case kGreedy:
-      return new GreedyFeatureSelector();
-    case kRandom:
-      return new RandomFeatureSelector();
-    default:
-      LOG(FATAL) << "unknown coordinate selector: " << choice;
-  }
-  return nullptr;
-}
-
-}  // namespace linear
-}  // namespace xgboost
diff --git a/ml-xgboost/src/linear/linear_updater.cc b/ml-xgboost/src/linear/linear_updater.cc
deleted file mode 100644
index 95c9908..0000000
--- a/ml-xgboost/src/linear/linear_updater.cc
+++ /dev/null
@@ -1,37 +0,0 @@
-/*!
- * Copyright 2018
- */
-#include <xgboost/linear_updater.h>
-#include <dmlc/registry.h>
-#include "./param.h"
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::LinearUpdaterReg);
-}  // namespace dmlc
-
-namespace xgboost {
-
-LinearUpdater* LinearUpdater::Create(const std::string& name, GenericParameter const* lparam) {
-  auto *e = ::dmlc::Registry< ::xgboost::LinearUpdaterReg>::Get()->Find(name);
-  if (e == nullptr) {
-    LOG(FATAL) << "Unknown linear updater " << name;
-  }
-  auto p_linear = (e->body)();
-  p_linear->learner_param_ = lparam;
-  return p_linear;
-}
-
-}  // namespace xgboost
-
-namespace xgboost {
-namespace linear {
-DMLC_REGISTER_PARAMETER(LinearTrainParam);
-
-// List of files that will be force linked in static links.
-DMLC_REGISTRY_LINK_TAG(updater_shotgun);
-DMLC_REGISTRY_LINK_TAG(updater_coordinate);
-#ifdef XGBOOST_USE_CUDA
-DMLC_REGISTRY_LINK_TAG(updater_gpu_coordinate);
-#endif  // XGBOOST_USE_CUDA
-}  // namespace linear
-}  // namespace xgboost
diff --git a/ml-xgboost/src/linear/param.h b/ml-xgboost/src/linear/param.h
deleted file mode 100644
index 7cf4430..0000000
--- a/ml-xgboost/src/linear/param.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*!
- * Copyright 2018 by Contributors
- * \file param.h
- * \brief training parameters.
- */
-#ifndef XGBOOST_LINEAR_PARAM_H_
-#define XGBOOST_LINEAR_PARAM_H_
-#include "xgboost/parameter.h"
-
-namespace xgboost {
-namespace linear {
-/**
- * \brief A set of available FeatureSelector's
- */
-enum FeatureSelectorEnum {
-  kCyclic = 0,
-  kShuffle,
-  kThrifty,
-  kGreedy,
-  kRandom
-};
-
-struct LinearTrainParam : public XGBoostParameter<LinearTrainParam> {
-  /*! \brief learning_rate */
-  float learning_rate;
-  /*! \brief regularization weight for L2 norm */
-  float reg_lambda;
-  /*! \brief regularization weight for L1 norm */
-  float reg_alpha;
-  int feature_selector;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(LinearTrainParam) {
-    DMLC_DECLARE_FIELD(learning_rate)
-        .set_lower_bound(0.0f)
-        .set_default(0.5f)
-        .describe("Learning rate of each update.");
-    DMLC_DECLARE_FIELD(reg_lambda)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe("L2 regularization on weights.");
-    DMLC_DECLARE_FIELD(reg_alpha)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe("L1 regularization on weights.");
-    DMLC_DECLARE_FIELD(feature_selector)
-        .set_default(kCyclic)
-        .add_enum("cyclic", kCyclic)
-        .add_enum("shuffle", kShuffle)
-        .add_enum("thrifty", kThrifty)
-        .add_enum("greedy", kGreedy)
-        .add_enum("random", kRandom)
-        .describe("Feature selection or ordering method.");
-    // alias of parameters
-    DMLC_DECLARE_ALIAS(learning_rate, eta);
-    DMLC_DECLARE_ALIAS(reg_lambda, lambda);
-    DMLC_DECLARE_ALIAS(reg_alpha, alpha);
-  }
-  /*! \brief Denormalizes the regularization penalties - to be called at each update */
-  void DenormalizePenalties(double sum_instance_weight) {
-    reg_lambda_denorm = reg_lambda * sum_instance_weight;
-    reg_alpha_denorm = reg_alpha * sum_instance_weight;
-  }
-  // denormalizated regularization penalties
-  float reg_lambda_denorm;
-  float reg_alpha_denorm;
-};
-
-}  // namespace linear
-}  // namespace xgboost
-
-#endif  // XGBOOST_LINEAR_PARAM_H_
diff --git a/ml-xgboost/src/linear/updater_coordinate.cc b/ml-xgboost/src/linear/updater_coordinate.cc
deleted file mode 100644
index 23f227a..0000000
--- a/ml-xgboost/src/linear/updater_coordinate.cc
+++ /dev/null
@@ -1,105 +0,0 @@
-/*!
- * Copyright 2018 by Contributors
- * \author Rory Mitchell
- */
-
-#include <xgboost/linear_updater.h>
-#include "./param.h"
-#include "../common/timer.h"
-#include "coordinate_common.h"
-#include "xgboost/json.h"
-
-namespace xgboost {
-namespace linear {
-
-DMLC_REGISTER_PARAMETER(CoordinateParam);
-DMLC_REGISTRY_FILE_TAG(updater_coordinate);
-
-// training parameter
-/**
- * \class CoordinateUpdater
- *
- * \brief Coordinate descent algorithm that updates one feature per iteration
- */
-
-class CoordinateUpdater : public LinearUpdater {
- public:
-  // set training parameter
-  void Configure(Args const& args) override {
-    const std::vector<std::pair<std::string, std::string> > rest {
-      tparam_.UpdateAllowUnknown(args)
-    };
-    cparam_.UpdateAllowUnknown(rest);
-    selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
-    monitor_.Init("CoordinateUpdater");
-  }
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("linear_train_param"), &tparam_);
-    FromJson(config.at("coordinate_param"), &cparam_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["linear_train_param"] = ToJson(tparam_);
-    out["coordinate_param"] = ToJson(cparam_);
-  }
-
-  void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
-              gbm::GBLinearModel *model, double sum_instance_weight) override {
-    tparam_.DenormalizePenalties(sum_instance_weight);
-    const int ngroup = model->learner_model_param->num_output_group;
-    // update bias
-    for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
-      auto grad = GetBiasGradientParallel(group_idx, ngroup,
-                                          in_gpair->ConstHostVector(), p_fmat);
-      auto dbias = static_cast<float>(tparam_.learning_rate *
-                                      CoordinateDeltaBias(grad.first, grad.second));
-      model->Bias()[group_idx] += dbias;
-      UpdateBiasResidualParallel(group_idx, ngroup,
-                                 dbias, &in_gpair->HostVector(), p_fmat);
-    }
-    // prepare for updating the weights
-    selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
-                    tparam_.reg_alpha_denorm,
-                    tparam_.reg_lambda_denorm, cparam_.top_k);
-    // update weights
-    for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
-      for (unsigned i = 0U; i < model->learner_model_param->num_feature; i++) {
-        int fidx = selector_->NextFeature
-          (i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
-           tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
-        if (fidx < 0) break;
-        this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), p_fmat, model);
-      }
-    }
-    monitor_.Stop("UpdateFeature");
-  }
-
-  inline void UpdateFeature(int fidx, int group_idx, std::vector<GradientPair> *in_gpair,
-                            DMatrix *p_fmat, gbm::GBLinearModel *model) {
-    const int ngroup = model->learner_model_param->num_output_group;
-    bst_float &w = (*model)[fidx][group_idx];
-    auto gradient =
-        GetGradientParallel(group_idx, ngroup, fidx, *in_gpair, p_fmat);
-    auto dw = static_cast<float>(
-        tparam_.learning_rate *
-        CoordinateDelta(gradient.first, gradient.second, w, tparam_.reg_alpha_denorm,
-                        tparam_.reg_lambda_denorm));
-    w += dw;
-    UpdateResidualParallel(fidx, group_idx, ngroup, dw, in_gpair, p_fmat);
-  }
-
- private:
-  CoordinateParam cparam_;
-  // training parameter
-  LinearTrainParam tparam_;
-  std::unique_ptr<FeatureSelector> selector_;
-  common::Monitor monitor_;
-};
-
-XGBOOST_REGISTER_LINEAR_UPDATER(CoordinateUpdater, "coord_descent")
-    .describe("Update linear model according to coordinate descent algorithm.")
-    .set_body([]() { return new CoordinateUpdater(); });
-}  // namespace linear
-}  // namespace xgboost
diff --git a/ml-xgboost/src/linear/updater_gpu_coordinate.cu b/ml-xgboost/src/linear/updater_gpu_coordinate.cu
deleted file mode 100644
index e7db2dc..0000000
--- a/ml-xgboost/src/linear/updater_gpu_coordinate.cu
+++ /dev/null
@@ -1,261 +0,0 @@
-/*!
- * Copyright 2018-2019 by Contributors
- * \author Rory Mitchell
- */
-
-#include <thrust/execution_policy.h>
-#include <thrust/inner_product.h>
-#include <xgboost/data.h>
-#include <xgboost/linear_updater.h>
-#include "xgboost/span.h"
-
-#include "coordinate_common.h"
-#include "../common/common.h"
-#include "../common/device_helpers.cuh"
-#include "../common/timer.h"
-#include "./param.h"
-
-namespace xgboost {
-namespace linear {
-
-DMLC_REGISTRY_FILE_TAG(updater_gpu_coordinate);
-
-/**
- * \class GPUCoordinateUpdater
- *
- * \brief Coordinate descent algorithm that updates one feature per iteration
- */
-
-class GPUCoordinateUpdater : public LinearUpdater {  // NOLINT
- public:
-  ~GPUCoordinateUpdater() {  // NOLINT
-    if (learner_param_->gpu_id >= 0) {
-      dh::safe_cuda(cudaSetDevice(learner_param_->gpu_id));
-    }
-  }
-
-  // set training parameter
-  void Configure(Args const& args) override {
-    tparam_.UpdateAllowUnknown(args);
-    coord_param_.UpdateAllowUnknown(args);
-    selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
-    monitor_.Init("GPUCoordinateUpdater");
-  }
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("linear_train_param"), &tparam_);
-    FromJson(config.at("coordinate_param"), &coord_param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["linear_train_param"] = ToJson(tparam_);
-    out["coordinate_param"] = ToJson(coord_param_);
-  }
-
-  void LazyInitDevice(DMatrix *p_fmat, const LearnerModelParam &model_param) {
-    if (learner_param_->gpu_id < 0) return;
-
-    num_row_ = static_cast<size_t>(p_fmat->Info().num_row_);
-
-    CHECK(p_fmat->SingleColBlock());
-    SparsePage const& batch = *(p_fmat->GetBatches<CSCPage>().begin());
-
-    if (IsEmpty()) {
-      return;
-    }
-
-    dh::safe_cuda(cudaSetDevice(learner_param_->gpu_id));
-    // The begin and end indices for the section of each column associated with
-    // this device
-    std::vector<std::pair<bst_uint, bst_uint>> column_segments;
-    row_ptr_ = {0};
-    // iterate through columns
-    for (size_t fidx = 0; fidx < batch.Size(); fidx++) {
-      common::Span<Entry const> col = batch[fidx];
-      auto cmp = [](Entry e1, Entry e2) {
-        return e1.index < e2.index;
-      };
-      auto column_begin =
-          std::lower_bound(col.cbegin(), col.cend(),
-                           xgboost::Entry(0, 0.0f), cmp);
-      auto column_end =
-          std::lower_bound(col.cbegin(), col.cend(),
-                           xgboost::Entry(num_row_, 0.0f), cmp);
-      column_segments.emplace_back(
-          std::make_pair(column_begin - col.cbegin(), column_end - col.cbegin()));
-      row_ptr_.push_back(row_ptr_.back() + (column_end - column_begin));
-    }
-    data_.resize(row_ptr_.back());
-    gpair_.resize(num_row_ * model_param.num_output_group);
-    for (size_t fidx = 0; fidx < batch.Size(); fidx++) {
-      auto col = batch[fidx];
-      auto seg = column_segments[fidx];
-      dh::safe_cuda(cudaMemcpy(
-          data_.data().get() + row_ptr_[fidx],
-          col.data() + seg.first,
-          sizeof(Entry) * (seg.second - seg.first), cudaMemcpyHostToDevice));
-    }
-  }
-
-  void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
-              gbm::GBLinearModel *model, double sum_instance_weight) override {
-    tparam_.DenormalizePenalties(sum_instance_weight);
-    monitor_.Start("LazyInitDevice");
-    this->LazyInitDevice(p_fmat, *(model->learner_model_param));
-    monitor_.Stop("LazyInitDevice");
-
-    monitor_.Start("UpdateGpair");
-    auto &in_gpair_host = in_gpair->ConstHostVector();
-    // Update gpair
-    if (learner_param_->gpu_id >= 0) {
-      this->UpdateGpair(in_gpair_host);
-    }
-    monitor_.Stop("UpdateGpair");
-
-    monitor_.Start("UpdateBias");
-    this->UpdateBias(p_fmat, model);
-    monitor_.Stop("UpdateBias");
-    // prepare for updating the weights
-    selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
-                     tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm,
-                     coord_param_.top_k);
-    monitor_.Start("UpdateFeature");
-    for (auto group_idx = 0; group_idx < model->learner_model_param->num_output_group;
-         ++group_idx) {
-      for (auto i = 0U; i < model->learner_model_param->num_feature; i++) {
-        auto fidx = selector_->NextFeature(
-            i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
-            tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
-        if (fidx < 0) break;
-        this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), model);
-      }
-    }
-    monitor_.Stop("UpdateFeature");
-  }
-
-  void UpdateBias(DMatrix *p_fmat, gbm::GBLinearModel *model) {
-    for (int group_idx = 0; group_idx < model->learner_model_param->num_output_group;
-         ++group_idx) {
-      // Get gradient
-      auto grad = GradientPair(0, 0);
-      if (learner_param_->gpu_id >= 0) {
-        grad = GetBiasGradient(group_idx, model->learner_model_param->num_output_group);
-      }
-      auto dbias = static_cast<float>(
-          tparam_.learning_rate *
-              CoordinateDeltaBias(grad.GetGrad(), grad.GetHess()));
-      model->Bias()[group_idx] += dbias;
-
-      // Update residual
-      if (learner_param_->gpu_id >= 0) {
-        UpdateBiasResidual(dbias, group_idx, model->learner_model_param->num_output_group);
-      }
-    }
-  }
-
-  void UpdateFeature(int fidx, int group_idx,
-                     std::vector<GradientPair> *in_gpair,
-                     gbm::GBLinearModel *model) {
-    bst_float &w = (*model)[fidx][group_idx];
-    // Get gradient
-    auto grad = GradientPair(0, 0);
-    if (learner_param_->gpu_id >= 0) {
-      grad = GetGradient(group_idx, model->learner_model_param->num_output_group, fidx);
-    }
-    auto dw = static_cast<float>(tparam_.learning_rate *
-                                 CoordinateDelta(grad.GetGrad(), grad.GetHess(),
-                                                 w, tparam_.reg_alpha_denorm,
-                                                 tparam_.reg_lambda_denorm));
-    w += dw;
-
-    if (learner_param_->gpu_id >= 0) {
-      UpdateResidual(dw, group_idx, model->learner_model_param->num_output_group, fidx);
-    }
-  }
-
-  // This needs to be public because of the __device__ lambda.
-  GradientPair GetBiasGradient(int group_idx, int num_group) {
-    dh::safe_cuda(cudaSetDevice(learner_param_->gpu_id));
-    auto counting = thrust::make_counting_iterator(0ull);
-    auto f = [=] __device__(size_t idx) {
-      return idx * num_group + group_idx;
-    };  // NOLINT
-    thrust::transform_iterator<decltype(f), decltype(counting), size_t> skip(
-        counting, f);
-    auto perm = thrust::make_permutation_iterator(gpair_.data(), skip);
-
-    return dh::SumReduction(perm, num_row_);
-  }
-
-  // This needs to be public because of the __device__ lambda.
-  void UpdateBiasResidual(float dbias, int group_idx, int num_groups) {
-    if (dbias == 0.0f) return;
-    auto d_gpair = dh::ToSpan(gpair_);
-    dh::LaunchN(learner_param_->gpu_id, num_row_, [=] __device__(size_t idx) {
-      auto &g = d_gpair[idx * num_groups + group_idx];
-      g += GradientPair(g.GetHess() * dbias, 0);
-    });
-  }
-
-  // This needs to be public because of the __device__ lambda.
-  GradientPair GetGradient(int group_idx, int num_group, int fidx) {
-    dh::safe_cuda(cudaSetDevice(learner_param_->gpu_id));
-    common::Span<xgboost::Entry> d_col = dh::ToSpan(data_).subspan(row_ptr_[fidx]);
-    size_t col_size = row_ptr_[fidx + 1] - row_ptr_[fidx];
-    common::Span<GradientPair> d_gpair = dh::ToSpan(gpair_);
-    auto counting = thrust::make_counting_iterator(0ull);
-    auto f = [=] __device__(size_t idx) {
-      auto entry = d_col[idx];
-      auto g = d_gpair[entry.index * num_group + group_idx];
-      return GradientPair(g.GetGrad() * entry.fvalue,
-                          g.GetHess() * entry.fvalue * entry.fvalue);
-    };  // NOLINT
-    thrust::transform_iterator<decltype(f), decltype(counting), GradientPair>
-        multiply_iterator(counting, f);
-    return dh::SumReduction(multiply_iterator, col_size);
-  }
-
-  // This needs to be public because of the __device__ lambda.
-  void UpdateResidual(float dw, int group_idx, int num_groups, int fidx) {
-    common::Span<GradientPair> d_gpair = dh::ToSpan(gpair_);
-    common::Span<Entry> d_col = dh::ToSpan(data_).subspan(row_ptr_[fidx]);
-    size_t col_size = row_ptr_[fidx + 1] - row_ptr_[fidx];
-    dh::LaunchN(learner_param_->gpu_id, col_size, [=] __device__(size_t idx) {
-      auto entry = d_col[idx];
-      auto &g = d_gpair[entry.index * num_groups + group_idx];
-      g += GradientPair(g.GetHess() * dw * entry.fvalue, 0);
-    });
-  }
-
- private:
-  bool IsEmpty() {
-    return num_row_ == 0;
-  }
-
-  void UpdateGpair(const std::vector<GradientPair> &host_gpair) {
-    dh::safe_cuda(cudaMemcpyAsync(
-        gpair_.data().get(),
-        host_gpair.data(),
-        gpair_.size() * sizeof(GradientPair), cudaMemcpyHostToDevice));
-  }
-
-  // training parameter
-  LinearTrainParam tparam_;
-  CoordinateParam coord_param_;
-  std::unique_ptr<FeatureSelector> selector_;
-  common::Monitor monitor_;
-
-  std::vector<size_t> row_ptr_;
-  dh::device_vector<xgboost::Entry> data_;
-  dh::caching_device_vector<GradientPair> gpair_;
-  size_t num_row_;
-};
-
-XGBOOST_REGISTER_LINEAR_UPDATER(GPUCoordinateUpdater, "gpu_coord_descent")
-    .describe(
-        "Update linear model according to coordinate descent algorithm. GPU "
-        "accelerated.")
-    .set_body([]() { return new GPUCoordinateUpdater(); });
-}  // namespace linear
-}  // namespace xgboost
diff --git a/ml-xgboost/src/linear/updater_shotgun.cc b/ml-xgboost/src/linear/updater_shotgun.cc
deleted file mode 100644
index 08604ce..0000000
--- a/ml-xgboost/src/linear/updater_shotgun.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-/*!
- * Copyright 2018 by Contributors
- * \author Tianqi Chen, Rory Mitchell
- */
-
-#include <xgboost/linear_updater.h>
-#include "coordinate_common.h"
-
-namespace xgboost {
-namespace linear {
-
-DMLC_REGISTRY_FILE_TAG(updater_shotgun);
-
-class ShotgunUpdater : public LinearUpdater {
- public:
-  // set training parameter
-  void Configure(Args const& args) override {
-    param_.UpdateAllowUnknown(args);
-    if (param_.feature_selector != kCyclic &&
-        param_.feature_selector != kShuffle) {
-      LOG(FATAL) << "Unsupported feature selector for shotgun updater.\n"
-                 << "Supported options are: {cyclic, shuffle}";
-    }
-    selector_.reset(FeatureSelector::Create(param_.feature_selector));
-  }
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("linear_train_param"), &param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["linear_train_param"] = ToJson(param_);
-  }
-
-  void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
-              gbm::GBLinearModel *model, double sum_instance_weight) override {
-    auto &gpair = in_gpair->HostVector();
-    param_.DenormalizePenalties(sum_instance_weight);
-    const int ngroup = model->learner_model_param->num_output_group;
-
-    // update bias
-    for (int gid = 0; gid < ngroup; ++gid) {
-      auto grad = GetBiasGradientParallel(gid, ngroup,
-                                          in_gpair->ConstHostVector(), p_fmat);
-      auto dbias = static_cast<bst_float>(param_.learning_rate *
-                               CoordinateDeltaBias(grad.first, grad.second));
-      model->Bias()[gid] += dbias;
-      UpdateBiasResidualParallel(gid, ngroup, dbias, &in_gpair->HostVector(), p_fmat);
-    }
-
-    // lock-free parallel updates of weights
-    selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
-                     param_.reg_alpha_denorm, param_.reg_lambda_denorm, 0);
-    for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
-      const auto nfeat = static_cast<bst_omp_uint>(batch.Size());
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nfeat; ++i) {
-        int ii = selector_->NextFeature
-          (i, *model, 0, in_gpair->ConstHostVector(), p_fmat, param_.reg_alpha_denorm,
-           param_.reg_lambda_denorm);
-        if (ii < 0) continue;
-        const bst_uint fid = ii;
-        auto col = batch[ii];
-        for (int gid = 0; gid < ngroup; ++gid) {
-          double sum_grad = 0.0, sum_hess = 0.0;
-          for (auto& c : col) {
-            const GradientPair &p = gpair[c.index * ngroup + gid];
-            if (p.GetHess() < 0.0f) continue;
-            const bst_float v = c.fvalue;
-            sum_grad += p.GetGrad() * v;
-            sum_hess += p.GetHess() * v * v;
-          }
-          bst_float &w = (*model)[fid][gid];
-          auto dw = static_cast<bst_float>(
-              param_.learning_rate *
-              CoordinateDelta(sum_grad, sum_hess, w, param_.reg_alpha_denorm,
-                              param_.reg_lambda_denorm));
-          if (dw == 0.f) continue;
-          w += dw;
-          // update grad values
-          for (auto& c : col) {
-            GradientPair &p = gpair[c.index * ngroup + gid];
-            if (p.GetHess() < 0.0f) continue;
-            p += GradientPair(p.GetHess() * c.fvalue * dw, 0);
-          }
-        }
-      }
-    }
-  }
-
- protected:
-  // training parameters
-  LinearTrainParam param_;
-
-  std::unique_ptr<FeatureSelector> selector_;
-};
-
-XGBOOST_REGISTER_LINEAR_UPDATER(ShotgunUpdater, "shotgun")
-    .describe(
-        "Update linear model according to shotgun coordinate descent "
-        "algorithm.")
-    .set_body([]() { return new ShotgunUpdater(); });
-}  // namespace linear
-}  // namespace xgboost
diff --git a/ml-xgboost/src/logging.cc b/ml-xgboost/src/logging.cc
deleted file mode 100644
index f03e1be..0000000
--- a/ml-xgboost/src/logging.cc
+++ /dev/null
@@ -1,109 +0,0 @@
-/*!
- * Copyright 2015-2018 by Contributors
- * \file logging.cc
- * \brief Implementation of loggers.
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-
-#include <iostream>
-#include <map>
-
-#include "xgboost/parameter.h"
-#include "xgboost/logging.h"
-
-#if !defined(XGBOOST_STRICT_R_MODE) || XGBOOST_STRICT_R_MODE == 0
-// Override logging mechanism for non-R interfaces
-void dmlc::CustomLogMessage::Log(const std::string& msg) {
-  const xgboost::LogCallbackRegistry* registry
-    = xgboost::LogCallbackRegistryStore::Get();
-  auto callback = registry->Get();
-  callback(msg.c_str());
-}
-
-namespace xgboost {
-
-ConsoleLogger::~ConsoleLogger() {
-  if (ShouldLog(cur_verbosity_)) {
-    dmlc::CustomLogMessage::Log(BaseLogger::log_stream_.str());
-  }
-}
-
-TrackerLogger::~TrackerLogger() {
-  log_stream_ << '\n';
-  rabit::TrackerPrint(log_stream_.str());
-}
-
-}  // namespace xgboost
-
-#endif  // !defined(XGBOOST_STRICT_R_MODE) || XGBOOST_STRICT_R_MODE == 0
-
-namespace xgboost {
-
-DMLC_REGISTER_PARAMETER(ConsoleLoggerParam);
-
-ConsoleLogger::LogVerbosity ConsoleLogger::global_verbosity_ =
-    ConsoleLogger::DefaultVerbosity();
-
-ConsoleLoggerParam ConsoleLogger::param_ = ConsoleLoggerParam();
-
-bool ConsoleLogger::ShouldLog(LogVerbosity verbosity) {
-  return verbosity <= global_verbosity_ || verbosity == LV::kIgnore;
-}
-
-void ConsoleLogger::Configure(Args const& args) {
-  param_.UpdateAllowUnknown(args);
-  switch (param_.verbosity) {
-    case 0:
-      global_verbosity_ = LogVerbosity::kSilent;
-      break;
-    case 1:
-      global_verbosity_ = LogVerbosity::kWarning;
-      break;
-    case 2:
-      global_verbosity_ = LogVerbosity::kInfo;
-      break;
-    case 3:
-      global_verbosity_ = LogVerbosity::kDebug;
-    default:
-      // global verbosity doesn't require kIgnore
-      break;
-  }
-}
-
-ConsoleLogger::LogVerbosity ConsoleLogger::DefaultVerbosity() {
-  return LogVerbosity::kWarning;
-}
-
-ConsoleLogger::LogVerbosity ConsoleLogger::GlobalVerbosity() {
-  return global_verbosity_;
-}
-
-ConsoleLogger::ConsoleLogger(LogVerbosity cur_verb) :
-    cur_verbosity_{cur_verb} {}
-
-ConsoleLogger::ConsoleLogger(
-    const std::string& file, int line, LogVerbosity cur_verb) {
-  cur_verbosity_ = cur_verb;
-  switch (cur_verbosity_) {
-    case LogVerbosity::kWarning:
-      BaseLogger::log_stream_ << "WARNING: "
-                              << file << ":" << line << ": ";
-      break;
-    case LogVerbosity::kDebug:
-      BaseLogger::log_stream_ << "DEBUG: "
-                              << file << ":" << line << ": ";
-      break;
-    case LogVerbosity::kInfo:
-      BaseLogger::log_stream_ << "INFO: "
-                              << file << ":" << line << ": ";
-      break;
-    case LogVerbosity::kIgnore:
-      BaseLogger::log_stream_ << file << ":" << line << ": ";
-      break;
-    case LogVerbosity::kSilent:
-      break;
-  }
-}
-
-}  // namespace xgboost
diff --git a/ml-xgboost/src/metric/elementwise_metric.cc b/ml-xgboost/src/metric/elementwise_metric.cc
deleted file mode 100644
index 0a3e673..0000000
--- a/ml-xgboost/src/metric/elementwise_metric.cc
+++ /dev/null
@@ -1,8 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-// Dummy file to keep the CUDA conditional compile trick.
-
-#if !defined(XGBOOST_USE_CUDA)
-#include "elementwise_metric.cu"
-#endif  // !defined(XGBOOST_USE_CUDA)
diff --git a/ml-xgboost/src/metric/elementwise_metric.cu b/ml-xgboost/src/metric/elementwise_metric.cu
deleted file mode 100644
index df6e665..0000000
--- a/ml-xgboost/src/metric/elementwise_metric.cu
+++ /dev/null
@@ -1,389 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file elementwise_metric.cc
- * \brief evaluation metrics for elementwise binary or regression.
- * \author Kailong Chen, Tianqi Chen
- *
- *  The expressions like wsum == 0 ? esum : esum / wsum is used to handle empty dataset.
- */
-#include <rabit/rabit.h>
-#include <xgboost/metric.h>
-#include <dmlc/registry.h>
-#include <cmath>
-
-#include "metric_common.h"
-#include "../common/math.h"
-#include "../common/common.h"
-
-#if defined(XGBOOST_USE_CUDA)
-#include <thrust/execution_policy.h>  // thrust::cuda::par
-#include <thrust/functional.h>        // thrust::plus<>
-#include <thrust/transform_reduce.h>
-#include <thrust/iterator/counting_iterator.h>
-
-#include "../common/device_helpers.cuh"
-#endif  // XGBOOST_USE_CUDA
-
-namespace xgboost {
-namespace metric {
-// tag the this file, used by force static link later.
-DMLC_REGISTRY_FILE_TAG(elementwise_metric);
-
-template <typename EvalRow>
-class ElementWiseMetricsReduction {
- public:
-  explicit ElementWiseMetricsReduction(EvalRow policy) : policy_(std::move(policy)) {}
-
-  PackedReduceResult CpuReduceMetrics(
-      const HostDeviceVector<bst_float>& weights,
-      const HostDeviceVector<bst_float>& labels,
-      const HostDeviceVector<bst_float>& preds) const {
-    size_t ndata = labels.Size();
-
-    const auto& h_labels = labels.HostVector();
-    const auto& h_weights = weights.HostVector();
-    const auto& h_preds = preds.HostVector();
-
-    bst_float residue_sum = 0;
-    bst_float weights_sum = 0;
-
-#pragma omp parallel for reduction(+: residue_sum, weights_sum) schedule(static)
-    for (omp_ulong i = 0; i < ndata; ++i) {
-      const bst_float wt = h_weights.size() > 0 ? h_weights[i] : 1.0f;
-      residue_sum += policy_.EvalRow(h_labels[i], h_preds[i]) * wt;
-      weights_sum += wt;
-    }
-    PackedReduceResult res { residue_sum, weights_sum };
-    return res;
-  }
-
-#if defined(XGBOOST_USE_CUDA)
-
-  PackedReduceResult DeviceReduceMetrics(
-      const HostDeviceVector<bst_float>& weights,
-      const HostDeviceVector<bst_float>& labels,
-      const HostDeviceVector<bst_float>& preds) {
-    size_t n_data = preds.Size();
-
-    thrust::counting_iterator<size_t> begin(0);
-    thrust::counting_iterator<size_t> end = begin + n_data;
-
-    auto s_label = labels.DeviceSpan();
-    auto s_preds = preds.DeviceSpan();
-    auto s_weights = weights.DeviceSpan();
-
-    bool const is_null_weight = weights.Size() == 0;
-
-    auto d_policy = policy_;
-
-    dh::XGBCachingDeviceAllocator<char> alloc;
-    PackedReduceResult result = thrust::transform_reduce(
-        thrust::cuda::par(alloc),
-        begin, end,
-        [=] XGBOOST_DEVICE(size_t idx) {
-          bst_float weight = is_null_weight ? 1.0f : s_weights[idx];
-
-          bst_float residue = d_policy.EvalRow(s_label[idx], s_preds[idx]);
-          residue *= weight;
-          return PackedReduceResult{ residue, weight };
-        },
-        PackedReduceResult(),
-        thrust::plus<PackedReduceResult>());
-
-    return result;
-  }
-
-#endif  // XGBOOST_USE_CUDA
-
-  PackedReduceResult Reduce(
-      const GenericParameter &tparam,
-      int device,
-      const HostDeviceVector<bst_float>& weights,
-      const HostDeviceVector<bst_float>& labels,
-      const HostDeviceVector<bst_float>& preds) {
-    PackedReduceResult result;
-
-    if (device < 0) {
-      result = CpuReduceMetrics(weights, labels, preds);
-    }
-#if defined(XGBOOST_USE_CUDA)
-    else {  // NOLINT
-      device_ = device;
-      preds.SetDevice(device_);
-      labels.SetDevice(device_);
-      weights.SetDevice(device_);
-
-      dh::safe_cuda(cudaSetDevice(device_));
-      result = DeviceReduceMetrics(weights, labels, preds);
-    }
-#endif  // defined(XGBOOST_USE_CUDA)
-    return result;
-  }
-
- private:
-  EvalRow policy_;
-#if defined(XGBOOST_USE_CUDA)
-  int device_{-1};
-#endif  // defined(XGBOOST_USE_CUDA)
-};
-
-struct EvalRowRMSE {
-  char const *Name() const {
-    return "rmse";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float label, bst_float pred) const {
-    bst_float diff = label - pred;
-    return diff * diff;
-  }
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? std::sqrt(esum) : std::sqrt(esum / wsum);
-  }
-};
-
-struct EvalRowRMSLE {
-  char const* Name() const {
-    return "rmsle";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float label, bst_float pred) const {
-    bst_float diff = std::log1p(label) - std::log1p(pred);
-    return diff * diff;
-  }
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? std::sqrt(esum) : std::sqrt(esum / wsum);
-  }
-};
-
-struct EvalRowMAE {
-  const char *Name() const {
-    return "mae";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float label, bst_float pred) const {
-    return std::abs(label - pred);
-  }
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? esum : esum / wsum;
-  }
-};
-
-struct EvalRowLogLoss {
-  const char *Name() const {
-    return "logloss";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float y, bst_float py) const {
-    const bst_float eps = 1e-16f;
-    const bst_float pneg = 1.0f - py;
-    if (py < eps) {
-      return -y * std::log(eps) - (1.0f - y)  * std::log(1.0f - eps);
-    } else if (pneg < eps) {
-      return -y * std::log(1.0f - eps) - (1.0f - y)  * std::log(eps);
-    } else {
-      return -y * std::log(py) - (1.0f - y) * std::log(pneg);
-    }
-  }
-
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? esum : esum / wsum;
-  }
-};
-
-struct EvalError {
-  explicit EvalError(const char* param) {
-    if (param != nullptr) {
-      CHECK_EQ(sscanf(param, "%f", &threshold_), 1)
-          << "unable to parse the threshold value for the error metric";
-      has_param_ = true;
-    } else {
-      threshold_ = 0.5f;
-      has_param_ = false;
-    }
-  }
-  const char *Name() const {
-    static std::string name;
-    if (has_param_) {
-      std::ostringstream os;
-      os << "error";
-      if (threshold_ != 0.5f) os << '@' << threshold_;
-      name = os.str();
-      return name.c_str();
-    } else {
-      return "error";
-    }
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(
-      bst_float label, bst_float pred) const {
-    // assume label is in [0,1]
-    return pred > threshold_ ? 1.0f - label : label;
-  }
-
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? esum : esum / wsum;
-  }
-
- private:
-  bst_float threshold_;
-  bool has_param_;
-};
-
-struct EvalPoissonNegLogLik {
-  const char *Name() const {
-    return "poisson-nloglik";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float y, bst_float py) const {
-    const bst_float eps = 1e-16f;
-    if (py < eps) py = eps;
-    return common::LogGamma(y + 1.0f) + py - std::log(py) * y;
-  }
-
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? esum : esum / wsum;
-  }
-};
-
-struct EvalGammaDeviance {
-  const char *Name() const {
-    return "gamma-deviance";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float label, bst_float pred) const {
-    bst_float epsilon = 1.0e-9;
-    bst_float tmp = label / (pred + epsilon);
-    return tmp - std::log(tmp) - 1;
-  }
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return 2 * esum;
-  }
-};
-
-struct EvalGammaNLogLik {
-  static const char *Name() {
-    return "gamma-nloglik";
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float y, bst_float py) const {
-    bst_float psi = 1.0;
-    bst_float theta = -1. / py;
-    bst_float a = psi;
-    bst_float b = -std::log(-theta);
-    bst_float c = 1. / psi * std::log(y/psi) - std::log(y) - common::LogGamma(1. / psi);
-    return -((y * theta - b) / a + c);
-  }
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? esum : esum / wsum;
-  }
-};
-
-struct EvalTweedieNLogLik {
-  explicit EvalTweedieNLogLik(const char* param) {
-    CHECK(param != nullptr)
-        << "tweedie-nloglik must be in format tweedie-nloglik@rho";
-    rho_ = atof(param);
-    CHECK(rho_ < 2 && rho_ >= 1)
-        << "tweedie variance power must be in interval [1, 2)";
-  }
-  const char *Name() const {
-    static std::string name;
-    std::ostringstream os;
-    os << "tweedie-nloglik@" << rho_;
-    name = os.str();
-    return name.c_str();
-  }
-
-  XGBOOST_DEVICE bst_float EvalRow(bst_float y, bst_float p) const {
-    bst_float a = y * std::exp((1 - rho_) * std::log(p)) / (1 - rho_);
-    bst_float b = std::exp((2 - rho_) * std::log(p)) / (2 - rho_);
-    return -a + b;
-  }
-  static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return wsum == 0 ? esum : esum / wsum;
-  }
-
- protected:
-  bst_float rho_;
-};
-/*!
- * \brief base class of element-wise evaluation
- * \tparam Derived the name of subclass
- */
-template<typename Policy>
-struct EvalEWiseBase : public Metric {
-  EvalEWiseBase() = default;
-  explicit EvalEWiseBase(char const* policy_param) :
-    policy_{policy_param}, reducer_{policy_} {}
-
-  bst_float Eval(const HostDeviceVector<bst_float>& preds,
-                 const MetaInfo& info,
-                 bool distributed) override {
-    if (info.labels_.Size() == 0) {
-      LOG(WARNING) << "label set is empty";
-    }
-    CHECK_EQ(preds.Size(), info.labels_.Size())
-        << "label and prediction size not match, "
-        << "hint: use merror or mlogloss for multi-class classification";
-    int device = tparam_->gpu_id;
-
-    auto result =
-        reducer_.Reduce(*tparam_, device, info.weights_, info.labels_, preds);
-
-    double dat[2] { result.Residue(), result.Weights() };
-
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    return Policy::GetFinal(dat[0], dat[1]);
-  }
-
-  const char* Name() const override {
-    return policy_.Name();
-  }
-
- private:
-  Policy policy_;
-  ElementWiseMetricsReduction<Policy> reducer_{policy_};
-};
-
-XGBOOST_REGISTER_METRIC(RMSE, "rmse")
-.describe("Rooted mean square error.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalRowRMSE>(); });
-
-XGBOOST_REGISTER_METRIC(RMSLE, "rmsle")
-.describe("Rooted mean square log error.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalRowRMSLE>(); });
-
-XGBOOST_REGISTER_METRIC(MAE, "mae")
-.describe("Mean absolute error.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalRowMAE>(); });
-
-XGBOOST_REGISTER_METRIC(LogLoss, "logloss")
-.describe("Negative loglikelihood for logistic regression.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalRowLogLoss>(); });
-
-XGBOOST_REGISTER_METRIC(PossionNegLoglik, "poisson-nloglik")
-.describe("Negative loglikelihood for poisson regression.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalPoissonNegLogLik>(); });
-
-XGBOOST_REGISTER_METRIC(GammaDeviance, "gamma-deviance")
-.describe("Residual deviance for gamma regression.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalGammaDeviance>(); });
-
-XGBOOST_REGISTER_METRIC(GammaNLogLik, "gamma-nloglik")
-.describe("Negative log-likelihood for gamma regression.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalGammaNLogLik>(); });
-
-XGBOOST_REGISTER_METRIC(Error, "error")
-.describe("Binary classification error.")
-.set_body([](const char* param) { return new EvalEWiseBase<EvalError>(param); });
-
-XGBOOST_REGISTER_METRIC(TweedieNLogLik, "tweedie-nloglik")
-.describe("tweedie-nloglik@rho for tweedie regression.")
-.set_body([](const char* param) {
-  return new EvalEWiseBase<EvalTweedieNLogLik>(param);
-});
-
-}  // namespace metric
-}  // namespace xgboost
diff --git a/ml-xgboost/src/metric/metric.cc b/ml-xgboost/src/metric/metric.cc
deleted file mode 100644
index 941a0d9..0000000
--- a/ml-xgboost/src/metric/metric.cc
+++ /dev/null
@@ -1,88 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file metric_registry.cc
- * \brief Registry of objective functions.
- */
-#include <dmlc/registry.h>
-#include <xgboost/metric.h>
-#include <xgboost/generic_parameters.h>
-
-#include "metric_common.h"
-
-namespace xgboost {
-template <typename MetricRegistry>
-Metric* CreateMetricImpl(const std::string& name, GenericParameter const* tparam) {
-  std::string buf = name;
-  std::string prefix = name;
-  const char* param;
-  auto pos = buf.find('@');
-  if (pos == std::string::npos) {
-    if (!buf.empty() && buf.back() == '-') {
-      // Metrics of form "metric-"
-      prefix = buf.substr(0, buf.length() - 1);  // Chop off '-'
-      param = "-";
-    } else {
-      prefix = buf;
-      param = nullptr;
-    }
-    auto *e = ::dmlc::Registry<MetricRegistry>::Get()->Find(prefix.c_str());
-    if (e == nullptr) {
-      return nullptr;
-    }
-    auto p_metric = (e->body)(param);
-    return p_metric;
-  } else {
-    std::string prefix = buf.substr(0, pos);
-    auto *e = ::dmlc::Registry<MetricRegistry>::Get()->Find(prefix.c_str());
-    if (e == nullptr) {
-      return nullptr;
-    }
-    auto p_metric = (e->body)(buf.substr(pos + 1, buf.length()).c_str());
-    return p_metric;
-  }
-}
-
-Metric *
-Metric::Create(const std::string& name, GenericParameter const* tparam) {
-  auto metric = CreateMetricImpl<MetricReg>(name, tparam);
-  if (metric == nullptr) {
-    LOG(FATAL) << "Unknown metric function " << name;
-  }
-
-  metric->tparam_ = tparam;
-  return metric;
-}
-
-Metric *
-GPUMetric::CreateGPUMetric(const std::string& name, GenericParameter const* tparam) {
-  auto metric = CreateMetricImpl<MetricGPUReg>(name, tparam);
-  if (metric == nullptr) {
-    LOG(WARNING) << "Cannot find a GPU metric builder for metric " << name
-                 << ". Resorting to the CPU builder";
-    return metric;
-  }
-
-  // Narrowing reference only for the compiler to allow assignment to a base class member.
-  // As such, using this narrowed reference to refer to derived members will be an illegal op.
-  // This is moot, as this type is stateless.
-  static_cast<GPUMetric *>(metric)->tparam_ = tparam;
-  return metric;
-}
-}  // namespace xgboost
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::MetricReg);
-DMLC_REGISTRY_ENABLE(::xgboost::MetricGPUReg);
-}
-
-namespace xgboost {
-namespace metric {
-// List of files that will be force linked in static links.
-DMLC_REGISTRY_LINK_TAG(elementwise_metric);
-DMLC_REGISTRY_LINK_TAG(multiclass_metric);
-DMLC_REGISTRY_LINK_TAG(rank_metric);
-#ifdef XGBOOST_USE_CUDA
-DMLC_REGISTRY_LINK_TAG(rank_metric_gpu);
-#endif
-}  // namespace metric
-}  // namespace xgboost
diff --git a/ml-xgboost/src/metric/metric_common.h b/ml-xgboost/src/metric/metric_common.h
deleted file mode 100644
index d676797..0000000
--- a/ml-xgboost/src/metric/metric_common.h
+++ /dev/null
@@ -1,90 +0,0 @@
-/*!
- * Copyright 2018-2020 by Contributors
- * \file metric_common.h
- */
-#ifndef XGBOOST_METRIC_METRIC_COMMON_H_
-#define XGBOOST_METRIC_METRIC_COMMON_H_
-
-#include <limits>
-#include <string>
-
-#include "../common/common.h"
-
-namespace xgboost {
-
-// This creates a GPU metric instance dynamically and adds it to the GPU metric registry, if not
-// present already. This is created when there is a device ordinal present and if xgboost
-// is compiled with CUDA support
-struct GPUMetric : Metric {
-  static Metric *CreateGPUMetric(const std::string& name, GenericParameter const* tparam);
-};
-
-/*!
- * \brief Internal registry entries for GPU Metric factory functions.
- *  The additional parameter const char* param gives the value after @, can be null.
- *  For example, metric map@3, then: param == "3".
- */
-struct MetricGPUReg
-  : public dmlc::FunctionRegEntryBase<MetricGPUReg,
-                                      std::function<Metric * (const char*)> > {
-};
-
-/*!
- * \brief Macro to register metric computed on GPU.
- *
- * \code
- * // example of registering a objective ndcg@k
- * XGBOOST_REGISTER_GPU_METRIC(NDCG_GPU, "ndcg")
- * .describe("NDCG metric computer on GPU.")
- * .set_body([](const char* param) {
- *     int at_k = atoi(param);
- *     return new NDCG(at_k);
- *   });
- * \endcode
- */
-
-// Note: Metric names registered in the GPU registry should follow this convention:
-// - GPU metric types should be registered with the same name as the non GPU metric types
-#define XGBOOST_REGISTER_GPU_METRIC(UniqueId, Name)                         \
-  ::xgboost::MetricGPUReg&  __make_ ## MetricGPUReg ## _ ## UniqueId ## __ =  \
-      ::dmlc::Registry< ::xgboost::MetricGPUReg>::Get()->__REGISTER__(Name)
-
-namespace metric {
-
-// Ranking config to be used on device and host
-struct EvalRankConfig {
- public:
-  // Parsed from metric name, the top-n number of instances within a group after
-  // ranking to use for evaluation.
-  unsigned topn{std::numeric_limits<unsigned>::max()};
-  std::string name;
-  bool minus{false};
-};
-
-class PackedReduceResult {
-  double residue_sum_ { 0 };
-  double weights_sum_ { 0 };
-
- public:
-  XGBOOST_DEVICE PackedReduceResult() {}  // NOLINT
-  XGBOOST_DEVICE PackedReduceResult(double residue, double weight)
-      : residue_sum_{residue}, weights_sum_{weight} {}
-
-  XGBOOST_DEVICE
-  PackedReduceResult operator+(PackedReduceResult const &other) const {
-    return PackedReduceResult{residue_sum_ + other.residue_sum_,
-                              weights_sum_ + other.weights_sum_};
-  }
-  PackedReduceResult &operator+=(PackedReduceResult const &other) {
-    this->residue_sum_ += other.residue_sum_;
-    this->weights_sum_ += other.weights_sum_;
-    return *this;
-  }
-  double Residue() const { return residue_sum_; }
-  double Weights() const { return weights_sum_; }
-};
-
-}  // namespace metric
-}  // namespace xgboost
-
-#endif  // XGBOOST_METRIC_METRIC_COMMON_H_
diff --git a/ml-xgboost/src/metric/multiclass_metric.cc b/ml-xgboost/src/metric/multiclass_metric.cc
deleted file mode 100644
index 7733a33..0000000
--- a/ml-xgboost/src/metric/multiclass_metric.cc
+++ /dev/null
@@ -1,8 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-// Dummy file to keep the CUDA conditional compile trick.
-
-#if !defined(XGBOOST_USE_CUDA)
-#include "multiclass_metric.cu"
-#endif  // !defined(XGBOOST_USE_CUDA)
diff --git a/ml-xgboost/src/metric/multiclass_metric.cu b/ml-xgboost/src/metric/multiclass_metric.cu
deleted file mode 100644
index 377a050..0000000
--- a/ml-xgboost/src/metric/multiclass_metric.cu
+++ /dev/null
@@ -1,241 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file multiclass_metric.cc
- * \brief evaluation metrics for multiclass classification.
- * \author Kailong Chen, Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <xgboost/metric.h>
-#include <cmath>
-
-#include "metric_common.h"
-#include "../common/math.h"
-#include "../common/common.h"
-
-#if defined(XGBOOST_USE_CUDA)
-#include <thrust/execution_policy.h>  // thrust::cuda::par
-#include <thrust/functional.h>        // thrust::plus<>
-#include <thrust/transform_reduce.h>
-#include <thrust/iterator/counting_iterator.h>
-
-#include "../common/device_helpers.cuh"
-#endif  // XGBOOST_USE_CUDA
-
-namespace xgboost {
-namespace metric {
-// tag the this file, used by force static link later.
-DMLC_REGISTRY_FILE_TAG(multiclass_metric);
-
-template <typename EvalRowPolicy>
-class MultiClassMetricsReduction {
-  void CheckLabelError(int32_t label_error, size_t n_class) const {
-    CHECK(label_error >= 0 && label_error < static_cast<int32_t>(n_class))
-        << "MultiClassEvaluation: label must be in [0, num_class),"
-        << " num_class=" << n_class << " but found " << label_error << " in label";
-  }
-
- public:
-  MultiClassMetricsReduction() = default;
-
-  PackedReduceResult CpuReduceMetrics(
-      const HostDeviceVector<bst_float>& weights,
-      const HostDeviceVector<bst_float>& labels,
-      const HostDeviceVector<bst_float>& preds,
-      const size_t n_class) const {
-    size_t ndata = labels.Size();
-
-    const auto& h_labels = labels.HostVector();
-    const auto& h_weights = weights.HostVector();
-    const auto& h_preds = preds.HostVector();
-
-    bst_float residue_sum = 0;
-    bst_float weights_sum = 0;
-    int label_error = 0;
-    bool const is_null_weight = weights.Size() == 0;
-
-#pragma omp parallel for reduction(+: residue_sum, weights_sum) schedule(static)
-    for (omp_ulong idx = 0; idx < ndata; ++idx) {
-      bst_float weight = is_null_weight ? 1.0f : h_weights[idx];
-      auto label = static_cast<int>(h_labels[idx]);
-      if (label >= 0 && label < static_cast<int>(n_class)) {
-        residue_sum += EvalRowPolicy::EvalRow(
-            label, h_preds.data() + idx * n_class, n_class) * weight;
-        weights_sum += weight;
-      } else {
-        label_error = label;
-      }
-    }
-    CheckLabelError(label_error, n_class);
-    PackedReduceResult res { residue_sum, weights_sum };
-
-    return res;
-  }
-
-#if defined(XGBOOST_USE_CUDA)
-
-  PackedReduceResult DeviceReduceMetrics(
-      const HostDeviceVector<bst_float>& weights,
-      const HostDeviceVector<bst_float>& labels,
-      const HostDeviceVector<bst_float>& preds,
-      const size_t n_class) {
-    size_t n_data = labels.Size();
-
-    thrust::counting_iterator<size_t> begin(0);
-    thrust::counting_iterator<size_t> end = begin + n_data;
-
-    auto s_labels = labels.DeviceSpan();
-    auto s_preds = preds.DeviceSpan();
-    auto s_weights = weights.DeviceSpan();
-
-    bool const is_null_weight = weights.Size() == 0;
-    auto s_label_error = label_error_.GetSpan<int32_t>(1);
-    s_label_error[0] = 0;
-
-    dh::XGBCachingDeviceAllocator<char> alloc;
-    PackedReduceResult result = thrust::transform_reduce(
-        thrust::cuda::par(alloc),
-        begin, end,
-        [=] XGBOOST_DEVICE(size_t idx) {
-          bst_float weight = is_null_weight ? 1.0f : s_weights[idx];
-          bst_float residue = 0;
-          auto label = static_cast<int>(s_labels[idx]);
-          if (label >= 0 && label < static_cast<int32_t>(n_class)) {
-            residue = EvalRowPolicy::EvalRow(
-                label, &s_preds[idx * n_class], n_class) * weight;
-          } else {
-            s_label_error[0] = label;
-          }
-          return PackedReduceResult{ residue, weight };
-        },
-        PackedReduceResult(),
-        thrust::plus<PackedReduceResult>());
-    CheckLabelError(s_label_error[0], n_class);
-
-    return result;
-  }
-
-#endif  // XGBOOST_USE_CUDA
-
-  PackedReduceResult Reduce(
-      const GenericParameter &tparam,
-      int device,
-      size_t n_class,
-      const HostDeviceVector<bst_float>& weights,
-      const HostDeviceVector<bst_float>& labels,
-      const HostDeviceVector<bst_float>& preds) {
-    PackedReduceResult result;
-
-    if (device < 0) {
-      result = CpuReduceMetrics(weights, labels, preds, n_class);
-    }
-#if defined(XGBOOST_USE_CUDA)
-    else {  // NOLINT
-      device_ = tparam.gpu_id;
-      preds.SetDevice(device_);
-      labels.SetDevice(device_);
-      weights.SetDevice(device_);
-
-      dh::safe_cuda(cudaSetDevice(device_));
-      result = DeviceReduceMetrics(weights, labels, preds, n_class);
-    }
-#endif  // defined(XGBOOST_USE_CUDA)
-    return result;
-  }
-
- private:
-#if defined(XGBOOST_USE_CUDA)
-  dh::PinnedMemory label_error_;
-  int device_{-1};
-#endif  // defined(XGBOOST_USE_CUDA)
-};
-
-/*!
- * \brief base class of multi-class evaluation
- * \tparam Derived the name of subclass
- */
-template<typename Derived>
-struct EvalMClassBase : public Metric {
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK(preds.Size() % info.labels_.Size() == 0)
-        << "label and prediction size not match";
-    const size_t nclass = preds.Size() / info.labels_.Size();
-    CHECK_GE(nclass, 1U)
-        << "mlogloss and merror are only used for multi-class classification,"
-        << " use logloss for binary classification";
-
-    int device = tparam_->gpu_id;
-    auto result = reducer_.Reduce(*tparam_, device, nclass, info.weights_, info.labels_, preds);
-    double dat[2] { result.Residue(), result.Weights() };
-
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    return Derived::GetFinal(dat[0], dat[1]);
-  }
-  /*!
-   * \brief to be implemented by subclass,
-   *   get evaluation result from one row
-   * \param label label of current instance
-   * \param pred prediction value of current instance
-   * \param nclass number of class in the prediction
-   */
-  XGBOOST_DEVICE static bst_float EvalRow(int label,
-                                          const bst_float *pred,
-                                          size_t nclass);
-  /*!
-   * \brief to be overridden by subclass, final transformation
-   * \param esum the sum statistics returned by EvalRow
-   * \param wsum sum of weight
-   */
-  inline static bst_float GetFinal(bst_float esum, bst_float wsum) {
-    return esum / wsum;
-  }
-
- private:
-  MultiClassMetricsReduction<Derived> reducer_;
-  // used to store error message
-  const char *error_msg_;
-};
-
-/*! \brief match error */
-struct EvalMatchError : public EvalMClassBase<EvalMatchError> {
-  const char* Name() const override {
-    return "merror";
-  }
-  XGBOOST_DEVICE static bst_float EvalRow(int label,
-                                          const bst_float *pred,
-                                          size_t nclass) {
-    return common::FindMaxIndex(pred, pred + nclass) != pred + static_cast<int>(label);
-  }
-};
-
-/*! \brief match error */
-struct EvalMultiLogLoss : public EvalMClassBase<EvalMultiLogLoss> {
-  const char* Name() const override {
-    return "mlogloss";
-  }
-  XGBOOST_DEVICE static bst_float EvalRow(int label,
-                                          const bst_float *pred,
-                                          size_t nclass) {
-    const bst_float eps = 1e-16f;
-    auto k = static_cast<size_t>(label);
-    if (pred[k] > eps) {
-      return -std::log(pred[k]);
-    } else {
-      return -std::log(eps);
-    }
-  }
-};
-
-XGBOOST_REGISTER_METRIC(MatchError, "merror")
-.describe("Multiclass classification error.")
-.set_body([](const char* param) { return new EvalMatchError(); });
-
-XGBOOST_REGISTER_METRIC(MultiLogLoss, "mlogloss")
-.describe("Multiclass negative loglikelihood.")
-.set_body([](const char* param) { return new EvalMultiLogLoss(); });
-}  // namespace metric
-}  // namespace xgboost
diff --git a/ml-xgboost/src/metric/rank_metric.cc b/ml-xgboost/src/metric/rank_metric.cc
deleted file mode 100644
index b55c764..0000000
--- a/ml-xgboost/src/metric/rank_metric.cc
+++ /dev/null
@@ -1,676 +0,0 @@
-/*!
- * Copyright 2020 XGBoost contributors
- */
-// When device ordinal is present, we would want to build the metrics on the GPU. It is *not*
-// possible for a valid device ordinal to be present for non GPU builds. However, it is possible
-// for an invalid device ordinal to be specified in GPU builds - to train/predict and/or compute
-// the metrics on CPU. To accommodate these scenarios, the following is done for the metrics
-// accelarated on the GPU.
-// - An internal GPU registry holds all the GPU metric types (defined in the .cu file)
-// - An instance of the appropriate gpu metric type is created when a device ordinal is present
-// - If the creation is successful, the metric computation is done on the device
-// - else, it falls back on the CPU
-// - The GPU metric types are *only* registered when xgboost is built for GPUs
-//
-// This is done for 2 reasons:
-// - Clear separation of CPU and GPU logic
-// - Sorting datasets containing large number of rows is (much) faster when parallel sort
-//   semantics is used on the CPU. The __gnu_parallel/concurrency primitives needed to perform
-//   this cannot be used when the translation unit is compiled using the 'nvcc' compiler (as the
-//   corresponding headers that brings in those function declaration can't be included with CUDA).
-//   This precludes the CPU and GPU logic to coexist inside a .cu file
-
-#include <rabit/rabit.h>
-#include <xgboost/metric.h>
-#include <dmlc/registry.h>
-#include <cmath>
-
-#include <vector>
-
-#include "xgboost/host_device_vector.h"
-#include "../common/math.h"
-#include "metric_common.h"
-
-namespace {
-
-using PredIndPair = std::pair<xgboost::bst_float, uint32_t>;
-using PredIndPairContainer = std::vector<PredIndPair>;
-
-/*
- * Adapter to access instance weights.
- *
- *  - For ranking task, weights are per-group
- *  - For binary classification task, weights are per-instance
- *
- * WeightPolicy::GetWeightOfInstance() :
- *   get weight associated with an individual instance, using index into
- *   `info.weights`
- * WeightPolicy::GetWeightOfSortedRecord() :
- *   get weight associated with an individual instance, using index into
- *   sorted records `rec` (in ascending order of predicted labels). `rec` is
- *   of type PredIndPairContainer
- */
-
-class PerInstanceWeightPolicy {
- public:
-  inline static xgboost::bst_float
-  GetWeightOfInstance(const xgboost::MetaInfo& info,
-                      unsigned instance_id, unsigned group_id) {
-    return info.GetWeight(instance_id);
-  }
-  inline static xgboost::bst_float
-  GetWeightOfSortedRecord(const xgboost::MetaInfo& info,
-                          const PredIndPairContainer& rec,
-                          unsigned record_id, unsigned group_id) {
-    return info.GetWeight(rec[record_id].second);
-  }
-};
-
-class PerGroupWeightPolicy {
- public:
-  inline static xgboost::bst_float
-  GetWeightOfInstance(const xgboost::MetaInfo& info,
-                      unsigned instance_id, unsigned group_id) {
-    return info.GetWeight(group_id);
-  }
-
-  inline static xgboost::bst_float
-  GetWeightOfSortedRecord(const xgboost::MetaInfo& info,
-                          const PredIndPairContainer& rec,
-                          unsigned record_id, unsigned group_id) {
-    return info.GetWeight(group_id);
-  }
-};
-
-}  // anonymous namespace
-
-namespace xgboost {
-namespace metric {
-// tag the this file, used by force static link later.
-DMLC_REGISTRY_FILE_TAG(rank_metric);
-
-/*! \brief AMS: also records best threshold */
-struct EvalAMS : public Metric {
- public:
-  explicit EvalAMS(const char* param) {
-    CHECK(param != nullptr)  // NOLINT
-        << "AMS must be in format ams@k";
-    ratio_ = atof(param);
-    std::ostringstream os;
-    os << "ams@" << ratio_;
-    name_ = os.str();
-  }
-
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK(!distributed) << "metric AMS do not support distributed evaluation";
-    using namespace std;  // NOLINT(*)
-
-    const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
-    PredIndPairContainer rec(ndata);
-
-    const auto &h_preds = preds.ConstHostVector();
-    #pragma omp parallel for schedule(static)
-    for (bst_omp_uint i = 0; i < ndata; ++i) {
-      rec[i] = std::make_pair(h_preds[i], i);
-    }
-    XGBOOST_PARALLEL_SORT(rec.begin(), rec.end(), common::CmpFirst);
-    auto ntop = static_cast<unsigned>(ratio_ * ndata);
-    if (ntop == 0) ntop = ndata;
-    const double br = 10.0;
-    unsigned thresindex = 0;
-    double s_tp = 0.0, b_fp = 0.0, tams = 0.0;
-    const auto& labels = info.labels_.ConstHostVector();
-    for (unsigned i = 0; i < static_cast<unsigned>(ndata-1) && i < ntop; ++i) {
-      const unsigned ridx = rec[i].second;
-      const bst_float wt = info.GetWeight(ridx);
-      if (labels[ridx] > 0.5f) {
-        s_tp += wt;
-      } else {
-        b_fp += wt;
-      }
-      if (rec[i].first != rec[i + 1].first) {
-        double ams = sqrt(2 * ((s_tp + b_fp + br) * log(1.0 + s_tp / (b_fp + br)) - s_tp));
-        if (tams < ams) {
-          thresindex = i;
-          tams = ams;
-        }
-      }
-    }
-    if (ntop == ndata) {
-      LOG(INFO) << "best-ams-ratio=" << static_cast<bst_float>(thresindex) / ndata;
-      return static_cast<bst_float>(tams);
-    } else {
-      return static_cast<bst_float>(
-          sqrt(2 * ((s_tp + b_fp + br) * log(1.0 + s_tp/(b_fp + br)) - s_tp)));
-    }
-  }
-
-  const char* Name() const override {
-    return name_.c_str();
-  }
-
- private:
-  std::string name_;
-  float ratio_;
-};
-
-/*! \brief Area Under Curve, for both classification and rank computed on CPU */
-struct EvalAuc : public Metric {
- private:
-  // This is used to compute the AUC metrics on the GPU - for ranking tasks and
-  // for training jobs that run on the GPU.
-  std::unique_ptr<xgboost::Metric> auc_gpu_;
-
-  template <typename WeightPolicy>
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed,
-                 const std::vector<unsigned> &gptr) {
-    const auto ngroups = static_cast<bst_omp_uint>(gptr.size() - 1);
-    // sum of all AUC's across all query groups
-    double sum_auc = 0.0;
-    int auc_error = 0;
-    const auto& labels = info.labels_.ConstHostVector();
-    const auto &h_preds = preds.ConstHostVector();
-
-    #pragma omp parallel reduction(+:sum_auc, auc_error) if (ngroups > 1)
-    {
-      // Each thread works on a distinct group and sorts the predictions in that group
-      PredIndPairContainer rec;
-      #pragma omp for schedule(static)
-      for (bst_omp_uint group_id = 0; group_id < ngroups; ++group_id) {
-        // Same thread can work on multiple groups one after another; hence, resize
-        // the predictions array based on the current group
-        rec.resize(gptr[group_id + 1] - gptr[group_id]);
-        #pragma omp parallel for schedule(static) if (!omp_in_parallel())
-        for (bst_omp_uint j = gptr[group_id]; j < gptr[group_id + 1]; ++j) {
-          rec[j - gptr[group_id]] = {h_preds[j], j};
-        }
-
-        XGBOOST_PARALLEL_SORT(rec.begin(), rec.end(), common::CmpFirst);
-        // calculate AUC
-        double sum_pospair = 0.0;
-        double sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
-        for (size_t j = 0; j < rec.size(); ++j) {
-          const bst_float wt = WeightPolicy::GetWeightOfSortedRecord(info, rec, j, group_id);
-          const bst_float ctr = labels[rec[j].second];
-          // keep bucketing predictions in same bucket
-          if (j != 0 && rec[j].first != rec[j - 1].first) {
-            sum_pospair += buf_neg * (sum_npos + buf_pos * 0.5);
-            sum_npos += buf_pos;
-            sum_nneg += buf_neg;
-            buf_neg = buf_pos = 0.0f;
-          }
-          buf_pos += ctr * wt;
-          buf_neg += (1.0f - ctr) * wt;
-        }
-        sum_pospair += buf_neg * (sum_npos + buf_pos * 0.5);
-        sum_npos += buf_pos;
-        sum_nneg += buf_neg;
-        // check weird conditions
-        if (sum_npos <= 0.0 || sum_nneg <= 0.0) {
-          auc_error += 1;
-        } else {
-          // this is the AUC
-          sum_auc += sum_pospair / (sum_npos * sum_nneg);
-        }
-      }
-    }
-
-    // Report average AUC across all groups
-    // In distributed mode, workers which only contains pos or neg samples
-    // will be ignored when aggregate AUC.
-    bst_float dat[2] = {0.0f, 0.0f};
-    if (auc_error < static_cast<int>(ngroups)) {
-      dat[0] = static_cast<bst_float>(sum_auc);
-      dat[1] = static_cast<bst_float>(static_cast<int>(ngroups) - auc_error);
-    }
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    CHECK_GT(dat[1], 0.0f)
-      << "AUC: the dataset only contains pos or neg samples";
-    return dat[0] / dat[1];
-  }
-
- public:
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size())
-        << "label size predict size not match";
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(info.labels_.Size());
-
-    const auto &gptr = info.group_ptr_.empty() ? tgptr : info.group_ptr_;
-    CHECK_EQ(gptr.back(), info.labels_.Size())
-        << "EvalAuc: group structure must match number of prediction";
-
-    // For ranking task, weights are per-group
-    // For binary classification task, weights are per-instance
-    const bool is_ranking_task =
-      !info.group_ptr_.empty() && info.weights_.Size() != info.num_row_;
-
-    // Check if we have a GPU assignment; else, revert back to CPU
-    if (tparam_->gpu_id >= 0) {
-      if (!auc_gpu_) {
-        // Check and see if we have the GPU metric registered in the internal registry
-        auc_gpu_.reset(GPUMetric::CreateGPUMetric(this->Name(), tparam_));
-      }
-
-      if (auc_gpu_) {
-        return auc_gpu_->Eval(preds, info, distributed);
-      }
-    }
-
-    if (is_ranking_task) {
-      return Eval<PerGroupWeightPolicy>(preds, info, distributed, gptr);
-    } else {
-      return Eval<PerInstanceWeightPolicy>(preds, info, distributed, gptr);
-    }
-  }
-
-  const char *Name() const override { return "auc"; }
-};
-
-/*! \brief Evaluate rank list */
-struct EvalRank : public Metric, public EvalRankConfig {
- private:
-  // This is used to compute the ranking metrics on the GPU - for training jobs that run on the GPU.
-  std::unique_ptr<xgboost::Metric> rank_gpu_;
-
- public:
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK_EQ(preds.Size(), info.labels_.Size())
-        << "label size predict size not match";
-
-    // quick consistency when group is not available
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(preds.Size());
-    const auto &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
-
-    CHECK_NE(gptr.size(), 0U) << "must specify group when constructing rank file";
-    CHECK_EQ(gptr.back(), preds.Size())
-        << "EvalRank: group structure must match number of prediction";
-
-    const auto ngroups = static_cast<bst_omp_uint>(gptr.size() - 1);
-    // sum statistics
-    double sum_metric = 0.0f;
-
-    // Check and see if we have the GPU metric registered in the internal registry
-    if (tparam_->gpu_id >= 0) {
-      if (!rank_gpu_) {
-        rank_gpu_.reset(GPUMetric::CreateGPUMetric(this->Name(), tparam_));
-      }
-      if (rank_gpu_) {
-        sum_metric = rank_gpu_->Eval(preds, info, distributed);
-      }
-    }
-
-    if (!rank_gpu_ || tparam_->gpu_id < 0) {
-      const auto &labels = info.labels_.ConstHostVector();
-      const auto &h_preds = preds.ConstHostVector();
-
-      #pragma omp parallel reduction(+:sum_metric)
-      {
-        // each thread takes a local rec
-        PredIndPairContainer rec;
-        #pragma omp for schedule(static)
-        for (bst_omp_uint k = 0; k < ngroups; ++k) {
-          rec.clear();
-          for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
-            rec.emplace_back(h_preds[j], static_cast<int>(labels[j]));
-          }
-          sum_metric += this->EvalGroup(&rec);
-        }
-      }
-    }
-
-    if (distributed) {
-      bst_float dat[2];
-      dat[0] = static_cast<bst_float>(sum_metric);
-      dat[1] = static_cast<bst_float>(ngroups);
-      // approximately estimate the metric using mean
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-      return dat[0] / dat[1];
-    } else {
-      return static_cast<bst_float>(sum_metric) / ngroups;
-    }
-  }
-
-  const char* Name() const override {
-    return name.c_str();
-  }
-
- protected:
-  explicit EvalRank(const char* name, const char* param) {
-    using namespace std;  // NOLINT(*)
-
-    if (param != nullptr) {
-      std::ostringstream os;
-      if (sscanf(param, "%u[-]?", &topn) == 1) {
-        os << name << '@' << param;
-        this->name = os.str();
-      } else {
-        os << name << param;
-        this->name = os.str();
-      }
-      if (param[strlen(param) - 1] == '-') {
-        minus = true;
-      }
-    } else {
-      this->name = name;
-    }
-  }
-
-  virtual double EvalGroup(PredIndPairContainer *recptr) const = 0;
-};
-
-/*! \brief Precision at N, for both classification and rank */
-struct EvalPrecision : public EvalRank {
- public:
-  explicit EvalPrecision(const char* name, const char* param) : EvalRank(name, param) {}
-
-  double EvalGroup(PredIndPairContainer *recptr) const override {
-    PredIndPairContainer &rec(*recptr);
-    // calculate Precision
-    std::stable_sort(rec.begin(), rec.end(), common::CmpFirst);
-    unsigned nhit = 0;
-    for (size_t j = 0; j < rec.size() && j < this->topn; ++j) {
-      nhit += (rec[j].second != 0);
-    }
-    return static_cast<double>(nhit) / this->topn;
-  }
-};
-
-/*! \brief NDCG: Normalized Discounted Cumulative Gain at N */
-struct EvalNDCG : public EvalRank {
- private:
-  double CalcDCG(const PredIndPairContainer &rec) const {
-    double sumdcg = 0.0;
-    for (size_t i = 0; i < rec.size() && i < this->topn; ++i) {
-      const unsigned rel = rec[i].second;
-      if (rel != 0) {
-        sumdcg += ((1 << rel) - 1) / std::log2(i + 2.0);
-      }
-    }
-    return sumdcg;
-  }
-
- public:
-  explicit EvalNDCG(const char* name, const char* param) : EvalRank(name, param) {}
-
-  double EvalGroup(PredIndPairContainer *recptr) const override {
-    PredIndPairContainer &rec(*recptr);
-    std::stable_sort(rec.begin(), rec.end(), common::CmpFirst);
-    double dcg = CalcDCG(rec);
-    std::stable_sort(rec.begin(), rec.end(), common::CmpSecond);
-    double idcg = CalcDCG(rec);
-    if (idcg == 0.0f) {
-      if (this->minus) {
-        return 0.0f;
-      } else {
-        return 1.0f;
-      }
-    }
-    return dcg/idcg;
-  }
-};
-
-/*! \brief Mean Average Precision at N, for both classification and rank */
-struct EvalMAP : public EvalRank {
- public:
-  explicit EvalMAP(const char* name, const char* param) : EvalRank(name, param) {}
-
-  double EvalGroup(PredIndPairContainer *recptr) const override {
-    PredIndPairContainer &rec(*recptr);
-    std::stable_sort(rec.begin(), rec.end(), common::CmpFirst);
-    unsigned nhits = 0;
-    double sumap = 0.0;
-    for (size_t i = 0; i < rec.size(); ++i) {
-      if (rec[i].second != 0) {
-        nhits += 1;
-        if (i < this->topn) {
-          sumap += static_cast<double>(nhits) / (i + 1);
-        }
-      }
-    }
-    if (nhits != 0) {
-      sumap /= nhits;
-      return sumap;
-    } else {
-      if (this->minus) {
-        return 0.0f;
-      } else {
-        return 1.0f;
-      }
-    }
-  }
-};
-
-/*! \brief Cox: Partial likelihood of the Cox proportional hazards model */
-struct EvalCox : public Metric {
- public:
-  EvalCox() = default;
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK(!distributed) << "Cox metric does not support distributed evaluation";
-    using namespace std;  // NOLINT(*)
-
-    const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
-    const auto &label_order = info.LabelAbsSort();
-
-    // pre-compute a sum for the denominator
-    double exp_p_sum = 0;  // we use double because we might need the precision with large datasets
-
-    const auto &h_preds = preds.ConstHostVector();
-    for (omp_ulong i = 0; i < ndata; ++i) {
-      exp_p_sum += h_preds[i];
-    }
-
-    double out = 0;
-    double accumulated_sum = 0;
-    bst_omp_uint num_events = 0;
-    const auto& labels = info.labels_.ConstHostVector();
-    for (bst_omp_uint i = 0; i < ndata; ++i) {
-      const size_t ind = label_order[i];
-      const auto label = labels[ind];
-      if (label > 0) {
-        out -= log(h_preds[ind]) - log(exp_p_sum);
-        ++num_events;
-      }
-
-      // only update the denominator after we move forward in time (labels are sorted)
-      accumulated_sum += h_preds[ind];
-      if (i == ndata - 1 || std::abs(label) < std::abs(labels[label_order[i + 1]])) {
-        exp_p_sum -= accumulated_sum;
-        accumulated_sum = 0;
-      }
-    }
-
-    return out/num_events;  // normalize by the number of events
-  }
-
-  const char* Name() const override {
-    return "cox-nloglik";
-  }
-};
-
-/*! \brief Area Under PR Curve, for both classification and rank computed on CPU */
-struct EvalAucPR : public Metric {
-  // implementation of AUC-PR for weighted data
-  // translated from PRROC R Package
-  // see https://doi.org/10.1371/journal.pone.0092209
- private:
-  // This is used to compute the AUCPR metrics on the GPU - for ranking tasks and
-  // for training jobs that run on the GPU.
-  std::unique_ptr<xgboost::Metric> aucpr_gpu_;
-
-  template <typename WeightPolicy>
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed,
-                 const std::vector<unsigned> &gptr) {
-    const auto ngroups = static_cast<bst_omp_uint>(gptr.size() - 1);
-
-    // sum of all AUC's across all query groups
-    double sum_auc = 0.0;
-    int auc_error = 0;
-
-    const auto &h_labels = info.labels_.ConstHostVector();
-    const auto &h_preds = preds.ConstHostVector();
-
-    #pragma omp parallel reduction(+:sum_auc, auc_error) if (ngroups > 1)
-    {
-      // Each thread works on a distinct group and sorts the predictions in that group
-      PredIndPairContainer rec;
-      #pragma omp for schedule(static)
-      for (bst_omp_uint group_id = 0; group_id < ngroups; ++group_id) {
-        double total_pos = 0.0;
-        double total_neg = 0.0;
-        // Same thread can work on multiple groups one after another; hence, resize
-        // the predictions array based on the current group
-        rec.resize(gptr[group_id + 1] - gptr[group_id]);
-        #pragma omp parallel for schedule(static) reduction(+:total_pos, total_neg) \
-          if (!omp_in_parallel())  // NOLINT
-        for (bst_omp_uint j = gptr[group_id]; j < gptr[group_id + 1]; ++j) {
-          const bst_float wt = WeightPolicy::GetWeightOfInstance(info, j, group_id);
-          total_pos += wt * h_labels[j];
-          total_neg += wt * (1.0f - h_labels[j]);
-          rec[j - gptr[group_id]] = {h_preds[j], j};
-        }
-
-        // we need pos > 0 && neg > 0
-        if (total_pos <= 0.0 || total_neg <= 0.0) {
-          auc_error += 1;
-          continue;
-        }
-
-        XGBOOST_PARALLEL_SORT(rec.begin(), rec.end(), common::CmpFirst);
-
-        // calculate AUC
-        double tp = 0.0, prevtp = 0.0, fp = 0.0, prevfp = 0.0, h = 0.0, a = 0.0, b = 0.0;
-        for (size_t j = 0; j < rec.size(); ++j) {
-          const bst_float wt = WeightPolicy::GetWeightOfSortedRecord(info, rec, j, group_id);
-          tp += wt * h_labels[rec[j].second];
-          fp += wt * (1.0f - h_labels[rec[j].second]);
-          if ((j < rec.size() - 1 && rec[j].first != rec[j + 1].first) || j  == rec.size() - 1) {
-            if (tp == prevtp) {
-              a = 1.0;
-              b = 0.0;
-            } else {
-              h = (fp - prevfp) / (tp - prevtp);
-              a = 1.0 + h;
-              b = (prevfp - h * prevtp) / total_pos;
-            }
-            if (0.0 != b) {
-              sum_auc += (tp / total_pos - prevtp / total_pos -
-                          b / a * (std::log(a * tp / total_pos + b) -
-                                   std::log(a * prevtp / total_pos + b))) / a;
-            } else {
-              sum_auc += (tp / total_pos - prevtp / total_pos) / a;
-            }
-            prevtp = tp;
-            prevfp = fp;
-          }
-        }
-        // sanity check
-        if (tp < 0 || prevtp < 0 || fp < 0 || prevfp < 0) {
-          CHECK(!auc_error) << "AUC-PR: error in calculation";
-        }
-      }
-    }
-
-    // Report average AUC-PR across all groups
-    // In distributed mode, workers which only contains pos or neg samples
-    // will be ignored when aggregate AUC-PR.
-    bst_float dat[2] = {0.0f, 0.0f};
-    if (auc_error < static_cast<int>(ngroups)) {
-      dat[0] = static_cast<bst_float>(sum_auc);
-      dat[1] = static_cast<bst_float>(static_cast<int>(ngroups) - auc_error);
-    }
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    CHECK_GT(dat[1], 0.0f)
-      << "AUC-PR: the dataset only contains pos or neg samples";
-    CHECK_LE(dat[0], dat[1]) << "AUC-PR: AUC > 1.0";
-    return dat[0] / dat[1];
-  }
-
- public:
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size())
-        << "label size predict size not match";
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(info.labels_.Size());
-
-    const auto &gptr = info.group_ptr_.empty() ? tgptr : info.group_ptr_;
-    CHECK_EQ(gptr.back(), info.labels_.Size())
-        << "EvalAucPR: group structure must match number of prediction";
-
-    // For ranking task, weights are per-group
-    // For binary classification task, weights are per-instance
-    const bool is_ranking_task =
-      !info.group_ptr_.empty() && info.weights_.Size() != info.num_row_;
-
-    // Check if we have a GPU assignment; else, revert back to CPU
-    if (tparam_->gpu_id >= 0 && is_ranking_task) {
-      if (!aucpr_gpu_) {
-        // Check and see if we have the GPU metric registered in the internal registry
-        aucpr_gpu_.reset(GPUMetric::CreateGPUMetric(this->Name(), tparam_));
-      }
-
-      if (aucpr_gpu_) {
-        return aucpr_gpu_->Eval(preds, info, distributed);
-      }
-    }
-
-    if (is_ranking_task) {
-      return Eval<PerGroupWeightPolicy>(preds, info, distributed, gptr);
-    } else {
-      return Eval<PerInstanceWeightPolicy>(preds, info, distributed, gptr);
-    }
-  }
-
-  const char *Name() const override { return "aucpr"; }
-};
-
-XGBOOST_REGISTER_METRIC(AMS, "ams")
-.describe("AMS metric for higgs.")
-.set_body([](const char* param) { return new EvalAMS(param); });
-
-XGBOOST_REGISTER_METRIC(Auc, "auc")
-.describe("Area under curve for both classification and rank.")
-.set_body([](const char* param) { return new EvalAuc(); });
-
-XGBOOST_REGISTER_METRIC(AucPR, "aucpr")
-.describe("Area under PR curve for both classification and rank.")
-.set_body([](const char* param) { return new EvalAucPR(); });
-
-XGBOOST_REGISTER_METRIC(Precision, "pre")
-.describe("precision@k for rank.")
-.set_body([](const char* param) { return new EvalPrecision("pre", param); });
-
-XGBOOST_REGISTER_METRIC(NDCG, "ndcg")
-.describe("ndcg@k for rank.")
-.set_body([](const char* param) { return new EvalNDCG("ndcg", param); });
-
-XGBOOST_REGISTER_METRIC(MAP, "map")
-.describe("map@k for rank.")
-.set_body([](const char* param) { return new EvalMAP("map", param); });
-
-XGBOOST_REGISTER_METRIC(Cox, "cox-nloglik")
-.describe("Negative log partial likelihood of Cox proportioanl hazards model.")
-.set_body([](const char* param) { return new EvalCox(); });
-}  // namespace metric
-}  // namespace xgboost
diff --git a/ml-xgboost/src/metric/rank_metric.cu b/ml-xgboost/src/metric/rank_metric.cu
deleted file mode 100644
index 290b1fb..0000000
--- a/ml-xgboost/src/metric/rank_metric.cu
+++ /dev/null
@@ -1,709 +0,0 @@
-/*!
- * Copyright 2020 by Contributors
- * \file rank_metric.cc
- * \brief prediction rank based metrics.
- * \author Kailong Chen, Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <dmlc/registry.h>
-
-#include <xgboost/metric.h>
-#include <xgboost/host_device_vector.h>
-#include <thrust/iterator/discard_iterator.h>
-
-#include <cmath>
-#include <vector>
-
-#include "metric_common.h"
-
-#include "../common/math.h"
-#include "../common/device_helpers.cuh"
-
-namespace xgboost {
-namespace metric {
-// tag the this file, used by force static link later.
-DMLC_REGISTRY_FILE_TAG(rank_metric_gpu);
-
-/*! \brief Evaluate rank list on GPU */
-template <typename EvalMetricT>
-struct EvalRankGpu : public Metric, public EvalRankConfig {
- public:
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    // Sanity check is done by the caller
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(preds.Size());
-    const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
-
-    const auto ngroups = static_cast<bst_omp_uint>(gptr.size() - 1);
-
-    auto device = tparam_->gpu_id;
-    dh::safe_cuda(cudaSetDevice(device));
-
-    info.labels_.SetDevice(device);
-    preds.SetDevice(device);
-
-    auto dpreds = preds.ConstDevicePointer();
-    auto dlabels = info.labels_.ConstDevicePointer();
-
-    // Sort all the predictions
-    dh::SegmentSorter<float> segment_pred_sorter;
-    segment_pred_sorter.SortItems(dpreds, preds.Size(), gptr);
-
-    // Compute individual group metric and sum them up
-    return EvalMetricT::EvalMetric(segment_pred_sorter, dlabels, *this);
-  }
-
-  const char* Name() const override {
-    return name.c_str();
-  }
-
-  explicit EvalRankGpu(const char* name, const char* param) {
-    using namespace std;  // NOLINT(*)
-    if (param != nullptr) {
-      std::ostringstream os;
-      if (sscanf(param, "%u[-]?", &this->topn) == 1) {
-        os << name << '@' << param;
-        this->name = os.str();
-      } else {
-        os << name << param;
-        this->name = os.str();
-      }
-      if (param[strlen(param) - 1] == '-') {
-        this->minus = true;
-      }
-    } else {
-      this->name = name;
-    }
-  }
-};
-
-/*! \brief Precision at N, for both classification and rank */
-struct EvalPrecisionGpu {
- public:
-  static double EvalMetric(const dh::SegmentSorter<float> &pred_sorter,
-                           const float *dlabels,
-                           const EvalRankConfig &ecfg) {
-    // Group info on device
-    const auto &dgroups = pred_sorter.GetGroupsSpan();
-    const auto ngroups = pred_sorter.GetNumGroups();
-    const auto &dgroup_idx = pred_sorter.GetGroupSegmentsSpan();
-
-    // Original positions of the predictions after they have been sorted
-    const auto &dpreds_orig_pos = pred_sorter.GetOriginalPositionsSpan();
-
-    // First, determine non zero labels in the dataset individually
-    auto DetermineNonTrivialLabelLambda = [=] __device__(uint32_t idx) {
-      return (static_cast<unsigned>(dlabels[dpreds_orig_pos[idx]]) != 0) ? 1 : 0;
-    };  // NOLINT
-
-    // Find each group's metric sum
-    dh::caching_device_vector<uint32_t> hits(ngroups, 0);
-    const auto nitems = pred_sorter.GetNumItems();
-    auto *dhits = hits.data().get();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-    // For each group item compute the aggregated precision
-    dh::LaunchN(device_id, nitems, nullptr, [=] __device__(uint32_t idx) {
-      const auto group_idx = dgroup_idx[idx];
-      const auto group_begin = dgroups[group_idx];
-      const auto ridx = idx - group_begin;
-      if (ridx < ecfg.topn && DetermineNonTrivialLabelLambda(idx)) {
-        atomicAdd(&dhits[group_idx], 1);
-      }
-    });
-
-    // Allocator to be used for managing space overhead while performing reductions
-    dh::XGBCachingDeviceAllocator<char> alloc;
-    return static_cast<double>(thrust::reduce(thrust::cuda::par(alloc),
-                                              hits.begin(), hits.end())) / ecfg.topn;
-  }
-};
-
-/*! \brief NDCG: Normalized Discounted Cumulative Gain at N */
-struct EvalNDCGGpu {
- public:
-  static void ComputeDCG(const dh::SegmentSorter<float> &pred_sorter,
-                         const float *dlabels,
-                         const EvalRankConfig &ecfg,
-                         // The order in which labels have to be accessed. The order is determined
-                         // by sorting the predictions or the labels for the entire dataset
-                         const xgboost::common::Span<const uint32_t> &dlabels_sort_order,
-                         dh::caching_device_vector<double> *dcgptr) {
-    dh::caching_device_vector<double> &dcgs(*dcgptr);
-    // Group info on device
-    const auto &dgroups = pred_sorter.GetGroupsSpan();
-    const auto &dgroup_idx = pred_sorter.GetGroupSegmentsSpan();
-
-    // First, determine non zero labels in the dataset individually
-    auto DetermineNonTrivialLabelLambda = [=] __device__(uint32_t idx) {
-      return (static_cast<unsigned>(dlabels[dlabels_sort_order[idx]]));
-    };  // NOLINT
-
-    // Find each group's DCG value
-    const auto nitems = pred_sorter.GetNumItems();
-    auto *ddcgs = dcgs.data().get();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-
-    // For each group item compute the aggregated precision
-    dh::LaunchN(device_id, nitems, nullptr, [=] __device__(uint32_t idx) {
-      const auto group_idx = dgroup_idx[idx];
-      const auto group_begin = dgroups[group_idx];
-      const auto ridx = idx - group_begin;
-      auto label = DetermineNonTrivialLabelLambda(idx);
-      if (ridx < ecfg.topn && label) {
-        atomicAdd(&ddcgs[group_idx], ((1 << label) - 1) / std::log2(ridx + 2.0));
-      }
-    });
-  }
-
-  static double EvalMetric(const dh::SegmentSorter<float> &pred_sorter,
-                           const float *dlabels,
-                           const EvalRankConfig &ecfg) {
-    // Sort the labels and compute IDCG
-    dh::SegmentSorter<float> segment_label_sorter;
-    segment_label_sorter.SortItems(dlabels, pred_sorter.GetNumItems(),
-                                   pred_sorter.GetGroupSegmentsSpan());
-
-    uint32_t ngroups = pred_sorter.GetNumGroups();
-
-    dh::caching_device_vector<double> idcg(ngroups, 0);
-    ComputeDCG(pred_sorter, dlabels, ecfg, segment_label_sorter.GetOriginalPositionsSpan(), &idcg);
-
-    // Compute the DCG values next
-    dh::caching_device_vector<double> dcg(ngroups, 0);
-    ComputeDCG(pred_sorter, dlabels, ecfg, pred_sorter.GetOriginalPositionsSpan(), &dcg);
-
-    double *ddcg = dcg.data().get();
-    double *didcg = idcg.data().get();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-    // Compute the group's DCG and reduce it across all groups
-    dh::LaunchN(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
-      if (didcg[gidx] == 0.0f) {
-        ddcg[gidx] = (ecfg.minus) ? 0.0f : 1.0f;
-      } else {
-        ddcg[gidx] /= didcg[gidx];
-      }
-    });
-
-    // Allocator to be used for managing space overhead while performing reductions
-    dh::XGBCachingDeviceAllocator<char> alloc;
-    return thrust::reduce(thrust::cuda::par(alloc), dcg.begin(), dcg.end());
-  }
-};
-
-/*! \brief Mean Average Precision at N, for both classification and rank */
-struct EvalMAPGpu {
- public:
-  static double EvalMetric(const dh::SegmentSorter<float> &pred_sorter,
-                           const float *dlabels,
-                           const EvalRankConfig &ecfg) {
-    // Group info on device
-    const auto &dgroups = pred_sorter.GetGroupsSpan();
-    const auto ngroups = pred_sorter.GetNumGroups();
-    const auto &dgroup_idx = pred_sorter.GetGroupSegmentsSpan();
-
-    // Original positions of the predictions after they have been sorted
-    const auto &dpreds_orig_pos = pred_sorter.GetOriginalPositionsSpan();
-
-    // First, determine non zero labels in the dataset individually
-    const auto nitems = pred_sorter.GetNumItems();
-    dh::caching_device_vector<uint32_t> hits(nitems, 0);
-    auto DetermineNonTrivialLabelLambda = [=] __device__(uint32_t idx) {
-      return (static_cast<unsigned>(dlabels[dpreds_orig_pos[idx]]) != 0) ? 1 : 0;
-    };  // NOLINT
-
-    thrust::transform(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                      thrust::make_counting_iterator(nitems),
-                      hits.begin(),
-                      DetermineNonTrivialLabelLambda);
-
-    // Allocator to be used by sort for managing space overhead while performing prefix scans
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    // Next, prefix scan the nontrivial labels that are segmented to accumulate them.
-    // This is required for computing the metric sum
-    // Data segmented into different groups...
-    thrust::inclusive_scan_by_key(thrust::cuda::par(alloc),
-                                  dh::tcbegin(dgroup_idx), dh::tcend(dgroup_idx),
-                                  hits.begin(),  // Input value
-                                  hits.begin());  // In-place scan
-
-    // Find each group's metric sum
-    dh::caching_device_vector<double> sumap(ngroups, 0);
-    auto *dsumap = sumap.data().get();
-    const auto *dhits = hits.data().get();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-    // For each group item compute the aggregated precision
-    dh::LaunchN(device_id, nitems, nullptr, [=] __device__(uint32_t idx) {
-      if (DetermineNonTrivialLabelLambda(idx)) {
-        const auto group_idx = dgroup_idx[idx];
-        const auto group_begin = dgroups[group_idx];
-        const auto ridx = idx - group_begin;
-        if (ridx < ecfg.topn) {
-          atomicAdd(&dsumap[group_idx],
-                    static_cast<double>(dhits[idx]) / (ridx + 1));
-        }
-      }
-    });
-
-    // Aggregate the group's item precisions
-    dh::LaunchN(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
-      auto nhits = dgroups[gidx + 1] ? dhits[dgroups[gidx + 1] - 1] : 0;
-      if (nhits != 0) {
-        dsumap[gidx] /= nhits;
-      } else {
-        if (ecfg.minus) {
-          dsumap[gidx] = 0;
-        } else {
-          dsumap[gidx] = 1;
-        }
-      }
-    });
-
-    return thrust::reduce(thrust::cuda::par(alloc), sumap.begin(), sumap.end());
-  }
-};
-
-/*! \brief Area Under Curve metric computation for ranking datasets */
-struct EvalAucGpu : public Metric {
- public:
-  // This function object computes the positive precision pair for each prediction group
-  class ComputePosPair : public thrust::unary_function<uint32_t, double> {
-   public:
-    XGBOOST_DEVICE ComputePosPair(const double *pred_group_pos_precision,
-                                  const double *pred_group_neg_precision,
-                                  const double *pred_group_incr_precision)
-      : pred_group_pos_precision_(pred_group_pos_precision),
-        pred_group_neg_precision_(pred_group_neg_precision),
-        pred_group_incr_precision_(pred_group_incr_precision) {}
-
-    // Compute positive precision pair for the prediction group at 'idx'
-    __device__ __forceinline__ double operator()(uint32_t idx) const {
-        return pred_group_neg_precision_[idx] *
-               (pred_group_incr_precision_[idx] + pred_group_pos_precision_[idx] * 0.5);
-    }
-
-   private:
-    // Accumulated positive precision for the prediction group
-    const double *pred_group_pos_precision_{nullptr};
-    // Accumulated negative precision for the prediction group
-    const double *pred_group_neg_precision_{nullptr};
-    // Incremental positive precision for the prediction group
-    const double *pred_group_incr_precision_{nullptr};
-  };
-
-  template <typename T>
-  void ReleaseMemory(dh::caching_device_vector<T> &vec) {  // NOLINT
-    dh::caching_device_vector<T>().swap(vec);
-  }
-
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    // Sanity check is done by the caller
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(info.labels_.Size());
-    const std::vector<unsigned> &gptr = info.group_ptr_.empty() ? tgptr : info.group_ptr_;
-
-    auto device = tparam_->gpu_id;
-    dh::safe_cuda(cudaSetDevice(device));
-
-    info.labels_.SetDevice(device);
-    preds.SetDevice(device);
-    info.weights_.SetDevice(device);
-
-    auto dpreds = preds.ConstDevicePointer();
-    auto dlabels = info.labels_.ConstDevicePointer();
-    auto dweights = info.weights_.ConstDevicePointer();
-
-    // Sort all the predictions (from one or more groups)
-    dh::SegmentSorter<float> segment_pred_sorter;
-    segment_pred_sorter.SortItems(dpreds, preds.Size(), gptr);
-
-    const auto &dsorted_preds = segment_pred_sorter.GetItemsSpan();
-    const auto &dpreds_orig_pos = segment_pred_sorter.GetOriginalPositionsSpan();
-
-    // Group info on device
-    const auto &dgroups = segment_pred_sorter.GetGroupsSpan();
-    uint32_t ngroups = segment_pred_sorter.GetNumGroups();
-
-    // Final values
-    double hsum_auc = 0.0;
-    unsigned hauc_error = 0;
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-
-    // Allocator to be used for managing space overhead while performing reductions
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    if (ngroups == 1) {
-      const auto nitems = segment_pred_sorter.GetNumItems();
-
-      // First, segment all the predictions in the group. This is required so that we can
-      // aggregate the positive and negative precisions within that prediction group
-      dh::caching_device_vector<unsigned> dpred_segs(nitems, 0);
-      auto *pred_seg_arr = dpred_segs.data().get();
-      // This is for getting the next segment number
-      dh::caching_device_vector<unsigned> seg_idx(1, 0);
-      auto *seg_idx_ptr = seg_idx.data().get();
-
-      dh::caching_device_vector<double> dbuf_pos(nitems, 0);
-      dh::caching_device_vector<double> dbuf_neg(nitems, 0);
-      auto *buf_pos_arr = dbuf_pos.data().get();
-      auto *buf_neg_arr = dbuf_neg.data().get();
-
-      dh::LaunchN(device_id, nitems, nullptr, [=] __device__(int idx) {
-        auto ctr = dlabels[dpreds_orig_pos[idx]];
-        // For ranking task, weights are per-group
-        // For binary classification task, weights are per-instance
-        const auto wt = dweights == nullptr ? 1.0f : dweights[dpreds_orig_pos[idx]];
-        buf_pos_arr[idx] = ctr * wt;
-        buf_neg_arr[idx] = (1.0f - ctr) * wt;
-        if (idx == nitems - 1 || dsorted_preds[idx] != dsorted_preds[idx + 1]) {
-          auto new_seg_idx = atomicAdd(seg_idx_ptr, 1);
-          auto pred_val = dsorted_preds[idx];
-          do {
-            pred_seg_arr[idx] = new_seg_idx;
-            idx--;
-          } while (idx >= 0 && dsorted_preds[idx] == pred_val);
-        }
-      });
-
-      auto nunique_preds = seg_idx.back();
-      ReleaseMemory(seg_idx);
-
-      // Next, accumulate the positive and negative precisions for every prediction group
-      dh::caching_device_vector<double> sum_dbuf_pos(nunique_preds, 0);
-      auto itr = thrust::reduce_by_key(thrust::cuda::par(alloc),
-                                       dpred_segs.begin(), dpred_segs.end(),  // Segmented by this
-                                       dbuf_pos.begin(),  // Individual precisions
-                                       thrust::make_discard_iterator(),  // Ignore unique segments
-                                       sum_dbuf_pos.begin());  // Write accumulated results here
-      ReleaseMemory(dbuf_pos);
-      CHECK(itr.second - sum_dbuf_pos.begin() == nunique_preds);
-
-      dh::caching_device_vector<double> sum_dbuf_neg(nunique_preds, 0);
-      itr = thrust::reduce_by_key(thrust::cuda::par(alloc),
-                                  dpred_segs.begin(), dpred_segs.end(),
-                                  dbuf_neg.begin(),
-                                  thrust::make_discard_iterator(),
-                                  sum_dbuf_neg.begin());
-      ReleaseMemory(dbuf_neg);
-      ReleaseMemory(dpred_segs);
-      CHECK(itr.second - sum_dbuf_neg.begin() == nunique_preds);
-
-      dh::caching_device_vector<double> sum_nneg(nunique_preds, 0);
-      thrust::inclusive_scan(thrust::cuda::par(alloc),
-                             sum_dbuf_neg.begin(), sum_dbuf_neg.end(),
-                             sum_nneg.begin());
-      double sum_neg_prec_val = sum_nneg.back();
-      ReleaseMemory(sum_nneg);
-
-      // Find incremental sum for the positive precisions that is then used to
-      // compute incremental positive precision pair
-      dh::caching_device_vector<double> sum_npos(nunique_preds + 1, 0);
-      thrust::inclusive_scan(thrust::cuda::par(alloc),
-                             sum_dbuf_pos.begin(), sum_dbuf_pos.end(),
-                             sum_npos.begin() + 1);
-      double sum_pos_prec_val = sum_npos.back();
-
-      if (sum_pos_prec_val <= 0.0 || sum_neg_prec_val <= 0.0) {
-        hauc_error = 1;
-      } else {
-        dh::caching_device_vector<double> sum_pospair(nunique_preds, 0);
-        // Finally, compute the positive precision pair
-        thrust::transform(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                          thrust::make_counting_iterator(static_cast<uint32_t>(nunique_preds)),
-                          sum_pospair.begin(),
-                          ComputePosPair(sum_dbuf_pos.data().get(),
-                                         sum_dbuf_neg.data().get(),
-                                         sum_npos.data().get()));
-        ReleaseMemory(sum_dbuf_pos);
-        ReleaseMemory(sum_dbuf_neg);
-        ReleaseMemory(sum_npos);
-        hsum_auc = thrust::reduce(thrust::cuda::par(alloc),
-                                  sum_pospair.begin(), sum_pospair.end())
-                     / (sum_pos_prec_val * sum_neg_prec_val);
-      }
-    } else {
-      // AUC sum for each group
-      dh::caching_device_vector<double> sum_auc(ngroups, 0);
-      // AUC error across all groups
-      dh::caching_device_vector<int> auc_error(1, 0);
-      auto *dsum_auc = sum_auc.data().get();
-      auto *dauc_error = auc_error.data().get();
-
-      // For each group item compute the aggregated precision
-      dh::LaunchN<1, 32>(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
-        double sum_pospair = 0.0, sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
-
-        for (auto i = dgroups[gidx]; i < dgroups[gidx + 1]; ++i) {
-          const auto ctr = dlabels[dpreds_orig_pos[i]];
-          // Keep bucketing predictions in same bucket
-          if (i != dgroups[gidx] && dsorted_preds[i] != dsorted_preds[i - 1]) {
-            sum_pospair += buf_neg * (sum_npos + buf_pos * 0.5);
-            sum_npos += buf_pos;
-            sum_nneg += buf_neg;
-            buf_neg = buf_pos = 0.0f;
-          }
-          // For ranking task, weights are per-group
-          // For binary classification task, weights are per-instance
-          const auto wt = dweights == nullptr ? 1.0f : dweights[gidx];
-          buf_pos += ctr * wt;
-          buf_neg += (1.0f - ctr) * wt;
-        }
-        sum_pospair += buf_neg * (sum_npos + buf_pos * 0.5);
-        sum_npos += buf_pos;
-        sum_nneg += buf_neg;
-
-        // Check weird conditions
-        if (sum_npos <= 0.0 || sum_nneg <= 0.0) {
-          atomicAdd(dauc_error, 1);
-        } else {
-          // This is the AUC
-          dsum_auc[gidx] = sum_pospair / (sum_npos * sum_nneg);
-        }
-      });
-
-      hsum_auc = thrust::reduce(thrust::cuda::par(alloc), sum_auc.begin(), sum_auc.end());
-      hauc_error = auc_error.back();  // Copy it back to host
-    }
-
-    // Report average AUC across all groups
-    // In distributed mode, workers which only contains pos or neg samples
-    // will be ignored when aggregate AUC.
-    bst_float dat[2] = {0.0f, 0.0f};
-    if (hauc_error < ngroups) {
-      dat[0] = static_cast<bst_float>(hsum_auc);
-      dat[1] = static_cast<bst_float>(ngroups - hauc_error);
-    }
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    CHECK_GT(dat[1], 0.0f)
-      << "AUC: the dataset only contains pos or neg samples";
-    return dat[0] / dat[1];
-  }
-
-  const char* Name() const override {
-    return "auc";
-  }
-};
-
-/*! \brief Area Under PR Curve metric computation for ranking datasets */
-struct EvalAucPRGpu : public Metric {
- public:
-  // This function object computes the item's positive/negative precision value
-  class ComputeItemPrecision : public thrust::unary_function<uint32_t, float> {
-   public:
-    // The precision type to be computed
-    enum class PrecisionType {
-      kPositive,
-      kNegative
-    };
-
-    XGBOOST_DEVICE ComputeItemPrecision(PrecisionType ptype,
-                                        uint32_t ngroups,
-                                        const float *dweights,
-                                        const xgboost::common::Span<const uint32_t> &dgidxs,
-                                        const float *dlabels)
-      : ptype_(ptype), ngroups_(ngroups), dweights_(dweights), dgidxs_(dgidxs), dlabels_(dlabels) {}
-
-    // Compute precision value for the prediction that was originally at 'idx'
-    __device__ __forceinline__ float operator()(uint32_t idx) const {
-      // For ranking task, weights are per-group
-      // For binary classification task, weights are per-instance
-      const auto wt = dweights_ == nullptr ? 1.0f : dweights_[ngroups_ == 1 ? idx : dgidxs_[idx]];
-      return wt * (ptype_ == PrecisionType::kPositive ? dlabels_[idx] : (1.0f - dlabels_[idx]));
-    }
-
-   private:
-    PrecisionType ptype_;  // Precision type to be computed
-    uint32_t ngroups_;  // Number of groups in the dataset
-    const float *dweights_;  // Instance/group weights
-    const xgboost::common::Span<const uint32_t> dgidxs_;  // The group a given instance belongs to
-    const float *dlabels_;  // Unsorted labels in the dataset
-  };
-
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    // Sanity check is done by the caller
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(info.labels_.Size());
-    const std::vector<unsigned> &gptr = info.group_ptr_.empty() ? tgptr : info.group_ptr_;
-
-    auto device = tparam_->gpu_id;
-    dh::safe_cuda(cudaSetDevice(device));
-
-    info.labels_.SetDevice(device);
-    preds.SetDevice(device);
-    info.weights_.SetDevice(device);
-
-    auto dpreds = preds.ConstDevicePointer();
-    auto dlabels = info.labels_.ConstDevicePointer();
-    auto dweights = info.weights_.ConstDevicePointer();
-
-    // Sort all the predictions
-    dh::SegmentSorter<float> segment_pred_sorter;
-    segment_pred_sorter.SortItems(dpreds, preds.Size(), gptr);
-
-    const auto &dsorted_preds = segment_pred_sorter.GetItemsSpan();
-    // Original positions of the predictions after they have been sorted
-    const auto &dpreds_orig_pos = segment_pred_sorter.GetOriginalPositionsSpan();
-
-    // Group info on device
-    const auto &dgroups = segment_pred_sorter.GetGroupsSpan();
-    uint32_t ngroups = segment_pred_sorter.GetNumGroups();
-    const auto &dgroup_idx = segment_pred_sorter.GetGroupSegmentsSpan();
-
-    // First, aggregate the positive and negative precision for each group
-    dh::caching_device_vector<double> total_pos(ngroups, 0);
-    dh::caching_device_vector<double> total_neg(ngroups, 0);
-
-    // Allocator to be used for managing space overhead while performing transformed reductions
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    // Compute each elements positive precision value and reduce them across groups concurrently.
-    ComputeItemPrecision pos_prec_functor(ComputeItemPrecision::PrecisionType::kPositive,
-                                          ngroups, dweights, dgroup_idx, dlabels);
-    auto end_range =
-      thrust::reduce_by_key(thrust::cuda::par(alloc),
-                            dh::tcbegin(dgroup_idx), dh::tcend(dgroup_idx),
-                            thrust::make_transform_iterator(
-                              // The indices need not be sequential within a group, as we care only
-                              // about the sum of positive precision values within a group
-                              dh::tcbegin(segment_pred_sorter.GetOriginalPositionsSpan()),
-                              pos_prec_functor),
-                            thrust::make_discard_iterator(),  // We don't care for the group indices
-                            total_pos.begin());  // Sum of positive precision values in the group
-    CHECK(end_range.second - total_pos.begin() == total_pos.size());
-
-    // Compute each elements negative precision value and reduce them across groups concurrently.
-    ComputeItemPrecision neg_prec_functor(ComputeItemPrecision::PrecisionType::kNegative,
-                                          ngroups, dweights, dgroup_idx, dlabels);
-    end_range =
-      thrust::reduce_by_key(thrust::cuda::par(alloc),
-                            dh::tcbegin(dgroup_idx), dh::tcend(dgroup_idx),
-                            thrust::make_transform_iterator(
-                              // The indices need not be sequential within a group, as we care only
-                              // about the sum of negative precision values within a group
-                              dh::tcbegin(segment_pred_sorter.GetOriginalPositionsSpan()),
-                              neg_prec_functor),
-                            thrust::make_discard_iterator(),  // We don't care for the group indices
-                            total_neg.begin());  // Sum of negative precision values in the group
-    CHECK(end_range.second - total_neg.begin() == total_neg.size());
-
-    const auto *dtotal_pos = total_pos.data().get();
-    const auto *dtotal_neg = total_neg.data().get();
-
-    // AUC sum for each group
-    dh::caching_device_vector<double> sum_auc(ngroups, 0);
-    // AUC error across all groups
-    dh::caching_device_vector<int> auc_error(1, 0);
-    auto *dsum_auc = sum_auc.data().get();
-    auto *dauc_error = auc_error.data().get();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-    // For each group item compute the aggregated precision
-    dh::LaunchN<1, 32>(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
-      // We need pos > 0 && neg > 0
-      if (dtotal_pos[gidx] <= 0.0 || dtotal_neg[gidx] <= 0.0) {
-        atomicAdd(dauc_error, 1);
-      } else {
-        auto gbegin = dgroups[gidx];
-        auto gend = dgroups[gidx + 1];
-        // Calculate AUC
-        double tp = 0.0, prevtp = 0.0, fp = 0.0, prevfp = 0.0, h = 0.0, a = 0.0, b = 0.0;
-        for (auto i = gbegin; i < gend; ++i) {
-          const auto wt = dweights == nullptr ? 1.0f
-                                              : dweights[ngroups == 1 ? dpreds_orig_pos[i] : gidx];
-          tp += wt * dlabels[dpreds_orig_pos[i]];
-          fp += wt * (1.0f - dlabels[dpreds_orig_pos[i]]);
-          if ((i < gend - 1 && dsorted_preds[i] != dsorted_preds[i + 1]) || (i == gend - 1)) {
-            if (tp == prevtp) {
-              a = 1.0;
-              b = 0.0;
-            } else {
-              h = (fp - prevfp) / (tp - prevtp);
-              a = 1.0 + h;
-              b = (prevfp - h * prevtp) / dtotal_pos[gidx];
-            }
-            if (0.0 != b) {
-              dsum_auc[gidx] += (tp / dtotal_pos[gidx] - prevtp / dtotal_pos[gidx] -
-                                 b / a * (std::log(a * tp / dtotal_pos[gidx] + b) -
-                                          std::log(a * prevtp / dtotal_pos[gidx] + b))) / a;
-            } else {
-              dsum_auc[gidx] += (tp / dtotal_pos[gidx] - prevtp / dtotal_pos[gidx]) / a;
-            }
-            prevtp = tp;
-            prevfp = fp;
-          }
-        }
-
-        // Sanity check
-        if (tp < 0 || prevtp < 0 || fp < 0 || prevfp < 0) {
-          // Check if we have any metric error thus far
-          auto current_auc_error = atomicAdd(dauc_error, 0);
-          KERNEL_CHECK(!current_auc_error);
-        }
-      }
-    });
-
-    const auto hsum_auc = thrust::reduce(thrust::cuda::par(alloc), sum_auc.begin(), sum_auc.end());
-    const auto hauc_error = auc_error.back();  // Copy it back to host
-
-    // Report average AUC-PR across all groups
-    // In distributed mode, workers which only contains pos or neg samples
-    // will be ignored when aggregate AUC-PR.
-    bst_float dat[2] = {0.0f, 0.0f};
-    if (hauc_error < static_cast<int>(ngroups)) {
-      dat[0] = static_cast<bst_float>(hsum_auc);
-      dat[1] = static_cast<bst_float>(static_cast<int>(ngroups) - hauc_error);
-    }
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    CHECK_GT(dat[1], 0.0f)
-      << "AUC-PR: the dataset only contains pos or neg samples";
-    CHECK_LE(dat[0], dat[1]) << "AUC-PR: AUC > 1.0";
-    return dat[0] / dat[1];
-  }
-
-  const char* Name() const override {
-    return "aucpr";
-  }
-};
-
-XGBOOST_REGISTER_GPU_METRIC(AucGpu, "auc")
-.describe("Area under curve for rank computed on GPU.")
-.set_body([](const char* param) { return new EvalAucGpu(); });
-
-XGBOOST_REGISTER_GPU_METRIC(AucPRGpu, "aucpr")
-.describe("Area under PR curve for rank computed on GPU.")
-.set_body([](const char* param) { return new EvalAucPRGpu(); });
-
-XGBOOST_REGISTER_GPU_METRIC(PrecisionGpu, "pre")
-.describe("precision@k for rank computed on GPU.")
-.set_body([](const char* param) { return new EvalRankGpu<EvalPrecisionGpu>("pre", param); });
-
-XGBOOST_REGISTER_GPU_METRIC(NDCGGpu, "ndcg")
-.describe("ndcg@k for rank computed on GPU.")
-.set_body([](const char* param) { return new EvalRankGpu<EvalNDCGGpu>("ndcg", param); });
-
-XGBOOST_REGISTER_GPU_METRIC(MAPGpu, "map")
-.describe("map@k for rank computed on GPU.")
-.set_body([](const char* param) { return new EvalRankGpu<EvalMAPGpu>("map", param); });
-}  // namespace metric
-}  // namespace xgboost
diff --git a/ml-xgboost/src/metric/survival_metric.cc b/ml-xgboost/src/metric/survival_metric.cc
deleted file mode 100644
index cc0614f..0000000
--- a/ml-xgboost/src/metric/survival_metric.cc
+++ /dev/null
@@ -1,105 +0,0 @@
-/*!
- * Copyright 2019 by Contributors
- * \file survival_metric.cc
- * \brief Metrics for survival analysis
- * \author Avinash Barnwal, Hyunsu Cho and Toby Hocking
- */
-
-#include <rabit/rabit.h>
-#include <xgboost/metric.h>
-#include <xgboost/host_device_vector.h>
-#include <dmlc/registry.h>
-#include <cmath>
-#include <memory>
-#include <vector>
-#include <limits>
-
-#include "xgboost/json.h"
-
-#include "../common/math.h"
-#include "../common/survival_util.h"
-
-using AFTParam = xgboost::common::AFTParam;
-using AFTLoss = xgboost::common::AFTLoss;
-
-namespace xgboost {
-namespace metric {
-// tag the this file, used by force static link later.
-DMLC_REGISTRY_FILE_TAG(survival_metric);
-
-/*! \brief Negative log likelihood of Accelerated Failure Time model */
-struct EvalAFT : public Metric {
- public:
-  explicit EvalAFT(const char* param) {}
-
-  void Configure(const Args& args) override {
-    param_.UpdateAllowUnknown(args);
-    loss_.reset(new AFTLoss(param_.aft_loss_distribution));
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String(this->Name());
-    out["aft_loss_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["aft_loss_param"], &param_);
-  }
-
-  bst_float Eval(const HostDeviceVector<bst_float> &preds,
-                 const MetaInfo &info,
-                 bool distributed) override {
-    CHECK_NE(info.labels_lower_bound_.Size(), 0U)
-      << "y_lower cannot be empty";
-    CHECK_NE(info.labels_upper_bound_.Size(), 0U)
-      << "y_higher cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_lower_bound_.Size());
-    CHECK_EQ(preds.Size(), info.labels_upper_bound_.Size());
-
-    /* Compute negative log likelihood for each data point and compute weighted average */
-    const auto& yhat = preds.HostVector();
-    const auto& y_lower = info.labels_lower_bound_.HostVector();
-    const auto& y_upper = info.labels_upper_bound_.HostVector();
-    const auto& weights = info.weights_.HostVector();
-    const bool is_null_weight = weights.empty();
-    const float aft_loss_distribution_scale = param_.aft_loss_distribution_scale;
-    CHECK_LE(yhat.size(), static_cast<size_t>(std::numeric_limits<omp_ulong>::max()))
-      << "yhat is too big";
-    const omp_ulong nsize = static_cast<omp_ulong>(yhat.size());
-
-    double nloglik_sum = 0.0;
-    double weight_sum = 0.0;
-    #pragma omp parallel for \
-     shared(weights, y_lower, y_upper, yhat) reduction(+:nloglik_sum, weight_sum)
-    for (omp_ulong i = 0; i < nsize; ++i) {
-      // If weights are empty, data is unweighted so we use 1.0 everywhere
-      const double w = is_null_weight ? 1.0 : weights[i];
-      const double loss
-        = loss_->Loss(y_lower[i], y_upper[i], yhat[i], aft_loss_distribution_scale);
-      nloglik_sum += loss;
-      weight_sum += w;
-    }
-
-    double dat[2]{nloglik_sum, weight_sum};
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    return static_cast<bst_float>(dat[0] / dat[1]);
-  }
-
-  const char* Name() const override {
-    return "aft-nloglik";
-  }
-
- private:
-  AFTParam param_;
-  std::unique_ptr<AFTLoss> loss_;
-};
-
-XGBOOST_REGISTER_METRIC(AFT, "aft-nloglik")
-.describe("Negative log likelihood of Accelerated Failure Time model.")
-.set_body([](const char* param) { return new EvalAFT(param); });
-
-}  // namespace metric
-}  // namespace xgboost
diff --git a/ml-xgboost/src/objective/aft_obj.cc b/ml-xgboost/src/objective/aft_obj.cc
deleted file mode 100644
index ff48fcd..0000000
--- a/ml-xgboost/src/objective/aft_obj.cc
+++ /dev/null
@@ -1,118 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file rank.cc
- * \brief Definition of aft loss.
- */
-
-#include <dmlc/omp.h>
-#include <xgboost/logging.h>
-#include <xgboost/objective.h>
-#include <vector>
-#include <limits>
-#include <algorithm>
-#include <memory>
-#include <utility>
-#include <cmath>
-
-#include "xgboost/json.h"
-
-#include "../common/math.h"
-#include "../common/random.h"
-#include "../common/survival_util.h"
-
-using AFTParam = xgboost::common::AFTParam;
-using AFTLoss = xgboost::common::AFTLoss;
-
-namespace xgboost {
-namespace obj {
-
-DMLC_REGISTRY_FILE_TAG(aft_obj);
-
-class AFTObj : public ObjFunction {
- public:
-  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
-    param_.UpdateAllowUnknown(args);
-    loss_.reset(new AFTLoss(param_.aft_loss_distribution));
-  }
-
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo& info,
-                   int iter,
-                   HostDeviceVector<GradientPair>* out_gpair) override {
-    /* Boilerplate */
-    CHECK_EQ(preds.Size(), info.labels_lower_bound_.Size());
-    CHECK_EQ(preds.Size(), info.labels_upper_bound_.Size());
-
-    const auto& yhat = preds.HostVector();
-    const auto& y_lower = info.labels_lower_bound_.HostVector();
-    const auto& y_upper = info.labels_upper_bound_.HostVector();
-    const auto& weights = info.weights_.HostVector();
-    const bool is_null_weight = weights.empty();
-
-    out_gpair->Resize(yhat.size());
-    std::vector<GradientPair>& gpair = out_gpair->HostVector();
-    CHECK_LE(yhat.size(), static_cast<size_t>(std::numeric_limits<omp_ulong>::max()))
-      << "yhat is too big";
-    const omp_ulong nsize = static_cast<omp_ulong>(yhat.size());
-    const float aft_loss_distribution_scale = param_.aft_loss_distribution_scale;
-
-    #pragma omp parallel for \
-      shared(weights, y_lower, y_upper, yhat, gpair)
-    for (omp_ulong i = 0; i < nsize; ++i) {
-      // If weights are empty, data is unweighted so we use 1.0 everywhere
-      const double w = is_null_weight ? 1.0 : weights[i];
-      const double grad = loss_->Gradient(y_lower[i], y_upper[i],
-                                          yhat[i], aft_loss_distribution_scale);
-      const double hess = loss_->Hessian(y_lower[i], y_upper[i],
-                                         yhat[i], aft_loss_distribution_scale);
-      gpair[i] = GradientPair(grad * w, hess * w);
-    }
-  }
-
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    // Trees give us a prediction in log scale, so exponentiate
-    std::vector<bst_float> &preds = io_preds->HostVector();
-    const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
-    #pragma omp parallel for shared(preds)
-    for (long j = 0; j < ndata; ++j) {  // NOLINT(*)
-      preds[j] = std::exp(preds[j]);
-    }
-  }
-
-  void EvalTransform(HostDeviceVector<bst_float> *io_preds) override {
-    // do nothing here, since the AFT metric expects untransformed prediction score
-  }
-
-  bst_float ProbToMargin(bst_float base_score) const override {
-    return std::log(base_score);
-  }
-
-  const char* DefaultEvalMetric() const override {
-    return "aft-nloglik";
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("survival:aft");
-    out["aft_loss_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["aft_loss_param"], &param_);
-    loss_.reset(new AFTLoss(param_.aft_loss_distribution));
-  }
-
- private:
-  AFTParam param_;
-  std::unique_ptr<AFTLoss> loss_;
-};
-
-// register the objective functions
-XGBOOST_REGISTER_OBJECTIVE(AFTObj, "survival:aft")
-.describe("AFT loss function")
-.set_body([]() { return new AFTObj(); });
-
-}  // namespace obj
-}  // namespace xgboost
-
-
diff --git a/ml-xgboost/src/objective/hinge.cc b/ml-xgboost/src/objective/hinge.cc
deleted file mode 100644
index 4476ff6..0000000
--- a/ml-xgboost/src/objective/hinge.cc
+++ /dev/null
@@ -1,18 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-
-// Dummy file to keep the CUDA conditional compile trick.
-
-#include <dmlc/registry.h>
-namespace xgboost {
-namespace obj {
-
-DMLC_REGISTRY_FILE_TAG(hinge_obj);
-
-}  // namespace obj
-}  // namespace xgboost
-
-#ifndef XGBOOST_USE_CUDA
-#include "hinge.cu"
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/objective/hinge.cu b/ml-xgboost/src/objective/hinge.cu
deleted file mode 100644
index b889cb8..0000000
--- a/ml-xgboost/src/objective/hinge.cu
+++ /dev/null
@@ -1,98 +0,0 @@
-/*!
- * Copyright 2018-2019 by Contributors
- * \file hinge.cc
- * \brief Provides an implementation of the hinge loss function
- * \author Henry Gouk
- */
-#include "xgboost/objective.h"
-#include "xgboost/json.h"
-#include "xgboost/span.h"
-#include "xgboost/host_device_vector.h"
-
-#include "../common/math.h"
-#include "../common/transform.h"
-#include "../common/common.h"
-
-namespace xgboost {
-namespace obj {
-
-#if defined(XGBOOST_USE_CUDA)
-DMLC_REGISTRY_FILE_TAG(hinge_obj_gpu);
-#endif  // defined(XGBOOST_USE_CUDA)
-
-class HingeObj : public ObjFunction {
- public:
-  HingeObj() = default;
-
-  void Configure(
-      const std::vector<std::pair<std::string, std::string> > &args) override {}
-
-  void GetGradient(const HostDeviceVector<bst_float> &preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size())
-        << "labels are not correctly provided"
-        << "preds.size=" << preds.Size()
-        << ", label.size=" << info.labels_.Size();
-
-    const size_t ndata = preds.Size();
-    const bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-    out_gpair->Resize(ndata);
-    common::Transform<>::Init(
-        [=] XGBOOST_DEVICE(size_t _idx,
-                           common::Span<GradientPair> _out_gpair,
-                           common::Span<const bst_float> _preds,
-                           common::Span<const bst_float> _labels,
-                           common::Span<const bst_float> _weights) {
-          bst_float p = _preds[_idx];
-          bst_float w = is_null_weight ? 1.0f : _weights[_idx];
-          bst_float y = _labels[_idx] * 2.0 - 1.0;
-          bst_float g, h;
-          if (p * y < 1.0) {
-            g = -y * w;
-            h = w;
-          } else {
-            g = 0.0;
-            h = std::numeric_limits<bst_float>::min();
-          }
-          _out_gpair[_idx] = GradientPair(g, h);
-        },
-        common::Range{0, static_cast<int64_t>(ndata)},
-        tparam_->gpu_id).Eval(
-            out_gpair, &preds, &info.labels_, &info.weights_);
-  }
-
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    common::Transform<>::Init(
-        [] XGBOOST_DEVICE(size_t _idx, common::Span<bst_float> _preds) {
-          _preds[_idx] = _preds[_idx] > 0.0 ? 1.0 : 0.0;
-        },
-        common::Range{0, static_cast<int64_t>(io_preds->Size()), 1},
-        tparam_->gpu_id)
-        .Eval(io_preds);
-  }
-
-  const char* DefaultEvalMetric() const override {
-    return "error";
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("binary:hinge");
-  }
-  void LoadConfig(Json const& in) override {}
-};
-
-// register the objective functions
-XGBOOST_REGISTER_OBJECTIVE(HingeObj, "binary:hinge")
-.describe("Hinge loss. Expects labels to be in [0,1f]")
-.set_body([]() { return new HingeObj(); });
-
-}  // namespace obj
-}  // namespace xgboost
diff --git a/ml-xgboost/src/objective/multiclass_obj.cc b/ml-xgboost/src/objective/multiclass_obj.cc
deleted file mode 100644
index ec66160..0000000
--- a/ml-xgboost/src/objective/multiclass_obj.cc
+++ /dev/null
@@ -1,18 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-
-// Dummy file to keep the CUDA conditional compile trick.
-
-#include <dmlc/registry.h>
-namespace xgboost {
-namespace obj {
-
-DMLC_REGISTRY_FILE_TAG(multiclass_obj);
-
-}  // namespace obj
-}  // namespace xgboost
-
-#ifndef XGBOOST_USE_CUDA
-#include "multiclass_obj.cu"
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/objective/multiclass_obj.cu b/ml-xgboost/src/objective/multiclass_obj.cu
deleted file mode 100644
index 29af5e0..0000000
--- a/ml-xgboost/src/objective/multiclass_obj.cu
+++ /dev/null
@@ -1,202 +0,0 @@
-/*!
- * Copyright 2015-2018 by Contributors
- * \file multi_class.cc
- * \brief Definition of multi-class classification objectives.
- * \author Tianqi Chen
- */
-#include <dmlc/omp.h>
-
-#include <vector>
-#include <algorithm>
-#include <limits>
-#include <utility>
-
-#include "xgboost/parameter.h"
-#include "xgboost/data.h"
-#include "xgboost/logging.h"
-#include "xgboost/objective.h"
-#include "xgboost/json.h"
-
-#include "../common/common.h"
-#include "../common/math.h"
-#include "../common/transform.h"
-
-namespace xgboost {
-namespace obj {
-
-#if defined(XGBOOST_USE_CUDA)
-DMLC_REGISTRY_FILE_TAG(multiclass_obj_gpu);
-#endif  // defined(XGBOOST_USE_CUDA)
-
-struct SoftmaxMultiClassParam : public XGBoostParameter<SoftmaxMultiClassParam> {
-  int num_class;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(SoftmaxMultiClassParam) {
-    DMLC_DECLARE_FIELD(num_class).set_lower_bound(1)
-        .describe("Number of output class in the multi-class classification.");
-  }
-};
-
-class SoftmaxMultiClassObj : public ObjFunction {
- public:
-  explicit SoftmaxMultiClassObj(bool output_prob)
-  : output_prob_(output_prob) {}
-
-  void Configure(Args const& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo& info,
-                   int iter,
-                   HostDeviceVector<GradientPair>* out_gpair) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK(preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size()))
-        << "SoftmaxMultiClassObj: label size and pred size does not match.\n"
-        << "label.Size() * num_class: "
-        << info.labels_.Size() * static_cast<size_t>(param_.num_class) << "\n"
-        << "num_class: " << param_.num_class << "\n"
-        << "preds.Size(): " << preds.Size();
-
-    const int nclass = param_.num_class;
-    const auto ndata = static_cast<int64_t>(preds.Size() / nclass);
-
-    auto device = tparam_->gpu_id;
-    out_gpair->SetDevice(device);
-    info.labels_.SetDevice(device);
-    info.weights_.SetDevice(device);
-    preds.SetDevice(device);
-
-    label_correct_.Resize(1);
-    label_correct_.SetDevice(device);
-
-    out_gpair->Resize(preds.Size());
-    label_correct_.Fill(1);
-
-    const bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-
-    common::Transform<>::Init(
-        [=] XGBOOST_DEVICE(size_t idx,
-                           common::Span<GradientPair> gpair,
-                           common::Span<bst_float const> labels,
-                           common::Span<bst_float const> preds,
-                           common::Span<bst_float const> weights,
-                           common::Span<int> _label_correct) {
-          common::Span<bst_float const> point = preds.subspan(idx * nclass, nclass);
-
-          // Part of Softmax function
-          bst_float wmax = std::numeric_limits<bst_float>::min();
-          for (auto const i : point) { wmax = fmaxf(i, wmax); }
-          double wsum = 0.0f;
-          for (auto const i : point) { wsum += expf(i - wmax); }
-          auto label = labels[idx];
-          if (label < 0 || label >= nclass) {
-            _label_correct[0] = 0;
-            label = 0;
-          }
-          bst_float wt = is_null_weight ? 1.0f : weights[idx];
-          for (int k = 0; k < nclass; ++k) {
-            // Computation duplicated to avoid creating a cache.
-            bst_float p = expf(point[k] - wmax) / static_cast<float>(wsum);
-            const float eps = 1e-16f;
-            const bst_float h = fmax(2.0f * p * (1.0f - p) * wt, eps);
-            p = label == k ? p - 1.0f : p;
-            gpair[idx * nclass + k] = GradientPair(p * wt, h);
-          }
-        }, common::Range{0, ndata}, device, false)
-        .Eval(out_gpair, &info.labels_, &preds, &info.weights_, &label_correct_);
-
-    std::vector<int>& label_correct_h = label_correct_.HostVector();
-    for (auto const flag : label_correct_h) {
-      if (flag != 1) {
-        LOG(FATAL) << "SoftmaxMultiClassObj: label must be in [0, num_class).";
-      }
-    }
-  }
-  void PredTransform(HostDeviceVector<bst_float>* io_preds) override {
-    this->Transform(io_preds, output_prob_);
-  }
-  void EvalTransform(HostDeviceVector<bst_float>* io_preds) override {
-    this->Transform(io_preds, true);
-  }
-  const char* DefaultEvalMetric() const override {
-    return "merror";
-  }
-
-  inline void Transform(HostDeviceVector<bst_float> *io_preds, bool prob) {
-    const int nclass = param_.num_class;
-    const auto ndata = static_cast<int64_t>(io_preds->Size() / nclass);
-    max_preds_.Resize(ndata);
-
-    auto device = tparam_->gpu_id;
-    if (prob) {
-      common::Transform<>::Init(
-          [=] XGBOOST_DEVICE(size_t _idx, common::Span<bst_float> _preds) {
-            common::Span<bst_float> point =
-                _preds.subspan(_idx * nclass, nclass);
-            common::Softmax(point.begin(), point.end());
-          },
-          common::Range{0, ndata}, device)
-        .Eval(io_preds);
-    } else {
-      io_preds->SetDevice(device);
-      max_preds_.SetDevice(device);
-      common::Transform<>::Init(
-          [=] XGBOOST_DEVICE(size_t _idx,
-                             common::Span<const bst_float> _preds,
-                             common::Span<bst_float> _max_preds) {
-            common::Span<const bst_float> point =
-                _preds.subspan(_idx * nclass, nclass);
-            _max_preds[_idx] =
-                common::FindMaxIndex(point.cbegin(),
-                                     point.cend()) - point.cbegin();
-          },
-          common::Range{0, ndata}, device, false)
-        .Eval(io_preds, &max_preds_);
-    }
-    if (!prob) {
-      io_preds->Resize(max_preds_.Size());
-      io_preds->Copy(max_preds_);
-    }
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    if (this->output_prob_) {
-      out["name"] = String("multi:softprob");
-    } else {
-      out["name"] = String("multi:softmax");
-    }
-    out["softmax_multiclass_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["softmax_multiclass_param"], &param_);
-  }
-
- private:
-  // output probability
-  bool output_prob_;
-  // parameter
-  SoftmaxMultiClassParam param_;
-  // Cache for max_preds
-  HostDeviceVector<bst_float> max_preds_;
-  HostDeviceVector<int> label_correct_;
-};
-
-// register the objective functions
-DMLC_REGISTER_PARAMETER(SoftmaxMultiClassParam);
-
-XGBOOST_REGISTER_OBJECTIVE(SoftmaxMultiClass, "multi:softmax")
-.describe("Softmax for multi-class classification, output class index.")
-.set_body([]() { return new SoftmaxMultiClassObj(false); });
-
-XGBOOST_REGISTER_OBJECTIVE(SoftprobMultiClass, "multi:softprob")
-.describe("Softmax for multi-class classification, output probability distribution.")
-.set_body([]() { return new SoftmaxMultiClassObj(true); });
-
-}  // namespace obj
-}  // namespace xgboost
diff --git a/ml-xgboost/src/objective/objective.cc b/ml-xgboost/src/objective/objective.cc
deleted file mode 100644
index 8f65cec..0000000
--- a/ml-xgboost/src/objective/objective.cc
+++ /dev/null
@@ -1,51 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file objective.cc
- * \brief Registry of all objective functions.
- */
-#include <xgboost/objective.h>
-#include <dmlc/registry.h>
-
-#include <sstream>
-
-#include "xgboost/host_device_vector.h"
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::ObjFunctionReg);
-}  // namespace dmlc
-
-namespace xgboost {
-// implement factory functions
-ObjFunction* ObjFunction::Create(const std::string& name, GenericParameter const* tparam) {
-  auto *e = ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->Find(name);
-  if (e == nullptr) {
-    std::stringstream ss;
-    for (const auto& entry : ::dmlc::Registry< ::xgboost::ObjFunctionReg>::List()) {
-      ss << "Objective candidate: " << entry->name << "\n";
-    }
-    LOG(FATAL) << "Unknown objective function: `" << name << "`\n"
-               << ss.str();
-  }
-  auto pobj = (e->body)();
-  pobj->tparam_ = tparam;
-  return pobj;
-}
-
-}  // namespace xgboost
-
-namespace xgboost {
-namespace obj {
-// List of files that will be force linked in static links.
-#ifdef XGBOOST_USE_CUDA
-DMLC_REGISTRY_LINK_TAG(regression_obj_gpu);
-DMLC_REGISTRY_LINK_TAG(hinge_obj_gpu);
-DMLC_REGISTRY_LINK_TAG(multiclass_obj_gpu);
-DMLC_REGISTRY_LINK_TAG(rank_obj_gpu);
-#else
-DMLC_REGISTRY_LINK_TAG(regression_obj);
-DMLC_REGISTRY_LINK_TAG(hinge_obj);
-DMLC_REGISTRY_LINK_TAG(multiclass_obj);
-DMLC_REGISTRY_LINK_TAG(rank_obj);
-#endif  // XGBOOST_USE_CUDA
-}  // namespace obj
-}  // namespace xgboost
diff --git a/ml-xgboost/src/objective/rank_obj.cc b/ml-xgboost/src/objective/rank_obj.cc
deleted file mode 100644
index 25cd9e6..0000000
--- a/ml-xgboost/src/objective/rank_obj.cc
+++ /dev/null
@@ -1,17 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-
-// Dummy file to keep the CUDA conditional compile trick.
-#include <dmlc/registry.h>
-namespace xgboost {
-namespace obj {
-
-DMLC_REGISTRY_FILE_TAG(rank_obj);
-
-}  // namespace obj
-}  // namespace xgboost
-
-#ifndef XGBOOST_USE_CUDA
-#include "rank_obj.cu"
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/objective/rank_obj.cu b/ml-xgboost/src/objective/rank_obj.cu
deleted file mode 100644
index 30cfdb0..0000000
--- a/ml-xgboost/src/objective/rank_obj.cu
+++ /dev/null
@@ -1,954 +0,0 @@
-/*!
- * Copyright 2015-2019 XGBoost contributors
- */
-#include <dmlc/omp.h>
-#include <dmlc/timer.h>
-#include <xgboost/logging.h>
-#include <xgboost/objective.h>
-#include <vector>
-#include <algorithm>
-#include <utility>
-
-#include "xgboost/json.h"
-#include "xgboost/parameter.h"
-
-#include "../common/math.h"
-#include "../common/random.h"
-
-#if defined(__CUDACC__)
-#include <thrust/sort.h>
-#include <thrust/gather.h>
-#include <thrust/iterator/discard_iterator.h>
-#include <thrust/random/uniform_int_distribution.h>
-#include <thrust/random/linear_congruential_engine.h>
-
-#include <cub/util_allocator.cuh>
-
-#include "../common/device_helpers.cuh"
-#endif
-
-namespace xgboost {
-namespace obj {
-
-#if defined(XGBOOST_USE_CUDA) && !defined(GTEST_TEST)
-DMLC_REGISTRY_FILE_TAG(rank_obj_gpu);
-#endif  // defined(XGBOOST_USE_CUDA)
-
-struct LambdaRankParam : public XGBoostParameter<LambdaRankParam> {
-  size_t num_pairsample;
-  float fix_list_weight;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(LambdaRankParam) {
-    DMLC_DECLARE_FIELD(num_pairsample).set_lower_bound(1).set_default(1)
-        .describe("Number of pair generated for each instance.");
-    DMLC_DECLARE_FIELD(fix_list_weight).set_lower_bound(0.0f).set_default(0.0f)
-        .describe("Normalize the weight of each list by this value,"
-                  " if equals 0, no effect will happen");
-  }
-};
-
-#if defined(__CUDACC__)
-// Helper functions
-
-template <typename T>
-XGBOOST_DEVICE __forceinline__ uint32_t
-CountNumItemsToTheLeftOf(const T *__restrict__ items, uint32_t n, T v) {
-  return thrust::lower_bound(thrust::seq, items, items + n, v,
-                             thrust::greater<T>()) -
-         items;
-}
-
-template <typename T>
-XGBOOST_DEVICE __forceinline__ uint32_t
-CountNumItemsToTheRightOf(const T *__restrict__ items, uint32_t n, T v) {
-  return n - (thrust::upper_bound(thrust::seq, items, items + n, v,
-                                  thrust::greater<T>()) -
-              items);
-}
-#endif
-
-/*! \brief helper information in a list */
-struct ListEntry {
-  /*! \brief the predict score we in the data */
-  bst_float pred;
-  /*! \brief the actual label of the entry */
-  bst_float label;
-  /*! \brief row index in the data matrix */
-  unsigned rindex;
-  // constructor
-  ListEntry(bst_float pred, bst_float label, unsigned rindex)
-    : pred(pred), label(label), rindex(rindex) {}
-  // comparator by prediction
-  inline static bool CmpPred(const ListEntry &a, const ListEntry &b) {
-    return a.pred > b.pred;
-  }
-  // comparator by label
-  inline static bool CmpLabel(const ListEntry &a, const ListEntry &b) {
-    return a.label > b.label;
-  }
-};
-
-/*! \brief a pair in the lambda rank */
-struct LambdaPair {
-  /*! \brief positive index: this is a position in the list */
-  unsigned pos_index;
-  /*! \brief negative index: this is a position in the list */
-  unsigned neg_index;
-  /*! \brief weight to be filled in */
-  bst_float weight;
-  // constructor
-  LambdaPair(unsigned pos_index, unsigned neg_index)
-    : pos_index(pos_index), neg_index(neg_index), weight(1.0f) {}
-  // constructor
-  LambdaPair(unsigned pos_index, unsigned neg_index, bst_float weight)
-    : pos_index(pos_index), neg_index(neg_index), weight(weight) {}
-};
-
-class PairwiseLambdaWeightComputer {
- public:
-  /*!
-   * \brief get lambda weight for existing pairs - for pairwise objective
-   * \param list a list that is sorted by pred score
-   * \param io_pairs record of pairs, containing the pairs to fill in weights
-   */
-  static void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
-                              std::vector<LambdaPair> *io_pairs) {}
-
-  static char const* Name() {
-    return "rank:pairwise";
-  }
-
-#if defined(__CUDACC__)
-  PairwiseLambdaWeightComputer(const bst_float *dpreds,
-                               const bst_float *dlabels,
-                               const dh::SegmentSorter<float> &segment_label_sorter) {}
-
-  class PairwiseLambdaWeightMultiplier {
-   public:
-    // Adjust the items weight by this value
-    __device__ __forceinline__ bst_float GetWeight(uint32_t gidx, int pidx, int nidx) const {
-      return 1.0f;
-    }
-  };
-
-  inline const PairwiseLambdaWeightMultiplier GetWeightMultiplier() const {
-    return {};
-  }
-#endif
-};
-
-#if defined(__CUDACC__)
-class BaseLambdaWeightMultiplier {
- public:
-  BaseLambdaWeightMultiplier(const dh::SegmentSorter<float> &segment_label_sorter,
-                             const dh::SegmentSorter<float> &segment_pred_sorter)
-    : dsorted_labels_(segment_label_sorter.GetItemsSpan()),
-      dorig_pos_(segment_label_sorter.GetOriginalPositionsSpan()),
-      dgroups_(segment_label_sorter.GetGroupsSpan()),
-      dindexable_sorted_preds_pos_(segment_pred_sorter.GetIndexableSortedPositionsSpan()) {}
-
- protected:
-  const common::Span<const float> dsorted_labels_;  // Labels sorted within a group
-  const common::Span<const uint32_t> dorig_pos_;  // Original indices of the labels
-                                                  // before they are sorted
-  const common::Span<const uint32_t> dgroups_;  // The group indices
-  // Where can a prediction for a label be found in the original array, when they are sorted
-  const common::Span<const uint32_t> dindexable_sorted_preds_pos_;
-};
-
-// While computing the weight that needs to be adjusted by this ranking objective, we need
-// to figure out where positive and negative labels chosen earlier exists, if the group
-// were to be sorted by its predictions. To accommodate this, we employ the following algorithm.
-// For a given group, let's assume the following:
-// labels:        1 5 9 2 4 8 0 7 6 3
-// predictions:   1 9 0 8 2 7 3 6 5 4
-// position:      0 1 2 3 4 5 6 7 8 9
-//
-// After label sort:
-// labels:        9 8 7 6 5 4 3 2 1 0
-// position:      2 5 7 8 1 4 9 3 0 6
-//
-// After prediction sort:
-// predictions:   9 8 7 6 5 4 3 2 1 0
-// position:      1 3 5 7 8 9 6 4 0 2
-//
-// If a sorted label at position 'x' is chosen, then we need to find out where the prediction
-// for this label 'x' exists, if the group were to be sorted by predictions.
-// We first take the sorted prediction positions:
-// position:      1 3 5 7 8 9 6 4 0 2
-// at indices:    0 1 2 3 4 5 6 7 8 9
-//
-// We create a sorted prediction positional array, such that value at position 'x' gives
-// us the position in the sorted prediction array where its related prediction lies.
-// dindexable_sorted_preds_pos_:  8 0 9 1 7 2 6 3 4 5
-// at indices:                    0 1 2 3 4 5 6 7 8 9
-// Basically, swap the previous 2 arrays, sort the indices and reorder positions
-// for an O(1) lookup using the position where the sorted label exists.
-//
-// This type does that using the SegmentSorter
-class IndexablePredictionSorter {
- public:
-  IndexablePredictionSorter(const bst_float *dpreds,
-                            const dh::SegmentSorter<float> &segment_label_sorter) {
-    // Sort the predictions first
-    segment_pred_sorter_.SortItems(dpreds, segment_label_sorter.GetNumItems(),
-                                   segment_label_sorter.GetGroupSegmentsSpan());
-
-    // Create an index for the sorted prediction positions
-    segment_pred_sorter_.CreateIndexableSortedPositions();
-  }
-
-  inline const dh::SegmentSorter<float> &GetPredictionSorter() const {
-    return segment_pred_sorter_;
-  }
-
- private:
-  dh::SegmentSorter<float> segment_pred_sorter_;  // For sorting the predictions
-};
-#endif
-
-// beta version: NDCG lambda rank
-class NDCGLambdaWeightComputer
-#if defined(__CUDACC__)
-  : public IndexablePredictionSorter
-#endif
-{
- public:
-#if defined(__CUDACC__)
-  // This function object computes the item's DCG value
-  class ComputeItemDCG : public thrust::unary_function<uint32_t, float> {
-   public:
-    XGBOOST_DEVICE ComputeItemDCG(const common::Span<const float> &dsorted_labels,
-                                  const common::Span<const uint32_t> &dgroups,
-                                  const common::Span<const uint32_t> &gidxs)
-      : dsorted_labels_(dsorted_labels),
-        dgroups_(dgroups),
-        dgidxs_(gidxs) {}
-
-    // Compute DCG for the item at 'idx'
-    __device__ __forceinline__ float operator()(uint32_t idx) const {
-      return ComputeItemDCGWeight(dsorted_labels_[idx], idx - dgroups_[dgidxs_[idx]]);
-    }
-
-   private:
-    const common::Span<const float> dsorted_labels_;  // Labels sorted within a group
-    const common::Span<const uint32_t> dgroups_;  // The group indices - where each group
-                                                  // begins and ends
-    const common::Span<const uint32_t> dgidxs_;  // The group each items belongs to
-  };
-
-  // Type containing device pointers that can be cheaply copied on the kernel
-  class NDCGLambdaWeightMultiplier : public BaseLambdaWeightMultiplier {
-   public:
-    NDCGLambdaWeightMultiplier(const dh::SegmentSorter<float> &segment_label_sorter,
-                               const NDCGLambdaWeightComputer &lwc)
-      : BaseLambdaWeightMultiplier(segment_label_sorter, lwc.GetPredictionSorter()),
-        dgroup_dcgs_(lwc.GetGroupDcgsSpan()) {}
-
-    // Adjust the items weight by this value
-    __device__ __forceinline__ bst_float GetWeight(uint32_t gidx, int pidx, int nidx) const {
-      if (dgroup_dcgs_[gidx] == 0.0) return 0.0f;
-
-      uint32_t group_begin = dgroups_[gidx];
-
-      auto pos_lab_orig_posn = dorig_pos_[pidx];
-      auto neg_lab_orig_posn = dorig_pos_[nidx];
-      KERNEL_CHECK(pos_lab_orig_posn != neg_lab_orig_posn);
-
-      // Note: the label positive and negative indices are relative to the entire dataset.
-      // Hence, scale them back to an index within the group
-      auto pos_pred_pos = dindexable_sorted_preds_pos_[pos_lab_orig_posn] - group_begin;
-      auto neg_pred_pos = dindexable_sorted_preds_pos_[neg_lab_orig_posn] - group_begin;
-      return NDCGLambdaWeightComputer::ComputeDeltaWeight(
-        pos_pred_pos, neg_pred_pos,
-        static_cast<int>(dsorted_labels_[pidx]), static_cast<int>(dsorted_labels_[nidx]),
-        dgroup_dcgs_[gidx]);
-    }
-
-   private:
-     const common::Span<const float> dgroup_dcgs_;  // Group DCG values
-  };
-
-  NDCGLambdaWeightComputer(const bst_float *dpreds,
-                           const bst_float *dlabels,
-                           const dh::SegmentSorter<float> &segment_label_sorter)
-    : IndexablePredictionSorter(dpreds, segment_label_sorter),
-      dgroup_dcg_(segment_label_sorter.GetNumGroups(), 0.0f),
-      weight_multiplier_(segment_label_sorter, *this) {
-    const auto &group_segments = segment_label_sorter.GetGroupSegmentsSpan();
-
-    // Allocator to be used for managing space overhead while performing transformed reductions
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    // Compute each elements DCG values and reduce them across groups concurrently.
-    auto end_range =
-      thrust::reduce_by_key(thrust::cuda::par(alloc),
-                            dh::tcbegin(group_segments), dh::tcend(group_segments),
-                            thrust::make_transform_iterator(
-                              // The indices need not be sequential within a group, as we care only
-                              // about the sum of items DCG values within a group
-                              dh::tcbegin(segment_label_sorter.GetOriginalPositionsSpan()),
-                              ComputeItemDCG(segment_label_sorter.GetItemsSpan(),
-                                             segment_label_sorter.GetGroupsSpan(),
-                                             group_segments)),
-                            thrust::make_discard_iterator(),  // We don't care for the group indices
-                            dgroup_dcg_.begin());  // Sum of the item's DCG values in the group
-    CHECK(end_range.second - dgroup_dcg_.begin() == dgroup_dcg_.size());
-  }
-
-  inline const common::Span<const float> GetGroupDcgsSpan() const {
-    return { dgroup_dcg_.data().get(), dgroup_dcg_.size() };
-  }
-
-  inline const NDCGLambdaWeightMultiplier GetWeightMultiplier() const {
-    return weight_multiplier_;
-  }
-#endif
-
-  static void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
-                              std::vector<LambdaPair> *io_pairs) {
-    std::vector<LambdaPair> &pairs = *io_pairs;
-    float IDCG;  // NOLINT
-    {
-      std::vector<bst_float> labels(sorted_list.size());
-      for (size_t i = 0; i < sorted_list.size(); ++i) {
-        labels[i] = sorted_list[i].label;
-      }
-      std::stable_sort(labels.begin(), labels.end(), std::greater<bst_float>());
-      IDCG = ComputeGroupDCGWeight(&labels[0], labels.size());
-    }
-    if (IDCG == 0.0) {
-      for (auto & pair : pairs) {
-        pair.weight = 0.0f;
-      }
-    } else {
-      for (auto & pair : pairs) {
-        unsigned pos_idx = pair.pos_index;
-        unsigned neg_idx = pair.neg_index;
-        pair.weight *= ComputeDeltaWeight(pos_idx, neg_idx,
-                                          sorted_list[pos_idx].label, sorted_list[neg_idx].label,
-                                          IDCG);
-      }
-    }
-  }
-
-  static char const* Name() {
-    return "rank:ndcg";
-  }
-
-  inline static bst_float ComputeGroupDCGWeight(const float *sorted_labels, uint32_t size) {
-    double sumdcg = 0.0;
-    for (uint32_t i = 0; i < size; ++i) {
-      sumdcg += ComputeItemDCGWeight(sorted_labels[i], i);
-    }
-
-    return static_cast<bst_float>(sumdcg);
-  }
-
- private:
-  XGBOOST_DEVICE inline static bst_float ComputeItemDCGWeight(unsigned label, uint32_t idx) {
-    return (label != 0) ? (((1 << label) - 1) / std::log2(static_cast<bst_float>(idx + 2))) : 0;
-  }
-
-  // Compute the weight adjustment for an item within a group:
-  // pos_pred_pos => Where does the positive label live, had the list been sorted by prediction
-  // neg_pred_pos => Where does the negative label live, had the list been sorted by prediction
-  // pos_label => positive label value from sorted label list
-  // neg_label => negative label value from sorted label list
-  XGBOOST_DEVICE inline static bst_float ComputeDeltaWeight(uint32_t pos_pred_pos,
-                                                            uint32_t neg_pred_pos,
-                                                            int pos_label, int neg_label,
-                                                            float idcg) {
-    float pos_loginv = 1.0f / std::log2(pos_pred_pos + 2.0f);
-    float neg_loginv = 1.0f / std::log2(neg_pred_pos + 2.0f);
-    bst_float original = ((1 << pos_label) - 1) * pos_loginv + ((1 << neg_label) - 1) * neg_loginv;
-    float changed = ((1 << neg_label) - 1) * pos_loginv + ((1 << pos_label) - 1) * neg_loginv;
-    bst_float delta = (original - changed) * (1.0f / idcg);
-    if (delta < 0.0f) delta = - delta;
-    return delta;
-  }
-
-#if defined(__CUDACC__)
-  dh::caching_device_vector<float> dgroup_dcg_;
-  // This computes the adjustment to the weight
-  const NDCGLambdaWeightMultiplier weight_multiplier_;
-#endif
-};
-
-class MAPLambdaWeightComputer
-#if defined(__CUDACC__)
-  : public IndexablePredictionSorter
-#endif
-{
- public:
-  struct MAPStats {
-    /*! \brief the accumulated precision */
-    float ap_acc{0.0f};
-    /*!
-     * \brief the accumulated precision,
-     *   assuming a positive instance is missing
-     */
-    float ap_acc_miss{0.0f};
-    /*!
-     * \brief the accumulated precision,
-     * assuming that one more positive instance is inserted ahead
-     */
-    float ap_acc_add{0.0f};
-    /* \brief the accumulated positive instance count */
-    float hits{0.0f};
-
-    XGBOOST_DEVICE MAPStats() {}  // NOLINT
-    XGBOOST_DEVICE MAPStats(float ap_acc, float ap_acc_miss, float ap_acc_add, float hits)
-      : ap_acc(ap_acc), ap_acc_miss(ap_acc_miss), ap_acc_add(ap_acc_add), hits(hits) {}
-
-    // For prefix scan
-    XGBOOST_DEVICE MAPStats operator +(const MAPStats &v1) const {
-      return {ap_acc + v1.ap_acc, ap_acc_miss + v1.ap_acc_miss,
-              ap_acc_add + v1.ap_acc_add, hits + v1.hits};
-    }
-
-    // For test purposes - compare for equality
-    XGBOOST_DEVICE bool operator ==(const MAPStats &rhs) const {
-      return ap_acc == rhs.ap_acc && ap_acc_miss == rhs.ap_acc_miss &&
-             ap_acc_add == rhs.ap_acc_add && hits == rhs.hits;
-    }
-  };
-
- private:
-  template <typename T>
-  XGBOOST_DEVICE inline static void Swap(T &v0, T &v1) {
-#if defined(__CUDACC__)
-    thrust::swap(v0, v1);
-#else
-    std::swap(v0, v1);
-#endif
-  }
-
-  /*!
-   * \brief Obtain the delta MAP by trying to switch the positions of labels in pos_pred_pos or
-   *        neg_pred_pos when sorted by predictions
-   * \param pos_pred_pos positive label's prediction value position when the groups prediction
-   *        values are sorted
-   * \param neg_pred_pos negative label's prediction value position when the groups prediction
-   *        values are sorted
-   * \param pos_label, neg_label the chosen positive and negative labels
-   * \param p_map_stats a vector containing the accumulated precisions for each position in a list
-   * \param map_stats_size size of the accumulated precisions vector
-   */
-  XGBOOST_DEVICE inline static bst_float GetLambdaMAP(
-    int pos_pred_pos, int neg_pred_pos,
-    bst_float pos_label, bst_float neg_label,
-    const MAPStats *p_map_stats, uint32_t map_stats_size) {
-    if (pos_pred_pos == neg_pred_pos || p_map_stats[map_stats_size - 1].hits == 0) {
-      return 0.0f;
-    }
-    if (pos_pred_pos > neg_pred_pos) {
-      Swap(pos_pred_pos, neg_pred_pos);
-      Swap(pos_label, neg_label);
-    }
-    bst_float original = p_map_stats[neg_pred_pos].ap_acc;
-    if (pos_pred_pos != 0) original -= p_map_stats[pos_pred_pos - 1].ap_acc;
-    bst_float changed = 0;
-    bst_float label1 = pos_label > 0.0f ? 1.0f : 0.0f;
-    bst_float label2 = neg_label > 0.0f ? 1.0f : 0.0f;
-    if (label1 == label2) {
-      return 0.0;
-    } else if (label1 < label2) {
-      changed += p_map_stats[neg_pred_pos - 1].ap_acc_add - p_map_stats[pos_pred_pos].ap_acc_add;
-      changed += (p_map_stats[pos_pred_pos].hits + 1.0f) / (pos_pred_pos + 1);
-    } else {
-      changed += p_map_stats[neg_pred_pos - 1].ap_acc_miss - p_map_stats[pos_pred_pos].ap_acc_miss;
-      changed += p_map_stats[neg_pred_pos].hits / (neg_pred_pos + 1);
-    }
-    bst_float ans = (changed - original) / (p_map_stats[map_stats_size - 1].hits);
-    if (ans < 0) ans = -ans;
-    return ans;
-  }
-
- public:
-  /*
-   * \brief obtain preprocessing results for calculating delta MAP
-   * \param sorted_list the list containing entry information
-   * \param map_stats a vector containing the accumulated precisions for each position in a list
-   */
-  inline static void GetMAPStats(const std::vector<ListEntry> &sorted_list,
-                                 std::vector<MAPStats> *p_map_acc) {
-    std::vector<MAPStats> &map_acc = *p_map_acc;
-    map_acc.resize(sorted_list.size());
-    bst_float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
-    for (size_t i = 1; i <= sorted_list.size(); ++i) {
-      if (sorted_list[i - 1].label > 0.0f) {
-        hit++;
-        acc1 += hit / i;
-        acc2 += (hit - 1) / i;
-        acc3 += (hit + 1) / i;
-      }
-      map_acc[i - 1] = MAPStats(acc1, acc2, acc3, hit);
-    }
-  }
-
-  static char const* Name() {
-    return "rank:map";
-  }
-
-  static void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
-                              std::vector<LambdaPair> *io_pairs) {
-    std::vector<LambdaPair> &pairs = *io_pairs;
-    std::vector<MAPStats> map_stats;
-    GetMAPStats(sorted_list, &map_stats);
-    for (auto & pair : pairs) {
-      pair.weight *=
-        GetLambdaMAP(pair.pos_index, pair.neg_index,
-                     sorted_list[pair.pos_index].label, sorted_list[pair.neg_index].label,
-                     &map_stats[0], map_stats.size());
-    }
-  }
-
-#if defined(__CUDACC__)
-  MAPLambdaWeightComputer(const bst_float *dpreds,
-                          const bst_float *dlabels,
-                          const dh::SegmentSorter<float> &segment_label_sorter)
-    : IndexablePredictionSorter(dpreds, segment_label_sorter),
-      dmap_stats_(segment_label_sorter.GetNumItems(), MAPStats()),
-      weight_multiplier_(segment_label_sorter, *this) {
-    this->CreateMAPStats(dlabels, segment_label_sorter);
-  }
-
-  void CreateMAPStats(const bst_float *dlabels,
-                      const dh::SegmentSorter<float> &segment_label_sorter) {
-    // For each group, go through the sorted prediction positions, and look up its corresponding
-    // label from the unsorted labels (from the original label list)
-
-    // For each item in the group, compute its MAP stats.
-    // Interleave the computation of map stats amongst different groups.
-
-    // First, determine postive labels in the dataset individually
-    auto nitems = segment_label_sorter.GetNumItems();
-    dh::caching_device_vector<uint32_t> dhits(nitems, 0);
-    // Original positions of the predictions after they have been sorted
-    const auto &pred_original_pos = this->GetPredictionSorter().GetOriginalPositionsSpan();
-    // Unsorted labels
-    const float *unsorted_labels = dlabels;
-    auto DeterminePositiveLabelLambda = [=] __device__(uint32_t idx) {
-      return (unsorted_labels[pred_original_pos[idx]] > 0.0f) ? 1 : 0;
-    };  // NOLINT
-
-    thrust::transform(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                      thrust::make_counting_iterator(nitems),
-                      dhits.begin(),
-                      DeterminePositiveLabelLambda);
-
-    // Allocator to be used by sort for managing space overhead while performing prefix scans
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    // Next, prefix scan the positive labels that are segmented to accumulate them.
-    // This is required for computing the accumulated precisions
-    const auto &group_segments = segment_label_sorter.GetGroupSegmentsSpan();
-    // Data segmented into different groups...
-    thrust::inclusive_scan_by_key(thrust::cuda::par(alloc),
-                                  dh::tcbegin(group_segments), dh::tcend(group_segments),
-                                  dhits.begin(),  // Input value
-                                  dhits.begin());  // In-place scan
-
-    // Compute accumulated precisions for each item, assuming positive and
-    // negative instances are missing.
-    // But first, compute individual item precisions
-    const auto *dhits_arr = dhits.data().get();
-    // Group info on device
-    const auto &dgroups = segment_label_sorter.GetGroupsSpan();
-    auto ComputeItemPrecisionLambda = [=] __device__(uint32_t idx) {
-      if (unsorted_labels[pred_original_pos[idx]] > 0.0f) {
-        auto idx_within_group = (idx - dgroups[group_segments[idx]]) + 1;
-        return MAPStats{static_cast<float>(dhits_arr[idx]) / idx_within_group,
-                        static_cast<float>(dhits_arr[idx] - 1) / idx_within_group,
-                        static_cast<float>(dhits_arr[idx] + 1) / idx_within_group,
-                        1.0f};
-      }
-      return MAPStats{};
-    };  // NOLINT
-
-    thrust::transform(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                      thrust::make_counting_iterator(nitems),
-                      this->dmap_stats_.begin(),
-                      ComputeItemPrecisionLambda);
-
-    // Lastly, compute the accumulated precisions for all the items segmented by groups.
-    // The precisions are accumulated within each group
-    thrust::inclusive_scan_by_key(thrust::cuda::par(alloc),
-                                  dh::tcbegin(group_segments), dh::tcend(group_segments),
-                                  this->dmap_stats_.begin(),  // Input map stats
-                                  this->dmap_stats_.begin());  // In-place scan and output here
-  }
-
-  inline const common::Span<const MAPStats> GetMapStatsSpan() const {
-    return { dmap_stats_.data().get(), dmap_stats_.size() };
-  }
-
-  // Type containing device pointers that can be cheaply copied on the kernel
-  class MAPLambdaWeightMultiplier : public BaseLambdaWeightMultiplier {
-   public:
-    MAPLambdaWeightMultiplier(const dh::SegmentSorter<float> &segment_label_sorter,
-                              const MAPLambdaWeightComputer &lwc)
-      : BaseLambdaWeightMultiplier(segment_label_sorter, lwc.GetPredictionSorter()),
-        dmap_stats_(lwc.GetMapStatsSpan()) {}
-
-    // Adjust the items weight by this value
-    __device__ __forceinline__ bst_float GetWeight(uint32_t gidx, int pidx, int nidx) const {
-      uint32_t group_begin = dgroups_[gidx];
-      uint32_t group_end = dgroups_[gidx + 1];
-
-      auto pos_lab_orig_posn = dorig_pos_[pidx];
-      auto neg_lab_orig_posn = dorig_pos_[nidx];
-      KERNEL_CHECK(pos_lab_orig_posn != neg_lab_orig_posn);
-
-      // Note: the label positive and negative indices are relative to the entire dataset.
-      // Hence, scale them back to an index within the group
-      auto pos_pred_pos = dindexable_sorted_preds_pos_[pos_lab_orig_posn] - group_begin;
-      auto neg_pred_pos = dindexable_sorted_preds_pos_[neg_lab_orig_posn] - group_begin;
-      return MAPLambdaWeightComputer::GetLambdaMAP(
-        pos_pred_pos, neg_pred_pos,
-        dsorted_labels_[pidx], dsorted_labels_[nidx],
-        &dmap_stats_[group_begin], group_end - group_begin);
-    }
-
-   private:
-    common::Span<const MAPStats> dmap_stats_;  // Start address of the map stats for every sorted
-                                               // prediction value
-  };
-
-  inline const MAPLambdaWeightMultiplier GetWeightMultiplier() const { return weight_multiplier_; }
-
- private:
-  dh::caching_device_vector<MAPStats> dmap_stats_;
-  // This computes the adjustment to the weight
-  const MAPLambdaWeightMultiplier weight_multiplier_;
-#endif
-};
-
-#if defined(__CUDACC__)
-class SortedLabelList : dh::SegmentSorter<float> {
- private:
-  const LambdaRankParam &param_;                      // Objective configuration
-
- public:
-  explicit SortedLabelList(const LambdaRankParam &param)
-    : param_(param) {}
-
-  // Sort the labels that are grouped by 'groups'
-  void Sort(const HostDeviceVector<bst_float> &dlabels, const std::vector<uint32_t> &groups) {
-    this->SortItems(dlabels.ConstDevicePointer(), dlabels.Size(), groups);
-  }
-
-  // This kernel can only run *after* the kernel in sort is completed, as they
-  // use the default stream
-  template <typename LambdaWeightComputerT>
-  void ComputeGradients(const bst_float *dpreds,   // Unsorted predictions
-                        const bst_float *dlabels,  // Unsorted labels
-                        const HostDeviceVector<bst_float> &weights,
-                        int iter,
-                        GradientPair *out_gpair,
-                        float weight_normalization_factor) {
-    // Group info on device
-    const auto &dgroups = this->GetGroupsSpan();
-    uint32_t ngroups = this->GetNumGroups() + 1;
-
-    uint32_t total_items = this->GetNumItems();
-    uint32_t niter = param_.num_pairsample * total_items;
-
-    float fix_list_weight = param_.fix_list_weight;
-
-    const auto &original_pos = this->GetOriginalPositionsSpan();
-
-    uint32_t num_weights = weights.Size();
-    auto dweights = num_weights ? weights.ConstDevicePointer() : nullptr;
-
-    const auto &sorted_labels = this->GetItemsSpan();
-
-    // This is used to adjust the weight of different elements based on the different ranking
-    // objective function policies
-    LambdaWeightComputerT weight_computer(dpreds, dlabels, *this);
-    auto wmultiplier = weight_computer.GetWeightMultiplier();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-    // For each instance in the group, compute the gradient pair concurrently
-    dh::LaunchN(device_id, niter, nullptr, [=] __device__(uint32_t idx) {
-      // First, determine the group 'idx' belongs to
-      uint32_t item_idx = idx % total_items;
-      uint32_t group_idx =
-          thrust::upper_bound(thrust::seq, dgroups.begin(),
-                              dgroups.begin() + ngroups, item_idx) -
-          dgroups.begin();
-      // Span of this group within the larger labels/predictions sorted tuple
-      uint32_t group_begin = dgroups[group_idx - 1];
-      uint32_t group_end = dgroups[group_idx];
-      uint32_t total_group_items = group_end - group_begin;
-
-      // Are the labels diverse enough? If they are all the same, then there is nothing to pick
-      // from another group - bail sooner
-      if (sorted_labels[group_begin] == sorted_labels[group_end - 1]) return;
-
-      // Find the number of labels less than and greater than the current label
-      // at the sorted index position item_idx
-      uint32_t nleft  = CountNumItemsToTheLeftOf(
-        sorted_labels.data() + group_begin, item_idx - group_begin + 1, sorted_labels[item_idx]);
-      uint32_t nright = CountNumItemsToTheRightOf(
-        sorted_labels.data() + item_idx, group_end - item_idx, sorted_labels[item_idx]);
-
-      // Create a minstd_rand object to act as our source of randomness
-      thrust::minstd_rand rng((iter + 1) * 1111);
-      rng.discard(((idx / total_items) * total_group_items) + item_idx - group_begin);
-      // Create a uniform_int_distribution to produce a sample from outside of the
-      // present label group
-      thrust::uniform_int_distribution<int> dist(0, nleft + nright - 1);
-
-      int sample = dist(rng);
-      int pos_idx = -1;  // Bigger label
-      int neg_idx = -1;  // Smaller label
-      // Are we picking a sample to the left/right of the current group?
-      if (sample < nleft) {
-        // Go left
-        pos_idx = sample + group_begin;
-        neg_idx = item_idx;
-      } else {
-        pos_idx = item_idx;
-        uint32_t items_in_group = total_group_items - nleft - nright;
-        neg_idx = sample + items_in_group + group_begin;
-      }
-
-      // Compute and assign the gradients now
-      const float eps = 1e-16f;
-      bst_float p = common::Sigmoid(dpreds[original_pos[pos_idx]] - dpreds[original_pos[neg_idx]]);
-      bst_float g = p - 1.0f;
-      bst_float h = thrust::max(p * (1.0f - p), eps);
-
-      // Rescale each gradient and hessian so that the group has a weighted constant
-      float scale = __frcp_ru(niter / total_items);
-      if (fix_list_weight != 0.0f) {
-        scale *= fix_list_weight / total_group_items;
-      }
-
-      float weight = num_weights ? dweights[group_idx - 1] : 1.0f;
-      weight *= weight_normalization_factor;
-      weight *= wmultiplier.GetWeight(group_idx - 1, pos_idx, neg_idx);
-      weight *= scale;
-      // Accumulate gradient and hessian in both positive and negative indices
-      const GradientPair in_pos_gpair(g * weight, 2.0f * weight * h);
-      dh::AtomicAddGpair(&out_gpair[original_pos[pos_idx]], in_pos_gpair);
-
-      const GradientPair in_neg_gpair(-g * weight, 2.0f * weight * h);
-      dh::AtomicAddGpair(&out_gpair[original_pos[neg_idx]], in_neg_gpair);
-    });
-
-    // Wait until the computations done by the kernel is complete
-    dh::safe_cuda(cudaStreamSynchronize(nullptr));
-  }
-};
-#endif
-
-// objective for lambda rank
-template <typename LambdaWeightComputerT>
-class LambdaRankObj : public ObjFunction {
- public:
-  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo& info,
-                   int iter,
-                   HostDeviceVector<GradientPair>* out_gpair) override {
-    CHECK_EQ(preds.Size(), info.labels_.Size()) << "label size predict size not match";
-
-    // quick consistency when group is not available
-    std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(info.labels_.Size());
-    const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
-    CHECK(gptr.size() != 0 && gptr.back() == info.labels_.Size())
-          << "group structure not consistent with #rows" << ", "
-          << "group ponter size: " << gptr.size() << ", "
-          << "labels size: " << info.labels_.Size() << ", "
-          << "group pointer back: " << (gptr.size() == 0 ? 0 : gptr.back());
-
-#if defined(__CUDACC__)
-    // Check if we have a GPU assignment; else, revert back to CPU
-    auto device = tparam_->gpu_id;
-    if (device >= 0) {
-      ComputeGradientsOnGPU(preds, info, iter, out_gpair, gptr);
-    } else {
-      // Revert back to CPU
-#endif
-      ComputeGradientsOnCPU(preds, info, iter, out_gpair, gptr);
-#if defined(__CUDACC__)
-    }
-#endif
-  }
-
-  const char* DefaultEvalMetric() const override {
-    return "map";
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String(LambdaWeightComputerT::Name());
-    out["lambda_rank_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["lambda_rank_param"], &param_);
-  }
-
- private:
-  bst_float ComputeWeightNormalizationFactor(const MetaInfo& info,
-                                             const std::vector<unsigned> &gptr) {
-    const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
-    bst_float sum_weights = 0;
-    for (bst_omp_uint k = 0; k < ngroup; ++k) {
-      sum_weights += info.GetWeight(k);
-    }
-    return ngroup / sum_weights;
-  }
-
-  void ComputeGradientsOnCPU(const HostDeviceVector<bst_float>& preds,
-                             const MetaInfo& info,
-                             int iter,
-                             HostDeviceVector<GradientPair>* out_gpair,
-                             const std::vector<unsigned> &gptr) {
-    LOG(DEBUG) << "Computing " << LambdaWeightComputerT::Name() << " gradients on CPU.";
-
-    bst_float weight_normalization_factor = ComputeWeightNormalizationFactor(info, gptr);
-
-    const auto& preds_h = preds.HostVector();
-    const auto& labels = info.labels_.HostVector();
-    std::vector<GradientPair>& gpair = out_gpair->HostVector();
-    const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
-    out_gpair->Resize(preds.Size());
-
-    #pragma omp parallel
-    {
-      // parallel construct, declare random number generator here, so that each
-      // thread use its own random number generator, seed by thread id and current iteration
-      std::minstd_rand rnd((iter + 1) * 1111);
-      std::vector<LambdaPair> pairs;
-      std::vector<ListEntry>  lst;
-      std::vector< std::pair<bst_float, unsigned> > rec;
-
-      #pragma omp for schedule(static)
-      for (bst_omp_uint k = 0; k < ngroup; ++k) {
-        lst.clear(); pairs.clear();
-        for (unsigned j = gptr[k]; j < gptr[k+1]; ++j) {
-          lst.emplace_back(preds_h[j], labels[j], j);
-          gpair[j] = GradientPair(0.0f, 0.0f);
-        }
-        std::stable_sort(lst.begin(), lst.end(), ListEntry::CmpPred);
-        rec.resize(lst.size());
-        for (unsigned i = 0; i < lst.size(); ++i) {
-          rec[i] = std::make_pair(lst[i].label, i);
-        }
-        std::stable_sort(rec.begin(), rec.end(), common::CmpFirst);
-        // enumerate buckets with same label, for each item in the lst, grab another sample randomly
-        for (unsigned i = 0; i < rec.size(); ) {
-          unsigned j = i + 1;
-          while (j < rec.size() && rec[j].first == rec[i].first) ++j;
-          // bucket in [i,j), get a sample outside bucket
-          unsigned nleft = i, nright = static_cast<unsigned>(rec.size() - j);
-          if (nleft + nright != 0) {
-            int nsample = param_.num_pairsample;
-            while (nsample --) {
-              for (unsigned pid = i; pid < j; ++pid) {
-                unsigned ridx = std::uniform_int_distribution<unsigned>(0, nleft + nright - 1)(rnd);
-                if (ridx < nleft) {
-                  pairs.emplace_back(rec[ridx].second, rec[pid].second,
-                      info.GetWeight(k) * weight_normalization_factor);
-                } else {
-                  pairs.emplace_back(rec[pid].second, rec[ridx+j-i].second,
-                      info.GetWeight(k) * weight_normalization_factor);
-                }
-              }
-            }
-          }
-          i = j;
-        }
-        // get lambda weight for the pairs
-        LambdaWeightComputerT::GetLambdaWeight(lst, &pairs);
-        // rescale each gradient and hessian so that the lst have constant weighted
-        float scale = 1.0f / param_.num_pairsample;
-        if (param_.fix_list_weight != 0.0f) {
-          scale *= param_.fix_list_weight / (gptr[k + 1] - gptr[k]);
-        }
-        for (auto & pair : pairs) {
-          const ListEntry &pos = lst[pair.pos_index];
-          const ListEntry &neg = lst[pair.neg_index];
-          const bst_float w = pair.weight * scale;
-          const float eps = 1e-16f;
-          bst_float p = common::Sigmoid(pos.pred - neg.pred);
-          bst_float g = p - 1.0f;
-          bst_float h = std::max(p * (1.0f - p), eps);
-          // accumulate gradient and hessian in both pid, and nid
-          gpair[pos.rindex] += GradientPair(g * w, 2.0f*w*h);
-          gpair[neg.rindex] += GradientPair(-g * w, 2.0f*w*h);
-        }
-      }
-    }
-  }
-
-#if defined(__CUDACC__)
-  void ComputeGradientsOnGPU(const HostDeviceVector<bst_float>& preds,
-                             const MetaInfo& info,
-                             int iter,
-                             HostDeviceVector<GradientPair>* out_gpair,
-                             const std::vector<unsigned> &gptr) {
-    LOG(DEBUG) << "Computing " << LambdaWeightComputerT::Name() << " gradients on GPU.";
-
-    auto device = tparam_->gpu_id;
-    dh::safe_cuda(cudaSetDevice(device));
-
-    bst_float weight_normalization_factor = ComputeWeightNormalizationFactor(info, gptr);
-
-    // Set the device ID and copy them to the device
-    out_gpair->SetDevice(device);
-    info.labels_.SetDevice(device);
-    preds.SetDevice(device);
-    info.weights_.SetDevice(device);
-
-    out_gpair->Resize(preds.Size());
-
-    auto d_preds = preds.ConstDevicePointer();
-    auto d_gpair = out_gpair->DevicePointer();
-    auto d_labels = info.labels_.ConstDevicePointer();
-
-    SortedLabelList slist(param_);
-
-    // Sort the labels within the groups on the device
-    slist.Sort(info.labels_, gptr);
-
-    // Initialize the gradients next
-    out_gpair->Fill(GradientPair(0.0f, 0.0f));
-
-    // Finally, compute the gradients
-    slist.ComputeGradients<LambdaWeightComputerT>
-      (d_preds, d_labels, info.weights_, iter, d_gpair, weight_normalization_factor);
-  }
-#endif
-
-  LambdaRankParam param_;
-};
-
-#if !defined(GTEST_TEST)
-// register the objective functions
-DMLC_REGISTER_PARAMETER(LambdaRankParam);
-
-XGBOOST_REGISTER_OBJECTIVE(PairwiseRankObj, PairwiseLambdaWeightComputer::Name())
-.describe("Pairwise rank objective.")
-.set_body([]() { return new LambdaRankObj<PairwiseLambdaWeightComputer>(); });
-
-XGBOOST_REGISTER_OBJECTIVE(LambdaRankNDCG, NDCGLambdaWeightComputer::Name())
-.describe("LambdaRank with NDCG as objective.")
-.set_body([]() { return new LambdaRankObj<NDCGLambdaWeightComputer>(); });
-
-XGBOOST_REGISTER_OBJECTIVE(LambdaRankObjMAP, MAPLambdaWeightComputer::Name())
-.describe("LambdaRank with MAP as objective.")
-.set_body([]() { return new LambdaRankObj<MAPLambdaWeightComputer>(); });
-#endif
-
-}  // namespace obj
-}  // namespace xgboost
diff --git a/ml-xgboost/src/objective/regression_loss.h b/ml-xgboost/src/objective/regression_loss.h
deleted file mode 100644
index f8ef24f..0000000
--- a/ml-xgboost/src/objective/regression_loss.h
+++ /dev/null
@@ -1,142 +0,0 @@
-/*!
- * Copyright 2017-2019 XGBoost contributors
- */
-#ifndef XGBOOST_OBJECTIVE_REGRESSION_LOSS_H_
-#define XGBOOST_OBJECTIVE_REGRESSION_LOSS_H_
-
-#include <dmlc/omp.h>
-#include <xgboost/logging.h>
-#include <algorithm>
-#include "../common/math.h"
-
-namespace xgboost {
-namespace obj {
-
-// common regressions
-// linear regression
-struct LinearSquareLoss {
-  // duplication is necessary, as __device__ specifier
-  // cannot be made conditional on template parameter
-  XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return x; }
-  XGBOOST_DEVICE static bool CheckLabel(bst_float x) { return true; }
-  XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
-    return predt - label;
-  }
-  XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
-    return 1.0f;
-  }
-  template <typename T>
-  static T PredTransform(T x) { return x; }
-  template <typename T>
-  static T FirstOrderGradient(T predt, T label) { return predt - label; }
-  template <typename T>
-  static T SecondOrderGradient(T predt, T label) { return T(1.0f); }
-  static bst_float ProbToMargin(bst_float base_score) { return base_score; }
-  static const char* LabelErrorMsg() { return ""; }
-  static const char* DefaultEvalMetric() { return "rmse"; }
-
-  static const char* Name() { return "reg:squarederror"; }
-};
-
-struct SquaredLogError {
-  XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return x; }
-  XGBOOST_DEVICE static bool CheckLabel(bst_float label) {
-    return label > -1;
-  }
-  XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
-    predt = fmaxf(predt, -1 + 1e-6);  // ensure correct value for log1p
-    return (std::log1p(predt) - std::log1p(label)) / (predt + 1);
-  }
-  XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
-    predt = fmaxf(predt, -1 + 1e-6);
-    float res = (-std::log1p(predt) + std::log1p(label) + 1) /
-                std::pow(predt + 1, 2);
-    res = fmaxf(res, 1e-6f);
-    return res;
-  }
-  static bst_float ProbToMargin(bst_float base_score) { return base_score; }
-  static const char* LabelErrorMsg() {
-    return "label must be greater than -1 for rmsle so that log(label + 1) can be valid.";
-  }
-  static const char* DefaultEvalMetric() { return "rmsle"; }
-
-  static const char* Name() { return "reg:squaredlogerror"; }
-};
-
-// logistic loss for probability regression task
-struct LogisticRegression {
-  // duplication is necessary, as __device__ specifier
-  // cannot be made conditional on template parameter
-  XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return common::Sigmoid(x); }
-  XGBOOST_DEVICE static bool CheckLabel(bst_float x) { return x >= 0.0f && x <= 1.0f; }
-  XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
-    return predt - label;
-  }
-  XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
-    const float eps = 1e-16f;
-    return fmaxf(predt * (1.0f - predt), eps);
-  }
-  template <typename T>
-  static T PredTransform(T x) { return common::Sigmoid(x); }
-  template <typename T>
-  static T FirstOrderGradient(T predt, T label) { return predt - label; }
-  template <typename T>
-  static T SecondOrderGradient(T predt, T label) {
-    const T eps = T(1e-16f);
-    return std::max(predt * (T(1.0f) - predt), eps);
-  }
-  static bst_float ProbToMargin(bst_float base_score) {
-    CHECK(base_score > 0.0f && base_score < 1.0f)
-        << "base_score must be in (0,1) for logistic loss, got: " << base_score;
-    return -logf(1.0f / base_score - 1.0f);
-  }
-  static const char* LabelErrorMsg() {
-    return "label must be in [0,1] for logistic regression";
-  }
-  static const char* DefaultEvalMetric() { return "rmse"; }
-
-  static const char* Name() { return "reg:logistic"; }
-};
-
-// logistic loss for binary classification task
-struct LogisticClassification : public LogisticRegression {
-  static const char* DefaultEvalMetric() { return "error"; }
-  static const char* Name() { return "binary:logistic"; }
-};
-
-// logistic loss, but predict un-transformed margin
-struct LogisticRaw : public LogisticRegression {
-  // duplication is necessary, as __device__ specifier
-  // cannot be made conditional on template parameter
-  XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return x; }
-  XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
-    predt = common::Sigmoid(predt);
-    return predt - label;
-  }
-  XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
-    const float eps = 1e-16f;
-    predt = common::Sigmoid(predt);
-    return fmaxf(predt * (1.0f - predt), eps);
-  }
-  template <typename T>
-    static T PredTransform(T x) { return x; }
-  template <typename T>
-    static T FirstOrderGradient(T predt, T label) {
-    predt = common::Sigmoid(predt);
-    return predt - label;
-  }
-  template <typename T>
-    static T SecondOrderGradient(T predt, T label) {
-    const T eps = T(1e-16f);
-    predt = common::Sigmoid(predt);
-    return std::max(predt * (T(1.0f) - predt), eps);
-  }
-  static const char* DefaultEvalMetric() { return "auc"; }
-
-  static const char* Name() { return "binary:logitraw"; }
-};
-
-}  // namespace obj
-}  // namespace xgboost
-
-#endif  // XGBOOST_OBJECTIVE_REGRESSION_LOSS_H_
diff --git a/ml-xgboost/src/objective/regression_obj.cc b/ml-xgboost/src/objective/regression_obj.cc
deleted file mode 100644
index 663989f..0000000
--- a/ml-xgboost/src/objective/regression_obj.cc
+++ /dev/null
@@ -1,18 +0,0 @@
-/*!
- * Copyright 2018 XGBoost contributors
- */
-
-// Dummy file to keep the CUDA conditional compile trick.
-
-#include <dmlc/registry.h>
-namespace xgboost {
-namespace obj {
-
-DMLC_REGISTRY_FILE_TAG(regression_obj);
-
-}  // namespace obj
-}  // namespace xgboost
-
-#ifndef XGBOOST_USE_CUDA
-#include "regression_obj.cu"
-#endif  // XGBOOST_USE_CUDA
diff --git a/ml-xgboost/src/objective/regression_obj.cu b/ml-xgboost/src/objective/regression_obj.cu
deleted file mode 100644
index 414f9d2..0000000
--- a/ml-xgboost/src/objective/regression_obj.cu
+++ /dev/null
@@ -1,569 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file regression_obj.cu
- * \brief Definition of single-value regression and classification objectives.
- * \author Tianqi Chen, Kailong Chen
- */
-
-#include <dmlc/omp.h>
-#include <xgboost/logging.h>
-#include <xgboost/objective.h>
-#include <cmath>
-#include <memory>
-#include <vector>
-
-#include "xgboost/host_device_vector.h"
-#include "xgboost/json.h"
-#include "xgboost/parameter.h"
-#include "xgboost/span.h"
-
-#include "../common/transform.h"
-#include "../common/common.h"
-#include "./regression_loss.h"
-
-
-namespace xgboost {
-namespace obj {
-
-#if defined(XGBOOST_USE_CUDA)
-DMLC_REGISTRY_FILE_TAG(regression_obj_gpu);
-#endif  // defined(XGBOOST_USE_CUDA)
-
-struct RegLossParam : public XGBoostParameter<RegLossParam> {
-  float scale_pos_weight;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(RegLossParam) {
-    DMLC_DECLARE_FIELD(scale_pos_weight).set_default(1.0f).set_lower_bound(0.0f)
-      .describe("Scale the weight of positive examples by this factor");
-  }
-};
-
-template<typename Loss>
-class RegLossObj : public ObjFunction {
- protected:
-  HostDeviceVector<int> label_correct_;
-
- public:
-  RegLossObj() = default;
-
-  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair>* out_gpair) override {
-    if (info.labels_.Size() == 0U) {
-      LOG(WARNING) << "Label set is empty.";
-    }
-    CHECK_EQ(preds.Size(), info.labels_.Size())
-        << " " << "labels are not correctly provided"
-        << "preds.size=" << preds.Size() << ", label.size=" << info.labels_.Size() << ", "
-        << "Loss: " << Loss::Name();
-    size_t const ndata = preds.Size();
-    out_gpair->Resize(ndata);
-    auto device = tparam_->gpu_id;
-    label_correct_.Resize(1);
-    label_correct_.Fill(1);
-
-    bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-    auto scale_pos_weight = param_.scale_pos_weight;
-    common::Transform<>::Init(
-        [=] XGBOOST_DEVICE(size_t _idx,
-                           common::Span<int> _label_correct,
-                           common::Span<GradientPair> _out_gpair,
-                           common::Span<const bst_float> _preds,
-                           common::Span<const bst_float> _labels,
-                           common::Span<const bst_float> _weights) {
-          bst_float p = Loss::PredTransform(_preds[_idx]);
-          bst_float w = is_null_weight ? 1.0f : _weights[_idx];
-          bst_float label = _labels[_idx];
-          if (label == 1.0f) {
-            w *= scale_pos_weight;
-          }
-          if (!Loss::CheckLabel(label)) {
-            // If there is an incorrect label, the host code will know.
-            _label_correct[0] = 0;
-          }
-          _out_gpair[_idx] = GradientPair(Loss::FirstOrderGradient(p, label) * w,
-                                          Loss::SecondOrderGradient(p, label) * w);
-        },
-        common::Range{0, static_cast<int64_t>(ndata)}, device).Eval(
-            &label_correct_, out_gpair, &preds, &info.labels_, &info.weights_);
-
-    // copy "label correct" flags back to host
-    std::vector<int>& label_correct_h = label_correct_.HostVector();
-    for (auto const flag : label_correct_h) {
-      if (flag == 0) {
-        LOG(FATAL) << Loss::LabelErrorMsg();
-      }
-    }
-  }
-
- public:
-  const char* DefaultEvalMetric() const override {
-    return Loss::DefaultEvalMetric();
-  }
-
-  void PredTransform(HostDeviceVector<float> *io_preds) override {
-    common::Transform<>::Init(
-        [] XGBOOST_DEVICE(size_t _idx, common::Span<float> _preds) {
-          _preds[_idx] = Loss::PredTransform(_preds[_idx]);
-        }, common::Range{0, static_cast<int64_t>(io_preds->Size())},
-        tparam_->gpu_id)
-        .Eval(io_preds);
-  }
-
-  float ProbToMargin(float base_score) const override {
-    return Loss::ProbToMargin(base_score);
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String(Loss::Name());
-    out["reg_loss_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["reg_loss_param"], &param_);
-  }
-
- protected:
-  RegLossParam param_;
-};
-
-// register the objective functions
-DMLC_REGISTER_PARAMETER(RegLossParam);
-
-XGBOOST_REGISTER_OBJECTIVE(SquaredLossRegression, LinearSquareLoss::Name())
-.describe("Regression with squared error.")
-.set_body([]() { return new RegLossObj<LinearSquareLoss>(); });
-
-XGBOOST_REGISTER_OBJECTIVE(SquareLogError, SquaredLogError::Name())
-.describe("Regression with root mean squared logarithmic error.")
-.set_body([]() { return new RegLossObj<SquaredLogError>(); });
-
-XGBOOST_REGISTER_OBJECTIVE(LogisticRegression, LogisticRegression::Name())
-.describe("Logistic regression for probability regression task.")
-.set_body([]() { return new RegLossObj<LogisticRegression>(); });
-
-XGBOOST_REGISTER_OBJECTIVE(LogisticClassification, LogisticClassification::Name())
-.describe("Logistic regression for binary classification task.")
-.set_body([]() { return new RegLossObj<LogisticClassification>(); });
-
-XGBOOST_REGISTER_OBJECTIVE(LogisticRaw, LogisticRaw::Name())
-.describe("Logistic regression for classification, output score "
-          "before logistic transformation.")
-.set_body([]() { return new RegLossObj<LogisticRaw>(); });
-
-// Deprecated functions
-XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear")
-.describe("Regression with squared error.")
-.set_body([]() {
-    LOG(WARNING) << "reg:linear is now deprecated in favor of reg:squarederror.";
-    return new RegLossObj<LinearSquareLoss>(); });
-// End deprecated
-
-// declare parameter
-struct PoissonRegressionParam : public XGBoostParameter<PoissonRegressionParam> {
-  float max_delta_step;
-  DMLC_DECLARE_PARAMETER(PoissonRegressionParam) {
-    DMLC_DECLARE_FIELD(max_delta_step).set_lower_bound(0.0f).set_default(0.7f)
-        .describe("Maximum delta step we allow each weight estimation to be." \
-                  " This parameter is required for possion regression.");
-  }
-};
-
-// poisson regression for count
-class PoissonRegression : public ObjFunction {
- public:
-  // declare functions
-  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
-    size_t const ndata = preds.Size();
-    out_gpair->Resize(ndata);
-    auto device = tparam_->gpu_id;
-    label_correct_.Resize(1);
-    label_correct_.Fill(1);
-
-    bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-    bst_float max_delta_step = param_.max_delta_step;
-    common::Transform<>::Init(
-        [=] XGBOOST_DEVICE(size_t _idx,
-                           common::Span<int> _label_correct,
-                           common::Span<GradientPair> _out_gpair,
-                           common::Span<const bst_float> _preds,
-                           common::Span<const bst_float> _labels,
-                           common::Span<const bst_float> _weights) {
-          bst_float p = _preds[_idx];
-          bst_float w = is_null_weight ? 1.0f : _weights[_idx];
-          bst_float y = _labels[_idx];
-          if (y < 0.0f) {
-            _label_correct[0] = 0;
-          }
-          _out_gpair[_idx] = GradientPair{(expf(p) - y) * w,
-                                          expf(p + max_delta_step) * w};
-        },
-        common::Range{0, static_cast<int64_t>(ndata)}, device).Eval(
-            &label_correct_, out_gpair, &preds, &info.labels_, &info.weights_);
-    // copy "label correct" flags back to host
-    std::vector<int>& label_correct_h = label_correct_.HostVector();
-    for (auto const flag : label_correct_h) {
-      if (flag == 0) {
-        LOG(FATAL) << "PoissonRegression: label must be nonnegative";
-      }
-    }
-  }
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    common::Transform<>::Init(
-        [] XGBOOST_DEVICE(size_t _idx, common::Span<bst_float> _preds) {
-          _preds[_idx] = expf(_preds[_idx]);
-        },
-        common::Range{0, static_cast<int64_t>(io_preds->Size())},
-        tparam_->gpu_id)
-        .Eval(io_preds);
-  }
-  void EvalTransform(HostDeviceVector<bst_float> *io_preds) override {
-    PredTransform(io_preds);
-  }
-  bst_float ProbToMargin(bst_float base_score) const override {
-    return std::log(base_score);
-  }
-  const char* DefaultEvalMetric() const override {
-    return "poisson-nloglik";
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("count:poisson");
-    out["poisson_regression_param"] = ToJson(param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    FromJson(in["poisson_regression_param"], &param_);
-  }
-
- private:
-  PoissonRegressionParam param_;
-  HostDeviceVector<int> label_correct_;
-};
-
-// register the objective functions
-DMLC_REGISTER_PARAMETER(PoissonRegressionParam);
-
-XGBOOST_REGISTER_OBJECTIVE(PoissonRegression, "count:poisson")
-.describe("Possion regression for count data.")
-.set_body([]() { return new PoissonRegression(); });
-
-
-// cox regression for survival data (negative values mean they are censored)
-class CoxRegression : public ObjFunction {
- public:
-  void Configure(
-      const std::vector<std::pair<std::string, std::string> > &args) override {}
-
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
-    const auto& preds_h = preds.HostVector();
-    out_gpair->Resize(preds_h.size());
-    auto& gpair = out_gpair->HostVector();
-    const std::vector<size_t> &label_order = info.LabelAbsSort();
-
-    const omp_ulong ndata = static_cast<omp_ulong>(preds_h.size()); // NOLINT(*)
-    const bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-
-    // pre-compute a sum
-    double exp_p_sum = 0;  // we use double because we might need the precision with large datasets
-    for (omp_ulong i = 0; i < ndata; ++i) {
-      exp_p_sum += std::exp(preds_h[label_order[i]]);
-    }
-
-    // start calculating grad and hess
-    const auto& labels = info.labels_.HostVector();
-    double r_k = 0;
-    double s_k = 0;
-    double last_exp_p = 0.0;
-    double last_abs_y = 0.0;
-    double accumulated_sum = 0;
-    for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
-      const size_t ind = label_order[i];
-      const double p = preds_h[ind];
-      const double exp_p = std::exp(p);
-      const double w = info.GetWeight(ind);
-      const double y = labels[ind];
-      const double abs_y = std::abs(y);
-
-      // only update the denominator after we move forward in time (labels are sorted)
-      // this is Breslow's method for ties
-      accumulated_sum += last_exp_p;
-      if (last_abs_y < abs_y) {
-        exp_p_sum -= accumulated_sum;
-        accumulated_sum = 0;
-      } else {
-        CHECK(last_abs_y <= abs_y) << "CoxRegression: labels must be in sorted order, " <<
-                                      "MetaInfo::LabelArgsort failed!";
-      }
-
-      if (y > 0) {
-        r_k += 1.0/exp_p_sum;
-        s_k += 1.0/(exp_p_sum*exp_p_sum);
-      }
-
-      const double grad = exp_p*r_k - static_cast<bst_float>(y > 0);
-      const double hess = exp_p*r_k - exp_p*exp_p * s_k;
-      gpair.at(ind) = GradientPair(grad * w, hess * w);
-
-      last_abs_y = abs_y;
-      last_exp_p = exp_p;
-    }
-  }
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    std::vector<bst_float> &preds = io_preds->HostVector();
-    const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
-#pragma omp parallel for schedule(static)
-    for (long j = 0; j < ndata; ++j) {  // NOLINT(*)
-      preds[j] = std::exp(preds[j]);
-    }
-  }
-  void EvalTransform(HostDeviceVector<bst_float> *io_preds) override {
-    PredTransform(io_preds);
-  }
-  bst_float ProbToMargin(bst_float base_score) const override {
-    return std::log(base_score);
-  }
-  const char* DefaultEvalMetric() const override {
-    return "cox-nloglik";
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("survival:cox");
-  }
-  void LoadConfig(Json const&) override {}
-};
-
-// register the objective function
-XGBOOST_REGISTER_OBJECTIVE(CoxRegression, "survival:cox")
-.describe("Cox regression for censored survival data (negative labels are considered censored).")
-.set_body([]() { return new CoxRegression(); });
-
-// gamma regression
-class GammaRegression : public ObjFunction {
- public:
-  void Configure(
-      const std::vector<std::pair<std::string, std::string> > &args) override {}
-
-  void GetGradient(const HostDeviceVector<bst_float> &preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
-    const size_t ndata = preds.Size();
-    auto device = tparam_->gpu_id;
-    out_gpair->Resize(ndata);
-    label_correct_.Resize(1);
-    label_correct_.Fill(1);
-
-    const bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-    common::Transform<>::Init(
-        [=] XGBOOST_DEVICE(size_t _idx,
-                           common::Span<int> _label_correct,
-                           common::Span<GradientPair> _out_gpair,
-                           common::Span<const bst_float> _preds,
-                           common::Span<const bst_float> _labels,
-                           common::Span<const bst_float> _weights) {
-          bst_float p = _preds[_idx];
-          bst_float w = is_null_weight ? 1.0f : _weights[_idx];
-          bst_float y = _labels[_idx];
-          if (y < 0.0f) {
-            _label_correct[0] = 0;
-          }
-          _out_gpair[_idx] = GradientPair((1 - y / expf(p)) * w, y / expf(p) * w);
-        },
-        common::Range{0, static_cast<int64_t>(ndata)}, device).Eval(
-            &label_correct_, out_gpair, &preds, &info.labels_, &info.weights_);
-
-    // copy "label correct" flags back to host
-    std::vector<int>& label_correct_h = label_correct_.HostVector();
-    for (auto const flag : label_correct_h) {
-      if (flag == 0) {
-        LOG(FATAL) << "GammaRegression: label must be nonnegative";
-      }
-    }
-  }
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    common::Transform<>::Init(
-        [] XGBOOST_DEVICE(size_t _idx, common::Span<bst_float> _preds) {
-          _preds[_idx] = expf(_preds[_idx]);
-        },
-        common::Range{0, static_cast<int64_t>(io_preds->Size())},
-        tparam_->gpu_id)
-        .Eval(io_preds);
-  }
-  void EvalTransform(HostDeviceVector<bst_float> *io_preds) override {
-    PredTransform(io_preds);
-  }
-  bst_float ProbToMargin(bst_float base_score) const override {
-    return std::log(base_score);
-  }
-  const char* DefaultEvalMetric() const override {
-    return "gamma-nloglik";
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("reg:gamma");
-  }
-  void LoadConfig(Json const&) override {}
-
- private:
-  HostDeviceVector<int> label_correct_;
-};
-
-// register the objective functions
-XGBOOST_REGISTER_OBJECTIVE(GammaRegression, "reg:gamma")
-.describe("Gamma regression for severity data.")
-.set_body([]() { return new GammaRegression(); });
-
-
-// declare parameter
-struct TweedieRegressionParam : public XGBoostParameter<TweedieRegressionParam> {
-  float tweedie_variance_power;
-  DMLC_DECLARE_PARAMETER(TweedieRegressionParam) {
-    DMLC_DECLARE_FIELD(tweedie_variance_power).set_range(1.0f, 2.0f).set_default(1.5f)
-      .describe("Tweedie variance power.  Must be between in range [1, 2).");
-  }
-};
-
-// tweedie regression
-class TweedieRegression : public ObjFunction {
- public:
-  // declare functions
-  void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
-    param_.UpdateAllowUnknown(args);
-    std::ostringstream os;
-    os << "tweedie-nloglik@" << param_.tweedie_variance_power;
-    metric_ = os.str();
-  }
-
-  void GetGradient(const HostDeviceVector<bst_float>& preds,
-                   const MetaInfo &info,
-                   int iter,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
-    CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
-    CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
-    const size_t ndata = preds.Size();
-    out_gpair->Resize(ndata);
-
-    auto device = tparam_->gpu_id;
-    label_correct_.Resize(1);
-    label_correct_.Fill(1);
-
-    const bool is_null_weight = info.weights_.Size() == 0;
-    if (!is_null_weight) {
-      CHECK_EQ(info.weights_.Size(), ndata)
-          << "Number of weights should be equal to number of data points.";
-    }
-
-    const float rho = param_.tweedie_variance_power;
-    common::Transform<>::Init(
-        [=] XGBOOST_DEVICE(size_t _idx,
-                           common::Span<int> _label_correct,
-                           common::Span<GradientPair> _out_gpair,
-                           common::Span<const bst_float> _preds,
-                           common::Span<const bst_float> _labels,
-                           common::Span<const bst_float> _weights) {
-          bst_float p = _preds[_idx];
-          bst_float w = is_null_weight ? 1.0f : _weights[_idx];
-          bst_float y = _labels[_idx];
-          if (y < 0.0f) {
-            _label_correct[0] = 0;
-          }
-          bst_float grad = -y * expf((1 - rho) * p) + expf((2 - rho) * p);
-          bst_float hess =
-              -y * (1 - rho) * \
-              std::exp((1 - rho) * p) + (2 - rho) * expf((2 - rho) * p);
-          _out_gpair[_idx] = GradientPair(grad * w, hess * w);
-        },
-        common::Range{0, static_cast<int64_t>(ndata), 1}, device)
-        .Eval(&label_correct_, out_gpair, &preds, &info.labels_, &info.weights_);
-
-    // copy "label correct" flags back to host
-    std::vector<int>& label_correct_h = label_correct_.HostVector();
-    for (auto const flag : label_correct_h) {
-      if (flag == 0) {
-        LOG(FATAL) << "TweedieRegression: label must be nonnegative";
-      }
-    }
-  }
-  void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
-    common::Transform<>::Init(
-        [] XGBOOST_DEVICE(size_t _idx, common::Span<bst_float> _preds) {
-          _preds[_idx] = expf(_preds[_idx]);
-        },
-        common::Range{0, static_cast<int64_t>(io_preds->Size())},
-        tparam_->gpu_id)
-        .Eval(io_preds);
-  }
-
-  bst_float ProbToMargin(bst_float base_score) const override {
-    return std::log(base_score);
-  }
-
-  const char* DefaultEvalMetric() const override {
-    return metric_.c_str();
-  }
-
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["name"] = String("reg:tweedie");
-    out["tweedie_regression_param"] = ToJson(param_);
-  }
-  void LoadConfig(Json const& in) override {
-    FromJson(in["tweedie_regression_param"], &param_);
-  }
-
- private:
-  std::string metric_;
-  TweedieRegressionParam param_;
-  HostDeviceVector<int> label_correct_;
-};
-
-// register the objective functions
-DMLC_REGISTER_PARAMETER(TweedieRegressionParam);
-
-XGBOOST_REGISTER_OBJECTIVE(TweedieRegression, "reg:tweedie")
-.describe("Tweedie regression for insurance data.")
-.set_body([]() { return new TweedieRegression(); });
-
-}  // namespace obj
-}  // namespace xgboost
diff --git a/ml-xgboost/src/predictor/cpu_predictor.cc b/ml-xgboost/src/predictor/cpu_predictor.cc
deleted file mode 100644
index ebc1512..0000000
--- a/ml-xgboost/src/predictor/cpu_predictor.cc
+++ /dev/null
@@ -1,487 +0,0 @@
-/*!
- * Copyright by Contributors 2017-2020
- */
-#include <dmlc/omp.h>
-#include <dmlc/any.h>
-
-#include <cstddef>
-#include <limits>
-#include <mutex>
-
-#include "xgboost/base.h"
-#include "xgboost/data.h"
-#include "xgboost/predictor.h"
-#include "xgboost/tree_model.h"
-#include "xgboost/tree_updater.h"
-#include "xgboost/logging.h"
-#include "xgboost/host_device_vector.h"
-
-#include "../data/adapter.h"
-#include "../common/math.h"
-#include "../gbm/gbtree_model.h"
-
-namespace xgboost {
-namespace predictor {
-
-DMLC_REGISTRY_FILE_TAG(cpu_predictor);
-
-bst_float PredValue(const SparsePage::Inst &inst,
-                    const std::vector<std::unique_ptr<RegTree>> &trees,
-                    const std::vector<int> &tree_info, int bst_group,
-                    RegTree::FVec *p_feats, unsigned tree_begin,
-                    unsigned tree_end) {
-  bst_float psum = 0.0f;
-  p_feats->Fill(inst);
-  for (size_t i = tree_begin; i < tree_end; ++i) {
-    if (tree_info[i] == bst_group) {
-      int tid = trees[i]->GetLeafIndex(*p_feats);
-      psum += (*trees[i])[tid].LeafValue();
-    }
-  }
-  p_feats->Drop(inst);
-  return psum;
-}
-
-template <size_t kUnrollLen = 8>
-struct SparsePageView {
-  SparsePage const* page;
-  bst_row_t base_rowid;
-  static size_t constexpr kUnroll = kUnrollLen;
-
-  explicit SparsePageView(SparsePage const *p)
-      : page{p}, base_rowid{page->base_rowid} {
-    // Pull to host before entering omp block, as this is not thread safe.
-    page->data.HostVector();
-    page->offset.HostVector();
-  }
-  SparsePage::Inst operator[](size_t i) { return (*page)[i]; }
-  size_t Size() const { return page->Size(); }
-};
-
-template <typename Adapter, size_t kUnrollLen = 8>
-class AdapterView {
-  Adapter* adapter_;
-  float missing_;
-  common::Span<Entry> workspace_;
-  std::vector<size_t> current_unroll_;
-
- public:
-  static size_t constexpr kUnroll = kUnrollLen;
-
- public:
-  explicit AdapterView(Adapter *adapter, float missing,
-                       common::Span<Entry> workplace)
-      : adapter_{adapter}, missing_{missing}, workspace_{workplace},
-        current_unroll_(omp_get_max_threads() > 0 ? omp_get_max_threads() : 1, 0) {}
-  SparsePage::Inst operator[](size_t i) {
-    bst_feature_t columns = adapter_->NumColumns();
-    auto const &batch = adapter_->Value();
-    auto row = batch.GetLine(i);
-    auto t = omp_get_thread_num();
-    auto const beg = (columns * kUnroll * t) + (current_unroll_[t] * columns);
-    size_t non_missing {beg};
-    for (size_t c = 0; c < row.Size(); ++c) {
-      auto e = row.GetElement(c);
-      if (missing_ != e.value && !common::CheckNAN(e.value)) {
-        workspace_[non_missing] =
-            Entry{static_cast<bst_feature_t>(e.column_idx), e.value};
-        ++non_missing;
-      }
-    }
-    auto ret = workspace_.subspan(beg, non_missing - beg);
-    current_unroll_[t]++;
-    if (current_unroll_[t] == kUnroll) {
-      current_unroll_[t] = 0;
-    }
-    return ret;
-  }
-
-  size_t Size() const { return adapter_->NumRows(); }
-
-  bst_row_t const static base_rowid = 0;  // NOLINT
-};
-
-template <typename DataView>
-void PredictBatchKernel(DataView batch, std::vector<bst_float> *out_preds,
-                        gbm::GBTreeModel const &model, int32_t tree_begin,
-                        int32_t tree_end,
-                        std::vector<RegTree::FVec> *p_thread_temp) {
-  auto& thread_temp = *p_thread_temp;
-  int32_t const num_group = model.learner_model_param->num_output_group;
-
-  std::vector<bst_float> &preds = *out_preds;
-  CHECK_EQ(model.param.size_leaf_vector, 0)
-      << "size_leaf_vector is enforced to 0 so far";
-  // parallel over local batch
-  const auto nsize = static_cast<bst_omp_uint>(batch.Size());
-  auto constexpr kUnroll = DataView::kUnroll;
-  const bst_omp_uint rest = nsize % kUnroll;
-  if (nsize >= kUnroll) {
-#pragma omp parallel for schedule(static)
-    for (bst_omp_uint i = 0; i < nsize - rest; i += kUnroll) {
-      const int tid = omp_get_thread_num();
-      RegTree::FVec &feats = thread_temp[tid];
-      int64_t ridx[kUnroll];
-      SparsePage::Inst inst[kUnroll];
-      for (size_t k = 0; k < kUnroll; ++k) {
-        ridx[k] = static_cast<int64_t>(batch.base_rowid + i + k);
-      }
-      for (size_t k = 0; k < kUnroll; ++k) {
-        inst[k] = batch[i + k];
-      }
-      for (size_t k = 0; k < kUnroll; ++k) {
-        for (int gid = 0; gid < num_group; ++gid) {
-          const size_t offset = ridx[k] * num_group + gid;
-          preds[offset] += PredValue(inst[k], model.trees, model.tree_info, gid,
-                                     &feats, tree_begin, tree_end);
-        }
-      }
-    }
-  }
-  for (bst_omp_uint i = nsize - rest; i < nsize; ++i) {
-    RegTree::FVec &feats = thread_temp[0];
-    const auto ridx = static_cast<int64_t>(batch.base_rowid + i);
-    auto inst = batch[i];
-    for (int gid = 0; gid < num_group; ++gid) {
-      const size_t offset = ridx * num_group + gid;
-      preds[offset] += PredValue(inst, model.trees, model.tree_info, gid,
-                                 &feats, tree_begin, tree_end);
-    }
-  }
-}
-
-class CPUPredictor : public Predictor {
- protected:
-  // init thread buffers
-  static void InitThreadTemp(int nthread, int num_feature, std::vector<RegTree::FVec>* out) {
-    int prev_thread_temp_size = out->size();
-    if (prev_thread_temp_size < nthread) {
-      out->resize(nthread, RegTree::FVec());
-      for (int i = prev_thread_temp_size; i < nthread; ++i) {
-        (*out)[i].Init(num_feature);
-      }
-    }
-  }
-
-  void PredictDMatrix(DMatrix *p_fmat, std::vector<bst_float> *out_preds,
-                      gbm::GBTreeModel const &model, int32_t tree_begin,
-                      int32_t tree_end) {
-    std::lock_guard<std::mutex> guard(lock_);
-    const int threads = omp_get_max_threads();
-    InitThreadTemp(threads, model.learner_model_param->num_feature, &this->thread_temp_);
-    for (auto const& batch : p_fmat->GetBatches<SparsePage>()) {
-      CHECK_EQ(out_preds->size(),
-               p_fmat->Info().num_row_ * model.learner_model_param->num_output_group);
-      size_t constexpr kUnroll = 8;
-      PredictBatchKernel(SparsePageView<kUnroll>{&batch}, out_preds, model, tree_begin,
-                         tree_end, &thread_temp_);
-    }
-  }
-
-  void InitOutPredictions(const MetaInfo& info,
-                          HostDeviceVector<bst_float>* out_preds,
-                          const gbm::GBTreeModel& model) const {
-    CHECK_NE(model.learner_model_param->num_output_group, 0);
-    size_t n = model.learner_model_param->num_output_group * info.num_row_;
-    const auto& base_margin = info.base_margin_.HostVector();
-    out_preds->Resize(n);
-    std::vector<bst_float>& out_preds_h = out_preds->HostVector();
-    if (base_margin.size() == n) {
-      CHECK_EQ(out_preds->Size(), n);
-      std::copy(base_margin.begin(), base_margin.end(), out_preds_h.begin());
-    } else {
-      if (!base_margin.empty()) {
-        std::ostringstream oss;
-        oss << "Ignoring the base margin, since it has incorrect length. "
-            << "The base margin must be an array of length ";
-        if (model.learner_model_param->num_output_group > 1) {
-          oss << "[num_class] * [number of data points], i.e. "
-              << model.learner_model_param->num_output_group << " * " << info.num_row_
-              << " = " << n << ". ";
-        } else {
-          oss << "[number of data points], i.e. " << info.num_row_ << ". ";
-        }
-        oss << "Instead, all data points will use "
-            << "base_score = " << model.learner_model_param->base_score;
-        LOG(WARNING) << oss.str();
-      }
-      std::fill(out_preds_h.begin(), out_preds_h.end(),
-                model.learner_model_param->base_score);
-    }
-  }
-
- public:
-  explicit CPUPredictor(GenericParameter const* generic_param) :
-      Predictor::Predictor{generic_param} {}
-  // ntree_limit is a very problematic parameter, as it's ambiguous in the context of
-  // multi-output and forest.  Same problem exists for tree_begin
-  void PredictBatch(DMatrix* dmat, PredictionCacheEntry* predts,
-                    const gbm::GBTreeModel& model, int tree_begin,
-                    uint32_t const ntree_limit = 0) override {
-    // tree_begin is not used, right now we just enforce it to be 0.
-    CHECK_EQ(tree_begin, 0);
-    auto* out_preds = &predts->predictions;
-    CHECK_GE(predts->version, tree_begin);
-    if (out_preds->Size() == 0 && dmat->Info().num_row_ != 0) {
-      CHECK_EQ(predts->version, 0);
-    }
-    if (predts->version == 0) {
-      // out_preds->Size() can be non-zero as it's initialized here before any tree is
-      // built at the 0^th iterator.
-      this->InitOutPredictions(dmat->Info(), out_preds, model);
-    }
-
-    uint32_t const output_groups =  model.learner_model_param->num_output_group;
-    CHECK_NE(output_groups, 0);
-    // Right now we just assume ntree_limit provided by users means number of tree layers
-    // in the context of multi-output model
-    uint32_t real_ntree_limit = ntree_limit * output_groups;
-    if (real_ntree_limit == 0 || real_ntree_limit > model.trees.size()) {
-      real_ntree_limit = static_cast<uint32_t>(model.trees.size());
-    }
-
-    uint32_t const end_version = (tree_begin + real_ntree_limit) / output_groups;
-    // When users have provided ntree_limit, end_version can be lesser, cache is violated
-    if (predts->version > end_version) {
-      CHECK_NE(ntree_limit, 0);
-      this->InitOutPredictions(dmat->Info(), out_preds, model);
-      predts->version = 0;
-    }
-    uint32_t const beg_version = predts->version;
-    CHECK_LE(beg_version, end_version);
-
-    if (beg_version < end_version) {
-      this->PredictDMatrix(dmat, &out_preds->HostVector(), model,
-                           beg_version * output_groups,
-                           end_version * output_groups);
-    }
-
-    // delta means {size of forest} * {number of newly accumulated layers}
-    uint32_t delta = end_version - beg_version;
-    CHECK_LE(delta, model.trees.size());
-    predts->Update(delta);
-
-    CHECK(out_preds->Size() == output_groups * dmat->Info().num_row_ ||
-          out_preds->Size() == dmat->Info().num_row_);
-  }
-
-  template <typename Adapter>
-  void DispatchedInplacePredict(dmlc::any const &x,
-                                const gbm::GBTreeModel &model, float missing,
-                                PredictionCacheEntry *out_preds,
-                                uint32_t tree_begin, uint32_t tree_end) const {
-    auto threads = omp_get_max_threads();
-    auto m = dmlc::get<Adapter>(x);
-    CHECK_EQ(m.NumColumns(), model.learner_model_param->num_feature)
-        << "Number of columns in data must equal to trained model.";
-    MetaInfo info;
-    info.num_col_ = m.NumColumns();
-    info.num_row_ = m.NumRows();
-    this->InitOutPredictions(info, &(out_preds->predictions), model);
-    std::vector<Entry> workspace(info.num_col_ * 8 * threads);
-    auto &predictions = out_preds->predictions.HostVector();
-    std::vector<RegTree::FVec> thread_temp;
-    InitThreadTemp(threads, model.learner_model_param->num_feature, &thread_temp);
-    size_t constexpr kUnroll = 8;
-    PredictBatchKernel(AdapterView<Adapter, kUnroll>(
-                           &m, missing, common::Span<Entry>{workspace}),
-                       &predictions, model, tree_begin, tree_end, &thread_temp);
-  }
-
-  void InplacePredict(dmlc::any const &x, const gbm::GBTreeModel &model,
-                      float missing, PredictionCacheEntry *out_preds,
-                      uint32_t tree_begin, unsigned tree_end) const override {
-    if (x.type() == typeid(data::DenseAdapter)) {
-      this->DispatchedInplacePredict<data::DenseAdapter>(
-          x, model, missing, out_preds, tree_begin, tree_end);
-    } else if (x.type() == typeid(data::CSRAdapter)) {
-      this->DispatchedInplacePredict<data::CSRAdapter>(
-          x, model, missing, out_preds, tree_begin, tree_end);
-    } else {
-      LOG(FATAL) << "Data type is not supported by CPU Predictor.";
-    }
-  }
-
-  void PredictInstance(const SparsePage::Inst& inst,
-                       std::vector<bst_float>* out_preds,
-                       const gbm::GBTreeModel& model, unsigned ntree_limit) override {
-    if (thread_temp_.size() == 0) {
-      thread_temp_.resize(1, RegTree::FVec());
-      thread_temp_[0].Init(model.learner_model_param->num_feature);
-    }
-    ntree_limit *= model.learner_model_param->num_output_group;
-    if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
-      ntree_limit = static_cast<unsigned>(model.trees.size());
-    }
-    out_preds->resize(model.learner_model_param->num_output_group *
-                      (model.param.size_leaf_vector + 1));
-    // loop over output groups
-    for (uint32_t gid = 0; gid < model.learner_model_param->num_output_group; ++gid) {
-      (*out_preds)[gid] = PredValue(inst, model.trees, model.tree_info, gid,
-                                    &thread_temp_[0], 0, ntree_limit) +
-                          model.learner_model_param->base_score;
-    }
-  }
-
-  void PredictLeaf(DMatrix* p_fmat, std::vector<bst_float>* out_preds,
-                   const gbm::GBTreeModel& model, unsigned ntree_limit) override {
-    const int nthread = omp_get_max_threads();
-    InitThreadTemp(nthread, model.learner_model_param->num_feature, &this->thread_temp_);
-    const MetaInfo& info = p_fmat->Info();
-    // number of valid trees
-    ntree_limit *= model.learner_model_param->num_output_group;
-    if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
-      ntree_limit = static_cast<unsigned>(model.trees.size());
-    }
-    std::vector<bst_float>& preds = *out_preds;
-    preds.resize(info.num_row_ * ntree_limit);
-    // start collecting the prediction
-    for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-      // parallel over local batch
-      const auto nsize = static_cast<bst_omp_uint>(batch.Size());
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nsize; ++i) {
-        const int tid = omp_get_thread_num();
-        auto ridx = static_cast<size_t>(batch.base_rowid + i);
-        RegTree::FVec &feats = thread_temp_[tid];
-        feats.Fill(batch[i]);
-        for (unsigned j = 0; j < ntree_limit; ++j) {
-          int tid = model.trees[j]->GetLeafIndex(feats);
-          preds[ridx * ntree_limit + j] = static_cast<bst_float>(tid);
-        }
-        feats.Drop(batch[i]);
-      }
-    }
-  }
-
-  void PredictContribution(DMatrix* p_fmat, std::vector<bst_float>* out_contribs,
-                           const gbm::GBTreeModel& model, uint32_t ntree_limit,
-                           std::vector<bst_float>* tree_weights,
-                           bool approximate, int condition,
-                           unsigned condition_feature) override {
-    const int nthread = omp_get_max_threads();
-    InitThreadTemp(nthread,  model.learner_model_param->num_feature, &this->thread_temp_);
-    const MetaInfo& info = p_fmat->Info();
-    // number of valid trees
-    ntree_limit *= model.learner_model_param->num_output_group;
-    if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
-      ntree_limit = static_cast<unsigned>(model.trees.size());
-    }
-    const int ngroup = model.learner_model_param->num_output_group;
-    CHECK_NE(ngroup, 0);
-    size_t const ncolumns = model.learner_model_param->num_feature + 1;
-    CHECK_NE(ncolumns, 0);
-    // allocate space for (number of features + bias) times the number of rows
-    std::vector<bst_float>& contribs = *out_contribs;
-    contribs.resize(info.num_row_ * ncolumns * model.learner_model_param->num_output_group);
-    // make sure contributions is zeroed, we could be reusing a previously
-    // allocated one
-    std::fill(contribs.begin(), contribs.end(), 0);
-    // initialize tree node mean values
-    #pragma omp parallel for schedule(static)
-    for (bst_omp_uint i = 0; i < ntree_limit; ++i) {
-      model.trees[i]->FillNodeMeanValues();
-    }
-    const std::vector<bst_float>& base_margin = info.base_margin_.HostVector();
-    // start collecting the contributions
-    for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-      // parallel over local batch
-      const auto nsize = static_cast<bst_omp_uint>(batch.Size());
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint i = 0; i < nsize; ++i) {
-        auto row_idx = static_cast<size_t>(batch.base_rowid + i);
-        RegTree::FVec &feats = thread_temp_[omp_get_thread_num()];
-        std::vector<bst_float> this_tree_contribs(ncolumns);
-        // loop over all classes
-        for (int gid = 0; gid < ngroup; ++gid) {
-          bst_float* p_contribs = &contribs[(row_idx * ngroup + gid) * ncolumns];
-          feats.Fill(batch[i]);
-          // calculate contributions
-          for (unsigned j = 0; j < ntree_limit; ++j) {
-            std::fill(this_tree_contribs.begin(), this_tree_contribs.end(), 0);
-            if (model.tree_info[j] != gid) {
-              continue;
-            }
-            if (!approximate) {
-              model.trees[j]->CalculateContributions(feats, &this_tree_contribs[0],
-                                                     condition, condition_feature);
-            } else {
-              model.trees[j]->CalculateContributionsApprox(feats, &this_tree_contribs[0]);
-            }
-            for (size_t ci = 0 ; ci < ncolumns ; ++ci) {
-                p_contribs[ci] += this_tree_contribs[ci] *
-                    (tree_weights == nullptr ? 1 : (*tree_weights)[j]);
-            }
-          }
-          feats.Drop(batch[i]);
-          // add base margin to BIAS
-          if (base_margin.size() != 0) {
-            p_contribs[ncolumns - 1] += base_margin[row_idx * ngroup + gid];
-          } else {
-            p_contribs[ncolumns - 1] += model.learner_model_param->base_score;
-          }
-        }
-      }
-    }
-  }
-
-  void PredictInteractionContributions(DMatrix* p_fmat, std::vector<bst_float>* out_contribs,
-                                       const gbm::GBTreeModel& model, unsigned ntree_limit,
-                                       std::vector<bst_float>* tree_weights,
-                                       bool approximate) override {
-    const MetaInfo& info = p_fmat->Info();
-    const int ngroup = model.learner_model_param->num_output_group;
-    size_t const ncolumns = model.learner_model_param->num_feature;
-    const unsigned row_chunk = ngroup * (ncolumns + 1) * (ncolumns + 1);
-    const unsigned mrow_chunk = (ncolumns + 1) * (ncolumns + 1);
-    const unsigned crow_chunk = ngroup * (ncolumns + 1);
-
-    // allocate space for (number of features^2) times the number of rows and tmp off/on contribs
-    std::vector<bst_float>& contribs = *out_contribs;
-    contribs.resize(info.num_row_ * ngroup * (ncolumns + 1) * (ncolumns + 1));
-    std::vector<bst_float> contribs_off(info.num_row_ * ngroup * (ncolumns + 1));
-    std::vector<bst_float> contribs_on(info.num_row_ * ngroup * (ncolumns + 1));
-    std::vector<bst_float> contribs_diag(info.num_row_ * ngroup * (ncolumns + 1));
-
-    // Compute the difference in effects when conditioning on each of the features on and off
-    // see: Axiomatic characterizations of probabilistic and
-    //      cardinal-probabilistic interaction indices
-    PredictContribution(p_fmat, &contribs_diag, model, ntree_limit,
-                        tree_weights, approximate, 0, 0);
-    for (size_t i = 0; i < ncolumns + 1; ++i) {
-      PredictContribution(p_fmat, &contribs_off, model, ntree_limit,
-                          tree_weights, approximate, -1, i);
-      PredictContribution(p_fmat, &contribs_on, model, ntree_limit,
-                          tree_weights, approximate, 1, i);
-
-      for (size_t j = 0; j < info.num_row_; ++j) {
-        for (int l = 0; l < ngroup; ++l) {
-          const unsigned o_offset = j * row_chunk + l * mrow_chunk + i * (ncolumns + 1);
-          const unsigned c_offset = j * crow_chunk + l * (ncolumns + 1);
-          contribs[o_offset + i] = 0;
-          for (size_t k = 0; k < ncolumns + 1; ++k) {
-            // fill in the diagonal with additive effects, and off-diagonal with the interactions
-            if (k == i) {
-              contribs[o_offset + i] += contribs_diag[c_offset + k];
-            } else {
-              contribs[o_offset + k] = (contribs_on[c_offset + k] - contribs_off[c_offset + k])/2.0;
-              contribs[o_offset + i] -= contribs[o_offset + k];
-            }
-          }
-        }
-      }
-    }
-  }
-
- private:
-  std::mutex lock_;
-  std::vector<RegTree::FVec> thread_temp_;
-};
-
-XGBOOST_REGISTER_PREDICTOR(CPUPredictor, "cpu_predictor")
-.describe("Make predictions using CPU.")
-.set_body([](GenericParameter const* generic_param) {
-            return new CPUPredictor(generic_param);
-          });
-}  // namespace predictor
-}  // namespace xgboost
diff --git a/ml-xgboost/src/predictor/gpu_predictor.cu b/ml-xgboost/src/predictor/gpu_predictor.cu
deleted file mode 100644
index 15d974b..0000000
--- a/ml-xgboost/src/predictor/gpu_predictor.cu
+++ /dev/null
@@ -1,562 +0,0 @@
-/*!
- * Copyright 2017-2020 by Contributors
- */
-#include <thrust/copy.h>
-#include <thrust/device_ptr.h>
-#include <thrust/device_vector.h>
-#include <thrust/fill.h>
-#include <memory>
-
-#include "xgboost/data.h"
-#include "xgboost/predictor.h"
-#include "xgboost/tree_model.h"
-#include "xgboost/tree_updater.h"
-#include "xgboost/host_device_vector.h"
-
-#include "../gbm/gbtree_model.h"
-#include "../data/ellpack_page.cuh"
-#include "../data/device_adapter.cuh"
-#include "../common/common.h"
-#include "../common/device_helpers.cuh"
-
-namespace xgboost {
-namespace predictor {
-
-DMLC_REGISTRY_FILE_TAG(gpu_predictor);
-
-struct SparsePageView {
-  common::Span<const Entry> d_data;
-  common::Span<const bst_row_t> d_row_ptr;
-
-  XGBOOST_DEVICE SparsePageView(common::Span<const Entry> data,
-                                common::Span<const bst_row_t> row_ptr) :
-      d_data{data}, d_row_ptr{row_ptr} {}
-};
-
-struct SparsePageLoader {
-  bool use_shared;
-  common::Span<const bst_row_t> d_row_ptr;
-  common::Span<const Entry> d_data;
-  bst_feature_t num_features;
-  float* smem;
-  size_t entry_start;
-
-  __device__ SparsePageLoader(SparsePageView data, bool use_shared, bst_feature_t num_features,
-                              bst_row_t num_rows, size_t entry_start)
-      : use_shared(use_shared),
-        d_row_ptr(data.d_row_ptr),
-        d_data(data.d_data),
-        num_features(num_features),
-        entry_start(entry_start) {
-    extern __shared__ float _smem[];
-    smem = _smem;
-    // Copy instances
-    if (use_shared) {
-      bst_uint global_idx = blockDim.x * blockIdx.x + threadIdx.x;
-      int shared_elements = blockDim.x * num_features;
-      dh::BlockFill(smem, shared_elements, nanf(""));
-      __syncthreads();
-      if (global_idx < num_rows) {
-        bst_uint elem_begin = d_row_ptr[global_idx];
-        bst_uint elem_end = d_row_ptr[global_idx + 1];
-        for (bst_uint elem_idx = elem_begin; elem_idx < elem_end; elem_idx++) {
-          Entry elem = d_data[elem_idx - entry_start];
-          smem[threadIdx.x * num_features + elem.index] = elem.fvalue;
-        }
-      }
-      __syncthreads();
-    }
-  }
-  __device__ float GetFvalue(int ridx, int fidx) const {
-    if (use_shared) {
-      return smem[threadIdx.x * num_features + fidx];
-    } else {
-      // Binary search
-      auto begin_ptr = d_data.begin() + (d_row_ptr[ridx] - entry_start);
-      auto end_ptr = d_data.begin() + (d_row_ptr[ridx + 1] - entry_start);
-      common::Span<const Entry>::iterator previous_middle;
-      while (end_ptr != begin_ptr) {
-        auto middle = begin_ptr + (end_ptr - begin_ptr) / 2;
-        if (middle == previous_middle) {
-          break;
-        } else {
-          previous_middle = middle;
-        }
-
-        if (middle->index == fidx) {
-          return middle->fvalue;
-        } else if (middle->index < fidx) {
-          begin_ptr = middle;
-        } else {
-          end_ptr = middle;
-        }
-      }
-      // Value is missing
-      return nanf("");
-    }
-  }
-};
-
-struct EllpackLoader {
-  EllpackDeviceAccessor const& matrix;
-  XGBOOST_DEVICE EllpackLoader(EllpackDeviceAccessor const& m, bool use_shared,
-                               bst_feature_t num_features, bst_row_t num_rows,
-                               size_t entry_start)
-      : matrix{m} {}
-  __device__ __forceinline__ float GetFvalue(int ridx, int fidx) const {
-    auto gidx = matrix.GetBinIndex(ridx, fidx);
-    if (gidx == -1) {
-      return nan("");
-    }
-    // The gradient index needs to be shifted by one as min values are not included in the
-    // cuts.
-    if (gidx == matrix.feature_segments[fidx]) {
-      return matrix.min_fvalue[fidx];
-    }
-    return matrix.gidx_fvalue_map[gidx - 1];
-  }
-};
-
-struct CuPyAdapterLoader {
-  data::CupyAdapterBatch batch;
-  bst_feature_t columns;
-  float* smem;
-  bool use_shared;
-
-  DEV_INLINE CuPyAdapterLoader(data::CupyAdapterBatch const batch, bool use_shared,
-                               bst_feature_t num_features, bst_row_t num_rows, size_t entry_start) :
-    batch{batch},
-    columns{num_features},
-    use_shared{use_shared} {
-      extern __shared__ float _smem[];
-      smem = _smem;
-      if (use_shared) {
-        uint32_t global_idx = blockDim.x * blockIdx.x + threadIdx.x;
-        size_t shared_elements = blockDim.x * num_features;
-        dh::BlockFill(smem, shared_elements, nanf(""));
-        __syncthreads();
-        if (global_idx < num_rows) {
-          auto beg = global_idx * columns;
-          auto end = (global_idx + 1) * columns;
-          for (size_t i = beg; i < end; ++i) {
-            smem[threadIdx.x * num_features + (i - beg)] = batch.GetElement(i).value;
-          }
-        }
-      }
-      __syncthreads();
-    }
-
-  DEV_INLINE float GetFvalue(bst_row_t ridx, bst_feature_t fidx) const {
-    if (use_shared) {
-      return smem[threadIdx.x * columns + fidx];
-    }
-    return batch.GetElement(ridx * columns + fidx).value;
-  }
-};
-
-struct CuDFAdapterLoader {
-  data::CudfAdapterBatch batch;
-  bst_feature_t columns;
-  float* smem;
-  bool use_shared;
-
-  DEV_INLINE CuDFAdapterLoader(data::CudfAdapterBatch const batch, bool use_shared,
-                               bst_feature_t num_features,
-                               bst_row_t num_rows, size_t entry_start)
-      : batch{batch}, columns{num_features}, use_shared{use_shared} {
-    extern __shared__ float _smem[];
-    smem = _smem;
-    if (use_shared) {
-      uint32_t global_idx = blockDim.x * blockIdx.x + threadIdx.x;
-      size_t shared_elements = blockDim.x * num_features;
-      dh::BlockFill(smem, shared_elements, nanf(""));
-      __syncthreads();
-      if (global_idx < num_rows) {
-        for (size_t i = 0; i < columns; ++i) {
-          smem[threadIdx.x * columns + i] = batch.GetValue(global_idx, i);
-        }
-      }
-    }
-    __syncthreads();
-  }
-  DEV_INLINE float GetFvalue(bst_row_t ridx, bst_feature_t fidx) const {
-    if (use_shared) {
-      return smem[threadIdx.x * columns + fidx];
-    }
-    return batch.GetValue(ridx, fidx);
-  }
-};
-
-template <typename Loader>
-__device__ float GetLeafWeight(bst_uint ridx, const RegTree::Node* tree,
-                               Loader* loader) {
-  RegTree::Node n = tree[0];
-  while (!n.IsLeaf()) {
-    float fvalue = loader->GetFvalue(ridx, n.SplitIndex());
-    // Missing value
-    if (isnan(fvalue)) {
-      n = tree[n.DefaultChild()];
-    } else {
-      if (fvalue < n.SplitCond()) {
-        n = tree[n.LeftChild()];
-      } else {
-        n = tree[n.RightChild()];
-      }
-    }
-  }
-  return n.LeafValue();
-}
-
-template <typename Loader, typename Data>
-__global__ void PredictKernel(Data data,
-                              common::Span<const RegTree::Node> d_nodes,
-                              common::Span<float> d_out_predictions,
-                              common::Span<size_t> d_tree_segments,
-                              common::Span<int> d_tree_group,
-                              size_t tree_begin, size_t tree_end, size_t num_features,
-                              size_t num_rows, size_t entry_start,
-                              bool use_shared, int num_group) {
-  bst_uint global_idx = blockDim.x * blockIdx.x + threadIdx.x;
-  Loader loader(data, use_shared, num_features, num_rows, entry_start);
-  if (global_idx >= num_rows) return;
-  if (num_group == 1) {
-    float sum = 0;
-    for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
-      const RegTree::Node* d_tree =
-          &d_nodes[d_tree_segments[tree_idx - tree_begin]];
-      float leaf = GetLeafWeight(global_idx, d_tree, &loader);
-      sum += leaf;
-    }
-    d_out_predictions[global_idx] += sum;
-  } else {
-    for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
-      int tree_group = d_tree_group[tree_idx];
-      const RegTree::Node* d_tree =
-          &d_nodes[d_tree_segments[tree_idx - tree_begin]];
-      bst_uint out_prediction_idx = global_idx * num_group + tree_group;
-      d_out_predictions[out_prediction_idx] +=
-          GetLeafWeight(global_idx, d_tree, &loader);
-    }
-  }
-}
-
-class DeviceModel {
- public:
-  dh::device_vector<RegTree::Node> nodes;
-  dh::device_vector<size_t> tree_segments;
-  dh::device_vector<int> tree_group;
-  size_t tree_beg_;  // NOLINT
-  size_t tree_end_;  // NOLINT
-  int num_group;
-
-  void CopyModel(const gbm::GBTreeModel& model,
-                 const thrust::host_vector<size_t>& h_tree_segments,
-                 const thrust::host_vector<RegTree::Node>& h_nodes,
-                 size_t tree_begin, size_t tree_end) {
-    nodes.resize(h_nodes.size());
-    dh::safe_cuda(cudaMemcpyAsync(nodes.data().get(), h_nodes.data(),
-                                  sizeof(RegTree::Node) * h_nodes.size(),
-                                  cudaMemcpyHostToDevice));
-    tree_segments.resize(h_tree_segments.size());
-    dh::safe_cuda(cudaMemcpyAsync(tree_segments.data().get(), h_tree_segments.data(),
-                                  sizeof(size_t) * h_tree_segments.size(),
-                                  cudaMemcpyHostToDevice));
-    tree_group.resize(model.tree_info.size());
-    dh::safe_cuda(cudaMemcpyAsync(tree_group.data().get(), model.tree_info.data(),
-                                  sizeof(int) * model.tree_info.size(),
-                                  cudaMemcpyHostToDevice));
-    this->tree_beg_ = tree_begin;
-    this->tree_end_ = tree_end;
-    this->num_group = model.learner_model_param->num_output_group;
-  }
-
-  void Init(const gbm::GBTreeModel& model, size_t tree_begin, size_t tree_end, int32_t gpu_id) {
-    dh::safe_cuda(cudaSetDevice(gpu_id));
-    CHECK_EQ(model.param.size_leaf_vector, 0);
-    // Copy decision trees to device
-    thrust::host_vector<size_t> h_tree_segments{};
-    h_tree_segments.reserve((tree_end - tree_begin) + 1);
-    size_t sum = 0;
-    h_tree_segments.push_back(sum);
-    for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
-      sum += model.trees.at(tree_idx)->GetNodes().size();
-      h_tree_segments.push_back(sum);
-    }
-
-    thrust::host_vector<RegTree::Node> h_nodes(h_tree_segments.back());
-    for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
-      auto& src_nodes = model.trees.at(tree_idx)->GetNodes();
-      std::copy(src_nodes.begin(), src_nodes.end(),
-                h_nodes.begin() + h_tree_segments[tree_idx - tree_begin]);
-    }
-    CopyModel(model, h_tree_segments, h_nodes, tree_begin, tree_end);
-  }
-};
-
-class GPUPredictor : public xgboost::Predictor {
- private:
-  void PredictInternal(const SparsePage& batch, size_t num_features,
-                       HostDeviceVector<bst_float>* predictions,
-                       size_t batch_offset) {
-    batch.offset.SetDevice(generic_param_->gpu_id);
-    batch.data.SetDevice(generic_param_->gpu_id);
-    const uint32_t BLOCK_THREADS = 128;
-    size_t num_rows = batch.Size();
-    auto GRID_SIZE = static_cast<uint32_t>(common::DivRoundUp(num_rows, BLOCK_THREADS));
-
-    auto shared_memory_bytes =
-        static_cast<size_t>(sizeof(float) * num_features * BLOCK_THREADS);
-    bool use_shared = true;
-    if (shared_memory_bytes > max_shared_memory_bytes_) {
-      shared_memory_bytes = 0;
-      use_shared = false;
-    }
-    size_t entry_start = 0;
-    SparsePageView data{batch.data.DeviceSpan(), batch.offset.DeviceSpan()};
-    dh::LaunchKernel {GRID_SIZE, BLOCK_THREADS, shared_memory_bytes} (
-        PredictKernel<SparsePageLoader, SparsePageView>,
-        data,
-        dh::ToSpan(model_.nodes), predictions->DeviceSpan().subspan(batch_offset),
-        dh::ToSpan(model_.tree_segments), dh::ToSpan(model_.tree_group),
-        model_.tree_beg_, model_.tree_end_, num_features, num_rows,
-        entry_start, use_shared, model_.num_group);
-  }
-  void PredictInternal(EllpackDeviceAccessor const& batch, HostDeviceVector<bst_float>* out_preds,
-                       size_t batch_offset) {
-    const uint32_t BLOCK_THREADS = 256;
-    size_t num_rows = batch.n_rows;
-    auto GRID_SIZE = static_cast<uint32_t>(common::DivRoundUp(num_rows, BLOCK_THREADS));
-
-    bool use_shared = false;
-    size_t entry_start = 0;
-    dh::LaunchKernel {GRID_SIZE, BLOCK_THREADS} (
-        PredictKernel<EllpackLoader, EllpackDeviceAccessor>,
-        batch,
-        dh::ToSpan(model_.nodes), out_preds->DeviceSpan().subspan(batch_offset),
-        dh::ToSpan(model_.tree_segments), dh::ToSpan(model_.tree_group),
-        model_.tree_beg_, model_.tree_end_, batch.NumFeatures(), num_rows,
-        entry_start, use_shared, model_.num_group);
-  }
-
-  void DevicePredictInternal(DMatrix* dmat, HostDeviceVector<float>* out_preds,
-                             const gbm::GBTreeModel& model, size_t tree_begin,
-                             size_t tree_end) {
-    dh::safe_cuda(cudaSetDevice(generic_param_->gpu_id));
-    if (tree_end - tree_begin == 0) {
-      return;
-    }
-    model_.Init(model, tree_begin, tree_end, generic_param_->gpu_id);
-    out_preds->SetDevice(generic_param_->gpu_id);
-
-    if (dmat->PageExists<SparsePage>()) {
-      size_t batch_offset = 0;
-      for (auto &batch : dmat->GetBatches<SparsePage>()) {
-        this->PredictInternal(batch, model.learner_model_param->num_feature,
-                              out_preds, batch_offset);
-        batch_offset += batch.Size() * model.learner_model_param->num_output_group;
-      }
-    } else {
-      size_t batch_offset = 0;
-      for (auto const& page : dmat->GetBatches<EllpackPage>()) {
-        this->PredictInternal(
-            page.Impl()->GetDeviceAccessor(generic_param_->gpu_id), out_preds,
-            batch_offset);
-        batch_offset += page.Impl()->n_rows;
-      }
-    }
-  }
-
- public:
-  explicit GPUPredictor(GenericParameter const* generic_param) :
-      Predictor::Predictor{generic_param} {}
-
-  ~GPUPredictor() override {
-    if (generic_param_->gpu_id >= 0) {
-      dh::safe_cuda(cudaSetDevice(generic_param_->gpu_id));
-    }
-  }
-
-  void PredictBatch(DMatrix* dmat, PredictionCacheEntry* predts,
-                    const gbm::GBTreeModel& model, int tree_begin,
-                    unsigned ntree_limit = 0) override {
-    // This function is duplicated with CPU predictor PredictBatch, see comments in there.
-    // FIXME(trivialfis): Remove the duplication.
-    std::lock_guard<std::mutex> const guard(lock_);
-    int device = generic_param_->gpu_id;
-    CHECK_GE(device, 0) << "Set `gpu_id' to positive value for processing GPU data.";
-    ConfigureDevice(device);
-
-    CHECK_EQ(tree_begin, 0);
-    auto* out_preds = &predts->predictions;
-    CHECK_GE(predts->version, tree_begin);
-    if (out_preds->Size() == 0 && dmat->Info().num_row_ != 0) {
-      CHECK_EQ(predts->version, 0);
-    }
-    if (predts->version == 0) {
-      this->InitOutPredictions(dmat->Info(), out_preds, model);
-    }
-
-    uint32_t const output_groups =  model.learner_model_param->num_output_group;
-    CHECK_NE(output_groups, 0);
-
-    uint32_t real_ntree_limit = ntree_limit * output_groups;
-    if (real_ntree_limit == 0 || real_ntree_limit > model.trees.size()) {
-      real_ntree_limit = static_cast<uint32_t>(model.trees.size());
-    }
-
-    uint32_t const end_version = (tree_begin + real_ntree_limit) / output_groups;
-
-    if (predts->version > end_version) {
-      CHECK_NE(ntree_limit, 0);
-      this->InitOutPredictions(dmat->Info(), out_preds, model);
-      predts->version = 0;
-    }
-    uint32_t const beg_version = predts->version;
-    CHECK_LE(beg_version, end_version);
-
-    if (beg_version < end_version) {
-      this->DevicePredictInternal(dmat, out_preds, model,
-                                  beg_version * output_groups,
-                                  end_version * output_groups);
-    }
-
-    uint32_t delta = end_version - beg_version;
-    CHECK_LE(delta, model.trees.size());
-    predts->Update(delta);
-
-    CHECK(out_preds->Size() == output_groups * dmat->Info().num_row_ ||
-          out_preds->Size() == dmat->Info().num_row_);
-  }
-
-  template <typename Adapter, typename Loader, typename Batch>
-  void DispatchedInplacePredict(dmlc::any const &x,
-                                const gbm::GBTreeModel &model, float missing,
-                                PredictionCacheEntry *out_preds,
-                                uint32_t tree_begin, uint32_t tree_end) const {
-    auto max_shared_memory_bytes = dh::MaxSharedMemory(this->generic_param_->gpu_id);
-    uint32_t const output_groups =  model.learner_model_param->num_output_group;
-    DeviceModel d_model;
-    d_model.Init(model, tree_begin, tree_end, this->generic_param_->gpu_id);
-
-    auto m = dmlc::get<Adapter>(x);
-    CHECK_EQ(m.NumColumns(), model.learner_model_param->num_feature)
-        << "Number of columns in data must equal to trained model.";
-    CHECK_EQ(this->generic_param_->gpu_id, m.DeviceIdx())
-        << "XGBoost is running on device: " << this->generic_param_->gpu_id << ", "
-        << "but data is on: " << m.DeviceIdx();
-    MetaInfo info;
-    info.num_col_ = m.NumColumns();
-    info.num_row_ = m.NumRows();
-    this->InitOutPredictions(info, &(out_preds->predictions), model);
-
-    const uint32_t BLOCK_THREADS = 128;
-    auto GRID_SIZE = static_cast<uint32_t>(common::DivRoundUp(info.num_row_, BLOCK_THREADS));
-
-    auto shared_memory_bytes =
-        static_cast<size_t>(sizeof(float) * m.NumColumns() * BLOCK_THREADS);
-    bool use_shared = true;
-    if (shared_memory_bytes > max_shared_memory_bytes) {
-      shared_memory_bytes = 0;
-      use_shared = false;
-    }
-    size_t entry_start = 0;
-
-    dh::LaunchKernel {GRID_SIZE, BLOCK_THREADS, shared_memory_bytes} (
-        PredictKernel<Loader, Batch>,
-        m.Value(),
-        dh::ToSpan(d_model.nodes), out_preds->predictions.DeviceSpan(),
-        dh::ToSpan(d_model.tree_segments), dh::ToSpan(d_model.tree_group),
-        tree_begin, tree_end, m.NumColumns(), info.num_row_,
-        entry_start, use_shared, output_groups);
-  }
-
-  void InplacePredict(dmlc::any const &x, const gbm::GBTreeModel &model,
-                      float missing, PredictionCacheEntry *out_preds,
-                      uint32_t tree_begin, unsigned tree_end) const override {
-    if (x.type() == typeid(data::CupyAdapter)) {
-      this->DispatchedInplacePredict<data::CupyAdapter, CuPyAdapterLoader, data::CupyAdapterBatch>(
-          x, model, missing, out_preds, tree_begin, tree_end);
-    } else if (x.type() == typeid(data::CudfAdapter)) {
-      this->DispatchedInplacePredict<data::CudfAdapter, CuDFAdapterLoader, data::CudfAdapterBatch>(
-          x, model, missing, out_preds, tree_begin, tree_end);
-    } else {
-      LOG(FATAL) << "Only CuPy and CuDF are supported by GPU Predictor.";
-    }
-  }
-
- protected:
-  void InitOutPredictions(const MetaInfo& info,
-                          HostDeviceVector<bst_float>* out_preds,
-                          const gbm::GBTreeModel& model) const {
-    size_t n_classes = model.learner_model_param->num_output_group;
-    size_t n = n_classes * info.num_row_;
-    const HostDeviceVector<bst_float>& base_margin = info.base_margin_;
-    out_preds->SetDevice(generic_param_->gpu_id);
-    out_preds->Resize(n);
-    if (base_margin.Size() != 0) {
-      CHECK_EQ(base_margin.Size(), n);
-      out_preds->Copy(base_margin);
-    } else {
-      out_preds->Fill(model.learner_model_param->base_score);
-    }
-  }
-
-  void PredictInstance(const SparsePage::Inst& inst,
-                       std::vector<bst_float>* out_preds,
-                       const gbm::GBTreeModel& model, unsigned ntree_limit) override {
-    LOG(FATAL) << "[Internal error]: " << __func__
-               << " is not implemented in GPU Predictor.";
-  }
-
-  void PredictLeaf(DMatrix* p_fmat, std::vector<bst_float>* out_preds,
-                   const gbm::GBTreeModel& model,
-                   unsigned ntree_limit) override {
-    LOG(FATAL) << "[Internal error]: " << __func__
-               << " is not implemented in GPU Predictor.";
-  }
-
-  void PredictContribution(DMatrix* p_fmat,
-                           std::vector<bst_float>* out_contribs,
-                           const gbm::GBTreeModel& model, unsigned ntree_limit,
-                           std::vector<bst_float>* tree_weights,
-                           bool approximate, int condition,
-                           unsigned condition_feature) override {
-    LOG(FATAL) << "[Internal error]: " << __func__
-               << " is not implemented in GPU Predictor.";
-  }
-
-  void PredictInteractionContributions(DMatrix* p_fmat,
-                                       std::vector<bst_float>* out_contribs,
-                                       const gbm::GBTreeModel& model,
-                                       unsigned ntree_limit,
-                                       std::vector<bst_float>* tree_weights,
-                                       bool approximate) override {
-    LOG(FATAL) << "[Internal error]: " << __func__
-               << " is not implemented in GPU Predictor.";
-  }
-
-  void Configure(const std::vector<std::pair<std::string, std::string>>& cfg) override {
-    Predictor::Configure(cfg);
-  }
-
- private:
-  /*! \brief Reconfigure the device when GPU is changed. */
-  void ConfigureDevice(int device) {
-    if (device >= 0) {
-      max_shared_memory_bytes_ = dh::MaxSharedMemory(device);
-    }
-  }
-
-  std::mutex lock_;
-  DeviceModel model_;
-  size_t max_shared_memory_bytes_;
-};
-
-XGBOOST_REGISTER_PREDICTOR(GPUPredictor, "gpu_predictor")
-.describe("Make predictions using GPU.")
-.set_body([](GenericParameter const* generic_param) {
-            return new GPUPredictor(generic_param);
-          });
-
-}  // namespace predictor
-}  // namespace xgboost
diff --git a/ml-xgboost/src/predictor/predictor.cc b/ml-xgboost/src/predictor/predictor.cc
deleted file mode 100644
index eb68d38..0000000
--- a/ml-xgboost/src/predictor/predictor.cc
+++ /dev/null
@@ -1,72 +0,0 @@
-/*!
- * Copyright 2017-2020 by Contributors
- */
-#include <dmlc/registry.h>
-#include <mutex>
-
-#include "xgboost/predictor.h"
-#include "xgboost/data.h"
-#include "xgboost/generic_parameters.h"
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::PredictorReg);
-}  // namespace dmlc
-namespace xgboost {
-
-void PredictionContainer::ClearExpiredEntries() {
-  std::vector<DMatrix*> expired;
-  for (auto& kv : container_) {
-    if (kv.second.ref.expired()) {
-      expired.emplace_back(kv.first);
-    }
-  }
-  for (auto const& ptr : expired) {
-    container_.erase(ptr);
-  }
-}
-
-PredictionCacheEntry &PredictionContainer::Cache(std::shared_ptr<DMatrix> m, int32_t device) {
-  std::lock_guard<std::mutex> guard { cache_lock_ };
-  this->ClearExpiredEntries();
-  container_[m.get()].ref = m;
-  if (device != GenericParameter::kCpuId) {
-    container_[m.get()].predictions.SetDevice(device);
-  }
-  return container_[m.get()];
-}
-
-PredictionCacheEntry &PredictionContainer::Entry(DMatrix *m) {
-  CHECK(container_.find(m) != container_.cend());
-  CHECK(container_.at(m).ref.lock())
-      << "[Internal error]: DMatrix: " << m << " has expired.";
-  return container_.at(m);
-}
-
-decltype(PredictionContainer::container_) const& PredictionContainer::Container() {
-  this->ClearExpiredEntries();
-  return container_;
-}
-
-void Predictor::Configure(
-    const std::vector<std::pair<std::string, std::string>>& cfg) {
-}
-Predictor* Predictor::Create(
-    std::string const& name, GenericParameter const* generic_param) {
-  auto* e = ::dmlc::Registry<PredictorReg>::Get()->Find(name);
-  if (e == nullptr) {
-    LOG(FATAL) << "Unknown predictor type " << name;
-  }
-  auto p_predictor = (e->body)(generic_param);
-  return p_predictor;
-}
-}  // namespace xgboost
-
-namespace xgboost {
-namespace predictor {
-// List of files that will be force linked in static links.
-#ifdef XGBOOST_USE_CUDA
-DMLC_REGISTRY_LINK_TAG(gpu_predictor);
-#endif  // XGBOOST_USE_CUDA
-DMLC_REGISTRY_LINK_TAG(cpu_predictor);
-}  // namespace predictor
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/constraints.cc b/ml-xgboost/src/tree/constraints.cc
deleted file mode 100644
index a1aa9dc..0000000
--- a/ml-xgboost/src/tree/constraints.cc
+++ /dev/null
@@ -1,103 +0,0 @@
-/*!
- * Copyright 2018-2019 by Contributors
- */
-#include <algorithm>
-#include <unordered_set>
-#include <vector>
-
-#include "xgboost/span.h"
-#include "xgboost/json.h"
-#include "constraints.h"
-#include "param.h"
-
-namespace xgboost {
-void FeatureInteractionConstraintHost::Configure(tree::TrainParam const& param,
-                                                 bst_feature_t const n_features) {
-  if (param.interaction_constraints.empty()) {
-    enabled_ = !param.interaction_constraints.empty();
-    return;  // short-circuit if no constraint is specified
-  }
-  enabled_ = true;
-
-  this->interaction_constraint_str_ = param.interaction_constraints;
-  this->n_features_ = n_features;
-  this->Reset();
-}
-
-void FeatureInteractionConstraintHost::Reset() {
-  if (!enabled_) {
-    return;
-  }
-  // Read std::vector<std::vector<bst_feature_t>> first and then
-  //   convert to std::vector<std::unordered_set<bst_feature_t>>
-  std::vector<std::vector<bst_feature_t>> tmp;
-  try {
-    ParseInteractionConstraint(this->interaction_constraint_str_, &tmp);
-  } catch (dmlc::Error const &e) {
-    LOG(FATAL) << "Failed to parse feature interaction constraint:\n"
-               << this->interaction_constraint_str_ << "\n"
-               << "With error:\n" << e.what();
-  }
-  for (const auto& e : tmp) {
-    interaction_constraints_.emplace_back(e.begin(), e.end());
-  }
-
-  // Initialise interaction constraints record with all variables permitted for the first node
-  node_constraints_.clear();
-  node_constraints_.resize(1, std::unordered_set<bst_feature_t>());
-  node_constraints_[0].reserve(n_features_);
-  for (bst_feature_t i = 0; i < n_features_; ++i) {
-    node_constraints_[0].insert(i);
-  }
-
-  // Initialise splits record
-  splits_.clear();
-  splits_.resize(1, std::unordered_set<bst_feature_t>());
-}
-
-void FeatureInteractionConstraintHost::SplitImpl(
-    bst_node_t node_id, bst_feature_t feature_id, bst_node_t left_id, bst_node_t right_id) {
-  bst_node_t newsize = std::max(left_id, right_id) + 1;
-
-  // Record previous splits for child nodes
-  auto feature_splits = splits_[node_id];  // fid history of current node
-  feature_splits.insert(feature_id);  // add feature of current node
-  splits_.resize(newsize);
-  splits_[left_id] = feature_splits;
-  splits_[right_id] = feature_splits;
-
-  // Resize constraints record, initialise all features to be not permitted for new nodes
-  CHECK_NE(newsize, 0);
-  node_constraints_.resize(newsize, std::unordered_set<bst_feature_t>());
-
-  // Permit features used in previous splits
-  for (bst_feature_t fid : feature_splits) {
-    node_constraints_[left_id].insert(fid);
-    node_constraints_[right_id].insert(fid);
-  }
-
-  // Loop across specified interactions in constraints
-  for (const auto &constraint : interaction_constraints_) {
-    // flags whether the specified interaction is still relevant
-    bst_uint flag = 1;
-
-    // Test relevance of specified interaction by checking all previous
-    // features are included
-    for (bst_uint checkvar : feature_splits) {
-      if (constraint.count(checkvar) == 0) {
-        flag = 0;
-        break;   // interaction is not relevant due to unmet constraint
-      }
-    }
-
-    // If interaction is still relevant, permit all other features in the
-    // interaction
-    if (flag == 1) {
-      for (bst_uint k : constraint) {
-        node_constraints_[left_id].insert(k);
-        node_constraints_[right_id].insert(k);
-      }
-    }
-  }
-}
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/constraints.cu b/ml-xgboost/src/tree/constraints.cu
deleted file mode 100644
index b6db0ed..0000000
--- a/ml-xgboost/src/tree/constraints.cu
+++ /dev/null
@@ -1,335 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- */
-#include <thrust/copy.h>
-#include <thrust/device_vector.h>
-#include <thrust/execution_policy.h>
-#include <thrust/iterator/counting_iterator.h>
-
-#include <algorithm>
-#include <string>
-#include <set>
-
-#include "xgboost/logging.h"
-#include "xgboost/span.h"
-#include "constraints.cuh"
-#include "param.h"
-#include "../common/device_helpers.cuh"
-
-namespace xgboost {
-
-size_t FeatureInteractionConstraintDevice::Features() const {
-  return d_sets_ptr_.size() - 1;
-}
-
-void FeatureInteractionConstraintDevice::Configure(
-    tree::TrainParam const& param, int32_t const n_features) {
-  has_constraint_ = true;
-  if (param.interaction_constraints.length() == 0) {
-    has_constraint_ = false;
-    return;
-  }
-  // --- Parse interaction constraints
-  // Interaction constraints parsed from string parameter.  After
-  // parsing, this looks like {{0, 1, 2}, {2, 3 ,4}}.
-  std::vector<std::vector<bst_feature_t>> h_feature_constraints;
-  try {
-    ParseInteractionConstraint(param.interaction_constraints, &h_feature_constraints);
-  } catch (dmlc::Error const& e) {
-    LOG(FATAL) << "Failed to parse feature interaction constraint:\n"
-               << param.interaction_constraints << "\n"
-               << "With error:\n" << e.what();
-  }
-  n_sets_ = h_feature_constraints.size();
-
-  size_t const n_feat_storage = LBitField64::ComputeStorageSize(n_features);
-  if (n_feat_storage == 0 && n_features != 0) {
-    LOG(FATAL) << "Wrong storage size, n_features: " << n_features;
-  }
-
-  // --- Initialize allowed features attached to nodes.
-  int32_t n_nodes { param.MaxNodes() };
-  node_constraints_.resize(n_nodes);
-  node_constraints_storage_.resize(n_nodes);
-  for (auto& n : node_constraints_storage_) {
-    n.resize(LBitField64::ComputeStorageSize(n_features));
-  }
-  for (size_t i = 0; i < node_constraints_storage_.size(); ++i) {
-    auto span = dh::ToSpan(node_constraints_storage_[i]);
-    node_constraints_[i] = LBitField64(span);
-  }
-  s_node_constraints_ = common::Span<LBitField64>(node_constraints_.data(),
-                                               node_constraints_.size());
-
-  // Represent constraints as CSR format, flatten is the value vector,
-  // ptr is row_ptr vector in CSR.
-  std::vector<uint32_t> h_feature_constraints_flatten;
-  for (auto const& constraints : h_feature_constraints) {
-    for (uint32_t c : constraints) {
-      h_feature_constraints_flatten.emplace_back(c);
-    }
-  }
-  std::vector<size_t> h_feature_constraints_ptr;
-  size_t n_features_in_constraints = 0;
-  h_feature_constraints_ptr.emplace_back(n_features_in_constraints);
-  for (auto const& v : h_feature_constraints) {
-    n_features_in_constraints += v.size();
-    h_feature_constraints_ptr.emplace_back(n_features_in_constraints);
-  }
-  // Copy the CSR to device.
-  d_fconstraints_.resize(h_feature_constraints_flatten.size());
-  thrust::copy(h_feature_constraints_flatten.cbegin(), h_feature_constraints_flatten.cend(),
-               d_fconstraints_.begin());
-  s_fconstraints_ = dh::ToSpan(d_fconstraints_);
-  d_fconstraints_ptr_.resize(h_feature_constraints_ptr.size());
-  thrust::copy(h_feature_constraints_ptr.cbegin(), h_feature_constraints_ptr.cend(),
-               d_fconstraints_ptr_.begin());
-  s_fconstraints_ptr_ = dh::ToSpan(d_fconstraints_ptr_);
-
-  // --- Compute interaction sets attached to each feature.
-  // Use a set to eliminate duplicated entries.
-  std::vector<std::set<int32_t> > h_features_set(n_features);
-  int32_t cid = 0;
-  for (auto const& constraints : h_feature_constraints) {
-    for (auto const& feat : constraints) {
-      h_features_set.at(feat).insert(cid);
-    }
-    cid++;
-  }
-  // Compute device sets.
-  std::vector<int32_t> h_sets;
-  int32_t ptr = 0;
-  std::vector<int32_t> h_sets_ptr {ptr};
-  for (auto const& feature : h_features_set) {
-    for (auto constraint_id : feature) {
-      h_sets.emplace_back(constraint_id);
-    }
-    // empty set is well defined here.
-    ptr += feature.size();
-    h_sets_ptr.emplace_back(ptr);
-  }
-  d_sets_ = h_sets;
-  d_sets_ptr_ = h_sets_ptr;
-  s_sets_ = dh::ToSpan(d_sets_);
-  s_sets_ptr_ = dh::ToSpan(d_sets_ptr_);
-
-  d_feature_buffer_storage_.resize(LBitField64::ComputeStorageSize(n_features));
-  feature_buffer_ = LBitField64{dh::ToSpan(d_feature_buffer_storage_)};
-
-  // --- Initialize result buffers.
-  output_buffer_bits_storage_.resize(LBitField64::ComputeStorageSize(n_features));
-  output_buffer_bits_ = LBitField64(dh::ToSpan(output_buffer_bits_storage_));
-  input_buffer_bits_storage_.resize(LBitField64::ComputeStorageSize(n_features));
-  input_buffer_bits_ = LBitField64(dh::ToSpan(input_buffer_bits_storage_));
-  result_buffer_.resize(n_features);
-  s_result_buffer_ = dh::ToSpan(result_buffer_);
-}
-
-FeatureInteractionConstraintDevice::FeatureInteractionConstraintDevice(
-    tree::TrainParam const& param, int32_t const n_features) :
-    has_constraint_{true}, n_sets_{0} {
-  this->Configure(param, n_features);
-}
-
-void FeatureInteractionConstraintDevice::Reset() {
-  for (auto& node : node_constraints_storage_) {
-    thrust::fill(node.begin(), node.end(), 0);
-  }
-}
-
-__global__ void ClearBuffersKernel(
-    LBitField64 result_buffer_output, LBitField64 result_buffer_input) {
-  auto tid = blockIdx.x * blockDim.x + threadIdx.x;
-  if (tid < result_buffer_output.Size()) {
-    result_buffer_output.Clear(tid);
-  }
-  if (tid < result_buffer_input.Size()) {
-    result_buffer_input.Clear(tid);
-  }
-}
-
-void FeatureInteractionConstraintDevice::ClearBuffers() {
-  CHECK_EQ(output_buffer_bits_.Size(), input_buffer_bits_.Size());
-  CHECK_LE(feature_buffer_.Size(), output_buffer_bits_.Size());
-  uint32_t constexpr kBlockThreads = 256;
-  auto const n_grids = static_cast<uint32_t>(
-      common::DivRoundUp(input_buffer_bits_.Size(), kBlockThreads));
-  dh::LaunchKernel {n_grids, kBlockThreads} (
-      ClearBuffersKernel,
-      output_buffer_bits_, input_buffer_bits_);
-}
-
-common::Span<bst_feature_t> FeatureInteractionConstraintDevice::QueryNode(int32_t node_id) {
-  if (!has_constraint_) { return {}; }
-  CHECK_LT(node_id, s_node_constraints_.size());
-
-  ClearBuffers();
-
-  thrust::counting_iterator<int32_t> begin(0);
-  thrust::counting_iterator<int32_t> end(result_buffer_.size());
-  auto p_result_buffer = result_buffer_.data();
-  LBitField64 node_constraints = s_node_constraints_[node_id];
-
-  thrust::device_ptr<bst_feature_t> const out_end = thrust::copy_if(
-      thrust::device,
-      begin, end,
-      p_result_buffer,
-      [=]__device__(int32_t pos) {
-        bool res = node_constraints.Check(pos);
-        return res;
-      });
-  size_t const n_available = std::distance(result_buffer_.data(), out_end);
-
-  return {s_result_buffer_.data(), s_result_buffer_.data() + n_available};
-}
-
-__global__ void SetInputBufferKernel(common::Span<bst_feature_t> feature_list_input,
-                                     LBitField64 result_buffer_input) {
-  uint32_t tid = threadIdx.x + blockIdx.x * blockDim.x;
-  if (tid < feature_list_input.size()) {
-    result_buffer_input.Set(feature_list_input[tid]);
-  }
-}
-
-__global__ void QueryFeatureListKernel(LBitField64 node_constraints,
-                                       LBitField64 result_buffer_input,
-                                       LBitField64 result_buffer_output) {
-  result_buffer_output |= node_constraints;
-  result_buffer_output &= result_buffer_input;
-}
-
-common::Span<bst_feature_t> FeatureInteractionConstraintDevice::Query(
-    common::Span<bst_feature_t> feature_list, int32_t nid) {
-  if (!has_constraint_ || nid == 0) {
-    return feature_list;
-  }
-
-  ClearBuffers();
-
-  LBitField64 node_constraints = s_node_constraints_[nid];
-  CHECK_EQ(input_buffer_bits_.Size(), output_buffer_bits_.Size());
-
-  uint32_t constexpr kBlockThreads = 256;
-  auto n_grids = static_cast<uint32_t>(
-      common::DivRoundUp(output_buffer_bits_.Size(), kBlockThreads));
-  dh::LaunchKernel {n_grids, kBlockThreads} (
-      SetInputBufferKernel,
-      feature_list, input_buffer_bits_);
-  dh::LaunchKernel {n_grids, kBlockThreads} (
-      QueryFeatureListKernel,
-      node_constraints, input_buffer_bits_, output_buffer_bits_);
-
-  thrust::counting_iterator<int32_t> begin(0);
-  thrust::counting_iterator<int32_t> end(result_buffer_.size());
-
-  LBitField64 local_result_buffer = output_buffer_bits_;
-
-  thrust::device_ptr<bst_feature_t> const out_end = thrust::copy_if(
-      thrust::device,
-      begin, end,
-      result_buffer_.data(),
-      [=]__device__(int32_t pos) {
-        bool res = local_result_buffer.Check(pos);
-        return res;
-      });
-  size_t const n_available = std::distance(result_buffer_.data(), out_end);
-
-  common::Span<bst_feature_t> result =
-      {s_result_buffer_.data(), s_result_buffer_.data() + n_available};
-  return result;
-}
-
-// Find interaction sets for each feature, then store all features in
-// those sets in a buffer.
-__global__ void RestoreFeatureListFromSetsKernel(
-    LBitField64 feature_buffer,
-
-    bst_feature_t fid,
-    common::Span<bst_feature_t> feature_interactions,
-    common::Span<size_t> feature_interactions_ptr,  // of size n interaction set + 1
-
-    common::Span<bst_feature_t> interactions_list,
-    common::Span<size_t> interactions_list_ptr) {
-  auto const tid_x = threadIdx.x + blockIdx.x * blockDim.x;
-  auto const tid_y = threadIdx.y + blockIdx.y * blockDim.y;
-  // painful mapping: fid -> sets related to it -> features related to sets.
-  auto const beg = interactions_list_ptr[fid];
-  auto const end = interactions_list_ptr[fid+1];
-  auto const n_sets = end - beg;
-  if (tid_x < n_sets) {
-    auto const set_id_pos = beg + tid_x;
-    auto const set_id = interactions_list[set_id_pos];
-    auto const set_beg = feature_interactions_ptr[set_id];
-    auto const set_end = feature_interactions_ptr[set_id + 1];
-    auto const feature_pos = set_beg + tid_y;
-    if (feature_pos < set_end) {
-      feature_buffer.Set(feature_interactions[feature_pos]);
-    }
-  }
-}
-
-__global__ void InteractionConstraintSplitKernel(LBitField64 feature,
-                                                 int32_t feature_id,
-                                                 LBitField64 node,
-                                                 LBitField64 left,
-                                                 LBitField64 right) {
-  auto tid = threadIdx.x + blockDim.x * blockIdx.x;
-  if (tid > node.Size()) {
-    return;
-  }
-  // enable constraints from feature
-  node |= feature;
-  // clear the buffer after use
-  if (tid < feature.Size()) {
-    feature.Clear(tid);
-  }
-
-  // enable constraints from parent
-  left  |= node;
-  right |= node;
-
-  if (tid == feature_id) {
-    // enable the split feature, set all of them at last instead of
-    // setting it for parent to avoid race.
-    node.Set(feature_id);
-    left.Set(feature_id);
-    right.Set(feature_id);
-  }
-}
-
-void FeatureInteractionConstraintDevice::Split(
-    bst_node_t node_id, bst_feature_t feature_id, bst_node_t left_id, bst_node_t right_id) {
-  if (!has_constraint_) { return; }
-  CHECK_NE(node_id, left_id)
-      << " Split node: " << node_id << " and its left child: "
-      << left_id << " cannot be the same.";
-  CHECK_NE(node_id, right_id)
-      << " Split node: " << node_id << " and its left child: "
-      << right_id << " cannot be the same.";
-  CHECK_LT(right_id, s_node_constraints_.size());
-  CHECK_NE(s_node_constraints_.size(), 0);
-
-  LBitField64 node = s_node_constraints_[node_id];
-  LBitField64 left = s_node_constraints_[left_id];
-  LBitField64 right = s_node_constraints_[right_id];
-
-  dim3 const block3(16, 64, 1);
-  dim3 const grid3(common::DivRoundUp(n_sets_, 16),
-                   common::DivRoundUp(s_fconstraints_.size(), 64));
-  dh::LaunchKernel {grid3, block3} (
-      RestoreFeatureListFromSetsKernel,
-      feature_buffer_, feature_id,
-      s_fconstraints_, s_fconstraints_ptr_,
-      s_sets_, s_sets_ptr_);
-
-  uint32_t constexpr kBlockThreads = 256;
-  auto n_grids = static_cast<uint32_t>(common::DivRoundUp(node.Size(), kBlockThreads));
-
-  dh::LaunchKernel {n_grids, kBlockThreads} (
-      InteractionConstraintSplitKernel,
-      feature_buffer_,
-      feature_id,
-      node, left, right);
-}
-
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/constraints.cuh b/ml-xgboost/src/tree/constraints.cuh
deleted file mode 100644
index cd5fbf2..0000000
--- a/ml-xgboost/src/tree/constraints.cuh
+++ /dev/null
@@ -1,168 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- *
- * \file Various constraints used in GPU_Hist.
- */
-#ifndef XGBOOST_TREE_CONSTRAINTS_H_
-#define XGBOOST_TREE_CONSTRAINTS_H_
-
-#include <dmlc/json.h>
-
-#include <cinttypes>
-#include <vector>
-
-#include "param.h"
-#include "xgboost/span.h"
-#include "../common/bitfield.h"
-#include "../common/device_helpers.cuh"
-
-namespace xgboost {
-
-// This class implements monotonic constraints, L1, L2 regularization.
-struct ValueConstraint {
-  double lower_bound;
-  double upper_bound;
-  XGBOOST_DEVICE ValueConstraint()
-      : lower_bound(-std::numeric_limits<double>::max()),
-        upper_bound(std::numeric_limits<double>::max()) {}
-  inline static void Init(tree::TrainParam *param, unsigned num_feature) {
-    param->monotone_constraints.resize(num_feature, 0);
-  }
-  template <typename ParamT>
-  XGBOOST_DEVICE inline double CalcWeight(const ParamT &param, tree::GradStats stats) const {
-    double w = xgboost::tree::CalcWeight(param, stats);
-    if (w < lower_bound) {
-      return lower_bound;
-    }
-    if (w > upper_bound) {
-      return upper_bound;
-    }
-    return w;
-  }
-
-  template <typename ParamT>
-  XGBOOST_DEVICE inline double CalcGain(const ParamT &param, tree::GradStats stats) const {
-    return tree::CalcGainGivenWeight<ParamT, float>(param, stats.sum_grad, stats.sum_hess,
-                                                    CalcWeight(param, stats));
-  }
-
-  template <typename ParamT>
-  XGBOOST_DEVICE inline double CalcSplitGain(const ParamT &param, int constraint,
-                                             tree::GradStats left, tree::GradStats right) const {
-    const double negative_infinity = -std::numeric_limits<double>::infinity();
-    double wleft = CalcWeight(param, left);
-    double wright = CalcWeight(param, right);
-    double gain =
-      tree::CalcGainGivenWeight<ParamT, float>(param, left.sum_grad, left.sum_hess, wleft) +
-      tree::CalcGainGivenWeight<ParamT, float>(param, right.sum_grad, right.sum_hess, wright);
-    if (constraint == 0) {
-      return gain;
-    } else if (constraint > 0) {
-      return wleft <= wright ? gain : negative_infinity;
-    } else {
-      return wleft >= wright ? gain : negative_infinity;
-    }
-  }
-
-  inline void SetChild(const tree::TrainParam &param, bst_uint split_index,
-                       tree::GradStats left, tree::GradStats right, ValueConstraint *cleft,
-                       ValueConstraint *cright) {
-    int c = param.monotone_constraints.at(split_index);
-    *cleft = *this;
-    *cright = *this;
-    if (c == 0) {
-      return;
-    }
-    double wleft = CalcWeight(param, left);
-    double wright = CalcWeight(param, right);
-    double mid = (wleft + wright) / 2;
-    CHECK(!std::isnan(mid));
-    if (c < 0) {
-      cleft->lower_bound = mid;
-      cright->upper_bound = mid;
-    } else {
-      cleft->upper_bound = mid;
-      cright->lower_bound = mid;
-    }
-  }
-};
-
-// Feature interaction constraints built for GPU Hist updater.
-struct FeatureInteractionConstraintDevice {
- protected:
-  // Whether interaction constraint is used.
-  bool has_constraint_;
-  // n interaction sets.
-  size_t n_sets_;
-
-  // The parsed feature interaction constraints as CSR.
-  dh::device_vector<bst_feature_t> d_fconstraints_;
-  common::Span<bst_feature_t> s_fconstraints_;
-  dh::device_vector<size_t> d_fconstraints_ptr_;
-  common::Span<size_t> s_fconstraints_ptr_;
-  /* Interaction sets for each feature as CSR.  For an input like:
-   * [[0, 1], [1, 2]], this will have values:
-   *
-   * fid:                                |0 | 1  | 2|
-   * sets a feature belongs to(d_sets_): |0 |0, 1| 1|
-   *
-   * d_sets_ptr_:                        |0, 1, 3, 4|
-   */
-  dh::device_vector<bst_feature_t> d_sets_;
-  common::Span<bst_feature_t> s_sets_;
-  dh::device_vector<size_t> d_sets_ptr_;
-  common::Span<size_t> s_sets_ptr_;
-
-  // Allowed features attached to each node, have n_nodes bitfields,
-  // each of size n_features.
-  std::vector<dh::device_vector<LBitField64::value_type>> node_constraints_storage_;
-  std::vector<LBitField64> node_constraints_;
-  common::Span<LBitField64> s_node_constraints_;
-
-  // buffer storing return feature list from Query, of size n_features.
-  dh::device_vector<bst_feature_t> result_buffer_;
-  common::Span<bst_feature_t> s_result_buffer_;
-
-  // Temp buffers, one bit for each possible feature.
-  dh::device_vector<LBitField64::value_type> output_buffer_bits_storage_;
-  LBitField64 output_buffer_bits_;
-  dh::device_vector<LBitField64::value_type> input_buffer_bits_storage_;
-  LBitField64 input_buffer_bits_;
-  /*
-   * Combined features from all interaction sets that one feature belongs to.
-   * For an input with [[0, 1], [1, 2]], the feature 1 belongs to sets {0, 1}
-   */
-  dh::device_vector<LBitField64::value_type> d_feature_buffer_storage_;
-  LBitField64 feature_buffer_;  // of Size n features.
-
-  // Clear out all temp buffers except for `feature_buffer_', which is
-  // handled in `Split'.
-  void ClearBuffers();
-
- public:
-  size_t Features() const;
-  FeatureInteractionConstraintDevice() = default;
-  void Configure(tree::TrainParam const& param, int32_t const n_features);
-  FeatureInteractionConstraintDevice(tree::TrainParam const& param, int32_t const n_features);
-  FeatureInteractionConstraintDevice(FeatureInteractionConstraintDevice const& that) = default;
-  FeatureInteractionConstraintDevice(FeatureInteractionConstraintDevice&& that) = default;
-  /*! \brief Reset before constructing a new tree. */
-  void Reset();
-  /*! \brief Return a list of features given node id */
-  common::Span<bst_feature_t> QueryNode(int32_t nid);
-  /*!
-   * \brief Return a list of selected features from given feature_list and node id.
-   *
-   * \param feature_list A list of features
-   * \param nid node id
-   *
-   * \return A list of features picked from `feature_list' that conform to constraints in
-   * node.
-   */
-  common::Span<bst_feature_t> Query(common::Span<bst_feature_t> feature_list, int32_t nid);
-  /*! \brief Apply split for node_id. */
-  void Split(bst_node_t node_id, bst_feature_t feature_id, bst_node_t left_id, bst_node_t right_id);
-};
-
-}      // namespace xgboost
-#endif  // XGBOOST_TREE_CONSTRAINTS_H_
diff --git a/ml-xgboost/src/tree/constraints.h b/ml-xgboost/src/tree/constraints.h
deleted file mode 100644
index facc5eb..0000000
--- a/ml-xgboost/src/tree/constraints.h
+++ /dev/null
@@ -1,65 +0,0 @@
-/*!
- * Copyright 2018-2019 by Contributors
- */
-#ifndef XGBOOST_TREE_CONSTRAINTS_H_
-#define XGBOOST_TREE_CONSTRAINTS_H_
-
-#include <string>
-#include <unordered_set>
-#include <vector>
-
-#include "xgboost/span.h"
-#include "xgboost/base.h"
-
-#include "param.h"
-
-namespace xgboost {
-/*!
- * \brief Feature interaction constraint implementation for CPU tree updaters.
- *
- * The interface is similiar to the one for GPU Hist.
- */
-class FeatureInteractionConstraintHost {
- protected:
-  // interaction_constraints_[constraint_id] contains a single interaction
-  //   constraint, which specifies a group of feature IDs that can interact
-  //   with each other
-  std::vector< std::unordered_set<bst_feature_t> > interaction_constraints_;
-  // int_cont_[nid] contains the set of all feature IDs that are allowed to
-  //   be used for a split at node nid
-  std::vector< std::unordered_set<bst_feature_t> > node_constraints_;
-  // splits_[nid] contains the set of all feature IDs that have been used for
-  //   splits in node nid and its parents
-  std::vector< std::unordered_set<bst_feature_t> > splits_;
-  // string passed by user.
-  std::string interaction_constraint_str_;
-  // number of features in DMatrix/Booster
-  bst_feature_t n_features_;
-  bool enabled_{false};
-
-  void SplitImpl(int32_t node_id, bst_feature_t feature_id, bst_node_t left_id,
-                 bst_node_t right_id);
-
- public:
-  FeatureInteractionConstraintHost() = default;
-  void Split(int32_t node_id, bst_feature_t feature_id, bst_node_t left_id,
-             bst_node_t right_id) {
-    if (!enabled_) {
-      return;
-    } else {
-      this->SplitImpl(node_id, feature_id, left_id, right_id);
-    }
-  }
-
-  bool Query(bst_node_t nid, bst_feature_t fid) const {
-    if (!enabled_) { return true; }
-    return node_constraints_.at(nid).find(fid) != node_constraints_.at(nid).cend();
-  }
-
-  void Reset();
-
-  void Configure(tree::TrainParam const& param, bst_feature_t const n_features);
-};
-}  // namespace xgboost
-
-#endif  // XGBOOST_TREE_CONSTRAINTS_H_
diff --git a/ml-xgboost/src/tree/gpu_hist/gradient_based_sampler.cu b/ml-xgboost/src/tree/gpu_hist/gradient_based_sampler.cu
deleted file mode 100644
index 6c70af9..0000000
--- a/ml-xgboost/src/tree/gpu_hist/gradient_based_sampler.cu
+++ /dev/null
@@ -1,382 +0,0 @@
-/*!
- * Copyright 2019 by XGBoost Contributors
- */
-#include <thrust/functional.h>
-#include <thrust/random.h>
-#include <thrust/transform.h>
-#include <xgboost/host_device_vector.h>
-#include <xgboost/logging.h>
-
-#include <algorithm>
-#include <limits>
-
-#include "../../common/compressed_iterator.h"
-#include "../../common/random.h"
-#include "gradient_based_sampler.cuh"
-
-namespace xgboost {
-namespace tree {
-
-/*! \brief A functor that returns random weights. */
-class RandomWeight : public thrust::unary_function<size_t, float> {
- public:
-  explicit RandomWeight(size_t seed) : seed_(seed) {}
-
-  XGBOOST_DEVICE float operator()(size_t i) const {
-    thrust::default_random_engine rng(seed_);
-    thrust::uniform_real_distribution<float> dist;
-    rng.discard(i);
-    return dist(rng);
-  }
-
- private:
-  uint32_t seed_;
-};
-
-/*! \brief A functor that performs a Bernoulli trial to discard a gradient pair. */
-class BernoulliTrial : public thrust::unary_function<size_t, bool> {
- public:
-  BernoulliTrial(size_t seed, float p) : rnd_(seed), p_(p) {}
-
-  XGBOOST_DEVICE bool operator()(size_t i) const {
-    return rnd_(i) > p_;
-  }
-
- private:
-  RandomWeight rnd_;
-  float p_;
-};
-
-/*! \brief A functor that returns true if the gradient pair is non-zero. */
-struct IsNonZero : public thrust::unary_function<GradientPair, bool> {
-  XGBOOST_DEVICE bool operator()(const GradientPair& gpair) const {
-    return gpair.GetGrad() != 0 || gpair.GetHess() != 0;
-  }
-};
-
-/*! \brief A functor that clears the row indexes with empty gradient. */
-struct ClearEmptyRows : public thrust::binary_function<GradientPair, size_t, size_t> {
-  XGBOOST_DEVICE size_t operator()(const GradientPair& gpair, size_t row_index) const {
-    if (gpair.GetGrad() != 0 || gpair.GetHess() != 0) {
-      return row_index;
-    } else {
-      return std::numeric_limits<std::size_t>::max();
-    }
-  }
-};
-
-/*! \brief A functor that combines the gradient pair into a single float.
- *
- * The approach here is based on Minimal Variance Sampling (MVS), with lambda set to 0.1.
- *
- * \see Ibragimov, B., & Gusev, G. (2019). Minimal Variance Sampling in Stochastic Gradient
- * Boosting. In Advances in Neural Information Processing Systems (pp. 15061-15071).
- */
-class CombineGradientPair : public thrust::unary_function<GradientPair, float> {
- public:
-  XGBOOST_DEVICE float operator()(const GradientPair& gpair) const {
-    return sqrtf(powf(gpair.GetGrad(), 2) + kLambda * powf(gpair.GetHess(), 2));
-  }
-
- private:
-  static constexpr float kLambda = 0.1f;
-};
-
-/*! \brief A functor that calculates the difference between the sample rate and the desired sample
- * rows, given a cumulative gradient sum.
- */
-class SampleRateDelta : public thrust::binary_function<float, size_t, float> {
- public:
-  SampleRateDelta(common::Span<float> threshold, size_t n_rows, size_t sample_rows)
-      : threshold_(threshold), n_rows_(n_rows), sample_rows_(sample_rows) {}
-
-  XGBOOST_DEVICE float operator()(float gradient_sum, size_t row_index) const {
-    float lower = threshold_[row_index];
-    float upper = threshold_[row_index + 1];
-    float u = gradient_sum / static_cast<float>(sample_rows_ - n_rows_ + row_index + 1);
-    if (u > lower && u <= upper) {
-      threshold_[row_index + 1] = u;
-      return 0.0f;
-    } else {
-      return std::numeric_limits<float>::max();
-    }
-  }
-
- private:
-  common::Span<float> threshold_;
-  size_t n_rows_;
-  size_t sample_rows_;
-};
-
-/*! \brief A functor that performs Poisson sampling, and scales gradient pairs by 1/p_i. */
-class PoissonSampling : public thrust::binary_function<GradientPair, size_t, GradientPair> {
- public:
-  PoissonSampling(common::Span<float> threshold, size_t threshold_index, RandomWeight rnd)
-      : threshold_(threshold), threshold_index_(threshold_index), rnd_(rnd) {}
-
-  XGBOOST_DEVICE GradientPair operator()(const GradientPair& gpair, size_t i) {
-    // If the gradient and hessian are both empty, we should never select this row.
-    if (gpair.GetGrad() == 0 && gpair.GetHess() == 0) {
-      return gpair;
-    }
-    float combined_gradient = combine_(gpair);
-    float u = threshold_[threshold_index_];
-    float p = combined_gradient / u;
-    if (p >= 1) {
-      // Always select this row.
-      return gpair;
-    } else {
-      // Select this row randomly with probability proportional to the combined gradient.
-      // Scale gpair by 1/p.
-      if (rnd_(i) <= p) {
-        return gpair / p;
-      } else {
-        return GradientPair();
-      }
-    }
-  }
-
- private:
-  common::Span<float> threshold_;
-  size_t threshold_index_;
-  RandomWeight rnd_;
-  CombineGradientPair combine_;
-};
-
-NoSampling::NoSampling(EllpackPageImpl* page) : page_(page) {}
-
-GradientBasedSample NoSampling::Sample(common::Span<GradientPair> gpair, DMatrix* dmat) {
-  return {dmat->Info().num_row_, page_, gpair};
-}
-
-ExternalMemoryNoSampling::ExternalMemoryNoSampling(EllpackPageImpl* page,
-                                                   size_t n_rows,
-                                                   const BatchParam& batch_param)
-    : batch_param_(batch_param),
-      page_(new EllpackPageImpl(batch_param.gpu_id, page->Cuts(), page->is_dense,
-                                page->row_stride, n_rows)) {}
-
-GradientBasedSample ExternalMemoryNoSampling::Sample(common::Span<GradientPair> gpair,
-                                                     DMatrix* dmat) {
-  if (!page_concatenated_) {
-    // Concatenate all the external memory ELLPACK pages into a single in-memory page.
-    size_t offset = 0;
-    for (auto& batch : dmat->GetBatches<EllpackPage>(batch_param_)) {
-      auto page = batch.Impl();
-      size_t num_elements = page_->Copy(batch_param_.gpu_id, page, offset);
-      offset += num_elements;
-    }
-    page_concatenated_ = true;
-  }
-  return {dmat->Info().num_row_, page_.get(), gpair};
-}
-
-UniformSampling::UniformSampling(EllpackPageImpl* page, float subsample)
-    : page_(page), subsample_(subsample) {}
-
-GradientBasedSample UniformSampling::Sample(common::Span<GradientPair> gpair, DMatrix* dmat) {
-  // Set gradient pair to 0 with p = 1 - subsample
-  thrust::replace_if(dh::tbegin(gpair), dh::tend(gpair),
-                     thrust::counting_iterator<size_t>(0),
-                     BernoulliTrial(common::GlobalRandom()(), subsample_),
-                     GradientPair());
-  return {dmat->Info().num_row_, page_, gpair};
-}
-
-ExternalMemoryUniformSampling::ExternalMemoryUniformSampling(EllpackPageImpl* page,
-                                                             size_t n_rows,
-                                                             const BatchParam& batch_param,
-                                                             float subsample)
-    : original_page_(page),
-      batch_param_(batch_param),
-      subsample_(subsample),
-      sample_row_index_(n_rows) {}
-
-GradientBasedSample ExternalMemoryUniformSampling::Sample(common::Span<GradientPair> gpair,
-                                                          DMatrix* dmat) {
-  // Set gradient pair to 0 with p = 1 - subsample
-  thrust::replace_if(dh::tbegin(gpair), dh::tend(gpair),
-                     thrust::counting_iterator<size_t>(0),
-                     BernoulliTrial(common::GlobalRandom()(), subsample_),
-                     GradientPair());
-
-  // Count the sampled rows.
-  size_t sample_rows = thrust::count_if(dh::tbegin(gpair), dh::tend(gpair), IsNonZero());
-
-  // Compact gradient pairs.
-  gpair_.resize(sample_rows);
-  thrust::copy_if(dh::tbegin(gpair), dh::tend(gpair), gpair_.begin(), IsNonZero());
-
-  // Index the sample rows.
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair), sample_row_index_.begin(), IsNonZero());
-  thrust::exclusive_scan(sample_row_index_.begin(), sample_row_index_.end(),
-                         sample_row_index_.begin());
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair),
-                    sample_row_index_.begin(),
-                    sample_row_index_.begin(),
-                    ClearEmptyRows());
-
-  // Create a new ELLPACK page with empty rows.
-  page_.reset();  // Release the device memory first before reallocating
-  page_.reset(new EllpackPageImpl(
-      batch_param_.gpu_id, original_page_->Cuts(), original_page_->is_dense,
-                                  original_page_->row_stride, sample_rows));
-
-  // Compact the ELLPACK pages into the single sample page.
-  thrust::fill(dh::tbegin(page_->gidx_buffer), dh::tend(page_->gidx_buffer), 0);
-  for (auto& batch : dmat->GetBatches<EllpackPage>(batch_param_)) {
-    page_->Compact(batch_param_.gpu_id, batch.Impl(), dh::ToSpan(sample_row_index_));
-  }
-
-  return {sample_rows, page_.get(), dh::ToSpan(gpair_)};
-}
-
-GradientBasedSampling::GradientBasedSampling(EllpackPageImpl* page,
-                                             size_t n_rows,
-                                             const BatchParam& batch_param,
-                                             float subsample)
-    : page_(page),
-      subsample_(subsample),
-      threshold_(n_rows + 1, 0.0f),
-      grad_sum_(n_rows, 0.0f) {}
-
-GradientBasedSample GradientBasedSampling::Sample(common::Span<GradientPair> gpair,
-                                                  DMatrix* dmat) {
-  size_t n_rows = dmat->Info().num_row_;
-  size_t threshold_index = GradientBasedSampler::CalculateThresholdIndex(
-      gpair, dh::ToSpan(threshold_), dh::ToSpan(grad_sum_), n_rows * subsample_);
-
-  // Perform Poisson sampling in place.
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair),
-                    thrust::counting_iterator<size_t>(0),
-                    dh::tbegin(gpair),
-                    PoissonSampling(dh::ToSpan(threshold_),
-                                    threshold_index,
-                                    RandomWeight(common::GlobalRandom()())));
-  return {n_rows, page_, gpair};
-}
-
-ExternalMemoryGradientBasedSampling::ExternalMemoryGradientBasedSampling(
-    EllpackPageImpl* page,
-    size_t n_rows,
-    const BatchParam& batch_param,
-    float subsample)
-    : original_page_(page),
-      batch_param_(batch_param),
-      subsample_(subsample),
-      threshold_(n_rows + 1, 0.0f),
-      grad_sum_(n_rows, 0.0f),
-      sample_row_index_(n_rows) {}
-
-GradientBasedSample ExternalMemoryGradientBasedSampling::Sample(common::Span<GradientPair> gpair,
-                                                                DMatrix* dmat) {
-  size_t n_rows = dmat->Info().num_row_;
-  size_t threshold_index = GradientBasedSampler::CalculateThresholdIndex(
-      gpair, dh::ToSpan(threshold_), dh::ToSpan(grad_sum_), n_rows * subsample_);
-
-  // Perform Poisson sampling in place.
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair),
-                    thrust::counting_iterator<size_t>(0),
-                    dh::tbegin(gpair),
-                    PoissonSampling(dh::ToSpan(threshold_),
-                                    threshold_index,
-                                    RandomWeight(common::GlobalRandom()())));
-
-  // Count the sampled rows.
-  size_t sample_rows = thrust::count_if(dh::tbegin(gpair), dh::tend(gpair), IsNonZero());
-
-  // Compact gradient pairs.
-  gpair_.resize(sample_rows);
-  thrust::copy_if(dh::tbegin(gpair), dh::tend(gpair), gpair_.begin(), IsNonZero());
-
-  // Index the sample rows.
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair), sample_row_index_.begin(), IsNonZero());
-  thrust::exclusive_scan(sample_row_index_.begin(), sample_row_index_.end(),
-    sample_row_index_.begin());
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair),
-                    sample_row_index_.begin(),
-                    sample_row_index_.begin(),
-                    ClearEmptyRows());
-
-  // Create a new ELLPACK page with empty rows.
-  page_.reset();  // Release the device memory first before reallocating
-  page_.reset(new EllpackPageImpl(batch_param_.gpu_id, original_page_->Cuts(),
-                                  original_page_->is_dense,
-                                  original_page_->row_stride, sample_rows));
-
-  // Compact the ELLPACK pages into the single sample page.
-  thrust::fill(dh::tbegin(page_->gidx_buffer), dh::tend(page_->gidx_buffer), 0);
-  for (auto& batch : dmat->GetBatches<EllpackPage>(batch_param_)) {
-    page_->Compact(batch_param_.gpu_id, batch.Impl(), dh::ToSpan(sample_row_index_));
-  }
-
-  return {sample_rows, page_.get(), dh::ToSpan(gpair_)};
-}
-
-GradientBasedSampler::GradientBasedSampler(EllpackPageImpl* page,
-                                           size_t n_rows,
-                                           const BatchParam& batch_param,
-                                           float subsample,
-                                           int sampling_method) {
-  monitor_.Init("gradient_based_sampler");
-
-  bool is_sampling = subsample < 1.0;
-  bool is_external_memory = page->n_rows != n_rows;
-
-  if (is_sampling) {
-    switch (sampling_method) {
-      case TrainParam::kUniform:
-        if (is_external_memory) {
-          strategy_.reset(new ExternalMemoryUniformSampling(page, n_rows, batch_param, subsample));
-        } else {
-          strategy_.reset(new UniformSampling(page, subsample));
-        }
-        break;
-      case TrainParam::kGradientBased:
-        if (is_external_memory) {
-          strategy_.reset(
-              new ExternalMemoryGradientBasedSampling(page, n_rows, batch_param, subsample));
-        } else {
-          strategy_.reset(new GradientBasedSampling(page, n_rows, batch_param, subsample));
-        }
-        break;
-      default:LOG(FATAL) << "unknown sampling method";
-    }
-  } else {
-    if (is_external_memory) {
-      strategy_.reset(new ExternalMemoryNoSampling(page, n_rows, batch_param));
-    } else {
-      strategy_.reset(new NoSampling(page));
-    }
-  }
-}
-
-// Sample a DMatrix based on the given gradient pairs.
-GradientBasedSample GradientBasedSampler::Sample(common::Span<GradientPair> gpair,
-                                                 DMatrix* dmat) {
-  monitor_.StartCuda("Sample");
-  GradientBasedSample sample = strategy_->Sample(gpair, dmat);
-  monitor_.StopCuda("Sample");
-  return sample;
-}
-
-size_t GradientBasedSampler::CalculateThresholdIndex(
-    common::Span<GradientPair> gpair, common::Span<float> threshold,
-    common::Span<float> grad_sum, size_t sample_rows) {
-  thrust::fill(dh::tend(threshold) - 1, dh::tend(threshold),
-               std::numeric_limits<float>::max());
-  thrust::transform(dh::tbegin(gpair), dh::tend(gpair), dh::tbegin(threshold),
-                    CombineGradientPair());
-  thrust::sort(dh::tbegin(threshold), dh::tend(threshold) - 1);
-  thrust::inclusive_scan(dh::tbegin(threshold), dh::tend(threshold) - 1,
-                         dh::tbegin(grad_sum));
-  thrust::transform(dh::tbegin(grad_sum), dh::tend(grad_sum),
-                    thrust::counting_iterator<size_t>(0), dh::tbegin(grad_sum),
-                    SampleRateDelta(threshold, gpair.size(), sample_rows));
-  thrust::device_ptr<float> min =
-      thrust::min_element(dh::tbegin(grad_sum), dh::tend(grad_sum));
-  return thrust::distance(dh::tbegin(grad_sum), min) + 1;
-}
-
-};  // namespace tree
-};  // namespace xgboost
diff --git a/ml-xgboost/src/tree/gpu_hist/gradient_based_sampler.cuh b/ml-xgboost/src/tree/gpu_hist/gradient_based_sampler.cuh
deleted file mode 100644
index 7394804..0000000
--- a/ml-xgboost/src/tree/gpu_hist/gradient_based_sampler.cuh
+++ /dev/null
@@ -1,151 +0,0 @@
-/*!
- * Copyright 2019 by XGBoost Contributors
- */
-#pragma once
-#include <xgboost/base.h>
-#include <xgboost/data.h>
-#include <xgboost/span.h>
-
-#include "../../common/device_helpers.cuh"
-#include "../../data/ellpack_page.cuh"
-
-namespace xgboost {
-namespace tree {
-
-struct GradientBasedSample {
-  /*!\brief Number of sampled rows. */
-  size_t sample_rows;
-  /*!\brief Sampled rows in ELLPACK format. */
-  EllpackPageImpl* page;
-  /*!\brief Gradient pairs for the sampled rows. */
-  common::Span<GradientPair> gpair;
-};
-
-class SamplingStrategy {
- public:
-  /*! \brief Sample from a DMatrix based on the given gradient pairs. */
-  virtual GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) = 0;
-  virtual ~SamplingStrategy() = default;
-};
-
-/*! \brief No sampling in in-memory mode. */
-class NoSampling : public SamplingStrategy {
- public:
-  explicit NoSampling(EllpackPageImpl* page);
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
-
- private:
-  EllpackPageImpl* page_;
-};
-
-/*! \brief No sampling in external memory mode. */
-class ExternalMemoryNoSampling : public SamplingStrategy {
- public:
-  ExternalMemoryNoSampling(EllpackPageImpl* page,
-                           size_t n_rows,
-                           const BatchParam& batch_param);
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
-
- private:
-  BatchParam batch_param_;
-  std::unique_ptr<EllpackPageImpl> page_;
-  bool page_concatenated_{false};
-};
-
-/*! \brief Uniform sampling in in-memory mode. */
-class UniformSampling : public SamplingStrategy {
- public:
-  UniformSampling(EllpackPageImpl* page, float subsample);
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
-
- private:
-  EllpackPageImpl* page_;
-  float subsample_;
-};
-
-/*! \brief No sampling in external memory mode. */
-class ExternalMemoryUniformSampling : public SamplingStrategy {
- public:
-  ExternalMemoryUniformSampling(EllpackPageImpl* page,
-                                size_t n_rows,
-                                const BatchParam& batch_param,
-                                float subsample);
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
-
- private:
-  EllpackPageImpl* original_page_;
-  BatchParam batch_param_;
-  float subsample_;
-  std::unique_ptr<EllpackPageImpl> page_;
-  dh::device_vector<GradientPair> gpair_{};
-  dh::caching_device_vector<size_t> sample_row_index_;
-};
-
-/*! \brief Gradient-based sampling in in-memory mode.. */
-class GradientBasedSampling : public SamplingStrategy {
- public:
-  GradientBasedSampling(EllpackPageImpl* page,
-                        size_t n_rows,
-                        const BatchParam& batch_param,
-                        float subsample);
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
-
- private:
-  EllpackPageImpl* page_;
-  float subsample_;
-  dh::caching_device_vector<float> threshold_;
-  dh::caching_device_vector<float> grad_sum_;
-};
-
-/*! \brief Gradient-based sampling in external memory mode.. */
-class ExternalMemoryGradientBasedSampling : public SamplingStrategy {
- public:
-  ExternalMemoryGradientBasedSampling(EllpackPageImpl* page,
-                                      size_t n_rows,
-                                      const BatchParam& batch_param,
-                                      float subsample);
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
-
- private:
-  EllpackPageImpl* original_page_;
-  BatchParam batch_param_;
-  float subsample_;
-  dh::caching_device_vector<float> threshold_;
-  dh::caching_device_vector<float> grad_sum_;
-  std::unique_ptr<EllpackPageImpl> page_;
-  dh::device_vector<GradientPair> gpair_;
-  dh::caching_device_vector<size_t> sample_row_index_;
-};
-
-/*! \brief Draw a sample of rows from a DMatrix.
- *
- * \see Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017).
- * Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information
- * Processing Systems (pp. 3146-3154).
- * \see Zhu, R. (2016). Gradient-based sampling: An adaptive importance sampling for least-squares.
- * In Advances in Neural Information Processing Systems (pp. 406-414).
- * \see Ohlsson, E. (1998). Sequential poisson sampling. Journal of official Statistics, 14(2), 149.
- */
-class GradientBasedSampler {
- public:
-  GradientBasedSampler(EllpackPageImpl* page,
-                       size_t n_rows,
-                       const BatchParam& batch_param,
-                       float subsample,
-                       int sampling_method);
-
-  /*! \brief Sample from a DMatrix based on the given gradient pairs. */
-  GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat);
-
-  /*! \brief Calculate the threshold used to normalize sampling probabilities. */
-  static size_t CalculateThresholdIndex(common::Span<GradientPair> gpair,
-                                        common::Span<float> threshold,
-                                        common::Span<float> grad_sum,
-                                        size_t sample_rows);
-
- private:
-  common::Monitor monitor_;
-  std::unique_ptr<SamplingStrategy> strategy_;
-};
-};  // namespace tree
-};  // namespace xgboost
diff --git a/ml-xgboost/src/tree/gpu_hist/histogram.cu b/ml-xgboost/src/tree/gpu_hist/histogram.cu
deleted file mode 100644
index edc3046..0000000
--- a/ml-xgboost/src/tree/gpu_hist/histogram.cu
+++ /dev/null
@@ -1,201 +0,0 @@
-/*!
- * Copyright 2020 by XGBoost Contributors
- */
-#include <thrust/reduce.h>
-#include <thrust/iterator/transform_iterator.h>
-#include <algorithm>
-#include <ctgmath>
-#include <limits>
-
-#include "xgboost/base.h"
-#include "row_partitioner.cuh"
-
-#include "histogram.cuh"
-
-#include "../../data/ellpack_page.cuh"
-#include "../../common/device_helpers.cuh"
-
-namespace xgboost {
-namespace tree {
-// Following 2 functions are slightly modifed version of fbcuda.
-
-/* \brief Constructs a rounding factor used to truncate elements in a sum such that the
-   sum of the truncated elements is the same no matter what the order of the sum is.
-
- * Algorithm 5: Reproducible Sequential Sum in 'Fast Reproducible Floating-Point
- * Summation' by Demmel and Nguyen
-
- * In algorithm 5 the bound is calculated as $max(|v_i|) * n$.  Here we use the bound
- *
- * \begin{equation}
- *   max( fl(\sum^{V}_{v_i>0}{v_i}), fl(\sum^{V}_{v_i<0}|v_i|) )
- * \end{equation}
- *
- * to avoid outliers, as the full reduction is reproducible on GPU with reduction tree.
- */
-template <typename T>
-DEV_INLINE __host__ T CreateRoundingFactor(T max_abs, int n) {
-  T delta = max_abs / (static_cast<T>(1.0) - 2 * n * std::numeric_limits<T>::epsilon());
-
-  // Calculate ceil(log_2(delta)).
-  // frexpf() calculates exp and returns `x` such that
-  // delta = x * 2^exp, where `x` in (-1.0, -0.5] U [0.5, 1).
-  // Because |x| < 1, exp is exactly ceil(log_2(delta)).
-  int exp;
-  std::frexp(delta, &exp);
-
-  // return M = 2 ^ ceil(log_2(delta))
-  return std::ldexp(static_cast<T>(1.0), exp);
-}
-
-namespace {
-struct Pair {
-  GradientPair first;
-  GradientPair second;
-};
-DEV_INLINE Pair operator+(Pair const& lhs, Pair const& rhs) {
-  return {lhs.first + rhs.first, lhs.second + rhs.second};
-}
-}  // anonymous namespace
-
-struct Clip : public thrust::unary_function<GradientPair, Pair> {
-  static DEV_INLINE float Pclip(float v) {
-    return v > 0 ? v : 0;
-  }
-  static DEV_INLINE float Nclip(float v) {
-    return v < 0 ? abs(v) : 0;
-  }
-
-  DEV_INLINE Pair operator()(GradientPair x) const {
-    auto pg = Pclip(x.GetGrad());
-    auto ph = Pclip(x.GetHess());
-
-    auto ng = Nclip(x.GetGrad());
-    auto nh = Nclip(x.GetHess());
-
-    return { GradientPair{ pg, ph }, GradientPair{ ng, nh } };
-  }
-};
-
-template <typename GradientSumT>
-GradientSumT CreateRoundingFactor(common::Span<GradientPair const> gpair) {
-  using T = typename GradientSumT::ValueT;
-  dh::XGBCachingDeviceAllocator<char> alloc;
-
-  thrust::device_ptr<GradientPair const> gpair_beg {gpair.data()};
-  thrust::device_ptr<GradientPair const> gpair_end {gpair.data() + gpair.size()};
-  auto beg = thrust::make_transform_iterator(gpair_beg, Clip());
-  auto end = thrust::make_transform_iterator(gpair_end, Clip());
-  Pair p = thrust::reduce(thrust::cuda::par(alloc), beg, end, Pair{});
-  GradientPair positive_sum {p.first}, negative_sum {p.second};
-
-  auto histogram_rounding = GradientSumT {
-    CreateRoundingFactor<T>(std::max(positive_sum.GetGrad(), negative_sum.GetGrad()),
-                            gpair.size()),
-    CreateRoundingFactor<T>(std::max(positive_sum.GetHess(), negative_sum.GetHess()),
-                            gpair.size()) };
-  return histogram_rounding;
-}
-
-template GradientPairPrecise CreateRoundingFactor(common::Span<GradientPair const> gpair);
-template GradientPair CreateRoundingFactor(common::Span<GradientPair const> gpair);
-
-template <typename GradientSumT>
-__global__ void SharedMemHistKernel(EllpackDeviceAccessor matrix,
-                                    common::Span<const RowPartitioner::RowIndexT> d_ridx,
-                                    GradientSumT* __restrict__ d_node_hist,
-                                    const GradientPair* __restrict__ d_gpair,
-                                    size_t n_elements,
-                                    GradientSumT const rounding,
-                                    bool use_shared_memory_histograms) {
-  using T = typename GradientSumT::ValueT;
-  extern __shared__ char smem[];
-  GradientSumT* smem_arr = reinterpret_cast<GradientSumT*>(smem);  // NOLINT
-  if (use_shared_memory_histograms) {
-    dh::BlockFill(smem_arr, matrix.NumBins(), GradientSumT());
-    __syncthreads();
-  }
-  for (auto idx : dh::GridStrideRange(static_cast<size_t>(0), n_elements)) {
-    int ridx = d_ridx[idx / matrix.row_stride];
-    int gidx =
-        matrix.gidx_iter[ridx * matrix.row_stride + idx % matrix.row_stride];
-    if (gidx != matrix.NumBins()) {
-      GradientSumT truncated {
-        TruncateWithRoundingFactor<T>(rounding.GetGrad(), d_gpair[ridx].GetGrad()),
-        TruncateWithRoundingFactor<T>(rounding.GetHess(), d_gpair[ridx].GetHess()),
-      };
-      // If we are not using shared memory, accumulate the values directly into
-      // global memory
-      GradientSumT* atomic_add_ptr =
-          use_shared_memory_histograms ? smem_arr : d_node_hist;
-      dh::AtomicAddGpair(atomic_add_ptr + gidx, truncated);
-    }
-  }
-
-  if (use_shared_memory_histograms) {
-    // Write shared memory back to global memory
-    __syncthreads();
-    for (auto i : dh::BlockStrideRange(static_cast<size_t>(0), matrix.NumBins())) {
-      GradientSumT truncated {
-        TruncateWithRoundingFactor<T>(rounding.GetGrad(), smem_arr[i].GetGrad()),
-        TruncateWithRoundingFactor<T>(rounding.GetHess(), smem_arr[i].GetHess()),
-      };
-      dh::AtomicAddGpair(d_node_hist + i, truncated);
-    }
-  }
-}
-
-template <typename GradientSumT>
-void BuildGradientHistogram(EllpackDeviceAccessor const& matrix,
-                            common::Span<GradientPair const> gpair,
-                            common::Span<const uint32_t> d_ridx,
-                            common::Span<GradientSumT> histogram,
-                            GradientSumT rounding) {
-  // decide whether to use shared memory
-  int device = 0;
-  dh::safe_cuda(cudaGetDevice(&device));
-  int max_shared_memory = dh::MaxSharedMemoryOptin(device);
-  size_t smem_size = sizeof(GradientSumT) * matrix.NumBins();
-  bool shared = smem_size <= max_shared_memory;
-  smem_size = shared ? smem_size : 0;
-
-  // opt into maximum shared memory for the kernel if necessary
-  auto kernel = SharedMemHistKernel<GradientSumT>;
-  if (shared) {
-    dh::safe_cuda(cudaFuncSetAttribute
-                  (kernel, cudaFuncAttributeMaxDynamicSharedMemorySize,
-                   max_shared_memory));
-  }
-
-  // determine the launch configuration
-  unsigned block_threads = shared ? 1024 : 256;
-  int n_mps = 0;
-  dh::safe_cuda(cudaDeviceGetAttribute(&n_mps, cudaDevAttrMultiProcessorCount, device));
-  int n_blocks_per_mp = 0;
-  dh::safe_cuda(cudaOccupancyMaxActiveBlocksPerMultiprocessor
-                (&n_blocks_per_mp, kernel, block_threads, smem_size));
-  unsigned grid_size = n_blocks_per_mp * n_mps;
-
-  auto n_elements = d_ridx.size() * matrix.row_stride;
-  dh::LaunchKernel {grid_size, block_threads, smem_size} (
-      kernel, matrix, d_ridx, histogram.data(), gpair.data(), n_elements,
-      rounding, shared);
-  dh::safe_cuda(cudaGetLastError());
-}
-
-template void BuildGradientHistogram<GradientPair>(
-    EllpackDeviceAccessor const& matrix,
-    common::Span<GradientPair const> gpair,
-    common::Span<const uint32_t> ridx,
-    common::Span<GradientPair> histogram,
-    GradientPair rounding);
-
-template void BuildGradientHistogram<GradientPairPrecise>(
-    EllpackDeviceAccessor const& matrix,
-    common::Span<GradientPair const> gpair,
-    common::Span<const uint32_t> ridx,
-    common::Span<GradientPairPrecise> histogram,
-    GradientPairPrecise rounding);
-
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/gpu_hist/histogram.cuh b/ml-xgboost/src/tree/gpu_hist/histogram.cuh
deleted file mode 100644
index d8673a8..0000000
--- a/ml-xgboost/src/tree/gpu_hist/histogram.cuh
+++ /dev/null
@@ -1,29 +0,0 @@
-/*!
- * Copyright 2020 by XGBoost Contributors
- */
-#ifndef HISTOGRAM_CUH_
-#define HISTOGRAM_CUH_
-#include <thrust/transform.h>
-#include "../../data/ellpack_page.cuh"
-
-namespace xgboost {
-namespace tree {
-
-template <typename GradientSumT>
-GradientSumT CreateRoundingFactor(common::Span<GradientPair const> gpair);
-
-template <typename T>
-DEV_INLINE T TruncateWithRoundingFactor(T const rounding_factor, float const x) {
-  return (rounding_factor + static_cast<T>(x)) - rounding_factor;
-}
-
-template <typename GradientSumT>
-void BuildGradientHistogram(EllpackDeviceAccessor const& matrix,
-                            common::Span<GradientPair const> gpair,
-                            common::Span<const uint32_t> ridx,
-                            common::Span<GradientSumT> histogram,
-                            GradientSumT rounding);
-}  // namespace tree
-}  // namespace xgboost
-
-#endif  // HISTOGRAM_CUH_
diff --git a/ml-xgboost/src/tree/gpu_hist/row_partitioner.cu b/ml-xgboost/src/tree/gpu_hist/row_partitioner.cu
deleted file mode 100644
index 25a7294..0000000
--- a/ml-xgboost/src/tree/gpu_hist/row_partitioner.cu
+++ /dev/null
@@ -1,188 +0,0 @@
-/*!
- * Copyright 2017-2019 XGBoost contributors
- */
-#include <thrust/iterator/discard_iterator.h>
-#include <thrust/iterator/transform_output_iterator.h>
-#include <thrust/sequence.h>
-#include <vector>
-#include "../../common/device_helpers.cuh"
-#include "row_partitioner.cuh"
-
-namespace xgboost {
-namespace tree {
-
-struct IndicateLeftTransform {
-  bst_node_t left_nidx;
-  explicit IndicateLeftTransform(bst_node_t left_nidx) : left_nidx(left_nidx) {}
-  __host__ __device__ __forceinline__ size_t
-  operator()(const bst_node_t& x) const {
-    return x == left_nidx ? 1 : 0;
-  }
-};
-
-struct IndexFlagTuple {
-  size_t idx;
-  size_t flag;
-};
-
-struct IndexFlagOp {
-  __device__ IndexFlagTuple operator()(const IndexFlagTuple& a,
-                                       const IndexFlagTuple& b) const {
-    return {b.idx, a.flag + b.flag};
-  }
-};
-
-struct WriteResultsFunctor {
-  bst_node_t left_nidx;
-  common::Span<bst_node_t> position_in;
-  common::Span<bst_node_t> position_out;
-  common::Span<RowPartitioner::RowIndexT> ridx_in;
-  common::Span<RowPartitioner::RowIndexT> ridx_out;
-  int64_t* d_left_count;
-
-  __device__ IndexFlagTuple operator()(const IndexFlagTuple& x) {
-    // the ex_scan_result represents how many rows have been assigned to left
-    // node so far during scan.
-    int scatter_address;
-    if (position_in[x.idx] == left_nidx) {
-      scatter_address = x.flag - 1;  // -1 because inclusive scan
-    } else {
-      // current number of rows belong to right node + total number of rows
-      // belong to left node
-      scatter_address = (x.idx - x.flag) + *d_left_count;
-    }
-    // copy the node id to output
-    position_out[scatter_address] = position_in[x.idx];
-    ridx_out[scatter_address] = ridx_in[x.idx];
-
-    // Discard
-    return {};
-  }
-};
-
-// Change the value type of thrust discard iterator so we can use it with cub
-class DiscardOverload : public thrust::discard_iterator<IndexFlagTuple> {
- public:
-  using value_type = IndexFlagTuple;  // NOLINT
-};
-
-// Implement partitioning via single scan operation using transform output to
-// write the result
-void RowPartitioner::SortPosition(common::Span<bst_node_t> position,
-                                  common::Span<bst_node_t> position_out,
-                                  common::Span<RowIndexT> ridx,
-                                  common::Span<RowIndexT> ridx_out,
-                                  bst_node_t left_nidx, bst_node_t right_nidx,
-                                  int64_t* d_left_count, cudaStream_t stream) {
-  WriteResultsFunctor write_results{left_nidx, position, position_out,
-                                    ridx,      ridx_out, d_left_count};
-  auto discard_write_iterator =
-      thrust::make_transform_output_iterator(DiscardOverload(), write_results);
-  auto counting = thrust::make_counting_iterator(0llu);
-  auto input_iterator = dh::MakeTransformIterator<IndexFlagTuple>(
-      counting, [=] __device__(size_t idx) {
-        return IndexFlagTuple{idx, position[idx] == left_nidx};
-      });
-  size_t temp_bytes = 0;
-  cub::DeviceScan::InclusiveScan(nullptr, temp_bytes, input_iterator,
-                                 discard_write_iterator, IndexFlagOp(),
-                                 position.size(), stream);
-  dh::TemporaryArray<int8_t> temp(temp_bytes);
-  cub::DeviceScan::InclusiveScan(temp.data().get(), temp_bytes, input_iterator,
-                                 discard_write_iterator, IndexFlagOp(),
-                                 position.size(), stream);
-}
-
-RowPartitioner::RowPartitioner(int device_idx, size_t num_rows)
-    : device_idx_(device_idx) {
-  dh::safe_cuda(cudaSetDevice(device_idx_));
-  ridx_a_.resize(num_rows);
-  ridx_b_.resize(num_rows);
-  position_a_.resize(num_rows);
-  position_b_.resize(num_rows);
-  ridx_ = dh::DoubleBuffer<RowIndexT>{&ridx_a_, &ridx_b_};
-  position_ = dh::DoubleBuffer<bst_node_t>{&position_a_, &position_b_};
-  ridx_segments_.emplace_back(Segment(0, num_rows));
-
-  thrust::sequence(
-      thrust::device_pointer_cast(ridx_.CurrentSpan().data()),
-      thrust::device_pointer_cast(ridx_.CurrentSpan().data() + ridx_.Size()));
-  thrust::fill(
-      thrust::device_pointer_cast(position_.Current()),
-      thrust::device_pointer_cast(position_.Current() + position_.Size()), 0);
-  left_counts_.resize(256);
-  thrust::fill(left_counts_.begin(), left_counts_.end(), 0);
-  streams_.resize(2);
-  for (auto& stream : streams_) {
-    dh::safe_cuda(cudaStreamCreate(&stream));
-  }
-}
-RowPartitioner::~RowPartitioner() {
-  dh::safe_cuda(cudaSetDevice(device_idx_));
-  for (auto& stream : streams_) {
-    dh::safe_cuda(cudaStreamDestroy(stream));
-  }
-}
-
-common::Span<const RowPartitioner::RowIndexT> RowPartitioner::GetRows(
-    bst_node_t nidx) {
-  auto segment = ridx_segments_.at(nidx);
-  // Return empty span here as a valid result
-  // Will error if we try to construct a span from a pointer with size 0
-  if (segment.Size() == 0) {
-    return common::Span<const RowPartitioner::RowIndexT>();
-  }
-  return ridx_.CurrentSpan().subspan(segment.begin, segment.Size());
-}
-
-common::Span<const RowPartitioner::RowIndexT> RowPartitioner::GetRows() {
-  return ridx_.CurrentSpan();
-}
-
-common::Span<const bst_node_t> RowPartitioner::GetPosition() {
-  return position_.CurrentSpan();
-}
-std::vector<RowPartitioner::RowIndexT> RowPartitioner::GetRowsHost(
-    bst_node_t nidx) {
-  auto span = GetRows(nidx);
-  std::vector<RowIndexT> rows(span.size());
-  dh::CopyDeviceSpanToVector(&rows, span);
-  return rows;
-}
-
-std::vector<bst_node_t> RowPartitioner::GetPositionHost() {
-  auto span = GetPosition();
-  std::vector<bst_node_t> position(span.size());
-  dh::CopyDeviceSpanToVector(&position, span);
-  return position;
-}
-
-void RowPartitioner::SortPositionAndCopy(const Segment& segment,
-                                         bst_node_t left_nidx,
-                                         bst_node_t right_nidx,
-                                         int64_t* d_left_count,
-                                         cudaStream_t stream) {
-  SortPosition(
-      // position_in
-      common::Span<bst_node_t>(position_.Current() + segment.begin,
-                               segment.Size()),
-      // position_out
-      common::Span<bst_node_t>(position_.Other() + segment.begin,
-                               segment.Size()),
-      // row index in
-      common::Span<RowIndexT>(ridx_.Current() + segment.begin, segment.Size()),
-      // row index out
-      common::Span<RowIndexT>(ridx_.Other() + segment.begin, segment.Size()),
-      left_nidx, right_nidx, d_left_count, stream);
-  // Copy back key/value
-  const auto d_position_current = position_.Current() + segment.begin;
-  const auto d_position_other = position_.Other() + segment.begin;
-  const auto d_ridx_current = ridx_.Current() + segment.begin;
-  const auto d_ridx_other = ridx_.Other() + segment.begin;
-  dh::LaunchN(device_idx_, segment.Size(), stream, [=] __device__(size_t idx) {
-    d_position_current[idx] = d_position_other[idx];
-    d_ridx_current[idx] = d_ridx_other[idx];
-  });
-}
-};  // namespace tree
-};  // namespace xgboost
diff --git a/ml-xgboost/src/tree/gpu_hist/row_partitioner.cuh b/ml-xgboost/src/tree/gpu_hist/row_partitioner.cuh
deleted file mode 100644
index 7a68616..0000000
--- a/ml-xgboost/src/tree/gpu_hist/row_partitioner.cuh
+++ /dev/null
@@ -1,203 +0,0 @@
-/*!
- * Copyright 2017-2019 XGBoost contributors
- */
-#pragma once
-#include "xgboost/base.h"
-#include "../../common/device_helpers.cuh"
-
-namespace xgboost {
-namespace tree {
-
-/*! \brief Count how many rows are assigned to left node. */
-__forceinline__ __device__ void AtomicIncrement(int64_t* d_count, bool increment) {
-#if __CUDACC_VER_MAJOR__ > 8
-  int mask = __activemask();
-  unsigned ballot = __ballot_sync(mask, increment);
-  int leader = __ffs(mask) - 1;
-  if (threadIdx.x % 32 == leader) {
-    atomicAdd(reinterpret_cast<unsigned long long*>(d_count),    // NOLINT
-              static_cast<unsigned long long>(__popc(ballot)));  // NOLINT
-  }
-#else
-  unsigned ballot = __ballot(increment);
-  if (threadIdx.x % 32 == 0) {
-    atomicAdd(reinterpret_cast<unsigned long long*>(d_count),    // NOLINT
-              static_cast<unsigned long long>(__popc(ballot)));  // NOLINT
-  }
-#endif
-}
-
-/** \brief Class responsible for tracking subsets of rows as we add splits and
- * partition training rows into different leaf nodes. */
-class RowPartitioner {
- public:
-  using RowIndexT = bst_uint;
-  struct Segment;
-  static constexpr bst_node_t kIgnoredTreePosition = -1;
-
- private:
-  int device_idx_;
-  /*! \brief In here if you want to find the rows belong to a node nid, first you need to
-   * get the indices segment from ridx_segments[nid], then get the row index that
-   * represents position of row in input data X.  `RowPartitioner::GetRows` would be a
-   * good starting place to get a sense what are these vector storing.
-   *
-   * node id -> segment -> indices of rows belonging to node
-   */
-  /*! \brief Range of row index for each node, pointers into ridx below. */
-  std::vector<Segment> ridx_segments_;
-  dh::caching_device_vector<RowIndexT> ridx_a_;
-  dh::caching_device_vector<RowIndexT> ridx_b_;
-  dh::caching_device_vector<bst_node_t> position_a_;
-  dh::caching_device_vector<bst_node_t> position_b_;
-  /*! \brief mapping for node id -> rows.
-   * This looks like:
-   * node id  |    1    |    2   |
-   * rows idx | 3, 5, 1 | 13, 31 |
-   */
-  dh::DoubleBuffer<RowIndexT> ridx_;
-  /*! \brief mapping for row -> node id. */
-  dh::DoubleBuffer<bst_node_t> position_;
-  dh::caching_device_vector<int64_t>
-      left_counts_;  // Useful to keep a bunch of zeroed memory for sort position
-  std::vector<cudaStream_t> streams_;
-
- public:
-  RowPartitioner(int device_idx, size_t num_rows);
-  ~RowPartitioner();
-  RowPartitioner(const RowPartitioner&) = delete;
-  RowPartitioner& operator=(const RowPartitioner&) = delete;
-
-  /**
-   * \brief Gets the row indices of training instances in a given node.
-   */
-  common::Span<const RowIndexT> GetRows(bst_node_t nidx);
-
-  /**
-   * \brief Gets all training rows in the set.
-   */
-  common::Span<const RowIndexT> GetRows();
-
-  /**
-   * \brief Gets the tree position of all training instances.
-   */
-  common::Span<const bst_node_t> GetPosition();
-
-  /**
-   * \brief Convenience method for testing
-   */
-  std::vector<RowIndexT> GetRowsHost(bst_node_t nidx);
-
-  /**
-   * \brief Convenience method for testing
-   */
-  std::vector<bst_node_t> GetPositionHost();
-
-  /**
-   * \brief Updates the tree position for set of training instances being split
-   * into left and right child nodes. Accepts a user-defined lambda specifying
-   * which branch each training instance should go down.
-   *
-   * \tparam  UpdatePositionOpT
-   * \param nidx        The index of the node being split.
-   * \param left_nidx   The left child index.
-   * \param right_nidx  The right child index.
-   * \param op          Device lambda. Should provide the row index as an
-   * argument and return the new position for this training instance.
-   */
-  template <typename UpdatePositionOpT>
-  void UpdatePosition(bst_node_t nidx, bst_node_t left_nidx,
-                      bst_node_t right_nidx, UpdatePositionOpT op) {
-    Segment segment = ridx_segments_.at(nidx);  // rows belongs to node nidx
-    auto d_ridx = ridx_.CurrentSpan();
-    auto d_position = position_.CurrentSpan();
-    if (left_counts_.size() <= nidx) {
-      left_counts_.resize((nidx * 2) + 1);
-      thrust::fill(left_counts_.begin(), left_counts_.end(), 0);
-    }
-    // Now we divide the row segment into left and right node.
-
-    int64_t* d_left_count = left_counts_.data().get() + nidx;
-    // Launch 1 thread for each row
-    dh::LaunchN<1, 128>(device_idx_, segment.Size(), [=] __device__(size_t idx) {
-      // LaunchN starts from zero, so we restore the row index by adding segment.begin
-      idx += segment.begin;
-      RowIndexT ridx = d_ridx[idx];
-      bst_node_t new_position = op(ridx);  // new node id
-      KERNEL_CHECK(new_position == left_nidx || new_position == right_nidx);
-      AtomicIncrement(d_left_count, new_position == left_nidx);
-      d_position[idx] = new_position;
-    });
-    // Overlap device to host memory copy (left_count) with sort
-    int64_t left_count;
-    dh::safe_cuda(cudaMemcpyAsync(&left_count, d_left_count, sizeof(int64_t),
-                                  cudaMemcpyDeviceToHost, streams_[0]));
-
-    SortPositionAndCopy(segment, left_nidx, right_nidx, d_left_count,
-                        streams_[1]);
-
-    dh::safe_cuda(cudaStreamSynchronize(streams_[0]));
-    CHECK_LE(left_count, segment.Size());
-    CHECK_GE(left_count, 0);
-    ridx_segments_.resize(std::max(static_cast<bst_node_t>(ridx_segments_.size()),
-                                   std::max(left_nidx, right_nidx) + 1));
-    ridx_segments_[left_nidx] =
-        Segment(segment.begin, segment.begin + left_count);
-    ridx_segments_[right_nidx] =
-        Segment(segment.begin + left_count, segment.end);
-  }
-
-  /**
-   * \brief Finalise the position of all training instances after tree
-   * construction is complete. Does not update any other meta information in
-   * this data structure, so should only be used at the end of training.
-   *
-   * \param op          Device lambda. Should provide the row index  and current
-   * position as an argument and return the new position for this training
-   * instance.
-   */
-  template <typename FinalisePositionOpT>
-  void FinalisePosition(FinalisePositionOpT op) {
-    auto d_position = position_.Current();
-    const auto d_ridx = ridx_.Current();
-    dh::LaunchN(device_idx_, position_.Size(), [=] __device__(size_t idx) {
-      auto position = d_position[idx];
-      RowIndexT ridx = d_ridx[idx];
-      bst_node_t new_position = op(ridx, position);
-      if (new_position == kIgnoredTreePosition) return;
-      d_position[idx] = new_position;
-    });
-  }
-
-  /**
-   * \brief Optimised routine for sorting key value pairs into left and right
-   * segments. Based on a single pass of exclusive scan, uses iterators to
-   * redirect inputs and outputs.
-   */
-  void SortPosition(common::Span<bst_node_t> position,
-                    common::Span<bst_node_t> position_out,
-                    common::Span<RowIndexT> ridx,
-                    common::Span<RowIndexT> ridx_out, bst_node_t left_nidx,
-                    bst_node_t right_nidx, int64_t* d_left_count,
-                    cudaStream_t stream = nullptr);
-
-  /*! \brief Sort row indices according to position. */
-  void SortPositionAndCopy(const Segment& segment, bst_node_t left_nidx,
-                           bst_node_t right_nidx, int64_t* d_left_count,
-                           cudaStream_t stream);
-  /** \brief Used to demarcate a contiguous set of row indices associated with
-   * some tree node. */
-  struct Segment {
-    size_t begin { 0 };
-    size_t end { 0 };
-
-    Segment() = default;
-
-    Segment(size_t begin, size_t end) : begin(begin), end(end) {
-      CHECK_GE(end, begin);
-    }
-    size_t Size() const { return end - begin; }
-  };
-};
-};  // namespace tree
-};  // namespace xgboost
diff --git a/ml-xgboost/src/tree/param.cc b/ml-xgboost/src/tree/param.cc
deleted file mode 100644
index 6f5080e..0000000
--- a/ml-xgboost/src/tree/param.cc
+++ /dev/null
@@ -1,110 +0,0 @@
-/*!
- * Copyright by Contributors 2019
- */
-#include <iostream>
-#include <vector>
-#include <utility>
-
-#include "xgboost/json.h"
-#include "param.h"
-
-namespace std {
-std::istream &operator>>(std::istream &is, std::vector<int> &t) {
-  t.clear();
-  // get (
-  while (true) {
-    char ch = is.peek();
-    if (isdigit(ch)) {
-      int idx;
-      if (is >> idx) {
-        t.emplace_back(idx);
-      }
-      return is;
-    }
-    is.get();
-    if (ch == '(') {
-      break;
-    }
-    if (!isspace(ch)) {
-      is.setstate(std::ios::failbit);
-      return is;
-    }
-  }
-  int idx;
-  std::vector<int> tmp;
-  while (true) {
-    char ch = is.peek();
-    if (isspace(ch)) {
-      is.get();
-    } else {
-      break;
-    }
-  }
-  if (is.peek() == ')') {
-    is.get();
-    return is;
-  }
-  while (is >> idx) {
-    tmp.push_back(idx);
-    char ch;
-    do {
-      ch = is.get();
-    } while (isspace(ch));
-    if (ch == 'L') {
-      ch = is.get();
-    }
-    if (ch == ',') {
-      while (true) {
-        ch = is.peek();
-        if (isspace(ch)) {
-          is.get();
-          continue;
-        }
-        if (ch == ')') {
-          is.get();
-          break;
-        }
-        break;
-      }
-      if (ch == ')') {
-        break;
-      }
-    } else if (ch == ')') {
-      break;
-    } else {
-      is.setstate(std::ios::failbit);
-      return is;
-    }
-  }
-  t = std::move(tmp);
-  return is;
-}
-}  // namespace std
-
-namespace xgboost {
-void ParseInteractionConstraint(
-    std::string const &constraint_str,
-    std::vector<std::vector<bst_feature_t>> *p_out) {
-  auto &out = *p_out;
-  auto j_inc = Json::Load({constraint_str.c_str(), constraint_str.size()});
-  auto const &all = get<Array>(j_inc);
-  out.resize(all.size());
-  for (size_t i = 0; i < all.size(); ++i) {
-    auto const &set = get<Array const>(all[i]);
-    for (auto const &v : set) {
-      if (XGBOOST_EXPECT(IsA<Integer>(v), true)) {
-        uint32_t u = static_cast<uint32_t const>(get<Integer const>(v));
-        out[i].emplace_back(u);
-      } else if (IsA<Number>(v)) {
-        double d = get<Number const>(v);
-        CHECK_EQ(std::floor(d), d)
-            << "Found floating point number in interaction constraints";
-        out[i].emplace_back(static_cast<uint32_t const>(d));
-      } else {
-        LOG(FATAL) << "Unknown value type for interaction constraint:"
-                   << v.GetValue().TypeStr();
-      }
-    }
-  }
-}
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/param.h b/ml-xgboost/src/tree/param.h
deleted file mode 100644
index 6df6a94..0000000
--- a/ml-xgboost/src/tree/param.h
+++ /dev/null
@@ -1,582 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2019 by Contributors
- * \file param.h
- * \brief training parameters, statistics used to support tree construction.
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_TREE_PARAM_H_
-#define XGBOOST_TREE_PARAM_H_
-
-#include <cmath>
-#include <cstring>
-#include <limits>
-#include <string>
-#include <vector>
-
-#include "xgboost/parameter.h"
-#include "xgboost/data.h"
-
-namespace xgboost {
-namespace tree {
-
-/*! \brief training parameters for regression tree */
-struct TrainParam : public XGBoostParameter<TrainParam> {
-  // learning step size for a time
-  float learning_rate;
-  // minimum loss change required for a split
-  float min_split_loss;
-  // minimum loss ratio relative to previous round to make split
-  float min_loss_ratio;
-  // maximum depth of a tree
-  int max_depth;
-  // maximum number of leaves
-  int max_leaves;
-  // if using histogram based algorithm, maximum number of bins per feature
-  int max_bin;
-  // growing policy
-  enum TreeGrowPolicy { kDepthWise = 0, kLossGuide = 1, kDepthWiseLossLtd = 2 };
-  int grow_policy;
-
-  //----- the rest parameters are less important ----
-  // minimum amount of hessian(weight) allowed in a child
-  float min_child_weight;
-  // L2 regularization factor
-  float reg_lambda;
-  // L1 regularization factor
-  float reg_alpha;
-  // default direction choice
-  int default_direction;
-  // maximum delta update we can add in weight estimation
-  // this parameter can be used to stabilize update
-  // default=0 means no constraint on weight delta
-  float max_delta_step;
-  // whether we want to do subsample 
-  // (for GOSS strategy this value also stands for subsample_upper)
-  float subsample;
-  // sampling method
-  enum SamplingMethod { kUniform = 0, kGradientBased = 1 };
-  int sampling_method;
-  // sampling strategy
-  int sampling_strategy;
-  int sampling_step;
-
-  bool enable_bbgen;
-  // subsample_lower - effective only for GOSS strategy
-  float subsample_lower;
-
-  // whether to subsample columns in each split (node)
-  float colsample_bynode;
-  // whether to subsample columns in each level
-  float colsample_bylevel;
-  // whether to subsample columns during tree construction
-  float colsample_bytree;
-  // accuracy of sketch
-  float sketch_eps;
-  // accuracy of sketch
-  float sketch_ratio;
-  // option to open cacheline optimization
-  bool cache_opt;
-  // whether refresh updater needs to update the leaf values
-  bool refresh_leaf;
-
-  // 2 (1/2), 3 (1/3), 4(1/4), 5 (1/5)
-  int random_split_denom;
-
-  std::vector<int> monotone_constraints;
-  // Stored as a JSON string.
-  std::string interaction_constraints;
-
-  // the criteria to use for ranking splits
-  std::string split_evaluator;
-
-  // ------ From cpu quantile histogram -------.
-  // percentage threshold for treating a feature as sparse
-  // e.g. 0.2 indicates a feature with fewer than 20% nonzeros is considered sparse
-  double sparse_threshold;
-  // use feature grouping? (default yes)
-  int enable_feature_grouping;
-  // when grouping features, how many "conflicts" to allow.
-  // conflict is when an instance has nonzero values for two or more features
-  // default is 0, meaning features should be strictly complementary
-  double max_conflict_rate;
-  // when grouping features, how much effort to expend to prevent singleton groups
-  // we'll try to insert each feature into existing groups before creating a new group
-  // for that feature; to save time, only up to (max_search_group) of existing groups
-  // will be considered. If set to zero, ALL existing groups will be examined
-  unsigned max_search_group;
-
-  // declare the parameters
-  DMLC_DECLARE_PARAMETER(TrainParam) {
-    DMLC_DECLARE_FIELD(learning_rate)
-        .set_lower_bound(0.0f)
-        .set_default(0.3f)
-        .describe("Learning rate(step size) of update.");
-    DMLC_DECLARE_FIELD(min_split_loss)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe(
-            "Minimum loss reduction required to make a further partition.");
-    DMLC_DECLARE_FIELD(min_loss_ratio)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe(
-            "Minimum loss ratio; 0 indicates no limit; a limit is for "
-            "depthwiselossltd policy");
-    DMLC_DECLARE_FIELD(max_depth)
-        .set_lower_bound(0)
-        .set_default(6)
-        .describe(
-            "Maximum depth of the tree; 0 indicates no limit; a limit is required "
-            "for depthwise policy");
-    DMLC_DECLARE_FIELD(max_leaves).set_lower_bound(0).set_default(0).describe(
-        "Maximum number of leaves; 0 indicates no limit.");
-    DMLC_DECLARE_FIELD(max_bin).set_lower_bound(2).set_default(256).describe(
-        "if using histogram-based algorithm, maximum number of bins per feature");
-    DMLC_DECLARE_FIELD(grow_policy)
-        .set_default(kDepthWise)
-        .add_enum("depthwise", kDepthWise)
-        .add_enum("lossguide", kLossGuide)
-        .add_enum("depthwiselossltd", kDepthWiseLossLtd)
-        .describe(
-            "Tree growing policy. 0: favor splitting at nodes closest to the node, "
-            "i.e. grow depth-wise. 1: favor splitting at nodes with highest loss "
-            "change. (cf. LightGBM). 2: favor splitting at nodes closest to the node, "
-            "stopping splitting nodes with low loss improvement");
-    DMLC_DECLARE_FIELD(min_child_weight)
-        .set_lower_bound(0.0f)
-        .set_default(1.0f)
-        .describe("Minimum sum of instance weight(hessian) needed in a child.");
-    DMLC_DECLARE_FIELD(reg_lambda)
-        .set_lower_bound(0.0f)
-        .set_default(1.0f)
-        .describe("L2 regularization on leaf weight");
-    DMLC_DECLARE_FIELD(reg_alpha)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe("L1 regularization on leaf weight");
-    DMLC_DECLARE_FIELD(default_direction)
-        .set_default(TrainParamKernel::learn)
-        .add_enum("learn", TrainParamKernel::learn)
-        .add_enum("left", TrainParamKernel::left)
-        .add_enum("right", TrainParamKernel::right)
-        .describe("Default direction choice when encountering a missing value");
-    DMLC_DECLARE_FIELD(max_delta_step)
-        .set_lower_bound(0.0f)
-        .set_default(0.0f)
-        .describe("Maximum delta step we allow each tree's weight estimate to be. "\
-                  "If the value is set to 0, it means there is no constraint");
-    DMLC_DECLARE_FIELD(subsample)
-        .set_range(0.0f, 1.0f)
-        .set_default(1.0f)
-        .describe("Row subsample ratio of training instance.");
-    DMLC_DECLARE_FIELD(sampling_method)
-        .set_default(kUniform)
-        .add_enum("uniform", kUniform)
-        .add_enum("gradient_based", kGradientBased)
-        .describe(
-            "Sampling method. 0: select random training instances uniformly. "
-            "1: select random training instances with higher probability when the "
-            "gradient and hessian are larger. (cf. CatBoost)");
-    DMLC_DECLARE_FIELD(colsample_bynode)
-        .set_range(0.0f, 1.0f)
-        .set_default(1.0f)
-        .describe("Subsample ratio of columns, resample on each node (split).");
-    DMLC_DECLARE_FIELD(colsample_bylevel)
-        .set_range(0.0f, 1.0f)
-        .set_default(1.0f)
-        .describe("Subsample ratio of columns, resample on each level.");
-    DMLC_DECLARE_FIELD(colsample_bytree)
-        .set_range(0.0f, 1.0f)
-        .set_default(1.0f)
-        .describe("Subsample ratio of columns, resample on each tree construction.");
-    DMLC_DECLARE_FIELD(sketch_eps)
-        .set_range(0.0f, 1.0f)
-        .set_default(0.03f)
-        .describe("EXP Param: Sketch accuracy of approximate algorithm.");
-    DMLC_DECLARE_FIELD(sketch_ratio)
-        .set_lower_bound(0.0f)
-        .set_default(2.0f)
-        .describe("EXP Param: Sketch accuracy related parameter of approximate algorithm.");
-    DMLC_DECLARE_FIELD(random_split_denom)
-        .set_lower_bound(1)
-        .set_default(1)
-        .describe("Ratio of split candidates that must be processed (1/r)");
-    DMLC_DECLARE_FIELD(cache_opt)
-        .set_default(true)
-        .describe("EXP Param: Cache aware optimization.");
-    DMLC_DECLARE_FIELD(enable_bbgen)
-        .set_default(false)
-        .describe("flag to enable batch Bernoulli generation");
-    DMLC_DECLARE_FIELD(sampling_strategy)
-        .set_default(TrainParamKernel::eachTree)
-        .add_enum("eachTree", TrainParamKernel::eachTree)
-        .add_enum("eachIteration", TrainParamKernel::eachIteration)
-        .add_enum("allIteration", TrainParamKernel::allIteration)
-        .add_enum("multiIteration", TrainParamKernel::multiIteration)
-        .add_enum("gossStyle", TrainParamKernel::gossStyle)
-        .describe("eachTree       - new sampling for each tree for each iteration,"
-                  "eachIteration  - new sampling each iteration,"
-                  "allIteration   - single sampling for all trees for all iterations,"
-                  "multiIteration - change sampling per sampling_step iterations,"
-                  "gossStyle      - GOSS-like strategy.");
-    DMLC_DECLARE_FIELD(sampling_step)
-        .set_default(1)
-        .describe("Sampling step for sampling_strategies - 3,4");
-    DMLC_DECLARE_FIELD(subsample_lower)
-        .set_lower_bound(0.0f)
-        .set_default(0.2f)
-        .describe("Portion of lower gradients");
-    DMLC_DECLARE_FIELD(refresh_leaf)
-        .set_default(true)
-        .describe("Whether the refresh updater needs to update leaf values.");
-    DMLC_DECLARE_FIELD(monotone_constraints)
-        .set_default(std::vector<int>())
-        .describe("Constraint of variable monotonicity");
-    DMLC_DECLARE_FIELD(interaction_constraints)
-        .set_default("")
-        .describe("Constraints for interaction representing permitted interactions."
-                  "The constraints must be specified in the form of a nest list,"
-                  "e.g. [[0, 1], [2, 3, 4]], where each inner list is a group of"
-                  "indices of features that are allowed to interact with each other."
-                  "See tutorial for more information");
-    DMLC_DECLARE_FIELD(split_evaluator)
-        .set_default("elastic_net,monotonic")
-        .describe("The criteria to use for ranking splits");
-
-    // ------ From cpu quantile histogram -------.
-    DMLC_DECLARE_FIELD(sparse_threshold).set_range(0, 1.0).set_default(0.2)
-        .describe("percentage threshold for treating a feature as sparse");
-    DMLC_DECLARE_FIELD(enable_feature_grouping).set_lower_bound(0).set_default(0)
-        .describe("if >0, enable feature grouping to ameliorate work imbalance "
-                  "among worker threads");
-    DMLC_DECLARE_FIELD(max_conflict_rate).set_range(0, 1.0).set_default(0)
-        .describe("when grouping features, how many \"conflicts\" to allow."
-       "conflict is when an instance has nonzero values for two or more features."
-       "default is 0, meaning features should be strictly complementary.");
-    DMLC_DECLARE_FIELD(max_search_group).set_lower_bound(0).set_default(100)
-        .describe("when grouping features, how much effort to expend to prevent "
-                  "singleton groups. We'll try to insert each feature into existing "
-                  "groups before creating a new group for that feature; to save time, "
-                  "only up to (max_search_group) of existing groups will be "
-                  "considered. If set to zero, ALL existing groups will be examined.");
-
-    // add alias of parameters
-    DMLC_DECLARE_ALIAS(reg_lambda, lambda);
-    DMLC_DECLARE_ALIAS(reg_alpha, alpha);
-    DMLC_DECLARE_ALIAS(min_split_loss, gamma);
-    DMLC_DECLARE_ALIAS(learning_rate, eta);
-  }
-
-  /*! \brief given the loss change, whether we need to invoke pruning */
-  bool NeedPrune(double loss_chg, int depth) const {
-    return loss_chg < this->min_split_loss ||
-           (this->max_depth != 0 && depth > this->max_depth);
-  }
-  /*! \brief maximum sketch size */
-  inline unsigned MaxSketchSize() const {
-    auto ret = static_cast<unsigned>(sketch_ratio / sketch_eps);
-    CHECK_GT(ret, 0U);
-    return ret;
-  }
-
-  bst_node_t MaxNodes() const {
-    if (this->max_depth == 0 && this->max_leaves == 0) {
-      LOG(FATAL) << "Max leaves and max depth cannot both be unconstrained.";
-    }
-    bst_node_t n_nodes{0};
-    if (this->max_leaves > 0) {
-      n_nodes = this->max_leaves * 2 - 1;
-    } else {
-      n_nodes = (1 << (this->max_depth + 1)) - 1;
-    }
-    CHECK_NE(n_nodes, 0);
-    return n_nodes;
-  }
-};
-
-/*! \brief Loss functions */
-
-// functions for L1 cost
-template <typename T1, typename T2>
-XGBOOST_DEVICE inline static T1 ThresholdL1(T1 w, T2 alpha) {
-  if (w > + alpha) {
-    return w - alpha;
-  }
-  if (w < - alpha) {
-    return w + alpha;
-  }
-  return 0.0;
-}
-
-template <typename T>
-XGBOOST_DEVICE inline static T Sqr(T a) { return a * a; }
-
-// calculate the cost of loss function
-template <typename TrainingParams, typename T>
-XGBOOST_DEVICE inline T CalcGainGivenWeight(const TrainingParams &p,
-                                            T sum_grad, T sum_hess, T w) {
-  return -(T(2.0) * sum_grad * w + (sum_hess + p.reg_lambda) * Sqr(w));
-}
-
-// calculate the cost of loss function
-template <typename TrainingParams, typename T>
-XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
-  if (sum_hess < p.min_child_weight) {
-    return T(0.0);
-  }
-  if (p.max_delta_step == 0.0f) {
-    if (p.reg_alpha == 0.0f) {
-      return Sqr(sum_grad) / (sum_hess + p.reg_lambda);
-    } else {
-      return Sqr(ThresholdL1(sum_grad, p.reg_alpha)) /
-          (sum_hess + p.reg_lambda);
-    }
-  } else {
-    T w = CalcWeight(p, sum_grad, sum_hess);
-    T ret = CalcGainGivenWeight(p, sum_grad, sum_hess, w);
-    if (p.reg_alpha == 0.0f) {
-      return ret;
-    } else {
-      return ret + p.reg_alpha * std::abs(w);
-    }
-  }
-}
-
-template <typename TrainingParams,
-          typename StatT, typename T = decltype(StatT().GetHess())>
-XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, StatT stat) {
-  return CalcGain(p, stat.GetGrad(), stat.GetHess());
-}
-
-// calculate weight given the statistics
-template <typename TrainingParams, typename T>
-XGBOOST_DEVICE inline T CalcWeight(const TrainingParams &p, T sum_grad,
-                                   T sum_hess) {
-  if (sum_hess < p.min_child_weight || sum_hess <= 0.0) {
-    return 0.0;
-  }
-  T dw;
-  if (p.reg_alpha == 0.0f) {
-    dw = -sum_grad / (sum_hess + p.reg_lambda);
-  } else {
-    dw = -ThresholdL1(sum_grad, p.reg_alpha) / (sum_hess + p.reg_lambda);
-  }
-  if (p.max_delta_step != 0.0f) {
-    if (dw > p.max_delta_step) {
-      dw = p.max_delta_step;
-    }
-    if (dw < -p.max_delta_step) {
-      dw = -p.max_delta_step;
-    }
-  }
-  return dw;
-}
-
-// Used in gpu code where GradientPair is used for gradient sum, not GradStats.
-template <typename TrainingParams, typename GpairT>
-XGBOOST_DEVICE inline float CalcWeight(const TrainingParams &p, GpairT sum_grad) {
-  return CalcWeight(p, sum_grad.GetGrad(), sum_grad.GetHess());
-}
-
-/*! \brief core statistics used for tree construction */
-struct XGBOOST_ALIGNAS(16) GradStats {
-  /*! \brief sum gradient statistics */
-  double sum_grad { 0 };
-  /*! \brief sum hessian statistics */
-  double sum_hess { 0 };
-
- public:
-  XGBOOST_DEVICE double GetGrad() const { return sum_grad; }
-  XGBOOST_DEVICE double GetHess() const { return sum_hess; }
-
-  friend std::ostream& operator<<(std::ostream& os, GradStats s) {
-    os << s.GetGrad() << "/" << s.GetHess();
-    return os;
-  }
-
-  XGBOOST_DEVICE GradStats() {
-    static_assert(sizeof(GradStats) == 16,
-                  "Size of GradStats is not 16 bytes.");
-  }
-
-  template <typename GpairT>
-  XGBOOST_DEVICE explicit GradStats(const GpairT &sum)
-      : sum_grad(sum.GetGrad()), sum_hess(sum.GetHess()) {}
-  explicit GradStats(const double grad, const double hess)
-      : sum_grad(grad), sum_hess(hess) {}
-  /*!
-   * \brief accumulate statistics
-   * \param p the gradient pair
-   */
-  inline void Add(GradientPair p) { this->Add(p.GetGrad(), p.GetHess()); }
-
-  /*! \brief add statistics to the data */
-  inline void Add(const GradStats& b) {
-    sum_grad += b.sum_grad;
-    sum_hess += b.sum_hess;
-  }
-  /*! \brief same as add, reduce is used in All Reduce */
-  inline static void Reduce(GradStats& a, const GradStats& b) { // NOLINT(*)
-    a.Add(b);
-  }
-  /*! \brief set current value to a - b */
-  inline void SetSubstract(const GradStats& a, const GradStats& b) {
-    sum_grad = a.sum_grad - b.sum_grad;
-    sum_hess = a.sum_hess - b.sum_hess;
-  }
-  /*! \return whether the statistics is not used yet */
-  inline bool Empty() const { return sum_hess == 0.0; }
-  /*! \brief add statistics to the data */
-  inline void Add(double grad, double hess) {
-    sum_grad += grad;
-    sum_hess += hess;
-  }
-};
-
-/*!
- * \brief statistics that is helpful to store
- *   and represent a split solution for the tree
- */
-template<typename GradientT>
-struct SplitEntryContainer {
-  /*! \brief loss change after split this node */
-  bst_float loss_chg {0.0f};
-  /*! \brief split index */
-  bst_feature_t sindex{0};
-  bst_float split_value{0.0f};
-
-  GradientT left_sum;
-  GradientT right_sum;
-
-  SplitEntryContainer() = default;
-
-  friend std::ostream& operator<<(std::ostream& os, SplitEntryContainer const& s) {
-    os << "loss_chg: " << s.loss_chg << ", "
-       << "split index: " << s.SplitIndex() << ", "
-       << "split value: " << s.split_value << ", "
-       << "left_sum: " << s.left_sum << ", "
-       << "right_sum: " << s.right_sum;
-    return os;
-  }
-  /*!\return feature index to split on */
-  bst_feature_t SplitIndex() const { return sindex & ((1U << 31) - 1U); }
-  /*!\return whether missing value goes to left branch */
-  bool DefaultLeft() const { return (sindex >> 31) != 0; }
-  /*!
-   * \brief decides whether we can replace current entry with the given statistics
-   *
-   *   This function gives better priority to lower index when loss_chg == new_loss_chg.
-   *   Not the best way, but helps to give consistent result during multi-thread
-   *   execution.
-   *
-   * \param new_loss_chg the loss reduction get through the split
-   * \param split_index the feature index where the split is on
-   */
-  bool NeedReplace(bst_float new_loss_chg, unsigned split_index) const {
-    if (std::isinf(new_loss_chg) || std::isnan(new_loss_chg)) {
-      // in some cases new_loss_chg can be NaN or Inf, for example when lambda = 0 & min_child_weight = 0
-      // skip value in this case
-      return false;
-    } else if (this->SplitIndex() <= split_index) {
-      return new_loss_chg > this->loss_chg;
-    } else {
-      return !(this->loss_chg > new_loss_chg);
-    }
-  }
-  /*!
-   * \brief update the split entry, replace it if e is better
-   * \param e candidate split solution
-   * \return whether the proposed split is better and can replace current split
-   */
-  inline bool Update(const SplitEntryContainer &e) {
-    if (this->NeedReplace(e.loss_chg, e.SplitIndex())) {
-      this->loss_chg = e.loss_chg;
-      this->sindex = e.sindex;
-      this->split_value = e.split_value;
-      this->left_sum = e.left_sum;
-      this->right_sum = e.right_sum;
-      return true;
-    } else {
-      return false;
-    }
-  }
-  /*!
-   * \brief update the split entry, replace it if e is better
-   * \param new_loss_chg loss reduction of new candidate
-   * \param split_index feature index to split on
-   * \param new_split_value the split point
-   * \param default_left whether the missing value goes to left
-   * \return whether the proposed split is better and can replace current split
-   */
-  bool Update(bst_float new_loss_chg, unsigned split_index,
-              bst_float new_split_value, bool default_left,
-              const GradientT &left_sum,
-              const GradientT &right_sum) {
-    if (this->NeedReplace(new_loss_chg, split_index)) {
-      this->loss_chg = new_loss_chg;
-      if (default_left) {
-        split_index |= (1U << 31);
-      }
-      this->sindex = split_index;
-      this->split_value = new_split_value;
-      this->left_sum = left_sum;
-      this->right_sum = right_sum;
-      return true;
-    } else {
-      return false;
-    }
-  }
-
-  /*! \brief same as update, used by AllReduce*/
-  inline static void Reduce(SplitEntryContainer &dst,         // NOLINT(*)
-                            const SplitEntryContainer &src) { // NOLINT(*)
-    dst.Update(src);
-  }
-};
-
-using SplitEntry = SplitEntryContainer<GradStats>;
-}  // namespace tree
-
-/*
- * \brief Parse the interaction constraints from string.
- * \param constraint_str String storing the interfaction constraints:
- *
- *  Example input string:
- *
- *    "[[1, 2], [3, 4]]""
- *
- * \param p_out Pointer to output
- */
-void ParseInteractionConstraint(
-    std::string const &constraint_str,
-    std::vector<std::vector<xgboost::bst_feature_t>> *p_out);
-}  // namespace xgboost
-
-// define string serializer for vector, to get the arguments
-namespace std {
-inline std::ostream &operator<<(std::ostream &os, const std::vector<int> &t) {
-  os << '(';
-  for (auto it = t.begin(); it != t.end(); ++it) {
-    if (it != t.begin()) {
-      os << ',';
-    }
-    os << *it;
-  }
-  // python style tuple
-  if (t.size() == 1) {
-    os << ',';
-  }
-  os << ')';
-  return os;
-}
-
-std::istream &operator>>(std::istream &is, std::vector<int> &t);
-}  // namespace std
-
-#endif  // XGBOOST_TREE_PARAM_H_
diff --git a/ml-xgboost/src/tree/split_evaluator.cc b/ml-xgboost/src/tree/split_evaluator.cc
deleted file mode 100644
index be16615..0000000
--- a/ml-xgboost/src/tree/split_evaluator.cc
+++ /dev/null
@@ -1,279 +0,0 @@
-/*!
- * Copyright 2018 by Contributors
- * \file split_evaluator.cc
- * \brief Contains implementations of different split evaluators.
- */
-#include <dmlc/json.h>
-#include <dmlc/registry.h>
-
-#include <algorithm>
-#include <unordered_set>
-#include <vector>
-#include <limits>
-#include <memory>
-#include <string>
-#include <sstream>
-#include <utility>
-
-#include "xgboost/logging.h"
-#include "xgboost/parameter.h"
-
-#include "param.h"
-#include "split_evaluator.h"
-#include "../common/common.h"
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::tree::SplitEvaluatorReg);
-}  // namespace dmlc
-
-namespace xgboost {
-namespace tree {
-
-SplitEvaluator* SplitEvaluator::Create(const std::string& name) {
-  std::stringstream ss(name);
-  std::string item;
-  SplitEvaluator* eval = nullptr;
-  // Construct a chain of SplitEvaluators. This allows one to specify multiple constraints.
-  while (std::getline(ss, item, ',')) {
-    auto* e = ::dmlc::Registry< ::xgboost::tree::SplitEvaluatorReg>
-        ::Get()->Find(item);
-    if (e == nullptr) {
-      LOG(FATAL) << "Unknown SplitEvaluator " << name;
-    }
-    eval = (e->body)(std::unique_ptr<SplitEvaluator>(eval));
-  }
-  return eval;
-}
-
-// Default implementations of some virtual methods that aren't always needed
-void SplitEvaluator::Init(const TrainParam* param) {}
-void SplitEvaluator::Reset() {}
-void SplitEvaluator::AddSplit(bst_uint nodeid,
-                              bst_uint leftid,
-                              bst_uint rightid,
-                              bst_uint featureid,
-                              bst_float leftweight,
-                              bst_float rightweight) {}
-
-bst_float SplitEvaluator::ComputeSplitScore(bst_uint nodeid,
-                                            bst_uint featureid,
-                                            const GradStats& left_stats,
-                                            const GradStats& right_stats) const {
-  bst_float left_weight = ComputeWeight(nodeid, left_stats);
-  bst_float right_weight = ComputeWeight(nodeid, right_stats);
-  return ComputeSplitScore(nodeid, featureid, left_stats, right_stats, left_weight, right_weight);
-}
-
-/*! \brief Applies an elastic net penalty and per-leaf penalty. */
-class ElasticNet final : public SplitEvaluator {
- public:
-  explicit ElasticNet(std::unique_ptr<SplitEvaluator> inner) {
-    if (inner) {
-      LOG(FATAL) << "ElasticNet does not accept an inner SplitEvaluator";
-    }
-  }
-  void Init(const TrainParam* param) override {
-    params_ = param;
-  }
-
-  SplitEvaluator* GetHostClone() const override {
-    auto r = new ElasticNet(nullptr);
-    r->params_ = this->params_;
-    CHECK(r->params_);
-
-    return r;
-  }
-
-  bst_float ComputeSplitScore(bst_uint nodeid,
-                             bst_uint featureid,
-                             const GradStats& left_stats,
-                             const GradStats& right_stats,
-                             bst_float left_weight,
-                             bst_float right_weight) const override {
-    return ComputeScore(nodeid, left_stats, left_weight) +
-      ComputeScore(nodeid, right_stats, right_weight);
-  }
-
-  bst_float ComputeSplitScore(bst_uint nodeid,
-                             bst_uint featureid,
-                             const GradStats& left_stats,
-                             const GradStats& right_stats) const override {
-    return ComputeScore(nodeid, left_stats) + ComputeScore(nodeid, right_stats);
-  }
-
-  bst_float ComputeScore(bst_uint parentID, const GradStats &stats, bst_float weight)
-      const override {
-    auto loss = weight * (2.0 * stats.sum_grad + stats.sum_hess * weight
-        + params_->reg_lambda * weight)
-        + 2.0 * params_->reg_alpha * std::abs(weight);
-    return -loss;
-  }
-
-  bst_float ComputeScore(bst_uint parentID, const GradStats &stats) const {
-    if (params_->max_delta_step == 0.0f) {
-      return Sqr(ThresholdL1(stats.sum_grad)) / (stats.sum_hess + params_->reg_lambda);
-    } else {
-      return ComputeScore(parentID, stats, ComputeWeight(parentID, stats));
-    }
-  }
-
-  bst_float ComputeWeight(bst_uint parentID, const GradStats& stats)
-      const override {
-    bst_float w = -ThresholdL1(stats.sum_grad) / (stats.sum_hess + params_->reg_lambda);
-    if (params_->max_delta_step != 0.0f && std::abs(w) > params_->max_delta_step) {
-      w = std::copysign(params_->max_delta_step, w);
-    }
-    return w;
-  }
-
- private:
-  TrainParam const* params_;
-
-  inline double ThresholdL1(double g) const {
-    if (g > params_->reg_alpha) {
-      return g - params_->reg_alpha;
-    } else if (g < -params_->reg_alpha) {
-      return g + params_->reg_alpha;
-    } else {
-      return 0.0;
-    }
-  }
-};
-
-XGBOOST_REGISTER_SPLIT_EVALUATOR(ElasticNet, "elastic_net")
-.describe("Use an elastic net regulariser")
-.set_body([](std::unique_ptr<SplitEvaluator> inner) {
-    return new ElasticNet(std::move(inner));
-  });
-
-/*! \brief Enforces that the tree is monotonically increasing/decreasing with respect to a user specified set of
-      features.
-*/
-class MonotonicConstraint final : public SplitEvaluator {
- public:
-  explicit MonotonicConstraint(std::unique_ptr<SplitEvaluator> inner) {
-    if (!inner) {
-      LOG(FATAL) << "MonotonicConstraint must be given an inner evaluator";
-    }
-    inner_ = std::move(inner);
-  }
-
-  void Init(const TrainParam* param) override {
-    inner_->Init(param);
-    params_ = param;
-    Reset();
-  }
-
-  void Reset() override {
-    lower_.resize(1, -std::numeric_limits<bst_float>::max());
-    upper_.resize(1, std::numeric_limits<bst_float>::max());
-  }
-
-  SplitEvaluator* GetHostClone() const override {
-    if (params_->monotone_constraints.size() == 0) {
-      // No monotone constraints specified, just return a clone of inner to speed things up
-      return inner_->GetHostClone();
-    } else {
-      auto c = new MonotonicConstraint(
-        std::unique_ptr<SplitEvaluator>(inner_->GetHostClone()));
-      c->params_ = this->params_;
-      CHECK(c->params_);
-      c->Reset();
-      return c;
-    }
-  }
-
-  bst_float ComputeSplitScore(bst_uint nodeid,
-                             bst_uint featureid,
-                             const GradStats& left_stats,
-                             const GradStats& right_stats,
-                             bst_float left_weight,
-                             bst_float right_weight) const override {
-    bst_float infinity = std::numeric_limits<bst_float>::infinity();
-    bst_int constraint = GetConstraint(featureid);
-    bst_float score = inner_->ComputeSplitScore(
-      nodeid, featureid, left_stats, right_stats, left_weight, right_weight);
-
-    if (constraint == 0) {
-      return score;
-    } else if (constraint > 0) {
-      return left_weight <= right_weight ? score : -infinity;
-    } else {
-      return left_weight >= right_weight ? score : -infinity;
-    }
-  }
-
-  bst_float ComputeScore(bst_uint parentID, const GradStats& stats, bst_float weight)
-      const override {
-    return inner_->ComputeScore(parentID, stats, weight);
-  }
-
-  bst_float ComputeWeight(bst_uint parentID, const GradStats& stats)
-      const override {
-    bst_float weight = inner_->ComputeWeight(parentID, stats);
-
-    if (parentID == ROOT_PARENT_ID) {
-      // This is the root node
-      return weight;
-    } else if (weight < lower_.at(parentID)) {
-      return lower_.at(parentID);
-    } else if (weight > upper_.at(parentID)) {
-      return upper_.at(parentID);
-    } else {
-      return weight;
-    }
-  }
-
-  void AddSplit(bst_uint nodeid,
-                bst_uint leftid,
-                bst_uint rightid,
-                bst_uint featureid,
-                bst_float leftweight,
-                bst_float rightweight) override {
-    inner_->AddSplit(nodeid, leftid, rightid, featureid, leftweight, rightweight);
-    bst_uint newsize = std::max(leftid, rightid) + 1;
-    lower_.resize(newsize);
-    upper_.resize(newsize);
-    bst_int constraint = GetConstraint(featureid);
-
-    bst_float mid = (leftweight + rightweight) / 2;
-    CHECK(!std::isnan(mid));
-    CHECK(nodeid < upper_.size());
-
-    upper_[leftid] = upper_.at(nodeid);
-    upper_[rightid] = upper_.at(nodeid);
-    lower_[leftid] = lower_.at(nodeid);
-    lower_[rightid] = lower_.at(nodeid);
-
-    if (constraint < 0) {
-      lower_[leftid] = mid;
-      upper_[rightid] = mid;
-    } else if (constraint > 0) {
-      upper_[leftid] = mid;
-      lower_[rightid] = mid;
-    }
-  }
-
- private:
-  TrainParam const* params_;
-  std::unique_ptr<SplitEvaluator> inner_;
-  std::vector<bst_float> lower_;
-  std::vector<bst_float> upper_;
-
-  inline bst_int GetConstraint(bst_uint featureid) const {
-    if (featureid < params_->monotone_constraints.size()) {
-      return params_->monotone_constraints[featureid];
-    } else {
-      return 0;
-    }
-  }
-};
-
-XGBOOST_REGISTER_SPLIT_EVALUATOR(MonotonicConstraint, "monotonic")
-.describe("Enforces that the tree is monotonically increasing/decreasing "
-    "w.r.t. specified features")
-.set_body([](std::unique_ptr<SplitEvaluator> inner) {
-    return new MonotonicConstraint(std::move(inner));
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/split_evaluator.h b/ml-xgboost/src/tree/split_evaluator.h
deleted file mode 100644
index 2b38764..0000000
--- a/ml-xgboost/src/tree/split_evaluator.h
+++ /dev/null
@@ -1,99 +0,0 @@
-/*!
- * Copyright 2018 by Contributors
- * \file split_evaluator.h
- * \brief Used for implementing a loss term specific to decision trees. Useful for custom regularisation.
- * \author Henry Gouk
- */
-
-#ifndef XGBOOST_TREE_SPLIT_EVALUATOR_H_
-#define XGBOOST_TREE_SPLIT_EVALUATOR_H_
-
-#include <dmlc/registry.h>
-#include <xgboost/base.h>
-#include <functional>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "param.h"
-
-#define ROOT_PARENT_ID (-1 & ((1U << 31) - 1))
-
-namespace xgboost {
-namespace tree {
-
-// Should GradStats be in this header, rather than param.h?
-struct GradStats;
-
-class SplitEvaluator {
- public:
-  // Factory method for constructing new SplitEvaluators
-  static SplitEvaluator* Create(const std::string& name);
-
-  virtual ~SplitEvaluator() = default;
-
-  // Used to initialise any regularisation hyperparameters provided by the user
-  virtual void Init(const TrainParam* param);
-
-  // Resets the SplitEvaluator to the state it was in after the Init was called
-  virtual void Reset();
-
-  // This will create a clone of the SplitEvaluator in host memory
-  virtual SplitEvaluator* GetHostClone() const = 0;
-
-  // Computes the score (negative loss) resulting from performing this split
-  virtual bst_float ComputeSplitScore(bst_uint nodeid,
-                                      bst_uint featureid,
-                                      const GradStats& left_stats,
-                                      const GradStats& right_stats,
-                                      bst_float left_weight,
-                                      bst_float right_weight) const = 0;
-
-  virtual bst_float ComputeSplitScore(bst_uint nodeid,
-                                      bst_uint featureid,
-                                      const GradStats& left_stats,
-                                      const GradStats& right_stats) const;
-
-  // Compute the Score for a node with the given stats
-  virtual bst_float ComputeScore(bst_uint parentid,
-                                const GradStats &stats,
-                                bst_float weight) const = 0;
-
-  // Compute the weight for a node with the given stats
-  virtual bst_float ComputeWeight(bst_uint parentid, const GradStats& stats)
-      const = 0;
-
-  virtual void AddSplit(bst_uint nodeid,
-                        bst_uint leftid,
-                        bst_uint rightid,
-                        bst_uint featureid,
-                        bst_float leftweight,
-                        bst_float rightweight);
-};
-
-struct SplitEvaluatorReg
-    : public dmlc::FunctionRegEntryBase<SplitEvaluatorReg,
-        std::function<SplitEvaluator* (std::unique_ptr<SplitEvaluator>)> > {};
-
-/*!
- * \brief Macro to register tree split evaluator.
- *
- * \code
- * // example of registering a split evaluator
- * XGBOOST_REGISTER_SPLIT_EVALUATOR(SplitEval, "splitEval")
- * .describe("Some split evaluator")
- * .set_body([]() {
- *     return new SplitEval();
- *   });
- * \endcode
- */
-#define XGBOOST_REGISTER_SPLIT_EVALUATOR(UniqueID, Name) \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::tree::SplitEvaluatorReg& \
-  __make_ ## SplitEvaluatorReg ## _ ## UniqueID ## __ = \
-      ::dmlc::Registry< ::xgboost::tree::SplitEvaluatorReg>::Get()->__REGISTER__(Name)  //NOLINT
-
-}  // namespace tree
-}  // namespace xgboost
-
-#endif  // XGBOOST_TREE_SPLIT_EVALUATOR_H_
diff --git a/ml-xgboost/src/tree/tree_model.cc b/ml-xgboost/src/tree/tree_model.cc
deleted file mode 100644
index 8f45621..0000000
--- a/ml-xgboost/src/tree/tree_model.cc
+++ /dev/null
@@ -1,1045 +0,0 @@
-/*!
- * Copyright 2015-2019 by Contributors
- * \file tree_model.cc
- * \brief model structure for tree
- */
-#include <dmlc/registry.h>
-#include <dmlc/json.h>
-
-#include <xgboost/tree_model.h>
-#include <xgboost/logging.h>
-#include <xgboost/json.h>
-
-#include <sstream>
-#include <limits>
-#include <cmath>
-#include <iomanip>
-#include <stack>
-
-#include "param.h"
-#include "../common/common.h"
-
-namespace xgboost {
-// register tree parameter
-DMLC_REGISTER_PARAMETER(TreeParam);
-
-namespace tree {
-DMLC_REGISTER_PARAMETER(TrainParam);
-}
-
-/*!
- * \brief Base class for dump model implementation, modeling closely after code generator.
- */
-class TreeGenerator {
- protected:
-  static int32_t constexpr kFloatMaxPrecision =
-      std::numeric_limits<bst_float>::max_digits10;
-  FeatureMap const& fmap_;
-  std::stringstream ss_;
-  bool const with_stats_;
-
-  template <typename Float>
-  static std::string ToStr(Float value) {
-    static_assert(std::is_floating_point<Float>::value,
-                  "Use std::to_string instead for non-floating point values.");
-    std::stringstream ss;
-    ss << std::setprecision(kFloatMaxPrecision) << value;
-    return ss.str();
-  }
-
-  static std::string Tabs(uint32_t n) {
-    std::string res;
-    for (uint32_t i = 0; i < n; ++i) {
-      res += '\t';
-    }
-    return res;
-  }
-  /* \brief Find the first occurance of key in input and replace it with corresponding
-   *        value.
-   */
-  static std::string Match(std::string const& input,
-                           std::map<std::string, std::string> const& replacements) {
-    std::string result = input;
-    for (auto const& kv : replacements) {
-      auto pos = result.find(kv.first);
-      CHECK_NE(pos, std::string::npos);
-      result.replace(pos, kv.first.length(), kv.second);
-    }
-    return result;
-  }
-
-  virtual std::string Indicator(RegTree const& tree, int32_t nid, uint32_t depth) const {
-    return "";
-  }
-  virtual std::string Integer(RegTree const& tree, int32_t nid, uint32_t depth) const {
-    return "";
-  }
-  virtual std::string Quantitive(RegTree const& tree, int32_t nid, uint32_t depth) const {
-    return "";
-  }
-  virtual std::string NodeStat(RegTree const& tree, int32_t nid) const {
-    return "";
-  }
-
-  virtual std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const = 0;
-
-  virtual std::string SplitNode(RegTree const& tree, int32_t nid, uint32_t depth) {
-    auto const split_index = tree[nid].SplitIndex();
-    std::string result;
-    if (split_index < fmap_.Size()) {
-      switch (fmap_.TypeOf(split_index)) {
-        case FeatureMap::kIndicator: {
-          result = this->Indicator(tree, nid, depth);
-          break;
-        }
-        case FeatureMap::kInteger: {
-          result = this->Integer(tree, nid, depth);
-          break;
-        }
-        case FeatureMap::kFloat:
-        case FeatureMap::kQuantitive: {
-          result = this->Quantitive(tree, nid, depth);
-          break;
-        }
-        default:
-          LOG(FATAL) << "Unknown feature map type.";
-      }
-    } else {
-      result = this->PlainNode(tree, nid, depth);
-    }
-    return result;
-  }
-
-  virtual std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const = 0;
-  virtual std::string BuildTree(RegTree const& tree, int32_t nid, uint32_t depth) = 0;
-
- public:
-  TreeGenerator(FeatureMap const& _fmap, bool with_stats) :
-      fmap_{_fmap}, with_stats_{with_stats} {}
-  virtual ~TreeGenerator() = default;
-
-  virtual void BuildTree(RegTree const& tree) {
-    ss_ << this->BuildTree(tree, 0, 0);
-  }
-
-  std::string Str() const {
-    return ss_.str();
-  }
-
-  static TreeGenerator* Create(std::string const& attrs, FeatureMap const& fmap,
-                               bool with_stats);
-};
-
-struct TreeGenReg : public dmlc::FunctionRegEntryBase<
-  TreeGenReg,
-  std::function<TreeGenerator* (
-      FeatureMap const& fmap, std::string attrs, bool with_stats)> > {
-};
-}  // namespace xgboost
-
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::TreeGenReg);
-}  // namespace dmlc
-
-namespace xgboost {
-
-TreeGenerator* TreeGenerator::Create(std::string const& attrs, FeatureMap const& fmap,
-                                     bool with_stats) {
-  auto pos = attrs.find(':');
-  std::string name;
-  std::string params;
-  if (pos != std::string::npos) {
-    name = attrs.substr(0, pos);
-    params = attrs.substr(pos+1, attrs.length() - pos - 1);
-    // Eliminate all occurances of single quote string.
-    size_t pos = std::string::npos;
-    while ((pos = params.find('\'')) != std::string::npos) {
-      params.replace(pos, 1, "\"");
-    }
-  } else {
-    name = attrs;
-  }
-  auto *e = ::dmlc::Registry< ::xgboost::TreeGenReg>::Get()->Find(name);
-  if (e == nullptr) {
-    LOG(FATAL) << "Unknown Model Builder:" << name;
-  }
-  auto p_io_builder = (e->body)(fmap, params, with_stats);
-  return p_io_builder;
-}
-
-#define XGBOOST_REGISTER_TREE_IO(UniqueId, Name)                        \
-  static DMLC_ATTRIBUTE_UNUSED ::xgboost::TreeGenReg&                   \
-  __make_ ## TreeGenReg ## _ ## UniqueId ## __ =                        \
-                  ::dmlc::Registry< ::xgboost::TreeGenReg>::Get()->__REGISTER__(Name)
-
-
-class TextGenerator : public TreeGenerator {
-  using SuperT = TreeGenerator;
-
- public:
-  TextGenerator(FeatureMap const& fmap, std::string const& attrs, bool with_stats) :
-      TreeGenerator(fmap, with_stats) {}
-
-  std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string kLeafTemplate = "{tabs}{nid}:leaf={leaf}{stats}";
-    static std::string kStatTemplate = ",cover={cover}";
-    std::string result = SuperT::Match(
-        kLeafTemplate,
-        {{"{tabs}",  SuperT::Tabs(depth)},
-         {"{nid}",   std::to_string(nid)},
-         {"{leaf}",  SuperT::ToStr(tree[nid].LeafValue())},
-         {"{stats}", with_stats_ ?
-          SuperT::Match(kStatTemplate,
-                        {{"{cover}", SuperT::ToStr(tree.Stat(nid).sum_hess)}}) : ""}});
-    return result;
-  }
-
-  std::string Indicator(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string const kIndicatorTemplate = "{nid}:[{fname}] yes={yes},no={no}";
-    int32_t nyes = tree[nid].DefaultLeft() ?
-                   tree[nid].RightChild() : tree[nid].LeftChild();
-    auto split_index = tree[nid].SplitIndex();
-    std::string result = SuperT::Match(
-        kIndicatorTemplate,
-        {{"{nid}",   std::to_string(nid)},
-         {"{fname}", fmap_.Name(split_index)},
-         {"{yes}",   std::to_string(nyes)},
-         {"{no}",    std::to_string(tree[nid].DefaultChild())}});
-    return result;
-  }
-
-  std::string SplitNodeImpl(
-      RegTree const& tree, int32_t nid, std::string const& template_str,
-      std::string cond, uint32_t depth) const {
-    auto split_index = tree[nid].SplitIndex();
-    std::string const result = SuperT::Match(
-        template_str,
-        {{"{tabs}",    SuperT::Tabs(depth)},
-         {"{nid}",     std::to_string(nid)},
-         {"{fname}",   split_index < fmap_.Size() ? fmap_.Name(split_index) :
-                                                    std::to_string(split_index)},
-         {"{cond}",    cond},
-         {"{left}",    std::to_string(tree[nid].LeftChild())},
-         {"{right}",   std::to_string(tree[nid].RightChild())},
-         {"{missing}", std::to_string(tree[nid].DefaultChild())}});
-    return result;
-  }
-
-  std::string Integer(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string const kIntegerTemplate =
-        "{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}";
-    auto cond = tree[nid].SplitCond();
-    const bst_float floored = std::floor(cond);
-    const int32_t integer_threshold
-        = (floored == cond) ? static_cast<int>(floored)
-        : static_cast<int>(floored) + 1;
-    return SplitNodeImpl(tree, nid, kIntegerTemplate,
-                         std::to_string(integer_threshold), depth);
-  }
-
-  std::string Quantitive(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string const kQuantitiveTemplate =
-        "{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}";
-    auto cond = tree[nid].SplitCond();
-    return SplitNodeImpl(tree, nid, kQuantitiveTemplate, SuperT::ToStr(cond), depth);
-  }
-
-  std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    auto cond = tree[nid].SplitCond();
-    static std::string const kNodeTemplate =
-        "{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}";
-    return SplitNodeImpl(tree, nid, kNodeTemplate, SuperT::ToStr(cond), depth);
-  }
-
-  std::string NodeStat(RegTree const& tree, int32_t nid) const override {
-    static std::string const kStatTemplate = ",gain={loss_chg},cover={sum_hess}";
-    std::string const result = SuperT::Match(
-        kStatTemplate,
-        {{"{loss_chg}", SuperT::ToStr(tree.Stat(nid).loss_chg)},
-         {"{sum_hess}", SuperT::ToStr(tree.Stat(nid).sum_hess)}});
-    return result;
-  }
-
-  std::string BuildTree(RegTree const& tree, int32_t nid, uint32_t depth) override {
-    if (tree[nid].IsLeaf()) {
-      return this->LeafNode(tree, nid, depth);
-    }
-    static std::string const kNodeTemplate = "{parent}{stat}\n{left}\n{right}";
-    auto result = SuperT::Match(
-        kNodeTemplate,
-        {{"{parent}", this->SplitNode(tree, nid, depth)},
-         {"{stat}",   with_stats_ ? this->NodeStat(tree, nid) : ""},
-         {"{left}",   this->BuildTree(tree, tree[nid].LeftChild(), depth+1)},
-         {"{right}",  this->BuildTree(tree, tree[nid].RightChild(), depth+1)}});
-    return result;
-  }
-
-  void BuildTree(RegTree const& tree) override {
-    static std::string const& kTreeTemplate = "{nodes}\n";
-    auto result = SuperT::Match(
-        kTreeTemplate,
-        {{"{nodes}", this->BuildTree(tree, 0, 0)}});
-    ss_ << result;
-  }
-};
-
-XGBOOST_REGISTER_TREE_IO(TextGenerator, "text")
-.describe("Dump text representation of tree")
-.set_body([](FeatureMap const& fmap, std::string const& attrs, bool with_stats) {
-            return new TextGenerator(fmap, attrs, with_stats);
-          });
-
-class JsonGenerator : public TreeGenerator {
-  using SuperT = TreeGenerator;
-
- public:
-  JsonGenerator(FeatureMap const& fmap, std::string attrs, bool with_stats) :
-      TreeGenerator(fmap, with_stats) {}
-
-  std::string Indent(uint32_t depth) const {
-    std::string result;
-    for (uint32_t i = 0; i < depth + 1; ++i) {
-      result += "  ";
-    }
-    return result;
-  }
-
-  std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string const kLeafTemplate =
-        R"L({ "nodeid": {nid}, "leaf": {leaf} {stat}})L";
-    static std::string const kStatTemplate =
-        R"S(, "cover": {sum_hess} )S";
-    std::string result = SuperT::Match(
-        kLeafTemplate,
-        {{"{nid}",  std::to_string(nid)},
-         {"{leaf}", SuperT::ToStr(tree[nid].LeafValue())},
-         {"{stat}", with_stats_ ? SuperT::Match(
-             kStatTemplate,
-             {{"{sum_hess}",
-               SuperT::ToStr(tree.Stat(nid).sum_hess)}})  : ""}});
-    return result;
-  }
-
-  std::string Indicator(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    int32_t nyes = tree[nid].DefaultLeft() ?
-                   tree[nid].RightChild() : tree[nid].LeftChild();
-    static std::string const kIndicatorTemplate =
-        R"ID( "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no})ID";
-    auto split_index = tree[nid].SplitIndex();
-    auto result = SuperT::Match(
-        kIndicatorTemplate,
-        {{"{nid}",   std::to_string(nid)},
-         {"{depth}", std::to_string(depth)},
-         {"{fname}", fmap_.Name(split_index)},
-         {"{yes}",   std::to_string(nyes)},
-         {"{no}",    std::to_string(tree[nid].DefaultChild())}});
-    return result;
-  }
-
-  std::string SplitNodeImpl(RegTree const &tree, int32_t nid,
-                            std::string const &template_str, std::string cond,
-                            uint32_t depth) const {
-    auto split_index = tree[nid].SplitIndex();
-    std::string const result = SuperT::Match(
-        template_str,
-        {{"{nid}",     std::to_string(nid)},
-         {"{depth}",   std::to_string(depth)},
-         {"{fname}",   split_index < fmap_.Size() ? fmap_.Name(split_index) :
-                                                    std::to_string(split_index)},
-         {"{cond}",    cond},
-         {"{left}",    std::to_string(tree[nid].LeftChild())},
-         {"{right}",   std::to_string(tree[nid].RightChild())},
-         {"{missing}", std::to_string(tree[nid].DefaultChild())}});
-    return result;
-  }
-
-  std::string Integer(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    auto cond = tree[nid].SplitCond();
-    const bst_float floored = std::floor(cond);
-    const int32_t integer_threshold
-        = (floored == cond) ? static_cast<int32_t>(floored)
-        : static_cast<int32_t>(floored) + 1;
-    static std::string const kIntegerTemplate =
-        R"I( "nodeid": {nid}, "depth": {depth}, "split": "{fname}", )I"
-        R"I("split_condition": {cond}, "yes": {left}, "no": {right}, )I"
-        R"I("missing": {missing})I";
-    return SplitNodeImpl(tree, nid, kIntegerTemplate,
-                         std::to_string(integer_threshold), depth);
-  }
-
-  std::string Quantitive(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string const kQuantitiveTemplate =
-        R"I( "nodeid": {nid}, "depth": {depth}, "split": "{fname}", )I"
-        R"I("split_condition": {cond}, "yes": {left}, "no": {right}, )I"
-        R"I("missing": {missing})I";
-    bst_float cond = tree[nid].SplitCond();
-    return SplitNodeImpl(tree, nid, kQuantitiveTemplate, SuperT::ToStr(cond), depth);
-  }
-
-  std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    auto cond = tree[nid].SplitCond();
-    static std::string const kNodeTemplate =
-        R"I( "nodeid": {nid}, "depth": {depth}, "split": {fname}, )I"
-        R"I("split_condition": {cond}, "yes": {left}, "no": {right}, )I"
-        R"I("missing": {missing})I";
-    return SplitNodeImpl(tree, nid, kNodeTemplate, SuperT::ToStr(cond), depth);
-  }
-
-  std::string NodeStat(RegTree const& tree, int32_t nid) const override {
-    static std::string kStatTemplate =
-        R"S(, "gain": {loss_chg}, "cover": {sum_hess})S";
-    auto result = SuperT::Match(
-        kStatTemplate,
-        {{"{loss_chg}", SuperT::ToStr(tree.Stat(nid).loss_chg)},
-         {"{sum_hess}", SuperT::ToStr(tree.Stat(nid).sum_hess)}});
-    return result;
-  }
-
-  std::string SplitNode(RegTree const& tree, int32_t nid, uint32_t depth) override {
-    std::string properties = SuperT::SplitNode(tree, nid, depth);
-    static std::string const kSplitNodeTemplate =
-        "{{properties} {stat}, \"children\": [{left}, {right}\n{indent}]}";
-    auto result = SuperT::Match(
-        kSplitNodeTemplate,
-        {{"{properties}", properties},
-         {"{stat}",   with_stats_ ? this->NodeStat(tree, nid) : ""},
-         {"{left}",   this->BuildTree(tree, tree[nid].LeftChild(), depth+1)},
-         {"{right}",  this->BuildTree(tree, tree[nid].RightChild(), depth+1)},
-         {"{indent}", this->Indent(depth)}});
-    return result;
-  }
-
-  std::string BuildTree(RegTree const& tree, int32_t nid, uint32_t depth) override {
-    static std::string const kNodeTemplate = "{newline}{indent}{nodes}";
-    auto result = SuperT::Match(
-        kNodeTemplate,
-        {{"{newline}", depth == 0 ? "" : "\n"},
-         {"{indent}", Indent(depth)},
-         {"{nodes}",  tree[nid].IsLeaf() ? this->LeafNode(tree, nid, depth) :
-                                           this->SplitNode(tree, nid, depth)}});
-    return result;
-  }
-};
-
-XGBOOST_REGISTER_TREE_IO(JsonGenerator, "json")
-.describe("Dump json representation of tree")
-.set_body([](FeatureMap const& fmap, std::string const& attrs, bool with_stats) {
-            return new JsonGenerator(fmap, attrs, with_stats);
-          });
-
-struct GraphvizParam : public XGBoostParameter<GraphvizParam> {
-  std::string yes_color;
-  std::string no_color;
-  std::string rankdir;
-  std::string condition_node_params;
-  std::string leaf_node_params;
-  std::string graph_attrs;
-
-  DMLC_DECLARE_PARAMETER(GraphvizParam){
-    DMLC_DECLARE_FIELD(yes_color)
-        .set_default("#0000FF")
-        .describe("Edge color when meets the node condition.");
-    DMLC_DECLARE_FIELD(no_color)
-        .set_default("#FF0000")
-        .describe("Edge color when doesn't meet the node condition.");
-    DMLC_DECLARE_FIELD(rankdir)
-        .set_default("TB")
-        .describe("Passed to graphiz via graph_attr.");
-    DMLC_DECLARE_FIELD(condition_node_params)
-        .set_default("")
-        .describe("Conditional node configuration");
-    DMLC_DECLARE_FIELD(leaf_node_params)
-        .set_default("")
-        .describe("Leaf node configuration");
-    DMLC_DECLARE_FIELD(graph_attrs)
-        .set_default("")
-        .describe("Any other extra attributes for graphviz `graph_attr`.");
-  }
-};
-
-DMLC_REGISTER_PARAMETER(GraphvizParam);
-
-class GraphvizGenerator : public TreeGenerator {
-  using SuperT = TreeGenerator;
-  GraphvizParam param_;
-
- public:
-  GraphvizGenerator(FeatureMap const& fmap, std::string const& attrs, bool with_stats) :
-      TreeGenerator(fmap, with_stats) {
-    param_.UpdateAllowUnknown(std::map<std::string, std::string>{});
-    using KwArg = std::map<std::string, std::map<std::string, std::string>>;
-    KwArg kwargs;
-    if (attrs.length() != 0) {
-      std::istringstream iss(attrs);
-      try {
-        dmlc::JSONReader reader(&iss);
-        reader.Read(&kwargs);
-      } catch(dmlc::Error const& e) {
-        LOG(FATAL) << "Failed to parse graphviz parameters:\n\t"
-                   << attrs << "\n"
-                   << "With error:\n"
-                   << e.what();
-      }
-    }
-    // This turns out to be tricky, as `dmlc::Parameter::Load(JSONReader*)` doesn't
-    // support loading nested json objects.
-    if (kwargs.find("condition_node_params") != kwargs.cend()) {
-      auto const& cnp = kwargs["condition_node_params"];
-      for (auto const& kv : cnp) {
-        param_.condition_node_params += kv.first + '=' + "\"" + kv.second + "\" ";
-      }
-      kwargs.erase("condition_node_params");
-    }
-    if (kwargs.find("leaf_node_params") != kwargs.cend()) {
-      auto const& lnp = kwargs["leaf_node_params"];
-      for (auto const& kv : lnp) {
-        param_.leaf_node_params += kv.first + '=' + "\"" + kv.second + "\" ";
-      }
-      kwargs.erase("leaf_node_params");
-    }
-
-    if (kwargs.find("edge") != kwargs.cend()) {
-      if (kwargs["edge"].find("yes_color") != kwargs["edge"].cend()) {
-        param_.yes_color = kwargs["edge"]["yes_color"];
-      }
-      if (kwargs["edge"].find("no_color") != kwargs["edge"].cend()) {
-        param_.no_color = kwargs["edge"]["no_color"];
-      }
-      kwargs.erase("edge");
-    }
-    auto const& extra = kwargs["graph_attrs"];
-    static std::string const kGraphTemplate = "    graph [ {key}=\"{value}\" ]\n";
-    for (auto const& kv : extra) {
-      param_.graph_attrs += SuperT::Match(kGraphTemplate,
-                                     {{"{key}", kv.first},
-                                      {"{value}", kv.second}});
-    }
-
-    kwargs.erase("graph_attrs");
-    if (kwargs.size() != 0) {
-      std::stringstream ss;
-      ss << "The following parameters for graphviz are not recognized:\n";
-      for (auto kv : kwargs) {
-        ss << kv.first << ", ";
-      }
-      LOG(WARNING) << ss.str();
-    }
-  }
-
- protected:
-  // Only indicator is different, so we combine all different node types into this
-  // function.
-  std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    auto split = tree[nid].SplitIndex();
-    auto cond = tree[nid].SplitCond();
-    static std::string const kNodeTemplate =
-        "    {nid} [ label=\"{fname}{<}{cond}\" {params}]\n";
-
-    // Indicator only has fname.
-    bool has_less = (split >= fmap_.Size()) || fmap_.TypeOf(split) != FeatureMap::kIndicator;
-    std::string result = SuperT::Match(kNodeTemplate, {
-        {"{nid}",    std::to_string(nid)},
-        {"{fname}",  split < fmap_.Size() ? fmap_.Name(split) :
-                                           'f' + std::to_string(split)},
-        {"{<}",      has_less ? "<" : ""},
-        {"{cond}",   has_less ? SuperT::ToStr(cond) : ""},
-        {"{params}", param_.condition_node_params}});
-
-    static std::string const kEdgeTemplate =
-        "    {nid} -> {child} [label=\"{is_missing}\" color=\"{color}\"]\n";
-    auto MatchFn = SuperT::Match;  // mingw failed to capture protected fn.
-    auto BuildEdge =
-        [&tree, nid, MatchFn, this](int32_t child) {
-          bool is_missing = tree[nid].DefaultChild() == child;
-          std::string buffer = MatchFn(kEdgeTemplate, {
-              {"{nid}",        std::to_string(nid)},
-              {"{child}",      std::to_string(child)},
-              {"{color}",      is_missing ? param_.yes_color : param_.no_color},
-              {"{is_missing}", is_missing ? "yes, missing": "no"}});
-          return buffer;
-        };
-    result += BuildEdge(tree[nid].LeftChild());
-    result += BuildEdge(tree[nid].RightChild());
-    return result;
-  };
-
-  std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
-    static std::string const kLeafTemplate =
-        "    {nid} [ label=\"leaf={leaf-value}\" {params}]\n";
-    auto result = SuperT::Match(kLeafTemplate, {
-        {"{nid}",        std::to_string(nid)},
-        {"{leaf-value}", ToStr(tree[nid].LeafValue())},
-        {"{params}",     param_.leaf_node_params}});
-    return result;
-  };
-
-  std::string BuildTree(RegTree const& tree, int32_t nid, uint32_t depth) override {
-    if (tree[nid].IsLeaf()) {
-      return this->LeafNode(tree, nid, depth);
-    }
-    static std::string const kNodeTemplate = "{parent}\n{left}\n{right}";
-    auto result = SuperT::Match(
-        kNodeTemplate,
-        {{"{parent}", this->PlainNode(tree, nid, depth)},
-         {"{left}",   this->BuildTree(tree, tree[nid].LeftChild(), depth+1)},
-         {"{right}",  this->BuildTree(tree, tree[nid].RightChild(), depth+1)}});
-    return result;
-  }
-
-  void BuildTree(RegTree const& tree) override {
-    static std::string const kTreeTemplate =
-        "digraph {\n"
-        "    graph [ rankdir={rankdir} ]\n"
-        "{graph_attrs}\n"
-        "{nodes}}";
-    auto result = SuperT::Match(
-        kTreeTemplate,
-        {{"{rankdir}",     param_.rankdir},
-         {"{graph_attrs}", param_.graph_attrs},
-         {"{nodes}",       this->BuildTree(tree, 0, 0)}});
-    ss_ << result;
-  };
-};
-
-XGBOOST_REGISTER_TREE_IO(GraphvizGenerator, "dot")
-.describe("Dump graphviz representation of tree")
-.set_body([](FeatureMap const& fmap, std::string attrs, bool with_stats) {
-            return new GraphvizGenerator(fmap, attrs, with_stats);
-          });
-
-constexpr bst_node_t RegTree::kRoot;
-
-std::string RegTree::DumpModel(const FeatureMap& fmap,
-                               bool with_stats,
-                               std::string format) const {
-  std::unique_ptr<TreeGenerator> builder {
-    TreeGenerator::Create(format, fmap, with_stats)
-  };
-  builder->BuildTree(*this);
-
-  std::string result = builder->Str();
-  return result;
-}
-
-bool RegTree::Equal(const RegTree& b) const {
-  if (NumExtraNodes() != b.NumExtraNodes()) {
-    return false;
-  }
-  auto const& self = *this;
-  bool ret { true };
-  this->WalkTree([&self, &b, &ret](bst_node_t nidx) {
-    if (!(self.nodes_.at(nidx) == b.nodes_.at(nidx))) {
-      ret = false;
-      return false;
-    }
-    return true;
-  });
-  return ret;
-}
-
-bst_node_t RegTree::GetNumLeaves() const {
-  bst_node_t leaves { 0 };
-  auto const& self = *this;
-  this->WalkTree([&leaves, &self](bst_node_t nidx) {
-                   if (self[nidx].IsLeaf()) {
-                     leaves++;
-                   }
-                   return true;
-                 });
-  return leaves;
-}
-
-bst_node_t RegTree::GetNumSplitNodes() const {
-  bst_node_t splits { 0 };
-  auto const& self = *this;
-  this->WalkTree([&splits, &self](bst_node_t nidx) {
-                   if (!self[nidx].IsLeaf()) {
-                     splits++;
-                   }
-                   return true;
-                 });
-  return splits;
-}
-
-void RegTree::Load(dmlc::Stream* fi) {
-  CHECK_EQ(fi->Read(&param, sizeof(TreeParam)), sizeof(TreeParam));
-  nodes_.resize(param.num_nodes);
-  stats_.resize(param.num_nodes);
-  CHECK_NE(param.num_nodes, 0);
-  CHECK_EQ(fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()),
-           sizeof(Node) * nodes_.size());
-  CHECK_EQ(fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()),
-           sizeof(RTreeNodeStat) * stats_.size());
-  // chg deleted nodes
-  deleted_nodes_.resize(0);
-  for (int i = 1; i < param.num_nodes; ++i) {
-    if (nodes_[i].IsDeleted()) {
-      deleted_nodes_.push_back(i);
-    }
-  }
-  CHECK_EQ(static_cast<int>(deleted_nodes_.size()), param.num_deleted);
-}
-void RegTree::Save(dmlc::Stream* fo) const {
-  CHECK_EQ(param.num_nodes, static_cast<int>(nodes_.size()));
-  CHECK_EQ(param.num_nodes, static_cast<int>(stats_.size()));
-  fo->Write(&param, sizeof(TreeParam));
-  CHECK_EQ(param.deprecated_num_roots, 1);
-  CHECK_NE(param.num_nodes, 0);
-  fo->Write(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size());
-  fo->Write(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * nodes_.size());
-}
-
-void RegTree::LoadModel(Json const& in) {
-  FromJson(in["tree_param"], &param);
-  auto n_nodes = param.num_nodes;
-  CHECK_NE(n_nodes, 0);
-  // stats
-  auto const& loss_changes = get<Array const>(in["loss_changes"]);
-  CHECK_EQ(loss_changes.size(), n_nodes);
-  auto const& sum_hessian = get<Array const>(in["sum_hessian"]);
-  CHECK_EQ(sum_hessian.size(), n_nodes);
-  auto const& base_weights = get<Array const>(in["base_weights"]);
-  CHECK_EQ(base_weights.size(), n_nodes);
-  auto const& leaf_child_counts = get<Array const>(in["leaf_child_counts"]);
-  CHECK_EQ(leaf_child_counts.size(), n_nodes);
-  // nodes
-  auto const& lefts = get<Array const>(in["left_children"]);
-  CHECK_EQ(lefts.size(), n_nodes);
-  auto const& rights = get<Array const>(in["right_children"]);
-  CHECK_EQ(rights.size(), n_nodes);
-  auto const& parents = get<Array const>(in["parents"]);
-  CHECK_EQ(parents.size(), n_nodes);
-  auto const& indices = get<Array const>(in["split_indices"]);
-  CHECK_EQ(indices.size(), n_nodes);
-  auto const& conds = get<Array const>(in["split_conditions"]);
-  CHECK_EQ(conds.size(), n_nodes);
-  auto const& default_left = get<Array const>(in["default_left"]);
-  CHECK_EQ(default_left.size(), n_nodes);
-
-  stats_.clear();
-  nodes_.clear();
-
-  stats_.resize(n_nodes);
-  nodes_.resize(n_nodes);
-  for (int32_t i = 0; i < n_nodes; ++i) {
-    auto& s = stats_[i];
-    s.loss_chg = get<Number const>(loss_changes[i]);
-    s.sum_hess = get<Number const>(sum_hessian[i]);
-    s.base_weight = get<Number const>(base_weights[i]);
-    s.leaf_child_cnt = get<Integer const>(leaf_child_counts[i]);
-
-    auto& n = nodes_[i];
-    bst_node_t left = get<Integer const>(lefts[i]);
-    bst_node_t right = get<Integer const>(rights[i]);
-    bst_node_t parent = get<Integer const>(parents[i]);
-    bst_feature_t ind = get<Integer const>(indices[i]);
-    float cond { get<Number const>(conds[i]) };
-    bool dft_left { get<Boolean const>(default_left[i]) };
-    n = Node{left, right, parent, ind, cond, dft_left};
-  }
-
-  deleted_nodes_.clear();
-  for (bst_node_t i = 1; i < param.num_nodes; ++i) {
-    if (nodes_[i].IsDeleted()) {
-      deleted_nodes_.push_back(i);
-    }
-  }
-
-  auto& self = *this;
-  for (auto nid = 1; nid < n_nodes; ++nid) {
-    auto parent = self[nid].Parent();
-    CHECK_NE(parent, RegTree::kInvalidNodeId);
-    self[nid].SetParent(self[nid].Parent(), self[parent].LeftChild() == nid);
-  }
-  CHECK_EQ(static_cast<bst_node_t>(deleted_nodes_.size()), param.num_deleted);
-}
-
-void RegTree::SaveModel(Json* p_out) const {
-  auto& out = *p_out;
-  CHECK_EQ(param.num_nodes, static_cast<int>(nodes_.size()));
-  CHECK_EQ(param.num_nodes, static_cast<int>(stats_.size()));
-  out["tree_param"] = ToJson(param);
-  CHECK_EQ(get<String>(out["tree_param"]["num_nodes"]), std::to_string(param.num_nodes));
-  using I = Integer::Int;
-  auto n_nodes = param.num_nodes;
-
-  // stats
-  std::vector<Json> loss_changes(n_nodes);
-  std::vector<Json> sum_hessian(n_nodes);
-  std::vector<Json> base_weights(n_nodes);
-  std::vector<Json> leaf_child_counts(n_nodes);
-
-  // nodes
-  std::vector<Json> lefts(n_nodes);
-  std::vector<Json> rights(n_nodes);
-  std::vector<Json> parents(n_nodes);
-  std::vector<Json> indices(n_nodes);
-  std::vector<Json> conds(n_nodes);
-  std::vector<Json> default_left(n_nodes);
-
-  for (int32_t i = 0; i < n_nodes; ++i) {
-    auto const& s = stats_[i];
-    loss_changes[i] = s.loss_chg;
-    sum_hessian[i] = s.sum_hess;
-    base_weights[i] = s.base_weight;
-    leaf_child_counts[i] = static_cast<I>(s.leaf_child_cnt);
-
-    auto const& n = nodes_[i];
-    lefts[i] = static_cast<I>(n.LeftChild());
-    rights[i] = static_cast<I>(n.RightChild());
-    parents[i] = static_cast<I>(n.Parent());
-    indices[i] = static_cast<I>(n.SplitIndex());
-    conds[i] = n.SplitCond();
-    default_left[i] = n.DefaultLeft();
-  }
-
-  out["loss_changes"] = std::move(loss_changes);
-  out["sum_hessian"] = std::move(sum_hessian);
-  out["base_weights"] = std::move(base_weights);
-  out["leaf_child_counts"] = std::move(leaf_child_counts);
-
-  out["left_children"] = std::move(lefts);
-  out["right_children"] = std::move(rights);
-  out["parents"] = std::move(parents);
-  out["split_indices"] = std::move(indices);
-  out["split_conditions"] = std::move(conds);
-  out["default_left"] = std::move(default_left);
-}
-
-void RegTree::FillNodeMeanValues() {
-  size_t num_nodes = this->param.num_nodes;
-  if (this->node_mean_values_.size() == num_nodes) {
-    return;
-  }
-  this->node_mean_values_.resize(num_nodes);
-  this->FillNodeMeanValue(0);
-}
-
-bst_float RegTree::FillNodeMeanValue(int nid) {
-  bst_float result;
-  auto& node = (*this)[nid];
-  if (node.IsLeaf()) {
-    result = node.LeafValue();
-  } else {
-    result  = this->FillNodeMeanValue(node.LeftChild()) * this->Stat(node.LeftChild()).sum_hess;
-    result += this->FillNodeMeanValue(node.RightChild()) * this->Stat(node.RightChild()).sum_hess;
-    result /= this->Stat(nid).sum_hess;
-  }
-  this->node_mean_values_[nid] = result;
-  return result;
-}
-
-void RegTree::CalculateContributionsApprox(const RegTree::FVec &feat,
-                                           bst_float *out_contribs) const {
-  CHECK_GT(this->node_mean_values_.size(), 0U);
-  // this follows the idea of http://blog.datadive.net/interpreting-random-forests/
-  unsigned split_index = 0;
-  // update bias value
-  bst_float node_value = this->node_mean_values_[0];
-  out_contribs[feat.Size()] += node_value;
-  if ((*this)[0].IsLeaf()) {
-    // nothing to do anymore
-    return;
-  }
-  bst_node_t nid = 0;
-  while (!(*this)[nid].IsLeaf()) {
-    split_index = (*this)[nid].SplitIndex();
-    nid = this->GetNext(nid, feat.GetFvalue(split_index), feat.IsMissing(split_index));
-    bst_float new_value = this->node_mean_values_[nid];
-    // update feature weight
-    out_contribs[split_index] += new_value - node_value;
-    node_value = new_value;
-  }
-  bst_float leaf_value = (*this)[nid].LeafValue();
-  // update leaf feature weight
-  out_contribs[split_index] += leaf_value - node_value;
-}
-
-// Used by TreeShap
-// data we keep about our decision path
-// note that pweight is included for convenience and is not tied with the other attributes
-// the pweight of the i'th path element is the permuation weight of paths with i-1 ones in them
-struct PathElement {
-  int feature_index;
-  bst_float zero_fraction;
-  bst_float one_fraction;
-  bst_float pweight;
-  PathElement() = default;
-  PathElement(int i, bst_float z, bst_float o, bst_float w) :
-    feature_index(i), zero_fraction(z), one_fraction(o), pweight(w) {}
-};
-
-// extend our decision path with a fraction of one and zero extensions
-void ExtendPath(PathElement *unique_path, unsigned unique_depth,
-                bst_float zero_fraction, bst_float one_fraction,
-                int feature_index) {
-  unique_path[unique_depth].feature_index = feature_index;
-  unique_path[unique_depth].zero_fraction = zero_fraction;
-  unique_path[unique_depth].one_fraction = one_fraction;
-  unique_path[unique_depth].pweight = (unique_depth == 0 ? 1.0f : 0.0f);
-  for (int i = unique_depth - 1; i >= 0; i--) {
-    unique_path[i+1].pweight += one_fraction * unique_path[i].pweight * (i + 1)
-                                / static_cast<bst_float>(unique_depth + 1);
-    unique_path[i].pweight = zero_fraction * unique_path[i].pweight * (unique_depth - i)
-                             / static_cast<bst_float>(unique_depth + 1);
-  }
-}
-
-// undo a previous extension of the decision path
-void UnwindPath(PathElement *unique_path, unsigned unique_depth,
-                unsigned path_index) {
-  const bst_float one_fraction = unique_path[path_index].one_fraction;
-  const bst_float zero_fraction = unique_path[path_index].zero_fraction;
-  bst_float next_one_portion = unique_path[unique_depth].pweight;
-
-  for (int i = unique_depth - 1; i >= 0; --i) {
-    if (one_fraction != 0) {
-      const bst_float tmp = unique_path[i].pweight;
-      unique_path[i].pweight = next_one_portion * (unique_depth + 1)
-                               / static_cast<bst_float>((i + 1) * one_fraction);
-      next_one_portion = tmp - unique_path[i].pweight * zero_fraction * (unique_depth - i)
-                               / static_cast<bst_float>(unique_depth + 1);
-    } else {
-      unique_path[i].pweight = (unique_path[i].pweight * (unique_depth + 1))
-                               / static_cast<bst_float>(zero_fraction * (unique_depth - i));
-    }
-  }
-
-  for (auto i = path_index; i < unique_depth; ++i) {
-    unique_path[i].feature_index = unique_path[i+1].feature_index;
-    unique_path[i].zero_fraction = unique_path[i+1].zero_fraction;
-    unique_path[i].one_fraction = unique_path[i+1].one_fraction;
-  }
-}
-
-// determine what the total permuation weight would be if
-// we unwound a previous extension in the decision path
-bst_float UnwoundPathSum(const PathElement *unique_path, unsigned unique_depth,
-                         unsigned path_index) {
-  const bst_float one_fraction = unique_path[path_index].one_fraction;
-  const bst_float zero_fraction = unique_path[path_index].zero_fraction;
-  bst_float next_one_portion = unique_path[unique_depth].pweight;
-  bst_float total = 0;
-  for (int i = unique_depth - 1; i >= 0; --i) {
-    if (one_fraction != 0) {
-      const bst_float tmp = next_one_portion * (unique_depth + 1)
-                            / static_cast<bst_float>((i + 1) * one_fraction);
-      total += tmp;
-      next_one_portion = unique_path[i].pweight - tmp * zero_fraction * ((unique_depth - i)
-                         / static_cast<bst_float>(unique_depth + 1));
-    } else if (zero_fraction != 0) {
-      total += (unique_path[i].pweight / zero_fraction) / ((unique_depth - i)
-               / static_cast<bst_float>(unique_depth + 1));
-    } else {
-      CHECK_EQ(unique_path[i].pweight, 0)
-        << "Unique path " << i << " must have zero weight";
-    }
-  }
-  return total;
-}
-
-// recursive computation of SHAP values for a decision tree
-void RegTree::TreeShap(const RegTree::FVec &feat, bst_float *phi,
-                       unsigned node_index, unsigned unique_depth,
-                       PathElement *parent_unique_path,
-                       bst_float parent_zero_fraction,
-                       bst_float parent_one_fraction, int parent_feature_index,
-                       int condition, unsigned condition_feature,
-                       bst_float condition_fraction) const {
-  const auto node = (*this)[node_index];
-
-  // stop if we have no weight coming down to us
-  if (condition_fraction == 0) return;
-
-  // extend the unique path
-  PathElement *unique_path = parent_unique_path + unique_depth + 1;
-  std::copy(parent_unique_path, parent_unique_path + unique_depth + 1, unique_path);
-
-  if (condition == 0 || condition_feature != static_cast<unsigned>(parent_feature_index)) {
-    ExtendPath(unique_path, unique_depth, parent_zero_fraction,
-               parent_one_fraction, parent_feature_index);
-  }
-  const unsigned split_index = node.SplitIndex();
-
-  // leaf node
-  if (node.IsLeaf()) {
-    for (unsigned i = 1; i <= unique_depth; ++i) {
-      const bst_float w = UnwoundPathSum(unique_path, unique_depth, i);
-      const PathElement &el = unique_path[i];
-      phi[el.feature_index] += w * (el.one_fraction - el.zero_fraction)
-                                 * node.LeafValue() * condition_fraction;
-    }
-
-  // internal node
-  } else {
-    // find which branch is "hot" (meaning x would follow it)
-    unsigned hot_index = 0;
-    if (feat.IsMissing(split_index)) {
-      hot_index = node.DefaultChild();
-    } else if (feat.GetFvalue(split_index) < node.SplitCond()) {
-      hot_index = node.LeftChild();
-    } else {
-      hot_index = node.RightChild();
-    }
-    const unsigned cold_index = (static_cast<int>(hot_index) == node.LeftChild() ?
-                                 node.RightChild() : node.LeftChild());
-    const bst_float w = this->Stat(node_index).sum_hess;
-    const bst_float hot_zero_fraction = this->Stat(hot_index).sum_hess / w;
-    const bst_float cold_zero_fraction = this->Stat(cold_index).sum_hess / w;
-    bst_float incoming_zero_fraction = 1;
-    bst_float incoming_one_fraction = 1;
-
-    // see if we have already split on this feature,
-    // if so we undo that split so we can redo it for this node
-    unsigned path_index = 0;
-    for (; path_index <= unique_depth; ++path_index) {
-      if (static_cast<unsigned>(unique_path[path_index].feature_index) == split_index) break;
-    }
-    if (path_index != unique_depth + 1) {
-      incoming_zero_fraction = unique_path[path_index].zero_fraction;
-      incoming_one_fraction = unique_path[path_index].one_fraction;
-      UnwindPath(unique_path, unique_depth, path_index);
-      unique_depth -= 1;
-    }
-
-    // divide up the condition_fraction among the recursive calls
-    bst_float hot_condition_fraction = condition_fraction;
-    bst_float cold_condition_fraction = condition_fraction;
-    if (condition > 0 && split_index == condition_feature) {
-      cold_condition_fraction = 0;
-      unique_depth -= 1;
-    } else if (condition < 0 && split_index == condition_feature) {
-      hot_condition_fraction *= hot_zero_fraction;
-      cold_condition_fraction *= cold_zero_fraction;
-      unique_depth -= 1;
-    }
-
-    TreeShap(feat, phi, hot_index, unique_depth + 1, unique_path,
-             hot_zero_fraction * incoming_zero_fraction, incoming_one_fraction,
-             split_index, condition, condition_feature, hot_condition_fraction);
-
-    TreeShap(feat, phi, cold_index, unique_depth + 1, unique_path,
-             cold_zero_fraction * incoming_zero_fraction, 0,
-             split_index, condition, condition_feature, cold_condition_fraction);
-  }
-}
-
-void RegTree::CalculateContributions(const RegTree::FVec &feat,
-                                     bst_float *out_contribs,
-                                     int condition,
-                                     unsigned condition_feature) const {
-  // find the expected value of the tree's predictions
-  if (condition == 0) {
-    bst_float node_value = this->node_mean_values_[0];
-    out_contribs[feat.Size()] += node_value;
-  }
-
-  // Preallocate space for the unique path data
-  const int maxd = this->MaxDepth(0) + 2;
-  std::vector<PathElement> unique_path_data((maxd * (maxd + 1)) / 2);
-
-  TreeShap(feat, out_contribs, 0, 0, unique_path_data.data(),
-           1, 1, -1, condition, condition_feature, 1);
-}
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/tree_updater.cc b/ml-xgboost/src/tree/tree_updater.cc
deleted file mode 100644
index 24ff2ab..0000000
--- a/ml-xgboost/src/tree/tree_updater.cc
+++ /dev/null
@@ -1,43 +0,0 @@
-/*!
- * Copyright 2015 by Contributors
- * \file tree_updater.cc
- * \brief Registry of tree updaters.
- */
-#include <dmlc/registry.h>
-
-#include "xgboost/tree_updater.h"
-#include "xgboost/host_device_vector.h"
-
-namespace dmlc {
-DMLC_REGISTRY_ENABLE(::xgboost::TreeUpdaterReg);
-}  // namespace dmlc
-
-namespace xgboost {
-
-TreeUpdater* TreeUpdater::Create(const std::string& name, GenericParameter const* tparam) {
-  auto *e = ::dmlc::Registry< ::xgboost::TreeUpdaterReg>::Get()->Find(name);
-  if (e == nullptr) {
-    LOG(FATAL) << "Unknown tree updater " << name;
-  }
-  auto p_updater = (e->body)();
-  p_updater->tparam_ = tparam;
-  return p_updater;
-}
-
-}  // namespace xgboost
-
-namespace xgboost {
-namespace tree {
-// List of files that will be force linked in static links.
-DMLC_REGISTRY_LINK_TAG(updater_colmaker);
-DMLC_REGISTRY_LINK_TAG(updater_skmaker);
-DMLC_REGISTRY_LINK_TAG(updater_refresh);
-DMLC_REGISTRY_LINK_TAG(updater_prune);
-DMLC_REGISTRY_LINK_TAG(updater_quantile_hist);
-DMLC_REGISTRY_LINK_TAG(updater_histmaker);
-DMLC_REGISTRY_LINK_TAG(updater_sync);
-#ifdef XGBOOST_USE_CUDA
-DMLC_REGISTRY_LINK_TAG(updater_gpu_hist);
-#endif  // XGBOOST_USE_CUDA
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_basemaker-inl.h b/ml-xgboost/src/tree/updater_basemaker-inl.h
deleted file mode 100644
index 66ab919..0000000
--- a/ml-xgboost/src/tree/updater_basemaker-inl.h
+++ /dev/null
@@ -1,484 +0,0 @@
-/*!
- * Copyright 2014 by Contributors
- * \file updater_basemaker-inl.h
- * \brief implement a common tree constructor
- * \author Tianqi Chen
- */
-#ifndef XGBOOST_TREE_UPDATER_BASEMAKER_INL_H_
-#define XGBOOST_TREE_UPDATER_BASEMAKER_INL_H_
-
-#include <rabit/rabit.h>
-
-
-#include <vector>
-#include <algorithm>
-#include <string>
-#include <limits>
-#include <utility>
-
-#include "xgboost/base.h"
-#include "xgboost/json.h"
-#include "xgboost/tree_updater.h"
-#include "param.h"
-#include "constraints.h"
-
-#include "../common/io.h"
-#include "../common/random.h"
-#include "../common/quantile.h"
-
-namespace xgboost {
-namespace tree {
-/*!
- * \brief base tree maker class that defines common operation
- *  needed in tree making
- */
-class BaseMaker: public TreeUpdater {
- public:
-  void Configure(const Args& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("train_param"), &this->param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["train_param"] = ToJson(param_);
-  }
-
- protected:
-  // helper to collect and query feature meta information
-  struct FMetaHelper {
-   public:
-    /*! \brief find type of each feature, use column format */
-    inline void InitByCol(DMatrix* p_fmat,
-                          const RegTree& tree) {
-      fminmax_.resize(tree.param.num_feature * 2);
-      std::fill(fminmax_.begin(), fminmax_.end(),
-                -std::numeric_limits<bst_float>::max());
-      // start accumulating statistics
-      for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-        for (bst_uint fid = 0; fid < batch.Size(); ++fid) {
-          auto c = batch[fid];
-          if (c.size() != 0) {
-            CHECK_LT(fid * 2, fminmax_.size());
-            fminmax_[fid * 2 + 0] =
-                std::max(-c[0].fvalue, fminmax_[fid * 2 + 0]);
-            fminmax_[fid * 2 + 1] =
-                std::max(c[c.size() - 1].fvalue, fminmax_[fid * 2 + 1]);
-          }
-        }
-      }
-    }
-    /*! \brief synchronize the information */
-    inline void SyncInfo() {
-      rabit::Allreduce<rabit::op::Max>(dmlc::BeginPtr(fminmax_), fminmax_.size());
-    }
-    // get feature type, 0:empty 1:binary 2:real
-    inline int Type(bst_uint fid) const {
-      CHECK_LT(fid * 2 + 1, fminmax_.size())
-          << "FeatHelper fid exceed query bound ";
-      bst_float a = fminmax_[fid * 2];
-      bst_float b = fminmax_[fid * 2 + 1];
-      if (a == -std::numeric_limits<bst_float>::max()) return 0;
-      if (-a == b) {
-        return 1;
-      } else {
-        return 2;
-      }
-    }
-    bst_float MaxValue(bst_uint fid) const {
-      return fminmax_[fid *2 + 1];
-    }
-
-    void SampleCol(float p, std::vector<bst_feature_t> *p_findex) const {
-      std::vector<bst_feature_t> &findex = *p_findex;
-      findex.clear();
-      for (size_t i = 0; i < fminmax_.size(); i += 2) {
-        const auto fid = static_cast<bst_uint>(i / 2);
-        if (this->Type(fid) != 0) findex.push_back(fid);
-      }
-      auto n = static_cast<unsigned>(p * findex.size());
-      std::shuffle(findex.begin(), findex.end(), common::GlobalRandom());
-      findex.resize(n);
-      // sync the findex if it is subsample
-      std::string s_cache;
-      common::MemoryBufferStream fc(&s_cache);
-      dmlc::Stream& fs = fc;
-      if (rabit::GetRank() == 0) {
-        fs.Write(findex);
-      }
-      rabit::Broadcast(&s_cache, 0);
-      fs.Read(&findex);
-    }
-
-   private:
-    std::vector<bst_float> fminmax_;
-  };
-  // ------static helper functions ------
-  // helper function to get to next level of the tree
-  /*! \brief this is  helper function for row based data*/
-  inline static int NextLevel(const SparsePage::Inst &inst, const RegTree &tree, int nid) {
-    const RegTree::Node &n = tree[nid];
-    bst_uint findex = n.SplitIndex();
-    for (const auto& ins : inst) {
-      if (findex == ins.index) {
-        if (ins.fvalue < n.SplitCond()) {
-          return n.LeftChild();
-        } else {
-          return n.RightChild();
-        }
-      }
-    }
-    return n.DefaultChild();
-  }
-  //  ------class member helpers---------
-  /*! \brief initialize temp data structure */
-  inline void InitData(const std::vector<GradientPair> &gpair,
-                       const DMatrix &fmat,
-                       const RegTree &tree) {
-    {
-      // setup position
-      position_.resize(gpair.size());
-      std::fill(position_.begin(), position_.end(), 0);
-      // mark delete for the deleted datas
-      for (size_t i = 0; i < position_.size(); ++i) {
-        if (gpair[i].GetHess() < 0.0f) position_[i] = ~position_[i];
-      }
-      // mark subsample
-      if (param_.subsample < 1.0f) {
-        CHECK_EQ(param_.sampling_method, TrainParam::kUniform)
-          << "Only uniform sampling is supported, "
-          << "gradient-based sampling is only support by GPU Hist.";
-        std::bernoulli_distribution coin_flip(param_.subsample);
-        auto& rnd = common::GlobalRandom();
-        for (size_t i = 0; i < position_.size(); ++i) {
-          if (gpair[i].GetHess() < 0.0f) continue;
-          if (!coin_flip(rnd)) position_[i] = ~position_[i];
-        }
-      }
-    }
-    {
-      // expand query
-      qexpand_.reserve(256); qexpand_.clear();
-      qexpand_.push_back(0);
-      this->UpdateNode2WorkIndex(tree);
-    }
-    this->interaction_constraints_.Configure(param_, fmat.Info().num_col_);
-  }
-  /*! \brief update queue expand add in new leaves */
-  inline void UpdateQueueExpand(const RegTree &tree) {
-    std::vector<int> newnodes;
-    for (int nid : qexpand_) {
-      if (!tree[nid].IsLeaf()) {
-        newnodes.push_back(tree[nid].LeftChild());
-        newnodes.push_back(tree[nid].RightChild());
-      }
-    }
-    // use new nodes for qexpand
-    qexpand_ = newnodes;
-    this->UpdateNode2WorkIndex(tree);
-  }
-  // return decoded position
-  inline int DecodePosition(bst_uint ridx) const {
-    const int pid = position_[ridx];
-    return pid < 0 ? ~pid : pid;
-  }
-  // encode the encoded position value for ridx
-  inline void SetEncodePosition(bst_uint ridx, int nid) {
-    if (position_[ridx] < 0) {
-      position_[ridx] = ~nid;
-    } else {
-      position_[ridx] = nid;
-    }
-  }
-  /*!
-   * \brief this is helper function uses column based data structure,
-   *        reset the positions to the lastest one
-   * \param nodes the set of nodes that contains the split to be used
-   * \param p_fmat feature matrix needed for tree construction
-   * \param tree the regression tree structure
-   */
-  inline void ResetPositionCol(const std::vector<int> &nodes,
-                               DMatrix *p_fmat,
-                               const RegTree &tree) {
-    // set the positions in the nondefault
-    this->SetNonDefaultPositionCol(nodes, p_fmat, tree);
-    this->SetDefaultPostion(p_fmat, tree);
-  }
-  /*!
-   * \brief helper function to set the non-leaf positions to default direction.
-   *  This function can be applied multiple times and will get the same result.
-   * \param p_fmat feature matrix needed for tree construction
-   * \param tree the regression tree structure
-   */
-  inline void SetDefaultPostion(DMatrix *p_fmat,
-                                const RegTree &tree) {
-    // set default direct nodes to default
-    // for leaf nodes that are not fresh, mark then to ~nid,
-    // so that they are ignored in future statistics collection
-    const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
-
-#pragma omp parallel for schedule(static)
-    for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
-      const int nid = this->DecodePosition(ridx);
-      if (tree[nid].IsLeaf()) {
-        // mark finish when it is not a fresh leaf
-        if (tree[nid].RightChild() == -1) {
-          position_[ridx] = ~nid;
-        }
-      } else {
-        // push to default branch
-        if (tree[nid].DefaultLeft()) {
-          this->SetEncodePosition(ridx, tree[nid].LeftChild());
-        } else {
-          this->SetEncodePosition(ridx, tree[nid].RightChild());
-        }
-      }
-    }
-  }
-  /*!
-   * \brief this is helper function uses column based data structure,
-   *  to CORRECT the positions of non-default directions that WAS set to default
-   *  before calling this function.
-   * \param batch The column batch
-   * \param sorted_split_set The set of index that contains split solutions.
-   * \param tree the regression tree structure
-   */
-  inline void CorrectNonDefaultPositionByBatch(
-      const SparsePage &batch, const std::vector<bst_uint> &sorted_split_set,
-      const RegTree &tree) {
-    for (size_t fid = 0; fid < batch.Size(); ++fid) {
-      auto col = batch[fid];
-      auto it = std::lower_bound(sorted_split_set.begin(), sorted_split_set.end(), fid);
-
-      if (it != sorted_split_set.end() && *it == fid) {
-        const auto ndata = static_cast<bst_omp_uint>(col.size());
-        #pragma omp parallel for schedule(static)
-        for (bst_omp_uint j = 0; j < ndata; ++j) {
-          const bst_uint ridx = col[j].index;
-          const bst_float fvalue = col[j].fvalue;
-          const int nid = this->DecodePosition(ridx);
-          CHECK(tree[nid].IsLeaf());
-          int pid = tree[nid].Parent();
-
-          // go back to parent, correct those who are not default
-          if (!tree[nid].IsRoot() && tree[pid].SplitIndex() == fid) {
-            if (fvalue < tree[pid].SplitCond()) {
-              this->SetEncodePosition(ridx, tree[pid].LeftChild());
-            } else {
-              this->SetEncodePosition(ridx, tree[pid].RightChild());
-            }
-          }
-        }
-      }
-    }
-  }
-  /*!
-   * \brief this is helper function uses column based data structure,
-   * \param nodes the set of nodes that contains the split to be used
-   * \param tree the regression tree structure
-   * \param out_split_set The split index set
-   */
-  inline void GetSplitSet(const std::vector<int> &nodes,
-                          const RegTree &tree,
-                          std::vector<unsigned>* out_split_set) {
-    std::vector<unsigned>& fsplits = *out_split_set;
-    fsplits.clear();
-    // step 1, classify the non-default data into right places
-    for (int nid : nodes) {
-      if (!tree[nid].IsLeaf()) {
-        fsplits.push_back(tree[nid].SplitIndex());
-      }
-    }
-    std::sort(fsplits.begin(), fsplits.end());
-    fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
-  }
-  /*!
-   * \brief this is helper function uses column based data structure,
-   *        update all positions into nondefault branch, if any, ignore the default branch
-   * \param nodes the set of nodes that contains the split to be used
-   * \param p_fmat feature matrix needed for tree construction
-   * \param tree the regression tree structure
-   */
-  virtual void SetNonDefaultPositionCol(const std::vector<int> &nodes,
-                                        DMatrix *p_fmat,
-                                        const RegTree &tree) {
-    std::vector<unsigned> fsplits;
-    this->GetSplitSet(nodes, tree, &fsplits);
-    for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-      for (auto fid : fsplits) {
-        auto col = batch[fid];
-        const auto ndata = static_cast<bst_omp_uint>(col.size());
-        #pragma omp parallel for schedule(static)
-        for (bst_omp_uint j = 0; j < ndata; ++j) {
-          const bst_uint ridx = col[j].index;
-          const bst_float fvalue = col[j].fvalue;
-          const int nid = this->DecodePosition(ridx);
-          // go back to parent, correct those who are not default
-          if (!tree[nid].IsLeaf() && tree[nid].SplitIndex() == fid) {
-            if (fvalue < tree[nid].SplitCond()) {
-              this->SetEncodePosition(ridx, tree[nid].LeftChild());
-            } else {
-              this->SetEncodePosition(ridx, tree[nid].RightChild());
-            }
-          }
-        }
-      }
-    }
-  }
-  /*! \brief helper function to get statistics from a tree */
-  template<typename TStats>
-  inline void GetNodeStats(const std::vector<GradientPair> &gpair,
-                           const DMatrix &fmat,
-                           const RegTree &tree,
-                           std::vector< std::vector<TStats> > *p_thread_temp,
-                           std::vector<TStats> *p_node_stats) {
-    std::vector< std::vector<TStats> > &thread_temp = *p_thread_temp;
-    thread_temp.resize(omp_get_max_threads());
-    p_node_stats->resize(tree.param.num_nodes);
-#pragma omp parallel
-    {
-      const int tid = omp_get_thread_num();
-      thread_temp[tid].resize(tree.param.num_nodes, TStats());
-      for (unsigned int nid : qexpand_) {
-        thread_temp[tid][nid] = TStats();
-      }
-    }
-    // setup position
-    const auto ndata = static_cast<bst_omp_uint>(fmat.Info().num_row_);
-#pragma omp parallel for schedule(static)
-    for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
-      const int nid = position_[ridx];
-      const int tid = omp_get_thread_num();
-      if (nid >= 0) {
-        thread_temp[tid][nid].Add(gpair[ridx]);
-      }
-    }
-    // sum the per thread statistics together
-    for (int nid : qexpand_) {
-      TStats &s = (*p_node_stats)[nid];
-      s = TStats();
-      for (size_t tid = 0; tid < thread_temp.size(); ++tid) {
-        s.Add(thread_temp[tid][nid]);
-      }
-    }
-  }
-  /*! \brief common helper data structure to build sketch */
-  struct SketchEntry {
-    /*! \brief total sum of amount to be met */
-    double sum_total;
-    /*! \brief statistics used in the sketch */
-    double rmin, wmin;
-    /*! \brief last seen feature value */
-    bst_float last_fvalue;
-    /*! \brief current size of sketch */
-    double next_goal;
-    // pointer to the sketch to put things in
-    common::WXQuantileSketch<bst_float, bst_float> *sketch;
-    // initialize the space
-    inline void Init(unsigned max_size) {
-      next_goal = -1.0f;
-      rmin = wmin = 0.0f;
-      sketch->temp.Reserve(max_size + 1);
-      sketch->temp.size = 0;
-    }
-    /*!
-     * \brief push a new element to sketch
-     * \param fvalue feature value, comes in sorted ascending order
-     * \param w weight
-     * \param max_size
-     */
-    inline void Push(bst_float fvalue, bst_float w, unsigned max_size) {
-      if (next_goal == -1.0f) {
-        next_goal = 0.0f;
-        last_fvalue = fvalue;
-        wmin = w;
-        return;
-      }
-      if (last_fvalue != fvalue) {
-        double rmax = rmin + wmin;
-        if (rmax >= next_goal && sketch->temp.size != max_size) {
-          if (sketch->temp.size == 0 ||
-              last_fvalue > sketch->temp.data[sketch->temp.size-1].value) {
-            // push to sketch
-            sketch->temp.data[sketch->temp.size] =
-                common::WXQuantileSketch<bst_float, bst_float>::
-                Entry(static_cast<bst_float>(rmin),
-                      static_cast<bst_float>(rmax),
-                      static_cast<bst_float>(wmin), last_fvalue);
-            CHECK_LT(sketch->temp.size, max_size)
-                << "invalid maximum size max_size=" << max_size
-                << ", stemp.size" << sketch->temp.size;
-            ++sketch->temp.size;
-          }
-          if (sketch->temp.size == max_size) {
-            next_goal = sum_total * 2.0f + 1e-5f;
-          } else {
-            next_goal = static_cast<bst_float>(sketch->temp.size * sum_total / max_size);
-          }
-        } else {
-          if (rmax >= next_goal) {
-            LOG(TRACKER) << "INFO: rmax=" << rmax
-                         << ", sum_total=" << sum_total
-                         << ", naxt_goal=" << next_goal
-                         << ", size=" << sketch->temp.size;
-          }
-        }
-        rmin = rmax;
-        wmin = w;
-        last_fvalue = fvalue;
-      } else {
-        wmin += w;
-      }
-    }
-    /*! \brief push final unfinished value to the sketch */
-    inline void Finalize(unsigned max_size) {
-      double rmax = rmin + wmin;
-      if (sketch->temp.size == 0 || last_fvalue > sketch->temp.data[sketch->temp.size-1].value) {
-        CHECK_LE(sketch->temp.size, max_size)
-            << "Finalize: invalid maximum size, max_size=" << max_size
-            << ", stemp.size=" << sketch->temp.size;
-        // push to sketch
-        sketch->temp.data[sketch->temp.size] =
-            common::WXQuantileSketch<bst_float, bst_float>::
-            Entry(static_cast<bst_float>(rmin),
-                  static_cast<bst_float>(rmax),
-                  static_cast<bst_float>(wmin), last_fvalue);
-        ++sketch->temp.size;
-      }
-      sketch->PushTemp();
-    }
-  };
-  /*! \brief training parameter of tree grower */
-  TrainParam param_;
-  /*! \brief queue of nodes to be expanded */
-  std::vector<int> qexpand_;
-  /*!
-   * \brief map active node to is working index offset in qexpand,
-   *   can be -1, which means the node is node actively expanding
-   */
-  std::vector<int> node2workindex_;
-  /*!
-   * \brief position of each instance in the tree
-   *   can be negative, which means this position is no longer expanding
-   *   see also Decode/EncodePosition
-   */
-  std::vector<int> position_;
-
-  FeatureInteractionConstraintHost interaction_constraints_;
-
- private:
-  inline void UpdateNode2WorkIndex(const RegTree &tree) {
-    // update the node2workindex
-    std::fill(node2workindex_.begin(), node2workindex_.end(), -1);
-    node2workindex_.resize(tree.param.num_nodes);
-    for (size_t i = 0; i < qexpand_.size(); ++i) {
-      node2workindex_[qexpand_[i]] = static_cast<int>(i);
-    }
-  }
-};
-}  // namespace tree
-}  // namespace xgboost
-#endif  // XGBOOST_TREE_UPDATER_BASEMAKER_INL_H_
diff --git a/ml-xgboost/src/tree/updater_colmaker.cc b/ml-xgboost/src/tree/updater_colmaker.cc
deleted file mode 100644
index 43b6050..0000000
--- a/ml-xgboost/src/tree/updater_colmaker.cc
+++ /dev/null
@@ -1,633 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2019 by Contributors
- * \file updater_colmaker.cc
- * \brief use columnwise update to construct a tree
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <memory>
-#include <vector>
-#include <cmath>
-#include <algorithm>
-
-#include "xgboost/parameter.h"
-#include "xgboost/tree_updater.h"
-#include "xgboost/logging.h"
-#include "xgboost/json.h"
-#include "param.h"
-#include "constraints.h"
-#include "../common/random.h"
-#include "split_evaluator.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_colmaker);
-
-struct ColMakerTrainParam : XGBoostParameter<ColMakerTrainParam> {
-  // speed optimization for dense column
-  float opt_dense_col;
-  DMLC_DECLARE_PARAMETER(ColMakerTrainParam) {
-    DMLC_DECLARE_FIELD(opt_dense_col)
-        .set_range(0.0f, 1.0f)
-        .set_default(1.0f)
-        .describe("EXP Param: speed optimization for dense column.");
-  }
-
-  /*! \brief whether need forward small to big search: default right */
-  inline bool NeedForwardSearch(int default_direction, float col_density,
-                                bool indicator) const {
-    return default_direction == TrainParamKernel::right ||
-           (default_direction == TrainParamKernel::learn && (col_density < opt_dense_col) &&
-            !indicator);
-  }
-  /*! \brief whether need backward big to small search: default left */
-  inline bool NeedBackwardSearch(int default_direction) const {
-    return default_direction != TrainParamKernel::right;
-  }
-};
-
-DMLC_REGISTER_PARAMETER(ColMakerTrainParam);
-
-/*! \brief column-wise update to construct a tree */
-class ColMaker: public TreeUpdater {
- public:
-  void Configure(const Args& args) override {
-    param_.UpdateAllowUnknown(args);
-    colmaker_param_.UpdateAllowUnknown(args);
-    if (!spliteval_) {
-      spliteval_.reset(SplitEvaluator::Create(param_.split_evaluator));
-    }
-    spliteval_->Init(&param_);
-  }
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("train_param"), &this->param_);
-    FromJson(config.at("colmaker_train_param"), &this->colmaker_param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["train_param"] = ToJson(param_);
-    out["colmaker_train_param"] = ToJson(colmaker_param_);
-  }
-
-  char const* Name() const override {
-    return "grow_colmaker";
-  }
-
-  void LazyGetColumnDensity(DMatrix *dmat) {
-    // Finds densities if we don't already have them
-    if (column_densities_.empty()) {
-      std::vector<size_t> column_size(dmat->Info().num_col_);
-      for (const auto &batch : dmat->GetBatches<SortedCSCPage>()) {
-        for (auto i = 0u; i < batch.Size(); i++) {
-          column_size[i] += batch[i].size();
-        }
-      }
-      column_densities_.resize(column_size.size());
-      for (auto i = 0u; i < column_densities_.size(); i++) {
-        size_t nmiss = dmat->Info().num_row_ - column_size[i];
-        column_densities_[i] =
-            1.0f - (static_cast<float>(nmiss)) / dmat->Info().num_row_;
-      }
-    }
-  }
-
-  void Update(HostDeviceVector<GradientPair> *gpair,
-              DMatrix* dmat,
-              const std::vector<RegTree*> &trees, int iter,
-              float* p_last_round_best_loss) override {
-    if (rabit::IsDistributed()) {
-      LOG(FATAL) << "Updater `grow_colmaker` or `exact` tree method doesn't "
-                    "support distributed training.";
-    }
-    this->LazyGetColumnDensity(dmat);
-    // rescale learning rate according to size of trees
-    float lr = param_.learning_rate;
-    param_.learning_rate = lr / trees.size();
-    interaction_constraints_.Configure(param_, dmat->Info().num_row_);
-    // build tree
-    for (auto tree : trees) {
-      Builder builder(
-        param_,
-        colmaker_param_,
-        std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone()),
-        interaction_constraints_, column_densities_);
-      builder.Update(gpair->ConstHostVector(), dmat, tree);
-    }
-    param_.learning_rate = lr;
-  }
-
- protected:
-  // training parameter
-  TrainParam param_;
-  ColMakerTrainParam colmaker_param_;
-  // SplitEvaluator that will be cloned for each Builder
-  std::unique_ptr<SplitEvaluator> spliteval_;
-  std::vector<float> column_densities_;
-
-  FeatureInteractionConstraintHost interaction_constraints_;
-  // data structure
-  /*! \brief per thread x per node entry to store tmp data */
-  struct ThreadEntry {
-    /*! \brief statistics of data */
-    GradStats stats;
-    /*! \brief last feature value scanned */
-    bst_float last_fvalue { 0 };
-    /*! \brief current best solution */
-    SplitEntry best;
-    // constructor
-    ThreadEntry() = default;
-  };
-  struct NodeEntry {
-    /*! \brief statics for node entry */
-    GradStats stats;
-    /*! \brief loss of this node, without split */
-    bst_float root_gain { 0.0f };
-    /*! \brief weight calculated related to current data */
-    bst_float weight { 0.0f };
-    /*! \brief current best solution */
-    SplitEntry best;
-    // constructor
-    NodeEntry() = default;
-  };
-  // actual builder that runs the algorithm
-  class Builder {
-   public:
-    // constructor
-    explicit Builder(const TrainParam& param,
-                     const ColMakerTrainParam& colmaker_train_param,
-                     std::unique_ptr<SplitEvaluator> spliteval,
-                     FeatureInteractionConstraintHost _interaction_constraints,
-                     const std::vector<float> &column_densities)
-        : param_(param), colmaker_train_param_{colmaker_train_param},
-          nthread_(omp_get_max_threads()),
-          spliteval_(std::move(spliteval)),
-          interaction_constraints_{std::move(_interaction_constraints)},
-          column_densities_(column_densities) {}
-    // update one tree, growing
-    virtual void Update(const std::vector<GradientPair>& gpair,
-                        DMatrix* p_fmat,
-                        RegTree* p_tree) {
-      std::vector<int> newnodes;
-      this->InitData(gpair, *p_fmat, *p_tree);
-      this->InitNewNode(qexpand_, gpair, *p_fmat, *p_tree);
-      for (int depth = 0; depth < param_.max_depth; ++depth) {
-        this->FindSplit(depth, qexpand_, gpair, p_fmat, p_tree);
-        this->ResetPosition(qexpand_, p_fmat, *p_tree);
-        this->UpdateQueueExpand(*p_tree, qexpand_, &newnodes);
-        this->InitNewNode(newnodes, gpair, *p_fmat, *p_tree);
-        for (auto nid : qexpand_) {
-          if ((*p_tree)[nid].IsLeaf()) {
-            continue;
-          }
-          int cleft = (*p_tree)[nid].LeftChild();
-          int cright = (*p_tree)[nid].RightChild();
-          spliteval_->AddSplit(nid,
-                               cleft,
-                               cright,
-                               snode_[nid].best.SplitIndex(),
-                               snode_[cleft].weight,
-                               snode_[cright].weight);
-          interaction_constraints_.Split(nid, snode_[nid].best.SplitIndex(), cleft, cright);
-        }
-        qexpand_ = newnodes;
-        // if nothing left to be expand, break
-        if (qexpand_.size() == 0) break;
-      }
-      // set all the rest expanding nodes to leaf
-      for (const int nid : qexpand_) {
-        (*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
-      }
-      // remember auxiliary statistics in the tree node
-      for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
-        p_tree->Stat(nid).loss_chg = snode_[nid].best.loss_chg;
-        p_tree->Stat(nid).base_weight = snode_[nid].weight;
-        p_tree->Stat(nid).sum_hess = static_cast<float>(snode_[nid].stats.sum_hess);
-      }
-    }
-
-   protected:
-    // initialize temp data structure
-    inline void InitData(const std::vector<GradientPair>& gpair,
-                         const DMatrix& fmat,
-                         const RegTree& tree) {
-      {
-        // setup position
-        position_.resize(gpair.size());
-        CHECK_EQ(fmat.Info().num_row_, position_.size());
-        std::fill(position_.begin(), position_.end(), 0);
-        // mark delete for the deleted datas
-        for (size_t ridx = 0; ridx < position_.size(); ++ridx) {
-          if (gpair[ridx].GetHess() < 0.0f) position_[ridx] = ~position_[ridx];
-        }
-        // mark subsample
-        if (param_.subsample < 1.0f) {
-          CHECK_EQ(param_.sampling_method, TrainParam::kUniform)
-            << "Only uniform sampling is supported, "
-            << "gradient-based sampling is only support by GPU Hist.";
-          std::bernoulli_distribution coin_flip(param_.subsample);
-          auto& rnd = common::GlobalRandom();
-          for (size_t ridx = 0; ridx < position_.size(); ++ridx) {
-            if (gpair[ridx].GetHess() < 0.0f) continue;
-            if (!coin_flip(rnd)) position_[ridx] = ~position_[ridx];
-          }
-        }
-      }
-      {
-        column_sampler_.Init(fmat.Info().num_col_, param_.colsample_bynode,
-                             param_.colsample_bylevel, param_.colsample_bytree);
-      }
-      {
-        // setup temp space for each thread
-        // reserve a small space
-        stemp_.clear();
-        stemp_.resize(this->nthread_, std::vector<ThreadEntry>());
-        for (auto& i : stemp_) {
-          i.clear(); i.reserve(256);
-        }
-        snode_.reserve(256);
-      }
-      {
-        // expand query
-        qexpand_.reserve(256); qexpand_.clear();
-        qexpand_.push_back(0);
-      }
-    }
-    /*!
-     * \brief initialize the base_weight, root_gain,
-     *  and NodeEntry for all the new nodes in qexpand
-     */
-    inline void InitNewNode(const std::vector<int>& qexpand,
-                            const std::vector<GradientPair>& gpair,
-                            const DMatrix& fmat,
-                            const RegTree& tree) {
-      {
-        // setup statistics space for each tree node
-        for (auto& i : stemp_) {
-          i.resize(tree.param.num_nodes, ThreadEntry());
-        }
-        snode_.resize(tree.param.num_nodes, NodeEntry());
-      }
-      const MetaInfo& info = fmat.Info();
-      // setup position
-      const auto ndata = static_cast<bst_omp_uint>(info.num_row_);
-      #pragma omp parallel for schedule(static)
-      for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
-        const int tid = omp_get_thread_num();
-        if (position_[ridx] < 0) continue;
-        stemp_[tid][position_[ridx]].stats.Add(gpair[ridx]);
-      }
-      // sum the per thread statistics together
-      for (int nid : qexpand) {
-        GradStats stats;
-        for (auto& s : stemp_) {
-          stats.Add(s[nid].stats);
-        }
-        // update node statistics
-        snode_[nid].stats = stats;
-      }
-      // calculating the weights
-      for (int nid : qexpand) {
-        bst_uint parentid = tree[nid].Parent();
-        snode_[nid].weight = static_cast<float>(
-            spliteval_->ComputeWeight(parentid, snode_[nid].stats));
-        snode_[nid].root_gain = static_cast<float>(
-            spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
-      }
-    }
-    /*! \brief update queue expand add in new leaves */
-    inline void UpdateQueueExpand(const RegTree& tree,
-                                  const std::vector<int> &qexpand,
-                                  std::vector<int>* p_newnodes) {
-      p_newnodes->clear();
-      for (int nid : qexpand) {
-        if (!tree[ nid ].IsLeaf()) {
-          p_newnodes->push_back(tree[nid].LeftChild());
-          p_newnodes->push_back(tree[nid].RightChild());
-        }
-      }
-    }
-
-    // update enumeration solution
-    inline void UpdateEnumeration(int nid, GradientPair gstats,
-                                  bst_float fvalue, int d_step, bst_uint fid,
-                                  GradStats &c, std::vector<ThreadEntry> &temp) const { // NOLINT(*)
-      // get the statistics of nid
-      ThreadEntry &e = temp[nid];
-      // test if first hit, this is fine, because we set 0 during init
-      if (e.stats.Empty()) {
-        e.stats.Add(gstats);
-        e.last_fvalue = fvalue;
-      } else {
-        // try to find a split
-        if (fvalue != e.last_fvalue &&
-            e.stats.sum_hess >= param_.min_child_weight) {
-          c.SetSubstract(snode_[nid].stats, e.stats);
-          if (c.sum_hess >= param_.min_child_weight) {
-            bst_float loss_chg {0};
-            if (d_step == -1) {
-              loss_chg = static_cast<bst_float>(
-                  spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
-                  snode_[nid].root_gain);
-              bst_float proposed_split = (fvalue + e.last_fvalue) * 0.5f;
-              if ( proposed_split == fvalue ) {
-                e.best.Update(loss_chg, fid, e.last_fvalue,
-                              d_step == -1, c, e.stats);
-              } else {
-                e.best.Update(loss_chg, fid, proposed_split,
-                              d_step == -1, c, e.stats);
-              }
-            } else {
-              loss_chg = static_cast<bst_float>(
-                  spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
-                  snode_[nid].root_gain);
-              bst_float proposed_split = (fvalue + e.last_fvalue) * 0.5f;
-              if ( proposed_split == fvalue ) {
-                e.best.Update(loss_chg, fid, e.last_fvalue,
-                            d_step == -1, e.stats, c);
-              } else {
-                e.best.Update(loss_chg, fid, proposed_split,
-                            d_step == -1, e.stats, c);
-              }
-            }
-          }
-        }
-        // update the statistics
-        e.stats.Add(gstats);
-        e.last_fvalue = fvalue;
-      }
-    }
-    // same as EnumerateSplit, with cacheline prefetch optimization
-    void EnumerateSplit(const Entry *begin,
-                        const Entry *end,
-                        int d_step,
-                        bst_uint fid,
-                        const std::vector<GradientPair> &gpair,
-                        std::vector<ThreadEntry> &temp)  const { // NOLINT(*)
-      CHECK(param_.cache_opt) << "Support for `cache_opt' is removed in 1.0.0";
-      const std::vector<int> &qexpand = qexpand_;
-      // clear all the temp statistics
-      for (auto nid : qexpand) {
-        temp[nid].stats = GradStats();
-      }
-      // left statistics
-      GradStats c;
-      // local cache buffer for position and gradient pair
-      constexpr int kBuffer = 32;
-      int buf_position[kBuffer] = {};
-      GradientPair buf_gpair[kBuffer] = {};
-      // aligned ending position
-      const Entry *align_end;
-      if (d_step > 0) {
-        align_end = begin + (end - begin) / kBuffer * kBuffer;
-      } else {
-        align_end = begin - (begin - end) / kBuffer * kBuffer;
-      }
-      int i;
-      const Entry *it;
-      const int align_step = d_step * kBuffer;
-      // internal cached loop
-      for (it = begin; it != align_end; it += align_step) {
-        const Entry *p;
-        for (i = 0, p = it; i < kBuffer; ++i, p += d_step) {
-          buf_position[i] = position_[p->index];
-          buf_gpair[i] = gpair[p->index];
-        }
-        for (i = 0, p = it; i < kBuffer; ++i, p += d_step) {
-          const int nid = buf_position[i];
-          if (nid < 0 || !interaction_constraints_.Query(nid, fid)) { continue; }
-          this->UpdateEnumeration(nid, buf_gpair[i],
-                                  p->fvalue, d_step,
-                                  fid, c, temp);
-        }
-      }
-
-      // finish up the ending piece
-      for (it = align_end, i = 0; it != end; ++i, it += d_step) {
-        buf_position[i] = position_[it->index];
-        buf_gpair[i] = gpair[it->index];
-      }
-      for (it = align_end, i = 0; it != end; ++i, it += d_step) {
-        const int nid = buf_position[i];
-        if (nid < 0 || !interaction_constraints_.Query(nid, fid)) { continue; }
-        this->UpdateEnumeration(nid, buf_gpair[i],
-                                it->fvalue, d_step,
-                                fid, c, temp);
-      }
-      // finish updating all statistics, check if it is possible to include all sum statistics
-      for (int nid : qexpand) {
-        ThreadEntry &e = temp[nid];
-        c.SetSubstract(snode_[nid].stats, e.stats);
-        if (e.stats.sum_hess >= param_.min_child_weight &&
-            c.sum_hess >= param_.min_child_weight) {
-          bst_float loss_chg;
-          const bst_float gap = std::abs(e.last_fvalue) + kRtEps;
-          const bst_float delta = d_step == +1 ? gap: -gap;
-          if (d_step == -1) {
-            loss_chg = static_cast<bst_float>(
-                spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
-                snode_[nid].root_gain);
-            e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1, c,
-                          e.stats);
-          } else {
-            loss_chg = static_cast<bst_float>(
-                spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
-                snode_[nid].root_gain);
-            e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1,
-                          e.stats, c);
-          }
-        }
-      }
-    }
-
-    // update the solution candidate
-    virtual void UpdateSolution(const SparsePage &batch,
-                                const std::vector<bst_feature_t> &feat_set,
-                                const std::vector<GradientPair> &gpair,
-                                DMatrix*p_fmat) {
-      // start enumeration
-      const auto num_features = static_cast<bst_omp_uint>(feat_set.size());
-#if defined(_OPENMP)
-      const int batch_size =  // NOLINT
-          std::max(static_cast<int>(num_features / this->nthread_ / 32), 1);
-#endif  // defined(_OPENMP)
-
-      {
-#pragma omp parallel for schedule(dynamic, batch_size)
-        for (bst_omp_uint i = 0; i < num_features; ++i) {
-          bst_feature_t const fid = feat_set[i];
-          int32_t const tid = omp_get_thread_num();
-          auto c = batch[fid];
-          const bool ind = c.size() != 0 && c[0].fvalue == c[c.size() - 1].fvalue;
-          if (colmaker_train_param_.NeedForwardSearch(
-                  param_.default_direction, column_densities_[fid], ind)) {
-            this->EnumerateSplit(c.data(), c.data() + c.size(), +1,
-                                 fid, gpair, stemp_[tid]);
-          }
-          if (colmaker_train_param_.NeedBackwardSearch(param_.default_direction)) {
-            this->EnumerateSplit(c.data() + c.size() - 1, c.data() - 1, -1,
-                                 fid, gpair, stemp_[tid]);
-          }
-        }
-      }
-    }
-    // find splits at current level, do split per level
-    inline void FindSplit(int depth,
-                          const std::vector<int> &qexpand,
-                          const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat,
-                          RegTree *p_tree) {
-      auto feat_set = column_sampler_.GetFeatureSet(depth);
-      for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-        this->UpdateSolution(batch, feat_set->HostVector(), gpair, p_fmat);
-      }
-      // after this each thread's stemp will get the best candidates, aggregate results
-      this->SyncBestSolution(qexpand);
-      // get the best result, we can synchronize the solution
-      for (int nid : qexpand) {
-        NodeEntry const &e = snode_[nid];
-        // now we know the solution in snode[nid], set split
-        if (e.best.loss_chg > kRtEps) {
-          bst_float left_leaf_weight =
-              spliteval_->ComputeWeight(nid, e.best.left_sum) *
-              param_.learning_rate;
-          bst_float right_leaf_weight =
-              spliteval_->ComputeWeight(nid, e.best.right_sum) *
-              param_.learning_rate;
-          p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
-                             e.best.DefaultLeft(), e.weight, left_leaf_weight,
-                             right_leaf_weight, e.best.loss_chg,
-                             e.stats.sum_hess,
-                             e.best.left_sum.GetHess(), e.best.right_sum.GetHess(),
-                             0);
-        } else {
-          (*p_tree)[nid].SetLeaf(e.weight * param_.learning_rate);
-        }
-      }
-    }
-    // reset position of each data points after split is created in the tree
-    inline void ResetPosition(const std::vector<int> &qexpand,
-                              DMatrix* p_fmat,
-                              const RegTree& tree) {
-      // set the positions in the nondefault
-      this->SetNonDefaultPosition(qexpand, p_fmat, tree);
-      // set rest of instances to default position
-      // set default direct nodes to default
-      // for leaf nodes that are not fresh, mark then to ~nid,
-      // so that they are ignored in future statistics collection
-      const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
-
-#pragma omp parallel for schedule(static)
-      for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
-        CHECK_LT(ridx, position_.size())
-            << "ridx exceed bound " << "ridx="<<  ridx << " pos=" << position_.size();
-        const int nid = this->DecodePosition(ridx);
-        if (tree[nid].IsLeaf()) {
-          // mark finish when it is not a fresh leaf
-          if (tree[nid].RightChild() == -1) {
-            position_[ridx] = ~nid;
-          }
-        } else {
-          // push to default branch
-          if (tree[nid].DefaultLeft()) {
-            this->SetEncodePosition(ridx, tree[nid].LeftChild());
-          } else {
-            this->SetEncodePosition(ridx, tree[nid].RightChild());
-          }
-        }
-      }
-    }
-    // customization part
-    // synchronize the best solution of each node
-    virtual void SyncBestSolution(const std::vector<int> &qexpand) {
-      for (int nid : qexpand) {
-        NodeEntry &e = snode_[nid];
-        for (int tid = 0; tid < this->nthread_; ++tid) {
-          e.best.Update(stemp_[tid][nid].best);
-        }
-      }
-    }
-    virtual void SetNonDefaultPosition(const std::vector<int> &qexpand,
-                                       DMatrix *p_fmat,
-                                       const RegTree &tree) {
-      // step 1, classify the non-default data into right places
-      std::vector<unsigned> fsplits;
-      for (int nid : qexpand) {
-        if (!tree[nid].IsLeaf()) {
-          fsplits.push_back(tree[nid].SplitIndex());
-        }
-      }
-      std::sort(fsplits.begin(), fsplits.end());
-      fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
-      for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-        for (auto fid : fsplits) {
-          auto col = batch[fid];
-          const auto ndata = static_cast<bst_omp_uint>(col.size());
-#pragma omp parallel for schedule(static)
-          for (bst_omp_uint j = 0; j < ndata; ++j) {
-            const bst_uint ridx = col[j].index;
-            const int nid = this->DecodePosition(ridx);
-            const bst_float fvalue = col[j].fvalue;
-            // go back to parent, correct those who are not default
-            if (!tree[nid].IsLeaf() && tree[nid].SplitIndex() == fid) {
-              if (fvalue < tree[nid].SplitCond()) {
-                this->SetEncodePosition(ridx, tree[nid].LeftChild());
-              } else {
-                this->SetEncodePosition(ridx, tree[nid].RightChild());
-              }
-            }
-          }
-        }
-      }
-    }
-    // utils to get/set position, with encoded format
-    // return decoded position
-    inline int DecodePosition(bst_uint ridx) const {
-      const int pid = position_[ridx];
-      return pid < 0 ? ~pid : pid;
-    }
-    // encode the encoded position value for ridx
-    inline void SetEncodePosition(bst_uint ridx, int nid) {
-      if (position_[ridx] < 0) {
-        position_[ridx] = ~nid;
-      } else {
-        position_[ridx] = nid;
-      }
-    }
-    //  --data fields--
-    const TrainParam& param_;
-    const ColMakerTrainParam& colmaker_train_param_;
-    // number of omp thread used during training
-    const int nthread_;
-    common::ColumnSampler column_sampler_;
-    // Instance Data: current node position in the tree of each instance
-    std::vector<int> position_;
-    // PerThread x PerTreeNode: statistics for per thread construction
-    std::vector< std::vector<ThreadEntry> > stemp_;
-    /*! \brief TreeNode Data: statistics for each constructed node */
-    std::vector<NodeEntry> snode_;
-    /*! \brief queue of nodes to be expanded */
-    std::vector<int> qexpand_;
-    // Evaluates splits and computes optimal weights for a given split
-    std::unique_ptr<SplitEvaluator> spliteval_;
-
-    FeatureInteractionConstraintHost interaction_constraints_;
-    const std::vector<float> &column_densities_;
-  };
-};
-
-XGBOOST_REGISTER_TREE_UPDATER(ColMaker, "grow_colmaker")
-.describe("Grow tree with parallelization over columns.")
-.set_body([]() {
-    return new ColMaker();
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_gpu_common.cuh b/ml-xgboost/src/tree/updater_gpu_common.cuh
deleted file mode 100644
index 63da94a..0000000
--- a/ml-xgboost/src/tree/updater_gpu_common.cuh
+++ /dev/null
@@ -1,182 +0,0 @@
-/*!
- * Copyright 2017-2019 XGBoost contributors
- */
-#pragma once
-#include <thrust/random.h>
-#include <cstdio>
-#include <cub/cub.cuh>
-#include <stdexcept>
-#include <string>
-#include <vector>
-#include "../common/device_helpers.cuh"
-#include "../common/random.h"
-#include "param.h"
-
-namespace xgboost {
-namespace tree {
-
-struct GPUTrainingParam {
-  // minimum amount of hessian(weight) allowed in a child
-  float min_child_weight;
-  // L2 regularization factor
-  float reg_lambda;
-  // L1 regularization factor
-  float reg_alpha;
-  // maximum delta update we can add in weight estimation
-  // this parameter can be used to stabilize update
-  // default=0 means no constraint on weight delta
-  float max_delta_step;
-
-  GPUTrainingParam() = default;
-
-  XGBOOST_DEVICE explicit GPUTrainingParam(const TrainParam& param)
-      : min_child_weight(param.min_child_weight),
-        reg_lambda(param.reg_lambda),
-        reg_alpha(param.reg_alpha),
-        max_delta_step(param.max_delta_step) {}
-};
-
-using NodeIdT = int32_t;
-
-/** used to assign default id to a Node */
-static const bst_node_t kUnusedNode = -1;
-
-/**
- * @enum DefaultDirection node.cuh
- * @brief Default direction to be followed in case of missing values
- */
-enum DefaultDirection {
-  /** move to left child */
-  kLeftDir = 0,
-  /** move to right child */
-  kRightDir
-};
-
-struct DeviceSplitCandidate {
-  float loss_chg {-FLT_MAX};
-  DefaultDirection dir {kLeftDir};
-  int findex {-1};
-  float fvalue {0};
-
-  GradientPair left_sum;
-  GradientPair right_sum;
-
-  XGBOOST_DEVICE DeviceSplitCandidate() {}  // NOLINT
-
-  template <typename ParamT>
-  XGBOOST_DEVICE void Update(const DeviceSplitCandidate& other,
-                             const ParamT& param) {
-    if (other.loss_chg > loss_chg &&
-        other.left_sum.GetHess() >= param.min_child_weight &&
-        other.right_sum.GetHess() >= param.min_child_weight) {
-      *this = other;
-    }
-  }
-
-  XGBOOST_DEVICE void Update(float loss_chg_in, DefaultDirection dir_in,
-                             float fvalue_in, int findex_in,
-                             GradientPair left_sum_in,
-                             GradientPair right_sum_in,
-                             const GPUTrainingParam& param) {
-    if (loss_chg_in > loss_chg &&
-        left_sum_in.GetHess() >= param.min_child_weight &&
-        right_sum_in.GetHess() >= param.min_child_weight) {
-      loss_chg = loss_chg_in;
-      dir = dir_in;
-      fvalue = fvalue_in;
-      left_sum = left_sum_in;
-      right_sum = right_sum_in;
-      findex = findex_in;
-    }
-  }
-  XGBOOST_DEVICE bool IsValid() const { return loss_chg > 0.0f; }
-
-  friend std::ostream& operator<<(std::ostream& os, DeviceSplitCandidate const& c) {
-    os << "loss_chg:" << c.loss_chg << ", "
-       << "dir: " << c.dir << ", "
-       << "findex: " << c.findex << ", "
-       << "fvalue: " << c.fvalue << ", "
-       << "left sum: " << c.left_sum << ", "
-       << "right sum: " << c.right_sum << std::endl;
-    return os;
-  }
-};
-
-struct DeviceSplitCandidateReduceOp {
-  GPUTrainingParam param;
-  explicit DeviceSplitCandidateReduceOp(GPUTrainingParam param) : param(std::move(param)) {}
-  XGBOOST_DEVICE DeviceSplitCandidate operator()(
-      const DeviceSplitCandidate& a, const DeviceSplitCandidate& b) const {
-    DeviceSplitCandidate best;
-    best.Update(a, param);
-    best.Update(b, param);
-    return best;
-  }
-};
-
-struct DeviceNodeStats {
-  GradientPair sum_gradients;
-  float root_gain {-FLT_MAX};
-  float weight {-FLT_MAX};
-
-  /** default direction for missing values */
-  DefaultDirection dir {kLeftDir};
-  /** threshold value for comparison */
-  float fvalue {0.0f};
-  GradientPair left_sum;
-  GradientPair right_sum;
-  /** \brief The feature index. */
-  int fidx{kUnusedNode};
-  /** node id (used as key for reduce/scan) */
-  NodeIdT idx{kUnusedNode};
-
-  XGBOOST_DEVICE DeviceNodeStats() {}  // NOLINT
-
-  template <typename ParamT>
-  HOST_DEV_INLINE DeviceNodeStats(GradientPair sum_gradients, NodeIdT nidx,
-                                  const ParamT& param)
-      : sum_gradients(sum_gradients),
-        idx(nidx) {
-    this->root_gain =
-        CalcGain(param, sum_gradients.GetGrad(), sum_gradients.GetHess());
-    this->weight =
-        CalcWeight(param, sum_gradients.GetGrad(), sum_gradients.GetHess());
-  }
-
-  HOST_DEV_INLINE void SetSplit(float fvalue, int fidx, DefaultDirection dir,
-                                GradientPair left_sum, GradientPair right_sum) {
-    this->fvalue = fvalue;
-    this->fidx = fidx;
-    this->dir = dir;
-    this->left_sum = left_sum;
-    this->right_sum = right_sum;
-  }
-
-  HOST_DEV_INLINE void SetSplit(const DeviceSplitCandidate& split) {
-    this->SetSplit(split.fvalue, split.findex, split.dir, split.left_sum,
-                   split.right_sum);
-  }
-
-  /** Tells whether this node is part of the decision tree */
-  HOST_DEV_INLINE bool IsUnused() const { return (idx == kUnusedNode); }
-
-  /** Tells whether this node is a leaf of the decision tree */
-  HOST_DEV_INLINE bool IsLeaf() const {
-    return (!IsUnused() && (fidx == kUnusedNode));
-  }
-};
-
-template <typename T>
-struct SumCallbackOp {
-  // Running prefix
-  T running_total;
-  // Constructor
-  XGBOOST_DEVICE SumCallbackOp() : running_total(T()) {}
-  XGBOOST_DEVICE T operator()(T block_aggregate) {
-    T old_prefix = running_total;
-    running_total += block_aggregate;
-    return old_prefix;
-  }
-};
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_gpu_hist.cu b/ml-xgboost/src/tree/updater_gpu_hist.cu
deleted file mode 100644
index 67848b1..0000000
--- a/ml-xgboost/src/tree/updater_gpu_hist.cu
+++ /dev/null
@@ -1,1138 +0,0 @@
-/*!
- * Copyright 2017-2020 XGBoost contributors
- */
-#include <thrust/copy.h>
-#include <thrust/reduce.h>
-#include <xgboost/tree_updater.h>
-#include <algorithm>
-#include <cmath>
-#include <memory>
-#include <limits>
-#include <queue>
-#include <utility>
-#include <vector>
-
-#include "xgboost/host_device_vector.h"
-#include "xgboost/parameter.h"
-#include "xgboost/span.h"
-#include "xgboost/json.h"
-
-#include "../common/device_helpers.cuh"
-#include "../common/hist_util.h"
-#include "../common/timer.h"
-#include "../data/ellpack_page.cuh"
-#include "param.h"
-#include "updater_gpu_common.cuh"
-#include "constraints.cuh"
-#include "gpu_hist/gradient_based_sampler.cuh"
-#include "gpu_hist/row_partitioner.cuh"
-#include "gpu_hist/histogram.cuh"
-
-namespace xgboost {
-namespace tree {
-#if !defined(GTEST_TEST)
-DMLC_REGISTRY_FILE_TAG(updater_gpu_hist);
-#endif  // !defined(GTEST_TEST)
-
-// training parameters specific to this algorithm
-struct GPUHistMakerTrainParam
-    : public XGBoostParameter<GPUHistMakerTrainParam> {
-  bool single_precision_histogram;
-  bool deterministic_histogram;
-  bool debug_synchronize;
-  // declare parameters
-  DMLC_DECLARE_PARAMETER(GPUHistMakerTrainParam) {
-    DMLC_DECLARE_FIELD(single_precision_histogram).set_default(false).describe(
-        "Use single precision to build histograms.");
-    DMLC_DECLARE_FIELD(deterministic_histogram).set_default(true).describe(
-        "Pre-round the gradient for obtaining deterministic gradient histogram.");
-    DMLC_DECLARE_FIELD(debug_synchronize).set_default(false).describe(
-        "Check if all distributed tree are identical after tree construction.");
-  }
-};
-#if !defined(GTEST_TEST)
-DMLC_REGISTER_PARAMETER(GPUHistMakerTrainParam);
-#endif  // !defined(GTEST_TEST)
-
-struct ExpandEntry {
-  int nid;
-  int depth;
-  DeviceSplitCandidate split;
-  uint64_t timestamp;
-  ExpandEntry() = default;
-  ExpandEntry(int nid, int depth, DeviceSplitCandidate split,
-              uint64_t timestamp)
-      : nid(nid), depth(depth), split(std::move(split)), timestamp(timestamp) {}
-  bool IsValid(const TrainParam& param, int num_leaves) const {
-    if (split.loss_chg <= kRtEps) return false;
-    if (split.left_sum.GetHess() == 0 || split.right_sum.GetHess() == 0) {
-      return false;
-    }
-    if (split.loss_chg < param.min_split_loss) { return false; }
-    if (param.max_depth > 0 && depth == param.max_depth) {return false; }
-    if (param.max_leaves > 0 && num_leaves == param.max_leaves) { return false; }
-    return true;
-  }
-
-  static bool ChildIsValid(const TrainParam& param, int depth, int num_leaves) {
-    if (param.max_depth > 0 && depth >= param.max_depth) return false;
-    if (param.max_leaves > 0 && num_leaves >= param.max_leaves) return false;
-    return true;
-  }
-
-  friend std::ostream& operator<<(std::ostream& os, const ExpandEntry& e) {
-    os << "ExpandEntry: \n";
-    os << "nidx: " << e.nid << "\n";
-    os << "depth: " << e.depth << "\n";
-    os << "loss: " << e.split.loss_chg << "\n";
-    os << "left_sum: " << e.split.left_sum << "\n";
-    os << "right_sum: " << e.split.right_sum << "\n";
-    return os;
-  }
-};
-
-inline static bool DepthWise(const ExpandEntry& lhs, const ExpandEntry& rhs) {
-  if (lhs.depth == rhs.depth) {
-    return lhs.timestamp > rhs.timestamp;  // favor small timestamp
-  } else {
-    return lhs.depth > rhs.depth;  // favor small depth
-  }
-}
-inline static bool LossGuide(const ExpandEntry& lhs, const ExpandEntry& rhs) {
-  if (lhs.split.loss_chg == rhs.split.loss_chg) {
-    return lhs.timestamp > rhs.timestamp;  // favor small timestamp
-  } else {
-    return lhs.split.loss_chg < rhs.split.loss_chg;  // favor large loss_chg
-  }
-}
-
-// With constraints
-template <typename GradientPairT>
-XGBOOST_DEVICE float inline LossChangeMissing(
-    const GradientPairT& scan, const GradientPairT& missing, const GradientPairT& parent_sum,
-    const float& parent_gain, const GPUTrainingParam& param, int constraint,
-    const ValueConstraint& value_constraint,
-    bool& missing_left_out) {  // NOLINT
-  float missing_left_gain = value_constraint.CalcSplitGain(
-      param, constraint, GradStats(scan + missing),
-      GradStats(parent_sum - (scan + missing)));
-  float missing_right_gain = value_constraint.CalcSplitGain(
-      param, constraint, GradStats(scan), GradStats(parent_sum - scan));
-
-  if (missing_left_gain >= missing_right_gain) {
-    missing_left_out = true;
-    return missing_left_gain - parent_gain;
-  } else {
-    missing_left_out = false;
-    return missing_right_gain - parent_gain;
-  }
-}
-
-/*!
- * \brief
- *
- * \tparam ReduceT     BlockReduce Type.
- * \tparam TempStorage Cub Shared memory
- *
- * \param begin
- * \param end
- * \param temp_storage Shared memory for intermediate result.
- */
-template <int BLOCK_THREADS, typename ReduceT, typename TempStorageT, typename GradientSumT>
-__device__ GradientSumT ReduceFeature(common::Span<const GradientSumT> feature_histogram,
-                                      TempStorageT* temp_storage) {
-  __shared__ cub::Uninitialized<GradientSumT> uninitialized_sum;
-  GradientSumT& shared_sum = uninitialized_sum.Alias();
-
-  GradientSumT local_sum = GradientSumT();
-  // For loop sums features into one block size
-  auto begin = feature_histogram.data();
-  auto end = begin + feature_histogram.size();
-  for (auto itr = begin; itr < end; itr += BLOCK_THREADS) {
-    bool thread_active = itr + threadIdx.x < end;
-    // Scan histogram
-    GradientSumT bin = thread_active ? *(itr + threadIdx.x) : GradientSumT();
-    local_sum += bin;
-  }
-  local_sum = ReduceT(temp_storage->sum_reduce).Reduce(local_sum, cub::Sum());
-  // Reduction result is stored in thread 0.
-  if (threadIdx.x == 0) {
-    shared_sum = local_sum;
-  }
-  __syncthreads();
-  return shared_sum;
-}
-
-/*! \brief Find the thread with best gain. */
-template <int BLOCK_THREADS, typename ReduceT, typename ScanT,
-          typename MaxReduceT, typename TempStorageT, typename GradientSumT>
-__device__ void EvaluateFeature(
-    int fidx, common::Span<const GradientSumT> node_histogram,
-    const EllpackDeviceAccessor& matrix,
-    DeviceSplitCandidate* best_split,  // shared memory storing best split
-    const DeviceNodeStats& node, const GPUTrainingParam& param,
-    TempStorageT* temp_storage,  // temp memory for cub operations
-    int constraint,              // monotonic_constraints
-    const ValueConstraint& value_constraint) {
-  // Use pointer from cut to indicate begin and end of bins for each feature.
-  uint32_t gidx_begin = matrix.feature_segments[fidx];  // begining bin
-  uint32_t gidx_end = matrix.feature_segments[fidx + 1];  // end bin for i^th feature
-
-  // Sum histogram bins for current feature
-  GradientSumT const feature_sum = ReduceFeature<BLOCK_THREADS, ReduceT>(
-      node_histogram.subspan(gidx_begin, gidx_end - gidx_begin), temp_storage);
-
-  GradientSumT const parent_sum = GradientSumT(node.sum_gradients);
-  GradientSumT const missing = parent_sum - feature_sum;
-  float const null_gain = -std::numeric_limits<bst_float>::infinity();
-
-  SumCallbackOp<GradientSumT> prefix_op =
-      SumCallbackOp<GradientSumT>();
-  for (int scan_begin = gidx_begin; scan_begin < gidx_end;
-       scan_begin += BLOCK_THREADS) {
-    bool thread_active = (scan_begin + threadIdx.x) < gidx_end;
-
-    // Gradient value for current bin.
-    GradientSumT bin =
-        thread_active ? node_histogram[scan_begin + threadIdx.x] : GradientSumT();
-    ScanT(temp_storage->scan).ExclusiveScan(bin, bin, cub::Sum(), prefix_op);
-
-    // Whether the gradient of missing values is put to the left side.
-    bool missing_left = true;
-    float gain = null_gain;
-    if (thread_active) {
-      gain = LossChangeMissing(bin, missing, parent_sum, node.root_gain, param,
-                               constraint, value_constraint, missing_left);
-    }
-
-    __syncthreads();
-
-    // Find thread with best gain
-    cub::KeyValuePair<int, float> tuple(threadIdx.x, gain);
-    cub::KeyValuePair<int, float> best =
-        MaxReduceT(temp_storage->max_reduce).Reduce(tuple, cub::ArgMax());
-
-    __shared__ cub::KeyValuePair<int, float> block_max;
-    if (threadIdx.x == 0) {
-      block_max = best;
-    }
-
-    __syncthreads();
-
-    // Best thread updates split
-    if (threadIdx.x == block_max.key) {
-      int split_gidx = (scan_begin + threadIdx.x) - 1;
-      float fvalue;
-      if (split_gidx < static_cast<int>(gidx_begin)) {
-        fvalue =  matrix.min_fvalue[fidx];
-      } else {
-        fvalue = matrix.gidx_fvalue_map[split_gidx];
-      }
-      GradientSumT left = missing_left ? bin + missing : bin;
-      GradientSumT right = parent_sum - left;
-      best_split->Update(gain, missing_left ? kLeftDir : kRightDir, fvalue,
-                         fidx, GradientPair(left), GradientPair(right), param);
-    }
-    __syncthreads();
-  }
-}
-
-template <int BLOCK_THREADS, typename GradientSumT>
-__global__ void EvaluateSplitKernel(
-    common::Span<const GradientSumT> node_histogram,  // histogram for gradients
-    common::Span<const bst_feature_t> feature_set,    // Selected features
-    DeviceNodeStats node,
-    xgboost::EllpackDeviceAccessor matrix,
-    GPUTrainingParam gpu_param,
-    common::Span<DeviceSplitCandidate> split_candidates,  // resulting split
-    ValueConstraint value_constraint,
-    common::Span<int> d_monotonic_constraints) {
-  // KeyValuePair here used as threadIdx.x -> gain_value
-  using ArgMaxT = cub::KeyValuePair<int, float>;
-  using BlockScanT =
-      cub::BlockScan<GradientSumT, BLOCK_THREADS, cub::BLOCK_SCAN_WARP_SCANS>;
-  using MaxReduceT = cub::BlockReduce<ArgMaxT, BLOCK_THREADS>;
-
-  using SumReduceT = cub::BlockReduce<GradientSumT, BLOCK_THREADS>;
-
-  union TempStorage {
-    typename BlockScanT::TempStorage scan;
-    typename MaxReduceT::TempStorage max_reduce;
-    typename SumReduceT::TempStorage sum_reduce;
-  };
-
-  // Aligned && shared storage for best_split
-  __shared__ cub::Uninitialized<DeviceSplitCandidate> uninitialized_split;
-  DeviceSplitCandidate& best_split = uninitialized_split.Alias();
-  __shared__ TempStorage temp_storage;
-
-  if (threadIdx.x == 0) {
-    best_split = DeviceSplitCandidate();
-  }
-
-  __syncthreads();
-
-  // One block for each feature. Features are sampled, so fidx != blockIdx.x
-  int fidx = feature_set[blockIdx.x];
-
-  int constraint = d_monotonic_constraints[fidx];
-  EvaluateFeature<BLOCK_THREADS, SumReduceT, BlockScanT, MaxReduceT>(
-      fidx, node_histogram, matrix, &best_split, node, gpu_param, &temp_storage,
-      constraint, value_constraint);
-
-  __syncthreads();
-
-  if (threadIdx.x == 0) {
-    // Record best loss for each feature
-    split_candidates[blockIdx.x] = best_split;
-  }
-}
-
-/**
- * \struct  DeviceHistogram
- *
- * \summary Data storage for node histograms on device. Automatically expands.
- *
- * \tparam GradientSumT      histogram entry type.
- * \tparam kStopGrowingSize  Do not grow beyond this size
- *
- * \author  Rory
- * \date    28/07/2018
- */
-template <typename GradientSumT, size_t kStopGrowingSize = 1 << 26>
-class DeviceHistogram {
- private:
-  /*! \brief Map nidx to starting index of its histogram. */
-  std::map<int, size_t> nidx_map_;
-  dh::device_vector<typename GradientSumT::ValueT> data_;
-  int n_bins_;
-  int device_id_;
-  static constexpr size_t kNumItemsInGradientSum =
-      sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT);
-  static_assert(kNumItemsInGradientSum == 2,
-                "Number of items in gradient type should be 2.");
-
- public:
-  void Init(int device_id, int n_bins) {
-    this->n_bins_ = n_bins;
-    this->device_id_ = device_id;
-  }
-
-  void Reset() {
-    auto d_data = data_.data().get();
-      dh::LaunchN(device_id_, data_.size(),
-                  [=] __device__(size_t idx) { d_data[idx] = 0.0f; });
-    nidx_map_.clear();
-  }
-  bool HistogramExists(int nidx) const {
-    return nidx_map_.find(nidx) != nidx_map_.cend();
-  }
-  int Bins() const {
-    return n_bins_;
-  }
-  size_t HistogramSize() const {
-    return n_bins_ * kNumItemsInGradientSum;
-  }
-
-  dh::device_vector<typename GradientSumT::ValueT>& Data() {
-    return data_;
-  }
-
-  void AllocateHistogram(int nidx) {
-    if (HistogramExists(nidx)) return;
-    // Number of items currently used in data
-    const size_t used_size = nidx_map_.size() * HistogramSize();
-    const size_t new_used_size = used_size + HistogramSize();
-    if (data_.size() >= kStopGrowingSize) {
-      // Recycle histogram memory
-      if (new_used_size <= data_.size()) {
-        // no need to remove old node, just insert the new one.
-        nidx_map_[nidx] = used_size;
-        // memset histogram size in bytes
-      } else {
-        std::pair<int, size_t> old_entry = *nidx_map_.begin();
-        nidx_map_.erase(old_entry.first);
-        nidx_map_[nidx] = old_entry.second;
-      }
-      // Zero recycled memory
-      auto d_data = data_.data().get() + nidx_map_[nidx];
-      dh::LaunchN(device_id_, n_bins_ * 2,
-                  [=] __device__(size_t idx) { d_data[idx] = 0.0f; });
-    } else {
-      // Append new node histogram
-      nidx_map_[nidx] = used_size;
-      // Check there is enough memory for another histogram node
-      if (data_.size() < new_used_size + HistogramSize()) {
-        size_t new_required_memory =
-            std::max(data_.size() * 2, HistogramSize());
-        data_.resize(new_required_memory);
-      }
-    }
-
-    CHECK_GE(data_.size(), nidx_map_.size() * HistogramSize());
-  }
-
-  /**
-   * \summary   Return pointer to histogram memory for a given node.
-   * \param nidx    Tree node index.
-   * \return    hist pointer.
-   */
-  common::Span<GradientSumT> GetNodeHistogram(int nidx) {
-    CHECK(this->HistogramExists(nidx));
-    auto ptr = data_.data().get() + nidx_map_[nidx];
-    return common::Span<GradientSumT>(
-        reinterpret_cast<GradientSumT*>(ptr), n_bins_);
-  }
-};
-
-struct CalcWeightTrainParam {
-  float min_child_weight;
-  float reg_alpha;
-  float reg_lambda;
-  float max_delta_step;
-  float learning_rate;
-  XGBOOST_DEVICE explicit CalcWeightTrainParam(const TrainParam& p)
-      : min_child_weight(p.min_child_weight),
-        reg_alpha(p.reg_alpha),
-        reg_lambda(p.reg_lambda),
-        max_delta_step(p.max_delta_step),
-        learning_rate(p.learning_rate) {}
-};
-
-// Manage memory for a single GPU
-template <typename GradientSumT>
-struct GPUHistMakerDevice {
-  int device_id;
-  EllpackPageImpl* page;
-  BatchParam batch_param;
-
-  std::unique_ptr<RowPartitioner> row_partitioner;
-  DeviceHistogram<GradientSumT> hist{};
-
-  /*! \brief Gradient pair for each row. */
-  common::Span<GradientPair> gpair;
-
-  dh::caching_device_vector<int> monotone_constraints;
-  dh::caching_device_vector<bst_float> prediction_cache;
-
-  /*! \brief Sum gradient for each node. */
-  std::vector<GradientPair> host_node_sum_gradients;
-  dh::caching_device_vector<GradientPair> node_sum_gradients;
-  bst_uint n_rows;
-
-  TrainParam param;
-  bool deterministic_histogram;
-
-  GradientSumT histogram_rounding;
-
-  dh::PinnedMemory pinned_memory;
-
-  std::vector<cudaStream_t> streams{};
-
-  common::Monitor monitor;
-  std::vector<ValueConstraint> node_value_constraints;
-  common::ColumnSampler column_sampler;
-  FeatureInteractionConstraintDevice interaction_constraints;
-
-  using ExpandQueue =
-      std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
-                          std::function<bool(ExpandEntry, ExpandEntry)>>;
-  std::unique_ptr<ExpandQueue> qexpand;
-
-  std::unique_ptr<GradientBasedSampler> sampler;
-
-  GPUHistMakerDevice(int _device_id,
-                     EllpackPageImpl* _page,
-                     bst_uint _n_rows,
-                     TrainParam _param,
-                     uint32_t column_sampler_seed,
-                     uint32_t n_features,
-                     bool deterministic_histogram,
-                     BatchParam _batch_param)
-      : device_id(_device_id),
-        page(_page),
-        n_rows(_n_rows),
-        param(std::move(_param)),
-        column_sampler(column_sampler_seed),
-        interaction_constraints(param, n_features),
-        deterministic_histogram{deterministic_histogram},
-        batch_param(_batch_param) {
-    sampler.reset(new GradientBasedSampler(page,
-                                           n_rows,
-                                           batch_param,
-                                           param.subsample,
-                                           param.sampling_method));
-    monitor.Init(std::string("GPUHistMakerDevice") + std::to_string(device_id));
-  }
-
-  void InitHistogram();
-
-  ~GPUHistMakerDevice() {  // NOLINT
-    dh::safe_cuda(cudaSetDevice(device_id));
-    for (auto& stream : streams) {
-      dh::safe_cuda(cudaStreamDestroy(stream));
-    }
-  }
-
-  // Get vector of at least n initialised streams
-  std::vector<cudaStream_t>& GetStreams(int n) {
-    if (n > streams.size()) {
-      for (auto& stream : streams) {
-        dh::safe_cuda(cudaStreamDestroy(stream));
-      }
-
-      streams.clear();
-      streams.resize(n);
-
-      for (auto& stream : streams) {
-        dh::safe_cuda(cudaStreamCreate(&stream));
-      }
-    }
-
-    return streams;
-  }
-
-  // Reset values for each update iteration
-  // Note that the column sampler must be passed by value because it is not
-  // thread safe
-  void Reset(HostDeviceVector<GradientPair>* dh_gpair, DMatrix* dmat, int64_t num_columns) {
-    if (param.grow_policy == TrainParam::kLossGuide) {
-      qexpand.reset(new ExpandQueue(LossGuide));
-    } else {
-      qexpand.reset(new ExpandQueue(DepthWise));
-    }
-    this->column_sampler.Init(num_columns, param.colsample_bynode,
-      param.colsample_bylevel, param.colsample_bytree);
-    dh::safe_cuda(cudaSetDevice(device_id));
-    this->interaction_constraints.Reset();
-    std::fill(host_node_sum_gradients.begin(), host_node_sum_gradients.end(),
-              GradientPair());
-
-    auto sample = sampler->Sample(dh_gpair->DeviceSpan(), dmat);
-    n_rows = sample.sample_rows;
-    page = sample.page;
-    gpair = sample.gpair;
-
-    if (deterministic_histogram) {
-      histogram_rounding = CreateRoundingFactor<GradientSumT>(this->gpair);
-    } else {
-      histogram_rounding = GradientSumT{0.0, 0.0};
-    }
-
-    row_partitioner.reset();  // Release the device memory first before reallocating
-    row_partitioner.reset(new RowPartitioner(device_id, n_rows));
-    hist.Reset();
-  }
-
-  std::vector<DeviceSplitCandidate> EvaluateSplits(
-      std::vector<int> nidxs, const RegTree& tree,
-      size_t num_columns) {
-    auto result_all = pinned_memory.GetSpan<DeviceSplitCandidate>(nidxs.size());
-
-    // Work out cub temporary memory requirement
-    GPUTrainingParam gpu_param(param);
-    DeviceSplitCandidateReduceOp op(gpu_param);
-
-    dh::TemporaryArray<DeviceSplitCandidate> d_result_all(nidxs.size());
-    dh::TemporaryArray<DeviceSplitCandidate> split_candidates_all(nidxs.size()*num_columns);
-
-    auto& streams = this->GetStreams(nidxs.size());
-    for (auto i = 0ull; i < nidxs.size(); i++) {
-      auto nidx = nidxs[i];
-      auto p_feature_set = column_sampler.GetFeatureSet(tree.GetDepth(nidx));
-      p_feature_set->SetDevice(device_id);
-      common::Span<bst_feature_t> d_sampled_features =
-          p_feature_set->DeviceSpan();
-      common::Span<bst_feature_t> d_feature_set =
-          interaction_constraints.Query(d_sampled_features, nidx);
-      common::Span<DeviceSplitCandidate> d_split_candidates(
-          split_candidates_all.data().get() + i * num_columns,
-          d_feature_set.size());
-
-      DeviceNodeStats node(host_node_sum_gradients[nidx], nidx, param);
-
-      common::Span<DeviceSplitCandidate> d_result(d_result_all.data().get() + i, 1);
-      if (d_feature_set.empty()) {
-        // Acting as a device side constructor for DeviceSplitCandidate.
-        // DeviceSplitCandidate::IsValid is false so that ApplySplit can reject this
-        // candidate.
-        auto worst_candidate = DeviceSplitCandidate();
-        dh::safe_cuda(cudaMemcpyAsync(d_result.data(), &worst_candidate,
-                                      sizeof(DeviceSplitCandidate),
-                                      cudaMemcpyHostToDevice));
-        continue;
-      }
-
-      // One block for each feature
-      uint32_t constexpr kBlockThreads = 256;
-      dh::LaunchKernel {uint32_t(d_feature_set.size()), kBlockThreads, 0, streams[i]} (
-          EvaluateSplitKernel<kBlockThreads, GradientSumT>,
-          hist.GetNodeHistogram(nidx), d_feature_set, node, page->GetDeviceAccessor(device_id),
-          gpu_param, d_split_candidates, node_value_constraints[nidx],
-          dh::ToSpan(monotone_constraints));
-
-      // Reduce over features to find best feature
-      size_t cub_bytes = 0;
-      cub::DeviceReduce::Reduce(nullptr,
-                                cub_bytes, d_split_candidates.data(),
-                                d_result.data(), d_split_candidates.size(), op,
-                                DeviceSplitCandidate(), streams[i]);
-      dh::TemporaryArray<char> cub_temp(cub_bytes);
-      cub::DeviceReduce::Reduce(reinterpret_cast<void*>(cub_temp.data().get()),
-                                cub_bytes, d_split_candidates.data(),
-                                d_result.data(), d_split_candidates.size(), op,
-                                DeviceSplitCandidate(), streams[i]);
-    }
-
-    dh::safe_cuda(cudaMemcpy(result_all.data(), d_result_all.data().get(),
-                             sizeof(DeviceSplitCandidate) * d_result_all.size(),
-                             cudaMemcpyDeviceToHost));
-    return std::vector<DeviceSplitCandidate>(result_all.begin(), result_all.end());
-  }
-
-  void BuildHist(int nidx) {
-    hist.AllocateHistogram(nidx);
-    auto d_node_hist = hist.GetNodeHistogram(nidx);
-    auto d_ridx = row_partitioner->GetRows(nidx);
-    BuildGradientHistogram(page->GetDeviceAccessor(device_id), gpair, d_ridx, d_node_hist,
-                           histogram_rounding);
-  }
-
-  void SubtractionTrick(int nidx_parent, int nidx_histogram,
-                        int nidx_subtraction) {
-    auto d_node_hist_parent = hist.GetNodeHistogram(nidx_parent);
-    auto d_node_hist_histogram = hist.GetNodeHistogram(nidx_histogram);
-    auto d_node_hist_subtraction = hist.GetNodeHistogram(nidx_subtraction);
-
-    dh::LaunchN(device_id, page->Cuts().TotalBins(), [=] __device__(size_t idx) {
-      d_node_hist_subtraction[idx] =
-          d_node_hist_parent[idx] - d_node_hist_histogram[idx];
-    });
-  }
-
-  bool CanDoSubtractionTrick(int nidx_parent, int nidx_histogram,
-                             int nidx_subtraction) {
-    // Make sure histograms are already allocated
-    hist.AllocateHistogram(nidx_subtraction);
-    return hist.HistogramExists(nidx_histogram) &&
-           hist.HistogramExists(nidx_parent);
-  }
-
-  void UpdatePosition(int nidx, RegTree::Node split_node) {
-    auto d_matrix = page->GetDeviceAccessor(device_id);
-
-    row_partitioner->UpdatePosition(
-        nidx, split_node.LeftChild(), split_node.RightChild(),
-        [=] __device__(bst_uint ridx) {
-          // given a row index, returns the node id it belongs to
-          bst_float cut_value =
-              d_matrix.GetFvalue(ridx, split_node.SplitIndex());
-          // Missing value
-          int new_position = 0;
-          if (isnan(cut_value)) {
-            new_position = split_node.DefaultChild();
-          } else {
-            if (cut_value <= split_node.SplitCond()) {
-              new_position = split_node.LeftChild();
-            } else {
-              new_position = split_node.RightChild();
-            }
-          }
-          return new_position;
-        });
-  }
-
-  // After tree update is finished, update the position of all training
-  // instances to their final leaf. This information is used later to update the
-  // prediction cache
-  void FinalisePosition(RegTree const* p_tree, DMatrix* p_fmat) {
-    dh::TemporaryArray<RegTree::Node> d_nodes(p_tree->GetNodes().size());
-    dh::safe_cuda(cudaMemcpy(d_nodes.data().get(), p_tree->GetNodes().data(),
-                             d_nodes.size() * sizeof(RegTree::Node),
-                             cudaMemcpyHostToDevice));
-
-    if (row_partitioner->GetRows().size() != p_fmat->Info().num_row_) {
-      row_partitioner.reset();  // Release the device memory first before reallocating
-      row_partitioner.reset(new RowPartitioner(device_id, p_fmat->Info().num_row_));
-    }
-    if (page->n_rows == p_fmat->Info().num_row_) {
-      FinalisePositionInPage(page, dh::ToSpan(d_nodes));
-    } else {
-      for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
-        FinalisePositionInPage(batch.Impl(), dh::ToSpan(d_nodes));
-      }
-    }
-  }
-
-  void FinalisePositionInPage(EllpackPageImpl* page, const common::Span<RegTree::Node> d_nodes) {
-    auto d_matrix = page->GetDeviceAccessor(device_id);
-    row_partitioner->FinalisePosition(
-        [=] __device__(size_t row_id, int position) {
-      if (!d_matrix.IsInRange(row_id)) {
-        return RowPartitioner::kIgnoredTreePosition;
-      }
-      auto node = d_nodes[position];
-
-      while (!node.IsLeaf()) {
-        bst_float element = d_matrix.GetFvalue(row_id, node.SplitIndex());
-        // Missing value
-        if (isnan(element)) {
-          position = node.DefaultChild();
-        } else {
-          if (element <= node.SplitCond()) {
-            position = node.LeftChild();
-          } else {
-            position = node.RightChild();
-          }
-        }
-        node = d_nodes[position];
-      }
-      return position;
-    });
-  }
-
-  void UpdatePredictionCache(bst_float* out_preds_d) {
-    dh::safe_cuda(cudaSetDevice(device_id));
-    auto d_ridx = row_partitioner->GetRows();
-    if (prediction_cache.size() != d_ridx.size()) {
-      prediction_cache.resize(d_ridx.size());
-      dh::safe_cuda(cudaMemcpyAsync(prediction_cache.data().get(), out_preds_d,
-                                    prediction_cache.size() * sizeof(bst_float),
-                                    cudaMemcpyDefault));
-    }
-
-    CalcWeightTrainParam param_d(param);
-
-    dh::safe_cuda(
-        cudaMemcpyAsync(node_sum_gradients.data().get(), host_node_sum_gradients.data(),
-                        sizeof(GradientPair) * host_node_sum_gradients.size(),
-                        cudaMemcpyHostToDevice));
-    auto d_position = row_partitioner->GetPosition();
-    auto d_node_sum_gradients = node_sum_gradients.data().get();
-    auto d_prediction_cache = prediction_cache.data().get();
-
-    dh::LaunchN(
-        device_id, prediction_cache.size(), [=] __device__(int local_idx) {
-          int pos = d_position[local_idx];
-          bst_float weight = CalcWeight(param_d, d_node_sum_gradients[pos]);
-          d_prediction_cache[d_ridx[local_idx]] +=
-              weight * param_d.learning_rate;
-        });
-
-    dh::safe_cuda(cudaMemcpy(
-        out_preds_d, prediction_cache.data().get(),
-        prediction_cache.size() * sizeof(bst_float), cudaMemcpyDefault));
-    row_partitioner.reset();
-  }
-
-  void AllReduceHist(int nidx, dh::AllReducer* reducer) {
-    monitor.StartCuda("AllReduce");
-    auto d_node_hist = hist.GetNodeHistogram(nidx).data();
-    reducer->AllReduceSum(
-        reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
-        reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
-        page->Cuts().TotalBins() * (sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT)));
-    reducer->Synchronize();
-
-    monitor.StopCuda("AllReduce");
-  }
-
-  /**
-   * \brief Build GPU local histograms for the left and right child of some parent node
-   */
-  void BuildHistLeftRight(const ExpandEntry &candidate, int nidx_left,
-        int nidx_right, dh::AllReducer* reducer) {
-    auto build_hist_nidx = nidx_left;
-    auto subtraction_trick_nidx = nidx_right;
-
-    // Decide whether to build the left histogram or right histogram
-    // Use sum of Hessian as a heuristic to select node with fewest training instances
-    bool fewer_right = candidate.split.right_sum.GetHess() < candidate.split.left_sum.GetHess();
-    if (fewer_right) {
-      std::swap(build_hist_nidx, subtraction_trick_nidx);
-    }
-
-    this->BuildHist(build_hist_nidx);
-    this->AllReduceHist(build_hist_nidx, reducer);
-
-    // Check whether we can use the subtraction trick to calculate the other
-    bool do_subtraction_trick = this->CanDoSubtractionTrick(
-        candidate.nid, build_hist_nidx, subtraction_trick_nidx);
-
-    if (do_subtraction_trick) {
-      // Calculate other histogram using subtraction trick
-      this->SubtractionTrick(candidate.nid, build_hist_nidx,
-                             subtraction_trick_nidx);
-    } else {
-      // Calculate other histogram manually
-      this->BuildHist(subtraction_trick_nidx);
-      this->AllReduceHist(subtraction_trick_nidx, reducer);
-    }
-  }
-
-  void ApplySplit(const ExpandEntry& candidate, RegTree* p_tree) {
-    RegTree& tree = *p_tree;
-
-    GradStats left_stats{};
-    left_stats.Add(candidate.split.left_sum);
-    GradStats right_stats{};
-    right_stats.Add(candidate.split.right_sum);
-    GradStats parent_sum{};
-    parent_sum.Add(left_stats);
-    parent_sum.Add(right_stats);
-    node_value_constraints.resize(tree.GetNodes().size());
-    auto base_weight = node_value_constraints[candidate.nid].CalcWeight(param, parent_sum);
-    auto left_weight =
-        node_value_constraints[candidate.nid].CalcWeight(param, left_stats)*param.learning_rate;
-    auto right_weight =
-        node_value_constraints[candidate.nid].CalcWeight(param, right_stats)*param.learning_rate;
-    tree.ExpandNode(candidate.nid, candidate.split.findex,
-                    candidate.split.fvalue, candidate.split.dir == kLeftDir,
-                    base_weight, left_weight, right_weight,
-                    candidate.split.loss_chg, parent_sum.sum_hess,
-                    left_stats.GetHess(), right_stats.GetHess());
-    // Set up child constraints
-    node_value_constraints.resize(tree.GetNodes().size());
-    node_value_constraints[candidate.nid].SetChild(
-        param, tree[candidate.nid].SplitIndex(), left_stats, right_stats,
-        &node_value_constraints[tree[candidate.nid].LeftChild()],
-        &node_value_constraints[tree[candidate.nid].RightChild()]);
-    host_node_sum_gradients[tree[candidate.nid].LeftChild()] =
-        candidate.split.left_sum;
-    host_node_sum_gradients[tree[candidate.nid].RightChild()] =
-        candidate.split.right_sum;
-
-    interaction_constraints.Split(candidate.nid, tree[candidate.nid].SplitIndex(),
-                                  tree[candidate.nid].LeftChild(),
-                                  tree[candidate.nid].RightChild());
-  }
-
-  void InitRoot(RegTree* p_tree, dh::AllReducer* reducer, int64_t num_columns) {
-    constexpr bst_node_t kRootNIdx = 0;
-    dh::XGBCachingDeviceAllocator<char> alloc;
-    GradientPair root_sum = thrust::reduce(
-        thrust::cuda::par(alloc),
-        thrust::device_ptr<GradientPair const>(gpair.data()),
-        thrust::device_ptr<GradientPair const>(gpair.data() + gpair.size()));
-    dh::safe_cuda(cudaMemcpyAsync(node_sum_gradients.data().get(), &root_sum, sizeof(root_sum),
-                                  cudaMemcpyHostToDevice));
-    reducer->AllReduceSum(
-        reinterpret_cast<float*>(node_sum_gradients.data().get()),
-        reinterpret_cast<float*>(node_sum_gradients.data().get()), 2);
-    reducer->Synchronize();
-    dh::safe_cuda(cudaMemcpyAsync(host_node_sum_gradients.data(),
-                                  node_sum_gradients.data().get(), sizeof(GradientPair),
-                                  cudaMemcpyDeviceToHost));
-
-    this->BuildHist(kRootNIdx);
-    this->AllReduceHist(kRootNIdx, reducer);
-
-    // Remember root stats
-    p_tree->Stat(kRootNIdx).sum_hess = host_node_sum_gradients[kRootNIdx].GetHess();
-    auto weight = CalcWeight(param, host_node_sum_gradients[kRootNIdx]);
-    p_tree->Stat(kRootNIdx).base_weight = weight;
-    (*p_tree)[kRootNIdx].SetLeaf(param.learning_rate * weight);
-
-    // Initialise root constraint
-    node_value_constraints.resize(p_tree->GetNodes().size());
-
-    // Generate first split
-    auto split = this->EvaluateSplits({kRootNIdx}, *p_tree, num_columns);
-    qexpand->push(
-        ExpandEntry(kRootNIdx, p_tree->GetDepth(kRootNIdx), split.at(0), 0));
-  }
-
-  void UpdateTree(HostDeviceVector<GradientPair>* gpair_all, DMatrix* p_fmat,
-                  RegTree* p_tree, dh::AllReducer* reducer) {
-    auto& tree = *p_tree;
-
-    monitor.StartCuda("Reset");
-    this->Reset(gpair_all, p_fmat, p_fmat->Info().num_col_);
-    monitor.StopCuda("Reset");
-
-    monitor.StartCuda("InitRoot");
-    this->InitRoot(p_tree, reducer, p_fmat->Info().num_col_);
-    monitor.StopCuda("InitRoot");
-
-    auto timestamp = qexpand->size();
-    auto num_leaves = 1;
-
-    while (!qexpand->empty()) {
-      ExpandEntry candidate = qexpand->top();
-      qexpand->pop();
-      if (!candidate.IsValid(param, num_leaves)) {
-        continue;
-      }
-      this->ApplySplit(candidate, p_tree);
-
-      num_leaves++;
-
-      int left_child_nidx = tree[candidate.nid].LeftChild();
-      int right_child_nidx = tree[candidate.nid].RightChild();
-      // Only create child entries if needed
-      if (ExpandEntry::ChildIsValid(param, tree.GetDepth(left_child_nidx),
-                                    num_leaves)) {
-        monitor.StartCuda("UpdatePosition");
-        this->UpdatePosition(candidate.nid, (*p_tree)[candidate.nid]);
-        monitor.StopCuda("UpdatePosition");
-
-        monitor.StartCuda("BuildHist");
-        this->BuildHistLeftRight(candidate, left_child_nidx, right_child_nidx, reducer);
-        monitor.StopCuda("BuildHist");
-
-        monitor.StartCuda("EvaluateSplits");
-        auto splits = this->EvaluateSplits({left_child_nidx, right_child_nidx},
-                                           *p_tree, p_fmat->Info().num_col_);
-        monitor.StopCuda("EvaluateSplits");
-
-        qexpand->push(ExpandEntry(left_child_nidx,
-                                   tree.GetDepth(left_child_nidx), splits.at(0),
-                                   timestamp++));
-        qexpand->push(ExpandEntry(right_child_nidx,
-                                   tree.GetDepth(right_child_nidx),
-                                   splits.at(1), timestamp++));
-      }
-    }
-
-    monitor.StartCuda("FinalisePosition");
-    this->FinalisePosition(p_tree, p_fmat);
-    monitor.StopCuda("FinalisePosition");
-  }
-};
-
-template <typename GradientSumT>
-inline void GPUHistMakerDevice<GradientSumT>::InitHistogram() {
-  if (!param.monotone_constraints.empty()) {
-    // Copy assigning an empty vector causes an exception in MSVC debug builds
-    monotone_constraints = param.monotone_constraints;
-  }
-  host_node_sum_gradients.resize(param.MaxNodes());
-  node_sum_gradients.resize(param.MaxNodes());
-
-  // Init histogram
-  hist.Init(device_id, page->Cuts().TotalBins());
-}
-
-template <typename GradientSumT>
-class GPUHistMakerSpecialised {
- public:
-  GPUHistMakerSpecialised() = default;
-  void Configure(const Args& args, GenericParameter const* generic_param) {
-    param_.UpdateAllowUnknown(args);
-    generic_param_ = generic_param;
-    hist_maker_param_.UpdateAllowUnknown(args);
-    dh::CheckComputeCapability();
-
-    monitor_.Init("updater_gpu_hist");
-  }
-
-  ~GPUHistMakerSpecialised() {  // NOLINT
-    dh::GlobalMemoryLogger().Log();
-  }
-
-  void Update(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
-              const std::vector<RegTree*>& trees) {
-    monitor_.StartCuda("Update");
-
-    // rescale learning rate according to size of trees
-    float lr = param_.learning_rate;
-    param_.learning_rate = lr / trees.size();
-    ValueConstraint::Init(&param_, dmat->Info().num_col_);
-    // build tree
-    try {
-      for (xgboost::RegTree* tree : trees) {
-        this->UpdateTree(gpair, dmat, tree);
-
-        if (hist_maker_param_.debug_synchronize) {
-          this->CheckTreesSynchronized(tree);
-        }
-      }
-      dh::safe_cuda(cudaGetLastError());
-    } catch (const std::exception& e) {
-      LOG(FATAL) << "Exception in gpu_hist: " << e.what() << std::endl;
-    }
-
-    param_.learning_rate = lr;
-    monitor_.StopCuda("Update");
-  }
-
-  void InitDataOnce(DMatrix* dmat) {
-    device_ = generic_param_->gpu_id;
-    CHECK_GE(device_, 0) << "Must have at least one device";
-    info_ = &dmat->Info();
-    reducer_.Init({device_});  // NOLINT
-
-    // Synchronise the column sampling seed
-    uint32_t column_sampling_seed = common::GlobalRandom()();
-    rabit::Broadcast(&column_sampling_seed, sizeof(column_sampling_seed), 0);
-
-    BatchParam batch_param{
-      device_,
-      param_.max_bin,
-      generic_param_->gpu_page_size
-    };
-    auto page = (*dmat->GetBatches<EllpackPage>(batch_param).begin()).Impl();
-    dh::safe_cuda(cudaSetDevice(device_));
-    maker.reset(new GPUHistMakerDevice<GradientSumT>(device_,
-                                                     page,
-                                                     info_->num_row_,
-                                                     param_,
-                                                     column_sampling_seed,
-                                                     info_->num_col_,
-                                                     hist_maker_param_.deterministic_histogram,
-                                                     batch_param));
-
-    monitor_.StartCuda("InitHistogram");
-    dh::safe_cuda(cudaSetDevice(device_));
-    maker->InitHistogram();
-    monitor_.StopCuda("InitHistogram");
-
-    p_last_fmat_ = dmat;
-    initialised_ = true;
-  }
-
-  void InitData(DMatrix* dmat) {
-    if (!initialised_) {
-      monitor_.StartCuda("InitDataOnce");
-      this->InitDataOnce(dmat);
-      monitor_.StopCuda("InitDataOnce");
-    }
-  }
-
-  // Only call this method for testing
-  void CheckTreesSynchronized(RegTree* local_tree) const {
-    std::string s_model;
-    common::MemoryBufferStream fs(&s_model);
-    int rank = rabit::GetRank();
-    if (rank == 0) {
-      local_tree->Save(&fs);
-    }
-    fs.Seek(0);
-    rabit::Broadcast(&s_model, 0);
-    RegTree reference_tree {};  // rank 0 tree
-    reference_tree.Load(&fs);
-    CHECK(*local_tree == reference_tree);
-  }
-
-  void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* p_fmat,
-                  RegTree* p_tree) {
-    monitor_.StartCuda("InitData");
-    this->InitData(p_fmat);
-    monitor_.StopCuda("InitData");
-
-    gpair->SetDevice(device_);
-    maker->UpdateTree(gpair, p_fmat, p_tree, &reducer_);
-  }
-
-  bool UpdatePredictionCache(const DMatrix* data, HostDeviceVector<bst_float>* p_out_preds) {
-    if (maker == nullptr || p_last_fmat_ == nullptr || p_last_fmat_ != data) {
-      return false;
-    }
-    monitor_.StartCuda("UpdatePredictionCache");
-    p_out_preds->SetDevice(device_);
-    maker->UpdatePredictionCache(p_out_preds->DevicePointer());
-    monitor_.StopCuda("UpdatePredictionCache");
-    return true;
-  }
-
-  TrainParam param_;   // NOLINT
-  MetaInfo* info_{};   // NOLINT
-
-  std::unique_ptr<GPUHistMakerDevice<GradientSumT>> maker;  // NOLINT
-
- private:
-  bool initialised_ { false };
-
-  GPUHistMakerTrainParam hist_maker_param_;
-  GenericParameter const* generic_param_;
-
-  dh::AllReducer reducer_;
-
-  DMatrix* p_last_fmat_ { nullptr };
-  int device_{-1};
-
-  common::Monitor monitor_;
-};
-
-class GPUHistMaker : public TreeUpdater {
- public:
-  void Configure(const Args& args) override {
-    // Used in test to count how many configurations are performed
-    LOG(DEBUG) << "[GPU Hist]: Configure";
-    hist_maker_param_.UpdateAllowUnknown(args);
-    // The passed in args can be empty, if we simply purge the old maker without
-    // preserving parameters then we can't do Update on it.
-    TrainParam param;
-    if (float_maker_) {
-      param = float_maker_->param_;
-    } else if (double_maker_) {
-      param = double_maker_->param_;
-    }
-    if (hist_maker_param_.single_precision_histogram) {
-      float_maker_.reset(new GPUHistMakerSpecialised<GradientPair>());
-      float_maker_->param_ = param;
-      float_maker_->Configure(args, tparam_);
-    } else {
-      double_maker_.reset(new GPUHistMakerSpecialised<GradientPairPrecise>());
-      double_maker_->param_ = param;
-      double_maker_->Configure(args, tparam_);
-    }
-  }
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("gpu_hist_train_param"), &this->hist_maker_param_);
-    if (hist_maker_param_.single_precision_histogram) {
-      float_maker_.reset(new GPUHistMakerSpecialised<GradientPair>());
-      FromJson(config.at("train_param"), &float_maker_->param_);
-    } else {
-      double_maker_.reset(new GPUHistMakerSpecialised<GradientPairPrecise>());
-      FromJson(config.at("train_param"), &double_maker_->param_);
-    }
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["gpu_hist_train_param"] = ToJson(hist_maker_param_);
-    if (hist_maker_param_.single_precision_histogram) {
-      out["train_param"] = ToJson(float_maker_->param_);
-    } else {
-      out["train_param"] = ToJson(double_maker_->param_);
-    }
-  }
-
-  void Update(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
-              const std::vector<RegTree*>& trees) override {
-    if (hist_maker_param_.single_precision_histogram) {
-      float_maker_->Update(gpair, dmat, trees);
-    } else {
-      double_maker_->Update(gpair, dmat, trees);
-    }
-  }
-
-  bool UpdatePredictionCache(
-      const DMatrix* data, HostDeviceVector<bst_float>* p_out_preds) override {
-    if (hist_maker_param_.single_precision_histogram) {
-      return float_maker_->UpdatePredictionCache(data, p_out_preds);
-    } else {
-      return double_maker_->UpdatePredictionCache(data, p_out_preds);
-    }
-  }
-
-  char const* Name() const override {
-    return "grow_gpu_hist";
-  }
-
- private:
-  GPUHistMakerTrainParam hist_maker_param_;
-  std::unique_ptr<GPUHistMakerSpecialised<GradientPair>> float_maker_;
-  std::unique_ptr<GPUHistMakerSpecialised<GradientPairPrecise>> double_maker_;
-};
-
-#if !defined(GTEST_TEST)
-XGBOOST_REGISTER_TREE_UPDATER(GPUHistMaker, "grow_gpu_hist")
-    .describe("Grow tree with GPU.")
-    .set_body([]() { return new GPUHistMaker(); });
-#endif  // !defined(GTEST_TEST)
-
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_histmaker.cc b/ml-xgboost/src/tree/updater_histmaker.cc
deleted file mode 100644
index 3996acd..0000000
--- a/ml-xgboost/src/tree/updater_histmaker.cc
+++ /dev/null
@@ -1,764 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2019 by Contributors
- * \file updater_histmaker.cc
- * \brief use histogram counting to construct a tree
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <vector>
-#include <algorithm>
-
-#include "xgboost/tree_updater.h"
-#include "xgboost/base.h"
-#include "xgboost/logging.h"
-
-#include "../common/quantile.h"
-#include "../common/group_data.h"
-#include "./updater_basemaker-inl.h"
-#include "constraints.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_histmaker);
-
-class HistMaker: public BaseMaker {
- public:
-  void Update(HostDeviceVector<GradientPair> *gpair,
-              DMatrix *p_fmat,
-              const std::vector<RegTree*> &trees, int iter,
-              float* p_last_round_best_loss) override {
-    interaction_constraints_.Configure(param_, p_fmat->Info().num_col_);
-    // rescale learning rate according to size of trees
-    float lr = param_.learning_rate;
-    param_.learning_rate = lr / trees.size();
-    // build tree
-    for (auto tree : trees) {
-      this->UpdateTree(gpair->ConstHostVector(), p_fmat, tree);
-    }
-    param_.learning_rate = lr;
-  }
-  char const* Name() const override {
-    return "grow_histmaker";
-  }
-
- protected:
-    /*! \brief a single column of histogram cuts */
-  struct HistUnit {
-    /*! \brief cutting point of histogram, contains maximum point */
-    const float *cut;
-    /*! \brief content of statistics data */
-    GradStats *data;
-    /*! \brief size of histogram */
-    uint32_t size;
-    // default constructor
-    HistUnit() = default;
-    // constructor
-    HistUnit(const float *cut, GradStats *data, uint32_t size)
-        : cut{cut}, data{data}, size{size} {}
-    /*! \brief add a histogram to data */
-    void Add(float fv, const std::vector<GradientPair> &gpair,
-             const MetaInfo &info, const size_t ridx) {
-      unsigned bin = std::upper_bound(cut, cut + size, fv) - cut;
-      CHECK_NE(size, 0U) << "try insert into size=0";
-      CHECK_LT(bin, size);
-      data[bin].Add(gpair[ridx]);
-    }
-  };
-  /*! \brief a set of histograms from different index */
-  struct HistSet {
-    /*! \brief the index pointer of each histunit */
-    const uint32_t *rptr;
-    /*! \brief cutting points in each histunit */
-    const bst_float *cut;
-    /*! \brief data in different hist unit */
-    std::vector<GradStats> data;
-    /*! \brief return a column of histogram cuts */
-    inline HistUnit operator[](size_t fid) {
-      return {cut + rptr[fid], &data[0] + rptr[fid], rptr[fid+1] - rptr[fid]};
-    }
-  };
-  // thread workspace
-  struct ThreadWSpace {
-    /*! \brief actual unit pointer */
-    std::vector<unsigned> rptr;
-    /*! \brief cut field */
-    std::vector<bst_float> cut;
-    // per thread histset
-    std::vector<HistSet> hset;
-    // initialize the hist set
-    inline void Configure(const TrainParam &param, int nthread) {
-      hset.resize(nthread);
-      // cleanup statistics
-      for (int tid = 0; tid < nthread; ++tid) {
-        for (auto& d : hset[tid].data) { d = GradStats(); }
-        hset[tid].rptr = dmlc::BeginPtr(rptr);
-        hset[tid].cut = dmlc::BeginPtr(cut);
-        hset[tid].data.resize(cut.size(), GradStats());
-      }
-    }
-    /*! \brief clear the workspace */
-    inline void Clear() {
-      cut.clear(); rptr.resize(1); rptr[0] = 0;
-    }
-    /*! \brief total size */
-    inline size_t Size() const {
-      return rptr.size() - 1;
-    }
-  };
-  // workspace of thread
-  ThreadWSpace wspace_;
-  // reducer for histogram
-  rabit::Reducer<GradStats, GradStats::Reduce> histred_;
-  // set of working features
-  std::vector<bst_feature_t> selected_features_;
-  // update function implementation
-  virtual void UpdateTree(const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat,
-                          RegTree *p_tree) {
-    CHECK(param_.max_depth > 0) << "max_depth must be larger than 0";
-    this->InitData(gpair, *p_fmat, *p_tree);
-    this->InitWorkSet(p_fmat, *p_tree, &selected_features_);
-    // mark root node as fresh.
-    (*p_tree)[0].SetLeaf(0.0f, 0);
-
-    for (int depth = 0; depth < param_.max_depth; ++depth) {
-      // reset and propose candidate split
-      this->ResetPosAndPropose(gpair, p_fmat, selected_features_, *p_tree);
-      // create histogram
-      this->CreateHist(gpair, p_fmat, selected_features_, *p_tree);
-      // find split based on histogram statistics
-      this->FindSplit(depth, gpair, p_fmat, selected_features_, p_tree);
-      // reset position after split
-      this->ResetPositionAfterSplit(p_fmat, *p_tree);
-      this->UpdateQueueExpand(*p_tree);
-      // if nothing left to be expand, break
-      if (qexpand_.size() == 0) break;
-    }
-    for (int const nid : qexpand_) {
-      (*p_tree)[nid].SetLeaf(p_tree->Stat(nid).base_weight * param_.learning_rate);
-    }
-  }
-  // this function does two jobs
-  // (1) reset the position in array position, to be the latest leaf id
-  // (2) propose a set of candidate cuts and set wspace.rptr wspace.cut correctly
-  virtual void ResetPosAndPropose(const std::vector<GradientPair> &gpair,
-                                  DMatrix *p_fmat,
-                                  const std::vector <bst_feature_t> &fset,
-                                  const RegTree &tree) = 0;
-  // initialize the current working set of features in this round
-  virtual void InitWorkSet(DMatrix *p_fmat,
-                           const RegTree &tree,
-                           std::vector<bst_feature_t> *p_fset) {
-    p_fset->resize(tree.param.num_feature);
-    for (size_t i = 0; i < p_fset->size(); ++i) {
-      (*p_fset)[i] = static_cast<unsigned>(i);
-    }
-  }
-  // reset position after split, this is not a must, depending on implementation
-  virtual void ResetPositionAfterSplit(DMatrix *p_fmat,
-                                       const RegTree &tree) {
-  }
-  virtual void CreateHist(const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat,
-                          const std::vector <bst_feature_t> &fset,
-                          const RegTree &tree)  = 0;
-
- private:
-  void EnumerateSplit(const HistUnit &hist,
-                      const GradStats &node_sum,
-                      bst_uint fid,
-                      SplitEntry *best,
-                      GradStats *left_sum) const {
-    if (hist.size == 0) return;
-
-    double root_gain = CalcGain(param_, node_sum.GetGrad(), node_sum.GetHess());
-    GradStats s, c;
-    for (bst_uint i = 0; i < hist.size; ++i) {
-      s.Add(hist.data[i]);
-      if (s.sum_hess >= param_.min_child_weight) {
-        c.SetSubstract(node_sum, s);
-        if (c.sum_hess >= param_.min_child_weight) {
-          double loss_chg = CalcGain(param_, s.GetGrad(), s.GetHess()) +
-                            CalcGain(param_, c.GetGrad(), c.GetHess()) - root_gain;
-          if (best->Update(static_cast<bst_float>(loss_chg), fid, hist.cut[i], false, s, c)) {
-            *left_sum = s;
-          }
-        }
-      }
-    }
-    s = GradStats();
-    for (bst_uint i = hist.size - 1; i != 0; --i) {
-      s.Add(hist.data[i]);
-      if (s.sum_hess >= param_.min_child_weight) {
-        c.SetSubstract(node_sum, s);
-        if (c.sum_hess >= param_.min_child_weight) {
-          double loss_chg = CalcGain(param_, s.GetGrad(), s.GetHess()) +
-                            CalcGain(param_, c.GetGrad(), c.GetHess()) - root_gain;
-          if (best->Update(static_cast<bst_float>(loss_chg), fid, hist.cut[i-1], true, c, s)) {
-            *left_sum = c;
-          }
-        }
-      }
-    }
-  }
-
-  void FindSplit(int depth,
-                 const std::vector<GradientPair> &gpair,
-                 DMatrix *p_fmat,
-                 const std::vector <bst_feature_t> &feature_set,
-                 RegTree *p_tree) {
-    const size_t num_feature = feature_set.size();
-    // get the best split condition for each node
-    std::vector<SplitEntry> sol(qexpand_.size());
-    std::vector<GradStats> left_sum(qexpand_.size());
-    auto nexpand = static_cast<bst_omp_uint>(qexpand_.size());
-#pragma omp parallel for schedule(dynamic, 1)
-    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
-      const int nid = qexpand_[wid];
-      CHECK_EQ(node2workindex_[nid], static_cast<int>(wid));
-      SplitEntry &best = sol[wid];
-      GradStats &node_sum = wspace_.hset[0][num_feature + wid * (num_feature + 1)].data[0];
-      for (size_t i = 0; i < feature_set.size(); ++i) {
-        // Query is thread safe as it's a const function.
-        if (!this->interaction_constraints_.Query(nid, feature_set[i])) {
-          continue;
-        }
-
-        EnumerateSplit(this->wspace_.hset[0][i + wid * (num_feature+1)],
-                       node_sum, feature_set[i], &best, &left_sum[wid]);
-      }
-    }
-    // get the best result, we can synchronize the solution
-    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
-      const bst_node_t nid = qexpand_[wid];
-      SplitEntry const& best = sol[wid];
-      const GradStats &node_sum = wspace_.hset[0][num_feature + wid * (num_feature + 1)].data[0];
-      this->SetStats(p_tree, nid, node_sum);
-      // set up the values
-      p_tree->Stat(nid).loss_chg = best.loss_chg;
-      // now we know the solution in snode[nid], set split
-      if (best.loss_chg > kRtEps) {
-        bst_float base_weight = CalcWeight(param_, node_sum);
-        bst_float left_leaf_weight =
-            CalcWeight(param_, best.left_sum.sum_grad, best.left_sum.sum_hess) *
-            param_.learning_rate;
-        bst_float right_leaf_weight =
-            CalcWeight(param_, best.right_sum.sum_grad,
-                       best.right_sum.sum_hess) *
-            param_.learning_rate;
-        p_tree->ExpandNode(nid, best.SplitIndex(), best.split_value,
-                           best.DefaultLeft(), base_weight, left_leaf_weight,
-                           right_leaf_weight, best.loss_chg,
-                           node_sum.sum_hess,
-                           best.left_sum.GetHess(), best.right_sum.GetHess());
-        GradStats right_sum;
-        right_sum.SetSubstract(node_sum, left_sum[wid]);
-        auto left_child = (*p_tree)[nid].LeftChild();
-        auto right_child = (*p_tree)[nid].RightChild();
-        this->SetStats(p_tree, left_child, left_sum[wid]);
-        this->SetStats(p_tree, right_child, right_sum);
-        this->interaction_constraints_.Split(nid, best.SplitIndex(), left_child, right_child);
-      } else {
-        (*p_tree)[nid].SetLeaf(p_tree->Stat(nid).base_weight * param_.learning_rate);
-      }
-    }
-  }
-
-  inline void SetStats(RegTree *p_tree, int nid, const GradStats &node_sum) {
-    p_tree->Stat(nid).base_weight =
-        static_cast<bst_float>(CalcWeight(param_, node_sum));
-    p_tree->Stat(nid).sum_hess = static_cast<bst_float>(node_sum.sum_hess);
-  }
-};
-
-class CQHistMaker: public HistMaker {
- public:
-  CQHistMaker()  = default;
-  char const* Name() const override {
-    return "grow_local_histmaker";
-  }
-
- protected:
-  struct HistEntry {
-    HistMaker::HistUnit hist;
-    unsigned istart;
-    /*!
-     * \brief add a histogram to data,
-     * do linear scan, start from istart
-     */
-    inline void Add(bst_float fv,
-                    const std::vector<GradientPair> &gpair,
-                    const MetaInfo &info,
-                    const bst_uint ridx) {
-      while (istart < hist.size && !(fv < hist.cut[istart])) ++istart;
-      CHECK_NE(istart, hist.size);
-      hist.data[istart].Add(gpair[ridx]);
-    }
-    /*!
-     * \brief add a histogram to data,
-     * do linear scan, start from istart
-     */
-    inline void Add(bst_float fv,
-                    GradientPair gstats) {
-      if (fv < hist.cut[istart]) {
-        hist.data[istart].Add(gstats);
-      } else {
-        while (istart < hist.size && !(fv < hist.cut[istart])) ++istart;
-        if (istart != hist.size) {
-          hist.data[istart].Add(gstats);
-        } else {
-          LOG(INFO) << "fv=" << fv << ", hist.size=" << hist.size;
-          for (size_t i = 0; i < hist.size; ++i) {
-            LOG(INFO) << "hist[" << i << "]=" << hist.cut[i];
-          }
-          LOG(FATAL) << "fv=" << fv << ", hist.last=" << hist.cut[hist.size - 1];
-        }
-      }
-    }
-  };
-  // sketch type used for this
-  using WXQSketch = common::WXQuantileSketch<bst_float, bst_float>;
-  // initialize the work set of tree
-  void InitWorkSet(DMatrix *p_fmat,
-                   const RegTree &tree,
-                   std::vector<bst_feature_t> *p_fset) override {
-    if (p_fmat != cache_dmatrix_) {
-      feat_helper_.InitByCol(p_fmat, tree);
-      cache_dmatrix_ = p_fmat;
-    }
-    feat_helper_.SyncInfo();
-    feat_helper_.SampleCol(this->param_.colsample_bytree, p_fset);
-  }
-  // code to create histogram
-  void CreateHist(const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat,
-                  const std::vector<bst_feature_t> &fset,
-                  const RegTree &tree) override {
-    const MetaInfo &info = p_fmat->Info();
-    // fill in reverse map
-    feat2workindex_.resize(tree.param.num_feature);
-    std::fill(feat2workindex_.begin(), feat2workindex_.end(), -1);
-    for (size_t i = 0; i < fset.size(); ++i) {
-      feat2workindex_[fset[i]] = static_cast<int>(i);
-    }
-    // start to work
-    this->wspace_.Configure(this->param_, 1);
-    // if it is C++11, use lazy evaluation for Allreduce,
-    // to gain speedup in recovery
-    auto lazy_get_hist = [&]() {
-      thread_hist_.resize(omp_get_max_threads());
-      // start accumulating statistics
-      for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-        // start enumeration
-        const auto nsize = static_cast<bst_omp_uint>(fset.size());
-#pragma omp parallel for schedule(dynamic, 1)
-        for (bst_omp_uint i = 0; i < nsize; ++i) {
-          int fid = fset[i];
-          int offset = feat2workindex_[fid];
-          if (offset >= 0) {
-            this->UpdateHistCol(gpair, batch[fid], info, tree,
-                                fset, offset,
-                                &thread_hist_[omp_get_thread_num()]);
-          }
-        }
-      }
-      // update node statistics.
-      this->GetNodeStats(gpair, *p_fmat, tree,
-                         &thread_stats_, &node_stats_);
-      for (int const nid : this->qexpand_) {
-        const int wid = this->node2workindex_[nid];
-        this->wspace_.hset[0][fset.size() + wid * (fset.size() + 1)]
-                .data[0] = node_stats_[nid];
-      }
-    };
-    // sync the histogram
-    this->histred_.Allreduce(dmlc::BeginPtr(this->wspace_.hset[0].data),
-                             this->wspace_.hset[0].data.size(), lazy_get_hist);
-  }
-
-  void ResetPositionAfterSplit(DMatrix *p_fmat,
-                                 const RegTree &tree) override {
-    this->GetSplitSet(this->qexpand_, tree, &fsplit_set_);
-  }
-  void ResetPosAndPropose(const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat,
-                          const std::vector<bst_feature_t> &fset,
-                          const RegTree &tree) override {
-    const MetaInfo &info = p_fmat->Info();
-    // fill in reverse map
-    feat2workindex_.resize(tree.param.num_feature);
-    std::fill(feat2workindex_.begin(), feat2workindex_.end(), -1);
-    work_set_.clear();
-    for (auto fidx : fset) {
-      if (feat_helper_.Type(fidx) == 2) {
-        feat2workindex_[fidx] = static_cast<int>(work_set_.size());
-        work_set_.push_back(fidx);
-      } else {
-        feat2workindex_[fidx] = -2;
-      }
-    }
-    const size_t work_set_size = work_set_.size();
-
-    sketchs_.resize(this->qexpand_.size() * work_set_size);
-    for (auto& sketch : sketchs_) {
-      sketch.Init(info.num_row_, this->param_.sketch_eps);
-    }
-    // intitialize the summary array
-    summary_array_.resize(sketchs_.size());
-    // setup maximum size
-    unsigned max_size = this->param_.MaxSketchSize();
-    for (size_t i = 0; i < sketchs_.size(); ++i) {
-      summary_array_[i].Reserve(max_size);
-    }
-    {
-      // get smmary
-      thread_sketch_.resize(omp_get_max_threads());
-
-      // TWOPASS: use the real set + split set in the column iteration.
-      this->SetDefaultPostion(p_fmat, tree);
-      work_set_.insert(work_set_.end(), fsplit_set_.begin(), fsplit_set_.end());
-      std::sort(work_set_.begin(), work_set_.end());
-      work_set_.resize(std::unique(work_set_.begin(), work_set_.end()) - work_set_.begin());
-
-      // start accumulating statistics
-      for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-        // TWOPASS: use the real set + split set in the column iteration.
-        this->CorrectNonDefaultPositionByBatch(batch, fsplit_set_, tree);
-
-        // start enumeration
-        const auto nsize = static_cast<bst_omp_uint>(work_set_.size());
-        #pragma omp parallel for schedule(dynamic, 1)
-        for (bst_omp_uint i = 0; i < nsize; ++i) {
-          int fid = work_set_[i];
-          int offset = feat2workindex_[fid];
-          if (offset >= 0) {
-            this->UpdateSketchCol(gpair, batch[fid], tree,
-                                  work_set_size, offset,
-                                  &thread_sketch_[omp_get_thread_num()]);
-          }
-        }
-      }
-      for (size_t i = 0; i < sketchs_.size(); ++i) {
-        common::WXQuantileSketch<bst_float, bst_float>::SummaryContainer out;
-        sketchs_[i].GetSummary(&out);
-        summary_array_[i].SetPrune(out, max_size);
-      }
-      CHECK_EQ(summary_array_.size(), sketchs_.size());
-    }
-    if (summary_array_.size() != 0) {
-      size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_size);
-      sreducer_.Allreduce(dmlc::BeginPtr(summary_array_), nbytes, summary_array_.size());
-    }
-    // now we get the final result of sketch, setup the cut
-    this->wspace_.cut.clear();
-    this->wspace_.rptr.clear();
-    this->wspace_.rptr.push_back(0);
-    for (size_t wid = 0; wid < this->qexpand_.size(); ++wid) {
-      for (unsigned int i : fset) {
-        int offset = feat2workindex_[i];
-        if (offset >= 0) {
-          const WXQSketch::Summary &a = summary_array_[wid * work_set_size + offset];
-          for (size_t i = 1; i < a.size; ++i) {
-            bst_float cpt = a.data[i].value - kRtEps;
-            if (i == 1 || cpt > this->wspace_.cut.back()) {
-              this->wspace_.cut.push_back(cpt);
-            }
-          }
-          // push a value that is greater than anything
-          if (a.size != 0) {
-            bst_float cpt = a.data[a.size - 1].value;
-            // this must be bigger than last value in a scale
-            bst_float last = cpt + fabs(cpt) + kRtEps;
-            this->wspace_.cut.push_back(last);
-          }
-          this->wspace_.rptr.push_back(static_cast<unsigned>(this->wspace_.cut.size()));
-        } else {
-          CHECK_EQ(offset, -2);
-          bst_float cpt = feat_helper_.MaxValue(i);
-          this->wspace_.cut.push_back(cpt + fabs(cpt) + kRtEps);
-          this->wspace_.rptr.push_back(static_cast<unsigned>(this->wspace_.cut.size()));
-        }
-      }
-      // reserve last value for global statistics
-      this->wspace_.cut.push_back(0.0f);
-      this->wspace_.rptr.push_back(static_cast<unsigned>(this->wspace_.cut.size()));
-    }
-    CHECK_EQ(this->wspace_.rptr.size(),
-             (fset.size() + 1) * this->qexpand_.size() + 1);
-  }
-
-  inline void UpdateHistCol(const std::vector<GradientPair> &gpair,
-                            const SparsePage::Inst &col,
-                            const MetaInfo &info,
-                            const RegTree &tree,
-                            const std::vector<bst_feature_t> &fset,
-                            bst_uint fid_offset,
-                            std::vector<HistEntry> *p_temp) {
-    if (col.size() == 0) return;
-    // initialize sbuilder for use
-    std::vector<HistEntry> &hbuilder = *p_temp;
-    hbuilder.resize(tree.param.num_nodes);
-    for (int const nid : this->qexpand_) {
-      const unsigned wid = this->node2workindex_[nid];
-      hbuilder[nid].istart = 0;
-      hbuilder[nid].hist = this->wspace_.hset[0][fid_offset + wid * (fset.size()+1)];
-    }
-    if (this->param_.cache_opt != 0) {
-      constexpr bst_uint kBuffer = 32;
-      bst_uint align_length = col.size() / kBuffer * kBuffer;
-      int buf_position[kBuffer];
-      GradientPair buf_gpair[kBuffer];
-      for (bst_uint j = 0; j < align_length; j += kBuffer) {
-        for (bst_uint i = 0; i < kBuffer; ++i) {
-          bst_uint ridx = col[j + i].index;
-          buf_position[i] = this->position_[ridx];
-          buf_gpair[i] = gpair[ridx];
-        }
-        for (bst_uint i = 0; i < kBuffer; ++i) {
-          const int nid = buf_position[i];
-          if (nid >= 0) {
-            hbuilder[nid].Add(col[j + i].fvalue, buf_gpair[i]);
-          }
-        }
-      }
-      for (bst_uint j = align_length; j < col.size(); ++j) {
-        const bst_uint ridx = col[j].index;
-        const int nid = this->position_[ridx];
-        if (nid >= 0) {
-          hbuilder[nid].Add(col[j].fvalue, gpair[ridx]);
-        }
-      }
-    } else {
-      for (const auto& c : col) {
-        const bst_uint ridx = c.index;
-        const int nid = this->position_[ridx];
-        if (nid >= 0) {
-          hbuilder[nid].Add(c.fvalue, gpair, info, ridx);
-        }
-      }
-    }
-  }
-  inline void UpdateSketchCol(const std::vector<GradientPair> &gpair,
-                              const SparsePage::Inst &col,
-                              const RegTree &tree,
-                              size_t work_set_size,
-                              bst_uint offset,
-                              std::vector<BaseMaker::SketchEntry> *p_temp) {
-    if (col.size() == 0) return;
-    // initialize sbuilder for use
-    std::vector<BaseMaker::SketchEntry> &sbuilder = *p_temp;
-    sbuilder.resize(tree.param.num_nodes);
-    for (int const nid : this->qexpand_) {
-      const unsigned wid = this->node2workindex_[nid];
-      sbuilder[nid].sum_total = 0.0f;
-      sbuilder[nid].sketch = &sketchs_[wid * work_set_size + offset];
-    }
-    // first pass, get sum of weight, TODO, optimization to skip first pass
-    for (const auto& c : col) {
-        const bst_uint ridx = c.index;
-        const int nid = this->position_[ridx];
-        if (nid >= 0) {
-          sbuilder[nid].sum_total += gpair[ridx].GetHess();
-      }
-    }
-    // if only one value, no need to do second pass
-    if (col[0].fvalue  == col[col.size()-1].fvalue) {
-      for (int const nid : this->qexpand_) {
-        sbuilder[nid].sketch->Push(
-            col[0].fvalue, static_cast<bst_float>(sbuilder[nid].sum_total));
-      }
-      return;
-    }
-    // two pass scan
-    unsigned max_size = this->param_.MaxSketchSize();
-    for (int const nid : this->qexpand_) {
-      sbuilder[nid].Init(max_size);
-    }
-    // second pass, build the sketch
-    if (this->param_.cache_opt != 0) {
-      constexpr bst_uint kBuffer = 32;
-      bst_uint align_length = col.size() / kBuffer * kBuffer;
-      int buf_position[kBuffer];
-      bst_float buf_hess[kBuffer];
-      for (bst_uint j = 0; j < align_length; j += kBuffer) {
-        for (bst_uint i = 0; i < kBuffer; ++i) {
-          bst_uint ridx = col[j + i].index;
-          buf_position[i] = this->position_[ridx];
-          buf_hess[i] = gpair[ridx].GetHess();
-        }
-        for (bst_uint i = 0; i < kBuffer; ++i) {
-          const int nid = buf_position[i];
-          if (nid >= 0) {
-            sbuilder[nid].Push(col[j + i].fvalue, buf_hess[i], max_size);
-          }
-        }
-      }
-      for (bst_uint j = align_length; j < col.size(); ++j) {
-        const bst_uint ridx = col[j].index;
-        const int nid = this->position_[ridx];
-        if (nid >= 0) {
-          sbuilder[nid].Push(col[j].fvalue, gpair[ridx].GetHess(), max_size);
-        }
-      }
-    } else {
-      for (const auto& c : col) {
-        const bst_uint ridx = c.index;
-        const int nid = this->position_[ridx];
-        if (nid >= 0) {
-          sbuilder[nid].Push(c.fvalue, gpair[ridx].GetHess(), max_size);
-        }
-      }
-    }
-    for (int const nid : this->qexpand_) { sbuilder[nid].Finalize(max_size); }
-  }
-  // cached dmatrix where we initialized the feature on.
-  const DMatrix* cache_dmatrix_{nullptr};
-  // feature helper
-  BaseMaker::FMetaHelper feat_helper_;
-  // temp space to map feature id to working index
-  std::vector<int> feat2workindex_;
-  // set of index from fset that are current work set
-  std::vector<bst_feature_t> work_set_;
-  // set of index from that are split candidates.
-  std::vector<bst_uint> fsplit_set_;
-  // thread temp data
-  std::vector<std::vector<BaseMaker::SketchEntry> > thread_sketch_;
-  // used to hold statistics
-  std::vector<std::vector<GradStats> > thread_stats_;
-  // used to hold start pointer
-  std::vector<std::vector<HistEntry> > thread_hist_;
-  // node statistics
-  std::vector<GradStats> node_stats_;
-  // summary array
-  std::vector<WXQSketch::SummaryContainer> summary_array_;
-  // reducer for summary
-  rabit::SerializeReducer<WXQSketch::SummaryContainer> sreducer_;
-  // per node, per feature sketch
-  std::vector<common::WXQuantileSketch<bst_float, bst_float> > sketchs_;
-};
-
-// global proposal
-class GlobalProposalHistMaker: public CQHistMaker {
- public:
-  char const* Name() const override {
-    return "grow_histmaker";
-  }
-
- protected:
-  void ResetPosAndPropose(const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat,
-                          const std::vector<bst_feature_t> &fset,
-                          const RegTree &tree) override {
-    if (this->qexpand_.size() == 1) {
-      cached_rptr_.clear();
-      cached_cut_.clear();
-    }
-    if (cached_rptr_.size() == 0) {
-      CHECK_EQ(this->qexpand_.size(), 1U);
-      CQHistMaker::ResetPosAndPropose(gpair, p_fmat, fset, tree);
-      cached_rptr_ = this->wspace_.rptr;
-      cached_cut_ = this->wspace_.cut;
-    } else {
-      this->wspace_.cut.clear();
-      this->wspace_.rptr.clear();
-      this->wspace_.rptr.push_back(0);
-      for (size_t i = 0; i < this->qexpand_.size(); ++i) {
-        for (size_t j = 0; j < cached_rptr_.size() - 1; ++j) {
-          this->wspace_.rptr.push_back(
-              this->wspace_.rptr.back() + cached_rptr_[j + 1] - cached_rptr_[j]);
-        }
-        this->wspace_.cut.insert(this->wspace_.cut.end(), cached_cut_.begin(), cached_cut_.end());
-      }
-      CHECK_EQ(this->wspace_.rptr.size(),
-               (fset.size() + 1) * this->qexpand_.size() + 1);
-      CHECK_EQ(this->wspace_.rptr.back(), this->wspace_.cut.size());
-    }
-  }
-
-  // code to create histogram
-  void CreateHist(const std::vector<GradientPair> &gpair,
-                  DMatrix *p_fmat,
-                  const std::vector<bst_feature_t> &fset,
-                  const RegTree &tree) override {
-    const MetaInfo &info = p_fmat->Info();
-    // fill in reverse map
-    this->feat2workindex_.resize(tree.param.num_feature);
-    this->work_set_ = fset;
-    std::fill(this->feat2workindex_.begin(), this->feat2workindex_.end(), -1);
-    for (size_t i = 0; i < fset.size(); ++i) {
-      this->feat2workindex_[fset[i]] = static_cast<int>(i);
-    }
-    // start to work
-    this->wspace_.Configure(this->param_, 1);
-    // to gain speedup in recovery
-    {
-      this->thread_hist_.resize(omp_get_max_threads());
-
-      // TWOPASS: use the real set + split set in the column iteration.
-      this->SetDefaultPostion(p_fmat, tree);
-      this->work_set_.insert(this->work_set_.end(), this->fsplit_set_.begin(),
-                             this->fsplit_set_.end());
-      XGBOOST_PARALLEL_SORT(this->work_set_.begin(), this->work_set_.end(),
-                            std::less<decltype(this->work_set_)::value_type>{});
-      this->work_set_.resize(
-          std::unique(this->work_set_.begin(), this->work_set_.end()) - this->work_set_.begin());
-
-      // start accumulating statistics
-      for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-        // TWOPASS: use the real set + split set in the column iteration.
-        this->CorrectNonDefaultPositionByBatch(batch, this->fsplit_set_, tree);
-
-        // start enumeration
-        const auto nsize = static_cast<bst_omp_uint>(this->work_set_.size());
-#pragma omp parallel for schedule(dynamic, 1)
-        for (bst_omp_uint i = 0; i < nsize; ++i) {
-          int fid = this->work_set_[i];
-          int offset = this->feat2workindex_[fid];
-          if (offset >= 0) {
-            this->UpdateHistCol(gpair, batch[fid], info, tree,
-                                fset, offset,
-                                &this->thread_hist_[omp_get_thread_num()]);
-          }
-        }
-      }
-
-      // update node statistics.
-      this->GetNodeStats(gpair, *p_fmat, tree,
-                         &(this->thread_stats_), &(this->node_stats_));
-      for (const int nid : this->qexpand_) {
-        const int wid = this->node2workindex_[nid];
-        this->wspace_.hset[0][fset.size() + wid * (fset.size()+1)]
-            .data[0] = this->node_stats_[nid];
-      }
-    }
-    this->histred_.Allreduce(dmlc::BeginPtr(this->wspace_.hset[0].data),
-                            this->wspace_.hset[0].data.size());
-  }
-
-  // cached unit pointer
-  std::vector<unsigned> cached_rptr_;
-  // cached cut value.
-  std::vector<bst_float> cached_cut_;
-};
-
-XGBOOST_REGISTER_TREE_UPDATER(LocalHistMaker, "grow_local_histmaker")
-.describe("Tree constructor that uses approximate histogram construction.")
-.set_body([]() {
-    return new CQHistMaker();
-  });
-
-// The updater for approx tree method.
-XGBOOST_REGISTER_TREE_UPDATER(HistMaker, "grow_histmaker")
-.describe("Tree constructor that uses approximate global of histogram construction.")
-.set_body([]() {
-    return new GlobalProposalHistMaker();
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_prune.cc b/ml-xgboost/src/tree/updater_prune.cc
deleted file mode 100644
index f47395b..0000000
--- a/ml-xgboost/src/tree/updater_prune.cc
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2020 by Contributors
- * \file updater_prune.cc
- * \brief prune a tree given the statistics
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <xgboost/tree_updater.h>
-
-#include <string>
-#include <memory>
-
-#include "xgboost/base.h"
-#include "xgboost/json.h"
-#include "./param.h"
-#include "../common/io.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_prune);
-
-/*! \brief pruner that prunes a tree after growing finishes */
-class TreePruner: public TreeUpdater {
- public:
-  TreePruner() {
-    syncher_.reset(TreeUpdater::Create("sync", tparam_));
-  }
-  char const* Name() const override {
-    return "prune";
-  }
-
-  // set training parameter
-  void Configure(const Args& args) override {
-    param_.UpdateAllowUnknown(args);
-    syncher_->Configure(args);
-  }
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("train_param"), &this->param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["train_param"] = ToJson(param_);
-  }
-  bool CanModifyTree() const override {
-    return true;
-  }
-
-  // update the tree, do pruning
-  void Update(HostDeviceVector<GradientPair> *gpair,
-              DMatrix *p_fmat,
-              const std::vector<RegTree*> &trees, int iter,
-              float* p_last_round_best_loss) override {
-    // rescale learning rate according to size of trees
-    float lr = param_.learning_rate;
-    param_.learning_rate = lr / trees.size();
-    for (auto tree : trees) {
-      this->DoPrune(tree);
-    }
-    param_.learning_rate = lr;
-    syncher_->Update(gpair, p_fmat, trees, iter, p_last_round_best_loss);
-  }
-
- private:
-  // try to prune off current leaf
-  bst_node_t TryPruneLeaf(RegTree &tree, int nid, int depth, int npruned) { // NOLINT(*)
-    CHECK(tree[nid].IsLeaf());
-    if (tree[nid].IsRoot()) {
-      return npruned;
-    }
-    bst_node_t pid = tree[nid].Parent();
-    CHECK(!tree[pid].IsLeaf());
-    RTreeNodeStat const &s = tree.Stat(pid);
-    // Only prune when both child are leaf.
-    auto left = tree[pid].LeftChild();
-    auto right = tree[pid].RightChild();
-    bool balanced = tree[left].IsLeaf() &&
-                    right != RegTree::kInvalidNodeId && tree[right].IsLeaf();
-    if (balanced && param_.NeedPrune(s.loss_chg, depth)) {
-      // need to be pruned
-      tree.ChangeToLeaf(pid, param_.learning_rate * s.base_weight);
-      // tail recursion
-      return this->TryPruneLeaf(tree, pid, depth - 1, npruned + 2);
-    } else {
-      return npruned;
-    }
-  }
-  /*! \brief do pruning of a tree */
-  void DoPrune(RegTree* p_tree) {
-    auto& tree = *p_tree;
-    bst_node_t npruned = 0;
-    for (int nid = 0; nid < tree.param.num_nodes; ++nid) {
-      if (tree[nid].IsLeaf() && !tree[nid].IsDeleted()) {
-        npruned = this->TryPruneLeaf(tree, nid, tree.GetDepth(nid), npruned);
-      }
-    }
-    LOG(INFO) << "tree pruning end, "
-              << tree.NumExtraNodes() << " extra nodes, " << npruned
-              << " pruned nodes, max_depth=" << tree.MaxDepth();
-  }
-
- private:
-  // synchronizer
-  std::unique_ptr<TreeUpdater> syncher_;
-  // training parameter
-  TrainParam param_;
-};
-
-XGBOOST_REGISTER_TREE_UPDATER(TreePruner, "prune")
-.describe("Pruner that prune the tree according to statistics.")
-.set_body([]() {
-    return new TreePruner();
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_quantile_hist.cc b/ml-xgboost/src/tree/updater_quantile_hist.cc
deleted file mode 100644
index fadaf75..0000000
--- a/ml-xgboost/src/tree/updater_quantile_hist.cc
+++ /dev/null
@@ -1,1299 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2017-2018 by Contributors
- * \file updater_quantile_hist.cc
- * \brief use quantized feature values to construct a tree
- * \author Philip Cho, Tianqi Checn, Egor Smirnov
- */
-#include <dmlc/timer.h>
-#include <rabit/rabit.h>
-#include <boostkit_xgboost_kernel/bbgen.h>
-#include <boostkit_xgboost_kernel/update_quantile_hist_kernel.h>
-
-#include <cmath>
-#include <memory>
-#include <vector>
-#include <algorithm>
-#include <queue>
-#include <iomanip>
-#include <numeric>
-#include <string>
-#include <utility>
-
-#include "xgboost/logging.h"
-#include "xgboost/tree_updater.h"
-
-#include "constraints.h"
-#include "param.h"
-#include "./updater_quantile_hist.h"
-#include "./split_evaluator.h"
-#include "../common/random.h"
-#include "../common/hist_util.h"
-#include "../common/row_set.h"
-#include "../common/column_matrix.h"
-#include "../common/threading_utils.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_quantile_hist);
-
-void QuantileHistMaker::Configure(const Args& args) {
-  // initialize pruner
-  if (!pruner_) {
-    pruner_.reset(TreeUpdater::Create("prune", tparam_));
-  }
-  pruner_->Configure(args);
-  param_.UpdateAllowUnknown(args);
-
-  // initialize the split evaluator
-  if (!spliteval_) {
-    spliteval_.reset(SplitEvaluator::Create(param_.split_evaluator));
-  }
-
-  spliteval_->Init(&param_);
-}
-
-void QuantileHistMaker::Update(HostDeviceVector<GradientPair> *gpair,
-                               DMatrix *dmat,
-                               const std::vector<RegTree *> &trees,
-                               int iter,
-                               float* p_last_round_best_loss) {
-  if (dmat != p_last_dmat_ || is_gmat_initialized_ == false) {
-    gmat_.Init(dmat, static_cast<uint32_t>(param_.max_bin));
-    column_matrix_.Init(gmat_, param_.sparse_threshold);
-
-    if (param_.enable_feature_grouping > 0) {
-      gmatb_.Init(gmat_, column_matrix_, param_);
-    }
-    // A proper solution is puting cut matrix in DMatrix, see:
-    // https://github.com/dmlc/xgboost/issues/5143
-    is_gmat_initialized_ = true;
-  }
-  // rescale learning rate according to size of trees
-  float lr = param_.learning_rate;
-  param_.learning_rate = lr / trees.size();
-  int_constraint_.Configure(param_, dmat->Info().num_col_);
-  // build tree
-  if (!builder_) {
-    builder_.reset(new Builder(
-        param_,
-        std::move(pruner_),
-        std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone()),
-        int_constraint_, dmat));
-  }
-  for (auto tree : trees) {
-    builder_->Update(gmat_, gmatb_, column_matrix_, gpair, dmat, tree, iter, p_last_round_best_loss);
-  }
-  param_.learning_rate = lr;
-
-  p_last_dmat_ = dmat;
-}
-
-bool QuantileHistMaker::UpdatePredictionCache(
-    const DMatrix* data,
-    HostDeviceVector<bst_float>* out_preds, int iter) {
-  // don't save cache
-  if (SaveCacheChecker(param_.sampling_strategy, param_.subsample, iter, param_.sampling_step)) {
-    // save cache
-    return builder_->UpdatePredictionCache(data, out_preds);
-  } else {
-    // don't save cache
-    return false;
-  }
-}
-
-void QuantileHistMaker::Builder::SyncHistograms(
-    int starting_index,
-    int sync_count,
-    RegTree *p_tree) {
-  builder_monitor_.Start("SyncHistograms");
-
-  const bool isDistributed = rabit::IsDistributed();
-
-  const size_t nbins = hist_builder_.GetNumBins();
-  common::BlockedSpace2d space(nodes_for_explicit_hist_build_.size(), [&](size_t node) {
-    return nbins;
-  }, 1024);
-
-  common::ParallelFor2d(space, this->nthread_, [&](size_t node, common::Range1d r) {
-    const auto entry = nodes_for_explicit_hist_build_[node];
-    auto this_hist = hist_[entry.nid];
-    // Merging histograms from each thread into once
-    hist_buffer_.ReduceHist(node, r.begin(), r.end());
-
-    if (!(*p_tree)[entry.nid].IsRoot() && entry.sibling_nid > -1 && !isDistributed) {
-      auto parent_hist = hist_[(*p_tree)[entry.nid].Parent()];
-      auto sibling_hist = hist_[entry.sibling_nid];
-
-      SubtractionHist(sibling_hist, parent_hist, this_hist, r.begin(), r.end());
-    }
-  });
-
-  if (isDistributed) {
-    this->histred_.Allreduce(hist_[starting_index].data(), hist_builder_.GetNumBins() * sync_count);
-    // use Subtraction Trick
-    for (auto const& node : nodes_for_subtraction_trick_) {
-      SubtractionTrick(hist_[node.nid], hist_[node.sibling_nid],
-                       hist_[(*p_tree)[node.nid].Parent()]);
-    }
-  }
-
-  builder_monitor_.Stop("SyncHistograms");
-}
-
-void QuantileHistMaker::Builder::BuildHistogramsLossGuide(
-                        ExpandEntry entry,
-                        const GHistIndexMatrix &gmat,
-                        const GHistIndexBlockMatrix &gmatb,
-                        RegTree *p_tree,
-                        const std::vector<GradientPair> &gpair_h) {
-  nodes_for_explicit_hist_build_.clear();
-  nodes_for_subtraction_trick_.clear();
-  nodes_for_explicit_hist_build_.push_back(entry);
-
-  if (entry.sibling_nid > -1) {
-    nodes_for_subtraction_trick_.emplace_back(entry.sibling_nid, entry.nid,
-        p_tree->GetDepth(entry.sibling_nid), 0.0f, 0);
-  }
-
-  int starting_index = std::numeric_limits<int>::max();
-  int sync_count = 0;
-
-  AddHistRows(&starting_index, &sync_count);
-  BuildLocalHistograms(gmat, gmatb, p_tree, gpair_h);
-  SyncHistograms(starting_index, sync_count, p_tree);
-}
-
-
-void QuantileHistMaker::Builder::AddHistRows(int *starting_index, int *sync_count) {
-  builder_monitor_.Start("AddHistRows");
-
-  for (auto const& entry : nodes_for_explicit_hist_build_) {
-    int nid = entry.nid;
-    hist_.AddHistRow(nid);
-    (*starting_index) = std::min(nid, (*starting_index));
-  }
-  (*sync_count) = nodes_for_explicit_hist_build_.size();
-
-  for (auto const& node : nodes_for_subtraction_trick_) {
-    hist_.AddHistRow(node.nid);
-  }
-
-  builder_monitor_.Stop("AddHistRows");
-}
-
-
-void QuantileHistMaker::Builder::BuildLocalHistograms(
-    const GHistIndexMatrix &gmat,
-    const GHistIndexBlockMatrix &gmatb,
-    RegTree *p_tree,
-    const std::vector<GradientPair> &gpair_h) {
-  builder_monitor_.Start("BuildLocalHistograms");
-
-  const size_t n_nodes = nodes_for_explicit_hist_build_.size();
-  // create space of size (# rows in each node)
-  common::BlockedSpace2d space(n_nodes, [&](size_t node) {
-    const int32_t nid = nodes_for_explicit_hist_build_[node].nid;
-    return row_set_collection_[nid].Size();
-  }, 256);
-
-  std::vector<GHistRow> target_hists(n_nodes);
-  for (size_t i = 0; i < n_nodes; ++i) {
-    const int32_t nid = nodes_for_explicit_hist_build_[i].nid;
-    target_hists[i] = hist_[nid];
-  }
-
-  hist_buffer_.Reset(this->nthread_, n_nodes, space, target_hists);
-
-  // Parallel processing by nodes and data in each node
-  common::ParallelFor2d(space, this->nthread_, [&](size_t nid_in_set, common::Range1d r) {
-    const auto tid = static_cast<unsigned>(omp_get_thread_num());
-    const int32_t nid = nodes_for_explicit_hist_build_[nid_in_set].nid;
-
-    auto start_of_row_set = row_set_collection_[nid].begin;
-    auto rid_set = RowSetCollection::Elem(start_of_row_set + r.begin(),
-                                      start_of_row_set + r.end(),
-                                      nid);
-    BuildHist(gpair_h, rid_set, gmat, gmatb, hist_buffer_.GetInitializedHist(tid, nid_in_set));
-  });
-
-  builder_monitor_.Stop("BuildLocalHistograms");
-}
-
-
-void QuantileHistMaker::Builder::BuildNodeStats(
-    const GHistIndexMatrix &gmat,
-    DMatrix *p_fmat,
-    RegTree *p_tree,
-    const std::vector<GradientPair> &gpair_h) {
-  builder_monitor_.Start("BuildNodeStats");
-  for (auto const& entry : qexpand_depth_wise_) {
-    int nid = entry.nid;
-    this->InitNewNode(nid, gmat, gpair_h, *p_fmat, *p_tree);
-    // add constraints
-    if (!(*p_tree)[nid].IsLeftChild() && !(*p_tree)[nid].IsRoot()) {
-      // it's a right child
-      auto parent_id = (*p_tree)[nid].Parent();
-      auto left_sibling_id = (*p_tree)[parent_id].LeftChild();
-      auto parent_split_feature_id = snode_[parent_id].best.SplitIndex();
-      spliteval_->AddSplit(parent_id, left_sibling_id, nid, parent_split_feature_id,
-                           snode_[left_sibling_id].weight, snode_[nid].weight);
-      interaction_constraints_.Split(parent_id, parent_split_feature_id,
-                                     left_sibling_id, nid);
-    }
-  }
-  builder_monitor_.Stop("BuildNodeStats");
-}
-
-void QuantileHistMaker::Builder::AddSplitsToTree(
-          const GHistIndexMatrix &gmat,
-          RegTree *p_tree,
-          int *num_leaves,
-          int depth,
-          unsigned *timestamp,
-          std::vector<ExpandEntry>* nodes_for_apply_split,
-          std::vector<ExpandEntry>* temp_qexpand_depth, 
-          float* p_last_round_best_loss) {
-  for (auto const& entry : qexpand_depth_wise_) {
-    int nid = entry.nid;
-
-    if ((snode_[nid].best.loss_chg < kRtEps) ||
-        (param_.max_depth > 0 && depth == param_.max_depth) ||
-        (param_.max_leaves > 0 && (*num_leaves) == param_.max_leaves)) {
-      (*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
-    } else if ((param_.grow_policy == TrainParam::kDepthWiseLossLtd) &&
-        KDepthWiseLossLtdLeafChecker(param_.min_loss_ratio,
-            *p_last_round_best_loss, snode_[nid].best.loss_chg)) {
-      (*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
-    } else {
-      nodes_for_apply_split->push_back(entry);
-
-      NodeEntry& e = snode_[nid];
-      bst_float left_leaf_weight =
-          spliteval_->ComputeWeight(nid, e.best.left_sum) * param_.learning_rate;
-      bst_float right_leaf_weight =
-          spliteval_->ComputeWeight(nid, e.best.right_sum) * param_.learning_rate;
-      p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
-                         e.best.DefaultLeft(), e.weight, left_leaf_weight,
-                         right_leaf_weight, e.best.loss_chg, e.stats.sum_hess,
-                         e.best.left_sum.GetHess(), e.best.right_sum.GetHess());
-
-      int left_id = (*p_tree)[nid].LeftChild();
-      int right_id = (*p_tree)[nid].RightChild();
-      temp_qexpand_depth->push_back(ExpandEntry(left_id, right_id,
-                                                p_tree->GetDepth(left_id), 0.0, (*timestamp)++));
-      temp_qexpand_depth->push_back(ExpandEntry(right_id, left_id,
-                                                p_tree->GetDepth(right_id), 0.0, (*timestamp)++));
-      // - 1 parent + 2 new children
-      (*num_leaves)++;
-    }
-  }
-}
-
-
-void QuantileHistMaker::Builder::EvaluateAndApplySplits(
-    const GHistIndexMatrix &gmat,
-    const ColumnMatrix &column_matrix,
-    RegTree *p_tree,
-    int *num_leaves,
-    int depth,
-    unsigned *timestamp,
-    std::vector<ExpandEntry> *temp_qexpand_depth, int _iter, float* p_last_round_best_loss) {
-  EvaluateSplits(qexpand_depth_wise_, gmat, hist_, *p_tree, _iter);
-
-  std::vector<ExpandEntry> nodes_for_apply_split;
-  AddSplitsToTree(gmat, p_tree, num_leaves, depth, timestamp,
-                  &nodes_for_apply_split, temp_qexpand_depth, p_last_round_best_loss);
-  ApplySplit(nodes_for_apply_split, gmat, column_matrix, hist_, p_tree);
-}
-
-// Split nodes to 2 sets depending on amount of rows in each node
-// Histograms for small nodes will be built explicitly
-// Histograms for big nodes will be built by 'Subtraction Trick'
-// Exception: in distributed setting, we always build the histogram for the left child node
-//    and use 'Subtraction Trick' to built the histogram for the right child node.
-//    This ensures that the workers operate on the same set of tree nodes.
-void QuantileHistMaker::Builder::SplitSiblings(const std::vector<ExpandEntry>& nodes,
-                   std::vector<ExpandEntry>* small_siblings,
-                   std::vector<ExpandEntry>* big_siblings,
-                   RegTree *p_tree) {
-  for (auto const& entry : nodes) {
-    int nid = entry.nid;
-    RegTree::Node &node = (*p_tree)[nid];
-    if (rabit::IsDistributed()) {
-      if (node.IsRoot() || node.IsLeftChild()) {
-        small_siblings->push_back(entry);
-      } else {
-        big_siblings->push_back(entry);
-      }
-    } else {
-      if (!node.IsRoot() && node.IsLeftChild() &&
-          (row_set_collection_[nid].Size() <
-           row_set_collection_[(*p_tree)[node.Parent()].RightChild()].Size())) {
-        small_siblings->push_back(entry);
-      } else if (!node.IsRoot() && !node.IsLeftChild() &&
-                 (row_set_collection_[nid].Size() <=
-                  row_set_collection_[(*p_tree)[node.Parent()].LeftChild()].Size())) {
-        small_siblings->push_back(entry);
-      } else if (node.IsRoot()) {
-        small_siblings->push_back(entry);
-      } else {
-        big_siblings->push_back(entry);
-      }
-    }
-  }
-}
-
-void QuantileHistMaker::Builder::ExpandWithDepthWise(
-  const GHistIndexMatrix &gmat,
-  const GHistIndexBlockMatrix &gmatb,
-  const ColumnMatrix &column_matrix,
-  DMatrix *p_fmat,
-  RegTree *p_tree,
-  const std::vector<GradientPair> &gpair_h,
-  int _iter,
-  float* p_last_round_best_loss) {
-  unsigned timestamp = 0;
-  int num_leaves = 0;
-
-  // in depth_wise growing, we feed loss_chg with 0.0 since it is not used anyway
-  qexpand_depth_wise_.emplace_back(ExpandEntry(ExpandEntry::kRootNid, ExpandEntry::kEmptyNid,
-      p_tree->GetDepth(ExpandEntry::kRootNid), 0.0, timestamp++));
-  ++num_leaves;
-  for (int depth = 0; depth < param_.max_depth + 1; depth++) {
-    int starting_index = std::numeric_limits<int>::max();
-    int sync_count = 0;
-    std::vector<ExpandEntry> temp_qexpand_depth;
-
-    SplitSiblings(qexpand_depth_wise_, &nodes_for_explicit_hist_build_,
-                  &nodes_for_subtraction_trick_, p_tree);
-    AddHistRows(&starting_index, &sync_count);
-
-    BuildLocalHistograms(gmat, gmatb, p_tree, gpair_h);
-    SyncHistograms(starting_index, sync_count, p_tree);
-
-    BuildNodeStats(gmat, p_fmat, p_tree, gpair_h);
-    EvaluateAndApplySplits(gmat, column_matrix, p_tree, &num_leaves, depth, &timestamp,
-                   &temp_qexpand_depth, _iter, p_last_round_best_loss);
-    // clean up
-    qexpand_depth_wise_.clear();
-    nodes_for_subtraction_trick_.clear();
-    nodes_for_explicit_hist_build_.clear();
-    if (temp_qexpand_depth.empty()) {
-      break;
-    } else {
-      qexpand_depth_wise_ = temp_qexpand_depth;
-      temp_qexpand_depth.clear();
-    }
-  }
-
-  // update best_split_loss_chg
-  if (p_last_round_best_loss != nullptr) {
-    size_t size_ = snode_.size();
-    auto* nodes_best_loss_chg = new bst_float[size_];
-    for (size_t i = 0; i < size_; i++) {
-      nodes_best_loss_chg[i] = snode_[i].best.loss_chg;
-    }
-    UpdateBestSplitLossChange(*p_last_round_best_loss, size_, *nodes_best_loss_chg);
-    delete[] nodes_best_loss_chg;
-  }
-}
-
-void QuantileHistMaker::Builder::ExpandWithLossGuide(
-    const GHistIndexMatrix& gmat,
-    const GHistIndexBlockMatrix& gmatb,
-    const ColumnMatrix& column_matrix,
-    DMatrix* p_fmat,
-    RegTree* p_tree,
-    const std::vector<GradientPair>& gpair_h,
-    int _iter) {
-
-  unsigned timestamp = 0;
-  int num_leaves = 0;
-
-  ExpandEntry node(ExpandEntry::kRootNid, ExpandEntry::kEmptyNid,
-      p_tree->GetDepth(0), 0.0f, timestamp++);
-  BuildHistogramsLossGuide(node, gmat, gmatb, p_tree, gpair_h);
-
-  this->InitNewNode(ExpandEntry::kRootNid, gmat, gpair_h, *p_fmat, *p_tree);
-
-  this->EvaluateSplits({node}, gmat, hist_, *p_tree, _iter);
-  node.loss_chg = snode_[ExpandEntry::kRootNid].best.loss_chg;
-
-  qexpand_loss_guided_->push(node);
-  ++num_leaves;
-
-  while (!qexpand_loss_guided_->empty()) {
-    const ExpandEntry candidate = qexpand_loss_guided_->top();
-    const int nid = candidate.nid;
-    qexpand_loss_guided_->pop();
-    if (candidate.IsValid(param_, num_leaves)) {
-      (*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
-    } else {
-      NodeEntry& e = snode_[nid];
-      bst_float left_leaf_weight =
-          spliteval_->ComputeWeight(nid, e.best.left_sum) * param_.learning_rate;
-      bst_float right_leaf_weight =
-          spliteval_->ComputeWeight(nid, e.best.right_sum) * param_.learning_rate;
-      p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
-                         e.best.DefaultLeft(), e.weight, left_leaf_weight,
-                         right_leaf_weight, e.best.loss_chg, e.stats.sum_hess,
-                         e.best.left_sum.GetHess(), e.best.right_sum.GetHess());
-
-      this->ApplySplit({candidate}, gmat, column_matrix, hist_, p_tree);
-
-      const int cleft = (*p_tree)[nid].LeftChild();
-      const int cright = (*p_tree)[nid].RightChild();
-
-      ExpandEntry left_node(cleft, cright, p_tree->GetDepth(cleft),
-                            0.0f, timestamp++);
-      ExpandEntry right_node(cright, cleft, p_tree->GetDepth(cright),
-                            0.0f, timestamp++);
-
-      if (rabit::IsDistributed()) {
-        // in distributed mode, we need to keep consistent across workers
-        BuildHistogramsLossGuide(left_node, gmat, gmatb, p_tree, gpair_h);
-      } else {
-        if (row_set_collection_[cleft].Size() < row_set_collection_[cright].Size()) {
-          BuildHistogramsLossGuide(left_node, gmat, gmatb, p_tree, gpair_h);
-        } else {
-          BuildHistogramsLossGuide(right_node, gmat, gmatb, p_tree, gpair_h);
-        }
-      }
-
-      this->InitNewNode(cleft, gmat, gpair_h, *p_fmat, *p_tree);
-      this->InitNewNode(cright, gmat, gpair_h, *p_fmat, *p_tree);
-      bst_uint featureid = snode_[nid].best.SplitIndex();
-      spliteval_->AddSplit(nid, cleft, cright, featureid,
-                           snode_[cleft].weight, snode_[cright].weight);
-      interaction_constraints_.Split(nid, featureid, cleft, cright);
-
-      this->EvaluateSplits({left_node, right_node}, gmat, hist_, *p_tree, _iter);
-      left_node.loss_chg = snode_[cleft].best.loss_chg;
-      right_node.loss_chg = snode_[cright].best.loss_chg;
-
-      qexpand_loss_guided_->push(left_node);
-      qexpand_loss_guided_->push(right_node);
-
-      ++num_leaves;  // give two and take one, as parent is no longer a leaf
-    }
-  }
-}
-
-void QuantileHistMaker::Builder::Update(const GHistIndexMatrix& gmat,
-                                        const GHistIndexBlockMatrix& gmatb,
-                                        const ColumnMatrix& column_matrix,
-                                        HostDeviceVector<GradientPair>* gpair,
-                                        DMatrix* p_fmat,
-                                        RegTree* p_tree, 
-                                        int iter,
-                                        float* p_last_round_best_loss) {
-  builder_monitor_.Start("Update");
-
-  const std::vector<GradientPair>& gpair_h = gpair->ConstHostVector();
-
-  spliteval_->Reset();
-  interaction_constraints_.Reset();
-
-  this->InitData(gmat, gpair_h, *p_fmat, *p_tree, iter);
-
-  if (param_.grow_policy == TrainParam::kLossGuide) {
-    ExpandWithLossGuide(gmat, gmatb, column_matrix, p_fmat, p_tree, gpair_h, iter);
-  } else {
-    ExpandWithDepthWise(gmat, gmatb, column_matrix, p_fmat, p_tree, gpair_h, iter, p_last_round_best_loss);
-  }
-
-  for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
-    p_tree->Stat(nid).loss_chg = snode_[nid].best.loss_chg;
-    p_tree->Stat(nid).base_weight = snode_[nid].weight;
-    p_tree->Stat(nid).sum_hess = static_cast<float>(snode_[nid].stats.sum_hess);
-  }
-
-  pruner_->Update(gpair, p_fmat, std::vector<RegTree*>{p_tree}, iter, p_last_round_best_loss);
-
-  builder_monitor_.Stop("Update");
-}
-
-bool QuantileHistMaker::Builder::UpdatePredictionCache(
-    const DMatrix* data,
-    HostDeviceVector<bst_float>* p_out_preds) {
-  // p_last_fmat_ is a valid pointer as long as UpdatePredictionCache() is called in
-  // conjunction with Update().
-  if (!p_last_fmat_ || !p_last_tree_ || data != p_last_fmat_) {
-    return false;
-  }
-  builder_monitor_.Start("UpdatePredictionCache");
-
-  std::vector<bst_float>& out_preds = p_out_preds->HostVector();
-
-  if (leaf_value_cache_.empty()) {
-    leaf_value_cache_.resize(p_last_tree_->param.num_nodes,
-                             std::numeric_limits<float>::infinity());
-  }
-
-  CHECK_GT(out_preds.size(), 0U);
-
-  size_t n_nodes = row_set_collection_.end() - row_set_collection_.begin();
-
-  common::BlockedSpace2d space(n_nodes, [&](size_t node) {
-    return row_set_collection_[node].Size();
-  }, 1024);
-
-  common::ParallelFor2d(space, this->nthread_, [&](size_t node, common::Range1d r) {
-    const RowSetCollection::Elem rowset = row_set_collection_[node];
-    if (rowset.begin != nullptr && rowset.end != nullptr) {
-      int nid = rowset.node_id;
-      bst_float leaf_value;
-      // if a node is marked as deleted by the pruner, traverse upward to locate
-      // a non-deleted leaf.
-      if ((*p_last_tree_)[nid].IsDeleted()) {
-        while ((*p_last_tree_)[nid].IsDeleted()) {
-          nid = (*p_last_tree_)[nid].Parent();
-        }
-        CHECK((*p_last_tree_)[nid].IsLeaf());
-      }
-      leaf_value = (*p_last_tree_)[nid].LeafValue();
-
-      for (const size_t* it = rowset.begin + r.begin(); it < rowset.begin + r.end(); ++it) {
-        out_preds[*it] += leaf_value;
-      }
-    }
-  });
-
-  builder_monitor_.Stop("UpdatePredictionCache");
-  return true;
-}
-
-void InitSampling(const std::vector<GradientPair>& gpair,
-                  const DMatrix& fmat,
-                  std::vector<size_t>* row_indices,
-                  const size_t nthread,
-                  const bool enable_bbgen,
-                  const float subsample,
-                  const bool goss) {
-  const auto& info = fmat.Info();
-  auto& rnd = common::GlobalRandom();
-  std::vector<size_t>& row_indices_local = *row_indices;
-  size_t* p_row_indices = row_indices_local.data();
-#if XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-  std::bernoulli_distribution coin_flip(subsample);
-  size_t j = 0;
-  for (size_t i = 0; i < info.num_row_; ++i) {
-    if (gpair[i].GetHess() >= 0.0f && coin_flip(rnd)) {
-      p_row_indices[j++] = i;
-    }
-  }
-  /* resize row_indices to reduce memory */
-  row_indices_local.resize(j);
-#else
-  float threshold_grad_val = goss ? ThresholdGradientCal(gpair, rnd, subsample) :
-      std::numeric_limits<float>::max();
-
-  std::vector<size_t> row_offsets(nthread, 0);
-  /* usage of mt19937_64 give 2x speed up for subsampling */
-  std::vector<std::mt19937> rnds(nthread);
-  /* create engine for each thread */
-  for (std::mt19937& r : rnds) {
-    r = rnd;
-  }
-  const size_t discard_size = info.num_row_ / nthread;
-  #pragma omp parallel num_threads(nthread)
-  {
-    const size_t tid = omp_get_thread_num();
-    const size_t ibegin = tid * discard_size;
-    const size_t iend = (tid == (nthread - 1)) ?
-                        info.num_row_ : ibegin + discard_size;
-    if (enable_bbgen) {
-      struct BbgenSamplingParam bbgenParam = {.subsample = subsample, .tid = tid,
-          .discardSize = discard_size, .ibegin = ibegin, .iend = iend,
-          .thresholdGradVal = threshold_grad_val};
-      BbgenSampling(bbgenParam, rnds, gpair, *p_row_indices, row_offsets);
-    } else {
-      std::bernoulli_distribution coin_flip(subsample);
-
-      rnds[tid].discard(2 * discard_size * tid);
-      if (goss) {
-        for (size_t i = ibegin; i < iend; ++i) {
-          if (gpair[i].GetHess() >= 0.0f &&
-            ((fabs(gpair[i].GetGrad()) > threshold_grad_val) || coin_flip(rnds[tid]))) {
-            p_row_indices[ibegin + row_offsets[tid]++] = i;
-          }
-        }
-      } else {
-        for (size_t i = ibegin; i < iend; ++i) {
-          if (gpair[i].GetHess() >= 0.0f && coin_flip(rnds[tid])) {
-            p_row_indices[ibegin + row_offsets[tid]++] = i;
-          }
-        }
-      }
-    }
-  }
-  /* discard global engine */
-  rnd = rnds[nthread - 1];
-  size_t prefix_sum = row_offsets[0];
-  for (size_t i = 1; i < nthread; ++i) {
-    const size_t ibegin = i * discard_size;
-
-    for (size_t k = 0; k < row_offsets[i]; ++k) {
-      row_indices_local[prefix_sum + k] = row_indices_local[ibegin + k];
-    }
-    prefix_sum += row_offsets[i];
-  }
-  /* resize row_indices to reduce memory */
-  row_indices_local.resize(prefix_sum);
-#endif  // XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-}
-
-void QuantileHistMaker::Builder::InitData(const GHistIndexMatrix& gmat,
-                                          const std::vector<GradientPair>& gpair,
-                                          const DMatrix& fmat,
-                                          const RegTree& tree,
-                                          int iter) {
-  CHECK((param_.max_depth > 0 || param_.max_leaves > 0))
-      << "max_depth or max_leaves cannot be both 0 (unlimited); "
-      << "at least one should be a positive quantity.";
-  if (param_.grow_policy == TrainParam::kDepthWise || param_.grow_policy == TrainParam::kDepthWiseLossLtd) {
-    CHECK(param_.max_depth > 0) << "max_depth cannot be 0 (unlimited) "
-                                << "when grow_policy is depthwise or depthwiselossltd.";
-  }
-  builder_monitor_.Start("InitData");
-  const auto& info = fmat.Info();
-
-  {
-    // initialize the row set
-    row_set_collection_.Clear();
-    // clear local prediction cache
-    leaf_value_cache_.clear();
-    // initialize histogram collection
-    uint32_t nbins = gmat.cut.Ptrs().back();
-    hist_.Init(nbins);
-    hist_buffer_.Init(nbins);
-
-    // initialize histogram builder
-#pragma omp parallel
-    {
-      this->nthread_ = omp_get_num_threads();
-    }
-    hist_builder_ = GHistBuilder(this->nthread_, nbins);
-
-    std::vector<size_t>& row_indices = *row_set_collection_.Data();
-    row_indices.resize(info.num_row_);
-    size_t* p_row_indices = row_indices.data();
-    // mark subsample and build list of member rows
-
-    if ((param_.subsample < 1.0f) || (param_.sampling_step > 1)) {
-      CHECK_EQ(param_.sampling_method, TrainParam::kUniform)
-        << "Only uniform sampling is supported, "
-        << "gradient-based sampling is only support by GPU Hist.";
-      struct InitSamplingKernelParam initParam = {.nthread = (size_t)this->nthread_, .iter = iter,
-          .lastIterSampling = &last_iter_sampling, .samplingStrategy = param_.sampling_strategy,
-          .samplingStep = param_.sampling_step, .enableBbgen = param_.enable_bbgen, .subsample = param_.subsample};
-      InitSamplingKernel(gpair, fmat, row_indices, last_sampling, initParam);
-    } else {
-      MemStackAllocator<bool, 128> buff(this->nthread_);
-      bool* p_buff = buff.Get();
-      std::fill(p_buff, p_buff + this->nthread_, false);
-
-      const size_t block_size = info.num_row_ / this->nthread_ + !!(info.num_row_ % this->nthread_);
-
-      #pragma omp parallel num_threads(this->nthread_)
-      {
-        const size_t tid = omp_get_thread_num();
-        const size_t ibegin = tid * block_size;
-        const size_t iend = std::min(static_cast<size_t>(ibegin + block_size),
-            static_cast<size_t>(info.num_row_));
-
-        for (size_t i = ibegin; i < iend; ++i) {
-          if (gpair[i].GetHess() < 0.0f) {
-            p_buff[tid] = true;
-            break;
-          }
-        }
-      }
-
-      bool has_neg_hess = false;
-      for (int32_t tid = 0; tid < this->nthread_; ++tid) {
-        if (p_buff[tid]) {
-          has_neg_hess = true;
-        }
-      }
-
-      if (has_neg_hess) {
-        size_t j = 0;
-        for (size_t i = 0; i < info.num_row_; ++i) {
-          if (gpair[i].GetHess() >= 0.0f) {
-            p_row_indices[j++] = i;
-          }
-        }
-        row_indices.resize(j);
-      } else {
-        #pragma omp parallel num_threads(this->nthread_)
-        {
-          const size_t tid = omp_get_thread_num();
-          const size_t ibegin = tid * block_size;
-          const size_t iend = std::min(static_cast<size_t>(ibegin + block_size),
-              static_cast<size_t>(info.num_row_));
-          for (size_t i = ibegin; i < iend; ++i) {
-           p_row_indices[i] = i;
-          }
-        }
-      }
-    }
-  }
-
-  row_set_collection_.Init();
-
-  {
-    /* determine layout of data */
-    const size_t nrow = info.num_row_;
-    const size_t ncol = info.num_col_;
-    const size_t nnz = info.num_nonzero_;
-    // number of discrete bins for feature 0
-    const uint32_t nbins_f0 = gmat.cut.Ptrs()[1] - gmat.cut.Ptrs()[0];
-    if (nrow * ncol == nnz) {
-      // dense data with zero-based indexing
-      data_layout_ = kDenseDataZeroBased;
-    } else if (nbins_f0 == 0 && nrow * (ncol - 1) == nnz) {
-      // dense data with one-based indexing
-      data_layout_ = kDenseDataOneBased;
-    } else {
-      // sparse data
-      data_layout_ = kSparseData;
-    }
-  }
-  // store a pointer to the tree
-  p_last_tree_ = &tree;
-  if (data_layout_ == kDenseDataOneBased) {
-    column_sampler_.Init(info.num_col_, param_.colsample_bynode, param_.colsample_bylevel,
-            param_.colsample_bytree, true);
-  } else {
-    column_sampler_.Init(info.num_col_, param_.colsample_bynode, param_.colsample_bylevel,
-            param_.colsample_bytree,  false);
-  }
-  if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
-    /* specialized code for dense data:
-       choose the column that has a least positive number of discrete bins.
-       For dense data (with no missing value),
-       the sum of gradient histogram is equal to snode[nid] */
-    const std::vector<uint32_t>& row_ptr = gmat.cut.Ptrs();
-    const auto nfeature = static_cast<bst_uint>(row_ptr.size() - 1);
-    uint32_t min_nbins_per_feature = 0;
-    for (bst_uint i = 0; i < nfeature; ++i) {
-      const uint32_t nbins = row_ptr[i + 1] - row_ptr[i];
-      if (nbins > 0) {
-        if (min_nbins_per_feature == 0 || min_nbins_per_feature > nbins) {
-          min_nbins_per_feature = nbins;
-          fid_least_bins_ = i;
-        }
-      }
-    }
-    CHECK_GT(min_nbins_per_feature, 0U);
-  }
-  {
-    snode_.reserve(256);
-    snode_.clear();
-  }
-  {
-    if (param_.grow_policy == TrainParam::kLossGuide) {
-      qexpand_loss_guided_.reset(new ExpandQueue(LossGuide));
-    } else {
-      qexpand_depth_wise_.clear();
-    }
-  }
-  builder_monitor_.Stop("InitData");
-}
-
-// if sum of statistics for non-missing values in the node
-// is equal to sum of statistics for all values:
-// then - there are no missing values
-// else - there are missing values
-bool QuantileHistMaker::Builder::SplitContainsMissingValues(const GradStats e,
-                                                            const NodeEntry& snode) {
-  if (e.GetGrad() == snode.stats.GetGrad() && e.GetHess() == snode.stats.GetHess()) {
-    return false;
-  } else {
-    return true;
-  }
-}
-
-// nodes_set - set of nodes to be processed in parallel
-void QuantileHistMaker::Builder::EvaluateSplits(const std::vector<ExpandEntry>& nodes_set,
-                                               const GHistIndexMatrix& gmat,
-                                               const HistCollection& hist,
-                                               const RegTree& tree,
-                                               int _iter) {
-  builder_monitor_.Start("EvaluateSplits");
-
-  const size_t n_nodes_in_set = nodes_set.size();
-  const size_t nthread = std::max(1, this->nthread_);
-
-  using FeatureSetType = std::shared_ptr<HostDeviceVector<bst_feature_t>>;
-  std::vector<FeatureSetType> features_sets(n_nodes_in_set);
-  best_split_tloc_.resize(nthread * n_nodes_in_set);
-
-  // Generate feature set for each tree node
-  for (size_t nid_in_set = 0; nid_in_set < n_nodes_in_set; ++nid_in_set) {
-    const int32_t nid = nodes_set[nid_in_set].nid;
-    features_sets[nid_in_set] = column_sampler_.GetFeatureSet(tree.GetDepth(nid));
-
-    for (unsigned tid = 0; tid < nthread; ++tid) {
-      best_split_tloc_[nthread*nid_in_set + tid] = snode_[nid].best;
-    }
-  }
-
-  // Create 2D space (# of nodes to process x # of features to process)
-  // to process them in parallel
-  const size_t grain_size = std::max<size_t>(1, features_sets[0]->Size() / nthread);
-  common::BlockedSpace2d space(n_nodes_in_set, [&](size_t nid_in_set) {
-      return features_sets[nid_in_set]->Size();
-  }, grain_size);
-
-  // Start parallel enumeration for all tree nodes in the set and all features
-  std::vector<int> proc_grid_idx(n_nodes_in_set * 2);
-  if (param_.random_split_denom > 1) {
-    for (auto& e : proc_grid_idx) {
-      e = _iter;
-    }
-  }
-  common::ParallelFor2d(space, this->nthread_, [&](size_t nid_in_set, common::Range1d r) {
-    const int32_t nid = nodes_set[nid_in_set].nid;
-    const auto tid = static_cast<unsigned>(omp_get_thread_num());
-    GHistRow node_hist = hist[nid];
-
-    for (auto idx_in_feature_set = r.begin(); idx_in_feature_set < r.end(); ++idx_in_feature_set) {
-      const auto fid = features_sets[nid_in_set]->ConstHostVector()[idx_in_feature_set];
-      if (interaction_constraints_.Query(nid, fid)) {
-        if (param_.default_direction == TrainParamKernel::learn) {
-          auto grad_stats = this->EnumerateSplit<+1>(gmat, node_hist, snode_[nid],
-              &best_split_tloc_[nthread*nid_in_set + tid], fid, nid, 
-              (param_.random_split_denom > 1 ? proc_grid_idx[2 * nid_in_set] : 0));
-          if (SplitContainsMissingValues(grad_stats, snode_[nid])) {
-            this->EnumerateSplit<-1>(gmat, node_hist, snode_[nid],
-                &best_split_tloc_[nthread*nid_in_set + tid], fid, nid,
-                (param_.random_split_denom > 1 ? proc_grid_idx[2 * nid_in_set + 1] : 0));
-          }
-        } else if (param_.default_direction == TrainParamKernel::right) {
-          auto grad_stats = this->EnumerateSplit<+1>(gmat, node_hist, snode_[nid],
-              &best_split_tloc_[nthread*nid_in_set + tid], fid, nid, 
-              (param_.random_split_denom > 1 ? proc_grid_idx[2 * nid_in_set] : 0));
-        } else {
-          this->EnumerateSplit<-1>(gmat, node_hist, snode_[nid],
-              &best_split_tloc_[nthread*nid_in_set + tid], fid, nid, 
-              (param_.random_split_denom > 1 ? proc_grid_idx[2 * nid_in_set + 1] : 0));
-        }
-      }
-    }
-  });
-
-  // Find Best Split across threads for each node in nodes set
-  for (unsigned nid_in_set = 0; nid_in_set < n_nodes_in_set; ++nid_in_set) {
-    const int32_t nid = nodes_set[nid_in_set].nid;
-    for (unsigned tid = 0; tid < nthread; ++tid) {
-      snode_[nid].best.Update(best_split_tloc_[nthread*nid_in_set + tid]);
-    }
-  }
-
-  builder_monitor_.Stop("EvaluateSplits");
-}
-
-// split row indexes (rid_span) to 2 parts (left_part, right_part) depending
-// on comparison of indexes values (idx_span) and split point (split_cond)
-// Handle dense columns
-// Analog of std::stable_partition, but in no-inplace manner
-template <bool default_left, typename BinIdxType>
-inline std::pair<size_t, size_t> PartitionDenseKernel(const common::DenseColumn<BinIdxType>& column,
-      common::Span<const size_t> rid_span, const int32_t split_cond,
-      common::Span<size_t> left_part, common::Span<size_t> right_part) {
-  const int32_t offset = column.GetBaseIdx();
-  const BinIdxType* idx = column.GetFeatureBinIdxPtr().data();
-  size_t* p_left_part = left_part.data();
-  size_t* p_right_part = right_part.data();
-  size_t nleft_elems = 0;
-  size_t nright_elems = 0;
-
-  for (auto rid : rid_span) {
-    if (column.IsMissing(rid)) {
-      if (default_left) {
-        p_left_part[nleft_elems++] = rid;
-      } else {
-        p_right_part[nright_elems++] = rid;
-      }
-    } else {
-      if ((static_cast<int32_t>(idx[rid]) + offset) <= split_cond) {
-        p_left_part[nleft_elems++] = rid;
-      } else {
-        p_right_part[nright_elems++] = rid;
-      }
-    }
-  }
-  return {nleft_elems, nright_elems};
-}
-
-// Split row indexes (rid_span) to 2 parts (left_part, right_part) depending
-// on comparison of indexes values (idx_span) and split point (split_cond).
-// Handle sparse columns
-template<bool default_left, typename BinIdxType>
-inline std::pair<size_t, size_t> PartitionSparseKernel(
-  common::Span<const size_t> rid_span, const int32_t split_cond,
-  const common::SparseColumn<BinIdxType>& column, common::Span<size_t> left_part,
-  common::Span<size_t> right_part) {
-  size_t* p_left_part  = left_part.data();
-  size_t* p_right_part = right_part.data();
-
-  size_t nleft_elems = 0;
-  size_t nright_elems = 0;
-  const size_t* row_data = column.GetRowData();
-  const size_t column_size = column.Size();
-  if (rid_span.size()) {  // ensure that rid_span is nonempty range
-    // search first nonzero row with index >= rid_span.front()
-    const size_t* p = std::lower_bound(row_data, row_data + column_size,
-                                       rid_span.front());
-
-    if (p != row_data + column_size && *p <= rid_span.back()) {
-      size_t cursor = p - row_data;
-
-      for (auto rid : rid_span) {
-        while (cursor < column_size
-               && column.GetRowIdx(cursor) < rid
-               && column.GetRowIdx(cursor) <= rid_span.back()) {
-          ++cursor;
-        }
-        if (cursor < column_size && column.GetRowIdx(cursor) == rid) {
-          if (static_cast<int32_t>(column.GetGlobalBinIdx(cursor)) <= split_cond) {
-            p_left_part[nleft_elems++] = rid;
-          } else {
-            p_right_part[nright_elems++] = rid;
-          }
-          ++cursor;
-        } else {
-          // missing value
-          if (default_left) {
-            p_left_part[nleft_elems++] = rid;
-          } else {
-            p_right_part[nright_elems++] = rid;
-          }
-        }
-      }
-    } else {  // all rows in rid_span have missing values
-      if (default_left) {
-        std::copy(rid_span.begin(), rid_span.end(), p_left_part);
-        nleft_elems = rid_span.size();
-      } else {
-        std::copy(rid_span.begin(), rid_span.end(), p_right_part);
-        nright_elems = rid_span.size();
-      }
-    }
-  }
-
-  return {nleft_elems, nright_elems};
-}
-
-template <typename BinIdxType>
-void QuantileHistMaker::Builder::PartitionKernel(
-    const size_t node_in_set, const size_t nid, common::Range1d range,
-    const int32_t split_cond, const ColumnMatrix& column_matrix, const RegTree& tree) {
-  const size_t* rid = row_set_collection_[nid].begin;
-  common::Span<const size_t> rid_span(rid + range.begin(), rid + range.end());
-  common::Span<size_t> left  = partition_builder_.GetLeftBuffer(node_in_set,
-                                                                range.begin(), range.end());
-  common::Span<size_t> right = partition_builder_.GetRightBuffer(node_in_set,
-                                                                 range.begin(), range.end());
-  const bst_uint fid = tree[nid].SplitIndex();
-  const bool default_left = tree[nid].DefaultLeft();
-  const auto column_ptr = column_matrix.GetColumn<BinIdxType>(fid);
-
-  std::pair<size_t, size_t> child_nodes_sizes;
-
-  if (column_ptr->GetType() == xgboost::common::kDenseColumn) {
-    const common::DenseColumn<BinIdxType>& column =
-          static_cast<const common::DenseColumn<BinIdxType>& >(*(column_ptr.get()));
-    if (default_left) {
-      child_nodes_sizes = PartitionDenseKernel<true>(column, rid_span, split_cond, left, right);
-    } else {
-      child_nodes_sizes = PartitionDenseKernel<false>(column, rid_span, split_cond, left, right);
-    }
-  } else {
-    const common::SparseColumn<BinIdxType>& column
-      = static_cast<const common::SparseColumn<BinIdxType>& >(*(column_ptr.get()));
-    if (default_left) {
-      child_nodes_sizes = PartitionSparseKernel<true>(rid_span, split_cond, column, left, right);
-    } else {
-      child_nodes_sizes = PartitionSparseKernel<false>(rid_span, split_cond, column, left, right);
-    }
-  }
-
-  const size_t n_left  = child_nodes_sizes.first;
-  const size_t n_right = child_nodes_sizes.second;
-
-  partition_builder_.SetNLeftElems(node_in_set, range.begin(), range.end(), n_left);
-  partition_builder_.SetNRightElems(node_in_set, range.begin(), range.end(), n_right);
-}
-
-
-void QuantileHistMaker::Builder::FindSplitConditions(const std::vector<ExpandEntry>& nodes,
-                                                     const RegTree& tree,
-                                                     const GHistIndexMatrix& gmat,
-                                                     std::vector<int32_t>* split_conditions) {
-  const size_t n_nodes = nodes.size();
-  split_conditions->resize(n_nodes);
-
-  for (size_t i = 0; i < nodes.size(); ++i) {
-    const int32_t nid = nodes[i].nid;
-    const bst_uint fid = tree[nid].SplitIndex();
-    const bst_float split_pt = tree[nid].SplitCond();
-    const uint32_t lower_bound = gmat.cut.Ptrs()[fid];
-    const uint32_t upper_bound = gmat.cut.Ptrs()[fid + 1];
-    int32_t split_cond = -1;
-    // convert floating-point split_pt into corresponding bin_id
-    // split_cond = -1 indicates that split_pt is less than all known cut points
-    CHECK_LT(upper_bound,
-             static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
-    for (uint32_t i = lower_bound; i < upper_bound; ++i) {
-      if (split_pt == gmat.cut.Values()[i]) {
-        split_cond = static_cast<int32_t>(i);
-      }
-    }
-    (*split_conditions)[i] = split_cond;
-  }
-}
-
-void QuantileHistMaker::Builder::AddSplitsToRowSet(const std::vector<ExpandEntry>& nodes,
-                                                   RegTree* p_tree) {
-  const size_t n_nodes = nodes.size();
-  for (size_t i = 0; i < n_nodes; ++i) {
-    const int32_t nid = nodes[i].nid;
-    const size_t n_left = partition_builder_.GetNLeftElems(i);
-    const size_t n_right = partition_builder_.GetNRightElems(i);
-
-    row_set_collection_.AddSplit(nid, (*p_tree)[nid].LeftChild(),
-        (*p_tree)[nid].RightChild(), n_left, n_right);
-  }
-}
-
-
-void QuantileHistMaker::Builder::ApplySplit(const std::vector<ExpandEntry> nodes,
-                                            const GHistIndexMatrix& gmat,
-                                            const ColumnMatrix& column_matrix,
-                                            const HistCollection& hist,
-                                            RegTree* p_tree) {
-  builder_monitor_.Start("ApplySplit");
-
-  // 1. Find split condition for each split
-  const size_t n_nodes = nodes.size();
-  std::vector<int32_t> split_conditions;
-  FindSplitConditions(nodes, *p_tree, gmat, &split_conditions);
-
-  // 2.1 Create a blocked space of size SUM(samples in each node)
-  common::BlockedSpace2d space(n_nodes, [&](size_t node_in_set) {
-    int32_t nid = nodes[node_in_set].nid;
-    return row_set_collection_[nid].Size();
-  }, kPartitionBlockSize);
-
-  // 2.2 Initialize the partition builder
-  // allocate buffers for storage intermediate results by each thread
-  partition_builder_.Init(space.Size(), n_nodes, [&](size_t node_in_set) {
-    const int32_t nid = nodes[node_in_set].nid;
-    const size_t size = row_set_collection_[nid].Size();
-    const size_t n_tasks = size / kPartitionBlockSize + !!(size % kPartitionBlockSize);
-    return n_tasks;
-  });
-
-  // 2.3 Split elements of row_set_collection_ to left and right child-nodes for each node
-  // Store results in intermediate buffers from partition_builder_
-  common::ParallelFor2d(space, this->nthread_, [&](size_t node_in_set, common::Range1d r) {
-    const int32_t nid = nodes[node_in_set].nid;
-      switch (column_matrix.GetTypeSize()) {
-      case common::kUint8BinsTypeSize:
-        PartitionKernel<uint8_t>(node_in_set, nid, r,
-                  split_conditions[node_in_set], column_matrix, *p_tree);
-        break;
-      case common::kUint16BinsTypeSize:
-        PartitionKernel<uint16_t>(node_in_set, nid, r,
-                  split_conditions[node_in_set], column_matrix, *p_tree);
-        break;
-      case common::kUint32BinsTypeSize:
-        PartitionKernel<uint32_t>(node_in_set, nid, r,
-                  split_conditions[node_in_set], column_matrix, *p_tree);
-        break;
-      default:
-        CHECK(false);  // no default behavior
-    }
-    });
-
-  // 3. Compute offsets to copy blocks of row-indexes
-  // from partition_builder_ to row_set_collection_
-  partition_builder_.CalculateRowOffsets();
-
-  // 4. Copy elements from partition_builder_ to row_set_collection_ back
-  // with updated row-indexes for each tree-node
-  common::ParallelFor2d(space, this->nthread_, [&](size_t node_in_set, common::Range1d r) {
-    const int32_t nid = nodes[node_in_set].nid;
-    partition_builder_.MergeToArray(node_in_set, r.begin(),
-        const_cast<size_t*>(row_set_collection_[nid].begin));
-  });
-
-  // 5. Add info about splits into row_set_collection_
-  AddSplitsToRowSet(nodes, p_tree);
-
-  builder_monitor_.Stop("ApplySplit");
-}
-
-void QuantileHistMaker::Builder::InitNewNode(int nid,
-                                             const GHistIndexMatrix& gmat,
-                                             const std::vector<GradientPair>& gpair,
-                                             const DMatrix& fmat,
-                                             const RegTree& tree) {
-  builder_monitor_.Start("InitNewNode");
-  {
-    snode_.resize(tree.param.num_nodes, NodeEntry(param_));
-  }
-
-  {
-    auto& stats = snode_[nid].stats;
-    GHistRow hist = hist_[nid];
-    if (tree[nid].IsRoot()) {
-      if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
-        const std::vector<uint32_t>& row_ptr = gmat.cut.Ptrs();
-        const uint32_t ibegin = row_ptr[fid_least_bins_];
-        const uint32_t iend = row_ptr[fid_least_bins_ + 1];
-        auto begin = hist.data();
-        for (uint32_t i = ibegin; i < iend; ++i) {
-          const GradStats et = begin[i];
-          stats.Add(et.sum_grad, et.sum_hess);
-        }
-      } else {
-        const RowSetCollection::Elem e = row_set_collection_[nid];
-        for (const size_t* it = e.begin; it < e.end; ++it) {
-          stats.Add(gpair[*it]);
-        }
-      }
-      histred_.Allreduce(&snode_[nid].stats, 1);
-    } else {
-      int parent_id = tree[nid].Parent();
-      if (tree[nid].IsLeftChild()) {
-        snode_[nid].stats = snode_[parent_id].best.left_sum;
-      } else {
-        snode_[nid].stats = snode_[parent_id].best.right_sum;
-      }
-    }
-  }
-
-  // calculating the weights
-  {
-    bst_uint parentid = tree[nid].Parent();
-    snode_[nid].weight = static_cast<float>(
-            spliteval_->ComputeWeight(parentid, snode_[nid].stats));
-    snode_[nid].root_gain = static_cast<float>(
-            spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
-  }
-  builder_monitor_.Stop("InitNewNode");
-}
-
-
-// Enumerate the split values of specific feature.
-// Returns the sum of gradients corresponding to the data points that contains a non-missing value
-// for the particular feature fid.
-template <int d_step>
-GradStats QuantileHistMaker::Builder::EnumerateSplit(
-    const GHistIndexMatrix &gmat, const GHistRow &hist, const NodeEntry &snode,
-    SplitEntry *p_best, bst_uint fid, bst_uint nodeID, int proc_grid_idx) const {
-  CHECK(d_step == +1 || d_step == -1);
-
-  // aliases
-  const std::vector<uint32_t>& cut_ptr = gmat.cut.Ptrs();
-  const std::vector<bst_float>& cut_val = gmat.cut.Values();
-
-  // statistics on both sides of split
-  GradStats c;
-  GradStats e;
-  // best split so far
-  SplitEntry best;
-
-  // bin boundaries
-  CHECK_LE(cut_ptr[fid],
-           static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
-  CHECK_LE(cut_ptr[fid + 1],
-           static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
-  // imin: index (offset) of the minimum value for feature fid
-  //       need this for backward enumeration
-  const auto imin = static_cast<int32_t>(cut_ptr[fid]);
-  // ibegin, iend: smallest/largest cut points for feature fid
-  // use int to allow for value -1
-  int32_t ibegin, iend;
-  if (d_step > 0) {
-    ibegin = static_cast<int32_t>(cut_ptr[fid]);
-    iend = static_cast<int32_t>(cut_ptr[fid + 1]);
-  } else {
-    ibegin = static_cast<int32_t>(cut_ptr[fid + 1]) - 1;
-    iend = static_cast<int32_t>(cut_ptr[fid]) - 1;
-  }
-
-  // index of grid that should be processed
-  int eff_split_denom = EffSplitDenomCal(param_.random_split_denom, param_.max_bin);
-
-  for (int32_t i = ibegin; i != iend; i += d_step) {
-    // start working
-    // try to find a split
-    e.Add(hist[i].GetGrad(), hist[i].GetHess());
-    if (e.sum_hess >= param_.min_child_weight) {
-      c.SetSubstract(snode.stats, e);
-
-      // skip some splits
-      if (!SplitSkipChecker(eff_split_denom, i, proc_grid_idx)) {
-        if (c.sum_hess >= param_.min_child_weight) {
-          bst_float loss_chg;
-          bst_float split_pt;
-          if (d_step > 0) {
-            // forward enumeration: split at right bound of each bin
-            loss_chg = static_cast<bst_float>(
-                spliteval_->ComputeSplitScore(nodeID, fid, e, c) -
-                snode.root_gain);
-            split_pt = cut_val[i];
-            best.Update(loss_chg, fid, split_pt, d_step == -1, e, c);
-          } else {
-            // backward enumeration: split at left bound of each bin
-            loss_chg = static_cast<bst_float>(
-                spliteval_->ComputeSplitScore(nodeID, fid, c, e) -
-                snode.root_gain);
-            if (i == imin) {
-              // for leftmost bin, left bound is the smallest feature value
-              split_pt = gmat.cut.MinValues()[fid];
-            } else {
-              split_pt = cut_val[i - 1];
-            }     
-            best.Update(loss_chg, fid, split_pt, d_step == -1, c, e);
-          }
-        }
-      }
-    }
-  }
-  p_best->Update(best);
-
-  return e;
-}
-
-XGBOOST_REGISTER_TREE_UPDATER(FastHistMaker, "grow_fast_histmaker")
-.describe("(Deprecated, use grow_quantile_histmaker instead.)"
-          " Grow tree using quantized histogram.")
-.set_body(
-    []() {
-      LOG(WARNING) << "grow_fast_histmaker is deprecated, "
-                   << "use grow_quantile_histmaker instead.";
-      return new QuantileHistMaker();
-    });
-
-XGBOOST_REGISTER_TREE_UPDATER(QuantileHistMaker, "grow_quantile_histmaker")
-.describe("Grow tree using quantized histogram.")
-.set_body(
-    []() {
-      return new QuantileHistMaker();
-    });
-
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_quantile_hist.h b/ml-xgboost/src/tree/updater_quantile_hist.h
deleted file mode 100644
index 35c6056..0000000
--- a/ml-xgboost/src/tree/updater_quantile_hist.h
+++ /dev/null
@@ -1,397 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2017-2018 by Contributors
- * \file updater_quantile_hist.h
- * \brief use quantized feature values to construct a tree
- * \author Philip Cho, Tianqi Chen, Egor Smirnov
- */
-#ifndef XGBOOST_TREE_UPDATER_QUANTILE_HIST_H_
-#define XGBOOST_TREE_UPDATER_QUANTILE_HIST_H_
-
-#include <dmlc/timer.h>
-#include <rabit/rabit.h>
-#include <xgboost/tree_updater.h>
-
-#include <memory>
-#include <vector>
-#include <string>
-#include <queue>
-#include <iomanip>
-#include <unordered_map>
-#include <utility>
-
-#include "xgboost/data.h"
-#include "xgboost/json.h"
-#include "constraints.h"
-#include "./param.h"
-#include "./split_evaluator.h"
-#include "../common/random.h"
-#include "../common/timer.h"
-#include "../common/hist_util.h"
-#include "../common/row_set.h"
-#include "../common/column_matrix.h"
-
-namespace xgboost {
-
-/*!
- * \brief A C-style array with in-stack allocation. As long as the array is smaller than MaxStackSize, it will be allocated inside the stack. Otherwise, it will be heap-allocated.
- */
-template<typename T, size_t MaxStackSize>
-class MemStackAllocator {
- public:
-  explicit MemStackAllocator(size_t required_size): required_size_(required_size) {
-  }
-
-  T* Get() {
-    if (!ptr_) {
-      if (MaxStackSize >= required_size_) {
-        ptr_ = stack_mem_;
-      } else {
-        ptr_ =  reinterpret_cast<T*>(malloc(required_size_ * sizeof(T)));
-        do_free_ = true;
-      }
-    }
-
-    return ptr_;
-  }
-
-  ~MemStackAllocator() {
-    if (do_free_) free(ptr_);
-  }
-
-
- private:
-  T* ptr_ = nullptr;
-  bool do_free_ = false;
-  size_t required_size_;
-  T stack_mem_[MaxStackSize];
-};
-
-namespace tree {
-
-using xgboost::common::GHistIndexMatrix;
-using xgboost::common::GHistIndexBlockMatrix;
-using xgboost::common::GHistIndexRow;
-using xgboost::common::HistCollection;
-using xgboost::common::RowSetCollection;
-using xgboost::common::GHistRow;
-using xgboost::common::GHistBuilder;
-using xgboost::common::ColumnMatrix;
-using xgboost::common::Column;
-
-/*! \brief construct a tree using quantized feature values */
-class QuantileHistMaker: public TreeUpdater {
- public:
-  QuantileHistMaker() = default;
-  void Configure(const Args& args) override;
-
-  void Update(HostDeviceVector<GradientPair>* gpair,
-              DMatrix* dmat,
-              const std::vector<RegTree*>& trees, int iter,
-              float* p_last_round_best_loss) override;
-
-  bool UpdatePredictionCache(const DMatrix* data,
-                             HostDeviceVector<bst_float>* out_preds, int iter) override;
-
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("train_param"), &this->param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["train_param"] = ToJson(param_);
-  }
-
-  char const* Name() const override {
-    return "grow_quantile_histmaker";
-  }
-
- protected:
-  // training parameter
-  TrainParam param_;
-  // quantized data matrix
-  GHistIndexMatrix gmat_;
-  // (optional) data matrix with feature grouping
-  GHistIndexBlockMatrix gmatb_;
-  // column accessor
-  ColumnMatrix column_matrix_;
-  DMatrix const* p_last_dmat_ {nullptr};
-  bool is_gmat_initialized_ {false};
-
-  // data structure
-  struct NodeEntry {
-    /*! \brief statics for node entry */
-    GradStats stats;
-    /*! \brief loss of this node, without split */
-    bst_float root_gain;
-    /*! \brief weight calculated related to current data */
-    float weight;
-    /*! \brief current best solution */
-    SplitEntry best;
-    // constructor
-    explicit NodeEntry(const TrainParam& param)
-        : root_gain(0.0f), weight(0.0f) {}
-  };
-  // actual builder that runs the algorithm
-
-  struct Builder {
-   public:
-    // constructor
-    explicit Builder(const TrainParam& param,
-                     std::unique_ptr<TreeUpdater> pruner,
-                     std::unique_ptr<SplitEvaluator> spliteval,
-                     FeatureInteractionConstraintHost int_constraints_,
-                     DMatrix const* fmat)
-      : param_(param), pruner_(std::move(pruner)),
-        spliteval_(std::move(spliteval)),
-        interaction_constraints_{std::move(int_constraints_)},
-        p_last_tree_(nullptr), p_last_fmat_(fmat),
-        last_iter_sampling(-1) {
-      builder_monitor_.Init("Quantile::Builder");
-    }
-    // update one tree, growing
-    virtual void Update(const GHistIndexMatrix& gmat,
-                        const GHistIndexBlockMatrix& gmatb,
-                        const ColumnMatrix& column_matrix,
-                        HostDeviceVector<GradientPair>* gpair,
-                        DMatrix* p_fmat,
-                        RegTree* p_tree,
-                        int iter,
-                        float* p_last_round_best_loss);
-
-    inline void BuildHist(const std::vector<GradientPair>& gpair,
-                          const RowSetCollection::Elem row_indices,
-                          const GHistIndexMatrix& gmat,
-                          const GHistIndexBlockMatrix& gmatb,
-                          GHistRow hist) {
-      if (param_.enable_feature_grouping > 0) {
-        hist_builder_.BuildBlockHist(gpair, row_indices, gmatb, hist);
-      } else {
-        hist_builder_.BuildHist(gpair, row_indices, gmat, hist, data_layout_ != kSparseData);
-      }
-    }
-
-    inline void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
-      builder_monitor_.Start("SubtractionTrick");
-      hist_builder_.SubtractionTrick(self, sibling, parent);
-      builder_monitor_.Stop("SubtractionTrick");
-    }
-
-    bool UpdatePredictionCache(const DMatrix* data,
-                               HostDeviceVector<bst_float>* p_out_preds);
-
-   protected:
-    /* tree growing policies */
-    struct ExpandEntry {
-      static const int kRootNid  = 0;
-      static const int kEmptyNid = -1;
-      int nid;
-      int sibling_nid;
-      int depth;
-      bst_float loss_chg;
-      unsigned timestamp;
-      ExpandEntry(int nid, int sibling_nid, int depth, bst_float loss_chg, unsigned tstmp):
-        nid(nid), sibling_nid(sibling_nid), depth(depth), loss_chg(loss_chg), timestamp(tstmp) {}
-
-      bool IsValid(TrainParam const& param, int32_t num_leaves) const {
-        bool ret = loss_chg <= kRtEps ||
-                   (param.max_depth > 0 && this->depth == param.max_depth) ||
-                   (param.max_leaves > 0 && num_leaves == param.max_leaves);
-        return ret;
-      }
-    };
-
-    // initialize temp data structure
-    virtual void InitData(const GHistIndexMatrix& gmat,
-                  const std::vector<GradientPair>& gpair,
-                  const DMatrix& fmat,
-                  const RegTree& tree, int iter = 0);
-
-    void EvaluateSplits(const std::vector<ExpandEntry>& nodes_set,
-                        const GHistIndexMatrix& gmat,
-                        const HistCollection& hist,
-                        const RegTree& tree,
-                        int _iter = 0);
-
-    void ApplySplit(std::vector<ExpandEntry> nodes,
-                        const GHistIndexMatrix& gmat,
-                        const ColumnMatrix& column_matrix,
-                        const HistCollection& hist,
-                        RegTree* p_tree);
-
-    template <typename BinIdxType>
-    void PartitionKernel(const size_t node_in_set, const size_t nid, common::Range1d range,
-                         const int32_t split_cond,
-                         const ColumnMatrix& column_matrix, const RegTree& tree);
-
-    void AddSplitsToRowSet(const std::vector<ExpandEntry>& nodes, RegTree* p_tree);
-
-
-    void FindSplitConditions(const std::vector<ExpandEntry>& nodes, const RegTree& tree,
-                             const GHistIndexMatrix& gmat, std::vector<int32_t>* split_conditions);
-
-    void InitNewNode(int nid,
-                     const GHistIndexMatrix& gmat,
-                     const std::vector<GradientPair>& gpair,
-                     const DMatrix& fmat,
-                     const RegTree& tree);
-
-    // Enumerate the split values of specific feature
-    // Returns the sum of gradients corresponding to the data points that contains a non-missing
-    // value for the particular feature fid.
-    template <int d_step>
-    GradStats EnumerateSplit(const GHistIndexMatrix &gmat, const GHistRow &hist,
-                             const NodeEntry &snode, SplitEntry *p_best,
-                             bst_uint fid, bst_uint nodeID, int proc_grid_idx) const;
-
-    // if sum of statistics for non-missing values in the node
-    // is equal to sum of statistics for all values:
-    // then - there are no missing values
-    // else - there are missing values
-    bool SplitContainsMissingValues(const GradStats e, const NodeEntry& snode);
-
-    void ExpandWithDepthWise(const GHistIndexMatrix &gmat,
-                             const GHistIndexBlockMatrix &gmatb,
-                             const ColumnMatrix &column_matrix,
-                             DMatrix *p_fmat,
-                             RegTree *p_tree,
-                             const std::vector<GradientPair> &gpair_h, int _iter,
-                             float* p_last_round_best_loss);
-
-    void BuildLocalHistograms(const GHistIndexMatrix &gmat,
-                              const GHistIndexBlockMatrix &gmatb,
-                              RegTree *p_tree,
-                              const std::vector<GradientPair> &gpair_h);
-
-    void AddHistRows(int *starting_index, int *sync_count);
-
-    void BuildHistogramsLossGuide(
-                        ExpandEntry entry,
-                        const GHistIndexMatrix &gmat,
-                        const GHistIndexBlockMatrix &gmatb,
-                        RegTree *p_tree,
-                        const std::vector<GradientPair> &gpair_h);
-
-    // Split nodes to 2 sets depending on amount of rows in each node
-    // Histograms for small nodes will be built explicitly
-    // Histograms for big nodes will be built by 'Subtraction Trick'
-    void SplitSiblings(const std::vector<ExpandEntry>& nodes,
-                   std::vector<ExpandEntry>* small_siblings,
-                   std::vector<ExpandEntry>* big_siblings,
-                   RegTree *p_tree);
-
-    void SyncHistograms(int starting_index,
-                        int sync_count,
-                        RegTree *p_tree);
-
-    void BuildNodeStats(const GHistIndexMatrix &gmat,
-                        DMatrix *p_fmat,
-                        RegTree *p_tree,
-                        const std::vector<GradientPair> &gpair_h);
-
-    void EvaluateAndApplySplits(const GHistIndexMatrix &gmat,
-                                const ColumnMatrix &column_matrix,
-                                RegTree *p_tree,
-                                int *num_leaves,
-                                int depth,
-                                unsigned *timestamp,
-                                std::vector<ExpandEntry> *temp_qexpand_depth, 
-                                int _iter, 
-                                float* p_last_round_best_loss);
-
-    void AddSplitsToTree(
-              const GHistIndexMatrix &gmat,
-              RegTree *p_tree,
-              int *num_leaves,
-              int depth,
-              unsigned *timestamp,
-              std::vector<ExpandEntry>* nodes_for_apply_split,
-              std::vector<ExpandEntry>* temp_qexpand_depth,
-              float* p_last_round_best_loss);
-
-    void ExpandWithLossGuide(const GHistIndexMatrix& gmat,
-                             const GHistIndexBlockMatrix& gmatb,
-                             const ColumnMatrix& column_matrix,
-                             DMatrix* p_fmat,
-                             RegTree* p_tree,
-                             const std::vector<GradientPair>& gpair_h, 
-                             int _iter);
-
-    inline static bool LossGuide(ExpandEntry lhs, ExpandEntry rhs) {
-      if (lhs.loss_chg == rhs.loss_chg) {
-        return lhs.timestamp > rhs.timestamp;  // favor small timestamp
-      } else {
-        return lhs.loss_chg < rhs.loss_chg;  // favor large loss_chg
-      }
-    }
-    //  --data fields--
-    const TrainParam& param_;
-    // number of omp thread used during training
-    int nthread_;
-    common::ColumnSampler column_sampler_;
-    // the internal row sets
-    RowSetCollection row_set_collection_;
-
-    int last_iter_sampling;
-    std::vector<size_t> last_sampling;
-    
-    // the temp space for split
-    std::vector<RowSetCollection::Split> row_split_tloc_;
-    std::vector<SplitEntry> best_split_tloc_;
-    /*! \brief TreeNode Data: statistics for each constructed node */
-    std::vector<NodeEntry> snode_;
-    /*! \brief culmulative histogram of gradients. */
-    HistCollection hist_;
-    /*! \brief feature with least # of bins. to be used for dense specialization
-               of InitNewNode() */
-    uint32_t fid_least_bins_;
-    /*! \brief local prediction cache; maps node id to leaf value */
-    std::vector<float> leaf_value_cache_;
-
-    GHistBuilder hist_builder_;
-    std::unique_ptr<TreeUpdater> pruner_;
-    std::unique_ptr<SplitEvaluator> spliteval_;
-    FeatureInteractionConstraintHost interaction_constraints_;
-
-    static constexpr size_t kPartitionBlockSize = 2048;
-    common::PartitionBuilder<kPartitionBlockSize> partition_builder_;
-
-    // back pointers to tree and data matrix
-    const RegTree* p_last_tree_;
-    DMatrix const* const p_last_fmat_;
-
-    using ExpandQueue =
-       std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
-                           std::function<bool(ExpandEntry, ExpandEntry)>>;
-
-    std::unique_ptr<ExpandQueue> qexpand_loss_guided_;
-    std::vector<ExpandEntry> qexpand_depth_wise_;
-    // key is the node id which should be calculated by Subtraction Trick, value is the node which
-    // provides the evidence for substracts
-    std::vector<ExpandEntry> nodes_for_subtraction_trick_;
-    // list of nodes whose histograms would be built explicitly.
-    std::vector<ExpandEntry> nodes_for_explicit_hist_build_;
-
-    enum DataLayout { kDenseDataZeroBased, kDenseDataOneBased, kSparseData };
-    DataLayout data_layout_;
-
-    common::Monitor builder_monitor_;
-    common::ParallelGHistBuilder hist_buffer_;
-    rabit::Reducer<GradStats, GradStats::Reduce> histred_;
-  };
-
-  std::unique_ptr<Builder> builder_;
-  std::unique_ptr<TreeUpdater> pruner_;
-  std::unique_ptr<SplitEvaluator> spliteval_;
-  FeatureInteractionConstraintHost int_constraint_;
-};
-
-}  // namespace tree
-}  // namespace xgboost
-
-#endif  // XGBOOST_TREE_UPDATER_QUANTILE_HIST_H_
diff --git a/ml-xgboost/src/tree/updater_refresh.cc b/ml-xgboost/src/tree/updater_refresh.cc
deleted file mode 100644
index 937be40..0000000
--- a/ml-xgboost/src/tree/updater_refresh.cc
+++ /dev/null
@@ -1,167 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014 by Contributors
- * \file updater_refresh.cc
- * \brief refresh the statistics and leaf value on the tree on the dataset
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <xgboost/tree_updater.h>
-
-#include <vector>
-#include <limits>
-
-#include "xgboost/json.h"
-#include "./param.h"
-#include "../common/io.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_refresh);
-
-/*! \brief pruner that prunes a tree after growing finishs */
-class TreeRefresher: public TreeUpdater {
- public:
-  void Configure(const Args& args) override {
-    param_.UpdateAllowUnknown(args);
-  }
-  void LoadConfig(Json const& in) override {
-    auto const& config = get<Object const>(in);
-    FromJson(config.at("train_param"), &this->param_);
-  }
-  void SaveConfig(Json* p_out) const override {
-    auto& out = *p_out;
-    out["train_param"] = ToJson(param_);
-  }
-  char const* Name() const override {
-    return "refresh";
-  }
-  bool CanModifyTree() const override {
-    return true;
-  }
-  // update the tree, do pruning
-  void Update(HostDeviceVector<GradientPair> *gpair,
-              DMatrix *p_fmat,
-              const std::vector<RegTree*> &trees, int iter,
-              float* p_last_round_best_loss) override {
-    if (trees.size() == 0) return;
-    const std::vector<GradientPair> &gpair_h = gpair->ConstHostVector();
-    // thread temporal space
-    std::vector<std::vector<GradStats> > stemp;
-    std::vector<RegTree::FVec> fvec_temp;
-    // setup temp space for each thread
-    const int nthread = omp_get_max_threads();
-    fvec_temp.resize(nthread, RegTree::FVec());
-    stemp.resize(nthread, std::vector<GradStats>());
-    #pragma omp parallel
-    {
-      int tid = omp_get_thread_num();
-      int num_nodes = 0;
-      for (auto tree : trees) {
-        num_nodes += tree->param.num_nodes;
-      }
-      stemp[tid].resize(num_nodes, GradStats());
-      std::fill(stemp[tid].begin(), stemp[tid].end(), GradStats());
-      fvec_temp[tid].Init(trees[0]->param.num_feature);
-    }
-    // if it is C++11, use lazy evaluation for Allreduce,
-    // to gain speedup in recovery
-    auto lazy_get_stats = [&]() {
-      const MetaInfo &info = p_fmat->Info();
-      // start accumulating statistics
-      for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
-        CHECK_LT(batch.Size(), std::numeric_limits<unsigned>::max());
-        const auto nbatch = static_cast<bst_omp_uint>(batch.Size());
-        #pragma omp parallel for schedule(static)
-        for (bst_omp_uint i = 0; i < nbatch; ++i) {
-          SparsePage::Inst inst = batch[i];
-          const int tid = omp_get_thread_num();
-          const auto ridx = static_cast<bst_uint>(batch.base_rowid + i);
-          RegTree::FVec &feats = fvec_temp[tid];
-          feats.Fill(inst);
-          int offset = 0;
-          for (auto tree : trees) {
-            AddStats(*tree, feats, gpair_h, info, ridx,
-                     dmlc::BeginPtr(stemp[tid]) + offset);
-            offset += tree->param.num_nodes;
-          }
-          feats.Drop(inst);
-        }
-      }
-      // aggregate the statistics
-      auto num_nodes = static_cast<int>(stemp[0].size());
-      #pragma omp parallel for schedule(static)
-      for (int nid = 0; nid < num_nodes; ++nid) {
-        for (int tid = 1; tid < nthread; ++tid) {
-          stemp[0][nid].Add(stemp[tid][nid]);
-        }
-      }
-    };
-    reducer_.Allreduce(dmlc::BeginPtr(stemp[0]), stemp[0].size(), lazy_get_stats);
-    // rescale learning rate according to size of trees
-    float lr = param_.learning_rate;
-    param_.learning_rate = lr / trees.size();
-    int offset = 0;
-    for (auto tree : trees) {
-      this->Refresh(dmlc::BeginPtr(stemp[0]) + offset, 0, tree);
-      offset += tree->param.num_nodes;
-    }
-    // set learning rate back
-    param_.learning_rate = lr;
-  }
-
- private:
-  inline static void AddStats(const RegTree &tree,
-                              const RegTree::FVec &feat,
-                              const std::vector<GradientPair> &gpair,
-                              const MetaInfo &info,
-                              const bst_uint ridx,
-                              GradStats *gstats) {
-    // start from groups that belongs to current data
-    auto pid = 0;
-    gstats[pid].Add(gpair[ridx]);
-    // tranverse tree
-    while (!tree[pid].IsLeaf()) {
-      unsigned split_index = tree[pid].SplitIndex();
-      pid = tree.GetNext(pid, feat.GetFvalue(split_index), feat.IsMissing(split_index));
-      gstats[pid].Add(gpair[ridx]);
-    }
-  }
-  inline void Refresh(const GradStats *gstats,
-                      int nid, RegTree *p_tree) {
-    RegTree &tree = *p_tree;
-    tree.Stat(nid).base_weight =
-        static_cast<bst_float>(CalcWeight(param_, gstats[nid]));
-    tree.Stat(nid).sum_hess = static_cast<bst_float>(gstats[nid].sum_hess);
-    if (tree[nid].IsLeaf()) {
-      if (param_.refresh_leaf) {
-        tree[nid].SetLeaf(tree.Stat(nid).base_weight * param_.learning_rate);
-      }
-    } else {
-      tree.Stat(nid).loss_chg = static_cast<bst_float>(
-          xgboost::tree::CalcGain(param_, gstats[tree[nid].LeftChild()]) +
-          xgboost::tree::CalcGain(param_, gstats[tree[nid].RightChild()]) -
-          xgboost::tree::CalcGain(param_, gstats[nid]));
-      this->Refresh(gstats, tree[nid].LeftChild(), p_tree);
-      this->Refresh(gstats, tree[nid].RightChild(), p_tree);
-    }
-  }
-  // training parameter
-  TrainParam param_;
-  // reducer
-  rabit::Reducer<GradStats, GradStats::Reduce> reducer_;
-};
-
-XGBOOST_REGISTER_TREE_UPDATER(TreeRefresher, "refresh")
-.describe("Refresher that refreshes the weight and statistics according to data.")
-.set_body([]() {
-    return new TreeRefresher();
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_skmaker.cc b/ml-xgboost/src/tree/updater_skmaker.cc
deleted file mode 100644
index 73eede3..0000000
--- a/ml-xgboost/src/tree/updater_skmaker.cc
+++ /dev/null
@@ -1,404 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014 by Contributors
- * \file updater_skmaker.cc
- * \brief use approximation sketch to construct a tree,
-          a refresh is needed to make the statistics exactly correct
- * \author Tianqi Chen
- */
-#include <rabit/rabit.h>
-#include <xgboost/base.h>
-#include <xgboost/tree_updater.h>
-#include <vector>
-#include <algorithm>
-
-#include "../common/quantile.h"
-#include "../common/group_data.h"
-#include "./updater_basemaker-inl.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_skmaker);
-
-class SketchMaker: public BaseMaker {
- public:
-  char const* Name() const override {
-    return "grow_skmaker";
-  }
-  void Update(HostDeviceVector<GradientPair> *gpair,
-              DMatrix *p_fmat,
-              const std::vector<RegTree*> &trees, int iter,
-              float* p_last_round_best_loss) override {
-    // rescale learning rate according to size of trees
-    float lr = param_.learning_rate;
-    param_.learning_rate = lr / trees.size();
-    // build tree
-    for (auto tree : trees) {
-      this->Update(gpair->ConstHostVector(), p_fmat, tree);
-    }
-    param_.learning_rate = lr;
-  }
-
- protected:
-  inline void Update(const std::vector<GradientPair> &gpair,
-                     DMatrix *p_fmat,
-                     RegTree *p_tree) {
-    this->InitData(gpair, *p_fmat, *p_tree);
-    for (int depth = 0; depth < param_.max_depth; ++depth) {
-      this->GetNodeStats(gpair, *p_fmat, *p_tree,
-                         &thread_stats_, &node_stats_);
-      this->BuildSketch(gpair, p_fmat, *p_tree);
-      this->SyncNodeStats();
-      this->FindSplit(depth, gpair, p_fmat, p_tree);
-      this->ResetPositionCol(qexpand_, p_fmat, *p_tree);
-      this->UpdateQueueExpand(*p_tree);
-      // if nothing left to be expand, break
-      if (qexpand_.size() == 0) break;
-    }
-    if (qexpand_.size() != 0) {
-      this->GetNodeStats(gpair, *p_fmat, *p_tree,
-                         &thread_stats_, &node_stats_);
-      this->SyncNodeStats();
-    }
-    // set all statistics correctly
-    for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
-      this->SetStats(nid, node_stats_[nid], p_tree);
-      if (!(*p_tree)[nid].IsLeaf()) {
-        p_tree->Stat(nid).loss_chg = static_cast<bst_float>(
-            node_stats_[(*p_tree)[nid].LeftChild()].CalcGain(param_) +
-            node_stats_[(*p_tree)[nid].RightChild()].CalcGain(param_) -
-            node_stats_[nid].CalcGain(param_));
-      }
-    }
-    // set left leaves
-    for (int nid : qexpand_) {
-      (*p_tree)[nid].SetLeaf(p_tree->Stat(nid).base_weight * param_.learning_rate);
-    }
-  }
-  // define the sketch we want to use
-  using WXQSketch = common::WXQuantileSketch<bst_float, bst_float>;
-
- private:
-  // statistics needed in the gradient calculation
-  struct SKStats {
-    /*! \brief sum of all positive gradient */
-    double pos_grad { 0 };
-    /*! \brief sum of all negative gradient */
-    double neg_grad { 0 };
-    /*! \brief sum of hessian statistics */
-    double sum_hess { 0 };
-
-    SKStats() = default;
-
-    // accumulate statistics
-    void Add(const GradientPair& gpair) {
-      if (gpair.GetGrad() >= 0.0f) {
-        pos_grad += gpair.GetGrad();
-      } else {
-        neg_grad -= gpair.GetGrad();
-      }
-      sum_hess += gpair.GetHess();
-    }
-    /*! \brief calculate gain of the solution */
-    inline double CalcGain(const TrainParam &param) const {
-      return xgboost::tree::CalcGain(param, pos_grad - neg_grad, sum_hess);
-    }
-    /*! \brief set current value to a - b */
-    inline void SetSubstract(const SKStats &a, const SKStats &b) {
-      pos_grad = a.pos_grad - b.pos_grad;
-      neg_grad = a.neg_grad - b.neg_grad;
-      sum_hess = a.sum_hess - b.sum_hess;
-    }
-    // calculate leaf weight
-    inline double CalcWeight(const TrainParam &param) const {
-      return xgboost::tree::CalcWeight(param, pos_grad - neg_grad, sum_hess);
-    }
-    /*! \brief add statistics to the data */
-    inline void Add(const SKStats &b) {
-      pos_grad += b.pos_grad;
-      neg_grad += b.neg_grad;
-      sum_hess += b.sum_hess;
-    }
-    /*! \brief same as add, reduce is used in All Reduce */
-    inline static void Reduce(SKStats &a, const SKStats &b) { // NOLINT(*)
-      a.Add(b);
-    }
-  };
-  inline void BuildSketch(const std::vector<GradientPair> &gpair,
-                          DMatrix *p_fmat,
-                          const RegTree &tree) {
-    const MetaInfo& info = p_fmat->Info();
-    sketchs_.resize(this->qexpand_.size() * tree.param.num_feature * 3);
-    for (auto & sketch : sketchs_) {
-      sketch.Init(info.num_row_, this->param_.sketch_eps);
-    }
-    thread_sketch_.resize(omp_get_max_threads());
-    // number of rows in
-    const size_t nrows = p_fmat->Info().num_row_;
-    // start accumulating statistics
-    for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
-      // start enumeration
-      const auto nsize = static_cast<bst_omp_uint>(batch.Size());
-      #pragma omp parallel for schedule(dynamic, 1)
-      for (bst_omp_uint fidx = 0; fidx < nsize; ++fidx) {
-        this->UpdateSketchCol(gpair, batch[fidx], tree,
-                              node_stats_,
-                              fidx,
-                              static_cast<size_t>(batch[fidx].size()) == nrows,
-                              &thread_sketch_[omp_get_thread_num()]);
-      }
-    }
-    // setup maximum size
-    unsigned max_size = param_.MaxSketchSize();
-    // synchronize sketch
-    summary_array_.resize(sketchs_.size());
-    for (size_t i = 0; i < sketchs_.size(); ++i) {
-      common::WXQuantileSketch<bst_float, bst_float>::SummaryContainer out;
-      sketchs_[i].GetSummary(&out);
-      summary_array_[i].Reserve(max_size);
-      summary_array_[i].SetPrune(out, max_size);
-    }
-    size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_size);
-    sketch_reducer_.Allreduce(dmlc::BeginPtr(summary_array_), nbytes, summary_array_.size());
-  }
-  // update sketch information in column fid
-  inline void UpdateSketchCol(const std::vector<GradientPair> &gpair,
-                              const SparsePage::Inst &col,
-                              const RegTree &tree,
-                              const std::vector<SKStats> &nstats,
-                              bst_uint fid,
-                              bool col_full,
-                              std::vector<SketchEntry> *p_temp) {
-    if (col.size() == 0) return;
-    // initialize sbuilder for use
-    std::vector<SketchEntry> &sbuilder = *p_temp;
-    sbuilder.resize(tree.param.num_nodes * 3);
-    for (unsigned int nid : this->qexpand_) {
-      const unsigned wid = this->node2workindex_[nid];
-      for (int k = 0; k < 3; ++k) {
-        sbuilder[3 * nid + k].sum_total = 0.0f;
-        sbuilder[3 * nid + k].sketch = &sketchs_[(wid * tree.param.num_feature + fid) * 3 + k];
-      }
-    }
-    if (!col_full) {
-      for (const auto& c : col) {
-        const bst_uint ridx = c.index;
-        const int nid = this->position_[ridx];
-        if (nid > 0) {
-          const GradientPair &e = gpair[ridx];
-          if (e.GetGrad() >= 0.0f) {
-            sbuilder[3 * nid + 0].sum_total += e.GetGrad();
-          } else {
-            sbuilder[3 * nid + 1].sum_total -= e.GetGrad();
-          }
-          sbuilder[3 * nid + 2].sum_total += e.GetHess();
-        }
-      }
-    } else {
-      for (unsigned int nid : this->qexpand_) {
-        sbuilder[3 * nid + 0].sum_total = static_cast<bst_float>(nstats[nid].pos_grad);
-        sbuilder[3 * nid + 1].sum_total = static_cast<bst_float>(nstats[nid].neg_grad);
-        sbuilder[3 * nid + 2].sum_total = static_cast<bst_float>(nstats[nid].sum_hess);
-      }
-    }
-    // if only one value, no need to do second pass
-    if (col[0].fvalue  == col[col.size()-1].fvalue) {
-      for (int nid : this->qexpand_) {
-        for (int k = 0; k < 3; ++k) {
-          sbuilder[3 * nid + k].sketch->Push(col[0].fvalue,
-                                             static_cast<bst_float>(
-                                                 sbuilder[3 * nid + k].sum_total));
-        }
-      }
-      return;
-    }
-    // two pass scan
-    unsigned max_size = param_.MaxSketchSize();
-    for (int nid : this->qexpand_) {
-      for (int k = 0; k < 3; ++k) {
-        sbuilder[3 * nid + k].Init(max_size);
-      }
-    }
-    // second pass, build the sketch
-    for (const auto& c : col) {
-      const bst_uint ridx = c.index;
-      const int nid = this->position_[ridx];
-      if (nid >= 0) {
-        const GradientPair &e = gpair[ridx];
-        if (e.GetGrad() >= 0.0f) {
-          sbuilder[3 * nid + 0].Push(c.fvalue, e.GetGrad(), max_size);
-        } else {
-          sbuilder[3 * nid + 1].Push(c.fvalue, -e.GetGrad(), max_size);
-        }
-        sbuilder[3 * nid + 2].Push(c.fvalue, e.GetHess(), max_size);
-      }
-    }
-    for (int nid : this->qexpand_) {
-      for (int k = 0; k < 3; ++k) {
-        sbuilder[3 * nid + k].Finalize(max_size);
-      }
-    }
-  }
-  inline void SyncNodeStats() {
-    CHECK_NE(qexpand_.size(), 0U);
-    std::vector<SKStats> tmp(qexpand_.size());
-    for (size_t i = 0; i < qexpand_.size(); ++i) {
-      tmp[i] = node_stats_[qexpand_[i]];
-    }
-    stats_reducer_.Allreduce(dmlc::BeginPtr(tmp), tmp.size());
-    for (size_t i = 0; i < qexpand_.size(); ++i) {
-      node_stats_[qexpand_[i]] = tmp[i];
-    }
-  }
-  inline void FindSplit(int depth,
-                        const std::vector<GradientPair> &gpair,
-                        DMatrix *p_fmat,
-                        RegTree *p_tree) {
-    const bst_uint num_feature = p_tree->param.num_feature;
-    // get the best split condition for each node
-    std::vector<SplitEntry> sol(qexpand_.size());
-    auto nexpand = static_cast<bst_omp_uint>(qexpand_.size());
-    #pragma omp parallel for schedule(dynamic, 1)
-    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
-      const int nid = qexpand_[wid];
-      CHECK_EQ(node2workindex_[nid], static_cast<int>(wid));
-      SplitEntry &best = sol[wid];
-      for (bst_uint fid = 0; fid < num_feature; ++fid) {
-        unsigned base = (wid * p_tree->param.num_feature + fid) * 3;
-        EnumerateSplit(summary_array_[base + 0],
-                       summary_array_[base + 1],
-                       summary_array_[base + 2],
-                       node_stats_[nid], fid, &best);
-      }
-    }
-    // get the best result, we can synchronize the solution
-    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
-      const int nid = qexpand_[wid];
-      const SplitEntry &best = sol[wid];
-      // set up the values
-      this->SetStats(nid, node_stats_[nid], p_tree);
-      // now we know the solution in snode[nid], set split
-      if (best.loss_chg > kRtEps) {
-        bst_float base_weight = node_stats_[nid].CalcWeight(param_);
-        bst_float left_leaf_weight =
-            CalcWeight(param_, best.left_sum.sum_grad, best.left_sum.sum_hess) *
-            param_.learning_rate;
-        bst_float right_leaf_weight =
-            CalcWeight(param_, best.right_sum.sum_grad,
-                       best.right_sum.sum_hess) *
-            param_.learning_rate;
-        p_tree->ExpandNode(nid, best.SplitIndex(), best.split_value,
-                           best.DefaultLeft(), base_weight, left_leaf_weight,
-                           right_leaf_weight, best.loss_chg,
-                           node_stats_[nid].sum_hess,
-                           best.left_sum.GetHess(), best.right_sum.GetHess());
-      } else {
-        (*p_tree)[nid].SetLeaf(p_tree->Stat(nid).base_weight * param_.learning_rate);
-      }
-    }
-  }
-  // set statistics on ptree
-  inline void SetStats(int nid, const SKStats &node_sum, RegTree *p_tree) {
-    p_tree->Stat(nid).base_weight = static_cast<bst_float>(node_sum.CalcWeight(param_));
-    p_tree->Stat(nid).sum_hess = static_cast<bst_float>(node_sum.sum_hess);
-  }
-  inline void EnumerateSplit(const WXQSketch::Summary &pos_grad,
-                             const WXQSketch::Summary &neg_grad,
-                             const WXQSketch::Summary &sum_hess,
-                             const SKStats &node_sum,
-                             bst_uint fid,
-                             SplitEntry *best) {
-    if (sum_hess.size == 0) return;
-    double root_gain = node_sum.CalcGain(param_);
-    std::vector<bst_float> fsplits;
-    for (size_t i = 0; i < pos_grad.size; ++i) {
-      fsplits.push_back(pos_grad.data[i].value);
-    }
-    for (size_t i = 0; i < neg_grad.size; ++i) {
-      fsplits.push_back(neg_grad.data[i].value);
-    }
-    for (size_t i = 0; i < sum_hess.size; ++i) {
-      fsplits.push_back(sum_hess.data[i].value);
-    }
-    std::sort(fsplits.begin(), fsplits.end());
-    fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
-    // sum feature
-    SKStats feat_sum;
-    feat_sum.pos_grad = pos_grad.data[pos_grad.size - 1].rmax;
-    feat_sum.neg_grad = neg_grad.data[neg_grad.size - 1].rmax;
-    feat_sum.sum_hess = sum_hess.data[sum_hess.size - 1].rmax;
-    size_t ipos = 0, ineg = 0, ihess = 0;
-    for (size_t i = 1; i < fsplits.size(); ++i) {
-      WXQSketch::Entry pos = pos_grad.Query(fsplits[i], ipos);
-      WXQSketch::Entry neg = neg_grad.Query(fsplits[i], ineg);
-      WXQSketch::Entry hess = sum_hess.Query(fsplits[i], ihess);
-      SKStats s, c;
-      s.pos_grad = 0.5f * (pos.rmin + pos.rmax - pos.wmin);
-      s.neg_grad = 0.5f * (neg.rmin + neg.rmax - neg.wmin);
-      s.sum_hess = 0.5f * (hess.rmin + hess.rmax - hess.wmin);
-      c.SetSubstract(node_sum, s);
-      // forward
-      if (s.sum_hess >= param_.min_child_weight &&
-          c.sum_hess >= param_.min_child_weight) {
-        double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
-        best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], false,
-                     GradStats(s.pos_grad - s.neg_grad , s.sum_hess),
-                     GradStats(c.pos_grad - c.neg_grad, c.sum_hess));
-      }
-      // backward
-      c.SetSubstract(feat_sum, s);
-      s.SetSubstract(node_sum, c);
-      if (s.sum_hess >= param_.min_child_weight &&
-          c.sum_hess >= param_.min_child_weight) {
-        double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
-        best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], true,
-                     GradStats(s.pos_grad - s.neg_grad, s.sum_hess),
-                     GradStats(c.pos_grad - c.neg_grad, c.sum_hess));
-      }
-    }
-    {
-      // all including
-      SKStats s = feat_sum, c;
-      c.SetSubstract(node_sum, s);
-      if (s.sum_hess >= param_.min_child_weight &&
-          c.sum_hess >= param_.min_child_weight) {
-        bst_float cpt = fsplits.back();
-        double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
-        best->Update(static_cast<bst_float>(loss_chg), fid,
-                     cpt + std::abs(cpt) + 1.0f, false,
-                     GradStats(s.pos_grad - s.neg_grad, s.sum_hess),
-                     GradStats(c.pos_grad - c.neg_grad, c.sum_hess));
-      }
-    }
-  }
-
-  // thread temp data
-  // used to hold temporal sketch
-  std::vector<std::vector<SketchEntry> > thread_sketch_;
-  // used to hold statistics
-  std::vector<std::vector<SKStats> > thread_stats_;
-  // node statistics
-  std::vector<SKStats> node_stats_;
-  // summary array
-  std::vector<WXQSketch::SummaryContainer> summary_array_;
-  // reducer for summary
-  rabit::Reducer<SKStats, SKStats::Reduce> stats_reducer_;
-  // reducer for summary
-  rabit::SerializeReducer<WXQSketch::SummaryContainer> sketch_reducer_;
-  // per node, per feature sketch
-  std::vector<common::WXQuantileSketch<bst_float, bst_float> > sketchs_;
-};
-
-XGBOOST_REGISTER_TREE_UPDATER(SketchMaker, "grow_skmaker")
-.describe("Approximate sketching maker.")
-.set_body([]() {
-    return new SketchMaker();
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/src/tree/updater_sync.cc b/ml-xgboost/src/tree/updater_sync.cc
deleted file mode 100644
index d476994..0000000
--- a/ml-xgboost/src/tree/updater_sync.cc
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
-* Copyright (C) 2021. Huawei Technologies Co., Ltd.
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-* */
-/*!
- * Copyright 2014-2019 by Contributors
- * \file updater_sync.cc
- * \brief synchronize the tree in all distributed nodes
- */
-#include <xgboost/tree_updater.h>
-#include <vector>
-#include <string>
-#include <limits>
-
-#include "xgboost/json.h"
-#include "../common/io.h"
-
-namespace xgboost {
-namespace tree {
-
-DMLC_REGISTRY_FILE_TAG(updater_sync);
-
-/*!
- * \brief syncher that synchronize the tree in all distributed nodes
- * can implement various strategies, so far it is always set to node 0's tree
- */
-class TreeSyncher: public TreeUpdater {
- public:
-  void Configure(const Args& args) override {}
-
-  void LoadConfig(Json const& in) override {}
-  void SaveConfig(Json* p_out) const override {}
-
-  char const* Name() const override {
-    return "prune";
-  }
-
-  void Update(HostDeviceVector<GradientPair> *gpair,
-              DMatrix* dmat,
-              const std::vector<RegTree*> &trees, int iter,
-              float* p_last_round_best_loss) override {
-    if (rabit::GetWorldSize() == 1) return;
-    std::string s_model;
-    common::MemoryBufferStream fs(&s_model);
-    int rank = rabit::GetRank();
-    if (rank == 0) {
-      for (auto tree : trees) {
-        tree->Save(&fs);
-      }
-    }
-    fs.Seek(0);
-    rabit::Broadcast(&s_model, 0);
-    for (auto tree : trees) {
-      tree->Load(&fs);
-    }
-  }
-};
-
-XGBOOST_REGISTER_TREE_UPDATER(TreeSyncher, "sync")
-.describe("Syncher that synchronize the tree in all distributed nodes.")
-.set_body([]() {
-    return new TreeSyncher();
-  });
-}  // namespace tree
-}  // namespace xgboost
diff --git a/ml-xgboost/tests/cpp/common/test_partition_builder.cc b/ml-xgboost/tests/cpp/common/test_partition_builder.cc
deleted file mode 100644
index fb2f366..0000000
--- a/ml-xgboost/tests/cpp/common/test_partition_builder.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-#include <gtest/gtest.h>
-#include <vector>
-#include <string>
-#include <utility>
-
-#include "../../../src/common/row_set.h"
-#include "../helpers.h"
-
-namespace xgboost {
-namespace common {
-
-TEST(PartitionBuilder, BasicTest) {
-  constexpr size_t kBlockSize = 16;
-  constexpr size_t kNodes = 5;
-  constexpr size_t kTasks = 3 + 5 + 10 + 1 + 2;
-
-  std::vector<size_t> tasks = { 3, 5, 10, 1, 2 };
-
-  PartitionBuilder<kBlockSize> builder;
-  builder.Init(kTasks, kNodes, [&](size_t i) {
-    return tasks[i];
-  });
-
-  std::vector<size_t> rows_for_left_node = { 2, 12, 0, 16, 8 };
-
-  for(size_t nid = 0; nid < kNodes; ++nid) {
-    size_t value_left = 0;
-    size_t value_right = 0;
-
-    size_t left_total = tasks[nid] * rows_for_left_node[nid];
-
-    for(size_t j = 0; j < tasks[nid]; ++j) {
-      size_t begin = kBlockSize*j;
-      size_t end = kBlockSize*(j+1);
-
-      auto left  = builder.GetLeftBuffer(nid, begin, end);
-      auto right = builder.GetRightBuffer(nid, begin, end);
-
-      size_t n_left   = rows_for_left_node[nid];
-      size_t n_right = kBlockSize - rows_for_left_node[nid];
-
-      for(size_t i = 0; i < n_left; i++) {
-        left[i] = value_left++;
-      }
-
-      for(size_t i = 0; i < n_right; i++) {
-        right[i] = left_total + value_right++;
-      }
-
-      builder.SetNLeftElems(nid, begin, end, n_left);
-      builder.SetNRightElems(nid, begin, end, n_right);
-    }
-  }
-  builder.CalculateRowOffsets();
-
-  std::vector<size_t> v(*std::max_element(tasks.begin(), tasks.end()) * kBlockSize);
-
-  for(size_t nid = 0; nid < kNodes; ++nid) {
-
-    for(size_t j = 0; j < tasks[nid]; ++j) {
-      builder.MergeToArray(nid, kBlockSize*j, v.data());
-    }
-
-    for(size_t j = 0; j < tasks[nid] * kBlockSize; ++j) {
-      ASSERT_EQ(v[j], j);
-    }
-    size_t n_left  = builder.GetNLeftElems(nid);
-    size_t n_right = builder.GetNRightElems(nid);
-
-    ASSERT_EQ(n_left, rows_for_left_node[nid] * tasks[nid]);
-    ASSERT_EQ(n_right, (kBlockSize - rows_for_left_node[nid]) * tasks[nid]);
-  }
-}
-
-}  // namespace common
-}  // namespace xgboost
diff --git a/pom.xml b/pom.xml
index de3041e..79ee0f4 100644
--- a/pom.xml
+++ b/pom.xml
@@ -12,7 +12,7 @@
     <maven.compiler.source>1.8</maven.compiler.source>
     <maven.compiler.target>1.8</maven.compiler.target>
     <encoding>UTF-8</encoding>
-    <spark.version>spark2.3.2</spark.version>
+    <spark.version>spark3.1.1</spark.version>
   </properties>
   <modules>
     <module>ml-kernel-client-core</module>
@@ -23,8 +23,8 @@
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>spark-mllib_2.11</artifactId>
-      <version>2.3.2</version>
+      <artifactId>spark-mllib_2.12</artifactId>
+      <version>3.1.1</version>
     </dependency>
     <dependency>
       <groupId>it.unimi.dsi</groupId>
@@ -33,8 +33,8 @@
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
-      <artifactId>scalatest_2.11</artifactId>
-      <version>3.0.3</version>
+      <artifactId>scalatest_2.12</artifactId>
+      <version>3.2.0</version>
     </dependency>
   </dependencies>
   <build>
diff --git a/tools/kal-test/README.md b/tools/kal-test/README.md
deleted file mode 100644
index 8303236..0000000
--- a/tools/kal-test/README.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# kal-test
-
-
-### Description
-The Kunpeng algorithm library test tool can be used to test machine learning and graph analysis algorithms.
-
-
-### Compilation Tutorial
-
-#### Prerequisites
-1.  The Maven compilation environment has been configured.
-2.  The algorithm software package has been obtained.
-#### Procedure
-1.  Go to the Spark-ml-algo-lib/tools/kal-test directory in the compilation environment.
-2.  Install the dependencies.<br/>
-    mvn install:install-file -DgroupId=org.apache.spark.graphx.lib -DartifactId=boostkit-graph-kernel-client_2.11 -Dversion=2.1.0 -Dfile=lib/boostkit-graph-kernel-client_2.11-1.2.0.jar -Dpackaging=jar**_
-3.  Run the compile command:<br/>
-    mvn clean install -DskipTests_**
-4.  View the kal-test_2.11-0.1.jar file generated in Spark-ml-algo-lib/tools/kal-test/target.
-
-### Deployment and Usage Description
-
-1.  Deploy the kal-test folder in the test environment, for example, in the /home/test/boostkit/ directory. If the directory does not exist, create one.<br/>mkdir -p /home/test/boostkit/_**
-2.  Go to the directory.<br/>
-    cd /home/test/boostkit/kal-test/_**
-3.  Save the obtained boostkit-graph-kernel-scala_version-kal_version-spark_version-aarch64.jar, boostkit-graph-acc_scala_version-kal_version-spark_version.jar, and boostkit-graph-core_scala_version-kal_version-spark_version.jar files to /home/test/boostkit/kal-test/lib.
-4.  Go to the /home/test/boostkit/kal-test directory.<br/>
-    cd /home/test/boostkit/kal-test_**
-5.  Run the following command in the /home/test/boostkit/kal-test/ directory (taking the PageRank algorithm as an example):<br/>bash bin/graph/pr_run.sh uk_2002 run_**
-6.  Check the algorithm running status.
-
-### Algorithm and Dataset
-
-
-| Algorithm | Dataset | Interface |
-| :-----| ----: | :----: |
-| PageRank | uk_2002 | run |
-
-
-### References
-
-1. Open source KNN: https://github.com/saurfang/spark-knn.git
-2. Open source BFS: https://github.com/prasad223/GraphxBFS
-3. Open source DBSCAN: https://github.com/alitouka/spark_dbscan
-4. Open source ClusteringCoefficient: https://github.com/apache/spark/pull/9150/files
-5. Open source Betweenness: https://github.com/Sotera/distributed-graph-analytics/tree/master/dga-graphx/src/main/scala/com/soteradefense/dga/graphx/hbse
-6. Open source Node2Vec: https://github.com/QuanLab/node2vec-spark
-7. Open source SubgraphMatching: https://datalab.snu.ac.kr/pegasusn/scala-apidoc/#pegasus.spark.subgraph.PSE
-8. Open source XGBoost: https://github.com/dmlc/xgboost/tree/v1.1.0
diff --git a/tools/kal-test/bin/graph/betweenness_run.sh b/tools/kal-test/bin/graph/betweenness_run.sh
deleted file mode 100644
index 9637c1a..0000000
--- a/tools/kal-test/bin/graph/betweenness_run.sh
+++ /dev/null
@@ -1,121 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ];then
-  echo "Usage:<dataset name>"
- 	echo "dataset name:cit_patents,enwiki_2018,uk_2002"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/betweenness/betweenness_spark.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "enwiki_2018" ] &&
-   [ ${dataset_name} != "uk_2002" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,enwiki_2018,uk_2002"
-  exit 1
-fi
-outputPath=/tmp/graph/result/betweenness/${dataset_name}
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_numPartitions_${cpu_name}"
-spark_task_cpus="${dataset_name}_SparkTaskCpus_${cpu_name}"
-thread_num="${dataset_name}_ThreadNum_${cpu_name}"
-betweenness_part_num="${dataset_name}_BetweennessPartNum_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-driver_memory_val=${!driver_memory}
-spark_task_cpus_val=${!spark_task_cpus}
-thread_num_val=${!thread_num}
-betweenness_part_num_val=${!betweenness_part_num}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${spark_task_cpus}:${spark_task_cpus_val}"
-echo "${thread_num}:${thread_num_val}"
-echo "${betweenness_part_num}:${betweenness_part_num_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${spark_task_cpus_val} ] ||
-  [ ! ${thread_num_val} ] ||
-  [ ! ${betweenness_part_num_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-gt_path="${dataset_name}_gt"
-data_path_val=${!dataset_name}
-gt_path_val=${!gt_path}
-echo "${dataset_name} : ${data_path_val}"
-echo "${gt_path} : ${gt_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs--betweenness_${dataset_name}"
-spark-submit \
---class com.bigdata.graph.BetweennessRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.rpc.message.maxSize=2046 \
---conf spark.worker.timeout=3600 \
---conf spark.driver.maxResultSize=200g \
---conf spark.rpc.askTimeout=36000 \
---conf spark.network.timeout=6000s \
---conf spark.broadcast.blockSize=4m \
---conf spark.shuffle.manager=SORT \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.locality.wait.node=0 \
---conf spark.boostkit.bigdata.graph.betweenness.partnum=${betweenness_part_num_val} \
---conf spark.boostkit.bigdata.graph.betweenness.threadnum=${thread_num_val} \
---conf spark.task.cpus=${spark_task_cpus_val} \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} "no" ${num_partitions_val} ${data_path_val} "no" ${outputPath} ${gt_path_val}  | tee ./log/log
diff --git a/tools/kal-test/bin/graph/betweenness_run_opensource.sh b/tools/kal-test/bin/graph/betweenness_run_opensource.sh
deleted file mode 100644
index 6774daa..0000000
--- a/tools/kal-test/bin/graph/betweenness_run_opensource.sh
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "Usage:<dataset name>"
-  echo "dataset name:cit_patents,enwiki_2018,uk_2002"
-  exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/betweenness/betweenness_spark_opensource.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "cit_patents" ] &&
-  [ ${dataset_name} != "enwiki_2018" ] &&
-  [ ${dataset_name} != "uk_2002" ]; then
-  echo "invalid dataset name,dataset name:cit_patents,enwiki_2018,uk_2002"
-  exit 1
-fi
-outputPath=/tmp/graph/result/betweenness/${dataset_name}_opensource
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_numPartitions_${cpu_name}"
-pivots="${dataset_name}_pivots_${cpu_name}"
-iteration="${dataset_name}_iteration_${cpu_name}"
-graph_split="${dataset_name}_graphSplit_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-driver_memory_val=${!driver_memory}
-pivots_val=${!pivots}
-iteration_val=${!iteration}
-graph_split_val=${!graph_split}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${pivots}:${pivots_val}"
-echo "${iteration}:${iteration_val}"
-echo "${graph_split}:${graph_split_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${pivots_val} ] ||
-  [ ! ${iteration_val} ] ||
-  [ ! ${graph_split_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-gt_path="${dataset_name}_gt"
-data_path_val=${!dataset_name}
-gt_path_val=${!gt_path}
-echo "${dataset_name} : ${data_path_val}"
-echo "${gt_path} : ${gt_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs--betweenness_${dataset_name}_opensource"
-
-spark-submit \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---name "Betweenness_${dataset_name}_opensource" \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.kryoserializer.buffer.max=2047m \
---conf spark.driver.maxResultSize=0 \
---conf spark.ui.showConsoleProgress=true \
---conf spark.driver.extraJavaOptions="-Xms${driver_memory_val} -XX:hashCode=0" \
---conf spark.executor.extraJavaOptions="-Xms${executor_memory_val} -XX:hashCode=0" \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---jars "./lib/scopt_2.11-3.2.0.jar" \
-./lib/hbse_2.11-0.1.jar \
--m yarn \
--s ${graph_split_val} \
--n ${num_partitions_val} \
--i ${data_path_val} \
--o ${outputPath} \
--g ${gt_path_val} \
--p ${pivots_val} \
--b ${iteration_val} > betweenness_temp.log
-CostTime=$(cat betweenness_temp.log |grep "CostTime of Top-K" | awk '{print $6}')
-Accuracy=$(cat betweenness_temp.log |grep "Accuracy of Top-K" | awk '{print $6}')
-currentTime=$(date "+%Y%m%d_H%M%S")
-rm -rf betweenness_temp.log
-echo -e "algorithmName: Betweenness\ncostTime: $CostTime\ndatasetName: ${dataset_name}\nisRaw: 'yes'\nAccuracy: ${Accuracy}\ntestcaseType: Betweenness_opensource_${dataset_name}\n" > ./report/"Betweenness_${currentTime}.yml"
-echo "Exec Successful: end." > ./log/log
diff --git a/tools/kal-test/bin/graph/bfs_run.sh b/tools/kal-test/bin/graph/bfs_run.sh
deleted file mode 100644
index a41493e..0000000
--- a/tools/kal-test/bin/graph/bfs_run.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ];then
-  echo "Usage:<dataset name>"
- 	echo "dataset name:cit_patents,enwiki_2018,arabic_2005,graph500_22,graph500_23,graph500_25"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/bfs/bfs_spark.properties
-source conf/graph/bfs/bfs_source_id.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "enwiki_2018" ] &&
-   [ ${dataset_name} != "arabic_2005" ] &&
-   [ ${dataset_name} != "graph500_22" ] &&
-   [ ${dataset_name} != "graph500_23" ] &&
-   [ ${dataset_name} != "graph500_25" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,enwiki_2018,arabic_2005,graph500_22,graph500_23,graph500_25"
-  exit 1
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_numPartitions_${cpu_name}"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-extra_java_options_val="-Xms${executor_memory_val}"
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "extra_java_options_val : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-source_ids="${dataset_name}_SourceID"
-source_ids_val=${!source_ids}
-IFS=,
-source_ids_arr=($source_ids_val)
-
-echo "${source_ids}:${source_ids_val}"
-for source_id in ${source_ids_arr[@]}
-do
-  hdfs dfs -rm -r -f "/tmp/graph/result/bfs/${dataset_name}_${source_id}"
-
-  echo "start to clean cache and sleep 30s"
-  ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-  sleep 30
-
-  mkdir -p log
-
-  echo "start to submit spark jobs--${dataset_name}_${source_id}"
-  spark-submit \
-  --class com.bigdata.graph.BFSRunner \
-  --master yarn \
-  --deploy-mode ${deploy_mode_val} \
-  --num-executors ${num_executors_val} \
-  --executor-memory ${executor_memory_val} \
-  --executor-cores ${executor_cores_val} \
-  --driver-memory 200g \
-  --conf "spark.driver.maxResultSize=200g" \
-  --conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
-  --conf "spark.driver.extraJavaOptions=-Xms200g" \
-  --conf spark.locality.wait.node=0 \
-  --jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --driver-class-path "lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --conf "spark.executor.extraClassPath=boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${source_id} ${num_partitions_val} "no" ${data_path_val} | tee ./log/log
-done
diff --git a/tools/kal-test/bin/graph/bfs_run_opensource.sh b/tools/kal-test/bin/graph/bfs_run_opensource.sh
deleted file mode 100644
index d3426e0..0000000
--- a/tools/kal-test/bin/graph/bfs_run_opensource.sh
+++ /dev/null
@@ -1,151 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ];then
-  echo "Usage:<dataset name>"
- 	echo "dataset name:cit_patents,enwiki_2018,arabic_2005,graph500_22,graph500_23,graph500_25"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/bfs/bfs_spark_opensource.properties
-source conf/graph/bfs/bfs_source_id.properties
-source conf/graph/bfs/bfs_opensource.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "enwiki_2018" ] &&
-   [ ${dataset_name} != "arabic_2005" ] &&
-   [ ${dataset_name} != "graph500_22" ] &&
-   [ ${dataset_name} != "graph500_23" ] &&
-   [ ${dataset_name} != "graph500_25" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,enwiki_2018,arabic_2005,graph500_22,graph500_23,graph500_25"
-  exit 1
-fi
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors"
-executor_cores="${dataset_name}_executorCores"
-executor_memory="${dataset_name}_executorMemory"
-num_partitions="${dataset_name}_numPartitions"
-deploy_mode="deployMode"
-split="${dataset_name}_split"
-q="${dataset_name}_q"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-extra_java_options_val="-Xms${executor_memory_val}"
-split_val=${!split}
-q_val=${!q}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "extra_java_options_val : ${extra_java_options_val}"
-echo "${split}: ${split_val}"
-echo "${q}: ${q_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-source_ids="${dataset_name}_SourceID"
-source_ids_val=${!source_ids}
-IFS=,
-source_ids_arr=($source_ids_val)
-
-echo "${source_ids}:${source_ids_val}"
-for source_id in ${source_ids_arr[@]}
-do
-  hdfs dfs -rm -r -f "/tmp/graph/result/bfs/${dataset_name}_opensource_${source_id}"
-
-  echo "start to clean cache and sleep 30s"
-  ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-  sleep 30
-
-  mkdir -p log
-
-  echo "start to submit spark jobs--${dataset_name}_${source_id}"
-  spark-submit \
-  --class Driver \
-  --master yarn \
-  --deploy-mode ${deploy_mode_val} \
-  --num-executors ${num_executors_val} \
-  --executor-memory ${executor_memory_val} \
-  --executor-cores ${executor_cores_val} \
-  --driver-memory 80g \
-  --conf "spark.driver.maxResultSize=80g" \
-  --conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
-  --conf "spark.driver.extraJavaOptions=-Xms80g" \
-  --conf spark.locality.wait.node=0 \
-  --jars "lib/scopt_2.10-3.5.0.jar" \
-  --driver-class-path "lib/scopt_2.10-3.5.0.jar" \
-  --conf "spark.executor.extraClassPath=scopt_2.10-3.5.0.jar" \
-  ./lib/bfs_2.10-0.1.2.jar \
-  -g 'EdgeList' \
-  -p 'EdgePartition2D' \
-  -n ${num_partitions_val} \
-  -i ${source_id} \
-  -d 10 \
-  -l 'yarn' \
-  -f "${data_path_val}" \
-  -t "${split_val}" \
-  -o "/tmp/graph/result/bfs/${dataset_name}_opensource_${source_id}" \
-  -z ${num_partitions_val} \
-  -q ${q_val} > bfs_temp_1.log
-
-  cat bfs_temp_1.log | grep "BFS: Time:" > bfs_temp_2.log
-  if [ -s bfs_temp_2.log ];then
-    echo "start to report result"
-  else
-    exit 1
-  fi
-
-  while read line
-  do
-    echo $line
-    IFS=" "
-    line_arr=($line)
-    time_temp=${line_arr[${#line_arr[@]}-1]}
-    bfs_time=$(echo -e "scale=4;${time_temp}/1000" | bc)
-    echo "CostTime(s): ${bfs_time}"
-    if [ ! -d "./report" ]; then
-      mkdir report
-    fi
-    currentTime=$(date "+%Y%m%d_%H%M%S")
-    echo -e "algorithmName: BFS\ncostTime: $bfs_time\ndatasetName: ${dataset_name}\nisRaw: 'yes'\nsourceID: ${source_id}\ntestcaseType: BFS_opensource_${dataset_name}_${source_id}\n" > ./report/"BFS_${currentTime}.yml"
-  done < bfs_temp_2.log
-  rm -rf "bfs_temp_1.log"
-  rm -rf "bfs_temp_2.log"
-done
-echo "Exec Successful: end." > ./log/log
diff --git a/tools/kal-test/bin/graph/cc_run.sh b/tools/kal-test/bin/graph/cc_run.sh
deleted file mode 100755
index 596590c..0000000
--- a/tools/kal-test/bin/graph/cc_run.sh
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage: <dataset name>"
- echo "dataset name: graph500_25 or graph500_26 or liveJournal"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name."
-  echo "dataset name: graph500_25 or graph500_26 or liveJournal"
-	exit 0
-fi
-
-dataset_name=$1
-if [ $dataset_name != 'graph500_25' ] && [ $dataset_name != 'graph500_26' ] && [ $dataset_name != 'liveJournal' ];
-then
-  echo 'invalid dataset'
-  echo "dataset name: graph500_25 or graph500_26 or liveJournal"
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/cc/cc_spark.properties
-num_executors_val="numExecutors_${cpu_name}"
-executor_cores_val="executorCores_${cpu_name}"
-executor_memory_val="executorMemory_${cpu_name}"
-executor_extra_javaopts_val="executorExtraJavaopts_${cpu_name}"
-default_parallelism_val="defaultParallelism_${dataset_name}_${cpu_name}"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-default_parallelism=${!default_parallelism_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-echo "${default_parallelism_val}:${default_parallelism}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/cc/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-
-spark-submit \
---class com.bigdata.graph.ConnectedComponentsRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.driver.maxResultSize=100g \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.kryoserializer.buffer=48m \
---conf spark.default.parallelism=${default_parallelism} \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf spark.executor.extraClassPath="fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "no" ${cpu_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/cc_run_raw.sh b/tools/kal-test/bin/graph/cc_run_raw.sh
deleted file mode 100755
index ca6f975..0000000
--- a/tools/kal-test/bin/graph/cc_run_raw.sh
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/bin/bash
-set -e
-case "$1" in
--h | --help | ?)
- echo "Usage: <dataset name>"
- echo "dataset name: graph500_25 or graph500_26 or liveJournal"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name."
-  echo "dataset name: graph500_25 or graph500_26 or liveJournal"
-	exit 0
-fi
-
-dataset_name=$1
-if [ $dataset_name != 'graph500_25' ] && [ $dataset_name != 'graph500_26' ] && [ $dataset_name != 'liveJournal' ];
-then
-  echo 'invalid dataset'
-  echo "dataset name: graph500_25 or graph500_26 or liveJournal"
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/cc/cc_spark_raw.properties
-num_executors_val="numExecutors"
-executor_cores_val="executorCores"
-executor_memory_val="executorMemory"
-executor_extra_javaopts_val="executorExtraJavaopts"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-
-source ${CONF_DIR}/graph_datasets.properties
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/cc/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-
-spark-submit \
---class com.bigdata.graph.ConnectedComponentsRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.driver.maxResultSize=100g \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.kryoserializer.buffer=48m \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "yes" ${cpu_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/cd_run.sh b/tools/kal-test/bin/graph/cd_run.sh
deleted file mode 100755
index eee98dc..0000000
--- a/tools/kal-test/bin/graph/cd_run.sh
+++ /dev/null
@@ -1,102 +0,0 @@
-#!/bin/bash
-set -e
-case "$1" in 
--h | --help | ?)
- echo "Usage:<dataset name>"
- echo "dataset name: simulate1 or simulate2 or usaRoad"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name: simulate1 or simulate2 or usaRoad"
-	exit 0
-fi
-dataset_name=$1
-if [ $dataset_name != 'simulate1' ] && [ $dataset_name != 'simulate2' ] && [ $dataset_name != 'usaRoad' ];
-then
-  echo 'invalid dataset'
-  echo "please input dataset name: simulate1 or simulate2 or usaRoad"
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/cd/cd_spark_${cpu_name}.properties
-num_executors_val="numExecutors_${dataset_name}"
-executor_cores_val="executorCores_${dataset_name}"
-executor_memory_val="executorMemory_${dataset_name}"
-executor_extra_javaopts_val="executorExtraJavaopts_${dataset_name}"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/cd/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.CycleDetectionWithConstrainsRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf spark.rpc.askTimeout=3600 \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=3600000 \
---conf spark.worker.timeout=3600 \
---conf spark.network.timeout=6000s \
---conf spark.storage.blockManagerSlaveTimeoutMs=600000 \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.driver.maxResultSize=100g \
---conf spark.shuffle.manager=SORT \
---conf spark.broadcast.blockSize=25g \
---conf spark.core.connection.ack.wait.timeout=60000s \
---conf spark.shuffle.memoryFraction=0.6 \
---conf spark.storage.memoryFraction=0.2 \
---conf spark.rdd.compress=true \
---conf spark.executor.memoryOverhead=5g \
---conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "run" "no" ${cpu_name} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/graph/closeness_run.sh b/tools/kal-test/bin/graph/closeness_run.sh
deleted file mode 100644
index 1c56ffe..0000000
--- a/tools/kal-test/bin/graph/closeness_run.sh
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><weight or not>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  echo "2nd argument: weight or not: e.g. weighted,unweighted"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><weight or not>"
- 	echo "dataset name:cit_patents,graph500_23,uk_2002"
-  echo "weight or not:weighted,unweighted"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/closeness/closeness_spark.properties
-
-dataset_name=$1
-weight=$2
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "graph500_23" ] &&
-   [ ${dataset_name} != "uk_2002" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,graph500_23,uk_2002"
-  exit 1
-fi
-if [ ${weight} != "weighted" ] && [ ${weight} != "unweighted" ];then
-  echo "invalid argument value,must be: weighted or unweighted"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/closeness/${dataset_name}_${weight}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_${weight}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_${weight}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_${weight}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_${weight}_numPartitions_${cpu_name}"
-ratio="${dataset_name}_${weight}_ratio_${cpu_name}"
-output_node_num="outputNodeNum"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-ratio_val=${!ratio}
-output_node_num_val=${!output_node_num}
-driver_memory_val=${!driver_memory}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${ratio} : ${ratio_val}"
-echo "${output_node_num} : ${output_node_num_val}"
-echo "${driver_memory}:${driver_memory_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${num_partitions_val} ] ||
-  [ ! ${ratio_val} ] ||
-  [ ! ${output_node_num_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.ClosenessRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.worker.timeout=3600 \
---conf spark.driver.maxResultSize=200g \
---conf spark.rpc.askTimeout=36000 \
---conf spark.rdd.compress=true \
---conf spark.network.timeout=6000s \
---conf spark.broadcast.blockSize=4m \
---conf spark.shuffle.manager=SORT \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.locality.wait.node=0 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${num_partitions_val} ${weight} ${output_node_num_val} ${ratio_val} "no" ${data_path_val} "false" | tee ./log/log
diff --git a/tools/kal-test/bin/graph/closeness_run_hive.sh b/tools/kal-test/bin/graph/closeness_run_hive.sh
deleted file mode 100644
index 2c50f0d..0000000
--- a/tools/kal-test/bin/graph/closeness_run_hive.sh
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
- echo "Usage:<table name> <col1> <col2> <colWeight> <weighted> <k> <p> <partition> <save_mode> <save_arg>"
- exit 0
- ;;
-esac
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-if [ ${cpu_name} == "aarch64" ]
-then
-  cpu_name="aarch_64"
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-table_name=$1
-col1=$2
-col2=$3
-colWeight=$4
-weighted=$5
-k=$6
-p=$7
-partition=$8
-save_mode=$9
-save_arg=${10}
-
-echo "table_name: $table_name"
-echo "col1: $col1"
-echo "col2: $col2"
-echo "colWeight: $colWeight"
-echo "weighted: $weighted"
-echo "k: $k"
-echo "p: $p"
-echo "partition: $partition"
-echo "save_mode: $save_mode"
-echo "save_arg: $save_arg"
-
-spark-submit \
---class com.bigdata.graph.ClosenessHiveRunner \
---master yarn \
---deploy-mode "client" \
---num-executors 35 \
---executor-memory "25g" \
---executor-cores 4 \
---driver-memory "16g" \
---conf spark.worker.timeout=3600 \
---conf spark.driver.maxResultSize=200g \
---conf spark.rpc.askTimeout=36000 \
---conf spark.network.timeout=6000s \
---conf spark.broadcast.blockSize=4m \
---conf spark.shuffle.manager=SORT \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.rdd.compress=true \
---conf spark.shuffle.compress=true \
---conf spark.shuffle.spill.compress=true \
---conf spark.io.compression.codec=lz4 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${table_name} ${col1} ${col2} ${colWeight} ${weighted} ${k} ${p} ${partition} ${save_mode} ${save_arg}
diff --git a/tools/kal-test/bin/graph/clusteringcoefficient_run.sh b/tools/kal-test/bin/graph/clusteringcoefficient_run.sh
deleted file mode 100644
index 65ff744..0000000
--- a/tools/kal-test/bin/graph/clusteringcoefficient_run.sh
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><api name><weight or not>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. lcc,avgcc,globalcc"
-  echo "3nd argument: weight or not: e.g. weighted,unweighted"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ];then
-  echo "Usage:<dataset name><api name><weight or not>"
- 	echo "dataset name:cit_patents,uk_2002,arabic_2005,graph500_22,graph500_23,graph500_24,graph500_25"
- 	echo "api name:lcc,avgcc,globalcc"
-  echo "weight or not:weighted,unweighted"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/clusteringcoefficient/clusteringcoefficient_spark.properties
-
-dataset_name=$1
-api_name=$2
-weight=$3
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "uk_2002" ] &&
-   [ ${dataset_name} != "arabic_2005" ] &&
-   [ ${dataset_name} != "graph500_22" ] &&
-   [ ${dataset_name} != "graph500_23" ] &&
-   [ ${dataset_name} != "graph500_24" ] &&
-   [ ${dataset_name} != "graph500_25" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,uk_2002,arabic_2005,graph500_22,graph500_23,graph500_24,graph500_25"
-  exit 1
-fi
-if [ ${api_name} != "lcc" ] &&
-   [ ${api_name} != "avgcc" ] &&
-   [ ${api_name} != "globalcc" ] ;then
-  echo "invalid argument value,api name: lcc,avgcc,globalcc"
-  exit 1
-fi
-if [ ${weight} != "weighted" ] && [ ${weight} != "unweighted" ];then
-  echo "invalid argument value,must be: weighted or unweighted"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/clusteringcoefficient/${dataset_name}_${3}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_numPartitions_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-driver_memory_val=${!driver_memory}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.ClusteringCoefficientRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.executor.memoryOverhead=2048 \
---conf spark.executor.extraJavaOptions="-Xms12g" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${num_partitions_val} ${weight} "no" ${data_path_val} ${api_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/clusteringcoefficient_run_opensource.sh b/tools/kal-test/bin/graph/clusteringcoefficient_run_opensource.sh
deleted file mode 100644
index fc72b8b..0000000
--- a/tools/kal-test/bin/graph/clusteringcoefficient_run_opensource.sh
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><api name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. lcc,avgcc,globalcc"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:graph500_22,graph500_23,graph500_24,graph500_25"
- 	echo "api name:lcc,avgcc,globalcc"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/clusteringcoefficient/clusteringcoefficient_spark_opensource.properties
-
-dataset_name=$1
-api_name=$2
-
-if
-   [ ${dataset_name} != "graph500_22" ] &&
-   [ ${dataset_name} != "graph500_23" ] &&
-   [ ${dataset_name} != "graph500_24" ] &&
-   [ ${dataset_name} != "graph500_25" ] ;then
-  echo "invalid dataset name,dataset name:graph500_22,graph500_23,graph500_24,graph500_25"
-  exit 1
-fi
-if [ ${api_name} != "lcc" ] &&
-   [ ${api_name} != "avgcc" ] &&
-   [ ${api_name} != "globalcc" ] ;then
-  echo "invalid argument value,api name: lcc,avgcc,globalcc"
-  exit 1
-fi
-outputPath="/tmp/graph/result/clusteringcoefficient/${dataset_name}"
-
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_numPartitions_${cpu_name}"
-split="${dataset_name}_split"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-split_val=${!split}
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-driver_memory_val=${!driver_memory}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${split}:${split_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.huawei.bigdata.graphx.Test \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---name "clusteringcoefficient_${1}_${2}" \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.rpc.askTimeout=36000 \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=3600000 \
---conf spark.worker.timeout=3600 \
---conf spark.network.timeout=6000s \
---conf spark.storage.blockManagerSlaveTimeoutMs=600000 \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.driver.maxResultSize=100g \
---conf spark.shuffle.manager=SORT \
---conf spark.broadcast.blockSize=25g \
---conf spark.network.timeout=1200s \
---conf spark.rpc.message.maxSize=2046 \
---conf spark.core.connection.ack.wait.timeout=60000s \
---conf spark.executor.extraJavaOptions="-Xms35g" \
---conf spark.rdd.compress=true \
---jars "lib/lcc_kaiyuan.jar" \
---driver-class-path "lib/lcc_kaiyuan.jar:lib/graph_test_2.11-1.0.0.jar" \
---conf "spark.executor.extraClassPath=lcc_kaiyuan.jar" \
-./lib/graph_test_2.11-1.0.0.jar clusteringcoefficient yarn ${data_path_val} "${split_val}" ${outputPath} ${num_partitions_val} ${api_name} > clusteringcoefficient_temp.log
-CostTime=$(cat clusteringcoefficient_temp.log | grep "LabelPropagation is finished" | awk '{print $8}')
-currentTime=$(date "+%Y%m%d_%H%M%S")
-rm -rf clusteringcoefficient_temp.log
-echo -e "algorithmName: clusteringcoefficient\ncostTime: $CostTime\ndatasetName: ${dataset_name}\napi_name: ${api_name}\nisRaw: 'yes'\ntestcaseType: clusteringcoefficient_opensource_${dataset_name}_${api_name}\n" > ./report/"ClusteringCoefficient_${currentTime}.yml"
-echo "Exec Successful: end." > ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/graph/degree_run.sh b/tools/kal-test/bin/graph/degree_run.sh
deleted file mode 100644
index 7e91b9d..0000000
--- a/tools/kal-test/bin/graph/degree_run.sh
+++ /dev/null
@@ -1,123 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. it_2004"
-  echo "2nd argument: name of api: e.g. degrees,inDegrees,outDegrees"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:it_2004,twitter7,uk_2007_05,mycielskian20,gap_kron,com_friendster"
-  echo "api name:degrees,inDegrees,outDegrees"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/degree/degree_spark.properties
-
-dataset_name=$1
-api_name=$2
-
-if [ ${dataset_name} != "it_2004" ] &&
-   [ ${dataset_name} != "twitter7" ] &&
-   [ ${dataset_name} != "uk_2007_05" ] &&
-   [ ${dataset_name} != "mycielskian20" ] &&
-   [ ${dataset_name} != "gap_kron" ] &&
-   [ ${dataset_name} != "com_friendster" ] ;then
-  echo "invalid dataset name,dataset name:it_2004,twitter7,uk_2007_05,mycielskian20,gap_kron,com_friendster"
-  exit 1
-fi
-
-if [ ${api_name} != "degrees" ] &&
-   [ ${api_name} != "inDegrees" ] &&
-   [ ${api_name} != "outDegrees" ];then
-  echo "invalid api name,api name: degrees,inDegrees,outDegrees"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/degree/${dataset_name}_${api_name}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors="${api_name}_${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${api_name}_${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${api_name}_${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${api_name}_${dataset_name}_numPartitions_${cpu_name}"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-extra_java_options_val="-Xms${executor_memory_val}"
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "extra_java_options_val : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.DegreeRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 200g \
---conf spark.locality.wait.node=0 \
---conf spark.driver.maxResultSize=0 \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.broadcast.blockSize=1m \
---conf spark.reducer.maxSizeInFlight=59mb \
---conf spark.shuffle.file.buffer=17k \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.io.compression.codec=lzf \
---conf spark.shuffle.compress=true \
---conf spark.rdd.compress=false \
---conf spark.shuffle.io.preferDirectBufs=true \
---conf spark.shuffle.spill.compress=true \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar,lib/boostkit-graph-acc_2.11-1.3.0-${spark_version_val}.jar,lib/fastutil-8.3.1.jar" \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar:lib/boostkit-graph-acc_2.11-1.3.0-${spark_version_val}.jar:lib/fastutil-8.3.1.jar" \
---conf "spark.executor.extraClassPath=boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar:boostkit-graph-acc_2.11-1.3.0-${spark_version_val}.jar:fastutil-8.3.1.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} "no" ${data_path_val} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/degree_run_raw.sh b/tools/kal-test/bin/graph/degree_run_raw.sh
deleted file mode 100644
index d5247f0..0000000
--- a/tools/kal-test/bin/graph/degree_run_raw.sh
+++ /dev/null
@@ -1,120 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. it_2004"
-  echo "2nd argument: name of api: e.g. degrees,inDegrees,outDegrees"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:it_2004,twitter7,uk_2007_05,mycielskian20,gap_kron,com_friendster"
-  echo "api name:degrees,inDegrees,outDegrees"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/degree/degree_spark_raw.properties
-
-dataset_name=$1
-api_name=$2
-
-if [ ${dataset_name} != "it_2004" ] &&
-   [ ${dataset_name} != "twitter7" ] &&
-   [ ${dataset_name} != "uk_2007_05" ] &&
-   [ ${dataset_name} != "mycielskian20" ] &&
-   [ ${dataset_name} != "gap_kron" ] &&
-   [ ${dataset_name} != "com_friendster" ] ;then
-  echo "invalid dataset name,dataset name:it_2004,twitter7,uk_2007_05,mycielskian20,gap_kron,com_friendster"
-  exit 1
-fi
-
-if [ ${api_name} != "degrees" ] &&
-   [ ${api_name} != "inDegrees" ] &&
-   [ ${api_name} != "outDegrees" ];then
-  echo "invalid api name,api name: degrees,inDegrees,outDegrees"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/degree/${dataset_name}_${api_name}_raw"
-
-# concatnate strings as a new variable
-num_executors="${api_name}_${dataset_name}_numExecutors"
-executor_cores="${api_name}_${dataset_name}_executorCores"
-executor_memory="${api_name}_${dataset_name}_executorMemory"
-num_partitions="${api_name}_${dataset_name}_numPartitions"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-num_partitions_val=${!num_partitions}
-extra_java_options_val="-Xms${executor_memory_val}"
-
-deploy_mode_val=${!deploy_mode}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "extra_java_options_val : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.DegreeRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 200g \
---conf spark.locality.wait.node=0 \
---conf spark.driver.maxResultSize=0 \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.broadcast.blockSize=1m \
---conf spark.reducer.maxSizeInFlight=59mb \
---conf spark.shuffle.file.buffer=17k \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.io.compression.codec=lzf \
---conf spark.shuffle.compress=true \
---conf spark.rdd.compress=false \
---conf spark.shuffle.io.preferDirectBufs=true \
---conf spark.shuffle.spill.compress=true \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---driver-class-path "lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} "yes" ${data_path_val} | tee ./log/log
-
diff --git a/tools/kal-test/bin/graph/incpr_run.sh b/tools/kal-test/bin/graph/incpr_run.sh
deleted file mode 100644
index 16c3037..0000000
--- a/tools/kal-test/bin/graph/incpr_run.sh
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. run"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name: twitter-2010"
-  echo "api name:run or runUntilConvergence"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/incpr/incpr_spark.properties
-
-dataset_name=$1
-api_name=$2
-
-input_path=${basic_input_path}
-output_path=${basic_output_path}
-echo ${input_path}
-echo ${output_path}
-exit 0
-result=$(echo ${dataset_name} | grep "twitter_2010")
-if [[ "$result" != "" ]]
-then
-    echo "correct dataset name ${dataset_name}, continue kal test."
-else
-    echo "invalid dataset name ${dataset_name}, expect twitter-2010*."
-    exit 1
-fi
-
-if [ ${api_name} != "run" ] && [ ${api_name} != "runUntilConvergence" ];then
-  echo "invalid api name ${api_name}, expect run or runUntilConvergence"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/incpr/${dataset_name}_${api_name}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-prefix="run"
-if [ ${api_name} == "runUntilConvergence" ]
-then
-  prefix="convergence"
-fi
-
-# concatnate strings as a new variable
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-executor_memory_overhead="execMemOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-num_partitions="numPartitions_"${cpu_name}
-echo $num_executors
-echo $executor_cores
-echo $executor_memory
-echo $extra_java_options
-echo $num_partitions
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-kal_version=kalVersion
-kal_version_val=${!kal_version}
-echo "${dataset_name} : ${data_path_val}"
-
-
-echo "start to submit spark jobs"
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.graph.IncPageRankRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 100g \
---conf spark.driver.maxResultSize=200g \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.kryoserializer.buffer.max=2040m \
---conf spark.driver.extraJavaOptions="-Xms100G" \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.locality.wait.node=0 \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.memory.fraction=0.5 \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-${kal_version_val}-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-${kal_version_val}-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-${kal_version_val}-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} "no" | tee ./log/log
-
diff --git a/tools/kal-test/bin/graph/kcore_run.sh b/tools/kal-test/bin/graph/kcore_run.sh
deleted file mode 100755
index 63dfc7e..0000000
--- a/tools/kal-test/bin/graph/kcore_run.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage: <dataset name>"
- echo "dataset name: graph500_22 or graph500_23 or graph500_25 or graph500_26"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name."
-  echo "dataset name: graph500_22 or graph500_23 or graph500_25 or graph500_26"
-	exit 0
-fi
-
-dataset_name=$1
-if [ $dataset_name != 'graph500_22' ] && [ $dataset_name != 'graph500_23' ] && [ $dataset_name != 'graph500_25' ] && [ $dataset_name != 'graph500_26' ];
-then
-  echo 'invalid dataset'
-  echo 'dataset name: graph500_22 or graph500_23 or graph500_25 or graph500_26'
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/kcore/kcore_spark.properties
-num_executors_val="numExecutors_${dataset_name}_${cpu_name}"
-executor_cores_val="executorCores_${dataset_name}_${cpu_name}"
-executor_memory_val="executorMemory_${dataset_name}_${cpu_name}"
-executor_extra_javaopts_val="executorExtraJavaopts_${dataset_name}_${cpu_name}"
-executor_emoryOverhead_val="executorMemoryOverhead_${dataset_name}_${cpu_name}"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-executor_emoryOverhead=${!executor_emoryOverhead_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-echo "${executor_emoryOverhead_val}:${executor_emoryOverhead}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/kcore/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-
-spark-submit \
---class com.bigdata.graph.KCoreDecompositionRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
---conf spark.driver.maxResultSize=200g \
---conf spark.locality.wait.node=0 \
---conf spark.executor.memoryOverhead=${executor_emoryOverhead} \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf spark.executor.extraClassPath="boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "no" ${cpu_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/kcore_run_hive.sh b/tools/kal-test/bin/graph/kcore_run_hive.sh
deleted file mode 100755
index 4c648ce..0000000
--- a/tools/kal-test/bin/graph/kcore_run_hive.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
- echo "Usage:<table name> <col1> <col2> <partition> <save_mode> <save_arg>"
- exit 0
- ;;
-esac
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-if [ ${cpu_name} == "aarch64" ]
-then
-  cpu_name="aarch_64"
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-table_name=$1
-col1=$2
-col2=$3
-partition=$4
-save_mode=$5
-save_arg=$6
-
-echo "table_name: $table_name"
-echo "col1: $col1"
-echo "col2: $col2"
-echo "partition: $partition"
-echo "save_mode: $save_mode"
-echo "save_arg: $save_arg"
-
-spark-submit \
---class com.bigdata.graph.KCoreDecompositionHiveRunner \
---deploy-mode "client" \
---driver-memory "16g" \
---num-executors 35 \
---executor-cores 4 \
---executor-memory "25g" \
---conf spark.driver.maxResultSize=200g \
---conf spark.locality.wait.node=0 \
---conf spark.executor.memoryOverhead=10240 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.rdd.compress=true \
---conf spark.shuffle.compress=true \
---conf spark.shuffle.spill.compress=true \
---conf spark.io.compression.codec=lz4 \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf spark.executor.extraClassPath="boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${table_name} ${col1} ${col2} ${partition} ${save_mode} ${save_arg}
diff --git a/tools/kal-test/bin/graph/kcore_run_raw.sh b/tools/kal-test/bin/graph/kcore_run_raw.sh
deleted file mode 100755
index 9a4aef9..0000000
--- a/tools/kal-test/bin/graph/kcore_run_raw.sh
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
- echo "Usage: <dataset name>"
- echo "dataset name: graph500_22 or graph500_23 or graph500_25 or graph500_26"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name."
-  echo "dataset name: graph500_22 or graph500_23 or graph500_25 or graph500_26"
-	exit 0
-fi
-
-dataset_name=$1
-if [ $dataset_name != 'graph500_22' ] && [ $dataset_name != 'graph500_23' ] && [ $dataset_name != 'graph500_25' ] && [ $dataset_name != 'graph500_26' ];
-then
-  echo 'invalid dataset'
-  echo 'dataset name: graph500_22 or graph500_23 or graph500_25 or graph500_26'
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/kcore/kcore_spark_raw.properties
-num_executors_val="numExecutors_${dataset_name}"
-executor_cores_val="executorCores_${dataset_name}"
-executor_memory_val="executorMemory_${dataset_name}"
-executor_extra_javaopts_val="executorExtraJavaopts_${dataset_name}"
-executor_emoryOverhead_val="executorMemoryOverhead_${dataset_name}"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-executor_emoryOverhead=${!executor_emoryOverhead_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-echo "${executor_emoryOverhead_val}:${executor_emoryOverhead}"
-
-source ${CONF_DIR}/graph_datasets.properties
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/kcore/raw/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-
-spark-submit \
---class com.bigdata.graph.KCore \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
---conf spark.driver.maxResultSize=200g \
---conf spark.locality.wait.node=0 \
---conf spark.executor.memoryOverhead=${executor_emoryOverhead} \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "yes" ${cpu_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/louvain_run.sh b/tools/kal-test/bin/graph/louvain_run.sh
deleted file mode 100644
index 72c2401..0000000
--- a/tools/kal-test/bin/graph/louvain_run.sh
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. graph500_22"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ];then
-  echo "Usage:<dataset name>"
- 	echo "dataset name:graph500_22,graph500_24,graph500_25,cit_patents,uk_2002 or arabic_2005"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/louvain/louvain_spark.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "graph500_22" ] &&
-   [ ${dataset_name} != "graph500_24" ] &&
-   [ ${dataset_name} != "graph500_25" ] &&
-   [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "uk_2002" ] &&
-   [ ${dataset_name} != "arabic_2005" ];then
-  echo "invalid dataset name,dataset name:graph500_22,graph500_24,graph500_25,cit_patents,uk_2002 or arabic_20055"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/louvain/${dataset_name}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-extra_java_options="${dataset_name}_extraJavaOptions_${cpu_name}"
-num_partitions="${dataset_name}_numPartitions_${cpu_name}"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-echo "start to submit spark jobs"
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.graph.LouvainRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 16g \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=3600000 \
---conf spark.worker.timeout=3600 \
---conf spark.driver.maxResultSize=200g \
---conf spark.rpc.askTimeout=36000 \
---conf spark.rdd.compress=true \
---conf spark.network.timeout=6000s \
---conf spark.broadcast.blockSize=4m \
---conf spark.shuffle.manager=SORT \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.locality.wait.node=0 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${num_partitions_val} "no" ${data_path_val}
diff --git a/tools/kal-test/bin/graph/louvain_run_hive.sh b/tools/kal-test/bin/graph/louvain_run_hive.sh
deleted file mode 100644
index c52547a..0000000
--- a/tools/kal-test/bin/graph/louvain_run_hive.sh
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
- echo "Usage:<table name> <col1> <col2> <colWeight> <iterNum> <isDirected> <partition> <save_mode> <save_arg>"
- exit 0
- ;;
-esac
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-if [ ${cpu_name} == "aarch64" ]
-then
-  cpu_name="aarch_64"
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-table_name=$1
-col1=$2
-col2=$3
-colWeight=$4
-iterNum=$5
-isDirected=$6
-partition=$7
-save_mode=$8
-save_arg=$9
-
-echo "table_name: $table_name"
-echo "col1: $col1"
-echo "colWeight: $colWeight"
-echo "iterNum: $iterNum"
-echo "isDirected: $isDirected"
-echo "partition: $partition"
-echo "save_mode: $save_mode"
-echo "save_arg: $save_arg"
-
-spark-submit \
---class com.bigdata.graph.LouvainHiveRunner \
---master yarn \
---deploy-mode "client" \
---num-executors 35 \
---executor-memory "25g"  \
---executor-cores 8 \
---driver-memory "16g" \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=3600000 \
---conf spark.worker.timeout=3600 \
---conf spark.driver.maxResultSize=200g \
---conf spark.rpc.askTimeout=36000 \
---conf spark.network.timeout=6000s \
---conf spark.broadcast.blockSize=4m \
---conf spark.shuffle.manager=SORT \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.rdd.compress=true \
---conf spark.shuffle.compress=true \
---conf spark.shuffle.spill.compress=true \
---conf spark.io.compression.codec=lz4 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${table_name} ${col1} ${col2} ${colWeight} ${iterNum} ${isDirected} ${partition} ${save_mode} ${save_arg}
diff --git a/tools/kal-test/bin/graph/lpa_run.sh b/tools/kal-test/bin/graph/lpa_run.sh
deleted file mode 100755
index f20c9f0..0000000
--- a/tools/kal-test/bin/graph/lpa_run.sh
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage:<api> <dataset name>"
- echo "api: run or runConvergence"
- echo "dataset name: graph500_22 or graph500_24 or graph500_25"
- exit 0
- ;;
-esac
-
-if [ $# -ne 2 ];then
-	echo "please input intput and dataset name."
-	echo "api: run or runConvergence"
-  echo "dataset name: graph500_22 or graph500_24 or graph500_25"
-	exit 0
-fi
-api=$1
-if [ $api != "run" ] && [ $api != "runConvergence" ];
-then
-  echo "invalid api."
-  echo "api: run or runConvergence"
-  exit 0
-fi
-
-dataset_name=$2
-if [ $dataset_name != 'graph500_22' ] && [ $dataset_name != 'graph500_24' ] && [ $dataset_name != 'graph500_25' ];
-then
-  echo 'invalid dataset'
-  echo "dataset name: graph500_22 or graph500_24 or graph500_25"
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/lpa/lpa_spark.properties
-num_executors_val="numExecutors_${cpu_name}"
-executor_cores_val="executorCores_${cpu_name}"
-executor_memory_val="executorMemory_${cpu_name}"
-executor_extra_javaopts_val="executorExtraJavaopts_${cpu_name}"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/lpa/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-
-if [ ${api} == "runConvergence" ];
-then
-  spark-submit \
-  --class com.bigdata.graph.LabelPropagationRunner \
-  --deploy-mode ${deploy_mode} \
-  --driver-memory ${driver_memory} \
-  --num-executors ${num_executors} \
-  --executor-cores ${executor_cores} \
-  --executor-memory ${executor_memory} \
-  --conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
-  --jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --conf "spark.boostkit.graph.lpa.convergence=true" \
-  ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} ${api} "no" ${cpu_name} | tee ./log/log
-else
-  spark-submit \
-  --class com.bigdata.graph.LabelPropagationRunner \
-  --deploy-mode ${deploy_mode} \
-  --driver-memory ${driver_memory} \
-  --num-executors ${num_executors} \
-  --executor-cores ${executor_cores} \
-  --executor-memory ${executor_memory} \
-  --conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
-  --jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} ${api} "no" ${cpu_name} | tee ./log/log
-fi
diff --git a/tools/kal-test/bin/graph/lpa_run_raw.sh b/tools/kal-test/bin/graph/lpa_run_raw.sh
deleted file mode 100755
index d6eaf17..0000000
--- a/tools/kal-test/bin/graph/lpa_run_raw.sh
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage:<dataset name>"
- echo "dataset name: graph500_22 or graph500_24 or graph500_25"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name: graph500_22 or graph500_24 or graph500_25"
-	exit 0
-fi
-dataset_name=$1
-if [ $dataset_name != 'graph500_22' ] && [ $dataset_name != 'graph500_24' ] && [ $dataset_name != 'graph500_25' ];
-then
-  echo 'invalid dataset'
-  echo "dataset name: graph500_22 or graph500_24 or graph500_25"
-  exit 0
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/lpa/lpa_spark_raw.properties
-num_executors_val="numExecutors_${dataset_name}"
-executor_cores_val="executorCores_${dataset_name}"
-executor_memory_val="executorMemory_${dataset_name}"
-executor_extra_javaopts_val="executorExtraJavaopts_${dataset_name}"
-
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-executor_extra_javaopts=${!executor_extra_javaopts_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${executor_extra_javaopts_val}:${executor_extra_javaopts}"
-
-source ${CONF_DIR}/graph_datasets.properties
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/lpa/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-
-mkdir -p log
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.LabelPropagationRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${executor_extra_javaopts}" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "run" "yes" ${cpu_name} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/graph/mce_run.sh b/tools/kal-test/bin/graph/mce_run.sh
deleted file mode 100644
index be539cb..0000000
--- a/tools/kal-test/bin/graph/mce_run.sh
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/bin/bash
-set -e
-case "$1" in 
--h | --help | ?)
- echo "Usage:<dataset name>"
- echo "dataset name:graph500_23,or graph500_24,or graph500_25"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name:graph500_23,or graph500_24,or graph500_25"
-	exit 0
-fi
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/mce/mce_spark.properties
-num_executors_val="numExecutors_${cpu_name}"
-executor_cores_val="executorCores"
-executor_memory_val="executorMemory_${cpu_name}"
-extra_java_options_val="extraJavaOptions"
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-extra_java_options=${!extra_java_options_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${extra_java_options_val}:${extra_java_options}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path_val="${dataset_name}_mce"
-output_path=${!output_path_val}
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.MaximalCliqueEnumerationRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${extra_java_options}" \
---conf spark.locality.wait=10 \
---conf spark.rdd.compress=false \
---conf spark.io.compression.codec=lz4 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} | tee ./log/log
-
-
-
-
-
diff --git a/tools/kal-test/bin/graph/mce_run_hive.sh b/tools/kal-test/bin/graph/mce_run_hive.sh
deleted file mode 100644
index b7fe542..0000000
--- a/tools/kal-test/bin/graph/mce_run_hive.sh
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/bin/bash
-set -e
-case "$1" in
--h | --help | ?)
- echo "Usage:<table name> <col1> <col2> <minK> <maxDegree> <partition> <save_mode> <save_arg>"
- exit 0
- ;;
-esac
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-if [ ${cpu_name} == "aarch64" ]
-then
-  cpu_name="aarch_64"
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-table_name=$1
-col1=$2
-col2=$3
-minK=$4
-maxDegree=$5
-partition=$6
-save_mode=$7
-save_arg=$8
-
-echo "table_name: $table_name"
-echo "col1: $col1"
-echo "col2: $col2"
-echo "minK: $minK"
-echo "maxDegree: $maxDegree"
-echo "partition: $partition"
-echo "save_mode: $save_mode"
-echo "save_arg: $save_arg"
-
-spark-submit \
---class com.bigdata.graph.MaximalCliqueEnumerationHiveRunner \
---master yarn \
---deploy-mode "client" \
---driver-memory "80g" \
---num-executors 59 \
---executor-cores 4 \
---executor-memory "15g" \
---conf spark.locality.wait=10 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.rdd.compress=true \
---conf spark.shuffle.compress=true \
---conf spark.shuffle.spill.compress=true \
---conf spark.io.compression.codec=lz4 \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${table_name} ${col1} ${col2} ${minK} ${maxDegree} ${partition} ${save_mode} ${save_arg}
-
-
-
-
-
diff --git a/tools/kal-test/bin/graph/modularity_run.sh b/tools/kal-test/bin/graph/modularity_run.sh
deleted file mode 100755
index 9aafdee..0000000
--- a/tools/kal-test/bin/graph/modularity_run.sh
+++ /dev/null
@@ -1,75 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. grah500_22"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. graph500_22"
-  exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-
-source conf/graph/modularity/modularity_spark.properties
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExectuors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-extra_java_options="${dataset_name}_extraJavaOptions_${cpu_name}"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-mkdir -p log
-echo "start to submit spark jobs"
-
-
-spark-submit \
---class com.bigdata.graph.ModularityRunner \
---driver-memory 80g \
---master yarn \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/mssp_run.sh b/tools/kal-test/bin/graph/mssp_run.sh
deleted file mode 100755
index e2459b8..0000000
--- a/tools/kal-test/bin/graph/mssp_run.sh
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <source number>"
-  echo "1st argument: name of dataset: e.g. uk_2002 5 or uk_2002 5 raw or livejournal 5 or arabic_2005 5"
-  exit 0
-  ;;
-esac
-
-if [ $# -lt 2 ]; then
-  echo "please input 2 argument: <dataset name> <source number>"
-  echo "1st argument: name of dataset: e.g. uk_2002 5; uk_2002 5 raw"
-  exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/mssp/mssp_spark.properties
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-dataset_name=$1
-source_num=$2
-is_raw=$3
-
-hdfs dfs -rm -r -f /tmp/graph/result/mssp/${dataset_name}_${source_num}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_${source_num}_numExectuors_${cpu_name}${is_raw}"
-executor_cores="${dataset_name}_${source_num}_executorCores_${cpu_name}${is_raw}"
-executor_memory="${dataset_name}_${source_num}_executorMemory_${cpu_name}${is_raw}"
-driver_memory="${dataset_name}_${source_num}_driverMemory_${cpu_name}${is_raw}"
-extra_java_options="${dataset_name}_${source_num}_extraJavaOptions_${cpu_name}${is_raw}"
-compute_partition="${dataset_name}_${source_num}_computePartition_${cpu_name}${is_raw}"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-driver_memory_val=${!driver_memory}
-extra_java_options_val=${!extra_java_options}
-compute_partition_val=${!compute_partition}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${compute_partition} : ${compute_partition_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${driver_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${compute_partition_val} ] ||
-  [ ! ${executor_memory_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-echo "start to submit spark jobs"
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-if [[ ${is_raw} == "raw" ]]; then
-spark-submit \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
---class com.bigdata.graph.MSSPRunner \
---master yarn \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf spark.driver.maxResultSize=100g \
-./lib/kal-test_2.11-0.1.jar ${dataset_name}_${source_num}  ${compute_partition_val} ${is_raw}  | tee ./log/log
-else
-spark-submit \
---class com.bigdata.graph.MSSPRunner \
---master yarn \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf spark.driver.maxResultSize=100g \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name}_${source_num}  ${compute_partition_val}  | tee ./log/log
-fi
diff --git a/tools/kal-test/bin/graph/node2vec_run.sh b/tools/kal-test/bin/graph/node2vec_run.sh
deleted file mode 100644
index a2d9ac6..0000000
--- a/tools/kal-test/bin/graph/node2vec_run.sh
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ];then
-  echo "Usage:<dataset name>"
- 	echo "dataset name:cit_patents,soc_liveJournal,uk_2002"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/node2vec/node2vec_spark.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "soc_liveJournal" ] &&
-   [ ${dataset_name} != "uk_2002" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,soc_liveJournal,uk_2002"
-  exit 1
-fi
-outputPath=/tmp/graph/result/node2vec/${dataset_name}
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}-${cpu_name}
-
-
-# concatnate strings as a new variable
-
-master_="master"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-driver_cores="driverCores_${cpu_name}"
-executor_cores="executorCores_${cpu_name}"
-executor_memory="executorMemory"
-num_executors="numExecutors"
-
-
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-driver_memory_val=${!driver_memory}
-driver_cores_val=${!driver_cores}
-
-echo "${cpu_name}"
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${driver_cores}:${driver_cores_val}"
-
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${driver_memory_val} ] ||
-  [ ! ${driver_cores_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs--node2vec_${model_conf}"
-spark-submit \
---class com.bigdata.graph.Node2VecRunner \
---master ${master_val} \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---driver-cores ${driver_cores_val} \
---conf spark.kryoserializer.buffer.max=2047m \
---conf spark.ui.showConsoleProgress=true \
---conf spark.driver.maxResultSize=0 \
---conf spark.driver.extraJavaOptions="-Xms300G -XX:hashCode=0" \
---conf spark.executor.extraJavaOption="-Xms315G -XX:hashCode=0" \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-core_2.11-1.3.0-${spark_version_val}.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/node2vec_run_opensource.sh b/tools/kal-test/bin/graph/node2vec_run_opensource.sh
deleted file mode 100644
index 67c0ab2..0000000
--- a/tools/kal-test/bin/graph/node2vec_run_opensource.sh
+++ /dev/null
@@ -1,126 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ];then
-  echo "Usage:<dataset name>"
- 	echo "dataset name:cit_patents,soc_liveJournal,uk_2002"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/node2vec/node2vec_spark_opensource.properties
-
-dataset_name=$1
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "soc_liveJournal" ] &&
-   [ ${dataset_name} != "uk_2002" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,soc_liveJournal,uk_2002"
-  exit 1
-fi
-
-outputPath=/tmp/graph/result/node2vec/${dataset_name}_"opensource"/${dataset_name}-"test.txt"
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}-"opensource"
-# concatnate strings as a new variable
-
-master_="master"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-driver_cores="driverCores"
-executor_cores="executorCores"
-executor_memory="executorMemory"
-num_executors="numExecutors"
-
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-driver_memory_val=${!driver_memory}
-driver_cores_val=${!driver_cores}
-
-echo "${cpu_name}"
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${driver_cores}:${driver_cores_val}"
-
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${driver_memory_val} ] ||
-  [ ! ${driver_cores_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-negEdge_path="${dataset_name}_negEdge"
-data_path_val=${!dataset_name}
-negEdge_path_val=${!negEdge_path}
-
-echo "${dataset_name} : ${data_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs--node2vec_${model_conf}"
-spark-submit \
---class vn.five9.Main \
---master ${master_val} \
---name "Node2Vec_${model_conf}" \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---driver-cores ${driver_cores_val} \
---conf spark.kryoserializer.buffer.max=2047m \
---conf spark.ui.showConsoleProgress=true \
---conf spark.driver.maxResultSize=0 \
---conf spark.driver.extraJavaOptions="-Xms300G -XX:hashCode=0" \
---conf spark.executor.extraJavaOption="-Xms315G -XX:hashCode=0" \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---jars "lib/fastutil-8.3.1.jar,lib/spark-mllib_2.11-2.3.2.jar,lib/smile-core-2.5.3.jar,lib/scopt_2.11-3.5.0.jar,lib/smile-math-2.5.3.jar" \
-./lib/node2vec-baseline.jar \
---cmd node2vec --indexed true --directed true --degree 1000000000 \
---p 1.0 --q 1.0 --walkLength 5 --numWalks 10 \
---input ${data_path_val} --output ${outputPath} > node2vec_tmp.log
-
-CostTime=$(cat node2vec_tmp.log |grep "total time" | awk '{print $7}')
-currentTime=$(date "+%Y%m%d_H%M%S")
-rm -rf node2vec_tmp.log
-echo -e "algorithmName: Node2vec\ncostTime: $CostTime\ndatasetName: ${dataset_name}\nisRaw: 'yes'\ntestcaseType: Node2vec_opensource_${dataset_name}\n" > ./report/"Node2vec_${currentTime}.yml"
-if [ $? -eq 0 ];then
-  echo "Exec Successful: end." > ./log/log
-else
-  echo "Exec Failure: please check the code" > ./log/log
-fi
diff --git a/tools/kal-test/bin/graph/ppr_run.sh b/tools/kal-test/bin/graph/ppr_run.sh
deleted file mode 100644
index 63b07ab..0000000
--- a/tools/kal-test/bin/graph/ppr_run.sh
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><api name><sourceCnt>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. fixMS"
-  echo "3nd argument: sourceCnt or null: e.g. 10"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ] &&
-   [ $# -ne 3 ] ;then
-  echo "Usage:<dataset name><api name><sourceCnt>"
- 	echo "dataset name:cit_patents,uk_2002,arabic_2005"
- 	echo "api name:fixMS,fixSS,conSS"
-  echo "sourceCnt or null:1,5,10,50,100"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/ppr/ppr_spark.properties
-
-dataset_name=$1
-api_name=$2
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "uk_2002" ] &&
-   [ ${dataset_name} != "arabic_2005" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,uk_2002,arabic_2005"
-  exit 1
-fi
-if [ ${api_name} != "fixMS" ] &&
-   [ ${api_name} != "fixSS" ] &&
-   [ ${api_name} != "conSS" ] ;then
-  echo "invalid argument value,api name: fixMS,fixSS,conSS"
-  exit 1
-fi
-
-
-if [ $# -eq 3 ]; then
-    src=$3
-    if [ ${src} != "1" ] &&
-       [ ${src} != "5" ] &&
-       [ ${src} != "10" ] &&
-       [ ${src} != "50" ] &&
-       [ ${src} != "100" ] ;then
-      echo "invalid argument value,must be: 1, 5, 10, 50 or 100"
-      exit 1
-    fi
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${api_name}_${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${api_name}_${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${api_name}_${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${api_name}_${dataset_name}_numPartitions_${cpu_name}"
-extra_Java_Options="${api_name}_${dataset_name}_extraJavaOptions_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-driver_memory_val=${!driver_memory}
-extra_Java_Options_val=${!extra_Java_Options}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_Java_Options_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-if [ ${api_name} == "fixMS" ]; then
-  outputPath="/tmp/graph/result/ppr/${dataset_name}/${api_name}_${src}"
-  hdfs dfs -rm -r -f ${outputPath}
-  echo "start to clean cache and sleep 30s"
-  ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-  sleep 30
-
-  mkdir -p log
-
-  echo "start to submit spark jobs--${api_name}_${dataset_name}_${src}"
-  spark-submit \
-  --class com.bigdata.graph.PersonalizedPageRankRunner \
-  --master yarn \
-  --deploy-mode ${deploy_mode_val} \
-  --num-executors ${num_executors_val} \
-  --executor-memory ${executor_memory_val} \
-  --executor-cores ${executor_cores_val} \
-  --driver-memory ${driver_memory_val} \
-  --conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
-  --conf spark.driver.maxResultSize=80g \
-  --conf spark.driver.extraJavaOptions="-Xms80G" \
-  --conf spark.locality.wait.node=0 \
-  --jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  --conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-  ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} ${data_path_val} "no" ${src} ${outputPath} | tee ./log/log
-else
-  source conf/graph/ppr/ppr_source_id.properties
-  IFS=,
-  source_ids="${dataset_name}_SourceID"
-  source_ids_val=${!source_ids}
-  source_ids_arr=($source_ids_val)
-
-  echo "${source_ids}:${source_ids_val}"
-  for source_id in ${source_ids_arr[@]}
-  do
-    outputPath="/tmp/graph/result/ppr/${dataset_name}/${api_name}_${source_id}"
-    hadoop fs -rm -r -f ${outputPath}
-    echo "start to clean cache and sleep 30s"
-    ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-    ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-    ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-    ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-    sleep 30
-
-    mkdir -p log
-
-    echo "start to submit spark jobs--${api_name}_${dataset_name}_${source_id}"
-    spark-submit \
-    --class com.bigdata.graph.PersonalizedPageRankRunner \
-    --master yarn \
-    --deploy-mode ${deploy_mode_val} \
-    --num-executors ${num_executors_val} \
-    --executor-memory ${executor_memory_val} \
-    --executor-cores ${executor_cores_val} \
-    --driver-memory ${driver_memory_val} \
-    --conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
-    --conf spark.driver.maxResultSize=80g \
-    --conf spark.driver.extraJavaOptions="-Xms80G" \
-    --conf spark.locality.wait.node=0 \
-    --jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-    --driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-    --conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-    ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} ${data_path_val} "no" ${source_id} ${outputPath} | tee ./log/log
-  done
-fi
diff --git a/tools/kal-test/bin/graph/ppr_run_raw.sh b/tools/kal-test/bin/graph/ppr_run_raw.sh
deleted file mode 100644
index b1d4ac5..0000000
--- a/tools/kal-test/bin/graph/ppr_run_raw.sh
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><api name><sourceCnt>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. fixMS"
-  echo "3nd argument: sourceCnt or null: e.g. 10"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ] &&
-   [ $# -ne 3 ] ;then
-  echo "Usage:<dataset name><api name><sourceCnt>"
- 	echo "dataset name:cit_patents,uk_2002,arabic_2005"
- 	echo "api name:fixMS,fixSS,conSS"
-  echo "sourceCnt or null:1,5,10,50,100"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/ppr/ppr_spark_raw.properties
-
-dataset_name=$1
-api_name=$2
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "uk_2002" ] &&
-   [ ${dataset_name} != "arabic_2005" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,uk_2002,arabic_2005"
-  exit 1
-fi
-if [ ${api_name} != "fixMS" ] &&
-   [ ${api_name} != "fixSS" ] &&
-   [ ${api_name} != "conSS" ] ;then
-  echo "invalid argument value,api name: fixMS,fixSS,conSS"
-  exit 1
-fi
-
-
-if [ $# -eq 3 ]; then
-    src=$3
-    if [ ${src} != "1" ] &&
-       [ ${src} != "5" ] &&
-       [ ${src} != "10" ] &&
-       [ ${src} != "50" ] &&
-       [ ${src} != "100" ] ;then
-      echo "invalid argument value,must be: 1, 5, 10, 50 or 100"
-      exit 1
-    fi
-fi
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${api_name}_${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${api_name}_${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${api_name}_${dataset_name}_executorMemory_${cpu_name}"
-num_partitions="${api_name}_${dataset_name}_numPartitions_${cpu_name}"
-extra_Java_Options="${api_name}_${dataset_name}_extraJavaOptions_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-driver_memory_val=${!driver_memory}
-extra_Java_Options_val=${!extra_Java_Options}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_Java_Options_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-if [ ${api_name} == "fixMS" ]; then
-  outputPath="/tmp/graph/result/ppr/${dataset_name}/${api_name}_${src}_raw"
-  hdfs dfs -rm -r -f ${outputPath}
-  echo "start to clean cache and sleep 30s"
-  ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-  ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-  sleep 30
-
-  mkdir -p log
-
-  echo "start to submit spark jobs--${api_name}_${dataset_name}_${src}_raw"
-  spark-submit \
-  --class com.bigdata.graph.PersonalizedPageRankRunner \
-  --master yarn \
-  --deploy-mode ${deploy_mode_val} \
-  --num-executors ${num_executors_val} \
-  --executor-memory ${executor_memory_val} \
-  --executor-cores ${executor_cores_val} \
-  --driver-memory ${driver_memory_val} \
-  --conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
-  --conf spark.driver.maxResultSize=80g \
-  --conf spark.driver.extraJavaOptions="-Xms80G" \
-  --conf spark.locality.wait.node=0 \
-  --jars "lib/fastutil-8.3.1.jar" \
-  --driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
-  --conf "spark.executor.extraClassPath=fastutil-8.3.1.jar" \
-  ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} ${data_path_val} "yes" ${src} ${outputPath} | tee ./log/log
-else
-  source conf/graph/ppr/ppr_source_id.properties
-  IFS=,
-  source_ids="${dataset_name}_SourceID"
-  source_ids_val=${!source_ids}
-  source_ids_arr=($source_ids_val)
-
-  echo "${source_ids}:${source_ids_val}"
-  for source_id in ${source_ids_arr[@]}
-  do
-    outputPath="/tmp/graph/result/ppr/${dataset_name}/${api_name}_${source_id}_raw"
-    hadoop fs -rm -r -f ${outputPath}
-    echo "start to clean cache and sleep 30s"
-    ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-    ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-    ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-    ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-    sleep 30
-
-    mkdir -p log
-
-    echo "start to submit spark jobs--${api_name}_${dataset_name}_${source_id}_raw"
-    spark-submit \
-    --class com.bigdata.graph.PersonalizedPageRankRunner \
-    --master yarn \
-    --deploy-mode ${deploy_mode_val} \
-    --num-executors ${num_executors_val} \
-    --executor-memory ${executor_memory_val} \
-    --executor-cores ${executor_cores_val} \
-    --driver-memory ${driver_memory_val} \
-    --conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
-    --conf spark.driver.maxResultSize=80g \
-    --conf spark.driver.extraJavaOptions="-Xms80G" \
-    --conf spark.locality.wait.node=0 \
-    --jars "lib/fastutil-8.3.1.jar" \
-    --driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
-    --conf "spark.executor.extraClassPath=fastutil-8.3.1.jar" \
-    ./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} ${data_path_val} "yes" ${source_id} ${outputPath} | tee ./log/log
-  done
-fi
diff --git a/tools/kal-test/bin/graph/pr_run.sh b/tools/kal-test/bin/graph/pr_run.sh
deleted file mode 100644
index 645a732..0000000
--- a/tools/kal-test/bin/graph/pr_run.sh
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. run"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:cit_patents,or uk_2002,or arabic_2005"
-  echo "api name:run or runUntilConvergence"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/pr/pr_spark.properties
-
-dataset_name=$1
-api_name=$2
-
-if [ ${dataset_name} != "cit_patents" ] && [ ${dataset_name} != "uk_2002" ] && [ ${dataset_name} != "arabic_2005" ];then
-  echo "invalid dataset name,dataset name:cit_patents,or uk_2002,or arabic_2005"
-  exit 1
-fi
-if [ ${api_name} != "run" ] && [ ${api_name} != "runUntilConvergence" ];then
-  echo "invalid api name,api name: run or runUntilConvergence"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/pr/${dataset_name}_${api_name}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-prefix="run"
-if [ ${api_name} == "runUntilConvergence" ]
-then
-  prefix="convergence"
-fi
-
-# concatnate strings as a new variable
-num_executors="${prefix}_${dataset_name}_numExecutors_${cpu_name}"
-executor_cores="${prefix}_${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${prefix}_${dataset_name}_executorMemory_${cpu_name}"
-extra_java_options="${prefix}_${dataset_name}_extraJavaOptions_${cpu_name}"
-num_partitions="${prefix}_${dataset_name}_numPartitions_${cpu_name}"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-
-echo "start to submit spark jobs"
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.graph.PageRankRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 100g \
---conf spark.driver.maxResultSize=200g \
---conf spark.driver.extraJavaOptions="-Xms100G" \
---conf spark.locality.wait.node=0 \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} "no" ${data_path_val} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/pr_run_hive.sh b/tools/kal-test/bin/graph/pr_run_hive.sh
deleted file mode 100644
index be41fe3..0000000
--- a/tools/kal-test/bin/graph/pr_run_hive.sh
+++ /dev/null
@@ -1,62 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
- echo "Usage:<table name> <col1> <col2> <api> <tol> <resetProb> <numIter> <partition> <save_mode> <save_arg>"
- exit 0
- ;;
-esac
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-if [ ${cpu_name} == "aarch64" ]
-then
-  cpu_name="aarch_64"
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-table_name=$1
-col1=$2
-col2=$3
-api=$4
-tol=$5
-resetProb=$6
-numIter=$7
-partition=$8
-save_mode=$9
-save_arg=${10}
-
-echo "table_name: $table_name"
-echo "col1: $col1"
-echo "col2: $col2"
-echo "api: $api"
-echo "tol: $tol"
-echo "resetProb: $resetProb"
-echo "numIter: $numIter"
-echo "partition: $partition"
-echo "save_mode: $save_mode"
-echo "save_arg: $save_arg"
-
-spark-submit \
---class com.bigdata.graph.PageRankHiveRunner \
---master yarn \
---deploy-mode "client" \
---num-executors 36 \
---executor-memory "25g" \
---executor-cores 4 \
---driver-memory 100g \
---conf spark.driver.maxResultSize=200g \
---conf spark.driver.extraJavaOptions="-Xms100G" \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.rdd.compress=true \
---conf spark.shuffle.compress=true \
---conf spark.shuffle.spill.compress=true \
---conf spark.io.compression.codec=lz4 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${table_name} ${col1} ${col2} ${api} ${tol} ${resetProb} ${numIter} ${partition} ${save_mode} ${save_arg}
diff --git a/tools/kal-test/bin/graph/pr_run_raw.sh b/tools/kal-test/bin/graph/pr_run_raw.sh
deleted file mode 100644
index 948b376..0000000
--- a/tools/kal-test/bin/graph/pr_run_raw.sh
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. cit_patents"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:cit_patents,or uk_2002,or arabic_2005"
-  echo "api name:run or runUntilConvergence"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/pr/pr_spark_raw.properties
-
-dataset_name=$1
-api_name=$2
-
-hdfs dfs -rm -r -f "/tmp/graph/result/pr/${dataset_name}_${api_name}_raw"
-
-prefix="run"
-if [ ${api_name} == "runUtilConvergence" ]
-then
-  prefix="convergence"
-fi
-
-# concatnate strings as a new variable
-num_executors="${prefix}_${dataset_name}_numExecutors"
-executor_cores="${prefix}_${dataset_name}_executorCores"
-executor_memory="${prefix}_${dataset_name}_executorMemory"
-extra_java_options="${prefix}_${dataset_name}_extraJavaOptions"
-num_partitions="${prefix}_${dataset_name}_numPartitions"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-num_partitions_val=${!num_partitions}
-
-deploy_mode_val=${!deploy_mode}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-echo "start to submit spark jobs"
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.graph.PageRankRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 100g \
---conf spark.driver.maxResultSize=200g \
---conf spark.driver.extraJavaOptions="-Xms100G" \
---conf spark.locality.wait.node=0 \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---driver-class-path "lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} ${num_partitions_val} "yes" ${data_path_val} | tee ./log/log
-
diff --git a/tools/kal-test/bin/graph/scc_run.sh b/tools/kal-test/bin/graph/scc_run.sh
deleted file mode 100755
index 9045d56..0000000
--- a/tools/kal-test/bin/graph/scc_run.sh
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage:<dataset name>"
- echo "dataset name: enwiki_2018 or arabic_2005 or cit_patents"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name: enwiki_2018 or arabic_2005 or cit_patents"
-	exit 0
-fi
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-
-num_executors_val="numExecutors_${cpu_name}"
-executor_cores_val="executorCores_${cpu_name}"
-executor_memory_val="executorMemory_${cpu_name}"
-if [ ${dataset_name} == "arabic_2005" ]
-then
-  num_executors_val="numExecutors_${cpu_name}_arabic_2005"
-  executor_cores_val="executorCores_${cpu_name}_arabic_2005"
-  executor_memory_val="executorMemory_${cpu_name}_arabic_2005"
-fi
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-source ${CONF_DIR}/scc/scc_spark.properties
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "executor_extra_javaopts:${executor_extra_javaopts}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/scc/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.StronglyConnectedComponentsRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.driver.maxResultSize=100g \
---conf spark.ui.showConsoleProgress=false \
---conf spark.driver.extraJavaOptions="-Xms12g -XX:hashCode=0" \
---conf spark.executor.extraJavaOptions="${executor_extra_javaopts}" \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.memory.fraction=0.24939583270092516 \
---conf spark.memory.storageFraction=0.5849745294783253 \
---conf spark.broadcast.blockSize=1m \
---conf spark.reducer.maxSizeInFlight=59mb \
---conf spark.shuffle.file.buffer=17k \
---conf spark.io.compression.codec=lzf \
---conf spark.shuffle.compress=true \
---conf spark.rdd.compress=false \
---conf spark.shuffle.io.preferDirectBufs=true \
---conf spark.shuffle.spill.compress=true \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "run" "no" ${cpu_name} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/graph/scc_run_raw.sh b/tools/kal-test/bin/graph/scc_run_raw.sh
deleted file mode 100755
index 35b766c..0000000
--- a/tools/kal-test/bin/graph/scc_run_raw.sh
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage:<dataset name>"
- echo "dataset name: enwiki_2018 or arabic_2005 or cit_patents"
- exit 0
- ;;
-esac
-
-if [ $# -ne 1 ];then
-	echo "please input dataset name: enwiki_2018 or arabic_2005 or cit_patents"
-	exit 0
-fi
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-
-num_executors_val="numExecutors_${cpu_name}"
-executor_cores_val="executorCores_${cpu_name}"
-executor_memory_val="executorMemory_${cpu_name}"
-master_val="master"
-deploy_mode_val="deployMode"
-driver_memory_val="driverMemory"
-source ${CONF_DIR}/scc/scc_spark.properties
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-master=${!master_val}
-driver_memory=${!driver_memory_val}
-deploy_mode=${!deploy_mode_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll, please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "executor_extra_javaopts:${executor_extra_javaopts}"
-
-source ${CONF_DIR}/graph_datasets.properties
-input_path=${!dataset_name}
-output_path="${output_path_prefix}/scc/${dataset_name}"
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.StronglyConnectedComponentsRunner \
---deploy-mode ${deploy_mode} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf spark.locality.wait.node=0 \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.driver.maxResultSize=100g \
---conf spark.ui.showConsoleProgress=false \
---conf spark.driver.extraJavaOptions="-Xms12g -XX:hashCode=0" \
---conf spark.executor.extraJavaOptions="${executor_extra_javaopts}" \
---conf spark.rpc.askTimeout=1000000s \
---conf spark.network.timeout=1000000s \
---conf spark.executor.heartbeatInterval=100000s \
---conf spark.rpc.message.maxSize=1000 \
---conf spark.memory.fraction=0.24939583270092516 \
---conf spark.memory.storageFraction=0.5849745294783253 \
---conf spark.broadcast.blockSize=1m \
---conf spark.reducer.maxSizeInFlight=59mb \
---conf spark.shuffle.file.buffer=17k \
---conf spark.io.compression.codec=lzf \
---conf spark.shuffle.compress=true \
---conf spark.rdd.compress=false \
---conf spark.shuffle.io.preferDirectBufs=true \
---conf spark.shuffle.spill.compress=true \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} "run" "yes" ${cpu_name} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/graph/sgm_run.sh b/tools/kal-test/bin/graph/sgm_run.sh
deleted file mode 100644
index 2b2e71c..0000000
--- a/tools/kal-test/bin/graph/sgm_run.sh
+++ /dev/null
@@ -1,182 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><queryGraph name><identical or not>"
-  echo "1st argument: name of dataset: name of dataset: e.g. graph500_19"
-  echo "2nd argument: name of queryGraph: e.g. 4dgn"
-  echo "3nd argument: match mode: e.g. identical"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ];then
-  echo "Usage:<dataset name><queryGraph name><identical or not>"
- 	echo "dataset name:graph500_19,liveJournal,com_orkut"
- 	echo "queryGraph name:[ for Identical: 4dgn/4sqr/5tree/6star; for unIdentical: 4dgn/4clique/5clique/6clique]"
-  echo "match mode:Identical or unIdentical"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/sgm/sgm_spark.properties
-
-dataset_name=$1
-queryGraph=$2
-match_mode=$3
-queryGraph_path="query_${2}"
-
-
-if [ ${dataset_name} != "graph500_19" ] &&
-   [ ${dataset_name} != "liveJournal" ] &&
-   [ ${dataset_name} != "com_orkut" ] ;then
-  echo "invalid dataset name,dataset name:graph500_19,liveJournal,com_orkut"
-  exit 1
-fi
-if [ ${match_mode} != "Identical" ] &&
-   [ ${match_mode} != "unIdentical" ] ;then
-  echo "invalid argument value,match mode:identical or unidentical"
-  exit 1
-fi
-if [ ${match_mode} == "Identical" ] ; then
-  if [ ${queryGraph} != "4dgn" ] &&
-     [ ${queryGraph} != "4sqr" ] &&
-     [ ${queryGraph} != "5tree" ] &&
-     [ ${queryGraph} != "6star" ] ; then
-    echo "invalid queryGraph,queryGraph name:4dgn,4sqr,5tree,6star"
-    exit 1
-  fi
-elif [ ${match_mode} == "unIdentical" ]; then
-  if [ ${queryGraph} != "4dgn" ] &&
-     [ ${queryGraph} != "4clique" ] &&
-     [ ${queryGraph} != "5clique" ] &&
-     [ ${queryGraph} != "6clique" ] ; then
-    echo "invalid queryGraph,queryGraph name:4dgn,4clique,5clique,6clique"
-    exit 1
-  fi
-fi
-
-outputPath="/tmp/graph/result/sgm/${1}_${2}_${3}"
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_${queryGraph}_${match_mode}_numExecutors_${cpu_name}"
-executor_cores="${dataset_name}_${queryGraph}_${match_mode}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_${queryGraph}_${match_mode}_executorMemory_${cpu_name}"
-num_partitions="${dataset_name}_${queryGraph}_${match_mode}_numPartitions_${cpu_name}"
-extra_Java_Options="${dataset_name}_${queryGraph}_${match_mode}_executorExtraJavaOptions_${cpu_name}"
-num_Task="${dataset_name}_${queryGraph}_${match_mode}_numberTask_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-rpc_askTime="rpcAskTime"
-scheduler_maxRegisteredResourcesWaitingTime="schedulerMaxRegisteredResourcesWaitingTime"
-worker_timeout="workerTimeout"
-network_timeout="networkTimeout"
-storage_blockManagerSlaveTimeoutMs="storageBlockManagerSlaveTimeoutMs"
-shuffle_blockTransferService="shuffleBlockTransferService"
-driver_maxResultSize="driverMaxResultSize"
-shuffle_manager="shuffleManager"
-broadcast_blockSize="broadcastBlockSize"
-rpc_message_maxSize="rpcMessageMaxSize"
-core_connection_ack_wait_timeout="coreConnectionAckWaitTimeout"
-storage_memoryFraction="storageMemoryFraction"
-shuffle_memoryFraction="shuffleMemoryFraction"
-rdd_compress="rddCompress"
-memory_useLegacyMode="memoryUseLegacyMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-num_task_val=${!num_Task}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-extra_Java_Options_val=${!extra_Java_Options}
-driver_memory_val=${!driver_memory}
-rpc_askTime_val=${!rpc_askTime}
-scheduler_maxRegisteredResourcesWaitingTime_val=${!scheduler_maxRegisteredResourcesWaitingTime}
-worker_timeout_val=${!worker_timeout}
-network_timeout_val=${!network_timeout}
-storage_blockManagerSlaveTimeoutMs_val=${!storage_blockManagerSlaveTimeoutMs}
-shuffle_blockTransferService_val=${!shuffle_blockTransferService}
-driver_maxResultSize_val=${!driver_maxResultSize}
-shuffle_manager_val=${!shuffle_manager}
-broadcast_blockSize_val=${!broadcast_blockSize}
-rpc_message_maxSize_val=${!rpc_message_maxSize}
-core_connection_ack_wait_timeout_val=${!core_connection_ack_wait_timeout}
-storage_memoryFraction_val=${!storage_memoryFraction}
-shuffle_memoryFraction_val=${!shuffle_memoryFraction}
-rdd_compress_val=${!rdd_compress}
-memory_useLegacyMode_val=${!memory_useLegacyMode}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${extra_Java_Options}:${extra_Java_Options_val}"
-echo "${num_Task}:${num_task_val}"
-
-if [ ! ${num_executors_val} ] ||
-   [ ! ${executor_cores_val} ] ||
-   [ ! ${executor_memory_val} ] ||
-   [ ! ${num_partitions_val} ] ||
-   [ ! ${num_task_val} ] ||
-   [ ! ${extra_Java_Options_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-queryGraph_path_val=${!queryGraph_path}
-echo "${dataset_name} : ${data_path_val}"
-echo "${queryGraph_path} : ${queryGraph_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs--SGM_${1}_${2}_${3}"
-spark-submit \
---class com.bigdata.graph.SubgraphMatchingRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
---conf spark.rpc.askTime=${rpc_askTime_val} \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=${scheduler_maxRegisteredResourcesWaitingTime_val} \
---conf spark.worker.timeout=${worker_timeout_val} \
---conf spark.network.timeout=${network_timeout_val} \
---conf spark.storage.blockManagerSlaveTimeoutMs=${storage_blockManagerSlaveTimeoutMs_val} \
---conf spark.shuffle.blockTransferService=${shuffle_blockTransferService_val} \
---conf spark.driver.maxResultSize=${driver_maxResultSize_val} \
---conf spark.shuffle.manager=${shuffle_manager_val} \
---conf spark.broadcast.blockSize=${broadcast_blockSize_val} \
---conf spark.rpc.message.maxSize=${rpc_message_maxSize_val} \
---conf spark.core.connection.ack.wait.timeout=${core_connection_ack_wait_timeout_val} \
---conf spark.storage.memoryFraction=${storage_memoryFraction_val} \
---conf spark.shuffle.memoryFraction=${shuffle_memoryFraction_val} \
---conf spark.rdd.compress=${rdd_compress_val} \
---conf spark.memory.useLegacyMode=${memory_useLegacyMode_val} \
---conf spark.executor.memoryOverhead=5g \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${queryGraph} "no" ${match_mode} ${outputPath} ${data_path_val} ${num_partitions_val} ${num_task_val} ${queryGraph_path_val} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/sgm_run_opensource.sh b/tools/kal-test/bin/graph/sgm_run_opensource.sh
deleted file mode 100644
index 04c2c7d..0000000
--- a/tools/kal-test/bin/graph/sgm_run_opensource.sh
+++ /dev/null
@@ -1,171 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><queryGraph name>"
-  echo "1st argument: name of dataset: name of dataset: e.g. graph500_19"
-  echo "2nd argument: name of queryGraph: e.g. 4dgn"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><queryGraph name>"
- 	echo "dataset name:graph500_19,liveJournal,com_orkut"
- 	echo "queryGraph name:4dgn,4clique,5clique,6clique"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-dataset_name=$1
-queryGraph=$2
-
-source conf/graph/sgm/sgm_spark_opensource.properties
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-if [ ${dataset_name} != "graph500_19" ] &&
-   [ ${dataset_name} != "liveJournal" ] &&
-   [ ${dataset_name} != "com_orkut" ] ;then
-  echo "invalid dataset name,dataset name:graph500_19,liveJournal,com_orkut"
-  exit 1
-fi
-
-if [ ${queryGraph} != "4dgn" ] &&
-   [ ${queryGraph} != "4clique" ] &&
-   [ ${queryGraph} != "5clique" ] &&
-   [ ${queryGraph} != "6clique" ] ; then
-  echo "invalid queryGraph,queryGraph name:4dgn,4clique,5clique,6clique"
-  exit 1
-fi
-
-outputPath="/tmp/graph/result/sgm/${1}_${2}"
-hdfs dfs -rm -r -f ${outputPath}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="numExecutors_${cpu_name}"
-executor_cores="executorCores_${cpu_name}"
-executor_memory="executorMemory_${cpu_name}"
-num_partitions="numPartitions_${cpu_name}"
-extra_Java_Options="executorExtraJavaOptions_${cpu_name}"
-num_Colors="numberColors_${cpu_name}"
-graph_Split="${dataset_name}_split"
-queryGraph_Path="query_${queryGraph}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-rpc_askTime="rpcAskTime"
-scheduler_maxRegisteredResourcesWaitingTime="schedulerMaxRegisteredResourcesWaitingTime"
-worker_timeout="workerTimeout"
-network_timeout="networkTimeout"
-storage_blockManagerSlaveTimeoutMs="storageBlockManagerSlaveTimeoutMs"
-shuffle_blockTransferService="shuffleBlockTransferService"
-driver_maxResultSize="driverMaxResultSize"
-shuffle_manager="shuffleManager"
-broadcast_blockSize="broadcastBlockSize"
-rpc_message_maxSize="rpcMessageMaxSize"
-core_connection_ack_wait_timeout="coreConnectionAckWaitTimeout"
-storage_memoryFraction="storageMemoryFraction"
-shuffle_memoryFraction="shuffleMemoryFraction"
-rdd_compress="rddCompress"
-memory_useLegacyMode="memoryUseLegacyMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-num_colors_val=${!num_Colors}
-graph_split_val=${!graph_Split}
-queryGraph_path_val=${!queryGraph_Path}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-extra_Java_Options_val=${!extra_Java_Options}
-driver_memory_val=${!driver_memory}
-rpc_askTime_val=${!rpc_askTime}
-scheduler_maxRegisteredResourcesWaitingTime_val=${!scheduler_maxRegisteredResourcesWaitingTime}
-worker_timeout_val=${!worker_timeout}
-network_timeout_val=${!network_timeout}
-storage_blockManagerSlaveTimeoutMs_val=${!storage_blockManagerSlaveTimeoutMs}
-shuffle_blockTransferService_val=${!shuffle_blockTransferService}
-driver_maxResultSize_val=${!driver_maxResultSize}
-shuffle_manager_val=${!shuffle_manager}
-broadcast_blockSize_val=${!broadcast_blockSize}
-rpc_message_maxSize_val=${!rpc_message_maxSize}
-core_connection_ack_wait_timeout_val=${!core_connection_ack_wait_timeout}
-storage_memoryFraction_val=${!storage_memoryFraction}
-shuffle_memoryFraction_val=${!shuffle_memoryFraction}
-rdd_compress_val=${!rdd_compress}
-memory_useLegacyMode_val=${!memory_useLegacyMode}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${extra_Java_Options}:${extra_Java_Options_val}"
-echo "${num_Colors}:${num_colors_val}"
-echo "${graph_Split}:${graph_split_val}"
-echo "${queryGraph_Path}:${queryGraph_path_val}"
-
-if [ ! ${num_executors_val} ] ||
-   [ ! ${executor_cores_val} ] ||
-   [ ! ${executor_memory_val} ] ||
-   [ ! ${num_partitions_val} ] ||
-   [ ! ${num_colors_val} ] ||
-   [ ! ${extra_Java_Options_val} ] ||
-   [ ! ${graph_split_val} ] ||
-   [ ! ${queryGraph_path_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs--SGM_${1}_${2}"
-spark-submit \
---class pegasus.spark.subgraph.TestOriginal \
---name "SGM_${1}_${2}_opensource" \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
---conf spark.rpc.askTime=${rpc_askTime_val} \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=${scheduler_maxRegisteredResourcesWaitingTime_val} \
---conf spark.worker.timeout=${worker_timeout_val} \
---conf spark.network.timeout=${network_timeout_val} \
---conf spark.storage.blockManagerSlaveTimeoutMs=${storage_blockManagerSlaveTimeoutMs_val} \
---conf spark.shuffle.blockTransferService=${shuffle_blockTransferService_val} \
---conf spark.driver.maxResultSize=${driver_maxResultSize_val} \
---conf spark.shuffle.manager=${shuffle_manager_val} \
---conf spark.broadcast.blockSize=${broadcast_blockSize_val} \
---conf spark.rpc.message.maxSize=${rpc_message_maxSize_val} \
---conf spark.core.connection.ack.wait.timeout=${core_connection_ack_wait_timeout_val} \
---conf spark.storage.memoryFraction=${storage_memoryFraction_val} \
---conf spark.shuffle.memoryFraction=${shuffle_memoryFraction_val} \
---conf spark.rdd.compress=${rdd_compress_val} \
---conf spark.memory.useLegacyMode=${memory_useLegacyMode_val} \
-./lib/pegasus-spark_2.11-0.1.0-SNAPSHOT_openSource.jar yarn ${data_path_val} ${outputPath} ${queryGraph_path_val} ${num_colors_val} 232 "," ${graph_split_val} 10000 > sgm_temp.log
-num_subgraphs=$(cat sgm_temp.log | grep "number of matched subgraphs" | awk -F '[\t]' '{print $2}')
-costTime=$(cat sgm_temp.log | grep "cost time" | awk -F '[\t]' '{print $2}')
-currentTime=$(date "+%Y%m%d_H%M%S")
-rm -rf sgm_temp.log
-echo -e "algorithmName: SGM\ncostTime: $costTime\ndatasetName: ${dataset_name}\nisRaw: 'yes'\nnum_subgraphs: $num_subgraphs\ntestcaseType: SGM_opensource_${1}_${2}_opensource\n" > ./report/"SGM_${currentTime}.yml"
-echo "Exec Successful: End." > ./log/log
diff --git a/tools/kal-test/bin/graph/tc_run.sh b/tools/kal-test/bin/graph/tc_run.sh
deleted file mode 100644
index 1fc9b1b..0000000
--- a/tools/kal-test/bin/graph/tc_run.sh
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in 
--h | --help | ?)
- echo "Usage:<dataset name>"
- echo "dataset name:graph500_22,or graph500_23,or graph500_24,or graph500_25,or graph500_26"
- echo "api name:run or preCanonical"
- exit 0
- ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:graph500_22,or graph500_23,or graph500_24,or graph500_25,or graph500_26"
-  echo "api name:run or preCanonical"
-	exit 0
-fi
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/tc/tc_spark.properties
-num_executors_val="numExecutors_${cpu_name}"
-executor_cores_val="executorCores"
-executor_memory_val="executorMemory_${cpu_name}"
-extra_java_options_val="extraJavaOptions_${cpu_name}"
-master_val="master"
-deploy_mode_val="deployMode"
-driver_cores_val="driverCores"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-extra_java_options=${!extra_java_options_val}
-master=${!master_val}
-deploy_mode=${!deploy_mode_val}
-driver_cores=${!driver_cores_val}
-driver_memory=${!driver_memory_val}
-if [ ! ${num_executors} ] \
-	|| [ ! ${executor_cores} ] \
-  || [ ! ${executor_memory} ] \
-	|| [ ! ${deploy_mode} ] \
-	|| [ ! ${driver_cores} ] \
-	|| [ ! ${driver_memory} ] \
-	|| [ ! ${master} ]; then
-   echo "Some values are NUll,please confirm with the property files"
-   exit 0
-fi
-echo "${master_val}:${master}"
-echo "${deploy_mode_val}:${deploy_mode}"
-echo "${num_executors_val}:${num_executors}"
-echo "${executor_cores_val}:${executor_cores}"
-echo "${executor_memory_val}:${executor_memory}"
-echo "${extra_java_options_val}:${extra_java_options}"
-echo "${driver_memory_val}:${driver_memory}"
-echo "${driver_cores_val}:${driver_cores}"
-
-source ${CONF_DIR}/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-input_path=${!dataset_name}
-output_path_val="${dataset_name}_tc"
-output_path=${!output_path_val}
-echo "${dataset_name}: ${input_path},${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.TriangleCountRunner \
---deploy-mode ${deploy_mode} \
---driver-cores ${driver_cores} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${extra_java_options} -XX:SurvivorRatio=4 -XX:ParallelGCThreads=6" \
---conf spark.rpc.askTimeout=36000 \
---conf spark.akka.timeout=3600 \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=3600000 \
---conf spark.worker.timeout=3600 \
---conf spark.network.timeout=6000s \
---conf spark.storage.blockManagerSlaveTimeoutMs=600000 \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.driver.maxResultSize=100g \
---conf spark.shuffle.manager=SORT \
---conf spark.broadcast.blockSize=25g \
---conf spark.akka.frameSize=2046 \
---conf spark.core.connection.ack.wait.timeout=60000s \
---conf spark.storage.memoryFraction=0.2 \
---conf spark.shuffle.memoryFraction=0.6 \
---conf spark.rdd.compress=true \
---jars "lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} ${api_name} | tee ./log/log
-
-
-
-
-
diff --git a/tools/kal-test/bin/graph/tc_run_raw.sh b/tools/kal-test/bin/graph/tc_run_raw.sh
deleted file mode 100644
index 784277a..0000000
--- a/tools/kal-test/bin/graph/tc_run_raw.sh
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <api name>"
-  echo "dataset name: graph500_22, or graph500_23, or graph500_24, or graph500_25, or graph500_26"
-  echo "api name: run, or preCanonical"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "Usage: <dataset name> <api name>"
-  echo "dataset name: graph500_22, or graph500_23, or graph500_24, or graph500_25, or graph500_26"
-  echo "api name: run, or preCanonical"
-  exit 0
-fi
-dataset_name=$1
-api_name=$2
-
-BIN_DIR="$(cd "$(dirname "$0")"; pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source ${CONF_DIR}/tc/tc_spark_raw.properties
-num_executors_val="numExectuors"
-executor_cores_val="executorCores"
-executor_memory_val="executorMemory"
-extra_java_options_val="extraJavaOptions"
-master_val="master"
-deploy_mode_val="deployMode"
-driver_cores_val="driverCores"
-driver_memory_val="driverMemory"
-num_executors=${!num_executors_val}
-executor_cores=${!executor_cores_val}
-executor_memory=${!executor_memory_val}
-extra_java_options=${!extra_java_options_val}
-master=${!master_val}
-deploy_mode=${!deploy_mode_val}
-driver_cores=${!driver_cores_val}
-driver_memory=${!driver_memory_val}
-if [ ! ${num_executors} ] \
-    || [ ! ${executor_cores} ] \
-    || [ ! ${executor_memory} ] \
-    || [ ! ${driver_cores} ] \
-    || [ ! ${driver_memory} ] \
-    || [ ! ${deploy_mode} ] \
-    || [ ! ${master} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-echo "${master_val} : ${master}"
-echo "${deploy_mode_val} : ${deploy_mode}"
-echo "${num_executors_val} : ${num_executors}"
-echo "${executor_cores_val}: ${executor_cores}"
-echo "${executor_memory_val} : ${executor_memory}"
-echo "${extra_java_options_val} : ${extra_java_options}"
-echo "${driver_memory_val} : ${driver_memory}"
-echo "${driver_cores_val} : ${driver_cores}"
-
-source ${CONF_DIR}/graph_datasets.properties
-input_path=${!dataset_name}
-output_path_val="${dataset_name}_tc"
-output_path=${!output_path_val}
-echo "${dataset_name} : ${input_path}, ${output_path}"
-
-echo "start to clean exist output"
-hdfs dfs -rm -r -f -skipTrash ${output_path}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.TriangleCountRunner \
---deploy-mode ${deploy_mode} \
---driver-cores ${driver_cores} \
---driver-memory ${driver_memory} \
---num-executors ${num_executors} \
---executor-cores ${executor_cores} \
---executor-memory ${executor_memory} \
---conf "spark.executor.extraJavaOptions=${extra_java_options} -XX:SurvivorRatio=4 -XX:ParallelGCThreads=6" \
---conf spark.rpc.askTimeout=36000 \
---conf spark.akka.timeout=3600 \
---conf spark.scheduler.maxRegisteredResourcesWaitingTime=3600000 \
---conf spark.worker.timeout=3600 \
---conf spark.network.timeout=6000s \
---conf spark.storage.blockManagerSlaveTimeoutMs=600000 \
---conf spark.shuffle.blockTransferService=nio \
---conf spark.driver.maxResultSize=100g \
---conf spark.shuffle.manager=SORT \
---conf spark.broadcast.blockSize=25g \
---conf spark.akka.frameSize=2046 \
---conf spark.core.connection.ack.wait.timeout=60000s \
---conf spark.storage.memoryFraction=0.2 \
---conf spark.shuffle.memoryFraction=0.6 \
---conf spark.rdd.compress=true \
---driver-class-path "lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${input_path} ${output_path} ${api_name} "yes" | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/graph/tr_run.sh b/tools/kal-test/bin/graph/tr_run.sh
deleted file mode 100644
index 4d8d407..0000000
--- a/tools/kal-test/bin/graph/tr_run.sh
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name><api name><seeds count>"
-  echo "1st argument: name of dataset: name of dataset: e.g. cit_patents"
-  echo "2nd argument: name of api: e.g. run,runUntilConvergence"
-  echo "3nd argument: seeds count: e.g. 100,500,1000"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ];then
-  echo "Usage:<dataset name><api name><seeds count>"
- 	echo "dataset name:cit_patents,uk_2002,arabic_2005"
- 	echo "api name:run,runUntilConvergence"
-  echo "seeds count:100,500,1000"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/tr/tr_spark.properties
-
-dataset_name=$1
-api_name=$2
-seedsCount=$3
-
-if [ ${dataset_name} != "cit_patents" ] &&
-   [ ${dataset_name} != "uk_2002" ] &&
-   [ ${dataset_name} != "arabic_2005" ] ;then
-  echo "invalid dataset name,dataset name:cit_patents,uk_2002,arabic_2005"
-  exit 1
-fi
-if [ ${api_name} != "run" ] &&
-   [ ${api_name} != "runUntilConvergence" ] ;then
-  echo "invalid argument value,api name: run,runUntilConvergence"
-  exit 1
-fi
-if [ ${seedsCount} != "100" ] && [ ${seedsCount} != "500" ] && [ ${seedsCount} != "1000" ];then
-  echo "invalid argument value,must be: 100 or 500 or 1000"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graph/result/tr/${dataset_name}_${seedsCount}_${api_name}"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-# concatnate strings as a new variable
-num_executors="${api_name}_${dataset_name}_${seedsCount}_numExecutors_${cpu_name}"
-executor_cores="${api_name}_${dataset_name}_${seedsCount}_executorCores_${cpu_name}"
-executor_memory="${api_name}_${dataset_name}_${seedsCount}_executorMemory_${cpu_name}"
-num_partitions="${api_name}_${dataset_name}_${seedsCount}_numPartitions_${cpu_name}"
-extra_Java_Options="${api_name}_${dataset_name}_${seedsCount}_extraJavaOptions_${cpu_name}"
-deploy_mode="deployMode"
-driver_memory="driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-extra_Java_Options_val=${!extra_Java_Options}
-driver_memory_val=${!driver_memory}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "${driver_memory}:${driver_memory_val}"
-echo "${extra_Java_Options}:${extra_Java_Options_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${num_partitions_val} ] ; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs"
-spark-submit \
---class com.bigdata.graph.TrustRankRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory ${driver_memory_val} \
---conf spark.executor.extraJavaOptions=${extra_Java_Options_val} \
---conf spark.driver.maxResultSize=200g \
---conf spark.driver.extraJavaOptions="-Xms100G" \
---conf spark.locality.wait.node=0 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${num_partitions_val} "no" ${data_path_val} ${api_name} ${seedsCount}| tee ./log/log
diff --git a/tools/kal-test/bin/graph/wce_run.sh b/tools/kal-test/bin/graph/wce_run.sh
deleted file mode 100755
index 72ef27d..0000000
--- a/tools/kal-test/bin/graph/wce_run.sh
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. grah500_22"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. graph500_22"
-  exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-
-source conf/graph/wce/wce_spark.properties
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-dataset_name=$1
-
-hdfs dfs -rm -r -f /tmp/result/${dataset_name}
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors="${dataset_name}_numExectuors_${cpu_name}"
-executor_cores="${dataset_name}_executorCores_${cpu_name}"
-executor_memory="${dataset_name}_executorMemory_${cpu_name}"
-extra_java_options="${dataset_name}_extraJavaOptions_${cpu_name}"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-mkdir -p log
-echo "start to submit spark jobs"
-
-spark-submit \
---class com.bigdata.graph.WCERunner \
---driver-memory 80g \
---master yarn \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} | tee ./log/log
diff --git a/tools/kal-test/bin/graph/wce_run_hive.sh b/tools/kal-test/bin/graph/wce_run_hive.sh
deleted file mode 100755
index 0ceb8e0..0000000
--- a/tools/kal-test/bin/graph/wce_run_hive.sh
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
- echo "Usage:<table name> <col1> <col2> <maxIter> <maxDegree> <save_mode> <save_arg>"
- exit 0
- ;;
-esac
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-if [ ${cpu_name} == "aarch64" ]
-then
-  cpu_name="aarch_64"
-fi
-
-source conf/graph/graph_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-
-table_name=$1
-col1=$2
-col2=$3
-maxIter=$4
-maxDegree=$5
-save_mode=$6
-save_arg=$7
-
-spark-submit \
---class com.bigdata.graph.WCEHiveRunner \
---driver-memory 80g \
---master yarn \
---num-executors 35 \
---executor-cores 8 \
---executor-memory "25g" \
---conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
---conf spark.rdd.compress=true \
---conf spark.shuffle.compress=true \
---conf spark.shuffle.spill.compress=true \
---conf spark.io.compression.codec=lz4 \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/kal-test_2.11-0.1.jar:lib/boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${table_name} ${col1} ${col2} ${maxIter} ${maxDegree} ${save_mode} ${save_arg}
diff --git a/tools/kal-test/bin/graph/wpr_run.sh b/tools/kal-test/bin/graph/wpr_run.sh
deleted file mode 100644
index d727cb2..0000000
--- a/tools/kal-test/bin/graph/wpr_run.sh
+++ /dev/null
@@ -1,124 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. cage14"
-  echo "2nd argument: name of api: e.g. static"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ];then
-  echo "Usage:<dataset name><api name>"
- 	echo "dataset name:GAP_road or cage14"
-  echo "api name:static  or convergence"
-	exit 0
-fi
-
-current_path=$(dirname $(readlink -f "$0"))
-echo "current folder path: ${current_path}"
-BIN_DIR="$(cd "$(dirname "$0")";pwd)"
-CONF_DIR="${BIN_DIR}/../../conf/graph"
-source conf/graph/wpr/wpr_spark.properties
-
-dataset_name=$1
-api_name=$2
-
-if [ ${dataset_name} != "GAP_road" ] && [ ${dataset_name} != "cage14" ];then
-  echo "invalid dataset name,dataset name:GAP_road,or cage14"
-  exit 1
-fi
-if [ ${api_name} != "static" ] && [ ${api_name} != "convergence" ];then
-  echo "invalid api name,api name: static or convergence"
-  exit 1
-fi
-
-hdfs dfs -rm -r -f "/tmp/graphhData/wpr/output"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-prefix="run"
-if [ ${api_name} == "runUntilConvergence" ]
-then
-  prefix="convergence"
-fi
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-executor_memory_overhead="execMemOverhead_"${cpu_name}
-num_partitions="numPartitions_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-echo $num_executors
-echo $executor_cores
-echo $executor_memory
-echo $extra_java_options
-echo $num_partitions
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-deploy_mode_val=${!deploy_mode}
-num_partitions_val=${!num_partitions}
-
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-
-if [ ! ${num_executors_val} ] ||
-  [ ! ${executor_cores_val} ] ||
-  [ ! ${executor_memory_val} ] ||
-  [ ! ${extra_java_options_val} ] ||
-  [ ! ${num_partitions_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/graph/graph_datasets.properties
-kal_version=kalVersion
-kal_version_val=${!kal_version}
-
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-
-
-echo "start to submit spark jobs"
-echo "start to clean cache and sleep 3s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 3
-
-mkdir -p log
-spark-submit \
---class com.bigdata.graph.WeightedPageRankRunner \
---master yarn \
---deploy-mode ${deploy_mode_val} \
---num-executors ${num_executors_val} \
---executor-memory ${executor_memory_val} \
---executor-cores ${executor_cores_val} \
---driver-memory 100g \
---conf spark.driver.maxResultSize=200g \
---conf spark.driver.extraJavaOptions="-Xms100G" \
---conf spark.locality.wait.node=0 \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf spark.shuffle.blockTransferService=nio \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-graph-kernel-2.11-${kal_version_val}-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-graph-kernel-2.11-${kal_version_val}-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-graph-kernel-2.11-${kal_version_val}-{$spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${dataset_name} ${api_name} "no" | tee ./log/log
-
diff --git a/tools/kal-test/bin/ml/als_run.sh b/tools/kal-test/bin/ml/als_run.sh
deleted file mode 100755
index ccbc43f..0000000
--- a/tools/kal-test/bin/ml/als_run.sh
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. als"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. als"
-  exit 0
-fi
-
-
-source conf/ml/als/als_spark.properties
-
-data_structure=$1
-dataset_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${data_structure}_${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs --- ALS-${model_conf}"
-spark-submit \
---class com.bigdata.ml.ALSRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---driver-java-options "-Xms20g -Xss5g" \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=-Xms20g -Xss5g" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.driver.maxResultSize=256G" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/als_run_raw.sh b/tools/kal-test/bin/ml/als_run_raw.sh
deleted file mode 100755
index f78398e..0000000
--- a/tools/kal-test/bin/ml/als_run_raw.sh
+++ /dev/null
@@ -1,102 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. als"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. als"
-  exit 0
-fi
-
-
-source conf/ml/als/als_spark.properties
-
-data_structure=$1
-dataset_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${data_structure}_${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs --- ALS_raw-${model_conf}"
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.ALSRunner \
---driver-java-options "-Xms15g -Xss5g" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=-Xms20g -Xss5g" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/cov_run.sh b/tools/kal-test/bin/ml/cov_run.sh
deleted file mode 100644
index 5aafe56..0000000
--- a/tools/kal-test/bin/ml/cov_run.sh
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 arguments: <dataset name>"
-  echo "argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-fi
-
-source conf/ml/cov/cov_spark.properties
-
-dataset_name=$1
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-${platform_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${platform_name}
-executor_cores="executorCores_"${platform_name}
-executor_memory="executorMemory_"${platform_name}
-executor_memory_overhead="executorMemOverhead_"${platform_name}
-extra_java_options="extraJavaOptions_"${platform_name}
-driver_max_result_size="driverMaxResultSize"
-driver_cores="driverCores_"${platform_name}
-driver_memory="driverMemory_"${platform_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_max_result_size_val=${!driver_max_result_size}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${driver_max_result_size} : ${driver_max_result_size_val}"
-
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${driver_max_result_size_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- Cov-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---class com.bigdata.ml.CovRunner \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.driver.maxResultSize=${driver_max_result_size_val}" \
---conf "spark.network.timeout=3600s" \
---conf "spark.executor.heartbeatInterval=1000s" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
-
diff --git a/tools/kal-test/bin/ml/cov_run_raw.sh b/tools/kal-test/bin/ml/cov_run_raw.sh
deleted file mode 100644
index 1995e37..0000000
--- a/tools/kal-test/bin/ml/cov_run_raw.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 arguments: <dataset name>"
-  echo "argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-fi
-
-source conf/ml/cov/cov_spark.properties
-
-dataset_name=$1
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-"raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${platform_name}
-executor_cores="executorCores_"${platform_name}
-executor_memory="executorMemory_"${platform_name}
-executor_memory_overhead="executorMemOverhead_"${platform_name}
-extra_java_options="extraJavaOptions_"${platform_name}
-driver_max_result_size="driverMaxResultSize"
-driver_cores="driverCores_"${platform_name}
-driver_memory="driverMemory_"${platform_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_max_result_size_val=${!driver_max_result_size}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${driver_max_result_size} : ${driver_max_result_size_val}"
-
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${driver_max_result_size_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- Cov-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar" \
---class com.bigdata.ml.CovRunner \
---jars "lib/fastutil-8.3.1.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.driver.maxResultSize=${driver_max_result_size_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/dbscan_run.sh b/tools/kal-test/bin/ml/dbscan_run.sh
deleted file mode 100644
index a700fef..0000000
--- a/tools/kal-test/bin/ml/dbscan_run.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "argument: name of dataset: e.g. bremenSmall/farm/house"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 arguments: <dataset name>"
-  echo "argument: name of dataset: e.g. bremenSmall/farm/house"
-  exit 0
-fi
-
-source conf/ml/dbscan/dbscan_spark.properties
-
-dataset_name=$1
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-${platform_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${platform_name}
-executor_cores="executorCores_"${platform_name}
-executor_memory="executorMemory_"${platform_name}
-extra_java_options="extraJavaOptions_"${platform_name}
-driver_cores="driverCores_"${platform_name}
-driver_memory="driverMemory_"${platform_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- DBSCAN-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---class org.apache.spark.ml.clustering.DBSCANRunner \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.task.maxFailures=1" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
-
diff --git a/tools/kal-test/bin/ml/dbscan_run_opensource.sh b/tools/kal-test/bin/ml/dbscan_run_opensource.sh
deleted file mode 100644
index e904de3..0000000
--- a/tools/kal-test/bin/ml/dbscan_run_opensource.sh
+++ /dev/null
@@ -1,124 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "argument: name of dataset: e.g. bremenSmall/farm/house"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 arguments: <dataset name>"
-  echo "argument: name of dataset: e.g. bremenSmall/farm/house"
-  exit 0
-fi
-
-source conf/ml/dbscan/dbscan_spark_opensource.properties
-
-outputPath="hdfs:///tmp/ml/result/dbscan/alitoukaDBSCAN/output_${dataset_name}"
-hdfsJarPath="hdfs:///tmp/ml/test/dbscan"
-
-hdfs dfs -rm -r -f ${outputPath}
-hdfs dfs -mkdir -p ${hdfsJarPath}
-hdfs dfs -ls ${hdfsJarPath}
-if [ $? -eq 0 ];then
-  hdfs dfs -rm -r -f ${hdfsJarPath}/alitouka_dbscan_2.11-0.1.jar
-  hdfs dfs -put ./lib/alitouka_dbscan_2.11-0.1.jar ${hdfsJarPath}
-fi
-
-dataset_name=$1
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-"opensource"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${platform_name}
-executor_cores="executorCores_"${platform_name}
-executor_memory="executorMemory_"${platform_name}
-extra_java_options="extraJavaOptions_"${platform_name}
-driver_max_result_size="driverMaxResultSize"
-driver_cores="driverCores_"${platform_name}
-driver_memory="driverMemory_"${platform_name}
-master_="master"
-deploy_mode="deployMode"
-epsilon="epsilon_"${dataset_name}
-min_points="minPoints_"${dataset_name}
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_max_result_size_val=${!driver_max_result_size}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-epsilon_val=${!epsilon}
-min_points_val=${!min_points}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${driver_max_result_size} : ${driver_max_result_size_val}"
-echo "${epsilon} : ${epsilon_val}"
-echo "${min_points} : ${min_points_val}"
-
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${driver_max_result_size_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${epsilon_val} ] \
-    || [ ! ${min_points_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- DBSCAN-${model_conf}"
-
-spark-submit \
---jars "lib/scopt_2.11-3.5.0.jar" \
---class org.alitouka.spark.dbscan.DbscanDriver \
---deploy-mode ${deploy_mode_val} \
---name "alitouka_DBSCAN_${model_conf}" \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.driver.maxResultSize=${driver_max_result_size_val}" \
-${hdfsJarPath}/alitouka_dbscan_2.11-0.1.jar --ds-master ${master_val} --ds-jar ${hdfsJarPath}/alitouka_dbscan_2.11-0.1.jar --ds-input ${data_path} --ds-output ${outputPath} --eps ${epsilon_val} --numPts ${min_points_val} >dbscan_tmp.log
-CostTime=$(cat dbscan_tmp.log | grep "train total" | awk '{print $3}')
-currentTime=$(date "+%Y%m%d_%H%M%S")
-rm -rf dbscan_tmp.log
-echo -e "algorithmName: DBSCAN\ncostTime: ${CostTime}\ndatasetName: ${dataset_name}\nisRaw: 'yes'\ntestcaseType: DBSCAN_opensource_${dataset_name}\n" > ./report/"DBSCAN_${currentTime}.yml"
-echo "Exec Successful: costTime: ${CostTime}" > ./log/log
diff --git a/tools/kal-test/bin/ml/dt_run.sh b/tools/kal-test/bin/ml/dt_run.sh
deleted file mode 100755
index f9adcab..0000000
--- a/tools/kal-test/bin/ml/dt_run.sh
+++ /dev/null
@@ -1,125 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for dataframe: fit/fit1; for rdd: trainClassifier/trainRegressor]"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 4 ]; then
-  echo "please input 4 arguments: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for dataframe: fit/fit1; for rdd: trainClassifier/trainRegressor]"
-  exit 0
-fi
-
-
-source conf/ml/dt/dt_spark.properties
-
-algorithm_type=$1
-data_structure=$2
-dataset_name=$3
-api_name=$4
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-model_conf=${algorithm_type}_${data_structure}_${dataset_name}_${api_name}
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}_${algorithm_type}"_"${dataset_name}"_numExectuors"
-executor_cores=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorCores"
-executor_memory=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorMemory"
-extra_java_options=${cpu_name}_${algorithm_type}"_"${dataset_name}"_extraJavaOptions"
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-max_failures="maxFailures"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-max_failures_val=${!max_failures}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${max_failures} : ${max_failures_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${max_failures_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-echo "start to submit spark jobs"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.DTRunner \
---driver-java-options "-Xms15g" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.taskmaxFailures=${max_failures_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/dt_run_raw.sh b/tools/kal-test/bin/ml/dt_run_raw.sh
deleted file mode 100755
index b8f273f..0000000
--- a/tools/kal-test/bin/ml/dt_run_raw.sh
+++ /dev/null
@@ -1,123 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for dataframe: fit/fit1; for rdd: trainClassifier/trainRegressor]"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 4 ]; then
-  echo "please input 4 arguments: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for dataframe: fit/fit1; for rdd: trainClassifier/trainRegressor]"
-  exit 0
-fi
-
-
-source conf/ml/dt/dt_spark_raw.properties
-
-algorithm_type=$1
-data_structure=$2
-dataset_name=$3
-api_name=$4
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-model_conf=${algorithm_type}_${data_structure}_${dataset_name}_${api_name}
-
-# concatnate strings as a new variable
-num_executors=${algorithm_type}"_"${dataset_name}"_numExectuors"
-executor_cores=${algorithm_type}"_"${dataset_name}"_executorCores"
-executor_memory=${algorithm_type}"_"${dataset_name}"_executorMemory"
-extra_java_options=${algorithm_type}"_"${dataset_name}"_extraJavaOptions"
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-max_failures="maxFailures"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-max_failures_val=${!max_failures}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${max_failures} : ${max_failures_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${max_failures_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-echo "start to submit spark jobs"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.DTRunner \
---driver-java-options "-Xms15g" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.taskmaxFailures=${max_failures_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/gbdt_run.sh b/tools/kal-test/bin/ml/gbdt_run.sh
deleted file mode 100755
index 685505c..0000000
--- a/tools/kal-test/bin/ml/gbdt_run.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: e.g. fit"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 4 ]; then
-  echo "please input 3 arguments: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: e.g. fit"
-  exit 0
-fi
-
-
-source conf/ml/gbdt/gbdt_spark.properties
-
-algorithm_type=$1
-data_structure=$2
-dataset_name=$3
-api_name=$4
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${algorithm_type}-${data_structure}-${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors"
-executor_cores=${dataset_name}"_executorCores_"${cpu_name}
-executor_memory="executorMemory"
-extra_java_options="extraJavaOptions"
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-echo "start to submit spark jobs"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.GBDTRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.driver.maxResultSize=256G" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/gbdt_run_raw.sh b/tools/kal-test/bin/ml/gbdt_run_raw.sh
deleted file mode 100755
index 5458f22..0000000
--- a/tools/kal-test/bin/ml/gbdt_run_raw.sh
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: e.g. fit"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 4 ]; then
-  echo "please input 3 arguments: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: e.g. fit"
-  exit 0
-fi
-
-
-source conf/ml/gbdt/gbdt_spark.properties
-
-algorithm_type=$1
-data_structure=$2
-dataset_name=$3
-api_name=$4
-
-model_conf=${algorithm_type}-${data_structure}-${dataset_name}-${api_name}"-raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors"
-executor_cores=${dataset_name}"_executorCores_x86_64"
-executor_memory="executorMemory"
-extra_java_options="extraJavaOptions"
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-echo "start to submit spark jobs"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar" \
---class com.bigdata.ml.GBDTRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/idf_run.sh b/tools/kal-test/bin/ml/idf_run.sh
deleted file mode 100755
index deafdd4..0000000
--- a/tools/kal-test/bin/ml/idf_run.sh
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/bin/bash
-set -e
-
-ifRaw="opt"
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <if checkModel>"
-  echo "1rd argument: name of dataset: e.g. D10m200m"
-  echo "2st argument: if checkModel: e.g. false"
-  echo "Note: If you are sure you want to check the result(model), please run the idf_run_raw.sh script at least once first."
-  echo "This script will save the model in \${workspace}/output/models/\${datasetName}/${ifRaw}/"
-  exit 0
-esac
-
-if [ $# -ne 2 ]; then
-  echo "Usage: <dataset name> <if checkModel>"
-  echo "1rd argument: name of dataset: e.g. D10m200m"
-  echo "2st argument: if checkModel: e.g. false"
-  echo "Note: If you are sure you want to check the result(model), please run the idf_run_raw.sh script at least once first."
-  echo "This script will save the model in \${workspace}/output/models/\${datasetName}/${ifRaw}/"
-  exit 0
-fi
-
-
-source conf/ml/idf/idf_spark.properties
-
-dataset_name=$1
-ifCheckModel=$2
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}_${ifRaw}"_"${dataset_name}"_numExecutors"
-executor_cores=${cpu_name}_${ifRaw}"_"${dataset_name}"_executorCores"
-executor_memory=${cpu_name}_${ifRaw}"_"${dataset_name}"_executorMemory"
-executor_extra_java_options=${cpu_name}_${ifRaw}"_"${dataset_name}"_extraJavaOptions"
-driver_cores=${cpu_name}_${ifRaw}"_"${dataset_name}"_driverCores"
-driver_memory=${cpu_name}_${ifRaw}"_"${dataset_name}"_driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_extra_java_options_val=${!executor_extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-
-echo "master : ${master}"
-echo "deployMode : ${deployMode}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_extra_java_options} : ${executor_extra_java_options_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master} ] \
-    || [ ! ${deployMode} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-models_path=${dataset_name}_modelsPath
-models_path_val=${!models_path}
-echo "${dataset_name} : ${data_path_val}"
-echo "spark_version : ${spark_version_val}"
-echo "Is raw? ${ifRaw}"
-echo "If checkoutModels? ${ifCheckModel}"
-echo "models_path : ${models_path_val}"
-echo "start to submit spark jobs"
-
-echo "start to clean cache and sleep 3s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 3
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.IDFRunner \
---master ${master} \
---deploy-mode ${deployMode} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---conf spark.executor.extraJavaOptions=${executor_extra_java_options_val} \
---driver-java-options "-Xms15g" \
---driver-class-path "lib/json4s-ext_2.11-3.2.11.jar:lib/boostkit-ml-acc_2.11-${kalVersion}-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-${kalVersion}-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-${kalVersion}-${spark_version_val}-${cpu_name}.jar" \
---jars "lib/json4s-ext_2.11-3.2.11.jar,lib/boostkit-ml-acc_2.11-${kalVersion}-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-${kalVersion}-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-${kalVersion}-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${ifRaw} ${dataset_name} ${data_path_val} ${models_path_val} ${ifCheckModel}| tee ./log/log
diff --git a/tools/kal-test/bin/ml/idf_run_raw.sh b/tools/kal-test/bin/ml/idf_run_raw.sh
deleted file mode 100755
index 88bc9d8..0000000
--- a/tools/kal-test/bin/ml/idf_run_raw.sh
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/bin/bash
-set -e
-
-ifRaw="pri"
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1rd argument: name of dataset: e.g. D10m200m"
-  echo "This script will save the model in \${workspace}/output/models/\${datasetName}/${ifRaw}/"
-  exit 0
-esac
-
-if [ $# -ne 1 ]; then
-  echo "Usage: <dataset name>"
-  echo "1rd argument: name of dataset: e.g. D10m200m"
-  echo "This script will save the model in \${workspace}/output/models/\${datasetName}/${ifRaw}/"
-  exit 0
-fi
-
-
-source conf/ml/idf/idf_spark.properties
-
-dataset_name=$1
-ifCheckModel="false"
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}_${ifRaw}"_"${dataset_name}"_numExecutors"
-executor_cores=${cpu_name}_${ifRaw}"_"${dataset_name}"_executorCores"
-executor_memory=${cpu_name}_${ifRaw}"_"${dataset_name}"_executorMemory"
-executor_extra_java_options=${cpu_name}_${ifRaw}"_"${dataset_name}"_extraJavaOptions"
-driver_cores=${cpu_name}_${ifRaw}"_"${dataset_name}"_driverCores"
-driver_memory=${cpu_name}_${ifRaw}"_"${dataset_name}"_driverMemory"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_extra_java_options_val=${!executor_extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-
-
-echo "master : ${master}"
-echo "deploy_mode : ${deployMode}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_extra_java_options} : ${executor_extra_java_options_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master} ] \
-    || [ ! ${deployMode} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-models_path=${dataset_name}_modelsPath
-models_path_val=${!models_path}
-echo "${dataset_name} : ${data_path_val}"
-echo "spark_version : ${spark_version_val}"
-echo "Is raw? -${ifRaw}"
-echo "If checkoutModels? -${ifCheckModel}"
-echo "models_path : ${models_path_val}"
-echo "start to submit spark jobs"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 3
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.IDFRunner \
---master ${master} \
---deploy-mode ${deployMode} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---conf spark.executor.extraJavaOptions=${executor_extra_java_options_val} \
---driver-java-options "-Xms15g" \
---jars "lib/json4s-ext_2.11-3.2.11.jar,lib/fastutil-8.3.1.jar" \
-./lib/kal-test_2.11-0.1.jar ${ifRaw} ${dataset_name} ${data_path_val} ${models_path_val} ${ifCheckModel}| tee ./log/log
diff --git a/tools/kal-test/bin/ml/kmeans_run.sh b/tools/kal-test/bin/ml/kmeans_run.sh
deleted file mode 100755
index 034d2a4..0000000
--- a/tools/kal-test/bin/ml/kmeans_run.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D200M20"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D200M20"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/kmeans/kmeans_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-driver_java_options="driverJavaOptions_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_java_options_val=${!driver_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${driver_java_options} : ${driver_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- KMeans-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.KMeansRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---driver-java-options ${driver_java_options_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/mahout-core-0.9.jar,lib/mahout-math-0.9.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/mahout-math-0.9.jar:lib/mahout-core-0.9.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:lib/mahout-core-0.9.jar:lib/mahout-math-0.9.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/kmeans_run_raw.sh b/tools/kal-test/bin/ml/kmeans_run_raw.sh
deleted file mode 100755
index c485d30..0000000
--- a/tools/kal-test/bin/ml/kmeans_run_raw.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D200M20"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D200M20"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/kmeans/kmeans_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}"-raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-driver_java_options="driverJavaOptions_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_java_options_val=${!driver_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${driver_java_options} : ${driver_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- KMeans-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.KMeansRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---driver-java-options ${driver_java_options_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/mahout-core-0.9.jar,lib/mahout-math-0.9.jar" \
---driver-class-path "lib/mahout-math-0.9.jar:lib/mahout-core-0.9.jar:lib/snakeyaml-1.19.jar" \
---conf "spark.executor.extraClassPath=lib/mahout-core-0.9.jar:lib/mahout-math-0.9.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/knn_run.sh b/tools/kal-test/bin/ml/knn_run.sh
deleted file mode 100644
index ebe9907..0000000
--- a/tools/kal-test/bin/ml/knn_run.sh
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. glove"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. glove"
-  exit 0
-fi
-
-
-source conf/ml/knn/knn_spark.properties
-
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-echo "start to submit spark jobs --- KNN-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.ml.KNNRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/knn_run_raw.sh b/tools/kal-test/bin/ml/knn_run_raw.sh
deleted file mode 100644
index f6d065d..0000000
--- a/tools/kal-test/bin/ml/knn_run_raw.sh
+++ /dev/null
@@ -1,111 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. glove"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. glove"
-  exit 0
-fi
-
-
-source conf/ml/knn/knn_spark_raw.properties
-
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-echo "start to submit spark jobs --- KNN-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.ml.KNNRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/lda_run.sh b/tools/kal-test/bin/ml/lda_run.sh
deleted file mode 100644
index 3fa8d1b..0000000
--- a/tools/kal-test/bin/ml/lda_run.sh
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. nytimes/pubmed/D20M200K"
-  echo "3rd argument: name of API: e.g. fit/run"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. nytimes/pubmed/D20M200K"
-  echo "3rd argument: name of API: e.g. fit/run"
-  exit 0
-fi
-
-source conf/ml/lda/lda_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- LDA-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---class com.bigdata.ml.LDARunner \
---jars "lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.driver.cores=${driver_cores_val}" \
---conf "spark.task.cpus=${executor_cores_val}" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/lda_run_raw.sh b/tools/kal-test/bin/ml/lda_run_raw.sh
deleted file mode 100644
index dab2aa2..0000000
--- a/tools/kal-test/bin/ml/lda_run_raw.sh
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. nytimes/pubmed/D20M200K"
-  echo "3rd argument: name of API: e.g. fit/run"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. nytimes/pubmed/D20M200K"
-  echo "3rd argument: name of API: e.g. fit/run"
-  exit 0
-fi
-
-source conf/ml/lda/lda_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}"-raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- LDA-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar" \
---class com.bigdata.ml.LDARunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/linR_run.sh b/tools/kal-test/bin/ml/linR_run.sh
deleted file mode 100644
index 0b27ea2..0000000
--- a/tools/kal-test/bin/ml/linR_run.sh
+++ /dev/null
@@ -1,115 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments:<dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/linR/linR_spark.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}_${api_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-max_failures="maxFailures"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-max_failures_val=${!max_failures}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${max_failures} : ${max_failures_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${max_failures_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-echo "start to submit spark jobs --- LinR-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.ml.LinRRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.taskmaxFailures=${max_failures_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf}
diff --git a/tools/kal-test/bin/ml/linR_run_raw.sh b/tools/kal-test/bin/ml/linR_run_raw.sh
deleted file mode 100644
index 617478a..0000000
--- a/tools/kal-test/bin/ml/linR_run_raw.sh
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments:<dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/linR/linR_spark_raw.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}_${api_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-max_failures="maxFailures"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-max_failures_val=${!max_failures}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${max_failures} : ${max_failures_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${max_failures_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-echo "start to submit spark jobs --- LinR-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.LinRRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.taskmaxFailures=${max_failures_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf}
diff --git a/tools/kal-test/bin/ml/logR_run.sh b/tools/kal-test/bin/ml/logR_run.sh
deleted file mode 100644
index ce8a9bc..0000000
--- a/tools/kal-test/bin/ml/logR_run.sh
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments:<dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/logR/logR_spark.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- LogR-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.LogRRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/logR_run_raw.sh b/tools/kal-test/bin/ml/logR_run_raw.sh
deleted file mode 100644
index d36bb71..0000000
--- a/tools/kal-test/bin/ml/logR_run_raw.sh
+++ /dev/null
@@ -1,97 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments:<dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. mnist8m"
-  echo "2nd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/logR/logR_spark.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}-${api_name}-"raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- LogR-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.LogRRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/pca_run.sh b/tools/kal-test/bin/ml/pca_run.sh
deleted file mode 100644
index f13643a..0000000
--- a/tools/kal-test/bin/ml/pca_run.sh
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D10M1K"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D10M1K"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/pca/pca_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors=${dataset_name}"_numExectuors_"${cpu_name}
-executor_cores=${dataset_name}"_executorCores_"${cpu_name}
-executor_memory=${dataset_name}"_executorMemory_"${cpu_name}
-extra_java_options=${dataset_name}"_extraJavaOptions_"${cpu_name}
-driver_cores=${dataset_name}"_driverCores_"${cpu_name}
-driver_memory=${dataset_name}"_driverMemory_"${cpu_name}
-executor_memory_overhead=${dataset_name}"_executorMemoryOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- PCA-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.PCARunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/pca_run_kml.sh b/tools/kal-test/bin/ml/pca_run_kml.sh
deleted file mode 100644
index bfb6d34..0000000
--- a/tools/kal-test/bin/ml/pca_run_kml.sh
+++ /dev/null
@@ -1,112 +0,0 @@
-#!/bin/bash
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D10M1K"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D10M1K"
-  echo "3rd argument: name of API: e.g. fit/fit1"
-  exit 0
-fi
-
-
-source conf/ml/pca/pca_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors=${dataset_name}"_numExectuors_"${cpu_name}
-executor_cores=${dataset_name}"_executorCores_"${cpu_name}
-executor_memory=${dataset_name}"_executorMemory_"${cpu_name}
-extra_java_options=${dataset_name}"_extraJavaOptions_"${cpu_name}
-driver_cores=${dataset_name}"_driverCores_"${cpu_name}
-driver_memory=${dataset_name}"_driverMemory_"${cpu_name}
-executor_memory_overhead=${dataset_name}"_executorMemoryOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-scp lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar root@agent1:/opt/lib/ml
-scp lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar root@agent2:/opt/lib/ml
-scp lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar root@agent3:/opt/lib/ml
-
-
-echo "start to submit spark jobs --- PCA-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.PCARunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.driver.extraLibraryPath=/opt/netlib" \
---conf "spark.executor.extraLibraryPath=/opt/netlib" \
---conf "spark.driver.extraClassPath=/opt/netlib/*:lib/kal-test_2.11-0.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=/opt/lib/ml/*:/opt/netlib/*" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/pca_run_raw.sh b/tools/kal-test/bin/ml/pca_run_raw.sh
deleted file mode 100644
index 252bd00..0000000
--- a/tools/kal-test/bin/ml/pca_run_raw.sh
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D10M1K"
-  echo "3rd argument: name of API: e.g. fit"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 3 ]; then
-  echo "please input 3 arguments: <data structure> <dataset name> <api name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. D10M1K"
-  echo "3rd argument: name of API: e.g. fit"
-  exit 0
-fi
-
-
-source conf/ml/pca/pca_spark.properties
-
-data_structure=$1
-dataset_name=$2
-api_name=$3
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${data_structure}-${dataset_name}-${api_name}-"raw"
-
-# concatnate strings as a new variable
-num_executors=${dataset_name}"_numExectuors_"${cpu_name}
-executor_cores=${dataset_name}"_executorCores_"${cpu_name}
-executor_memory=${dataset_name}"_executorMemory_"${cpu_name}
-extra_java_options=${dataset_name}"_extraJavaOptions_"${cpu_name}
-driver_cores=${dataset_name}"_driverCores_"${cpu_name}
-driver_memory=${dataset_name}"_driverMemory_"${cpu_name}
-executor_memory_overhead=${dataset_name}"_executorMemoryOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- PCA-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar" \
---class com.bigdata.ml.PCARunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/pearson_run.sh b/tools/kal-test/bin/ml/pearson_run.sh
deleted file mode 100755
index 28f32e8..0000000
--- a/tools/kal-test/bin/ml/pearson_run.sh
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K"
-  exit 0
-fi
-
-
-source conf/ml/pearson/pearson_spark.properties
-
-data_structure=$1
-dataset_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${data_structure}_${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-memory_overhead="execMemOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-dataset_output_=${dataset_name}"_output"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs --- Pearson-${model_conf}"
-spark-submit \
---class com.bigdata.ml.PearsonRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memoryOverhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/pearson_run_raw.sh b/tools/kal-test/bin/ml/pearson_run_raw.sh
deleted file mode 100755
index bc2fc56..0000000
--- a/tools/kal-test/bin/ml/pearson_run_raw.sh
+++ /dev/null
@@ -1,115 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K"
-  exit 0
-fi
-
-
-source conf/ml/pearson/pearson_spark_raw.properties
-
-data_structure=$1
-dataset_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${data_structure}_${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-memory_overhead="execMemOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-dataset_output_=${dataset_name}"_output"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-echo "start to submit spark jobs --- Pearson_raw-${model_conf}"
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar" \
---jars "lib/snakeyaml-1.19.jar,lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.PearsonRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memoryOverhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/ps_run.sh b/tools/kal-test/bin/ml/ps_run.sh
deleted file mode 100644
index 70ad9f0..0000000
--- a/tools/kal-test/bin/ml/ps_run.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "argument: name of dataset: e.g. kosarak/IBM10M47/IBM100M47"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 arguments: <dataset name>"
-  echo "argument: name of dataset: e.g. kosarak/IBM10M47/IBM100M47"
-  exit 0
-fi
-
-source conf/ml/ps/ps_spark.properties
-
-dataset_name=$1
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-${platform_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${platform_name}
-executor_cores="executorCores_"${platform_name}
-executor_memory="executorMemory_"${platform_name}
-extra_java_options="extraJavaOptions_"${platform_name}
-driver_cores="driverCores_"${platform_name}
-driver_memory="driverMemory_"${platform_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- PrefixSpan-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---class com.bigdata.ml.PrefixSpanRunner \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.rdd.compress=false" \
---conf "spark.network.timeout=600s" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/ps_run_raw.sh b/tools/kal-test/bin/ml/ps_run_raw.sh
deleted file mode 100644
index 9ce5d2f..0000000
--- a/tools/kal-test/bin/ml/ps_run_raw.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "argument: name of dataset: e.g. kosarak/IBM10M47/IBM100M47"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 arguments: <dataset name>"
-  echo "argument: name of dataset: e.g. kosarak/IBM10M47/IBM100M47"
-  exit 0
-fi
-
-source conf/ml/ps/ps_spark.properties
-
-dataset_name=$1
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-"raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${platform_name}
-executor_cores="executorCores_"${platform_name}
-executor_memory="executorMemory_"${platform_name}
-extra_java_options="extraJavaOptions_"${platform_name}
-driver_cores="driverCores_"${platform_name}
-driver_memory="driverMemory_"${platform_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- PrefixSpan-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar" \
---class com.bigdata.ml.PrefixSpanRunner \
---jars "lib/fastutil-8.3.1.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.rdd.compress=false" \
---conf "spark.network.timeout=600s" \
---conf "spark.driver.maxResultSize=256G" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/rf_run.sh b/tools/kal-test/bin/ml/rf_run.sh
deleted file mode 100755
index a18eb95..0000000
--- a/tools/kal-test/bin/ml/rf_run.sh
+++ /dev/null
@@ -1,126 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for classification: fit/fit1/fit2/fit3; for regression: train/train1/train2]"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 4 ]; then
-  echo "please input 4 arguments: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for classification: fit/fit1/fit2/fit3; for regression: train/train1/train2]"
-  exit 0
-fi
-
-
-source conf/ml/rf/rf_spark.properties
-
-algorithm_type=$1
-data_structure=$2
-dataset_name=$3
-api_name=$4
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-
-model_conf=${algorithm_type}_${data_structure}_${dataset_name}_${api_name}
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}_${algorithm_type}"_"${dataset_name}"_numExectuors"
-executor_cores=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorCores"
-executor_memory=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorMemory"
-extra_java_options=${cpu_name}_${algorithm_type}"_"${dataset_name}"_extraJavaOptions"
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-max_failures="maxFailures"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-max_failures_val=${!max_failures}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${max_failures} : ${max_failures_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${max_failures_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-echo "start to submit spark jobs"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.RFRunner \
---driver-java-options "-Xms15g" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.taskmaxFailures=${max_failures_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/rf_run_raw.sh b/tools/kal-test/bin/ml/rf_run_raw.sh
deleted file mode 100755
index a802e7e..0000000
--- a/tools/kal-test/bin/ml/rf_run_raw.sh
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for classification: fit/fit1/fit2/fit3; for regression: train/train1/train2]"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 4 ]; then
-  echo "please input 4 arguments: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: type of algorithm: [classification/regression]"
-  echo "2nd argument: type of data structure: [dataframe/rdd]"
-  echo "3rd argument: name of dataset: e.g. epsilon"
-  echo "4th argument: name of API: [for classification: fit/fit1/fit2/fit3; for regression: train/train1/train2]"
-  exit 0
-fi
-
-
-source conf/ml/rf/rf_spark_raw.properties
-
-algorithm_type=$1
-data_structure=$2
-dataset_name=$3
-api_name=$4
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${algorithm_type}_${data_structure}_${dataset_name}_${api_name}
-
-# concatnate strings as a new variable
-num_executors=${algorithm_type}"_"${dataset_name}"_numExectuors"
-executor_cores=${algorithm_type}"_"${dataset_name}"_executorCores"
-executor_memory=${algorithm_type}"_"${dataset_name}"_executorMemory"
-extra_java_options=${algorithm_type}"_"${dataset_name}"_extraJavaOptions"
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-max_failures="maxFailures"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-max_failures_val=${!max_failures}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${max_failures} : ${max_failures_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${max_failures_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-echo "start to submit spark jobs"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.RFRunner \
---driver-java-options "-Xms15g" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.taskmaxFailures=${max_failures_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/simrank_run.sh b/tools/kal-test/bin/ml/simrank_run.sh
deleted file mode 100644
index 0d83eca..0000000
--- a/tools/kal-test/bin/ml/simrank_run.sh
+++ /dev/null
@@ -1,120 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. simrank3w"
-  echo "2nd argument: type of Algorithm: e.g. boostkit/raw"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments:<dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. simrank3w"
-  echo "2nd argument: type of Algorithm: e.g. boostkit/raw"
-  exit 0
-fi
-
-
-source conf/ml/simrank/simrank_spark.properties
-
-dataset_name=$1
-algorithm_type=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-case_name=${dataset_name}-${algorithm_type}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${cpu_name}
-executor_cores="executorCores_"${cpu_name}
-executor_memory="executorMemory_"${cpu_name}
-extra_java_options="extraJavaOptions_"${cpu_name}
-driver_cores="driverCores_"${cpu_name}
-driver_memory="driverMemory_"${cpu_name}
-executor_memory_overhead="execMemOverhead_"${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to submit spark jobs --- SimRank-${case_name}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-if [[ ${algorithm_type} == "boostkit" ]]; then
-spark-submit \
---class com.bigdata.ml.SimRankRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-kernel-2.11-2.1.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-kernel-2.11-2.1.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-kernel-2.11-2.1.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${case_name} ${data_path} | tee ./log/log
-else
-spark-submit \
---class com.bigdata.ml.SimRankRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/kal-test_2.11-0.1.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:kal-test_2.11-0.1.jar" \
-./lib/kal-test_2.11-0.1.jar ${case_name} ${data_path} | tee ./log/log
-fi
\ No newline at end of file
diff --git a/tools/kal-test/bin/ml/spca_run.sh b/tools/kal-test/bin/ml/spca_run.sh
deleted file mode 100644
index 61bb59c..0000000
--- a/tools/kal-test/bin/ml/spca_run.sh
+++ /dev/null
@@ -1,115 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. Kemelmacher"
-  echo "If you want to save the results, plz change the configuration [saveRes] in <spca.yml>"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. Kemelmacher"
-  echo "If you want to save the results, plz change the configuration <saveRes>"
-  exit 0
-fi
-
-
-source conf/ml/spca/spca_spark.properties
-
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-echo "${dataset_name} : ${data_path_input}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-echo "start to submit spark jobs --- SPCA-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.ml.SPCARunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-2.1.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-2.1.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-2.1.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-2.1.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-2.1.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-2.1.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-2.1.0-${spark_version_val}.jar:boostkit-ml-core_2.11-2.1.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-2.1.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_input} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/spca_run_raw.sh b/tools/kal-test/bin/ml/spca_run_raw.sh
deleted file mode 100644
index c1d7920..0000000
--- a/tools/kal-test/bin/ml/spca_run_raw.sh
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. Kemelmacher"
-  echo "If you want to save the results, plz change the configuration [saveRes] in <spca.yml>"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. Kemelmacher"
-  echo "If you want to save the results, plz change the configuration <saveRes>"
-  exit 0
-fi
-
-
-source conf/ml/spca/spca_spark_raw.properties
-
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-echo "${dataset_name} : ${data_path_input}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-echo "start to submit spark jobs --- SPCA-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.SPCARunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_input} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/spearman_run.sh b/tools/kal-test/bin/ml/spearman_run.sh
deleted file mode 100644
index 94b72a0..0000000
--- a/tools/kal-test/bin/ml/spearman_run.sh
+++ /dev/null
@@ -1,111 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-fi
-
-source conf/ml/spearman/spearman_spark.properties
-
-data_structure=$1
-dataset_name=$2
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${data_structure}-${dataset_name}-${platform_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${platform_name}
-executor_cores="executorCores_"${dataset_name}
-executor_memory="executorMemory_"${dataset_name}_${platform_name}
-executor_memory_overhead="executorMemOverhead_"${dataset_name}_${platform_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${platform_name}
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-dataset_output_="SpearMan"_${dataset_name}_"output"
-data_path=${!dataset_name}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- SpearMan-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---class com.bigdata.ml.SpearManRunner \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${platform_name}.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256g" \
---conf "spark.network.timeout=3600s" \
---conf "spark.task.maxFailures=1" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} | tee ./log/log
-
diff --git a/tools/kal-test/bin/ml/spearman_run_raw.sh b/tools/kal-test/bin/ml/spearman_run_raw.sh
deleted file mode 100644
index 3a7610c..0000000
--- a/tools/kal-test/bin/ml/spearman_run_raw.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <data structure> <dataset name>"
-  echo "1st argument: type of data structure: [dataframe/rdd]"
-  echo "2nd argument: name of dataset: e.g. CP10M1K/CP2M5K/CP1M10K"
-  exit 0
-fi
-
-source conf/ml/spearman/spearman_spark.properties
-
-data_structure=$1
-dataset_name=$2
-platform_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${data_structure}-${dataset_name}-"raw"
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${platform_name}
-executor_cores="executorCores_"${dataset_name}
-executor_memory="executorMemory_"${dataset_name}_${platform_name}
-executor_memory_overhead="executorMemOverhead_"${dataset_name}_${platform_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${platform_name}
-driver_cores="driverCores"
-driver_memory="driverMemory"
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-executor_memory_overhead_val=${!executor_memory_overhead}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${platform_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${executor_memory_overhead} : ${executor_memory_overhead_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${executor_memory_overhead_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-dataset_output_="SpearMan"_${dataset_name}_"output"
-data_path=${!dataset_name}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-
-echo "${dataset_name} : ${data_path}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-echo "start to submit spark jobs --- SpearMan-${model_conf}"
-
-spark-submit \
---driver-class-path "lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar" \
---class com.bigdata.ml.SpearManRunner \
---jars "lib/fastutil-8.3.1.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar" \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.memoryOverhead=${executor_memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256g" \
---conf "spark.network.timeout=3600s" \
---conf "spark.task.maxFailures=1" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/svd_run.sh b/tools/kal-test/bin/ml/svd_run.sh
deleted file mode 100644
index e0c5981..0000000
--- a/tools/kal-test/bin/ml/svd_run.sh
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. D10M1K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. D10M1K"
-  exit 0
-fi
-
-
-source conf/ml/svd/svd_spark.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-dataset_output_=${dataset_name}"_output"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/U"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/V"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/s"
-hdfs dfs -mkdir -p "${data_path_output}_${cpu_name}_${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-echo "start to submit spark jobs --- SVD-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.ml.SVDRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/svd_run_kml.sh b/tools/kal-test/bin/ml/svd_run_kml.sh
deleted file mode 100644
index 4df4ac4..0000000
--- a/tools/kal-test/bin/ml/svd_run_kml.sh
+++ /dev/null
@@ -1,127 +0,0 @@
-#!/bin/bash
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. D10M1K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. D10M1K"
-  exit 0
-fi
-
-
-source conf/ml/svd/svd_spark.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-dataset_output_=${dataset_name}"_output"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/U"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/V"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/s"
-hdfs dfs -mkdir -p "${data_path_output}_${cpu_name}_${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-scp lib/fastutil-8.3.1.jar lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar root@agent1:/opt/lib/ml
-scp lib/fastutil-8.3.1.jar lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar root@agent2:/opt/lib/ml
-scp lib/fastutil-8.3.1.jar lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar root@agent3:/opt/lib/ml
-
-
-mkdir -p log
-echo "start to submit spark jobs --- SVD-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---class com.bigdata.ml.SVDRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
---conf "spark.driver.extraLibraryPath=/opt/netlib" \
---conf "spark.executor.extraLibraryPath=/opt/netlib" \
---conf "spark.driver.extraClassPath=/opt/netlib/*:lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=/opt/lib/ml/*:/opt/netlib/*" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/svd_run_raw.sh b/tools/kal-test/bin/ml/svd_run_raw.sh
deleted file mode 100644
index 371acc8..0000000
--- a/tools/kal-test/bin/ml/svd_run_raw.sh
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name>"
-  echo "1st argument: name of dataset: e.g. D10M1K"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 1 ]; then
-  echo "please input 1 argument: <dataset name>"
-  echo "1st argument: name of dataset: e.g. D10M1K"
-  exit 0
-fi
-
-
-source conf/ml/svd/svd_spark_raw.properties
-
-dataset_name=$1
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-model_conf=${dataset_name}
-
-# concatnate strings as a new variable
-num_executors="numExectuors_"${dataset_name}_${cpu_name}
-executor_cores="executorCores_"${dataset_name}_${cpu_name}
-executor_memory="executorMemory_"${dataset_name}_${cpu_name}
-extra_java_options="extraJavaOptions_"${dataset_name}_${cpu_name}
-driver_cores="driverCores_"${dataset_name}_${cpu_name}
-driver_memory="driverMemory_"${dataset_name}_${cpu_name}
-memory_overhead="execMemOverhead_"${dataset_name}_${cpu_name}
-master_="master"
-deploy_mode="deployMode"
-compress_="compress"
-
-dataset_output_=${dataset_name}"_output"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-memory_overhead_val=${!memory_overhead}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-compress_val=${!compress_}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${memory_overhead} : ${memory_overhead_val}"
-echo "${compress_} : ${compress_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${memory_overhead_val} ] \
-    || [ ! ${compress_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_input=${!dataset_name}
-data_path_output=${!dataset_output_}
-data_path_val=${data_path_input}","${data_path_output}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/U"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/V"
-hdfs dfs -rm -r -f "${data_path_output}_${cpu_name}_${is_raw}/s"
-hdfs dfs -mkdir -p "${data_path_output}_${cpu_name}_${is_raw}"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}
-
-mkdir -p log
-echo "start to submit spark jobs --- SVD-${model_conf}"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar:lib/fastutil-8.3.1.jar" \
---class com.bigdata.ml.SVDRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.executor.instances=${num_executors_val}" \
---conf "spark.executor.memory_overhead=${memory_overhead_val}" \
---conf "spark.driver.maxResultSize=256G" \
---conf "spark.rdd.compress=${compress_val}" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/svm_run.sh b/tools/kal-test/bin/ml/svm_run.sh
deleted file mode 100755
index 843b08d..0000000
--- a/tools/kal-test/bin/ml/svm_run.sh
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. 10M4096"
-  echo "2nd argument: name of API: e.g. fit"
-  exit 0
-  ;;
-esac
-
-
-
-source conf/ml/svm/svm_spark.properties
-
-dataset_name=$1
-api_name=$2
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-model_conf=${dataset_name}-${api_name}-${cpu_name}
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}"_numExectuors"
-executor_cores=${cpu_name}"_executorCores"
-executor_memory=${cpu_name}"_executorMemory"
-extra_java_options=${cpu_name}"_extraJavaOptions"
-driver_cores=${cpu_name}"_driverCores"
-driver_memory=${cpu_name}"_driverMemory"
-master_=${cpu_name}"_master"
-deploy_mode=${cpu_name}"_deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo ${cpu_name}
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-echo "start to submit spark jobs"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.SVMRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val}" \
---conf "spark.LinearSVC.inertiaCofficient=0.5" \
---jars "lib/fastutil-8.3.1.jar,lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar,lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---driver-class-path "lib/kal-test_2.11-0.1.jar:lib/fastutil-8.3.1.jar:lib/snakeyaml-1.19.jar:lib/boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:lib/boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar:boostkit-ml-acc_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-core_2.11-1.3.0-${spark_version_val}.jar:boostkit-ml-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/svm_run_raw.sh b/tools/kal-test/bin/ml/svm_run_raw.sh
deleted file mode 100755
index 2d17400..0000000
--- a/tools/kal-test/bin/ml/svm_run_raw.sh
+++ /dev/null
@@ -1,90 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <algorithm type> <data structure> <dataset name> <api name>"
-  echo "1st argument: name of dataset: e.g. 10M4096"
-  echo "2nd argument: name of API: e.g. fit"
-  exit 0
-  ;;
-esac
-
-
-source conf/ml/svm/svm_spark.properties
-
-dataset_name=$1
-api_name=$2
-
-model_conf=${dataset_name}-${api_name}"-raw"
-
-# concatnate strings as a new variable
-num_executors="x86_64_numExectuors"
-executor_cores="x86_64_executorCores"
-executor_memory="x86_64_executorMemory"
-extra_java_options="x86_64_extraJavaOptions"
-driver_cores="x86_64_driverCores"
-driver_memory="x86_64_driverMemory"
-master_="x86_64_master"
-deploy_mode="x86_64_deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path=${!dataset_name}
-
-echo "${dataset_name} : ${data_path}"
-echo "start to submit spark jobs"
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---driver-class-path "lib/snakeyaml-1.19.jar" \
---jars "lib/fastutil-8.3.1.jar" \
---conf "spark.executor.extraClassPath=fastutil-8.3.1.jar" \
---class com.bigdata.ml.SVMRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf "spark.executor.extraJavaOptions=${extra_java_options_val} -XX:SurvivorRatio=4 -XX:ParallelGCThreads=6" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/xgbt_run.sh b/tools/kal-test/bin/ml/xgbt_run.sh
deleted file mode 100755
index eb5d554..0000000
--- a/tools/kal-test/bin/ml/xgbt_run.sh
+++ /dev/null
@@ -1,121 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <algorithm type>"
-  echo "1rd argument: name of dataset: e.g. higgs"
-  echo "2st argument: type of algorithm: [classification/regression]"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <dataset name> <algorithm type>"
-  echo "1rd argument: name of dataset: e.g. higgs"
-  echo "2st argument: type of algorithm: [classification/regression]"
-  exit 0
-fi
-
-
-source conf/ml/xgbt/xgbt_spark.properties
-
-dataset_name=$1
-algorithm_type=$2
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-
-model_conf=${algorithm_type}_${dataset_name}
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}_${algorithm_type}"_"${dataset_name}"_numExecutors"
-executor_cores=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorCores"
-executor_memory=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorMemory"
-extra_java_options=${cpu_name}_${algorithm_type}"_"${dataset_name}"_extraJavaOptions"
-driver_cores=${cpu_name}_${algorithm_type}"_"${dataset_name}"_driverCores"
-driver_memory=${cpu_name}_${algorithm_type}"_"${dataset_name}"_driverMemory"
-task_cpus=${cpu_name}_${algorithm_type}"_"${dataset_name}"_taskCpus"
-num_partitions=${cpu_name}_${algorithm_type}"_"${dataset_name}"_numPartitions"
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-task_cpus_val=${!task_cpus}
-num_partitions_val=${!num_partitions}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${task_cpus} : ${task_cpus_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${task_cpus_val} ] \
-    || [ ! ${num_partitions_val} ] \
-    || [ ! ${deploy_mode_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="no"
-echo "-Is raw? -${is_raw}"
-echo "start to submit spark jobs"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}_${extra_java_options_val}_${driver_cores_val}_${driver_memory_val}_${task_cpus_val}_${num_partitions_val}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.XGBTRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf spark.task.cpus=${task_cpus_val} \
---conf spark.executor.extraJavaOptions=${extra_java_options_val} \
---conf spark.executorEnv.LD_LIBRARY_PATH="./lib/:${LD_LIBRARY_PATH}" \
---conf spark.executor.extraLibraryPath="./lib" \
---conf spark.driver.extraLibraryPath="./lib" \
---files=lib/libboostkit_xgboost_kernel.so  \
---jars "lib/boostkit-xgboost4j-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar,lib/boostkit-xgboost4j_2.11-1.3.0-${cpu_name}.jar,lib/boostkit-xgboost4j-${spark_version_val}_2.11-1.3.0-${cpu_name}.jar,lib/snakeyaml-1.19.jar,lib/kal-test_2.11-0.1.jar" \
---conf spark.executor.extraClassPath="boostkit-xgboost4j-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar:boostkit-xgboost4j_2.11-1.3.0-${cpu_name}.jar:boostkit-xgboost4j-${spark_version_val}_2.11-1.3.0-${cpu_name}.jar:snakeyaml-1.19.jar" \
---conf spark.driver.extraClassPath="lib/kal-test_2.11-0.1.jar:lib/boostkit-xgboost4j-kernel-2.11-1.3.0-${spark_version_val}-${cpu_name}.jar:lib/boostkit-xgboost4j_2.11-1.3.0-${cpu_name}.jar:lib/boostkit-xgboost4j-${spark_version_val}_2.11-1.3.0-${cpu_name}.jar:lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/ml/xgbt_run_raw.sh b/tools/kal-test/bin/ml/xgbt_run_raw.sh
deleted file mode 100755
index bda00a7..0000000
--- a/tools/kal-test/bin/ml/xgbt_run_raw.sh
+++ /dev/null
@@ -1,117 +0,0 @@
-#!/bin/bash
-set -e
-
-case "$1" in
--h | --help | ?)
-  echo "Usage: <dataset name> <algorithm type>"
-  echo "1rd argument: name of dataset: e.g. higgs"
-  echo "2st argument: type of algorithm: [classification/regression]"
-  exit 0
-  ;;
-esac
-
-if [ $# -ne 2 ]; then
-  echo "please input 2 arguments: <dataset name> <algorithm type>"
-  echo "1rd argument: name of dataset: e.g. higgs"
-  echo "2st argument: type of algorithm: [classification/regression]"
-  exit 0
-fi
-
-
-source conf/ml/xgbt/xgbt_spark_raw.properties
-
-dataset_name=$1
-algorithm_type=$2
-
-cpu_name=$(lscpu | grep Architecture | awk '{print $2}')
-
-
-
-model_conf=${algorithm_type}_${dataset_name}
-
-# concatnate strings as a new variable
-num_executors=${cpu_name}_${algorithm_type}"_"${dataset_name}"_numExecutors"
-executor_cores=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorCores"
-executor_memory=${cpu_name}_${algorithm_type}"_"${dataset_name}"_executorMemory"
-extra_java_options=${cpu_name}_${algorithm_type}"_"${dataset_name}"_extraJavaOptions"
-driver_cores=${cpu_name}_${algorithm_type}"_"${dataset_name}"_driverCores"
-driver_memory=${cpu_name}_${algorithm_type}"_"${dataset_name}"_driverMemory"
-task_cpus=${cpu_name}_${algorithm_type}"_"${dataset_name}"_taskCpus"
-num_partitions=${cpu_name}_${algorithm_type}"_"${dataset_name}"_numPartitions"
-master_="master"
-deploy_mode="deployMode"
-
-num_executors_val=${!num_executors}
-executor_cores_val=${!executor_cores}
-executor_memory_val=${!executor_memory}
-extra_java_options_val=${!extra_java_options}
-driver_cores_val=${!driver_cores}
-driver_memory_val=${!driver_memory}
-task_cpus_val=${!task_cpus}
-num_partitions_val=${!num_partitions}
-master_val=${!master_}
-deploy_mode_val=${!deploy_mode}
-
-
-echo "${master_} : ${master_val}"
-echo "${deploy_mode} : ${deploy_mode_val}"
-echo "${driver_cores} : ${driver_cores_val}"
-echo "${driver_memory} : ${driver_memory_val}"
-echo "${num_executors} : ${num_executors_val}"
-echo "${executor_cores}: ${executor_cores_val}"
-echo "${executor_memory} : ${executor_memory_val}"
-echo "${extra_java_options} : ${extra_java_options_val}"
-echo "${task_cpus} : ${task_cpus_val}"
-echo "${num_partitions} : ${num_partitions_val}"
-echo "cpu_name : ${cpu_name}"
-
-if [ ! ${num_executors_val} ] \
-    || [ ! ${executor_cores_val} ] \
-    || [ ! ${executor_memory_val} ] \
-    || [ ! ${extra_java_options_val} ] \
-    || [ ! ${driver_cores_val} ] \
-    || [ ! ${driver_memory_val} ] \
-    || [ ! ${master_val} ] \
-    || [ ! ${task_cpus_val} ] \
-    || [ ! ${num_partitions_val} ] \
-    || [ ! ${deploy_mode_val} ] \
-    || [ ! ${cpu_name} ]; then
-  echo "Some values are NULL, please confirm with the property files"
-  exit 0
-fi
-
-
-source conf/ml/ml_datasets.properties
-spark_version=sparkVersion
-spark_version_val=${!spark_version}
-data_path_val=${!dataset_name}
-echo "${dataset_name} : ${data_path_val}"
-is_raw="yes"
-echo "-Is raw? -${is_raw}"
-echo "start to submit spark jobs"
-
-spark_conf=${master_val}_${deploy_mode_val}_${num_executors_val}_${executor_cores_val}_${executor_memory_val}_${extra_java_options_val}_${driver_cores_val}_${driver_memory_val}_${task_cpus_val}_${num_partitions_val}
-
-echo "start to clean cache and sleep 30s"
-ssh server1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent1 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent2 "echo 3 > /proc/sys/vm/drop_caches"
-ssh agent3 "echo 3 > /proc/sys/vm/drop_caches"
-sleep 30
-
-mkdir -p log
-spark-submit \
---class com.bigdata.ml.XGBTRunner \
---deploy-mode ${deploy_mode_val} \
---driver-cores ${driver_cores_val} \
---driver-memory ${driver_memory_val} \
---num-executors ${num_executors_val} \
---executor-cores ${executor_cores_val} \
---executor-memory ${executor_memory_val} \
---master ${master_val} \
---conf spark.task.cpus=${task_cpus_val} \
---conf spark.executor.extraJavaOptions=${extra_java_options_val} \
---jars "lib/xgboost4j_2.11-1.1.0.jar,lib/xgboost4j-spark_2.11-1.1.0.jar,lib/snakeyaml-1.19.jar,lib/kal-test_2.11-0.1.jar" \
---conf spark.executor.extraClassPath="xgboost4j_2.11-1.1.0.jar:xgboost4j-spark_2.11-1.1.0.jar:snakeyaml-1.19.jar" \
---conf spark.driver.extraClassPath="lib/kal-test_2.11-0.1.jar:lib/xgboost4j_2.11-1.1.0.jar:lib/xgboost4j-spark_2.11-1.1.0.jar:lib/snakeyaml-1.19.jar" \
-./lib/kal-test_2.11-0.1.jar ${model_conf} ${data_path_val} ${cpu_name} ${is_raw} ${spark_conf} | tee ./log/log
diff --git a/tools/kal-test/bin/workflow.sh b/tools/kal-test/bin/workflow.sh
deleted file mode 100644
index 7a4e6da..0000000
--- a/tools/kal-test/bin/workflow.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-
-./bin/ml/gbdt_run.sh classification dataframe epsilon fit
-./bin/ml/gbdt_run.sh classification dataframe epsilon fit1
-./bin/ml/gbdt_run.sh classification dataframe epsilon fit2
-./bin/ml/gbdt_run.sh classification dataframe epsilon fit3
-
-./bin/ml/gbdt_run.sh regression dataframe epsilon fit
-./bin/ml/gbdt_run.sh regression dataframe epsilon fit1
-./bin/ml/gbdt_run.sh regression dataframe epsilon fit2
-./bin/ml/gbdt_run.sh regression dataframe epsilon fit3
-
-./bin/ml/gbdt_run.sh classification rdd epsilon rdd
-./bin/ml/gbdt_run.sh classification rdd epsilon javardd
-./bin/ml/gbdt_run.sh regression rdd epsilon rdd
-./bin/ml/gbdt_run.sh regression rdd epsilon javardd
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/betweenness/betweenness.yml b/tools/kal-test/conf/graph/betweenness/betweenness.yml
deleted file mode 100644
index 6443d10..0000000
--- a/tools/kal-test/conf/graph/betweenness/betweenness.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-# Betweenness dataset parameters arm
-
-betweenness:
-  cit_patents:
-    outputPath: hdfs:///tmp/graph/result/betweenness/cit_patents
-    splitGraph: "\t"
-    p: 0.156
-    k: 10000
-
-  enwiki_2018:
-    outputPath: hdfs:///tmp/graph/result/betweenness/enwiki_2018
-    splitGraph: "\t"
-    p: 0.005
-    k: 10000
-
-  uk_2002:
-    outputPath: hdfs:///tmp/graph/result/betweenness/uk_2002
-    splitGraph: "\t"
-    p: 0.0014
-    k: 10000
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/betweenness/betweenness_spark.properties b/tools/kal-test/conf/graph/betweenness/betweenness_spark.properties
deleted file mode 100644
index c1095ac..0000000
--- a/tools/kal-test/conf/graph/betweenness/betweenness_spark.properties
+++ /dev/null
@@ -1,52 +0,0 @@
-# Spark parameters
-deployMode=client
-driverMemory=80g
-
-cit_patents_numExecutors_aarch64=71
-cit_patents_executorCores_aarch64=4
-cit_patents_executorMemory_aarch64=12G
-cit_patents_numPartitions_aarch64=240
-cit_patents_BetweennessPartNum_aarch64=864
-cit_patents_ThreadNum_aarch64=2
-cit_patents_SparkTaskCpus_aarch64=2
-
-enwiki_2018_numExecutors_aarch64=71
-enwiki_2018_executorCores_aarch64=4
-enwiki_2018_executorMemory_aarch64=12G
-enwiki_2018_numPartitions_aarch64=576
-enwiki_2018_BetweennessPartNum_aarch64=3168
-enwiki_2018_ThreadNum_aarch64=2
-enwiki_2018_SparkTaskCpus_aarch64=2
-
-uk_2002_numExecutors_aarch64=71
-uk_2002_executorCores_aarch64=4
-uk_2002_executorMemory_aarch64=12G
-uk_2002_numPartitions_aarch64=240
-uk_2002_BetweennessPartNum_aarch64=864
-uk_2002_ThreadNum_aarch64=2
-uk_2002_SparkTaskCpus_aarch64=2
-
-cit_patents_numExecutors_x86_64=59
-cit_patents_executorCores_x86_64=4
-cit_patents_executorMemory_x86_64=15G
-cit_patents_numPartitions_x86_64=240
-cit_patents_BetweennessPartNum_x86_64=5040
-cit_patents_ThreadNum_x86_64=1
-cit_patents_SparkTaskCpus_x86_64=2
-
-
-enwiki_2018_numExecutors_x86_64=59
-enwiki_2018_executorCores_x86_64=4
-enwiki_2018_executorMemory_x86_64=15G
-enwiki_2018_numPartitions_x86_64=480
-enwiki_2018_BetweennessPartNum_x86_64=1440
-enwiki_2018_ThreadNum_x86_64=2
-enwiki_2018_SparkTaskCpus_x86_64=2
-
-uk_2002_numExecutors_x86_64=59
-uk_2002_executorCores_x86_64=5
-uk_2002_executorMemory_x86_64=15G
-uk_2002_numPartitions_x86_64=240
-uk_2002_BetweennessPartNum_x86_64=720
-uk_2002_ThreadNum_x86_64=2
-uk_2002_SparkTaskCpus_x86_64=2
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/betweenness/betweenness_spark_opensource.properties b/tools/kal-test/conf/graph/betweenness/betweenness_spark_opensource.properties
deleted file mode 100644
index 218bd73..0000000
--- a/tools/kal-test/conf/graph/betweenness/betweenness_spark_opensource.properties
+++ /dev/null
@@ -1,28 +0,0 @@
-# Spark parameters
-deployMode=client
-driverMemory=80g
-
-cit_patents_numExecutors_x86_64=29
-cit_patents_executorCores_x86_64=8
-cit_patents_executorMemory_x86_64=31G
-cit_patents_numPartitions_x86_64=240
-cit_patents_pivots_x86_64=10300
-cit_patents_iteration_x86_64=10000
-cit_patents_graphSplit_x86_64="\t"
-
-enwiki_2018_numExecutors_x86_64=59
-enwiki_2018_executorCores_x86_64=4
-enwiki_2018_executorMemory_x86_64=15G
-enwiki_2018_numPartitions_x86_64=240
-enwiki_2018_pivots_x86_64=30
-enwiki_2018_iteration_x86_64=10
-enwiki_2018_graphSplit_x86_64="\t"
-
-
-uk_2002_numExecutors_x86_64=23
-uk_2002_executorCores_x86_64=10
-uk_2002_executorMemory_x86_64=38G
-uk_2002_numPartitions_x86_64=240
-uk_2002_pivots_x86_64=35
-uk_2002_iteration_x86_64=13
-uk_2002_graphSplit_x86_64="\t"
diff --git a/tools/kal-test/conf/graph/bfs/bfs.yml b/tools/kal-test/conf/graph/bfs/bfs.yml
deleted file mode 100644
index 97306e5..0000000
--- a/tools/kal-test/conf/graph/bfs/bfs.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-# BFS dataset parameters
-
-bfs:
-  cit_patents:
-    outputPath: hdfs:///tmp/graph/result/bfs/cit_patents
-    splitGraph: "\t"
-    isDirect: true
-    depthLimit: 10
-
-  enwiki_2018:
-    outputPath: hdfs:///tmp/graph/result/bfs/enwiki_2018
-    splitGraph: "\t"
-    isDirect: true
-    depthLimit: 10
-
-  arabic_2005:
-    outputPath: hdfs:///tmp/graph/result/bfs/arabic_2005
-    splitGraph: "\t"
-    isDirect: true
-    depthLimit: 10
-
-  graph500_22:
-    outputPath: hdfs:///tmp/graph/result/bfs/graph500_22
-    splitGraph: " "
-    isDirect: false
-    depthLimit: 10
-
-  graph500_23:
-    outputPath: hdfs:///tmp/graph/result/bfs/graph500_23
-    splitGraph: " "
-    isDirect: false
-    depthLimit: 10
-
-  graph500_25:
-    outputPath: hdfs:///tmp/graph/result/bfs/graph500_25
-    splitGraph: " "
-    isDirect: false
-    depthLimit: 10
diff --git a/tools/kal-test/conf/graph/bfs/bfs_opensource.properties b/tools/kal-test/conf/graph/bfs/bfs_opensource.properties
deleted file mode 100644
index 7ec6666..0000000
--- a/tools/kal-test/conf/graph/bfs/bfs_opensource.properties
+++ /dev/null
@@ -1,12 +0,0 @@
-cit_patents_split="\t"
-cit_patents_q=true
-enwiki_2018_split="\t"
-enwiki_2018_q=true
-arabic_2005_split="\t"
-arabic_2005_q=true
-graph500_22_split=" "
-graph500_22_q=false
-graph500_23_split=","
-graph500_23_q=false
-graph500_25_split=" "
-graph500_25_q=false
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/bfs/bfs_source_id.properties b/tools/kal-test/conf/graph/bfs/bfs_source_id.properties
deleted file mode 100644
index e92a5e0..0000000
--- a/tools/kal-test/conf/graph/bfs/bfs_source_id.properties
+++ /dev/null
@@ -1,6 +0,0 @@
-cit_patents_SourceID=5907828,5415318,5187336,5833578,4392749,4841212,5076293,4040028,4372582,4795119
-enwiki_2018_SourceID=746,8772,21772,120250,622934,1551636,3965549,1604602,1604609,1604631
-arabic_2005_SourceID=1670,51860,152713,241498,4301061,10012301,22633966,22671309,22671985,11919106
-graph500_22_SourceID=801590,2673237,254471,1610346,3625091,2079512,2999118,2079626,2788395,644668,2404500,2098295,2353233,3009625,1273090
-graph500_23_SourceID=7343958,1320614,2134798,4771523,5113815,7826138,468980,3608955,7634969,4120652,7935287,4633288,2703467,4672090,710114,1036350,6538582,7410401,7713575,5723238
-graph500_25_SourceID=2984368,28051905,23288850,8133358,30465165,32077192,3588225,10567776,15564773,22821566,16473042,1057188,3618262,10779632,31184653,10803749,30374117,3015466,18845296,20096970,15527064,25432179,12294489,25461250,16997339,798169
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/bfs/bfs_spark.properties b/tools/kal-test/conf/graph/bfs/bfs_spark.properties
deleted file mode 100644
index 0534674..0000000
--- a/tools/kal-test/conf/graph/bfs/bfs_spark.properties
+++ /dev/null
@@ -1,62 +0,0 @@
-# Spark parameters
-deployMode=client
-
-cit_patents_numExecutors_aarch64=35
-cit_patents_executorCores_aarch64=8
-cit_patents_executorMemory_aarch64=25G
-cit_patents_numPartitions_aarch64=280
-
-enwiki_2018_numExecutors_aarch64=35
-enwiki_2018_executorCores_aarch64=8
-enwiki_2018_executorMemory_aarch64=25G
-enwiki_2018_numPartitions_aarch64=280
-
-arabic_2005_numExecutors_aarch64=35
-arabic_2005_executorCores_aarch64=8
-arabic_2005_executorMemory_aarch64=25G
-arabic_2005_numPartitions_aarch64=280
-
-graph500_22_numExecutors_aarch64=35
-graph500_22_executorCores_aarch64=8
-graph500_22_executorMemory_aarch64=25G
-graph500_22_numPartitions_aarch64=280
-
-graph500_23_numExecutors_aarch64=35
-graph500_23_executorCores_aarch64=8
-graph500_23_executorMemory_aarch64=25G
-graph500_23_numPartitions_aarch64=280
-
-graph500_25_numExecutors_aarch64=35
-graph500_25_executorCores_aarch64=8
-graph500_25_executorMemory_aarch64=25G
-graph500_25_numPartitions_aarch64=560
-
-cit_patents_numExecutors_x86_64=12
-cit_patents_executorCores_x86_64=19
-cit_patents_executorMemory_x86_64=78G
-cit_patents_numPartitions_x86_64=75
-
-enwiki_2018_numExecutors_x86_64=12
-enwiki_2018_executorCores_x86_64=19
-enwiki_2018_executorMemory_x86_64=78G
-enwiki_2018_numPartitions_x86_64=228
-
-arabic_2005_numExecutors_x86_64=59
-arabic_2005_executorCores_x86_64=4
-arabic_2005_executorMemory_x86_64=15G
-arabic_2005_numPartitions_x86_64=236
-
-graph500_22_numExecutors_x86_64=29
-graph500_22_executorCores_x86_64=8
-graph500_22_executorMemory_x86_64=31G
-graph500_22_numPartitions_x86_64=232
-
-graph500_23_numExecutors_x86_64=29
-graph500_23_executorCores_x86_64=8
-graph500_23_executorMemory_x86_64=31G
-graph500_23_numPartitions_x86_64=232
-
-graph500_25_numExecutors_x86_64=12
-graph500_25_executorCores_x86_64=19
-graph500_25_executorMemory_x86_64=78G
-graph500_25_numPartitions_x86_64=456
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/bfs/bfs_spark_opensource.properties b/tools/kal-test/conf/graph/bfs/bfs_spark_opensource.properties
deleted file mode 100644
index f21e9ad..0000000
--- a/tools/kal-test/conf/graph/bfs/bfs_spark_opensource.properties
+++ /dev/null
@@ -1,32 +0,0 @@
-# Spark parameters
-deployMode=client
-
-cit_patents_numExecutors=11
-cit_patents_executorCores=20
-cit_patents_executorMemory=78G
-cit_patents_numPartitions=220
-
-enwiki_2018_numExecutors=11
-enwiki_2018_executorCores=20
-enwiki_2018_executorMemory=78G
-enwiki_2018_numPartitions=220
-
-arabic_2005_numExecutors=29
-arabic_2005_executorCores=8
-arabic_2005_executorMemory=31G
-arabic_2005_numPartitions=232
-
-graph500_22_numExecutors=12
-graph500_22_executorCores=19
-graph500_22_executorMemory=78G
-graph500_22_numPartitions=228
-
-graph500_23_numExecutors=11
-graph500_23_executorCores=20
-graph500_23_executorMemory=78G
-graph500_23_numPartitions=508
-
-graph500_25_numExecutors=39
-graph500_25_executorCores=6
-graph500_25_executorMemory=23G
-graph500_25_numPartitions=468
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cc/cc.yml b/tools/kal-test/conf/graph/cc/cc.yml
deleted file mode 100644
index bcb0c9e..0000000
--- a/tools/kal-test/conf/graph/cc/cc.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-partition:
-  liveJournal_x86_64_yes: 210
-  liveJournal_x86_64_no: 78
-  liveJournal_aarch64_no: 78
-  graph500_25_x86_64_yes: 210
-  graph500_25_x86_64_no: 210
-  graph500_25_aarch64_no: 210
-  graph500_26_x86_64_yes: 210
-  graph500_26_x86_64_no: 210
-  graph500_26_aarch64_no: 210
-split:
-  liveJournal: "\t"
-  graph500_25: " "
-  graph500_26: " "
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cc/cc_spark.properties b/tools/kal-test/conf/graph/cc/cc_spark.properties
deleted file mode 100644
index 1878568..0000000
--- a/tools/kal-test/conf/graph/cc/cc_spark.properties
+++ /dev/null
@@ -1,19 +0,0 @@
-numExecutors_aarch64=71
-executorMemory_aarch64=12g
-executorCores_aarch64=4
-executorExtraJavaopts_aarch64="-Xms12g"
-defaultParallelism_liveJournal_aarch64=78
-defaultParallelism_graph500_25_aarch64=230
-defaultParallelism_graph500_26_aarch64=280
-
-numExecutors_x86_64=59
-executorMemory_x86_64=15g
-executorCores_x86_64=4
-executorExtraJavaopts_x86_64="-Xms15g"
-defaultParallelism_liveJournal_x86_64=470
-defaultParallelism_graph500_25_x86_64=210
-defaultParallelism_graph500_26_x86_64=460
-
-master=yarn
-deployMode=client
-driverMemory=200g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cc/cc_spark_raw.properties b/tools/kal-test/conf/graph/cc/cc_spark_raw.properties
deleted file mode 100644
index 5934181..0000000
--- a/tools/kal-test/conf/graph/cc/cc_spark_raw.properties
+++ /dev/null
@@ -1,11 +0,0 @@
-numExecutors=12
-executorMemory=78g
-executorCores=18
-executorExtraJavaopts="-Xms78g"
-defaultParallelism_liveJournal=470
-defaultParallelism_graph500_25=210
-defaultParallelism_graph500_26=460
-
-master=yarn
-deployMode=client
-driverMemory=200g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cd/cd.yml b/tools/kal-test/conf/graph/cd/cd.yml
deleted file mode 100644
index 594d083..0000000
--- a/tools/kal-test/conf/graph/cd/cd.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-partition:
-  simulate1_x86_64_yes: 228
-  simulate1_x86_64_no: 236
-  simulate1_aarch64_no: 284
-  simulate2_x86_64_yes: 232
-  simulate2_x86_64_no: 236
-  simulate2_aarch64_no: 284
-  usaRoad_x86_64_yes: 228
-  usaRoad_x86_64_no: 228
-  usaRoad_aarch64_no: 282
-split:
-  simulate1: ","
-  simulate2: ","
-  usaRoad: " "
-minLoopLen: 3
-maxLoopLen: 10
-minRate: 0.8
-maxRate: 1.2
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cd/cd_spark_aarch64.properties b/tools/kal-test/conf/graph/cd/cd_spark_aarch64.properties
deleted file mode 100644
index fe537a5..0000000
--- a/tools/kal-test/conf/graph/cd/cd_spark_aarch64.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-numExecutors_simulate1=71
-executorMemory_simulate1=12g
-executorCores_simulate1=4
-executorExtraJavaopts_simulate1="-Xms12g"
-
-numExecutors_simulate2=71
-executorMemory_simulate2=12g
-executorCores_simulate2=4
-executorExtraJavaopts_simulate2="-Xms12g"
-
-numExecutors_usaRoad=47
-executorMemory_usaRoad=19g
-executorCores_usaRoad=6
-executorExtraJavaopts_usaRoad="-Xms19g"
-
-master=yarn
-deployMode=client
-driverMemory=80g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cd/cd_spark_raw.properties b/tools/kal-test/conf/graph/cd/cd_spark_raw.properties
deleted file mode 100644
index 9b158f1..0000000
--- a/tools/kal-test/conf/graph/cd/cd_spark_raw.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-numExecutors_simulate1=12
-executorMemory_simulate1=78g
-executorCores_simulate1=19
-executorExtraJavaopts_simulate1="-Xms78g"
-
-numExecutors_simulate2=29
-executorMemory_simulate2=31g
-executorCores_simulate2=8
-executorExtraJavaopts_simulate2="-Xms31g"
-
-numExecutors_usaRoad=12
-executorMemory_usaRoad=78g
-executorCores_usaRoad=19
-executorExtraJavaopts_usaRoad="-Xms78g"
-
-master=yarn
-deployMode=client
-driverMemory=80g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/cd/cd_spark_x86_64.properties b/tools/kal-test/conf/graph/cd/cd_spark_x86_64.properties
deleted file mode 100644
index 7ab8908..0000000
--- a/tools/kal-test/conf/graph/cd/cd_spark_x86_64.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-numExecutors_simulate1=59
-executorMemory_simulate1=15g
-executorCores_simulate1=4
-executorExtraJavaopts_simulate1="-Xms15g"
-
-numExecutors_simulate2=59
-executorMemory_simulate2=15g
-executorCores_simulate2=4
-executorExtraJavaopts_simulate2="-Xms15g"
-
-numExecutors_usaRoad=12
-executorMemory_usaRoad=78g
-executorCores_usaRoad=19
-executorExtraJavaopts_usaRoad="-Xms78g"
-
-master=yarn
-deployMode=client
-driverMemory=80g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/closeness/closeness.yml b/tools/kal-test/conf/graph/closeness/closeness.yml
deleted file mode 100644
index ae0b370..0000000
--- a/tools/kal-test/conf/graph/closeness/closeness.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-# Closeness dataset parameters arm
-
-closeness:
-  cit_patents:
-    outputPath: hdfs:///tmp/graph/result/closeness/cit_patents
-    splitGraph: "\t"
-    comparePathWeighted: hdfs:///tmp/graph/dataset/closeness/groundtruth-cit-weighted.txt
-    comparePathUnweighted: hdfs:///tmp/graph/dataset/closeness/groundtruth-cit-unweighted.txt
-  graph500_23:
-    outputPath: hdfs:///tmp/graph/result/closeness/graph500_23
-    splitGraph: " "
-    comparePathWeighted: hdfs:///tmp/graph/dataset/closeness/groundtruth-23-weighted.txt
-    comparePathUnweighted: hdfs:///tmp/graph/dataset/closeness/groundtruth-23-unweighted.txt
-  uk_2002:
-    outputPath: hdfs:///tmp/graph/result/closeness/uk_2002
-    splitGraph: "\t"
-    comparePathWeighted: hdfs:///tmp/graph/dataset/closeness/groundtruth-uk-weighted.txt
-    comparePathUnweighted: hdfs:///tmp/graph/dataset/closeness/groundtruth-uk-unweighted.txt
diff --git a/tools/kal-test/conf/graph/closeness/closeness_spark.properties b/tools/kal-test/conf/graph/closeness/closeness_spark.properties
deleted file mode 100644
index 777dd89..0000000
--- a/tools/kal-test/conf/graph/closeness/closeness_spark.properties
+++ /dev/null
@@ -1,76 +0,0 @@
-# Spark parameters
-deployMode=client
-outputNodeNum=10000
-driverMemory=80g
-
-cit_patents_weighted_numExecutors_aarch64=71
-cit_patents_weighted_executorMemory_aarch64=12G
-cit_patents_weighted_executorCores_aarch64=4
-cit_patents_weighted_numPartitions_aarch64=288
-cit_patents_weighted_ratio_aarch64=0.00001
-
-cit_patents_unweighted_numExecutors_aarch64=71
-cit_patents_unweighted_executorMemory_aarch64=12G
-cit_patents_unweighted_executorCores_aarch64=4
-cit_patents_unweighted_numPartitions_aarch64=288
-cit_patents_unweighted_ratio_aarch64=0.00001
-
-graph500_23_weighted_numExecutors_aarch64=71
-graph500_23_weighted_executorMemory_aarch64=12G
-graph500_23_weighted_executorCores_aarch64=4
-graph500_23_weighted_numPartitions_aarch64=288
-graph500_23_weighted_ratio_aarch64=0.0001
-
-graph500_23_unweighted_numExecutors_aarch64=71
-graph500_23_unweighted_executorMemory_aarch64=12G
-graph500_23_unweighted_executorCores_aarch64=4
-graph500_23_unweighted_numPartitions_aarch64=288
-graph500_23_unweighted_ratio_aarch64=0.0001
-
-uk_2002_weighted_numExecutors_aarch64=35
-uk_2002_weighted_executorMemory_aarch64=25G
-uk_2002_weighted_executorCores_aarch64=8
-uk_2002_weighted_numPartitions_aarch64=288
-uk_2002_weighted_ratio_aarch64=0.001
-
-uk_2002_unweighted_numExecutors_aarch64=71
-uk_2002_unweighted_executorMemory_aarch64=12G
-uk_2002_unweighted_executorCores_aarch64=4
-uk_2002_unweighted_numPartitions_aarch64=288
-uk_2002_unweighted_ratio_aarch64=0.001
-
-cit_patents_weighted_numExecutors_x86_64=18
-cit_patents_weighted_executorMemory_x86_64=52G
-cit_patents_weighted_executorCores_x86_64=12
-cit_patents_weighted_numPartitions_x86_64=240
-cit_patents_weighted_ratio_x86_64=0.00001
-
-cit_patents_unweighted_numExecutors_x86_64=18
-cit_patents_unweighted_executorMemory_x86_64=52G
-cit_patents_unweighted_executorCores_x86_64=12
-cit_patents_unweighted_numPartitions_x86_64=240
-cit_patents_unweighted_ratio_x86_64=0.00001
-
-graph500_23_weighted_numExecutors_x86_64=59
-graph500_23_weighted_executorMemory_x86_64=15G
-graph500_23_weighted_executorCores_x86_64=4
-graph500_23_weighted_numPartitions_x86_64=480
-graph500_23_weighted_ratio_x86_64=0.0001
-
-graph500_23_unweighted_numExecutors_x86_64=29
-graph500_23_unweighted_executorMemory_x86_64=31G
-graph500_23_unweighted_executorCores_x86_64=8
-graph500_23_unweighted_numPartitions_x86_64=240
-graph500_23_unweighted_ratio_x86_64=0.0001
-
-uk_2002_weighted_numExecutors_x86_64=59
-uk_2002_weighted_executorMemory_x86_64=15G
-uk_2002_weighted_executorCores_x86_64=4
-uk_2002_weighted_numPartitions_x86_64=240
-uk_2002_weighted_ratio_x86_64=0.0001
-
-uk_2002_unweighted_numExecutors_x86_64=29
-uk_2002_unweighted_executorMemory_x86_64=31G
-uk_2002_unweighted_executorCores_x86_64=8
-uk_2002_unweighted_numPartitions_x86_64=240
-uk_2002_unweighted_ratio_x86_64=0.0001
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient.yml b/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient.yml
deleted file mode 100644
index 6a3b99d..0000000
--- a/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-clusteringCoefficient:
-  cit_patents:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/cit_patents
-    splitGraph: "\t"
-    isDirect: true
-
-  uk_2002:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/uk_2002
-    splitGraph: "\t"
-    isDirect: true
-
-  arabic_2005:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/arabic_2005
-    splitGraph: "\t"
-    isDirect: true
-
-  graph500_22:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/graph500_22
-    splitGraph: " "
-    isDirect: false
-
-  graph500_23:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/graph500_23
-    splitGraph: " "
-    isDirect: false
-
-  graph500_24:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/graph500_24
-    splitGraph: " "
-    isDirect: false
-
-  graph500_25:
-    outputPath: hdfs:///tmp/graph/result/clusteringcoefficient/graph500_25
-    splitGraph: " "
-    isDirect: false
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient_spark.properties b/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient_spark.properties
deleted file mode 100644
index 233feaa..0000000
--- a/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient_spark.properties
+++ /dev/null
@@ -1,73 +0,0 @@
-deployMode=client
-driverMemory=50g
-master=yarn
-
-cit_patents_numExecutors_aarch64=71
-cit_patents_executorCores_aarch64=4
-cit_patents_executorMemory_aarch64=12G
-cit_patents_numPartitions_aarch64=720
-
-uk_2002_numExecutors_aarch64=71
-uk_2002_executorCores_aarch64=4
-uk_2002_executorMemory_aarch64=12G
-uk_2002_numPartitions_aarch64=720
-
-arabic_2005_numExecutors_aarch64=71
-arabic_2005_executorCores_aarch64=4
-arabic_2005_executorMemory_aarch64=12G
-arabic_2005_numPartitions_aarch64=720
-
-graph500_22_numExecutors_aarch64=71
-graph500_22_executorCores_aarch64=4
-graph500_22_executorMemory_aarch64=12G
-graph500_22_numPartitions_aarch64=720
-
-graph500_23_numExecutors_aarch64=71
-graph500_23_executorCores_aarch64=4
-graph500_23_executorMemory_aarch64=12G
-graph500_23_numPartitions_aarch64=720
-
-graph500_24_numExecutors_aarch64=71
-graph500_24_executorCores_aarch64=4
-graph500_24_executorMemory_aarch64=12G
-graph500_24_numPartitions_aarch64=720
-
-graph500_25_numExecutors_aarch64=71
-graph500_25_executorCores_aarch64=4
-graph500_25_executorMemory_aarch64=12G
-graph500_25_numPartitions_aarch64=720
-
-cit_patents_numExecutors_x86_64=71
-cit_patents_executorCores_x86_64=4
-cit_patents_executorMemory_x86_64=12G
-cit_patents_numPartitions_x86_64=720
-
-uk_2002_numExecutors_x86_64=71
-uk_2002_executorCores_x86_64=4
-uk_2002_executorMemory_x86_64=12G
-uk_2002_numPartitions_x86_64=720
-
-arabic_2005_numExecutors_x86_64=71
-arabic_2005_executorCores_x86_64=4
-arabic_2005_executorMemory_x86_64=12G
-arabic_2005_numPartitions_x86_64=720
-
-graph500_22_numExecutors_x86_64=59
-graph500_22_executorCores_x86_64=4
-graph500_22_executorMemory_x86_64=15G
-graph500_22_numPartitions_x86_64=720
-
-graph500_23_numExecutors_x86_64=59
-graph500_23_executorCores_x86_64=4
-graph500_23_executorMemory_x86_64=15G
-graph500_23_numPartitions_x86_64=720
-
-graph500_24_numExecutors_x86_64=59
-graph500_24_executorCores_x86_64=4
-graph500_24_executorMemory_x86_64=15G
-graph500_24_numPartitions_x86_64=720
-
-graph500_25_numExecutors_x86_64=59
-graph500_25_executorCores_x86_64=4
-graph500_25_executorMemory_x86_64=15G
-graph500_25_numPartitions_x86_64=720
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient_spark_opensource.properties b/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient_spark_opensource.properties
deleted file mode 100644
index a2b8704..0000000
--- a/tools/kal-test/conf/graph/clusteringcoefficient/clusteringcoefficient_spark_opensource.properties
+++ /dev/null
@@ -1,31 +0,0 @@
-deployMode=client
-driverMemory=50g
-master=yarn
-
-graph500_22_numExecutors_x86_64=29
-graph500_22_executorCores_x86_64=8
-graph500_22_executorMemory_x86_64=35G
-graph500_22_numPartitions_x86_64=232
-graph500_22_extraJavaOptions_x86_64=-Xms35g
-graph500_22_split=" "
-
-graph500_23_numExecutors_x86_64=29
-graph500_23_executorCores_x86_64=8
-graph500_23_executorMemory_x86_64=35G
-graph500_23_numPartitions_x86_64=300
-graph500_23_extraJavaOptions_x86_64=-Xms35g
-graph500_23_split=","
-
-graph500_24_numExecutors_x86_64=29
-graph500_24_executorCores_x86_64=8
-graph500_24_executorMemory_x86_64=35G
-graph500_24_numPartitions_x86_64=300
-graph500_24_extraJavaOptions_x86_64=-Xms35g
-graph500_24_split=" "
-
-graph500_25_numExecutors_x86_64=29
-graph500_25_executorCores_x86_64=8
-graph500_25_executorMemory_x86_64=35G
-graph500_25_numPartitions_x86_64=300
-graph500_25_extraJavaOptions_x86_64=-Xms35g
-graph500_25_split=" "
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/degree/degree.yml b/tools/kal-test/conf/graph/degree/degree.yml
deleted file mode 100644
index b02db62..0000000
--- a/tools/kal-test/conf/graph/degree/degree.yml
+++ /dev/null
@@ -1,26 +0,0 @@
-# Degrees dataset parameters
-
-degree:
-  it_2004:
-    outputPath: hdfs:///tmp/graph/result/degree/it_2004
-    splitGraph: " "
-
-  twitter7:
-    outputPath: hdfs:///tmp/graph/result/degree/twitter7
-    splitGraph: " "
-
-  uk_2007_05:
-    outputPath: hdfs:///tmp/graph/result/degree/uk_2007_05
-    splitGraph: "\t"
-
-  mycielskian20:
-    outputPath: hdfs:///tmp/graph/result/degree/mycielskian20
-    splitGraph: " "
-
-  gap_kron:
-    outputPath: hdfs:///tmp/graph/result/degree/gap_kron
-    splitGraph: " "
-
-  com_friendster:
-    outputPath: hdfs:///tmp/graph/result/degree/com_friendster
-    splitGraph: " "
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/degree/degree_spark.properties b/tools/kal-test/conf/graph/degree/degree_spark.properties
deleted file mode 100644
index 3a627ac..0000000
--- a/tools/kal-test/conf/graph/degree/degree_spark.properties
+++ /dev/null
@@ -1,93 +0,0 @@
-# Spark parameters
-deployMode=client
-
-inDegrees_it_2004_numExecutors_aarch64=35
-inDegrees_it_2004_executorCores_aarch64=8
-inDegrees_it_2004_executorMemory_aarch64=25G
-inDegrees_it_2004_numPartitions_aarch64=150
-
-inDegrees_twitter7_numExecutors_aarch64=35
-inDegrees_twitter7_executorCores_aarch64=8
-inDegrees_twitter7_executorMemory_aarch64=25G
-inDegrees_twitter7_numPartitions_aarch64=150
-
-inDegrees_uk_2007_05_numExecutors_aarch64=35
-inDegrees_uk_2007_05_executorCores_aarch64=8
-inDegrees_uk_2007_05_executorMemory_aarch64=25G
-inDegrees_uk_2007_05_numPartitions_aarch64=300
-
-outDegrees_it_2004_numExecutors_aarch64=35
-outDegrees_it_2004_executorCores_aarch64=8
-outDegrees_it_2004_executorMemory_aarch64=25G
-outDegrees_it_2004_numPartitions_aarch64=150
-
-outDegrees_twitter7_numExecutors_aarch64=59
-outDegrees_twitter7_executorCores_aarch64=4
-outDegrees_twitter7_executorMemory_aarch64=15G
-outDegrees_twitter7_numPartitions_aarch64=240
-
-outDegrees_uk_2007_05_numExecutors_aarch64=35
-outDegrees_uk_2007_05_executorCores_aarch64=8
-outDegrees_uk_2007_05_executorMemory_aarch64=25G
-outDegrees_uk_2007_05_numPartitions_aarch64=150
-
-degrees_mycielskian20_numExecutors_aarch64=47
-degrees_mycielskian20_executorCores_aarch64=6
-degrees_mycielskian20_executorMemory_aarch64=19G
-degrees_mycielskian20_numPartitions_aarch64=300
-
-degrees_gap_kron_numExecutors_aarch64=35
-degrees_gap_kron_executorCores_aarch64=8
-degrees_gap_kron_executorMemory_aarch64=25G
-degrees_gap_kron_numPartitions_aarch64=240
-
-degrees_com_friendster_numExecutors_aarch64=35
-degrees_com_friendster_executorCores_aarch64=8
-degrees_com_friendster_executorMemory_aarch64=25G
-degrees_com_friendster_numPartitions_aarch64=150
-
-inDegrees_it_2004_numExecutors_x86_64=59
-inDegrees_it_2004_executorCores_x86_64=4
-inDegrees_it_2004_executorMemory_x86_64=15G
-inDegrees_it_2004_numPartitions_x86_64=240
-
-inDegrees_twitter7_numExecutors_x86_64=29
-inDegrees_twitter7_executorCores_x86_64=8
-inDegrees_twitter7_executorMemory_x86_64=31G
-inDegrees_twitter7_numPartitions_x86_64=150
-
-inDegrees_uk_2007_05_numExecutors_x86_64=29
-inDegrees_uk_2007_05_executorCores_x86_64=8
-inDegrees_uk_2007_05_executorMemory_x86_64=31G
-inDegrees_uk_2007_05_numPartitions_x86_64=300
-
-outDegrees_it_2004_numExecutors_x86_64=29
-outDegrees_it_2004_executorCores_x86_64=8
-outDegrees_it_2004_executorMemory_x86_64=31G
-outDegrees_it_2004_numPartitions_x86_64=150
-
-outDegrees_twitter7_numExecutors_x86_64=29
-outDegrees_twitter7_executorCores_x86_64=8
-outDegrees_twitter7_executorMemory_x86_64=31G
-outDegrees_twitter7_numPartitions_x86_64=240
-
-outDegrees_uk_2007_05_numExecutors_x86_64=39
-outDegrees_uk_2007_05_executorCores_x86_64=6
-outDegrees_uk_2007_05_executorMemory_x86_64=23G
-outDegrees_uk_2007_05_numPartitions_x86_64=240
-
-degrees_mycielskian20_numExecutors_x86_64=39
-degrees_mycielskian20_executorCores_x86_64=6
-degrees_mycielskian20_executorMemory_x86_64=23G
-degrees_mycielskian20_numPartitions_x86_64=300
-
-degrees_gap_kron_numExecutors_x86_64=29
-degrees_gap_kron_executorCores_x86_64=8
-degrees_gap_kron_executorMemory_x86_64=31G
-degrees_gap_kron_numPartitions_x86_64=240
-
-degrees_com_friendster_numExecutors_x86_64=59
-degrees_com_friendster_executorCores_x86_64=4
-degrees_com_friendster_executorMemory_x86_64=15G
-degrees_com_friendster_numPartitions_x86_64=150
-
diff --git a/tools/kal-test/conf/graph/degree/degree_spark_raw.properties b/tools/kal-test/conf/graph/degree/degree_spark_raw.properties
deleted file mode 100644
index 6ecb621..0000000
--- a/tools/kal-test/conf/graph/degree/degree_spark_raw.properties
+++ /dev/null
@@ -1,47 +0,0 @@
-# Spark parameters
-deployMode=client
-
-inDegrees_it_2004_numExecutors=59
-inDegrees_it_2004_executorCores=4
-inDegrees_it_2004_executorMemory=15G
-inDegrees_it_2004_numPartitions=300
-
-inDegrees_twitter7_numExecutors=59
-inDegrees_twitter7_executorCores=4
-inDegrees_twitter7_executorMemory=15G
-inDegrees_twitter7_numPartitions=240
-
-inDegrees_uk_2007_05_numExecutors=39
-inDegrees_uk_2007_05_executorCores=6
-inDegrees_uk_2007_05_executorMemory=23G
-inDegrees_uk_2007_05_numPartitions=240
-
-outDegrees_it_2004_numExecutors=29
-outDegrees_it_2004_executorCores=8
-outDegrees_it_2004_executorMemory=31G
-outDegrees_it_2004_numPartitions=240
-
-outDegrees_twitter7_numExecutors=39
-outDegrees_twitter7_executorCores=6
-outDegrees_twitter7_executorMemory=23G
-outDegrees_twitter7_numPartitions=240
-
-outDegrees_uk_2007_05_numExecutors=59
-outDegrees_uk_2007_05_executorCores=4
-outDegrees_uk_2007_05_executorMemory=15G
-outDegrees_uk_2007_05_numPartitions=240
-
-degrees_mycielskian20_numExecutors=39
-degrees_mycielskian20_executorCores=6
-degrees_mycielskian20_executorMemory=23G
-degrees_mycielskian20_numPartitions=240
-
-degrees_gap_kron_numExecutors=59
-degrees_gap_kron_executorCores=4
-degrees_gap_kron_executorMemory=15G
-degrees_gap_kron_numPartitions=240
-
-degrees_com_friendster_numExecutors=59
-degrees_com_friendster_executorCores=4
-degrees_com_friendster_executorMemory=15G
-degrees_com_friendster_numPartitions=150
diff --git a/tools/kal-test/conf/graph/graph_datasets.properties b/tools/kal-test/conf/graph/graph_datasets.properties
deleted file mode 100644
index 236f774..0000000
--- a/tools/kal-test/conf/graph/graph_datasets.properties
+++ /dev/null
@@ -1,76 +0,0 @@
-# sparkVersion
-sparkVersion=spark2.3.2
-
-# kalVersion
-kalVersion=2.1.0
-
-# undirected graph dataset path
-graph500_19=hdfs:///tmp/graph/dataset/graph500-19.e
-graph500_22=hdfs:///tmp/graph_data/graph500-22.e
-graph500_23=hdfs:///tmp/graph_data/graph500-23.e
-graph500_24=hdfs:///tmp/graph_data/graph500-24.e
-graph500_25=hdfs:///tmp/graph_data/graph500-25.e
-graph500_26=hdfs:///tmp/graph_data/graph500-26.e
-liveJournal=hdfs:///tmp/graph/dataset/com-lj.ungraph.txt
-mycielskian20=hdfs:///tmp/graph/dataset/mycielskian20.mtx
-gap_kron=hdfs:///tmp/graph/dataset/GAP-kron.mtx
-twitter_2010=hdfs:///tmp/graph/
-com_friendster=hdfs:///tmp/graph/dataset/com-Friendster.mtx
-com_orkut=hdfs:///tmp/graph/dataset/com-orkut.ungraph.txt
-GAP_road=hdfs:///tmp/graphData/wpr/GAP-road.mtx
-twitter_2010=hdfs:///tmp/incpr/incData/twitter-2010_0.001_batch_1
-
-# directed graph dataset path
-cit_patents=hdfs:///tmp/graph/dataset/cit-Patents.txt
-uk_2002=hdfs:///tmp/graph/dataset/uk-2002-edgelist.txt
-arabic_2005=hdfs:///tmp/graph/dataset/arabic-2005-edgelist.txt
-enwiki_2018=hdfs:///tmp/graph/dataset/enwiki-2018-edgelist.txt
-simulate1=hdfs:///tmp/graph/dataset/Simulate1.csv
-simulate2=hdfs:///tmp/graph/dataset/Simulate2.csv
-usaRoad=hdfs:///tmp/graph/dataset/USA-road-d.USA.gr
-it_2004=hdfs:///tmp/graph/dataset/it-2004.mtx
-twitter7=hdfs:///tmp/graph/dataset/twitter7.mtx
-uk_2007_05=hdfs:///tmp/graph/dataset/uk-2007-05-edgelist.txt
-soc_liveJournal=hdfs:///tmp/graph/dataset/soc-LiveJournal1.txt
-
-
-# community/edgelist data path
-uk=hdfs:///tmp/modularity/graph/uk-2002-edgelist.txt
-arabic=hdfs:///tmp/modularity/graph/arabic-2005-edgelist.txt
-twitter=hdfs:///tmp/modularity/graph/twitter-2010-edgelist.txt
-
-# groundTruth data path
-cit_patents_gt=hdfs:///tmp/graph/dataset/cit_gt.txt
-enwiki_2018_gt=hdfs:///tmp/graph/dataset/enwiki_gt.txt
-uk_2002_gt=hdfs:///tmp/graph/dataset/uk_gt.txt
-
-# negEdge data path
-cit_patents_negEdge=hdfs:///tmp/graph/dataset/cit-Patents-negEdges.txt
-soc_liveJournal_negEdge=hdfs:///tmp/graph/dataset/soc-livejournal-negEdges.txt
-uk_2002_negEdge=hdfs:///tmp/graph/dataset/uk-2002-negEdges.txt
-
-# sgm query graph path
-query_4clique=hdfs:///tmp/graph/dataset/sgmQueryGraph/4clique.txt
-query_4sqr=hdfs:///tmp/graph/dataset/sgmQueryGraph/4sqr.txt
-query_4dgn=hdfs:///tmp/graph/dataset/sgmQueryGraph/4sqr-dgn.txt
-query_5clique=hdfs:///tmp/graph/dataset/sgmQueryGraph/5clique.txt
-query_5tree=hdfs:///tmp/graph/dataset/sgmQueryGraph/5tree.txt
-query_6clique=hdfs:///tmp/graph/dataset/sgmQueryGraph/6clique.txt
-query_6star=hdfs:///tmp/graph/dataset/sgmQueryGraph/6star.txt
-
-# output data path
-uk_community=hdfs:///tmp/modularity/community/directed/community_louvain_uk-2002-edgelist
-arabic_community=hdfs:///tmp/modularity/community/directed/community_louvain_arabic-2005-edgelist
-twitter_community=hdfs:///tmp/modularity/community/directed/community_louvain_twitter-2010-edgelist
-graph500_23_community=hdfs:///tmp/modularity/community/undirected/community_louvain_graph500-23
-graph500_25_community=hdfs:///tmp/modularity/community/undirected/community_louvain_graph500-25
-graph500_26_community=hdfs:///tmp/modularity/community/undirected/community_louvain_graph500-26
-graph500_23_mce=hdfs:///tmp/test/mce/graph500_23_test
-graph500_24_mce=hdfs:///tmp/test/mce/graph500_24_test
-graph500_25_mce=hdfs:///tmp/test/mce/graph500_25_test
-graph500_22_tc=hdfs:///tmp/test/tc/graph500_22_test
-graph500_23_tc=hdfs:///tmp/test/tc/graph500_23_test
-graph500_24_tc=hdfs:///tmp/test/tc/graph500_24_test
-graph500_25_tc=hdfs:///tmp/test/tc/graph500_25_test
-graph500_26_tc=hdfs:///tmp/test/tc/graph500_26_test
-output_path_prefix=hdfs:///tmp/graph/result/
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/incpr/incpr.yml b/tools/kal-test/conf/graph/incpr/incpr.yml
deleted file mode 100644
index 68817a3..0000000
--- a/tools/kal-test/conf/graph/incpr/incpr.yml
+++ /dev/null
@@ -1,10 +0,0 @@
-# incPageRank dataset parameters arm
-
-incpr:
-  twitter_2010:
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    partNum: 273
-    inputPath: hdfs:///tmp/incpr/incData/twitter-2010_0.001_batch_1
-    outputPath: hdfs:///tmp/incpr/res/output
diff --git a/tools/kal-test/conf/graph/incpr/incpr_spark.properties b/tools/kal-test/conf/graph/incpr/incpr_spark.properties
deleted file mode 100644
index 41ccd43..0000000
--- a/tools/kal-test/conf/graph/incpr/incpr_spark.properties
+++ /dev/null
@@ -1,21 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=71
-executorCores_aarch64=4
-executorMemory_aarch64=12G
-execMemOverhead_aarch64=2G
-extraJavaOptions_aarch64=-Xms12g
-
-driverCores_x86_64=36
-driverMemory_x86_64=50G
-numExectuors_x86_64=71
-executorCores_x86_64=4
-executorMemory_x86_64=15G
-execMemOverhead_x86_64=2G
-extraJavaOptions_x86_64=-Xms15g
-numPartitions_x86_64=100
-basic_input_path=hdfs:///tmp
-basic_output_path=hdfs:///out
diff --git a/tools/kal-test/conf/graph/kcore/kcore.yml b/tools/kal-test/conf/graph/kcore/kcore.yml
deleted file mode 100644
index 2ee40af..0000000
--- a/tools/kal-test/conf/graph/kcore/kcore.yml
+++ /dev/null
@@ -1,25 +0,0 @@
-partition:
-  graph500_22_x86_64_yes: 216
-  graph500_23_x86_64_yes: 216
-  graph500_25_x86_64_yes: 236
-  graph500_26_x86_64_yes: 236
-
-  graph500_22_aarch64_no: 240
-  graph500_23_aarch64_no: 240
-  graph500_25_aarch64_no: 552
-  graph500_26_aarch64_no: 280
-
-  graph500_22_x86_64_no: 228
-  graph500_23_x86_64_no: 228
-  graph500_25_x86_64_no: 232
-  graph500_26_x86_64_no: 232
-split:
-  graph500_22: " "
-  graph500_23: " "
-  graph500_25: " "
-  graph500_26: " "
-iterNum:
-  graph500_22: 28
-  graph500_23: 44
-  graph500_25: 85
-  graph500_26: 38
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/kcore/kcore_spark.properties b/tools/kal-test/conf/graph/kcore/kcore_spark.properties
deleted file mode 100644
index 35d8d69..0000000
--- a/tools/kal-test/conf/graph/kcore/kcore_spark.properties
+++ /dev/null
@@ -1,45 +0,0 @@
-numExecutors_graph500_22_aarch64=12
-numExecutors_graph500_23_aarch64=12
-numExecutors_graph500_25_aarch64=23
-numExecutors_graph500_26_aarch64=35
-executorMemory_graph500_22_aarch64=78g
-executorMemory_graph500_23_aarch64=78g
-executorMemory_graph500_25_aarch64=38g
-executorMemory_graph500_26_aarch64=25g
-executorCores_graph500_22_aarch64=20
-executorCores_graph500_23_aarch64=20
-executorCores_graph500_25_aarch64=12
-executorCores_graph500_26_aarch64=8
-executorExtraJavaopts_graph500_22_aarch64="-Xms78g"
-executorExtraJavaopts_graph500_23_aarch64="-Xms78g"
-executorExtraJavaopts_graph500_25_aarch64="-Xms38g"
-executorExtraJavaopts_graph500_26_aarch64="-Xms25g"
-executorMemoryOverhead_graph500_22_aarch64=7985
-executorMemoryOverhead_graph500_23_aarch64=7985
-executorMemoryOverhead_graph500_25_aarch64=3892
-executorMemoryOverhead_graph500_26_aarch64=4096
-
-numExecutors_graph500_22_x86_64=12
-numExecutors_graph500_23_x86_64=12
-numExecutors_graph500_25_x86_64=29
-numExecutors_graph500_26_x86_64=29
-executorMemory_graph500_22_x86_64=78g
-executorMemory_graph500_23_x86_64=78g
-executorMemory_graph500_25_x86_64=31g
-executorMemory_graph500_26_x86_64=31g
-executorCores_graph500_22_x86_64=19
-executorCores_graph500_23_x86_64=19
-executorCores_graph500_25_x86_64=8
-executorCores_graph500_26_x86_64=8
-executorExtraJavaopts_graph500_22_x86_64="-Xms78g"
-executorExtraJavaopts_graph500_23_x86_64="-Xms78g"
-executorExtraJavaopts_graph500_25_x86_64="-Xms31g"
-executorExtraJavaopts_graph500_26_x86_64="-Xms29g"
-executorMemoryOverhead_graph500_22_x86_64=7985
-executorMemoryOverhead_graph500_23_x86_64=7985
-executorMemoryOverhead_graph500_25_x86_64=3175
-executorMemoryOverhead_graph500_26_x86_64=4096
-
-master=yarn
-deployMode=client
-driverMemory=200g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/kcore/kcore_spark_raw.properties b/tools/kal-test/conf/graph/kcore/kcore_spark_raw.properties
deleted file mode 100644
index 1c542ed..0000000
--- a/tools/kal-test/conf/graph/kcore/kcore_spark_raw.properties
+++ /dev/null
@@ -1,24 +0,0 @@
-numExecutors_graph500_22=18
-numExecutors_graph500_23=18
-numExecutors_graph500_25=59
-numExecutors_graph500_26=59
-executorMemory_graph500_22=52g
-executorMemory_graph500_23=52g
-executorMemory_graph500_25=15g
-executorMemory_graph500_26=15g
-executorCores_graph500_22=12
-executorCores_graph500_23=12
-executorCores_graph500_25=4
-executorCores_graph500_26=4
-executorExtraJavaopts_graph500_22="-Xms52g"
-executorExtraJavaopts_graph500_23="-Xms52g"
-executorExtraJavaopts_graph500_25="-Xms15g"
-executorExtraJavaopts_graph500_26="-Xms15g"
-executorMemoryOverhead_graph500_22=5325
-executorMemoryOverhead_graph500_23=5325
-executorMemoryOverhead_graph500_25=1536
-executorMemoryOverhead_graph500_26=4096
-
-master=yarn
-deployMode=client
-driverMemory=200g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/louvain/louvain.yml b/tools/kal-test/conf/graph/louvain/louvain.yml
deleted file mode 100644
index 0f4e6d0..0000000
--- a/tools/kal-test/conf/graph/louvain/louvain.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-# Louvain dataset parameters
-
-louvain:
-  graph500_22:
-    outputPath: hdfs:///tmp/graph/result/louvain/graph500_22
-    splitGraph: " "
-    maxIterations: 20
-    isDirected: false
-
-  graph500_24:
-    outputPath: hdfs:///tmp/graph/result/louvain/graph500_24
-    splitGraph: " "
-    maxIterations: 20
-    isDirected: false
-
-  graph500_25:
-    outputPath: hdfs:///tmp/graph/result/louvain/graph500_25
-    splitGraph: " "
-    maxIterations: 20
-    isDirected: false
-
-  cit_patents:
-    outputPath: hdfs:///tmp/graph/result/louvain/cit_patents
-    splitGraph: "\t"
-    maxIterations: 20
-    isDirected: true
-
-  uk_2002:
-    outputPath: hdfs:///tmp/graph/result/louvain/uk_2002
-    splitGraph: "\t"
-    maxIterations: 20
-    isDirected: true
-
-  arabic_2005:
-    outputPath: hdfs:///tmp/graph/result/louvain/arabic_2005
-    splitGraph: "\t"
-    maxIterations: 20
-    isDirected: true
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/louvain/louvain_spark.properties b/tools/kal-test/conf/graph/louvain/louvain_spark.properties
deleted file mode 100644
index 082d0be..0000000
--- a/tools/kal-test/conf/graph/louvain/louvain_spark.properties
+++ /dev/null
@@ -1,74 +0,0 @@
-# Spark parameters
-deployMode=client
-
-graph500_22_numExecutors_aarch64=35
-graph500_22_executorCores_aarch64=8
-graph500_22_executorMemory_aarch64=25G
-graph500_22_extraJavaOptions_aarch64=-Xms25G
-graph500_22_numPartitions_aarch64=280
-
-graph500_24_numExecutors_aarch64=71
-graph500_24_executorCores_aarch64=4
-graph500_24_executorMemory_aarch64=12G
-graph500_24_extraJavaOptions_aarch64=-Xms12G
-graph500_24_numPartitions_aarch64=284
-
-graph500_25_numExecutors_aarch64=71
-graph500_25_executorCores_aarch64=4
-graph500_25_executorMemory_aarch64=12G
-graph500_25_extraJavaOptions_aarch64=-Xms12G
-graph500_25_numPartitions_aarch64=568
-
-cit_patents_numExecutors_aarch64=35
-cit_patents_executorCores_aarch64=8
-cit_patents_executorMemory_aarch64=25G
-cit_patents_extraJavaOptions_aarch64=-Xms25G
-cit_patents_numPartitions_aarch64=280
-
-uk_2002_numExecutors_aarch64=71
-uk_2002_executorCores_aarch64=4
-uk_2002_executorMemory_aarch64=12G
-uk_2002_extraJavaOptions_aarch64=-Xms12G
-uk_2002_numPartitions_aarch64=284
-
-arabic_2005_numExecutors_aarch64=71
-arabic_2005_executorCores_aarch64=4
-arabic_2005_executorMemory_aarch64=12G
-arabic_2005_extraJavaOptions_aarch64=-Xms12G
-arabic_2005_numPartitions_aarch64=284
-
-graph500_22_numExecutors_x86_64=29
-graph500_22_executorCores_x86_64=8
-graph500_22_executorMemory_x86_64=31G
-graph500_22_extraJavaOptions_x86_64=-Xms31G
-graph500_22_numPartitions_x86_64=232
-
-graph500_24_numExecutors_x86_64=59
-graph500_24_executorCores_x86_64=4
-graph500_24_executorMemory_x86_64=15G
-graph500_24_extraJavaOptions_x86_64=-Xms15G
-graph500_24_numPartitions_x86_64=236
-
-graph500_25_numExecutors_x86_64=59
-graph500_25_executorCores_x86_64=4
-graph500_25_executorMemory_x86_64=15G
-graph500_25_extraJavaOptions_x86_64=-Xms15G
-graph500_25_numPartitions_x86_64=472
-
-cit_patents_numExecutors_x86_64=29
-cit_patents_executorCores_x86_64=8
-cit_patents_executorMemory_x86_64=31G
-cit_patents_extraJavaOptions_x86_64=-Xms31G
-cit_patents_numPartitions_x86_64=232
-
-uk_2002_numExecutors_x86_64=59
-uk_2002_executorCores_x86_64=4
-uk_2002_executorMemory_x86_64=15G
-uk_2002_extraJavaOptions_x86_64=-Xms15G
-uk_2002_numPartitions_x86_64=236
-
-arabic_2005_numExecutors_x86_64=59
-arabic_2005_executorCores_x86_64=4
-arabic_2005_executorMemory_x86_64=15G
-arabic_2005_extraJavaOptions_x86_64=-Xms15g
-arabic_2005_numPartitions_x86_64=236
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/lpa/lpa.yml b/tools/kal-test/conf/graph/lpa/lpa.yml
deleted file mode 100644
index 09c5e0d..0000000
--- a/tools/kal-test/conf/graph/lpa/lpa.yml
+++ /dev/null
@@ -1,12 +0,0 @@
-partition:
-  graph500_22_x86_64_yes: 472
-  graph500_22_x86_64_no: 235
-  graph500_22_aarch64_no: 282
-  graph500_24_x86_64_yes: 702
-  graph500_24_x86_64_no: 225
-  graph500_24_aarch64_no: 282
-  graph500_25_x86_64_yes: 472
-  graph500_25_x86_64_no: 225
-  graph500_25_aarch64_no: 282
-split: " "
-maxSteps: 10
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/lpa/lpa_spark.properties b/tools/kal-test/conf/graph/lpa/lpa_spark.properties
deleted file mode 100644
index 7d43f59..0000000
--- a/tools/kal-test/conf/graph/lpa/lpa_spark.properties
+++ /dev/null
@@ -1,13 +0,0 @@
-numExecutors_aarch64=47
-executorMemory_aarch64=19g
-executorCores_aarch64=6
-executorExtraJavaopts_aarch64="-Xms19g"
-
-numExecutors_x86_64=47
-executorMemory_x86_64=19g
-executorCores_x86_64=5
-executorExtraJavaopts_x86_64="-Xms19g"
-
-master=yarn
-deployMode=client
-driverMemory=300g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/lpa/lpa_spark_raw.properties b/tools/kal-test/conf/graph/lpa/lpa_spark_raw.properties
deleted file mode 100644
index c0065f4..0000000
--- a/tools/kal-test/conf/graph/lpa/lpa_spark_raw.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-numExecutors_graph500_22=59
-executorMemory_graph500_22=15g
-executorCores_graph500_22=4
-executorExtraJavaopts_graph500_22="-Xms15g"
-
-numExecutors_graph500_24=78
-executorMemory_graph500_24=12g
-executorCores_graph500_24=3
-executorExtraJavaopts_graph500_24="-Xms12g"
-
-numExecutors_graph500_25=59
-executorMemory_graph500_25=15g
-executorCores_graph500_25=4
-executorExtraJavaopts_graph500_25="-Xms15g"
-
-master=yarn
-deployMode=client
-driverMemory=300g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/mce/mce.yml b/tools/kal-test/conf/graph/mce/mce.yml
deleted file mode 100644
index 3d80481..0000000
--- a/tools/kal-test/conf/graph/mce/mce.yml
+++ /dev/null
@@ -1,11 +0,0 @@
-partition:
-  graph500_23: 284
-  graph500_24: 284
-  graph500_25: 568
-minK: 3
-maxDegree: 2000
-split:
-  graph500_23: " "
-  graph500_24: " "
-  graph500_25: " "
-
diff --git a/tools/kal-test/conf/graph/mce/mce_spark.properties b/tools/kal-test/conf/graph/mce/mce_spark.properties
deleted file mode 100644
index 9cba801..0000000
--- a/tools/kal-test/conf/graph/mce/mce_spark.properties
+++ /dev/null
@@ -1,9 +0,0 @@
-numExecutors_aarch64=71
-numExecutors_x86_64=59
-executorCores=4
-executorMemory_aarch64=13G
-executorMemory_x86_64=15g
-extraJavaOptions=""
-master=yarn
-deployMode=client
-driverMemory=80g
diff --git a/tools/kal-test/conf/graph/modularity/modularity.yml b/tools/kal-test/conf/graph/modularity/modularity.yml
deleted file mode 100644
index 3df1dc1..0000000
--- a/tools/kal-test/conf/graph/modularity/modularity.yml
+++ /dev/null
@@ -1,56 +0,0 @@
-# WCE dataset parameters
-
-modularity:
-  graph500_23:
-    inputPath: hdfs:///tmp/modularity/graph/graph500-23.e
-    inputCommunity: hdfs:///tmp/modularity/lpa_community/graph500-23.e
-    splitGraph: " "
-    splitCommunity: " "
-    isWeighted: false
-    isDirected: false
-    numPartitions: 200
-
-  graph500_25:
-    inputPath: hdfs:///tmp/modularity/graph/graph500-25.e
-    inputCommunity: hdfs:///tmp/modularity/lpa_community/graph500-25.txt
-    splitGraph: " "
-    splitCommunity: ","
-    isWeighted: false
-    isDirected: false
-    numPartitions: 500
-
-  graph500_26:
-    inputPath: hdfs:///tmp/modularity/graph/graph500-26.e
-    inputCommunity: hdfs:///tmp/modularity/lpa_community/graph500-26.e
-    splitGraph: " "
-    splitCommunity: " "
-    isWeighted: false
-    isDirected: false
-    numPartitions: 500
-
-  uk:
-    inputPath: hdfs:///tmp/modularity/graph/uk-2002-edgelist.txt
-    inputCommunity: hdfs:///tmp/modularity/lpa_community/uk-2002-edgelist.txt
-    splitGraph: "\t"
-    splitCommunity: ","
-    isWeighted: false
-    isDirected: true
-    numPartitions: 500
-
-  arabic:
-    inputPath: hdfs:///tmp/modularity/graph/arabic-2005-edgelist.txt
-    inputCommunity: hdfs:///tmp/modularity/lpa_community/arabic-2005-edgelist.txt
-    splitGraph: "\t"
-    splitCommunity: ","
-    isWeighted: false
-    isDirected: true
-    numPartitions: 500
-
-  twitter:
-    inputPath: hdfs:///tmp/modularity/graph/twitter-2010-edgelist.txt
-    inputCommunity: hdfs:///tmp/modularity/lpa_community/twitter-2010-edgelist.txt
-    splitGraph: "\t"
-    splitCommunity: ","
-    isWeighted: false
-    isDirected: true
-    numPartitions: 1000
diff --git a/tools/kal-test/conf/graph/modularity/modularity_spark.properties b/tools/kal-test/conf/graph/modularity/modularity_spark.properties
deleted file mode 100644
index 838bae3..0000000
--- a/tools/kal-test/conf/graph/modularity/modularity_spark.properties
+++ /dev/null
@@ -1,60 +0,0 @@
-# Spark parameters
-graph500_23_numExectuors_aarch64=35
-graph500_23_executorCores_aarch64=8
-graph500_23_executorMemory_aarch64=25G
-graph500_23_extraJavaOptions_aarch64=-Xms25g
-
-graph500_25_numExectuors_aarch64=35
-graph500_25_executorCores_aarch64=8
-graph500_25_executorMemory_aarch64=25G
-graph500_25_extraJavaOptions_aarch64=-Xms25g
-
-graph500_26_numExectuors_aarch64=90
-graph500_26_executorCores_aarch64=3
-graph500_26_executorMemory_aarch64=11G
-graph500_26_extraJavaOptions_aarch64=-Xms11g
-
-uk_numExectuors_aarch64=47
-uk_executorCores_aarch64=6
-uk_executorMemory_aarch64=19G
-uk_extraJavaOptions_aarch64=-Xms19g
-
-arabic_numExectuors_aarch64=47
-arabic_executorCores_aarch64=6
-arabic_executorMemory_aarch64=19G
-arabic_extraJavaOptions_aarch64=-Xms19g
-
-twitter_numExectuors_aarch64=95
-twitter_executorCores_aarch64=3
-twitter_executorMemory_aarch64=9G
-twitter_extraJavaOptions_aarch64=-Xms9g
-
-graph500_23_numExectuors_x86_64=33
-graph500_23_executorCores_x86_64=7
-graph500_23_executorMemory_x86_64=28G
-graph500_23_extraJavaOptions_x86_64=-Xms28g
-
-graph500_25_numExectuors_x86_64=33
-graph500_25_executorCores_x86_64=7
-graph500_25_executorMemory_x86_64=28G
-graph500_25_extraJavaOptions_x86_64=-Xms28g
-
-graph500_26_numExectuors_x86_64=78
-graph500_26_executorCores_x86_64=3
-graph500_26_executorMemory_x86_64=11G
-graph500_26_extraJavaOptions_x86_64=-Xms11g
-
-uk_numExectuors_x86_64=33
-uk_executorCores_x86_64=7
-uk_executorMemory_x86_64=28G
-uk_extraJavaOptions_x86_64=-Xms28g
-
-arabic_numExectuors_x86_64=47
-arabic_executorCores_x86_64=5
-arabic_executorMemory_x86_64=19G
-arabic_extraJavaOptions_x86_64=-Xms19g
-
-twitter_numExectuors_x86_64=78
-twitter_executorCores_x86_64=3
-twitter_executorMemory_x86_64=11G
-twitter_extraJavaOptions_x86_64=-Xms11g
diff --git a/tools/kal-test/conf/graph/mssp/mssp.yml b/tools/kal-test/conf/graph/mssp/mssp.yml
deleted file mode 100644
index 7186939..0000000
--- a/tools/kal-test/conf/graph/mssp/mssp.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-# MSSP dataset parameters
-
-mssp:
-  uk_2002_5:
-    inputPath: hdfs:///tmp/graph/dataset/uk-2002-edgelist.txt
-    sourcePath: hdfs:///tmp/graph/mssp/source/uk-random_5
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/mssp/uk_2002_5
-
-  uk_2002_50:
-    inputPath: hdfs:///tmp/graph/dataset/uk-2002-edgelist.txt
-    sourcePath: hdfs:///tmp/graph/mssp/source/uk-random_50
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/mssp/uk_2002_50
-
-  arabic_2005_5:
-    inputPath: hdfs:///tmp/graph/dataset/arabic-2005-edgelist.txt
-    sourcePath: hdfs:///tmp/graph/mssp/source/arabic-random_5
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/mssp/arabic_2005_5
-
-  arabic_2005_50:
-    inputPath: hdfs:///tmp/graph/dataset/arabic-2005-edgelist.txt
-    sourcePath: hdfs:///tmp/graph/mssp/source/arabic-random_50
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/mssp/arabic_2005_50
-
-  livejournal_5:
-    inputPath: hdfs:///tmp/graph/dataset/soc-LiveJournal1.txt
-    sourcePath: hdfs:///tmp/graph/mssp/source/livejournal-random_5
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/mssp/livejournal_5
-
-  livejournal_50:
-    inputPath: hdfs:///tmp/graph/dataset/soc-LiveJournal1.txt
-    sourcePath: hdfs:///tmp/graph/mssp/source/livejournal-random_50
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/mssp/livejournal_50
diff --git a/tools/kal-test/conf/graph/mssp/mssp_spark.properties b/tools/kal-test/conf/graph/mssp/mssp_spark.properties
deleted file mode 100644
index d2233da..0000000
--- a/tools/kal-test/conf/graph/mssp/mssp_spark.properties
+++ /dev/null
@@ -1,128 +0,0 @@
-# Spark parameters
-
-uk_2002_5_numExectuors_aarch64=35
-uk_2002_5_executorCores_aarch64=8
-uk_2002_5_executorMemory_aarch64=25g
-uk_2002_5_driverMemory_aarch64=200g
-uk_2002_5_extraJavaOptions_aarch64=-Xms25g
-uk_2002_5_computePartition_aarch64=280
-
-uk_2002_50_numExectuors_aarch64=35
-uk_2002_50_executorCores_aarch64=8
-uk_2002_50_executorMemory_aarch64=25g
-uk_2002_50_driverMemory_aarch64=200g
-uk_2002_50_extraJavaOptions_aarch64=-Xms25g
-uk_2002_50_computePartition_aarch64=280
-
-arabic_2005_5_numExectuors_aarch64=35
-arabic_2005_5_executorCores_aarch64=8
-arabic_2005_5_executorMemory_aarch64=25g
-arabic_2005_5_driverMemory_aarch64=200g
-arabic_2005_5_extraJavaOptions_aarch64=-Xms25g
-arabic_2005_5_computePartition_aarch64=280
-
-arabic_2005_50_numExectuors_aarch64=35
-arabic_2005_50_executorCores_aarch64=8
-arabic_2005_50_executorMemory_aarch64=25g
-arabic_2005_50_driverMemory_aarch64=200g
-arabic_2005_50_extraJavaOptions_aarch64=-Xms25g
-arabic_2005_50_computePartition_aarch64=280
-
-livejournal_5_numExectuors_aarch64=35
-livejournal_5_executorCores_aarch64=8
-livejournal_5_executorMemory_aarch64=25g
-livejournal_5_driverMemory_aarch64=200g
-livejournal_5_extraJavaOptions_aarch64=-Xms25g
-livejournal_5_computePartition_aarch64=280
-
-livejournal_50_numExectuors_aarch64=35
-livejournal_50_executorCores_aarch64=8
-livejournal_50_executorMemory_aarch64=25g
-livejournal_50_driverMemory_aarch64=200g
-livejournal_50_extraJavaOptions_aarch64=-Xms25g
-livejournal_50_computePartition_aarch64=280
-
-uk_2002_5_numExectuors_x86_64=59
-uk_2002_5_executorCores_x86_64=4
-uk_2002_5_executorMemory_x86_64=15g
-uk_2002_5_driverMemory_x86_64=200g
-uk_2002_5_extraJavaOptions_x86_64=-Xms15g
-uk_2002_5_computePartition_x86_64=236
-
-uk_2002_50_numExectuors_x86_64=59
-uk_2002_50_executorCores_x86_64=4
-uk_2002_50_executorMemory_x86_64=15g
-uk_2002_50_driverMemory_x86_64=200g
-uk_2002_50_extraJavaOptions_x86_64=-Xms15g
-uk_2002_50_computePartition_x86_64=236
-
-arabic_2005_5_numExectuors_x86_64=59
-arabic_2005_5_executorCores_x86_64=4
-arabic_2005_5_executorMemory_x86_64=15g
-arabic_2005_5_driverMemory_x86_64=200g
-arabic_2005_5_extraJavaOptions_x86_64=-Xms15g
-arabic_2005_5_computePartition_x86_64=236
-
-arabic_2005_50_numExectuors_x86_64=59
-arabic_2005_50_executorCores_x86_64=4
-arabic_2005_50_executorMemory_x86_64=15g
-arabic_2005_50_driverMemory_x86_64=200g
-arabic_2005_50_extraJavaOptions_x86_64=-Xms15g
-arabic_2005_50_computePartition_x86_64=236
-
-livejournal_5_numExectuors_x86_64=29
-livejournal_5_executorCores_x86_64=8
-livejournal_5_executorMemory_x86_64=31g
-livejournal_5_driverMemory_x86_64=200g
-livejournal_5_extraJavaOptions_x86_64=-Xms31g
-livejournal_5_computePartition_x86_64=232
-
-livejournal_50_numExectuors_x86_64=29
-livejournal_50_executorCores_x86_64=8
-livejournal_50_executorMemory_x86_64=31g
-livejournal_50_driverMemory_x86_64=200g
-livejournal_50_extraJavaOptions_x86_64=-Xms31g
-livejournal_50_computePartition_x86_64=232
-
-#raw params
-uk_2002_5_numExectuors_x86_64raw=35
-uk_2002_5_executorCores_x86_64raw=8
-uk_2002_5_executorMemory_x86_64raw=25g
-uk_2002_5_driverMemory_x86_64raw=200g
-uk_2002_5_extraJavaOptions_x86_64raw=-Xms25g
-uk_2002_5_computePartition_x86_64raw=280
-
-uk_2002_50_numExectuors_x86_64raw=35
-uk_2002_50_executorCores_x86_64raw=8
-uk_2002_50_executorMemory_x86_64raw=25g
-uk_2002_50_driverMemory_x86_64raw=200g
-uk_2002_50_extraJavaOptions_x86_64raw=-Xms25g
-uk_2002_50_computePartition_x86_64raw=280
-
-arabic_2005_5_numExectuors_x86_64raw=35
-arabic_2005_5_executorCores_x86_64raw=8
-arabic_2005_5_executorMemory_x86_64raw=25g
-arabic_2005_5_driverMemory_x86_64raw=200g
-arabic_2005_5_extraJavaOptions_x86_64raw=-Xms25g
-arabic_2005_5_computePartition_x86_64raw=280
-
-arabic_2005_50_numExectuors_x86_64raw=35
-arabic_2005_50_executorCores_x86_64raw=8
-arabic_2005_50_executorMemory_x86_64raw=25g
-arabic_2005_50_driverMemory_x86_64raw=200g
-arabic_2005_50_extraJavaOptions_x86_64raw=-Xms25g
-arabic_2005_50_computePartition_x86_64raw=280
-
-livejournal_5_numExectuors_x86_64raw=35
-livejournal_5_executorCores_x86_64raw=8
-livejournal_5_executorMemory_x86_64raw=25g
-livejournal_5_driverMemory_x86_64raw=200g
-livejournal_5_extraJavaOptions_x86_64raw=-Xms25g
-livejournal_5_computePartition_x86_64raw=280
-
-livejournal_50_numExectuors_x86_64raw=35
-livejournal_50_executorCores_x86_64raw=8
-livejournal_50_executorMemory_x86_64raw=25g
-livejournal_50_driverMemory_x86_64raw=200g
-livejournal_50_extraJavaOptions_x86_64raw=-Xms25g
-livejournal_50_computePartition_x86_64raw=280
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/node2vec/node2vec.yml b/tools/kal-test/conf/graph/node2vec/node2vec.yml
deleted file mode 100644
index c4578b3..0000000
--- a/tools/kal-test/conf/graph/node2vec/node2vec.yml
+++ /dev/null
@@ -1,44 +0,0 @@
-# Node2vec dataset parameters arm
-
-node2vec:
-  cit_patents:
-    partitions: 240
-    directed: true
-    weighted: false
-    walkLength: 20
-    numWalks: 5
-    p: 1.0
-    q: 1.0
-    iteration: 10
-    dimension: 128
-    windowSize: 20
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/node2vec/cit_patents/cit-test.txt
-
-  soc_liveJournal:
-    partitions: 240
-    directed: true
-    weighted: false
-    walkLength: 5
-    numWalks: 5
-    p: 1.0
-    q: 1.0
-    iteration: 10
-    dimension: 128
-    windowSize: 20
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/node2vec/soc_liveJournal/soc-test.txt
-
-  uk_2002:
-    partitions: 240
-    directed: true
-    weighted: false
-    walkLength: 5
-    numWalks: 1
-    p: 1.0
-    q: 1.0
-    iteration: 10
-    dimension: 128
-    windowSize: 20
-    splitGraph: "\t"
-    outputPath: hdfs:///tmp/graph/result/node2vec/uk_2002/uk-test.txt
diff --git a/tools/kal-test/conf/graph/node2vec/node2vec_spark.properties b/tools/kal-test/conf/graph/node2vec/node2vec_spark.properties
deleted file mode 100644
index 0fa2904..0000000
--- a/tools/kal-test/conf/graph/node2vec/node2vec_spark.properties
+++ /dev/null
@@ -1,13 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverMemory=300G
-executorMemory=315g
-numExecutors=3
-
-driverCores_aarch64=91
-executorCores_aarch64=94
-
-driverCores_x86_64=75
-executorCores_x86_64=78
-
diff --git a/tools/kal-test/conf/graph/node2vec/node2vec_spark_opensource.properties b/tools/kal-test/conf/graph/node2vec/node2vec_spark_opensource.properties
deleted file mode 100644
index a846046..0000000
--- a/tools/kal-test/conf/graph/node2vec/node2vec_spark_opensource.properties
+++ /dev/null
@@ -1,8 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverMemory=300G
-numExecutors=21
-executorCores=8
-executorMemory=43G
-driverCores=75G
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/ppr/ppr.yml b/tools/kal-test/conf/graph/ppr/ppr.yml
deleted file mode 100644
index 33d4e62..0000000
--- a/tools/kal-test/conf/graph/ppr/ppr.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-# PersonalizedPageRank dataset parameters arm
-
-ppr:
-  cit_patents:
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
-    sourcesPath: hdfs:///tmp/graph/dataset/ppr_sources/cit_patents
-
-  uk_2002:
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
-    sourcesPath: hdfs:///tmp/graph/dataset/ppr_sources/uk_2002
-
-  arabic_2005:
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
-    sourcesPath: hdfs:///tmp/graph/dataset/ppr_sources/arabic_2005
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/ppr/ppr_source_id.properties b/tools/kal-test/conf/graph/ppr/ppr_source_id.properties
deleted file mode 100644
index 82e2808..0000000
--- a/tools/kal-test/conf/graph/ppr/ppr_source_id.properties
+++ /dev/null
@@ -1,3 +0,0 @@
-cit_patents_SourceID=5622600,5481601,5675700,5900101,4930200,4956300,5861707,5877028,5573854,5901425,5837429
-uk_2002_SourceID=16450200,13157400,1074900,3930300,17115000,11099400,4335300,10651502,9378301,1895701,4714502,14070301,4997400
-arabic_2005_SourceID=278400,4056900,10197600,19206300,7866300,11463600,7872900,12360300,1567500,13875000,16428900,17001300,8457300,3451800,10769700,20033100,20018700,16322400,5136900,6093600
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/ppr/ppr_spark.properties b/tools/kal-test/conf/graph/ppr/ppr_spark.properties
deleted file mode 100644
index c1717ae..0000000
--- a/tools/kal-test/conf/graph/ppr/ppr_spark.properties
+++ /dev/null
@@ -1,111 +0,0 @@
-# Spark parameters
-driverMemory=80g
-deployMode=client
-
-fixMS_cit_patents_numExecutors_aarch64=12
-fixMS_cit_patents_executorCores_aarch64=23
-fixMS_cit_patents_executorMemory_aarch64=79G
-fixMS_cit_patents_extraJavaOptions_aarch64=-Xms79G
-fixMS_cit_patents_numPartitions_aarch64=72
-
-fixMS_uk_2002_numExecutors_aarch64=12
-fixMS_uk_2002_executorCores_aarch64=23
-fixMS_uk_2002_executorMemory_aarch64=79G
-fixMS_uk_2002_extraJavaOptions_aarch64=-Xms79G
-fixMS_uk_2002_numPartitions_aarch64=102
-
-fixMS_arabic_2005_numExecutors_aarch64=12
-fixMS_arabic_2005_executorCores_aarch64=23
-fixMS_arabic_2005_executorMemory_aarch64=79G
-fixMS_arabic_2005_extraJavaOptions_aarch64=-Xms79G
-fixMS_arabic_2005_numPartitions_aarch64=102
-
-fixSS_cit_patents_numExecutors_aarch64=12
-fixSS_cit_patents_executorCores_aarch64=23
-fixSS_cit_patents_executorMemory_aarch64=79G
-fixSS_cit_patents_extraJavaOptions_aarch64=-Xms79G
-fixSS_cit_patents_numPartitions_aarch64=72
-
-fixSS_uk_2002_numExecutors_aarch64=12
-fixSS_uk_2002_executorCores_aarch64=23
-fixSS_uk_2002_executorMemory_aarch64=79G
-fixSS_uk_2002_extraJavaOptions_aarch64=-Xms79G
-fixSS_uk_2002_numPartitions_aarch64=72
-
-fixSS_arabic_2005_numExecutors_aarch64=36
-fixSS_arabic_2005_executorCores_aarch64=7
-fixSS_arabic_2005_executorMemory_aarch64=26G
-fixSS_arabic_2005_extraJavaOptions_aarch64=-Xms26G
-fixSS_arabic_2005_numPartitions_aarch64=120
-
-conSS_cit_patents_numExecutors_aarch64=12
-conSS_cit_patents_executorCores_aarch64=23
-conSS_cit_patents_executorMemory_aarch64=79G
-conSS_cit_patents_extraJavaOptions_aarch64=-Xms79G
-conSS_cit_patents_numPartitions_aarch64=72
-
-conSS_uk_2002_numExecutors_aarch64=12
-conSS_uk_2002_executorCores_aarch64=23
-conSS_uk_2002_executorMemory_aarch64=79G
-conSS_uk_2002_extraJavaOptions_aarch64=-Xms79G
-conSS_uk_2002_numPartitions_aarch64=72
-
-conSS_arabic_2005_numExecutors_aarch64=36
-conSS_arabic_2005_executorCores_aarch64=7
-conSS_arabic_2005_executorMemory_aarch64=26G
-conSS_arabic_2005_extraJavaOptions_aarch64=-Xms26G
-conSS_arabic_2005_numPartitions_aarch64=120
-
-fixMS_cit_patents_numExecutors_x86_64=12
-fixMS_cit_patents_executorCores_x86_64=19
-fixMS_cit_patents_executorMemory_x86_64=79G
-fixMS_cit_patents_extraJavaOptions_x86_64=-Xms79G
-fixMS_cit_patents_numPartitions_x86_64=72
-
-fixMS_uk_2002_numExecutors_x86_64=12
-fixMS_uk_2002_executorCores_x86_64=19
-fixMS_uk_2002_executorMemory_x86_64=79G
-fixMS_uk_2002_extraJavaOptions_x86_64=-Xms79G
-fixMS_uk_2002_numPartitions_x86_64=102
-
-fixMS_arabic_2005_numExecutors_x86_64=12
-fixMS_arabic_2005_executorCores_x86_64=19
-fixMS_arabic_2005_executorMemory_x86_64=79G
-fixMS_arabic_2005_extraJavaOptions_x86_64=-Xms79G
-fixMS_arabic_2005_numPartitions_x86_64=102
-
-fixSS_cit_patents_numExecutors_x86_64=12
-fixSS_cit_patents_executorCores_x86_64=19
-fixSS_cit_patents_executorMemory_x86_64=79G
-fixSS_cit_patents_extraJavaOptions_x86_64=-Xms79G
-fixSS_cit_patents_numPartitions_x86_64=72
-
-fixSS_uk_2002_numExecutors_x86_64=12
-fixSS_uk_2002_executorCores_x86_64=19
-fixSS_uk_2002_executorMemory_x86_64=79G
-fixSS_uk_2002_extraJavaOptions_x86_64=-Xms79G
-fixSS_uk_2002_numPartitions_x86_64=72
-
-fixSS_arabic_2005_numExecutors_x86_64=36
-fixSS_arabic_2005_executorCores_x86_64=7
-fixSS_arabic_2005_executorMemory_x86_64=26G
-fixSS_arabic_2005_extraJavaOptions_x86_64=-Xms26G
-fixSS_arabic_2005_numPartitions_x86_64=120
-
-conSS_cit_patents_numExecutors_x86_64=12
-conSS_cit_patents_executorCores_x86_64=19
-conSS_cit_patents_executorMemory_x86_64=79G
-conSS_cit_patents_extraJavaOptions_x86_64=-Xms79G
-conSS_cit_patents_numPartitions_x86_64=72
-
-conSS_uk_2002_numExecutors_x86_64=12
-conSS_uk_2002_executorCores_x86_64=19
-conSS_uk_2002_executorMemory_x86_64=79G
-conSS_uk_2002_extraJavaOptions_x86_64=-Xms79G
-conSS_uk_2002_numPartitions_x86_64=72
-
-conSS_arabic_2005_numExecutors_x86_64=36
-conSS_arabic_2005_executorCores_x86_64=7
-conSS_arabic_2005_executorMemory_x86_64=26G
-conSS_arabic_2005_extraJavaOptions_x86_64=-Xms26G
-conSS_arabic_2005_numPartitions_x86_64=120
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/ppr/ppr_spark_raw.properties b/tools/kal-test/conf/graph/ppr/ppr_spark_raw.properties
deleted file mode 100644
index c37a78a..0000000
--- a/tools/kal-test/conf/graph/ppr/ppr_spark_raw.properties
+++ /dev/null
@@ -1,57 +0,0 @@
-# Spark parameters
-driverMemory=80g
-deployMode=client
-
-fixMS_cit_patents_numExecutors_x86_64=12
-fixMS_cit_patents_executorCores_x86_64=19
-fixMS_cit_patents_executorMemory_x86_64=79G
-fixMS_cit_patents_extraJavaOptions_x86_64=-Xms79G
-fixMS_cit_patents_numPartitions_x86_64=72
-
-fixMS_uk_2002_numExecutors_x86_64=12
-fixMS_uk_2002_executorCores_x86_64=19
-fixMS_uk_2002_executorMemory_x86_64=79G
-fixMS_uk_2002_extraJavaOptions_x86_64=-Xms79G
-fixMS_uk_2002_numPartitions_x86_64=102
-
-fixMS_arabic_2005_numExecutors_x86_64=12
-fixMS_arabic_2005_executorCores_x86_64=19
-fixMS_arabic_2005_executorMemory_x86_64=79G
-fixMS_arabic_2005_extraJavaOptions_x86_64=-Xms79G
-fixMS_arabic_2005_numPartitions_x86_64=102
-
-fixSS_cit_patents_numExecutors_x86_64=12
-fixSS_cit_patents_executorCores_x86_64=19
-fixSS_cit_patents_executorMemory_x86_64=79G
-fixSS_cit_patents_extraJavaOptions_x86_64=-Xms79G
-fixSS_cit_patents_numPartitions_x86_64=72
-
-fixSS_uk_2002_numExecutors_x86_64=12
-fixSS_uk_2002_executorCores_x86_64=19
-fixSS_uk_2002_executorMemory_x86_64=79G
-fixSS_uk_2002_extraJavaOptions_x86_64=-Xms79G
-fixSS_uk_2002_numPartitions_x86_64=102
-
-fixSS_arabic_2005_numExecutors_x86_64=12
-fixSS_arabic_2005_executorCores_x86_64=19
-fixSS_arabic_2005_executorMemory_x86_64=79G
-fixSS_arabic_2005_extraJavaOptions_x86_64=-Xms79G
-fixSS_arabic_2005_numPartitions_x86_64=102
-
-conSS_cit_patents_numExecutors_x86_64=12
-conSS_cit_patents_executorCores_x86_64=19
-conSS_cit_patents_executorMemory_x86_64=79G
-conSS_cit_patents_extraJavaOptions_x86_64=-Xms79G
-conSS_cit_patents_numPartitions_x86_64=72
-
-conSS_uk_2002_numExecutors_x86_64=12
-conSS_uk_2002_executorCores_x86_64=19
-conSS_uk_2002_executorMemory_x86_64=79G
-conSS_uk_2002_extraJavaOptions_x86_64=-Xms79G
-conSS_uk_2002_numPartitions_x86_64=102
-
-conSS_arabic_2005_numExecutors_x86_64=12
-conSS_arabic_2005_executorCores_x86_64=19
-conSS_arabic_2005_executorMemory_x86_64=79G
-conSS_arabic_2005_extraJavaOptions_x86_64=-Xms79G
-conSS_arabic_2005_numPartitions_x86_64=102
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/pr/pr.yml b/tools/kal-test/conf/graph/pr/pr.yml
deleted file mode 100644
index 7a9f601..0000000
--- a/tools/kal-test/conf/graph/pr/pr.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-# PageRank dataset parameters arm
-
-pr:
-  cit_patents:
-    outputPath: hdfs:///tmp/graph/result/pr/cit_patents
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
-
-  uk_2002:
-    outputPath: hdfs:///tmp/graph/result/pr/uk_2002
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
-
-  arabic_2005:
-    outputPath: hdfs:///tmp/graph/result/pr/arabic_2005
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/pr/pr_spark.properties b/tools/kal-test/conf/graph/pr/pr_spark.properties
deleted file mode 100644
index 176637a..0000000
--- a/tools/kal-test/conf/graph/pr/pr_spark.properties
+++ /dev/null
@@ -1,74 +0,0 @@
-# Spark parameters
-deployMode=client
-
-run_cit_patents_numExecutors_aarch64=39
-run_cit_patents_executorCores_aarch64=7
-run_cit_patents_executorMemory_aarch64=26G
-run_cit_patents_extraJavaOptions_aarch64=-Xms26G
-run_cit_patents_numPartitions_aarch64=240
-
-run_uk_2002_numExecutors_aarch64=54
-run_uk_2002_executorCores_aarch64=5
-run_uk_2002_executorMemory_aarch64=19G
-run_uk_2002_extraJavaOptions_aarch64=-Xms19G
-run_uk_2002_numPartitions_aarch64=180
-
-run_arabic_2005_numExecutors_aarch64=36
-run_arabic_2005_executorCores_aarch64=7
-run_arabic_2005_executorMemory_aarch64=28G
-run_arabic_2005_extraJavaOptions_aarch64=-Xms28G
-run_arabic_2005_numPartitions_aarch64=180
-
-run_cit_patents_numExecutors_x86_64=27
-run_cit_patents_executorCores_x86_64=8
-run_cit_patents_executorMemory_x86_64=38G
-run_cit_patents_extraJavaOptions_x86_64=-Xms38G
-run_cit_patents_numPartitions_x86_64=165
-
-run_uk_2002_numExecutors_x86_64=48
-run_uk_2002_executorCores_x86_64=4
-run_uk_2002_executorMemory_x86_64=21G
-run_uk_2002_extraJavaOptions_x86_64=-Xms21G
-run_uk_2002_numPartitions_x86_64=120
-
-run_arabic_2005_numExecutors_x86_64=33
-run_arabic_2005_executorCores_x86_64=7
-run_arabic_2005_executorMemory_x86_64=31G
-run_arabic_2005_extraJavaOptions_x86_64=-Xms31g
-run_arabic_2005_numPartitions_x86_64=165
-
-convergence_cit_patents_numExecutors_aarch64=12
-convergence_cit_patents_executorCores_aarch64=23
-convergence_cit_patents_executorMemory_aarch64=79G
-convergence_cit_patents_extraJavaOptions_aarch64=-Xms79G
-convergence_cit_patents_numPartitions_aarch64=72
-
-convergence_uk_2002_numExecutors_aarch64=12
-convergence_uk_2002_executorCores_aarch64=23
-convergence_uk_2002_executorMemory_aarch64=79G
-convergence_uk_2002_extraJavaOptions_aarch64=-Xms79G
-convergence_uk_2002_numPartitions_aarch64=72
-
-convergence_arabic_2005_numExecutors_aarch64=36
-convergence_arabic_2005_executorCores_aarch64=7
-convergence_arabic_2005_executorMemory_aarch64=26G
-convergence_arabic_2005_extraJavaOptions_aarch64=-Xms26G
-convergence_arabic_2005_numPartitions_aarch64=120
-
-convergence_cit_patents_numExecutors_x86_64=12
-convergence_cit_patents_executorCores_x86_64=19
-convergence_cit_patents_executorMemory_x86_64=79G
-convergence_cit_patents_extraJavaOptions_x86_64=-Xms79g
-convergence_cit_patents_numPartitions_x86_64=102
-
-convergence_uk_2002_numExecutors_x86_64=12
-convergence_uk_2002_executorCores_x86_64=19
-convergence_uk_2002_executorMemory_x86_64=79G
-convergence_uk_2002_extraJavaOptions_x86_64=-Xms79g
-convergence_uk_2002_numPartitions_x86_64=72
-
-convergence_arabic_2005_numExecutors_x86_64=12
-convergence_arabic_2005_executorCores_x86_64=19
-convergence_arabic_2005_executorMemory_x86_64=79G
-convergence_arabic_2005_extraJavaOptions_x86_64=-Xms79g
-convergence_arabic_2005_numPartitions_x86_64=102
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/pr/pr_spark_raw.properties b/tools/kal-test/conf/graph/pr/pr_spark_raw.properties
deleted file mode 100644
index 7764dca..0000000
--- a/tools/kal-test/conf/graph/pr/pr_spark_raw.properties
+++ /dev/null
@@ -1,39 +0,0 @@
-# Spark parameters
-deployMode=client
-
-run_cit_patents_numExecutors=12
-run_cit_patents_executorCores=19
-run_cit_patents_executorMemory=79G
-run_cit_patents_extraJavaOptions=-Xms79G
-run_cit_patents_numPartitions=72
-
-run_uk_2002_numExecutors=12
-run_uk_2002_executorCores=19
-run_uk_2002_executorMemory=79G
-run_uk_2002_extraJavaOptions=-Xms79G
-run_uk_2002_numPartitions=102
-
-run_arabic_2005_numExecutors=12
-run_arabic_2005_executorCores=19
-run_arabic_2005_executorMemory=79G
-run_arabic_2005_extraJavaOptions=-Xms79g
-run_arabic_2005_numPartitions=102
-
-
-convergence_cit_patents_numExecutors=12
-convergence_cit_patents_executorCores=19
-convergence_cit_patents_executorMemory=79G
-convergence_cit_patents_extraJavaOptions=-Xms79g
-convergence_cit_patents_numPartitions=72
-
-convergence_uk_2002_numExecutors=12
-convergence_uk_2002_executorCores=19
-convergence_uk_2002_executorMemory=79G
-convergence_uk_2002_extraJavaOptions=-Xms79g
-convergence_uk_2002_numPartitions=102
-
-convergence_arabic_2005_numExecutors=12
-convergence_arabic_2005_executorCores=19
-convergence_arabic_2005_executorMemory=79G
-convergence_arabic_2005_extraJavaOptions=-Xms79g
-convergence_arabic_2005_numPartitions=102
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/scc/scc.yml b/tools/kal-test/conf/graph/scc/scc.yml
deleted file mode 100644
index 571485f..0000000
--- a/tools/kal-test/conf/graph/scc/scc.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-partition: 300
-split:
-  cit_patents: "\t"
-  enwiki_2018: "\t"
-  arabic_2005: "\t"
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/scc/scc_spark.properties b/tools/kal-test/conf/graph/scc/scc_spark.properties
deleted file mode 100644
index 7ea3e02..0000000
--- a/tools/kal-test/conf/graph/scc/scc_spark.properties
+++ /dev/null
@@ -1,20 +0,0 @@
-executorMemory_aarch64=12g
-executorCores_aarch64=4
-numExecutors_aarch64=71
-
-executorMemory_aarch64_arabic_2005=25g
-executorCores_aarch64_arabic_2005=8
-numExecutors_aarch64_arabic_2005=35
-
-executorMemory_x86_64=15g
-executorCores_x86_64=4
-numExecutors_x86_64=59
-
-executorMemory_x86_64_arabic_2005=23g
-executorCores_x86_64_arabic_2005=6
-numExecutors_x86_64_arabic_2005=39
-
-master=yarn
-deployMode=client
-driverMemory=100g
-executor_extra_javaopts="-Xms${!executor_memory_val} -XX:hashCode=0"
diff --git a/tools/kal-test/conf/graph/scc/scc_x86.yml b/tools/kal-test/conf/graph/scc/scc_x86.yml
deleted file mode 100644
index 915ed30..0000000
--- a/tools/kal-test/conf/graph/scc/scc_x86.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-partition: 400
-split:
-  cit_patents: "\t"
-  enwiki_2018: "\t"
-  arabic_2005: "\t"
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/sgm/sgm.yml b/tools/kal-test/conf/graph/sgm/sgm.yml
deleted file mode 100644
index be8a81d..0000000
--- a/tools/kal-test/conf/graph/sgm/sgm.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-splitDataGraph:
-  graph500_19: ","
-  liveJournal: "\t"
-  com_orkut: "\t"
-splitQueryGraph: ","
-resultNum: 10000
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/sgm/sgm_spark.properties b/tools/kal-test/conf/graph/sgm/sgm_spark.properties
deleted file mode 100644
index 97558b8..0000000
--- a/tools/kal-test/conf/graph/sgm/sgm_spark.properties
+++ /dev/null
@@ -1,354 +0,0 @@
-# Spark parameters
-deployMode=client
-driverMemory=16g
-rpcAskTime=36000
-schedulerMaxRegisteredResourcesWaitingTime=3600000
-workerTimeout=3600
-networkTimeout=6000s
-storageBlockManagerSlaveTimeoutMs=600000
-shuffleBlockTransferService=nio
-driverMaxResultSize=200g
-shuffleManager=SORT
-broadcastBlockSize=25g
-rpcMessageMaxSize=2046
-coreConnectionAckWaitTimeout=60000s
-storageMemoryFraction=0.2
-shuffleMemoryFraction=0.6
-rddCompress=true
-memoryUseLegacyMode=true
-
-graph500_19_4dgn_unIdentical_numberTask_aarch64=1500
-graph500_19_4dgn_unIdentical_numExecutors_aarch64=35
-graph500_19_4dgn_unIdentical_executorCores_aarch64=8
-graph500_19_4dgn_unIdentical_executorMemory_aarch64=25g
-graph500_19_4dgn_unIdentical_numPartitions_aarch64=280
-graph500_19_4dgn_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_4clique_unIdentical_numberTask_aarch64=500
-graph500_19_4clique_unIdentical_numExecutors_aarch64=35
-graph500_19_4clique_unIdentical_executorCores_aarch64=8
-graph500_19_4clique_unIdentical_executorMemory_aarch64=25g
-graph500_19_4clique_unIdentical_numPartitions_aarch64=280
-graph500_19_4clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_5clique_unIdentical_numberTask_aarch64=500
-graph500_19_5clique_unIdentical_numExecutors_aarch64=35
-graph500_19_5clique_unIdentical_executorCores_aarch64=8
-graph500_19_5clique_unIdentical_executorMemory_aarch64=25g
-graph500_19_5clique_unIdentical_numPartitions_aarch64=280
-graph500_19_5clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_6clique_unIdentical_numberTask_aarch64=1000
-graph500_19_6clique_unIdentical_numExecutors_aarch64=47
-graph500_19_6clique_unIdentical_executorCores_aarch64=6
-graph500_19_6clique_unIdentical_executorMemory_aarch64=19g
-graph500_19_6clique_unIdentical_numPartitions_aarch64=282
-graph500_19_6clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms19g
-
-liveJournal_4dgn_unIdentical_numberTask_aarch64=1000
-liveJournal_4dgn_unIdentical_numExecutors_aarch64=47
-liveJournal_4dgn_unIdentical_executorCores_aarch64=6
-liveJournal_4dgn_unIdentical_executorMemory_aarch64=19g
-liveJournal_4dgn_unIdentical_numPartitions_aarch64=282
-liveJournal_4dgn_unIdentical_executorExtraJavaOptions_aarch64=-Xms19g
-
-liveJournal_4clique_unIdentical_numberTask_aarch64=500
-liveJournal_4clique_unIdentical_numExecutors_aarch64=35
-liveJournal_4clique_unIdentical_executorCores_aarch64=8
-liveJournal_4clique_unIdentical_executorMemory_aarch64=25g
-liveJournal_4clique_unIdentical_numPartitions_aarch64=280
-liveJournal_4clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-liveJournal_5clique_unIdentical_numberTask_aarch64=1500
-liveJournal_5clique_unIdentical_numExecutors_aarch64=35
-liveJournal_5clique_unIdentical_executorCores_aarch64=8
-liveJournal_5clique_unIdentical_executorMemory_aarch64=25g
-liveJournal_5clique_unIdentical_numPartitions_aarch64=280
-liveJournal_5clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-liveJournal_6clique_unIdentical_numberTask_aarch64=2000
-liveJournal_6clique_unIdentical_numExecutors_aarch64=35
-liveJournal_6clique_unIdentical_executorCores_aarch64=8
-liveJournal_6clique_unIdentical_executorMemory_aarch64=25g
-liveJournal_6clique_unIdentical_numPartitions_aarch64=280
-liveJournal_6clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_4dgn_unIdentical_numberTask_aarch64=1000
-com_orkut_4dgn_unIdentical_numExecutors_aarch64=35
-com_orkut_4dgn_unIdentical_executorCores_aarch64=8
-com_orkut_4dgn_unIdentical_executorMemory_aarch64=25g
-com_orkut_4dgn_unIdentical_numPartitions_aarch64=280
-com_orkut_4dgn_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_4clique_unIdentical_numberTask_aarch64=500
-com_orkut_4clique_unIdentical_numExecutors_aarch64=35
-com_orkut_4clique_unIdentical_executorCores_aarch64=8
-com_orkut_4clique_unIdentical_executorMemory_aarch64=25g
-com_orkut_4clique_unIdentical_numPartitions_aarch64=280
-com_orkut_4clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_5clique_unIdentical_numberTask_aarch64=500
-com_orkut_5clique_unIdentical_numExecutors_aarch64=35
-com_orkut_5clique_unIdentical_executorCores_aarch64=8
-com_orkut_5clique_unIdentical_executorMemory_aarch64=25g
-com_orkut_5clique_unIdentical_numPartitions_aarch64=280
-com_orkut_5clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_6clique_unIdentical_numberTask_aarch64=500
-com_orkut_6clique_unIdentical_numExecutors_aarch64=47
-com_orkut_6clique_unIdentical_executorCores_aarch64=6
-com_orkut_6clique_unIdentical_executorMemory_aarch64=19g
-com_orkut_6clique_unIdentical_numPartitions_aarch64=282
-com_orkut_6clique_unIdentical_executorExtraJavaOptions_aarch64=-Xms19g
-
-graph500_19_4dgn_Identical_numberTask_aarch64=3000
-graph500_19_4dgn_Identical_numExecutors_aarch64=35
-graph500_19_4dgn_Identical_executorCores_aarch64=8
-graph500_19_4dgn_Identical_executorMemory_aarch64=25g
-graph500_19_4dgn_Identical_numPartitions_aarch64=280
-graph500_19_4dgn_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_4sqr_Identical_numberTask_aarch64=1000
-graph500_19_4sqr_Identical_numExecutors_aarch64=35
-graph500_19_4sqr_Identical_executorCores_aarch64=8
-graph500_19_4sqr_Identical_executorMemory_aarch64=25g
-graph500_19_4sqr_Identical_numPartitions_aarch64=280
-graph500_19_4sqr_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_5tree_Identical_numberTask_aarch64=500
-graph500_19_5tree_Identical_numExecutors_aarch64=35
-graph500_19_5tree_Identical_executorCores_aarch64=8
-graph500_19_5tree_Identical_executorMemory_aarch64=25g
-graph500_19_5tree_Identical_numPartitions_aarch64=280
-graph500_19_5tree_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_6star_Identical_numberTask_aarch64=500
-graph500_19_6star_Identical_numExecutors_aarch64=35
-graph500_19_6star_Identical_executorCores_aarch64=8
-graph500_19_6star_Identical_executorMemory_aarch64=25g
-graph500_19_6star_Identical_numPartitions_aarch64=280
-graph500_19_6star_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-liveJournal_4dgn_Identical_numberTask_aarch64=1000
-liveJournal_4dgn_Identical_numExecutors_aarch64=35
-liveJournal_4dgn_Identical_executorCores_aarch64=8
-liveJournal_4dgn_Identical_executorMemory_aarch64=25g
-liveJournal_4dgn_Identical_numPartitions_aarch64=280
-liveJournal_4dgn_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-liveJournal_4sqr_Identical_numberTask_aarch64=1000
-liveJournal_4sqr_Identical_numExecutors_aarch64=35
-liveJournal_4sqr_Identical_executorCores_aarch64=8
-liveJournal_4sqr_Identical_executorMemory_aarch64=25g
-liveJournal_4sqr_Identical_numPartitions_aarch64=280
-liveJournal_4sqr_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-liveJournal_5tree_Identical_numberTask_aarch64=1000
-liveJournal_5tree_Identical_numExecutors_aarch64=35
-liveJournal_5tree_Identical_executorCores_aarch64=8
-liveJournal_5tree_Identical_executorMemory_aarch64=25g
-liveJournal_5tree_Identical_numPartitions_aarch64=280
-liveJournal_5tree_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-liveJournal_6star_Identical_numberTask_aarch64=1000
-liveJournal_6star_Identical_numExecutors_aarch64=35
-liveJournal_6star_Identical_executorCores_aarch64=8
-liveJournal_6star_Identical_executorMemory_aarch64=25g
-liveJournal_6star_Identical_numPartitions_aarch64=280
-liveJournal_6star_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_4dgn_Identical_numberTask_aarch64=3000
-com_orkut_4dgn_Identical_numExecutors_aarch64=35
-com_orkut_4dgn_Identical_executorCores_aarch64=8
-com_orkut_4dgn_Identical_executorMemory_aarch64=25g
-com_orkut_4dgn_Identical_numPartitions_aarch64=280
-com_orkut_4dgn_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_4sqr_Identical_numberTask_aarch64=3000
-com_orkut_4sqr_Identical_numExecutors_aarch64=35
-com_orkut_4sqr_Identical_executorCores_aarch64=8
-com_orkut_4sqr_Identical_executorMemory_aarch64=25g
-com_orkut_4sqr_Identical_numPartitions_aarch64=280
-com_orkut_4sqr_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_5tree_Identical_numberTask_aarch64=1000
-com_orkut_5tree_Identical_numExecutors_aarch64=35
-com_orkut_5tree_Identical_executorCores_aarch64=8
-com_orkut_5tree_Identical_executorMemory_aarch64=25g
-com_orkut_5tree_Identical_numPartitions_aarch64=280
-com_orkut_5tree_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-com_orkut_6star_Identical_numberTask_aarch64=1000
-com_orkut_6star_Identical_numExecutors_aarch64=35
-com_orkut_6star_Identical_executorCores_aarch64=8
-com_orkut_6star_Identical_executorMemory_aarch64=25g
-com_orkut_6star_Identical_numPartitions_aarch64=280
-com_orkut_6star_Identical_executorExtraJavaOptions_aarch64=-Xms25g
-
-graph500_19_4dgn_unIdentical_numberTask_x86_64=1500
-graph500_19_4dgn_unIdentical_numExecutors_x86_64=29
-graph500_19_4dgn_unIdentical_executorCores_x86_64=8
-graph500_19_4dgn_unIdentical_executorMemory_x86_64=31g
-graph500_19_4dgn_unIdentical_numPartitions_x86_64=232
-graph500_19_4dgn_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_4clique_unIdentical_numberTask_x86_64=500
-graph500_19_4clique_unIdentical_numExecutors_x86_64=29
-graph500_19_4clique_unIdentical_executorCores_x86_64=8
-graph500_19_4clique_unIdentical_executorMemory_x86_64=31g
-graph500_19_4clique_unIdentical_numPartitions_x86_64=232
-graph500_19_4clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_5clique_unIdentical_numberTask_x86_64=1000
-graph500_19_5clique_unIdentical_numExecutors_x86_64=29
-graph500_19_5clique_unIdentical_executorCores_x86_64=8
-graph500_19_5clique_unIdentical_executorMemory_x86_64=31g
-graph500_19_5clique_unIdentical_numPartitions_x86_64=232
-graph500_19_5clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_6clique_unIdentical_numberTask_x86_64=1000
-graph500_19_6clique_unIdentical_numExecutors_x86_64=29
-graph500_19_6clique_unIdentical_executorCores_x86_64=8
-graph500_19_6clique_unIdentical_executorMemory_x86_64=31g
-graph500_19_6clique_unIdentical_numPartitions_x86_64=232
-graph500_19_6clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_4dgn_unIdentical_numberTask_x86_64=1000
-liveJournal_4dgn_unIdentical_numExecutors_x86_64=29
-liveJournal_4dgn_unIdentical_executorCores_x86_64=8
-liveJournal_4dgn_unIdentical_executorMemory_x86_64=31g
-liveJournal_4dgn_unIdentical_numPartitions_x86_64=232
-liveJournal_4dgn_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_4clique_unIdentical_numberTask_x86_64=1500
-liveJournal_4clique_unIdentical_numExecutors_x86_64=29
-liveJournal_4clique_unIdentical_executorCores_x86_64=8
-liveJournal_4clique_unIdentical_executorMemory_x86_64=31g
-liveJournal_4clique_unIdentical_numPartitions_x86_64=232
-liveJournal_4clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_5clique_unIdentical_numberTask_x86_64=1000
-liveJournal_5clique_unIdentical_numExecutors_x86_64=39
-liveJournal_5clique_unIdentical_executorCores_x86_64=6
-liveJournal_5clique_unIdentical_executorMemory_x86_64=23g
-liveJournal_5clique_unIdentical_numPartitions_x86_64=234
-liveJournal_5clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms23g
-
-liveJournal_6clique_unIdentical_numberTask_x86_64=2000
-liveJournal_6clique_unIdentical_numExecutors_x86_64=29
-liveJournal_6clique_unIdentical_executorCores_x86_64=8
-liveJournal_6clique_unIdentical_executorMemory_x86_64=31g
-liveJournal_6clique_unIdentical_numPartitions_x86_64=232
-liveJournal_6clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_4dgn_unIdentical_numberTask_x86_64=1500
-com_orkut_4dgn_unIdentical_numExecutors_x86_64=29
-com_orkut_4dgn_unIdentical_executorCores_x86_64=8
-com_orkut_4dgn_unIdentical_executorMemory_x86_64=31g
-com_orkut_4dgn_unIdentical_numPartitions_x86_64=232
-com_orkut_4dgn_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_4clique_unIdentical_numberTask_x86_64=500
-com_orkut_4clique_unIdentical_numExecutors_x86_64=29
-com_orkut_4clique_unIdentical_executorCores_x86_64=8
-com_orkut_4clique_unIdentical_executorMemory_x86_64=31g
-com_orkut_4clique_unIdentical_numPartitions_x86_64=232
-com_orkut_4clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_5clique_unIdentical_numberTask_x86_64=500
-com_orkut_5clique_unIdentical_numExecutors_x86_64=29
-com_orkut_5clique_unIdentical_executorCores_x86_64=8
-com_orkut_5clique_unIdentical_executorMemory_x86_64=31g
-com_orkut_5clique_unIdentical_numPartitions_x86_64=232
-com_orkut_5clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_6clique_unIdentical_numberTask_x86_64=1000
-com_orkut_6clique_unIdentical_numExecutors_x86_64=39
-com_orkut_6clique_unIdentical_executorCores_x86_64=6
-com_orkut_6clique_unIdentical_executorMemory_x86_64=23g
-com_orkut_6clique_unIdentical_numPartitions_x86_64=234
-com_orkut_6clique_unIdentical_executorExtraJavaOptions_x86_64=-Xms23g
-
-graph500_19_4dgn_Identical_numberTask_x86_64=2000
-graph500_19_4dgn_Identical_numExecutors_x86_64=29
-graph500_19_4dgn_Identical_executorCores_x86_64=8
-graph500_19_4dgn_Identical_executorMemory_x86_64=31g
-graph500_19_4dgn_Identical_numPartitions_x86_64=232
-graph500_19_4dgn_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_4sqr_Identical_numberTask_x86_64=1000
-graph500_19_4sqr_Identical_numExecutors_x86_64=29
-graph500_19_4sqr_Identical_executorCores_x86_64=8
-graph500_19_4sqr_Identical_executorMemory_x86_64=31g
-graph500_19_4sqr_Identical_numPartitions_x86_64=232
-graph500_19_4sqr_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_5tree_Identical_numberTask_x86_64=500
-graph500_19_5tree_Identical_numExecutors_x86_64=29
-graph500_19_5tree_Identical_executorCores_x86_64=8
-graph500_19_5tree_Identical_executorMemory_x86_64=31g
-graph500_19_5tree_Identical_numPartitions_x86_64=232
-graph500_19_5tree_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_6star_Identical_numberTask_x86_64=1000
-graph500_19_6star_Identical_numExecutors_x86_64=29
-graph500_19_6star_Identical_executorCores_x86_64=8
-graph500_19_6star_Identical_executorMemory_x86_64=31g
-graph500_19_6star_Identical_numPartitions_x86_64=232
-graph500_19_6star_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_4dgn_Identical_numberTask_x86_64=2000
-liveJournal_4dgn_Identical_numExecutors_x86_64=29
-liveJournal_4dgn_Identical_executorCores_x86_64=8
-liveJournal_4dgn_Identical_executorMemory_x86_64=31g
-liveJournal_4dgn_Identical_numPartitions_x86_64=232
-liveJournal_4dgn_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_4sqr_Identical_numberTask_x86_64=2000
-liveJournal_4sqr_Identical_numExecutors_x86_64=29
-liveJournal_4sqr_Identical_executorCores_x86_64=8
-liveJournal_4sqr_Identical_executorMemory_x86_64=31g
-liveJournal_4sqr_Identical_numPartitions_x86_64=232
-liveJournal_4sqr_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_5tree_Identical_numberTask_x86_64=1000
-liveJournal_5tree_Identical_numExecutors_x86_64=29
-liveJournal_5tree_Identical_executorCores_x86_64=8
-liveJournal_5tree_Identical_executorMemory_x86_64=31g
-liveJournal_5tree_Identical_numPartitions_x86_64=232
-liveJournal_5tree_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-liveJournal_6star_Identical_numberTask_x86_64=1000
-liveJournal_6star_Identical_numExecutors_x86_64=29
-liveJournal_6star_Identical_executorCores_x86_64=8
-liveJournal_6star_Identical_executorMemory_x86_64=31g
-liveJournal_6star_Identical_numPartitions_x86_64=232
-liveJournal_6star_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_4dgn_Identical_numberTask_x86_64=3000
-com_orkut_4dgn_Identical_numExecutors_x86_64=29
-com_orkut_4dgn_Identical_executorCores_x86_64=8
-com_orkut_4dgn_Identical_executorMemory_x86_64=31g
-com_orkut_4dgn_Identical_numPartitions_x86_64=232
-com_orkut_4dgn_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_4sqr_Identical_numberTask_x86_64=3000
-com_orkut_4sqr_Identical_numExecutors_x86_64=29
-com_orkut_4sqr_Identical_executorCores_x86_64=8
-com_orkut_4sqr_Identical_executorMemory_x86_64=31g
-com_orkut_4sqr_Identical_numPartitions_x86_64=232
-com_orkut_4sqr_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_5tree_Identical_numberTask_x86_64=1000
-com_orkut_5tree_Identical_numExecutors_x86_64=29
-com_orkut_5tree_Identical_executorCores_x86_64=8
-com_orkut_5tree_Identical_executorMemory_x86_64=31g
-com_orkut_5tree_Identical_numPartitions_x86_64=232
-com_orkut_5tree_Identical_executorExtraJavaOptions_x86_64=-Xms31g
-
-com_orkut_6star_Identical_numberTask_x86_64=500
-com_orkut_6star_Identical_numExecutors_x86_64=29
-com_orkut_6star_Identical_executorCores_x86_64=8
-com_orkut_6star_Identical_executorMemory_x86_64=31g
-com_orkut_6star_Identical_numPartitions_x86_64=232
-com_orkut_6star_Identical_executorExtraJavaOptions_x86_64=-Xms31g
diff --git a/tools/kal-test/conf/graph/sgm/sgm_spark_opensource.properties b/tools/kal-test/conf/graph/sgm/sgm_spark_opensource.properties
deleted file mode 100644
index 22c0417..0000000
--- a/tools/kal-test/conf/graph/sgm/sgm_spark_opensource.properties
+++ /dev/null
@@ -1,32 +0,0 @@
-# Spark parameters
-deployMode=client
-driverMemory=16g
-rpcAskTime=36000
-schedulerMaxRegisteredResourcesWaitingTime=3600000
-workerTimeout=3600
-networkTimeout=6000s
-storageBlockManagerSlaveTimeoutMs=600000
-shuffleBlockTransferService=nio
-driverMaxResultSize=200g
-shuffleManager=SORT
-broadcastBlockSize=25g
-rpcMessageMaxSize=2046
-coreConnectionAckWaitTimeout=60000s
-storageMemoryFraction=0.2
-shuffleMemoryFraction=0.6
-rddCompress=true
-memoryUseLegacyMode=true
-
-numberColors_x86_64=12
-numExecutors_x86_64=29
-executorCores_x86_64=8
-executorMemory_x86_64=31g
-numPartitions_x86_64=232
-executorExtraJavaOptions_x86_64=-Xms31g
-
-graph500_19_split=","
-liveJournal_split="\t"
-com_orkut_split="\t"
-
-
-
diff --git a/tools/kal-test/conf/graph/tc/tc.yml b/tools/kal-test/conf/graph/tc/tc.yml
deleted file mode 100644
index fa3438c..0000000
--- a/tools/kal-test/conf/graph/tc/tc.yml
+++ /dev/null
@@ -1,8 +0,0 @@
-partition: 720
-split:
-  graph500_22: " "
-  graph500_23: " "
-  graph500_24: " "
-  graph500_25: " "
-  graph500_26: " "
-
diff --git a/tools/kal-test/conf/graph/tc/tc_spark.properties b/tools/kal-test/conf/graph/tc/tc_spark.properties
deleted file mode 100644
index 2f3a8b6..0000000
--- a/tools/kal-test/conf/graph/tc/tc_spark.properties
+++ /dev/null
@@ -1,11 +0,0 @@
-numExecutors_aarch64=74
-numExecutors_x86_64=59
-executorCores=4
-executorMemory_aarch64=12g
-executorMemory_x86_64=16g
-extraJavaOptions_aarch64=-Xms12g
-extraJavaOptions_x86_64=-Xms16g
-master=yarn
-deployMode=client
-driverCores=36
-driverMemory=50g
diff --git a/tools/kal-test/conf/graph/tr/tr.yml b/tools/kal-test/conf/graph/tr/tr.yml
deleted file mode 100644
index 68fe762..0000000
--- a/tools/kal-test/conf/graph/tr/tr.yml
+++ /dev/null
@@ -1,72 +0,0 @@
-trustRank:
-  cit_patents_100:
-    outputPath: hdfs:///tmp/graph/result/tr/cit_patents_100
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/cit-Patents_100
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  cit_patents_500:
-    outputPath: hdfs:///tmp/graph/result/tr/cit_patents_500
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/cit-Patents_500
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  cit_patents_1000:
-    outputPath: hdfs:///tmp/graph/result/tr/cit_patents_1000
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/cit-Patents_1000
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  uk_2002_100:
-    outputPath: hdfs:///tmp/graph/result/tr/uk_2002_100
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/uk-2002-edgelist_100
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  uk_2002_500:
-    outputPath: hdfs:///tmp/graph/result/tr/uk_2002_500
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/uk-2002-edgelist_500
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  uk_2002_1000:
-    outputPath: hdfs:///tmp/graph/result/tr/uk_2002_1000
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/uk-2002-edgelist_1000
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  arabic_2005_100:
-    outputPath: hdfs:///tmp/graph/result/tr/arabic_2005_100
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/arabic-2005-edgelist_100
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  arabic_2005_500:
-    outputPath: hdfs:///tmp/graph/result/tr/arabic_2005_500
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/arabic-2005-edgelist_500
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
-
-  arabic_2005_1000:
-    outputPath: hdfs:///tmp/graph/result/tr/arabic_2005_1000
-    seedsPath: hdfs:///tmp/graph/dataset/tr_seeds/arabic-2005-edgelist_1000
-    splitGraph: "\t"
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-8
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/tr/tr_spark.properties b/tools/kal-test/conf/graph/tr/tr_spark.properties
deleted file mode 100644
index 00b920d..0000000
--- a/tools/kal-test/conf/graph/tr/tr_spark.properties
+++ /dev/null
@@ -1,219 +0,0 @@
-# Spark parameters
-deployMode=client
-driverMemory=100g
-
-runUntilConvergence_cit_patents_100_numExecutors_aarch64=12
-runUntilConvergence_cit_patents_100_executorCores_aarch64=23
-runUntilConvergence_cit_patents_100_executorMemory_aarch64=79G
-runUntilConvergence_cit_patents_100_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_cit_patents_100_numPartitions_aarch64=72
-
-runUntilConvergence_cit_patents_500_numExecutors_aarch64=12
-runUntilConvergence_cit_patents_500_executorCores_aarch64=23
-runUntilConvergence_cit_patents_500_executorMemory_aarch64=79G
-runUntilConvergence_cit_patents_500_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_cit_patents_500_numPartitions_aarch64=102
-
-runUntilConvergence_cit_patents_1000_numExecutors_aarch64=12
-runUntilConvergence_cit_patents_1000_executorCores_aarch64=23
-runUntilConvergence_cit_patents_1000_executorMemory_aarch64=79G
-runUntilConvergence_cit_patents_1000_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_cit_patents_1000_numPartitions_aarch64=72
-
-runUntilConvergence_uk_2002_100_numExecutors_aarch64=12
-runUntilConvergence_uk_2002_100_executorCores_aarch64=23
-runUntilConvergence_uk_2002_100_executorMemory_aarch64=79G
-runUntilConvergence_uk_2002_100_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_uk_2002_100_numPartitions_aarch64=120
-
-runUntilConvergence_uk_2002_500_numExecutors_aarch64=12
-runUntilConvergence_uk_2002_500_executorCores_aarch64=23
-runUntilConvergence_uk_2002_500_executorMemory_aarch64=79G
-runUntilConvergence_uk_2002_500_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_uk_2002_500_numPartitions_aarch64=102
-
-runUntilConvergence_uk_2002_1000_numExecutors_aarch64=12
-runUntilConvergence_uk_2002_1000_executorCores_aarch64=23
-runUntilConvergence_uk_2002_1000_executorMemory_aarch64=79G
-runUntilConvergence_uk_2002_1000_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_uk_2002_1000_numPartitions_aarch64=120
-
-runUntilConvergence_arabic_2005_100_numExecutors_aarch64=12
-runUntilConvergence_arabic_2005_100_executorCores_aarch64=23
-runUntilConvergence_arabic_2005_100_executorMemory_aarch64=79G
-runUntilConvergence_arabic_2005_100_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_arabic_2005_100_numPartitions_aarch64=102
-
-runUntilConvergence_arabic_2005_500_numExecutors_aarch64=12
-runUntilConvergence_arabic_2005_500_executorCores_aarch64=23
-runUntilConvergence_arabic_2005_500_executorMemory_aarch64=79G
-runUntilConvergence_arabic_2005_500_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_arabic_2005_500_numPartitions_aarch64=120
-
-runUntilConvergence_arabic_2005_1000_numExecutors_aarch64=12
-runUntilConvergence_arabic_2005_1000_executorCores_aarch64=23
-runUntilConvergence_arabic_2005_1000_executorMemory_aarch64=79G
-runUntilConvergence_arabic_2005_1000_extraJavaOptions_aarch64=-Xms79G
-runUntilConvergence_arabic_2005_1000_numPartitions_aarch64=120
-
-runUntilConvergence_cit_patents_100_numExecutors_x86_64=12
-runUntilConvergence_cit_patents_100_executorCores_x86_64=19
-runUntilConvergence_cit_patents_100_executorMemory_x86_64=79G
-runUntilConvergence_cit_patents_100_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_cit_patents_100_numPartitions_x86_64=72
-
-runUntilConvergence_cit_patents_500_numExecutors_x86_64=12
-runUntilConvergence_cit_patents_500_executorCores_x86_64=19
-runUntilConvergence_cit_patents_500_executorMemory_x86_64=79G
-runUntilConvergence_cit_patents_500_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_cit_patents_500_numPartitions_x86_64=72
-
-runUntilConvergence_cit_patents_1000_numExecutors_x86_64=12
-runUntilConvergence_cit_patents_1000_executorCores_x86_64=19
-runUntilConvergence_cit_patents_1000_executorMemory_x86_64=79G
-runUntilConvergence_cit_patents_1000_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_cit_patents_1000_numPartitions_x86_64=72
-
-runUntilConvergence_uk_2002_100_numExecutors_x86_64=12
-runUntilConvergence_uk_2002_100_executorCores_x86_64=19
-runUntilConvergence_uk_2002_100_executorMemory_x86_64=79G
-runUntilConvergence_uk_2002_100_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_uk_2002_100_numPartitions_x86_64=120
-
-runUntilConvergence_uk_2002_500_numExecutors_x86_64=12
-runUntilConvergence_uk_2002_500_executorCores_x86_64=19
-runUntilConvergence_uk_2002_500_executorMemory_x86_64=79G
-runUntilConvergence_uk_2002_500_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_uk_2002_500_numPartitions_x86_64=102
-
-runUntilConvergence_uk_2002_1000_numExecutors_x86_64=12
-runUntilConvergence_uk_2002_1000_executorCores_x86_64=19
-runUntilConvergence_uk_2002_1000_executorMemory_x86_64=79G
-runUntilConvergence_uk_2002_1000_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_uk_2002_1000_numPartitions_x86_64=102
-
-runUntilConvergence_arabic_2005_100_numExecutors_x86_64=12
-runUntilConvergence_arabic_2005_100_executorCores_x86_64=19
-runUntilConvergence_arabic_2005_100_executorMemory_x86_64=79G
-runUntilConvergence_arabic_2005_100_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_arabic_2005_100_numPartitions_x86_64=120
-
-runUntilConvergence_arabic_2005_500_numExecutors_x86_64=12
-runUntilConvergence_arabic_2005_500_executorCores_x86_64=19
-runUntilConvergence_arabic_2005_500_executorMemory_x86_64=79G
-runUntilConvergence_arabic_2005_500_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_arabic_2005_500_numPartitions_x86_64=102
-
-runUntilConvergence_arabic_2005_1000_numExecutors_x86_64=12
-runUntilConvergence_arabic_2005_1000_executorCores_x86_64=19
-runUntilConvergence_arabic_2005_1000_executorMemory_x86_64=79G
-runUntilConvergence_arabic_2005_1000_extraJavaOptions_x86_64=-Xms79G
-runUntilConvergence_arabic_2005_1000_numPartitions_x86_64=120
-
-run_cit_patents_100_numExecutors_aarch64=12
-run_cit_patents_100_executorCores_aarch64=23
-run_cit_patents_100_executorMemory_aarch64=79G
-run_cit_patents_100_extraJavaOptions_aarch64=-Xms79G
-run_cit_patents_100_numPartitions_aarch64=102
-
-run_cit_patents_500_numExecutors_aarch64=12
-run_cit_patents_500_executorCores_aarch64=23
-run_cit_patents_500_executorMemory_aarch64=79G
-run_cit_patents_500_extraJavaOptions_aarch64=-Xms79G
-run_cit_patents_500_numPartitions_aarch64=72
-
-run_cit_patents_1000_numExecutors_aarch64=12
-run_cit_patents_1000_executorCores_aarch64=23
-run_cit_patents_1000_executorMemory_aarch64=79G
-run_cit_patents_1000_extraJavaOptions_aarch64=-Xms79G
-run_cit_patents_1000_numPartitions_aarch64=102
-
-run_uk_2002_100_numExecutors_aarch64=12
-run_uk_2002_100_executorCores_aarch64=23
-run_uk_2002_100_executorMemory_aarch64=79G
-run_uk_2002_100_extraJavaOptions_aarch64=-Xms79G
-run_uk_2002_100_numPartitions_aarch64=120
-
-run_uk_2002_500_numExecutors_aarch64=12
-run_uk_2002_500_executorCores_aarch64=23
-run_uk_2002_500_executorMemory_aarch64=79G
-run_uk_2002_500_extraJavaOptions_aarch64=-Xms79G
-run_uk_2002_500_numPartitions_aarch64=120
-
-run_uk_2002_1000_numExecutors_aarch64=12
-run_uk_2002_1000_executorCores_aarch64=23
-run_uk_2002_1000_executorMemory_aarch64=79G
-run_uk_2002_1000_extraJavaOptions_aarch64=-Xms79G
-run_uk_2002_1000_numPartitions_aarch64=102
-
-run_arabic_2005_100_numExecutors_aarch64=12
-run_arabic_2005_100_executorCores_aarch64=23
-run_arabic_2005_100_executorMemory_aarch64=79G
-run_arabic_2005_100_extraJavaOptions_aarch64=-Xms79G
-run_arabic_2005_100_numPartitions_aarch64=120
-
-run_arabic_2005_500_numExecutors_aarch64=12
-run_arabic_2005_500_executorCores_aarch64=23
-run_arabic_2005_500_executorMemory_aarch64=79G
-run_arabic_2005_500_extraJavaOptions_aarch64=-Xms79G
-run_arabic_2005_500_numPartitions_aarch64=120
-
-run_arabic_2005_1000_numExecutors_aarch64=12
-run_arabic_2005_1000_executorCores_aarch64=23
-run_arabic_2005_1000_executorMemory_aarch64=79G
-run_arabic_2005_1000_extraJavaOptions_aarch64=-Xms79G
-run_arabic_2005_1000_numPartitions_aarch64=120
-
-run_cit_patents_100_numExecutors_x86_64=12
-run_cit_patents_100_executorCores_x86_64=19
-run_cit_patents_100_executorMemory_x86_64=79G
-run_cit_patents_100_extraJavaOptions_x86_64=-Xms79G
-run_cit_patents_100_numPartitions_x86_64=102
-
-run_cit_patents_500_numExecutors_x86_64=12
-run_cit_patents_500_executorCores_x86_64=19
-run_cit_patents_500_executorMemory_x86_64=79G
-run_cit_patents_500_extraJavaOptions_x86_64=-Xms79G
-run_cit_patents_500_numPartitions_x86_64=72
-
-run_cit_patents_1000_numExecutors_x86_64=12
-run_cit_patents_1000_executorCores_x86_64=19
-run_cit_patents_1000_executorMemory_x86_64=79G
-run_cit_patents_1000_extraJavaOptions_x86_64=-Xms79G
-run_cit_patents_1000_numPartitions_x86_64=72
-
-run_uk_2002_100_numExecutors_x86_64=12
-run_uk_2002_100_executorCores_x86_64=19
-run_uk_2002_100_executorMemory_x86_64=79G
-run_uk_2002_100_extraJavaOptions_x86_64=-Xms79G
-run_uk_2002_100_numPartitions_x86_64=120
-
-run_uk_2002_500_numExecutors_x86_64=12
-run_uk_2002_500_executorCores_x86_64=19
-run_uk_2002_500_executorMemory_x86_64=79G
-run_uk_2002_500_extraJavaOptions_x86_64=-Xms79G
-run_uk_2002_500_numPartitions_x86_64=102
-
-run_uk_2002_1000_numExecutors_x86_64=12
-run_uk_2002_1000_executorCores_x86_64=19
-run_uk_2002_1000_executorMemory_x86_64=79G
-run_uk_2002_1000_extraJavaOptions_x86_64=-Xms79G
-run_uk_2002_1000_numPartitions_x86_64=102
-
-run_arabic_2005_100_numExecutors_x86_64=12
-run_arabic_2005_100_executorCores_x86_64=19
-run_arabic_2005_100_executorMemory_x86_64=19G
-run_arabic_2005_100_extraJavaOptions_x86_64=-Xms19G
-run_arabic_2005_100_numPartitions_x86_64=120
-
-run_arabic_2005_500_numExecutors_x86_64=12
-run_arabic_2005_500_executorCores_x86_64=19
-run_arabic_2005_500_executorMemory_x86_64=79G
-run_arabic_2005_500_extraJavaOptions_x86_64=-Xms79G
-run_arabic_2005_500_numPartitions_x86_64=120
-
-run_arabic_2005_1000_numExecutors_x86_64=12
-run_arabic_2005_1000_executorCores_x86_64=19
-run_arabic_2005_1000_executorMemory_x86_64=79G
-run_arabic_2005_1000_extraJavaOptions_x86_64=-Xms79G
-run_arabic_2005_1000_numPartitions_x86_64=120
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/wce/wce.yml b/tools/kal-test/conf/graph/wce/wce.yml
deleted file mode 100644
index a7c4ef0..0000000
--- a/tools/kal-test/conf/graph/wce/wce.yml
+++ /dev/null
@@ -1,26 +0,0 @@
-# WCE dataset parameters
-
-wce:
-  graph500_24:
-    inputPath: hdfs:///tmp/graph_data/graph500-24.e
-    outputPath: hdfs:///tmp/result/graph500_24
-    splitGraph: " "
-    maxIterations: 1000
-    maxDegree: 2000
-    numPartitions: 284
-
-  graph500_25:
-    inputPath: hdfs:///tmp/graph_data/graph500-25.e
-    outputPath: hdfs:///tmp/result/graph500_25
-    splitGraph: " "
-    maxIterations: 1000
-    maxDegree: 2000
-    numPartitions: 284
-
-  graph500_26:
-    inputPath: hdfs:///tmp/graph_data/graph500-26.e
-    outputPath: hdfs:///tmp/result/graph500_26
-    splitGraph: " "
-    maxIterations: 1000
-    maxDegree: 2000
-    numPartitions: 284
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/wce/wce_spark.properties b/tools/kal-test/conf/graph/wce/wce_spark.properties
deleted file mode 100644
index f5c27d9..0000000
--- a/tools/kal-test/conf/graph/wce/wce_spark.properties
+++ /dev/null
@@ -1,30 +0,0 @@
-# Spark parameters
-graph500_24_numExectuors_aarch64=71
-graph500_24_executorCores_aarch64=4
-graph500_24_executorMemory_aarch64=12G
-graph500_24_extraJavaOptions_aarch64=-Xms12g
-
-graph500_25_numExectuors_aarch64=71
-graph500_25_executorCores_aarch64=4
-graph500_25_executorMemory_aarch64=12G
-graph500_25_extraJavaOptions_aarch64=-Xms12g
-
-graph500_26_numExectuors_aarch64=71
-graph500_26_executorCores_aarch64=4
-graph500_26_executorMemory_aarch64=12G
-graph500_26_extraJavaOptions_aarch64=-Xms12g
-
-graph500_24_numExectuors_x86_64=59
-graph500_24_executorCores_x86_64=4
-graph500_24_executorMemory_x86_64=15G
-graph500_24_extraJavaOptions_x86_64=-Xms15g
-
-graph500_25_numExectuors_x86_64=59
-graph500_25_executorCores_x86_64=4
-graph500_25_executorMemory_x86_64=15G
-graph500_25_extraJavaOptions_x86_64=-Xms15g
-
-graph500_26_numExectuors_x86_64=59
-graph500_26_executorCores_x86_64=4
-graph500_26_executorMemory_x86_64=15G
-graph500_26_extraJavaOptions_x86_64=-Xms15g
\ No newline at end of file
diff --git a/tools/kal-test/conf/graph/wpr/wpr.yml b/tools/kal-test/conf/graph/wpr/wpr.yml
deleted file mode 100644
index a0d889c..0000000
--- a/tools/kal-test/conf/graph/wpr/wpr.yml
+++ /dev/null
@@ -1,22 +0,0 @@
-# PageRank dataset parameters arm
-
-wpr:
-  static:
-    inputpath: hdfs:///tmp/graphData/wpr/GAP-road.mtx
-    splitGraph: "\\s+"
-    partitionNum: 102
-    executor_nums: 12
-    outputPath: hdfs:///tmp/graphData/wpr/output
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
-
-  convergent:
-    inputpath: hdfs:///tmp/graphData/wpr/cage14.mtx
-    splitGraph: "\\s+"
-    partitionNum: 60
-    executor_nums: 12
-    outputPath: hdfs:///tmp/330alg/graph/wpr/output
-    numIter: 100
-    resetProb: 0.15
-    tolerance: 1e-7
diff --git a/tools/kal-test/conf/graph/wpr/wpr_spark.properties b/tools/kal-test/conf/graph/wpr/wpr_spark.properties
deleted file mode 100644
index e8d7bbc..0000000
--- a/tools/kal-test/conf/graph/wpr/wpr_spark.properties
+++ /dev/null
@@ -1,23 +0,0 @@
-#spark parameters
-deployMode=client
-
-#run_numExecutors_aarch64=39
-#run_executorCores_aarch64=7
-#run_executorMemory_aarch64=26G
-#run_extraJavaOptions_aarch64=-Xms26G
-#run_numPartitions_aarch64=100
-
-#run_numExecutors_x86_64=39
-#run_executorCores_x86_64=7
-#run_executorMemory_x86_64=26G
-#run_extraJavaOptions_x86_64=-Xms26G
-#deployMode : client
-#run_numPartitions_x86_64=100
-driverCores_x86_64=36
-driverMemory_x86_64=50G
-numExectuors_x86_64=71
-executorCores_x86_64=4
-executorMemory_x86_64=15G
-execMemOverhead_x86_64=2G
-extraJavaOptions_x86_64=-Xms15g
-numPartitions_x86_64=100
diff --git a/tools/kal-test/conf/ml/als/als.yml b/tools/kal-test/conf/ml/als/als.yml
deleted file mode 100644
index efad96e..0000000
--- a/tools/kal-test/conf/ml/als/als.yml
+++ /dev/null
@@ -1,128 +0,0 @@
-#ALS model params
-
-als:
-  aarch64:
-    dataframe:
-      als:
-        pt: 276
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsbs:
-        pt: 276
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsh:
-        pt: 276
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-    rdd:
-      als:
-        pt: 276
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsbs:
-        pt: 276
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsh:
-        pt: 276
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-  x86_64:
-    dataframe:
-      als:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsbs:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsh:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-    rdd:
-      als:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsbs:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsh:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
diff --git a/tools/kal-test/conf/ml/als/als_raw.yml b/tools/kal-test/conf/ml/als/als_raw.yml
deleted file mode 100644
index 03675d9..0000000
--- a/tools/kal-test/conf/ml/als/als_raw.yml
+++ /dev/null
@@ -1,65 +0,0 @@
-#ALS model params
-
-als:
-  x86_64:
-    dataframe:
-      als:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsbs:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsh:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-    rdd:
-      als:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsbs:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
-
-      alsh:
-        pt: 228
-        numIterations: 200
-        nonnegative: false
-        implicitPrefs: false
-        numItemBlocks: 228
-        numUserBlocks: 228
-        regParam: 0.0
-        alpha: 1.0
diff --git a/tools/kal-test/conf/ml/als/als_spark.properties b/tools/kal-test/conf/ml/als/als_spark.properties
deleted file mode 100644
index e5ed257..0000000
--- a/tools/kal-test/conf/ml/als/als_spark.properties
+++ /dev/null
@@ -1,15 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=12
-executorCores_aarch64=23
-executorMemory_aarch64=79G
-
-driverCores_x86_64=30
-driverMemory_x86_64=50G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=79G
diff --git a/tools/kal-test/conf/ml/als/als_spark_raw.properties b/tools/kal-test/conf/ml/als/als_spark_raw.properties
deleted file mode 100644
index 751453f..0000000
--- a/tools/kal-test/conf/ml/als/als_spark_raw.properties
+++ /dev/null
@@ -1,9 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-
-driverCores_x86_64=30
-driverMemory_x86_64=50G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=79G
diff --git a/tools/kal-test/conf/ml/cov/cov.yml b/tools/kal-test/conf/ml/cov/cov.yml
deleted file mode 100644
index 6ffb284..0000000
--- a/tools/kal-test/conf/ml/cov/cov.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-#Covariance model params
-
-cov:
-  CP10M1K_aarch64:
-    numPartitions: 280
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP10M1K
-    verifiedDataPath: None
-
-  CP2M5K_aarch64:
-    numPartitions: 280
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP2M5K
-    verifiedDataPath: None
-
-  CP1M10K_aarch64:
-    numPartitions: 280
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP1M10K
-    verifiedDataPath: None
-
-  CP10M1K_x86_64:
-    numPartitions: 234
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP10M1K
-    verifiedDataPath: None
-
-  CP2M5K_x86_64:
-    numPartitions: 234
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP2M5K
-    verifiedDataPath: None
-
-  CP1M10K_x86_64:
-    numPartitions: 234
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP1M10K
-    verifiedDataPath: None
-
-  CP10M1K_raw:
-    numPartitions: 234
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP10M1K/raw
-    verifiedDataPath: hdfs:///tmp/ml/result/verified/cov/CP10M1K
-
-  CP2M5K_raw:
-    numPartitions: 234
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP2M5K/raw
-    verifiedDataPath: hdfs:///tmp/ml/result/verified/cov/CP2M5K
-
-  CP1M10K_raw:
-    numPartitions: 234
-    saveDataPath: hdfs:///tmp/ml/result/cov/CP1M10K/raw
-    verifiedDataPath: hdfs:///tmp/ml/result/verified/cov/CP1M10K
diff --git a/tools/kal-test/conf/ml/cov/cov_spark.properties b/tools/kal-test/conf/ml/cov/cov_spark.properties
deleted file mode 100644
index bb32470..0000000
--- a/tools/kal-test/conf/ml/cov/cov_spark.properties
+++ /dev/null
@@ -1,19 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverMaxResultSize=256g
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=15
-executorCores_aarch64=19
-executorMemory_aarch64=63G
-executorMemOverhead_aarch64=5G
-extraJavaOptions_aarch64=-Xms63g
-
-driverCores_x86_64=36
-driverMemory_x86_64=50G
-numExectuors_x86_64=18
-executorCores_x86_64=13
-executorMemory_x86_64=50G
-executorMemOverhead_x86_64=5G
-extraJavaOptions_x86_64=-Xms50g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/dbscan/dbscan.yml b/tools/kal-test/conf/ml/dbscan/dbscan.yml
deleted file mode 100644
index 6c6581f..0000000
--- a/tools/kal-test/conf/ml/dbscan/dbscan.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-#Dbscan model params
-
-dbscan:
-  bremenSmall:
-    numPartitions: 3
-    epsilon: 100
-    minPoints: 312
-    sampleRate: 1.0
-
-  farm:
-    numPartitions: 3
-    epsilon: 1000
-    minPoints: 10
-    sampleRate: 1.0
-
-  house:
-    numPartitions: 3
-    epsilon: 3200
-    minPoints: 10
-    sampleRate: 1.0
diff --git a/tools/kal-test/conf/ml/dbscan/dbscan_spark.properties b/tools/kal-test/conf/ml/dbscan/dbscan_spark.properties
deleted file mode 100644
index b15e341..0000000
--- a/tools/kal-test/conf/ml/dbscan/dbscan_spark.properties
+++ /dev/null
@@ -1,16 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=36
-driverMemory_aarch64=200G
-numExectuors_aarch64=3
-executorCores_aarch64=94
-executorMemory_aarch64=316G
-extraJavaOptions_aarch64=-Xms316g
-
-driverCores_x86_64=30
-driverMemory_x86_64=200G
-numExectuors_x86_64=3
-executorCores_x86_64=78
-executorMemory_x86_64=316G
-extraJavaOptions_x86_64=-Xms316g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/dbscan/dbscan_spark_opensource.properties b/tools/kal-test/conf/ml/dbscan/dbscan_spark_opensource.properties
deleted file mode 100644
index 8325002..0000000
--- a/tools/kal-test/conf/ml/dbscan/dbscan_spark_opensource.properties
+++ /dev/null
@@ -1,17 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverMaxResultSize=256g
-driverCores_x86_64=30
-driverMemory_x86_64=200G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=79G
-extraJavaOptions_x86_64=-Xms79g
-
-epsilon_bremenSmall=100
-minPoints_bremenSmall=312
-epsilon_farm=1000
-minPoints_farm=10
-epsilon_house=3200
-minPoints_house=10
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/dt/dt_arm.yml b/tools/kal-test/conf/ml/dt/dt_arm.yml
deleted file mode 100644
index b1d1708..0000000
--- a/tools/kal-test/conf/ml/dt/dt_arm.yml
+++ /dev/null
@@ -1,177 +0,0 @@
-#DT model params
-
-dt:
-  classification:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 5120
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 5120
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-  regression:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
diff --git a/tools/kal-test/conf/ml/dt/dt_spark.properties b/tools/kal-test/conf/ml/dt/dt_spark.properties
deleted file mode 100644
index 613781c..0000000
--- a/tools/kal-test/conf/ml/dt/dt_spark.properties
+++ /dev/null
@@ -1,71 +0,0 @@
-# Spark parameters
-
-driverCores=36
-driverMemory=50g
-master=yarn
-deployMode=client
-
-maxFailures=1
-compress=false
-
-numFeaturesOptFindSplits=8192
-
-aarch64_classification_higgs_numExectuors=12
-aarch64_classification_higgs_executorCores=23
-aarch64_classification_higgs_executorMemory=79G
-aarch64_classification_higgs_extraJavaOptions=-Xms79g
-
-x86_64_classification_higgs_numExectuors=12
-x86_64_classification_higgs_executorCores=19
-x86_64_classification_higgs_executorMemory=79G
-x86_64_classification_higgs_extraJavaOptions=-Xms79g
-
-aarch64_regression_higgs_numExectuors=12
-aarch64_regression_higgs_executorCores=23
-aarch64_regression_higgs_executorMemory=79G
-aarch64_regression_higgs_extraJavaOptions=-Xms79g
-
-x86_64_regression_higgs_numExectuors=29
-x86_64_regression_higgs_executorCores=8
-x86_64_regression_higgs_executorMemory=31G
-x86_64_regression_higgs_extraJavaOptions=-Xms31g
-
-aarch64_classification_mnist8m_numExectuors=35
-aarch64_classification_mnist8m_executorCores=8
-aarch64_classification_mnist8m_executorMemory=26G
-aarch64_classification_mnist8m_extraJavaOptions=-Xms26g
-
-x86_64_classification_mnist8m_numExectuors=12
-x86_64_classification_mnist8m_executorCores=19
-x86_64_classification_mnist8m_executorMemory=79G
-x86_64_classification_mnist8m_extraJavaOptions=-Xms79g
-
-aarch64_regression_mnist8m_numExectuors=35
-aarch64_regression_mnist8m_executorCores=8
-aarch64_regression_mnist8m_executorMemory=26G
-aarch64_regression_mnist8m_extraJavaOptions=-Xms26g
-
-x86_64_regression_mnist8m_numExectuors=12
-x86_64_regression_mnist8m_executorCores=19
-x86_64_regression_mnist8m_executorMemory=79G
-x86_64_regression_mnist8m_extraJavaOptions=-Xms79g
-
-aarch64_classification_epsilon_numExectuors=12
-aarch64_classification_epsilon_executorCores=23
-aarch64_classification_epsilon_executorMemory=79G
-aarch64_classification_epsilon_extraJavaOptions=-Xms79g
-
-x86_64_classification_epsilon_numExectuors=12
-x86_64_classification_epsilon_executorCores=19
-x86_64_classification_epsilon_executorMemory=79G
-x86_64_classification_epsilon_extraJavaOptions=-Xms79g
-
-aarch64_regression_epsilon_numExectuors=12
-aarch64_regression_epsilon_executorCores=23
-aarch64_regression_epsilon_executorMemory=79G
-aarch64_regression_epsilon_extraJavaOptions=-Xms79g
-
-x86_64_regression_epsilon_numExectuors=12
-x86_64_regression_epsilon_executorCores=19
-x86_64_regression_epsilon_executorMemory=79G
-x86_64_regression_epsilon_extraJavaOptions=-Xms79g
diff --git a/tools/kal-test/conf/ml/dt/dt_spark_raw.properties b/tools/kal-test/conf/ml/dt/dt_spark_raw.properties
deleted file mode 100644
index ab9896c..0000000
--- a/tools/kal-test/conf/ml/dt/dt_spark_raw.properties
+++ /dev/null
@@ -1,39 +0,0 @@
-# Spark parameters
-
-driverCores=36
-driverMemory=50g
-master=yarn
-deployMode=client
-
-maxFailures=1
-compress=false
-
-classification_higgs_numExectuors=12
-classification_higgs_executorCores=19
-classification_higgs_executorMemory=79G
-classification_higgs_extraJavaOptions=-Xms79g
-
-regression_higgs_numExectuors=12
-regression_higgs_executorCores=19
-regression_higgs_executorMemory=79G
-regression_higgs_extraJavaOptions=-Xms79g
-
-classification_mnist8m_numExectuors=12
-classification_mnist8m_executorCores=19
-classification_mnist8m_executorMemory=79G
-classification_mnist8m_extraJavaOptions=-Xms79g
-
-regression_mnist8m_numExectuors=12
-regression_mnist8m_executorCores=19
-regression_mnist8m_executorMemory=79G
-regression_mnist8m_extraJavaOptions=-Xms79g
-
-classification_epsilon_numExectuors=12
-classification_epsilon_executorCores=19
-classification_epsilon_executorMemory=79G
-classification_epsilon_extraJavaOptions=-Xms79g
-
-regression_epsilon_numExectuors=29
-regression_epsilon_executorCores=8
-regression_epsilon_executorMemory=31G
-regression_epsilon_extraJavaOptions=-Xms31g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/dt/dt_x86.yml b/tools/kal-test/conf/ml/dt/dt_x86.yml
deleted file mode 100644
index 5891ca7..0000000
--- a/tools/kal-test/conf/ml/dt/dt_x86.yml
+++ /dev/null
@@ -1,177 +0,0 @@
-#DT model params
-
-dt:
-  classification:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 5120
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 5120
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-  regression:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 6
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
diff --git a/tools/kal-test/conf/ml/dt/dt_x86_raw.yml b/tools/kal-test/conf/ml/dt/dt_x86_raw.yml
deleted file mode 100644
index 40114fb..0000000
--- a/tools/kal-test/conf/ml/dt/dt_x86_raw.yml
+++ /dev/null
@@ -1,177 +0,0 @@
-#DT model params
-
-dt:
-  classification:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 72
-        numCopiesInput: 1
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 72
-        numCopiesInput: 1
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 14
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-  regression:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 17
-        maxBins: 512
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 1
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featuresType: "array"
-        copyStrategy: "normal"
-        useDFCollPtner: "true"
diff --git a/tools/kal-test/conf/ml/gbdt/gbdt.yml b/tools/kal-test/conf/ml/gbdt/gbdt.yml
deleted file mode 100644
index 0ff0aaf..0000000
--- a/tools/kal-test/conf/ml/gbdt/gbdt.yml
+++ /dev/null
@@ -1,119 +0,0 @@
-#GBDT model params
-
-gbdt:
-  epsilon_aarch64:
-    numPartitions: 228
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 20
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  epsilon_x86_64:
-    numPartitions: 228
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 20
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  rcv_aarch64:
-    numPartitions: 228
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 20
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  rcv_x86_64:
-    numPartitions: 228
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 20
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  epsilon_raw:
-    numPartitions: 66
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 20
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  rcv_raw:
-    numPartitions: 66
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 20
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  D10M4096libsvm_aarch64:
-    numPartitions: 276
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 64
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  D10M4096libsvm_x86_64:
-    numPartitions: 228
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 64
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
-
-  D10M4096libsvm_raw:
-    numPartitions: 228
-    maxIter: 100
-    maxDepth: 5
-    maxBins: 64
-    stepSize: 0.1
-    cacheNodeIds: true
-    maxMemoryInMB: 2048
-    minInstancesPerNode: 1
-    minInfoGain: 0.0
-    subsamplingRate: 1.0
-    featureSubsetStrategy: "auto"
diff --git a/tools/kal-test/conf/ml/gbdt/gbdt_spark.properties b/tools/kal-test/conf/ml/gbdt/gbdt_spark.properties
deleted file mode 100644
index e273621..0000000
--- a/tools/kal-test/conf/ml/gbdt/gbdt_spark.properties
+++ /dev/null
@@ -1,16 +0,0 @@
-# Spark parameters
-numExectuors=12
-
-epsilon_executorCores_aarch64=19
-epsilon_executorCores_x86_64=19
-rcv_executorCores_aarch64=19
-rcv_executorCores_x86_64=19
-D10M4096libsvm_executorCores_aarch64=23
-D10M4096libsvm_executorCores_x86_64=19
-
-executorMemory=77G
-extraJavaOptions=-Xms77g
-driverCores=40
-driverMemory=50G
-master=yarn
-deployMode=client
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/idf/idf.yml b/tools/kal-test/conf/ml/idf/idf.yml
deleted file mode 100644
index e07741f..0000000
--- a/tools/kal-test/conf/ml/idf/idf.yml
+++ /dev/null
@@ -1,28 +0,0 @@
-#IDF model params
-idf:
-  opt:
-    D10m200m:
-      pt: 0
-      combineStrategy: "auto"
-      fetchMethod: "reduce"
-      orcFormat: true
-      Flag: [idf, "D10m200m"]
-    D2g250m:
-      pt: 0
-      combineStrategy: "auto"
-      fetchMethod: "fold"
-      orcFormat: true
-      Flag: [idf, "D2g250m"]
-  pri:
-    D10m200m:
-      pt: 30
-      combineStrategy: "not used"
-      fetchMethod: "not used"
-      orcFormat: true
-      Flag: [idf, "D10m200m"]
-    D2g250m:
-      pt: 57
-      combineStrategy: "not used"
-      fetchMethod: "not used"
-      orcFormat: true
-      Flag: [idf, "D2g250m"]
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/idf/idf_spark.properties b/tools/kal-test/conf/ml/idf/idf_spark.properties
deleted file mode 100644
index 4a4d1fc..0000000
--- a/tools/kal-test/conf/ml/idf/idf_spark.properties
+++ /dev/null
@@ -1,59 +0,0 @@
-# Spark parameters
-
-master=yarn
-deployMode=client
-aarch64_opt_D10m200m_driverCores=36
-aarch64_opt_D10m200m_driverMemory=50G
-aarch64_opt_D10m200m_numExecutors=12
-aarch64_opt_D10m200m_executorCores=23
-aarch64_opt_D10m200m_executorMemory=79G
-aarch64_opt_D10m200m_extraJavaOptions=-Xms79g
-
-aarch64_pri_D10m200m_driverCores=36
-aarch64_pri_D10m200m_driverMemory=50G
-aarch64_pri_D10m200m_numExecutors=12
-aarch64_pri_D10m200m_executorCores=23
-aarch64_pri_D10m200m_executorMemory=79G
-aarch64_pri_D10m200m_extraJavaOptions=-Xms79g
-
-aarch64_opt_D2g200m_driverCores=36
-aarch64_opt_D2g200m_driverMemory=50G
-aarch64_opt_D2g200m_numExecutors=12
-aarch64_opt_D2g200m_executorCores=23
-aarch64_opt_D2g200m_executorMemory=79G
-aarch64_opt_D2g200m_extraJavaOptions=-Xms79g
-
-aarch64_pri_D2g200m_driverCores=36
-aarch64_pri_D2g200m_driverMemory=50G
-aarch64_pri_D2g200m_numExecutors=12
-aarch64_pri_D2g200m_executorCores=23
-aarch64_pri_D2g200m_executorMemory=79G
-aarch64_pri_D2g200m_extraJavaOptions=-Xms79g
-
-x86_64_opt_D10m200m_driverCores=30
-x86_64_opt_D10m200m_driverMemory=50G
-x86_64_opt_D10m200m_numExecutors=12
-x86_64_opt_D10m200m_executorCores=19
-x86_64_opt_D10m200m_executorMemory=79G
-x86_64_opt_D10m200m_extraJavaOptions=-Xms79g
-
-x86_64_pri_D10m200m_driverCores=30
-x86_64_pri_D10m200m_driverMemory=50G
-x86_64_pri_D10m200m_numExecutors=12
-x86_64_pri_D10m200m_executorCores=19
-x86_64_pri_D10m200m_executorMemory=79G
-x86_64_pri_D10m200m_extraJavaOptions=-Xms79g
-
-x86_64_opt_D2g200m_driverCores=30
-x86_64_opt_D2g200m_driverMemory=50G
-x86_64_opt_D2g200m_numExecutors=12
-x86_64_opt_D2g200m_executorCores=19
-x86_64_opt_D2g200m_executorMemory=79G
-x86_64_opt_D2g200m_extraJavaOptions=-Xms79g
-
-x86_64_pri_D2g200m_driverCores=30
-x86_64_pri_D2g200m_driverMemory=50G
-x86_64_pri_D2g200m_numExecutors=12
-x86_64_pri_D2g200m_executorCores=19
-x86_64_pri_D2g200m_executorMemory=79G
-x86_64_pri_D2g200m_extraJavaOptions=-Xms79g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/kmeans/kmeans.yml b/tools/kal-test/conf/ml/kmeans/kmeans.yml
deleted file mode 100644
index 3bc75db..0000000
--- a/tools/kal-test/conf/ml/kmeans/kmeans.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-#KMeans model params
-
-kmeans:
-  D1200M20_aarch64:
-    numPartitions: 280
-    maxIterations: 200
-    k: 200
-
-  D200M20_aarch64:
-    numPartitions: 280
-    maxIterations: 200
-    k: 200
-
-  D200M100_aarch64:
-    numPartitions: 280
-    maxIterations: 200
-    k: 200
-
-  D1200M20_x86_64:
-    numPartitions: 232
-    maxIterations: 200
-    k: 200
-
-  D200M20_x86_64:
-    numPartitions: 232
-    maxIterations: 200
-    k: 200
-
-  D200M100_x86_64:
-    numPartitions: 232
-    maxIterations: 200
-    k: 200
-
-  D1200M20_raw:
-    numPartitions: 232
-    maxIterations: 200
-    k: 200
-
-  D200M20_raw:
-    numPartitions: 232
-    maxIterations: 200
-    k: 200
-
-  D200M100_raw:
-    numPartitions: 232
-    maxIterations: 200
-    k: 200
diff --git a/tools/kal-test/conf/ml/kmeans/kmeans_spark.properties b/tools/kal-test/conf/ml/kmeans/kmeans_spark.properties
deleted file mode 100644
index 535ff97..0000000
--- a/tools/kal-test/conf/ml/kmeans/kmeans_spark.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=50
-driverMemory_aarch64=36G
-numExectuors_aarch64=35
-executorCores_aarch64=8
-executorMemory_aarch64=26G
-extraJavaOptions_aarch64=-Xms12g
-driverJavaOptions_aarch64=-Xms15g
-
-driverCores_x86_64=50
-driverMemory_x86_64=30G
-numExectuors_x86_64=29
-executorCores_x86_64=8
-executorMemory_x86_64=30G
-extraJavaOptions_x86_64=-Xms30g
-driverJavaOptions_x86_64=-Xms15g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/knn/knn.yml b/tools/kal-test/conf/ml/knn/knn.yml
deleted file mode 100644
index 6da5100..0000000
--- a/tools/kal-test/conf/ml/knn/knn.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-#KNN model params
-
-knn:
-  aarch64:
-    glove:
-      pt: 280
-      k: 100
-      testNum: 50000
-      testBatchSize: 5000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 30
-
-    gist:
-      pt: 280
-      k: 100
-      testNum: 50000
-      testBatchSize: 5000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 30
-
-    deep1b:
-      pt: 280
-      k: 100
-      testNum: 50000
-      testBatchSize: 2000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 30
-
-
-  x86_64:
-    glove:
-      pt: 234
-      k: 100
-      testNum: 50000
-      testBatchSize: 5000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 30
-
-    gist:
-      pt: 228
-      k: 100
-      testNum: 50000
-      testBatchSize: 5000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 30
-
-    deep1b:
-      pt: 234
-      k: 100
-      testNum: 50000
-      testBatchSize: 2000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 30
diff --git a/tools/kal-test/conf/ml/knn/knn_raw.yml b/tools/kal-test/conf/ml/knn/knn_raw.yml
deleted file mode 100644
index 5f2b797..0000000
--- a/tools/kal-test/conf/ml/knn/knn_raw.yml
+++ /dev/null
@@ -1,30 +0,0 @@
-#KNN model params
-
-knn:
-  x86_64:
-    glove:
-      pt: 236
-      k: 100
-      testNum: 50000
-      testBatchSize: 5000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 100
-
-    gist:
-      pt: 232
-      k: 100
-      testNum: 50000
-      testBatchSize: 5000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 1000
-
-    deep1b:
-      pt: 236
-      k: 100
-      testNum: 50000
-      testBatchSize: 2000
-      topTreeSizeRate: 10.0
-      topTreeLeafSize: 10
-      subTreeLeafSize: 1000
diff --git a/tools/kal-test/conf/ml/knn/knn_spark.properties b/tools/kal-test/conf/ml/knn/knn_spark.properties
deleted file mode 100644
index ecedc58..0000000
--- a/tools/kal-test/conf/ml/knn/knn_spark.properties
+++ /dev/null
@@ -1,54 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-compress=false
-
-driverCores_glove_aarch64=36
-driverMemory_glove_aarch64=50G
-numExectuors_glove_aarch64=35
-executorCores_glove_aarch64=8
-executorMemory_glove_aarch64=26G
-extraJavaOptions_glove_aarch64=-Xms26g
-execMemOverhead_glove_aarch64=3G
-
-driverCores_gist_aarch64=36
-driverMemory_gist_aarch64=50G
-numExectuors_gist_aarch64=35
-executorCores_gist_aarch64=8
-executorMemory_gist_aarch64=26G
-extraJavaOptions_gist_aarch64=-Xms26g
-execMemOverhead_gist_aarch64=3G
-
-driverCores_deep1b_aarch64=36
-driverMemory_deep1b_aarch64=50G
-numExectuors_deep1b_aarch64=35
-executorCores_deep1b_aarch64=8
-executorMemory_deep1b_aarch64=26G
-extraJavaOptions_deep1b_aarch64=-Xms26g
-execMemOverhead_deep1b_aarch64=3G
-
-
-
-driverCores_glove_x86_64=30
-driverMemory_glove_x86_64=50G
-numExectuors_glove_x86_64=18
-executorCores_glove_x86_64=13
-executorMemory_glove_x86_64=50G
-extraJavaOptions_glove_x86_64=-Xms50g
-execMemOverhead_glove_x86_64=4G
-
-driverCores_gist_x86_64=30
-driverMemory_gist_x86_64=50G
-numExectuors_gist_x86_64=12
-executorCores_gist_x86_64=19
-executorMemory_gist_x86_64=79G
-extraJavaOptions_gist_x86_64=-Xms79g
-execMemOverhead_gist_x86_64=5G
-
-driverCores_deep1b_x86_64=30
-driverMemory_deep1b_x86_64=50G
-numExectuors_deep1b_x86_64=18
-executorCores_deep1b_x86_64=13
-executorMemory_deep1b_x86_64=50G
-extraJavaOptions_deep1b_x86_64=-Xms50g
-execMemOverhead_deep1b_x86_64=5G
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/knn/knn_spark_raw.properties b/tools/kal-test/conf/ml/knn/knn_spark_raw.properties
deleted file mode 100644
index b54649d..0000000
--- a/tools/kal-test/conf/ml/knn/knn_spark_raw.properties
+++ /dev/null
@@ -1,29 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-compress=false
-
-
-driverCores_glove_x86_64=30
-driverMemory_glove_x86_64=50G
-numExectuors_glove_x86_64=59
-executorCores_glove_x86_64=4
-executorMemory_glove_x86_64=15G
-extraJavaOptions_glove_x86_64=-Xms15g
-execMemOverhead_glove_x86_64=4G
-
-driverCores_gist_x86_64=30
-driverMemory_gist_x86_64=50G
-numExectuors_gist_x86_64=29
-executorCores_gist_x86_64=8
-executorMemory_gist_x86_64=31G
-extraJavaOptions_gist_x86_64=-Xms31g
-execMemOverhead_gist_x86_64=5G
-
-driverCores_deep1b_x86_64=30
-driverMemory_deep1b_x86_64=50G
-numExectuors_deep1b_x86_64=59
-executorCores_deep1b_x86_64=4
-executorMemory_deep1b_x86_64=15G
-extraJavaOptions_deep1b_x86_64=-Xms15g
-execMemOverhead_deep1b_x86_64=5G
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/lda/lda.yml b/tools/kal-test/conf/ml/lda/lda.yml
deleted file mode 100644
index ae82d48..0000000
--- a/tools/kal-test/conf/ml/lda/lda.yml
+++ /dev/null
@@ -1,83 +0,0 @@
-#LDA model params
-
-lda:
-  nytimes_aarch64:
-    numPartitions: 12
-    numPartitionsTest: 276
-    numFeatures: 102660
-    checkpointInterval: -1
-    inputDataType: libsvm
-    maxIter: 100
-    k: 100
-
-  pubmed_aarch64:
-    numPartitions: 12
-    numPartitionsTest: 276
-    numFeatures: 141043
-    checkpointInterval: -1
-    inputDataType: libsvm
-    maxIter: 100
-    k: 100
-
-  D20M200K_aarch64:
-    numPartitions: 12
-    numPartitionsTest: 276
-    numFeatures: 200000
-    checkpointInterval: -1
-    inputDataType: obj
-    maxIter: 100
-    k: 100
-
-  nytimes_x86_64:
-    numPartitions: 12
-    numPartitionsTest: 228
-    numFeatures: 102660
-    checkpointInterval: -1
-    inputDataType: libsvm
-    maxIter: 100
-    k: 100
-
-  pubmed_x86_64:
-    numPartitions: 12
-    numPartitionsTest: 228
-    numFeatures: 141043
-    checkpointInterval: -1
-    inputDataType: libsvm
-    maxIter: 100
-    k: 100
-
-  D20M200K_x86_64:
-    numPartitions: 12
-    numPartitionsTest: 228
-    numFeatures: 200000
-    checkpointInterval: -1
-    inputDataType: obj
-    maxIter: 100
-    k: 100
-
-  nytimes_raw:
-    numPartitions: 96
-    numPartitionsTest: 228
-    numFeatures: 102660
-    checkpointInterval: -1
-    inputDataType: libsvm
-    maxIter: 100
-    k: 100
-
-  pubmed_raw:
-    numPartitions: 216
-    numPartitionsTest: 228
-    numFeatures: 141043
-    checkpointInterval: -1
-    inputDataType: libsvm
-    maxIter: 100
-    k: 100
-
-  D20M200K_raw:
-    numPartitions: 228
-    numPartitionsTest: 228
-    numFeatures: 200000
-    checkpointInterval: -1
-    inputDataType: obj
-    maxIter: 100
-    k: 100
diff --git a/tools/kal-test/conf/ml/lda/lda_spark.properties b/tools/kal-test/conf/ml/lda/lda_spark.properties
deleted file mode 100644
index 162149d..0000000
--- a/tools/kal-test/conf/ml/lda/lda_spark.properties
+++ /dev/null
@@ -1,16 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=40
-driverMemory_aarch64=100G
-numExectuors_aarch64=12
-executorCores_aarch64=23
-executorMemory_aarch64=75G
-extraJavaOptions_aarch64=-Xms75g
-
-driverCores_x86_64=40
-driverMemory_x86_64=100G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=75G
-extraJavaOptions_x86_64=-Xms75g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/linR/linR.yml b/tools/kal-test/conf/ml/linR/linR.yml
deleted file mode 100644
index 94715c9..0000000
--- a/tools/kal-test/conf/ml/linR/linR.yml
+++ /dev/null
@@ -1,58 +0,0 @@
-#Linear Regression model params
-
-linR:
-  aarch64:
-    mnist8m:
-      pt: 276
-      numFeatures: 784
-      loss: "huber"
-      regParam: 0.01
-      elasticNetParam: 0.0
-      maxIter: 400
-      tolerance: 1E-6
-
-    Twitter:
-      pt: 276
-      numFeatures: 9866
-      loss: "squaredError"
-      regParam: 0.01
-      elasticNetParam: 0.0
-      maxIter: 400
-      tolerance: 1E-6
-
-    rcv:
-      pt: 276
-      numFeatures: 47236
-      loss: "squaredError"
-      regParam: 0.01
-      elasticNetParam: 1.0
-      maxIter: 400
-      tolerance: 1E-6
-
-  x86_64:
-    mnist8m:
-      pt: 228
-      numFeatures: 784
-      loss: "huber"
-      regParam: 0.01
-      elasticNetParam: 0.0
-      maxIter: 400
-      tolerance: 1E-6
-
-    Twitter:
-      pt: 228
-      numFeatures: 9866
-      loss: "squaredError"
-      regParam: 0.01
-      elasticNetParam: 0.0
-      maxIter: 400
-      tolerance: 1E-6
-
-    rcv:
-      pt: 228
-      numFeatures: 47236
-      loss: "squaredError"
-      regParam: 0.01
-      elasticNetParam: 1.0
-      maxIter: 400
-      tolerance: 1E-6
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/linR/linR_raw.yml b/tools/kal-test/conf/ml/linR/linR_raw.yml
deleted file mode 100644
index cd5badf..0000000
--- a/tools/kal-test/conf/ml/linR/linR_raw.yml
+++ /dev/null
@@ -1,30 +0,0 @@
-#Linear Regression model params
-
-linR:
-  x86_64:
-    mnist8m:
-      pt: 228
-      numFeatures: 784
-      loss: "huber"
-      regParam: 0.01
-      elasticNetParam: 0.0
-      maxIter: 400
-      tolerance: 1E-6
-
-    Twitter:
-      pt: 228
-      numFeatures: 9866
-      loss: "squaredError"
-      regParam: 0.01
-      elasticNetParam: 0.0
-      maxIter: 400
-      tolerance: 1E-6
-
-    rcv:
-      pt: 228
-      numFeatures: 47236
-      loss: "squaredError"
-      regParam: 0.01
-      elasticNetParam: 1.0
-      maxIter: 400
-      tolerance: 1E-6
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/linR/linR_spark.properties b/tools/kal-test/conf/ml/linR/linR_spark.properties
deleted file mode 100644
index ca066c9..0000000
--- a/tools/kal-test/conf/ml/linR/linR_spark.properties
+++ /dev/null
@@ -1,19 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-maxFailures=1
-compress=false
-
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=12
-executorCores_aarch64=23
-executorMemory_aarch64=79G
-extraJavaOptions_aarch64=-Xms79g
-
-driverCores_x86_64=30
-driverMemory_x86_64=50G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=79G
-extraJavaOptions_x86_64=-Xms79g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/linR/linR_spark_raw.properties b/tools/kal-test/conf/ml/linR/linR_spark_raw.properties
deleted file mode 100644
index 7fbce0c..0000000
--- a/tools/kal-test/conf/ml/linR/linR_spark_raw.properties
+++ /dev/null
@@ -1,13 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-maxFailures=1
-compress=false
-
-
-driverCores_x86_64=30
-driverMemory_x86_64=50G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=79G
-extraJavaOptions_x86_64=-Xms79g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/logR/logR.yml b/tools/kal-test/conf/ml/logR/logR.yml
deleted file mode 100644
index 3f58773..0000000
--- a/tools/kal-test/conf/ml/logR/logR.yml
+++ /dev/null
@@ -1,110 +0,0 @@
-#Logistic Regression model params
-
-logR:
-  mnist8m_aarch64:
-    numPartitions: 276
-    numLabels: 10
-    numFeatures: 784
-    regParam: 0.01
-    elasticNetParam: 0.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: true
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  Twitter_aarch64:
-    numPartitions: 276
-    numLabels: 1
-    numFeatures: 9866
-    regParam: 0.01
-    elasticNetParam: 0.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: false
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  rcv_aarch64:
-    numPartitions: 276
-    numLabels: 1
-    numFeatures: 47236
-    regParam: 0.01
-    elasticNetParam: 1.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: false
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  mnist8m_x86_64:
-    numPartitions: 228
-    numLabels: 10
-    numFeatures: 784
-    regParam: 0.01
-    elasticNetParam: 0.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: true
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  Twitter_x86_64:
-    numPartitions: 228
-    numLabels: 1
-    numFeatures: 9866
-    regParam: 0.01
-    elasticNetParam: 0.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: false
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  rcv_x86_64:
-    numPartitions: 228
-    numLabels: 1
-    numFeatures: 47236
-    regParam: 0.01
-    elasticNetParam: 1.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: false
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  mnist8m_raw:
-    numPartitions: 228
-    numLabels: 10
-    numFeatures: 784
-    regParam: 0.01
-    elasticNetParam: 0.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: true
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  Twitter_raw:
-    numPartitions: 228
-    numLabels: 1
-    numFeatures: 9866
-    regParam: 0.01
-    elasticNetParam: 0.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: false
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
-
-  rcv_raw:
-    numPartitions: 228
-    numLabels: 1
-    numFeatures: 47236
-    regParam: 0.01
-    elasticNetParam: 1.0
-    maxIter: 400
-    tolerance: 1E-6
-    isSetBound: false
-    coefficientLowerBound: -1E32
-    coefficientUpperBound: 1E32
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/logR/logR_spark.properties b/tools/kal-test/conf/ml/logR/logR_spark.properties
deleted file mode 100644
index 0576b9e..0000000
--- a/tools/kal-test/conf/ml/logR/logR_spark.properties
+++ /dev/null
@@ -1,16 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=12
-executorCores_aarch64=23
-executorMemory_aarch64=79G
-extraJavaOptions_aarch64=-Xms79g
-
-driverCores_x86_64=30
-driverMemory_x86_64=50G
-numExectuors_x86_64=12
-executorCores_x86_64=19
-executorMemory_x86_64=79G
-extraJavaOptions_x86_64=-Xms79g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/ml_datasets.properties b/tools/kal-test/conf/ml/ml_datasets.properties
deleted file mode 100644
index eb63b86..0000000
--- a/tools/kal-test/conf/ml/ml_datasets.properties
+++ /dev/null
@@ -1,62 +0,0 @@
-# sparkVersion
-sparkVersion=spark2.3.2
-kalVersion=2.1.0
-
-# data path
-epsilon=hdfs:///tmp/ml/dataset/epsilon_train,hdfs:///tmp/ml/dataset/epsilon_test
-rcv=hdfs:///tmp/ml/dataset/rcv1bin_train,hdfs:///tmp/ml/dataset/rcv1bin_test
-mnist8m=hdfs:///tmp/ml/dataset/mnist8m_train,hdfs:///tmp/ml/dataset/mnist8m_test
-higgs=hdfs:///tmp/ml/dataset/higgs_train,hdfs:///tmp/ml/dataset/higgs_test
-D10M4096libsvm=hdfs:///tmp/ml/dataset/10M4096libsvm,hdfs:///tmp/ml/dataset/10M4096libsvm
-D10M4096=hdfs:///tmp/ml/dataset/svm_10m4096_train,hdfs:///tmp/ml/dataset/svm_10m4096_test
-D40M1K=hdfs:///tmp/ml/dataset/svm_40m_1k_train,hdfs:///tmp/ml/dataset/svm_40m_1k_test
-D280M118=hdfs:///tmp/ml/dataset/svm_280m_118_train,hdfs:///tmp/ml/dataset/svm_280m_118_test
-D1200M20=hdfs:///tmp/ml/dataset/kmeans_1200m20
-D200M20=hdfs:///tmp/ml/dataset/kmeans_200m20
-D200M100=hdfs:///tmp/ml/dataset/kmeans_200m100
-Twitter=hdfs:///tmp/ml/dataset/Twitter_train,hdfs:///tmp/ml/dataset/Twitter_test
-D10M1K=hdfs:///tmp/ml/dataset/pca_svd_10M1K
-D1M10K=hdfs:///tmp/ml/dataset/pca_svd_1M10K
-MESH=hdfs:///tmp/ml/dataset/MESH_DEFORM/mesh_deform.mtx
-RUCCI=hdfs:///tmp/ml/dataset/RUCCI/Rucci1.mtx
-D20M200K=hdfs:///tmp/ml/dataset/lda_20m200k,hdfs:///tmp/ml/dataset/lda_20m200k
-nytimes=hdfs:///tmp/ml/dataset/nytimes,hdfs:///tmp/ml/dataset/nytimes
-pubmed=hdfs:///tmp/ml/dataset/pubmed,hdfs:///tmp/ml/dataset/pubmed
-als=hdfs:///tmp/ml/dataset/ALS
-alsbs=hdfs:///tmp/ml/dataset/ALS_bs
-alsh=hdfs:///tmp/ml/dataset/ALS_h
-glove=hdfs:///tmp/ml/dataset/GloVe
-gist=hdfs:///tmp/ml/dataset/GIST
-deep1b=hdfs:///tmp/ml/dataset/DEEP1B
-kosarak=hdfs:///tmp/ml/dataset/Kosarak
-IBM100M47=hdfs:///tmp/ml/dataset/IBM_Seq_c100000it100t5s10
-IBM10M47=hdfs:///tmp/ml/dataset/IBM_Seq_c10000it100t5s10
-CP10M1K=hdfs:///tmp/ml/dataset/CP10M1K
-CP2M5K=hdfs:///tmp/ml/dataset/CP2M5K
-CP1M10K=hdfs:///tmp/ml/dataset/CP1M10K
-bremenSmall=hdfs:///tmp/ml/dataset/bremenSmall
-farm=hdfs:///tmp/ml/dataset/farm
-house=hdfs:///tmp/ml/dataset/house
-mesh_deform=hdfs:///tmp/hxy_test/sparse_matrix/mesh_deform_234024_9394
-Kemelmacher=hdfs:///tmp/hxy_test/sparse_matrix/Kemelmacher_28453_9694
-wathen100=hdfs:///tmp/hxy_test/sparse_matrix/wathen100_30402_30402
-MOLIERE=hdfs:///tmp/hxy_test/sparse_matrix/MOLIERE_2016_30239688_30239688
-D10m200m=hdfs:///tmp/ml/dataset/idf_10m_200m_orc
-D2g250m=hdfs:///tmp/ml/dataset/idf_2g_250m_orc
-simrank3w=hdfs:///tmp/wc_test/simrank/HibenchRating3wx3w
-simrank5w=hdfs:///tmp/wc_test/simrank/HibenchRating5wx5w
-simrank7w=hdfs:///tmp/wc_test/simrank/HibenchRating7wx7w
-
-#output data path
-D10M1K_output=hdfs:///tmp/ml/output/svd/D10M1K
-D1M10K_output=hdfs:///tmp/ml/output/svd/D1M10K
-MESH_output=hdfs:///tmp/ml/output/svd/MESH
-RUCCI_output=hdfs:///tmp/ml/output/svd/RUCCI
-CP10M1K_output=hdfs:///tmp/ml/output/pearson/CP10M1K
-CP2M5K_output=hdfs:///tmp/ml/output/pearson/CP2M5K
-CP1M10K_output=hdfs:///tmp/ml/output/pearson/CP1M10K
-SpearMan_CP10M1K_output=hdfs:///tmp/ml/result/spearman/CP10M1K
-SpearMan_CP2M5K_output=hdfs:///tmp/ml/result/spearman/CP2M5K
-SpearMan_CP1M10K_output=hdfs:///tmp/ml/result/spearman/CP1M10K
-D10m200m_modelsPath="./output/models/D10m200m"
-D2g250m_modelsPath="./output/models/D2g250m"
diff --git a/tools/kal-test/conf/ml/pca/pca.yml b/tools/kal-test/conf/ml/pca/pca.yml
deleted file mode 100644
index 79616e2..0000000
--- a/tools/kal-test/conf/ml/pca/pca.yml
+++ /dev/null
@@ -1,75 +0,0 @@
-#pca model params
-
-pca:
-  D10M1K_aarch64:
-    numPartitions: 280
-    k: 500
-    numCols: 0
-    numRows: 0
-    dataFormat: dense
-    sep: ","
-    
-  D1M10K_aarch64:
-    numPartitions: 285
-    k: 500
-    numCols: 0
-    numRows: 0
-    dataFormat: dense
-    sep: ","
-    
-  MESH_aarch64:
-    numPartitions: 285
-    k: 500
-    numCols: 9394
-    numRows: 0
-    dataFormat: coo
-    sep: " "
-
-  D10M1K_x86_64:
-    numPartitions: 236
-    k: 500
-    numCols: 0
-    numRows: 0
-    dataFormat: dense
-    sep: ","
-
-  D1M10K_x86_64:
-    numPartitions: 234
-    k: 500
-    numCols: 0
-    numRows: 0
-    dataFormat: dense
-    sep: ","
-
-  MESH_x86_64:
-    numPartitions: 234
-    k: 500
-    numCols: 9394
-    numRows: 0
-    dataFormat: coo
-    sep: " "
-
-  D10M1K_raw:
-    numPartitions: 236
-    k: 500
-    numCols: 0
-    numRows: 0
-    dataFormat: dense
-    sep: ","
-
-  D1M10K_raw:
-    numPartitions: 234
-    k: 500
-    numCols: 0
-    numRows: 0
-    dataFormat: dense
-    sep: ","
-
-  MESH_raw:
-    numPartitions: 234
-    k: 500
-    numCols: 9394
-    numRows: 0
-    dataFormat: coo
-    sep: " "
-
diff --git a/tools/kal-test/conf/ml/pca/pca_spark.properties b/tools/kal-test/conf/ml/pca/pca_spark.properties
deleted file mode 100644
index 3e664f2..0000000
--- a/tools/kal-test/conf/ml/pca/pca_spark.properties
+++ /dev/null
@@ -1,55 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-
-D10M1K_driverCores_aarch64=50
-D10M1K_driverMemory_aarch64=36G
-D10M1K_numExectuors_aarch64=35
-D10M1K_executorCores_aarch64=8
-D10M1K_executorMemory_aarch64=26G
-D10M1K_executorMemoryOverhead_aarch64=3G
-D10M1K_extraJavaOptions_aarch64=-XX:+UseNUMA
-
-
-D10M1K_driverCores_x86_64=36
-D10M1K_driverMemory_x86_64=50G
-D10M1K_numExectuors_x86_64=59
-D10M1K_executorCores_x86_64=4
-D10M1K_executorMemory_x86_64=15G
-D10M1K_executorMemoryOverhead_x86_64=2G
-D10M1K_extraJavaOptions_x86_64=-XX:+UseNUMA
-
-
-D1M10K_driverCores_aarch64=36
-D1M10K_driverMemory_aarch64=50G
-D1M10K_numExectuors_aarch64=15
-D1M10K_executorCores_aarch64=19
-D1M10K_executorMemory_aarch64=61G
-D1M10K_executorMemoryOverhead_aarch64=7G
-D1M10K_extraJavaOptions_aarch64=-XX:+UseNUMA
-
-D1M10K_driverCores_x86_64=36
-D1M10K_driverMemory_x86_64=50G
-D1M10K_numExectuors_x86_64=18
-D1M10K_executorCores_x86_64=13
-D1M10K_executorMemory_x86_64=50G
-D1M10K_executorMemoryOverhead_x86_64=5G
-D1M10K_extraJavaOptions_x86_64=-XX:+UseNUMA
-
-
-MESH_driverCores_aarch64=36
-MESH_driverMemory_aarch64=50G
-MESH_numExectuors_aarch64=15
-MESH_executorCores_aarch64=19
-MESH_executorMemory_aarch64=61G
-MESH_executorMemoryOverhead_aarch64=7G
-MESH_extraJavaOptions_aarch64=-Xms61g
-
-MESH_driverCores_x86_64=36
-MESH_driverMemory_x86_64=50G
-MESH_numExectuors_x86_64=18
-MESH_executorCores_x86_64=13
-MESH_executorMemory_x86_64=50G
-MESH_executorMemoryOverhead_x86_64=5G
-MESH_extraJavaOptions_x86_64=-Xms50g
-
diff --git a/tools/kal-test/conf/ml/pearson/pearson.yml b/tools/kal-test/conf/ml/pearson/pearson.yml
deleted file mode 100644
index 00b5449..0000000
--- a/tools/kal-test/conf/ml/pearson/pearson.yml
+++ /dev/null
@@ -1,36 +0,0 @@
-#Pearson model params
-
-pearson:
-  aarch64:
-    dataframe:
-      CP10M1K:
-        pt: 280
-      CP2M5K:
-        pt: 280
-      CP1M10K:
-        pt: 280
-
-    rdd:
-      CP10M1K:
-        pt: 280
-      CP2M5K:
-        pt: 280
-      CP1M10K:
-        pt: 280
-
-  x86_64:
-    dataframe:
-      CP10M1K:
-        pt: 234
-      CP2M5K:
-        pt: 234
-      CP1M10K:
-        pt: 234
-
-    rdd:
-      CP10M1K:
-        pt: 234
-      CP2M5K:
-        pt: 234
-      CP1M10K:
-        pt: 234
diff --git a/tools/kal-test/conf/ml/pearson/pearson_raw.yml b/tools/kal-test/conf/ml/pearson/pearson_raw.yml
deleted file mode 100644
index dc2c9ab..0000000
--- a/tools/kal-test/conf/ml/pearson/pearson_raw.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-#Pearson model params
-
-pearson:
-  x86_64:
-    dataframe:
-      CP10M1K:
-        pt: 234
-      CP2M5K:
-        pt: 234
-      CP1M10K:
-        pt: 234
-
-    rdd:
-      CP10M1K:
-        pt: 234
-      CP2M5K:
-        pt: 234
-      CP1M10K:
-        pt: 234
diff --git a/tools/kal-test/conf/ml/pearson/pearson_spark.properties b/tools/kal-test/conf/ml/pearson/pearson_spark.properties
deleted file mode 100644
index 166ce32..0000000
--- a/tools/kal-test/conf/ml/pearson/pearson_spark.properties
+++ /dev/null
@@ -1,19 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=15
-executorCores_aarch64=19
-executorMemory_aarch64=63G
-extraJavaOptions_aarch64=-Xms63g
-execMemOverhead_aarch64=5G
-
-driverCores_x86_64=36
-driverMemory_x86_64=50G
-numExectuors_x86_64=18
-executorCores_x86_64=13
-executorMemory_x86_64=50G
-extraJavaOptions_x86_64=-Xms50g
-execMemOverhead_x86_64=5G
diff --git a/tools/kal-test/conf/ml/pearson/pearson_spark_raw.properties b/tools/kal-test/conf/ml/pearson/pearson_spark_raw.properties
deleted file mode 100644
index df83502..0000000
--- a/tools/kal-test/conf/ml/pearson/pearson_spark_raw.properties
+++ /dev/null
@@ -1,11 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-
-driverCores_x86_64=36
-driverMemory_x86_64=50G
-numExectuors_x86_64=18
-executorCores_x86_64=13
-executorMemory_x86_64=50G
-extraJavaOptions_x86_64=-Xms50g
-execMemOverhead_x86_64=5G
diff --git a/tools/kal-test/conf/ml/ps/ps.yml b/tools/kal-test/conf/ml/ps/ps.yml
deleted file mode 100644
index ea1d09a..0000000
--- a/tools/kal-test/conf/ml/ps/ps.yml
+++ /dev/null
@@ -1,111 +0,0 @@
-#PrefixSpan model params
-
-ps:
-  kosarak_aarch64:
-    numPartitions: 280
-    minSupport: 0.001
-    maxPatternLength: 10
-    maxLocalProjDBSize: 100000000
-    localTimeout: 15
-    filterCandidates: true
-    projDBstep: 10
-    redistributeData: true
-    saveDataPath: hdfs:///tmp/ml/result/ps/kosarak
-    verifiedDataPath: None
-
-  IBM10M47_aarch64:
-    numPartitions: 280
-    minSupport: 0.002
-    maxPatternLength: 10
-    maxLocalProjDBSize: 10000000
-    localTimeout: 70
-    filterCandidates: true
-    projDBstep: 10
-    redistributeData: false
-    saveDataPath: hdfs:///tmp/ml/result/ps/IBM10M47
-    verifiedDataPath: None
-
-  IBM100M47_aarch64:
-    numPartitions: 280
-    minSupport: 0.0021
-    maxPatternLength: 10
-    maxLocalProjDBSize: 100000000
-    localTimeout: 70
-    filterCandidates: true
-    projDBstep: 10
-    redistributeData: false
-    saveDataPath: hdfs:///tmp/ml/result/ps/IBM100M47
-    verifiedDataPath: None
-
-  kosarak_x86_64:
-    numPartitions: 232
-    minSupport: 0.001
-    maxPatternLength: 10
-    maxLocalProjDBSize: 10000000
-    localTimeout: 15
-    filterCandidates: true
-    projDBstep: 10
-    redistributeData: true
-    saveDataPath: hdfs:///tmp/ml/result/ps/kosarak
-    verifiedDataPath: None
-
-  IBM10M47_x86_64:
-    numPartitions: 232
-    minSupport: 0.002
-    maxPatternLength: 10
-    maxLocalProjDBSize: 10000000
-    localTimeout: 150
-    filterCandidates: true
-    projDBstep: 10
-    redistributeData: false
-    saveDataPath: hdfs:///tmp/ml/result/ps/IBM10M47
-    verifiedDataPath: None
-
-  IBM100M47_x86_64:
-    numPartitions: 232
-    minSupport: 0.0021
-    maxPatternLength: 10
-    maxLocalProjDBSize: 100000000
-    localTimeout: 150
-    filterCandidates: true
-    projDBstep: 1
-    redistributeData: false
-    saveDataPath: hdfs:///tmp/ml/result/ps/IBM100M47
-    verifiedDataPath: None
-
-  kosarak_raw:
-    numPartitions: 232
-    minSupport: 0.001
-    maxPatternLength: 10
-    maxLocalProjDBSize: 10000000
-    localTimeout: None
-    filterCandidates: None
-    projDBstep: None
-    redistributeData: true
-    saveDataPath: None
-    verifiedDataPath: hdfs:///tmp/ml/result/verified/ps/kosarak
-
-  IBM10M47_raw:
-    numPartitions: 232
-    minSupport: 0.002
-    maxPatternLength: 10
-    maxLocalProjDBSize: 1000000
-    localTimeout: None
-    filterCandidates: None
-    projDBstep: None
-    redistributeData: false
-    saveDataPath: None
-    verifiedDataPath: hdfs:///tmp/ml/result/verified/ps/IBM10M47
-
-  IBM100M47_raw:
-    numPartitions: 232
-    minSupport: 0.0021
-    maxPatternLength: 10
-    maxLocalProjDBSize: 10000000
-    localTimeout: None
-    filterCandidates: None
-    projDBstep: None
-    redistributeData: false
-    saveDataPath: None
-    verifiedDataPath: hdfs:///tmp/ml/result/verified/ps/IBM100M47
-
diff --git a/tools/kal-test/conf/ml/ps/ps_spark.properties b/tools/kal-test/conf/ml/ps/ps_spark.properties
deleted file mode 100644
index 9f4b3cc..0000000
--- a/tools/kal-test/conf/ml/ps/ps_spark.properties
+++ /dev/null
@@ -1,16 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=35
-executorCores_aarch64=8
-executorMemory_aarch64=26G
-extraJavaOptions_aarch64=-Xms26g
-
-driverCores_x86_64=30
-driverMemory_x86_64=100G
-numExectuors_x86_64=29
-executorCores_x86_64=8
-executorMemory_x86_64=31G
-extraJavaOptions_x86_64=-Xms31g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/rf/rf_arm.yml b/tools/kal-test/conf/ml/rf/rf_arm.yml
deleted file mode 100644
index 5817216..0000000
--- a/tools/kal-test/conf/ml/rf/rf_arm.yml
+++ /dev/null
@@ -1,233 +0,0 @@
-#RF model params
-
-rf:
-  classification:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      rcv:
-        genericPt: 300
-        maxMemoryInMB: 16384
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      mnist8m:
-        genericPt: 250
-        maxMemoryInMB: 2048
-        pt: 20
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 20
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      rcv:
-        genericPt: 220
-        maxMemoryInMB: 10240
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-  regression:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 11
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      rcv:
-        genericPt: 220
-        maxMemoryInMB: 16384
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 20
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 11
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      rcv:
-        genericPt: 220
-        maxMemoryInMB: 10240
-        pt: 36
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
diff --git a/tools/kal-test/conf/ml/rf/rf_spark.properties b/tools/kal-test/conf/ml/rf/rf_spark.properties
deleted file mode 100644
index 4eec5d6..0000000
--- a/tools/kal-test/conf/ml/rf/rf_spark.properties
+++ /dev/null
@@ -1,89 +0,0 @@
-# Spark parameters
-
-driverCores=36
-driverMemory=50g
-master=yarn
-deployMode=client
-
-maxFailures=1
-compress=false
-
-aarch64_classification_higgs_numExectuors=35
-aarch64_classification_higgs_executorCores=8
-aarch64_classification_higgs_executorMemory=26G
-aarch64_classification_higgs_extraJavaOptions=-Xms26g
-
-x86_64_classification_higgs_numExectuors=29
-x86_64_classification_higgs_executorCores=8
-x86_64_classification_higgs_executorMemory=31G
-x86_64_classification_higgs_extraJavaOptions=-Xms31g
-
-aarch64_regression_higgs_numExectuors=35
-aarch64_regression_higgs_executorCores=8
-aarch64_regression_higgs_executorMemory=26G
-aarch64_regression_higgs_extraJavaOptions=-Xms26g
-
-x86_64_regression_higgs_numExectuors=12
-x86_64_regression_higgs_executorCores=19
-x86_64_regression_higgs_executorMemory=79G
-x86_64_regression_higgs_extraJavaOptions=-Xms79g
-
-aarch64_classification_mnist8m_numExectuors=35
-aarch64_classification_mnist8m_executorCores=8
-aarch64_classification_mnist8m_executorMemory=26G
-aarch64_classification_mnist8m_extraJavaOptions=-Xms26g
-
-x86_64_classification_mnist8m_numExectuors=29
-x86_64_classification_mnist8m_executorCores=8
-x86_64_classification_mnist8m_executorMemory=31G
-x86_64_classification_mnist8m_extraJavaOptions=-Xms31g
-
-aarch64_regression_mnist8m_numExectuors=12
-aarch64_regression_mnist8m_executorCores=23
-aarch64_regression_mnist8m_executorMemory=79G
-aarch64_regression_mnist8m_extraJavaOptions=-Xms79g
-
-x86_64_regression_mnist8m_numExectuors=12
-x86_64_regression_mnist8m_executorCores=19
-x86_64_regression_mnist8m_executorMemory=79G
-x86_64_regression_mnist8m_extraJavaOptions=-Xms79g
-
-aarch64_classification_epsilon_numExectuors=12
-aarch64_classification_epsilon_executorCores=23
-aarch64_classification_epsilon_executorMemory=79G
-aarch64_classification_epsilon_extraJavaOptions=-Xms79g
-
-x86_64_classification_epsilon_numExectuors=12
-x86_64_classification_epsilon_executorCores=19
-x86_64_classification_epsilon_executorMemory=79G
-x86_64_classification_epsilon_extraJavaOptions=-Xms79g
-
-aarch64_regression_epsilon_numExectuors=35
-aarch64_regression_epsilon_executorCores=8
-aarch64_regression_epsilon_executorMemory=26G
-aarch64_regression_epsilon_extraJavaOptions=-Xms26g
-
-x86_64_regression_epsilon_numExectuors=12
-x86_64_regression_epsilon_executorCores=19
-x86_64_regression_epsilon_executorMemory=79G
-x86_64_regression_epsilon_extraJavaOptions=-Xms79g
-
-aarch64_classification_rcv_numExectuors=35
-aarch64_classification_rcv_executorCores=8
-aarch64_classification_rcv_executorMemory=26G
-aarch64_classification_rcv_extraJavaOptions=-Xms26g
-
-x86_64_classification_rcv_numExectuors=12
-x86_64_classification_rcv_executorCores=19
-x86_64_classification_rcv_executorMemory=79G
-x86_64_classification_rcv_extraJavaOptions=-Xms79g
-
-aarch64_regression_rcv_numExectuors=35
-aarch64_regression_rcv_executorCores=8
-aarch64_regression_rcv_executorMemory=26G
-aarch64_regression_rcv_extraJavaOptions=-Xms26g
-
-x86_64_regression_rcv_numExectuors=12
-x86_64_regression_rcv_executorCores=19
-x86_64_regression_rcv_executorMemory=79G
-x86_64_regression_rcv_extraJavaOptions=-Xms79g
diff --git a/tools/kal-test/conf/ml/rf/rf_spark_raw.properties b/tools/kal-test/conf/ml/rf/rf_spark_raw.properties
deleted file mode 100644
index c73a423..0000000
--- a/tools/kal-test/conf/ml/rf/rf_spark_raw.properties
+++ /dev/null
@@ -1,49 +0,0 @@
-# Spark parameters
-
-driverCores=36
-driverMemory=50g
-master=yarn
-deployMode=client
-
-maxFailures=1
-compress=false
-
-classification_higgs_numExectuors=29
-classification_higgs_executorCores=8
-classification_higgs_executorMemory=31G
-classification_higgs_extraJavaOptions=-Xms31g
-
-regression_higgs_numExectuors=12
-regression_higgs_executorCores=19
-regression_higgs_executorMemory=79G
-regression_higgs_extraJavaOptions=-Xms79g
-
-classification_mnist8m_numExectuors=29
-classification_mnist8m_executorCores=8
-classification_mnist8m_executorMemory=31G
-classification_mnist8m_extraJavaOptions=-Xms31g
-
-regression_mnist8m_numExectuors=12
-regression_mnist8m_executorCores=19
-regression_mnist8m_executorMemory=79G
-regression_mnist8m_extraJavaOptions=-Xms79g
-
-classification_epsilon_numExectuors=12
-classification_epsilon_executorCores=19
-classification_epsilon_executorMemory=79G
-classification_epsilon_extraJavaOptions=-Xms79g
-
-regression_epsilon_numExectuors=12
-regression_epsilon_executorCores=19
-regression_epsilon_executorMemory=79G
-regression_epsilon_extraJavaOptions=-Xms79g
-
-classification_rcv_numExectuors=12
-classification_rcv_executorCores=19
-classification_rcv_executorMemory=79G
-classification_rcv_extraJavaOptions=-Xms79g
-
-regression_rcv_numExectuors=12
-regression_rcv_executorCores=19
-regression_rcv_executorMemory=79G
-regression_rcv_extraJavaOptions=-Xms79g
diff --git a/tools/kal-test/conf/ml/rf/rf_x86.yml b/tools/kal-test/conf/ml/rf/rf_x86.yml
deleted file mode 100644
index b27fe24..0000000
--- a/tools/kal-test/conf/ml/rf/rf_x86.yml
+++ /dev/null
@@ -1,233 +0,0 @@
-#RF model params
-
-rf:
-  classification:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      rcv:
-        genericPt: 300
-        maxMemoryInMB: 16384
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 23
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 22
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 18
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      rcv:
-        genericPt: 220
-        maxMemoryInMB: 10240
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-  regression:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 11
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      rcv:
-        genericPt: 180
-        maxMemoryInMB: 16384
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 18
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 22
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 18
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 11
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      rcv:
-        genericPt: 180
-        maxMemoryInMB: 10240
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
diff --git a/tools/kal-test/conf/ml/rf/rf_x86_raw.yml b/tools/kal-test/conf/ml/rf/rf_x86_raw.yml
deleted file mode 100644
index 4af5665..0000000
--- a/tools/kal-test/conf/ml/rf/rf_x86_raw.yml
+++ /dev/null
@@ -1,233 +0,0 @@
-#RF model params
-
-rf:
-  classification:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 72
-        numCopiesInput: 1
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 1024
-        pt: 72
-        numCopiesInput: 1
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      rcv:
-        genericPt: 300
-        maxMemoryInMB: 16384
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 23
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 22
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 18
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 15
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      rcv:
-        genericPt: 220
-        maxMemoryInMB: 10240
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-  regression:
-    dataframe:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 36
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 36
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 11
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: ""
-        featuresType: "array"
-      rcv:
-        genericPt: 180
-        maxMemoryInMB: 16384
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
-    rdd:
-      higgs:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 18
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 17
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      mnist8m:
-        genericPt: 300
-        maxMemoryInMB: 2048
-        pt: 22
-        numCopiesInput: 6
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 10
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      epsilon:
-        genericPt: 300
-        maxMemoryInMB: 4096
-        pt: 18
-        numCopiesInput: 5
-        numTrees: 20
-        maxDepth: 11
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "auto"
-        featuresType: "array"
-      rcv:
-        genericPt: 180
-        maxMemoryInMB: 10240
-        pt: 32
-        numCopiesInput: 7
-        numTrees: 20
-        maxDepth: 13
-        maxBins: 128
-        useNodeIdCache: false
-        checkpointInterval: 10
-        numClasses: 2
-        bcVariables: false
-        featureSubsetStrategy: "0.1"
-        featuresType: "fasthashmap"
diff --git a/tools/kal-test/conf/ml/simrank/simrank.yml b/tools/kal-test/conf/ml/simrank/simrank.yml
deleted file mode 100644
index 9821db3..0000000
--- a/tools/kal-test/conf/ml/simrank/simrank.yml
+++ /dev/null
@@ -1,86 +0,0 @@
-#simrank model params
-
-simrank:
-  simrank3w-boostkit-aarch64:
-    numPartitions: 284
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-    
-  simrank5w-boostkit-aarch64:
-    numPartitions: 284
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank7w-boostkit-aarch64:
-    numPartitions: 284
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank3w-boostkit-x86_64:
-    numPartitions: 236
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank5w-boostkit-x86_64:
-    numPartitions: 236
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank7w-boostkit-x86_64:
-    numPartitions: 236
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank3w-raw-aarch64:
-    numPartitions: 284
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank5w-raw-aarch64:
-    numPartitions: 284
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank7w-raw-aarch64:
-    numPartitions: 284
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank3w-raw-x86_64:
-    numPartitions: 236
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank5w-raw-x86_64:
-    numPartitions: 236
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
-
-  simrank7w-raw-x86_64:
-    numPartitions: 236
-    damp: 0.6
-    maxIter: 5
-    saveResult: false
-    savePath: ""
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/simrank/simrank_spark.properties b/tools/kal-test/conf/ml/simrank/simrank_spark.properties
deleted file mode 100644
index 458fa73..0000000
--- a/tools/kal-test/conf/ml/simrank/simrank_spark.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores_aarch64=36
-driverMemory_aarch64=50G
-numExectuors_aarch64=71
-executorCores_aarch64=4
-executorMemory_aarch64=12G
-execMemOverhead_aarch64=2G
-extraJavaOptions_aarch64=-Xms12g
-
-driverCores_x86_64=36
-driverMemory_x86_64=50G
-numExectuors_x86_64=59
-executorCores_x86_64=4
-executorMemory_x86_64=15G
-execMemOverhead_x86_64=2G
-extraJavaOptions_x86_64=-Xms15g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/spca/spca.yml b/tools/kal-test/conf/ml/spca/spca.yml
deleted file mode 100644
index 70aed20..0000000
--- a/tools/kal-test/conf/ml/spca/spca.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-#SPCA model params
-
-spca:
-  Kemelmacher:
-    pt: 284
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 9694
-    pcPath: "pc_boostkit_Kemelmacher.txt"
-    sigmaPath: "sigma_boostkit_Kemelmacher"
-    saveRes: false
-
-  mesh_deform:
-    pt: 284
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 9394
-    pcPath: "pc_boostkit_Mesh.txt"
-    sigmaPath: "sigma_boostkit_Mesh"
-    saveRes: false
-
-  wathen100:
-    pt: 284
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 30402
-    pcPath: "pc_boostkit_wathen.txt"
-    sigmaPath: "sigma_boostkit_wathen"
-    saveRes: false
-
-  MOLIERE:
-    pt: 284
-    k: 10
-    sep: " "
-    dataFormat: "coo"
-    numCols: 30239688
-    pcPath: "pc_boostkit_MOLIERE.txt"
-    sigmaPath: "sigma_boostkit_MOLIERE"
-    saveRes: false
diff --git a/tools/kal-test/conf/ml/spca/spca_raw.yml b/tools/kal-test/conf/ml/spca/spca_raw.yml
deleted file mode 100644
index ece70f9..0000000
--- a/tools/kal-test/conf/ml/spca/spca_raw.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-#SPCA model params
-
-spca:
-  Kemelmacher:
-    pt: 236
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 9694
-    pcPath: "pc_baseline_Kemelmacher.txt"
-    sigmaPath: "sigma_baseline_Kemelmacher"
-    saveRes: false
-
-  mesh_deform:
-    pt: 236
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 9394
-    pcPath: "pc_baseline_Mesh.txt"
-    sigmaPath: "sigma_baseline_Mesh"
-    saveRes: false
-
-  wathen100:
-    pt: 236
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 30402
-    pcPath: "pc_baseline_wathen.txt"
-    sigmaPath: "sigma_baseline_wathen"
-    saveRes: false
-
-  MOLIERE:
-    pt: 236
-    k: 10
-    sep: " "
-    dataFormat: "coo"
-    numCols: 30239688
-    pcPath: "pc_baseline_MOLIERE.txt"
-    sigmaPath: "sigma_baseline_MOLIERE"
-    saveRes: false
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/spca/spca_spark.properties b/tools/kal-test/conf/ml/spca/spca_spark.properties
deleted file mode 100644
index 40a372b..0000000
--- a/tools/kal-test/conf/ml/spca/spca_spark.properties
+++ /dev/null
@@ -1,70 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-compress=false
-
-driverCores_Kemelmacher_aarch64=50
-driverMemory_Kemelmacher_aarch64=50G
-numExectuors_Kemelmacher_aarch64=71
-executorCores_Kemelmacher_aarch64=4
-executorMemory_Kemelmacher_aarch64=12G
-extraJavaOptions_Kemelmacher_aarch64=-Xms12g
-execMemOverhead_Kemelmacher_aarch64=2G
-
-driverCores_mesh_deform_aarch64=50
-driverMemory_mesh_deform_aarch64=50G
-numExectuors_mesh_deform_aarch64=71
-executorCores_mesh_deform_aarch64=4
-executorMemory_mesh_deform_aarch64=12G
-extraJavaOptions_mesh_deform_aarch64=-Xms12g
-execMemOverhead_mesh_deform_aarch64=2G
-
-driverCores_wathen100_aarch64=50
-driverMemory_wathen100_aarch64=50G
-numExectuors_wathen100_aarch64=71
-executorCores_wathen100_aarch64=4
-executorMemory_wathen100_aarch64=12G
-extraJavaOptions_wathen100_aarch64=-Xms12g
-execMemOverhead_wathen100_aarch64=2G
-
-driverCores_MOLIERE_aarch64=50
-driverMemory_MOLIERE_aarch64=50G
-numExectuors_MOLIERE_aarch64=71
-executorCores_MOLIERE_aarch64=4
-executorMemory_MOLIERE_aarch64=12G
-extraJavaOptions_MOLIERE_aarch64=-Xms12g
-execMemOverhead_MOLIERE_aarch64=2G
-
-
-
-driverCores_Kemelmacher_x86_64=50
-driverMemory_Kemelmacher_x86_64=50G
-numExectuors_Kemelmacher_x86_64=59
-executorCores_Kemelmacher_x86_64=4
-executorMemory_Kemelmacher_x86_64=15G
-extraJavaOptions_Kemelmacher_x86_64=-Xms15g
-execMemOverhead_Kemelmacher_x86_64=2G
-
-driverCores_mesh_deform_x86_64=50
-driverMemory_mesh_deform_x86_64=50G
-numExectuors_mesh_deform_x86_64=59
-executorCores_mesh_deform_x86_64=4
-executorMemory_mesh_deform_x86_64=15G
-extraJavaOptions_mesh_deform_x86_64=-Xms15g
-execMemOverhead_mesh_deform_x86_64=2G
-
-driverCores_wathen100_x86_64=50
-driverMemory_wathen100_x86_64=50G
-numExectuors_wathen100_x86_64=59
-executorCores_wathen100_x86_64=4
-executorMemory_wathen100_x86_64=15G
-extraJavaOptions_wathen100_x86_64=-Xms15g
-execMemOverhead_wathen100_x86_64=2G
-
-driverCores_MOLIERE_x86_64=50
-driverMemory_MOLIERE_x86_64=50G
-numExectuors_MOLIERE_x86_64=59
-executorCores_MOLIERE_x86_64=4
-executorMemory_MOLIERE_x86_64=15G
-extraJavaOptions_MOLIERE_x86_64=-Xms15g
-execMemOverhead_MOLIERE_x86_64=2G
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/spca/spca_spark_raw.properties b/tools/kal-test/conf/ml/spca/spca_spark_raw.properties
deleted file mode 100644
index b6365b2..0000000
--- a/tools/kal-test/conf/ml/spca/spca_spark_raw.properties
+++ /dev/null
@@ -1,37 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-compress=false
-
-
-driverCores_Kemelmacher_x86_64=50
-driverMemory_Kemelmacher_x86_64=50G
-numExectuors_Kemelmacher_x86_64=59
-executorCores_Kemelmacher_x86_64=4
-executorMemory_Kemelmacher_x86_64=15G
-extraJavaOptions_Kemelmacher_x86_64=-Xms15g
-execMemOverhead_Kemelmacher_x86_64=2G
-
-driverCores_mesh_deform_x86_64=50
-driverMemory_mesh_deform_x86_64=50G
-numExectuors_mesh_deform_x86_64=59
-executorCores_mesh_deform_x86_64=4
-executorMemory_mesh_deform_x86_64=15G
-extraJavaOptions_mesh_deform_x86_64=-Xms15g
-execMemOverhead_mesh_deform_x86_64=2G
-
-driverCores_wathen100_x86_64=50
-driverMemory_wathen100_x86_64=50G
-numExectuors_wathen100_x86_64=59
-executorCores_wathen100_x86_64=4
-executorMemory_wathen100_x86_64=15G
-extraJavaOptions_wathen100_x86_64=-Xms15g
-execMemOverhead_wathen100_x86_64=2G
-
-driverCores_MOLIERE_x86_64=50
-driverMemory_MOLIERE_x86_64=50G
-numExectuors_MOLIERE_x86_64=59
-executorCores_MOLIERE_x86_64=4
-executorMemory_MOLIERE_x86_64=15G
-extraJavaOptions_MOLIERE_x86_64=-Xms15g
-execMemOverhead_MOLIERE_x86_64=2G
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/spearman/spearman.yml b/tools/kal-test/conf/ml/spearman/spearman.yml
deleted file mode 100644
index b693f1b..0000000
--- a/tools/kal-test/conf/ml/spearman/spearman.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-#Spearman model params
-
-spearman:
-  CP10M1K_aarch64:
-    numPartitions: 2840
-
-  CP2M5K_aarch64:
-    numPartitions: 1420
-
-  CP1M10K_aarch64:
-    numPartitions: 1400
-
-  CP10M1K_x86_64:
-    numPartitions: 2360
-
-  CP2M5K_x86_64:
-    numPartitions: 1180
-
-  CP1M10K_x86_64:
-    numPartitions: 1160
-
-  CP10M1K_raw:
-    numPartitions: 2360
-
-  CP2M5K_raw:
-    numPartitions: 2360
-
-  CP1M10K_raw:
-    numPartitions: 2320
diff --git a/tools/kal-test/conf/ml/spearman/spearman_spark.properties b/tools/kal-test/conf/ml/spearman/spearman_spark.properties
deleted file mode 100644
index 242d2f3..0000000
--- a/tools/kal-test/conf/ml/spearman/spearman_spark.properties
+++ /dev/null
@@ -1,54 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-driverCores=36
-driverMemory=50G
-
-executorCores_CP10M1K=4
-executorCores_CP2M5K=4
-executorCores_CP1M10K=8
-
-numExectuors_CP10M1K_aarch64=71
-executorMemory_CP10M1K_aarch64=9G
-executorMemOverhead_CP10M1K_aarch64=5G
-extraJavaOptions_CP10M1K_aarch64=-Xms9g
-
-numExectuors_CP2M5K_aarch64=71
-executorMemory_CP2M5K_aarch64=9G
-executorMemOverhead_CP2M5K_aarch64=5G
-extraJavaOptions_CP2M5K_aarch64=-Xms9g
-
-numExectuors_CP1M10K_aarch64=35
-executorMemory_CP1M10K_aarch64=18G
-executorMemOverhead_CP1M10K_aarch64=10G
-extraJavaOptions_CP1M10K_aarch64=-Xms18g
-
-numExectuors_CP10M1K_x86_64=59
-executorMemory_CP10M1K_x86_64=12G
-executorMemOverhead_CP10M1K_x86_64=5G
-extraJavaOptions_CP10M1K_x86_64=-Xms12g
-
-numExectuors_CP2M5K_x86_64=59
-executorMemory_CP2M5K_x86_64=12G
-executorMemOverhead_CP2M5K_x86_64=5G
-extraJavaOptions_CP2M5K_x86_64=-Xms12g
-
-numExectuors_CP1M10K_x86_64=29
-executorMemory_CP1M10K_x86_64=23G
-executorMemOverhead_CP1M10K_x86_64=10G
-extraJavaOptions_CP1M10K_x86_64=-Xms23g
-
-numExectuors_CP10M1K_raw=59
-executorMemory_CP10M1K_raw=15G
-executorMemOverhead_CP10M1K_raw=2G
-extraJavaOptions_CP10M1K_raw=-Xms15g
-
-numExectuors_CP2M5K_raw=59
-executorMemory_CP2M5K_raw=15G
-executorMemOverhead_CP2M5K_raw=2G
-extraJavaOptions_CP2M5K_raw=-Xms15g
-
-numExectuors_CP1M10K_raw=29
-executorMemory_CP1M10K_raw=23G
-executorMemOverhead_CP1M10K_raw=10G
-extraJavaOptions_CP1M10K_raw=-Xms23g
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/svd/svd.yml b/tools/kal-test/conf/ml/svd/svd.yml
deleted file mode 100644
index 8ecb576..0000000
--- a/tools/kal-test/conf/ml/svd/svd.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-#SVD model params
-
-svd:
-  D10M1K:
-    pt: 280
-    k: 500
-    sep: ","
-    dataFormat: "dense"
-    numCols: 0
-    numRows: 0
-
-  D1M10K:
-    pt: 285
-    k: 500
-    sep: ","
-    dataFormat: "dense"
-    numCols: 0
-    numRows: 0
-
-  MESH:
-    pt: 284
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 9394
-    numRows: 0
-
-  RUCCI:
-    pt: 284
-    k: 100
-    sep: " "
-    dataFormat: "coo"
-    numCols: 109901
-    numRows: 0
diff --git a/tools/kal-test/conf/ml/svd/svd_raw.yml b/tools/kal-test/conf/ml/svd/svd_raw.yml
deleted file mode 100644
index cb13e2e..0000000
--- a/tools/kal-test/conf/ml/svd/svd_raw.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-#SVD model params
-
-svd:
-  D10M1K:
-    pt: 280
-    k: 500
-    sep: ","
-    dataFormat: "dense"
-    numCols: 0
-    numRows: 0
-
-  D1M10K:
-    pt: 285
-    k: 500
-    sep: ","
-    dataFormat: "dense"
-    numCols: 0
-    numRows: 0
-
-  MESH:
-    pt: 284
-    k: 500
-    sep: " "
-    dataFormat: "coo"
-    numCols: 9394
-    numRows: 0
-
-  RUCCI:
-    pt: 284
-    k: 100
-    sep: " "
-    dataFormat: "coo"
-    numCols: 109901
-    numRows: 0
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/svd/svd_spark.properties b/tools/kal-test/conf/ml/svd/svd_spark.properties
deleted file mode 100644
index d0c8407..0000000
--- a/tools/kal-test/conf/ml/svd/svd_spark.properties
+++ /dev/null
@@ -1,69 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-compress=false
-
-driverCores_D10M1K_aarch64=35
-driverMemory_D10M1K_aarch64=50G
-numExectuors_D10M1K_aarch64=35
-executorCores_D10M1K_aarch64=8
-executorMemory_D10M1K_aarch64=26G
-extraJavaOptions_D10M1K_aarch64=-XX:+UseNUMA
-execMemOverhead_D10M1K_aarch64=3G
-
-driverCores_D1M10K_aarch64=35
-driverMemory_D1M10K_aarch64=50G
-numExectuors_D1M10K_aarch64=15
-executorCores_D1M10K_aarch64=19
-executorMemory_D1M10K_aarch64=61G
-extraJavaOptions_D1M10K_aarch64=-XX:+UseNUMA
-execMemOverhead_D1M10K_aarch64=7G
-
-driverCores_MESH_aarch64=35
-driverMemory_MESH_aarch64=50G
-numExectuors_MESH_aarch64=71
-executorCores_MESH_aarch64=4
-executorMemory_MESH_aarch64=12G
-extraJavaOptions_MESH_aarch64=-Xms12g
-execMemOverhead_MESH_aarch64=2G
-
-driverCores_RUCCI_aarch64=35
-driverMemory_RUCCI_aarch64=50G
-numExectuors_RUCCI_aarch64=71
-executorCores_RUCCI_aarch64=4
-executorMemory_RUCCI_aarch64=12G
-extraJavaOptions_RUCCI_aarch64=-Xms12g
-execMemOverhead_RUCCI_aarch64=2G
-
-
-driverCores_D10M1K_x86_64=30
-driverMemory_D10M1K_x86_64=50G
-numExectuors_D10M1K_x86_64=29
-executorCores_D10M1K_x86_64=8
-executorMemory_D10M1K_x86_64=31G
-extraJavaOptions_D10M1K_x86_64=-XX:+UseNUMA
-execMemOverhead_D10M1K_x86_64=4G
-
-driverCores_D1M10K_x86_64=30
-driverMemory_D1M10K_x86_64=50G
-numExectuors_D1M10K_x86_64=18
-executorCores_D1M10K_x86_64=13
-executorMemory_D1M10K_x86_64=50G
-extraJavaOptions_D1M10K_x86_64=-XX:+UseNUMA
-execMemOverhead_D1M10K_x86_64=5G
-
-driverCores_MESH_x86_64=30
-driverMemory_MESH_x86_64=50G
-numExectuors_MESH_x86_64=18
-executorCores_MESH_x86_64=13
-executorMemory_MESH_x86_64=50G
-extraJavaOptions_MESH_x86_64=-Xms50g
-execMemOverhead_MESH_x86_64=5G
-
-driverCores_RUCCI_x86_64=30
-driverMemory_RUCCI_x86_64=50G
-numExectuors_RUCCI_x86_64=18
-executorCores_RUCCI_x86_64=13
-executorMemory_RUCCI_x86_64=50G
-extraJavaOptions_RUCCI_x86_64=-Xms50g
-execMemOverhead_RUCCI_x86_64=5G
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/svd/svd_spark_raw.properties b/tools/kal-test/conf/ml/svd/svd_spark_raw.properties
deleted file mode 100644
index d5e38fa..0000000
--- a/tools/kal-test/conf/ml/svd/svd_spark_raw.properties
+++ /dev/null
@@ -1,37 +0,0 @@
-# Spark parameters
-master=yarn
-deployMode=client
-compress=false
-
-
-driverCores_D10M1K_x86_64=30
-driverMemory_D10M1K_x86_64=50G
-numExectuors_D10M1K_x86_64=29
-executorCores_D10M1K_x86_64=8
-executorMemory_D10M1K_x86_64=31G
-extraJavaOptions_D10M1K_x86_64=-XX:+UseNUMA
-execMemOverhead_D10M1K_x86_64=4G
-
-driverCores_D1M10K_x86_64=30
-driverMemory_D1M10K_x86_64=50G
-numExectuors_D1M10K_x86_64=18
-executorCores_D1M10K_x86_64=13
-executorMemory_D1M10K_x86_64=50G
-extraJavaOptions_D1M10K_x86_64=-XX:+UseNUMA
-execMemOverhead_D1M10K_x86_64=5G
-
-driverCores_MESH_x86_64=30
-driverMemory_MESH_x86_64=50G
-numExectuors_MESH_x86_64=18
-executorCores_MESH_x86_64=13
-executorMemory_MESH_x86_64=50G
-extraJavaOptions_MESH_x86_64=-Xms50g
-execMemOverhead_MESH_x86_64=5G
-
-driverCores_RUCCI_x86_64=30
-driverMemory_RUCCI_x86_64=50G
-numExectuors_RUCCI_x86_64=18
-executorCores_RUCCI_x86_64=13
-executorMemory_RUCCI_x86_64=50G
-extraJavaOptions_RUCCI_x86_64=-Xms50g
-execMemOverhead_RUCCI_x86_64=5G
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/svm/svm.yml b/tools/kal-test/conf/ml/svm/svm.yml
deleted file mode 100644
index cc30003..0000000
--- a/tools/kal-test/conf/ml/svm/svm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-#GBDT model params
-svm:
-  D280M118_aarch64:
-    numPartitions: 142
-    regParam: 0.01
-    maxIter: 50
-
-  D40M1K_aarch64:
-    numPartitions: 142
-    regParam: 0.01
-    maxIter: 100
-
-  D10M4096_aarch64:
-    numPartitions: 284
-    regParam: 0.01
-    maxIter: 100
-
-  D280M118_x86_64:
-    numPartitions: 232
-    regParam: 0.01
-    maxIter: 50
-
-  D40M1K_x86_64:
-    numPartitions: 232
-    regParam: 0.01
-    maxIter: 100
-
-  D10M4096_x86_64:
-    numPartitions: 232
-    regParam: 0.01
-    maxIter: 100
-
-  D280M118_raw:
-    numPartitions: 232
-    regParam: 0.01
-    maxIter: 50
-
-  D40M1K_raw:
-    numPartitions: 232
-    regParam: 0.01
-    maxIter: 100
-
-  D10M4096_raw:
-    numPartitions: 232
-    regParam: 0.01
-    maxIter: 100
diff --git a/tools/kal-test/conf/ml/svm/svm_spark.properties b/tools/kal-test/conf/ml/svm/svm_spark.properties
deleted file mode 100644
index 1ad0f0f..0000000
--- a/tools/kal-test/conf/ml/svm/svm_spark.properties
+++ /dev/null
@@ -1,18 +0,0 @@
-# Spark parameters
-aarch64_numExectuors=71
-aarch64_executorCores=4
-aarch64_executorMemory=12G
-aarch64_extraJavaOptions=-Xms12g
-aarch64_driverCores=36
-aarch64_driverMemory=50G
-aarch64_master=yarn
-aarch64_deployMode=client
-
-x86_64_numExectuors=29
-x86_64_executorCores=8
-x86_64_executorMemory=30G
-x86_64_extraJavaOptions=-Xms30g
-x86_64_driverCores=30
-x86_64_driverMemory=50G
-x86_64_master=yarn
-x86_64_deployMode=client
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/xgbt/xgbt_arm.yml b/tools/kal-test/conf/ml/xgbt/xgbt_arm.yml
deleted file mode 100644
index bbc9f54..0000000
--- a/tools/kal-test/conf/ml/xgbt/xgbt_arm.yml
+++ /dev/null
@@ -1,66 +0,0 @@
-#XGBT model params
-xgbt:
-  optimized:
-    classification:
-      higgs:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "binary:logistic"
-        num_round: 500
-        num_workers: 51
-        nthread: 4
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
-      mnist8m:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "multi:softprob"
-        num_round: 500
-        num_class: 10
-        num_workers: 24
-        nthread: 9
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
-    regression:
-      higgs:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "reg:squarederror"
-        num_round: 500
-        num_workers: 51
-        nthread: 4
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
-      mnist8m:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "reg:squarederror"
-        num_round: 500
-        num_workers: 120
-        nthread: 2
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/xgbt/xgbt_spark.properties b/tools/kal-test/conf/ml/xgbt/xgbt_spark.properties
deleted file mode 100644
index bcd3299..0000000
--- a/tools/kal-test/conf/ml/xgbt/xgbt_spark.properties
+++ /dev/null
@@ -1,77 +0,0 @@
-# Spark parameters
-
-master=yarn
-deployMode=client
-
-x86_64_classification_higgs_driverCores=40
-x86_64_classification_higgs_driverMemory=300G
-x86_64_classification_higgs_numExecutors=33
-x86_64_classification_higgs_executorCores=7
-x86_64_classification_higgs_taskCpus=7
-x86_64_classification_higgs_executorMemory=27G
-x86_64_classification_higgs_extraJavaOptions=-Xms27g
-x86_64_classification_higgs_numPartitions=33
-
-aarch64_classification_higgs_driverCores=40
-aarch64_classification_higgs_driverMemory=300G
-aarch64_classification_higgs_numExecutors=51
-aarch64_classification_higgs_executorCores=4
-aarch64_classification_higgs_taskCpus=4
-aarch64_classification_higgs_executorMemory=17G
-aarch64_classification_higgs_extraJavaOptions=-Xms17g
-aarch64_classification_higgs_numPartitions=51
-
-x86_64_classification_mnist8m_driverCores=40
-x86_64_classification_mnist8m_driverMemory=300G
-x86_64_classification_mnist8m_numExecutors=18
-x86_64_classification_mnist8m_executorCores=9
-x86_64_classification_mnist8m_taskCpus=9
-x86_64_classification_mnist8m_executorMemory=50G
-x86_64_classification_mnist8m_extraJavaOptions=-Xms50g
-x86_64_classification_mnist8m_numPartitions=18
-
-aarch64_classification_mnist8m_driverCores=40
-aarch64_classification_mnist8m_driverMemory=300G
-aarch64_classification_mnist8m_numExecutors=24
-aarch64_classification_mnist8m_executorCores=9
-aarch64_classification_mnist8m_taskCpus=9
-aarch64_classification_mnist8m_executorMemory=37G
-aarch64_classification_mnist8m_extraJavaOptions=-Xms37g
-aarch64_classification_mnist8m_numPartitions=24
-
-
-x86_64_regression_higgs_driverCores=40
-x86_64_regression_higgs_driverMemory=300G
-x86_64_regression_higgs_numExecutors=33
-x86_64_regression_higgs_executorCores=7
-x86_64_regression_higgs_taskCpus=7
-x86_64_regression_higgs_executorMemory=27G
-x86_64_regression_higgs_extraJavaOptions=-Xms27g
-x86_64_regression_higgs_numPartitions=33
-
-aarch64_regression_higgs_driverCores=40
-aarch64_regression_higgs_driverMemory=300G
-aarch64_regression_higgs_numExecutors=51
-aarch64_regression_higgs_executorCores=4
-aarch64_regression_higgs_taskCpus=4
-aarch64_regression_higgs_executorMemory=17G
-aarch64_regression_higgs_extraJavaOptions=-Xms17g
-aarch64_regression_higgs_numPartitions=51
-
-x86_64_regression_mnist8m_driverCores=40
-x86_64_regression_mnist8m_driverMemory=300G
-x86_64_regression_mnist8m_numExecutors=33
-x86_64_regression_mnist8m_executorCores=7
-x86_64_regression_mnist8m_taskCpus=7
-x86_64_regression_mnist8m_executorMemory=27G
-x86_64_regression_mnist8m_extraJavaOptions=-Xms27g
-x86_64_regression_mnist8m_numPartitions=33
-
-aarch64_regression_mnist8m_driverCores=40
-aarch64_regression_mnist8m_driverMemory=300G
-aarch64_regression_mnist8m_numExecutors=120
-aarch64_regression_mnist8m_executorCores=2
-aarch64_regression_mnist8m_taskCpus=2
-aarch64_regression_mnist8m_executorMemory=7G
-aarch64_regression_mnist8m_extraJavaOptions=-Xms7g
-aarch64_regression_mnist8m_numPartitions=120
diff --git a/tools/kal-test/conf/ml/xgbt/xgbt_spark_raw.properties b/tools/kal-test/conf/ml/xgbt/xgbt_spark_raw.properties
deleted file mode 100644
index 03311c7..0000000
--- a/tools/kal-test/conf/ml/xgbt/xgbt_spark_raw.properties
+++ /dev/null
@@ -1,40 +0,0 @@
-# Spark parameters
-
-master=yarn
-deployMode=client
-
-x86_64_classification_higgs_driverCores=40
-x86_64_classification_higgs_driverMemory=300G
-x86_64_classification_higgs_numExecutors=33
-x86_64_classification_higgs_executorCores=7
-x86_64_classification_higgs_taskCpus=7
-x86_64_classification_higgs_executorMemory=27G
-x86_64_classification_higgs_extraJavaOptions=-Xms27g
-x86_64_classification_higgs_numPartitions=33
-
-x86_64_classification_mnist8m_driverCores=40
-x86_64_classification_mnist8m_driverMemory=300G
-x86_64_classification_mnist8m_numExecutors=24
-x86_64_classification_mnist8m_executorCores=9
-x86_64_classification_mnist8m_taskCpus=9
-x86_64_classification_mnist8m_executorMemory=37G
-x86_64_classification_mnist8m_extraJavaOptions=-Xms37g
-x86_64_classification_mnist8m_numPartitions=24
-
-x86_64_regression_higgs_driverCores=40
-x86_64_regression_higgs_driverMemory=300G
-x86_64_regression_higgs_numExecutors=33
-x86_64_regression_higgs_executorCores=7
-x86_64_regression_higgs_taskCpus=7
-x86_64_regression_higgs_executorMemory=27G
-x86_64_regression_higgs_extraJavaOptions=-Xms27g
-x86_64_regression_higgs_numPartitions=33
-
-x86_64_regression_mnist8m_driverCores=40
-x86_64_regression_mnist8m_driverMemory=300G
-x86_64_regression_mnist8m_numExecutors=33
-x86_64_regression_mnist8m_executorCores=7
-x86_64_regression_mnist8m_taskCpus=7
-x86_64_regression_mnist8m_executorMemory=27G
-x86_64_regression_mnist8m_extraJavaOptions=-Xms27g
-x86_64_regression_mnist8m_numPartitions=33
\ No newline at end of file
diff --git a/tools/kal-test/conf/ml/xgbt/xgbt_x86.yml b/tools/kal-test/conf/ml/xgbt/xgbt_x86.yml
deleted file mode 100644
index eccecda..0000000
--- a/tools/kal-test/conf/ml/xgbt/xgbt_x86.yml
+++ /dev/null
@@ -1,122 +0,0 @@
-#XGBT model params
-xgbt:
-  primary:
-    classification:
-      higgs:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        objective: "binary:logistic"
-        num_round: 500
-        num_workers: 33
-        nthread: 7
-        tree_method: "hist"
-        grow_policy: "depthwise"
-      mnist8m:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        objective: "multi:softprob"
-        num_round: 500
-        num_class: 10
-        num_workers: 24
-        nthread: 9
-        tree_method: "hist"
-        grow_policy: "depthwise"
-    regression:
-      higgs:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        objective: "reg:squarederror"
-        num_round: 500
-        num_workers: 33
-        nthread: 7
-        tree_method: "hist"
-        grow_policy: "depthwise"
-      mnist8m:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        objective: "reg:squarederror"
-        num_round: 500
-        num_workers: 33
-        nthread: 7
-        tree_method: "hist"
-        grow_policy: "depthwise"
-  optimized:
-    classification:
-      higgs:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "binary:logistic"
-        num_round: 500
-        num_workers: 33
-        nthread: 7
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
-      mnist8m:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "multi:softprob"
-        num_round: 500
-        num_class: 10
-        num_workers: 18
-        nthread: 9
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
-    regression:
-      higgs:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "reg:squarederror"
-        num_round: 500
-        num_workers: 33
-        nthread: 7
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
-      mnist8m:
-        eta: 0.1
-        gamma: 1
-        min_child_weight: 1
-        max_depth: 6
-        allow_non_zero_for_missing: true
-        vectorType: "sparse"
-        enable_bbgen: true
-        rabit_enable_tcp_no_delay: true
-        objective: "reg:squarederror"
-        num_round: 500
-        num_workers: 33
-        nthread: 7
-        tree_method: "hist"
-        grow_policy: "depthwiselossltd"
\ No newline at end of file
diff --git a/tools/kal-test/pom.xml b/tools/kal-test/pom.xml
deleted file mode 100644
index 4f10f7a..0000000
--- a/tools/kal-test/pom.xml
+++ /dev/null
@@ -1,91 +0,0 @@
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-    <groupId>com.bigdata</groupId>
-    <artifactId>kal-test_2.11</artifactId>
-    <version>0.1</version>
-    <name>${project.artifactId}</name>
-    <description>Spark KAL algorithm test</description>
-    <inceptionYear>2020</inceptionYear>
-    <packaging>jar</packaging>
-
-    <properties>
-        <maven.compiler.source>1.8</maven.compiler.source>
-        <maven.compiler.target>1.8</maven.compiler.target>
-        <encoding>UTF-8</encoding>
-        <scala.version>2.11.8</scala.version>
-    </properties>
-
-    <dependencies>
-        <dependency>
-            <groupId>ml.dmlc</groupId>
-            <artifactId>xgboost4j-spark_2.12</artifactId>
-            <version>1.3.1</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark.graphx.lib</groupId>
-            <artifactId>boostkit-graph-kernel-client_2.11</artifactId>
-            <version>2.1.0</version>
-            <classifier>spark2.3.2</classifier>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>boostkit-ml-kernel-client_2.11</artifactId>
-            <version>2.1.0</version>
-            <classifier>spark2.3.2</classifier>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-graphx_2.11</artifactId>
-            <version>2.3.2</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.spark</groupId>
-            <artifactId>spark-mllib_2.11</artifactId>
-            <version>2.3.2</version>
-        </dependency>
-        <dependency>
-            <groupId>it.unimi.dsi</groupId>
-            <artifactId>fastutil</artifactId>
-            <version>8.3.1</version>
-        </dependency>
-        <dependency>
-            <groupId>org.yaml</groupId>
-            <artifactId>snakeyaml</artifactId>
-            <version>1.19</version>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.mahout</groupId>
-            <artifactId>mahout-core</artifactId>
-            <version>0.9</version>
-            <exclusions>
-                <exclusion>
-                    <groupId>org.apache.hadoop</groupId>
-                    <artifactId>hadoop-core</artifactId>
-                </exclusion>
-            </exclusions>
-        </dependency>
-    </dependencies>
-    <build>
-        <sourceDirectory>src/main/scala</sourceDirectory>
-        <plugins>
-            <plugin>
-                <groupId>net.alchim31.maven</groupId>
-                <artifactId>scala-maven-plugin</artifactId>
-                <version>3.2.0</version>
-                <executions>
-                    <execution>
-                        <goals>
-                            <goal>compile</goal>
-                        </goals>
-                        <configuration>
-                            <args>
-                                <arg>-dependencyfile</arg>
-                                <arg>${project.build.directory}/.scala_dependencies</arg>
-                            </args>
-                        </configuration>
-                    </execution>
-                </executions>
-            </plugin>
-        </plugins>
-    </build>
-</project>
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/BFSRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/BFSRunner.scala
deleted file mode 100644
index 3c2be7a..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/BFSRunner.scala
+++ /dev/null
@@ -1,124 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter, InputStreamReader}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.BFS
-import org.apache.spark.rdd.RDD
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class BFSConfig extends Serializable {
-  @BeanProperty var bfs: util.HashMap[String, Object] = _
-}
-
-class BFSParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isDirect: Boolean = _
-  @BeanProperty var sourceID: Long = _
-  @BeanProperty var depthLimit: Int = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object BFSRunner {
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName: String = args(0)
-      val sourceID: Long = args(1).toLong
-      val numPartitions: Int = args(2).toInt
-      val isRaw: String = args(3)
-      val inputPath: String = args(4)
-
-      val stream: InputStreamReader = Utils.getStream("conf/graph/bfs/bfs.yml")
-      val representer = new Representer
-      representer.addClassTag(classOf[BFSParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[BFSConfig]), representer, options)
-      val description = new TypeDescription(classOf[BFSParams])
-      yaml.addTypeDescription(description)
-      val config: BFSConfig = yaml.load(stream).asInstanceOf[BFSConfig]
-      val paramsMap: util.HashMap[String, Object] =
-        config.bfs
-          .get(datasetName)
-          .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new BFSParams()
-
-      val outputPath: String = paramsMap.get("outputPath").toString
-      val splitGraph: String = paramsMap.get("splitGraph").toString
-      val isDirect: Boolean = paramsMap.get("isDirect").toString.toBoolean
-      val depthLimit: Int = paramsMap.get("depthLimit").toString.toInt
-
-      var newOutput = s"${outputPath}_${sourceID}"
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(newOutput)
-      params.setSplitGraph(splitGraph)
-      params.setDatasetName(datasetName)
-      params.setNumPartitions(numPartitions)
-      params.setIsDirect(isDirect)
-      params.setSourceID(sourceID)
-      params.setDepthLimit(depthLimit)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("BFS")
-      params.setTestcaseType(s"BFS_${datasetName}_${sourceID}")
-      println("inputPath: " + inputPath)
-      println("outputPath: " + newOutput)
-
-      var appName = s"BFS_${datasetName}_${sourceID}"
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime: Long = System.currentTimeMillis()
-      val inputRdd: RDD[(String, String)] =
-        Util.readUndirectDataFromHDFS(sc, inputPath, splitGraph, numPartitions)
-      val graph: Graph[Int, Int] = Util.buildUnweightedGraph(inputRdd, isDirect)
-
-      val result: RDD[String] =
-        BFS
-          .run(graph, sourceID, isDirect, depthLimit)
-          .vertices
-          .filter(_._2._1 != Integer.MAX_VALUE)
-          .map(f => f._1.toString + ";" + f._2._2.mkString(",") + ";" + f._2._1)
-
-      Util.saveDataToHDFS(result, newOutput)
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/BFS_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/BetweennessRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/BetweennessRunner.scala
deleted file mode 100644
index 8d30fcb..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/BetweennessRunner.scala
+++ /dev/null
@@ -1,118 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter, InputStreamReader}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.lib.Betweenness
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class BetweennessConfig extends Serializable{
-  @BeanProperty var betweenness: util.HashMap[String, Object] = _
-}
-
-class BetweennessParams extends Serializable{
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var computePartitions: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var p: Float = _
-  @BeanProperty var computeTopK = "no"
-  @BeanProperty var groundTruthPath: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-
-
-object BetweennessRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val isRaw = args(1)
-      val partition = args(2).toInt
-      val inputPath = args(3)
-      val computeTopK = args(4)
-      val outputPath = args(5)
-      val groundTruthPath = args(6)
-
-      val representer = new Representer
-      representer.addClassTag(classOf[BetweennessParams], Tag.MAP)
-
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val stream: InputStreamReader = Utils.getStream("conf/graph/betweenness/betweenness.yml")
-      val yaml = new Yaml(new Constructor(classOf[BetweennessConfig]), representer, options)
-      val description = new TypeDescription(classOf[BetweennessParams])
-      yaml.addTypeDescription(description)
-      val config: BetweennessConfig = yaml.load(stream).asInstanceOf[BetweennessConfig]
-      val paramsMap: util.HashMap[String, Object] =
-        config.betweenness
-          .get(datasetName)
-          .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new BetweennessParams()
-
-      params.setDatasetName(datasetName)
-      params.setInputPath(inputPath)
-      params.setK(paramsMap.get("k").toString.toInt)
-      params.setP(paramsMap.get("p").toString.toFloat)
-      params.setComputeTopK(computeTopK)
-      params.setIsRaw(isRaw)
-      params.setComputePartitions(partition)
-      params.setOutputPath(outputPath)
-      params.setSplitGraph(paramsMap.get("splitGraph").toString)
-      params.setGroundTruthPath(groundTruthPath)
-      params.setAlgorithmName("Betweenness")
-      params.setTestcaseType(s"Betweenness_${datasetName}")
-      val conf = new SparkConf().setAppName(s"Betweenness_${datasetName}")
-      val sc = new SparkContext(conf)
-
-      val startTime = System.currentTimeMillis()
-
-      val edgeRDD = Util.readEdgeListFromHDFS(sc, inputPath, params.getSplitGraph, isWeighted = false, partition)
-
-      val result = Betweenness.run(edgeRDD, params.getK, params.getP)
-      Util.saveDataToHDFS(result, ",", outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-      params.setCostTime(costTime)
-      if (computeTopK.equals("yes")) {
-        val groundTruthSet = Util.readTopKResultFromHDFS(sc,
-          groundTruthPath, ",", partition).collect().toSet
-        val resultSet = Util.readTopKResultFromHDFS(sc,
-          outputPath, ",", partition).collect().toSet
-        val accuracy = groundTruthSet.intersect(resultSet).size.toDouble / groundTruthSet.size
-
-        println(s"Top-K Betweenness Computing Finished. Accuracy ${accuracy}.\nExec Successful: costTime: ${costTime}")
-      } else {
-        println(s"Exec Successful: costTime: ${costTime}")
-      }
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/Betweenness_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessHiveRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessHiveRunner.scala
deleted file mode 100644
index 22f8542..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessHiveRunner.scala
+++ /dev/null
@@ -1,85 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.graphx.lib.Closeness
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{DoubleType, LongType, StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-
-object ClosenessHiveRunner {
-  def main(args: Array[String]): Unit = {
-    if (args.length < 10) {
-      println(args.mkString(","))
-      println("Usage:KCoreDecompositionRunner <tableName> <col1> <col2> <colWeight> <weighted> <k> <p> <partition> <save_mode> <save_arg>")
-      System.exit(-1)
-    }
-    val tableName: String = args(0)
-    val col1: String = args(1)
-    val col2: String = args(2)
-    val colWeight: String = args(3)
-    val weighted: Boolean = args(4).toBoolean
-    val k: Int = args(5).toInt
-    val p: Double = args(6).toDouble
-    val partition: Int = args(7).toInt
-    val saveMode: String = args(8)
-    val saveArg: String = args(9)
-
-    try {
-      val appName = s"Closeness_${tableName}_${weighted}"
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val spark: SparkSession = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
-      // record start time
-      val startTime: Long = System.currentTimeMillis()
-
-      val sql = s"select * from ${tableName}"
-      var edgesRDD: RDD[(Long, Long, Double)] = null
-      var verticesRDD: RDD[(String, Long)] = null
-      if (colWeight == "none") {
-        val edges: DataFrame = spark.sql(sql).select(col1, col2)
-        val tmpRDD1: RDD[(String, String)] = edges.rdd.map(row => (row(0).toString, row(1).toString))
-        verticesRDD = tmpRDD1.flatMap(f => Iterator(f._1, f._2)).distinct.zipWithIndex.cache
-        verticesRDD.foreachPartition(_ => {})
-        println("vertices count:" +verticesRDD.count())
-        edgesRDD = tmpRDD1.leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get)).leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get, 1.0))
-      } else {
-        val edges: DataFrame = spark.sql(sql).select(col1, col2, colWeight)
-        val tmpRDD: RDD[(String, (String, Double))] = edges.rdd.map(row => (row(0).toString, (row(1).toString, row(2).toString.toDouble)))
-        verticesRDD = tmpRDD.flatMap(f => Iterator(f._1, f._2._1)).distinct().zipWithIndex().cache()
-        verticesRDD.foreachPartition(_ => {})
-        println("vertices count:" + verticesRDD.count())
-        edgesRDD = tmpRDD.leftOuterJoin(verticesRDD, partition).map(f => (f._2._1._1, (f._2._2.get, f._2._1._2))).leftOuterJoin(verticesRDD, partition).map(f => (f._2._1._1, f._2._2.get, f._2._1._2))
-      }
-
-      val result: RDD[(Long, Double)] =
-        Closeness.run(edgesRDD, weighted, k, p)
-      val finalResult = result.leftOuterJoin(verticesRDD.map(_.swap), 200).map(f => Row(f._2._2.get, f._2._1))
-
-      val _ = saveMode match {
-        case "hive" => {
-          val schema_resultMap: StructType = StructType(List(StructField("_node_id", StringType, true), StructField("_closeness", DoubleType, true)))
-          val resultMapDF: DataFrame = spark.createDataFrame(finalResult, schema_resultMap)
-          resultMapDF.createOrReplaceTempView("ClosenessMapTmpV")
-          val outputTableName: String = saveArg
-          val createSql = s"create table ${outputTableName}_closeness(_node_id varchar(250), _closeness double)"
-          spark.sql(createSql)
-          spark.sql(s"insert into ${outputTableName}_closeness select * from ClosenessMapTmpV")
-        }
-        case "hdfs" => {
-          val outputPath: String = saveArg
-          Util.saveDataToHDFS(result, ",", outputPath)
-        }
-        case _ => throw new Exception("illegal save mode")
-      }
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      println(s"Exec Successful: costTime: ${costTime}s")
-      spark.close()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessRunner.scala
deleted file mode 100644
index be8bc16..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/ClosenessRunner.scala
+++ /dev/null
@@ -1,151 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.Closeness
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class ClosenessConfig extends Serializable {
-  @BeanProperty var closeness: util.HashMap[String, Object] = _
-}
-
-class ClosenessParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var weighted: String = _
-  @BeanProperty var outputNodeNum: Int = _
-  @BeanProperty var ratio: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var comparePathWeighted: String = _
-  @BeanProperty var comparePathUnweighted: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object ClosenessRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val numPartitions = args(1).toInt
-      val weighted = args(2)
-      val outputNodeNum = args(3).toInt
-      val ratio = args(4).toDouble
-      val isRaw = args(5)
-      val inputPath = args(6)
-      val compare = args(7).toBoolean
-
-      val stream = Utils.getStream("conf/graph/closeness/closeness.yml")
-      val representer = new Representer
-      representer.addClassTag(classOf[ClosenessParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[ClosenessConfig]), representer, options)
-      val description = new TypeDescription(classOf[ClosenessParams])
-      yaml.addTypeDescription(description)
-      val config: ClosenessConfig =
-        yaml.load(stream).asInstanceOf[ClosenessConfig]
-      val paramsMap =
-        config.closeness
-          .get(datasetName)
-          .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new ClosenessParams()
-
-      val outputPath = paramsMap.get("outputPath").toString
-      val newOutput = s"${outputPath}_${weighted}"
-      val splitGraph = paramsMap.get("splitGraph").toString
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(newOutput)
-      params.setSplitGraph(splitGraph)
-      params.setDatasetName(datasetName)
-      params.setNumPartitions(numPartitions)
-      params.setWeighted(weighted)
-      params.setOutputNodeNum(outputNodeNum)
-      params.setRatio(ratio)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("Closenness")
-      params.setTestcaseType(s"Closenness_${datasetName}_${weighted}")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + newOutput)
-
-      val weightedBool = weighted match {
-        case "weighted"   => true
-        case "unweighted" => false
-        case _            => throw new Exception("illegal weighted value")
-      }
-
-      var appName = s"Closeness_${datasetName}_${weighted}"
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-
-      val edgeRDD =
-        Util.readEdgeListFromHDFS(sc,
-                                  inputPath,
-                                  splitGraph,
-                                  weightedBool,
-                                  numPartitions)
-      val result =
-        Closeness.run(edgeRDD, weightedBool, outputNodeNum, ratio)
-      Util.saveDataToHDFS(result, ",", newOutput)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/Closeness_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-      if (compare) {
-        val groundTruthPath =
-          if (weightedBool) { paramsMap.get("comparePathWeighted").toString } else {
-            paramsMap.get("comparePathUnweighted").toString
-          }
-
-        val groundTruthSet = Util
-          .readTopKResultFromHDFS(sc, groundTruthPath, ",", numPartitions)
-          .collect()
-          .toSet
-        val resultSet = Util
-          .readTopKResultFromHDFS(sc, newOutput, ",", numPartitions)
-          .collect()
-          .toSet
-        val accuracy = groundTruthSet
-          .intersect(resultSet)
-          .size
-          .toDouble / groundTruthSet.size
-        println(s"Accuracy:${accuracy}")
-      }
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/ClusteringCoefficientRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/ClusteringCoefficientRunner.scala
deleted file mode 100644
index 9bbd54a..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/ClusteringCoefficientRunner.scala
+++ /dev/null
@@ -1,141 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter, InputStreamReader}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.ClusteringCoefficient
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-class clusteringCoefficientConfig extends Serializable {
-  @BeanProperty var clusteringCoefficient: util.HashMap[String, Object] = _
-}
-class clusteringCoefficientParms extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var isDirect: Boolean = _
-  @BeanProperty var isWeight: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var computePartitions: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var LocalClusteringCoefficient: AnyVal = _
-  @BeanProperty var AverageClusteringCoefficient: AnyVal = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-object ClusteringCoefficientRunner {
-
-  def main(args: Array[String]): Unit = {
-    try{
-      val datsetName = args(0)
-      val computePartitions = args(1).toInt
-      val isWeight = args(2)
-      val isRaw = args(3)
-      val inputPath = args(4)
-      val api = args(5)
-
-      val weightedBool = isWeight match {
-        case "weighted"   => true
-        case "unweighted" => false
-        case _            => throw new Exception("illegal weighted value")
-      }
-
-      val stream: InputStreamReader = Utils.getStream("conf/graph/clusteringcoefficient/clusteringcoefficient.yml")
-      val representer = new Representer
-      representer.addClassTag(classOf[clusteringCoefficientParms], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[clusteringCoefficientConfig]), representer, options)
-      val description = new TypeDescription(classOf[clusteringCoefficientParms])
-      yaml.addTypeDescription(description)
-      val config: clusteringCoefficientConfig = yaml.load(stream).asInstanceOf[clusteringCoefficientConfig]
-      val paramsMap: util.HashMap[String, Object] = config.clusteringCoefficient
-        .get(datsetName)
-        .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new clusteringCoefficientParms
-
-      val outputPath: String = paramsMap.get("outputPath").toString
-      val splitGraph: String = paramsMap.get("splitGraph").toString
-      val isDirect: Boolean = paramsMap.get("isDirect").toString.toBoolean
-
-      params.setDatasetName(datsetName)
-      params.setComputePartitions(computePartitions)
-      params.setIsWeight(isWeight)
-      params.setInputPath(inputPath)
-      params.setIsRaw(isRaw)
-      params.setApiName(api)
-      params.setOutputPath(s"${outputPath}_${isWeight}")
-      params.setSplitGraph(splitGraph)
-      params.setIsDirect(isDirect)
-      params.setAlgorithmName("ClusteringCoefficient")
-      params.setTestcaseType(s"ClusteringCoefficient_${datsetName}_${api}_${isWeight}")
-
-      println("inputPath:" + inputPath)
-      println("outputPath:" + outputPath)
-
-      val appName = s"ClusteringCoefficient_${api}_${isWeight}_${datsetName}"
-
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      val startTime: Long = System.currentTimeMillis()
-
-      val inputRDD = Util.readUndirectDataFromHDFS(sc, inputPath, splitGraph, params.getComputePartitions)
-        .map(f => (f._1.toLong, f._2.toLong))
-
-      val graph = if (weightedBool) {
-        Graph.fromEdgeTuples(inputRDD, 0.0).mapEdges(f => 1.0)
-      } else {
-        Graph.fromEdgeTuples(inputRDD, 0.0)
-      }
-
-      val result = api match {
-        case "lcc" =>
-          val result = ClusteringCoefficient
-            .runLocalClusteringCoefficient(graph, isDirect, weightedBool).vertices
-          Util.saveDataToHDFS(result, ",", params.outputPath)
-        case "avgcc" => ClusteringCoefficient
-            .runAverageClusteringCoefficient(graph, isDirect, weightedBool)
-        case "globalcc" => ClusteringCoefficient.runGlobalClusteringCoefficient(graph)
-        case _ => throw new Exception("illegal api")
-      }
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/ClusteringCoefficient_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      if (api.equals("lcc")){
-        println(s"Exec Successful: costTime: ${costTime}")
-      } else if (api.equals("avgcc")){
-        params.setAverageClusteringCoefficient(result)
-        println("Exec Successful: costTime: \t%.5f\nAvgerageClusteringCoefficient\t%.5f".format(costTime, result))
-      } else {
-        params.setLocalClusteringCoefficient(result)
-        println("Exec Successful: costTime: \t%.5f\nGlobalClusteringCoefficient\t%.5f".format(costTime, result))
-      }
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/ConnectedComponentsRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/ConnectedComponentsRunner.scala
deleted file mode 100644
index 599e7cf..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/ConnectedComponentsRunner.scala
+++ /dev/null
@@ -1,98 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.ConnectedComponents
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.Graph
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class CCParams extends Serializable {
-  @BeanProperty var partition = new JHashMap[String, Int]
-  @BeanProperty var split = new JHashMap[String, String]
-
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var curPartition: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object ConnectedComponentsRunner {
-  private val RESULT_SPLIT = ","
-  private val PARAM_FILEPATH = "conf/graph/cc/cc.yml"
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 5) {
-      println(args.mkString(","))
-      println("Usage:ConnectedComponentsRunner <dataset_name> <input_path> <output_path> <is_raw> <cpu_name>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-    val isRaw = args(3)
-    val cpuName = args(4)
-
-    val representer = new Representer
-    representer.addClassTag(classOf[CCParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[CCParams]), representer, options)
-    val description = new TypeDescription(classOf[CCParams])
-    yaml.addTypeDescription(description)
-    val params = yaml.load(Utils.getStream(PARAM_FILEPATH)).asInstanceOf[CCParams]
-    val partition = params.getPartition.get(s"${dataset}_${cpuName}_${isRaw}")
-    val split = params.getSplit.get(dataset)
-    val appName = if ("yes".equals(isRaw)) {
-      s"CC_RAW_${dataset}_${cpuName}"
-    } else {
-      s"CC_${dataset}_${cpuName}"
-    }
-
-    try {
-      val sc = new SparkContext(new SparkConf().setAppName(appName))
-      val startTime = System.currentTimeMillis()
-
-      val inputRdd = Util.readUndirectDataFromHDFS(sc, inputPath, split, partition).map(x => (x._1.toLong, x._2.toLong))
-      val graph = Graph.fromEdgeTuples(inputRdd, 0)
-      val result = ConnectedComponents.run(graph).vertices
-      Util.saveDataToHDFS(result, RESULT_SPLIT, outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      println(s"Exec Successful: connected components costTime: ${costTime}s")
-
-      params.setDatasetName(dataset)
-      params.setIsRaw(isRaw)
-      params.setCurPartition(s"$partition")
-      params.setAlgorithmName("CC")
-      params.setTestcaseType(appName)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/CC_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-
-      sc.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/CycleDetectionWithConstrainsRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/CycleDetectionWithConstrainsRunner.scala
deleted file mode 100644
index 620e3c1..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/CycleDetectionWithConstrainsRunner.scala
+++ /dev/null
@@ -1,121 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.lib.CycleDetectionWithConstrains
-
-import scala.beans.BeanProperty
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class CDParams extends Serializable {
-  @BeanProperty var partition = new JHashMap[String, Int]
-  @BeanProperty var split = new JHashMap[String, String]
-  @BeanProperty var minLoopLen: Int = _
-  @BeanProperty var maxLoopLen: Int = _
-  @BeanProperty var minRate: Double = _
-  @BeanProperty var maxRate: Double = _
-
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object CycleDetectionWithConstrainsRunner {
-  private val CD_RESULT_SPLIT = ","
-  private val CD_PARAM_FILEPATH = "conf/graph/cd/cd.yml"
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 6) {
-      println(args.mkString(","))
-      println("Usage:CycleDetectionWithConstrainsRunner <dataset_name> <input_path> <output_path> <api> <is_raw> <cpu_name>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-    val api = args(3)
-    val isRaw = args(4)
-    val cpuName = args(5)
-
-    val representer = new Representer
-    representer.addClassTag(classOf[CDParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[CDParams]), representer, options)
-    val description = new TypeDescription(classOf[CDParams])
-    yaml.addTypeDescription(description)
-    val params = yaml.load(Utils.getStream(CD_PARAM_FILEPATH)).asInstanceOf[CDParams]
-    val split = params.getSplit.get(dataset)
-    val partition = params.getPartition.get(s"${dataset}_${cpuName}_${isRaw}")
-    val appName = s"CD_${dataset}_${api}_${cpuName}"
-    try {
-      val sc = new SparkContext(new SparkConf().setAppName(appName))
-      val startTime = System.currentTimeMillis()
-
-      val input = dataset match {
-        case "usaRoad" =>
-          sc.textFile(inputPath, partition)
-            .filter(_.startsWith("a"))
-            .flatMap{x =>
-              val lines = x.split(split)
-              if (lines.length != 4) {
-                Iterator.empty
-              } else {
-                Iterator.single((lines(1).toLong, lines(2).toLong, lines(3).toDouble))
-              }
-
-            }
-        case _ =>
-          sc.textFile(inputPath, partition)
-            .filter(!_.startsWith("#"))
-            .flatMap{x =>
-              val lines = x.split(split)
-              if (lines.length != 3) {
-                Iterator.empty
-              } else {
-                Iterator.single((lines(0).toLong, lines(1).toLong, lines(2).toDouble))
-              }
-
-            }
-      }
-      val result = CycleDetectionWithConstrains.run(input, partition, params.minLoopLen,params.maxLoopLen,
-        params.minRate,params.maxRate)
-
-      Util.saveDataToHDFS(result.map(_.mkString(CD_RESULT_SPLIT)), outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      params.setDatasetName(dataset)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("CD")
-      params.setTestcaseType(s"CD_${dataset}")
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/CD_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: cycle detection costTime: ${costTime}s")
-
-      sc.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/DegreeRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/DegreeRunner.scala
deleted file mode 100644
index 29dfb2d..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/DegreeRunner.scala
+++ /dev/null
@@ -1,123 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.Graph
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class DegreeConfig extends Serializable {
-  @BeanProperty var degree: util.HashMap[String, Object] = _
-}
-
-class DegreeParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-
-}
-
-object DegreeRunner {
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val api = args(1)
-      val numPartitions = args(2).toInt
-      val isRaw = args(3)
-      val inputPath = args(4)
-
-      val stream = Utils.getStream("conf/graph/degree/degree.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[DegreeParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[DegreeConfig]), representer, options)
-      val description = new TypeDescription(classOf[DegreeParams])
-      yaml.addTypeDescription(description)
-      val config: DegreeConfig = yaml.load(stream).asInstanceOf[DegreeConfig]
-      val paramsMap =
-        config.degree
-          .get(datasetName)
-          .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new DegreeParams()
-
-      val outputPath = paramsMap.get("outputPath").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-
-      var newOutput = s"${outputPath}_${api}"
-      if (isRaw == "yes") {
-        newOutput = s"${outputPath}_${api}_raw"
-      }
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(newOutput)
-      params.setSplitGraph(splitGraph)
-      params.setDatasetName(datasetName)
-      params.setNumPartitions(numPartitions)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("Degree")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + newOutput)
-
-      var appName = s"Degree_${datasetName}_${api}"
-      if (isRaw == "yes") {
-        appName = s"Degree_${datasetName}_${api}_raw"
-      }
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-      params.setTestcaseType(appName)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-      val edgeInfo =
-        Util.readDataFromHDFSForDegree(sc, inputPath, splitGraph, numPartitions)
-      val graph = Graph.fromEdgeTuples(edgeInfo, 0)
-      val result = api match {
-        case "degrees"    => graph.degrees
-        case "inDegrees"  => graph.inDegrees
-        case "outDegrees" => graph.outDegrees
-        case _            => throw new Exception("illegal api")
-      }
-
-      Util.saveDataToHDFS(result, ",", newOutput)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/Degree_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/IncPageRankRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/IncPageRankRunner.scala
deleted file mode 100644
index 2989f69..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/IncPageRankRunner.scala
+++ /dev/null
@@ -1,139 +0,0 @@
-
-package com.bigdata.graph
-
-import com.bigdata.utils.Utils
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.IncPageRank
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.storage.StorageLevel
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import java.io.{File, FileWriter}
-import java.util
-import scala.beans.BeanProperty
-import scala.collection.mutable
-
-
-class IncPrConfig extends Serializable {
-  @BeanProperty var incpr: util.HashMap[String, Object] = _
-}
-
-class IncPrParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var numIter: Int = _
-  @BeanProperty var resetProb: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-  @BeanProperty var partNum: Int = _
-}
-
-object IncPageRankRunner {
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val api = args(1)
-      val isRaw = args(2)
-      val inputPath = args(3)
-      val outputPath = args(4)
-
-      val stream = Utils.getStream("conf/graph/incpr/incpr.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[IncPrParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[IncPrConfig]), representer, options)
-      val description = new TypeDescription(classOf[IncPrParams])
-      yaml.addTypeDescription(description)
-      val config: IncPrConfig = yaml.load(stream).asInstanceOf[IncPrConfig]
-      val paramsMap =
-        config.incpr.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new IncPrParams()
-
-      // val inputPath = paramsMap.get("inputPath").toString
-      // val outputPath = paramsMap.get("outputPath").toString
-      val numIter = paramsMap.get("numIter").toString.toInt
-      val resetProb = paramsMap.get("resetProb").toString.toDouble
-      var partNum = paramsMap.get("partNum").toString.toInt
-
-      var newOutput = s"${outputPath}_${api}"
-      if (isRaw == "yes") {
-        newOutput = s"${outputPath}_${api}_raw"
-      }
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(newOutput)
-      params.setNumIter(numIter)
-      params.setResetProb(resetProb)
-      params.setDatasetName(datasetName)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("IncPr")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + newOutput)
-
-      var appName = s"IncPageRank_${datasetName}_${api}"
-      if (isRaw == "yes") {
-        appName = s"IncPageRank_${datasetName}_${api}_raw"
-      }
-      params.setTestcaseType(appName)
-
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-      val spark = SparkSession.builder().config(sparkConf).getOrCreate()
-      implicit val graph = spark.read
-        .orc(inputPath)
-        .rdd
-        .map(row => (row.getAs[Long]("srcId"),
-          row.getAs[Int]("srcStatus"),
-          row.getAs[mutable.WrappedArray[Long]]("dstId").toArray[Long],
-          row.getAs[mutable.WrappedArray[Int]]("dstStatus").toArray[Int],
-          row.getAs[Double]("pr")))
-        .persist(StorageLevel.MEMORY_ONLY_SER)
-      graph.foreachPartition(f => {})
-
-      val res = IncPageRank.run(graph, partNum, numIter, resetProb)
-      res.map(f => f._1 + "\t" + f._2.formatted("%.6f")).saveAsTextFile(outputPath)
-
-      val finishTime = System.currentTimeMillis()
-      val costTime = (finishTime - startTime) / 1000
-      println(s"IncPageRank Computing Finished. CostTime = $costTime's.")
-
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/IncPR_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionHiveRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionHiveRunner.scala
deleted file mode 100644
index 3598e53..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionHiveRunner.scala
+++ /dev/null
@@ -1,72 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.graphx.lib.KCoreDecomposition
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{IntegerType, LongType, StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-
-object KCoreDecompositionHiveRunner {
-  private val RESULT_SPLIT = ","
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 6) {
-      println(args.mkString(","))
-      println("Usage:KCoreDecompositionRunner <table name> <col1> <col2> <partition> <save_mode> <save_arg>")
-      System.exit(-1)
-    }
-    val tableName: String = args(0)
-    val col1: String = args(1)
-    val col2: String = args(2)
-    val partition: Int = args(3).toInt
-    val saveMode: String = args(4)
-    val saveArg: String = args(5)
-
-    val appName = s"KCORE_${tableName}"
-
-    try {
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName(appName)
-        .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true")
-      val spark: SparkSession = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
-
-      val startTime: Long = System.currentTimeMillis()
-      val sql = s"select * from ${tableName}"
-      val edges: DataFrame = spark.sql(sql).select(col1, col2)
-      // string to long
-      val tmpRDD: RDD[(String, String)] = edges.rdd.map(row => (row(0).toString, row(1).toString))
-      val verticesRDD: RDD[(String, Long)] = tmpRDD.flatMap(f => Iterator(f._1, f._2)).distinct.zipWithIndex.cache
-      verticesRDD.foreachPartition(_ => {})
-      println("vertices count:" + verticesRDD.count())
-      val indexEdgeRDD: RDD[(Long, Long)] = tmpRDD.leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get)).leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get))
-
-      val result: RDD[(Long, Int)] = KCoreDecomposition.run(indexEdgeRDD)
-      println("result count: " + result.count())
-      val finalResult: RDD[Row] = result.leftOuterJoin(verticesRDD.map(_.swap), partition).map(f => Row(f._2._2.get, f._2._1))
-      val _ = saveMode match {
-        case "hive" => {
-          val schema_resultMap: StructType = StructType(List(StructField("_node_id", StringType, true), StructField("_coreness", IntegerType, true)))
-          val resultMapDF: DataFrame = spark.createDataFrame(finalResult, schema_resultMap)
-          resultMapDF.createOrReplaceTempView("coreMapTmpV")
-          val outputTableName: String = saveArg
-          val createSql = s"create table ${outputTableName}_kcore(_node_id varchar(250), _coreness int)"
-          spark.sql(createSql)
-          spark.sql(s"insert into ${outputTableName}_kcore select * from coreMapTmpV")
-        }
-        case "hdfs" => {
-          val outputPath: String = saveArg
-          Util.saveDataToHDFS(result, RESULT_SPLIT, outputPath)
-        }
-        case _ => throw new Exception("illegal save mode")
-      }
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      println(s"Exec Successful: KCore Decomposition costTime: ${costTime}s")
-      spark.close()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionRunner.scala
deleted file mode 100644
index 0a85c71..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/KCoreDecompositionRunner.scala
+++ /dev/null
@@ -1,95 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.KCoreDecomposition
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class KCoreParams extends Serializable {
-  @BeanProperty var partition = new JHashMap[String, Int]
-  @BeanProperty var split = new JHashMap[String, String]
-  @BeanProperty var iterNum = new JHashMap[String, Int]
-
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var curPartition: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-
-}
-
-object KCoreDecompositionRunner {
-  private val RESULT_SPLIT = ","
-  private val PARAM_FILEPATH = "conf/graph/kcore/kcore.yml"
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 5) {
-      println(args.mkString(","))
-      println("Usage:KCoreDecompositionRunner <dataset_name> <input_path> <output_path> <is_raw> <cpu_name>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-    val isRaw = args(3)
-    val cpuName = args(4)
-
-    val representer = new Representer
-    representer.addClassTag(classOf[KCoreParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[KCoreParams]), representer, options)
-    val description = new TypeDescription(classOf[KCoreParams])
-    yaml.addTypeDescription(description)
-    val params = yaml.load(Utils.getStream(PARAM_FILEPATH)).asInstanceOf[KCoreParams]
-    val partition = params.getPartition.get(s"${dataset}_${cpuName}_${isRaw}")
-    val split = params.getSplit.get(dataset)
-    val appName = s"KCORE_${dataset}_${cpuName}"
-
-    try {
-      val sc = new SparkContext(new SparkConf().setAppName(appName))
-      val startTime = System.currentTimeMillis()
-
-      val inputRdd = Util.readUndirectDataFromHDFS(sc, inputPath, split, partition)
-        .map(x => (x._1.trim.toLong, x._2.trim.toLong))
-      val result = KCoreDecomposition.run(inputRdd)
-      Util.saveDataToHDFS(result, RESULT_SPLIT, outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      println(s"Exec Successful: KCore Decomposition costTime: ${costTime}s")
-
-      params.setDatasetName(dataset)
-      params.setIsRaw(isRaw)
-      params.setCurPartition(s"$partition")
-      params.setAlgorithmName("KCore")
-      params.setTestcaseType(s"KCore_${dataset}")
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/KCORE_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-
-      sc.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/KcoreMain.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/KcoreMain.scala
deleted file mode 100644
index e1f00a7..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/KcoreMain.scala
+++ /dev/null
@@ -1,152 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark._
-import org.apache.spark.graphx._
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.collection.immutable.IntMap
-import org.apache.spark.storage.StorageLevel
-
-import scala.beans.BeanProperty
-
-case class Index(clic:String)
-
-/**
- * refer to [[https://github.com/DMGroup-IUPUI/Spark-kCore/blob/be0465f11201bb099747dfa32c212609f8b2bb8b/src/main/scala/KcoreMain.scala]],
- * add cycle check and change result store
- */
-class KCoreRawParams extends Serializable {
-    @BeanProperty var partition = new JHashMap[String, Int]
-    @BeanProperty var split = new JHashMap[String, String]
-    @BeanProperty var iterNum = new JHashMap[String, Int]
-
-    @BeanProperty var datasetName: String = _
-    @BeanProperty var costTime: Double = _
-    @BeanProperty var isRaw: String = _
-    @BeanProperty var curPartition: String = _
-    @BeanProperty var algorithmName: String = _
-    @BeanProperty var testcaseType: String = _
-}
-
-object KCore {
-
-    val initialMsg="-10"
-    def mergeMsg(msg1: String, msg2:String): String = msg1+":"+msg2
-
-    def vprog(vertexId: VertexId, value: (Int, Int,IntMap[Int],Int), message: String): (Int, Int,IntMap[Int],Int) = {
-        if (message == initialMsg){
-            return (value._1,value._2,value._3,value._4)
-        }
-        else{
-            val msg=message.split(":")
-            val elems=msg //newWeights.values
-            var counts:Array[Int]=new Array[Int](value._1+1)
-            for (m <-elems){
-                val im=m.toInt
-                if(im<=value._1)
-                {
-                    counts(im)=counts(im)+1
-                }
-                else{
-                    counts(value._1)=counts(value._1)+1
-                }
-            }
-            var curWeight =  0 //value._4-newWeights.size
-            for(i<-value._1 to 1 by -1){
-                curWeight=curWeight+counts(i)
-                if(i<=curWeight){
-                    return (i, value._1,value._3,value._4)  
-                }
-            }
-            return (0, value._1,value._3,value._4)
-            }
-    }
-
-    def sendMsg(triplet: EdgeTriplet[(Int, Int,IntMap[Int],Int), Int]): Iterator[(VertexId, String)] = {
-        val sourceVertex = triplet.srcAttr
-        val destVertex=triplet.dstAttr
-        return Iterator((triplet.dstId,sourceVertex._1.toString),(triplet.srcId,destVertex._1.toString))
-
-    }
-
-
-
-    def main(args: Array[String]){
-        val RESULT_SPLIT = ","
-        val PARAM_FILEPATH = "conf/graph/kcore/kcore.yml"
-        if (args.length < 5) {
-            println(args.mkString(","))
-            println("Usage:KCore <dataset_name> <input_path> <output_path> <is_raw> <cpu_name>")
-            System.exit(-1)
-        }
-        val dataset = args(0)
-        val inputPath = args(1)
-        val outputPath = args(2)
-        val isRaw = args(3)
-        val cpuName = args(4)
-
-        val representer = new Representer
-        representer.addClassTag(classOf[KCoreRawParams], Tag.MAP)
-        val options = new DumperOptions
-        options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-        val yaml = new Yaml(new Constructor(classOf[KCoreRawParams]), representer, options)
-        val description = new TypeDescription(classOf[KCoreRawParams])
-        yaml.addTypeDescription(description)
-        val params = yaml.load(Utils.getStream(PARAM_FILEPATH)).asInstanceOf[KCoreRawParams]
-        val partition = params.getPartition.get(s"${dataset}_${cpuName}_${isRaw}")
-        val split = params.getSplit.get(dataset)
-        val appName = s"KCORE_RAW_${dataset}_${cpuName}"
-        val maxIter = params.iterNum.get(dataset)
-
-        //setting up spark environment
-        val conf: SparkConf = new SparkConf()
-            .setAppName(appName)
-        val sc = new SparkContext(conf)
-
-        val startTime = System.currentTimeMillis()
-        val graphData = Util.readUndirectDataFromHDFS(sc, inputPath, split, partition)
-          .map(x => (x._1.toLong, x._2.toLong))
-          .persist(StorageLevel.MEMORY_AND_DISK_SER)
-        val ygraph = Util.convertToGraphXGraph(graphData)
-          .partitionBy(PartitionStrategy.RandomVertexCut)
-          .groupEdges((e1, e2) => e1)
-          .subgraph(epred = edge => edge.srcId != edge.dstId)
-
-        val deg = ygraph.degrees
-
-        val mgraph=ygraph.outerJoinVertices(deg)((id, oldattr, newattr) =>newattr.getOrElse(0)).mapVertices((id, attr) =>(attr,-1,IntMap[Int](),attr))
-        ygraph.unpersist()
-        val minGraph = mgraph.pregel(initialMsg,maxIter,EdgeDirection.Either)(vprog,sendMsg,mergeMsg)
-
-        minGraph.vertices.map(x => s"${x._1}${RESULT_SPLIT}${x._2._1}").saveAsTextFile(outputPath)
-
-        val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-        params.setCostTime(costTime)
-
-        println(s"Exec Successful: KCore Decomposition Raw costTime: ${costTime}s")
-
-        params.setDatasetName(dataset)
-        params.setIsRaw(isRaw)
-        params.setCurPartition(s"$partition")
-        params.setAlgorithmName("KCoreRaw")
-        params.setTestcaseType(s"KCore_Raw_${dataset}")
-        val folder = new File("report")
-        if (!folder.exists()) {
-            val mkdir = folder.mkdirs()
-            println(s"Create dir report ${mkdir}")
-        }
-        val writer = new FileWriter(s"report/KCORE_RAW_${
-            Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-                System.currentTimeMillis())
-        }.yml")
-        yaml.dump(params, writer)
-        sc.stop()
-    }
-}
\ No newline at end of file
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/LabelPropagationRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/LabelPropagationRunner.scala
deleted file mode 100644
index 9988665..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/LabelPropagationRunner.scala
+++ /dev/null
@@ -1,109 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.PartitionStrategy.EdgePartition2D
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.lib.{LabelPropagation, Modularity}
-
-import scala.beans.BeanProperty
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class LPAParams extends Serializable {
-  @BeanProperty var partition = new JHashMap[String, Int]
-  @BeanProperty var split: String = _
-  @BeanProperty var maxSteps: Int = _
-
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var modularity: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object LabelPropagationRunner {
-  private val RESULT_SPLIT = ","
-  private val PARAM_FILEPATH = "conf/graph/lpa/lpa.yml"
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 6) {
-      println(args.mkString(","))
-      println("Usage:LabelPropagationRunner <dataset_name> <input_path> <output_path> <api> <is_raw> <cpu_name>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-    val api = args(3)
-    val isRaw = args(4)
-    val cpuName = args(5)
-
-    val representer = new Representer
-    representer.addClassTag(classOf[LPAParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[LPAParams]), representer, options)
-    val description = new TypeDescription(classOf[LPAParams])
-    yaml.addTypeDescription(description)
-    val params = yaml.load(Utils.getStream(PARAM_FILEPATH)).asInstanceOf[LPAParams]
-    val partition = params.getPartition.get(s"${dataset}_${cpuName}_${isRaw}")
-    val appName = s"LPA_${dataset}_${api}_${cpuName}"
-    try {
-      val sc = new SparkContext(new SparkConf().setAppName(appName))
-      val startTime = System.currentTimeMillis()
-
-      val input = Util.readUndirectDataFromHDFS(sc, inputPath, params.getSplit, partition)
-        .flatMap(x => Iterator((x._1.toLong, x._2.toLong)))
-      val inputGraph =
-      if ("yes".equals(isRaw)) {
-        Graph.fromEdgeTuples(input, 0).partitionBy(EdgePartition2D)
-      } else {
-        Graph.fromEdgeTuples(input, 0)
-      }
-      val result = LabelPropagation.run(inputGraph, params.maxSteps).vertices
-      Util.saveDataToHDFS(result, RESULT_SPLIT, outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      println(s"Exec Successful: label propagation costTime: ${costTime}s")
-
-      if (!"yes".equals(isRaw)) {
-        val nodes = Util.readCommFromHDFS(sc, outputPath, RESULT_SPLIT, partition)
-        val edges = Util.readGraphFromHDFS(sc, inputPath, params.getSplit, false, partition)
-        val modularity = Modularity.run(nodes, edges, false, partition)
-        params.setModularity(modularity)
-        println(s"Modularity: ${modularity}.")
-      }
-      params.setDatasetName(dataset)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("LPA")
-      params.setTestcaseType(appName)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/LPA_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-
-      sc.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/LouvainHiveRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/LouvainHiveRunner.scala
deleted file mode 100644
index 89d0f0d..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/LouvainHiveRunner.scala
+++ /dev/null
@@ -1,87 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.graphx.lib.Louvain
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{LongType, StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-
-object LouvainHiveRunner {
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 9) {
-      println("Usage:LouvainRunner <table_name> <col1> <col2> <colWeight> <iterNum> <isDirected> <partition> <save_mode> <save_arg>")
-      System.exit(-1)
-    }
-    val tableName: String = args(0)
-    val col1: String = args(1)
-    val col2: String = args(2)
-    val colWeight: String = args(3)
-    val iterNum: Int = args(4).toInt
-    val isDirected: Boolean = args(5).toBoolean
-    val partition: Int = args(6).toInt
-    val saveMode: String = args(7)
-    val saveArg: String = args(8)
-
-    try {
-      val appName = s"Louvain_${tableName}"
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val spark: SparkSession = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
-      // record start time
-      val startTime: Long = System.currentTimeMillis()
-
-      val sql = s"select * from ${tableName}"
-      var edgesRDD: RDD[(Long, Long, Double)] = null
-      var verticesRDD: RDD[(String, Long)] = null
-      if (colWeight == "none") {
-        // 类型是string，转化为long
-        val edges: DataFrame = spark.sql(sql).select(col1, col2)
-        val tmpRDD: RDD[(String, String)] = edges.rdd.map(row => (row(0).toString, row(1).toString))
-        verticesRDD = tmpRDD.flatMap(f => Iterator(f._1, f._2)).distinct.zipWithIndex().cache()
-        edgesRDD = tmpRDD.leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get)).leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get, 1.0))
-      } else {
-        // 类型是string，转化为long
-        val edges: DataFrame = spark.sql(sql).select(col1, col2, colWeight)
-        val tmpRDD: RDD[(String, (String, Double))] = edges.rdd.map(row => (row(0).toString, (row(1).toString, row(2).toString.toDouble)))
-        verticesRDD = tmpRDD.flatMap(f => Iterator(f._1, f._2._1)).distinct().zipWithIndex().cache()
-        verticesRDD.foreachPartition(_ => {})
-        println("vertices count:" + verticesRDD.count())
-        edgesRDD = tmpRDD.leftOuterJoin(verticesRDD, partition).map(f => (f._2._1._1, (f._2._2.get, f._2._1._2))).leftOuterJoin(verticesRDD, partition).map(f => (f._2._1._1, f._2._2.get, f._2._1._2))
-      }
-
-      val (q, comm) = Louvain.run(edgesRDD, iterNum, isDirected, partition)
-      // long to string
-      val finalResult: RDD[Row] = comm.leftOuterJoin(verticesRDD.map(_.swap), 200).map(f => Row(f._2._2.get, f._2._1))
-
-      val _ = saveMode match {
-        case "hive" => {
-          val schema_commMap: StructType = StructType(List(StructField("_node_id", StringType, true), StructField("_comm_id", LongType, true)))
-          val commMapDF: DataFrame = spark.createDataFrame(finalResult, schema_commMap)
-          commMapDF.createOrReplaceTempView("commMapTmpV")
-          val outputTableName: String = saveArg
-          val createSql = s"create table ${outputTableName}_louvain(_node_id varchar(250), _comm_id long)"
-          spark.sql(createSql)
-          spark.sql(s"insert into ${outputTableName}_louvain select * from commMapTmpV")
-        }
-        case "hdfs" => {
-          val outputPath: String = saveArg
-          Util.saveDataToHDFS(comm, ",", outputPath)
-        }
-        case _ => throw new Exception("illegal save mode")
-      }
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      val commNum: Int = comm.map(_._2).distinct().count().toInt
-
-      println("louvain modularity:\t%.5f\nComm num:\t%d\n".format(q, commNum))
-      println(s"Exec Successful: costTime: ${costTime}s")
-      spark.close()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/LouvainRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/LouvainRunner.scala
deleted file mode 100644
index 90380ab..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/LouvainRunner.scala
+++ /dev/null
@@ -1,120 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.Louvain
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class LouvainConfig extends Serializable {
-  @BeanProperty var louvain: util.HashMap[String, Object] = _
-}
-
-class LouvainParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var maxIterations: Int = _
-  @BeanProperty var isDirected: Boolean = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var modularity: Double = _
-  @BeanProperty var communityNum: Int = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-
-}
-
-object LouvainRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val numPartitions = args(1).toInt
-      val isRaw = args(2)
-      val inputPath = args(3)
-
-      val stream = Utils.getStream("conf/graph/louvain/louvain.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[LouvainParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[LouvainConfig]), representer, options)
-      val description = new TypeDescription(classOf[LouvainParams])
-      yaml.addTypeDescription(description)
-      val config: LouvainConfig = yaml.load(stream).asInstanceOf[LouvainConfig]
-      val paramsMap =
-        config.louvain
-          .get(datasetName)
-          .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new LouvainParams()
-
-      val outputPath = paramsMap.get("outputPath").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-      val maxIterations = paramsMap.get("maxIterations").toString.toInt
-      val isDirected = paramsMap.get("isDirected").toString.toBoolean
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(outputPath)
-      params.setSplitGraph(splitGraph)
-      params.setMaxIterations(maxIterations)
-      params.setIsDirected(isDirected)
-      params.setDatasetName(datasetName)
-      params.setNumPartitions(numPartitions)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("Louvain")
-      params.setTestcaseType(s"Louvain_${datasetName}")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + outputPath)
-
-      var appName = s"Louvain_${datasetName}"
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-
-      val edgeRDD =
-        Util.readGraphFromHDFS(sc, inputPath, splitGraph, false, numPartitions)
-      val (q, comm) = Louvain.run(edgeRDD, maxIterations, isDirected, numPartitions)
-      Util.saveDataToHDFS(comm, ",", outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      val commNum = comm.map(_._2).distinct().count().toInt
-      println("louvain modularity:\t%.5f\nComm num:\t%d\nCost time:\t%.5f".format(q, commNum, costTime))
-      params.setCostTime(costTime)
-      params.setModularity(q)
-      params.setCommunityNum(commNum)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/Louvain_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/MSSPRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/MSSPRunner.scala
deleted file mode 100644
index 01a100d..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/MSSPRunner.scala
+++ /dev/null
@@ -1,132 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.ShortestPaths
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class MsspConfig extends Serializable {
-  @BeanProperty var mssp: util.HashMap[String, Object] = _
-}
-
-class MsspParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var sourcePath: String = _
-  @BeanProperty var computePartition: Int = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object MSSPRunner {
-  private val ARGS_LEN_RAW = 3
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val computePartition = args(1).toInt
-      var isRaw = "noraw"
-      if(args.length == ARGS_LEN_RAW){
-        isRaw = args(2)
-      }
-
-      val stream = Utils.getStream("conf/graph/mssp/mssp.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[MsspParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[MsspConfig]), representer, options)
-      val description = new TypeDescription(classOf[MsspParams])
-      yaml.addTypeDescription(description)
-
-      val config: MsspConfig = yaml.load(stream).asInstanceOf[MsspConfig]
-      val paramsMap = config.mssp.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new MsspParams()
-
-      val inputPath = paramsMap.get("inputPath").toString
-      val sourcePath = paramsMap.get("sourcePath").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-      val outputPath = paramsMap.get("outputPath").toString
-
-
-      params.setInputPath(inputPath)
-      params.setSourcePath(sourcePath)
-      params.setComputePartition(computePartition)
-      params.setSplitGraph(splitGraph)
-      params.setOutputPath(outputPath)
-      params.setDatasetName(datasetName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("Mssp")
-
-      println("inputPath: " + inputPath)
-      println("sourcePath: " + sourcePath)
-      println("computePartition: " + computePartition)
-      println("splitGraph: " + splitGraph)
-      println("outputPath: " + outputPath)
-      println("datasetName: " + datasetName)
-      println("isRaw: " + isRaw)
-      var appName = s"MSSP_${datasetName}"
-      if (isRaw.equals("yes")){
-        appName = s"MSSP_RAW_${datasetName}"
-      }
-
-      params.setTestcaseType(appName)
-
-      val sparkConf = new SparkConf().setAppName(appName).setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-
-      val edgeRDD = Util.readUndirectDataFromHDFS(sc, inputPath, splitGraph, computePartition)
-        .map(f => (f._1.trim.toLong, f._2.trim.toLong)).persist(StorageLevel.MEMORY_AND_DISK_SER)
-
-      val inputGraph = Util.convertToGraphXGraph(edgeRDD).persist()
-      val source = Util.loadSourceVertices(sc, sourcePath)
-
-      val res = ShortestPaths.run(inputGraph, source.collect.toSeq).vertices
-      res.map(f => {
-        var s = f._1.toString + ":"
-          f._2.foreach(x => s = s + "(" + x._1.toString + "," + x._2.toString + ")")
-        s
-      }).saveAsTextFile(outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      println("Short Path result = true, and costTime = " + costTime + "s")
-
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/MSSP_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationHiveRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationHiveRunner.scala
deleted file mode 100644
index 6ece3d5..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationHiveRunner.scala
+++ /dev/null
@@ -1,84 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.SparkConf
-import org.apache.spark.graphx.lib.MaximalCliqueEnumeration
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType, StringType}
-
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-
-object MaximalCliqueEnumerationHiveRunner {
-  private val MCE_OUTPUT_SPLIT = ","
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 8) {
-      println("Usage:MaxcliqueEnumerationRunner <table_name> <col1> <col2> <mink> <maxDegree> <partition> <save_mode> <save_arg>")
-      System.exit(-1)
-    }
-    val tableName = args(0)
-    val col1 = args(1)
-    val col2 = args(2)
-    val mink = args(3).toInt
-    val maxDegree: Int = args(4).toInt
-    val partition: Int = args(5).toInt
-    val saveMode = args(6)
-    val saveArg = args(7)
-
-    try {
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName("mce")
-        .setMaster("yarn")
-      val spark = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
-      val startTime = System.currentTimeMillis()
-      val sql = s"select * from ${tableName}"
-
-      val edges: DataFrame = spark.sql(sql).select(col1, col2)
-      val edgesRDD: RDD[(String, String)] = edges.rdd.map(row => (row(0).toString, row(1).toString))
-
-      val result: (RDD[(Int, String)], RDD[(Int, String)]) = MaximalCliqueEnumeration.run(edgesRDD, mink, maxDegree, partition)
-      val _ = saveMode match {
-        case "hive" => {
-          val nodeMap: RDD[Row] = result._1.asInstanceOf[RDD[(Int, String)]].map(t => Row(t._2, t._1))
-          val cliqueMap: RDD[Row] = result._2.asInstanceOf[RDD[(Int, String)]].map(t => Row(t._1, t._2))
-          val schema_nodeMap: StructType = StructType(List(StructField("_id", StringType, true), StructField("_id_int", IntegerType, true)))
-          val schema_cliqueMap: StructType = StructType(List(StructField("_id_int", IntegerType, true), StructField("_clique_id", StringType, true)))
-
-          val nodeMapDF: DataFrame = spark.createDataFrame(nodeMap, schema_nodeMap)
-          val cliqueMapDF: DataFrame = spark.createDataFrame(cliqueMap, schema_cliqueMap)
-          nodeMapDF.createOrReplaceTempView("nodeMapTmpV")
-          cliqueMapDF.createOrReplaceTempView("cliqueMapTmpV")
-
-          val outputTableName: String = saveArg
-          val createSql = s"create table ${outputTableName}_mce_nodeMap(_id varchar(250), _id_int int)"
-          val createSql2 = s"create table ${outputTableName}_mce_cliqueMap(_id_int int,_clique_id varchar(20))"
-          spark.sql(createSql)
-          spark.sql(createSql2)
-
-          spark.sql(s"insert into ${outputTableName}_mce_nodeMap select * from nodeMapTmpV")
-          spark.sql(s"insert into ${outputTableName}_mce_cliqueMap select * from cliqueMapTmpV")
-        }
-
-        case "hdfs" => {
-          val outputPath = saveArg
-          val mapInfoPath = s"${outputPath}/${tableName}/map_info"
-          val cliquePath = s"${outputPath}/${tableName}/clique_info"
-
-          Util.saveDataToHDFS(result._1, MCE_OUTPUT_SPLIT, mapInfoPath)
-          Util.saveDataToHDFS(result._2, MCE_OUTPUT_SPLIT, cliquePath)
-
-        }
-        case _ => throw new Exception("illegal save mode")
-      }
-
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      println(s"Exec Successful:maximal clique detection costTime:${costTime}s")
-      spark.close()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationRunner.scala
deleted file mode 100644
index 22f803f..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/MaximalCliqueEnumerationRunner.scala
+++ /dev/null
@@ -1,89 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.MaximalCliqueEnumeration
-
-import scala.beans.BeanProperty
-import org.apache.spark.sql.SparkSession
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class MaximalCliqueEnumerationParams extends Serializable {
-  @BeanProperty var partition: JHashMap[String, Int] = new JHashMap[String, Int]()
-  @BeanProperty var minK: Int = _
-  @BeanProperty var maxDegree: Int = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var split: JHashMap[String, String] = new JHashMap[String, String]
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object MaximalCliqueEnumerationRunner {
-  private val MCE_PARAM_FILEPATH = "conf/graph/mce/mce.yml"
-  private val MCE_OUTPUT_SPLIT = ","
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 3) {
-      println("Usage:MaxcliqueEnumerationRunner <dataset-name><input-path><output-path>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-
-    val representer = new Representer
-    representer.addClassTag(classOf[MaximalCliqueEnumerationParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[MaximalCliqueEnumerationParams]), representer, options)
-    val description = new TypeDescription(classOf[MaximalCliqueEnumerationParams])
-    yaml.addTypeDescription(description)
-    val params = yaml.load(Utils.getStream(MCE_PARAM_FILEPATH)).asInstanceOf[MaximalCliqueEnumerationParams]
-    val split = params.getSplit.get(dataset)
-    val partition = params.getPartition.get(dataset)
-    val mapInfoPath = s"${outputPath}/map_info"
-    val cliquePath = s"${outputPath}/clique_info"
-
-    try {
-      val spark = SparkSession.builder().getOrCreate()
-      val startTime = System.currentTimeMillis()
-
-      println(s"##start to run test.params:${inputPath},${split},${partition}")
-      val inputRdd = Util.readUndirectDataFromHDFS(spark.sparkContext, inputPath, split, partition)
-      println(s"##start to run.params:${params.getMinK},${params.getMaxDegree},${partition}")
-      val result = MaximalCliqueEnumeration.run(inputRdd, params.getMinK, params.getMaxDegree, partition)
-      Util.saveDataToHDFS(result._1, MCE_OUTPUT_SPLIT, mapInfoPath)
-      Util.saveDataToHDFS(result._2, MCE_OUTPUT_SPLIT, cliquePath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setDatasetName(dataset)
-      params.setCostTime(costTime)
-      params.setAlgorithmName("MaximalCliqueEnumeration")
-      params.setTestcaseType(s"MaximalCliqueEnumeration_${dataset}")
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/MCE_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful:maximal clique detection costTime:${costTime}s")
-      spark.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/ModularityRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/ModularityRunner.scala
deleted file mode 100644
index 1271161..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/ModularityRunner.scala
+++ /dev/null
@@ -1,109 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.Modularity
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class ModularityConfig extends Serializable {
-  @BeanProperty var modularity: util.HashMap[String, Object] = _
-}
-
-class ModularityParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var inputCommunity: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var splitCommunity: String = _
-  @BeanProperty var isWeighted: Boolean = _
-  @BeanProperty var isDirected: Boolean = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object ModularityRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val sparkConf = new SparkConf().setAppName("Modularity").setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      val datasetName = args(0)
-
-      val stream = Utils.getStream("conf/graph/modularity/modularity.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[ModularityParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[ModularityConfig]), representer, options)
-      val description = new TypeDescription(classOf[ModularityParams])
-      yaml.addTypeDescription(description)
-      val config: ModularityConfig = yaml.load(stream).asInstanceOf[ModularityConfig]
-      val paramsMap = config.modularity.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new ModularityParams()
-
-      val inputPath = paramsMap.get("inputPath").toString
-      val inputCommunity = paramsMap.get("inputCommunity").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-      val splitCommunity = paramsMap.get("splitCommunity").toString
-      val isWeighted = paramsMap.get("isWeighted").toString.toBoolean
-      val isDirected = paramsMap.get("isDirected").toString.toBoolean
-      val numPartitions = paramsMap.get("numPartitions").toString.toInt
-
-      params.setInputPath(inputPath)
-      params.setInputCommunity(inputCommunity)
-      params.setSplitGraph(splitGraph)
-      params.setSplitCommunity(splitCommunity)
-      params.setIsDirected(isDirected)
-      params.setIsWeighted(isWeighted)
-      params.setNumPartitions(numPartitions)
-      params.setDatasetName(datasetName)
-      params.setAlgorithmName("Modularity")
-      params.setTestcaseType(s"Modularity_${datasetName}")
-
-      println("inputPath: " + inputPath)
-      println("inputCommunity: " + inputCommunity)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-      val graphRDD = Util.readGraphFromHDFS(sc, inputPath, splitGraph, isWeighted, numPartitions)
-      val communityRDD = Util.readCommFromHDFS(sc, inputCommunity, splitCommunity, numPartitions)
-      val q = Modularity.run(communityRDD, graphRDD, isDirected, numPartitions)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      println("modularity: %.5f\nCost time: %.5f".format(q, costTime))
-
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/Modularity_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/Node2VecRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/Node2VecRunner.scala
deleted file mode 100644
index 7fbf948..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/Node2VecRunner.scala
+++ /dev/null
@@ -1,113 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter, InputStreamReader}
-import java.util
-import com.bigdata.utils.Utils
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.lib.{Node2Vec, Params}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class Node2VecConfig extends Serializable{
-  @BeanProperty var node2vec: util.HashMap[String, Object] = _
-}
-
-class Node2VecParams extends Serializable{
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var partitions: Int = _
-  @BeanProperty var directed: Boolean = _
-  @BeanProperty var weighted: Boolean = _
-  @BeanProperty var walkLength: Int = _
-  @BeanProperty var numWalks: Int = _
-  @BeanProperty var p: Double = _
-  @BeanProperty var q: Double = _
-  @BeanProperty var iteration: Int = _
-  @BeanProperty var dimension: Int = _
-  @BeanProperty var windowSize: Int = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object Node2VecRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (datasetName, platformName) = (modelConfSplit(0), modelConfSplit(1))
-      val inputPath = args(1)
-
-      val representer = new Representer
-      representer.addClassTag(classOf[Node2VecParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val stream: InputStreamReader = Utils.getStream("conf/graph/node2vec/node2vec.yml")
-      val yaml = new Yaml(new Constructor(classOf[Node2VecConfig]), representer, options)
-      val description = new TypeDescription(classOf[Node2VecParams])
-      yaml.addTypeDescription(description)
-      val config: Node2VecConfig = yaml.load(stream).asInstanceOf[Node2VecConfig]
-      val paramsMap: util.HashMap[String, Object] =
-        config.node2vec
-          .get(datasetName)
-          .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new Node2VecParams()
-
-      params.setDatasetName(datasetName)
-      params.setInputPath(inputPath)
-      params.setOutputPath(paramsMap.get("outputPath").toString)
-      params.setPartitions(paramsMap.get("partitions").toString.toInt)
-      params.setDirected(paramsMap.get("directed").toString.toBoolean)
-      params.setWeighted(paramsMap.get("weighted").toString.toBoolean)
-      params.setWalkLength(paramsMap.get("walkLength").toString.toInt)
-      params.setNumWalks(paramsMap.get("numWalks").toString.toInt)
-      params.setP(paramsMap.get("p").toString.toDouble)
-      params.setQ(paramsMap.get("q").toString.toDouble)
-      params.setIteration(paramsMap.get("iteration").toString.toInt)
-      params.setDimension(paramsMap.get("dimension").toString.toInt)
-      params.setWindowSize(paramsMap.get("windowSize").toString.toInt)
-      params.setSplitGraph(paramsMap.get("splitGraph").toString)
-      params.setAlgorithmName("Node2Vec")
-      params.setTestcaseType(s"Node2Vec_${datasetName}")
-
-      val conf = new SparkConf().setAppName(s"Node2Vec_${datasetName}_${platformName}")
-      val sc = new SparkContext(conf)
-
-      val startTime = System.currentTimeMillis()
-
-      val edgeRDD = Util.readEdgeListFromHDFS(sc, inputPath, params.getSplitGraph, params.getWeighted, params.getPartitions)
-
-      val n2vParams = Params(params.getDirected, params.getWeighted, params.getP, params.getQ, params.getWalkLength, params.getNumWalks, params.getIteration, params.getDimension, params.getWindowSize)
-
-      val node2vecModel = Node2Vec.run(edgeRDD,n2vParams)
-      Util.saveNode2VecModel(node2vecModel, params.getOutputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-      params.setCostTime(costTime)
-      println(s"Exec Successful: costTime: ${costTime}")
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/Node2Vec_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/PageRankHiveRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/PageRankHiveRunner.scala
deleted file mode 100644
index 36f7b1d..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/PageRankHiveRunner.scala
+++ /dev/null
@@ -1,84 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.graphx.{Edge, Graph}
-import org.apache.spark.graphx.lib.PageRank
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{DoubleType, LongType, StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-
-object PageRankHiveRunner {
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 10) {
-      println("Usage:PageRankRunner <tableName> <col1> <col2> <api> <tol> <resetProb> <numIter> <partition> <save_mode> <save_arg>")
-      System.exit(-1)
-    }
-    val tableName: String = args(0)
-    val col1: String = args(1)
-    val col2: String = args(2)
-    val api: String = args(3)
-    val tol: Double = args(4).toDouble
-    val resetProb: Double = args(5).toDouble
-    val numIter: Int = args(6).toInt
-    val partition: Int = args(7).toInt
-    val saveMode: String = args(8)
-    val saveArg: String = args(9)
-
-    try {
-      val appName = s"PageRank_${tableName}_${api}"
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-        .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true")
-      val spark: SparkSession = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
-
-      // record start time
-      val startTime: Long = System.currentTimeMillis()
-
-      val sql = s"select * from ${tableName}"
-      val edges: DataFrame = spark.sql(sql).select(col1, col2)
-      val edgesRDD: RDD[(String, String)] = edges.rdd.map(row => (row(0).toString, row(1).toString))
-      val verticesRDD: RDD[(String, Long)] = edgesRDD.flatMap(f => Iterator(f._1, f._2)).distinct.zipWithIndex().cache()
-      verticesRDD.foreachPartition(_ => {})
-      println("vertices count:" + verticesRDD.count())
-      val indexEdgesRDD: RDD[Edge[Double]] = edgesRDD.leftOuterJoin(verticesRDD, partition).map(f => (f._2._1, f._2._2.get)).leftOuterJoin(verticesRDD, partition).map(f => Edge(f._2._1, f._2._2.get, 1.0))
-
-      val graph: Graph[Double, Double] = Graph.fromEdges(indexEdgesRDD, 1.0)
-      val result: Graph[Double, Double] = api match {
-        case "runUntilConvergence" =>
-          PageRank.runUntilConvergence(graph, tol, resetProb)
-        case "run" => PageRank.run(graph, numIter, resetProb)
-        case _ => throw new Exception("illegal api")
-      }
-      val resultRDD = result.vertices.map(f => (f._1, f._2))
-      val finalResult: RDD[Row] = resultRDD.leftOuterJoin(verticesRDD.map(_.swap), partition).map(f => Row(f._2._2.get, f._2._1))
-
-
-      val _ = saveMode match {
-        case "hive" => {
-          val schema_resultMap: StructType = StructType(List(StructField("_node_id", StringType, true), StructField("_pagerank", DoubleType, true)))
-          val resultMapDF: DataFrame = spark.createDataFrame(finalResult, schema_resultMap)
-          resultMapDF.createOrReplaceTempView("PageRankMapTmpV")
-          val outputTableName: String = saveArg
-          val createSql = s"create table ${outputTableName}_pagerank(_node_id varchar(250), _pagerank double)"
-          spark.sql(createSql)
-          spark.sql(s"insert into ${outputTableName}_pagerank select * from PageRankMapTmpV")
-        }
-        case "hdfs" => {
-          val outputPath: String = saveArg
-          result.vertices.map(f => s"${f._1}\t${f._2}").saveAsTextFile(outputPath)
-        }
-        case _ => throw new Exception("illegal save mode")
-      }
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      println(s"Exec Successful: costTime: ${costTime}s")
-      spark.close()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/PageRankRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/PageRankRunner.scala
deleted file mode 100644
index 363fb30..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/PageRankRunner.scala
+++ /dev/null
@@ -1,138 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.PageRank
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class PrConfig extends Serializable {
-  @BeanProperty var pr: util.HashMap[String, Object] = _
-}
-
-class PrParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var numIter: Int = _
-  @BeanProperty var resetProb: Double = _
-  @BeanProperty var tolerance: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object PageRankRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val api = args(1)
-      val numPartitions = args(2).toInt
-      val isRaw = args(3)
-      val inputPath = args(4)
-
-      val stream = Utils.getStream("conf/graph/pr/pr.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[PrParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[PrConfig]), representer, options)
-      val description = new TypeDescription(classOf[PrParams])
-      yaml.addTypeDescription(description)
-      val config: PrConfig = yaml.load(stream).asInstanceOf[PrConfig]
-      val paramsMap =
-        config.pr.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new PrParams()
-
-      val outputPath = paramsMap.get("outputPath").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-      val numIter = paramsMap.get("numIter").toString.toInt
-      val resetProb = paramsMap.get("resetProb").toString.toDouble
-      var tolerance = paramsMap.get("tolerance").toString.toDouble
-
-      var newOutput = s"${outputPath}_${api}"
-      if (isRaw == "yes") {
-        newOutput = s"${outputPath}_${api}_raw"
-      }
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(newOutput)
-      params.setSplitGraph(splitGraph)
-      params.setNumIter(numIter)
-      params.setResetProb(resetProb)
-      params.setTolerance(tolerance)
-      params.setDatasetName(datasetName)
-      params.setNumPartitions(numPartitions)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("Pr")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + newOutput)
-
-      var appName = s"PageRank_${datasetName}_${api}"
-      if (isRaw == "yes") {
-        appName = s"PageRank_${datasetName}_${api}_raw"
-      }
-      params.setTestcaseType(appName)
-
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-
-      implicit val context = sc
-      val edges =
-        Util
-          .loadLines2EdgesT(inputPath, numPartitions, 1, false, splitGraph)
-          .setName("OriginalEdges")
-      val graph = Graph.fromEdges(edges, 0.15D)
-      val result = api match {
-        case "runUntilConvergence" =>
-          PageRank.runUntilConvergence(graph, tolerance, resetProb)
-        case "run" => PageRank.run(graph, numIter, resetProb)
-        case _     => throw new Exception("illegal api")
-      }
-
-      result.vertices.map(f => s"${f._1}\t${f._2}").saveAsTextFile(newOutput)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      println("pagerank costTime = " + costTime + "s")
-
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/PR_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/PersonalizedPageRankRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/PersonalizedPageRankRunner.scala
deleted file mode 100644
index 0d09183..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/PersonalizedPageRankRunner.scala
+++ /dev/null
@@ -1,138 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.ml.ALSParams
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkContext
-import org.apache.spark.SparkConf
-import org.apache.spark.graphx.{Edge, Graph}
-
-import scala.beans.BeanProperty
-import org.apache.spark.graphx.lib.PageRank
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-class PPrConfig extends Serializable {
-  @BeanProperty var ppr: util.HashMap[String, Object] = _
-}
-
-class PPrParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var numIter: Int = _
-  @BeanProperty var resetProb: Double = _
-  @BeanProperty var tolerance: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var sourcesPath: String = _
-  @BeanProperty var source: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-object PersonalizedPageRankRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val datasetName = args(0)
-      val api = args(1)
-      val numPartitions = args(2).toInt
-      val inputPath = args(3)
-      val isRaw = args(4)
-      val source = args(5)
-      var outputPath = args(6)
-
-      val stream = Utils.getStream("conf/graph/ppr/ppr.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[PPrParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[PPrConfig]), representer, options)
-      val description = new TypeDescription(classOf[PPrParams])
-      yaml.addTypeDescription(description)
-
-      val config: PPrConfig = yaml.load(stream).asInstanceOf[PPrConfig]
-
-      val paramsMap = config.ppr.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new PPrParams()
-
-      var appName = s"PPR_${api}_${datasetName}_${source}"
-      if (isRaw.equals("yes")) {
-        appName = s"PPR_${api}_${datasetName}_${source}_raw"
-      }
-
-      val splitGraph = paramsMap.get("splitGraph").toString
-      val numIter = paramsMap.get("numIter").toString.toInt
-      val resetProb = paramsMap.get("resetProb").toString.toDouble
-      val tolerance = paramsMap.get("tolerance").toString.toDouble
-      val sourcesPath = s"${paramsMap.get("sourcesPath").toString}/${source}.txt"
-
-      params.setApiName(api)
-      params.setDatasetName(datasetName)
-      params.setInputPath(inputPath)
-      params.setIsRaw(isRaw)
-      params.setNumIter(numIter)
-      params.setNumPartitions(numPartitions)
-      params.setOutputPath(outputPath)
-      params.setResetProb(resetProb)
-      params.setSourcesPath(sourcesPath)
-      params.setTolerance(tolerance)
-      params.setSource(source)
-      params.setAlgorithmName("PPr")
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf().setAppName(appName)
-      val sc = new SparkContext(conf)
-
-      val startTime = System.currentTimeMillis()
-
-      val inputRDD = Util.readUndirectDataFromHDFS(sc, inputPath, splitGraph, numPartitions)
-        .map(f => (f._1.toLong, f._2.toLong))
-      val graph = Util.convertToGraphXGraph(inputRDD)
-
-      val result = api match {
-        case "fixMS" =>
-          val sourcesId = sc.textFile(sourcesPath).map(_.toLong).collect()
-          PageRank.runParallelPersonalizedPageRank(graph, numIter,
-            resetProb, sourcesId)
-        case "fixSS" =>
-          PageRank.runWithOptions(graph, numIter, resetProb, Option(source.toLong))
-        case "conSS" =>
-          PageRank.runUntilConvergenceWithOptions(graph, tolerance, resetProb, Option(source.toLong))
-        case _ =>
-          val empty = sc.emptyRDD[Edge[Double]]
-          Graph.fromEdges(empty, 0.0)
-      }
-
-      Util.saveDataToHDFS(result.vertices.map(f => s"${f._1}\t${f._2}"), outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/PPR_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/StronglyConnectedComponentsRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/StronglyConnectedComponentsRunner.scala
deleted file mode 100644
index 86993f8..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/StronglyConnectedComponentsRunner.scala
+++ /dev/null
@@ -1,97 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-
-import com.bigdata.utils.Utils
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.StronglyConnectedComponents
-
-import scala.beans.BeanProperty
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class SCCParams extends Serializable {
-  @BeanProperty var partition: Int = _
-  @BeanProperty var split: JHashMap[String, String] = new JHashMap[String, String]
-
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object StronglyConnectedComponentsRunner {
-  private val SCC_RESULT_SPLIT = ","
-  private val SCC_NUM_ITER = Integer.MAX_VALUE
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 6) {
-      println(args.mkString(","))
-      println("Usage:StronglyConnectedComponents <dataset_name> <input_path> <output_path> <api> <is_raw> <cpu_name>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-    val api = args(3)
-    val isRaw = args(4)
-    val cpuName = args(5)
-    val paramFilepath = if ("aarch64".equals(cpuName)) {
-      "conf/graph/scc/scc.yml"
-    } else {
-      "conf/graph/scc/scc_x86.yml"
-    }
-
-    val representer = new Representer
-    representer.addClassTag(classOf[SCCParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[SCCParams]), representer, options)
-    val description = new TypeDescription(classOf[SCCParams])
-    yaml.addTypeDescription(description)
-    val params = yaml.load(Utils.getStream(paramFilepath)).asInstanceOf[SCCParams]
-    val split = params.getSplit.get(dataset)
-    val appName = s"SCC_${dataset}_${api}_${cpuName}"
-    try {
-      val sc = new SparkContext(new SparkConf().setAppName(appName))
-      val startTime = System.currentTimeMillis()
-
-      val input = Util.readUndirectDataFromHDFS(sc, inputPath, split, params.getPartition)
-        .flatMap(x => Iterator((x._1.toLong, x._2.toLong)))
-      val graph = Graph.fromEdgeTuples(input, 0)
-      val result = StronglyConnectedComponents.run(graph, SCC_NUM_ITER)
-      Util.saveDataToHDFS(result.vertices, SCC_RESULT_SPLIT, outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      params.setDatasetName(dataset)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("SCC")
-      params.setTestcaseType(s"SCC_${dataset}")
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/SCC_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: strongly connected component costTime: ${costTime}s")
-
-      sc.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/SubgraphMatchingRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/SubgraphMatchingRunner.scala
deleted file mode 100644
index 8aa72d5..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/SubgraphMatchingRunner.scala
+++ /dev/null
@@ -1,112 +0,0 @@
-package com.bigdata.graph
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.SubgraphMatching
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import java.io.{File, FileWriter}
-import java.util
-import scala.beans.BeanProperty
-
-class SubgraphMatchingParams extends Serializable{
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitDataGraph: util.HashMap[String, String] = new util.HashMap[String, String]()
-  @BeanProperty var splitQueryGraph: String = _
-  @BeanProperty var taskNum: Int = _
-  @BeanProperty var resultNum: Int = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-  @BeanProperty var isIdentical: String = _
-}
-
-
-object SubgraphMatchingRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val datasetName = args(0)
-      val queryGraphName = args(1)
-      val isRaw = args(2)
-      val isIdentical = args(3)
-      val outputPath = args(4)
-      val inputPath = args(5)
-      val partitionNum = args(6).toInt
-      val taskNum = args(7).toInt
-      val queryGraphPath = args(8)
-      val testcaseType = s"SGM_${datasetName}_${queryGraphName}_${isIdentical}"
-
-      val representer = new Representer
-      representer.addClassTag(classOf[SubgraphMatchingParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[SubgraphMatchingParams]), representer, options)
-      val description = new TypeDescription(classOf[SubgraphMatchingParams])
-      yaml.addTypeDescription(description)
-      val params = yaml.load(Utils.getStream("conf/graph/sgm/sgm.yml")).asInstanceOf[SubgraphMatchingParams]
-
-      val splitDataGraph = params.getSplitDataGraph.get(datasetName)
-      val resultNum = params.getResultNum
-      val splitQueryGraph = params.getSplitQueryGraph
-
-      params.setAlgorithmName("SGM")
-      params.setDatasetName(datasetName)
-      params.setInputPath(inputPath)
-      params.setOutputPath(outputPath)
-      params.setIsRaw(isRaw)
-      params.setNumPartitions(partitionNum)
-      params.setTestcaseType(testcaseType)
-      params.setIsIdentical(isIdentical)
-      params.setTaskNum(taskNum)
-
-      val isIdenticalBool = isIdentical match {
-        case "Identical" => true
-        case "unIdentical" => false
-      }
-
-      val conf = new SparkConf()
-        .setAppName(testcaseType)
-      val sc = new SparkContext(conf)
-
-      val startTime = System.currentTimeMillis()
-
-      val inputRDD = Util.readUndirectDataFromHDFS(sc, inputPath, splitDataGraph, partitionNum)
-        .map(f => (f._1.toLong, f._2.toLong))
-      val queryGraphRDD = sc.textFile(queryGraphPath)
-      val edgelist: Array[(Long, Long)] = queryGraphRDD.map(line => {
-        val strings = line.split(splitQueryGraph)
-        (strings(0).toLong, strings(1).toLong)
-      }).collect()
-
-      val (numSubgraphs, subgraphs) =
-        SubgraphMatching.run(inputRDD, edgelist, taskNum, resultNum, isIdenticalBool)
-
-      println("total matched results:\t%d".format(numSubgraphs))
-      subgraphs.map(x => x.mkString("\t")).saveAsTextFile(outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/SGM_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-
-    }
-  }
-}
\ No newline at end of file
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/TrangleCountRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/TrangleCountRunner.scala
deleted file mode 100644
index ac2d58c..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/TrangleCountRunner.scala
+++ /dev/null
@@ -1,111 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util.{HashMap => JHashMap}
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.lib.TriangleCount
-
-import scala.beans.BeanProperty
-import org.apache.spark.sql.SparkSession
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class TriangleCountParams extends Serializable {
-  @BeanProperty var partition: Int = _
-  @BeanProperty var split: JHashMap[String, String] = new JHashMap[String, String]
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object TriangleCountRunner {
-  private val TC_PARAM_FILEPATH = "conf/graph/tc/tc.yml"
-  private val TC_RESULT_SPLIT = ","
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 4) {
-      println(args.mkString(","))
-      println("Usage:TriangleCountRunner <dataset_name><input_path><api_name>")
-      System.exit(-1)
-    }
-    val dataset = args(0)
-    val inputPath = args(1)
-    val outputPath = args(2)
-    val api = args(3)
-    val isRaw = if (args.length > 4) {
-      args(4)
-    } else {
-      "no"
-    }
-
-    val representer = new Representer
-    representer.addClassTag(classOf[TriangleCountParams], Tag.MAP)
-    val options = new DumperOptions
-    options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-    val yaml = new Yaml(new Constructor(classOf[TriangleCountParams]), representer, options)
-    val description = new TypeDescription(classOf[TriangleCountParams])
-    yaml.addTypeDescription(description)
-
-    val params = yaml.load(Utils.getStream(TC_PARAM_FILEPATH)).asInstanceOf[TriangleCountParams]
-    val split = params.getSplit.get(dataset)
-    try {
-      var appName = s"TC_${api}_${dataset}"
-      if (isRaw.equals("yes")){
-        appName = s"TC_RAW_${api}_${dataset}"
-      }
-      val conf = new SparkConf().setAppName(appName)
-      val spark = SparkSession.builder().config(conf).getOrCreate()
-      val startTime = System.currentTimeMillis()
-
-      val inputRDD = Util.readUndirectDataFromHDFS(spark.sparkContext, inputPath, split, params.getPartition)
-        .flatMap { x =>
-          if (x._1.toLong == x._2.toLong) {
-            Iterator.empty
-          } else if (x._1.toLong > x._2.toLong) {
-            Iterator((x._2.toLong, x._1.toLong))
-          } else {
-            Iterator((x._1.toLong, x._2.toLong))
-          }
-
-        }
-      val graph = Graph.fromEdgeTuples(inputRDD, 0)
-      val result = api match {
-        case "preCanonical" => TriangleCount.runPreCanonicalized(graph).vertices.repartition(params.getPartition)
-        case _ => TriangleCount.run(graph).vertices.repartition(params.getPartition)
-      }
-      Util.saveDataToHDFS(result, TC_RESULT_SPLIT, outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      params.setDatasetName(dataset)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("TriangleCount")
-      params.setTestcaseType(appName)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/TC_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: triangle count costTime: ${costTime}s")
-
-      spark.stop()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure:${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/TrustRankRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/TrustRankRunner.scala
deleted file mode 100644
index db7b28f..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/TrustRankRunner.scala
+++ /dev/null
@@ -1,132 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter, InputStreamReader}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.graphx.lib.TrustRank
-import org.apache.spark.graphx.Graph
-
-import scala.beans.BeanProperty
-
-
-class TrustRankConfig extends Serializable{
-  @BeanProperty var trustRank: util.HashMap[String, Object] = _
-}
-
-class TrustRankParams extends Serializable{
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var seedsPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var computePartitions: Int = _
-  @BeanProperty var tol: Double = _
-  @BeanProperty var numIter: Int = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var resetProb: Double = _
-  @BeanProperty var seedsCnt: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-
-object TrustRankRunner {
-
-  def main(args: Array[String]): Unit = {
-    try{
-      val datsetName = args(0)
-      val computePartitions = args(1).toInt
-      val isRaw = args(2)
-      val inputPath = args(3)
-      val api = args(4)
-      val seedsCnt = args(5)
-
-      val stream: InputStreamReader = Utils.getStream("conf/graph/tr/tr.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[TrustRankParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[TrustRankConfig]), representer, options)
-      val description = new TypeDescription(classOf[TrustRankParams])
-      yaml.addTypeDescription(description)
-      val config: TrustRankConfig = yaml.load(stream).asInstanceOf[TrustRankConfig]
-      val paramsMap: util.HashMap[String, Object] = config.trustRank
-        .get(s"${datsetName}_${seedsCnt}")
-        .asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new TrustRankParams
-
-      val seedsPath: String = paramsMap.get("seedsPath").toString
-      val splitGraph: String = paramsMap.get("splitGraph").toString
-      val tol: Double = paramsMap.get("tolerance").toString.toDouble
-      val outputPath: String = paramsMap.get("outputPath").toString
-      val numIter = paramsMap.get("numIter").toString.toInt
-      val resetProb = paramsMap.get("resetProb").toString.toDouble
-
-      params.setSeedsCnt(seedsCnt)
-      params.setSeedsPath(seedsPath)
-      params.setSplitGraph(splitGraph)
-      params.setOutputPath(s"${outputPath}_${api}")
-      params.setDatasetName(datsetName)
-      params.setComputePartitions(computePartitions)
-      params.setIsRaw(isRaw)
-      params.setInputPath(inputPath)
-      params.setApiName(api)
-      params.setTol(tol)
-      params.setNumIter(numIter)
-      params.setResetProb(resetProb)
-      params.setAlgorithmName("TrustRank")
-      params.setTestcaseType(s"TrustRank_${datsetName}_${api}_${seedsCnt}")
-
-      val appName = s"TrustRank_${datsetName}_${api}_${seedsCnt}"
-
-      val sparkConf = new SparkConf()
-        .setMaster("yarn")
-        .setAppName(appName)
-      val sc = new SparkContext(sparkConf)
-      implicit val context = sc
-
-      val startTime = System.currentTimeMillis()
-
-      val edges = Util.loadLines2EdgesT(inputPath, computePartitions, 1.0D, false, splitGraph)
-        .setName("OriginalEdges")
-      val g = Graph.fromEdges(edges, 1.0D)
-      val seeds = sc.textFile(seedsPath, 10).map(_.split("\t")(0).trim.toLong)
-
-      api match {
-        case "run" => TrustRank.run(g, seeds, numIter, resetProb)
-            .vertices.map(f => s"${f._1}\t${f._2}")
-            .saveAsTextFile(params.outputPath)
-        case "runUntilConvergence" => TrustRank.runUntilConvergence(g, seeds, tol, resetProb)
-            .vertices.map(f => s"${f._1}\t${f._2}")
-            .saveAsTextFile(params.outputPath)
-        case _ => throw new Exception("illegal api")
-      }
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/TrustRank_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}")
-    }catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/Util.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/Util.scala
deleted file mode 100644
index c55b9bc..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/Util.scala
+++ /dev/null
@@ -1,281 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.SparkContext
-import org.apache.spark.rdd.RDD
-import org.apache.spark.graphx.Graph
-import org.apache.spark.graphx.Edge
-import org.apache.spark.graphx.VertexId
-import org.apache.spark.ml.linalg.Vector
-import java.util.regex.Pattern
-
-import scala.reflect.ClassTag
-
-object Util {
-
-  def readDirectDataFromHDFS(sc: SparkContext,
-                             filePath: String,
-                             split: String,
-                             partition: Int): RDD[(String, String, String)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          val x = line.split(split)
-          if (x.length < 3) {
-            Iterator.empty
-          } else {
-            val node1 = x(0)
-            val node2 = x(1)
-            val weight = x(2)
-            Iterator((node1, node2, weight))
-          }
-        }
-      })
-  }
-
-  def readUndirectDataFromHDFS(sc: SparkContext,
-                               filePath: String,
-                               split: String,
-                               partition: Int): RDD[(String, String)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          val x = line.split(split)
-          if (x.length < 2) {
-            Iterator.empty
-          } else {
-            val node1 = x(0)
-            val node2 = x(1)
-            Iterator((node1, node2))
-          }
-        }
-      })
-  }
-
-  def readCommFromHDFS(sc: SparkContext,
-                       filePath: String,
-                       split: String,
-                       partition: Int): RDD[(Long, Long)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          var mLine = line
-          if (mLine.contains("(") || mLine.contains(")")) {
-            mLine = mLine.replaceAll("\\(", "")
-            mLine = mLine.replaceAll("\\)", "")
-          }
-          val x = mLine.split(split)
-          if (x.length < 2) {
-            Iterator.empty
-          } else {
-            Iterator((x(0).toLong, x(1).toLong))
-          }
-        }
-      })
-  }
-
-  def readGraphFromHDFS(sc: SparkContext,
-                        filePath: String,
-                        split: String,
-                        isWeighted: Boolean,
-                        partition: Int): RDD[(Long, Long, Double)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          val x = line.split(split)
-          if (x.length < 2) {
-            Iterator.empty
-          } else {
-            var w = 1.0
-            if (isWeighted) {
-              w = x(2).toDouble
-            }
-            Iterator((x(0).toLong, x(1).toLong, w))
-          }
-        }
-      })
-  }
-
-  def loadLines2EdgesT[ED](
-      path: String,
-      partNum: Int,
-      defaultValue: ED,
-      weighted: Boolean,
-      split: String = ",")(implicit sc: SparkContext): RDD[Edge[ED]] = {
-    var lines = sc.textFile(path, partNum)
-    lines =
-      if (lines.getNumPartitions > partNum) lines.coalesce(partNum) else lines
-    lines.mapPartitions(iterator => {
-      val pattern = Pattern.compile(split)
-      iterator.flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          val contents = pattern.split(line)
-          val srcId = contents(0).trim.toLong
-          val dstId = contents(1).trim.toLong
-          if (srcId == dstId) {
-            Iterator.empty
-          } else {
-            if (weighted && contents.length >= 3) {
-              Iterator(Edge(srcId, dstId, contents(2).asInstanceOf[ED]))
-            } else {
-              Iterator(Edge(srcId, dstId, defaultValue))
-            }
-          }
-        }
-      })
-    })
-  }
-
-  def readEdgeListFromHDFS(
-      sc: SparkContext,
-      filePath: String,
-      split: String,
-      isWeighted: Boolean,
-      partition: Int): RDD[(VertexId, VertexId, Double)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          val x = line.trim.split(split)
-          if (x.length < 2) {
-            Iterator.empty
-          } else {
-            var w = 1.0
-            if (isWeighted && x.length > 2) {
-              w = x(2).toDouble
-            }
-            Iterator.single((x(0).toLong, x(1).toLong, w))
-          }
-        }
-      })
-  }
-
-  def readTopKResultFromHDFS(sc: SparkContext,
-                             filePath: String,
-                             split: String,
-                             partition: Int): RDD[VertexId] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#")) {
-          Iterator.empty
-        } else {
-          val x = line.trim.split(split)
-          if (x.length < 2) {
-            Iterator.empty
-          } else {
-            Iterator.single(x.head.toLong)
-          }
-        }
-      })
-  }
-
-  def readDataFromHDFSForDegree(sc: SparkContext,
-                                filePath: String,
-                                split: String,
-                                partition: Int): RDD[(Long, Long)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#") || line.startsWith("%")) {
-          Iterator.empty
-        } else {
-          val x = line.split(split)
-          if (x.length < 2) {
-            Iterator.empty
-          } else {
-            val node1 = x(0)
-            val node2 = x(1)
-            Iterator((node1.toLong, node2.toLong))
-          }
-        }
-      })
-  }
-
-  def saveNode2VecModel(modelRDD: RDD[(Long, Vector)],output: String):Unit = {
-
-    modelRDD
-      .map{ case (u, vec) => s"$u ${vec.toArray.mkString("(",",",")")}"}
-      .saveAsTextFile(output)
-  }
-
-  def saveDataToHDFS[T: ClassTag](data: RDD[T], filePath: String): Unit = {
-    data.saveAsTextFile(filePath)
-  }
-
-  def saveDataToHDFS[T: ClassTag, V: ClassTag](data: RDD[(T, V)],
-                                               split: String,
-                                               filePath: String): Unit = {
-    data.map(f => f._1 + split + f._2).saveAsTextFile(filePath)
-  }
-
-  def loadSourceVertices(sc: SparkContext, filePath: String): RDD[Long] = {
-    sc.textFile(filePath).flatMap { line =>
-      if (line.startsWith("#")) {
-        Iterator.empty
-      } else {
-        Iterator(line.trim.toLong)
-      }
-    }
-  }
-
-  def convertToGraphXGraph(edgeRdd: RDD[(Long, Long)]): Graph[Int, Int] = {
-    val edges = edgeRdd.map(x => Edge(x._1, x._2, 1))
-    val defaultUser = (1)
-    val graph = Graph.fromEdges(edges, defaultUser)
-    graph
-  }
-
-  def buildUnweightedGraph(inputRdd: RDD[(String, String)],
-                           isDirected: Boolean,
-                           defaultVertex: Int = 0,
-                           defaultEdge: Int = 0): Graph[Int, Int] = {
-    val edges: RDD[Edge[Int]] = inputRdd.flatMap(f => {
-      val src: VertexId = f._1.toLong
-      val dst: VertexId = f._2.toLong
-
-      if (isDirected) {
-        Iterator(Edge(src, dst, defaultEdge))
-      } else {
-        if (src < dst) {
-          Iterator(Edge(src, dst, defaultEdge))
-        } else {
-          Iterator(Edge(dst, src, defaultEdge))
-        }
-      }
-    })
-
-    val graph: Graph[Int, Int] = Graph.fromEdges(edges, defaultVertex)
-    graph
-  }
-
-  def readDataUnwieightedFromHDFS(sc: SparkContext,
-                                 filePath: String,
-                                 split: String,
-                                 partition: Int): RDD[(VertexId, VertexId)] = {
-    sc.textFile(filePath, partition)
-      .flatMap(line => {
-        if (line.startsWith("#") || line.startsWith("%")){
-          Iterator.empty
-        } else {
-          val x = line.split(split)
-          if (x.length < 2){
-            Iterator.empty
-          } else {
-            val node1 = x(0)
-            val node2 = x(1)
-            Iterator((node1.toLong, node2.toLong))
-          }
-        }
-      })
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/WCEHiveRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/WCEHiveRunner.scala
deleted file mode 100644
index 86123a9..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/WCEHiveRunner.scala
+++ /dev/null
@@ -1,78 +0,0 @@
-package com.bigdata.graph
-
-import org.apache.spark.graphx.lib.WeakCliqueEnumeration
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-
-object WCEHiveRunner {
-
-  def main(args: Array[String]): Unit = {
-    if (args.length < 7) {
-      println("Usage:WCERunner <table name> <col1> <col2> <maxIter> <maxDegree> <save_mode> <save_arg>")
-      System.exit(-1)
-    }
-
-    val tableName: String = args(0)
-    val col1: String = args(1)
-    val col2: String = args(2)
-    val maxIter: Int = args(3).toInt
-    val maxDegree: Int = args(4).toInt
-    val saveMode: String = args(5)
-    val saveArg: String = args(6)
-
-    try {
-      val sparkConf: SparkConf = new SparkConf()
-        .setAppName("WCE")
-        .setMaster("yarn")
-        .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true")
-      val spark: SparkSession = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
-
-      // record start time
-      val startTime: Long = System.currentTimeMillis()
-      val sql = s"select * from ${tableName}"
-      val edges: DataFrame = spark.sql(sql).select(col1, col2)
-      val edgesRDD: RDD[(String, String)] = edges.rdd.map(row => (row(0).toString, row(1).toString))
-
-      val result: (RDD[(Int, String)], RDD[(Int, Int)]) = WeakCliqueEnumeration.run(edgesRDD, maxIter, maxDegree)
-      val _ = saveMode match {
-        case "hive" => {
-          val nodeMap: RDD[Row] = result._1.asInstanceOf[RDD[(Int, String)]].map(t => Row(t._2, t._1))
-          val cliqueMap: RDD[Row] = result._2.asInstanceOf[RDD[(Int, Int)]].map(t => Row(t._1, t._2))
-          val schema_nodeMap: StructType = StructType(List(StructField("_id", StringType, true), StructField("_id_int", IntegerType, true)))
-          val schema_cliqueMap: StructType = StructType(List(StructField("_id_int", IntegerType, true), StructField("_clique_id", IntegerType, true)))
-
-          val nodeMapDF: DataFrame = spark.createDataFrame(nodeMap, schema_nodeMap)
-          val cliqueMapDF: DataFrame = spark.createDataFrame(cliqueMap, schema_cliqueMap)
-          nodeMapDF.createOrReplaceTempView("nodeMapTmpV")
-          cliqueMapDF.createOrReplaceTempView("cliqueMapTmpV")
-
-          val outputTableName: String = saveArg
-          val createSql = s"create table ${outputTableName}_wce_nodeMap(_id varchar(250), _id_int int)"
-          val createSql2 = s"create table ${outputTableName}_wce_cliqueMap(_id_int int,_clique_id int)"
-          spark.sql(createSql)
-          spark.sql(createSql2)
-
-          spark.sql(s"insert into ${outputTableName}_wce_nodeMap select * from nodeMapTmpV")
-          spark.sql(s"insert into ${outputTableName}_wce_cliqueMap select * from cliqueMapTmpV")
-        }
-        case "hdfs" => {
-          val outputPath: String = saveArg
-          Util.saveDataToHDFS(result._1, ",", outputPath + "/map_info")
-          Util.saveDataToHDFS(result._2, ",", outputPath + "/clique_info")
-        }
-        case _ => throw new Exception("illegal save mode")
-      }
-
-      val costTime: Double = (System.currentTimeMillis() - startTime) / 1000.0
-      println("weak clique detection result = true")
-      println(s"Exec Successful: costTime: ${costTime}s")
-      spark.close()
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/WCERunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/WCERunner.scala
deleted file mode 100644
index c941f19..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/WCERunner.scala
+++ /dev/null
@@ -1,108 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.lib.WeakCliqueEnumeration
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class WceConfig extends Serializable {
-  @BeanProperty var wce: util.HashMap[String, Object] = _
-}
-
-class WceParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var maxIterations: Int = _
-  @BeanProperty var maxDegree: Int = _
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object WCERunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val sparkConf = new SparkConf().setAppName("WCE").setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      val datasetName = args(0)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-
-      val stream = Utils.getStream("conf/graph/wce/wce.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[WceParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[WceConfig]), representer, options)
-      val description = new TypeDescription(classOf[WceParams])
-      yaml.addTypeDescription(description)
-      val config: WceConfig = yaml.load(stream).asInstanceOf[WceConfig]
-      val paramsMap = config.wce.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new WceParams()
-
-      val inputPath = paramsMap.get("inputPath").toString
-      val outputPath = paramsMap.get("outputPath").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-      val maxIterations = paramsMap.get("maxIterations").toString.toInt
-      val maxDegree = paramsMap.get("maxDegree").toString.toInt
-      val numPartitions = paramsMap.get("numPartitions").toString.toInt
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(outputPath)
-      params.setSplitGraph(splitGraph)
-      params.setMaxIterations(maxIterations)
-      params.setMaxDegree(maxDegree)
-      params.setNumPartitions(numPartitions)
-      params.setDatasetName(datasetName)
-      params.setAlgorithmName("Wce")
-      params.setTestcaseType(s"Wce_${datasetName}")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + outputPath)
-
-      val inputRdd = Util.readUndirectDataFromHDFS(sc, inputPath, splitGraph, numPartitions)
-      val result = WeakCliqueEnumeration.run(inputRdd, maxIterations.toInt, maxDegree.toInt)
-      Util.saveDataToHDFS(result._1, ",", outputPath + "/map_info")
-      Util.saveDataToHDFS(result._2, ",", outputPath + "/clique_info")
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      println("weak clique detection result = true, and costTime = " + costTime + "s")
-
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/WCE_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/graph/WeightedPageRankRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/graph/WeightedPageRankRunner.scala
deleted file mode 100644
index 43d6cca..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/graph/WeightedPageRankRunner.scala
+++ /dev/null
@@ -1,146 +0,0 @@
-package com.bigdata.graph
-
-import java.io.{File, FileWriter}
-import java.util
-import com.bigdata.utils.Utils
-import org.apache.spark.graphx.{Edge, Graph}
-import org.apache.spark.graphx.lib.WeightedPageRank
-import org.apache.spark.{SparkConf, SparkContext}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class WprConfig extends Serializable {
-  @BeanProperty var wpr: util.HashMap[String, Object] = _
-}
-
-class WprParams extends Serializable {
-  @BeanProperty var inputPath: String = _
-  @BeanProperty var outputPath: String = _
-  @BeanProperty var splitGraph: String = _
-  @BeanProperty var numIter: Int = _
-  @BeanProperty var resetProb: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var partitionNum: Int = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-  @BeanProperty var algoTolerance: String = _
-  @BeanProperty var computeType: String = _
-}
-
-object WeightedPageRankRunner {
-  def main(args: Array[String]): Unit = {
-    try {
-      val datasetName = args(0)
-      val api = args(1)
-      val isRaw = args(2)
-
-      val stream = Utils.getStream("conf/graph/wpr/wpr.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[WprParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[WprConfig]), representer, options)
-      val description = new TypeDescription(classOf[WprParams])
-      yaml.addTypeDescription(description)
-      val config: WprConfig = yaml.load(stream).asInstanceOf[WprConfig]
-      // 通过算法名字获取对应配置文件的内容
-      val paramsMap =
-        config.wpr.get(api).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new WprParams()
-
-      val inputPath = paramsMap.get("inputpath").toString
-      val splitGraph = paramsMap.get("splitGraph").toString
-      println(s"splitGraph is : $splitGraph")
-      val partitionNum = paramsMap.get("partitionNum").toString.toInt
-      println(s"partitionNum is : $partitionNum")
-      val outputPath = paramsMap.get("outputPath").toString
-      val numIter = paramsMap.get("numIter").toString.toInt
-      println(s"numIter is : $numIter")
-      val resetProb = paramsMap.get("resetProb").toString.toDouble
-      println(s"resetProb is : $resetProb")
-      val algoTolerance = paramsMap.get("tolerance").toString.toDouble
-      println(s"algoTolerance is : $algoTolerance")
-      var newOutput = s"${outputPath}_${api}"
-      if (isRaw == "yes") {
-        newOutput = s"${outputPath}_${api}_raw"
-      }
-
-      params.setInputPath(inputPath)
-      params.setOutputPath(newOutput)
-      params.setSplitGraph(splitGraph)
-      params.setNumIter(numIter)
-      params.setResetProb(resetProb)
-      params.setDatasetName(datasetName)
-      params.setPartitionNum(partitionNum)
-      params.setApiName(api)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("wpr")
-
-      println("inputPath: " + inputPath)
-      println("outputPath: " + newOutput)
-
-      var appName = s"WeightedPageRank_${datasetName}_${api}"
-      if (isRaw == "yes") {
-        appName = s"WeightedPageRank_${datasetName}_${api}_raw"
-      }
-      params.setTestcaseType(appName)
-
-      val sparkConf = new SparkConf()
-        .setAppName(appName)
-        .setMaster("yarn")
-      val sc = new SparkContext(sparkConf)
-
-      // record start time
-      val startTime = System.currentTimeMillis()
-
-      implicit val context = sc
-      val edgeRdd = sc.textFile(inputPath, partitionNum).filter(!_.startsWith("%")).map(f => {
-        val arr = f.split(splitGraph)
-        if(arr.length == 3) {
-          Edge(arr(0).toLong, arr(1).toLong, arr(2).toDouble)
-        } else {
-          Edge(arr(0).toLong, arr(1).toLong, 1.0D)
-        }
-      })
-      val g = Graph.fromEdges(edgeRdd, 1.0)
-      val result = if (api.toLowerCase().equals("static")) {
-        org.apache.spark.graphx.lib.WeightedPageRank.run(g, numIter.toInt, 0.15)
-      } else {
-        org.apache.spark.graphx.lib.WeightedPageRank.runUntilConvergence(g, numIter.toDouble, 0.15)
-      }
-      val sumresult = result.vertices.values.sum()
-      result.vertices.map(f => s"${f._1},${f._2}").saveAsTextFile(outputPath)
-
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      println(s"WeightedPageRank Computing Finished. sum of WPR result: ${sumresult}")
-      println(s"WeightedPageRank Computing Finished. total Time Spend:  ${costTime} s")
-
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(
-        s"report/WPR_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss", System.currentTimeMillis())}.yml")
-      yaml.dump(params, writer)
-
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/ALSRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/ALSRunner.scala
deleted file mode 100644
index 3c9e7ef..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/ALSRunner.scala
+++ /dev/null
@@ -1,279 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util
-
-import scala.beans.BeanProperty
-import scala.collection.mutable
-
-import com.bigdata.utils.Utils
-import org.apache.spark.ml.recommendation.ALS
-import org.apache.spark.mllib.recommendation.Rating
-import org.apache.spark.mllib.linalg.SparseVector
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class ALSConfig extends Serializable {
-  @BeanProperty var als: util.HashMap[String, util.HashMap[String, util.HashMap[String, util.HashMap[String, Object]]]] = _
-}
-
-class ALSParams extends Serializable {
-  @BeanProperty var pt: Int = _
-  @BeanProperty var nonnegative: Boolean = _
-  @BeanProperty var implicitPrefs: Boolean = _
-  @BeanProperty var numItemBlocks: Int = _
-  @BeanProperty var numUserBlocks: Int = _
-  @BeanProperty var numIterations: Int = _
-  @BeanProperty var regParam: Double = _
-  @BeanProperty var alpha: Double = _
-
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var dataStructure: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var startTime: Long = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object ALSRunner {
-
-  def Vector2Rating(rawdata: RDD[SparseVector]) : RDD[Rating] = {
-    val Ratingdata: RDD[Rating] = rawdata.zipWithIndex().flatMap{
-      case (v, i) =>
-        val arr = mutable.ArrayBuilder.make[Rating]
-        arr.sizeHint(v.numActives)
-        v.foreachActive{(ii, vi) =>
-          arr += Rating(i.toInt, ii, vi.toFloat)
-        }
-        arr.result()
-    }
-    Ratingdata
-  }
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val modelConfSplit = args(0).split("_")
-      val (dataStructure, datasetName) = (modelConfSplit(0), modelConfSplit(1))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val trainingDataPath = dataPathSplit(0)
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = (cpuName, isRaw) match {
-        case ("aarch64", "no") =>
-          Utils.getStream("conf/ml/als/als.yml")
-        case ("x86_64", "no") =>
-          Utils.getStream("conf/ml/als/als.yml")
-        case ("x86_64", "yes") =>
-          Utils.getStream("conf/ml/als/als_raw.yml")
-      }
-
-      val representer = new Representer
-      representer.addClassTag(classOf[ALSParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[ALSConfig]), representer, options)
-      val description = new TypeDescription(classOf[ALSParams])
-      yaml.addTypeDescription(description)
-      val configs: ALSConfig = yaml.load(stream).asInstanceOf[ALSConfig]
-      val params = new ALSParams()
-
-      val alsParamMap: util.HashMap[String, Object] = configs.als.get(cpuName).get(dataStructure).get(datasetName)
-      params.setPt(alsParamMap.getOrDefault("pt", "1000").asInstanceOf[Int])
-      params.setNumIterations(alsParamMap.getOrDefault("numIterations", "200").asInstanceOf[Int])
-      params.setNonnegative(alsParamMap.getOrDefault("nonnegative", "false").asInstanceOf[Boolean])
-      params.setImplicitPrefs(alsParamMap.getOrDefault("implicitPrefs", "false").asInstanceOf[Boolean])
-      params.setNumItemBlocks(alsParamMap.getOrDefault("numItemBlocks", "228").asInstanceOf[Int])
-      params.setNumUserBlocks(alsParamMap.getOrDefault("numUserBlocks", "228").asInstanceOf[Int])
-      params.setRegParam(alsParamMap.getOrDefault("regParam", "0.0").asInstanceOf[Double])
-      params.setAlpha(alsParamMap.getOrDefault("alpha", "1.0").asInstanceOf[Double])
-
-      params.setTrainingDataPath(trainingDataPath)
-      params.setDataStructure(dataStructure)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("ALS")
-      params.setTestcaseType(s"ALS_${dataStructure}_${datasetName}")
-      if (isRaw.equals("yes")){
-        params.setTestcaseType(s"ALS_raw_${dataStructure}_${datasetName}")
-      }
-
-
-      val conf = isRaw match {
-        case "yes" =>
-          new SparkConf().setAppName(s"ALS_raw_${dataStructure}_${datasetName}").setMaster(master)
-        case "no" =>
-          new SparkConf().setAppName(s"ALS_${dataStructure}_${datasetName}").setMaster(master)
-      }
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-      println(s"Initialized spark session.")
-      val startTime = System.currentTimeMillis()
-      params.setStartTime(startTime)
-      val sc = spark.sparkContext
-
-      import spark.implicits._
-      val rawdata: RDD[SparseVector] = sc.objectFile(dataPath).repartition(params.pt)
-
-      val (predictions, costTime) = dataStructure match {
-        case "dataframe" =>
-          params.apiName = "fit"
-          val ratings = Vector2Rating(rawdata).toDF().cache()
-          println("count: " + ratings.count())
-          val mapTime = System.currentTimeMillis()
-          println("map cost Time[seconds]: " + (mapTime - startTime).toDouble / 1000.0)
-          new ALSKernel().alsDataframeJob(spark, ratings, params)
-        case "rdd" =>
-          params.apiName = "run"
-          val ratings: RDD[Rating] = Vector2Rating(rawdata).cache()
-          println("count: " + ratings.count())
-          val mapTime = System.currentTimeMillis()
-          println("map cost Time[seconds]: " + (mapTime - startTime).toDouble / 1000.0)
-          new ALSKernel().alsRDDJob(spark, ratings, params)
-      }
-      params.setEvaluation(predictions)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/ALS_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${predictions}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-    }
-  }
-}
-
-class ALSKernel {
-
-  def alsDataframeJob(spark: SparkSession, ratings: DataFrame, params: ALSParams): (Double, Double) = {
-    val numIterations = params.numIterations
-    val nonnegative = params.nonnegative
-    val implicitPrefs = params.implicitPrefs
-    val numItemBlocks = params.numItemBlocks
-    val numUserBlocks = params.numUserBlocks
-    val regParam = params.regParam
-    val alpha = params.alpha
-
-    val als = new ALS()
-      .setMaxIter(numIterations)
-      .setUserCol("user")
-      .setItemCol("product")
-      .setRatingCol("rating")
-      .setNonnegative(nonnegative)
-      .setImplicitPrefs(implicitPrefs)
-      .setNumItemBlocks(numItemBlocks)
-      .setNumUserBlocks(numUserBlocks)
-      .setRegParam(regParam)
-      .setAlpha(alpha)
-
-    val model = als.fit(ratings)
-    val costTime = (System.currentTimeMillis() - params.startTime) / 1000.0
-
-    model.setColdStartStrategy("drop")
-    val predictions = model.transform(ratings)
-
-    val predictValue = if (params.implicitPrefs) {
-      val p = predictions.select("rating", "prediction").rdd
-        .map{ case Row(label: Double, prediction: Float) => (label, prediction) }
-        .map{ case (r1, r2) =>
-          val pr = if (r1 > 0.0) 1.0f else 0.0f
-          val err = (pr - r2)
-          err * err
-        }.mean()
-      println("implicitPrefs Mean Squared Error = " + p)
-      p
-    } else {
-      val p = predictions.select("rating", "prediction").rdd
-        .map{ case Row(label: Double, prediction: Float) => (label, prediction) }
-        .map {t =>
-          val err = (t._1 - t._2)
-          err * err
-        }.mean()
-      println("Mean Squared Error = " + p)
-      p
-    }
-    (predictValue, costTime)
-  }
-
-  def alsRDDJob(spark: SparkSession, ratings: RDD[Rating], params: ALSParams): (Double, Double) = {
-
-    val numIterations = params.numIterations
-    val nonnegative = params.nonnegative
-    val implicitPrefs = params.implicitPrefs
-    val numItemBlocks = params.numItemBlocks
-    val numUserBlocks = params.numUserBlocks
-    val regParam = params.regParam
-    val alpha = params.alpha
-
-    import org.apache.spark.mllib.recommendation.ALS
-    val rank = 10
-    val model = ALS.train(ratings, rank, numIterations, regParam, numUserBlocks)
-    val costTime = (System.currentTimeMillis() - params.startTime) / 1000.0
-
-    val usersProducts = ratings.map { case Rating(user, product, rate) =>
-      (user, product)
-    }
-    val predictions =
-      model.predict(usersProducts).map{ case Rating(user, product, rate) =>
-        ((user, product), rate)
-      }
-    val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
-      ((user, product), rate)
-    }.join(predictions)
-
-    val predictValue = if (implicitPrefs) {
-      val p = ratesAndPreds.map{ case ((user, product), (r1, r2)) => (r1, r2)}
-        .map{ case (r1, r2) =>
-          val pr = if (r1 > 0.0) 1.0f else 0.0f
-          val err = (pr - r2)
-          err * err
-        }.mean()
-      println("implicitPrefs Mean Squared Error = " + p)
-      p
-    } else {
-      val p = ratesAndPreds.map{ case ((user, product), (r1, r2)) => (r1, r2)}
-        .map {t =>
-          val err = (t._1 - t._2)
-          err * err
-        }.mean()
-      println("Mean Squared Error = " + p)
-      p
-    }
-    (predictValue, costTime)
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/CovRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/CovRunner.scala
deleted file mode 100644
index 76990ab..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/CovRunner.scala
+++ /dev/null
@@ -1,164 +0,0 @@
-package com.bigdata.ml
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.linalg.distributed.RowMatrix
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.storage.StorageLevel
-import org. apache.hadoop.fs.{FileSystem, Path}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import java.io.{File, FileWriter}
-import java.util
-import scala.beans.BeanProperty
-
-class CovConfig extends Serializable {
-
-  @BeanProperty var cov: util.HashMap[String, Object] = _
-}
-
-class CovParams extends Serializable {
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var resultSum: Double = _
-  @BeanProperty var evaluation: String = "mismatch"
-  @BeanProperty var verifiedDataPath: String = _
-  @BeanProperty var saveDataPath: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-
-
-}
-
-class CovKernel {
-  def runJob(spark: SparkSession,params: CovParams): (Double, Double) = {
-    val sc = spark.sparkContext
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-
-    val startTime = System.currentTimeMillis()
-
-    val data = sc.textFile(params.dataPath)
-      .map(x => Vectors.dense(x.split(",").map(_.toDouble)))
-      .repartition(params.numPartitions)
-      .persist(StorageLevel.MEMORY_ONLY)
-
-    val matrix = new RowMatrix(data)
-    val covMat = matrix.computeCovariance()
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val result = covMat.toArray
-
-    val saveFile = new Path(params.saveDataPath)
-    if (fs.exists(saveFile)) {
-      fs.delete(saveFile, true)
-    }
-    val res = new Array[String](covMat.numRows)
-    for(i <- 0 until covMat.numRows) {
-      val row = result.slice(i * covMat.numRows, (i + 1) * covMat.numCols).mkString(";")
-      res(i) = i.toString + " " + row
-    }
-    sc.parallelize(res).repartition(100).saveAsTextFile(params.saveDataPath)
-
-    if (params.isRaw == "yes") {
-      val verifiedFile = new Path(params.verifiedDataPath)
-      if (fs.exists(verifiedFile)) {
-        val ori = sc.textFile(params.saveDataPath).map(line => {
-          val arr = line.split(" ")
-          (arr(0).toInt, arr(1).split(";").map(_.toDouble))}).collect().sortBy(_._1).map(_._2)
-        val opt = sc.textFile(params.verifiedDataPath).map(line => {
-          val arr = line.split(" ")
-          (arr(0).toInt, arr(1).split(";").map(_.toDouble))}).collect().sortBy(_._1).map(_._2)
-
-        Array.range(0, ori.length).foreach(i => {
-          val arr1:Array[Double] = ori(i)
-          val arr2:Array[Double] = opt(i)
-          for (j <- 0 until arr1.length) {
-            if(math.abs(arr1(j) - arr2(j)) <= 1.0e-7) {
-              params.setEvaluation("match")
-            }
-          }
-        })
-      }
-      else {
-        params.setEvaluation("no verified data")
-      }
-    }
-    (result.sum, costTime)
-  }
-}
-
-object CovRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (datasetName, platformName) = (modelConfSplit(0), modelConfSplit(1))
-
-      val dataPath = args(1)
-
-      val datasetCpuName = s"${datasetName}_${platformName}"
-      val stream = Utils.getStream("conf/ml/cov/cov.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[CovParams], Tag.MAP)
-
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-
-      val yaml = new Yaml(new Constructor(classOf[CovConfig]), representer, options)
-      val description = new TypeDescription(classOf[CovParams])
-      yaml.addTypeDescription(description)
-      val config: CovConfig = yaml.load(stream).asInstanceOf[CovConfig]
-      val paramsMap = config.cov.get(datasetCpuName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new CovParams()
-
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setAlgorithmName("Cov")
-      params.setTestcaseType(s"Cov_${datasetName}_${platformName}")
-
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setSaveDataPath(paramsMap.get("saveDataPath").toString)
-      params.setVerifiedDataPath(paramsMap.get("verifiedDataPath").toString)
-
-      val conf = new SparkConf().setAppName(s"Cov_${datasetName}_${platformName}")
-
-      if (platformName == "raw") {
-        params.setIsRaw("yes")
-      }
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val cov = new CovKernel()
-      val tuple = cov.runJob(spark, params)
-      params.setResultSum(tuple._1)
-      params.setCostTime(tuple._2)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/Cov${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${params.getCostTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/DTRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/DTRunner.scala
deleted file mode 100644
index 8e920a4..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/DTRunner.scala
+++ /dev/null
@@ -1,403 +0,0 @@
-package com.bigdata.ml
-import java.io.{File, FileWriter}
-
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.Pipeline
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.ml.classification.{DecisionTreeClassifier, RandomForestClassifier}
-import org.apache.spark.ml.evaluation.{MulticlassClassificationEvaluator, RegressionEvaluator}
-import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
-import org.apache.spark.ml.param.{ParamMap, ParamPair}
-import org.apache.spark.ml.regression.{DecisionTreeRegressor, RandomForestRegressor}
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.{DecisionTree, RandomForest}
-import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
-import org.apache.spark.mllib.tree.impurity.{Gini, Variance}
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.storage.StorageLevel
-
-class DTConfig extends Serializable {
-  @BeanProperty var dt: util.HashMap[String, util.HashMap[String, util.HashMap[String, util.HashMap[String, Object]]]] = _
-}
-
-class DTParams extends Serializable {
-  @BeanProperty var genericPt: Int = _
-  @BeanProperty var maxMemoryInMB: Int = _
-  @BeanProperty var pt: Int = _
-  @BeanProperty var numCopiesInput: Int = _
-  @BeanProperty var maxDepth: Int = _
-  @BeanProperty var maxBins: Int = _
-  @BeanProperty var numClasses: Int = _
-  @BeanProperty var useNodeIdCache: Boolean = _
-  @BeanProperty var checkpointInterval: Int = _
-  @BeanProperty var featuresType: String = _
-  @BeanProperty var bcVariables: Boolean = _
-  @BeanProperty var copyStrategy: String = _
-  @BeanProperty var useDFCollPtner: String = _
-
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var algorithmType: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object DTRunner {
-  def main(args: Array[String]): Unit = {
-    try {
-      val modelConfSplit = args(0).split("_")
-      val (algorithmType, dataStructure, datasetName, apiName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2), modelConfSplit(3))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = (cpuName, isRaw) match {
-        case ("aarch64", "no") =>
-          Utils.getStream("conf/ml/dt/dt_arm.yml")
-        case ("x86_64", "no") =>
-          Utils.getStream("conf/ml/dt/dt_x86.yml")
-        case ("x86_64", "yes") =>
-          Utils.getStream("conf/ml/dt/dt_x86_raw.yml")
-      }
-
-      val representer = new Representer
-      representer.addClassTag(classOf[DTParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[DTConfig]), representer, options)
-      val description = new TypeDescription(classOf[DTParams])
-      yaml.addTypeDescription(description)
-
-      val configs: DTConfig = yaml.load(stream).asInstanceOf[DTConfig]
-      val params = new DTParams()
-
-      val dtParamMap: util.HashMap[String, Object] = configs.dt.get(algorithmType).get(dataStructure).get(datasetName)
-      params.setGenericPt(dtParamMap.getOrDefault("genericPt", "1000").asInstanceOf[Int])
-      params.setMaxMemoryInMB(dtParamMap.getOrDefault("maxMemoryInMB", "256").asInstanceOf[Int])
-      params.setPt(dtParamMap.getOrDefault("pt", "1000").asInstanceOf[Int])
-      params.setNumCopiesInput(dtParamMap.getOrDefault("numCopiesInput", "1").asInstanceOf[Int])
-      params.setMaxDepth(dtParamMap.getOrDefault("maxDepth", "5").asInstanceOf[Int])
-      params.setMaxBins(dtParamMap.getOrDefault("maxBins", "32").asInstanceOf[Int])
-      params.setNumClasses(dtParamMap.get("numClasses").asInstanceOf[Int])
-      params.setUseNodeIdCache(dtParamMap.getOrDefault("useNodeIdCache", "false").asInstanceOf[Boolean])
-      params.setCheckpointInterval(dtParamMap.getOrDefault("checkpointInterval", "10").asInstanceOf[Int])
-      params.setFeaturesType(dtParamMap.getOrDefault("featuresType", "array").asInstanceOf[String])
-      params.setBcVariables(dtParamMap.getOrDefault("bcVariables", "false").asInstanceOf[Boolean])
-      params.setCopyStrategy(dtParamMap.getOrDefault("copyStrategy", "normal").asInstanceOf[String])
-      params.setUseDFCollPtner(dtParamMap.getOrDefault("useDFCollPtner", "true").asInstanceOf[String])
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setAlgorithmType(algorithmType)
-      params.setApiName(apiName)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("DT")
-
-      var appName = s"DT_${algorithmType}_${datasetName}_${dataStructure}_${apiName}"
-      if (isRaw.equals("yes")){
-        appName = s"DT_RAW_${algorithmType}_${datasetName}_${dataStructure}_${apiName}"
-      }
-      params.setTestcaseType(appName)
-
-
-      val conf = new SparkConf().setAppName(appName).setMaster(master)
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-      if ("no" == isRaw.asInstanceOf[String]) {
-        conf.set("spark.boostkit.ml.rf.binnedFeaturesDataType",
-          dtParamMap.get("featuresType").asInstanceOf[String])
-        conf.set("spark.boostkit.ml.rf.numTrainingDataCopies",
-          dtParamMap.get("numCopiesInput").asInstanceOf[Int].toString)
-        conf.set("spark.boostkit.ml.rf.numPartsPerTrainingDataCopy",
-          dtParamMap.get("pt").asInstanceOf[Int].toString)
-        conf.set("spark.boostkit.ml.rf.broadcastVariables",
-          dtParamMap.get("bcVariables").asInstanceOf[Boolean].toString)
-        conf.set("spark.boostkit.ml.rf.copyStrategy",
-          dtParamMap.get("copyStrategy").asInstanceOf[String])
-        conf.set("spark.boostkit.ml.rf.useDFCollPartitioner",
-          dtParamMap.get("useDFCollPtner").asInstanceOf[String])
-        if (dataStructure == "rdd") {
-          conf.set("spark.boostkit.ml.rf.maxBins",
-            dtParamMap.get("maxBins").asInstanceOf[Int].toString)
-          conf.set("spark.boostkit.ml.rf.maxMemoryInMB",
-            dtParamMap.get("maxMemoryInMB").asInstanceOf[Int].toString)
-        }
-      }
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = dataStructure match {
-        case "dataframe" => new DTKernel().dtDataframeJob(spark, params)
-        case "rdd" => new DTKernel().dtRDDJob(spark, params)
-      }
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/DT_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-    }
-  }
-}
-
-class DTKernel {
-  def dtDataframeJob(spark: SparkSession, params: DTParams): (Double, Double) = {
-    val pt = params.pt
-    val trainingDataPath = params.trainingDataPath
-    val testDataPath = params.testDataPath
-    val maxDepth = params.maxDepth
-    val maxBins = params.maxBins
-    val useNodeIdCache = params.useNodeIdCache
-    val checkpointInterval = params.checkpointInterval
-    val maxMemoryInMB = params.maxMemoryInMB
-    val genericPt = params.genericPt
-
-    println(s"Initialized spark session.")
-    val startTime = System.currentTimeMillis()
-
-    val indexLabel: Boolean = params.datasetName match {
-      case "mnist8m" =>
-        false
-      case "higgs" =>
-        false
-      case "epsilon" =>
-        true
-      case "rcv" =>
-        true
-      case _ =>
-        true
-    }
-    var indexLabelDone = false
-    val trainingLabelColName = if (indexLabel) "indexedLabel" else "label"
-
-    val reader = spark.read.format("libsvm")
-    if (params.datasetName == "mnist8m") {
-      reader.option("numFeatures",784)
-    } else if (params.datasetName == "higgs") {
-      reader.option("numFeatures",28)
-    } else if (params.datasetName == "epsilon") {
-      reader.option("numFeatures", 2000)
-    } else if (params.datasetName == "rcv") {
-      reader.option("numFeatures", 47236)
-    }
-
-    val numPtTrainData = if ("no" == params.isRaw) genericPt else pt
-    println(s"numPtTrainData = ${(numPtTrainData)}")
-    val trainingData = {
-      var trainingData = reader
-      .load(trainingDataPath)
-      .repartition(numPtTrainData)
-      if (indexLabel && params.algorithmType == "classification") {
-        params.datasetName match {
-          case "epsilon" | "rcv" =>
-            trainingData = trainingData.selectExpr("if(label < 0.0, 0, 1) as indexedLabel", "features")
-            indexLabelDone = true
-          case _ =>
-            println(s"index Label by StringIndexer because of unknown dataset")
-        }
-      }
-      trainingData.persist(StorageLevel.MEMORY_AND_DISK_SER)
-      trainingData
-    }
-
-    //for implementing different fit APIs
-    val maxBinsJY = 10
-    val maxDepthJY = 3
-
-    // Train a RandomForest model
-    val dTree = params.algorithmType match {
-      case "classification" =>{
-        val oldDt = new DecisionTreeClassifier()
-          .setLabelCol(trainingLabelColName)
-          .setFeaturesCol("features")
-          .setMaxDepth(maxDepthJY)
-          .setMaxBins(maxBinsJY)
-          .setCacheNodeIds(useNodeIdCache)
-          .setCheckpointInterval(checkpointInterval)
-          .setMaxMemoryInMB(maxMemoryInMB)
-        oldDt
-      }
-      case "regression" =>{
-        val oldDt = new DecisionTreeRegressor()
-          .setLabelCol("label")
-          .setFeaturesCol("features")
-          .setMaxDepth(maxDepthJY)
-          .setMaxBins(maxBinsJY)
-          .setCacheNodeIds(useNodeIdCache)
-          .setCheckpointInterval(checkpointInterval)
-          .setMaxMemoryInMB(maxMemoryInMB)
-        oldDt
-      }
-    }
-
-    if (params.apiName == "fit"){
-      dTree.setMaxBins(maxBins)
-      dTree.setMaxDepth(maxDepth)
-    }
-
-    val pipeline = if (!indexLabelDone && params.algorithmType == "classification") {
-      val labelIndexer = new StringIndexer()
-        .setInputCol("label")
-        .setOutputCol("indexedLabel")
-        .fit(trainingData)
-      new Pipeline()
-      .setStages(Array(labelIndexer, dTree))
-    } else {
-      new Pipeline()
-        .setStages(Array(dTree))
-    }
-
-    val paramMaps = new Array[ParamMap](2)
-    for (i <- 0 until paramMaps.size){
-      paramMaps(i) = ParamMap(dTree.maxDepth -> maxDepth)
-        .put(dTree.maxBins, maxBins)
-    }
-
-    val model = params.apiName match {
-      case "fit" => pipeline.fit(trainingData)
-      case "fit1" =>
-        val models = pipeline.fit(trainingData, paramMaps)
-        models(0)
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val testData = {
-      var testData = reader
-        .load(testDataPath)
-        .repartition(genericPt)
-      if (indexLabel && params.algorithmType == "classification") {
-        params.datasetName match {
-          case "epsilon" | "rcv" =>
-            testData = testData.selectExpr("if(label < 0.0, 0, 1) as indexedLabel", "features")
-          case _ =>
-            println(s"index Label by StringIndexer because of unknown dataset")
-        }
-      }
-      testData.persist(StorageLevel.MEMORY_AND_DISK_SER)
-      testData
-    }
-
-    // Make predictions.
-    val predictions = model.transform(testData)
-
-    // Select (prediction, true label) and compute test error.
-    val evaluator = params.algorithmType match {
-      case "classification" =>
-        new MulticlassClassificationEvaluator()
-          .setLabelCol (trainingLabelColName)
-          .setPredictionCol ("prediction")
-          .setMetricName ("accuracy")
-
-      case "regression" =>
-        new RegressionEvaluator()
-          .setLabelCol ("label")
-          .setPredictionCol ("prediction")
-          .setMetricName ("rmse")
-    }
-    val res = evaluator.evaluate(predictions)
-    (res, costTime)
-  }
-
-  def dtRDDJob(spark: SparkSession, params: DTParams): (Double, Double) = {
-
-    val pt = params.pt
-    val trainingDataPath = params.trainingDataPath
-    val testDataPath = params.testDataPath
-    var maxDepth = params.maxDepth
-    val maxBins = params.maxBins
-    val genericPt = params.genericPt
-    var numClasses = params.numClasses
-
-    val sc = spark.sparkContext
-    println(s"Initialized spark session.")
-    val startTime = System.currentTimeMillis()
-
-    val numFeatures = params.datasetName match {
-      case "mnist8m" => 784
-      case "higgs" => 28
-      case "epsilon" =>2000
-      case "rcv" => 47236
-    }
-
-    val numPtTrainData = if ("no" == params.isRaw) genericPt else pt
-    val trainingData = MLUtils.loadLibSVMFile(sc, trainingDataPath, numFeatures)
-      .repartition(numPtTrainData)
-      .persist(StorageLevel.MEMORY_AND_DISK_SER)
-    val trainingLabelPositive = trainingData.map(i=> if (i.label < 0) {
-      LabeledPoint(0.0, i.features)
-    } else {
-      LabeledPoint (i.label, i.features)
-    })
-
-
-    val model = params.algorithmType match {
-      case "classification" =>
-        DecisionTree.trainClassifier(trainingLabelPositive, numClasses, Map.empty[Int, Int], "gini", maxDepth, maxBins)
-      case "regression" =>
-        DecisionTree.trainRegressor(trainingLabelPositive, Map.empty[Int, Int], "variance", maxDepth, maxBins)
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val testData = MLUtils.loadLibSVMFile(sc, testDataPath, numFeatures)
-      .repartition(genericPt)
-      .persist(StorageLevel.MEMORY_AND_DISK_SER)
-    val testLabelPositive = testData.map(i=> if (i.label < 0) {
-      LabeledPoint(0.0, i.features)
-    } else {
-      LabeledPoint (i.label, i.features)
-    })
-
-    val labeleAndPreds = testLabelPositive.map{ point =>
-      val prediction = model.predict(point.features)
-      (point.label, prediction)
-    }
-    val res = params.algorithmType match {
-      case "classification" => 1.0 - labeleAndPreds.filter(r => r._1 == r._2).count.toDouble / testLabelPositive.count()
-      case "regression" => math.sqrt(labeleAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.mean())
-    }
-    (res, costTime)
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/GBDTRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/GBDTRunner.scala
deleted file mode 100644
index dbdf9ee..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/GBDTRunner.scala
+++ /dev/null
@@ -1,322 +0,0 @@
-package com.bigdata.ml
-
-import java.io.FileWriter
-import java.io.File
-import java.util.HashMap
-
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.classification.GBTClassifier
-import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
-import org.apache.spark.ml.regression.GBTRegressor
-import org.apache.spark.ml.param.{ParamPair, ParamMap}
-import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.configuration.BoostingStrategy
-import org.apache.spark.mllib.tree.GradientBoostedTrees
-
-import com.bigdata.utils.Utils
-
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class GBDTConfig extends Serializable {
-
-  @BeanProperty var gbdt: HashMap[String, Object] = _
-}
-
-class GBDTParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var maxIter: Int = _
-  @BeanProperty var maxDepth: Int = _
-  @BeanProperty var maxBins: Int = _
-  @BeanProperty var stepSize: Double = _
-  @BeanProperty var cacheNodeIds: Boolean = _
-  @BeanProperty var maxMemoryInMB: Int = _
-  @BeanProperty var minInstancesPerNode: Int = _
-  @BeanProperty var minInfoGain: Double = _
-  @BeanProperty var subsamplingRate: Double = _
-  @BeanProperty var featureSubsetStrategy: String = _
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var algorithmType: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-
-class GBDTKernel {
-  
-  def gbdtDataframeJob(spark: SparkSession, params: GBDTParams): (Double, Double) = {
-
-    val startTime = System.currentTimeMillis()
-
-    val trainingData = spark
-      .read
-      .format("libsvm")
-      .option("vectorType", "dense")
-      .load(params.trainingDataPath)
-      .repartition(params.numPartitions)
-
-    val labelIndexer = new StringIndexer()
-      .setInputCol("label")
-      .setOutputCol("indexedLabel")
-      .fit(trainingData)
-
-    val featureIndexer = new VectorIndexer()
-      .setInputCol("features")
-      .setOutputCol("indexedFeatures")
-      .setHandleInvalid("skip")
-      .setMaxCategories(2)
-      .fit(trainingData)
-
-    val labelConverter = new IndexToString()
-      .setInputCol("prediction")
-      .setOutputCol("predictedLabel")
-      .setLabels(labelIndexer.labels)
-
-    val gbdt = params.algorithmType match {
-      case "classification" =>{
-        new GBTClassifier()
-          .setLabelCol("indexedLabel")
-          .setFeaturesCol("indexedFeatures")
-          .setMaxIter(params.maxIter)
-          .setMaxDepth(params.maxDepth)
-          .setMaxBins(params.maxBins)
-          .setStepSize(params.stepSize)
-          .setMinInstancesPerNode(params.minInstancesPerNode)
-          .setMinInfoGain(params.minInfoGain)
-          .setMaxMemoryInMB(params.maxMemoryInMB)
-          .setSubsamplingRate(params.subsamplingRate)
-          .setCacheNodeIds(params.cacheNodeIds)
-          .setFeatureSubsetStrategy(params.featureSubsetStrategy)
-          .setSeed(2020)
-      }
-      case "regression" =>{
-        new GBTRegressor()
-          .setLabelCol("indexedLabel")
-          .setFeaturesCol("indexedFeatures")
-          .setMaxIter(params.maxIter)
-          .setMaxDepth(params.maxDepth)
-          .setMaxBins(params.maxBins)
-          .setStepSize(params.stepSize)
-          .setMinInstancesPerNode(params.minInstancesPerNode)
-          .setMinInfoGain(params.minInfoGain)
-          .setMaxMemoryInMB(params.maxMemoryInMB)
-          .setSubsamplingRate(params.subsamplingRate)
-          .setCacheNodeIds(params.cacheNodeIds)
-          .setFeatureSubsetStrategy(params.featureSubsetStrategy)
-          .setSeed(2020)
-      }
-    }
-
-    val pipeline = new Pipeline()
-      .setStages(Array(labelIndexer, featureIndexer, gbdt, labelConverter))
-
-    val paramMap = ParamMap(gbdt.maxDepth -> params.maxDepth)
-      .put(gbdt.maxIter, params.maxIter)
-
-    val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-    for (i <- 0 to paramMaps.size -1) {
-      paramMaps(i) = ParamMap(gbdt.maxDepth -> params.maxDepth)
-        .put(gbdt.maxIter, params.maxIter)
-    }
-
-    val maxDepthParamPair = ParamPair(gbdt.maxDepth, params.maxDepth)
-    val maxIterParamPair = ParamPair(gbdt.maxIter, params.maxIter)
-    val maxBinsParamPair = ParamPair(gbdt.maxBins, params.maxBins)
-
-    val model = params.apiName match {
-      case "fit" => pipeline.fit(trainingData)
-      case "fit1" => pipeline.fit(trainingData, paramMap)
-      case "fit2" =>
-        val models = pipeline.fit(trainingData, paramMaps)
-        models(0)
-      case "fit3" => pipeline.fit(trainingData, maxDepthParamPair, maxIterParamPair, maxBinsParamPair)
-
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val testData = spark
-      .read
-      .format("libsvm")
-      .option("vectorType", "dense")
-      .load(params.testDataPath)
-      .repartition(params.numPartitions)
-
-    // Make predictions.
-    val predictions = model.transform(testData)
-
-    // Select (prediction, true label) and compute test error.
-    val evaluator = params.algorithmType match {
-      case "classification" =>
-        new MulticlassClassificationEvaluator()
-          .setLabelCol ("indexedLabel")
-          .setPredictionCol ("prediction")
-          .setMetricName ("accuracy")
-
-      case "regression" =>
-        new RegressionEvaluator()
-          .setLabelCol ("indexedLabel")
-          .setPredictionCol ("prediction")
-          .setMetricName ("rmse")
-    }
-    val res = evaluator.evaluate(predictions)
-    (res, costTime)
-}
-
-  def gbdtRDDJob(spark: SparkSession, params: GBDTParams): (Double, Double) = {
-
-    val sc = spark.sparkContext
-    val startTime = System.currentTimeMillis()
-
-    val trainingData = MLUtils.loadLibSVMFile(sc, params.trainingDataPath).repartition(params.numPartitions)
-    val trainingLabelPositive = trainingData.map(i => if (i.label < 0) {
-      LabeledPoint(0.0, i.features)
-    } else {
-      LabeledPoint(i.label, i.features)
-    })
-
-    val boostingStrategy = params.algorithmType match {
-      case "classification" => BoostingStrategy.defaultParams("Classification")
-      case "regression" => BoostingStrategy.defaultParams("Regression")
-    }
-
-    boostingStrategy.numIterations = params.maxIter
-    boostingStrategy.learningRate = params.stepSize
-    boostingStrategy.treeStrategy.maxDepth = params.maxDepth
-    boostingStrategy.treeStrategy.maxBins = params.maxBins
-    boostingStrategy.treeStrategy.minInstancesPerNode = params.minInstancesPerNode
-    boostingStrategy.treeStrategy.maxMemoryInMB = params.maxMemoryInMB
-    boostingStrategy.treeStrategy.subsamplingRate = params.subsamplingRate
-    boostingStrategy.treeStrategy.useNodeIdCache = params.cacheNodeIds
-
-    val gbdt = new GradientBoostedTrees(boostingStrategy)
-    val model = params.apiName match {
-      case "rdd" => gbdt.run(trainingLabelPositive)
-      case "javardd" => gbdt.run(trainingLabelPositive.toJavaRDD)
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val testData = MLUtils.loadLibSVMFile(sc, params.testDataPath).repartition(params.numPartitions)
-    val testLabelPositive = testData.map(i => if (i.label < 0) {
-      LabeledPoint(0.0, i.features)
-    } else {
-      LabeledPoint(i.label, i.features)
-    })
-
-    val labeleAndPreds = testLabelPositive.map{ point =>
-      val prediction = model.predict(point.features)
-      (point.label, prediction)
-    }
-    val res = params.algorithmType match {
-      case "classification" => labeleAndPreds.filter(r => r._1 == r._2).count.toDouble / testLabelPositive.count()
-      case "regression" => math.sqrt(labeleAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.mean())
-    }
-    (res, costTime)
-  }
-}
-
-
-
-object GBDTRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (algorithmType, dataStructure, datasetName, apiName, cpuName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2), modelConfSplit(3), modelConfSplit(4))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val datasetCpuName = s"${datasetName}_${cpuName}"
-      val stream = Utils.getStream("conf/ml/gbdt/gbdt.yml")
-      val representer = new Representer
-      representer.addClassTag(classOf[GBDTParams], Tag.MAP)
-
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[GBDTConfig]), representer, options)
-      val description = new TypeDescription(classOf[GBDTParams])
-      yaml.addTypeDescription(description)
-      val config: GBDTConfig = yaml.load(stream).asInstanceOf[GBDTConfig]
-      val paramsMap = config.gbdt.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new GBDTParams()
-
-      params.setAlgorithmType(algorithmType)
-      params.setApiName(apiName)
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setAlgorithmName("GBDT")
-      if (cpuName == "raw") {
-        params.setIsRaw("yes")
-      }
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setMaxIter(paramsMap.get("maxIter").toString.toInt)
-      params.setMaxDepth(paramsMap.get("maxDepth").toString.toInt)
-      params.setMaxBins(paramsMap.get("maxBins").toString.toInt)
-      params.setStepSize(paramsMap.get("stepSize").toString.toDouble)
-      params.setCacheNodeIds(paramsMap.get("cacheNodeIds").toString.toBoolean)
-      params.setMaxMemoryInMB(paramsMap.get("maxMemoryInMB").toString.toInt)
-      params.setMinInstancesPerNode(paramsMap.get("minInstancesPerNode").toString.toInt)
-      params.setMinInfoGain(paramsMap.get("minInfoGain").toString.toDouble)
-      params.setSubsamplingRate(paramsMap.get("subsamplingRate").toString.toDouble)
-      params.setFeatureSubsetStrategy(paramsMap.get("featureSubsetStrategy").toString)
-
-      var appName = s"GBDT_${algorithmType}_${dataStructure}_${datasetName}_${apiName}"
-      if (cpuName.equals("raw")){
-        appName = s"GBDT_RAW_${algorithmType}_${dataStructure}_${datasetName}_${apiName}"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(appName)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = dataStructure match {
-        case "dataframe" => new GBDTKernel().gbdtDataframeJob(spark, params)
-        case "rdd" => new GBDTKernel().gbdtRDDJob(spark, params)
-      }
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/GBDT_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
\ No newline at end of file
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/IDFRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/IDFRunner.scala
deleted file mode 100644
index 58183f9..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/IDFRunner.scala
+++ /dev/null
@@ -1,97 +0,0 @@
-package com.bigdata.ml
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.{Encoders, SparkSession}
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.feature.{IDF, IDFModel}
-import org.yaml.snakeyaml.Yaml
-
-import java.io.{File, FileInputStream, FileOutputStream, FileWriter, ObjectInputStream, ObjectOutputStream}
-import java.util
-import scala.beans.BeanProperty
-
-class IDFConfig extends Serializable {
-  @BeanProperty var pr: util.HashMap[String, Object] = _
-}
-
-object IDFRunner{
-  def main(args: Array[String]): Unit = {
-    //get parameters
-    val ifRaw = args(0)
-    val datasetName = args(1)
-    val stream = Utils.getStream("conf/ml/idf/idf.yml")
-    val yaml = new Yaml
-    val yamlCfg = yaml.load(stream).asInstanceOf[util.HashMap[String, Object]].get("idf")
-      .asInstanceOf[util.HashMap[String, Object]].get(ifRaw)
-      .asInstanceOf[util.HashMap[String, Object]].get(datasetName)
-      .asInstanceOf[util.HashMap[String, Object]]
-    val pt = yamlCfg.get("pt").asInstanceOf[Int]
-    val combineStrategy = yamlCfg.get("combineStrategy").asInstanceOf[String]
-    val fetchMethod = yamlCfg.get("fetchMethod").asInstanceOf[String]
-
-    val dataPath = args(2)
-    val modelPath = args(3)
-    val checkModel = java.lang.Boolean.parseBoolean(args(4))
-
-    //init
-    val conf = new SparkConf().setAppName(s"IDF_${datasetName}_${ifRaw}")
-    conf.set("spark.driver.maxResultSize", "256G")
-    if (ifRaw == "opt"){
-      conf.set("spark.sophon.ml.idf.combineStrategy", combineStrategy)
-      conf.set("spark.sophon.ml.idf.fetchMethod", fetchMethod)
-    }
-    val spark = SparkSession.builder().config(conf).getOrCreate()
-
-    val startTime = System.currentTimeMillis()
-    //convert to orc(The orc data type is used by default.To change the data type, modify the code.)
-    val orcData = spark.read.schema(Encoders.product[DocSchema].schema).format("orc").load(dataPath)
-    //repatition
-    val data = if (pt > 0){
-      orcData.select("tf").repartition(pt)
-    } else {
-      orcData.select("tf")
-    }
-    val idf = new IDF().setInputCol("tf").setOutputCol("tf_idf")
-    val model = idf.fit(data)
-    val trainFinishTime = System.currentTimeMillis()
-    //save model
-    saveModelToLocal(model, s"${modelPath}/${ifRaw}/")
-    //check Models
-    if (checkModel){
-      val expectModel = loadModelFromLocal(s"${modelPath}/pri/IDFModel")
-      val numMismatch = expectModel.idf.toArray.zip(model.idf.toArray).count(v => math.abs(v._1 - v._2) > 1E-10)
-      if (numMismatch != 0){
-        println("IDF model mismatch!")
-      } else {
-        println("IDF model matched!")
-      }
-    }
-    val endTime = System.currentTimeMillis()
-    val algTime = (trainFinishTime - startTime)/1000
-    val allTime = (endTime - startTime)/1000
-    println(s"IDF run complete, algTime = ${algTime}s, allTime = ${allTime}s")
-    yamlCfg.put("algTime", algTime.toString+"s")
-    yamlCfg.put("allTime", allTime.toString+"s")
-    yamlCfg.put("algorithmName", "IDF")
-    yamlCfg.put("dataset", datasetName)
-    yamlCfg.put("Flag", Array[String]("idf", datasetName))
-    val writer = new FileWriter(s"report/IDF_${Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",System.currentTimeMillis())}.yml")
-    yaml.dump(yamlCfg, writer)
-  }
-  def saveModelToLocal(model: IDFModel, dirPath: String): Unit = {
-    val dir = new File(dirPath)
-    if (!dir.exists()){
-      dir.mkdirs()
-    }
-    val outObj = new ObjectOutputStream(new FileOutputStream(dirPath+"/IDFModel"))
-    outObj.writeObject(model)
-    outObj.close()
-  }
-  def loadModelFromLocal(modelPath: String): IDFModel = {
-    val inObj = new ObjectInputStream(new FileInputStream(modelPath))
-    val model = inObj.readObject().asInstanceOf[IDFModel]
-    model
-  }
-  case class DocSchema(id: Long, tf: Vector)
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/KMeansRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/KMeansRunner.scala
deleted file mode 100644
index 4ee458e..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/KMeansRunner.scala
+++ /dev/null
@@ -1,192 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util.HashMap
-
-import com.bigdata.utils.Utils
-import org.apache.hadoop.io.LongWritable
-import org.apache.mahout.math.VectorWritable
-import org.apache.spark.ml.clustering.{KMeans => MlKMeans}
-import org.apache.spark.ml.linalg.{Vectors => MlVectors}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.mllib.clustering.{KMeans => MlibKMeans}
-import org.apache.spark.mllib.linalg.{Vectors => MlibVectors}
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.functions.udf
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class KMeansConfig extends Serializable {
-
-  @BeanProperty var kmeans: HashMap[String, Object] = _
-}
-
-class KMeansParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var maxIterations: Int = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-class KMeansKernel {
-
-  def runDataFrameJob(spark: SparkSession, params: KMeansParams): (Double, Double) = {
-
-    val sc = spark.sparkContext
-
-    val startTime = System.currentTimeMillis()
-
-    val data = sc.sequenceFile[LongWritable, VectorWritable](params.dataPath)
-
-    val dataRDD = data.map{ case (k, v) =>
-      var vector: Array[Double] = new Array[Double](v.get().size)
-      for (i <- 0 until v.get().size) vector(i) = v.get().get(i)
-      vector
-    }.repartition(params.numPartitions).persist()
-
-    import spark.implicits._
-    val dataDF = dataRDD.toDF("features")
-    val convertToVector = udf((array: Seq[Double]) => {
-      MlVectors.dense(array.toArray)
-    })
-    val trainingData = dataDF.withColumn("features", convertToVector($"features"))
-    
-    println("count: " + trainingData.count())
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    
-    val kmeans = new MlKMeans().setK(params.k).setMaxIter(params.maxIterations)
-
-    val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-    for (i <- 0 to paramMaps.size -1) {
-      paramMaps(i) = ParamMap(kmeans.k -> params.k)
-        .put(kmeans.maxIter, params.maxIterations)
-    }
-
-    val model = params.apiName match {
-      case "fit" => kmeans.fit(trainingData)
-      case "fit1" =>
-        val models = kmeans.fit(trainingData, paramMaps)
-        models(0)
-    }
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-    val res = model.computeCost(trainingData)
-    (res, costTime)
-  }
-
-  def runRDDJob(spark: SparkSession, params: KMeansParams): (Double, Double) = {
-
-    val sc = spark.sparkContext
-
-    val startTime = System.currentTimeMillis()
-
-    val data = sc.sequenceFile[LongWritable, VectorWritable](params.dataPath)
-
-    val dataRDD = data.map{ case (k, v) =>
-      var vector: Array[Double] = new Array[Double](v.get().size)
-      for (i <- 0 until v.get().size) vector(i) = v.get().get(i)
-      MlibVectors.dense(vector)
-    }.repartition(params.numPartitions).cache()
-    println("count: " + dataRDD.count())
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val model = new MlibKMeans()
-      .setK(params.k)
-      .setMaxIterations(params.maxIterations)
-      .run(dataRDD)
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    params.setLoadDataTime(loadDataTime)
-    val res = model.computeCost(dataRDD)
-    (res, costTime)
-  }
-}
-
-object KMeansRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (dataStructure, datasetName, apiName, cpuName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2), modelConfSplit(3))
-
-      val dataPath = args(1)
-
-      val datasetCpuName = s"${datasetName}_${cpuName}"
-      val stream = Utils.getStream("conf/ml/kmeans/kmeans.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[KMeansParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[KMeansConfig]), representer, options)
-      val description = new TypeDescription(classOf[KMeansParams])
-      yaml.addTypeDescription(description)
-      val config: KMeansConfig = yaml.load(stream).asInstanceOf[KMeansConfig]
-      val paramsMap = config.kmeans.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new KMeansParams()
-
-      params.setApiName(apiName)
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setAlgorithmName("KMeans")
-      if (cpuName == "raw") {
-        params.setIsRaw("yes")
-      }
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setMaxIterations(paramsMap.get("maxIterations").toString.toInt)
-      params.setK(paramsMap.get("k").toString.toInt)
-
-      var appName = s"KMeans_${dataStructure}_${datasetName}_${apiName}_${cpuName}"
-      if (cpuName.equals("raw")){
-        appName = s"KMeans_RAW_${dataStructure}_${datasetName}_${apiName}_${cpuName}"
-      }
-      params.setTestcaseType(appName)
-      val conf = new SparkConf()
-        .setAppName(s"KMeans_${dataStructure}_${datasetName}_${apiName}_${cpuName}")
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = dataStructure match {
-        case "dataframe" => new KMeansKernel().runDataFrameJob(spark, params)
-        case "rdd" => new KMeansKernel().runRDDJob(spark, params)
-      }
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/KMeans_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/KNNRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/KNNRunner.scala
deleted file mode 100644
index 70d8e8a..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/KNNRunner.scala
+++ /dev/null
@@ -1,249 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter, PrintWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.neighbors.KNN
-import org.apache.spark.ml.linalg.Vectors
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class KNNConfig extends Serializable {
-
-  @BeanProperty var knn: util.HashMap[String, util.HashMap[String, util.HashMap[String, Object]]] = _
-}
-
-class KNNParams extends Serializable {
-
-  @BeanProperty var pt: Int = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var testNum: Int = _
-  @BeanProperty var testBatchSize: Int = _
-  @BeanProperty var featuresCol: String = _
-  @BeanProperty var distanceCol: String = _
-  @BeanProperty var neighborsCol: String = _
-  @BeanProperty var topTreeSizeRate: Double = _
-  @BeanProperty var topTreeLeafSize: Int = _
-  @BeanProperty var subTreeLeafSize: Int = _
-
-  @BeanProperty var inputDataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object KNNRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val datasetName = args(0)
-
-      val inputDataPath = args(1)
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = isRaw match {
-        case "no" =>
-          Utils.getStream("conf/ml/knn/knn.yml")
-        case "yes" =>
-          Utils.getStream("conf/ml/knn/knn_raw.yml")
-      }
-      val representer = new Representer
-      representer.addClassTag(classOf[KNNParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[KNNConfig]), representer, options)
-      val description = new TypeDescription(classOf[KNNParams])
-      yaml.addTypeDescription(description)
-      val configs: KNNConfig = yaml.load(stream).asInstanceOf[KNNConfig]
-      val params = new KNNParams()
-
-      val knnParamMap: util.HashMap[String, Object] = configs.knn.get(cpuName).get(datasetName)
-      params.setPt(knnParamMap.getOrDefault("pt", "200").asInstanceOf[Int])
-      params.setK(knnParamMap.getOrDefault("k", "10").asInstanceOf[Int])
-      params.setTestNum(knnParamMap.getOrDefault("testNum", "100000").asInstanceOf[Int])
-      params.setTestBatchSize(knnParamMap.getOrDefault("testBatchSize", "10").asInstanceOf[Int])
-      params.setTopTreeSizeRate(knnParamMap.getOrDefault("topTreeSizeRate", "10.0").asInstanceOf[Double])
-      params.setTopTreeLeafSize(knnParamMap.getOrDefault("topTreeLeafSize", "10").asInstanceOf[Int])
-      params.setSubTreeLeafSize(knnParamMap.getOrDefault("subTreeLeafSize", "30").asInstanceOf[Int])
-      params.setFeaturesCol("features")
-      params.setDistanceCol("distances")
-      params.setNeighborsCol("neighbors")
-
-      params.setInputDataPath(inputDataPath)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("KNN")
-
-      var appName = s"KNN_${datasetName}"
-      if (isRaw == "yes") {
-        appName = s"KNN_${datasetName}_raw"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(appName).setMaster(master)
-
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val costTime = if (isRaw == "no") {
-        new KNNKernel().runJob(spark, params)
-      } else {
-        new KNNKernel().runRawJob(spark, params)
-      }
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/knn_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
-class KNNKernel {
-
-  def runJob(spark: SparkSession, params: KNNParams): Double = {
-
-    import spark.implicits._
-    val startTime = System.currentTimeMillis()
-    val dataPath = params.inputDataPath
-    val featuresCol = params.featuresCol
-    val testNum = params.testNum
-    val pt = params.pt
-    val neighborsCol = params.neighborsCol
-    val distanceCol = params.distanceCol
-    val testBatchSize = params.testBatchSize
-    val k = params.k
-
-    //read data
-    val rawData = spark.sparkContext.textFile(dataPath)
-      .map(line => {
-        val arr = line.split("\t")
-        val id = arr(0).toLong
-        val feature = Vectors.dense(arr(1).split(",").map(_.toDouble))
-        (id, feature)
-      }).toDF("id", featuresCol).cache()
-
-    //split train/test datasets
-    val trainDataDF = rawData.filter($"id" >= testNum).repartition(pt).cache()
-    val testDataDF = rawData.filter($"id" < testNum).repartition(pt).cache()
-    trainDataDF.count()
-    testDataDF.count()
-
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    //fit
-    val model = new KNN()
-      .setFeaturesCol(featuresCol)
-      .setAuxiliaryCols(Array("id"))
-      .fit(trainDataDF)
-
-    //transform
-    val testResults = model
-        .setNeighborsCol(neighborsCol)
-        .setDistanceCol(distanceCol)
-        .setK(k)
-        .setTestBatchSize(testBatchSize)
-        .transform(testDataDF).cache()
-    testResults.count()
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    costTime
-  }
-
-  def runRawJob(spark: SparkSession, params: KNNParams): Double = {
-
-    import spark.implicits._
-    val startTime = System.currentTimeMillis()
-    val dataPath = params.inputDataPath
-    val featuresCol = params.featuresCol
-    val testNum = params.testNum
-    val pt = params.pt
-    val neighborsCol = params.neighborsCol
-    val distanceCol = params.distanceCol
-    val k = params.k
-    val topTreeSizeRate = params.topTreeSizeRate
-    val topTreeLeafSize = params.topTreeLeafSize
-    val subTreeLeafSize = params.subTreeLeafSize
-
-    //read data
-    val rawData = spark.sparkContext.textFile(dataPath)
-      .map(line => {
-        val arr = line.split("\t")
-        val id = arr(0).toLong
-        val feature = Vectors.dense(arr(1).split(",").map(_.toDouble))
-        (id, feature)
-      }).toDF("id", featuresCol).cache()
-
-    //split train/test datasets
-    val trainDataDF = rawData.filter($"id" >= testNum).repartition(pt).cache()
-    val testDataDF = rawData.filter($"id" < testNum).repartition(pt).cache()
-    trainDataDF.count()
-    testDataDF.count()
-
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    //fit
-    import org.apache.spark.ml.knn.KNN
-    val model = new KNN()
-      .setTopTreeSize((pt * topTreeSizeRate).toInt)
-      .setTopTreeLeafSize(topTreeLeafSize)
-      .setSubTreeLeafSize(subTreeLeafSize)
-      .setBalanceThreshold(0.0)
-      .setFeaturesCol(featuresCol)
-      .setAuxCols(Array("id"))
-      .fit(trainDataDF)
-
-    //transform
-    val testResults = model
-      .setBufferSize(Double.MaxValue)
-      .setNeighborsCol(neighborsCol)
-      .setDistanceCol(distanceCol)
-      .setK(k)
-      .transform(testDataDF).cache()
-    testResults.count()
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    costTime
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/LDARunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/LDARunner.scala
deleted file mode 100644
index 2e3b3d6..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/LDARunner.scala
+++ /dev/null
@@ -1,264 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util.HashMap
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.ml.clustering.{LDA => MLLDA, LDAModel}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.rdd.RDD
-import org.apache.spark.mllib.linalg.{Vector => MLlibVector}
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.mllib.clustering.{LocalLDAModel, LDA => MLlibLDA}
-import org.apache.spark.sql.SparkSession
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-
-class LDAConfig extends Serializable {
-
-  @BeanProperty var lda: HashMap[String, Object] = _
-}
-
-class LDAParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var numPartitionsTest: Int = _
-  @BeanProperty var numFeatures: Int = _
-  @BeanProperty var maxIter: Int = _
-  @BeanProperty var checkpointInterval: Int = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var inputDataType: String = _
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var dataStructure: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-class LDAKernel {
-
-  def runDataFrameJob(spark: SparkSession, params: LDAParams): (LDAModel, Double) = {
-    val sc = spark.sparkContext
-    val startTime = System.currentTimeMillis()
-    val trainingData = if (params.inputDataType == "libsvm") {
-      spark.read.format("libsvm")
-        .option("numFeatures", params.numFeatures)
-        .load(params.trainingDataPath)
-        .repartition(params.numPartitions)
-        .persist()
-    } else {
-      import spark.implicits._
-      val corpus: RDD[(Long, MLlibVector)] = sc.objectFile(params.trainingDataPath)
-      corpus
-        .map(i => (i._1, i._2.asML))
-        .toDF()
-        .repartition(params.numPartitions)
-        .withColumnRenamed("_1", "label")
-        .withColumnRenamed("_2", "features")
-        .persist()
-    }
-
-    println("count: " + trainingData.count())
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-    val lda = new MLLDA()
-      .setSeed(2020L)
-      .setK(params.k)
-      .setMaxIter(params.maxIter)
-      .setCheckpointInterval(params.checkpointInterval)
-
-    val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-    for (i <- 0 to paramMaps.size - 1) {
-      paramMaps(i) = ParamMap(lda.k -> params.k)
-        .put(lda.maxIter, params.maxIter)
-    }
-    val model = params.apiName match {
-      case "fit" => lda.fit(trainingData)
-      case "fit1" =>
-        val models = lda.fit(trainingData, paramMaps)
-        models(0)
-    }
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    println("load and train costTime: " + costTime)
-
-    trainingData.unpersist()
-    spark.close()
-    (model, costTime)
-  }
-
-  def runPredictJob(spark: SparkSession, params: LDAParams, model: LDAModel): Double = {
-    val sc = spark.sparkContext
-
-    println("====== Start Predict =======: ")
-    val startTime = System.currentTimeMillis()
-    val testData = if (params.inputDataType == "libsvm") {
-      spark.read.format("libsvm")
-        .option("numFeatures", params.numFeatures)
-        .load(params.testDataPath)
-        .repartition(params.numPartitionsTest)
-        .persist()
-    } else {
-      import spark.implicits._
-      val corpus: RDD[(Long, MLlibVector)] = sc.objectFile(params.testDataPath)
-      corpus
-        .map(i => (i._1, i._2.asML))
-        .toDF()
-        .repartition(params.numPartitionsTest)
-        .withColumnRenamed("_1", "label")
-        .withColumnRenamed("_2", "features")
-        .persist()
-    }
-    val testDataSize = testData.count()
-    val fraction = if (testDataSize <= 10000) {
-      1.0
-    } else {
-      10000.0 / testDataSize.toDouble
-    }
-    val testDataSample = testData.sample(fraction, 2020L)
-
-    val res = model.logLikelihood(testDataSample)
-    println("====== End Predict =======: ")
-    val predictTime = (System.currentTimeMillis() - startTime) / 1000.0
-    println("Predict costTime: " + (predictTime))
-    testData.unpersist()
-    spark.close()
-    res
-  }
-
-  def runRDDJob(spark: SparkSession, params: LDAParams): Double = {
-    val sc = spark.sparkContext
-    val startTime = System.currentTimeMillis()
-    val trainingData = if (params.inputDataType == "libsvm") {
-      MLUtils.loadLibSVMFile(sc, params.trainingDataPath, params.numFeatures)
-        .map(i => (i.label.toLong, i.features))
-        .repartition(params.numPartitions)
-        .persist()
-    } else {
-      val corpus: RDD[(Long, MLlibVector)] = sc.objectFile(params.trainingDataPath)
-      corpus
-        .repartition(params.numPartitions)
-        .persist()
-    }
-    println("count: " + trainingData.count())
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    val model = new MLlibLDA()
-      .setSeed(2020L)
-      .setK(params.k)
-      .setMaxIterations(params.maxIter)
-      .setCheckpointInterval(params.checkpointInterval)
-      .setOptimizer("online")
-      .run(trainingData)
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    trainingData.unpersist()
-    spark.close()
-    costTime
-  }
-}
-
-
-object LDARunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (dataStructure, datasetName, apiName, cpuName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2), modelConfSplit(3))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val datasetCpuName = s"${datasetName}_${cpuName}"
-      val stream = Utils.getStream("conf/ml/lda/lda.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[LDAParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[LDAConfig]), representer, options)
-      val description = new TypeDescription(classOf[LDAParams])
-      yaml.addTypeDescription(description)
-      val config: LDAConfig = yaml.load(stream).asInstanceOf[LDAConfig]
-      val paramsMap = config.lda.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new LDAParams()
-
-      params.setApiName(apiName)
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setDataStructure(dataStructure)
-      params.setAlgorithmName("LDA")
-      if (cpuName == "raw") {
-        params.setIsRaw("yes")
-      }
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setNumPartitionsTest(paramsMap.get("numPartitionsTest").toString.toInt)
-      params.setNumFeatures(paramsMap.get("numFeatures").toString.toInt)
-      params.setCheckpointInterval(paramsMap.get("checkpointInterval").toString.toInt)
-      params.setK(paramsMap.get("k").toString.toInt)
-      params.setMaxIter(paramsMap.get("maxIter").toString.toInt)
-      params.setInputDataType(paramsMap.get("inputDataType").toString)
-
-      var appName = s"LDA_${dataStructure}_${datasetName}_${apiName}_${cpuName}"
-      if (cpuName.equals("raw")){
-        appName = s"LDA_RAW_${dataStructure}_${datasetName}_${apiName}_${cpuName}"
-      }
-      params.setTestcaseType(appName)
-
-      val confTrain = new SparkConf()
-        .setAppName(s"LDA_${dataStructure}_${datasetName}_${apiName}_${cpuName}")
-      val sparkTrain = SparkSession.builder.config(confTrain).getOrCreate()
-
-      val (res, costTime) = dataStructure match {
-        case "dataframe" => {
-          val (trainModel, dataFrameCostTime) = new LDAKernel().runDataFrameJob(sparkTrain, params)
-          val confPredict = new SparkConf()
-            .setAppName(s"LDA_${dataStructure}_${datasetName}_${apiName}_${cpuName}_predict").set("spark.task.cpus", "1")
-          val sparkPredict = SparkSession.builder.config(confPredict).getOrCreate()
-          val dataFrameRes = new LDAKernel().runPredictJob(sparkPredict, params, trainModel)
-          (dataFrameRes, dataFrameCostTime)
-        }
-        case "rdd" => (0.0, new LDAKernel().runRDDJob(sparkTrain, params))
-      }
-
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/LDA_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/LinRRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/LinRRunner.scala
deleted file mode 100644
index 018bac4..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/LinRRunner.scala
+++ /dev/null
@@ -1,225 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.linalg.DenseMatrix
-import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.regression.LinearRegression
-import org.apache.spark.sql.functions.udf
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class LinRConfig extends Serializable {
-
-  @BeanProperty var linR: util.HashMap[String, util.HashMap[String, util.HashMap[String, Object]]] = _
-}
-
-class LinRParams extends Serializable {
-
-  @BeanProperty var pt: Int = _
-  @BeanProperty var numFeatures: Int = _
-  @BeanProperty var loss: String = _
-  @BeanProperty var regParam: Double = _
-  @BeanProperty var elasticNetParam: Double = _
-  @BeanProperty var maxIter: Int = _
-  @BeanProperty var tolerance: Double = _
-
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object LinRRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("_")
-      val (datasetName, apiName) =
-        (modelConfSplit(0), modelConfSplit(1))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = (cpuName, isRaw) match {
-        case ("aarch64", "no") =>
-          Utils.getStream("conf/ml/linR/linR.yml")
-        case ("x86_64", "no") =>
-          Utils.getStream("conf/ml/linR/linR.yml")
-        case ("x86_64", "yes") =>
-          Utils.getStream("conf/ml/linR/linR_raw.yml")
-      }
-      val representer = new Representer
-      representer.addClassTag(classOf[LinRParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[LinRConfig]), representer, options)
-      val description = new TypeDescription(classOf[LinRParams])
-      yaml.addTypeDescription(description)
-      val configs: LinRConfig = yaml.load(stream).asInstanceOf[LinRConfig]
-      val params = new LinRParams()
-
-      val linRParamMap: util.HashMap[String, Object] = configs.linR.get(cpuName).get(datasetName)
-      params.setPt(linRParamMap.getOrDefault("pt", "2000").asInstanceOf[Int])
-      params.setNumFeatures(linRParamMap.getOrDefault("numFeatures", "500").asInstanceOf[Int])
-      params.setLoss(linRParamMap.getOrDefault("loss", "squaredError").asInstanceOf[String])
-      params.setRegParam(linRParamMap.getOrDefault("regParam", "0.01").asInstanceOf[Double])
-      params.setElasticNetParam(linRParamMap.getOrDefault("elasticNetParam", "0.0").asInstanceOf[Double])
-      params.setMaxIter(linRParamMap.getOrDefault("maxIter", "500").asInstanceOf[Int])
-      params.setTolerance(linRParamMap.getOrDefault("tolerance", "1E-6").asInstanceOf[Double])
-
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setApiName(apiName)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("LinR")
-
-      var appName = s"LinR_${datasetName}_${apiName}"
-      if (isRaw.equals("yes")){
-        appName = s"LinR_RAW_${datasetName}_${apiName}"
-      }
-      params.setTestcaseType(appName)
-      val conf = new SparkConf()
-        .setAppName(appName).setMaster(master)
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = new LinRKernel().runJob(spark, params)
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/LinRegression_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
-class LinRKernel {
-
-  def runJob(spark: SparkSession, params: LinRParams): (Double, Double) = {
-
-    import spark.implicits._
-    val startTime = System.currentTimeMillis()
-    val trainData = spark
-      .read
-      .format("libsvm")
-      .option("vectorType", "dense")
-      .option("numFeatures", params.numFeatures)
-      .load(params.trainingDataPath)
-      .repartition(params.pt)
-      .cache()
-
-    val trainingData = if (params.datasetName == "rcv") {
-      val label_convert = udf((x: Double) => if (x < 0.0) 0.0 else 1.0)
-      trainData.withColumn("label", label_convert($"label"))
-    } else {
-      trainData
-    }
-    println(s"trainingData: ${trainingData.count()}")
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    val numIterationJY = 10
-    val regParamJY = 0.01
-
-    val linR = new LinearRegression()
-      .setSolver("l-bfgs")
-      .setLoss(params.loss)
-      .setRegParam(regParamJY)
-      .setElasticNetParam(params.elasticNetParam)
-      .setMaxIter(numIterationJY)
-      .setTol(params.tolerance)
-
-    if (params.apiName == "fit"){
-      linR.setRegParam(params.regParam)
-      linR.setMaxIter(params.maxIter)
-    }
-
-    val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-    for (i <- 0 to paramMaps.size -1) {
-      paramMaps(i) = ParamMap(linR.maxIter -> params.maxIter)
-        .put(linR.regParam, params.regParam)
-    }
-
-    val model = params.apiName match {
-      case "fit" => linR.fit(trainingData)
-      case "fit1" =>
-        val models = linR.fit(trainingData, paramMaps)
-        models(0)
-    }
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    val testData = spark
-      .read
-      .format("libsvm")
-      .option("vectorType", "dense")
-      .option("numFeatures", params.numFeatures)
-      .load(params.testDataPath)
-      .repartition(params.pt)
-      .cache()
-
-    val testingData = if (params.datasetName == "rcv") {
-      val label_convert = udf((x: Double) => if (x < 0.0) 0.0 else 1.0)
-      testData.withColumn("label", label_convert($"label"))
-    } else {
-      testData
-    }
-
-    val getSquaredError = udf((v1: Double, v2: Double) => {
-      math.pow((v1 - v2), 2)
-    })
-    val predictions = model.transform(testingData)
-      .withColumn("squaredError", getSquaredError($"label", $"prediction"))
-      .select("squaredError").summary("mean")
-
-    val res = predictions.select("squaredError").first().getString(0).toDouble
-
-    (res, costTime)
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/LogRRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/LogRRunner.scala
deleted file mode 100644
index bcdca98..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/LogRRunner.scala
+++ /dev/null
@@ -1,207 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util.HashMap
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.linalg.DenseMatrix
-import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.sql.functions.udf
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class LogRConfig extends Serializable {
-
-  @BeanProperty var logR: HashMap[String, Object] = _
-}
-
-class LogRParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var numLabels: Int = _
-  @BeanProperty var numFeatures: Int = _
-  @BeanProperty var maxIter: Int = _
-  @BeanProperty var regParam: Double = _
-  @BeanProperty var elasticNetParam: Double = _
-  @BeanProperty var tolerance: Double = _
-  @BeanProperty var isSetBound: Boolean = _
-  @BeanProperty var coefficientLowerBound: Double = _
-  @BeanProperty var coefficientUpperBound: Double = _
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-
-}
-
-class LogRKernel {
-
-  def runJob(spark: SparkSession, params: LogRParams): (Double, Double) = {
-
-    import spark.implicits._
-    val startTime = System.currentTimeMillis()
-    val trainData = spark
-      .read
-      .format("libsvm")
-      .option("vectorType", "dense")
-      .option("numFeatures", params.numFeatures)
-      .load(params.trainingDataPath)
-      .repartition(params.numPartitions)
-      .cache()
-
-    val trainingData = if (params.datasetName == "rcv") {
-      val label_convert = udf((x: Double) => if (x < 0.0) 0.0 else 1.0)
-      trainData.withColumn("label", label_convert($"label"))
-    } else {
-      trainData
-    }
-    println(s"trainingData: ${trainingData.count()}")
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val logR = if (params.isSetBound){
-      new LogisticRegression()
-        .setRegParam(params.regParam)
-        .setElasticNetParam(params.elasticNetParam)
-        .setMaxIter(params.maxIter)
-        .setTol(params.tolerance)
-        .setLowerBoundsOnCoefficients(new DenseMatrix(params.numLabels, params.numFeatures, Array.fill(params.numLabels * params.numFeatures)(params.coefficientLowerBound), true))
-        .setUpperBoundsOnCoefficients(new DenseMatrix(params.numLabels, params.numFeatures, Array.fill(params.numLabels * params.numFeatures)(params.coefficientUpperBound), true))
-    }
-    else {
-      new LogisticRegression()
-        .setRegParam(params.regParam)
-        .setElasticNetParam(params.elasticNetParam)
-        .setMaxIter(params.maxIter)
-        .setTol(params.tolerance)
-    }
-
-    val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-    for (i <- 0 to paramMaps.size -1) {
-      paramMaps(i) = ParamMap(logR.maxIter -> params.maxIter)
-        .put(logR.regParam, params.regParam)
-    }
-
-    val model = params.apiName match {
-      case "fit" => logR.fit(trainingData)
-      case "fit1" =>
-        val models = logR.fit(trainingData, paramMaps)
-        models(0)
-    }
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    val testData = spark
-      .read
-      .format("libsvm")
-      .option("vectorType", "dense")
-      .option("numFeatures", params.numFeatures)
-      .load(params.testDataPath)
-      .repartition(params.numPartitions)
-      .cache()
-
-    val testingData = if (params.datasetName == "rcv") {
-      val label_convert = udf((x: Double) => if (x < 0.0) 0.0 else 1.0)
-      testData.withColumn("label", label_convert($"label"))
-    } else {
-      testData
-    }
-
-    val predictions = model.transform(testingData)
-    val res = predictions.filter($"label" === $"prediction").count().toDouble / predictions.count()
-
-    (res, costTime)
-  }
-}
-
-object LogRRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (datasetName, apiName, cpuName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val datasetCpuName = s"${datasetName}_${cpuName}"
-      val stream = Utils.getStream("conf/ml/logR/logR.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[LogRParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[LogRConfig]), representer, options)
-      val description = new TypeDescription(classOf[LogRParams])
-      yaml.addTypeDescription(description)
-      val config: LogRConfig = yaml.load(stream).asInstanceOf[LogRConfig]
-      val paramsMap = config.logR.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new LogRParams()
-
-      params.setApiName(apiName)
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      if (cpuName == "raw") {
-        params.setIsRaw("yes")
-      }
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setMaxIter(paramsMap.get("maxIter").toString.toInt)
-      params.setNumLabels(paramsMap.get("numLabels").toString.toInt)
-      params.setNumFeatures(paramsMap.get("numFeatures").toString.toInt)
-      params.setRegParam(paramsMap.get("regParam").toString.toDouble)
-      params.setElasticNetParam(paramsMap.get("elasticNetParam").toString.toDouble)
-      params.setTolerance(paramsMap.get("tolerance").toString.toDouble)
-      params.setIsSetBound(paramsMap.get("isSetBound").toString.toBoolean)
-      params.setCoefficientLowerBound(paramsMap.get("coefficientLowerBound").toString.toDouble)
-      params.setCoefficientUpperBound(paramsMap.get("coefficientUpperBound").toString.toDouble)
-      params.setAlgorithmName("LogR")
-      var appName = s"LogR_${datasetName}_${apiName}"
-      if (cpuName.equals("raw")){
-        appName = s"LogR_RAW_${datasetName}_${apiName}"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(appName)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = new LogRKernel().runJob(spark, params)
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/LogRegression_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/PCARunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/PCARunner.scala
deleted file mode 100644
index 714a392..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/PCARunner.scala
+++ /dev/null
@@ -1,217 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter, PrintWriter}
-import java.util.HashMap
-
-import com.bigdata.utils.Utils
-import org.apache.spark.ml.feature.{PCA => MLPCA}
-import org.apache.spark.ml.linalg.{Vectors => MLVectors}
-import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.mllib.feature.{PCA => MLibPCA}
-import org.apache.spark.mllib.linalg.{Vectors => MLibVectors}
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.types.{StringType, StructField, StructType}
-import org.apache.spark.sql.functions.udf
-import org.apache.spark.storage.StorageLevel
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class PCAConfig extends Serializable {
-
-  @BeanProperty var pca: HashMap[String, Object] = _
-}
-
-class PCAParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var numCols: Int = _
-  @BeanProperty var numRows: Int = _
-  @BeanProperty var dataFormat: String = _
-  @BeanProperty var sep: String = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var dataStructure: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-class PCAKernel {
-
-  def runDataFrameJob(spark: SparkSession, params: PCAParams): (Array[Double], Double) = {
-
-    val sc = spark.sparkContext
-    val numColsBC = sc.broadcast(params.numCols)
-    val sepBC = sc.broadcast(params.sep)
-
-    val startTime = System.currentTimeMillis()
-
-    val data = if (params.dataFormat == "coo") {
-      spark.createDataFrame(sc.textFile(params.dataPath, params.numPartitions)
-        .map(line => {
-          val entry = line.split(sepBC.value)
-          (entry(0).toInt, (entry(1).toInt, entry(2).toDouble))
-        }).groupByKey()
-        .map { case (_, vectorEntries) => MLVectors.sparse(numColsBC.value, vectorEntries.toSeq) }
-        .repartition(params.numPartitions)
-        .map(Tuple1.apply))
-        .toDF("matrix").persist(StorageLevel.MEMORY_ONLY)
-    } else {
-      spark.createDataFrame(sc.textFile(params.dataPath)
-        .map(row => Row(MLVectors.dense(row.split(sepBC.value).map(_.toDouble))))
-        .repartition(params.numPartitions),
-        StructType(List(StructField("matrix", VectorType)))
-      ).persist(StorageLevel.MEMORY_ONLY)
-    }
-
-    println("count: " + data.count())
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val pca = new MLPCA().setK(params.k).setInputCol("matrix")
-
-    val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-    for (i <- 0 to paramMaps.size - 1) {
-      paramMaps(i) = ParamMap(pca.k -> params.k)
-        .put(pca.inputCol, "matrix")
-    }
-
-    val model = params.apiName match {
-      case "fit" => pca.fit(data)
-      case "fit1" =>
-        val models = pca.fit(data, paramMaps)
-        models(0)
-    }
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-    val res = model.pc.values
-    (res, costTime)
-  }
-
-  def runRDDJob(spark: SparkSession, params: PCAParams): (Array[Double], Double) = {
-
-    val sc = spark.sparkContext
-    val numColsBC = sc.broadcast(params.numCols)
-    val sepBC = sc.broadcast(params.sep)
-
-    val startTime = System.currentTimeMillis()
-
-    val data = if (params.dataFormat == "coo") {
-      sc.textFile(params.dataPath, params.numPartitions)
-        .map(line => {
-          val entry = line.split(sepBC.value)
-          (entry(0).toInt, (entry(1).toInt, entry(2).toDouble))
-        }).groupByKey()
-        .map { case (_, vectorEntries) => MLibVectors.sparse(numColsBC.value, vectorEntries.toSeq) }
-        .repartition(params.numPartitions).persist(StorageLevel.MEMORY_ONLY)
-    } else {
-      sc.textFile(params.dataPath)
-        .map(row => MLibVectors.dense(row.split(sepBC.value).map(_.toDouble)))
-        .repartition(params.numPartitions).persist(StorageLevel.MEMORY_ONLY)
-    }
-
-    println("count: " + data.count())
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val pca = new MLibPCA(params.k).fit(data)
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-    val res = pca.pc.values
-    (res, costTime)
-  }
-}
-
-object PCARunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (dataStructure, datasetName, apiName, cpuName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2), modelConfSplit(3))
-
-      val dataPath = args(1)
-
-      val datasetCpuName = s"${datasetName}_${cpuName}"
-      val stream = Utils.getStream("conf/ml/pca/pca.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[PCAParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[PCAConfig]), representer, options)
-      val description = new TypeDescription(classOf[PCAParams])
-      yaml.addTypeDescription(description)
-      val config: PCAConfig = yaml.load(stream).asInstanceOf[PCAConfig]
-      val paramsMap = config.pca.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new PCAParams()
-
-      params.setApiName(apiName)
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setDataStructure(dataStructure)
-      params.setAlgorithmName("PCA")
-      params.setTestcaseType(s"PCA_${dataStructure}_${datasetName}_${apiName}")
-      if (cpuName == "raw") {
-        params.setIsRaw("yes")
-      }
-
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setK(paramsMap.get("k").toString.toInt)
-      params.setNumCols(paramsMap.get("numCols").toString.toInt)
-      params.setNumRows(paramsMap.get("numRows").toString.toInt)
-      params.setDataFormat(paramsMap.get("dataFormat").toString)
-      params.setSep(paramsMap.get("sep").toString)
-
-      var appName = s"PCA_${dataStructure}_${datasetName}_${apiName}"
-      if (cpuName.equals("raw")){
-        appName = s"PCA_RAW_${dataStructure}_${datasetName}_${apiName}"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(appName)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = dataStructure match {
-        case "dataframe" => new PCAKernel().runDataFrameJob(spark, params)
-        case "rdd" => new PCAKernel().runRDDJob(spark, params)
-      }
-      params.setCostTime(costTime)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/PCA_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      val resOutput = new File(s"report/PCAResult_${dataStructure}_${datasetName}_${apiName}_${cpuName}.txt")
-      val pw = new PrintWriter(resOutput, "UTF-8")
-      res.foreach(pw.println)
-      pw.close()
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/PearsonRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/PearsonRunner.scala
deleted file mode 100644
index a8f5186..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/PearsonRunner.scala
+++ /dev/null
@@ -1,207 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util
-
-import scala.beans.BeanProperty
-import com.bigdata.utils.Utils
-import org.apache.spark.ml.stat
-import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
-import org.apache.spark.ml.linalg.{Matrix, Vectors, Vector}
-import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
-import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.types.{StructField, StructType}
-import org.apache.spark.storage.StorageLevel
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-class PearsonConfig extends Serializable {
-  @BeanProperty var pearson: util.HashMap[String, util.HashMap[String, util.HashMap[String, util.HashMap[String, Object]]]] = _
-}
-
-class PearsonParams extends Serializable {
-  @BeanProperty var pt: Int = _
-  @BeanProperty var sum: Double = _
-
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var outputDataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var startTime: Long = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object PearsonRunner {
-
-  def main(args: Array[String]): Unit = {
-    try {
-      val modelConfSplit = args(0).split("_")
-      val (dataStructure, datasetName) = (modelConfSplit(0), modelConfSplit(1))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, outputDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = (cpuName, isRaw) match {
-        case ("aarch64", "no") =>
-          Utils.getStream("conf/ml/pearson/pearson.yml")
-        case ("x86_64", "no") =>
-          Utils.getStream("conf/ml/pearson/pearson.yml")
-        case ("x86_64", "yes") =>
-          Utils.getStream("conf/ml/pearson/pearson_raw.yml")
-      }
-      val representer = new Representer
-      representer.addClassTag(classOf[PearsonParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[PearsonConfig]), representer, options)
-      val description = new TypeDescription(classOf[PearsonParams])
-      yaml.addTypeDescription(description)
-      val configs: PearsonConfig = yaml.load(stream).asInstanceOf[PearsonConfig]
-      val params = new PearsonParams()
-
-      val pearsonParamMap: util.HashMap[String, Object] = configs.pearson.get(cpuName).get(dataStructure).get(datasetName)
-      params.setPt(pearsonParamMap.getOrDefault("pt", "1000").asInstanceOf[Int])
-
-      params.setTrainingDataPath(trainingDataPath)
-      params.setOutputDataPath(outputDataPath)
-      params.setApiName(dataStructure)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("Pearson")
-      params.setTestcaseType(s"Pearson_${dataStructure}_${datasetName}")
-
-      var appName = s"Pearson_${dataStructure}_${datasetName}"
-      if (isRaw.equals("yes")){
-        appName = s"Pearson_RAW_${dataStructure}_${datasetName}"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = isRaw match {
-        case "yes" =>
-          new SparkConf().setAppName(s"Pearson_raw_${dataStructure}_${datasetName}").setMaster(master)
-        case "no" =>
-          new SparkConf().setAppName(s"Pearson_${dataStructure}_${datasetName}").setMaster(master)
-      }
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-      println(s"Initialized spark session.")
-      val startTime = System.currentTimeMillis()
-      params.setStartTime(startTime)
-      val sc = spark.sparkContext
-
-      val (sum, costTime) = dataStructure match {
-        case "dataframe" =>
-          new PearsonKernel().runDataframeJob(spark, params)
-        case "rdd" =>
-          new PearsonKernel().runRddJob(spark, params)
-      }
-      params.setSum(sum)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/Pearson_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; Matrix sum: ${sum}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-    }
-  }
-}
-
-class PearsonKernel {
-
-  def runDataframeJob(spark: SparkSession, params: PearsonParams): (Double, Double) = {
-    val pt = params.pt
-
-    val data = spark.createDataFrame(
-      spark.sparkContext
-        .textFile(params.trainingDataPath)
-        .map(x=>Row(Vectors.dense(x.split(",").map(_.toDouble))))
-        .repartition(pt),
-      StructType(List(StructField("matrix", VectorType)))
-    ).persist(StorageLevel.MEMORY_ONLY)
-
-    val mat = stat.Correlation.corr(data, "matrix")
-
-    val costTime = (System.currentTimeMillis() - params.startTime) / 1000.0
-
-    val result = mat.collect()(0).getAs[Matrix](0).toArray
-    val sum = result.sum
-
-    //save result
-    val res = new Array[String](mat.count().toInt)
-    for(i <- 0 until mat.count().toInt) {
-      val row = result.slice(i * mat.count().toInt, (i + 1) * mat.first().size).mkString(";")
-      res(i) = i.toString + " " + row
-    }
-    spark.sparkContext.parallelize(res).repartition(100).saveAsTextFile(s"${params.outputDataPath}_${params.cpuName}_${params.isRaw}")
-
-    (sum, costTime)
-  }
-
-  def runRddJob(spark: SparkSession, params: PearsonParams): (Double, Double) = {
-
-    val pt = params.pt
-
-    val data = spark.createDataFrame(
-      spark.sparkContext
-        .textFile(params.trainingDataPath)
-        .map(x=>Row(Vectors.dense(x.split(",").map(_.toDouble))))
-        .repartition(pt),
-      StructType(List(StructField("matrix", VectorType)))
-    ).persist(StorageLevel.MEMORY_ONLY)
-
-    val rdd = data.select("matrix").rdd.map{
-      case Row(v: Vector) => OldVectors.fromML(v)
-    }
-
-    import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
-    val oldM = OldStatistics.corr(rdd, "pearson")
-    val costTime = (System.currentTimeMillis() - params.startTime) / 1000.0
-
-    val result = oldM.toArray
-    val sum = result.sum
-
-    //save result
-    val res = new Array[String](oldM.numRows)
-    for(i <- 0 until oldM.numRows) {
-      val row = result.slice(i * oldM.numRows, (i + 1) * oldM.numCols).mkString(";")
-      res(i) = i.toString + " " + row
-    }
-    spark.sparkContext.parallelize(res).repartition(100).saveAsTextFile(s"${params.outputDataPath}_${params.cpuName}_${params.isRaw}")
-
-    (sum, costTime)
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/PrefixSpanRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/PrefixSpanRunner.scala
deleted file mode 100644
index ff1e3f3..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/PrefixSpanRunner.scala
+++ /dev/null
@@ -1,200 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util.HashMap
-
-import com.bigdata.utils.Utils
-import io.airlift.compress.lz4.Lz4Codec
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.hadoop.io.NullWritable
-import org.apache.spark.SparkConf
-import org.apache.spark.mllib.fpm.PrefixSpan
-import org.apache.spark.sql.SparkSession
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-
-class PrefixSpanConfig extends Serializable {
-
-  @BeanProperty var ps: HashMap[String, Object] = _
-}
-
-class PrefixSpanParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var minSupport: Double = _
-  @BeanProperty var maxPatternLength: Int = _
-  @BeanProperty var maxLocalProjDBSize: Int = _
-  @BeanProperty var localTimeout: String = _
-  @BeanProperty var filterCandidates: String = _
-  @BeanProperty var projDBstep: String = _
-  @BeanProperty var redistributeData: Boolean = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var verifiedDataPath: String = _
-  @BeanProperty var saveDataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var evaluation: String = "mismatch"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var numFreqSeqs: Long = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-class PrefixSpanKernel {
-
-  def runJob(spark: SparkSession, params: PrefixSpanParams): Unit = {
-
-    val sc = spark.sparkContext
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-
-    val startTime = System.currentTimeMillis()
-    val oriSequences = sc.textFile(params.dataPath).map{line =>
-      var trans = Array.empty[Array[Int]]
-      var items = Array.empty[Int]
-      line.split(" ").foreach{itemStr =>
-        val item = itemStr.toInt
-        if (item >= 0) {
-          items :+= item
-        } else if (-1 == item) {
-          trans :+= items
-          items = Array.empty
-        } else if (-2 == item) {
-          assert(items.isEmpty)
-        }
-      }
-      trans
-    }
-
-    val sequences = if (params.redistributeData) {
-      sc.parallelize(oriSequences.collect(), params.numPartitions).cache()
-    } else {
-      oriSequences.repartition(params.numPartitions).cache()
-    }
-    println(s"Data count: ${sequences.count()}")
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-    val prefixSpan = new PrefixSpan()
-        .setMinSupport(params.minSupport)
-        .setMaxPatternLength(params.maxPatternLength)
-        .setMaxLocalProjDBSize(params.maxLocalProjDBSize)
-    val model = prefixSpan.run(sequences)
-
-    val newPatterns = model.freqSequences.map{freqSequence =>
-      val freqSeqs = freqSequence.sequence.map{v =>
-         v.sorted.mkString("(", ",", ")")
-      }.mkString("[", ",", "]")
-      s"$freqSeqs,${freqSequence.freq}"
-    }.persist()
-
-    val numFreqSeqs = newPatterns.count()
-    params.setNumFreqSeqs(numFreqSeqs)
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setCostTime(costTime)
-    if (params.isRaw == "yes") {
-      val verifiedFile = new Path(params.verifiedDataPath)
-      if (fs.exists(verifiedFile)){
-        val histPatterns = sc.sequenceFile[NullWritable, String](params.verifiedDataPath).map(_._2).persist()
-        if (histPatterns.subtract(newPatterns).take(1).isEmpty &&
-          newPatterns.subtract(histPatterns).take(1).isEmpty) {
-          params.setEvaluation("match")
-        }
-      } else {
-        params.setEvaluation("no verified data")
-      }
-    } else {
-      val saveFile = new Path(params.saveDataPath)
-      if (fs.exists(saveFile)) {
-        fs.delete(saveFile, true)
-      }
-      newPatterns.repartition(15).map((NullWritable.get(), _))
-        .saveAsSequenceFile(params.saveDataPath, Some(classOf[Lz4Codec]))
-    }
-  }
-}
-
-object PrefixSpanRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (datasetName, platformName) =
-        (modelConfSplit(0), modelConfSplit(1))
-
-      val dataPath = args(1)
-
-      val datasetCpuName = s"${datasetName}_${platformName}"
-      val stream = Utils.getStream("conf/ml/ps/ps.yml")
-      val representer = new Representer
-      representer.addClassTag(classOf[PrefixSpanParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[PrefixSpanConfig]), representer, options)
-      val description = new TypeDescription(classOf[PrefixSpanParams])
-      yaml.addTypeDescription(description)
-      val config: PrefixSpanConfig = yaml.load(stream).asInstanceOf[PrefixSpanConfig]
-      val paramsMap = config.ps.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new PrefixSpanParams()
-
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setAlgorithmName("PrefixSpan")
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setMinSupport(paramsMap.get("minSupport").toString.toDouble)
-      params.setMaxPatternLength(paramsMap.get("maxPatternLength").toString.toInt)
-      params.setMaxLocalProjDBSize(paramsMap.get("maxLocalProjDBSize").toString.toInt)
-      params.setLocalTimeout(paramsMap.get("localTimeout").toString)
-      params.setFilterCandidates(paramsMap.get("filterCandidates").toString)
-      params.setProjDBstep(paramsMap.get("projDBstep").toString)
-      params.setRedistributeData(paramsMap.get("redistributeData").toString.toBoolean)
-      params.setSaveDataPath(paramsMap.get("saveDataPath").toString)
-      params.setVerifiedDataPath(paramsMap.get("verifiedDataPath").toString)
-
-      var appName = s"PrefixSpan_${datasetName}_${platformName}"
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(s"PrefixSpan_${datasetName}_${platformName}")
-
-      if (platformName == "raw") {
-        params.setIsRaw("yes")
-      } else {
-        conf.set("spark.boostkit.ml.ps.localTimeout", params.localTimeout)
-        conf.set("spark.boostkit.ml.ps.filterCandidates", params.filterCandidates)
-        conf.set("spark.boostkit.ml.ps.projDBstep", params.projDBstep)
-      }
-
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val ps = new PrefixSpanKernel()
-      ps.runJob(spark, params)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/PrefixSpan${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${params.costTime}s; evaluation: ${params.evaluation}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/RFRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/RFRunner.scala
deleted file mode 100644
index 322a348..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/RFRunner.scala
+++ /dev/null
@@ -1,404 +0,0 @@
-package com.bigdata.ml
-import java.io.{File, FileWriter}
-
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.Pipeline
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.ml.classification.RandomForestClassifier
-import org.apache.spark.ml.evaluation.{MulticlassClassificationEvaluator, RegressionEvaluator}
-import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
-import org.apache.spark.ml.param.{ParamMap, ParamPair}
-import org.apache.spark.ml.regression.RandomForestRegressor
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.RandomForest
-import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
-import org.apache.spark.mllib.tree.impurity.{Gini, Variance}
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.storage.StorageLevel
-
-class RFConfig extends Serializable {
-  @BeanProperty var rf: util.HashMap[String, util.HashMap[String, util.HashMap[String, util.HashMap[String, Object]]]] = _
-}
-
-class RFParams extends Serializable {
-  @BeanProperty var genericPt: Int = _
-  @BeanProperty var maxMemoryInMB: Int = _
-  @BeanProperty var pt: Int = _
-  @BeanProperty var numCopiesInput: Int = _
-  @BeanProperty var numTrees: Int = _
-  @BeanProperty var maxDepth: Int = _
-  @BeanProperty var maxBins: Int = _
-  @BeanProperty var numClasses: Int = _
-  @BeanProperty var useNodeIdCache: Boolean = _
-  @BeanProperty var checkpointInterval: Int = _
-  @BeanProperty var featureSubsetStrategy: String = _
-  @BeanProperty var featuresType: String = _
-  @BeanProperty var bcVariables: Boolean = _
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var algorithmType: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object RFRunner {
-  def main(args: Array[String]): Unit = {
-    try {
-      val modelConfSplit = args(0).split("_")
-      val (algorithmType, dataStructure, datasetName, apiName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2), modelConfSplit(3))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = (cpuName, isRaw) match {
-        case ("aarch64", "no") =>
-          Utils.getStream("conf/ml/rf/rf_arm.yml")
-        case ("x86_64", "no") =>
-          Utils.getStream("conf/ml/rf/rf_x86.yml")
-        case ("x86_64", "yes") =>
-          Utils.getStream("conf/ml/rf/rf_x86_raw.yml")
-      }
-      val representer = new Representer
-      representer.addClassTag(classOf[RFParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[RFConfig]), representer, options)
-      val description = new TypeDescription(classOf[RFParams])
-      yaml.addTypeDescription(description)
-      val configs: RFConfig = yaml.load(stream).asInstanceOf[RFConfig]
-      val params = new RFParams()
-
-      val rfParamMap: util.HashMap[String, Object] = configs.rf.get(algorithmType).get(dataStructure).get(datasetName)
-      params.setGenericPt(rfParamMap.getOrDefault("genericPt", "1000").asInstanceOf[Int])
-      params.setMaxMemoryInMB(rfParamMap.getOrDefault("maxMemoryInMB", "256").asInstanceOf[Int])
-      params.setPt(rfParamMap.getOrDefault("pt", "1000").asInstanceOf[Int])
-      params.setNumCopiesInput(rfParamMap.getOrDefault("numCopiesInput", "1").asInstanceOf[Int])
-      params.setNumTrees(rfParamMap.getOrDefault("numTrees", "20").asInstanceOf[Int])
-      params.setMaxDepth(rfParamMap.getOrDefault("maxDepth", "5").asInstanceOf[Int])
-      params.setMaxBins(rfParamMap.getOrDefault("maxBins", "32").asInstanceOf[Int])
-      params.setNumClasses(rfParamMap.get("numClasses").asInstanceOf[Int])
-      params.setUseNodeIdCache(rfParamMap.getOrDefault("useNodeIdCache", "false").asInstanceOf[Boolean])
-      params.setCheckpointInterval(rfParamMap.getOrDefault("checkpointInterval", "10").asInstanceOf[Int])
-      params.setFeatureSubsetStrategy(rfParamMap.getOrDefault("featureSubsetStrategy", "auto").asInstanceOf[String])
-      params.setFeaturesType(rfParamMap.getOrDefault("featuresType", "array").asInstanceOf[String])
-      params.setBcVariables(rfParamMap.getOrDefault("bcVariables", "false").asInstanceOf[Boolean])
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setAlgorithmType(algorithmType)
-      params.setApiName(apiName)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("RF")
-
-      var appName = s"RF_${algorithmType}_${datasetName}_${dataStructure}_${apiName}"
-      if (isRaw.equals("yes")){
-        appName = s"RF_RAW_${algorithmType}_${datasetName}_${dataStructure}_${apiName}"
-      }
-      params.setTestcaseType(appName)
-
-
-      val conf = new SparkConf().setAppName(appName).setMaster(master)
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-      if ("no" == isRaw.asInstanceOf[String]) {
-        conf.set("spark.boostkit.ml.rf.binnedFeaturesDataType",
-          rfParamMap.get("featuresType").asInstanceOf[String])
-        conf.set("spark.boostkit.ml.rf.numTrainingDataCopies",
-          rfParamMap.get("numCopiesInput").asInstanceOf[Int].toString)
-        conf.set("spark.boostkit.ml.rf.numPartsPerTrainingDataCopy",
-          rfParamMap.get("pt").asInstanceOf[Int].toString)
-        conf.set("spark.boostkit.ml.rf.broadcastVariables",
-          rfParamMap.get("bcVariables").asInstanceOf[Boolean].toString)
-      }
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val (res, costTime) = dataStructure match {
-        case "dataframe" => new RFKernel().rfDataframeJob(spark, params)
-        case "rdd" => new RFKernel().rfRDDJob(spark, params)
-      }
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/RF_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-    }
-  }
-}
-
-class RFKernel {
-  def rfDataframeJob(spark: SparkSession, params: RFParams): (Double, Double) = {
-    val pt = params.pt
-    val trainingDataPath = params.trainingDataPath
-    val testDataPath = params.testDataPath
-    val numTrees = params.numTrees
-    val maxDepth = params.maxDepth
-    val maxBins = params.maxBins
-    val useNodeIdCache = params.useNodeIdCache
-    val checkpointInterval = params.checkpointInterval
-    val maxMemoryInMB = params.maxMemoryInMB
-    val featureSubsetStrategy = params.featureSubsetStrategy
-    val genericPt = params.genericPt
-
-    val startTime = System.currentTimeMillis()
-
-    val reader = spark.read.format("libsvm")
-    if (params.datasetName == "mnist8m") {
-      reader.option("numFeatures",784)
-    } else if (params.datasetName == "higgs") {
-      reader.option("numFeatures",28)
-    } else if (params.datasetName == "epsilon") {
-      reader.option("numFeatures", 2000)
-    } else if (params.datasetName == "rcv") {
-      reader.option("numFeatures", 47236)
-    }
-
-    val numPtTrainData = if ("no" == params.isRaw) genericPt else pt
-    val trainingData = reader
-      .load(trainingDataPath)
-      .repartition(numPtTrainData)
-      .persist(StorageLevel.MEMORY_AND_DISK_SER)
-
-    val labelIndexer = new StringIndexer()
-      .setInputCol("label")
-      .setOutputCol("indexedLabel")
-      .fit(trainingData)
-
-    val labelConverter = new IndexToString()
-      .setInputCol("prediction")
-      .setOutputCol("predictedLabel")
-      .setLabels(labelIndexer.labels)
-
-    // for implementing different fit APIs
-    val numTreesJY = 5
-    val maxDepthJY = 3
-
-    // Train a RandomForest model
-    val rf = params.algorithmType match {
-      case "classification" =>{
-        val oldRf = new RandomForestClassifier()
-          .setLabelCol("indexedLabel")
-          .setFeaturesCol("features")
-          .setNumTrees(numTreesJY)
-          .setMaxDepth(maxDepthJY)
-          .setMaxBins(maxBins)
-          .setCacheNodeIds(useNodeIdCache)
-          .setCheckpointInterval(checkpointInterval)
-          .setMaxMemoryInMB(maxMemoryInMB)
-        if (featureSubsetStrategy.nonEmpty)
-          oldRf.setFeatureSubsetStrategy(featureSubsetStrategy)
-        oldRf
-      }
-      case "regression" =>{
-        val oldRf = new RandomForestRegressor()
-          .setLabelCol("indexedLabel")
-          .setFeaturesCol("features")
-          .setNumTrees(numTreesJY)
-          .setMaxDepth(maxDepthJY)
-          .setMaxBins(maxBins)
-          .setCacheNodeIds(useNodeIdCache)
-          .setCheckpointInterval(checkpointInterval)
-          .setMaxMemoryInMB(maxMemoryInMB)
-        if (featureSubsetStrategy.nonEmpty)
-          oldRf.setFeatureSubsetStrategy(featureSubsetStrategy)
-        oldRf
-      }
-    }
-
-    if (params.apiName == "fit"){
-      rf.setNumTrees(numTrees)
-      rf.setMaxDepth(maxDepth)
-    }
-
-    val pipeline = new Pipeline()
-      .setStages(Array(labelIndexer, rf, labelConverter))
-
-    val paramMap = ParamMap(rf.maxDepth -> maxDepth)
-      .put(rf.numTrees, numTrees)
-    val maxDepth1 = maxDepthJY
-    val maxDepth2 = maxDepth
-    val firstParamPair = ParamPair(rf.maxDepth, maxDepth1)
-    val otherParamPairs_1st = ParamPair(rf.maxDepth, maxDepth2)
-    val otherParamPairs_2nd = ParamPair(rf.numTrees, numTrees)
-    val paramMaps = new Array[ParamMap](2)
-    for (i <- 0 until paramMaps.size){
-      paramMaps(i) = ParamMap(rf.maxDepth -> maxDepth)
-        .put(rf.numTrees, numTrees)
-    }
-
-    val model = params.apiName match {
-      case "fit" => pipeline.fit(trainingData)
-      case "fit1" => pipeline.fit(trainingData, paramMap)
-      case "fit2" =>
-        val models = pipeline.fit(trainingData, paramMaps)
-        models(0)
-      case "fit3" => pipeline.fit(trainingData, firstParamPair, otherParamPairs_1st, otherParamPairs_2nd)
-
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val testData = reader
-      .load(testDataPath)
-      .repartition(genericPt)
-      .persist(StorageLevel.MEMORY_AND_DISK_SER)
-
-    // Make predictions.
-    val predictions = model.transform(testData)
-
-    // Select (prediction, true label) and compute test error.
-    val evaluator = params.algorithmType match {
-      case "classification" =>
-        new MulticlassClassificationEvaluator()
-          .setLabelCol ("indexedLabel")
-          .setPredictionCol ("prediction")
-          .setMetricName ("accuracy")
-
-      case "regression" =>
-        new RegressionEvaluator()
-          .setLabelCol ("indexedLabel")
-          .setPredictionCol ("prediction")
-          .setMetricName ("rmse")
-    }
-    val res = evaluator.evaluate(predictions)
-    (res, costTime)
-  }
-
-  def rfRDDJob(spark: SparkSession, params: RFParams): (Double, Double) = {
-
-    val pt = params.pt
-    val trainingDataPath = params.trainingDataPath
-    val testDataPath = params.testDataPath
-    val numTrees = params.numTrees
-    var maxDepth = params.maxDepth
-    val maxBins = params.maxBins
-    val useNodeIdCache = params.useNodeIdCache
-    val checkpointInterval = params.checkpointInterval
-    val maxMemoryInMB = params.maxMemoryInMB
-    val featureSubsetStrategy = params.featureSubsetStrategy
-    val genericPt = params.genericPt
-    var numClasses = params.numClasses
-
-    val sc = spark.sparkContext
-    val startTime = System.currentTimeMillis()
-
-    val numFeatures = params.datasetName match {
-      case "mnist8m" => 784
-      case "higgs" => 28
-      case "epsilon" =>2000
-      case "rcv" => 47236
-    }
-
-    val numPtTrainData = if ("no" == params.isRaw) genericPt else pt
-    val trainingData = MLUtils.loadLibSVMFile(sc, trainingDataPath, numFeatures)
-      .repartition(numPtTrainData)
-      .persist(StorageLevel.MEMORY_AND_DISK_SER)
-    val trainingLabelPositive = trainingData.map(i=> if (i.label < 0) {
-      LabeledPoint(0.0, i.features)
-    } else {
-      LabeledPoint (i.label, i.features)
-    })
-
-
-    val model = params.algorithmType match {
-      case "classification" =>
-        val seed = "org.apache.spark.ml.classification.RandomForestClassifier".hashCode
-        params.apiName match {
-          case "train" =>
-            val strategy = new Strategy (Algo.Classification, Gini, maxDepth = maxDepth,
-              numClasses = numClasses, maxBins = maxBins, useNodeIdCache = useNodeIdCache,
-              checkpointInterval = checkpointInterval, maxMemoryInMB = maxMemoryInMB)
-            RandomForest.trainClassifier(trainingLabelPositive, strategy, numTrees, featureSubsetStrategy, seed)
-          case "train1" =>
-            val categoricalFeaturesInfo = Map[Int, Int]()
-            RandomForest.trainClassifier(trainingLabelPositive, numClasses, categoricalFeaturesInfo,
-              numTrees, featureSubsetStrategy, "gini", maxDepth, maxBins, seed)
-          case "train2" =>
-            val categoricalFeaturesInfo = new java.util.HashMap[java.lang.Integer, java.lang.Integer]()
-            RandomForest.trainClassifier(trainingLabelPositive.toJavaRDD, numClasses, categoricalFeaturesInfo,
-              numTrees, featureSubsetStrategy, "gini", maxDepth, maxBins, seed)
-        }
-      case "regression" =>
-        val seed = "org.apache.spark.ml.regression.RandomForestRegressor".hashCode
-        params.apiName match {
-          case "train" =>
-            val strategy = new Strategy (Algo.Regression, Variance, maxDepth = maxDepth,
-              numClasses = 0, maxBins = maxBins, useNodeIdCache = useNodeIdCache,
-              checkpointInterval = checkpointInterval, maxMemoryInMB = maxMemoryInMB)
-            RandomForest.trainRegressor(trainingLabelPositive, strategy, numTrees, featureSubsetStrategy, seed)
-          case "train1" =>
-            val categoricalFeaturesInfo = Map[Int, Int]()
-            RandomForest.trainRegressor(trainingLabelPositive, categoricalFeaturesInfo,
-              numTrees, featureSubsetStrategy, "variance", maxDepth, maxBins, seed)
-          case "train2" =>
-            val categoricalFeaturesInfo = new java.util.HashMap[java.lang.Integer, java.lang.Integer]()
-            RandomForest.trainRegressor(trainingLabelPositive.toJavaRDD, categoricalFeaturesInfo,
-              numTrees, featureSubsetStrategy, "variance", maxDepth, maxBins, seed)
-        }
-
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val testData = MLUtils.loadLibSVMFile(sc, testDataPath)
-      .repartition(genericPt)
-      .persist(StorageLevel.MEMORY_AND_DISK_SER)
-    val testLabelPositive = testData.map(i=> if (i.label < 0) {
-      LabeledPoint(0.0, i.features)
-    } else {
-      LabeledPoint (i.label, i.features)
-    })
-
-    val labeleAndPreds = testLabelPositive.map{ point =>
-      val prediction = model.predict(point.features)
-      (point.label, prediction)
-    }
-    val res = params.algorithmType match {
-      case "classification" => 1.0 - labeleAndPreds.filter(r => r._1 == r._2).count.toDouble / testLabelPositive.count()
-      case "regression" => math.sqrt(labeleAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.mean())
-    }
-    (res, costTime)
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/SPCARunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/SPCARunner.scala
deleted file mode 100644
index b75de10..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/SPCARunner.scala
+++ /dev/null
@@ -1,192 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.feature.SPCA
-import org.apache.spark.ml.feature.PCA
-import org.apache.spark.ml.linalg.Vectors
-import org.apache.spark.mllib.linalg.{DenseMatrix, DenseVector}
-import org.apache.spark.storage.StorageLevel
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class SPCAConfig extends Serializable {
-
-  @BeanProperty var spca: util.HashMap[String, util.HashMap[String, Object]] = _
-}
-
-class SPCAParams extends Serializable {
-
-  @BeanProperty var pt: Int = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var sep: String = _
-  @BeanProperty var numCols: Int = _
-  @BeanProperty var pcPath: String = _
-  @BeanProperty var sigmaPath: String = _
-  @BeanProperty var saveRes: Boolean = _
-
-  @BeanProperty var inputDataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object SPCARunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val datasetName = args(0)
-
-      val inputDataPath = args(1)
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = isRaw match {
-        case "no" =>
-          Utils.getStream("conf/ml/spca/spca.yml")
-        case "yes" =>
-          Utils.getStream("conf/ml/spca/spca_raw.yml")
-      }
-      val representer = new Representer
-      representer.addClassTag(classOf[SPCAParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[SPCAConfig]), representer, options)
-      val description = new TypeDescription(classOf[SPCAParams])
-      yaml.addTypeDescription(description)
-      val configs: SPCAConfig = yaml.load(stream).asInstanceOf[SPCAConfig]
-      val params = new SPCAParams()
-
-      val spcaParamMap: util.HashMap[String, Object] = configs.spca.get(datasetName)
-      params.setPt(spcaParamMap.getOrDefault("pt", "250").asInstanceOf[Int])
-      params.setK(spcaParamMap.getOrDefault("k", "10").asInstanceOf[Int])
-      params.setSep(spcaParamMap.getOrDefault("sep", " ").asInstanceOf[String])
-      params.setNumCols(spcaParamMap.getOrDefault("numCols", "0").asInstanceOf[Int])
-      params.setPcPath(spcaParamMap.getOrDefault("pcPath", null.asInstanceOf[String]).asInstanceOf[String])
-      params.setSigmaPath(spcaParamMap.getOrDefault("sigmaPath", null.asInstanceOf[String]).asInstanceOf[String])
-      params.setSaveRes(spcaParamMap.getOrDefault("saveRes", "false").asInstanceOf[Boolean])
-
-      params.setInputDataPath(inputDataPath)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("SPCA")
-
-      var appName = s"SPCA_${datasetName}"
-      if (isRaw == "yes") {
-        appName = s"SPCA_${datasetName}_raw"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(appName).setMaster(master)
-
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val costTime = new SPCAKernel().runJob(spark, params)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/SPCA_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
-class SPCAKernel {
-
-  def runJob(spark: SparkSession, params: SPCAParams): Double = {
-
-    import spark.implicits._
-    val startTime = System.currentTimeMillis()
-    val trainingData = if (params.isRaw == "yes"){
-      val numColsBC = spark.sparkContext.broadcast(params.numCols)
-      val sepBC = spark.sparkContext.broadcast(params.sep)
-      val data = spark.createDataFrame(spark.sparkContext.textFile(params.inputDataPath, params.pt)
-        .map(line => {
-          val entry = line.split(sepBC.value)
-          (entry(0).toInt, (entry(1).toInt, entry(2).toDouble))
-        }).groupByKey()
-        .map { case (_, vectorEntries) => Vectors.sparse(numColsBC.value, vectorEntries.toSeq) }
-        .repartition(params.pt)
-        .map(Tuple1.apply))
-        .toDF("matrix").persist(StorageLevel.MEMORY_ONLY)
-      data
-    } else {
-      val data = spark.createDataFrame(spark.sparkContext.textFile(params.inputDataPath, params.pt)
-        .map(line => {
-          val entry = line.split(params.sep)
-          (entry(0).toInt, (entry(1).toInt, entry(2).toDouble))
-        }).groupByKey()
-        .map{case (_, vectorEntries) => Vectors.sparse(params.numCols, vectorEntries.toSeq)}
-        .repartition(params.pt)
-        .map(Tuple1.apply))
-        .toDF("matrix")
-        .persist(StorageLevel.MEMORY_ONLY)
-      data
-    }
-
-
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    val model = if (params.isRaw == "no"){
-      new SPCA()
-        .setK(params.k)
-        .setInputCol("matrix")
-        .fit(trainingData)
-    } else {
-      new PCA()
-        .setK(params.k)
-        .setInputCol("matrix")
-        .fit(trainingData)
-    }
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(costTime)
-
-    // save result
-    if (params.saveRes && params.pcPath != null && params.sigmaPath != null) {
-      Utils.writeMatrix(new DenseMatrix(model.pc.numRows, model.pc.numCols,
-        model.pc.values, model.pc.isTransposed), params.pcPath)
-      Utils.writeVector(new DenseVector(model.explainedVariance.toArray), params.sigmaPath)
-    }
-
-    costTime
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/SVDRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/SVDRunner.scala
deleted file mode 100644
index 17cdf5d..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/SVDRunner.scala
+++ /dev/null
@@ -1,179 +0,0 @@
-package com.bigdata.ml
-
-import java.io.{File, FileWriter, PrintWriter}
-import java.util
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.mllib.linalg.{DenseMatrix, DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.linalg.distributed.RowMatrix
-import org.apache.spark.storage.StorageLevel
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, norm => brzNorm}
-
-import scala.beans.BeanProperty
-
-class SVDConfig extends Serializable {
-
-  @BeanProperty var svd: util.HashMap[String, util.HashMap[String, Object]] = _
-}
-
-class SVDParams extends Serializable {
-
-  @BeanProperty var pt: Int = _
-  @BeanProperty var k: Int = _
-  @BeanProperty var sep: String = _
-  @BeanProperty var dataFormat: String = _
-  @BeanProperty var numCols: Int = _
-  @BeanProperty var numRows: Int = _
-
-  @BeanProperty var inputDataPath: String = _
-  @BeanProperty var outputDataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = _
-  @BeanProperty var cpuName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-object SVDRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val datasetName = args(0)
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (inputDataPath, outputDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master, deployMode, numExec, execCores, execMem) =
-        (sparkConfSplit(0), sparkConfSplit(1), sparkConfSplit(2), sparkConfSplit(3), sparkConfSplit(4))
-
-      val stream = isRaw match {
-        case "no" =>
-          Utils.getStream("conf/ml/svd/svd.yml")
-        case "yes" =>
-          Utils.getStream("conf/ml/svd/svd_raw.yml")
-      }
-      val representer = new Representer
-      representer.addClassTag(classOf[SVDParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[SVDConfig]), representer, options)
-      val description = new TypeDescription(classOf[SVDParams])
-      yaml.addTypeDescription(description)
-      val configs: SVDConfig = yaml.load(stream).asInstanceOf[SVDConfig]
-      val params = new SVDParams()
-
-      val svdParamMap: util.HashMap[String, Object] = configs.svd.get(datasetName)
-      params.setPt(svdParamMap.getOrDefault("pt", "250").asInstanceOf[Int])
-      params.setK(svdParamMap.getOrDefault("k", "10").asInstanceOf[Int])
-      params.setSep(svdParamMap.getOrDefault("sep", ",").asInstanceOf[String])
-      params.setDataFormat(svdParamMap.getOrDefault("dataFormat", "dense").asInstanceOf[String])
-      params.setNumCols(svdParamMap.getOrDefault("numCols", "0").asInstanceOf[Int])
-      params.setNumRows(svdParamMap.getOrDefault("numRows", "0").asInstanceOf[Int])
-
-      params.setInputDataPath(inputDataPath)
-      params.setOutputDataPath(outputDataPath)
-      params.setDatasetName(datasetName)
-      params.setCpuName(cpuName)
-      params.setIsRaw(isRaw)
-      params.setAlgorithmName("SVD")
-
-      var appName = s"SVD_${datasetName}"
-      if (isRaw == "yes") {
-        appName = s"SVD_${datasetName}_raw"
-      }
-      params.setTestcaseType(appName)
-
-      val conf = new SparkConf()
-        .setAppName(appName).setMaster(master)
-
-      val commonParas = Array (
-        ("spark.submit.deployMode", deployMode),
-        ("spark.executor.instances", numExec),
-        ("spark.executor.cores", execCores),
-        ("spark.executor.memory", execMem)
-      )
-      conf.setAll(commonParas)
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val costTime = new SVDKernel().runJob(spark, params)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/SVD_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
-class SVDKernel {
-
-  def runJob(spark: SparkSession, params: SVDParams): Double = {
-
-    import spark.implicits._
-    val startTime = System.currentTimeMillis()
-    val numColsBC = spark.sparkContext.broadcast(params.numCols)
-    val sepBC = spark.sparkContext.broadcast(params.sep)
-    val trainingData = if (params.dataFormat == "coo") {
-      spark.sparkContext.textFile(params.inputDataPath, params.pt)
-        .map(line => {
-          val entry = line.split(sepBC.value)
-          (entry(0).toInt, (entry(1).toInt, entry(2).toDouble))
-        }).groupByKey()
-        .map{case (_, vectorEntries) => {Vectors.sparse(numColsBC.value, vectorEntries.toSeq)}}
-        .persist(StorageLevel.MEMORY_ONLY)
-    } else {
-      spark.sparkContext.textFile(params.inputDataPath)
-        .map(row => Vectors.dense(row.split(sepBC.value).map(_.toDouble)))
-        .repartition(params.pt)
-        .persist(StorageLevel.MEMORY_ONLY)
-    }
-
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    val matrix = new RowMatrix(trainingData, params.numRows, params.numCols)
-
-    val svd = matrix.computeSVD(params.k, computeU  = true)
-
-    //触发U的计算
-    svd.U.rows.count()
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(costTime)
-
-    // save V and s
-    if (params.outputDataPath != null){
-      spark.sparkContext.parallelize(svd.s.toArray, 1).saveAsTextFile(s"${params.outputDataPath}_${params.cpuName}_${params.isRaw}/s")
-      spark.sparkContext.parallelize(Utils.toRowMajorArray(svd.V.asInstanceOf[DenseMatrix]).map(_.mkString(","))).saveAsTextFile(s"${params.outputDataPath}_${params.cpuName}_${params.isRaw}/V")
-    }
-
-    println(s"Results have been saved at ${params.outputDataPath}")
-
-    costTime
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/SVMRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/SVMRunner.scala
deleted file mode 100644
index d9de249..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/SVMRunner.scala
+++ /dev/null
@@ -1,151 +0,0 @@
-package com.bigdata.ml
-import java.io.{File, FileWriter}
-import java.util.HashMap
-
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.param.{ParamMap, ParamPair}
-import org.apache.spark.ml.classification.LinearSVC
-import org.apache.spark.ml.linalg.Vectors
-
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import com.bigdata.utils.Utils
-import scala.beans.BeanProperty
-
-class SVMConfig extends Serializable {
-  @BeanProperty var svm: HashMap[String, Object] = _
-}
-
-class SVMParams extends Serializable {
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var regParam: Double = _
-  @BeanProperty var maxIter: Int = _
-  @BeanProperty var trainingDataPath: String = _
-  @BeanProperty var testDataPath: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-
-}
-
-object SVMRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (datasetName, apiName, cpuName) =
-        (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2))
-
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainingDataPath, testDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val datasetCpuName = s"${datasetName}_${cpuName}"
-      val stream = Utils.getStream("conf/ml/svm/svm.yml")
-      val representer = new Representer
-      representer.addClassTag(classOf[SVMParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[SVMConfig]), representer, options)
-      val description = new TypeDescription(classOf[SVMParams])
-      yaml.addTypeDescription(description)
-      val config: SVMConfig = yaml.load(stream).asInstanceOf[SVMConfig]
-      val paramsMap = config.svm.get(datasetCpuName).asInstanceOf[HashMap[String, Object]]
-      val params = new SVMParams()
-      params.setApiName(apiName)
-      params.setTrainingDataPath(trainingDataPath)
-      params.setTestDataPath(testDataPath)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setDatasetName(datasetName)
-      params.setAlgorithmName("SVM")
-      params.setTestcaseType(s"SVM_${datasetName}_${apiName}")
-      if (cpuName == "raw") {
-        params.setIsRaw("yes")
-        params.setTestcaseType(s"SVM_raw_${datasetName}_${apiName}")
-      }
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setMaxIter(paramsMap.get("maxIter").toString.toInt)
-      params.setRegParam(paramsMap.get("regParam").toString.toDouble)
-
-      val startTime = System.currentTimeMillis()
-      val conf = new SparkConf()
-        .setAppName(s"SVM_${datasetName}_${apiName}")
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-      val sc = spark.sparkContext
-      import spark.implicits._
-
-//      val startTime = System.currentTimeMillis()
-      val training = sc.textFile(trainingDataPath).repartition(params.numPartitions)
-      val test = sc.textFile(testDataPath).repartition(params.numPartitions)
-      val parsedData0 = training.map { line =>
-        val parts = line.split(',')
-        (parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
-      }
-        .persist()
-      val parsedData = parsedData0.toDF("label", "features")
-
-      val svm = new LinearSVC()
-        .setMaxIter(params.maxIter)
-        .setRegParam(params.regParam)
-
-      val paramMap = ParamMap(svm.maxIter -> params.maxIter)
-        .put(svm.regParam, params.regParam)
-
-      val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
-      for (i <- 0 to paramMaps.size -1) {
-        paramMaps(i) = ParamMap(svm.maxIter -> params.maxIter)
-          .put(svm.regParam, params.regParam)
-      }
-
-      val maxIterParamPair = ParamPair(svm.maxIter, params.maxIter)
-      val regParamPair = ParamPair(svm.regParam, params.regParam)
-      val model = params.apiName match {
-        case "fit" => svm.fit(parsedData)
-        case "fit1" => svm.fit(parsedData, paramMap)
-        case "fit2" =>
-          val models = svm.fit(parsedData, paramMaps)
-          models(0)
-        case "fit3" => svm.fit(parsedData, maxIterParamPair, regParamPair)
-      }
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-      val parsedTest = test.map { line =>
-        val parts = line.split(',')
-        (parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
-      }
-        .toDF("label","features")
-        .cache()
-      val res = model.transform(parsedTest)
-      val evaluation = res.filter($"label"===$"prediction").count().toDouble/res.count
-
-      params.setEvaluation(evaluation)
-      params.setCostTime(costTime)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/SVM_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${evaluation}")
-      sc.stop
-    }catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
\ No newline at end of file
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/SimRankRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/SimRankRunner.scala
deleted file mode 100644
index 4c01d38..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/SimRankRunner.scala
+++ /dev/null
@@ -1,151 +0,0 @@
-//scalastyle:off
-package com.bigdata.ml
-
-import java.io.{File, FileWriter}
-import java.util.HashMap
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.ml.recommendation.ALS.Rating
-import org.apache.spark.ml.recommendation.{SimRank, SimRankOpenSource}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import scala.beans.BeanProperty
-
-class SimRankConfig extends Serializable {
-
-  @BeanProperty var simrank: HashMap[String, Object] = _
-}
-
-class SimRankParams extends Serializable {
-
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var maxIter: Int = _
-  @BeanProperty var damp: Double = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var savePath: String = _
-  @BeanProperty var saveResult: Boolean = false
-  @BeanProperty var algorithmType: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var caseName: String = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var loadDataTime: Double = _
-  @BeanProperty var algorithmName: String = _
-}
-
-class SimRankKernel {
-
-  def runJob(spark: SparkSession, params: SimRankParams): Double = {
-    val startTime = System.currentTimeMillis()
-
-    import spark.implicits._
-    val userCol = "user"
-    val itemCol = "item"
-    val df = spark.sparkContext.objectFile[Rating[Int]](params.getDataPath).repartition(params.getNumPartitions)
-      .map(row => {
-        ("user-" + row.user.toString, "item-" + row.item.toString)
-      }).toDF(userCol, itemCol)
-
-    val loadDataTime = (System.currentTimeMillis() - startTime) / 1000.0
-    params.setLoadDataTime(loadDataTime)
-
-    if (params.getAlgorithmType.equals("boostkit")) {
-      val simrank = new SimRank()
-        .setDamp(params.getDamp)
-        .setNumIter(params.getMaxIter)
-        .setUserCol(userCol)
-        .setItemCol(itemCol)
-
-      val simrankRes = simrank.computeSimilarity(df)
-      simrankRes.itemSimilarity.foreach(_ => {})
-      simrankRes.userSimilarity.foreach(_ => {})
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      if(params.getSaveResult) {
-        simrankRes.userSimilarity.write
-          .mode("overwrite").option("header", value = true).csv(params.savePath)
-        simrankRes.itemSimilarity.write
-          .mode("overwrite").option("header", value = true).csv(params.savePath)
-      }
-
-    } else {
-      val simrankRes = new SimRankOpenSource().execute(df, (userCol, itemCol), params.getDamp, params.getMaxIter)
-      simrankRes._1.foreach(_ => {})
-      simrankRes._2.foreach(_ => {})
-      val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-      params.setCostTime(costTime)
-      if(params.getSaveResult) {
-        simrankRes._1.write
-          .mode("overwrite").option("header", value = true).csv(params.savePath)
-        simrankRes._2.write
-          .mode("overwrite").option("header", value = true).csv(params.savePath)
-      }
-    }
-    params.getCostTime
-  }
-}
-
-object SimRankRunner {
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val caseName = args(0)
-      val caseArray = caseName.split("-")
-      val (datasetName, algorithmType) = (caseArray(0), caseArray(1))
-
-      val dataPath = args(1)
-
-      val stream = Utils.getStream("conf/ml/simrank/simrank.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[SimRankParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[SimRankConfig]), representer, options)
-      val description = new TypeDescription(classOf[SimRankParams])
-      yaml.addTypeDescription(description)
-      val config: SimRankConfig = yaml.load(stream).asInstanceOf[SimRankConfig]
-      val paramsMap = config.simrank.get(caseName).asInstanceOf[HashMap[String, Object]]
-
-      val params = new SimRankParams()
-
-      params.setAlgorithmType(algorithmType)
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setCaseName(caseName)
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-      params.setMaxIter(paramsMap.get("maxIter").toString.toInt)
-      params.setDamp(paramsMap.get("damp").toString.toDouble)
-      params.setSaveResult(paramsMap.get("saveResult").toString.toBoolean)
-      params.setSavePath(paramsMap.get("savePath").toString)
-      params.setAlgorithmName("simrank")
-
-      val conf = new SparkConf()
-        .setAppName(s"SimRank_${caseName}")
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val costTime = new SimRankKernel().runJob(spark, params)
-
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/SimRank_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/SpearManRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/SpearManRunner.scala
deleted file mode 100644
index 057aa70..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/SpearManRunner.scala
+++ /dev/null
@@ -1,215 +0,0 @@
-package com.bigdata.ml
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.ml
-import org.apache.spark.ml.linalg.{Matrix, Vectors, Vector}
-import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
-import org.apache.spark.mllib
-import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
-import org.apache.spark.sql.{Row, SparkSession}
-import org.apache.spark.sql.types.{StructField, StructType}
-import org.apache.spark.storage.StorageLevel
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import java.io.{File, FileWriter}
-import java.util
-import scala.beans.BeanProperty
-
-class SpearManConfig extends Serializable {
-
-  @BeanProperty var spearman: util.HashMap[String, Object] = _
-}
-
-class SpearManParams extends Serializable {
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var apiName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var resultSum: Double = _
-  @BeanProperty var resultAvg: Double = _
-  @BeanProperty var saveDataPath: String = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-class SpearManKernel {
-  def runDataframeJob(spark: SparkSession,params: SpearManParams): (Double, Double, Double) = {
-    val sc = spark.sparkContext
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-    val startTime = System.currentTimeMillis()
-
-    val data = spark.createDataFrame(
-      spark
-        .sparkContext
-        .textFile(params.dataPath)
-        .map(x=>Row(Vectors.dense(x.split(",").map(_.toDouble))))
-        .repartition(params.numPartitions),
-       StructType(List(StructField("matrix", VectorType)))
-    ).persist(StorageLevel.MEMORY_ONLY)
-
-    val mat_df = ml.stat.Correlation.corr(data,"matrix", method = "spearman")
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val result = mat_df.collect()(0).getAs[Matrix](0).toArray
-    val result_avg = result.sum/result.length
-
-    val saveFile = new Path(s"${params.saveDataPath}/${params.apiName}")
-    val saveFileRaw = new Path(s"${params.saveDataPath}/raw/${params.apiName}")
-    if (fs.exists(saveFile) || fs.exists(saveFileRaw)) {
-      fs.delete(saveFile, true)
-      fs.delete(saveFileRaw, true)
-    }
-
-    val res_new = mat_df.collect()(0).getAs[Matrix](0)
-    val res_new2 = res_new.toArray
-    val res_new3 = new Array[String](res_new.numCols)
-
-    for(i <- 0 until res_new.numRows) {
-      val row = res_new2.slice(i * res_new.numRows, (i + 1) * res_new.numRows).mkString(";")
-      res_new3(i) = i + " " + row
-    }
-    if(params.isRaw == "yes"){
-      sc.parallelize(res_new3).repartition(100).saveAsTextFile(s"${params.saveDataPath}/raw/${params.apiName}")
-    }
-    else {
-      sc.parallelize(res_new3).repartition(100).saveAsTextFile(s"${params.saveDataPath}/${params.apiName}")
-    }
-
-    (result.sum,result_avg, costTime)
-  }
-
-  def runRddJob(spark: SparkSession,params: SpearManParams): (Double, Double, Double) = {
-    val sc = spark.sparkContext
-    val fs = FileSystem.get(sc.hadoopConfiguration)
-
-    val startTime = System.currentTimeMillis()
-
-    val data = spark.createDataFrame(
-      spark
-        .sparkContext
-        .textFile(params.dataPath)
-        .map(x=>Row(Vectors.dense(x.split(",").map(_.toDouble))))
-        .repartition(params.numPartitions),
-      StructType(List(StructField("matrix", VectorType)))
-    ).persist(StorageLevel.MEMORY_ONLY)
-
-    val rdd = data.select("matrix").rdd.map{
-      case Row(v: Vector) => OldVectors.fromML(v)
-    }
-    val mat_rdd = mllib.stat.Statistics.corr(rdd,method = "spearman")
-
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-
-    val result = mat_rdd.toArray
-    val result_avg = result.sum/result.length
-
-    val saveFile = new Path(s"${params.saveDataPath}/${params.apiName}")
-    val saveFileRaw = new Path(s"${params.saveDataPath}/raw/${params.apiName}")
-    if (fs.exists(saveFile) || fs.exists(saveFileRaw)) {
-      fs.delete(saveFile, true)
-      fs.delete(saveFileRaw, true)
-    }
-    val res = new Array[String](mat_rdd.numRows)
-    for(i <- 0 until mat_rdd.numRows) {
-      val row = result.slice(i * mat_rdd.numRows, (i + 1) * mat_rdd.numCols).mkString(";")
-      res(i) = i.toString + " " + row
-    }
-    if(params.isRaw == "yes"){
-      sc.parallelize(res).repartition(100).saveAsTextFile(s"${params.saveDataPath}/raw/${params.apiName}")
-    }
-    else {
-      sc.parallelize(res).repartition(100).saveAsTextFile(s"${params.saveDataPath}/${params.apiName}")
-    }
-
-    (result.sum,result_avg,costTime)
-  }
-}
-
-
-
-object SpearManRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (dataStructure, datasetName, platformName) = (modelConfSplit(0), modelConfSplit(1), modelConfSplit(2))
-
-      val dataAllPath = args(1)
-      val dataPathSplit = dataAllPath.split(",")
-      val (dataPath, saveDataPath) = (dataPathSplit(0), dataPathSplit(1))
-
-      val datasetCpuName = s"${datasetName}_${platformName}"
-      val stream = Utils.getStream("conf/ml/spearman/spearman.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[SpearManParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[SpearManConfig]), representer, options)
-      val description = new TypeDescription(classOf[SpearManParams])
-      yaml.addTypeDescription(description)
-      val config: SpearManConfig = yaml.load(stream).asInstanceOf[SpearManConfig]
-      val paramsMap = config.spearman.get(datasetCpuName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new SpearManParams()
-
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setApiName(dataStructure)
-      params.setSaveDataPath(saveDataPath)
-      params.setAlgorithmName("SpearMan")
-      params.setTestcaseType(s"SpearMan_${dataStructure}_${datasetName}")
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-
-      val conf = new SparkConf().setAppName(s"SpearMan_${datasetName}_${dataStructure}_${platformName}")
-
-      if (platformName == "raw") {
-        params.setIsRaw("yes")
-      }
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-
-      val tuple = dataStructure match {
-        case "dataframe" =>
-          new SpearManKernel().runDataframeJob(spark, params)
-        case "rdd" =>
-          new SpearManKernel().runRddJob(spark, params)
-      }
-
-      params.setResultSum(tuple._1)
-      params.setResultAvg(tuple._2)
-      params.setCostTime(tuple._3)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/SpearMan${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${params.getCostTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
-
-
-
diff --git a/tools/kal-test/src/main/scala/com/bigdata/ml/XGBTRunner.scala b/tools/kal-test/src/main/scala/com/bigdata/ml/XGBTRunner.scala
deleted file mode 100644
index ce77146..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/ml/XGBTRunner.scala
+++ /dev/null
@@ -1,206 +0,0 @@
-package com.bigdata.ml
-
-import com.bigdata.utils.Utils
-import ml.dmlc.xgboost4j.scala.spark.{XGBoostClassifier, XGBoostRegressor}
-import org.apache.spark.SparkConf
-import org.apache.spark.ml.evaluation.{MulticlassClassificationEvaluator, RegressionEvaluator}
-import org.apache.spark.sql.{Dataset, Row, SparkSession}
-import org.apache.spark.storage.StorageLevel
-
-import org.yaml.snakeyaml.{DumperOptions,TypeDescription,Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import java.io.{File, FileWriter, PrintWriter, StringWriter}
-import scala.beans.BeanProperty
-import java.util
-
-class XGBTConfig extends Serializable{
-  @BeanProperty var xgbt: util.HashMap[String,util.HashMap[String,util.HashMap[String,util.HashMap[String,Object]]]]=_
-}
-class XGBTParams extends Serializable{
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-  @BeanProperty var mode: String = _
-  @BeanProperty var eta: Double = _
-  @BeanProperty var gamma: Double = _
-  @BeanProperty var min_child_weight: Int = _
-  @BeanProperty var max_depth: Int = _
-  @BeanProperty var verbosity: Int = _
-  @BeanProperty var allow_non_zero_for_missing: Boolean = _
-  @BeanProperty var vectorType: String = _
-  @BeanProperty var enable_bbgen: Boolean = _
-  @BeanProperty var rabit_enable_tcp_no_delay: Boolean = _
-  @BeanProperty var tree_method: String = _
-  @BeanProperty var grow_policy: String = _
-  @BeanProperty var tr_fname: String = _
-  @BeanProperty var ts_fname: String = _
-  @BeanProperty var objective: String = _
-  @BeanProperty var num_round: Int = _
-  @BeanProperty var num_workers: Int = _
-  @BeanProperty var num_class: Int = _
-  @BeanProperty var nthread: Int = _
-  @BeanProperty var evaluation: Double = _
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var isRaw: String = _
-
-}
-object XGBTRunner {
-  def main(args: Array[String]): Unit = {
-    try{
-      val modelConf = args(0)
-      val modelConfSplit = modelConf.split("_")
-      val (algType,datasetName)=(modelConfSplit(0),modelConfSplit(1))
-      val dataPath = args(1)
-      val dataPathSplit = dataPath.split(",")
-      val (trainDataPath,testDataPath)=(dataPathSplit(0),dataPathSplit(1))
-      val cpuName = args(2)
-      val isRaw = args(3)
-      val sparkConfSplit = args(4).split("_")
-      val (master,deployMode)=(sparkConfSplit(0),sparkConfSplit(1))
-
-      val OptOrPri = isRaw match{
-        case "yes" => "primary"
-        case "no" => "optimized"
-      }
-      val stream = cpuName match{
-        case "x86_64" => Utils.getStream("conf/ml/xgbt/xgbt_x86.yml")
-        case "aarch64" => Utils.getStream("conf/ml/xgbt/xgbt_arm.yml")
-      }
-
-      val representer =new Representer
-      representer.addClassTag(classOf[XGBTParams],Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[XGBTConfig]),representer,options)
-      val description = new TypeDescription(classOf[XGBTParams])
-      yaml.addTypeDescription(description)
-      val configs: XGBTConfig = yaml.load(stream).asInstanceOf[XGBTConfig]
-      val params = new XGBTParams()
-      val xgbtParamMap: util.HashMap[String,Object] = cpuName match{
-        case "x86_64" => configs.xgbt.get(OptOrPri).get(algType).get(datasetName)
-        case "aarch64" => configs.xgbt.get(OptOrPri).get(algType).get(datasetName)
-      }
-
-      params.setEta(xgbtParamMap.get("eta").asInstanceOf[Double])
-      params.setGamma(xgbtParamMap.get("gamma").asInstanceOf[Int])
-      params.setMin_child_weight(xgbtParamMap.get("min_child_weight").asInstanceOf[Int])
-      params.setMax_depth(xgbtParamMap.get("max_depth").asInstanceOf[Int])
-      params.setAllow_non_zero_for_missing(xgbtParamMap.get("allow_non_zero_for_missing").asInstanceOf[Boolean])
-      params.setVectorType(xgbtParamMap.get("vectorType").asInstanceOf[String])
-      params.setObjective(xgbtParamMap.get("objective").asInstanceOf[String])
-      params.setNum_round(xgbtParamMap.get("num_round").asInstanceOf[Int])
-      params.setNum_workers(xgbtParamMap.get("num_workers").asInstanceOf[Int])
-      params.setNthread(xgbtParamMap.get("nthread").asInstanceOf[Int])
-      params.setTree_method(xgbtParamMap.get("tree_method").asInstanceOf[String])
-      params.setGrow_policy(xgbtParamMap.get("grow_policy").asInstanceOf[String])
-      if(modelConf=="classification_mnist8m"){
-        params.setNum_class(xgbtParamMap.get("num_class").asInstanceOf[Int])
-      }
-      if(isRaw=="no"){
-        params.setEnable_bbgen(xgbtParamMap.get("enable_bbgen").asInstanceOf[Boolean])
-        params.setRabit_enable_tcp_no_delay(xgbtParamMap.get("rabit_enable_tcp_no_delay").asInstanceOf[Boolean])
-      }
-      params.setTr_fname(trainDataPath)
-      params.setTs_fname(testDataPath)
-
-      params.setIsRaw(isRaw)
-
-      val appName = isRaw match {
-        case "yes" => s"XGBT_${datasetName}_${algType}_openSource"
-        case "no" => s"XGBT_${datasetName}_${algType}_${cpuName}"
-      }
-
-      val conf = new SparkConf().setAppName(appName).setMaster(master)
-      val commonParas =Array(
-        ("spark.submit.deployMode",deployMode)
-      )
-      conf.setAll(commonParas)
-      val spark= SparkSession.builder().config(conf).getOrCreate()
-      var paramsAnyMap: scala.Predef.Map[String,Any]=Map[String,Any]()
-      paramsAnyMap += ("eta" -> xgbtParamMap.get("eta"))
-      paramsAnyMap += ("gamma" -> xgbtParamMap.get("gamma"))
-      paramsAnyMap += ("min_child_weight" -> xgbtParamMap.get("min_child_weight"))
-      paramsAnyMap += ("max_depth" -> xgbtParamMap.get("max_depth"))
-      paramsAnyMap += ("allow_non_zero_for_missing" -> xgbtParamMap.get("allow_non_zero_for_missing"))
-      paramsAnyMap += ("vectorType" -> xgbtParamMap.get("vectorType"))
-      paramsAnyMap += ("objective" -> xgbtParamMap.get("objective"))
-      paramsAnyMap += ("num_round" -> xgbtParamMap.get("num_round"))
-      paramsAnyMap += ("num_workers" -> xgbtParamMap.get("num_workers"))
-      paramsAnyMap += ("nthread" -> xgbtParamMap.get("nthread"))
-      paramsAnyMap += ("tree_method" -> xgbtParamMap.get("tree_method"))
-      paramsAnyMap += ("grow_policy" -> xgbtParamMap.get("grow_policy"))
-      if(modelConf=="classification_mnist8m") {
-        paramsAnyMap += ("num_class" -> xgbtParamMap.get("num_class"))
-      }
-      if (isRaw=="no"){
-        paramsAnyMap += ("enable_bbgen" -> xgbtParamMap.get("enable_bbgen"))
-        paramsAnyMap += ("rabit_enable_tcp_no_delay" -> xgbtParamMap.get("rabit_enable_tcp_no_delay"))
-      }
-      val start_time = System.currentTimeMillis()
-      val XGBTrain = algType match {
-        case "classification" =>
-          new XGBoostClassifier(paramsAnyMap).setLabelCol("label").setFeaturesCol("features")
-        case "regression" =>
-          new XGBoostRegressor(paramsAnyMap).setLabelCol("label").setFeaturesCol("features")
-      }
-
-      val train_data = getTrainData(spark,params).persist(StorageLevel.MEMORY_AND_DISK_SER)
-      val model = XGBTrain.fit(train_data)
-      val fitEndTime = System.currentTimeMillis()
-      val test_data = getTestData(spark,params).persist(StorageLevel.MEMORY_AND_DISK_SER)
-      val predictions = model.transform(test_data)
-
-      val evaluator = algType match {
-        case "classification" =>
-          new MulticlassClassificationEvaluator().setLabelCol("label").setPredictionCol("prediction").setMetricName("accuracy")
-        case "regression" =>
-          new RegressionEvaluator().setLabelCol("label").setPredictionCol("prediction").setMetricName("rmse")
-      }
-      val res = evaluator.evaluate(predictions)
-      println(s"Test Error =  ${(1.0-res)}")
-
-      val costTime = (fitEndTime-start_time)/1000.0
-      params.setEvaluation(res)
-      params.setCostTime(costTime)
-      params.setAlgorithmName("xgboost")
-      params.setTestcaseType(appName)
-
-      predictions.select("prediction","label","features").show(5)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/XGBT_${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${costTime}s; evaluation: ${res}")
-    }catch {
-      case e: Throwable=>
-        println(s"Exec Failure: ${e.getMessage}")
-        val sw: StringWriter = new StringWriter()
-        val pw: PrintWriter = new PrintWriter(sw)
-        e.printStackTrace(pw)
-        println("=============>>printStackTraceStr Exception: " + e.getClass + "\n===>" + sw.toString)
-    }
-  }
-  def getTrainData(spark:SparkSession,config:XGBTParams): Dataset[Row] ={
-    val tr_fname = config.getTr_fname
-    println("tr_fname",tr_fname)
-    var reader = spark.read.format("libsvm").option("vectorType",config.getVectorType)
-    val tr_data = reader.load(tr_fname)
-    tr_data
-  }
-  def getTestData(spark: SparkSession, config: XGBTParams): Dataset[Row] = {
-    val ts_fname = config.getTs_fname
-    println("ts_fname" , ts_fname)
-    var reader= spark.read.format("libsvm").option("vectorType",config.getVectorType)
-    val ts_data = reader.load(ts_fname)
-    ts_data
-  }
-}
diff --git a/tools/kal-test/src/main/scala/com/bigdata/utils/Utils.scala b/tools/kal-test/src/main/scala/com/bigdata/utils/Utils.scala
deleted file mode 100644
index 7363c48..0000000
--- a/tools/kal-test/src/main/scala/com/bigdata/utils/Utils.scala
+++ /dev/null
@@ -1,83 +0,0 @@
-package com.bigdata.utils
-
-import java.io.{File, FileInputStream, InputStreamReader, PrintWriter}
-import java.nio.charset.StandardCharsets
-import java.text.SimpleDateFormat
-import java.util.{Date, TimeZone}
-
-import org.apache.spark.mllib.linalg._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.SparkSession
-
-
-object Utils {
-  /**
-   *
-   * @param filename The resource name
-   * @return
-   */
-
-  def getStream(filename: String): InputStreamReader = {
-
-    val file = new File(filename)
-    if (!file.exists() || file.isDirectory) {
-      throw  new Exception(s"Fail to find prorerty file[${file}]")
-    }
-    val inputStreamReader = new InputStreamReader(new FileInputStream(file), StandardCharsets.UTF_8)
-
-    inputStreamReader
-
-    }
-
-  /**
-   *
-   * @param dateFmt date format
-   * @param utcMilliseconds "yyyy-MM-dd HH:mm:ss"
-   * @return String date
-   */
-  def getDateStrFromUTC(dateFmt: String, utcMilliseconds: Long): String = {
-    val sf = new SimpleDateFormat(dateFmt)
-    sf.setTimeZone(TimeZone.getTimeZone("Asia/Shanghai"))
-    sf.format(new Date(utcMilliseconds))
-  }
-
-
-  /**
-    *  Convert DenseMatrix to 2-dimension array, stored in row major
-    * @param matrix Input matrix
-    * @return 2-dimension array, stored in row major
-    */
-  def toRowMajorArray(matrix: DenseMatrix): Array[Array[Double]] = {
-    val nRow = matrix.numRows
-    val nCol = matrix.numCols
-    val arr = new Array[Array[Double]](nRow).map(_ => new Array[Double](nCol))
-    if(matrix.isTransposed){
-      var srcOffset = 0
-      for{i <- 0 until nRow} {
-        System.arraycopy(matrix.values, srcOffset, arr(i), 0, nCol)
-        srcOffset += nCol
-      }
-    } else {
-      matrix.values.indices.foreach(idx => {
-        val j = math.floor(idx / nRow).toInt
-        val i = idx % nRow
-        arr(i)(j) = matrix.values(idx)
-      })
-    }
-    arr
-  }
-
-  def writeMatrix(mat: DenseMatrix, path: String): Unit = {
-    val writer =new PrintWriter(path)
-    val arr = toRowMajorArray(mat)
-    arr.foreach(vec => writer.write(vec.mkString(",") + "\n"))
-    writer.close()
-  }
-
-  def writeVector(vector: DenseVector, path: String): Unit = {
-    val writer =new PrintWriter(path)
-    vector.values.foreach(d => writer.write(d + "\n"))
-    writer.close()
-  }
-
-  }
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/ml/classification/KNNClassifier.scala b/tools/kal-test/src/main/scala/org/apache/spark/ml/classification/KNNClassifier.scala
deleted file mode 100644
index 4c020f0..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/ml/classification/KNNClassifier.scala
+++ /dev/null
@@ -1,239 +0,0 @@
-package org.apache.spark.ml.classification
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.knn._
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.param.shared.HasWeightCol
-import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
-import org.apache.spark.ml.linalg._
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{DoubleType, StructType}
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.SparkException
-
-import scala.collection.mutable.ArrayBuffer
-
-/**
-  * [[https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]] for classification.
-  * An object is classified by a majority vote of its neighbors, with the object being assigned to
-  * the class most common among its k nearest neighbors.
-  */
-class KNNClassifier(override val uid: String) extends ProbabilisticClassifier[Vector, KNNClassifier, KNNClassificationModel]
-with KNNParams with HasWeightCol {
-
-  def this() = this(Identifiable.randomUID("knnc"))
-
-  /** @group setParam */
-  override def setFeaturesCol(value: String): this.type = set(featuresCol, value)
-
-  /** @group setParam */
-  override def setLabelCol(value: String): this.type = {
-    set(labelCol, value)
-
-    if ($(weightCol).isEmpty) {
-      set(inputCols, Array(value))
-    } else {
-      set(inputCols, Array(value, $(weightCol)))
-    }
-  }
-
-  //fill in default label col
-  setDefault(inputCols, Array($(labelCol)))
-
-  /** @group setWeight */
-  def setWeightCol(value: String): this.type = {
-    set(weightCol, value)
-
-    if (value.isEmpty) {
-      set(inputCols, Array($(labelCol)))
-    } else {
-      set(inputCols, Array($(labelCol), value))
-    }
-  }
-
-  setDefault(weightCol -> "")
-
-  /** @group setParam */
-  def setK(value: Int): this.type = set(k, value)
-
-  /** @group setParam */
-  def setTopTreeSize(value: Int): this.type = set(topTreeSize, value)
-
-  /** @group setParam */
-  def setTopTreeLeafSize(value: Int): this.type = set(topTreeLeafSize, value)
-
-  /** @group setParam */
-  def setSubTreeLeafSize(value: Int): this.type = set(subTreeLeafSize, value)
-
-  /** @group setParam */
-  def setBufferSizeSampleSizes(value: Array[Int]): this.type = set(bufferSizeSampleSizes, value)
-
-  /** @group setParam */
-  def setBalanceThreshold(value: Double): this.type = set(balanceThreshold, value)
-
-  /** @group setParam */
-  def setSeed(value: Long): this.type = set(seed, value)
-
-  override protected def train(dataset: Dataset[_]): KNNClassificationModel = {
-    // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
-    val instances = extractLabeledPoints(dataset).map {
-      case LabeledPoint(label: Double, features: Vector) => (label, features)
-    }
-    val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
-    if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
-
-    val labelSummarizer = instances.treeAggregate(
-      new MultiClassSummarizer)(
-      seqOp = (c, v) => (c, v) match {
-        case (labelSummarizer: MultiClassSummarizer, (label: Double, features: Vector)) =>
-          labelSummarizer.add(label)
-      },
-      combOp = (c1, c2) => (c1, c2) match {
-        case (classSummarizer1: MultiClassSummarizer, classSummarizer2: MultiClassSummarizer) =>
-          classSummarizer1.merge(classSummarizer2)
-      })
-
-    val histogram = labelSummarizer.histogram
-    val numInvalid = labelSummarizer.countInvalid
-    val numClasses = histogram.length
-
-    if (numInvalid != 0) {
-      val msg = s"Classification labels should be in {0 to ${numClasses - 1} " +
-        s"Found $numInvalid invalid labels."
-      logError(msg)
-      throw new SparkException(msg)
-    }
-
-    val knnModel = copyValues(new KNN()).fit(dataset)
-    knnModel.toNewClassificationModel(uid, numClasses)
-  }
-
-  override def fit(dataset: Dataset[_]): KNNClassificationModel = {
-    // Need to overwrite this method because we need to manually overwrite the buffer size
-    // because it is not supposed to stay the same as the Classifier if user sets it to -1.
-    transformSchema(dataset.schema, logging = true)
-    val model = train(dataset)
-    val bufferSize = model.getBufferSize
-    copyValues(model.setParent(this)).setBufferSize(bufferSize)
-  }
-
-  override def copy(extra: ParamMap): KNNClassifier = defaultCopy(extra)
-}
-
-class KNNClassificationModel private[ml](
-                                          override val uid: String,
-                                          val topTree: Broadcast[Tree],
-                                          val subTrees: RDD[Tree],
-                                          val _numClasses: Int
-                                        ) extends ProbabilisticClassificationModel[Vector, KNNClassificationModel]
-with KNNModelParams with HasWeightCol with Serializable {
-  require(subTrees.getStorageLevel != StorageLevel.NONE,
-    "KNNModel is not designed to work with Trees that have not been cached")
-
-  /** @group setParam */
-  def setK(value: Int): this.type = set(k, value)
-
-  /** @group setParam */
-  def setBufferSize(value: Double): this.type = set(bufferSize, value)
-
-  override def numClasses: Int = _numClasses
-
-  //TODO: This can benefit from DataSet API
-  override def transform(dataset: Dataset[_]): DataFrame = {
-    val getWeight: Row => Double = {
-      if($(weightCol).isEmpty) {
-        r => 1.0
-      } else {
-        r => r.getDouble(1)
-      }
-    }
-
-    val neighborRDD : RDD[(Long, Array[(Row, Double)])] = transform(dataset, topTree, subTrees)
-    val merged = neighborRDD
-      .map {
-        case (id, labelsDists) =>
-          val (labels, _) = labelsDists.unzip
-          val vector = new Array[Double](numClasses)
-          var i = 0
-          while (i < labels.length) {
-            vector(labels(i).getDouble(0).toInt) += getWeight(labels(i))
-            i += 1
-          }
-          val rawPrediction = Vectors.dense(vector)
-          lazy val probability = raw2probability(rawPrediction)
-          lazy val prediction = probability2prediction(probability)
-
-          val values = new ArrayBuffer[Any]
-          if ($(rawPredictionCol).nonEmpty) {
-            values.append(rawPrediction)
-          }
-          if ($(probabilityCol).nonEmpty) {
-            values.append(probability)
-          }
-          if ($(predictionCol).nonEmpty) {
-            values.append(prediction)
-          }
-
-          (id, values)
-      }
-
-    dataset.sqlContext.createDataFrame(
-      dataset.toDF().rdd.zipWithIndex().map { case (row, i) => (i, row) }
-        .leftOuterJoin(merged) //make sure we don't lose any observations
-        .map {
-        case (i, (row, values)) => Row.fromSeq(row.toSeq ++ values.get)
-      },
-      transformSchema(dataset.schema)
-    )
-  }
-
-  override def transformSchema(schema: StructType): StructType = {
-    var transformed = schema
-    if ($(rawPredictionCol).nonEmpty) {
-      transformed = SchemaUtils.appendColumn(transformed, $(rawPredictionCol), new VectorUDT)
-    }
-    if ($(probabilityCol).nonEmpty) {
-      transformed = SchemaUtils.appendColumn(transformed, $(probabilityCol), new VectorUDT)
-    }
-    if ($(predictionCol).nonEmpty) {
-      transformed = SchemaUtils.appendColumn(transformed, $(predictionCol), DoubleType)
-    }
-    transformed
-  }
-
-  override def copy(extra: ParamMap): KNNClassificationModel = {
-    val copied = new KNNClassificationModel(uid, topTree, subTrees, numClasses)
-    copyValues(copied, extra).setParent(parent)
-  }
-
-  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
-    rawPrediction match {
-      case dv: DenseVector =>
-        var i = 0
-        val size = dv.size
-
-        var sum = 0.0
-        while (i < size) {
-          sum += dv.values(i)
-          i += 1
-        }
-
-        i = 0
-        while (i < size) {
-          dv.values(i) /= sum
-          i += 1
-        }
-
-        dv
-      case sv: SparseVector =>
-        throw new RuntimeException("Unexpected error in KNNClassificationModel:" +
-          " raw2probabilitiesInPlace encountered SparseVector")
-    }
-  }
-
-  override protected def predictRaw(features: Vector): Vector = {
-    throw new SparkException("predictRaw function should not be called directly since kNN prediction is done in distributed fashion. Use transform instead.")
-  }
-}
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/ml/clustering/DBSCANRunner.scala b/tools/kal-test/src/main/scala/org/apache/spark/ml/clustering/DBSCANRunner.scala
deleted file mode 100644
index 97dec50..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/ml/clustering/DBSCANRunner.scala
+++ /dev/null
@@ -1,143 +0,0 @@
-package org.apache.spark.ml.clustering
-
-import com.bigdata.utils.Utils
-import org.apache.spark.SparkConf
-import org.apache.spark.ml.linalg.{VectorUDT, Vectors}
-import org.apache.spark.sql.{Row, SparkSession}
-import org.apache.spark.sql.types.{StructField, StructType}
-import org.yaml.snakeyaml.{DumperOptions, TypeDescription, Yaml}
-import org.yaml.snakeyaml.constructor.Constructor
-import org.yaml.snakeyaml.nodes.Tag
-import org.yaml.snakeyaml.representer.Representer
-
-import java.io.{File, FileWriter}
-import java.util
-import scala.beans.BeanProperty
-
-class DBSCANConfig extends Serializable {
-
-  @BeanProperty var dbscan: util.HashMap[String, Object] = _
-}
-
-class DBSParams extends Serializable {
-  @BeanProperty var numPartitions: Int = _
-  @BeanProperty var epsilon: Double = _
-  @BeanProperty var minPoints: Int = _
-  @BeanProperty var sampleRate: Double = _
-  @BeanProperty var dataPath: String = _
-  @BeanProperty var datasetName: String = _
-  @BeanProperty var datasetCpuName: String = _
-  @BeanProperty var isRaw: String = "no"
-  @BeanProperty var costTime: Double = _
-  @BeanProperty var numTotal: util.HashMap[String, Int] = _
-  @BeanProperty var algorithmName: String = _
-  @BeanProperty var testcaseType: String = _
-}
-
-class DBSCANKernel {
-  def runJob(spark: SparkSession, params: DBSParams): (util.HashMap[String, Int],Double) = {
-    val startTime = System.currentTimeMillis()
-
-    val sc = spark.sparkContext
-
-    val inputSchema = StructType(
-      Seq(
-        StructField("features", new VectorUDT, false)
-      )
-    )
-    val dataRow = sc.textFile(params.dataPath).map(x => Row(Vectors.dense(x.split(" ").map(_.toDouble))))
-    val data = spark.createDataFrame(dataRow, inputSchema)
-      .repartition(params.numPartitions).cache()
-
-    val model = new DBSCAN()
-      .setEpsilon(params.epsilon)
-      .setMinPoints(params.minPoints)
-      .setSampleRate(params.sampleRate)
-
-    val globalClustersDF = model.fitPredict(data)
-    globalClustersDF.foreachPartition(_ => {})
-
-    globalClustersDF.cache()
-    val pointTypeArr = globalClustersDF.select("prediction").collect().map(_(0)).map(x => x.toString.toInt)
-    val clusterIDArr = globalClustersDF.select("label").collect().map(_(0)).map(x => x.toString.toInt)
-    val costTime = (System.currentTimeMillis() - startTime) / 1000.0
-    val numClusters: Int = clusterIDArr.toSet.size
-    val numCore: Int = pointTypeArr.count(_ == 0)
-    val numBorder: Int = pointTypeArr.count(_ == 1)
-    val numNoise: Int = pointTypeArr.count(_ == -1)
-    val numTotal = new util.HashMap[String, Int]()
-    numTotal.put(s"numClusters",numClusters)
-    numTotal.put(s"numCore",numCore)
-    numTotal.put(s"numBorder",numBorder)
-    numTotal.put(s"numNoise",numNoise)
-    (numTotal,costTime)
-  }
-}
-
-object DBSCANRunner {
-
-  def main(args: Array[String]): Unit = {
-
-    try {
-      val modelConfSplit = args(0).split("-")
-      val (datasetName, platformName) = (modelConfSplit(0), modelConfSplit(1))
-
-      val dataPath = args(1)
-
-      val datasetCpuName = s"${datasetName}_${platformName}"
-      val stream = Utils.getStream("conf/ml/dbscan/dbscan.yml")
-
-      val representer = new Representer
-      representer.addClassTag(classOf[DBSParams], Tag.MAP)
-      val options = new DumperOptions
-      options.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK)
-      val yaml = new Yaml(new Constructor(classOf[DBSCANConfig]), representer, options)
-      val description = new TypeDescription(classOf[DBSParams])
-      yaml.addTypeDescription(description)
-
-      val config: DBSCANConfig = yaml.load(stream).asInstanceOf[DBSCANConfig]
-      val paramsMap = config.dbscan.get(datasetName).asInstanceOf[util.HashMap[String, Object]]
-
-      val params = new DBSParams()
-
-      params.setDataPath(dataPath)
-      params.setDatasetName(datasetName)
-      params.setDatasetCpuName(datasetCpuName)
-      params.setAlgorithmName("DBSCAN")
-      params.setTestcaseType(s"DBSCAN_${datasetName}")
-      params.setEpsilon(paramsMap.get("epsilon").toString.toDouble)
-      params.setMinPoints(paramsMap.get("minPoints").toString.toInt)
-      params.setSampleRate(paramsMap.get("sampleRate").toString.toDouble)
-
-      params.setNumPartitions(paramsMap.get("numPartitions").toString.toInt)
-
-
-      val conf = new SparkConf().setAppName(s"DBSCAN_${datasetName}_${platformName}")
-
-      val spark = SparkSession.builder.config(conf).getOrCreate()
-
-      val dbscan = new DBSCANKernel()
-      val tuple = dbscan.runJob(spark, params)
-      params.setNumTotal(tuple._1)
-      params.setCostTime(tuple._2)
-      val folder = new File("report")
-      if (!folder.exists()) {
-        val mkdir = folder.mkdirs()
-        println(s"Create dir report ${mkdir}")
-      }
-      val writer = new FileWriter(s"report/DBSCAN${
-        Utils.getDateStrFromUTC("yyyyMMdd_HHmmss",
-          System.currentTimeMillis())
-      }.yml")
-      yaml.dump(params, writer)
-      println(s"Exec Successful: costTime: ${params.getCostTime}s")
-    } catch {
-      case e: Throwable =>
-        println(s"Exec Failure: ${e.getMessage}")
-        throw e
-    }
-  }
-}
-
-
-
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/ml/knn/KNN.scala b/tools/kal-test/src/main/scala/org/apache/spark/ml/knn/KNN.scala
deleted file mode 100644
index 54c9f79..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/ml/knn/KNN.scala
+++ /dev/null
@@ -1,588 +0,0 @@
-package org.apache.spark.ml.knn
-
-import breeze.linalg.{DenseVector, Vector => BV}
-import breeze.stats._
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.classification.KNNClassificationModel
-import org.apache.spark.ml.knn.KNN.{KNNPartitioner, RowWithVector, VectorWithNorm}
-import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.regression.KNNRegressionModel
-import org.apache.spark.ml.util._
-import org.apache.spark.ml.{Estimator, Model}
-import org.apache.spark.ml.linalg.{Vector, VectorUDT, Vectors}
-import org.apache.spark.mllib.rdd.MLPairRDDFunctions._
-import org.apache.spark.rdd.{RDD, ShuffledRDD}
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.random.XORShiftRandom
-import org.apache.spark.{HashPartitioner, Partitioner}
-import org.apache.log4j
-import org.apache.spark.mllib.knn.KNNUtils
-
-import scala.annotation.tailrec
-import scala.collection.mutable.ArrayBuffer
-import scala.util.hashing.byteswap64
-
-// features column => vector, input columns => auxiliary columns to return by KNN model
-private[ml] trait KNNModelParams extends Params with HasFeaturesCol with HasInputCols {
-  /**
-    * Param for the column name for returned neighbors.
-    * Default: "neighbors"
-    *
-    * @group param
-    */
-  val neighborsCol = new Param[String](this, "neighborsCol", "column names for returned neighbors")
-
-  /** @group getParam */
-  def getNeighborsCol: String = $(neighborsCol)
-
-  /**
-    * Param for distance column that will create a distance column of each nearest neighbor
-    * Default: no distance column will be used
-    *
-    * @group param
-    */
-  val distanceCol = new Param[String](this, "distanceCol", "column that includes each neighbors' distance as an additional column")
-
-  /** @group getParam */
-  def getDistanceCol: String = $(distanceCol)
-
-  /**
-    * Param for number of neighbors to find (> 0).
-    * Default: 5
-    *
-    * @group param
-    */
-  val k = new IntParam(this, "k", "number of neighbors to find", ParamValidators.gt(0))
-
-  /** @group getParam */
-  def getK: Int = $(k)
-
-  /**
-   * Param for maximum distance to find neighbors
-   * Default: Double.PositiveInfinity
-    *
-    * @group param
-   */
-  val maxDistance = new DoubleParam(this, "maxNeighbors", "maximum distance to find neighbors", // todo: maxDistance or maxNeighbors?
-                                     ParamValidators.gt(0))
-
-  /** @group getParam */
-  def getMaxDistance: Double = $(maxDistance)
-
-  /**
-    * Param for size of buffer used to construct spill trees and top-level tree search.
-    * Note the buffer size is 2 * tau as described in the paper.
-    *
-    * When buffer size is 0.0, the tree itself reverts to a metric tree.
-    * -1.0 triggers automatic effective nearest neighbor distance estimation.
-    *
-    * Default: -1.0
-    *
-    * @group param
-    */
-  val bufferSize = new DoubleParam(this, "bufferSize",
-    "size of buffer used to construct spill trees and top-level tree search", ParamValidators.gtEq(-1.0))
-
-  /** @group getParam */
-  def getBufferSize: Double = $(bufferSize)
-
-  private[ml] def transform(data: RDD[Vector], topTree: Broadcast[Tree], subTrees: RDD[Tree]): RDD[(Long, Array[(Row,Double)])] = {
-    val searchData = data.zipWithIndex()
-      .flatMap {
-        case (vector, index) =>
-          val vectorWithNorm = new VectorWithNorm(vector)
-          val idx = KNN.searchIndices(vectorWithNorm, topTree.value, $(bufferSize))
-            .map(i => (i, (vectorWithNorm, index)))
-
-          assert(idx.nonEmpty, s"indices must be non-empty: $vector ($index)")
-          idx
-      }
-      .partitionBy(new HashPartitioner(subTrees.partitions.length))
-
-    // for each partition, search points within corresponding child tree
-    val results = searchData.zipPartitions(subTrees) {
-      (childData, trees) =>
-        val tree = trees.next()
-        assert(!trees.hasNext)
-        childData.flatMap {
-          case (_, (point, i)) =>
-            tree.query(point, $(k)).collect {
-              case (neighbor, distance) if distance <= $(maxDistance) =>
-                (i, (neighbor.row, distance))
-            }
-        }
-    }
-
-    // merge results by point index together and keep topK results
-    results.topByKey($(k))(Ordering.by(-_._2))
-      .map { case (i, seq) => (i, seq) }
-  }
-
-  private[ml] def transform(dataset: Dataset[_], topTree: Broadcast[Tree], subTrees: RDD[Tree]): RDD[(Long, Array[(Row, Double)])] = {
-    transform(dataset.select($(featuresCol)).rdd.map(_.getAs[Vector](0)), topTree, subTrees)
-  }
-
-}
-
-private[ml] trait KNNParams extends KNNModelParams with HasSeed {
-  /**
-    * Param for number of points to sample for top-level tree (> 0).
-    * Default: 1000
-    *
-    * @group param
-    */
-  val topTreeSize = new IntParam(this, "topTreeSize", "number of points to sample for top-level tree", ParamValidators.gt(0))
-
-  /** @group getParam */
-  def getTopTreeSize: Int = $(topTreeSize)
-
-  /**
-    * Param for number of points at which to switch to brute-force for top-level tree (> 0).
-    * Default: 5
-    *
-    * @group param
-    */
-  val topTreeLeafSize = new IntParam(this, "topTreeLeafSize",
-    "number of points at which to switch to brute-force for top-level tree", ParamValidators.gt(0))
-
-  /** @group getParam */
-  def getTopTreeLeafSize: Int = $(topTreeLeafSize)
-
-  /**
-    * Param for number of points at which to switch to brute-force for distributed sub-trees (> 0).
-    * Default: 20
-    *
-    * @group param
-    */
-  val subTreeLeafSize = new IntParam(this, "subTreeLeafSize",
-    "number of points at which to switch to brute-force for distributed sub-trees", ParamValidators.gt(0))
-
-  /** @group getParam */
-  def getSubTreeLeafSize: Int = $(subTreeLeafSize)
-
-  /**
-    * Param for number of sample sizes to take when estimating buffer size (at least two samples).
-    * Default: 100 to 1000 by 100
-    *
-    * @group param
-    */
-  val bufferSizeSampleSizes = new IntArrayParam(this, "bufferSizeSampleSize",  // todo: should this have an 's' at the end?
-    "number of sample sizes to take when estimating buffer size", { arr: Array[Int] => arr.length > 1 && arr.forall(_ > 0) })
-
-  /** @group getParam */
-  def getBufferSizeSampleSizes: Array[Int] = $(bufferSizeSampleSizes)
-
-  /**
-    * Param for fraction of total points at which spill tree reverts back to metric tree
-    * if either child contains more points (0 <= rho <= 1).
-    * Default: 70%
-    *
-    * @group param
-    */
-  val balanceThreshold = new DoubleParam(this, "balanceThreshold",
-    "fraction of total points at which spill tree reverts back to metric tree if either child contains more points",
-    ParamValidators.inRange(0, 1))
-
-  /** @group getParam */
-  def getBalanceThreshold: Double = $(balanceThreshold)
-
-  setDefault(topTreeSize -> 1000, topTreeLeafSize -> 10, subTreeLeafSize -> 30,
-    bufferSize -> -1.0, bufferSizeSampleSizes -> (100 to 1000 by 100).toArray, balanceThreshold -> 0.7,
-    k -> 5, neighborsCol -> "neighbors", distanceCol -> "", maxDistance -> Double.PositiveInfinity)
-
-  /**
-    * Validates and transforms the input schema.
-    *
-    * @param schema input schema
-    * @return output schema
-    */
-  protected def validateAndTransformSchema(schema: StructType): StructType = {
-    SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
-    val auxFeatures = $(inputCols).map(c => schema(c))
-    val schemaWithNeighbors = SchemaUtils.appendColumn(schema, $(neighborsCol), ArrayType(StructType(auxFeatures)))
-
-    if ($(distanceCol).isEmpty) {
-      schemaWithNeighbors
-    } else {
-      SchemaUtils.appendColumn(schemaWithNeighbors, $(distanceCol), ArrayType(DoubleType))
-    }
-  }
-}
-
-/**
-  * kNN Model facilitates k-Nestrest Neighbor search by storing distributed hybrid spill tree.
-  * Top level tree is a MetricTree but instead of using back tracking, it searches all possible leaves in parallel
-  * to avoid multiple iterations. It uses the same buffer size that is used in model training, when the search
-  * vector falls into the buffer zone of the node, it dispatches search to both children.
-  *
-  * A high level overview of the search phases is as follows:
-  *
-  *  1. For each vector to search, go through the top level tree to output a pair of (index, point)
-  *  1. Repartition search points by partition index
-  *  1. Search each point through the hybrid spill tree in that particular partition
-  *  1. For each point, merge results from different partitions and keep top k results.
-  *
-  */
-class KNNModel private[ml](
-                            override val uid: String,
-                            val topTree: Broadcast[Tree],
-                            val subTrees: RDD[Tree]
-                          ) extends Model[KNNModel] with KNNModelParams {
-  require(subTrees.getStorageLevel != StorageLevel.NONE,
-    "KNNModel is not designed to work with Trees that have not been cached")
-
-  /** @group setParam */
-  def setNeighborsCol(value: String): this.type = set(neighborsCol, value)
-
-  /** @group setParam */
-  def setDistanceCol(value: String): this.type = set(distanceCol, value)
-
-  /** @group setParam */
-  def setK(value: Int): this.type = set(k, value)
-
-  /** @group setParam */
-  def setMaxDistance(value: Double): this.type = set(maxDistance, value)
-
-  /** @group setParam */
-  def setBufferSize(value: Double): this.type = set(bufferSize, value)
-
-  //TODO: All these can benefit from DataSet API
-  override def transform(dataset: Dataset[_]): DataFrame = {
-    val merged: RDD[(Long, Array[(Row,Double)])] = transform(dataset, topTree, subTrees)
-
-    val withDistance = $(distanceCol).nonEmpty
-
-    dataset.sqlContext.createDataFrame(
-      dataset.toDF().rdd.zipWithIndex().map { case (row, i) => (i, row) }
-        .leftOuterJoin(merged)
-        .map {
-          case (i, (row, neighborsAndDistances)) =>
-            val (neighbors, distances) = neighborsAndDistances.map(_.unzip).getOrElse((Array.empty[Row], Array.empty[Double]))
-            if (withDistance) {
-              Row.fromSeq(row.toSeq :+ neighbors :+ distances)
-            } else {
-              Row.fromSeq(row.toSeq :+ neighbors)
-            }
-        },
-      transformSchema(dataset.schema)
-    )
-  }
-
-  override def transformSchema(schema: StructType): StructType = {
-    val auxFeatures = $(inputCols).map(c => schema(c))
-    val schemaWithNeighbors = SchemaUtils.appendColumn(schema, $(neighborsCol), ArrayType(StructType(auxFeatures)))
-    if ($(distanceCol).isEmpty) {
-      schemaWithNeighbors
-    } else {
-      SchemaUtils.appendColumn(schemaWithNeighbors, $(distanceCol), ArrayType(DoubleType))
-    }
-  }
-
-  override def copy(extra: ParamMap): KNNModel = {
-    val copied = new KNNModel(uid, topTree, subTrees)
-    copyValues(copied, extra).setParent(parent)
-  }
-
-  def toNewClassificationModel(uid: String, numClasses: Int): KNNClassificationModel = {
-    copyValues(new KNNClassificationModel(uid, topTree, subTrees, numClasses))
-  }
-
-  def toNewRegressionModel(uid: String): KNNRegressionModel = {
-    copyValues(new KNNRegressionModel(uid, topTree, subTrees))
-  }
-}
-
-/**
-  * k-Nearest Neighbors (kNN) algorithm
-  *
-  * kNN finds k closest observations in training dataset. It can be used for both classification and regression.
-  * Furthermore it can also be used for other purposes such as input to clustering algorithm.
-  *
-  * While the brute-force approach requires no pre-training, each prediction requires going through the entire training
-  * set resulting O(n log(k)) runtime per individual prediction using a heap keep track of neighbor candidates.
-  * Many different implementations have been proposed such as Locality Sensitive Hashing (LSH), KD-Tree, Metric Tree and etc.
-  * Each algorithm has its shortcomings that prevent them to be effective on large-scale and/or high-dimensional dataset.
-  *
-  * This is an implementation of kNN based upon distributed Hybrid Spill-Trees where training points are organized into
-  * distributed binary trees. The algorithm is designed to support accurate approximate kNN search but by tuning parameters
-  * an exact search can also be performed with cost of additional runtime.
-  *
-  * Each binary tree node is either a
-  *
-  * '''Metric Node''':
-  * Metric Node partition points exclusively into two children by finding two pivot points and divide by middle plane.
-  * When searched, the child whose pivot is closer to query vector is searched first. Back tracking is required to
-  * ensure accuracy in this case, where the other child should be searched if it can possibly contain better neighbor
-  * based upon candidates picked during previous search.
-  *
-  * '''Spill Node''':
-  * Spill Node also partitions points into two children however there are an overlapping buffer between the two pivot
-  * points. The larger the buffer size, the less effective the node eliminates points thus could increase tree height.
-  * When searched, defeatist search is used where only one child is searched and no back tracking happens in this
-  * process. Because of the buffer between two children, we are likely to end up with good enough candidates without
-  * searching the other part of the tree.
-  *
-  * While Spill Node promises O(h) runtime where h is the tree height, the tree is deeper than Metric Tree's O(log n)
-  * height on average. Furthermore, when it comes down to leaves where points are more closer to each other, the static
-  * buffer size means more points will end up in the buffer. Therefore a Balance Threshold (rho) is introduced: when
-  * either child of Spill Node makes up more than rho fraction of the total points at this level, Spill Node is reverted
-  * back to a Metric Node.
-  *
-  * A high level overview of the algorithm is as follows:
-  *
-  *  1. Sample M data points (M is relatively small and can be held in driver)
-  *  1. Build the top level metric tree
-  *  1. Repartition RDD by assigning each point to leaf node of the above tree
-  *  1. Build a hybrid spill tree at each partition
-  *
-  * This concludes the training phase of kNN.
-  * See [[KNNModel]] for details on prediction phase.
-  *
-  *
-  * This algorithm is described in [[http://dx.doi.org/10.1109/WACV.2007.18]] where it was shown to scale well in terms of
-  * number of observations and dimensions, bounded by the available memory across clusters (billions in paper's example).
-  * This implementation adapts the MapReduce algorithm to work with Spark.
-  *
-  */
-class KNN(override val uid: String) extends Estimator[KNNModel] with KNNParams {
-  def this() = this(Identifiable.randomUID("knn"))
-
-  /** @group setParam */
-  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
-
-  /** @group setParam */
-  def setK(value: Int): this.type = set(k, value)
-
-  /** @group setParam */
-  def setAuxCols(value: Array[String]): this.type = set(inputCols, value)
-
-  /** @group setParam */
-  def setTopTreeSize(value: Int): this.type = set(topTreeSize, value)
-
-  /** @group setParam */
-  def setTopTreeLeafSize(value: Int): this.type = set(topTreeLeafSize, value)
-
-  /** @group setParam */
-  def setSubTreeLeafSize(value: Int): this.type = set(subTreeLeafSize, value)
-
-  /** @group setParam */
-  def setBufferSizeSampleSizes(value: Array[Int]): this.type = set(bufferSizeSampleSizes, value)
-
-  /** @group setParam */
-  def setBalanceThreshold(value: Double): this.type = set(balanceThreshold, value)
-
-  /** @group setParam */
-  def setSeed(value: Long): this.type = set(seed, value)
-
-  override def fit(dataset: Dataset[_]): KNNModel = {
-    val rand = new XORShiftRandom($(seed))
-    //prepare data for model estimation
-    val data = dataset.selectExpr($(featuresCol), $(inputCols).mkString("struct(", ",", ")"))
-      .rdd
-      .map(row => new RowWithVector(row.getAs[Vector](0), row.getStruct(1)))
-    //sample data to build top-level tree
-    val sampled = data.sample(withReplacement = false, $(topTreeSize).toDouble / dataset.count(), rand.nextLong()).collect()
-    val topTree = MetricTree.build(sampled, $(topTreeLeafSize), rand.nextLong())
-    //build partitioner using top-level tree
-    val part = new KNNPartitioner(topTree)
-    //noinspection ScalaStyle
-    val repartitioned = new ShuffledRDD[RowWithVector, Null, Null](data.map(v => (v, null)), part).keys
-
-    val tau =
-      if ($(balanceThreshold) > 0 && $(bufferSize) < 0) {
-        KNN.estimateTau(data, $(bufferSizeSampleSizes), rand.nextLong())
-      } else {
-        math.max(0, $(bufferSize))
-      }
-    logInfo("Tau is: " + tau)
-
-    val trees = repartitioned.mapPartitionsWithIndex {
-      (partitionId, itr) =>
-        val rand = new XORShiftRandom(byteswap64($(seed) ^ partitionId))
-        val childTree =
-          HybridTree.build(itr.toIndexedSeq, $(subTreeLeafSize), tau, $(balanceThreshold), rand.nextLong())
-
-        Iterator(childTree)
-    }.persist(StorageLevel.MEMORY_AND_DISK)
-    // TODO: force persisting trees primarily for benchmark. any reason not to do this for regular runs?
-    trees.count()
-
-    val model = new KNNModel(uid, trees.context.broadcast(topTree), trees).setParent(this)
-    copyValues(model).setBufferSize(tau)
-  }
-
-  override def transformSchema(schema: StructType): StructType = {
-    validateAndTransformSchema(schema)
-  }
-
-  override def copy(extra: ParamMap): KNN = defaultCopy(extra)
-}
-
-
-object KNN {
-
-  val logger = log4j.Logger.getLogger(classOf[KNN])
-
-  /**
-    * VectorWithNorm can use more efficient algorithm to calculate distance
-    */
-  case class VectorWithNorm(vector: Vector, norm: Double) {
-    def this(vector: Vector) = this(vector, Vectors.norm(vector, 2))
-
-    def this(vector: BV[Double]) = this(Vectors.fromBreeze(vector))
-
-    def fastSquaredDistance(v: VectorWithNorm): Double = {
-      KNNUtils.fastSquaredDistance(vector, norm, v.vector, v.norm)
-    }
-
-    def fastDistance(v: VectorWithNorm): Double = math.sqrt(fastSquaredDistance(v))
-  }
-
-  /**
-    * VectorWithNorm plus auxiliary row information
-    */
-  case class RowWithVector(vector: VectorWithNorm, row: Row) {
-    def this(vector: Vector, row: Row) = this(new VectorWithNorm(vector), row)
-  }
-
-  /**
-    * Estimate a suitable buffer size based on dataset
-    *
-    * A suitable buffer size is the minimum size such that nearest neighbors can be accurately found even at
-    * boundary of splitting plane between pivot points. Therefore assuming points are uniformly distributed in
-    * high dimensional space, it should be approximately the average distance between points.
-    *
-    * Specifically the number of points within a certain radius of a given point is proportionally to the density of
-    * points raised to the effective number of dimensions, of which manifold data points exist on:
-    * R_s = \frac{c}{N_s ** 1/d}
-    * where R_s is the radius, N_s is the number of points, d is effective number of dimension, and c is a constant.
-    *
-    * To estimate R_s_all for entire dataset, we can take samples of the dataset of different size N_s to compute R_s.
-    * We can estimate c and d using linear regression. Lastly we can calculate R_s_all using total number of observation
-    * in dataset.
-    *
-    */
-  def estimateTau(data: RDD[RowWithVector], sampleSize: Array[Int], seed: Long): Double = {
-    val total = data.count()
-
-    // take samples of points for estimation
-    val samples = data.mapPartitionsWithIndex {
-      case (partitionId, itr) =>
-        val rand = new XORShiftRandom(byteswap64(seed ^ partitionId))
-        itr.flatMap {
-          p => sampleSize.zipWithIndex
-            .filter { case (size, _) => rand.nextDouble() * total < size }
-            .map { case (size, index) => (index, p) }
-        }
-    }
-    // compute N_s and R_s pairs
-    val estimators = samples
-      .groupByKey()
-      .map {
-        case (index, points) => (points.size, computeAverageDistance(points))
-      }.collect().distinct
-
-    // collect x and y vectors
-    val x = DenseVector(estimators.map { case (n, _) => math.log(n) })
-    val y = DenseVector(estimators.map { case (_, d) => math.log(d) })
-
-    // estimate log(R_s) = alpha + beta * log(N_s)
-    val xMeanVariance: MeanAndVariance = meanAndVariance(x)
-    val xmean = xMeanVariance.mean
-    val yMeanVariance: MeanAndVariance = meanAndVariance(y)
-    val ymean = yMeanVariance.mean
-
-    val corr = (mean(x *:* y) - xmean * ymean) / math.sqrt((mean(x *:* x) - xmean * xmean) * (mean(y *:* y) - ymean * ymean))
-
-    val beta = corr * yMeanVariance.stdDev / xMeanVariance.stdDev
-    val alpha = ymean - beta * xmean
-    val rs = math.exp(alpha + beta * math.log(total))
-
-    if (beta > 0 || beta.isNaN || rs.isNaN) {
-      val yMax = breeze.linalg.max(y)
-      logger.error(
-        s"""Unable to estimate Tau with positive beta: $beta. This maybe because data is too small.
-            |Setting to $yMax which is the maximum average distance we found in the sample.
-            |This may leads to poor accuracy. Consider manually set bufferSize instead.
-            |You can also try setting balanceThreshold to zero so only metric trees are built.""".stripMargin)
-      yMax
-    } else {
-      // c = alpha, d = - 1 / beta
-      rs / math.sqrt(-1 / beta)
-    }
-  }
-
-  // compute the average distance of nearest neighbors within points using brute-force
-  private[this] def computeAverageDistance(points: Iterable[RowWithVector]): Double = {
-    val distances = points.map {
-      point => points.map(p => p.vector.fastSquaredDistance(point.vector)).filter(_ > 0).min
-    }.map(math.sqrt)
-
-    distances.sum / distances.size
-  }
-
-  /**
-    * Search leaf index used by KNNPartitioner to partition training points
-    *
-    * @param v one training point to partition
-    * @param tree top tree constructed using sampled points
-    * @param acc accumulator used to help determining leaf index
-    * @return leaf/partition index
-    */
-  @tailrec
-  private[knn] def searchIndex(v: RowWithVector, tree: Tree, acc: Int = 0): Int = {
-    tree match {
-      case node: MetricTree =>
-        val leftDistance = node.leftPivot.fastSquaredDistance(v.vector)
-        val rightDistance = node.rightPivot.fastSquaredDistance(v.vector)
-        if (leftDistance < rightDistance) {
-          searchIndex(v, node.leftChild, acc)
-        } else {
-          searchIndex(v, node.rightChild, acc + node.leftChild.leafCount)
-        }
-      case _ => acc // reached leaf
-    }
-  }
-
-  //TODO: Might want to make this tail recursive
-  private[ml] def searchIndices(v: VectorWithNorm, tree: Tree, tau: Double, acc: Int = 0): Seq[Int] = {
-    tree match {
-      case node: MetricTree =>
-        val leftDistance = node.leftPivot.fastDistance(v)
-        val rightDistance = node.rightPivot.fastDistance(v)
-
-        val buffer = new ArrayBuffer[Int]
-        if (leftDistance - rightDistance <= tau) {
-          buffer ++= searchIndices(v, node.leftChild, tau, acc)
-        }
-
-        if (rightDistance - leftDistance <= tau) {
-          buffer ++= searchIndices(v, node.rightChild, tau, acc + node.leftChild.leafCount)
-        }
-
-        buffer
-      case _ => Seq(acc) // reached leaf
-    }
-  }
-
-  /**
-    * Partitioner used to map vector to leaf node which determines the partition it goes to
-    *
-    * @param tree `Tree` used to find leaf
-    */
-  class KNNPartitioner[T <: RowWithVector](tree: Tree) extends Partitioner {
-    override def numPartitions: Int = tree.leafCount
-
-    override def getPartition(key: Any): Int = {
-      key match {
-        case v: RowWithVector => searchIndex(v, tree)
-        case _ => throw new IllegalArgumentException(s"Key must be of type Vector but got: $key")
-      }
-    }
-
-  }
-
-}
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/ml/knn/MetricTree.scala b/tools/kal-test/src/main/scala/org/apache/spark/ml/knn/MetricTree.scala
deleted file mode 100644
index b7e3bd2..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/ml/knn/MetricTree.scala
+++ /dev/null
@@ -1,397 +0,0 @@
-package org.apache.spark.ml.knn
-
-import breeze.linalg._
-import org.apache.spark.ml.knn.KNN._
-import org.apache.spark.ml.linalg.{Vector, Vectors}
-import org.apache.spark.util.random.XORShiftRandom
-
-import scala.collection.mutable
-
-/**
- * A [[Tree]] is used to store data points used in k-NN search. It represents
- * a binary tree node. It keeps track of the pivot vector which closely approximate
- * the center of all vectors within the node. All vectors are within the radius of
- * distance to the pivot vector. Finally it knows the number of leaves to help
- * determining partition index.
- */
-private[ml] abstract class Tree extends Serializable {
-  val leftChild: Tree
-  val rightChild: Tree
-  val size: Int
-  val leafCount: Int
-  val pivot: VectorWithNorm
-  val radius: Double
-
-  def iterator: Iterator[RowWithVector]
-
-  /**
-   * k-NN query using pre-built [[Tree]]
-   * @param v vector to query
-   * @param k number of nearest neighbor
-   * @return a list of neighbor that is nearest to the query vector
-   */
-  def query(v: Vector, k: Int = 1): Iterable[(RowWithVector, Double)] = query(new VectorWithNorm(v), k)
-  def query(v: VectorWithNorm, k: Int): Iterable[(RowWithVector, Double)] = query(new KNNCandidates(v, k)).toIterable
-
-  /**
-   * Refine k-NN candidates using data in this [[Tree]]
-   */
-  private[knn] def query(candidates: KNNCandidates): KNNCandidates
-
-  /**
-   * Compute QueryCost defined as || v.center - q || - r
-   * when >= v.r node can be pruned
-   * for MetricNode this can be used to determine which child does queryVector falls into
-   */
-  private[knn] def distance(candidates: KNNCandidates): Double = distance(candidates.queryVector)
-
-  private[knn] def distance(v: VectorWithNorm): Double =
-    if(pivot.vector.size > 0) pivot.fastDistance(v) else 0.0
-}
-
-private[knn]
-case object Empty extends Tree {
-  override val leftChild = this
-  override val rightChild = this
-  override val size = 0
-  override val leafCount = 0
-  override val pivot = new VectorWithNorm(Vectors.dense(Array.empty[Double]))
-  override val radius = 0.0
-
-  override def iterator: Iterator[RowWithVector] = Iterator.empty
-  override def query(candidates: KNNCandidates): KNNCandidates = candidates
-}
-
-private[knn]
-case class Leaf (data: IndexedSeq[RowWithVector],
-                    pivot: VectorWithNorm,
-                    radius: Double) extends Tree {
-  override val leftChild = Empty
-  override val rightChild = Empty
-  override val size = data.size
-  override val leafCount = 1
-
-  override def iterator: Iterator[RowWithVector] = data.iterator
-
-  // brute force k-NN search at the leaf
-  override def query(candidates: KNNCandidates): KNNCandidates = {
-    val sorted = data
-      .map{ v => (v, candidates.queryVector.fastDistance(v.vector)) }
-      .sortBy(_._2)
-
-    for((v, d) <- sorted if candidates.notFull ||  d < candidates.maxDistance)
-      candidates.insert(v, d)
-
-    candidates
-  }
-}
-
-private[knn]
-object Leaf {
-  def apply(data: IndexedSeq[RowWithVector]): Leaf = {
-    val vectors = data.map(_.vector.vector.asBreeze)
-    val (minV, maxV) = vectors.foldLeft((vectors.head, vectors.head)) {
-      case ((accMin, accMax), bv) =>
-        (min(accMin, bv), max(accMax, bv))
-    }
-    val pivot = new VectorWithNorm((minV + maxV) / 2.0)
-    val radius = math.sqrt(squaredDistance(minV, maxV)) / 2.0
-    Leaf(data, pivot, radius)
-  }
-}
-
-/**
- * A [[MetricTree]] represents a MetricNode where data are split into two partitions: left and right.
- * There exists two pivot vectors: leftPivot and rightPivot to determine the partitioning.
- * Pivot vector should be the middle of leftPivot and rightPivot vectors.
- * Points that is closer to leftPivot than to rightPivot belongs to leftChild and rightChild otherwise.
- *
- * During search, because we have information about each child's pivot and radius, we can see if the
- * hyper-sphere intersects with current candidates sphere. If so, we search the child that has the
- * most potential (i.e. the child which has the closest pivot).
- * Once that child has been fully searched, we backtrack to the remaining child and search if necessary.
- *
- * This is much more efficient than naive brute force search. However backtracking can take a lot of time
- * when the number of dimension is high (due to longer time to compute distance and the volume growing much
- * faster than radius).
- */
-private[knn]
-case class MetricTree(leftChild: Tree,
-                         leftPivot: VectorWithNorm,
-                         rightChild: Tree,
-                         rightPivot: VectorWithNorm,
-                         pivot: VectorWithNorm,
-                         radius: Double
-                          ) extends Tree {
-  override val size = leftChild.size + rightChild.size
-  override val leafCount = leftChild.leafCount + rightChild.leafCount
-
-  override def iterator: Iterator[RowWithVector] = leftChild.iterator ++ rightChild.iterator
-  override def query(candidates: KNNCandidates): KNNCandidates = {
-    lazy val leftQueryCost = leftChild.distance(candidates)
-    lazy val rightQueryCost = rightChild.distance(candidates)
-    // only query if at least one of the children is worth looking
-    if(candidates.notFull ||
-        leftQueryCost - candidates.maxDistance < leftChild.radius ||
-        rightQueryCost - candidates.maxDistance < rightChild.radius ){
-      val remainingChild = {
-        if (leftQueryCost <= rightQueryCost) {
-          leftChild.query(candidates)
-          rightChild
-        } else {
-          rightChild.query(candidates)
-          leftChild
-        }
-      }
-      // check again to see if the remaining child is still worth looking
-      if (candidates.notFull ||
-          remainingChild.distance(candidates) - candidates.maxDistance < remainingChild.radius) {
-        remainingChild.query(candidates)
-      }
-    }
-    candidates
-  }
-}
-
-object MetricTree {
-  /**
-   * Build a (metric)[[Tree]] that facilitate k-NN query
-   *
-   * @param data vectors that contain all training data
-   * @param seed random number generator seed used in pivot point selecting
-   * @return a [[Tree]] can be used to do k-NN query
-   */
-  def build(data: IndexedSeq[RowWithVector], leafSize: Int = 1, seed: Long = 0L): Tree = {
-    val size = data.size
-    if(size == 0) {
-      Empty
-    } else if(size <= leafSize) {
-      Leaf(data)
-    } else {
-      val rand = new XORShiftRandom(seed)
-      val randomPivot = data(rand.nextInt(size)).vector
-      val leftPivot = data.maxBy(v => randomPivot.fastSquaredDistance(v.vector)).vector
-      if(leftPivot == randomPivot) {
-        // all points are identical (or only one point left)
-        Leaf(data, randomPivot, 0.0)
-      } else {
-        val rightPivot = data.maxBy(v => leftPivot.fastSquaredDistance(v.vector)).vector
-        val pivot = new VectorWithNorm(Vectors.fromBreeze((leftPivot.vector.asBreeze + rightPivot.vector.asBreeze) / 2.0))
-        val radius = math.sqrt(data.map(v => pivot.fastSquaredDistance(v.vector)).max)
-        val (leftPartition, rightPartition) = data.partition{
-          v => leftPivot.fastSquaredDistance(v.vector) < rightPivot.fastSquaredDistance(v.vector)
-        }
-
-        MetricTree(
-          build(leftPartition, leafSize, rand.nextLong()),
-          leftPivot,
-          build(rightPartition, leafSize, rand.nextLong()),
-          rightPivot,
-          pivot,
-          radius
-        )
-      }
-    }
-  }
-}
-
-/**
- * A [[SpillTree]] represents a SpillNode. Just like [[MetricTree]], it splits data into two partitions.
- * However, instead of partition data into exactly two halves, it contains a buffer zone with size of tau.
- * Left child contains all data left to the center plane + tau (in the leftPivot -> rightPivot direction).
- * Right child contains all data right to the center plane - tau.
- *
- * Search doesn't do backtracking but rather adopt a defeatist search where it search the most prominent
- * child and that child only. The buffer ensures such strategy doesn't result in a poor outcome.
- */
-private[knn]
-case class SpillTree(leftChild: Tree,
-                                      leftPivot: VectorWithNorm,
-                                      rightChild: Tree,
-                                      rightPivot: VectorWithNorm,
-                                      pivot: VectorWithNorm,
-                                      radius: Double,
-                                      tau: Double,
-                                      bufferSize: Int
-                                       ) extends Tree {
-  override val size = leftChild.size + rightChild.size - bufferSize
-  override val leafCount = leftChild.leafCount + rightChild.leafCount
-
-  override def iterator: Iterator[RowWithVector] =
-    leftChild.iterator ++ rightChild.iterator.filter(childFilter(leftPivot, rightPivot))
-
-  override def query(candidates: KNNCandidates): KNNCandidates = {
-    if (size <= candidates.k - candidates.candidates.size) {
-      iterator.foreach(candidates.insert)
-    } else {
-      val leftQueryCost = candidates.queryVector.fastSquaredDistance(leftPivot)
-      val rightQueryCost = candidates.queryVector.fastSquaredDistance(rightPivot)
-
-      (if (leftQueryCost <= rightQueryCost) leftChild else rightChild).query(candidates)
-
-      // fill candidates with points from other child excluding buffer so we don't double count.
-      // depending on K and how high we are in the tree, this can be very expensive and undesirable
-      // TODO: revisit this idea when we do large scale testing
-      if(candidates.notFull) {
-        (if (leftQueryCost <= rightQueryCost) {
-          rightChild.iterator.filter(childFilter(leftPivot, rightPivot))
-        } else {
-          leftChild.iterator.filter(childFilter(rightPivot, leftPivot))
-        }).foreach(candidates.tryInsert)
-      }
-    }
-    candidates
-  }
-
-  private[this] val childFilter: (VectorWithNorm, VectorWithNorm) => RowWithVector => Boolean =
-    (p1, p2) => p => p.vector.fastDistance(p1) - p.vector.fastDistance(p2) > tau
-}
-
-
-object SpillTree {
-  /**
-   * Build a (spill)[[Tree]] that facilitate k-NN query
-   *
-   * @param data vectors that contain all training data
-   * @param tau overlapping size
-   * @param seed random number generators seed used in pivot point selecting
-   * @return a [[Tree]] can be used to do k-NN query
-   */
-  def build(data: IndexedSeq[RowWithVector], leafSize: Int = 1, tau: Double, seed: Long = 0L): Tree = {
-    val size = data.size
-    if (size == 0) {
-      Empty
-    } else if (size <= leafSize) {
-      Leaf(data)
-    } else {
-      val rand = new XORShiftRandom(seed)
-      val randomPivot = data(rand.nextInt(size)).vector
-      val leftPivot = data.maxBy(v => randomPivot.fastSquaredDistance(v.vector)).vector
-      if (leftPivot == randomPivot) {
-        // all points are identical (or only one point left)
-        Leaf(data, randomPivot, 0.0)
-      } else {
-        val rightPivot = data.maxBy(v => leftPivot.fastSquaredDistance(v.vector)).vector
-        val pivot = new VectorWithNorm(Vectors.fromBreeze((leftPivot.vector.asBreeze + rightPivot.vector.asBreeze) / 2.0))
-        val radius = math.sqrt(data.map(v => pivot.fastSquaredDistance(v.vector)).max)
-        val dataWithDistance = data.map(v =>
-          (v, leftPivot.fastDistance(v.vector), rightPivot.fastDistance(v.vector))
-        )
-        val leftPartition = dataWithDistance.filter { case (_, left, right) => left - right <= tau }.map(_._1)
-        val rightPartition = dataWithDistance.filter { case (_, left, right) => right - left <= tau }.map(_._1)
-
-        SpillTree(
-          build(leftPartition, leafSize, tau, rand.nextLong()),
-          leftPivot,
-          build(rightPartition, leafSize, tau, rand.nextLong()),
-          rightPivot,
-          pivot,
-          radius,
-          tau,
-          leftPartition.size + rightPartition.size - size
-        )
-      }
-    }
-  }
-}
-
-object HybridTree {
-  /**
-   * Build a (hybrid-spill) `Tree` that facilitate k-NN query
-   *
-   * @param data vectors that contain all training data
-   * @param seed random number generator seed used in pivot point selecting
-   * @param tau overlapping size
-   * @param rho balance threshold
-   * @return a `Tree` can be used to do k-NN query
-   */
-  //noinspection ScalaStyle
-  def build(data: IndexedSeq[RowWithVector],
-                            leafSize: Int = 1,
-                            tau: Double,
-                            rho: Double = 0.7,
-                            seed: Long = 0L): Tree = {
-    val size = data.size
-    if (size == 0) {
-      Empty
-    } else if (size <= leafSize) {
-      Leaf(data)
-    } else {
-      val rand = new XORShiftRandom(seed)
-      val randomPivot = data(rand.nextInt(size)).vector
-      val leftPivot = data.maxBy(v => randomPivot.fastSquaredDistance(v.vector)).vector
-      if (leftPivot == randomPivot) {
-        // all points are identical (or only one point left)
-        Leaf(data, randomPivot, 0.0)
-      } else {
-        val rightPivot = data.maxBy(v => leftPivot.fastSquaredDistance(v.vector)).vector
-        val pivot = new VectorWithNorm(Vectors.fromBreeze((leftPivot.vector.asBreeze + rightPivot.vector.asBreeze) / 2.0))
-        val radius = math.sqrt(data.map(v => pivot.fastSquaredDistance(v.vector)).max)
-        lazy val dataWithDistance = data.map(v =>
-          (v, leftPivot.fastDistance(v.vector), rightPivot.fastDistance(v.vector))
-        )
-        // implemented boundary is parabola (rather than perpendicular plane described in the paper)
-        lazy val leftPartition = dataWithDistance.filter { case (_, left, right) => left - right <= tau }.map(_._1)
-        lazy val rightPartition = dataWithDistance.filter { case (_, left, right) => right - left <= tau }.map(_._1)
-
-        if(rho <= 0.0 || leftPartition.size > size * rho || rightPartition.size > size * rho) {
-          //revert back to metric node
-          val (leftPartition, rightPartition) = data.partition{
-            v => leftPivot.fastSquaredDistance(v.vector) < rightPivot.fastSquaredDistance(v.vector)
-          }
-          MetricTree(
-            build(leftPartition, leafSize, tau, rho, rand.nextLong()),
-            leftPivot,
-            build(rightPartition, leafSize, tau, rho, rand.nextLong()),
-            rightPivot,
-            pivot,
-            radius
-          )
-        } else {
-          SpillTree(
-            build(leftPartition, leafSize, tau, rho, rand.nextLong()),
-            leftPivot,
-            build(rightPartition, leafSize, tau, rho, rand.nextLong()),
-            rightPivot,
-            pivot,
-            radius,
-            tau,
-            leftPartition.size + rightPartition.size - size
-          )
-        }
-      }
-    }
-  }
-}
-
-/**
- * Structure to maintain search progress/results for a single query vector.
- * Internally uses a PriorityQueue to maintain a max-heap to keep track of the
- * next neighbor to evict.
- *
- * @param queryVector vector being searched
- * @param k number of neighbors to return
- */
-private[knn]
-class KNNCandidates(val queryVector: VectorWithNorm, val k: Int) extends Serializable {
-  private[knn] val candidates = mutable.PriorityQueue.empty[(RowWithVector, Double)] {
-    Ordering.by(_._2)
-  }
-
-  // return the current maximum distance from neighbor to search vector
-  def maxDistance: Double = if(candidates.isEmpty) 0.0 else candidates.head._2
-  // insert evict neighbor if required. however it doesn't make sure the insert improves
-  // search results. it is caller's responsibility to make sure either candidate list
-  // is not full or the inserted neighbor brings the maxDistance down
-  def insert(v: RowWithVector, d: Double): Unit = {
-    while(candidates.size >= k) candidates.dequeue()
-    candidates.enqueue((v, d))
-  }
-  def insert(v: RowWithVector): Unit = insert(v, v.vector.fastDistance(queryVector))
-  def tryInsert(v: RowWithVector): Unit = {
-    val distance = v.vector.fastDistance(queryVector)
-    if(notFull || distance < maxDistance) insert(v, distance)
-  }
-  def toIterable: Iterable[(RowWithVector, Double)] = candidates
-  def notFull: Boolean = candidates.size < k
-}
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/ml/recommendation/SimRankOpenSource.scala b/tools/kal-test/src/main/scala/org/apache/spark/ml/recommendation/SimRankOpenSource.scala
deleted file mode 100644
index 8d08e47..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/ml/recommendation/SimRankOpenSource.scala
+++ /dev/null
@@ -1,151 +0,0 @@
-// scalastyle:off
-package org.apache.spark.ml.recommendation
-
-import org.apache.spark.{Dependency, ShuffleDependency, SparkContext}
-import org.apache.spark.graphx._
-import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.storage.StorageLevel
-import org.slf4j.LoggerFactory
-
-/**
- * simrank
- *
- */
-class SimRankOpenSource() {
-
-  private val PARAM_NAME_DAMP = "damp"
-  private val INPUT_NAME_NODES_INFO = "nodes"
-  private val PARAM_NAME_MAX_ITERATOR = "maxIterator"
-
-  def execute(input: DataFrame, colNames: (String, String), damp: Double, maxIter: Int): (DataFrame, DataFrame) = {
-    import input.sparkSession.implicits._
-    val nodes = input.select(colNames._1, colNames._2).distinct().map(one => (one.getString(0), one.getString(1))).rdd.cache()
-    val sim = SimRankOpenSource.getSimilarity(nodes, damp, maxIter)
-
-    (sim._1.toDF(colNames._1 + "1", colNames._1 + "2", "similarity"), sim._2.toDF(colNames._2 + "1", colNames._2 + "2", "similarity"))
-  }
-
-}
-
-class SimRankGraphOpenSource() {
-  /**
-   * 创建图的结构
-   *
-   * @param node2Index 节点idx信息
-   * @param nodes      用户商品二部图
-   * @return
-   */
-  def graphStruct(node2Index: RDD[(String, Long)], nodes: RDD[(String, String)]): Graph[String, Int] = {
-
-    val indexedNodes = nodes.join(node2Index).map(r => (r._2._1, r._2._2)).join(node2Index).map(r => (r._2._1, r._2._2))
-
-    val relationShips: RDD[Edge[Int]] = indexedNodes.map { x =>
-      val x1 = x._1
-      val x2 = x._2
-      Edge(x1, x2, 1)
-    }
-    val users: RDD[(VertexId, String)] = node2Index.map { x =>
-      (x._2, x._1)
-    }
-
-    val graph = Graph(users, relationShips)
-    graph
-  }
-}
-
-object SimRankOpenSource {
-  private val LOGGER = LoggerFactory.getLogger(this.getClass)
-
-  /**
-   * 获取item相似图
-   *
-   * @param nodes 用户商品节点信息[device_id, goods_id]
-   * @param damp  阻尼系数
-   */
-  def getSimilarity(nodes: RDD[(String, String)],
-                    damp: Double, maxIte: Int): (RDD[(String, String, Double)], RDD[(String, String, Double)]) = {
-    val itemSet = nodes.map(x => (x._2, "-")).distinct()
-    val userSet = nodes.map(x => (x._1, "-")).distinct()
-    // 用户商品index化
-    val node2IndexArray = (nodes.map(_._1) union nodes.map(_._2)).distinct.collect().zipWithIndex.map(one => (one._1, one._2.toLong))
-    val nodesNum = node2IndexArray.length
-    val node2Index = nodes.sparkContext.parallelize(node2IndexArray)
-
-    val graph = new SimRankGraphOpenSource().graphStruct(node2Index, nodes)
-    val outs = graph.outDegrees.map(x => (x._1, 1 / x._2.toDouble))
-    val ins = graph.inDegrees.map(x => (x._1, 1 / x._2.toDouble))
-    val rdd_out = graph.outerJoinVertices(outs)((id, _, degin) => (id.toString, degin.getOrElse(0))).triplets.map { x =>
-      (x.dstId, x.srcId, x.srcAttr._2.toString.toDouble * x.attr)
-    }
-    val rdd_int = graph.outerJoinVertices(ins)((id, _, degin) => (id.toString, degin.getOrElse(0))).triplets.map { x =>
-      (x.srcId, x.dstId, x.dstAttr._2.toString.toDouble * x.attr)
-    }
-
-    val rdd_all = rdd_out.union(rdd_int)
-
-    val blockSize = 2048
-    val transferMatrix = new CoordinateMatrix(rdd_all.map { x =>
-      MatrixEntry(x._1, x._2, x._3)
-    }.repartition(1000)).toBlockMatrix(blockSize, blockSize).persist(StorageLevel.MEMORY_AND_DISK_SER)
-
-    // 初始化相似度矩阵
-    var S_k = new CoordinateMatrix(nodes.sparkContext.parallelize(0 until nodesNum).map { x =>
-      MatrixEntry(x, x, 1.0)
-    }.repartition(1000))
-    // K+1次迭代相似度矩阵
-    var S_kp1 = S_k.entries
-
-    for (i <- 0 until maxIte) {
-      S_kp1 = transferMatrix.transpose.multiply(S_k.toBlockMatrix(blockSize, blockSize))
-        .multiply(transferMatrix).toCoordinateMatrix().entries.map(entry => {
-        if (entry.i == entry.j) {
-          MatrixEntry(entry.i, entry.j, 1.0)
-        } else {
-          MatrixEntry(entry.i, entry.j, entry.value * damp)
-        }
-      }).persist(StorageLevel.MEMORY_AND_DISK_SER).setName("S_Kp1")
-      S_kp1.foreachPartition(_ => {})
-      cleanShuffleDependencies(S_kp1.sparkContext, S_kp1.dependencies)
-      S_k = new CoordinateMatrix(S_kp1, nodesNum, nodesNum)
-    }
-
-    // RDD[(idx, device_id/goods_id)]
-    val index2Node = node2Index.map(x => (x._2, x._1))
-
-    val nodeSim = S_kp1.map(x => (x.i, x.j, x.value)).map(x => (x._1, (x._2, x._3)))
-      .join(index2Node)
-      .map(x => (x._2._1._1, (x._2._1._2, x._2._2)))
-      .join(index2Node)
-      .map(x => (x._2._1._2, (x._2._2, x._2._1._1)))
-    val userSim = nodeSim.join(userSet).map(x => (x._1, x._2._1._1, x._2._1._2)).filter(x => !x._1.equals(x._2))
-    val itemSim = nodeSim.join(itemSet).map(x => (x._1, x._2._1._1, x._2._1._2)).filter(x => !x._1.equals(x._2))
-    (userSim, itemSim)
-  }
-
-  def cleanShuffleDependencies[T](
-    sc: SparkContext,
-    deps: Seq[Dependency[_]],
-    blocking: Boolean = false): Unit = {
-    // If there is no reference tracking we skip clean up.
-    sc.cleaner.foreach { cleaner =>
-      /**
-       * Clean the shuffles & all of its parents.
-       */
-      def cleanEagerly(dep: Dependency[_]): Unit = {
-        if (dep.isInstanceOf[ShuffleDependency[_, _, _]]) {
-          val shuffleId = dep.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
-          cleaner.doCleanupShuffle(shuffleId, blocking)
-        }
-        val rdd = dep.rdd
-        val rddDeps = rdd.dependencies
-        if (rdd.getStorageLevel == StorageLevel.NONE && rddDeps != null) {
-          rddDeps.foreach(cleanEagerly)
-        }
-      }
-      deps.foreach(cleanEagerly)
-    }
-  }
-}
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/ml/regression/KNNRegression.scala b/tools/kal-test/src/main/scala/org/apache/spark/ml/regression/KNNRegression.scala
deleted file mode 100644
index a5f397a..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/ml/regression/KNNRegression.scala
+++ /dev/null
@@ -1,156 +0,0 @@
-package org.apache.spark.ml.regression
-
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.knn.{KNN, KNNModelParams, KNNParams, Tree}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.param.shared.HasWeightCol
-import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.ml.{PredictionModel, Predictor}
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.storage.StorageLevel
-
-/**
-  * [[https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]] for regression.
-  * The output value is simply the average of the values of its k nearest neighbors.
-  */
-class KNNRegression(override val uid: String) extends Predictor[Vector, KNNRegression, KNNRegressionModel]
-with KNNParams with HasWeightCol {
-  def this() = this(Identifiable.randomUID("knnr"))
-
-  /** @group setParam */
-  override def setFeaturesCol(value: String): this.type = set(featuresCol, value)
-
-  /** @group setParam */
-  override def setLabelCol(value: String): this.type = {
-    set(labelCol, value)
-
-    if ($(weightCol).isEmpty) {
-      set(inputCols, Array(value))
-    } else {
-      set(inputCols, Array(value, $(weightCol)))
-    }
-  }
-
-  //fill in default label col
-  setDefault(inputCols, Array($(labelCol)))
-
-  /** @group setWeight */
-  def setWeightCol(value: String): this.type = {
-    set(weightCol, value)
-
-    if (value.isEmpty) {
-      set(inputCols, Array($(labelCol)))
-    } else {
-      set(inputCols, Array($(labelCol), value))
-    }
-  }
-
-  setDefault(weightCol -> "")
-
-  /** @group setParam */
-  def setK(value: Int): this.type = set(k, value)
-
-  /** @group setParam */
-  def setTopTreeSize(value: Int): this.type = set(topTreeSize, value)
-
-  /** @group setParam */
-  def setTopTreeLeafSize(value: Int): this.type = set(topTreeLeafSize, value)
-
-  /** @group setParam */
-  def setSubTreeLeafSize(value: Int): this.type = set(subTreeLeafSize, value)
-
-  /** @group setParam */
-  def setBufferSizeSampleSizes(value: Array[Int]): this.type = set(bufferSizeSampleSizes, value)
-
-  /** @group setParam */
-  def setBalanceThreshold(value: Double): this.type = set(balanceThreshold, value)
-
-  /** @group setParam */
-  def setSeed(value: Long): this.type = set(seed, value)
-
-  override protected def train(dataset: Dataset[_]): KNNRegressionModel = {
-    val knnModel = copyValues(new KNN()).fit(dataset)
-    knnModel.toNewRegressionModel(uid)
-  }
-
-  override def fit(dataset: Dataset[_]): KNNRegressionModel = {
-    // Need to overwrite this method because we need to manually overwrite the buffer size
-    // because it is not supposed to stay the same as the Regressor if user sets it to -1.
-    transformSchema(dataset.schema, logging = true)
-    val model = train(dataset)
-    val bufferSize = model.getBufferSize
-    copyValues(model.setParent(this)).setBufferSize(bufferSize)
-  }
-
-  override def copy(extra: ParamMap): KNNRegression = defaultCopy(extra)
-}
-
-class KNNRegressionModel private[ml](
-                                      override val uid: String,
-                                      val topTree: Broadcast[Tree],
-                                      val subTrees: RDD[Tree]
-                                    ) extends PredictionModel[Vector, KNNRegressionModel]
-with KNNModelParams with HasWeightCol with Serializable {
-  require(subTrees.getStorageLevel != StorageLevel.NONE,
-    "KNNModel is not designed to work with Trees that have not been cached")
-
-  /** @group setParam */
-  def setK(value: Int): this.type = set(k, value)
-
-  /** @group setParam */
-  def setBufferSize(value: Double): this.type = set(bufferSize, value)
-
-  //TODO: This can benefit from DataSet API in Spark 1.6
-  override def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val getWeight: Row => Double = {
-      if($(weightCol).isEmpty) {
-        r => 1.0
-      } else {
-        r => r.getDouble(1)
-      }
-    }
-
-    val neighborDataset : RDD[(Long, Array[(Row, Double)])] = transform(dataset, topTree, subTrees)
-    val merged = neighborDataset
-      .map {
-        case (id, labelsDists) =>
-          val (labels, _) = labelsDists.unzip
-          var i = 0
-          var weight = 0.0
-          var sum = 0.0
-          val length = labels.length
-          while (i < length) {
-            val row = labels(i)
-            val w = getWeight(row)
-            sum += row.getDouble(0) * w
-            weight += w
-            i += 1
-          }
-
-          (id, sum / weight)
-      }
-
-    dataset.sqlContext.createDataFrame(
-      dataset.toDF().rdd.zipWithIndex().map { case (row, i) => (i, row) }
-        .leftOuterJoin(merged) //make sure we don't lose any observations
-        .map {
-        case (i, (row, value)) => Row.fromSeq(row.toSeq :+ value.get)
-      },
-      transformSchema(dataset.schema)
-    )
-  }
-
-  override def copy(extra: ParamMap): KNNRegressionModel = {
-    val copied = new KNNRegressionModel(uid, topTree, subTrees)
-    copyValues(copied, extra).setParent(parent)
-  }
-
-  override def predict(features: Vector): Double = {
-    val neighborDataset : RDD[(Long, Array[(Row, Double)])] = transform(subTrees.context.parallelize(Seq(features)), topTree, subTrees)
-    val results = neighborDataset.first()._2
-    val labels = results.map(_._1.getDouble(0))
-    labels.sum / labels.length
-  }
-}
diff --git a/tools/kal-test/src/main/scala/org/apache/spark/mllib/knn/KNNUtils.scala b/tools/kal-test/src/main/scala/org/apache/spark/mllib/knn/KNNUtils.scala
deleted file mode 100644
index 16aa41f..0000000
--- a/tools/kal-test/src/main/scala/org/apache/spark/mllib/knn/KNNUtils.scala
+++ /dev/null
@@ -1,20 +0,0 @@
-package org.apache.spark.mllib.knn
-
-import org.apache.spark.ml.{linalg => newlinalg}
-import org.apache.spark.mllib.{linalg => oldlinalg}
-import org.apache.spark.mllib.util.MLUtils
-
-object KNNUtils {
-
-  import oldlinalg.VectorImplicits._
-
-  def fastSquaredDistance(
-                           v1: newlinalg.Vector,
-                           norm1: Double,
-                           v2: newlinalg.Vector,
-                           norm2: Double,
-                           precision: Double = 1e-6): Double = {
-    MLUtils.fastSquaredDistance(v1, norm1, v2, norm2, precision)
-  }
-
-}